esa-sceva/satcom-chunk-collection huggingface dataset:
# MR Images, Brain Lesions, and Deep Learning
## 2 The Literature Review: Selection Criteria
### Computer-Aided Diagnosis in Medical Imaging (CADx System)
#### 3.2.2 Image Preprocessing  
There are several preprocessing steps necessary to reduce noise and artifacts in the medical images, which should be performed before the segmentation [40; 166; 167]. The preprocessing steps commonly used are (1) grayscale conversion and image resizing [167] to get better contrast and enhancement; (2) bias field correction to correct the intensity inhomogeneity [30; 166]; (3) image registration, a process for spatial alignment [166]; and (4) removal of nonbrain tissue such as fat, skull, or neck, which has intensities overlapping with intensities of brain tissues [27; 166; 168]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Details** & **Type:** & & **Reference/ Support** \\
**Name** & **Details** & **Public** & & **[[https://www.nitrc](https://www.nitrc)]([https://www.nitrc](https://www.nitrc)).** \\ \hline MICCAI & MS lesion segmentation challenge: data for competition in order to compare algorithms to segment the MS lesions since 2008 & Public and private.Some data require subscription. & (accessed on 10 December 2020) & [PERSON] et al. (2017) [9] \\ \hline BRATS & Brain tumor segmentation: MRI dataset for challenge BRATS since 2012.MRI modalities: T1, T1C, T2, FLAIR. & & & \\  & BRATS 2015 contains 220 brains with high-grade and 54 brains with low-grade gliomas for training and 53 brains with mixed high- and low-grade gliomas for testing. & & \\ \hline  & Dataset used for ischemic stroke lesion segmentation challenge since 2015 in order to evaluate stroke lesion/clinical outcome prediction from acute MRI scans. & Public, require registration and approval & [[https://www.smir.ch/](https://www.smir.ch/)]([https://www.smir.ch/](https://www.smir.ch/)) (accessed on 10 December 2020) & [PERSON] et al. (2017) [26] \\ ISLES & Res has two categories with individual datasets: & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of datasets dedicated to ischemia (stroke) and demyelinating diseases (multiple sclerosis (MS)). Brain medical image datasets are also listed.
## Materials and Methods
### Data Analysis  
Chromatographic signals were normalized for each sample weight, and noise was subtracted for each run. Unmatched chromatographic peaks were heightened to values close to zero. Both chromatographic peak areas and microbial counts were log-transformed before performing a data-fusion process. First, a multifactorial model was built in order to evaluate the effect of the factors (can volume, time of sampling, preservative treatment) and their interactions on the distributions of parameters analyzed by chemical, physical, and microbiological analysis. Since results of the full factorial model did not show a significant effect (\(p\) > 0.05) on composition for the factor \"can volume\", results were presented pooling samples based on the preservative treatment (NC, OAM and BSM). When the significance of the effect due to the interaction between \"time of sampling\" and \"preservative treatment\" was observed (\(p\) < 0.05), multiple comparisons were performed over group least square means by the [PERSON]'s HSD test. Then, a multiparametric approach was developed. The whole dataset was submitted to power transformation, chosen as a nonlinear conversion process that allowed the reduction of data heteroscedasticity. Subsequently, Pareto scaling was used to adjust significance of variables represented by large and small fold changes [21]. Finally, multilevel principal component analysis (multilevel-PCA) was applied as a multiparametric explorative tool, in order to reduce the dimensionality of the dataset while retaining the maximum of the original information and preserving the structure of the dataset (time and treatment). The final dataset consisted of a 48 (volatile organic compounds + pH + water activity + microbial counts) \(\times\) 44 (samples) matrix. Univariate statistical analysis was performed by the JMP\({}^{\boxtimes}\) 15.2.0 tool of SAS (SAS Institute Inc., Cary, NC, USA). The multivariate analysis was performed by the mixOmics package of R and the PLS Toolbox of MATLAB.
## 3 Discussion
It should be noted that, in the present study, 2.5% NaOCl was used as the final irrigation, which was activated with an ultrasonic system. No discoloration was observed in any of the teeth in follow-ups beyond 60 days. The second limitation is the volume of residual PS at the time of light activation. For optimal performance, it is necessary to aspirate the excess PS, as was performed in our protocol. This prevents the excess solution from absorbing the applied light, thereby making the disinfection process more effective. Post-operative pain is a fundamental variable to analyze when evaluating new antimicrobial therapies. LLLT combined with PBMT is recognized as one of the most potent non-pharmacologic methods for alleviating pain [47]. Several articles have reported that applying aPDT in endodontic treatments does not ensure a decrease in post-operative pain [48; 49; 50]. In contrast, [PERSON] et al. (2019) [25] concluded, through a randomized controlled clinical trial, that PBMT after endodontic treatment had a significant effect on post-operative pain. [PERSON] et al. (2018) [51] observed a decrease in pain within the LLLT group on both the first and third days. Similarly, [PERSON] et al. (2018) [52] and [PERSON] et al. (2011) [53] found that pain following endodontic treatment was notably reduced in the LLLT group at intervals of 4, 8, 12, 24, and 48 h post-treatment. Additional studies have confirmed significant pain relief from 4 to 48 h in LLLT groups [25; 54; 55]. Similarly, none of the patients in this study reported signs of pain after completing the therapeutic regimen. This may be due to the analgesic action that PBMT can exert. However, characteristics such as wavelength, energy and power density, beam spot area, and the time and number of irradiations should be taken into consideration, as they may affect the outcomes. Research has suggested that the analgesic effects are attributed to the anti-inflammatory and neurological actions of LLLT, such as enhancing lymphocyte and nerve cell respiration, stabilizing membrane potentials, and releasing neurotransmitters into inflamed tissues [53]. Moreover, LLLT significantly increases fibroblast activity, accelerates the healing of connective tissues, and exerts anti-inflammatory properties [56].
# Real-time Neural Networks Implementation Proposal for Microcontrollers
## 2 System Description
### Basic Operations  
This section describes the implementation of the basic operations used in this work. First, Algorithm 3 implements the matrix product. A very important detail is that all the modules were implemented following pointer arithmetic procedures. With this in mind, the source code is very well optimized for memory usage, reducing the overall duration of execution from each module. Every matrix used in this implementation is floating-point (IEEE754) [26] representation values.  
Algorithm 4 shows the pseudocode of an element-wise product between two matrices used in this work, more specifically in the BPM-\(k\) module implementation. This same pseudocode in Algorithm 4 can be slightly altered to perform subtraction or addition by editing the operation between matrices on line 5. It is important to notice that Algorithm 4 requires that both matrices have the same dimensions.  
```
1:function\(prod(\mathbf{Y^{k}},\mathbf{E},\mathbf{g^{k}},P,M)\)
2: Initialise (\(i,j\)) \(\leftarrow\) 0
3:for\(i\gets 1\) to P do
4:for\(j\gets 1\) to M do
5:\(\mathbf{g^{k}}[i][j]=\mathbf{Y^{k}}[i][j]\times\mathbf{E}[i][j]\)
6:endfor
7:endfor
8:endfunction
```  
**Algorithm 4** Element-wise matrix product implementation algorithm.  
Algorithm 5 presents to us the implementation of the calculation of the _Trace_ and _Product_ between two matrices at a single operation inside a nested loop. This simple optimization saved a few \(\upmu\)s of processing time for the overall training process.  
Algorithm 6 shows the implemented steps for calculating the activation function of the induced-local-field of each neuron, an element-wise operation. This Algorithm 6 is implementing a sigmoid activation function.  
```
1:function\(\mathit{trace}(\mathbf{E^{k}},\mathbf{E^{kT}},P,M)\)
2: Initialize (\(i\), \(j\), \(\mathrm{trace}\)) \(\leftarrow\) 0
3:for\(i\gets 1\) to P do
4:for\(j\gets 1\) to M do
5:\(\mathrm{trace}\) = \(\mathrm{trace}\) + \(\mathbf{E^{k}}[i][j]\times\)\(\mathbf{E^{k}}\)T\([i][j]\)
6:endfor
7:endfor
8:endfunction
```  
**Algorithm 6** Pseudocode implementation of the sigmoid activation function.  
```
1:function\(\mathit{activfun}(\mathbf{Y^{k}},\mathbf{Y^{k+1}},P,M)\)
2: Initialise (\(i\), \(j\)) \(\leftarrow\) 0
3:for\(i\gets 1\) to P do
4:for\(j\gets 1\) to M do
5:\(\mathbf{y^{k+1}}[i][j]=(1.0)/(1.0+\mathrm{exponential}(-\mathbf{Y^{k}}[i][j]))\)
6:endfor
7:endfor
8:endfunction
```  
**Algorithm 7** Pseudocode implementation of the trace of the readout between two matrices.
# Screening of Plants and Indigenous Bacteria to Improve Arsenic Phytoextraction
## 3 Results and Discussion
### Microcosm Tests with Oxalate
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Soil**} & \multirow{2}{*}{**Treatment**} & \multicolumn{2}{c}{_Z. mays_} & \multicolumn{2}{c}{_C. sativa_} \\ \cline{3-6}  & & **Roots** & **Shoots** & **Roots** & **Shoots** \\ \hline \multirow{2}{*}{S2} & CT++ & 0.21 \(\pm\) 0.02a & 0.26 \(\pm\) 0.01a & 0.12 \(\pm\) 0.01a & 0.04 \(\pm\) 0.01a \\  & O++ & 8.01 \(\pm\) 0.72b & 4.11 \(\pm\) 0.16b & 11.4 \(\pm\) 0.91b & 5.57 \(\pm\) 0.22b \\ \hline \multirow{2}{*}{SW} & CT++ & 0.11 \(\pm\) 0.01a & 0.07 \(\pm\) 0.01a & 0.11 \(\pm\) 0.01a & 0.05 \(\pm\) 0.01a \\  & O++ & 15.6 \(\pm\) 1.09b & 6.69 \(\pm\) 0.27b & 7.82 \(\pm\) 0.63b & 3.85 \(\pm\) 0.19b \\ \hline \end{tabular}
\end{table} 
Table 5: Arsenic concentration (mg kg\({}^{-1}\)) in roots and shoots of _Z. mays_ and _C. sativa_ grown on soil control (CT) and As-contaminated soil treated with oxalate (O). PGPR bacteria (second set of isolates) were added to all soil samples, as indicated by the “++” sign. Values are reported as mean (\(n\) = 3) \(\pm\) SD. Values with different letters are significantly different at the 5% probability level (Tukey’s test).
###### Abstract  
The main purpose of electromagnetic interference (EMI) shielding coatings is the insulation of sensitive devices and protect people from electromagnetic field exposure due to its effects on the human body. This paper investigates the dielectric properties, and electromagnetic shielding performances of graphite and carbon black (CB) filled epoxy matrix composites produced by the mechanical mixing method. The sample compositions were created at rates ranging from 1 to 7 wt%. Samples were analyzed by Vector Network Analyzer (VNA) using the coaxial method in the range of 1-14 GHz, including L band, S band, C band, X band and partially Ku band. After determining the scattering parameters with VNA, AC conductivity, absorption, reflection and total shielding efficiency values were calculated. At high frequencies, almost all the samples showed higher AC conductivity. CB-filled samples show higher AC conductivity than graphite-filled samples. The total shielding efficiency (\(SET_{T}\)) of the graphite-added samples (19-21 dB) is slightly higher than the carbon black-added samples (8-17 dB). Distinct filling ratios in graphite-added samples result in closer shielding behavior in contrast to carbon black-added samples. However, higher shielding efficiency is observed as the CB filler ratio increases. The shielding efficiencies of the samples with both types of filling materials vary little depending on the frequency. Reflection is the main mechanism of the shielding efficiency, which constitutes the majority of total efficiency for all types of samples.  
coating; electromagnetic interference shielding; composites; carbon black; graphite Article
###### Abstract  
Due to the dynamic and harsh underwater environment, which involves a long propagation delay, high bit error rate, and limited bandwidth, it is challenging to achieve reliable communication in underwater wireless sensor networks (UWSNs) and network support applications, like environmental monitoring and natural disaster prediction, which require energy efficiency and low latency. To tackle these challenges, we introduce AC-RL-based power control (ACRLPC), a novel hybrid MAC protocol that can efficiently integrate Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA)-based MAC and Time Division Multiple Access (TDMA) with Actor-Critic Reinforcement Learning (AC-RL). The proposed framework employs adaptive strategies, utilizing adaptive power control and intelligent access methods, which adjust to fluctuating conditions on the network. Harsh and dynamic underwater environment performance evaluations of the proposed scheme confirm a significant outperformance of ACRLPC compared to the current protocols of FDUMAC, TCH-MAC, and UW-ALOHA-QM in all major performance measures, like energy consumption, throughput, accuracy, latency, and computational complexity. The ACRLPC is an ultra-energy-efficient protocol since it provides higher-grade power efficiency by maximizing the throughput and limiting the latency. Its overcoming of computational complexity makes it an approach that greatly relaxes the processing requirement, especially in the case of large, scalable underwater deployments. The unique hybrid architecture that is proposed effectively combines the best of both worlds, leveraging TDMA for reliable access, and the flexibility of CSMA/CA serves as a robust and holistic mechanism that meets the desired enablers of the system.  
**Keywords:** underwater wireless sensor networks (UWSNs); medium access control (MAC) protocol; collision sensing multiple access with collision avoidance (CSMA/CA); energy efficiency; actor-critic reinforcement learning (AC-RL); computational complexity
###### Abstract  
Accurate wind speed prediction is of importance for long-span cross-sea bridges. To this end, data decomposition techniques are usually employed to promote accuracy of the prediction model. Since wind speed data come sequentially, real-time decomposition should be adopted. However, real-time decomposition may degrade the accuracy due to the end effect. In this paper, a novel scheme of real-time decomposition that is a combination of truncated real-time decomposition and multi-resolution data is developed. Specifically, truncated real-time decomposition firstly denoises the data and eliminates the end effect; high-resolution data are then introduced to compensate for the information loss. Further, a novel wind speed prediction model that consists of the proposed scheme and neural networks is proposed. Specifically, two gated recurrent unit neural networks are employed to extract features from the obtained original-resolution and high-resolution data, respectively, and a multi-layer perceptron is adopted to utilize extracted features and make predictions. The proposed model is validated on the monitoring wind speed data of two long-span cross-sea bridges. Specifically, the mean absolute error and the root of mean square error of the proposed model on the two datasets are 0.334, 0.445 and 0.233 and 0.316 m/s, which are smaller than benchmark models and demonstrate superiority of the proposed model.  
wind speed prediction; data decomposition; multi-resolution data; machine learning; long-span bridge +
Footnote †: journal: Medical Imaging  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 2 Materials and Methods
### Sensitivity and Performance of the Device  
The device's sensitivity is in the range of 9 to 18 mV/mN, depending on the location of the embedding region (near or within the sensitive area of the strain gauge as shown in Figure 1). Graphically, Figure 3 shows that the change in device output signal explains more than 99% of the load variability for strain gauges G1 to G6. Therefore, there is a high positive correlation between the load and the instrumental signal of the new device, where the variation in sensor sensitivity is attributable to manufacturing differences. There is high linearity within 30% to 80% of the maximum load because the sensors are, by design, within the elastic range.  
In general, the sensor was tested for loads between 0 and 100 mN, showing high levels of linearity between load and instrumental signal and of lateral displacement between 0 and 1 mm, without generating gauge delamination or residual deformation effect and with an affordable cost and consumption. However, the experimental setup to measure shear stress in channels' beds is expensive. The order of magnitude of detectable forces is less than 1 N/m\({}^{2}\). On the other hand, the bed shear stress in open channels is in the order of 1-10 N/m\({}^{2}\)[25; 26; 27; 28]. In this case, the most relevant characteristic is that the dimension of the device is small enough to avoid significant interferences in the flow. It has to be noticed that there is room for improving: (1) the sensor embedding procedure to attain perfect embedding and (2) the capture of the device's output signal.  
Currently, it is difficult to provide an accurate estimation of the resolution and the minimum detectable force for the proposed device. However, an appropriate estimate is that the precision for the force is 2.5 mN and for the device's output signal is 0.17 mV; therefore, the shear stress resolution would be 0.1 mN.
## 4 Results and Discussion
### Exhaust Gas Temperature and Energy  
In CCHP system, the exhaust gas is used to provide heat for the absorption refrigerator; thus, the exhaust temperature is a key parameter. When the ignition timing is delayed, the combustion process is thus delayed, and as a result, the temperature of the cylinder gas changes accordingly. Figure 9 shows the changed cylinder gas temperature.  
The exhaust valve opens at 55 \({}^{\circ}\)CA BBDC. The delayed combustion process also results in higher exhaust temperatures in the pipe. For example, if the ignition timing is delayed to 10 \({}^{\circ}\)CA BTDC, compared with MBT case, the exhaust temperature increases by 130 K. This indicates that the exhaust temperature can be adjusted flexibly by changing the spark timing.  
As it is known, the heat energy of natural gas burned in the cylinder can be simply divided into three parts: the brake power, the exhaust heat energy, and the heat transferred to the cooling system. Figure 10 shows the ratio of the simulated exhaust heat energy. In the figure, the marked powers are the engine power at MBT. The delayed ignition timing increases the ratio of the exhaust heat energy/power. The tendencies are quite similar. The ratio of the exhaust heat energy at 34 \({}^{\circ}\)CA BBDC is about 32%, while the ignition is delayed to 8 \({}^{\circ}\)CA BBDC, the ratio increases to 50%. Figure 11 shows the variation trend of exhaust energy with load at different ignition advance angles. It can be seen from the figure that with the increase in the load, the exhaust energy increases significantly. For example, when the ignition advance angle is 22 \({}^{\circ}\)CA BTDC, when the power increases from 400 kW to 500 kW, the exhaust energy increases by 88.7 kW.  
Figure 10: Exhaust gas energy.  
Figure 9: Cylinder temperatures.  
The increased exhaust heat energy can be used by the CCHP system for heating or refrigerating. That is to say that when there is a higher cooling demand in the summer or heating demand in the winter of a CCHP system, it can be simply treated by delaying the engine spark timing without extra heating equipment.
## 5 Result Analysis
### Case 3  
For Case 3, the conditions of the boundary were set as follows: 75% of the maximum heat load in the heat flow boundary condition was imposed symmetrically, meaning that \(q_{adm}\) = 0.729. As in previous cases, the minimum and maximum air flow velocity were applied. In the case of the minimum air flow velocity, the temperature field predictions can be seen, respectively, in Figures 13 and 14 for the \(y\) and \(z\) planes.  
Figure 12: The results of the field of temperature in the \(x\)–\(y\) plane through the planes: (**a**) \(z_{adm}\) = 0; (**b**) \(z_{adm}\) = 0.244; (**c**) \(z_{adm}\) = 0.489; and (**d**) \(z_{adm}\) = 1.  
Figure 14: The results of the field of temperature in the \(x\)-\(y\) plane through the planes: (**a**) \(z_{adm}=0\); (**b**) \(z_{adm}=0.244\); (**c**) \(z_{adm}=0.489\); and (**d**) \(z_{adm}=1\).  
Figure 13: The results of the field of temperature in the \(x\)-\(z\) plane through the planes: (**a**) \(y_{adm}=0.100\); (**b**) \(y_{adm}=0.485\); and (**c**) \(y_{adm}=0.900\).  
In Figure 13, it is possible to observe the temperature of the server racks above the established value for hot spots and average dimensionless temperatures in the hot aisles of \(T_{adm}\) = 0.350. Several hot spots occur with the minimum air flow velocity. It is possible to observe in Figure 14 that, for a minimum air flow velocity, when 75% is set for the maximum load, several hot spots are foreseen, as confirmed by Figure 13, in the server racks at the end of all the rows. Regarding the extraction temperature, an average dimensionless temperature around \(T_{adm}\) = 0.200 is expected, and, since it is higher than \(T_{adm}\) = 0.125, it leads to the prediction of hot spots.  
When the air flow velocity is simulated at the maximum level, the temperature field predictions show a different result, as can be seen, respectively, in Figures 15 and 16 for the \(y\) and \(z\) planes.  
By observing Figure 15, it is possible to assess that some of the racks in the data center room exhibit temperature values similar to the inlet air temperature, especially in the intermediate server racks of each row.  
As shown in the plane temperature fields of Figure 16, when applying the maximum level of air flow velocity, the thermal behavior of the data center room does not provide hot spots, neither for the server racks nor for the extraction zone, which has an average temperature similar to the inlet air temperature. Both figures confirm that no hot spots occur in this scenario.  
Figure 15: The results of the field of temperature in the \(x\)–\(z\) plane through the planes: (**a**) \(y_{adm}\) = 0.100; (**b**) \(y_{adm}\) = 0.485; and (**c**) \(y_{adm}\) = 0.900.
## 4 Results and Discussions
### Key Findings
#### 4.2.3 Cluster and Sorting Time Analysis  
In the analysis of the clustering and sorting time of the algorithm, Table 4 is used as a guide in determining the specific number of items in each label.  
Figure 14: Configuration 3. _This scenario shows the improvement in terms of dwell time for 100 sloppers, looped 5 times. The shopping lists for the shoppers were generated from a pool of 555 items._  
Figure 13: Configuration 2. _This scenario shows the improvement in terms of dwell time for 100 sloppers, looped 5 times. The shopping lists for the shoppers were generated from a pool of 60 items._From the graph shown in Figure 15, DProSA-AG shows a better clustering time than DProSA-AP. DProSA-AP takes an average of 20% more time than DProSA-AG. However, during the sorting time of each algorithm shown in Figure 16, DProSA-AP shows better sorting time than DProSA-AG, with an average of 45% less time than DProSA-AG. Although both algorithms have differences in the clustering and sorting time, it should be noted that these values are almost negligible when it comes to their actual usage.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Label** & **Number of Items** \\ \hline Small & 5–7 \\ \hline Medium & 10–14 \\ \hline Large & 15–21 \\ \hline Mixed & 5–21 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of items based on the label.  
Figure 16: Average sorting times of DProSA-AG and DProSA-AP. _Each bar is differentiated based on the size of the list and the algorithm used._  
Figure 15: Clustering times of DProSA-AG and DProSA-AP. _The graph is based on the 60 item pool data._