esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data
## 3 Results  
### Distribution of Haze Identification  
Two haze scenarios in winter were selected to test the performance of our proposed method, as shown in Figure 8. The first row of images is 2 December 2019, and the second row is 9 February 2020. The left panel is a true color map, and the right one is the distribution of the identified haze. On 2 December 2019, most parts of eastern China and India were shrouded by haze. Except for the clear sky area in Inner Mongolia, Xinjiang and the Qinghai-Tibet Plateau, the other parts of the whole study area were covered by cloud pixels. All of the above phenomena could be identified by the algorithm, and the boundary between the clear sky area and the cloud area could be clearly identified by the algorithm. According to the true color map, on 9 February 2020, the haze in the eastern coastal areas of China, such as Shandong and Tianjin, was relatively serious, while other areas were basically experiencing the state of light haze. In India, medium haze and light haze appeared simultaneously, and the distribution of clouds, clear skies and haze were also accurately identified. However, it is easy to miss cloud features when a thin or broken cloud covers a clear sky (e.g., South Korea and North Korea), and there is a slight error in distinguishing light haze from clear sky (e.g., Central India).  
In addition, the performance of clear pixels on a brighter surface in 19 October 2020 is further presented as the third row of images in Figure 8. Compared with the true color map, it can be clearly seen that the algorithm can accurately identify the clear sky pixels in high-altitude areas such as Inner Mongolia, northwest China, Afghanistan, Pakistan and  
Figure 8: True color map and corresponding identification map based on MHAM algorithm, these three rows represent the distributions on 2 December 2019, 9 February 2020 and 19 October 2020, respectively.  
the Qinghai-Tibet Plateau, as well as the clear sky in low-value areas of the three provinces of northeastern China. It shows that the variation of 0.865 and 1.64 \(\upmu\)m and NDVI\({}_{\text{swir}}\) introduced in this algorithm are effective.  
Meanwhile, the corresponding operational cloud mask with the same dates as Figure 8 derived from MYD35 and MERSI-II were also presented for a cross-comparison. As depicted in Figure 9, the cloud, probably cloud, clear and probably clear pixels are drawn in yellow, gray, green and aqua, respectively. The recognition values of the clear sky and cloud pixels are basically the same, and the difference is mainly reflected in the processing of possible cloud and possible clear sky pixels. Compared with the classifications in Figure 8, neither the MODIS nor MERSI-II operational cloud mask identified the haze regions that appeared in India and in eastern China. It can be seen that MODIS treated those as probably cloud and probably clear pixels, while the processing conditions of MERSI-II were not as strict as that of MODIS, thus most of the haze pixels were labeled as clear.
## 5 Research Case Study and Simulation Parameters  
The proposed energy system planning and management approach are investigated on an undeserved Marsabit county isolated microgrid in Kenya, which is currently served by conventional diesel-based generators. The goal of this work is to investigate the best flexibility management incorporated hybrid VRE energy supply combination that will completely replace the existing diesel generators considering the cost and reliability criteria that are described above. The hourly meteorological data of the locality (2.3369\({}^{\circ}\) N, 37.9904\({}^{\circ}\) E) was obtained from online sources [55; 56] for 2015 to 2018. The meteorological data set consists of wind speed, wind direction, air pressure, relative humidity, solar irradiance, and the temperature variables. The economic and technical parameters were obtained from [57] through desk research and consultation with energy sector employees and policymakers in the region. Table 1 shows the details of simulation parameters and Table 2 shows the considered self and cross-price elasticity of demand, which is adopted from [52] modified to fit the Kenyan case. The price elasticity of demand entails self and cross-elasticity; the self-elasticity defines the sensitivity of demand with respect to price within the same pricing interval while cross-elasticity (\(PE_{\phi(i,j)}\)) define the load demand sensitivity of the (\(i\)th) pricing period in response to the electricity price variation in the (\(j\)th) pricing period. The cross-elasticity of demand is given by [50];The Kenyan tariff structure of 2018 was obtained from [58; 59]. The current electricity rate of 15.80 US Cents per kWh for ordinary domestic consumers was considered to be the flat rate \(E_{stat}^{pr}\). For this work, the CPP DRP pricing scheme was considered to be 20.00 US Cents per kWh for peak period from 7:00 p.m. to 10:00 p.m. while the rest of the day adopted a flat pricing of 15.80 US Cents per kWh. TADP DRP implemented a time-ahead hourly variable pricing scheme with a maximum and minimum electricity price of 20.00 US Cents per kWh and 10.00 US cents per kWh, respectively. PHES has no geographical limitations [60] and have been found to be a viable ESS technology option for both large and small-scale energy management applications. Its prospects in terms of cost-effectiveness and flexibility provision has also been verified in [48], thus, it has been determined to be one of the most suitable ESS options for application in isolated places such as the Kenyan microgrid case under study. PHES stores electricity as sensible heat in thermally insulated and closed-looped thermal storage systems which ensures that the system is isolated; hence, based on the design aspects outlined in [47], there is a guarantee that the model is feasible for deployment for our case study. The analysis of a proposed commercial PHES design with a maximum capacity of 16 MWh as detailed in [45; 61] has been adopted as the benchmark for many studies in the literature; thus, the system technical and economic specifications are used in our work for the Kenyan microgrid under study.
# Automated Pneumothorax Segmentation with a Spatial Prior Contrast Adapter
## 5 Discussion
### Limitations  
In this study, SPCA-enhanced nnUNet primarily focuses on a single dataset, CANDID-PTX, and its evaluation is limited to pneumothorax on this specific dataset. As such, the model's ability to generalize to other datasets or modalities remains untested. While the model achieves promising results within the context of the chosen dataset, its effectiveness and robustness in handling different types of data or in diverse application scenarios have not been explored. This limitation raises concerns about the model's potential to perform well when applied to other domains or tasks, as the features and patterns inherent in different datasets might vary significantly.  
Additionally, SPCA-enhanced nnUNet has not been evaluated on multimodal data, which could include variations in data types such as images and text. Medical reports generated by physicians are also included in the dataset CANDID-PTX. Many clinical applications involve complex, multimodal inputs, and a model that is restricted to a single modality may struggle to adapt to such environments. A model named ConTEXTual Net was proposed by [PERSON] et al., which uses medical reports from the CANDID-PTX dataset as attention to prompt the location of pneumothorax regions, ultimately improving segmentation accuracy. However, the paper has a logical issue in practical medical applications; in real-world scenarios, we do not have pre-existing medical reports to guide the deep learning model to focus on specific areas. If medical reports were available, there would be no need to perform pneumothorax segmentation [34]. Therefore, future work should aim to test the model across a variety of datasets from different domains and modalities to assess its adaptability and scalability. By doing so, we can better understand its strengths and limitations, ultimately improving its performance and ensuring its applicability to a wider range of practical tasks. Furthermore, incorporating cross-modal data could lead to more comprehensive insights, making the model more versatile and useful in clinical applications.
## 3 Results
### Exposure Findings  
Overall, most of the articles (71%) reported at least one sample had detectable levels of a hazardous drug. When stratified by study type, 78% of case-control studies reported detectable levels, whereas 61% of cross-sectional studies reported detecting a hazardous drug in at least one sample. For those case-control studies which reported positive urinary contamination results, three concluded that their findings were statistically significant [9; 43; 48]. Several others found detectable levels in some case subjects, whereas all of the controls in the study had non-detectable levels of urinary contamination [10; 23; 25; 27; 46; 50].  
There was no apparent correlation between sample size and whether detectable drug levels were reported. However, it bears mentioning that the majority (11/15 or 73%) of those studies which collected 24-h urine voids had at least one urine sample with detectable levels of a hazardous drug. Of note, the two longitudinal studies included in this review reported decreases in the percentage of positive urine samples over time [11; 26].  
It was not possible to compare exposure results between studies, even if they were of the same design, because of different sample collection methods, differences in the analytical limit of detection, and/or different analytes examined. In fact, in their methodologies, [PERSON] et al. [31] and [PERSON] et al. [34] utilized various urine sampling methods _within_ the study itself.  
Urinary contamination was often detected even though control measures were reportedly implemented. For instance, several studies found detectable urinary drug levels despite gloves being worn by workers as personal protection, including [PERSON] et al. [50], [PERSON] et al. [38], [PERSON] et al. [25], and [PERSON] et al. [35] among others. In fact, in the study by [PERSON] et al., a revised standard operating procedure (SOP) was introduced which included the use of gloves, but detectable levels of urinary contamination were found in healthcare workers both before and after the SOP implementation [29]. Furthermore, although the use of a closed-system transfer device reduced healthcare workers' urinary contamination levels, detectable results were still found in two studies [30; 41].
# Intrusion Detection for in-Vehicle Communication Networks: An Unsupervised Kohonen SOM Approach
## 5 Materials and Methods
### Performance Evaluation Metrics  
Traditional classification metrics were used to evaluate the performances of the two explained methods tested on the described datasets.  
In particular, given TP (true positive) and TN (true negative)--the numbers of CAN messages correctly classified, respectively, as attack or normal; and FP (false positive) and FN (false negative)--the numbers of CAN messages incorrectly classified, respectively, as attack or normal, we calculated:  
\[Accuracy\ =\ \frac{TP+TF}{TP+FP+TN+FN}\, \tag{7}\]  
the ratio of correctly classified instances,  
\[Precision\ =\ \frac{TP}{TP+FP}\, \tag{8}\]  
the ratio of correctly detected errors to the total of detected errors,  
\[Recall\ =\ \frac{TP}{TP+FN}\, \tag{9}\]  
the ratio of the correctly detected errors to the total of actual errors including not-detected ones,  
\[F1\ =\ \frac{2\ (Precision\text{-}Recall)}{Precision+Recall}, \tag{10}\]  
a weighted average of precision and recall.  
We also computed the false negative rate (FNR), that is, the fraction of undetected attacks, as-follows:  
\[FNR\ =\ \frac{FN}{TP+FN}. \tag{11}\]  
The FRN measure has great importance in attack detection for in-vehicle CAN buses and its value should be very small since even a very small number of undetected attacks can cause damage in the vehicle, impairing safety.
## 1 Introduction
Therefore, in this work we aim to shed insight on how scaffold manufacturing parameters and flow culturing conditions affect the O2 transport and uptake by the cells in realistic bone tissue engineering \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scaffold Type** & \begin{tabular}{c} **Simulated** \\ **Geometry** \\ \end{tabular} & \begin{tabular}{c} **O2** \\ **Diffusion** \\ \end{tabular} & \
\begin{tabular}{c} **O2** \\ **Convection** \\ \end{tabular} &
\begin{tabular}{c} **O2** \\ **Reaction** \\ \end{tabular} & **Varied Parameter** & **Citation** \\ \hline
4555 Bioglas-PCL. Robcast, & \begin{tabular}{c} Micro-computed \\ **I**onsquency \\ \end{tabular} & Yes & No & No & Void Fraction & \begin{tabular}{c} [PERSON] et \\ al. [12] \\ \end{tabular} \\ \hline Cardiac Tissue Eng. & Idealized & Yes & Yes & No & \begin{tabular}{c} Squeeze Pressure \\ \end{tabular} & \begin{tabular}{c} [PERSON] et \\ \end{tabular} \\ \hline Microchanneled Hydrogel & Idealized & Yes & No & No & \begin{tabular}{c} Microchannel \\ Configuration \\ \end{tabular} & \begin{tabular}{c} [PERSON] \\ et al. [13] \\ \end{tabular} \\ \hline Periodically Self-Repeated & Idealized & Yes & No & Yes & \begin{tabular}{c} Geometry of the \\ Repeating Element \\ \end{tabular} & \begin{tabular}{c} Li et \\ \end{tabular} \\ \hline Bone Tissue Eng. Molded Tantalum & Idealized & Yes & Yes & Yes & Flow rate & \
\begin{tabular}{c} [PERSON] \\ et al. [4] \\ \end{tabular} \\ \hline Homogeneous Porous Medium & Idealized & Yes & Yes & Yes & Flow rate, Porosity &
\begin{tabular}{c} Yan et \\ [15] \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature overview of O2 simulations in tissue engineering scaffolds, shows that image-based simulation of convection with diffusion and reaction has not yet been done. scaffolds. To do this, we use two types of commonly-implemented types: the salt leached foam and the non-woven fiber mesh poly-L-lactic acid (PLLA) scaffolds. Their geometries are scanned in 3D using high resolution micro-computed tomography (\(\mu\)CT), and are imported into our image-based Lattice Boltzmann Method (LBM) flow [16; 17; 18; 19] and reactive Lagrangian Scalar Tracking (rLST) mass transport [20] solvers. A big advantage of the latter is it can model particles with a range of reactivity, which is informative about how cells that are not necessarily starved for O\({}_{2}\) consume it. In this way, a more complete picture of O\({}_{2}\) transport within the different types of BTE scaffolds can be constructed. The overall computational scheme is depicted in Figure 1.
## 2 Methods
### Parameter Selection  
We used in-house Python implementations of the algorithms for all the connectivity measures studied (The \(\text{TE}^{\theta}_{\text{xa}}\) implementation is available at [[https://github.com/ide270](https://github.com/ide270)]([https://github.com/ide270](https://github.com/ide270)) 4/Kernel_Phase_Transfer_Entropy, accessed on 13 July 2021), except for \(\text{TE}^{\theta}_{KSG}\). In that case, we used the implementation provided by the open access toolbox TRENTOOL, a TE estimation and analysis toolbox for Matlab [34].  
Regarding the selection of parameters involved in the different effective connectivity estimation methods, we proceeded as follows: For the TE methods, we estimated all parameters from the real-valued time series, i.e., before extracting the phase time series. The embedding delay \(\tau\) was set to 1 autocorrelation time (ACT), as proposed in [31]. The embedding dimension \(d\) was selected from the range \(d=\{1,2,\ldots,10\}\) using [PERSON]'s criterion [34,53]. Note that for any signal pair, the embedding parameters selected are those of the driven or target time series, i.e., to estimate \(\text{TE}(\mathbf{x}\rightarrow\mathbf{y})\) we use for both time series the embedding parameters found for \(\mathbf{y}\). The interaction delay \(u\) was set as the value generating the largest TE from ranges that varied depending on the experiment: \(u=\{1,2,\ldots,10\}\) for the NMMs, \(u=\{1,4,\ldots,25\}\) for the MI data, and \(u=\{50,60,\ldots,250\}\) for the WM data. Note that the meaning of \(u\) in terms of the time delay of the directed interaction between the driving and driven systems is associated with the sampling frequency, e.g., \(u=\{1,2,\ldots,10\}\) for data sampled at 250 Hz translates to a time range between 4 ms and 40 ms. For \(\text{TE}^{\theta}_{\text{xa}}\) we select a value of \(\alpha=2\), which is neutral to weighting, a convenient choice when there is no previous knowledge about the values of the \(\alpha\) parameter better suited for a particular application [10,28]. In addition, as kernel function, we employ an RBF kernel with Euclidean distance (see Equation (13)). The bandwidth \(\sigma\) was set in each case as the median distance of the data [54]. For \(\text{TE}^{\theta}_{KSG}\) the Theiler correction window and the number of neighbors were left at their default values in TRENTOOL, 4 and 1 ACT,respectively [34]. For the GC methods the order of the autoregressive models \(o\) was selected from the range \(o=\{1,3,\ldots,9\}\) using Akaike information criterion [55; 56]. Furthermore, in order to estimate the PSI we employed a sliding window 5 frequency bins long (3 bins long for the WM data), centered on the frequency of interest. Finally, for all the connectivity methods involving the extraction of phase time series through Morlet wavelets, we varied the parameter \(m\) (see Equation (10)) from 3 to 10 in a logarithmic scale, according to the selected frequency of the filter.
## 2 Materials and Methods  
### Materials  
As mentioned above, the developed self-healing concrete is based on organic microcapsules. For the core material, in this study, bisphenol A type epoxy resin E-51 (Shenzhen Yoshida Chemical Co., Ltd., Shenzhen, China) was adopted for the microcapsule component materials due to its high bond strength, wide adhesion, good stability, low shrinkage of curing products, and good mechanical properties [25]. However, due to its viscosity at room temperature, epoxy resin E-51 is not conducive to the siphon phenomenon when microcapsules rupture in the matrix. Accordingly, it is necessary to add a diluent to reduce the viscosity of the resin E-51, so that the resin glue is more prone to infiltrate the surface of the crack wall and have good compatibility with the epoxy resin E-51. BGE (Shanghai Bangcheng Chemical Co., Ltd., Shanghai, China) is a liquid at room temperature, and its viscosity is very low. Due to the molecular structure of the ether bond and epoxy bond, it can participate in the curing reaction of epoxy resin, therefore BGE was adopted as an active diluent for the epoxy resin.  
BGE was added to the epoxy resin at different mass fractions, and the viscosity was measured after mixing at room temperature (26 \({}^{\circ}\)C) (Figure 1). When the content of BGE was 0-10%, the viscosity of E-51 decreased sharply with the increase of the BGE content. When the content of BGE was 10-20%, the viscosity of E-51 decreased slowly. When the BGE content was greater than 20%, the E-51 viscosity no longer changed. This is because E-51 is an oligomer, which has hydrogen bonds and other active groups on the molecular chain, thus the intermolecular force is relatively strong. The diluent may play a role in the dispersion of E-51 when present at a certain dosage range, at which the intermolecular force is decreased. However, if the diluent content exceeds a certain level, this effect is no longer obvious. The results that are shown in Figure 1 reveal that the suitable content of BGE is between 10 to 20%. The specific content of epoxy resin may be determined through analysis of the curing kinetics.  
For the wall material, as the urea-formaldehyde resin has good mechanical properties, heat resistance, and tightness, water can also be used as a dispersion medium to synthesize the wall material by the solution polymerization method, in this study, urea-formaldehyde resin was used as the wall of microcapsules.  
The epoxy resin as the core material can be converted into a three-dimensional mesh structure only by adding a curing agent. In order to ensure the self-healing effect, the latent curing agent MC120D was selected for this study.  
Figure 1: Effect of the n-butyl glycidyl ether (BGE) diluent on the viscosity of epoxy E-51.  
In the test for the healing agent, the content of MC120D was fixed at 20% of the diluted epoxy resin, and the mass fraction of BGE to epoxy resin was fixed at 10.0%, 12.5%, 15.0%, 17.5%, and 20.0%. After mixing, the mixture was sealed and allowed to stand for one day for the test.
# Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video
## 6 Simulation Results
### Dataset 2
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Adaptation Algorithms** & **Proposed** & **FDASH** & **DBT** & **QLSA** & **DASH-Google** \\ \hline Average Video Rate (kbps) & 1418.92 & 1274.77 & 1229.03 & 1380.94 & 1394.02 \\ Switching Ratio & 0.28 & 0.07 & 0.13 & 0.42 & 0.48 \\ Inefficiency & 0.11 & 0.30 & 0.25 & 0.19 & 0.14 \\ Average of Switches & 504.56 & 583.10 & 427.44 & 494.58 & 383.51 \\ QoE & 1286.39 & 1112.9 & 1093.30 & 1205.33 & 1135.70 \\ MOS & 3.67 & 2.97 & 3.07 & 3.43 & 3.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of algorithms at a buffer size of 15 s and a segment duration of 2 s.  
Figure 10: Comparison of (**a**) rebufferings per client, and average number of interruptions, and (**b**) average buffering time when the clients downloaded Dataset 2. The buffer size and segment duration were set to 15 s and 2 s, respectively.
# A Novel Bi-Dual Inference Approach for Detecting Six-Element Emotions
## 4 Experiment
### Experimental Results and Analysis
#### 4.4.5 \(\alpha\) Parameter Analysis  
In this subsection, we present an experimental discussion and analysis of the parameter \(\alpha\) in Equation (10). The detailed experimental results are illustrated in Figure 10.  
Figure 10a displays the comparison of the \(F1\) scores between our proposed model and other baseline models for the interval \(\alpha\in[0,0.1]\). Similarly, Figure 10b presents the comparison of the \(F1\) scores for the interval \(\alpha\in[0,1]\). It is evident that our proposed model achieves the best performance when \(\alpha=0.02\). While the other models are not significantly affected by the parameter \(\alpha\), as their \(F1\) scores remain relatively stable and unchanged. From Figure 10b, we can conclude that our model outperforms the other baseline models across a wider range of \(\alpha\) values.
## 5 Experimental Analysis and Verification
### Experiments on the RDCA with Noise  
To verify the noise immunity performance of the model, we trained both the three-layer RDCA (RDCA-3) and the three layer regular convolutional auto-encoder (CAE-3) without a denoising strategy. It is to be noted that the reconstructed signal in the decoder is exactly the same as the input for the CAE-3, where the size of the filter is optimally set to \(11\times 11\) for the first convolutional layer and 9 \(\times\) 9 for the rest layers. Further, the depth of the RDCA and CAE in different layers are sequentially 8, 16, and 24, respectively. The input signal SNR range from 10 dB to 30 dB with a sliding stride of 5 dB. The classification result with different layers and noise is depicted in Figure 10, where the error bar denotes the standard deviation.  
It is clear from Figure 10 that the classification accuracy increases as the noise level decreases for both RDCA and CAE. The result is in line with expectations, in which a lower noise level means a cleaner signal. Specifically, when the SNR value is below 20 dB, the advantages of the RDCA-3 are more obvious than the CAE-3. Further, the performance of the RDCA-3 is close to the CAE-3 when the SNR value is higher than 20 dB. Given the difference in reconstruction goals, the classification result indicates that the RDCA has stronger PQD feature extraction capabilities.