esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Gluten Detection in Bread Images Using Convolutional Neural Networks
## 3 Materials and Methods  
### Gluten-Free and Gluten-Containing Bread  
Due to the absence of gluten, gluten-free doughs differ from gluten-based doughs in terms of their structure and properties [35]. Gluten consists primarily of interstitial disordered storage proteins (glutenins and gliadins) that can form networks of megadalton size. These networks are responsible for the unique viscoelastic properties of wheat dough, which affect its elasticity and extensibility [36; 37; 38]. These networks enable the dough to trap gas [38; 39], resulting in a foam structure in which gas cells are separated by a continuous starch-gluten matrix. This structure results in a spongy texture in the bread, resulting in a light and even crumb texture [38; 39; 40; 41]. In contrast, gluten-free doughs, which lack this protein network, are characterized as being less cohesive, less elastic, with a more dense structure [42].  
There is a noticeable difference between images of gluten-containing breads (see Figure 1(1-3)) and gluten-free breads (see Figure 1(4-6)). We can see that the gluten-free bread lacks this cohesive structure, resulting in irregular air bubbles and a less uniform texture compared to gluten-containing breads. Through the use of these imaging techniques, it is possible to effectively distinguish between gluten-based and gluten-free breads based on their visual appearance.
# Feasibility of LED-Assisted CMOS Camera: Contrast Estimation for Laser Tattoo Treatment
## 4 Discussion
Although the current study demonstrated the feasible application of an LED-assisted camera to detect variations in tattoo contrast after laser treatment, experimental limitations still remain prior to clinical translation. As only two types of tattoo colors (black and red) were used for the current study, the proposed technique needs to be examined with various tattoo colors (e.g., blue, green, and yellow) as well as multicolored (mixed colors) tattoos to confirm the diagnostic validation. Owing to the ease of handling, a mouse was selected as the animal model for the proof of concept [5]. However, compared with human skin (dermis of \(\geq\)1 mm), the small animal has typically thinner dermis (\(\leq\)1 mm). Upon laser treatment, tattoo pigments are fragmented and removed from the skin through the lymphatic system or trans-epidermal elimination [2; 13]. Thus, the thinner skin could accelerate the pigment fragmentation owing to the rapid wound healing. To emulate clinical conditions and to validate clinical translations, larger animal models should be tested further with the proposed method. Hence, a contrast scale and a dynamic range for tattoo monitoring need to be validated with other extrinsic factors such as the melanin concentration in the skin, ink concentration, spatial distribution of ink within the tissue, and geometrical conditions of the tissue.
# Safety Analysis and Condition Assessment of Corroded Energy Pipelines under Landslide Disasters
###### Abstract  
Corrosion poses a significant risk to the safety of energy pipelines, while landslide disasters emerge as the primary threat responsible for triggering pipeline failures across mountainous areas. To date, there is limited research focused on the safety of energy pipelines considering the synergistic effect of corrosion and landslides. The present study proposes a finite element (FE)-based model to assess the condition of corroded pipelines under landslides. The effects of corrosion dimensions (length and depth) and location are determined. A novel equation is finally developed to predict the maximum stress and determine the most disadvantageous position for corroded pipelines under various landslide displacements. The results demonstrate that (1) as the landslide progresses, the pipeline's stress significantly increases; (2) corrosion depth has a more significant impact on the pipeline condition than the corrosion length, and it is positively correlated with the pipe's stress; (3) the maximum stress exhibits a nonlinear relationship with the landslide-facing position and the corrosion circumferential location; and (4) when the axial position of the corrosion is more than 6.5 m away from the center of the landslide, the location of maximum stress shifts from the corrosion region to the central section of the pipeline within the landslide. This work contributes to helping pipeline owners to understand the applicability of energy pipelines subjected to the combined effects of corrosion and landslides and provides support for future risk assessment efforts in pipeline integrity management.  
energy pipelines; corrosion; landslides; safety analysis; condition assessment Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], Y. Safety Analysis and Condition Assessment of Corroded Energy Pipelines under Landslide Disasters. _Appl. Sci._**2023**, _13_, 12880. [[https://doi.org/10.3390/appl3212880](https://doi.org/10.3390/appl3212880)]([https://doi.org/10.3390/appl3212880](https://doi.org/10.3390/appl3212880))
# GNSS-Based Vertically Monitoring of Super-Tall Buildings
## 4 Simulation
Finally, based on the above static baseline vectors and the covariance matrix from the three reference stations, we calculate the reference points' coordinates with a minimally constrained least-squares adjustment. We can fix the reference points' coordinates as a constraint and then process \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**XYZ Directions**} & \multicolumn{3}{c}{**\(xyH\) Directions**} \\ \hline  & \(w_{X}\) & \(w_{Y}\) & \(w_{Z}\) & \(w_{x}\) & \(w_{y}\) & \(w_{H}\) \\ \hline \(<\)1 cm & 71.38 & 39.51 & 76.79 & 94.51 & 70.99 & 38.23 \\
1–2 cm & 26.26 & 31.34 & 20.27 & 5.35 & 26.39 & 30.18 \\
2–3 cm & 2.21 & 17.96 & 2.78 & 0.13 & 2.49 & 18.02 \\
3–4 cm & 0.14 & 7.63 & 0.12 & 0.01 & 0.13 & 8.50 \\
4–5.2 cm & 0.01 & 2.30 & 0.02 & 0 & 0 & 3.16 \\ \(\geq\)5.2 cm & 0 & 1.26 & 0.02 & 0 & 0 & 1.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics of misclosures of the spatial GNSS monitoring network (%). Figure 4: GNSS monitoring network in simulation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Before Processing**} & \multicolumn{3}{c}{**After Processing**} \\ \hline  & \(V_{\Delta X}\) & \(V_{\Delta Y}\) & \(V_{\Delta Z}\) & \(V_{\Delta X}\) & \(V_{\Delta Y}\) & \(V_{\Delta Z}\) \\ \hline \(<\)1 cm & 93.79 & 64.38 & 93.09 & 94.19 & 65.27 & 94.20 \\
1–2 cm & 6.16 & 27.37 & 6.62 & 5.78 & 28.08 & 5.76 \\
2–3 cm & 0.05 & 6.54 & 0.29 & 0.03 & 6.65 & 0.04 \\ \(\geq\)3 cm & 0 & 1.71 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of baseline vectors’ corrections of the spatial GNSS monitoring network (%). the GNSS monitoring network with a least-squares adjustment at each epoch. In order to analyze the differences in position of the monitoring point in the horizontal and vertical directions, we transform the coordinate time series of the monitoring point from the WGS84 system(\(X\),\(Y\),\(Z\)) to the Gaussian coordinate system(\(x\),\(y\),\(H\)). Table 4 lists the statistics of the differences in position with respect to the coordinate time series of the monitoring point as shown in Figure 5 in the \(x\), \(y\), and \(H\) directions without and with processing. Referring to the statistics listed in Table 4, one may see that the gross errors of baseline vectors that more significantly affect the \(H\) direction are almost eliminated. After processing, the differences of all \(x\) coordinates and 95.36% of the \(y\) coordinates are within \(\pm\)2.0 cm, while the differences of 94.18% of the \(H\) coordinates are within \(\pm\)3.0 cm. In addition, the STD values of differences in position with respect to the monitoring coordinates are \(\pm\)2.9, \(\pm\)4.6, and \(\pm\)10.1 mm in the \(x\), \(y\), and \(H\) directions, respectively. It can be seen that the monitoring result is quite similar to the standard precision of GNSS receivers.
## 1 Introduction  
Taste and odor (T & O) problems mainly caused by secondary metabolites of microorganisms in aquatic ecosystems have been frequently reported worldwide [1; 2; 3; 4]. Geosmin (trans-1, 10-dimethyl-trans-9-decalol), a sesquiterpene derivative with an earthy/musty smell, is a common odor compound in surface water. Since its discovery in _Streptomyces griseus_ LP-16 [5], geosmin has been regarded to cause frequent T & O incidents in water supplies, because of its strong earthy smell and extraordinarily low sensory threshold of 4-20 ng\(\cdot\)L\({}^{-1}\)[6]. Cyanobacteria is the main phytoplankton group in many eutrophic waters; members of this group, along with myxobacteria, actinomycetes and fungi, are the principal producers of geosmin [4; 7; 8].  
A 726-amino acid cyclase encoded by _sco6073_ in _Streptomyces coelicolor_ A3 (2) catalyzes the Mg\({}^{2+}\)-dependent cyclization of farnesyl diphosphate (FPP) to geosmin [9]. According to [PERSON] _et al_. [10], this type of cyclase is a bifunctional enzyme composed of two similar domains at the N-terminal and C-terminal positions, and FPP is converted to geosmin through the catalysis of these two domains. In cyanobacteria, two putative geosmin synthase genes (_geoA1_ and _geoA2_) homologous to _sco6073_ were identified from the geosmin-producing cyanobacterium, _Phormidium_ sp., by using degenerating primers for PCR and reverse PCR [11]. [PERSON] _et al_. [12] and [PERSON] _et al_. [13] cloned and successfully expressed the germacrene/germacradienol and geosmin synthase genes (_npunmod_) from the geosmin-producing cyanobacterium, _Nostoc punctiforme_ PCC 73102, in _Escherichia coli_. Apart from the benthic/periphytic groups represented above, planktonic (bloom-forming) cyanobacterium species are also likely to produce geosmin [2; 7]. However, only [PERSON] _et al_. [14] have analyzed the expression of geosmin synthase in _Anabaena circinalis_. Further in-depth studies on the genetics of geosmin synthesis from bloom-forming cyanobacteria have yet to be conducted.  
Water blooms have become a frequent occurrence in Chinese waters, particularly in large shallow lakes. T & O episodes that result from these water blooms have become a major environmental problem, with the worst case recorded in Lake Taihu in May, 2007 [15]. Two plateau lakes (_i.e._, Bianchi and Erhai) in Yunnan Province have experienced severe odor problems caused by water blooms in the past decade [3]. Our previous work found that the planktonic heterocystous species, _Anabaena_ _ucrainica_ (Schhorb.) [PERSON], is involved in the odor problems of these two lakes (unpublished data). In the present study, two strains of _A.__ucrainica_ with high geosmin productivities were isolated from Bianchi and Erhai lakes and used to investigate the genes responsible for geosmin synthesis. The present study aimed to identify and characterize geosmin synthase genes from the bloom-forming _A.__ucrainica_ strains, to explore whether or not other genes are structurally or functionally related to this gene and to preliminarily investigate the origin and evolution of these genes.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Designing an Energy-Conscious Algorithm
#### 5.1.4 Parameter Quantization
In another approach, an integer Haar wavelet transform (IHT) was used by the author of [79] for denoising the ECG signal. This works by using integer coefficients, thereby eliminating FP operations. Some other authors [83; 87; 88; 89; 90; 91] also replaced the FP operation with fixed-point or integer operations to reduce resource consumption, computational load, and power consumption. In contrast, the authors of [92; 93] kept the FP arithmetic to maintain the dynamic properties of the ECG signal in their ECG denoising system. However, to reduce resource consumption and the off-chip bandwidth, they used half-precision (16 bit) FP instead of single-precision (32 bit) FP. Quantization significantly reduces the size of a model by using lower-precision arithmetic operations, which are faster and consume less power. This process is crucial for deploying complex models on wearable devices, which are constrained by limited resources and battery life. FPGAs can be programmed to use any bit width for their operations, allowing for more precise control over the quantization process.
# Turn-to-Turn Fault Diagnosis on Three-Phase Power Transformer Using Hybrid Detection Algorithm
## 4 Simulation and Comparative Analysis
### External Fault Scheme  
In this scheme, the external fault happened at t = 3 s in the secondary side phase A. The external fault was simulated with a short line between the line-to-line and line-to-ground in the secondary side. The resistor was used to mimic the short circuit in the simulation, with several values (50, 100, 500, 600, 700, and 800 ohms). The external fault condition created a massive differential current. The primary and secondary current on the terminal voltage (see in Figure 16) showed a significant difference before and after the fault. The conventional differential protection detected the external fault because the differential current between the primary and secondary sides exceeded the limit as shown in Figure 17. The phase difference between the primary and secondary negative sequence current also stayed above 85\({}^{\circ}\).The external fault scheme, the negative sequence current from the primary (Ip2) and secondary (Is2) side, rose significantly. The phase difference between both sides stabilized at 118\({}^{\circ}\) (see in Figure 18). Based on that, the directional algorithm was declared an external fault condition.  
Figure 16: The fundamental signal under the external fault.  
Figure 17: The negative sequence current from the primary and secondary side under an external fault.  
The EPVA and differential negative-sequence algorithm were unable to distinguish between a normal and an external fault. When an external fault happened, iepva, the differential negative sequence current did not increase or exceed the threshold value. Therefore, the transformer condition was declared as a non-internal fault. However, this condition also occurred at normal conditions, as shown in Figures 19 and 20. The fuzzy algorithm generated a crisp external value more significant than the crisp internal value. The internal and external fault crisp values were 15.26 and 7918, respectively like shown in Figure 21. Therefore, the fuzzy algorithm generated the external fault status.  
Figure 21: The internal and external crisp value under an external fault.  
Figure 19: iepva under an external fault.  
Figure 20: The differential negative sequence current under an external fault.  
This limitation can be overcome by the negative-sequence directional algorithm, conventional differential protection, and fuzzy algorithm, where three sub algorithms detect the external fault. Therefore, the hybrid detection algorithm chooses an external fault as the result. The hybrid detection algorithm generated the correct result about the transformer condition in the external fault scheme (see in Figure 22). The summary of the results is shown as either the sub algorithms or the hybrid algorithm that declared the external fault status at the PT (see in Figure 23).  
Figure 23: A summary of the results from the external fault scheme.  
Figure 22: The hybrid algorithm result in the external fault scheme.
# Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification
###### Abstract  
The abnormal conduction of cardiac activity in the lower chamber of the heart (ventricular) can cause cardiac diseases and sometimes leads to sudden death. In this paper, the author proposed the Reservoir Computing (RC) based Echo State Networks (ESNs) for ventricular heartbeat classification based on a single Electrocardiogram (ECG) lead. The Association for the Advancement of Medical Instrumentation (AAMI) standards were used to preprocesses the standardized diagnostic tool (ECG signals) based on the interpatient scheme. Despite the extensive efforts and notable experiments that have been done on machine learning techniques for heartbeat classification, ESNs are yet to be considered for heartbeat classification as a is fast, scalable, and reliable approach for real-time scenarios. Our proposed method was especially designed for Medical Internet of Things (MIoT) devices, for instance wearable wireless devices for ECG monitoring or ventricular heart beat detection systems and so on. The experiments were conducted on two public datasets, namely AHA and MIT-BIH-SVDM. The performance of the proposed model was evaluated using the MIT-BIH-AR dataset and it achieved remarkable results. The positive predictive value and sensitivity are 98.98% and 98.98%, respectively for the modified lead II (MLII) and 98.96% and 97.95 for the V1 lead, respectively. However, the experimental results of the state-of-the-art approaches, namely the patient-adaptable method, improved generalization, and the multiview learning approach obtained 92.8%, 87.0%, and 98.0% positive predictive values, respectively. These obtained results of the existing studies exemplify that the performance of this method achieved higher accuracy. We believe that the improved classification accuracy opens up the possibility for implementation of this methodology in Medical Internet of Things (MIoT) devices in order to bring improvements in e-health systems.  
reservoir computing; echo state networks; bio signals, bio sensors; heart beat classification; medical internet of things; medical wearable wireless devices +
Footnote †: journal: Medical Internet of Things (MIoT) devices
# Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors
## 2 Literature Review
### Machine Learning in WSNs  
A refined version of the Levenberg-Marquardt neural network [7] was proposed to improve energy efficiency by finding anomalies in WSNs. The work was also equipped with LEACH and energy-efficient sensor routing for making a reliable network connection between the source and the destination. The experimental work indicated a better performance with the model included with the machine learning approach over the regular methods [7]. An energy-efficient machine learning-based regression algorithm was included with the fuzzy based intelligent routing approach [8] for improving the energy efficiency in WSNs. A fine cluster head was identified in the work, through a fuzzy interference system using the hop-count method. The experimental analysis indicated a betterment in terms of packet delivery ratio and throughput with energy efficiency [8]. A reinforcement-learning-based multi-objective routing approach [9] was proposed with dynamic objective selection algorithm for optimizing energy consumption in IoT networks. The algorithm utilized informative-shaped rewards with correlated objectives for an efficient learning process. The work indicated an excellent delivery ratio along with a perfect data delivery latency [9].  
An artificial-neural-network-based approach was implemented [10] to select a better cluster head from the connected WSNs. The artificial neural network algorithm was equipped to analyze residual energy, number of nearby nodes, and distance to the base station for selecting the best cluster head. The algorithm was also included with energy-efficient clustering algorithm for minimizing the energy consumption. It was achieved by switching OFF the cluster heads and their corresponding nodes, when there is no signal received for transmission, for a certain duration. The experimental work indicated a betterment in terms of energy enhancement and network lifetime over the traditional methods [10]. An enhanced energy optimization approach was structured with a machine learning technique [11] to improve the energy efficiency in industrial wireless sensor networks. A knowledge-based learning technique was included in the work to detect the sensor node, one that can deliver the data information with minimum energy usage. The model also had a feedback control system for estimating the best routing process in the network. Hence, the work reduced the network traffic and improved the transmission energy consumption to 64.72% when compared to the regular methods [11]. A multi-agent-based reinforcement learning approach was designed [12] to predict best energy efficient routing in WSNs. The work reduced the transmission delay and thus improved the average latency and network lifetime [12]. A novel self-driven continual learning framework [13] was designed for motor fault diagnosis. The model can detect faults in WSN by making considerable customizations [13].
## 2 Subjects and Methods
### Statistical Analysis  
Power analysis was set as follows: power > 0.80 (beta < 0.2) for alpha < 0.05 and to test whether the analysis was not underpowered. ANCOVA model consisting of Subject factor (explaining the inter-individual variability), between subject factors Sex (males vs. females), Group (experimental vs. control), within-subject factor Stage (after intervention (Stage 2) vs. before intervention (Stage 1), covariate Age and a full set of between-factor interactions was used. For post hoc analysis, the least significant multiple comparisons were used. To eliminate skewed data distribution and heteroscedasticity, the original data were transformed to attain symmetric distribution in dependent variables and, at the same time, to stabilize the variance (attaining homoscedasticity), by a power transformation, as described in detail previously [35]. After performing the statistical tests, the data were then retransformed into the original scale using a recurrent formula. A robust Mann-Whitney test was used to evaluate the differences between the experimental and control groups at the beginning of the trial. The NCSS 12 statistical software from (Number Cruncher Statistical Systems, Kaysville, UT, USA) was used for the Mann-Whitney test and power analysis. Power transformations were accomplished using the statistical software Statgraphics Centurion 18 from Statgraphics Technologies, Inc. (The Plains, VA, USA) and ANCOVA testing was performed with the use of jamovi statistical software (The jamovi project (2021). jamovi. (Version 2.2) (Computer Software). Retrieved from [[https://www.jamovi.org](https://www.jamovi.org)]([https://www.jamovi.org](https://www.jamovi.org)) accessed on 20 March 2022).