esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The electromagnetic data using the geomagnetic vertical intensity polarization method still contain annual variations, monthly variations, and semimonthly variations, making the true high values of electromagnetic radiation submerged by noise or appear only under specific conditions or excited by external factors. In addition to the electromagnetic signals [33; 34], various forms of energy can be used as precursors to regular earthquakes for short-term forecasting, such as the acoustic (e.g., [35; 36; 37; 38]) and neutron emission (e.g., [39; 40; 41; 42]). The geological structure and acoustic emission (AE) can imply earthquake forecasting, for example, a large abrupt increase in the AE signals was observed at the Assisi earthquake [43]. The neutron flux variations can correspond to seismic activity in the Pamir region [44], and the relationship between neutron flux variations and the processes of the Earth's crust can be used for the short-term prediction and monitoring of earthquakes (e.g., [41]). Furthermore, the geothermal variation near the surface was suggested to greatly affect the geomagnetic anomalies and indicates a potential for achieving specific understanding in earthquake prediction (e.g., [45; 46]). Considering the spatial gradient of thermal sources beneath the active tectonic settings, geothermal variation including Joule heat is a candidate to explain the spatiotemporal distribution of geomagnetic changes and the time delay of the earthquake precursors (e.g., [47]). These approaches, from acoustic and neutron emission to the shallow geothermal investigation, are also important for understanding the observed earthquake precursors in Qinghai China, and these concerns and the potential mechanism (e.g., [48, 49]) are left open for future multidisciplinary studies.
# An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts
## 3 Methodology  
### Backpropagation Neural Network (BPNN)  
BPNN is a multi-layer feedforward neural network proposed in 1986 [1]. Its main structure comprises an input layer, one or more hidden layers and an output layer. The training process of BPNN includes two processes: signal forward propagation and error backpropagation. For the forward direction, the signal from the input layer is transmitted to the output layer through the hidden layer. If the actual output is different from the expected output, the error between the actual output value and the expected output value will be propagated back. Neural networks modify the weights of neurons by the sum of the squares of gradient descent on the errors. Training and adjustments are repeated in this way until the error between the actual output and the expected output reaches the minimum and is acceptable. After reviewing the previous study, BPNN has been used in many fields, including the path recognition of automated guided vehicles [38], air quality prediction and assessment [39], and stability analysis of underground mine hard rock pillars [40].
# A Small Implantable Compact Antenna for Wireless Telemetry Applied to Wireless Body Area Networks
###### Abstract  
WIBANs are human-centric wireless networks, and implantable antennas represent a vital communication component within WBANs. The dielectric properties of human tissue are highly complex, with each layer exhibiting distinct dielectric constants that significantly influence the performance of implanted antennas. It is therefore imperative that a compact broadband implantable antenna be designed in order to address the instability in communication of medical implant devices. The antenna, coated in silicone, is a single-layer structure fed by a coaxial cable, with a volume of just 6 mm \(\times\) 6 mm\(\times\) 0.53 mm. A metallic patch is etched on the upper surface of the substrate, and the compact antenna design is enhanced with the introduction of S-shaped, F-shaped, and rectangular slots on the patch. The bottom side of the substrate is etched with rectangular ground planes, which broaden the impedance bandwidth of the antenna. The simulation results demonstrate that the antenna attains an impedance bandwidth of 23.8% (2.08-2.64 GHz), encompassing the entirety of the Industrial, Scientific, and Medical (ISM) band (2.4-2.48 GHz). In order to simulate the working environment of the antenna within the human body, physical tests were conducted on the antenna in pork tissue. The test results demonstrate that the antenna exhibits a measured bandwidth of 28% (2.3-3.03 GHz), with a radiation pattern that displays omnidirectional radiation characteristics. The antenna's impedance matching and radiation characteristics remain essentially consistent in both bent and unbent states, indicating structural robustness. In comparison to other implantable antennas, this antenna displays a wider impedance bandwidth, a lower Specific Absorption Rate (SAR), and superior implant performance.  
implantable antenna; compactification; broadband; robust characteristics +
Footnote †: journal: Applied sciences  
_Article_  
A Small Implantable Compact Antenna for Wireless Telemetry Applied to Wireless Body Area Networks.
## 2 Distribution in Nature
### Aquatic Animals  
Endocellular symbiotic bacteria have been proposed as a possible source of eukaryotic TTX by means of an exogenous pathway. Many of the TTX-containing marine species such as pufferfish [41] and xanthid crabs [14] have been found to harbour TTX producing bacteria within their microbiome as determined by chemical analysis and toxicity assay of media inoculated with isolated bacteria [41; 42]. Such TTX-producing bacteria include species identified from the following genera: _Actinomyces_, _Aeromonas_, _Alteromonas_, _Bacillus_, _Pseudomonas_ and _Vibrio_. Some of them were isolated in particular from a determined specie; for instance, _Vibrio alginolyticus_ was isolated from starfish, _Vibrio_ spp. and _Aeromonas_ from the pufferfish and _Vibrio_ and _Pseudomonas_ spp. from gastropods [34]. Both _Bacillus_ and _Pseudomonas_ species from the venom-producing posterior salivary gland of blue-ringed octopus were found to produce TTX as confirmed by mouse toxicity assay [43].  
The statement that TTX has an exogenous origin in fresh water and marine organisms is supported by several studies. An exogenous origin for TTX is suspected for certain toxic crabs which feed on small gastropods known to contain TTX and on marine sediments containing TTX-positivebacteria. Therefore, it is assumed that crabs obtain TTX from the food chain. Toxicity in snails, on which crabs feed, suggests that there is a regional variation that subsequently correlates with the toxicity of crabs. This would imply an exogenous origin of TTX in both snails and the crabs, higher up the food chain [41].  
In a recent report, samples of the grey side-gilled sea slug _Pleurobranchaaea maculata_ were collected from 10 populations around New Zealand and TTX levels assessed using liquid chromatography-mass spectrometry (LC-MS). This study shows that the occurrence of TTX may involve symbiotic TTX-producing bacteria, though the possibility that TTX is accumulated through the food chain or produced endogenously cannot be fully excluded [40]. These sea slugs become non-toxic when they are fed on a TTX-free diet [41]. A small gastropod, _Umberium suturale_, a known progenitor of TTX and anhydroTTX, was isolated from the digestive gland of starfish suggesting an exogenous origin for TTX in this starfish.  
Nevertheless, an evidence for a TTX endogenous origin for gastropods is that TTX was higher in the muscle than in the digestive gland of the snail _Natica lineata_ and the edible gastropod, _Polinices didyma_. Of course, in the case of the gastropods, it is possible that after initial ingestion of TTX, the toxin could have then migrated and remained in tissue compartments outside of the digestive region [34].
## 3 Results and Discussion
### Identification of Aroma-Active Compounds in WRTs with Different Cultivars
Overall, the AIs of the aroma-active compounds ranged from 20\(-\)3.4 (DHP), 2.3\(-\)3.4 (RG), 1.0\(-\)3.0 (SX) and 1.5\(-\)3.3 (JF) in class A; 2.0\(-\)3.3 (DHP), 2.3\(-\)3.3 (RG), 1.8\(-\)3.3 (SX), and 2.0\(-\)3.4 (JF) in class B; 2.3\(-\)3.3 (DHP), 2.4\(-\)3.0 (RG), 2.4\(-\)3.3 (SX), and 2.0\(-\)3.2 (JF) in class C; 2.0\(-\)3.4 (DHP), 2.4\(-\)3.2 (RG), 2.0\(-\)3.2 (SX), and 2.3\(-\)3.2 (JF) in class D; 2.0\(-\)3.0 (DHP), 2.0\(-\)2.8 (RG), 2.0\(-\)3.0 (SX), and 1.7\(-\)3.0 (JF) in class E; 2.0\(-\)2.8 (DHP), 2.0\(-\)3.3 (RG), 2.3\(-\)3.7 (SX), and 2.2\(-\)3.0 (JF) in class F. Furthermore, the summarized AI values of the above six odor classes for different WRTs were presented as radar graphs (Figure 4), and four similar aroma profiles were observed with the predominant performances in \"floral & fruity\" (42.0\(-\)49.1 of total AI values) and \"green & fresh\" attributes (43.2\(-\)46.0 of total AI values), moderate performance in \"roasted and caramel\" (21.8\(-\)26.4 of total AI values), and nearly equal and lower proportions in other attributes (9.6\(-\)16.8 of total AI values). Specifically, the AIs of the \"floral & fruity\" attribute of RG and JF were obviously higher than those of DHP and SX, while the opposite appearance was observed for the \"roasted and caramel\" attribute. The highest AI values of \"floral & fruity\" (49.1) and \"green & fresh\" (46.0) were exhibited by the RG sample. Additionally, the lowest AI value of the \"green & fresh\" attribute was obtained in JF (43.2), while its \"sweet\" attribute (16.8) was significantly higher than in other types of WRTs. For details, benzeneacetaldehyde (3.4, class A), methyl salicylate (3.3, class B), 2-methyl-3,5-diethylpyrazine (3.3, class C), acetophenone (3.4, class D) in DHP; phenethyl acetate (3.4, class A), _trans_-alloocimene (3.3, class B), acetophenone (3.2, class D), and (_E_,_E_)-2,4-heptalienal (3.3, class F) in RG; (_E_,_Z_)-2,6-nonadienal (3.3, class B), 2-methyl-3,5-diethylpyrazine (3.3, class C), acetophenone (3.2, class D), (_E_,_E_)-2,4-heptalienal (3.7, class F) in SX; _trans_-\(\beta\)-ocimene (3.3, class A), linalool (3.3, class A), _trans_-alloocimene (3.4, class B), 2-acetylpyrrole (3.2, class C), and acetophenone (3.2, class D) in JF presented superior (>3.0) and the highest intensities in the corresponding WRT samples. By comparing the intensities of the aroma-active compounds in WRTs with different cultivars, methyl octanoate (3.0\(-\)3.2), _trans_-linal oxide (furanoid) (3.0\(-\)3.2), benzeneacetaldehyde (3.0\(-\)3.4), _trans_-\(\beta\)-ocimene (3.0\(-\)3.3), _trans_-alloocimene (3.2\(-\)3.4), 2-acetylpyrrole (3.0\(-\)3.2), and acetophenone (3.2\(-\)3.4) generally had strong aroma intensities (AI \(\geq\) 3.0), but there were no significant differences among the samples, which may be due to the common and basic aroma-active components of WRTs. The above compounds also play important roles in the aroma formation of most kinds of teas, especially in black and  
\begin{table}
# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
## 3 Methodology
### Model Development
#### 3.7.1 Cross-Validation for Robust Model Assessment  
A detailed fivefold cross-validation process was rigorously applied to test the effectiveness and generalizability of our models across diverse data subsets. This method not only confirmed the models' robustness but also ensured their reliability for practical applications. The choice of fivefold cross-validation, specifically, was driven by its ability to provide a balanced assessment, reducing both variance and bias. Each fold significantly contributed to tuning the model parameters and selecting the most robust model, addressing potential data variability and imbalance. This approach maximized the use of the dataset, ensuring that each data point was utilized in both training and validation, which minimized biases in model evaluation and enhanced the findings' applicability and generalizability.
## 5 Experimental Evaluation
### Experimental Setup
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Settings** \\ \hline \multicolumn{3}{c}{Simulation Environment} \\ \hline Operation system & Ubuntu 18.04 (64 bit Linux) \\ Programming language & Python 3.8 \\ Framework for model design & PyTorch 2.0.1 \\ Network design emulator & Mininet 3.6.5 \\ \hline \multicolumn{3}{c}{Network Emulator} \\ \hline Network topology & Mesh network of switches \\ IP suite & Transmission control protocol (TCP) \\ Software switch type & Open vSwitch 2.9.8 \\ SDN-based controller & OVS-controller \\ SDN controller protocol & OpenFlow \\ Link delay & Shortest route 1.6 ms \\ Link bandwidth & 20\(\sim\)100 Mbps \\ \hline \multicolumn{3}{c}{UA-FedRL} \\ \hline Learning rate (\(\alpha\)) & \(0.1,0.5,0.9\) \\ Discount factor (\(\gamma\)) & \(0.1,0.5,0.9\) \\ Episodes (\(T\)) & \(20,100\) \\ \hline \multicolumn{3}{c}{CNN} \\ \hline Convolution layer & 2 \\ Kernel size & 5 \\ Optimizer & SGD \\ Learning rate & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of experimental parameters and simulation settings used in this study. Figure 5: Hardware specifications of the selected IoT devices used in this study. side to further process the model parameters. The overall data flow of the implemented communication simulator between the client and server can be seen in Figure 6.
## 4 Experiments and Results  
### Training Experiment Setup  
The current work employs Gazebo as the simulation environment for training and validation, within which a robot, \"Turtlebot3 Waffle\", is selected as the experimental robot. The selected robot is equipped with encoders, an inertial measurement unit, and a laser radar, for which the parameters are shown in Table 1. The laser radar provides 24 equally angular spaced scan data, which cover an angular range of (\(-90^{\circ}\), \(90^{\circ}\)) and a distance range of (0.12 m, 3.0 m). In this study, the authors successfully implemented the proposed algorithm by coding. Furthermore, the improved DDPG and D* algorithms were integrated using the move_base package in the Robot Operating System (ROS). The training was conducted on a platform equipped with an NVIDIA GTX 2080 Ti, using the Adam optimizer with a learning rate set to 0.0001.
# Evaluation of Satellite Imagery for Monitoring Pacific Walruses at a Large Coastal Haulout
Prior to initiating the study, we consulted with the Eskimo Walrus Commission [26], an Alaska Native co-management group that represents coastal communities with cultures that rely on walruses, to ensure that our methods would not adversely affect conservation efforts nor infringe on the privacy of local residents.
## 6 Field Industrial Test
### Observation Result
#### 6.3.2 Observation Results of Roadway Deformation  
Figures 13 and 14 show the surface position-shift curves of 3203 track channeling and transport channeling at measuring points No. 21 and No. 40, respectively. As can be seen from the figure, the deformation of the surrounding rock of the roadway increases slowly at first and then rapidly as the working face approaches the roadway. By analyzing the monitoring results of all stations, it can be concluded that the total deformation law of the channeling at the side of the lane-retention side is as follows: during the observation period, the displacement of the top and floor is about 85-400 mm, with an average of 264 mm, and the displacement of the floor is about 80%. The proximity between the two sides is about 70-545 mm, with an average of 328 mm. The total deformation law of the longitudinal trough at the side of the wide coal pillar is as follows: during the observation period, the displacement of the top and bottom is about 160-250 mm, with an average of 240 mm, and the displacement of the bottom is about 80%. The proximity between the two sides is about 20-220 mm, with an average of 141 mm. A large number of scholars have analyzed and concluded that the deformation of goaf roadway under the same geological conditions is much greater than that of roadway with wide coal pillar. Through the analysis of the top and floor displacement of the two longitudinal trays, it can be seen that the deformation control effect of track longitudinal trays on the side of flexible-mold concrete wall is close to that of transport longitudinal trays on the side of 50 m wide coal pillar after top cutting and pressure relief, and the deformation control effect of both sides is relatively obvious. By comparing the displacement convergence of the gob-side entry without roof cutting (as shown in Figure 2) with that after roof cutting, it can be seen that the average convergence of the two sidewalls decreased by 24.0% after roof cutting, and the average convergence of the roof and floor decreased by 27.0%. Combined with the previous numerical simulation  
Figure 12: Distribution diagram of resistance between hydraulic supports at both ends of the working face.  
results, roof cutting effectively reduces the deformation and failure of the surrounding rock on the gob-side-entry roadway.  
Figure 15 shows the deformation of the surrounding rock in the advance section of the channeling of track 3203 at the lane retaining side after top cutting and pressure relief. It can be seen from the figure that the deformation control effect of surrounding rock in the channeling of track is good.
# An Overview of GIS-Based Assessment and Mapping of Mining-Induced Subsidence
## 3 Mine Subsidence Susceptibility/Hazard Assessment and Mapping
### Literature Review According to Analytic Methods and Approaches
#### Comparative Studies  
Some comparative studies have been reported in the assessment of subsidence susceptibility. [PERSON] et al. [32] and [PERSON] et al. [33] developed software called ArcMine as an extension toolbar in the ArcMap program that enables users to select up to 15 contributing factors as well as to select one to all methods of FR, FMFs, and the analytic hierarchy process (AHP) technique for comparative analysis and visualization of the mining subsidence susceptibility in a GIS environment. As a case study, the prediction accuracies of the two models were compared, such as the fuzzy model and fuzzy AHP model. The validation result showed that the fuzzy AHP model showed a higher prediction accuracy than the fuzzy model.  
[PERSON] et al. [34] assessed and compared ground subsidence hazards near an abandoned underground coal mine in Korea using FR and logistic regression (LR) models. This study compared the characteristics of the two models in the data processing step. The FR model is simple, and the application procedure is easy to understand. There is no need to convert attribute values to another format because the FR value can be used as a rating. Conversely, the LR model requires data to be converted to ASCII format for use in the statistical package and later reconverted to be incorporated into the GIS database. The verification results showed that the LR model (95.01%) had a better prediction accuracy than the FR model (93.29%) in the study area. The validation results revealed that both techniques showed a high predictive performance. Unfortunately, this study did not provide an interpretation of why the LR model showed a higher prediction accuracy than the FR model. [PERSON] et al. [35] applied two methods (FR vs. FR-AHP) to generate subsidence hazard maps and compared two maps to determine the most accurate subsidence hazard map, which is one of the elements needed to compile a subsidence risk map. The CFD-AUC validation technique revealed that the FR model showed a higher prediction accuracy than the FR-AHP integrated model. Hence, the FR model-based subsidence hazard map was selected as one of the elements to map subsidence risk.  
In terms of the four machine learning algorithms compared by [PERSON] et al. [36] in predicting subsidence susceptibility, the ROC-AUC validation technique revealed that the BLR model produced a higher prediction accuracy compared to other applied models, even though the other models also had reasonable results. Similarly, [PERSON] et al. [37] compared the predictive capabilities of different models in generating mining-induced subsidence susceptibility maps. The ROC-AUC validation technique revealed that the map generated by the logit boost model, one of the meta-ensemble machine learning models, showed the highest prediction accuracy (91.44%). This was followed by the logistic (88.92%), multilayer perceptron (86.76%), Bayes net (86.42%), and naive Bayes (85.39%) models.
###### Abstract
As a result, it suppresses proliferation of cells and favors in vitro and in vivo apoptosis [11]. Another RAS family inhibitor is farnesylthiosalicylic acid (FTS; salirasib), which functions as a mimic of the C-terminal S-farnesyl cysteine modification of Ras. As a result, it decreases plasma membrane association, reduces RAS-GTP levels and protein stability, and lowers effector signaling [7]. Another approach to the inhibition of RAS function is blocking RAS-effector interaction. [PERSON] et al. used the hydrazinescardioamide (Kobe065) to inhibit interaction between RAS-GTP and the RAS binding domain of Raf-1 with a Ki = 46 \(\mu\)M. In vitro models showed it decreased the phosphorylation of downstream proteins in the RAS pathway (i.e., MEK, ERK, and AKT) and suppressed the allosteric RAS binding site of Sos [12]. However, until now, only one RAS GTPase family inhibitor, sotorasib, has been granted accelerated approval by the Food and Drug Administration (FDA) for use in adult patients with _KRAS G12C_-mutated locally advanced or metastatic NSCLC [13]. Given the important role of RAS proteins in the development of lung cancer, it is important to better understand RAS-targeted therapies. Therefore, the present study investigates the in vitro effects of possible RAS inhibitors known to disturb RAS oncogenic signaling on NSCLC. Although some studies have evaluated the effects of deltarasin, FTS and Kobe0065 on cell proliferation in NSCLC [14, 15, 16], they have not examined its effect on _KRAS_ and _HRAS_ gene expression. The study first performs a bioinformatic analysis of _KRAS_ and _HRAS_ gene expression levels in NSCLC and evaluates the prognostic and therapeutic significance of these genes; it then examines the impact of three selected chemical compounds (deltarasin, FTS and Kobe0065) on NSCLC A549 cell line viability and on _KRAS_ and _HRAS_ mRNA expression levels, during a 48-h observation period after stimulation.