esa-sceva/satcom-chunk-collection huggingface dataset:
# Lithium Battery SOH Estimation Based on Manifold Learning and LightGBM
## 3 Li-Ion Battery SOH Prediction Process  
For Li-ion battery SOH prediction, this paper designs a Li-ion battery SOH prediction flow based on manifold learning and LightGBM. This prediction flow is shown in Figure 1.  
As can be seen from Figure 1, the battery SOH prediction model proposed in this paper includes four major parts: data acquisition, feature engineering establishment, manifold learning, and model prediction. Firstly, the battery charging and discharging cycle experiment is designed to obtain the dataset B. Secondly, the feature engineering is established, and this paper chooses to extract a total of three types of features from different angles, which are the capacity degradation features, entropy value features, and correlation coefficient features. Among them, the capacity degradation features mainly focus on the discharge voltage curve in the charge/discharge cycle and extract different feature parameters, including the average voltage decay and IC curve feature parameters; the entropy value features are the generalized multiscale sample entropy of each attribute using the algorithm described in the previous paper, which measures the information uncertainty; finally, the autocorrelation coefficients and partial autocorrelation coefficients are extracted in the time domain; then, the L-ISOMAP manifold learning algorithm is used. Dimensional simplification is performed on the input feature set to solve the dimensional explosion problem; finally, a LightGBM prediction model is established to perform SOH prediction on different datasets, and model evaluation is performed.  
Figure 1: Manifold learning and LightGBM-based SOH estimation process for a Li-ion battery.
# Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks
## 3 Data Collections  
This study uses data from a report on seismic wave velocity (P wave) published by the Engineering Research Institute of Iowa State University, Ames, Iowa [51]. Field measurement of seismic wave velocity was conducted on highway embankments. The embankments were constructed with three types of soil namely, clay loam, silty clay (two weathering variations, gray and brown), and silty loam. For laboratory measurements, samples were collected from the side slope of the highway embankment adjacent to the field measurement location. One additional soil type was used for the laboratory measurementsdefined as loess. The types of soils investigated, and their liquid limit and plasticity index are shown in Table 1.  
Micro-seismic refraction tests were conducted at 34 different locations to measure seismic velocities in the field. The equipment consists of three components: an impact source, a receiving transducer, and a seismic timer. A model 217 Micro-Seismic timer and a transducer were used for these micro-seismic refraction tests. A tack hammer was used as the impact source on a 5/8-inch diameter steel ball-bearing to transmit the energy into the ground. Seismic measurements were taken along a 2 ft line by moving the receiver in 3-in intervals. A total of 10 first-arrival measurements were collected at each station. The seismic wave velocities were calculated from slope of the distance-time plots. At the midpoint of the seismic line, a standard rubber balloon volumetric density measurement and an in situ water content measurement were performed [52]. The range of values associated with the field measurements is listed in Table 2.  
In the laboratory, 35 different soils samples were compacted in 4-inch diameter by 4.58 inch higholds. Standard and modified AASHO compaction tests procedures were followed to prepare the samples. Water content, dry density of all the samples (total 35) were determined in lab. Liquid limit (LL), plasticity index (total 5 samples) were determined on for the different soil types. Seismic velocities were measured on all samples (total 35) using the pulse-transmission method.
## 2 Materials and Methods
### Data and Methodology  
The methodology flow-chart (Figure 2) for this study and details on the data processing and the methods to achieve the aim of the study are described in the following sections. This study utilized medium-resolution remote sensing satellite imageries to monitor changes in mangrove cover and fragmentation, covering the years 2000 to 2019. Support Vector Machine (SVM) was selected as the classifier for the image classification process. A patch analysis plugin was implemented for fragmentation analysis based on ArcGIS software. The land cover, LAI, and GPP Products of Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 006 were used to define the impact of loss and fragmentation on mangrove forests in IM. The MODIS Land Cover Product is based on a supervised decision-tree classification method using inputs from other MODIS products, including the land/water mask, nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectance, Enhanced Vegetation Index (EVI), snow cover, land surface temperature, terrain elevation information, etc. [37]. The MODIS LAI product incorporated the MODIS Land Cover Product and MODIS Surface Reflectance Product into the radiative transfer model to derive LAI [38]. The MODIS GPP product is derived by relating the absorbed photosynthetically active radiation (APAR) calculated from LAI with the energy conversion efficiency (\(\epsilon\)) [26]. More information of the MODIS products and the method to access these products are described below.
# A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems
## 4 Two Case Studies of the Proposed Framework
### Traffic Light System for Smart Transportation
#### 4.1.2 System Architecture and Implementation  
We demonstrate the effectiveness of our approach using a busy crossroads as a testbed, as shown in Figure 2. Sensors strategically placed at the intersection act as event producers, continuously capturing data on vehicle presence, speed, and pedestrian movements. These data points are then transmitted as event messages to roadside units (RSUs) acting as event consumers. The RSUs handle real-time decision making and adjustments based on the received information.  
The core functionality revolves around event propagation and response:  
* When congestion or an accident is detected, the closest RSU publishes a unique event message to the MQTT broker.
* This triggers downstream RSUs on the same street to receive the message and dynamically calculate new route plans for affected vehicles, potentially miles away from the initial event.
* The edge server, running on Elixir, executes immediate control actions based on the updated route plans.
* Centralized cloud systems oversee the broader traffic network and initiate larger adjustments when necessary.  
Elixir plays a crucial role in ensuring data integrity and consistency throughout this process. Its functional programming paradigm, featuring immutable data structures,  
Figure 2: Typical use case of a traffic light system.  
safeguards data from inadvertent modifications. For instance, an intersection sensor's real-time vehicle density data are represented as an immutable structure shared with two processes: one analyzing city-wide traffic flow and another managing the specific intersection. If the centralized analysis process predicts modifications elsewhere based on this shared data, it does not alter the original structure. This ensures that the intersection control process operates with unaltered data, maintaining consistent and accurate signal timing adjustments based on real-time conditions.
## 3 Methodology and Materials
### Scene Recognition  
The recognition stage in scene recognition is the culmination of the entire process, where the model employs the extracted and refined features to make informed decisions about the category or label of a given scene or image. This stage plays a pivotal role in the overall success of a scene recognition system, and its effectiveness directly hinges on the quality of the features obtained through prior processing, including feature selection and fusion. After feature selection, the recognition stage begins by taking the optimized feature representation as the input. These features are intended to capture the most discriminative and relevant aspects of the scene, having undergone a rigorous selection process to eliminate noise and redundancy. The choice of features is critical, as it greatly influences the model's ability to discern subtle differences between scenes.  
Typically, the recognition stage is characterized by the presence of a SoftMax layer, which is the final layer of our deep model. The SoftMax layer transforms the output of the previous layers into probability distributions over the possible scene categories. Each output neuron corresponds to a specific scene class, and the SoftMax function ensures that the class probabilities sum up to one. Consequently, the class with the highest probability is assigned as the predicted label for the input scene.  
To train the recognition model, a suitable loss function is employed, such as categorical cross-entropy, which quantifies the dissimilarity between predicted and ground truth labels. During training, the model learns to adjust its parameters (weights and biases) to minimize this loss, improving its ability to make accurate predictions. The model's performance is rigorously evaluated during the recognition stage using a separate validation or test dataset. Metrics such as accuracy, precision, recall, and \(F1\) score are commonly used to assess the model's ability to correctly classify scenes into their respective categories.
# Endowed Polyphenols in Advanced Delivery Systems for Vaginal Infections
## 6 Polyphenols as Antimicrobial Agents
### Quercetin  
Quercetin (3,3,4,5,7-pentahydroxflavone), first isolated in 1936 by [PERSON], belongs to the flavonoid subclass of flavonoids, and it is found in several fruits and vegetables. The highest quantities of quercetin can be found in onions, asparagus, and red-leaf lettuce, while the lower ones are found in broccoli, green peppers, peas, and tomatoes [97; 98]. Various medicinal plants, like _Hypericum perforatum_ and _Ginkgo biloba_, also contain quercetin [99]. This polyphenol is one of the most studied [97], mainly due to its identified biological properties, such as antioxidant, anticancer, antimicrobial (antiviral, antibacterial, antifungal), and anti-inflammatory activities [97; 98; 99; 100]. Furthermore, and highly relevant, the United States FDA has granted quercetin GRAS (Generally Recognized As Safe) status [99].  
Particularly, regarding antimicrobial activity, a large variety of bacterial strains are susceptible to quercetin. Its solubility and interaction with the bacterial cell membrane have both been connected to this biological property [99]. The bactericidal action of quercetin is generally less effective against Gram-negative bacteria than against Gram-positive [101]. Similar results have been observed for some quercetin derivates [102]. A study by [PERSON] et al. (2011) showed that _Streptococcus mutans_, _Streptococcus sobrinus_, _Lactobacillus acidophilus_, _Streptococcus sanguis_, _Actinobacillus actinomycetemcomitans_, and _Prevotella intermedia_ were inhibited by quercetin, with minimum inhibitory concentrations (MICs) ranging from 1 to 8 mg/mL [103]. _S. aureus_ (including MRSA and MSSA (methicillin-sensitive _S. aureus_) [104]) and _Pseudomonas aeruginosa_ were likewise susceptible to the inhibitory effects of quercetin at MIC levels of 20 \(\upmu\)g/mL [105]. Moreover, _Candidia_ spp. [106], _C. albicans_ and _Saccharomyces cerevisiae_[104] have all been shown to be susceptible to quercetin antifungal effects.
# Lyapunov-Guided Energy Scheduling and Computation Offloading for Solar-Powered WSN
###### Abstract  
To satisfy the continuously high energy consumption and high computational capacity requirements for IoT applications, such as video monitoring, we integrate solar harvesting and multi-access edge computing (MEC) technologies to develop a solar-powered MEC system. Considering the stochastic nature of solar arrivals and channel conditions, we formulate a stochastic optimization problem to maximize network energy efficiency under the constraints of energy queue stability, task queue stability, peak transmission power, and maximum CPU frequency of each sensor. To solve the long-term stochastic optimization problem, we propose a Lyapunov-based online joint computational offloading and resource scheduling optimization algorithm, transforming the long-term stochastic problem into a series of deterministic subproblems in each time slot. Simulation results show that the proposed algorithm can find the optimal solution to tradeoff long-term energy efficiency and queueing backlog without requiring a priori knowledge of the channel state and energy arrival, which is a more realistic solution for practical solar-powered MEC systems.  
multiple access edge computing (MEC); solar energy; resource scheduling; partial computation offloading; long-term energy efficiency; queue stability +
Footnote †: journal: Applied
# Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery
## 2 Materials and Methods
### Drought Classification Based on Soil Relative Humidity  
The study area was divided into five regions with different moisture gradients according to the drought level standard formulated by the Ministry of Water Resources of the People's Republic of China (Table 2).
# Gated Recurrent Attention for Multi-Style Speech Synthesis
## 2 Backgrounds
### Location-Sensitive Attention  
LSA [21], which extends the additive content-based attention [16] to cumulate attention weights from the previous decoder time-step, was applied to the Tacotron 2. The LSA stochastically generates a decoder outputs \(y=(y_{1},y_{2},...,y_{T})\) from an encoded input sequence \(x=(x_{1},x_{2},...,x_{N})\). Note that the encoded input sequence and the decoder state sequence may have different time-scales, e.g., for speech synthesis, the decoder state represents a latent acoustic feature at each frame, whereas the encoded input represents a character or phoneme. The LSA updates a recurrent state \(f_{n,t}\in\mathbb{R}^{k}\) at each decoder time-step \(t\) with the attention weights \(\alpha_{n,t}\) by convolving it with a matrix \(F\in\mathbb{R}^{k\times r}\):  
\[f_{n,t}=f_{n,t-1}+F*\alpha_{n,t} \tag{7}\]  
where \(*\) denotes the convolution operation and the attention weights \(\alpha_{n,t}\), which are also called as alignment between the encoder and decoder are computed by the score \(e_{n,t}\) as follows,  
\[\alpha_{n,t}=\frac{\exp{(e_{n,t})}}{\sum_{v=1}^{N}\exp{(e_{v,t})}}. \tag{8}\]  
The score \(e_{n,t}\) rates how much the encoder output at the location \(n\) is to be attended for generating the decoder state around the location \(t\). The LSA uses the previous recurrent state \(f_{n,t-1}\) to compute the score \(e_{n,t}\):  
\[e_{n,t}=v^{T}\tanh{(Ws_{t-1}+Vx_{n}+Uf_{n,t-1}+b)} \tag{9}\]  
where \(s_{t-1}\) is the \((t-1)\)-th decoder recurrent state; \(v\) and \(b\) are vectors; and \(U\), \(V\), and \(W\) are matrices. The attention-based decoder generates an output \(y_{t}\) by focusing on the relevant elements of \(x\) as follows,  
\[g_{t}=\sum_{n=1}^{N}\alpha_{n,t}x_{n}, \tag{10}\]  
\[s_{t}\sim recurrency\left(s_{t-1},g_{t},y_{t}\right), \tag{11}\]  
\[y_{t}\sim generator\left(s_{t-1},g_{t}\right) \tag{12}\]  
where \(g_{t}\) is the glimpse [23]. The recurrent activation and output function of the LSTM [27] are typically used as the _recurrency_ and _generator_, respectively.The score \(e_{n,t}\) is location-sensitive in the sense that the previous recurrent state \(f_{n,t-1}\) is determined by the convolved previous attention weights \(F*\alpha_{n,t-1}\), which contain the location of the focus from the previous step. The LSA is also known as hybrid attention because the score \(e_{n,t}\) is a function of both the content \(x_{n}\) and the previous recurrent state \(f_{n,t-1}\).
## 5 Dynamic Analysis
### Simulation Results
#### 5.3.2 Test Case 2--3-Steps Burr Profile \"Low-Low-High\"  
A 3-steps burr profile \"low-low-high\" (see Figure 11 (top)) is used as an input for the dynamic simulation. \"Low\"/\"high\" means that the burr height is not/is sufficiently high  
Figure 10: Step burr profile (\(h=5\) mm, **top**); position (**middle**) and velocity (**bottom**) of the grinding wheel support.  
to make the projection of the cutting force along the sliding direction exceed the elastic force threshold of the mechanism. In this test case, steps with increasing height are investigated. In the first two steps the cutting force is below the elastic force threshold, whereas in the third step the cutting force is sufficiently high (above the threshold) to make the mechanism work in the compliant mode of operation. The simulation results are presented in Figure 11 (middle and bottom). It can be noticed that the grinding wheel support does not move while the first two steps are processed (precision cut), and it moves backwards thanks to the compliance of the mechanism while the third step is processed. Similarly to the previous test case, an equilibrium condition is achieved (after a transient of about 0.3 s after the beginning of step 3) and a cut profile with constant height is obtained for step 3, as it can be noticed in Figure 11. Additionally, in this test case the simulation results confirm the expected dynamic behavior of the system: the grinding wheel support moves backward only when the cutting force projection exceeds the elastic force threshold, and when this happens a constant cut profile is obtained, after an initial transient (Condition 1 and Condition 2 of Section 5.3). From another point of view, for small burr heights the mechanism is (ideally) infinitely stiff, whereas it becomes compliant for high burr heights. Some small oscillations can be noticed when the burr height changes value but is not sufficiently high to allow the mechanism to work in the compliant mode (i.e., the grinding wheel support does not move and the cutting precision is ensured). This is due to the presence of the mechanical stop, which is not infinitely stiff. Nevertheless, this effect causes a negligible effect on the grinding wheel position, as it can be noticed in Figure 11 (middle). The maximum (normal) deburring force reduces again to 148 N from the nominal value of 240 N (corresponding to the nominal height of 4 mm), i.e., roughly a 38% (automatic) force reduction is obtained, which is related to the 38% reduction of the cutting height (2.5 instead of 4 mm).
# Enhancing Security of Online Interfaces: Adversarial Handwritten Arabic CAPTCHAs Generation
## 5 Discussion
### Security Level
#### 5.1.1 Meaningless Words  
The results of applying various adversarial perturbations to meaningless words and testing recognition were examined. The key findings were as follows:  
* CTC achieved a comparable 43.33% level of partial recognition, with a lower 23.33% rate of unrecognized results, indicating that it perturbed the text more efficiently than EOT in confounding machine vision systems.
* For EOT, none of the images were completely recognized correctly, indicating this technique successfully introduced some distortion. However, the high 66.66% figure of partial recognition suggests that the distortion had only a minimal impact, allowing the text to remain partially legible to machines.
* IAN resulted in a 26.66% incorrect recognition rate and a higher partial recognition rate of 60%, implying that its distortions did not completely obscure the text, unlike more successful techniques. IAN needs to improve its obscuring capabilities.
* JSMA uniquely had only one image 3.33% completely recognized. The highest percentage of 30% of images unrecognized aligns with the following characteristics:
* JSMA cannot identify the same level of influential pixels/features, as the meaningless words do not have inherent semantic meaning that the model has learned to recognize robustly.
* With less identifiable influential features, JSMA can introduce smaller perturbations to cause a misclassification.
* This results in a lower non-recognition rate (30%) for meaningless words, as the adversary can more easily perturb the input to bypass the CAPTCHA.
* Finally, SGTCS achieved a high partial recognition of 63.33% but a similar incorrect or unrecognized split to IAN, implying a comparable obscuring ability.  
JSMA showed the strongest protection against automated solvers. The varying outcomes between techniques also support the concept that each distortion type impacts recognition differently.
# RGB-D Camera for 3D Laser Point Cloud Hole Repair in Mine Access Shaft Roadway
## 5 Repair of Point Cloud  
The point cloud data collected by the three-dimensional laser scanner was largely missing, which had a certain impact on the establishment of three-dimensional point cloud visualization of underground shaft roadway, as shown in Figure 13. The missing parts of point cloud data were collected and repaired by RGB-D camera to finally ensure that the overall point cloud data of the manhole roadway was intuitive and true.  
Generally, point cloud data repair is mainly reflected in the shape, position, and fit degree. The main steps of repair are coarse registration and fine registration of the two types of point clouds and then good repairing of the missing parts. In our study, the algorithm of FPFH [27] + ICP, ISS [28] + ICP, SVD [29] + ICP, and the 3d-NDT algorithm [30] were used to register and fuse the point cloud data of the RGB-D depth camera and 3D laser scanning point cloud to carry out further visual repair.  
Figure 12: Point cloud data measurement map. (**a**) 3D laser point cloud data measurement; (**b**) Depth camera point cloud data measurement.  
Figure 13: Missing 3D laser point cloud data. (**a**) Side view; (**b**) Front view.
## 7 Module III: Temporal Privacy
### Simple Global Anti Temporal Scheme (SGAT)
If we presume \(\tau_{proc}\) is much smaller than \(\tau_{trans}\), then \(\tau_{d}\) can be rewritten as the following:  
\[\tau_{d}=\tau_{w}+\tau_{trans} \tag{36}\]  
If the message needs to go through (\(U\)) hops to the BS, and if we assume that the transmission only happens at the end of the Interval \(I_{i}\), then \(t_{s}\), equals to \(\omega\), and the total delivery time (\(\tau_{d\textit{-total}}\)) can be calculated according to the expression below [42]:  
\[\tau_{d\textit{-total}}=\sum_{u=1}^{U}\tau_{w_{u}}+\tau_{trans_{u}} \tag{37}\]  
Having \(t_{s}\) equals to \(\omega\); _i.e._, sending message at the end of the interval, will increase the delay of the delivery presuming that every \(\tau_{trans}\) is equal [42]. Thus, optimizing \(\tau_{d\textit{-total}}\) is a function of \(\tau_{w}\) according to Equation (38):  
\[\tau_{d\textit{-total}}=\mathrm{f}(\tau_{w})=\sum_{u=1}^{U}\tau_{w_{u}} \tag{38}\]  
Each SN will be informed during the setup phase about \(\omega\): for the lifetime of the network. The BS also can alter this value by broadcast when the conditions of the WSN changes (closed-loop control). The value of \(\omega_{i}\) should be calculated to achieve at least the minimum expected lifetime span \(\Omega_{\mathrm{min}}\) without jeopardizing the privacy and data integrity. Thus:  
\[\Omega_{\mathrm{high\text{-th}}}\geq\Omega_{i}\geq\Omega_{\mathrm{low\text{-th }}} \tag{39}\]  
where \(\Omega_{\mathrm{high\text{-th}}}\) is the highest possible value for \(\Omega_{i}\) and, \(\Omega_{\mathrm{low\text{-th}}}\) is the lowest expected value for \(\Omega_{i}\). When SN does not have a real message to send before the end of the interval period \(I_{i}\), it will send a fake message according to the procedure explained in the anonymity module. When SN has a real message, it will send it to one node from the neighborhood set (\(\mathrm{N}_{i}\)).
# Performance Analysis of Cooperative Non-Orthogonal Multiple Access in Visible Light Communication
## 3 BER Analytical Expression  
In this section, we discuss the equations used for calculating the BER of non-cooperative and cooperative PD-NOMA.