esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The greatest risk associated with the possibility of microbiological contamination of juices is due to the lack of thermal processing. They are usually pasteurized [1; 2; 3]. However, in recent decades, the possibility of applying cold atmospheric pressure plasma--CAP (a mixture of electrons and ions, positively and negatively charged atoms and molecules, generated during an electric discharge at atmospheric pressure)--for food treatment has been investigated [4; 5; 6]. From the microbial inactivation point of view, the hydroxyl group (OH\(\bullet\)) plays the most important role. It attracts H\({}^{+}\) ions from the structural proteins of a bacterium's cell wall [7]. Plasma also leads to the degradation of microbial DNA. Ultraviolet rays, also generated during an electric discharge, are characterized by a high energy value and a high degree of absorbance by DNA and RNA molecules, which leads to the formation of thymine dimers and fragmentation of the nucleoid [8; 9; 10; 11; 12; 13].  
From the point of view of promoting a healthy lifestyle, an important issue is the availability of food containing phytochemicals, i.e., substances with biological activity (clean label products) [14]. The spice prepared from the fruit of a shrub plant belonging to the genus _Rhus coriaria_ L. has strong antioxidative and antibacterial properties. It contains flavonoids such as gallic acid, methyl gallate, kaempfer, fustine, fisetin, sulfuretin, butein, quercetin and myrectin. Sumac also contains mainly monounsaturated oleic acid (omega-9) and polyunsaturated linolenic acid (omega-6). Micronutrients include certain amounts of vitamins C, B\({}_{1}\), B\({}_{2}\) and B\({}_{6}\), as well as potassium, calcium, magnesium, iron, zinc, manganese and copper [15; 16; 17; 18; 19]. This combination of nutrients could have beneficial properties supporting the defense against free radicals, which, according to the published literature, are partly responsible for formation of diseases such as cancer, diabetes or heart problems [20]. Consuming this oriental spice ensures proper blood sugar levels and regulates cholesterol levels [21]. Sumac fruit is also used as a natural diuretic, which helps in the proper elimination of toxins and cleansing the whole body [22; 23]. Several positive effects of using sumac in meat dishes, fish, cooking oils or dairy products have been described in the literature [24; 25; 26; 27; 28].  
In this study, the addition of sumac powder combined with cold plasma treatment was used for juice products for the first time. The aim of the study was to investigate the effect of using this type of treatment on the number of microorganisms and the nutritional value of carrot juice during refrigerated storage.
# Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning
## 2 Background
### Performance Evaluation Metrics  
To examine the behaviors of classification algorithms, certain evaluation measures were utilized. In this work, we incorporate F-score and AUC to assess the algorithms, but we also provide the short definitions of Precision, Recall, Sensitivity, Specificity, and Receiver Operator Characteristic (ROC) curve, since they are essential for the computation and definition of F-score and AUC [33].  
\[Precision=\frac{TruePositive}{TruePositive+FalsePositive} \tag{12}\]  
\[Recall=\frac{TruePositive}{TruePositive+FalseNegative} \tag{13}\]  
_Precision_ is defined as the proportion of true positives to all anticipated positive instances (12). _Recall_ is the proportion of true positives to all positive cases (13).  
\[Fscore=2*\frac{Precision*Recall}{Precision+Recall} \tag{14}\]  
The harmonic mean of accuracy and recall is computed using the F-score. This score considers both false positives and false negatives (14).  
\[Sensitivity=\frac{TruePositive}{TruePositive+FalseNegative} \tag{15}\]  
ROC curve is a binary classification problem assessment measure. It is a probability curve that shows the True Positive Rate (_Sensitivity_) (15) against the False Positive Rate (1-Specificity) (16) at various threshold levels, separating the'signal' from 'noise.'  
\[Specificity=\frac{TrueNegative}{TrueNegative+FalsePositive} \tag{16}\]  
AUC is a measure that discriminates between classes and is used to summarize the ROC curve. The greater the AUC, the stronger the model's ability to differentiate between positive and negative classifications. When AUC = 1, the classifier successfully distinguishes between all positive and negative class points. On the other hand, if the AUC is 0, the classifier classifies all positives as negatives and all negatives as positives.
## 2 The Proposed Method
### Invertible Denoising Network
#### 2.2.5 Image Reconstruction  
Given the loss-less nature of information in reversible networks [18], if we align the first three channels of the transformed latent representation with the down-sampled clean image, the remaining channels will encode high-frequency information. As high-frequency information typically contains noise, we discard all high-frequency signals before reconstructing a clean image from a low-resolution image. This process can be formulated as follows:  
\[\mathrm{p}(\mathrm{y})\!=\mathrm{p}(\mathrm{x}_{\mathrm{LR}},\mathrm{x}_{ \mathrm{HF}},\mathrm{n})\!=\mathrm{p}(\mathrm{x}_{\mathrm{LR}})\mathrm{p}( \mathrm{x}_{\mathrm{HF}},\mathrm{n}|\mathrm{x}_{\mathrm{LR}}) \tag{10}\]In this context, \(x_{LR}\) denotes a low-resolution clean image, while \(x_{HF}\) represents the high-frequency signal that cannot be included by \(x_{LR}\) during the reconstruction of the original high-frequency image. Due to the inherent challenge in disentangling \(x_{HF}\) and \(n\), we discard all channels representing \(z\sim p(x_{HF},n|x_{LR})\) to completely eliminate spatially varying noise. However, this leads to the loss of \(x_{HF}\). To restore \(x_{HF}\), we sample from \(N\sim(0,I)\) and train the InvDN to transform \(z_{HF}\) alongside \(x_{LR}\) for achieving clean image restoration. Consequently, the lost high-frequency clean image details that \(x_{HF}\) are effectively embedded into \(z_{HF}\).
## 3 Results and Discussion
### Contact Angles  
Contact angles can be employed to determine the gradient structure of the IPN because EP and PU have different surface wettability [42; 43; 44]. After the fracture surface of the IPN sample was well polished using a 5000 mesh abrasive paper, contact angles between the dried gradient IPN and redistilled water were measured (Figure 4).  
Our previous work have reported that the water contact angles of EP (LY-1564) and PU (130 T) are \(75.42^{\circ}\) and \(110.06^{\circ}\), respectively. As shown in Figure 4, surface water contact angle of the IPN sample at the region of 3/4 h, 2/4 h and 1/4h are \(78.56^{\circ}\), \(83.56^{\circ}\) and \(93.27^{\circ}\), Which increases along with the gradient direction.  
Previous reports have indicated that surface tensions impurity and curing rate discrepancy of PU and EP are the main driving force in the self-gradient process [38; 45]. PU phase has higher surface tension and shorter gel time than that of EP phase in the IPN. Therefore, the earlier gelled PU phase will mainly distribute in the bottom of the IPN according to the thermodynamics, while the EP phase will be extruded to the top side of the IPN, which leds to the continues gradient distributions of the components. Thus, the water contact angle shows the same trend along the gradient direction.
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 6 Open Research Issues and Challenges  
In this section, important open research issues and challenges are addressed and technically discussed in the context of routing. The routing protocols for UAWSNs are still in their developmental phase and are subject to extensive continuing research. The major design goals of routing protocols in UAWSNs are prolonged sensor lifetime, improved network availability, reduced energy consumption, decreased delivery latency, and reduced routing complexity. Because a UAV has higher mobility, a longer deployment range, and a longer operation time than traditional mobile sensor nodes, important challenging issues are raised in routing for UAWSNs.
# The Impact of Spoofing Attacks in Connected Autonomous Vehicles under Traffic Congestion Conditions
## 4 Evaluation
### Effects of Spoofing Attack
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Kind of Attack** & **Density of 10 vehicles/km** & **Density of 15 vehicles/km** & **Density of 20 vehicles/km** & **Density of 25 vehicles/km** \\ \hline Normal Scenario & 664.9341 & 678.5202 & 679.1850 & 679.1850 \\ Spoofing Attack Scenario & \(-\)682.9962 & \(-\)678.0775 & \(-\)680.5207 & \(-\)694.6582 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _CoV_ values for acceleration. Figure 11: Acceleration of platoon of vehicles with 25 vehicles/km in normal scenario. Figure 12: Acceleration of platoon of vehicles in spoofing attack scenario with a density of 25 vehicles/km. distances than they would normally travel in the same amount of time, but at the same time significantly disrupts the wireless transmission medium (increasing the number of packets the vehicles transmit). Figure 14 illustrates the average total traveled distance for all nodes with sporadic random values (\(n=10,15,20,25\) vehicles/km) in a range of 10 to 25 vehicles, which is reasonable for an urban area. In this series of figures, the main conclusions are identical to those of Figure 13 above. What is clear is that the spoofing attack only affects victim node (1), causing it to move at minimal speeds throughout the rest of the simulation, and the effects on the following  
Figure 14: Bar graphs for average total traveled distance for all nodes within a range of vehicle density. Figure 13: Mean value of PDR and average total distance for different vehicle density values. vehicles are significant but not that large. In addition, at these levels of density increase, we do not see a significant increase in distance traveled.
###### Abstract
Over time, the GSLs physiological roles have been studied using various biophysical, genetic, cell biology, and biochemical methods. However, the GSLs involvement in the etiopathogenesis of these diseases is not extensively known and, therefore, this represents a field with much promise for the future. Mass spectrometry (MS) has had a great impact in the structural elucidation and quantitative profiling of GSLs within the recent past. Based on innovative MS-approaches coupled with chromatographic separation [63; 64; 65], intact GSLs can be investigated to provide at the same time the carbohydrate core sequence and the ceramide moiety composition. A first analytical challenge in investigating GSLs is represented by their structural complexity, which results in highly variable physicochemical properties. In addition, the quality of the laborious GSL extraction/purification procedures from biological matrices greatly influences their ionization (to remove the interference of other lipids, proteins, and nucleic acids). Another obstacle is the lack of commercially available standards and the reduced availability and quantity of normal and pathological tissue sample, making it difficult to achieve a quantitative, comprehensive, and large-scale profiling of GSLs in various tissues/organisms under normal or pathological conditions. Among the soft ionization methods for mass spectrometric GLS analysis, matrix-assisted laser desorption ionization (MALDI) offers significant advantages, such as the simplicity and speed of the analysis; an adequate molecular mass lipids range, which is high enough that background matrix signal interferences are minimal; and the tolerance of higher quantities of impurities in comparison to other MS-based methods, reducing the necessity for sample purification, and offering high reproducibility [66]. Moreover, choosing the right matrix plays a pivotal analysis role, as a potential risk is represented by the post-source decay of GSLs, which is associated with noticeable decarboxylation and desialylation reactions, especially for sialic acid-containing GSLs [67; 68]. Thus, the present review highlights representative literature results related to GSL profiles in normal and pathological conditions using MALDI non-imaging MS (non-IMS) approaches, together with their advantages and limitations.
# Evaluation of Gasket Surface Contact Stress Uniformity Based on Gray-Level Co-Occurrence Matrix
###### Abstract  
Due to the high demand for compression recovery tests of metal gaskets, it is difficult to provide and evaluate uniform stress on metal gaskets. To effectively evaluate the uniformity of surface stress on a metal gasket, we propose a method of evaluating gasket surface stress based on gray-level co-occurrence matrix theory. Angular second moment and entropy are selected as the main characteristic parameters, and the evaluation indexes (\(f_{1}\) and \(f_{2}\)) are proposed. The analysis is based on finite element calculation under different parameters of a gasket test rig. The angular second moment and entropy curve of the stress distribution cloud map are drawn according to the result of finite element calculation. The results show that the method can evaluate the uniformity of stress distribution. The best parameters of the gasket testing rig are  
stress distribution through visual comparison [5], parameter evaluation [6], and digital image evaluation. Visual comparison can only judge stress changes in the cloud map that are obvious and can be directly identified by the naked eye. Parameter evaluation is used to quantitatively evaluate the distribution uniformity in the stress distribution cloud map by taking samples in a direction and distance on the surface, then calculating the average value and standard deviation. Parameter evaluation is applicable to situations where the variation range of stress is small. The digital image method uses digital image processing technology to analyze the characteristics of the cloud map and extract the image characteristic information, so as to evaluate the stress distribution. The gray-level co-occurrence matrix is a kind of digital image method. By extracting image texture features, the texture distribution is measured by the value of the image's characteristic parameters. This method has high accuracy, more information, and requires less calculation time [7]. In this work, the gray-level co-occurrence matrix method was selected to extract the characteristic parameters of the cloud map based on finite element calculation to evaluate the stress distribution. The analysis was conducted on various compression conditions and test rig parameters. The computational model of the gasket test rig was established to apply pressure on a gasket. By extracting characteristic parameters, the cloud map of surface stress was evaluated based on different test conditions from three aspects, namely the inner radius of the test rig's compression rod, the compression condition, and the height of the compression plate. Finally, optimization guidance for the gasket test rig is provided.
## 2 Methodology
### Nernst Equation  
In this work, the Nernst model is used to predict the pattern of a future discharge cycle using data from the reference cycle and the estimated degradation data. If the SOC is known, the terminal cell voltage can be determined by applying the Nernst equation [40,41] as  
\[v_{n}(t)=E_{0}-Ri_{n}(t)+\mu_{1}\ln[SOC(t)]+\mu_{2}\ln[1-SOC(t)] \tag{11}\]  
where \(v_{n}(t)\) and \(i_{n}(t)\) are the terminal cell voltage and current (which is positive during the discharge stage and negative during the charge stage) at the \(n\)-th cycle, \(E_{0}\) is the standard cell potential, \(R\) is the internal resistance of the cell, and \(\mu_{1}\) and \(\mu_{2}\) are constant parameters. For a set of \(m\) measurements at different times \(j\Delta T\) in the same cycle, where \(j=1\), \(2\), \(3\), \(\ldots\), \(m\), the set of parameters in (11) can be determined as  
\[\begin{bmatrix}E_{0}\\ R\\ n_{1}\\ \mu_{2}\end{bmatrix}=\begin{bmatrix}1&i_{n,1}&\ln(SOC_{n,1})&\ln(1-SOC_{n,1} )\\ 1&i_{n,2}&\ln(SOC_{n,2})&ln(1-SOC_{n,2})\\ \ldots&\ldots&\ldots&\ldots\\ \ldots&\ldots&\ldots&\ldots\\ 1&i_{n,m}&\ln(SOC_{n,m})&\ln(1-SOC_{n,m})\end{bmatrix}^{-1}\begin{bmatrix}v _{n,1}\\ v_{n,2}\\ \ldots\\ \ldots\\ v_{n,m}\end{bmatrix} \tag{12}\]  
where \(v_{ij}\) is the terminal cell voltage at cycle number \(i\), with \(i=1\), \(2\), \(\ldots\), \(n\), and at the \(j\)-th sample of the cycle, where \(j=1\), \(2\), \(\ldots\), \(m\).  
To predict the behavior of a future cycle (terminal voltage vs. SOC) at different stages of cell aging, the following steps are proposed:  
* Reference cycle. The instantaneous values of current and battery terminal voltage are measured during the full discharge cycle and the instantaneous values of the SOC are calculated. From these, the four parameters of the Nernst equation are determined.
* Future cycle \(n\). By applying the LF or ARIMA forecasting methods, the estimated values of the \(\Delta V_{RMS,n}\) and the capacity fade \(\Delta C_{n}\) of cycle \(n\) are determined. If the rated capacity \(C_{rated}\) of the cell and the capacity fade at the \(n\)-th cycle \(\Delta C_{n}\) are known, the state of charge SOC\({}_{n}\) at the \(n\)-th cycle can be calculated as \[SOC_{n}(t)=\frac{1}{C_{rated}-\Delta C_{n}}\int_{0}^{t}i(t)dt\] (13)  
Finally, once the SOC\({}_{n}\) of the \(n\)-th cycle is known, the terminal cell voltage \(v_{n}\) for this cycle is obtained from \(\Delta V_{RMS,n}\) and (11) so that the terminal voltage is predicted vs. the SOC characteristic curve of the \(n\)-th cycle.
## 5 Simulation Results and Analysis
### Network Throughput with an Obstacle  
We also evaluated the network throughput in an area with obstacles. ICBM-UAV maintains the throughput better in an obstacle environment. When compared to the performance in an obstacle-free environment, the flocking model performs marginally worse, as illustrated in Figure 11. When the UAVs travel at 10 m/s, the DFM flocking model was not built for the obstacle environment, which has an impact on network throughput. AODV and OSLR perform a little worse than the non-obstacle environment, as the UAVs change their positions due to obstacles, which causes the UAVs to change coordinates; rebuilding the path will have an effect on the throughput of a network, where the DFM model works below the non-obstacle environment as the appearance of obstacle between the leader and follower will affect the transmission of packets and throughput if a network is lower.
# Multi-Sensor Data Fusion Method Based on Self-Attention Mechanism
## 3 Method
### Point Cloud Module  
The point cloud feature extraction module in the CNN-SA utilizes three one-dimensional (1D) convolutional layers to extract features from the point cloud. To avoid offsets and bias, batch normalization layers were applied to normalize the outputs of the 1D convolutional layers, ensuring that the outputs had similar distributions. Subsequently, an activation function was applied for nonlinear transformation, processing the normalized results to retain positive values and enhance the nonlinear expressive capability of the network. The structure of this module is illustrated in Figure 2, where \(P\in\mathbb{R}^{n\times 3}\) represents 3D point cloud data, \(n\) denotes the number of points, and \(F_{p}\in\mathbb{R}^{256\times n}\) represents point cloud features.  
Figure 1: Multi-sensor data fusion model.  
The 3D point cloud was input into the point cloud feature extraction module and underwent three sets of nonlinear mapping operations:  
\[\mathrm{F_{1}}=\mathrm{ReLu}(\mathrm{BN}(\mathrm{f^{1}}(\mathrm{P}))) \tag{1}\]  
\[\mathrm{F_{2}}=\mathrm{ReLu}(\mathrm{BN}(\mathrm{f^{1}}(\mathrm{F_{1}}))) \tag{2}\]  
\[\mathrm{F_{P}}=\mathrm{ReLu}(\mathrm{BN}(\mathrm{f^{1}}(\mathrm{F_{2}}))) \tag{3}\]  
where \(\mathrm{f^{1}}\) is a standard 1D convolution operation with a kernel size of one and stride of one. The convolution operation does not alter the size of the feature map; however, the number of channels changes from 3 to 256. BN represents batch normalization, which normalizes the input to a batch size to stabilize the data distribution. ReLu denotes the activation function that transforms the input values into non-negative values.
###### Abstract  
Carbon and water fluxes in ecosystems are tightly coupled by gas diffusion through stomata. However, carbon-water (C-W) relationships vary largely across time scales, vegetation types, and regions. Subtropical forests in China play an important role in the global carbon and water cycles, yet studies of C-W relationships in this region remain limited. Here, we investigated summer-time C-W relationships in this region at two subtropical sites: the evergreen broadleaved forest at Dinghushan (23.17\({}^{\circ}\) N, 112.53\({}^{\circ}\) E, 300 m) and the evergreen coniferous forest at Qianyanzhou (26.74\({}^{\circ}\) N, 115.06\({}^{\circ}\) W, 106 m), using the flux tower data from the FLUXNET2015. The C-W relationship was examined using two measures. The first was daily water use efficiency (WUE), which is the ratio of daily gross primary productivity (GPP) to evapotranspiration (ET). The second was the correlation coefficient (\(r\)) of hourly GPP and ET. Our analysis showed that the daily WUE in the two forests ranged over 4-14 mg CO\({}_{2}\) per g H\({}_{2}\)O, higher in the coniferous forest than in the broadleaved forest. The mean values of \(r\) for hourly C-W coupling were similar at the two forests, being 0.5-0.6, which suggests asynchronous diurnal variations in GPP and ET. Both daily WUE and \(r\) were modulated by meteorological conditions. In general, high radiation, air temperature, and humidity can reduce WUE at both sites. For the broadleaved forest, the most influential factor on WUE was VPD, followed by radiation, while in the coniferous forest, VPD, air temperature, and radiation were almost equally important. For hourly C-W coupling, VPD plays a significant role. The drier the air is, the weaker the coupling in the two forests. The daily WUE and hourly C-W coupling reflect the C-W relationship from different perspectives. Both showed the strongest response to VPD but with different sensitivity.  
gross primary productivity; evapotranspiration; meteorological factors; subtropical forest; water use efficiency
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 1 Introduction
Because of the limited battery capacity and high power consumption of UAVs, however, an energy-efficient routing protocol for UAWSNs is necessarily required both in military domains and in commercial applications. In addition, the deployment and the trajectory planning of UAV significantly influence the routing protocol performance [20].
## 3 Energy Efficient Data Transmission in WSN
### Cluster Head Selection Using AWOA
#### 3.2.3 Fitness Calculation  
During the encryption procedure, the fitness of every whale is calculated. Fitness is defined based on standard deviation (SD) and average distance. The fitness function can be written as follows:  
\[Fitness=\frac{1}{A_{Dist}}\times\frac{1}{\sigma} \tag{6}\]  
where,  
\(A_{Dist}\rightarrow\) Average distance between CH and SN  
\(\sigma\rightarrow\) SD  
Based on the above fitness function, all SNs should be assigned to their closest CH. The \(A_{Dist}\) is calculated using Equation (7).  
\[A_{v}Dist=\frac{1}{n}\sum_{i=1}^{n}D_{i} \tag{7}\]  
where,  
\(n\rightarrow\) Number of SN  
\(D_{i}\rightarrow\) Distance between SN and CH.  
The SD is designed based on the ratio of residual energy and the number of the member SNs of a CH if the gateway \(G_{i}\) has \(N_{i}\) number of member SNs and residual energy \(E_{\text{Residual}}(G_{i})\). The shorter the SD, the higher the fitness value [23]. Then, the SD can be calculated follows:  
\[\sigma=\sqrt{\frac{1}{m}\sum_{i=1}^{m}(\mu-R_{i})^{2}} \tag{8}\]\[\mu =\frac{1}{m}\sum_{i=1}^{m}R_{i} \tag{9}\] \[R_{i} =\frac{E_{Residual(G_{i})}}{n_{i}} \tag{10}\]  
where,  
\(m\rightarrow\) Number of CHs