esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping of Kill Erosion of the Middle Volga (Russia) Region Using Deep Neural Network
## 1 Introduction
Recently, there has been a rapid increase in the number of works related to deep neural networks (DNNs) for the semantic segmentation of remote sensing data. This has been facilitated by the increased quality of remote sensing data, user-friendly deep-learning framework availability, and the increased computing power available to researchers. At present, DNNs enable successful automated interpretation of anthropogenic objects [47; 48; 49], shorelines [50; 51], land use [52; 53; 54], vegetation cover dynamics [55; 56], and exogenous processes [57; 58]. In all cases, the authors note the higher recognition accuracy of the objects of interest than other methods and emphasize scaling the trained models. Deep neural networks have not been applied to the recognition and mapping of rill erosion. Nevertheless, the importance of the problem under study and the promising use of artificial intelligence for solving this problem determines the necessity of developing an appropriate methodology. Given the intensification of rill erosion, the necessity of mapping it, and the limitations of field survey and modeling, this study aims to develop and apply a deep neural network-based methodology for automated rill erosion detection from remote sensing data.
# Research on Safety Prediction of Sector Traffic Operation Based on a Long Short Term Memory Model
## 2 Air Traffic Parameters in Sectors
### Definition of Safety Parameters  
From the perspective of deterministic conflict detection, conflict events are identified by an aircraft safety zone [29]. The safety zone and relative position between aircrafts are shown in Figure 2.  
The conflict event is defined by the separation index in Equation (4). According to the separation standard from the Chinese rules of air traffic control, \(d_{0}\) = 10 km and \(h_{0}\) = 300 m are assigned:  
\[index=\max(\frac{|\Delta h|}{h_{0}},\frac{d}{d_{0}})-1 \tag{4}\]  
On the basis of index, the calculation process is shown in Figure 3. The specific definitions are:  
(1) Number of flight conflicts: the number of conflict events during statistical period, where \(num\) represents the total number of aircraft pairs, and \(is\_conflict_{i}\) represents whether there is a conflict event between pair of aircrafts:  
\[is\_conflict_{i}=\left\{\begin{array}{ll}1&\text{if}\quad index<0\\ 0&\text{if}\quad index\geq 0\end{array}\right. \tag{5}\]  
Figure 3: Calculation process of safety parameters.  
Figure 2: Relative position relationship between aircrafts.  
\[count=\sum_{i=1}^{num}is\_conflict_{i} \tag{6}\]  
(2) Minimum safety index: the minimum value of index during statistical period with respect to the pair of aircrafts which hold the closest distance, where \(min\_index\) represents the minimum index itself, \(num\) represents the total number of aircraft pairs, and \(index_{i}\) represents the index between the pair of aircrafts:  
\[min\_index=\min(index_{1},\cdots,index_{i},\cdots,index_{num}) \tag{7}\]
# Embedded Based Real-Time Monitoring in the High-Pressure Resin Transfer Molding Process for CFRP
## 4 Results and Discussion
### Process Analysis
The resin injected at a high pressure of 120 bar or greater diffused radially by approximately 200 mm far from the injection port (Figure 12a) to the center (Figure 12b) of CFRP specimen. In Figure 12c, the resin quickly arrived at the edge parts along the wall with a smaller resistance than the central part of mold as the resin viscosity increased due to the mold temperature and frictional resistance of the fiber. In particular, the mold wall without carbon fiber had almost no resistance; furthermore, in Figure 12d, the resin that had already reached the outer wall bumped against the wall and then flowed back to the center. The CFRP specimen was entirely impregnated by compression force after the resin injection had completed as shown in Figure 12e,f. As described above, the reason the cavity pressure sensor CP2 increased after 100 s in the curing step was that stagnation of the resin flow  
Figure 9: Results of the HP-RTM injection process: (**a**) Cavity pressures and injection pressure; (**b**) Resin temperature. occurred at the center of the mold and the constant pressure compressed the cured resin. The arrival time of the resin flow was two seconds from the CP1 sensor to the CP2 sensor and three seconds to the CP3-4 sensors. In addition, the maximum reaction temperature uniformly increased for sensors T1-4. Therefore, it is inferable that the resin was completely impregnated in the CFRP laminate from the change in the cavity pressure and the reaction temperature of the resin inside the mold.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Application Areas
#### 4.2.6 Intensive Care Units  
The critical nature of Intensive Care Units (ICUs) necessitates precise capacity planning and resource management. [PERSON] et al. [42] documented DES applications in optimizing bed occupancy, allocating staff efficiently, and improving patient flow. Their study showed that DES-based capacity planning reduced ICU admission delays by 28%, emphasizing the role of simulation in mitigating bottlenecks in high-acuity care settings.
# Design of a SIMO Deep Learning-Based Chaos Shift Keying (Dlcsk) Communication System
## 4 Simulation Results  
In this section, we first provide a comparison between the BER performance of the Single-input Single-output (SISO) DLCSK and the conventional DCSK over AWGN and multi-path Rayleigh fading channels. Then, evaluation of various parameters such as bifurcation parameters and the number of antennas on the system performance takes place. In all simulations the classifiers are trained only at a limited SNR range, i.e., \(\sigma_{tr}^{(n)}\in[\sigma_{tr,min},\ \sigma_{tr,max}]\) dB, while tested over a wide range of \(E_{b}/N_{0}\) values greater than 0 dB.  
In order to generate chaotic signals, we choose two discrete time recursive functions, i.e., Chebyshev and Logistic maps, which have been used extensively in practical communication systems [40; 83]. The transmitter creates two sets of chaotic signals using these two maps during the training phase, according to the values in Table 1. For example, in case 1, each of these sets includes the number of \(N=2000\) chaotic signals, each with length \(\beta=50\) samples. In other words, we use two sets of the training chaotic signals, each with total length \(S=50\times 2000=10^{5}\) samples. All of the chaotic signals have equivalent class labels as \(j\in\{0,1\}\). The receiver obtains the corresponding altered signals and forms the final training set as input of the NN. Therefore, after the number of \(2\times 2000=\text{r}\ 4000\) signal transmissions, we will obtain a training set, consisting of 4000 received signals along with their corresponding class labels j. In the test phase, a binary \"0\" will be sent, transmitting a Chebyshev map, and if \"1\" is to be sent, a Logistic map is transmitted.  
In our simulations, to evaluate the system's generalization capability and robustness against parameter mismatch, we use different parameter settings for the training and testing phases. Therefore, to generate Logistic maps, the bifurcation parameters are, respectively, chosen as \(\rho_{train}=3.6\) and \(\rho_{test}=3.3\) for the training and the deployment phases, with an initial state as \(\tilde{x}_{1}=0.3\). To generate Chebyshev maps, initial states are chosen as \(\tilde{x}_{1}=0.3535\) and \(\tilde{x}_{1}=0.3\) for the training and the deployment phases, respectively.  
Predefined functions of the MATLAB Neural Network Toolbox can be used to define an LSTM network and specify training options, including Learning Rate (\(\eta\)), and the number of hidden layers (\(H\)). In all experiments, the learning rate is set to \(\eta=0.01\). The other DL parameters are selected based on the SIMO DLCSK parameters. Although we consider the AWGN and Rayleigh fading channels, SIMO DLCSK can also be applied to any other channel model as well. The considered network parameters are listed in Table 1.
## 4 Results
### Part III  
Lastly, we will examine the DCAI strategies used to improve the results. As discussed in the Section 3, DCAI is a paradigm shift in artificial intelligence that focuses on data as the primary source of information. We examined five DCAI approaches: cleanlab-OOD,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network** & **Train** & **Test** & **F-Score** & **Precision** & **Recall** & **Train and Test Size** \\ \hline IsrPoliceDB & 0.97 & 0.96 & 0.96 & 0.96 & 0.96 & 1016/255 \\ \hline NIST-DB4 & 0.94 & 0.90 & 0.82 & 0.86 & 0.79 & 800/200 \\ \hline NIST-302 & 0.88 & 0.84 & 0.81 & 0.81 & 0.81 & 1583/396 \\ \hline SOCOfing & 0.84 & 0.80 & 0.68 & 0.69 & 0.68 & 4800/1200 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of gender classification using the selected VGG19 model on the four different datasets: our internal IsrPoliceDB and the three public datasets—NIST-DB4, NIST-302, and SOCOfing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Train Accuracy** & **Test Accuracy** & **F-Score** & **Precision** & **Recall** \\ \hline VGG16 & 0.92 & 0.83 & 0.8 & 0.8 & 0.81 \\ \hline VGG19 & 0.87 & 0.84 & 0.81 & 0.81 & 0.81 \\ \hline ResNet18 & 0.97 & 0.76 & 0.77 & 0.78 & 0.78 \\ \hline ResNet50 & 0.85 & 0.75 & 0.76 & 0.78 & 0.74 \\ \hline ResNet101 & 0.87 & 0.76 & 0.77 & 0.79 & 0 FLIP (Easy and Hard), and MOC (Easy and Hard). For each of the five approaches, we scored every instance in the training set and filtered out the highest 5% of instances in the dataset. Subsequently, we re-trained each model using the resulting clean dataset. The newly trained model was then evaluated on the original test-set.  
As evident from Figure 3, two out of the five approaches--cleanlab-OOD and FLIP-Easy--showed consistent improvements, with an average F-score improvement of 2.5% and 2.75%, respectively. The cleanlab-OOD approach improved the F-score across the four datasets, except for the NIST-302 dataset, where the values were equal to the baseline. The most significant improvement was observed for the NIST-DB4 dataset, where the F-score increased from 0.82 to 0.87. FLIP-Easy approaches showed noticeable improvements in the NIST-DB4 and NIST-302 datasets. These findings provide compelling evidence of the successful filtering capability of DCAI approaches, effectively eliminating the data that negatively impacted the model's performance.  
Figure 3: A percentage change in F-score compared to baseline results is shown in Table 2. For each database we examined five DCAI approaches: cleanlab-OOD, FLIP (Easy and Hard), and MOC (Easy and Hard).  
Figure 2: The percentage change in F-score compared to baseline results is shown in Table 2. For each database we examined four cases. Gender classification based on (a) 50% of the inner region (first row), (b) 60% of the inner region (second row), (c) 50% of the outer region (third row), (d) 40% of the outer region (forth row).
# True Equalization of Polarization-Dependent Loss in Presence of Fast Rotation of SOP +
## 4 Simulation and Analysis
The results are shown in Figure 7d,e (the solid point is the worst polarization case and the dashed point is the best polarization case). We study both the weak impairment condition (RSOP = 300 krad/s, PDL = 4 dB, and OSNR = 15 dB), as shown in Figure 7d, and the severe impairment condition (RSOP = 1 Mrad/s, PDL = 6 dB, and OSNR = 15 dB), as shown in Figure 7e. We can see that the proposed Golden code + KF method can effectively mitigate the different BERs of the two polarization branches in both weak and extreme scenarios. Figure 7: \(Cont.\)
# Research on Authentic Signature Identification Method Integrating Dynamic and Static Features
## 3 Materials and Methods
### Classification Model  
After feature selection, a variety of classification methods is used for verification. The training data are input into the classifier to learn the model and then the model predicts the label. Finally, the accuracy of model prediction is calculated to achieve the effect of signature identification.  
Machine learning is a way to realize artificial intelligence, including different kinds of algorithms. It is a method of using data, training models and then using models to predict. For smaller datasets, classical machine learning algorithms are usually better than deep learning, which often require a large amount of data. The experiments are mainly conducted using four traditional algorithms: discriminant analysis, K-nearest neighbor, random forest and support vector machine.  
Convolutional neural network (CNN) is a kind of feedforward neural network with convolution calculation and deep structure, which has been used to varying degrees in image processing, natural language processing, etc. [25]. The advantage is that multiple convolutional filters are used to extract high-level information from low-level information and the disadvantage is that encapsulation is not conducive to network performance improvement. The model used in the experiment has 9 layers, including 4 convolution layers, 4 maximum pooling layers and 1 fully connected layer.  
Long short-term memory (LSTM) is a special recurrent neural network (RNN) model, which solves the short-term memory problem of RNN for solving the gradient disappearance and gradient explosion problems during the training of long sequences [26]. LSTM is very suitable for dealing with problems highly related to time series, such as machine translation, conversation generation, encoding and decoding, etc. The model used in the experiment has 4 layers, including 1 input layer, 2 hidden layers and 1 output layer.
## 4 Discussion
We previously reported the storage stability of three different types (strongly acidic, slightly acidic, and neutral) of electrolyzed water at standard concentrations [25]. Even the neutral type (NW), which had the highest storage stability, was affected by differences in storage environment, causing a significant decrease in its properties when stored at room temperature without light shielding when the bottle was open [25]. As previously reported [25], test NW preparations from the same two-stage electrolysis were used in this study. We standardized the sealing of all storage containers for eight NW preparations with different ACCs containing standard concentrations and examined the effects of the initial ACC, light shielding, and temperature during storage on long-term storage stability. Consistently with previous findings [23; 25; 38], our findings confirmed the effectiveness of shaded and refrigerated storage conditions. As previously mentioned, the pH and ACC of NW during actual use change in a complex manner depending on the initial ACC at the time of preparation and the storage conditions. High-concentration NW preparations are particularly susceptible to a significant reduction in ACC during storage. Consequently, an inappropriately diluted NW (i.e., diluted with insufficiently effective ACC) may be used for disinfection of the oral cavity, potentially resulting in insufficient microbicidal activity. Therefore, when using stored high-concentration NW, it is necessary to measure ACC and dilute it appropriately before use. Neutral electrolyzed water containing HOCI as the main ingredient has a pH equivalent to that of the oral cavity at rest, which holds promise for potential applications in dental practice. By accurately assessing antimicrobial components based on properties such as pH and ACC at the time of use and maximizing the effectiveness of NW, it is possible to replace traditional disinfectants and contribute to SDGs by preventing the overuse of chemical substances. In this study, the reason for the whitening of the PET containers storing NW\({}_{1000}\) was not identified by chemical analysis. The effects of storage conditions for neutral electrolyzed water on containers will be investigated in detail by adding containers made of other materials. Future research will focus on evaluating the biological safety of NW and gels containing NW [35] for intraoral treatment using cytotoxicity tests on oral mucosa cells. Additionally, we plan to specify the optimal usage conditions (concentration, form, treatment, and time) of NW for each intraoral procedure and further evaluate their efficacy and biocompatibility through clinical trials.
## 6 Evaluation
### Experimental Environment  
This subsection explains how to set up the experimental environment, including the tools for simulating the network, attacks, and parameters, all experiment scenarios, evaluation of detection performance metrics, and the methods to be compared, are described.  
The experiment setup in [21] was used for our testbed, along with Mininet to simulate the DetNet SDN environment and one SDN controller, OpenDayLight. The core of the network is composed of switches in an Ubuntu system 16.04 virtual machine. Because we considered the limitations of the experimental equipment, the bandwidth of all links in our testbed is 10 Mbps. In addition, we set up botnet access requests to the target area in SDN to generate legitimate traffic using iPerf [21] and trafgen [26], which are network tools, to simulate hybrid attacks on the testbed, where we could directly control all hosts. iPerf and trafgen are tools to generate and observe traffic based on the maximum achievable bandwidth on IP networks. This traffic supports different timings, protocols, and buffers, and it can collect corresponding performance results, including throughput,loss, and other parameters. We employed five botnets that played the role of an SEA and LFA and twenty-five hosts as normal usage. The SDN controller and bot were built by a container deployed in several Virtual Private Cloud (VPC) networks in different cloud service providers to emulate the DetNet architecture. The container is a lightweight virtualized service containing only the application and its libraries and dependencies, and VPC is a virtual version of a physical network.  
The SEA botnets with trafgen [26] send at least 10,000 SYN packets, and LFA botnets send traceroute packets to the target area to find target links before sending TCP or UDP packets with iPerf [21] at both ends of the target link to congest the target links. During the attack phase, the bandwidth of attack traffic from bots is 2 Mbps. At the same time, the remaining twenty-five normal hosts randomly access the three servers in the target area because attacks will exist simultaneously with normal usage in the real world.
## 1 Introduction
### Routing Protocols: A Brief Overview
#### 1.3.2. Destination-Sequenced Distance Vector Routing (DSDV)  
It is the most widely used table-driven routing protocol in Wireless Sensor Networks. In this protocol, every node maintains routing tables with all necessary information required to reach a particular destination in the network, along with the minimum hops required to reach that destination. Every table entry is associated with a sequence number that will be used to identify the previous existence of the root. Every node will send a periodic update with the increased sequence number to advertise its location and status in the network. Route with the most recent sequence number is considered the final route for any destination [11; 12].
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 5 Management and Countermeasures
### Fireighting
In summary, the previous studies mainly focused on the comparison of fire extinguishing effects among different extinguishing agents, such as Halon, water, carbon dioxide, foam, dry powders and so on. However, some deficiencies still exist and challenging questions are being proposed regarding fighting battery fire. For example, the spray time of the agent, dose of agent, damage degree of the battery, cost and scenario, etc. shall be quantitatively or qualitatively estimated. Furthermore, standard specifications and guidelines for the firefighting of battery fire shall be formulated and promulgated as soon as possible to meet the requirements of the market.
## 4 Discussion
### Remote Sensing for Biodiversity Conservation
#### 4.3.2 Linking Ecological and Remote Sensing Communities  
Interdisciplinary approaches between biodiversity conservation and remote sensing fields are developing [30; 126; 127; 128]. Remote sensing data, due to their great diversity, can be used for many applications [30]. For example, the plant specific diversity can be assessed from data characterizing the soil [129]. Maps produced by species distribution models are more reliable when the models integrate environmental, ecosystem structure or habitat quality variables [1; 128; 130]. Furthermore, remote sensing data allows to detect the effects of possible habitat modifications [54] and to follow the consequences of protection actions implemented [30]. Finally, the data collected in situ, for instance, physiological data from biomimetic loggers use or experiments, demographic data from counts, tracking movements data from transmitter collars, complement those acquired by conventional remote sensing methods and are essential for improving conservation strategies [127; 131]. For example, defining the activity time (related to physiology) and the persistence index can allow the construction of a mechanistic species distribution model [88]. The activity time mechanistic variable can also be integrated in the same way as climate and habitat variables (possibly measured by remote sensing) in a distribution model [83]. This last modeling method, called the hybrid method, is growing rapidly because it combines the advantages of the two main distribution model types, the ease to use of correlative models and the explanatory power of mechanistic models [132; 133].
###### Abstract  
Damage models, particularly the Gurson-Tvergaard-Needleman (GTN) model, are widely used in numerical simulation of material deformations. Each damage model has some constants which must be identified for each material. The direct identification methods are costly and time consuming. In the current work, a combination of experimental, numerical simulation and optimization were used to determine the constants. Quasi-static and dynamic tests were carried out on notched specimens. The experimental profiles of the specimens were used to determine the constants. The constants of GTN damage model were identified through the proposed method and using the results of quasi-static tests. Numerical simulation of the dynamic test was performed utilizing the constants obtained from quasi-static experiments. The results showed a high precision in predicting the specimen's profile in the dynamic testing. The sensitivity analysis was performed on the constants of GTN model to validate the proposed method. Finally, the experiments were simulated using the Johnson-Cook (J-C) damage model and the results were compared to those obtained from GTN damage model.  
Keywords:damage model; Gurson model; Kriging method; simulation; optimization +
Footnote †: journal: Applied Sciences