esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Internet has grown explosively and rapidly with the ability to provide numerous services for a wide range of Internet applications. The Internet includes a huge amount of applications and protocols such as http, https, file transfer, e-mail, databases, VOIP, etc.  
Internet Server Provider (ISP) and network operators are usually interested in knowing the traffic carried in their networks for the purpose of optimizing network performance and security issues. Therefore, network traffic classification is an important foundation for identifying unknown Internet applications which have abnormal behaviors. In particular, network classification can detect traffic which includes threats, such as denial of service, flooding attacks and other such threats [1, 2].  
With the increase in Internet usage, a lot of Internet applications have been developed. However, these new applications can carry abnormal Internet traffic, which has a negative effect on network performance. Some of the Internet applications generate several types of versions with different traffic attributes. For instance, online games usually constitute a huge number of the overall games across the world each year. Network traffic classification is invaluable for identifying these large applications. Network traffic management is another issue which shows the importance of Internet traffic classification. When network managers plan to control network users through fair usage of bandwidth, they need to first know which type of applications they are dealing with. Thus, the managers cannot achieve their administrative tasks, unless they classify the network traffic. In the home network, traffic classification can help to enhance the Quality of Service (QoS) of Internet services. In general, the identification of Internet traffic helps in classifying of different types of attackers [1].
## 2 Background and Preliminary
### Anomaly Types in MTS
#### 2.2.2 Pattern-Wise Anomalies  
Shapelet anomaly: Shapelet anomalies are circumstance that occurs in a particular environment or context. Unlike individual data point anomalies, shapelet anomaliy indicates that the collective behavior of a group of related data significantly deviates from the expected or normal pattern, as shown in Figure 2b.  
\[diss_{p}(p,\hat{p})>\delta \tag{6}\]  
where \(\hat{p}\) specifies the expected shape of the subsequence. _diss_ is a function that measures the difference between two subsequences.  
Trend anomaly: Trend anomalies focus on sudden changes or deviations from the long-term trend in the data. These anomalies may result from sudden events, systemic changes, or other factors leading to abrupt shifts or abnormal growth or decline in the trend direction.  
\[diss_{\tau}(\tau,\hat{\tau})>\delta \tag{7}\]  
where \(\hat{\tau}\) is the trend of normal subsequences.  
Cycle Anomaly: Cycle anomalies occur when there are abnormal changes in the periodic patterns of time series data. For example, there is anomaly in the frequency or intensity of a seasonal event.  
\[diss_{s}(s,\hat{s})>\delta \tag{8}\]  
Figure 2: **(a)** Point-wise anomalies, the red dots indicate anomalies, and **(b)** Patten-wise anomalies, the red areas represent anomalies.  
where \(\delta\) is the cyclicity of expected subsequences.
# Communication Architecture in Mixed-Reality Simulations of Unmanned Systems
## 6 Addressing the Precision Aspects of the Proposed Architecture  
The proposed architecture enables validation process of complex multi-vehicle systems through high-fidelity testing in MR simulations. It also provides functionality of precise modeling of wireless channel utilization by (i) employing networking simulation for SWEs and (ii) injecting an RF signal into the wireless channel, as described in Section 5.3. Even though the process of injecting the RF signal into the wireless channel is necessary for the high-fidelity modeling of the medium utilization, it also produces undesirable side effects caused by the fact that multiple SWEs use a single static modem for the communication with HWEs. It produces interference that is inconsistent with the reality and could lower the precision of the simulations. These simulation aspects need to be addressed, and we propose the following solutions.
## 4 Conclusions  
In this study, we focused on the revegetation of waste slag heaps at XKS, the world's largest antimony mine, investigating the response of environmental geochemical behaviors and the evolution of comprehensive ecological risks. Results indicated that revegetation could positively improve the geochemical characteristics of the slag heap, and with the increase in the treatment time, the pH value gradually decreased from strongly alkaline to nearly neutral, while the TN, TP, and Corg content gradually increased. Compared with the unremediated slag heap, the total content of metal(loid)s such as As and Sb decreased significantly after revegetation and gradually approached the background level of the original vegetation area. BCR analysis showed that the exchangeable fraction of Sb and As decreased significantly after revegetation, while the residual fraction increased. Based on the MCR method, the ecological risk of the area was reduced from the very high risk level to the less risky level. Generally, the revegetation technology in antimony mining areas can effectively improve the environmental quality and reduce the risk level. This study provides an effective insight into the ecological restoration of antimony mines, based on the multidimensional perspective of \"geochemistry--speciation transformation--risk evolution\", which can provide a basis for the precise control of the remediation process. Based on the key findings of this study, the research topics that need to be further explored are (1) decoding rhizosphere processes, integrating metagenomics and metabolomics to explore how root exudates regulate metal(loid) speciation, and identifying functional microbiomes and (2) establishing long-term resilience monitoring, tracking ecosystem stability, and establishing the dynamic MCR forecasting models.  
Conceptualization, X.Z. and F.H.; methodology, X.Z., J.L., F.H., D.W. and L.D.; formal analysis, X.Z., C.C., L.X. and W.W.; investigation, X.Z., J.L., F.H. and C.C.; resources, X.Z., J.L., F.H., D.W. and L.D.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z., J.L., F.H., D.W. and L.D.; visualization, X.Z., C.C. and L.X.; supervision, F.H.; project administration, X.Z., D.W. and L.D.; funding acquisition, X.Z., D.W. and L.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Research Fund Program of Hunan Mine Carbon Sequestation and Sink Enhancement Engineering Technology Research Center (No. 2024 KSCTZH02) and the public welfare geological survey project of China Geological Survey (DD20230701305).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
All the authors thank the reviewers and editors for their constructive comments and suggestions in improving the quality of the work presented.  
The authors declare no conflicts of interest.
# An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions
## 3 Weather Effects
### Humidity  
Humidity is broken down into two different types: absolute and relative. Absolute humidity is the amount of water vapor in a cubic meter of air. Depending on the temperature of the environment, the amount of water vapor and miscellaneous particles that can be contained within a volume could vary. When observing absolute humidity with the current temperature, relative humidity is attained. Relative humidity is 100 percent or saturated when the dew point temperature and actual temperature match. With this in mind, the ratio of the actual vapor pressure to the saturated vapor pressure will also be the relative humidity [41]. If saturation is achieved along with certain weather conditions, fog forma  
Figure 12: Range degradation curve observed when 905 nm and 1550 nm are subjected to 0.5 km and 0.2 km visibility fog.  
tions could be facilitated. Since pressure is directly affected by humidity, humidity should affect ultrasonic sensors. The levels of humidity have a variable effect on the attenuation of ultrasonic waves. This effect is not necessarily the same for all frequency to humidity levels. For example, at 200 kHz, maximum attenuation is observed at saturation but at 60 kHz, max attenuation is observed at 60% humidity. The latter frequency is relatively close to modern AV sensor frequencies. In conditions of no humidity, attenuation is approximately 0.25 dB/ft. Another phenomenon observed by [39] was at 20 \({}^{\circ}\)C; attenuation seemed to rise sharply.  
Based on water's high absorption coefficient, high humidity should have a noticeable contribution to the attenuation of LiDAR performance. Under normal conditions, the extinction coefficient of the 905 nm is typically about double the 1550 nm's one. Table 2 shows the atmospheric extinction coefficients subjected to varying levels of humidity, as reported by [PERSON] et al. As can be observed, the extinction coefficients of the 905 nm and the 1550 nm approximately keep their ratios the same. That being said, different levels of humidity have negligible effects on the performance of LiDAR. Since LiDAR is not affected by water vapor content of humidity, RADAR, being a more robust system, will also not be affected by humidity levels [33].
# First-Principles Study of the Contact Resistance at 2D Metal/2D Semiconductor Heterojunctions
## 2 Computational Methods  
The first-principles simulations of the 2D metal/semiconductor lateral heterostructures were performed using density functional theory (DFT), as implemented in the Siesta code [28], using the generalized gradient approximation (GGA) for the exchange-correlation functional [29]. Valence electrons were described using double-zeta polarized basis sets, while core electrons were described by norm-conserving pseudopotentials [30]. The slab models investigated in this work may include a net dipole moment, induced by defects or adsorbates on the surface; the calculations on such systems are performed by including self-consistent dipole corrections [31]. Long-range van der Waals forces were also included, using Grimme dispersion corrections [32].  
The non-equilibrium Green's function (NEGF) method was used for the ballistic transport simulations of the heterostructures, as implemented in the TranSiesta code [33], with single-zeta polarized basis sets [34; 35; 36]. The system was divided into three different parts, i.e., the left electrode, the central channel region, and the right electrode, as schematically pictured in Figure 1.  
The ballistic current-voltage (I-V) characteristics are computed using the Landauer equation [37; 38],  
\[I=\frac{2q}{h}\int Trace\Big{[}\Gamma_{1}G^{R}\Gamma_{2}G^{A}\Big{]}(f_{1}(E)- f_{2}(E))\;dE \tag{1}\]  
where \(q\) and \(h\) are the electron charge and Planck constant, respectively, \(G^{R}\) (\(G^{A}\)) is the retarded (advanced) [PERSON]'s function, and \(\Gamma_{i}\) and \(f_{i}\) are the self-energies and electronic distribution functions at the contacts, respectively.  
The resistance R of the 2D metal/semiconductor heterojunction is obtained from the simulated current-voltage characteristics in the linear regime (R = V/I), as shown for a typical 1T/2H/1T-MoS2 structure in Figure 2a. The calculated resistance (multiplied by the contact width W) is shown in Figure 2b as a function of the channel length. The resistance presents an almost linear dependence on the channel length, as expected in the (quasi-) ballistic regime [39; 40].  
\[R(L_{diamond})\;W=2(R_{c}W)+R_{diamond}(L_{diamond})\;W. \tag{2}\]  
Figure 1: Schematic two terminal device used for the non-equilibrium Green’s function (NEGF) transport simulation. The electrochemical potentials of the left (\(\upmu_{1}\)) and right (\(\upmu_{2}\)) electrodes are shifted by qV when a bias V is applied to the structure. The self-energies \(\Gamma_{1}\) and \(\Gamma_{2}\) at the contacts are also indicated.  
The factor 2 in this equation accounts for the two 1T/2H interfaces in the two-terminal device. The contact resistance \(R_{\text{c}}W\) is then obtained by extrapolating the resistance RW to the limit \(L_{channel}\)\(\rightarrow\)0 nm.
# Fluorescence Hyperspectral Imaging for Early Diagnosis of Heat-Stressed Ginseng Plants
## 4 Discussion  
### Spectral Analysis  
In the current investigation, emissions occurred between 450 and 600 nm and 625 and 750 nm. However, as demonstrated in the SNV-based preprocessed spectral data in Figure 6b, reflectance increased substantially, with wavelengths from around 680 to 731 nm, with sharper peaks due to internal light scattering by the leaf cells. At the same time, the reflectance in the visible range was lower due to light absorption by leaf pigments in the visible region between 450 and 636 nm. There are significant intensity variations across the spectral curve of the samples, with control plants having higher reflectance in the near-infrared region than stressed plants. Since healthy plants reflect more light than unhealthy plants in the near-infrared region over 700 nm, heat-stressed plants that presented a higher intensity in the visible range might have less greenness and chlorophyll composition, which reflects more light from the surface of the leaves. However, in the visible region, there were some overlaps between control and stressed ginseng spectral mapping due to a higher moisture content in healthy plants.  
In comparison, the sensitive group was vulnerable to heat treatment, because the peaks represent changes in order, with Figure 6b mapping order changes in the peaks at 683 nm in the visible region. Apart from peaks at the near-infrared region of 731 nm, higher peaks correspond to control, resistance, and sensitive plants. Various studies have reported peaks at around 680 and 735 nm corresponding to plants' chlorophyll absorption. For instance, early-stage disease detection in graprefruit plants appeared at around 686 nm and 735 nm [39], and early blight and late blight disease in tomato leaves' chlorophyll bands were detected at 680 nm [65]. In another study, drought-stressed soybean plants presented chlorophyll (a) peaks at around 686 and 738 nm [30], and for assessment yield in cowpea plants, chlorophyll peaks were observed at the same peaks, at around 683 nm and 731 nm [66]. Overall, we concluded that peaks at 683 nm and 731 nm corresponded with chlorophyll peaks in control and heat-stressed ginseng plant leaves.
## 2 Materials and Methodology
### Electrical Connections and Data Measurements  
In practice, many measuring devices were used during the experiment (digital clamp meter, a single-end glass seal (NTC) thermistor temperature sensor, digital temperature humidity meter) integrated with a microcontroller to obtain more accurate results. Heat sensors (thermocouples) are attached at various places on the panel to determine the temperature distribution on the test panel. All of the mentioned sensors here are sending their measurement signals to an Arduino Mega which is shown in Figure 8.  
The recorded data is shown by Arduino IDE serial monitor and saved into a text file. To optimize the TEG panel output voltage, two approaches are used in this study. A microcontroller (Mega) was used to measure the current and voltage in real-time in conjunction with some manual measurements.
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 4 Fault-Types Classification (FC)
### Artificial Neural Network (ANN) Based FC
#### 4.1.2 Radial basis Function Network (RBFN)  
A radial basis function network (RBFN) is a type of FNN that uses radial basis activation functions for hidden nodes as shown in Figure 3 [64]. Typically, activation functions are Gaussian functions and an RBFN has one hidden layer [65]. RBFN used to construct fault classifiers considering the inability of FNN shown with sigmoid activation functions [66]. [PERSON] et al. trained two RBFNs separately to classify faults involved with and without earth, respectively [67].
# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
## 4 Experiments
### Datasets  
The proposed RS-YOLOX model is evaluated on three aerial remote sensing image datasets: DOTA-v1.5 [42; 43; 44], TGRS-HRSRD [45; 46], and RSOD [47; 48].  
DOTA-v1.5 includes 2806 large-size images, 16 categories, and 403,318 object instances. The DOTA images were collected from Google Earth, GF-2, and JL-1 satellite provided by the China Center for Resources Satellite Data and Application. It is widely used in the research of remote sensing image object detection. The image size of this dataset is large (800 \(\times\) 800 to 20,000 \(\times\) 20,000 pixels), and the images could not be directly used for model training. So we cropped each image to a size of 640 \(\times\) 640. Then we used data augmentation techniques to increase the number of small categories. The number of images after cropping and augmentation was 42,831. We split the images into a training set, a validation set, and a test set in a ratio of 6:2:2. The training set had 25,699 images, the validation set had 8566 images, and the test set had 8566 images.  
Figure 7: Execution process of the SAHI framework.  
TGRS-HRRSD includes 21,761 images, 13 categories, and 55,740 object instances. This dataset was acquired from Google Earth and Baidu Map. The image size of this dataset is from 150 \(\times\) 150 to 1200 \(\times\) 1200. The advantage of this dataset is that the number of categories is balanced and each category has about 4000 instances. The officially announced training set had 5401 images, validation set had 5417 images, and test set had 10,943 images.  
RSOD includes 976 images, 4 categories, and 6950 object instances. The image size of this dataset is about 1000 \(\times\) 1000. The number of different categories in this dataset is not balanced. To achieve better experimental results, we used data augmentation techniques to increase the number of images in small categories. The augmented dataset has a total of 1916 images, with approximately 400 images per category. We split the images into a training set, a validation set, and a test set in a ratio of 6:2:2. The training set had 1150 images, the validation set had 383 images, and the test set had 383 images.
# Research on the Performance of an End-to-End Intelligent Receiver with Reduced Transmitter Data
###### Abstract  
A large amount of data transmission is one of the challenges faced by communication systems. In this paper, we revisit the intelligent receiver consisting of a neural network, and we find that the intelligent receiver can reduce the data at the transmitting end while improving the decoding accuracy. Specifically, we first construct a smart receiver model, and then design two ways to reduce the data at the transmitter side, namely, end-of-transmitter data trimming and equal-interval data trimming, to investigate the decoding performance of the receiver under the different trimming methods. The simulation results show that the receiver still has an accurate decoding performance with a small amount of trimming at the end of the transmitter data, while the decoding performance of the smart receiver is better than that of the conventional receiver with complete data when the data is trimmed at equal intervals. Moreover, the receiver with equally-spaced data cropping has a lower BER when the data at the transmitter side is reduced by the same data length. This paper provides a new solution to reduce the amount of data at the transmitter side.  
neural network; intelligent receiver; reduce data; decoding; bit error rate +
Footnote †: journal: Medical Imaging  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 3 Signal Measurement Techniques
### Time-Based Methods  
3.1.1. Time of Arrival (TOA)  
The time of arrival (TOA) [24] method calculates the distance between the emitter and the receiving node considering the time elapsed between the emission and reception of the signal. It is also called time of flight (ToF) because it measures the signal transmission time between the receiver and the transmitter. Knowing the speed of propagation of the signal in the middle and the time taken to get from one point to the other (time of flight or flight time), the space traveled is directly calculable as follows:  
\[\text{R}_{i}=v*t, \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameter** & **Wi-Fi** & **Ultrasound** & **Infrared** & **Bluetooth** & **Rigid** & **ZigBee** & **UWB** \\ \hline Accuracy & 1–10 & 0.01–0.1 & 5–10 & 2–15 & 0.5 (passive) & 1–5 & 0.1–1 \\ \hline Coverage & 20–50 & 2–10 & 1–5 & 1–30 & 1–100 & 10–100 & 0.50–10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Range of accuracy (meters) and coverage (meters) for technology.  
where \(v\) is the speed of the signal employed, which is the transmission time of the signal received by node \(i\), and \(\text{R}_{i}\) is the distance of signal transmission received by node \(i\). Then, the location of the target is estimated using triangulation. For this method to be effective, it is necessary to have three reference points (also called anchors). Graphically representing the system, the node of which we want to calculate the coordinates would be given by the intersection of the three circumferences with the center of the reference nodes (Figure 4). In that case, we would have the following equations:  
\[\begin{array}{l}\left(x_{1}-x\right)^{2}+\left(y_{1}-y\right)^{2}=R_{1}^{2} \\ \left(x_{2}-x\right)^{2}+\left(y_{2}-y\right)^{2}=R_{2}^{2}\\ \left(x_{3}-x\right)^{2}+\left(y_{3}-y\right)^{2}=R_{3}^{2}\\ \end{array}, \tag{2}\]  
where \(\left(x,y\right)\) are the coordinates of the target node.  
This approach, however, has the big flaw of requiring the transmitter and receiver to be synchronized, which is not always feasible. To achieve high accuracy, this issue of the TOA technique can be compensated for by combining it with UWB (ultrawideband, a radio-based communication technology). This technology uses a short pulse duration to filter the signals caused by reflection to improve the overall performance.