esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Transmission for Buffer-Aided Relay Networks in the Internet of Things
## 2 System Model and Relay Selection Policy  
### System Model  
Let us consider the uplink transmission for the buffer-aided relay network in IoT application, as illustrated in Figure 1, which consists of a source sensor \(S\), a controller \(D\), \(M\) half-duplex intermediate relay sensors \(\{R_{m}\}_{m=1}^{M}\) and \(K\) passive eavesdroppers \(\{E_{k}\}_{k=1}^{K}\). In the network, all nodes are equipped with a single antenna and each relay is also equipped with a data buffer \(B_{m}\) of finite size \(L\). Note that the data packets in the buffer obey the \"first-in-first-out\" rule. Therefore, the time that a data packet is transmitted from the relay sensor to the controller depends on the length of the queue. On the other hand, it takes only one time slot to transmit a packet from the source sensor to the relay sensor. Furthermore, the \(S\to R\) and \(R\to D\) links are referred to as the main channel, and the \(S\to E\) and \(R\to E\) links are referred to as the wiretap channels. All channels are modeled as the quasi-static flat Rayleigh fading, hence the channel coefficients keep unchanged in the coherent time of the channels [30]. Since the impact of significant path loss, the direct link between \(S\) and \(D\) is assumed unavailable. That is to say, the source sensor \(S\) has to communicate with the controller \(D\) via the assistance of multiple intermediate relay sensors [27; 31; 32]. In this paper, we denote the complex Gaussian random variable \(h_{ab}\) as the channel coefficient of link \(a\to b\). According to this, the channel gain \(\left|h_{ab}\right|^{2}\) can be regarded as an exponentially distributed random variable, which mean it is equal to \(E\left[\left|h_{ab}\right|^{2}\right]=1/\lambda_{ab}=d_{ab}^{-\kappa}\), where \(E[\cdot]\) denotes the expectation operation, and \(d_{ab}\) and \(\kappa\) represent the distance of the link and the path loss factor, respectively. Specifically, the main channels are assumed independent and identically distributed (i.i.d), i.e., \(\lambda_{SR}=\lambda_{RD}\). Besides, due to the energy limitation of the sensors nodes in IoT networks, we consider the total power constraint \(P_{S}+P_{R}=P_{Total}\), where \(P_{S}\) and \(P_{R}\) represent the maximum transmit power of the source and the relay sensor, and \(P_{Total}\) denotes the total power.
## 2 Materials and Methods
### Verification Experiment of the Treatment System
#### 2.3.2 In Vivo Experiment  
In mammalian proteins, the effects of HIFU can be classified into three distinct categories. The first category involves low-intensity thermal damage, which is caused by prolonged exposure to HIFU. The second category involves high-intensity damage that occurs over a short period and is the result of cavitation. The third and final category involves moderate-intensity and -duration mechanically induced damage. When implementing vascular therapy, it is common practice to utilize low-intensity and prolonged treatment regimens. This approach is employed to achieve vessel shrinkage while simultaneously mitigating the risk of complications such as vessel rupture, which can arise as a result of cavitation and other mechanical effects [27; 28; 29]. In this study, we conducted an in vivo experiment on rabbit marginal ear veins using low-intensity and prolonged ultrasound therapy, utilizing its thermal effect on proteins, to assess the treatment effectiveness of the therapy system. The experimental setup comprised a laboratory lift, the integrated therapy system, a signal-triggering and -receiving device, and a displacement platform controlled by a stepper motor control box (Zolix SC300 series, Zolix, Beijing, China).  
The experimental setup for verifying the accuracy of the treatment using rabbit marginal ear veins is shown in Figure 8. A New Zealand white rabbit weighing 2 kg (\(\pm\)0.5 kg) was anesthetized intramuscularly with ketamine (30 mg/kg) and intubated with an endotracheal tube which was ventilated with isoflurane (0.2 L/kg/min) to keep the rabbit under anesthesia throughout the procedure with an R500 small animal anesthesia machine (RWD). The fur on both sides of the ear was shaved with a hair trimmer and then carefully depilated with a depilatory cream to ensure proper acoustic coupling and prevent skin burns. One ear was immobilized in an experimental vessel filled with deionized water using medical tape, and the marginal ear vein was exposed on the integrated probe movement path. The imaging probe was used to position the protruding marginal ear vein, after which the mechanical structure drove the integrated probe to the designated treatment site. An electric power of 10 \(\pm\) 1 W was applied for 4 min. After the experiment, the effectiveness was evaluated by comparing the results before and after treatment using the Vevo 2100 ultra-high resolution small animal ultrasound imaging system produced by VisualSonics with a 50 MHz linear array probe.  
Following the guidelines of the Declaration of Helsinki and approved by the Laboratory Animal Center of Huazhong University of Science and Technology (SYXK-2021-0057), an in vivo experiment on rabbits was conducted. The animals were kept under controlled environmental conditions, including a 12/12 h light/dark cycle and a temperature of 22 \(\pm\) 2 \({}^{\circ}\)C. Food and water were well supplied during the experiment.  
Figure 7: The experimental platform of the reset error measurement.
## 3 Case Analysis and Discussion
### Driving Behavior Risk Measurement Model
#### 3.4.2 Weight Calculation Results  
According to the above formula, the weights of various indicators (e.g., the driving stability indicator, the car-following risk indicator, and the lane-changing risk indicator) were calculated, and the results are shown in Table 3.  
The driving behavior risk measurement of each vehicle in frame \(i\) was calculated according to the weight in Table 3. \(MOR\) refers to the comprehensive risk evaluation results of all driving behaviors for each vehicle in frame \(i\). The calculation formula and distribution of \(MOR\) are presented in Equation (21)  
\[MOR=0.397R_{1}+0.310R_{2}+0.082R_{3}+0.210R_{4} \tag{21}\]  
where \(R_{1}\) and \(R_{2}\) represent the indicators of the lateral and longitudinal stability, respectively, both of which determine the driving stability indicator, and \(R_{3}\) and \(R_{4}\) are the indicators of car-following and lane-changing risk, respectively. Based on the dataset, the frequency distribution of the driving behavior risk measurements was obtained, as shown in Figure 6.  
Figure 6 shows that the overall data presented a normal distribution, the risk measurement for most drivers' driving behavior was between 0.1 and 0.3, and the remaining samples showed a lower frequency. For the MOR data, the lower their value, the more conservative a driver's driving behavior. Conversely, the higher their value, the more aggressive a driver's driving behavior.  
Figure 5: Normalization results for the driving behavior risk indicators.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indicator** & \(S_{j}\) & \(R_{j}\) & \(C_{j}\) & \(W_{j}\) \\ \hline \(R_{1}\) & 0.65652 & 2.916 & 1.91442 & 0.397 \\ \hline \(R_{2}\) & 0.54565 & 2.744 & 1.4972
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Windows**} & \multicolumn{6}{c}{**Classification Type and Performance (\%)**} \\ \hline
**Type** & **Size (ms)** & **CNN** & **MLP** & **Naive Bayes** & **SVM** & **Random Forest** & **k-NN** \\ \hline \multirow{4}{*}{Hamming} & 20 & 92.60 & 84.57 & 74.37 & 83.14 & 85.56 & 84.94 \\  & 25 & 92.11 & 84.19 & 74.63 & 82.91 & 86.36 & 85.42 \\  & 30 & 92.04 & 84.54 & 73.80 & 82.93 & 86.41 & 84.08 \\  & 35 & 92.59 & 84.12 & 74.66 & 83.36 & 86.37 & 84.43 \\ \hline \multirow{4}{*}{Hanning} & 20 & 92.59 & 84.40 & 74.51 & 83.16 & 85.41 & 84.50 \\  & 25 & 92.40 & 84.38 & 74.61 & 83.14 & 86.10 & 84.89 \\  & 30 & 92.18 & 85.11 & 74.48 & 83.20 & 86.36 & 84.72 \\  & 35 & 92.80 & 85.38 & 74.51 & 83.38 & 87.10 & 83.42 \\ \hline \multirow{4}{*}{Rectangular} & 20 & 92.05 & 85.24 & 73.80 & 83.30 & 85.63 & 84.49 \\  & 25 & 92.24 & 85.26 & 74.70 & 83.38 & 86.25 & 83.70 \\ \cline{1-1}  & 30 & 92.44 & 85.41 & 74.01 & 83.66 & 86.43 & 83.64 \\ \cline{1-1}  & 35 & 92.85 & 84.32 & 74.12 & 83.33 & 86.56 & 83.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance accuracy for male speakers based on GRU+EZMFCC. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Windows**} & \multicolumn{6}{c}{**Classification Type and Performance (\%)**} \\ \hline
**Type** & **Size (ms)** & **CNN** & **MLP** & **Naive Bayes** & **SVM** & **Random Forest** & **k-NN** \\ \hline \multirow{4}{*}{Hamming} & 20 & 93.98 & 88.52 & 77.76 & 85.20 & 89.23 & 86.71 \\  & 25 & 94.71 & 88.37 & 78.49 & 85.44 & 88.28 & 85.01 \\  & 30 & 94.44 & 88.51 & 77.24 & 86.15 & 88.38 & 84.58 \\  & 35 & 94.00 & 87.65 & 77.71 & 86.03 & 89.14 & 84.17 \\ \hline \multirow{4}{*}{Hanning} & 20 & 93.33 & 88.08 & 77.75 & 86.39 & 88.35 & 86.19 \\  & 25 & 94.00 & 87.72 & 78.22 & 86.30 & 88.89 & 87.03 \\  & 30 & 94.55 & 88.63 & 77.29 & 85.01 & 88.09 & 86.79 \\  & 35 & 94.21 & 87.70 & 77.01 & 85.47 & 88.23 & 86.29 \\ \hline \multirow{4}{*}{Rectangular} & 20 & 94.61 & 86.97 & 77.35 & 85.55 & 88.82 & 85.18 \\  & 25 & 93.74 & 88.05 & 77.73 & 86.19 & 88.43 & 84.54 \\ \cline{1-1}  & 30 & 93.79 & 88.13 & 77.08 & 84.62 & 87.62 & 84.61 \\ \cline{1-1}  & 35 & 94.18 & 87.23 & 77.55 & 84.03 & 88.12 & 85.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance accuracy for male speakers based on GRU+EZDMFCC.
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 2 Literature Review
### Over/Under Voltage and/or Frequency:
#### 2.2.2 Active Method  
The active method of islanding detection deals with the perturbation to the system directly. It means that an additional external variable, positive feedback or controlled change is introduced, which interact with the power system operation providing the significant change in system parameters during islanded conditions and negligible change during proper operation of the DG in grid. It involves the feedback technique or some mechanism to find islanding through parameters change [58]. This method is more accurate and effective than the passive method due to relatively small NDZs, but it has slow response toward the perturbation. In addition, the power quality and system stability may degrade due to the external variable introduced in the system [59]. However, this method requires a remarkably higher cost of implementation [49].
## 3 Experiments
### Video Datasets and Compared Algorithms  
The datasets employed are captured by the Jilin-1 satellite constellation. The satellite video sequences are all cropped from the original video sequences, and most of them contain three types of moving objects: moving vehicles, airplanes in the sky, and ships in the ocean. There are three airport objects, two moving vehicle objects, and ships in the ocean. The total number of objects moving in the airport is approximately 10. The maximum size of the object in all satellite videos is 32 \(\times\) 27 pixels, and the minimum  
Figure 6: Visualization of an object occlusion process. (**a**) not occluded object. (**b**) partially occluded object. (**c**) end of object occlusion.  
is about 10 \(\times\) 10 pixels. We number every frame and select the object in a frame as a representative to zoom in to see it more clearly.  
```
Input: frames: video datasets. \(T\): number of processed frames. \(F_{T}\): current frame \(T\). \(P_{T-1}\): the object position of frame \(T-1\). Output: \(P_{T}\): the current frame object position. Select the region of interest (ROI) and set the position of first frame. Set the occlusion threshold \(T_{h}\). for i in range (len(frames)): ifi == 1: (first frame)  Initialize the KCF tracker, VGG network, and Kalman filter. \(F_{HOG}\): extract HOG features. \(F_{VGG}\): VGG features selection and enhancement. \(P_{T}\): the position of current frame. else: Crop image patch from frames [i] according to \(\mathbf{P_{T}}\). Fuse-response: Fusion strategy for feature (\(F_{HOG}\), \(F_{VGG}\)) responses. \(P_{\text{peak}}\): the position of the max fuse-response. SDM: Calculate the SDM to detect occlusion. ifSDM > \(T_{h}\): /* the object is unoccluded */ \(P_{sub\text{-peak}}\): Subpixel location for \(P_{\text{peak}}\). \(P_{\text{final}}\): Motion trajectory compensation and correction. else: \(P_{\text{final}}\): The object position obtained by Kalman filter. \(P_{T}\gets P_{final}\) return\(P_{T}\) break
```  
**Algorithm 1** The proposed tracking scheme  
We challenge our algorithm against the current object trackers: CSK [11], KCF [12], HCF [26], TLD [27], DSST [28], SiamFC [29], SiamRPN [30]. The CSK algorithm and KCF algorithm are improved based on the MOSSE algorithm. The TLD algorithm has a better effect on the tracker when the object is partially occluded, while the HCF, SiamFC, and SiamRPN algorithms all extract the object's depth features during tracking, achieving appealing tracking results. The subsequent trials highlight that our proposed method is more effective for object tracking in satellite videos than current classic algorithms.
## 4 Conclusions  
The effect of the concentration of fine sediments on the hydraulics of the energy dissipation of a turbulent jet is an important and urgent problem for dams on hyperconcentrated sediment-laden rivers. Experiments were conducted using a deep outlet in an upstream tank and a plunge pool consisting of 12 test cases at four sediment concentrations (0 kg/m\({}^{3}\), 50 kg/m\({}^{3}\), 150 kg/m\({}^{3}\) and 250 kg/m\({}^{3}\)) under three upstream water heads (1.80 m, 1.85 m and 1.90 m). The results of these tests were used to study the hydraulic characteristics of energy dissipation in terms of flow discharge, jet properties, and hydrodynamic pressure. The main conclusions are:  
* Flow discharge is solely related to the upstream water head for a deep outlet and is independent of sediment concentration.
* The horizontal length of the jet from the deep outlet to the plunge pool increases with increasing upstream water head, and the width of the jet decreases with increasing sediment concentrations. The nappe of the jet also becomes smoother and exhibits decreased air entrainment as a result of the higher viscosity of suspensions with higher sediment concentrations.
* The time-averaged pressure in the plunge pool exhibits a peak around the impact region of the falling jet and the magnitude and distance from the outlet of this peak increases with increasing sediment concentration due to increased viscosity. The distance from the outlet to the peak also increases with increasing upstream water head.
* The RMS fluctuating pressure exhibits the same trend as the time-averaged pressure. The probability density, power spectrum, and coefficient of correlation of hydrodynamic pressure was calculated and the results demonstrate that as sediment concentration increases, the fluctuating energy increases, as does the scale of the vortexes, and the dominant frequency decreases.  
The findings of this study provide invaluable insights into the effects of sediment on the behavior of energy dissipators in large river dams. The behaviors and relationships determined in this study establish a foundation for the informed design of dam energy dissipator structures such as plunge pools, improving the operational safety of the overall dam. In the future, the effects of sediment on the velocity profile of the flow will be studied further.  
All authors contributed to the research work. [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the tests; [PERSON] analyzed the data; and [PERSON] drafted the paper.  
The authors are grateful to the National Natural Science Foundation of China (Grant Nos. 51509180, 51579172 and 51409187) for financial support.  
The authors dedicate the manuscript to the memory of [PERSON] who gave much advice.  
The authors declare no conflict of interest.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 1 Introduction
### Background
Our approach involves two days ahead wind speed forecasting, using both deterministic and probabilistic approaches on a dataset containing wind meteorology data from a wind farm in Western Cape province, South Africa. Statistical and ML methods are used for point and interval forecasting of wind speed. These methods are evaluated individually and combined to improve the forecasts. Statistical methods, such as statistical learning, make use of models such as autoregressive (AR), moving average (MA) or both (ARMA) and for non-stationary data makes use of autoregressive integrated moving average models (ARIMA). The conventional statistical learning techniques are the ARIMA models [2]. Other statistical learning techniques involving generalised additive models (GAMs) can also be used for wind speed forecasting [12]. Furthermore, a combination of forecasts from these methods, with the evaluated level of certainty using various point and interval forecasting metrics are discussed in this paper.
# Named Entity Recognition Using Conditional Random Fields
## 3 Characteristics of Urdu Language  
Urdu is a prominent South Asian language with several unique characteristics that play a notable role in the development of Urdu NER systems. For instance, Urdu is based on Arabic script, which flows from right to left, whereas Western languages flow from left to right. The differences between three prominent Western languages and the Urdu language are illustrated in Table 1 using an example sentence.  
Another key characteristic of the Urdu language that makes it different from the Western languages is its high contextual sensitivity. For instance, in the Urdu language, a letter can be written in more than one shape, where the shape is determined by two factors: the position of the letter in the text and the surrounding letters. This indicates that a letter can take on a single form if it appears at the beginning of a word and another shape if it appears in the middle of a word. Similarly, a letter can have another shape if it appears at the end of a word. For further understanding, consider the example sentence presented in Table 2.  
The table contains an example English sentence, its Urdu translation, and the Urdu letters used in the sentence in their original form. It can be observed from the table that although the sentence is composed of merely three words, the shapes of several letters (letters 2, 8, 9, 10, 12, and 14) are changed due to their position.  
Furthermore, Urdu has a lot of inflections because it draws terms from Arabic and Persian, as well as Turkish and English [21; 30; 31; 32; 33; 34]. As a result of its morphology, Urdu combines several other spoken languages with its own [21; 35].  
Besides the Urdu language characteristics discussed above, there are multiple other reasons that Urdu language processing (ULP) in general and NER in Urdu in particular require the special attention of the research community. For instance, Urdu is a free word order language, meaning that a single sentence can be structured in more than one way without any change in the meaning of the sentence. Therefore, in contrast to the Western languages, the position of a word in an Urdu sentence may not play a significant role in recognizing named entities [7]. Additionally, Urdu does not have capital letters, which is typically a key indicator of NE in the Western languages. Nevertheless, Urdu is widely acknowledged as a low-resource language with a scarcity of publicly available NER corpora, which has thwarted the advancement of supervised learning techniques for Urdu NER.  
To summarize, the main characteristics that make the Urdu NER task more challenging and complex are [7]: the absence of capitalization, its free word order nature, borrowed vocabulary, affixes, nested entities, and a lack of standard linguistic resources. Therefore, the current study aimed to develop techniques and computational resources for named entity recognition in Urdu.
# Use of Granulated Rubber Tyre Waste as Lightweight Backfill Material for Retaining Walls
## 2 Properties of Rubber Aggregates
### Classification of ELTs  
Traditional soils are usually classified according to the size range of the particles present in the soil, generally referred to as follows: clays \(<\) 0.002 mm; silts 0.002-0.06 mm; sands 0.06-2.00 mm; gravels 2.0-60.0 mm. Nevertheless, rubber granulates are classified by the shredding process of the ELTs. In most cases, this is carried out by two or more parallel shafts of blades rotating at different speeds to enable the incorporation of the tyre. The size of the resulting pieces is defined by the separation of the shafts. During the grinding process stages, steel and textile are separated, and the granulated rubber is obtained as a result [8].  
The particle size and distribution of shredded tyres can differ, and these materials are called Scrap Tyre Derived (STD) or Tyre-Derived Aggregate (TDA). Rubber shredding systems are established according to the current market demand. The materials produced in the various stages of the treatment processes, mainly through the size reduction of ELTs, can be largely classified in terms of their dimension(s). These are described in ASTM D6270 [26] and presented in Table 1, while Figure 1 shows the particle sizes typically produced for civil engineering applications. Given the limited number of investigations using rubber particles smaller than 12 mm in diameter as a lightweight backfill material for retaining walls, this research is focused on the study of the properties of this class of material, classified as granulated rubber (C).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Product** & **Size** \\ \hline Whole tyre & Unprocessed \\ Tyre shreds (A) & Between 50 and 305 mm \\ Tyre chips (B) & Between 12 and 50 mm \\ Granulated rubber (C) & Between 425 μm (40 mesh) and 12 mm \\ Powdered rubber (D) & Below 425 μm (40 mesh) \\ \hline \hline \end{tabular}
\end{table}
Table 1: ELTs classification by size in accordance with ASTM D6270 [26].