esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Simulation and Real-Time Experimental Results from the Employed Localization Algorithm  
In order to evaluate the performance of the VIO algorithm, the Malaga dataset [48] was used. At the second step, a real-time experiment with our designed electric vehicle prototype was performed to evaluate the performance of the integrated localization algorithm (VIO plus wheel odometry). The Malaga dataset was collected in different urban scenarios with a car equipped with a variety of different sensors including one stereo camera, an IMU, and laser sensors. This dataset provides different driving scenarios. In this study, a scenario named \"short avenue loop closure\" was used. Figure 9a shows the employed  
Figure 8: The prototype of the electric vehicle.  
Figure 7: Electric vehicle system configuration.  
driving scenario. The size of the vehicle estimation trajectory was intentionally chosen to be bigger than that of the true trajectory, because we wanted to evaluate the ability of the filter for scale estimation. In this test, the ratio was defined to be three. Figure 9b shows the performance of the employed visual-inertial odometry in simulation as compared with the ground truth from a GPS in the dataset.  
As it can be seen in Figure 9, although the scale of the VO trajectory was set to be three times bigger than the ground truth trajectory, the EKF was able to do scale estimation and provided correct estimation for VIO. In order to evaluate the performance of the proposed localization algorithm, an experiment using our designed electric vehicle was conducted. Figure 10 shows the result of the experiment that was conducted on the Kunsan National University campus.  
From the result of the real-time experiment, it can be seen that the combination of the VIO and wheel odometry provides a far better estimation as compared with both the VO and VIO.
## 4 DRL Based TDIA Transmission Scheduling Algorithm (DRLSA-IA)
### The Describe of DRLSA-IA
We calculate the distance between the current Q-value and the target Q-value through the MSE loss function. Finally, all the parameters (\(\omega\)) of the eval-net \(Q_{1}\) are updated through gradient backpropagation of the neural network. \[Loss=\frac{1}{T\;ktimes}\sum_{j=1}^{T\;ktimes}\Bigl{(}Q_{target}-Q_{1}\Bigl{(} S^{j}(t),a^{j}(t),\omega\Bigr{)}\Bigr{)}^{2}. \tag{12}\]  
```
1:Iteration times \(Itimes\), episode size \(ktimes\), the number of nodes \(N\), the number of time slots \(T\). 2:Input: discount factor \(\gamma\), learning rate \(\alpha\), exploration probability \(\epsilon\), the updated frequency of target-net \(Q_{2}\) parameters \(c\). 3:Output: the parameters of the eval-net \(Q_{1}\). 4:Initialize eval-net \(Q_{1}\) parameters \(\theta\) with random weights \(\omega\leftarrow\omega_{0}\);
5:Set target-net \(Q_{2}\) parameters \(\omega^{{}^{\prime}}\leftarrow\omega^{{}^{\prime}}_{0}\);
6:for Iteration \(m=1,\cdots,\)\(Itimes\)do
7:for episode \(k=1,\cdots,\)\(Itimes\)do
8:for timeslot \(t=1,\cdots,T\)do
9:for node \(i=1,\cdots,N\)do
10: Feed network state \(S_{i}(t)\) into the \(Q_{1}\);
11:\(Q_{1}\) generates an estimation of Q-values for all actions \(A=\{0,1,2,\cdots,N\}\);
12: Use \(\epsilon\)-greedy method to select action \(a_{i}(t)\);
13: Take action \(a_{i}(t)\), then update network state \(S_{i}(t+1)\) and obtain reward \(R(t)\);
14: Feed state \(S_{i}(t+1)\) into the \(Q_{1}\), \(Q_{2}\);
15:\(Q_{1}\) and \(Q_{2}\) generate estimations of Q-value, for all available actions;
16: Form a target Q-value of \(a_{i}(t)\) by (11);
17:endfor
18:endfor
19: Train \(Q_{1}\) with the all experiences in \(ktimes\) episode;
20: Every \(c\) iterations update target Q-Network \(Q_{2}\) parameter \(\omega^{{}^{\prime}}=\omega\);
21:endfor
```  
**Algorithm 1** Training Process of DRLSA-IA  
Each node uses the trained DRLSA-IA to distributely decide on transmission in real-time. In each time slot \(t\), node \(i\) obtains observation \(o_{i}(t)\), and then feeds observed state \(S_{i}(t)\) into the trained \(Q_{1}\). Q-values are generated by \(Q_{1}\) for all available actions \(a_{i}(t)\in\{0,1,2,\ldots,N\}\). Then, node \(i\) selects an action according to the \(\epsilon\)-greedy policy. The network state is updated.
## 3 Results
### Relocated Seismicity  
Figure 11 shows the distribution of relocated seismicity with the LOTOS algorithm. In general, the relocated hypocentral depths were slightly larger compared to the original depths from ROMPLUS Catalog [33; 34]. Several clusters of earthquakes were observed at the extremities of the map (left), as well as the cluster of events from the center of the map associated with the Targu Jiu earthquake sequence. Vertical sections (right) show the relocated hypocenters corresponding to the marked profiles. Depths are given in km. Most seismicity is concentrated between 5 and 25 km depth, with a dominant cluster around 15-25 km. There is also a correlation of the earthquakes with the fault paths in the area; the main cluster in the center seems to have a NE-SW orientation.  
\begin{table}
\begin{tabular}{c c c} \hline
**Depth (km)** & **Vp (km/s)** & **Vs (km/s)** \\ \hline
0 & 5.33 & 3.05 \\ \hline \(-5\) & 5.5918 & 3.2525 \\ \hline \(-10\) & 5.8036 & 3.3729 \\ \hline \(-15\) & 5.9989 & 3.4787 \\ \hline \(-20\) & 6.1647 & 3.5644 \\ \hline \(-25\) & 6.6443 & 3.8253 \\ \hline \(-31\) & 7.1269 & 4.0849 \\ \hline \(-35\) & 7.3346 & 4.1981 \\ \hline \(-40\) & 7.4671 & 4.2787 \\ \hline \end{tabular}
\end{table}
Table 2: P- and S-velocities in the reference 1D model after optimization.  
Figure 11: LOTOS relocated seismicity. Red dots are the resulting locations, bars are the mean errors with respect to the initial locations, blue lines are fault lines. Locations of the vertical cross sections are indicated in the left-side map.
## 1 Introduction
### Contribution in CO2 Emissions
In their study, [PERSON] and [PERSON] [37] introduced a power management technique for a residential integrated PV-storage battery system. This technique allows for both grid-connected and off-grid operation, and incorporates multimode control. The concept of integrating a smart house system with an EV as a storage device has been proposed. This system enables vehicle-to-home and vehicle-to-gadget (V2G) operations, providing advantages for both utility companies and consumers [38; 39; 40]. Renewable-energy-based charging stations are considered the most viable solution for the charging of EVs. However, their integration into the existing charging system necessitates an additional power conversion stage. This, in turn, leads to an increase in system complexity and power loss. Furthermore, it is imperative to integrate the current control system with the controllers dedicated to each stage of the conversion process. Hence, the establishment of a cohesive control and co-ordination mechanism is imperative for facilitating the development of a versatile and multifaceted operational integrated system [41]. The proliferation of EVs has given rise to various challenges within the domains of energy, transportation, and manufacturing. The development of charging platforms and infrastructure is necessary to facilitate the charging of EVs, both in residential settings and public locations. With the growing prevalence of EVs in circulation, there arises a pressing requirement to allocate resources towards the development of an intelligent grid infrastructure. The significant dimensions of EV batteries result in substantial power consumption during the charging process [42]. The existing body of literature provides various recommendations for mitigating the impact of EV charging on the distribution system. There are two primary categories of mitigation methods. The initial approach involves the implementation of time-of-use (TOU) pricing by utilities to discrerely manage EV charging [43; 44]. This approach incentivizes EV owners to charge their vehicles during non-peak hours by reducing electricity rates during off-peak periods in a time-of-use pricing framework [45]. The utilization of this technique results in a significant reduction in peak load demand, thereby mitigating concerns related to transformer overloading and heating. The second strategy involves the utilization of smart charging algorithms by utilities to actively control the rates and start times of EV charging [46]. The advantages of an EV charging system encompass the optimization of customer benefits and enhancement of utility benefits through the strategic scheduling of EV charging during off-peak-load hours. These are some of the articles whose contributions are highlighted here, but there are many more in which numerous experts in the field of EVs have produced high-caliber work. Currently, the field of research pertaining to EVs and their associated charging infrastructure is experiencing significant growth and development. Even so, EVs will encounter several difficulties, which have been explained further.
## 5 Solution Methodology Based on NSGA-II and TOPSIS  
The proposed OPP model is nonlinear because SCADA measurements and ZIBs are introduced into the problem. The multi-objective model is usually transformed into a single-objective model by unifying the dimensions and setting the corresponding weights [44]. However, the selection of weight requires prior knowledge which has strong subjectivity. Therefore, the non-dominated sorting genetic algorithm-II (NSGA-II) is employed to perform the optimization and get the Pareto optimal solution set in this paper.  
To minimize the number of PMUs required and state estimation error comprehensively, the compromise solution is selected by TOPSIS [45]. If the installation cost is limited, the solution with the minimum number of PMUs in the Pareto set is selected as the optimal solution.
# Detecting Cryptojacking Web Threats: An Approach with Autoencoders and Deep Dense Neural Networks
## 4 Detecting Browser-Based Cryptojacking and Related Works
After presenting the list of advances that have been made historically in terms of cryptojacking detection on the web, it can be summarized that there is currently no consensus on the type of features to evaluate in order to generalize the picture. Similarly, there is no clear context to address which pre-processing steps may be the most ideal to employ to improve data quality, and finally it has been shown that although shallow algorithms can quickly generalize the sample set, by their nature, they tend to work only on well-structured data, losing learning power if the proportion of information contains variable multidata [51]. For this reason, a multi-layered viewpoint (host-based and network-based) is proposed in this project, with a new scope in the characterization of the cryptojacking samples, as a fundamental element in a proper detection. Thus, it can be said that the following hypothesis arises:  
**Research hypothesis**: by addressing the two feature selection flanks on which the cryptojacking attack vector extends, the host and the network, and by representing their best latent information employing algorithms that maximizes them, such as an AE and a deep dense neural network (DNN), the detection radius can increase significantly.
# Polar Code Parameter Recognition Algorithm Based on Dual Space
## 4 Numerical Simulation
### Influence of Code Length  
The recognition rate curve of the proposed algorithm was simulated under different code lengths with 1/3 code rate, 2000 codewords, the SNR range of \(-\)2-8 dB, and the recognition rate output every 0.5 dB. The results of 2000 simulations are shown in Figure 10.  
As shown in the figure, as the code length increases, the recognition performance of the algorithm deteriorates. This is because the longer the code length and the larger the code weight, the more easily the accuracy of the statistic calculation is affected by bit errors, and thus the recognition performance decreases.
# TBRNet: Two-Stream BiLSTM Residual Network for Video Action Recognition
## 4 Experiments
### Ablation Study
#### 4.2.2 Analysis of Cross-Stream Connections
As we can see from rows 4, 6, and 7 of Table 2, under the circumstance of multiplicative gating, the fusing direction from the motion to the appearance stream achieves superior performance compared with the other two types. Similar to the additive gating (rows 3 and 5 of Table 2), letting the motion signals flow to the appearance stream outperforms the same schema but in the opposite direction. Since the appearance features have stronger effects on recognizing the hidden patterns in frames, when enabling the flow of appearance signals to the motion stream, the whole network is dominated by appearance signals, which makes the loss of motion stream quickly reduce to a low value and finally leads to the overfitting to appearance patterns. Things are different when using the \(\leftarrow\) direction, where both pathways learn the motion patterns together without suffering from the overfitting resulting from the domination of motion signals, thus modeling more appropriate spatiotemporal features. In the \(\leftarrow\)\(\rightarrow\) bidirectional connections, the same problem occurs in which the appearance signals flowing in both streams force the whole network to overemphasize appearance information from RGB frames. Therefore, we employed the multiplicative cross-stream residual connections illustrated in Figure 5d with a fusion of directions from the motion to the appearance stream for the two-stream CNN encoder, which is verified to be more effective for extracting spatiotemporal features than other variants. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Connection Case** & **Figure** & **UCF101** & **HMDB51** \\ \hline direct \(\oplus\leftarrow\) & Figure 5a & 21.72 & 57.15 \\ direct \(\oslash\leftarrow\) & Figure 5b & 75.26 & 79.43 \\ residual \(\oplus\leftarrow\) & Figure 5c & 9.04 & 39.91 \\ residual \(\oslash\leftarrow\) & Figure 5d & 7.53 & 36.69 \\ residual \(\oplus\rightarrow\) & Figure 5c & 14.51 & 48.03 \\ residual \(\oslash\rightarrow\) & Figure 5d & 14.22 & 46.82 \\ residual \(\oslash\leftarrow\) & Figure 5e & 13.87 & 46.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification errors (%) on UCF101 and HMDB51 of the two-stream encoder with different cross-stream connections. We use \(\oplus\) to denote an addition operation and use \(\oslash\) to denote an element-wise multiplication operation. The directions from the motion to the appearance stream and from the appearance to the motion stream are denoted as \(\leftarrow\) and \(\rightarrow\), respectively, while we use \(\leftarrow\)\(\rightarrow\) to denote the bidirectional connection.
# A Novel Feeder Link Handover Strategy for Backhaul in LEO Satellite Networks
## 3 System Model
### Backhaul Model
The overall capacity of the ULs is assumed to be C\({}^{u}\) at most. Accordingly, each satellite needs to transmit traffic to its adjacent satellites and its connecting GSs, which is denoted by \(f_{n}^{out}(t)\) as follows:  
\[f_{n}^{out}(t)=\sum_{j=1,j\
eq n}^{N}l_{n,j}f_{n,j}(t)+\sum_{m=1}^{M}x_{n,m}(t )f_{n,m}^{g}(t),\qquad\forall S_{n}\in\mathbf{V}(t) \tag{10}\]  
where \(f_{n,m}^{g}(t)\) is the traffic flow from \(S_{n}\) to \(G_{m}\). Based on Equations (3) and (10), the available backhaul capacity in the time slot \(t\) is defined as the overall traffic flow capacity of all feeder links as follows:  
\[\sum_{m=1}^{M}\sum_{n=1}^{N}x_{n,m}(t)f_{n,m}^{g}(t) \tag{11}\]
###### Abstract  
Background:This study investigates differences in muscle co-contraction and peak electromyography (EMG) activity between novice and advanced Taekwondo athletes during consecutive roundhouse (_bundal chaguu_) kicks, examining the influence of body composition and experience level. **Methods:** Sixteen Taekwondo athletes (12 males, 4 females; mean age: 20.5 \(\pm\) 4.3 years) were divided into novice (\(n=8\)) and advanced (\(n=8\)) groups. Muscle co-contraction indices and peak EMG activity across 15 consecutive kicks were assessed in key lower limb muscles, including the _biceps femoris_ (BF), _lateral gastrocnemius_ (LG), _retts femoris_ (RF), _soleus_ (SO), _semictindinosus_ (ST), _tibials anterior_ (TA), vastus lateralis (VL), and vastus medialis (VM). **Results:** Advanced athletes exhibited significantly higher co-contraction indices in BF-RF, VM-BF, and SO-TA pairs (\(p<0.05\)) and increased peak EMG trends in the BF and LG (\(p<0.05\)). Novice athletes showed significantly reduced peak EMG increases in the RF, VM, and VL. EMG trends were influenced by body composition, with principal component analysis indicating that higher fat mass and lower muscle mass were associated with greater variations in muscle activation. **Conclusions:** These findings suggest that advanced athletes refine motor control through increased co-contraction, improving stability and efficiency, while novices exhibit less optimized coordination patterns. This study provides insights into the neuromechanical adaptations associated with expertise development in Taekwondo.  
s _Applied sciences_  
_Article_
# Infrared Small Moving Target Detection via Saliency Histogram and Geometrical Invariability
## 5 Conclusions  
It is easy for conventional IR small target detection methods to generate large quantities of false alarms due to the small size of target, the lack of color or texture information and the interference of clutters. Furthermore, the existing algorithms scarcely have the ability to detect both bright and dark IR small targets accurately at the same time, and the inter-frame motion information is also ignored by most of the researchers.  
In this paper, an IR small moving target detection method using saliency histogram and geometrical invariability is proposed. For the in-frame detection part, a saliency histogram is established by averaging the cumulative saliency value of each gray level so that a single-frame segmentation can be made via an adaptive threshold of the histogram, and the centroid position of candidate targets is calculated via a connected components labeling algorithm and a gray-weighted criterion. For the inter-frame detection part, false alarms are further removed according to the geometrical invariability existing between two relatively still points. Large numbers of experiments convincingly prove that our method has robustness and satisfactory precision under various natural backgrounds compared with other state-of-the-art methods.  
In our future work, we plan to concentrate on investigating more well-performed features of the IR small target in single-frame detection so as to reduce the computing quantity of the inter-frame detection and further improve the final detection precision.  
Figure 13: Receiving Operating Characteristic (ROC) curves of the four tested sequences: (**a**) Seq.1, (**b**) Seq.2, (**c**) Seq.3, (**d**) Seq.4.  
**Acknowledgments:** This paper is financially supported by the National Natural Science Foundation of China (61675099) and the Open Foundation of State Key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) (SKLNST-2016-2-07).  
**Author Contributions:** [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents/materials/analysis tools; and [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Tooth extractions, especially of impacted lower third molars, are among the most common procedures performed in dental practices. The continuity of the patient's oral mucosa, which is interrupted during them, can manifest itself in general discomfort, pain, swelling, and even trismus. In the age of cosmetic dentistry, when lost teeth are restored through implant, prosthetic, and orthodontic treatment, each tooth extraction actually reduces the amount of available alveolar bone. This has prompted researchers to develop extraction sockets treatment procedures that reduce the negative consequences of surgical intervention while also enhancing the rate of alveolar bone and soft tissue regeneration using minimally invasive approaches. This is expected to enable or significantly facilitate further stages of treatment. The aim of this paper is to review the literature on the use of autologous blood preparations, which are considered to aid regenerative processes when applied to extraction sockets.  
advanced platelet-rich plasma; concentrated growth factors; post-extraction sockets; A-PRF; CGF; regeneration; autologous blood products; dentistry +
Footnote †: journal:  
not seem to make sense), and gels and mouthwash with chlorhexidine reduce the incidence of alveolar osteitis [3].  
Previously, the only way to heal the alveoli of extracted teeth was a natural dressing made of a blood clot. The recovery period was associated with swelling, discomfort and a dry socket, and there was a risk of hemorrhage and nerve damage during the procedure [4]. This prompted critics to seek alternative treatments such as dressings that modulate inflammatory responses and accelerate healing [5].  
Human plasma derivatives, also known as tissue fibrin adhesives, were the first of their kind to be introduced in Europe in the late 1970s. The general mechanism of action relies on the formation of a fibrin clot, which is the end product of the blood clotting process. They are used to seal wound edges and provide a homeostatic surface, as well as a bonding substance for biomaterial particles in bone augmentation surgery. The commercial preparation was made from cryoprecipitated donor plasma and contained two lyophilized components: fibrinogen/fibronectin/factor-XIII dissolved in an antifibrinolytic solution (e.g., aprotinin), and thrombin concentrate dissolved in a dilute calcium chloride solution. Activated factor XIII catalyzes crosslinking between fibrin molecules in the presence of calcium cations, resulting in a water-insoluble fibrin matrix that serves as a clot scaffold.  
However, the use of such treatment puts patients at risk of infection with pathogens such as hepatitis (HBV, HCV). This led researchers to seek procedures based on the use of autologous preparations derived from patients' serum. Combining whole plasma from patients with bovine thrombin produced fibrin glues. Unfortunately, their manufacture was characterized by very low physical reproducibility (in terms of viscosity and elasticity, for example) [6].