esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Vehicle Tracking Based on Monocular Camera in Driver View
## 4 Implementation Details
### Training and Testing  
In the training phase, the model is trained for a total of 80 epochs on the synthetic dataset. The multi-step learning rate is adopted and initialized to \(10^{-2}\), reducing the learning rate of each parameter group by the decay factor once the number of epochs reaches one of the milestones. We set the milestones in the middle and in three-quarters of the epochs, and the decay factor is set at 0.1. The input images are resized to 1088 \(\times\) 608 and some data augmentation techniques such as rotation, scaling and color dithering are used.  
In the testing phase, the IoU threshold required to qualify as detected and the object confidence threshold are set to 0.5. The IoU threshold for non-maximum suppression is set to 0.4. The deletion condition for the final unmatched tracks is 30 frames, which means that the tracks will be removed if the condition is not satisfied. Each tracked target is assigned a Kalman filter as the motion model. Therefore, the number of objects can also affect tracking speed.
## 6 Estimating the Logistical Security Needs of the Operation  
As previously stated, estimating the logistical needs to support the aviation technology operation process consists of four stages. The first three stages are quite simple in terms of the algorithm for determining needs. The major difficulty is getting through Stage IV.  
The research presented in the article can decisively facilitate this process, without needing a specialized tool in the form of dedicated IT software or specialist knowledge in the basics of reliability, logistical planning, or statistics. A prerequisite is the ongoing collection of information about failures that have occurred and the recording of specified, characteristic information about failures and how they were rectified. Based on the collected data, the MTTF\({}_{\text{D}}\) should be determined for the device in question (taking into account the P/N of the unit), and an additional analysis carried out by determining the unit's replacement rate over the analyzed period (e.g., 1 year), W, from the formula:  
\[W=\frac{number\ of\ replacements\ of\ the\ i-th\ element}{number\ of\ failures\ of\ the\ i-th\ element}. \tag{2}\]  
The logistics consumption for the \(i\)-th element \(L_{pi}\) can be determined from the values: \(MTTF_{Di}\) and \(W_{i}\), with the help of the relationship:  
\[Lp_{i}=\frac{N}{MTTF_{Di}}\cdot W_{i}, \tag{3}\]  
where \(N\) is the planning number of flight hours of the fleet.  
The complex compilation of logistical needs resulting from the planning process carried out for Phase IV follows:  
\[Lp=\sum Lp_{i} \tag{4}\]  
However, it should be remembered that Formulas (2)-(4) apply to Stage IV, i.e., the estimation of needs using the MTTF\({}_{\text{D}}\) reliability index estimated on the basis of past operational data and knowledge of emergent failures.  
A comprehensive statement of the logistical maintenance needs to be estimated, based on the MTTF\({}_{\text{D}}\) indicator, which should be reduced by the number of assortments (P/N) held in the user's warehouse. Only the result of this operation will constitute a complete list of assortments necessary to be procured for the planned aircraft's lifetime.
## 4 Results  
### System Functionality  
The assessment included testing system functionalities across various end-user systems (such as web apps, desktops, etc.), different hardware for end users, and alternative hardware for the Gateway API. The evaluation process involved comparing the requests sent from the devices with the packages received by the server.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Test Category** & **Description** \\ \hline Smoke Testing & A regular load test configured for minimal load. \\ Load Testing & Primarily focused on assessing the current performance of the system in terms of concurrent users or requests per second. \\ Stress Testing & The simulation of extreme scenarios to evaluate the system’s availability and stability under heavy load \\ Soak Testing & Revealing performance and reliability issues that arise from prolonged system pressure \\ \hline \hline \end{tabular}
\end{table}
Table 2: k6 tool tests categories.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline data\_received & The size of data returned by the server. \\ data\_sent & The size of data sent to the server. \\ http\_req\_blocked & The time the server spent processing a request. \\ http\_req\_connecting & The time it took to establish a connection with the server. \\ http\_req\_duration & The time taken for the request. \\ http\_req\_failed & The percentage of failed requests and the total count of \\  & both failed and successful requests. \\ expected\_response & The time it took to create the response in relation to the \\  & value of http\_req\_duration. \\ http\_req\_receiving & The time it took to receive the response from the server. \\ http\_req\_sending & The time it took to send the request to the server. \\ http\_req\_waiting & The time it took for the server to accept the request. \\ http\_req\_s & The number of requests made per second. \\ iteration\_duration & The time taken for a group of tests. \\ iterations & The total number of tests. \\ vus & Virtual users created. \\ vus\_max & Maximum number of virtual users. \\ \hline \hline \end{tabular}
\end{table}
Table 3: k6 tool analysis includes the following fields.  
Three key parameters were considered: the accuracy of collected information, the precision of reported errors, and the service execution time under 300 ms. Each test iteration was conducted after implementing optimization suggestions for the service. The final results confirmed successful transmission of all requests to the intended destination without any loss or duplication, with accurate responses. Some minor deviations in service timing intervals were observed, likely attributed to noise in the link budget.  
The evaluation of different hardware configurations for the server was based on user scenarios, which identified the most time-consuming set of services. Consequently, the minimum requirements for the Gateway API and Identity Server were determined to be a 1.6 GHz processor, 2 GB of RAM, and 5 GB of available hard disk space.
## 5 Concluding Remarks  
In this study, a two-sided interface is proposed for both the system operator and end-users to improve the participation of end-users in DR programs. The structures and specifications of the system operator and end-user interfaces are explained in detail and their advantages over the similar tools are presented.  
The main contributions of this study are as follows:  
* The interface is developed for both the system operator and end-user sides by taking their mutual benefits into account.  
Figure 17: Energy costs with and without the proposed management algorithm.  
Figure 18: Price history screen of the HEM module.  
* The interface provides the system operator with all the essential and detailed information about the status of end-users in real time, so that an efficient and timely scheduling is realized to prevent the unfavorable effects of peak power demands.
* The user-driven mobile phone-based interface has the capability of increasing the awareness of end-users and thus their participation, compared to the tools with complicated and time-consuming decision-making processes.
* In addition to enabling the interactions with the system operator, the versatile interface has the ability to be used as an access point to a HEM system that can control appliances and show their visualized historical measurements to end-users.
* The proposed interface also allows to receive the notifications and then to participate in the DR event through mobile internet.
* The proposed interface has a compact and modular structure; in other words, it allows including any controllable appliance into the interface.  
As a future study, storage systems can be included in the system for exploiting the residential renewable energy potential and for improving the efficiency of the proposed structure by storing the energy at low-cost periods and using it later at peak-demand periods. Furthermore, an advanced forecasting tool can be integrated into the proposed structure for better matching the consumption and generation values.  
This work was supported by FEDER funds through COMPETE 2020 and by Portuguese funds through FCT, under Projects. SAICT-PAC/0004/2015-POCI-01-0145-FEDER-016434, POCI-01-0145-FEDER-006961, UID/EEA/50014/2013, UID/CEC/50021/2013, UID/EMS/00151/2013, and SFRH/BPD/103744/2014, and by The Scientific and Technological Research Council of Turkey (TUBITAK) under Project Grant Number 116E115. Also, the research leading to these results has received funding from the EU Seventh Framework Programme FP7/2007-2013 under grant agreement No. 309048.  
[PERSON] mainly contributed by realizing the relevant implementations regarding the interfaces and HEMS integration. [PERSON] and [PERSON] provided the main idea, wrote the main parts of the paper and coordinated the overall study. [PERSON] and [PERSON] contributed by overall technical suggestions through the research steps and also contributed to the writing of the final research paper.  
The authors declare no conflict of interest
# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 4 Proposed Methodology
### Stream-2: Sensor Stream Methodology--Bi-LSTM Integration with Channel Attention (CA) Model
#### 4.5.2 Model Derivation  
In this model, the input sensor data are represented as a sequence  
\(x=\{x_{1},x_{2},\ldots,x_{T}\}\), where \(T\) is the total number of time steps in the sequence.  
First, the sensor data \(x\) are passed through the Bi-LSTM layer, which captures temporal dependencies in both directions (past and future):  
\[h_{Bilstm}=\text{BiLSTM}(h_{x}) \tag{7}\]  
The output of the Bi-LSTM is considered as Feature (8):  
\[f_{1}=h_{bilstm} \tag{8}\]  
Next, Feature (8) is passed through a CA mechanism, which refines the feature representations by selectively focusing on important channels while suppressing irrelevant ones. The output after applying CA is given by:  
\[f_{2}=\text{ChannelAttention}(f_{1}) \tag{9}\]  
The refined feature \(f_{2}\) is then passed through a fully connected layer, followed by ReLU activation in Equation (10)  
\[f_{3}=\text{ReLU}(W_{1}f_{2}+b_{1}) \tag{10}\]  
where \(W_{1}\) and \(b_{1}\) are the weights and bias of the first linear layer.  
The output of the fully connected layer is passed through a sigmoid activation function to produce a final scaled output:  
\[y_{1}=\sigma(W_{2}f_{3}+b_{2}) \tag{11}\]  
where \(\sigma\) is the sigmoid activation function, and \(W_{2}\) and \(b_{2}\) are the weights and biases of the second linear layer.  
Then, a residual (skip) connection is implemented by multiplying Feature-1 \(f_{1}\) with the output from the sigmoid layer \(y_{1}\):  
\[f_{sensor}=f_{1}\odot y_{1} \tag{12}\]  
where \(\odot\) denotes element-wise multiplication.  
Finally, for the single modality or sensor modality output, the residual final feature vector \(f_{sensor}\) is passed through another linear layer followed by Softmax activation to obtain the final classification probabilities:  
\[y_{final}=\text{Softmax}(W_{3}f_{sensor}+b_{3}) \tag{13}\]  
where \(W_{3}\) and \(b_{3}\) are the weights and bias of the final linear layer, and \(y_{final}\) represents the output vector containing the classification probabilities.  
For the multimodal case, the concatenated skeleton and sensor features are passed through the linear layer and concatenated with the GSTCAN feature to produce the multimodal feature fusion.
## 1 Introduction
### Novel Contributions  
This research makes several significant contributions to the field of information diffusion modeling:  
First, we systematically compare three distinct delay mechanisms (constant, uniform, and exponential) across diverse network topologies--a gap in the analysis of the existing literature. This comparative approach reveals unexpected insights about the relative importance of delay types.  
Second, we develop a robust methodological framework that integrates real social network data (Reddit threads) with controlled synthetic networks, enabling both ecological validity and experimental rigor in our analysis.  
Third, we identify the critical role of temporal dynamics, particularly demonstrating that peak_speed_time serves as the dominant predictor of information coverage (importance = 0.848), a finding with substantial implications for the real-time monitoring of information spread.  
Fourth, we provide concrete and actionable recommendations for optimizing information dissemination strategies based on network structure and spread probability, with direct applications for social media platforms, marketing campaigns, and crisis communication.
## 5 Performance Evaluation
### Impact of the PHY Data Transmission Rate  
Figure 4 shows the saturation energy efficiency of the evaluated mechanisms as a function of the PHY data transmission rate and the number of STAs. More specifically, Figure 4a focuses on the PHY data transmission rate ranging from 6 Mbps to 54 Mbps. Results for the energy efficiency of DCF and PSM are plotted together with a single line and marker. On the contrary, the energy efficiency of TXOP PSM is shown with a different line and marker since its behavior is significantly different from those of DCF and PSM. It is also worth noting that the results of the saturation throughput as a function of the PHY data transmission rate are not shown due to their similarities to those presented for the MSDU length. A throughput improvement ranging from 7% to 32% for DCF, PSM, and TXOP PSM using \(\alpha=3\) as compared to these with \(\alpha=1\) is achieved as the PHY data transmission rate increases from 6 Mbps to 54 Mbps.  
Figure 4a shows that all the mechanisms attain higher energy efficiency with higher PHY data transmission rates. This is due to the fact that the time to send a data frame becomes shorter, hence resulting in higher efficiency of data transmission. In addition, it can be seen that DCF and PSM using \(\alpha=3\) achieve an increase in energy efficiency from 7% to 29% as compared to DCF and PSM with \(\alpha=1\) when the PHY data transmission rate increases from 6 Mbps to 54 Mbps.  
As it can be seen in Figure 4a, TXOP PSM with \(\alpha=1\) significantly improves the energy efficiency of DCF and PSM for PHY data transmission rates lower than 36 Mbps. The highest increase of 235% in energy efficiency is achieved for the lowest PHY data transmission rate of 6 Mbps. Such increase in energy efficiency decreases from 235% to 60% for higher PHY data transmission rates up to 24 Mbps. The reason is that the transmission duration is shorter or longer depending on whether the PHY data transmission rates increases or decreases, respectively. Shorter data transmission times decrease  
Figure 4: Saturation energy efficiency of the evaluated mechanisms with \(\alpha=1\) and \(\alpha=3\) rounds of data transmissions as a function of the PHY data transmission rate and the number of STAs. **(a)** PHY data transmission rate. **(b)** Number of STAs.  
the time that the STAs can sleep whereas longer transmission times increase the microsleep period. For higher PHY data transmission rates ranging from 36 Mbps to 54 Mbps, TXOP PSM with \(\alpha=1\) performs as DCF and PSM. This occurs because these PHY data transmission rates do not produce data transmission times enabling microsleep operation based on the awake and sleep transition times of the STAs.  
In addition, it can be seen in Figure 4a that TXOP PSM using \(\alpha=3\) can allow the STAs to sleep for all the PHY data transmission rates, achieving the highest energy efficiency value. The improvement ranging from 424% to 110% for TXOP PSM when compared to DCF and PSM is obtained when the PHY data transmission rate increases from 6 Mbps to 54 Mbps.
# Study on Preparation of Aluminum Ash Coating Based on Plasma Spray
## 3 Results and Discussion
### Effects of Spray Process on Coating Properties
#### 3.2.3 Energy Spectrum Analysis  
As shown in Figure 9, there are different regions on the surface of the ultimate coating, which are divided into the gray region, light region, and dark region. The SEM and energy spectrum are applied for analysis.  
The energy spectrum analysis was conducted by energy dispersive X-ray spectroscopy (EDS) to analyze the composition and content of different regions of the coating, and the analysis results are shown in Figure 10. Figure 10a shows the gray region. The region's main elements are 60.6 wt.%  
Figure 8: Metallographic photomicrograph: (**a**) surface microstructure of the coating; (**b**) section microstructure of the coating.  
Figure 9: SEM images of coating numbered 9.  
Al and 6.2 wt.% Si, and the hardness is about 400-800 HV. Figure 10b shows a light region with 43.9 wt.% Al and 53.0 wt.% O. By preliminarily concluding, this region is dominated by alumina, and its hardness is about 500-900 HV. Figure 10c is a dark region comprising 85.1 wt.% C and 13.8 wt.% O, and it has a low hardness of about 100-200 HV. During the spraying process, the spraying direction is not perpendicular to the surface of the workpiece, and there is an angle of 60\({}^{\circ}\). Therefore, the particle motion trajectory is similar to the parabolic shape. Under the action of deposition and gravity effects, particles of different masses are separated, while particles of similar mass produce agglomeration, leading to the difference in particle quality between different regions. The dark region contains a lot of carbon, about 85.1 wt.%, and the origin of carbon in the coating includes two primary aspects. First, the experimental raw materials come from electrolytic aluminum waste. In the process of electrolysis, the carbon reacts with alumina under the action of the current to form primary aluminum and carbon dioxide, thus some carbon remains in the aluminum ash. Second, from Figure 1, we can see that a certain amount of SiC is present in the ultimate aluminum ash. Under the high temperature of atmospheric plasma spraying, which will generate a high temperature above 2000 \({}^{\circ}\)C in the plasma arc outer flame, the SiC further reacts to form C and SiO\({}_{2}\). Furthermore, under the action of spray angle and gravity, carbon is concentrated, and the dark region with a high carbon content is formed in the coating.  
Figure 10: _Cont._
## 3 Proposed Improved ADRC Strategy
### Proposed Improved ADRC Strategy Design in Cascaded ESO System  
As mentioned, tolerance to disturbances is crucial in designing the outer loop voltage controller. Therefore, this article proposes using ADRC to achieve robust tracking performance of the DC bus voltage and resilience against interference. ADRC is effective at handling various uncertainties. As illustrated in Figure 6, the estimation capability of disturbances is further enhanced. In this figure, \(Z_{2}\) represents a preliminary estimate of the generalized interference provided by the traditional ESO, while \(v_{2}\) addresses the dynamic differences between the estimated value and the actual disturbance. By combining ESO1 and ESO2, the system achieves more accurate disturbance estimation, thereby improving overall control performance.  
By correctly designing cascaded ESO and observer bandwidth \(\omega_{0}\) can effectively capture the disturbance dynamics of the system. For disturbance compensation, it should meet the following feedback control law.  
\[u=\frac{k_{p}(\gamma-y)-(z_{2}+v_{2})}{b_{0}} \tag{18}\]  
In the formula, \(k_{p}\) is the proportional gain, and \(r\) means DC bus voltage reference, \(u_{dc}^{*}\). If ignoring the estimation error of disturbance, the outer loop transfer function of the voltage based on ADRC control is:  
\[G_{cl}=\frac{k_{p}}{s+k_{p}} \tag{19}\]  
Figure 7 shows a block diagram of the outer circle control of the voltage.  
The ADRC compensates the feed-forward of the voltage outside loop object with complex nonlinear characteristics into a first-order integrator regulated by a proportional controller.  
Figure 6: Illustration of the Structure of the Improved ADRC Scheme.  
Figure 7: Voltage outside Circle Control according to the proposed Method (\({}^{**}\) in the figure means conjugate complex number).
# Hybrid-Recursive Feature Elimination for Efficient Feature Selection
## 3 Results
### Experiments  
We compared the previous RFE methods (SVM-RFE, RF-RFE, GBM-RFE) and the proposed Hybrid-RFE method. Hybrid-RFE was tested using simple sum and weighted sum functions. All experiments were performed using R language ([[http://www.r-project.org](http://www.r-project.org)]([http://www.r-project.org](http://www.r-project.org))). The SVM-RFE code was referenced from multiple SVM-RFE [11], and we implemented the code for the others as modified versions of SVM-RFE (_See_ Supplementary Materials).  
The performance evaluation metric for each method was the classification accuracy obtained on the selected features from the RFE methods. Four classification algorithms were used: k-nearest neighbor (KNN), SVM, RF, and naive Bayes (NB). The default tuning parameters were used for each classifier. Table 2 lists these classifiers and their corresponding R packages.  
Five-fold cross-validation was used not only to evaluate the accuracy of each classifier after feature selection, but also to perform the RFE process. In other words, the final feature importance list of an RFE method was calculated by averaging the five feature importance lists from each cross-validation fold.  
All of our RFE algorithms have a \"halve.aboves\" parameter. If the number of features is larger than the parameter value, RFE removes half of the least important features from the feature list. This approach was suggested by [11] as a means to speed up the feature selection process. In the regular RFE process, only the least important feature was removed from the feature list. We set the \"halve.aboves\" parameter to 200.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Dataset** & **\# of Observations** & **\# of Features** & **\# of Classes** \\ \hline
1 & satellite & 6435 & 36 & 6 \\
2 & messidor & 351 & 33 & 2 \\
3 & sonar & 208 & 60 & 2 \\
4 & messidor & 1151 & 20 & 2 \\
5 & GDS1027 & 154 & 1000 & 4 \\
6 & GDS2546 & 167 & 1000 & 4 \\
7 & GDS2547 & 164 & 1000 & 4 \\
8 & GDS3715 & 109 & 1000 & 3 \\ \hline \end{tabular}
\end{table}
Table 1: List of benchmark datasets.  
\begin{table}
\begin{tabular}{c c} \hline
**Classifier** & **R package** \\ \hline KNN & class \\ SVM & e1071 \\ RF & randomForest \\ NB & e1071 \\ \hline \end{tabular}
\end{table}
Table 2: Classifiers and their corresponding R packages.