esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Architecture of the Tethered Hexacopter System
### Tethered Multicopter  
The platform carrying the proposed system was an unmanned aerial vehicle of the multirotor helicopter type. After analyzing the aspects of reliability and lifting capacity in the design phase, it was decided to use a hexator configuration. This offered a good combination of drive redundancy and compact dimensions. The hexarotor is shown in Figure 2.  
Figure 1: Overview diagram of the tethered hexacopter system.  
In Figure 2, the frame of the multicopter (Figure 2A); one of the six arms (Figure 2B); the drive unit consisting of a propeller, a BLDC motor, and an electronic speed controller (Figure 2C); the converter power supply system (Figure 2D); the landing gear (Figure 2E); and the control system comprising an autopilot with a GNSS receiver (Figure 2F) are presented.  
This unmanned aerial vehicle was characterized by the fact that it was powered by wire from the ship's deck. Electricity was transmitted via a special cable from a ground source to dedicated electricity converters onboard the UAV. In addition, the hexarotor had a battery buffer in the event of damage to the tether, so that it was able to perform a quick but controlled landing. The weight of the helicopter, depending on the optoelectronic equipment and the length of the power cable, oscillated between 10.5 and 13.5 kg. The demand for electricity in the hover phase, depending on the prevailing weather conditions, was 1500-2000 W. At peak power consumption, which occurred during the take-off phase and the fight against turbulence, the multirotor could consume up to 3.6 kW. The main specifications of the multirotor unmanned aerial vehicle are listed in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Distance between opposite motors & 960 mm \\ Wheelbase diameter (top view) & 1400 mm \\ Max. single-drive thrust & 40 N \\ Max. take-off mass & 13.5 kg \\ Payload & 3 kg \\ Power type and source & Electric, tethered from ground (vessel) \\ Motor type & Brushless DC motor \\ Wind resistance & 16.6 m/s \\ Flight duration & Unlimited \({}^{\star}\) \\ \hline \hline \end{tabular} \({}^{\star}\)Depends only on the reliability of power supply via the cable.  
\end{table}
Table 1: Multirotor basic characteristics.  
Figure 2: Multirotor in six-arm configuration.  
The hexacopter could be controlled manually from the level of the remote control apparatus and the pilot-operator, or automatically from the level of the computer, in which case the operator performed only a supervisory function. The unmanned aerial system (with other components described below) on the helipad deck of the \"Wolin\" ferry is shown in Figure 3.
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 3 Factors Influencing Sticking in MXene Electrodes
### Electrode Processing Techniques  
The choice of electrode processing techniques can significantly impact the sticking behavior in MXene electrodes, as well as the overall performance of MXene-based supercapacitors. Below are some key details:
# Kinematic In Situ Self-Calibration of a Backpack-Based Multi-Beam LiDAR System
## 4 Experiment Description and Result Analysis
### Analysis of Simulation Experiment Results
The reduction rate varied from 50 to 99%, which gave the number of used points as 11,275 to 226. The redundancy linearly reduced as the number of points decreased. As shown in Figure 11, a clear inverse relationship between the RMSE of the estimated parameters and the number of used points was found. We also found that there were dramatic increases in RMSE for angular offsets from Calibration III-9 to III-10, while range offset showed a relatively small increase. To further investigate this phenomenon, additional calibration tests were repeatedly conducted. More specifically, Calibration III-10 (using 226 points) ran five times. At this stage, one should note that the involved number of points was kept as 226 but the points were randomly picked from the whole dataset (i.e., from 22,550 points) for each run. For the comparison, Calibration III-5 (using 2255 points) ran five times as well while picking the involved points randomly for each run. Calibration III-5 was chosen because it corresponded to the inflection point as seen in Figure 11. The results of these additional tests were shown in Figure 12. In the case of repetition of Calibration III-5 (in Figure 12a), RMSE values of the estimated APs for five runs were very similar and the variance of the values was low. On the other hand, repetition of Calibration III-10 (in Figure 12b) showed fluctuating RMSE results, which were too many dataset-dependent outcomes. After these additional tests, it was found that  
Figure 10: RMSE of estimated APs for Calibration II. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Used Planes** & **Total Points** & **Used Points** & **Reduction (\%)** & **Redundancy** \\ \hline III-1 & & 11,275 & 50 & 11,214 \\ III-2 & & 9020 & 60 & 8959 \\ III-3 & & 6765 & 70 & 6704 \\ III-4 & & 4510 & 80 & 4449 \\ III-5 & 2, 3, and 5 & 22,550 & 2  
if the number of used points was too small, the calibration process provided unreliable solutions. In this regard, at least 2000 or more points is recommended to estimate reliable angular offsets.
## 3 Motivation and Reuse Scenario
### Wood Reuse Scenario  
To effectively demonstrate our proposed framework, we use a wood construction supply chain scenario that exemplifies our system's capabilities. This scenario, as depicted in Figure 1, involves a series of events spanning seven distinct entities, each representing an interconnected role in the supply chain. It starts with the manufacturer, who sources raw materials and creates the initial product while also generating an MP for it. The MP serves as a comprehensive digital document that describes the product's characteristics. The manufactured goods are then routed through a network of warehouses that serve as intermediaries, temporarily storing the products before they reach end users in the construction industry.  
When the products reach the end of their intended life cycle, they enter a reuse loop and are rigorously evaluated. If appropriate, they are refurbished to meet strict reuse standards before being reintegrated into the supply chain and used for new purposes. This loop not only extends the materials' life cycle but also emphasises the principle of circularity. When materials are deemed unsuitable for reuse, they are recycled, effectively closing the loop. The MP is an essential component of this process as it is constantly updated to reflect current ownership, transaction timestamps, and the product's operational history. Such provenance reinforces trust and confidence in the wood construction supply chain, leading to more sustainable practices.  
Figure 1: A wood recycling and reuse scenario in the construction supply chain [8].
# Brain-Inspired Data Transmission in Dense Wireless Network
###### Abstract  
In this paper, the authors investigate the innovative concept of a dense wireless network supported by additional functionalities inspired by the human nervous system. The nervous system controls the entire human body due to reliable and energetically effective signal transmission. Among the structure and modes of operation of such an ultra-dense network of neurons and glial cells, the authors selected the most worthwhile when planning a dense wireless network. These ideas were captured, modeled in the context of wireless data transmission. The performance of such an approach have been analyzed in two ways, first, the theoretic limits of such an approach has been derived based on the stochastic geometry, in particular--based on the percolation theory. Additionally, computer experiments have been carried out to verify the performance of the proposed transmission schemes in four simulation scenarios. Achieved results showed the prospective improvement of the reliability of the wireless networks while applying proposed bio-inspired solutions and keeping the transmission extremely simple.  
dense wireless network; percolation threshold; reliability; stochastic geometry
# Depth Estimation of a Deformable Object via a Monocular Camera
## 6 Experiments and Discussion
### Experiments on a Human  
Experiments are also undertaken on a human to verify the proposed approach. We produce the 3D reconstruction of the human in his still pose at the very beginning. Subsequently, the human changes his pose and the camera captures the corresponding monocular image for each pose. We use these images to test the proposed approach and the previous algorithms [14; 25]. The program to implement this experiment is the same as that for the NAO robot experiment.  
The depth estimation results by the proposed method and other approaches are shown in Figure 6. Additionally, the depth accuracies of the proposed method and other three approaches are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approaches** & **Abs Relative Difference** & **Squared Relative Difference** & **RMSE (linear)** & **RMSE (log)** \\ \hline The approach in [25] & 4.8693 & 510.8358 & 452.1486 & 3.3712 \\ \hline The approach in [14] & 1.1972 & 53.7397 & 182.3361 & 1.2484 \\ \hline The proposed approach & 0.9987 & 31.7892 & 152.0740 & 1.0743 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative comparison on the aspects of accuracy and efficiency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approaches** & **Abs Relative Difference** & **Squared Relative Difference** & **RMSE (linear)** & **RMSE (log)** \\ \hline The approach in [25] & 5.9473 & 638.9376 & 367.0673 & 3.7256 \\ \hline The approach in [14] & 2.0792 & 197.2335 & 216,7147 & 2.1638 \\ \hline The proposed approach & 1.8274 & 83.5462 & 187.4539 & 2.0132 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative comparison on the aspects of accuracy and efficiency.  
Figure 6: The depth estimation results for the human volunteer by the approaches in [14; 25] and the proposed method.  
Similar to the experiments taken on NAO robot, the experiments results can validate the effectiveness of the proposed method. The proposed method uses the 3D point cloud reconstructed ahead of time as the priori model. Subsequently, it depends on the image features to estimate the pose changes of the priori model, therefore being capable of estimating the depth information accurately.
###### Abstract  
Multimodal functional near-infrared spectroscopy-functional magnetic resonance imaging (fNIRS-fMRI) studies have been highly beneficial for both the fNIRS and fMRI field as, for example, they shed light on the underlying mechanism of each method. However, several noise sources exist in both methods. Motion artifact removal is an important preprocessing step in fNIRS analysis. Several manual motion-artifact removal methods have been developed which require time and are highly dependent on expertise. Only a few automatic methods have been proposed. AMARA (acceleration-based movement artifact reduction algorithm) is one of the most promising automatic methods and was originally tested in an fNIRS sleep study with long acquisition times (~8 h). However, it relies on accelerometry data, which is problematic when performing concurrent fNIRS-fMRI experiments. Most accelerometers are not MR compatible, and in any case, existing datasets do not have this data. Here, we propose a new way to retrospectively determine acceleration data for motion correction methods, such as AMARA in multimodal fNIRS-fMRI studies. We do so by considering the individual slice stack acquisition times of simultaneous multislice (SMS) acquisition and reconstructing high-resolution motion traces from each slice stack time. We validated our method on 10 participants during a memory task (2- and 3-back) with 6 fNIRS channels over the prefrontal cortex (limited field of view with fMRI). We found that this motion correction significantly improved the detection of activation in deoxyhemoglobin and outperformed up-sampled motion traces. However, we found no improvement in oxyhemoglobin. Furthermore, our data show a high overlap with fMRI activation when considering activation in channels according to both deoxyhemoglobin and oxyhemoglobin.  
Keywords:NIRS-fMRI; automatic motion correction; multimodal; AMARA; post-hoc analysis; simultaneous multislice/multiband +
Footnote †: journal: _Algorithms_
# TYCOS: A Specialized Dataset for Typical Components of Satellites
## 4 Evaluation
### Key Component Detection Benchmark  
To establish the initial detection benchmark for the TYCOS dataset, we conducted experiments using eight models. These models encompassed various versions of the YOLO series, including the YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOF, and YOLOX detection models. YOLOv3 represents an early- and single-stage model aimed at improving the real-time performance. YOLOF and YOLOX, on the other hand, are advances on YOLOv3, integrating anchor-free or encoder structures. Inspired by YOLOX, YOLOv8 adopts an anchor-free approach, which excels in detecting irregularly shaped objects, thereby further enhancing the detection accuracy and speed.  
Table 6 presents the AP results for the eight detection models, with the highest AP values highlighted in bold. From Table 6, we note that the accuracy of the YOLO series models on the spatial object dataset was consistently above 75.34%, with the maximum accuracy reaching 93.94%. Regarding the accuracy for each key component, 1 (solar panel) achieved its highest accuracy in YOLOv8, reaching 91.07%; meanwhile, its lowest accuracy was observed in YOLOv3, at 64.31%. Component 2 (body) attained its highest accuracy in YOLOv8, reaching 99.67%. Similarly, 3 (radar) also achieved its highest accuracy in YOLOv8, reaching 91.09%. YOLOv8 exhibited the highest AP values among single-stage detection models, with an average increase of 16.98% compared to other detection models. In summary, the YOLOv8 single-stage detection model demonstrated advantages in detecting the key components of satellites.  
In Table 6, it can be seen that the precision of detection for 1 (solar panel)--was lower compared to 2 (body) and 3 (radar). The solar panel has a rectangular shape, and there is a large variation in the aspect ratio within the image field of view, indicating that lighting, motion, and occlusion have a greater impact on the features of the solar panel.
## 6 Results and Discussion  
By applying the proposed approach and combining the three levels of the three control factors presented in the previous section, 27 alternatives of the same workplace are generated. Figure 10 presents three examples of the generated layouts which differ for the position of the logistic spaces. The application of the proposed approach allows obtaining 27 layout alternatives all of them in compliance with the reference standards. The values of the minimum distance among the resources are greater than the one established by the standards. Furthermore, the approach considers the relationship between minimum distances and some ergonomic aspects: the minimum distance is greater than the safety threshold value, whereas it is less than the maximum load carrying distance defined as a function of the carried cumulative mass (see Section 4.3). The productive aspect is also taken into account since the minimization of the occupied floor allows taking the resources closer, minimizing travel time of operator and reducing the cycle time. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Function** & **Objective** & **Summary** \\ \hline Impact on space & \(\frac{A_{c}}{A_{t}}100\) & Minimize & \begin{tabular}{c} Percentage of the total plant available area \\ occupied by the collaborative workplace \\ Percentage of the total needed execution \\ time characterized by the simultaneous \\ working of human and robot \\ Total needed execution time \\ \end{tabular} \\ \multirow{2}{*}{\begin{tabular}{c} Time \\ Cost \\ \end{tabular} } & \(\sum\limits_{i=1}^{R}c_{i}n_{i}\) & Minimize & \
\begin{tabular}{c} Total cost as the sum of all the active and passive resources \\ \end{tabular} \\ \multirow{2}{*}{SMART} & \(\sum\limits_{i=1}^{F}a_{i}f_{i}\) & Maximize &
\begin{tabular}{c} Weighted combination of the \\ previous functions \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation functions [34; 35]. All the 27 scenarios are evaluated independently through the four KPIs presented in Section 5.2. The _impact on space_, the _HRC relevance_, the _time_ and the _cost_ are calculated for each configuration and a synthetic value given by the combination of them is pulled out by means of the SMART technique. In Table 7, the results of this assessment are presented. The combination of the three parameters provides the configuration number. The corresponding value of each function is presented, as well as the SMART utility value, calculated by the weighted combination of the other functions as depicted in Table 6.
## 1 Introduction: From Heterogeneous Sensors to the Sensor Web
The SWE specifications provide the functionality to integrate sensors into Spatial Data Infrastructures (SDI). The integration of sensor assets into SDIs makes it possible to couple available sensor data with other spatio-temporal resources (e.g., maps, raster as well as vector data) at the application level, which maximizes the information effectiveness for decision support. Due to this integration, Sensor Webs and the geosensors they comprise represent a real-time link of Geoinformation Systems (GIS) into the physical world. Thereby, _geos_ensors are defined as sensors delivering an observation with georeferenced location [2]. This work builds upon but is different from [3, 8, 16]. While those former papers in this research area describe the architecture of the first generation of OGC's SWE specification framework, this article goes beyond that and analyzes the _new generation SWE_ by pointing out differences with the preceding versions of the standards and by describing newly introduced specifications (Section 3). Before we describe those changes, this work relates SWE to other approaches for linking sensor resources to the Web (Section 2). Section 4 sketches how the SWE services can be applied to build a Sensor Web infrastructure and presents conducted projects which utilized the SWE framework. In Section 5, we identify challenges and future work for SWE including the improvement of interoperability, the integration of sensors and services, and new paradigms such as humans as sensors and the Semantic Sensor Web. The article ends with a conclusion in Section 6.
## 3 Experiments
### Assessment of Indicators  
In IDSs, the following metrics are commonly used to assess the performance of a method: true positives (_TP_) represent correctly predicted attack samples, true negatives (_TN_) represent correctly predicted normal samples, false positives (_FP_) indicate samples where the normal class was incorrectly predicted as an attack, and false negatives (_FN_) represent samples where the attack class was incorrectly predicted as normal.  
Accuracy is the ratio of correctly classified samples to all samples, as shown in Equation (8).  
\[\text{Accuracy}=\frac{TP+TN}{TR+TN+FP+FN} \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Traffic Class** & **Label** & **Numbers** & **Ratio** \\ \hline Benign & Benign & 2,273,097 & 80.30\% \\ DDoS & DDoS & 128,027 & 4.52\% \\  & DoS Hulk & 231,073 & 8.16\% \\ DoS & DoS GoldenEye & 10,293 & 0.36\% \\ DoS Slowloris & 5796 & 0.20\% \\  & DoS Slowhttptest & 5499 & 0.19\% \\ Port Scan & Port Scan & 158,930 & 5.61\% \\ Botnet & Bot & 1966 & 0.07\% \\ Fritpe Force & FTP-Patator & 7938 & 0.28\% \\  & SSH-Patator & 5897 & 0.20\% \\  & Web Attack—Brute Force & 1507 & 0.05\% \\ Web Attack & Web Attack—Sql Injection & 21 & 0.001\% \\  & Web Attack—XSS & 652 & 0.02\% \\ Infiltration & Infiltration & 36 & 0.002\% \\ Heartbleed & Heartbleed & 11 & 0.001\% \\ Total & N & 2,830,743 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed distribution of CICIDS2017 dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Set** & **Testing Dataset** & **Testing Set** & **Testing Dataset** \\ \hline Benign (50\%) & 10,000 & & 2000 + 8000 (generated) \\  & 20,000 & & 2000 + 18,000 (generated) \\ Benign (50\%) & 30,000 & Benign 100 & 2000 + 28,000 (generated) \\  & 40,000 & & 2000 + 38,000 (generated) \\  & 50,000 & & 2000 + 48,000 (generated) \\ \hline Attack (50\%) & 60,000 & & 2000 + 58,000 (generated) \\  & 70,000 & & 2000 + 68,000 (generated) \\  & 80,000 & Attack 1900 & 2000 + 78,000 (generated) \\  & 90,000 & & 2000 + 88,000 (generated) \\ \hline \hline \end{tabular}
\end{table}
Table 2: A detailed distribution of the dataset.  
Precision is the ratio of samples correctly predicted as attacks to the total number of positively predicted samples, as shown in Equation (9).  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{9}\]  
Recall is the ratio of samples correctly predicted as attacks to the total number of negatively predicted samples, as shown in Equation (10).  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
The \(F1\) score is the metric used to balance precision and recall and is the most important metric to evaluate a method. \(P\) means precision, and \(R\) means recall, as shown in Equation (11).  
\[F1=2\times\frac{PR}{P+R} \tag{11}\]
# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 5 Experimental Results and Comparison
### Traffic Status Dataset
#### 5.2.3 Random Forests  
The same set of parameters was used for the random forest classifier, and the final values for each that give the best results were also the same as for the \"lane detection\" dataset. The results are listed and plotted in Table 7 and Figure 8.  
The results are very similar in characteristics to those obtained with the \"lane detection\" dataset. We obtained encouraging metric curves but with linear relations that were slightly too steep, as observed in Figure 8a. The class 1 curve is quite different from the others. In Figure 8b, it can be noticed that the ROC curve area is average. The curves are inclined to the extreme top left at the middle, which is preferred, but the variation is too steep and short lived. Here again, the ability of the model to be able to provide consistent results has to be questioned.  
The random forest metrics above suggest good results as well, but they are quite varied in terms of the precision, recall and F1-score, giving different levels of performance. It can also be noted that the performances across the classes are also quite unevenly distributed.
## 3 State-of-the-Art Results on the Detection of Identity Deception
### Detection Technique Objectives  
Based on our survey of papers, we identified two main contexts for which models have been developed: either for industry or as part of academic research. This does not mean that there are not many studies that include both academic and industry professionals, but rather that the objective of a study is often influenced by the individuals who are involved in the work.  
Studies conducted for industry use have models that have been developed in order to improve user experience while simultaneously minimizing work for OSN administrators [22]. As a consequence of trying to meet these goals, an industry-oriented model tends to have a low false positive rate (i.e., high precision), which may come at the expense of letting a few malicious users remain undetected (i.e., low recall).  
Academic researcher models are being developed with a different perspective. Since the model's objective is not tied to the performance of any one OSN, the focus is more on the general soundness of the detection method, rather than having to frame the model in the context of a marketable product. As a result, studies from academia tend to focus more on making sure that their model can select all of the malicious actors who may be present in the dataset. This intuitively leads to a \"sound\" model, making sure that the detection mechanism is as correct as possible (i.e., high recall).