esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Gated Recurrent Neural Network
#### 2.3.2 Gated Recurrent Unit  
LSTM is known for its excellent ability to capture the long- and short-term dependence of sequence data. However, the tensor calculation of LSTM networks is complicated, which will cause the training process to often take a long time. To reduce the training time, gated recurrent unit (GRU) networks improve the inherent gate structure of LSTM network, and shows better results on certain tasks [17].  
Similar to the LSTM, GRU also controls the information exchange of hidden cells through gate units; however, the difference is that GRU does not have a separate memory cell. The detailed implementation of GRU is shown in Figure 3. The difference from LSTM is that GRU combines the hidden state (\(h_{t}\)) and the cell state (\(c_{t}\)) into one. Therefore, there are only two special gate structures left in the GRU, namely the update gate (\(z_{t}\)) and the reset gate (\(r_{t}\)). The update gate (\(z_{t}\)) determines the degree to which the hidden layer state  
Figure 2: The LSTM architecture: (**a**) the operating mechanism of forget gate; (**b**) the operating mechanism of input gate; and (**c**) the operating mechanism of output gate.  
information in the previous time step \(t-1\) is accepted by the hidden layer in the current time step \(t\). Reset gate (\(r_{t}\)) determines the extent to which the hidden layer state information from the previous moment is recorded in the candidate hidden layer state \(\hat{c}_{t}\) at the current moment. The values of both the update gate and the reset gate are directly proportional to the amount of state information introduced in the previous time step.  
In Figure 3, the GRU networks are calculated as follows:  
\[r_{t} =\sigma(W_{xx}x_{t}+W_{hr}h_{t-1}+b_{r}) \tag{8}\] \[z_{t} =\sigma(W_{xx}x_{t}+W_{hz}h_{t-1}+b_{z})\] (9) \[\hat{c}_{t} =\tanh(W_{xx}x_{t}+W_{hc}(r_{t}\otimes h_{t-1})+b_{c})\] (10) \[c_{t} =(1-z_{t})\otimes c_{t-1}+z_{t}\otimes\hat{c}_{t}\] (11) \[h_{t} =c_{t} \tag{12}\]  
where \(W_{xx}\), \(W_{hr}\), \(W_{xz}\), and \(W_{hz}\) are the network weight matrices. \(b_{r}\) and \(b_{s}\) are bias vectors, and \(r_{t}\) and \(z_{t}\) are vectors for the activation values of the update gate and reset gate, respectively.
## 3 The Proposed Approaches
### Proposed MSU-CNN Model  
Network architecture of the proposed MSU-CNN model is given in Figure 3. The description of the blocks in the proposed architecture is provided below.  
_Input Layer:_ This layer defines the input to the model and specifies the dimensions of the input images. Additionally, normalization is applied so that each feature has a mean of zero and a standard deviation of one.  
_First Convolutional Block:_ This block consists of a convolution2 dLayer, batchNormalizationLayer, and reluLayer. The convolution2 dLayer applies convolution using a 3 \(\times\) 3 kernel with 32 filters, deriving basic features from the input. The batchNormalizationLayer normalizes activations to improve training stability and accelerate convergence. The reluLayer introduces non-linearity using the Rectified Linear Unit (ReLU) activation function, which replaces negative values with zero, facilitating efficient gradient propagation.  
_Block 1:_ This block comprises a convolution2 dLayer, batchNormalizationLayer, reluLayer, and maxPooling2 dLayer. A 3 \(\times\) 3 convolution is performed with 64 filters and a stride of 1, keeping the output size similar to the input. The Max pooling (2 \(\times\) 2, stride = 2) reduces the spatial dimensions by half, preserving essential features while lowering computational complexity.  
_Block 2:_ This block increases the number of filters to 128, maintaining the 3 \(\times\) 3 kernel size. Batch normalization and ReLU activation are applied as before. Pooling further reduces the feature map dimensions, capturing higher-level feature representations.  
_Block 3:_ The number of filters is increased to 256, and convolution is applied. Batch normalization and ReLU activation follow. Pooling continues to reduce spatial dimensions.  
_Block 4:_ This block utilizes 512 filters for convolution, followed by average pooling to aggregate spatial information more effectively.  
_Fully Connected Layers:_ These layers refine the extracted features and perform classification. The initial fully connected layer consists of 1024 neurons, followed by another with 512 neurons. Dropout (0.5) is applied between these layers to prevent overfitting by randomly deactivating 50% of the neurons. The softmax layer converts the output into probability distributions over the class labels. Finally, the classification layer assigns the predicted class based on the highest probability.
## 5 Discussion and Conclusions
### Limitations and Future Research  
This study's limitations include the country context in which our data was solicited, the size of our sample, and the industry context. To this end, the results are not generalizable globally but are valid and reliable for future studies and continuation of this field of research. Another limiting factor may be the methodological side of research. As for the selection of companies for the analysis it was limited by the availability of data. When analysing the data, it was assumed that the companies that did not disclose their data (the authors asked all GPOs companies for them) do not differ significantly in terms of financial results from the companies from which the data was obtained. According to the authors, the sizes of the compared groups are not very small (46 and 25 cases), in statistics, groups of at least 30 cases are conventionally considered \"large\", so here we are very close to this condition. The authors are aware that increasing the sample size would have a positive effect on the value of work. Always, the fuller the data, the better. On the other hand, the results of statistical tests--as we know--dependon the sample size in such a way that the smaller the size of the compared groups, the more difficult it is to obtain statistically significant differences. The small size of the groups (except for really extreme cases) does not affect the reliability of the statistical test results. Therefore, undertaking the analysis of small groups is really risky on the part of the researcher, while the fact of obtaining statistically significant differences and relationships for a small sample is somewhat more valuable than for large samples. The authors are also aware that some of the differences between GPOs and stand-alone companies may be apparent. However, during the statistical analysis of the data held, no results appeared that would indicate this in any way.  
Despite these limitations, however, the presented research offers a relatively new perspective on the quantitative analysis of GPO. It can serve as one of the starting points for a more in-depth analysis of the financial situation in purchasing groups. The authors hope that the research directions initiated in the article will be continued. It seems particularly important to compare the obtained results with organizations forming purchasing groups in Western Europe and other industries.  
Conceptualization, G.Z; methodology, G.Z; formal analysis, G.Z; data curation, G.Z; writing--original draft preparation, G.Z; writing--review and editing, G.Z, D.Z.; visualization, G.Z; supervision, G.Z.; project administration, G.Z; funding acquisition, G.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
## 3 Continuous to Discrete Dynamics: Return Map  
### Lorenz Map  
To describe the time evolution for interpersonal coordination, we introduce a method to explore regularity by reducing the dimensionality of continuous dynamics (e.g., three-dimensional flow) to discrete dynamics (e.g., two- or one-dimensional). The most popular method is the Lorenz map, which was a seminal work that contributed to the foundation of the chaos theory [36].  
The meteorologist, [PERSON], developed a simplified mathematical model for atmospheric convection [36]. The model is a system of three ordinary differential equations now known as the Lorenz equations:  
\[\frac{dx}{dt} = \sigma(y-x) \tag{1}\] \[\frac{dy}{dt} = x(r-z)-y\] (2) \[\frac{dz}{dt} = xy-bz \tag{3}\]  
Here \(x\), \(y\), and \(z\) are proportional to the intensity of convection motion, the temperature difference between the ascending and descending currents, and distortion of the vertical temperature profile from linearity, respectively. \(\sigma\), \(r\), and \(b\) are the system parameters. [PERSON] focused on a \"single feature ([36], p. 138)\", that is, \(z_{n}\), of his three-dimensional strange attractor (Figure 2a). Figure 2b shows a time series of \(z(t)\), and the \(z(t)\) peaks were plotted as \(z_{n}\) vs. \(z_{n+1}\) (Figure 2c). The function \(z_{n+1}=f(z_{n})\) is called the Lorenz map. The Lorenz map shows the road to chaos through the bifurcation [33].  
Figure 1: Abrupt changes in the interpersonal coordination pattern corresponding to an interpersonal distance of 2.7–3.0 m while playing kendo. The frequencies of the relative phase per 0.1 m interval at interpersonal distances of 2.7–3.0 m were calculated, and the means and standard deviations are presented. The relative phases were divided into nine ranges (0\({}^{\circ}\)–20\({}^{\circ}\), 20\({}^{\circ}\)–40\({}^{\circ}\), 40\({}^{\circ}\)–60\({}^{\
## 3 Materials and Methods
### Experiment Design  
The wayfinding experiment design is illustrated in Figure 1. The experiment took approximately one hour and had three phases (as shown in Figure 1):  
1. Pre-Wayfinding Task Phase (15-20 min): Before the task, all participants read and signed a consent form and filled out a demographic questionnaire. The participants were also required to complete the Santa Barbara Sense of Direction Scale (SBSOD) [25] and two spatial ability tests to evaluate the sense of direction, spatial orientation and spatial reasoning (see Appendix A) of each individual.
2. Wayfinding Task (30-35 min): This phase focuses on performing the actual wayfinding task. The study used an independent group design, and participants were divided into two groups (control and experiment) of eleven participants each. Participants were randomly assigned to either the control or the experiment group and were required to complete a wayfinding task using either an outdoor or indoor-outdoor navigation app, depending on the assigned group. Both groups were provided with a clear set of instructions concerning the experiment and performed the same wayfinding task. The only difference was that the control group used an outdoor navigation app (Google Maps), and the experiment group used an indoor/outdoor navigation app (MazeMap) to complete the same wayfinding task. Each participant was tested individually.  
The wayfinding task selected for the experiment included navigating to two different meeting rooms in a specified order within the campus (see Appendix B for details of the task). The wayfinding task routes were all located on the Gloshaugen campus of the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway (shown in Appendix B). The starting points and destinations were pre-defined. The participants were instructed to find their way to the destinations from the starting point by referring to the mobile navigation app (Google Maps or MazeMap), depending on their designated group.  
Figure 1: Wayfinding experiment design.  
The task finished when the participant reached the second venue. Each participant individually performed this task where an observer followed them silently (without intervening) to take field notes (recording wayfinding performance parameters, i.e., task completion, time, mistakes, hesitation, and help) and observations. The participants had 30 min to complete this task. The task scenario and route maps are presented in Appendix B.
3. Post-Wayfinding Task Phase (15-20 min): After completing the wayfinding task, participants were asked to complete mental workload questionnaires (NASA-TLX rating and subjective workload questionnaire) and a short follow-up interview.
## 2 Related Work  
### Wireless Networks for Industrial Control  
Real-time control systems are often best designed around time-triggered (TT) principles due to their periodic nature and the requirements for low sampling and actuation jitter [15]. In WSANs, hybrid (or flexible) variations on TT behavior have found to better suited than strict TDMA [8, 9, 10, 11, 12]. The most existing wireless protocols for industrial applications (such as WirelessHART and ISA 100.11a) employ TT-based schemes [4]. At this stage, it is also worth acknowledging that there has also been recent interest in event-based triggering in WSANs, such as variations on the \"Send-On-Delta\" strategy [16]. However, such event-based schemes are not directly considered further in this paper as they only provide extremely limited facilities for fault tolerance (an implicit assumption is that a packet omission indicates that the measured variable has not changed by a magnitude greater than a pre-specified bound [16]). Additionally, they can experience excessive jitter during worst-case scenarios [14], and will normally default to loose time-triggering behavior anyway during process steady-states through the use of timeout mechanisms [16, 17]. Although TT-based systems have been found effective to help reduce levels of packet jitter, the effects of packet loss and channel dropout still required special consideration. In wired networks such as CAN, Bit Error Rates (BERs) may be in the region of \(10^{-6}\), normally arriving in short correlated bursts of approximately 5-20 bits [18]. As such, packet loss probability in these situations is comparatively low, and the omission of multiple consecutive samples can effectively be considered to be a \"rare\" event. However the nature of a wireless channel is such that packet loss probabilities are comparatively higher, and--importantly--will typically be strongly correlated [3, 8, 19, 20, 21]. For example, experimental data have been reported in the literature that shows that wireless channels having Packet Error Rates (PERs) as low as 0.01% will regularly experience losses of over 50 consecutive packets, rising in some extreme cases to over 1000 [3]. Even though such multiple consecutive packet losses are not likely to impact packets from the same control loop, the omission of multiple consecutive samples in a loop should nevertheless be considered a \"routine\" event. In many types of system, the loss of the wireless channel for a time duration exceeding the feedback controllability limit of the process may become an occurrence that cannot be neglected from the design. Various off-line and online techniques such as interference-aware routing and scheduling, dynamic topology control and retransmission techniques along with multi-channel MAC protocols (e.g., [3, 8, 9, 13, 21]) have all been shown help to ameliorate these issues; however they cannot be completely eliminated. Therefore, application-levelcompensation schemes are also desirable, especially if wireless control systems are to be employed in more critical control applications in future.
## 3 Energy-Sensitive X-ray and Gamma-ray Imaging in Wide FoV
### Directional Response, Field-of-View  
The directional response of the instrument was evaluated in frame of the ground-based tests prior to integration for the CubeSat VZLUSAT-2 launch. The angular acceptance and directional response expressed in terms of the Field-of-View (FoV) was tested by scanning between \(-35\) to \(+60\) degrees in both directions (vertical, horizontal and diagonal scan). Measurements were performed with a laboratory X-ray RTG unit. The directional response in wide FoV range is shown in Figure 9. The optical-collimator pattern registered by the pixel detector gradually shifts along the indicated directions. This effect can be more clearly seen in the expanded detector images at selected directional positions shown in Figure 10. The FoV of the imager, given by the optics/collimator acceptance, is \(35\times 35\) deg.
## 1 Introduction
RF-signal-based detection involves analyzing the communication signals that UAVs emit and receive during their operation by capturing the RF transmissions between the UAV and its controller. Machine learning techniques are typically used to classify and identify whether the signals are coming from a UAV. An example is the study by [PERSON] et al., which detected the presence of UAVs communicating using the FHSS technique in the 2.4 GHz band. This was performed through an initial cyclostationarity method that reduces false positives and a variant of the STFT that extracts the necessary parameters to be fed into the classification algorithms [10]. Lastly, UAV detection using computer vision involves using cameras and advanced algorithms to identify UAVs in real-time images and videos. These computer vision systems are mainly based on convolutional neural networks (CNNs) that are trained with large datasets containing images of UAVs under different conditions. [PERSON] et al. analyzed six object detection models to evaluate the accuracy of each in detecting UAVs using a PTZ camera [11]. After conducting an in-depth analysis of each of the existing techniques for UAV detection, it was concluded that none of them is perfect, as they all have some limitations. However, computer vision techniques and RF signal analysis techniques offer the best performance, making them the most effective for UAV detection. Furthermore, it was thought that, since a single technique may have limitations, using both methods together could enhance the system's effectiveness. This is because the limitations of one process can be compensated for by the other, resulting in a final system with high performance capabilities for UAV detection. Therefore, this article proposes a hybrid approach integrating a radio spectrum analysis module employing autoencoders for UAV detection and a computer vision module based on the YOLOv10 (You Only Look Once) algorithm. Additionally, a localization algorithm is developed, where, when two fixed cameras detect a UAV, they indicate the exact position to the PTZ mobile camera, along with a tracking algorithm that allows the PTZ camera to follow the UAV continuously. The rest of the paper is organized as follows: Section 2 reviews the state of the art of the technologies used in this paper. Section 3 delves into the methodology used to develop the UAV detection system, explaining the system's logical and physical architecture and the algorithms designed for the two modules. Section 4 contains the tests conducted and an analysis of the results, and, finally, Section 5 presents the conclusions and future directions.
# A Methodology for the Design of Application-Specific Cyber-Physical Social Sensing Co-Simulators
## 2 State of the Art
### CPSS Co-Simulation  
A CPSS simulator should be able to simulate all the subsystems composing a CPSS. That implies not only representing elements belonging to each domain, but also considering specific and precise models for them. For example, as we are seeing, various social simulators allow users to include smartphones in simulations, however, no model for the cellular network, congestion, data flow, communication protocols, handovers, etc., is proposed. Physical phenomena, such as heat propagation, cannot either be represented using the appropriate dynamical model. Network and physical simulators, on the other hand, present the opposite problem. The inclusion of all these details into a unique simulation causes simulators to be heavier and slower, but results closer to reality are obtained.  
In that way, as previously mentioned, works about methodologies for designing co-simulation tools are scarce. In fact, most works are focused on the implementation process, which it is a later stage to the design phase. Thus, most existing ones are focused on very particular aspects, such as the coordination of continuous and discrete-events simulators [61], the problem of mixing tools implementing time with different data structures (and accuracy) [62], or the creation of a multi-domain description language adequate for the co-simulation [63]. Works on requirements capture can be also found [64; 65] and final users' needs evaluation [66; 67], but they are focused on generic software, often resulting inefficient. Finally, papers addressing a complete methodology focused on the design process usually investigate procedures to create equivalent models in order to load the entire scenario (including all domains) in a unique simulator [68] (for example, representing people as mobile nodes in the network simulator). We are sure that no work proposes a complete methodology which allows software architects to design their own co-simulators.  
In the next subsection, we review the state of the art on the simulation of the three main domains which compose a CPSS.
# Design Considerations and Performance Evaluation of Gossi Routing in LoRa-Based Linear Networks
## 6 Impact of Hardware Imperfection on Network Performance  
Hardware imperfection can significantly impact the coverage overlapping estimation. To explore the hardware imperfection and its impact on the RSSI and SNR, we ran a few trials by using different Arduino boards and LoRa shields (with a constant distance of 175 cm approximately between two nodes). We used two types of Arduino boards (one with standard USB and the other with a micro USB port) and two types of LoRa shields (chip manufacturer H RF96 and SX 1276). We used the same network configuration and kept changing the physical nodes (Arduino + LoRa shields) to explore the hardware imperfection. The impact of hardware imperfection on the RSSI and SNR is shown in Table 4.  
However, it is not guaranteed that using identical nodes (with the same Arduino boards and LoRa shields from the same manufacturers) will yield consistent RSSI values.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source Arduino Board** & **Destination Arduino Board** & **Source LoRa Shield** & **Destination LoRa Shield** & **RSSI** & **SNR** \\ \hline Arduino UNO & Arduino UNO & H RF96 & H RF96 & \(-\)40 & 5 \\ Arduino UNO (Micro USB) & Arduino UNO (Micro USB) & H RF96 & H RF96 & \(-\)35 & 5 \\ Arduino UNO & Arduino UNO & SX 1276 & SX 1276 & \(-\)35 & 5 \\ Arduino UNO (Micro USB) & Arduino UNO (Micro USB) & SX 1276 & SX 1276 & \(-\)77 & 5 \\ Arduino UNO & Arduino UNO & H RF96 & SX 1276 & \(-\)35 & 5 \\ Arduino UNO (Micro USB) & Arduino UNO (Micro USB) & H RF96 & SX 1276 & \(-\)40 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Impact of hardware imperfection on the RSSI and SNR using different manufacturers.  
Figure 14: Performance comparison using a one-hop model.  
We observed variations in the RSSI values among three identical nodes, primarily due to hardware imperfections, as shown in Table 5.
# On Fast Converging Data-Selective Adaptive Filtering
## 3 Data-Selective Adaptive Filtering Algorithms
### Online Conjugate Gradient  
Minimizing the objective function in (30) is equivalent to  
\[\min_{\mathbf{w}}\frac{1}{2}\mathbf{w}^{T}(k+1)\mathbf{R}\mathbf{w}(k+1)- \mathbf{p}^{T}\mathbf{w}(k+1) \tag{37}\]  
in which \(\mathbf{R}=\mathbb{E}[\mathbf{x}(k)\mathbf{x}^{T}(k)]\) is the \(N\times N\) autocorrelation matrix of the input signal and \(\mathbf{p}=\mathbb{E}[d(k)\mathbf{x}(k)]\) is the cross-correlation vector between the input and reference signals. Similarly, our goal is to solve the linear equation  
\[\mathbf{R}\mathbf{w}(k+1)=\mathbf{p}. \tag{38}\]  
The CG method can solve this problem by expressing the solution  
\[\mathbf{w}_{\text{o}}=\sum_{i=0}^{N-1}\alpha(i)\mathbf{c}(i) \tag{39}\]in a basis formed by a set of vectors \(\mathbf{c}_{i},\ i\in\{0,\ldots N-1\}\) that present \(\mathbf{R}\)-conjugacy, that is, \(\mathbf{c}^{T}(i)\mathbf{R}\mathbf{c}(j)=0\) for all \(i\
eq j\). By premultiplying Equation (39) by \(\mathbf{c}^{T}(k)\mathbf{R}\) and using conjugate definition:  
\[\mathbf{c}^{T}(k)\mathbf{R}\mathbf{w}_{\mathrm{o}} = \mathbf{c}^{T}(k)\mathbf{R}\left(\sum_{i=0}^{N-1}\alpha(i) \mathbf{c}(