esa-sceva/satcom-chunk-collection huggingface dataset:
# Clustering for Lifetime Enhancement in Wireless Sensor Networks
## 4 Simulation Results
### First Scenario: The Base Station (BS) Is Located at the Center of the Area (50, 50)  
Figure 7 illustrates the WSN topology for this scenario.  
According to our study, the most effective approach for this topology is where all nodes transmit their data individually. This approach results in a longer network lifetime compared to the clustering-based approach with cluster sizes of 2 and 3. Figures 8 and 9 illustrate these topologies, respectively.  
The three topologies illustrate the WSN construction and node distribution, including individual nodes, pairs, and trios. The WSN's performance is evaluated based on five parameters: lifetime, throughput (packets sent to the BS), First Dead Node (FDN), Middle Dead Node (MDN), and Last Dead Node (LDN). Figure 10 depicts the lifetime of each topology, with the graphs representing the lifetime for individual nodes, Cluster-2, and Cluster-3, respectively.  
Figure 7: Topology of the first scenario with individual nodes and BS at (50, 50).  
As discussed in Section 5, the individual transmission approach yields the highest lifetime, followed by the cluster with the lowest density. Specifically, the topology with clusters of two nodes (Cluster-2) performs better in terms of lifetime than the topology with clusters of three nodes (Cluster-3), and so on. Figure 11 shows the throughput for each of the three topologies, while Table 3 provides the values for the remaining three parameters: FDN, MDN, and LDN. The individual topology consistently outperforms the clustering-based topologies, with the cluster with the lowest density performing better than those with higher densities.  
Figure 8: Topology of the first scenario with pair nodes and BS at (50, 50).  
Figure 9: Topology of the first scenario with trio nodes and BS at (50, 50).  
To summarize, in a WSN composed of 100 nodes distributed within a 100 m \(\times\) 100 m area with a base station located at the center (50, 50), the individual transmission approach for sending packets to the base station outperforms cluster-based topologies across all criteria (lifetime, throughput, FDN, MDN, and LDN).  
Figure 11: Throughput for individual nodes, Cluster-2, and Cluster-3 topologies with BS at (50, 50).  
Figure 10: Lifetime for individual nodes, Cluster-2, Cluster-3 topologies with BS at (50, 50).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Topology** & **FDN** & **MDN** & **LDN** \\ \hline Individual & 1340 & 1912 & 2496 \\ \hline Cluster-2 & 1229 & 1361 & 1541 \\ \hline Cluster-3 & 816 & 1118 & 1137 \\ \hline \hline \end{tabular}
\end{table}
Table 3: FDN, MDN, LDN for individual nodes, Cluster-2, and Cluster-3 topologies.
# Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools
## 3 Application Example
### Feature Selection  
An analysis of the linear correlation between the variables was performed (Figure 8). The correlation coefficient varies between \(-1\) and \(1\). This coefficient makes it possible to see whether one variable justifies the linear variation of another. When it is close to \(1\), it means that there is a strong positive correlation, that is, if a given feature \(A\) increases, then feature \(B\) also increases and if \(A\) decreases, \(B\) it also decreases.  
In this case, it appears that the correlation between the features is mostly low or nonexistent (correlation coefficient close to zero). Even so, in the case of the features pressure-mean_3h and pressuremean_24h, the value of the correlation coefficient is approximately \(0.5\) and a more detailed analysis will be relevant.  
Thus, Figure 9 shows the failures, by type of components, according to the evolution of the features pressuremean_3h and pressuremean_24h. It is possible to observe that, for components \(1\) and \(3\), there are clusters of points.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Datetime** & **machineID** & **comp1** & **comp2** & **comp3** & **comp4** \\ \hline
0 & 2015-01-01 06:00:00 & 1 & 19.00000 & 214.000000 & 154.00000 & 169.000000 \\
1 & 2015-01-01 07:00:00 & 1 & 19.041667 & 214.041667 & 154.041667 & 169.041667 \\
2 & 2015-01-01 08:00:00 & 1 & 19.083333 & 214.083333 & 154.083333 & 169.083333 \\
3 & 2015-01-01 09:00:00 & 1 & 19.125000 & 214.125000 & 154.125000 & 169.125000 \\
4 & 2015-01-01 10:00:00 & 1 & 19.166667 & 214.166667 & 154.166667 & 169.166667 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Time since the last replacement, by type of component.  
Figure 8: Features correlation.  
However, the same is not true for components 2 and 4. It is likewise possible to observe that most failures for component 3 occur for higher pressuremean_3h and pressuremean_24h values, when compared with the other components. Thus, it was decided to keep both features, since there is a clear relationship between these and the occurrence of failures in at least some of the components
## 3 Results
### RQ7: What Are the Outcomes Obtained by the Application of the Proposed Solutions?  
Table 4 depicts the major outcomes of the primary studies. In total, 22.2% of them reported results of the development of an app, prototype framework and concept, while did not conduct user studies [72; 76; 77; 78; 79; 80; 83]. The majority of research (42%, \(n=15\)) shown an improvement of participant's ability and performance. In the study conducted in [51; 56; 73], the qualitative analysis shows that participants enhanced their social interaction and communication skills. Of the studies, 16.7% found that they have improved continuous attention and engagement [26; 54; 55; 70; 73; 74; 82]. In the studies conducted by [PERSON] et al. [59] and [PERSON] et al. [62], the authors found that all participants were more independent after the AR training. The statistical analysis in [57] shows a reduction in irritability of ASD children. The results of [68] revealed no statistically significant differences between control and experimental groups with or without using AR-based intervention; however, the qualitative feedback provided by the researchers revealed an improvement in the focus of attention and motivation among children through AR, which can provide fruitful results in the development of skills for children with ASD. The quantitative analysis using tests in [58; 60; 62; 70] shows that the test score of all the participants was improved, while [PERSON] et al. [57] revealed that the outcome is the decreased ABC-H score. Furthermore, in the study conducted in [52; 69; 71; 85], the authors found that the usability of the applied mobile AR application. [PERSON] et al. [65] explored the relationship between student performance and validated clinical measures and then demonstrated game performance is related to the severity of ADHD symptoms.
## 2 Past  
### Digitization  
In the 1960s, digital computers began to migrate slowly into medical imaging, but the transforming event was the introduction of computed tomography (CT) into medical imaging in the early 1970s. In CT, mathematical algorithms created images from many X-ray measurements across multiple projections. Apart from imaging acquisition and formation, digital imaging also opened the possibility for multiple advances including image processing and improvement (i.e., noise suppression), multiplanar reconstruction, three-dimensional (3D) imaging, and transmission to remote places accompanied by the pertinent information of the patients [1]. Nowadays, there are three potentially transformative innovations. One is in the field of archiving and communications, where there will be interesting developments related to storage in the picture archiving communication systems (PACS) and also to the speed of transmission and recovery of images, as well as technologies at lower costs. The second is the increasing interconnection of all types of devices and objects through the network. Medical reports can be written in support (tablet, telephone, etc.) that will not necessarily have to be located in a hospital or other health center. A third group of innovations will be related to technologies that may improve diagnosis and treatment, either by lowering the price of the medical services themselves, helping people with disabilities, improving the quality of life of the elderly, or developing algorithms for computer-aided diagnosis and quantitative extraction of imaging features from medical scans (\"radiomics\") (Figure 1). Digitization strategy in the health environment rests on two strengths: the logical integration of the image data (PACS) with the patient's clinical and demographic data and the transmission of data and images from one location to another [2].
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 5 Discussion
The semi-automatic approach allows selecting the best threshold that defines a geomorphological based pattern of the flooded area. Conversely, the semi-automatic approach requires specific operator experience in flood dynamics. * Results also show that the direct visual mapping of flooding areas can be performed only when the flooded area is visible in co-flood images. Otherwise, it is better to use semi-automatic detection followed by the refinement step. In the presented study, we used only cost-free satellite data that allowed us to create reliable flood maps. One of the most important features of the used satellite data is their continuous temporal sampling and the rapid revisiting time. For example, MODIS satellites provide multispectral images on a daily basis, which allows having free cloud images close to the flood peak for the Ebro and Po flood areas. The Sentinel-1 system continually provides SAR data with short revisit times and global coverage, and Sentinel-2 satellites allowed the creation of flood maps with very good spatial resolution. However, if we must map floods affecting dense urban areas with high accuracy or produce an exact flood water depth map for damage assessment, we must exploit high-resolution (satellite, aerial or terrestrial) data. Very often, the required high-resolution data are not free, for example, the 2016 Chisola stream, which flooded the Moncalieri town, as described in previous work [57]. In this work, we also exploited free ancillary data such as DEM, river gauge data, and geolocated information. Our areas of study were covered by free and high-resolution DEMs provided by local authorities and, for the Ebro and Po basins, were covered by gauge station networks providing river level data with a good density. As described earlier, these ancillary datasets are fundamental for a better definition of the limits of the flooded area.
## 4 NFV and SFC Frameworks Taxonomy
### Resilience and Fault Recovery Frameworks
#### 4.3.4 Resilient SFCs  
[PERSON] et al. [48] proposed an OpenStack and OpenDaylight-based environment to deploy and orchestrate resilient SFCs in cloud environments. The proposed framework follows the ETSI NFV model, which is capable of traffic rerouting, in the case of faults occurring at runtime. The authors proposed an extension to the ETSI NFV framework for  
Figure 13: REINFORCE framework.  
SFC orchestration and management, with the implementation of a service chain consisting of two firewall network functions in standby and active modes.  
They used the Open Baton framework [72] as the NFVO, which uses a messaging queue to communicate with the SFC orchestrator (the OpenDaylight SDN Controller), and the Zabbix network monitoring tool. Service function failure is simulated by abruptly terminating the process running the NF, then the Open Baton Fault Management System (FMS) switches to the standby network function, and the failed NF is recovered by the Orchestrator.
## 2 Experimental Setup
### Characterization Calibration of PSP  
The pressure sensitivity of the PSP can be obtained with a static calibration system. The schematic of the calibration chamber is shown in Figure 3. A UV LED light source with a wavelength of 405 nm is used as the excitation light source to promote the oxygen quenching reaction. The sample is placed on the heating/cooling table, and the pressure and temperature can be changed by air source and liquid nitrogen. Pressure and temperature sensors are used to accurately read the pressure and temperature in the chamber. Pressure sensitivity is calibrated by changing the pressure under constant temperature conditions.  
Figure 1: Structure schematic of the fast response PSP.  
Figure 2: A group of samples with sweep times increased gradually.  
The pressure and luminescent intensity can be modeled by a simplified form of the Stern-Volmer equation, which can be expressed as:  
\[\frac{I_{ref}}{I}=A(T)+B(T)\frac{P}{P_{ref}} \tag{1}\]  
where \(I_{ref}\) and \(P_{ref}\) are the luminescent intensity and the air pressure at a reference condition, respectively. The temperature-dependent coefficients \(A\) and \(B\) are determined through calibration experiments. During the pressure calibration, we found that there seems to be a strong correlation between coefficients \(A\) and \(B\). The changes of coefficients \(A\) and \(B\) of the 30 samples are shown in Figure 4, and they change simultaneously and regularly.  
The pressure sensitivity is approximately equal to coefficient B. So, we try to predict the pressure sensitivity using the paint thickness and roughness of the model surface based on the ANN, then use the pressure sensitivity to predict coefficient A. We substitute the values of coefficient A and pressure sensitivity (coefficient B) into the Stern-Volmer equation  
Figure 4: Changes of coefficients A and B of 30 samples in pressure calibration.  
Figure 3: Schematic of the PSP static calibration system.  
to calculate the pressure. In this way, we can avoid the problem of paint thickness and roughness differences between samples and models in PSP pressure measurement tests.
# Multi-Tag Fusion Localization Method Based on Geometric Constraints
## 4 Fusion of Multiple Observations
### Geometric Constraint Effectiveness
#### 5.2.2 Process Validation
This substantial enhancement in orientation accuracy further underscores the effectiveness of geometric constraints in refining the precision and stability of the positioning system. We have selected a specific dataset to visually demonstrate the comparative performance between our proposed method and the method without geometric constraints. The visualization focuses on the X, Y, and YAW positioning data for both methods. As depicted in Figure 9, our method exhibits a smoother trajectory, indicative of more stable and precise localization. In contrast, the method lacking geometric constraints shows noticeable spikes and abrupt changes, revealing its susceptibility to inaccuracies.
## 2 Related Works  
### Patient Severity Analysis Based on Medical Data  
To analyze patient severity, hospitals utilize clinical data and various machine learning algorithms. Studies are being conducted in areas such as predicting cardiovascular events in patients [19], analyzing the severity of COVID-positive patients [20; 21], and assessing the condition of emergency patients [22; 23] using data and predictive models. [PERSON] et al. [24] explain that artificial intelligence models applied in these studies include logistic regression, random forests, XGBoost, and deep learning, among other types. According to [PERSON] et al. [25], the outcomes of such models are determined by the quality of the data used. [PERSON] et al. [26] state that clinical data from hospitals typically involve a small number of patients and exhibit a non-IID nature. [PERSON] et al. [27] explain that when data are imbalanced, models may not learn effectively, leading to decreased performance in predictions. To address this issue, it is necessary to increase the amount of trainable data and minimize bias to enhance predictions. For this reason, recent research starts with collecting as much data as possible [28]. However, in the field of medical research, there is opposition to centralizing data storage, and concerns about data sharing and privacy protection remain [29; 30].  
In this study, we used the NEDIS dataset. NEDIS is a system designed to support emergency medical policy decisions related to the occurrence, transportation, treatment, and discharge of emergency patients. Data from the emergency rooms of 172 hospitals, collected from 2017 to 2022, were used. The dataset includes vital information about emergency patients, with personally identifiable information and codes removed. The data are labeled using the Korean Triage and Acuity Scale (KTAS) to classify the severity levels of patients, and a total of 2,051,908 data entries were used.
# Dual Auto-Encoder GAN-Based Anomaly Detection for Industrial Control System
## 4 Dual Auto-Encoder GAN-Based Anomaly Detection for Industrial Control System (DAGAN)
### Model Training
#### 4.2.2 Training Process  
Based on the above loss functions, the training process of our DAGAN model is an alternate iterative training between two GANs. The detailed process is as follows: First, the weights of our DAGAN model are randomly initialized. Second, the first GAN starts being trained. The generator \(G_{1}\) and the encoder \(En_{1}\) are fixed, and a batch of samples with label 0 is generated by \(G_{1}\). The generated samples with label 0 and real samples with label 1 are input into the discriminator \(D_{1}\) for training to complete a binary classification. After the discriminator \(D_{1}\) is trained, fix \(D_{1}\) and use the real samples to train \(G_{1}\) and \(En_{1}\). According to the encoder-decoder-encoder pipeline, each real sample \(x\) is encoded by \(G_{En}\) into a low-dimensional feature \(z\), antigen \(z\) is reconstructed by \(G_{Dn1}\) into \(x^{\prime}_{1}\), and finally \(x^{\prime}_{1}\) is encoded by \(En_{1}\) into a low-dimensional feature \(z^{\prime}_{1}\). According to the loss function (Equation (10)), the parameters of \(G_{En}\), \(G_{Dn1}\) and \(En_{1}\) are updated. Third, start training the second GAN after training the first GAN. The discriminator \(D_{1}\) of the first GAN is used to score all real samples to get the marginal samples and calculate the threshold \(\mu\). According to the threshold \(\mu\), the discriminator \(D_{2}\) and generator \(G_{2}\) of the second GAN are trained alternately. The training process is the same as the first GAN, the loss function of the \(D_{2}\) is Formula (17), and the loss function of the generator \(G2\) is Formula (16). Finally, the first GAN and the second GAN are trained alternately for multiple rounds. After each round of training of the first GAN, the marginal samples are recalculated and the threshold \(\mu\) is updated. Based on the updated \(\mu\), the second GAN is trained. When two GANs converge, the training of our DAGAN model ends.
# Monolithically Integrated THz Detectors Based on High-Electron-Mobility Transistors
###### Abstract  
We present THz direct detectors based on an AlGaN/GaN high electron mobility transistor (HEMT), featuring excellent optical sensitivity and low noise-equivalent power (NEP). These detectors are monolithically integrated with various antenna designs and exhibit state-of-the-art performance at room temperature. Their architecture enables straightforward scaling to two-dimensional formats, paving the way for terahertz focal plane arrays (FPAs). In particular, for one detector type, a fully realized THz FPA has been demonstrated in this paper. Theoretical and experimental characterizations are provided for both single-pixel detectors (0.1-1.5 THz) and the FPA (0.1-1.1 THz). The broadband single detectors achieve optical sensitivities exceeding 20 mA/W up to 1 THz and NEP values below 100 \(\mathrm{pW}/\sqrt{\mathrm{Hz}}\). The best optical NEP is below 10 \(\mathrm{pW}/\sqrt{\mathrm{Hz}}\) at 175 GHz. The reported sensitivity and NEP values were achieved including antenna and optical coupling losses, underlining the excellent overall performance of the detectors.  
room-temperature THz direct detectors; THz HEMT antenna integration; resistive mixing; on-chip antenna; TeraFET 2025253539
## 3 Basic Concepts
### Trajectory  
A trajectory, also known as a raw trajectory, is a sequence of spatio-temporal points, defined in Definition 1. Each point, detailed in Definition 2, includes spatial coordinates and a timestamp, referred to as trajectory features in this work. A segment of a trajectory is called a sub-trajectory, as described in Definition 3, which can also be considered a trajectory.  
**Definition 1** (Trajectory).: _A trajectory \(T\) is a sequence of spatio-temporal points \(T=(p_{0}\langle x_{0},y_{0},t_{0}\rangle,\ldots,p_{n}\langle x_{n},y_{n},t_{n} \rangle)\), where \((x_{i},y_{i})\) are spatial coordinates, and \(t_{i}\) represents time, with \(t_{0}<t_{1}<\ldots<t_{n}\) to maintain chronological order._  
**Definition 2** (Point): _A point \(p\) is a tuple \(\langle x,y,t\rangle\), where \(x\) and \(y\) are spatial coordinates representing a location, and \(t\) is the time of the visit._  
**Definition 3** (Sub-trajectory): _A sub-trajectory \(s\) of a trajectory \(T\) is an ordered sequence of points from \(T\), defined as \(s=(p_{i_{1}},p_{i_{2}},\ldots,p_{i_{k}})\), where \(s\) contains at least one point but fewer than all points of \(T\)._  
In this work, we use the terms _point_ or _visit_ to refer to a single element of a trajectory, while, by the term _location \(l\)_, we refer to the point's spatial information. A subsequence of locations (Definition 4) is an ordered list of locations. We denote by \(U_{set}=\{u_{1},\ldots,u_{n}\}\) the set of the distinct individuals represented in the mobility dataset \(D\), formally described in Definition 5.  
We use, in this work, the terms _point_ or _visit_ refer to an individual element of a trajectory, while the term _location \(l\)_ specifically denotes the spatial aspect of a point. A subsequence of locations (Definition 4) is defined as an ordered sequence of locations. The set \(U_{set}=u_{1},\ldots,u_{n}\) represents the distinct individuals captured in the mobility dataset \(D\), as outlined in Definition 5.  
**Definition 4** (Subsequence): _Let \(\mathcal{L}=\{l_{1},l_{2},\ldots,l_{w}\}\) represent a set of locations. A sequence \(S=\langle s_{1},s_{2},\ldots,s_{m}\rangle\) is an ordered list of locations from \(\mathcal{L}\), where each location can appear multiple times._  
_A sequence \(T=\langle t_{1},t_{2},\ldots,t_{z}\rangle\) is called a subsequence of \(S\) (denoted \(T\preccurlyeq S\)) if there are indices \(1\leq i_{1}<i_{2}<\cdots<i_{z}\leq m\) such that_  
\[t_{j}=s_{i_{j}}\quad\text{for}\quad j=1,2,\ldots,z.\]  
_This ensures that \(T\) maintains the order of \(S\)._  
**Definition 5** (Mobility Dataset): _A mobility dataset \(D\) is a collection of trajectories, \(D=\{T_{1},T_{2},\ldots,T_{n}\}\), where each \(T_{u}\) represents the trajectory of a moving object \(u\) (\(1\leq u\leq n\)). For multiple-aspect trajectories, the dataset is represented as \(D=\{MAT_{1},MAT_{2},\ldots,MAT_{n}\}\)._