esa-sceva/satcom-chunk-collection huggingface dataset:
# Radiometric Cross-Calibration of GF-1 PMS Sensor with a New BRDF Model
## 2 Satellites, Test Sites, and Datasets
### Satellites
The'sensor' in this paper represents PMS, OLI, and MODIS. \(\lambda\) is the wavelength (unit: nm). The top of atmosphere (TOA) solar irradiance is computed by Equation (2). \[E_{\text{sensor,i}}=\frac{\int E(\lambda)*\tau_{sensor,i}(\lambda)\text{d} \lambda}{\int\tau_{sensor,i}(\lambda)\text{d}\lambda} \tag{2}\]  
where \(E_{\text{sensor,i}}\) is the TOA solar irradiance of sensor \(i\)-th band. \(E(\lambda)\) is the TOA solar irradiance profile range from 400 to 1000 nm (unit: \(W/\text{m}^{2}/\text{nm}\)) based on Thuillier solar irradiance model [30]. Figure 1: Relative spectral response (RSR) of Panchromatic and Multi-Spectral (PMS), Operational Land Imager (OLI), and MODIS sensor.
## 3 Overview of HO Management in 5G UDSC Network
### 5G Technologies and Features  
In 5G network, cells are divided into large and small cells to provide more network efficiency. Worldwide wireless web concept is used by 5G which is user-centric in contrast to service-centric used in 4G. By using user-centric concepts, 5G enable to support several applications and different services to connect the world. 5G mobile communication embed number of different technologies such as device-to-device (D2D), machine-to-machine (M2M), multiple-input multiple-output (MIMO), edge computing (EC), small cell (SC), beamforming (BF), convergence of WiFi and cellular, nonorthogonal multiple access (NOMA), subscribe software-defined networking (SDN), network functions virtualization (NFV) and channel coding (CC) [33]. 5G also introduce some features i.e., 5G architecture will include cloud computing, device centric and distributed system, provides higher data rates almost in gigabits, support large number of devices, utilization of battery is low, and it has low infrastructure cost [34]. To provide the ubiquitous communication in mobile systems, 5G include machine-to-machine communication technology which almost connect 100 billion devices. To provide higher throughput and gives higher spectrum efficiency, 5G uses MIMO technology [35]. 5G also take advantages of other technologies like OFDM, IoT, UDSC network, and mm Wave [36]. To gain the higher data rate and direct connectivity between devices, 5G use device to device communication technology. Succeeding four behaviors make sure D2D communication in 5G beyond scenarios i.e., device relaying with operator-controlled link establishment (DR-OC), direct D2D communication with operator-controlled link establishment (DC-OC), device relaying with device-controlled link establishment (DR-DC), and direct D2D communication with device-controlled link establishment (DCDC) [37; 38].  
Small cell technology is a promising yet economically driven approach to overcome the hurdles of mobile network operators, 5G coverage, capacity and IoT devices. Although small cells practice short range BSs, they have the potential to handle high data rates of mobile subscribers, monitor connected things and roll out both 5G and 6G. Small cells, for instance, picocells, femtocells and microcells, are deployed over the macrocell networks positioned with the same geographical region to form HetNets [39]. These HetNets have the capacity to deliver uninterrupted communications having high data rates and quality services, ensuring users to exploit reliable mobile networks services while moving from one cell to another. However, the deployment of large number of small cells to design HetNets creates issues, such as high inter-cellular interaction and expansion of cell boundaries, resulting in HO failures and radio link failure. All in all, the greater the number of small cells in HetNets, the greater will be the mobility management issues [40].
# The Privacy Flag Observatory: A Crowdsourcing Tool for Real Time Privacy Threats Evaluation
## 4 Implementation and Operation of the Observatory
### Confidentiality
Otherwise, if the initial connection is made using the standard HTTP protocol in an insecure network, it is possible that an eavesdropper can intercept the initial request and redirect the traffic to a malicious website. Furthermore, the HSTS protocol can significantly reduce the risk of SSL stripping attacks. However, as expected, the HSTS is not a panacea. Sophisticated attacks, such as _BEAST_ and _CRIME_, cannot be eliminated, but the deployment of HSTS lowers their success rate. Finally, HSTS should be enabled and supported both on the client and server sides. * Use of a trustworthy certificate chain: A website should have a _valid_ and _trusted_ certificate. The process of validating the certificate is based on a chain of trust that links back to a _Root Certification Authority (CA)_ trusted by the user's browser [30]. Websites that use _self-issued, expired_ or _non-recognizable_ certificates from unknown or not trusted CAs [31, 32] are not suitable for web applications that handle sensitive data and content. On the other hand, a self-issued certificate might be a better alternative to the deployment of the simple HTTP connection which offers no protection at all, allowing all information to be transmitted as plaintext. A compromising solution may be to use certificates, such as the ones provided by the _Let's Encrypt_ initiative, which are _free encryption certificates_. However, such certificates have limited support and lifetime compared to the commercial certificates [33]. The adoption of valid and trusted certificates is a key aspect in the efforts to increase the security of the Web. * Public key pinning: _HTTP Public Key Pinning (HPKP)_ is a security mechanism which allows HTTPS websites to defend against impersonation attacks [34]. These attacks are based on malformed or invalid, i.e., fraudulent, certificates [35]. For example, attackers might compromise a Certificate Authority and then issue fraudulent certificates for any domain. To defend against this threat, the web server can provide a list of \"pinned\" public key hashes. In this way, in subsequent connections, web clients will expect the server to use one or more of those public values (keys) in its certificate chain [36]. In Figure 4, we see examples of how this information is provided in graphical form (pie charts) by the platform.
# GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme Drought in the Amazon River Basin
## 3 Data and Methods
### Data
#### 3.1.2 Swarm Data  
In our study, the Swarm SH solution was provided by the International Combination Service for Time-Variable Gravity, which combines Swarm solutions from different institutions using variance component estimation to achieve the optimal solution. This combination includes solutions from four institutions: the Astronomical Institute at the Czech Academy of Sciences, the University of Bern, the Institute of Geodesy at the Graz University of Technology, and Ohio State University [37]. The process for calculating regional \(1^{\circ}\times 1^{\circ}\) TWSC gridded data using the Swarm SH solution is the same as that for the GRACE/GFO solution. The only difference is that the GRACE/GFO SH solution uses a 300 km Gaussian filter and a P3M6 polynomial filter, whereas the Swarm solution uses a 1000 km fan filter [29].
# Vibration Reduction on Circular Disks with Vibroacoustic Metamaterials
## 2 Numerical Design of the Circular Disk with a Vibroacoustic Metamaterial  
In order to investigate the behavior of a VAMM for out-of-plane vibration reduction within a spinning disk shaped rotor, a test structure needs to be evaluated. For this, a circular disk with similar dimensions like those of a typical circular saw blade used in woodworking on table saws and chop saws is used. The circular disk features an outer diameter (\(d_{\mathrm{o}}\)) of 300 mm, an inner diameter (\(d_{\mathrm{i}}\)) of 30 mm and a thickness (\(t\)) of 2 mm (see Figure 3). This circular disk is used for integration of a VAMM. The design by numerical methods (finite element method) of the circular disk with an integrated VAMM is presented in the following sections.
## 3 Methods
### Switching Mode
#### 3.2.2 SSVEP Parameter Training and Optimization  
Different flickering frequencies and duty cycles result in different \(RC\) values, and the best combination of the two SSVEP parameters for each user may be different. To find the best parameters for each user, an offline training stage is performed to test 11 flickering frequencies (20 Hz, 20.41 Hz, 20.83 Hz, 21.28 Hz, 21.72 Hz, 22.22 Hz, 22.73 Hz, 23.26 Hz, 23.81 Hz, 24.39 Hz, and 25 Hz) and five different duty cycles (10%, 30%, 50%, 70%, and 90%) for each user. Figure 4 shows the spectrum of the 3-s EEG signal recorded at Oz, respectively, where the flickering frequency of the LED is 24.39 Hz. We can observe from this spectrum the peaks at its first (24.39 Hz) and the second (48.78 Hz) harmonic frequencies. Additionally, the peak corresponding to the second harmonic is much smaller than the one corresponding to the first harmonic. However, there is no peak at its third harmonic (73.17 Hz), which could be due to the fact that the EEG signal has been band-passed (1-70 Hz) in an EEG preprocessing circuit board. The best combination of the two SSVEP parameters results in the largest \(RC\) value. When the BCI system performs online with a user, the best flickering frequency and the best duty cycle associated with this user are adopted. By doing so, optimum sensitivity of online SSVEP detection is achieved.  
Figure 3: The Arduino board and the LED lamp used in our system.
# Dual Auto-Encoder GAN-Based Anomaly Detection for Industrial Control System
## 4 Dual Auto-Encoder GAN-Based Anomaly Detection for Industrial Control System (DAGAN)
### Anomaly Detection  
When the training of our DAGAN model is complete, by considering both the normal distribution and the marginal distribution, the model can detect whether a test sample is an outlier or not from the feature space.  
A test sample \(\hat{x}\) is first fed into the first GAN to obtain its low-dimensional feature \(z\). Second, \(z\) is reconstructed into a new sample \(\hat{x}^{\prime}_{1}\) according to the normal distribution learned by \(G_{1}\). Finally, \(\hat{x}^{\prime}_{1}\) is encoded again by \(En_{1}\) as a low-dimensional feature \(z^{\prime}_{1}\). Therefore, the first anomaly score measures the similarity between two features \(z\) and \(z^{\prime}_{1}\), defined as:  
\[\begin{split} S_{1}=\left\|G_{En}(\hat{x})-En(G_{1}(\hat{x})) \right\|_{2}\\ Score_{1}=\frac{S_{1}-S_{1}^{Min}}{S_{1}^{Max}-S_{1}^{Min}}\end{split} \tag{18}\]  
When the test sample \(\hat{x}\) is a normal sample, \(Score_{1}\) is small and close to 0. When the test sample \(\hat{x}\) is a marginal sample, \(Score_{1}\) is large. When the test sample \(\hat{x}\) is outlier, \(Score_{1}\) is very large.  
According to the first anomaly score, marginal samples and outliers cannot be well identified, which may easily lead to the misjudgment of marginal samples. For this, the test sample \(\hat{x}\) is again fed into the second GAN and reconstructed to generate marginal samples \(\hat{x}^{\prime}_{2}\) according to the marginal distribution learned by \(G_{2}\). To compare \(\hat{x}\) and \(\hat{x}^{\prime}_{2}\) from the feature space, the output features of an intermediate layer in discriminator \(D_{2}\) are used. Therefore, the first anomaly score measures the similarity between two features of \(\hat{x}\) and \(\hat{x}^{\prime}_{2}\), defined as:  
\[\begin{split} S_{2}=\left\|f(\hat{x})-f(G_{2}(\hat{x}))\right\|_{2 }\\ Score_{2}=\frac{S_{2}-S_{2}^{Min}}{S_{2}^{Max}-S_{2}^{Min}}\end{split} \tag{19}\]  
When the test sample \(\hat{x}\) is a normal sample, \(Score_{2}\) is small. When the test sample \(\hat{x}\) is a marginal sample, the \(Score_{2}\) is close to 0. When the test sample \(\hat{x}\) is an outlier, the \(Score_{2}\) is large.  
Combining the above two scores, the final anomaly score of our DAGAN model is defined as follows:  
\[A(\hat{x})=(1-\gamma)Score_{1}+\gamma Score_{2} \tag{20}\]where \(\gamma\) is a hyper-parameter with a value of [0-1], representing the importance of \(Score_{2}\).  
Based on the above anomaly score, we can quickly identify whether a test sample is an outlier, and greatly reduce the probability of unseen marginal samples being misjudged. Because when both normal samples and marginal samples are tested, the anomaly score is smaller. Conversely, when anomalous samples are tested, the anomaly score becomes significantly larger.
# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 6 The Potential Applications of Voxel-Based Navigation  
Although this paper mainly focuses on voxel-based navigation, other research fields would benefit from advancements in these studies. In this section, we list a set of applications of voxel-based navigation techniques that could serve as a basic function and lead to the generation of new models and algorithms.
###### Abstract  
The integration of modern computing and advanced communication with power grids has led to the emergence of electrical cyber-physical systems (ECPSs). However, the massive application of communication technologies makes the power grids become more vulnerable to cyber attacks. In this paper, we study the vulnerability of ECPSs and develop defence strategies against cyber attacks. Detection and protection algorithms are proposed to deal with the emergency of cascading failures. Moreover, we propose a weight adjustment strategy to solve the unbalanced power flows problem which is caused by splitting incidents. A MATLAB-based platform with advantages of easy programming, fast calculation, and no damage to systems is built for the offline simulation and analysis of the vulnerability of ECPSs. We also propose a five-aspect method of vulnerability assessment which includes the robustness, economic costs, degree of damage, vulnerable equipment, and trip point. The study is of significance to decision makers as they can get specific advice and defence strategies about a special power system.  
electrical cyber-physical system; protection procedure; vulnerability assessment; simulation platform +
Footnote †: journal: Applied Sciences
## 4 Experimental Results
### Experimental Results  
#### 4.2.1 Modularity  
We evaluate the results of topological partitioning based on communication relationships by calculating the modularity [1]. The modularity is widely used because it is simple to calculate and can accurately reflect the quality of a global partition. The modularity can be calculated by the following formula.  
\[Q=\frac{1}{2m}\sum_{i\
eq j}\left(\text{A}_{\text{ij}}-\frac{k_{i}k_{j}}{2m} \right)\delta\left(C_{i},C_{j}\right) \tag{6}\]  
where \(i\) and \(j\) represent any two nodes in the graph; \(m\) is the number of sides in the entire network; \(A_{ij}\) is the value of the row and \(C_{i},C_{j}\) column in the adjacency matrix (1 or 0); and \(k_{i},k_{j}\) represent the modularity of nodes \(i\) and \(j\), i.e., the number of adjacent nodes. The letter \(i\) represents the numbering of the subgraphs \(j\) and \(\delta\left(C_{i},C_{j}\right)\). The definition is  
\[\delta\left(C_{i},C_{j}\right)=\left\{\begin{array}{ll}1&C_{i}=C_{j}\\ 0&C_{i}\
eq C_{j}\end{array}\right. \tag{7}\]  
Figure 3 shows the results of every hour of data per day, including 214,735 traffic flows and 16,145 IPs. The community identification was performed every 5 min. The modularity of the obtained  
division result is above 0.8. Therefore, the proposed method is simple and effective when comparing the sizes of \(|P_{i}|\) and \(|Q_{i}|\) to determine the identity of node \(i\) and divide the network accordingly.
# Wireless Mesh Networking: An IoT-Oriented Perspective Survey on Relevant Technologies
## Appendix A  
In the following, we detail the procedure we used to derive the values \(v_{i,k}\) involved in calculation of Equation (1) and shown in Figure 17. These values, as previously highlighted, have been obtained through a literature analysis on several research works [79; 80; 81; 82; 83; 84; 85; 86; 87]). In each of these papers, at least one technology and one performance metric were considered. In Table A1, for each considered metric we indicate the reference where relevant information is reported. Then, we assign a value (in an arbitrary, but reasonable scale decided by us) representative of the performance for the corresponding metric. For each technology, we consider the arithmetic average among the collected performance values and, finally, we divide this average by the maximum among all values. In this way, the final result is normalized between 0 and 1.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**LoRa**} & \multicolumn{3}{c}{**IEEE 802.15.4**} & \multicolumn{3}{c}{**IEEE 802.11**} & \multicolumn{1}{c}{**IEEE 802.15.1**} \\ \hline  & [79] & [80] & [81] & [82] & [83] & [82] & [84] & [85] & [86] & [87] \\ \hline
**Scalability** & 4 & 3.5 & — & — & — & 3 & 3 & 3 & 3 & 3 \\ \hline
**Range** & 5.5 & 5 & 4.5 & 8 & 3.5 & — & — & 3 & 3 & 4 \\ \hline
**Coverage** & 5.5 & 5 & — & 7 & 1 & 3 & 3 & 4 & 4 & 4.5 \\ \hline
**Deployment** & 5.5 & 5.5 & — & 7 & 2 & — & — & 4 & 4 & 2 \\ \hline
**Latency** & 4 & 4 & 5 & 4 & 5.5 & — & — & 4 & 4 & 4 \\ \hline
**Power** & **—** & **—** & 6 & 7 & 4 & — & — & 4 & 4 & 2 \\ \hline
**Battery Life** & 5 & 5.5 & — & — & — & 3.5 & — & 1 & 1 & 4 \\ \hline
**Network** & **—** & **—** & 4 & — & — & 2.5 & — & 3 & 3 & 4 \\ \hline
**Data Rate** & **—** & **—** & 1 & 1.5 & — & — & 5.5 & 5.5 & 2.5 \\ \hline
**Max Scale** (\(\bar{v}_{i,k}^{\max}\)) & 6 & 6 & 7 & 8 & 8 & 6 & 6 & 6 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance metrics used to evaluate the analyzed WMN protocols.  
For example, in terms of scalability, from the values in Table 1 one can conclude that: for LoRa the value will be \(((4/6)+(3.5/6))/2=0.625\); for IEEE 802.15.4 the value will be \(((3/6)+(3/6)+(3/6))/3=0.5\); for IEEE 802.11 the value will be \((3/6)=0.5\); for IEEE 802.15.1 the value will be \((3/6)=0.5\).
# A Novel Algorithm to Optimize the Energy Consumption Using IoT and Based on Ant Colony Algorithm
## 4 The Proposed Method
This paper aims to improve the traditional idea of clustering (spatial clustering) to achieve the primary goal of wireless sensor networks, i.e., to increase network life while maintaining network coverage and provide an integrated method for clustering based on energy location. We believe that in presenting the new algorithm, energy-based clustering could create clusters with the same energy level and distribute energy consumption across network nodes. The flowchart of Figure 2 shows the main steps of the proposed method for routing in the target network. Figure 2: Flowchart of proposed routing algorithm using Internet of Things (IoT). As can be seen in the diagram, the routing in the proposed method has three necessary steps as follows:  
* Clustering;
* Optimization of cluster centers;
* Data transfer. In the continuation of this section, each of these steps will be explained.
## 5 Data Acquisition and Processing
### Data Acquisition
The interspacing between the electrodes was 10 m.  
The distribution of the single-station and array measurements was made according to the available geophysical and geological data. The aim was to optimize the data coverage for the three zones. Zone 1. The Oued-Fodda plain. Ambient vibrations were recorded at 67 sites, including 6 in the village of Zbabda and 9 in Oued-Fodda city (Figure 4), and 58 of these are distributed over 8 profiles oriented in a WNW-ESE direction. As an attempt to image the rupture trace of the 1980 El-Asnam earthquake in the upper sedimentary layers, the profiles, Pr 6, Pr 7, and Pr 8 (Figure 3), are crossing the surface trace around the village of Zmoul (Figure 4), with 11 measurement points on the Sara-El-Maarouf anticline, which corresponds to the overlapping block. Ambient vibrations were also recorded using the array measurement technique at 5 sites, including 2 (AR3 and AR4) within Oued-Fodda city (Figure 4). Zone 2. The Carnot Plain. A total set of 64 single-station measurement points were conducted in this zone, distributed over 8 profiles oriented in a NW-SE direction (Figures 3 and 5). An array measurement in a circular configuration was carried out in the city of El-Attaf, with apertures of 20 m and 40 m, along with two single-station measurement points at the same site, in order to perform a joint inversion. An electrical resistivity survey was also carried out in the Mechta-Nouasser locality (Figure 5). Figure 4: Compiled data in Zone 1. In addition, an array ambient vibration measurement was carried out near the town of El-Amra, with apertures of 20 m and 50 m (AR7). Furthermore, an electrical resistivity survey (E14) was performed south of the El-Amra locality to better constrain the thickness of the shallow Quaternary layers in this area.
## 3 DSP Algorithm
### DSP Operations  
To describe the operations of the DSP algorithm, a sample network with seven nodes and link weights is considered, as shown in Figure 1. In this case, the DSP algorithm is applied to the network in order to establish two separate paths between source node 1 and destination node 5.  
First, the DSP applies the _Search Path_ algorithm to find all possible paths from node 1 to node 5. There were 21 possible paths between nodes 1 and 5 found by the _Search Path_ algorithm. The possible path list is then sorted using _Quicksort_. The searched paths and sorted paths are shown in Table 1. Based on the sorted path list, the DSP selects two separate paths that have the best path distances, and that have no common nodes, by applying the _Select DP_ algorithm to the sorted path list. The selected dual paths are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Possible Path List**} & \multicolumn{2}{c}{**Sorted Path List**} \\ \cline{2-5}  & **Path** & **Distance** & **Path** & **Distance** \\ \hline
1 & \(\{1,2,3,4,5\}\) & 13 & \(\{1,2,3,5\}\) & 8 \\
2 & \(\{1,2,3,4,6,7,5\}\) & 17 & \(\{1,6,4,5\}\) & 9 \\
3 & \(  
The dual paths between source 1 and destination 5 found by DSP are shown in Figure 2.  
When source node 1 sends unicast frames to destination node 5, the frames are forwarded to the destination through the pre-established dual paths. In the failure-free case, the destination receives two identical copies of each sent frame, processes the first copy, and discards the duplicate. When the link between node 4 and node 5 fails, only one copy of each frame delivered through path 2 is lost; the other copy still reaches the destination node without switchover delay, as shown in Figure 3. Therefore, the DSP provides fault-tolerant communication in the case of network failure.  
Once the source node detects the failure, it updates its link table and re-runs the DSP algorithm to find the second path to the destination, as shown in Figure 4.  
Figure 3: Fault-tolerant communication in the failure case.  
Figure 2: The dual separate paths (DSPs) from source 1 to destination 5.