esa-sceva/satcom-chunk-collection huggingface dataset:
# Reviewing the Potential of Sentinel-2 in Assessing the Drought
## 2 Properties of Sentinel-2  
Compared to the available remote sensing data including Landsat and MODIS information, the introduction of Sentinel-2, the twin satellite with a higher spatial and temporal resolution, brought revolutionary changes in the field of remote sensing, especially in the observation of land-surface processes [56]. The sentinel-2 mission includes two identical satellites Sentinel-2A and Sentinel-2B. Sentinel-2A was released in November 2015 and Sentinel-2B in March 2017, respectively. Although the MSI (Multi-Spectral Instrument) on both Sentinel-2A and 2B are designed identically, the measured spectral response function recorded a variation especially in B1, B2, B11, and more than 1% for NDVI [57]. Sentinel-2 has a wide range of applications and is generally used for land monitoring, maritime monitoring, emergency management, and security. The spatial resolution of Sentinel-2 varies from 10 m to 60 m and the radiometric resolution of MSI is 12 bit with a combined revisiting time of 5 days. Detailed band information with the associated signal-to-noise ratio is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Band No.**} & \multirow{2}{*}{**Spatial Resolution (m)**} & \multicolumn{2}{c}{**Sentinel-2 A**} & \multicolumn{2}{c}{**Sentinel-2 B**} & \multirow{2}{*}{**SNR @ Lref**} \\ \cline{3-3} \cline{5-6}  & & **Central** & **Band WIDTH** & **Central** & **Band** & **Width (nm)** & \\ \hline B1 & 60 & 442.7 & 21 & 442.2 & 21 & 129 \\ \hline B2 & 10 & 492.4 & 66 & 492.1 & 66 & 158 \\ \hline B3 & 10 & 559.8 & 36 & 559.0 & 36 & 168 \\ \hline B4 & 10 & 664.6 & 31 & 664.9 & 31 & 148 \\ \hline B5 & 20 & 704.1 & 15 & 103.8 & 16 & 117 \\ \hline B6 & 20 & 740.5 & 15 & 739.1 & 15 & 89 \\ \hline B7 & 20 & 782.8 & 20 & 779.7 & 20 & 105 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sentinel-2 band characteristics.  
Figure 1: Methodological concept.
## 7 Results
### Frame Size  
A temporary fault in the communication subsystem only results in an error if a frame is being transmitted while the fault happens. Therefore, we can assume that the size of the frame impacts the reliability, as larger frames occupy the communication subsystem for a longer time than smaller frames. The following experiments allow us to quantify this impact. Figure 12 shows the reliability achievable by TSN, approach A and approach B when transmitting frames of 64, 782 and 1500 bytes in two cases, namely, our case of reference with BER equal to \(1\times 10^{-10}\) and another case with a BER of \(1\times 10^{-6}\).  
If we look at the results obtained when the BER is \(1\times 10^{-10}\), we can see that the frame size has an important impact on the reliability achievable by TSN, which drops more than a 20% with larger frames. In contrast, we can see that in this case the impact for approaches A and B is negligible. This shows that, when the BER is low, using the minimum level of temporal redundancy (number of proactive replicas, \(k\), equal to 2) is enough to compensate the negative impact of using large frames. Moreover, the results of approach A further indicate that bridges do not need to regenerate replicas at their output ports (as done in B) to compensate this negative impact; at least when the path does not include more than six bridges.  
This benefit of temporal redundancy is corroborated, to some extent, when we consider a BER of \(1\times 10^{-6}\). However, in this case, the reliability of A and B drops around 98% and 50% respectively when the frame size is 1500 bytes instead of 64. This indicates that when the BER is high, it is necessary to increase the number of time replicas to keep a high level of reliability; especially when using approach A instead of B. Later on, we demonstrate this assertion (for the reference frame size of 782 B) when analysing the impact of the number of replicas.  
As a general recommendation, we can say that in critical applications that operate in harsh electromagnetic environments, it may be convenient to reduce the size of frames whenever possible. Nonetheless, this is not always possible. In those cases, approach B is a more adequate solution than approach A, but even with approach B the reliability  
Figure 12: Reliability when varying the frame size. The left side of the figure shows the reliability for a BER of \(1\times 10^{-10}\), while the right shows the reliability for a BER of \(1\times 10^{-6}\). In both cases, TSN is represented in grey, approach A in orange and approach B in green. The \(x\) axis represents the frame sizes in bytes, while the \(y\) axis represents the reliability.  
decreases dramatically. Thus, in such environments larger frames should have a higher level of redundancy than shorter ones to provide adequate levels of reliability.
# Efficient Traffic Flow Guidance Feedback Strategy Considering Drivers' Disobedience in ITS
## 4 Simulation Results and Discussion
### Experimental Design  
The traffic simulator is chosen to be SUMO, \"simulation of urban mobility.\" The SVM and \(k\)-means are implemented in MATLAB. An open-source toolbox of MATLAB, LIBSVM [19], is used in our experimentation. Although MATLAB has its own function for implementing SVM, the functions in LIBSVM are considered more powerful than those in the MATLAB package. Thanks to TraCI4 Matlab [20], which serves as an implementation of the Traffic Control Interface (TraCI) for MATLAB, an integration between MATLAB and SUMO is realized. TraCI4 Matlab is an application programming interface (API) developed in MATLAB that allows communication between any application developed in this language and a microscopic road traffic simulator such as SUMO.
# Long-Term Forecasting Using MAMTF: A Matrix Attention Model Based on the Time and Frequency Domains
## 4 Experiments
### Our Model and Implementation Details  
The overall architecture of our model is shown in Figure 1. Due to the difference between the independent matrix attention mechanism and the merged matrix attention mechanism, the experimental model is divided into two versions: the merged matrix attention mechanism version M-MAMFT and the independent matrix attention mechanism version I-MAMFT. We set the number of layers of the encoder (including the frequency domain encoder and time domain encoder) and decoder (including the frequency domain decoder and time domain decoder) to 1. The look-back window c of our model is set to 720. The parameter n is set to 192, and the parameter l = n. We use the cosine-decaying learning rate and set the initial learning rate differently for different datasets, and the specific learning rate settings are shown in Table 2. The Dropout is set to 0.1. We use MSE and MAE as the loss functions and optimize using the Adam optimizer. The smaller the value of the MSE and MAE, the better.
## 5 Discussion
### Implications for Policy and Practice  
A comparison with the results of the 2024 field survey, which demonstrated indications of slum area mapping in several locations, suggests that the accuracy achieved through field survey methods is on par with, if not superior to, the use of remote sensing imagery and street view imagery alone. This finding indicates that the combination of the most recent survey results also provides substantial support for the incorporation of more inclusive mapping results prior to the formulation of a slum upgrading policy by the city government. It is evident that utilizing satellite and street view imagery has been advantageous in terms of reducing analysis time and enhancing the extent of the area covered. It is evident that the implementation of the field survey method necessitates a greater investment of time and results in a more circumscribed area of coverage when undertaken over a period of one year.  
The findings of this study are expected to encourage district and city administrations to adopt remote sensing imagery (RSI) and street view imagery (SVI) for delineating slum areas, optimizing field survey data collection while saving time and costs. Future research may explore the applicability of FCN and FCN-DK models with RSI and SVI in diverse contexts. By developing a comprehensive overview of identifiable slum characteristics, this study offers insights that can be adapted to local contexts, aiding in the identification and resolution of slum conditions. Engaging stakeholders can enhance tailored interventions, while the application of RSI and SVI in mapping and monitoring can support urban planners and policymakers in improving slum areas, ultimately fostering community resilience and sustainability.  
The significance of the classification conceptualization of slum areas cannot be overlooked, as it must be adapted to the local context that exists in each region [28]. In Indonesia, slum classification is determined based on seven criteria, which include building condition, neighborhood road accessibility, drainage system, drinking water supply, domestic waste management, waste management, and availability of fire protection system. In addition, the limitations in terms of social complexity in slums, which are considered to be inhabited by people with lower-middle socio-economic conditions, are also difficult to measure only from satellite imagery. Further research is expected to combine it with population density or poverty level maps to produce a more representative mapping for policymakers. Therefore, the development of a model that is in accordance with the conceptualization of slums based on the local context in Indonesia is crucial in the process of making slum maps.
## 4 Solution Approach
### Objective and Constrains
#### 4.1.3 Subproblem 3 (Related Decision Variable \(c_{sh}\)\(g_{sl}\) and \(h_{ssl}\))  
\[\begin{array}{ll}\hskip-14.226378 pt\mbox{$Z_{\mbox{\scriptsize sub1.3}}$}( \ \mu_{ssl}^{1},\mu_{sl}^{2},\mu_{ssl}^{3},\mu_{l}^{4},\mu_{l}^{5},\mu_{sd}^{7}, \mu_{sl}^{5})=\\ \hskip-14.226378 pt\mbox{$\min$}\ \sum_{s\in S}\sum_{l\in L}(\sum_{d=D_{s}}(\mu_{sl}^{2}+ \mu_{ssl}^{3}r_{sl}+\mu_{sd}^{5}D_{sl}(c_{sl},g_{sl})-\mu_{ssl}^{1})h_{ssl}+( \mu_{l}^{4}-\mu_{sl}^{7})c_{sl}+(\mu_{sl}^{7}-\sum_{d\in D_{s}}\mu_{ssl}^{3}) \mbox{$g_{sl}$}),\end{array}\]  
**Subject to:**  
\[h_{ssl}=0\ \mbox{or}\ 1 \forall s\in S,\forall d\in D_{s},l\in L\] (LR 1.3) \[g_{sl}\in[0,\max_{d\in D_{s}}r_{sl}] \forall s\in S,l\in L.\] (LR 1.5)  
Subproblem 3 can be decomposed into \(/S|/L/\) independent subproblems involved a delay function. For each link \(l\in L\) in a multicast group \(s\in S\):  
\[\min\ \sum_{d\in D_{s}}(\mu_{sl}^{2}+\mu_{ssl}^{3}r_{sl}+\mu_{sd}^{5}D_{sl}(c_{ sl},g_{sl})-\mu_{ssl}^{1})h_{ssl}+(\mu_{l}^{4}-\mu_{sl}^{7})c_{sl}+(\mu_{sl}^{7}- \sum_{d\in D_{s}}\mu_{ssl}^{3})\mbox{$g_{sl}$}.\]  
However, the decomposed subproblem is a complicated problem due to the coupling of \(h_{ssl}\), \(c_{sl}\) and \(g_{sl}\). Since the auxiliary variable \(g_{sl}\) is a discrete and finite set. The optimal solution can be computed and compared from all finite results to find out. Then \(h_{ssl}\) and \(g_{sl}\) are considered. According to the algorithm developed in [23], subproblems can be solved and decomposed by the following steps:  
**Step 1:** First of all, the parts of decision variable \(h_{ssl}\) can be solved by ( \(\mu_{sl}^{2}+\mu_{ssl}^{3}r_{sl}+\mu_{sd}^{5}D_{sl}(c_{sl},g_{sl})-\mu_{ssl}^{1 }\)) for each destination \(d\) in multicast group \(s\). These \(/D_{s}\)/ numbers of \(c_{sl}\) is the so-called break points illustrated in Figure 9.  
**Step 2:** Sort these breaking points and denote them as \(c_{sl}^{1},c_{sl}^{2}......c_{sl}^{|D_{s}|}\) with the sequence from smallest to largest.  
**Step 3:** At each interval, \(c_{sl}^{i}\!\leq\!c_{sl}\!\leq\!c_{sl}^{i+1}\), the corresponding \(h_{ssl}\) is 1 if \(\mu_{sl}^{2}+\mu_{ssl}^{3}r_{sl}+\mu_{sd}^{5}D_{sl}(c_{sl},g_{sl})-\mu_{ssl}^{1 }\!\leq\!0,\mbox{otherwise $h_{ssl}$ is 0.}\)  
**Step 4:** Until now we only need to determine the value of \(c_{sl}\). We regard these \(/S|/L/\) independent subproblems as an auxiliary function \(\mbox{$\theta_{sl}$}(c_{sl})\) denoted as following: \(\mbox{$\theta_{sl}$}(c_{sl})=\sum_{d\in D_{s}}(\mu_{sl}^{2}+\mu_{ssl}^{3}r_{sl} +\mu_{sd}^{5}D_{sl}(c_{sl},g_{sl})-\mu_{ssl}^{1})h_{ssl}+(\mu_{l}^{4}-\mu_{sl} ^{7})c_{sl}+(\mu_{sl}^{7}-\sum_{d\in D_{s}}\mu_{ssl}^{3})\mbox{$\[\sum_{d\in D_{s}}\mu_{sd}^{5}h_{sd}=a_{1},\text{and the local minimal }c_{sl}\text{ must be either at the smaller boundary point, }c_{sl}^{i}\text{ or }c_{sl}^{i+1},\text{or at point }c_{sl}^{*}=g_{sl}+\sqrt{\frac{a_{1}}{(\mu_{l}^{4}-\mu_{sl}^{ 7})}}.\]  
**Step 5:** The global minimum point can be found by comparing these \(/D_{s}/\) local minimum points for each \(/S/|L/\) iteration.
# Improving Streaming Video with Deep Learning-Based Network Throughput Prediction
## 3 Theoretical Background
### Adaptation Strategies
\]  
The algorithm scans the available qualities \(Q\) and selects the representation with the highest average bit rate lower than or equal to the estimated throughput \(r\) modified by a factor \(\lambda\), as presented in (3a). The algorithm will switch to the best achievable video bit rate if it differs from the current bit rate, excluding two scenarios: network throughput increases but the buffer level remains low (3b) or network throughput decreases but the buffer level remains high (3c). For ExoPlayer, the default values for \(B_{\min}\) and \(B_{\max}\) are 10 and 25 s.  
The third algorithm, which we will refer to as _Tian2016_, avoids video bit rate variability resulting from throughput fluctuations and estimation errors. Additionally, when the available network bandwidth remains higher for a longer time than the current video bit rate, the algorithm increases the video bit rate steadily and decreases it more aggressively when the throughput falls. A proportional-integral-derivative (PID) controller is responsible for the player's decision regarding video bit rate selection. A PID implements a control loop with a feedback mechanism, which transfers the controller's output value to the system input. The system adjusts its output by computing the difference between a set point and a measured process variable. The authors claim that their algorithm quickly responds to congestion level shifts and maintains a stable video rate during throughput variations. We took the parameters for the algorithm from [23] from sections V and VI-D.  
As was stated earlier, a DASH system fragments a video into segments. The segment length \(L\) is usually between 2 and 10 s; for example, the MSS algorithm uses 2-5 s [25]. In our work, we set the video segment length to 4 s.
# Secure and Scalable Internet of Things Model Using Post-Quantum MACsec
## 3 Related Work  
### MACsec (802.1 AE, MAC Security)  
MACsec represents a Layer 2 security protocol that ensures the authenticity and integrity of data frames to provide robust protection against replay attacks [22]. It offers multiple advantages in terms of network security, such as reduced header size and support for physical port-level encryption and decryption. Since Layer 2 IoT networks, such as LoRa WAN (Long Range Wide Area Network), are characterized by limited bandwidth, the smaller size of headers improves transmission efficiency compared with IPSec. Additionally, physical port-level encryption and decryption in MACsec significantly enhances network security by protecting data right from their point of origin, ensuring confidentiality, integrity, and authenticity. On top of that, MACsec contributes to high-speed network connections by directly ensuring security at the data link layer. Although there are several implementation options, such as unicast, broadcast, and multicast communications, MACsec's security mechanisms are independent of upper-layer processes to avoid unnecessary modifications when user applications change.  
In MACsec communication, hosts within the same CA share a common CAK. The CA leader periodically initiates an EAP request to other hosts to ensure all nodes within the CA use the same CAK. A CAK distributed among the hosts within the same CA is used to generate an Integrity Check Value-Key (ICK) and a Key Encrypting Key (KEK) through AES-ECB. The KEK, derived from the CAK, encrypts the Secure Association Key (SAK) using AES Key Wrap, while the ICK, also derived from the CAK, facilitates message authentication. The SAK encrypts the user data alongside the Security Tag (SecTAG) values in a sequence similar to that of the distributed SAKs, determined by the Association Number (AN) [23].
## 3 Results and Discussion
### Electrochemical Characterization of the Electrodes  
Cyclic voltammetry (CV) was performed to characterize the electrochemical properties of both electrodes. Figure 3 shows the CV curves of the AC and the AC-CPL at a scan rate of 2 mV s\({}^{-1}\) in the voltage range of \(-\)0.7 to 0.7 V. The CV curve for the AC represents typical capacitor behavior and has a nearly rectangular shape. In contrast to the symmetrical AC, the curve for AC-CPL has a relatively high current value at the whole potential. Additionally, anodic and cathodic peaks were observed at \(\sim\)0.20 and \(-\)0.52 V, respectively, which corresponds to the Faradaic reactions provided by the surface functionalities of the cation-exchange layer. The specific capacitance of the AC-CPL calculated from the CV result was 113.94 F g\({}^{-1}\), which had an increased value compared with that of the AC (73.67 F g\({}^{-1}\)). The presence of the cation-exchange layer contributed to improving the capacitance of the carbon electrode.  
Electrochemical impedance spectroscopy (EIS) analysis was carried out to investigate the electrical resistance of the electrode materials. Figure 4 shows the Nyquist plots of the alternating current (AC) impedance spectroscopy for the prepared electrodes. The intercept of the curve at the real axis (Z') in the high-frequency range corresponds to the solution resistance (R\({}_{\text{s}}\)). The diameter of the semicircle represents the charge-transfer resistance (R\({}_{\text{ct}}\)), also known as the interfacial reaction resistance. The linear part of the low-frequency region represents the Warburg impedance corresponding to the frequency dependence of ion diffusion/transport in aqueous solution [29]. The R\({}_{\text{s}}\) values were measured at 7.8 ohms and 9.0 ohms for the AC and AC-CPL, respectively. The R\({}_{\text{ct}}\) associated with the electrode-electrolyte interface was determined from the width of the semicircle that appears at high frequencies. The R\({}_{\text{ct}}\) was lower in the AC-CPL than in the AC, which might be related to the pseudo-capacitive contribution increased by the cation-ex  
Figure 3: Comparison of cyclic voltammetry curves of AC and AC-CPL in 0.5 M CsNO\({}_{3}\) at 2 mV s\({}^{-1}\) scan rate.  
Figure 2: SEM image of surface of activated carbon electrode (**a**); SEM images of surface (**b**) and cross-section (**c**) of the activated carbon electrode coated by the cation polymer layer.  
firmly coated cation-exchange layer played a positive role in reducing the charge resistance of the AC.
# Long-Term Forecasting Using MAMTF: A Matrix Attention Model Based on the Time and Frequency Domains
## 3 A Matrix Attention Model Based on the Time and Frequency Domains
### The Overall Model Architecture  
The overall architecture of our model is shown in Figure 1. First, we borrowed the decomposition structure from the Autoformer model [15]. We decompose the time series data into seasonal and trend components using Equation (2). Since it is difficult to capture complex and variable seasonal component features in the time domain, mapping them to the frequency domain reduces the complexity of the seasonal components themselves and facilitates the extraction of the data features. Therefore, we utilize frequency domain blocks to extract seasonal information with high stochasticity and poor regularity to help the model capture local dynamics. The time domain block is utilized to extract smooth floating trend component information to help the model capture the long-term change patterns.  
\[X^{t}=AvgPool(padding(X)),X^{s}=X-X^{t}, \tag{2}\]  
\(X^{t}\) represents the trend term, \(X^{s}\) represents the seasonal term, \(X\) is the original data, and \(AvgPool(\cdot)\) is the average pooling operation performed. We designed a frequency domain block to extract the seasonal component features and a time domain block to extract the trend component features and finally fused the output of the two extracted features as the final prediction result.
# Computational Evaluation of Shock Wave Interaction with a Liquid Droplet
## 3 Results
### Qualitative Analysis
For each instantaneous picture, both front (a) and back (b) views are shown. Figure 4: Instantaneous contours for normalized mean flow variables at the midplane \(x=0\), corresponding to \(t^{*}=0.010\), 0.044, 0.060, 0.099 and 0.17 (from top to bottom). Vertical planes show speed (left) and temperature (right), while horizontal planes show pressure (left) and vorticity magnitude (right). Main flow is from bottom left to top right corners. Figure 5: Instantaneous contours for normalized mean flow variables at the midplane \(x=0\), corresponding to \(t^{*}=0.25\), 0.35, 0.44, 0.57 and 0.65 (from top to bottom). Vertical planes show speed (left) and temperature (right), while horizontal planes show pressure (left) and vorticity magnitude (right). Main flow is from bottom left to top right corners. By inspection of the snapshots in Figure 4, corresponding to the time interval \(0.01\leq t^{*}\leq 0.17\), during which the water body changes in shape very slightly, both incident and reflected moving wave fronts are evident, as well as the transmitted wave inside the water body [12]. Immediately after the passage of the main shock, the air flow is accelerated so that the speed approximately becomes \(1.5V_{2}\) at the droplet's equator, which is the exact value for potential flow past a rigid sphere. As it is clearly illustrated in Figure 5, corresponding to the time interval \(0.25\leq t^{*}\leq 0.65\), during the early stages of the interaction process, the droplet flattening is a consequence of the nonuniform pressure distribution around the droplet surface, with high pressures at the forward and rear stagnation regions and low pressures at the equator, which is due to the acceleration of the surrounding gas. Furthermore, as it is illustrated by the temperature contour maps, the liquid phase is practically maintained at the constant temperature corresponding to the initial conditions. It is worth stressing that the above qualitative analysis for the initial stages of the interaction process, in terms of unsteady mean flow variables, is fully consistent with the results provided by more sophisticated high-fidelity numerical simulations performed at the same Mach number [15], as well as the experimental visualizations of the SIE phenomenology [37]. In particular, differently from the classical description of the breakup process as corresponding to two consecutive stages [36], the present CFD study confirms that droplet flattening and stripping of liquid material from the parent body occur simultaneously, while being complexly connected, as was already observed in [15].