esa-sceva/satcom-chunk-collection huggingface dataset:
# Gamma-ray Bursts: 50 Years and Counting!
## 7 The High-Energy (AGILE and Fermi) Era (2007-Ongoing)
### Fermi (2008-Ongoing)
The ability to carry out highly accurate spectral analysis over such a wide range of energies (7 orders of magnitude, including the hitherto unexplored range \(>\) 10 GeV) has provided fundamental information for understanding the composition of the GRB jet and the emission mechanisms of the prompt emission, triggering a real theoretical renaissance in trying to understand the large number of unexplained observational effects. The observation of the LAT's first bright GRB, GRB080916C, already showed almost-featureless, time-resolved spectra and spectral features divergent from the predictions put forward by the conventional fireball internal-shock model, thus necessitating a reevaluation of the fundamentals of the theoretical framework [137]. Subsequent observations of other GRBs (e.g., GRB090510, 090902B, and 090926A) have introduced a layer of complexity into the observed GRB spectra: these analyses have shown that the spectra of the observed GRBs are composed of the superposition of at least three distinct spectral components [138; 139]. In addition to the conventional non-thermal component of the Band function, in some GRBs, a further quasi-thermal component is present, assuming a dominant [140] or sub-dominant role [141]. The observation of other signals (e.g., GRB090902B, 090510, and 090926A), moreover, suggested the existence of a third component of the power-law spectrum, which extends to both high and low (GBM) energies [142]. The origins of this additional spectral component remain elusive. Recently, more in-depth analyses of low- energy spectra have been carried out, going down to the optical, highlighting the existence of a spectral break in the low-energy part of prompt spectra. This is consistent with emission from synchrotron radiation in the moderately rapid cooling regime, identifying the spectral break with the cooling frequency [143; 144].The Band spectrum thus modified is in better agreement with the data compared to the addition of ad hoc thermal and non-thermal components to the total spectrum. This era can be summarized as follows:  
* By fully exploiting the production of electron-positron pairs in the detectors, it was possible to extend the energy range to hundreds of GeV. * Comparing GRBs observed by both the GBM and LAT, it was found that the LAT band emission usually lasts much longer than the GBM band emission. * Comparing the GBM and LAT data also shows that the GeV emission has a delayed start compared to the MeV emission. * The possibility of performing unprecedentedly detailed spectral analysis in a wide spectral window, including HE, has provided very important information for understanding the composition of GRB jets and the prompt-emission mechanisms. * The use of modern machine-learning techniques applied to AGILE detectors led to the identification of previously unidentified GRB signals.
While general consensus affirms that the first method represents the most economic and rapid method for obtaining fuel parameters, it has been equally well demonstrated that fire simulation could be affected by a great variety of biases given the non-site-specific input data. On the contrary, the estimation of fuel parameters ad hoc for the landscape under investigation was shown to be the optimal method to obtain robust and precise data for site-specific fire simulation. However, field surveys cannot be repeated everywhere and frequently, as they are expensive and time consuming [19; 20; 21; 22]. Therefore, new methods need to be developed to regularly estimate fuel parameters across multiple scales and vegetation types. This gap has been partially filled by the growing availability of new sensors and remote sensing detection capable of providing data for modeling live and dead fuel load estimates. In light of the above, a number of authors [23; 24; 25; 26; 27; 28; 29] have attempted to estimate vegetative fuel loads using different remote sensing predictors. For example, [PERSON] et al. [27] employed radar data (i.e., Synthetic Aperture Radar) to measure the distribution of forest biomass and canopy fuel loads in Yellowstone National Park (USA). [PERSON] et al. [28] developed a methodological framework to estimate the forest stand mean height and above-ground biomass in China by integrating field plots, Sentinel imagery, as well as topographic and geographical data. [PERSON] et al. [30] used aerial photography, airborne laser scanning, and cadastral datasets to test a reliable method for characterizing fuel loads at a local scale in New Jersey (USA). [PERSON] et al. [31] presented a methodology to map coarse woody debris in the boreal forest of Alberta (Canada) using optical imagery and an infra-canopy vegetation-index layer derived from LIDAR. However, to our knowledge, few studies have attempted to estimate fine dead fuel loads by integrating multiple sources of remote sensors (LIDAR, RADAR, and optical products) and machine learning techniques [32], even though these fine dead fuel loads represent the major carrier of fire ignition and spread [33; 34; 35; 36; 37; 38]. To fill this gap, we tested machine learning models to estimate fine dead fuel load (i.e., 1-h fuel load) using multi-source remote sensing data in a Mediterranean landscape of Southern Europe. Thus, the specific objectives of the study were to: (1) estimate 1-h fuel loads using remote sensing predictors and machine learning techniques; (2) evaluate the performance of each machine learning technique compared to traditional linear regression models; (3) assess the importance of each remote sensing predictor; and (4) map the 1-h fuel load in a pilot area of the Apulia region in southern Italy. Our work intends to corroborate evolving new methods to regularly estimate fuel loads for which remote sensing predictors have become extremely important. The increasing ability to regularly acquire fuel load distribution across multiple scales and vegetation types would improve fire risk assessment, fire behavior prediction, and fire management plans.
## 3 Proposed Approach
### Application Layer (SCADA/SIS Software) Attacks and Countermeasures  
The application layer, essential to SCADA and SIS software control, is a key target for malware such as NotPetya, which exploits software vulnerabilities to cripple operations. Countermeasures include automated patch management systems to ensure timely software updates, zero-downtime patching for continuous operations, and real-time monitoring of application behavior to detect anomalies. To enhance the response, this layer integrates an automated patching protocol and AI-driven behavior analysis tools for proactive anomaly identification. Attacks on the application layer can result in widespread system outages and physical damage to critical infrastructure, severely impacting essential services.  
According to Figure 5, the diagram illustrates the attack vectors and response processes in the application layer, particularly focusing on SCADA and SIS software. Systems that are not updated with security patches remain vulnerable, allowing malware to exploit these weaknesses. Due to the nature of industrial control systems, update cycles are often lengthy or complex, leaving vulnerabilities exposed for extended periods. To address these issues and strengthen security, the following guidelines should be implemented.  
Figure 4: Attack vectors and response processes of the network layer.  
Outdated software within the application layer represents a significant vulnerability, especially when security patches are delayed or not applied due to operational dependencies. Malware such as NotPetya exploits these weaknesses to disrupt critical operations or cause irreversible damage. Unpatched SCADA and SIS systems, in particular, are prime targets for cyberattacks, often leading to data loss, economic damage, and prolonged recovery times. Furthermore, insider negligence or exploitation by cybercriminals through social engineering tactics further heightens these risks. Thus, regular software updates and continuous monitoring of application behavior are essential to minimize potential threats.
* Implement an automated patch management system to ensure that SCADA and SIS software are always updated with the latest security patches. This will proactively eliminate vulnerabilities and reduce the likelihood of malware infiltration.
* Implement a zero-downtime patching system that allows security patches to be applied without interrupting operations. This ensures continuous service availability while enhancing system security.
* Monitor all software activities to detect abnormal behavior early. Real-time detection systems prevent malware spread or insider manipulation from going unnoticed.
# Performance Evaluation of IEEE 802.11 ah Networks With High-Throughput Bidirectional Traffic
## 4 Performance Evaluation and Discussion
### Video Streaming Scenario
#### 4.2.2 Video Streaming Scenario: Results
If the streams have to be reliable but are generally unidirectional, then increasing the number of TIM groups is a better choice than increasing the contention inside a RAW slot. However, care must be taken when increasing the number of TIM groups because, at some point, latency will become too large and the network performance will degrade. The exact point depends on the network configuration and traffic load. Figure 6: Average IP camera data rate per station for various TCP segment sizes and MCSs for (**a**) 10, (**b**) 20 and (**c**) 40 stations declines 45–70% when doubling the network density. Best overall performance is measured for a TCP segment size of 2144 bytes as it is the largest segment not fragmented at the network layer. (**a**) 10 stations; (**b**) 20 stations; (**c**) 40 stations.
# Study on the Elimination Method of Wind Field Influence in Retrieving a Sea Surface Current Field
## 5 Data Processing
### HFSWR Data Processing  
Since the measurement range of the HFSWR data is not necessarily within the observation range of the satellite radar, the HFSWR data need to be interpolated into the SAR image area, according to the latitude and longitude of the SAR image. It can be seen from Figure 2 that scene 1 and scene 2 cover the largest HFSWR measurement area. Therefore, only the HFSWR data of these two scenes were processed. The HFSWR data has four vital parameters, namely longitude, latitude, U, and V. According to the user manual, U is the east-direction current speed, and V is the north-direction current speed. Since the velocity of our retrieval was in the radial direction, it was then converted to the surface direction, and the current field also needed to be converted in this way. The range direction is shown in the following:  
\[Range=norm(P_{image}^{\rightarrow})\times norm(P_{sat}^{\rightarrow}), \tag{12}\]  
where \(P_{image}^{\rightarrow}\) is the azimuth vector of SAR imaging, which can be obtained from the initial position of satellite imaging. \(P_{sat}^{\rightarrow}\) is a vector from the center of the earth pointing to the center of the satellite trajectory. [\(\times\)] represents the cross-product, and [norm] represents a normalized operation.  
Thus, \(U\) and \(V\) can be converted to the ocean surface current field in the SAR image range direction:  
\[V\_current=\overset{\rightarrow}{U}*Range+\overset{\rightarrow}{V}*Range, \tag{13}\]  
where [\(*\)] represents the dot product of vectors.  
Utilizing Formulas (12) and (13), the result of HFSWR data processing is shown in Figure 6.
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 5 Results and Comparison
### Performance Comparison  
To facilitate a performance comparison with state-of-the-art methods and techniques, we employed Xilinx FPGA devices to implement our proposed design. The synthesis results of the proposed design are presented in Table 6. The first column of Table 6 provides references. The next column (second) specifies the FPGA platform (for synthesis). Column 3 represents the operational clock frequency in MHz. The resource utilization is presented in columns 4 and 5. Column 6 provides the time, measured in microseconds (\(\upmu\)s), required to complete a single PM operation. Finally, the last column presents the throughput/ratio, which quantifies the number of computations executed per unit of time. By analyzing the results in Table 6, we can make an accurate and unbiased comparison between the proposed design and the existing solutions.
## 2 Materials and Methods
### System Performance and Robustness Analysis
#### 2.4.4 Robust Stability Analysis  
Robust stability is a very important criterion when evaluating the performance of a control system. Since the models used for analyzing and designing are normally imperfect matches with the real processes due to many sources of uncertainties. For fractional order control systems, in previous works, most authors focus exclusively on two criteria including maximum sensitivity (\(M_{s}\)) and maximum peak (\(M_{p}\)) in the frequency domain. In this study, the structured singular value (SSV) or \(\mu\)-synthesis with the \(M\)-\(\Delta\) structure, which is usually employed to evaluate the robustness of integer order systems, is also adopted to analyze the robust stability of the fractional order control systems. In addition, perturbations due to the multiplicative output uncertainty in each loop of multivariable processes are also considered as Figure 3.  
The transfer function matrix from the outputs to the inputs of \(\Delta(s)\) can be easily obtained by:  
\[\mathbf{M}(s)=-\mathbf{W}_{o}(s)\mathbf{Q}(s)\mathbf{G}_{c}(s)\bigl{[} \mathbf{I}+\mathbf{Q}_{o}(s)\mathbf{G}_{c}(s)\bigr{]}^{-1} \tag{39}\]  
where \(\mathbf{W_{o}}(s)\) is a weighted matrix representing the output uncertainties.  
According to the \(\mu\)-synthesis, the control system will remain stable under multiplicative output uncertainty if the following constraint inequality is satisfied  
\[\mu[\mathbf{M}(j\omega)]=\mu\bigl{\{}\mathbf{W}_{o}j\omega)\mathbf{Q}(j\omega )\mathbf{G}_{c}(j\omega)\bigl{[}\mathbf{I}+\mathbf{Q}_{o}(j\omega)\mathbf{G}_ {c}(j\omega)\bigr{]}^{-1}\bigr{\}}<1,\ \ \forall\omega \tag{40}\]  
Note that, in this work, \(\mathbf{Q}(s)\), \(\mathbf{Q}_{o}(s)\) and \(\mathbf{G}_{c}(s)\) are in fractional order forms.
# Development of Zirconium-Based Alloys with Low Elastic Modulus for Dental Implant Materials
## 3 Results and Discussion  
### Microstructure and Phase Observation  
Figure 1 shows the XRD profile of the Zr-7 Cu-xSn alloys. In the diffraction peak, a high Zr\({}_{2}\)Cu diffraction peak was observed around 20 = 37\({}^{\circ}\), and the addition of Sn did not change the crystalline structure of Zr. However, as the Sn content increased, the peak intensity of the \(\alpha\)-Zr phase lowered. Figure 2 shows the microstructural photograph of Zr-7 Cu-xSn alloy. The \(\alpha\)-Zr phase and dark gray phase Zr\({}_{2}\)Cu intermetallic compounds were observed. As Sn content increases, the Zr\({}_{2}\)Cu grows and changes from dendritic structure to large plate phase as shown in Figure 2d.  
Figure 3 shows a scanning electron microstructural photograph of each composition. The lamellar structure in the Zr alloy can be clearly seen, which is identified to be \(\alpha\)-Zr + Zr\({}_{2}\)Cu eutectic structure. The Zr\({}_{2}\)Cu eutectic phase is circular around the interface. Based on the Zr-Cu binary phase diagram [27], the intermetallic phase Zr\({}_{2}\)Cu is generated when the Cu content is low. These Zr\({}_{2}\)Cu phases can be classified into two phases: precipitated phase (formed due to decrease in the solubility of Cu and Zr as the temperature decreases) and eutectoid phase (formed at temperatures below 820 \({}^{\circ}\)C). As the solubility of Cu atoms decreases as the temperature decreases, they dissolve into \(\beta\)-Zr, and Cu atoms diffuse into the grain boundary region to form a Zr\({}_{2}\)Cu phase [27]. According to the Zr-Sn binary phase diagram, Sn raises the \(\alpha\)\(\rightarrow\)\(\beta\) transformation temperature and it has a solid solubility of 8.3 wt% in \(\alpha\)-Zr [28]. Therefore, even if a small amount of Sn is added, the structure of Zr does not change and Sn is dissolved in Zr.  
Figure 1: X-ray diffraction (XRD) profiles of Zr-7 Cu–XSn (x = 1, 5, 10, 15 wt%).  
Figure 2: Optical microscopy (OM) micrographs of Zirconium-base alloys; Zr-7 Cu–XSn (x = 1, 5, 10, 15 wt%): (**a**) Zr-7 Cu-1 Sn, (**b**) Zr-7 Cu-5 Sn, (**c**) Zr-7 Cu-10 Sn, (**d**) Zr-7 Cu-15 Sn.
## 4 Review: Slant Links Rain Fade Models
### Limitations
#### 4.4.4 Dao Model  
This model [99] for the fade slope was developed based on the VDK model, and a modification of the ITU-R P.1623 [98] model was proposed due to various parameters playing a dominant role in the fade slope. The datasets that were used were collected through an experimental setup in Kualalumpur, Malaysia. Details about the experimental setup description are available in [99]. The scintillation is filtered out from the attenuation through a moving average filter, whose cutoff frequency is defined by:  
\[f_{\text{B}}=\frac{0.445}{t_{a}} \tag{62}\]  
After the scintillation is filtered out, the fade slope is calculated according to:  
\[\zeta(t)=\frac{A\Big{(}t+\frac{1}{2}\Delta t\Big{)}-A\Big{(}t-\frac{1}{2} \Delta t\Big{)}}{\Delta t}\quad\frac{\text{dB}}{\text{s}} \tag{63}\]  
where \(\Delta t\) is the time interval length and \(t\) is sample number. It is noted that the climatic parameters need to be defined for the tropical regions as:  
\[s=\frac{\sigma_{\zeta}}{F(f_{\text{B}},\Delta t)\,A} \tag{64}\]  
Further, this assumption (Equation (64)) has been used to modify the ITU-R model:  
\[\sigma_{\zeta}=s(\Delta t)\cdot F(f_{\text{B}},\Delta t)\cdot\mathbb{Q}\bigg{(} \frac{\text{dB}}{\text{s}}\bigg{)} \tag{65}\]  
where \(\mathbb{Q}\) is used as the \"adjusting parameter\" with the standard deviation corresponding to the measured data-matching capability.  
\[\mathbb{Q}=0.0035A^{3}-0.0868A^{2}+0.567A+0.1792 \tag{66}\]  
The detailed steps of the model are given in Algorithm 22.  
```
1 Calculate \(F\) /* Equation (59) */
2 Calculate \(\sigma_{\zeta}\) /* Equation (60) */
3 Calculate \(P(\zeta|A)\) /* Equation (61) */ Return \(A\)
```  
**Algorithm 22**Dao model [99]
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 4 Centralized Protocols
### Vtsa  
Researchers developed the virtual time-slot allocation (VTSA) to make use of mmWave's free space path loss [6]. In the event of omnidirectional antennas, the VTSA technique can also be used. When established communication links are more than 1 m apart, the proposed approach can schedule time slots so that multiple links can use the same time slot and reuse space. As it is a centralized protocol, a PNC manages co-channel interference (CCI), which is generated by the sharing of the channel time allocation (CTA). As a consequence, the same CTA can most likely be reassigned to multiple long-distance connections. Furthermore, the PNC employs a probing signal broadcasting period (PSBP) method that includes headers to determine the possible CCI. However, the PSBP must employ modulation and coding techniques to evade header packet loss.  
* Advantages: The VTSA is meant to allow several communication channels to reuse TDMA time slots simultaneously to enhance the system throughput, while also monitoring the potential performance degradation owing to co-channel interference, thus increasing the transmission efficiency.
* Disadvantages: Since the VTSA uses multiple simultaneous links and uses the same CTA, it can experience signal interference because of the sharing. In addition, it is impossible to ensure that each connection receives access to a single superframe.
# Data Extraction Method for Industrial Data Matrix Codes Based on Local Adjacent Modules Structure
## 6 Experiment Results  
Our algorithm experiments related to this article use MATLAB 2020a on Win10 operation system with Intel(R) Core(TM) i5-9400 and CPU @2.90 GHz. The decoding software is Intellect 1.5.1, a machine vision software from COGNEX.  
Samples used in this experiment come from the database of barcodes marked in the aluminum alloy by our laboratory. The marking equipment is Ndd: YAG-T80C laser. The type of barcode is the Data Matrix ECC200 barcode. The barcode images are captured by mobile phones under indoor lighting in a lab. In addition, the camera's pixels are 20 million. We chose RGB as the image format, and the size of the images is \(400\times 400\). The size of our database is 250, including 52 barcodes with the size of \(12\times 12\), 37 barcodes with the size of \(14\times 14\), and 161 barcodes with the size of \(16\times 16\). We divide the samples into a training set and a test set. The size of the training set is 50, and the size of the test set is 200. Some samples of our database are shown in Figure 14.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Symbol Size**} & **Max Numeric Capacity** & **Max Alphanumer Capacity** & **Max Binary Capacity** & **Max** \\  & **Max Numeric Capacity** & **Max Binary Capacity** & **Max Binary Capacity** & **Conrectable Error/Erasure** \\ \hline \(10\times 10\) & 6 & 3 & 1 & 2 \\ \(12\times 12\) & 10 & 6 & 3 & 3 \\ \(14\times 14\) & 16 & 10 & 6 & 5/7 \\ \(16\times 16\) & 24 & 16 & 10 & 6/9 \\ \(18\times 18\) & 36 & 25 & 20 & 7/11 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The size, capacity, and error correction features of each Data Matrix format.  
Figure 13: Barcode reconstruction. (**a**) Colors of modules of the Finder Pattern and the Timing Pattern; (**b**) the reconstructed barcode image; (**c**) incorrect modules in the reconstructed barcode image.
# Real-Time Path Planning of Driverless Mining Trains with Time-Dependent Physical Constraints
## 3 Problem Description
### System Model  
We define a physical network \(G^{*}=(S,W)\) with a set of nodes \(S\) and a set of links \(W\). The set \(S\) consists of switch stations (\(S_{u}\subset S\)), and load-outs (\(S_{l}\subset S\)) in the physical network. \(W=\{(i,j)\}\) is the set of links in physical network, where \((i,j)\) stands for link connecting node \(i\in S\) to node \(j\in S\). The set \(W\) consists of mainline tracks (\(W_{u}\subset W\)), sliding tracks (\(W_{w}\subset W\)) and crossovers (\(W_{o}\subset W\)) in the physical network. For each link \((i,j)\), let  
* \(d_{ij}\) be the capacity of \((i,j)\) where capacity is the maximum number of trains that can stand on the link \((i,j)\) at any point of time;
* \(f(i,j)\) be the travel time from i to j, where f is the speed profile-based function; and
* \(f(j,i)\) be the travel time from j to i, where f is the speed profile-based function.  
In order to model the business operation of mine loading, for each load-out \(i\in S_{l}\), let \(P_{i}\) be the average loading time for a train.  
Let \(Q=\{0,1,\cdots,|Q|-1\}\) be the set of time instants, where time is discretized into discrete time instants of length g minutes. For instance, if we take g = 5 then a period of 1 h would be represented by discrete set \(Q=\{0,1,2,\cdots,12\}\) in our model.
## 2 Materials and Methods
### Samples Study
#### 2.3.7 Optical Properties  
The Helios Gamma UV-Vis Spectrophotometer (Thermo Electron Corporation, Beverly, USA) was used in order to make the transmission measurements of the samples. The apparatus was equipped with the quartz-coated single-beam optical system with the 2 nm spectral bandwidth. The tungsten lamp and the deuterium lamp were used as light sources. The measurements were made at room temperature in the wavelength range from 190 nm to 800 nm with the 0.5 nm step, which corresponds to the ultraviolet C (UV-C) and visible (VIS) light. The obtained data were recorded by the VISION software and the baseline subtraction was made automatically.