esa-sceva/satcom-chunk-collection huggingface dataset:
# Sentinel-1 GRD Preprocessing Workflow +
## 1 Introduction  
The establishment of the Copernicus Programme by the European Commission created a new paradigm in the availability and accessibility of data information, offering services based on Earth observation satellites and in situ data under six thematic Copernicus services. The Copernicus Programme has become the world's largest space data provider, providing complete, free and open access to satellite data, mainly acquired by Sentinel satellites. The main advantages offered by Sentinel data are the improved spatial resolution and high revisit frequency, making them useful for a wide range of applications.  
The Sentinel-1 mission is a constellation of two polar-orbiting satellites (Sentinel-1A and Sentinel-1B), which operate day and night, sensing with a C-band synthetic aperture radar instrument operating at a centre frequency of 5.405 GHz, allowing the acquisition of imagery regardless of weather and illumination conditions. Sentinel-1 satellite constellations acquire Synthetic Aperture Radar (SAR) data in single or dual polarization with a revisit time of 6 days. Sentinel-1 Level 1 data are distributed by the Copernicus Open Access Hub under two product types: Ground Range Detected (GRD) and Single Look Complex (SLC).  
Sentinel-1 level-1 GRD products consist of focused SAR data that have been detected, multi-looked and projected to ground range using an Earth ellipsoid model. The ellipsoid projectionof the GRD products is corrected using the terrain height, specified in the product general annotation, that varies in azimuth but is constant in range. After performing a multi-looking separately for each burst, a ground range detected image is generated merging all bursts in all sub-swaths. The Sentinel-1 GRD scene is composed of square pixels with reduced speckle, due to the multi-look processing, representing only the detected amplitude (the phase information is discarded).
## 3 Mathematical Concept and Modeling
### Mathematical Model of CV-CNN-TCN
#### 3.1.2 Complex-Valued Convolutional Neural Network (CV-CNN)  
The CV-CNN extracts spatial and spectral features from the complex input. For a convolutional layer, let \(\mathbf{W}=\mathbf{W}_{R}+j\mathbf{W}_{I}\) be the complex-valued filter weights, and \(\mathbf{b}=\mathbf{b}_{R}+j\mathbf{b}_{I}\) be the biases. The convolution operation is given by  
\[\mathbf{H}=f(\mathbf{W}*\mathbf{X}+\mathbf{b}) \tag{2}\]  
where \(*\) denotes the convolution operation, and \(f(.)\) is a complex activation function, such as the complex-modulus activation:  
\[f(\mathbf{Z})=\sqrt{\mathbf{Z}_{R}^{2}+\mathbf{Z}_{I}^{2}} \tag{3}\]  
Expanding the convolution into real and imaginary parts:  
\[\mathbf{H}_{R}=f(\mathbf{W}_{R}*\mathbf{X}_{R}+\mathbf{W}_{I}*\mathbf{X}_{I}+ \mathbf{b}_{R}), \tag{4}\]  
\[\mathbf{H}_{I}=f(\mathbf{W}_{R}*\mathbf{X}_{I}+\mathbf{W}_{I}*\mathbf{X}_{R}+ \mathbf{b}_{I}). \tag{5}\]  
The output of the \(k\)-th convolutional layer is the complex feature map:  
\[\mathbf{H}_{k}=\mathbf{H}_{R}^{k}+j\mathbf{H}_{I}^{k},k=1,2,\ldots,K. \tag{6}\]
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
## 4 Baseline Clustering Strategies under Jamming
### Cooperation Domain
#### 4.2.2 Power Allocation and Cooperation (PAC)  
The fourth strategy uses both techniques, cooperation and power allocation, to route the data. Therefore, the nodes that participate in the cooperation optimize the transmission power to reach the next relay node. Consequently, the nodes involved in the routing chain can reduce their energy consumption and increase their lifetime.
# Optimizing the Scheduling of Teaching Activities in a Faculty
## 8 Discussions and Future Studies  
The evolution of automated timetable generation systems has emerged as a vital area of research, particularly in the context of educational institutions facing increasing demands for efficient scheduling solutions. While existing studies have significantly contributed to the understanding and development of these systems, they have primarily focused on smaller datasets and less complex scenarios. This study distinguishes itself by tackling the intricate challenges posed by large-scale scheduling, integrating hundreds of courses, instructors, rooms, and time slots. The ability to generate effective and efficient timetables despite these complexities is an important advancement for institutions with large student and faculty populations, as it directly impacts their operational efficiency and overall academic performance.  
Several enhancements are planned to further improve the timetable generation system, ensuring it meets the diverse needs of educational institutions effectively. Users will be able to interact with the timetable, making manual adjustments or edits based on specific requirements, providing greater flexibility and customization to meet unique needs. The application will enable the generation of reports and summaries of the generated timetables, which can be used by administrators, faculty members, and students for various purposes such as academic planning, resource allocation, and schedule communication. Additionally, it will support communication features to notify stakeholders about timetable updates or changes.  
Implementing load balancing [59] will enhance the system's performance, particularly when handling large datasets or multiple simultaneous users. An automated way to retrieve rooms, timeslots, student groups, teachers, and lessons (e.g., via Excel) will be added, streamlining data entry processes and ensuring accuracy. Furthermore, a feature to export the generated timetable will be included. Current issues related to the implemented constraints and updating entities using Spring JPA will be addressed to enhance overall system performance and accuracy.  
Fine-tuning the algorithm and adding a filtered move selection for faster construction of the initial solution after the Construction Heuristics phase will be undertaken to improve efficiency. A benchmarking feature will be added, allowing users to test various configurations and evaluate their impact on performance. Finally, the application will be extended to enable centralized timetable generation for schools, high schools, and other colleges, each with potentially different constraints, thereby expanding the system's applicability and utility across various educational institutions.
# Potential Role of Plant Extracts and Phytochemicals Against Foodborne Pathogens
## 3 Plant Extracts and Phytochemicals against Bacteria Causing Foodborne Diseases
### Bacillus cereus  
_B. cereus_, a Gram-positive spore-forming bacterium, is frequently implicated in foodborne infections. Among the pathogenic aspects of the bacterium is the production of tissue-damaging exo-enzymes. The bacterium secretes toxins including hemolysis, phospholipases, emesis-inducing toxins and proteases [175]. In the in vitro antibacterial assay against _B. cereus_, the extract of _Drypoteris erythrosora_ (D.C. Eaton) [PERSON] exhibited an MIC of 0.0156 mg/mL, followed by _Siegesbeckia glabrescens_ (SG) Makino leaf (0.0313 mg/mL), _Morus alba_ L. bark (0.0313 mg/mL), _Carex pumila_ Thunb. root (0.0625 mg/mL) and _Citrus paradisi_ Macfad. seed (0.0625 mg/mL) extracts. The combined inhibitory effects of extracts against _B. cereus_ were also determined. A combination of _D. erythrosora_ and _C. pumila_ extracts showed a partial synergistic inhibition, with a fractional inhibitory concentration index of 0.75. The single and combined inhibitory activities of selected plant extracts against _B. cereus_ in reconstituted infant rice cereal were also investigated and showed the MICs of _S. glabrescens_, _M. alba_, _D. erythrosora_ and _C. pumila_ extracts against _B. cereus_ as 1.0, 2.0, 2.0 and 8.0 mg/mL, respectively [176]. The plant extracts of _Rhus coriaria_ L. and _Hipiscus sabdarifa_ L. were investigated against six presumptive _Bacilluscereus_ isolates that were isolated from 49 samples of food, soil, manure and eggshells in Amman, Jordan. It was observed that the inhibition of the growth of bacteria was proportional to the increase in extract concentrations. Complete inhibition of the growth was demonstrated at the highest concentrations. The authors concluded that _Rhus criaria_ and _Hibiscus sabdariffa_ have the potential to be used as food preservatives against wider spectra of spoilage microorganisms in food [177].
## 6 Limitations  
This method is limited by the computational time and space needed. PCAP files for large networks over a single day are usually at least 10-20 gigabytes in size, and producing the netflow for such a daylong PCAP can be a challenge. However, modern monitoring solutions already incorporate netflow collection at sensors (e.g., Suricata IDS can also collect netflow information).  
ERGM calculations, especially MCMCMLE iterations, are extremely time-consuming, often requiring 20 min per iteration. Therefore, it is advisable to have access to computational resources before this method can be used on a real-time scale for a large network.  
In addition, the availability and difficulty in procuring high-quality \"normal\" data can limit the effectiveness of the method. Ideally, this method would be trained on a week-long continuous anomaly-free sample of packets from a large organization's computer network. In our study, we were limited to using a PCAP file as our baseline for producing hour normal hourly traffic baseline.  
The ARMA model is limited in its temporal applicability, losing predictive power after just a few hours in the test set. This suggests any viable future model might need continuous new data every few hours to produce an up-to-date time series for ARMA. In other words, a real-time solution that aims at hourly anomaly detection should be fed with hourly up-to-date data. The ARMA model could also be potentially paired with a model with longer memory, such as an LSTM, which also specializes in analyzing time series data.  
Finally, a 5%, 10%, or even 25% data injection on a network with several thousand nodes might prove to be too insignificant for the ERGM model to produce an anomalous coefficient for certain terms which do not take into account network geometry. Larger size networks should also be tested to establish the efficacy of such a method.
## 5 Behavior-Score Analysis Model
### Behavior-Score Analysis Model Based on Polynomial Regression  
To analyze the impact of non-directional online behavior on students' academic performance, it is necessary to analyze the relationship R between two key features of non-directional online behavior and their score, that is \"time-score\" R\({}_{\text{d-s}}\) and \"flow-score\" R\({}_{\text{f-s}}\).  
There are 82 attributes in the score set. Except for the user name attribute, the other 81 attributes are all courses. The corresponding score of each course is \(S_{x}\), (\(x\) = 1,2,..., 81). Due to the different subjects and numbers of final examinations for each user, to simplify the scores, we first calculated the average final score of each user, which is recorded as \(AVE_{score}\), and the calculation method is shown in Formula (4).  
Then, we added up the daily net flow in all the effective online records of the same user, divided by the online days, and obtained the daily average flow of each user, which is recorded as \(AVE_{flow}\), and the calculation method is shown in Formula (5). These two types of data are used as the basis for the correlation analysis of user average score and daily average net flow.  
In the same way, we added the daily online time in all the effective online records of the same user and divided by days to obtain the daily average online time of different users, which is recorded as \(AVE_{length}\). The calculation method is shown in Formula (6). This kind of data is taken as the basis of the correlation analysis between the average score of users and the average online time.  
\[AVE_{score}=\frac{\sum S_{x}}{m} \tag{4}\]  
where \(m\) is the number of scores.  
\[AVE_{flow}=\frac{F}{d} \tag{5}\]  
\[AVE_{length}=\frac{T}{d} \tag{6}\]  
where \(d\) is the number of days users are online, m is the number of courses.  
According to empirical assumptions, the polynomial regression equation of flow-score is  
\[y=ax^{3}+bx^{2}+cx+d \tag{7}\]  
The polynomial regression equation of length-score is  
\[y=mx^{4}+nx^{3}+px^{2}+qx+s \tag{8}\]
### Structure and Contributions  
To summarize, the following contributions are presented in this manuscript:  
* Development of a novel sensor-fusion-based experimental apparatus designed to collect biometric features from touchscreen handwriting to fill an existing gap in the literature and further advance the field of online signature verification systems.
* Curation of the preliminary dataset for biometric analysis, comprising diverse sensor readings and providing a comprehensive resource for analyzing handwriting dynamics for user authentication purposes.
* Experimental validation of the proposed apparatus through preliminary testing in a controlled experiment involving multiple participants.  
Our apparatus offers several key advantages that set it apart in the field of biometric data collection. By integrating multiple sensors, including magnetometers, accelerometers,gyroscopes, cameras, microphones, and piezo sensors, it captures a comprehensive range of biometric data beyond what single-modality solutions offer. The system enables the selection of specific phrases or types of handwriting (letters, words, sentences) for data collection, which is not a common feature in many related studies, and it is mostly focused only on signatures. The modular design of the apparatus allows an easy adjustment of sensor types and numbers, offering high customizability for diverse applications, research needs, and experimental conditions. Its flexibility in hardware and software, along with support for both stylus and finger inputs, enhances its versatility for advancing biometric research.  
The system's limitations include a small initial dataset, which affects the generalizability of the findings and will be addressed by expanding to a larger, more diverse participant pool. The S Pen Remote SDK restricts data access, particularly for pressure and tilt measurements, though piezo sensors have been used as alternatives. Power consumption is a concern with multiple devices, especially for the smartwatch, though current research shows manageable battery life. Finally, the apparatus's current configuration is less portable due to its multi-device arrangement, but future iterations may improve portability by eliminating the need for a smartphone and using more integrated devices.  
This manuscript is structured as follows. Section 2 describes the proposed apparatus, utilized sensors, and additional accessories in the study. In Section 3, the results are then presented. Section 4 discusses the obtained results. Finally, concluding remarks are given in Section 5.
## 8 5G Potentials and Promises for Robot Applications
### Low Latency  
Ultra-reliable low-latency communication (URLL) offered in 5G new radio allows for future applications which on-demand for aggressive latency for a quick reaction. The existing 4G cellular network is not appropriate for this purpose.  
Low latency means a slight delay between sending and receiving information indispensable for autonomous driving and flying. For example, 1 ms minimum latency (average of 10 ms) is expected to be provided by 5G, which is a substantial breakthrough compared to the 200 ms latency typical of 4G. The central station that might perform the localisation, planning, or control must receive and send back data and command fast enough to the vehicles operating at high speed. The time for transmitting and receiving data is vital for autonomous control, where the car moves rapidly. For example, for a drone to be controlled, both localisation and control commands need to be processed and sent back to the UAV with an acceptable slight delay, resulting in a fast reaction of the UAV.
# Participatory Exhibition-Viewing Using Augmented Reality and Analysis of Visitor Behavior
## 4 Data Analysis
### Duration Analysis  
The test statistic of duration was t(61) = 0.286, \(p\) > 0.05. That is, at the \(\alpha\) = 0.05 level of significance, the durations of use in the exhibition-viewing and message modes showed no significant differences. The mean durations for viewing and messaging activities were 71.895 s and 86.575 s, respectively. This suggests that, on average, participants were more engaged or took longer when it came to leaving messages compared to simply viewing the artworks. The slightly longer duration for messaging could indicate a deeper level of engagement, as participants may have taken additional time to compose their thoughts or interact with the messaging interface. Both activities exhibited significant variability among participants, as indicated by the standard deviations (72.9296 s for viewing and 92.5035 s for messaging). This wide range underscores the personalized nature of the exhibition experience, with some participants choosing to engage more superficially, while others dove deeper into both viewing and messaging.  
Despite the broad variability, the small standard errors of the mean (9.3377 s for viewing and 11.8439 s for messaging) highlight the statistical reliability of these averages. This implies that our calculated mean values accurately represent the central tendencies of how participants are spending their time, providing confidence in these figures despite the individual differences.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Mean** & **N** & **Std. Deviation** & **Std. Error Mean** \\ \hline Duration & Viewing Duration & 71.895 & 61 & 72.9296 & 9.3377 \\ (seconds) & Message Duration & 86.575 & 61 & 92.5035 & 11.8439 \\ \hline Distance & Viewing Distance & 25.580 & 61 & 155.3080 & 19.8851 \\ (meters) & Message Distance & 163.476 & 61 & 1156.3489 & 148.0553 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The distance and duration sample statistics.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Mean** & **Std. Deviation** & **Std. Error Mean** & \multicolumn{3}{c}{**95\% Confidence Interval**} & \multirow{2}{*}{**t**} & \multirow{2}{*}{**df**} & \multirow{2}{*}{**Sig. (2 tailed)**} \\  & & & & & **Lower** & & **Upper** & \\ \hline Distance & \(-\)14.6801 & 124.5373 & 15.9454 & \(-\)46.5756 & 17.2154 & \(-\)0.921 & 60 & 0.361 \\ (meters) & \(-\)14.6801 & 124.5373 & 15.9454 & \(-\)46.5756 & 17.2154 & \(-\)0.921 & 60 & 0.361 \\ (meters) & \(-\)137.8966 & 1001.4421 & 128.2215 & \(-\)394.3779 & 118.5846 & \(-\)1.075 & 60 & 0.286 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary table of \(t\) tests for visitor distances and durations (N = 61).
## 2 Materials and Methods
### Analytical Procedures  
#### 2.3.1 Phytochemical Analyses  
The two extracts of _T. capitatus_ were analyzed by HPLC-DAD as described by [PERSON] et al. [17]. Briefly, the analyses were conducted with a system that consisted of an autosampler (Agilent Infinity 1260, Agilent, Santa Clara, CA, USA), a gradient quaternary pump (HP 1100, Hewlett Packard, Waldbronn, Germany), and a diode array detector (HP 1100), which was connected to a ZORBAX Eclipse XDB-C18 column (5 \(\upmu\)m, 250 \(\times\) 4.6 mm). The solvent system comprised water (A), methanol (B), and acetonitrile (C), each containing 0.2% trifluoroacetic acid. The initial composition of the mobile phase was 90% A, 6% B, and 4% C, and changed with linear gradients to 85% A, 9% B, and 6% C within 5 min, 71% A, 17.4% B, and 11.6% C within 30 min, and 0% A, 85% B, and 15% C within 60 min. The solvent flow rate was 1 mL/min, and the injection volume was 20 \(\upmu\)L. Detection was performed at 280 nm, and the data were processed by the ChemStation for LC 3D software (Agilent, Rev. B.04.03). Standard rosmarinic acid, carvacrol, and quercetin were used as external standards for the quantifications of phenolics in extracts. Specifically, quercetin was used for the determination of total flavonoids.  
The GC/MS analyses of EOs were carried out according to [PERSON] et al. [18]. An Agilent 6890 GC system (plus+) coupled to an Agilent 5973 mass selective detector (Agilent, Santa Clara, CA, USA), equipped with an HP-5 MS column (30 m \(\times\) 20 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m), was used for the analyses. The oven temperature was started at 50 \({}^{\circ}\)C, increased to 100 \({}^{\circ}\)at a 10 \({}^{\circ}\)C min\({}^{-1}\) rate, then to 220 \({}^{\circ}\)C at a 15 \({}^{\circ}\)C min\({}^{-1}\) rate, and held at 220 \({}^{\circ}\)C for 7 min. Helium was used as a carrier gas at 1 mL min\({}^{-1}\) column flow, inlet temperature 220 \({}^{\circ}\)C, and split 20:1. The mass range was 40-400, and compounds were identified by comparison of their mass spectra with the data of NIST and Wiley mass spectral libraries.  
3.2 Determination of Minimum Inhibition Concentration (MIC) and Minimum Bactericidal Concentration (MBC)  
The MIC and MBC values, expressed as mg/mL, were determined using the microdilution method, as previously described [6; 19]. All compounds were tested in one microplate per microorganism and concentrations ranged from 0.02 to 10 mg/mL for EOs and 3.1-100 mg/mL for WSTRTc extracts. Standard polystyrene microplates with lids were used to avoid the transition of the vapors between the wells [20; 21; 22].
# Charge-Line Dual-FET High-Repetition-Rate Pulsed Laser Driver
## 1 Introduction  
Nowadays, high-power short laser pulses are the subject of great interest in many fields of application, e.g., Light Detection and Ranging (LIDAR) [1, 2], time resolved spectroscopy [3], or picosecond gain switched lasers [4]. The pulsed current source might also be applied to the electrically tunable Near Infra-Red (NIR) [4] or Quantum Cascade Lasers [5]. However, the main application of the solution presented in this paper is the seed laser driver for high-power (kW-level) fiber lasers. Optical pulse quality and reproducibility are vital for a stable and reliable operation of the whole fiber laser system.  
The direct modulation of the semiconductor laser current is a commonly used technique to produce fast optical pulses [1, 2, 6, 7, 8] and has the advantage of simple and inexpensive operation. Many different topologies have been discussed so far [1, 2, 6, 7]; however, noise parameters are rarely recalled. Considering those facts, a novel direct laser current modulator was designed and constructed resulting in the outstanding noise and switching properties.
# Chosen Plaintext Combined Attack against SM4 Algorithm
## 3 Methodologies
### Differential Analysis
In summary, by selecting different plaintext inputs, taking the S-box output of the second and fourth rounds as the attack objects, and combining with differential analysis, the key of the first four rounds of SM4 encryption algorithm can be obtained. Finally, the initial key of SM4 can be recovered by the key expansion algorithm.