esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Educational Sector  
Globally, digital technologies have changed the operations of the educational sector. Digital and distance learning have opened up programs to students who would have otherwise been deprived of education, especially in developing countries. In Nigeria, however, the use of digital technologies is still at a low ebb, possibly due to the poor digital infrastructure, inadequate funding of the educational sector, limited expertise, resistance to change by the educational sector, overdependence of the educational institutions on the government and disconnection among the various digital learning enhancement initiatives [24]. The report of the Economist Intelligence Unit Limited, which ranks the ability of a nation to leverage information and communication technologies to promote a digital learning environment, revealed that Nigeria ranks 79 th out of the 81 countries in 2018, with a digital readiness score of 2.125 [25]. This simply suggests that the Nigerian educational sector lags in the adoption of the digital learning system. Despite the digital backwardness of the educational sector, there are still a few notable efforts to promote a digital learning environment in the country. For instance, the National Open University Nigeria (NOUN), the most recognized distance learning institution in Nigeria, is changing the modus operandi of the Nigerian universities. Unlike the conventional universities, NOUN delivers lectures virtually through a combination of Web-based modules, including audio, videotapes, and textual materials [26]. This makes it possible for students who would have been excluded to have access to education in Nigeria. Digital learning also helps the institution to be resilient, especially during the COVID-19 pandemic. In response to the challenges posed by the COVID-19 pandemic, a digital education company, commonly known as uLesson, leveraged digital technologies to create a virtual learning environment for students, most notably primary and secondary school students in Nigeria. This has greatly changed how primary and secondary school students learn and study for their examinations in the country and has made them resilient against internal and external attacks that may disrupt their studies. The implementation of computer-based tests by the joint admission and matriculation board has also helped advance the use of digital technologies in the Nigerian educational sector and these methods are being implemented in major universities today.
## 2 Experimental Setup  
### Test Materials  
The GRS used in this study was extracted from the edge of a railway subgrade, representing typical soils therein. The GRS was dried for 48 h, pulverized and then sieved through a 5.0 mm sieve to generate the prepared soils.  
The specific soil gravity (\(G_{8}\)) is 2.72. The particle size distribution of the soil obtained from sieve analysis exhibits a composition of 37.2% fines (<0.075 mm), 57.3% sand (0.075-4.75 mm), and 5.5% Gravel grain (>4.75 mm). The values of liquid limit (\(LL\)), plastic limit (\(PL\)) and \(PI\) are 46.6%, 23.8% and 22.8%, respectively. The maximum dry density and optimal moisture content (OMC) of the soil are 1.65 g/cm\({}^{3}\) and 20.3%. According to the Criteria for classification of soils (GBJ145-90) [45], the soil is classified as residual soil. In order to study the influence of fines content, the soil is divided into seven particle groups (5-2 mm, 2-1 mm, 1-0.5 mm, 0.5-0.25 mm, 0.25-0.1 mm, 0.1-0.075 mm and <0.075 mm) by the sieving method. In order to meet the specification requirements for high-speed railroad bed fillers [46], the fines content varies from 0%, 10%, 20%, to 30% by adding fine particles (particle size < 0.075 mm). The percentages refer to the ratio of the dry mass of fine particles to the dry mass of soil. The Standard Proctor tests were conducted to obtain the optimal moisture content and maximum dry density of the adjusted soils. The particle size distributions and physical properties of soils with different fines contents are illustrated in Table 1.
## 4 Discussion and Future Works
### Limitations and Considerations for Future Research
Notably, the cryptography library [74] is widely recognized and frequently employed for implementing cryptographic operations in Python. Its high-level Application Programming Interface (API) encompasses various cryptographic tasks including AES encryption, authentication, digital signatures, key management, and secure networking. Furthermore, SSL/TLS, commonly referred to as SSL, is a protocol that leverages a session-level layer on the Internet to establish a secure channel. The Python Standard Library includes the ssl module [75], which facilitates the creation of SSL/TLS connections by offering functionality for authentication, encryption, and secure socket communication using SSL/TLS protocols. Furthermore, exploring Fast Dynamic Time Warping (FDTW) techniques [76] shows promise for reducing the computational costs involved in achieving precise time alignment. By delving into FDTW methods, researchers can potentially streamline the synchronization process while maintaining high levels of accuracy. This avenue of investigation holds significant potential for advancing the state-of-the-art in time synchronization within WSNs, paving the way for improved performance and scalability in practical applications. Moreover, in the context of anomaly detection in time series data, particularly for applications in damage detection within SHM, the utilization of ML techniques offers opportunities for improvement. Ensemble methods, such as Random Forests or Gradient Boosting, have the potential to overcome the limitations of individual models, thereby enhancing robustness and reliability. Additionally, the combination of K-Nearest Neighbors (KNN) with DTW or FDTW allows for effective classification by leveraging the temporal alignment capabilities of DTW or FDTW and the nearest neighbor approach of KNN. By incorporating these ML techniques, the accuracy and effectiveness of anomaly detection and damage identification in SHM applications can be significantly enhanced.
# Development of the Romanian Radar Sensor for Space Surveillance and Tracking Activities +
## 6 Initial Tests and Results
In order to estimate the accuracy of the measured values for range and Doppler speed in comparison with the theoretical estimated values, the differences \(\Delta_{\text{Rmeasured}}=\text{R}_{\text{measured}}\)\(-\)\(\text{R}_{\text{estimated}}\) and \(\Delta_{\text{Vmeasured}}=\text{V}_{\text{measured}}\)\(-\)\(\text{V}_{\text{estimated}}\) were also computed and plotted in Figures 9 and 10. The estimated values were obtained from the TLE values. It was shown that these errors were quite small. The range error was inside an interval of less than \(\pm\)0.6 km compared to the mean range of 1923 km for COSMOS2932. The Doppler speed error belonged to an interval of \(\pm\)0.02 km/s compared to the mean speed of \(-\)1.785 km/s  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Object**} & **Estimated** & **Range Min** & **Range Max** & **Lowest** & **Highest** \\  & \(\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\for COSMOS2932. Similar corresponding values were obtained for the other four spatial objects targeted. Figure 8: (**a**) Measured and filtered range values for COSMOS2932; (**b**) speed derived from measured and filtered Doppler values for COSMOS2932. Figure 9: Difference between the measured and the TLE-computed values for range as a function of trigger number (time) for COSMOS2932.
## 4 Results and Analysis
### Overview of Simulation Results
#### Results Summary
Figure 9 illustrates the comparative analysis of latency and throughput between the ONSSO framework and traditional methods. The figure compares the traditional model and an ONSSO model across two key performance metrics: latency and throughput. The left graph shows the latency comparison, measured in milliseconds (ms):  
* The traditional model has a much higher latency of around 75 ms, with error bars indicating some variation;
* The ONSSO model shows significantly lower latency at approximately 18 ms;
* This indicates the ONSSO model is about 4 times faster in terms of response time. The right graph shows throughput comparison, measured in Megabits per second (Mbps):  
* The traditional model has relatively low throughput at around 30-40 Mbps;
* The ONSSO model demonstrates dramatically higher throughput at about 375 Mbps;
* This suggests the ONSSO model can handle roughly 10 times more data per second. Overall, these results strongly suggest that the ONSSO model outperforms the traditional model in both aspects. It responds much faster (lower latency) and can process much more data per unit time (higher throughput). The error bars on both graphs indicate the variance in measurements, with the ONSSO model showing a particularly large variation in its throughput performance.
# Image Steganography Using LSB and Hybrid Encryption Algorithms
## 4 Results and Discussion  
The embedding and extracting algorithms were implemented using Python 3.6 on VScode IDE utilising a web page and applied to bitmapped images with 256 colours of the same size (\(500\times 500\)) with a 24-bit depth. Different secret texts were embedded to evaluate the impact of the embedding process on the images. It has to be noted that the analysis included three cover images, which were partitioned into different blocks, and two images were used as encrypted messages and secret patterns.
# Detection of Mineralization Zones Using Aeromagnetic Data
## 2 Geologic Setting  
The study region is in Egypt's central Eastern Desert, and it includes the Barramiya, Abu Marwa, and Umm Miriewah areas, with a concentration on the Barramiya. In the Barramiya District, tectonically deformed metasedimentary and volcano-sedimentary rocks host a neoproterozoic ophiolitic melange of allochthonous serpentinite blocks and clasts, as well as carbonatized/silicified derivatives [14]. According to the Conoco map (Figure 1), the study region has ophiolitic serpentinite rocks, talc carbonate rocks, metagabbro to metadiorite rocks, gabbroic rocks, ophiolitic basic metavolcanic rocks, intermediate-to-acid metavolcanic rocks, calc-alkaline rocks, metasediments, and sedimentary rocks on the top, serpentinite in the Barramiya area run N-SW, and produce lithotectonic terrain dominated by oceanic affinity rocks [15]. They form elongate fault bounded by sheets.  
The mountainous El-Barramiya-Um Salatite belt is made up of serpentinites with the remains of hurzburgite, pyroxenite, and subordinate metabasalts. The belt generates folded tabular bodies or sheets that are elongated in the ENE and aligned with the ophiolitic melange matrix's major foliation (metasediments and metavolcanics) (Figure 1). Serpentinites come in two types: huge and severely sheared, deformed, and concentrated across the area in close contact with the other melange components. Because the strength of carbonatization increases near steeply dipping transgressive faults, listvenite rocks in the gold mine area developed from the alteration of tectonized serpentinite, which is represented by talc-actinolite schists and talc-carbonate rocks. Syn-orogenic rocks, like quartz diorite and granodiorite, have intruded the local ophiidic rocks. At last, unconformity is brought about by sedimentary strata from the western part of the study region, as shown by the Tarif and Quseir formations. The research region is heavily deformed and sheared, with gold, sulphides, and chromite deposits. Shear zones and general faults regulate gold deposition in the area, which is controlled by auriferous quartz veins [16; 17]. In the northern part of the El-Barramiya area, syn-orogenic quartz diorite and granodiorite intrusions subsequently intruded the ophiolitic melange phase. The Barramiya's structural setting illustrates the Eastern Desert's complicated deformational history. The study of structural fabrics revealed that the El-Barramiya region was overprinted by syn- and post-accretion deformation periods (D1-D3) [18].
# Wireless Multi-Node uRLLc B5G/6G Networks for Critical Services in Electrical Power Systems
## 2 The OFDM/FHMA System Based on Mini-Slot Frame Architecture  
### Design of Reliable OFDM/FHMA System  
In wireless 4G/5G systems, multi-carrier OFDM has been adopted as the basic signal waveform in the physical layer [8; 10]. However, the prototypical OFDM gives less consideration on how to guarantee the high reliability when the OFDM system suffers from adverse interference or jamming. Particularly in the power system scenario, the reliability of OFDM is more critical than the case of civilian communications. This paper attempts to design a secure and reliable OFDM signal by integrating with the hopping subcarriers' frequency, i.e., OFDM/FH, which is shown in Figure 2. Furthermore, as the various service nodes in power systems adopt different FH sequences (strategies), a multi-node OFDM/FH could also be implemented, the so-called OFDM/FHMA system [10; 18].  
The integration of OFDM and FH can be depicted in detail as follows. In the transmitter, the serial data \(\left\{D_{i}^{(k)}\mid i=0,1,\ldots,N_{f}-1\right\}\) with \(N_{f}\) bits are converted into the parallel data by a serial-to-parallel converter. The whole available bandwidth is evenly divided into \(N_{f}\) non-overlapping sub-bands, i.e., \(F\), and each of the sub-bands contains \(N_{h}\) contiguous frequency slots, that is \[\mathbf{F}=\Big{\{}\mathbf{F}_{I}\mid l=0,1,\cdots,N_{f}-1\Big{\}},\mathbf{F}_{I}\
eq\mathbf{F}_{I },l\
eq j,\text{and }\|\mathbf{F}_{I}\|=N_{h}. \tag{1}\]  
The subcarrier in the \(l\)-th subchannel of OFDM is hopped over these \(N_{h}\) frequency slots in the set \(\mathbf{F}_{I}\) according to a specific hopping strategy (FH sequence). By such a sophisticated design, these subchannels can maintain the orthogonality; in addition, the subchannels of the OFDM/FH system will experience independent fading, and FH helps reduce the malicious threats resulting from the interferer/jammer. The receiver of OFDM/FH consists of three parts: \(N_{f}\) parallel correlators, the dehoppers in the subchannels, and the decision module. At the receiver, it is reasonable to assume that the local FH sequence is perfectly synchronized with the transmitter of interest, and the conjugation of fading channel gain \(g_{m}^{(1)^{*}},m=0,1,\cdots,N_{f}-1\) is obtained by the pilot signals. The signal processing in the OFDM/FH transceiver will be presented in detail in Section 3.
# Review on the Recent Numerical Studies of Liquid Atomization
## 3 Interface Capturing  
The interface capturing methods try to locate the interface shape and position at each numerical iteration step based on the volume fraction or signed distance function, depending on if volume of fluid (VOF) or level set (LS) method is used, which are representatives of interface capturing methods. The VOF method is able to guarantee the conservation in nature but requires more computation cost when calculating the normal interface and curvature. The LS makes up this disadvantage but loses in conservation nature. Therefore, they are frequently coupled together in liquid atomization called the CLSVOF method. The smoothed particle hydrodynamics (SPH) method and lattice Boltzmann method (LBM), which are going to be discussed in following sections, also have the ability to capture interface, so in some literature [35] they are also referred to as the interface capture method.
## 5 Discussion
### Result Analysis for the Fuzzy MCDM Model

Table 10: Closeness coefficients among different fall protection plan alternatives. Figure 6: Sensitivity analysis results of sub-criteria C1, C2, C7, C12, C13 and C14. Figure 7: Sensitivity analysis results of sub-criteria C3, C4, C6, C9, and C11. Figure 7 demonstrated the sensitivity results of the sub-criteria of C3, C4, C6, C9, and C11. In these cases, as the sub-criteria weights fluctuate, the optimal solution does not change, but the ranking of other alternatives may change. For example, in the C3 changing condition shown in Figure 7, the overall ranking does not change significantly when the C3 weight decreases. Therefore, the optimal solution is still A3. However, when the C3 weight increases, the Cci value of A2 and A3 increases rapidly while that of A1 increases slowly. On the other hand, the performance of A4 becomes worse. While the weight of C3 increases more, the performance of A2 becomes better than A1 and A4. As a result, the overall ranking may change during the sensitivity simulation. However, in these changing conditions, A3 is always the optimal fall protection plan no matter how the criteria weights change. From the other three sub-criteria changing conditions shown in Figure 8, it can be concluded that in C5, C8 and C10 changing conditions, the optimal solution may change. For example, in the C5 weight changing conditions as shown in Figure 9, the optimal solution may change with the criteria weights fluctuating. Specifically, the C5 weight changing scenario is illustrated in a 3D plot as shown in Figure 9, where the \(x\)-axis represents the weight changing condition number while the \(y\)-axis represents the \(CC_{i}\). The weight of C5 changes from 1%, 2%, 5%, 10%, 20%, 50%, 100%, 150%, 200%, 250%, 300%, 350%, 400% and 450% of the base weight, which corresponds to condition 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, and 14, respectively. As the weight changes, the closeness coefficient of A3 decreases but the A4 score increases. Finally, the performance of A4 becomes better than that of A3. In this condition, A4 is the optimal fall protection plan. Similarly, in the C5, C8, C10 changing conditions, the final ranking results heavily depend on the sub-criteria weights, and the optimal solution should be analyzed and selected based on decision makers' preference on criteria weights and further analysis. To summarize, from the above sensitivity analysis, A3 is the optimal fall protection plan alternative in most cases while A4 ranks the top in several conditions, and A1 and A2 are never the best fall protection plans. Therefore, planners and decision makers can select the optimal solution in A3 and A4 considering the actual construction conditions. The final result and alternative ranking of the proposed fuzzy MCDM model is effective and robust. Figure 8: Sensitivity analysis results of sub-criteria C5, C8 and C10.
# Development of Analytical Model for Bonding of CFRP Rod in Concrete Subjected to Cyclic Loads
## 3 Development of Finite Element Model for G40 Concrete and UHPFRC Using Non-Interface Element
### Interactions  
To compare the commonly assumed \"Tie\" interaction between CFRP and concrete found in most literature studies with the newly developed interface element investigated in this article, two numerical models were constructed. One interaction scenario involved embedding CFRP rods directly into the concrete without utilising an interface element, while the other employed an interaction definition based on analytical results and stiffness values derived from experimental data. The interaction between CFRP bars partially embedded in both concrete segments required a distinct approach, namely, partitioning. Since CFRP is considered an external reinforcement to concrete, full embedment akin to conventional reinforcement is not viable; however, perfect bonding between CFRP and concrete is essential. Consequently, for the CFRP-concrete interaction, the concrete section was partitioned into four pieces, and the externally extruded hole for CFRP was sealed. The Tie interaction between these segments was implemented with the surface-to-surface discretisation method (Figure 4). This approach allows the CFRP bar to exhibit behaviour consistent with testing and material properties within the concrete layers. Additionally, Table 8 summarises the contact behaviour parameters. Similarly, for UHPFRC and normal G40 concrete, the same modelling and simulation approach was employed, with the only difference being the material properties.  
Figure 3: (**a**) Mesh of Concrete; (**b**) CFRP rod mesh as a 3D element with 15 mm mesh size.
# Animal Movement Prediction Based on Predictive Recurrent Neural Network
## 3 Methods
### Dataset  
For the collection of animal movement data, we used geo records of animals from the Movebank website [26]. Movebank is an online database of animal tracking data. In particular, we used the \"Long-billed curfew migration from Idaho\" dataset, which contains 64 trajectories of long-billed curfews. The dataset was collected over six years, from 2013 to 2019, and it consists of ARGOS latitude, ARGOS longitude, timestamp, and tag local identifier. According to the dataset description, the number of deployed locations is 148,983, and there are no outliers. The coordinate system follows the WGS84 reference format.  
To represent the movement range of long-billed curfews, we use the abundance map [27]. Figure 2 shows the abundance distribution map of long-billed curfews. In the map, the four seasons are represented by different colors, and the distribution density is expressed by the contrast of color. The distribution itself is calculated based on geo records from individuals that have moved more than one kilometer over one hour. For instance, the dark red indicates high-density areas during the breeding season (e.g., Montana, Idaho). During the non-breeding season, San Francisco and California show high density.  
Animal migration routes are closely related to weather and terrain. Hence, for prediction, we collected weather and terrain data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the US National Oceanic and Atmospheric Administration (NOAA) and selected eight features for our feature set: Humidity, vertical wind speed, horizontal wind speed, temperature, relative humidity, cloud coverage, land classification label (GlobCover), and land-sea mask.  
In this paper, we use four typical features to represent animal movements: Heading, speed, acceleration, and moving distance. Such movement-related features can be easily calculated from the collected time-location records by using the \"move\" package of R statistics [28].  
Figure 2: Seasonally averaged abundance map of long-billed curfews.