esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of the Organization and Implementation of E-Learning in Wartime Inside Ukraine
###### Abstract  
The article provides a factual foundation for the possibility of organizing and implementing e-learning in Ukrainian higher educational institutions during the war. The current research topicality is supported by the urgent need for training experience, organization and implementation during wartime because of the fact that both the educational process and the opportunity to obtain an education should not be halted. The study's goal is to assess the current state of the e-learning organization and implementation, as well as to examine students' attitude towards the educational process during wartime. Methods such as scientific source analysis, generalization and systematization of the e-learning experience and its practical application were used to achieve the goal. Furthermore, empirical methods such as interviewing and observation were used. Questionnaires have been proposed as important research tools for this purpose. Four structured groups for the use of e-learning have been formed and identified based on the findings. We created an e-learning organization and support model based on them. Furthermore, we identified ten poignant factors as the sources of difficulties for teachers when implementing innovations, with limited resources and a lack of time being among the most significant.  
e-learning; war; web service; educational process; teaching staff +
Footnote †: journal: _Future Internet_**2022, 14, 295. [[https://doi.org/10.3390/fi4100295](https://doi.org/10.3390/fi4100295)]([https://doi.org/10.3390/fi4100295](https://doi.org/10.3390/fi4100295))**
the university administration. Accordingly, the demand for internet technologies and cloud technology tools (web services) has increased significantly [1].  
Even during Ukraine's martial law, the importance of obtaining an education has been demonstrated. The war destroyed educational facilities, depriving students and their teachers of them. The majority of them lacked the resources necessary to complete their education. During the war, they found their studies uncomfortable. The difficulties that prevent students from studying properly have been described, and the need for significant improvements in distance learning has been highlighted. Opportunities to encourage students during the war have been revealed. They manifest themselves in the balance of time periods, which have been increased for completing educational tasks, reducing home (independent) tasks, extending sessions and so on. Following a mix of scientists' and personal experiences, recommendations for effective and useful web tools for teachers who lack special knowledge and skills in working with applications were made, as they are excellent for improving their own educational electronic environment.
# Virtual Design and Construction (VDC) Framework: A Current Review, Update and Discussion
## 4 VDC Case Studies
### Case Study 7: 'Ovalo Monitor Bridge' (Peru)  
The 'Ovalo Monitor Bridge' project is located at the Monitor Huascar roundabout, built between 2021 and 2022 in Lima, Peru. With an 870 meter-span and a budget of $21M, this project serves as a bypass with the main purpose of alleviating traffic in the roundabout, which is the intersection between two big town districts [69].  
To reduce the execution time of the structural elements, the VDC implementation focused on increasing the industrialization of the bridge elements (footings, columns and head beams) through the design, construction, assembly and installation of prefabricated rebar cages (PRC). ICE sessions were carried out with the main stakeholders involved in delivering the PCR elements to bring solutions, confirm details and make decisions regarding the structural trade. The project's BIM helped better visualize the pre-assembled PRC elements, those modeled with a minimum of LOD 350. All elements were prefabricated from the information provided by the BIM, with zero rework. Moreover, PPM was focused on tracking the progress of the PRC elements, following up on the status of the industrialization level and the overall time reduction in the structural trade [69].  
The VDC implementation outcomes showed a project time saving of 31% in the structural trade by designing, constructing, assembling and installing prefabricated reinforcement cage elements.  
Production Metrics (Production Objectives) from 279 VDC Case Studies Classified into Common Production Metrics Obtained from the Literature (PML)  
A conceptual content analysis was performed based on empirical evidence of applying production metrics (production objectives) in the construction industry [63]. This analysis was based on 279 VDC reports from Scandinavia and Latin American professionals in 2019 and 2020.  
The study's main objective was to know the homogeneity and discrepancy of the Production Metrics found in the Literature (PML) for each VDC implementation. The purpose of the research is supported by the fact that, since production metrics are the key element in quantifying and monitoring project performance, there should be a certain level of consensus or standardization in their use [63].  
The research results showed a considerable discrepancy in PML reported across professionals. Of the 17 PML (Table 2), three PML (detailed schedule conformance, stakeholder involvement and meeting efficiency) were reported by more than 40% of the professionals. The largest discrepancy was observed in meeting efficiency (Scandinavia: 44%, Latin America: 78%), meeting effectiveness (Scandinavia: 40%, Latin America: 3%) and BIM coordination consistency (Scandinavia: 23%, Latin America: 40%).  
Finally, the authors reinforced the need to develop a common vocabulary of production metrics for different project types, sizes and phases so that Architecture, Engineering and Construction (AEC) professionals can track those useful for their specific projects [63].
# Wideband Eight-Antenna Array Designs for 5G Smartphone Applications
## 7 Indoor Throughput Measurement  
To evaluate the proposed antenna's throughput in a real propagation environment, an indoor measurement was conducted by BWant Co., Ltd. in Taipei, Taiwan. Due to equipment limitations, only 4 \(\times\) 4 channel measurements were performed instead of 8 \(\times\) 8. As shown in Figure 21, the experiment setup involved four Vivaldi antennas with an operating frequency band of 0.5-6 GHz as transmitting antennas, mounted 0.9 m above the ground. The receiving antennas (the proposed antenna) were positioned 2.5 m away from the transmitter, also at a height of 0.9 m. Figure 21 illustrates the arrangement of the transmitting antennas, with a pair of Vivaldi antennas placed vertically and the other pair positioned orthogonally (horizontally) for this measurement. At different frequencies of 3.6 GHz, 4 GHz, and 5.1 GHz, the measured throughput for the 4 \(\times\) 4 channel measurement was consistently 2023 Mbps. Although the ideal 4 \(\times\) 4 channel throughput is calculated to be 4430 Mbps, considering the indoor multipath fading effects, the measured throughput of 2023 Mbps (or 2.023 Gbps) is considered a very good result.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{\begin{tabular}{c} **Ant. Size** \\ **(mm\({}^{2}\))** \\ \end{tabular} } & \begin{tabular}{c} **Frame** \\ **Profile** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **BW** \\ **(GHz)** \\ \end{tabular} & \
\begin{tabular}{c} **Iso.** \\ **(dB)** \\ \end{tabular} &
\begin{tabular}{c} **ECC** \\ **(dB)** \\ \end{tabular} \\ \hline
[39] \({}^{\ast}\) & 2 \(\times\) 7\({}^{\text{frame}}\) & 7 & 3.2–5.93 \({}^{\text{10-dB}}\) & \textgreater{}12 & \textless{}0.1 & 43.93 (8 \(\times\) 8) \\
[40] \({}^{\ast}\) & 17 \(\times\) 7\({}^{\text{frame}}\) & 7 & 3.3–6 \({}^{\text{6-dB}}\) & \textgreater{}18 & \textless{}0.05 & NA \\
[41] & 13.9 \(\times\) 6 & 7 & 3.1–6.0 \({}^{\text{10-dB}}\) & \textgreater{}10 & \textless{}0.1 & 39 (8 \(\times\) 8) \\
[42] & 16 \(\times\) 5 & 7 & 3.3–6 \({}^{\text{6-dB}}\) & \textgreater{}12 & 0 & NA \\
[43] & 30 \(\times\) 6.2 \({}^{\ast}\) & 7 & 3.3–7.5 \({}^{\text{6-dB}}\) & \textgreater{}10 & \textless{}0.06 & NA \\
[44] & 28 \(\times\) 7 \(\&\) & 5 & 3.3–5 \({}^{\text{6-dB}}\) & \textgreater{}10 & \textless{}0.3 & NA \\
[46] & 21.5 \(\times\) 6 & 7 & 3.3–5 \({}^{\text{6-dB}}\) & \textgreater{}14.5 & \textless{}0.1 & 38 (8 \(\times\) 8) \\ PA & 21 \(\times\) 6 & 7 & 3.1–6 \({}^{\text{6-dB}}\) & \textgreater{}10 & \textless{}0.1 & 40 (8 \(\times\) 8) \\ \hline \end{tabular}  
* PA: propose antenna, NA: not applicable, \({}^{\ast}\) IFA pair, \({}^{\ast}\) metallic frame, and \({}^{\text{k}}\) integrated slot antenna pair with connected line of width 1.8 mm.  
\end{table}
Table 1: Comparison with other reported sub-6 GHz 8-antenna array designs.  
Figure 21: _Cont._
## 4 Extension of Traditional Impedance Tracking Topology
### Analysis of CCM Mode  
Based on Equation (16), the relationship between the current iLb in inductor Lb and time t in CCM can be modeled and analyzed by adjusting the numerical parameters. Figure 8 shows the relationship between iLb and t in CCM, where iLb represents the current flowing through inductor Lb; ton is the conduction time of the switch transistor; and Tb is the end point of one period.  
Figure 8: CCM Mode.  
Figure 7: Buck circuit topology.  
The mathematical analysis shown in Figure 8 reveals that the average voltage across the inductor within one period is zero, leading to the following equation:  
\[\int_{0}^{t_{\text{on}}}{(\text{Vb}-\text{Vo})\text{dt}+\int_{t_{\text{on}}}^{t_{ \text{b}}}{(-\text{Vo})\text{dt}}}=0 \tag{13}\]  
According to Equation (18), the output voltage of the buck circuit in CCM can be obtained as follows:  
\[\text{Vo}=\text{D}\text{Vb} \tag{14}\]
# A Psychoacoustic-Based Multiple Audio Object Coding Approach via Intra-Object Sparsity
## 2 Proposed Compression Framework
### MDCT and Active Object Detection  
In \(n^{\text{th}}\) frame, an input audio object \(\mathbf{s}_{n}=[s_{n}(1),\,s_{n}(2),\,\ldots,\,s_{n}(M)]\) is transformed into the MDCT domain, denoted by \(S(n,l)\), where \(n\) (\(1\leq n\leq N\)) and \(l\) (\(1\leq l\leq L\)) are frame number and frequency index, respectively. \(M=1024\) is the frame length. Here, a 2048-points MDCT is applied with 50% overlapped [22]. By this overlap, discontinuity at block boundary is smoothed out without increasing the number of transform coefficients. Afterwards, MDCT of an original signal \(s_{n}\) can be formulated as:  
\[S(n,l)=2\bigg{[}\mathbf{s}_{n}\cdot\Big{(}\mathbf{\varphi}_{l}^{1}\Big{)}^{T}+\mathbf{s}_{n +1}\cdot\Big{(}\mathbf{\varphi}_{l}^{2}\Big{)}^{T}\bigg{]} \tag{1}\]  
where \(L=1024\), \(\mathbf{\varphi}_{l}^{1}\triangleq\big{\{}\varphi_{l}^{1}(1),\varphi_{l}^{1}(2), \cdots,\varphi_{l}^{1}(M)\big{\}}\), \(\mathbf{\varphi}_{l}^{2}\triangleq\big{\{}\varphi_{l}^{2}(1),\varphi_{l}^{2}(2),\cdots,\varphi_{l}^{2}(M)\big{\}}\) are the basis functions corresponding to \(n^{\text{th}}\) frame and \((n+1)^{\text{th}}\) frame. \(\varphi_{l}^{1}(m)=\omega(m)\cdot\cos\Big{[}\frac{\pi}{M}\cdot\Big{(}m+\frac{ M+1}{2}\Big{)}\cdot\Big{(}l-\frac{1}{2}\Big{)}\Big{]}\), \(\varphi_{l}^{2}(m)=\omega(m+M)\cdot\cos\Big{[}\frac{\pi}{M}\cdot\Big{(}m+\frac {3M+1}{2}\Big{)}\cdot\Big{(}l-\frac{1}{2}\Big{)}\Big{]}\) and \({}^{\text{T}}\) is the transpose operation. In addition, a Kaiser-Bessel derived (KBD) short-time window slid along the time axis with 50% overlapping between frames is used as window function \(\omega(m)\).  
In order to ensure the encoding scheme only encodes active frames without processing the silence frames, an Active Object Detection technique is applied to check the active audio objects in the current frame. Hence, Voice Activity Detection (VAD) [23] is utilized in this work, which is based on the short-time energy of audio in the current frame and comparison with the estimated background noise level. Each source uses a flag to indicate whether it is active in current frame. i.e.,  
\[flag=\begin{cases}1,&\text{if the current object is active}\\ 0,&\text{otherwise}\end{cases} \tag{2}\]  
Afterwards, only the frames which are detected as active will be sent into the next module. In contrast, the mute frames will be ignored in the proposed codec. This procedure ensures that silence frames cannot be selected.  
Figure 1: The block diagram for the proposed compression framework. (MDCT, Modified Discrete Cosine Transform; IMDCT, Inverse Modified Discrete Cosine Transform; NPTF, Number of Preserved Time-Frequency Bins; SQVH, Scalar Quantized Vector Huffman Coding; TF, Time-Frequency).
# Effective Strategies for Enhancing Real-Time Weapons Detection in Industry
## 4 Results
### First Phase
#### 4.1.1 Models
This decision was based on two factors: firstly, balancing the trade-off between the model's inference speed and its accuracy, and secondly, minimising the training time required for the model. The small variant of YOLOv8 necessitates the least duration of training but demonstrates comparable parameters to its larger alternatives, while notably improving the inference speed. This model is appealing for deployment on peripheral devices due to its compactness. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **AP50** & **AP75** & **AP** & **F1** & **FP** \\ \hline YOLOv8l & 75.77 & 71.35 & 62.34 & 71.10 & 401 \\ YOLOv8m & 73.12 & 69.22 & 60.94 & 68.70 & 196 \\ YOLOv8s & 77.12 & 68.10 & 59.15 & 72.50 & 348 \\ YOLOv7-d6 & 59.00 & 47.00 & 41.40 & 72.00 & 106 \\ YOLOv7-e6 & 59.40 & 49.50 & 40.80 & 72.90 & 139 \\ YOLOv7-w6 & 59.80 & 50.90 & 42.80 & 72.80 & 256 \\ YOLOv7x & 19.90 & 18.90 & 14.60 & 58.00 & 60 \\ YOLOv7 & 40.10 & 38.80 & 32.90 & 56.60 & 31 \\ YOLOv5l & 66.50 & 58.80 & 48.40 & 61.40 & 356 \\ YOLOv5m & 67.60 & 58.10 & 48.30 & 61.90 & 186 \\ YOLOv5s & 59.70 & 48.60 & 40.30 & 49.00 & 214 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of each YOLO model trained with _Disarm-Dataset_. The metrics used were Average Precision (AP), AP50, AP75, F1 score (F1), and False Positives (FP). These were calculated using an IoU of 0.7 and a confidence threshold of 0.5. Note that the results in red are the best and those in blue are the second best. Figure 6: Comparison between models using _Disarm-Dataset_ according to AP and mFPS. Note that the mFPS was obtained using a 720 p video on an RTX 3060.
# A Lightweight Detection Method for Meretirix Based on an Improved YOLOv8 Algorithm
## 4 Experiment and Analysis
### Experimental Results and Analysis  
#### 4.2.1 Comparison of Different Algorithms  
To verify the effectiveness of the improved YOLOv8 algorithm proposed in this study, a series of evaluation metrics were adopted, including precision, recall, [EMAIL_ADDRESS], [EMAIL_ADDRESS]:0.95, the number of parameters, and GFLOPs. A comparative experiment was conducted with Faster R-CNN, YOLOv5s, and YOLOv8n on the self-constructed clam dataset. All algorithms were tested under the same experimental conditions, with the batch size set to 8 and the total number of epochs set to 50. The Faster R-CNN algorithm used ResNet50 as its backbone network. The experimental results are presented in Table 1.  
Based on the experimental results presented in Table 1, it can be concluded that although the two-stage object detection algorithm Faster R-CNN demonstrates outstanding detection accuracy, it suffers from high computational complexity and a large number of parameters, resulting in excessively large model weight files. Therefore, it is not suitable for the lightweight and real-time detection requirements of the dataset used in this study. In contrast, YOLOv8n outperforms YOLOv5s in terms of parameter count and computational efficiency, while also achieving slightly higher detection accuracy, indicating better overall performance. The proposed YOLOv8-RFD algorithm achieves comparable detection accuracy to YOLOv8n, but shows improved performance under the more stringent [EMAIL_ADDRESS]:0.95 metric, while significantly reducing both the number of parameters and computational cost. In summary, the improved YOLOv8 algorithm proposed in this study demonstrates superiority across multiple key metrics and is better suited for lightweight real-time detection tasks.
## 4 Results and Discussion
### Effects of SFD Parameters
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bifurcation Point** & \(\widetilde{\Omega}\) & **Floquet Multiplier \(u_{max}\)** \\ \hline A & 1.0369 & 1.0589 \\ B & 1.0982 & 0.5344 \(\pm\) 0.8634 i \\ C & 1.2977 & 1.0008 \\ D & 1.1660 & 1.0716 \\ E & 1.1760 & 1.0669 \\ F & 1.1669 & 1.0818 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Floquet multiplier variations at bifurcation points. Figure 22: Frequency–response curves at SFD nodes under base pitching motion (\(C/R=0.728\%\), \(U=252\) g \(\cdot\) mm, \(\omega_{by}=10\) rad/s): (**a**) horizontal displacement; (**b**) vertical displacement. Figure 24 illustrates the frequency-response curves at the SFD node under fixed conditions of clearance-to-radius ratio \(C/R=0.728\%\) and base pitching angular velocity \(\omega_{by}=10\) rad/s, with varying unbalanced magnitudes of \(U=144.02\) g - mm and 360.04 g - mm. Circle markers in the figure denote saddle-node bifurcation points. Analysis reveals distinct dynamic behaviors across different unbalanced magnitudes. At lower unbalanced magnitudes, the system exhibits single-valued stable responses without bifurcation phenomena or unstable solutions. When the unbalanced magnitude increases to 252 \(g\cdot\)mm (shown in Figure 22), multiple regions of unstable solutions emerge, characterized by both secondary Hopf bifurcations and saddle-node bifurcations. Further increase in unbalanced magnitude to 360.04 g \(\cdot\)mm results in a region of unstable solution, which Floquet multiplier analysis identifies as a saddle-node bifurcation. This significant variation in dynamic behavior demonstrates the crucial influence of unbalanced magnitude on system bifurcation characteristics, indicating that specific parameter combinations can lead to fundamentally different bifurcation patterns.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis regarding each halophyte species was performed using PRIMER v6 with the PERMANOVA+ add-on. A resemblance matrix using the content (\(\upmu\)g mg\({}^{-1}\) DW) of each FA in each halophyte was performed using the Bray-Curtis similarity coefficient, following a log (x + 1) transformation to empathize the compositional differences rather than on quantitative differences [32]. Permutational multivariate analysis of variance (PERMANOVA) was used to assess the differences between FA profiles of wild versus cultured halophytes, considering edible parts and roots and the three sampling dates (spring, summer and autumn). Three factors were included in test design: (1) condition was introduced as a fixed factor, with cultured and wild being used as levels; (2) sampling dates were introduced as a fixed factor, with spring, summer, and autumn being used as levels; (3) plant organs were introduced as a random factor nested in conditions, with edible parts and roots being used as levels. The statistical significance of multivariate variance components was tested using 9999 permutations of residuals under a reduced model, with significance level of 0.05. Therefore, a permutation analysis of multivariate dispersions (PERMDISP) was used if the PERMANOVA result showed a significant difference [33]. A Principal Coordinates Analysis (PCO) was performed to calculate the variability in FA content considering the factors. This analysis enables to plot the inter-individual differences in FA content along the first two axes into the multidimensional space. For a detailed description of the statistical analysis described above please refer to Clarke & Gorley [34].
# Towards a Near-Real-Time Protocol Tunneling Detector Based on Machine Learning Techniques +
## 5 Experimental Evaluation
The result of the filtering operation is clearly shown in Table 3, where the number of processed PCAP packets is more than 54% less than the ones received in input by the traffic analyzer. Since the bit-stream lengths are different from the datasets [37, 39], we retrained our ANN to be fed with 44 B sequences. On the contrary, for this evaluation, we maintained the same hyperparameters for the different SVMs, reported in Table 2, and the same threshold \(c\), used in the other experiments. In particular, for all our experimental evaluations, we set \(c\) to 0.99999 in order to maximize the algorithm sensitivity and to compensate for the lesser information provided by the processing of [38]. This explains why, in the experimental evaluations, we were not able to achieve a very low false positive rate, as shown in Table 4.  
However, in context where a high number of false positives could be detrimental, \(c\) can be tuned to obtain a 0.5% false positive rate or lesser without losing accuracy on protocol tunneling attacks. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **No. of PCAP** & **No. of Processed** & \multirow{2}{*}{**No. of Connections**} & \multirow{2}{*}{**FP**} & \multirow{2}{*}{\(FPR(\%)\)} \\  & **Packets** & **PCAP Packets** & & & \\ \hline Legitimate traffic & 5.4 M & 2.8 M & 51,459 & 2966 & 5.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Benign test set summary. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Tunnel Type**} & **No. of PCAP Packets** & **No. of Processed PCAP Packets** & **No. of Connections** & **TP** & \(TPR(\%)\) \\ \hline Telnet over DNS tunnel [37] & 2.4 M & 2.2 M & 457 & 457 & 100\% \\ \hline SFTP over DNS tunnel [37] & 2 M & 1 M & 209 & 209 & 100\% \\ \hline SSH over DNS tunnel [37] & 2.8 M & 2.7 M & 545 & 545 & 100\% \\ \hline Light file exfiltration [38] & 187,500 & 102,000 & 7617 & 7361 & 96.6\% \\ \hline Heavy file exfiltration [38] & 1.34 M & 765,000 & 43,964 & 42,441 & 96.5\% \\ \hline Data exfiltration over Iodine DNS tunnel [39] & 438 & 247 & 1 & 1 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Malicious test set summary.
## 2 Materials and Methods
### Quantitative Real-Time PCR (qRT-PCR)  
RAW 264.7 macrophages designated for quantitative real-time PCR were exposed to ACE and the EtOAc fraction at concentrations of 0, 25, 50, and 100 ug/mL for a duration of 6 hr. Following this treatment, the cells were subsequently stimulated with 100 ng/mL of LPS for a period of 3 h, both in the presence and absence of ACE and the EtOAc fraction. Total RNA was isolated from RAW 264.7 macrophages using a homemade Trizol reagent. Subsequently, the RNA (1 ug) was reverse-transcribed into cDNA using the Compact cDNA Synthesis Kit (Smart Gene, Daejeon, Republic of Korea) within the GeneAmp PCR System 9700 (PerkinElmer, Inc., Waltham, MA, USA). Quantitative real-time PCR (qRT-PCR) was performed using the SYBR Green Q-PCR Master Mix (Smart Gene) on a QuantStudio(tm 1 Real-Time PCR system (Thermo Fisher Scientific). The qRT-PCR process followed standard thermal cycling conditions, including polymerase activation at 95 \({}^{\circ}\)C for 2 min, followed by 40 cycles of denaturation at 95 \({}^{\circ}\)C for 5 s and annealing/extension at 60 \({}^{\circ}\)C for 30 s. The primers utilized were sourced from Macrogen Co., Ltd. (Seoul, Republic of Korea). To ensure consistency, all mRNA levels were normalized using glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as an internal control.