esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Payment Verification and Browsing Phase
#### 3.6.2 Case 2: Proxy Authorization
The Proxy calculates:  
\[BC_{C-P}=h(r_{C-P},s_{C-P}), \tag{151}\]  
\[(ID_{BC},BC_{C-P})\] will also be uploaded to the blockchain center. * Step 7: The Proxy generates a random value \(k_{P-L}\) and calculates:  
\[z_{P-L}=h(ID_{P},M_{P-L},(C_{1},C_{2}),TS_{P-L},ID_{BC}), \tag{152}\]  
\[(x_{P-L},y_{P-L})=k_{P-L}G, \tag{153}\]  
\[r_{P-L}=x_{P-L}\text{mod}n, \tag{154}\]  
\[s_{P-L}=k_{P-L}{}^{-1}(z_{P-L}+r_{P-L}d_{P})\text{mod}n, \tag{155}\]  
\[Enc_{P-L}=E_{PK_{L}}(ID_{P},M_{P-L},(C_{1},C_{2}),TS_{P-L},ID_{BC}), \tag{156}\]and sends \(ID_{P},Enc_{P-L},(r_{P-L},s_{P-L})\) to the Licensee. * Step 8: The Licensee first calculates: \[(ID_{P},M_{P-L},(C_{1},C_{2}),TS_{P-L},ID_{BC})=D_{SK_{L}}(Enc_{P-L}),\] (157) uses \[TS_{NOW}-TS_{P-L}\leq\Delta T\] (158) to confirm whether the timestamp is valid, verifies the correctness of the ECDSA signature, and then calculates: \[{z_{P-L}}^{\prime}=h(ID_{P},M_{P-L},(C_{1},C_{2}),TS_{P-L},ID_{BC}),\] (159) \[(ID_{BC},BC_{C-P})\]0 (160) \[(ID_{BC},BC_{C-P})\]1 (161) \[(ID_{BC},BC_{C-P})\]2 (162) \[(ID_{BC},BC_{C-P})\]3 (163) If the verification is passed, the authorization information is confirmed by Licensee, and the smart contracts plans and plchk will be sent. The content is as follows (Scheme 14):  
function insert smart contract plans(  
string pl_id, string pl_detail,  
string pl_tid, string pl_tsp) {  
count ++;  
pl[count].id = id;  
pl[count].detail = detail;  
pl[count].key = key;  
pl[count].tsp = step;  
sign string p_key (pl_id, pl_detail,  
pl_key, pl_tsp);  
verify string p_key (pl_id, pl_detail,  
pl_key, pl_tsp);  
function check smart contract plchk(  
string pl_id, string pl_detail,  
string pl_key, string pl_tsp) {  
return pl_id.exist;  
return pl_detail.exist;  
return pl_key.exist;  
return pl_tsp.exist;  
**Scheme 14. ** The smart contracts plans and plchk. The Licensee calculates:  
\[(ID_{BC},BC_{C-P})\]4  
\((ID_{BC},BC_{P-L})\) will also be uploaded to the blockchain center. Finally, the APP calculates:  
\[(ID_{BC},BC_{C-P})\]5  
to obtain the identity of the digital content successfully. This step is performed automatically by the smart contract, and the Licensee cannot skip the verification process privately. **4. Analysis**  
In this section, we analyze the requirements of digital rights management as follows.
## 2 Methodology
### Feature Selection  
In this study, 5 different models were created in the analysis by utilizing machine learning algorithms. While creating these models, we used auto-correlation and cross-correlation methods. Each model was created according to the values obtained from these methods and the results obtained are shown in Figure 3 and Table 2. According to the cross-correlation results, the input data that have the most impact on the output parameter  
Figure 1: Study area.  
Figure 2: Maximum monthly wind speed for study area.  
are the t-12 lagged data. It is understood from these results that the most second effective parameters are the data t-11 and t-2 lagged. The results obtained from cross-correlation and auto-correlation are very close to each other. The input variables based on results obtained from both methods were created as in Table 2. Many methods can be used to determine the input variables and also the model diversity can be increased. In the preliminary analysis, many different data input combinations were tried and as a result, it was decided to use the model inputs given in Table 2. The input variables that gave negative results as seen in Figure 3 were not added to the input structure because they did not give effective results in the preliminary analysis. Considering Table 2, the t-12, t-11, t-2, and t-1 indicate lagged time for 12 months, 11 months, etc. For instance, t-11 shows the input from the lagged time of 11 months before the output variable's time t. Both methods here were applied to the entire dataset, and no pre-processing was applied to the data. The most successful models were chosen for this study.
## 2 Related Work
### Chatbots for Arabic Settings  
Arabic is a complex language; therefore, creating Arabic chatbots has proven to be difficult for researchers [57]. To date, only a few researchers have attempted to develop Arabic chatbots. According to a survey [55], Arabic chatbots are still in their infancy; all of the current work is rule-based or retrieval-based, and the lack of accessible datasets impedes their growth [25].  
However, some studies have been able to overcome Arabic language limitations by using translation tools. For example, [PERSON] et al. [56] developed a question-answering method, and [PERSON] et al. [58] developed an Arabic language model. The authors of [57] developed MidoBot, a deep learning-based generative chatbot in Arabic that uses a sequence-to-sequence model to generate new responses from a dataset. The performance of these developments and that of [59] demonstrates the potential of neural models for understanding the Arabic language.  
In addition, [PERSON] et al. [60] highlighted the potential of neural models for Arabic language comprehension. This prompted an investigation of neural solutions for open tasks in Arabic, such as empathic response production. Recent advances in transformer-based models have shown that language-specific BERT-based models are incredibly efficient in understanding language, provided they are pre-trained on an extensive corpus [59]. These models have established new benchmarks and have achieved state-of-the-art results for most NLP tasks [41].  
Recent research has focused entirely on data-driven end-to-end systems that use neural generative models to generate the appropriate responses [41]. The most popular choices are seq2 seq and transformer, and pre-trained models [33; 37] and GPT3 [42] are two examples of encoder-decoder models. To explore these models for Arabic, large corpora of conversations with verified and cleaned annotations are needed [44] by creating contextualized word embeddings, transformers, and pre-trained models that can compensate for data deficiencies [33].  
Their low computational cost makes them suitable for small datasets that Arabic can handle. Recent contextualized embeddings can be taught on unannotated materials, thereby improving various Arabic NLP tasks [41]. Multilingual BERT [61], AraT5 [62], AraGPT2 [63], ARBERT & MARBERT [64], AraELECTRA [41], and AraBERT [59] are contextualized embedding models that support Arabic.  
Controllability is another aspect that can be used to construct sound Arabic conversational AI systems [33]. Hybrid models are an excellent alternative to this, as they integrate the capabilities of retrieval and generative models, where returned answers can be matched with generated answers to help retrieval models find a better answer [37].
# Coupled Vibration Analysis of Submerged Floating Tunnel System in Wave and Current
## 3 Case Study
### Analysis Results
#### 3.2.1 Amplification under Combined Parametric and Hydrodynamic Excations  
Figure 3a depicts the vibration curves of the SFT system under combined parametric excitation and external hydraulic loads. After the steady vibration state is reached, the mid-span amplitude of the cables is about 0.402 m. In addition, the time-history curves of tube displacement and cable stress variation exhibit similar trends, which indicates that the tension fluctuation of the cables is mainly affected by the motion of the tube.  
As the wave force acting on the tube shows a logarithmic attenuation with the increases of the installation depth of the tube [22], Figure 3b also gives the vibration responses of the system without wave force. Comparing Figure 3a with Figure 3b, it can be found that the VIV of the cables drives the SFT system into a stable resonance status. However, since the wave period (10.8 s) is much longer than the natural vibration period of the tube (0.899 s), the influence of wave force on the system is very limited. The results are listed in Table 2.
## 2 Background
### Applied Optimization Methods
#### 3.4.1 Deterministic Methods
These methods simulate the behavior of the infrastructure assets after applying alternative management strategies/interventions and explore the uncertainties and risks during the management. A Markov chain is a popular choice for simulation based methods to simulate the infrastructure performance [77; 78; 79]. Simulation based methods can better describe the deterioration of infrastructure condition and, therefore, can consider the uncertainties during the optimization process. Yet, for large networks or complicated problems, the simulations may be too complex and these methods may not be efficient. Hence, these methods are recommended for small and simple DM, where infrastructure asset condition and uncertainties are important considerations in the DM. In general, deterministic methods are the most popular choice for IAM among the reviewed articles. A main reason of their popularity is that they are effective and efficient when supporting management decisions. They can generate high-quality solutions for both SOO and MOO. According to the reviewed articles, their solution quality is stable and not affect by problem size and the restrictiveness of constraints. With the help of powerful computers, they are able to complete life-cycle analysis of large infrastructure networks in reasonable times. Considering the solution quality, deterministic methods are highly recommended for DM in IAM. However, deterministic methods follow specific algorithms with application limitations and restrictions. Hence, they may be ineffective for some types of problems in IAM.
## 5 Simulation and Analysis
### Performance of the Proposed ACE Method with Different Numbers of Terminals  
Figure 9 illustrates the RACH success probability and power consumption under different NB-IoT terminal load conditions. According to the specifications in the 3 GPP document, the density of NB-IoT terminals in rural areas is set at 0.1 devices/km\({}^{2}\), with a beam coverage radius of 45 km. It can be calculated that the number of terminals within the beam coverage range is approximately 600-700. Setting the simulation terminal quantity based on this number is reasonable. From this figure, with a growing number of terminals, the collision probability of the preamble increases and leads to a continuous decline in RACH success probability. Meanwhile, the power consumption of NB-IoT terminals also rises. However, the NSGA-II jointly optimized system with two sets of parameters achieves an improvement in RACH success probability. In addition, the proposed ACE method consumes less power and makes it more suitable for LEO satellite-based IoT networks. This  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Service** & **Maximum Number of Repetitions** & **Back-Off Window Size** \\ \hline Alerting Service (Satellite & 12 & [0, 512] MS \\ Altitude 600 km) & & \\ Data Collection Service & 4 & [0, 1024] MS \\ (Satellite Altitude 600 km) & & \\ Alerting Service (Satellite & 18 & [0, 2048] MS \\ Altitude 1200 km) & & \\ Data Collection Service & 8 & [0, 1024] MS \\ (Satellite Altitude 1200 km) & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The proposed Adaptive Coverage Enhancement method.  
is accomplished by reducing the maximum number of repetitions and shortening the size of the back-off window. As can be seen, the proposed ACE method can meet the demands of RACH success probability and power consumption in LEO satellite-based IoT networks.
## 4 Global Error Model Verification by Numerical Simulations
### First Case Analysis: \(\Delta A_{\mathrm{s}}\) Equals \(\Delta h_{\mathrm{s}}\)
#### 4.1.2 Black Region  
First, the black areas in Figures 10a and 11a contained the region where the proposed error model was not valid. As noted in Section 3.2, the region where the error model was not valid was a circular ring. In Figures 10 and 11, the time was 27 May 2024, \(UTC=21\) h, and \(E_{\omega}=169.8116/60/60\) h. According to Equation (16), the \(\eta_{N=0,\cos\omega=0}\) value was either \(-45.7^{\circ}\) or \(134.3^{\circ}\). The black areas in Figures 10a and 11a are distributed around the circular ring defined by the longitude value, \(\eta_{N=0,\cos\omega=0}\). Therefore, the black areas contained the region where the proposed error model was invalid.  
Then, in Figures 10 and 11, the areas in black, excluding the circular ring, were examined; these black areas outside the circular ring were called the valid black areas. In Figures 10a and 11a, the valid black area was characterized by a gradual increase in the absolute value of the ratio of total differentials of latitude and longitude (i.e., the TDRLON and TDRLAT values), ranging from four to positive infinity. By zooming in on this area, it was observed that the contour line distribution patterns in Figure 10a,b were consistent, as well as those in Figure 11a,b, meaning that the distribution pattern of total increments was consistent with the distribution pattern of total differential ratios. Consequently, it could be inferred that the error model remained applicable within the valid black area. Nevertheless, it should be noted that due to the excessively high absolute value of the total differential ratio (the TDRLON and TDRLAT values), even minimal measurement errors in the independent variables could cause significant deviations in latitude and longitude.  
Further research indicated that when \(\Delta A_{\mathrm{s}}=\Delta h_{\mathrm{s}}\
eq 0.1^{\circ}\), for instance, or \(\Delta A_{\mathrm{s}}=\Delta h_{\mathrm{s}}=0.5^{\circ}\) or \(\Delta A_{\mathrm{s}}=\Delta h_{\mathrm{s}}=3^{\circ}\), the conclusions regarding the valid black areas and the invalid circular ring still held true.
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
## 2 Fundamentals of FSO Signal Transmission
### Pointing Error  
Airborne FSO links are subject to pointing errors due to mobility. In certain scenarios, such as when the spot size of the received beam is much larger than the aperture of the detector, the effects of pointing errors can be neglected. In other words, when the received beam can be treated as a plane wave due to its large spot size, the intensity of the beam remains relatively constant over the detector's aperture, resulting in reduced sensitivity to pointing errors. This assumption holds true only when the spot size is significantly larger than the detector's aperture. If the spot size becomes comparable to, or smaller than, the detector's aperture, even minor pointing errors can cause fluctuations in the received signal intensity, leading to potential errors and performance degradation. Pointing errors in HAPS-to-ground FSO links arise from issues with platform stability, tracking and feedback control, and beam divergence. Unlike HAPS, rotary-wing UAVs operate at lower altitudes and are subject to even more significant platform movements and fluctuations. In particular, rotary-wing UAVs rely on propulsion systems that push air down to generate lift and maintain airborne positions. This propulsion mechanism introduces inherent platform movements and vibrations, leading to fluctuations in the UAV's position, altitude, and orientation. The combination of semi-fixed positions and dynamic motions, including yaw, pitch, and roll, can result in considerable pointing errors that impact the alignment between the transmitter and receiver in the communication link. These pointing errors require continuous adjustments to maintain a stable and reliable connection. Therefore, in rotary-wing UAV-to-ground links, it becomes crucial to implement robust tracking and stabilization systems to mitigate the effects of pointing errors. In Table 6, we provide a comparison of various factors related to pointing errors in HAPS-to-ground FSO links and rotary wing UAV-to-ground FSO links, in addition to mitigation techniques. In terrestrial FSO links, the primary source of pointing error is building sway, where movement can have a significant impact, primarily in one direction while being minor
## 2 Materials and Methods
### Multichannel Orthogonal Correlation Detection Method
#### 2.2.2 Multichannel Orthogonal Correlation
We performed a sliding correlation of 150 ms between the three frequency signals (\(f_{1}\), \(f_{2}\), and \(f_{3}\)) outputted by the notch filter and the corresponding orthogonal carrier signals, which resulted in three correlation results: \(R_{1}(t),\ R_{2}(t),\) and \(R_{3}(t)\). Figure 3: Correlation peaks at different correlation lengths.
## 2 Methodology
### Grey System Theory
When larger the better, x\({}_{i}\)(j) is transformed into x\({}_{i}^{*}\)(j), the formula is as follows: \[\rm{x}_{i}^{*}(j)=\frac{x_{i}(j)\!-\!min_{j}x_{i}(j)}{max_{j}x_{i}(j)\!-\!min_{j}x _{i}(j)}\] (2) When Nominal the best, when the target value is x\({}_{\rm{ob}}\)(j) and \(\rm{max_{j}x_{i}(j)\geq x_{ob}(j)\geq min_{j}x_{i}(j)}\), then its formula is as follows: \[\rm{x}_{i}^{*}(j)=\frac{|x_{i}(j)\!-\!x_{ob}(j)|}{max_{j}x_{i}(j)\!-\!x_{ob}( j)}\] (3) When smaller the better, its formula is as follows: \[\rm{x}_{i}^{*}(j)=\frac{max_{j}x_{i}(j)\!-\!min_{j}x_{i}(j)}{max_{j}x_{i}(j)\! -\!min_{j}x_{i}(j)}\] (4) When implement computation, the sequential data also needs standardization. Use Equation (2) to Equation (4) as the standardized transformation, in which x\({}_{0}\)(j) replaces the original x\({}_{i}\)(j). (4) Implement the computation of grey relational coefficient, the computational formula is as follows: \[\rm{\Delta_{0i}(j)=|x_{0}^{*}(j)\!-\!x_{i}^{*}(j)|}\] (5) The degree of grey relation is expressed as \(\rm{\gamma_{0i}(j)}\), its computational formula is as follows: \[\rm{\gamma_{0i}(j)=\frac{\Delta min+\zeta\Delta max}{\Delta_{0i}(j)+\zeta \Delta max}}\] (6) Among which \(\rm{\Delta max=max_{j}max_{j}\Delta_{0i}(j)}\), then \(\rm{\Delta min=min_{j}min_{j}\Delta_{0i}(j)}\) and \(\zeta\in[0,1]\). (6) Compute the degree of grey relation \(\rm{\Gamma_{0i}}\) and consider the computation of weight \(\rm{W_{i}}\). If the weight value is different, the overall decision making of design will be measured on the basis of the degree of grey relation \(\rm{\Gamma_{0i}}\) and establish data distance space. It could also be expressed as \(\rm{\widetilde{\gamma}}\), the computational formula is as follows: \[\rm{\Gamma_{0i}=\sum_{i=1}^{n}[\rm{W_{i}(j)\times\gamma_{0i}(j)}]}\] (7)
# E-Waste Challenges in India: Environmental and Human Health Impacts
## 5 Negative Impact of E-Waste on the Environment
### Negative Impact on Water Quality:  
Water is the ecosystem of many aquatic microbes, plants, and animals. However, soil degradation occurs whereby hazardous toxic metals from electronic waste, including arsenic, lead, mercury, cadmium, and lithium, seep into the soil, polluting groundwater. For example, the decomposition of the cathode ray tube releases heavy metals such as lead and mercury into groundwater [62; 63]. These toxic substances travel to other water bodies like rivers, reservoirs, ponds, wetlands, and lakes; This pathway creates acidic proliferation that leads to acidification and toxic accumulation. Acidification can kill organisms and microorganisms in marine and freshwater ecosystems. Toxicants travel miles from recycling sites through the soil and into water bodies, making the water unfit for drinking and other uses, creating a challenge to find clean water [62; 63; 66]. Due to acidification and contamination, freshwater and marine organisms such as fish and phytoplankton die, which disrupts the biodiversity of that affected area and damages the water ecosystem. The toxification of water can harm aquatic ecosystems to such an extent that recovery becomes uncertain and perhaps unattainable.  
Through the process of bioleaching, toxicants can enter water bodies where metal slowly leaches out from informally-disposed-of e-waste and pollutes nearby areas [64]. Organisms living in this area consume toxic heavy metals and store them in their bodies in trace amounts for a long time [64]. These heavy metals (Hg, Pb, As, Cd, etc.) are transferred to the next level in the food chain. For example, when small organisms such as fish consume heavy metals such as mercury, these toxicants pass through the food chain, increasing the concentration through biomagnification and accumulating in the body through bioaccumulation. Bioaccumulation is a process in which heavy metals, pollutants, or pesticides gradually build up within a single organism over time, persisting for a lifetime because the body cannot excrete or break them down. For example, a fish in polluted water absorbs mercury; it accumulates in its tissues, cannot be easily excreted, and continues to accumulate overtime, making it harmful for large predators or humans who consume it. This disturbs the whole ecosystem and causes serious health problems in humans,including neurological damage, especially in children, kidney damage, stomach problems, etc. Many of these toxic heavy metals, including arsenic, lithium, and mercury, act as carcinogens [66].
# Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder +
## 6 Conclusions  
In this paper, we proposed an unsupervised domain adaptation framework based on a stacked convolution sparse autoencoder, called SCSA. Our method can learn more transferable and discriminative representations across domains. Firstly, the original data is pre-processed by the layer-wise RICA with whitening. Then, the labeled data information in the source domain is encoded via softmax encoder weight regularization in a sparse autoencoder model. Finally, the convolutional kernels are used to reserve the local relevance for learning abstract representations. The proposed method was extensively tested on several datasets and was found to be more effective than state-of-the-art domain adaptation methods. The proposed method was extensively tested on several datasets and an accuracy of up to 89.3% was obtained, outperforming other state-of-the-art autoencoder-based domain adaptation methods, such as SSRLDA.  
The designed SCSA is only concerned with the unsupervised domain adaptation of image data and is not concerned with other types of data, such as text data. In the future, we intend to focus on learning better feature representations in text data for unsupervised domain adaptation.  
Figure 6: Parameter influence on \(\beta\) of SCSA on ImageNet dataset. The y-axis represents the classification accuracy of the target domain; the x-axis represents the value range of \(\beta\).  
Figure 7: Parameter influence on \(\lambda\) of SCSA on ImageNet dataset. The y-axis represents the classification accuracy of the target domain; the x-axis represents the value range of \(\lambda\).