esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering
## 2 Survey  
A survey was conducted to determine whether users read opinions, even if a product has been recommended to them. The survey was conducted with 350 people from different countries, such as Spain, Colombia, Argentina, Brazil, Portugal, Italy, France, Belgium, Chile, Uruguay and England. Ten questions were asked, as shown in Table 1.  
10. Why do you read the opinions of recommended products?  
The results presented in the table show that 98% of respondents read reviews of recommended products before buying. Specifically, 91% read movie reviews on the most popular streaming platforms and 99% rely on the opinions of other users before booking a hotel, restaurant or leisure activity. In the survey, users were asked about the amount of time they spend searching for and reading reviews of recommended products. The average time is 1.50 h. Participants in the survey noted that they spend around15 min reading movie reviews to 4 h reading reviews of hotels and leisure activities. Analyzing the reasons for reading the opinions of recommended products, they were grouped and summarized as follows: to learn about the quality of the product; to determine if the article is really useful for your needs; to determine whether the seller is honest about the functionality of the product; to learn about the experiences that other buyers had; to learn about the negative characteristics of the product; and to learn about the context or conditions of use.  
To summarize the results of the survey, we highlight that:  
1. People who use platforms where products are recommended read opinions regarding recommended products before buying them.
2. For each recommended product, they spend up to 1.5 h reading opinions.
3. They read user feedback to learn about the conditions of use of the product, the quality of the product, the reliability of the seller, to obtain a general overview of the product, to learn about the negative characteristics of the product, among others.
4. It is necessary to provide recommender systems with methods that present information about the experiences of other users along with the presentation of the product itself. These methods should help users by filtering reviews and presenting only the necessary information.
## 2 Experimental Section
### Instrumentation and Synthesis
#### Synthesis of 1-Fluoro-4-(Nitroprop-1-Enyl)Benzene **5**  
Compound **5** was prepared [23] in 30% yield, yellow crystals, mp. 65-66 \({}^{\circ}\)C. \({}^{1}\)H NMR (300 MHz, CDCl\({}_{3}\)): \(\delta_{\rm H}\) (ppm): 8.06 (s, 1H, H-C=C), 7.46 (dd, \(J\) = 3.30, 5.37 Hz, 2H, Ar-H), 7.17 (t, \(J\) = 8.66 Hz, 2H, Ar-H), 2.46 (s, 3H, C-H, \(E\)), 1.59 (s, 3H, C-H, \(Z\)). \({}^{13}\)C NMR (300 MHz, CDCl\({}_{3}\)) \(\delta_{\rm C}\) (ppm): 165.2 (d, \(J\) = 252.21 Hz, 1C, C-F), 147.5 (C=C, \(\beta\) carbon), 132.5 (C=C, \(\alpha\) carbon), 132.2 (Ar), 128.5 (Ar), 128.5 (Ar), 116.3 (Ar), 116.0 (Ar), 14.0 (CH\({}_{3}\)). GC/MS \(m\)/z (M\({}^{+}\)) 181.
## 4 Model Prediction Results and Analysis
### Prediction Results
Generally, soils with higher LL values exhibit greater compressibility. In zones 1 and 4--where a single water system predominantly influences deposition--soils are relatively homogeneous, and thus the typical geotechnical characteristic of higher LL correlating with greater compressibility is reflected, resulting in a positive LL coefficient. In contrast, zones 2 and 3, which are influenced by multiple water systems, receive sediments from diverse sources that mix and reduce overall homogeneity. Consequently, the LL coefficient in these zones appears as negative, deviating from the usual geotechnical trend. Furthermore, the LL coefficients in zones 2 and 3 are comparatively smaller than those in zones 1 and 4. In an environment with multiple water systems, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Zone** & **Best Model** & \multicolumn{2}{c}{**Regression Equation**} & \multicolumn{1}{c}{**R\({}^{2}\)**} \\ \hline
1 & LR & \(C_{c}=-0.017697(w_{n})+0.035058(\mathrm{LL})+0.001132(\mathrm{PI})+0.094264(e_{0 })+0.387972\) & 0.7308 \\ \hline
2 & MLR & \(C_{c}=-0.019399(w_{n})-0.008944(\mathrm{LL})+0.027466(\mathrm{PI})+0.029309(e_{ 0})+0.339065\) & 0.5539 \\ \hline
3 & MLR & \(C_{c}=-0.047816(w_{n})-0.005584(\mathrm{LL})+0.044939(\mathrm{PI})+0.093734(e_{ 0})+0.292654\) & 0.7575 \\ \hline
4 & LR & \(C_{c}=0.009149(\mathrm{LL})+0.046717(\mathrm{PI})+0.038494(e_{0})+0.317704\) & 0.8546 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Regression equations of best models by zone. the influence of LL on soil compressibility tends to be less predictable. Therefore, to reduce uncertainty, the LL coefficient is lowered, thereby minimizing its contribution to the compression index. When comparing the absolute values of the coefficients (excluding their signs), zone 1--characterized by a homogeneous single-water-system environment with limited clay mineral diversity and a strong influence of physical structure--exhibits the initial void ratio as the most dominant property, resulting in a markedly larger coefficient gap compared with other properties. In contrast, zone 2, where diverse sediments intermix, shows the smallest difference among coefficients, indicating that all properties exert relatively similar influences on the soil. Although zone 3 is influenced by a complex water system, its proximity to land leads to a greater accumulation of coarse-grained sediments and thus a pronounced impact of physical structure. As a result, as in zone 1, the initial void ratio dominates in zone 3, producing a substantial coefficient gap relative to other properties.
## 3 Results
### General Characteristics of Identified Studies
#### 3.1.2 Geographic Distribution of Studies  
As can be observed in Figure 4, the analyzed publications were written by authors hailing from 43 countries from all inhabited continents. The largest pool of papers has been delivered by authors from the United Kingdom (15 publications in total), followed by the USA (14 publications) and Germany (11 publications). No other country has surpassed the threshold of 10 contributed publications.
# A Lunar-Orbiting Satellite Constellation for Wireless Energy Supply
## 3 System Architecture and Key Technologies
### Power Scaling by Laser Beam Combination
The harsh radiation and high-temperature gradient that the system would have to endure shorten the list of materials and components that are appropriate for the application. To enlarge the laser beam from its fiber-output diameter to its final diameter suitable for long-distance propagation, the telescope should be designed with mirrors instead of the more commonly used lens, mainly because of the degradation that the latter would undergo when subject to such high radiation.
# Symmetric-Key-Based Security for Multicast Communication in Wireless Sensor Networks +
## 4 Security Scheme
### Group Key Construction Phase
#### 4.3.2 Unicast Approach  
In the unicast approach, \(P\) shares the random value \(k_{g}\) through the following message with all the subscribers (\(S_{s},s=1,\ldots,n\)):  
\[T_{g},h_{w},E_{k_{s}}(k_{g}\oplus H(x)),H(ID_{G}\|T_{g}\|k_{g}\|h_{w})\]  
Each subscriber can decrypt this message using its secret key. After XORing the obtained result with the stored value \(H(x)\), the subscriber retrieves the group key.  
To complete the process (for both the unicast and multicast approaches), each subscriber \(S_{s},(s=1,\ldots,n)\) must send an ACK of reception to the publisher \(P\).  
\[B_{s},E_{k_{ps}}(H(k_{g},T_{g},h_{w}),H(T_{1}\|T_{2}\|h_{w}\|k_{s}))\]  
If after decryption of the second part of the message, the hash value of the original timestamp \(T_{g}\) and hash value \(h_{w}\) are recovered, the publisher stores the rest of the ciphertext \(H(T_{g}\|h_{w}\|k_{s})\). If all acknowledgements have been collected, \(P\) computes  
\[\bigoplus_{s=1}^{n}H(T_{1}\|T_{2}\|h_{w}\|k_{s})\]  
and sends this result, together with the value \(h_{w}\), to the NMM. The NMM can compute the individual values of this sum and thus it can verify whether the resulting sum corresponds with the received value. If so, the NMM approves the multicast group and sends a confirmation using unicast communication to each of the members. If not, the individual messages \((B_{s},H(T_{g}\|h_{w}\|k_{s}))\) should be transmitted to the NMM to identify the potential source of the problem. The members of the multicast group now store \(k_{g},h_{w}\).  
A summary of the group key construction phase can be found in Figures 3 and 4, for the multicast and unicast approaches, respectively.  
Figure 3: Summary of the multicast approach in the group key construction phase.
## 3 Experimental Setup  
The experimental setup consisted of two full-scale RC interior joints of a moment-resisting frame with moderate ductility (Figure 1) prepared according to ACI codes (440.2R-17) [41]. A plain specimen (CS1) was ordered as the control specimen, and another section that was later retrofitted with a flange-bonded scheme (RS1) was constructed and tested under monotonic loading until sample failure. The joints were chosen from a typical four-story RC structure and consisted of half-length lower and upper floor columns that were 2700 mm (2.7 m) in height with half-length left and right beams with a length of 4000 mm (4 m) and dimensions of column joint cross-sections of 400 \(\times\) 400 mm\({}^{2}\). Additionally, 8\(\Phi\)25 bars were used to longitudinally reinforce the columns, complying with a 2.4% reinforcement ratio. Rectangular \(\Phi\)10 ties were used as transverse reinforcements. Each two-column ties were 170 mm apart inside the joint, 600 mm above and below the joint, and 200 mm in the general length of the column. All beams had a length of 1800 mm (1.8 m) from the column to the free end, and the cross-section selected was 300 \(\times\) 300 mm\({}^{2}\). Two bars (i.e., \(\Phi\)25) were employed for reinforcement of the beams at the top and two bars (i.e., \(\Phi\)20) at the bottom. \(\Phi\)8 rectangular stirrups were used as transverse reinforcements of the beams (Figure 1). The stirrups were 50 mm from one another in the first 600 mm. The yield strengths of the reinforcing steel were 400 MPa for the bars and ties. However, the concrete compressive strength reached 21.5 MPa for the standard cylinder samples. The ultimate stress and strain and elasticity modulus of the unit-directional CFRP used in the models were, respectively, f_FRP = 3900 MPa, 0.0155 mm/mm, and 240 GPa. In the retrofitted specimen with the flange-bonded scheme (RS1), FRP laminates covered the top and bottom flanges of the ends of the beams, which joined the connection, extending so as to cover the adjoining column faces. The joint was covered by 5 FRP layers. The total thickness for 5 layers of FRP is 0.825 mm. The length of CFRP sheets was kept constant at 300 mm for the joint based on recommendations by [PERSON] and [PERSON] [31] for desirable plastic hinge relocation. In the design of this experiment, an attempt has been made to investigate the relocation of plastic hinges when the load is applied to the end of beams. Therefore, to maintain the same strength for the beam against both in-plane and out-of-plane loads, the square section has been employed to extend the results of this study for the frame exposed to lateral loads in future studies.
## 5 Conclusions  
Traditional algorithms (Mean, CO, Hart85, and FHMM) offer computational efficiency and speed, making them suitable for real-time applications or resource-constrained environments; however, their accuracy decreases with increasingly complex consumption patterns. In contrast, deep learning models (DAE, RNN, Seq2 Point, Seq2 Seq, and WindowGRU) significantly improve the accuracy and adaptability to heterogeneous loads. Still, they entail high computational costs and prolonged execution times, which limit their applicability in latency-sensitive scenarios.  
The accuracy of NILM models is influenced by critical factors: higher sampling rates enable the more precise capture of transient events; the inclusion of harmonics enhances the discrimination in classical models and does not negatively impact neural network-based models; power filters can marginally benefit traditional algorithms but often degrade the event detection in low loads, whereas deep models maintain robustness without filtering; and the appliance type determines the method suitability, with stable loads favoring classical algorithms and cyclic or complex loads requiring deep learning, subject to the computational capacity.  
In the evaluation of open hardware, the oZm v1 demonstrated the best overall F1-score performance, particularly for specific devices. In contrast, the oZm v3 showed a lower accuracy, attributed to the increased complexity of the dataset. The OMPM yielded intermediate results, with a high precision in energy assignment metrics (EAE). However, an increased number of devices negatively impacted metrics such as the MNEAP and RMSE, highlighting the challenge of simultaneous disaggregation in complex scenarios.  
In conclusion, the effectiveness of NILM depends on striking a balance between accuracy, the computational capacity, and the characteristics of the dataset and hardware. The design and study of new datasets, alongside the integration of open hardware and the consideration of harmonics, are essential to advancing the NILM accuracy and applicability, particularly in smart home and industrial energy monitoring contexts.
## 4 Results
### Feature Visualization Analysis before and after Model Transfer  
T-SNE method was employed in this study to visually analyze the spatiotemporal fusion features extracted by each model, evaluate the effectiveness of the features in the model transfer process, and provide a basis for which layers to adjust in the model fine-tuning. Figure 21a shows the t-SNE feature dimensionality reduction results for rice and other classes in the Mississippi imp alluvial plain (Region A) in 2019. It could be found that the features extracted by the attTFBS model (Model A) could effectively distinguish rice from other ground objects. Figure 21b shows the t-SNE dimensionality reduction results of the features of rice and other types of ground objects in the Sacramento area (Region B) in 2019 extracted using the attTFBS model (Model B) with the same hidden layers as the model used in the Mississippi alluvial plain (Model A). Although this area (Region A) is thousands of kilometers from the source area (Region B), the features extracted by the model could still be effectively used to distinguish rice from other classes. However, when the model (Model A or B) was applied directly to the inter-continental (Region C), it could no longer extract effective features for rice classification, which was confirmed by the visualization results of the t-SNE feature dimension reduction (Figure 21c-f). It is not possible to map rice in Region C directly using model A. Then, a fine-tuned model (Model C) was used to extract the spatiotemporal fusion features of rice in the Shuangcheng area (Region C) from 2017 to 2020; the t-SNE method was used to reduce their dimensions and visualize them; the results are shown in Figure 21g-j. This indicates that the features extracted by the fine-tuned model (Model C) significantly improved the rice identification ability compared with the model without adjusting the parameters of the feature extraction layer (Model A or B), thus significantly improving the confusion of rice and non-rice pixels. The results showed that we must fine-tune the feature extraction layer, as well as the output layer, to realize the inter-continental transfer of the pre-trained model.  
Figure 20: The comparison of Sentinel-1 VV time-series curve of rice in Shuangcheng (blue lines in Maps (**a–c**)) with those in the Sanjiang Plain (red lines in Map (**a–c**)). The red lines in Maps (**a–c**) are the VV curves of correctly classified rice pixels, non-rice pixels misclassified as rice pixels, and rice pixels incorrectly classified as non-rice, respectively. The blue and red arrows, respectively, represent the lowest VV value of rice pixels in the Shuangcheng region and the Sanjiang Plain, indicating the transplanting period of rice. The light-colored areas around the curves represent \(\pm 1\sigma\) from the average.
# Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
## 6 Conclusions  
In this study, a time delay optimization algorithm for compressing the shipborne radar digital video using deep learning was proposed. First, the difference between shipborne radar digital video and traditional screen video was analyzed and a large number of shipborne radar digital video sequences was collected. We encoded the shipborne radar digital video using SCM8.7, the official test platform of SCC, and established a CTU depth interval data set. The dataset contains CTU division depth interval information for encoding shipborne radar digital video under different QP values. Second, an optimization algorithm flow for the reducing the shipborne radar digital video time delay based on deep learning was introduced. Finally, we built a neural network model and used a large  
Figure 8: R-D curves contrast.  
number of samples to train the model. In the process of compressing shipborne radar digital video, the encoder predicts the division depth interval of CTUs in advance by invoking the trained model, which reduces a lot of unnecessary RD-cost calculation, and accelerates the speed of encoding radar video. The actual radar transmission and display results show that, compared with the official SCC test platform SCM8.7, our optimization method results in a significant increase in encoding speed, saving about 39.84% of the encoding time overall, whereas BD-BR and BD-PSNR result in a reduction of only 2.26% and 0.19 dB.  
Methodology, H.L. and Y.Z.; investigation, H.L. and Z.W.; data curation, H.L. and Z.W.; writing--original draft preparation, H.L.; writing--review and editing, Y.Z. and Z.W.; supervision, Y.Z.; project administration, H.L. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Key R&D Program of China (No. 2018 YFB1601502), the Liao Ning Revitalization Talents Program (No. XLYC1902071), the Fundamental Research Funds for the Central Universities (No. 3132019313).  
Not applicable.  
The authors declare no conflict of interest.
# Classification Method of Plug Seedlings Based on Transfer Learning
## 2 Materials and Methods
### Classification Method of Plug Seedlings  
#### 2.4.1 Convolutional Neural Networks  
Convolutional neural networks (CNN) is a neural network with convolutional structure. The convolution structure can reduce the amount of memory occupied by deep networks, and can also reduce the number of parameters of the network and alleviate the over-fitting of the model. This advantage is more obvious when the input of the network is a multi-dimensional image. The image can be directly used as the input of the network, avoiding the complicated feature extraction and data reconstruction process in the traditional recognition algorithm. Currently, convolutional neural networks are widely used in agriculture. This study is based on four convolutional neural networks (Alexnet, Inception-v3, VGG16, Resnet-18) [21; 22; 23; 24]. The transfer model is used to construct the seedling model. The main parameters of the four network structures are shown in Table 2. Taking the Alexnet convolutional neural network as an example, the structure of the convolutional neural network is as shown in Figure 6a. The network has eight layers, of which the first five layers are convolutional layers and the last three layers are 2 fully connected layers and 1 softmax layer.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Depth** & **Size** & **Parameters (Millions)** & **Image Input Size** \\ \hline Alexnet & 8 & 227 M & 61.0 & \(227\times 227\) \\ Inception-v3 & 48 & 89 M & 23.9 & \(299\times 299\) \\ Resnet-18 & 18 & 44 M & 11.7 & \(224\times 224\) \\ VGG16 & 16 & 515 M & 138 & \(224\times 224\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main parameters of 4 convolutional neural networks.  
Figure 6: Alexnet convolutional neural network and transfer learning model structure. (**a**) Alexnet convolutional neural network structure. (**b**) Transfer learning model structure based on Alexnet.
# Influence of Soil Parameters on Detecting Voids behind a Tunnel Lining Using an Impact Echo Method
## 5 Conclusions  
The following conclusions can be drawn from this study:  
(1) A comprehensive analysis of vibration responses in the time, frequency and time-frequency domains was useful for void detection, and the box-counting dimensions were useful for quantitative evaluation of the void.  
(2) With increasing soil density and water content, the acceleration response of the concrete slab decreased. Soils with a large density differences had a certain influence on the void detection, while changing the water content had little effect.  
(3) By calculating the box-counting dimensions of the vibration responses, the value was found to be stable within the area where the void existed behind the tunnel. When the detection point was beyond twice the void dimension, it became difficult to locate the void.  
(4) Using numerical simulation, the change in soil density and Young's modulus had some influence on void condition detection, while a change in Poisson's ratio had little effect.  
**Author Contributions:** Formal analysis, C.N.; funding acquisition, H.Z.; investigation, R.C. and C.N.; methodology, M.M. and W.L.; software, H.Z.; supervision, W.L.; writing--review and editing, M.M. and R.C.  
**Funding:** This research was funded by the Fundamental Research Funds for the Central Universities (grant number 2018 JBM036) and the Beijing Natural Science Foundation (grant number 3184047).  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders played role in the design of the study, the collection of the results or the decision to publish the results.