esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Hybrid VLC/RF Networks: Features, Applications, and Future Directions
###### Abstract
On the other hand, the papers marked as deeply analyzed are those that we consider to address each of the topics mentioned in detail, providing characteristics of the technologies, pending challenges, an analysis of what has been achieved by other authors, and possible solutions that can be carried out to optimize VLC/RF networks. Therefore, in this work an extensive study of the state of the art of hybrid VLC/RF networks in ideal indoor scenarios in the scientific literature is carried out and the characteristics and implementation of these networks in outdoor scenarios with harsh conditions are analyzed. The main applications of hybrid VLC/RF networks in the technological, economic, and social-environmental fields are considered. In addition, the main challenges faced by hybrid VLC/RF networks and future research directions are discussed. Figure 1: Bibliometric analysis of the VLC/RF networks studies by countries. The rest of this paper is organized as follows. In Section 2, the state of the art of hybrid VLC/RF networks in indoor scenarios with ideal conditions is reviewed, including analysis of aspects such as lighting requirements, hybrid channel model, load balancing, network selection, and resource allocation, and hybrid network topologies. In Section 3, a review of the state of the art of hybrid VLC/RF networks in outdoor scenarios with harsh conditions is presented. Section 4 analyzes the possible applications of hybrid VLC/RF networks in the technological, economic, and social-environmental fields. Section 5 establishes the main challenges and future directions of these hybrid networks. Finally, the conclusions of this review are presented in Section 6.
# Three-Dimensional Resource Allocation in D2D-Based V2V Communication
###### Abstract  
Device-to-Device (D2D) communication is the major enabler of Vehicle-to-Everything communication in 3 rd Generation Partnership Project (3 GPP) Release 14. The user equipment/device can engage either in direct communication with the infrastructure, use a relay node, or it can communicate directly with another device with or without infrastructure support. The user equipment can be either a hand-held cellular device or a moving vehicle. The coexistence of cellular user equipment with the vehicular user equipment imposes different Quality of Service (QOS) requirements due to the rapid mobility of the vehicles and interference. Resource allocation is an important task by which the user equipment is allocated the required resources based on different QOS parameters. In this paper, we introduced the case of three types of users which share uplink resources: two types of vehicular users, and a third user that acts as a handheld cellular phone, which is nearly static. By keeping in mind, the differential QOS requirements for the three types of users, we have calculated the optimum power and then applied a 3-dimensional graph-based matching and hypergraph coloring based resource block (RB) allocation.  
Device to Device (D2D); Long-Term Evolution (LTE); Vehicle to Everything (V2X); Vehicle to Vehicle (V2V); Vehicle to Infrastructure (V2I); Vehicle to Pedestrians (V2P); cellular user equipment (CUE); user equipment (UE); base station (BS) +
Footnote †: journal: Electronics Letters
# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 7 Evaluation
### Characteristics of Well-Crafted Summary  
Summarizing videos is a highly subjective endeavor since a summary created manually reflects the individual preferences of the annotator or evaluator. However, it is possible to identify specific characteristics of a well-crafted summary that can be considered essential  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset Name** & **Year** & **Short Description** & **Num. Vids** & **Length per Video (Min.)** \\ \hline OVP [84] & 1999 & Home, entertainment and news videos & 50 & \([1,4]\) \\ \hline Sports-1M [85] & 2014 & YouTube sports videos & \(10^{6}\) & 5.6 (average) \\ \hline UTE [82] & 2012 & Egocentric videos capture Physical daily activities & 10 & \([180,300]\) \\ \hline SumMe [80] & 2014 & Sport, event and holiday videos & 25 & \([1,6]\) \\ \hline TVSum [81] & 2015 & Documentaries, news and egocentric videos & 50 & \([2,10]\) \\ \hline FineGym [98] & 2020 & YouTube gymnasium videos & 156 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Video datasets used for the personalized summarization task.  
requirements [56]. The following characteristics can provide valuable information for the development of a successful video summarization model.  
* _Representativeness:_ The summary should effectively convey the main points of the original video. Although some information may be lost in the process of summarizing, it is essential to create a concise summary that clearly distinguishes the original video from others. Each video consists of various sub-videos, each presenting a distinct storyline. Therefore, the ideal summary is a blend of sub-videos that closely represents all narratives within the original video while remaining distinct from sub-videos in other videos [56].
* _Sparsity:_ Original videos could come from various sources, including video news, a video blog, long online streams, or a television program. In all instances, the preferred synopsis should be brief, typically lasting only a few seconds, as that is the typical duration a viewer will invest before determining whether to engage further or move on. Although, for concise videos, the ideal summary might be approximately 15% of the total duration, this proportion may decrease considerably for longer videos [56].
* _Diversity:_ Another essential characteristic of a video summary is the diversity present in its frames. While it may be feasible to generate a summary for certain videos and datasets that predominantly consists of similar frames and clips, it is still preferable to have a summary that offers a broader range of visual content. Although a viewer can grasp the essence of the video by examining a single frame, it is advantageous to have a summary that encompasses a diverse set of visual information [56].
# A Study on the Effect of Drift Factor on Feature Optimization in Electronic Nose Detection
## 3 Results and Discussion
### Drift Effects of Feature Optimization  
At the same time, we believe that the influence of the drift factor on the e-nose data also extends to the feature optimization part, which ultimately affects the e-nose compensation effect after feature optimization. In order to prove our conjecture, we compared the score plots of the sensors under different experimental conditions for side-by-side comparison, in the expectation of visualizing the influence of the drift factor on the feature optimization through the scores of the sensors and capturing more patterns and details. The results we obtained are shown in Figure 5.  
From the results, we can observe that the sensor scores under different experimental conditions show some variability. Taking the score plot at 80% humidity as an example, the W2W sensor scores much higher than the other sensors, but the sensor scores of this type of sensors in all other humidity conditions are at a lower level. This partly proves our conjecture that the effect of drift on the data collected by the e-nose ultimately extends to the feature optimization part, and thus further processing of this part is likely to be able to further improve the efficiency of the e-nose.  
Moreover, we observed that the sensor scores were more concentrated in certain experimental conditions, most notably the sensor scores in the temperature at 5 \({}^{\circ}\)C condition, where only the W1W and W1S sensors received higher scores, with the exception of W2S, which received a score of 3.06, all of which were zero. This may imply that there is some  
Figure 5: Plot of sensor scores for different experimental conditions.  
connection between the sensor scores and the classification effect of the electronic nose in this condition, e.g., the more concentrated the sensor scores are, the better the classification effect is, and the drift effect is lower. This needs to be further demonstrated by applying the mathematical model classification in the next section.
# Application of Collision Warning Algorithm Alarm in Fishing Vessel's Waterway
## 4 Application in Fishing Vessel's Waterway Based on Vpass Data
### Case Study of the Collision Warning Algorithm Application Based on Vpass Data
#### 4.2.1 Overview of the Maritime Risk Assessment Tools
\[\text{N}_{\text{G}}^{\text{head-on}}=\text{Lw}\sum_{\text{ij}}\text{P}_{\text {G}\,\text{ij}}^{\text{head-on}}\frac{\text{V}_{\text{ij}}}{\text{V}_{\text{i} }^{(1)}\text{V}_{\text{j}}^{(2)}}\left(\text{Q}_{\text{i}}^{(1)}\text{Q}_{ \text{j}}^{(2)}\right) \tag{6}\]  
where  
\(\text{Lw}\): : Length of the segment;  
\(Q_{i}Q_{j}\): : The number of passages per unit time for each ship type and size;  
\(V_{i}V_{j}\): : Speed of the ships;  
\(P_{\text{G}}\): : The probability that two ships will collide in a head-on situation;  
\[\text{N}_{\text{G}}^{\text{crossing}}=\sum_{\text{ij}}\frac{\text{Q}_{\text{i }}^{(1)}\text{Q}_{\text{j}}^{(2)}}{\text{V}_{\text{i}}^{(1)}\text{V}_{\text{ j}}^{(2)}}\text{D}_{\text{ij}}\text{V}_{\text{ij}}\frac{1}{\sin\theta}\text{ for }10^{\circ}<|\theta|<170^{\circ} \tag{7}\]  
where  
\(\text{D}_{\text{ij}}\): : Apparent collision diameter;  
\(\theta\): : Encounter angle of two ships. IWRAP MkII imports maritime traffic data and sets the length and width of legs according to traffic flow. Then, it is possible to develop an IWRAP model that predicts the annual collision frequency after analyzing the distribution of traffic by converting it to the number of traffic vessels per year.
# Propagation of Cosh-Airy and Cos-Airy Beams in Parabolic Potential
###### Abstract  
The analytical expressions of one-dimensional cosh-Airy and cos-Airy beams in the parabolic potential are derived in the general and the phase transition points. The expression in the phase transition point shows a symmetric Gaussian intensity profile and is independent of any Airy features, which is completely different from that in the general point. The intensity, the center of gravity, and the effective beam size of the cosh-Airy and cos-Airy beams in the parabolic potential are periodic and have the same period. The effects of the transverse displacement, the cosh factor, and the cosine factor on these periodic behaviors are also investigated. The direction of self-acceleration reverses every half-period. The phase transition point is also the inversion point of the intensity distribution, which indicates that the intensity distributions before and after the phase transition point are mirror symmetrical. The periodic behaviors of the normalized intensity, the center of gravity, and the effective beam size of the cosh-Airy and cos-Airy beams in the parabolic potential are attractive and well displayed. The results obtained here may have potential applications in particle manipulation, signal processing, and so on.  
Keywords:cosh-Airy beam; cos-Airy beam; parabolic potential; beam propagation +
Footnote †: journal: Applied Physics Letters
# Lateral Dynamic Response of Helical Pile in Viscoelastic Foundation Considering Shear Deformation
## 1 Introduction
Therefore, the lateral bearing behavior of the helical pile is also worth further research, especially the dynamic problems of helical piles widely used in wind power foundations, transportation foundations, and other engineering projects [37; 38; 39; 40; 41]. However, most of the existing research on the dynamic response of the helical pile and the dynamic interaction theory between helical pile and soil is based on the Euler beam model, and the breadth and depth of research are still insufficient. Further research is urgently needed on the lateral dynamic response (LDR) of the helical pile considering the pile-soil interaction [42]. In order to investigate the lateral vibration characteristics of the helical pile more realistically, this paper takes the shear deformation effect of the helical pile into account, and develops an equivalent stiffness method to establish the dynamic governing equations of the helical pile embedded in a viscoelastic foundation based on the Winkler foundation theory and the Timoshenko beam theory. Then, the analytical solutions for the lateral dynamic displacement (LDD), bending moment (BM), and shear force (SF) of the helical pile are strictly derived, and the rationality of the analytical solutions is also verified by comparing it with existing solutions. Based on the obtained solutions, the influence of pile and soil parameters on the lateral vibration characteristics of the helical pile with space and time response is investigated in detail. The relevant results can be utilized to guide the dynamic design of helical piles under lateral loads, especially suitable for the helical piles in marine engineering and the design of dynamic foundations on helical piles.
# Organizations towards the Evaluation of Environmental Management Tools ISO 14001 and EMAS
## 2 Similarities and Differences between ISO 14001:2015 and EMAS Regulation

Table 1: Selected differences between EMAS and ISO 14001:2015. Source: Own elaboration based on [36; 37; 38; 39; 40]. and have global geographical applicability [36]. They can be implemented by large and small companies, public and private organizations alike. The new EMAS version includes all the requirements of ISO 14001:2015, with additional elements, such as an environmental declaration and the commitment of all employees. Both standards also cover new areas: life cycle perspective, organizational context, needs and expectations of the stakeholders. Similarities between the systems include the monitoring of environmental performance indicators (e.g., ISO 14001:2015, items 6.2.2 and 9.1.1), and the use of audits to monitor environmental processes in terms of compliance and improvement. ISO 14001:2015 requires the identification of environmental aspects and impacts, while EMAS requires an initial comprehensive environmental review of processes [37]. EMAS provides a record of all registered organizations, which is not available for ISO 14001:2015. Additionally, EMAS has a stricter interpretation of environmental processes planning and management [36]. Benefits of EMS implementation are to some extent common to both systems; however, the EMAS implementation may generate additional benefits, as shown in Table 2.
## 3 Model Architecture
### Embedding Layer  
In this layer, in order to obtain richer semantic information and word embedding representations of different granularity for each input, we use the Chinese pre-trained model, RoBERTa. RoBERTa has the same model structure compared to the pre-trained model--Bert--but has the optimizations below:  
* The model adopts the Whole Word Mask (WWM) mechanism, which improves the learning ability by constantly changing the masking position during training.
* The model uses the full-sentences and the doc-sentences as input to dynamically increase the batch size to replace the Next Sentence Prediction objective (NSP) in Bert.
* The model is trained using a larger batch size with more training data and longer training time.  
Figure 3: The overall architecture of the AT-CRF Reader reading comprehension model, with the bidirectional attention flow mechanism on the right, where \(x\) and \(q\) are the input context and query, \(c\) and \(u\) are the initial vector representations of the context and the query obtained after the embedding layer processing, \(H\) and \(V\) are the contextual vector representations of the context and the query respectively, \(\hat{H}\) contains the most important word information of the query in the context, \(\hat{V}\) contains the correlation representation of the query word and each context word, and \(H^{\prime}\) is the self-attention representation of the whole context, \(G\) is the splicing and fusion of the output of the semantic interaction layer and the contextual self-attentive layer. \(G_{b}\) embeds information about the entire context and query through the Bi-LSTM.  
* The model uses a larger Byte to construct the vocabulary table.  
In practical applications, the RoBERTa outperforms the Bert on various downstream tasks. The model used in this paper contains a 12-layer Transformer encoder, and the vector dimension is 768 dimensions.First, splicing the input context \(X=\{\ x_{1},x_{2},\cdots,x_{n}\}\) with query \(Q=\{\ q_{1},q_{2},\cdots,q_{m}\}\), by adding a \([CLS]\) token at the start position, and an \([SEP]\) token at the end and split position. Then, take the corresponding positional encoding, word vector, and contextual vector as input, and then processing by RoBERTa, use the output of the last layer of the encoder as the vector representation \(O\) of the context and query. Finally, the initial vector representations of the context \(C=\{\ c_{1},c_{2},\cdots,c_{n}\}\) and the query \(U=\big{\{}\ u_{1},u_{2},\cdots,u_{n}\}\) are output, respectively. The calculation process is as follows:  
\[input=[CLS]+X+[SEP]+Q+[SEP] \tag{3}\]  
\[O=RoBERTa_{-}wwm_{-}ext(input) \tag{4}\]
## 4 Application
### Clustering Evaluation Metrics  
To quantitatively evaluate the quality of the selected feature subsets, we adopted three widely used internal clustering validation indices that measure the compactness, separation, and balance of clusters without requiring ground truth labels:  
* Silhouette Coefficient (SC): The Silhouette Coefficient quantifies how well each sample lies within its cluster compared to other clusters. For a sample \(i\), it is defined as \[s(i)=\frac{b(i)-a(i)}{\max\{a(i),b(i)\}}\] (34) where \(a(i)\) is the average intra-cluster distance (compactness) and \(b(i)\) is the smallest average inter-cluster distance (separation) between sample \(i\) and all samples in other clusters. The goal SC is the mean of \(s(i)\) overall samples, ranging from \(-1\) (worst) to \(1\) (best);
* Davies-Bouldin Index (DBI): The Davies-Bouldin Index evaluates the average similarity between each cluster and its most similar counterpart. It is defined as follows: \[DBI=\frac{1}{k}\sum_{i=1}^{k}\max_{j\
eq i}\left(\frac{\sigma_{i}+\sigma_{j}}{ d(c_{i},c_{j})}\right)\] (35) where \(\sigma_{i}\) is the average distance from samples in cluster \(i\) to its centroid \(c_{i}\), and \(d(c_{i},c_{j})\) is the distance between centroids \(c_{i}\) and \(c_{j}\). Lower DBI values indicate better clustering performance.
* Calinski-Harabasz Index (CHI): The Calinski-Harabasz Index measures the ratio of between-cluster dispersion to within-cluster dispersion: \[CHI=\frac{Tr(B_{k})}{Tr(W_{k})}\times\frac{N-k}{k-1}\] (36) where \(B_{k}\) and \(W_{k}\) are the between-cluster and within-cluster covariance matrices, \(k\) is the number of clusters, and N is the total number of samples. A higher CHI value indicates better-defined clusters. These metrics collectively assess the trade-off between intra-cluster cohesion and inter-cluster separation, providing a comprehensive evaluation of feature subsets in unsupervised scenarios.
# Chemical Signatures of Ge in Magnetic of Wugang BIF, China
## 2 Geological Setting and Methods
### Analytical Methods  
Laser ablation inductively coupled mass spectrometry (LA-ICP-MS) analyses were applied to evaluate trace elements used as petrogenetic indicators [12; 14; 33]. LA-ICP-MS is a rapid and precise method with a spatial resolution of <0.1 mm and a sub-ppm detection limit [22]. Many previous studies have focused on trace elements in ore minerals using in situ LA-ICP-MS analyses [34; 35; 36; 37; 38].  
Concentrations of major element oxides (FeO, MnO, Cr\({}_{2}\)O\({}_{3}\), MgO, SiO\({}_{2}\), K\({}_{2}\)O, CaO, TiO\({}_{2}\), Na\({}_{2}\)O, and Al\({}_{2}\)O\({}_{3}\)) (Table 1) and trace elements (Mg\({}^{24+}\), Al\({}^{27+}\), Si\({}^{29+}\), Ca\({}^{43+}\), Sc\({}^{45+}\), Ti\({}^{48+}\), V\({}^{51+}\), Cr\({}^{53+}\), Mn\({}^{55+}\), Co\({}^{59+}\), Ni\({}^{60+}\), Cu\({}^{63+}\), Zn\({}^{66+}\), Ga\({}^{69+}\), Ge\({}^{73+}\), Y\({}^{89+}\), Zr\({}^{90+}\), Nb\({}^{93+}\), Mo\({}^{95+}\), Hf\({}^{178+}\), W\({}^{182+}\), and Pb\({}^{208+}\)) (Table 2) were analyzed using field-emission electron probe microanalyzer (FE-EPMA; JEOL JXA-8530F, JEOL) and quadrupole inductively coupled plasma mass spectrometry (iCapQ ICP-MS, Thermo Fisher Scientific, Waltham, MA, USA) equipped with femtosecond laser ablation systems (J200 LA model, Applied Spectra Inc., West Sacramento, CA, USA), respectively, at the Core Research Facility of the Pusan National University (Busan, Republic of Korea). The samples were analyzed at a pulse repetition rate of 10 Hz, and a pulse width of 480 fs for trace element analysis. An ablated spot size of 70 \(\upmu\)m with a laser energy density of approximately 100 \(\upmu\)J cm\({}^{-2}\), for approximately 15 s, was used on the sample. NIST 610 glass was used as an external standard for the calculation of elemental concentrations in magnetite. Data reduction was performed using the trace element data reduction scheme of IOLITE4 software. Analyzed data are presented by Supplementary Tables S1 and S2.  
Figure 2: Petrographic features of iron ores from Wugang BIF. (**a–f**) Macroscopic aspect of iron ores and (**g–i**) microscopic aspect of magnetite, mt—magnetite. Iron ore has both a distinctive layer texture and a massive texture. Coarse-grained subhedral magnetite-associated quartz is also observed.
## 5 Result and Discussion
### VULREM Performance Assessment  
The vulnerability classification accuracies of the proposed model and bag-of-words-based models were compared, as shown in Table 5. All models of the proposed VULREM with different lengths showed higher vulnerability classification performance than the bag-of-words models. At the same time, the VULREM classification model was presented with inputs of different string lengths according to the number of source-code lines, and the highest accuracy performance was achieved at a string size of 320.  
In the dataset obtained from six different web applications specific to the study, there was a total of eight different CVE-numbered vulnerabilities. The distribution of the sample numbers of these vulnerabilities was not homogeneous. Therefore, in the training and testing phase of the model, it was seen that there was a change in the F1-score values in direct proportion to the number of samples, as given in Figure 3, but the accuracy changes were very small.  
In the test phase of the VULREM source-code scanning tool, scanning time performance was evaluated along with accuracy performance. According to the results given in Table 6, the model had a detection time that increased in direct proportion to the number of vulnerabilities detected in the source code. In the test phase of the dataset obtained from six different web applications, a maximum of 20 vulnerabilities were detected and the total detection time of these vulnerabilities was measured to be 12.4 s maximum. In terms of continuous vulnerability testing in software development processes, this time is acceptable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** & **F1-Score** & **MCC** \\ \hline k-NN & 0.796 & 0.798 & 0.783 & 0.790 & 0.530 \\ NB & 0.810 & 0.828 & 0.840 & 0.823 & 0.590 \\ SVM & 0.8723 & 0.870 & 0.880 & 0.872 & 0.660 \\ RoBERTa & 0.9112 & 0.911 & 0.925 & 0.911 & 0.712 \\ BiLSTM & 0.9265 & 0.922 & 0.931 & 0.926 & 0.730 \\ VULREM-64 & 0.940 & 0.945 & 0.951 & 0.952 & 0.780 \\ VULREM-128 & 0.957 & 0.954 & 0.961 & 0.954 & 0.850 \\ VULREM-256 & 0.970 & 0.968 & 0.972 & 0.973 & 0.861 \\ VULREM-320 & 0.982 & 0.983 & 0.982 & 0.990 & 0.870 \\ VULREM-384 & 0.960 & 0.968 & 0.962 & 0.963 & 0.840 \\ VULREM-512 & 0.947 & 0.945 & 0.951 & 0.946 & 0.820 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation performance of models.