esa-sceva/satcom-chunk-collection huggingface dataset:
# Boating Tourism and Fishing Interactions: A Social Network Analysis Using AIS Data
## 4 Findings
### SNA Relationships with Vessels  
From the data collected (Table 2), regarding fishing vessels with high interaction, whether intra their typology or with other typology of vessels (inter), an SNA can be constructed to infer the interaction that occurs between vessels and consequently between economic activities (i.e., fisheries and tourism/recreation).  
Recreational nodes: From the data analysis, it can be seen that three passenger vessels are responsible for over 56% of the AIS pings related to recreational vessels. On average, this typology of vessels also has the overall highest average number of pings per vessel (889 pings/vessel).  
Fisheries nodes: About 87% of all sets of AIS pings come from the fishing fleet. Within this fleet, seiners and trawlers are the ones with the most records. The seiners have the highest average of the AIS pings, closely followed by trawlers (782 pings/vessel and 723 pings/vessel, respectively).  
The vessels (nodes) with the most pings also have the most edges. In fishing boats, the domain is divided between three types of boats, while in the recreational, there is a clear dominance for passenger boats.  
From (Figure 3) it can be seen that there are three distinct communities: the first is composed of 22 vessels in outer waters as recurrent trawlers (pinkish and bluish); the second has 16 seiners (greenish) and three passenger boats (orangish) closer to the coast; the third is composed of only four fishing vessels (multi-gear for multipurpose fisheries). Most of the interaction takes place with vessels of the same category, with sporadic cases of vessels of other categories occurring in nearby locations in the same time frame. Table 3 shows the main metrics calculated.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Activity** & **AIS Classification** & **Vessel Typology** & **No. of Vessels** & **No. of Pings** & **Observed Interaction** \\ \hline \multirow{3}{*}{Recreational} & Passenger & Passenger & 3 & 2666 & High (inter) \\  & Pleasure Craft & Pleasure Craft & 41 & 1411 & Low \\  & Sailing & Sailing & 21 & 681 & Low \\  & **Total** & & **65** & **4758** & \\ \hline \multirow{3}{*}{Fisheries} & Fishing & Trawler & 22 & 15,910 & High (intra) \\  & Fishing & Seiner & 16 & 12,519 & High (inter) \\  & Fishing & Multi-gear & 4 & 2383 & High (intra) \\  & Fishing & Longliner & 1 & 84 & Not known \\  & **Total** & & **43** & **30,896** & \\ \hline \hline \end{tabular} Note: Pings consist of the summation of hourly records for a period of two consecutive years (2022/3).  
\end{table}
Table 2: Data distribution and classification by vessel category.  
\begin{table}
\begin{tabular}{l c} \hline \hline Nodes: & 45 \\ Edges: & 175 \\ Average degree: & 3.889 \\ Average weighted degree: & 3.889 \\ Network diameter: & 6 \\ Graph density: & 0.088 \\ Connected components: & 3 \\ Modularity: & 0.292 \\ Statistical inference: & 315.321 \\ Average clustering coefficient: & 0.508 \\ Average path length: & 2.182 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main network metrics used in the SNA.
###### Abstract  
Water use efficiency (WUE) of ecosystems plays a crucial role in balancing carbon storage and water consumption. The Yunnan-Guizhou Plateau, a karst landscape region with relatively fragile ecosystems in China, requires a better understanding of the evolution of WUE and the factors driving it for the region's ecological sustainability. This study employs Theil-Sen slope estimation and Mann-Kendall significance analysis to investigate the temporal trends and spatial patterns of WUE in the study area. Additionally, a machine learning model, XGBoost, is used to establish driving relationships, and the SHAP model is applied to interpret the importance of the driving factors and their specific relationship with WUE. The results show that (1) WUE exhibits an increasing trend, with a slope of 0.002, indicating improved water absorption and utilization capacity of vegetation in the region. (2) The spatial distribution of WUE follows a \"high-low-high\" pattern from southwest to northeast, with 6.68% of the area showing a significant increase, 50.80% showing a weak increase, 4.60% showing a significant decrease, and 37.92% showing a weak decrease. (3) The importance of the driving factors is ranked as follows: normalized difference vegetation index (NDVI), maximum temperature (TMAX), shortwave radiation (SRAD), Palmer drought severity index (PDSI), vapor pressure deficit (VPD), and precipitation (PRE). The NDVI has a linear positive relationship with WUE; SRAD has a decreasing effect on WUE, with this effect weakening at higher values; and TMAX, PRE, the PDSI, and VPD show a non-monotonic relationship with WUE, increasing and then decreasing. The findings of this study are significant for ecological civilization construction and sustainable development in the region.  
WUE; spatiotemporal evolution; driving analysis; machine learning; interpretable models +
Footnote †: journal: Journal of the Accepted: 19 December 2024
# An Efficient Library for Reliability Block Diagram Evaluation
## 3 RBD Computation Library-librbd
### librbd Usage
#### 3.3.1. API for Generic Components  
The following interfaces are used to evaluate the reliability of RBD blocks with generic components:  
* int _rbdSeriesGeneric_(double *R, double *O, unsigned char **N**, unsigned int **T**)
* int _rbdParallelGeneric_(double *R, double *O, unsigned char **N**, unsigned int **T**)
* int _rbdKoNoGeneric_(double *R, double *O, unsigned char **N**, unsigned char **K**, unsigned int **T**)
* int _rbdBridgeGeneric_(double *R, double *O, unsigned char **N**, unsigned int **T**)  
The capitalized and bold characters identify the following input parameters:  
* **N**: the number of components inside the block. Note that, for the bridge block, the number of components must be equal to 5.
* **K**: the minimum number of components inside the block. Note that this parameter is available for _KooN_ blocks.
* **T**: the number of time instants over which the reliability curve is computed.
* **R**: the reliability curves for the input components. Since this is the generic components case, this parameter underlies an **N**\(\times\)**T** matrix.
* **O**: the computed reliability curve, returned as an array of **T** elements.  
All interfaces return 0 in case of successful computation and a negative number otherwise.
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
###### Abstract  
Privacy and verifiability are crucial security requirements in e-voting systems and combining them is considered to be a challenge given that they seem to be contradictory. On one hand, privacy means that cast votes cannot be traced to the corresponding voters. On the other hand, linkability of voters and their votes is a requirement of verifiability which has the consequence that a voter is able to check their vote in the election result. These two contradictory features can be addressed by adopting privacy-preserving cryptographic primitives, which at the same time as achieving privacy, achieve verifiability. Many end-to-end schemes that support verifiability and privacy have the need for some voter action. This makes ballot casting more complex for voters. We propose the PVPBC voting system, which is an e-voting system that preserves privacy and verifiability without affecting voter usability. The PVPBC voting system uses an effective and distributed method of authorization, which is based on revocable anonymity, by making use of a permissioned distributed ledger and smart contract. In addition, the underlying PVPBC voting system satisfies election verifiability using the Seleme voting scheme. The Seleme protocol is a verifiable e-voting protocol. It publishes votes in plaintext accompanied by tracing numbers. This enables voters to confirm that their votes have been captured correctly by the system. Numerical experiments support the claim that PVPBC scales well as a function of the number of voters and candidates. In particular, PVPBC's authorization time increases linearly as a function of the population size. The average latency associated with accessing the system also increases linearly with the voter population size. The latency incurred when a valid authentication transaction is created and sent on the DLT network is 6.275 ms. Empirical results suggest that the cost in GBP for casting and storing an encrypted ballot alongside a tracker commitment is a linear function of the number of candidates, which is an attractive aspect of PVPBC.  
verifiable voting; online voting; Seleme; distributed ledger technology Article  
PVPBC Privacy and Verifiability Preserving E-Voting  
Permissioned Blockchain
## 7 Results
It is important to bear in mind that the best transmission signals are observed in values close to \(-75\) dBm. This makes sense considering that the LoRa technology is the one with the best coverage and, therefore, will present the least number of collisions. The one that presents worse coverage is WiFi and is understandable due to the large number of devices connected to campus networks, it is logical that the characteristics of a campus present a smaller scale than the characteristics of a city. Of the three technologies that are in the same frequency band, ZigBee has better signal coverage (between good and medium coverage). These figures help to better understand Tables 2 and 3 due to the metrics related to the interference of the channel such as: collisions, packets retransmissions, resilience, among others. Figure 8: Received Signal Strength Indication (RSSI) values in dBm under cooperation scheme for each technology. Figure 9: RSSI values in dBm under collaboration scheme for each technology. As mentioned above, one of the key metrics in any sensor network is energy consumption. Figures 10 and 11 show a summary of the energy consumption for each protocol under the cooperative and collaborative schemes. Figure 10 shows that under the cooperative scheme the protocol with the lowest energy consumption is ZigBee, where the minimum consumption value is 0.2 Joules, but most of the sensors consume between 0.8 and 2.1 Joules, with the median being 1.5 Joules. Zigbee consumes about half of the LoRa protocol, since the latter has an average value of 2.4 Joules; however, 50% of the sensors consume between 2.4 and 4.3 Joules. On the other hand, BLE and WiFi have very scattered data, reaching to consume values as low as 1.1 Joules up to 6.8 Joules as in the case of WiFi, with the median  
Figure 11: Energy in Joules under collaboration scheme for each technology. Figure 10: Energy in Joules under cooperation scheme for each technology. value of 3.2 Joules for BLE and 4.8 Joules for WiFi. In this way, it is clear that the protocol with the best consumption is ZigBee, additionally it has less dispersed data so that a more exact battery life calculation can be made, otherwise in WiFi the scattered data would cause different battery life time for each sensor. By contrast, Figure 11 shows the protocols under the collaborative scheme; however, the results are very similar to those in Figure 10, with the ZigBee protocol having the lowest energy consumption. It is important to mention that in a comparison between schemes, the cooperative has a lower energy consumption in all protocols except for WiFi, in which values are very similar. The biggest difference is the dispersion of the data, for the ZigBee protocol the range in the cooperative scheme is 3.6 Joules and in the collaborative scheme is 0.7 Joules, something similar occurs for both LoRa and BLE. This low dispersion indicates that all the sensors consume practically the same energy (at least in ZigBee), so that the lifetime of their batteries will be very similar, being able to program a more efficient maintenance of the network.
## 3 Results and Discussion
### Effect of Genotype on Total and Individual Anthocyanins
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Cyanidin 3-Glucoside (mg kg\({}^{-1}\) Fresh Weight)**} \\ \hline
**Location** & **Year** & **Can** & **Cle** & **Flo** & **Fri** & **Gar** & **Son** &
\begin{tabular}{c} _Yearly_ \\ _Mean per_ \\ _Location_ \\ \end{tabular} \\ \hline
**Norway** & **2017** & 9.6 \(\pm\) 2.6 & 3.6 \(\pm\) 1.5 & 9.4 \(\pm\) 2.8 & 14.1 \(\pm\) 2.4 & 3.2 \(\pm\) 0.7 & 5.7 \(\pm\) 1.7 & _7.6 A_ \\  & **2018** & 12.7 \(\pm\) 3 & 7.4 \(\pm\) 5.2 & 14.3 \(\pm\) 2.2 & 13.2 \(\pm\) 1.1 & 4.4 \(\pm\) 1.2 & 6.0 \(\pm\) 2.3 & _9.6 A_ \\  & _mean_ & _11.2 b_ & _5.5 a_ & _11.8 b_ & _13.7 b_ & _3.8 a_ & _5.8 a_ & \\ \hline
**Poland** & **2017** & 9.7 \(\pm\) 1.7 & 3.2 \(\pm\) 0.6 & 12.7 \(\pm\) 4.5 & 15.0 \(\pm\) 3.3 & 3.3 \(\pm\) 0.5 & 6.3 \(\pm\) 2.6 & _8.4 A_ \\  & **2018** & 29.2 \(\pm\) 2.6 & 8.8 \(\pm\) 1.9 & 47.8 \(\pm\) 6.7 & 27.8 \(\pm\) 4.1 & 9.2 \(\pm\) 2.1 & 13.4 \(\pm\) 5.9 & _22.7 B_ \\  & _mean_ & _19.4 bc_ & _6.0 a_ & _30.3 c_ & _21.4 bc_ & _6.2 a_ & _9.9 ab_ & \\ \hline
**Germany** & **2017** & 10.7 \(\pm\) 2.4 & 2.5 \(\pm\) 0.6 & 17.4 \(\pm\) 3.6 & 12.8 \(\pm\) 1.2 & 2.5 \(\pm\) 1.3 & 4.4 \(\pm\) 1.6 & _8.4 A_ \\  & **2018** & 12.9 \(\pm\) 4.8 & 5.0 \(\pm\) 4.8 & 26.4 \(\pm\) 4.4 & 16.9 \(\pm\) 2.0 & 4.0 \(\pm\) 0.5 & 6.0 \(\pm\) 2.4 & _11.9 A_ \\  & _mean_ & _11.8 b_ & _3.7 a_ & _21.9 c_ & _14.8 b_ & _3.3 a_ & _5.2 a_ & \\ \hline
**Italy** & **2017** & - & 1.6 \(\pm\) 0.3 & - & 11.9 \(\pm\) 4.6 & 2.5 \(\pm\) 0.5 & 2.8 \(\pm\) 0.5 & _4.7 B_ \\  & **2018** & - & 1.6 \(\pm\) 0.7 & - & 2.4 \(\pm\) 1.5 & 1.5 \(\pm\) 0.7 & 2.7 \(\pm\) 1.8 & _2.0 A_ \\  & _mean_ & - & _1.6 a_ & - & _7.0 b_ & _2.0 a_ & _2.7 a_ & \\ \hline
**France** & **2017** & 11.3 \(\pm\) 1.5 & 1.3 \(\pm\) 0.1 & 19.0 \(\pm\) 14.5 & 12.5 \(\pm\) 1.7 & 1.7 \(\pm\) 0.3 & 2.6 \(\pm\) 0.7 & _8.0 A_ \\  & **2018** & 10.7 \(\pm\) 3.0 & 1.8 \(\pm\) 0.4 & 16.9 \(\pm\) 5.8 & 9.0 \(\pm\) 2.1 & 2.7 \(\pm\) 0.6 & 3.1 \(\pm\) 1.3 & _7.4 A_ \\  & _mean_ & _11.0 b_ & _1.6 a_ & _18.0 c_ & _10.7 b_ & _2.2 a_ & _2.8 a_ & \\ \hline \multicolumn{2}{c}{_Cultivar mean over all locations_} & _13.3 b_ & _3.7 a_ & _20.5 c_ & _13.5 b_ & _3.5 a_ & _5.3 a_ & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effect of growing location and year on cyanidin 3-Glucoside content as the mean of two picking dates in fruits of six strawberry cultivars.
# Screen-Cam Robust Image Watermarking with Feature-Based Synchronization
## 3 Proposed Watermarking Scheme
### Local Square Feature Region Construction
#### 3.1.1 Gaussian Function Preprocess  
In the embedding and extraction processes, the detection of feature points will be performed on the Gaussian filtered images. A two-dimensional Gaussian function \(G(x,y)\) is obtained by the product of two one-dimensional Gaussian functions and can be defined as:  
\[G(x,y)=\frac{1}{2\pi\sigma^{2}}e^{-\frac{x^{2}+y^{2}}{2\sigma^{2}}} \tag{1}\]  
where \(\sigma\) is the standard deviation. The Gaussian kernel \(H_{G}\), whose sigma is set to 2 and window size to 7, is employed here. The image convolution process is defined as:  
\[I^{\prime}(x,y)=H_{G}*I(x,y) \tag{2}\]  
where \(I\) is the input image and \(I^{\prime}\) is the convolution result. \(*\) denotes the convolution operator.  
Figure 4: The construction process of local square feature regions in \(1024\times 1024\) Lena image. (**a**) Luminance image of Lena after a Gaussian function. (**b**) The extracted feature points by modified Harris–Laplace. (**c**) The associated orientation. (**d**) The associated LSFRs.  
Figure 3: The variations of feature points in screen-cam process.
# Spatiotemporal Feature Enhancement for Lip-Reading: A Survey
## 2 Classification of Lip-Reading Methods
### Lip-Reading Methods Based on Visual Features  
Lip-reading methods based on visual features, as illustrated in Figure 4b, extract visual features from lip image sequences (videos) in deep learning, paying more attention to morphological changes in lip contours and textures, which are indicative of speech-related movements. The recognition process consists of three main stages: visual feature extraction, temporal feature modeling, and classification. First, salient visual features are extracted from the front end or autoencoders by encoding lip-reading images in 2D CNNs [37], 3D CNNs [38] through these architectures like ResNet [39], DenseNet [40], EfficientNet [41], MobileNets [42], and ShuffleNet [43]. Temporal modeling of the visual feature is performed from the back end of deep models such as RNNs [44], LSTMs [45], and GRUs [46]. Finally, the classification task is carried out by using Softmax or CTC [47].  
In the early stages of deep learning, researchers explored popular technologies such as ResNet18 and RNN for lip-reading, achieving notable improvements over traditional machine learning methods. Unfortunately, these methods primarily focus on extracting visual features, overlooking temporal information. Although the back end of these models employs temporal methods like RNN and LSTM to capture the sequential features of lip movements, significant temporal information is often lost due to the front-end networks' insufficient attention to temporal dynamics. As a result, the modeling of temporal features might be suboptimal. Consequently, there has been a gradual shift in focus toward extracting spatiotemporal features at the front ends of these models.
# Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm
## 3 Preliminaries
### Differential Electromagnetic Analysis (DEMA)  
DEMA [30,31] is a classical method in electromagnetic analysis for analyzing electromagnetic consumption to identify data independent correlations. It uses electromagnetic signal traces \(T\) to determine whether a key byte guess \(K_{s}\) is correct. The hypothesis is that small variations may be observed in an electromagnetic trace based on the output of the  
Figure 2: The basic process of PSO.  
cryptographic algorithm. The main idea of this approach is to partition the electromagnetic traces into two sets, then compute the difference of the average of the two sets. If the two sets are uncorrelated, the difference is close to 0; if the two sets are correlated, the difference is a non-zero value. Given a sufficient number of traces, even tiny correlation can be found, no matter how much noise is in the traces, since the noise will effectively cancel out during the process of averaging. The rule of partition is based on the output of DEMA selection function \(D(C_{i},b,K_{s})\). The function \(D(C_{i},b,K_{s})\) is defined as computing the \(b\)-bit value of the cryptographic algorithm intermediate for ciphertext \(C_{i}\). The attacker record \(m\) ciphertexts (or plaintexts) and \(m\) electromagnetic traces. Each trace contains \(k\) samples. The attacker computes a \(k\)-sample differential trace \(\Delta D[1\dots k]\) by finding the difference between the average of the traces for which \(D(C_{i},b,K_{s})\) is 1 and the average of the traces for which \(D(C_{i},b,K_{s})\) is 0. Thus, \(\Delta D[j]\) is the average over \(C_{1\dots m}\) of the effect due to the value represented by the selection function \(D(C_{i},b,K_{s})\) on the electromagnetic radiation traces at point \(j\):  
\[\Delta D[j]=\frac{\sum_{i=1}^{m}D(C_{i},b,K_{s})T_{i}[j]}{\sum_{i=1}^{m}D(C_{ i},b,K_{s})}-\frac{\sum_{i=1}^{m}(1-D(C_{i},b,K_{s}))T_{i}[j]}{\sum_{i=1}^{m}(1-D(C_ {i},b,K_{s}))} \tag{4}\]  
where \(C_{i}\) is the \(i\)th ciphertext, \(T_{i}[j]\) is the point \(j\) in \(i\)th electromagnetic traces.
# Enhancing Embedded Space with Low-Level Features for Speech Emotion Recognition
## 3 Experiments and Results  
We have tested the proposed mechanism in various proportions \(\beta=K/M\), where \(K\) is the number of injected features and \(M\) specifies the total number of attributes in the final classification stage. Then, we compared the results with those obtained by base architectures with the same dimensionality of the final feature space but without an injection mechanism. The size of the final feature space \(M\) took values from two different sets. For the base models, these were the values 32, 64, 128, 256, 512, 1024 and 2048. These models with an injection mechanism were the same, omitting the first two, 32 and 64. It was necessary due to the number of injected features \(K=99\), which would be greater than the target feature space. Additionally, to obtain more reliable results for each variant, we have prepared five models with different starting seeds, then a part of results we have presented as an average. As a result, jointly for both base representations and different values of \(M\), we have prepared 120 models.  
We used the following parameters to calculate the CQT representation: number of bins equal to 84, 12 bins per octave and minimum frequency equal to 32.70 Hz. In the case of Mel spectrograms, we have used 128 filters. In training individual models, we used the following parameters: 100 epochs, a learning rate equal to 0.001, an Adam optimizer and cross entropy as a loss function. The data was randomly divided into a train, validation and test subsets as 60%, 20% and 20%, respectively, for all recordings.
## 1 Introduction
In this paper, a novel non-coherent communication scheme called differential chaotic shift keying based on orthogonal double-rate noise reduction (NR-ODBR-DCSK) is presented. The aim is to enhance the information transmission rate of the NR-DCSK system. The design of the transmitter and receiver mechanisms is accomplished by analyzing the signal structure, and the system performance is evaluated by simulation. The results show that the proposed method not only retains a lower BER, but also enhances the information transmission rate of the system without using chaotic synchronization by incorporating the Walsh quadrature into the NR-DCSK system. The main contributions of this paper are as follows:  
Firstly, a novel non-coherent communication scheme, i.e., the reference orthogonal double-rate noise reduction based on the differential chaotic shift keying (NR-ODBR-DCSK) communication system and method, was proposed to address the low information transmission rate of the NR-DCSK system. The article described the signal frame structure, modulator, and receiver design of the system. Secondly, the article derived the data energy ratio (DBR) and theoretical bit error rate of the NR-ODBR-DCSK system in Gaussian channels and analyzed its energy efficiency. Compared with the DCSK and NR-DCSK systems, the NR-ODBR-DCSK system saved transmission bit energy and reduced the system BER. Finally, the article explored the optimal number of replications and the semi-diffusion factor of the NR-ODBR-DCSK system through simulation analysis and verified the physical feasibility of the NR-ODBR-DCSK system through USRP. It was found that the NR-ODBR-DCSK system outperformed the NR-DCSK and DCSK systems in terms of transmitted data rate and BER under specific SNR conditions.