esa-sceva/satcom-chunk-collection huggingface dataset:
# Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme
## 3 Network Model and Problem Formulation
### Problem Formulation  
We model the HMMN-EC as a directed graph \(\overset{\rightarrow}{\mathrm{G}}=(\mathrm{V},\overset{\rightarrow}{ \mathrm{E}})\), where V represents the set of nodes, i.e., BSs, ARNs, ECNs, SSNs, URNs, and MPs, and \(\overset{\rightarrow}{\mathrm{E}}\) represents directed edges between two nodes that are within the communication radius. To differentiate nodes, the subsets of BSs, ARNs, ECNs, SSNs, URNs, and MPs are denoted as \(\mathrm{V}_{\mathrm{BS}},\mathrm{V}_{\mathrm{ARN}},\mathrm{V}_{\mathrm{ECN}}, \mathrm{V}_{\mathrm{SSN}},\mathrm{V}_{\mathrm{URN}},\mathrm{V}_{\mathrm{URN}},\) and \(\mathrm{V}_{\mathrm{MP}}\). Thus, the whole set of nodes \(\mathrm{V}=\mathrm{V}_{\mathrm{BS}}\cup\mathrm{V}_{\mathrm{ARN}}\cup \mathrm{V}_{\mathrm{ECN}}\cup\mathrm{V}_{\mathrm{SSN}}\cup\mathrm{V}_{\mathrm{URN}}\cup \mathrm{V}_{\mathrm{MP}}\). The edge \(e_{ij}\overset{\rightarrow}{\mathrm{E}}\) is a binary variable, \(e_{ij}=1\) indicates there exists a direct communication link from node \(i\) to node \(j\), and vice versa. \(|\mathrm{V}_{x}|\) represents the number of nodes in set \(\mathrm{V}_{x}\). The main notations used in the paper are listed in Table 1.  
As the marine monitoring devices--especially the battery-powered underwater monitoring devices--are expensive, it is desirable to reduce the total number of devices for deployment to minimize the total deployment cost. In the meantime, it is hard if not impossible to replace batteries of underwater nodes, and thus, it is important to maximize the operation time of network nodes. In this paper, we will formulate MO problem under the HMMN-EC architecture. The primary objectives are to minimize the total deployment cost while maximizing the network lifetime subjected to the limited node communication radius and battery capacities. Here, the network lifetime is defined as the time until the first node runs out of energy [36].
# Research on Space-Based Visible Detection for Conical Space Targets
## 1 Introduction
Therefore, this paper studied the space-based visible light detection of conical space targets, and presented a method for determining the caliber of the space-based visible light detection system. Firstly, we established a three-dimensional conical space target radiation transfer model. Secondly, the effects of the phase angle between the solar incident direction, the bore sight direction of the detector, and the conical target inclination on the radiation intensity of the space target received by the detector were analyzed. Finally, we used the system signal-to-noise ratio as the evaluation standard to obtain a threshold caliber that can meet the system detection requirements, and took the SBV sensor as an example to verify that the caliber determination method in this paper is effective. Therefore, the research in this paper has a certain generalization effect and can be applied to the conventional visible light detection system.
# Throughput/Area Optimized Architecture for Elliptic-Curve Diffie-Hellman Protocol
## 3 Proposed ECDH Architecture
### Arithmetic and Logic Unit (ALU)
Therefore, ten multiplications require ten clock cycles. The \(m-1\) clock cycles are required for the squaring as one square takes only the one clock cycle. The total clock cycles to perform one inversion are \((10+m-1)\). Using our ECDH architecture (given in Figure 3), \(2\times(10+m-1)\) clock cycles are needed to perform one polynomial inversion over \(GF(2^{233})\) because we used only the one _MULT_ unit for both multiplication and squaring computations. On the other hand, the Itoh-Tsujii requires repeated squaring computations. Apart from one _MULT_ unit, we have employed the pipeline registers in the proposed ECDH design. These two factors (use of one _MULT_ and pipelining) determine the limitation of our architecture for the inversion computation.
## 3 Results and Discussion  
The performance of the proposed SAS model was illustrated by computing the complex flow configurations in three cases with boundary layer separation from curved surfaces, namely, the 3D asymmetric diffuser flow, 2D periodic hills flow, and 2D U-turn duct flow. The motivation for choosing these cases is that they present challenges to conventional RANS models. The SST-SAS and SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) models were adopted for comparison. In general, many factors may influence the results. For a reasonable comparison, for each case, all of the calculation conditions, such as the computational mesh, timestep size, discretization scheme, and Courant-Friedrichs-Lewy (CFL) number, were set the same or as close as possible for all three turbulence models.
## 1 Introduction
In addition, the force-velocity profile variables showed high inter-unit reliability (ICC [0.93-0.99]; typical error [0.5-2%]), demonstrating that the most recent GPS units can be used for reliably measuring force-velocity profiles. The authors concluded that this GPS type (Vector S7, Catapult(r), Melbourne, Australia, 10 Hz) was a valid and reliable alternative to the reference method to determine force-velocity profiles [8]. Finally, a very recent study has confirmed this high inter-trial reliability, for the same GPS and radar models, provided that the same tool, the same measurements and the same analysis procedures are used [13]. The type of GPS used in the present study (K-AI Wearable Tech, KSport(r) World SrI, Fano, Italy, 50 Hz) has never been tested for analyzing force-velocity profiles. This study has three aims: first, to assess the inter-unit and the inter-trial reliability of the force-velocity profile variables derived from K-AI Wearable Tech data. The second aim was to assess the concurrent validity of the input variables (maximal sprint speed (V\({}_{\text{max}}\)) and acceleration time constant (\(\tau\))). The third aim was to assess the concurrent validity of the computed output variables (theoretical maximal horizontal force (F\({}_{0}\)), theoretical maximal horizontal speed (V\({}_{0}\)) and maximal horizontal power (P\({}_{\text{max}}\))) obtained with the GPS compared to the radar device. Our hypothesis was that due to its high sampling frequency, this GPS system is valid and reliable, compared with the reference device, for establishing a sprint force-velocity profile.
## 3 Results and Discussions
### Design Considerations  
Based on the above simulation results and analysis, we provide the overall design considerations for the UOWC system.  
* Tx considerations: The optical source determines the optical output power and light divergence. The choice of LED or laser is scenario-specific and depends on the degree of alignment difficulty, if the tracking and pointing equipment is available for just point-to-point link, the collimation property and large power of laser (Figure 6) can help mitigate the temporal dispersion to achieve longer range and higher data rate. If the alignment is hard or more Rx users need to be covered, LED is a good choice. However, in turbid waters, illumination patterns of LED and laser both show significant spatial spread at the Rx plane (Figure 7), thus, the LED no longer has the merit of easy alignment and laser should be used under such a condition.
* Rx considerations: Although more optical signals can be received by using larger aperture size and wider FOV, they also result in more solar noises captured at the Rx in the shallow UOWC environment. Based on the simulation results of BER performance (Figures 11 and 12), we suggest employing a large aperture combined with a narrow FOV angle to guarantee enough optical power at the Rx plane (Figure 10) and mitigate the SNR degradation. In addition, the optical filter in front of the detector is also important in determining the Rx's sensitivity to solar irradiance radiation. An optical filter with narrow bandwidth can reduce the system FOV and suppress ambient noises.
# Monitoring-System Development for a Bottom-Set Gillnet through Time-Domain Dynamic Simulations
## 6 Identification of Monitoring Parameters in Line-Failure Scenarios
Figure 9:1 Time histories of the X (**top**) and Y (**bottom**) directional accelerations of the net assembly in the middle location (Y = 0 m) as mooring lines in the middle location (Y = 0 m) are broken after 600 s (\(H_{s}\) = 2.0 m, \(T_{p}\) = 7.0 s, current velocity = 0.6 m/s). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Mooring-Line-Breakage Location** & \multicolumn{2}{c}{**Key Parameters**} \\ \hline
**Y Location** & **Z Location** & \\ \hline
0 m & TOP & TEN (Y = 0 m), AX \\
0 m & TOP and BOT & TEN (Y = 0 m), DX, AX \\ \(-\)50 m & TOP & AY \\ \(-\)50 m & BOT & AY \\ \(-\)50 m & TOP and BOT & AY, TEN (Y = 0 m) \\ \(-\)100 m & TOP & AY \\ \(-\)100 m & BOT & AY \\ \(-\)100 m & TOP and BOT & AY, TEN (Y = \(-\)150 m) \\ \(-\)150 m & TOP & AY, TEN (Y = \(-\)150 m) \\ \(-\)150 m & BOT & AY, TEN (Y = \(-\)150 m) \\ \(-\)150 m & TOP and BOT & AY, TEN (Y = \(-\)150 m) \\ \hline \end{tabular}
\end{table} 
Table 3: Key monitoring parameters in different mooring-line-breakage scenarios (TEN = tension of top mooring line, DX = surge motion of location buoy in the middle location of the net assembly, AX = X-directional acceleration in the middle location of the net assembly, AY = Y-directional acceleration in the middle location of the net assembly, TOP = top mooring line, and BOT = bottom mooring line). Figure 17: Time histories of X (**top**) and Y (**bottom**) directional accelerations of the net assembly in the middle location (Y = 0 m) as mooring lines at Y = \(-\)50 m are broken after 600 s (\(H_{\rm S}\) = 2.0 m, \(T_{p}\) = 7.0 s, current velocity = 0.6 m/s). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Mooring-Line-Breakage**} & \multicolumn{2}{c}{**TEN**} & \multicolumn{2}{c}{**TEN**} & \multicolumn{2}{c}{**DX**} & \multicolumn{2}{c}{**AX**} & \multicolumn{2}{c}{**AY**} \\  & **Location** & & **(Y = 0 m)** & **(Y = \(-\)150 m)** & **(Y = 0 m)** & **(Y = 0 m)** & **(Y = 0 m)** \\ \hline Y & Z & AVE & STD & AVE & STD & AVE & STD & STD \\ Location & Location & (kN) & (kN) & (kN) & (kN) & (m) & (m) & (m/s\({}^{2}\)) & (m/s\({}^{2}\)) \\ No breakage & 0.179 & 0.127 & 0.116 & 0.085 & 96.868 & 0.265 & 0.131 & 0.000 \\
0 m & TOP & 0.000 & 0.003 & 0.115 & 0.083 & 97.154 & 0.305 & 0.339 & 0.000 \\
0 m & BOT & 0.289 & 0.191 & 0.115 & 0.083 & 96.881 & 0.265 & 0.201 & 0.000 \\
0 m & TOP and BOT & 0.000 & 0.003 & 0.121 & 0.084 & 114.518 & 1.763 & 0.470 & 0.000 \\ \(-\)50 m & TOP & 0.177 & 0.121 & 0.115 & 0.082 & 96.867 & 0.266 & 0.164 & 0.067 \\ \(-\)50 m & BOT & 0.178 & 0.124 & 0.115 & 0.082 & 96.867 & 0.266 & 0.164 & 0.049 \\ \(-\)50 m & TOP and BOT & 0.232 & 0.153 & 0.125 & 0.087 & 96.836 & 0.266 & 0.129 & 0.074 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistical data of mooring tension, surge motion of the location buoy, and acceleration of the net assembly in the X and Y directions in different mooring-line-breakage scenarios at \(H_{s}\) = 3.0 m, \(T_{p}\) = 8.0 s, and current velocity = 0.6 m/s.
# Green Communication in IoT for Enabling Next-Generation Wireless Systems
## 4 Numerical Results and Discussion
### System Evaluation
#### 4.3.3 A Comparison between EFRP and DSR Algorithm  
To demonstrate the effectiveness of our proposed protocol (EFPR) and show how it is able to overcome the DSR protocol, a comparison from two different perspectives will be introduced. First, the comparison will be made based on the total energy consumption that the system needs to spend to deliver a packet from a given source to the destination (BS). Then, we will present a comparison between both protocols based on the total number of alive nodes in the system during the lifetime of the network.
## 1 Introduction  
Hybrid _Pennisetum_ (_Pennisetum americanum_\(\times\)_Pennisetum purpureum_, HP) is a perennial herb, which is widely planted in tropical areas because of its large size, rapid growth, good adaptability to the environment and high yield [1]. HP can produce a large amount of biomass of about 40 tons per hectare per year [2]. Because of its high utilization efficiency of water and nitrogen, huge biomass, and high quality, it can be used as feed for herbivores and has research value [3]. In addition, it has been found that the silage HP can improve the daily gain and meat quality of meat rabbits [4].  
However, the yield of grasses such as _Pennisetum_ is greatly affected by the seasons, and it grows fast when it is rainy in summer and slow when it is dry in winter [5]. If the water content is high, the plants are easy to mildew and rot, so the storage mode of forage after harvest is very important. Ensiling is a method of preserving green plants by anaerobic fermentation, which allows the quality of the grass to be maintained for an extended period of time [6]. When the yield of the HP is high, silage can be processed to store the forage, and the nutritional ingredients of the forage can be retained to the greatest extent, the shelf life can be improved, and the palatability can be improved. However, tropical Gramineae plants have a rough stem and leaf structure and high cellulose content [5], Moreover, HPhas the characteristics of high buffering capacity and low water-soluble carbohydrate (WSC) content [7], Therefore, it is difficult to produce excellent silage.  
Either directly adding sucrose [5] or adding cellulase to hydrolyze cellulose to produce glucose [8; 9] can provide substrate for ensilage of hybrids _Pennisetum_ and promote the proliferation of lactic acid bacteria. _Lactobacillus plantarum_ has been widely used in silage experiments and production [10; 11; 12; 13]. By consuming WSC, lactic acid (LA) is produced to reduce the pH of silage, so as to control the growth of other miscellaneous bacteria and improve the silage quality.  
Therefore, the main purpose of this study is to evaluate the effects of different additives and mixed additives on the silage quality of HP. Pay special attention to the fermentation quality and bacterial community among different treatments.
## 4 Materials and Methods
### Gene Expression Analysis by RNA Sequencing and Real-Time qPCR  
For sequencing, total RNA was isolated from the ten finger citron tissues according to the protocol described by [PERSON] et al. [24]. RNA was digested by Turbo-DNase (Ambion, Foster City, CA, USA). RNA quality was evaluated on a NanoPhotometer(r) spectrophotometer (IMPLEN, Munchen, Germany). The integrity and quantity of total RNA were assessed using a BioAnalyzer (Agilent Technologies, La Jolla, CA, USA). RNA-Seq was performed by LC-bio (Hangzhou, China) on the Illumina HiSeq 4000 platform. Raw reads obtained from RNA-Seq were pre-processed, adapters were trimmed; low-quality and shorter reads were removed. The _Citrus medica_ genome was selected as reference for finger citron. Clean reads were aligned to the _C. medica_ genome available online ([[http://citrus.hzau.edu.cn/orange/](http://citrus.hzau.edu.cn/orange/)]([http://citrus.hzau.edu.cn/orange/](http://citrus.hzau.edu.cn/orange/))) by [PERSON]. Q20, Q30 and GC contents of the clean data were calculated. The expression profile of assembled gene was attached in Table S6. Functional annotation of genes was obtained from two public databases: KEGG (The Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology). Gene expression level was evaluated by fragments per kilobase of exon model per million mapped reads (FPKM). Three different RNA isolations were used as replicates for libraries construction and RNA sequencing.  
To confirm the expression profiles obtained from the transcriptome data, the expression levels of four TPS genes were randomly selected for real-time qPCR (RT-qPCR) analysis. Total RNA was extracted as mentioned above. The first strand cDNA was synthesized using iScript(tm) cDNA synthesis kit (Bio-Rad, Hercules, CA, USA). The reaction system of qPCR was constructed using reaction mix SsoFast(tm) EvaGreen(r) Supermix (Bio-Rad, Hercules, CA, USA) in a volume of 20 \(\upmu\)L, according to the manufacturer's instructions. The qPCR reaction program on CFX96 TM Real-time system (Bio-Rad, Hercules, CA, USA) was set as follows: 95 \({}^{\circ}\)C for 3 min, 45 cycles of 95 \({}^{\circ}\)C for 10 s and 60 \({}^{\circ}\)C for 30 s, with a final melting curve step from 65 \({}^{\circ}\)C to 95 \({}^{\circ}\)C. Melting curve analysis for each pair of primers was performed to evaluate the PCR product specificity. Moreover, the PCR products were cloned into pMD18-T vectors (TaKaRa, Dalian, China) and sequence analysis confirmed the correct amplicons produced from each pair of primers. The primers were designed at the 3'UTR and sequences were listed in Table S7. At least three different RNA isolations and cDNA syntheses were used as replicates for the RT-qPCR. The relative expression levels were calculated as 2\({}^{\upmu\)CT against internal reference citrus actin [24].
# Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis
## 6 Conclusions & Future Work  
In this research paper, we propose a novel recommender system that combines sentiment analysis using the Bi-LSTM deep learning model with collaborative filtering techniques in the e-commerce domain. The objective of this system is to enhance the accuracy and personalization of recommendation systems for online shopping. The proposed architecture offers flexibility in incorporating various techniques, including preprocessing strategies, sentiment analysis with Glove+Bi-LSTM, and different recommender system methods. By leveraging sentiment analysis on user opinions and reviews, the architecture enables the development of a recommender system specifically tailored to e-commerce platforms.  
The experiments were conducted using reviews from the Amazon Kindle book and Amazon digital music datasets to evaluate the effectiveness and practicality of the proposed approaches. The results demonstrate the value and applicability of the proposed methods in generating personalized recommendations on e-commerce websites. Specifically, the combination of deep learning-based sentiment analysis and collaborative filtering techniques significantly improves the performance of the recommender system by utilizing the additional information extracted from user reviews and comments.  
By incorporating sentiment analysis, the study demonstrates improved performance metrics compared to traditional collaborative filtering methods without sentiment analysis across all tested algorithms. The optimal results were achieved with a value of \(\alpha=0.7\) for the user-based CF technique and \(\alpha=0.3\) for the item-based CF technique for both datasets.  
In future work, we plan to further enhance this study by extending the system to handle various types of ratings and comments, thus increasing its adaptability and application. To achieve this goal, we will enhance the sentiment analysis component by employing more advanced algorithms and incorporating additional information for a comprehensive evaluation across different domains, types of ratings and comments, and user characteristics.  
Conceptualization, I.K. and A.E.-A. and N.A. and M.E.M.; methodology, I.K. and N.D.; software, I.K. and N.D. and N.A. and A.E.-A.; validation, A.E.-A., N.A. and M.E.M.; formal analysis, I.K., N.D., and A.E.-A.; investigation, I.K. and N.A. and A.E.-A. and M.E.M.; data curation, I.K. and N.D.; writing--original draft preparation, I.K. and A.E.-A. and N.A.; writing--review and editing, I.K. and N.D. and A.E.-A. and N.A.; visualization, I.K. and N.D.; supervision, A.E.-A. and N.A. and M.E.M.; project administration, M.E.M. All authors have read and agreed to the published version of the manuscript.
## Appendix A
^{T}0^{T}0^{T}]^{T}\) resulting in \(\|\Delta\mathbf{x}_{k}^{E}\|=\|\Delta\mathbf{x}_{k}\|\) which transforms (12) into  
\[\|\Delta\mathbf{u}_{k}\|\leq B_{\mathrm{c}\mathrm{x}}\|\Delta\mathbf{x}_{k}\|+\varepsilon. \tag{13}\]  
Next, by the mean value theorem, there is a \(0<\Gamma<1\) ensuring that  
\[\Delta\mathbf{x}_{k}=\overset{\wedge}{\mathbf{x}}_{k}-\widetilde{\mathbf{x}} _{k}=(\mathbf{s}_{x})^{-1}(\overset{\wedge}{\mathbf{y}}_{k})-(\mathbf{s}_{x })^{-1}(\widetilde{\mathbf{y}}_{k})=\frac{\partial(\mathbf{s}_{x})^{-1}( \mathbf{y}_{k}^{\Gamma})}{\partial\mathbf{y}_{k}}(\overset{\wedge}{\mathbf{y }}_{k}-\widetilde{\mathbf{y}}_{k}),\mathbf{y}_{k}^{\Gamma}=\Gamma\overset{ \wedge}{\mathbf{y}}_{k}+(1-\Gamma)\widetilde{\mathbf{y}}_{k}.. \tag{14}\]  
It then follows that  
\[\|\Delta\mathbf{x}_{k}\|\leq B_{\mathrm{s}\mathrm{y}}\|\Delta\mathbf{y}_{k}\|. \tag{15}\]  
Using (13) and (15) in (10) it results  
\[\|\Delta\mathbf{y}_{k}\|\leq B_{\mathrm{sx}}B_{\mathrm{s}\mathrm{y}}\|\Delta \mathbf{y}_{k}\|+B_{\mathrm{s}\mathrm{u}}(B_{\mathrm{c}\mathrm{x}}\|\Delta \mathbf{x}_{k-1}\|+\varepsilon)\leq B_{\mathrm{s}\mathrm{x}}B_{\mathrm{s} \mathrm{y}}\|\Delta\mathbf{y}_{k}\|+B_{\mathrm{s}\mathrm{u}}B_{\mathrm{c} \mathrm{x}}B_{\mathrm{s}\mathrm{y}}\|\Delta\mathbf{y}_{k-1}\|+B_{\mathrm{s} \mathrm{u}}\varepsilon, \tag{16}\]  
which is equivalent to  
\[\|\Delta\mathbf{y}_{k}\|\leq\underbrace{\frac{B_{\mathrm{s}\mathrm{u}}B_{ \mathrm{c}\mathrm{x}}B_{\mathrm{s}\mathrm{y}}}{1-B_{\mathrm{s}\mathrm{x}}B_{ \mathrm{s}\mathrm{y}}}}_{B_{1}}\|\Delta\mathbf{y}_{k-1}\|+\underbrace{\frac{B_{ \mathrm{s}\mathrm{u}}}{1-B_{\mathrm{s}\mathrm{x}}B_{\mathrm{s}\mathrm{y}}}}_{B_ {2}}\varepsilon. \tag{17}\]  
One can write that  
\[J_{\mathrm{MR}}^{N}(\overset{\wedge}{\mathbf{\theta}})=\sum_{k=1}^{N}\bigl{\|} \Delta y_{k}\bigr{\|}^{2}\leq\left(B_{2}\sum_{k=1}^{N}\sum_{j=0}^{k-1}B_{1}^{j }\right)^{2}\varepsilon^{2}=B\varepsilon^{2}. \tag{18}\]  
which is the conclusion (10), and the proof of _Theorem 1_ is completed.
###### Abstract
This study focused on improving stress prediction performance by applying a three-dimensional (3D) convolutional gated self-attention deep neural network (3 DCGSA) to the spatiotemporal frequency distribution of emotional multi-channel EEG signals evoked by audiovisual stimuli. This study makes the following primary contributions:  
* A 3D convolutional self-attention DNN is developed that integrates features learned from the spatiotemporal and frequency domains of multi-channel EEG signals to significantly improve emotional stress recognition performance. * The proposed 3D convolutional self-attention neural network is comprised of a 3D convolutional block (3 DConvB) and a gated self-attention block. 3 DConvB is applied to each frequency band individually, rather than to the entire frequency bands, to capture internal variation in the spatiotemporal relationship between electrodes within each frequency band. Additionally, nonlocal operations are performed using a gated self-attention block to reliably extract both long-distance dependencies and global information. * We combine the inter-electrode correlation information according to the emotional stress response extracted from each frequency band of the EEG signal using inter-frequency mapping. This allows us to additionally learn complementary and inter-connected information between frequency bands, as well as to model the internal dependencies between salient EEG features related to emotional stress. The rest of this paper is organized as follows: Section 2 presents the details of the proposed method. Section 3 introduces the experimental data and results, and Section 4 summarizes the conclusions of the study and future research directions.