esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Technology for NFV Converged Networks
## 3 Silicon Photonics Devices in Optical Transport: Challenges and Opportunities
### Thermal Management in Silicon Photonics
Solving those issues will require significant technology advances and, indeed, if silicon photonics will really be able to meet its cost reduction promises is still an open question across the scientific community and, even more, across the industry. We think that, despite the outstanding difficulty of some of those challenges, they can and will be eventually won, based on the solutions we outlined in the previous sections. These will be further detailed in the next sections for two specific use cases (full tunable transceivers and reconfigurable optical add-drop multiplexers). The major obstacles to the success of silicon photonics are not technical, in our opinion. The lack of widely accepted standards and design libraries, contrary to electrical ICs, and the difficulties of setting up an economically sustainable business model, appear to play a major role. Silicon fabs are reluctant to divert resources from a profitable consumer market to products with much lower volumes and more uncertain business. On the other hand, telecommunication systems vendors seldom have all the competences in house. Even so, a vertical production model, where everything is made in house, from design to production, passing through prototyping and sampling, could hardly be sustainable in absence of certain selling volumes (that, in turn, cannot be guaranteed in absence of products, creating a detrimental vicious circle). Avoiding an impasse calls for alternatives, creative multi-player models, where: (i) industry can leverage on design competences provided by universities and research centers, (ii) sampling and prototyping are supported by publicly funded pilot lines, and (iii) standardization organizations facilitate the availability of multi-vendor interoperable solutions based on standard libraries and design practices.
## 2 Materials and Methods
### Data Analysis  
Participants who did not complete the protocol as specified (12 sessions over 6 weeks, twice a week on non-consecutive days) were excluded from the final analysis to ensure that the outcomes accurately reflected full exposure to the intervention, as originally intended. Statistical analyses were performed using JASP software (version 0.18.3.0). Descriptive statistics, demographic variables, and outcomes were summarized using counts, percentages, means, and standard deviations (SD). Participants were stratified into high-, medium-, or low-risk categories based on their baseline scores on the KSMT. Responders to the iVRE treatment were identified using a 30% reduction from baseline NRS as the cut-off criterion [33]. Given the small sample size, mean differences (MD) and 95% confidence intervals for mean differences (CI-MD) were reported as primary measures. The assumption of normality for all variables was tested using the Shapiro-Wilk test before conductingpre-test (baseline) and post-test comparisons (15 min after the first session for PPTs and after six weeks of intervention for NRS and PPTs). As complementary analyses, paired Student's \(t\)-tests were conducted to compare pre- and post-intervention NRS scores, while a repeated measures analysis of variance (ANOVA) was performed for PPTs. The significance level was set at \(p<0.05\). The effect size (ES) of the intervention, calculated using Cohen's d, was categorized as small (0.20-0.49), medium (0.50-0.79), or large (>0.80) [50]. Finally, to assess the feasibility of this exploratory study, the following indicators were considered: recruitment rate, enrollment rate, and retention rate [51].
## 3 Results and Discussions
### Tennessee Eastman Process
#### 3.2.1 Influence of Different Feature Selection Algorithms on the Performance of Fault Diagnosis
Although the PSO-SVM-RFE model does not have an advantage over the SVM-RFE model for categories 8, 10, 15, and 16, its diagnostic rate is still above 80.00%. In the other fault categories, the difference between them is not significant. Thus, the PSO-SVM-RFE model with initial pre-screening by PSO-SVM performs better in feature selection than the single SVM-RFE model. 3. PSO-SVM-RFE had an average diagnostic rate of 91%, whereas RFtb had an average diagnostic rate of 90%. The former has a minor edge in the average accuracy rate if there is little difference in the diagnostic rate of 21 fault categories. PSO-SVM-RFE has a better diagnostic rate for several fault types than RFtb. Figure 5 shows that the PSO-SVM-RFE algorithm gives a better diagnosis rate for the 21 fault categories while keeping a more or less smooth quasi-break rate. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **PSO-SVM-RFE** & **SVM-RFE** & **RFtb** & **Unoptimized** \\ \hline
1 & 0.99 & 1.00 & 0.93 & 0.99 \\
2 & 0.99 & 0.99 & 0.99 & 0.99 \\
3 & 0.98 & 0.69 & 0.98 & 0.85 \\
4 & 1.00 & 1.00 & 1.00 & 1.00 \\
5 & 1.0
## 1 Introduction
It has been reported in our previous feasibility study that this program can be safely and effectively used for communication training [35]. VR has been widely studied as a tool with characteristics of high accessibility and low-cost in providing psychological interventions. With technological advancements, VR can easily provide realistic environments where individuals can communicate with others anytime, anywhere, and can increase the effectiveness of repetitive training by generating high immersion and strong motivation [36]. Considering these advantages, VR has been used to promote emotional/social adaptation skills in patients with mental illness, including autism spectrum disorder [37], schizophrenia [38], and social anxiety disorder [39]. VR has also well-established effectiveness in educational training for the general population, making it suitable as a tool to deliver practical and valid communication theory [40]. In addition, the advantages of VR include suitability for presenting emotional stimuli that can induce aggression [41]. Recent studies have actually reported that VR can be effectively used to manage aggressive behaviors in the context of emotional experiences [10; 42; 43; 44]. Unlike these previous VR-based aggression management studies, our program attempts to modulate aggression using VR as a means of embodying the here-and-now experience of [PERSON]'s theory. The purpose of this study was to investigate whether the participants' behaviors in the virtual environment related to dysfunctional communication reflect the characteristics related to aggression. Through this investigation, we tried to evaluate whether the VR-based communication modification program can be used for aggression management. The following research questions were addressed: (1) Will participants with high aggression demonstrate different patterns in the behavioral parameters of the program than those with low aggression? (2) Will the correlations between the behavioral parameters and psychological assessments scores for aggression and anger in participants with high aggression be significant and reflect the characteristics of high aggression?
# Estimation of River Velocity and Discharge Based on Video Images and Deep Learning
###### Abstract  
Space-time image velocimetry (STIV) plays an important role in river velocity measurement due to its safety and efficiency. However, its practical application is affected by complex scene conditions, resulting in significant errors in the accurate estimation of texture angles. This paper proposes a method to predict the texture angles in frequency domain images based on an improved ShuffleNetV2. The second \(1\times 1\) convolution in the main branch of the downsampling unit and basic unit is deleted, the kernel size of the depthwise separable convolution is adjusted, and a Bottleneck Attention Module (BAM) is introduced to enhance the ability of capturing important feature information, effectively improving the precision of texture angles. In addition, the measured data from a current meter are used as the standard for comparison with established and novel approaches, and this study further validates its methodology through comparative experiments conducted in both artificial and natural river channels. The experimental results at the Agu, Panxi, and Mengxing hydrological stations demonstrate that the relative errors of the discharge measured by the proposed method are 2.20%, 3.40%, and 2.37%, and the relative errors of the mean velocity are 1.47%, 3.64%, and 1.87%, which affirms it has higher measurement accuracy and stability compared with other methods.  
deep learning; discharge; river surface velocity; ShuffleNetV2; space-time image 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol., 2020, pp. 1-10. IEEE.
## 5 RIS Implementation Structures
### Reflectarray RIS Implementation  
Most of the reported RIS work assumes a reflectarray-based implementation, where each part is a configurable load-terminated antenna (Figure 3a,b) [41]. However, this execution would require a great deal of productivity to be successful.
# Improved Recommendations Based on Trust Relationships in Social Networks
## Factors in Trust Relationships
### Similarity of Users  
A recommendation from a trusted user is usually reliable, but since the target user and trusted users may differ in interests and preferences, the recommendation might not be a suitable one; that is, a trusted and similar user can better affect the target user's ratings. Therefore, to improve the prediction accuracy, we should consider the similarities between users.  
In recommendation systems, we can generally obtain a set of users \(U\), a set of items \(I\) and a user-item rating matrix \(R\), which records user experiences; in particular, we denote the rating value on item \(i_{j}\) by user \(u_{a}\) as \(r_{aj}\). Since \(R\) implies the factors of user interests and preferences, we can calculate the similarities between users based on it by utilizing typical methods. In this paper, we adopt the approach proposed in [4] to address the problem.  
The rating matrix \(R\) is usually very large; thus, to effectively realize dimension reduction, \(R\) can be decomposed into two lower dimension metrics \(S\) and \(Q\) by matrix factorization. That is, \(R=SQ^{T}\), where \(S\) and \(Q\), respectively, denote implicit feature matrices of users and items, in which each line is a low dimensional feature vector of a user or an item. Then, for a given user \(u_{a}\) and user \(u_{b}\), we calculate the similarity between them as follows:  
\[USim_{ab}=\frac{\mathbf{v}_{a}\bullet\mathbf{v}_{b}}{\|\mathbf{v}_{a}\|\bullet\|\mathbf{v}_{ b}\|} \tag{4}\]  
Figure 1: A trust relation sample.  
where \(USim_{\text{ab}}\) is the similarity between \(u_{a}\) and \(u_{b}\), \(v_{a}\in S\), \(v_{b}\in S\), and they respectively denote the feature vector of \(u_{a}\) and \(u_{b}\).
## 3 Holistic Scene Graphs
### Edges  
Edges of the graph refer to a relation that exists between nodes. This dependence among entities, actions, and attributes is what gives conceptual value to a scene. The edges can be used to demonstrate different types of relations as described below:  
* **Entity-entity:** Relations among entities can be broadly categories into two types. Firstly, spatial relations signify the position and orientation of one entity with respect to another entity. These can be directly extracted from the scene itself. Some examples of such relations are \"car on road\" or \"dog beside person\". Secondly, meronymic relations highlight part-whole connections between various objects. Examples of such relations can be \"bike with wheels\" or \"trees have leaves\".
* **Entity-attribute:** Relations such as these serve as entity descriptors and mostly correspond to the qualitative or quantitative attributes of the entities such as \"red car\" or \"tall building\".
* **Action-attribute:** Attributes can not only describe entities, but also various actions. Examples of such attributes are \"rashly driving\" or \"jumping high\".
* _Agent-action relation:_ This kind of relation connects the action with the entity that is performing the action. For the sentence \"The boy is playing\", \"boy\" is the subject or agent who is performing the action of \"playing\". In most general cases, actions are always associated with some implicitly or explicitly defined subject.
- _Action-patient relations:_ It is often seen that various objects are directly affected as a result of an action. This object or patient of an action can be represented using this kind of relation. However, the presence of a patient of an action may not always occur in a scene. A simple example for this can be the sentence: \"He is driving a car\". In this sentence, the person referred to by the pronoun \"He\" is the agent causing the action \"driving\". However, \"car\" is the object that is being affected by the said action. Hence, \"car\" is the patient of the the action \"driving\".
- _Spatial relations:_ Not all entities will directly be associated with an action, but can still be present in the context. An action can be carried out in a specific location that also reveals important information about the scene. For example, \"driving on a highway\" and \"driving on a street\" correspond to slightly different visual stimuli, which can be significantly important for a specific task. In each of these cases, entities such as \"highway\" and \"street\" are not directly involved in the action of driving, but their spatial context is informative nonetheless. An example of the desired holistic graph is demonstrated in Figure 1.
## 2 Fundamentals
### Empirical Wavelet Transform (EWT)  
[PERSON] proposed the EWT based on EMD (empirical mode decomposition) using the wavelet transform as the theoretical framework [27]. The detail coefficients \(W_{f}^{e}(n,t)\) and the approximation coefficients \(W_{f}^{e}(0,t)\) of EWT can be expressed as follows:  
\[W_{f}^{e}(n,t)=\langle f(t),\psi_{n}(t)\rangle=\int f(\tau)\overline{\psi_{n}( \tau-t)}d\tau \tag{2}\]  
\[W_{f}^{e}(0,t)=\langle f(t),\varphi_{1}(t)\rangle=\int f(\tau)\overline{\varphi _{1}(\tau-t)}d\tau \tag{3}\]  
where \(\psi_{n}(t)\) and \(\varphi_{1}(t)\) are the empirical wavelet function and the scale function, respectively, and \(f(t)\) can be reconstructed by the following formula:  
\[f(t)=W_{f}^{e}(0,t)*\varphi_{1}(t)+\sum_{n=1}^{N}W_{f}^{e}(n,t)*\varphi_{n}(t) \tag{4}\]  
In the field of structural modal parameter identification, the intrinsic mode function (IMF) component can be separated from the response signal by EWT, and the modal parameters of the structure can be obtained by analyzing the IMF [28].
# Improving Performance of Cluster Heads Selection in DEC Protocol Using K-Means Algorithm for WSN
###### Abstract  
Wireless sensor networks (WSN) have found more and more applications in remote control and monitoring systems. Energy management in the network is crucial because all nodes in the WSN are energy constrained. Therefore, the design and implementation of WSN protocols that reduce energy depletion in the network is still an open scientific problem. In this paper, we propose a new clustering protocol that combines DEC (deterministic energy-efficient clustering) protocol with K-means clustering, called DEC-KM (deterministic energy-efficient clustering protocol with K-means). DEC is a very energy-efficient clustering protocol that outperforms its predecessors, such as LEACH and SEP. K-means ensures more effective clustering and shorter data transmission distances within the network. The shorter distances improve the network's lifetime and stability and reduce power consumption. Additional heuristic rules in DEC-KM ensure improved cluster head selection, taking into account node energy level and position and minimising the risk of premature cluster head exhaustion. The simulation results for the DEC-KM protocol using MATLAB show that cluster heads have shorter distances to nodes in cluster areas than for the original DEC protocol. The proposed protocol ensures reduced energy consumption, outperforms the standard DEC, and extends the stability period and lifetime of the network.  
wireless sensor networks; deterministic energy-efficient clustering; K-means; energy consumption; network's stability period +
Footnote †: journal: Sensors  
1
Footnote 1: institutetext: Department of Physics, University of California, Berkeley, CA 94062-8999, USA
## 2 Results
### Optimization of Solid-Phase Microextraction (SPME) Parameters
#### 2.1.2 Effect of Ethanol and Sodium Chloride Concentration
On the other hand, many authors ([PERSON], 2018; 2018) report application of saturated salt during extraction as a factor increasing the ionic strength of analytes. In this study, four concentrations of NaCl, i.e., 0%, 10%, 20%, and 30% _w/v_ were tested in terms of the VSCs adsorption during SPME. Most of the analyzed compounds achieved the highest peak areas at 20% _w/v_ sodium chloride concentration (Table 1), and further increasing the salt concentration led to a decrease in the obtained signals. Similar results were obtained by [PERSON] et al. (2018), where HS-SPME was optimized to analyze terpenids in Madera wines. Authors reported that the addition of salt up to 30% improves extraction of the majority of analytes, while a decrease was observed at 40% concentration. In this work, 2-methyltetrahydrodrothiophene-3-one, 2-thiophenecarboxaldehyde, ethyl 3-(methylthio)propionate, and benzothiazole were the only analytes for which the highest response was obtained at 30% NaCl addition. Different results were obtained also for thiophenol, where the addition of salt significantly decreased the peak area, regardless of the salt concentration (approximately 80%-85% lower peak areas). What is more, the presence of ethanethiol and dimethyl sulfide was reported only in samples with 2.5% _v/v_ of ethanol and with NaCl concentration up to 20% _w/v_. In every other sample, the peak of ethanol made identification of these components impossible. One of the techniques used to improve SPME efficiency is the addition of EDTA to the samples (2018). According to [PERSON] et al. (2018), this procedure stabilizes the matrix by preventing metal-catalyzed oxidation of the compounds and increasing the pH. Considering the best results were obtained when 20% _w/v_ NaCl addition was used, this concentration was chosen to access the effect of EDTA addition on the extraction efficiency. Moreover, the addition of EDTA to the samples without salt addition was investigated to check whether the exclusive addition of EDTA would increase the performance of SPME to the extent that no salt treatment would be necessary. It was previously stated that, especially in the case of the DI-SPME method, the addition of salt to the sample can shorten the lifetime of the fiber ([PERSON], 2018). Figure 2 presents the effect of EDTA on the SPME absorption of VSCs tested in this study. The addition of EDTA did not change the total peak area of tested compounds and also resulted in the improvement in peak shape. Samples to which EDTA was added were characterized by sharper peaks, and what is more, the peak of thiophenol reach area was close to the one obtained when no addition of NaCl was used (approx. \(1.5\times 10^{8}\)). Thus, in the next stages of experiments, the 2.5% _v/v_ ethanol concentration and addition of 2 g NaCl along with 0.1 g EDTA to 10 mL of sample was selected.
# User Acceptance of Information Feed Advertising: A Hybrid Method Based on SEM and QCA
## Research Design
### Research Method
Moreover, the effective integration of the fsQCA method and mainstream statistical analysis methods have provided a broad opportunity to expand the social science theory's descriptive, predictive, and explanatory power [29]. For example, the previous research analyzed the influencing factors of customer complaint behavior on customer loyalty in B2C E-commerce. It used the SEM model and fsQCA to test the relationship among distribution, interaction and procedural justice, positive and negative emotions, service compensation satisfaction, and trust [30]. With the help of fsQCA, another work analyzed the influencing of user characteristics and intensive factors on user behavior [31]. [PERSON] et al. (2018) used the SEM model and fsQCA to analyze the cancellation of sports sponsorship in sports virtual brand community [32]. The study of [PERSON] et al. (2019) showed that six combinations of seven antecedent variables were sufficient for users' willingness to accept mobile medical care [33]. [PERSON] et al. (2020) considered customers' background and explored two methods of qualitative comparative analysis, including fsQCA and the multiple regression analysis, to explain the factors influencing customers' participation in the brand advertising [34]. Based on the SEM analysis and the summary of different levels of influencing factors, this paper explores the combination of factors influencing user acceptance of information feed with the QCA method's help. The introduction of the QCA method makes us not limited to studying a single factor, but also does not rely on researchers' subjective will. The conclusion is more objective and exploratory. Through the combination of the SEM model and QCA method, we can not only find the independent influence of a single factor but also mine the combination effect of user's intention, which can further analyze the user's willingness to accept information feed advertising.
## 2 Related Work
### Relevant Research on Chinese Address Parsing  
#### 2.1.1 Dictionary-Based Address Parsing  
The dictionary-based approach for addressing parsing stands as one of the earliest instances of applying mechanical text segmentation techniques to this task. This methodology involves breaking down addresses through string matching. Generally, the process initiates by first splitting the address text into individual characters and then sequentially comparing these characters with the placenames listed in an address dictionary. Should a match be identified, the term is retained; conversely, the method attempts to establish a match by either adding or removing a single character until only one character remains. If the string still resists segmentation, it is treated as an out-of-vocabulary term [12]. The development of the dictionary predominantly relies on a substantial repository of pre-existing toponym data, and the parsing procedure deliberately sidesteps the integration of address rule knowledge or statistical insights [13]. Currently, there is an ongoing refinement of the dictionary-based address-parsing technique. As an illustration, [PERSON] et al. [14] augmented this approach by forming sets of potential placenames based on shared character features among toponyms and constructing a single-character index to elevate query efficiency and parsing precision. In another vein, [PERSON] et al. [15] adopted a forward adaptive length matching algorithm grounded in address indicator words to curtail redundant data input and heighten parsing efficiency.  
The efficacy of these dictionary-based methods heavily hinges on the comprehensiveness of the dictionary, leaving them incapable of recognizing out-of-vocabulary terms within addresses. In the face of the incessant emergence of novel toponyms and addresses, traditional toponym dictionaries grapple with staying aligned with the evolving landscape [16].