esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Load Dynamics on Converter-Dominated Isolated Power Systems
## 4 Load Modeling Influence on Grid Dynamics
### Static and Dynamic Load Modeling--Sensitivity Analysis
#### 4.2.1 Base Case  
The network load distribution of the base case scenario can be observed in Table 2, where it is considered that the IM loads account for 26.5% of the total system load. Regarding the loads modeled by the exponential model, the value of its power coefficients was considered equal to 1.  
Taking into account the load distribution presented in Table 2, it is intended to determine the minimum required BESS power converter capacity that ensures the post-fault recovery of all IM loads. As can be observed in Figure 9, for a BESS power converter sizing lower than 38 MVA, the small industrial motor connected to L3 stalls following the fault clearance. This is due to the large reactive power consumption required by the IM after the network fault clearance, which demands a large reactive power from the BESS power converter for about 2-3 s after the voltage recovery, as it can be observed in Figure 10. Furthermore, in Figure 11, the network frequency and voltage measured at bus B1 are depicted, with the large voltage oscillations for the 37 MVA BESS power converter case being notable, which is a consequence of the L3 small industrial motor stall. In both cases, a similar network frequency drop induced by the short circuit is observed. From this analysis, it is concluded that the initial BESS power converter sizing is not suitable when considering dynamic load modeling (it is intended for the post-fault recovery of all IM loads).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Load Modeling** & \\ \hline
**Load** & **Active Power (MW)** & **Load Model** \\ \hline L1 & 5.4 & Exponential \\ L2 & 7.3 & Constant Impedance \\ L3 & 3.1 & Induction Motor \\ L4 & 2 & Exponential \\ L5 & 2.2 & Induction Motor \\ \hline \hline \end{tabular}
\end{table}
Table 2: Static and dynamic load distribution.  
Figure 9: L3 small industrial motor speed and reactive power consumption—base case scenario.
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 5 Evaluation
### Impact of Proposed Algorithm on RA KPIs
#### 5.4.3 Waiting Time Distribution  
The dropped packet rate can be explained in terms of the waiting time distribution. It could be defined as the probability that the waiting time of a request does not exceed the delay budget of the user. We measured the waiting time distribution for different delay budgets of packets at different time points. In Figure 8, we grouped the episodes into 50 units. Group 3 requires more training time. The DQN allowed the waiting time to decrease sharply, which also means fewer drop rates. The sharp decrease is more pronounced for the latter groups. In addition, Figure 9 illustrates the comparison between DQN and the other algorithms on the waiting time distribution. DQN again shows the best results.  
Figure 6: Comparison of successful access probability: DQN-based vs. others in random access network environment.  
Figure 8: DQN’s waiting time distribution measured at different groups of episodes in random access network environment.  
Figure 7: Comparison of access delay: DQN based vs. others in random access network environment.  
Figure 9: Comparison of waiting time distribution: DQN-based vs. others in random access network environment.
## Abstract  
One of the main design challenges in wireless sensor networks (WSNs) is achieving a high-data-rate transmission for individual sensor devices. The high altitude platform (HAP) is an important communication relay platform for WSNs and next-generation wireless networks. Multiple-input multiple-output (MIMO) techniques provide the diversity and multiplexing gain, which can improve the network performance effectively. In this paper, a virtual MIMO (V-MIMO) model is proposed by networking multiple HAPs with the concept of multiple assets in view (MAV). In a shadowed Rician fading channel, the diversity performance is investigated. The probability density function (PDF) and cumulative distribution function (CDF) of the received signal-to-noise ratio (SNR) are derived. In addition, the average symbol error rate (ASER) with BPSK and QPSK is given for the V-MIMO model. The system capacity is studied for both perfect channel state information (CSI) and unknown CSI individually. The ergodic capacity with various SNR and Rician factors for different network configurations is also analyzed. The simulation results validate the effectiveness of the performance analysis. It is shown that the performance of the HAPs network in WSNs can be significantly improved by utilizing the MAV to achieve overlapping coverage, with the help of the V-MIMO techniques.  
keywords: wireless sensor networks; high altitude platform; virtual multiple-input multiple-output; system capacity; shadowed Rician fading; multiple assets in view; average symbol error rate; channel state information +
Footnote †: journal: Journal of Signal Processing
## 4 Results and Analysis
### Analysis of NonCompliance Behaviors
#### 4.2.1 Neutralization, SRS and Noncompliance
\begin{table}
\begin{tabular}{c c c c} \hline
**Authors** & **Research Method** & **Sample Size** & **Findings** \\ \hline
[94] & The quantitative research methodology was adopted. SEM is used for model testing. & 1449 employees participated & Neutralization is a significant predictor of employees’ intention to violate ISP. \\ \hline
[95] & The quantitative methodology was adopted. SPSS used for data analysis; PLS used for hypothesis testing & 179 employees from 10 different industries participated & Neutralization techniques should be considered when designing an ISPC model. Attitude and response efficacy are also helpful. Self-efficacy was not found to be effective. \\ \hline
[20] & Quantitative method used. SEM is used for results and analysis & 539 usable responses of computer using professionals collected from different organization & The stress of security requirements leads to moral disengagement, which increases violating ISB. \\ \hline
[52] & The quantitative research methodology was adopted. SEM is used for hypothesis and model testing & 415 usable responses were collected from manufacturing and services firms & Noncompliance behaviors of peers, work impediments, and security system anxiety is the causes of noncompliance with ISP. \\ \hline
[96] & A scenario-based quantitative study. SPSS and PROC MIXED used for hypothesis and data testing & 968 complete responses collected & Procedural, organizational injustice causes computer abuse behavior; sanction certainty reduces injustice effect and intention to abuse ISP. \\ \hline
[97] & Mixed method research design employees participated. & 924 Finnish significant predictor of reactance towards ISPC. \\ \hline \end{tabular}
\end{table} 
Table 11: Studies on SRS, neutralization, and noncompliance.
## 3 Results
### GC-MS Analysis  
GC-MS methodologies allowed us to determine volatile compounds, alkanes, and fatty acids, and these are listed in Tables 2-4.  
The SPME approach for the GC-MS analysis was a robust and effective method for detecting volatile metabolites in the untreated matrix, whereas the analysis of hexane extract allowed us to identify a class of molecules peculiar to insects, namely methyl-branched hydrocarbons (MBCHs).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{N}^{\circ}\) & **Component 1** & **LRI 2** & **LRI 3** & **(\%)** \\ \hline
1 & pentanoic acid & 1758 & 1762 & 0.2 \(\pm\) 0.03 \\
2 & palmitic acid & 2941 & 2946 & 27.8 \(\pm\) 0.05 \\
3 & stearic acid & 3183 & 3181 & 10.4 \(\pm\) 0.03 \\
4 & oleic acid & 3190 & 3184 & 21.0 \(\pm\) 0.07 \\
5 & linoleic acid & 3217 & * & 38.1 \(\pm\) 0.06 \\
6 & linolenic acid & 3289 & 3292 & 2.5 \(\pm\) 0.02 \\  & SUM & & & 100.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fatty acid composition (percentage mean value of the three batches \(\pm\)SD) of _A. domesticus_.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{N}^{\circ}\) & **Component 1** & **LRI 2** & **LRI 3** & **(\%)** \\ \hline
1 & methylcyclopentane & 635 & 629 & 62.9 \(\pm\) 0.04 \\
2 & 4-methyl-heptane & 771 & 768 & 0.2 \(\pm\) 0.02 \\
3 & octane & 810 & * & 0.1 \(\pm\) 0.02 \\
4 & 2,4-dimethyl-l-heptane & 822 & 821 & 0.2 \(\pm\) 0.02 \\
5 & 2,3,4-trimethyl-hexane & 854 & 850 & 0.1 \(\pm\) 0.02 \\
6 & 3,3-dimethyl-octane & 930 & 935 & 0.2 \(\pm\) 0.01 \\
7 & 2,3,6,7-tetramethyl-octane & 928 & 935.5 & 0.3 \(\pm\) 0.02 \\
8 & decane & 1010 & * & 0.3 \(\pm\) 0.01 \\
9 & 4,7-dimethyl-undecane & 1121 & * & 0.2 \(\pm\) 0.02 \\
10 & 4-methyl-undecane & 1167 & 1160 & 0.6 \(\pm\) 0.02 \\
11 & 2,5-dimethyl-benzaldehyde & 1215 & 1208 & 0.2 \(\pm\) 0.02 \\
12 & 4,6-dimethyl-dodecane & 1335 & 1325* & 0.7 \(\pm\) 0.02 \\
13 & 2,4-di-tert-butylphenol & 1529 & 1521 & 0.7 \(\pm\) 0.02 \\
14 & hexadecanoic acid & 1980 & 1973 & 32.8 \(\pm\) 0.04 \\  & SUM & & & 99.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Chemical composition (percentage mean value of the three batches \(\pm\)SD) of _A. domesticus_ hexane extract.  
The GC-MS technique allowed us to identify six fatty acids: linoleic acid (38.1%) and palmitic acid (27.8%) were the most abundant, followed by oleic acid (21.0%), stearic acid (10.4%), and linoleic acid (2.5%), and, with a much lower percentage value, pentanoic acid (0.2%).
## 4 Comparison and Discussion  
In the previous section, we classify DTN solutions for IoT into three categories: BP-based, Routing-based and X-DTN. We also review a number of proposed solutions in the literature. In this section, we summarize these solutions in Tables 1 and 2. In Table 1, we highlight targeted environment and used technologies for each solution. This table gives an overall view of DTN solutions for IoT organized by our classification categories. As can be seen, several reference technologies are available but there is not a standard unified DTN solution for IoT that combines both a DTN architecture with a routing protocol. In Table 2, we compare the solutions based on the following IoT constraints: delivery, mobility, heterogeneity, scalability and resources. We choose theses constraints since they are common to both IoT and DTN. We observe that most solutions focus on improving delivery and resources consumption. Using mobility still needs more investigation while the most persistent problem is the heterogeneity and scalability in IoT environments.
## 4 The Proposed Development Model: Holistic Security and Accessibility Layered Architecture (HSALA)
### Summary of Comparative Analysis  
The comparison highlights the significant advantages of HSALA over the three-layered model in addressing contemporary challenges in web application development. By integrating accessibility and cybersecurity at each architectural layer, HSALA ensures the following:  
* Comprehensive accessibility: meeting or exceeding accessibility standards, enhancing inclusivity for all users.
* Robust cybersecurity: implementing proactive and layered security measures, reducing vulnerabilities and building trust.
* Scalability and flexibility: facilitating growth and the adaptation to new technologies without compromising performance or maintainability.
* Enhanced user experience: providing a seamless, intuitive, and secure interface, balancing aesthetics and functionality.
* Regulatory compliance: aligning with legal and industry standards, mitigating compliance risks and potential legal challenges.  
In contrast, the three-layered model, while simpler, may fall short in effectively integrating accessibility and security, potentially leading to fragmented user experiences, security vulnerabilities, scalability issues, and higher long-term costs due to technical debt.
# Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture
## 3 Dataset and Methods
### Improvements to SlowFast Network Structure
#### 3.2.2 Action Attention Modules
By introducing the ACTION module to capture diverse types of activation signals before each original residual block (Figure 13a) in the fast branch, and subsequently performing convolution, finer-grained features can be obtained. This refinement contributes to improved accuracy in multi-label behavior classification. In this study, an ACTION module is introduced into each original residual block of the fast branch, referred to as the ACTION residual block (Figure 13b).
# AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation
## 6 Discussion
### Clinical Integration and End-Users  
We assume our framework will be utilized as a supplementary tool in stroke protocols, providing likelihood scores and calibrated alarms directly to stroke neurologists and emergency physicians through interactions with their PACS viewer or electronic health record dashboard. This scenario features a traffic light interface (green/amber/red) aligned with calibrated risk levels, offering swift, visual decision support that allows physicians to prioritize high-risk patients for prompt imaging examination and management. By integrating AI outputs with current clinical workflows and decision thresholds, our solution can improve the real-time triage without interrupting existing practices.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 4 5G and Healthcare Opportunities
### 5G and Universal Coverage  
Currently, 5G is in a formative stage and is anticipated to materialize by 2020. 5G will be the first technology to be developed in an age when the vision of global Internet connectivity is close to reality (as almost 60% of the world's population is already covered by a mobile 2G/3G/4G signal). 5G should materialize on this legacy and aim to provide universal coverage to digitize societies. By 2020, it is expected that 5G will provide connectivity to 50 billion devices, 212 billion sensors and enable access to 44 zettabytes of data [48]. Similarly, the 5G cloud is likely to cover one-third of world's population with 1.1 billion connections by 2025 [49]. In a report by the Global mobile Suppliers Association (GSA) [50], it is estimated that the commercial deployment of 5G will begin after the completion of standardization process in 2020. They also projected that over 270 operators will have deployed the commercial services of 5G over the globe. In the context of universal access, 5G is exploring the integration of various technologies such as Device-to-Device (D2D) communications, IoT, Massive Multiple Input-Multiple-Output (MIMO), millimetre wave communications and full-duplex transmissions to increase coverage in all urban and rural areas, as well as on roads and on railway tracks. Additionally, Unmanned Aerial Vehicles (UAV) and drone communications will also be utilized to provide coverage in rural areas.  
Despite its great potential for universal access, we have noted in our previous work [51] that many 5G technologies--including the big three 5G technologies [44] of ultra-densification, millimetre Wave (mmWave) and massive MIMO--are primed towards increased performance rather than coverage. In fact, technologies such as ultra-densification and mmWave will make universal coverage even more challenging; even though massive MIMO can be used for coverage gains and energy reduction if the associated computational cost of massive MIMO can be overcome. The problem of universal reliable coverage for 5G will be an important concern for healthcare applications due to the criticality of its subject domain that directly affects human lives. In particular, research on extending coverage, and providing reliability guarantees is key for enabling the various e-health applications. In terms of technologies, energy optimization (e.g., via advanced MIMO and beamforming) will become important and will require further innovations.
# Robust Text-to-Cypher Using Combination of BERT, GraphSAGE, and Transformer (CoBGT) Model
## 1 Introduction
This method tries to discover which parts of the natural language question (can be words or phrases) refer to which database elements (tables and columns). This method is a good and popular approach in text-to-SQL because SQL is designed for relational databases, which organize data in tables with predefined schemas. The databases structured are created with clear relationships defined by foreign keys while Cypher queries involve pattern matching, where the query looks for specific structures or paths within the graph. This adds another layer of complexity to schema linking, as the model must not only identify the correct schema elements but also understand how they relate to each other within the graph's structure. To address the above issue, we proposed a novel model to translate natural language questions into Cypher queries, making it easier for users to interact with graph databases without needing to learn the query language. This model is the combination of bidirectional encoder representations from Transformers (BERT), Graph Sample and AggregatE (GraphSAGE), and the Transformers assigned as CoBGT model. Firstly, due to BERT achieving high performance in word-embedding processing, BERT model was utilized to extract the key value in natural language. Since Cypher is a query language for graph databases, GraphSAGE, which handles graph data, is suitable for exploiting relations and properties in Cypher. Finally, the features from BERT and GraphSAGE models were supplied to Transformer to generate the Cypher query (text-to-Cypher). In addition, although there was a dataset for text-to-Cypher in [18], this dataset is in the Chinese language. Thus, we have created a comprehensive text-to-Cypher dataset in English, which serves as a critical resource for training and evaluating text-to-Cyphermodels. This dataset is not only instrumental in developing our model but also provides a valuable benchmark for future research in this domain. Overall, the contributions of this paper can be summarized as follows:  
* Provided a novel method to translate natural language into Cypher queries (text-to- Cypher). * Proposed a new dataset for designing text-to-Cypher models. * Analyzed the compatibility of BERT, GraphSAGE, and Transformer in text-to-Cypher translation. * Conducted experiments to verify the performance of the proposed model. The rest of this paper is organized as follows. In Section 2, we provide some background about Cypher query language, BERT, GraphSAGE, and Transformers. Section 3 describes our proposed model in detail. The simulation results and datasets are presented in Section 4 to evaluate the proposed model. Finally, in Section 5, we present the conclusions and suggestions for future studies.
## 3 Results and Discussion  
### XRD Analysis  
Figure 1 shows the XRD patterns of the ignimbrite precursor material, zeolite, TiO\({}_{2}\)/zeolite, and pure TiO\({}_{2}\) obtained with the sol-gel method. The ignimbrite pattern reveals that its main identified crystalline phase is anorthoclase, followed by cristobalite and albite. Anorthoclase is a feldspar composed of aluminum silicates with varying percentages of potassium, sodium, and calcium [36; 37]. Furthermore, the obtained peaks are primarily located between angles of 15\({}^{\circ}\)and 40\({}^{\circ}\) (20), which is consistent with zeolite synthesis studies by [PERSON] et al. [38] and [PERSON] et al. [39]. The open halo that was observed suggests thermodynamically metastable amorphous aluminosilicate structures with high pozzolanic activity [40].  
The zeolite pattern shows that the peaks of ignimbrite decreased, whereas new peaks appeared. This reduction in initial crystalline phases may be attributed to the dissolution of precursors with an alkaline solution, contributing to the increase in silicate and aluminate solutions present in these [41]. This assumption is supported by the study by [PERSON] et al. [42], where a reduction in the intensity of quartz and mullite was observed after the synthesis process using NaOH. The zeolite spectrum indicated characteristic peaks at 20 = 15.7\({}^{\circ}\), 18.1\({}^{\circ}\), 25.8\({}^{\circ}\), and 30.4\({}^{\circ}\) corresponding to the analcime-type zeolite structure (JCPDS no. 76-0904) [43].  
Finally, the XRD patterns of the TiO\({}_{2}\) and TiO\({}_{2}\)/zeolite catalysts evidence characteristic peaks for the anatase phase (JCPDS no. 96-901-5930) that were observed at approximately 25.3\({}^{\circ}\), 37.7\({}^{\circ}\), 48.0\({}^{\circ}\) 54.1\({}^{\circ}\), and 62.6\({}^{\circ}\)[44]; a rutile phase (JCPDS no. 01-078-1510) was detected in a small proportion [45]. No significant changes were observed in the TiO\({}_{2}\) peaks after incorporating zeolite. However, in the TiO\({}_{2}\)/zeolite compound, two additional peaks corresponding to analcime zeolite were observed. Therefore, the TiO\({}_{2}\)/zeolite photocatalytic material exhibits the structure of the TiO\({}_{2}\) component in the anatase phase along with peaks corresponding to the analytical structure of zeolite.
# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
## 3 Traditional Feature-Based Mosaic Process for SAR Images
### Keypoint Detection and Matching  
The classic SIFT is an efficient feature detection algorithm [13]. First, the potential interest points that are invariant to scale and orientation were identified by using a difference of Gaussians function and select the keypoints based on their stability. Then, one or more orientations were assigned to each keypoint location based on local image gradient directions and form a feature vector with 128 elements for each keypoint. Finally, the candidate keypoints were matched by finding their nearest neighbor, which is defined as the keypoint with the minimum Euclidean distance for the descriptor vector. In addition, the speeded-up robust features (SURF) algorithm and the oriented features from the accelerated segment test (FAST) and rotated binary robust independent elementary features (BRIEF) algorithm were also used in SAR image mosaics [32; 33].
# Infrared Small Moving Target Detection via Saliency Histogram and Geometrical Invariability
## 2 In-Frame Detection Based on Saliency Histogram
### Saliency Histogram  
Considering the saliency map S and the input IR image I share the same size, it is obvious that there is a pixel-wise correspondence between S and I in the spatial domain. Based on this, a normalized saliency histogram \(\chi\) is developed via averaging the accumulating saliency value of each gray level, which is illustrated by Equation (6):  
\[\chi(k)=\left\{\begin{array}{cc}\frac{\rm{S}_{a}(k)/N(k)}{\rm{sum}(\chi)}& \rm{if}\ S_{a}(k)\
eq 0\\ 0&\rm{others}\end{array}\right. \tag{6}\]  
where \(\chi(k)\in\) [0,1] is the bin value for an arbitrary gray level k (\(k=1,2,3,\ldots,\) L and L is the maximum gray level value) in the saliency histogram; \(\rm{sum}(x)\) means the sum of all the bin values which is used to make a normalization; \(\rm{N}(k)=NUM\{(x,y)|I(x,y)=k\}\) refers to the total number of the pixels whose gray levels are k in I, where \(\rm{NUM}\{\cdot\}\) means counting the number of elements in a set; \(\rm{S}_{a}(k)\) is defined as the cumulative sum of a set of saliency values in S, and  
\[\rm{S}_{a}(k)=\sum_{x}\sum_{y}S(x,y),\ (x,y)\in\{(x,y)|I(x,y)=k\} \tag{7}\]  
Now, let us have a further discussion for the design of our saliency histogram. First of all, the ideal result of saliency histogram should meet the request that only those bins whose gray levels correspond to IR target are assigned with large saliency values. Enlightened by this idea, we calculate the saliency map S, which provides a quantitative measurement of saliency for each pixel and develop Equation (7) to measure the degree of saliency for each gray level. However, \(\rm{S}_{a}(k)\) is not a rigorous metric because there is a special case where the gray level \(\rm{k^{\prime}}\) in the background area has a low saliency value but quite a large quantity of pixels, and the \(\rm{S}_{a}(k^{\prime})\) is still large. To address this problem, we take the number of pixels with different gray levels into consideration and use the averaging procedure to propose the final saliency histogram as Equation (6).