esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology and Data
### Qualitative Interview
#### 3.1.1 Sampling Process  
Twelve SMEs have been selected as sample companies, which belong to different industries in order to obtain more diverse information [24]. With reference to [PERSON] [24], the interviewed SMEs were required to meet the following criteria:  
1. Legal enterprises in the country and meeting the definition of SMEs standards (the paid-in capital is less than about USD$35,000, or the number of regular employees is less than 200 people).
2. In order to ensure the consistency of environmental conditions (e.g., local policy, mobile communication network facilities, and support from system service providers), the selected enterprises should be located in or around a major metropolitan area.
3. At least three years of internet connection within the company, to ensure that the company possesses the basic capabilities and knowledge of fixed network and information applications. In addition, according to the suggestions of [PERSON] et al., four categories of SMEs were included in the samples [22].  
1. Provider: Companies which develop and provide broadband mobile applications as a business.
2. Adopter: Companies which have adopted broadband mobile applications already.
3. Initiator: Companies which are beginning to understand and look for related information about broadband mobile applications or are willing to adopt and pay for a broadband mobile application in the next 2 years.
4. No Intention: Companies which are completely unwilling to use broadband mobile applications.  
Apart from the status quo of their company, the respondents in the provider group were asked about their experiences in providing broadband mobile applications in both successful and unsuccessful cases. As [PERSON] et al. indicates, if the company plans to adopt e-business within two years, it may be considered an initiator [45].  
The National Association of Small and Medium Enterprises assisted in selecting the company interviewees. All research objectives and the criteria of respondent selection were disclosed in advance. Respondents were required to be in the management level, be suitably familiar with the enterprise, and have adequate experience in using computers and smartphones. However, because of the internal users' context in the research model, it was not expected that we would interview the top level of the company management, in order to avoid the error occurring between research results and the staff's actual perceptions. In the process of making contact, after the researcher's instructions and communication, all selected SMEs agreed to be interviewed and sent a qualified person to participate. The Table A1 in Appendix A provides a summary of the SMEs interviewed; all participants and company names are redacted for the purposes of the study.
# AI-Enabled Compact and Efficient Dynamic Light Scattering System for Precise Microparticle Sizing
## 2 Materials and Methods
### DLS Measurements  
The manufacturer of the commercial DLS instrument used for performance comparison in this study was Brookhaven Instruments, with the model number NanoBrook Omni. We diluted the microsphere solution with pure water (18 M\(\Omega\)\(\times\) cm) until its mass fraction was below 0.001%, and the microsphere test liquid that can be used for DLS measurement was obtained. The liquid was introduced into a cuvette, followed by ultrasonic treatment at a frequency of 40 kHz and a power output of 100 W for 1 min. Ultrasound at room temperature for 1 min does not cause a change in liquid temperature exceeding 1 degree Celsius. Therefore, temperature changes can be ignored. This process yielded a homogeneously dispersed microsphere suspension, which is suitable for DLS analysis. SEM images were obtained for each microsphere sample to serve as a reference for comparing the particle sizes determined by DLS. Measurements were performed for 15 microsphere sizes, ranging from 0 nm (pure water as the reference) to 1500 nm in 100 nm increments. Each size was measured five times over a 10 s interval, yielding a total of 80 datasets for ACF calculation and subsequent analysis. We expanded the original set of 80 ACF datasets to 160 by introducing random perturbations, aiming to enhance both dataset diversity and robustness. Specifically, we utilized Python's random number generator (version 3.9.7) with a fixed seed to ensure reproducibility. For each original dataset, a 2% random error was applied to each value, generating a new, slightly perturbed dataset. Having 5 sets of data allowed us to conduct statistical analysis and provide deeper information on data variability, which helps neural networks learn more data features and improve network accuracy.
# TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks
## 6 Conclusions and Future Work  
In this paper, we developed a new traffic management model, called Traffic-Aware Clustering-based Routing Protocol (TACRP), for large-scale and dynamic VANETs. The traffic management unit (TMU) is introduced in the protocol to control the entire network traffic, which includes the mobility information, neighborhood details of vehicles, RSUs' information, and cluster management. Intra-cluster communication and traffic management are controlled by the cluster head (CH) and the CH is directed by the TMU. The TMU separates vehicles of similar speeds and directions so that cluster reliability is increased. Communication links become stable and, during communication links, failures and retransmissions are reduced, which leads to improved energy efficiency in the network. The performance of the TACRP routing protocol was compared with earlier works, notably the ECHS and NRHCS protocols, in terms of energy efficiency, network throughput, packet delivery ratio, and end-to-end delay of the network. The TACRP routing protocol showed superior performance in the comparative analysis which was achieved using the construction of a traffic management unit and stable clusters in the dynamically varying VANET topography. In future research, analyzing the performance of the proposed TACRP routing protocol with more multifaceted situations and other VANET challenges will be conducted.  
Conceptualization, funding acquisition, resources, writing--original draft, M.I.H.; data curation, funding acquisition, A.J.A.; funding acquisition, funding acquisition, methodology, validation, writing--review & editing, visualization, supervision, A.H.A.; investigation, funding acquisition, S.A.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Current Status, Challenges and Future Developments
#### 3.2.5 Renewable Hydrogen Cross-Cutting Issues
Hydrogen safety standards establish minimum safety requirements in the event of a hydrogen flame. However, the propagation of a hydrogen flame depends on environmental conditions (e.g., temperature and wind) as well as on the hydrogen itself (e.g., pressure and state of aggregation); therefore, further research is needed to characterize the flame under different operating conditions. Noteworthy in this line of research is the work carried out by [179], who propose a system based on a high-speed VR camera and IRT to characterize the morphology and temperature of the hydrogen flame. The research proposes a test of the venting of a spherical hydrogen tank and compares it with standards such as NFPA 68. The comparison shows that the standard is perfectly applicable in an industrial environment and is aligned with the observations resulting from the tests. Leak detection is another key cross-cutting issue in the hydrogen sector. Because the hydrogen molecule is very small and operating conditions are demanding, the likelihood of gas leaks at joints is increased. Additionally, due to these characteristics, automating hydrogen detection is particularly challenging. The studies analyzed propose a combination of geospatial tools such as DT, VR, IoT sensors, and LiDAR in different components of the HVC. [PERSON], [PERSON] et al. [181] propose adaptations of existing leak detection and monitoring technologies for tanks specifically used in mining environments, using LiDAR detection in geostationary orbit with a laser radiation wavelength of 532 nm. On the other hand, [PERSON] et al., D. [182] propose a system of IoT sensors distributed at different coordinates along a hydrogen pipeline to detect possible leaks based on changes in operating parameters such as pressure or temperature. The shortage of professionals trained in hydrogen technologies poses a significant barrier for companies undertaking projects in this field. Training in hydrogen technologies must follow a broad strategy, focused on all levels of education. In this sense, geospatial techniques such as VR or AR can be used to create highly visual and robust educational material, suitable for different educational levels. [PERSON], [PERSON] et al. [186] use AR with physical elements and virtual animations to provide an interactive experience that allows users to explore and better understand the technical and operational challenges of these physical and virtual systems. Finally, [PERSON], [PERSON] et al. [187] use AR to learn how to work in the laboratory with a fuel cell-based educational system. The aim is to provide a visual interface that allows different students to perform different experiments in a virtual environment as if they were in the laboratory. It is considered to increase the scope and hours of training and reduce costs, but without compromising educational quality.
# Influence of Milling Techniques on the Performance of Wheat Straw Ash in Cement Composites
## 3 Results and Discussions
### Mercury Intrusion Porosimetry  
Mercury intrusion porosimetry was carried out on the mortar specimens to examine the effect of WSA fineness on the overall porosity. Figure 7 illustrates the results of MIP analysis in terms of total porosity (%) measured at 7 days, 28 days, and 90 days. It is obvious that porosity decreases with increasing curing age irrespective of the constituents of mixes. It is primarily credited to the formation of additional hydration products with age, thus resulted in a densified microstructure. At the age of 7 days, reference mortar specimen presented 15.82% total porosity whereas at the age of 28 days and 90 days this value reduced to 15.01% and 14.22%, respectively. Furthermore, the inclusion of WSA triggered a decrease in the porosity of the mortar mixes irrespective of the replacement level. The decrease was more noticeable with the addition of finer WSA particles. For example, the total porosity for W1/20, W2/20, and W3/20 were measured 13.12%, 16.22%, and 12.16% at 7 days; 12.27%, 15.88%, and 11.78% at 28 days; 11.54%, 15.05%, and 10.68% at 90 days, respectively. It can be seen that total porosity reduced at the early ages as well. It means mainly filler effect attributed to the porosity  
Figure 6: TG curves and calcium hydroxide content of paste specimens at 28 days.  
reduction for the early ages and pozzolanic effect and filler effect imparted reduced porosity in the later ages [32; 39]. [PERSON] et al. [33] reported that adding ultra-fine BA improved the pore structure and resulted in an enhanced durability-related performance.
# Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China
## 2 Methods
### Construction of Daily NEE Estimation Model
#### 2.3.2 Attention  
DMLP Based NEE Modeling  
DMLP models, which stand for deep multilayer perceptron models, possess the ability to acquire intricate data representations. They excel at addressing regression and classifica  
Figure 2: Overview of the test process.  
tion tasks by effectively modeling the input data [29]. MLP stands out as the predominant method employed for analyzing quantitative relationships among multiple variables. Several researchers have conducted comparisons between MLP and other models such as autoregressive integrated moving average (ARIMA) and SVM for time series forecasting. Notably, MLPs have demonstrated superior accuracy in these comparisons [30; 31].  
Attention, a technique that enables models to focus on important information and fully learn to absorb it, is widely used in artificial intelligence and deep learning. When dealing with long sequence data, traditional RNNs may encounter problems such as gradient vanishing or gradient explosion, making it difficult to capture long-term dependencies in the sequence. The attention mechanism effectively solves the long sequence problem by weighting and averaging the information from different time steps, allowing the model to focus on and process the more important parts of the sequence attention [32]. Introducing the attention mechanism into neural networks can significantly improve the performance of the model. By allowing the model to assign different attentional weights at each time step or each location, attention enables the model to focus more on key information, and thus better understand and interpret the input data [33]. The attention mechanism makes the model's prediction results more interpretable. By observing the distribution of attention weights, we can learn about the model's focus on the input data and thus gain insight into the model's decision-making process. In this study, we combined the attention mechanism with DMLP (Figure 3) to construct this model for modeling the NEE.  
In recent years, there has been a surge in studies leveraging deep learning techniques for time series forecasting across a range of models [21]. This study conducted a compara  
Figure 3: Model structure.  
tive analysis of different deep learning models, including the 1D CNN, LSTM, MLP models, and RF models, in the context of time series forecasting. The machine learning approach of random forests has obtained excellent results for NEE modeling studies [3; 18; 24], so the random forest approach was selected as a comparative test for machine learning models.
# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 4 The Proposed Framework for Travel Time Prediction
### DeepST-Net: A Deep Neural Network Model for Urban Travel Time Prediction  
DeepST-Net, a Deep Spatiotemporal Neural Network, is designed to address the challenges of urban travel time prediction by effectively capturing the variability and dynamics of urban traffic patterns. Leveraging advanced spatiotemporal data preprocessing and refinement techniques, the model utilizes high-quality input data to model complex, non-linear dependencies, overcoming the limitations of traditional approaches.  
The architecture of DeepST-Net features four fully connected hidden layers, optimized through extensive experimentation and error analysis. This multilayer design is tailored to learn intricate relationships between factors influencing travel time variability, enabling the  
Figure 7: Spatial distribution of pickup and drop-off trips.  
model to deliver accurate and reliable predictions. The general architecture of DeepST-Net is illustrated in Figure 8.  
The input layer of the network processes key data, including pickup and drop-off coordinates, trip dates, and temporal features at daily, weekly, and yearly scales. These inputs are transformed and integrated within the hidden layers, allowing the network to capture complex relationships between input features and the target variable--travel time. The output layer utilizes the learned representations from the hidden layers to generate the final travel time prediction.  
To ensure input comparability and prevent bias in the model's predictions, a Min-Max Scaler was applied to normalize the input data to a range from 0 to 1. The same normalization parameters were used on the test data to evaluate the model's performance. Model training involved trial and error and utilized the following parameters:  
* The Adamax optimizer with the default value of the learning rate 0.001 (selected based on experimentation and gradient descent optimization);
* A batch size of 64 (chosen to balance computational efficiency and training stability).
* A number of training epochs equal to 120 (determined by iterative experimentation to achieve convergence).
* Early stopping callback: Used with patience of 5 to stop training when the validation loss stops improving, preventing overfitting.
* Learning rate on plateau callback: Reduces the learning rate with a patience of 5 if validation loss stops improving, aiding finer adjustments and preventing overshooting during training.
* Activation function in the hidden layers: ReLU (Rectified Linear Unit), defined as \(ReLU(x)=\max(0,x)\), which introduces non-linearity and helps capture complex patterns.
* Activation function in the output layer: Linear activation (\(f(x)=x\)), appropriate for regression tasks to predict continuous travel time values.
# Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos +
## 3 Data Collection and Methodology
### Message Tracing Model
#### 3.3.2 Sequence Tracing  
Sequence tracing tracks transaction data to aggregate the timestamp values for each inter-blockchain communication transaction. The information is recorded as IBC Msg Relay Time. Several transaction datasets, including IBC Tx Generation, IBC Transfer Tx, Valid IBC Recv Tx, and Valid IBC Ack Tx, share the precise Sequence identification [10]. The Tzhash from the IBC Tx Generation identifies a specific fungible token transfer. It is a 1:1 match with the Txhash of the IBC Transfer Tx. Every IBC Tx Generation includes a timestamp indicating the start of inter-blockchain communication transactions.  
Figure 8: Example of a block-level cross-chain transaction.  
Figure 9: Example of filtering valid transactions.  
In contrast, the IBC Transfer Tx, Valid IBC Recv Tx, and Valid IBC Ack Tx sequences are matched based on the Source_channel and Destination_channel. The timestamp of the block containing these transactions is included. In the IBC Msg Relay Time, a sequence trace on the transaction data stores the timestamp value for each fungible token transmission. This trace ensures that the timestamp data has been correctly aggregated. The sequence tracing process used to obtain and preserve timestamp values in the IBC Msg Relay Time is displayed in Figure 10.
# Joint AP Association and Bandwidth Allocation Optimization Algorithm in High-Dense WLANs
## 5 Performance Evaluation
### Evaluation Results
#### 5.2.3 Fairness Index  
Finally, we also evaluate the fairness index in terms of transmission time for all algorithms as the client density increases. As shown in Figures 9 and 10, our algorithm obtains higher fairness than the other two algorithms do regardless of the client distribution and density. The fairness index is about 0.8, 0.66 and 0.52 for CAA-BD, Categorized and SSF algorithms under both uniform and hotspot distributions respectively. Obviously, the fairness index for the three algorithms is almost unchanged and has the similar tendency under the uniform distribution, and in the hotspot distribution case, the Categorized algorithm even happens to decline when the number of clients grows beyond 140. The reason is that the SSF and Categorized algorithms do not show solicitude for load balancing among  
Figure 8: Delay in the hotspot distribution.  
Figure 7: Delay in the uniform distribution.  
APs. Inevitably, when the client density is high, most of the clients gather on a few APs, which leads to the rest of the APs to be underutilized and wastes network resource.
## 3 Proposed Design
### Application Traffic Patterns Identification
#### 3.1.7 Blood Glucose  
The blood glucose level determines the amount of glucose (sugar) present in the blood. It is observed that the risk for hypergly excursions for individuals with type 1 diabetes can be reduced by continuously monitoring the glucose level. Moreover, the required dose of insulin can be estimated. Using implantable wireless sensor, the glucose level can be monitor. From Table 1, considering sampling rate 0.004 Hz and sampling rate 8 bits we have SIT = 250,000 ms; MPDS = 0.004 bytes/s; DNS after 250,000 ms = 1 byte; DNS after 250,000 ms including minimum header size = 132 bits; Total data rate required by Pulse oximeter packet = 528 bps.
# No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System
## 3 Materials and Methods
### Proposed Method
#### 3.2.2 Edge-Based Perceptual Difference Measure
For the second part of our experiment, an edge was selected as the most significant perceptual feature for consideration and Sobel edge detection as the most prominent and widely used mask for detecting edges in an image. In our proposed method, the edges of the current frame were compared with those of the previous one to determine the distortion between them. The edges of a frame detected using Equations (8) and (9) are shown in Figure 9, in which those of one distorted and one original frame from Figure 5 are presented. Each edge-detected frame was compared with the previous frame in the same view to observe edge distortions. From our experimental observations, it was noted that, for undistorted videos, the perceptual edge difference was small. However, if any prominent error-induced distortions were present in a frame, this measure demonstrated a significant difference compared with the previous one and may have surpassed a predefined threshold in terms of edge accumulation. According to Equations (10) and (11), the threshold at which we can observe the magnitude of the edge differences was half the standard deviation of the current edge magnitude (\(\sigma G_{c}\)). Based on Equation (12), the maximum edge difference considered was that between the separate left and right frames. It was obvious that the motions in a video affected the accumulation of edge differences, as shown by the differences between the Sobel edge-detected original right views in frames 4 and 5 in Figure 10 in which the edges indicate relative motions between the frames. Figure 10: Difference in Sobel edge accumulation due to motion. Figure 9: Sobel edge detection in a stereoscopic frame.