esa-sceva/satcom-chunk-collection huggingface dataset:
# A Large Language Model and Qualitative Comparative Analysis-Based Study of Trust in E-Commerce
## 5 Discussion
### Contributions to Academia and Practice  
The main academic contribution of this study lies in utilizing LLMs and QCA to enhance the understanding of customer trust in e-commerce. Traditional research on trust in e-commerce has often relied on regression models, which are limited in capturing the complexity and non-linear interactions among different stages of the customer journey. By incorporating LLMs, we introduce a powerful machine learning approach that can process large volumes of unstructured data, specifically customer reviews, to accurately predict trust-related outcomes. This represents a methodological advancement by utilizing neural computation to model and predict trust sentiment from natural language, offering richer insights into customer behavior than conventional structured data methods like surveys.  
A key academic contribution of this study is the synergy between predictive performance (LLMs) and explanatory analysis (QCA). While LLMs excel in predicting trust based on textual data, QCA provides a tool to uncover the configurations of factors--such as selection, delivery, and recovery stages--that combine to influence trust. This combination of methods bridges the gap between predictive analytics and causal explanation, enabling a more comprehensive understanding of the factors that drive trust in e-commerce.  
This study provides valuable practical insights for e-commerce businesses, particularly in how they can leverage advanced tools to predict and understand customer trust. First, the ability of LLMs to process large volumes of unstructured text data, such as customer reviews, offers businesses a highly scalable solution for understanding customer sentiment in real time. This provides a real-time pulse on customer trust levels and enables businesses to respond proactively to emerging trust issues before they become widespread.  
In addition to predictive insights from LLMs, this study highlights the importance of configurations of customer experience elements across the selection, delivery, and recovery stages. For businesses, this means that improving trust requires a holistic view of the customer journey, rather than focusing on any single touchpoint. For example, this research shows that while an efficient delivery process is critical to fostering trust, businesses must also pay attention to pre-purchase decisions (selection) and post-purchase support (recovery). This means that a seamless and cohesive experience across these stages is key to building long-term customer trust. Therefore, businesses should aim to optimize all touchpoints, ensuring that customers have positive experiences at every stage of their journey.
## 3 Proposed Method
### Regression via Classification  
Conventionally, the prediction of age falls into the category of a regression problem because age represents a continuous value, preferably a set of discrete classes. The off-the-shelf pre-trained models evaluated for ImageNet classification consist of neurons for every object class, normalized employing the Softmax function. In the case of the regression task, we employed a Euclidean loss function with the replacement of the output layer with one neuron. Unfortunately, training a CNN solely for age regression tasks experiences a high error rate due to the instability while handling outliers. Consequently, the model convergence had difficulty owing to high gradients and unstable predictions as well.  
Under these circumstances, we considered the age regression task via a standard classification approach where the ages were discretized into \(K\) categories. Hence, the Softmax function was used in the output layer of the network to normalize the arbitrary output values into probabilities across predicted age classes. Equation (1) describes the procedure of the probability generation for each labeled class by the Softmax activation function, mentioned below:  
\[S(\mathbf{v})_{i}=\frac{e^{\mathbf{v}_{i}}}{\sum_{j=1}^{N}e^{\mathbf{v}_{j}}} \tag{1}\]  
where \(i\) denotes the present element index of the input vector \(\mathbf{v}\), \(N\) is the total number of classes for the particular task, and all \(v\) values are the elements of the input vector.  
Figure 5: The entire face detection process over raw face images using MTCNN.  
Following this procedure, the learning conducted by the CNN model for age classification and quantifying the regression value out of the expected value formulated by means of Softmax probabilities belongs to the \(K\) neurons, as shown in Equation (2).  
\[E=\sum_{i=1}^{|k|}y_{i}\cdot p_{i} \tag{2}\]  
where \(k\) belongs to 101 age categories, \(y_{i}\in[0,\,100]\), and \(i=1,2,\ldots 101\). \(p_{i}\) denote the Softmax-normalized output probability of neuron \(i\). From the experimental results, it is proven that robustness throughout training and accuracy during testing is ensured by this formula.
## 7 Experimental Evaluation
### Streaming ML Model Evaluation
Figure 4 presents the confusion matrix for the Next Offset Class that shows how many predictions are correct and incorrect per class. As we can observe, the classification for the Next Offset correctly classifies most of the file requests as sequential requests, since many of the traces have large sequential reads. Even though there is an apparent imbalance problem with the training data, the model can accurately predict the class, even for the minority classes. This fact is also evident by the high Precision (0.995), Recall (0.996), and F1 score (0.996). Similarly, Figure 5 presents the confusion matrix for the Next File Hotness Class. About 63% of the file requests are classified in class _ONE_ and 26% in class _TWO_. Hence, the imbalance problem is visible, but the model is still able to achieve a minimal number of miss-classifications, as well as high Precision (0.993), Recall (0.991), and F1 score (0.992). Figure 3: The (**a**) Mean Absolute Error (MAE), (**b**) maximum model size, and (**c**) runtime when predicting the _Next Offset_ with ARF using all features with optimized configuration, selected features with default configuration, and selected features with optimized configuration. Further, we present two representative line graphs showing the original and predicted values for the two regression scenarios. In particular, Figure 6 presents the results for _Next Offset_ for Trace 11, while Figure 7 presents the results for _Next File Hotness_ for Trace 9. These figures visually demonstrate that, even in the presence of erratic and complex behavior, the predictions offered by the two models can faithfully follow the trends. At the beginning of the trace, there is a slightly higher difference between the original and the predicted values, which reduces as time goes by. This showcases the ability of the streaming ML models to learn and improve as they are trained with more instances. This is especially evident in the second half of Trace 11 in Figure 6, where the predicted values match almost exactly the original values. Trace 11 also serves as a good example of a concept drift in the access patterns of the workload. The first half of the trace is characterized by complex non-sequential access patterns, followed by a sudden switch to a sequential access pattern. The streaming ML model immediately adjusts and starts making correct sequential Next Offset predictions. Figure 4: Confusion matrix for _Next Offset Class_ target. Figure 5: Confusion matrix for _Next File Hotness Class_ target. **Key takeaways:** The optimized configurations for the selected features for all four models produce highly accurate models (0.98 F1 score and 0.07 MAE on average), which are very memory efficient (<400 KB), even after processing millions of requests. The classification models work well, even in a highly imbalanced dataset. Over time, streaming ML models learn and improve as they are trained with more instances.
# High-Speed Surface Property Recognition with a 140 GHz Frequency
###### Abstract  
In the field of integrated sensing and communication, there is a growing need for advanced environmental perception. The terahertz (THz) frequency band, significant for ultra-high-speed data connections, shows promise in environmental sensing, particularly in detecting surface textures crucial for autonomous systems' decision-making. However, traditional numerical methods for parameter estimation in these environments struggle with accuracy, speed, and stability, especially in high-speed scenarios like vehicle-to-everything communications. This study introduces a deep learning approach for identifying surface roughness using a 140-GHz setup tailored for such conditions. A high-speed data acquisition system was developed to mimic real-world scenarios, and a diverse set of rough surface samples was prepared for realistic high-speed datasets to train the models. The model was trained and validated in three challenging scenarios: random occlusions, sparse data, and narrow-angle observations. The results demonstrate the method's effectiveness in high-speed conditions, suggesting terahertz frequencies' potential in future sensing and communication applications.  
terahertz (THz); surface property recognition; high-speed scenario; diffuse scattering; deep learning +
Footnote †: journal: Applied sciences
## 3 Results
### Wettability of the Deposited CYTOP NPs  
The wettability of the network and monolayer structures composed of CYTOP NPs deposited onto glass slides was evaluated. Again, five-dot parallel beams were irradiated onto the same areas of the CYTOP surface to deposit NPs onto the glass slides. The pulse energy and the number of pulses were 5 \(\upmu\)J/pulse and 10,000, respectively. The CYTOP-glass distance was selected as 100 or 4000 \(\upmu\)m for deposition of the network or monolayer structure on each glass substrate, respectively. Figure 6 shows an overall SEM image of NPs deposited onto the glass slide following fs laser irradiation at a CYTOP-glass distance of 100 \(\upmu\)m and a schematic of the NP-distributed areas.  
Figure 6a shows that the CYTOP NPs were deposited radially with a diameter of 1200 \(\upmu\)m onto the glass slide. Changes in the surface morphology of the deposited NPs are discernible depending on the distance from the center of the NP-distributed areas, as shown in Figure 6b. Figure 6i-iii shows that melted NP aggregates are agglomerated within a distance of 170 \(\upmu\)m from the center of the NP-deposited areas. Dense network structures are deposited at 170-520 \(\upmu\)m, and short, chain-like NP aggregates are observed at distances between 520 and 600 \(\upmu\)m; that is, debris of network structures is formed. Thus almost 70% of the NP-deposited areas are composed of network structures. In the case of a CYTOP-glass distance of 4000 \(\upmu\)m, the monolayer structure (see Figure 4b) is deposited radially, with a diameter of 3000 \(\upmu\)m.  
Figure 5: SEM images of NPs on glass slides after fs laser irradiation with different pulse energies.  
Figure 6: (**a**) Overall SEM image of deposited NPs on the glass and (**b**) a schematic of the NP-distributed areas. (**i–iii**) Enlarged SEM images corresponding to NPs deposited in areas i–iii in subfigure (**b**).  
After NP deposition, we evaluated the wettability of the CYTOP structures by measuring the water contact angle of each structure. Figure 7 shows microscopic images of 1 \(\upmu\)L water droplets on (a) a normal glass slide, (b) a CYTOP thin film [23], (c) monolayer, and (d) network structures composed of CYTOP NPs deposited onto glass slides. Table 2 summarizes the surface roughness, contact angle, and wettability of each substrate.  
The results in Figure 7 and Table 2 show that a low contact angle was observed for the glass surface, which was expected because the glass slide surface generally exhibits hydrophilic behavior. However, almost the same contact angle was observed on the CYTOP thin film and on the monolayer structure composed of CYTOP NPs on glass, as shown in Figure 7b and c. The highest contact angle of 158\({}^{\circ}\) was observed for the network structures deposited onto glass. These results indicate that the wettability of the glass surface can be varied from hydrophilic to superhydrophobic by altering the laser-processing parameters used for CYTOP NP deposition.
# Performance of Granulated Ti-Dopped Manganese Oxide Oxygen Carriers in Chemical Looping Processes
## 2 Materials and Methods
### Characterization of Oxygen Carriers  
The main phases of the manganese-titanium (Mn-Ti) mixed oxides were identified by X-ray diffraction (XRD). For that purpose, a Bruker D8 Advance X-ray powder diffractometer with an X-ray source (Cu anode) working at 40 kV and 40 mA was used. It included an energy-dispersive one-dimensional detector. In order to obtain the diffractogram of each sample, the scanning step selected was 0.019\({}^{\circ}\) over the 20 range of 10-80\({}^{\circ}\)DIFFRAC.EVA.V5.2 and TOPAS6 software, on the basis of the Joint Committee on Powder Diffraction Standards, were used in the assignation and quantification of crystalline phases.  
Crushing strength determination of the different oxygen carriers was carried out in a Shimpo FGN-5 digital force gauge apparatus as the force required to fracture a particle. According to the procedure, the value of crushing strength presented in this work was the average value of 20 measurements of randomly chosen particles within the desired range of 0.1 and 0.3 mm.  
Other material properties determined were the specific surface area in ASAP 2020 from Micromeritics, applying the BET method to the isotherm obtained according to ISO 9277. The porosity of the particles was determined by Hg intrusion in a Quantachrome PoreMaster 33, and their real density was measured with a Micromeritics AccuPycc II 1340 helium pycnometer.  
A scanning electron microscope SEM EDX Hitachi S-3400N equipped with an EDX analyzer Rontec XFlash of Si(Li) was used to investigate the microstructure of the oxygen carrier materials. Both the morphology of the particles and their cross section were observed after cutting once embedded in resin.
# Policy-Based Smart Contracts Management for IoT Privacy Preservation
## 4 Implementation and Validation
### Validation  
The Hyperledger Fabric provides a tool called Explorer, used to visualize the network that has been constructed. It shows the list of peers, metrics, and transactions by organization panels, followed by the most recent activity. Each panel displays the channel's history activity, stats, and the most recent activity. The process of linking based on Explorer is shown in Figure 6.  
The metrics panel is divided into four tabs; each one displays the most current block and transaction data for the selected channel. Figure 6 also shows the total number of counts per hour (or minute). The results of this experiment revealed that the time in terms of block and transaction minutes is insignificant in all evaluation measures, and indicated the number of blocks, eight, added to the fabric network in the 3.00 p.m. timeframe.  
Figure 7 depicts the blocks, which indicate the information saved in the ledger for each block. We can see the hash value of each block, as well as the previous hash value of block 19, for example. We can monitor and control our Fabric network with this tool.  
Since we did not modify the Hyperledger Fabric blockchain code, the behavior of the blockchain is similar to existing benchmarking, such as [PERSON] et al. (2018). They conducted a performance study of Hyperledger Fabric 2.0. Figure 8 shows the behavior of the blockchain network for one organization, such as a service provider in our case, with an increased number of peers (nodes) verifying each received smart contract (transaction). The blue, orange, gray, and yellow bars show the transaction times of two nodes, four nodes, eight nodes, and sixteen nodes, respectively.  
Figure 6: The Hyperledger Explorer.  
Figure 7: Blockchain block.  
We notice the increase in execution time each time we add new peer nodes within one organization. This effect is predicted, since adding more peers increases the network's communication and synchronization costs.  
Figure 9 shows the average throughput and latency for different block sizes across various transaction arrival rates. As the transaction arrival rate increases, we observe that the throughput grows proportionally.
# Global Formulation of the Synthetic Storm Technique Oriented to Satellite Link-Budget Design
###### Abstract
\begin{table}
\begin{tabular}{c l} \hline \hline
**Reference, Year** & **SST Application** \\ \hline
1996 [1] & Mathematical theory of the Synthetic Storm Technique (SST), with two vertical layers of precipitation: 0 \({}^{\circ}\)C (melting layer), 20 \({}^{\circ}\)C ( rainfall) \\
2005 [2] & SST-derived \(A(t)\) can substitute missing experimental data (Italast). \\
1994 [3] & The power spectrum of experimental \(A(t)\) falls off as \(-\)20 dB/dec \\
1997 [4] & Fade durations. \\
1998 [5] & Satellite communications with mobile transmitters/receivers. \\
1998 [6] & Diurnal distribution of rain attenuation in communication and broadcasting satellite systems. \\
1998 [7] & Wide area joint probability of rain attenuation in satellite systems with a common on-board resource. \\
1998 [8] & Worst-month statistics of rain attenuation. \\
1999 [9] & Large distance site diversity. \\
2000 [10] & Matching service quality and system design to the time of the day. \\
2003 [11] & Small scale (“micro”) site diversity. \\
2003 [12] & Statistics of interruption time due to rain attenuation. \\
2004 [13] & Service oriented statistics of interruption time. \\
2006 [14] & Annual and diurnal satellite rain attenuation statistics in Athens. \\
2006 [15] & Low elevation angle (5\({}^{\circ}\)) terrestrial paths. \\
2006 [16] & Time diversity as a rain attenuation countermeasure. \\
2007 [17] & Rain storms speed and temporal properties of precipitation. \\
2007 [18] & Time diversity gain in satellite links. \\
2007 [19] & SST as part of a time series generator. \\
2008 [20] & Tropical attenuation statistics. \\
2009 [21] & Phase delay and attenuation in deep-space tracking. \\
2011 [22] & Phase delay and differential attenuation in phased array antennas for deep-space communications. \\
2013 [23] & Small scale site diversity in Guam. \\
2013 [24] & Efficiency of satellite channels and ideal fade-countermeasure. \\
2016 [25] & Space communications to the Sun–Earth Lagrangian point L1 from Spino \\
2016 [26] & Space communications to the Sun–Earth Lagrangian point L1 from Madrid. \\
2016 [27] & A method to achieve clear-sky data-volume download in satellite links \\
2016 [28] & Rain Attenuation Statistics at Ka and Q band in Athens. \\
2017 [29] & Linear combining techniques using time diversity. \\
2018 [30] & Rain attenuation statistics for micro and millimeter wavelengths. \\
2018 [31] & Fade dynamics in equatorial Malaysia. \\
2019 [32] & Space diversity in Attica, Greece. \\
2021 [33] & Time series of rain attenuation in a tropical location. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: References and applications of the Synthetic Storm Technique (SST). a discussion on the results. Finally, Section 6 draws some conclusions. In Appendix A, we show the scatter plots of the error for each site considered.
## 4 Engineering Application  
### Geometry and Finite Element Model  
During the construction at the Yonglian tunnel entrance up to the F2(ZK91 + 325-ZK91 + 325) fault fracture zone in Jiangxi Province of China [37; 38], water and mud inrush catastrophe occurred. The F2 fault surface crosses the section ZK91 + 350, trending SSE, intersecting the tunnel axis at a 45\({}^{\circ}\) angle, dipping to the east with an angle of 84\({}^{\circ}\), and has a width of 15 to 35 m, predominantly filled with argillaceous materials, and the stratum has a high water content.  
Figure 7 depicts the finite element model and geometry of the fault and fractured zones in the Yong Lian tunnel, which measures 400 m in length and 360 m in height. A fault fracture zone (F2:ZK91 + 325-ZK91 + 325) with high water pressure exists in front of the heading face, with a dip angle of 84\({}^{\circ}\) and a width of 25 m from the tunnel face. Table 2 outlines the physical parameters based on literature reference [38]. Initial settings include porosity (\(\varphi\)) at 0.3 and fluidized grain concentration (\(c\)) at 0.05. Boundary conditions entail pore pressures of 0, 0, and 2 MPa applied separately at the outlet, upper, and bottom boundaries, with the others having no flow boundary. The Dirichlet condition is adopted at the interface of surrounding rock (P1, V1) with fault (P2, V2), ensuring continuous boundary pressure and velocity. After 550 min of initial steady seepage computation, we derive the initial pore pressure for the entire region. Figures 8 and 9 illustrate the temporal fluctuations of porosity, pore pressure, and fluidized grain concentration, respectively. Figures 10-12 demonstrate the development of the water inrush channel.  
Figure 8: _Cont_.  
Figure 7: Tunnel water inrush model.  
Figure 8: Time variation in the porosity.  
Figure 9: _Cont_.  
Figure 10: Changes in the porosity over 0.5 in the fault zone over time.  
Figure 9: Time variations in the pore water pressure \(p\) and fluidized grain concentration \(c\).
## 3 Results
### Horizontal Jump Dynamics Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Parameter**} & **EG (\(\mu\) = 15)** & **CG (\(\mu\) = 15)** & **F-Value** & \(p\)**-Value** & \(\eta^{2}\) \\  & **M \(\pm\) SD** & **M \(\pm\) SD** & **F-Value** & **\(p\)**-Value** & \(\eta^{2}\) \\ \hline JD (m) & & & & & \\ L-R Pre & 1.47 \(\pm\) 0.12 & 1.48 \(\pm\) 0.10 & 89.37 \({}^{*}\) & \(<\)0.05 & 0.761 \\ L-R Post & 1.66 \(\pm\) 0.08 & 1.49 \(\pm\) 0.10 & & & \\ \hline H-GRF (N) & & & & & \\ L-Pre & 735 \(\pm\) 82 & 733 \(\pm\) 86 & 13.98 \({}^{*}\) & \(<\)0.05 & 0.333 \\ L-Post & 823 \(\pm\) 53 & 737 \(\pm\) 83 & 13.98 \({}^{*}\) & \(<\)0.05 & 0.333 \\ R-Pre & 741 \(\pm\) 78 & 740 \(\pm\) 91 & & & \\ R-Post & 810 \(\pm\) 75 & 738 \(\pm\) 62 & 7.39 \({}^{*}\) & \(<\)0.05 & 0.209 \\ \hline V-GRF (N) & & & & & \\ L-Pre & 1719 \(\pm\) 198 & 1718 \(\pm\) 189 & 12.51 \({}^{*}\) & \(<\)0.05 & 0.309 \\ L-Post & 1928 \(\pm\) 130 & 1721 \(\pm\) 142 & 12.51 \({}^{*}\) & \(<\)0.05 & 0.309 \\ R-Pre & 1733 \(\pm\) 183 & 1735 \(\pm\) 160 & 16.69 \({}^{*}\) & \(<\)0.05 & 0.373 \\ R-Post & 1903 \(\pm\) 174 & 1739 \(\pm\) 171 & & & \\ \hline R-GRF (N) & & & & & \\ L-Pre & 1870 \(\pm\) 209 & 1869 \(\pm\) 201 & 13.26 \({}^{*}\) & \(<\)0.05 & 0.321 \\ L-Post & 2096 \(\pm\) 134 & 1873 \(\pm\) 159 & 13.26 \({}^{*}\) & \(<\)0.05 & 0.381 \\ R-Pre & 1885 \(\pm\) 194 & 1887 \(\pm\) 176 & 17.42 \({}^{*}\) & \(<\)0.05 & 0.384 \\ R-Post & 2069 \(\pm\) 180 & 1890 \(\pm\) 176 & & & \\ \hline T-PRF (\(\theta\)) & & & & & \\ L-Pre & 66.7 \(\pm\) 1.3 & 66.8 \(\pm\) 1.5 & & & \\ L-Post & 66.7 \(\pm\) 1.1 & 66.7 \(\pm\) 1.2 & & 0.024 & \(>\)0.05 & 0.001 \\ R-Pre & 66.7 \(\pm\) 1.3 & 66.8 \(\pm\) 1.5 & & & \\ R-Post & 66.1 \(\pm\) 1.5 & 66.8 \(\pm\) 1.4 & & 0.778 & \(>\)0.05 & 0.027 \\ \hline \end{tabular}
\begin{tabular}{c} EG represents the experimental group, and CG represents the control group. Abbreviations: The jump distance (m) is JD (m). The horizontal force of take-off is H-GRF. The vertical ground reaction force (N) is V-GRF (N). The resultant ground reaction force (N) is R-GRF (N). The trajectory at peak resultant force (degrees) is T-PRF (\(\theta\)). The data are presented as the mean \(\pm\) standard deviation (M \(\pm\) SD), and were statistically analyzed using the F-test, with the significance level set at \({}^{*}p<0.05\). \\ \end{tabular}
\end{table} 
Table 4: Analysis of dynamic parameters of lateral jump.