esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Testing and Test Case Generation  
A testing procedure tries to locate errors in the code or specification. In this sense, errors can lead to failures in the system execution, deviating between the actual and expected outcomes.  
For software, a testing method generally relies on four main components: an information artifact, a generation mechanism, test data adequacy criteria, and a test oracle. Figure 1 sketches this process.  
The definitions of these components alongside fundamental testing concepts used on the paper are as follows [13]:  
* **Test**: The act of exercising software with test cases to find faults or demonstrate correctness.
* **Test case**: A set of inputs, preconditions, and expected outcomes associated with a particular program behavior.
* **Test suite**: A collection of test cases.
* **Test execution**: An execution of the system-under-test constrained to the conditions specified by a given test case.
* **Information artifact**: A source of information used as a guide for generating test cases. It can be a formal specification, a design document, or source code, and provides information about the system's functionality or goals necessary to produce meaningful test cases.
* **Generation mechanism**: An algorithm or strategy adopted to generate a test case from an information artifact.  
Figure 1: An overview of the testing process, adapted from [13] with permission from Elsevier.  
* **Test data adequacy criterion**: A measure of test case quality concerning a testing goal such as coverage. A test data adequacy criterion is often used to guide test case generation towards achieving the defined goal.
* **Coverage criteria**: It is a measure of test adequacy focusing on how thoroughly a test suite covers a specified portion of the information artifact that represents some functionality in the system-under-test.
* **Test oracle**: A procedure to evaluate if given a test execution is correct or not. It may be specified, derived, implicit, or human.
# Server Operation and Virtualization to Save Energy and Cost in Future Sustainable Computing
## 2 Background
### Synchronization of Virtual EPC Model and Virtualization Candidate Component
Since the standard 4 core server blade can handle traffic of about 5 Gbit/s, if the traffic can be dispersed at multiple points, the virtual EPC model can be sufficiently operated. Since packet processing in the server platform is not sufficiently stable (unable to provide stable packet processing speed due to load and other elements), however, mobile network service providers are concerned about it. Thus, the first virtual EPC application scenario is expected to be used in an enterprise that operates independently without much impact on the existing mobile network infrastructure or in a new dedicated EPC business model that does not have a large load such as M2M. In most mobile core networks, multiple appliances are located between the 3 GPP core and external network/service, especially SGi interface. Solutions in the form of middle box that provide multiple functions exist and require a complicated setting. Most settings are performed manually. Since all traffic belonging to a specific APN (Access Point Name), i.e., traffic that does not require specific middle box processing, has to pass the same processing route, the efficiency of equipment operation is low. Accurately speaking, it is not an EPC function defined in the 3 GPP standard, and it does not have much effect on service supply compared with the EPC functions. Therefore, mobile network service providers are less burdened with regard to the application of new solutions. As such, it is suitable to apply virtualization technology in particular, together with SDN technology as service chaining that enables flow level traffic routing. Not all mobile network service providers pay attention to it, however. Some embodied DPI and TCP performance optimization functions in the gateway itself, operating a simple SGi interface wherein a firewall is operated in external networks such as mobile core network and Internet.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**P2G**} & \multirow{2}{*}{**CCS**} & \multirow{2}{*}{**HCNG**} & \multicolumn{2}{c}{**Fixed**} & \multicolumn{2}{c}{**Continuous**} & \multicolumn{2}{c}{**Time-**} & \multirow{2}{*}{**Uncertainty**} & \multirow{2}{*}{**Multi-timescale**} \\  & & & & **Hydrogen** & & & & **Hydrogen** & & **Method** \\  & & & & **Blending** & & & **Blending** & & \\ \hline \([5]\) & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & SO \& RO & \(\times\) \\ \([10]\) & ✓ & \(\times\) & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \([11]\) & ✓ & ✓ & ✓ & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) \\ \([18]\) & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & DRO & ✓ \\ \([19]\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & RO & ✓ \\ \([20]\) & ✓ & \(\times\) & ✓ & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) \\ This Paper & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & DRO & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparative summary of this paper and related research. The symbol \(\checkmark\) indicates that the corresponding aspect is considered, while \(\times\) indicates it is not.
## 1 Introduction
### Diffusion Kalman Filtering for Comparison with Proposed Method  
In this paper, we analyze the performance of the information fusion method proposed in our previous work [11] under various criteria, further proposing it to be compatible for SGs that can improve the information accuracy and reduce the energy consumption. We incorporate the statistical properties of three major error sources that occur in IoT platform: measurement noise, quantization noise, and transmission noise to the information fusion function. With the proposed statistical information fusion approach, our main intention is to achieve the maximum improvement in information accuracy with a single information transmission in each hop. Therefore, we can save the excessive resource consumptions in conventional approaches that are used to improve the information accuracy Even though similar approach is found in [47], it lacks a performance analysis considering the channel bit errors. As it was analyzed in the motivation section, SGs favor high bit error rates. Therefore, the accuracy of the proposed method under high bit errors must be considered. We evaluate the performance of the proposed method in chain and tree network structures comparing its performance with conventional Average fusion and very recently proposed diffusion Kalman Filtering given in [48]. This mechanism is well suitable for comparison since it also does not exchange raw measurements with neighbors like the proposed method. Work presented in [46,48] has been proved to be simpler and accurate than [45] as the former algorithms do not exchange raw measurements. They are also proven to be energy and bandwidth efficient than exchanging raw measurements. Instead of broadcasting observations, they have an individual update and local update of state. Algorithm 1 presented in [46] has been proved to be more accurate than Estimated Based Diffusion Kalman Filter (EBDKF) in [48], but at the expense of communication and computational resources. EBDKF shows more accuracy and have less communication burden compared to Algorithm 2 given in [46] under low sampling periods. Therefore, we choose EBDKF along with conventional Average method to compare with the proposed method for smart grids.  
Improvements in both information accuracy and network lifetime of the proposed statistical approach are shown through extensive simulations in bit level and numerical level. The computational complexity of the proposed statistical approach is also investigated comparing with preceding two fusion functions. We also derive analytical upper bounds for variances of the final fused information.
# End-to-End Service Availability in Heterogeneous Multi-Tier Cloud-Fog-Edge Networks
## 1 Introduction
### Objective and Contributions of the Paper  
This paper rises to the occasion, bridging this gap with a tailored methodology of modeling for comprehensive analysis of end-to-end availability in multi-tier communication setups. This technique not only sheds light on current systems but also provides additional insight for future designs, ensuring the reliability and efficacy of next-generation systems.  
While traditional cloud-fog-edge models have three main tiers, modern multi-level architectures can be more complex. In large-scale systems covering expansive geographic areas, additional hierarchical levels may emerge.  
However, traditional reliability modeling techniques have limitations when applied to complex, multi-level architectures.  
This work aims to address this gap through a novel modeling approach tailored to analyze end-to-end availability in multi-tier communication systems. The proposed technique offers new ways to evaluate existing systems and set availability requirements during the design phase.
## 4 Discussion
### Limitations and Future Work  
The study of the factors influencing ESVs currently needs further improvement. This paper analyzes the importance of nine driving factors (HAI, GPP, Slope, DEM, POP, Temp, GDP, Pre and PM) on ESVs; however, ESVs also respond to other influencing factors such as oxygen release [57], the normalized difference vegetation index [41] and soil conditions [40]. Therefore, further refinement is still needed to comprehensively consider the effects of driving factors on ESVs in future studies to fully reflect the values of the ESVs. In addition, the data used in this paper may have had accuracy uncertainties of their own due to the limitations and effects of the raw data and other conditions. Therefore, data with a higher inversion accuracy can be used to improve the analysis results in future studies.  
Furthermore, this study only analyzed the effect of the HAI in synergy with other driving factors on ESVs and did not investigate the effects of other driving factors in synergy with each other on the ESVs. In future work, the effects on ESVs of the synergies of these other driving factors with each other will be developed. In addition, although this study analyzed the impact of driver synergies on ESVs, drivers such as other natural conditions and policies in the different study areas could not be quantified, and the impacts of these drivers were not analyzed. In future research, some policies and other natural conditions can be included as impact factor indicators.
###### Abstract  
There is limited evidence comparing the effects of manual and instrumented-assisted manipulations among adults with neck pain. Our purpose was to determine the effects of a multisession regime of manual and instrument-assisted cervical manipulation on pain, disability, perception of change, and muscle properties in subjects with nonspecific neck pain. We conducted a double-blind, randomized, placebo-controlled study in 32 subjects with nonspecific neck pain. Two groups received three sessions of cervical (C3/C4) manipulation, one group manual and the other instrument-assisted, a third group received three sessions of sham manipulation, and a fourth group served as a control. Self-reported pain, pressure pain thresholds, neck disability, patient perception of change, and properties (tous, stiffness, and elasticity) of the upper trapezius and biceps brachii were assessed at baseline, immediately after the first session and 15 days after the end of the intervention. After the end of the intervention, the percentage of changes in the visual analogue scale score, Neck Disability Index, and Patient Global Perception of Change score were significantly higher in the manual group in comparison with the other groups (\(p<0.05\)). No between-group differences were observed in the percentage of changes in touns, stiffness, and elasticity of the four muscles at the end of the intervention. We concluded that three sessions of C3/C4 manual manipulation improved pain and disability in subjects with nonspecific neck pain.  
s 2023 2  
maybe also at sites not related to the site of manipulation [12]. A widespread effect and one observed at sites not related to the level of manipulation raises the question of whether spinal manipulation should be applied at a specific level, or could be applied at a non-specific spinal level, without a previous clinical examination of motion restriction [14].  
Spinal manipulation can be performed manually or instrument-assisted. While some studies showed better short-term results in self-reported disability and pain with manual manipulation [15], others found no differences between these two interventions [16]. However, the available studies in the literature refer to short-term results, and little is known on middle-term effects. Hence, this study aims to assess the short and medium-term effects of a pre-selected spinal level manual and instrument-assisted cervical manipulation on pain, neck disability, patient perception of change, and myotonometer parameters (tonus, stiffness, and elasticity) in subjects with nonspecific neck pain. By assessing regions related and not related to the level manipulated, this study aims also to determine whether the effects of manipulation are region-specific or widespread.
# Digital Twin-Driven Stability Optimization Framework for Large Underground Caverns
## 3 Practical Implementation and Case Study
### Digital Twin Application for Monitoring and Stability Assessment
#### 3.2.7 BIM-Based As-Built Information  
As the construction and excavation progressed, the real-world construction data of actual deformation, as-built rock support, and geological model data within the DT framework were integrated in real time to ensure continuous updates and refinements (Figure 12). A multi-BIM model was developed to replicate actual construction conditions, dynamically updating the excavation and support processes in Steps 2, 3, and 4 based on real-world feedback. The BIM-based visualization deformation monitoring system enabled engineers to track convergence trends in the powerhouse cavern, identifying zones of excessive deformation and assessing the effectiveness of installed support systems. Instrumentation data from extensometers, load cells, and convergence arrays were fed into the FEM models, allowing for the real-time assessment of deformation behavior and the triggering of necessary modifications to the rock support strategy based on defined trigger levels. As-built support elements, including rock bolts, shotcrete layers, and anchors, were continuously updated in the model to reflect field adjustments. Additionally, a BIM-based as-built geological model was developed, incorporating detailed lithology, geological structures, and weak zones, ensuring that excavation and support strategies remained data-driven.  
This feedback loop informed earlier DT phases, particularly geological modeling, numerical analysis, and construction sequencing, optimizing safety and stability. The BIM-DT integration improved excavation efficiency, enhanced rock support effectiveness, and ensured long-term structural resilience, offering a scalable framework for future underground construction.  
Figure 11: (**a**) Location of extensometers, convergence stations, and load cells; sample of deformation data over months in flat format with graphical representation. (**b**) Extensometer data; (**c**) convergence station data; (**d**) load cell data.
# Multi-Class Intrusion Detection Based on Transformer for IoT Networks Using CIC-IoT-2023 Dataset
## 3 Deep Learning Model
### Transformer
#### 3.2.4 Layer Normalization  
Layer Normalization is a technique that normalizes each input feature independently, aiming to eliminate scale differences between different features and maintain output stability. Layer normalization helps control the output of each layer to keep it within a smaller range, helping to prevent gradient explosion. Sometimes, it can accelerate the convergence of the model and improve the training speed. Compared with Batch Normalization, Layer Normalization does not need to consider batch information.
# SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation
## 3 Methodology
### Stacked Hourglass Module
#### 3.3.3 Spatial Weighting  
Generally, depthwise convolution applies filters independently to each channel, resulting in a lack of interaction between channels. Therefore, many existing studies have performed a \(1\times 1\) convolution on feature maps generated through depthwise convolution to combine all the channels and create new information, enhancing channel interaction. However, this operation requires significant computational resources. Spatial weighting [17] was used to minimize the computational cost in the proposed model (Figure 5).  
Figure 5: Multi-dilation block comprising the decoder (D: Dilation Rate, GAP: Global Average Pooling, and C: Channel Dimension).  
The operation of spatial weighting is described as follows: All spatial feature maps are collected using global average pooling, and \(1\times 1\) convolution and ReLU activation functions are then used to reduce the channels. The feature map of the reduced channels is restored using \(1\times 1\) convolution and sigmoid activation functions. Afterward, element-wise multiplication is applied to the initial input feature map to effectively integrate the feature map while minimizing the computational channels.
## 3 Results
### Cross-Validation  
The performance of the GPP models, using cross-validation procedures, was examined for the model that combined the PRI with the SIF (red), since it produced the best empirical performance. The k-fold cross-validation approach to the model (Figure 6) consistently provided satisfactory statistics (r\({}^{2}\)cv = 0.79, RMSE\({}_{\text{cv}}\) = 0.188 mg CO\({}_{2}\)/m\({}^{2}\)/s). When repeating this analysis for the same model using only 2008 data, evaluated by comparison with the remaining dataset, showed comparable results for the training data (Figure 7a) and validation data (Figure 7b). These results were also similar to the original model presented previously (Figure 5), demonstrating the consistency and robustness of the model across multiple growing seasons, including different corn cultivars and variable environmental conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Output Variable** & **Predictor Variable** & **r\({}^{2}\)** & **RMSE (mg CO\({}_{2}\)/m\({}^{2}\)/s)** \\ \hline GPP & **PRI** & **0.54** & **0.2770** \\  & SIF (red) & 0.31 & 0.3598 \\  & SIF (far-red) & 0.28 & 0.3891 \\  & SIF (red) yield & 0.21 & 0.3877 \\  & SIF (far-red) yield & 0.20 & 0.4107 \\ \hline
**PRI, SIF (red)** & **0.80** & **0.1894** \\  & **PRI, SIF (far-red)** & **0.78** & **0.1994** \\  & PRI, SIF (red) yield & 0.67 & 0.2055 \\  & PRI, SIF (far-red) yield & 0.66 & 0.2099 \\ \hline \end{tabular}
\end{table}
Table 4: Summary of statistics (r\({}^{2}\) and RMSE) of GPP models examined in this study. Note: the highest performing model in each group is shown in bold type.  
Figure 5: Performance of the GPP model (r\({}^{2}\) = 0.80, RMSE = 0.186 mg CO\({}_{2}\)/m\({}^{2}\)/s) using both the PRI and the SIF (red) observations from four growing seasons (2008, 2010–2012) in the USDA BARC cornfield.
## 5 Conclusions and Final Considerations  
This study analyzed waiting times in Greater Valparaiso's public transportation system using an approach that combines exploratory analysis, spatial autocorrelation, and clustering. The results are consistent with prior research [20,22] highlighting the importance of service frequency and geographic location in shaping the user experience.  
The findings indicate that waiting times vary substantially depending on the mode of transportation and geographic location. Modes such as Metro Valparaiso, Trolleybuses, and Funiculars display short, stable waiting periods (3.14-5.90 min), whereas urban buses exhibit longer delays (up to 60 min) in areas such as Santa Julia, Vina del Mar Oriente, and Miraflores. Spatial autocorrelation confirms that these prolonged waits cluster in specific sectors, influenced by low service frequency and limited infrastructure. From a sociodemographic perspective, employees, laborers, and unpaid family members, typically low-income groups, are most affected, revealing an equity gap in access to transportation.
## 4 Results and Discussions  
To generalize the results and conclusions, the depth \(z\), pile deflection \(y\), bending moment \(M\), earth pressure \(p_{\mathrm{a}}\), and subgrade reaction \(p_{\mathrm{P}}\) are nondimensionalized as their corresponding dimensionless responses \(z_{\mathrm{a}}\), \(y_{\mathrm{a}}\), \(M_{\mathrm{a}}\), \(p_{\mathrm{a},\mathrm{a}}\), and \(p_{\mathrm{P},\mathrm{a}}\), given by Equations (1) to (5), respectively. Particularly, the normalizations were realized on the basis of the coefficient of pile deformation, \(\alpha\). Note that some unexpected pile deflection points are observed in these experiments, and they can be classified into two types for different reasons. The type-I abnormal point occurred when the sagging probe of the dial indicator with untigthened bolts lost close contact with the pile and was unexpectedly contacted or even stuck with the horizontal PVC pipe. In this scenario, the reading was not the real one. It can only be eliminated by tightening the bolt to guarantee a horizontal probe and expanding the diameters of the PVC pipe to guarantee the free movement of the probe. This type of abnormal point is erroneous and should be deleted. The type-II abnormal point was produced by the inherent limitation of the dial indicator because it can only read under compression. That is, it can only measure one-way displacement. Consequently, the reading of the dial indicator was always zero and did not change with the displacement of the pile, failing to reflect the actual deflection of the pile when the pile moved away from the dial indicator. This error can be minimized by setting a certain preload of compression on the dial indicator so that the probe can move with the pile when the pile departs away from the dial indicator. However, this method is not applicable for large displacement cases. In addition, if interested readers want to know the actual negative displacement, it can be approximately estimated based on the linear variation of displacement with the depth of a rigid pile.  
\[z_{\mathrm{a}}=\alpha z \tag{1}\]\[y_{\alpha} =\frac{y}{b} \tag{2}\] \[M_{\alpha} =\frac{M}{\alpha EI}\] (3) \[p_{\alpha,\alpha} =\frac{p_{\mathrm{a}}}{\alpha^{3}EI}\] (4) \[p_{\mathrm{P,\alpha}} =\frac{p_{\mathrm{P}}}{\alpha^{3}EI} \tag{5}\]