esa-sceva/satcom-chunk-collection huggingface dataset:
# Wall Structure Geometry Verification Using TLS Data and BIM Model
## 3 Case Study  
In the case study, a three-room apartment was used (Figure 14). The apartment is part of a residential complex NUPPU in Bratislava with 800 apartments created by eight separate buildings. The BIM model of the apartment was provided by the contractor. The LOD of the model is 300, which means that the elements are graphically represented in the model as a separate system or object defined by quantity, size, shape, location, and orientation. The interior of the apartment was scanned using a terrestrial laser scanner Trimble TX5 with a scanning resolution of 1/16, which means 24.5 mm \(\times\) 24.5 mm/10 m. The ranging error of the instrument defined by the producer is \(\pm\)2 mm at 10 m. The whole interior was scanned from eight positions of the instrument. The scanned data were registered to the coordinate system of the design using surface-based registration (using overlapping areas) and target-based registration (using identical reference points) with a registration error of 3 mm. The described approach was performed on selected walls of the case study object. The numbering of the walls is shown in Figure 14.  
The procedure described in Section 2 was used for this case study. The first step was the creation of wall segments from the derived corners from the IFC file. Segmentation of the corresponding subsets of point cloud was performed consequently. The threshold was set as 40 mm for distances and 4\({}^{\circ}\) for angles between normal vectors (for normal vector-based filtration). Figures 15 and 16 show the original point cloud (result of scanning) of selected walls colored in RGB. The walls are plastered with gray gypsum plaster. The color of the scanned surface in combination with the ambient light generates bright colors of points. Figure 14: Case study object—BIM model with scanned point cloud and numbers of walls. Figure 13: Deviation map—quantification of flatness of the wall. Results of the comparison of planes from the BIM model and planes estimated from the segmented subsets of points are listed in Tables 1 and 2. Table 1 contains parameters (a, b, c, and d) of the planes from IFC as well as the planes segmented and estimated from the point cloud. Table 2 shows the deviations in orientation and the deviation between the coefficients d (representing the distance of the plane from the origin of the coordinate system) of the corresponding planes and contains the maximum, minimum, and average deviations between the IFC plane and the segmented point cloud.
## 3 Experimental Setup
### Evaluation Metrics  
The classification performance of DL models can be evaluated by metrics such as accuracy, precision, sensitivity, F1 score and [PERSON]'s kappa coefficient. These metrics are derived from the confusion matrix to analyze the prediction results of the model by class. The classes in the confusion matrix are as follows. True Positive (TP): instances that the model predicts as positive and are actually positive. True Negative (TN): instances that the model predicts as negative and are actually negative. False Positive (FP): instances that the model predicts as positive but are actually negative. False Negative (FN): instances that the model predicts as negative but are actually positive. Table 6 summarizes the classification metrics, including their mathematical formulas and detailed explanations.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Metric** & **Equation** & **Description** \\ \hline Accuracy & \(\frac{TP+TN}{TP+TN+TN}\) & Refers to the proportion of the model’s total predictions that are correct. \\ Precision & \(\frac{TP}{TP+TP}\) & Refers to the proportion of positive predicted samples that are actually positive. \\ Recall & \(\frac{TP}{TP+TN}\) & Measures how many true positive samples are correctly predicted. \\ F1-Score & \(\frac{2\times(Precision\cdot Recall)}{Precision\cdot Recall}\) & Provides the balance between precision and sensitivity. \\ [PERSON]’s kappa score & \(\frac{\text{D}-\text{D}_{p}}{\text{T}-\text{P}_{p}}\) & Assesses how well the model performs compared to random guessing and is calculated based on observed accuracy (p\({}_{0}\)) and expected accuracy (p\({}_{0}\)). \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance metrics, equations and detailed descriptions.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Configuration** & **Parameter** \\ \hline Operating System & Windows 11, 64 Bit \\ Programming Language & Python version 3.11.4 \\ Frameworks & Tensorflow 2.14.0, Keras version 2.11.4, Matplotlib 3.7.1 \\ GPU & 2 x Nvidia RTX 3090 24 GB \\ CPU & Intel(B) Core(TM) 9i-10920X CPU @ 3.50 GHz \\ RAM & 128 GB \\ CUDA & v12.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specifications of the hardware and software environment employed for model training and evaluation.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Batch Size** & **Epoch** & **Learning Rate** & **Optimizer** & **Weights\_Decay\_Rate** \\ \hline
64 & 100 & 0.0003 & Adam & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hyperparameters used for training CNN models, including batch size, number of epochs, learning rate, optimizer and weight decay rate.
# Enhancing Robustness of Per-Packet Load-Balancing for Fat-Tree
## 3 Load Balancing Based on Symmetric Path Groups (LBSP)
### Handling Asymmetry due to Failures  
#### 3.3.1 Failure Notification  
For failure detection, each switch sends hello messages to its neighbor switches. On detecting a failure, it floods a failure message in the network. A failure message contains the information on the failure type and the address range affected by the failure. With the failure types, a switch can figure out which link group should not be selected. Table 2 summarizes the failure types.  
A failure message from a switch detecting a failure of a downlink contains the address range associated with the port incident to the failed link. In contrast, a failure message from a switch detecting a failure of an uplink contains the address range of all the hosts. In the fat-tree shown in Figure 2, suppose that the link between A31 and T31 is down. The failure message sent from A31 will contain the address range of [124... 127] and the failure type of 1. Suppose that Switch T3 receives the failure message. Then, T3 cannot select Link Group 1 for the flows with the destination address in the range 124-127.  
In the case of a three-tier fat-tree, the failure messages take at most three hops to reach all the aggregation switches and ToR switches that need the failure information to select a link group.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Type of Failed Link** & **Affected Link Group** \\ \hline
0 & ToR switch–aggregation switch & Link Group 0 \\
1 & ToR switch–aggregation switch & Link Group 1 \\
2 & aggregation switch–core switch & Link Group 0 \\
3 & aggregation switch–core switch & Link Group 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Failure Types.  
Figure 4: (**a**) Illustrations of destination address ranges associated with downlink ports of the core switch C15 and the aggregation Switch A3 in the fat-tree shown in Figure 2; and (**b**) an illustration of partitioning 16 shortest paths into four path groups and assigning path groups according to the least significant bits of the destination address.
## 3 Literature Review
### Hybrid Techniques  
SMOTEBoost and RUSBoost are popular hybrid techniques that combine SMOTE and RUS with AdaBoost, respectively. The main idea of these techniques is to apply SMOTE or RUS at each boosting iteration to solve the imbalance problem and improve the performance of the software defect prediction. [PERSON] et al. [PERSON] et al. (2016) utilized SMOTEBoost and RUSBoost to address the software defect imbalance problem. Experimental results proved that SMOTEBoost and RUSBoost improved the prediction performance of imbalance defect classification.  
EasyEnsemble [PERSON] et al. (2018) is a hybrid model that solves the imbalance defect issue by creating a balanced dataset using random undersampling. The model uses Adaboost on the balanced subsets to classify the modules. EasyEnsemble was combined with neighbor cleaning (NCL) to gain an improved prediction performance by using EasyEnsemble to overcome the class imbalance problem. In contrast, NCL was used to solve the class overlapping problem. This combination showed a better performance than ensemble random undersampling, NB, NB + log, RF, and Dynamic AdaBoost.NC, SMOTE+NB, RUS+NB, SMOTEBoost, and RUSBoost.  
[PERSON] et al. [PERSON] et al. (2018) proposed a hybrid ensemble model to handle the imbalance problem and empower the performance of software defect prediction. This hybrid model combines three oversampling techniques and then applies RF as a base learner for the ensemble model. Oversampling techniques include ROS, Majority Weighted Minority Oversampling Technique, and Fuzzy-Based Feature-Instance Recovery. Results showed that the hybrid model yielded good results compared to multiple other approaches, such as ROS.
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 7 Emerging Research Areas and Future Directions
### Integration of Emerging Data Sources and Technologies  
Recent studies have shed light on the prevalence and impact of distracted driving in the United States. Cambridge Mobile Telematics (CMT) and Arity, two companies aggregating data from mobile phones and vehicle telematics, provide alarming insights:  
* CMT's 2023 report reveals that 34% of all drivers who crash interact with their phone in the minute before the crash [130].
* Arity's 2023 report notes a 30% increase in distracted driving per mile from 2019 to 2023 [131].  
These findings contrast with the National Highway Traffic Safety Administration's (NHTSA) 2022 research note, which reports lower percentages of distraction-affected crashes [132]. The discrepancy can be attributed to different methodologies and data sources, highlighting the value of telematics data in supplementing traditional police crash reports.  
The studies also reveal interesting patterns in distracted-driving behavior, including seasonal and geographic variations. However, it is important to note potential limitations in the data sample collected by companies like Arity and CMT, such as selection bias and the focus on phone-based distractions.  
These findings underscore the urgent need for continued efforts to combat distracted driving through legislation, enforcement, education, and technology-based solutions.
## 2 Related Work and Theoretical Background
### Multi-Objective Optimization (MOO) in Sustainable Manufacturing  
Balancing conflicting goals is essential in sustainable manufacturing, and that is where Multi-Objective Optimization (MOO) techniques shine. Various MOO approaches, including genetic algorithms such as NSGA-II, particle swarm optimization, and other metaheuristics, have been used to optimize trade-offs. For example, some research has aimed to minimize cost, energy use, and ensure quality at the same time by adjusting the machining parameters or the scheduling [5; 17]. Other studies have focused on creating production schedules that reduce delays and reduce emissions [18; 19]. MOO has helped optimize supply chain design by choosing the best locations, transport methods, and inventory strategies while accounting for both cost and environmental impact [20].  
Limitation: While MOO offers tools to manage trade-offs, its success is highly dependent on up-to-date, high-quality input data. Many existing applications rely on estimates or historical averages instead of real-time insights. Environmental goals are often based on broad models or static calculations rather than live sensor data tied to current process conditions. Moreover, many implementations lack an automatic loop to bring fresh data into the optimization engine on a continuous basis.
## 3 Research Method
### Development of the Research Hypotheses
#### 3.1.3 Relationship between E-Service Quality and Continuous Usage Intention  
The relationship between e-SQ and CUI is typically positive and significant. Higher levels of e-SQ tend to lead to greater CS, e-trust, and loyalty, thereby increasing customer intentions to continue using a service over time. Conversely, lower levels of e-SQ can result in dissatisfaction, distrust, and eventual discontinuation of usage. Several studies have explored the correlation between e-SQ and CUI. [PERSON] et al. [43] examined the association between e-SQ and continuance intention specifically within eTax services. Similarly, [PERSON] et al. [44] observed the impact of e-SQ on continuance intentions in the business-to-consumer mHealth domain. [PERSON] et al. [6] demonstrated how e-SQ influences CUI in community group buying platforms. Consequently, the current study hypothesizes the following relationship between e-SQ and CUI in e-commerce marketplace platforms:  
**Hypothesis 3 (H3).**_E-SQ is positively related to CUI._
# Nonconvex Nonlinear Transformation of Low-Rank Approximation for Tensor Completion
## 5 Numerical Experiments
### Color Image Recovery  
We applied our method to recovering color images at extremely low sampling rates (SR) of 2.5%, 5%, 10%, and 15%, and compared the results with those of TNN, UTNN, FTNN, and NTTNN, as shown in Figure 4, demonstrating that our method has superior color and detail restoration capabilities. We utilize PSNR and SSIM to assess the quality of recovery performance. The evaluation results are presented in Table 2. Our method achieves the highest indices in both metrics at low sampling rates of SR = 2.5%, 5%, and 10%. However, at SR = 15%, FTNN obtains the highest indices in SSIM. In Figure 4, our model demonstrates superior capability in restoring the colors of the image under low sampling rates compared to other models, while preserving more image details within the calibrated window.
# Incremental Formula-Based Fix Localization
## 6 Related Work
### Formula-Based Fault Localization
Second, instead of trying to check all possible diverted paths by exhaustively diverting bounded subsets of conditions on the counterexample path, we encode the possible effect of diverting operations into the trace formula to leverage the search capacity of the solver. As a result, by analyzing MCSs obtained from the solver, a much smaller number of diversion attempts is needed to derive correct paths than is required using [PERSON]'s approach. [PERSON] and [PERSON] [16] proposed two techniques called on-demand formula computation (OFC) and clause weighting (CW) to mitigate the computational expensive and improve the accuracy of formula-based fault localization. Specifically, OFC (1) encodes only statement instances in the original failing trace into the error trace formula, (2) computes all MCS of the constructed formula. (3) If there is a conditional statement \(st\) such that (i) \(st\) is found in an MCS and (ii) a branch \(b\) of \(st\) is still not encoded in the formula, then the OFC expands the formula by encoding all statement instances in the branch \(b\) and go back to step (2). Otherwise, the obtained MCSs are reported as the final output. OFC and our proposed ATF encoding similar in encoding only partial program into formula in an increasing manner. There are two main differences between the OFC and our method ATF. First, the formula in OFC approach is expanded to encode all branches of conditional statements that occurred in an MCS, while the ATF formula is expanded to refine abstracted conditional branches that are included in an angelic execution of the angelic program. Second, our approach does not require computing all MCS of the intermediate formula, as the OFC does, instead, it computes one MCS at a time and stops computing MCS when the formula needs more refinement. In the Angelic Debugging approach [21], program expression(s) in suspicious scope (provided in advance) are replaced with a nondeterministic expression (i.e., an angelic choice which can return arbitrary value). Then, they use symbolic execution to find a successful execution in the transformed program with the input is fixed to a given failing test input. If such a successful execution exists, the translated suspicious expression is considered a fix candidate, and the concrete values of the non-deterministic expressions is reported as angelic values, or a suggestion to fix. The limitation of this method is that it performs on each expression separately; thus, they need to run the symbolic execution many times, each time for checking one expression. Thus, they have to call to SMT solver many times for solving different formulas. One limitation of this method is that it does not return minimal results. Indeed, it can output a successful angelic execution by replacing all statements in the suspicious scope with angelic value.
## 4 Seismic Vulnerability of Wall Pier Girder Bridges
### Seismic Vulnerability of Wall Pier Bridges
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Minor Damage**} & \multicolumn{2}{c}{**Medium Damage**} & \multicolumn{2}{c}{**Severe Damage**} & \multicolumn{2}{c}{**Complete Destruction**} \\ \cline{2-9}
**Component** & \multirow{2}{*}{\begin{tabular}{c} **Median** \\ **Value (g)** \\ \end{tabular} } & \begin{tabular}{c} **Logarithmic** \\ **Standard** \\ **Deviation** \\ \end{tabular} & \begin{tabular}{c} **Median** \\ **Standard** \\ **Value (g)** \\ \end{tabular} & \begin{tabular}{c} **Median** \\ **Value (g)** \\ \end{tabular} & \begin{tabular}{c} **Median** \\ **Standard** \\ **Deviation** \\ \end{tabular} & \
\begin{tabular}{c} **Logarithmic** \\ **Standard** \\ **Value (g)** \\ \end{tabular} &
\begin{tabular}{c} **Median** \\ **Standard** \\ **Deviation** \\ \end{tabular} \\ \hline Pier (out of plane) & 0.2541 & 0.8003 & 0.7618 & 0.4712 & 1.3108 & 0.7875 & 3.1159 & 1.1027 \\ Pier (in plane) & 0.316 & 1.5414 & 1.0808 & 0.6534 & 3.3743 & 0.7875 & 3.3743 & 0.7875 \\ Pier (overall) & 0.1892 & 0.9701 & 0.695 & 0.463 & 1.3108 & 0.7875 & 3.1159 & 1.1027 \\ Bearing & 0.2312 & 0.8775 & 0.3966 & 0.6135 & 0.5306 & 0.5739 & 0.6435 & 0.4278 \\ Abutment & 0.5759 & 0.8253 & 0.6877 & 0.4962 & 0.8648 & 0.3866 & 1.4911 & 0.4057 \\ Bridge system & 0.1767 & 0.8758 & 0.3682 & 0.5241 & 0.5031 & 0.5449 & 0.6435 & 0.4278 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The median and logarithmic standard deviation in the fragility models of bridge components and bridge systems to different damage states. Figure 9: Seismic fragility curves of the bridges. damage state, severe damage state and complete destruction state. The statistical parameters of the in-plane vulnerability curve in severe damage state and the complete destruction state are the same, and the primary reason is that the in-plane bearing capacities of the structures is rapidly decreased after the wall piers reach a serious damage state, and the structures quickly enter the complete destruction state. In the medium damage state, severe damage state and complete destruction state, the vulnerability curve of the bridge system and the bearings are closest or coincident, that is, their seismic vulnerabilities are closely related.
## 3 State-of-the-Art Results on the Detection of Identity Deception
### Review Analysis Method  
In the analysis that follows, we identify representative papers from the literature that offer different approaches to identity deception detection. The list is intentionally not exhaustive because the scope of this paper is to highlight what we deem to be serious shortcomings for the state of research in identity deception detection.  
The literature review was conducted by a faculty member and a graduate student at Western Washington University. The initial article search involved terms such as \"identity deception detection\" and \"malicious account detection\" using well-known databases such as IEEE Xplore, ACMDigital Library, and Google Scholar. We selected articles of interest based on their accuracy (i.e., such as the article falling within the scope of our investigation) and impact. The latter means that the article was either published in a venue of high impact, a reputable venue, and claimed to have substantially improved on past identity deception detection models. For example, reference [17] was both published in a reputable venue and has demonstrated a high malicious account detection accuracy of 99%. As such, the selection process led to representative papers with diverse approaches that have been or are likely to be cited frequently by other papers. In the end, we focused on and analyzed the methodological approaches described in 20 papers. We used threats to internal validity, external validity, and construct validity as the primary criteria for evaluating the 20 selected articles. These threats have been repeatedly identified as a serious source of concern for studies primarily in the medical field [18; 19], but also in the field of cybersecurity [20]. Typical threats to external validity involve faulty sampling practices (e.g., not having a random or representative sample), inability to control online environmental conditions (e.g., different online social network designs have different features), and the interaction of history with a study (e.g., the implementation of a detection system can affect behavior and vice versa, and as such, data history becomes important). Additionally, construct validity, which relates to external validity, focuses on how operational definitions of variables arebeing conceptualized. In other words, are the features selected for identity deception detection models representative of the definitions the researchers have set for them? A typical example where construct validity may be threatened is when a study uses proxy variables due to the inability to measure an effect accurately.  
We proceed by discussing the techniques used, the criteria for evaluation used by these techniques, and the goals of these detection methods. Figure 1 depicts a high-level diagram for these items.
The time-varying elevation correction to the specular point positioning based on the main decision factor of SSH real-time variation, ocean tide, has not been thoroughly studied. Additionally, the different effects of the geographical tidal difference on the positioning have not been considered into discussion. Differing from previous studies based on ocean tide, in order to reduce the time-varying elevation error of the reflection reference surface and improve the positioning of the specular reflection point, this study proposes the ocean tidal correction positioning method. Firstly, the initial specular reflection point is positioned on the earth ellipsoid. In the iterative positioning process, the geoid undulation calculated based on the earth gravitational model is introduced. The basic static-state elevation difference between the instantaneous sea surface and the ellipsoid surface is corrected. Secondly, on this basis, the ocean tidal model is applied to predict the total tidal height relative to geoid. Then the tidal height is introduced in iterative positioning. The reflection reference surface is corrected to the ocean tidal surface, reducing the positioning error resulted from time-varying elevation difference. Thirdly, based on the elevation correction, the reflection reference surface is corrected from the radial to the normal direction of the specular reflection point without approximation. The positioning error caused by the normal-radial difference is reduced. Fourthly, the different effects of the tidal height gradient modulo in the offshore and deep sea on the positioning correction are discussed.