esa-sceva/satcom-chunk-collection huggingface dataset:
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 6 Applications and Policy Implications
### Emerging Technology Applications and Connected Vehicle Integration
#### 6.5.1 Autonomous Vehicle Crash Patterns and Safety Implications  
[PERSON] et al.'s [124] analysis of California crash data revealed that 61.1% of autonomous vehicle-including accidents were rear-end collisions. Environmental factors, such as mixed land use and proximity to schools, play a significant role in crash propensity. These findings support targeted safety system improvements and deployment strategies, particularly enhanced rear-end collision avoidance through automatic emergency braking systems in conventional vehicles.
# A Novel Magnetic Equivalent Circuit Model for the Corner of Inductor Core
## 4 Results and Discussion
### Discuss the Number of Corner Branches \(R_{c,i}\)  
We divided the corner \(R_{c,i}\) into two, three, four, and five branches and calculated the inductance from 1-1000A which compared with the inductance of the measured square inductor. The average relative error was calculated by Equation (11) and the program running time in MATLAB under the computer configuration of Intel Xeon E5-2670 CPUs @2.6 GHz 48 GB RAM is shown in Table 3.  
It can be seen that as the number of branches increases, the average relative error decreases slightly. This is because when the corner has two branches, the distribution of the corner branch flux can be automatically changed by the reluctance of the inner branch and the outer branch due to the change in the flux density. Multiple corner branching can better describe the magnetic density distribution in different regions of the corner, but the accuracy improvement of the flux calculation is not obvious. However, the calculation time of iteration increases significantly when a branch is added. The calculation time is \(26^{\prime}30^{\prime\prime}\) at two branches, and the time doubles when it reaches five branches. Based on the above experimental data, we chose three corner branches, which can effectively describe the magnetic density distribution between the corners, and show a high calculation accuracy of magnetic flux and acceptable calculation time.
## 3 Results
### Efficiency Outcomes
#### 3.1.4 Bracket Bond Failure  
A two-arm parallel trial was used in [PERSON] et al.'s study [56] to compare the effectiveness of therapy between PSLBs and CBs systems. In three orthodontic hospital departments in the UK, a prospective multicenter randomized controlled clinical trial was carried out. An edgewise appliance bracket system with either a PSLB (3M SmartClip) or a CB (3M Victory) preadjusted system was used to treat participants. According to the study, there were no appreciable variations in treatment effectiveness between the SmartClip (PSLB) and Victory (CB) appliance systems, and the total bond failure rates for both systems were comparable. No serious harm was observed with either bracket system. The study concluded that there was no clinically significant difference in treatment efficiency between the two bracket systems.
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## 3 Algorithm Design
### Fitness Function  
Genetic algorithms determine the selection and reproduction of populations based on the fitness function. Defining the fitness function of a genetic algorithm requires considering the objective function of the optimization problem and whether the constraints are satisfied. For this optimization problem \(\mathbf{P}2\), the fitness function \(f(\chi,K)\) is defined as follows  
\[f(\chi,K)=\frac{1}{\left\lfloor\frac{90}{\Delta\Psi}\right\rfloor}\sum_{p=1}^{ \left\lfloor\frac{80}{\Delta\Psi}\right\rfloor}\sum_{i=1}^{N}c_{i}[p]+E\sum_{p= 1}^{\left\lfloor\frac{80}{\Delta\Psi}\right\rfloor}I((\chi[p],K[p])\in C_{p}), \tag{20}\]  
where \(C_{p}\) represents the constraint of problem \(\mathbf{P}2\), and \(E\) represents the cost factor of constraint violation. \(I\) is an indicative function. When \((I[p],K[p])\in C_{p}\), \(I\) is \(1\), otherwise \(I\) is \(0\), where the constraints \(C_{p}\) include the constraints (19a), (19c), (19e), (19f), (19g) and (19h).
# Warpage Behavior on Silicon Semiconductor Device:
## 2 Sample and Test Description  
The considered samples for warpage analysis are 50 \(\times\) 10 \(\times\) 0.75 mm beam, made by bulk Silicon 730 \(\upmu\)m-thick, TiW 0.3 \(\upmu\)m, Cu seed 0.2 \(\upmu\)m, and ECD Copper 20 \(\upmu\)m-thick. These portions are sliced from wafer just after copper electro-deposition at room temperature; therefore, copper is not thermally treated before samples formation. The slices are annealed following three different temperature profile, shown in Figure 1. Considering that the experiments analyze the impact of annealing process on wafer warpage, it is assumed that the trends discovered by these measurements on rectangular slices are valid also for wafer. As first approach, according to [PERSON]'s theory later reported in Section 4.1, the ratio between 8-inches (200 mm) wafer and rectangular slice (50 mm) deflection could be calculated from Equation (7), resulting equal to 8.  
The ramps are characterized by fixed heating and cooling rate (respectively, 10 and 5 \(\lx@math@degree\)C/min) and setting the maximum temperature at 250 (in profiles \"W/ plateau\" and \"W/o plateau\") or 150 \(\lx@math@degree\)C (in profile \"150 \(\lx@math@degree\)C\"). The profile \"W/ plateau\" has a period of 30 min (the so called \"plateau\") in which temperature dwells at 250 \(\lx@math@degree\)C. The two temperature values are quite lower than the real process annealing temperature and they are chosen to analyze the warpage behavior due to copper film modification induced by Cu grain growth. As later shown by DSC analysis, an exothermic reaction happens at around 160 \(\lx@math@degree\)C.  
Figure 1: Temperature profile considered for annealing the samples and measuring the warpage in temperature.  
The profile \"W/o plateau\" is repeated three times to investigate on hysteretic behavior after the first cycle. Warpage is measured continuously during the annealing, according to methodology explained in the following paragraph. Due to simple geometrical considerations, beam curvature is derived from warpage and compared with the calculated by Finite Element Model in linear elastic regime. Physical analysis is performed to clarify the physical modifications that occurs in copper during thermal profile. With respect to the samples considered for warpage measurements, it is used for physical analysis samples with AlCu metal and TEOS passivations layers placed between the TiW layer of 0.3 \(\upmu\)m and the silicon substrate. The difference between warpage and physical analysis sample is negligible for the sake of the present work because the scope of physical analysis is to highlight thick copper micro-structure modification due to different temperature input. These interleaved layers between thick copper and silicon substrate do not affect copper structure modification due to thermal budget.
## 6 Simulation Results  
This section presents the simulation results of our algorithm. We compare our solution to the geographical algorithms of the literature that assume no position information: VCost [9], which is the best algorithm known regarding energy-efficiency, and LTP [10], which is one of the first known to guarantee delivery. In order to further evaluate the energy saving contributions of HECTOR, we also compare it to its variant HECTOR', which selects as the next hop the node that maximizes the progress towards the destination (_i.e._, it considers that \(cost(|uv|)=1\ \forall\,u,v\) and tries to minimize \(COP_{T}\) or \(COP_{V}\)). HECTOR' guarantees delivery but uses hop count as metric and is not energy aware. We first present the simulation setup and then give some performance results about energy consumption overhead, mean path length and mean hop length.
# Proxy-Based Adaptive Transmission of MP-QUIC in Internet-of-Things Environment
## 6 Conclusions  
In this paper, we described a proxy-based adaptive MP-QUIC transmission for throughput enhancement in the IoT environment. In the proposed scheme, a proxy device is employed between IoT clients and IoT server to aggregate the traffics of many clients in the access network. From the testbed experimentations, we can see that the proposed scheme outperforms the normal QUIC (using a single path) and the existing MP-QUIC scheme (using the round-robin policy) in terms of response delay and total transmission delay. This is because the proposed adaptive scheme can transmit data packets over the best path with lower RTTs and/or lower utilization among the multiple paths by considering the network conditions, such as link delay and packet loss rate. The proposed proxy-based adaptive MP-QUIC transmission scheme tends to give higher throughput performance than the existing QUIC schemes.  
In the proposed scheme, many IoT clients are connected to the pre-configured proxy. This can be regarded as a centralized scenario, which may be subject to the issue of a single point of failure. For further study, the distributed IoT scenario can be considered as a more elaborated scheme to handle a single point of failure.  
[PERSON] wrote the initial manuscript; [PERSON] revised the manuscript; [PERSON] proofread the manuscript; All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R11A3057509).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Orthogonal Experiment for Manganese Dioxide Lithium Ion Sieve Preparation
#### 3.1.2 Surface Morphology Analysis  
MnO\({}_{2}\) and LiOH, with a mass ratio of 10:1, were placed into a crucible, thoroughly mixed, and then water was added to form a paste, which was baked in a muffle furnace at 800 \({}^{\circ}\)C for 60 min. The surface morphology of the lithium ion sieve obtained by grinding was analyzed, and the SEM morphology under different magnification is shown in Figure 2.  
Figure 2 shows that the particles of the prepared manganese dioxide lithium ion sieve were relatively loose, and the sample size was relatively uniform, with agglomeration and an irregular polyhedron shape. There were many voids in the particle structure, which could be attributed to the release of O\({}_{2}\) during the calcination process. The phenomenon is consistent with [PERSON]'s research [37], and these voids were favorable for the entry and exit of ions in the ion sieve.  
Figure 1: Experimental procedure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experimental Number** & \
\begin{tabular}{c} **Lithium Adsorption** \\ **Efficiency/\%** \\ \end{tabular} &
\begin{tabular}{c} **Average Adsorption** \\ **Efficiency/\%** \\ \end{tabular} \\ \hline
1 & 100 & \\
2 & 100 & 99.98 \\
3 & 99.94 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Result of confirmative experiment for manganese dioxide lithium ion sieve preparation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Experimental Number** & \begin{tabular}{c} **Factors** \\ **A** \\ \end{tabular} & \begin{tabular}{c} **Lithium Adsorption** \\ **B/\({}^{\circ}\)C** \\ \end{tabular} & \begin{tabular}{c} **C/min** \\ **C/min** \\ \end{tabular} & \begin{tabular}{c} **D/mol/L** \\ \end{tabular} & \begin{tabular}{c} **E/h** \\ \end{tabular} & \
\begin{tabular}{c} **F/h** \\ \end{tabular} &
\begin{tabular}{c} **Efficiency/\%** \\ \end{tabular} \\ \hline
1 & 6:1 & 100 & 60 & 0.1 & 6 & 6 & 18.46 \\
2 & 6:1 & 200 & 120 & 0.5 & 12 & 12 & 3
# The Quest for the Best Explanation: Comparing Models and XAI Methods in Air Quality Modeling Tasks
## 2 Materials and Methods
### Feature Engineering and SHAP-Based Feature Reduction  
To enhance model performance while preserving interpretability, we applied automated feature engineering using lagged variables and evaluated their effect dynamically. Lagged features were created for basic environmental variables--PM\({}_{10}\), TEMP, PRES, DEWP, and WSPM--at multiple time intervals (1, 2, 3, 6, 9, 12, 15, 18, 24, 36, and 48 h). These lags capture temporal dependencies and correlations that are often relevant in air quality dynamics as well as in time-series analyses. We also ensured that this process did not negatively impact our modeling performance as we compared the model's R\({}^{2}\) and RMSE values before and after the generation of lagged variables and showed a positive impact on the model's metrics.  
After this step, SHAP-based feature reduction was applied. SHAP values were computed using the training data, and only the subset of features accounting for 95% of the total SHAP importance was retained. The model was then retrained on this dimensionality-reduced feature set. This entire procedure was applied independently for each monitoring station, allowing the model to adapt feature selection to the specific environmental context of each location. We evaluated performance before and after SHAP-based feature reduction using RMSE and R\({}^{2}\) scores for each station but also collectively as an average of all stations.
## 7 Discussion
### System and Technical Challenges
#### 7.1.4 System Interoperability  
Achieving seamless interoperability across various DT systems and transportation infrastructure components is essential for realizing a truly integrated and efficient transportation network throughout its entire lifecycle. Currently, many DTs operate in isolation, focusing on specific aspects of transportation, such as road traffic management or bridge health monitoring [28]. These standalone systems limit the overall effectiveness of DTs, as they often fail to account for the interdependencies and interactions between different infrastructure components across the planning, construction, operation, and decommissioning phases.  
To address this challenge, future research must focus on developing interoperability frameworks that enable communication and data sharing across diverse DT systems. Establishing common standards for data exchange and developing APIs that allow different systems to interact seamlessly are critical steps in this direction [27]. For example, an interoperable urban transportation DT could integrate real-time data from roads, railways, public transit systems, and pedestrian pathways, providing a comprehensive view of the entire network [96].  
A centralized transportation infrastructure data repository could further enhance interoperability by serving as a shared resource for various DT systems. However, achieving true interoperability requires overcoming technical challenges related to data integration, as well as addressing regulatory issues concerning data privacy and security [126]. By fostering collaboration among stakeholders and promoting the adoption of universal standards, the transportation sector can move closer to a fully interconnected DT ecosystem that supports infrastructure management throughout its entire lifecycle.
# Monitoring and Calibrating Building Materials Drying Kinetics with Capacitance Sensors
## 3 Results and Discussion
### Drying Moisture Content Results  
In general, drying processes involve two basic parts [30]. Many different mathematical models are proposed for describing the process of drying. In general, drying kinetics of building materials depend on the environmental conditions (air temperature, relative humidity, air velocity), material, and surface characteristics [31].  
1. The drying rate is constant up to critical moisture content. This period depends mainly on the material's microstructure and the boundary conditions;
2. The falling drying rate period. This is the period of unsaturated surface drying, determined by the difference between material moisture content and the surrounding water vapor pressures [32].  
The moisture content _Xsat_ (%) and voltage difference _Vsat_ are the values for the saturated samples, at the beginning of the experiment, while _Xo_ (%), _Vo_ are the corresponding values at the end of the experiment, which correspond to the equilibrium values for the environmental conditions under investigation. These values are shown in Table 3.  
As the moisture content of the materials increases, the capacitance increases as well. In Figure 6, the results for the variations of the materials' moisture content in relation to time are presented, with the average values for each material. For some materials, like ML and MC, the beginning of the experiment showed no change due to the calibrated range of the measurement circuit. There were no changes for the first few hours, as the capacitance circuit was tuned for a maximum range of 600 mV [22]. Especially for the cement mortar, although it presents lower moisture content in respect to the other mortars, the values of voltage difference remain at a high level even after 41 h.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Xsat (\%)** & **Stdev** & **Xo (\%)** & **Stdev** & **Vsat** & **Stdev** & **Vo** & **Stdev** \\  & **(kg/kg db)** & **(kg/kg db)** & **(kg/kg db)** & **(kg/kg db)** & **(mV/mV db)** & **(mV/mV db)** & **(mV/mV db)** \\ \hline _BRI_ & 8.39 & 0.005 & 0.20 & 0.0004 & 0.390 & 0.019 & 0.192 & 0.026 \\ _BRM_ & 12.9 & 0.014 & 0.18 & 0.00005 & 0.393 & 0.134 & 0.122 & 0.001 \\ _SRY_ & 13.3 & 0.050 & 0.73 & 0.005 & 0.649 & 0.011 & 0.159 & 0.029 \\ _SST_ & 3.80 & 0.009 & 0.45 & 0.040 & 0.350 & 0.031 & 0.231 & 0.007 \\ _ML_ & 13.2 & 0.002 & 0.83 & 0.060 & 0.646 & 0.008 & 0.169 & 0.029 \\ _MHL_ & 14.7 & 0.676 & 1.80 & 0.160 & 0.639 & 0.033 & 0.186 & 0.044 \\ _MLP_ & 15.8 & 0.090 & 1.10 & 0.090 & 0.635 & 0.003 & 0.160 & 0.005 \\ MC & 7.70 & 0.160 & 1.01 & 0.120 & 0.636 & 0.013 & 0.271 & 0.006 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Different materials’ moisture content and voltage difference values for the samples saturated with water.  
Figure 6: _Cont._
# Integrative Factors of E-Health Laboratory Adoption: A Case of Indonesia
## 3 Research Model and Hypotheses
### The Technology Acceptance Model Issue  
Behavioral intention aspects of the TAM, namely, perceived usefulness and perceived ease of use [107] in literature and studies are often used. Many successful studies show the relationship of these factors in the use of information systems. In this study, perceived usefulness and perceived ease of use follow the works in [107,108], and adoption refers to the study in [36]. In the context of e-health laboratories, the ease and usefulness of using e-health laboratories will certainly have an influence on the user's intention to use e-health laboratories.  
In this study, perceived usefulness can be described as the relative usefulness perceived from an e-health laboratory system. The easier the e-health laboratory technology, the greater the usefulness to consumers. E-health laboratories are easy to learn and easy to use because of the simple way of use. Users find it easy to use e-health laboratories because they are easy to navigate. Users adopt and desire to use e-health laboratories in the future. Thus, two hypotheses are formed:  
**Hypothesis 13 (H13).**_Perceived usefulness has a positive impact on the adoption of the e-health laboratory._  
**Hypothesis 14 (H14).**_Perceived ease of use has a positive impact on the adoption of the e-health laboratory._
# Required Evidence for Clinical Applications of Liquid Biopsy Using Especially CTCs in Lung Cancer
## 3 Liquid Biopsies
### Circulating Tumor Cells  
The predominant method of CTC detection is the CellSearch system which identifies CTCs by their expression of EpCAM and cytokeratin, while lacking CD45 [11]. In 2004 this system received FDA clearance and it is the only FDA-cleared technique to identify and enumerate CTC from a tube of blood (7.5 mL) for metastatic prostate, breast and colon carcinoma.  
Not only in these malignancies but also in lung cancer, the number of CTCs is predictive for a shorter progression-free and overall survival [12; 13; 14]. CTC persistence after treatment is associated with therapy failure for many malignancies [12; 15; 16; 17; 18; 19; 20; 21; 22; 23]. In fact, their counts and change after therapy are stronger correlated with survival than response evaluation by computed tomography (CT) in metastatic breast cancer patients [12; 14; 15]. In addition, several morphological changes in CTCs have been associated with chemotherapy resistance [23; 24; 25; 26]. Moreover, in small cell lung cancer, genomic analysis (assessment of copy number anomalies (can)) of CTCs may be used to predict response to chemotherapy [27]. Driver mutations and PD-L1 expression can be detected in and on CTCs (therapeutic marker) [28; 29; 30; 31; 32].  
However, while CTCs are a clear prognostic factor in NSCLC, it is a weak or not a predictor for tumor response at all (Table 1) [17; 29; 33; 34; 35; 36]. Moreover, their clinical application is limited by the low detection rate [35]. Even in advanced stage NSCLC, CTCs are only detected in 30% of patients and in almost all cases in low numbers per 7.5 mL of peripheral blood (Table 1). Other limitations are the standardization of CTC isolation or enrichment, the laborious and time-consuming process of isolating CTCs, being less flexible than applying tests for the determination of circulating DNA.  
When CTCs are captured in sufficient numbers, the heterogeneity of tumors can be studied by analyzing these cells at an individual cell level. They could be used to study tumor development and evolution. Unlike conventional biopsies, which can only contain tumor material from one location, CTCs probably represent the most relevant tumor cells in the body [37]. Another advantage of CTCs as compared with tumor biopsies is that they can be measured sequentially to assess tumor activity under therapy as they can be obtained in a minimally invasive manner [38].  
Figure 2: Different mechanisms that lead to the release of tumor cells and tumor DNA from the primary tumor into the blood stream. Circulating tumor cells (CTC) and circulating tumor DNA (ctDNA) can then be obtained by a normal blood draw.