esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Effects of Micronutrient-Deficient Diet on Obesity  
Several studies have suggested that the rate of increase in obesity is greater in regions of the world with greater micronutrient deficiencies [17], while others reported significant inverse associations between the dietary intake of micronutrients and Body Mass Index (BMI) and/or obesity [18; 19; 20; 21; 22]. These studies were cross-sectional and hence failed to demonstrate the cause-and-effect relationship between micronutrient deficiencies and excess weight unequivocally.  
Stronger research evidence showing that a micronutrient-deficient diet may induce obesity in animals was published. [PERSON] et al. (2014) [23] examined the effect of simultaneously reducing the content of vitamins (B1, B2, B3, B5, B6, B7, B9, B12, A, D3, E, and K) in the diet of C57 BL/6 J Rj mice by 50% over 12 weeks. At the end of the experiment, test mice were characterized by 6% higher weight and twice as much body fat content when compared to control mice on standard feed. At the same time, no significant difference in feed consumption in both groups was noted. The authors suggested that vitamin deficiencies in the diet favored the deposition of adipose tissue by reducing the intensity of its breakdown processes.  
Another murine study that investigated the influence of micronutrient inadequacy in the development of obesity focused on vitamin D and Ca [24]. In this work, mice were fed a \"new Western diet\" with an elevated fat content to induce obesity. A group of mice with Ca and vitamin D insufficiency developed greater levels of inflammatory markers in blood plasma when compared to the control group. Nevertheless, both groups had similar adiposity and mean body weight throughout the experiment. Therefore, the dietary adequacy of vitamin D and Ca did not protect mice from the development of obesity due to a high-fat diet.  
Summarizing, studies suggest that inadequacy of micronutrient intake may lead to excessive weight gain but adequacy may not protect from obesity if other dietary recommendations--such as relatively low-fat intake--are not met.
## 5 Discussion and Suggestions  
(1) Interlayer isolation structures in engineering applications should consider the SSI effect, and in the design of such structures, the research focus should be on the low structures and isolation layers. Based on the law of the response of acceleration and displacement, the soft land base has a certain amplification effect, which significantly affects the lower structure and isolation layer. The structural displacement effect of the upper and lower parts of the soft land base is greater than that of the upper structure, and the displacement is concentrated at the isolation layer. The isolation layer and lower structure are weak parts, and a large diameter isolation bearing is recommended. In addition, the stiffness of the lower structure is increased, and measures must be considered for the isolation layer in the high intensity area.  
(2) The SSI effect is significant on the soft soil foundation and, therefore, must be considered in seismic-isolation buildings built on this foundation type. The displacement response of interlayer isolation structure of the soft soil foundation is obviously greater than that of the rigid foundation, and thus, measures must be taken to improve the damping effect, for example, optimization of the parameters of the isolation layer and the layout of the support.  
(3) The interlayer isolation structure of the soft soil foundation is prone to foundation settlement under the action of earthquake, resulting in the inclination of the building. Therefore, studies must focus on the stability of the foundation for the seismic-isolation structure on soft soil foundation.  
Figure 28: Comparison between the interlayer shear responses under the single and bidirectional seismic excitations (PGA = 0.2 g). (**a**) Near-field ordinary ground motion; (**b**) Far-field ordinary ground motion; (**c**) Near-field pulse ground motion; (**d**) Far-field harmonic ground motion.
# Study on the Influence of Deep Soil Liquefaction on the Seismic Response of Subway Stations
## 3 Calculation Results Analysis of Soil Layers
### Pore Pressure Ratio  
The liquefaction characteristics of the soil layer beneath the station structure's bottom slab have been investigated. The pore pressure ratio (ppr) is employed to depict the liquefaction behavior of the soil. The soil layer is fully liquefied when the ppr = 1. As demonstrated in Figure 7, the liquefied region is principally located below the structure's bottom slab under the impact of the design earthquake. Under the influence of the high-level earthquake, the liquefied areas primarily extend directly below the structure and, to a certain extent, on both sides of the bottom slab.  
The pore pressure ratio curve is shown in Figure 8. P1 is located at the bottom slab of the station, P2 is located 10 m under the bottom slab of the station, and P3 is located 20 m under the bottom slab of the station. The magnitude of the pore pressure ratio under the effect of the design earthquake is P1 > P2 > P3, where the peak pore pressure ratio of P1 is close to 0.5. The magnitude of the pore pressure ratio under the effect of the high-level earthquake is P1 > P2 > P3, where the peak pore pressure ratio of P1 reaches 0.8. This indicates that the soil experiences a high degree of liquefaction at the bottom slab of the station structure under the action of the high-level earthquake. The degree of liquefaction in the deep liquefied soil layers is directly proportional to the seismic intensity. Under the action of the high-level earthquake, the soil layer at the bottom slab of the station structure is close to complete liquefaction.  
Figure 6: Vertical distribution of peak acceleration of soil. (**a**) design earthquake; (**b**) high-level earthquake.
## 3 Data and Methodology  
### Study Area and Data Sources  
Weifang is located in the middle of the Shandong Peninsula (Figure 1), with Zibo to the west, Linyi to the south, Qingdao to the east and Laizhou Bay and the Bohai Sea to the north, covering a total area of 16,000 square kilometers. Weifang City is high in the south (with ground elevation 100-1032 m) and low in the north (with ground elevation under 7 m). The south is mainly covered with hills and low mountains, while the northeast is mainly characterized by plains and lakes. Weifang City has been experiencing industrial and economic advancements and was one of the most rapidly developing cities in Shandong Province. As of 2019, the number of registered vehicles had exceeded 2 million. This growth has resulted in damage to the Weifang City environment. Existing studies on the characteristics of air pollution in Weifang have mainly focused on spatiotemporal patterns, while few reports have described the transport trajectories and mechanisms as well as the potential sources of air pollution. This paper utilizes hourly PM\({}_{2.5}\) monitoring data and a back-trajectory model to analyze the transport pathways and potential source contributions of PM\({}_{2.5}\) pollution in Weifang and can provide important scientific support for joint atmospheric pollution control and management.  
The PM\({}_{2.5}\) monitoring data used in this paper were acquired from 5 national monitoring stations, 4 provincial monitoring stations, and 29 city monitoring stations in Weifang (Table A1 in Appendix A). The data were obtained by automatic air quality monitors through 24-h continuous monitoring and cover the period from 1 March 2015 to 29 February 2016. The data were acquired from the Data Center of the Ministry of Environmental Protection of the People's Republic of China ([[http://datacenter.mpe.gov.cn/index](http://datacenter.mpe.gov.cn/index)]([http://datacenter.mpe.gov.cn/index](http://datacenter.mpe.gov.cn/index))). Thermo Fisher 1405F monitoring devices were used to measure the PM\({}_{2.5}\) concentrations, and this instrument operates on the principle of measuring PM\({}_{2.5}\) concentrations by a filter dynamic measurement system (FDMS) with the tapered element oscillating microbalance (TEOM) and the beta-attenuation method [6].  
The major data structure is illustrated in Figure 1(a). The meteorological data used in the back-trajectory model were obtained from GDAS data provided by the National Centers for Environmental Prediction (NCEP). The 1.0\({}^{\circ}\) resolution global reanalysis data are adopted. These data are recorded every 6 h, namely, at 00:00, 06:00, 12:00 and 18:00 (UTC). Their structure is shown in Figure 1(b).  
Figure 1: Study area and spatial distribution of monitoring sites.  
Figure 2: Structures of monitoring station data and GDAS data.
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Feature Attribution and Game Theory
#### 6.6.2 Datasets  
The datasets listed in Table 14 reveal critical insights into the application scope and validation strategies of feature attribution methods for drift detection. Notably, the Electricity and Adult datasets are frequently reused across studies, highlighting their utility as benchmarks for temporal or demographic shifts. These datasets likely offer structured, time-varying features (e.g., hourly electricity demand or income demographics) that align well with contribution-trajectory monitors (e.g., smoothed Shapley values or iSAGE loss decompositions). Similarly, the Bike Sharing and Airlines datasets provide cyclical temporal patterns, enabling researchers to test how attribution shifts reflect seasonal or operational changes. Domain diversity is also evident: cybersecurity studies (e.g., CICIDS2017, IoTID20) leverage attribution to detect abrupt feature importance shifts during attacks (e.g., DNS payload length surges in INSOMNIA), while healthcare datasets like PROMISE12 Prostate MRI and Southampton General Hospital's ED demonstrate how drift in clinical feature relevance can signal evolving diagnostic practices. However, the predominance of cybersecurity and transactional data (e.g., Brazilian E-Commerce) suggests potential gaps in domains like environmental science or social systems, where drift patterns may differ. A limitation lies in the reliance on static or synthetically partitioned datasets (e.g., CoverType), which may not fully capture real-world temporal dynamics. These datasets underscore the adaptability of game-theoretic attribution frameworks but also call for broader domain inclusion and richer temporal granularity to advance robustness evaluations.
# Establishment of a Standard Method for Boundary Slip Measurement on Smooth Surfaces Based on AFM
## 3 Standard Process of Measuring Boundary Slip
### Data Processing
Since the slip length was calculated based on Equation (4) with fitting range as D \(\geq\) 100 nm, and this distance was much larger than the scope of van der Waals force (0.5 nm), the van der Waals force can be ignored. Since the cantilever always stayed away from solid surface (the distance was greater than or equal to the diameter of the sphere; in this example, the closest distance was 58.6 \(\upmu\)m), the Stokes force on cantilever was not affected by the separation distance. It can be expressed as [18]:  
Figure 3.9  
where \(F_{S}\) is Stokes force, \(l\) is effective length of the cantilever and \(\dot{z}\) is drive speed of piezoelectric ceramics. As the Stokes force was obtained by fitting method, the parameters in Equation (10) were not measured. When the sphere was moving far away from the surface, the force acting on the probe consisted of hydrodynamic force and Stokes force. Based on Equation (1), hydrodynamic force is a function of separation distance, denoted as \(f(D)\). Based on Equation (10), Stokes force was not affected by separation distance, thus the force acting on the probe can be expressed as:  
Figure 3.0  
When the sphere was moving far away from the surface, \(V\) can be considered as constant and the Stokes force is also constant. Then, the force on the sphere can be expressed as follows, where C is a constant:  
Figure 3.1  
Then, the Stokes force can be obtained by fitting the curve with Equation (12). Finally, the influences including electrostatic force and Stokes force can be removed from the curve. (6) Access to slip length  
It should be noted that the driving velocity was not equal to approaching velocity due to the force on the sphere. As a result, the approaching velocity was obtained from the experimental data. The relative value of time for each data point was obtained, and then the approaching velocity of the sphere was obtained by taking the derivative of separation distance of each data point. Finally, the curve of \(V/F_{H}\) was obtained, as shown in Figure 3.2 Eventually, \(b\) was obtained by fitting the certain distance of the curve. The slip length was the intersection coordinate value of the horizontal axis and curve. The two different curves in Figure 10 are the results of boundary slip measurements on two kinds of surfaces: surfaces of octadecyltrichlorosilane (OTS) and mica. The former is a hydrophobic surface, on which nanobubbles may exist [39; 40] that lead to a finite slip length, while the latter is a hydrophilic surface in non-slip condition. Their original data were so similar to the data in Figure 3 that the slip length could only be calculated using the data in Figure 10, which were processed.
# A Review of Transformer-Based Approaches for Image Captioning
## 4 Transformers in Captioning
### Captioning Transformer with Stacked Attention Modules  
[PERSON] et al. [77] apply a standard transformer to perform image captioning. The encoder is a ResNext CNN [78] and the image features are taken from the final layers. These are then used as the keys and values in the decoder. Thus, the encoder is a CNN and the  
Figure 1: Transformers in image captioning: evolution from (**a**) traditional encoder-decoder-based captioning to (**b**) basic transformer-based captioning models to (**c**) captioning transformers based on vision-language pre-training.  
decoder is a transformer, with a design and parameters closely following those used by [PERSON] et al. [36].  
The decoder design envisions a stacking of several individual decoder layers; the overall output is taken as a combination of the outputs of the different layers. The decoder layers are separated by dropout layers to avoid overfitting.  
Based on this stacking, they introduce multi-level supervision to take advantage of multi-layer outputs of the transformer. Every layer can be used to generate the current word. During training, the outputs of each layer are passed through a linear transformation and are then taken together to calculate the cross-entropy loss of the model as shown in Figure 2. The training objective is to minimize the cross-entropy loss.
## 5 Discussion
### Insights into the Three Sampling Strategies  
This study employed three different sampling strategies, corresponding to Scenario 1, Scenario 2, and Scenario 3. Scenario 1 utilized different sampling ratios of landslideto non-landslide points, while Scenarios 2 and 3 were based on different approaches to refining the dataset.  
In Scenario 1, the 1:5 ratio yielded the best results, with an AUC value of 0.914. This ratio also avoided overemphasizing high-susceptibility areas, a limitation seen with a 1:1 ratio. The success of the 1:5 ratio can be attributed to the fact that in the study area, the spatial distribution of high- and low-susceptibility areas aligns closely with 1:5 (1:4.23)--consistently with the regional geological conditions. Increasing the number of non-landslide samples beyond a 1:50 ratio makes it harder to capture high-susceptibility areas. Based on this, avoiding excessively high negative sample ratios and instead adopting sampling strategies that mirror the actual proportions in the study area, such as the 1:5 ratio, can effectively improve model performance in this region.
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
## 3 Analytical Modeling and Performance Evaluation
### Modeling the Handover Latency
#### 3.2.6 Handover Latency Analysis  
If the different handover latencies are compared, the order of latencies can be given as [31]:  
\[\text{MIPv6}>\text{FMIPv6}>\text{HMIPv6}.\]  
When comparing HMIPv6 and PMIPv6, the order of latencies can be given as [32]:  
\[\text{HMIPv6}>\text{PMIPv6}.\]  
When comparing PMIPv6 and FMIPv6, FMIPv6 outperforms PMIPv6 [33]. Therefore, we can say:  
\[\text{FMIPv6}>\text{PMIPv6}.\]  
Our evaluation above shows that TFMIPv6 outperforms other mobility management protocols. Therefore, we can give the order of latencies as follows:  
\[\text{MIPv6}>\text{FMIPv6}>\text{HMIPv6}>\text{PMIPv6}>\text{TMIPv6}.\]For comparison purposes, the relative gain is defined here for the handover latency to the MIPv6 process as:  
\[G_{H}=\frac{L_{HO}}{T_{HO}}, \tag{17}\]  
where \(L_{HO}\) is the handover latency for MIPv6 and \(T_{HO}\) is the handover latency for the proposed approach. If \(G_{H}\) is greater than 0.1, signifies that the proposed handover technique outperforms the basic one.
# Cross-Layer Optimization for Heterogeneous MU-MIMO/OFDMA Networks
## 4 Optimization Framework
### Decision Variables
#### 4.1.1 Data Rate
Let \(z_{llfk}\) be a binary variable that indicates whether or not subchannel \(k\) is allocated to flow \(f\) on link \(l\) at time slot \(t\). Then, using \(z_{llfk}\), we can derive \(b_{llf}\) as follows:  
\[b_{llf}=B^{\min}\sum_{k}z_{llfs}\leq B^{\max}_{l}\cdot z_{llf}, \hskip 28.452756 pt\forall l,t,f \tag{6}\]  
where the rightmost term means that the value of \(b_{llf}\) is limited to the maximum bandwidth of the link, denoted as \(B^{\max}_{l}\). Here, \(z_{llf}\) is a binary variable indicating whether flow \(f\) is active on link \(l\) at time slot \(t\). This value has the following relationship to the variables \(z_{llfs}\) and \(z_{llfk}\):  
\[z_{llf}=\sum_{s}z_{llfs}\leq 1,\hskip 28.452756 pt\forall l,t,f \tag{7}\] \[z_{llf}=\max_{k}(z_{llfk}),\hskip 28.452756 pt\forall l,t,f,k. \tag{8}\]  
For the flow \(f\) on link \(l\) to be activated at time slot \(t\), it should be allocated to at least one spatial stream (constraint (7)) and one subchannel (constraint (8)). Please note that constraint (8) is expressed in a nonlinear form, and we therefore convert it to the following linear constraint:  
\[z_{llfk}\leq z_{llf}\leq\sum_{k}z_{llfk},\hskip 28.452756 pt\forall l,t,f,k. \tag{9}\]  
Although the variables for the link capacity have now been defined, one remaining problem is that constraint (5) is expressed as the product of two variables, and is therefore inherently nonlinear. However, it can be also equivalently represented as a set of linear constraints, as follows:  
\[B^{\min}\cdot c_{llfs}\leq s\cdot B^{\max}_{l}\cdot z_{llfs}, \hskip 28.452756 pt\forall l,t,f,s \tag{10}\] \[s\left(b_{llf}-B^{\max}_{l}\cdot(1-z_{llfs})\right)\leq B^{\min} \cdot c_{llfs}\leq s\cdot b_{llf}, \hskip 28.452756 pt\forall l,t,f,s. \tag{11}\]  
Please note that \(B^{\min}\) is multiplied with \(c_{llfs}\), since we assume a unit data rate, which is achieved when a single spatial stream (i.e., \(s=1\)) and a bandwidth of \(B^{\min}\) are used.
## 4 Experiment and Application: Lane-Level Trajectory Matching
### Lane-Level Trajectory Matching
#### 4.2.3 Trajectory Matching Test  
In this experiment, the smart On-board Unit (OBU) device is mounted on the probe vehicle. The OBU is used as the data acquisition unit in the experiment, and its built-in ZED-F9K high-precision positioning module is capable of providing decimeter-level accuracy positioning data, as shown in Figure 13b. Moreover, the speed, acceleration,  
Figure 11: Third-order Bezier curve channel change model.  
Figure 12: Lane-level topological relationship at an intersection. (**a**) Topological relationship of lane A; (**b**) Topological relationship of lane D.  
latitude and longitude, and wheel speed as well as other data obtained by using the IMU heading prediction module integrated into the device can provide crucial information for monitoring driving behavior such as lane changing behavior.  
The device, shown in Figure 13a, was installed in the front windshield of the vehicle (a), while a camera recording was used during the test to probe the actual driving condition of the vehicle and provide a base for the subsequent trajectory verification. Based on the map constructed in the previous step, the test was run over the range of the constructed lane-level road network. The experimental test range is about 9 km long, covering part of the roads in Pingshan District, Shenzhen. The lane-level map covered during data collection is shown in Figure 14a, and a total of 14,928 trajectory points were acquired.  
The trajectory coordinates are matched to the constructed lane-level network by the algorithm in Section 3. A sample map matching result for trajectory 2 is shown in Figure 14b.  
In order to verify the effectiveness of the proposed method, the accuracy of the trajectory matching results is estimated by calculating the proportion of correctly matched trajectory points. Specifically, the trajectory matching accuracy rate R can be calculated through Equation (13).  
Figure 14: Trajectory matching results. (**a**) The tested road sections; (**b**) Lane-level map matching results.  
Figure 13: Experimental platform. (**a**) Installation of the equipment; (**b**) Detailed components of OBU.  
\[R=\frac{\sum C_{i}}{\sum A_{i}} \tag{13}\]  
where \(C_{i}\) is the total number of correctly matched trajectory points for trajectory \(i\) and \(A_{i}\) represents all trajectory points covered in trajectory \(i\).  
In our test, we have collected vehicle trajectory two times and the estimated trajectory matching accuracy are shown in Table 1.
## 4 Parameterization and Analysis of the Proposed TDL Model
### Delay-Doppler Spectrum
Thus, we present the normalized averaged Doppler spectrum of the scenarios FS1 to FS4 conditions in Figure 7. The obvious difference between the A2G channel and other channels is that when the UAV is flying at high altitude, its A2G propagation link is not blocked by any obstacles (trees, houses, etc. ), the LoS path persists and the relative power of the LoS path is much higher than that of the other multipaths (e.g., scattering, reflection, etc.). Therefore, in an A2G channel, this is usually represented by the presence of only one peak in its Doppler. Figure 6: Normalized delay-Doppler spectrum. Subfigures (**a–f**) correspond to the normalized delay-Doppler spectrum of the considered typical UAV flight phases in FS1 to FS6 models. Moreover, it can be observed that in all four considered scenarios, there exists strong Doppler component that is relevant to the first channel tap containing the strong LoS path. In scenario FS1, the maximum power of Doppler spectrum is around 30 dB higher than the rest Doppler components. From the averaged Doppler spectrum, Doppler components caused by MPCs can be also noticed for scenarios FS2 and FS3. It is worth mentioning that the strong Doppler component corresponding to the first tap containing the LoS path is centralized at the Doppler shift frequency according to Equation (14). For all four considered scenarios, the Doppler components corresponding to the first channel taps are significantly stronger than the rest of the components corresponding to other multipath taps. It also confirms the conclusions for the relatively high K-factor values analyzed in the previous section. In addition, the \"Jakes\"- and \"flat\"-type spectrum shapes show the poor performance among the four considered spectral distributions, which is consistent with the propagation conditions with the presence of a strong LoS component. The \"Bell\"- and \"Gaussian\"-type spectrum shapes fit the LoS component better, especially the \"Bell\" spectrum with smaller a mean square error. Therefore, we propose to use the \"Bell\"-type spectrum for modeling the Doppler spectrum at various UAV flight scenarios.