esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Constructing an AT Representation Model Containing Tolerance-Zone DOF Layers
### OWL/SWRL Representation for AT Information Based on Tolerance-Zone DOFs
#### 4.5.1 SWRL Representation of Generation Rules for AT Specifications  
Tolerance types are usually classified into two main categories: shape tolerance and directional positional tolerance. The inference rules for tolerance type generation are constructed based on the classes and object properties and data attributes built by the protege tool.  
1. SWRL representation of rules for generating shape tolerance types. The form tolerance type is related to the target feature element type. SWRL rules are constructed according to the correspondence between the type of the target feature element and the form tolerance [3], and the automatically generated SWRL rule base for form tolerance items can be obtained [5; 23; 31]. For example, the commonly used shape tolerance types for cylinders are cylindricity, centre element axis straightness, cross-section roundness and rotation bus straightness; the inference rules are as follows: RCylindrical(?x) \(\wedge\) ICylindrical(?y) \(\wedge\) hasNON(?x,?y) \(\rightarrow\) hasCylindricity(?x,?y) \(\wedge\) HasRoundness(?x,?y) \(\wedge\) hasStrahitness(?x,?y).
2. SWRL representation of rules for generating orientation-position tolerance types. The orientation-position tolerance type of the target feature element is closely related to the orientation and position of the constrained feature element. The mapping relationship between the assembly tolerance type and the spatial relationship is expressed as the corresponding SWRL inference rule, and the SWRL rule base for the automatic generation of the orientation-position tolerance type can be established. For example, if the target feature plane and the constraint feature plane are perpendicular to each  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Serial Number** & **Object Properties** & **Domain** & **Range** \\ \hline
1 & hasZone & RealFeature & ToleranceZone \\
2 & hasSpatialRelations & ConstrainedFeature/RealFeature & TargetFeature/RealFeature \\
3 & hasDOFs & ToleranceType & DOFs \\
4 & hasToleranceType & TargetFeature/RealFeature & ToleranceType \\
5 & hasADF & ConstrainedFeature & TargetFeature \\
6 & HasACR & RealFeature & RealFeature \\
7 & hasFuncRequirement & Assembly & FuncRequirement \\  & Data Properties & Domain & Data Types \\ \hline
8 & hasDOF\_AroundX & DOFs & Boolean \\
9 & hasDOF\_AroundY & DOFs & Boolean \\
10 & hasDOF\_AroundZ & DOFs & Boolean \\
11 & hasDOF\_X & DOFs & Boolean \\
12 & hasDOF\_Y & DOFs & Boolean \\
13 & hasDOF\_Z & DOFs & Boolean \\
14 & hasDirection & ToleranceZone & String \\
15 & Select\_of\_effectiveness & ToleranceType & Boolean \\ \hline \hline \end{tabular}
\end{table}
Table 3: Domain and range domains of some target and data attributes.  
other, the tolerance types that can be selected for the target feature elements are positional and perpendicular. The inference rules are expressed as follows: TPlane(?x) \(\wedge\) Cplane(?y) \(\wedge\) hasPER(?x,?y)\(\rightarrow\) hasPerpendicularity(?x,?y) \(\wedge\) hasPosition(?x,?y).
# The Quest for the Best Explanation: Comparing Models and XAI Methods in Air Quality Modeling Tasks
## 3 Results
### XGBoost Model Feature Importance vs. XAI Methods
Some conditions show moderate agreement but differ in magnitude. For instance, \"1000.00 \(<\) CO \(\leq\) 1700.00\" shows a strong positive SHAP value but a slightly weaker LIME weight, suggesting this condition contributes overall but may have less impact in some local contexts. Conversely, \"600.00 \(<\) CO \(\leq\) 1200.00\" has a strong positive LIME weight but negligible SHAP value, which could mean it has local importance in specific data regions but not when averaged globally. There are also features where SHAP and LIME diverge notably. For example, \"DEWP \(\leq\) \(-\)10.20\" and nearby dew point ranges show strong negative LIME weights but only small negative SHAP values. This suggests that a very low dew point may strongly influence some individual predictions but is not consistently impactful across the whole dataset. Coming to O\({}_{3}\) (ozone), value ranges such as \"O\({}_{3}\)\(\leq\) 7.00\" and \"O\({}_{3}\)\(\leq\) 8.00\" show modest SHAP contributions but higher LIME values, again highlighting a localized effect. This finding can be attributed to the fact that low O\({}_{3}\) levels are usually associated with winter conditions and low photochemical activity, both related to low PM production. The most significant factors, where both methods strongly agree on their impact, are primarily in the low-temperature and high particulate matter (PM\({}_{10}\)) ranges. These appear to be key drivers of model estimations across both local and global perspectives. In contrast, disagreement in features such as dew point and certain CO and SO\({}_{2}\) ranges shows certain conditions are more important in some instances than throughout the data.
# In-Depth Evaluation of Association between Crash and Hand Arthritis via Naturalistic Driving Study
## 2 Methodology
### Random-Effect Logistic Regression Model  
A logistic regression model was adopted for predicting binary response variables. Two types of binary response variables were evaluated: whether an event is a crash or not, and whether the driver was engaged in a secondary task or not,  
\[Y_{ij}=\left\{\begin{array}{c}1\text{ {crash }(or\text{ {secondary task usage}})}\\ 0\text{ {no crash }(or\text{ {no secondary task usage}})},\text{ {i=1, }\ldots,\text{ {I}},\text{ {j=1, }\ldots,\text{ {J}}_{i}}\\ \text{ {or}}\end{array}\right.\]  
\[Y_{ij}=\left\{\begin{array}{c}1\text{ {the driver was engaged in secondary task}})\\ 0\text{ {The driver was not engated in secondary task}})^{\text{ }},\text{ {i=1, }\ldots,\text{ {I}},\text{ {j=1, }\ldots,\text{ {J}}_{i}}\end{array}\right.\]  
for \(i\)th driver, \(i=1,\ldots,\text{ {I}}\) and \(j\)th event for driver \(i,j=1,\ldots,\text{ {J}}_{i}\).  
The logistic regression model can be formulated as follows. Assuming that the response variable \(Y_{ij}\) follows a Bernoulli distribution:  
\[Y_{ij}\sim Bernoulli\left(p_{ij}\right)\]  
where \(I\) is the number of drivers and \(J_{i}\) is the number of observations for driver \(i\); \(p_{ij}\) is the probability of \(Y_{ij}=1\). A logit link function was used to link the probability of having a crash (\(p_{ij}\)) with a set of covariates, as shown below.  
\[logit\big{(}p_{ij}\big{)}=log\left(\frac{p_{ij}}{1-p_{ij}}\right)=\beta_{0}+ \beta_{1}X_{1 ij}+\beta_{2}X_{2 ij}+\ldots+\beta_{k}X_{kij}+z_{ij}u_{i},\]  
where \(X_{ki}\) is the variable for potential factor \(k\), and \(\beta_{k}\) is the corresponding regression coefficient. \(\beta_{k}=0\) indicates there is no significant association between the response variable and factor \(X_{k}\). For the binary factors, \(\exp(\beta_{k})\) is the OR associated with the factor. Several factors were included in the model, including arthritis status, age, and sex. The \(u_{i}\) is a vector of mixed effects and \(z_{ij}\) is the corresponding design matrix. The term \(z_{ij}u_{i}\) ensures that all observations from driver \(i\) share the same random term \(u_{i}\), adding the unique characteristics of a driver to the model. Commonly, \(\mu_{i}\) is assumed to follow a normal distribution with a mean that is equal to zero and the variance being estimated from the data.
# Infrared Imaging System with a Local Polarization Channel for Target Detection
## 3 System Design  
The proposed infrared imaging system with a local polarization channel has a full FOV of 45\({}^{\circ}\) with an infrared intensity imaging function and an FOI of 2.6\({}^{\circ}\) with a local polarization imaging function. The specifications of the system are listed in Table 1. Here, to demonstrate the imaging characteristics, three typical FOIs were simulated, namely, Areas 1, 2, and 3. The FOIs of the selected areas were (0\({}^{\circ}\), \(-\)1.3\({}^{\circ}\))-(0\({}^{\circ}\), 1.3\({}^{\circ}\)), (0\({}^{\circ}\), 8.5\({}^{\circ}\))-(0\({}^{\circ}\), 11.1\({}^{\circ}\)), and (0\({}^{\circ}\), 16.4\({}^{\circ}\))-(0\({}^{\circ}\), 19.0\({}^{\circ}\)), respectively. These areas covered the FOI from the center to the periphery of the target scene.  
Figure 2 shows the infrared imaging system with a local polarization channel designed in this study. The entire effective FOV was 45\({}^{\circ}\), as shown in Figure 2a, whereas the FOI of each area was 2.6\({}^{\circ}\), as shown in Figure 2b-d.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **EFL/mm** & **FOV/\({}^{\circ}\)** & **F\#** & **Local Magnification** \\ \hline
**Peripheral channel** & 10 & 45 & F/2 & / \\ \hline
**Local polarization** & 20 & 2.6 & F/4 & 2\(\times\) \\
**channel** & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Specifications of the infrared imaging system with a local polarization channel.  
Figure 2: Layout of the infrared imaging system with a local polarization channel: (**a**) peripheral channel with full FOV; (**b**) local polarization channel for Area 1; (**c**) local polarization channel for Area 2; and (**d**) local polarization channel for Area 3.  
The spot diagrams and modulation transfer function (MTF) plots, which revealed the imaging quality of the peripheral and local polarization channels of the proposed system, are shown in Figure 3. All spot diagrams in the peripheral and local polarization channels are comparable to those of the Airy disk. The MTF, which is the module of the optical transfer function (OTF), characterizes the capability of an imaging system to resolve the observed scene details. The pixel size of the detector used was 25 \(\upmu\)m, corresponding to a cut-off spatial frequency of 20 cycles/mm, namely, the Nyquist frequency. The MTF values were greater than 0.25 at 20 cycles/mm. Hence, this imaging system can maintain good image performance in both peripheral and local polarization channels.  
Figure 3: _Cont._
# New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System
## 2 Quality Control Algorithm and Quality Measures
### Quality Control Algorithm Procedure
#### 2.2.3 Adaptation  
To facilitate adaptation of the null hypothesis through estimation using the least squares method, the adaptation step is required to remove measurements, including outliers, which are identified through the data snooping technique. Figure 3 depicts the whole of the quality control procedure.  
Figure 1: PDF under hypothesis testing.  
Figure 2: w-test for data snooping.
# Multi-Connectivity-Based Adaptive Fractional Packet Duplication in Cellular Networks
## 3 Multi-Connectivity in Cellular Networks
### Packet Duplication  
Packet duplication is implemented at the packet data convergence protocol (PDCP) layer and can be performed for both the control and the data plane. With a UE having a DC, the source node is responsible for duplicating packets and sending over the two independent networks. These are then combined at the receiver with duplicate ones discarded. The split-bearer design, which is also shown in Figure 3, allows for the less invasive implementation of packet duplication in DC. The identical PDCP packet data unit (PDU) is transferred across the two distinct radio link control (RLC)/medium access control (MAC) entities or the two nodes in the PD, which is analogous to the split bearer operation.  
The radio resource configuration (RRC) layer sets up the PD operation, which is typically performed at the radio bearer level. An new RLC entity and an additional logical channel are added to the radio bearer to handle the duplicated PDCP PDUs when duplication is enabled for a radio bearer via RRC signaling. The two legs in the case of DC correspond to the MCG and SCG cell groups, respectively. Throughout the lifespan of a carrier, packet duplication is not always useful and basically depends on the channel  
Figure 3: Adaptive packet duplication.  
Figure 2: Carrier aggregation and dual connectivity.  
conditions and state of the radio bearer. Thus, it is preferred to have control over whether or not packet duplication occurs. In order to save air interface resources, packet duplication must be activated or deactivated on the fly.  
User data is divided into numerous carriers at the MAC layer in CA rather than DC. The RRC layer configures packet duplication in a manner similar to the DC situation. In order to manage the duplicated PDCP PDUs, an extra RLC entity and an additional logical channel are added to the original RLC entity and the logical channel associated with a radio bearer when RRC configures duplication for that radio bearer. Yet, as opposed to DC, where there are two distinct MAC entities, there is only one MAC entity. PDCP duplication on the same carrier is not supported, according to 3 GPP RAN2. Hence, in contrast to the DC situation, the RRC layer must additionally set the mapping of the original and duplicate logical channels to distinct carriers. Packet duplication in CA is not supported if it has already been set up in DC according to a 3 GPP RAN2 agreement.  
It is worth noting that the PDCP layer in LTE already provides duplication detection based on the sequence number. As a result, if the transmitter provides multiple PDCP PDUs (through separate legs), the receiver can only analyze the earlier received PDCP PDU. The PDCP PDU that arrives later is simply discarded with no modifications to the specification required. As a result, packet duplication may be extended to the LTE-NR DC situation.
# Bio-Based Polymers with Potential for Biodegradability
## 3 Poly(lactic acid) (PLA) and Related Polymers
### Degradation of PLA Composites and PLA Blends  
Pure PLA is difficult to use in most applications due to its brittleness (high modulus of elasticity coupled with low strains at break) [146]. A common strategy for overcoming the brittleness of PLA is blending with other polymers that will improve the overall mechanical properties. Another technique is to add nanofillers derived from clays, carbon, cellulose, or other sources to form PLA-based nanocomposites [117; 147]. In certain applications, chemical additives or modified polymers are deliberately added to biodegradable polymers in order to limit the degradation rate of polymers when durability or extended shelf life is needed [148; 149]. Since the presence of other polymers and/or nanofillers can impact biodegradation, there have been investigations on biodegradation of PLA nanocomposites and PLA blends.  
Polymer blends may have different morphologies based on blending ratios, which will impact biodegradation processes. Furthermore, various biodegradable polymers degrade at different rates [150; 151]. PLA/poly(hydroxybutyrate) (PHB) blends, which are promising food packaging materials, have been shown to be compostable under normal compositing conditions [152]. Certain PLA/thermoplastic polyester polyurethane (TPU) blends have also demonstrated biodegradability. The degradation profiles of the PLA/TPU blends were correlated to the different polymer morphologies based on the blend ratios [153]. Poly(butylene-adipate-_co_-terephthalate) (PBAT), an aliphatic-aromatic copolyester that is also biodegradable, has been blended with PLA to increase the toughness of PLA [154; 155]. Two recent studies on the biodegradability of a PLA/PBAT blends shows that the blends biodegradable at a slower rate than either PLA or PBAT [156; 151].  
Biodegradation studies have found that PLA nanocomposite films, containing either native or organo-modified montmorillonitees (MMT) nanoclay fillers, show faster biodegradation onset than pure PLA films [123; 157]. Another group, [PERSON] et al., also found that organically modified fluoro-hectorite increased the degradation rate compared to pure PLA [158; 159]. This indicates that PLA composites containing nanoclay fillers may be treated at composting facilities alongside pure PLA.
## 2 Materials and Methods
### Data  
#### 2.2.1 Study Sites  
Four EC tower sites covering the temperate steppe (NM and XL) and alpine meadow (GCT and SD) were selected for this study (Figure 1 and Table 2). The NM and XL sites are located at the Inner Mongolia Grassland Ecosystem Research Station (IMGERS) in the eastern Inner Mongolian Plateau, which has a semiarid continental climate. These two sites represent the typical grassland type in Inner Mongolia. The mean annual temperature (MAT) and precipitation (MAP) are 2 \({}^{\circ}\)C and 350 mm, respectively. Chestnut and dark chestnut soils were the zonal soil types found in this region [76]. Soil moisture is 0.18-0.26 m\({}^{3}\) m\({}^{-3}\) and 0.05-0.07 m\({}^{3}\) m\({}^{-3}\) are the field capacity and wilting point, respectively ([[http://www.chinaflux.org/](http://www.chinaflux.org/)]([http://www.chinaflux.org/](http://www.chinaflux.org/))). The GCT and SD sites are situated in the Haibei Alpine Meadow Ecosystem Research Station (HBAMERS) in the eastern Tibet-Qinghai Plateau, which has a plateau continental climate (MAT = -1.7 \({}^{\circ}\)C and MAP = 600 mm). These two sites represent the typical grassland type in the northeastern Tibet-Qinghai Plateau. The growing season temperature at these two sites (5-7 \({}^{\circ}\)C) is much lower than that in the temperate steppe (13 \({}^{\circ}\)C). Alpine shrub meadow (GCT) is distributed on mountain shadow slopes and dominated by mollic cryic cambsiol soil with a high soil water content. Distributed in surface depressions, alpine swamp meadow (SD) is dominated by orthic spodosol soil, the soil surface of which is waterlogged with a water depth of approximately 0-5 cm during the growing season [77]. All four sites are member stations of the Chinese Terrestrial Ecosystem Flux Observational Network (ChinaFLUX), and detailed site information is available in [PERSON] et al. [61], [PERSON] et al. [78], and [PERSON] et al. [4].
# An Optimization Model for a Desert Railway Route Scheme Based on Interval Number and TOPSIS
## 3 Case Study
### Index Weight Calculation  
#### 3.2.1 Weight of the AHP Method  
According to the evaluation index system established in this paper, six experts from design and construction are invited to utilize the AHP approach to analyze the relative importance of the indexes. The judgment matrix of the secondary indicators has been summarized in Table 4.  
The subjective weights of the indicator are calculated by using Equations (7)-(12). Each judgment matrix should pass the consistency test.  
The indicators that have the largest weight are length of the railway line, impact on nature reserves, and project investment, among which the length of the railway line has the greatest influence on the scheme decision-making. This is because the railway divides the original surface of the desert area. The longer the route mileage, the more noticeable the damaging effect. Increasing the length of the railway also increases the investment and the risk of sand damage. The Niya National Wetland Park not only has a strong function in regulating groundwater, but also can purify sewage and regulate regional climate. As a result, the geographical environment is very sensitive to the project construction, and the cost of recovery after the damage is very high. Due to the large scale of the railway project investment, the railway design scheme should achieve economic rationality by ensuring performance and advanced technology.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicators.** & **Scheme A** & **Scheme B** & **Scheme C** \\ \hline Length of the railway line (km) & 39.50 & 40.97 & 38.88 \\ Land requisition amount (hec) & 115.4 & 122.1 & 137.5 \\ Project investment (\$million) & 172.29 & 162.91 & 156.71 \\ Minimum curve radius (m) & 2000 & 2200 & 2500 \\ The ratio of the bridges and tunnels (\%) & 28.59 & 10.42 & 20.33 \\ \hline Railway length in the moveable dune area & & & \\ (km) & 31.952 & 28.558 & 27.452 \\ The angle between the railway and the & & & \\ wind direction)/\({}^{\circ}\) & & & \\ Temporary land occupation (hec) & 1202.0 & 1399.0 & 1513.0 \\ The scale of the sand control work & Larger & Large & Small \\ Feasibility of plant sand control & Bad & Average & Better \\ \hline Impact on nature reserves & Smaller & Large & Larger \\ Impact on the water environment & Average & Large & Large \\ Impact on the wildlife & Average & Larger & Large \\ Impact on the vegetation cover & Smaller & Large & Larger \\ Impact on the basic farmland & Small & Average & Small \\ \hline Speed limit & Small & Large & Average \\ Track maintenance & Average & Small & Small \\ Operation safety & Average & Good & Good \\ \hline \hline \end{tabular}
\end{table}
Table 3: Engineering information of route schemes.
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 3 Methods
### Datasets
#### 3.2.1. Levir-Cd  
As a key dataset in the field of remote sensing change monitoring, LeVIR-CD has significant advantages over similar studies due to its large-scale multi-temporal image pair characteristics. The dataset consists of 637 pairs of high-resolution Google Earth images, each with a spatial resolution of half a meter. Each image has a size of 1024 \(\times\) 1024 pixels. The time span of the dataset spans 16 years, from 2002 to 2018, capturing the significant morphological changes in buildings during urbanization.  
The image content is highly diverse, covering a variety of building structures such as villa communities, high-rise apartment clusters, decentralized garages, and industrial storage buildings, focusing on the dynamic process of building expansion and morphological changes. To enhance the algorithm's adaptability, all images are standardized and cropped to a consistent input size of 256 \(\times\) 256 pixels. The dataset is then split into 7120 training sets, 1024 validation sets, and 2048 test sets.  
Figure 7 showcases a selection of data from the dataset, illustrating the transformation over time. Notably, the images depict the emergence of buildings on previously vacant land, highlighting the dynamic nature of urban development. This visual representation underscores the dataset's capability to capture and document the evolving landscape, making it an invaluable resource for change detection studies.
# Data-Aware Path Planning for Autonomous Vehicles Using Reinforcement Learning +
###### Abstract  
This paper addresses the challenge of optimizing path planning for autonomous vehicles in urban environments by considering both traffic and bandwidth variability on the road. Traditional path planning methods are inadequate for the needs of interconnected vehicles that require significant real-time data transfer. We propose a reinforcement learning approach for path planning, formulated to use road traffic conditions and bandwidth availability. This approach optimizes routes by minimizing travel time while maximizing data transfer capability. We create a realistic simulation environment using GraphML, incorporating real-world map data and vehicle mobility patterns to evaluate the effectiveness of our approach. Through comprehensive testing against various baselines, our reinforcement learning model demonstrates the ability to adapt and find optimal paths that significantly outperform conventional strategies. These results emphasize the feasibility of using reinforcement learning for dynamic path optimization and highlight its potential to improve both the efficiency of travel and the reliability of data-driven decisions in autonomous vehicular networks.  
Keywords:autonomous vehicles; vehicle routing and navigation; vehicular networks; reinforcement learning +
Footnote †: This paper is an extended version of paper published in the IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS), Denver, CO, USA, 20-22 October 2022.