esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Organization and Contributions of This Paper  
In this paper, state-of-the-art research of automated parking space allocation and route selection is reviewed in Section 2. Section 3 describes the four factors that affect the choice of parking space for drivers. In Section 4, a fuzzy comprehensive evaluation method is proposed to evaluate and score (i.e., rank) the available parking spaces in an intelligent parking infrastructure [16]. The fuzzy algorithm is based on first predicting which space the driver would select and then allocating one of the remaining spaces to the autonomous vehicle. In Section 5, the Floyd algorithm is introduced to provide path navigation for autonomous vehicles [14] according to the nodes of available parking spaces and road information in the parking infrastructure. Section 6 provides three examples of a parking lot on the campus of Jiangsu University to illustrate the step-by-step implementation of the proposed algorithm via Python. Concluding remarks of this work are given in Section 7.  
The contributions of this paper are listed as follows:  
1. In this paper, a fuzzy comprehensive evaluation method is used to first predict the parking preference of a driver based on four main factors that influence their choice of parking spaces. This procedure prevents conflict of allocation of parking space to the AV with that chosen by the HV.
2. Based on the node information in a parking infrastructure, the Floyd algorithm is used to provide path navigation for the AVs.
3. The proposed methodology, combining fuzzy theory and the Floyd algorithm, provides a novel scheme for a central command station to assign parking spaces for AVs in the presence of HVs. The merit of this work thus provides a foundation for future work to investigate the problem of automated parking for multiple vehicles.
## 2 Torque Transmission Mechanism and Experimental Scheme
### Torque Load Measurement Method  
The torsion deformation of the circular shaft will occur under the torque load, which needs to be simplified to a certain degree when calculating its stress distribution. According to the flat cross-section theorem, it is assumed that the cross-section of the circular shaft still remains a circular plane after twisting and the original radius remains a straight line.  
In this experiment, the torque load on the foundation ring was measured using the elastic shaft strain measurement method, i.e., when the circular shaft is subjected to the torque load, the cross-section is subjected to shear stress, and the maximum tensile stress \(\sigma_{1}\) and compressive stress \(\sigma_{2}\) are generated in the direction at an angle of 45\({}^{\circ}\) to the axis, and their values are equal to the maximum shear stress \(\tau_{max}\) on the original section.  
The strains along the \(\sigma_{1}\) and \(\sigma_{2}\) directions are  
\[\epsilon_{1}=\frac{\sigma_{1}}{E}-\mu\frac{\sigma_{3}}{E};\;\epsilon_{3}=\frac {\sigma_{3}}{E}-\mu\frac{\sigma_{1}}{E} \tag{5}\]  
where \(E\) is the elastic modulus of the material, and \(\mu\) is [PERSON]'s ratio.  
Based on the mechanics of the materials, the following relation can be obtained:  
\[\sigma_{1}=\frac{E\cdot\epsilon_{1}}{1+\mu}=-\sigma_{3} \tag{6}\]  
As for the physical relationship and stress distribution, [PERSON]'s law is applied, i.e.,  
\[\tau_{\text{max}}=|\sigma|=\frac{E\cdot\epsilon_{1}}{1+\mu} \tag{7}\]  
Figure 2: Torque load transfer process within the foundation.  
The calculation formula for torque load \(M\) is as follows:  
\[M=\tau_{\text{max}}\cdot W_{p}=\bigg{|}\frac{E\cdot\varepsilon_{1}}{1+\mu}\bigg{|} \cdot W_{p}=\bigg{|}\frac{E\cdot\varepsilon_{3}}{1+\mu}\bigg{|}\cdot W_{p} \tag{8}\]  
For a hollow section circular shaft, the calculation formula for its torsion section coefficient \(W_{p}\) is as follows:  
\[W_{p}=\frac{\pi\cdot D^{3}}{16}\Big{(}1-\alpha^{4}\Big{)} \tag{9}\]  
where \(\alpha\) is the ratio between the inner and outer diameters of the same section.
## 4 Experiments and Results
### Ablation Experiment
#### 4.4.4 Ablation Experiment on Loss Function  
To validate the proposed DFL-SIoU loss function, we compared its performance against several mainstream loss functions in dense pedestrian detection, with experimental results presented in Table 5. The results demonstrate that our DFL-SIoU achieves superior performance across all evaluation metrics. Specifically, the proposed loss function holistically addresses the characteristics of small-scale pedestrians in dense scenarios by mitigating gradient explosion issues while accelerating convergence.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **AP** & **Recall** & **MR\({}^{-2}\)** \\ \hline No attention mechanism & 91.5 & 95.1 & 40.6 \\ Add SENet [41] & 91.7 & 95.5 & 40.2 \\ Add CBAM [42] & 92.0 & 95.8 & 39.9 \\ Add CA [43] & 92.3 & 96.2 & 39.7 \\ Add EMA [44] & 92.6 & 96.5 & 39.3 \\ Add MLSA (Ours) & 93.3 & 97.1 & 38.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiment results for attention mechanisms (%).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **AP** & **MR\({}^{-2}\)** & **GFLOPS** & **FPS** \\ \hline C2f + MLSA + SPDH + DFL-SIoU & 91.8 & 39.8 & 166.9 & 62.8 \\ C2f + Deformable Conv + MLSA + SPDH + DFL-SIoU & 92.3 & 39.4 & 168.2 & 59.9 \\ C2f + Dynamic Conv + MLSA+ SPDH + DFL-SIoU & 92.7 & 39.1 & 167.9 & 59.7 \\ C2f\_D2 CN + MLSA + SPDH + DFL-SIoU (ours) & 93.3 & 38.7 & 169.3 & 57.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study of the C2f_D2 CN module on the CrowdHuman dataset.
# Comparison of Land Cover Categorical Data Stored in OSM and Authoritative Topographic Data
## 5 Discussion  
### Semantic Uncertainty  
The comparative analysis of data requires semantic similarity in the investigated concepts. As emphasized by many researchers [34; 50; 51], this assumption is a prerequisite for proper comparative data quality assessment. Semantic similarity in GIScience enables not only data comparison, but also the integration of data from different sources and their further analysis. If the semantic similarity between concepts is ignored, the evaluation or data comparison will be inaccurate [52]. The semantic consistency of concepts in IG can be analyzed at the general level, i.e., only the definition of the object and its geometrical representation are given [53], or at the detailed level, which also comprises definitions of attributes [34; 54]. In general, semantic inconsistency results from the classification used, as well as scaling [53]. Scientists use numerous methods to quantify and visualize geodata uncertainties, many of which rely on mathematical models. Semantic uncertainty results from incomplete knowledge of spatial features and phenomena. Traditionally, the boundaries of geographical features are treated as discrete and mutually exclusive, and often, the true location of the boundary is unknown and subject to positional and semantic errors. [PERSON] et al. [52] introduced a conceptual framework of ontology that includes the definition, semantic relationship, nature, and attributes of geographic features (concept). The authors defined semantic similarity as \"calculated on the basis of the feature similarity of ontology concept and the semantic distance of concept to measure the semantic similarity of ontology concept. The smaller the semantic distance between concepts, the higher the similarity between concepts\".  
Based on OSMwiki [55] and Ministry Regulation [56], it is seen that in both datasets, a \"building\" is a man-made structure with a roof, standing (more or less) permanently in one place and geometrically represented by a polygon. Differences in definition only arise when \"tag:building use\" is considered. During forest surveys, two tags, \"natural = wood\" and \"landuse = forest\" are used to map. They represent forests or other areas of trees. Both tags together are compliant with the forest definition in BCDT10k. Water bodies are represented in OSM by the following tags: \"natural = water\", \"landuse = reservoir\" and \"water = reservoir\"; these are equivalent to \"surface water\", i.e., areas occupied by rivers, canals and reservoirs. \"Highway = {motorway, trunk, primary, secondary, tertiary, unclassified, residential}\" is the principal tag for the road network and corresponds to \"road\", while \"tag:railway = rail\" matches \"track or set of tracks\" in BCDT10k. Finally, streams, rivers and other watercourses in OSM have the tag \"waterway = {river, stream, tidal_channel}\", and in BCDT10k are named \"river and stream/channel\" [37].
## 3 Experiment and Analyses
### Dynamic Experiment
#### 3.2.1 Fault Identification
Therefore, pseudorange-based RAIM is not applicable in high-precision indoor PL positioning, which illustrates the significance and purpose of this study again. Since the cycle slip is the most threatening fault for the carrier phase-based positioning system and it can frequently occur in reality, the analysis under cycle slip fault cases is also included. The simulated cycle slips (3 cycles) on PL1-L1, PL4-L1, PL5-L1 and PL6-L1 during the last 20 epochs are added in the dynamic data collection for further analysis. In order to avoid the influence of PL8, PL8 is excluded first, and the simulated cycle slips are added respectively. The corresponding test statistics are shown in Figure 14.  
In this case, the error source is only simulated cycle slips. According to Figure 14, the data quality of the other four pseudolites except PL8 is good. What's more, the test statistics based on carrier phase observations are sensitive to cycle slips and this kind of fault can be also effectively detected.
# Credible Navigation Algorithm for GNSS Attack Detection Using Auxiliary Sensor System
## 6 Conclusions  
In order to detect GNSS attacks and obtain a credible navigation result for mobile terminals, this paper proposes a credible navigation algorithm for GNSS attack detection using an auxiliary sensor system. The credible navigation algorithm is constructed based on the credible Kalman filter model. By adding a credible decision-making system to the Kalman filter framework and utilizing the complementary characteristics of the GNSS and the auxiliary sensor system, the algorithm overcomes the shortcomings of a single sensor and can detect GNSS attacks. By using a credible verification window to detect different kinds of GNSS attacks, including GNSS jump attacks and GNSS slow-change attacks, our proposed algorithm adaptively chooses an updated model to perform filter measurement correction and position prediction according to the outcome. Finally, the uninterrupted position information of the mobile terminal and the credible navigation result can be obtained.  
In addition, two data sets are collected during real driving for simulation and evaluation. Through these data sets, the parameter settings of the proposed algorithm are determined, and the advantage of the proposed algorithm is verified. We show that the proposed algorithm has better positioning accuracy, a lower positioning accuracy deterioration factor, higher navigation credibility and lower detection latency compared with conventional algorithms under GNSS jump attacks or GNSS slow-change attacks.  
Conceptualization, J.S. and H.W.; data curation, X.G.; formal analysis, J.S. and S.L.; investigation, X.G.; methodology, J.S.; project administration, Y.G.; resources, H.W. and Y.G.; software, Y.Z.; supervision, H.W.; validation, J.S., X.G. and S.L.; visualization, Y.L.; writing--original draft, J.S. and S.L.; writing--review and editing, X.G. and H.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & \
\begin{tabular}{c} **After GNSS** \\ **Jump Attacks** \\ \end{tabular} &
\begin{tabular}{c} **After GNSS** \\ **low-Change Attacks** \\ \end{tabular} \\ \hline EKF & / & / \\ RIO & 1 s & 27 s \\ ACNA & 2 s & 5 s \\ \hline \hline \end{tabular}
\end{table}
Table 7: Detection latency for the three algorithms.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
This research was carried out in the project: China's Second-Generation Satellite Navigation System Major Project (Y9E0153M26). Additionally, we wish to thank [PERSON] for the data transform, and [PERSON] for advice on experimental design.  
The authors declare no conflict of interest.
# Methodology for Studying the Reliability of Interlocking Devices in Bulgarian Railways
## 3 Results
### Statistical Assessment of Failures of the Interlocking Devices and Signallinig Equipment  
#### 3.1.1 Statistical Assessment of Failures in STD--Sofia  
Based on the number of failures given in the Annual consolidated activity reports and using Equations (1) and (2), the total number of failures of all device types and the corresponding percentage ratio was calculated. The results obtained are shown graphically in Figure 1a-d (respectively, for years 2020, 2021, 2022 and for the entire period).  
Similarly, the total duration of failures of all device types and the corresponding percentage ratio for the four periods was calculated using the Equations (3) and (4).  
It can be seen that the failures of interlocking devices and their duration have a relatively low proportion, as well as a relatively short recovery time. These are indoor devices located in special rooms at railway stations, unlike all other equipment, which is located outdoors (excluding ATC) and is subject to the effects of climatic conditions (rain, snow, humidity, temperature, etc.) [49; 50; 51]. There is also a greater likelihood of theft, vandalism, encroachment, and damage due to activities of external organisations, etc.  
It can also be noted that despite the low number and proportion of failures of TCD, their duration is relatively high. These failures are mainly on the external telecommunications lines (only 3.74% of failures, but around 11% of the total recovery time), only seven failures are on the ATC for the entire period, or 0.13%, respectively; their recovery time is also short (only 50.48 h or 0.18%). In general, the duration of failures is the longest for all such devices (rail current circuits--23.24%, lines for providing semi-automatic and automatic interlocking--23.95%, automatic crossing devices--15%, etc.) due to the time required to locate the exact location of the fault and the need to move the repair team there.  
The results obtained are shown graphically in Figure 2a-d (respectively, for years 2020, 2021, 2022, and for the entire period).  
Figure 1: Number and percentage distribution of failures of devices of signalling systems for STD—Sofa: **(a)** 2020; **(b)** 2021; **(c)** 2022; **(d)** period 2020–2022.  
Figure 2: Duration of failures of devices of signalling systems and their percentage distribution for STD—Sofa: **(a)** 2020; **(b)** 2021; **(c)** 2022; **(d)** period 2020–2022.  
It should be noted that 100% are the values accepted only for IIDs, not for all devices.  
The largest number of failures and, accordingly, the longest recovery time are observed with RRID--72.1% of failures and 79.2% of duration, but these are only 6.06% and 6.17% of failures and duration of all signalling devices for the three-year period.  
It should be mentioned the low number and share of failures in route computer interlocking devices, as well as their duration. A trend towards a decrease, especially in their number, can be observed. One of the main reasons is a software crash and the need to restart some modules (applications) or the entire system.
# Integrated Information in Relational Quantum Dynamics (RQD)
## Appendix A Computational Scalability and Practical Estimators for \(\Phi\)
### Empirical Timing Benchmarks  
To substantiate the scaling improvements afforded by the above methods, we measured the wall-clock runtime of both the exact and MPS-based approaches on systems of increasing size. In particular, we benchmarked (i) the exhaustive bipartition search and (ii) the tensor network estimator \(\tilde{\Phi}_{\chi}\) (with \(\chi=128\)) for system sizes \(n=6,8,\ldots,18\) qubits for MPS and \(n=6,8,10,12\) qubits for the exact method. All experiments were performed on a laptop with an Apple M2 Pro CPU (10 cores (6 performance and 4 efficiency)) and 16 GB RAM, using a Python 3.13.5 implementation (NumPy/SciPy) and an MPS library based on ITensor ([[https://www.python.org/](https://www.python.org/)]([https://www.python.org/](https://www.python.org/))). Each timing result is the median of 5 in dependent runs with identical random seeds for state preparation (to limit variability). The median runtime for each \(n\) is summarized in Table A1.  
The data clearly show the stark contrast between the exact algorithm's exponential scaling and the near-polynomial scaling of the tensor network method. The plots of runtime vs. \(n\) (Figure A1,b) highlight the difference of the two approaches. Below we summarize a few key observations from these benchmarks:  
1. **Exponential vs. near-polynomial:** The exact search time grows roughly as \(O(2.45^{n})\), in agreement with the combinatorial estimate \(\binom{n}{\lfloor n/2\rfloor}\sim 2^{n}/\sqrt{n}\). By contrast, the MPS-based estimator exhibits only mild superlinear growth with \(n\) (dominated by the \(O(n,\chi^{3})\) cost of environment tensor updates), much closer to polynomial scaling.
2. **Tensor network speedup:** For small systems (\(n\lesssim 6\)), the overhead of building the MPS means the exact method and MPS method are comparable in speed. But for larger sizes, the tensor network approach quickly pulls ahead. By \(n=14\) and \(16\), the MPS estimator is already orders of magnitude faster than exhaustive search for the same system.
3. **Practical crossover:** Beyond about \(n\approx 12\), the exact brute-force method becomes infeasible on a laptop (e.g., 18 qubits required days vs. a few seconds with MPS). Meanwhile, the MPS approach remains tractable up to at least \(n\approx 30\) on the same hardware, offering a clear practical advantage for larger systems.
## 2 Theoretical Background
### Models  
In soil, water can fill pore spaces with various degrees of saturation, and dielectric constant of this multiphase system is proportional to the percentage of water filling the voids. A simple mathematical model that considers the contributes of every phase was developed by [PERSON] [27], and it is named CRIM (Complex Refractive Index Model):  
\[\varepsilon_{b}^{\alpha}\;=\;\varepsilon_{s}^{\alpha}(1\;-\;\varphi)\;+\;[ \varepsilon_{w}^{\alpha}S_{w}\;+\;\varepsilon_{a}^{\alpha}(1\;-\;S_{w})]\varphi \tag{7}\]  
where \(\varphi\) is the porosity, \(S\) is the saturation, and subscripts \(b\), s, \(w\), \(a\) refer respectively to bulk, soil grains, water, and air. The exponent \(\alpha\) (usually taken \(\simeq\) 0.5) comes from the relationship between the velocity (\(v\)) of an electromagnetic wave and the dielectric constant (\(\varepsilon_{r}\)) of the material in which the wave propagates. A simplified form can be:  
\[v=\frac{c}{\sqrt{\varepsilon_{r}}} \tag{8}\]  
where \(c\) is the light speed. In a medium with high dielectric constant, like water, electromagnetic waves propagate slower than in a low dielectric constant material. The logic of the CRIM model is to calculate a weighted average of the travel time of a wave passing through its various phases and to relate this time to dielectric permittivity through Equation (8).
# Low-Cost RSAC and Adsorption Characteristics in the Removal of Copper Ions from Wastewater
## 3 Results and Discussion
### Adsorption Experiments
#### 3.1.2 Effect of Adsorbent Amount  
Figure 5 shows the correlation between adsorbent amount and copper ion adsorption. Firstly, the copper ion removal rate increased continuously with the increase of the RSAC particle amount, but the slope decreased at the same time. Meanwhile, the adsorption amount per unit mass of RSAC particles showed a different trend: the adsorption amount per unit mass of RSAC particles increased at the beginning stage and decreased after  
Figure 4: Effect of adsorption time on the adsorption of copper ions by RSAC particles.  
reaching a specific point with the increase of the adsorbent amount. The two trends indicated that there was a balance point between efficiency and performance. The amount aimed best removal rate may lead to inefficient usage of RSAC particles: the adsorption capacity per mass of RSAC particles was only 1.43 mg/g at 10 g, which indicated that the adsorption performance of RSAC particles was not fully utilized. The reason may be that the copper ion removal rate increased because of the increase of RSPRC particle amount, the increase in contact area, and the increase in the number of adsorption sites [25].  
The adsorption amount of copper ions per unit mass of adsorbent showed a different trend. This may be due to the fact that when the concentration of copper ions in the solution is constant and the adsorbent dosage is low, the adsorption sites are not significant in driving the diffusion and adsorption reaction caused by the atmosphere of copper ion concentration. With the increase in the dosage, the total adsorption sites provided to copper ions in the solid-liquid system increased and the adsorption amount per unit mass of RSPRC particles also increased [26]. Considering economic factors, the dosage of 5 g/150 mL was chosen as a balance point and used in the subsequent experiments.
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 3 System Model
### Two Nodes Reliable Model (2n-Rm)  
For establishing reliable links, 2N-RM is introduced in which three important factors are considered distance, Expected Transmission Count (ETX), and residual energy.
## 3 Results and Discussion
### Investigations of the Thermal Degradation
#### 3.4.2 Differential Scanning Calorimetry Measurement Results  
It has been demonstrated that differential scanning calorimetry is a good opportunity to disclose the thermal behavior and degradation of samples [18; 37]. In order to correctly understand and reveal the thermal degradation of the samples, differential scanning calorimetry measurements were performed. From the DSC curves in Figure 10, in the 30-350 \({}^{\circ}\)C temperature range the curves had a wide peak after the amorphous stage between 50 and 100 \({}^{\circ}\)C, and above this, a valley was present in the curves until 170 \({}^{\circ}\)C, except in the curve of the orange samples where a small peak (a melting event) at about 110 \({}^{\circ}\)C was also shown. It is supposed that this deviation also resulted from the different components of the orange sample (e.g., acryl). Above 110 \({}^{\circ}\)C, similar peaks were detected until 350 \({}^{\circ}\)C, possibly due to the crystallization (semi-crystalline part) of the samples.