esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Smart Urban Logistics and Access Management Strategies
Thirdly, certain parking practices, including unauthorized stops or the presence of delivery trucks and buses during loading or unloading, limit roadway availability and lead to disruption and uncertainty for other road users [14]. From this perspective, the proposed model incorporates dynamic parking management measures based on the implementation of policies and technologies aimed at regulating parking spaces according to demand and contextual factors. Dynamic parking management is configured as a flexible tool, capable of adjusting in real time variables such as pricing, availability, and time constraints, to maximize the efficiency of parking space utilization [15]. This approach contributes to reducing urban congestion, improving traffic flow, and enhancing the overall performance of parking systems. Regulatory strategies can be adopted either upstream, through scheduled time restrictions, or in real time via the use of Intelligent Transportation Systems (ITS). Recognized as one of the most significant innovations in the field of urban logistics, dynamic parking management provides multiple benefits. It allows for the rational use of available space, ensuring more efficient utilization of loading and unloading areas by logistics operators. This leads to minimized waiting times, enhanced delivery scheduling accuracy, and, more broadly, increased efficiency in the urban freight distribution process. Furthermore, this type of management promotes a higher turnover of commercial vehicles, increasing access to operational areas for a larger number of logistics providers. However, the implementation of such systems presents challenges that must be addressed. Among these are the high initial costs associated with investments in technological infrastructure, as well as the need for structured cooperation between public and private stakeholders to ensure effective governance and ongoing maintenance of the installed systems. Despite these obstacles, dynamic parking management stands out as a strategic solution to optimize urban logistics processes, enhance the efficiency of the supply chain, and mitigate the environmental and social impacts of urban mobility [16]. In summary, the effective management of last-mile logistics, supported by emerging Information and Communication Technologies (ICT) underpinned by Industry 4.0 and 5.0 paradigms, is confirmed as a cornerstone for achieving a sustainable urban future.
# Magnetic Field-Based Vehicle Positioning System in Long Tunnel Environment
## 4 Experimental Results and Discussion  
### Test Bed  
The proposed system was verified with an experiment conducted at Gangnam Beltway in Seoul, Korea. Figure 3 shows the test bed. Figure 10 presents the experimental setup, where the smartphone was attached to the dashboard using a holder. The data from the magnetic sensor of the smartphone were stored in an internal SD card at 50 Hz. In addition, the vehicle velocity data of on-board diagnostic 2 (OBD2) were stored together at approximately 2 Hz. OBD2 data are transmitted to the smartphone through USB serial communication. The sensor data and OBD2 data are simultaneously stored in the smartphone, and when data are saved, the internal clock time of the smartphone is also stored. Since the sampling time of sensor data is faster than that of OBD2 data, OBD2 data is interpolated to generate synchronized data with the sensor data. The vehicle position calculated using OBD2 velocity are utilized for DB generation and experimental verification.  
Figure 9: Minor DB generation using previous pair index and RP margin, \(R\).
## Abbreviations  
ABCOA  
Artificial Bee Colony Algorithm  
AI  
Artificial Intelligence  
AMLM  
Alternative Maximum Likelihood Method  
ANFIS  
Adaptive Neuro-Fuzzy Inference System  
ANN  
Artificial Neural Network  
BOA  
Bat Optimization Algorithm  
BP  
Back-Propagation  
CS  
Cuckoo Search Algorithm  
DBNGA  
Deep Belief Network with Genetic Algorithms  
ECM  
Equivalent Energy Method  
ELM  
Extreme Learning Machine  
EM  
Empirical Method  
EMJ  
Empirical Method of Justus  
EML  
Empirical Method of Lysen  
EPFM  
Energy Pattern Factor Method  
EPM  
Energy Pattern Method  
FS
Fourier Series  
GA  
Genetic Algorithm  
GM  
Graphical Method  
GRNN  
Generalized Regression Neural Network  
GWO  
Gray Wolf Optimizer  
KNN  
K-Nearest Neighbor  
KS  
Kolmogorov-Smirnov Distance  
LASSO  
Least Absolute Shrinkage Selector Operator  
LSR  
Least-Squares Regression  
LSSVRGA  
Least-Squares Support Vector Regression with Genetic Algorithms  
LSSVRTSGA  
Least-Squares Support Vector Regression for Time Series with Genetic Algorithms  
MAE  
Mean Absolute Error  
MAPE  
Mean Absolute Percentage Error  
MBE  
Mean Bias Error  
MLFFNN  
Multi-Layer Feed-Forward Neural Network  
MLM  
Maximum Likelihood Method  
MLP  
Multi-Layer Perceptron  
MLPNN  
Multi-Layer Perceptron Neural Network  
Mmab  
Mabchour's Method  
MMLM  
Modified Maximum Likelihood Method  
MOM  
Method of Moments  
MPE  
Mean Percentage Error  
MSE  
Mean Square Error
## 4 Target Model and Frequency Offset Extraction Method
### Model Establishment
#### 4.1.2 The Model of the Received Signal  
In the actual environment, the receiving antenna will receive the direct wave signal from the base station and the multipath interference caused by the reflection of other obstacles in the environment while receiving the target echo. The transmission diagram of the 5G signal from the transmitting base station to the receiving antenna is shown in Figure 4.  
The signal received by the receiving antenna can be represented as follows:  
\[s_{\text{echo}}(t)=\alpha s(t-t_{0})+\sum_{m}a_{m}s(t-t_{0}-\tau_{m})+\sum_{n }b_{n}s(t-t_{0}-\tau_{n})\exp[j2\pi f_{d_{n}}(t)]+n(t) \tag{5}\]  
Figure 4: Signal propagation diagram. The 5G base station is the signal transmission location, and the receiving antenna is the signal arrival location. The direct wave is the signal that directly reaches the receiving antenna from the 5G base station. Multipath signals are signals reflected by static obstacles. The target echo is reflected by the target.  
Figure 3: Bistatic radar model. TX and RX are the transmitting and receiving antennas, and the speed of the target is \(v\). \(\beta\) is the double base angle, and \(\delta\) is the angle between the direction of the target velocity and the bisector of the double base angle.  
where \(s(t)\) is the transmitted signal, \(t_{0}\) is the time delay of the signal propagating in a straight line between the receiving antenna and the base station, \(t_{0}+\tau_{m}\) is the time delay generated by the transmitted signal reflecting from the \(m\)-th stationary obstacle to the receiving antenna, \(t_{0}+\tau_{n}\) is the time delay generated by the transmitted signal reflecting from the \(n\)-th moving target to the receiving antenna, \(f_{d_{n}}\) is the Doppler frequency shift caused by the radial velocity generated by the motion of the \(n\)-th moving target, and \(n(t)\) is noises in the environment.
## 3 Results
### Effects of Metal Ions on Bioproducts Synthesis  
#### 3.3.1 Impact of Metal Ions on Chlorophyll Content  
At the end of experiments, total chlorophyll concentrations along with chlorophyll ratios (chlorophyll a/chlorophyll b) were determined and are shown in Figure 2.  
Exposure to Cd induced a significant increase in total chlorophyll content ranging from 8.33% to 15.23% in the presence of 3 and 1.5 mg/L of Cd, respectively. However, for Cr and Pb, total chlorophyll concentrations were lower than control, particularly for the highest Pb tested concentration. Chlorophyll ratios remained constant in the presence of 10 mg/L of Pb and 3 mg/L of Cd, but decreased under all other conditions.  
\begin{table}
\begin{tabular}{c c c} \hline
**Metal Ion** & **Concentration (mg/L)** & **Growth Rates \(\mu\) (Day\({}^{-1}\))** \\ \hline Control & 0 & 0.2090 \(\pm\)0.0033 **\({}^{\ast}\)** \\ \hline \multirow{2}{*}{Cd} & 1.5 & 0.1962 \(\pm\)0.0033 \({}^{\rm c}\) \\ \cline{2-3}  & 3 & 0.1862 \(\pm\)0.0006 \({}^{\rm d}\) \\ \hline \multirow{2}{*}{Cr} & 4 & 0.1999 \(\pm\)0.0004 \({}^{\rm b}\) \\ \cline{2-3}  & 8 & 0.1883 \(\pm\)0.0011 \({}^{\rm d}\) \\ \hline \multirow{2}{*}{Pb} & 5 & 0.2073 \(\pm\)0.0011 \({}^{\rm a}\) \\ \cline{2-3}  & 10 & 0.1992 \(\pm\)0.0002 \({}^{\rm b}\) \\ \hline \end{tabular} \({}^{\ast}\) Different lower-case letters in the third column indicate statically significant differences among growth rates in the series of Cd, Cr or Pb exposures. Values followed by the same letter(s) are not significantly different (\(p<0.05\), ANOVA, [PERSON]’s multiple comparison test) (Mean \(\pm\) S.E.).  
\end{table}
Table 2: Growth rates (\(\mu\)) of _C. dorsiventrale_ incubated at different concentrations of Cd, Cr or Pb for 14 days.  
Figure 2: Effect of Cd, Cr and Pb single metal ion exposure on chlorophyll content and chlorophyll ratio in _C. dorsiventrale_ after 14 days of exposure. Data are means \(\pm\) SE. Columns labeled with different lower-case letters indicate statistically significant differences (\(p<0.05\), ANOVA, [PERSON]’s multiple comparison test).
## 2 Passive Filter Design
### AC Filter  
The inductor-capacitor-inductor (LCL) filter is designed as an AC filtering to filter out the ripples and improve power quality for the bidirectional multi-purpose inverter. The two inductors and one capacitor are used as filters per phase: \(L_{inv}\) is inverter side inductor, \(C_{f}\) is a parallel capacitor, and \(L_{load}\) is the load side inductor calculated as in Equations (1) and (2), referred to in Figure 3.  
\[L_{inv}=\frac{V_{DC}}{nf_{S}I_{ACrated}\%ripple} \tag{1}\]  
\[I_{ACrated}=\frac{\sqrt{2}P_{load}}{3V_{AC}} \tag{2}\]  
In Equation (1), the value of \(n\) would be 4 [23] for a three-phase inverter, \(I_{ACrated}\) (A) is the rated current at the load side, \(P_{load}\) (W) is load power, \(f_{S}\) (Hz) is operating switching frequency, \(\%\)\(ripple\) is 40%, and \(V_{AC}\) (V) is phase-to-neutral voltage. AC capacitor filter at three phases could be calculated as Equation (3).  
\[C_{f}=\frac{1\%\ P_{rated}}{2\pi f_{AC}\ V_{AC}^{2}} \tag{3}\]  
where \(f_{AC}\) (Hz) is AC frequency and \(P_{rated}\) (W) is rated load power.  
Figure 3: Passive filters of the inverter.  
Attenuation factor \((I_{att})\) which would indicate an allowable ripple in the inductor, and a switching inductor is represented in Equation (4). \(I_{att}\) has to be minimized while maintaining a stable and cost-effective filter [24].  
\[I_{att}=\left|\frac{1}{1+r\left(1-L_{inv}C_{f}(2\pi f_{s})^{2}\right)}\right|\% \tag{4}\]  
\(I_{att}\) is considered 5% for this design. The factor \(r\) is derived by rearranging the above equation and represented in Equation (5).  
\[r=\left|\frac{\frac{1}{I_{att}}-1}{\left(1-L_{rect}C_{f}(2\pi f_{s})^{2}\right)}\right| \tag{5}\]  
The other load side inductor \(L_{load}\) is determined by multiplying \(r\) with \(L_{inv}\), as expressed in Equation (6).  
\[L_{load}=rL_{inv} \tag{6}\]  
After filter design, it is essential to calculate the resonance frequency \(f_{res}\) (Hz) as shown in Equation (7), which must meet the required criteria as well as validate the LCL filter design. The stable region of \(f_{res}\) is between 10 times the rated AC frequency (i.e., 50 Hz frequency in the case of grid applications, or maximum rated speed of the motor can be considered for electric motor applications) and half of switching frequency. These criteria avoid issues in the upper and the lower harmonic spectrums.  
\[f_{res}=\frac{1}{2\pi\sqrt{L_{inv}C_{f}}} \tag{7}\]  
The remaining variable describes passive damping that could be added in order to avoid oscillations. The damping resistor \(r_{d}\) can be calculated by the use of capacitor and resonance frequency value as represented in Equation (8).  
\[r_{d}=\frac{1}{6\pi f_{res}C_{f}} \tag{8}\]
# Fast and Robust Asynchronous Rendezvous Scheme for Cognitive Radio Networks
## 3 Preliminary
### Enhanced Jump-Stay Algorithm  
The same authors improve the performance of JS by proposing the EJS algorithm that can reduce the upper bounds of both MTTR and ETTR values from \(O(P^{3})\) to \(O(P^{2})\) under the asymmetric model, while keeping the same order of rendezvous times of the symmetric case. Unlike JS, EJS's jump pattern lasts \(3P\) time slots, i.e., each round takes \(4P\) time slots which consists of three jump patterns for \(3P\) time slots and one stay pattern for \(P\) time slots. In addition, the starting index \(i\) is randomly selected from \([1,P]\) and switched to the next number every round (\(4P\) time slots) in the round-robin fashion, while the step length \(r\) remains the same in every round after randomly selecting from the available channels for both symmetric and asymmetric models. Therefore, the channel \(c\) of the jump pattern is determined as follows. \(c=((i+t\times r-1)\ mod\ P)+1\) where \(c\) is the channel of time \(t=t\ mod\ 4P\). If \(c\) is greater than \(M\), then it is remapped to \([1,M]\) (i.e., \(c=((c-1)\ mod\ M)+1\) for \(c>M\)). For the asymmetric model in EJS, additional replacement process is performed to increase the probability of rendezvous. That is to say, if \(c_{j}\) is the computed channel in the sequence of User \(k\) and it does not belong to \(C_{k}\), then it is replaced by \(((j-1)\ mod\ |C_{k}|)+1)th\) channel in \(C_{k}\). For example, given \(C_{k}=\{c_{1},c_{3},c_{4}\}\) and \(j=5\), since \(c_{5}\) does not belong to \(C_{k}\), it is replaced by \(c_{3}\) (Here, \(((j-1)\ mod\ |C_{k}|)+1)=2\) and \(c_{3}\) is the \(2 nd\) channel in \(C_{k}\)). Figure 2 illustrates an example rendezvous of the symmetric EJS system where \(M=4\) and \(P=5\). The rendezvous process is very similar to the JS system, except the length of one round of EJS is longer than that of the JS system. For the asymmetric scenario, two users' CH sequences under EJS scheme are shown in Figure 3 when \(M=2\) and \(P=3\) without substitution process. User 1 as a sender has two available channels \(\{c_{1},c_{2}\}\) and User 2 as a receiver has only one available channel \(\{c_{2}\}\). Two different step lengths for User 1 are considered in this example. When the step lengths of the sender are 1 and 2, the sender and receiver rendezvous at slot numbers 14 and 10, respectively. The upper bounds of MTTR for symmetric and asymmetric models are proved to be \(4P\) and \(4P(P+1-G)\) time slots, respectively in [PERSON]'s work [9].  
Figure 1: An example of symmetric JS Algorithm.
# Multi-Source Data Fusion for Vehicle Maintenance Project Prediction
###### Abstract  
Ensuring road safety is heavily reliant on the effective maintenance of vehicles. Accurate predictions of maintenance requirements can substantially reduce ownership costs for vehicle owners. Consequently, this field has attracted increasing attention from researchers in recent years. However, existing studies primarily focus on predicting a limited number of maintenance needs, predominantly based solely on vehicle mileage and driving time. This approach often falls short, as it does not comprehensively monitor the overall health condition of vehicles, thus posing potential safety risks. To address this issue, we propose a deep fusion network model that utilizes multi-source data, including vehicle maintenance record data and vehicle base information data, to provide comprehensive predictions for vehicle maintenance projects. To capture the relationships among various maintenance projects, we create a correlation representation using the maintenance project co-occurrence matrix. Furthermore, building on the correlation representation, we propose a deep fusion network that employs the attention mechanism to efficiently merge vehicle mileage and vehicle base information. Experiments conducted on real data demonstrate the superior performance of our proposed model relative to competitive baseline models in predicting vehicle maintenance projects.  
deep learning; multi-source data; vehicle maintenance; projects prediction +
Footnote †: journal:
## 4 Case Study: The Kimberley Climate Adaptation Project (KCAP)
### Policy Outcomes  
The KCAP, through the stakeholder and community workshops, produced more than 70 recommendations for climate change mitigation and adaptation, some of which were informed by the visualizations in Google Earth and Biosphere3D. Figure 7 shows possible tracks for public bus transit, Figure 8 a landscape design for flood-proof green space and infrastructure in the city center, and Figure 9 possible visual outcome of sustainable forest management practices adapting to climate change. It is not possible to link policy outcomes exclusively to the case study project because we are not looking at a controlled experiment here, but at a planning process that is informed through multiple stakeholders and policies. Nevertheless, follow-up interviews with key decision makers showed that at least six projects in advanced planning state for implementation were informed by it. In addition, three operational changes were initiated; one outreach event followed the KCAP; and five policy changes, e.g., a new by-law and follow-up studies commissioned by the City, may have been informed by the KCAP.
## 4 Experiments
### Experiments on the Performance of Descriptors
#### 4.2.4 Comparison between Different Feature Descriptors  
We compare the performance of our proposed covariance matrix descriptor to the performance of state-of-the-art feature descriptors like spin image [7], 3 DSC [8], PFH [9], RoPS [11], FHPH [12], BRoPH [13], and KDD [14], which are regarded as classical feature descriptors in the literature for successful surface matching and are implemented on Point Cloud Library (PCL) [38; 39]. Any required parameter (support radius, bin size) for the compared feature descriptors is set according to their original recommendations or similar values for our proposed feature descriptor, with the goal of providing the most fair comparison possible.  
Figure 5 clearly shows that the spin image descriptor performs worse than the other three descriptors under three different noise circumstances. The fundamental explanation for this is that the spin image descriptor is sensitive to non-uniform sampling [40], whereas the keypoints in this set of trials are not produced via uniform sampling. Simultaneously, when noise levels rise, PFH's performance falls significantly. There are two reasons for this. First, the PFH establishes a Darboux coordinate system and computes four features between two surrounding points that are rotation and translation invariant and noise resistant. Second, PFH combines three of its four feature variables to build a histogram with 16 intervals, which further increases PFH's robustness to noise.  
Meanwhile, Figure 8 shows that the performance of KDD is better than that of our proposed feature descriptor in the case of 0.1 mr noise, but as the noise increases, particularly when the noise is 0.3 mr and 0.5 mr, the performance of our proposed feature descriptor clearly outperforms all of the other feature descriptors. Our proposed feature descriptor is based on a covariance matrix, which is noise resistant for two reasons. First, the feature vectors that comprise the covariance matrix are noise robust and rotation and translation insensitive. Second, the mean operation in the process of producing covariance filters out most of the noise-damaged samples, directly improving the robustness of the feature descriptor to noise.  
Figure 8: Performance comparison between our proposed covariance matrix descriptor and the state-of-art feature descriptors under different noise conditions.
## 4 Discussion
### Materials  
A wide range of thermoplastic materials was assessed across the included studies. Each material possesses unique molecular characteristics that, in combination with external factors such as thermoforming, liquid absorption, temperature changes, and varying loading and unloading conditions, can alter physical properties such as flexural modulus and, consequently, affect the applied force/moment system [33].  
Several studies reported significant differences between materials. [PERSON] et al. [33] noted varying impacts of thermoforming on flexural properties depending on the polymer tested: PET-G remained stable, slight changes were observed in PET, and a significant increase in modulus was seen in TPU. [PERSON] and [PERSON] [28] observed a reduction in flexural strength due to thermoforming and aging in both Zendura (PU) and Duran (PET-G). However, single-layer materials such as TPU or PET-G generally demonstrated superior mechanical behavior compared to multilayer configurations. [PERSON] et al. [19] and [PERSON] et al. [20] found that single-layer (TPU, PET-G) materials exhibited higher stiffness and lower stress relaxation than multilayer ones (PET-G/TPU, TPU/PC). This might suggest that internal layering of different polymers may reduce homogeneity and compromise the ability to maintain consistent forces over time. [PERSON] et al. [27] expanded on this by comparing single-layer TPU with multilayer materials composed of combinations of TPU/PC and PET-G/TPU at 30 \({}^{\circ}\)C and 37 \({}^{\circ}\)C. They observed that single-layer TPU delivered higher forces and was less sensitive to temperature variation, while multilayer materials exhibited greater mechanical degradation under the same conditions. These findings reinforce the notion that layered materials may not always offer mechanical advantages.  
These results highlight the importance of comparing different materials to better understand their mechanical behavior and clinical implications. Duran, a PET-G material, emerged as the most extensively studied material in this review. However, despite the demonstrated need for comparative data, independent evaluation of the most widely used aligner system, Invisalign, which utilizes the proprietary multilayer thermoplastic polyurethane SmartTrack(r), remains unavailable due to patent protection and trade secrecy. The absence of accessible performance data for this material poses a significant barrier to scientific validation, prevents objective comparison with other systems, and creates a critical gap in the literature. This limitation not only restricts research efforts but also hampers evidence-based material selection in clinical settings, where clinicians must often rely on manufacturer claims rather than peer-reviewed data.
## 4 Results and Discussion
### The Grid-Connected Photovoltaic Systems (On Grid-PV) Design
#### 4.3.5 Operating Voltage of The String System  
(a) RMPPV (Rated Maximum Power Point Voltage)  
The operating voltage is obtained by multiplying the RMPPV of the module by the number of modules in a series source circuit, obtaining 562 volts.  
(b) The Maximum System Voltage is calculated by multiplying the value of Voc, and then multiplying that value by the number of modules in a series string.  
Module Voc = 49.35 Volts Nominal temperature = 25\({}^{\circ}\)C Number of Modules = 14. For the above, 690.9 volts per strings <1000 Volts was obtained (compliant for a 1000 Vmax inverter). Due to the fact that the maximum open circuit voltage per string was much lower than the 1000 Volts supported by the system, the temperature correction was not calculated.