esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior
## 5 Traffic Simulation Models
### Traffic Simulation Algorithms
When the vehicle intends to change lanes, the model assesses the existing headway space to determine whether changing lanes is achievable. If it is, then the process happens. If not, then the vehicle remains in the current lane [73]. A simple  
Figure 5: Basic car-following model. illustration of the lane-changing decision of a vehicle is depicted in Figure 6. The model must meet certain criteria such that for a given adjacent lane, both the space headway for the following and preceding cars must be more than the unsafe distance, which can be computed as follows:  
\[d_{(safe)}=\frac{s_{n^{2}}^{2}-s_{n+1}^{2}+3s_{n+1}b\lambda}{2b} \tag{7}\]  
\[d=x_{n}-L_{n}-x_{n+1} \tag{8}\]  
Acceptable Headway Gap if \(:d\geq Cd_{(safe)}\) (9)  
Let \(s_{n^{2}}\) and \(s_{n+1}\) denote the following and preceding vehicle speeds, respectively, and \(b\) be the vehicle's maximum deceleration. We refer to the actual vehicle following distance if the vehicle moved into the adjacent lane by \(d\). Equation (9) is derived from Equations (7) and (8), which compute the smallest acceptable headway gap between each vehicle \(C\) and the minimum safe distance \(d_{(safe)}\) between the subject vehicle and the following vehicle [66; 74]. * Gap acceptance models: Gap-acceptance models are mainly used to determine the traffic conditions in adjacent lanes prior to a vehicle accessing the available space. They are used to estimate the amount of space and time required to cross a junction, enter a roundabout, or change lanes [75]. These two factors are dependent on the traffic conditions, such as the road characteristics, the speed, and the lengths of the following and preceding vehicles as well as the passive vehicle. The minimum safe distance \(d_{(safe)}\) between the subject vehicle and the following vehicle, which is also known as the critical gap, is a significant parameter affecting gap acceptance behavior. An important assumption that has to be addressed in the gap acceptance models is the headway distribution in the circulating flow to measure the road capacity [66; 76]:  
\[Y_{n}(t)=\left\{ \begin{array}{ll}1,&\text{if }d_{n(t)}\geq d_{n_{(safe)}}(t), \\ 0,&\text{if }d_{n(t)}<d_{n_{(safe)}}(t),\end{array} \right. \tag{10}\]  
where \(Y\) denotes the vehicle's decision of whether or not to overtake the adjacent lane at a given time \((t)\), \(d_{n}(t)\) is the available headway gap, and \(d_{n_{(safe)}}(t)\) is the critical gap. The vehicle forces entry when the gap size is equally likely to be accepted \((Y,(t)=1)\); otherwise, the vehicle rejects \((Y,(t)=0)\) the observed gap and stays in the same lane [75].
## 3 Results
### Effects of LAI and Climatic Factors on WUE
According to the statistics on the grey relational degree between the WUE and any of the climate factors at the same place, the areas where precipitation, relative humidity, wind speed, temperature, water vapor pressure and sunshine duration were the most strongly correlated climate factors with WUE accounted for 62.41%, 12.99%, 11.52%, 5.67%, 5.61% and 1.79% of the study area, respectively (Figure 6i). Relative humidity was the most strongly correlated climate factor with WUE in north Baotou, west Xilingol League, east Hinggan League, and north Chifeng. Wind speed was the most strongly correlated climate factor with WUE in Erdos, Hohhot, south Chifeng and central Hulunbuir. Among all the vegetation types, meadow showed the highest grey relational degree between WUE and LAI (0.852), which was significantly higher than those of forest (0.724) and swamp (0.711) (Table 1). The correlation of the WUE with precipitation was next to that with LAI in all the vegetation types. The grey relational degree between the WUE and precipitation was relatively high in meadow and shrub, but it was the lowest in swamp; that between the WUE and relative humidity was the highest in meadow, but the lowest in swamp. The grey relational degrees of the WUE of all the vegetation types with temperature, wind speed, sunshine duration, and water vapor pressure were lower than 0.66. Sunshine duration was the least correlated with WUE in all the vegetation types. Temperature was more strongly correlated with the WUE of forest and shrub than wind speed. However, opposite phenomena were observed in meadow and cultivated land. To summarize, the grey relational degrees of the WUE of all the vegetation types, except swamp with LAI and precipitation, were higher than 0.7, showing strong correlations. In other words, LAI and precipitation were strongly correlated with WUE and they were more important to WUE than other factors, followed by relative humidity and wind speed. Temperature, water vapor pressure and sunshine duration were less important to WUE. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Vegetation Types**} \\ \cline{2-5}
**Influencing** & \multicolumn{1}{c}{**Forest**} & \multicolumn{1}{c}{**Swamp**} & \multicolumn{1}{c}{**Shrub**} & \multicolumn{1}{c}{**Meadow**} & \multicolumn{1}{c}{**Cultivated Vegetation**} \\ \hline LAI & 0.724 & 0.711 & 0.826 & 0.852 & 0.813 \\ PRE & 0.711 & 0.699 & 0.729 & 0.741 & 0.719 \\ TEM & 0.651 & 0.647 & 0.647 & 0.633 & 0.649 \\ WS & 0.645 & 0.653 & 0.635 & 0.650 & 0.655 \\ SD & 0.600 & 0.599 & 0.599 & 0.607 & 0.618 \\ RH & 0.677 & 0.671 & 0.690 & 0.697 & 0.678 \\ EA & 0.632 & 0.630 & 0.636 & 0.632 & 0.645 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Grey relational degrees between WUE and LAI and climate factors for different vegetation types. Figure 6: Spatial distribution of grey relational degree between WUE and (**a**) LAI, (**b**) PRE, (**c**) RH, (**d**) WS, (**e**) TEM, (**f**) EA, and (**g**) SD and spatial patterns of the max grey relational degree in (**h**) all factors and (**i**) all climate factors.
# Lightweight End-to-End Speech Enhancement Generative Adversarial Network Using Sinc Convolutions
## 6 Experimental Setup
### Implementation Details  
The networks are trained for 100 epochs with the batchsize 100. Different from previous works [19; 30; 31], we utilize the Adam optimizer [46] (\(\beta_{1}=0\) and \(\beta_{2}=0.9\)), with the two-timescale update rule (TTUR) [47]. According to the recent successful approach to training GANs quickly and stably [48], we set the learning rate of the discriminator to 0.0004 and that of the generator to 0.0001, i.e., the discriminator has a four-times-faster learning rate to virtually emulate numerous iterations in the discriminator prior to updating the generator. We extract raw speech chunks of length \(L=16,384\) (approximately 1 s) over 50% overlap as the input, to avoid any speech distortion caused by handcrafted features. A high-frequency pre-emphasis filter of coefficient 0.95 is applied to each waveform chunk before presenting it to the networks, as this is proved to help correct with some high-frequency artifacts in the de-noising setup. During testing, raw speech chunks are extracted from testing utterances without overlap, and outputs are correspondingly de-emphasized and concatenated as the enhanced waveforms. The number of filters varies along with the depth. In the situation of the addition architecture, they are \(\{64,64,128,256,512,1024\}\), resulting in the output size of the feature map \(\{8092\times 64,2048\times 64,512\times 128,128\times 256,32\times 512,8 \times 1024\}\). In contrast, in the instance of the substitution architecture, they are \(\{64,128,256,512,1024\}\), resulting in the output size of the feature map \(\{4096\times 64,1024\times 128,256\times 256,64\times 512,16\times 1024\}\). We also conduct ablation tests on the influence of the input length, the filter number and the kernel size. All experiments are implemented in PyTorch [49].
## 5 Chain Synchronization  
### Sequence Number  
The MHC method needs to add a sequence field to ensure the reliability of the transmission process. According to the position of the sequence number, when the node value of the message hash chain is calculated, there are two ways to calculate the chain. The first  
Figure 3: Construction method of the message hash chain.  
way is that the sequence number \(seq_{i}\) can be included in the message header \(hdr_{i}\), and the node value \(HC_{i}\) of message hash chain is obtained by MHC calculation. In this way, the non-repudiation of the sequence field can be guaranteed, but it will cause difficulties when tracing the message's contents. The message hash chain expression constructed in this way is the following:  
\[HC_{i}=h(h(m_{i}||seq_{i})||HC_{i-1}). \tag{3}\]  
The second way is to concatenate the digest of the sequence number and the message's digest to construct the message's node value of the message hash chain. This way ensures that the message and sequence cannot be tampered with, and makes it easier to trace the message's content. Therefore, it is recommended to use the second way when constructing the node value of the message hash chain. The expression of the node value of the message hash chain constructed in this way is the following:  
\[HC_{i}=h(h(m_{i})||h(seq_{i})||HC_{i-1}). \tag{4}\]
# Torque Characteristic Analysis of a Transverse Flux Motor Using a Combined-Type Stator Core
## 3 Theoretical Analysis of Torque Characteristic of TFM
### Cogging Torque  
The cogging torque is a result of the interaction between core teeth and permanent magnet in permanent magnet motors. Similar to the traditional permanent magnet synchronous motor (PMSM), the proposed CS-TFM contains a teeth-slot structure. The difference between them is that the teeth-slot structure of CS-TFM is on the rotor side instead of the one on the stator side in the PMSM. Therefore, the cogging torque of CS-TFM is caused by the interaction between the permanent magnet and rotor teeth.  
The fundamental frequency of the cogging torque is the least common multiple of the rotor teeth number and the stator pole number [17]; in the CS-TFM, the teeth number is \(p\) and the pole number is \(2p\). Thus, the fundamental frequency of cogging torque is \(2p\) and the period is \(\tau_{\rm m}\).  
The cogging torque of each single-phase can be expressed as:  
\[\left\{\begin{array}{l}T_{\rm cogA}=T_{\rm i}\sin\left(2\theta+\theta_{1} \right)+T_{\rm 2}\sin\left(2\theta+\theta_{2}\right)+T_{\rm 3}\sin\left(2 \theta+\theta_{3}\right)+...\\ T_{\rm cogB}=T_{\rm i}\sin\left(2\theta+\theta_{1}+2\pi/3\right)+T_{\rm 2} \sin\left(2\theta+\theta_{2}+2\pi/3\right)+T_{\rm 3}\sin\left(2\theta+ \theta_{3}+2\pi/3\right)+...\\ T_{\rm cogC}=T_{\rm i}\sin\left(2\theta+\theta_{1}+4\pi/3\right)+T_{\rm 2} \sin\left(2\theta+\theta_{2}+4\pi/3\right)+T_{\rm 3}\sin\left(2\theta+ \theta_{3}+4\pi/3\right)+...\end{array}\right., \tag{20}\]  
where \(T_{i}\) and \(\theta_{\rm i}\) are the amplitude and initial phase angle of the \(i\)th order harmonic, respectively.  
Then, the total cogging torque of three-phase TFM is:  
\[T_{cog}=T_{\rm cogA}+T_{\rm cogB}+T_{\rm cogC}=3T_{\rm 3}\sin 3\left(2\omega t+ \theta_{3}\right)+3T_{\rm 6}\sin 6\left(2\omega t+\theta_{3}\right)+... \tag{21}\]  
Compared to Equations (20) and (21), the harmonic component besides the \(3i\)th order is counteracted, and the amplitude of the three-phase cogging torque is reduced significantly.
## 3 Experimental Results and Theoretical Analysis
### Acoustic Emission Parameters
#### 3.3.2 Distribution of Acoustic Emission Energies
From Figure 9, the optimum exponents of the second stage of the selected samples with different flaw inclination angles have some differences, which may result from the calculation of standard error with the maximum likelihood method. When \(Q_{\min}\) is within the range of 100 aJ to 1000 aJ, the fitted exponents tend to a stable value, and the standard error is mainly within 0.03 using Equation (8). Therefore, we believe that the flaw inclination angle has no effect on the optimum exponent and that the optimum exponents of the specimens are 1.65 \(\pm\) 0.03.
## 3 Spectrum Management Issues
### A. Carrier Aggregation
#### 3.1.3 Capacity Improvement  
The authors in [97] have focused on the issue of network capacity maximization in the CA. The approach is based on the joint cell association and user scheduling; in this approach, the user can connect BSs by using multiple carrier bands. This technique helps solve convex optimization issues, but it suffers from high computational complexity when the number of users in the cell is high. Another study on increasing radio resource efficiency has focused on adaptive CA with differentiated cloud services [98]. It proposes a cross-layer scheduling approach based on three mechanisms: (1) Markov Decision Process-Based Cost Reward Packet Selection (MDP-PS), (2) Adaptive Packet Scheduling (APS), and (3) Adaptive Component Carrier Scheduling (ACCS). The simulation results have proved that the suggested method delivers improved results for capacity, network reward, and packet failure rate; however, authors have suggested that an analytical method for C-RAN (Cloud/Centralized Radio Access Network) and energy-efficient CR techniques are needed for future cellular networks.
## 4 Materials and Methods
### Accuracy Evaluation Metrics  
Different accuracy measures have been calculated to evaluate experimental results, such as Root Mean Square Error (_RMSE_), Mean Absolute Percentage Error (_MAPE_) and _F1-score_.  
_RMSE_ is simply the square root of the mean square error, with the only difference being that _MSE_ measures the variance of the residuals, while _RMSE_ measures the standard deviation of the residuals:  
\[RMSE=\sqrt{MSE},\text{ where }MSE=\frac{1}{n}\sum_{t=1}^{n}\lvert y_{t}-\hat{y}_{ t}\rvert^{2} \tag{8}\]  
where _n_-the number of time point, \(y_{t}\)-is the actual value at a given observation in a dataset \(t\), and \(\hat{y}_{t}\)- is the predicted value.  
_MAPE_ is the most commonly used metric for evaluating the accuracy of a prediction model. It calculates the average percentage difference between the actual and predicted values of a variable:  
\[MAPE=\frac{100\%}{n}\sum_{t=1}^{n}\left\lvert\frac{y_{t}-\hat{y}_{t}}{y_{t}}\right\rvert \tag{9}\]  
The _F1-score_ is a widely used metric for evaluating the performance of a classification model, especially in scenarios in which we want to balance both precision and recall.  
\[Precision=\frac{TP}{TP+FP} \tag{10}\]  
\[Recall=\frac{TP}{TP+FN} \tag{11}\]  
\[F1\text{ }score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{12}\]  
For multi-class classification, the _F1-score_ for each class is calculated using the one-against-one (OvR) method. In this approach, the metrics for each class are determined separately, as if a separate classifier were used for each class. However, instead of assigning several _F1-scores_ to each class, it is more appropriate to derive an average and obtain a single value to describe the overall performance. There are three types of averaging methods commonly used for _F1-score_ calculation in multi-class classification, but weighted averaging is most relevant for such unbalanced data. Weighted averaging calculates the _F1-score_ for each class separately and then takes the weighted average of these scores, where the weight for each class is proportional to the number of samples in that class. In this case, the _F1-score_ result is biased toward the larger classes.  
\[Weighted_{avg}F1\text{ }Score=\sum_{i=1}^{n}w_{i}\times F1\text{ }Score_{i} \tag{13}\]  
\[w_{i}=\frac{k_{i}}{N} \tag{14}\]  
where _N_-total number of samples, number of samples _k_\({}_{i}\) in class \(i\).  
The macro average calculates the _F1-score_ for each class separately and derives an unweighted average of these scores. This means that each class is treated equally, regardless of the number of samples it contains (support value):  
\[Macro_{avg}F1\text{ }Score=\frac{\sum_{i=1}^{n}F1\text{ }Score_{i}}{n} \tag{15}\]  
where _n_-number of classes.
# Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data
###### Abstract  
Cropland is fundamental to food security, and monitoring cropland non-agriculturalization through satellite enforcement can effectively manage and protect cropland. However, existing research primarily focuses on optical imagery, and there are problems such as low data processing efficiency and long updating cycles, which make it difficult to meet the needs of large-scale rapid monitoring. To comprehensively and accurately obtain cropland change information, this paper proposes a method based on the Google Earth Engine (GEE) cloud platform, combining optical imagery and synthetic aperture radar (SAR) data for quick and accurate detection of cropland non-agriculturalization. The method uses existing land-use/land cover (LULC) products to quickly update cropland mapping, employs change vector analysis (CVA) for detecting non-agricultural changes in cropland, and introduces vegetation indices to remove pseudo-changes. Using Shanwei City, Guangdong Province, as a case study, the results show that (1) the cropland map generated in this study aligns well with the actual distribution of cropland, achieving an accuracy of 90.8%; (2) compared to using optical imagery alone, the combined optical and SAR data improves monitoring accuracy by 22.7%, with an overall accuracy of 73.65%; (3) in the past five years, cropland changes in Shanwei followed a pattern of an initial increase followed by a decrease. The research in this paper can provide technical reference for the rapid monitoring of cropland non-agriculturalization on a large scale, so as to promote cropland protection and rational utilization of cropland.  
G 2025 15 147 +
Footnote †: journal: Applied sciences  
Article
# Investigation of Crosstalk between Data Signals and Feed Light in Power-over-Fiber
###### Abstract  
In power-over-fiber systems, simultaneous data signals and power transmission using a single optical fiber is an attractive step towards simplifying the systems. However, when simultaneously transmitting data signals and feed light in a single core, the transmission quality of the data signal degrades owing to the crosstalk based on the characteristics of the feed light source. In this study, we investigate and evaluate the crosstalk between the data signal and high-power feed light in a power-over-fiber system. To observe the effect of the crosstalk more clearly, we use a power-over-fiber link using a single-core, conventional multimode fiber, and a high-power Raman fiber laser as the feed light source. Consequently, it is found that the feed light induces relative intensity noise related to the longitudinal mode beating and mode partition noise determined by the characteristics of the laser. In addition, it depends on the input power and transmission distance, and has a significant impact on the quality of the data signal by the noise transfer. To evaluate these noises quantitatively, we also evaluate the error-vector-magnitude and bit-error-rate characteristics of analog and digital data signals, which show that similar characteristics are observed.  
power-over-fiber; multimode fibers; fiber lasers; relative intensity noise; longitudinal mode beating; mode partition noise; cross-phase-modulation +
Footnote †: journal: _Photonics_
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 3 Problem Description and Contributions  
Current mobile networks are often over-dimensioned to accommodate varying traffic demands, resulting in the under-utilization of base station resources during off-peak hours. It leads to increased costs for operators opting for SingleRAN technology, including the expenses for network sites, spectrum, and electricity. Furthermore, the deployment of 5G alongside existing technologies exacerbates these costs. While operators have been working on network optimization solutions to improve resource utilization, there is still room for more energy savings. This paper aims to answer the following questions:  
* How can RAN sharing improve the energy savings for an operator as compared to using independent hardware?
* What could be the impact of independent resource optimization by the operators with different KPIs on the energy savings obtained?  
Our contributions in this work are as follows:  
1. Through simulations we evaluate the benefits, complexity, and challenges of the RANaaS RAN-sharing scenario from the TowerCo perspective, specifically on the energy savings obtained. We analyze the benefits of providing independence in the sharing process by allowing each operator to control their share of resources and other cell-level parameters, thus enabling service differentiation.
2. We conduct a benefit analysis of using AI for energy-efficient network optimization from an operator's perspective.
3. We propose an approach to distribute the energy cost among the participating MNOs in the RAN-sharing scenario of interest.
4. We discuss the implications and challenges of independent resource optimization by MNOs in the RANaaS RAN-sharing scenario by highlighting the potential benefits and drawbacks.
# The Use of Autogenous Teeth for Alveolar Ridge Preservation: A Literature Review
## 6 Undemineralized Dentin Matrix (UDD)
### Clinical Studies
#### 6.2.2 Radiologic Outcomes  
Radiologically, CBCT images showed that alveolar ridge dimensions were preserved in most cases [54; 55; 57]. In the study by [PERSON] et al. the vertical and horizontal dimensions of the sockets grafted were preserved, and in some cases increased [57]. One particular study by [PERSON] et al. performed a retrospective radiographic cone-beam computed tomography in order to better assess the efficacy of this graft in terms of volume stability [54]. Comparing the preoperative and the postoperative (4 months after ARP procedure) dimensions, the reduction in the buccal bone plate thickness at 1 mm, 3 mm and 5 mm bellow the buccal crest was, respectively: \(-\)0.87 \(\pm\) 0.84 mm; \(-\)0.60 \(\pm\) 0.70 mm and \(-\)0.41 \(\pm\) 0.55 mm [54]. Following the same level measurements, the mean ridge width changes were, respectively: 1.38 \(\pm\) 1.24 mm, 0.82 \(\pm\) 1.13 mm, and 0.43 \(\pm\) 0.89 mm [54]. Finally, the authors concluded that the average mid-buocal bone height gain was 1.1% and the mid-lingual height gain was 5.6% [54]. Another study that evaluated through CBCT analysis the alveolar ridge dimensions before and 4 months after the ARP procedure reported a loss of 0.76 mm in the vertical dimension and a loss of 1.1 mm in the horizontal dimension [69].
## 3 Materials and Methodology
### Machine Learning Models
#### 3.1.6 Random Forest  
The random forest (RF) model is an ensemble learning technique that constructs multiple trees during training and outputs the class that is the mean prediction (regression) or the mode of the classes (classification) of the individual trees [40]. It uses an average of several decision trees to improve accuracy and manage overfitting [41].