esa-sceva/satcom-chunk-collection huggingface dataset:
# 3 GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach
## 5 Simulation Results
### Throughput for Minimum Reliability  
In this section, we study the throughput (in Kbps) of the 5G V2X communication system for each coding scheme and QoS scenario, as defined in [35]. For the calculation of the throughput, we assumed that the transmitter power is 23 dBm [9] (p. 9), the distance between the transmitter and the receiver is computed according to the specifications of each QoS scenario (Table 2) and the minimum reliability requirements are fulfilled. Additionally, we limited our observation to the maximum possible throughput that each QoS scenario can achieve. The results are presented in Table 6 for each QoS scenario. Table 6 shows that the maximum possible throughput in QoS scenarios Q1 and Q4 is achieved for all simulation scenarios and coding schemes. From the same table, we conclude that the urban-NLOS scenario fails totally for QoS scenarios Q2, Q3, Q5 and Q6 since the distance is too long for non-line-of-sight signals to meet the reliability requirements. Apart from that, these QoS scenarios behave excellently in all other simulation scenarios. Scenario Q6 is the worst throughput-effective scenario, being the only one that cannot reach the maximum throughput in the highway-NLOSv scenario, but its performance marginally reaches the optimum.  
Figure 6: Maximum range for minimum reliability per QoS scenario for simulation scenarios S1-S5 and 128-bit frame length. (**a**) QoS scenario Q1; (**b**) QoS scenario Q2; (**c**) QoS scenario Q3; (**d**) QoS scenario Q4; (**e**) QoS scenario Q5; (**f**) QoS scenario Q6.
# Pavement Defect Segmentation in Orthoframes with a Pipeline of Three Convolutional Neural Networks
## 4 Road Area Extraction  
### Architecture  
Road area extraction (RAE) is necessary in order to define the region of interest (ROI) and is performed by RSN which is a ConvNet inspired by U-Net [25] architecture (Figure 4).  
The RSN consists of five encoding blocks, five decoding blocks, a final segmentation block and shortcut connections between the encoding and decoding blocks. Each encoding block consists of six layers--(1) a batch normalization layer, (2) first convolutional layer, (3) first LeakyReLU activation, (4) second convolutional layer, (5) second LeakyReLU activation and (6) a max pooling layer. The decoding blocks have a similar structure except that max pooling layer is replaced with upsampling layer which is followed by a concatenation layer, thus an encoding block consists of seven layers. Encoding blocks have two outputs: (1) feature maps after max pooling connected to the next block and (2) feature maps before max pooling connected to the corresponding decoding block concatenation layer. The final segmentation block has only two layers--(1) a batch normalization layer and (2) a convolutional layer. Within a block, each convolutional layer has the same number of convolutional kernels, but not all blocks have the same number of kernels. However, an encoding block and its corresponding decoding block have the same number of convolutional kernels. Thus the first encoding and the last decoding block have 32 convolutional kernels. In the next encoding block the number of kernels is doubled (64) until it reaches 512 in the last encoding block. In contrast, the segmentation block's convolutional layer only has a single kernel.
## 4 Results
### Identification of Sensitive Features
In order to judge the surface deformation anomalies in a more refined way, it is defined that two or fewer deformation anomalies occurring at the same time at the same coordinates of the pixels in the same grid in the rainy season of 2021  
Figure 4: Area maps (**a**,**c**,**e**,**g**) of the sampling areas of the four PS points and their corresponding cumulative changes of all PS points in the sampling frame (**b**,**d**,**f**,**h**), different colors represent the cumulative surface deformation of all PS points at different positions within the sampling area, and the box-and-line plots are the deformation rate distributions of all all PS points in the sampling frame. are recognized as low-sensitive pixels of surface deformation, and more than two are recognized as high-sensitive pixels; at the same time, the anomalies occurring are classified into subsidence anomalies and upliftment anomalies (e.g., Figure 5d). The area and number of anomalies of various features are shown in Figure 5c and Table 2. Among them, \"High sensitive areas\" represent the number of times anomalies occur in pixels where different ground objects are located, and 05, 04, and 03 represent the number of times abnormal subsidence occurs, which is 5, 4, and 3. Thus, 5, 4, and 3 indicate that the number of times abnormal uplift occurs is 5, 4, and 3. \"Total area\" represents the total area of pixels with different abnormal occurrences. \"Crops\" and \"Rangeland\" represent the areas where different abnormal deformations occur in crop and grassland areas, respectively. \"Number of buildings\" represents the number of houses with different abnormal deformations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Highly** & \multirow{2}{*}{**Total Area (m)\({}^{2}\)**} & \multirow{2}{*}{**Crops (m)\({}^{2}\)**} & \multirow{2}{*}{**Rangeland (m)\({}^{2}\)**} & **Number of** \\
**Sensitive Areas** & & & & Buildings \\ \hline
05 & 8800 & 319 & 80 & 0 \\
04 & 45,600 & 2543.45 & 11,764 & 21 \\
03 & 2,764,400 & 2,326,908 & 86,070 & 468 \\
3 & 702,800 & 228,980 & 33,773 & 376 \\
4 & 46,800 & 21,001 & 1476 & 10 \\
5 & 3600 & 1464 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Area and number of physical anomalies at each site. Figure 5: (**a**) Shows the variation in the number of anomalous pixels, (**b**) shows the result of time-series decomposition of a PS point, which is used to analyze the phenomenon of thick heads and thick tails of the number of anomalous pixels in (**a**), and (**c**) shows the statistics of different anomalies in different features, with the red background representing the anomalous subsidence phenomenon and the blue background representing the anomalous uplift phenomenon. (**d**) shows the number of times surface deformation anomalies occur at different pixel positions, with 0 before the number indicating the number of abnormal subsidence times and 0 without indicating the number of abnormal uplift times.
###### Abstract  
Resource prediction algorithms have been recently proposed in Network Function Virtualization architectures. A prediction-based resource allocation is characterized by higher operation costs due to: (i) Resource underestimate that leads to quality of service degradation; (ii) used cloud resource over allocation when a resource overestimate occurs. To reduce such a cost, we propose a cost-aware prediction algorithm able to minimize the sum of the two cost components. The proposed prediction solution is based on a convolutional and Long Short Term Memory neural network to handle the spatial and temporal correlations of the need processing capacities. We compare in a real network and traffic scenario the proposed technique to a traditional one in which the aim is to exactly predict the needed processing capacity. We show how the proposed solution allows for cost advantages in the order of 20%.  
Keywords:Network Function Virtualization; computing resources; machine learning; long short term memory; convolutional network +
Footnote †: journal: Future Internet
# A Novel Optimal Control Strategy of Four Drive Motors for an Electric Vehicle
## 3 Discussion of Simulation Results
### Performance Analysis of the Optimal Energy Management for the Electric Vehicle
\begin{table}
\begin{tabular}{c c c c c} \hline  & \begin{tabular}{c} **Average Torque** \\ **(Four-Wheel)** \\ \end{tabular} & \begin{tabular}{c} **Rule-Based Control** \\ **(Front-Rear Axle)** \\ \end{tabular} & \
\begin{tabular}{c} **Optimal** \\ **(Front-Rear Axle)** \\ \end{tabular} &
\begin{tabular}{c} **Optimal** \\ **(Four-Wheel)** \\ \end{tabular} \\ \hline Energy Efficiency (km/kWh) & 4.7862 & 4.9749 & 5.1122 & 5.2985 \\ Improvement Rate (\%) & 3.9 & 6.8 & 10.7 \\ \hline \end{tabular}
\end{table} 
Table 4: The simulation results of different control strategies for a 95 kW motor under the NEDC driving cycle. \begin{table}
\begin{tabular}{c c c c c} \hline  & \begin{tabular}{c} **Average Torque** \\ **(Four-Wheel)** \\ \end{tabular} & \begin{tabular}{c} **Rule-Based Control** \\ **(Front-Rear Axle)** \\ \end{tabular} & \
\begin{tabular}{c} **Optimal** \\ **(Front-Rear Axle)** \\ \end{tabular} &
\begin{tabular}{c} **Optimal** \\ **(Four-Wheel)** \\ \end{tabular} \\ \hline Energy Efficiency (km/kWh) & 4.8028 & 4.8245 & 5.0319 & 5.0710 \\ Improvement Rate (\%) & – & 0.8 & 4.8 & 5.6 \\ \hline \end{tabular}
\end{table} 
Table 5: The HIL test results of different control strategies for a 45 kW motor under the NEDC driving cycle. \begin{table}
\begin{tabular}{c c c c c} \hline  & \begin{tabular}{c} **Average Torque** \\ **(Four-Wheel)** \\ \end{tabular} & \begin{tabular}{c} **Rule-Based Control** \\ **(Front-Rear Axle)** \\ \end{tabular} & \
\begin{tabular}{c} **Optimal** \\ **(Front-Rear Axle)** \\ \end{tabular} &
\begin{tabular}{c} **Optimal** \\ **(Four-Wheel)** \\ \end{tabular} \\ \hline Energy Efficiency (km/kWh) & 4.8517 & 5.0261 & 5.1319 & 5.1467 \\ Improvement Rate (\%) & – & 3.6 & 5.8 & 6.1 \\ \hline \end{tabular}
\end{table} 
Table 5: The HIL test results of different control strategies for a 45 kW motor under the NEDC driving cycle. Algorithms were established for four-wheel average torque mode, rule-based control, front and rear axle motor optimization, and four-wheel motor optimization. These optimizations were developed based on the ECMS method. Compared to the four-wheel average torque mode, rule-based control and the optimized system achieved significant improvements in a pure simulation environment, with increases in pure electric efficiency of 0.096 0.096%/0.535559%/1.261334% and 0.169%/1.50289%/3.917369%, respectively, as shown in Tables 7 and 8; in a HIL environment, the increases were 0.070  
may not allow it to reach optimal efficiency, instead showing a situation of excess power, causing a slight decrease in efficiency. Furthermore, the four-wheel optimization algorithm showed a higher improvement rate compared to the front and rear axle optimization algorithm, mainly due to the four-wheel optimization algorithm's ability to more delicately control the output of each motor, reducing losses in the energy distribution process, thereby significantly enhancing overall efficiency.
## 1 Introduction
Also, 126 Landsat scenes were processed to classify and map land-cover changes in each city. Since Thailand has experienced significant changes in urban development during the last 20 years, this study points out the spatiotemporal SUHI trend for each city and intercity (trend among cities) and performs a comparison with the built-up surface growth derived from Landsat imagery. To examine the SUHI patterns and their relevance in terms of temporal and spatial trends, empirical metrics were retrieved to quantify the phenomenon and to perform comparative analyses. The study was carried out by means of a two-dimensional least-square Gaussian fitting technique, whose parameterization and visualization proves to be useful in SUHI pattern insight.
# Potential and Limitations of the New European Ground Motion Service in Landslides at a Local Scale
###### Abstract  
Mass movements represent one of the most significant geohazards worldwide. The aim of this research is to highlight the potential and limitations of the European Ground Motion Service (EGMS) in detecting and monitoring mass movements at a local scale, especially in cases where data from in situ instrumental devices are unavailable. The study area corresponds to the La Miera landslide, located in Asturias (NW Spain). The multidisciplinary methodology applied involved the following steps: (1) downloading, acquiring, and analyzing Sentinel-1 A-DInSAR datasets (2015-2021) through the EGMS; (2) conducting a detailed geomorphological map and identifying evidence of movement; (3) classifying building damage by means of a damage inventory; (4) compiling and analyzing daily rainfall records with respect to deformation time series. Sentinel-1 A-DInSAR results revealed maximum LOS and East-West velocities of \(-11.6\) and \(-7.9\) mm/yr related to the landslide activity. Geomorphological mapping allowed for the updating of the landslide boundaries and its characterization as an active, complex movement. Registered building damage, which ranged from moderate to serious, was correlated with LOS and East-West velocities. The displacement recorded by the EGMS closely corresponds with rainfall periods, while periods of reduced rainfall coincide with the stabilization and recovery phases of displacement. This emphasizes a noteworthy quantitative correlation between rainfall events and EGMS data, evident both spatially and temporally. This work highlights that areas in which the EGMS data indicate deformation but lack in situ instrumental records, geomorphological techniques, and building damage surveys can provide spatial validation of the EGMS displacement, while rainfall records can provide temporal validation.  
EGMS; Sentinel-1 A-DInSAR; mass movement; rainfall; asturias +
Footnote †: journal: Applied Sciences
## 3 Current Status of the Circuit Component in the MMW Regime
### Power Amplifier (PA)
#### 3.2.1 PA in CMOS Technology  
Figure 5 illustrates the saturation power performance of the published MMW CMOS PAs. In [87], a 2-D distributed power-combining network with right/left-handed transmission line is proposed to offer distributed amplification and power combining for low loss and wideband performances of the PA. In [88], a Q-band stack PA is reported. The more transistors stack, the more supply voltage and output power allow. However, the high supply voltage is still an issue for this topology. In [89], a parallel power combiner based on a transmission-line transformer is proposed. Also, a cascode power cell structure with the inter-stage matching inductor is exploited to gain high output power and efficiency. In [90], a 45 GHz Doherty PA was reported. A cascode amplifier and slow-wave coplanar waveguides are exploited to improve the performances. The efficiency is boosted 8% compared with the conventional Doherty PA. In [91], the high output power is achieved by 32 ways combining differential PA. Wilkinson  
combiners, and a multi-port argyle transformer are deployed to minimize the path loss for high output power. In [92], a lumped-element \(\lambda\)/4 combiner is proposed. The inductor can realize higher Zo and lower loss with respect to the thin high-Z0 transmission lines owing to their self-magnetic coupling. Note that the combiner poses a 50 \(\Omega\) load to each PA. In [93], the novel devices layout is presented to minimize the parasitics of the transistors. By utilizing the transistors in the proposed transformer-based power combining MMW PA, which operates in Class AB, the efficiency is boosted. Moreover, one of the amplifier cell in this PA will be turned off while operating in low power mode. In this fashion, the efficiency and the loss of the combiner will be further reduced. In [94], the stability, broadband width are achieved by using cascode drain-source neutralization while implementing the CMOS 28 nm 60 GHz PA. Except for the recent published works, a representative MMW PA will be illustrated.  
**A three-stage, six-way combining CS W-band PA** is presented with 14.8-dBm Psat with 65-nm CMOS technology [73]. The peak gain is 12 dB and the 3-dB bandwidth is from 79 to 106 GHz with a chip size of 0.28 mm\({}^{2}\). Likewise, an eight-way combing matching networks is proposed to achieve the impedance transformation [74]. With the proposed method, a CS D-band 65-nm CMOS PA is implemented with 13.2-dBm Psat at 140 GHz. Figure 6 shows the chip photo, and the chip size is 0.38 mm\({}^{2}\).  
Figure 5: Saturation power of the recent published MMW CMOS PAs. Data taken from [87, 88, 89, 90, 91, 92, 93, 94, 95].  
Figure 6: Chip photo of the D-band eight-way combining common-source power amplifier (CS PA) [74].
# Adaptive Filtering for Multi-Track Audio Based on Time-Frequency Masking Detection
## 1 Introduction
Mixing remains one of the most challenging aspects of the production of music. Algorithms dedicated to supporting this process are still developing [28], leaving ample room for advancements to fully leverage the accessibility to computing power. In this article, we propose a simple masking detection method that systematically compares each track with high temporal and spectral resolutions, incorporates the interchangeability between masker and maskee and uses simple user-defined parameters to produce an intricate equalisation filter to improve the clarity of a mix.
## 1 Current Role of CCTA in Clinical Practice and Guidelines
### Plaque Burden  
Several studies highlighted the optimal negative predictive value of CCTA, reporting annualized event rates ranging from 0.02% to 0.3% for short [13], intermediate [14], and long-term [15] outcomes in patients with normal CCTA. Contrarywise, the CONFIRM(Coronary CT Angiography Evaluation for Clinical Outcomes: An InteRnational Multicenter) registry indicated a strong association between the extension and distribution of atherosclerotic plaques detected by CCTA and long-term prognosis, suggesting as the CCTA plaque burden might represent a significant predictor of patient outcomes [16]. Several studies demonstrated how patients might be stratified across a risk continuum beyond a simple classification based on the presence of stenoses [17; 18]. Indeed, in comparison to people without detectable plaques, the recognition of non-obstructive CAD on CCTA, which is typically undetected by stress test methodologies, has been related to an
# A Review of Document Image Enhancement Based on Document Degradation Problem
## 3 Document Image Degradation Methods
### Metrics
It is the minimum number of character-level operations required to transform the ground truth or reference text into the OCR output text. CER is formulated as follows: \[\text{CER}=\frac{\text{S}+\text{D}+\text{I}}{\text{N}}\] (7) where S is the number of substitutions, D is the number of deletions, I is the number of insertions, and N is the number of characters quoted or ground truth text. The lower the CER value, the better the OCR performance model. CER can be normalized to ensure that it does not fall out of the range of 0-100 and is not affected by insertion errors. In normalizing CER, C is the number of correctly identified in the text sentence. The normalized CER is formulated as follows: \[\text{CER}_{\text{normalized}}=\frac{\text{S}+\text{D}+\text{I}}{\text{S}+\text {D}+\text{I}+\text{C}}\] (8) * Word error rate (WER) [84]: In order to maintain consistency between the identified word sequences and the standard word sequences, certain words need to be replaced, deleted, or inserted, and the total number of these inserted, replaced, or deleted words, divided by the percentage of the total number of words in the standard word sequences, is the WER. \[\text{WER}=\frac{\text{S}_{\text{w}}+\text{D}_{\text{w}}+\text{I}_{\text{w}}} {\text{N}}\] (9) where \(\text{S}_{\text{w}}\) is the number of substitutions, \(\text{D}_{\text{w}}\) is the words deleted, \(\text{I}_{\text{w}}\) is the words inserted, and N is the number of words. * Structural similarity (SSIM) [85]: The structural similarity index defines structural information from the perspective of image composition as an attribute that reflects the structure of objects in a scene independently of luminance and contrast and models distortion as a combination of three different factors: luminance, contrast, and structure. Given patch \"x\" from one image and corresponding patch \"y\" from another image. The formula is as follows:  
\[\text{SSIM}(\mathbf{x},\mathbf{y})=\frac{\left(2\mu_{x}\mu_{y}+C_{1}\right)\left(2\sigma_ {xy}+C_{2}\right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+C_{1}\right)\left(\sigma_{x}^ {2}+\sigma_{y}^{2}+C_{2}\right)} \tag{10}\]  
where \(\mu_{x}\) is the average of \(\mathbf{x}\), \(\mu_{y}\) is the average of \(\mathbf{y}\), \(\sigma_{x}^{2}\) is the variance of \(\mathbf{x}\), \(\sigma_{y}^{2}\) is the variance of \(\mathbf{y}\), and \(\sigma_{xy}\) is the covariance of \(\mathbf{x}\) and \(\mathbf{y}\). \(C_{1}\), \(C_{2}\) stand for constant.
# Mutual Influence of Users Credibility and News Spreading in Online Social Networks
## 4 Simulations and Results  
To evaluate the proposed model we performed several experiments to assess news propagation both for highly connected and semi-isolated nodes. All experiments were made on a two-layers scale-free network with 1000 nodes. A scale-free network is a network characterized by a power-law degree distribution (i.e., the fraction \(P(k)\) of nodes with degree \(k\) goes as \(P(k)\sim k^{-\gamma}\), where \(\gamma\) is a parameter that typically assumes a value in the range \(2<\gamma<3\)). It means that in such a network the majority of nodes have few connections, while there are a few number of nodes, called hubs, with extraordinary high number of links. Specifically, in our experiments we employed scale-free networks since online social networks usually exhibit such a kind of behaviour [69]. Networks were generated by using the JGraphT Java Library [70]. In detail, the first layer was an undirected network representing OSN acquaintances while the second layer was the credibility network. All weights of the CN layer were initially set to 0.5 that represented a neutral credibility (\(C_{ij}=0.5\ \forall\ (i,j)\)).  
To understand how local credibility updates affects news propagation, we considered a highly connected seed node since news propagation for semi-isolated nodes were influenced by both low contact rate and credibility, making it difficult to distinguish which one of them actually affected the lack of news propagation. We simulated the spreading of 150 different news in 5 scenarios, as reported in Table 1. All the presented results were the average over 10 different simulations for each scenario.  
Scenarios \(A\) and \(B\) (i.e., all news spread are respectively true or false) were used to understand how a variation in the credibility of the spreader node affected its news spreading. Indeed, in real OSNs if a node lost its credibility fewer and fewer nodes were willing to receive and propagate its news (and vice versa).  
Simulations reported in Cases \(C\) and \(D\) aimed at checking to what extent a change in a node's behavior impacted its news spreading process and how fast changes happened; for instance, when the node initially spread only fakes, and after a while, it propagated only true news, or vice-versa.  
All simulation results are reported in Figure 3. As shown, the number of infected nodes for the first news (i.e., \(C_{news_{1}}\)) was similar in all the presented scenarios. As soon as nodes spread other news, \(C_{\bar{j}}\) played a fundamental role in the network evolution, as detailed in the following.