esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of Critical Road Links Based on Static and Dynamic Features [PERSON]
## 3 Methodology
### Traffic Link Representation Learning
\(\varepsilon_{flow\_x}\) is the traffic demand coefficient, \(flow_{x}\) is the traffic flow of link node \(x\) and \(flow_{v}\) is the traffic flow of link node \(v\). \(\varepsilon_{speed\_x}\) is traffic state coefficient, \(speed_{x}\) is the average speed of link node \(x\) and \(speed_{design}\) denotes the design speed of link node \(x\). As shown in Figure 2, the traffic flow and traffic speed on the nodes represent the dynamic attributes of the link nodes. The connection relationship between nodes represents the static attributes of the link nodes. In terms of static features that affect the walk, the label values on the edges characterize the control parameters of the next link node \(x\) passing link node \(v\). Where the control parameter for returning to the previous link node \(t\) is \(\frac{1}{p}\). The control parameter for exploring further nodes such as link node \(x_{2}\) and \(x_{3}\) is \(\frac{1}{q}\). The control parameter for exploring other link nodes such as link node \(x_{1}\) is 1. In terms of dynamic features that affect the walk, link nodes with higher traffic flow and lower traffic speed are found to have a higher transition probability. For instance, in the case of link nodes \(x_{2}\) and \(x_{3}\), the transition probability from \(v\) to \(x_{2}\) is higher than from \(v\) to \(x_{3}\). Conversely, for link nodes \(t\) and \(x_{3}\), the transition probability from \(v\) to \(t\) is higher than from \(v\) to \(x_{3}\). Therefore, with \(v_{i}(i=1,2,\ldots,n)\) as the starting link node, the walk sequence \(W_{v_{i}}=\left\{w_{v_{i}}^{1},w_{v_{i}}^{2},\ldots,w_{v_{i}}^{l}\right\}\) can be obtained by simulating a random walk through the transition probability function, where \(w_{v_{i}}^{j}(j=1,2,\ldots,l)\) denotes the \(j^{th}\) link node passed in the walk procedure with \(v_{i}\) as the starting link node, \(l\) denotes the length of the walk. Repeat \(r\) times for each node in the traffic network, and finally obtain \(r\) walk sequences \(W=\left\{W_{v_{i},1},W_{v_{i},2},\ldots,W_{v_{i},r}\right\}\) of length \(l\) for each node, where \(W_{v_{i},k}\) denotes the \(k^{th}\) repeat of the walk sequence with \(v_{i}\) as the starting link node. Finally, the walk sequences obtained from each node in the traffic network are utilized as input, and the skip-gram model, which is commonly used in natural language processing (NLP), is employed for training. This  
Figure 2: Illustration of random walk process in TraLink2 vec. allows for the mapping of road links with dynamic and static attributes into d-dimensional vectors.
## 3 Materials and Methods
### Survey Overview and Reliability
* Questions about the content of MaGraDa (C): these covered aspects related to the information provided by the procedures and algorithms (C1), the need for the information contained in MaGraDa (C2), its organisation (C3), and the functionalities and capabilities offered by MaGraDa (C4). * Questions about the didactic level of MaGraDa (D): the users answered questions about whether MaGraDa helped them to understand the concepts of graphs (D1), whether MaGraDa facilitated independent work (D2), whether MaGraDa helped them to identify, diagnose and correct errors in their graph exercises (D3), whether MaGraDa helped them in terms of their academic performance (D4), whether they would prefer to use MaGraDa rather than performing the exercises without computer assistance (D5), and finally their opinions about the help that MaGraDa could provide in terms of scoring better on the exams (D6). * Questions about general opinions on MaGraDa (G): three questions were included about whether they would recommend MaGraDa for learning concepts about graphs (G1), whether MaGraDa made learning about graphs more interesting (G2), and an overall question about their level of satisfaction with MaGraDa (G3). The anonymous questionnaire was distributed to students in laboratory classes using Google Forms. To avoid bias in the study, the students completed the questionnaire about MaGraDa when all the content related to graphs had been explained. A total of 116 students answered the questionnaire and were included in the study. The collected data were analysed with IBM SPSS Statistics 28.0 for Windows [50], using descriptive statistics, appropriate statistical tests, and multiple linear regression (MLR) with dummy variables [51]. Figure 14: Questionnaire items about MaGraDa using a five-point Likert scale. Using a numeric scale from one to five to represent the values of the 18 input variables in Table 3, related to opinions about MaGraDa, we calculated the determinant of the Pearson correlation matrix. This determinant was approximately zero, suggesting that there were linear relationships among the variables. The variables for which the highest correlation coefficients were found were G1, G2 and G3, with coefficients ranging from 0.565 to 0.692 (general opinion block); the pairs (D3,D6), (D5,D6), (D2,D4), (D3,D4) and (D4,D6) of the didactic level block, with correlation coefficients of between 0.515 and 0.623; the pair (E1,E2) of the block related to the ease of use and friendliness of the software, with a correlation coefficient of 0.625; and the pairs (E2,G3), (E1,G3), (C2,G2), (D6,G1) and (C2,G1), with
# Face Spoofing Detection with Stacking Ensembles in Work Time Registration System
## 3 The Proposed Method
### Image Analysis Using CNN  
A dedicated CNN was developed to analyze full images and assess face authenticity based on subtle cues, such as face quality, edge presence, and visual consistency. The goal of this module is to analyze elements of the image that would otherwise be difficult to process with algorithms, such as the quality of the face, the appearance of objects or edges close to the face, or the cohesion of the image.  
The network architecture consists of two sets of convolutional layers followed by batch normalization layers, ReLU activation functions, pooling, and dropout layers (CONV => RELU => CONV => RELU => POOL => DROPOUT). Then, a single fully connected layer is added to the network. Finally, a SoftMax classifier is deployed to output the probabilities of the face being authentic or spoofed. The full architecture is shown in Figure 3. The face score is determined by the highest of those probabilities.  
To better account for real use cases, the images were augmented. The enhancements included a random horizontal flip (mirror flip), a random rotation by up to 5 degrees, and a random change in the brightness and saturation of the image (by up to 10%). Such parameters were intentionally small and were selected to represent real-world conditions. The operations also included scaling the images down to a size of 64 \(\times\) 64 pixels.  
The images used for training and testing were additionally preprocessed by centering them around the detected face; this is to ensure that the network can analyze the face and its close surroundings, and is especially useful if the face is only part of a bigger image. Experimental evaluation has shown a great increase in accuracy after this centering step,compared to when the images are scaled down without centering. This is therefore an integral part of this module that makes it perform better.  
The output layer of the convolutional neural network contains a SoftMax classifier, which has two outputs. Each output can be interpreted as the probability that the image belongs to the specific class, which in the case of this system is \"authentic\" or \"spoof\".
## 3 Results and Discussions
### RORC Thermal Performance: Effect of the Evaporator Pinch Point Effect  
This unit considers the influence of engine load on the performance of the heat recovery system based on a RORC cycle. The results presented above that correspond to a sensitivity analysis were obtained based on the most representative operating condition of the gas engine. Internal engine variables such as pressure, mixture temperature, and mixture recirculation percentage are controlled by the engine power control system to provide high efficiency in partial load engine operations. Global energy indicators with an equal evaporating pressure configuration for the three fluids under study of 675.8 kPa were chosen as study variables, as shown in Figure 10. To avoid liquid corrosion in the expander, for safety reasons, all feasible operating points of the proposed configurations of the different engine loads ensure that the working fluids at the evaporator outlet evaporate completely, in addition to a gas temperature at the evaporator outlet, i.e., current 11, higher than the acid dew temperature (200 \({}^{\circ}\)C) to prevent acid corrosion of the exhaust.  
As well as the absolute thermal efficiency increases, the overall energy conversion efficiency decreases with increasing engine load. The maximum net output power achieved for RORC configurations with respect to engine load percentages is 78.28 kW and 97.8% for pentane, 75.52 kW and 97.8% for hexane, and 86.29 kW and 68.82% for octane. However, over a period of motor operation, the thermal efficiency increase, for the RORC configuration, first increases, and then decreases, achieving a maximum of 78.5% motor load.  
These results correspond to the fact that, at a higher engine load, a greater flow of exhaust gases is achieved, which increases the energy loss in the recuperator, due to the limited evaporation pressure and temperatures of the thermal coupling oil. As the operating load increases, there is an increase in the fluid evaporation temperature in the ITC 2. Consequently, the power increases, which is the primary element for increasing thermal and exergetic efficiency. However, the isentropic efficiency of the turbine decreases slightly due to the increase in thermal oil temperature, causing a decrease in the indicators at high engine loads. This trend of increasing power at engine load is a result of both the increase in thermal oil inlet temperature to the evaporator, which causes an increase in the working fluid mass flow, and the difference in enthalpy between the pump and turbine inlet and outlet of the system, but the latter is more clearly seen in the turbine.  
Figure 9: Performance of different fluids for a RORC system with varying AP. (**a**) Net power; (**b**) Absolute increase in thermal efficiency; (**c**) RORC thermal efficiency; and (**d**) Global exergetic efficiency.
## 4 Experimental Evaluation
### Model Comparison Experiments
#### 4.3.2 Results and Analysis
Based on the experience of the first set of experiments, the recognition rate of YOLOv5 was high, but the test results of images were not superb, hence, we continued with the image test. Figure 7 shows an example of the test images. It was found that all three methods could detect the text in the ancient images of the water book well, but the YOLO model still had more misrecognition problems. For example, the test images of YOLOv5s and YOLOv5s both misidentified two classes of characters, misidentifying all the \"si\" as \"shuz\" and all the \"wuqvx\" were incorrectly identified as \"wug\". In addition, YOLOv5x also misidentified \"chen\" as \"xiong2\", whereas Faster R-CNN recognized all the words in the test images correctly. According to our analysis, the error rate of YOLOv5 is due to the use of two data augmentation techniques in the YOLOv5 model--one is Mosaic and the other is random erasure. These two methods contribute greatly to the detection of person, animals, or other objects, but are not applicable to Shui characters. Because human characters have their own specificity, when some features of characters are erased, they may lose their original meanings and become new characters, so there is a high possibility of misclassification. Shui manuscript characters recognition is a small-target and multi-target detection problem, with the focus being on whether the characters in Shui manuscripts can be accurately detected and recognized. The above experimental results show that YOLO and SSD have many problems detecting small and multiple targets. Thus, Faster R-CNN is the most suitable for the detection and recognition of Shui manuscript characters. Furthermore, in 2018, [PERSON] and [PERSON] [25] proposed the use of Faster R-CNN as a method for detecting symbols in handwritten graphics, and experimentally evaluated it on graphical and mathematical expression datasets. Their results showed that Faster R-CNN can be effectively used for handwritten symbol recognition. In 2019, [PERSON] et al. [26] proposed an improved Faster R-CNN algorithm for natural scene text detection. They reported \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Backbone Network** & **mAP (\%)** \\ \hline YOLOv5s & CSP-Darknet & 80.3 \\ YOLOv5x & CSP-Darknet & 84.1 \\ Faster R-CNN & VGG-16 & 85.8 \\ Faster R-CNN & ResNet-101 & 87.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the second set of experiments. Figure 7: Image test results of YOLOv5s, YOLOv5x, and Faster R-CNN. experimental results demonstrating that it improved the detection of small target characters. Considering the successful application of Faster R-CNN in text detection reported by these two studies and the results of our evaluation experiments, we chose Faster R-CNN to train the Shui manuscript characters recognition model and applied it to the development of the mobile applet.
# A Layer-Wise Strategy for Indoor As-Built Modeling Using Point Clouds
###### Abstract  
The automatic modeling of as-built building interiors, known as indoor building reconstruction, is gaining increasing attention because of its widespread applications. With the development of sensors to acquire high-quality point clouds, a new modeling scheme called scan-to-BIM (building information modeling) emerged as well. However, the traditional scan-to-BIM process is time-tedious and labor-intensive. Most existing automatic indoor building reconstruction solutions can only fit the specific data or lack of detailed model representation. In this paper, we propose a layer-wise method, on the basis of 3D planar primitives, to create 2D floor plans and 3D building models. It can deal with different types of point clouds and retain many structural details with respect to protruding structures, complicated ceilings, and fine corners. The experimental results indicate the effectiveness of the proposed method and the robustness against noises and sparse data.  
Keywords: indoor modeling; layer-wise strategy; point clouds; 3D primitives; as-built BIM +
Footnote †: journal: Applied
## 4 Experiments
### Datasets and Metrics  
In this work, the RESIDE-indoor and RESIDE-6K datasets are used to train the proposed CCD-Net. RESIDE (REalistic Single-Image-Dehazing Dataset) [48] is a benchmark dataset designed for image-dehazing tasks, containing 13,990 pairs of hazy images paired with corresponding clear images from real-world scenes. The dataset offers a diverse set of samples from various environments, covering different lighting conditions, scenes, and visibility distances. By simulating and collecting images under various hazy weather conditions, RESIDE aims to provide a challenging benchmark for the development of dehazing algorithms. With a large scale that includes both synthetic and real image pairs, the dataset is well suited for training and testing deep learning models, particularly for evaluating the performance of dehazing algorithms in complex real-world scenarios.  
We evaluated the dehazing effect of our method using three commonly used image-quality metrics: Peak Signal-to-Noise Ratio (PSNR, dB), Structural Similarity Index (SSIM), and CIEDE2000 (CIEDE). PSNR quantifies the ratio between the maximum possible signal power and the power of corrupting noise. SSIM is designed to measure the structural similarity between two images by considering luminance, contrast, and structural components. CIEDE [49] is a perceptual color-difference metric proposed by the International Commission on Illumination (CIE). Unlike traditional color-difference formulas, CIEDE incorporates luminance, chroma, and hue adjustments, making it more consistent with human visual perception. It is particularly useful for evaluating color fidelity in image-processing tasks, as it quantifies the perceptual difference between two colors. The calculation formula is as follows:  
\[\Delta E_{00}=\sqrt{\left(\frac{\Delta L^{\prime}}{k_{L}S_{L}}\right)^{2}+ \left(\frac{\Delta C^{\prime}}{k_{C}S_{C}}\right)^{2}+\left(\frac{\Delta H^{ \prime}}{k_{H}S_{H}}\right)^{2}+R_{T}\left(\frac{\Delta C^{\prime}}{k_{C}S_{C} }\right)\left(\frac{\Delta H^{\prime}}{k_{H}S_{H}}\right)} \tag{7}\]  
where \(\Delta E_{00}\) indicates the similarity between two colors, with a smaller value signifying a closer color match. \(\Delta L^{\prime}\) represents the difference in lightness, while \(\Delta C^{\prime}\) denotes the chroma difference, and \(\Delta H^{\prime}\) corresponds to the hue difference. The weighting coefficients \(k_{L}\), \(k_{C}\), and \(k_{H}\) are typically set to 1 to balance the contributions of different components. The scaling factors \(S_{L}\), \(S_{C}\), and \(S_{H}\) are used to normalize the respective differences in lightness, chroma, and hue. Additionally, \(R_{T}\) is a rotation term specifically designed to adjust color differences in the blue region, ensuring better perceptual accuracy.  
A higher PSNR or SSIM value (closer to 1) suggests that the dehazed image preserves more structural details of the ground-truth image, while a lower CIEDE value indicates better color preservation.
###### Abstract  
People with insomnia frequently underestimate the duration of their sleep compared to objective polysomnography-measured sleep duration. Cognitive behavioural therapy for insomnia (CBT-I) is the most effective treatment for insomnia and also reduces the degree of sleep underestimation. Obstructive sleep apnoea (OSA) is a highly prevalent sleep disorder characterised by frequent narrowing (hypopnoea) and closure (apnoea) of the upper airway during sleep. Comorbid insomnia and sleep apnoea (COMISA) is a prevalent and debilitating disorder. No study has investigated subjectively (sleep diary) versus objectively (polysomnography) measured sleep discrepancies (SOSD) in individuals with COMISA before or following CBT-I. This randomised waitlist-controlled trial investigated SOSD in 145 participants with COMISA before and 6-weeks after CBT-I (\(n=72\)) versus control (\(n=73\)). All participants were studied prior to continuous positive airway pressure treatment for sleep apnoea. At baseline, participants underestimated their total sleep time (mean \(\pm\) SD \(-51.9\pm 94.1\) min) and sleep efficiency (\(-9.6\pm 18.3\%\)), and overestimated sleep onset latency (\(34.5\pm 86.1\) min; all \(p=<0.001\)). Mixed models indicated a main effect of time on reduction of SOSD in both groups, but no between-group difference in the reduction of any SOSD parameters. These findings may indicate that untreated OSA contributes to a discrepancy between perceived and objective sleep parameters in people with COMISA that is not amenable to CBT-I alone (ACTRN12613001178730).  
insomnia; obstructive sleep apnoea; CBT-I; subjective-objective sleep discrepancy
# Post-Quantum Migration of the Tor Application
## Glossary \begin{tabular}{l l}
**Acronym** & **Meaning** \\ ACCE & Authenticated and Confidential Channel Establishment \\ ACME & Automated Certificate Management Environment \\ AEAD & Authenticated Encryption with Associated Data \\ AES & Advanced Encryption Standard \\ AKE & Authenticated Key Exchange \\ ARTI & A pure-Rust Tor Implementation \\ CA & Certificate Authorities \\ CCA & Chosen-Ciphertext Attack \\ SVP & Shortest Vector Problem \\ CRYSTALS & Cryptographic Suite for Algebraic Lattices \\ DH & Diffie-Hellman \\ DHCP & Dynamic Host Configuration Protocol \\ DNS & Domain Name System \\ DSA & Digital Signature Algorithm \\ ECC & Elliptic Curve Cryptography \\ ECDH & Elliptic Curve Diffie-Hellman \\ EES & Efficient Embedded Security \\ FALCON & Fast Fourier Lattice-based Compact signatures over NTRU \\ FO & Fujisaki-Okamoto transform \\ FS & Forward Secrecy \\ HHK & Hofheinz-Hovelmanns-Kiltz transform \\ \end{tabular}   
HQC  
Hamming Quasi-Cyclic  
IETF  
Internet Engineering Task Force  
IND-CCA  
INDDistinguishability under Chosen-Ciphertext Attack  
IND-CCA2  
INDDistinguishability under Adaptive-Ciphertext Attack  
IND-CPA  
INDDistinguishability under Chosen-Plaintext Attack  
IP  
Internet Protocol  
KEM  
Key Encapsulation Mechanism  
LMS  
Leighton-Micali Signature  
MLWE  
Module Learning With Error  
ML-KEM  
Module-Lattice-based Key Encapsulation Mechanism  
ML-DSA  
Module-Lattice-based Digital Signature Algorithm  
MTU  
Maximum Transmission Unit  
NAT  
Network Address Translation  
NIST  
National Institute of Standards and Technology  
NSA  
National Security Agency  
NTRU  
Nth degree TRUncated polynomial ring  
OSs  
Onion Services  
PDK  
Pre-Distributed (public) Key  
PFS  
Perfect Forward Secrecy  
PKE  
Public Key Encryption  
PQC  
Post-Quantum Cryptography  
PRNG  
Pseudo-Random Number Generator  
QC  
Quantum Computer  
QKD  
Quantum Key Distribution  
RAM  
Random-Access Memory  
RSA  
Rivest-Shamir-Adleman  
RTT  
Round-Trip Time  
SHA  
Secure Hash Algorithm  
SSL  
Secure Sockets Layer  
TAP  
Tor Authentication Protocol  
TCP  
Transmission Control Protocol  
TLS  
Transport Layer Security  
TOR  
The Onion Routing  
XMS  
eXtended Merkle Signature Scheme  
1W-AKE  
One-Way Authenticated Key Exchange  
* **AES-128-CTR**: in counter mode as stream cipher; IV of all 0 bytes. * **RSA**: exponent of 65,537. OAEP-MGF1 padding, with SHA-1 as its digest function (label left unset). Tor paper plans to move away from SHA-1 to align with evolving cryptographic standards and best practices. * **SHA-1** as \"hash of a public key\", DER encoding of an ASN.1 RSA public key (as specified in PKCS.1). * **Relay identity keys**: currently, two types are still deployed: RSA1024 is legacy and is being replaced by Ed25519, which is the one advertised in the table.
## 5 RT Real-Time Simulation and Experimental Validation
The proposed sensorless control system, including the adaptive laws and SMO, has been testified in hard operating circumstances: speed reversion, rotor resistance mismatch (up to 150%), high and low speed operation, and load torque change.
## 4 Case Study of Data Collaboration
### Visual Traffic Flow Detection Based on DFA
#### 4.3.2 Boundary Detection  
For the dilated binary image, we performed contour extraction based on boundary detection [34]. Its contour is drawn in Figure 8d, and the number of contours of the region of interest (ROI) is counted as the original data of the traffic flow at the intersection.
## 6 Forecasting Results and Discussion
### Aggregated Forecasting with Clean Data  
Considering different types of energy consumption applied in buildings, different time series behaviors were related to the type of buildings. In this experiment, we categorized the type of customers in buildings into residential and commercial. Additionally, we considered the clean data period from April to August 2018, when the data did not have many outliers and missing values. We performed the proposed model on these categories, as demonstrated in Table 5. The average forecasting errors for all residential buildings achieve 0.154, 71.73, 0.089, and 33.74 RMSE, NRMSE, MAE, and MAPE, respectively. The average errors for all commercial buildings achieve 0.135, 26.67, 0.084, and 27,37 RMSE, NRMSE, MAE, and MAPE, respectively.  
From these error rates, we can conclude that aggregated forecasting for all commercial buildings obtains the smallest error rates in comparison to residential buildings. This is because most of the individual commercial buildings have relatively consistent behaviors and load profiles during this clean data period, while most of the individual residential buildings have the opposite, where their load profiles seem nonuniform and nonstationary.