esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
Regarding the estimation of the clock skew to contact the device disconnected from the network for a long time period, a similar approach has been presented in the paper [29], in which the estimation of the clock skew distribution is also based on the normal distribution. However, instead of defining the window length, this paper just proposes a method to estimate the probability mass within a specific time frame and does not consider the shift or different sizes of the time window. Therefore it provides a more exact mathematical model to the calculation's reference uniform scenario (repetition of the same reception window three times) method. Our proposition allows us to decrease the energy cost by up to 30% compared to such a simple solution, as was presented in Figure 5.  
The continuous-time synchronization problem in most of the research papers is solved by messages updating the clocks in the network, e.g., in the flooding proposition presented in [30]. The algorithm proposed in this paper is more suitable for networks with rare communication (e.g., devices transmitting data once or twice per day), where clock synchronization would be energy demanding. A similar approach for a synchronization algorithm for a multihop network, which dynamically changes the window size to match clock drift, is presented in [31]. While the proposition is interesting, it assumes regular communication between nodes with additional synchronization communications exchanged between parent-child nodes and even more to obtain the global network synchronization. This is denoted as algorithm A1 on the plots. A recent paper by [PERSON] et al. [29] proposed a novel maximum likelihood estimation (MLE) with an innovative implementation to  
Figure 12: The degree of improvement brought by the algorithm (W3) in relation to the algorithm (W1) —algorithm (W2) is omitted in this statement because it did not bring a significant improvement.  
minimize the clock skew estimation error caused by delay. The method presented in this paper allows selecting the listening window size to estimate the probability density of clock skew based on Gaussian delay. We further extend this concept by proposing how to synchronize the clocks during a continuous transmission, simplifying the decision threshold to decrease and increase the reception window size. We leave the possible merging of the two approaches for the proposed estimation method in [29], with our proposition of averaging the latest measurements for further study.
## 4 Case Study
### System Reliability Evaluation
#### 4.2.3 Monte Carlo Simulation  
The accuracy of the system reliability evaluation of the Monte Carlo simulation is determined by the simulation time and amount of CNC grinding machine. The evaluation result of different simulation times and different amounts of CNC grinding machine by the algorithm mentioned in Section 2 is shown in Figure 6.  
The result will fluctuate severely from that in Figure 6 when the simulation time and amount of the CNC grinding machine are small, and it will tend to be stable when the simulation time is more than 10 years and the amount of the CNC grinding machine is more than 10, which fluctuates slightly between 530 and 560 h.  
Figure 4: Probability density curve of time between failures of total machine. MTBF—mean time between failures.  
Figure 5: Cumulative distribution curve of time between failures of whole machine.  
The point estimation of system reliability is 551 h on condition that the simulation time is 20 years, the amount of the CNC grinding machine is 20, and the interval estimation is between 511 h and 585 h.  
\[MTBF_{s}^{mk}=\frac{T_{sum}}{n_{sum}}=551 \tag{44}\]
## 3 Methodology
### Watermark Detection  
Watermark detection is the inverse process of watermark creation, and the settings of each execution step must be compatible with the parameters established during the construction process, the specific process is shown in Figure 3. The feature points set \(V^{\prime}\) is obtained by applying the same degree of Douglas-Peucker compression to the vector map to be detected, then performing a block operation on \(V^{\prime}\) with the same parameters as when constructing the zero watermark, and constructing a CDTN within each sub-block, recording the maximum and minimum interior angles of each triangle to form a sequence of angle values \(A^{\prime}_{r}\). According to Equations (8) and (9), the complex sequence  
Figure 2: Procedure of the proposed watermarking scheme.  
of angle values \(A_{r}^{u\prime}\) of each sub-block \(A_{r}^{\prime}\) are constructed, DFT is applied to acquire the Fourier coefficient sequence \(A_{r}^{v\prime}\), and then the phase value sequence \(P_{r}^{v\prime}\) of each sub-block is obtained. The angular values in \(P_{r}^{v\prime}\) are rounded down and converted to binary to form the feature sequence set \(P_{r}^{binary^{\prime}}\).  
All zero-watermark images \(W_{r}^{zero}\) are obtained from the IPR matrix \(W_{r}^{zero}\) and matched with the elements in \(P_{r}^{binary^{\prime}}\). The Xor operation is then applied to the successfully matched sequence to obtain multiple encrypted watermark images \(W_{inf}^{ccy^{\prime}}\) (Equation (14)).  
\[W_{inf}^{ccy^{\prime}}=P_{r}^{binary^{\prime}}\oplus W_{r}^{zero} \tag{14}\]  
The inverted Arnold scrambling is employed on an encrypted watermark image \(W_{inf}^{ccy^{\prime}}\) to obtain the decrypted watermark image \(W^{\prime}\), and then \(W^{\prime}\) is compared with the original watermark image \(W\) for verification. Normalized Correlation (NC) is used to measure the similarity between images [3] (Equation (15)).  
\[NC=\frac{\sum\limits_{i,j}W_{i,j}*W_{i,j}^{\prime}}{\sqrt{\sum\limits_{i,j}W_{ i,j}^{2}}\sqrt{\sum\limits_{i,j}W_{i,j}^{2}}} \tag{15}\]  
where \(W_{i,j}\) and \(W_{i,j}^{\prime}\) are original and extracted watermark bit information at the coordinates of \((i,j)\), respectively. Bit error rate (BER) is used to evaluate the specific error size of the extracted watermarks [3] (Equation (16)).  
\[BER=\frac{100}{l}\sum\limits_{i,j=0}^{l-1}\begin{cases}0\ W_{i,j}=W_{i,j}^{ \prime}\\ 1\ W_{i,j}\
eq W_{i,j}^{\prime}\end{cases} \tag{16}\]  
where \(l\) denotes the length of watermark information.
# Experimental Investigation of a Device to Restrain the Horizontal Sliding of U-FREIs
## 8 Test Results and Observations
### Shear Test Results
#### 8.2.3 Dynamic Shear Test  
Figure 17 shows the load-displacement curves of all the performed dynamic shear tests. In this figure, it can be observed that, in all cases, the isolator behavior stabilized at the third cycle, a residual displacement is observed at the end of every cycle, and the Q specimens were more dissipative than the B specimens, as observed in the quasi-static shear test.  
No further damages due to the dynamic shear tests were observed in the specimens.  
Table 5 reports the isolator stiffness, \(K_{b}\), and viscous damping ratio, \(\xi_{b}\), calculated in the third loading cycle according to Equations (4) and (5), respectively. Table 5 also reports the \(K_{b}\) and \(\xi_{b}\) variations between the test configuration with one constraint and the configuration with no constraint (\(\Delta\)K\({}_{\rm b}\)-0  
Based on the values reported in Table 5, the following observations can be drawn:  
* For both the Q and B specimens, the introduction of one constraint produced no significant variations in the isolator horizontal stiffness; in fact, these variations were lower than 5%. The same applies to the B specimens, considering the variations between the two constraints and no constraint.
* Regarding the damping ratio, the variation in the presence of one constraint was negligible for the Q specimens, while an increase of 9% was observed for the B specimens.  
Figure 17: Force–displacement curves of dynamic shear test: (**a**) without constraint; (**b**) with constraint on one contact surface; (**c**) with constraint on both contact surfaces.  
Figure 18: (**a**) Average K\({}_{\text{b}}\) values at cycle III and K\({}_{\text{b}}\) variations due to the presence of the constraints; (**b**) average \(\upxi_{\text{b}}\) values at cycle III and \(\upxi_{\text{b}}\) variations due to the presence of the constraints.  
In the presence of two constraints, the increase in the damping ratio in comparison to the configuration without constraints was 7.4% for the B specimens.
# Are Clinical Balance Measures Linked to Cycling Performance?
###### Abstract  
Balance is paramount to safe and successful cycling, both in competition and recreation. Balance has been investigated in the return-to-cycling context, but its relationship to cycling performance is unknown. Our study aimed to analyse the relationship between balance, measured by common clinical balance tests, and cycling performance. Seven competitive cyclists participated in this cross-sectional correlational study. We collected field-based measures of cycling performance, including mean velocity, minimum and maximum velocity, mean corner speed, average lap time, and lean angle. Also, we measured balance via the balance error scoring system, the star excursion balance test, the lateral reach test, and the modified Bass test of dynamic balance. Strong correlations between cornering performance and cycling performance (r = 0.65-0.87, \(p<0.01\)) were detected. Weak correlations between clinical balance tests and cycling performance (r = 0.33-0.53, \(p<0.05\)) were observed. In conclusion, our study showed that improving cornering performance has the potential to improve overall cycling performance. We also found no clear correlations between our clinical balance tests and cycling performance. These weak correlations between postural control in standing and cycling performance suggest that standing balance and balancing during cycling are distinct motor control processes.  
bicycle handling; cycling biomechanics; elite cycling; star excursion balance test 2024
# A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection
## 2 Related Works
The deep object detection model is the right way to simultaneously solve both problems of fire classification and localization, as evidenced by [PERSON]'s method. In DTA [3], a Faster R-CNN object detection model was also adopted to localize SRoFs. Then, the dynamic behavior of fire extracted by long short-term memory (LSTM) [25] in the localized regions was exploited in the short-term fire decision. Thereafter, to make the decision reliable, the short-term decisions were then combined for a final fire decision over a long-term period by majority voting. Note that the DTA method exploits both spatial and short-term temporal features, and uses them for fire decision in an ensemble fashion. However, it was not enough because only a limited amount of visual information included in video clips was exploited, and the environmental information from other sensors for fire decision was not included. In addition, the specific domain knowledge where the fire detection system operates was hard to incorporate with the previous DTA method.
## 2 Internet of Vehicles and Smart Cities
### Applications
#### 2.3.1 Taxonomy
It utilizes parameters such as real-time traffic information, fuel consumption, speed, and road condition data, among others, to choose the best route for the vehicle. * _cooperative driving_ applications that aim at coordinating a group of vehicles, so they drive in the same way as one. This improves energy efficiency, traffic flow and helps prevent accidents. * _parking navigation_ applications that use algorithms to track optimal routes that lead to the closest available parking zones. [PERSON] et al. [23] developed a cooperative adaptive driving application, a variation of cooperative adaptive cruise control, utilizing mobile edge computing and platooning techniques to avoid accidents and improve traffic flow. [PERSON] et al. [24] present an algorithm for multiple vehicles that enforces safety in intelligent intersections through time slot allocation. [PERSON] et al. [25] developed algorithms that schedule vehicles with V2V communications based on distributed mutual exclusion. [PERSON] et al. [26] and [PERSON] et al. [27] present route navigation systems based on the vehicle's fuel consumption and traffic congestion on roads, respectively.
## 3 Results
### End-to-end SIF Retrievals of the Optimal Empirical Parameters  
End-to-end SIF retrieval for TECIS-like simulations was carried out to test the retrieval ability of the selected optimal combinations of empirical parameters. We calculated the mean and standard deviation of SIF retrieved under 1280 different atmospheric conditions (\(\mathrm{SIF}_{\mathrm{retrieved}}\)) and took 126 SIF values simulated by the SCOPE model as true values (\(\mathrm{SIF}_{\mathrm{true}}\)). Here, we show end-to-end SIF retrievals using the optimal empirical parameters for SIFIS onboard TECIS-1 (Figure 9).  
The scatter points distribute very close to the 1:1 line for both simulations in the far-red and red band. For far-red SIF retrieval, the optimized parameters setting in the data-driven algorithm are given with a 735-758 nm fitting window, second-order polynomial, and four feature vectors. When the SNR is 322, the standard error of SIF retrieval is 0.62 mW \(\mathrm{m^{-2}~{}sr^{-1}~{}nm^{-1}}\) and the RMSE value is 0.63 mW \(\mathrm{m^{-2}~{}sr^{-1}~{}nm^{-1}}\). For red SIF retrieval, the optimized parameters are second-order polynomial and seven feature vectors using a fitting window of 682-697 nm. The standard error of SIF retrieval was 0.44 mW \(\mathrm{m^{-2}~{}sr^{-1}~{}nm^{-1}}\), and the RMSE value was 0.53 mW \(\mathrm{m^{-2}~{}sr^{-1}~{}nm^{-1}}\). Furthermore, the error lines of different SIF intensities have almost the same amount under a certain noise  
Figure 9: The end-to-end SIF retrieval for the TECIS-1 satellite using (**a**) far-red and (**b**) red fitting windows with optimized parameter setting (SNR = 322). For far-red SIF, the fitting window is 735–758 nm, the order of the polynomial is 2, and the number of feature vectors is 4. For red SIF, the fitting window is 682–697 nm, the order of the polynomial is 2, and the number of feature vectors is 7. The standard deviations of retrieved SIF are depicted by the error bars.  
level (e.g., SNR = 322), which indicates that the larger SIF levels have a smaller relative error and the absolute error is mainly affected by noise while independent of SIF intensity.  
We also evaluated the fitting accuracy of the reconstructed radiance spectra between 735-758 nm and 682-697 nm with and without the filling of fluorescence under an SNR of 322, as shown in Figure 10. Figure 10 also shows excellent fitting accuracy of the forward model to reconstruct the TOA radiance. The residual value was less than 0.11 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\) for far-red SIF retrieval and less than 0.04 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\) for red SIF retrieval. The forward model fitted with SIF fit the radiance better with a lower residual error.
# Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
## 3 Materials and Methods
### Computer Hardware and Software  
The MLP classification model was developed using the PyTorch library [37] and ran on a GPU, whilst the other ML models were developed using the SkLearn library [38]. All the models were trained on a standard laptop equipped with INTEL core i7 CPU, 8 GB of RAM, and an NVIDIA GEFORCE 940 MX graphics card. PyTorch is a library developed by Meta (formerly Facebook) that facilitates the construction of deep learning projects through models coded in Python. For the scientific community, PyTorch has become an outstanding deep learning tool due to its accessibility, ease of use, and wide range of applications [37].
# Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study
## 2 Literature Review
### Research Novelty  
This section highlights the novelty of this empirical study, which is viewed from three perspectives: specification of parameter settings in neural network architectures, optimization process of hyper-parameters for DL algorithms, and performance metrics for phishing detection model evaluation. Even though DL offers various advantages, one of its major drawbacks is manual parameter tuning. There is no standard and holistic guideline for selecting these hyper-parameters to achieve the highest performance accuracy. Table 1 shows some of the previous research works utilizing DL for phishing detection. These studies are categorized into four groups according to their level of specifying the parameter settings in the neural network architecture. The four categories include Not Specified (NS), Rarely Specified (RS), Partly Specified (PS), and Fully Specified (FS). Studies belonging to the first group (NS) applied DL algorithms without mentioning any hyper-parameters, while those in the second group (RS) only specified one or two. The third group (PS) showed the highest number of studies, yet not all of the parameters were specified or described. Unlike previous research, our study specified all of the hyper-parameters in DL architectures and described the tuning process step-by-step, as provided in Section 3.4. In addition, the previous studies were also categorized based on the number and type of DL algorithms used. Some studies employed only one technique, while others applied dual or multiple methods. The authors of [41] used four different DL algorithms, but they belonged to the same type of machine learning (unsupervised). Our research covers the highest number of DL techniques, and from various categories, such as supervised (CNN), un-supervised (LSTM, GRU), and hybrid (DNN), to provide more comprehensive research on numerous DL algorithms. Table 2 provides a more in-depth analysis of the parameter optimization process in the related studies. Most studies focused on describing how to achieve the parameter settings for neural network architecture and learning rate, while dropout rate, batch size, and the number of epochs were neglected. Unlike the previous works from Table 1, those in Table 2 described in detail how to obtain some of the hyper-parameters, including the neural network architecture, learning rate, dropout rate, batch size, and epoch. In contrast, our study described the step-by-step process of fine-tuning all the parameters above, to further investigate how these hyper-parameters can affect the performance accuracy of a phishing detection model. Table 3 shows a list of performance metrics frequently used by previous authors in their studies. The most common metrics adopted by researchers to evaluate the performance of DL-based phishing detection models were ACC, PR, RC, and F1-Score. Other metrics, such as FPR, FNR, training time, and testing time, were measured by only some authors. Meanwhile, GPU memory requirement, parameter size, number of URLs per second, epoch
## 4 Performance Evaluation and Comparative Analysis
### PF-ClusterCache Performance Analysis
Second, the eviction rate has remarkable behavior as it starts increasing with the CS size until attaining its maximum, and then declines with a further increase in the CS size. This is essentially due to the specifics of the underlying used LPF replacement scheme. The LPF does not allow a replacement (and therefore an eviction) unless the incoming data content has a higher popularity count than the least popular data in cache. As such, the CS of any SCN within the cluster is always occupied by the most popular data. As a result, when the CS size is small, only the most popular content is cached and therefore no replacement is permitted. As the CS size becomes larger, more popular content (or, equivalently, more content with a lower popularity count) is cached and consequently more replacements can take place. When we reach a CS size sufficient to cache all of the most popular content, the eviction rate becomes very small as virtually all requested content is retrieved from its SCNs within its cluster. Once again, PF-ClusterCache is very efficient, with a rather reduced CS size that should be enough to allow the most frequently requested (most popular) data content to be cached at its SCNs. PF-ClusterCache caches requested popular content near the edge of the network. It does not allow caching at the core routers, leaving them as fast name-based forwarding structures. Figure 8: Average hop count to retrieve popular content as a function of CS size. Now, we turn to investigating the required maximum popularity table size as a function of the Zipf parameter value. Recall that PF-ClusterCache uses a finite predefined size of the popularity table at each SCN. Recall also that the popularity table is managed as LFU finite storage. An Interest that finds the popularity table of its SCN full and whose name prefix does not have an entry replaces the oldest, least frequently used prefix name found in the table. Table 3 shows, for 10 popular traffic streams, the maximum popularity table size needed (used in the simulation) for different values of the content popularity. No more than a few hundred places are needed, even for low Zipf parameter values.
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 3 OCR Process
### Segmentation
#### 3.2.1 Line Segmentation  
In line segmentation, the skew-corrected image is divided into lines. Line segmentation is an important step in OCR as it allows the separation of the image into individual lines so that the OCR system can process them one-by-one and improve the recognition of the text in the image. Connected component analysis, project profile, and machine learning-based approaches can be used to perform line segmentation. Once the lines of text have been segmented, the OCR system can process each line individually, which can improve the accuracy of the character recognition process.  
A method for line segmentation of printed Arabic text with diacritics using a divide-and-conquer algorithm is presented in [70]. It breaks the image of printed text into smaller blocks, applies image processing techniques to extract the text lines, and then applies a set of heuristic rules to remove false positives and adjust the segments as necessary. It uses image processing techniques such as thresholding, morphological operations, and connected component analysis. The research results show that the proposed method can accurately segment printed Arabic text with diacritics into text lines with a high degree of accuracy. The research uses several evaluation metrics such as F-score, precision, recall, and F-Measure to evaluate the performance of the proposed method.  
[PERSON] et al. [71; 72] proposed a basic standardized test framework for evaluating the quality of text line segmentation algorithms in OCR systems for accurate handwritten text recognition. Their proposed framework includes experiments for measuring the accuracy of text line segmentation, skew rate, and reference text line evaluation.
## 2 Background
### Deployment in Wireless Sensor Network
#### 2.5.1 Static Deployment  
When the information regarding the application area is known in advance or the sensor node position can be determined, then the deterministic deployment will be applied as shown in Figure 5. Most of the research studies that depend on deterministic deployment set up the node location based on geometrical structure. This can be in a two-dimensional plane, such as a square, triangular, hexagon, and tri-hexagon tiling grids; it was proven that the regular hexagon is the best topology in the two-dimensional plane [25]. The other deployment can be in three-dimensional space by using the three-dimensional mathematical model and space geometric theory to find a solution for the coverage problem in many three-dimensional applications, which can be classified as binary and probabilistic coverage models. The space geometric methods can be divided into the volumetric quotient-based approach and spherical overlap approach (k-coverage) [26].