esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy Policy Analysis of Banks and Mobile Money Services in the Middle East
## 4 Methodology
### Readability  
To evaluate the readability of privacy policies, we leveraged the popular and widely used Flesch-Kinkaid readability tests, namely, reading grade level score and reading ease score [20]. The Flesch-Kinkaid reading grade level determines the required US grade-level education to understand a piece of text. The metric's values range from 0 to 20, where a lower value means that the text is easier to comprehend for the reader, whereas a higher value means that the text is hard to read. The Flesch reading ease score gives a score between 1 and 100, with 100 being the highest readability score. A score between 70 to 80 is equivalent to school grade level 8. Higher reading ease scores, therefore, indicate that text is easier to read, whereas, lower numbers mark passages that are more difficult to read.  
We calculated the readability metrics using a publicly available online tool [31]. This tool also computes other readability scores, such as the Coleman-Liau index, Automated Readability Index, and SMOG index. We also leveraged the basic text statistics generated by this tool such as the word count of a policy statement.  
Moreover, we looked at the language distribution of a privacy policy, i.e., whether the privacy policy is available in English, the official language of the country where the bank or mobile money service is deployed, or both. The official languages of Middle Eastern countries are Arabic, Turkish, Persian, or Hebrew, and the common second language in these countries is English [32]. We performed our analysis on the English version of the privacy policies for the following reasons:  
1. English was a common language among the investigators. Moreover, the majority of mobile money services and banks in the Middle East provide their policy statements in English as well as the official language. Only three Turkish apps in our dataset did not have an English version of the policy. Therefore, we used their English translated versions.
2. There is no widely used readability metric for the official languages of Middle East. Although there has been some work on developing a readability metric (e.g., OSMAN proposed by [PERSON] et al. [23]),for Arabic, this work shows that the readability of Arabic text was similar to the readability of its English version. Similarly, for Turkish, [PERSON] et al. [24] found that the Atesman Reading Ease Formula for Turkish text was compatible with the Flesch Reading ease score of its English translation.
# An Improved V-Net Model for Thyroid Nodule Segmentation
## 3 Materials and Methods
### SEV-Net  
SEV-Net is a model created by incorporating SE mechanisms into the existing V-Net architecture. Figure 5 shows a schematic representation of SEV-Net for segmenting nodules in two-dimensional thyroid images.  
SE mechanisms have been employed in numerous medical image segmentation studies using the V-Net model across various stages and functional modules [36; 37]. As in the improved V-Net method, this architecture integrates the SE mechanism into all encoder blocks, as well as the last decoder block prior to the output. By incorporating the SE mechanism exclusively in the last decoder block, significant features are emphasized, and insignificant ones are suppressed quickly and accurately.  
Figure 6 illustrates the integration of the SE mechanism into the encoder blocks containing three convolution operations.  
The SEV-Net architecture comprises five encoder layers and four decoder layers. It utilizes two convolutional operations in the first, second, eighth, and ninth layers while employing three convolutional operations in the third through seventh layers. Integration of the SE mechanism occurs within encoder blocks that feature two convolutional operations in the same manner. By integrating the SE mechanism into each encoder block, the network effectively learns critical features. In the SE mechanism, the squeeze operation is first performed by applying global average pooling to generate scalar values that contain channel-wise information. Subsequently, the excitation phase commences with two fully connected layers. The ReLU activation function was implemented in the first fully connected layer, while the second utilized a sigmoid activation function to yield  
Figure 5: The schematic representation of SEV-Net for segmenting nodules in two-dimensional thyroid images.  
normalized weights ranging from 0 to 1 through feature-dimension loading. To emphasize the key features, normalized weights were then adjusted for each channel characteristic and transformed into outputs.
# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 3 Face Anti-Spoofing
### FAS Based on Spoof Trace Separation
#### 3.1.3 FAS Based on Generalization  
The current DL-based FAS methods have achieved satisfactory results in the evaluation of various datasets. However, these models are based on the assumption that the source and target datasets are independent and identically distributed (i.i.d.) [24], which often results in the trained model using a large number of domain-specific features for classification, such as background, lighting. The inability to learn features that can truly distinguish between real faces and spoof faces easily leads to the model overfitting on various datasets. In the real world, there are a lot of differences between the samples used for inference testing and the data used for training, such as environments, attack methods. Therefore, algorithms that overfit on the training datasets cannot be generalized to real usage sc  
Figure 9: The flowchart of FAS based on spoof trace separation.  
narios. In other words, the model faces the problem of a weak generalization ability in out-of-domain scenarios.  
This section will systematically summarize and analyze recent research progress on generalization models in the field of DL-based FAS. Starting from the difficulties and challenges encountered by FAS in real-world scenarios, the problems are divided into two categories: encountering unknown domains and encountering unknown attacks. Different methods for each problem are summarized, and then the methods for solving unknown domains are divided into two categories, domain adaptation and domain generalization. The methods for solving unknown attacks are divided into zero-shot/few-shot learning and anomaly detection. In this review, the term unknown domains refer to external variations unrelated to spoofing (e.g., background, lighting, and sensor noise) that nevertheless affect the visual quality of images. Generally speaking, the most significant domain discrepancy is covariate shift [113]. In contrast, unknown attacks typically refer to attack types that do not appear during the training phase and possess novel physical properties, such as material and geometry. The principles, advantages, and disadvantages of representative methods are detailed.
## 2 Design and Simulations
### Grid Independence Validation  
The geometry of the electric heater is created by the software CREO and pretreated by the software Ansys SCDM. The Ansys Fluent meshing mode is used to generate the unstructured poly mesh of the electric heater. The grid independence test is conducted with 4.4 M (million), 5.1 M, 6.6 M, and 8.7 M grids, as shown in Figure 2(a). It is found that the growth rate of \(\Delta P\) decreases with the increase of the grid number, and \(\Delta P\) stabilizes when the grid number is 8.7 M. Considering the computational efficiency and precision, the final grid number is selected approximately 8.7 M. Fluent meshing provides an efficient and intelligent meshing solutions. Figure 2(b) shows the mesh grids of the inlet part of the electric heater.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Physical Parameters** & \(\mathbf{A_{0}}\) & \(\mathbf{A_{1}}\) & \(\mathbf{A_{2}}\) & \(\mathbf{A_{3}}\) & \(\mathbf{A_{4}}\) \\ \hline \(\rho\) (\(\mathrm{kg/m^{3}}\)) & 346.2 & \(-1.284\) & \(0.002130\) & \(-1.623\times 10^{-6}\) & \(4.609\times 10^{-10}\) \\ \(k\) (\(\mathrm{W/m\cdot K}\)) & 0.02062 & \(2.284\times 10^{-5}\) & \(5.489\times 10^{-8}\) & \(-4.225\times 10^{-11}\) & \(1.118\times 10^{-14}\) \\ \(C_{p}\) (\(\mathrm{J/kg\cdot K}\)) & 2057 & \(-5.108\) & \(0.009570\) & \(-7.465\times 10^{-6}\) & \(2.125\times 10^{-9}\) \\ \(\mu\) (\(\mathrm{Pa\cdot s}\)) & \(9.105\times 10^{-6}\) & \(3.566\times 10^{-8}\) & \(5.645\times 10^{-12}\) & \(-1.339\times 10^{-14}\) & \(4.991\times 10^{-18}\) \\  & & \(F(x)=\mathrm{A_{0}}+\mathrm{A_{1}}\)\(T+\mathrm{A_{2}}\)\(T^{2}+\mathrm{A_{3}}\)\(T^{3}+\mathrm{A_{4}}\)\(T^{4}\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The polynomial fitting of the thermophysical parameters of nitrogen versus temperature when the working pressure is 10 MPa. Here, the Kelvin temperature is adopted.  
Figure 3: _Cont._
## 4 Data Transformation Impact on Gray Values Compression
### Delta Coding  
Delta coding is a data stream transformation in which symbols are stored as the difference between the current character and the previous character [47]. Fewer bits are needed to represent the differences between successive characters than the characters themselves. The histogram of the image _horse_ can be seen in Figure 8.  
After applying delta coding on the histogram data, the results can be seen of Figure 9.  
When analyzing Figure 9, the frequency distribution is better for entropy coders because the differences in the frequencies of occurrence of each element are greater. For example, in Figure 9, there are about 250 elements with value 0 and about 10 elements with value 100. This difference should be exploited by Huffman coding and increase the effectiveness of the compression.  
Figure 8: Histogram of the image _horse_.  
Figure 9: Histogram of the image _horse_ after applying delta coding.
## 2 AUV Design and Construction
### Control System and Sensors  
Figure 4 illustrates the structure of the control system of the AUV. The microprocess computing unit (MCU) composed of Psoc inside the AUV receives data from a Raspberry Pi (RPI) and sends pulse-width modulation (PWM) signals to the servomotor. The MCU also receives data from the depth sensor and the attitude heading reference system (AHRS) sensor, which measures the attitude of the AUV, and transmits them to the RPI. The RPI collects the camera image and sensor data received from the MCU and displays the collected sensor data on the screen through the GUI. The control of the AUV is transmitted through the UWOC system according to an external command. The GUI screen was built such that the camera image, sensor data, and communication status could be confirmed, and CRC32 was applied to check the communication data. Figure 5 shows the GUI screen, which shows the orientation and position of the AUV with the visual information of the underwater camera on the AUV.  
Figure 4: Structure of AUV control system.  
The sensors used for attitude control of the AUV include the AHRS and depth sensors. Adafruit's BNO055 model was used for the AHRS, which provides roll, pitch, and yaw values.
# Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete
## 3 Materials and Methods
### Quality Assessment Criteria  
In this study, three common-used quality assessment criteria such as R, MAE and RMSE were calculated to evaluate the prediction performance of the models. Detailed information and formulations of these criteria could be found in the literature [84; 85; 86; 87; 88; 89; 90; 91].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Compressive** & **Tensile Strength** & **Curing** & **D\({}_{\text{max}}\) of** & **Stone Powder** & **Fineness Modulus** \\  & **Strength of Cement** & **of Cement** & **Age** & **Crushed** & **Sand** & **of Sand** \\ \hline Unit & MPa & MPa & Days & mm & \% & [-] \\ \hline Notation & I\({}_{1}\) & I\({}_{2}\) & I\({}_{3}\) & I\({}_{4}\) & I\({}_{5}\) & I\({}_{6}\) \\ \hline Min & 35.50 & 6.90 & 3.00 & 16.00 & 0.00 & 2.20 \\ \hline Average & 47.95 & 8.25 & 80.93 & 28.31 & 7.54 & 3.06 \\ \hline Median & 46.80 & 8.00 & 28.00 & 31.50 & 6.60 & 3.15 \\ \hline Max & 63.40 & 10.20 & 388.00 & 31.50 & 20.00 & 3.50 \\ \hline Std & 4.29 & 0.60 & 102.36 & 3.68 & 4.48 & 0.27 \\ \hline CV (\%) & 8.95 & 7.29 & 126.48 & 12.99 & 59.42 & 8.98 \\ \hline \multirow{2}{*}{**Parameter**} & **Water to Binder** & **Water to Cement** & **Water** & **Sand Ratio** & **Slump** & **Cubic Compressive Strength of** \\  & **Ratio** & **Ratio** & **Water** & **Sand Ratio** & **Slump** & **Concete * \\ \hline Unit & [-] & [-] & kg/m\({}^{3}\) & \% & mm & MPa \\ \hline Notation & I\({}_{7}\) & I\({}_{8}\) & I\({}_{9}\) & I\({}_{10}\) & I\({}_{11}\) & Y \\ \hline Min & 0.25 & 0.31 & 120.00 & 28.00 & 11.00 & 19.00 \\ \hline Average & 0.43 & 0.46 & 175.49 & 37.23 & 98.34 & 55.80 \\ \hline Median & 0.45 & 0.45 & 180.00 & 36.00 & 70.00 & 56.45 \\ \hline Max & 0.69 & 0.69 & 291.00 & 44.00 & 260.00 & 96.30 \\ \hline Std & 0.09 & 0.07 & 15.16 & 4.00 & 66.64 & 16.70 \\ \hline CV (\%) & 20.81 & 14.42 & 8.64 & 10.74 & 67.77 & 29.93 \\ \hline \hline \end{tabular}  
* \({}^{*}\)Cylinder compressive strength = 0.82 \(\times\) Cubic compressive strength.  
\end{table}
Table 1: Statistical analysis of inputs and output used in this study.
## 6 Results and Discussion
### Comparison Between Versions of bWSO  
In Table 5, the average accuracy value, the best standard deviation, and the worst result of the binary bWSO algorithm are given as a result of 30 independent runs. The best results are highlighted in bold font. Considering the average success value, it was seen that using the appropriate transfer function increased the success rate when comparing the bWSO-0 version with the value bWSO versions. It has been seen that the bWSO versions proposed using the V-shape transfer function are more successful than the bWSO versions proposed with the S-shape transfer functions. Considering the average ranking value in total, it is seen that the proposed logarithmic transfer function is in the first place. In the bWSO versions whose Recall values are given in Table 6, the recommended bWSO-log method achieved successful results in nine of the nineteen datasets. In the bWSO versions with the _F1-Score_ values in Table 7, the proposed bWSO-log method was found to be better in ten of the nineteen datasets.
# FLGQM: Robust Federated Learning Based on Geometric and Qualitative Metrics
## 3 Results and Discussion  
### Experimental Setup  
#### 3.1.1 Datasets  
In our experiments, we utilize two publicly available classification datasets.  
**MINST**: [35]: A widely used dataset in machine learning, it serves as the standard benchmark for digit recognition tasks. It comprises 60,000 training images and 10,000 test images, with each grayscale image representing a handwritten digit (ranging from 0 to 9) in a 28 \(\times\) 28 pixel format.  
**CIFAR-10**: [36]: Curated by the Canadian Institute for Advanced Research (CIFAR), it is a widely used dataset in machine learning. It consists of 60,000 32 \(\times\) 32 pixel color images categorized into 10 distinct classes. These classes represent objects such as airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks.  
To simulate the distribution of non-IID data among clients, we follow previous work [5] and assign a random dominant label \(l\) to each client, i.e., the number of labels \(l\) dominates within the client and the number of other labels is uniformly distributed. Specifically, we group all clients according to the number of classes within the dataset, i.e., if there are \(Y\) classes in the dataset, the clients are divided into \(Y\) groups. Then, for label \(l\), it is assigned to group \(l\) with a probability of \(q\); otherwise, it is assigned to other groups.  
Each client within the same group receives an equal and balanced distribution of the data. \(q\) controls differences in the distribution of training data across clients. When \(q=1/Y\), it indicates that the client's local training data follow an independent and identically distributed (IID) pattern. Conversely, if \(q\) takes any other value, the client's local training data are considered non-IID. Moreover, as the value of \(q\) increases, the degree of non-IIDness in the client's local training data also escalates. In our experiments, we deliberately set \(q\) to 0.5 for both MNIST and CIFAR-10, resulting in a higher degree of non-IID characteristics in the data.
# Nonlinear Model Predictive Control with Terminal Cost for Autonomous Vehicles Trajectory Follow
###### Abstract  
This paper presents a nonlinear model predictive control with terminal cost (NMPC-WTC) algorithm and its open/closed-loop system analysis and simulation validation for accurate and stable path tracking of autonomous vehicles. The path tracking issue is formulated as an optimal control problem. In order to improve the squeezing phenomenon of traditional NMPC, a discrete-time nonlinear model predictive controller with terminal cost is then designed, in which the state error of last step is augmented. The cost function of NMPC-WTC consists of two parts: (1) the traditional NMPC cost function responding to tracking errors and controller output, and (2) the augmented terminal cost. The algorithm was implemented on CasADi numerical optimization framework, which is free, open-source and developed for nonlinear optimization. The open-loop and closed-loop simulation results are then presented to demonstrate the improved performance in tracking accuracy and stability compared to traditional model predictive controller.  
terminal cost; nonlinear model predictive control; path tracking +
Footnote †: journal: Applied Sciences
## 3 Building Load Type
### Classify Based on Time Span
#### 3.1.4 Long Term Load Forecasting  
Long-term load forecasting plays a pivotal role in the production, operation, planning, and construction of power systems. It not only needs to be closely integrated with long-term macroeconomic trends but also requires a thorough consideration of various complex factors [72,73], including uncertainties in future scenarios and trends, diverse end-uses, fuel types, and driving forces [74]. When the latest trends in time series data are difficult to obtain, accurately predicting long-term loads becomes challenging. Figure 5 presents an example of long-term load forecasting.  
Figure 5: Example of predicting long-term loads using different models. Among them, (**A**) uses a BP neural network, (**B**) uses an ELMAN network, (**C**) uses an LSTM network, (**D**) uses an SVM, (**E**) uses a CNN-LSTM model, (**F**) uses an SSA-CNN-LSTM model, (**G**) uses a CNN-SVM model, and (**H**) uses an SSA-CNN-SVM model [75].  
As the time horizon of prediction extends, the likelihood of dynamic changes becomes increasingly significant, leading to a gradual decrease in prediction accuracy [76]. As a crucial tool for ensuring the safe and economic operation of the system, long-term load forecasting has a profound impact on enterprises' formulation of production and business objectives as well as development plans [77]. Its prediction results serve as an important basis for the planning and renovation of construction projects and can also provide guidance for the selection of construction sites, power generation methods, determination of construction scales, and scheduling of construction progress for power plant projects [78]. Therefore, long-term load forecasting holds an irreplaceable significance in grid investment decisions and energy system planning, playing a vital role in optimizing resource allocation, enhancing system efficiency, and ensuring energy security [79].
## 4 Proposed Efficient Monitoring System (EMS)
### Neighbor's Assessment Component  
This component's primary goal is to share reputations with immediate neighbors. The \(RVN\) value of each node is computed by the neighbor assessment component. Equation (7) can be used to compute the \(RVN\) value. This component of EMS comprises two types of tables; namely, information table \(I_{Table}\) and references table \(R_{Table}\). Each entry in \(I_{Table}\) comprises a node ID and the \(RVN\) value for that node. This component uses three different types of messages to share reputations with its neighbors, namely \(Reputation_{request}\), \(Reputation_{reply}\) message, and \(ALARM_{message}\). The neighbor's assessment component asks \(N\) neighbors to offer their comments on the member nodes at each communication opportunity through \(Reputation_{request}\) message. The \(RVN\) value for the node responding to the neighbor's assessment component query is returned by these neighbors through \(Reputation_{reply}\) message. These values are saved in the \(R_{T}able\), which each neighbor is responsible for maintaining throughout its time on the network. The \(ALARM_{message}\) message is sent to neighbors regarding a node who performs selfish behavior repeatedly and is currently punished. Each time a neighbor makes direct communication with a node \(i\), the \(RVN\) value of a such node is updated.  
\[RVN_{i}=\frac{\Sigma_{n=1}^{N}R_{v}}{N} \tag{7}\]  
The detail of the neighbor's module is discussed in Algorithm 2. In this algorithm, nodes send requests to obtain the reputation of certain nodes from their neighbor's nodes. The neighbor node checks the reputation of nodes in \(Ref_{T}able\) and its view on the reputation of those specific nodes. After sending \(reply_{m}essage\), the neighbor's node updates the \(R_{T}able\).  
```
1:INPUT:\(N\) neighbor's to determine node \(i\)OUTPUT:\(RVN\) of node \(i\)
2:view=0;
3:node send \(Reputation_{request}\) to its neighbor's
4:for each node \(i\in N\)do
5:\(veiw=view+node_{i}(RVN)\)
6:\(RVN=\frac{view}{N}\)
7: Neighbor's send back \(Reputation_{reply}\) to node asked reputation of \(node_{i}\)
8: update \(R_{T}able\)
9: update \(I_{T}able\)
10:endfor
11:End
```  
**Algorithm 2** Algorithm for Neighbor's Assessment Component
# Upper Extremity Kinematics and Electromyographic Activity in Uninjured Tennis Players
## 2 Materials and Methods  
### Subjects  
The study was reviewed and approved by the Mayo Clinic Institutional Review Board. Prior to participation in the study, the subjects signed a written consent form. To be eligible for the study, the subjects had to be between ages 12 and 21, actively participate in USTA-sanctioned events, have pain-free range of motion (ROM) of the bilateral upper and lower limbs, and not have any current musculoskeletal upper extremity injuries. In total, 20 subjects participated in this study (11/20 Male, Age = 15.0 \(\pm\) 1.8 years, Height = 1.7 \(\pm\) 1.1 m, BMI = 21.3 \(\pm\) 3.4 kg/m\({}^{2}\), 18/20 right-arm dominant). This sample size is consistent with ranges established by previous sport kinematic normative database studies, in which the sample sizes ranged from 7 to 29 subjects (mean = 21) [14; 18; 19; 20; 21; 22].  
Male subjects were asked to perform testing shirtless while females wore a tight-fitting sports bra or tank top. All subjects wore tight-fitting shorts. Subjects used their own racket to keep swing patterns consistent with match play. Retroreflective markers were placed on anatomic landmarks and were used to define joint centers and joint axes, measure segment length, and track motion. All markers were placed directly onto the skin, if possible. The markers were secured with double-sided tape to allow for unrestricted movement. Four additional markers were attached to the subject's racket to assist with event detection.