esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Impact of Monitoring Point Location on Prediction Results  
As shown in Figure 5, the four monitoring points are distributed in the same administrative district, but there are different features around different monitoring points. It is evident from Tables 4 and 5 that the noise levels monitored by the four points are different. Among them, the 01 and 04 points are close to the road and construction site, the surrounding environment is generally noisy, and the average weekly noise is about 60 dB(A). The 02 point is relatively quiet because of the proximity to the water surface and residential areas. Its average weekly noise is about 50 dB(A). In overall, as illustrated in Figure 6, the model performs well on each dataset, but for the 1-s interval dataset, the prediction model has a larger error on the dataset of monitoring point 02. This is because the environment around point 02 is relatively quiet. When the model is making noise prediction, and it is actually predicting the  
Figure 8: Comparison of prediction results of different models at different time intervals: (**a**) 1-s interval; (**b**) 1-min interval; (**c**) 10-min interval; (**d**) 30-min interval.  
typical value of this monitoring point noise, once there are other noise sources at point 02, the value predicted is much lower than the actual value, and the difference between the observed values and the predicted values is larger in contrast with other data sets, resulting in a larger error. On the other hand, when the time interval increases, this situation disappears, owing to the fact that some of the high noise values are averaged, and the model still predicts the typical value of this monitoring point. The error becomes infinitesimal when the model is fully trained.
# A Study of Gender Bias in Face Presentation Attack and Its Mitigation
###### Abstract  
In biometric systems, the process of identifying or verifying people using facial data must be highly accurate to ensure a high level of security and credibility. Many researchers investigated the fairness of face recognition systems and reported demographic bias. However, there was not much study on face presentation attack detection technology (PAD) in terms of bias. This research sheds light on bias in face spoofing detection by implementing two phases. First, two CNN (convolutional neural network)-based presentation attack detection models, ResNet50 and VGG16 were used to evaluate the fairness of detecting imposer attacks on the basis of gender. In addition, different sizes of Spoof in the Wild (SiW) testing and training data were used in the first phase to study the effect of gender distribution on the models' performance. Second, the debiasing variational autoencoder (DB-VAE) (Amini, A., et al., Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure) was applied in combination with VGG16 to assess its ability to mitigate bias in presentation attack detection. Our experiments exposed minor gender bias in CNN-based presentation attack detection methods. In addition, it was proven that imbalance in training and testing data does not necessarily lead to gender bias in the model's performance. Results proved that the DB-VAE approach (Amini, A., et al., Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure) succeeded in mitigating bias in detecting spoof faces.  
gender bias; presentation attack detection; debiasing variational autoencoder; convolutional neural network +
Footnote †: journal: _Future Internet_  
1
Footnote 1: [[https://www.mdpi.com/journal/futureinternet](https://www.mdpi.com/journal/futureinternet)]([https://www.mdpi.com/journal/futureinternet](https://www.mdpi.com/journal/futureinternet))
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 6 Graph-Based RA in CR Networks
### RA Methods Based GSP  
A GSP procedure for DRL-based cooperative spectrum sensing (CSS) of the PU signal is developed in [79]. Each SU is a vertex in the coordination graph which establishes connections among all nodes, the decisions (on spectrum occupancy) of which influence the throughput of their neighbors. Then, the problem is defined as maximizing the CR network's potential throughput, subjected to the SUs' decisions. These are exchanged between the graph's vertices via message passing so as to determine the optimal decision (action) to train the DRL algorithm. Fast convergence to very good probabilities of misdetection \(P_{MD}\) and of false alarm \(P_{FA}\) is observed. A DRL procedure for GNN generating joint channel and power allocation for the throughput maximization of an underlay CR network is developed in [80]. The graph is composed of the associated links (each user is constantly connected to the same AP) as vertices, with the interference links between them being their edges. The vertices' attributes (which are the GNN's inputs) are the distance between the PUs and the SUs, as well as the channels they occupy. With this data, the GNN produces channel and power allocations for both the SUs and PUs. Then, the DRL procedure ensures the PUs' throughput requirements are met. The proposed algorithm achieves stable network throughput \(R_{T}\) that is much higher than that of alternative learning procedures.
## 3 The Model Validation
### Addendum Modification Coefficient
#### 4.2.2 The Influence on Mesh Stiffness  
The relationship between the mesh stiffness of the helical gear and the addendum modification coefficient given in Table 3 is studied. It can be found that in the whole mesh process, with an increasing addendum modification coefficient of the driving gear (Case 1), the driven gear decreases, the mesh stiffness curve increases first and then decreases while moving toward the positive of the \(x\)-direction. For Case 2, in which the positive coefficient is adopted in the driving and driven gears, the mesh stiffness curve is unchanged with the different coefficients. For Case 3, in which the negative coefficient is adopted, the mesh stiffness curve decreases slightly with an increasing absolute value of the negative coefficient, as shown in Figure 20.  
The fluctuation value and mean value of mesh stiffness in a mesh period can be obtained. For Case 1 with \(x_{1}+x_{2}=0\), the fluctuation value of mesh stiffness is always approximately \(10\%\), and the mean value increases first and then decreases; the maximum of the mean value is \(3.5\times 10^{8}\) N/m with \(x_{1}=x_{2}=0\), and the minimum value is \(2.4\times 10^{8}\) N/m with \(x_{1}=-x_{2}=0.8\) or \(x_{1}=-x_{2}=-0.8\). For Case 2 with \(x_{1}>0\) and \(x_{2}>0\), with an increasing addendum modification coefficient, the fluctuation value decreases from \(10\%\) to \(2\%\), which indicates that the larger the positive modification, the smaller the fluctuation value. The mean value of mesh stiffness for Case 2 remains approximately \(3.5\times 10^{8}\) N/m. For Case 3 with \(x_{1}<0\) and \(x_{2}<0\), the fluctuation value remains unchanged at approximately \(10\%\), as shown in Figures 21 and 22. Synthetically speaking, adopting a positive coefficient for the driving and driven gears is good for reducing the fluctuation of mesh stiffness.  
Figure 21: The influence of addendum modification on the fluctuation. (**a**) Case 1, (**b**) Case 2, (**c**) Case 3.  
Figure 22: The influence of addendum modification on the mean value. (**a**) Case 1, (**b**) Case 2, (**c**) Case 3.
## 2 Literature Review
### Padding and Masking Methods  
Time series data have a multidimensional tensor shape that changes with time and are analyzed using specialized methods, including RNN. The preprocessing required for the RNN method involves transforming the data into a specified tensor form. Most RNN methods accept data as three-dimensional tensors of batch size, timestep, and feature dimension. In other words, all data in one mini batch must contain the same number of timesteps on the time axis and the same dimension. However, testbeds in most fields include different timesteps. For instance, in the field of natural language processing, the word count varies in different sentences. This discrepancy is compensated using several methods. Padding is one such method, wherein a dummy is attached to the dataset to fit the longest step length. Figure 2 illustrates a conceptual diagram of zero-padding. The padding method fills the amount corresponding to the insufficient step using a dummy. This method requires additional work because the dataset must be transformed during the padding process.  
Another method is masking, which excludes the padded dummy in the model training process. Figure 3 depicts a conceptual diagram of the masking method. The mask value represents real and dummy data. The masking method sets the learning weight of the  
Figure 1: Conceptual structure of the long short-term memory (LSTM).  
Figure 2: Conceptual structure of the padding method.  
dummy data to zero during training. However, padding is generally set to zero as well, which poses a potential problem. When the index of the training data is expressed as zero, distinguishing between the dummy and real data becomes difficult. This is referred to as the zero-indexing problem, which can be avoided by setting a special symbol, which must be a number that does not overlap with input data.
# Deep-Learning Multiscale Digital Holographic Intensity and Phase Reconstruction
###### Abstract  
Addressing the issue of the simultaneous reconstruction of intensity and phase information in multiscale digital holography, an improved deep-learning model, Mimo-Net, is proposed. For holograms with uneven distribution of useful information, local feature extraction is performed to generate holograms of different scales, branch input training is used to realize multiscale feature learning, and feature information of different receptive fields is obtained. The up-sampling path outputs multiscale intensity and phase information simultaneously through dual channels. The experimental results show that compared to Y-Net, which is a network capable of reconstructing intensity and phase information simultaneously, Mimo-Net can perform intensity and phase reconstruction simultaneously on three different scales of holograms with only one training, improving reconstruction efficiency. The peak signal-to-noise ratio and structural similarity of the Mimo-Net reconstruction for three different scales of intensity and phase information are higher than those of the Y-Net reconstruction, improving the reconstruction performance.  
digital holography; holographic reconstruction; deep learning; multiscale; feature extraction +
Footnote †: journal: Applied sciences
# Homogeneity and Trend Analysis of Climatic Variables in Cap-Bon Region of Tunisia
## 3 Results and Discussion
### Trend in Climatic Variables
#### 3.2.2 Trend in Annual, Seasonal and Monthly Precipitation  
The results of the statistical tests applied for annual and monthly precipitation for the period (1982-2020) are summarized in Table 4. A non-significant decreasing trend in annual precipitation was detected, with an amount equal to \(-37.5\) mm/decade. This result is in agreement with those obtained by [PERSON] and [PERSON] (2018), who found a non-significant decreasing trend in annual precipitation for most of the studied stations in central Italy during the period 1951-2012. The estimated average of magnitude of the decreasing trend was equal to \(-15.2\) mm/decade, which is lower than that obtained in our case.  
In addition, [PERSON] et al. (2019), in their assessment of annual and seasonal precipitation trends over the Macta basin (northwest of Algeria), showed that 17 stations among 48 presented insignificant downward trends during the study period with Sen's slope values ranging from \(-0.08\) to \(-2.38\) mm/year. As it can be inferred from the results, a mix of positive and negative tendencies, which were overall insignificant, characterizes monthly precipitation. Similar findings were reported by [PERSON] and [PERSON] (2018), who indicated that there is no significant trend in annual and monthly precipitation in Northeast Iran. Regarding the case of December, where a significant downward shift was recorded around the year 2007, the divided parts did not exhibit any significant trend. Thus, the declining trend observed for the whole data is mainly attributed to the shift break in the mean. It is worthwhile to emphasize that hundreds of years are highly required for better precipitation trend detection. However, our collected precipitation data cover a short time interval, which could impede the exact trend detection.
# A Survey on Mission-Profile-Based Stress Testing of Electric Motor Drives
## 2 Influential Factors on the Aging and Reliability of EMD Components
### Non-Electromagnetic Stress
#### 2.2.2 Mechanical Stress  
Mechanical stress has the greatest impact on the electric motor due to the presence of moving parts and its inertia. Due to the high angular velocities, the rotor conductors are subjected to significant centrifugal forces [47]. Centrifugal forces, along with magnetic forces, contribute to the deformation, breakdown, and degradation of rotor windings. The magnitude of the centrifugal force can be evaluated based on the speed of the rotor and the radius at which the conductors are located. As the rotor spins faster, the centrifugal force increases with the square of the velocity, leading to higher stress on the winding conductors. This increased force contributes to the deformation and wear of the rotor windings, particularly in high-speed motors. In extreme cases, excessive centripetal force can lead to the breaking of conductor strands, further compromising the integrity of the motor. Therefore, high-speed motors are most often designed as two-pole machines with a small rotor diameter. As stated in [48], machines with a higher number of poles require a higher supply frequency in order to maintain a constant rotational speed. Therefore, the motor cannot be directly connected to the constant-frequency mains voltage, and the use of a voltage source inverter (VSI) becomes necessary.  
Mechanical stress also includes friction, torque ripples, vibrations, and motor overload. The friction in the bearings and other mechanical components of the electric motor contributes to energy losses, additional heating, and potential wear over time [49]. The authors [50] explain the importance of water cooling in reducing heat generated by mechanical losses due to friction. Water cooling is more efficient than air cooling due to the highly specific heat capacity of water, low maintenance costs, and easy implementation.  
As reported in [51], bearing failures are the major contributors to electric machine degradation. Bearing vibrations contribute to additional stress in terms of increased friction, thermal stress, bearing current flow, and degradation of the shaft. Bearing currents are a result of induced voltage caused by the stray magnetic field and the existence of a capacitive coupling between the rotor and stator. As stated in [52], this capacitive connection causes a potential difference, generating a current that seeks the path of least resistance, often through the motor bearings.  
The dynamics of the EMD are influenced by mechanical behavior, including inertia and vibrations, which contribute to mechanical stress. As noted in [13; 53], inertia limits rapid speed changes, while vibrations cause torque oscillations, both affecting control quality. Since mechanical responses are slower than electrical ones, varying operating conditions further impact performance. This stress accelerates wear on components, highlighting the importance of accurately modeling mechanical behaviors for reliable operation.
# Educational Escape Rooms as an Active Learning Tool for Teaching Telecommunications Engineering
## 2 Methodology
### Escape Room 1
#### 2.1.2 Longitudinal Redundancy Check (LRC)  
The second puzzle results in the computation of an XOR based longitudinal redundancy check (LRC) on a block of data, although students do not know this at the start. LRC's a are more robust form of error detection compared to parity and have less transmission overhead. They involve performing a sequential mathematical operation on each byte of data to generate a checksum which is appended to the end of the data.  
Students are presented with a series of sequential hex bytes followed by an ASCII table. When the students look at each of the hex bytes in the ASCII table it will instruct them to compute the LRC checksum on all the data bytes provided. Students then need to convert all the bytes to binary and compute a sequential XOR as they travel through each of the bytes as demonstrated in Table 1.  
The result for this truncated example (00101101b) is converted to decimal (45) and serves as the answer to our puzzle.
## 3 Algorithms and Implementation
### Dual Extended Kalman Filter (DEKF)  
In the domain of parameter estimation with EKF, there are several approaches to achieve the same overall objective. Joint EKF runs a single filter that directly implements the augmented state-space shown above. The DEKF performs two coupled and simultaneous filters for state and parameters estimation. In general, there seems to a consensus that dual EKF demonstrates a computational advantage, while joint EKF demonstrates an accuracy advantage. For the purposes of this paper, the DEKF is implemented as the easiest solution to code and debug, where its process map has been clarified as follows [46; 47; 48; 49; 50].  
State-Space Model:  
\[x_{k+1}=f(x_{k},u_{k},\theta_{k})+w_{k}\text{ and }\theta_{k+1}=\theta_{k}+r_{k}\]  
\[y_{k}=\mathrm{g}(x_{k},u_{k},\theta_{k})+v_{k}\text{ and }d_{k}=\mathrm{g}(x_{k},u_{k}, \theta_{k})+e_{k}\]where \(w_{k}\), \(v_{k}\), \(r_{k}\) and \(e_{k}\) are independent zero-mean Gaussian noise processes of covariance matrices \(\sum_{uv}\), \(\sum_{v^{\prime}}\sum_{v^{\prime}}\sum_{v^{\prime}}\), respectively.  
Definitions:  
\[A_{k-1}=\frac{\partial f\big{(}x_{k-1},u_{k-1},\hat{\theta}_{k}^{-}\big{)}}{ \partial x_{k-1}}C_{k}^{x}=\frac{\partial\text{g}\big{(}x_{k},u_{k},\hat{ \theta}_{k}^{-}\big{)}}{\partial x_{k}}C_{k}^{\theta}=\frac{\text{dg}(\hat{x}_ {k}^{-},u_{k},\theta)}{dx_{k}}\]  
Computation:  
For \(k\) = 1, 2,... compute:  
Weight filter time update:  
\[\hat{\theta}_{k}^{+} =\hat{\theta}_{k-1}^{+}\] \[\hat{\theta}_{k}^{\pm} =\sum_{\theta,k-1}+\sum_{r}\]  
State filter time update:  
\[\hat{x}_{k}^{-}=f\Big{(}\hat{x}_{k-1}^{+},\;u_{k-1},\hat{\theta}_{k}^{-}\Big{)}\]  
\[\sum_{x,k}^{-}=A_{k-1}\sum_{x,k-1}^{\pm}A^{T}{}_{k-1}+\sum_{w}\]  
State filter measurement update:  
\[L_{k}^{x}=\sum_{\overline{x},k}^{-}(C_{k}^{x})^{T}\Bigg{[}C_{k}^{x}\sum_{ \overline{x},k}^{-}(C_{k}^{x})^{T}+\sum_{v}\Bigg{]}^{-1}\]  
\[\hat{x}_{k}^{+}=\hat{x}_{k}^{-}+L_{k}^{x}\big{[}y_{k}-\text{g}\big{(}\hat{x}_ {k}^{-},u_{k},\hat{\theta}_{k}^{-}\big{)}\big{]}\]  
\[\sum_{\overline{x},k}^{+}=(I-L_{k}^{x}C_{k}^{x})\sum_{\overline{x},k}^{-}\]  
Weight filter measurement update:  
\[L_{k}^{\theta}=\sum_{\overline{\theta},k}^{-}\Big{(}C_{k}^{\theta}\Big{)}^{T }\Bigg{[}C_{k}^{\theta}\sum_{\overline{\theta},k}^{-}\Big{(}C_{k}^{\theta} \Big{)}^{T}+\sum_{e}\Bigg{]}^{-1}\]  
\[\hat{\theta}_{k}^{+}=\hat{\theta}_{k}^{-}+L_{k}^{x}\big{[}y_{k}-\text{g}\big{(} \hat{x}_{k}^{-},u_{k},\hat{\theta}_{k}^{-}\big{)}\big{]}\]  
\[\sum_{\overline{\theta},k}^{+}=\Big{(}I-L_{k}^{\theta}C_{k}^{\theta}\Big{)} \sum_{\overline{\theta},k}^{-}\]
###### Abstract  
The probe tip of a micro-coordinate Measuring Machine (micro-CMM) is a microsphere with a diameter of hundreds of microns, and its sphericity is generally controlled within tens to hundreds of nanometers. However, the accurate measurement of the microsphere morphology is difficult because of the small size and high precision requirement. In this study, a measurement method with two scanning probes is proposed to obtain dimensions including the diameter and sphericity of microsphere. A series of maximum cross-sectional profiles of the microsphere in different angular directions are scanned simultaneously and differently by the scanning probes. By integrating the data of these maximum profiles, the dimensions of the microsphere can be calculated. The scanning probe is fabricated by combining a quartz tuning fork and a tungsten tip, which have a fine vertical resolution at a sub-nano scale. A commercial ruby microsphere is measured with the proposed method. Experiments that involve the scanning of six section profiles are carried out to estimate the dimensions of the ruby microsphere. The repeatability error of one section profile is 15.1 nm, which indicates that the measurement system has favorable repeatability. The mainly errors in the measurement are eliminated. The measured diameter and roundness are all consistent with the size standard of the commercial microsphere. The measurement uncertainty is evaluated, and the measurement results show that the method can be used to measure the dimensions of microspheres effectively.  
microsphere; dimension; sphericity; micro-coordinate measuring machine; scanning probe 1
# Recognition and Classification of Handwritten Urdu Numerals Using Deep Learning Techniques
## 5 Experiment and Discussion
### Expanded Testing Set  
As our model is tested on real-life examples thus, it is deemed necessary to validate our results with a separate dataset. This dataset consists of 90 images captured with the same tools as were used for the training data. The preprocessing techniques similar to training data were applied to it. The only difference here is that it contained 4 classes of zero, one, two, and five instead of 10 classes. So, during training and testing of the currency dataset, the training data for Urdu numerals was minimized to the same 4 classes. The same CNN network was used as proposed in Figure 10. An accuracy of 97.97% was achieved on the training set along with 89.41% on the test set. To obtain the best results, we retuned the hyperparameters of the model. We chose different batch sizes ranging from 16 to 128 to find the one where we achieved the best results while keeping the learning rate at 0.001. Furthermore, the number of epochs was varied accordingly which resulted in getting momentum 0.7 with SGD.  
Along with CNN, ResNet, GoogLeNet, and SVM were also applied to this expanded test set to obtain an insight into their accuracies and losses. The parameter batch size was kept to 32 for both ResNet and GoogLeNet. The bar graph in Figure 16 shows that our proposed CNN model with the SVM worked best on a real-life dataset with a validation accuracy of 91%. On the other hand, the validation accuracies of GoogLeNet (53.47%), ResNet50 (64.24%), and Softmax (89.41%) are worth cons  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Systems** & **Dataset** & **Classifier** & **Accuracy Achieved** \\ \hline
[11] & UNHD(Urdu characters and ligatures) & BLSTM & 93.96\% \\
[40] & Sindhi handwritten numbers & Self-organizing map neural network & 86.89\% \\  & & & 97.4\% \\
[21] & Handwritten Urdu numerals & Rule based technique, HMM & (Rule based technique), \\  & & & 96.2\% (HMM) \\
[41] & Handwritten Urdu numerals & Daubechies wavelet & 92.05\% \\
[42] & Undu handwritten characters and numerals & Convolutional neural network & 98.3\% \\
[26] & Undu handwritten characters and numerals & Convolutional neural network & 98.3\% \\ Our Approach & Handwritten Urdu numerals & GoogLeNet and ResNet & 95.7\% \\  & Feature extraction using convolution layer & 98.41\% \\ Our Approach & Handwritten Urdu numerals & and Softmax Activation for classi cation & **99.3\%** \\  & & **Feature extraction using convolution layer** & **99.3\%** \\  & & **and SVM for classi cation** & **99.3\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of handwritten Urdu numerals recognition on different classifiers.  
on particular classes instead of all the 10 classes and the data within these 4 classes was also less in quantity, so this caused a reduction in accuracies for real-life data.