esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Supervised Learning
#### 4.1.4 Beam Selection  
The authors in [51] propose a combined supervised ML approach for beams selection in mm-wave communications. The beam selection problem was addressed as a multi-class problem, using two supervised learning algorithms (kNN and Support Vector Classifier-SVC) to address the issue, with simulation results showing that the proposed ML schemes can retain 90% of the sum rate with optimal beam selection. In [52], a supervised SVM for beam selection is proposed, aiming to achieve high sum-rate at lower computational complexity. The results verified that the proposed ML approach can achieve higher Average Sum Rate (ASR) with substantially lower computational complexity than conventional approaches. In [53], the authors propose a DNN model for beam selection in mm-wave systems, to reduce space required for the initial beam. The results show that the proposed beam selection reduces the beam overhead by up 79.3%. In [54], a DNN for optimal downlink beam in mm-wave networks is proposed, to enhance prediction accuracy and data rate. The simulation results show superior performance and robustness of the proposed model. The conventional approaches mostly rely on the sub 6 GHz information, especially in the low signal-to-noise ratio (SNR) regions. In [55], a novel deep learning solution  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **ML Approach** & **Application Problem** & **Description** \\ \hline \([40]\) & DNN & User’s requirements & Prediction of user’s requirements in high dynamic UAV networks \\ \hline \([41]\) & DNN\& RNN & Traffic load, power allocation, Load Balancing & Prediction of traffic load and optimization of power allocation \\ \hline \([42]\) & ANN, SVM & Cooperative Sensing Schemes (CSSs) & Prediction of trasmit power and boundary decision classifier \\ \hline \([43,44]\) & ANN, Random Forest, SVM & Data rate & Accuraty prediction of data rate with lowest possible prediction error \\ \hline \([45]\) & DNN & Beamforming Schemes in MISO channels & Prediction of trasmit power and trasmitter’s beamforming \\ \hline \([46]\) & k-means & Clustering in MIMO-NOMA systems & Efficient clustering with higher throughput and lower SNR \\ \hline \([39]\) & ANN & DBA in Passive Optical Networks (PONs) & Bandwidth and uplink latency prediction \\ \hline \end{tabular}
\end{table}
Table 5: _Cont._based on a RNN, namely the Gated Recurrent Unit (GRU) is proposed for beam selection. The model can predict the serving base station and beam for each drone based on their prior trajectories and locations, extending their coverage. Simulation results show that the proposed scheme can achieve more than 90% accuracy for beam prediction.
## 1 Introduction
### Message Transmission Overhead in OppNets  
Portable handheld user devices (e.g., smartphones and tablets) have become inseparable components of OppNets. Besides their ability to serve as routers, they present the primary user interface, as they are the main platform for human communication today. These devices can now be perceived as the users themselves: their attachment to humans allows them to follow and learn user movement patterns, gather social information about the user, as well as maintain user contextual information (e.g., contact information, occupation and preferences). While the presence of these devices contributes to the feasibility of collecting sensed delay-tolerant data with OppNets, it is also responsible for certain challenges that befall network protocol design.  
Due to freedom of human movement and long intervals that may exist between successive encounters, messages may remain buffered in relay nodes for relatively long periods in the order of hours to days. Although delay is tolerated in OppNets, it is preferable to keep it minimal, in order to reduce message loss due to time-to-live (TTL) exhaustion or buffer overflows at congested nodes. This is often curbed by generating multiple copies of a message (referred to as \"message replication\" in this paper) and sending them through different routes. However, an optimal performance requires a careful selection of the number of copies for each message. Too many copies may lead to excessive transmissions, frequent buffer overflows, and rapid battery depletion. Fewer copies, on the other hand, may reduce throughput, due to less delivery guarantees and increased delays.  
Therefore, in order for nodes to collectively optimize overall network performance, a major challenge lies in their ability to decide how many copies to generate per message, and when to generate a copy. Nodes require knowledge about the network to make such decisions. Unfortunately, due to the infrastructure-less nature of the network and high dynamicity of user movement, global knowledge about the network is not available to nodes. Attempts to obtain global knowledge may be impractical [25] and often threatens the feasibility of the technology [26]. Such attempts may result in excessive bandwidth utilization, transmission overheads, and may also compromise throughput. Furthermore, the dynamicity of the network poses difficulties in obtaining up-to-date knowledge [27]. Routing solutions also need to cope with portable handheld user device characteristics typical of the application scenario, such as high population and degrees of spatial locality [28].
# A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans
## 2 Materials and Methods
### Main Aspects of Convolutional Neural Networks  
One of the most powerful deep-learning techniques is the use of CNNs and they are common in analyzing and classifying images. Their architecture consists of the perceptron model, which means that it has fully connected layers, with each neuron connecting to every other neuron to the next layer. It is interesting to examine all the different types of layers that it contains.  
First, there is the convolutional layer, whose name is similar to the name of the neural network. This specific type of layer is fundamental for CNNs, because it creates activation maps. That means that it learns the patterns of the image, helping the algorithm to classify any new image based on these patterns [78; 79].  
Second comes the pooling layer, which is very important for down-sampling the image and removing any noise that may fuzzy the algorithm. It works on a specific threshold, where it keeps any pixel value higher than the threshold and discards any value lower than the threshold.  
The last layer of a CNN is the fully connected layer, which \"flattens\" the output of previous node, which in definition, means that it converts it into a vector, so it will be ready for an output. Following this, our algorithm gives a label to each image. There are subcategories to the fully connected layer, as the first one does the vectorization and the last one gives the probabilities for each of the given class. [80; 81; 82].  
Figure 1: Image breast cancer bone metastasis samples in the dataset (Label: (**a**,**b**) malignant and (**c**,**d**) benign).
# Fault Detection Method for Wind Turbine Generators Based on Attention-Based Modeling
## 1 Introduction
Through extensive experiments, the proposed AW-LSTM is verified, to further improve the fault prediction accuracy of time series bearing signals.
# Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?
## 5 Test and Results  
This section presents results of the described robust SPP estimators. Particularly, Section 5.1 reports a set of simulated experiments to highlight certain aspects of these estimators and provide further insights on their application to GNSS SPP. Additionally, we provide results with an experimental dataset using real data recorded over harsh propagation conditions in Section 5.2.
# Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD
## 3 Experimental Results and Analysis
### Results of Defect Detection Network  
The trained network parameters were adopted for the MobileNet-SSD defect detection network. The test set image contained four different types of defect samples, each of which had 30 images obtained through resampling. Each sample involved one or more defects. The detection results of the trained MobileNet-SSD defect detection network on the four kinds of defect samples are show in Table 4.  
It can be seen from Table 3 that the surface defect detection network completes the defect marking of 120 defect samples with a 95.00% accuracy rate. There were missing and false samples in dent and burr defects and missing samples in abrasion defects. This is because the notches are more obvious than the other defects, and related to the image quality and subjective feelings of humans.  
When the filling line was in operation, the container passed by the image acquisition device within a certain distance, triggering the CCD camera to take photos. The defect detection network performed forward operation. If there were defects in the image, the alarm would buzz and the defect type and location were identified by the host (Figure 8). The single forward operation of the network was at the rate of 0.12 s/image.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Defect Type** & \begin{tabular}{c} **Sample** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **Successful** \\ **Detection** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **Leakage** \\ **Number** \\ \end{tabular} & \
\begin{tabular}{c} **Error** \\ **Detection** \\ **Number** \\ \end{tabular} &
\begin{tabular}{c} **Positive** \\ **Rate (\%)** \\ \end{tabular} \\ \hline Breach & 30 & 30 & 0 & 0 & 100.00 \\ Dent & 30 & 27 & 2 & 1 & 90.00 \\ Burr & 30 & 28 & 1 & 1 & 93.33 \\ Abrasion & 30 & 39 & 1 & 0 & 96.67 \\ Total & 120 & 115 & 4 & 2 & 95.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detection results of the trained MobileNet-SSD algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **VGG-16** & **MobileNet** & **MobileNet-SSD** \\ \hline Basic learning rate & 0.003 & 0.003 & 0.003 \\ Number of iterations of training & 10,000 & 10,000 & 10,000 \\ Verifying the number of iterations & 50 & 50 & 50 \\ Batch quantity & 128 & 128 & 128 \\ Optimization algorithm & SGD & SGD & SGD \\ Network parameter (million) & \(\approx\)12.30 & \(\approx\)1.76 & \(\approx\)1.92 \\ Amount of calculation (million) & \(\approx\)1325.00 & \(\approx\)127.54 & \(\approx\)157.32 \\ Mean of loss function & 0.14 & 0.19 & 0.09 \\ Mean value (\%) & 93.91 & 92.33 & 96.73 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training parameters and results of the three detection networks. SGD = stochastic gradient descent.
## 2 Materials and Methods
### Power Consumption Modeling
#### 2.1.2 Material Removal Power Modeling  
Material removal power refers to the power consumed in the interaction between cutting tools and workpiece materials to achieve specific shapes and meet certain technical requirements. There are several factors that influence material removal power, making accurate modeling challenging. The power consumption in the cutting process is directly influenced by process parameters. Therefore, establishing the material removal power based on specific process parameters offers the advantages of simplicity, directness, and accuracy, facilitating the further optimization of process parameters. In this paper, both exponential and polynomial models are used to fit and analyze cutting power, as shown in Equations (14) and (15), respectively, and a comparison of their merits is presented.  
\[P_{\text{m}}=a\cdot n^{c_{1}}\cdot f_{2}^{c_{2}}\cdot a_{p}^{c_{3}} \tag{14}\]  
\[P_{\text{m}}=p_{0}+p_{1}\cdot n+p_{2}\cdot a_{p}+p_{3}\cdot f_{2}+ \tag{15}\]  
\[p_{4}\cdot n\cdot a_{p}+p_{5}\cdot n\cdot f_{2}+p_{6}\cdot a_{p}\cdot f_{2}\]  
where \(n,f_{2}\), and \(a_{p}\) represent the spindle speed, feed per tooth, and depth of cut, respectively, and \(a,c_{1}\), \(c_{2}\), \(c_{3}\), \(p_{0}\), \(p_{1}\), \(p_{2}\), \(p_{3}\), \(p_{4}\), \(p_{5}\), and \(p_{6}\) are coefficients that are closely related to the cutting tools, materials, and process conditions.
## 3 Case Study and Parametric Analysis
### Calculation and Analysis of Critical State
#### 3.2.2 Critical Leakage Pressure and Self-Sealing Stress  
The critical leakage pressure is equal to the self-sealing stress, which is supplied by the viscous resistance of the percolation channel to the liquid. If it is assumed that the liquid pressure will not  
Figure 8: The variation curves of the critical percolation channel height \(u_{c}\) and critical magnification \(\zeta_{c}\) with increasing nominal contact stress; the critical magnification of the sealing interface under a nominal contact stress of 2 MPa is calculated as 627250.  
cause deformation of the seal interface; the sealing surface may be considered an absolute seal when the liquid pressure is below the critical leakage pressure. The critical leakage pressure (also equal to the self-sealing stress) of the horizontal percolation channel (no elevation head change) is calculated here and depicted in Figure 9.  
As shown in Figure 9, when the nominal contact stress is on the brink of 0, the self-sealing effect is observed. The beginning of the black curve is almost vertical; after the obvious self-sealing effect, the critical percolation pressure increases exponentially, with increasing contact stress. The trend of the black curve and color curves is consistent, but the prediction is more substantial than the test of the literature [39]. This may be because the ideal conditions for the calculation hypothesis are challenging to achieve in the trial. For example, the calculation does not consider the joints of gaskets, and the specimen may have defects.  
The authentic leakage process is much more complicated when the liquid pressure is large enough; it will split the seal interface to form a leakage channel, so, in theory, the real critical leakage pressure is much smaller than the calculation results.
# Secure Convolution Neural Network Inference Based on Homomorphic Encryption
## 3 System Model, Encryption Scheme and Design Goals
### Homomorphic Encryption Scheme  
The concept of homomorphism was proposed by [PERSON] et al. [2], and it means that certain operations can be performed on data when the data is in ciphertext. Homomorphic encryption refers to allowing a third party to perform certain types of operations on ciphertext and to ensure that the ciphertext after the operation is decrypted, as well as making the obtained plaintext equivalent to performing the same operation on the unencrypted plaintext; in this process, no third party can obtain any information in plain text.  
Homomorphic encryption schemes can be classified into three categories based on the depth of computation: (1) partially homomorphic encryption: this scheme can only perform one type of operation (addition or multiplication) on ciphertexts [21]. (2) Somewhat homomorphic encryption: this scheme allows for multiple operations on ciphertexts, but limits the depth of computation [22]. (3) Fully homomorphic encryption: this scheme enables arbitrary operations to be performed on ciphertexts an unlimited number of times [4]. As homomorphic encryption has evolved over time, these schemes can be divided into four generations based on their time of development. In Table 1, some characteristics of these generations of homomorphic encryption schemes are presented.  
Figure 2: Inference procedure.  
**Definition 1**.: _Given input data \(x\) and any operation \(f\), if there exists an encryption scheme \(\varepsilon\) that satisfies the following equation, where \(Enc\) is the encryption operation, \(Dec\) is the decryption operation, \(f^{\prime}\) is the corresponding ciphertext operation of \(f\), then the scheme \(\varepsilon\) is a homomorphic encryption scheme if it meets the following equation:_  
\[f(x)=Dec(f^{\prime}(Enc(x))) \tag{1}\]  
_In the initial stage of the combination of homomorphic encryption technology and neural network reasoning, many schemes used homomorphic encryption schemes that only supported integers. The unified image representation is also a floating point number. This requires adding a floating-point-to-integer mapping process._
## 3 Results and Discussion
### Effect of Different Amplitudes
#### 3.2.2 X-Direction Specimens  
Figure 9 compares and analyzes the stress-strain relationship of \(X^{0}_{0\_i}\)_, \(X^{0.1}_{700\_i}\), \(X^{0.2}_{700\_i}\), and \(X^{0.3}_{700\_i}\) (_i_ = 1-9). The influence of the vibration field with the same frequency (700 Hz) but different amplitudes (0.1-0.3 g) on the tensile properties of the X-direction specimens can be determined. It can be seen that there is almost no change in the specimens' tensile properties with increasing amplitude. Details are shown in Table 5. When vibration is introduced, the tensile properties of the X-direction specimens are only slightly changed. The average tensile strength of \(X^{0.1}_{700\_i}\)_, \(X^{0.2}_{700\_i}\), and \(X^{0.3}_{700\_i}\) are, respectively, 55.45, 55.13, and 54.32 MPa, increasing only by 2.97, 2.38, and 0.87%, respectively, compared with \(X^{0}_{0\_i}\). In terms of plasticity, the corresponding values are 10.9, 10.8, and 11.0%, increasing by 7.92, 6.93, and 8.91% respectively. This can be explained as proposed in Section 3.1.2.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Specimens (_i = 1–9_)** & \(Z^{0}_{0\_i}\) & \(Z^{0.1}_{700\_i}\) & \(Z^{0.2}_{700\_i}\) & \(Z^{0.3}_{700\_i}\) \\ \hline Average tensile strength (MPa) & 23.68 & 34.65 & 36.54 & 38.25 \\ Standard deviation & 1.61 & 0.85 & 0.82 & 0.78 \\ Growth (\%) & - & 46.3 & 54.3 & 61.5 \\ Average plasticity (\%) & 5.7 & 7.14 & 7.35 & 7.7 \\ Standard deviation & 0.36 & 0.18 & 0.17 & 0.14 \\ Growth (\%) & - & 25.3 & 28.9 & 35.1 \\ \hline \end{tabular}
\end{table}
Table 4: The tensile properties of the Z-direction specimens processed without applied vibration and with different applied vibrations.  
Figure 7: The effect of applied vibration (700 Hz frequency with different amplitudes) on the tensile properties of Z-direction specimens.  
Figure 8: SEM micrographs of specimens’ cross sections. (**a**) Without applied vibration; (**b**) effect of 700 Hz and 0.1 g vibration applied; (**c**) effect of 700 Hz and 0.2 g vibration applied; and (**d**) effect of 700 Hz and 0.3 g vibration applied.  
Figure 9: The effect of applied vibration (700 Hz frequency with different amplitudes) on the tensile properties of X-direction specimens.
# FASTSET: A Fast Data Structure for the Representation of Sets of Integers
## 2 Implementation
### Initialization  
A FASTSET is initialized by specifying the range for the elements value, and then simply allocating memory for two arrays:  
FASTSET* Create( int N ) {  FASTSET* p := calloc( FASTSET, 1 )  p->pos := calloc( int, N )  p->elem := malloc( (N+1) * sizeof(int) )  p->elem[0] := 0 // empty set  return p  }  
We assume that calloc allocates a block of memory initialized to 0s, in which case there is nothing else to be done. If, on the other hand, calloc returns a block of memory not initialized to 0, we must perform a for cycle to initialize pos[] to 0s (note that there is no need to initialize the other entries of elem[], since they will be written before read).  
Finally, sometimes the following operation is useful for re-initializing a FASTSET, since, for large \(N\), it can be faster (namely \(O(|S|)\)) than creating an empty FASTSET from scratch (that is \(O(N)\) because of calloc zeroing):  
void Clean( FASTSET* s ) {  for ( int i:=1; i <= s->elem[0]; i++ )  s->pos[s->elem[i]] := 0  s->elem[0] := 0  }  
It is clear that both creating and cleaning algorithms for FASTSET, as they are outlined, require \(O(N)\) time, because of zeroing the pos[] array (by calloc or a loop, respectively). It is possible, however, to use a trick originally outlined in [7] (exercise 2.12) to avoid the initialization while leaving \"garbage\" in pos[]:  
FASTSET* Create( int N ) { // now O(1)  FASTSET* p := malloc( sizeof( FASTSET) )  p->pos := malloc( N * sizeof(int) ) // unknown data in pos  p->elem := malloc( (N+1) * sizeof(int) )  p->elem[0] := 0 // empty set  return p  }  
void Clean( FASTSET* s ) { // now O(1)  s->elem[0] := 0; // no loop needed: garbage left in pos  }  
We only need to make a slightly more complex belonging check, that must now handle the garbage possibly present in pos:  
Boolean Belongs( FASTSET* s, int v ) { // STILL O(1)  k := s->pos[v]  return ( k > 0 AND k <= s->elem[0] AND s->elem[k] == v )  }
## 3 Simulation Results of the Elastoplastic SW-DVIB Method
### Elastoplastic SW-DVIB Fracture Simulation  
To assess the effectiveness of the model in simulating material fractures, a uniaxial tension test was performed on a rock specimen containing a pre-crack [66]. A simplified representation of the material samples is shown in Figure 11. The primary objective of this test was to examine the effect of the pre-crack dip angle on the failure condition of the material. According to test result [66], the micro bond parameters are confirmed as \(E=5.17\) GPa, \(\widetilde{\varepsilon}_{y}=5.1\times 10^{-4}\), \(\widetilde{\varepsilon}_{b}=8.67\times 10^{-4}\), \(\alpha=0.6\), \(v=0.2\), and \(\rho=2700\) kg/m\({}^{3}\).  
As shown in Figure 12a, the simulated uniaxial stress-strain relationship for various pre-crack cases reveals a decrease in the specimen tensile strength with an increase in the pre-crack dip angle. The corresponding simulated relationship between the uniaxial tensile strength and pre-crack dip angle (Figure 12b) is largely consistent with experimental observations [66].  
Figure 11: Uniaxial tension model of a pre-cracked rock specimen.  
Figure 10: Simulated fracture morphology by the method with fracture energy: (**a**) coarse mesh; (**b**) middle mesh; (**c**) fine mesh.  
The simulated fracture process is illustrated in Figure 13. The model findings indicate that tensile fracture is the primary failure mechanism for pre-cracked specimens subjected to uniaxial tensile loading. Although the crack-propagation process remained consistent across varying pre-crack dip angles, the resulting propagation trajectories differed. Specifically, at the \(\alpha=30^{\circ}\) and \(\alpha=45^{\circ}\) angles, the first crack growth exhibited a slight horizontal deviation. By contrast, at \(\alpha=90^{\circ}\), the initial propagation was horizontal. The comparison between the simulated fracture status and the experimental results [66] is shown in Figure 14. It is seen that the simulated fracture status is consistent with experimental observation.  
Figure 12: Simulated outcomes for cases with various pre-crack dip angles: (**a**) stress–strain curves; (**b**) peak stresses [67].  
Figure 13: Simulated fracture process where the pre-crack exhibits varying dip angle: (**a**) \(\alpha=30^{\circ}\); (**b**) \(\alpha=45^{\circ}\); (**c**) \(\alpha=90^{\circ}\).  
Simulation examples suggest that the proposed method can be used to simulate the fracture behavior of a material.