esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminary and Related Works
### Unsupervised Anomaly Detection  
Unsupervised anomaly detection in industrial contexts has emerged as a critical field of research, offering practical solutions for scenarios where labelling anomalies is infeasible and anomaly examples are scarce or entirely absent in the training data.  
[PERSON]'s comprehensive review provides an extensive overview of anomaly analysis techniques for both images and videos [24]. It covers various methods and their applications, serving as a valuable resource for understanding the state of the art in unsupervised visual anomaly detection.  
[PERSON] et al. [25] address the challenge of anomaly detection in industrial image data when anomaly samples are scarce. Their unsupervised ensemble method generates high-quality pseudo-anomaly images for training and demonstrates performance improvements on real datasets.  
[PERSON]'s work addresses the challenge of detecting cracks in infrastructure using industrial UAVs [26]. The authors propose an unsupervised domain adaptive approach that enables robust crack recognition, even in real-world site conditions, making it suitable for practical infrastructural inspections.  
[PERSON]'s work presents a novel approach for industrial anomaly detection that incorporates knowledge distillation and self-supervised masking [27]. By leveraging these techniques, the authors achieve improved anomaly detection performance, contributing to the advancement of unsupervised methods.  
[PERSON] and [PERSON] explore techniques to enhance the accuracy and efficiency of anomaly detection in the manufacturing of printed circuit boards [28; 29]. [PERSON] proposes a method for detecting modifications in assembled PCBs using deep convolutional autoencoders, showcasing the potential of autoencoders in industrial anomaly detection.  
The recent contributions underscore the array of approaches and techniques deployed in the realm of unsupervised visual anomaly detection for industrial applications, laying the foundation for more robust and efficient anomaly detection solutions across diverse industrial settings [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. It is essential to emphasize that, distinct from unsupervised learning in other vision tasks, unsupervised anomaly detection tasks leverage anomaly-free images for training, leading to a paradigm where models inherently operate under the out-of-distribution concept.
# Diagnosis of Intracranial Tumors via the Selective CNN Data Modeling Technique
## 4 Experimentation and Results
### Experimentation and Analysis  
We implemented various configured CNNs to find the optimal model. For various implementations, we had a variety of hyperparameters, such as artificial layers and convolutional layers, as well as regularization parameters, including Level 1 regularization (L1), Level 2 regularization (L2), image input sizes (IS), batch norm (BN), dropout (DO), kernel sizes (KS), and pooling matrix sizes (PS). We analyzed all the CNN architectures based on maximum accuracy (_MVA_), least cross-entropy loss (_LVCEL_), and time taken (_TT_) for training. The optimal architecture was selected on the basis of the objective function value (_OFV_), which is expressed in Equation (5) as follows:  
\[\textit{Objective Function Value }(OFV)=\frac{MVA}{TT+LVCEL} \tag{5}\]  
Table 2 contains all the information regarding all 15 different CNNs with their _MVA_, _LVCEL_, and _TT_ in seconds. Figure 5 shows the _MVA_, _LVCEL_ and _TT_ for each architecture. Figure 6 shows the validation accuracy, and Figure 7 depicts the validation loss of each CNN model for each epoch. In Table 2, we notice that architecture 4 had the lowest _OFV_ of 0.6073. This might be due to batch normalization, as it reduces training time. We also observed a directly proportional connection between the training time and our different CNN architectures. As in architectures 5, 6, and 13, we fed image sizes of (64, 64) according to which training time was reduced drastically. Hence, we can claim that training time is highly dependent upon IS. Earlier, as we mentioned, _TT_ (Figure 5c) changed drastically when there was an increase or decrease in architecture size, i.e., when we expanded or reduced the layers of CNN and ANN or applied different parameters. According to Table 2, it can also be observed that there is no drastic change in the _MVA_ and _LVCEL_. However, the maximum _OFV_ of 1.84 was achieved by the 13 th CNN architecture, and the minimum _OFV_ was scored by the 4 th model as 0.6073. Therefore, we can finally conclude that the simple architecture performed better and was more stable and accurate. For further consideration, architecture 13 was considered an optimal CNN model to diagnose and classify brain tumors.
## 2 Materials and Methods
### Hydrophilicity Test Analysis  
It is crucial to assess the polymers' compatibility with water-based solutions, their potential for biomedical applications, and how they will function in aqueous reaction environments [51]. The hydrophilicity test is commonly employed to evaluate polymers' capacity to attract and hold water. There are multiple methods to examine how a polymer interacts with water, encompassing water absorption testing, surface energy calculation, and the predominant one, contact angle measurement. The final method measures the contact angle between the water droplet and the polymer surface by placing the droplet on the polymer surface. This contact angle serves as a pivotal indicator of the polymer's hydrophilicity. If the water droplet beads up on the polymer surface, this high contact angle demonstrates low hydrophilicity, while a low contact angle, where the water spreads out on the polymer surface, indicates high hydrophilicity. Since PSS is considered a hydrophilic polymer, the samples were also tested for hydrophilicity by adding 3 microliter drops to dried samples and observing the resulting contact angle of the liquid.
# Characterization of Tiled Architecture for C-Band 1-Bit Beam-Steering Transmitarray
## 1 Introduction
A successful attempt to extend the application of low-cost transmitarrays to V-band was experimentally demonstrated in [9], although no electronic control was available at the time for two-dimensional beam-steering. Most of the above concepts have been demonstrated using integrated transmitarrays fabricated in planar printed-circuit technology. However, fabricating large single-panel transmitarrays raises the cost of proof-of-concept prototyping and makes the technology unaffordable for teaching laboratories. Our research is aimed at adopting the transmitarray architecture for MIMO communications in C-band. In our previous publications [10; 11], we reported on a low-frequency prototype of novel 1-bit dual-polarized tiled transmitarray, whereby the required phase distribution across the array aperture was built from standalone unit cells manufactured individually and assembled in the required pattern using a rectangular latticed plastic frame, Figure 1. Some preliminary simulation and measurement results were presented, and it appeared that the tiled architecture can be a viable solution for fast prototyping and teaching experiments, without significant performance deterioration, as compared with a similar single-panel transmitarray. Moreover, the possibility of replacing and adding individual elements in the tiled array makes it both repairable and adjustable for a specific focal distance and feed type. This paper revisits previous simulations and provides new results of modelling and experimental characterization of the tiled transmitarray.
# Research on Performance Evaluation and Optimization Theory for Thermal Microscope Imaging Systems
## 4 Analysis
### Difference Analysis
#### 4.1.2 Relationship with Telephoto Mode Thermal Imaging System MDTD\({}^{\prime}\)  
The MDTD of the telephoto mode thermal imaging system can be represented as  
\[MDTD^{\prime}=\sqrt{2}\frac{SNR_{TH}NETD^{\prime}}{\overline{I}(x,y)}(\frac{f \beta\Delta f_{eye}}{t_{e}f_{p}\Delta f_{n}})^{1/2} \tag{34}\]  
similarly, the relationship between MDTD of the thermal imaging system in microscopic mode and the telephoto mode MDTD\({}^{\prime}\) can be expressed as  
\[MDTD=\frac{1}{D_{NA}{}^{2}}\ \times\frac{\tau_{0}^{\prime}}{4F^{2}\tau_{0}} \times MDTD^{\prime} \tag{35}\]  
according to Equations (33) and (35), the changes of MRTD and MDTD of the TMISs concerning \(D_{NA}\) can be analyzed. For example, using an uncooled focal plane detector imaging device with MRTD\({}^{\prime}\) = 100 \(mK\) (F = 1) and MDTD\({}^{\prime}\) = 150 \(mK\) (F = 1) in telephoto mode, if the difference in objective transmittance is ignored, the MRTD-\(D_{NA}\) and MDTD-\(D_{NA}\) curves of the TMISs can be obtained, as shown in Figure 6.  
It can be seen from Figure 6 that the MRTD and MDTD of the microscopic mode are monotonically decreasing functions of \(D_{NA}\). When a 2x infrared microscope objective is used, its \(D_{NA}\) = 0.24, then we get MRTD = 4.34 MRTD\({}^{\prime}\) = 434 \(mK\) and MDTD = 4.34 MDTD\({}^{\prime}\) = 651 \(mK\). Because \(D_{NA}\) will decrease with the increase of the magnification of the microscope objective lens, combining Equations (33) and (35) and Figure 6 shows that the MRTD and MDTD of the TMISs increase with the increase of the magnification of the microscope objective lens; that is, the temperature resolution of the TMISs decreases with the increase of the magnification.  
Therefore, when designing the TMISs, it is necessary to consider the magnification and temperature resolution of the system comprehensively. Simply increasing the magnification of the objective lens to increase the spatial resolution of the system will cause the temperature resolution of the system to decrease. Therefore, when the system is applied to the field of microthermal analysis, it is necessary to consider improving the spatial resolution of the system by other means (such as adding micro-scanning equipment) on the premise of ensuring that the system also has a high-temperature resolution.
# Simple Ultrasonic-Based Localization System for Mobile Robots
## 2 Localization Options
Simultaneous Localization and Mapping (SLAM) are rarely considered separately; in most cases, a robot gradually adds new information from its sensor (lidar) to the map and uses map-based localization to determine its position. This is called simultaneous localization and mapping or concurrent mapping and localization (CML). Since localization errors affect the quality of the map and vice versa, the key problem of SLAM relates to the uncertainty of the robot's position and map. Probability methods are used to update the robot's knowledge of its position and environment. Two main approaches to probabilistic modeling have been used: Extended Kalman Filtering and Particle Filtering. Both approaches work sequentially; probability distributions are updated in so-called prediction and measurement steps corresponding to the robot's movement and sensor. Numerous SLAM methods have been implemented, some focusing on speed, others on accuracy, or other aspects [25; 26]. At our department, we focused on developing several different types of applications with mobile robots, whether it was group robotics with mobile robots or the development of walking platforms for mobile robots. Therefore, there was a need to use an easily integrable localization system into our prototypes for testing the behavior of robots in their operational environment [27; 28; 29; 30; 31].
###### Abstract
However, if Si diffuses into the SrM matrix during the high-temperature sintering process, the M\({}_{\rm S}\) could be lowered owing to the weakening of ferromagnetism. In our previous study [20], M-hexaferrite sintered bodies were manufactured using two different methods by changing the order of SiO\({}_{2}\) addition. In the first method (pre-Si process), 1 wt% of SiO\({}_{2}\) powder was added during the mixing of the initial raw materials, and in the second method (post-Si process), the same amount of SiO\({}_{2}\) powder was added as a sintering additive during the ball milling step after calcination. When the samples were sintered at T = 1250 \({}^{\circ}\)C, the M\({}_{\rm S}\) values decreased by 4.7% and 4.8% in the post-Si and pre-Si samples, respectively, compared to that of sample where Si was not added (non-Si). The coercivity (H\({}_{\rm C}\)) of both the pre-Si and post-Si processed samples significantly improved compared to the non-Si case. The results of previous studies suggested that Si diffusion into the SrM matrix causes a decrease in M\({}_{\rm S}\), and grain growth can be effectively controlled during sintering regardless of whether SiO\({}_{2}\) is added as a sintering additive (post-Si) or in the initial precursor mixing stage (pre-Si) [20]. In general, apart from SiO\({}_{2}\), sintering additives, such as SrCO\({}_{3}\) (or SrO) and CaCO\({}_{3}\) (or CaO), are also mixed in an appropriate ratio. The simultaneous additions of CaCO\({}_{3}\) and SrCO\({}_{3}\) are known to be beneficial in tailoring a dense microstructure with relatively fine microstructure [15]. The SiO\({}_{2}\) effectively suppresses grain growth and increases the H\({}_{\rm C}\) and the Ca or Sr oxides (or carbonates) promote the densification, which increases the residual magnetic flux density (B\({}_{\rm r}\)) of the magnet. Therefore, Si was used as a substitute rather than as a sintering additive in this study. For satisfying the charge neutrality conditions, Si\({}^{4+}\) and either Li\({}^{+}\), K\({}^{+}\), or Mg\({}^{2+}\) ions were co-substituted into the SrM structure to find an optimal substitution composition with excellent hard magnetic properties. Thus, nominal compositions of SrFe\({}_{12-2x}\)Si\({}_{x}\)Mg\({}_{x}\)O\({}_{19}\), Sr\({}_{1-y}\)Fe\({}_{12-y}\)Si\({}_{y}\)K\({}_{y}\)O\({}_{19}\), and SrFe\({}_{12-z}\)(Si\({}_{0.6}\)Li\({}_{0.6}\))\({}_{2}\)O\({}_{19}\) were designed. Notably, in the composition of SrFe\({}_{12-z}\)(Si\({}_{0.6}\)Li\({}_{0.6}\))\({}_{2}\)O\({}_{19}\), the substitution ions are expected to be partly employed in the interstitial sites because the total number of Li\({}^{+}\) and Siions is 20% more than that of the substitution site (Fe\({}^{3+}\)). For the sintered samples, changes in the microstructure and magnetic properties according to each substitution level were studied. For a substitution composition that exhibits optimal hard magnetic properties, anisotropic magnets were manufactured, and the performance of the permanent magnets was evaluated.
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 8 Simulation Set-Up and Results
### Results
#### 8.2.1 UAV @ 50 m  
In this section, we discuss the results when the UAVs are flying at a height of 50 m. This is a relatively lower altitude; therefore, the number of interfering elements is relatively lower.  
Figure 12 shows SINR values for UAV @ 50 m. When no link adaptation is employed, i.e., when the FPA method is used, the values vary from 6.52 dB to 7.15 dB. This is a relatively steady performance; however, the SINR values are the lowest compared to Q-learning and DQL. This is expected as it is a lower altitude and the interference conditions do not change drastically, except for a brief 27 s peak between the 26 s and 53 s marks. Q-learning starts with a higher value of 9.7 dB and even goes up to 11.6 dB with an average value of 10.45 dB. This is indicative of good performance for the link; however, after about 95 s, it suffers a drastic dip in performance in addition to presenting erratic patterns till the end of the simulation. This can be seen in Figure 12 as the annotated text in the red circle. When the simulation progresses, the Q-table becomes large and achieving the optimal solution becomes difficult. This is also reflected in the link latency as shown in Figure 13.  
Figure 11: CCDF vs. \(\delta_{eff}\) for FPA, tabular Q-learning, and DQL.  
Finally, DQL performs the best with average, minimum, and maximum values of 12.06 dB, 11.74, and 12.19 db, respectively, which is indicative of steady performance with high SINR values. It also shows a really low latency value of 25.61 ms. Consequently, interference is best mitigated by DQL amongst the three methods.  
Figure 12: SINR vs. time @ 50 m.  
Figure 13: Latency at 50 m.
## 3 Simulation Signal Analysis
### Numerical Simulation Analysis  
Generally, vibration signal is composed of a modulation component and a noise component. Therefore, the simulation signal is generated to verify the effectiveness of the improved total variation denoising method, which is listed as follows.  
\[x=x_{1}+x_{2} \tag{21}\]  
\[x_{1}=\sin(2\pi f_{1}t+\cos(2\pi f_{2}t)) \tag{22}\]  
where \(x_{1}\) is a typical modulating signal with \(f_{1}=15\) Hz and \(f_{2}=30\) Hz. \(x_{2}\) is Gaussian white noise, its mean value is 0 and variance is \(\delta=0.5\). The clean signal \(x_{1}\) and noisy signal \(x_{2}\) are shown in Figure 1, respectively. It can be seen that the modulation characteristics of the original signal in noisy signal are difficult to identify in Figure 1. Using the preferred regularization parameter selection method based on the weights between permutation entropy and cross correlation coefficient defined in Equation (18), the parameter can be selected as \(\lambda=1.3\).  
Figure 1: The clean signal and noisy signal with \(\delta=0.5\). (**a**) The time response of the clean signal; (**b**) The time response of the noisy signal.  
To verify the validity of the different denoising methods, the performance is evaluated by signal-to-noise ratio (SNR). Comparing with the traditional wavelet denoising method and the typical total variation denoising method using first-order total variation, the proposed second-order total variation noise reduction method by non-convex penalty function defined in Equations (9) and (10) is used for simulation signal analysis. It is important to note that the 'wden' function is chosen as the automatic 1-D denoising function and the wavelet basis function is set as'sym5' in wavelet denoising. Moreover, the wavelet basis function selection and parameter setting are the same in the remaining part of this paper. The analysis results provided by different methods are shown in Figure 2. We can make the conclusion that the proposed method can largely reduce the noise components from original signal and maintain the key characteristics to the greatest extent. The proposed improved total variation noise reduction method can acquire a larger value of SNR to 11.31 db under the condition of \(\delta=0.5\).  
In order to evaluate the effect of different noise level for the simulated signal, we also calculate the SNR after denoising by different methods in Table 1. It can be seen that the proposed method has better noise reduction performance than other methods.  
Figure 2: Comparison of the different denoising methods. (**a**) Simulation results of TV denoising; (**b**) Simulation results of wavelet transforms denoising; (**c**) The results provided by the proposed method.
# Load Forecasting Based on LVMD-DBFCM Load Curve Clustering and the CNN-IVIA-BLSTM Model
## 3 Methodology
### The Proposed CNN-IVIA-BLSTM Forecasting Model
#### 3.3.1 CNN: Convolutional Neural Network  
A CNN can automatically extract potential features between massive loads of continuous and discontinuous data to build a compressed complete feature vector for the top fully connected layer [40]. It is a hierarchical neural feedforward network and consists of a series of network layers with different functions. It mainly comprises the input layer, implied layer, and output layer. Among these, the implied layer takes the convolutional layer as the core, and the main function of the convolution layer is to extract the feature fusion of the input data as an arithmetic of the convolution layer. In addition, the CNN includes pooling layers (to reduce the number of parameters of the neural network) and the Dropout layer (to prevent data overfitting). The 1D convolutional kernel is two-dimensional and has a length and width; however, there is a sliding window in the width or height direction, and multiplication is transformed into addition. In this paper, a 1D CNN is used, with a convolutional kernel size of \(3\times 3\), a sliding window of 10, and a step size of 1. The overall structure of the convolutional network is shown in Figure 9. A convolutional block is a combination of \(M\) convolutional layers and \(b\) convergence layers. It can be stacked with \(N\) consecutive convolutional blocks followed by \(K\) fully connected layers.  
Figure 8: The gradient graph of _SSE_ with increasing cluster number \(q\) from 1 to 10.
# Point Cloud Deep Learning Network Based on Local Domain Multi-Level Feature
## 3 Proposed Method  
Previous research shows that some information of the point cloud will be lost if the max pooling operation is performed directly. In this paper, a local domain relational convolution is designed to further extract more features from the input point cloud data and weaken the useless features. The RS-CNN randomly samples from the raw point cloud data to construct local neighborhoods, and the convolution that can be directly applied in the point cloud is designed. In this paper, we first use graph convolution to extract features, and then obtain feature points containing more information. Then, we construct the local domain and extract features from feature points again by relational convolution.
## 3 Background and Related Works
### HIP DEX Packet's Format Analysis and Proposed Compression
#### 3.4.1 HIP Header Fix Parameters Compressibility
According to [19], _HIT_ fields can be reduced to 96 bits, due to the omission of the HIT prefix (the first 32 bits) and maintaining only the suitable generated value. We propose to minimize the _packet type_ field from seven bits to five bits, as proposed by the authors of [32]. As the packet type field cannot exceed the actual standardization of HIP DEX protocol because there are eight types of HIP packets, where the maximal value is 19 (the packet type values in II, R1, I2, and R2 messages are respectively 1, 2, 3, and 4. With UPDATE, NOTIFY, CLOSE, and CLOSE_ACK, the packet type field takes the values 16, 17, 18, and 19 respectively). The performed amelioration in the proposed compression in this paper and obtained results are presented in Table 2.
# Analysis and Design of Functional Device for Vehicular Cloud Computing
###### Abstract  
Relay technology application becomes prevalent nowadays, as it can effectively extend the communication distance, especially for vehicular networks with a limited communication range. Combined with vehicular cloud (VC), transmission efficiency can be improved by offloading partial data. Hence, designing a vehicle relay algorithm and implementation embedded vehicle device is critical. In this paper, VC is considered to deal with the complexity computation in our proposed system model. Without a loss of generality, an end-to-end vehicle communication with one assisted vehicle is analyzed firstly on a transmission link based on VC. Here, the signal-to-noise ratio (SNR) on the receiving end and link outage probability is obtained to enhance the link reliability. The VC computing helps us further simplify computational complexity. Subsequently, an embedded vehicle-enabled device is designed to achieve the optimal path relay selection in realistic vehicular environments. In the functional device framework, we display an optimal path relay selection algorithm according to the link quality. Finally, the performance of the transmission link on the outage probability related with SNR is verified in the simulation results. Meanwhile, the effect of the relay gain is also analyzed. The application of a vehicle-enabled embedded device could improve the performance of vehicular networks.  
relay-assisted; embedded device unit; optimal path; outage probability Article