esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Problem Solution
### Power Control Scheme
Therefore, in solving the subproblem **SP2**, we improve the simulated annealing algorithm to solve the power control scheme to accelerate the cooling process of the system. Specifically, if the system finds a better feasible solution during the iterative search at the current temperature, we give the system an additional cooling rate \(v_{decrease}\) to accelerate the system annealing; when the iteration at that temperature ends, if the system does not find a better feasible solution, then the system continues the annealing process with the original cooling rate \(V_{decrease}\) to find a better solution. We summarize the improved power control scheme based on the simulated annealing algorithm in Algorithm 2. In Algorithm 2, the system is first initialized and the initial solution is randomly generated, the value of the objective function corresponding to the initial solution is calculated (lines 2-4), and the process of outer loop annealing is started. Next, at the current temperature, it enters an inner loop to generate new feasible solutions and uses the Metropolis criterion to determine whether to accept the new feasible solutions until the end of the inner loop when the number of iterations is reached (lines 9-22). Then, the temperature is updated using the improved strategy (lines 23-26) and re-entered into the inner loop until the current temperature is lower than the termination temperature, the outer loop is ended and the algorithm is terminated to obtain the optimal power control scheme for all PDs under the current time slot. ```
1:Input: PD power maximum \(P_{\max}\) and the minimum transmission rate requirement of the PDs \(R_{th}\). Output: Optimal power control scheme \(\{\mathbf{P}(t)\}\). 2:Initialize: Initial temperature \(T_{0}\), original cooling rate \(V_{decrease}\), additional cooling rate \(v_{decrease}\), termination temperature \(T_{end}\) and the number of iterations \(L_{iteration}\). 3:Randomly generate a random initial solution \(\boldsymbol{p}_{1}\) to the subproblem **SP2**. 4:Calculate \(f(\boldsymbol{p}_{1})\). 5:repeat
6:for temperature \(T_{0}\)do
7: Set \(k=0\), \(r=0\). 8:repeat
9: Randomly generate a random initial solution \(\boldsymbol{p}_{2}\) to the subproblem **SP2**. 10: Calculate \(f(\boldsymbol{p}_{2})\). 11: Calculate \(\Delta f=f(\boldsymbol{p}_{2})-f(\boldsymbol{p}_{1})\). 12:if\(\Delta f>0\)then
13:\(\boldsymbol{p}_{1}=\boldsymbol{p}_{2}\). 14:\(r=r+1\). 15:else
16: Generate a uniformly distributed random number \(\partial\) in the interval [0,1]. 17:if\(\frac{\Delta f}{v_{0}}>\partial\)then
18:\(\boldsymbol{p}_{1}=\boldsymbol{p}_{2}\). 19:endif
20:endif
21:\(k=k+1\). 22:until\(k>L_{iteration}\). 23:if\(r>0\)then
24:\(T_{0}=T_{0}\times v_{decrease}\). 25:endif
26:\(T_{0}=T_{0}\times V_{decrease}\). 27:endfor
28:until\(T_{0}<T_{end}\). 29:Return\(\boldsymbol{p}_{1}\). ```  
**Algorithm 2** Improved power control scheme based on simulated annealing algorithm
## 4 Findings and Discussion
### ITS/IoV Intrusion Detection Systems (IDS)
#### 4.1.2 Application of XAI-Based IDSs for ICVs  
The explainability and call for the trustworthiness of the ML have motivated the need for XAI. Table 3 is a summary of some current studies on XAI-based IDS for ITS, showing their performance, approach, and year of publication.  
The works of [54] proposed the use of SHapley Additive exPlanations (SHAP) to enhance the transparency and resilience of deep learning-based IDS for IoV networks. This approach gave an accuracy of 99.15% regarding its ability to protect IoV networks against attacks. However, the major obstacle is the challenge of computational complexity.  
Recently (September 2022), the authors in [102] employed the XAI for trustworthy Invehicle IDS leveraging CAN bus data. They further enriched the body of XAI by proposing a novel \"VisExp\" approach for visualizing the explanations necessary for gaining trust in the system. However, the limitation of the approach is the reliance on a survey formatted using Google forms. This has the potential of skewing the results.  
In [103], the authors proposed a rule-based XAI leveraging the adaptive neuro-fuzzy inference system (ANFIS) for understanding unmanned aerial vehicles (UAV) maneuvering decisions. In addition, they developed a human-computer interface (HCI) where the outputs of the ANFIS are translated to linguistic values. This is a promising approach to aiding the monitoring and mitigation of possible intrusion into UAV networks.  
Interestingly, the authors of [104] proposed novel XAI-based deep learning solutions for IDSs in the IoT generally not limited to the IoV. However, the approach is similar to other authors as they used SHAP and RuleFit. The use of SHAP has enjoyed dominance due to its ease of use and flexibility in generating the basis for the visualization of explainable results [86].
## 2 Methods
### Path Optimization
#### 2.4.1 Collision Detection  
In terms of collision detection, the end effector trajectories are first input into a simulation environment built based on the real working environment of the robotic arm. The simulated robotic arm moves along the planned path, and the interference between each link and obstacle is detected. This process can generally be carried out using a geometric envelope method. As shown in Figure 7, the robotic arm is wrapped with simple geometric shapes, and collision detection is performed using envelope models. The advantage of this method is that it requires less computational effort for collision detection, as it only needs to calculate the minimum distance between the envelope model and the obstacles to determine if a collision occurs. If a collision is detected at a certain point along the trajectory, the inverse kinematics of the robotic arm at that point can be traversed to check if all joint combinations lead to collisions. If a collision-free inverse solution cannot be found, the trajectory is discarded.
# Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder +
## 4 Our Proposed Method
### Convolution and Pool Layer  
After feature learning via the stacked sparse autoencoder, the convolution and pool kernel is utilized to preserve the local relevance of features. Given \(x^{(l)}\in R^{m_{1}\times m_{2}\times d}\) are the whole sample representation of both source and target domain maps of layer \(l\), where \(m_{1}\) and \(m_{2}\) represent the height and width of each input map, respectively, and \(d\) represents the number of channels. The patches \(P\in R^{(n_{1}\times n_{2}\times d)\times K}\) are extracted from \(x^{(l)}\) to compose the training set for learning latent features, where \(K\) denotes the number of patches, \(n_{1}\) and \(n_{2}\) are the size of patches, respectively, and \(n_{1}\times n_{2}\times d\) denotes the convolution kernel size. Each input is reshaped to the vector of \((n_{1}\times n_{2}\times d)\times 1\) for the convenience of training the autoencoder. The number of neurons in hidden layer 1 can be manually designed.  
After the convolved features are extracted, we divide the input features into disjoint \(n_{1}\times n_{2}\) regions, and the mean (or maximum) activation function is utilized to obtain the pooled convolution feature representations, where \(n_{1}\) and \(n_{2}\) denote the size of patches. Different pooling methods are selected for different distributed datasets. For example, the mean pool objective function is (12):  
\[P=\frac{P-mean(P)}{\text{std}(P)} \tag{12}\]  
In the experiments performed, the parameter \(\theta\) in the objective function \(J(\theta)\) is updated as (13):  
\[\theta=\theta-\gamma\
abla_{\theta}J\left(\theta;x^{(i)}\right) \tag{13}\]  
where \(x_{i}\) is derived from the projection and \(\gamma\) is the learning rate, which is usually much lower than the corresponding learning rate in batch gradient descent due to larger variance in the update. In the experiments undertaken, the momentum method is introduced to rapidly facilitate the objective along the shallow ravine.  
\[\begin{split} v&=\phi v+\gamma\
abla_{\theta}J \left(\theta;x^{(i)}\right)\\ \theta&=\theta-v\end{split} \tag{14}\]  
where \(v\) is the current velocity vector with the same dimension as the parameter vector \(\theta\). \(\phi\in(0,1]\) determines how many iterations from the previous gradients are incorporated into the current update.  
It is of note that the pooling operation can both reduce the representation dimensions and select more significant features. For example, the pervasive pooling tools, such as max-pooling [49], mean-pooling [50] and stochastic pooling [51] have achieved promising performance in feature representation learning. Therefore, in the experiments, two different pooling tools, max and mean pooling, were used according to the distribution of the datasets.
## 3 Developed Equipment and Experimental Setup  
### Schematic of Developed Equipment  
Commonly employed FRF tests are performed by attaching an accelerometer to the structure and striking the measurer with an impact hammer by the user. However, due to the inaccessibility of the porcelain insulator owing to the field conditions in which high voltages pass, it is necessary to develop suitable equipment that can measure over long-distance ranges according to basic operating principles. The device developed in this study is shown in Figure 2. The developed device is divided into three parts: the head, including the auto impact hammer and signal receiver; the body, made to extend the length of the insulating material; and the tail, consisting of the impact button and handle. It also includes a controller that can adjust the impact strength of the auto impact hammer and a device for power supply. The auto impact hammer (330 AE-05) in the head region, manufactured by AISYSTEMS Ltd. In Korea, can control the impact strength up to 100 N, and includes a force sensor to measure the input energy during striking. The MEMS microphone (Zero-Height SiSonicTM Microphone) was manufactured by KNOWLED Ltd. In USA, and reduces weight for user convenience in the signal receiver. The contactless MEMS microphone was used in consideration of field situations where direct  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Categorization** & **Cristobalite** & **Alumina** & **Sum** \\ \hline Total & 63 & 25 & 88 \\ Normal & 59 & 22 & 81 \\ \hline \multirow{3}{*}{Defect type} & Sub total & 4 & 3 & 7 \\  & Cap & 3 & 3 & 6 \\ \cline{1-1}  & A. P & 1 & - & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Categorization of specimens according to type of material and defect.  
contact is difficult [28]. Four sensors are used to reduce data measurement errors and ensure data reliability, and the frequency range of the sensors is 0 to 80 kHz. The insulation stick of the body part is made to extend up to 5 m.  
The signal conditioner (PCB 482C16) and DAQ (NI PXIe-6366) were used in the developed device for data acquisition. The measurement program used NI Labview signal express to store data at a sampling rate of 500 kS/s, which was analyzed using the MATLAB signal process toolbox, because the stored data are values in the time domain.
## 4 Experiment
### Experimental Setup and Evaluation Metrics  
During the training process, we utilized BERT-base [17] and BERT-tiny [22] as pre-trained language models. BERT-base is a pre-trained deep bidirectional Transformer model with 110 million parameters, consisting of 12 Transformer encoder layers and 768 hidden units. On the other hand, BERT-tiny is a smaller-scale pre-trained bidirectional Transformer model. Compared to BERT-base, BERT-tiny has a smaller model size with only two Transformer encoder layers and 10 million parameters, making it suitable for resource-constrained environments. We trained the CAKGC model with the BERT-base version on an NVIDIA GeForce RTX 3090 GPU (NVIDIA, Santa Clara, CA, USA) and the BERT-tiny version on an NVIDIA Tesla K80 GPU (NVIDIA, Santa Clara, CA, USA).  
In the entity prediction task of knowledge graph completion, MRR, Hits@1, Hits@3, and Hits@10 are commonly used evaluation metrics to measure the performance of models. They are defined as follows:  
MRR: MRR is calculated by sorting the candidate entity list for each query entity in descending order based on the predicted probabilities from the model. Then, the reciprocal rank of the correct entity for each query entity in the candidate entity list is computed.  
\[MRR=\frac{1}{|S|}\sum_{i=1}^{|S|}\frac{1}{rank_{i}} \tag{19}\]  
where \(N\) is the number of candidate entities and \(rank_{i}\) represents the rank of the correct entity in the candidate entity list based on the descending order of probabilities.  
Hits@k: Hits@k is calculated by sorting the candidate entity list for each query entity in descending order based on the predicted probabilities from the model. Then, it checks if the correct entity is present within the top k predicted entities for each query entity. Hits@k focuses solely on whether the correct entity is among the top k predicted entities.  
\[Hits@k=\frac{1}{|S|}\sum_{i=1}^{|S|}\Gamma(rank_{i}\leq k) \tag{20}\]  
where \(N\) represents the number of candidate entities, and \(I(x)\) takes values from the set \(\{0,1\}\). Specifically, when \(rank_{i}\leq k\), \(I\) takes the value of \(1\); otherwise, \(I\) takes the value of \(0\).
## 3 Results and Discussion
### Infrared Spectroscopic and Chemical Analysis
The disappearance of the bands at about 3010 and 1654 cm\({}^{-1}\) originated from vibrations of -CH=CH- (cis), which led to the conclusion that these bonds took part in the polymerization reaction, contributing to a permanent connection of these structural elements. Some additional insight into the chemical properties of the synthesized materials was provided by an analysis of characteristic properties such as acid values and iodine values. Table 3 presents the values obtained for the prepared materials. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **AV** & **IV** \\
**Polymer** & **(mg KOH/g)** & **(mg I2/100 g)** \\ \hline C-DVB & 9.26 & 19.61 \\ L-DVB & 4.12 & 31.59 \\ P-DVB & 7.21 & 10.04 \\ Ao-DVB & 21.24 & 17.00 \\ Lo-DVB & 43.90 & 19.77 \\ Ro-DVB & 22.86 & 20.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristic chemical parameters: acid values (AV) and iodine values (IV). The acid value indicates the presence of free carboxylic acids in the polymer sample. Polymers C-DVB, L-DVB, and P-DVB carboxylic species can arise as an oxidation product of methyl groups or some C=C bonds, and in the case of citral derivatives, also by oxidation of aldehyde groups. Such processes are undesired side reactions. Slightly higher results of acid values were obtained for the oil-based polymers, which were a consequence of the slow hydrolysis of fats heated in the aqueous solution during synthesis. The course of such a side reaction was also proved by the above-described broadening of the characteristic band of the ester group. The iodine value is informative of the availability of residual C=C in the polymer network. During the determination, bromine, and iodine contained in the Hanus reagent yielded quantitative addition to unsaturated bonds in the polymer network. The obtained results were within the range of 10-32 mgl\({}_{2}\) per 100 g of a polymer sample, which is much lower than for vegetable oils (Table 1). Low values of IV indicate a high conversion of the reactants. However, non-zero values mean that some amounts of unsaturated bonds remained in the polymer structures. These could be equally unreacted double bonds of monomers or bonds that arose during the termination of polymerization, e.g., as a result of disproportionation reactions.
# Automatic Medical Image Segmentation with Vision Transformer
## 2 Related Works
### Medical Segmentation Studies  
Medical segmentation tasks pose several challenges, including the scarcity of manual annotations and high memory requirements for processing high resolution image scans. In contrast with natural images frameworks like DeepLab [16] and PSPNet [17], U-Net [2]-based architectures like VNet [18], U-Net++ [19], HDenseU-Net [20], and nnU-Net [21] demonstrate superior performance in preserving fine anatomical details while being more memory efficient.  
Attention mechanisms have gained popularity for U-Nets in medical image segmentation, enabling models to better capture informative features and spatial dependencies [22]. Recurrent attention in RAAGR2-Net [23] focuses on relevant regions during segmentation. Multi-scale attention gates in M-Unet [24] extract local and global context across scales. PsLSNet [25] leverages position-sensitive self-attention to model intricate spatial relationships. Additionally, MSAU-Net [26] attends to features across multiple scales simultaneously, while SegR-Net [27] reweights features spatially and channel-wise via attention. U-NetPlus [28] incorporates attention gates and dense skip connections for enhanced information flow. Moreover, U-Det [29] jointly performs detection and segmentation by integrating attention. Overall, these various attention mechanisms improve U-Nets for medical images by enabling more effective feature representation and spatial dependency modeling, leading to gains in segmentation accuracy.  
Traditional semi-supervised medical image segmentation methods primarily rely on handcrafted features to contrive models, including the prior-based [30] and clustering-based models [31]. Performance often depends heavily on the quality of the feature representation. Adversarial learning is also leveraged to exploit the unlabeled data. For instance, with uncertainty guidance [32], the mean teacher model [33] is extended for left atrium segmentation in a semi-supervised way. [PERSON] et al. [34] introduced a shape-aware strategy leveraging unlabeled data and geometric constraints. However, our method synergizes local precision and global modeling via a simple yet effective CNN-transformer fusion tailored for medical images. By combining complementary strengths, it achieves accurate analysis without hand-tuning features or complex adversarial objectives. Unlabeled data improve the consistency regularization.
## 3 Spectrum Management Issues
### Cognitive Radio
#### 3.2.1 Spectrum Sensing  
Another spectrum sensing approach has been presented for real-time configuration [116]. The idea is to utilize the Filter Bank Multicarrier (FBMC) approach, which is based on the adjustment by using the non-linear fractional program and stationery Karush-Kuhn-Tucker (KKT) condition. It helps achieve the efficient utilization of network resources that can be used for IoT applications. The authors have suggested that in the future, various bio-inspired optimization techniques can be used for efficient CR integration. Another method to minimize interference and enhance sensing accuracy is discussed in [117]. It is a group-based multichannel synchronized spectrum sensing approach that is based on the Dynamic Multi-Channel Slot Allocation (DMCSA) algorithm. The idea is to introduce a special entity, i.e., a spectrum agent that is used to execute only spectrum sensing and report to the fusion center. The extracted results have proved that the proposed technique could deliver optimal performance in terms of throughput, detection probability, delay, and sensing overhead.
## 3 Results and Discussion
### Structural Analysis  
Structural analyses of as-synthesized materials and sintered pellets were performed using XRPD and the diffraction patterns are presented in Figure 1. The phases are indexed to Bi\({}_{2}\)Te\({}_{3}\) (ICDD: 01-082-0358) and Sb\({}_{2}\)Te\({}_{3}\) (ICDD: 01-071-0393) with rhombohedral crystal structure, and the corresponding indexing is designated on the powder diffraction patterns in Figure 1a. No impurity peaks are observed, revealing the high purity of the as-made materials. Due to the similarity of reduction potential of Bi\({}^{3+}\) and Sb\({}^{3+}\) ions, step-wise transformation of the synthetic chemistry from pure Bi\({}_{2}\)Te\({}_{3}\) to pure Sb\({}_{2}\)Te\({}_{3}\) phase was very smoothly handled at the reaction temperature. The shift of the position for the major peak indexed to (015), going from Bi\({}_{2}\)Te\({}_{3}\) to Sb\({}_{2}\)Te\({}_{3}\), can be observed in Figure 1b, which is ascribed to systematic shrinkage of the Bi\({}_{2}\)Te\({}_{3}\) lattice by the introduction of Sb, as Sb\({}_{2}\)Te\({}_{3}\) has slightly smaller lattice dimensions (a = b = 4.395 A for Bi\({}_{2}\)Te\({}_{3}\) and 4.264 A for Sb\({}_{2}\)Te\({}_{3}\), crystal structure, as displayed in Figure S4a). Based on the lattice parameters the shrinkage in a and b corresponds to about 3% by increasing x (in Bi\({}_{2-x}\)Sb\({}_{x}\)Te\({}_{3}\)) from 0 to 2, while going from Bi\({}_{2}\)Te\({}_{3}\) to Sb\({}_{2}\)Te\({}_{3}\)_following Vegard's rule_ (see Figure S4). The width of the diffraction peaks with the same index vary from sample to sample, which is ascribed to the small crystallite size and non-uniform strain within the materials. XRPD analyses performed on the sintered samples showed narrower diffraction peaks, variation of peak intensities from powder samples, due to the crystal/grain growth (Figure 1c), and reduced strain within the material upon exposure to high T and pressure for a brief period.  
Figure 1: XRD powder patterns of powder Bi\({}_{2-x}\)Sb\({}_{x}\)Te\({}_{3}\) samples: (**a**) in the 2\(\theta\) range of 15–60; and (**b**) 2 \(\theta\) range of 26–29, focusing on the peak with the miller indices of (015), showing the shift of the peak position towards larger 2\(\theta\) values due to shrinking lattice by substituting Bi with Sb. (**c,d**) The XRD patterns of the sintered pellets measured perpendicular to the pressing direction. The crystalline phases are indexed to Bi\({}_{2}\)Te\({}_{3}\) (ICDD: 01-082-0358) (for x = 0) and Sb\({}_{2}\)Te\({}_{3}\) (ICDD: 01-071-0393) (for x = 2) with rhombohedral crystal structure.
# Investigation of the Maturity Changes of Cherry Tomato Using Magnetic Resonance Imaging
## 2 Materials and Methods
### MRI System and Pulse Sequence  
An industrial-grade MRI system (M10, ASPECT Magnetic Technologies Ltd., Shoham, Israel) installed at Jeonbuk National University in Republic of Korea was used to acquire a series of MR images of intact fresh fruits nondestructively. The MRI system was equipped with a permanent magnet, which has 1 Tesla magnetic field strength, and a radio frequency coil holding up to 80 mm outer diameter sample. A Gradient Echo (GRE) pulse sequence with three-dimensional (3D) capability was used to acquire a series of 20 two-dimensional (2D) image sets of a target sample. The repetition and echo times were set to 40 and 15 ms, respectively. The pixel size of the MR image was \(256\times 256\) pixels. The slice thickness was 0.001 m and the field of view (FOV) of the acquired image was \(0.05\times 0.05\) m. Thus, we obtained 3D volume data for cherry tomato with spatial voxels of \(3.8\times 10^{-11}\) m\({}^{3}\). The acquisition duration of a series of 20 MR images was 388 s. A total of 54 MR image datasets were acquired and examined to investigate internal structure changes of cherry tomato with different maturity stages. Figure 2 shows a series of 20 cherry tomato MR images with an olive oil reference tube, the oil reference was used for checking sample loading direction and magnet homogeneity.  
Figure 1: Cross-cut-section photos of all samples showing different maturity stages.  
Figure 2: Cherry tomato magnetic resonance images.