esa-sceva/satcom-chunk-collection huggingface dataset:
# Similarity-Based Malware Classification Using Graph Neural Networks
## 3 Proposed Problem-Solving Mechanism
### Similarity Model
#### 3.5.2 Similarity Model Architecture  
This study traverses each sample and randomly selects one sample of the same family and a sample of one other family to form a pair. In conclusion, it generates one same family pair and one different family pair. Therefore, the number of sample pairs is twice the original sample number, and the pairs of the same family and other families each account for half. As shown in Figure 9, the similarity model has two graph neural networks with the same parameters and weights in the Siamese network. Malware sample pairing first uses GraphSAGE to learn the structural information. Secondly, Set2 Set calculates the graph feature of the sample graph. Thirdly, the vector is passed into the linear transformation layer and converted into a 32-dimensional vector. Then, the contrastive loss function yields the loss value as the distance between the embedding vectors of two samples. When the two samples are of the same family, a smaller distance between them corresponds to a smaller loss value. When the two samples are of different families, a greater distance between them correspond to a smaller loss value, which is zero when the distance exceeds the margin. Finally, the weights of GNNs are updated using contrastive loss backpropagation to make the model more accurate.  
Figure 8: Applied contrastive loss concept.  
Figure 9: Applied similarity model architecture.  
The similarity model is constructed, and the distances between the embedding vectors of the samples can be evaluated to determine whether the samples are of the same family. Table 2 presents the parameters of the similarity model in this study. The training learning rate is 10-3, the batch size is 64; the optimization function is Adam; and the Euclidean distance is used to calculate the distance in contrastive loss. Notably, the margin's condition is set to 1; the distance is 0.5 as the threshold value. Thus, a margin less than or equal to 0.5 belongs to the same family; otherwise, greater than 0.5 belongs to different families.
## 3 The Optimization and Reconfiguration of UDNs Incorporating VPPs
### Constraints  
The operation of UDNs incorporating VPPs must satisfy certain operational constraints, which mainly include power flow balance constraints, voltage and current constraints, DG power constraints, and VPP participation in demand response constraints.
## 2 Materials and Methods
### Analytical Methods  
Dissolved oxygen was determined using the Winkler method [41]. N-NH\({}_{4}\)\({}^{+}\)[42], N-NO\({}_{\mathrm{x}}\)[41], SRP [43], and DSi [44] were determined with standard spectrophotometric methods (Perkin Elmer, Lambda 35). An analytical blank that underwent the same procedure as the samples, including filtration, was always analyzed to correct for sample contamination.  
Chl-a and Pha were determined spectrophotometrically according to [45] from 1 cm\({}^{3}\) sediment slurry extracted with 10 mL 90% water-acetone.  
Organic matter was determined as loss on ignition at 550 \({}^{\circ}\)C with a standard procedure [46]. Total N content was determined using a CHN elemental analyser (CHNS-O EA 1108 Carlo Erba), while organic P was determined following [47].  
The BSi content of primary producers was analyzed according to [48]. In short, 30 mg of dry plant tissue were digested with 30 mL 0.1 M Na\({}_{2}\)CO\({}_{3}\) in polypropylene (PP) bottles. During the extraction, samples were constantly shaken for 4 h at 150 rpm in a shaking water bath at 85 \({}^{\circ}\)C. Before the analysis, each sample (1 mL of extraction solution) was neutralized with 9 mL of 0.021 M HCl and analyzed using the molybdate blue spectrophotometric method [44].  
The total BSi content of sediments was analyzed following [49], by digesting 30 mg of dry sediment with 30 mL of 0.1 M Na\({}_{2}\)CO\({}_{3}\) in PP bottles for 5 h at 85 \({}^{\circ}\)C. Subsamples were collected after 3, 4 and 5 h and analyzed using the molybdate blue spectrophotometric method [44]. Before the analysis, each sample (1 mL of extraction solution) was neutralized with 9 mL of HCl 0.021 M. To correct for the amount of Si resulting from mineral dissolution, the Si content of the subsamples was plotted against dissolution time. The \(y\)-axis intercept of the linear regression line represents the estimated BSi content. All BSi dissolved within the first 3 h.
## 5 Evaluation Metrics
### Spearman Rank-Order Correlation Coefficient (SROCC)
#### 5.1.1 Linear Correlation Coefficient (LCC)  
The linear connection between two variables is measured by LCC. It is used in BIQA to assess the relationship between expected and actual quality ratings. The following is a definition of LCC,  
\[LCC=\frac{\sum_{l=1}^{m}(p_{i}-\bar{p})(q_{l}-\bar{q})}{\sqrt{\sum_{l=1}^{m}(p_{l }-\bar{p})^{2}\sum_{l=1}^{m}(q_{l}-\bar{q})^{2}}}, \tag{10}\]  
where \(p_{l}\) and \(q_{l}\) are the forecasted and true quality ratings of the \(l\)th image, and \(\bar{p}\) and \(\bar{q}\) are the average of the predicted and ground truth quality scores. LCC has a value between \(-1\) and \(1\), with \(1\) indicating perfect positive correlation, \(0\) indicating no correlation, and \(-1\) indicating a perfect negative correlation.
## 4 Architecture Design
### Continuous ML Pipeline in an Emulation Environment
#### 4.1.4 Model Training and Deployment  
The sequence diagram (in Figure 7) illustrates model training and deployment in the system. The steps of the sequence are congruent with the conceptual view of research artifacts (Figure 2), and they are described in the following:  
* Steps 1.2-1.3: The OWAMP client is started for measurement of OWD during emulation. During/after emulation, measurement data are saved into a database (Figure 6) at the edge node.  
Figure 6: Class diagram illustrating a data viewpoint of the architecture.  
* Step 2.1: The ML engineer/data scientist manages ML pipelines, which includes mainly model training with different parameters. Model training is started.
* Steps 2.11-2.3: A model training job reads training data from the database and trains a prediction model. The model is logged into MLFlow.
* Steps 2.4-2.5: The ML engineer/data scientist may evaluate different versions of models via MLFlow UI. After evaluation, the most suitable model is selected for deployment.
* Steps 2.5-2.53 The model is transformed into ONNX format. Subsequently, the model file is transferred to RedisAI (with a RedisAI client), where the model is loaded into memory.
## 3 The Proposed Algorithm
### SARSA for Solving the MDP Model
#### 3.3.2 The Departure Call Event  
When a departure call event occurred (\(e_{t}=e_{n,k,t}^{end}\)), the current state was \(s_{t}=\left(X_{t},\ e_{n,k,t}^{end}\right)\). The channel represented by the performed action with the minimal Q-value was selected and then the channel occupied by the departure call was released. The policy for the departure call event was expressed by:  
\[\pi\left(s_{t}=\left(X_{t},\ e_{n,k,t}^{end}\right)\right)=a_{t}=arg\ min\ Q (s_{t},\ a). \tag{11}\]  
If \(a_{t}\) was equal to \(k\), the channel occupied by the departure call was directly released. If \(a_{t}\) was not equal to \(k\), channel \(k\) was reallocated to the call occupying the channel represented by \(a_{t}\). The policy for the departure call event is shown in Algorithm 3.  
```
Input:\(e_{t}=e_{n,t}^{ent}\), \(s_{t}=(X_{t},e_{t})\) Output:\(a_{t}\) if\(\tilde{A}(t)=\phi\)  rejected //reject the current new call event else if\(rand(\ )<\varepsilon\) //select the eligible channel with \(\varepsilon\) \(a_{t}=random(\tilde{A}(n))\) //select the eligible channel for the performed action else for\(a\in\tilde{A}(t)\) do if\(Q(s_{t},a)>Q(s_{t},a_{t})\) \(a_{t}=a\) //select the channel represented by the selected action end if \(X_{t+1}\gets x_{t}(t,a_{t})=1\) //transform the channel status endif
```  
**Algorithm 2**Policy for a new call event  
```
Input:\(e_{t}=e_{n,t}^{ent}\), \(s_{t}=(X_{t},e_{t})\) Output:\(a_{t}\) for\(a\in\{a_{t},\ x_{t}(n,t)=1\}\) do if\(Q(s_{t},a)<Q(s_{t},a_{t})\) \(a_{t}=a\) //select the channel represented by the action with the minimal Q-value endif if\((a_{t}==k)\) //judge whether the value of the selected action is \(k\) \(x_{t}(n,k)=0\) //release the channel occupied by the current departure call event else  reallocation( ) //reallocate channel \(k\) to the call occupying the channel \(a_{t}\) endif
```  
**Algorithm 3**Policy for a departure call event
# Low-Level Laser Therapy for Tooth Sensitivity after Tooth Bleaching: A Systematic Review
## 1 Introduction
One mechanism involves the stimulation of the Na\({}^{+}\)/K\({}^{+}\) pump, which maintains the electrical balance across the cell membrane, inducing hyperpolarization, increasing the nerve pain threshold, and reducing sensitivity to stimuli [17]. Another theory suggests that laser application can completely inhibit the depolarization of A delta and C fibers [18], thereby suppressing the generation and transmission of nerve impulses and promoting direct nerve analgesia [19]. Additionally, lasers can inhibit the release of agents such as bradykinin, which sensitize pain receptors, while also increasing the production of endorphins, the body's natural pain-relieving substances. Lasers also promote tissue repair and reduce inflammation by enhancing local microcirculation and lymphatic drainage. Finally, they can stimulate pulp mesenchymal cells to differentiate into odontoblasts, producing reparative dentin [20; 21]. Low-level laser therapy (LLLT), also known as photobiomodulation (PBM), is a therapeutic technique that uses low-intensity lasers (1-500 mW) at specific wavelengths within the visible to near-infrared spectrum (600-1064 nm) to stimulate cellular function. Diode lasers are preferred for LLLT because they can penetrate to varying depths and deliver targeted light with minimal heating of the surrounding tissues. When the laser interacts with the target tissue, a photobiological reaction occurs involving the absorption of the specific wavelength by a photoreceptor molecule [22]. This initiates a cascade of biochemical reactions in the cells, primarily targeting the mitochondria and specifically cytochrome-c oxidase, leading to increased production of adenosine triphosphate (ATP). Various other cellular processes are stimulated, including modulation of signaling pathways, reduction of pro-inflammatory mediators, increase in anti-inflammatory mediators, proliferation of fibroblasts, endothelial and mesenchymal stem cells, reduction of oxidative stress, and modulation of reactive oxygen species (ROS) levels [23]. It has also been noted that LLLT can alter gene expression patterns within cells [24]. Consequently, LLLT, with its analgesic,vascular, biomodulatory, and anti-inflammatory effects, can promote tissue repair and wound healing [25]. The quest to determine whether LLLT can effectively alleviate tooth sensitivity following tooth bleaching has gathered significant attention in recent years. Despite some investigations that have been conducted in the past few years, the lack of standardized treatment protocols has led to varied findings across studies and thus to a lack of the robust evidence needed for conclusive recommendations. In the present systematic review, we apraise data provided by all available primary RCTs in order to evaluate whether LLLT is suitable for treating tooth sensitivity when administrated after in-office bleaching therapy.
## 2 Model and Parameter Estimation
### Positioning Function Model
\right]=\left[\begin{array}{ccccc}\frac{x_{ 0}-x^{1}}{\rho_{0}}&\frac{y_{0}-y^{1}}{\rho_{0}}&\frac{z_{0}-z^{1}}{\rho_{0}} &1&M_{uert}^{1}&0&\cdots&0\\ \frac{x_{0}-x^{1}}{\rho_{0}}&\frac{y_{0}-y^{1}}{\rho_{0}}&\frac{z_{0}-z^{1}}{ \rho_{0}}&1&M_{uert}^{1}&1&\cdots&0\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ \where \(M_{wet}\) is the tropospheric wet delay mapping function; \(dZTD_{\omega}\) is the zenith tropospheric wet delay; \(B\) is the ambiguity parameter. Formula (5) is abbreviated as \(\mathbf{y}=\mathbf{G}\mathbf{x}\).
## 2 Materials and Methods
### Quantitation of Benzenemethanethiol in the Fermentation Broth
#### 2.6.3 GC-SCD Analysis  
For the analysis of benzenemethanethiol generated by different yeast strains during fermentation, gas chromatography-sulfur chemiluminescence detection (GC-SCD) was employed. After the fermentation process was complete, 10 mL of the sample was sampled and centrifuged at 8000 r/min for 10 min at 4 \({}^{\circ}\)C before analysis. The supernatant was then harvested and preserved at \(-\)20 \({}^{\circ}\)C. Next, 8 mL of the supernatant was transferred into a 20 mL headspace vial containing 3 g of sodium chloride. A 75 \(\upmu\)m (CAR/PDMS) fiber was used to absorb the target compounds, and the sample was equilibrated for 30 min and extracted at 30 \({}^{\circ}\)C for 30 min. After extraction, to allow for desorption of the analytes via the split-less mode, the fiber was inserted into the injection port of the GC (250 \({}^{\circ}\)C) for 7 min.  
GC-SCD analysis of benzenemethanethiol was conducted on an Agilent 7890A GC (Agilent Technologies Inc., Santa Clara, CA, USA) and an SCD detector 8355. The instrument was equipped with a DB-WAX capillary column (30 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m film thickness). High-purity nitrogen served as carrier gas at a flow rate of 1 mL/min. The oven temperature program was as follows: the temperature was maintained at 50 \({}^{\circ}\)C for 2 min, increased to 150 \({}^{\circ}\)C at a speed of 3 \({}^{\circ}\)C/min, raised to 230 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min, and maintained for another 10 min [13]. For the Agilent 8355 SCD, the burner temperature is set at 800 \({}^{\circ}\)C, the base temperature is set at 200 \({}^{\circ}\)C, oxidant flow and hydrogen flow are set at 50 and 46, respectively. Chemical identification was conducted using a comparison of the retention times of target compounds on the columns with the authentic standards. Semi-quantization was conducted in line with the previous articles published by research groups [9; 22]. Disopropyl disulfide served as the internal standard (IS) and used at a final concentration of 1.35 mg/L in samples.
# Structural Design and Study of an Integrated Cutter System Based on Machine Operation
## 1 Introduction
The reaction force of the cutter breaking the rock is firstly transmitted to the cutter body through the cutter ring, then to the cutter shaft by the bearings in the cutter body and finally to the cutter holder, which is responsible for fastening the cutter shaft. Prolonged exposure of the cutter system to vibration can cause the fasteners in the system to loosen, which in turn can cause the cutter to disengage. This increases construction costs and may even lead to risky accidents. Therefore, when evaluating the reliability of a new cutter system, the fastening performance of the cutter holder structure is an important evaluation index. At present, in the field of anti-loosening performance testing, universal test standards and testing methods applicable to complex fasteners, such as the integrated cutter system, have not been established. In the structural program of the EICS, since the fastening of the structure relies on the connection between the screw and the locking wedge, the anti-loosening performance of the screw is directly related to the fastening performance of the structure. The main forms of failure of the bolt connection are loosening, fatigue fracture, corrosion, etc., loosening occurring with the highest probability. The essential reason for bolt loosening is that with the growth of service time, the bolt preload gradually decreases to the thread self-locking limit, and relative displacement between the bolt and the nut begins to occur, resulting in a significant decrease in the fastening capacity [15]. In summary, the structural design of integrated cutter systems is still in the exploratory stage. In the face of the harsh working environment of shield tunneling machines, the reliability of the structural design of most cutter systems needs to be tested in actual engineering applications. In addition, most of the design schemes do not establish evaluation systems for cutter systems, such that the advantages of integrated cutter systems over traditional cutter systems cannot be visually compared. Therefore, this paper proposes an eccentric integrated cutter system and objectively evaluates the three cutter schemes based on the analytic hierarchy process (AHP) [16]. Finally, a vibration test is designed to verify the anti-loosening performance of the integrated cutter system. The main contents of this paper are as follows. The second part concerns the design of the integrated cutter system, which mainly includes the structural design and working principle of the three cutter systems. The third part describes the establishment of the analytic hierarchy process and the evaluation process for the three cutter solutions. The fourth part tests the anti-loosening performance of the integrated cutter system by vibration testing. The fifth part provides a summary and future prospects.
## 4 Experimental Results
### SNR vs. Normalized Misalignment (NM) of the PAPMCC Algorithm  
NM versus SNR was used to analyze the performance of the devised PAPMCC under colored input for estimating network echo channel. The performance results of the PAPMCC with various SNRs are presented in Figure 10, which shows that the estimation error decreased as the SNR increased  
Figure 8: Performance comparisons of the proposed PAPMCC algorithm. Input signal: colored.  
Figure 9: Performance comparisons of the proposed PAPMCC algorithm. Input signal: speech signal.  
from 0 to 20 dB. Clearly, the steady-state performance of the PAPMCC was significantly better than the related algorithms in low SNR environments.
## 2 Study Area and Data
### Data
#### Time-Related Data  
In the study area, forest fire occurrences were concentrated in spring and winter while they were not frequent in summer and fall. Therefore, day of the year (DOY) was used as a variable to consider the seasonality of forest fires. DOY has been used as a variable after converting through sine or cosine functions to consider seasonality in many studies [42; 43]. In this study, the sine function was applied to DOY, which generated a value between \(-1\) (winter) and \(1\) (summer) with one year period.
# Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network
## 2 Additive Manufacturing Parts Inspection
### Convolutional Neural Network (CNN) Architecture
#### 2.4.2 Training Details  
The experiments in this work were conducted with one six-core Advanced Micro Devices (AMD, Santa Clara, CA, USA) Ryzen 5 2600 processor and one Nvidia (Santa Clara, CA, USA) GeForce 1070 GPU. The code was developed in Python 3.6.8 using TensorFlow (version 1.13.1) and Keras (version 2.2.4). Some parameters were common in all experiments and are described here. A batch size of 32 and a learning rate of \(1\times 10^{-4}\) were used. Each convolutional layer was followed by a max pooling layer with a filter size of \(2\times 2\) and a stride of 1. Batch normalization was used for centering and normalization of the images [40] and applied before the fully connected layers. An Adam optimizer [41] was used in the training process. Each network was identified with a unique Model #.
# Calibration of a Low-Cost 8\(\times\)8 Active Phased Array Antenna
## 3 Active Phased Array Calibration
### Passive Antenna Measurement  
The comparison between simulated and measured reflection coefficients of each column are presented in Figure 6. The simulated reflection coefficients are below \(-14\) dB for all the columns; however, column 3 of the prototype is not as well manufactured as the rest of the columns, resulting in a worse reflection coefficient. But, in our working band, it is below \(-10\) dB. The difference between simulation results and measurement results may be due to the following reasons:  
* Since the connector has a certain bend, it may be that the End Launch connector is not directly in the center of the feeding microstrip line of the columns.
* In the process of adhesive bonding of the PCBs, the alignment is not very precise and, when pressing the PCBs, the heights of the prepreg and foam were affected, differing from the height of the simulation.  
To evaluate the unbalanced amplitude and phase between columns, the parameter S\({}_{21}\) for each column is measured in the UPM Spherical Near Field System using a standard gain horn (SGH) as transmitter. Column 5 is selected as reference and the rest of the columns are normalized with respect to the reference. In Figure 7, the normalized unbalanced amplitude of the passive subarray is \(\pm 1.5\) dB in band. Regarding the phase difference between columns is \(\pm 30^{\circ}\). These discrepancies are caused by the manufacturing process. Since the prepreg bonding of the PCB may not adhered precisely, this could be one of the explanations for the encountered discrepancies (a line with an extra of 1 mm results in an extra of 11.37\({}^{\circ}\) in the transmission phase). Therefore, in the process of integrating  
Figure 5: Beamformer control and power supply schematic.  
the beamformer and the passive antenna, we have to compensate for the errors in the manufacturing of the passive antenna.
## 2 Related Evaluation Methods  
### AHP Method  
The AHP method is a systematic method that combines qualitative and quantitative analyses, particularly effective for complex systems with multiple evaluation indicators. Its core idea is to decompose a complex objective into hierarchical levels, simplifying the evaluation process by analyzing each level individually. This approach mirrors human decision making, reducing complexity and difficulty. The AHP method is widely used due to its intuitiveness, ease of understanding, and adaptability to diverse analytical requirements [28; 29; 30; 31].