esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Linear Classification Evaluation Protocol  
One common yet rough way to evaluate self-supervised learning methods is to carry out tests under the so-called linear classification evaluation protocol [8; 51]. The learned representations by self-supervised learning were utilized directly by a linear classifier on the main task, which was, in our case, the discrimination between CG images and NIs. More precisely, for a neural network (ResNet50 in our study) trained after self-supervised learning, we replaced the last layer of neural network by an appropriate linear layer and trained the linear layer on the main task of CG forensics, while keeping the other parameters of neural network frozen. This meant that only the new linear layer/classifier was trained on the training samples of the CG forensics datasets of Artlantis, Autodesk, Corona and VRay mentioned above, while the other layers of neural network obtained after self-supervised pre-training were untouched. Table 2 presents the experimental results for this linear classification evaluation protocol, where our proposed method is compared with several popular and representative self-supervised methods: [PERSON] et al.'s method [38], based on the image rotation prediction pretext task (denoted by RotNet), [PERSON] et al.'s method [41], based on the patch puzzles pretext task (denoted by Jigsaw), and three recent methods, based on contrastive learning (i.e., BarlowTwins [46], SimCLR [42] and MoCo-V2 [44]). We also compared with a neural network pre-trained by using the supervised ImageNet image classification task (denoted by ImageNetClassi). The linear classifier of all the methods was trained on the same data and with the same training parameters. From the results in Table 2 we can observe that the performance, after linear classifier training,was, in general, satisfying and that our proposed method performed consistently well and better than existing methods for the CG forensics problem. This was probably due to better appropriateness of our designed pretext task for the classification of CG images and NIs, leading to more effective representations learned after self-supervised pre-training.
###### Abstract  
It has been suggested that microorganisms present in honey are a potential source of antimicrobial compounds. This study aimed to isolate and characterize bacteria from 46 Greek honey samples of diverse botanical and geographical origin and to determine whether these bacteria demonstrate antibacterial activity against five important nosocomial and foodborne pathogens. In total, 2014 bacterial isolates were obtained and screened for antibacterial activity. Overall, 16% of the isolates inhibited the growth of _Staphylococcus aureus_, 11.2% inhibited the growth of _Pseudomonas aeruginosa_ and _Acinetobacter humannini_, 10.2% inhibited the growth of _Salmonella Typhimurium_ and 12.4% of the isolates affected the growth of _Citrobacter freundii_. In total, 316 isolates that inhibited the growth of more than two of the tested pathogens were grouped by restriction fragment length polymorphisms (RFLP) analysis of the 16S rRNA gene amplicon. Fifty of them were identified by 16S rRNA gene sequencing. The majority, 62% of the isolates, belonged to the genus _Basillus_. Only 10% of the isolates were identified as Gram-negative bacteria. Furthermore, in several bacterial isolates, genes encoding polyketide synthases and nonribosomal peptide synthetases that catalyze the biosynthesis of secondary metabolites which might contribute to the exerted antimicrobial activity, were detected. This study demonstrates that honey microbiota exerts antimicrobial activity and is a putative source of secondary metabolites against important nosocomial and food pathogens that warrants further investigation.  
Keywords:honey; microbiota; antimicrobial activity; 16S-tRNA gene; nonribosomal peptide synthetase; polyketide synthase; secondary metabolites +
Footnote †: journal: Applied Sciences
## 3 Background
### Adversary Models  
An ever-greater variety of attack surfaces and approaches make it easier for adversaries to launch serious intrusions. Adversaries can implement attacks through the approach of a physical interface, e.g., on-board diagnostic (OBD) connector and USB slot [38; 39]. Attackers can also invade a vehicle by the way of a remote wireless method, such as Wi-Fi [40; 41], Bluetooth [38; 42; 43; 44], cellular [38; 40; 41; 42; 44], radio data system [38; 40; 43; 45], and telematics [38; 40]. Moreover, sensor failures can also cause the anomalous behaviors of a vehicle. Therefore, in this study, we took into account the attacks and sensor failures that result in the anomalous behaviors on vehicles. According to the related literature [46; 47; 48], the anomalous behaviors can be represented as the following five main scenarios:  
_Hijack_: Adversaries hijack the sensors or ECUs and report the false messages. The adversaries attempt to simulate the normal messages' sending frequency and transmit the continuous tampered messages on the CAN bus.  
_Bias_: Sensor signals slightly deviate from the true value over a period of time. This anomalous behavior means the vehicle is suffering some attacks or that the sensor is faulty, which cannot reflect the accurate signal reading.  
_Injection_: This means that the vehicle suffers from a wireless message injection attack. The attacker transmits false or invalid messages on the the CAN bus randomly, which is the easiest approach for the adversary to implement attacks.  
_DOS_: Denial of Service (DOS) aims to result in the unavailable service or intended function of vehicular systems. An attacker can flood the in-vehicle network with high-frequency messages in a short period to launch a DOS attack.  
_Replay_: Attackers store the valid signal at a previous time and replay them at a later intrusion stage. The real values are overwritten with recorded message values of the same signal over a period of time.  
The visualization of the anomaly scenarios are shown in Figure 1, and the red rectangles refer to the anomaly intervals. In this study, we aimed to develop a vehicle anomaly detection model that could detect the five anomaly types above under different durations with a high accuracy.
# Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning
## 3 Research Design
### Data Collection  
For estimating Microsoft's stock price, we gathered financial data from the Yahoo Finance website and SM data, including Twitter data, as well as stock Twitter data from StockTwits, for the timeframe from 16 July 2020 to 31 October 2020 summing up to 108 days. The SMP will be divided into two sections. The first involves the utilization of Twitter data and financial data, while the second involves the utilization of StockTwits data and financial data.
# Deformations of FRP-Concrete Composite Beam: Experiment and Numerical Analysis
## 2 Test Program
### Test Apparatus  
The beam specimens were tested using a 5000 kN capacity servo-hydraulic machine at displacement control. The loading speed was equal to 0.4 mm/min. A load cell was used to measure the applied load. Figure 3 shows the loading scheme and test apparatus. Linear variable displacement transducers (LVDTs; \(L_{10}\)-\(L_{15}\), Figure 3), attached in two continuous lines to the side surface \"(1)\" of the specimen, were used to monitor flexural deformations of the concrete prism. Nine LVDTs (\(L_{1}\)-\(L_{9}\), Figure 3) measured the vertical displacements in pure bending zone. Such arrangement of the devices enables cross-validation of the test outcomes: the measurement outcomes enable to construct three each other independent moment-curvature diagrams suitable for the tension-stiffening analysis.  
The construction of the support joints of GFRP-concrete beams has prevented slip of the purtuded profile that was fixed over the supports using the rapid assembly mortar C\({}_{\text{{{C}}}\text{{{E}}}\text{{{E}}}\text{{{E}}}\text{{{E}}}\text{{{E}}}}\). The displacement was monitored using an LVDT devise attached to the boundary surface of the specimen, as shown in Figure 4.  
The data logger Almemo 5690-2 was used to collect the output data of all LVDT and the load cell. Deformations of the side surface \"(2)\" (Figure 3) were monitored using a digital image correlation (DIC) system that also enables tracking the development of cracks. Two cameras Imager Elite 5M were used for that purpose. The cameras were placed on a tripod at the 3 m distance from the specimen; the distance between the cameras was equal to 0.4 m. The cameras, incorporating a charge-coupled device (CCD) detector, have a resolution of 2456 \(\times\) 2085 pixel at the 12.2 fps rate.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Specimen** & \(h\)**, mm** & \(b\)**, mm** & \(d\) **(*)**, mm** & \(t\)**, mm** & \(A_{r}\)**, mm\({}^{2}\)** & \(E_{r}\)**, GPa** & \(E_{a}\)**, GPa** \\ \hline \(B1\)-GFRP & 104 & 197 & 109 & 1.0 & 800.0 & 58.1 & 7.47 \\ \(B2\)-GFRP & 106 & 200 & 111 & 1.0 & 800.0 & 58.1 & 7.47 \\ \(B1\) & 105 & 201 & 80 & – & 150.8 & 196 & – \\ \(B2\) & 102 & 198 & 75 & – & 150.8 & 196 & – \\ \hline \hline \end{tabular} (*) The effective depth is determined as the distance from the top edge of the cross-section to the gravity centre of the bar reinforcement or purtuded profile.  
\end{table}
Table 1: Geometry and mechanical parameters of the beam specimens.
# A Novel Deep Reinforcement Learning Approach to Traffic Signal Control with Connected Vehicles +
## 4 Simulation Results
### Convergence of DRL-Based Signal Controller Training  
The convergence of the proposed DRL training algorithm was demonstrated by evaluating the accumulated rewards for each episode. As shown in Figure 8, the rewards increased rapidly at the beginning and then leveled off as the training progressed. The average vehicle speed and average waiting time in each episode are also plotted to show the improvement and convergence of traffic measurements. As previously noted, the average waiting time is not part of the optimization objective due to its conflicting relationship with the average vehicle speed in signal control policy. As a result, the average waiting time increases slightly at the end of the training process.
###### Abstract  
High-intensity focused ultrasound (HIFU) ablation has been widely used in advanced pancreatic cancer in recent years. In numerous studies, HIFU has been shown to be safe, effective, and practicable, but there are a few HIFU-related adverse effects. The patients' discomfort during therapy increased proportionally with increasing delivered energy and pain levels seemed to be independent of the dose of anesthetic or sedation. If the power used in therapy is too high, the temperature of the target area will rise rapidly, and the boiling and cavitation make the shape of the necrosis area difficult to control. In recent years, moderate-intensity focused ultrasound (MIFU) has also been shown to heat the tumor locally for palliative treatment. Choosing the appropriate power and effectively controlling the tissue temperature until reaching the threshold for thermal necrosis are of great significance for ensuring the safety of palliative treatment. In this study, an infrared temperature measurement experimental system was set up to measure the temperature rise at different power in an ex vivo bovine liver. It was found that when the acoustic intensity of the focused ultrasound was lower than the cavitation threshold (within the range of the MIFU), the temperature of the tissue kept rising at a steady rate and could still reach the thermal damage threshold temperature within tens of seconds. The results showed that the temperature induced by the MIFU was almost entirely dependent on the thermal effect, and the temperature of the tissue at the end of sonication was linearly related to the power. Finally, this study considered the effect of blood perfusion on the temperature for the application of focused ultrasound in the palliative treatment of advanced pancreatic cancer with the goal of providing a reference for the application of focused ultrasound in the palliative treatment of advanced pancreatic cancer.  
pancreatic cancer; palliative treatment; moderate-intensity focused ultrasound; temperature +
Footnote †: journal: Applied Sciences
## 4 Results
### Measurement of Chloride Content  
Chloride content analysis was conducted on Specim A after 175 days of conditioning in an NaCl solution. The UGWL data showed the maximum variations for the points 38 cm (15 in) and 53 cm (21 in) away from the edge, as shown in Figure 3; therefore, cores were extracted from these two locations. An additional core was extracted at the 76 cm (30 in) location for comparison purposes to represent points with a lower UGWL amplitude change. The pulverized concrete samples were then sieved using a No. 20 sieve (850 uM) as recommended by ASTM C1152 [21], which provides guidelines on the acid-soluble test for the determination of chloride content in concrete. For each sample, 2 g of homogenized material was mixed with 5 mL of trace-metal-grade nitric acid and 3 drops of Methyl Orange indicator. After the color of the samples became pink, the samples were placed on a hotplate until they began boiling. The samples were then vacuum filtered using Whatman #1 filter paper. The final solutions were diluted to 250 mL using 18 Mohm water. The chloride content was subsequently determined using a Metrohm Eco IC ion chromatograph and a five-point linear calibration curve. Ion separation was achieved using a Metrosep A Supp 4-250/4.0 anion separation column, standard eluent (2.4 mM sodium carbonate and 2.5 mM sodium bicarbonate), and an eluent flow rate of 1.0 mL/minute, all at room temperature. Each sample was analyzed in duplicate along with a method blank.  
The results of the chloride content analysis are given in Tables 5 and 6 for the first and second iterations, respectively, while Table 7 presents the average percentage of chloride concentrations from the two iterations.  
As it can be seen Tables 5-7, the chloride content of all the samples exceeds the CTL specified by ACI 318, which ranges between 0.05 to 0.1% by weight of concrete. As such, it can be concluded that the chloride content in this specimen has exceeded the chloride threshold values and corrosion activity had definitely started in the rebar by day 175.  
Table 8 presents the correlation between the chloride content and the data from both the UGWL and HCP on day 175 (right before CTL check) for points 38 cm (15 in), 53 cm (21 in), and 76 cm (30 in) in Specimen A. There is a strong correlation between all three measurement types.  
Figure 13 provides a visual for the correlation between chloride content and the UGWL data, further illustrating the potential of this method in detecting corrosion activity as early as when the chloride levels are right at the threshold of 0.18%.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sample** & **Average \%CI per Concrete Weight** \\ \hline
38 cm (15 in) & 0.84\% \\
53 cm (21 in) & 0.55\% \\
76 cm (30 in) & 0.185\% \\ \hline \end{tabular}
\end{table}
Table 7: Percentage of chlorite content per weight of concrete.  
Figure 13: Correlation between UGWL data and chloride content.
## 2 Problem Description  
Reasonable and accurate trajectory prediction results can help ICVs understand the current driving scene, perceive driving risks in advance, and then make effective decisions and plans. In this paper, we assumed that the position coordinates and velocity information of vehicles in the scene had been obtained through the networked environment. We denote the feature information \(X\) of vehicles in the scene over a past time horizon \(t_{h}\) as:  
\[X=[P_{1},P_{2},\cdots,P_{t_{h}}] \tag{1}\]  
\[P_{t}=[(x_{t}^{1},y_{t}^{1},v_{t}^{1}),(x_{t}^{2},y_{t}^{2},v_{t}^{2}),\cdots,(x_{t}^{N},y_{t}^{N},v_{t}^{N})] \tag{2}\]  
where \(P_{t}\) is the feature information of all vehicles in the scene at the time \(t\), \(N\) is the number of vehicles in the scene, \(x\) and \(y\) are the position coordinates of the vehicle, and \(v\) is the velocity of the vehicle. We denote the predicted trajectories in the future time horizon \(t_{f}\) as:  
\[Y=[\hat{P}_{t_{h}+1},\hat{P}_{t_{h}+2},\cdots,\hat{P}_{t_{h}+t_{f}}] \tag{3}\]  
where,  
\[\hat{P}_{t_{h}+t^{\prime}}=[(\hat{x}_{t_{h}+t^{\prime}}^{1},\hat{y}_{t_{h}+t^ {\prime}}^{1}),(\hat{x}_{t_{h}+t^{\prime}}^{2},\hat{y}_{t_{h}+t^{\prime}}^{2}),\cdots,(\hat{x}_{t_{h}+t^{\prime}}^{N},\hat{y}_{t_{h}+t^{\prime}}^{N})] \tag{4}\]  
are the predicted vehicles coordinates at the time \(t_{h}+t^{\prime}\).  
Therefore, the vehicle trajectory prediction in this paper can be summarized as: enter the historical feature information \(X\) into the prediction model and hope that the model can generate the future position coordinates \(Y\) of the vehicles in the scene.
## 4 Results and Discussion
According to the previous studies [43; 44; 45], the spectral region from 3700 to 3000 cm\({}^{-1}\) for all samples, with a characteristic maximum of about 3300 cm\({}^{-1}\), shows bands characteristic of the stretching vibrations of the -OH group. For this type of food sample, this band is primarily attributable to the vibrations of the -OH groups in fats, carbohydrates, and, especially, water molecules. The region in the range from 3000 to about 2800 cm\({}^{-1}\) is another important area for food products rich in fats and carbohydrates, in which there are stretching vibrations of C-H, alkyl, and aromatic groups resulting from the vibrations of these groups in hydrocarbon molecules. These vibrations are attributable to both symmetric and asymmetric functional groups -CH\({}_{2}\). Vibrations with a maximum of \(\sim\)3300 cm\({}^{-1}\) attributable to the -OH groups, due to their high intensity, significantly underpin the C-H stretching vibrations in the -CH\({}_{2}\) groups. Figure 2: Fourier transform infrared FTIR spectra of ice cream samples subjected to UP ultrasound pasteurization and traditional PP pasteurization, presented in the spectral range from 3600 to 550 cm\({}^{-1}\) normalized at 3293 cm\({}^{-1}\). A fairly wide band of vibrations attributable to the -OH groups is a result of the formation of strong hydrogen bonds, which--in the case of the tested samples--are primarily found in carboxylic acid dimers. The band that is attributable to the same groups but occurs in a completely different area, i.e., with a maximum of \(\sim\)1650 cm\({}^{-1}\) (Figure 1), is the deformation vibrations of the -OH groups, the origin of which is analogous to those described above. In such samples, this band can also be built up by the asymmetric vibrations stretching COO- groups. Another important area of vibrations with a maximum of about 1743 cm\({}^{-1}\) is attributable to the stretching vibrations of the functional ketone groups C=O of fructose, one of the components of the tested samples. It is highly reliable in the analysis of the samples because its intensity strongly depends on whether traditional pasteurization or ultrasound pasteurization is used. The band with a maximum of 1743 cm\({}^{-1}\) is clearly underpinned from the short-wave side by a maximum of \(\sim\)1720 cm\({}^{-1}\). This substructure may result from the hydrogen bond formation between carbonyl groups and -OH groups,
# Fault Diagnosis of Planetary Gear Train Crack Based on DC-DRSN
## 3 Fault Diagnosis Method Based on DC-DRSN and Verification  
### Fault Diagnosis Method Based on DC-DRSN  
Although DRSN implements adaptive learning thresholds and achieves good results in fault diagnosis, it is often used to deal with the spatial features contained in two-dimensional images and it fails to fully explore the dynamic changes on one-dimensional time series. Therefore, a fault diagnosis method based on a double channel-deep residual shrinkage network (DC-DRSN) is proposed in this paper.  
The fault diagnosis process based on DC-DRSN is shown in Figure 3.  
Figure 3: Fault diagnosis process based on DC-DRSN.  
Step 1: For the one-dimensional signal and two-dimensional time-frequency diagram with different levels of noise, the training samples, verification samples, and test samples are randomly selected.  
Step 2: Construct the DC-DRSN model, initialize the network weights, and set parameters such as the number of iterations and the learning rate.  
The network structure of DC-DRSN is shown in Figure 3, which is composed of a two-channel neural network. The upper channel uses a two-dimensional deep residual contraction network, and its input is a two-dimensional time-frequency graph after the transformation of the original vibration signal to reflect the weak local characteristics of the signal at different angles and improve the feature learning ability of the signal. The lower channel uses a one-dimensional residual contraction network, which takes the original vibration signal as the input, retains the timing and correlation of the signal, and concisely and effectively expresses the global dynamic time domain characteristics. On this basis, the upper and lower channels are fused in the fusion layer, and the extracted time-frequency features are classified and identified by the SoftMax activation function.  
The DRSN hyperparameters are shown in Table 1. They consist of a convolutional layer, a batch normalization layer, and a max pooling layer.  
Step 3: The one-dimensional signal and the two-dimensional time-frequency graph training samples are input to the upper and lower channels for model training at the same time.  
Step 4: The model parameters are continuously optimized through training samples and validation samples until the network accuracy is satisfied and the number of iterations is reached to obtain a trained diagnostic model.  
Step 5: Input test samples to the model and obtain diagnostic results through confusion matrix and T-SNE visualization.