esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment
### Performance Evaluation
#### 5.4.2 Effect of Multi-Scale Kernels on the Model Performance  
To verify the effectiveness of the multi-scale module, we evaluate the performance of MST-VAE with short-scale only, long-scale only, and finally combine both as a multi-scale. The performance comparison between three modules are shown in Figure 8. To evaluate the model using a single scale, we removed the concatenate and feature reduction layers in the MST-VAE architecture. The overall results demonstrate that combining different kernel scales can enhance the anomaly detection performance. Since the long-scale kernel is able to extract longer temporal information, it helps the model to detect mild anomalies. Specifically, the long-scale module shows higher performance compared to the short-scale module in ASD and PSM datasets. In the datasets SMD and SWAT, there is not much difference between the short-scale and long-scale modules because anomalies in these datasets are easier to be detected (large spikes on several metrics).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Baselines** & **ASD** & **SMD** & **PSM** & **SWaT** & **WADI** & **Average** \\ \hline LOF & 0.7954 & 0.8954 & 0.7609 & 0.8167 & 0.4689 & 0.7475 \\ DAGMM & 0.7436 & 0.9528 & 0.9399 & 0.7997 & 0.4306 & 0.7733 \\ Isolation Forest & 0.7115 & 0.9516 & 0.9200 & 0.8413 & 0.6674 & 0.8184 \\ LSTM-NDT & 0.4061 & 0.7687 & 0.8319 & 0.8133 & 0.5067 & 0.6653 \\ MAD-GAN & 0.6325 & 0.8966 & 0.6158 & 0.8431 & 0.7085 & 0.7393 \\ LSTM-VAE & 0.5964 & 0.9501 & 0.7951 & 0.8223 & 0.5452 & 0.7418 \\ USAD & 0.7987 & 0.9024 & 0.7606 & 0.8227 & 0.4275 & 0.7424 \\ OmniAnomaly & 0.8344 & 0.9628 & 0.9076 & 0.7344 & 0.7927 & 0.8464 \\ MSCRED & 0.5948 & 0.8252 & 0.7468 & 0.8346 & 0.5469 & 0.7097 \\ InterFusion & 0.9531 & 0.9817 & 0.9511 & 0.9280 & 0.9103 & 0.9448 \\ \hline
**MST-VAE** & **0.9766** & **0.9849** & **0.9555** & **0.9407** & **0.9479** & **0.9611** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of the best F1 Scores of different baseline methods on five datasets.  
Figure 8: Performance comparison between short-scale, long-scale, and multi-scale modules.
# A Systematic Literature Review of LoRaWAN: Sensors and Applications
## 3 Results
### Applications of LoRaWAN  
LoRaWAN finds applications in diverse fields such as smart cities, industrial monitoring, agriculture, smart buildings, asset tracking, and environmental monitoring. In smart cities, it enables the efficient management of resources like lighting, waste, parking, and environmental conditions. In industrial settings, LoRaWAN enables remote monitoring of equipment, predictive maintenance, and supply-chain optimization. In agriculture, it facilitates soil and irrigation monitoring, crop management, and livestock tracking. In smart buildings, LoRaWAN is utilized for energy management, security systems, and occupancy sensing. Asset tracking across industries and environmental monitoring of air and water quality are additional areas where LoRaWAN plays a crucial role. To be more concrete, Table 6 classifies the different previous works analyzed in this review.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Database** & **Search String** & **Number of Articles** \\ \hline IEEE Xplore & 30 & 23 \\ Springer Link & 19 & 17 \\ SCOPUS & 23 & 18 \\ ACM & 14 & 3 \\ Mendeley & 0 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of articles: partial review vs. complete review.  
From Table 6, it can be seen that most of the previous works focus on the area of environmental monitoring, followed by works referring to other areas such as performance improvement in LoRaWAN technologies. Works about environmental monitoring applications are the most popular, perhaps due to the diversity of sensors on the market for the measurement of different parameters such as UV radiation, temperature, humidity, and CO\({}_{2}\), among others. The works belonging to \"Other areas\" are comparative studies of the general applications and performance of LoRaWAN technology.  
In the following subsections, we will describe the different works developed in the different areas of application.
# Study on the Effect of Anchor Cable Prestress Loss on Foundation Stability
## 3 Numerical Simulation  
### Model Size and Boundary Conditions  
The pit is a long structure, which basically satisfies the plane strain characteristics in the cross-section. Therefore, in order to ensure calculation accuracy and efficiency and, at the same time, try to eliminate the influence of the model boundary on the calculation results, one-third of the length of the pit is taken for calculation. The final size of the calculation model is 244 m \(\times\) 60 m \(\times\) 88 m, as shown in Figure 2. The stratum in the model is simulated by a solid unit, and the support system is simulated by a structural unit. Normal constraints are set on the perimeter boundaries of the model, and vertical and horizontal displacements are constrained on the bottom boundary. The maximum mesh size of the model is 2 m and the minimum is 0.5 m, and the grid distribution near the excavation foundation pit is more dense.
# An Improved Method for Ship Target Detection Based on YOLOv4
## 3 Methods
### RFB Module  
The RFB (Receptive Field Block) module is inspired by the structure of receptive fields in human visual systems. It refers to the ideas of the Inception network [24] and adds dilated convolution to Inception with the aim of enlarging the receptive field and extracting multi-scale features, which makes convolutional neural networks learn deep features more effectively. As shown in Figure 3, in the RFB structure, features are extracted using standard convolutions as well as dilated convolutions on multi-scales. The standard convolution simulates the receptive fields at different scales. The dilation convolution increases the receptive field while keeping the feature map size unchanged.  
In an effort to reduce parameters and non-linear layers, RFB_s replaces the 5 \(\times\) 5 convolutional layer with two stacked 3 \(\times\) 3 convolutional layers. Then, it uses one 1 \(\times\) 3 plus one 3 \(\times\) 1 convolutional layer to replace the 3 \(\times\) 3 convolutional layer for the same reason. As shown in Figure 4, the RFB_s structure has a four-branch structure consisting of four convolutional kernels with kernel sizes of 1 \(\times\) 1, 1 \(\times\) 3, 3 \(\times\) 1 and 5 \(\times\) 5. Dilated convolutions with different rates are introduced in each of its branches to extend the receptive field. The input features from the previous layer are first passed through the four-branch structure; then the output features are fused and passed through the 1 \(\times\) 1 convolutional layer, and the result is fused with the input features by a shortcut so that the original information can be retained; eventually, the result is provided by the activation function.
## 4 Materials and Methods
### Evaluation and Validation
#### 4.3.3 Evaluation Metrics  
Several metrics were employed to assess the effectiveness of the proposed model. These commonly used metrics enable comparisons to be made with other techniques in the literature. They include the precision, recall, F1 score and accuracy. Detailed definitions and descriptions of these metrics are provided in Table 3.  
Class imbalance affected the model's accuracy and recall of political hate speech and other categories as the minority. It was evident from precision-recall curves that the model had to make a compromise, resulting in precision and recall values of the minority classes as follows: political hate speech values were 79% and 74%, while those of religious hate speech were 91% and 89%, respectively. Further improvement might be achieved by mitigating this imbalance, using methods such as oversampling or by cost-sensitive learning.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Metrics** & **Equation** & **Description** \\ \hline \multirow{4}{*}{Accuracy} & \(\frac{TP+TN}{TP+FP+TN+TN}\) & Accuracy measures the proportion of correctly classified instances out of the total instances. It is calculated as the ratio of the sum of true positives (TP) and true negatives (TN) to the total number of instances, which includes true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) & Accuracy measures the proportion of true positives (TP) out of all positive predictions (TP + FP) \\ \multirow{4}{*}{Precision} & \(\frac{TP}{TP+FP}\) & Recall evaluates the proportion of true positives (TP) out of all actual positives (TP + FN) \\  & \(2*\frac{Precision*Recall}{Precision*Recall}\) & F1- score indicated as F1 is the average of precision and recall. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation metrics used for experiments.
## 3 Results and Discussion
### Air and CO\({}_{2}\) Reactivities  
#### 3.3.1 Air Reactivity  
During the production of aluminum, air can infiltrate from the top of the electrolysis cells and react with the anode top and sides in spite of the presence of the anode cover. This reaction produces CO\({}_{2}\), which results in the overconsumption of the anode [3; 27; 28; 29; 30], because carbon consumed by this reaction cannot be used to produce aluminum (carbon loss). Air reactivity results are given in Figure 4 for all anodes. As shown in this figure, air reactivity generally increased with increasing anode density for anodes containing 3% unmodified and modified biocoke (i.e., B3-A(0), B3-A(2), and B3-A(3)), with the exception of B3-A(1)3. These results are in agreement with the literature [5; 30], which states that air reactivity is a surface reaction. A higher density provides a greater surface for reaction. The air reactivity of the anode B3-A(1)3 did not seem to fit this trend. This type of behavior is quite common when heterogeneous industrial materials are used. In general, the addition of biocoke (modified or unmodified) reduced the air reactivity. Modification of biocoke with A(1) decreased the air reactivity the most.
## 4 A Case Study
### Validation of the Statistical Results by Field Monitoring  
Figure 8 shows the monitoring results from the distributed optical fiber in the deep borehole. The strain curves on 29 December 2020 are virtually identical to those on 6 December 2020 with no significant changes, as illustrated in Figure 8a. During this period, the deep borehole was 292 m-265 m away from working face 205 and it remained unaffected by the mining disturbances of the working face. On 4 January 2021, the strain curve of the optical fiber began to differ from the curve recorded on 6 December 2020, as shown in Figure 8b. At this time, the deep borehole was 255 m away from working face 205. Subsequently, as the distance between the borehole and the working face decreased, the divergence in the strain curves became more pronounced, as illustrated in Figure 8c-d.  
The results inferred above indicate that when the working face is within 255 m of the borehole, compression deformation starts to occur in the overburden strata. This distance of 255 m is identified as the advanced mining influence range for working face 205 during this mining period. This finding matches the statistical analysis results of mining-induced seismicity and confirms the effectiveness of the method described in Section 3.
## 4 Simulation  
In this section, the imaging by a space camera is simulated and analyzed. The center of the field of view is aligned with the target star when the pointing platform is at the initial angle of 0. As the satellite moves along its orbit and undergoes attitude variations, the pointing platform maintains the target star at the center of the field of view during a six-second integration time by compensating for image motion using the IMC model. The satellite operates in a low Earth orbit: the orbit height \(h\) is 400 km and the earth radius \(R\) is 6400 km. The satellite's attitude changes, caused by disturbance factors, are also taken into consideration. At 3 s, a disturbance of adding 100 arcseconds to the satellite's three-axis attitude angles is introduced. The sampling frequency of the simulation is 200 Hz. After calculating the IMC model, the two-axis reference position, actual position, and the position tracking errors of the pointing platform were determined, as shown in Figures 5 and 6, with the azimuth axis in Figure 5 and the elevation axis in Figure 6, respectively. The pointing platform was able to achieve high control accuracy and ensured compensation for the image motion.  
Figure 4: Block diagram of the imaging control.  
In using ideal point-source imaging, multiple image points within the exposure time were accumulated and displayed on the image plane. The optical coordinates of the stars within a 4 \(\times\) 4 degree field of view on the image plane, without compensation for image motion and post-image motion compensation using the pointing platform, are shown in Figures 7 and 8, respectively. From the figures, it can be observed that, after applying IMC, the camera produces star-like points, which closely resemble the ideal point sources. In contrast, when the camera does not utilize IMC, there is significant trailing observed in the images, thus rendering them ineffective for accurate observations of celestial objects.  
Figure 5: Position tracking and error of the azimuth angle.  
Figure 6: Position tracking and error of the elevation angle.
# Virtual Network Function Embedding under Nodal Outage Using Deep Q-Learning
## 3 VNF-FG Problem Formulation
### Constraints
#### 3.1.2 Nodal Balance  
In order to prevent any rise of unpredictable conditions, it is essential to establish an equilibrium on the substrate network. Placing all VNFs of a VNF-FG on a single substrate node will cause an overload on them as well as on its corresponding substrate links. This will lead to an inadequate performance by the networks. Thus, it is crucial to deploy the VNFs of a VNF-FG onto different substrate nodes, which will be the second constraint. This constraint ensures that only one VNF from a particular VNF-FG is embedded in a substrate node. Note that other VNFs from other VNF-FGs can also be embedded on the substrate node in question, but, for every instance of a VNF-FG, only one VNF belonging to it will be embedded on a particular substrate node:  
\[\sum_{h}Y_{h}^{v}\leq 1,\forall v \tag{2}\]
## 4 Conclusions
The paper is organized as follows:  
* AR: outlet-to-inlet area ratio
* AITD: aggressive interurbine transition duct
* \(C_{d}\): resistance coefficient of the sweeping rod
* \(C_{\rm p0}=\left(p_{0}-p_{\rm s,ref}\right)/\left(p_{0,\rm ref}-p_{\rm s,ref}\right)\): total pressure coefficient
* \(C_{\rm ps}=\left(p_{\rm s}-p_{\rm s,ref}\right)/\left(p_{0,\rm ref}-p_{\rm s, ref}\right)\): static pressure coefficient
* \(C_{\rm u\rm s}=\left(v_{\rm x}\omega_{\rm x}+v_{\rm y}\omega_{\rm y}+v_{\rm z }\omega_{\rm z}\right)\cdot\mathrm{H}/\left(u_{\rm e}\cdot\sqrt{v_{\rm x}^{2} +v_{\rm y}^{2}+v_{\rm z}^{2}}\right)\): streamwise vorticity coefficient
* \(C_{x}\): axial chord
* \(d\): diameter of the sweeping rod
* \(d\)/s: nondimensional diameter of the sweeping rod
* \(f\): reduced frequency
* \(f_{H\rm T}\): passing frequency of the HPT rotor
* FST: free stream turbulence intensity
* h: height relative to hub
* H: annulus height of the ITD inlet
* H\({}_{12}\): shape factor
* HP: high pressure
* HPT: high-pressure turbine
* ITD: interturbine transition duct
* L: axial length of the ITD
* L/H: nondimensional axial length of the ITD
## 3 System Model
From the above equation, we can notice that by increasing \(\theta_{u}\), the LoS probability increases, where:  
\[\theta_{u}=\frac{-180}{\pi}tan^{-1}\biggl{(}\frac{h_{m}}{r}\biggr{)} \tag{2}\]  
The horizontal distance \(d_{u,m}\) between the \(m\)th ABS and user \(u\) is calculated as  
\[d_{u,m}=\sqrt{(x_{u}-x_{m})^{2}+(y_{u}-y_{m})^{2}} \tag{3}\]  
where \((x_{u},y_{u}),(x_{m},y_{m})\) are the locations of a user and the ABS, respectively. By using the probability of LoS, the probability of NLoS communications can be calculated as  
\[P_{\text{NLoS}}=1-P_{\text{LoS}} \tag{4}\]  
The LoS path loss for a connected user is given as  
Figure 3:0  
Furthermore, due to buildings and tree, the LoS could disturb the communication, and the loss for a reflected signal with an NLoS component is calculated as:  
Figure 3:1  
Here, \(f_{c}\) is the carrier frequency, \(c\) is the speed of light, \(\eta_{\text{LoS}}\) means an additional loss for the LoS, and \(\eta_{\text{NLoS}}\) is the mean additional loss for the NLoS. As a result, the probabilistic mean path loss is defined as:  
Figure 3:2  
Let us assume \(A=\eta_{\text{LoS}}-\eta_{\text{NLoS}}\), \(B=20\log\frac{4\pi f_{c}}{c}+\eta_{\text{NLoS}}\), and \(h_{m}^{2}+d_{u,m}^{2}=\Bigl{(}\frac{d_{u,m}}{\cos\left(\theta_{u}\right)} \Bigr{)}^{2}\). By using Equations (5) and (6) in Equation (7) we have:  
Figure 3:3  
We observe from the above equation that the path loss changes by changing both the height and location of the ABS or by varying either one of them. Hence, the data rate between user \(u\) connected to the ABS is calculated as:  
Figure 3:4  
where \(P_{m}\) is the ABS power, \(I\) is the cochannel interference, \(G_{u}\) is the small-scale fading channel gain, and \(\sigma^{2}\) is the noise variance. We analyzed the outage probability considering the interference and the ABS main links, and it is given by:  
Figure 3:5  
where \(\gamma_{\text{th}}\) is the target SNR, defined as \(\gamma_{\text{th}}=2^{\frac{C_{\text{min}}}{B}}-1\) for the target minimum required rate \(C_{\text{min}}\) and the bandwidth \(B\).
## 3 Result And Discussion
### Predictive Models
#### 3.1.1 Evaluation Metric  
To comprehensively assess the efficacy of our predictive methodology, we incorporate four widely recognized error metrics. For fare prediction, the employed loss functions encompass the MSE (4) and its derivative, the RMSE (5), representing the square root of the MSE. In these equations, \(y_{predict}\) signifies the predicted fare amount, while \(y_{actual}\) represents the actual fare amount.  
\[\text{MSE}=\frac{1}{m}\sum_{i=1}^{m}(y_{actual}^{i}-y_{predict}^{i})^{2} \tag{4}\]  
\[\text{RMSE}=\sqrt{\frac{1}{m}\sum_{i=1}^{m}(y_{actual}^{i}-y_{predict}^{i})^{ 2}}=\sqrt{\text{MSE}_{target}} \tag{5}\]  
\[\text{MAE}=\frac{\sum_{i=1}^{m}|y_{test}^{i}-y_{predict}^{i}|}{m} \tag{6}\]  
\[\max_{\theta}-\ln\left\{\sum_{k=1}^{M}w_{k}(x)g_{k}(y_{t}|x)\right\} \tag{7}\]  
In the realm of demand prediction, the model's performance evaluation employs an array of metrics: MSE, RMSE, mean absolute error (MAE) (6), and negative log-likelihood (7). The negative log-likelihood is germane to the loss function of the MDN. In employing the maximum likelihood estimation, this loss function operates on the premise that the sampled values possess the highest probability of occurrence. In alignment with ML norms, the focus lies on minimizing, not maximizing. Thus, a negative sign is appended, effectively transposing the objective to minimize the negative log-likelihood. Notably, the summation of likelihood functions from each distribution (\(M\)) underpins this process which is the total number of areas in the city.  
The RMSE, MAE, and negative log-likelihood are justified evaluation metrics for our predictive models. The RMSE provides an overall measure of the prediction accuracy, while MAE captures average prediction errors. They possess intuitiveness and interpretability for numerical prediction. Moreover, the negative log-likelihood assesses the probabilistic predictions by measuring the logarithmic loss between the predicted probabilities and the actual outcomes. This metric is particularly useful when dealing with probabilistic predictions Using these metrics, we gain insights into the accuracy, precision, and probabilistic performance.
# Biochemistry of Terpenes and Recent Advances in Plant Protection
## 3 Role of terpenes in Plant Protection
### Role of Terpenes in Insect Modulation
\(\beta\)-1,3-glucan laminarin induced elicitation of volatile compounds that reduced the green leafhopper population by attracting the egg parasitoid wasp [87]. Terpene-based biopesticides have been in use for some time now. Some apply active ingredients from orange or citrus oils, essential oils obtained from the _Chenopodium ambrosioides_ variety, and neem extracts [88]. An essential oil recently purified from the floral whorls of _Cannabis sativa_ L by gas chromatography and gas chromatography-mass spectrometry methods proved effective against some insects. Aphids and mosquito larvae were all killed. A total of 66% of tobacco cutworms and 80% and 33% of houseflies and adult mosquitoes, respectively, failed to survive. The oil composition included 45.4% betacaryophyllene, 25.0% myrcene, and 17.9% \(\alpha\)-pinene-. There were also 8.3% humulene, 5.2% \(\beta\)-pinene, 5.1% ocimene, and 3.0% of farnesene. Intriguingly, only 3% of beneficial insects, such as ladybug larvae, were killed at the highest concentration [88]. Such a selective modulation nature makes a good starting material in isolation or combination for pesticide development. Figure 5a and Table 1 illustrate the role of terpen in defense against insects.
# Design and Signal-Decoding Test Verification of Dual-Channel Round Inductsyn Decoding Circuit
## 1 Introduction
This circuit processes both excitation and induced signals, utilizing two AD2S1210 chips to synchronously decode the coarse and fine channels of the dual-channel round inductsyn. The proposed design is validated through simulation and physical circuit experiments.