esa-sceva/satcom-chunk-collection huggingface dataset:
# LogEDL: Log Anomaly Detection via Evidential Deep Learning
## 5 Experiment and Analysis
### Experiment Setting
#### 5.1.4 Baselines  
To evaluate the performance of LogEDL, we compare it with the following log anomaly detection methods:  
Principal component analysis (PCA) [30]: A traditional machine learning method for dimensionality reduction-based anomaly detection, combined with item weighting techniques from the data retrieval field. Suitable for simpler data scenarios.  
Isolation Forest (iForest) [33]: An unsupervised learning algorithm for anomaly detection that represents features as tree structures.  
One-class SVM (OCSVM) [34]: A well-known model for one-class classification, widely used for anomaly detection in log data where only normal data are observed.  
LogCluster [4]: Based on similarity clustering methods, LogCluster automatically identifies similar log patterns and clusters them to achieve automatic anomaly recognition.  
DeepLog [7]: An anomaly detection model based on log templates which uses LSTM to model log data, automatically learns patterns of anomalous behavior, and compares predicted logs with real logs for anomaly detection.  
LogAnomaly [35]: Integrates log templates by analyzing synonyms in logs; encodes log templates into semantic vectors and inputs them into an LSTM model for anomaly detection.  
LogBERT [19]: A method based on BERT, LogBERT constructs two self-supervised training tasks to learn patterns of normal log sequences and detect anomalies deviating from the normal model.
## 4 Experimental Results
### Performance Evaluation
#### 4.3.1 Comparison of Original Data Classification Performance of Models Trained via Augmented Wafers
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Actual**Predicted** & **Center** & **Donut** & **Edge-Loc** & **Edge-Ring** & **Loc** & **Near-Full** & **Random** & **Scratch** & **None** \\ \hline Center & \begin{tabular}{c} 48 \\ (53.33\%) \\ \end{tabular} & 0 & 0 & 0 & 1 & 0 & 0 & 9 & \begin{tabular}{c} 32 \\ (35.56\%) \\ \end{tabular} \\ Donut & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & & & & & & & & (0\%) \\ Edge-Loc & 0 & 0 & 0 & 80 & & 34 & 0 & 0 & 19 & \begin{tabular}{c} 129 \\ (43.58\%) \\ \end{tabular} \\ Edge-Ring & 0 & 0 & 4 & \begin{tabular}{c} 18 \\ (58.06\%) \\ \end{tabular} & 0 & 0 & 0 & 0 & 9 & \begin{tabular}{c} 31 \\ (29.03\%) \\ \end{tabular} \\ Loc & 23 & 0 & 13 & 0 & \begin{tabular}{c} 67 \\ (22.55\%) \\ \end{tabular} & 0 & 2 & 69 & \begin{tabular}{c} 123 \\ (41.41\%) \\ \end{tabular} \\ Near-Full & 0 & 0 & 0 & 0 & 0 & \begin{tabular}{c} 16 \\ (100\%) \\ \end{tabular} & 0 & 0 & 0 & \begin{tabular}{c} 0 \\ (0\%) \\ \end{tabular} \\ Random & 2 & 2 & 26 & 0 & 7 & 2 & \begin{tabular}{c} 27 \\ (36.48\%) \\ \end{tabular} & 8 & \begin{tabular}{c} 0 \\ (0\%) \\ \end{tabular} \\ Scratch & 0 & 0 & 0 & 0 & 2 & 0 & \
\begin{tabular}{c} 19 \\ (26.38\%) \\ \end{tabular} & 51 \\ None & 3 & 0 & 11 & 1 & 27 & 0 & 0 & 108 &
\begin{tabular}{c} 13,399 \\ (98.88\%) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Confusion matrix of classifier trained with augmented data from CAE-based model. pattern-specific accuracy of a model trained via a WBM generated by a DCGAN-based model was always higher than each defect pattern-specific accuracy of a model trained via a WBM generated by a CAE-based model, as well as being 14.26% higher on average. Furthermore, the experimental results also showed that WBMs augmented using a CAE-based model were fully classified by defect patterns in a classifier trained with DCGAN-based augmented wafers, while some WBMs augmented with a DCGAN-based model were not accurately classified by a classifier trained through CAE-based models. These results intuitively show that the DCGAN-based model formed diverse defect patterns compared to the CAE-based model.
## 2 Methodology
### Horn and Schunck Algorithm  
The HS (Horn and Schunck) algorithm, extensively used for estimating the optical flow vector fields [20; 21; 22], computes velocity vectors that indicate each pixel's direction and speed between consecutive images in a sequence. Operating under the assumption of smooth motion within images, this algorithm calculates the optical flow vector field by minimizing an energy functional. The energy functional is composed of two components: the data term \(I_{x}u+I_{y}v+I_{t}\) and the smoothing term \(\alpha^{2}\Big{(}|\
abla u|^{2}+|\
abla v|^{2}\Big{)}\), expressed as:  
\[E=\iint\big{(}I_{x}u+I_{y}v+I_{t}\big{)}^{2}+\alpha^{2}\Big{(}|\
abla u|^{2}+ |\
abla v|^{2}\Big{)}dxdy \tag{4}\]  
In this formulation, \(I_{x}\) and \(I_{y}\) represent the spatial derivatives of the image brightness in the horizontal \((x)\) and vertical \((y)\) directions, respectively, while \(I_{t}\) denotes the temporal derivative. The variables \(u\) and \(v\) correspond to the horizontal and vertical components of the optical flow vector, respectively. The regularization parameter \(\alpha\) is employed to balance the influence between the data term and the smoothing term, where \(|\
abla u|^{2}+|\
abla v|^{2}\) imposes a smoothness constraint on the optical flow field. Consequently, at time \(t\), the optical flow vector at a given image coordinate \((x,y)\) can be represented as \((u_{x,y,t},v_{x,y,t})\).  
Estimating the optical flow vector field involves minimizing the energy functional presented in Equation (4). To achieve this, the Euler-Lagrange equation is applied to minimize \(E(u,v)\), resulting in a system of partial differential equations [23]. This system is then solved iteratively to derive the optical flow vector \(\big{(}u_{x,y,t},v_{x,y,t}\big{)}\).
# Statistical Characterization of Supraharmonics in Low-Voltage Distribution Networks
## 2 Materials and Methods
### Supraharmonic Evaluation  
The IEC 61000-4-30 standard [12] suggests three methods for the supraharmonic evaluation: (i) the CISPR 16-1-2 method [23], which is typically difficult to be applied by in-situ measurement systems; (ii) the extension of the grouping methodology, presented in the IEC 61000-4-7 for the frequency range 2-9 kHz, to the entire 2-150 kHz supraharmonic range; (iii) a method specifically presented in the standard [12], which considers measurements taken through cascaded high-pass and low-pass filters processed by a Discrete Fourier Transform (DFT).  
The third method is recalled hereinafter, as it is the method considered by the in-situ measurement systems that provide the data for the numerical experiments [24].  
The signal \(y(t)\) passes through cascaded high-pass and low-pass filters having proper poles to damp low-frequency components. A sliding window with an assigned width (multiple of the fundamental period, typically 10 cycles in 50-Hz power systems and 12 cycles in 60-Hz power systems) is applied to the filtered signal to obtain \(G\) equally-spaced measurement sets, determining the analysis intervals. Each measurement set consists of \(L\) discrete points that are processed through the DFT as:  
\[Y_{k\Delta f}=\sum_{n=0}^{L-1}y(n)e^{-j2\pi\frac{k}{L}n}, \tag{1}\]  
where \(y(n)\) is the \(n\)th sample of the signal, \(k=0,1,\ldots,L-1\), and \(\Delta f=1/LT_{s}\) with \(T_{s}\) as the sampling time. With proper settings, this is sufficient to estimate supraharmonic emissions and also to obtain some statistics upon them. For example, if \(L=512\) and \(T_{s}=1/1,024,000\) s, the DFT spectrum has a frequency resolution \(\Delta f=2\) kHz, and 256 frequency bins can be evaluated; the first seventy-five of these bins include the entirety of the supraharmonic range. Since \(G\) analysis intervals are considered, \(G\) spectra can be estimated. The minimum, average, and peak values of the spectral components across the \(G\) spectra can be calculated and recorded for statistical assessment purposes.
# Multi-Location Assortment Optimization with Drone and Human Courier Joint Delivery
## 1 Introduction
Limited to the shop capacity, delivery distance, and drone load capacity, the product assortments of shops are closely related to shop revenue. We assume the cost of delivery is borne by the customers and the online ordering platform. The objective of the multi-location assortment problem under the cooperative delivery by drone and human courier (MADH) is to maximize the revenue of the retailer network. In the following, we provide a feasible strategy for delivery operation mode. We use a multinomial logit (MNL) model to characterize customer purchase behavior and optimize product assortments for the MADH problem. The settings are as follows: a human courier delivers goods within \(D_{min}\) distance without any load capacity limit, a drone can have a trip of more than \(D_{min}\) and less than \(D_{max}\), and a load capacity \(W\). Under these settings, one can order the same product provided by different online stores. Then, we transform the mixed-integer problem (MIP) of the MADH into a mixed-integer linear programming problem. Moreover, we formulate the MADH problem as a mixed-integer conic problem and use McCormick inequalities to strengthen it and reduce the calculating time. In addition, we verify its effectiveness by numerical study and propose some tests to give some insights into drone delivery management. There are two questions we want to answer. The first one is how does the relationship between drone load capacity and product marginal revenue influence optimal revenue? The second is how does the delivery distance limit influence optimal revenue and how can it be adjusted for higher revenue and less drone wear and tear? The contribution of this paper is summarized below:  
* To the best of our knowledge, this paper is the first one to consider the assortment optimization problem under cooperative delivery by drone and human courier. * The drone and human courier joint delivery mode is proposed, and the load constraint of the drone, delivery distance settings, and storage capacity are concluded in the model. The MMNL model is used to characterize customer purchase behavior. * The MIP model is converted into MILP, and an accurate Conic + MC algorithm is designed to accelerate calculation. * In the numerical study, we first verify the effectiveness of our Conic + MC algorithm, then we conduct two cases to give some advice on product distribution optimization and delivery setting optimization. In Section 2, we review the relevant literature on drone delivery, assortment planning, and multi-location assortment planning. In Section 3, we present the MADH problem and show its complexity. In Section 4, we present its MILP formulation and mixed-integer conic formulation, which is strengthened by McCormick inequalities. In Section 5, we propose three numerical studies and some suggestions to retailers and online ordering platforms. In Section 6, we conclude.
# Lightweight and Robust Malware Detection Using Dictionaries of API Calls
## 8 Comparisons and Limitations  
To draw a comparative analysis with state-of-the-art works in the field, the traditional classifier CatBoost [30] and deep learning classifiers (LSTM and DGCNN) [31] are selected for the purpose. The selection is based on the success of these techniques in creating malware detection models using the sequences of API calls. To make fair comparisons, the same dataset and evaluation metrics are used. Hence, recall, F1 score, and precision are used to discuss the comparison results.  
As shown in Table 3, the proposed STC model performs very closely to deep learning algorithms. Deep learning model-based API calls could be built using thousands of sequences. Such a large number of API call sequences can be limited to a specific number. However, in both cases, it is important to note that these solutions often demand substantial computational power and resources. The simple representation of API calls in STC overcomes the curse of dimensionality problem that exists in representing API calls as a bag of words such as those found in CatBoost.  
The proposed STC model shows high performance for malware detection using the training and testing datasets and successfully passed the testing of model drift. However, the lack of testing of the model on a daily basis with evolved malware samples is considered a limitation of this work.
## 3 Inverter Modeling and Control
### Control System Design
#### 3.5.3 Current Control  
The IFOC has been designed based on space vector PWM control, with the fixed 10 kHz switching frequency for a manufacturer-based existing inverter. In this control scheme, measured \(\tilde{t}^{e}_{ds}\) and \(\tilde{t}^{e}_{qs}\) components have been compared to references \(\tilde{t}^{e*}_{ds}\) (by reference flux) and \(\tilde{t}^{e*}_{qs}\) (by reference torque) as presented in Figure 8. The two PI current controllers are working together and provide voltage references \(V^{*}_{ds}\) and \(V^{*}_{qs}\), which are used for the generation of PWM signals. As in Figure 8, computational time delay \(T_{d}\) models a time delay between ADC sampling instant as well as a consequent PWM duty cycle. Such time delay is the half of the PWM period, where the sampling period and the PWM period \(T_{sw}\) are the same, so a computation time delay \(T_{d}=0.5T_{Sw}\). The current controller is designed using computation time delay, plant model, and sensor delay, as shown in Figure 8, where \(T_{a}=\frac{\sigma L_{a}}{R_{1}}\) is the current model time constant and \(R_{1}=R_{S}+\frac{L_{m}^{2}}{L_{r}T_{c}}\) is the combined motor resistance [29].  
A closed-loop plant transfer function was used to perform a disturbance rejection analysis. The presence of input and output disturbances provided information about the designed controller. When a disturbance enters the system, the disturbance rejection plot could reveal information about the controller's effectiveness. The system responded quickly to controller disturbances in order to return to a stable state. The closed-loop step response of input and output disturbance rejection of speed control, current control, and flux control is shown in Figure 9. The formulation of each controller and their tuning is carried out by the use of the gain margin (\(g_{M}\)) with gain crossover frequency (\(\omega_{cg}\)) as well as the phase margin (\(P_{M}\)) with phase crossover frequency (\(\omega_{cg}\)). The PI controller's proportional gain and integral gain are set to achieve desirable controller performance (i.e., rise time, settling time, overshoot). The speed, flux, and current feedback controllers are depicted in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Control** & **Transfer Function** & \(g_{M}\) **(dB)** & \(P_{M}\) **(\(\omega_{cg}\) (rad/s))** \\ \hline Speed control & \(2.03+\frac{4.34}{s}\) & 54.7 (\(3.16\times 10^{3}\)) & \(83^{0}\) (20.4) \\ \hline Flux control & \(16,640+\frac{170.430}{s}\) & 84.3 (\(8\times 10^{5}\)) & \(71^{0}\) (17.4) \\ \hline Current control & \(0.08+\frac{37.2}{s}\) & 47.4 (\(5.9\times 10^{5}\)) & \(69^{0}\) (991) \\ \hline \hline \end{tabular}
\end{table}
Table 3: IFOC control of IM.  
Figure 8: Feedback current control loop.  
Figure 7: Feedback flux control loop.
## 3 The Proposed Resource Allocation Scheme for D2D Communication
### Computational Complexity Analysis  
We analyze the complexity of our proposed algorithm in this subsection. In Algorithm 1, first we need to calculate all \(p_{k,m}^{D^{*}}\) and \(R_{k,m}^{CD}\Big{(}p_{k,m}^{D^{*}}\Big{)}\), then we search the optimal power allocation and decide the subcarrier set \(\mathcal{M}_{k}\) for each D2D pair. The above two steps are irrelevant, and the complexity of both the two steps are \(\mathcal{O}(KM)\). Finally, we try to allocate the remaining subcarriers to all the D2D pairs, where the complexity of the final step in Algorithm 1 is \(\mathcal{O}(K\Omega),\left|\Omega\right|\leq\left|\mathcal{M}\right|\). Hence, the complexity of the subcarrier assignment algorithm is \(\mathcal{O}(KM)\). In Algorithm 2, with \(ie\) as the accuracy requirement for the bisection algorithm, the complexity of the power allocation for the \(k\)th D2D pair is \(\mathcal{O}(\log_{2}(1/\epsilon)\left|\mathcal{M}_{k}\right|)\). Therefore, the total complexity of our algorithm is \(\mathcal{O}(\log_{2}(1/\epsilon)KM)\). Obviously, our algorithm has polynomial complexity which has a linear relation with the scale of \(K\) and \(M\).
## 2 Methodology
### Power Generation Performance Loss Factors
#### 2.2.4 DC-AC Conversion Loss  
The conversion efficiency of the PCS is generally more than 90% when the input power is 30-50% of the rated power, and it is low when the input power is less than 10% of the rated power ([PERSON], 2012). In other words, to reduce the losses in the DC-to-AC conversion process of the PCS, the design of the PV system must ensure that the PCS input power meets the rated power of the PCS, through an optimal capacity design of the PV array and the PCS. In the BIPV system examined in this study, a PV array of 0.528 kWp was connected to a 3 kW PCS. That is, the input power of 17.6% of the rated capacity of PCS was connected. Therefore, it is expected that the normal conversion efficiency of the PCS cannot be realized. To analyze the DC-AC conversion loss, the PCS conversion efficiency depending on the input power was calculated using the PCS input power and the conversion efficiency.
# Deep Q-Learning for Two-Hop Communications of Drone Base Stations
## 5 Proposed Model
### Deep Q-Learning Model  
In the proposed Deep Q-learning model, we use the same action space as the Q-learning model, as a result, the drone can take any of the eight directions at any time step. However, the Q-learning limits on the number of states does not exist in DQN. Therefore, the states composed of the continuous 2D location of the user and the DBS anywhere in the target environment. The same decay epsilon greedy model is also applied in order to create a balance between exploration and exploitation.  
Instead of finding Q-values by Q-table, we used three dense layers, with _'Tunh'_ activation function. The number of hidden neurons of each layer is 128, 128, and 64, respectively. Obviously, for the last layer of our DQN model, the number of neurons is equal to the size of the action space, with _'linear'_ activation function.  
Moreover, both experience replay and target network model are applied to our model in order to avoid instability in training. The size of the replay buffer and more details on its implementation are discussed in Section 6. The pseudo-code of DQN is presented by Algorithm 1.  
```
1:Create a Replay Buffer \(B\)
2:Initiate randomly the local network weights \(\theta\)
3:Initiate target network weights \(\theta_{i}\leftarrow\theta\)
4:for each episode \(e\)do
5: Initiate a random state \(s\)
6:for each transition \(t\)do
7: Select action \(a\) according to \(c\)-greedy model
8: Find the next-state and reward
9: Store the experience in buffer \(B\)
10: Select batch-size random experiences from buffer \(B\)
11: Calculate \(Q(s,a)\) using local network and
12: the batch of experiences
13: Calculate \(Q^{\prime}(s^{\prime},a)\) using target network and
14: the batch of next-states
15: Calculate loss function using \(Q^{\prime}(s^{\prime},a)\) and \(Q(s,a)\)
16:if\(t\)%updating-interval \(==0\)then
17:\(\theta_{t}\leftarrow\theta\)
18:endif
19:endfor
20:endfor
```  
**Algorithm 1** DQN Algorithm
# Numerical Simulation Analysis of Wellbore Stability in Weak Interlayer of Igneous Rock
###### Abstract  
Wellbores drilled in igneous formation with weak interlayers face significant risk of instabilities. This study aims to investigate the underlying mechanisms of these instabilities by employing a combination of rock mechanics tests and numerical simulation techniques. The mechanical properties of igneous rocks are evaluated to determine core strength parameters and analyze the impact of drilling fluid immersion on core strength. The two-dimensional model of the igneous formation is refined, and theoretical derivations are made, including the linear elasticity principal equation and the extent of the plastic zone within the wellbore. A numerical simulation model is developed using ABAQUS to analyze the wellbore stability of the weak interlayer igneous formation, accounting for drilling fluid immersion and weak interlayer conditions. The numerical simulations focus on four key aspects of the weak interlayer formation: strength, permeability, horizontal in-situ stress anisotropy, and abnormal pore pressure. The study findings indicate that strengthening the weak interlayer effectively mitigates the risk of wellbore instability. Moreover, the permeability of the weak interlayer exhibits minimal impact on wellbore stability within the formation. However, an increase in horizontal in-situ stress anisotropy and the abnormal pore pressure both decrease wellbore stability along the direction of the maximum in-situ stress.  
ignore rock; wellbore stability; weak interlayer; numerical simulation; in-situ stress +
Footnote †: journal: Applied Sciences  
1
# Some Approaches for Light and Color on the Surface of Mars
## 3 Results
### Colorimetric Characteristics of Lights on Mars' Surface
#### 3.1.1 Correlated Color Temperature (CCT)  
Figure 2 shows the histogram of CCTs we computed using the method indicated in [17] for 3139 total spectral irradiances provided by COMIMART. We found that CCTs of lights on Mars' surface are in the range of 2333 K-5868 K with an average value of 3984 K (standard deviation of 953 K), which is consistent with CCTs in the range of 4600 K-5400 K reported by [PERSON] [7]. Therefore, CCTs of lights on Mars' surface are lower and in a shorter range than lights on Earth's surface. Specifically, daylight illuminants proposed by CIE [3] have CCTs in the range of 4000 K-25,000 K, and two currently recommended CIE standard illuminants are D65 and D50, which have approximate CCTs of 6500 K and 5000 K, respectively. Accordingly, we can state that the currently recommended D65 illuminant  
Figure 1: Examples of total (direct + diffuse) spectral irradiances on Mars’ surface from the COMIMART model [9], considering three values of opacity (Tau) [0.1 (a), 4.1 (b), and 8.1 (c)] and five values of the solar zenith angle (0\({}^{\circ}\), 18\({}^{\circ}\), 36\({}^{\circ}\), 54\({}^{\circ}\), the 72\({}^{\circ}\)).  
for use on Earth is not a realistic choice for work on Mars' surface. In Section 3.2 we will propose alternative sol-light illuminants which may be used in the future.  
Figure 3 illustrates changes in CCT for a few values of opacity (Tau) and solar zenith angles. As expected, we can see that the CCTs considerably decrease (more reddish lights) both when opacity (i.e., the amount of dust in the atmosphere) increases, and when the solar zenith angle increases from 0\({}^{\circ}\) to 72\({}^{\circ}\) (except for very low Tau values).