esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Framework of Object Tracking and Safety Evaluation  
The primary goal of this research is to develop a safety module with 3D Lidar to evaluate the severity of potential risks for an autonomous excavator with respect to time and distance by predicting the motions of the detected objects. Figure 1 presents the framework of this study to achieve our objectives. The framework comprises four components that include object detection and tracking (C1), calculation of excavation working area through kinematic analysis (C2), calculation of potential collision severity with respect to time and distance (C3), and safety evaluation (C4).  
The first component deals with the estimation and tracking of object positions and velocities through adopting the IMM (interacting multiple model) coupled with a UKF (unscented Kalman filter) and JPDA (joint probabilistic data association). This algorithm facilitates object tracking in a robust manner and the track management can automatically assign track IDs to each detected object [16]. The reason why the IMM-UKF-JPDA approach was selected was to handle the various motion models of different moving objects along with non-linear states. The second part of this work is to calculate the actual and predicted working areas of a modified mini hydraulic excavator by applying a kinematic analysis. Using the position states of the detected objects and working areas and applying the defined safety indices, the time and distance to a collision are estimated as the next step.  
Lastly, the collision risks at the current moment were categorized into safe, warning, and emergency based on the computed safety index values. The aforementioned algorithms were developed in the MATLAB environment. Detailed explanations on each step are provided in the following section.
## 3 Outage Probability and Asymptotic Analysis of the RF-UWOC Dual-Hop System
### System Average Outage Probability and Its Asymptotic Value  
When the signal-to-noise ratio \(\gamma^{DF}\) of the end-to-end link in the RF-UWOC hybrid dual-hop system is below a given threshold \(\gamma_{th}\), the system signal transmission is interrupted, and the probability of this event is calculated as the average outage probability of the system. Therefore, assuming Equation (25) \(\gamma=\gamma_{th}\), the outage probability of the RF-UWOC hybrid dual-hop system considering the DF relay protocol can be obtained as  
\[P_{out}^{DF}=F_{\gamma^{DF}}(\gamma_{th}). \tag{27}\]  
Approach\[F_{\gamma^{DF}}(\gamma_{th}) =\frac{1}{\Gamma(m)\Gamma(k)}G_{1,3}^{2}\Bigg{[}\frac{mkl_{IR}\gamma_{ th}}{\gamma_{1}}\Bigg{|}\begin{array}{c}1\\ k,m,0\end{array}\Bigg{]}+\frac{c^{2}}{c^{2}(a)}G_{2,3}^{2}\Bigg{[}\frac{1}{(bA_{ 0})^{\gamma}}\Big{(}\frac{\gamma_{th}}{\dot{\bar{\
u}}}\Big{)}^{\bar{\xi}} \Bigg{|}\begin{array}{c}1,\frac{\xi^{2}}{c}+1\\ \frac{\xi^{2}}{c},a,0\end{array}\Bigg{]} \tag{28}\] \[-\frac{g^{2}}{c^{2}(a)\Gamma(m)\Gamma(k)}G_{1,3}^{2,1}\Bigg{[} \frac{mkl_{IR}\gamma_{th}}{\gamma_{1}}\Bigg{|}\begin{array}{c}1\\ k,m,0\end{array}\Bigg{]}G_{2,3}^{2,1}\Bigg{[}\frac{1}{(bA_{0})^{\gamma}}\Big{(} \frac{\gamma_{th}}{\dot{\bar{\
u}}}\Big{)}^{\bar{\xi}}\Bigg{|}\begin{array}{c }1,\frac{\xi^{2}}{c}+1\\ \frac{\xi^{2}}{c},a,0\end{array}\Bigg{]}.\]  
When the average SNR is high, the asymptotic value \(P_{out}^{DF}\) of outage probability can be easily expressed by Equation (28) as  
\[F_{\gamma^{DF}}(\gamma_{th})\rightarrow\frac{\Delta_{1}\gamma_{th}^{k}}{\bar {\gamma}^{k}}+\frac{\Delta_{2}\gamma_{th}^{m}}{\bar{\gamma}^{m}}+\frac{\Delta_ {3}\gamma_{th}\frac{\xi^{2}}{\bar{\gamma}^{2}}}{\bar{\gamma}^{2}/r}+\frac{ \Delta_{4}\gamma_{th}\frac{\kappa}{\bar{\tau}}}{\bar{\gamma}^{2c/r}}. \tag{29}\]
## 4 Ransomware Detection Studies w.r.t Compromised Platforms
### Ransomware Attacks on IoT Ecosystem  
The Internet of Things (IoT) is a system of interrelated computing devices with limited power and memory constraints. The connected architecture of IoT devices made them more attractive for ransomware attackers, i.e., attackers can attack a large number of victims [48].  
A ransomware detection model utilizing CNN, LSTM and OCSVM was proposed in [62]. This detection model was designed to work on the fog layer. The detection was performed by vectorizing the malicious behavior of an application. The OCSVM utilized the generated vectors to find out the family of ransomware while LSTM and CNN performed classification of ransomware.  
Authors in [48] proposed a ransomware detection method designed by using the Deep Belief Network (DBN) in hardware. This method utilized the deep learning approach for ransomware detection. This was an online detection method designed for the IoT devices and embedded systems. It was a behavioral detection solution using memory assisted stochastic dynamic fixed-point arithmetic. Method stored the random bits streams in the memory.  
A deep learning based ransomware detection model was proposed in [89] for Industrial IoT. The high dimensional data was collected to perform hybrid feature engineering. Classical and variational auto-encoders were used to reduce the data dimension and features extraction. This study focused on the extraction of latent patterns, learning from these patterns, and data dimensionality reduction. DNN with BN was used to make the decision about ransomware and benign.  
A ransomware detection model was presented in [90] industrial IoT systems. This model utilized stacked variational autoencoder for unsupervised learning. This study utilized augmentation method to produce training data for fully connected network. Dynamic analysis was performed to capture API calls, file and directory operations, and registry keys. This model utilized fully connected neural network for the detection.  
A ransomware detection and prevention system for the backup systems, i.e., Cloud was proposed in [91]. This method utilized the file entropy method for ransomware detection. This method also provided the file restoration of original data if ransomware attack occurred. Machine learning methods were used along with optimal entropy reference value to detect the ransomware attack and its infected files
# Cooperative Path Planning for Multiple Mobile Robots via HAFSA and an Expansion Logic Strategy
## 2 Hybrid Artificial Fish Swarm Algorithm
### Experiential Learning  
In this section, an experiential learning strategy is presented to improve the performance of AFSA. Experiential learning strategies include adjustment of the step size and food concentration for the artificial fish. The step size of an artificial fish is fixed in traditional AFSA. However, as is known, the step size determines the convergence rate. If the step size is too small, the artificial fish will reach the optimal solution slowly and the global search ability will be decreased. Therefore, the artificial fish is easy to fall into a local optimum. Conversely, if the step size is too large, the convergence speed will be increased and oscillation will occur later in the algorithm iteration. Therefore, it is necessary to select an appropriate step size to ensure the global convergence speed and improve the accuracy of the optimal solution. In this article, a logarithmic function was used to update the step size by Equation (5);  
\[b=\log_{N}p,p=1,2,3\cdots N, \tag{5}\]  
where \(p\) indicates the current iteration number, and \(N\) is the maximum number of iterations.  
Then, the step size in the population update formula could be obtained by  
\[step=\log_{N}p\cdot(X_{\varepsilon}(t)-X_{i}(t))p=1,2,3\cdots N,i=1,2,3\cdots NUM, \tag{6}\]  
where \(X_{i}(t)\) is the current state of the artificial fish, \(X_{i+1}(t)\) is the next position of \(X_{i}(t)\) and \(X_{\varepsilon}(t)\) is the state that needs to be searched.  
Finally, the position of the updated solution could be expressed as follows:  
\[X_{i+1}(t)=X_{i}(t)+\log_{NC}p\cdot(X_{\varepsilon}(t)-X_{i}(t))p=1,2,3\cdots N,i=1,2,3\cdots NUM. \tag{7}\]  
As shown in Equation (7), with the increase in the number of iterations, the moving step gradually adapts to the change of the iteration numbers. The local search ability is increased by the improved moving factor. As a result, the artificial fish can locate the search direction quickly, move to the target area, maintain the global search ability of the optimal solution and accelerate the convergence speed.  
On the other hand, the food concentration of the artificial fish was represented as the ability of the solution to solve an optimization problem. The current position of the artificial fish with the highest food concentration was the optimal solution of the optimization problem. In this article, a weight coefficient function was used to design the food concentration, which could decrease the food concentration of the artificial fish and avoid the problem that the suboptimal solution of the fish swarm algorithm would interfere with the global solution. The food concentration equation was designed as follows:  
\[H_{\text{exp}}=w\sqrt{\left(x_{i}-x_{g}\right)^{2}+\left(y_{i}-y_{g}\right)^{2}}, \tag{8}\]  
where the weight coefficient is \(w\in[1,1.5)\).
## 3 Methodology
### DSO-Aggregator Interface  
The DSO-aggregator interface is a case-specific interface used by DSOs to communicate their flexibility needs with aggregators. This interface varies for different approaches towards utilising flexibility (e.g., day-ahead flexibility markets, capacity or curtailment agreements).  
The chosen approach towards enabling DSOs to utilise flexibility and the corresponding interface affect the baselining solution. It is therefore necessary to explain the assumptions behind the interface, as these assumptions lay at the basis of the measured load profile (including its flexibility):  
* Day-ahead flexibility market;
* Gate-closure at 12:00 (noon);
* DSO requests flexibility from aggregators, aggregators comply.
## 1 Introduction
### Contribution  
In this work, we propose a privacy-preserving outsourcing scheme of medical image classification based on a convolutional neural network to the cloud. It can reduce the computing burden of users by sending their medical images to the cloud service platform built with three servers. With lightweight cryptographic encryption technologies, the privacy of the input and the model can be protected. Our contribution can be summarized as follows:  
* It achieves outsourcing medical image classification based on a convolutional neural network. Users can get the result of a medical image with a lower computing burden;
* For different protocol blocks in medical image classification based on a convolutional neural network, we provide privacy-preserving schemes utilizing lightweight cryptography primitives-secret sharing, which can not only ensure security but also improve the efficiency of service;  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Security** & **Technology** & **Model** & **Domain** & **Efficiency** \\ \hline [PERSON] et al. [23] & \begin{tabular}{c} Semi-honest and \\ non-colluding \\ \end{tabular} & Yao’s Garbled Circuits (GC) & Two parties & \(\mathbb{Z}_{2^{i}}\) & Medium \\ \hline [PERSON] et al. [22] & \begin{tabular}{c} Honest-but-curious and \\ non-colluding \\ \end{tabular} & \begin{tabular}{c} Homomorphic Encryption (HE) \\ and Secret Sharing (SS) \\ \end{tabular} & Two parties & \(\mathbb{Z}_{2^{i}}\) & High \\ \hline [PERSON] et al. [24] & \
\begin{tabular}{c} Semi-honest and \\ non-colluding \\ \end{tabular} & Additive secret sharing (ASS) & Two parties & \(\mathbb{Z}_{2^{i}}\) & High \\ \hline Our scheme &
\begin{tabular}{c} Semi-honest and \\ non-colluding \\ \end{tabular} & Replicated Secret Sharing (RSS) & Three parties & \(\mathbb{Z}_{2^{i}}\) & High \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison of different privacy-preserving outsourcing convolutional neural network schemes.  
* A pathological section staining experiment with good accuracy is carried out, which proves the efficiency and security of the scheme in practice.  
The rest of the paper is organized as follows. Section 2 introduces some preliminary knowledge. Section 3 describes the system model and threat model. Section 4 introduces some building blocks of privacy-preserving outsourcing schemes for medical image classification based on convolutional neural networks. Section 5 describes the detailed scheme. Section 6 introduces the experimental configuration and analyzes the practical experimental performance, and shows the experimental results. Finally, Section 7 draws a conclusion of the paper and introduces the future application of our scheme.
## 2 Study Area and Data
### Data
#### 2.2.3 Climate Data  
Climate data were sourced from Science Information for Land Owners (SILO) which provides nationwide meteorological variables (e.g., maximum and minimum air temperature, and precipitation) at daily temporal frequencies by interpolating observations made by the Australian Bureau of Meteorology onto a 0.05\({}^{\circ}\) by 0.05\({}^{\circ}\) grid [37].
# The Recent Development of the Surgical Treatment for Hepatocellular Carcinoma
## 4 Surgical Treatment of Highly Advanced HCC Outside Milan Criteria
### Surgical Treatment of Multiple HCC  
In addition to tumor size, tumor number is a poor prognostic factor after liver resection of HCC [50]. In the BCLC staging system, all patients with multiple HCC outside the Milan criteria without distant metastasis or vascular invasion are classified as intermediate stage, and TACE is uniformly recommended. However, there are a wide variety of patients, some of whom may benefit from liver transplantation, and some may benefit from regional therapy such as liver resection or RFA compared to TACE [51; 52; 53; 54]. In 2019, [PERSON] et al. reported a nationwide study of the treatment outcomes of 3246 cases of multiple HCC in Japan [10]. Among them, subgroup analysis of the 1309 patients with HCC larger than 3 cm in size revealed that the 90-day mortality rate in the liver resection group was extremely low at 0.57%, and a five-year overall survival rate was significantly better in the liver resection group than the TACE group (53% vs. 32.7%). For subclassification of these heterogeneous populations, it has been reported that the combinatorial use of the number of tumors, size, tumor markers, and liver function can be useful [22; 23; 51; 55; 56; 57].
# Evaluating the Impact of the COVID-19 Pandemic on Residential Energy Use in Los Angeles
## 4 Discussion
The current findings show limited evidence of a higher increase in non-temperature sensitive load over the COVID-19 period for lower income areas than higher income areas. The expected effect of SIP response on energy by income is not clear, as various factors predict mixed results. For instance, more highly educated, higher-income professionals were more likely to be able to shift to working from home, while less-educated workers were more likely to either continue working outside the home (e.g., in essential service or manufacturing) or lose their jobs. Lower-income households tend to have more members to use devices if everyone is at home, but higher-income households have more square footage and more devices to be used per person; furthermore, lower-income households spend less money (and time) on entertainment and dining outside the home than higher-income households normally, and would thus experience less change. Residential use of portable space heaters and window AC units, more common among older housing stock in lower-income areas, also adds to electricity use. Given the limited number of neighborhoods sampled here and the small observed effect, this result is considered questionable, but suggestive of further consideration; additional research would be required to clearly ascertain the income differences in effect of SIP response on energy use. However, it is worth noting that even the same or lower increase in energy use is a greater hardship for lower-income households, as they already experience a significantly higher energy burden (that is, the proportion of their income spent on energy bills) and have little or no discretionary income to cover unexpected expenses. Overall, SIP compliance was initially strong, but this effect was temporary. Approximately one month elapsed between the rapid ramp-up of SIP response and a long-term decrease and eventually leveling out of SIP compliance. This occurred even with daily briefings from health experts and government officials reporting increasing caseloads in the LA area. Energy models considering change in occupancy must expect a ramp-up, peak, and an extended dynamic equilibrium for general change in occupancy. Considering the near future of the COVID-19 timeline, stay-at-home rates will continue to subside into  
Figure 10: Direct change in energy use (non-normalized for temperature) for Feeder E (large apartment complex) and Feeder F (commercial building). an extended equilibrium that is likely higher than pre-pandemic levels. This suggests that increased telecommuting from home will continue to raise the energy burden during high heat events. Mid-day energy use, compared to a pre-COVID-19 baseline has had a modest increase-this can help offset the increasing glut in solar energy mid-day during normal conditions. However peak conditions, especially in late afternoon when solar is switching to spinning reserves can still impact energy supplies during this critical ramp-up and source switching period.
# Single-Switch Non-Isolated Resonant DC-DC Converter for Single-Input Dual-Output Applications
## 3 Model and Experimental Prototype
### Experimental Results
Again, a load is connected to the positive output, and another equal one is connected to the negative output (two monopolar loads). In the same way, Figure 16 shows the input and output voltages for the input voltages of 10 V (on the left) and 14 V (on the right). The load is composed of two resistors of 20 \(\Omega\) connected to the positive and negative outputs. It can be seen that the designed control allows for an output voltage of 12 V with a small variation around \(\pm\)0.4 V. In these test conditions (and power used), in both step-down and step-up modes, the measured and obtained efficiency was 86%, which is a reasonable value for many applications. In this regard, Table 3 compares the proposed converter with some recently developed bipolar output DC-DC converters shown in References [18; 19; 20; 21; 22], taking into account characteristics such as the number of switches, diodes, capacitors and inductors used in the converter with bipolar output voltage capability, voltage stress, input/output conversion ability, use of an isolation transformer, power and efficiency tested, switching frequency, floating switch/es and soft-switching technique used. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Characteristics** & [18] & [19] & [20] & [21] & [22] & **Prop. Converter** \\ \hline Number of Switches & 2 & 4 & 4 & 4 & 1 & 1 \\ \hline Number of Diodes & 2 & - & 1 & - & 2 & 2 \\ \hline Number of Capacitors & 4 & 5 & 4 & 2 & 4 & 5 \\ \hline Number of Inductors & 2 & 2 & 1 & 1 & 3 & 3 \\ \hline Voltage Stress & \(V_{g}\) & \(V_{g}\) & \(V_{0}\) & \(V_{o}\) & \(V_{g}+V_{o}\) & \(>V_{g}+V_{o}\) \\ \hline Conversion Ratio & Step-Down & Step-Down & Step-up & Step-up & Step-Down & Step-Down \\ \hline Transformer & No & Yes & No & No & No \\ \hline Floating Switch/es & Yes & Yes & Yes & Yes & No & No \\ \hline Soft-Switching & ZVS & Yes & No & No & No & Yes \\ \hline Power & 60 W & 3.3 kW & 3.5 W & 200 mW, & 1 kW & 30 W \\ \hline Efficiency & 94.6\% & 95\% & 89.3\% & 83.4\% & 84.4\% & 86\% \\ \hline Switching Frequency & 1 MHz & 190 kHz & 1 MHz & 2.5 MHz & 25 kHz & 550 kHz \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison characteristics of the ZVS-QR SEPIC–Cuk converter with different converters with bipolar output voltage capability. Figure 16: ZVS-QR SEPIC–Cuk converter in step-down and step-up modes for an input voltage of 10 V (on the **left**) and 14 V (on the **right**): SEPIC-side output voltage (Channel C1: 10 V/div, 0 V offset), Cuk-side output voltage (Channel C2: 10 V/div, 0 V offset) and input voltage (Channel C3: 5 V/div, 0 V offset). As a result, although the proposed converter provides a lower efficiency and operates at an acceptable frequency when compared to the other topologies in the literature, it requires a single non-floating power switch and two freewheel diodes and provides a bipolar output voltage with a step-down/step-up conversion ratio.
## 4 Discussion  
### The Effect of Noise Type  
Although pink noise and speech noise have different frequency domain characteristics and spectral features, the differences between OE, CE, and MRT corresponding to different types of noise were not significant, that is, noise type was not significantly related to the inattention and impulsivity of college students. [PERSON] found insignificant difference in the CE of auditory sustained attention between ASD children and control children under different types of background sound conditions, and insignificant differences in OE and MRT correspond to ambient sound (running water) and speech sounds in control children [33], which is like the noise perception characteristics obtained in the paper.  
When the SNR is low, the processing speed of the subjects slows down, and there are more cases of not paying attention, resulting in an increase in OE. Compared with the condition without noise, OE increased by up to three times, CE increased by one time, and MRT increased by up to 7%, which indicates that noise has a more significant negative impact on college students' auditory sustained attention. [PERSON] et al. found that the higher the SNR, the longer the attention stabilization time for both non-disabled children and children with intellectual disabilities [34], which is like our research results. Although the test methods, distraction stimuli, and subjects of the above study are different from this experiment, the findings all indicated that environmental sounds and noise interfere with auditory sustained attention.  
In the absence of noise, the mean = 3.6, SD = 1.28 for OE and mean = 2.4, SD = 1.02 for CE, which are lower than the results obtained in the study of Chinese children [33]. A study applying the SAAAT [26] to adults in a noiseless environment found that compared with children, the number of different types of errors in adults decreased, which is consistent with our findings. This suggests that children's auditory sustained attention ability may continue to develop and improve with age and reach stability in adulthood. [PERSON] et al. [35] predicted that adults would have higher accuracy and faster response time in CPT tasks than children. The results of this study confirm the hypothesis of [PERSON] et al. to some extent. Adults performed better because they had lower levels of inattention and impulsive errors.  
Figure 10: Scatter diagram of measured and predicted number of total errors.