esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Simulations
### Curve Path Following  
Under the condition that the guidance parameters, the control parameters, the external disturbances and the initial state of USV remain the same, the curve path following simulation is carried out. The desired geometrical path is a curve expressed as \(p_{d}(\theta)=[50\sin(\theta/250),\theta]^{T}\). The simulation results of curve path following are plotted in Figure 5.  
Figure 4: The results of the straight-line path following.  
Figure 5a plots that the task of path following can still be implemented well without any navigation and control parameters unchanged, which shows that the proposed path following strategy has good adaptability. The heading tracking effect is shown in Figure 5b, and it still can maintain good control performance. The change curve of \(\Delta\) is similar to that of straight-line path following, which is depicted in Figure 5c. Figure 5d displays the estimated value of sideslip angle. Similarly, the control inputs \(\tau_{u}\) are shown in Figure 5e. The results of the curve path following are shown in Figure 5f.  
Figure 5: The results of the curve path following.  
and \(\tau_{r}\) are depicted in Figure 5e,f, respectively. They vary within a reasonable range, and \(\tau_{r}\) has an input saturation phenomenon.  
Thus far, the numerical simulations of straight-line path following and curve path following have achieved good results, indicating the correctness and effectiveness of the adaptive LOS navigation strategy and control strategy proposed in this paper.
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 3 AI and Ophthalmology: View of the Ensemble
### AI and Posterior Segment Diseases
#### 3.3.2 AI and Retinal Vein Occlusion  
Automatic diagnosis of Branch Retinal Vein Occlusion (BRVO) has been performed, e.g., by [PERSON] et al. using hierarchical local binary model (HLBP) and maximal pooling for characteristics selection. The AUC was 0.961 [43].  
[PERSON] et al. [44] utilized fundus ultrawide-field photographs of central retinal vein occlusion (CRVO) and healthy subjects to instruct a CNN with DL and an SVM to identify CRVO. The CNN with DL was superior to SVM: the AUC was 0.989, sensitivity was 98.4%, and specificity was97.9%.  
A Random Forest (RF) network was used to categorize images by means of vitreomacular adhesion (VMA) in BRVO patients. VMA is a salient biomarker that predicts answers to anti-VEGF (Anti-Vascular Endothelial Growth Factor) treatment. One could observe that eyes with VMA undergoing therapy had better visual acuity in comparison to eyes without VMA [45].
## 2 Materials and Methods
### Network Construction  
When constructing a WCS-based network, the critical values are used to determine whether WCS estimates are significant or not. If a WCS estimate is significant, that is, it is larger than its corresponding critical value, a pair of corresponding time series is connected by an edge. In this study, we constructed two kinds of WCS networks, each of which corresponds to a diurnal and semidiurnal period, respectively. Firstly, we computed a pair-wise WCS estimate matrix (\(S\times L\)), where \(S\) is the number of periods, and \(L\) denotes the sample size of a time series for a given pair of time series. Secondly, for specific diurnal and semidiurnal periods, we extracted a WCS row vector (\(1\times L\)) of a specific period from the WCS matrix (\(S\times L\)) and divided the WCS row vector into non-overlapping segments, with 24 h per segment. Thirdly, we averaged the WCS estimates belonging to each segment and assigned the mean WCS estimate to each segment. This mean WCS estimate was used to determine the connectivity for the corresponding pair of time series through comparison with the mean critical value of WCS. The critical value of WCS was computed from the WCS ensemble constructed via WCS computation (Equation (4)) performed on the 100 ensembles of surrogate times series following the iAAWT algorithm. Consequently,one network was constructed for every segment, and we obtained nine networks for the diurnal and semidiurnal periods (hereafter, DIT networks and SIT networks) because the sample size of our data was 225. This network construction procedure is schematically presented in Figure 4.
# Implementation of a Topology Independent MAC (TiMAC) Policy on a Low-Cost IoT System
## 5 Experimental Methodology and Results
### Results  
In Figure 6, the derived values of throughput for the eleven polynomial sets are depicted. It is observed here that, in all cases, throughput is higher than the minimum value guaranteed by the TiMAC policy (i.e., 0.04). In addition, it is clear that the first polynomial set (scenario) results in the highest value of throughput; however, it is smaller than the maximum value (i.e., 0.2). The latter result is expected since, on the one hand, the particular set of polynomials leads to a collision-free slot allocation. On the other hand, the channel interference, transmitter's accuracy, and receiver's sensitivity may provoke unsuccessful transmissions.  
Figure 7 depicts the average successful transmissions per frame per node for the eleven polynomial sets. The particular results confirm the TiMAC policy's lower bound of one successful transmission per frame per node. For the first polynomial set, it is observed that the successful transmissions are significantly more than the successful transmissions in the other ten scenarios. However, they are less than the maximum theoretically expected value of five, which is the number of successful transmissions per node per frame in cases where no packets are lost for the duration of the 100 frames.  
Figure 6: Throughput corresponding to various polynomial sets. In all cases, the throughput lays between the expected maximum (i.e., 0.2) and minimum (i.e., 0.04) values.  
Figure 7: Average number of successful transmissions per frame per node corresponding to various polynomial sets. In all cases, the number of successful transmissions is between the expected maximum (i.e., 5) and minimum (i.e., 1) values.  
The particular polynomials used in each set are listed in Table 1. Although the network topology remains unchanged throughout the experimental phase, each set leads to different number of successful transmissions.  
The successful transmissions per polynomial set are listed in Table 2. Each set's sum of successful transmissions was used to derive the aforementioned throughput values.
## 4 Data and Resources
### Safe Graph Data  
Safe Graph specializes in providing location data from a variety of public and private sources, ensuring that personal information is not disclosed or misused [41]. This allows for the analysis of consumer behavior and patterns without violating individuals' privacy. The data provided by Safe Graph are often utilized for market research, urban planning, and other purposes [44; 45]. Additionally, the company offers spending data, which include a comprehensive set of information that reflects the monetary expenditure of individuals or businesses on specific goods or services. These data can be broken down into categories, such as retail, dining, or entertainment, and can provide valuable insights into consumer spending patterns and trends.  
Our initial step was to acquire spending data for the year 2019 from Safe Graph hosted in AWS. We then proceeded to filter the data by dates, retaining only the records corresponding to the home and away games of the Saint Louis Cardinals played between Thursday, 28 March 2019, in Milwaukee against the Milwaukee Brewers, and Sunday, 29 September 2019, at the local Busch Stadium against the Chicago Cubs. Subsequently, we extracted the 'daily spend' data from the file, which contain information about the amount spent in a particular business each day across the entire SLMA.  
While this study leverages comprehensive datasets to analyze mobility patterns, it is crucial to acknowledge a significant limitation in these data sources. The differential cellphone data coverage across various demographic groups is a limitation of these data, potentially impacting the representativeness of the results.
# Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network
## 5 Simulation Evaluations
### Performance Comparison
#### 5.3.4 Impact of Charging Power  
An important factor that affects the charger's ability while performing charging tasks is the node's received charging rate, which can determine the node's full charging time. With the observation in Figure 8, when the charging rate is low, all of the five scheduling algorithms perform badly. This is because, with the lower charging rate, the charger takes more time to complete the charging tour, with the result that some high-energy-consumption nodes charged in the early time will run out of their energy before the charger's next round visiting. When the charging rate increases, the performances of these algorithms, especially WCI and TSP, are improved. For example, when \(p_{r}=5\), WCI's total disjointed duration, inactive duration, and data loss rate is 72%, 70%, and 69% of that delivered by TSP, respectively. When compared with NJNP, the three ratios are reduced to 42%, 34%, and 34%, respectively. Note that CI outperforms in disjointed duration when the charging power is low, while it suffers inferior performance in total inactive duration.
# The Impact of Leveee Openings on Storm Surge: A Numerical Analysis in Coastal Louisiana
###### Abstract  
The existence of the Mississippi River (MR) and Tributaries' levees in coastal Louisiana could block storm surge and cause surge setup in adjacent basins. In order to reduce storm surge amplification caused by these barriers, one possible solution is to build \"floodways\" through the mainstm MR levees to allow surge during tropical events to cross. The primary purpose of this study is to examine if these floodways/openings can help reduce storm surge in adjacent basins. Using Hurricane Isaac (2012) as an example, a pre-validated Delft3D-based hydrodynamic model was applied to study the effect of leveee openings on storm surge. Model results and flux analysis show that these levee openings were not effective in reducing storm surge in Barataria Basin and Breton Sound due to the complex interaction between the cross flow from the surge and the MR flow. During Isaac, the MR water could be diverted to Barataria and/or Breton, which resulted in an increase in storm surge, essentially defeating the primary objective of the levee openings. Overall, the impact of levee openings at the selected locations on storm surge reduction in adjacent basins of coastal Louisiana was minor and very limited.  
storm surge; Delft3D; flux analysis; Hurricane Isaac
## 3 Results and Discussion
In many studies, the effect of EOs on the construction of the cell wall has been reported. The results we obtained are consistent with the [PERSON] et al. (2011) [56] observation that EOs have stronger antibacterial effects on Gram-positive bacteria (for example, _Staphylococcus aureus_) than on Gram-negative bacteria (for example, _Escherichia coli_). This is attributed to the differences in the cell wall structure and composition of Gram-positive and Gram-negative bacteria. The irreversible damage to the bacterial cell membrane causes the leakage of cytoplasmic substances and ions and this, combined with the lack of energy substrates, leads to bacterial death. Due to the complex EO chemical composition, the mechanisms of their effects are also diverse, which makes it difficult to accurately determine the path of molecular action. It is also possible to suggest that each EO component has its own action mode [58; 59; 60]. Peppermint, lavender, raspberry seed, and ylang-ylang EOs exhibited suppression of the growth of pathogens causing gastrointestinal diseases and saprophyytes. These results are important for food production, since, by introducing EOs, additional contamination with pathogens and saprophyytes will be prevented and the growth of the accompanying microflora will be suppressed, which is important for food production. Determination of the Resistance of Lactobacillus helveticus 2/20 to Different Concentrations of EOs of Lavender, Peppermint, Raspberry Seed, and Yang-Ylang  
The resistance of the probiotic strain _Lactobacillus helveticus_ 2/20 to different concentrations of EOs of lavender, peppermint, raspberry seed, and ylang-ylang was also investigated. The experimental data indicate that the growth of _Lactobacillus helveticus_ 2/20 was not affected by EOs at concentrations of up to 1%. This demonstrates that the selected probiotic strain can be introduced into food emulsions, together with the EOs of lavender, peppermint, raspberry seed, and ylang-ylang. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Microorganism**} & \multicolumn{4}{c}{**MIC, \(\mathrm{\SIUnitSymbolMicro g/cm^{3}}\)**} \\ \cline{2-5}  & **Peppermint** & **Lavender** & **Yang-Yang** & **Raspberry Seed** \\ \hline _Bacillus subtilis_ ATCC 19659 & 500 & 2000 & \(\geq\)30,000 & \(\geq\)30,000 \\ \hline _Saccharomyces cerevisiae_ ATCC 9763 & 2000 & 2000 & \(\geq\)30,000 & \(\geq\)30,000 \\ \hline _Apergillus flavus_ ATCC 1015 & 500 & 500 & 7500 & 30,000 \\ \hline _Aspergillus flavus_ ATCC+ 9643 & 500 & 500 & 3750 & 30,000 \\ \hline _Penicillium chrysogenum_ ATCC 28089 & 500 & 500 & 7500 & 30,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Minimum inhibitory concentrations of peppermint, lavender, raspberry seed, and ylang-ylang EOs on the growth of _Bacillus subtilis_ ATCC 19659, _Saccharomyces cerevisiae_ ATCC 9763, _Penicillium chrysogenum_ ATCC 28089, _Aspergillus niger_ ATCC 1015, and _Aspergillus flavus_ ATCC 9643.
## 5 HPSO: Best-Fit Solution for Adaptive Filtering
### Advantages of HPSO
HPSO demonstrates adaptability in dynamic environments by dynamically adjusting parameters, incorporating updates based on local neighborhoods, and creating multiple swarms, allowing it to stay in sync with evolving optimization needs. This makes it a well-suited choice for scenarios where the underlying system's characteristics change over time. In cases where substantial computational power is needed, HPSO can be parallelized across multiple processors or nodes, leading to faster optimization processes. This is particularly useful for tasks that require real-time processing capabilities. HPSO is highly versatile in handling various problem types and dynamic conditions with limited prior knowledge. Its hybrid nature strikes a perfect balance between exploring a broad range of solutions and refining them, making it particularly advantageous in adaptive filtering tasks.
# Extracting the Secrets of OpenSSL with RAMBleed
## 3 RAMBleed
### Rowhammer  
Rowhammer is a phenomenon in which repeated access to multiple rows of DRAM induces bit flips in neighboring rows [4]. This phenomenon can be exploited to rewrite data without direct access to the memory region we desire to invert, which is a major problem that threatens the safety of computer systems. More specifically, the explanation for this process is as follows. As the cell density increases, the word lines become more closely packed. Consequently, when the voltage of one word line is increased, it causes the voltage of neighboring word lines to slightly increase, resulting in the loss of charge from the cell at that location. This effect can be magnified by activating the same row multiple times within a short period; if sufficient charge is lost to exceed the noise margin before a refresh takes place, a bit flip occurs.  
The ease of inducing a rowhammer bit flip depends on the values of the bits in the rows above and below the bit. Specifically, bit flipping is most likely to occur when there are uncharged cells above and below a charged cell [4]. For example, suppose that the values of three adjacent bits in the same column are denoted from top to bottom as \(x-y-z\). In true cells, cells with a bit value of one are charged. Therefore, the \(0-1-0\) state is the most prone to bit flipping. In this state, repeated access to the rows containing the upper and lower bits may cause a reversal of the middle bit, changing it to \(0-0-0\). However, the \(1-1-1\) state is less likely to experience bit flipping, and is more likely to remain \(1-1-1\) despite repeated access.  
Several DDR3 DRAM modules experience rowhammer-induced bit flipping. There are various tools to verify if installed DRAM is susceptible to rowhammer [4; 5].
# Seamless Handover Scheme for MEC/SDN-Based Vehicular Networks
## 3 MEC/SDN Vehicular Network Structure  
In this section, the considered structure of the VANET is introduced. Figure 1 presents the developed MEC/SDN vehicular network that runs over the 5G system. The system enables V2V and V2X communications, as vehicles can communicate with each other and with distributed roadside units (RSUs). Each vehicle deploys an onboard unit (OBU) whose structure is presented in Figure 2. This embedded unit facilitates the implementation and execution of the required communication standards and interfaces.  
Figure 1: System structure of the developed MEC-based vehicular network.  
The developed structure deploys distributed edge computing with two heterogeneous levels. The first level represents the multiple-access edge computing units connected to the RSUs, which are referred to as the vehicular edge computing units (V-MECs). The second level represents the distributed MEC units connected to the cellular base stations, which are referred to as the cellular MECs (M-MECs). Both levels of these distributed edge units are introduced to provide computing resources near to vehicles to achieve higher latency efficiency, higher reliability, and higher system availability. Moreover, the introduction of MEC units facilitates the implementation of the developed networking algorithms. In particular, the V-MEC implements our developed handover scheme, resulting in a higher execution efficiency than that of the traditional systems.  
The introduction of distributed edge units as presented in Figure 1 facilitates the achievement of the required quality of service (QoS) of V2X applications. The communication between the OBU and the V-MEC reduces the communication latency, increases the throughput, achieves a higher availability and reliability, and increases the spectral efficiency.  
The network deploys SDN technology for the control and management scheme. The considered core network deploys a control plane with multiple SDN controllers. The data-plane of the SDN deploys a distributed scheme of OpenFlow switches that receive their forwarding tables and their updates from the control scheme over the OpenFlow interface. The introduction of SDN technology is intended to provide the required network flexibility and an efficient management scheme for the network. For the developed handover scheme, the introduction of SDN achieves a seamless process, since the TCP connection is maintained.
###### Abstract  
Compared with the point prediction, the interval prediction of the load could more effectively guarantee the safe operation of the power system. In view of the problem that the correlation between adjacent load data is not fully utilized so that the prediction accuracy is reduced, this paper proposes the conditional copula function interval prediction method, which could make full use of the correlation relationship between adjacent load data so as to obtain the interval prediction result. At the same time, there are the different prediction results of the method under different parameters, and the evaluation results of the two accuracy evaluation indicators containing PICP (prediction interval coverage probability) and the PIAW (prediction interval average width) are inconsistent, the above result that the optimal parameters and prediction results cannot be obtained, therefore, the NSGA-II (Non-dominated Sorting Genetic Algorithm-II) multi-objective optimization algorithm is proposed to seek out the optimal solution set, and by evaluating the solution set, obtain the optimal prediction model parameters and the corresponding prediction results. Finally, the proposed method is applied to the three regions of Shaanxi Province, China to conduct ultra-short-term load prediction, and compare it with the commonly used load interval prediction method such as Gaussian process regression (GPR) algorithm, artificial neural network (ANN), extreme learning machine (ELM) and others, and the results show that the proposed method always has better prediction accuracy when applying it to different regions.  
Keywords:load prediction; interval prediction; conditional copula function; multi-objective optimization algorithm +
Footnote †: journal: Applied sciences  
_Article_  
Multi-Objective Interval Prediction of Load Based  
on the Conditional Copula Function
## 4 Experiments
### Attack Models  
We used three different DNN models to compare our method's adversarial example detection performance. These models were selected to ensure a diverse range of architectures, capturing different feature extraction and sequence modeling capabilities. Additionally, these models are widely used in the research literature for evaluating adversarial attack detection and robustness, providing a solid foundation for benchmarking the effectiveness of our proposed method [24, 42, 43]. For the first model, we implemented a CNN architecture similar to the one proposed by [PERSON] et al. [44]. This model includes a word-embedding layer with 300-dimensional vectors, followed by three convolutional layers with filter sizes of 3, 4, and 5, each containing 128 filters. It also features a max-pooling layer and a fully connected layer. The second model employs a Bidirectional Long Short-Term Memory (Bi-LSTM) recurrent neural network (RNN). This architecture consists of a word-embedding layer, two Bi-LSTM layers with 256 units each, and a fully connected layer. Both of these models used 300-dimensional GloVe pre-trained vectors in the embedding layer. For the third model, we used BERT [45] to validate the applicability of our method in the context of language models.
# Frequency-Shifted Optical Feedback Measurement Technologies Using a Solid-State Microchip Laser
## 3 Applications of the Laser Feedback Interferometer
### Thermal Expansion Coefficient Measurement
YAG MLFI\({}_{1}\) and YAG MLFI\({}_{2}\), Nd:YAG microchip laser feedback interferometry systems; LD, laser diode; ML, microchip laser; BS, beam splitter; PD, photo detector; AOM\({}_{1}\) and AOM\({}_{2}\), acousto-optic modulators; L\({}_{1}\), L\({}_{2}\), L\({}_{3}\), and L\({}_{4}\), lenses; Mr\({}_{1}\) and Mr\({}_{2}\), reference mirrors; S, sample. (Figure reproduced from Ref. [76]).