esa-sceva/satcom-chunk-collection huggingface dataset:
### Current Research on Load Prediction  
Many experts and scholars have made a lot of contributions to the development of short-term load forecasting. Generally, the means of prediction could be classified as traditional methods, classical prediction methods, and artificial intelligence prediction methods. Classical methods include the exponential smoothing method and time series method. Traditional methods include the trend extrapolation method and grey prediction method. Intelligent prediction methods include the artificial neural network (ANN) method, chaos theory method, SVM method, and combination prediction method [17]. ANN methods are briefly introduced below.  
The principle of ANN is proposed by studying the way of conveying neural signals in the brain. The solving model of the problem is established by a computer, and then the \"neurons\" are connected by weights according to the simulation of the brain learning process. By constantly changing the weight of the connection through the computer, the established model continuously approximates the nonlinear function of load prediction. Since [PERSON], D.C. et al. [18] first applied an ANN to load prediction in 1991, experts and scholars have continuously deepened their research on neural networks in load prediction and have made remarkable achievements.  
For an object with a more complex mapping relationship, ANN can also be used to describe it better and has a strong adaptive ability, so a large number of irregular data can also be processed through its adaptive ability. The disadvantage of the ANN is that it will have a slow convergence speed, and fall into local optimum solutions and overfitting issues.  
In [19], the dynamic correction method was proposed to evaluate the weights of neural networks, and the accuracy of load prediction can be effectively improved by using such a correction method. The authors in [20] combined the Hadoop architecture of database technology in the computer field with backpropagation (BP) neural networks. This has also been applied in a study using massive original data to realize load prediction. The combination of the two methods cannot only improve the accuracy of load prediction but also break through the scale of load prediction data. In [21], a short-term load forecasting model based on support vector regression (SVR) and the whale optimization algorithm (WOA) was proposed. However, the initial value of the original WOA algorithm lacks equilibrium stability. It is possible to fall into local optimum and low convergence accuracy. In literature [22], the fruit fly optimization algorithm (FOA) was improved, and the improved FOA (IFOA) algorithm was combined with the backpropagation neural network (BPNN) to build a wind power generation prediction model. The results show that the signal strength decreases and the packet loss rate increases with the increase in the transceiver distance, and the electromagnetic wave of the wind power plant will cause some interference with the signal strength. The model has limitations on the signal receiving and receiving distance and a high requirement in terms of equipment professionalism.
## 3 Modeling
### Calculation of Equivalent Load for Nonuniform Shrinkage  
To simplify the complexity and high computational demands for integrating the moisture transport analysis (_Model-1_) with the structural analysis (_Model-2_), the deflections owing to nonuniform shrinkage are converted into \"equivalent loads\" that can replicate the deflection induced by the nonuniform moisture loss. The equivalent loads can be calculated using the Bernoulli-Euler beam theory, under the assumption that plane sections remain plane. According to the theory, the curvature is a measure of how much a beam deforms at a point during bending.  
\[k(x)=\frac{1}{R}\approx\frac{d\theta}{dx}~{}=~{}\frac{\varepsilon}{y}=\frac{ \varepsilon_{t}-\varepsilon_{b}}{H}=\frac{M(x)}{E(x)I(x)} \tag{11}\]  
In the above, \(k(x)\) is a curvature equal to the slope of the strain profile at \(x\), \(R\) is the curve radius, \(y\) is a vertical distance from the neutral axis, \(\varepsilon\) is the strain at depth \(y\), \(\varepsilon_{t}\) is strain at the top fiber, \(\varepsilon_{b}\) strain the bottom fiber, \(H\) is the height of the beam, \(x\) is the distance along the member length, and \(\theta(x)\) is the rotation angle, as shown in Figure 1. The strain due to moisture loss is obtained according to Equation (8).  
\[\delta(x)=\int\theta(x)dx=\int\int k(x)dxdx=\int\int\frac{M(x)}{E(x)I(x)}dxdx. \tag{12}\]  
Here, \(\delta(x)\) is the deflection of a beam, \(M(x)\) is the moment, \(E(x)\) is the modulus of elasticity, and \(I(x)\) is the second moment of area of the section. Once the moment is formulated, the equivalent load \(Q(x)\) can be calculated as follows:  
\[Q(x)=\frac{d^{2}M(x)}{dx^{2}} \tag{13}\]  
Since the equivalent load is calculated without accounting for the reduction of stiffness due to cracking, when it is applied to the member with reduced stiffness in _Model-2_ it will induce a higher deflection than the actual; thus, the deflections will be overestimated when the equivalent load is applied to the cracked section if the girder is subjected to cracking in the structural analysis using _Model-2_. To account for this, the equivalent load is adjusted by multiplying by the ratio of the deflection of the uncracked member to that of the cracked member after the onset of cracking. The load-adjusting factor \(\gamma_{a}\) is defined as follows:  
\[\gamma_{a}=\frac{\delta_{ucr}}{\delta_{cr}} \tag{14}\]  
Here, \(\delta_{cr}\) and \(\delta_{ucr}\) are the deflections of a member at the time of crack initiation for the analysis performed with and without crack in _Model-2_, respectively.
# Immunomodulatory Properties of Probiotics and Their Derived Bioactive Compounds
## 5 Probiotic Derived Bioactive Compounds and Immunity
### Bacteriocins  
#### 5.1.1 General Features of Bacteriocins  
Bactericins comprise a miscellaneous group of bacterial peptides, which display antimicrobial activity against other bacteria. Their inhibitory activity can range from narrow to wide, whether they eliminate only bacterial strains that are closely related to the producer bacterium or non-related species [85]. Both Gram-positive and Gram-negative bacteria can secrete bacteriocins [86,87]. To protect themselves from being killed by their own bacteriocins, most of bacterial strains have developed self-defense mechanisms, such as the production of self-immunity proteins and the utilization of efflux pumps [13].  
Bactericins are small proteinaceous molecules, mostly ribosomally synthesized, with extraordinary and diverse characteristics in terms of molecular weight, net charge, pH, and heat tolerance, as well as physicochemical properties [88]. The secretion of bacteriocins can be modified by several abiotic factors, as well as the producer bacterial strain [89], and takes place mainly in the late exponential growth phase and the early stationary phase [88]. Bacteriocins are considered to be potential candidates for therapeutic use, due to their effectiveness against multidrug resistant bacteria (MDR) [87], since they form pores on the cell membrane of the target cell, a process that contributes to the induction of cell death and prevention of drug resistance development [90]. Bacteriocins bind directly on the cell membrane presumably in the absence of a specific receptor [91].  
The predominant producers of bacteriocins are lactic acid bacteria (LAB), a distinct group of Gram-positive bacteria with particular interest due to their \"generally recognized as safe\" (GRAS) status [79,92]. LAB strains produce lactic acid as a main product of carbohydrate fermentation and include various genera, such as _Pedicoccus_, _Leuconstoc_, _Lactococcus_, _Enterococcus_, _Streptococcus_, _Lactobacillus_, and _Bifidobacterium_[21,88]. They share common metabolic characteristics, including the ability of bacteriocins and SCFAs production [92,93]. LAB bacteriocins present great variety, specificity, potential in co-administration with other drugs [87,94], and stability in a wide range of physicochemical conditions. The lack of toxicity of bacteriocins and the absence of bacterial resistance to them has been attributed to their short biological half-life in the human body [88,92]. Bacteriocins selectively target pathogens, but not commensal microbiota [87], and present cytotoxic activity against cancer cells [95,96]. Finally, certain bacteriocins exhibit anti-inflammatory and immunomodulatory properties, thus participating in the maintenance of a balanced crosstalk between gut microbiota and immunity [90] (Figure 1).
# The Impact of Leveee Openings on Storm Surge: A Numerical Analysis in Coastal Louisiana
## 3 Model Results and Flux Analysis
### Hurricane Isaac (2012)  
As shown in the subfigure of Figure 1, Isaac originated from a tropical wave that moved off the coast of Africa on 16 August 2012 and entered the southeastern Gulf of Mexico early on 27 August. It gradually strengthened while moving across the Gulf of Mexico and became a Category 1 hurricane when located 140 km southeast of the mouth of the MR around 12:00 UTC 28 August. It slowed down considerably while it approached the coast of Louisiana, which prolonged the strong winds, dangerous storm surge and heavy rains along the northern Gulf coast. Isaac made two landfalls along the coast of Louisiana, the first one at Southwest Pass on the mouth of the MR around 00:00 UTC 29 August and the second one at just west of Port Fourchon around 08:00 UTC 29 August. Isaac then gradually weakened and dissipated inland. Refer to [14] for more information. Figure 3 shows the calculated distributions of hurricane winds when Isaac was passing through to the west of Barataria Basin on 29 August. Hurricane winds reached over 30 m/s for hours in coastal Louisiana. The prevailing wind direction over Breton Sound changed from east and southeast to south with the approaching of Isaac. In Barataria, the wind direction changed dramatically due to its short distance to the hurricane's center.
## 4 Experiment
### Experimental Indicators  
The evaluation metrics for this experiment include precision (\(P\)), recall (\(R\)), F1 score (\(F1\)), mean average precision (\(mAP\)), \(Rouge-N\), \(Rouge-L\), and \(BLEU\).  
\(P\) represents the proportion of samples classified as positive that are actually positive. \(R\) indicates the proportion of truly positive samples that are correctly classified as positive. The \(F1\) score takes both \(P\) and \(R\) into account and is their harmonic mean. The \(mAP\) value is the mean of average precision and is commonly used to evaluate the model's performance in fault detection. \(Rouge-N\) is a metric for evaluating the quality of text generation by the model. As shown in Equation (4), \(\text{Count}_{match}(gram_{n})\) represents the number of matching n-gram between the candidate text and the reference text.  
\[Rouge-N=\frac{\sum\limits_{S\in\{Reference\}}\sum\limits_{gram_{n}\in S}Count_{ match}(gram_{n})}{\sum\limits_{S\in\{Reference\}}\sum\limits_{gram_{n}\in S}Count(gram_{n})} \tag{4}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Configure** & **System Environment** \\ \hline \multirow{6}{*}{RTX4060(12 G)} & Pytorch1.13 \\  & Python3.7 \\ \cline{1-2}  & CUDA11.6 \\ \cline{1-2}  & CUDNN8.4.0 \\ \cline{1-2}  & Neo4j4.2.2 \\ \cline{1-2}  & windows11 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental configuration table.  
Figure 5: Displaying dataset samples.  
\(Rouge-L\) is a metric used to evaluate the similarity between generated text and reference text. It is based on the longest common subsequence (LCS) and calculates similarity by finding the longest subsequence shared between the generated and reference texts. \(Rouge-L\) can measure the sequential consistency and retention of key information between the texts. The calculation formula for \(Rouge-L\) is shown in Equation (5), where LCS(\(X,Y\)) represents the length of the longest common subsequence between the generated text \(X\) and the reference text \(Y\). \(|X|\) and \(|Y|\) are the lengths of the generated and reference texts, respectively.  
\[Rouge-L=\frac{LCS(X,Y)}{\text{max}(|X|,|Y|)} \tag{5}\]  
\(BLEU\) is a metric used to evaluate the difference between the generated text and the reference text. It is chosen to assess the quality of text generation due to its effectiveness in measuring the degree of match between the generated text and the reference text. As shown in Equations (6) and (7), \(max_{j\in m}hk(s_{ij})\) represents the maximum number of occurrences of an n-gram across multiple reference sentences, while \(\sum_{i}\sum_{k}min(h_{k}(c_{i}),max_{j\in m}hk(s_{ij}))\) represents the minimum number of occurrences of an n-gram between the generated sentence and the reference sentence. BP stands for the brevity penalty factor.  
\[P_{n}=\frac{\sum_{i}\sum_{k}min(h_{k}(c_{i}),max_{j\in m}hk(s_{ij}))}{\sum_{i }\sum_{k}min(h_{k}(c_{i}))} \tag{6}\]  
\[BLEU=BP\times exp(\sum_{n=1}^{N}W_{n}log(P_{n})) \tag{7}\]
###### Abstract
The description of the mPOC framework, based on its intelligent algorithm;
2. Combining the bottom-up and top-down approaches, leading to the calculations of the DRI and PEI values from end-user and OTC information gatherings;
3. Leveraging wearable IoMT devices, based on the Bio-Watch concept;
4. By combining wearable devices, intelligent algorithms, and real-time data analysis, the system contributes to enhancing disease prevention and control efforts, providing valuable insights and timely alerts to mitigate the impact of outbreaks. The structure of this paper is as follows: Section 2 presents a literature review on the technological interventions for COVID-19 monitoring. Section 3 explores non-invasive clinical and physiological presentations (CPPs) of COVID-19, discussing their specificity and sensitivity measures. Section 4 provides an overview of epidemiology and public health perspectives of COVID-19. Section 5 focuses on the sensory systems utilized in the mPOC framework. Section 6 delves into the mPOC algorithm and IoMT protocols. The feasibility, scalability, and performance evaluation of mPOC are discussed in Section 7. Section 8 outlines future directions for technology-driven pandemic response systems, followed by the conclusion and references. In this paper we aim to address and respond to the following two research questions. 1. How can the mPOC framework effectively utilize wearable and mobile devices to autonomously predict and monitor outbreaks in pandemic-stricken areas? 2. What are the key features and functionalities of the mPOC system that make it superior to existing technology-assisted intervention systems for monitoring COVID-19 and preventing the spread of the virus?These research questions focus on understanding the effectiveness and unique aspects of the mPOC framework in outbreak prediction and monitoring, as well as its advantages over other technology-assisted interventions.
## 7 Application to UWB Doppler Radar Simulation
### Tracking of Medium Maneuvering Target
#### 7.1.2 Filter Design  
The gain design for the UWB Doppler radar using the proposed strategy is presented here. We design an appropriate \(a_{\text{D}}\) and evaluate the resultant performance. We presume a rough approximate prediction of accelerations. For instance, when the maximum acceleration of the target in Figure 3b is approximately predicted as \(a_{\text{c}}=0.6\) m/s\({}^{2}\), \(a_{\text{D}}^{2}\) is then 0.04 from Equation (31). Using this \(a_{\text{D}}^{2}\), the above radar settings of \(R_{\text{xv}}=9.0\) (\(T=100\) ms, \(B_{\text{x}}=0.03^{2}\) m\({}^{2}\) and \(B_{\text{v}}=0.1^{2}\) m\({}^{2}\)/s\({}^{2}\)) and solving Equation (32), we have the optimal gains \(\alpha=0.315\), \(\beta=0.00801\), \(\eta=0.0721\) and \(\theta=1.15\). Optimal gains for other settings are similarly determined.
## 3 Methodology  
The details about the proposed anomaly detection system are presented in this section. First, the overview of the framework is shown in Figure 1, which includes two main stages:  
1. Offline model training: In this phase, the robotic manipulator functions normally when executing designated tasks. The corresponding energy consumption data is utilized by the reconstruction network to learn the distribution of normal data patterns. As part of the model training process, a loss function is defined to measure the disparity between the reconstructed output and the original energy consumption data. This loss function quantifying the reconstruction error is then utilized in backpropagation to refine and enhance the performance of the network iteratively.
2. Online anomaly detection: A sufficiently trained model should demonstrate the ability to accurately reconstruct input samples following normal patterns with minimal reconstruction errors. However, notable changes may occur in the energy consumption profiles of the robotic manipulator when subjected to attacks or other anomalous conditions. Consequently, the network may fail to accurately reconstruct these anomalous inputs using the normal patterns learned in the offline phase, leading to higher errors. Thus, a static threshold is determined to distinguish anomalies from normal data. Specifically, an input sample is flagged as an anomaly if its reconstruction error value is higher than the predefined threshold.  
In the following sections, we describe the essential processing steps involved in the offline training and online detection phases.
## 5 Performance Analysis  
In this section, we analyze the performance of RBCB scheme over the Rayleigh fading channel in terms of packet delivery probability (PDP) and average packet delay (APD). The packet delivery probability is defined as the probability that a node within the target region successfully receives a  
Figure 6: Cooperative retransmission.  
packet from the source node [38]. The average packet delay is defined as the average number of slots between a packet's generation and the time when it is cleared from the buffer of the source node.
# Resilience in the Internet of Medical Things: A Review and Case Study
## 4 Key Capabilities for Resilience in IoMT
### Global View  
A global view of the complete set of nodes and resources is crucial for resilient IoMT networks. SDN provides this feature to observe the entire network (routers, switches, and devices). With this feature, network administrators can monitor the different topologies of the network, traffic flows, security, and privacy of the IoMT networks [43]. Overall, resource management can be appropriately conducted while having a single global view of the IoMT networks.
# Quantum Circuit Synthesis Using Fuzzy-Logic-Assisted Genetic Algorithms
## 5 Results
### Experiment Setup  
The KU ITTC HPC cluster was used to run the evaluations, with each compiler provided with 20 CPUs and 8 GB of memory. The compute nodes consisted of AMD Opteron(tm) Processor 6274 and 6376 CPUs [82]. The nodes ran the Rocky Linux 9.4 (Blue Onyx) [83] operating system, and a Python environment, using version 3.10.9, was set up using Conda (version 24.9.2). The popular scientific Python library SciPy (version 1.11.4) [74] was utilized for the numerical BFGS-based numerical optimizer, while the SciKit Fuzzy library (version 0.5.0) [84] was used to implement the fuzzy logic controller. Finally, several libraries from Qiskit (version 1.2.4) [85] were used to implement the GA-based compiler. It, and BQSkit (version 1.2.0) [86], were also used for comparison and evaluation of the compiler.  
For evaluations, a test suite of random unitary matrices was generated for qubit sizes 2 and 3. These were generated using the random_unitary [87] library function from Qiskit, which ensures even distribution of generated unitaries. Five unitaries were generated per qubit size, and we refer to each as a _trial_. All experiments were conducted on this test suite, and all compiler comparisons were made based on the outcome of compiling this dataset.  
In addition to input matrices, target machines were also provided to the compilers. The target devices used ring-shaped topologies and bidirectional connectivity (i.e., CNOTs available in both direction) were used; see Figure 10. The basis gate set of \(\{I,X,R_{z}(\theta),\sqrt{X},CNOT\}\) was used for the targets. For the GA, Table 5 lists the gate elimination and gate fusion policies, noting that during both gate fusion and elimination, the right-most gate gets replaced with the \(I\) gate. All qubits and connections were assumed to be of the same quality.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Input Size** & \multicolumn{3}{c}{**2 Qubit Unitary**} & \multicolumn{3}{c}{**3 Qubit Unitary**} \\ \hline
**Population Size** & **10** & **20** & **40** & **10** & **20** & **40** \\ \hline GA & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline GA-F & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiments for analyzing effects of population size.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Input Size** & \multicolumn{3}{c}{**2 Qubit Unitary**} & \multicolumn{3}{c}{**3 Qubit Unitary**} \\ \hline
**Error Thresholds** & **0.1** & **0.01** & **0.001** & **10\({}^{-8}\)** & **0.1** & **0.01** & **0.001** & **10\({}^{-8}\)** \\ \hline GA & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline GA-F & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline Qiskit & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline BQSkit & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experiments for comparisons between all compilers.
# A Dual-Channel End-to-End Speech Enhancement Method Using Complex Operations in the Time Domain
## 2 Algorithm Description  
In this section, we introduce a dual-channel, end-to-end, time-domain speech enhancement method that employs complex operations, as illustrated in Figure 1. This approach comprises two stages: complex neural network adaptive beamformer (CNAB) and complex fully convolutional network (CFCN). In the first stage, the CNAB performs preliminary speech enhancement on the dual-channel noisy speech. In the second stage, the CFCN produces the final high-quality speech.