esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### qPCR Analysis of PPAR\(\gamma\), AhR, PXR, and NF-\(\kappa\)B Genes  
The qPCR analysis was performed according to the protocol described by [PERSON] et al. with minor modifications [33]. Briefly, the total RNA isolation was carried out after 6 h of treatment of the cells with the tested compounds as described above according to the manufacturer's protocol (Universal RNA Purification Kit, EURx, Gdansk, Poland). RNA quantity and quality were then determined using a NanoDrop device (ND/1000 UV/Vis; Thermo Fisher, Waltham, MA, USA). RT-PCR was performed using CFX Real Time (BioRad, Hercules, CA, USA), starting with transcription of mRNA to cDNA (with 800 ng of RNA template for CACO-2 and BJ) according to the manufacturer's protocol (Thermo Fisher). The qPCR analysis of genes was then performed in a volume of 20 \(\upmu\)L with 1 \(\upmu\)L of cDNA and specific probes and primers. The reaction was performed with the previously described reaction parameters [33]. The analyses were performed using the \(\Delta\Delta_{\text{Cl}}\) method (the sample \(\text{Cl}\) threshold was determined in the exponential phase). To evaluate the gene expression levels, the reference gene _ACTB_ was selected as the most stable gene.
## 2 Theoretical Models
### BPNN Prediction Model  
A typical BPNN is divided into three levels: input layer, hidden layer, and output layer. The training of BPNN is a type of supervised one, which contains the forward propagation of signals and the reverse propagation of errors. Figure 3 shows the specific prediction process of the BPNN.  
Figure 3 illustrates the specific prediction process of the BPNN. It can be observed that the BPNN prediction process is a training process where signal forward propagation and error back propagation occur iteratively. The whole process can be divided into four parts: forward propagation, back propagation, memory training, and learning convergence. Among them, the network's layers are updated sequentially, while back propagation involves layer-by-layer correction.  
In this section, the prediction model based on the BPNN was utilized to predict the capture performance of PM2.5 in the wire-plate ESP under the influence of a magnetic field. The specific parameters were set as follows: the number of hidden layer nodes is 6, the target error is 0.001, the learning rate is 0.01, and the number of iteration steps is 5000.  
Figure 2: Schematic diagram of multi-field coupling relationship inside high-temperature ESP.
# Embedding Security Awareness in IoT Systems: A Framework for Providing Change Impact Insights
## 5 Performance Analysis
### Robustness of the Framework  
To validate the robustness and model-agnostic nature of our framework, we demonstrate its ability to operate independently of any specific deep learning (DL) model trained to detect security threats. To this end, we build and train a convolutional neural network (CNN) using a separate benchmark dataset--the CSE-CIC-IDS2018 dataset [62]--which focuses on intrusion detection, particularly application-layer attacks. We modify the SAC instance of the security profile, which primarily targets DDoS attack prediction. Specifically, we extract data relevant to DDoS attack types from the dataset and adapt the security profile to incorporate the attribute set used in the CNN model. We also update the interconnections between the profile and the corresponding processes within the system architecture to ensure consistency.  
The trained CNN model is then deployed within the _security awareness_ component, _GenerateAttackStatus@ConsequenceResult_, and the interpretation workflow is executed successfully. Notably, this integration is achieved without requiring any modifications to the core code of the framework, thereby reinforcing its flexibility, extensibility, and model-independence.
## 4 Results and Discussion
### Analysis of Noise Reduction Effect of Permeable Asphalt Pavement  
Through the tire drop test, the tire noise level of the rut plates of the three gradation asphalt pavements of ordinary densely configured AC-13(Asphalt concrete with a nominal particle size of up to 13 mm), SMA(Stone Mastic Asphalt) and permeable asphalt pavement PAC-13(Fine-grained permeable asphalt concrete) was tested. The results are shown in Table 9.  
It can be seen from Table 9 that the noise of the common densely equipped AC-13 and SMA tires tested by the tire drop method is almost the same. The tire noise of the permeable PAC-13 rut board is significantly lower than the first two. This is due to the large internal voids in the permeable asphalt pavement. When the tire hits the road, the compression of the air at the contact point is lower than that of the densely distributed road, and the noise produced is less than that of the dense road. It can be seen that, compared with ordinary pavement, permeable asphalt pavement does have a good noise reduction effect.
# Elimination Properties for a Probabilistic Scheduling Problem
## 4 Blocks of Tasks
### Blocks of Tardy Tasks  
**Definition 2**. : _Subpermutation of tasks \(\pi^{\mathcal{D}}\) in a permutation \(\pi\in\Phi\) is called a \(\mathcal{D}\)**-block of tardy tasks**, if:_  
1. _every task_ \(j\in\pi^{\mathcal{D}}\) _is tardy and_ \(d_{j}<E(\tilde{\mathcal{S}}_{\text{first}}+\tilde{p}_{j})\)_, where_ \(\tilde{\mathcal{S}}_{\text{first}}\) _is a random variable--date of starting the execution of the first task from_ \(\pi^{\mathcal{D}}\)_,_
2. \(\pi^{\mathcal{D}}\) _is a maximum subpermutation meeting the constraint (a')._  
The following properties result directly from the tardy task block definition. **Corollary 2**. : _If \(\pi^{\mathcal{D}}=(\pi(a),\pi(a+1),\ldots,\pi(b))\) is a \(\mathcal{D}\)-block in permutation \(\pi\), then_1. _the following inequality is met_ \[\max\{d_{j}:\ j\in\pi^{\mathcal{D}}\}<E(\tilde{S}_{first})+\min\{E(\tilde{p}_{i}): \ i\in\pi^{\mathcal{D}}\}.\]
2. _any task (belonging to_ \(\pi^{\mathcal{D}}\)_), swapped in the first position, in permutation_ \(\pi\) _is tardy._  
Similarly as in the \(\mathcal{T}\)-block, we determine the first \(\mathcal{D}\)-block in permutation \(\pi\). After modifications (expected values of random variables \(E(\tilde{C}_{i})\) instead of deterministic values \(C_{i}\) and \(E(\tilde{S}_{first})\) instead of \(S_{first}\)), one can apply the \(\mathcal{AD}\)-**block algorithm** presented in the work of [PERSON] [29]. The computational complexity of this algorithm is \(O(n)\). Considering further elements in permutation, after the last of the first \(\mathcal{D}\)-blocks, we can designate the next block of tardy tasks. Theorem 1 ([26]): _For any permutation \(\pi\in\Phi\) there is such division \(\pi\) into subpermutations in which each of them is:_  
1. \(\mathcal{T}\)_-block, or_
2. \(\mathcal{D}\)_-block._  
The algorithm for division of an \(n\)-elemental permutation into blocks, based on the proof of Theorem 1, has a computational complexity \(O(n)\). It follows from the block definition and the Theorem 1 that after the division of permutation into blocks:  
1. Every task belongs to some \(\mathcal{T}\) or \(\mathcal{D}\) block. 2. Different blocks have different elements. 3. Two \(\mathcal{T}\) or \(\mathcal{D}\) blocks can appear directly next to each other. 4. A block can contain only one task. 5. The division of permutations into blocks is not interchangeable. It is easy to show that the order of occurrence of tasks in the \(\mathcal{D}\)-block is not optimal due to the criterion (6). Later in this section we present, we are presenting a method for determining the optimal order of elements in the \(\mathcal{D}\)-block. First, we will prove some auxiliary lemma. Lemma 2: _Let us assume that permutation \(\delta\) was generated from \(\pi\in\Phi\) by swapping the position of two random neighboring elements in the permutation.
# Overview of Voice Conversion Methods Based on Deep Learning
## 5 Future Directions  
The analysis of the presented methods revealed several solutions that are superior to others in specific ways. This report highlights some favorable sub-solutions and suggests selecting the most suitable ones to develop a technique that achieves a high conversion efficiency. Future work includes plans for implementing such a method. Moreover, the information in this report about the datasets used and the evaluation methods will establish an appropriate benchmark for testing the effectiveness of future algorithms.  
Although the problem of voice conversion has been faced by algorithm developers for a long time, some issues still require research. Here are some of them:  
* The complex individual nature of human speech voice conversion is a task of great complexity, as it requires an understanding of various aspects of sound, such as tone, timbre, intonation, and tempo.
* Real-time performance requirements--in some cases, voice conversion must be done in real time, meaning that the algorithm must run fast enough for the user to hear the result in real time.
* Satisfactory results--the resulting quality of voice conversion can be crucial, especially for commercial applications. Algorithm developers face the challenge of ensuring that the results are good enough to be helpful to users.
* The flexibility of operation--the algorithm's performance should adapt to our data. The final product should also adapt in the case of higher-quality data. Furthermore, if users have different lengths of statements, the algorithm should work smoothly.
* Developing appropriate metrics for evaluating performance--in order to put voice conversion algorithms into practice, it is necessary to determine how well they work. Therefore, algorithm developers must create appropriate quality assessment metrics that consider various aspects of voice conversion, such as speech fluency, naturalness, and intelligibility.  
Investigation, T.W.; methodology, T.W. and Z.P.; resources, T.W.; supervision, Z.P.; validation, Z.P.; writing--original draft, T.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Centre for Research and Development, grant number CYBERSECIDENT/ 381319/II/NCBR/ 2018 on \"The federal cyberspace threat detection and response system\" (acronym DET-RES) as part of the second competition of the CyberSeclCdent Research and Development Program--Cybersecurity and e-Identity.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 3 Efficiency and Accuracy of the Proposed Simulation Method
### Wind Tunnel Test Performed by [PERSON] and [PERSON]
#### 3.2.2 Comparison between the Simulated and Experimental Results  
The comparisons between the distributions of mean concentration along the streamline direction passing through the source in the street canyon is shown in Figure 13. It can be seen that the pollutant concentrations at three different heights simulated by the proposed method are in good agreement with the experimental results. Furthermore, the simulated concentration distributions at three different heights demonstrate a phenomenon that a high concentration region spread to the leeward side of the upwind building owing to the recirculation eddies inside the street canyon, which was also observed by [PERSON] and [PERSON] in the wind tunnel test.
## 3 Circuit Design
### Analog Front-End
#### 3.2. Analog to Digital Converter
The successive approximation logic has been implemented as a synchronous full-custom logic. The A/D converter works with a clock frequency of \(20\) MHz, providing \(1.25\) MS/s, _i.e._, about \(64\) channels \(\times\ 20\) kS/s per channel. Hence, a conversion period lasts \(16\) clock cycles (see Figure 5): The sampling phase lasts \(7\) clock periods to relax the specs of the input signal buffer, while every bit decision takesone clock period, starting from the most significant bit (\(MSB\)). The last clock cycle is reserved for the end-of-count (EOC) operations. The current consumption of input buffer and A/D converter are \(250\) and \(410\)\(\mu\)A, respectively.
## 2 Experimental Methodology and Apparatus
### Unsupervised Learning Approach
#### 2.3.2 Random Forest  
Random Forest is an ensemble learning method for both classification and regression tasks. It constructs multiple decision trees during training, each using a subset of the data and features. The final prediction is an average (regression) or majority vote (classification) of individual tree outputs. This technique reduces overfitting and enhances accuracy.
## 3 Methods
### Output Module  
#### 3.3.1 Prediction and Recommendation  
First, we take the courses that the top \(k\) nearest neighbors have learned but that the target learner has not learned as candidate courses.  
Moreover, some learners rate courses more conservatively, while others rate courses in an opposite way. As a second step, to ensure the consistency of learner course ratings, the predicted rating of unlearned course \(c\) by the target learner \(u_{i}\) can be predicted, as shown in Equation (9). Here, \(\overline{r_{i}}\) denotes the average rating for learned courses by learner \(u_{i}\); \(k_{i}\) denotes the set of target learner's top \(k\) nearest neighbor learners who rated course \(c\) in candidate courses; \(u_{j}\) denotes learner \(u_{j}\) in \(k_{i}\); \(r_{jc}\) denotes the rating of course \(c\) by learner \(u_{j}\); \(\overline{r_{j}}\) denotes the average rating of learned courses by learner \(u_{j}\); \(sim\big{(}u_{i},u_{j}\big{)}\) shown in Equation (8) denotes the comprehensive similarity between learner \(u_{i}\) and learner \(u_{j}\)Considering the influence of the similarity between learner \(u_{i}\) and learner \(u_{j}\), the predicted value of learner \(u_{i}\)'s course \(c\) rating \(\hat{r_{ic}}\) can be calculated based on \(\overline{r_{i}}\) and \(\frac{\sum_{j\in k_{i}}sim\left(u_{i},u_{j}\right)*\left(r_{jc}-\overline{r_{j} }\right)}{\sum_{j\in k_{i}}sim\left(u_{i},u_{j}\right)}\).  
\[\hat{r_{ic}}=\overline{r_{i}}+\frac{\sum_{j\in k_{i}}sim\left(u_{i},u_{j} \right)*\left(r_{jc}-\overline{r_{j}}\right)}{\sum_{j\in k_{i}}sim\left(u_{i}, u_{j}\right)} \tag{9}\]  
Finally, the top n courses with the highest predicted ratings were recommended for the target learners in Step 4.
## 3 Results
### Performance of the Robust Optimization Algorithm
#### 3.2.2 Isodose Lines  
Figures 6 and 7 illustrate the comparison of isodose lines calculated in the Oncentra Brachytherapy TPS of the nominal (manual) plan, inverse plan and robust plan for a cervical cancer patient. In all treatment plans, dwell times at each position for the desired target coverage were successfully encompassed based on EMBRACE protocol guidance. As shown in Figure 6, the bladder contour is encompassed by a minimum of percent isodose line in the robust planning while maintaining target-dose coverage and OAR sparing, rather than both coverage and sparing in the nominal and inverse planning.  
Figure 6: Isodose-line comparison of (**a**) nominal (manual) planning, (**b**) inverse planning, and (**c**) robust planning standard planning in EBRT brachytherapy boost treatment of cervical cancer: axial view. An EBRT total dose of 50.40 Gy was delivered in 28 fractions, and an ICR total dose of 30 Gy was delivered in 6 fractions using a TR applicator.  
Figure 7: Isodose-line comparison of (**a**) nominal (manual) planning, (**b**) inverse planning and (**c**) robust planning. Standard planning in EBRT brachytherapy boost treatment of cervical cancer: axial view. An EBRT total dose of 50.40 Gy was delivered in 28 fractions, and an ICR total dose of 30 Gy was delivered in 6 fractions using a TR applicator.  
In Figure 7, the cervical brachytherapy plan showed the isodose line from a coronal view. Figure 7 demonstrated a reduced dose to the international commission on radiation units (ICRU) vaginal right (R), left (L) point, and sigmoid than nominal and inverse plans (blue arrow with a single arrowhead). Furthermore, the robust plan not only minimized the dose delivery of the ICRU vaginal R and L point, but it also minimized doses to the OAR, while maintaining coverage of uneven HR- and IR-CTV contours. The most distinct difference between the methods in the results for the target and OAR is that the robust optimization method improved the dose distribution and decreased the dose delivery uncertainty. Table 1 displays the numerical results corresponding to Figures 6 and 7.  
The quantitative results in Table 1 indicate the EQD2 and the standard deviation of each nominal, inverse and robust plan strategy. For cervical cancer treatment, a prescription dose of 50.40 Gy to the target was delivered by EBRT as previously mentioned. Then, the total EQD2 (EBRT + BT) of HRCTV D98 for all plans (nominal, inverse, and robust plans) was estimated at 88.59 Gy, EQD2, 86.71 Gy, EQD2, and 84.84 Gy, EQD2, respectively, and the standard deviation for each plan was \(\pm\)1.0177, \(\pm\)0.9393, and \(\pm\)0.9085, respectively. Also, EQD2 and the standard deviation of the rectum \(D_{2 cc}<75^{*}\) for all plans were also estimated at 55.29 Gy, EQD2 (\(\pm\)0.4927), 54.94 Gy, EQD2 (\(\pm\)0.4620) and 54.09 Gy, EQD2 (\(\pm\)0.4052), respectively.
# A Novel Wireless-Netted UWB Life-Detection Radar System for Quasi-Static Person Sensing
## 2 Multi-Observation Point Detection System  
As shown in Figure 1, the architecture of the proposed wireless-netted multi-observation point detection system includes several independent single-channel Golay complementary coded UWB life-detection radars, which are taken as the network nodes controlled by a control host. The radars are responsible for the generation, transmission and reception of the Golay complementary coded signal. The detailed design of the system is discussed in Section 3. The control host is responsible for the human-machine interface, collaborative acquisition and data processing. By using time division multiplexing (TDM) and the star topology, the control host coordinates these network nodes during the multiple observation point detection process to prevent mutual interference.
## 2 MBR Types and Configuration
### Anaerobic MBR (AnMBR) for OMPs Removal  
Another arrangement known as anaerobic MBR (AnMBR), has become an attractive option for energy-neutral wastewater treatment. AnMBR is a hybrid process that integrates anaerobic process and membrane-based separation. AnMBR process converts organics present in sewage into biogas (methane) by biologic transformation employing basic sequences, such as hydrolysis, acidogenesis, acetogenesis and methanogenesis [81]. AnMBR has gained attention as more energy-efficient and effective process in contrast to aerobic MBR. Aerobic MBR utilizes huge energy to maintain dissolved oxygen and for membrane scouring. AnMBR could become energy saver by methane generation or even be a positive energy system by producing biogas for beneficial usage [82; 83]. The anaerobic process offers more challenges in terms of process disturbance because of retarding compounds, such as heavy metals, chlorinated hydrocarbons and cyanides frequently found in raw sewage [82]. The anaerobic process is less popular in sewage treatment due to fact that it rarely meets the required discharge standards specifically at low temperature. Also, it is too difficult to maintain sufficient slow growing MLSS with dilute wastewater and low HRT conditions [84]. Mitigation of membrane fouling and efficient dissolved methane recovery should be the key area for future developments in AnMBR technology [85]. Table 2 describes the fundamental difference between AnMBR and MBR. Anaerobic digestion was employed as an attractive option for excess sludge stabilization from conventional biologic process. The fate of OMPs present in settled sludge depends on characteristics and availability of EWGs and EDGs in the OMP when stabilized sludge is targeted for soil conditioning or in farm. The process governed by bacterial community consumes organics and OMPs in sludge as a food source and converts into carbon dioxide [67]. In order to remove OMPs, combined redox conditions can be applied as demonstrated by [PERSON] et al. (2016), when operated anaerobic and aerobic reactors in tandem [86]. Authors report that aerobic conditions are favorable for most of the OMP removal. Certain OMPs such as trimethoprim a pharmaceutical compound essentially needed anaerobic treatment [80]. Some recent AnMBR studies for OMP removal are summarized in Table 3. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Feature** & **AnMBR** & **MBR** \\ \hline Energy consumption (kWh/m3) & 0.03–5.7 a  & \(\sim\)2 b  \\ Biomass concentration (g/L) c  & 10–40 & 5–20 \\ Organic loading rate (kg COD/L/d) & 0.17–35.5 & 0.25–0.8 \\ Organic removal efficiency (\%) & \(>\)90 & \(>\)95 \\ Hydraulic retention time (hours) & \(>\)8 & 4–8 \\ Water flux, liters per square meter per hour (LMH) & 5–12 & 20–30 \\ Sludge retention time (d) & \(>\)100 & 5–20 \\ Operational temperature (\({}^{\circ}\)C) & 20–50 & 20–30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between anaerobic membrane bioreactors (AnMBRs) and MBRs for wastewater treatment (Adapted from [81]). Copyright Bioresource Technology, 2018.
## 4 Proposed Trust-Based Optimized Reporting Scheme
### Trust Calculation Mechanism
#### 4.2.1 Notations and Their Meanings  
* Energy (T\({}_{\text{e}}\)): This represents the residual energy of a specific node to participate in communication in the network. It is calculated by subtracting the current energy from the initial energy.
* Unselfishness (T\({}_{\text{u}}\)): This indicates how much a node has responded to transmission requests from neighboring nodes. It involves the trust value for energy and the number of successful communications.
* Honesty (T\({}_{\text{h}}\)): This assesses the node's trustworthiness based on the number of completed transmissions.
* Dishonesty (T\({}_{\text{dh}}\)): This assesses the node's trust based on the number of unsuccessful transmissions.
* Forgetting curve: This is a mechanism that prioritizes recent behavior over older feedback in trust evaluation.
* Similarity (T\({}_{\text{s}}\)): This measures the variation among the feedback propagated by the recommenders.
* E\({}_{\text{i}}\): a node's initial energy.
* E\({}_{\text{c}}\): a node's current energy.
* NT\({}_{\text{d}}\): the direct trust of a node.
* NT\({}_{\text{i}}\): the indirect trust of a node.
* ST: the number of successful tasks. In this paper, we represent each communication request as a \"task\".
* UT: the number of unsuccessful tasks.
* F\({}_{\text{h}}\): a set of honest feedback.
* F\({}_{\text{dh}}\): a set of dishonest feedback.
* T\({}_{\text{c}}\): the current trust value.
* T\({}_{\text{i}}\): the initial trust value.