esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Experiment 2: Evaluation of the Proposed Method on Cross-Database
#### 4.3.3 Comparison with State-of-the-Art Methods  
Table 6 summarizes recent studies on classifying arrhythmia rhythms using ECG signals. The PhysioNet 2017 challenge dataset, which evaluates the model's inter-patient scenario, has been employed in various studies. [PERSON] et al. (2018) and [PERSON] et al. (2019) used this dataset to classify arrhythmia rhythms, achieving an F1 score of 83.0% using ResNet and achieving 71.0% with CNN, respectively.  
When considering multiple datasets utilization for training, our method shows promising results. [PERSON] et al. (2019) showed that, when incorporating the MIT-BIH and SPH datasets into training, the F1 score substantially dropped to 60.3% compared to 89.5% when utilizing the SPH dataset alone. [PERSON] et al. (2018) used 12 leads with the two datasets, and this performed slightly better than our proposed method. However, no significant enhancement was seen even when using an additional 11 leads for rhythm classification.  
Our proposed method outperforms other studies that utilize a single lead and multiple datasets in inter-patient evaluation, achieving the highest F1 score of 87.4%. This is due to our approach's consideration of patient-dependent features in ECG signals and the application of adversarial learning to exclude these features during the beat classifier training process, unlike in other studies ([PERSON] et al., 2018; [PERSON] et al., 2018). By focusing more on beat-related features during beat-classifier training, our method demonstrates superior performance in arrhythmia classification in the inter-patient scheme.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Authors** & **Database** &
\begin{tabular}{c} **No. of** \\ **Classes** \\ \end{tabular} & **Method** & **Evaluation** & **Lead** & **F1-Score** & **Accuracy** \\ \hline [PERSON] et al. (2018) & PhysioNet 2017 & 3 & CNN & Inter-patient & 1 & 71.0 & Unknown \\ [PERSON] et al. (2019) & PhysioNet 2017 & 3 & ResNet & Inter-patient & 1 & 83.0 & Unknown \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of classification performance with the state-of-the-art methods.  
Figure 8: The first row consists of AFib patient’s 1D ECG signals derived from three different patients in the SPH dataset. The second and third rows depict the baseline n-BSMs and PI-BSMs transformed from the 1D ECG signals. PI-BSM images show more similarity than 1D ECG signals or baseline n-BSM.
# A Study on Design and Control of the Multi-Station Multi-Container Transportation System
## 3 Results
### Experimental Results  
After researching and designing the mechanical system and the control algorithm, some assumptions and constraints were set to verify the ability to transport containers among the stations:  
+ Some assumptions of the system:  
1. The containers can move through the gap in transition between the two conveyors.
2. The different height between the surfaces of the conveyors is negligible and can be ignored.
3. No abnormal errors occurred in the control model (PLC can handle the operation of the mechanical modules properly, TCP/IP transmission standard can function effectively without error, system data is constantly updated to the server and stored in the database).  
Figure 20: Handling situation 2 at the 4-line switching module.  
+ Some constraints of the system:  
1. Safety factor (the ceilings of buildings must be sufficiently rigid--related to construction site inspection; the system must be examined for durability to select the appropriate material).
2. The overhead conveyor system after being installed on the truss must operate effectively, without shaking or jamming the container during transport.
3. The system needs to be expandable and easy to install. The input/output for the control module need to be reduced to a minimum.
4. Containers must stop at the exact position (entrance and end position of modules, middle of the sub-conveyor).
5. The containers must always be in the middle of the conveyor width along the length of the conveying stroke (to ensure that there are no jams or entanglements during the transition where the conveyors are perpendicular to each other).  
From the assumptions and constraints of the system, we conducted several experiments to test the efficiency, precision, and accuracy of the whole transportation system. The experimental process is carried out by following these steps:  
1. Building a control function for the lifting module, the line switching module and performing experiments to check the operation of each mechanical module separately before installation. The experimental test of each module helps us to observe the other mechanical features that do not meet the previous assumptions and constraints. This issue is explained in detail in the Discussion section. After completing the installation:
2.
## 2 Materials and Methods
### Real-Time PCR Quantification (qPCR)  
Viral load and expression of defense genes was quantified by using specific primers (Table 1). The PCR reaction was carried out using 1\(\times\) of KAPA SYBR FAST Universal 2\(\times\) qPCR Master Mix (Kapa Biosystems, Wilmington, MA, USA) according to the manufacturer's instructions. Samples were added to a reaction volume of 15 \(\mu\)L, including 20 ng of cDNA, 530 nM of each primer, and sterile filtered molecular-grade water to reach 15 \(\mu\)L. The conditions used for the thermal reaction were 96 \({}^{\circ}\)C for 10 min, followed by 40 cycles at 95 \({}^{\circ}\)C for 15 s, 60 \({}^{\circ}\)C for 15 s, and 72 \({}^{\circ}\)C for 30 s. Real-time PCR assays were performed on a Stratagene Mx3000P thermal cycler (Agilent Technologies, Santa Clara, CA, USA), and the data were analyzed using MxPro software (Stratagene, Agilent Technologies, Santa Clara, CA, USA). The relative expression of each gene was calculated after normalization with an endogen gene (\(\beta\)-actin) as defined by [PERSON] [35]. The viral load was analyzed by the absolute quantification of DWV-A.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Primers** & \multicolumn{2}{c}{**Sequences**} & **Reference** \\ \hline DWV-A & F & TATCTTCATAAAGCCACCTGAA & [36] \\  & R & TTTCCTCATTAACTGTGGTGTGTGT & \\ \(\beta\)-Actin & F & ATGCACAACCTGCTTTCTG & [36] \\  & R & GACCACCAAATCATGGA & \\  & F & GCATGTTGAGGCTGAA & \\  & R & CCAATTCTGAAAGGCGTCCA & [37] \\ Cactus & F & CACAAGATCTGAGCAACGA & [37] \\  & R & GCATTCTTGAAGGAAACG & \\  & F & ACCTGATGTTATGCACAGT & \\  & R & AGCCTTTTAGAACTTTGTGTGT & [38] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nucleotide sequence of defense gene primers from _Apis mellifera_ and deformed wing virus (DWV-A) used in qRT-PCR.  
A standard curve by means of a purified PCR product Wizard(r) VR SV gel and PCR clean-up system (Promega, Madison, WI, USA), belonging to the viral target sequence, was utilized [34]. Purified amplicon was quantified by using spectrophotometry (EpochTM Microplate Spectrophotometer, BioTek, Winooski, VT, USA) and a copy number according to [PERSON], et al. [40] was calculated.  
Linear standard curves (95-100% efficiency) were applied using a serial dilution (1.0 \(\times\) 10\({}^{1}\) to 1.0 \(\times\) 10\({}^{9}\)) of viral copy numbers of purified cDNA. Subsequently, the cycle threshold (Ct) values were plotted against copy number values (log\({}_{10}\)). The sample copy numbers were estimated using the Ct values and comparisons with the linear equation of the standard curve and normalizing values of the housekeeping \(\beta\)-actin gene were made [36]. Data were expressed as the copy number of DWV per bee by considering the dilutions that were achieved in the cDNA synthesis and qPCR reaction.
## 4 Results and Discussion
### Experimental Setup  
#### 4.1.1 Datasets and Environment  
We conducted experiments using multiple benchmark datasets to ensure a comprehensive evaluation of our proposed methods. For retrieval tasks, we utilized the BEIR benchmark [39], which includes diverse retrieval scenarios across multiple domains. For document classification, we employed standard text classification datasets including 20 Newsgroups, Reuters-21578, and a proprietary official document dataset containing 5000 documents across 15 categories relevant to administrative workflows.  
The experimental platform consisted of a high-performance computing cluster with NVIDIA A100 GPUs, 128 GB RAM, and Intel Xeon processors (Intel, Santa Clara, CA, USA). All models were implemented using PyTorch 1.12 and Python 3.8, with experiments conducted under controlled conditions to ensure reproducibility.
## 1 Introduction
First, we pose a constrained optimization problem for selecting as many different technologies as possible, which do not share common risks while are capable of communicating the network nodes. We termed this problem as \"The optimal selection of the technology set.\" Second, we propose another constrained optimization problem for selecting the number of nodes to be used from each technology, in order to fulfill a network design requirement with a Capital Expenditure (CAPEX) restriction. We termed this problem as \"The fair technology distribution problem.\" Lastly, we state yet another constrained optimization problem for placing the nodes within the network topology so that its resilience is maximized. We termed this last problem as \"The reliable node placing problem.\" The correlated failures regarded in this paper are modeled using Shared Risk Node Groups (SRNGs). These failures represent here cyber attacks, which aim to take advantage of specific vulnerabilities shared in all the network nodes belonging to the same SRNG event. We comment that, in the eight real-world network topologies considered here, our multiculture design enhances network resilience as compared to a monoculture design. In fact, results show that the proposed multiculture network design, which regards individual and shared node risks, can cope with multiple node failures induced by correlated SRNG events. In doing so, it efficiently trades off the number of technologies to be used (i.e., the degree of heterogeneity in the network), the number of nodes to specify per technology, and their location in the network topology. We note that, when our algorithms select larger sets of technologies, an attacker should make more efforts to compromise the network integrity and the impact caused by a particular attack on the network infrastructure is reduced. Additionally, our results also show that the node placingalgorithm attempts to cluster nodes belonging to the same technology, and remarkably, it locates the most vulnerable technologies at sites where the impact of multiple node failures on the entire network connectivity is reduced. Lastly, we comment that the location method proposed here exhibits better results, concerning the Average Two-Terminal Reliability (ATTR) metric than our earlier method [13]. The rest of this paper is organized as follows. In Section 2, we present and summarize the related work in the area. In Section 3, we introduce the principles of multiculture network design that we have employed in our work for increasing resilience in the face of correlated attacks. In Section 4, we explain our methodology, mathematically define the problem of redesigning a network topology, introduce our correlated failure model, and formulate the three optimization problems used to carry out the multiculture network design. Next, we introduce the resilience metrics used here and present the search algorithms we developed for solving the above-mentioned optimization problems. In Section 5, we present and compare the numerical results achieved by our algorithms. Lastly, in Section 6, we draw the conclusions of the paper.
# Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion
## 4 Methodology and Model Development
### Diversion Model
#### 4.4.1 Roadway Geometry  
Roadway capacity is related to the number of lanes on the roadway. Additionally, lane width is an essential factor for maneuverability. In this study, the threshold for truck diversion was identified as lanes with a width of 9 feet. Roads with lane widths of 9 feet or less were added to the network as restrictions.  
Figure 6: Network building process.
## 2 Materials and Methods
### Environmental Assessment Using the Waste Reduction Algorithm (WAR)
#### 2.2.2 Atmospheric Impact Categories
\[i_{out}^{(t)}=i_{out}^{(cp)}+i_{out}^{(cp)}+i_{ave}^{(cp)}=\sum_{j}^{cp}M_{j} {}^{(out)}\sum_{k}^{cp}X_{kj}\forall_{k}+\sum_{j}^{cp-g}M_{j}{}^{(out)}\sum_{k }^{cp-g}X_{kj}\forall_{k} \tag{9}\]  
\[i_{out}^{(t)}=\frac{\left(i_{out}^{(cp)}+i_{out}^{(cp)}+i_{ave}^{(cp)}+i_{ave}^{ (cp)}\right)}{\sum_{P}P_{P}}=\frac{\sum_{j}^{cp}M_{j}{}^{(out)}\sum_{k}^{cp}X_{ kj}\forall_{k}+\sum_{j}^{cp-g}M_{j}{}^{(out)}\sum_{k}^{cp-g}X_{kj}\forall_{k}}{ \sum_{P}P_{P}} \tag{10}\]  
\[i_{gen}^{(t)} =i_{out}^{(cp)}-i_{in}^{(cp)}+i_{out}^{(cp)}-i_{in}^{(cp)}+i_{ave}^{ (cp)}+i_{ave}^{(cp)} \tag{11}\] \[=\sum_{j}^{cp}M_{j}{}^{(out)}\sum_{k}^{cp}X_{kj}\forall_{k}-\sum_ {j}^{cp}M_{j}{}^{(in)}\sum_{k}^{cp}X_{kj}\forall_{k}+\sum_{j}^{cp-g}M_{j}{}^{( out)}\sum_{k}^{cp-g}X_{kj}\forall_{k}\] (12) \[i_{gen}^{(t)} =\frac{\left(i_{out}^{(cp)}-i_{in}^{(cp)}+i_{out}^{(cp)}-i_{in}^{(cp )}+i_{ave}^{(cp)}+i_{ave}^{(cp)}\right)}{\sum_{P}P_{P}}\] (13) \[=\frac{\left(\sum_{j}^{cp}M_{j}{}^{(out)}\sum_{k}^{cp}X_{kj} \forall_{k}-\sum_{j}^{cp}M_{j}{}^{(in)}\sum_{k}^{cp}X_{kj}\forall_{k}+\sum_{j }^{cp-g}M_{j}{}^{(out)}\sum_{k}^{cp-g}X_{kj}\forall_{k}\right)}{\sum_{P}P_{P}} \tag{14}\]  
where \(i_{out}^{(cp)}\) and \(i_{in}^{(cp)}\) are the rate of PEIs out and into the system due to chemical interactions within the system, respectively; \(i_{out}^{(cp)}\) and \(i_{in}^{(cp)}\) are the rates of PEI out and into the system due to energy generation processes within the system; and \(i_{ave}^{(cp)}\) and \(i_{ave}^{(cp)}\) are the PEIs out of a system as a result of the release of waste energy due to energy generation and chemical processes within the system. Furthermore, \(M_{j}{}^{(in)}\) and \(M_{j}{}^{(out)}\) are the input and output mass flow rates of stream \(j\); \(X_{k}\) is the mass fraction of a component \(k\) in the stream \(j\); \(\forall_{k}\) is the overall Potential Environmental Impact of chemical \(k\); and \(P_{p}\) is the mass flowrate of product p [18]. To perform the environmental evaluation of a large scale production of magnetite (Fe\({}_{3}\)O\({}_{4}\)) nanoparticles via coprecipitation, four study cases were formulated. Case 1 is proposed without considering the energy and product streams. Case 2 considers the environmental impacts of the product streams without energy contribution. On the other hand, Case 3 considers the environmental impacts of the energy sources without product stream contribution and Case 4 considers the contribution of both energy and product streams to the potential environmental impacts. The combination of global impact analysis, impacts by category, effect of energy flow and source of energy allowed us to obtain a good diagnosis of the environmental viability of the process.
# Unsupervised Scene Image Text Segmentation Based on Improved CycleGAN
## 3 Scene Text Segmentation Image Generation
### Network Architecture
#### 3.2.2 Generator  
CycleGAN includes two generators, both of which have the same structure and are composed of an encoder, a transformer, an ASPP module and a decoder. However, the generator \(G_{A}\) realizes the conversion from a complex background image to no background image, \(G_{A}:A\to B\); the complex background is removed. The generator \(G_{A}\), on the other hand, restores the non-background image to a complex background image, \(G_{B}:B\to A\). Figure 4 shows the internal structure of the generator in detail.  
Both the input and output of the generator are RGB images. In order to reduce the computational effort, the input image is mapped from higher dimensions to lower dimensions. In other words, the input image is downsampled. Downsampling uses one convolutional layer of stride 1 and two convolutional layers of stride 2. In this way, the computational cost is the same, but you obtain a larger and more detailed network and receptive field. It can make the style transfer complete better. The converter consists of six residual blocks, which are responsible for extracting features in the image and converting the extracted image feature vector from domain \(A\) to domain \(B\). After the feature vector conversion of the image is completed, it will enter the newly introduced ASPP module. The ASPP module continues to process these features and obtain global features so as to fuse features from multiple scales and improve segmentation details. The decoder reconstructs the output of the converter to the size of the original image. The process of image processing by the decoder is the inverse process of the encoder. It uses deconvolution  
Figure 4: The structure of the new generator.  
Figure 3: ASPP module.  
layers to map images from low to high dimensions until the resulting image is equal in size to the original image.  
When CycleGAN is used to complete the task of scene text segmentation, the text edge is not clear enough, and there is a lot of noise. We consider it because in the convolution process, the global information is ignored and the edge contour is not obvious. The ability of ASPP to capture global information is well known, so we chose to introduce ASPP into the generator.
# NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research
## 7 Analysis of NTA Research (NTARC Database)
### Research Foci  
By querying metadata to explore where authors establish the focus of their proposals (Figure 4b), we found three main pillars: (a) the algorithm, (b) the methodology/framework, and (c) the features. The distribution in Figure 4b shows that the research effort mainly focuses on making NTA more accurate by the application of novel methodologies and algorithms (83.4%), and comparatively less attention is given to understanding network traffic phenomena or abstracting knowledge from collected data. The study of features is still relevant (13.8%), but the analysis of patterns, the study of outliers, or enhanced data descriptions are not core aspects of the research. These figures do not imply that papers do not tackle such low-rated tasks; instead, they emphasize that, when some kind of knowledge discovery is undertaken and disclosed, this is not claimed as the principal novelty of the scientific work. A closer look at what authors claim as their principal contributions corroborates this point (Section 7.6)  
Actually, discovering knowledge in data should be easier to face than designing effective detection systems, a task that implies some constraints that are difficult for scientists to overcome (see Section 8). Here, it is worth remarking that the high complexity of network traffic data justifies its use for testing novel analysis algorithms, frequently without taking into account the feasibility and transportability of the techniques used for real-life environments.
## 3 Analysis of Pumped Storage Transformation
### Pumped Storage Unit Selection  
The basis for installing pumped storage units is to expand the hydropower reversibility function while avoiding the influence on the original equipment as much as possible. There are two main types of pumped storage unit selection:  
1. Install reversible pump turbine. The installed reversible pump turbine is generally used for pumping conditions and can also serve as a backup for the power generation of the original generator set. Because of its power generation function, it is necessary to build corresponding transmission lines, and replacing the original turbine will cause a waste of conventional units.
2. Install water pump. A water pump can only be installed for pumping conditions, using reverse power transmission through existing transmission lines without incurring the cost of new transmission lines. However, it can only fill the valley in the power grid, and the original generator set must cooperate with the peak regulation. Reference data from China's Baishan Pumped Storage Project show that installing water pump reduces investment by approximately 10-20% compared to reversible pump turbine units [32].
# Design Approach for Composite Pavement Structure Incorporating Reflective Crack Considerations
## 4 Derivation of Calculation Formula for Load Stress
### Load Stress Analysis of Composite Pavement Considering Stress Absorption Layer  
While it has been acknowledged in engineering that the incorporation of stress-absorbing layers can enhance the crack resistance of road surfaces [27; 28], quantitatively characterizing this structural performance remains challenging. In this study, a three-dimensional finite element model is utilized, incorporating a stress-absorbing layer modulus of 600 MPa. The analysis focuses on varying \(h_{1}\) and the thickness of the stress-absorbing layer \(h_{3}\). Subsequently, the maximum tensile stress \(\sigma_{1}\), equivalent stress \(\sigma_{e}\), and maximum shear stress \(\tau_{max}\) of the asphalt layer when \(h_{1}=2\) cm, 4 cm, 6 cm, 12 cm, 16 cm, and 20 cm and \(h_{3}=0\) cm, 1 cm, 1.5 cm, 2 cm, and 3 cm are calculated. The calculation results are shown in Figure 18.  
Figure 18: _Cont._  
Based on the findings depicted in Figure 18, the following observations can be made:  
1. Setting a stress-absorbing layer can significantly reduce load stress. Setting a 1 cm stress-absorbing layer reduces \(\sigma_{1}\) by 69.7% and \(\sigma_{e}\) by 34.1% compared to the absence of a stress-absorbing layer, and \(\tau_{max}\) reduced by 30.9%.
2. Increasing \(h_{3}\) can effectively mitigate load stress. When \(h_{3}\) is less than 2 cm, the load stress decreases as thickness increases. However, once \(h_{3}\) is greater than 2 cm, the load stress remains relatively unchanged.
3. The greater the value of \(h_{1}\), the smaller the impact of the increase in \(h_{3}\) on load stress.  
Hence, the establishment of an appropriate thickness stress-absorbing layer is considered an effective approach for delaying the occurrence of reflection cracks. The research by [PERSON] [29] proposed that the stress-absorbing layer cannot be treated as a single structural layer and can be considered as a whole with the asphalt layer. The equivalent equation is given as follows:  
\[E_{c}=\left(\frac{E_{1}^{1/3}h_{1}+E_{3}^{1/3}h_{3}}{h_{1}+h_{3}}\right)^{3} \tag{29}\]  
where \(E_{c}\) is the equivalent elastic modulus.  
The aforementioned equation demonstrates that the equivalent elastic modulus is comparatively lower than that of the asphalt layer, while the tensile strength is higher. This disparity elucidates the ability of the stress-absorbing layer to impede or postpone cracking; however, it also underscores the limitation of excessive thickness in such a layer. As a result, while calculating the pavement structure when thinking about adding a stress-absorbing layer, the equivalent modulus conversion can be completed first and then the pavement structure design.