esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Examples
### Weight Calculation  
The function of weights is to describe the influence degree of several factors on the final path travel time distribution, which can reduce the errors caused by incomplete path  
Figure 10: Distribution factor and scaling factor dispersion of the whole data set.  
Figure 9: Distribution factor and scaling factor corresponding to each trajectory observation: (**a**) Distribution factor; (**b**) Scaling factor.  
coverage and uneven path traversal. The results of weighting coefficient \(\omega\) are shown in Figure 11.  
As can be seen from Figure 11a, due to the random distribution of float data overall, the weighting coefficient of float observation value under normal circumstances before the earthquake is evenly distributed, concentrated between 0.190 and 0.1975. It is not difficult to see from Figure 11b that, under the same condition of floating vehicle track, the weight coefficient of the observed value after the earthquake increases significantly compared with that before the earthquake, and different earthquake damage indexes have a great influence on the weight coefficient of the observed value. When the earthquake damage index is 0.1, the weight coefficient of the observed value is distributed between 0.790 and 0.832, and the median is 0.820, showing a left-biased distribution. When the damage index reached 0.9, the weighted coefficient of the observed values ranged from 0.870 to 0.910, with a median of 0.889, showing a right-skewed distribution. When the earthquake damage index was 0.5, the data were more concentrated and distributed between 0.905 and 0.920 with a median of 0.913, presenting a normal distribution. The interquartile interval IQR represents Q3-Q1, and the points in the figure are mild outliers, between Q3+1.5 IQR and Q3+3 IQR, or Q3+3 IQR. The median of the observed value is 0.820, showing a left-biased distribution. When the earthquake damage index reached 0.9, the weighted coefficient of the observed value ranged from 0.870 to 0.910, with a median of 0.889, showing a right-skewed distribution. When the earthquake damage index was 0.5, the data were more concentrated and distributed between 0.905 and 0.920 with a median of 0.913, presenting a normal distribution. The interquartile interval IQR represents Q3-  
between Q1-3 IQR and Q1-1.5 IQR. In general, the greater the earthquake damage index, the greater the weighted coefficient of observed values.
# Using Fast Frequency Hopping Technique to Improve Reliability of Underwater Communication System
## 3 Results of Underwater Experimental Tests
Figure 7 presents examples of decision values corresponding to a logical '0' and a logical '1' obtained for subsequent hops falling on the transmission of a single symbol '0' in the case of transmission through the \(d\) = 340 m and \(d\) = 550 m channel. The figure confirms the fact that the channels were characterized by different multipath propagations of acoustic wave. \begin{table}
\begin{tabular}{c c c} \hline
**Order of Diversity \(L\)** & **Transmission Rate [bps]** & **Bit Error Rate** \\  & & **BER** \\ \hline
3 & 83.3 & 0.024 \\
5 & 50 & 0.019 \\
7 & 35.7 & \(<\)0.001 \\
9 & 27.7 & \(<\)0.001 \\ \hline \end{tabular}
\end{table} 
Table 1: Experimental tests results for the \(d\) = 550 m channel (subsequent jumps, \(T_{H}\) = 4 ms, \(E_{H}\)/\(N_{0}\) = 31 dB). \begin{table}
\begin{tabular}{c c c} \hline
**Order of Diversity \(L\)** & **Transmission Rate [bps]** & **Bit Error Rate** \\  & & **BER** \\ \hline
3 & 83.3 & 0.022 \\
5 & 50 & \(<\)0.001 \\
7 & 35.7 & \(<\)0.001 \\
9 & 27.7 & \(<\)0.001 \\ \hline \end{tabular}
\end{table} 
Table 2: Experimental tests results for the \(d\) = 1035 m channel (subseq. jumps, \(T_{H}\) = 4 ms, \(E_{H}\)/\(N_{0}\) = 31 dB). Summarizing the obtained results of the experimental tests carried out on a lake, the FFH technique confirms high efficiency when working in shallow coastal waters. The commercial modems designed to work in horizontal channels that use other modulation techniques allow obtaining similar transmission rates of several dozen to hundreds of bits per second and require the use of strong channel coding. Tests gave an answer on the nature of the errors occurring. During the analysis of the FFH-BFSK technique with \(L=\{2,\ldots,9\}\), no burst errors were observed, only random errors were observed.
## 3 Results and Discussion
### Health Risk Assessment
In a study conducted in northeastern Iran, it was found that the health risks for children exceeded the acceptable limits set by health authorities, indicating a pressing need for intervention in areas with high nitrate levels [51]. Similarly, another study highlighted that approximately 14.28% of the groundwater samples were above the hazard quotient threshold for children, highlighting their heightened susceptibility [52].
# Multiple-Intelligent Reflective Surfaces (Multi-IRSs)-Based NOMA System
## 4 The Proposed Multi-IRS Approach  
This scenario proposes the case where more than one reflective plane exists in the communication environment. This is mainly to boost the received signal strength and to increase the chances of successful reception, especially on occasions where there is no direct link existing between the source and the destination. Having multiple IRSs would generate a coupling effect upon each other. In this situation, the effect of coupling among those IRSs is modeled using a factor \(\varrho\), which takes a value range of [0, 1]. For IRS-NOMA, since the resources are shared among the serving BSs, this factor value is always 1; hence, it will be omitted from the related expressions.  
For simplicity, the considered scenario will include only two IRSs, indexed with \(a\) and \(b\), respectively. At the destination, the receiving device receives reflected signals from \(N^{b}\)-reflective surfaces, in addition to the direct signal from the source, as clarified in Figure 2.  
This is to emulate the real-time case where the receiving device could be moving and transferring from the coverage area near one reflective surface into another with a different reflection surface installed, as illustrated in Figure 2. The received signal by the destination could be expressed as  
\[y^{u} =\left(h_{SD}^{u}+h_{SR_{a}}^{u(T)}\Theta h_{RD_{a}}^{u}+h_{SR_{b}}^ {u(T)}\Theta h_{RD_{b}}^{u}\right)*\] \[\sqrt{P_{IRS-NOMA}^{u}}x^{u}+n^{u}, \tag{12}\]  
the subscripts \(a\) and \(b\) refer to the IRS indices, while the rest of the variables have the same definitions as in (1).  
Following the concept of a multi-IRS scenario, the expression in (6) would be  
\[P_{IRS-NOMA}^{u}\left(N^{u},N^{b}\right)=\left(2^{SE_{IRS-NOMA}^{ u}}-1\right)* \tag{13}\] \[\frac{z^{u}+\sum_{v=u+1}^{U}P_{IRS-NOMA}^{v}\left(I_{SD}^{u}+N^{u }\alpha I_{IRS}^{u,d}+N^{b}\alpha I_{IRS}^{u,b}\right)^{2}}{\left(I_{SD}^{u}+N ^{u}\alpha I_{IRS}^{u,d}+N^{b}\alpha I_{IRS}^{u,b}\right)^{2}}\]  
the upper script \((^{a})\), which denotes the parameters associated with the first IRS and \(\left({}^{b}\right)\), is associated with the second IRS parameters. For instance, \(N^{a}\) and \(N^{b}\) refer to the number of reflective elements of the first and second IRS, respectively.
## 10 Shortcomings of Traveling Waves (TW) Based Methods
### Non-Traveling Wave (NTW) Method Based on Rate of Change of Voltage and Current  
When a dc fault occurs in MT-HVdc system, voltage and current undergo a decrease and an increase at a certain rate, respectively [109]. This change is basically a difference between voltage and current values measured at pre-fault and post-fault conditions of HVdc grid. This difference is then compared with the threshold or reference value to estimate the health of MT-HVdc system. If the voltage value is less than the reference value, fault is diagnosed in HVdc system via algorithm presented in Figure 22. In the similar fashion, if the current sample is greater than the reference value, fault is detected in the dc system as shown in Figure 23.  
Criteria for fault location and detection is given by:  
\[\Delta V=Vpost-Vpre \tag{117}\]  
Figure 21: Voltage distribution curves at different fault locations.  
\[\Delta V\leq Vref \tag{118}\]  
\[\Delta I=\ I_{post}-I_{pre} \tag{119}\]  
\[\Delta I\leq I_{ref} \tag{120}\]  
Information about fault is processed with in milliseconds but is affected by the noise which raise questions over accuracy of detection [110]. Normally, it is used for primary protection [109]. In a bipolar topology of HVdc system, this method is applied on each pole as its stable under any kind of disturbances.  
Rate of change of voltage and current methods depend upon the fault loop impedance. Therefore, in a fault where impedance is high or closer to terminals of converter station, this method does not provide satisfactory results because of minute rate of changes [110].  
Figure 23: Non-traveling wave (NTW) method based on rate of change of current.  
Figure 22: NTW method based on rate of change of voltage.
## 2 Reaction Mechanisms and Kinetics of Electrocatalytic Nitrate Reduction
### DFT Calculation as an Advanced Tool for eNO\({}_{3}\)R
#### 2.2.1 Insights into eNO\({}_{3}\)R: Descriptors and Rate Determining Steps (RDS)
They also constructed theoretical volcano plots using nitrogen binding energy (\(\Delta\)E\({}_{\text{N}}\)) and oxygen binding energy (\(\Delta\)E\({}_{\text{O}}\)) which serve as key descriptors for evaluating eNO\({}_{3}\)R performance on metal surfaces, obtained via DFT-based mean-field microkinetic modeling. The volcano plots reveal how the nitrate reduction rate depends on the adsorption strengths of N and oxygen O species at four different applied potentials. RDS in nitrate reduction varies with applied potential and metal type. At low potentials (\(-\)0.2 to 0 V), the RDS for Pt and Pd is the dissociation of *NO\({}_{3}\). As the potential increases to 0.4 V, *NO desorption becomes rate-controlling for these metals. For Rh, *N\({}_{2}\) formation is rate-controlling above 0.2 V. In contrast, for Fe and Co, the RDS shifts from *NO\({}_{3}\) dissociation to N\({}_{2}\) formation as the potential surpasses \(-\)0.2 V. On Cu, the RDS transitions from *NO\({}_{3}\) dissociation to *NO\({}_{2}\) dissociation at 0.2 V due to increased *NO\({}_{2}\) coverage. For Ag and Au, *NO\({}_{3}\) dissociation remains the RDS across all potentials, attributable to their weak adsorption and low nitrate coverage [67]. As previously demonstrated, descriptors and volcano plots play a critical role in identifying RDS and determining optimal catalysts. These approaches have been extensively utilized to predict new catalysts and effectively distinguish between different materials based on their catalytic activity and selectivity, as exemplified by the study conducted by [PERSON] et al. [68] who employed first-principles calculations to assess the electrocatalytic performance of dual-atom catalysts (DACs) based on TM\({}_{2}\)/N\({}_{6}-\)G (DACs supported by an N-doped graphene nanosheet where TM\({}_{2}\) ranges from Sc\({}_{2}\) to Zn\({}_{2}\)). Through high-throughput calculations, they identified Cr\({}_{2}\)/N\({}_{6}-\)G, Mn\({}_{2}\)/N\({}_{6}-\)G, and Cu\({}_{2}\)/N\({}_{6}-\)G as prominent candidates near the volcano plot as illustrated in Figure 5D, exhibiting impressive limiting potentials of \(-\)0.46, \(-\)0.45, and \(-\)0.36 V, respectively. Furthermore, significant energy barriers for byproduct formation (NO\({}_{2}\), NO, and N\({}_{2}\)O) which confirm their high selectivity [68].
# The Synergy of Renewable Energy and Desalination:
## 2 Overview of Desalination Technologies
### Membrane-Based Methods
Among other important criteria are the technology maturity level, fouling potential, and water recovery rate. Because the sustainability of the desalination industry depends on the environmental aspects, it is necessary to improve membrane-based methods taking into account environmental footprint, process integration, waste recycling, and integration of renewable energy sources. A comparison table with some characteristics of various membrane-based desalination technologies is given below in Table 2.  
Figure 2 shows the energy performance parameters of different desalination processes, including MSF, MED, RO, and NF. The orange color depicts the minimum and the grey color the maximum consumption for a given technology.
# Parallel-Based Corpus Annotation for Malay Health Documents
## 5 Discussion (Challenges)
### Ambiguous Entity Categorization  
There are certain words or phrases that can serve as entities for multiple category types, presenting a complex issue in named entity recognition (NER). For instance, consider the phrase \"sakit dada\" (chest pain), which could be perceived as an entity within either the disease or symptom categories. This duality generates a demand for context-specific interpretation by the NER system. If \"sakit dada\" appears within a disease diagnostic context, the NER should classify it within the disease entity category. Alternatively, if the phrase is cited in the description of symptoms, the NER should allocate it to the symptom entity category.  
In many scenarios, the NER system needs to analyze the broader context, taking into account related words in the sentence or document, to determine the most appropriate entity categorization. This is essentially utilizing the principles of co-reference resolution and word sense disambiguation to clarify semantic relationships and meanings.  
This context-sensitive entity categorization presents significant challenges in developing an accurate and reliable NER system. The complexity is amplified when dealing with the medical domain, given the vast range of terminologies and their potential overlap between categories. Furthermore, the NER system must also factor in the linguistic and cultural nuances that can influence the interpretation of certain words or phrases. Consequently, handling such ambiguities requires sophisticated models with robust context-understanding capabilities, well-crafted feature sets, and effective training methods. These requirements underscore the need for high-quality, annotated training data like the Malay Health Document Annotated Corpus.  
However, even with these resources, achieving a high level of accuracy in ambiguous entity categorization remains a demanding task. This is a significant area of research focus, with potential solutions exploring advanced techniques like deep learning and complex NLP models, as well as inter-disciplinary approaches integrating linguistics, medical knowledge, and computational methodologies.
# A 60-GHz Ultra-Thin and Flexible Metasurface for Frequency-Selective Wireless Applications
## 1 Introduction
In addition, the flexibility and conformity of such structures are anticipated as essential features to maximize the feasibility of integrating the wireless technology in numerous non-planar surfaces for the compact, robust, and reliable user experience [23; 24]. Due to the challenges involved in the large scale fabrication of the metasurfaces based on the flexible substrates or films, several advanced techniques, such as additive and subtractive lithographic processes and various nano-printing methods have been proposed [25; 26]. Among the fast and high-resolution printing techniques for bulk fabrication, screen printing is anticipated as a feasible choice due to the simplicity of the process, time-efficiency and reduced wastage, cost-effectiveness and reasonable accuracy [26]. The MMW spectrum has gathered huge research interest in moving toward the development of an ultra-fast infrastructure, with the potential to support a large number of applications and services for future networks [27]. Realization of such a massive migration from low-frequency bands to MMWs is a huge task and needs more intense efforts at different research forums to propose efficient MMW based devices and components. A high-attenuation 60 GHz band has been projected as well-suited to establish secure and high-speed point-to-point communication links for short-distance indoor networks [28]. This paper has proposed the flexible printed metasurface to be integrated into the indoor scenarios, such as walls and windows, in order to enhance the signal strength for the 60-GHz band. For a cost-effective and fast approach with reasonable accuracy, screen printing is exploited here due to its capability of bulk fabrication, making it a smart selection for EM surfaces with large surface area [23; 29]. The designed metasurface is characterized to validate a broadband performance by deploying the testing setup incorporating a quasi-optic test bench.
###### Abstract  
The success of the convolutional neural network (CNN) comes with a tremendous growth of diverse CNN structures, making it hard to deploy on limited-resource platforms. These over-sized models contain a large amount of filters in the convolutional layers, which are responsible for almost 99% of the computation. The key question here arises: Do we really need all those filters? By removing entire filters, the computational cost can be significantly reduced. Hence, in this article, a filter pruning method, a process of discarding a subset of unimportant or weak filters from the original CNN model, is proposed, which alleviates the shortcomings of over-sized CNN architectures at the cost of storage space and time. The proposed filter pruning strategy is adopted to compress the model by assigning additional importance weights to convolutional filters. These additional importance weights help each filter learn its responsibility and contribute more efficiently. We adopted different initialization strategies to learn more about filters from different aspects and prune accordingly. Furthermore, unlike existing pruning approaches, the proposed method uses a predefined error tolerance level instead of the pruning rate. Extensive experiments on two widely used image segmentation datasets: Inria and AIRS, and two widely known CNN models for segmentation: TernausNet and standard U-Net, verify that our pruning approach can efficiently compress CNN models with almost negligible or no loss of accuracy. For instance, our approach could significantly reduce 85% of all floating point operations (FLOPs) from TernausNet on Inria with a negligible drop of 0.32% in validation accuracy. This compressed network is six-times smaller and almost seven-times faster (on a cluster of GPUs) than that of the original TernausNet, while the drop in the accuracy is less than 1%. Moreover, we reduced the FLOPs by 84.34% without significantly deteriorating the output performance on the AIRS dataset for TernausNet. The proposed pruning method effectively reduced the number of FLOPs and parameters of the CNN model, while almost retaining the original accuracy. The compact model can be deployed on any embedded device without any specialized hardware. We show that the performance of the pruned CNN model is very similar to that of the original unpruned CNN model. We also report numerous ablation studies to validate our approach.  
convolutional neural network; filter pruning; image segmentation; network compression; weight initialization strategies +
Footnote †: journal: _Sciences_
# Temporal-Guided Label Assignment for Video Object Detection
## 3 Methodology
### Temporal-Guided Label Assignment Framework
#### 3.2.1 Weight Maps for Label Assignment  
As shown in Figure 2, we follow the initial setting in Faster R-CNN, which means our method is an anchor-based one.  
\(w\) is the weight map of one gt box of one image. Each location's weight represents the likelihood of a positive sample (negative sample). Here, \(w^{+}\) is the weight map of positives and \(w^{-}\) is the weight map for the negatives; these are generated by center weighting \(G\) and confidence weighting \(C\). Confidence weighting is generated by the RPN outputs. We follow the implementation in AutoAssign [18] for our label assignment. For location \(q\in S_{i}\), where \(S_{i}\) denotes all locations inside the bounding box of object \(i\) we have the following:  
\[w^{+}(q)=\frac{C(P^{+}(q))G(\overrightarrow{d(q)})}{\sum_{j\in S_{i}}C(P^{+} (j))G(\overrightarrow{d(j)})} \tag{1}\]  
where \(\overrightarrow{d(q)}\) denotes the offsets of a certain position inside an object to its box center along the x-axis and the y-axis. \(P^{+}(q)=P(cls,q)\cdot P(loc,q)\) is the positive confidence. \(P(cls,q)\) and \(P(loc,q)\) are the results of classification and coarse regression. Here, \(w^{+}(q)\) is normalized by all candidate locations of one object. \(w^{-}(q)\) is formulated by the IOU of prediction boxes on location \(q\) between gt boxes as follows:  
\[w^{-}(q)=1-f(iou(q)) \tag{2}\]  
where \(f(\cdot)\) denotes a function whose role is that the larger the IOU, the less likely the box will be a negative sample, and the smaller it is implies the opposite.  
But even this strategy of automatic label assignment encounters challenges in video object detection. When ab object's appearance in the video deteriorates, the label assignment strategy cannot obtain good weight maps because the distinction between the foreground and background is unclear in this condition. For example, the appearance of the rabbit in the target frame in Figure 2 is blurred, and the resulting weight maps are unfocused. Therefore, a framework that utilizes temporal information for the learning task of the RPN is put forward, and we call it temporal-guided label assignment.  
Figure 2: The architecture of our temporal guided label assignment (TGLA). The temporal-guided label assignment framework is presented by the upper blue box and the lower orange box, which denote forward propagation and loss computation separately. Note that the framework we mentioned below does not include feature instructing. Furthermore, we put forward an feature instructing module (FIM), which utilizes feature maps to guide the fusion of weight maps.
# Tensor Completion Based on Triple Tubal Nuclear Norm
## 2 Notations and Preliminaries
### Notations  
Vectors are denoted by bold lower case letters, e.g., \(\mathbf{v}\in\mathbb{R}^{n}\), matrices are denoted by bold upper case letters, e.g., \(\mathbf{M}\in\mathbb{R}^{n_{1}\times n_{2}}\), and tensors are denoted by calligraphy letters, e.g., \(\mathbf{\mathcal{T}}\in\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}\). Given a third-order tensor, its fiber is defined as a 1D vector obtained by fixing all indices, but one, and its slice is a 2D matrix defined by fixing all but two indices. Given a 3D tensor \(\mathbf{\mathcal{T}}\), \(\mathbf{\mathcal{T}}_{ijk}\) denotes the entry with index \((i,j,k)\); \(\mathbf{T}^{(k)}:=\mathbf{\mathcal{T}}(:,:,k)\) denotes the \(k\)-th frontal slice. \(\widetilde{\mathbf{\mathcal{T}}}\) denotes the tensor after performing the fast Fourier transformation along the tube fibers of \(\mathbf{\mathcal{T}}\). Notations \(\text{dft3}(\cdot)\) and \(\text{idft3}(\cdot)\) are used to represent the discrete Fourier transformation (DFT) and inverse discrete Fourier transformation (IDFT) along the tube fibers of 3D tensors.  
Given a matrix \(\mathbf{M}\in\mathbb{R}^{n_{1}\times n_{2}}\), its nuclear norm is defined as \(\|\mathbf{M}\|_{*}:=\sum_{i=1}^{p}\sigma_{i}(\mathbf{M})\), where \(p=\min\{n_{1},n_{2}\}\) and \(\sigma_{1}(\mathbf{M})\geq\cdots\geq\sigma_{p}(\mathbf{M})\) are the singular values of \(\mathbf{M}\) in non-ascending order. The inner product between two 3D tensors \(\mathbf{\mathcal{T}}_{1},\mathbf{\mathcal{T}}_{2}\in\mathbb{R}^{n_{1}\times n_{2}\times n _{3}}\) is defined as \(\langle\mathbf{\mathcal{T}}_{1},\mathbf{\mathcal{T}}_{2}\rangle:=\sum_{ijk}\mathbf{\mathcal{T}}_{1 }(i,j,k)\mathbf{\mathcal{T}}_{2}(i,j,k)\). The Frobenius norm of a tensor \(\mathbf{\mathcal{T}}\in\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}\) is defined as \(\|\mathbf{\mathcal{T}}\|_{\mathrm{F}}:=\sqrt{\sum_{ijk}\mathbf{\mathcal{T}}_{ijk}^{2}}\). The \(l_{\infty}\)-norm of a tensor \(\mathbf{\mathcal{T}}\in\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}\) is defined as \(\|\mathbf{\mathcal{T}}\|_{\infty}:=\max_{ijk}|\mathbf{\mathcal{T}}_{ijk}|\).