esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Analysis
### Evaluation Metrics  
Given the maximum \(FEs\) (\(MaxFEs\)) and accuracy \(\varepsilon\), the peak ratio (\(PR\)) and success rate (\(SR\)) were utilized to evaluate the performance of the multimodal optimization algorithms.  
\(PR\) reflects the average percentage of all global optima found over multiple runs, with the following formula:  
\[PR=\frac{\sum\limits_{num=1}^{NR}NPF_{i}}{NKP\times NR}. \tag{17}\]  
where \(NPF_{i}\) denotes the global optimum found at the end of the \(i\)th run, \(NKP\) represents the already known optimal value, \(NR\) denotes the number of runs, and \(NSR\) stands for the number of successful runs.  
\(SR\) is the percentage of successful runs (those in which all global optima are found). Its formula can be expressed as  
\[SR=\frac{NSR}{NR}. \tag{18}\]  
Five precision classes are often used in the literature: \(\varepsilon\) = 1.0 \(\times\) 10\({}^{-1}\), \(\varepsilon\) = 1.0 \(\times\) 10\({}^{-2}\), \(\varepsilon\) = 1.0 \(\times\) 10\({}^{-3}\), \(\varepsilon\) = 1.0 \(\times\) 10\({}^{-4}\), and \(\varepsilon\) = 1.0 \(\times\) 10\({}^{-5}\). However, since the precision levels \(\varepsilon\) = 1.0 \(\times\) 10\({}^{-1}\) and \(\varepsilon\) = 1.0 \(\times\) 10\({}^{-2}\) are not sufficiently high, only the last three precision levels \(\varepsilon\) = 1.0 \(\times\) 10\({}^{-3}\), \(\varepsilon\) = 1.0 \(\times\) 10\({}^{-4}\), and \(\varepsilon\) = 1.0 \(\times\) 10\({}^{-5}\) were used here.
# Hg Content in EU and Non-EU Processed Meat and Fish Foods
## 1 Introduction
There are several pathologies caused by mercury: lung damage because of acute Hg poisoning, neurological and psychological disorders (e.g., anxiety, sleep disorders and depression, irreversible kidney damage) due to chronic exposure to this element [14; 22; 23]. The largest number of studies in the literature concerns the determination of mercury content in fish products, both fresh and processed [24], while fewer studies are observed for processed meat products [10; 12; 14; 25; 26]. The common point of this research is the close correlation between the population's dietary exposure to mercury and different food habits. For mercury, European legislation (EU Regulation 1881/2006 and its subsequent amendments) sets maximum permitted levels only for fish muscle meat, food supplements, and salt, indicating no further limits for the remaining foodstuffs [27; 28]. There are various analytical techniques that allow the determination of Hg, which differ depending on the type of mercury compound to be determined and on the different types of sample processing. The most common methodologies for analyzing Hg include Cold vapor atomic absorption spectrometry (CV-AAS); Cold vapor atomic fluorescence spectrometry (CV-AFS); Inductively coupled plasma atomic emission spectrometry (ICP-AES) with hydride generation system; Inductively coupled plasma with mass spectrometry detector (ICP-MS) [29]. However, several analytical methods have now been developed that are characterized by their high versatility since they allow direct analysis of the sample without first treating it [29]. Among these analytical techniques is the Thermal decomposition amalgamation atomic absorption spectroscopy (TDA-AAS), the method on which the Direct Mercury Analyzer used in this study is based. Its use provides significant advantages compared with other analytical techniques: e.g., direct analysis of trace mercury; no sample mineralization required; rapid analysis; less loss of trace Hg; relatively low cost [29; 30]. Starting from this assumption, the objective of the following work was to determine the mercury content via Direct Mercury Analyzer (DMA-80) in processed meat and fish products purchased in supermarkets and ethnic food shops in Messina (Italy). Furthermore, to assess the possible risks derived from the excessive consumption of such products, the total Tolerable Weekly Intake values of Hg were calculated for each species analyzed in accordance with the EFSA guidelines.
# Positioning Information Privacy in Intelligent Transportation Systems:
## 2 Solutions for Spotting of Vehicles on the Road
### Human-Centric Localization  
Human-centric localization is targeted at people carrying gadgets [68; 69]. As soon as a person starts to approach the vehicle, a gadget (smartphone, smartwatch, or augmented reality glasses) is changing the state to pre-authentication phase [70]. From telecommunications perspective, the enablers for such authentication are already present in current 3 GPP LTE deployments [71] and ProSe service responsible for handling those as part of Device-to-Device (D2D) communications [72]. Therefore, the preliminary connection establishment could be automatically detected by the vehicle, and the last known location of the hand-held device could be utilized to improve the positioning of the vehicle.  
There have been numerous studies focusing on human-vehicle interaction ways of driver identification using sitting postures were investigated in [73], the interaction between a human and a vehicle is described in [74], and authors provide an overview of the importance of human-vehicle interaction in autonomous vehicles in [75].  
As positive aspects, we can distinguish the high accuracy in location determination owing to the availability of base station cell IDs and, consequently, the availability of data for calculation of the approximate vehicle location [76]. On the other hand, this method is highly dependent on the actual availability of devices, e.g., device utilization might become impossible due to insufficient battery power.
# An Optimization Routing Algorithm Based on Segment Routing in Software-Defined Networks
## 3 Algorithm Design
### Comprehensive Evaluation Model
#### 3.2.2 Calculate the Scheme Weight and the Criterion Weight  
In the fuzzy AHP hierarchy, the importance of different evaluation indicators of the same business is relevant, so we adopt the method of pairwise comparison to judge the matrix. But the values of different business programs are independent of each other. Therefore, we divide the scheme weight and the criterion weight separately in the fuzzy AHP. The weight of the business scheme is calculated by user preference or actual demand, but the criterion weight is still obtained through the establishment of the fuzzy judgment matrix.  
First, we determine the scheme weight. According to user preference or the network application, three weight values of the network application are given. Different users or business needs lead to different weight values of the network business.  
Then, we determine the criterion weight. Different network applications have different levels of sensitivity to each index. The business of voice communication requires data latency and jitter to be as small as possible; the real-time streaming media business has certain requirements for bandwidth and packet loss rates; the web interactive business requires a rapid response network. To provide differentiated service and high network availability, the evaluation performance of a network is necessary for each network application business. The specific steps are as follows:  
(1) Establish a fuzzy judgment matrix \(R=\left(r_{ij}\right)_{n\times n}\), where \(r_{ij}\) represents the relevance between the \(i\) element and the \(j\) element in this hierarchy. We use the number in the range of 0.1 to 0.9 to quantitatively describe the relative importance of any two QoS indicators. The greater the value of \(r_{ij}\)\(\left(0.1\leq r_{ij}\leq 0.9\right)\), the greater is the importance of the \(i\) element than the \(j\) element.  
(2) Transform into a fuzzy consistency matrix. First, according to (15), we calculate the sum of the rows of the judgment matrix, that is:  
\[\xi_{i}=\sum_{j=1}^{m}r_{ij};i=1,2,\ldots,n \tag{15}\]  
Then, we calculate fuzzy consistency matrix \(\mathbf{Q}\) by using (16):  
\[\xi_{ij}=\frac{\xi_{i}-\xi_{j}}{2n}+0.5 \tag{16}\]  
Figure 3: Evaluation hierarchy of Network Performance based on application.  
(3) Based on the normalization process of the sum of the rows, the weight vector is calculated by using (17):  
\[w_{i}=(\sum_{j=1}^{m}q_{ij})/(\sum_{i=1}^{n}\sum_{j=1}^{m}q_{ij});i=1,2,\ldots,m \tag{17}\]
###### Abstract
Previously, agricultural waste was employed as an adsorbent for Fe(III) adsorption. Rice straw was crushed and microwave-treated with distilled water (0.75 mg/g\({}^{-1}\)), oxal acid (0.81 mg/g\({}^{-1}\)), sulfuric acid (0.86 mg/g\({}^{-1}\)), and sodium hydroxide (0.59 mg/g\({}^{-1}\)) to be used as an adsorbent for Fe(III) adsorption [10]. Carbonised rice straw was produced by drying rice straw in an oven at 500 \({}^{\circ}\)C. After that, the carbonised rice straw was employed in the Fe(III) adsorption procedure. It is the Langmuir model and the \(q_{max}\) was 58.82 mg/g\({}^{-1}\)[3]. Waste from the sugar can industry was chemically treated and turned into powder for use as an adsorbent in the removal of Fe(III) from sample water as well as to study adsorption. Its sorption fitted well with the Langmuir model and \(q_{max}\) was 18.54 mg/g [18]. Coconut husk is an agricultural waste product that was cut and blended to a particle size range of 0.15-0.5 mm, after which it was treated for use as an adsorbent and in the adsorption process of Fe(III). The Freundlich model was utilized for adsorption [19]. Previous research found that agricultural waste can be crushed and treated, either chemically or by heat until carbonised, for use as an adsorbent. However, the amount of \(q_{max}\) is the same or less, except for rice straw charcoal and sugar cane. This study compares the adsorption of Fe(III) by the fibre of palmyra palms that have been untreated and chemically treated. Palmyra palms, also known as toddy palms, are mostly grown in Africa, South Asia, and Southeast Asia. The edible mesocarp, or ripe pulp, has a delicate orange-yellow hue that is rich in carotenoids and is considered delicious [20]. The fibre is derived from palm fruit. The chemical components of the fibre include \(\alpha\)-cellulase (53.4%), hemicellulose (29.6%) and lignin (17%). Strong alkalis can reduce the quantity of hemicellulose and lignin [21]. The goal of this work was to investigate the removal of Fe(III) by adsorption on natural palmyra palm fruit (_Borassus flabellifer_ L.). First, the equilibrium adsorption was calculated. The adsorption rate was then determined using the batch contact time approach. Certain parameters, such as solution pH and adsorbent dose, have an impact on the adsorption process. The equilibrium adsorption data were simulated using three distinct isotherm models: Langmuir, Freundlich, and Temkin. The results revealed that a simple chemical modification technique could be used to boost the adsorption capacity of biosorbents, resulting in low-cost and locally available natural adsorbents for the removal of Fe(III) ions.
# Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
###### Abstract  
Rolling bearings are one of the most important and indispensable components of a mechanical system, and an accurate prediction of their remaining life is essential to ensuring the reliable operation of a mechanical system. In order to effectively utilize the large amount of data collected simultaneously by multiple sensors during equipment monitoring and to solve the problem that global feature information cannot be fully extracted during the feature extraction process, this research presents a technique for forecasting the remaining lifespan of rolling bearings by integrating many features. Firstly, a parallel multi-branch feature learning network is constructed using TCN, LSTM, and Transformer, and a parallel multi-scale attention mechanism is designed to capture both local and global dependencies, enabling adaptive weighted fusing of output features from the three feature extractors. Secondly, the shallow features obtained by the parallel feature extractor are residually connected with the deep features through the attention mechanism to improve the efficiency of utilizing the information of the front and back features. Ultimately, the combined characteristics produce the forecasted findings for the RUL of the bearing using the fully connected layer, and RUL prediction studies were performed with the PHM 2012 bearing dataset and the XJTU-SY bearing accelerated life test dataset, and the experimental results demonstrate that the suggested method can effectively forecast the RUL of various types of bearings with reduced prediction errors.  
rolling bearings; remaining life prediction; multi-sensor fusion; feature fusion +
Footnote †: journal: Applied sciences  
1
# Automated Design of the Deep Neural Network Pipeline
## 4 Results
### Experiment 1--Basic Automated Design
#### 4.1.2 Image Processing  
Table 7 contains the precision, recall and F1 score values for the SPHH and Table 8 shows the performance of the SPHH automated design versus the manual design, as well as a comparison with the techniques listed in the literature survey in Section 2.6. The datasets listed only have one other state-of-the-art technique listed in the literature. Note that for ISU Rp1D and ISU Tilt, there are no published results available, making this research the current state of the art.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Precision** & **Recall** & **F1 Score** \\ \hline ACL & & & \\ IMDB & 0.79 & 0.95 & 0.86 \\ Movie & & & \\ Reviews & & & \\ \hline Amazon & & & \\ product & 0.74 & 0.92 & 0.82 \\ reviews & & & \\ \hline Coronavirus & 0.83 & 0.82 & 0.83 \\ \hline Sentiment-140 & 0.80 & 0.94 & 0.87 \\ \hline Yelp & 0.75 & 0.88 & 0.81 \\ \hline Enron & 0.89 & 0.90 & 0.89 \\ \hline SMS Spam & 0.84 & 0.93 & 0.88 \\ \hline Spam assassin & 0.83 & 0.82 & 0.82 \\ \hline YouTube & & & \\ comments & 0.82 & 0.98 & 0.89 \\ spam & & & \\ \hline \end{tabular}
\end{table}
Table 5: The precision, recall and F1 score values of the SPHH when applied to the text processing datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **SPHH** & **Literature** & **SPHH** & **Literature** \\  & **(Accuracy)** & **(Accuracy)** & **(Application Time)** & **(Application Time)** \\ \hline ACL & & 95.68 [51] & & \\ IMDB & 95.10 & **97.4**[33] & 127.73 & 900 [51] \\ Movie & & 90.3 [52] & & \\ Reviews & & 94 [53] & & \\ \hline Amazon & & 82.0 [52] & & \\ product & 86.89 & **96.54**[54] & 87.04 & 10 [34] \\ Reviews & & 87.86 [34] & & \\ \hline Coronavirus & **91.55** & 81.4 [55] & 51.41 & 395.27 [35] \\ \hline Sentiment-140 & 91.74 & 84.41 [56] & & \\  & 84.9 [57] & 200.04 & 1440 [56] \\  & **92.0**[36] & & \\ \hline Yelp & 86.84 & **89.0**[53] & 232.2 & Not specified \\ \hline Enron & 97.45 & **97.47**[43] & 90.93 & 170.75 [43] \\ \hline SMS Spam & 93.17 & 95.33 [59] & & \\  & **98.11**[44] & & \\  & 96.4 [46] & & \\ \hline Spam assassin & 91.37 & 95.9 [58] & 189.72 & Not specified \\  & **98.67**[45] & & \\ \hline YouTube & 94.7 [46] & & \\ comments & **98.78** & 96.61 [60] & 24.97 & Not specified \\ spam & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of SPHH compared with the state of the art and other deep neural network pipeline techniques for the text-processing datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Precision** & **Recall** & **F1 Score** \\ \hline Oral lesion & & & \\ dataset & 0.97 & 0.86 & 0.91 \\ (Classification) & & & \\ \hline UP oral lesion & & & \\ images dataset & 0.90 & 0.79 & 0.85 \\ (Classification) & & & \\ \hline ISU V1 & 0.67 & 0.82 & 0.74 \\ (Segmentation) & & & \\ \hline ISU Rp1d & 0.65 & 0.79 & 0.71 \\ (Segmentation) & 0.66 & 0.80 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The precision, recall and F1 score values of the SPHH when applied to the image processing datasets.
# Power Profiling of Smart Grid Users Using Dynamic Time Warping +
## 5 Power Profiling Model
### Load Data Clustering
Each load type is assigned a set of criteria, including a pattern set of the power usage time series, which represents the corresponding profile (workday or weekend) and a daily load factor. To determine the pattern set for each load type, we find a daily load time series among all sample days' load time series that has the minimum DTW distance to other similar days' load time series during the training phase. The average DTW distance to this representative time series is a criterion used in our power profiling model. For each load type, the selected day's load usage data time series is considered the load pattern. The second parameter used to identify the load profile is the hourly load factor, which is calculated based on Formula (12). Using these profiling criteria, a consumer's power consumption behavior is categorized into one of two types: a workday power profile or a weekend/holiday power profile. In Table 2, the characteristics of power load patterns sets, including DTW distance (average and standard deviation), power usage (peak and mean), and hourly load factor, are listed for both load profiles, WoLP and WeLP. In WoLP, the representative load pattern set has an average DTW distance of 1165.80 to other workdays' load data time series. The load pattern set in WeLP has an average DTW distance of 1244.75 to other weekends'/holidays' load data time series. As shown at this table, hourly load factors are 22.71% and 21.35% for workdays and weekends/holidays load types, respectively. It is noted that the workday load type has a higher hourly load factor, which indicates smoother power usage for this load type.
## 3 Cooperation Architecture and Models
### Hierarchical Task Model  
In this proposed architecture, one hierarchical task model is employed for automatically task parsing and allocating. This model composes of three layers from top to bottom: Mission \(M\), Sub-Mission \(T\), and Atomic Task \(\tau\), which are detailed in Table 1. A Mission \(M\) is that the action goal one swarm system should achieve, and is described by a structural template. According to parsing rules and policies, the set of Sub-Mission \(T\) and constraints implied within \(M\) can be further generated. Each Sub-Mission \(T\) represents a concrete type of function, such as area detecting, communication relaying, and so on. All these Sub-Missions are constrained by a group of conditions, typically covering the time sequence, efficiency, cost, etc. Furthermore, every Sub-Mission is finally resolved into a set of Atomic Tasks, which are all of the same type inherited from the parent Sub-Mission. Each Atomic Task \(\tau\) has only one indecomposable function, and during the scheduling, it is mapped to several service instances. These service instances are instantiated with particular parameters, especially, event(s) to trigger this task and be triggered by this task.  
Additionally, an event \(E\) is defined as a tuple \(<id,source,target,type,para>\), where \(source\) and \(target\) are the sender and receiver of this event separately, \(type\) indicates this is a point-to-point event (valued 0) or broadcast event (valued 1), and \(para\) composes of specific parameters.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Para** & **Definition** & **Para** & **Definition** \\ \hline \multicolumn{3}{c}{Mission \(M\)} \\ \(M.id\) & identification number. & \(M.beh\) & the behavior parameters of \(M\). \\ \(M.reg\) & region parameters of \(M\). & \(M.btw\) & behavior time window \([t_{s},t_{e}]\) of \(M\). \\ \(M.info\) & XML-based description of \(M\). & \(M.rule\) & parsing rules of \(M\). \\ \multicolumn{3}{c}{Sub-Mission \(T\)} \\ \(T.id\) & identification number. & \(T.\rho\) & the priority of \(T\). \\ \(T.reg\) & region parameters of \(T\). & \(T.etw\) & execution time window \([t_{s},t_{e}]\) of \(T\). \\ \(T.tar\) & target object (s) of \(T\). & \(T.act\) & the action of \(T\). \\ \(T.pre\) & direct predecessor sub-missions of \(T\). & \(T.suc\) & direct successor sub-missions of \(T\). \\ \(T.cons\) & constraints of \(T\). & \(T.ea\) & expected earning from the execution of \(T\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definitions of the Proposed Hierarchical Task Model.  
Figure 1: Virtualized-Service and Event-based Cooperation Architecture.
# Joint Optimization of Service Migration and Resource Allocation in Mobile Edge-Cloud Computing
## 3 System Model and Problem Definition
### System Model  
We assume that each ES in the edge-cloud collaboration system is associated with a corresponding radio access network (RAN) node. Let \(S=\{s_{1},\ldots,s_{i},\ldots,s_{n}\}\) denote the set of servers accessible to users via these RAN nodes. The required service functions (SFs) are deployed on the ESs to support emerging IoT applications. When users offload specific tasks to the ESs for processing, the corresponding SF creates a dedicated service instance (SI) to execute the task. The ESs support \(\Lambda\) types of application tasks, and their allocatable computational resources are represented as \(\{p^{1},\ldots,p^{\prime},\ldots,p^{n}\}\), measured in GHz.  
In the system, \(m\) users, denoted as \(U=\{u_{1},\ldots,u_{j},\ldots,u_{m}\}\), are continuously active in the environment. Each user has installed \(\Lambda\) types of intelligent applications on their respective devices. It is assumed that ESs periodically update the information regarding the users they serve. Time is segmented into discrete intervals \(T=\{1,\ldots,t,\ldots,T\}\), each with a duration of \(\tau\). After an ES updates the information of the users it serves in slot \(t\), the user selects one of the \(\Lambda\) application types to generate a task for offloading to the ES. Then, the ES creates an SI for the offloaded task, uploads the task information at this  
Figure 1: An example of an MECC environment.  
time slot, and inserts it into the task table _Task_ maintained by the control system, where \(\textit{Task}=\{\textit{Task}_{1},\ldots,\textit{Task}_{t},\ldots,\textit{Task}_{T}\}\). Here, \(\textit{Task}_{t}=\{\textit{task}^{1}_{t},\ldots,\textit{task}^{k}_{t},\ldots, \textit{task}^{k}_{t}\}\), where \(\textit{Task}_{t}\) represents the list of task information in time slot \(t\). We define by \(|task_{t}|=K_{t}\) the number of tasks in the system in time slot \(t\). And \(\textit{task}^{k}_{t}\) defines \(\{T_{W},\textit{task}^{k}_{t},V^{k}_{t},W,\textit{lock}^{k}_{t},X^{k}_{t},Z^{ k}_{t}\}\), which contains the response time constraints of the task type \(W\), the remaining time of the task in time slot \(t\), the remaining size of the task (measured in MB), the type of the task, the ESs associated with the user who offloaded the task, the location of the task, and \(Z^{k}_{t}=1\) indicates that the task is completed in time slot \(t\); otherwise, it remains unfinished.  
When service migration is required, the corresponding SI, including task and context information, must be migrated from the origin server to the destination server. The model and assumptions for service migration are elaborated below.