esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
## 2 Supporting Technologies in the IoT-Edge-Cloud Continuum  
The overall architectural approach of an IEC system is shown in Figure 3 (optional communication with a 5G network has been included as well). As can be observed, various IoT nodes from different operational scenarios may communicate with 5G access points (APs) via either public or private networks. The latter case can be more appealing in latency-demanding applications, such as smart manufacturing, since all network operations can be established within the premises of interest [34; 35]. It should also be noted at this point that inter-node communications can be supported as well, based on well-known communication protocols, such as Sigfox, LoRa, or narrow band (NB)-IoT [36]. It is also assumed that MEC servers can be either collocated with APs or alternately deployed in close proximity.  
IoT nodes, which are assumed to have sensing and transmitting capabilities, can offload a particular task to the MEC server either in cases of latency-demanding applications or in cases of extreme computational load. This offloading may also take place in the cloud domain if necessary. In all cases, optimum task offloading should take into consideration additional parameters that may have a direct effect on the system's performance, such as  
Figure 3: An IoT-edge-cloud operating system.  
the energy footprint and computational capabilities of the involved servers. Therefore, as depicted in Figure 3, efficient ML algorithms can be used for resource optimization and efficient task offloading. In all cases, it is essential to identify trusted IoT nodes and secure task offloading/data transfer procedures. Therefore, task offloading may also include the execution of advanced security protocols that might not always be feasible in resource-constraint IoT nodes. Finally, in highly demanding latency scenarios (e.g., autonomous driving in the cases of advanced 5G infrastructure [37]), the involved IoT devices should be in a position to operate autonomously and support network functionalities via NFV and ensure uninterrupted connectivity.  
In light of the above, the most important key enabling technologies for an efficient IEC deployment include distributed/decentralized ML approaches for efficient resource optimization, serverless computing to leverage software and hardware decoupling, blockchain technology to ensure security during data transfer among the various nodes of the continuum as well as trusted nodes identification, subnetworks for the provision of uninterrupted connectivity, and device to device (D2D) communications for inter-node data transfer and content caching if necessary.
# Fuzzy Clustering Approaches Based on Numerical Optimizations of Modified Objective Functions
## 6 Conclusions  
This study introduced a comprehensive framework for enhancing the classical Fuzzy C-Means algorithm via several options of modification for the objective function and various numerical optimization strategies applied to them. The incorporation of several modifications to the objective function, such as L2 regularization, entropy term, sparsity-inducing term, penalty for large clusters, and spatial regularization, improved the adaptability to datasets with noise and complex distributions. Each of these modifications was optimized numerically using gradient descent, root mean squared, and adaptive moment estimation, thus avoiding the necessity for analytically derived update rules.  
The experimental studies applied on a variety of real-world datasets demonstrated the effectiveness of the proposed modifications. Throughout these experimental studies, an extensive comparison was carried out across various modifications and various numerical optimization techniques. Two significant performance metrics, the fuzzy adjusted rand index and fuzzy normalized information, were applied for the assessment and comparison of the quality of the generated clusters. The findings highlight the practical advantages of combining adjustments on the objective functions with numerical optimization techniques in fuzzy clustering tasks. Among the employed modifications and optimization algorithms, it was noticed that generally, the spatial regularization (SPAT-REG) and large-cluster penalty (PEN-LSC) modifications combined with adaptive moment estimation performed better. Future research may explore hybrid approaches integrating multiple regularization strategies or applying this flexible optimization framework to hyper-parameter tuning problems too.  
Conceptualization, E.B. and S.H.; methodology, E.B. and S.H.; differentiation of regularization terms, S.H. and R.K.; numerical algorithm formulation, E.B. and S.H.; data preprocessing, R.R.; implementation, E.B. and R.R.; writing--original draft preparation, E.B., S.H. and R.R.; writing--review and editing, S.H. and R.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
All the used datasets are publicly available in the cited online repositories.  
The authors declare no conflicts of interest.
# Enhancing BER Performance Limit of BCH and RS Codes Using Multipath Diversity +
###### Abstract  
Modern wireless communication systems suffer from phase shifting and, more importantly, from interference caused by multipath propagation. Multipath propagation results in an antenna receiving two or more copies of the signal sequence sent from the same source but that has been delivered via different paths. Multipath components are treated as redundant copies of the original data sequence and are used to improve the performance of forward error correction (FEC) codes without extra redundancy, in order to improve data transmission reliability and increase the bit rate over the wireless communication channel. For a proof of concept Bose, Ray-Chaudhuri, and Hocquenghem (BCH) and Reed-Solomon (RS) codes have been used for FEC to compare their bit error rate (BER) performances. The results showed that the wireless multipath components significantly improve the performance of FEC. Furthermore, FEC codes with low error correction capability and employing the multipath phenomenon are enhanced to perform better than FEC codes which have a bit higher error correction capability and did not utilise the multipath. Consequently, the bit rate is increased, and communication reliability is improved without extra redundancy.  
multipath; propagation; phenomenon; FEC; BCH; Reed-solomon; column weight multipath combiner; SNR +
Footnote †: 6 April 2017; Accepted: 12 June 2017; Published: 16 June 2017
# Periodic Monitoring and Filtering Suppression of Signal Interference in Mine 5G Communication
## 3 Materials and Methods
### Interference Monitoring Based on Deep Complex Network
If the deep complex network is used for interference identification, its accuracy rate exceeds 90%, and here, we use it to monitor interference changes--it is almost completely credible.
# Innovative Approach Integrating Machine Learning Models for Coiled Tubing Fatigue Modeling
## 1 Introduction
In recent years, artificial intelligence (AI) and machine learning (ML) techniques have gained traction in tackling complex and non-linear data, including material fatigue behavior [5; 26]. With their ability to analyze large and complicated datasets, machine learning has emerged as a sophisticated tool that reduces the uncertainty present in traditional methods [27]. Unlike traditional modeling, machine learning and artificial neural network (ANN) techniques, such as back propagation algorithms with time series methods, have rarely been used. Studies from the literature [28] have used CT sample fatigue tests [29] and applied a BP neural network, showing promising results for predicting the number of cycles, even with a small amount of data and excluding the factor of welding type. Additionally, regression modeling along with experimental fatigue tests of non-welded CT specimens has demonstrated a strong correlation between CT deformation and fatigue progression [30]. Through the studies, we identified several experimental coiled tubing fatigue tests that were published, including CT fatigue lab tests and CT full-scale fatigue tests. These fatigue testing projects and studies aimed to improve the fatigue life of CT by studying different grades of material, various weld processes, and the role of internal pressure in CT durability (N is the number of cycles, where one cycle indicates the bending and straightening of the CT segment). However, most of those studies focused on one parameter, such as pressure, the grade, metal defect, etc., and used traditional modeling methods to predict the number of cycles N [20; 28; 31]. According to the literature and to the authors' knowledge, there is a significant lack of use of machine learning techniques. In this regard, the current study attempted to address this gap by applying advanced machine learning techniques. In the first step, we collected a large set of fatigue test data from different published experimental tests to obtain highly accurate modeling results. Then, we applied ML and ANN techniques. The dataset includes more than 350 tests covering various grades, wall thickness, CT diameters, pressure, and welding types. The dataset includes both lab- and full-scale tests to maximize accuracy in predicting the fatigue life of coiled tubing [13; 14; 21; 23; 29; 32; 33; 34; 35; 36]. In addition, this paper presents an approach that utilizes multiple machine learning approaches, such as ANNs and gradient-boosting techniques, and employs datasets to accurately predict the number of cycles to failure (N) while comparing different machine learning techniques for evaluation and recommendation.
## 3 Underwater Routing Protocols
### Vbva  
To address the void problem in three-dimensional mobile underwater sensor networks, the authors in [47] proposed a routing protocol called vector-based void avoidance (VBVA). This protocol has two features to cope with the void problem. Firstly, it uses a vector shift that directs the flow of data along the boundary of a void during routing. Secondly, when a concave void is along the way, a back-pressure strategy reverses the packets back along the paths. This leads to reliable delivery of data packets to the surface of the water. However, the energy consumption is not optimal due to the two selected features for void handling. This is because these features consume an extra amount of energy in selecting the routes and reversing the packets back along the paths with a concave void.
## 2 Materials and Methods  
### Materials  
Chitosan (CHT) with a medium molecular weight and a degree of deacetylation of 84.1%, polyethylene glycol diglycidyl ether (Mn 500), and glutaraldehyde (GA) solution Grade II, 25% in H\({}_{2}\)O were purchased from Sigma-Aldrich. Sodium Hyaluronate (HNa) Oral Grade LMW was obtained from Bioiberica (Barcelona, Spain) whereas acetic acid was provided by [PERSON] (Xalostoc, Mexico). MTS CellTiter 96(r) Aqueous Non-Radioactive Cell Proliferation kit was supplied by Promega (Madison, WI, USA). Human osteoblast hFOB1.19 from ATCC(r) CRL-11372(tm) (Manassas, VA, USA) were cultured in Dulbecco's modified Eagle's medium (DMEM) provided by Biowest (Riverside, MO, USA). For the antimicrobial assays, _Escherichia coli_ ATCC 25922, _Enterococcus faecalis_ ATCC 51299, _Staphylococcus aureus_ ATCC 25923, and _Salmonella typhimurium_ ATCC 14028 were used.
# Dynamic Job and Conveyor-Based Transport Joint Scheduling in Flexible Manufacturing Systems
## 3 Problem Description  
### Model  
The flexible job shop problem concerning no buffers and a transportation system can be described as follows. The facility has a set of \(m\) machines (\(M=\{1,2,\ldots,m\}\)) that are used to process a set of \(n\) jobs (\(J=\{1,2,\ldots,n\}\)). Since we consider a dynamic environment in which jobs continuously arrive, the set of jobs (\(J(t)\)) depends on the time (\(t\)), i.e., we have \(n(t)\). Such a dynamic set is composed of (i) the jobs that have arrived but have not been started yet and (ii) the newly arrived jobs.  
Each job (\(i\)) comprises \(h_{i}\) operations or _tasks_ (\(\tau_{ij}\), where \(i\) denotes the job, and \(j=1,2,\ldots,h_{i}\)), and each task can be executed on a subset of machines. The processing time (\(p_{ijk}\)) for a given task (\(j\)) of a job (\(i\)) on a machine (\(k\)) is considered known. If a task (\(\tau_{ij}\)) cannot be performed on machine (\(k\)), we set \(p_{ijk}=\infty\). The tasks within a job may have _precedence constraints_, meaning that a task cannot start until its preceding tasks (if any) are completed. For each job (\(i\)), the precedence constraints are summarized by a square matrix (\(U_{i}\)). An element (\(U_{jj^{\prime}}\)) is set to 1 if task \(\tau_{ij}\) precedes task \(\tau_{ij^{\prime}}\), and this is 0 otherwise.  
Each machine (\(k\)) is associated with a set of available times (\(A_{k}(t)\)), representing intervals during which tasks can be executed on that machine. Outside these intervals, the machine is unavailable either because it is busy with other tasks (e.g., a previous schedule is still being executed) or because it is undergoing maintenance. Each machine performs, at most, one task of any job at a time. With each new arrival, the availability sets for all machines are recomputed, considering the currently running jobs.  
The time required to move a job from machine \(k\) to machine \(l\), denoted by \(t_{kl}\), is a key aspect of our work, particularly as we assume that machines lack input or output buffers and a conveyor belt serves as the means of transportation. More specifically,  
\[t_{kl}=t_{kl}^{0}+t_{kl}^{c}\cdot n^{c} \tag{1}\]  
Here, \(t_{kl}^{0}\) represents the minimum time to travel from machine \(k\) to machine \(l\), \(t_{kl}^{c}\) is the cycle time in case machine \(l\) is unavailable, and \(n^{c}\) is the number of cycles the job needs to complete until the machine becomes available. These values are contingent on the layout of the production line (machines and conveyor belts) and can be readily measured once such a layout is defined. It is worth noting that by appropriately setting these values, we can encompass scenarios commonly explored in the literature: if \(t_{kl}^{c}=0\ \forall k,l\), the transport time remains constant; if \(t_{kl}=0\ \forall k,l\), the model excludes the transport system. Hence, our model is versatile, covering various layouts and accounting for cases where transport time is not considered.  
Table 1 summarizes the notation used for the model and the formulation of the problem.
## 1 Introduction  
In recent years, backfilling mining techniques have developed rapidly in China. Backfilling mining works well to control strata movement and is capable of reducing overburden subsidence, increasing the extraction rate of coal resources, and alleviating the environmental disruption caused by mining. Focusing on issues such as environmental protection and coal mining under buildings, railways, and water bodies, scientific researchers both in China and other countries have conducted numerous experimental studies on backfilling mining and made a series of progress [1, 2, 3, 4]. Coarse-grained wastes (from both mining and processing) are used as backfilling material in Poland [5]. The need for backfilling is a major issue in Australia, where 10 million cubic meters of underground voids are generated annually as a result of mining [6]. [PERSON] and [PERSON] attempt to provide a mathematical model while ensuring maximization of backfilled land area at minimum direct and economic costs in India [7]. A method is described that allows for a prospective evaluation of these backfilling materials [8]. A novel short-wall caving zone backfilling technique for controlling mining subsidence has been proposed [9]. At present, there are paste backfilling [10, 11], solid backfilling [12, 13], high water material backfilling [14, 15], slurry backfilling [16, 17], and many other backfilling mining techniques.  
However, backfilling technology in a goat also presents a series of problems, such as large consumption of backfilling material, slow advance speed, low production efficiency, and high comprehensive backfilling cost. A suitable partial backfilling method to achieve lower backfilling cost and higher backfilling efficiency is urgently needed. On the basis of the longwall strip filling method and spaced strip filling mining method proposed by [PERSON] et al. [18; 19], a longwall pier-column backfilling method has been creatively developed by the Wangzhuang coal mine in Zibo, China. The schematic diagram is shown in Figure 1. In the goat, the piers are filled at certain intervals, and each independent pier supports the overlying strata to control the overburden subsidence. In this method, the single hydraulic props are firstly used to support the roof of the goat after mining (Figure 2a), and then the slurry material is used to fill the pier-column bags. The single hydraulic props are withdrawn when the pier-columns reach a certain solidification strength (Figure 2b), and then the backfilling process is carried out in turn.  
With the passage of time, the Wangzhuang coal mine is about to shift to a new mining area. The mining height and width of the backfilling working face in the new mining area are both larger than those of the previous backfilling working faces. For this reason, it is necessary to analyze how changes in the geometric dimensions of the working face influence the control effect of overburden subsidence in pier-column backfilling, laying a scientific foundation for evaluating the backfilling effect in the future.
## 4 Results  
In this section, two methods are compared to our proposed method, including \(L_{EN}\) and \(L_{1}\). To evaluate the prediction accuracy of the three logistic regression models, we first used random partition to divide the samples. That is to say, the samples were divided into training samples (70%) and testing samples (30%). The detailed information of the three publicly available datasets used in the experiments are shown in Table 2. Secondly, in order to obtain the tuning parameter \(\lambda\), we applied 5-fold cross validation to the training set. Thirdly, the classification evaluation criteria were the corresponding average number at 50 runs. Table 3 shows that the results of the training set and testing set were obtained by \(L_{1}\), \(L_{EN}\), and \(L_{1/2}\). The results obtained by \(L_{1/2}\) were better those of \(L_{1}\) and \(L_{EN}\). For example, for the training set in the dataset GSE10072, the values of sensitivity, specificity, and accuracy of \(L_{1/2}\) were the same as for \(L_{1}\). Besides, the values of sensitivity and accuracy of \(L_{EN}\) were 0.98, and 0.99 lower than those of \(L_{1/2}\). For the testing set in dataset GSE4115, \(L_{1/2}\) and \(L_{EN}\) ranked first and second, respectively. \(L_{1}\) was the last. For instance, the value of accuracy of \(L_{1/2}\) was 0.80, higher than the 0.77 and 0.78 of \(L_{1}\) and \(L_{EN}\), respectively. Moreover, \(L_{1/2}\) was more sparse than \(L_{1}\) and \(L_{EN}\). As shown in Figure 1, In dataset GSE17084, the number of selected genes of \(L_{1/2}\) was 8, lower than the respective 33 and 82 of \(L_{1}\) and \(L_{EN}\). In a word, \(L_{1/2}\) was superior to \(L_{1}\) and \(L_{EN}\). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Datasets** & **No. of Samples** & **No. of Genes** & **Class** \\ \hline GSE10072 & 107 & 22283 & Normal/Tumor \\ GSE19804 & 120 & 54675 & Normal/Tumor \\ GSE4115 & 187 & 22215 & Normal/Tumor \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Three publicly available cancer datasets used in the experiments \begin{table}
\begin{tabular}{l c} \hline \hline
**Datasets** & **No. of Training (Class 1/Class 2)** & **No. of Testing (Class 1/Class 2)** \\ \hline GSE10072 & 75 (35 Normal/40 Tumor) & 32 (14 Normal/18 Tumor) \\ GSE19804 & 84 (46 Normal/38 Tumor) & 36 (14 Normal/22 Tumor) \\ GSE4115 & 131 (67 Normal/64 Tumor) & 56 (31 Normal/25 Tumor) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detailed information of the three publicly available datasets used in the experiments. In order to search the common gene signatures selected by the different methods, we used VENNY software (2.1.0 Centro Nacional de Biotecnologia, Madrid, Spain, 2015) [33] to generate Venn diagrams. As shown in Figure 2, we considered the common gene signatures selected by the logistic regression model with \(L_{1}\), \(L_{EN}\), and \(L_{1/2}\) regularization methods, which are the most relevant signatures of lung cancer. Hence, 2, 3, and 2 common genes were found in these methods for different datasets.
# Indoor Carrier Phase Positioning Technology Based on OFDM System
## 4 Numerical Results
Furthermore, the carrier phase measurement is not limited by the system sampling rate, which, combined with the correct integer ambiguity, makes the carrier phase algorithm suitable for scenarios with high accuracy requirements. Comparatively, both 'GMM EKF' and 'EKF' use only TDOA for user position tracking, which leads to lower positioning accuracy. The cumulative density function (CDF) curves of horizontal positioning errors are used as performance metrics in positioning evaluations. Define the positioning error as:  
\[e_{pos}=\sqrt{\left(\hat{x}-x_{\text{ture}}\right)^{2}+\left(\hat{y}-y_{\text{ture }}\right)^{2}} \tag{47}\]  
The CDFs for the localization error from both methods are shown in Figure 8. The 'TOA+CP EKF' method has the best performance, with 90% of the horizontal positioning errors within 0.27 m. Therefore, the carrier phase-based localization technique can meet the high accuracy localization requirements. The 'GMM EKF' method has the middle performance due to the algorithm using TDOA for user location tracking and NLOS elimination. Since the system sampling rate limits the TDOA measurement resolution, the positioning accuracy is low. 'EKF' method has the worst performance because it only uses the TDOA and has limited effectiveness in eliminating NLOS error. We simulated the localization accuracy of this algorithm with the different number of BSs. In our experiments, as shown in Figure 9, we set the length of the indoor scenario to 100 m and the width to 20 m. Furthermore, the coordinates of the six BSs are \([0,0],[40,0],[100,0],[0,20],[40,20],[100,20]\), respectively. The coordinate of the reference UE is \([50,10]\). The actual distance between the user and the BS determines the probability of LOS. Thus, the expansion of the simulation environment decreases the LOS probability and equivalently simulates the case of increasing obstacles. Figure 8: The CDF of horizontal localization error. When five BSs are used in the experiment, the BS located at \([40,0]\) is removed. When four BSs are involved in localization, the two BSs located at \([40,0]\), \([40,20]\) are removed. From Figure 10, it can be seen that the localization accuracy of the algorithm proposed in this paper decreases as the number of BSs decreases. The decrease in the number of available BSs leads to a more extended solution period for the integer ambiguity and thus decreases the localization accuracy. In addition, compared with Figure 8, the decrease in LOS probability does not cause severe degradation of the localization accuracy, so the NLOS error suppression scheme proposed in this paper is effective.