esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Implementation Proposal  
In response to the need to integrate and scale advanced technologies within the RAMI 4.0 framework, this article contributes by presenting a case study in which a 3.0 industry was successfully transformed into a 4.0 industry. The highlights of this article are as follows:  
* The proposed MES is responsible for controlling in-plant production, sourced from a cloud computing cloud service infrastructure, which stores and manages user data or requests through a web interface.
* This study demonstrates the convergence of a fog computing system in MES for cloud resource provisioning and container orchestration to fog slave nodes located at different stations and sub-processes within a modular production system.
* Through rigorous evaluation and validation, this study demonstrates that the reference model framework guide was key to establish a scaling from Industry 3.0 to Industry 4.0 by integrating and implementing different emerging technologies.  
The manuscript is designed to provide a comprehensive overview of the development and validation of the transition from Industry 3.0 to Industry 4.0 in a manufacturing process, based on the reference model framework for the integration of fog computing in a distributed MES. Section 2 examines the advantages and limitations of current technologies, setting the context for the approach presented in our research. Section 3 describes the design and integration of manufacturing execution systems, as well as both traditional and emerging computational technologies, including those that form the pillars of Industry 4.0, ITS, OTs and end-to-end communication systems. Section 4 evaluates the performance of the scale-up implementation compared to the original owner's plant, highlighting its flexibility, scalability, optimization and process effectiveness. The discussion in Section 5 analyzes the results obtained, highlighting the advantages of scaling an industrial system within the RAMI 4.0 framework, addressing possible limitations and proposing areas for future research. Finally, the conclusions (Section 6) synthesize the key findings of the case study, highlighting the potential impact of integrating advanced technologies into modular production processes and suggesting future research directions to deepen the technologies and methods employed.
# A Channel Measurement-Based Listen-Before-Talk Algorithm for LTE-LAA and WLAN Coexistence
## 2 Related Work
### Frame-Based LBT Schemes  
Because the existing LTE system operates on a fixed frame period, the FB-LBT scheme, which also follows a fixed frame period, is easier to implement in the LTE-LAA system compared to the LB-LBT scheme. In an FB-LBT-based LTE-LAA system, a single frame consists of 10 subframes. During the duration of a single frame, the LTE-LAA eNB transmits data over multiple subframes while including one or more idle subframes where no data transmission occurs. During the idle subframe duration, WLAN APs or nodes transmit data. For consecutive idle subframes, the LTE-LAA eNB begins channel sensing during a predefined sensing period before the end of the last subframe's duration. If the channel is  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Reference** & **LBT Type** & **Main Contributions** & **Traffic** \\ \hline
[5] & FB-LBT & Appropriate allocation of idle periods for WLAN & Unsaturated \\
[6] & FB-LBT & Mathematical model for analyzing FB-LBT & Unsaturated \\
[7] & LB-LBT & Evaluation of LB-LBT performance through simulation & Unsaturated \\
[8] & LB-LBT & Mathematical model for analyzing LB-LBT & Saturated \\
[1] & Non-LBT & Strategic allocation of resource blocks & Unsaturated \\
[11] & LB-LBT & Interference suppression techniques & Saturated \\
[12] & LB-LBT & Sensing techniques for operational parameters & Unsaturated \\
[13] & LB-LBT & Precoding and power control techniques & Saturated \\
[14] & LB-LBT & Joint optimization of transmission probability and rate & Saturated \\  & & Performance analysis considering the GAP period & Saturated \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of studies on the coexistence of LTE-LAA and WLAN systems.  
sensed as idle during the sensing period, the LTE-LAA eNB begins data transmission in the next subframe. However, if the channel is sensed as busy, the LTE-LAA eNB continues channel sensing for each subsequent subframe until it detects the channel as idle during the sensing period.  
Ref. [5] proposed an F-LBT scheme that allocates an appropriate number of consecutive idle subframes following the LTE-LAA eNB's data transmission, enabling WLAN APs or nodes to perform data transmission. The F-LBT scheme determines the number of idle subframes to allocate based on fairness between LTE-LAA and WLAN systems, as well as the overall system throughput. Ref. [6] proposed an analytical model to evaluate the throughput in a coexistence scenario involving FB-LBT-based LTE-LAA and WLAN systems.
## 3 Security Aspects Regarding Wireless Body Area Framework
### Data Transmission
#### 3.3.2 Fully Homomorphic Encryption  
Fully Homomorphic Encryption (FHE) represents a novel cryptographic technique that enables computations to be performed directly on encrypted data without requiring decryption [30]. FHE is still in its early stages of development, but its potential is immense. For instance, it could enable WBAN devices to perform advanced data analysis and processing without compromising patient privacy. Additionally, FHE could facilitate secure collaboration among healthcare providers, allowing them to share and analyze patient data without compromising confidentiality. However, FHE's computational complexity has been a significant hurdle. The processing demands for performing computations on encrypted data are substantially higher. This is expected to not be a hurdle as FHE is projected to be fast enough by 2025 for most use cases [31].
# Study on the Thermospheric Density Distribution Pattern during Geomagnetic Activity
## 4 Discussion  
Based on the results obtained from the EOF decomposition experiment of atmospheric density during a geomagnetic storm, as well as the analysis of the spatial-temporal distribution of atmospheric density during a geomagnetic storm, we have reached the following conclusions:  
(1) The first-order EOF characteristic vector field represents the most significant and large-scale changes in the original atmospheric density field while containing less detailed information. The addition of higher-order eigenvector fields gradually reveals the short-term and local characteristics of the thermosphere density and the more comprehensive description of the original atmospheric density distribution characteristics. The higher-order eigenvector field often reveals smaller amplitude characteristics, while the lower-order vector field reveals larger amplitude and longer duration features.  
(2) The analysis of atmospheric density time distribution during a geomagnetic storm showed that geomagnetic storms significantly influence the temporal distribution of atmospheric density in the thermosphere. The time coefficient curve of the day after the geomagnetic storm differs from that of the other two days. In contrast, the influence of geomagnetic storms on the spatial distribution of atmospheric density in the thermosphere is smaller than that on the temporal distribution. The spatial distribution of atmospheric density in the thermosphere is generally stable during a certain period, especially without a geomagnetic storm. Even if a geomagnetic storm occurs, the disturbance to the spatial distribution of atmospheric density is relatively small and cannot fundamentally change its state. Although the nighttime response to a geomagnetic storm is obvious, the continuous rotation of the Earth results in alternating day and night, thus making the geomagnetic storm's influence on atmospheric density's spatial distribution relatively small. Consequently, the spatial distribution stability of the Earth's thermosphere density is high, with slow short-term changes that accumulate over time. For the three geomagnetic storms, the spatial distribution curve of atmospheric density differs for each storm, resulting from long-term cumulative changes.  
(3) In the analysis of the spatial distribution of atmospheric density during a geomagnetic storm, the spatial distribution of atmospheric density in the daytime and nighttime regions is asymmetric in the northern and southern hemispheres before and after the storm. This asymmetry is related to the intensity of the geomagnetic storm itself, the area of solar wind energy injection, and the penetration height of energy particles. When the geomagnetic storm begins, the geomagnetic index rises sharply, and the disturbance increases. The spatial distribution of EOF atmospheric density reflects the heat conduction process of the atmosphere.
## 6 Experimental Evaluation and Discussion
### Open World Evaluation
#### 6.2.1 Effectiveness in a Real Network  
We deployed Tor user agents on virtual machines and implemented the defense, SMART, for the lightweight and reliable multi-path transmission mentioned in this paper. To defend against these WFP attacks, the defense does not employ padding packets or artificial delays. We chose the best-proven scheme (s = 4, m = 5, and the BWR strategy) and collected real Tor network traffic as a dataset. We applied the state-of-the-art classifier to classify this defended dataset and collected the undefended real traffic for classification, using the results as a reference group to evaluate the effectiveness of SMART. As can be observed in Table 4, the classifications obtained using the defense traces from the actual Tor network are only a little less accurate than the simulations. The analysis success rate of all these current state-of-the-art WFP classifiers has dropped from over 95% to less than 11% now. From this, we can reasonably infer that our defense SMART is effective in the real world and can render the current WFP attacks ineffective.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **k-NN** & **K-F** & **DF** & **Var-CNN** \\ \hline Undefended & 95.2 & 97.5 & 98.6 & 98.43 \\ SMART & 5.34 & 6.21 & 9.22 & 10.98 \\ \hline \end{tabular}
\end{table}
Table 4: Classification results before and after using SMART.  
Figure 6: Scores of the top nine features.  
Figure 7: Changes of the state-of-the-art WFP before and after the SMART defense: (**a**) WFP in the open-world setting (undefended); and (**b**) WFP in the open-world setting (defended).
## 2 Research Status  
### Traditional Traffic Prediction Models  
Time-series analysis models: Some studies [15] have used the Holt-Winters model to achieve time-series prediction and found that the traffic flow pattern not only involves random patient movements, but also shows trends and seasonal variation rules. Another study [16] proposed a multi-mean fusion prediction model that decomposed the traffic input into three parts: the trend sequence, the up-down sequence, and the noise sequence. Different models were used to fit and predict these sequences, and finally, a linear fusion of the three prediction results was carried out to obtain the changing characteristics of the predicted traffic pattern and improve the accuracy of the time-series prediction. However, satellite network traffic is extremely complex and is accompanied by high-frequency data streams. Although traditional methods perform well in predicting individual future time periods, it is very difficult for them to learn the traffic patterns in scenarios with multiple nodes, high-frequency data, and highly dynamic spatio-temporal topologies. Our model can better capture the potential hidden patterns of LEO constellations through multiple attention mechanisms, enabling accurate traffic prediction.  
Machine learning models: With the development of machine learning, this approach has been widely applied in the field of time-series prediction. [PERSON] et al. [17] developed an ITF model to assist ISPs (Internet service providers) in long-term business strategic planning and proactive network management. This model combines outlier detection and mitigation techniques with advanced gradient descent and boosting algorithms, significantly enhancing the model's performance and enabling real-world Internet traffic monitoring in high-speed ISP networks. [PERSON] et al. [18] compared the online sequential extreme learning machine (OS-ELM) with other machine learning models and demonstrated that OS-ELM outperformed other networks in terms of both the accuracy and computational efficiency, offering a promising solution for network traffic prediction problems. However, machine learning models typically train on only one type of prediction data. Our method separates the collected LEO constellation traffic into data on traffic volume at different times and data on traffic connection and disconnection through multi-source data separation. Time-series prediction models are then trained separately to learn the characteristics of the data with different physical meanings and combine them through splicing. Together with multiple attention mechanisms and GCN, our method can achieve more accurate network traffic prediction.
## 2 Methods
### Data Collection and Reduction  
The EEG was recorded from 32 electrodes, using an NVX-136 amplifier (MCS, Russia), according to the international 10-20 system (filters = 0.05-40 Hz; sampling rate = 1000 Hz; impedance \(\leq\) 10 k\(\Omega\); ground: AFz). Ocular movements were recorded through one electrode placed at the orbicular muscle below the right eye. Earlobe electrodes were used as an online reference.  
The EEG analysis was conducted off-line using EEGLAB [56]. The recording was split into 2000 ms epochs, with the baseline of 1000 ms. Eye-movement artefacts were corrected [57] and trials with activity exceeding \(\pm\)100 \(\upmu\)V were rejected. Trials with incorrect recognition were rejected. In total, 64.6% of trials were rejected. We decided to average ERP between electrodes (see below), as recommended by [PERSON] et al. [58]. We averaged frontal (Fp1, Fp2, F3, F4, F7, F8, and Fz), central (FC1, FC2, C3, Cz, C4, CP1, and CP2), temporal (FT9, FT10, FC5, FC6, T3, T4, T5, T6, CP5, CP6, TP9, and TP10) and posterior sites (P3, Pz, P4, O1, Oz, and O2). The visual examination of the grand averages of four regions permitted us to identify four waves during watching neutral faces (P1, N170, P2, EPN) and five waves during the gradual appearance of a facial emotional expression (P1, N170, P2, EPN and LPP).
## 4 Problem Description and Strategy
### Iterative Predictive-Reactive Strategy  
The proposed predictive-reactive rescheduling strategy is divided in two phases. The predictive phase consists of solving a static scheduling problem. In this phase, it is assumed that all the jobs' information is known in advance. The aim is to minimizes the _TWWT_ of the jobs which represents a schedule efficiency measure. The reactive phase starts after the instauration of an initial schedule. It consists of going iteratively through the simulation horizon \(T\). At each period, the process updates the schedule in response to a disruption caused by the new job arrival. In this phase, the strategy optimizes the schedule efficiency, but also the schedule stability by associating two criteria, the _TWWT_ as an efficiency measure and the _TWCTD_ as the stability measure. This strategy provides, at each rescheduling period, an optimal solution since the decisions are taken locally, unlike other rescheduling works that used a proactive approaches consisting in predicting the dates of the disruptions occurrences and taking the job into the consideration in the original schedule formulation [43].  
The proposed strategy consists of going iteratively through the simulation horizon period by period. At each period, a job can arrive. The new arrived job is combined with the set of uninitiated jobs for rescheduling. To handle, at each period, the arrival of the new jobs, firstly, we have considered a finite time horizon \([0,T]\), discretized into periods \(\Delta t\) (see Figure 2). For discrete production, the discrete flow models are often considered as more realistic models. They are used for performance evaluation and to track individual parts, or in real time flow control [72]. Indeed, \(\Delta t\) is the time period length, it also represents the time step. It is assumed that the occurrence of the jobs may be possible only at these times. Indeed, when a job arrives at time \(t\), this date becomes its release date. It is also assumed that \(\Delta t=1\) unit of time, the same time unit already in use for the jobs data \(p_{j}\) and \(r_{j}\), to simplify the calculations.  
We then defined \(\theta(t)\) a binary variable, equals to \(1\) if a job occurs at time \(t\) and \(0\) otherwise. The generation of its values is explained in the section of numerical results. The Algorithm 1 below describes the iterative predictive-reactive strategy.  
```
Solve the initial problem \(\text{LIST}=\{\)\(\{1_{\gamma},\ldots,\)\(\{n_{\pi}\}\) For (\(t\) in \(1\) to \(T\)) If (\(\theta(t)=1\)) - Add new job(s) to LIST. - eschedule the new problem with jobs in LIST (fixing already executed jobs by comparing their starting time). - Memorize the solution. End If End For
```  
**Algorithm 1** Rescheduling algorithm  
The algorithm traverses iteratively the time horizon period by period step. Thanks to the state of \(\theta(t)\), it checks whether a job arrives. If a job arrives, the algorithm updates the set of jobs LIST and reschedules the new set of jobs. Figure 3 summarizes the used predictive-reactive rescheduling strategy.  
Figure 2: Discretization of the time horizon.
## 2 Proposed CPM-GFDM Transceiver
### CPM-GFDM Receiver
#### 2.2.4. Zero Forcing Receiver  
For this particular receiver, \(Z=\mathbf{A}^{-1}\). In case \(\mathbf{A}\) is not square, its pseudo-inverse, \(\mathbf{A}^{+}\), is computed using the following relationship:  
\[\mathbf{A}^{+}=\Big{(}\mathbf{A}^{H}\mathbf{A}\Big{)}^{-1}\mathbf{A}^{H} \tag{15}\]  
where \(\mathbf{A}^{H}\) is the Hermitian of \(\mathbf{A}\). A zero forcing receiver reduces self-interference but amplifies noise.
# A Special Structural Based Weighted Network Approach for the Analysis of Protein Complexes
## 1 Introduction
Generally, this technique has two steps: namely, the identification of the complex core as a dense sub-network or maximal clique and then the characterization of the core of the protein complex. Although these two steps have been widely adopted in the detection of the protein complexes they tend to be inefficient when attempting to characterize the protein complex core of a dense sub-network [44]. Moreover, the majority of the core-attachment-based methods are based on the selection of proteins whose neighbors interact with more than half of the protein in the complex core in the sparse PPI networks [22]. However, this may result in high false-positive interactions and lead to the inaccurate detection of protein complexes [45; 46; 47]. The core-attachment structure is still being investigated; no studies have provided a clear distinction between overlapping proteins, core proteins, and peripheral proteins in terms of the weighted network structure [41]. The majority of studies simply focus on a few structural concepts of these protein complexes [20; 45; 46; 47; 48; 49]. Recently, method such as CALM has shown to be more efficient in the detection of overlapping protein complexes on large-scale PPI networks. However, this method only focuses on the detection of overlapping protein complexes and tends to ignore local attachment proteins to the complex core, as well as it does not consider the common neighborhood and high-order common neighborhood similarity measures when calculating the initial weight of the PPN. All those factors influence the reliability of the PPN and detection of the protein complexes which may result in false positive prediction. To address these limitations, we propose a special structural-based weighted network approach called the Weighted Edge algorithm, Core-Attachment, and Local Modularity (WECALM) for protein complex analysis. By our WECALM approach, our contributions are: First, we introduce a high-order similarity measure based on the Jaccard measure to compute the edge weights, which ensures the reliability of the PPI network. Second, we extend protein complex identification by using a weighted connectivity algorithm to discriminate and detect local attachment proteins to complex cores. Third, we extend the detection of protein complexes using the structural similarity measure concept. Fourth, we perform functional enrichment analysis by calculating the \(p\)-value of the detected complexes to validate their associated functions. This paper is organized as follows. In Section 2, we provide a preliminary overview of our approach. In Section 3, we give a detailed computational description of our approach to the detection of protein complexes. In Section 4, we describe our experimental PPI datasets and evaluation criteria of our proposed approach. In Section 5, we present the experimental results and discuss them. Lastly, in Section 6, we draw some conclusions and outline our future research plans.
# Experimental Study of Wave-Induced Response of Piles in Seabed with Various Permeability
## 3 Testing Results
### Responses of Sample 1 and Sample 2 Seabed  
Figure 3 shows the pore pressure (\(P\)) recorded in T1(f-_H8_) in pure Sample 1 seabed (without any piles) during the wave action of 640 s; note that waves started at \(t\)= 10 s. The  
Figure 2: Water-surface elevation.  
amplitude of pore pressure showed a value that was almost consistent with loading time, indicating that no substantial accumulation of pore pressure occurred. The pore-pressure amplitude decreased with seabed depth, as expected (see references [25,31]).  
Figure 4 shows the development of pore pressure in T7(s-\(H8\)) in pure Sample 2 seabed. Different from T1(f-\(H8\)) with Sample 1 seabed, pore-pressure build-ups could be clearly observed after an earlier stable period and the beginning time varied from about 250 s to 400 s with depth. Then, the pore pressure in the upper seabed reached a peak value and that in the deep seabed kept increasing during wave loading. As soil liquefaction is defined as the residual pore pressure being equivalent to effective soil stress (see dashed line in Figure 4), the typical liquidated response of seabed could be found at depth \(z\leq 15\) cm. Unlike in Sample 1 seabed with two-order-higher permeability, the results measured in the silty seabed (Sample 2) displayed two properties. (1) The pore-pressure amplitude build-up increased significantly at each depth, e.g., increasing from nearly 0.1 kPa to 0.5 kPa at \(z=5\) cm and from 0.1 kPa to 0.8 kPa at \(z=25\) cm. (2) The accumulation of pore pressure was apparently related to depth and showed a higher value in the deep seabed, indicating the significant effect of soil permeability on seabed response.