esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Islanding Detection Methods
### Signal Processing Based Methods
#### 2.5.4 Time-Time Transformer Method  
By providing a time-time distribution in a certain window, this method transforms a one-dimensional time-domain signal into a two-dimensional time-domain signal. The low- and high-frequency components are distributed differently in this manner. This technique provides a time-local perspective of the signal through the scaled window, making it effective even for noisy signals [38].
## 3 Results and Discussion
### Influence of Linear Velocity to the Removal of Cr(VI)  
The effect of linear velocity was examined in this experimental section, by using [Fe(II)]/[Cr(VI)] molar proportion of 9.6. The applied flocculant dose was 0.5 mg/L at an initial chromium concentration of 50 \(\mu\)g/L. With the increase of linear velocity, the time that is available in the tubular reactor for ferrous iron and hexavalent chromium to react is decreased. The linear velocity was adjusted to the values 4, 8, 12 and 15 m/h. A slight decrease in Cr(VI) removal with the increase of the linear velocity was noticed, as shown in Figure 2. For a linear velocity at 4 m/h the chromium removal rate was 88%, for linear velocity 8 m/h the removal rate was 85%, for linear velocity 12 m/h, the removal rate was at 86%, for linear velocity at 15 m/h the removal rate is at 83%. These results are summarized in Figure 3 and show that there is a slight decrease in the removal of hexavalent chromium by increasing the linear velocity, especially at values higher than 15 m/h. This is mainly attributed to the fact that by increasing the linear velocity to higher values, the contact time of the water with the reagent is decreased and therefore, the time allowed for the Fe(II) reaction with Cr(VI) to take place will be decreased. However, the percentage removal of 83% at 15 m/L is considered as a very satisfactory result, because this corresponds to residual Cr(VI) concentration of less than 10 \(\mu\)g/L and the daily water production accounts for about 1 m\({}^{3}\)/day.
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 3 Background in Programmable Data Plane and 5G and Beyond
### Network Function Virtualization, Network Slicing, and Multi-Access Edge Computing
#### 3.3.3 Multi-Access Edge Computing (MEC)  
Multi-access edge computing (MEC) [42] is a distributed computing paradigm aimed at extending the capabilities of cloud computing in proximity to end-user devices. By leveraging an infrastructure that enables the execution of applications and services at the network's edge, MEC reduces reliance on centralized cloud computing resources. These resources can be strategically located in diverse areas such as base stations, access points, and edge routers. This approach holds the potential to decrease network latency, optimize network bandwidth utilization, and facilitate the development of innovative applications and services that demand real-time data processing and low-latency response times. Within the realm of 5G, there exist several deployment scenarios wherein MEC can seamlessly integrate with 5 GS [43]. Figure 6 showcases four plausible physical implementations of a 5G + MEC network.
# Intelligent Prediction of Aeroengine Wear Based on the SVR Optimized by GMPSO
## 4 Aeroengine Wear Prediction Case
### Single-Step Prediction  
The parameter setting process of GMPSO is the same as that described in Section 4.1. Nevertheless, according to the definition of single-step prediction, the SVR will be trained five times to calculate five test samples. Table 4 shows the comparison between the multi-step and single-step predictions. The accuracy of single-step prediction is higher than that of multi-step prediction, but the elapsed time of multi-step prediction is better than that of single-step prediction. Thus, the proper prediction form can be determined by considering the performance and efficiency comprehensively.  
Figure 3: The influence of D on prediction accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**True Values**} & \multicolumn{4}{c}{**GMPSO-SVR**} & \multicolumn{4}{c}{**PSO-SVR**} \\  & \multicolumn{2}{c}{**Exp.No1**} & \multicolumn{2}{c}{**Exp.No2**} & \multicolumn{2}{c}{**Exp.No1**} & \multicolumn{2}{c}{**Exp.No2**} \\  & **Prediction** & \(RE\) & **Prediction** & \(RE\) & **Prediction** & \(RE\) & **Prediction** & \(RE\) \\ \hline
5.98 & 5.9842 & 0.07\% & 5.9842 & 0.07\% & 6.1838 & 3.41\% & 6.1387 & 2.65\% \\ \hline
6.02 & 6.0338 & 0.23\% & 6.0338 & 0.23\% & 5.9301 & 1.49\% & 6.0604 & 0.67\% \\ \hline
5.75 & 5.9962 & 4.28\% & 5.9962 & 4.28\% & 5.7172 & 0.57\% & 5.9905 & 4.18\% \\ \hline
6.23 & 5.8659 & 5.85\% & 5.8659 & 5.85\% & 5.6559 & 9.21\% & 5.8062 & 6.8\% \\ \hline
5.88 & 5.7425 & 2.34\% & 5.7425 & 2.34\% & 5.4062 & 8.06\% & 5.7914 & 0.15\% \\ \hline (\(C\), \(\gamma\), \(\varepsilon\), \(D\))\({}_{\text{best}}\) & (0.1, 13.5887, 0.01, 13) & (0.1, 13.5887, 0.01, 13) & (100, 0.01, 0.01, 11) & (0.1, 11.8511, 0.01, 11) \\ \hline Fitness value & 0.049\% & 0.049\% & 0.053\% & 0.051\% \\ \hline Average \(RE\) & 2.55\% & 2.55\% & 4.55\% & 2.89\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of multi-step prediction results.
# Leveraging Large Language Models for Sensor Data Retrieval
## 3 Architecture of the System
### Data Gathering
#### 3.1.2 Data Transformation  
When the sensor data returned in the previous step are unstructured, this second step allows these data to be transformed into reusable structured format. For this purpose, LLM is utilised to process raw sensor data, transforming them into structured information. This procedure involves creating a comprehensive prompt that outlines the specific data needed (e.g., \"meteorological information\") and specifying the desired output format. In this study, to seamlessly handle the subsequent retrieval process (see Section 3.2), data are transformed into CSV format.  
Figure 2 shows an example of a prompt defined for an LLM to transform meteorological raw sensor data into CSV format. In this example, {input} represents the raw data from the sensor that will be transformed into CSV. This prompt facilitates the parsing of sensor data provided in formats such as HTML. The generated output produces data in a more reusable format, in accordance with the FAIR principles.
# Design of a Dynamic Secondary Mirror Truss Adjustment Mechanism for Large Aperture Telescopes
## 2 Kinematic Modeling of the Mechanism
### Inverse Solution Model  
The position posture of the dynamic platform of the mechanism is known. The problem of solving the drive on the branch chain is done with the position inverse solution, as shown in Figure 1. The position vector \(\mathbf{r}=\left[\begin{array}{cccc}x&y&z\\ \end{array}\right]^{T}\) at the center point O\({}^{\prime}\) of the dynamic coordinate system O\({}^{\prime}\)-x\({}^{\prime}\)y\({}^{\prime}\)z\({}^{\prime}\) located in the fixed coordinate system O-xyz is expressed as:  
\[\mathbf{r}=\mathbf{a}_{i}+q_{i}\mathbf{u}_{i}+l_{i}\boldsymbol{\omega}_{i}- \mathbf{b}_{i},\ (i=1,2,3,4), \tag{4}\]  
Figure 2: Four PUR-branched chains.  
where \(\mathbf{a}_{i}\)\(\mathbf{b}_{i}\) are the measures of \(\mathrm{A}_{\mathrm{i}}\) and \(\mathrm{B}_{\mathrm{i}}\) in the fixed coordinate system O-xyz. The \(q_{i}\) is the length of the moving sub, \(\mathbf{u}_{i}\) is the unit vector of the moving sub, \(l_{i}\) is the length of the fixed bar on the branch \"\(i\)\", and \(\mathbf{\omega}_{i}\) is the unit vector of the fixed bar where \(l_{i}\) is located.  
\[\mathbf{a}_{i}=r_{a}\big{(}\cos\alpha_{i}\quad\sin\alpha_{i}\quad 0\big{)}^{T}, \tag{5}\]  
\[\mathbf{b}_{i}=\mathbf{R}\mathbf{b}_{i0}, \tag{6}\]  
\[\mathbf{b}_{i0}=r_{b}\big{(}\cos\alpha_{i}\quad\sin\alpha_{i}\quad 0\big{)}^{T}, \tag{7}\]  
where \(\alpha_{i}=(i-2)\frac{\pi}{2}\), \(i=1,2,3,4\) is the position angles of points \(\mathrm{A}_{\mathrm{i}}\) and \(\mathrm{B}_{\mathrm{i}}\) in the fixed and dynamic coordinate systems, respectively, and \(r_{a}\)\(r_{b}\) denote the radii of the static and dynamic platforms, respectively. Rewriting the closed-loop Equation (4) gives:  
\[\mathbf{r}+\mathbf{b}_{i}-\mathbf{a}_{i}-l_{i}\mathbf{\omega}_{i}=q_{i} \mathbf{u}_{i}, \tag{8}\]  
Taking the modulus of Equation (8) and squaring it produces:  
\[\mathbf{r}^{T}\mathbf{r}-2\big{(}\mathbf{a}_{i}+l_{i}\mathbf{\omega}_{i}- \mathbf{b}_{i}\big{)}^{T}\mathbf{r}+\mathbf{b}_{i}^{T}\mathbf{b}_{i}-2\big{(} \mathbf{a}_{i}+l_{i}\mathbf{\omega}_{i}\big{)}^{T}\mathbf{b}_{i}+\mathbf{a}_{ i}^{T}\mathbf{a}_{i}+2l_{i}\mathbf{a}_{i}^{T}\mathbf{\omega}_{i}+l_{i}^{2}-q_{i}^{2} =0,\ i=1,2,3,4 \tag{9}\]  
Solving Equation (9) according to the assembly pattern of the mechanism:  
\[q_{i}=\sqrt{\mathbf{r}^{T}\mathbf{r}-2\big{(}\mathbf{a}_{i}+l_{i}\mathbf{ \omega}_{i}-\mathbf{b}_{i}\big{)}^{T}\mathbf{r}+\mathbf{b}_{i}^{T}\mathbf{b}_ {i}-2\big{(}\mathbf{a}_{i}+l_{i}\mathbf{\omega}_{i}\big{)}^{T}\mathbf{b}_{i}+ \mathbf{a}_{i}^{T}\mathbf{a}_{i}+2l_{i}\mathbf{a}_{i}^{T}\mathbf{\omega}_{i}+l _{i}^{2}},\ i=1,2,3,4 \tag{10}\]  
\[\mathbf{u}_{i}=\frac{1}{q_{i}}(\mathbf{r}+\mathbf{b}_{i}-\mathbf{a}_{i}-l_{i }\mathbf{\omega}_{i}),i=1,2,3, \tag{11}\]
## 3 Materials and Methodology
### Signal Categorization in Personalized Learning Systems  
In this proposed system, signals are categorized into two types: explicit signals and implicit signals, which together form the foundation for constructing an accurate learner profile and predicting the next optimal learning path. Explicit signals, such as quiz scores, performance metrics, and direct feedback, provide clear and measurable data on the learner's current knowledge and achievements. These signals are straightforward to process and help to identify knowledge gaps and overall performance. Implicit signals are derived from the learner's behaviour and interaction patterns, such as time spent on modules, clickstream data, and study habits. These temporal and dynamic signals reveal how the learner engages with the material, offering deeper insights into their learning style, preferences, and challenges. To effectively use both types of signals, the system integrates them in a meaningful way using advanced machine learning models. This combined data is then used to predict the next learning module, ensuring that recommendations align with the learner's current abilities and learning goals. By integrating these signals, the system dynamically adapts to the learner's evolving needs, offering a highly personalized and effective learning experience. Details of different signals and their names are given below in Table 7.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Signal Type** & **Signal Name** & **Description** & **Example Source** \\ \hline \multirow{6}{*}{Explicit} & Pre-test Score & Measures prior knowledge before starting a module. & Pre-test assessment \\ \cline{2-4}  & Post-test Score & Assesses knowledge improvement after completing a module. & Post-test assessment \\ \cline{2-4}  & Satisfaction Rating & Indicates learner feedback on the module’s quality or difficulty. & Learner feedback form \\ \cline{2-4}  & Module Completion Status & Tracks whether the learner has successfully completed the module. & System logs \\ \cline{2-4}  & Initial Module & Content which the learner has chosen. & Content information \\ \hline \multirow{3}{*}{Implicit} & Time Spent on Module & Measures total time the learner spends engaging with a module. & System usage logs \\ \cline{2-4}  & Click Count & Tracks the number of clicks or interactions made within the learning system. & System interaction logs \\ \hline \hline \end{tabular}
\end{table}
Table 7: Signal categorisation.  
Figure 6: Model accuracy comparison.
## 7 Module III: Temporal Privacy
### Energy Controlled Anti Temporal Scheme (ECAT)
#### 7.2.2 Reducing the Amount of Fake Messages and Delay for Real Messages  
We have created a mechanism for dynamic interval allocation. Let us call \(\overline{\omega}\) the _big interval_ which has _subintervals_\(\omega_{i}\). It still makes sense to send fake messages at the end of each interval. However, having the real message wait until the end of the interval time, as exhibited in Figure 15, is not commendable because it increases the delay at each node. Let us presume the current subinterval \(\omega_{i}\) is the maximum, which is 8 time-units according to the example discussed earlier. Let us presume that the message was sensed at 2 time-units and it is ready to be sent at 4 time-units. Following the SGAT rules, it still needs to wait for another 4 time-units to be sent out! However, we know for sure that many other nodes have different \(\omega_{i}\) subinterval values. Thus, during the time subintervals 2, 4, 6, and 8 there is enough traffic in the network. We propose that when the data is ready, the SN\({}_{i}\) should send the data during the next subinterval slot within the current interval \(\omega_{i}\). Consequently, if we are at interval \(\omega_{max}=8\) which has four subintervals at (2, 4, 6, and 8), and for our example, at 6 time-units the data can be sent out. This way, we save about 2 time-units delay while we can guarantee that the adversary will not be able to infer the source of transmission because we have enough traffic distributed in the network.  
If we select higher values for \(\overline{\omega}\), then we can further reduce the number of fake messages transmitting at one subinterval, however, we are increasing the average delay as well. Selecting a value for \(\overline{\omega}\) could be a tool to adjust security _versus_ energy conservation. We have improved the fake message efficacy (_FME_) [27, 42] which could be calculated as indicated below:  
Figure 15: Any node assigned subinterval \(\omega_{i}=2\) will send a fake message if it does not have a real message to report. All the nodes in the network can send real messages within any subinterval.  
Sensors_**2015**, _15_  
\[\text{FME}=\frac{\sum_{i=1}^{i=\text{m}}\frac{\overline{\overline{\omega}}_{i}}{ \overline{\text{SI}}_{i}}-\text{m}}{\sum_{i=1}^{i=\text{m}}\frac{\overline{ \overline{\omega}}_{i}}{\overline{\text{SI}}_{i}}} \tag{45}\]  
where \(\overline{\overline{\omega}}\) is the big interval value, (SI) the subinterval value, (m) the total number of subintervals. For example, if SN assigned a sequence \(\omega_{i}\in[4,6,2,8]\), fake messages could be sent at the following subintervals [4, 10, 12, 20, 24, 30, 32,.....], and the real messages could be sent at the following sub intervals [2, 4, 6, 8, 10, 12,....]. By substituting \(\omega_{i}=8\), \(\text{SI}_{i}=2\) and \(\text{m}=4\), FME will be 60%. Figure 14 exhibits the transmission of fake and real messages for two consecutive subintervals.
# Implementation of a Blood Cold Chain System Using Blockchain Technology
## 3 Design of Blood Cold Chain System
### New Blood Cold Chain System Based on Blockchain Technology
#### 3.2.2 Architecture of New Blood Cold Chain System  
The blood cold chain consists of the blood management headquarters, blood banks, blood inspection centers, transportation vehicles, and hospitals. Because the participants are specified, the private blockchain approach is appropriate. The participants register using the membership service of Hyperledger Fabric. Figure 3 shows the existing blood cold chain and the architecture of the proposed new system. The left side of the figure shows the conventional centralized form, and the right side depicts the proposed blood cold chain system with the shared distributed branching technology.  
By this blockchain technology, the network participants share the same distributed ledger, and the blood management headquarters plays the role of information inquiry and transaction monitoring rather than participating directly in the supply chain.
# PIFNet: 3D Object Detection Using Joint Image and Point Cloud Features for Autonomous Driving
## 2 Related Works
### 3D Object Detection Based on LiDAR  
Many LiDAR-based methods are proposed in recent years. [PERSON] et al. [19] extended BirdNet to BirdNet+ to perform regression of height and vertical location of the center point of a 3D bounding box. Most LiDAR-based methods generate 3D proposals based on voxel-wise features [8; 16; 17; 18] or point-wise features [12; 13; 14; 15]. Voxel-based point encoding methods use 3D regular voxels against the disorder of point clouds, but they often miss precise point positions. [PERSON] et al. [16] developed a Sparsely Embedded CONvolution Detection (SECOND) technique to maximize the use of 3D information. [PERSON] et al. [18] presented a part-aware and aggregation neural network to predict high quality 3D proposals. [PERSON] et al. [17] developed a PointPillars network to convert the point cloud into a stacked pillar tensor and pillar index tensor. [PERSON] et al. [8] voxelized to extract voxel-wise features from point cloud, converted the 3D volumes into Bird Eye View (BEV) plane to obtain region proposals, and employed voxel Region of Interest (RoI) pooling to refine the bounding boxes. The LiDAR-based 3D object detectors directly utilize raw points, but the computation cost is large. [PERSON] et al. [10] introduced a pioneer, PointNet, that extracts point-wise features by Multi-Layer Perceptron (MLP) from raw point clouds without voxelization and developed hierarchical PointNet++ [11] that applied PointNet [10] recursively on raw point set. [PERSON] et al. [14] employed PointNet [10] to predict bottom-up 3D proposals and used RoI pooling to refine them. Sparse To Dense (STD) network [15] used PointNet [10] to yield the global features representing the geometric structure of the entire point set.
# Chapter Detection in Robotic Milling Using Entropy Features
## 1 Introduction
To overcome the shortcomings of EEMD and improve the decomposition performance, several improved methods based on EEMD have been proposed [30; 31; 32]. Among them, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method has a number of attractive features, including less residual noise, less spurious modes, and more physical meanings. Recently, the ICEEMDAN method has proven to be feasible for fault diagnosis [33; 34]. A set of IMFs can be obtained from the ICEEMDAN method. Signal reconstruction can be conducted using IMFs. However, the reconstructed signals cannot be directly used for chatter detection. Appropriate features should be extracted and used as chatter indicators. Fortunately, entropy, which is a measure of the uncertainty and irregularity of the data series, has been widely used as a chatter indicator [35; 36; 37; 38]. Specifically, dispersion entropy (DE) is another algorithm for evaluating and quantifying the randomness and complexity of one-dimensional time series, which is highly sensitive to signal changes [39; 40]. The above entropy features are commonly extracted under single-scale entropy. However, due to the strong nonstationary characteristics in vibration signals, the vibration modes are distributed among multi-scale. Therefore, useful information about chatter will be lost when only single-scale entropy is extracted. To address this issue, the refined composite multiscale dispersion (RCMDE) method has been proposed for estimating the complexity of time series under multi-scale [41]. In this work, the ICEEMDAN method is introduced for decomposing the milling vibration signals into a series of IMFs. Signal reconstruction is performed using IMFs, which are highly correlated with original signals. Weighted refined composite multiscale dispersion entropy (_wRCMDE_) is extracted from obtained reconstructed signals and acts as the chatter indicator. Then, a classification model is established for chatter detection. The remainder of this paper is organized as follows: In Section 2, the theoretical basis and framework for the chatter detection system are given. In Section 3, the experimental setup for robotic milling is presented, and an analysis of the vibration signals is performed. In Section 4, the proposed method is verified, and in Section 5, conclusions are drawn.
# Monitoring of Ionospheric Anomalies Using GNSS Observations to Detect Earthquake Precursors
## 4 Discussion
The comparison between the current SNR map with the mean of the previous 15 days reveal a trend that is not visible using the maps of the simple VTEC rate SNR. In Figure 8, high values of VTEC are present during DOYs 15 and 18; for DOY 15, this could be due to the recovery phase of the storm of DOYs 13 to 15, while for DOY 18 there does not seem to be a direct correlation with the solar activity. In Figure 12, the maps of DTEC from GIMs and from the proposed model at 13 UT show an increasing value during DOY 18 that can be read as an anomaly of a 2 hour duration. The lack of CORSs in the southern hemisphere prevents a deeper investigation into the events in this region and how they influenced the ionosphere over the earthquake area.
# Wireless Energy Harvesting Two-Way Relay Networks with Hardware Impairments
## 2 System and Signal Model
### TWR EH and Hardware-Impairment-Distortion Model  
The network employs a two-step multiple access broadcast transmission and assumes two common relaying protocols, decode-and-forward (DF) protocol and amplify-and-forward (AF). In this configuration, each round of transmission stage consist of two phases: multiple access (MA) phase and broadcast (BC) phase. In the MA phase, the relay \(R\) harvests energy and collects receives data from the wireless signals transmitted by both source nodes simultaneously; in the BC phase, the relay processing the collected data it received through the DF protocol or the AF protocol, and then broadcasts the processed information \(x_{R}\) with the harvested energy to both source nodes. Note that the power needed for information processing and for powering the relay hardware is ignored in the model. This kind of assumption has been applied in a lot of references, such as [11]).  
The power splitting energy harvesting protocol is considered in this paper, in which the receiver architecture of the relay consists of a power splitter and an information decoder (ID) for the DF protocol is adopted, or an information amplifier (IA) for the AF protocol is adopted. The power splitter divides the received signal (transmitted by the source nodes) into two portions: one portion \(\rho\) (\(0\leq\rho\leq 1\)) is used for EH and the other portion (\(1-\rho\)) for information processing.  
Figure 1: System model: (**a**) energy harvesting two way relaying networks with hardware impairments; (**b**) data frame structure of the PS protocol.  
Hardware impairments are considered both the transmitter and receiver of each node. Similar to [19; 23], we model the received signal in the presence of hardware impairments for an end-to-end transmission as  
\[y=h\sqrt{P}(x+\tau_{tx})+\tau_{rx}+n \tag{1}\]  
where \(\tau_{tx}\sim\mathcal{CN}(0,\kappa_{tx}^{2})\) and \(\tau_{rx}\sim\mathcal{CN}(0,\kappa_{rx}^{2}P_{\mathrm{rec}})\) are the aggregate distorta affecting noise caused by HI at the transmitter and receiver, respectively, \(\kappa_{tx}\) and \(\kappa_{rx}\) characterize the levels of impairments of the transmitter and receiver hardware, respectively, \(\mathcal{CN}(\cdot)\) stands for complex circularly symmetric Gaussian distributions, \(P_{\mathrm{rec}}=|h|^{2}P\) is power of the received signal expressed in (1), \(h\) is the channel coefficient and \(n\) is the additive white Gaussian noise. For simplicity of analysis, in the following discussion, we assume all nodes have the same structure so that the level of HI are the same, i.e., \(\kappa_{tx,i}=\kappa_{tx,j}=\kappa_{1}\), \(\kappa_{rx,i}=\kappa_{rx,j}=\kappa_{2}\), (\(i,j=1,2,r\) represent source \(S_{1}\), source \(S_{2}\) and relay \(R\) respectively).