esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Testbeds and Experimental Resources
**24078**  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Testbed** & \multicolumn{2}{c}{**Device Used**} & \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**Services Offered**} & \multicolumn{2}{c}{**Virtualization**} \\  & & \multicolumn{2}{c}{**Sensor Nodes**} & \multicolumn{2}{c}{**Support**} \\ \hline \multirow{4}{*}{Freie Universitat Berlin [162]} & \(\bullet\) & DES-WSN & \multirow{4}{*}{110} & \(\bullet\) & Temperature & \multirow{4}{*}{Yes} \\  & \(\bullet\) & DES-Mesh & & Humidity & & \\  & \(\bullet\) & DES-WMN & & & Humidity & \\  & & DEC-Mobile & & & & \\ \hline Braunschweig Institute of & \multirow{2}{*}{iSense} & 30 Nodes & \(\bullet\) & Load Sensors & \multirow{2}{*}{Yes} \\ Technology [163] & & 120 Sensors & & Weight & \\ \hline Research Academic Computer & \multirow{2}{*}{iSense} & \multirow{2}{*}{154} & \(\bullet\) & Environment & \multirow{2}{*}{Yes} \\ Technology Institute [164] & & TelosB & & & & \\  & & & & & \\ \hline Universitat Politecnica de & \multirow{2}{*}{iSense} & \multirow{2}{*}{10} & \(\bullet\) & Solar module & \multirow{2}{*}{Yes} \\ Catalunya [165] & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{Universität Bern [166]} & \(\bullet\) & TelosB & \multirow{4}{*}{47} & \(\bullet\) & Temperature & \multirow{4}{*}{Yes} \\  & & MSB-430 & & & & \\  & & & & & Humidity & \\  & & & & & Light & \\  & & & & & Accelerometer & \\ \hline \multirow{4}{*}{University of Geneva [167, 168]} & \(\bullet\) & Atom-Based & \multirow{4}{*}{25} & \(\bullet\) & Security & \multirow{4}{*}{Yes} \\  & \(\bullet\) & Servers & & & & \\  & \(\bullet\) & iSense & & & & \\ \hline \multirow{4}{*}{Delft University of [169, 170]} & \(\bullet\) & SOWNet & \multirow{4}{*}{140} & \(\bullet\) & Temperature & \multirow{4}{*}{Yes} \\  & & T-Node & & & & \\  & \(\bullet\) & SOWNet & \multirow{4}{*}{140} & \(\bullet\) & Temperature & \multirow{4}{*}{Yes} \\  & & G-Node & & & & \\ \cline{1-1} Technology [169, 170] & \(\bullet\) & Tmote Sky & & & & \\  & \(\bullet\) & TelobsB & & & & \\  & \(\bullet\) & Octopus I-II & & & & \\  & \(\bullet\) & MICAz & & & & \\ \hline Lancaster University [171] & \(\bullet\) & TelosB & 16 & \(\bullet\) & Temperature & \multirow{4}{*}{Yes} \\  & & & & \(\bullet\) & Humidity & \\ \hline \multirow{4}{*}{University of Cyprus [172]} & \(\bullet\) & MICAz & \multirow{4}{*}{14} & \(\bullet\) & Environment & \multirow{4}{*}{No} \\  & \(\bullet\) & Camera & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \multirow{4}{*}{KAIST University [161]} & \(\bullet\) & MTS300 & \multirow{4}{*}{50} & \(\bullet\) & Temperature & \multirow{4}{*}{Yes} \\ SmartFIRE [161] & \(\bullet\) & MTS420 & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \multirow{4}{*}{Washington University [176]} & \(\bullet\) & TelosB & 79 & \(\bullet\) & Light Radiation & \multirow{4}{*}{No} \\  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & &
## High Precision Signal Processing Algorithm for White Light Interferometry  
**[PERSON] ***  
Information and Communication Engineering Dept. Hansei University, 604-5 Dangjeong-dong Gunpo-city Kyunggi-do, Korea  
* Author to whom correspondence should be addressed; E-mail: [EMAIL_ADDRESS]; Tel.: 82-2-31-450-5174; Fax: 82-2-31-450-5172  
_Received: 22 April 2008; in revised form: 7 November 2008 / Accepted: 7 November 2008 / Published: 1 December 2008_  
**Abstract:** A new signal processing algorithm for absolute temperature measurement using white light interferometry has been proposed and investigated theoretically. The proposed algorithm determines the phase delay of an interferometer with very high precision (\(<<\) one fringe) by identifying the zero order fringe peak of cross-correlation of two fringe scans of white light interferometer. The algorithm features cross-correlation of interferometer fringe scans, hypothesis testing and fine tuning. The hypothesis test looks for a zero order fringe peak candidate about which the cross-correlation is symmetric minimizing the uncertainty of mis-identification. Fine tuning provides the proposed algorithm with high precision sub-sample resolution phase delay estimation capability. The shot noise limited performance of the proposed algorithm has been analyzed using computer simulations. Root-mean-square (RMS) phase error of the estimated zero order fringe peak has been calculated for the changes of three different parameters (_SNR_, fringe scan sample rate, coherence length of light source). Computer simulations showed that the proposed signal processing algorithm identified the zero order fringe peak with a miss rate of 3 x 10\({}^{\text{-4}}\) at 31 dB _SNR_ and the extrapolated miss rate at 35 dB was 3 x 10\({}^{\text{-8}}\). Also, at 35 dB _SNR_, RMS phase error less than 10\({}^{\text{-3}}\) fringe was obtained. The proposed signal processing algorithm uses a software approach that is potentially inexpensive, simple and fast.  
**Keywords**: Phase delay estimation, white light interferometry, signal processing algorithm, fine tuning
# Statistical Process Control with Intelligence Based on the Deep Learning Model
## 4 Experiment and Discussion
### Application in Real Production Data  
In order to verify the engineering value of the proposed method, it is applied to the quality control of real production. The diameter of the controlled object is the key to judge whether its quality is good or not, and its standard size is \(\varphi 750^{+0.235}_{+0.158}\) mm. The width of the detection window is still 25. The multilayer Bi-LSTM is used to monitor the data of its production process, and some recognition results are listed in Figure 12.  
Figure 11: The feature visualization results for (**a**) the expert features, (**b**) the features extracted by 1-CNN, and (**c**) the features extracted by multilayer Bi-LSTM.  
Figure 12: Abnormal HPs and CCPs found by the multilayer Bi-LSTM from production data.  
As shown in Figure 12, the proposed method can identify a variety of abnormal patterns from the quality data of the production environment. At the same time, the product quality distribution in this stage is skewed, which is also effectively recognized by the multilayer Bi-LSTM. It can be found that the key dimensions of this batch of products are generally smaller. According to the results of communication with factory engineers, the reason for this histogram pattern is likely to be the result of conscious processing by workers. It is worth noting that the multilayer Bi-LSTM trained with the above simulation data set is used in the quality control of real production data. So before the real data is input into the network, linear transformation is carried out to scale the data to the same range of simulation data. This transformation is very simple and will not affect the speed and automation of recognition. At the same time, it shows that the network trained by the simulation data can identify the real data well, and does not need to train the network repeatedly according to the different product specifications. The proposed method can be easily integrated into the existing industry 4.0 system to provide intelligent SPC data analysis scheme for manufacturing enterprises and improve production efficiency and product quality. In addition, the SYS pattern in Figure 12 was recognized as CYC pattern by 1D-CNN in the previous study [18], which shows that the recognition results of the previous method are not precise enough. In this paper, the CCP is increased to nine, which can achieve more refined CCPR.
## 5 Over-Voltage's Dependence on Penetration Levels of DG  
A core question is how much DG can be injected relative to its feeder's load without causing too much trouble? An analysis was performed on this feeder when both E-OLTCs and LVRs were on. The input data comes from only a random portion of the dataset.  
Five levels of DG penetration levels were simulated. Each level represents the percentage of the nominal installed DG. This is done by using scaling factors ranging from 0.6 Pn to 1.4 Pn in increments of 0.2 Pn. For each level, a count of over-voltage constraint violations has been taken. The results are presented in Table 2.  
Figure 13 is a graphical representation of the number of over-voltages depending on the percentage of DG.
# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
## 4 Results
### Results and Discussion  
Table 1 presents a comparative analysis of experimental results from various research methods, comparing the multimodal and unimodal methods proposed in this paper, ablation studies, and other research. Figure 7a illustrates the accuracy trend of different models during training. It can be clearly observed that the feature-level fusion method based on ResNet34 converges faster and achieves a higher final accuracy compared to other methods. Meanwhile, Figure 7b presents the ROC curve for the ResNet34 feature-level fusion model, with an area under the curve (AUC) of 0.99, indicating excellent classification performance. These results further validate the robustness of the proposed method in handling multimodal data. From Table 1 and the figures, it is evident that multimodal methods, especially the ResNet34-feature level fusion, significantly outperform unimodal methods in all evaluation metrics. The ResNet34-feature level fusion achieved a 95.85% accuracy, 97% sensitivity, 95% specificity, and an F1 score of 91%, demonstrating its strong overall performance. In comparison, the unimodal methods, such as LSTM in the time domain and CNN in the frequency domain, show noticeably lower results, with accuracy values of 85.42% and 88.41%, respectively. The fusion approach effectively leverages the complementary information among various features, thereby enhancing classification performance. Figure 6: The network diagram for the model-level fusion algorithm based on ML-LSTM. Moreover, the results of testing the ResNet34-feature level fusion algorithm on the ADFECGDB dataset are also impressive, achieving a high accuracy of 97.89%, with a sensitivity of 97% and a specificity of 98%. This indicates that the proposed method not only demonstrates high reliability but also possesses transferability, as it performs effectively across different datasets, validating its potential for broader applications in fetal heart monitoring. The ablation study further validates the importance of each feature to the model's performance. Removing the time-frequency domain features has the most significant impact on performance, indicating their crucial role in fetal heart signal classification. In contrast, removing time-domain, frequency-domain, and feature values has a relatively minor impact. A comparison with other studies shows that the methods proposed in this paper generally outperform others in various performance metrics, with all compared studies utilizing the same database, the PhysioNet/CinC Challenge 2013 dataset. Overall, the multimodal methods proposed in this paper demonstrate strong performance and wide applicability in the classification of fetal heart ECG signals. The comprehensive analysis across multiple databases and the ablation study further validate
# Radiation-Assisted Synthesis of Polymer-Based Nanomaterials
## 6 Controlling Radiation-Induced Grafting on Nanoscale  
Radiation-induced grafting is one of the most promising applications of radiation processing as it allows control of the surface properties of polymers and inorganic particles. Since the introduction of the idea of using ionizing radiation for the generation of polymer-bound radicals to initiate copolymerization of a different monomer by the leading polymer scientists [PERSON] and [PERSON], this technique has been widely used for the modification of polymers [67]. Until quite recently, controlling of grafting frequency and graft chain length were not possible due to random and irreversible character of free radical polymerization. With the introduction of controlled free radical polymerization techniques, especially Atom Transfer Radical Polymerization and Reversible Addition-Fragmentation Chain Transfer Polymerization, grafting of polymers has taken a new and unprecedented dimension. However, only the latter can be applied under the radiation conditions which make RAFT grafting possible using ionizing radiation. This technique allows the synthesis of polymers with precise, predetermined molecular weights with very narrow dispersity. In a recent paper, nanostructuring of polymers by controlling ionizing radiation-induced free radical polymerization, copolymerization, grafting and crosslinking by the RAFT mechanism has been discussed [68]. In this section, the unique advantage of controlling of radiation-induced grafting in nanoscale is discussed considering functionalization of track-etched membranes, modification of fuel cell membranes and synthesis of polymer brushes for cell sheet harvesting.
## 3 Results and Discussion
### Effects of AM Constraints and Objective Functions
#### 3.2.3 Compliance and Stress Minimization  
The weighted sum method is utilized for topology optimization to minimize compliance and stress simultaneously, and the multi-objective function for stress and compliance is defined as:  
\[f(x)=\frac{c(x)}{c_{0}}w_{1}+\frac{s(x)}{s_{0}}w_{2} \tag{18}\]  
where \(f(x)\) is the objective function, and \(s(x)\) and \(c(x)\) are the stress and compliance functions for the physical density variable \(x\). \(w_{1}\) and \(w_{2}\) are the weight combinations set to \(w_{1}\) = 5 and \(w_{2}\) = 1 for multi-objective optimization, and this weight combination was chosen based on the reference [28]. The relative importance of the two design objectives can be controlled in the overall objective function by multiplying each objective term by different weight factors, which would give designers more flexibility in terms of the area of application.  
Figure 10: Compliance Minimization (1: cantilever, 2: bridge shape, 3: L-shaped beam).  
Figure 14: Compliance minimization (**a**) without AM constraints and (**b**) with AM constraints.  
Figure 15: Stress minimization (**a**) without AM constraints and (**b**) with AM constraints.  
Figure 16: Compliance and stress minimization (**a**) without AM constraints and (**b**) with AM constraints.  
Figure 17: Comparison of the total volume after optimization (1, 4, 7: cantilever; 2, 5, 8: bridge shape; 3, 6, 9: L-shaped beam).  
Figure 18: Comparison of the part volume (1, 4, 7: cantilever; 2, 5, 8: bridge shape; 3, 6, 9: L-shaped beam).  
However, in certain cases, the optimizer may converge to a part geometry that has an increase in part volume in order to achieve a reduction in the volume of the support structure. This phenomenon can be observed in cases 2, 3, 5, 6, and 8 in Figure 18, where the optimizer converged to part geometries with increased volume to achieve a reduction in the support structure volume. Consequently, although the optimization process with AM constraints resulted in a significant reduction in support volume, the total volume of the design remained relatively unchanged. The optimizer tended to converge towards overly simplified shapes in order to minimize the support structures when AM constraints were added, as seen in Figures 14-16. This is because the topology optimization with AM constraints prioritized minimizing support structures over exploring more intricate designs to reduce part volume. Thus, topology optimization was performed to increase additive manufacturability and reduce printing complexity. These results show that there is a trade-off relationship between part volume and support structure volume during the optimization process. While the reduction in support volume is desirable, the increase in part volume may limit the potential benefits of AM.
# Pose Estimation of Primitive-Shaped Objects from a Depth Image Using Superquadric Representation
## 3 Methodology
### Geometric Segmentation  
As primitive shapes have only convex surfaces, we segment the depth map \(\mathcal{D}_{t}\) into convex shapes. [PERSON] et al. [30] adapted the concave region penalty to the normal edge-based segmentation in their SLAM pipeline. We employ their segmentation approach. They classify each pixel irrespective of whether the pixel is an object edge by employing two operators. The first operator detects concave boundaries by computing the dot product between the normal vector of target pixel and each normal of the eight-connected neighboring pixels. The second operator takes into account the maximum 3D point-to-plane distance between the target pixel and its eight neighbors. In order to establish the threshold of the second operator, we employ an uncertainty measure computed following the noise model [31].  
As a result, we obtain a binary geometric edge map \(\mathcal{B}_{t}\) at frame \(t\), \(\mathcal{B}_{t}\) from the input depth frame. To the edge map \(\mathcal{B}_{t}\), we apply a four-neighborhood connected component analysis algorithm to yield a label map \(\mathcal{L}_{t}\), where each element \(\mathbf{u}\) is associated with a segment label \(\mathcal{L}_{t}(\mathbf{u})=l_{j}\). Unlike the method in [30], we do not segment the points on the floor plane to extract the object point cloud.
# Hardware Acceleration of Tsunami Wave Propagation Modeling in the Southern Part of Japan
## 3 Numerical Results  
A number of numerical experiments using the MacCormack scheme were arranged to study the detailed distribution of tsunami wave maximal heights along the coast for several particular locations of the tsunami source. We moved the model tsunami source (sea surface displacement at the source) in the alongshore (four different positions \(i\) = 0, 1, 2, 3) and cross-shore (three different locations indicated by \(j\) = \(a\), \(b\), \(c\)) directions to compare the distributions of tsunami wave maxima along the coast line. The positions of all these 12 model tsunami sources are given in Figure 3. The position of the \(Si\)-\(b\) (\(i\) = 0,..., 3) source's negative wing (part of the initial water surface displacement) is the same as that of the \(Si\)-\(a\) positive wing, and the position of the \(Si\)-\(b\) positive wing is the same as that of the \(Si\)-\(c\) source's negative wing (\(i\) = 0,..., 3). Next, we describe how these experiments were conducted and discuss the obtained results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Platform** & **Before Optimization** & **After Optimization** \\ \hline Original MOST code on a regular PC & 3000 ms & 3000 ms \\ Intel (8 cores) & 300 ms & 180 ms \\ GPU Tesla K40 (Double Precision) & - & 20 ms \\ Calculator on FPGA xc6 vsc315-1 & - & 19 ms \\ Calculator on FPGA xc7 vx690-2 & - & 3.8 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of performance calculating tsunami wave propagation on different hardware platforms (reduced version from [14]).  
Figure 2: Image of the printed board—the designed FPGA-based “Calculator”.
# User-Information-Aware D2D Multicast File Distribution Mechanism
## 4 User's Demands Awareness
### Video Request Probability
#### 4.1.1 User's Interest  
The user's interest can be reflected by the accumulated duration information accessing video content to some extent. The larger watching times clearly indicate the higher interest degree for a certain interested video. Thus, the user's interest in the video can be shown in (3):  
\[p_{i,k}=\frac{t_{i,k}}{t_{i}} \tag{3}\]  
where \(p_{i,k}\) denotes the interest of user \(u_{i}\) about the \(k\)-th type of video, \(t_{i,k}\) is the total time of user \(u_{i}\) watching the \(k\)-th type of video, and \(t_{i}\) is the total time of user \(u_{i}\) watching all videos.  
In fact, the duration of different videos is very different, i.e. between over two hours for movies and a few minutes for a small video, so user's degree of interest just expressed by the ratio of viewing time is not accurate. The proportion, the number requesting this video to the total video requests, should be considered in the calculation of the user's interest degree. Let \(f_{i,k}\) denotes the ratio between the number of times user \(u_{i}\) watching the \(k\)-th type video and the total video views in the past period of time \(T\), it can be expressed as:  
\[f_{i,k}=\frac{x_{i,k}}{X_{i}} \tag{4}\]  
where, \(x_{i,k}\) denotes the number of times user \(u_{i}\) watching the \(k\)-th type video, and \(X_{i}\) indicates the total number of videos user \(u_{i}\) has watched in the past period of time \(T\). The user's interest can be measured more accurately by simultaneously considering the frequency information and duration information about the user watching the online videos.  
In addition, the calculation of the user's interest degree may be influenced by some interference information generated by the user's inadvertent touch. Therefore, based on filtered this part of information, let \(P_{i,k}^{interest}\) denotes the interest degree of user \(u_{i}\) about the \(k\)-th type video, and it can be given by:  
\[P_{i,k}^{interest}=\delta\frac{x_{i,k}}{X_{i}}+(1-\delta)\frac{\sum_{j=1}^{x_{i,k }}t_{i,k}^{j}}{t_{i}} \tag{5}\]  
where, \(\delta\) is the weight coefficient, \(t_{i,k}^{j}\) denotes the duration of user \(u_{i}\)\(j\)-th watching the \(k\)-th type video. If \(\delta\) > 0.5, the frequency information has greater influence on the interest degree, so user's random browsing may cause a large error in the calculation of the degree of interest. If \(\delta\) < 0.5, the duration information has greater influence on the interest degree, long videos have a great impact on the accuracy of the calculations of interest degree. Notably, each video viewing information in the Equation (5) should satisfy the constraint condition \(t_{i,k}^{j}>t_{th}\), \(t_{th}\) is the time threshold for judging whether a mistaken touch has occurred, namely, it is deemed to inadvertent touch when the time duration watching the video is less than the threshold, and this video viewing information will not be considered in the calculation of the user's degree of interest.
## 4 Outage Optimization Strategy Considering the Importance of Users
Equation (47) represents the island radial constraint:  
\[\sum\limits_{i_{1},i_{2}}c_{i_{1},i_{2},u,t}=\sum\limits_{i}y_{i,u,t}-1 \tag{47}\]  
where \(c_{i_{1},i_{2},u,t}\) is a 0-1 variable that indicates the connectivity status of the line on island \(u\) with nodes \(i_{1}\) and \(i_{2}\) as the first and last nodes at time \(t\), respectively; \(c_{i_{1},i_{2},u,t}\) is 1 if the line is connected and 0 if it is not. This constraint shows that on each island, the difference between the number of nodes and the number of lines is 1. This constraint ensures that the island operates in a radial fashion.