esa-sceva/satcom-chunk-collection huggingface dataset:
# Linking Off-Road Points to Routing Networks
## 2 Preliminaries
### Manifolds  
With regards to the vast number of encoding formats for spatial data, we describe our approach on a theoretical level, independently of any encoding. Therefore, we emphasize the concept of manifolds [20] in order to model space. The core idea is that we use several charts \(\left\{\varphi_{j}:V_{j}\to\mathbb{R}^{2}\right\}_{j\in J}\) that map locally to a plane. The images of these charts can be glued to transit local reasoning over multiple planes. The following definition gives a precise characterization:  
**Definition 4** ([20]).: _A compact \(n\)-dimensional manifold is a compact topological space \(M\) for which a finite family of homomorphisms \(\left\{\varphi_{j}:V_{j}\to\mathbb{R}^{n}\right\}_{j\in J}\) exists with \(V_{j}\subseteq M\) open for each \(j\in J\) such that:_  
* \(\bigcup_{j\in J}V_{j}=M\)_._
* \(\forall j,k\in J:V_{j}\cap V_{k}\
eq\emptyset\Rightarrow\varphi_{j}\circ \varphi_{k}^{-1}:\varphi_{k}(V_{k}\cap V_{j})\to\varphi_{j}(V_{k}\cap V_{j})\) _is a homomorphism, called the_ transition map_._ _Each_ \(\varphi_{i}\) _is called a_ chart_, and_ \(\left\{\varphi_{j}:\varphi_{j}:V_{j}\to\mathbb{R}^{n}\right\}_{j\in J}\) _is called an_ atlas _of_ \(M\)_._  
In differential geometry, the earth is often modeled as an oriented surface \(M\subset\mathbb{R}^{3}\), i.e., a two-dimensional, topological manifold that embeds to the space \(\mathbb{R}^{3}\) equipped with the Euclidean metric. We use the transition map in order to prolong geodesics over multiple maps. The \(V_{j}\) are open, connected subsets of \(M\) that cover \(M\). For example, any sphere or spheroid is a surface and \(\phi_{1}\) and \(\phi_{2}\) are the charts of this manifold. Further, every oriented surface has a Gauss map [21] that defines the normal field \(\
u:M\to\mathbb{R}^{3}\subset S^{2}\). Informally, \(M\) encodes the position on the surface, while \(\
u\) allows us to express elevation relative to the surface.
## 4 Experimental Environment and Results
### Experimental Results
The classification results can be seen in Table 5.  
To investigate the superior classification accuracy of the parallel feature extractor, different methods are investigated. One of the ways to explain this phenomenon is by investigating the class activation maps of the trained model during the inference. The following steps are taken during the inference:  
1. Test data images are input into the parallel model, and the activation maps from each layer of sub-networks and the parallel model are obtained separately. 2. Probability density function (PDF) of each activation map is calculated, and the mean PDF is generated for both the CNN, DCN, and the parallel model. 3. Entropy is calculated from the average PDF for three separate modules (CNN, DCN, and the parallel model). Entropy is considered a measure of randomness or uncertainty in an image, meaning the higher the entropy, the more complex the unpredictability [48]. According to Table 6, the parallel model has the lowest entropy for three classes, followed by the CNN and followed by the DCN. These values directly correspond to the corresponding model's classification performance of the specific class. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Models** & **Adult** & **Child** & **Elderly** \\ \hline EGBAD [28] & 20.334 & 21.510 & 19.817 \\ AnoGAN [4] & 19.439 & 20.145 & 18.056 \\ GANomaly [10] & 15.310 & 16.481 & 14.006 \\ Skip-GANomaly [30] & 8.365 & 9.931 & 6.480 \\ Previous Model [25] & 6.942 & 7.418 & 6.569 \\ Ablation Conv. & 8.621 & 10.015 & 6.714 \\ Ablation Dilated Conv. & 13.148 & 14.210 & 11.729 \\ Proposed Model & 5.128 & 6.332 & 4.765 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: FID score comparison for three different classes (Adult, Child, and Elderly) for the benchmark models, the proposed model, and the ablation study of the proposed model. Note that a lower score generally means better performance. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Classes** & **Full Model** & **Conv. Only** & **D. Conv. Only** & **DenseNet-169** [44] & **Inception-v4** [45] & **ResNet-101** & **VGG19 [47]** \\ \hline Adult & 89.91 & 83.34 & 78.38 & 87.46 & 86.51 & 83.45 & 82.20 \\ Elderly & 90.24 & 81.52 & 76.23 & 87.90 & 82.43 & 81.98 & 80.71 \\ Child & 86.80 & 84.27 & 77.56 & 84.53 & 80.92 & 80.26 & 78.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The classification accuracy results for the proposed model, the ablation study (convolutional pipeline-only, and dilated convolutional pipeline-only), and the benchmark classification models including DenseNet-169, Inception-v4, ResNet-101, and VGG19. Anomaly score comparison between three cases (normal-elderly vs. anomaly-child + adult, normal-child vs. anomaly-adult+elderly, normal-adult vs. anomaly-child+elderly) can be seen in Figure 4. Looking at the difference between the distributions between the normal and the abnormal cases, the proposed model is highly capable of distinguishing during the single-class anomaly detection inference.
# An Optimized Hierarchical Cluster Formation Approach for Management of Smart Cities
## 3 Proposed Approach
### Research Contributions  
According to the introduced protocol, the structure of each cluster may change after each round according to the properties of the nodes. Such properties will contribute to determining the optimized CH for each node and the optimized size of each cluster.  
However, as previously mentioned, allocating SNs to a suitable cluster based only on the distance factor to guarantee power savings is not precise. For more reliable results, this research proposes using all factors that may affect energy consumption during communication between two nodes. In this research, the communication energy cost (CEC) refers to the total effect of such factors.  
A high level of stored energy is not a sufficient factor for selecting the CH of a node. The eligibility of this stored energy (regardless of how high or low) for communication between two nodes is a critical factor during the cluster formation step. The first contribution is to construct clusters depending on all factors that affect energy consumption instead of considering distance as the only factor. To propose an efficient method to evaluate the adequacy of the remaining energy required to cover CEC during cluster formation is a second contribution of the present research. The third contribution is to adjust the optimal size of the cluster according to the energy requirements. Therefore, to extend the durability of CHs, this research does not allocate the node to the full-sized cluster. The allocation of nodes to the CH is allowed only if the cluster size permits, i.e., the addition of a node to the cluster will not affect the ability of the CH to complete the round. If the number of nodes is equal to or greater than this size, a node is allocated to the next suitable CH.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 6 Integration Mechanism for Fault Prediction and Scheduling Optimization
### Real-Time Data Integration  
The effectiveness of the integrated system relies on the continuous availability of the real-time machine state variables \(S(t)\), including the temperature, vibration, operating hours, and tool wear for each machine \(M_{i}\) at time \(t\). These states are collected through sensors and processed by the deep learning model to predict fault probabilities \(P(M_{i},J_{j},t)\). The SMT solver then uses these predictions to adjust the job schedules dynamically. This dynamic integration ensures that the system remains responsive to evolving machine conditions, allowing for proactive fault mitigation and optimized resource allocation.  
* Dynamic Updates: As the machine state data evolve, the fault probability \(P(M_{i},J_{j},t)\) is recalculated by the deep learning model. This ensures that scheduling optimization reflects the most current conditions of the machines.
* Real-Time Scheduling: The SMT solver receives the updated \(P(M_{i},J_{j},t)\) and re-optimizes the schedule to minimize overall fault risk while considering job deadlines and machine availability.
# Finite Element Modeling of the Soil-Nailing Process in Nailed-Soil Slopes
## 7 Sensitivity Analysis of Experimental Set-Up Dimensions  
The sensitivity analysis of the obtained results is carried out for plain strain conditions validation and dimensions selection. The numerical models using PLAXIS 3D were developed for sensitivity analysis by varying the dimensions of experimental set-up. The sensitivity of the experimental set-up is studied for the different to soil depth and for plain strain condition. For sensitivity investigation, the experimental results were compared with the numerical results for soil toe depths of 15 cm and 20 mm, and for 100-cm- and 120-cm-deep soil (in transverse direction). In three-dimension finite element models, the soil materials and other volume clusters are modeled using the 10-node tetrahedral elements. The six-node plate elements are used for foundation modeling. The nails in the 3D finite element model are simulated using two-node elastic spring elements with constant spring stiffness. The experimental set-up dimension and increase dimension models are shown in Figure 26.  
The plots of horizontal and vertical displacement of the nailed-soil slope using three-dimensional models in the working stage (stage 6) in the experimental set-up dimensions model, model with increased depth and model with increased width are depicted in Figures 27 and 28. The experimental set-up used in the investigation is found to be sensitive to the toe soil depth and is not fully under plain strain conditions.  
Figure 26: 3D model of the experimental set-up dimensions, model with increased depth and model with increased width. (**a**) Experimental set-up dimensions model. (**b**) Model with increased toe depth. (**c**) Model with increased width.
## 5 Multi-Source Fusion Bayesian Compressed Sampling (MSF-BCS)
### Multi-Source Bayesian Compressive Sampling (MS-BCS)
\begin{split} p\left(\mathbf{\alpha}^{m}\mid\hat{\mathbf{\omega}}^{2\text{D},m},\tau,\gamma,\mathbf{y}^{2\text{D},m}\right)\\ =\prod\limits_{t=1}^{N_{t}}\exp\left(-\frac{a_{t}\mathbf{a}_{t}^{m} +b_{t}(\mathbf{a}_{t}^{m})^{-1}}{2}\right)(\mathbf{a}_{t}^{m})^{p-1}\times\frac{(\mathbf{a}_ {t}^{m}/b_{t})^{p/2}}{2K_{p}\left(\sqrt{a_{t}^{m}b_{t}}\right)}\end{split}  \tag{18}\]  
where \(a_{t}=\left(\hat{\mathbf{\omega}}^{2\text{D},m}\right)^{2}\), \(b_{t}=\gamma\), \(p=-1/2\). In addition, \(K_{p}(\cdot)\) is a modified Bessel function of the second type with parameter \(p\). The distribution of \(\hat{\mathbf{\omega}}^{2\text{D},m}\) is based on \(\mathbf{\alpha}^{m}\)\(\tau\), and \(\gamma\) and is a multivariate Gaussian distribution. With these conditional probability density functions (PDFs) mentioned above, the posterior PDF of \(\hat{\mathbf{\omega}}^{2\text{D},m}\) can be obtained by MCMC and detailed procedure is referred to [PERSON] et al. (2015). In this process, the posterior PDF of \(\hat{\mathbf{\omega}}^{2\text{D},I}\) is calculated by determining the common hyperparameter \(\mathbf{\alpha}^{(I,II)}\), and the union \(\mathbf{A}^{(I,II)}\) of the non-trivial 2D basis functions. Finally, the estimated \(\hat{\mathbf{\omega}}^{2\text{D},I}\) and the chosen union \(\mathbf{A}^{(I,II)}\) can be used to obtain the geotechnical data \(\hat{\mathbf{F}}^{I}\) based on Equation (19). For 2D cross-sectional measurement data \(\mathbf{\hat{F}}^{I}\) are statistically sampled, and the mean \(\left(\mathbf{\mu}_{\mathbf{\hat{F}}^{I}}\right)\) and covariance matrix \(\left(\mathbf{COV}_{\mathbf{\hat{F}}^{I}}\right)\) of each sampled element are expressed as:  
\[\mathbf{\mu}_{\mathbf{\hat{F}}^{I}}=\mathbf{\hat{B}}^{I}\mathbf{\mu}_{\hat{\omega}^{ \text{2D},I}} \tag{19}\] \[\mathbf{COV}_{\mathbf{\hat{F}}^{I}}=\mathbf{\hat{B}}^{I}\mathbf{COV}_{ \hat{\omega}^{\text{2D},I}}\left(\mathbf{\hat{B}}^{I}\right)^{\text{T}}\]  
where \(\mathbf{\hat{B}}^{I}\) is a vector of length \(N_{t}\) corresponding to \(\mathbf{B}_{t}^{\text{2D},I}\) rearranged in increasing order of \(t\) (\(t=1,\ 2,\ \cdots,\ N_{t}\)). Equation (19) shows that given 2D cross-sectional measurement data \(\mathbf{Y}^{I}\), in particular those at unsampled positions of \(\mathbf{F}^{I}\), can be sampled according to the sampling standard deviation \(\mathbf{\sigma}_{\mathbf{\hat{F}}^{I}}\) given by \(\mathbf{\mu}_{\mathbf{\hat{F}}^{I}}\) and \(\mathbf{COV}_{\mathbf{\hat{F}}^{I}}\), while \(\mathbf{\mu}_{\mathbf{\hat{F}}^{I}}\) for each measurement in the 2D cross-section is a weighted sum of \(\mathbf{\mu}_{\hat{\omega}^{\text{2D},I}}\)[41]. A high-resolution \(\left(q_{\text{c1N}}\right)_{\text{cs}}\) cross-section is then generated from the \(q_{c}\) data (using \(\mathbf{\hat{F}}^{III}\) to represent the \(\left(q_{\text{c1N}}\right)_{\text{cs}}\) sample data) based on CPT's liquefaction probability assessment framework.
# Reconstructing Polarization Multiplexing Terahertz Holographic Images with Transmissive Metasurface
## 1 Introduction
In the reference [13], four TiO\({}_{2}\) nanopillars arranged on \(2\times 2\) square grids were used to define one metasurface superpixel. Based on the design, the authors demonstrated synchronous generation of four-channel nanoprinting-hologram images at the wavelength of 530 nm. In the reference [42], three hybrid all-dielectric meta-atoms were arranged interleaved to generate multi-channel polarization wavelength multiplexing metasurface holograms in the visible region. Furthermore, studies on the metasurface holograms with a single functionality are now mature, and there is a growing trend towards the development of subwavelength resolution and multiplexing metasurface holograms in the THz range. In this paper, we realize the reconstruction of high-resolution and polarization multiplexing THz holographic images by designing a multi-foci metalens, which is different from traditional metasurface holograms that were achieved based on iterative optimization algorithms. The metalens is composed of all-dielectric meta-atoms and the focal points of the metalens are utilized as the pixels of a holographic image. The effects of focal length and phase pixel number of the metalens on image quality are discussed to achieve high-resolution holographic images. Furthermore, by switching the polarization of incident lights, holographic images with different patterns are reconstructed on the focal plane. Our work achieves holographic images with both subwavelength resolution and polarization multiplexing holograms with advantages of miniaturization, integration, multiplicity, and high storage capacity, which has potential for applications in THz communications, information engineering, and encryption.
## 1 Introduction
Moreover, since the performance of VAD relies on that of SE, VAD must be re-trained whenever the SE is changed in its application or applied to different noise environments. This re-training of VAD also requires a new annotated dataset, which is impractical in terms of the development costs for VAD. In this paper, a single-channel SE method is proposed based on a multi-task learning U-Net (MTU-Net) architecture to provide a better estimate of the IRM and to simultaneously perform VAD. Inspired by the DRNN-based SE method [5], which jointly optimizes both the speech and noise spectra, the proposed MTU-Net extends the decoding network of a conventional U-Net so that it can estimate the speech and noise spectra together. Then, the IRM is estimated by using the spectra estimated for both clean speech and noise. Finally, the estimated IRM is applied to noisy input spectra to produce a clean speech spectrum estimate, which is more accurate than that obtained from the conventional U-Net. The speech quality and intelligibility of the proposed MTU-Net-based SE method are compared to those of statistical SE methods, especially SNMF [3], and several deep learning-based SE methods, including the conventional U-Net [15], SEGAN [8], and DRNN [5]. In addition to estimating the clean speech spectrum, the estimated IRM can be used to detect voiced intervals. This is because the speech presence probability for VAD can be represented by the mask. Thus, if the sum of IRMs along the frequency bins is greater than a given threshold, the noisy input speech at the time frame is detected as a speech frame. The performance of the proposed mask-based VAD is also compared with the performance of the VAD methods based on the boosted DNN (bDNN) [24] and long short-term memory (LSTM) [22]. This paper is organized as follows. Section 2 reviews a single-channel SE based on a conventional U-Net. Then, Section 3 proposes the MTU-Net-based SE method, where the MTU-Net is developed by extending the conventional U-Net under a framework of multi-tasking learning. After that, a VAD method is also proposed by incorporating the mask estimated from the MTU-Net-based SE. Section 4 evaluates the performance of the proposed MTU-Net-based SE method using objective measurements and comparing these measurements with those of a statistical and NN-based methods. In addition, the performance of the proposed mask-based VAD method is evaluated in terms of the area under the receiver operating characteristic curve objective measurements, which are compared with those of several NN-based methods. Finally, Section 5 concludes the paper.
## 4 Experimental Performance Evaluation
### Experimental Setup
All the samples were mixed recordings of two speakers from _Libri2 Mix_. a frame length of 25 milliseconds and a frame shift size of 10 milliseconds. The extractor was configured with a three-layer LSTM, where each layer had a hidden dimension of 256.
# Application Study of Adaptive Tracking Algorithm in Active Noise Control System of Transformer
## 4 Discussion
### Comparing Experiments of Algorithms
#### 4.1.2 Actual experiments  
In order to test the performance of the three algorithms in real sound field environment, the active noise reduction experiment is carried out. The noise source was simulated with 3 speakers, and the spacing between each speaker was 0.25 m; the secondary sound source array was composed  
Figure 7: Simulation results.  
of 6 speakers, each speaker having a vertical interval of 0.25 m and a horizontal spacing of 0.4 m. The primary sound source is the recorded transformer noise, and its volume is adjusted to 60 dB at a distance of 1 m. The parameter setting of the algorithm is the same as simulation. Two measuring points are set at 5 m and 10 m from the secondary sound source. The orientation diagram and experimental photos are shown in the Figure 8. The results are shown in the Table 2.  
From the measurement results of simulation experiment, it can be seen that 5.7 dB(A) noise reduction is obtained at the X1 measurement point and 4.7 dB(A) noise reduction is obtained at the X2 measurement point by using the proposed algorithm whereby 1.5 dB(A) noise reduction is obtained at the X1 measurement point and 0.8 dB(A) noise reduction is obtained at the X2 measurement point by using the LMS algorithm. Even the noise was intensified at two measurement points by using the FxLMS algorithm which means that the algorithm does not converge under this experimental condition, and that a secondary sources phenomenon screaming out of control appears multiple times in the experimental process. Although the convergence accuracy of the proposed algorithm is not the highest in the simulation experiment, but the proposed algorithm has the following advantages: on the one hand, it converges very fast; on the other hand, it can largely avoid the adverse effects of the noise in addition to those 5 single-frequency noise components which the proposed algorithm only considered as optimization objectives of noise reduction.
# Implementing GDPR-Compliant Surveys Using Blockchain
## 5 System Implementation
### Supporting Surveys  
To support surveys, we added a ledger in HLF (to hold metadata of the surveys and IPFS CID of the answers), separated files in IPFS, created specific methods in the API and included a mechanism to delete the information of expired surveys.  
In our Proof-of-Concept (PoC), the surveys are sets of questions created by the DC with an explicit purpose and a deadline. After the DC inserts the questions, the DS can read the survey description and decide whether to participate. To comply with article 9 of GDPR, the answer to each survey must be optional and time-constrained, and all the information about a survey must be deleted after the deadline [23].  
Each answer is stored separately on IPFS to allow deletion and editing independently from the user information entered in the registration and other user answers. One answer to a survey corresponds to a single IPFS file. This method also enables processing information without knowing which user generated the data.  
Figure 1: Olympus: HLF Network.  
Figure 2: Olympus: IPFS Network.  
To ensure that only registered users answer the survey, they must authenticate themselves before answering. However, the authentication information is not stored with the answer to the survey. The CID of the answers is stored within the survey ID in the user record on the blockchain to ensure that a user can answer each survey only once, and they can only delete and edit their own files.  
To comply with article 9 of GDPR, we request that the user provides specific consent for each survey after reading the description and the deadline. The consent information is stored along with the answer in the IPFS file.  
To ensure the automatic deletion of the surveys after the deadline expires, a cronjob requests their list from the blockchain and iterates over each one, comparing the deadline with the current time. The frequency of these checks can be easily changed; we choose two minutes for demonstration purposes. The structure of the survey list is as follows:  
[Survey1, Deadline1, Description1, [CID1, CID2, CID3]];  
[Survey2, Deadline2, Description2, [CID4, CID5, CID6]];  
When the cronjob detects an expired survey, it starts the deletion process: (1) iterates over the list of CIDs; (2) invokes the IPFS cluster to unpin each CID in the list; (3) invokes the IPFS cluster to run the garbage collector that removes every unpinned file; and (5) invokes the chaincode to delete the survey from their list. The list of answers in the users' records (with survey IDs and CIDs) remains intact.
# Grasp Analysis for the Robot-Based Manipulation of Pre-Assembled Cables with Electrical Connectors
## 2 State of the Art
### Evaluation of the State of the Art  
With the novel contact model by [11] presented in Section 2.1, the calculation of NSCs is possible. Using the thereby determined GWS and the findings of further work, the basis is provided to determine the required grasp force for an external disturbance.  
In addition, the feasibility of grasping and mating ECs using robotic systems is demonstrated by [15; 16; 17], among others. The approaches focus on the rigid components--the plugs and sockets. However, the cable and its mechanical effect on grasping and mating is not considered.  
Moreover, the works focusing on the manipulation of DLOs use various types of physics simulations to model the dynamic behavior of cables. However, except for [27], the approaches hardly address the mechanical characteristics of cables and often represent the cables in a simplified manner. Moreover, the works commonly focus only on the DLO while neglecting the EC. Overall, the approaches emphasize the demand for digital assembly planning for cables. However, an integration of the gripper system into the digital assembly planning of cables with ECs requires a combined consideration of the mechanical cable behavior and the EC.  
Currently, no approaches are known that enable us to investigate the mechanical influence of cables on the grasping of plugs. Therefore, with the current state of the art, grasp analysis is not yet possible for these hybrid components which consist of rigid plugs and deformable cables.