esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Measurement Setup  
In Figure 5, a block diagram providing an overview of the measurement setup is shown. The measurement is controlled from a PC outside the measurement chamber at the Institute of Microwave and Wireless Systems (IMW) at Leibniz University Hannover (LUH). It steers the rotation of the M\({}^{3}\)PA proposed in [40], which is mounted on a rotating arm. The corresponding antenna coordinate system and its location on the rotating arm can be seen in Figure 6. A signal generator is connected to the antenna port of the quad-ridged horn antenna used for the transmission of a vertically polarized wave. This antenna can be seen in the background of Figure 6. The transmitted signal is received by the M\({}^{3}\)PA. The M\({}^{3}\)PA is designed for a center frequency \(f_{\text{c}}=1060\) MHz. This allows its usage for both the interrogation as well as the reply of the transponders at 1030 MHz and 1090 MHz. Note that the performance is adaptable to other frequencies by changing the size of the antenna. The three antenna ports of the M\({}^{3}\)PA are connected to three ports of the SDR. The SDR is used to sample the received signals. The SDR under consideration is an Ettus USRP N310 [48]. It provides four ports and is capable of processing signals in the frequency domain of up to 6 GHz, offering a bandwidth of up to 100 MHz. A signal generator delivers an external local oscillator (LO) signal to the SDR, which is required for phase-coherent applications, as performed in [49]. This is due to the fact that inside the SDR two independent boards are used which lack mutual phase coherency. This can be mitigated by directly feeding the same clock signal to both boards. Phase coherency is mandatory when estimating the  
Figure 4: RMSE and CRB versus SNR of the estimated directions \([\boldsymbol{\phi}_{s},\boldsymbol{\theta}_{s}]\).  
incident angle, since the algorithms rely on the phase difference of the signal due to the radiation pattern [16]. Inside the SDR, the LO frequency of 2120 MHz is divided by two, which corresponds to an intermediate frequency (IF) of 1060 MHz. The transmission lines connecting the ports of the antenna to the SDR require an additional step of calibration. Prior to the measurements, a calibration of the signal paths needs to be conducted. An LO is used to deliver a synchronous signal via a splitter to two transmission lines connected to the ports of the SDR. The resulting differences in phases and amplitudes can than be compensated in software. The phases and attenuations of the feeding network of the antenna are known from simulations and can hence also be compensated by the SDR. A more detailed description of the calibration process can be found in [49]. For controlling the SDR and the record of the sampled signals, a Lenovo ThinkStation running an Ubuntu 20.04 with GNU Radio Companion and the Ettus UHD library is applied. Two 10 Gbps SFP+ links are used to transfer data from the SDR towards the Lenovo ThinkStation. The data streams of the sampled signals at each port are recorded in parallel by the ThinkStation via GNU Radio Companion.  
Figure 5: Measurement setup in antenna measurement chamber.
# RoPT: Route-Planning Model with Transformer
## 5 Experiments
### Hyper-Parameter Analysis  
In this section, the influence of \(\lambda\) and the number of GCN layers are investigated. The change in \(\lambda\) will affect the importance ratio of the passed nodes and destination. While a deeper GCN can capture more complex graph correlations, they may compromise the generalization performance. Exploring the appropriate number of GCN layers is helpful in order to promote the RoPT model. The results are shown in Figures 2a and 2b, respectively. Figure 2a demonstrates peak Recall at \(\lambda=0.5\), revealing an equivalent importance between the historical trajectory nodes and destinations. This suggests that temporal feature extraction and spatial modeling hold equal importance. From Figure 2b, the performance of Recall tends to be stable after the number of GCN layers reaches 4, indicating that the long-distance node has little influence on the next node. For this purpose, a _4-layer_ GCN is used to extract spatial features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Porto**} & \multicolumn{2}{c}{**Chengdu**} \\ \cline{2-5}  & **Acc** & **Recall** & **Acc** & **Recall** \\ \hline RoPT (Ours) & **75.81\%** & **71.47\%** & **62.91\%** & **61.82\%** \\ w/o M\({}_{\text{Des}}\) & 66.75\% & 60.38\% & 58.59\% & 55.65\% \\ w/o Transformer & 72.46\% & 67.91\% & 61.88\% & 60.17\% \\ w/o GCN & 70.80\% & 64.32\% & 60.97\% & 59.96\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation results (the values in bold represent the best).
## 3 Materials and Methods
### Automated Detection
#### 3.3.4 Fixed Camera Setup  
We also hypothesized that training with images collected by fixed cameras, as opposed to the current generalist scenario that simulates real data collection via a mobile device, would further improve the detection performance in the context of an automotive assembly line. In this fixed camera setup (FCS), besides keeping the image capturing conditions constant, it would be possible to have one object detector per camera and obtain more specialized and accurate models.  
In the current solution the detector needs to be able to detect 53 classes. We estimate that in a FCS each detector would have to detect a minimum of 12 and a maximum of 26 classes (This estimate was reached by considering that we have collected 10 samples/views/perspectives from the videos of each test car and by computing the average number of object classes visible in each view.). This would not only increase the overall detection performance, but the detection speed as well, due to the possibility of parallelization. To test the viability of this option, the following steps were followed:  
* each image in the real test was labeled with its visible vehicle view(s) and their corresponding objects
* any left/right discrepancies were corrected, by switching the incorrectly detected sides to match the one present in the corresponding view(s) (this was only applied to images with one visible side of the vehicle, not to images where both left and right sides are visible)
* the detected objects that did not belong to the given view(s) were eliminated  
Note that these situations would never occur in a FCS, because the corresponding detector would only have been trained on objects specific to that view, meaning it could not detect an object from the right side of the car if trained with images from the left view.  
Since the adopted class taxonomy separates front/back and left/right objects, there are no repeated classes (e.g., although there are 4 car rims, they each have their own Detectron2 macro class), which means that multiple detections of the same object stem from having 10 views of the same vehicle and not from more than one object with the same class. Therefore, we can use any of the detections (or combinations of them such as majority voting, for example) to produce the final CBIR code for a given object. Taking this into account, we post process (PP) the final list of predicted vehicle parts by the code with the minimum ratio between the CBIR distance (This corresponds to the Euclidean distance between the query object and its closest training data neighbor of the same macro class.) and its Detectron2's confidence score. This way we are able to choose the detections with the best trade-off between both Detectron2 and CBIR modules.
# Data Fusion of Multivariate Time Series: Application to Noisy 12-Lead ECG Signals
## 1 Introduction
developed a specific fusion method to detect multi-channel maternal ECG R-wave peak locations by a single-lead ECG R-peak detection algorithm [18]. For human activity recognition, feature fusion is commonly employed to measure the features of each lead of the multi-lead ECG signals [19]. However, the aforementioned methods can only be performed on a single-lead ECG signal. Once facing multi-lead ECG signals, the signal of each lead must be analyzed individually; thereby the computational efficiency is reduced. The motivation for this work originates from a purpose to comprehensively assess the quality of 12-lead ECG signals and effectively reduce the computational complexity. The key solution lies in converting the 12-lead ECG signals into a single-lead physiological signal with the quality characteristics of original signals being inherited in the single-lead signal as much as possible. Few papers in the literature have discussed the data fusion of multi-lead ECG signals. Analogously, in [20], [PERSON] et al. presented the multivariate data fusion algorithm (MDF), which was applied to in multivariate phase space reconstruction of oil film whirling in the rotor system. The features of the system in the fused results can be used in fault diagnosis. For the fusion of multi-lead ECG signals, we carried out some preliminary theoretical and experimental research [21; 22]. In these studies, the multi-lead ECG signals were converted into a single-lead signal via the local weighted zero-order model. The weighted coefficients of the model were further estimated by the information of the Euclidean distance between two adjacent state points and the angle between two neighboring vectors, respectively. Previous studies implied that ECG signals are chaotic [23]. For chaotic signals, the local weighted linear prediction algorithm (LWLPA) has been widely used in the prediction of chaotic time series. Inspired by LWLPA, in this paper, we propose a novel data fusion algorithm (NDFA) for 12-lead ECG signals, which can integrate the qualitative characteristics of 12-lead ECG signals into a single-lead signal adequately. The main contributions of the paper are threefold: (1) In this paper, how to compress the number of the leads for 12-lead ECG signals is addressed firstly; (2) as the ECG signals possess chaotic characteristics, based on the idea of LWLPA, NDFA is applied to fuse 12-lead ECG signals; and (3) Toreserve the quality characteristics of ECG signals as much as possible, in NDFA, two fuzzy inference systems are designed to calculate the weighted coefficient of each lead ECG signal properly. The outline of the rest of this paper is as follows: In Section 2, LWLPA is briefly discussed as preliminary; Section 3 introduces NDFA, based on LWLPA; the performance of NDFA is evaluated by synthetic ECG signals and realistic ECG signals in Section 4; and finally, Section 5 contains the conclusion.
# Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN +
## 3 Data Augmentation and Pattern Recognition Method Based on DAE-GAN  
### Deep Self-Encoder and CWGAN-GP Model  
The Deep Autoencoder (DAE) is based on the autoencoder architecture, with additional layers added to both the encoder and decoder to enhance their encoding and decoding capabilities. The DAE is optimized to minimize reconstruction error. The loss function for the DAE is defined in Equation (1).  
\[\left\{\begin{array}{l}\overset{\star}{\boldsymbol{x}_{i}}=De[En(\boldsymbol {x}_{i})]\\ \\ loss_{DAE}=\sum\limits_{i=1}^{n}{{{{\left({{\boldsymbol{x}}_{i}-\overset{ \star}{\boldsymbol{x}_{i}}}\right)}^{T}}}}\left({{{\boldsymbol{x}}_{i}- \overset{\star}{\boldsymbol{x}_{i}}}}\right)}\end{array}\right. \tag{1}\]  
In Equation (1), \(En\) and \(De\) represent the encoding and decoding functions, respectively, while \(x_{i}\) and \(x_{i}\) denote the original data and the reconstructed data, respectively.  
CWGAN-GP is an enhanced version of GAN, utilizing the Wasserstein distance as its objective function and incorporating a gradient penalty to enforce the 1-Lipschitz condition [25]. This addresses issues such as multi-class generation, gradient vanishing, and mode collapse in GANs. The CWGAN-GP loss function is presented in Equation (2).  
\[\left\{\begin{array}{l}L_{G}=-E_{z\sim P_{z}}[D(G(z|\boldsymbol {c})|\boldsymbol{c})]\\ L_{D}=E_{z\sim P_{z}}[D(G(z|\boldsymbol{c})|\boldsymbol{c})]-\\ E_{z\sim P_{z}}[D(\boldsymbol{x}|\boldsymbol{c})]+GP|_{\hat{\boldsymbol{x}}}\\ GP|_{\hat{\boldsymbol{x}}}=\lambda E_{z\sim P_{z}}\left[\left(\left\|\
abla_{z }D\left(\overset{\star}{\boldsymbol{x}}D\left(\overset{\star}{\boldsymbol{x} }\right|\boldsymbol{c}\right)\right\|_{p}-1\right)^{2}\right]\end{array}\right. \tag{2}\]  
In Equation (5), \(L_{G}\) represents the generator loss function, \(L_{D}\) represents the discriminator loss function, \(P_{z}\) denotes the prior distribution of the random noise \(z,\left.GP|_{\hat{\boldsymbol{x}}}\right\) is the gradient penalty term, and \(\lambda\) is the regularization coefficient.  
The network structures of the DAE and CWGAN-GP are shown in Figure 4:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & \
\begin{tabular}{c} **Number of Training Set** \\ **Pulse Samples** \\ \end{tabular} &
\begin{tabular}{c} **Number of Test Set** \\ **Pulse Samples** \\ \end{tabular} \\ \hline Tip discharge & 200 & 3000 \\ Floating discharge & 50 & 1200 \\ Bubble discharge & 120 & 2200 \\ Surface discharge & 80 & 1600 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample distribution of PD pulse training sample datasets.  
Figure 4: Structure diagrams of DAE and CWGAN-GP.
# Parameterised Enumeration for Modification Problems +
## 4 Generalisation to Modification Problems
### Closest String  
In the following, we consider a central NP complete problem in coding theory [32]. Given a set of binary strings \(I\), we want to find a string \(s\) with maximum Hamming distance \(\max\{\,d_{H}(s,s^{\prime})\mid s^{\prime}\in I\,\}\leq d\) for a \(d\in\mathbb{N}\), where \(d_{H}(s,s^{\prime})\) is the Hamming distance between two strings.  
**Definition 13** (Bit-Flip operation): _Given a string \(w=w_{1}\cdots w_{n}\) with \(w_{i}\in\{0,1\},n\in\mathbb{N}\) and a set \(S\subseteq\{1,\ldots,n\}\), \(S(w)\) denotes the string obtained from \(w\) by flipping the bits indicated by \(S\), more formally \(S(w):=S(w_{1})\cdots S(w_{n})\), where \(S(w_{i})=1-w_{i}\) if \(i\in S\) and \(S(w_{i})=w_{i}\) otherwise._  
The corresponding parametrised version is the following.  
\begin{tabular}{l l} \hline \hline Problem: & Closest-String \\ \hline Input: & A sequence \((s_{1},s_{2},...,s_{k})\) of \(k\) strings over \(\{0,1\}\) each of given length \(n\in N\) and \\  & an integer \(d\in N\). \\ Parameter: & The integer \(d\). \\ Question: & Does there exist \(S\subseteq\{1,\ldots,n\}\) such that \(d_{H}(S(s_{1}),s_{i})\leq d\) for all \(1\leq i\leq k\)? \\ \hline \hline \end{tabular}  
**Proposition 2** ([17]): _Closest-String is in FPT._  
Moreover, an exhaustive examination of a bounded search tree constructed from the idea of [PERSON] et al. [17] (Figure 1), allows to produce all minimal solutions of this problem in FPT. Accordingly, we get the following result for the corresponding enumeration problems.  
**Theorem 3**: \(\bullet\)__Enum-Closest-String\({}^{\textsc{lex}}\in\textsc{DelayFPT}\) _with polynomial space._  
\(\bullet\)__Enum-Closest-String\({}^{\textsc{size}}\in\textsc{DelayFPT}\) _with exponential space._  
**Proof.**\(\Omega\) is just the set of operations which flip the \(i\)th bit of a string for every \(i\in\mathbb{N}\). By combining Proposition 2 with Corollary 7 we get the desired result.
Furthermore, we introduce a novel validation method for visual style errors by utilizing [PERSON]'s method to segment CD datasets into subsets with varying degrees of visual style differences based on content and style similarity measurements of pre-trained backbone features. This enables quantitative assessment of the effectiveness of cross-visual style CD algorithms. We rigorously evaluate RCCD on the LEVIR-CD+ and S2 Looking datasets, including their subsets with significant visual style differences. Our results demonstrate that RCCD substantially improves cross-visual style CD performance, achieving state-of-the-art (SOTA) results. 1. RCCD framework: A novel fully convolutional Siamese neural network training framework with an encoder, decoder, and deep supervisor module, enabling efficient and effective mitigation of cross-visual style errors without incurring extra computational costs during inference. 2. Representation consistency deep supervised learning: A method integrating target local spatial context and global content awareness, constructing a loss function that effectively suppresses image pairs with significant visual style differences, promoting robust CD. 3. Visual style errors validation: A novel method leveraging pre-trained networks to partition datasets based on visual style differences, enabling quantitative assessment of cross-visual style CD algorithms. 4. SOTA performance: RCCD achieves SOTA accuracy, substantially improving cross-visual style error suppression in challenging scenarios. The code is available on GitHub: [[https://github.com/wjj282439449/RCCD](https://github.com/wjj282439449/RCCD)]([https://github.com/wjj282439449/RCCD](https://github.com/wjj282439449/RCCD)) (accessed on 3 October 2024). This paper proceeds as follows: Section 2 presents our methodology; Section 3 details our experimental setup and results; Section 4 discusses the implications and limitations; and Section 5 concludes the paper.
## 2 Materials and Methods
### Privacy Management
Therefore, they do not provide a more socio-technical perspective, despite the fact that for SNs, security and privacy is a major issue. [44], specifically, maintain that focusing on privacy requires not only the security of users' personal information and content, but also the security of SNs communication channels from internal or external attacks, as well as the unauthorized access to users' communication by third parties, using access control mechanisms. In this respect, [45] support that the following security and privacy requirements are immense, namely, the identity privacy, the location privacy, the node compromise attack, the layer-removing/adding attack, the forward and backward security, as well as the semi-trusted and malicious cloud security. Considering these, in order to expand previous works and to identify the adequate privacy-related requirements for a self-adaptive privacy protection scheme within SNs, the key issue is to examine users social and privacy needs. To our best knowledge, a measurement scale meeting these issues and focusing on self-adaptive privacy management within SNs in particular has not been developed in previous literature. To address that, taking into consideration that existing privacy scales could benefit from expansion manifold, while the combination of the advantages of previous privacy metrics may improve the level of privacy within cloud, we presented our scale. In the following Table 1 the comparison with these previous measurement scales is presented, indicating that social identity and social capital metrics are not included in other works. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Privacy and Sociological Metrics** & [27] & [28] & [38] & [39] & [40] & [41] & [42] & [43] & **Our Scale** \\ \hline Beliefs in Privacy Rights & & & X & & & & & X \\ Privacy Concerns & & X & & X & & & & X \\ Information Collection & & & & X & X & & & X \\ Self-disclosure & & & & X & & X & & X \\ Trusting Beliefs & & & & & & X & & & X \\ Privacy Control & X & X & & X & & & & X \\ Privacy Awareness & & X & & X & & & & X \\ Collaborative privacy management & & & & & & & X & \\ Self-disclosure/Cost-Benefit & & & & X & & & & X \\ Bonding Social Capital & & & & & & & & X \\ Bridging Social Capital & & & & & & & & X \\ Social identity metrics & & & & & & & & X \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison with previous privacy scales.
## 3 Results and Discussion
### Mechanical Behavior and Total Porosity
This phenomenon is important since the dissolution and activation processes of these residues are quite similar to the hydration mechanisms of fly ashes (a thermal input is needed to favor the formation of zeolitic precursors). The heat released during hydration in these hybrid systems is not enough for the appropriate formation of the N-A-S-H gel and perhaps, in these systems, to achieve better mechanical performance, it is necessary to increase the percentage of CEM I in their composition. From the point of view of the total porosity of these systems, Figure 4 shows the relationship between porosity at 28 days of curing and the mechanical strengths obtained. Observing the results, it can be seen that AAS-WG has lower percentages of porosities than the CEM I reference system. In this sense, the systems activated with NaOH present a total porosity of around 11.5% whereas the mortars activated with sodium silicate present a total porosity of around 2%. This is due to the formation of the C-A-S-H gel in both systems and the AAS-WG gel being richer in silicon, which allows a more compact and cohesive gel to be formed, thus promoting a very high compression strength [8; 29]. In relation to the hybrid systems, the fly ash systems also show high percentages of total porosity between 8 and 13%. The HFA-3% system presents the highest porosity, which is directly proportional to the low compression strength achieved. The hybrid ash system at 5% Na\({}_{2}\)SO\({}_{4}\) (HFA-5%) achieves slightly lower porosity values. This fact is indicative of the extra supply of Na\({}_{2}\)SO\({}_{4}\) (that increases the alkalinity of the system), thus favoring the precipitation of the main reaction products. However, the hybrid systems made of 80 wt % slag (HS-3% and HS-5%) present low total porosity values, such as the system with 5% of Na\({}_{2}\)SO\({}_{4}\), which seems to present a more uniform gel and adopts greater compactness to the system. Finally, regarding hybrid mortars from ceramic residues (HWT-5% and HPT-5%), it is observed that the total porosity is very similar to the HFA-3% system, indicating that, at room temperature, the hydration reaction of the aluminosilicates (with a low content of CaO) has not been completed to give the main reaction product, N-A-S-H gel.
## 2 Methods
### Adsorbent Characterization  
ZnO nanoparticle characterization was performed using powder X-Ray Diffraction (XRD). The ZnO nanomaterial was homogenized by grinding the sample into a fine powder in a mortar and pestle. The XRD data were collected using a Bruker D2 phaser diffractometer fitted with a cobalt source (K\(\alpha\) = 1.789A) and an iron filter. The patterns were collected from 10 to 80\({}^{\circ}\)\(2\theta\) with a 0.05\({}^{\circ}\) step and a 1s counting time. The fitting of the crystal structure was performed using the Le Bail fitting procedure in the Fullprof Suite software (Version January-2021) and crystallographic data from the literature [42; 43]. SEM data were collected using a Zeiss LEO LS 10 SEM microscope operating at 9 keV and a 2.5 A current with a working distance of 6.0 mm. The sample preparation for SEM characterization consisted of sputter coating the samples with a AuPd alloy for 30 s Zeta potential of the ZnO nanoparticles was measured by a Malvern Zetasizer Nano Series Nano ZS90 instrument at pH values between 2 and 6. The ZnO NPs were suspended in 18 M\(\Omega\) water while the pH of the ZnO aqueous solution was adjusted to the desired pH value using either dilute HNO3 or NaOH. The NPs were then equilibrated in solution to mimic the reaction conditions. The Zeta potential measurements were taken using the DTS1070 disposable cells. The RAMAN spectra were recorded using a Rigaku FirstGaurd hand-held RAMAN spectrometer equipped with a 754 nm laser. The spectra collection consisted of 100 co-additions at a 1000 ms sampling time and recorded from 200 cm\({}^{-1}\) to 1000 cm\({}^{-1}\) and the laser power was set to 100 mW.