esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Ultra-Low-Frequency Communication Based on the Rotating Shutter Antenna
## 3 Propagation Characteristics in Layered Media  
It is promising that RPMA can be used as the transmitting antenna in magnetic inductive underground communication and it is particularly important to study the radiation characteristics of RPMA in layered media. Layered media cause interface emission loss and transmission medium loss because of different permittivity and conductivity.  
This paper used FEKO to construct an equivalent model of a rotating permanent magnet antenna that rotated vertically on the ground and analyzed its magnetic field characteristics in three-layer media, such as air-soil-rock. The simulation parameters are shown in Table 2.  
The curves of magnetic flux intensity versus distance are shown in Figure 5 and it is clear that the maximum penetrating depth could reach 450 m for magnetic induction communication.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameters** & **Values** \\ \hline antenna & frequency & 320 Hz \\  & moment & 1 Am\({}^{2}\) \\  & depth & 1500 m \\ soil & permittivity & 10 \\  & conductivity & 0.01 S/m \\ rock & permittivity & 10 \\  & conductivity & 0.01 S/m \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of the simulation.  
Figure 4: B-field distribution from different viewing angles: (**a**) side view; (**b**) top view.  
Figure 5: Change in magnetic flux density with the distance at different depths.  
The curves of \(B_{\tau}\) and \(B_{\varphi}\) with azimuth at different depths shown in Figure 6 show that, at the same depth, the phase between the two magnetic field components was constant. If we used a two-axis magnetic field sensor to receive, we could obtain an enhanced signal by shifting the \(\varphi\)-direction component with a constant phase and then adding it to the \(r\)-direction component.
## 6 Conclusions  
This article identifies and summarizes the intuitive telerobotic frameworks proposed in the human-robot interaction (HRI) literature, which aim to remove human workers from harmful working environments by equipping complex robotic systems with human intelligence and command/control via intuitive and natural human-robot interaction, including the implementation of XR techniques to improve the user's situational awareness, depth perception, and spatial cognition, as fundamental to effective and efficient teleoperation. The article commences by delving into the prerequisites for achieving effective robotic teleoperation, highlighting two important requirements for an effective and intuitive teleoperation platform. First, natural motion retargeting, or the use of natural human behavior to teleoperate robotic systems, effectively reduces training costs and grants human-level dynamics to teleobotic systems; it is an important component of natural and intuitive human-robot interaction systems. Second, adequate multimodal feedback, especially visual feedback that provides depth information and situational awareness to enable the operator to know the dynamic situation in the robot's workspace, is also critical in interactive telemanipulation systems.  
This review has elucidated the state-of-the-art frameworks developed by the robotics community to integrate physical robotic platforms with XR technology. Specifically, it underscores MR-based 3D/2D vision mapping and merging as a method for providing immersive integration of diverse visual display modes for users. In the MR-enhanced telerobotic schemes, digital twins also play a pivotal role in facilitating intuitive teleoperation within the MR scene, in which users can interact with these digital twins naturally, enabling precise and intuitive control over robotic systems while obtaining real-time status information about the remote robot from the digital twins. We have presented the maincontributions of the reviewed articles on XR-enhanced telerobotic schemes in industrial systems and classified their application contexts into three main groups: effective and intuitive teleoperation, MR-enhanced intuitive telemanipulation, and MR-based robotic tele-welding. Additionally, this review highlights the pivotal role of XR, especially MR, in the domain of teleoperation systems and robotic welding. Identified as a promising development, the incorporation of haptic interfaces in tele-welding systems holds the potential to enhance task performance and operator perception.  
Conceptualization, Y.-P.S.; methodology, Y.-P.S., L.H.P. and J.G.C.; validation, Y.-P.S., X.-Q.C. and C.G.P.; writing--original draft preparation, Y.-P.S.; writing--review and editing, J.G.C. and C.Z.; supervision, J.G.C.; project administration, J.G.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 3 The Proposed Method
### Feature Fusion
#### 3.2.1 Exemplar Attention  
Exemplar attention [17] is employed instead of standard attention as the self-attention calculation layer in the encoder. This is to reduce computational complexity and improve tracking speed.  
Traditional feature fusion networks rely on standard attention to calculate the intrinsic relationships of features, as shown in Formula (1):  
\[Attention(X)=softmax\Bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}}\Bigg{)}V=softmax \Bigg{\{}\frac{\big{(}xW_{Q}\big{)}\big{(}W_{k}^{T}x^{T}\big{)}}{\sqrt{d_{k}}} \Bigg{\}}(xW_{V}) \tag{1}\]  
In the formula, \(Q\) represents the query vector, \(K\) represents the key vector, and \(V\) represents the value vector. By calculating the dot product of the \(Q\) and \(K\), scaling with a scaling factor \(\sqrt{d_{k}}\), and normalizing through the softmax function, the final weighted sum of the \(V\) effectively captures crucial information at each position in the input sequence.  
In natural language processing, each feature represents a specific word or token, necessitating the calculation of interrelations among all input sequences. However, in visual tasks like object tracking, adjacent spatial locations often represent the same object, leading to a corresponding reduction in the number of feature vectors. It enables the construction of a coarser visual representation, thereby reducing computational complexity.  
Exemplar attention, as shown in Figure 5, constructs the query vector \(Q\) by compressing the spatial dimensions of the input feature map \(X\) through average pooling to size S, followed by linear mapping. The key vector \(K\) relies on learning a small set of data information as exemplars, not solely dependent on relationships within the samples. Furthermore, the value vector \(V\) undergoes convolutional operations at the local level.  
Figure 4: Architecture of feature fusion sub-network.  
Figure 5: Standard attention (**a**) and exemplar attention (**b**).  
The calculation of \(Q\), \(K\), and \(V\) in exemplar attention is as shown in Formula (2):  
\[\begin{array}{c}Q=\Psi_{S}(X)W_{Q}\\ K=\hat{W}_{K}\\ V=W_{V}\otimes X\end{array} \tag{2}\]  
\(\Psi_{S}(X)\) represents the operation of mapping the feature map \(X\) to dimensions \(S\times S\) using a two-dimensional adaptive average pooling layer and obtaining the query vector \(Q\) by multiplying it with a parameter matrix \(W_{Q}\). \(\hat{W}_{K}\) represents the key vector \(K\) obtained after learning from the dataset, and \(W_{V}\otimes X\) represents the value vector \(V\) obtained by refining the input representation locally through convolution instead of the projection operation on the feature map \(X\). From Formulas (1) and (2), the calculation of exemplar attention can be deduced, as shown in Formula (3).  
\[Attention(X)=softmax\Bigg{\{}\frac{(\Psi_{S}(X)W_{Q})(\hat{W}_{K}^{T})}{\sqrt{d _{k}}}\Bigg{\}}(W_{V}\otimes X) \tag{3}\]
# Blockchain Technology for Intelligent Environments
## 3 Noteworthy Blockchain Implementations
### Private/Permissioned Blockchains
#### 3.2.2 Hyperledger Indy  
Hyperledger Indy [45] is a permissioned ledger used for managing _Decentralized Identifiers_ (_DIDs_) [46]. It originated from the Sovrin project [47] and it is currently under incubation by the Hyperledger foundation.  
DIDs is an identity technology that tries to overcome the limitations of centralized identity systems, including the federated ones. A key aspect of DIDs is that they are designed not to be dependent on a central issuing party that creates and controls the identity. Instead, DIDs are managed by the identity owner, an approach known as _self-sovereign identity_. The W3C Credentials Community Group has published a DIDs specification document, targeting the interoperability among various DID systems. The specification defines a DID as a random string associated with a _DID Document_ that includes public information about the DID owner and which is stored in a ledger, as well as with secret information used for the DID verification, stored in the DID owner's wallet. Furthermore, DIDs can be associated with some attributes referred to as _claims_ or _verifiable credentials_ (VCs). VC owners can securely prove to a third party that they are the legitimate owner of a specific credential (e.g., being an adult in a given jurisdiction, without necessarily revealing their identity).  
Hyperledger Indy abides by the W3C Credentials Community Group specification. It implements a permissioned ledger for storing DID documents, using the _Plenum_ consensus protocol, i.e., an implementation of the Redundant Byzantine Fault Tolerance algorithm [48] that can sustain \(F\) failures, where \(3F+1\) nodes are present. Furthermore, Indy provides a convenient SDK that can be used for creating, managing, and using digital identities. An Indy ledger is managed by a pool of _Stewards_. Stewards are in essence the entities that validate identifiers before they are recorded. Identifiers are generated by users and are forwarded to a Steward through a _Trust Anchor_, a specialized network node that has a trust relationship with a Steward and that vouches for the validity of an identifier.
## 3 Results and Discussion
### Adsorption Study
It can be seen in Table 3 that there is an excellent agreement between the computed and experimental values of \(q_{e}\). Since \(dq_{t}/dt\) is proportional to the second power of \((q_{e}-q_{t})\), the antibiotic removal will be much faster, initially owing to the higher driving force as compared to the later stages of contact as \(q_{t}\to q_{e}\). Moreover, the rate-limiting step is the surface adsorption, which involves the chemisorption of the antibiotics present in the aqueous solution to the adsorbent surface, due to a physiochemical interaction. Figure 5: Langmuir model for nitroimidazole adsorption onto SiC NPs (\(T\) = 25 \({}^{\circ}\)C; adsorbent dose = 0.1 g, pH = 8). For the intraparticle diffusion model represented by Equation (4), the nitroimidazolze antibiotic uptake at any instance of time depends upon on the intraparticle diffusion coefficient (\(K_{i}\)) and parameter \(C\), which is a measure of the thickness of the boundary layer. According to the model, the plot of uptake quantity versus \(\surd t\) would be linear if intraparticle diffusion governs the process of adsorption. Moreover, if the lines pass through the origin, then the intraparticle diffusion is the rate-controlling step. Plots that do not pass through the origin indicate some degree of boundary layer control. This indicates that other models may control the rate adsorption.
## 3 Design of Improved Harmonic Detection Method
### Hybrid Harmonic Detection Method Based on Bartlett-Hann Window DFT and DWT
For the high frequency part, considering whether it is doped with a steady-state signal, a Fourier transform is performed on the reconstructed high frequency part first, and three cases that may exist after the transformation are processed differently. Assume that the high frequency part after wavelet reconstruction is \(S(n)\), the average value of all peaks in the first half of \(S(n)\) is \(S_{1}\), the average value of all peaks in the second half is \(S_{2}\), and \((S_{2}-S_{1})/S_{2}\) is the attenuation rate. The degree is considered as a function of decay. The discrete Fourier transform of \(S(n)\) is \(F(n)\). After extracting the parameters of \(F(n)\), each harmonic is reduced to \(f_{k}(n)\). The three possible situations are handled as follows:  
1. Analysis of the reconstructed waveform from the wavelet transform shows that there is no attenuating signal in the high frequency part, but only the steady-state signal. In view of this situation, the parameters extracted from the data obtained by windowed interpolation DFT are used to restore the high frequency harmonics. The \(f_{k}(n)\) obtained is the result of harmonic analysis. 2. The high frequency part contains only time-varying signals with a tendency to decay. In view of this situation, the reconstruction result \(S(n)\) of the wavelet transform is used as the result of the final harmonic analysis. 3. There are both time-varying harmonic signals and steady-state harmonic signals in the high frequency part. In view of this situation, the parameters extracted from the data obtained by the windowed interpolation DFT are used to restore the high frequency harmonics to obtain \(f_{k}(n)\), and sequentially perform difference processing with the high frequency signal \(S(n)\) reconstructed by the wavelet. After each \(S(n)-\sum f_{k}(n)\), the attenuation rate is recalculated. When the attenuation rate shows a decreasing trend or reaches a minimum or even a negative value, this indicates that \(f_{k}(n)\) is an attenuation harmonic signal and is recorded as \(f_{k}^{*}(n)\). According to \(f_{k}^{*}(n)=S(n)-\sum f_{k}(n)\), it can restore time-varying harmonic signals. The flowchart of the improved harmonic detection method is shown in Figure 3.
# Simultaneous SAXS/SANS Method at D22 of ILL: Instrument Upgrade
## 1 Introduction
Unfortunately, in most cases for the latter instruments, high energy background signal on the X-ray detector is a significant challenge and hampers a simultaneous mode of combined X-ray and neutron methods. The X-ray detection system is often not optimized for simultaneous acquisition, thus both X-ray and neutron data sets are mainly acquired successively and not in a synchronous mode [29; 30]. An advanced SAXS system mounted on a standalone metal rack that makes it easily movable for use on the D22 SANS instrument has successfully been installed and tested at ILL [23]. For the first time, SAXS and SANS measurements in a simultaneous mode have been demonstrated [23]. One challenge is cutting down background signal that originates mainly from high-energy gamma radiation [31] in the experimental zone of the D22 instrument. A preliminary lead shielding design around our SAXS instrument at D22 of ILL has shown the capability of acquiring background discriminated SAXS data on a single energy threshold X-ray detector, enabling simultaneous SAXS/SANS experiments [23]. To enable a combined SAXS/SANS method in a simultaneous mode for all possible experimental conditions at the D22 instrument, an upgrade of the SAXS system was an essential requirement. In the current work, an optimized lead shielding has been implemented and the background suppression has been systematically examined. Additionally, a double energy threshold X-ray detector has been employed, replacing the single energy threshold detector. Overall reduced background noise and acceptable signal-to-noise ratios of SAXS data have been demonstrated upon combining both modifications, for all possible experimental configurations at the D22 instrument. Combined SAXS/SANS measurements were acquired for two nanoparticle (NP) dispersions of different sizes, to demonstrate the performance of the SAXS/SANS measurements under various experimental conditions.
## 4 Security Benefits of Directional Antennas in WSNs  
Directional antennas can mitigate or even eliminate the risks related to some categories of security attacks on WSNs due to their specific radiation pattern which can be materialized into mechanisms for localization of neighboring or malicious nodes, or can drastically reduce the areas from where an attack can be carried out. The main types of attacks that can be mitigated using directional antennas are: eavesdropping, jamming, Sybil attack and wormhole attack, but similar countermeasures can reduce the risks for traffic analysis, man-in-the-middle attack or node capturing attack.  
Directional antennas can reduce malicious attack risks in two ways, either directly by being immune to attacks launched from outside their narrow radiation region, or indirectly based on position verification procedures [39; 40] employing the received signal's direction of arrival. The main research in the field is briefly presented in Table 3 and discussed in the following subsections.
## 4 Results
### Predicting Goals and Assists
#### 4.2.3 Feature Importance  
In Table 3, '90 Minutes Played (90 s)' is identified as the most important feature in both the Decision Tree model and Gradient Boosting models, with a feature importance of 0.874 and 0.552, respectively. According to these two models, the total time a player spends on the field during a match is the key element impacting the prediction of goals and assists. The Decision Tree model also highlights the contribution of the aspect 'Starts', but its impact is less pronounced. However, the Random Forest model considers it the most significant, with a feature importance of 0.412, followed by 'Minutes Played (Min)' at 0.397. These features align with the consensus that a player's playing time and match involvement are critical factors influencing performance predictions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Hyper Parameter** & **RMSE** \\ \hline Naive Model & & 6.62 \\ \hline Decision Tree (DC\_1) & Default & 4.77 \\ \hline \multirow{3}{*}{Decision Tree (DC\_2)} & Max depth: None & \multirow{3}{*}{4.41} \\  & Min samples leaf: 4 & 4.41 \\  & Min samples split: 2 & \\ \hline Random Forest (RF\_1) & Default & 4.33 \\ \hline \multirow{3}{*}{Random Forest (RF\_2)} & Max depth: None & \multirow{3}{*}{4.24} \\  & Min samples leaf: 2 & \\  & Min samples split: 5 & \\  & N estimators: 50 & \\ \hline Gradient Boosting (GB\_1) & Default & 4.13 \\ \hline \multirow{3}{*}{Gradient Boosting (GB\_2)} & Learning rate: 0.1 & \multirow{3}{*}{4.05} \\  & Max depth: 3 & \\ \cline{1-1} Gradient Boosting (GB\_2) & Min samples leaf: 4 & \\ \cline{1-1}  & Min samples split: 10 & \\ \cline{1-1}  & N estimators: 50 & \\ \hline Ridge Regression (RR\_1) & Default & 3.91 \\ \hline
**Ridge Regression (RR\_2)** & **alpha: 10** & **3.90** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of default and fine-tuned ML models for predicting Goals and Assists.  
While different models assign different levels of importance to features, the analysis reveals a consensus across models regarding the central role of '90 min Played (90 s)' and the significance of playing time-related features in predicting goals and assists.
# Experimental and Numerical Study on UHPC-RC Decks within Hogging Moment Region
## 2 Stress State for Steel-Concrete Composite Grider Bridges Using an RC Deck  
Currently, the main girder of steel-concrete composite twin-I girder bridges is made of steel, and a prefabricated deck or a cast-in-place concrete deck is used. However, under service conditions, a tensile stress of more than 8 MPa is produced within the hogging moment region, which is much greater than the tensile strength of regular concrete (2\(\sim\)3 MPa). [PERSON] developed a parametric study on composite twin-I girder bridge systems. From the cases studied by [PERSON] et al. [26], several typical steel-concrete composite twin-I girder bridges were selected and analyzed to obtain the stress in the concrete deck, as shown in Figures 1 and 2.  
The respective stress states under the short-term strength limit state and the service limit state were calculated based on JTG D60-2015 [20], as shown in Figures 1 and 2. In Figure 1, the maximum tensile stress in the bridge deck is 3.5 MPa. Within a range of 57 m from both sides of the interior support (hogging moment region), the normal tensile  
Figure 1: Stress distribution in the concrete deck under the short-term strength limit state (unit: Pa).  
Figure 2: Stress distribution in the concrete deck under the service limit state (unit: Pa).  
stress of the concrete deck under the service limit state ranged from 5 to 8 Mpa, as shown in Figure 2. Note that the analyses were conducted using a linear elastic assumption for the material.  
Through the above analyses, the tensile stress of the concrete bridge deck was much greater than the tensile strength, and the concrete deck cracked. At present, the design check is generally controlled by the crack width. Although the crack width can meet the design requirements, cracking leads to internal steel-bar rust and other problems, affecting the durability of the structure due to the erosion of rainwater and other effects.  
The development and application of new materials, especially the application of UHPC and other materials with high tensile strength, provides a new idea to solve the cracking problems of regular concrete decks. This paper mainly used an experimental and numerical method to analyze the anti-cracking performance of UHPC-RC composite decks in steel-concrete composite bridge systems (e.g., twin-I girder bridge system), and discusses the feasibility of its application in steel-concrete composite bridge structures.
## 4 Experiments
### Comparison with Existing Methods
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Cross-Subject** & **Cross-View** & **Year** \\ \hline \hline \multicolumn{4}{c}{**Input: 3D Skeleton**} \\ GCA-LSTM [15] & 74.4 & 82.8 & 2017 \\ Two-stream attention LSTM [46] & 77.1 & 85.1 & 2018 \\ ST-GCN [23] & 81.5 & 88.3 & 2018 \\ SkeleMotion [5] & 69.6 & 80.1 & 2019 \\ AS-GCN [6] & 86.8 & 94.2 & 2019 \\
2s-AGCN [47] & 88.5 & 95.1 & 2019 \\ ST-TR (new) [24] & 89.9 & 96.1 & 2021 \\ DSwarm-Net (new) [51] & 85.5 & 90.0 & 2022 \\ ActionNet [30] & 73.2 & 76.1 & 2023 \\ SGMSN (new) [52] & 90.1 & 95.8 & 2023 \\ \multicolumn{4}{c}{**Input: Depth maps**} \\ HON4D[19] & 30.6 & 7.3 & 2013 \\ HOG\({}^{2}\)[35] & 32.2 & 22.3 & 2013 \\ SNV [50] & 31.8 & 13.6 & 2014 \\ Li [36] & 68.1 & 83.4 & 2018 \\ Wang. [2] & 87.1 & 84.2 & 2018 \\ MVDI [3] & 84.6 & 87.3 & 2019 \\
3 DV-PointNet++ [10] & 88.8 & 96.3 & 2020 \\ DOGV (new) [53] & 90.6 & 94.7 & 2021 \\
3 DFCNN [28] & 78.1 & 80.4 & 2022 \\
3D-Pruning [54] & 83.6 & 92.4 & 2022 \\ ConvLSTM (new) [29] & 80.4 & 79.9 & 2022 \\ CBBMC (new) [48] & 83.3 & 87.7 & 2023 \\ PointMapNet (new) [55] & 89.4 & 96.7 & 2023 \\ SFCNet (ours) & **89.9** & **96.7** & - \\ \multicolumn{4}{c}{**Input: Multimodalities**} \\ ED-MHI [31] & 85.6 & - & 2022 \\ TS-CNN-LSTM [49] & 87.3 & 91.8 & 2023 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of different methods for action recognition accuracy (%) on the NTU RGB+D 60 dataset.