esa-sceva/satcom-chunk-collection huggingface dataset:
# Asynchronous Quantum-Resistant Blockchain for Secure Intelligence Sharing
## 4 The Proposed Method
### Sharing Phase  
This phase illustrates the process of information sharing, as shown in Figure 8.  
Figure 5: Intelligence key generation process.  
Figure 6: Blockchain key generation process.  
Figure 7: Blockchain address generation process.  
Figure 8: Intelligence sharing flowchart.  
Figure 9: Intelligence purchase flowchart.  
Step 1: When \(B\) obtains the information from \(P\), \(B\) initiates a transaction with \(P\) to obtain the \(address_{P}\) and \(PK_{P}\), and then sends the purchase request to \(P\).  
Step 2: After receiving the request, \(P\) signs the message \(txt\) using its own private key \(SSK_{P}\), obtaining the signature \(Sig_{SSK_{p}}(txt)\). Subsequently, \(P\) encrypts both the original message \(txt\) and the signature \(Sig_{SSK_{p}}(txt)\) using \(PK_{B}\), resulting in \(Enc_{PK_{B}}\big{(}Sig_{SSK_{p}}(txt)\big{)}\), which is then sent to \(B\).  
Step 3: Once \(B\) receives the data from \(P\), it applies \(SK_{B}\) to decrypt and verify the identity of \(P\) by \(SPK_{P}\). If it is correct, \(B\) broadcasts the \(\mathrm{H}(txt)\) to the blockchain; otherwise, the transaction is not established.  
Step 4: The miners compete to become \(V\) through Equation (1), and determine the number \(\mathrm{n}\) of \(V\) when it reaches the condition as Equations (4) and (5).  
Step 5: \(B\) obtains \(HTX\) through performing hash function \(\mathrm{H}(\mathrm{H}(txt)\,||timestamp)\), and derives \(Sig_{SSK_{M}}(HTX)\).  
Step 6: \(B\) packs \(Sig_{SSK_{B}}(HTX)\) into broadcast information \(Data\) and broadcasts it to the AQRB, \(Data=(Sig_{SSK_{B}}(HTX),\,tx,\,timestamp,\,SPK_{B})\), including signature of \(B\), transaction information, timestamp, and public key of \(B\).  
\[R_{V}-R_{t}>0 \tag{4}\]  
\[R_{V}=\sum_{i=1}^{n}match_{i} \tag{5}\]
## 4 Numerical Analysis and Results
### Head Attachments
As it passes, the wave gradually propagates through the medium and moves away from the source. For the last time step of 0.001 s, the wavefront propagates a long distance from the cylindrical head. Its amplitude is significantly reduced and the wave energy further diffuses in the medium. The cylindrical shape of the head used has a direct effect on the stiffness, in turn affecting the depth of wave propagation. The phenomenon of wave propagation depends on the shape of the head. The conical head is a geometrical form through which it is possible to focus or scatter a wave. For the phenomenon under study, at a time step of 0.00016 s, an early stage of wave propagation from the head is observed. The area in which the wave occurs is limited and does not propagate to larger distances. As time progresses, the shape of the head used allows observation of wave dispersion inside the medium. For the last time step of 0.001 s, the area in which the wave is observable is the largest, but its intensity is weakened. For the first time step, a maximum displacement of 0.27 mm is observed, and for the last time step, it is 0.06 mm. Results were summarized for the three materials in an arrangement of two attachment shapes. The results are shown in Figure 15.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Medical Gel \#0 Material Attachment**} & \\ \multicolumn{2}{c}{**Cylindrical**} & \multicolumn{2}{c}{**Conical**} \\ \hline
**Inductor Frequency** & **Wave Velocity** & **Inductor Frequency** & **Wave Velocity** \\
**[MHz]** & **[m/s]** & **[MHz]** & **[m/s]** \\ \hline
1.0 & 1128 & 1.0 & 1237 \\
1.5 & 1180 & 1.5 & 1297 \\
2.0 & 1236 & 2.0 & 1326 \\
2.5 & 1314 &  
The designation P1 indicates a cylindrical attachment, while P2 indicates a conical attachment. In addition, the trend line is plotted as a linear function. The discrepancies may be due to the contact between the inductor and the attachment and the reading of the wave velocity data for individual equal time steps. Only for the Ecoflex 00-20 material was a large discrepancy observed between the cylindrical and conical attachments, at 25%. This may be due to the stiffness of the geometric system itself--that is, the effect of shape on the stiffness of the component, given the same material constants. In other cases, the results converge, and their difference does not exceed 2.4% for Medical Gel #0 or 5.2% for Agar Progel, which indicates a very good fit.
# Universally Composable Oblivious Transfer with Low Communication
## 1 Introduction
### Our Contribution  
Hence, a low communication OT protocol on lattice under Mod-LWR assumption was proposed in this paper. It obtained full-simulation security in the ROM. In addition, we extended it to a 1-out-of-N OT protocol with a minor tweak. Finally, we simulated this protocol and compared it with other protocols in the theoretical aspect. The simulation shows that the average communication and running time are only 2.45 kb and 0.5 ms per time, respectively. The comparison indicates that the protocol has the advantages of high efficiency and low communication.
## 4 Discussion
### WDF External Load  
Regarding the WDFs in the current study, they were found to cover a shorter TD than the CMFs. In contrast, [PERSON] et al. (2025) [25] observed that youth WDFs in a 1-4-3-3 formation covered more TD than the CMFs. Similarly, among professional players in the same formation (1-4-3-3), WDFs covered the greatest TD. In the 1-3-5-2 formation, WDFs covered similar distances to the FWDs and significantly shorter distances than the CMFs [16]. A study investigating the running demands of the 1-4-2-3-1 formation (same as the current study) found that WDFs covered less TD than the midfielders [11]. The tactical role of WDFs in the defensive line may vary and heavily depends on the midfield players and their roles. As mentioned earlier, formations with three midfielders require greater activity from the WDFs.  
In the current study, WDFs covered greater distances than the CDFs in high-speed zones. Additionally, WDFs performed fewer accelerations and decelerations than CMFs but more decelerations than FWDs. The findings of the present study are in agreement with those of previous research [8; 11; 16; 24; 25; 34]. In modern formations, WDFs actively contribute to both the attacking development and the defensive function of the team. Additionally, players in this position have ample space to carry out their actions. These two elements, which are directly related to the tactical role of the position, may also determine its running profile.
# Gaussian-Distributed Spread-Spectrum for Covert Communications
## 3 Proposed GDSS Scheme  
The block diagram of the proposed GDSS scheme is illustrated in Figure 4.  
The proposed scheme builds upon the standard QPSK DSSS-PN system by utilising the naturally occurring thermal white noise from the transmitter's circuity. The independently obtained noise sequences are applied to the in-phase (I) and quadrature-phase (Q) components of the signal after spreading to perform the Gaussian masking. The Gaussian masking process for the scheme illustrated in Figure 4 can be expressed as follows. For each QPSK spread complex chip **S**, a complex value **U**+**j**V is taken from a transmitter's  
Figure 4: Proposed GDSS system.  
Figure 3: Signal constellation of the DSSS-PN QPSK noise-free transmit signal, colour coded for each position in a quadrant.  
circuitry with amplified thermal white noise. Let \(\mathbf{I}\) and \(\mathbf{Q}\) be the result of element wise multiplication such that \(\mathbf{I}=\mathfrak{R}(\mathbf{S})\ast|\mathbf{U}|\) and \(\mathbf{Q}=\mathfrak{I}(\mathbf{S})\ast|\mathbf{V}|\), where \(\mathfrak{R}(\mathbf{S})\) and \(\mathfrak{I}(\mathbf{S})\) are the real and imaginary components of \(\mathbf{S}\), respectively. The resulting product to be transmitted is then expressed as \(\mathbf{I}+\mathbf{j}\mathbf{Q}\). The generated IQ values are upsampled, filtered, modulated with a RF carrier wave, amplified with a high power amplifier (HPA), and transmitted through the antenna. On the receiver side, the received signal is first amplified with a low noise amplifier (LNA) and passed through the RF demodulation and despreading steps to recover the transmitted message.  
A snapshot of the varying constellation points generated by masking the fixed QPSK points is shown in Figure 5, where each QPSK chip symbol (in a quadrant) is mapped to a non-deterministic location within the same quadrant every time. The Gaussian masking process moves the original symbol position in Figure 3 by the absolute values of the measured I and Q thermal noise values. It means the noise masking process does not move the symbol into a different quadrant, as illustrated in the colour matching scheme between the two figures. However, the cost of this method is an increase in the system BER, or for a same BER, an increase of the TX power. For a same average TX power, some symbols would be more susceptible to noise than they originally were due to being moved closer to the quadrant boundaries--a decreased Euclidean distance. The impacts to the BER will be quantified later. However, the adversaries detectablility is not necessarily scarified, depending on the detection method.
# Location Selection Methods for Urban Terminal Co-Distribution Centers with Air-Land Collaboration
## 5 Analysis
### Solution Results of GWO  
The parameter values listed in Section 4.2 were substituted into the GWO site selection model. After the iterative operation was completed, 28 of the 47 points in the preselected set of co-distribution centers were chosen for co-distribution centers. Among them, 15 were air co-distribution centers and 13 were ground co-distribution centers. The correspondence and specific structure of the co-distribution centers and demand points are listed in Table 6.  
### Solution Results of GWO  
The parameter values listed in Section 4.2 were substituted into the GWO site selection model. After the iterative operation was completed, 28 of the 47 points in the preselected set of co-distribution centers were chosen for co-distribution centers. Among them, 15 were air co-distribution centers and 13 were ground co-distribution centers. The correspondence and specific structure of the co-distribution centers and demand points are listed in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Selected Points** & \multicolumn{2}{c}{**Type of**} \\
**Selected** & **Co-Distribution** & & **Corresponding Set of Demand Points** \\ \hline
2 & Vehicle & 12, 14, 24, 28, 32, 37, 39, 40, 41, 43, 45, 55, 62, 63, 65, 70, 71, 72, 75, 76, 78, 83, 94, 95 \\ \hline
3 & Vehicle & 102, 108, 111, 127, 145, 183, 209, 212, 218, 221, 223, 225, 226, 243, 265, 268, 348 \\ \hline
4 & Vehicle & 279, 280, 301, 302, 327, 328, 329, 331, 334, 346, 367, 368 \\ \hline
7 & Vehicle & 378, 397, 398, 399, 413, 419, 427, 430, 455, 456, 466, 467, 468, 472, 473, 482, 484, 486, 488, 489 \\ \hline
8 & UAV & 320, 343, 349, 360, 365, 369, 370, 371, 373, 377, 380, 402, 405, 415, 438 \\ \hline
9 & Vehicle & 89, 101, 116, 117, 128, 137, 154, 162 \\ \hline
10 & Vehicle & 113, 140, 141, 142, 170, 171, 172, 204, 219, 229, 257, 263, 264, 304, 335 \\ \hline
14 & UAV & 107, 135, 184, 199, 205, 210, 238, 250, 255, 256, 267, 285, 287, 288, 289, 298, 311 \\ \hline
15 & UAV & 342, 355, 374, 375, 376, 379, 382, 388, 391, 406, 409, 410, 411, 425, 428, 432, 433, 434, 435, 445, 451, 453, 454, 453, 454, 459 \\ \hline
16 & UAV & 251, 254, 261, 266, 269, 286, 290, 291, 297, 299, 300, 308, 309, 310, 325, 336, 339, 340 \\ \hline
17 & UAV & 259, 270, 271, 292, 293, 313, 317, 318, 322, 323, 341, 351, 352, 354, 358 \\ \hline
18 & UAV & 67, 86, 88, 91, 93, 114, 118, 120, 121, 146, 147, 163, 196 \\ \hline
19 & Vehicle & 400, 401, 431, 457, 458, 469, 470, 477, 479 \\ \hline
20 & UAV & 149, 173, 174, 175, 190, 191, 192, 230, 233, 234, 235, 236 \\ \hline
22 & UAV & 364, 394, 395, 396, 408, 418, 422, 423, 429, 440, 442, 443, 446, 449, 463, 464, 465, 475, 490, 491, 492 \\ \hline
24 & UAV & 103, 104, 106, 119, 122, 125, 129, 130, 132, 134, 136, 138, 148, 153, 156, 157, 159, 161, 16 The final fitness value of the optimal solution was 0.1334, corresponding to a total time cost of 18.1012 h and a total economic cost of CNY 82,501,686. The co-distribution center site selection and demand point correspondence is shown in Figure 6.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 4 Trajectory Feature Extraction and Restoration Model Construction
### Construction of Seq2 Seq-Based Restoration Model  
The trajectory restoration problem can be viewed as a sequence regression problem, which aims to minimize the difference between the repaired trajectory and the true trajectory by training the dataset. This study utilizes the sequence-to-sequence structure of deep learning to build a trajectory restoration model. The Seq2 Seq structure extracts key information from the vessel trajectory through an encoder, which encodes it into a sequence of trajectory states. This sequence is an accurate representation of the trajectory information and is used to infer its original trajectory. The decoder is then utilized to repair the original trajectory based on the mined trajectory information. This method can effectively repair the vessel trajectory, which in turn improves the completeness and accuracy of the trajectory data.
## 4 Results
### Technical Feasibility
An overall share of 3D printable materials of 70% regarding commonly applied spare part materials (TOP 5 materials of companies' spare part production) is obtained through exclusion  
Figure 2: Applied spare part materials. of unspecified materials such as PAxx and by neglecting the possibility to substitute some of the mentioned materials with others (e.g., for cast irons). This value is based on a comparison of materials from Figure 2 with a material data base from 3D hubs [41] and additional sources. The following materials are considered 3D printable: SS316L (16%), Maraging 300 (3%), H13 (1%) [42], AlSi10 Mg (6%), Ti (less than 1%), In718 (less than 1%), Ag (less than 1%) [43], Au (less than 1%), PA6 (13%) [44], ABS (7%), PLA (4%), PA12 (2%), PA2200 (2%), Wax (2%), POM (1%), PA3200 GF (less than 1%), ASA (less than 1%), PETG (less than 1%), Visijet SL Impact (less than 1%), PC-ABS (less than 1%), ABS plus (less than 1%), HIPS (less than 1%), PC (less than 1%), Ultem 9085 (less than 1%), and ceramics (1%). The overall share of printable materials could be increased if a substitution of original materials was taken into account, potentially leading to inclines in costs and varying mechanical properties. Additionally, the printability of some special materials is demonstrated in research environments but is not (yet) available for industrial usage. In this regard, it can be expected that a larger share of materials is 3D printable in the future. The perceived second most crucial technical barrier arises from the non-fulfillment of accuracy levels and tolerances. To address this topic, Question 13 (Appendix A, Table A1) asks for minimum roughness values Ra (\(\mu\)m) for selected materials from Question 12 (Appendix A, Table A1). In Table 3, all gathered surface roughnesses for printable materials are listed, classified into material categories and analyzed by comparing surface roughness values of applicable AM machines/processes to figure out if post-processing is required. For some materials, e.g., SS316L, several surface roughness values are mentioned, showing that multiple participants require deviating values for the same material. The vast majority of materials (e.g., SS316 L, H13, AlSi10 Mg) demand for post-processing after being printed and does not fulfill the required minimum surface roughnesses directly. According to identified literature sources, only PLA (1 out of 14 cases, 7%) would fulfill the requirements without post-processing. However, it must be mentioned that surface qualities and achievable accuracy levels are significantly dependent on applied print settings, the AM machine/process, the placement (e.g., in PBF processes), and the orientation of the part in the build chamber, as well as the curvature of the
## 3 Preliminaries
### Dqn
This transfer of knowledge from one network to another contributes to more accurate estimations produced by the target network. This helps to stabilize the learning process and prevent the DQN from learning unstable or overestimated Q-values. During the learning process, DL minimizes the error estimated by the loss function by optimizing the weights \(\theta\). To compute the loss, Mean Squared Error (MSE) is used to find the difference between the target Q-value and the predicted Q-value as denoted in Equation (4):  
\[\text{Loss}=\text{MSE}(\text{Predicted Q-value, Target Q-value}) \tag{4}\]  
\[L(\theta)=E[(r+\gamma max_{a^{\prime}}\text{Q}_{target}(s^{\prime},a^{\prime} ;\theta^{\prime})-\text{Q}_{main}(s,a;\theta))^{2}] \tag{5}\]  
In Equation (5), \(\theta\) is the parameter of the main network, \(\theta^{\prime}\) is the parameter of the target network, \(Q_{main}(s,a;\theta)\) is the Q-values predicted by the main network, \(Q_{target}(s^{\prime},a^{\prime};\theta^{\prime})\) is the target Q-value predicted by the target network for the next state \(s^{\prime}\) and the action \(a^{\prime}\) that maximizes the Q-value, and \((0<\gamma\leq 1)\) is a discount factor that tells us how important future rewards are to the current state. Another crucial component and technique of DQN is known as replay memory, also referred to as experience replay. This technique enhances the learning process by storing the agent's experiences at each time-step. The agent's experience at each time-step is represented as a tuple as depicted in Equation (6):  
\[e_{t}=(s_{t},a_{t},r_{t}(s_{t},a_{t}),s_{t+1}) \tag{6}\]  
\[D=e_{1},\ldots,e_{N} \tag{7}\]  
Here, \(s_{t}\) represents the state at time \(t\), \(a_{t}\) is the action taken, \(r_{t}(s_{t},a_{t})\) is the corresponding reward given to the agent at time \(t\) as a result of the previous state-action pair \((s_{t},a_{t})\), and \(s_{t+1}\) is the next state. In Equation (7), D is the dataset where we store the agent's experiences at each time-step, pooled over many episodes into replay memory. This tuple indeed gives us a summary of the agent's experience at time \(t\). A key reason for using replay memory is to break the correlation between consecutive samples. If the network is learned only from consecutive samples of experience as they occurred sequentially in the environment, the samples would be highly correlated and would therefore lead to inefficient learning; randomizing the samples breaks these correlations and therefore reduces the variance of the updates [43].
## 3 Deep Learning Meta-Model
### Assessment of Meta-Model  
The following procedure was employed to assess different meta-models. Let \(\mathcal{D}=\{(\mathbf{x}_{i},c_{i})\}_{i=1}^{D}\) denote all available i.i.d. samples, \(\mathbf{x}_{i}\in\mathbb{R}^{|V|}\) be an input vector of the hydraulic conductivity values of an SRF, and \(c_{i}\in\mathbb{R}\) be the corresponding simulated concentration on the surface (see Section 2). Since we conduct supervised learning [33], \(\mathcal{D}\) is divided into learning (=training) samples \(\mathcal{L}\) and test samples \(\mathcal{T}\). Then, \(\mathcal{L}\) is evenly divided into \(\mathcal{L}_{s},s=1,\ldots,S\) to repeat the whole meta-model learning procedure \(S\) times. For each repetition, data are pre-processed to facilitate learning: \(c_{\mathcal{L}_{s}}=(c_{\mathcal{L}_{s}}-(c)_{\mathcal{L}_{s}})/\operatorname {std}^{2}(c_{\mathcal{L}_{s}})\), where std denotes the sample standard deviation.  
Given \(\mathcal{L}_{s}\), a meta-model learns a function \(f_{s}(\mathbf{x}):\mathbb{R}^{|\mathbf{x}|}\to\mathbb{R}\). The quality of the approximation is generally evaluated by a loss function, in our case, the mean squared error (MSE): \(\lambda(\mathcal{M},f_{s})=\frac{1}{|\mathcal{M}|}\|c_{\mathcal{M}}-f_{s}( \mathbf{x}_{\mathcal{M}})\|_{2}^{2}\), where \(\mathcal{M}\subseteq\mathcal{D}\). Namely, the training loss \(\lambda(\mathcal{L}_{s},f_{s})\) and test loss \(\lambda(\mathcal{T},f_{s})\) are observed. For the sake of comparing the accuracy of different meta-models, we use the normalized root mean squared error (\(NRMSE=\sqrt{MSE}/\operatorname{std}\)). The training NRMSE \(I_{\mathcal{L}}=\frac{1}{S}\sum_{s=1}^{S}\frac{\sqrt{\lambda(\mathcal{L}_{s} f_{s})}}{\operatorname{std}(c_{\mathcal{L}_{s}})}\) and test NRMSE \(I_{\mathcal{T}}=\frac{1}{S}\sum_{s=1}^{S}\frac{\sqrt{\lambda(\mathcal{T},f_{s })}}{\operatorname{std}(c_{\mathcal{T}})}\) are calculated. This metric allows us to draw a comparison among models on different mesh sizes. The lower the NRMSE, the better. Moreover, if the NRMSE is above 1, we can use a simple random generator instead of a complex meta-model.  
Figure 3: Diagram of the architecture of the used deep learning meta-model. The ChebNet graph convolutional neural network is followed by the global summation pool and the deep feed-forward neural network with hidden layers of 64 and 32 neurons. The convolutional kernel has \(Ch\) channels.
## 5 Conclusions  
This paper presents an improved algorithm called MS-DVSP to enhance the accuracy of the DVSP technique. The improvement in the algorithm lies in the fact that the two subsets that are correlated are shifted by nonintegral voxel values to obtain a maximum overlap between them. We tested the proposed scheme on red sandstone and woven composite specimens. Compared with DVSP, the accuracy and precision of MS-DVSP increase remarkably. In this study, with consideration of artifacts and noise in CT images, uncertainties of the displacement measurement and the strain measurement of the red sandstone specimen by using MS-DVSP algorithm, are 0.015 voxels and 2.34 \(\times\) 10\({}^{-4}\), respectively.  
The interior displacement and strain fields in a red sandstone specimen under compression and a woven composite specimen under 3-point bending are mapped using the MS-DVSP algorithm. From these plots, the characteristics of the interior deformation of the specimen are clearly depicted, thus elucidating the failure mechanism of the material. We believe the MS-DVSP algorithm will contribute to the further understanding of the physics and mechanics of opaque materials under stress.  
Conceptualization, L.M. and F.-P.C.; Methodology, L.M.; Software, L.M. and H.L.; Validation, H.L., Y.Z.; Z.Z. and R.G.; Formal Analysis, H.L., Y.Z.; Z.Z. and R.G.; Investigation, H.L., Y.Z.; Z.Z. and R.G.; Data Curation, L.M.; Writing--Original Draft Preparation, L.M.; Writing-Review & Editing, L.M. and F.-p.C.; Supervision, L.M.  
This work was financially supported by the Major Program for Research and Development of Scientific Instrument of National Natural Science Foundation of China (517/27807), the National Natural Science Foundation of China (51374211), the State Key Research Development Program of China (2016 YFC0600705), the Fundamental Research Funds for the Central Universities (2009 QM02), the US Office of Naval Research's Solid Mechanics Program grant No: N0014-14-1-0419, and the Laboratory for Experimental Mechanic Research of the Department of Mechanical Engineering at Stony Brook University. [PERSON] wishes to thank [PERSON], Director of the US Office of Naval Research's Solid Mechanics Program, for his support over the years for the advancement of the speckle technique.  
The authors declare no conflicts of interest.  
Figure 15: The standard deviation error of strain with different sizes of the strain calculation cubic box (**a**) Group 1; (**b**) Group 2.