esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Autoregressive Sparse Transformer Networks for Pedestrian Trajectory Prediction
## 3 Methods
### Sparse Spatial Transformer
Since \(A_{SI}^{t}\) is computed at every time step independently, it does not contain any temporal dependency information of the trajectories. Hence, we stack the dense interactions \(A_{SI}^{t}\) from every time step, and then fuse these stacked interactions with a \(1\times 1\) convolution along the temporal channel and a nonlinear function \(\sigma(\cdot)\). After that, we receive the spatial-temporal dense interaction \(A_{SI}\in R^{T_{obs}\times N\times N}\). \(A_{SI}\) models spatial interactions between the specific pedestrian and neighbors whose distances are smaller than \(D\). Nevertheless, not all these neighbors are involved in the interaction that might impact the future trajectory. In other words, there are superfluous interactions in \(A_{SI}\). To find the exact ones who are involved, we generate a sparse interaction mask \(M_{SP}\) to exclude the irrelevant neighbors. \[m_{ij}=\left\{ \begin{array}{ll}1&\alpha_{ij}\geq\gamma\text{ or }i=j\\ 0&\alpha_{ij}<\gamma\end{array} \right. \tag{5}\]  
where \(m_{ij}\) and \(\alpha_{ij}\) are the \((i,\,j)-th\) element in \(M_{SP}\) and \(A_{SI}\) at time \(t\), respectively. We omitted the superscript t for simplification. \(\gamma\in[0,1]\) is the element-wise threshold. After we obtained the sparse spatial mask \(M_{SP}\), the sparse spatial interaction matrix \(A_{SI}^{sparse}\) could be computed as  
\[A_{SI}^{sparse}=M_{SP}\odot A_{SI} \tag{6}\]  
where \(\odot\) denotes element-wise multiplication. Thus, a sparse spatial-temporal attention matrix representing the sparse directed interactions is eventually obtained from the inputs. The multi-head attention mechanism is also adopted to stabilize the process. Assume \(h_{i}\) is the embedding of node \(v_{i}\), and \(N_{i}\) is the neighbor set associated with \(v_{i}\). The final output feature of node \(v_{i}\) is calculated as:  
\[h_{i}^{\prime}=f\Big{(}h_{i}+\sum\
olimits_{j\in N_{i}\cup\{i\}}\alpha_{ij}^{ \prime}\cdot V_{j}\Big{)}+\Big{(}h_{i}+\sum\
olimits_{j\in N_{i}\cup\{i\}} \alpha_{ij}^{\prime}\cdot V_{j}\Big{)}\,\,i=1,\ldots,N \tag{7}\]  
Where \(\alpha_{ij}^{\prime}\) is the \((i,\,j)-\)th element in the sparse spatial interaction matrix \(A_{SI}^{sparse}\), and \(f(\cdot)\) is a fully connected layer. Finally, we can obtain nodes representation \(h^{\prime}=\{h_{1}^{\prime},\ldots,h_{N}^{\prime}\}\), where \(h_{i}^{\prime}\) is the updated embedding of node \(v_{i}\) output by the sparse spatial transformer. It captures the relative importance for different pedestrians. Therefore, the sparse spatial transformer can learn a self-adaptive attention that finds the exact set of pedestrians involved in social interactions in the scene.
# Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms
## 6 Application of Multi-Agent Deep Reinforcement Learning: State of the Art
### MADRL with Actors and Critics Applied to Distributed Entities in a Multi-Agent Environment
#### 6.5.3 [PERSON]'s MADRL Approach with Actors and Critics  
In [81], [PERSON] et al. embedded MADRL with actors and critics in distributed agents, which are the control units at road intersections in vehicular networks (A.2). The MADRL approach enables distributed agents to share information among themselves (X.1.2), estimate their policies, and use the estimation to select their own actions. Distributed agents select actions that maximize individual rewards in a collaborative manner (i.e., (X.2.2)). The MADRL approach has two main features: (a) enabling collaboration with neighboring distributed agents to provide a partial observation of the operating environment; and (b) addressing the curse of dimensionality due to the complexity of the joint actions of multiple neighboring agents. The proposed approach addresses the challenge of high dynamicity (C.2) due to the dynamic condition of the road traffic pattern.  
The control unit \(i\) represent state \(s_{t}^{i}\) with a two-tuple information, including: (a) the current traffic phase; and (b) the queue length of vehicles in each lane. The action \(a_{t}^{i}\) represents a control signal that extends the current traffic phase or switch to another traffic phase. The reward \(r_{t+1}^{i}(s_{t+1}^{i})\) represents the reduction of the average delay of vehicles at all incoming lanes associated with the control units.  
The proposed approach extends the traditional MADRL approach with enhanced knowledge sharing mechanisms. Firstly, each agent stores and updates its own observation at a centralized knowledge container. Secondly, each agent accesses the centralized knowledge container to obtain knowledge prior to action selection. During action selection, agents take into account their own observations and centralized knowledge. Since each agent interprets the collective knowledge differently, the centralized knowledge container can be reconstructed based on historical knowledge of all agents. In the proposed model, a single shared critic network learns using observations in the centralized knowledge container during training, and distributed actors make decisions during execution. The proposed approach has been shown to reduce the congestion level (P.6).
## 5 Theoretical Analyses
### Interval Analysis of \(\beta\)  
The upper bound of \(\beta\) is \(\frac{1}{2 ln2}\).  
There are two extreme cases for the relationship between \(P_{d}\) and \(P_{g}\). One is that the discriminator completely separates the two, and their distributions are disjunct. The other is that the discriminator cannot tell the two apart, and their distributions are almost indistinguishable. Obviously, what we want is the second extreme case. Therefore, for the minimization optimization problem of \(P_{g}\), after the introduction of \(\beta\), it is necessary to ensure that the extreme value obtained in the second case is smaller than the first case:  
When \(P_{d}\) and \(P_{g}\) do not intersect, the discriminator completely separates the two:  
\[\int p_{d}(x)[\mu_{D}(x)+\beta\mathrm{H}_{D}(x)]+p_{g}(x)[\mu_{\
eg D}(x)+\beta \mathrm{H}_{\
eg D}(x)]\mathrm{d}x=2\]When \(P_{d}\) and \(P_{g}\) are almost the same, the discriminator cannot separate them at all:  
\[\int p_{d}(x)[\mu_{D}(x)+\beta\text{H}_{D}(x)]+p_{g}(x)[\mu_{\
eg D}(x)+\beta \text{H}_{\
eg D}(x)]\text{d}x=1+2\beta\ln 2\]  
In order to ensure that the minimized optimization problem achieves the desired solution, there must be  
\[2>1+2\beta\ln 2\]  
So, \(\beta<\frac{1}{2\ln 2}\).  
Finally, we give an intuitive explanation of the hyperparameter \(\beta\). According to Proposition 1, the optimal discriminator is a sigmoid function about \(p_{d}(x)\) and \(p_{g}(x)\) with the control of \(\beta\). The relationship between \(\beta\) and discriminator \(D\) is shown in Figure 3. As mentioned before, \(\beta\) is used to control the tolerability of the discriminator. The larger \(\beta\) is, the more uncertain the soft borderline is and the higher the tolerance of the discriminator is, and vice versa.
# A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm
###### Abstract  
In the paradigm of mobile edge computing (MEC), providing low-latency and high-reliability services for users is garnering increasing attention. Appropriate edge-server placement is the crucial first step to realizing such services, as it can meet computation requirements and enhance resource utilization. This study delves into efficient and intelligent dynamic edge-server placement by taking into account time-varying network scenarios and deployment costs. Firstly, edge servers are classified into static and dynamic ones. Subsequently, an improved snake optimization algorithm is proposed to determine the number and placement locations of dynamic servers while adhering to delay requirements. Finally, a minimum placement-cost algorithm is put forward to further reduce the service cost. Experimental results demonstrate that compared to classic algorithms, the proposed algorithms can achieve a reduction in latency of 5% to 12%. And compared to the state-of-the-art methods, they can reduce service costs by 20% to 43%. This research offers an effective solution for dynamic edge-server placement and holds great theoretical and practical significance.  
edge computing; server placement; linear programming; snake optimization algorithm +
Footnote †: journal: Applied sciences  
0
Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 Result and Discussion
### Dynamic Multi-Stage Path Planning Experiments
#### 4.3.3 Average Path Cost  
Figure 10 shows the average path cost of the algorithms in different environments. ID A* produces optimal paths in low-AGV environments, but often leads to deadlocks when the number of AGV is greater than 30. CBS produces optimal paths in low-density environments, but struggles with congestion, often leading to deadlocks or excessive detours. Under low AGVs counts, distributed planning methods incur slightly higher average path costs than centralized planning methods, but remain within the acceptable limits. In comparison with PRIMAL2, PER-D3 QN-EDAgger produces higher-quality paths a majority of the time. Thanks to the A* expert demonstrations and extra A* rewards, PER-D3 QN-EDAgger's learned policy more closely mimics the A*centralized planning method, and PER-D3 QN-EDAgger has better dynamic obstacle adaptation. However, in high shelf-density scenarios, PRIMAL2 achieves lower average path costs and thus demonstrates stronger adaptability.  
Figure 8: Comparison of average task completion rate of algorithm under different obstacle densities and AGV quantities—the higher, the better.  
Figure 10: Comparison of average path cost of the algorithm under different obstacle densities and AGV quantities—the lower, the better.  
Figure 9: Comparison of average response time of algorithm under different obstacle densities and AGV quantities—the lower, the better.
# TYCOS: A Specialized Dataset for Typical Components of Satellites
## 2 Related Work
### Existing Space Scene Datasets
Thirdly, the most recent datasets lack open accessibility [29], which poses a significant obstacle for scholars to build upon existing research, thereby impeding a fair comparison of the performance of various methodologies. Overall, there is a notable absence of unified datasets tailored specifically for the detection of key satellite components. These datasets should meticulously replicate various illuminations, motion states, and scenarios that may give rise to occlusion of key components during detection tasks.
# Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator
## 1 Introduction
Section 3 elaborates on the principle and implementation architecture of the PAOC technique. Section 4 analyzes the multipath performance and thermal noise performance of the PAOC technique and provides several simulation results. The results and implications are discussed in Section 5. Finally, conclusions are given in Section 6.
## 2 Modern Physical Layer
### Acoustic Channel Equalization  
Because the acoustic channel is bandwidth limited, it distorts the transmitted signal, causing the transmitted symbol to spread to adjacent symbols. This interference is known as inter-symbol interference (ISI). Equalization is the process of filtering the received signal to cancel the ISI introduced by the channel impulse response.  
In addition, the inter-symbol interface caused by multipath can be avoided by inserting guard times between successive symbols [42]. Different echoes of the transmitted signal arrive at different times with different delays, and the modem should still be able to successfully decode the message. Moreover, to avoid multipath frequency spread, guard frequency bands are also needed [42]. These techniques are suitable for non-coherent communications, where no phase tracking is required. In real applications, spectrum efficiency is very low, because the data rate decreases, and higher bandwidth is needed to insert these guards.  
In coherent communications, another approach is explored. Conventional equalization algorithms require the transmission of a training data sequence to enable the receiver to estimate the channel.  
An adaptive decision feedback equalizer (DFE) can be used to compensate for the effects of multipath [43]. One of the drawbacks of DFE is that errors may propagate, because of wrong decisions fed into the feedback loop. Strong forward error correction (FEC) codes may be used to combat the error propagation and, as a result, reduce the bit error rate (BER) [10].  
As already described, RAKE filters used in DSSS, multi-carrier modulation and MIMO systems are also oriented towards solving the channel spread cancellation problem, because it is an essential issue that must be tackled in acoustic communications.  
The experiments run by the University of Birmingham and Newcastle, U.K., show that bit error rate (BER) can be reduced from \(10^{-2}\), without any equalization, up to \(10^{-3}\) using MIMO and LMStechniques, respectively [44].
###### Abstract  
This study proposed a novel approach for the offline dynamic parameter identification of parallel kinematics mechanisms in which the friction is significant and varying. Since the friction is significant, it should be incorporated to provide an accurate dynamic model. Furthermore, the varying normal forces as a result of the changing posture of the mechanism lead to varying friction forces, specifically varying static and Coulomb friction forces. By considering this variation, the static and Coulomb friction parameters are identified as coefficients instead of forces. A bound-constrained optimization technique using an iterative global search tool was employed in this work to minimize the residual errors while maintaining the physical feasibility of the solutions. Moreover, the friction was modeled by using the nonlinear Stribeck friction model since a linear friction model was not sufficient, whereas the variation of the friction followed the variation of the normal forces, which were evaluated through the Lagrange multipliers in the constrained dynamic model of the mechanism. The solutions obtained were verified by using some trajectories that were different from those used in the identification.  
dynamic parameter identification; dynamic parameters; parallel kinematics; parallel robot; parallel manipulator; varying friction +
Footnote †: journal: Applied
## 2 Related Works
### Unimodal Paradigms: Text-Only and Image-Only Models  
Early studies in crisis informatics predominantly focused on unimodal learning. Text-based models, such as DistilBERT [12], perform well on informativeness detection but overlook visual content critical to assessing crisis severity. [PERSON] et al. [10] adopted semi-supervised learning, reaching 90.5% F1 but ignoring visual context. [PERSON] et al. [13] employed TF-IDF with SVM, revealing susceptibility to linguistic ambiguity. [PERSON] et al. [14] addressed this using pre-trained language models (e.g., RoBERTa) with metaphor-aware prompts, achieving 89% F1 for classifying metaphor-rich disaster tweets. Conversely, image-only approaches use deep CNNs for visual classification. [PERSON] et al. [15] leveraged multispectral imagery to analyze disaster intensity with 97% precision. [PERSON] and [PERSON] [16] further demonstrated image-only value in flood assessment, using deep learning on remote sensing imagery to extract infrastructure features and identify damaged areas, achieving 85.6% accuracy. [PERSON] et al. [17] further used CNNs on multi-source geospatial data for Indonesian earthquake risk mapping, achieving 89.47% accuracy in high-risk zone identification. [PERSON] et al. [18] applied YOLOv4 and VGG-16 for damage classification. However, these lacked textual grounding, limiting semantic clarity.  
These unimodal limitations emphasize the need for multimodal approaches--combining visual and textual semantics, which are explored in the next section.
# Analytical Model to Calculate Radial Forces in Permanent-Magnet Synchronous Machines
## 4 Validation
### Analytical Results vs. FEM Results
#### 4.1.3 Magnetic Forces  
Finally, in this section, the magnetic forces calculated in a tooth of the stator and on the surface of the rotor are compared. In Figure 20, waveforms, and in Figure 21, spectra of the magnetic forces estimated in a tooth under different operating conditions, open-circuit (**OC**) and load (**LC**), are shown for both machines.  
Regarding the results shown in Figure 20, it can be concluded that the waveforms were properly calculated, but there were some differences in magnitudes, especially in load operation mode. This error in magnitudes was due to nonlinearities. As explained in Section 4.1.3, there was a significant concentration of magnetic flux at the edge of the tooth which led to errors in the computation of the magnitude of the flux density in these areas, and, in consequence, it also led to errors in the pressures computed in the edges. However, in the middle of the tooth, the flux density was estimated with rather high  
Figure 21: Harmonics of the magnetic forces created on a tooth base: (**a**) Qs48p8, (**b**) Qs36p15.  
Figure 19: Flux density on the tooth base in load condition: (**a**) Qs48p8, (**b**) Qs36p15.  
accuracy. Thus, this error in the magnitude of the magnetic forces was due to saturation effects that appeared at the side ends of the tooth of the stator. The spectra in Figure 21 show that the model was able to identify the main force harmonics.  
In Figure 22, the magnetic forces calculated on the rotor surface are shown. In this case, magnitudes of oscillation were very small in comparison with the average value of the magnetic field, and also in comparison with the forces in the stator. This makes sense because the main magnetic field created by the magnets is constant in the rotor frame, and harmonics in pressures and forces are produced by the armature field, which is much weaker than the field of the magnets.  
To summarize, the magnetic forces computed by the developed analytical model can be considered accurate enough, considering that nonlinearities were not taken into account.
# Tomographic Diffractive Microscopy: A Review of Methods and Recent Developments
## 3 The Resolution of Tomographic Diffractive Microscopy  
The aim of TDM is to obtain a 3D reconstruction of the investigated sample. The link between the measured scattered field and the 3D relative permittivity is given in Equation (9); by merging the measured components as synthetic aperture generation, the permittivity contrast of the sample is reconstructed through an inverse Fourier transform of the detected field under Born approximation [54; 55; 56]. In principle, for a given angle of illumination (e.g., normal incidence) with wave vector \(\mathbf{k}_{\mathrm{inc}}\), the Fourier components of the sample permittivity contrast were on a cap of sphere of radius \(2\mathrm{k}_{0}\), truncated by the numerical aperture (NA) of the objective. The sphere was centered on the extremity of wave vector \(-\mathbf{k}_{\mathrm{inc}}\), as in Figure 2. In order to increase the amount of detectable Fourier components, and thus improve the resolution of the object reconstruction, a variety of illumination angles must be used. Since the accessible frequency domain for each illumination depends on \(\mathbf{k}-\mathbf{k}_{\mathrm{inc}}\), we explain the resolution of TDM in three cases, see Figure 6.  
Figure 5: Images of a resin star sample, sample information: 97 nm wide rods of length 520 nm and height 140 nm on a Si substrate. (**a**) Image of the sample obtained by SEM. (**b**) Image of the sample obtained by Darkfield microscopy numerical aperture (NA) of objective 0.95. (**c**) Reconstruction obtained with a hybrid inversion method from tomographic diffraction microscopy data with NA equals to 0.95. (**d**) Permittivity reconstructed with a bounded inversion method using the prior knowledge of the resin permittivity from the same data as (**c**). (**e**) Permittivity distribution along the \(z\) direction versus the curvilinear abscissa of the dashed circle in (**d**) [10].
## Materials and Methods
### Data Collection and Statistical Analysis  
The dry mass, number of leaves per plant, number of nodes per plant, number of flowers per plant, plant height, leaf area, chlorophyll content, percent flowering, days of treatment needed to visible flower bud or days to visible buds (DVB), flower width, and photoreceptor gene expressions were measured after 41 days of the photoperiodic light treatments. All leaves with a length greater than 1 cm in were counted to determine the number of leaves per plant. Divided samples of the shoot and root were dried at 70 \({}^{\circ}\)C for 72 h in a Venticell-222 drying oven (MMM Medcenter Einrichtungen GmbH., Munich, Germany) before the dry mass measurements were taken with an EW 220-3 NM electronic scale (Kern and Sohn GmbH., Balingen, Germany). Leaf area measurements were taken with a LI-3000 leaf area meter (LI-COR Inc., Lincoln, NE, USA). The chlorophyll concentration was estimated from 10-mg samples of fresh, young, and fully developed leaves. Chlorophyll was extracted with 80% acetone at 4 \({}^{\circ}\)C. A Biochrom Libra S22 spectrophotometer (Biochrom Co. Ltd., Holliston, MA, USA) measured the absorbance of the supernatant at 645 and 663 nm, after the extracted chlorophyll was centrifuged at 3000 rpm. Calculations were performed according to the method described by [PERSON] et al. [23]. The statistical analysis was performed with the SAS 9.1 software (SAS Institute Inc., Cary, NC, USA). An analysis of variance (ANOVA) and [PERSON]'s multiple range test were performed with the results of this study. SigmaPlot 12.0 (Systat Software Inc., San Jose, CA, USA) was used for graphing.  
Total RNA Isolation, cDNA Synthesis, and Real-Time Polymerase Chain Reaction (PCR) of Selected Genes  
After 20 days of the photoperiodic light treatments, plants started displaying visible flower buds and the most recently matured 10 leaves per plant were collected for total RNA extraction. The latest leaf to be matured was collected an hour after the daily photoperiodic treatments began, at 9:00 a.m. This collection time was chosen because the photosynthetic rates are high at this time of the day. Equal amounts of cDNA using primers of _cryptochrome 1 (CRY1)_, _phytochrome A (PHYA)_, and _phytochrome B (PHYB)_, whose sequences are shown in Table 1, were used to perform the independent PCRs. As actin is frequently used to normalize molecular expression studies, it was used as an internal control. The \(2^{-\Delta\Delta\text{CI}}\) method [24] was used to determine the relative expression levels of each gene. At each sampling date, the individual gene expression levels in the plants grown with the light treatments were divided by the mean gene expression levels for plants in the control (SD10). The total RNA isolation and real-time quantitative PCR analysis of the selected genes were performed according to the method described in [PERSON] et al. [11].