esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The SDOF System with a Grounded TLIMD  
In general terms, a structure equipped with a grounded tuned lever inerter-like mass damper (TLIMD, first introduced in [22]) can be represented by an analogue TLIMD with a single-degree-of-freedom (SDOF) system (TLIMD-SDOF, shown in Figure 1a), with the DOF corresponding to the target mode, normally the fundamental mode of the primary structure, for the damper design. The \(m_{1}\), \(k_{1}\) and \(c_{1}\) are the mass, stiffness and damping coefficients of the host structure, respectively. The TLIMD comprises a rigid lever, having an auxiliary concentrated mass \(m_{2}\) (located at point \(P_{e}\)) and a parallel dashpot (\(c_{2}\))-spring (\(k_{2}\)) element with its terminals attached to the host structure and the lever (at point \(P_{r}\)), respectively, as shown in Figure 1b. The total mass of the lever is \(m_{3}=\rho L\), where \(\rho\) and \(L\) denote the mass per unit length and the overall lever length, respectively. Two Cartesian coordinate systems are introduced for the description of this coupled system. The (x,y)-coordinate system denotes the global fixed coordinate system, with the original point '\(O\)' located at the centre of the fulcrum \(P_{s}\). The (x',y')-coordinate system denotes the local rotating coordinate system fixed to the lever, with the original point coinciding with '\(O\)'.  
In Figure 1, the rotating angle of the lever, denoted as \(\theta\), is defined with a positive value representing clockwise rotation. The location of points \(P_{r}\), \(P_{e}\) and the lever right-side end point with respect to the fulcrum \(P_{s}\) in the rotating coordinate system are identified by \(l_{1}\), \(l_{2}\) and \(l_{3}\), respectively. Note that \(L=l_{1}+l_{3}\). The ground motion is denoted by \(x_{g}\), and the relative displacement responses (with respect to. the ground) of the host structure and the lever (at point \(P_{r}\)) are denoted by \(x_{1}\) and \(x_{2}\), respectively.  
Figure 1: (**a**) The analogue TLIMD-SDOF system. (**b**) Schematic configuration of the lever.
## 6 Existing Multi-UAV Communication Architectures
### Multi-Group Communication Architecture  
A multi-group architecture takes advantage of both centralized and single-group architectures. As shown in Figure 12, it extends the single-group architecture into several groups of homogeneous UAVs that communicate with each other according to the intra-UAV topology [19] seen in Section 6.2. Each group has a gateway UAV. In addition, the communication between groups of UAVs (group-to-group) is done through gateway UAVs that communicate bidirectionally with the GCS. The robustness of this architecture favors collaborative missions over large coverage areas while maintaining control from the GCS [28]. However, there are some limitations to be mentioned:  
* **Single point of failure**: if the gateway UAV fails, then all other UAVs in its group remain inactive. If the GCS fails, then the system will become totally paralyzed.
* **Homogeneity of UAVs**: in a group, the UAVs must be of the same type, and this is a hindrance for some missions. Moreover, this implies serious interoperability problems between groups of UAVs.  
In the next section, we will discuss a type of architecture that overcomes the limitations of the multi-group architecture.
# Design of a New Bilayer Multipole Electromagnetic Brake System for a Haptic Interface
## 5 Experimental Evaluation  
### Experimental Setup  
A prototype of the BLMP brake was manufactured for experimental verification of its characteristics and performance, as shown in Figure 10a. The AISI 420 magnetic steel was used for the core body due to its high fatigue strength and corrosion resistance properties, specifically chosen for the cyclic loading and shearing nature of the contact surfaces. The properties in a magnetic material are induced at the expense of high coercivity in its magnetization properties. The height of each pole of the core body is 30 mm; the remaining dimensions are given in Table 1. The friction pads having 2 mm thickness are made of aluminum alloy that has a coefficient of friction of about \(\mu=0.45\) with the steel, and they generate little wear upon contact. The enameled copper wire with 0.65 mm diameter is used for winding \(N=75\) turns on each coil of inner and outer layers. The higher resistance of AWG 22 wire helps in improving the inductive time constant of the coil. In the armature body, a 3 mm magnetic disc is connected with a non-magnetic rotor through a 0.3 mm thin sheet of 304 stainless steel that acts as a spring to allow relative motion in the air gap. The prototype of BLMP brake shown in Figure 10a weighs 646 g only.  
Figure 10: Experimental setup. (**a**) Prototype of proposed BLMP brake; (**b**) experimental platform for BLMP brake.  
Figure 9: Magnetic force as a function of the air gap. A comparison of simulation results with analytical model, for individual and combined actuation of layers.  
Figure 10b shows an experimental setup using BLMP brake. The stator of the brake is fixed with a support, whereas the armature is attached with motion-input handle through a six-axis force/torque sensor by Robotous co. and a 4096-bit position encoder by US Digital co. An 80 V, 120 A DC power supply is used in current control-mode to provide input to the brake. The computer system is used for data acquisition from sensors with a sampling time of 20 ms and an Arduino board provides a PWM control at the rate of 31 kHz.
# Multi-Granularity Semantic Collaborative Reasoning Network for Visual Dialog
## 1 Introduction
We first encode the question and dialog history into token- and sentence-granularity semantic representations. Then, we collaboratively assess the question-related information from the dialog history by considering the granular semantic representations of the question and dialog history. In this process, refined question representations with different granularities are obtained. Next, we perform collaborative reasoning to form an accurate alignment between visual objects and these two refined question representations. Finally, the target visual object representation-fused question features are delivered to the decoder for predicting answers. Figure 1: An example of visual dialog task. Based on an image and the dialog history, an agent needs to answer a series of questions by ranking a list of 100 candidate answers. Here, we only show top5 ranked answers. The major contributions of this work are summarized as follows. First, the joint use of the multi-granularity semantic representations of the new question and the image's dialog history allows MGSCRN to successfully complete a visual dialog task. The collaborative reasoning of the coreference relations among multiple inputs capitalizes on the heterogeneous informative relationships (i.e., token- and sentence-granularity semantic information) in the history, including those of the various objects in the image. Second, our experiments with the VisDial v1.0 dataset demonstrate outstanding performance results that are superior to other state-of-the-art methods. Third, our qualitative analysis shows that MGSCRN infers both historical and visual question-related information more accurately than the other methods, which notably utilize single semantically granular representations. This paper is structured in the following manner. The related works are shown in Section 2. Section 3 describes our proposed MGSCRN's model. In Section 4, we introduce our experimental setup and the visual dialog task measures. In Section 5, we empirically evaluate our proposed model using VisDial v1.0, VisPro, and VisDialConv datasets. Then, we report our experimental results and analyze the findings. This paper is concluded in Section 6.
## 2 LiCSAR System Architecture  
The LiCSAR system consists of several interconnected modules. The LiCSAR processing chain, described in Section 2.1, uses various custom tools and algorithms over the core processing functionality that is based on advanced commercial software for processing SAR data, GAMMA [46]. The GAMMA programs mentioned within this article are described in Appendix A. LiCSAR processing is performed over systematic geographical spatial extents termed frames. A frame is defined as a collection of Sentinel-1 IWS burst units imaged during the satellite pass within a given orbital track. We have created custom LiCSAR burst unit identifiers. Metadata for each of these burst units are extracted from Sentinel-1 Single Look Complex (SLC) acquisitions and registered in the LiCSInfo metadata database that handles burst and frame definitions (see Section 2.2).  
We extract and merge bursts covering a frame into SLC mosaics for each acquisition epoch. Afterwards, we coregister and resample these SLC mosaics to the geometry of a primary SLC acquisition, which is set during the initialisation of the frame. We then use the resampled SLC (RSLC) data to form interferometric products (wrapped and unwrapped interferograms, and coherence maps) by combining the new RSLC with, by default, four chronologically preceding ones, and georeference them to the World Geodetic System 1984 (WGS-84) coordinate system. The whole process is automated and optimised for effective batch processing in computer clusters, and identified as the LiCSAR FrameBatch toolbox, described in Section 2.3. LiCSAR FrameBatch is a standard tool used for updating frame datasets and is called by other specific tools, such as LiCSAR Earthquake InSAR Data Provider (EIDP), described in Section 2.4.  
We show a general overview of the LiCSAR system architecture in a flowchart in Figure 1. The whole system is closely aligned to the technical infrastructure offered by the Centre for Environmental Data Analysis (CEDA), see Section 2.5. Here, both data storage (CEDA Archive, internal and public LiCSAR data storages) and computing facilities are synchronised with the LiCSInfo database (see Section 2.2) and LiCSAR processing chain to prepare InSAR outputs.  
The flowchart in Figure 1 also shows a connection to the COMET Generic Atmospheric Correction Online Service (GACOS) system [47], allowing for optional routine corrections of tropospheric delay in interferograms using atmospheric weather models. The procedure to acquire GACOS data has not been fully automated yet, as the current API requests for generating GACOS data would prolong the LiCSAR product generation process. GACOS data per frame are currently being generated upon request and provided to the community. The impact of the optional GACOS corrections has been tested in Anatolia, where the correction reduced the phase standard deviation in interferograms by 20-30% [48].
## 3 Theory and Methods
### Calculation of Theoretical Orbital Period  
Previous studies have demonstrated that astronomical cycles controlled the deposition of fine-grained sedimentary strata within multiple continental lacustrine basins across China [21; 22; 23; 24; 25]. The Milankovitch cycle theory suggests that periodic variations in Earth's orbital parameters (eccentricity, obliquity, and precession) drive cyclic climate changes. These variations are preserved in climate-sensitive strata, allowing them to be identified in well log curves [26; 27; 28]. Over the past 250 million years of geological history, the most stable cycle has been the 405 ka long eccentricity cycle. Short eccentricity, obliquity, and precession cycles are influenced by Earth-Moon gravitational interactions and tidal forces, with their periods gradually increasing slightly over geological time. However, the ratio between these cycles remains relatively stable within specific geological periods and is reflected in variations in sedimentary structures, sedimentary features, and depositional thickness. Overall, the ratio of long eccentricity to short eccentricity, obliquity, and precession is approximately 20:5:2:1 [29; 30]. This study adopts the Cretaceous astronomical cycle model proposed by [PERSON] [31], integrating previous studies' [32] quantitative divisions of long eccentricity, short eccentricity, obliquity, and precession cycles to calculate the theoretical orbital parameters of the Cretaceous (Table 1). These parameters are used to identify Milankovitch cycle characteristics in the stratigraphic record and serve as the basis for astronomical cycle calculations.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Theoretical** & & & & **Cycle** & **Ratio** & & \\  & **Cycle/kyr** & & & & **Cycle** & **Ratio** & & \\ \hline \multirow{3}{*}{Eccentricity} & 405 & 1.00 & — & — & — & — & — & — & — \\  & 125 & 3.24 & 1.00 & — & — & — & — & — & — \\  & 100 & 4.05 & 1.25 & 1.00 & — & — & — & — & — \\ \hline \multirow{3}{*}{Obliquity} & 48.0 & 8.44 & 2.60 & 2.08 & 1.00 & — & — & — & — \\  & 40.0 & 10.13 & 3.13 & 2.5 & 1.20 & 1.00 & — & — & — \\  & 37.5 & 10.8 & 3.33 & 2.67 & 1.28 & 1.07 & 1.00 & — & — \\ \hline \multirow{3}{*}{Precession} & 22.5 & 18.00 & 5.56 & 4.44 & 2.13 & 1.78 & 1.67 & 1.00 & — \\  & 18.4 & 20.01 & 18.00 & 5.43 & 2.61 & 2.17 & 2.04 & 1.22 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The theoretical parameters of the Milankovitch cycle in the Cretaceous and their ratios (following reference [18; 19]).
## 5 Application Case
### Results of the Simulation
#### 5.3.2 Simulation Results of Hydraulic Head and Leakage
The relative error of leakage simulated by GA is much larger than that of the other three groups. The maximum value of the relative error in dam foundation leakage is 170% in 2007. The results simulated by PSO demonstrate the most excellent performance. The leakage relative error of PSO is basically between \(-10\%\) and \(10\%\). In the simulation of hydraulic head and leakage, the performance of GA is worse than that of PSO. This may be the result of the high dimensions of the model. It is challenging to meet the expected requirements in convergence accuracy by GA. The PSO strategy requires  
Figure 6: Comparison of measured and simulated values at the dam foundation: (**a**) leakage; (**b**) leakage relative error. no complex operations such as selection, crossover, and mutation, which adapts to this problem's solution better and achieves the global optimal solution more quickly.
# Evaluation of Parallel Computing on MPI Version PHITS Code
## 5 Conclusions  
In this study, verification of IAEA phase space data as radiation source was performed. The accuracy of this data as source was verified against ionization chamber measurements and PHITS calculations. We expect that phsp data is able to play the role of convenient accessibility for radiotherapists, medical physicists, or whoever is interested in radiation protection and the dose estimation of radiotherapy. Additionally, the MPI function provided by the PHITS code package was evaluated. While the increase in computational power according to the number of processors was identified, the cluster, composed of separate computers, had clear limitations stemming from differences in performance and parts that could not be processed in parallel, such as data transmission, reception, and collection. Considering that the MCNP method depends on the performance of the CPU used for calculation, it seems possible to overcome these limitations by improving the CPU performance of the nodes constituting the cluster.  
In the future, measures to reduce the K-factor should be devised through changes in communication methods between nodes and the efficient operation of data transmission and reception. Furthermore, it is necessary to study cluster efficiency in complex simulations, such as radiation treatment computer simulations, through PHITS code and phsp data.  
Conceptualization, H.-J.G. and K.-B.K.; methodology, H.-J.G. and K.-B.K.; software, H.-J.G.; validation, S.K., S.-B.H. and K.-B.K.; investigation, H.-J.G., S.-B.H., S.K. and K.-B.K.; data curation, H.-J.G. and K.-B.K.; writing--original draft preparation, H.-J.G. and K.-B.K.; writing--review and editing, H.-J.G., S.-B.H., S.K. and K.-B.K.; visualization, H.-J.G.; supervision, K.-B.K.; project administration, K.-B.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Nuclear Safety Research Program through the Korea Foundation Of Nuclear Safety (KoFONS) using the financial resource granted by the Nuclear Safety and Security Commission (NSSC) of the Republic of Korea (No. 2202012, No. 2103088).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
Author [PERSON] was employed by KIRAMS. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A Model and Quantitative Framework for Evaluating Iterative Steganography
## 1 Introduction  
Steganography is recognized as a well-established field of study [1] situated within the broader framework of information theory [2]. The primary focus of this discipline is the development and analysis of methods and techniques for concealing information within overt covers so that third parties remain unaware of the existence of the hidden data [3].
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 5 Adversarial Ant Colony Optimization Algorithm for Solving Non-TT Routing Problem
### Key Innovations and Contributions of AACO  
For the non-TT routing problem in TSNs, one of the contributions of this paper is the selection of ACO as the basis of algorithm optimization. By reasonably adjusting the impact factor of the pheromones of stronger ant colonies on the update of the pheromonesof other ant colonies, the improved ACO could effectively balance the relationship between the greedy principle and load balancing in order to better adapt to the TSN traffic shaping mechanisms, which is harder or even impossible for other algorithms to handle.
## 6 Nonthermal and Coulomb Focusing Effects in Lorentzian (Kappa) Plasmas
It is known that the states of free-electron in a dense plasma would be blocked by electrons occupying a quantum state by using the blocking factor [26; 27]. The quantum blocking effect on the bremsstrahlung spectrum in dense quantum plasmas will be treated elsewhere. Figure 1: The scaled form of the bremsstrahlung cross section per photon energy in units of \(\pi a_{0}^{2}/Ry\) as a function of the scaled photon energy \(\overline{\varepsilon}\) for W and Fe atoms when \(\overline{E}_{0}=15\). The black solid line is the electron-ion bremsstrahlung cross section \((d\overline{d}_{b}/d\overline{\varepsilon})_{e\in\overline{\varepsilon}}\) for the W atom. The blue dashed line is the electron-atom bremsstrahlung cross section \((d\overline{d}_{b}/d\overline{\varepsilon})_{e-n}^{C-\alpha}\) for the W atom including the influence of Coulomb focusing. The green dot-dashed line is the electron-ion bremsstrahlung cross section \((d\overline{d}_{b}/d\overline{\varepsilon})_{e-n}^{C-\alpha}\) for the Fe atom. The red dotted line is the electron-atom bremsstrahlung cross section \((d\overline{d}_{b}/d\overline{\varepsilon})_{e-n}^{C-\alpha}\) for the Fe atom including the influence of Coulomb focusing. Figure 2: The scaled form of the electron–atom bremsstrahlung cross section \((d\overline{\sigma}_{b}/d\overline{\varepsilon})_{e-a}\) per photon energy in units of \(\pi u_{0}^{2}/Ry\) as a function of the scaled photon energy \(\overline{\varepsilon}\) for W and Fe atoms when \(\overline{E}_{0}~{}=~{}15\). The black solid line is the Coulomb focused electron–atom bremsstrahlung cross section \((d\overline{\sigma}_{b}/d\overline{\varepsilon})_{e-a}^{CF}\) for the W atom including the influence of Coulomb focusing. The blue dashed line is the electron–atom bremsstrahlung cross section \((d\overline{\sigma}_{b}/d\overline{\varepsilon})_{e-a}\) for the W atom without the Coulomb focusing effect. The green dot-dashed line is the Coulomb focused electron–atom bremsstrahlung cross section \((d\overline{\sigma}_{b}/d\overline{\varepsilon})_{e-a}^{CF}\) for the Fe atom including the influence of Coulomb focusing. The red dotted line is the electron–atom bremsstrahlung cross section \((d\overline{\sigma}_{b}/d\overline{\varepsilon})_{e-a}\) for the Fe atom without the Coulomb focusing effect. Figure 4: The Coulomb focused bremsstrahlung emission rate \(\overline{P}_{\varepsilon}\) in units of \(P_{0}[\,=\,(32/3\pi^{1/2})r_{0}^{2}cn_{e}n_{a}]\) as a function of the scaled photon energy \(\overline{\varepsilon}\) for the W atom when \(\overline{E}_{0}\,=\,15\) including the influence of Coulomb focusing. The solid line is the case of the thermal case, i.e., \(\kappa\,\rightarrow\,\infty\). The dotted line is the case of \(\kappa\,=\,3\). The dashed is the case of \(\kappa\,=\,2\).
# Systematic Encoding and Shortening of PAC Codes
## 4 Shortening of PAC Codes  
PAC codes have native lengths that are powers of two, \(N=2^{n}\) for some \(n\geq 1\). In many applications, it is necessary to adjust the code length to some desired value other than \(2^{n}\). One method for adjusting code length is code shortening in which a portion \(\mathbf{x}_{\mathcal{C}}\) of the codeword \(\mathbf{x}\) is constrained to a predetermined value, say zero, and is not transmitted, effectively reducing the code length from \(N\) to \(N-|\mathcal{C}|\). A common method of code shortening for polar codes is to choose the set \(\mathcal{C}\) so that \(\mathbf{L}_{\mathcal{C}^{c},\mathcal{C}}=\mathbf{0}\)[7, 8]. The systematic encoding method for PAC codes presented above can be used to implement such a shortening method.  
Suppose we desire shortening of a PAC code in connection with non-systematic encoding. We partition the index set \(\{1,2,\ldots,N\}\) into three disjoint sets: a data index set \(\mathcal{A}\), a frozen index set \(\mathcal{B}\), and a shortening index set \(\mathcal{C}\) subject to the condition \(\mathbf{L}_{\mathcal{A},\mathcal{B},\mathcal{C}}=\mathbf{0}\). Then, we apply the systematic encoding method presented above to the problem  
\[\mathbf{x}=\mathbf{v}\mathbf{T}\mathbf{L},\quad(\mathbf{v}_{\mathcal{A}}, \mathbf{v}_{B})=(\mathbf{d},\mathbf{f}),\quad\mathbf{x}_{\mathcal{C}}= \mathbf{0}. \tag{11}\]  
In other words, the data word \(\mathbf{d}\) is treated as if it is part of the frozen part of the convolution input word \(\mathbf{v}\), and the part \(\mathbf{x}_{\mathcal{C}}\) of the codeword is treated as if it is the data part of the codeword in a systematic PAC code.  
If on the other hand, we desire to shorten a systematic PAC code, then the index set \(\{1,2,\ldots,N\}\) is partitioned into a data index set \(\mathcal{A}\), a frozen index set \(\mathcal{B}\), and a shortening index set \(\mathcal{C}\) subject to the condition \(\mathbf{L}_{\mathcal{B},\mathcal{A},\mathcal{C}}=\mathbf{0}\), and we apply the above systematic encoding method to the problem  
\[\mathbf{x}=\mathbf{v}\mathbf{T}\mathbf{L},\quad\mathbf{v}_{B}=\mathbf{f}, \quad(\mathbf{x}_{\mathcal{A}},\mathbf{x}_{\mathcal{C}})=(\mathbf{d},\mathbf{ 0}). \tag{12}\]  
In other words, we treat \((\mathbf{x}_{\mathcal{C}},\mathbf{x}_{\mathcal{A}})\) as if all of it is data in a systematic PAC code.  
Figure 2: Convolution circuit.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The author acknowledges helpful comments by reviewers.  
**Conflicts of Interest:** The author declares no conflicts of interest.