esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Three-dimensional compound measurement within a volume of interest is of great importance in industrial manufacturing and the biomedical field. However, there is no current method that can simultaneously perform spatial localization and 3D measurement in a non-scanning manner as well as the identification of material in a volume. In this paper, an Autostereoscopic-Raman Spectrometry-based (ARS) three-dimensional measurement system is proposed. The target object in a large depth range is initially positioned by the autostereoscopic 3D measurement method, and then the accurate position information is cross-checked and obtained by combining the spectral signal. Meanwhile, the spectral signal at the precise excitation position guided by the autostereoscopic signal also carries the material composition information. In order to verify the proposed ARS method, an associated measurement system was developed, and experimental studies of detecting various fibers of different depths in multi-layer glass structure were conducted. The spatial locations and dimensional information of multiple different targets can be measured in a volume, and their material can also be identified at the same time. The average error between the calculated position processed by the ARS system and the actual spatial position is within sub-micron levels, and the success rate of spectrum acquisition reaches 98%.  
autostereoscopy; Raman spectrometry; three-dimensional measurement
###### Abstract
It was found that the new model had an excellent ability to predict the movement of tracked vehicles with torsion bar suspension. [PERSON] et al. [16] examined how to avoid possible damage to the suspension system during an anti-tank mine explosion. An improved hyperbolic torsion spring model was proposed, which allowed a significant increase in body weight, enhanced the safety level, and improved the stability of the vehicle. Simulation analysis has been gradually applied to the field of mechanical structure design. With continuous research, it has been found that a single static or dynamic simulation analysis can not sufficiently express the motion and mechanical characteristics of system components, so most scholars have used the static-dynamic joint simulation method for simulation analysis. [PERSON] et al. [17] verified the accuracy of a newly developed multibody model by static-dynamic coupling simulation analysis. [PERSON] et al. [18], through static-dynamic joint simulation of the 5-degree-of-freedom manipulator, verified its load-bearing performance in practical work. [PERSON] [19] put forward a novel two degree of freedom (DOF) parallel manipulator with three legs, which was verified by the joint simulation of statics and dynamics, and the analytic results were verified by its simulation mechanism to be consistent with the calculated ones. To better adapt to the forest environment, deal with forest fires, and improve the track derailment of tracked fire engines in forest areas, and based on the research of robot kinematics and tracked vehicle suspension, this paper designed a new type of fish-bellied swing arm torsion bar suspension structure. Through an combination of disciplines, the fish-bellied structure, commonly used in bridge construction, was applied to the tracked wheel shaft swing arm design, and the torsion bar suspension theory was applied to tracked fire engines in forest areas, which filled the deficiency in the literature regarding climbing and overcoming obstacles in the walking mechanisms of forest tracked fire engines. Using the method of static-dynamic joint simulation, the proposed fish-bellied swing arm torsion bar suspension was simulated and analyzed, and the rationality of its structural design was verified. At the same time, the simulation results were evaluated by outdoor vehicle tests. It provided a theoretical reference for the kinematic characteristics of tracked vehicles in forest areas and technical support for developing high-passing tracked equipment.
# Quantum Synchronization and Entanglement of Dissipative Qubits Coupled to a Resonator
## 4 Weak Damping Rate Equation Approach  
We then present a typical example of the comparison between the RWA steady state and the summation of the rate equation series. We show the comparison between a single qubit interacting with a cavity in the case where both the spin and cavity are dissipative. Figure 3 shows the mean qubit spin-projection \(\left\langle S_{x}\right\rangle\) in the steady state as a function of the detuning \(\omega_{r}\). Similar results are obtained for other spin components \(\left\langle S_{y,z}\right\rangle\). The RWA steady state found using a Sylvester equation iteration shows a series of oscillations, where \(\omega_{r}\) is in the interval \([0,\Delta]\). These oscillations correspond to multiphoton resonances in the rotating frame \(k\omega_{r}=\Delta\) (where \(k\) is a positive integer). In the laboratory frame, these resonances correspond to transitions \((k+1)\omega=k\omega_{0}+\Omega\), where \(k+1\) photons are absorbed to excite \(k\) cavity levels and the qubit at energy \(\Omega\). The direct rate equation series \((\mathbf{R})\) converge only far from the resonance and fail to reproduce the multiphoton transitions but the corrected series \((\mathbf{R})^{*}\) reproduce this behavior correctly and fail to converge only in close vicinity to the cavity-driving field resonance when \(\omega_{r}\sim\gamma\).  
The comparison in Figure 3 is performed at relatively high frictions from the point of view of the rate equation perturbation theory and we see that the multi-photon resonances make the direct rate equation series \((\mathbf{R})\) unstable in a wide range of \(\omega_{r}\), highlighting the requirement \(\gamma\ll\min_{i,j}|e_{i}-e_{j}|\). For weaker damping, the rate equation series converge in a much wider range of detuning \(\omega_{r}\), as shown in Figure 4. In this case, even \((\mathbf{R})\) converges almost everywhere except at very narrow resonances, where some deviation from the exact steady state is still visible.  
Figure 3: Comparison between the RWA simulation and the summation of the rate equation series for \(F=\lambda\), \(\Omega-\omega_{0}=2\lambda\), and \(\gamma=\gamma_{s}=0.3\lambda\). The trace (R) corresponds to the summation of the series from the direct rate expansion in Equation (19), while \((\mathbf{R})^{*}\), which exhibits a larger radius of convergence, corresponds to Equation (30). The series (R) qualitatively reproduces the position of the multiphoton resonances, but with excessive amplitude, and it fails to converge. The series \((\mathbf{R})^{*}\) reproduces multiphoton resonance accurately but still fails to converge close to the cavity resonance \((\omega-\omega_{0})/\lambda\sim 1\) (further studies are needed to know if divergence occurs on energy scale \(\lambda\) or \(\gamma\) around the resonance).
## 3 Results
### Feasibility and User Experience During Follow-Up at Home
#### 3.2.2 User Experience Surveys  
We compared the mean participant SUS score at follow-up (83.3 \(\pm\) 15.2) to the passability threshold value of 68 (90% CI = [77.81,88.71], \(t\)(22) = 4.81, \(p\) < 0.001) and found that the system had acceptable usability in home settings (Figure 5B). We similarly compared mean QUEST score (4.5 \(\pm\) 0.5) to the midpoint threshold value of 3 (90% CI = [4.35,4.74], \(t\)(22) = 4.87, \(p\) < 0.001), finding that the system and its delivery were satisfying among stroke survivors in the home settings (Figure 5C). As with the survey results obtained after the IRF phase of this study, participants selected _ease of use_, _comfort_, and _effectiveness_ as items of most importance for determining satisfaction in home settings, whereas _troubleshooting_ and _dimensions_ were selected least often (Figure 6).
## 2 Materials and Methods
### Sequencing Performance and Evaluation  
Sequencing run was performed for 48 h on MinION sequencer (Oxford Nanopore Technologies) using R9 flow cell, connected to the appropriate personal computer meeting the recommended criteria. The MinKNOWtm software was used to check the number of active pores and used with the default settings. After sequencing, the Fast5.tmp files were converted to Fast5 files via command prompt. Reads were then base called using Albacore (Oxford Nanopore Technologies) and split by barcodes with EPI2 me Desktop Agent (Oxford Nanopore Technologies Metrichor). Taxonomic classification and quantitative analysis of reads derived from 28S rRNA amplicons were performed using \"What's in my pot\" tool (WIMP, Oxford Nanopore Technologies). In the case of bacterial taxa classification and quantification, the workflow for 16S rRNA gene encoded amplicon analysis in EPI2 ME Agent was chosen. The minimal quality score in both cases was set to 7.
## 4 Experiment
### Experimental Setup
#### 4.1.2 Dataset  
The application scenes of fire detection generally include indoor and outdoor fires, forest fires, and night-time fires; the classical fire pictures of these types of scenes were collected from the existing public fire datasets. To further validate the robustness of the proposed AEGG-FD, the images of other classical scenes where fire-like targets (such as bright lights, red objects, sun, etc.) are contained were also integrated into the dataset. These images are similar to the fire targets because of their colours, textures, and other features, which makes the fire detection algorithms have a higher possibility of misdetection and omission [47]. Therefore, the dataset constructed in this paper was enriched by adding these fire-like samples, which enabled the detection algorithm to maintain high performance in complex and diverse scenes.  
Our dataset has a total of 7149 images, of which 4248 are fire images and 2901 are fire-like images. After the image numbering of the above dataset was completed, the Labelme tool was used for manual labelling. To ensure uniform distribution of the dataset, the whole dataset was randomly divided into train and test sets in the ratio of 8 to 2. Some representative images from the dataset are shown in Figure 13.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Environment** & **Details** \\ \hline Language & Python 3.11 \\ Deep learning architecture & Torch 2.0 \\ Acceleration environment & CUDA 12.1 + cuDNN 8.9.1 \\ Operation system & Windows 11 64 bit \\ RAM & 16 GB \\ CPU & Intel(R) Core(TM) i9-13900 HX 2.20 GHz \\ GPU & NVIDIA GeForce RTX 4060 Laptop GPU \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental environment configuration.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Training Parameters** & **Details** \\ \hline Epochs & 150 \\ Batch Size & 16 \\ Img-size (pixels) & 640 \(\times\) 640 \\ Optimization algorithm & SGD \\ Initial learning rate & 0.01 \\ Momentum & 0.937 \\ Optimizer weight decay & 0.0005 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model training parameters.
## 2 Materials and Methods
### Fabrication of Silicon Nanowire Structure  
A boron-doped p-type single crystalline Si wafer was purchased from Namkang (with a thickness of 500 \(\upmu\)m, diameter 4 inch, resistivity of 10-15 \(\Omega\)cm, (100) orientation). AgNO\({}_{3}\) powder with a purity of 99.5% and 48-51 wt.% hydrogen fluoride (HF) were purchased from Sigma Aldrich, while a 70 wt.% concentrated nitric acid solution was purchased from Daejung Chemical (Seoul, Korea, 2018). Silicon wafers were cut to pieces with dimensions of 8 \(\times\) 8 mm\({}^{2}\) and cleaned with acetone, 2-propanol, and deionized water for 10 min each using a bath sonicator. To etch and form the nanowire structure on the planar silicon surface, a 20 mL aqueous solution of AgNO\({}_{3}\) (0.679 g, 0.02 M) and HF (5 M) were used. Finally, to remove impurities from Si surface, the Si nanostructure was carefully cleaned prior to photoelectrochemical cell measurement. Excess Ag remnants that could possibly contaminate the electrode surface during the electroless etching were completely washed by 70% nitric acid solution for two h to minimize Ag contaminants. No metal contamination (Ag residue) was confirmed by high-resolution energy-dispersive X-ray spectroscopy mapping.
## 3 Proposed Approach  
Exploration can be defined as the process of selection and execution of actions to maximize environmental awareness; therefore, models of the physical environment can be acquired. For a proper model generation, the agent has to interpret its sensors outcomes to make correct inferences about the surroundings, which corresponds to the map-building problem. The accuracy of the built map depends on the agent location when mapping and acquiring suitable models of the environment. This fundamental problem in mobile robotics is called Simultaneous Localization and Mapping (SLAM). SLAM is defined as a problem with an iterative solving strategy; while an agent navigates in an unknown environment, it must incrementally build a map of its surroundings; and localize itself within the built map. Additionally, as exploration is carried out, the agent must choose its viewpoints to ensure that the sensory measurements contain new and useful data. Thus, the accuracy of the map depends on the choice of viewpoints during exploration.  
In exploration applications, there is a trade-off between the amount of knowledge acquired and its acquisition cost. An explorer agent aims to obtain the maximum environment knowledge at a minimum cost (i.e., minimum time and/or power). This work proposes the design of a hybrid coordination mechanism based on the interaction of three actions for intelligent agents: communication, intelligent task allocation, and negotiation in a multi-agent system.  
Communication is important in any multi-agent system. The purpose is to integrate a market-based task allocation algorithm with cooperative negotiation for agent task completion. Figure 1 shows a diagram based on an individual agent analysis to carry out an overall mission considering the environment information provided by itself and its teammates. There are different degrees of cooperation in a multi-agent system. The highest is \"global cooperation,\" and corresponds to when an agent while making its own decision, keeps trying to maximize the global utility function that considers all agents actions in the system.  
The process of finding an appropriate task for an agent depends on three subprocesses in this approach. For any agent, the suitability of an agent task (\(S_{T\!I}\)) is proposed to be defined in terms of the Knowledge acquired from the teammates (\(K_{T\!M\!i}\)) and the Environment (\(K_{E\!I}\)), the accuracy of the communications system (\(a_{c}\)) and the sensors (\(a_{s}\)), the results obtained with the Decision-making Algorithm (\(DM_{Ri}\)), and the constraints given by negotiation within the Social Interactions (\(S_{Ni}\)). Each variable may change in time. This behavior determines the possibility of dynamic task allocation in the decision-making algorithm.  
\[S_{T\!I}=f(a_{c}K_{T\!M\!i},a_{s}K_{E\!i},\ DM_{Ri},\ SI_{Ni})\text{ for each task i} \tag{1}\]
## 1 Introduction  
In a fast-paced medical reality, biosciences and bioengineering have now become essential components in medical research and development. The drug discovery process is complex; it starts with high-throughput screening, then moves into preclinical testing and, eventually, first in-human phase 1 clinical trials to establish safety in healthy volunteers. If no safety signals emerge at this point, phase 2 trials are subsequently performed to further confirm safety, to establish pharmacokinetics and to look at preliminary efficacy data in the actual target patient population. These are followed by larger, more inclusive phase 3 pivotal clinical trials, which should be adequately powered to establish efficacy against the main clinically relevant endpoints. Only based on an extensive review of all this preclinical and clinical data are regulatory agency approvals provided. Even after the final approval of a new drug, research continues into phase 4 post-marketing clinical trials and independent studies to ensure that the real-world safety and efficacy data match those from the pivotal clinical trials [1]. Antimicrobial resistance is part of the World Health Organization's (WHO's) list of 10 global public health threats that humanity is facing [2] and the rates and burden of antimicrobial resistance are increasing globally [3; 4; 5]. With the advent of multidrug resistance, the WHO has defined a set of priority pathogens for which drug development should be fast tracked [2; 6]. These are classified based on priority into:  
* \"Priority 1 (critical): carbapenem-resistant _Acinetobacter baumannii_, carbapenem-resistant _Pseudomonas aeruginosa_ and carbapenem-resistant ESBL-producing Enterobacteriaceae *
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 6 Adapting the Cloud Detector to the Target Domain
### TTA on Satellite Hardware
#### 6.2.2 Tent  
Similar to DUA [71], Tent [69] also updates the the BN layers of \(\theta^{t}\) but with one minor difference--the affine transformation parameters are also updated. Recall \(\hat{\mu}\) and \(\hat{\sigma}\) are the running mean and running variance of an arbitrary BN layer of \(\theta^{t}\). We further denote \(\gamma\)and \(\beta\) as the affine transformation parameters of the BN layer. Given batch \(\mathcal{B}\), Tent [69] first estimates the Shannon entropy loss:  
\[H=-\sum_{x_{j}^{t}\in\mathcal{B}}\sum_{c=1}^{C}\hat{y}_{j,c}^{t}\cdot\log\hat{y }_{j,c}^{t}, \tag{10}\]  
where \(C\) is total number of classes, \(\hat{y}_{j}^{t}\) is the prediction of \(x_{j}^{t}\), and \(\hat{y}_{j,c}^{t}\) is the predicted probability of \(\hat{y}_{j}^{t}\) of class \(c\). Tent comprises as many iterations as the number of samples in a batch, where each iteration consists of a forward pass and a backward pass. During a forward pass, the normalisation statistics \(\hat{\mu},\hat{\sigma}\) for each BN layer are estimated/updated. A backward pass follows the prediction for the current batch. During a backward pass, the transformation parameters \(\alpha,\beta\) are updated, so the updated parameters only affect the next batch. In summary, each BN layer is updated in terms of  
\[\hat{\mu} \leftarrow\mathbb{E}_{\mathcal{B}}[x^{t}],\hskip 14.226378 pt\hat{ \sigma}^{2}\leftarrow\mathbb{E}_{\mathcal{B}}[(x^{t}-\mu)^{2}], \tag{11}\] \[\gamma \leftarrow\gamma+\frac{\partial H}{\partial\gamma},\hskip 14.226378 pt \beta\leftarrow\beta+\frac{\partial H}{\partial\beta}. \tag{12}\]  
In contrast to DUA [71], Tent [69] uses a fixed momentum, and the normalisation statistics \(\hat{\mu}_{0},\hat{\sigma}_{0}^{2}\) are recalculated from scratch on the target data.  
Excluding forward propagation and backward propagation, the computational complexity of Tent in one epoch, like that of DUA, is \(O(n_{\mathcal{B}}\cdot n_{\text{BN}})\) expectation operations, where \(n_{\text{BN}}\) is the number of BN layers.
## 5 Numerical Analysis Results and Comparison of Strong Wind Analysis with Real Tornado Action
### High Wind Speed Load, T1, T2, T3
#### 5.2.2 Analysis of Internal Forces
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Wind Direction—W1**} \\ \hline  & **Load** & & & **Stresses** & **Bending Moment** & **Shear Force** & **Normal Force** \\ \hline \multirow{2}{*}{**Type**} & \multicolumn{3}{c}{**Value**} & & & **\multicolumn{1}{c}{} & **\multicolumn{1}{c}{} & **\multicolumn{1}{c}{} & **\multicolumn{1}{c}{} \\  & **[kN/m\({}^{2}\)]** & **[km/h]** & **[m/s]** & **[kPa]** & **[kNm]** & **[kN]** & **[kN]** \\ \hline Dead load—Roo covering & 0.48 & - & - & 1.49 & 0.14 & 0.37 & 0.25 \\ Wind LWS & - & 79.2 & 22.00 & 4.05 & 0.44 & 0.54 & 0.11 \\ Wind T1 & - & 158.0 & 44.89 & 16.48 & 1.86 & 2.31 & 0.46 \\ Wind T2 & - & 178.0 & 49.44 & 20.13 & 2.29 & 2.84 & 0.57 \\ Wind T3 & - & 198.5 & 55.14 & 24.81 & 2.82 & 3.51 & 0.7 \\ \hline \hline \multicolumn{8}{c}{**Wind direction—W2**} \\ \hline  & \multicolumn{3}{c}{**Load**} & & & **\multicolumn{1}{c}{} & **\multicolumn{1}{c}{} & **\multicolumn{1}{c}{} & **\multicolumn{1}{c}{} \\ \cline{2-7}  & **[kN/m\({}^{2}\)]** & **[km/h]** & **[m/s]** & **[kPa]** & **[kNm]** & **[kN]** & **[kN]** \\ \hline Dead load—Roo covering & 0.48 & - & - & 1.49 & 0.14 & 0.37 & 0.25 \\ Wind LWS & - & 79.2 & 22.00 & 4.87 & 0.53 & 0.65 & 0.4 \\ Wind T1 & - & 158.0 & 44.89 & 21.25 & 2.31 & 2.82 & 1.73 \\ Wind T2 & - & 178.0 & 49.44 & 25.91 & 2.81 & 3.43 & 2.11 \\ Wind T3 & - & 198.5 & 55.14 & 32.09 & 3.49 & 4.25 & 2.59 \\ \hline \hline \multicolumn{8}{c}{**Wind direction—W3**} \\ \hline  & \multicolumn{3}{c}{**Load**} & & & **\multicolumn{1}{c}{} & **\multicolumn{1}{c}{} & **\multicolumn{1}{c}{} & **\multicolumn{1}{c}{} \\ \cline{2-7}  & **Value** & & & **\multicolumn{1}{c}{} & **\multicolumn{1}{c}{} & **\multicolumn{1}{c}{} \\ \cline{2-7}
**Type** & **[kN/m\({}^{2}\)]** & **[km/h]** & **[m/s]** & **[kPa]** & **[kNm]** & **[kN]** & **[kN]** \\ \hline Dead load—Roo covering & 0.48 & - & - & 1.49 & 0.14 & 0.37 & 0.25 \\ Wind LWS & - & 79.2 & 22.00 & 2.07 & 0.31 & 0.4 & 0.21 \\ Wind T1 & - & 158.0 & 44.89 & 9.03 & 1.31 & 1.68 & 0.91 \\ Wind T2 & - & 178.0 & 49.44 & 14.44 & 2.09 & 2.66 & 1.47 \\ Wind T3 & - & 198.5 & 55.14 & 17.65 & 2.55 & 3.26 & 1.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Tables presenting the list of maximum internal forces (stresses, bending moments, shear force, normal force) and deflection at different wind directions and speeds. Substituting into the formula, we obtain loads from permanent loads:  
\[F_{z}=4.25\;\text{kN}\times\cos(30^{\circ})+2.59\;\text{kN}\times\sin(30^{\circ}) =4.9756\;\text{kN} \tag{9}\]  
\[F_{x}=4.25\;\text{kN}\times\sin(30^{\circ})+2.59\;\text{kN}\times\cos(30^{\circ} )=4.368\;\text{kN} \tag{10}\]  
Substituting into the formula, we obtain wind loads:  
\[F_{z}=0.37\;\text{kN}\times\cos(30^{\circ})+0.25\;\text{kN}\times\sin(30^{ \circ})=0.4454\;\text{kN} \tag{11}\]  
\[F_{x}=0.37\;\text{kN}\times\sin(30^{\circ})+0.25\;\text{kN}\times\cos(30^{ \circ})=0.4015\;\text{kN} \tag{12}\]