esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Analysis of Seismic Pounding between Adjacent Buildings Accounting for SSI
## 2 Theoretical Model Framework
### Pounding Forces  
The equation of motion for Building \(A\) can be expressed in terms of translation and rocking along the \(x\) and \(y\) axes and twist along the \(z\) axis, as described by [PERSON] et al. [30], [PERSON] and [PERSON] [23], and [PERSON] et al. [35]. The static impedance functions have been used in this study (see [30]). Equation (3) shows the bounding-involved equation of motion for buildings with SSI:  
\[\begin{bmatrix}M^{a}&0\\ 0&M^{b}\end{bmatrix}\begin{Bmatrix}\dot{u}^{a}(t)\\ \dot{U}^{b}(t)\end{Bmatrix}+\begin{bmatrix}C^{a}&0\\ 0&C^{b}\end{bmatrix}\begin{Bmatrix}\dot{U}^{a}(t)\\ \dot{U}^{b}(t)\end{Bmatrix}+\begin{bmatrix}K^{a}&0\\ 0&K^{b}\end{bmatrix}\begin{Bmatrix}\dot{U}^{a}(t)\\ \dot{U}^{b}(t)\end{Bmatrix}+\begin{Bmatrix}F^{p}(t)\\ -F^{p}(t)\end{Bmatrix} \tag{3}\]  
Figure 2: An asymmetric view of adjacent shear buildings in two directions.  
where \(F^{p}(t)\), \(P^{q}(t)\), and \(P^{b}(t)\) are the vectors including the impact forces between floor masses \(\left(m_{i},m_{j}\right)\).  
In this study, the bounding force was simulated based on the nonlinear viscoelastic model [45, 48, 49, 50].
## 1 Introduction
It is defined as:  
\[\eta_{internal}=\frac{P_{DFG}}{P_{signal}}\times 100\% \tag{3}\]  
This internal efficiency is useful for characterizing the system's conversion performance, independent of pump power contributions [17]. By examining these three metrics--DFG efficiency, black-box efficiency, and internal efficiency--we obtain a comprehensive understanding of the system's performance, facilitating a fair comparison across DFG structures. In terms of efficiency metrics, our current work shows a notable advancement over previous studies. Compared to our previous paper (2023) [14], which reported both black-box efficiency (\(\eta_{BB}\)) and DFG efficiency at 2.2% and an internal efficiency of 12.5%, our new setup demonstrates substantial gains. Specifically, we achieved an \(\eta_{BB}\) and \(\eta_{DFG}\) of 3.3% and an \(\eta_{internal}\) of 18.8%. This reflects a -50% improvement in overall system effectiveness. [PERSON]'s study, a representative example of the state of the art for high-power, single-pass mid-infrared generation, reported a \(\eta_{BB}\) of 0.159% W\({}^{-1}\), \(\eta_{DFG}\) of 0.264%, and \(\eta_{internal}\) of 11.5% [18]. While their work emphasizes multi-watt power levels, our current \(\eta_{BB}\) is over twenty times higher, showcasing the significantly optimized efficiency of our intracavity system, even when accounting for both pump and signal source factors. Similarly, [PERSON]'s 2019 work achieved a \(\eta_{BB}\) of 2.43% W\({}^{-1}\) and a \(\eta_{DFG}\) of 6%, but with a lower \(\eta_{internal}\) at 6% [19]. While [PERSON]'s DFG efficiency remains relatively high, our work surpasses this with both a higher internal efficiency and a comparable BB efficiency. Lastly, [PERSON]'s 2024 study reports a record \(\eta_{internal}\) of 33.3% [20]. [PERSON] emphasizes that their highest conversion efficiency is observed at a specific threshold pump power and claims that their result represents the highest average power produced from any single-pass parametric conversion source with >3 um idler wavelength fed by a continuous-wave (CW) signal. However, [PERSON]'s \(\eta_{BB}\) is lower at 0.8%, and since this result was achieved under pulsed conditions, our results for continuous-wave operation signify a major advancement, demonstrating efficient power conversion without the gain enhancement seen in pulsed operation [20; 21]. In summary, our new setup demonstrates a respectable performance across all three metrics, making it highly competitive and efficient for DFG applications. This design shows potential for practical applications in GHG detection and free-space optical communication, addressing the increasing demands for compact size, low power consumption, wavelength tunability, and high efficiency. By systematically optimizing the overlap efficiency and other critical parameters, we demonstrate the potential of this design to excel in these applications. This work not only advances the existing body of knowledge in mid-IR generation but also opens new avenues for applications in environmental sensing and telecommunications [22; 23].
## 4 Conclusions  
This article reports on the development of an affordable portable instrument for the detection of VOCs using photoluminescence sensing materials. Determination is based on the phosphorescence changes undergone by a solid sensing material after interaction with the gaseous analyte. A portable instrument that is compatible with the sensing phase was designed and constructed using conventional optoelectronic components to be able to measure and process the changes in the photoluminescence of the sensing material, and to convert such changes into concentration information. The sensing system was evaluated in terms of its rapid and reversible acetone quantification in air gas flows. The results showed that the optical signal registered by the prototype was linearly dependent on the amount of acetone present in the gaseous stream, allowing for proper quantification of the acetone analyte with a very good detection limit (9 ppm).  
The low-cost sensor modules used to develop this instrumental prototype reduced its overall production costs to provide a comprehensive, portable, and real-time monitoring solution.  
Finally, we envisage simple and straightforward industrial applications of this instrument. Not only it is an affordable device but it is also highly versatile; by simply changing the sensing materials (e.g., using a different kind of luminescent nanoparticles, such as ZnS/CdSe quantum dots [36] or photoluminescence carbon dots [37], entrapped in a sol-gel matrix), the light source, and the firmware programming, the system could be directly adapted for the detection of other chemical species (e.g., different VOCs) in different industrial sectors (e.g., printing, paint manufacture, furniture coating, metal surface coating, etc.).  
Conceptualization, F.J.F. and J.M.C.; methodology, J.M.C and A.S.; software, A.L.; validation, M.V., A.S., and M.T.F.-A.; formal analysis, M.V.; investigation, P.L.-S. and M.V.; resources, J.C.C.; data curation, J.C.C.; writing--original draft preparation, F.J.F.; writing--review and editing, F.J.F.; visualization, F.J.F.; supervision, J.M.C.-F.; project administration, A.L.; funding acquisition, J.M.C.-F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by IDEPA (Economic Development Agency of the Principality of Asturias) and ArcelorMittal through grant number SV-PA-15-RIS3-5. Financial support from Principado de Asturias (Spain) through the project FC-GRUPIN-IDI/2018/000166, from the Ministry of Economy and Competitiveness (Spain) through CTQ2016-79412-P, and the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria Nacional de Investigaciones Aggararias (Spain) through RTA2015-00061/AEI/FEDER, co-financed with european regional development funds (FEDER), is gratefully acknowledged.  
The authors declare no conflict of interest.
## 1 Introduction
However, the very high rotating speed makes steel no more suitable due to the high mechanical stresses acting on the flywheel; consequently, titanium or carbon-fiber materials have to be employed, which increase cost, design and manufacturing complexity compared to steel-based solutions [20; 24; 27; 28]. Another FESS classification consists of non-integrated and integrated topologies [19; 29]; the former entails that the flywheel and the electrical machine devoted to its acceleration/deceleration are two independent devices, connected to each other through appropriate coupling devices. This configuration allows the flywheel and the electrical machine to be designed almost independently, but requires additional components for their coupling, as well as larger dimensions compared to integrated configurations [19]. The latter consists of integrating the flywheel into the rotor of the electrical machines, which could be either inner or outer depending on the machine configuration. As a result, a more compact and efficient FESS can be achieved, at the cost of increased design complexity and constraints [19; 30; 31]. In this scenario, this paper presents the control system design and an extensive energy analysis of an integrated FESS configuration for residential prosumers [32; 33]. In particular, the proposed FESS aims at storing 8 kWh of energy, to be exchanged with the main grid at 8 kW in order to be competitive in terms of both energy and power capabilities versus other battery-based solutions. In order to achieve such a high energy target, the proposed configuration relies on an inner-rotor large-airgap surface-mounted permanent magnet synchronous machine (IRLA-SPM), whose inner rotor integrates a tick carbon-fiber flywheel; this acts also as a sleeve for retaining PMs to the rotor surface at any speed. The design process and functionality tests of IRLA-SPM have been already detailed in [32; 33], whereas this paper focuses on its control system design and energy analysis over realistic operating scenarios. In particular, Section 2 presents a general overview of the proposed FESS, together with the definition of its operating modes according to its rotor speed and user's needs. Subsequently, its energy management system (EMS) is presented in detail (Section 3), as well as FESS control system; the latter consists of both machine- and grid-side control subsystems, which are devoted to reference current/power tracking in order to ensure proper FESS operation at any operating condition. The effectiveness of the proposed FESS has been validated through extensive simulation studies (Section 4), which have been carried out through MATLAB-Simulink by means of a real-time simulator in order to analyze FESS energy performance accurately over relatively long time horizons. The corresponding results are then presented and discussed (Sections 5 and 6), followed by some concluding remarks (Section 7).
# TGA-GS: Thermal Geometrically Accurate Gaussian Splatting
## 4 Experiments  
### Datasets and Baselines  
To evaluate the performance of 3D reconstruction and novel view synthesis under low-light conditions using multimodal RGB-thermal inputs, we constructed a specialized dataset captured by the commercial-grade FLIR E6 PRO handheld thermal imaging system. This advanced device simultaneously acquires aligned RGB and thermal imagery, featuring a thermal sensor with an operational range of \(-20\) \({}^{\circ}\)C to \(550\)\({}^{\circ}\)C and measurement accuracy of \(\pm 2\%\) of reading. The dataset includes five distinct indoor environments, specifically designed to simulate challenging low-light search-and-rescue scenarios. For each scene, we provide raw thermal images captured by the thermal camera, RGB images, thermal imaging data, MSX (Multi-Spectral Dynamic Imaging) images (edge-enhanced thermal-visual fusion), and camera pose data.  
For comprehensive performance evaluation, we select two types of baselines for our experiments: (1) High-rendering-quality methods, used exclusively in novel view synthesis experiments, including 3 DGS [2] and ThermalGaussian [62]. For ThermalGaussian, we opt for the OMMG method from the original paper due to its superior rendering quality within the ThermalGaussian framework. (2) High-reconstruction-quality methods, serving as baselines for both novel view synthesis and reconstruction experiments, including 2 DGS [94] and RaDe-GS [95].
## 2 Materials and Methods
### Mapping Soil Surface Changes Because of Mobilization or Accumulation Processes Using ISUM  
The ISUM allows obtaining a map of the current soil surface level along with the inter-row areas. When the measurements are tipped in an electronic format, the data are incorporated into ArcMap 10.5 (ESRI, Redlands, CA, USA) as a shapefile format. The points are digitalized using the tool \"fishnet\" taking as a reference, the beginning of each row, which was measured using a GPS. In order to obtain the most correct ISUM map, several interpolation methods were tested to get a current DEM (Digital Elevation Model): Ordinary Kriging (OK) with anisotropy, Multi-Quadratic (M-Q), Empirical Bayesian Kriging (EBK), Thin Plate Spline (TPS), IM-Q (Inverse Multi-Quadratic), Inverse Distance Weight (IDW), Completely Regularized Spline (CRS), and Spline with Tension (ST). For all the methods, the RMSE (root mean square error) and R\({}^{2}\) (coefficient of determination) were calculated. Table 1 shows the values referring to the mean, the mean square error, and the square error of each interpolation method. The lowest error and highest coefficients were obtained for the OK, obtaining a resolution of 0.03 m per pixel size.
# Entropy Weighted TOPSIS Based Cluster Head Selection in Wireless Sensor Networks under Uncertainty
## 2 Theoretical Background and the Related Work
### Finding the Best Alternative Using TOPSIS Method Based on TFNs  
To overcome an issue involving MCDM, this section describes how to use the TOPSIS strategy when the weights of the criteria are unknown and can be estimated by using Shannon entropy method.  
Assume that there exist \(m\) alternatives \(A_{1},A_{2},\ldots,A_{m}\) and \(n\) criteria \(C_{1},C_{2},\ldots,C_{n}\) with a weight vector \(w=(w_{1},w_{2},\ldots,w_{n})\), where \(0\leq w_{j}\leq 1\) and \(\sum w_{j}=1\). A decision matrix \(\widetilde{Z}=\left(\widetilde{z}_{ij}\right)_{m\times n}\) might be used to convey an alternative's characteristics in relation to the criteria expressed by a TFN \(\widetilde{z}_{ij}=\left(z_{ij}^{1},z_{ij}^{2},z_{ij}^{3}\right)\) where \(i=1,2,\ldots,m\) and \(j=1,2,\ldots,n\). Employing the signed distance method, mentioned in Section 2.2, the MCDM decision matrix (1) has been formed as follows:  
\[\left(D(\widetilde{Z},\widetilde{0})\right)_{m\times n}=\begin{pmatrix}D( \widetilde{z}_{11},\widetilde{0})&D(\widetilde{z}_{12},\widetilde{0})&\cdots&D( \widetilde{z}_{1n},\widetilde{0})\\ D(\widetilde{z}_{21},\widetilde{0})&D(\widetilde{z}_{22},\widetilde{0})&\cdots&D (\widetilde{z}_{2n},\widetilde{0})\\ \vdots&\vdots&\vdots&\vdots\\ D(\widetilde{z}_{m1},\widetilde{0})&D(\widetilde{z}_{m2},\widetilde{0})&\ldots&D (\widetilde{z}_{mn},\widetilde{0})\end{pmatrix} \tag{1}\]  
Let \(I^{+}=(1,1,\ldots,1)\) and \(I^{-}=(0,0,\ldots,0)\) represent consequently, the positive and negative ideal solutions for the \(m\) alternatives \(A_{1},A_{2},\ldots,A_{m}\). Here, we used the following formula to compute the separation measures \({sm_{i}}^{+}={sm_{i}}^{+}(I^{+},A_{i})\) and \({sm_{i}}^{-}={sm_{i}}^{-}(I^{-},A_{i})\) of each alternative from positive ideal and negative ideal solutions:  
\[{sm_{i}}^{+}=\sqrt{\sum_{j=1}^{n}\left(w_{j}(1-D(\widetilde{z}_{ij},\widetilde{0 })\right)^{2}} \tag{2}\]  
and  
\[{sm_{i}}^{-}=\sqrt{\sum_{j=1}^{n}\left(w_{j}D(\widetilde{z}_{ij},\widetilde{0 })\right)^{2}} \tag{3}\]  
Here \(D(\widetilde{z}_{ij},\widetilde{0})=0.25(z_{ij}^{1}+2z_{ij}^{2}+z_{ij}^{3})\), \(i=1,2,\ldots,m\), \(j=1,2,\ldots,n\), and \(w_{j},j=1,2,\ldots,n\) are calculated using the entropy-weighted method discussed in Section 2.3.  
Using Equations (2) and (3), the relative closeness of \(m\) alternatives \(A_{1},A_{2},\ldots,A_{m}\) with respect to the positive ideal solution \(I^{+}\) is computed as follows:  
\[RC_{i}(A_{i})=\frac{{sm_{i}^{-}}}{{sm_{i}^{-}+sm_{i}^{+}}},\;\;i=1,2,\ldots,m \tag{4}\]  
The best alternative among a group of specified possible alternatives can be identified using Equation (4), which also determines the ranking order of all alternatives. The alternatives may then be ranked according to the closeness coefficient, with the alternative with the higher rank being considered the best choice.
# A New Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF for Blockchains of HRRS Images
###### Abstract  
Aiming at the deficiency that blockchain technology is too sensitive to the binary-level changes of high resolution remote sensing (HRRS) images, we propose a new subject-sensitive hashing algorithm specially for HRRS image blockchains. To implement this subject-sensitive hashing algorithm, we designed and implemented a deep neural network model MultiRes-RCF (richer convolutional features) for extracting features from HRRS images. A MultiRes-RCF network is an improved RCF network that borrows the MultiRes mechanism of MultiResU-Net. The subject-sensitive hashing algorithm based on MultiRes-RCF can detect the subtle tampering of HRRS images while maintaining robustness to operations that do not change the content of the HRRS images. Experimental results show that our MultiRes-RCF-based subject-sensitive hashing algorithm has better tamper sensitivity than the existing deep learning models such as RCF, AAU-net, and Attention U-net, meeting the needs of HRRS image blockchains.  
subject-sensitive hashing; perceptual hashing; blockchain for HRRS image; integrity authentication +
Footnote †: journal:  
0000-0001-9991-4000]X.X.  
Subject-sensitive hashing [9] is a new type of hashing technology developed on the basis of perceptual hashing [10; 11; 12]. The main difference between subject-sensitive hashing and perceptual hashing is \"subject sensitivity\". Subject-sensitive hashing retains the robustness of perceptual hashing and can perform stricter integrity authentication on the image content that users pay attention to. However, the existing subject-sensitive hashing for HRRS images focuses on the overall performance of the algorithm, while the subject-sensitive hashing used in the blockchain should be more sensitive to tampering than in the conventional environment, because the data on the blockchain are difficult to change after they are on the chain.  
In this paper, based on the analysis of the blockchain's demand for subject-sensitive hashing, we propose a subject-sensitive hashing algorithm specially used for HRRS image blockchains, which overcomes the deficiency that mainstream blockchain technologies can only perform a binary-level authentication for HRRS images. The contributions of this paper can be summarized as follows:  
1. We provide an in-depth analysis of the requirements for blockchain-oriented subject-sensitive hashing.
2. We refine the improved blockchain model based on subject-sensitive hashing for HRRS images.
3. We propose a new deep neural network MultiRes-RCF for extracting subject-sensitive features of HRRS images and implement a subject-sensitive hashing algorithm based on MultiRes-RCF.  
The rest of the paper is composed as follows: The related works are described in Section 2. Section 3 discusses the details of our proposed method. The details of the experiments and discussion are presented in Section 4, and the conclusions are drawn in Section 5.
###### Abstract  
Predicting metro traffic flow is crucial for efficient urban planning and transit management. It enables cities to optimize resource allocation, reduce congestion, and enhance the overall commuter experience in rapidly urbanizing environments. Nevertheless, metro flow prediction is challenging due to the intricate spatial-temporal relationships inherent in the data and the varying influence of external factors. To model spatial-temporal correlations considering external factors, a novel spatial-temporal deep learning framework is proposed in this study. Firstly, mutual information is utilized to select the highly corrected stations of the examined station. Compared with the traditional correlation calculation methods, mutual information is particularly advantageous for analyzing nonlinear metro flow data. Secondly, metro flow data reflecting the historical trends from different time granularities are incorporated. Additionally, the external factor data that influence the metro flow are also considered. Finally, these multiple sources and dimensions of data are combined and fed into the deep neural network to capture the complex correlations of multi-dimensional data. Sufficient experiments are designed and conducted on the real dataset collected from Xi'an subway to verify the effectiveness of the proposed model. Experimental results are comprehensively analyzed according to the POI information around the subway station.  
metro flow prediction; mutual information; spatiotemporal correlations; external factors; transformer 2024
# An End-to-End Classifier Based on CNN for In-Air Handwritten-Chinese-Character Recognition
###### Abstract  
A convolutional neural network (CNN) has been successfully applied to in-air handwritten-Chinese-character recognition (IAHCCR). However, the existing models based on CNN for IAHCCR need to convert the coordinate sequence of a character into images. This conversion process increases training and classifying time, and leads to the loss of information. In order to solve this problem, we propose an end-to-end classifier based on CNN for IAHCCR in this paper, which, to knowledge, is novel for online handwritten-Chinese-character recognition (OLHCCR). Specifically, our model based on CNN directly takes the original coordinate sequence of an in-air handwritten-Chinese-character as input, and the output of the full connection layer is pooled by global average pooling to form a fixed-size feature vector, which is sent to softmax for classification. Our model can not only directly process coordinate sequences such as the models based on recurrent neural network (RNN), but can also obtain the global structure information of characters. We conducted experiments on two datasets, IAHCC-UCAS2016 and SCUT-COUCH2009. The experimental results show a comparison with existing CNN models based on image processing or RNN-based methods; our method does not require data augmentation techniques nor an ensemble of multiple trained models, and only uses a more compact structure to obtain higher recognition accuracy.  
onvolutional neural networks; in-air handwritten-Chinese-character recognition; end-to-end classifier; online handwritten-Chinese-character recognition; global average pooling