esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The intelligent transportation system is currently an active research area, and vehicle re-identification (Re-Id) is a fundamental task to implement it. It determines whether the given vehicle image obtained from one camera has already appeared over a camera network or not. There are many possible practical applications where the vehicle Re-Id system can be employed, such as intelligent vehicle parking, suspicious vehicle tracking, vehicle incident detection, vehicle counting, and automatic toll collection. This task becomes more challenging because of intra-class similarity, viewpoint changes, and inconsistent environmental conditions. In this paper, we propose a novel approach that re-identifies a vehicle in two steps: first we shortlist the vehicle from a gallery set on the basis of appearance, and then in the second step we verify the shortlisted vehicle's license plates with a query image to identify the targeted vehicle. In our model, the global channel extracts the feature vector from the whole vehicle image, and the local region channel extracts more discriminative and salient features from different regions. In addition to this, we jointly incorporate attributes like model, type, and color, etc. Lastly, we use a siamese neural network to verify license plates to reach the exact vehicle. Extensive experimental results on the benchmark dataset VeRi-776 demonstrate the effectiveness of the proposed model as compared to various state-of-the-art methods.  
vehicle re-identification; overlapping and non-overlapping cameras; global and local features; deep learning; license plate +
Footnote †: journal: Applied Soft 2019
## 2 Literature Review  
### Research on GTFP  
A method for evaluating productivity, known as the Malmquist productivity index, was first derived by [PERSON] in his seminal work [8]. As environmental awareness increases, industrial byproducts, such as pollutants, which are also called 'undesirable outputs in the production process', are being incorporated into the evaluation model [9] in a bid to obtain a more exact productivity index. It is also referred to as 'green productivity' because the negative consequences of environmental pollution are considered [10].  
However, the conventional Malmquist productivity method suffers two drawbacks. One is not satisfying circularity, and the other is that adjacent time productivity scores provide different measures of productivity change. To overcome the two problems, [PERSON] and [PERSON] [11] developed a new global productivity index, but still neglected to account for undesirable outputs. Subsequently, [PERSON] [7] proposed another new global Malmquist productivity index, incorporating both desirable and undesirable outputs, known as the Global Malmquist-Luenberger Index (GMLI) or GTFP. Since then, it has gained credibility in assessing the productivity of countries [12] and industrial sectors [13]. For example, [PERSON] et al. [14] investigated the GTFP changes in the metal industry of 30 Chinese provinces from 2000 to 2015. They found that GTFP increased by 11.52% every year. [PERSON] and [PERSON] [15] assessed the quality of growth of 30 Chinese provinces over the period of 1997-2015, measured by the GTFP index; conclusions showed that GTFP exhibited no positive growth for most provinces in most years, although GDP was growing fast. [PERSON] et al. [16] evaluated the agricultural GTFP of 30 Chinese provinces from 2005 to 2017 and found an inverted U-shaped trend over the sample period.  
Other researchers have focused on the driving factors of GTFP, such as factor market distortion [17], internet technology [18], land transfer marketization [19], political competition [20], foreign direct investment [21], energy consumption transition [22], financial agglomeration [23], green finance [24], green innovation [25], trade openness [26] investment in green technologies [27], and environmental regulation [28; 29]. In addition, some studies have taken a few policies as quasi-natural experiments and investigated the policy effects on GTFP, for example, SO\({}_{2}\) emissions trading scheme [30], free trade zone [31], e-commerce city pilot [32], emission trading system [33], smart city policy [34], and carbon trading [35]. Overall conclusions from empirical studies confirm that many factors can impact GTFP.
###### Abstract  
Strong winds or typhoon disasters pose significant risks to historical buildings in many countries and regions. Although numerous researchers have discussed the damage caused by natural wind disasters, little attention has been paid to the potential impact of renovation and adaptive reuse strategies on the wind resistance performance of historical buildings--an avoidable human-induced factor. This study selected the representative ancestral hall building in Guangzhou, namely Lujiang Academy, for on-site investigation and Computational Fluid Dynamics (CFD) simulation analysis. It examined two renovation approaches: installing glass curtain walls around the courtyard and enclosing the top of the courtyard with a glass canopy. This study also assessed the roof wind pressure distribution in its unaltered state to evaluate the impact of different renovation strategies on roof wind load distribution. The results showed that installing glass curtain walls can effectively reduce the upward suction effect on the main hall roof, thereby improving its wind resistance performance. Conversely, enclosing the courtyard with a glass canopy may increase negative pressure on areas such as the eaves and corridor roofs, raising the risk of roof damage. This study systematically evaluated the renovation strategies for historical buildings, detailing the specific impacts of different retrofit measures on the wind resistance performance of building roofs. The methods and findings are significant not only for the renovation of ancestral hall heritage buildings in Guangzhou but also for providing practical scientific methods and technical strategies for the conservation and renovation of similar historical structures. This research demonstrates an effective approach to enhancing wind resistance and preventive protection for historical buildings.  
historical buildings; ancestral hall building; Lujiang Academy; wind pressure; computational fluid dynamics (CFD) simulation; preventive conservation Article
# Factors That Affect Liquefaction-Induced Lateral Spreading in Large Subduction Earthquakes
## 2 Background of Lateral Spreading in Subduction Earthquakes
### Lateral Spreading Prediction Models  
Most of the lateral spreading prediction models are empirical. They use regression procedures to fit equations with field case histories ([PERSON] et al. (1987) [16], [PERSON] and [PERSON] (1995) [2], [PERSON] et al. (2002) [5]). These models take different algebraic forms, and they rely on parameters such as the liquefiable soil's thickness, density, and fines content; earthquake magnitude; and site-to-source distance. Semi-empirical models ([PERSON] et al. (2004) [17]; [PERSON] et al. (2006) [3]) use other variables, like shear strain ratios and earthquake intensity measures, such as peak surface acceleration. Table 1 shows a list of existing lateral spreading prediction models and their main variables.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Author(s)** & **Variables** \\ \hline [PERSON] et al. (1987) [16] & Ground slope, thickness of the liquefiable layer \\ [PERSON] and [PERSON] (1995) [2] & Ground slope, thickness of the liquefiable layer, fines content, \\  & average grain size, earthquake magnitude, horizontal distance \\  & from the site to the seismic energy source. \\ [PERSON] et al. (2002) [5] & Ground slope, thickness of the liquefiable layer, fines content, \\  & average grain size, earthquake magnitude, horizontal distance \\  & from the site to the seismic energy source. \\ [PERSON] et al. (2004) [17] & Ground slope, thickness of the liquefiable layer, shear strain, \\  & earthquake magnitude, depth to the liquefiable layer. \\ [PERSON] et al. (2006) [3] & Seismic coefficient, earthquake magnitude, horizontal distance \\  & from the site to the seismic energy source. \\ [PERSON] and [PERSON] (2008) [18] & Ground slope, thickness of the liquefiable layer, fines content, \\  & average grain size, earthquake magnitude, horizontal distance \\  & from the site to the seismic energy source, post-liquefaction \\  & undrained shear strength. \\ [PERSON] et al. (2012) [4] & Ground slope, thickness of the liquefiable layer, fines content, \\  & average grain size, pseudo-spectral displacement. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lateral spreading prediction models.  
Figure 3: Historical cases of lateral spread: (**a**) lateral spread ground failure near the west levee of Westside Main Canal in the 2010 Sierra El Mayor Mw7.1 earthquake (photo taken by [PERSON] on 4/7/10, [PERSON] et al. (2011) [14]); (**b**) lateral spread affecting the piers of the Muzoi Bridge in the 2005 Nias Island Mw 8.6 earthquake (modified from [PERSON] et al. (2005) [15]).
# Noiseless Attenuation for Continuous-Variable Quantum Key Distribution over Ground-Satellite Uplink
## 1 Introduction
The motivation of this work is to improve the performance of the uplink CV-QKD to achieve a complete quantum network with more efficiency.
# Generation of Pure State Photon Triplets in the C-Band
## 1 Introduction
The common pump source used to acquire polarization-entangled photon pairs from SPDC is narrow-band or continuous wave (CW) laser, but the subsequent photon pairs have a strong correlation in frequency [30]. A broadband pumping source is adopted in our work, and the optimal pumping duration is chosen by numerical investigation in Section 3. Finally, we obtain pure-state photon triplets with two kinds of periodically poled crystals under different parameters.
# A Novel Cloud Approach for Connected Vehicles
## 4 Analytic Model
### Approaches
#### 4.3.5 Network Overhead and Data Freshness Expressions for GeoVCDN  
With GeoVCDN, we keep the same mechanism as VCDNoCAM, and we add the V2V exchanges. This new feature requires adding new information. This information is the maximum number of hops that data can make between vehicles.  
**Definition of \(CR\)**  
Then, we obtain the network overhead which is caused by our approach:  
\[CR=CR_{i}+CR_{d0}+CR_{d1}+CR_{d2}\]  
The obtained result is an upper limit of the expected result, because we maximized the number of segments, without an RSU, which are contaminated at each stage.  
**Definition of \(\alpha_{D}\)**  
For the freshness, the worst case is the one in which the segments without an RSU do not receive the information. So, we suppose the following hypothesis: the length of a junction is higher than a vehicle's range multiplied by HL, which can be expressed by:  
\[L-P_{RSU}>P_{V}*HL \tag{34}\]  
The freshness is given by the number of vehicles being in the coverage area when the data are generated, to which we add, either the number of vehicles entering the coverage area, or the vehicles which receive the information through other vehicles, depending on the relation between the inter-distance and the range of the vehicles. Thus, on the one hand, if the inter-distance is higher than the vehicles' transmission range, \(\alpha_{D}\) is expressed as follows:  
\[\alpha_{D}=\frac{\alpha}{100}*\delta_{RSU}+\frac{F_{\lambda}}{\theta(\sigma, F_{CAM})} \tag{35}\]  
On the other hand, if the inter-distance is lower than the vehicles' transmission range, \(\alpha_{D}\) is obtained by the following expression:  
\[\begin{split}\alpha_{D}=\Big{(}\frac{\alpha}{100}*\delta_{RSU} \Big{)}+\frac{\alpha}{N_{S}}*P_{OR3}*N_{RSU}*\theta\Big{(}\frac{HM_{1}}{HL},1 \Big{)}+\\ \frac{\alpha}{N_{S}}*P_{OR2}*N_{0}*\theta\Big{(}\frac{HM_{2}}{HL },1\Big{)}\end{split} \tag{36}\]  
**Definition of \(FR\)**  
Then, if we come back to the general definition of the freshness (19), if the inter-distance is higher than the vehicles' range: \(FR=\frac{\frac{\alpha_{0}}{100}*\delta_{RSU}+\frac{\alpha}{N_{S}}*\theta\Big{(} \frac{HM_{1}}{HL},1\Big{)}}{\alpha}*100\). Otherwise:  
\[\begin{split} FR=\frac{\big{(}\frac{\alpha}{100}*\delta_{RSU} \big{)}+\frac{\alpha}{N_{S}}*P_{OR3}*N_{RSU}*\theta\Big{(}\frac{HM_{1}}{HL},1 \Big{)}}{\alpha}+\\ \frac{\frac{\alpha}{N_{S}}*P_{OR2}*N_{0}*\theta\Big{(}\frac{HM_{ 2}}{HL},1\Big{)}}{\alpha}*100\end{split} \tag{37}\]
# Impedance-Based Stability Analysis of Grid-Connected Inverters under the Unbalanced Grid Condition
## 2 Impedance Modeling of the DSOGI-PLL-Based Inverter and Verification
### Impedance Modeling under the Three-Phase Balanced Condition
#### 2.2.4 Inverse Park Transformation  
The function of the inverse Park transformation is to transform modulation signals from the \(dq\)-axis to the \(abc\)-axis. The phasor expressions of the \(a\)-phase modulation signal increments in the frequency domain are  
\[\mathbf{m}_{a}=\left\{\begin{array}{l}G_{\rm ma-Vp}\mathbf{V}_{\rm p}+G_{\rm ma-Vc}\mathbf{V }_{\rm c}^{*}+G_{\rm ma-lp}\mathbf{I}_{\rm p}+G_{\rm ma-lc}\mathbf{I}_{\rm c}^{*},f=f_{ \rm p}\\ G_{\rm mc-Vp}\mathbf{V}_{\rm p}+G_{\rm mc-Vc}\mathbf{V}_{\rm c}^{*}+G_{\rm mc-lp}\mathbf{I}_{ \rm p}+G_{\rm mc-lc}\mathbf{I}_{\rm c}^{*},f=f_{\rm c}\end{array}\right. \tag{12}\]  
where \(G_{\rm ma-Vp}=0.5\mathrm{j}M_{\rm d}T_{\rm p}+0.5\mathrm{\sigma}_{\rm md-Vp}-0.5M_ {\rm d}T_{\rm p}+0.5\mathrm{j}G_{\rm ma-Vp}\), \(G_{\rm ma-lp}=0.5\mathrm{j}G_{\rm md-Vp}+0.5\mathrm{j}G_{\rm ma-lg}\), \(G_{\rm ma-Vc}=0.5\mathrm{j}M_{\rm d}T_{\rm c}^{*}+0.5G_{\rm md-Vc}-0.5M_{\rm d}T_{ \rm c}^{*}+0.5\mathrm{j}G_{\rm ma-Vc}\), \(G_{\rm ma-lc}=0.5\mathrm{\sigma}_{\rm md-lc}+0.5\mathrm{\sigma}_{\rm md-lg}\), \(G_{\rm mc-Vp}=-0.5\mathrm{j}M_{\rm d}T_{\rm p}+0.5G_{\rm md-Vp}-0.5M_{\rm d}T_{ \rm p}-0.5\mathrm{j}G_{\rm ma-Vp}\), \(G_{\rm mc-lp}=0.5\mathrm{\sigma}_{\rm md-lg}-0.5\mathrm{j}G_{\rm ma-lg}\), \\(K_{\text{m}}\) represents the modulation coefficient. \(Z_{\text{p}}\) and \(Z_{\text{c}}\) represent the grid impedance at the perturbation frequency and coupled frequency, respectively.
## 5 Conclusions  
In this study, a magnetoelasticity-based (MB) sensor was used as a novel method for wirelessly and passively monitoring the degradation behavior of polylactic acid (PLA) artificial bone (PAB). The MB sensor strip was coated with PLA by a 3D printer to form PAB. Hydrolytic degradation of PAB samples was carried out in PBS (pH = 7.4) and NaOH (pH = 12) media over 15 days at 37 \({}^{\circ}\)C. The PAB diameter decreased to 14.92 \(\pm\) 0.02 and 14.96 \(\pm\) 0.02 mm, respectively. The varying value of the output power changed from 0.04 \(\pm\) 0.004 dbm to 0.2 \(\pm\) 0.04 dbm when the PAB diameter was changed from 15 \(\pm\) 0.05 mm to 14.92 \(\pm\) 0.02 mm under an external force of 100 N. The monitoring results with the MB sensor coincided with the theoretical analysis. This demonstrates the viability of the MB sensor for monitoring the degradation behavior of PAB in vitro. Importantly, the results showed that biological tissues had no effect on the output value of the MB sensor. There was no need for additional cables or power to collect and transmit the signal for the wireless and passive characteristics of the MB sensor. Importantly, compared with traditional methods (i.e., weight loss and SEM images), the MB sensor was convenient and had low cost to monitor the degradation behavior of PAB in vitro. There are currently many disadvantages that limit the use of this method for monitoring the degradation behavior of PAB in vivo, such as a lack of live animal samples, in vivo PAB implantation process, and related knowledge about transplant operations in vivo. Future research work will involve using the MB sensor to monitor the degradation behavior and rate of PAB in vivo.  
Figure 10: (**a**) Changes in the PAB diameter (mm) with the degradation time (days); (**b**) Relationship between the PAB diameter (mm) and varying value of the output power (dbm).  
**Author Contributions:** Conceptualization, L.R.; Methodology, Y.T.; Validation, J.W.; Formal analysis, K.Y.; Writing--original draft preparation, K.Y.; Writing--review and editing, Y.T.  
**Funding:** This work is supported in part by the National Natural Science Foundation of China (No. 51405074), and in part by the Natural Science Foundation of Jilin Province (No. 20190201103 JC).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Theory  
### Convolutional Neural Networks  
CNNs consist of three main components: a convolutional layer, a pooling layer, and a fully connected layer. The convolutional layer plays a crucial role in the feature extraction of the input features. By utilizing convolutional kernels, it captures valuable feature information from the input data, and the size and number of the convolutional kernels significantly influence the network's overall performance. The pooling layer is utilized to decrease the computational complexity by reducing the size of feature maps [18] while preserving the vital features. The two common forms of pooling layers are average pooling and maximal pooling. After the convolutional and pooling layers, towards the end of the CNN, one or more fully connected layers are often added to create global semantic information. The number of neurons and the selection of the activation function in the fully connected layers are some of the most critical hyperparameters influencing the network performance. Some of the more representative CNN models are LeNet-5 [19] and ResNet [20]. LeNet-5 is a classic CNN model with a straightforward architecture, as illustrated in Figure 1. It is widely used for recognizing handwritten digits in MNIST datasets.  
ResNet is a prominent CNN model that addresses the degradation problem of the network at deeper layers by introducing the residual structure and utilizing the jump connection feature. Owing to its high performance, ResNet is widely used in various research fields. ResNet-18, the simplest ResNet model, is depicted in Figure 2. ResNet-18 exhibits a remarkable performance in the tasks involving relatively simple data scenarios.  
Figure 1: LeNet-5 structure diagram.  
Figure 2: ResNet-18 structure diagram.
## 3 Sampling Strategy, Analytical Methods, and Results
### Analytical Methods  
Polished thin sections were prepared in the grinding room at the China University of Geosciences (Beijing, China). First, a detailed petrographic study was carried out to identify the crystal population of different types. To investigate the compositional variations within crystal interiors, as well as discrepancies between grains, representative grains of different amphibe types were selected for an electron probe profile analysis. In situ trace element compositions were also analyzed.  
The major element compositions of amphibe and clinopyroxene were determined by an electron probe microanalysis (EPMA) at the Mineral Laser Microprobe Analysis Laboratory, China University of Geosciences (Beijing, China), using an EPMA-1720 (beam current of 10 nA at 15 kV with a beam diameter of 5 \(\upmu\)m) electron microprobe. Corrections for inter-elemental effects were made using a ZAF (Z: atomic number; A: absorption; F: fluorescence) procedure. Analytical uncertainties were generally <2%.  
Trace element compositions of amphibe and clinopyroxene were determined by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) at the Key Laboratory of Mineralogy and Metallogeny, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences (Guangzhou, Chian). The LA-ICP-MS system consists of a GeoLas 2005 laser (Applied Spectra, West Sacramento, CA, USA) coupled to an Agilent 7500a ICP-MS (Agilent Technologies, Santa Clara, CA, USA). A detailed description of the method is provided in [43]. Trace element concentrations were calibrated using multiple reference materials (BCR-2G, BHVO-2G, and BIR-1G) as external standards, with Ca for internal standardization [44]. The off-line selection and integration of background and analyte signals, time-drift corrections, and quantitative calibrations were undertaken using the software ICPMSDataCal 11 [43; 44]. The analytical precision for most elements was <3%.  
An Excel 202 spreadsheet was used to calculate the chemical formulas and crystallization conditions of amphibes.  
Figure 2: Field photographs of the Sanggeda rocks. (**a**) The intrusion is in fault contact with the andesite. (**b**) Gabbro intruded into the ophiolite, causing significant serpentinization and schistosity in the latter. (**c**) The contact boundary between fine-grained hornblende gabbro and porphyritic hornblende gabbro. (**d**) Hornblendite cumulate enclave within the fine-grained hornblende gabbro. (**e**) Fine-grained hornblende gabbro. (**f**) Porphritic hornblende gabbro. (**g**) Detailed view of the contact boundary in (**b**). (**h**) Gabbro vein intruding into the ultramafic rock after the felsic rock. Dashed lines indicate the lithologic boundary. The diameter of the coin is 20.5 mm.
## 2 Theoretical Foundation and Hypotheses Development
### Legal Factors
#### 2.3.3 Economic Freedom and Regulatory Quality  
Studies suggest that economic freedom and regulatory quality are important factors in analyzing cross-country differences in economic standing [52; 53]. An economic freedom index assesses the degree to which legally acquired assets are protected and individuals are free to engage in open markets. Regulatory quality reflects perceptions of the government's capacity to establish and enforce solid rules and regulations that allow and support private-sector development. The deployment of CBDCs, notably wholesale CBDCs, requires cooperation between the central banks of various nations, and the economic standing, regulatory quality, and freedom of the nation play a significant role [54]. Legal systems that rigorously enforce contracts, including government contracts, tend to have better-developed financial intermediaries [55]. Economically free governments protect legal agreements and private property rights. However, the enforcement of individual rights in these nations is accompanied by regulatory delays that can prevent the launch of a project. [PERSON] [56] investigated the role of economic freedom in the fintech industry and discovered that many cryptocurrency-related activities occur in nations with less stringent capital controls and restrictions. High economic freedom and regulatory quality require various formalities and administrative processes, including the protection of individual rights, such as the General Data Protection Regulation (GDPR), for countries that comply, which is a primary concern with blockchain technology. The literature has long discussed the dichotomy between distributed ledger technology and privacy concerns [57; 58]. Considering this, we propose the following hypotheses:  
**H6.**_Countries with low economic freedom are highly likely to issue and engage in the adoption of a central bank digital currency_.  
**H7.**_Countries with low regulatory quality are highly likely to issue central bank digital currencies_.