esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Eight-State Continuous Variable Quantum Key Distribution by Applying Photon Subtraction
## 3 Security Analysis
### Secret Key Rate  
In this section, we calculate the secure key rate of the modified protocol. In this case, we performed heterodyne detection and homodyne detection with [PERSON] and [PERSON], respectively. While [PERSON] only performs a collective attack to get the best results, and the information obtained is limited to the Holevo range \(S(E:B)\). It should be noted that the state \(\rho_{AB_{1}}^{\tilde{T}_{1}}\) is no longer a Gaussian state after the photon subtraction operation is performed. Therefore, here, we cannot directly apply the traditional Gaussian CVQKD results to calculate the secret key rate. We derive the secure key rate based on the  
Figure 3: When \(d=110\) km, the relationship between \(V_{a}\) and secret key rate with different values of \(\epsilon\).  
Figure 2: When \(\epsilon=0.01\), the relationship between \(V_{a}\) and secret key rate with different values of \(d\).  
extremum of the Gaussian quantum states and the corresponding object with the same covariance matrix  
\[K=P_{(k)}^{\widehat{\Pi_{1}}}[\beta I(A:B)-S(E:B)], \tag{15}\]  
where \(I(A:B)\) and \(S(E:B)\) correspond to the mutual information between [PERSON] and Eve-Bob, and \(\beta\) is the efficiency of reverse reconciliation. When the state emitted by [PERSON] passes through the channel controlled by [PERSON], the covariance matrix \(\gamma_{AB_{3}}\) is expressed as  
\[\gamma_{AB_{3}}=\begin{bmatrix}aL_{2}&c\sigma_{z}\\ c\sigma_{z}&bL_{2}\end{bmatrix}, \tag{16}\]  
where \(a=X^{\prime}\), \(b=\eta(Y^{\prime}+X_{line})\), and \(c=\sqrt{\eta}Z^{\prime}_{8}\).  
Then, \(I(A:B)\) is derived based on [PERSON]'s measurement, which is calculated by  
\[I(A:B)= \frac{1}{2}log_{2}\frac{V_{A}}{V_{A|B}}, \tag{17}\]  
with \(V_{A}=(a+1)/2\) and \(V_{B}=b\) are the respective variances, and \(V_{A|B}\) is the conditional variance, namely  
\[V_{A|B}=V_{A}-\frac{\eta Z^{\prime}_{8}}{2V_{B}}=a-\frac{c^{2}}{2b}. \tag{18}\]  
[PERSON] can purify the system to interact with [PERSON] after [PERSON] performs homodyne detection. Based on this, the Holevo bound is given by  
\[\begin{split} S(E:B)=& S(E)-S(E|B)\\ =& G[(\lambda_{1}-1)/2]+G[(\lambda_{2}-1)/2]-G[( \lambda_{3}-1)/2],\end{split} \tag{19}\]  
where  
\[G(x)=(x+1)log_{2}(x+1)-xlog_{2}x, \tag{20}\]  
and the eigenvalues, \(\lambda_{1,2,3}\), in the equation can be calculated by  
\[\begin{split}\lambda_{1,2}^{2}&=\frac{1}{2}(A\pm \sqrt{A^{2}-4B}),\\ \lambda_{3}^{2}&=\frac{a^{2}-ac^{2}}{b},\end{split} \tag{21}\]  
with \(A=a^{2}+b^{2}-2c^{2}\) and \(B=ab-c^{2}\).
## 3 Materials and Methods  
The work made on Madonna del Sasso site was divided into four main packages:  
1. The production of an operative monography (OM). In this project, we decided to adopt the OM approach [23] for the acquisition and organization of all available information (e.g., monitoring data, thematic studies, publications, and maps). The first step of the project has been the collection, analysis, and organization of the available material. This approach is particularly useful when the analyzed site has a long history of studies, in situ exploration, and monitoring that could have produced a large amount of data that is not always well organized. The OM has been very useful for the definition of what has been already defined by the previous study. Thus, we used the collected literature data for the identification of better positions for GNSS installation.
2. The re-analysis of ARPA-Piemonte topographic network data. The topographic dataset acquired by ARPA Piemonte is available from 2006, and it represents the most important source of information for the definition of displacement trends. In the re-analysis, we removed some outlier measurements, and we recalculate linear regression of displacement time series (horizontal and 3D). This helped the comparison with other monitoring instruments like the PS InSAR and the new GNSS data.
3. Design, implementation, and test of GNSS low-cost sensors. The GNSS system has been developed and tested in the laboratory. After these first phases, the system has been installed in the unstable area, and it becomes the first in place continuous topographic monitoring solution adopted for the study of the evolution of the Madonna del Sasso cliff. Two low-cost GNSS sensors were installed on-site to measure the position of two different representative points continuously. The adopted monitoring network is equipped with hardware and software solutions that guarantee not only the in-situ acquisition but also the raw data transmission and processing.
4. Definition of a semi-automatic early warning (EW) procedure. The final step of the project has been the development of a GNSS results analysis aimed to implement an early warning (EW) procedure. The EW procedure is a step-by-step methodology that has the aim to firstly define critical alert thresholds indicative of a possible increase of blocks instability. After validation, the alert could be used to issue a bulletin for risk communications [29].
# Influence of Construction Joint and Bridge Geometry on Integral Abument Bridges
## 2 Parameters and Description  
The four design parameters listed in Table 1 were selected based on previous research [13; 15], IAB monitoring results [5; 26; 27], and transportation agency input [35]. In addition to the four design parameters, four uncontrollable environmental parameters including construction timeline, thermal expansion coefficient, backfill stiffness, and soil-pile interaction were considered in this study.  
Each bridge girder for varying bridge lengths and spans was designed in accordance with AASHTO LRFD Bridge Design Specifications (2014). The prestressed concrete (PSC) girder superstructure cross-section selected for this study consists of four or five girders with concrete compressive strength \(f_{\zeta}\)\({}^{\prime}\) = 55.2 MPa and a cast-in-place concrete deck of \(f_{\zeta}\)\({}^{\prime}\) = 27.6 MPa, as shown in Figure 1. The steel girder bridges have four or five steel plate girders of yield strength (\(F_{y}\)) = 345 MPa with a cross-section similar to that presented in Figure 1, and a detailed cross section for each bridge is presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Variable** \\ \hline Girder Material & Prestressed Concrete (PSC), Steel \\  & & 30.5 m \\ Bridge Span Lengths, \(L\) (m) & & 30.5 + 30.5 = 61.0 \\  & & 40.5 + 40.8 + 40.5 = 121.9 \\  & & 45.7 + 45.7 + 45.7 + 45.7 = 182.9 \\ Backfill Height, \(H\) (m) & & 3.1, 4.6, 6.1 \\  & & Min: Hinge \\ Construction Joint Stiffness & Intermediate: Rebars \#16@229 mm (PA standard) \\  & & Max: Rigid \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primary design parameters and ranges.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bridge Length** & **No. of Girders** & **PS Girder** & **Steel Plate Girder** \\
**(m)** & **No. of Girders** & **(D 1)** & **(Flange Width \(\times\) Flange Thickness** \(\times\) Web Depth \(\times\) Web Thickness)2 ** \\ \hline
30.5 & 4 & BT72 & 457.2 \(\times\) 31.7 \(\times\) 1168.4 \(\times\) 15.9 \\  & & (1372) & (+): 355.6 \(\times\) 31.7 \(\times\) 965.2 \(\times\) 9.5 \\
61.0 & 4 & BT72 & (+): 355.6 \(\times\) 38.1 \(\times\) 965.2 \(\times\) 9.5 \\  & & (1372) & (\(-\)): 355.6 \(\times\) 38.1 \(\times\) 965.2 \(\times\) 9.5 \\  & & BT81 & (+): 508 \(\times\) 38.1 \(\times\) 1371.6 \(\times\) 12.7 \\
121.9 & 5 & (1600) & (\(-\)): 508 \(\times\) 50.8 \(\times\) 1371.6 \(\times\) 12.7 \\
182.9 & 5 & BT90 & (+): 508 \(\times\) 31.7 \(\times\) 1676.4 \(\times\) 15.9 \\  & & (1829) & (\(-\)): 508 \(\times\) 44.4 \(\times\) 1676.4 \(\times\) 15.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Bridge girder sections.
# A Novel Cross-Latch Shift Register Scheme for Low Power Applications
## 2 The Proposed Cross-Latch Shift Register (CLSR) Design
Table 1 shows a comparison of the total number of transistors and the layout areas of the 2-bit shifted flip-flop designs, including the transistors used to generate differential clock signals and pulsed clock signals [27]. Figure 2: Previous master and slave (MS) latches based flip-flop (FF) designs. (**a**) Transmission gate flip-flop (TGFF). (**b**) Adaptive coupling flip-flop (ACFF) [28]. (**c**) True single-phase clock flip-flop (TSPCFF) [29]. (**d**) Charge sensing flip-flop (CSFF) [30]. Figure 1: Data transmission of a conventional shift register. (**a**) When CLK = 0. (**b**)When CLK = 1.
## 2 Study Area and Datasets
### Datasets  
Since the Gaofen-3 satellite was not designed for InSAR applications, few SAR images could be used for time-series processing. The dataset includes 8 scenes of Gaofen-3 SAR images acquired from fine strip I (FSI) mode from June 2020 to March 2021 [15] and 111 scenes of Sentinel-1 SAR images acquired from strip mode (SM) from June 2017 to April 2021. In total, 17 scenes of Sentinel-1 SAR images from June 2020 to April 2021 were applied to assess the accuracy of Gaofen-3 SAR images, and 94 scenes of Sentinel-1 SAR images from January 2018 to August 2020 were used to reveal the subsidence trend over the study area. The coverage of the two datasets is shown in Figure 1. Both Gaofen-3 and Sentinel-1 operate C-band, with wavelengths of ~5.5 cm. The details of two SAR data are shown in Table 2. A 30 m Copernicus DEM was used to remove the topographic phase from the generated interferograms.
## 1 Introduction
[28] proposed the vibration reduction measures of installing double dampers (i.e., HDR damper and viscous damper) on the cable and carried out research on the system. Recently, [PERSON] et al. [29] established a cable-two-HDR-damper system model by considering the cable's bending stiffness, and they analyzed the combined influence of two HDR dampers on the cable's first modal damping ratio. Compared with the damper installed on the cable end with support, tuned mass dampers (TMDs) or similar unsupported absorbers have better application prospects. [PERSON] and [PERSON] [30] investigated a system of a taut cable with two dampers, i.e., a viscous damper and a TMD, and they found that using two dampers on the cable can improve the modal damping of the cable system, compared with using only one of the dampers. Existing studies show that, for long cables, a single cable-end damper may no longer be able to provide enough modal damping for cable vibration control. In addition, there are also vibration-prone modes with lower damping, such as the first mode due to the influence of sag [31]. Therefore, a cable with more dampers at different positions is a feasible solution to improve the cable's multimode damping. At the same time, the tuned mass-high-damping rubber damper (TM-HDR-D), as a TMD-type device, provides a great effect in cable vibration reduction. The damping of this type of TMD device is provided by high-damping rubber; the damper does not have problems such as oil leakage, making it especially suitable for practical engineering. To study the combined control effect of the two dampers on the vibration of the cable, the model of a cable with an HDR damper and a TM-HDR-D system is first established in this paper. Then, based on typical systems--i.e., a cable with only an HDR damper, a cable with only a TM-HDR-D, and a cable with two dampers--the cable vibration reduction analysis is carried out. Based on an actual cable of a real-world cable-stayed bridge, the parameter design process of the HDR damper and TM-HDR-D is presented, and the practical feasibility of the combined vibration reduction measures of the two dampers is verified.
# Enhanced Pet Behavior Prediction via S2 GAN-Based Heterogeneous Data Synthesis
## 3 Pet Behavior Prediction via S2 GAN-based Data Synthesis  
Herein, we introduce a new method for pet behavior prediction via data synthesis based on the S2 GAN model. We aim to achieve precise behavior prediction in real-life environments, focusing on the typical living conditions of pets in Korea and using CCTV and wearable devices commonly used for observing behavior. We simulated an indoor domestic space where pets primarily reside and used one CCTV and one wearable device, as shown in Figure 1. To address the issue of differing data collection ranges between these devices, S2 GAN-based data synthesis was performed to compensate for any potential data loss, thereby ensuring high performance.  
The behavior prediction process is divided into four main stages, as shown in Figure 2. The first stage is data collection, where video data from CCTV and nine-axis-sensor (accelerometer, gyroscope, and magnetometer) data from wearable devices are gathered to capture the behavior of pets. In the second stage, namely data preprocessing, the collected data are processed to transform it into a format suitable for model training. This involves extracting the joints of pets from the video data and normalizing the sensor data to create sequences. The third stage is data synthesis, where, in cases of video data loss due to sensor data being collected outside the CCTV's range, S2 GAN-based data synthesis is performed to create fake skeleton feature sequences. In the final stage, existing data and synthesized data are combined to perform behavior prediction using a CNN-LSTM-based network. This model predicts the following nine behaviors: Standing on  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \multirow{2}{*}{**Research**} & \multicolumn{2}{c}{**Image Data**} & \multicolumn{2}{c}{**Sensor Data**} & \multirow{2}{*}{**Algorithms**} & \multirow{2}{*}{**Number of Behaviors**} \\ \cline{2-2} \cline{6-7}  & \multirow{2}{*}{**RGB**} & **RGB + Skeleton** & & & & \multicolumn{1}{p{56.9 pt}}{} & \\ \hline [PERSON] et al. (2022) [2] & & & O & & LSTM & 8 \\ \hline [PERSON] et al. (2022) [26] & & & O & O & CNN & 10 \\ \hline [PERSON] et al. (2023) [15] & & & O & O & O & TNGAN, CNN–LSTM & 9 \\ \hline [PERSON] et al. (2021) [25] & O & & O & O & CNN, LSTM & 11 \\ \hline [PERSON] et al. (2021) [22] & O & & & & & Yolov3 & 6 \\ \hline [PERSON] et al. (2022) [27] & & O & & & & Videopose3D & 1 \\ \hline [PERSON] et al. (2021) [28] & & O & & & & TRT-Net & 7 \\ \hline [PERSON] et al. (2022) [9] & O & & O & O & & CNN–LSTM & 7 \\ \hline Proposed Method & O & O & O & O & O & GAN, CNN–LSTM & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with related studies.  
Figure 1: Simulation research environment: Indoor space primarily inhabited by pets.
## 1 Introduction
* Optimization of the TTT and HOM settings involves applying the FLC rules based on previous expertise to determine their optimal values. This expertise includes an iterative process of applying different TTT and HOM values with varying ranges of input parameters to minimize RLF and HOPP. Specifically, different UE speed categories were considered for TTT optimization. It is crucial to set TTT and HOM in a manner that minimizes both RLF and HOPP levels simultaneously. Long TTT values may reduce HOPP occurrence but can lead to RLF due to serving signal deterioration and HO delay. Similarly, high HOM values can have the same effect. Conversely, a short TTT and low HOM may reduce the RLF but increase the overhead signaling and HOPP probabilities. This approach balances the mitigation of RLF and HOPP issues while ensuring the overall quality of service (QoS). * We present an algorithm that facilitates efficient HO decision-making in ultra-densely deployed SC HetNets, resulting in optimized KPIs compared to existing methods in the literature, with a focus on simultaneously mitigating RLF and HOPP levels. * Significantly, the UE speed thresholds considered in this study surpass those documented in prior literature. The carefully selected UE speeds are reflective of real-life scenarios, thus enhancing the practical applicability of this research. The remaining sections of this paper are organized as follows. Section 2 provides a summary of the relevant literature concerning the adjustment of HCPs, specifically focusing on TTT and HOM. Section 3 defines the system model used in this study, provides an overview of FLC, and explains the proposed algorithm. Section 4 lists the simulation parameters and presents a thorough discussion of the KPI results. Finally, Section 5 serves as the conclusion of the paper.
# DCSPose: A Dual-Channel Siamese Framework for Unseen Textures Object Pose Estimation
## 2 Related Work
### Template-Based Method  
Many deep-learning-based methods rely on large, annotated 3D model datasets to improve the accuracy and speed of object pose estimation. However, datasets with pose annotations are relatively scarce and have limited sample sizes. Annotating the pose parameters of objects is more challenging than annotating object categories, particularly in non-standard environmental conditions. This scarcity of annotated pose data makes it difficult to train high-quality pose estimators and significantly hampers the application of data-driven deep learning methods to new objects and scenarios.  
Indeed, when dealing with unknown shapes and types of objects and wishing to avoid retraining neural network models, template matching is a highly intuitive solution. Traditional template matching often requires manually designed matching features and may exhibit limited robustness against variations in background or lighting conditions. Therefore, combining deep learning feature extraction with the template matching concept is a logical and empirically proven approach.  
Deep learning methods based on the template matching concept have substantial potential in addressing the problem of pose estimation for unknown objects. These methods typically involve three key processes: template set creation, feature extraction and mapping, and feature matching.  
**Templates Creation.** To handle objects from different viewpoints and poses, a collection of templates needs to be created. These templates can be images representing the object from various poses. Each template in the set represents a possible pose of the object.  
**Feature Extraction and Mapping.** Deep Neural Networks are used to extract features from input images. These features capture essential information from the images, such as object edges and textures. To further compress and encode the features effectively, they can be mapped to a feature space where they maintain good discriminability. Feature mapping projects the extracted image features into the feature space of the template set.  
**Feature Matching.** Based on the results of feature mapping, the most similar template to the input image can be selected. This selection process often involves comparing feature similarity scores and identifying the template with the highest score. Once the most similar template is found, its pose information can be used to estimate the object's pose in the input image.  
This approach effectively combines the advantages of deep learning feature extraction and template matching, making it a powerful method for object pose estimation, especially in scenarios involving novel objects and diverse viewpoints. The similarity-based template matching process provides a reliable way to estimate object poses even when dealing with challenging conditions like variations in lighting, occlusions, and textureless objects.
# Clustering-Based Channel Allocation Method for Mitigating Inter-WBAN Interference
## 2 Related Works
### Research Gap  
In Table 1, we compare the channel allocation methods in a qualitative manner. We observe in the table that we still need an inter-WBAN coexistence strategy that has the following properties. The inter-WBAN coexistence method should be able to determine channels quicklyso that the total data rate of the coexisting WBANs are maximized. In addition, it should be adaptive to topology changes even when the number of coexisting WBANs is larger than the number of available channels. To increase the channel decision time, we take a centralized approach where a server collects necessary information from WBANs and determines the channels for them without iteration. To increase the adaptability, the parameters used in a channel allocation method need to be self-configured according to the topology changes. To tackle the issue, we adopt the modularity-based graph partitioning approach. Since an interference relationship graph formed by the coexisting WBANs is partitioned in a way that the modularity of the graph is maximized, the number of clusters is determined automatically regardless of the locations and the number of the WBANs in an area. In addition, we devise an adaptive module that can quicklyadjust to the changes in the topology of an interference relationship graph.
## 5 Case Studies
The summarization of statistical simulation results like power, voltage and current of the PV module under other partial shading conditions is presented in Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Other Different Shading** & **Parameter** & **ChoA** & **GWO** & **CSO** \\ \hline \multirow{5}{*}{\(G_{1}=\) [1000, 900, 800, 700]} & Maximum power @GMPP(W) & 525.13 & 525.13 & 525.13 \\ \cline{2-5}  & Output voltage(V) & 115.23 & 115.47 & 117.48 \\ \cline{2-5}  & Output current (A) & 4.54 & 4.52 & 4.412 \\ \cline{2-5}  & Output power (W) & 523.14 & 521.92 & 518.357 \\ \cline{2-5}  & Conversion efficiency (\%) & 99.62 & 99.38 & 98.71 \\ \hline \multirow{5}{*}{\(G_{2}=\) [900, 550, 100, 600]} & Maximum power @GMPP(W) & 336.61 & 336.61 & 336.61 \\ \cline{2-5}  & Output voltage(V) & 84.57 & 83.4V & 82.46 \\ \cline{2-5}  & Output current (A) & 3.9489 & 3.86A & 3.849 \\ \cline{2-5}  & Output power (W) & 333.95 & 321.92 & 317.38 \\ \cline{2-5}  & Conversion efficiency (\%) & 99.21 & 95.63 & 94.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summarization of statistical simulation results like power, voltage, and current of PV module. Figure 13: Comparison of settling time for the CSO, GWO and ChoA algorithms.
## 2 Feasibility Analysis and Modeling of Energy Storage in Transmission Network Expansion Planning
### Model Solving and Linearization
#### 3.4.2 Model Solving Methods  
In this paper, the proposed multi-stage collaborative planning model for transmission networks and energy storage is solved using the CPLEX 12.8.0 solver, called through the YALMIP R20230622 toolbox on the MATLAB 2023a platform. Equations (9)-(13) represent the objective functions of the model, and Equations (14)-(32) define the constraints for solving the model. Among them, \(x_{line,ij,Y}^{p}\) and \(x_{ess,i,Y}\) are integer variables that determine the transmission network expansion plans and energy storage locations at each planning stage. Besides, \(P_{ij,t,s,Y}^{o}\), \(P_{ij,t,s,Y}^{p}\), \(P_{gen,t,s,Y}^{p}\), \(P_{gen,t,s,Y}\), \(P_{\text{wind},j,t,s}\), \(P_{solar,i,t,s,Y}\), \(P_{c,j,t,s,Y}\), \(P_{d,i,t,s,Y}\), \(\theta_{i,t,s,Y}\), \(P_{ess,i,Y}\) and \(E_{ess,i,Y}\) are continuous variables, ensuring the system operates normally under various constraints at each stage, \(P_{ess,i,Y}\) and \(E_{ess,i,Y}\) specifically, determine the power and capacity configurations of the energy storage at each stage.