esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimentation and Performance Evaluation
### Hardware Co_Simulation Results Comparison  
In hardware co-simulation concept, the main treatment (processing module) is really implemented in the FPGA circuit and data acquisition and visualization are performed using Matlab. The system reads the image from MATLAB workspace; pass it to Xilinx Blockset where the different operations are performed and the result is sent over back to MATLAB workspace. The data used to evaluate the final system accuracy corresponds to the hardware co-simulation results which are really obtained from the FPGA circuit and which represent the final data that will be generated by the system.  
To implement the design on FPGA, ISE tool and core generator automatically generate a programming bit file using the System Generator blockset. A system block is created to point into the configuration file (bit) for the co_simulation. The generated co_simulation block (Figure 17) replaces  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Relative Measurements** & **Point 1 \(D\) = 40 cm** & **Point 2 \(D\) = 115 cm** & **Point 3 \(D\) = 415 cm** \\ \hline \multirow{3}{*}{\(\Delta x\) = 1 pixel} & \(\Delta r\) & 0.88 & 0.93 & 0.97 \\  & \(\Delta D\) & 0.81 & 0.96 & 2 \\  & \% & 1.02\% & 1.01\% & 1\% \\  & \(\Delta r\) & 8.83 & 9.28 & 10.73 \\ \cline{1-1}  & \(\Delta D\) & 10.38 & 12 & 27 \\ \cline{1-1}  & \% & 1.26\% & 1.10\% & 1.07\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example of error evaluation.  
Figure 16: Effect of spot center accuracy on distance measurements.  
the XSG model (Figure 8) and will be loaded on the FPGA device through the JTAG cable by closing the loop after connecting the board (Xilinx ML605).  
All along the robot movement, a stereo image is acquired and analyzed in order to generate 3D shape of the confined structure using the generated 3D world points. In order to verify if the behavior of the XSG model corresponds to the theoretical Matlab code, results of both models are compared noting that it could be relative errors according to the number of bits used. Figure 18 illustrates an example of square shape sparse reconstruction with both Matlab code (blue markers) and the proposed FPGA implemented system (red markers). An average error of about 0.46 cm is obtained between the software algorithm and the XSG architecture for the object of Figure 18.  
Figure 17: Hardware co_simulation bloc.  
Figure 18: Hw/Sw accuracy comparison: (**a**) Environment of test illustration; (**b**) 3D shapes reconstruction by simulation.
# Faster RCNN Target Detection Algorithm Integrating CBAM and FPN
## 4 Experimental Results and Analysis
### Analysis of Target Detection Experiment Results
#### 4.3.1 \(\text{AP}_{50}\) Detection Accuracy Comparison  
The experiment sets the initial learning rate to 0.005, the momentum parameter to 0.9, and the weight decay coefficient to 0.0005. \(\text{AP}_{50}\) refers to the detection accuracy when the IOU threshold is 0.5 [37; 38]. The batch size is 16 and the number of iterations is 20 k. The experimental results are shown in Table 4.
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 2 Locata Core Technologies
### TimeLoc  
Locata enables precise positioning by its high accuracy timing capability, which is referred to as TimeLoc technology. TimeLoc is a wireless synchronisation methodology that achieves time synchronisation to better than about 30 picoseconds among multiple LocataLites, without the need for expensive atomic clocks, external cables, or a master reference receiver [16]. [PERSON] et al. [17; 18; 24] and [PERSON] [25] provide a brief description of the TimeLoc procedure. Importantly, during the synchronisation procedure, the geometrical offset between two LocataLites must be precisely corrected for, which requires: (1) careful installation of LocataLites to avoid multipath; (2) precise survey of the coordinates of the antennas of each LocataLite; and, (3) accurate correction for the tropospheric delays. As shown in Figure 1, the master LocataLite can also be locked on to an external GNSS time, so that the LocataNet is seamlessly integrated with a GNSS constellation.
# First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields
## 3 Methodology
The use of four SAR dates to compute one mean VOD value was considered to decrease the noise in VOD computation (we assumed that VOD remains stable during 18 days using four S1 SAR images with a revisit time of 6 days over Europe). This assumption is made in many studies considering VOD retrievals from space-borne observations [28; 29]. For each SAR acquisition mode, each combination of two S1 images among the four S1 images (six combinations) was used to compute one VOD value (Equation (3)). This step can generate six possible VOD values (VOD\({}_{1}\),..., VOD\({}_{6}\)) for each SAR acquisition mode. Negative VOD values were eliminated, as well as VOD values when both \(\Delta\sigma_{\text{tot}}^{0}\) and \(\Delta\sigma_{\text{soil}}^{0}\) were lower than 0.5 dB. Indeed, a variation in \(\sigma_{\text{tot}}^{0}\) and \(\sigma_{\text{soil}}^{0}\) lower than 0.5 dB is assumed not to be significant and could be related to SAR radiometric noise. As a result, six VOD values from the four images in ascending mode (VOD\({}_{1\text{A}}\),..., VOD\({}_{6\text{A}}\)) and six VOD values from the four images in descending mode (VOD\({}_{1\text{D}}\),..., VOD\({}_{6\text{D}}\)) can be obtained at most for each plot. Finally, for each SAR acquisition mode, the mean of the six VOD values was calculated. It should be noted that VOD was computed using SAR images in VV and VH polarizations separately. In the following, VOD-VV\({}_{\text{A}}\) and VOD-VV\({}_{\text{D}}\) will denote VOD values computed from VV polarization of SAR data in ascending and descending modes, respectively. Similarly, VOD-VH\({}_{\text{A}}\) and VOD-V\({\rm{H}}_{\rm{D}}\) will denote VOD values computed from VH polarization of SAR data in ascending and descending modes, respectively. To assess the relevance of VOD estimates, the temporal evolution of VOD was analyzed in comparison to that of NDVI for the two crop growth cycles. Several studies have used vegetation indices derived from optical images to evaluate the relevance of VOD estimations over crops since optical indices are well-suited/efficient to monitor the crop development [9; 13]. For each polarization and each SAR overpass mode (Ascending and Descending), VOD values obtained over each crop plot were separately averaged for each non-irrigated crop type (barely, fallow, oat, rapeseed, and wheat) and the associated standard deviation (\(\sigma\)) is also calculated. For each crop type, the averaged value of VOD-V\({\rm{V}}_{\rm{A}}\), VOD-V\({\rm{V}}_{\rm{D}}\), VOD-V\({\rm{V}}_{\rm{A}}\), and VOD-V\({\rm{H}}_{\rm{D}}\) are denoted later as VOD-VV\({}_{\rm{A},\rm{av}}\), VOD-V\({\rm{V}}_{\rm{D},\rm{av}}\), VOD-V\({\rm{H}}_{\rm{A},\rm{av}}\), and VOD-V\({\rm{H}}_{\rm{D},\rm{av}}\), respectively. Similarly, for each crop type, NDVI values of plots were averaged for the study zone (\(\rm{NDVI}_{\rm{av}}\)). As a reminder, in this study VOD was computed only for non-irrigated crops (winter crops).
# An Image-Encryption Algorithm Based on Stage-Merging Bit Scrambling
## 2 Materials and Methods
### Encryption Process
#### 2.4.3 DNA Dynamic Coding and Operation  
1. The chaotic sequence \(L_{1}\) is converted into an integer sequence \(I_{1}\) with a value of 0-255 by Formula (12). \(I_{1}\) is used as a mask sequence. \[I_{1}(i)=\text{mod}(\text{floor}(L_{1}(i)\times 10^{14}),255)\] (12) where \(i=1,2,\ldots,MN\).
2. Calculate the DNA encoding rules \(rule_{1}\) and \(rule_{2}\), the DNA operation rule \(rule_{3}\), and the DNA decoding rules \(rule_{4}\); the calculation methods are as follows: \[rule_{1}(i)=\text{mod}(\text{floor}(L_{2}(i)\times x_{1}\times 10^{14}),8)+1\] (13) \[rule_{2}(i)=\text{mod}(\text{floor}(L_{2}(i)\times a\times 10^{14}),8)+1\] (14) \[rule_{3}(i)=\text{mod}(\text{floor}(L_{2}(i)\times{x_{1}}^{ \prime}\times 10^{14}),3)+1\] (15) \[rule_{4}(i)=\text{mod}(\text{floor}(L_{2}(i)\times{y_{1}}^{ \prime}\times 10^{14}),8)+1\] (16) where \(i=1,2,\ldots,MN\).
3. Traverse the sequences \(P\) and \(I_{1}\), and select the coding rules of \(rule_{1}(i)\) and \(rule_{2}(i)\) to encode the DNA for \(P(i)\) and \(I_{1}(i)\) respectively. The coding sequences \(P_{2}\) and \(I_{2}\) are obtained. Perform DNA operations on sequences \(P_{2}\) and \(I_{2}\) to obtain the coding sequence \(Q_{1}\). If \(rule_{3}(i)=1\), perform an addition operation. If \(rule_{3}(i)=2\), perform a subtraction operation. Otherwise, perform an XOR operation. Traverse the coding sequence \(Q_{1}\), take every four bases as a group, and decode according to the decoding rule of \(rule_{4}(i)\) to obtain the sequence.
4. The sequence \(Q_{2}\) is recombined into an image \(Q\) of size \(M\times N\) in the order of columns and rows. \(Q\) is the final cipher image.
###### Abstract  
A novel in situ stress monitoring method, based on rheological stress recovery (RSR) theory, was proposed to monitor the stress of rock mass in deep underground engineering. The RSR theory indicates that the tiny hole in the rock can close gradually after it was drilled due to the rheology characteristic, during which process the stress that existed in the rock can be monitored in real-time. Then, a three-dimensional stress monitoring sensor, based on the vibrating wire technique, was developed for in field measurement. Furthermore, the in-field monitoring procedures for the proposed technique are introduced, including hole drilling, sensor installation, grouting, and data acquisition. Finally, two in situ tests were carried out on deep roadways at the Pingdingshan (PDS) No. 1 and No. 11 coal mines to verify the feasibility and reliability of the proposed technique. The relationship between the recovery stress and the time for the six sensor faces are discussed and the final stable values are calculated. The in situ stress components of rock masses under geodetic coordinates were calculated via the coordinate transformation equation and the results are consistent with the in situ stress data by different methods, which verified the effectiveness of the proposed method.  
In situ stress; rheological stress recovery; three-dimensional sensor; monitoring; underground engineering
# A Searchable Encryption with Forward/Backward Security and Constant Storage
## 1 Introduction
[PERSON] et al. [9; 21] successively proposed a searchable encryption scheme based on a public key cryptosystem, which also satisfies forward and backward security. The pseudo-random permutation function is used as the core of the algorithm. The computational cost of the algorithm increases rapidly with the increase in keywords, and the existence of bilinear pairs in the algorithm also affects the computational efficiency, to some extent. However, these algorithms use a pseudo-random substitution function as the core of the algorithm, and the computational overhead of the algorithm grows faster with the growth in the number of keywords. In most of the current backward and forward secure DSE schemes, the user needs to maintain a local inverted index that stores the current state of each keyword to generate a search trapdoor. The state needs to be updated synchronously when keyword/document pair operations are performed to add or remove keywords. In the existing scheme, with the increase in keywords, the local storage increases sharply. This shows that it is necessary to design lightweight searchable encryption algorithms with low storage overhead. In summary, most of the current SEs possess the following problems: First, some proposed schemes achieve forward and backward security through pseudorandom functions [22], an inverted index, and other technologies, but the above schemes will create a serious server load under a large number of encryption and update operations. This is limited in practical application. Second, the client's state information may generate leakage. That is, DSE is still needed to improve the efficiency of the algorithm based on further reducing the possibility of leakage. Our main contributions are summarized as follows. We proposed a lightweight public-key searchable encryption scheme, which eliminates the bilinear pairing operation in the algorithm and improves the efficiency. Our scheme adopts a double-layer structure. The upper layer is responsible for the trapdoor of search keywords, which generates fields by an iterative hash function, and can effectively reduce the storage cost. The lower layer is responsible for storing the document index to optimize the computational cost and reduce the reconstruction overhead. Users can generate search trapdoors that satisfy forward and backward security, avoiding maintaining a state list for each keyword. We compare our scheme with the existing partially searchable encryption schemes in many aspects. The experimental results show that our scheme offers considerable advantages.
# Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
## 3 Materials and Methods
### Problem Description  
The path-planning problem investigated in this paper can be stated as follows: in continuous and uncertain environments, the problem is to plan rational paths for tracked robots from a start position to a goal position.  
In this paper, we used the Cartesian coordinate system. The schematic diagram of the tracked robot is shown in Figure 1. The position state of the tracked robot in this paper is defined as \(s=(x_{r},y_{r},\theta)\) by the global coordinate related to the map, where \(x_{r}\) and \(y_{r}\) are the tracked robot's abscissa and vertical coordinates, respectively, and \(\theta\) is the angle between the forward orientation of the tracked robot and abscissa axis. We equipped six ultrasonic sensors at the front of the tracked robot to perceive the external environment. The detection angle of each ultrasonic sensor was 30 degrees. The max detection distance of each sensor was \(D_{max}\). The tracked robot was driven by the left and right tracks. The kinematic equations of the tracked robot were  
\[\left(\begin{array}{c}\dot{x}\\ \dot{y}\\ \dot{\theta}\end{array}\right)=R\left[\begin{array}{cc}\frac{cos\theta}{2}& \frac{cos\theta}{2}\\ \frac{1}{I_{B}}&\frac{1}{I_{B}}\end{array}\right]\left(\begin{array}{c} \omega_{I}\\ \omega_{r}\end{array}\right), \tag{1}\]  
where \(R\) is the radius of the driving wheels in the tracks, \(\omega_{I}\) and \(\omega_{r}\) are the angular velocity of left tracks and right tracks (rad\(\cdot s^{-1}\)), respectively, and \(\mathrm{I}_{B}\) is the distance between left tracks and right tracks.
# A Robust \(H_{\infty}\) Application for Motor-Link Control Systems of Industrial Manipulators
## 2 \(H_{\infty}\) Control Approach Basics
### The Closed-Loop Control Architecture
It can be expressed as a third-order transfer function, relating the motor torque and the motor velocity as:  
\[G_{pm}(s)=\frac{s^{2}+s\frac{B^{\parallel}+\beta_{ml}}{J_{m}(s^{3}+s^{2}( \frac{\beta_{ml}}{J_{m}K_{r}^{2}}+\frac{B_{m}}{J_{m}}+\frac{B_{l}}{J}+\frac{B_ {m}}{J})+s(\frac{B_{l}\beta_{ml}}{J_{m}K_{r}^{2}}+\frac{K}{J_{m}K_{r}^{2}}+ \frac{B_{l}B_{m}}{J_{m}}+\frac{K}{J})+(\frac{B_{l}K}{J_{m}K_{r}^{2}}+\frac{B_ {m}K}{J_{m}}))}}{\left(\frac{B_{l}B_{m}}{K_{r}^{2}}+\frac{B_{l}B_{m}}{J_{m}}+ \frac{K}{J}+(\frac{B_{l}K}{J_{m}K_{r}^{2}}+\frac{B_{m}K}{J_{m}})\right)} \tag{2}\]  
where:  
* \(J_{m}\) is the motor inertia moment;
* \(J_{l}\) is the link inertia moment;
* \(K_{r}\) is the gear transmission;
* \(K\) is the stiffness;
* \(\beta_{ml}\) is the damping factor;
* \(B_{m}\) is the motor viscous friction;
* \(B_{l}\) is the link viscous friction. The position control loop derives from the velocity one. In this case, the plant is given by the closed-loop velocity function with the addition of the integral term that generates position from velocity. In Figure 2, \(G_{cv}(s)\) and \(G_{cp}(s)\) are, respectively, the \(H_{\infty}\) velocity and position controller transfer functions, which will be developed in the next sections. \(G_{a}(s)\) describes the actuator: it converts the current into the motor torque, representing the velocity plant input;  
Figure 3: General control software architecture of a COMAU industrial robot. in our case, it simply corresponds to a constant parameter \(K_{t}\). \(G_{pl}(s)\) is the link model transfer function relating the motor position to the link one, as:  
\[G_{pl}(s)=\frac{s\frac{\delta_{ml}}{K_{r}}+\frac{K}{K_{r}}}{s^{2}I_{t}+s(B_{l}+ \beta_{ml})+K} \tag{3}\]  
where the physical parameters \(I_{t}\), \(\beta_{ml}\), \(K_{r}\), \(K\), \(B_{l}\) are defined as for the plant model (2).
# Impacts of Diversity Technique Application in Order to Reduce the Fading in IEEE 802.15.4 Networks
## 3 BER in the Case of an SC Combiner  
The SC receiver (combiner) is used to reduce the impact of fading on the system performance. The signal at the output of the SC receiver is equal to the maximum of the signal at its inputs. The SC receiver can have two, three, or more inputs. The SC receiver is simpler for practical implementation than the MRC and EGC receivers and therefore has a large application [12; 20]. The SC combiner with two inputs can be represented as in Figure 1, where \(\gamma_{1}\) and \(\gamma_{2}\) are SNRs of signals at the input and \(\gamma\) the SNR of the signal at the output.  
The PDF of the signal at the output of the SC combiner can be represented as [21]:  
\[f_{SC}(\gamma)=f_{\gamma 1}(\gamma)F_{\gamma 2}(\gamma)+f_{\gamma 2}(\gamma)F_{ \gamma 1}(\gamma)=2f(\gamma)F(\gamma), \tag{7}\]  
where \(f(\gamma)\) is given by (5) and (6), and the cumulative distribution function (CDF) \(F(\gamma)\) by:  
\[F(\gamma)=\int\limits_{0}^{\gamma}f(t)dt, \tag{8}\]  
where \(f(t)\) is given by (5) and (6) for the Rayleigh and Nakagami-\(m\) distribution, respectively.
# Impedance-Based Stability Analysis of Grid-Connected Inverters under the Unbalanced Grid Condition
###### Abstract  
As a common interface circuit for renewable energy integrated into the power grid, the inverter is prone to work under a three-phase unbalanced weak grid. In this paper, the instability of grid-connected inverters under the unbalanced grid condition is investigated. First, a dual second-order generalized integrator phase-locked loop (DSOGI-PLL)-based inverter under balanced and unbalanced conditions is modeled. A fourth-order impedance model is established to describe its impedance characteristics under the unbalanced grid condition. To analyze this multi-input multi-output system, a simplified stability analysis method based on the generalized Nyquist stability criterion and matrix theory is proposed. Then, the influences of circuit and control parameters on the stability of the grid-connected inverter system under the unbalanced grid condition are investigated. Finally, the accuracy of the derived frequency-coupled impedance model is verified via simulations, and the effectiveness of the proposed simplified stability analysis method on the system stability analysis is verified via both simulations and hardware experiments.  
frequency coupling; inverter; stability analysis; unbalanced grid condition +
Footnote †: journal: Applied sciences
# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 4 Discussion and Analysis
### NMGs' Control  
Before delving into control aspects, it is essential to review communication, since the communication infrastructure plays a crucial role in monitoring and controlling NMGs. This infrastructure facilitates the exchange of information not only among microgrids but also with the central energy management system and end users. However, communication limitations, including bandwidth constraints, time delays, traffic congestion, and packet losses, can significantly impact overall system responses, especially in scenarios focusing on resilience, where response time and cooperation among network components are crucial. Additionally, NMGs are susceptible to cyberattacks. The cost of implementing advanced communication in NMGs poses another challenge. Therefore, when designing state-of-the-art control for NMGs, it is imperative to consider these challenges in the problem.  
The control of NMGs involves overseeing and managing network functions with the goals of energy trading, optimizing operational costs, maximizing power stability, ensuring reliability, enhancing user comfort, and achieving a resilience index. The assessment of networked microgrid control capabilities involves a multifaceted examination, encompassing perspectives such as control architecture, control modes, and control schemes. The control architecture and control modes provide insights into the structure of NMGs' control, while the control scheme outlines the strategy for overseeing interconnection and interchange among MGs.  
Centralized control architectures, while straightforward, suffer from low reliability due to single-point communication. On the other hand, decentralized controls, although reliable, become complex in large-scale systems. Distributed architectures, combining the advantages of both centralized and decentralized approaches, offer a promising solution. Despite potential privacy concerns in information exchange, optimizing information sharing can mitigate risks. In this control architecture, the P2P control mode is utilized to accelerate communication and cooperation among MGs.  
While hierarchical or droop control schemes are straightforward, they may struggle with scenarios involving changing control modes of DERs, dynamic microgrid structures, plug-and-play integration of neighboring microgrids, and stringent data privacy requirements, especially in islanded NMGs without slack buses. Addressing these challenges necessitates the use of optimization methods, with MIP being a commonly utilized approach. However, challenges persist, including the accuracy of uncertain parameters, time consumption, reliance on central controllers, and handling vast data in large power systems. To overcome these challenges, AI methods, particularly DQN [193; 194; 195; 131], present a promising solution. These methods are capable of managing extensive data, forecasting, learning, and reinforcing. However, it is important to note that these emerging methods are not yet fully mature in the context of power systems.
## Abstract  
This paper investigates the impact of environmental conditions on the possible output power of photovoltaic (PV) installations on top of hybrid electric vehicles (HEVs) and battery-powered electric vehicles (BEVs). First, we discuss the characteristics and behavior of PV cells in order to provide an understanding of the energy source that we aim to integrate into vehicles. Second, we elaborate on how PV cells and panels can be simulated to estimate the potential extension of the electrical driving range (ERE) of BEVs and HEVs. In particular, we concentrate on the impact of the vehicle's curved roof surface on the possible output of the PV installation. In this research, we present considerations for vehicles in both parking and driving conditions. More precisely, we demonstrate how the frequently changing environmental conditions that occur while driving represent significant challenges to the control of the operating voltage of PV cells. As the area for deploying PV cells on top of an electric vehicle is limited, attention needs to be paid to how to optimize and maximize the degree of efficiency of PV-powered electric vehicles.  
Keywords:battery-powered electric vehicle (BEV); charging; data acquisition; environmental data; hybrid electric vehicle (HEV); measurement; photovoltaic (PV) cell; photovoltaic (PV) energy; simulation +
Footnote †: journal: Applied Sciences