esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 The Subjective Evaluation Experiment
### Experimental Conditions for the Subjective Evaluation  
In the subjective evaluation, the experimental equipment and conditions were the same as those shown in Tables 1-3.  
Figure 7 illustrates the experimental arrangement for the real sound source (loudspeaker), and Figure 8 shows the experimental arrangement for the conventional and proposed methods. Figure 9 presents the experimental scene for the subjective evaluation.  
Figure 6: DRRs for the real sound source, the conventional method, and the proposed method. (**a**) Living room (\(d=2.0\) m, \(T_{60}=380\) ms). (**b**) Live studio (\(d=1.5\) m, \(T_{60}=765\) ms)  
Figure 7: Experimental arrangement for the real sound source (loudspeaker) in the subjective evaluation. (**a**) Living room (\(d=2.0\) m, \(T_{60}=380\) ms). (**b**) Live studio (\(d=1.5\) m, \(T_{60}=765\) ms).  
The experiment involved twelve male participants. The positions of the sound source were randomly selected, and the sound source was presented to each of the twelve participants once per position. The participants were then asked to report the perceived distance and direction of the virtual sound source. The frequency distribution of the responses was visualized using a bubble chart. The variation among subjects was evaluated using box plots for the correct answer rate. The distances chosen were 0.3, 1.0, or 1.5 m (in the living room environment) or 2.0 m (in the live studio environment), and the directions chosen were \(-45\), 0, or \(45^{\circ}\).  
The dry sound source was generated using ElectroBass as the instrument in Roland's Zenbeats DAW (Digital Audio Workstation) [49], creating a sound source with the C major key melody of [PERSON]'s Canon and then applying an effect in the same DAW to raise its pitch by one octave. The sampling frequency was 96 kHz, and the quantization was 16 bits. Figure 10 shows the waveform and spectrum of the generated dry sound source.  
Figure 8: Experimental arrangement for the conventional and proposed methods in the subjective evaluation. (**a**) Living room (\(d=2.0\) m, \(T_{60}=380\) ms). (**b**) Live studio (\(d=1.5\) m, \(T_{60}=765\) ms).  
Figure 10: Waveform and specrrum of the generated dry sound source.  
Figure 9: Experimental scene for subjective evaluation. (**a**) Living room (\(d=2.0\) m, \(T_{60}=380\) ms). (**b**) Live studio (\(d=1.5\) m, \(T_{60}=765\) ms).  
Additionally, the experiment was conducted without visual cues. Specifically, the listeners heard and responded to each test sound while wearing an eye mask. In other words, the device was hidden from view.  
We evaluated the following three conditions:  
* **Real**: The real sound source using the loudspeaker;
* **Conventional**: The conventional method [45];
* **Proposed**: The proposed method.
## 5 Performance Analysis
### Overall End-to-End Probability of Error  
We now aim to evaluate the overall probability of error for CF. An error occurs in CF transmission if the linear combination of source signals is not correctly obtained at the relay and/or the linear combination transmitted from the relay is not correctly obtained at the destination. Hence, both the phases of transmissions are required to be free from error. Further, the entire scheme requires \(L\) transmissions, because \(L\) linear combinations are required at the destination to obtain the original signals. Hence, the CF transmission is successful if and only if \(L\)-independent linear combinations are obtained at the destination without any error. Since we assumed that the channel is highly variant in time, hence, it is implicit that the \(L\) linear combinations will be independent. Therefore, the overall probability of error for CF scheme is given by (for \(n\,=\,\)1 dimension):  
\[P_{e}=1-\prod_{l=1}^{L}(1-P_{1}^{l})(1-P_{2}^{l})\]  
where the superscript indicates the \(l-\)th transmission. Therefore, inserting the expressions for \(P_{1}\) and \(P_{2}\) from Equations (26) and (29), respectively, we obtain:  
\[P_{e}=1-\left(\frac{2}{\pi}\left(\arctan\left(\frac{1}{2d}\right)\right) \right)^{L}\prod_{l=1}^{L}\left(1-\text{erfc}\left(\frac{c^{l}}{2\sqrt{2} \sigma_{1}}\right)\right) \tag{30}\]As in previous cases, the superscript indicates the value of the variable in the \(l\)th transmission. The above expression takes into account the necessity of all the linear combinations to be simultaneously error-free for error-free transmission. A simplified upper bound to the above expression can be given by (assuming a fixed gcd of one for all channel realizations):  
\[P_{e}^{u}=1-\left(\frac{2}{\pi}\left(\arctan\left(\frac{1}{2d}\right)\right) \right)^{L}\left(1-erfc\left(\frac{1}{2\sqrt{2}\sigma_{1}}\right)\right)^{L} \tag{31}\]  
In the next section, we will present the numerical results to demonstrate the performance of the proposed scheme.
# Modeling Intruder Reconnaissance Behavior through State Diagrams to Support Defensive Deception
## 4 Modeling Reconnaissance Activity
### Modeling Network Topology  
The other parameter that greatly influences the reconnaissance activity of an intruder is the protected network's structure and the knowledge that the intruder gains on it during reconnaissance. A syntax to describe the structure of the network on which an intruder performs reconnaissance activity is defined in the present subsection. The model describes the hosts that are up and running, as well as the ports (services) that are open for each host.  
The representation of the network topology including one element for each host of the network is  
\[NT=\{h_{1},h_{2},...,h_{k}\} \tag{4}\]  
Each one of these elements describes the IP address of the host and two sets of open ports (tcp and udp).  
\[h=\{ipAddress:\{[ports^{tcp}],[ports^{udp}]\}\} \tag{5}\]  
where  
\[ports=p_{1},p_{2},...,p_{t},p_{i}\in[1,65,535] \tag{6}\]  
An example of modeling a network topology is given in the following:  
NT = {  {  192.168.0.1:  [22, 80, 443],  [69]  },  {  192.168.0.5:  [22],  []  }  } This network comprises two hosts. The first one is 192.168.0.1 with three tcp ports open (22, 80, 443) and one udp port open (69), and the second one is 192.168.0.5, for which the only open port is TCP/22.
###### Abstract  
Ambient RF energy harvesting (RF-EH) is a particular case of wireless power transfer (WPT), which is characterized by ultra-low power operation. This work points out theoretical and practical aspects that should be considered in the design of RF rectifiers for ambient RF energy harvesting systems. The most common RF rectifier circuits are compared and discussed using simulations and experimental data. The efficiency is analyzed in terms of the input power and load resistance. It is demonstrated that the most efficient RF rectifier in ultra-low power conditions is the simple diode capacitor structure. As an illustrative example, an RF rectifier has been fabricated by designing an impedance-matching network to operate into the WiFi band. The fabricated prototype shows a measured 12% efficiency working at 2.47 GHz with around \(-30\) dBm ambient input power, which is higher than the reported efficiencies in the literature. The fabricated energy harvesting system delivers power between 25.6 nW and 129.6 nW to a resistive 10 k\(\Omega\) load. The obtained results are applicable to ambient RF up to 6 GHz.  
RF-DC conversion efficiency: RF ambient energy harvesting: RF energy scavenging: ultra-low RF power rectifier +
Footnote †: journal: Applied Sciences  
Accepted: 4 August 2022  
Published: 5 August 2022
# Vegetation Removal on 3D Point Cloud Reconstruction of Cut-Slopes Using U-Net
## 3 Methodology
### Colour Index  
The colour index is an efficient way for vegetation segmentation in 2D images [19; 26]. The widely-used colour index is the Excessive Greenness Index (\(ExG\)) [27; 28; 29]. \(ExG\) as shown in Equation (1) is a continuous index where a higher value indicates a green surface and a lower value indicates a bare ground.  
\[ExG=2G-R-B \tag{1}\]  
where \(R\), \(G\), \(B\) values are the normalised red, green, and blue values of each pixel in an image, \(0\leq R,G,B\leq 1\). \(ExG\) performs reasonably for vegetation segmentation from soil. However, the original \(ExG\) or any widely used colour index no longer showed a good performance in this study.  
A modified \(ExG\) (\(mExG\)) was developed in this study.  
\[mExG=G-1.2B \tag{2}\]  
where the coefficients 1.2 was determined by trial and error for the best performance. Figure 7 shows a performance comparison of the original \(ExG\) and \(mExG\), where any vegetation pixel was displayed as it was and any non-vegetation pixel was displayed as white. The yellow traffic line, the light blue guardrail and some of the slate grey rock were classified as vegetation by \(ExG\) as they also contained green. However, \(mExG\) segmented the traffic line, guardrail, and rock successfully as non-vegetation.  
Figure 8 shows an example that a single colour index was not sufficient for all kinds of vegetation. Two different colour indices, \(mExG\) and \(mExR\) (\(=R-1.2B\)), were used. The factor 1.2 in \(mExR\) was also identified by trial and error. Figure 8b showed the green mask generated with \(mExG\), Figure 8c showed the red mask generated with \(mExR\), and any pixel corresponding to either \(mExG\) or \(mExR\) was added up to get the entire vegetation, fresh or dried, as shown in Figure 8d.  
The colour index method may fail when colour of vegetation is close to the background as shown in Figures 9 and 10. In the red rectangle of Figure 9, both fresh and dry leaves were segmented with the colour index method successfully (vegetation as white, and non-vegetation as black), as the colours of them were different with the rock. However, in the blue rectangle, the colour of dry bush branch (grey) was close to the rock behind, and they were segmented wrongly as rock. In Figure 10, the dry grass in the red rectangle was segmented correctly, but the colour of weathered rock surface in the blue rectangle was close to yellow leaves, so that it was segmented wrongly as vegetation.  
Figure 6: Preparation a segmented dataset combining colour index and manual trimming.  
Figure 7: Comparison on the performance of the original \(ExG\) and modified \(ExG\): (**a**) original image, (**b**) vegetation segmented by \(ExG\), and (**c**) by \(mExG\).
# Recent Development of Mid-Infrared Supercontinuum Generation in Fluoroindate Glass Fibers
## 5 Mid-Infrared Supercontinuum Generation Using Fiber MOPA Seeded with 1.55 \(\upmu\)m Pulses
A power of 2.18 W (18.5% of the total power) was measured in the spectral region beyond 3.8 \(\upmu\)m. The authors emphasized that a power ratio beyond 3.8 \(\upmu\)m could be further improved by lowering the PRF.
## 4 Wind Loading and Vibration Mitigation  
Wind loading represents a characteristic load case for tall buildings. It is critical to ensure structural safety and occupational comfort during the design process. Due to the inherent slenderness, high-rises are prone to vibrations. This leads to the necessity to find ways to mitigate such oscillations. Added devices constitute a common way to counteract these effects, with them being either planned already in early design phases or sometimes even considered as part of retrofitting measures. In particular, passive AMDs represent a common solution to wind-induced vibrations. We choose the prototypical case of a sharp-cornered construction, the CAARC-B building. The advantage of this example is that the load conditions are thoroughly studied. Multiple references substantiate the knowledge base, as it represents a benchmark for building aerodynamics. Not only is the oncoming naturally turbulent wind described in detail, but also vortex-shedding characteristics are well-known. This cuboid shape is prone to such a phenomenon, typically resulting in a _von Karman_ vortex street [38]. Clear vortical structures (more dominant in the case of _low-to-no_ incoming turbulence) are linked to nearly harmonic cross-wind excitation forces.  
Figure 5: Showcasing the representative setup and qualitative results for the imposed motion of \(a=5\,\mathrm{mm}\) and \(h_{0}=40\,\mathrm{mm}\): the water surface being best identified by the border between the densities of air and water.
# Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm
## 4 Experimental Design and Result Analysis
### Experimental Settings
The specific experimental environment is as follows:  
The specific steps of the experiment are as follows: The learning rate was set to \(3\times 10^{-4}\) based on the default value of the Adam optimizer, which balances convergence \begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial Number** & **Category** & **Configuration** \\ \hline
1 & Processor & Intel Core i7-12700K \\
2 & Memory & 32 GB RAM \\
3 & Storage & 1 TB SSD \\
4 & Operating System & Windows 11/Ubuntu 20.04 \\
5 & Programming Language & Python 3.9 \\
6 & Libraries Used & baselines3, gym, NumPy, Matplotlib \\
7 & Learning Rate & 0.0003 \\
8 & Adjustment Step Size & 1024 \\
9 & Batch Size & 64 \\
10 & clip\_range & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: TD3 reinforcement learning wellbore trajectory design experimental environment. Figure 4: Three-stage well trajectory design training process. speed and stability. The batch size was set to 64, referencing similar continuous control studies in Paragraphs 1-75. The sensitivity analysis (Figure 5) shows that a \(\pm\)50% variation in the learning rate causes a \(\pm\)15% fluctuation in convergence steps, while Gaussian noise \(\alpha=0.2\) optimizes the exploration-exploitation trade-off. **Step 1:** The initial wellbore position and target reservoir position are set, and the initial state of the wellbore trajectory is defined. **Step 2:** The agent makes action decisions based on the TD3 strategy, including continuous adjustment of the inclination and azimuth. **Step 3:** The environment feeds back the new wellbore state and the corresponding reward value according to the action and records it into the experience pool. **Step 4:** The algorithm extracts empirical data to train the network model according to the priority experience playback mechanism and continuously optimizes the decision-making strategy. **Step 5:** Several training rounds are carried out, where each round contains multiple drilling simulation cycles, until the algorithm converges.
## 2 Preliminary Design  
### Parameters of the Battery Pack  
The battery pack was installed in a fully hybrid SUV, in which the electric power system requires a voltage rating of 288 V and 40 kW peak power. Considering the safety of NiMH batteries and the advantages of a wide temperature range, a NiMH battery was selected to meet the system requirement of the vehicle in this research project. Each single battery cell was a cylinder type and is used in HEVs, including the Toyota Prius I, Honda Insight, and Honda Civic; about 600 thousand sets have been mass produced so far. The manufacturer of this battery used to be Shonan Corun Energy Co., Ltd. (Kanagawa, Japan), but it was acquired and is now produced by Hunan Co-power EV Battery co., Ltd. (Changsha, China). The shape of the battery cells are shown in Figure 2a, and the electrical parameters are shown in Table 2. Each cell has a 6 Ah rated capacity, a 1.2 V rated voltage, and a peak discharge power of 185 W. A total of 240 battery cells were connected in serial to meet the system requirements of the vehicle. To improve the integration efficiency, a commercial L5 module, formed by welding five cells in series, and a total of 48 L5 modules, were used. These L5 modules are designed to be installed in two identical battery modules. Each battery module contains 24 L5 modules, which are arranged in two layers with 12 L5 modules in each layer. One of the two battery modules with a cooling plate is shown in Figure 2b.  
Figure 1: Organization of the study.  
Figure 2: The shape of the battery. (**a**) The battery cells; (**b**) the L5 battery module.
# QoE Optimization in a Live Cellular Network through RLC Parameter Tuning
## 4 Services
### Real-Time Video Streaming Service  
In a real-time video streaming service, the multimedia content is sent in a constant flow of data to the client as it is provided by the server. In this type of service, the content is reproduced as the data reach the client. For this reason, it is important that the data arrive on time, avoiding possible delays. Furthermore, in a real-time video streaming service, as the packets are played back, they are discarded; thus, the client does not get the complete multimedia file. This type of service is multicast oriented, similar to a television channel.  
QoE assessment in video services is usually performed using the mean opinion score (MOS). In this method, users rate the video quality using the following values: 1 (bad), 2 (poor), 3 (fair), 4 (good), and 5 (excellent). The traditional way to calculate the MOS consists of testing a group of users in a controlled environment; this method of computing the MOS is known as subjective MOS. However, this method presents some issues, as it is slow and expensive. To solve these problems, empirical mathematical models have been developed. These models use network or service performance metrics (e.g., the peak signal to noise  
Figure 3: TCP congestion control algorithms.  
ratio (PSNR) and the structural similarity (SSIM)) in order to obtain an approximation of real users' quality perception.  
As in [17], in this work, the model defined in [22] to approximate the MOS is used. In addition to the PSNR, this model considers aspects such as the packet loss rate and the frame degradation during transmission. As indicated in [22], this method achieves a Pearson correlation of 0.9509 with the subjective MOS, obtaining more accurate results than traditional MOS estimation techniques based only on the use of PSNR or SSIM.  
\[MOS=4.367-0.5040\cdot\frac{d}{dPSNR}-0.0517\cdot l, \tag{1}\]  
where \(d\) is the percentage of degraded frames, \(dPSNR\) is the PSNR average value of the distorted frames, and \(l\) is the lost frame rate.  
In this paper, a frame is considered as degraded when its PSNR is below the fifth percentile of the video frames' PSNR sent previously.
# Research on the Efficient Space Debris Observation Method Based on Optical Satellite Constellations
## 4 Cataloging Requirements  
The cataloging of space objects means that the monitoring network can continuously observe, match, correlate, and determine the orbits of space objects under normal operating conditions and can update the orbit information in a timely manner for each space object in the catalog. The number of space objects in the catalogue is an important index that reflects the ability of the monitoring network. The system performance is directly related to the number of space objects that are correctly cataloged.  
The conditions for the space objects being judged to be correct for cataloging were as follows. The maximum observable time interval of LEO objects was about 24-48 h, the maximum observable time interval of GEO objects was about 48-168 h, and the maximum observable time interval o other space objects was less than 168 h. If the vast majority of LEO space objects can be observed every 24 h, the orbital cataloguing for most LEO space objects can be achieved. In Section 5, the monitoring of space objects below 1000 km orbital altitude within 24 h on 26 February 2022 was calculated by the simulation, which was used to measure the cataloging ability of LEO space objects that were to be measured by the constellation.  
The calculation process was as follows: (1) Calculate the observation time interval for each space object. (2) Calculate the maximum revisit time interval for each space object. (3) Calculate the maximum observation time interval distribution function for the entire set of observation objects. (4) Calculate the percentage of the time interval for cataloging space objects that are less than the value defined above.
## 3 Finite Element Analysis
### Chord Length Effect  
It is known that the joint strength is affected by the boundary condition of the chord end and the chord length. [PERSON] and [PERSON] [34; 35] proposed a length of CHS equal to 10\(D\) to reduce the end effect for analysis. [PERSON] and [PERSON] [36] performed the FE analysis with the chord length, excluding the longitudinal plate width, which is equal to 10\(D\).  
In this paper, to check the effect of joint strength according to the chord length, the chord length (\(l_{0}\)) excluding the longitudinal plate width (\(l_{b}\)), was changed from 1.5\(D\) to 10\(D\). The chord length-to-diameter ratio (\(\alpha=\) 10/\(D\)) of the test specimen was 1.5, and the chord length of the test affected the joint strength, as shown in Figure 5. As \(\alpha\) increased, the joint strength decreased until \(\alpha\) became 6. The length of CHS for the FE analysis was, therefore, determined by selecting an \(\alpha\) of 6.  
Figure 4: Load–indentation curves of test and finite element (FE) models: (**a**) HSB600; (**b**) HSB600 and SS400.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{\(R_{test}\) (kN)} & \multicolumn{4}{c}{**Shell Element**} & \multicolumn{4}{c}{**Solid Element**} \\ \cline{3-8}  & & **[PERSON]/J.W.** & \(R_{FE}\) (kN) & \(R_{FE}\)/\(R_{test}\) & **O.J./J.W.** & \(R_{FE}\) (kN) & \(R_{FE}\)/\(R_{test}\) \\ \hline LH-N700-0.0 & \multirow{2}{*}{SS400} & 791.3 & 20/10 & 788.7 & 1.00 & 20/10 & 780.6 & 0.99 \\ LH-N700-0.6 & & 784.0 & 30/15 & 767.0 & 0.98 & 30/15 & 781.2 & 1.00 \\ LH-N350-0.0 & & 569.6 & 10/5 & 611.5 & 1.07 & 10/5 & 600.3 & 1.05 \\ LH-N350-0.6 & & 524.5 & 10/5 & 547.7 & 1.04 & 10/5 & 537.8 & 1.03 \\ LH-N700-0.0 & & 872.5 & 20/10 & 870.6 & 1.00 & 20/10 & 861.0 & 0.99 \\ LH-N700-0.6 & & 784.3 & 20/10 & 781.3 & 1.00 & 20/10 & 772.5 & 0.98 \\ Mean & & & & & 1.01 & & & 1.01 \\ CoV & & & & & 0.033 & & & 0.026 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of joint strength. CoV—coefficient of variation.
# IEEE 802.11 ax OFDMA Resource Allocation with Frequency-Selective Fading
## 5 Scheduling
### Greedy Algorithm
Assuming that the calculation of utility function values is done in constant time, the complexity of the algorithm is \(O(|C|\times\min\{N,M\})\), where \(M\) is the number of RUs in the considered frequency band and \(|C|\) is the number of MCSs in the MCS set. Thus, we see that the proposed greedy approach is much faster than an exhaustive search algorithm which can find an optimal solution but requires a cycle through all MCSs, and all possible RU allocations for all STAs, resulting in the complexity of \(O\big{(}|C|\times M^{N}\big{)}\).