esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 On-Site Experimental Measurement and Results Analysis of Converter Transformer
### Introduction to Converter Transformer Parameters
Its output is connected to the demagnetization data acquisition unit, and the data are ultimately fed back to the output control unit. The output control unit integrates information from the residual magnetization data acquisition unit, demagnetization data acquisition unit, and computer control unit to control the demagnetization transformer winding and achieve residual magnetization elimination function. The hardware logic diagram of the demagnetization device is shown in Figure 17.  
The operation process of using demagnetization device is as follows:  
1. Place the device in a safe environment, correctly connect the output voltage output terminal and ground wire before starting the device, and then connect the device to the power supply and start it. 2. Set the voltage value for impedance measurement and measure the impedance of the tested converter transformer at low voltage as a judgment for demagnetization completion. 3. Set the voltage of the capacitor and pre-charge it. 4. After the pre-charging is completed, release energy from the transformer to generate a demagnetizing current that decays and oscillates. During this period, observe the instrument and numerical changes. 5. Generate a demagnetization hysteresis loop based on the experimental data and calculate the residual magnetism. \begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Technical Specifications** \\ \hline Demagnetization voltage & 0–1500 V DC \\ Demagnetization current accuracy & 0.1 mA \\ Demagnetization voltage accuracy & 0.1 V \\ Measurement voltage & 0–1500 V AC \\ Measurement voltage accuracy & 1 V \\ Measurement current accuracy & 0.1 mA \\ Demagnetization time & \(\leq\)60 s \\ Measurement frequency & Adjustable from 10–100 Hz \\ Power supply & 220 V AC, 400 W \\ Applicable voltage level & Transformers rated from 10 kV to 1000 kV \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Main technical indicators of the device. Figure 17: Hardware logic diagram of the demagnetization device. (6) Perform impedance measurement under low voltage again to determine the demagnetization evaluation based on the changes in impedance magnitude twice. If it is not ideal, continue with the experimental steps in (2).
## 3 Research Methodology
### Data Collection  
Data taken for this study were collected retrospectively from the archival material of the SERU (Structural Engineering Research Unit) database [50], which was collected from the observational survey by a team of researchers from the Middle East Technical University (METU), Ankara, Turkey. The structural data used in this research is from Bingol survey that were collected immediately after the Bingol earthquake, specifically on 1 st May 2003 [51]. From that survey, the damage data of 28 reinforced concrete buildings had been selected for this research. The buildings consist primarily of 1 to 6 floors and were designed from the time-span 1970 to 2001. The data consist of various parameters such as irregularities, number of stories, openings, and so forth, verified by the available real images. All irregularities are identified by numbers \"1\" and \"0\" that stands for \"YES\" and \"NO\", respectively. The present condition of buildings indicated by \"0\", \"1\", and \"2\" to represent \"good\", \"moderate\", and \"poor\", accordingly [51]. Damage states of structures provided in the data are written as \"collapse\", \"severe\", \"moderate\", \"light\", and \"none\" to show the structural damage as well as infill damage, which were merged as a single damage grade as per EMS 98 [46]. Instead of numbers, grades are considered for EMS 98, shown in Table 14. Figure 1 represents the location of Bingol.  
As per the data, \"No\" has been written specifically for the absence of irregularities and buildings without a year of construction and assumed as it has been constructed after 1940. Bingol falls into a high seismic zone, which has a probability of 10 percent per 50 years, and PGA as 0.556 g (NS) and 0.28 g (EW) [53]. The velocity of soil Vs 30 for the Bingol earthquake was 529 m/s with an average Peak Ground Velocity (PGV) of 34.5 cm/s in the longitudinal and 21.9 cm/s in the transverse direction in a very dense soil type [54].
## 2 Methodology
### Data Normalization  
Data normalization is a commonly used data preprocessing technique aimed at handling data with different scales and units. This process normalizes the data to a uniform range and reduces the impact of differences in magnitude, features, and distributions on the model. Common normalization methods include Z-Score and Min-Max normalization.  
Considering that the data used in this study vary with depth, and variables such as well depth and deviation angle do not follow a normal distribution, Min-Max normalization was chosen as the data normalization method. This method linearly transforms the original data to constrain it within the range of [1], eliminating dimensional differences and mitigating the impact of varying magnitudes of parameters on model performance. The transformation is defined as follows:  
\[x*=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{4}\]  
Figure 3: Cramer’s \(V\) correlation coefficients between centralizer position and the cement bond quality of both interfaces.  
where the following are used:  
\(x\) represents the original data.  
\(x_{min}\) and \(x_{max}\) are the minimum and maximum values of the data, respectively.  
\(x*\) is the normalized data.
## 5 Results and Discussion
### Distributions Performance
For small mines, the minimum variation of the number of sensors is approximately \(-36.67\%\), and the maximum is \(-52.73\%\). For medium mines, the minimum variation is \(-39.02\%\), and the maximum is \(-53.52\%\). These variations show that the uniform function deploys fewer sensors than the exponential function. The average value of the percentage variation between the exponential and uniform functions is \(-44.70\%\) for small mines and \(-46.27\%\) for medium mines. This indicates that, on average, the uniform function deploys 44.70% fewer sensors in small mines and 46.27% fewer in medium mines than the exponential function. Consequently, the exponential distribution is the one that adds the most significant number of nodes, while the number of nodes located by the normal and uniform distributions are similar. The exponential distribution typically requires more nodes because it is designed to model events that occur independently and at a constant average rate but with the possibility of clustering or spreading over the area. This leads to more nodes being deployed to cover less predictable regions adequately. On the other hand, a uniform distribution assumes that the events (in this case, the locations of the nodes) are uniformly distributed over an interval. This distribution is consistent and varies less, meaning that fewer nodes can achieve coverage since each node covers a predictable space. The above data also suggest that the deployment strategy based on exponential distribution tends to result in a higher sensor density.
## 2 Polarization Control Structure Furthermore, Principle
### Comparisons of These Two Control Structures  
There are several other pivotal factors of polarization control devices that play a significant role in the control process of SOP. We provide a comparison of some performance parameters between the two types of PC in the table to illustrate the differences between them as Table 1. The PC consists of MZI based on thin-film lithium niobate (LN) has a more compact footprint, lower optical insertion loss, shorter response time, and lower half-wave voltage compared to the HWP-type PC. However, HWP can rotate endlessly to accomplish endless polarization manage. The PC based on MZI requires a reset process to ensure endless and stable polarization control, which may subjoin the complexity of DSP. These polarization control structures are promising to be further integrated with ICR. Nanofabrication technology is gradually mature. Integrated photonics that can modulate optical signals will lead to better performance of polarization controllers. Polarization controllers combining these technologies will have an ultra-compact size, enabling a high degree of integration and flexibility. Furthermore, it will have negative group velocity and anti-reflection and anti-scattering properties, which can realize precise manipulation and modulation of light [20].
# Software-Defined Heterogeneous Vehicular Networking: The Architectural Design and Open Challenges
## 4 Towards Software-Defined Heterogeneous Vehicular Networks
### Architectural Challenges
#### 4.2.6 Security  
Security is one of the critical concerns in a vehicular networking environment. Over the years, a number of security solutions have been envisaged for the VANETs, which primarily relied on the conventional cryptographic schemes utilizing public key infrastructures and certificates. Nevertheless, cryptographic-based solutions are not feasible for vehicular networks since vehicles are highly dynamic in nature and are distributed throughout the network, the availability of a networking infrastructure cannot be guaranteed at all times, and traditional cryptography-based solutions are also vulnerable to insider attacks. Hence, _trust_ has been recently introduced as an alternative for ensuring security in vehicular networks. In trust-based schemes, vehicles communicate and disseminate safety-critical messages with other vehicles based on trust (i.e., the confidence of one vehicle on the other) and constitute both direct trust and indirect trust. Direct trust is a vehicle's direct observation about the target vehicle, whereas indirect trust is computed by seeking recommendations from the one-hop neighbouring vehicles in the vicinity of the target vehicle. It is pertinent to mention that each one-hop neighbour that furnishes its recommendation has a different context (both conditions and capabilities), and thus, its recommendation segment should be weighted accordingly. Since the localized controllers have precise knowledge of the vehicles in their coverage area, they can act as a local trust management authority to weigh the individual recommendations and then aggregate them to obtain a trust segment. Moreover, once the trust value of a particular vehicle falls below a particular threshold, the localized controller can term it as malicious and ensure its elimination from the network. It can also intimate the same to the globalized controller for broadcasting this message to other roadside cloudlets and vehicular clouds to ensure that such malicious vehicles do not later become part of any network.
## 2 Materials and Methods
### Bacterial Strain Origins and Phylogenetic Characterization
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Origin** & **Strain** & **AN a** & **Origin b** & **Reference** \\ \hline _Microbacterium_ sp. C3W9B & KX000039 & Water & [18] \\ _Microbacterium_ sp. C3W23 & KX000040 & Water & [18] \\ _Microbacterium_ sp. C3W24A & KX000038 & Water & [18] \\ _Salinibacterium_ sp. C2W9 & KX000033 & Water & [18] \\ _Salinibacterium_ sp. C3W3 & KX000034 & Water & [18] \\ _Salinibacterium_ sp. C3W19 & KX000035 & Water & [18] \\ _Salinibacterium_ sp. C3W22 & KX000036 & Water & [18] \\ _Pseudomonas_ sp. C1W5 & KX000004 & Water & [18] \\ _Pseudomonas_ sp. C1W9 & KX000003 & Water & [18] \\ _Pseudomonas_ sp. C1W15 & KX000006 & Water & [18] \\ _Pseudomonas_ sp. C1W16 & KX000011 & Water & [18] \\ _Pseudomonas_ sp. C2W15B & KX000005 & Water & [18] \\ _Aequorivita_ sp. C1W3A & KX000015 & Water & [18] \\ _Aequorivita_ sp. C3W18A & KX000016 & Water & [18] \\ _Devesia_ sp. DS1 & KX000002 & Sediment & [18] \\ _Pseudomonas_ sp. BS19 & KX000007 & Sediment & [18] \\ _Pseudomonas_ sp. BS23 & KX000008 & Sediment & [18] \\ _Pseudomonas_ sp. C1S1 & KX000009 & Sediment & [18] \\ _Pseudomonas_ sp. C1S12B & KX000012 & Sediment & [18] \\ _Pseudomonas_ sp. C1S20 & KX000013 & Sediment & [18] \\ _Pseudomonas_ sp. C1S21A & KX000010 & Sediment & [18] \\ _Nocardioides_ sp. AS7 & KX000042 & Sediment & [18] \\ _Salinibacterium_ sp.BS9 & KX000027 & Sediment & [18] \\ _Salinibacterium_ sp.C1S5 & KX000031 & Sediment & [18] \\ _Salinibacterium_ sp.C1S22B & KX000030 & Sediment & [18] \\ _Salinibacterium_ sp.C1S23B & KX000037 & Sediment & [18] \\ _Bizionia_ sp. BS8B & KX000021 & Sediment & [18] \\ _Bizionia_ sp. BS22A & KX000022 & Sediment & [18] \\ _Bizionia_ sp. BS24 & KX000023 & Sediment & [18] \\ _Geldibacter_ sp. DS10 & KX000024 & Sediment & [18] \\ _Planococcus_ sp. AS5 & KX000045 & Sediment & [18] \\ _Planococcus_ sp. AS9 & KX000046 & Sediment & [18] \\ _Microbacterium_ sp.C1S13A & KX000041 & Sediment & [18] \\ \hline _Bacillus_ sp. PS62 & JX966213 & _P. spinosum_ & [22] \\ _Enterovibrio_ sp. PS152 & JX966212 & _P. spinosum_ & [22] \\ _Brevibacterium_ sp. PBE178 & KR185336 & _P. spinosum_ & [23] \\ _Brevibacterium_ sp. PBE181 & KR185337 & _P. spinosum_ & [23] \\ _Brevibacterium_ sp. PBE190 & KR185338 & _P. spinosum_ & [23] \\ _Brevibacterium_ sp. PBE209 & KR185339 & _P. spinosum_ & [23] \\ _Vibrio_ sp. PBN295 & KR185340 & _P. spinosum_ & [23] \\ \hline _Bacillus_ sp. SpN3 & LT604457 & _H. panica_ & [21] \\ _Thalassopira_ sp. A46 & JX298566 & _S._ & _S._ \\ _Thalassopira_ sp. A57 & JX298539 & _B. lutchosum_ & [19] \\ _Idiomarina_ sp. A19 & JX298543 & _M._ & _Laparedei_ & [19] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of Arctic and Mediterranean strains screened for inhibitory activity in this study.
# Improved Patch Packing and Refining Segmentation for the V-PCC Standard
## 1 Introduction
This method introduces a new voxel selection strategy that accurately selects voxels requiring refinement, eliminating the need for subsequent operations on voxels that do not require refinement. The VS-RS can reduce time consumption while ensuring rate-distortion (RD) performance and decreasing computation time by up to 43.50%. * In order to solve the problem of the original patch packaging method generating a lot of unoccupied areas and VS-RS generating additional small patches, this paper proposes a data-adaptive patch packing (DAPP), which includes more detailed sorting and positioning metrics as well as novel packing strategies. The DAPP can reduce the unoccupied size by up to 20.14%. They improve compression performance in a fully compatible manner. * Combining the two methods can lead to a significant improvement in overall performance. Extensive experiments have verified the proposed compression performance, which can save encoding time and achieve a BD-rate gain of \(-1.58\%\) in the V-PCC benchmark. * In addition to using common testing conditions to test the proposed method, we have also designed new evaluation metrics to measure improved performance. The remainder of this paper is organized as follows. Section 2 briefly describes the V-PCC and its related work. Section 3 first introduces refining segmentation and patch packing and their respective deficiencies, then focuses on the proposed method. Section 4 shows a series of experimental results obtained from the proposed method. Section 5 serves as the conclusion of the entire paper.
# Sample, Scalable Route to Produce Transparent Superhydrophobic/Hydrophilic Film Surfaces
## 3 Results and Discussion
### The Effects of Nanoparticle Nature on WCA and Transmittance with and without Thermal Curing  
To understand the effect of the silica nanoparticles' nature on the coating's transmittance and wettability, three of the following coatings were compared: sol-gel, sol-gel + HNT, and sol-gel + f-SiO\({}_{2}\). From Figure 3, we can observe that the sol-gel and sol-gel + HNT coatings remain hydrophilic before and after thermal treatment. However, the sol-gel + f-SiO\({}_{2}\) is superhydrophobic before and after heat treatment. This may be due to the air trapped in the rough surface when water is dropped onto it. According to Cassie-Baxter, a smaller particle's surface area interacting with water will result in a more significant contact angle. Chemically, polar molecules at the surface impacted wettability, causing an attraction between surface molecules and polar H\({}_{2}\)O [28]. As seen in Figure 3, the contact angle steadily increases with post-heat temperature. This combination's effectiveness is primarily due to the practical surface shape and chemistry's synergistic interaction. The main chemical adsorption occurred between the carboxyl groups of airborne organic molecules and the hydroxyl groups on the surface. In the meantime, the physical adsorption of airborne organic compounds on the surface may also be influenced by the hydrogen connection between the ester groups in those compounds and the hydroxyl groups. It has been widely reported that the apparent contact angle will decrease as the solid surface roughness increases, and the hydrophilicity is better when the solid surface is hydrophilic (contact angle 90\({}^{\circ}\)). When the solid surface is rough, the apparent contact angle will rise if the solid surface is hydrophobic (contact angle > 90\({}^{\circ}\)) [29]. In conclusion, the wettability change is due to both the chemistry and roughness of the films.  
Figure 2: TEM micrographs of the (**a,b**) silica and (**c,d**) HNT nanomaterials.
## 3 Deep Learning Structure Design
### Long Short-Term Memory (LSTM)  
LSTM is a special type of recurrent neural network (RNN) that resolves the issue of vanishing gradient that affects RNN topologies in general [27]. These types of networks learn long-term temporal-dependent data that are mostly used in the classification of sequential information. Popular applications include language modeling, sentiment analysis, and speech recognition. Figure 3 shows the LSTM architecture with forget and memory cells added on top of the traditional RNNs. These added cells allow the network to learn long-term sequential data relationships. The memory and forget cells determine the information to be retained and discarded, respectively.  
To compute the output of the forget cell \(F_{k}\), a sigmoid function is used on the input at the current time step \(x_{k}\) and the previous value of the hidden state \(h_{k-1}\):  
\[F_{k}=\sigma\Big{(}W_{f}\cdot[h_{k-1},x_{k}]\Big{)}+b_{f} \tag{1}\]  
where \(W_{f}\) and \(b_{f}\) represents the forget cell's weight and bias, respectively, \([h_{k-1},x_{k}]\) represents the concatenation of the previous hidden state and the current input, and \(\sigma\) is the sigmoid activation function. Afterwards, outputs of the forget cell, as well as the input cell \(I\) and the memory cell \(M\), are used to update the previous cell state \(n_{k-1}\) to a new cell state \(n_{k}\). This can be achieved by computing the following:  
\[I_{k}=\sigma(W_{I}\cdot[h_{k-1},x_{k}])+b_{I} \tag{2}\]  
\[\widetilde{M}_{k}=\tanh((W_{M}\cdot[h_{k-1},x_{k}])+b_{M}) \tag{3}\]  
\[M_{k}=(M_{k-1}\cdot F_{k})+\left(I_{k}\cdot\widetilde{M}_{k}\right) \tag{4}\]  
Figure 3: LSTM architecture.  
Figure 2: Typical CNN model.  
where \(W_{l}\) and \(b_{l}\) represent the input cell's weight and bias, respectively, and the \(W_{M}\) and \(b_{M}\) represent the memory cell's weight and bias, respectively. The final step is then to compute the value of the current hidden state \(h_{k}\), as described below.  
\[O_{k}=\sigma(W_{O}\cdot[h_{k-1},x_{k}])+b_{O} \tag{5}\]  
\[h_{k}=O_{k}\cdot\tanh M_{k} \tag{6}\]  
The purpose of the final step is to act as the network's memory, with information containing previous data to be used for prediction.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 2 Background
### Reinforcement Learning (RL) in Robotics
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **Model Type** & **Advantages** & **Disadvantages** \\ \hline AlphaZero & Model-Based & \begin{tabular}{l} Excels in structured tasks like planning and strategy \\ \end{tabular} & \begin{tabular}{l} High computational cost; less suited for dynamic environments \\ \end{tabular} \\ \hline IZA & Hybrid & \begin{tabular}{l} Enhances decision making by imagining \\ future states \\ \end{tabular} & \begin{tabular}{l} Computationally intensive; requires accurate \\ forward models \\ \end{tabular} \\ \hline MBMF & Model-Based & Combines planning with efficient learning & \begin{tabular}{l} Sensitive to inaccuracies in learned \\ dynamics models \\ \end{tabular} \\ \hline MBVE & Model-Based & Improves policy by refining value estimations & \begin{tabular}{l} Dependent on quality of learned \\ environment models \\ \end{tabular} \\ \hline DDPG & Model-Free & \begin{tabular}{l} Handles continuous actions; useful in \\ robotic control \\ \end{tabular} & Susceptible to instability and overfitting issues \\ \hline SAC & Model-Free & \begin{tabular}{l} Stable learning with entropy regularization \\ for exploration \\ \end{tabular} & \begin{tabular}{l} Higher computational cost than \\ simpler alternatives \\ \end{tabular} \\ \hline A2C & Model-Free & \begin{tabular}{l} Simplicity and reduced baseline variance \\ in updates \\ \end{tabular} & \begin{tabular}{l} Struggles with scalability in large \\ robotics systems \\ \end{tabular} \\ \hline A3C & Model-Free & Efficient in parallelized environments & High update variance; slower convergence \\ \hline REINFORCE & Model-Free & Simple implementation for small robotic tasks & \
\begin{tabular}{l} High variance; inefficient in complex \\ robotics tasks \\ \end{tabular} \\ \hline PPO & Model-Free &
\begin{tabular}{l} Robust, sample-efficient; works well in \\ robotics control \\ \end{tabular} & Requires careful tuning of hyperparameters \\ \hline DPO & Hybrid & Aligns directly with human-defined preferences & Limited adaptability to generic robotic tasks \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of popular RL algorithms.
# Interference-Aware Radio Resource Management for Cognitive High-Throughput Satellite Systems
###### Abstract  
By providing interactive broadband services to geographical areas underserved by terrestrial infrastructure, multi-beam satellite systems play a central role in future wireless communications. Targeting the terabit throughput requirements in satellite communications, we introduce a cognitive radio-based high-throughput satellite (HTS) system architecture where full frequency reuse is employed among beams. Moreover, by analyzing the characteristics of the considered architecture, we discuss the design challenges of radio resource management in cognitive HTS systems exposed to both intra-system and inter-system co-channel interference. Furthermore, to combat interference with low overhead, we propose a generic interference-aware resource management framework based on joint spatial division and multiplexing (JSDM). Under this framework, user grouping along with two-stage precoding is studied to achieve substantial improvement in the overall system throughput. Finally, some future research directions and challenges are also given.  
HTS system; cognitive radio; full frequency reuse; co-channel interference; radio resource management +
Footnote †: journal:  
_Article_