esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Illustrative Example
### Adaptive State Estimation  
The following section implements the control scheme outlined in Theorem 1 and illustrated in Figure 1. The proof demonstrates that the proposed control scheme can be executed with or without a fixed gain \((K)\) in the estimator. Utilizing a fixed gain can offer certain advantages, as it can influence the convergence of the state error. Both cases presented will utilize the same fixed gain, which is derived using a Linear Quadratic Regulator with \(Q=I_{3}\) and \(R=1\). In contrast, the adaptive laws will exhibit slight variations, as noted in Equations (56) and (71). Furthermore, the interaction tuning term \((\gamma_{y})\) controls the estimator's sensitivity to output error, directly impacting how the error state converges. There are numerical limits for \(\gamma_{y}\), if increased excessively, the estimator can diverge. For this example, \(\gamma_{y}=I_{2}\).
# Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films
###### Abstract  
The double-stranded B-form and A-form have long been considered the two most important native forms of DNA, each with its own distinct biological roles and hence the focus of many areas of study, from cellular functions to cancer diagnostics and drug treatment. Due to the heterogeneity and sensitivity of the secondary structure of DNA, there is a need for tools capable of a rapid and reliable quantification of DNA conformation in diverse environments. In this work, the second paper in the series that addresses conformational transitions in DNA thin films utilizing FTIR spectroscopy, we exploit popular chemometric methods: the principal component analysis (PCA), support vector machine (SVM) learning algorithm, and principal component regression (PCR), in order to quantify and categorize DNA conformation in thin films of different hydrated states. By complementing FTIR technique with multivariate statistical methods, we demonstrate the ability of our sample preparation and automated spectral analysis protocol to rapidly and efficiently determine conformation in DNA thin films based on the vibrational signatures in the 1800-935 cm\({}^{-1}\) range. Furthermore, we assess the impact of small hydration-related changes in FTIR spectra on automated DNA conformation detection and how to avoid discrepancies by careful sampling.  
Keywords:DNA thin films; FTIR; B-form quantification; principal component analysis (PCA); support vector machine (SVM); principal component regression (PCR); conformational transitions; base vibrations; A-form; B-form +
Footnote †: journal: Applied
## 5 Decentralized Decision-Making
### Decentralized Decision-Making Mechanisms  
5.3.1. Consensus Mechanisms  
Blockchain networks' ability to let participants achieve a collective agreement on the state of the blockchain in a distributed and trustless manner helps to explain its functioning in great part through consensus processes.  
The consensus method, known as proof-of-work (PoW), is one wherein miners compete to solve complex mathematical challenges. This mechanism authenticates transactions and adds new blocks to the blockchain. This method uses significant computer resources--also known as hashing--to find a hash value less than a designated target. The first miner  
Figure 12: Key principles of decentralized decision-making.  
who solves the challenge sends the response to the network. Should additional nodes validate the answer, the newly created block will be included on the blockchain. PoW is vulnerable to criticism despite its great security and durability due to its high energy consumption and scaling restrictions [60].  
Another well-known consensus system addressing some of PoW's shortcomings is proof-of-stake (PoS). Block validators in PoS are chosen depending on their Bitcoin count and willingness to \"stake,\" or lock down, as collateral. Based on their network connectivity, validators are selected to build fresh blocks and validate transactions. PoS hopes to lower energy use and increase scalability by measuring participation using stake instead of compute capability. Because they run the danger of losing their staked tokens should they approve false transactions, validators are financially motivated to operate honestly [61].  
A variation of PoS, delegated proof-of-stake (DPoS), improves scalability and efficiency even further. Token holders in DPoS can cast a restricted number of delegates or block producers to represent them in block generation and validation. Selected based on criteria like reputation, technical knowledge, and network contributions, delegates create blocks in a round-robin style, one at a time. Token holders' voting weight influences how much delegates affect block generation. By means of token-holder voting, DPoS seeks to preserve decentralization while lowering the validator count, hence optimizing scalability and efficiency [62].  
Every one of these consensus systems has benefits and drawbacks; their fit will depend on elements like security demands, the aims of decentralization, and scalability requirements. Comprehending how blockchain networks reach consensus and maintain the integrity of the distributed ledger depends on an awareness of the subtleties of these consensus algorithms.
## 4 Numerical Simulation of Strain Energy of Hard Roof
### Roof Energy Evolution Characteristics  
The spatial and temporal distribution characteristics of the elastic strain energy density under different propulsion distances are shown in Figure 4.  
The elastic strain energy of hard roof strata gradually increases with the advance of the working face. When the working face advances 10-40 m, energy accumulation occurs only in the two sides of the working face, and no elastic energy accumulation occurs in the main roof. When the working face advances to 50-60 m, the main roof of the working face begins to bend under the influence of the excavation of the working face, and the accumulation of strain energy occurs in the rock strata affected by the deformation. When the working face advances 70-80 m, the elastic strain energy density of the hard roof strata in front of the coal wall of the working face continues to increase, and the elastic energy accumulation reaches the maximum at 80 m. Finally, an 'n'-shaped energy gathering area is formed on the roof of the mining area.  
In the mining process of the lower part of the main roof (5 m above the goat), a plastic failure zone is produced in the lower part of the roof of the goat, and the strain energy accumulated in the lower part of the basic roof is absorbed and released by the coal body in the plastic zone. The strain energy accumulated in the hard roof strata in this area is always low, forming a circular energy accumulation area around the central failure zone. When the working face advances 50, 60, 70, and 80 m, the peak values of elastic strain energy density of hard roof strata are 9.12, 37.41, 63.7, and 82.02 kJ/m\({}^{3}\), respectively.  
During the mining process, the upper part of the main roof (20 m above the goat) remains largely undamaged in the middle of the rock layer, and energy accumulates after 50 m, forming an elliptical energy accumulation zone above the center of the goat. When the working face is advanced by 50, 60, 70, and 80 m, the peak elastic strain energy density of the hard roof rock layer is 9.24, 44.91, 89.21, and 140.54 kJ/m\({}^{3}\), respectively.  
Figure 4: _Cont._  
From the above analysis, it can be seen that as the working face advances to 70 and 80 m, the hard roof begins to bend and deform due to the influence of the excavation of the working face. The strain energy in the rock layer is rapidly accumulated due to the deformation, the peak value of the strain energy gradually increases, and the energy gradient gradually increases. The maximum strain energy of the roof reaches 140.54 kJ/m\({}^{3}\). At this moment in time, the working face advances to the first weighting interval. The sudden fracture of the roof will be accompanied by the release of elastic energy, causing roof vibration and inducing rock burst. Therefore, the basic roof should be cut before the first weighting interval to release energy.
# DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework
## 3 Proposed System
### Entities and Interconnection Model in Framework
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Existing Works** & **Pros** & **Cons** \\ \hline \multirow{6}{*}{[13]} & The proposed additional layer for selecting the processing level allows you to move the options for choosing the processing level closer to end users. & The additional level only performs functions for the choice of processing level, but the participation itself does not take part in the processing process. \\ \cline{2-3}  & The algorithm for choosing mobile devices based on their resources was proposed and described to form a cloudlet cluster. & The article does not consider the possibility of using the fog server for processing user requests. In addition, the article does not consider the problem when it is impossible to form a cluster to handle the user request. \\ \hline \multirow{6}{*}{[15]} & The article proposes a network hierarchy of the Framework for “foggy” systems, which consists of four levels. The atomization layer is responsible for forming a set of resources for cloud atomization from physical resources. & The proposed architecture uses only the concept of fog computing. Only mobile devices are used to process user requests, and the remote server and MEC servers are not used. \\ \cline{2-3}  & Our proposed architecture allows us to process user data at all 4 levels. The formation of a cluster is carried out on the basis of a larger number of parameters. & The proposed network architecture is more complex because it is based on the simultaneous use of MEC and fog technologies (in related works, only one technology was used). The load on the mini-cloud will be greater, since it will be responsible not only for the task of choosing the processing level, but also for processing user requests. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison systems from related works with our proposed system. that, fog-server receives the rights to use computing and network resources provided by fog-node for its own purposes (aggregation of computing resources). It is worth noting that further interaction takes place at a higher level of the computational structure, namely: periodic transmission of MicroCloud data about the controlled fog-zone upon request from MiniCloud. At the same time, the frequency of queries is variable which can be determined by the use of one or another algorithm for processing data on fog-zones in order to monitor and manage computing resources. The structure of the transmitted data fog-server (MicroCloud) is presented in the form of a resource tree in Figure 6.  
Thus, each MiniCloud has complete information about all controlled fog-zones. This solution allows the development and implementation of a number of monitoring and control algorithms in order to ensure the efficiency of the computing infrastructure, within the framework of the dynamism of fog-nodes, as well as the migration of microservices of the services provided.
## 2 Suggested Topology and Its Operation
### Output Voltage and Frequency Control
#### 2.3.1 Output Voltage Control  
In this control, the RMS value of the output voltage is controlled at a constant standard frequency. There are various voltage control techniques including phase angle (half and full-wave control), on-off cycle control, and PWM chopping. Their switching controls are depicted in Figure 8 with respect to the input-output voltage waveforms. The output of the half-wave controller as shown in Figure 8a is obtained by operating the developed topology in zero and non-inverting states respectively during the first quarter and reset cycle of the input voltage.  
The symmetrical output of Figure 8b can be achieved by alternating operating the converter in zero-state and non-inverting state in each half cycle of the input voltage. The output for the on-off cycle control as depicted in Figure 8c may be realized with the non-inverting and zero-state for one and two input cycles respectively. The behavior of the instantaneous output voltage waveforms of Figure 8a-c are mathematically explored in Equations (8) to (10) respectively.  
Figure 7: Elimination of inductor current interruption during the off state: (**a**) closed current loop to de-energize the inductor; (**b**) corresponding switching signals.  
\[v_{o}(\omega t)\ =\ \left\{\begin{array}{lr}0&0\leq\omega t\leq\frac{\pi}{2} \\ V_{m}\sin(\omega t)&\frac{\pi}{2}\leq\omega t\leq 2\pi\end{array}\right. \tag{8}\]  
\[v_{o}(\omega t)\ =\ \left\{\begin{array}{lr}0&0\leq\omega t\leq\frac{\pi}{2} \quad\&\pi\leq\omega t\leq\frac{3\pi}{2}\\ V_{m}\sin(\omega t)&\frac{\pi}{2}\leq\omega t\leq\pi&\&\frac{3\pi}{2}\leq \omega t\leq 2\pi\end{array}\right. \tag{9}\]  
\[v_{o}(\omega t)\ =\ \left\{\begin{array}{lr}V_{m}\sin(\omega t)&0\leq\omega t\leq 2 \pi\\ 0&2\pi\leq\omega t\leq 6\pi\end{array}\right. \tag{10}\]  
Similarly, the PWM chopping in the output voltage can be ensured by alternate operation in non-inverting and zero states and it can be viewed from Figure 8d.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref./Tech.** & **PA** & **AS** & **SE** & **BER** \\ \hline
[13; 14] & Pre-PA & Independent selection & MI analysis: Gaussian and FA & No \\
[15] & gradient descent-based iterative method & Independent selection & Concavity analysis & No \\  & & & For SM-LDM and & \\
[16] & Pre-PA & Independent selection & spatial-multiplexing LDM, and SM-TDM/FDM & No \\
[17] & Weighted sum Optimization & Independent selection & FA input with closed-form & PEP and SER analysis \\  & ESNR for NOMA-SM & & & \\  & involves identical & Independent and & Gaussian and FA & Simulation \\
[19] & modulation orders and no consideration for & shared AS & & \\  & SMED factor & & & \\  & ESNR, SM-LDM, & & & \\ Our study & different modulations, and data rate, considers & Shared AS, UL & FA & Simulation \\  & the SMED factor & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the most related literature. * Propose the shared antenna selection (SAS)-based SM-LDM to alleviate the SIC complexity and enhance the EE and SE. In the SAS system, the UL solely carries an SSK signal, and both layers cooperatively share the decision of selecting the active antenna. * Analyzes the spectral efficiency (SE) of the SAS-based SM-LDM system. Investigations are conducted through the SE and BER numerical results with Pre-PA and Bi-PA. The rest of this study is structured as follows: The second section describes the standard SM-LDM system. In Sections 3 and 4, respectively, we describe the proposed Bi-PA and SAS-based SM-LDM techniques. Section 5 discusses the SE analysis of the SAS-based SM-LDM, while Section 6 presents the numerical results. Finally, Section 7 delivers the paper's conclusion.
# Sound Event Detection Using Derivative Features in Deep Neural Networks
## 3 Network Architecture
### Mean-Teacher Model  
The mean-teacher model in this study is similar to that used as the baseline recognizer for SED in the DCASE 2019 challenge [1]. The architecture of the mean-teacher model is shown in Figure 3.  
Figure 3: Structure of the mean-teacher model.  
Figure 2: Structure of the basic convolutional recurrent neural network (CRNN).  
It consists of two CRNNs, the student model on the left, and the teacher model on the right. The student model updates the model parameters by calculating the classification and the consistency cost and by back-propagating the errors using gradient descent. The classification cost is calculated by comparing the output of the student model with the ground truth table using the weakly labeled and strongly labeled audio data, as shown in Figure 3. The consistency cost is calculated by comparing the output of the student model with that of the teacher model by using unlabeled, weakly, and strongly labeled data. The teacher model does not update its parameters by back propagation, but uses the ensemble moving average weights of the student model [21]. For the test, the teacher model generally produces more correct output and is used for prediction.  
An attention-based CRNN is used for the mean-teacher model. The mechanism is similar to that in [19]. A gated linear unit (GLU) is used in ConvBlock, and an attention layer at the output. The GLU is shown in more detail in Figure 4; it contains a screening module that passes input signals if related to the sound event of interest, and blocks all other the signals.  
The attention layer with the softmax activation function is used to compute the weighted average for the output of the classification layer in the 10 s clip. It emphasizes the dominant class at each time step in the computation of the average score of the posterior probability from the classification layer. The weighted average score \(O_{c}\) for the class C is computed by multiplying the attention layer output \(O_{c}^{att}(t)\) and the classification layer output \(O_{c}^{cla}(t)\) as follows.  
\[o_{c}(t)=\frac{\sum_{t=1}^{T}o_{c}^{att}(t)\times o_{c}^{cla}(t)}{\sum_{t=1}^ {T}o_{c}^{att}(t)} \tag{2}\]  
where \(T\) is the total number of time frames in the 10 s audio clip.
# A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images
###### Abstract  
Jakarta holds the distinction of being the largest capital city among ASEAN countries and ranks as the second-largest metropolitan area in the world, following Tokyo. Despite numerous studies examining the diverse urban land use and land cover patterns within the city, the recent state of urban green spaces has not been adequately assessed and mapped precisely. Most previous studies have primarily focused on urban built-up areas and manmade structures. In this research, the first-ever detailed map of Jakarta's urban green spaces as of 2023 was generated, with a resolution of three meters. This study employed a combination of supervised classification and evaluated two machine learning algorithms to achieve the highest accuracy possible. To achieve this, various satellite images were utilized, including VV and VH polarizations from Sentinel-1, multiple bands from Sentinel-2, and eight bands from Planet. The Planet data were subsequently transformed into the Red-Edge Triangulated Vegetation Index and Red-Edge Triangulated Wetness Index. The data training and testing samples for urban green spaces were obtained using the Street View images available on Google Maps. The results revealed that using the Random Forest classifier algorithm and only eight bands of Planet images achieved an accuracy rate of 84.9%, while a combination of multiple images achieved an impressive 95.9% accuracy rate. Jakarta's urban areas cover approximately 33.2% of green spaces. This study provides unprecedented insights into the type, size, and spatial distribution of Jakarta's urban green spaces, enabling urban residents and stakeholders to explore and promote healthier living and better manage these green areas. Additionally, a previously unexplored concept, Jakarta's urban green belt, is introduced.  
urban green space; Jakarta; Sentinel-1; Sentinel-2; Planet; Red-Edge Triangulated Index 1636. [[https://doi.org/10.3390/rs16081366](https://doi.org/10.3390/rs16081366)]([https://doi.org/10.3390/rs16081366](https://doi.org/10.3390/rs16081366))  
1
Footnote 1: [[https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing)]([https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing))
# Self-Sustainable Biomedical Devices Powered by RF Energy: A Review
## 2 RF Energy Harvesting Principles
### Antenna Design and Characteristics for RF Energy Harvesting
#### 2.1.2 Implantable Antenna Design for RF Energy Harvesting
Biocompatibility can be defined as the capability of the material to work with proper host response in a particular application. Two methods have been proposed to ensure the biocompatibility of the implantable antennas. The first method is based on covering the implantable antenna with biocompatible materials (PDMS) to accomplish the desirable biocompatible specifications [90; 91]. The second method is to use biocompatible substrates such as alumina, zirconia, and Macorin the fabrication of the implantable antenna [90; 92]. Although different groups have determined SAR for implantable antennas using different phantoms, input powers, etc., the safe limits established by the IEEE, FCC, and ICNIRP remain unchanged. Implantable antennas have similar SAR values to wearable antennas; however, the depth of the implantable device inside the body is a further degree of freedom for SAR determination [93]. A comparative study along with performative analysis of wearable and implantable antennas for RFEH are presented in Table 3, taking into account antenna characteristics: size, realized gain, operating frequency, SAR, and power conversion efficiency. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Antenna Type**} & \multirow{2}{*}{**Size**} & \multirow{2}{*}{**Substrate**} & \multirow{2}{*}{**Gain (dBi)**} & \multirow{2}{*}{**Frequency** (GHz)**} & \multirow{2}{*}{**SAR (1 g**} & \multirow{2}{*}{**PCE (\%)**} \\  & & & & & & & **W/Kg** \\ \hline \multirow{4}{*}{[83]} & Implantable & \multirow{4}{*}{10.8 mm} & \multirow{4}{*}{3210} & \multirow{4}{*}{\(-\)23.2} & \multirow{4}{*}{0.42–0.91} & \multirow{4}{*}{0.36} & \multirow{4}{*}{58} \\  & dual-band & & & & & & \\ miniaturized & miniaturized & & & & & & \\ circular & & & & & & & \\ antenna & & & & & & & \\ \hline \multirow{4}{*}{[94]} & Implantable & \multirow{4}{*}{30 \(\times\) 30 mm} & \multirow{4}{*}{Rogers 3010} & \multirow{4}{*}{\(-\)26} & \multirow{4}{*}{0.915} & \multirow{4}{*}{175} & \multirow{4}{*}{50} \\  & slot antenna array & & & & & & \\ \hline \multirow{4}{*}{[95]} & Silicon Carbide & \multirow{4}{*}{4.5 \(\times\) 4.5 mm} & Semi & \multirow{4}{*}{——} & \multirow{4}{*}{10} & \multirow{4}{*}{——} & \multirow{4}{*}{47.4} \\  & implantable & & insulating & & & & \\ antenna & & & & & & & \\ antenna & & & & & & & \\ \hline \multirow{4}{*}{[96]} & Quad-Band & \multirow{4}{*}{8.43 mm\({}^{3}\)} & \multirow{4}{*}{RO3010} & \multirow{4}{*}{–34, -29.6, \(\
# A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing
## 5 Evaluations
### Experiment Results
#### 5.2.1 Convergence  
We investigate the convergence for three groups of mobile users (of size 20, 30, and 40), and where each user has 20 trajectories in a timescale. The results are shown in Figures 5-7. We use a black dotted line to describe the convergence trend of delay with the increasing number of iterations for each group of users. Additionally, we have the following observations. (i) For the same group of users with the same trajectory, the delay of users guided by the DSP-DRL framework is far greater than the other two comparison algorithms. As shown in Figure 5, the red and yellow lines are the results of DSP-NM and DSP-AM, which are much higher than the beginning of DSP-DRL. The relationship between the results of DSP-NM and DSP-AM is influenced by the communication delay and the migration delay, which relates to the size of users' data and the configuration files of services. In our experiment, the users are set to send data packets uninterrupted at equal time intervals. Therefore, the communication delay increases sharply when the users move frequently, resulting in a very large delay under DSP-NM. (ii) The increasing number of users has an influence on the convergence. As shown in Figures 5-7, the speed of the convergence slows down as the number of users increases. As shown in Figure 5, the total delay is close to convergence after 250 iterations. However, as shown in Figures 6 and 7, the groups with 30 and 40 users approach convergence after 400 and 42