esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Development of Proposed Method
### Proposed Method
#### 4.2.2 Scaling Exponent Computation Using DFA  
After decomposing the signal into BLIMFs, the scaling exponent \(\alpha_{k}\) for each mode \(\mathbf{d}_{k}(n)\) using DFA is computed. The scaling exponent quantifies the correlation properties of the mode and helps distinguish between signal and noise.  
The DFA procedure for each mode \(\mathbf{d}_{k}(n)\) is as follows:  
1. **Integration**: Compute the cumulative sum (profile) of the mean-centered mode: \[y_{k}(i)=\sum_{n=1}^{i}(\mathbf{d}_{k}(n)-\mu_{k}),\quad i=1,2,\ldots,N,\] (6) where \(\mu_{k}\) is the mean of \(\mathbf{d}_{k}(n)\) and \(N\) is the length of the signal.
2. **Division into Segments**: Divide \(y_{k}(i)\) into \(N_{\text{s}}\) non-overlapping segments of equal length \(s\), where \(s\) ranges from \(s_{\text{min}}\) to \(s_{\text{max}}\).
3. **Detrending**: For each segment \(v\), fit a polynomial trend \(y_{k,v}^{(s)}(i)\) (usually linear) and compute the detrended fluctuation: \[F_{k}^{2}(v,s)=\frac{1}{s}\sum_{i=1}^{s}\Bigl{(}y_{k}((v-1)s+i)-y_{k,v}^{(s)}( i)\Bigr{)}^{2}.\] (7)
4. **Calculation of Fluctuation Function**: Compute the overall fluctuation function for scale \(s\): \[F_{k}(s)=\sqrt{\frac{1}{N_{\text{s}}}\sum_{v=1}^{N_{\text{s}}}F_{k}^{2}(v,s)}.\] (8)
5. **Estimation of Scaling Exponent**: Plot \(\log F_{k}(s)\) versus \(\log s\) and perform linear regression to estimate the scaling exponent \(\alpha_{k}\): \[\log F_{k}(s)=\alpha_{k}\log s+C_{k},\] (9) where \(C_{k}\) is an empirically selected constant.  
_Note_: A scaling exponent \(\alpha_{k}\) close to 0.5 indicates uncorrelated (white noise) behavior, while values greater than 0.5 indicate long-range correlations typical of signal components.
# Stochastic Approach to Hosting Limit of Transmission System and Improving Method Utilizing HVDC
## 2 Background Knowledge
### Test System and Scenarios  
The proposed methodology was simulated in the modified IEEE 39 bus test system illustrated in Figure 4 [27]. In this figure, regions can be seen to be divided to implement region-intensive integration scenarios.  
In the base case, no line reinforcement was performed, and in the point-to-point (P2P) HVDC reinforcement case, HVDC was installed on buses 6 and 19, which are the locations searched for by the above process. Finally, the HVDC system was expanded to a multi-station case in which the multi-terminal HVDC system (MTDC) was implemented on buses 6, 19, and 26.  
In addition, the simulation of the base case was performed for two scenarios in which the existing synchronous generator was converted to a synchronous condenser instead of retirement to evaluate the effect on the hosting limit due to the lack of reactive power from the retirement of the existing synchronous generator by the gradual integration of DERs. Experiments were performed for all scenarios using the proposed method via Monte Carlo-based simulations.  
Figure 3: Proposed methodology conduct procedure.
# Quartz Enhanced Conductance Spectroscopy for Polymer Nano-Mechanical Thermal Analysis
## 4 Conclusions  
In summary, a fast and highly sensitive method for polymer nanomechanical thermal analysis was developed based on a commercially available QTF which costs less than one dollar. The advantage of the new method compared with a conventional instrument is that the required mass of the polymer was only a few nanograms. The nanogram level samples greatly reduced the thermal equilibrium time for the measurement, making the new method a fast analysis tool. Sample pretreatment for the new method is also easier, because no annealing treatment is required before the measurement, which is an essential process for conventional thermal analysis to reduce the hardness, refine the grain, and eliminate the residual stress. Compared to micro-cantilevers or other optical measurement methods, QTF has the advantage of self-sensing; the resonance frequency and \(Q\) factor can be directly read out by means of its electrical conductance spectroscopy with respect to the frequency of the external excitation source and therefore no optical beam is required. Along with the resonance frequency and \(Q\) factor, the equivalent inductance and resistance of a MM-QTF can be simultaneously acquired from the conductance spectroscopy. Further improvement can be made by bridging two or more polymer microwires across the QTF prongs to analyze the thermodynamics of multiple kinds of materials simultaneously.  
[PERSON] and [PERSON] performed the experiments and wrote the manuscript; [PERSON], [PERSON], and [PERSON] analyzed the data; [PERSON], [PERSON], and [PERSON] reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (NSFC) (61805132, 11434007), the National Key R&D Program of China (2017 YFA0304203), Changjiang Scholars and Innovative Research Team at the University of Ministry of Education of China (IRT_17R70), 111 project (D18001), Outstanding Innovative Teams of Higher Learning Institutions of Shanxi, Foundation for Selected Young Scientists Studying Abroad, Sanjin Scholar (2017 QNSJXZ - 04), Applied basic research program of Shanxi Province (201801D221174), Scientific and Technological Innovation Programs of Higher Education Institutions In Shanxi (2019L0028), and Shanxi \"1331 KSC\".  
We thank [PERSON] and [PERSON] for valuable discussions.  
The authors declare no conflicts of interest.  
Figure 5: \(E^{\prime\prime}\) of disproportionately rosin (DPR), polymethyl methacrylate (PMMA), and high-density polyethylene (HDPE) microwire modified QTF as a function of temperature.
# A Dependable Localization Algorithm for Survivable Belt-Type Sensor Networks
## 3 Hop-Distance Estimation Correction
### Locating the Node and Fix the Result
According to the properties of the proposed Newtonian iteration, the magnitude of the gradient represents an approximation of an estimate and an optimal point. The smaller the gradient, the closer the estimate is to the best position. When the result of the algorithm converges to the least square solution, the normal anchor node will continue to search along the normal gradient direction rapidly, while the false anchor node will deviate from this gradient direction. This method can also eliminate some of the anchors that are interrupted or maliciously tampered with. The core principle of this algorithm is: to set up the equivalent of the equations to convert, to build a multivariate function of the minimum value for the problem, and then solve it. In terms of convergence speed, the convergence rate is faster when its initial value is far from the true value, and then approaches the initial value. Newton method convergence can basically meet the requirements of connectivity-based positioning  
Step 4: According to Equation (21), the difference quotient \((F_{(k)}/x_{r}F_{(k)}/y)\) is calculated:  
\[ \begin{array}{c}F_{(k)}/x=F(x_{(k)}+x,y_{(k)},d_{k1(k)},d_{k2(k)},\cdots,d_{kn( k)})/x\\ -F(x_{(k)},y_{(k)},d_{k1(k)},d_{k2(k)},\cdots,d_{kn(k)})/x\\ F_{(k)}/y=F(x_{(k)}+x,y_{(k)},d_{k1(k)},d_{k2(k)},\cdots,d_{kn(k)})/y\\ -F(x_{(k)},y_{(k)},d_{k1(k)},d_{k2(k)},\cdots,d_{kn(k)})/y\end{array}  \tag{21}\]  
Step 5: We calculate the one-step predictor, and then Equations (22) and (23) are established:  
\[x_{(k+1)}=x_{k}-W_{k}[F/x]_{k} \tag{22}\]  
\[y_{(k+1)}=y_{k}-W_{k}[F/y]_{k} \tag{23}\]  
In Equations (22) and (23), the following relationship exists, as shown in Equation (24):  
\[W_{k}=\frac{F(x_{(k)},y_{(k)},d_{k1(k)},d_{k2(k)},\cdots,d_{kn(k)})}{\left[F_ {(k)}/x\right]^{2}+\left[F_{(k)}/y\right]^{2}} \tag{24}\]  
Step 6: According to the need for positioning accuracy, jump to Step 2 again, the iterative cycle calculation. Otherwise, the algorithm should be terminated. While adopting the gradient descent algorithm in the Gaussian channel environment, we should select and search the optimal solution in the controllable range on the basis of the initial value [21]. At the same time, for the whole positioning process, we use the results of the least squares algorithm to provide more accurate initial information for gradient descent algorithm. More importantly, it has not been necessary to add additional communication overhead to obtain the other conditions required for the calculation, which is very useful for belt-type topologies with limited computing power.
## 7 Simulations and Discussion
### Packet Drop Percentage  
One of the important added values of our proposed algorithm is the improvement of the Packet Drop Percentage (PDP). The packet drop percentage in V2X routing reflects the reliability, QoS, congestion levels, and scalability of the network. Managing packet drop percentages is vital for ensuring the efficient and dependable delivery of data in V2X communication scenarios. A routing protocol should aim to guarantee a low packet drop percentage to ensure reliable data delivery, contribute to maintaining quality of service (QoS), optimize resource utilization, manage congestion, meet application requirements, and enhance network stability. Minimizing packet drops is essential for providing a dependable and efficient routing infrastructure.  
The simulation results demonstrate the effectiveness of our modified version of the OLSR protocol compared to the original version (see Figure 18). Our solution exhibits a high level of delivery quality by effectively resisting attacks. It enhances the OLSR protocol's resilience to attacks and ensures reliable packet transport under various attack  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Scenario 1** & **Scenario 2** \\ \hline
**Networks Metrics** & **Value** & **Value** \\ \hline Total Packets Sent & 10,512 & 10,512 \\ \hline Total Packets Received & 10,033 & 5063 \\ \hline Total Packets Dropped & 479 & 5449 \\ \hline Packets Delivery Ratio & 95.44\% & 48.16\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: OLSR performance evaluation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Networks Metrics** & **Value** \\ \hline Total Packets Sent & 10,512 \\ \hline Total Packets Received & 9197 \\ \hline Total Packets Dropped & 1333 \\ \hline Packets Delivery Ratio & 87.31\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: New-OLSR performance evaluation under Attack.  
conditions. This is evident from the significantly lower Packet Drop Percentage observed in our solution compared to the unmodified OLSR protocol. It should be noted that in extreme attack scenarios with more than 70% of attackers, our solution's performance approaches that of the unmodified OLSR protocol.  
The simulation results strongly corroborate the findings of the case study, confirming that the Byzantine version outperforms the unmodified OLSR protocol in terms of packet delivery ratio, which is highly advantageous for communication protocols. Consequently, the simulation results provide solid confirmation of the exceptional performance of our algorithm in effectively detecting and isolating malicious nodes. This leads to a reduced impact of malicious activities and ultimately enhances the security of the OLSR protocol's control plane.
# Application of HY-2B Satellite Data to Retrieve Snow Depth on Antarctic Sea Ice
## 3 Methods
### Snow Depth Retrieval Model
Consequently, a highly scattering layer is created, leading to an overestimation of the snow depth on sea ice [15]. Snow-covered ice can be re-covered by new snow. Additionally, the melt-refreeze phenomenon alters snow properties, including changes in snow grain size and density. These factors contribute to an overestimation of the snow depth on sea ice during the remote sensing retrieval from passive microwave data [33]. To mitigate the impact of these uncertainties on snow depth retrieval from HY-2B SMR data, this study employs a sliding 5-day box window for the retrieved snow depth [15; 17]. Figure 3: Comparison of fixed (traditional method) and dynamic (this study) tie points calculated from the HY-2B SMR measured brightness temperature at 19 and 37 GHz for open sea water in 2019.
## 2 Mobility Models in FANETs
### Group Mobility Models
#### 2.2.4 Pursue Mobility Model  
The Pursue (PRS) model seems similar to the concept of the NC mobility model [55]. The nodes in this environment attempt to track a particular object travelling in a specific way. During a pursuit of the target, the mobile nodes move in a random relative motion. UAV movement utilizing PRS is depicted in Figure 11. Moreover, to keep an accurate track of the subject being pursued, each mobile node's random behavior is restricted. For example, in the context of smart vehicles, whenever a collection of unmanned aerial vehicles tracks a suspect vehicle across a city, PRS could be used [55].
## 3 Method
### Implementation of Wav2 Vec  
Recently, transformers have outperformed RNNs and CNNs in many tasks related to natural language processing and computer vision. It was decided to check the performance of one of the transformer-based architectures, named Wav2 Vec2, developed and pre-trained by Facebook [54]. The model was implemented in the same way as other deep learning backbones, with the exception of extracting features. Wav2 Vec2 has its own built-in feature extractors, so it needs only a raw audio signal as input. The results compared to the best  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Split Variant** & **Class** & **Train** & **Val** & **Test** \\ \hline \multirow{8}{*}{Split by samples} & Prolongation & 6849 & 850 & 847 \\ \cline{2-5}  & Block & 9626 & 1183 & 1161 \\ \cline{2-5}  & Sound repetition & 4485 & 582 & 547 \\ \cline{2-5}  & Word repetition & 3694 & 445 & 482 \\ \cline{2-5}  & Interjection & 7770 & 921 & 967 \\ \hline \multirow{8}{*}{Split by episode} & Prolongation & 6941 & 787 & 715 \\ \cline{2-5}  & Block & 9522 & 1156 & 1132 \\ \cline{2-5}  & Sound repetition & 4507 & 529 & 515 \\ \cline{2-5}  & Word repetition & 3678 & 441 & 444 \\ \cline{2-5}  & Interjection & 7680 & 981 & 873 \\ \hline \multirow{8}{*}{Split by show} & Prolongation & 5723 & 1486 & 1353 \\ \cline{2-5}  & Block & 7848 & 2093 & 2027 \\ \cline{1-1} \cline{2-5}  & Sound repetition & 4120 & 589 & 903 \\ \cline{1-1} \cline{2-5}  & Word repetition & 3270 & 420 & 931 \\ \cline{1-1} \cline{2-5}  & Interjection & 6780 & 1016 & 1862 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Class distributions after different splits.  
performing ResNetBiLstm and ResNet18 are shown in Figure 16. The best model out of all the Wav2 Vec2 experiments was trained for 40 epochs with 32 samples in every batch. The initial weights come from Facebook's pretraining, so the model was only fine-tuned for this specific classification task. The learning rate was set to 5e-06. According to the results, the transformer-based model did not outperform previous methods. It achieved better results only for word and sound repetitions, which is promising for further research as there were issues with satisfactory results in those classes.  
It is worth noting that the ResNet18 model achieved an F1 measure score of 0.932 for the overall (disrupted) class. This is the highest score obtained in the studies conducted that has been seen in the literature.
# Passive Power Line Communication Filter Design and Benchmarking Using Scattering Parameters
## 2 Filters Integration, Topologies, and Impact in the Power Grid
### Services Present in the Power Grid
#### 2.3.1 Residual Current Protective Devices  
RCD (residual current protective device) is used in the installation as a protective device against faulty devices or the carelessness of users [30]. The tripping current of an RCD is \(I_{\text{An}}\leq 30\) mA, which can be an issue in the case of a PLC filter with a leakage current being installed after the main fuse. Of course, the tripping current can be adjusted in some RCDs but the leakage of the Y capacitors might change due to capacitor ageing causing the RCD to trip unreliably.
## 3 Small Size 1D-QDFT Algorithms
### Algorithm 3, \(N=4\)  
Let \(\mathbf{x}_{4}=[x_{0},x_{1},x_{2},x_{3}]^{T}\) and \(\mathbf{y}_{4}=[y_{0},y_{1},y_{2},y_{3}]^{T}\) be four-entry input and output vectors, and \(x_{n}\in H\), \(y_{m}\in H\), \(m,n=0,1,2,3\).  
The 4-point QDFT can be represented as  
\[\mathbf{y}_{4}=\mathbf{E}_{4}\mathbf{x}_{4}, \tag{6}\]  
where  
\[\mathbf{E}_{4}=\begin{bmatrix}1&1&1&1\\ 1&a_{4}&-1&-a_{4}\\ 1&-1&1&-1\\ 1&-a_{4}&-1&a_{4}\end{bmatrix},a_{4}=-0.5774i-0.5774j-0.5774k.\]  
To calculate expression (6), 4 quaternion-valued multiplications and 12 quaternion-valued additions are required. We will show how one can reduce the number of operations required when calculating a four-point QDFT.  
Let us reorder the rows of the matrix \(\mathbf{E}_{4}\) using the following permutation:  
\[\pi_{4}=\begin{pmatrix}0&1&2&3\\ 0&2&1&3\end{pmatrix}.\]  
As a result, we obtain the following matrix:  
\[\widetilde{\mathbf{E}}_{4}=\begin{bmatrix}1&1&1&1\\ 1&-1&1&-1\\ 1&a_{4}&-1&-a_{4}\\ 1&-a_{4}&-1&a_{4}\end{bmatrix}=\begin{bmatrix}\mathbf{A}_{2}^{(4)}&\mathbf{A} _{2}^{(4)}\\ \mathbf{B}_{2}^{(4)}&-\mathbf{B}_{2}^{(4)}\end{bmatrix},\]  
where  
\[\mathbf{A}_{2}^{(4)}=\mathbf{H}_{2},\ \mathbf{B}_{2}^{(4)}=\begin{bmatrix}1&a_{4} \\ 1&-a_{4}\end{bmatrix}=\mathbf{H}_{2}\begin{bmatrix}1&0\\ 0&a_{4}\end{bmatrix}.\]  
Figure 2: Data-flow diagram for the 3-point QDFT.  
Taking into account the structural properties of the resulting matrix, the four-point QDFT algorithm can be written in the following form:  
\[\mathbf{y}_{4}=\mathbf{E}_{4}\mathbf{x}_{4}=\mathbf{P}_{4}\widetilde{\mathbf{E} }_{4}\mathbf{x}_{4}=\mathbf{P}_{4}\mathbf{W}_{4}^{(2)}\widetilde{\mathbf{D}}_{4 }\mathbf{W}_{4}^{(1)}\mathbf{x}_{4}, \tag{7}\]  
where  
\[\widetilde{\mathbf{D}}_{4}=diag\Big{(}1,1,1,s_{0}^{(4)}\Big{)},s_{0}^{(4)}=a_ {4},\]  
\[\mathbf{W}_{4}^{(1)}=\mathbf{H}_{2}\otimes\mathbf{I}_{2}=\begin{bmatrix}1&0&1& 0\\ 0&1&0&1\\ 1&0&-1&0\\ 0&1&0&-1\end{bmatrix},\,\mathbf{W}_{4}^{(2)}=\mathbf{I}_{2}\otimes\mathbf{H}_{ 2}=\begin{bmatrix}1&1&0&0\\ 1&-1&0&0\\ 0&0&1&1\\ 0&0&1&-1\end{bmatrix},\]  
\[\mathbf{P}_{4}=\begin{bmatrix}1&0&0&0\\ 0&0&1&0\\ 0&1&0&0\\ 0&0&0&1\end{bmatrix}.\]  
Figure 3 shows a data-flow diagram of the proposed algorithm for implementing four-point QDFT. One can see that the algorithm requires performing only one multiplication and only eight additions of quaternion-valued operands.
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 7 Integrating RSA with Large Language Models (LLMs)
### Opportunities for Human-AI Collaboration in RSA with LLMs
#### 7.2.2 LLM-Based AI Supporting Human Agent  
In Section 6, we have pinpointed 10 emerging problems where AI supporting RSA can be applied, particularly in leveraging CV to aid human agents in terms of scene information acquisition and live video enhancement. Beyond the collaborative prospects outlined in Section 6, the enhanced image comprehension and reasoning abilities of LLMs offer significant potential for bolstering the perceptual and intellectual support provided to human agents.  
Regarding perceptual support, given the limitations of human cognitive capacity, human agents often struggle to swiftly identify target objects amid complex images featuring numerous elements. Leveraging LLM-based AI can expedite target localization within images, thanks to its robust computational capabilities. In [PERSON] et al. (2018), [PERSON] et al. presented an example of an input image featuring numerous items arranged on multiple shelves within a grocery store. LLMs can swiftly offer location cues for the target item (e.g., mustard) within such complex scenes.  
In terms of intellectual support, LLMs draw from vast amounts of Internet data for training, surpassing the knowledge scope of human agents by a considerable margin. A clear illustration of this is when a task involves specialized or unfamiliar information; human agents, lacking exposure to such data, are unable to offer meaningful assistance to PVI. For instance, if confronted with an unfamiliar trademark, human agents may struggle to provide product details to PVI or even find relevant information through search engines like Google. In contrast, LLMs-based AI likely possesses this information and can furnish it to human agents. Additionally, LLM-based AI inherently possesses multilingual capabilities, enabling communication with individuals speaking diverse languages, a feat beyond the reach of ordinary human agents.
# Development of Active Microvibration Isolation System for Precision Space Payload
## 4 Experimental Validation
### Discussion  
Compared to some vibration isolation systems used for ground experiments [22] with mass 1300 kg and resonant frequency 1.8 Hz, the active vibration isolation system designed in this paper is smaller in size and mass (25 kg) and higher in resonant frequency (10 Hz) which is advantageous for the limited resources available on the satellite. Although this design with high resonant frequency is not conducive to passive vibration isolation, the performance of the system can still get similar results with active control. In addition, compared to some other systems with similar structure [29], the decentralized control strategy adopted in this paper, which could do without the structural information of the system and matrix operations, reduces the complexity of the control loop. The experimental results in this paper also prove that the used hybrid control strategy has a better suppression effect for low frequency signals, extending the effective starting frequency of vibration isolation from 5 Hz to 0.5 Hz. According to the requirement of the precision payload, the acceleration after isolation should be small in enough, so the judgement of the isolator designed in this paper is not only on the suppression ratio, but also on the absolute magnitude of acceleration, which is different from these isolation systems above.  
However, it can be noticed that there are some prominent frequencies of acceleration on the load platform, such as 8 Hz in the X and Y directions and 24 Hz in the Z direction. These noises represent a significant component of vibration source which originated from the resonant frequencies of the optical stage. Under this circumstance, the performance of vibration isolation could be optimized by designing controllers with different gains for different frequency intervals. Besides, for further improving the performance of vibration isolation systems with the microvibration source, the noise from the system itself is not negligible, such as sensor noise, actuator noise and noise brought by cables [30]. The suppression of these disturbances were not considered in this paper which may limit the current performance of vibration isolation. Resolution of these issues will lead to better achievement in future research.