esa-sceva/satcom-chunk-collection huggingface dataset:
# Glove Prototype for Feature Extraction Applied to Learning by Demonstration Purposes
## 2 Literature Review
### Output Data  
The way the data captured by the glove can be transmitted as output can vary both in protocol and physically. The method that suits the application best needs to take in consideration speed and data integrity, but as communication technologies matured, this problem became less relevant, and mainstream solutions are well documented and available. It is possible to make the glove transmit the data via Bluetooth or other wireless protocols to make the glove prototype more portable and maneuverable instead of a wired transmission, but the wired transmission has the advantage of having faster solutions, is cheaper,and is usually more reliable.  
The output of an IMU is usually transferred via SPI or I\({}^{2}\)C, both well established communication protocols. Today, at the low end of the communication protocols, we find the inter-integrated circuit (I\({}^{2}\)C) and the serial peripheral interface (SPI) protocols. For communications between integrated circuits for slow communication with on-board peripherals, both protocols are well-suited, they coexist in modern digital electronics systems, and they most likely will continue to compete in the future, as both I\({}^{2}\)C and SPI complement each other for this kind of communication.
## 4 Result and Discussion
### Communication Performance  
A patient monitoring system requires the establishment of reliable bi-directional connections for information transfer; hence, we separately analyze the performance of our proposed technique for UL and DL scenarios.
# A Real Model of a Micro-Grid to Improve Network Stability
## 3 Description of the DC Micro-Grid
### Micro-Grid Storage
#### 3.2.1 Accumulators  
The former approach relies on accumulators, employing six batteries within the entire smart grid. The capacity of one battery corresponds to 120 Ah.
## 4 Discussion
To further expand the knowledge about the well-known antagonistic potential of EOs [19], TTO and EEO were chosen for this preliminary study. In our previous investigation, _M. alternifolia_ Cheel (tea tree) and _E. globulus_ Labill. essential oils showed the ability to combat antibiotic-resistant strains even when organized in biofilms, with the results showing promise regarding these two important public health problems [24]. Lastly, the role of \"trojan horse\" played by this protozoan is well known. Water (_Legionella, Aeromonas hydrophilic_) and food-borne (_Listeria monocytogenes, Salmonella enterica_ serovar Enteritidis, _Yersinia enterocolitica_) pathogens are examples of this endosymbiotic relationship, which is capable of implementing environmental spread and resistance [48; 49; 50]. This endosymbiotic relationship is beneficial, allowing both partners to survive and take advantage of the utilization of nutrients (exrected catabolites, remains of dead bacteria, etc. ), the development of new characteristics, and adaptation to new environments [51]. These host/parasite interactions indeed seem to be of considerable importance, increasing the parasites' potential virulence and their resistance to biocides and antibiotics [52; 53; 54; 55; 56; 57]. Therefore, based on all these concerns and given the challenges associated with the current AK treatment protocols, new and alternative biocides must be investigated for future clinical and environmental purposes. The data emerging from the present study suggest that both of the studied EOs are interesting natural compounds endowed with amoebicidal activity. TTO proved to be the most effective compound, with significant activity after only 1 h of exposure, especially for the concentrations 1 xMIC and 2 xMIC. EEO was less effective, and a reduction in viable amoeba cells of 62% was observed after 24 h for the highest concentration used (2 xMIC). The synergistic interaction of TTO and EEO employed in the present investigation once again highlights the fact that the adequate association of these two EOs can improve the biological activity of the single compounds. This increased amoebicidal activity confirms that the biological activity of these EOs is not given exclusively by the action of their main compound, but is also due to the synergism established between all their different phytochemical active components. This is consistent with other studies confirming the activity of EOs against _A. castellani_[27; 58] and _A. polyphaga_[59]. Other studies also agree that among the EOs with amoebicidal activity, tea tree EO is the most effective against different strains of _Acanthamoeba_, both individually [60] and in synergistic combination with compounds like dimethyl sulfoxide, which is used as a disinfect [61], and EO blends [44], similarly to what emerged in the present investigation.
###### Abstract  
The high penetration of renewable energy resources and power electronic-based components has led to a low-inertia power grid which would bring challenges to system operations. The new model of load frequency control (LFC) must be able to handle the modern scenario where controlled areas are interconnected by parallel AC/HVDC links and storage devices are added to provide virtual inertia. Notably, vulnerabilities within the communication channels for wide-area data exchange in LFC loops may make them exposed to various cyber attacks, while it still remains largely unexplored how the new LFC in the AC/HVDC interconnected system with emulated inertia would be affected under malicious intrusions. Thus, in this article, we are motivated to explore possible effects of the major types of data availability and integrity attacks--Denial of Service (DoS) and false data injection (FDI) attacks--on such a new LFC system. By using a system-theoretic approach, we explore the optimal strategies that attackers can exploit to launch DoS or FDI attacks to corrupt the system stability. Besides, a comparison study is performed to learn the impact of these two types of attacks on LFC models of power systems with or without HVDC link and emulated inertia. The simulation results on the the exemplary two-area system illustrate that both DoS and FDI attacks can cause large frequency deviations or even make the system unstable; moreover, the LFC system with AC/HVDC interconnections and emulated inertia could be more vulnerable to these two types of attacks in many adversarial scenarios.  
AC/HVDC interconnections; load frequency control; virtual inertia; Denial of Service; false data injection; attack impact Article
## 3 Proposed Collaborative FL Framework
### Proposed Grouping Scheme
#### 3.5.3 Grouping Edge Clients  
Next, we explain the process of the grouping scheme for all participating clients. In this study, we let the total groups be determined in advance, e.g., \(N\). However, since different group categories have different values of '\(k\)', this variation can easily attract the attention of attackers. In order to prevent the adversary from discovering the grouping rule, \(k\)s in both group types are as similar as possible. Hence, all groups use the same \(k\). Before computing \(k\), we should classify edge clients via _the distinguishing principle_ from which we have \(n_{n\_est}\) and \(n_{m\_est}\). In the context of the discussion about a false positive rate of classifying edge clients, \(n_{n\_est}\) and \(n_{m\_est}\) are almost equal to \(n_{n}\) and \(n_{m}\). Thus, we can compute \(k\), \(N_{n}\) and \(N_{m}\) in two cases as follows. Case 1: in this case, \(n_{n\_est}\geq n_{m\_est}\), \(k\) is equal to \(\lceil n/N\rceil\) and all clients are assigned to their corresponding group. However, \(kN\) is usually higher than \(n\), which means that the actual total number of groups is less than \(N\). For the sake of ensuring total number of groups almost equals to \(N\), some assigned normal edge clients should be assigned repeatedly into another normal group. The number of these repeatedly assigned normal edge clients is \(kN-n\). Hence, the total number of normal edge clients needed to be assigned into groups is equal to \(kN-n+n_{n\_est}\). Therefore, \(N_{n}\) is equal to \(\lceil(kN-n+n_{n\_est})/k\rceil\) and \(N_{m}\) is equal to \(\lceil n_{m\_est}/k\rceil\). Some assigned malicious edge clients are assigned repeatedly until \(N_{m}\) are filled with malicious edge clients. In summary, based on our discussion above, we can obtain the following equation. \[N_{n}=max(\frac{N}{2}+N_{o},\lceil(kN-n+n_{n\_est})/k\rceil) \tag{10}\]  
If \(N_{n}\) is \(N/2+N_{o}\), \(N_{m}\) should be \(N/2-N_{o}\). And if \(N_{n}\) is \(\lceil(kN-n+n_{n\_est})/k\rceil\), \(N_{m}\) is \(\lceil n_{m\_est}/k\rceil\). In the latter case, the maximum of \(N_{n}+N_{m}\) may be equal to \(N+1\). Case 2: in this case, \(n_{n\_est}<n_{m\_est}\), we directly set \(N_{n}\) to \(N/2+N_{o}\) and \(N_{m}\) to \(N/2-N_{o}\). Then \(k\) is computed via \(k=\lceil n_{m}/N_{m\_est}\rceil\). The total number of malicious edge clients, \(kN_{m}\), are assigned to malicious groups, where some malicious clients may be assigned repeatedly. Some normal edge clients are also assigned to normal groups repeatedly until all \(N_{n}\) are filled with normal edge clients. Basically, we only consider \(k\) to be larger than 2. In our grouping scheme, only the malicious client just be assigned to malicious groups. Based on this rule, the malicious clients cannot influence normal groups. The following shows the pseudo code of our grouping scheme (Algorithm 1).
# Image-Caption Model Based on Fusion Feature
## 4 Results
### Experimental Results and Analysis
Next, we performed three sets of comparison experiments to verify the effectiveness of the fusion properties. Firstly, we verified the add-fusion of region features and channel features by Grid + Regionadd. The model performance outperformed the traditional CNN-LSTM-Attention architecture, which verified the feasibility of fused features. Due to the fusion noise problem in add-fusion and multiplicative fusion, we obtained the best performance on the concatenate approach Grid + Regionconcat measured by CIDE scores. It was 3.2% higher than in the best-class comparison experiments. The above ablation experiment verifies the superiority of the image-caption-generation model based on fusion features. To further explore the role and importance of each part of the model, we performed a third set of ablation experiments at baseline, using a pure CNN + GCN + LSTM architecture, without adding the SE module, node embedding modules, attention mechanism fusion module, and pure vector stitching fusion vectors, as shown in Table 3. SE refers to the SE channel feature-extraction module, NE refers to the node-embedded regional feature module, and Att refers to the feature-fusion module based on the attention mechanism. As can be seen from the above experimental results' graphs and images, the attention mechanism of the feature-fusion module improves model performance the most among the various components of the model, by 1% and 3.4%, respectively, compared to the baseline \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **BLEU-1** & **BLEU-4** & **CIDEr** & **METEOR** & **ROUGE-L** \\ \hline High-Level Attention, 2019 [20] & 0.746 & 0.317 & 1.103 & 0.265 & 0.535 \\ CGVRG, 2020 [15] & 0.814 & 0.386 & 1.267 & 0.286 & 0.588 \\ M2-Transformer, 2020 [21] & 0.816 & 0.397 & 1.293 & 0.294 & 0.592 \\ RDN, 2019 [22] & 0.775 & 0.368 & 1.153 & 0.272 & 0.568 \\ Entangle-Transformer, 2019 [23] & 0.776 & 0.378 & 1.193 & 0.284 & 0.574 \\ DLCT, 2021 [24] & 0.816 & 0.392 & 1.297 & 0.298 & 0.598 \\ Ours & 0.821 & 0.399 & 1.311 & 0.297 & 0.601 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model performance on the MS COCO dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **BLEU-1** & **BLEU-4** & **CIDEr** & **METEOR** & **ROUGE-L** \\ \hline CNN–LSTM & 0.666 & 0.246 & 0.862 & 0.201 & - \\ CNN–LSTM–Attention & 0.726 & 0.310 & 0.920 & 0.235 & - \\ GCN–LSTM & 0.808 & 0.387 & 1.253 & 0.285 & 0.585 \\ GCN–LSTM–Attention & 0.816 & 0.393 & 1.279 & 0.288 & 0.590 \\ Grid + Regionadd & 0.801 & 0.344 & 1.237 & 0.273 & 0.577 \\ Grid + Regionmul & 0.811 & 0.389 & 1.271 & 0.280 & 0.591 \\ Grid + Regionconcat & 0.821 & 0.399 & 1.311 & 0.297 & 0.601 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of frame structure.
## 3 Probe Design and Calibration
The Friis formula for the probe gain should therefore be corrected as follows:  
\[G_{\text{probe on drone}}=\frac{1}{G_{\text{probe off drone}}}\bigg{(}\frac{4\pi r }{\lambda}\bigg{)}^{2}\frac{R_{0}}{R_{a2\text{ on drone}}(f)}\frac{\left|S_{21 \text{ on drone}}\right|^{2}}{\left|1-S_{22\text{ on drone}}\right|^{2}\left(1- \left|S_{11}\right|^{2}\right)}. \tag{2}\]7  
Since \(G_{\text{probe off drone}}\) computed as before is already a far-field figure, the on-drone gain can be found from near-field measurements as follows:  
\[G_{\text{probe on drone}}=\frac{1}{G_{\text{probe off drone}}}\bigg{(}\frac{4\pi r }{\lambda}\bigg{)}^{2}\frac{R_{0}}{R_{a2\text{ on drone}}(f)}\frac{\left|S_{21 \text{ on drone}}\right|^{2}}{\left|1-S_{22\text{ on drone}}\right|^{2}\left(1- \left|S_{11}\right|^{2}\right)}. \tag{2}\]8  
Once calibrated as presented above, the probe can be mounted on the drone together with a peak detector and a data-acquisition system. Near-field data can then be acquired by flying the drone at a fixed distance away from the HF antenna under test, as shown in \[G_{\text{probe on drone}}=\frac{1}{G_{\text{probe off drone}}}\bigg{(}\frac{4\pi r }{\lambda}\bigg{)}^{2}\frac{R_{0}}{R_{a2\text{ on drone}}(f)}\frac{\left|S_{21 \text{ on drone}}\right|^{2}}{\left|1-S_{22\text{ on drone}}\right|^{2}\left(1- \left|S_{11}\right|^{2}\right)}. \tag{2}\]9 Based on the near-field samples, one can calculate the antenna gain in the far-field zone. In highly dynamic scenarios, such as offshore wind farms, blade turning might impact the measured results, as their length is usually comparable to the wavelength. In that case, a statistical approach similar to mobile channel sounding can be developed. For this purpose, near-field measurements must be performed under calm weather conditions when the turbines are turning slowly, during several flight trips covering multiple turbine turnings.
# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
## 4 Discussion
### Effect of Different Number of Nodes  
For testing the effect of a different number of nodes in the network, two ablation experiments were introduced. Details are shown in Table 3.  
In the first experiment, each of these had 2400 segments, and each segment had 32 samples. Among them, 5-fold cross-validation was applied. Moreover, 19,200 segments were training segments, and the remaining 4800 segments were test segments.  
For the second experiment, each of these had 1200 segments, and each segment had 64 samples. Among them, 5-fold cross-validation was applied. Moreover, 9600 segments were training segments, and the remaining 2400 segments were test segments.  
In this part, testing was performed with different numbers of nodes, i.e., 5, 10, 15, and 20, in the fully connected layer and at 3000 epochs to validate the model. There were five groups. The final model was obtained through training data and was tested with the remaining data from this model.  
As shown in Figure 10, the best performance was obtained for a 20-layer ANN, which achieved 99.8% for 32 samples and 99.7% for 64 samples with a model size of around 5 kb. Therefore, the next step was to confirm the testing time so that the experiment could achieve real-time detection.
# Blockchain-Based Distributed Computing Consistency Verification for IoT Mobile Applications
## 8 Performance Analysis
### Communication Overhead Evaluation  
We conduct similar experiments to evaluate the communication overhead of BDCA with Fortress and CPVPA. Figure 8 demonstrates that the communication overhead of the BDCA in preprocessing data is lower compared to the Fortress scheme. This is attributed to the fact that the Fortress scheme requires the generation of various commitments of zero-knowledge proofs (ZKPs), which results in a significant communication burden. In contrast, BDCA only requires transferring the BV-MHT, which incurs a considerably lower communication overhead. Additionally, the communication overhead of the CPVPA scheme grows linearly with the number of blocks, resulting in a higher communication overhead compared to BDCA. Figure 9 highlights that both the Fortress and CPVPA schemes require transferring more combined blocks against each challenge in auditing data, resulting in a higher communication overhead compared to BDCA. This is due to the fact that BDCA employs a more efficient method for generating and transmitting combined blocks. Specifically, BDCA generates and transmits only the necessary combined blocks for each challenge, resulting in a significantly lower communication overhead. In general, BDCA is efficient in communication.  
Figure 6: The computation overhead of BDCA in preprocessing data vs. CPVPA and Fortress.  
Figure 7: The computation overhead of BDCA in auditing data vs. CPVPA and Fortress.
# A Multi-Layer Erbium-Doped Air-Hole-Assisted Few-Mode Fiber with Ultra-Low Differential Modal Gain
## 1 Introduction
It theoretically analyzed the fiber performance under core pumping and cladding pumping conditions [15]. The relationship between DMG and pump mode selection was investigated. It is verified that the cladding pumping is independent to the pump mode selection and it is promising in constructing a stable all-fiber scheme. Compared with the uniform ion doping, the DMG is reduced to lower than 2 dB with a multi-layered ion profile under cladding pumping. Then, [PERSON] et al. proposed a cladding-pumped air-hole-assisted erbium-doped fiber amplifier (AH-EDFA), which further verified the influence of cladding pumping on the amplifier [25]. In the analysis, a 4 m length of air-hole-assisted erbium-doped fiber (AH-EDF) and the 976 nm multimode pump was selected. A peak gain of 15.7 dB is obtained at 1565 nm, and in the wavelength range of 1555 nm to 1590 nm, the gain remains higher than 10 dB. It only focuses on the specific output signal power, while the DMG is not further discussed. From the former research, it can be found that when the central air hole is introduced to the core, the high RI difference between the air hole and silica core can effectively adjust the mode field distribution and restrict them into the ring-shaped core. It can initially reduce the differences between mode field distribution, thereby achieving similar gains and low DMG combined with the doping design. In this paper, we proposed an air-hole-assisted few-mode erbium-doped fiber (AH-FM-EDF). The mode field distribution of different guided modes, especially the gaussian distribution LP\({}_{01}\) mode, can be adjusted and is beneficial to gain equalization. With multi-layer erbium-ion doping in the ring-shaped core based on the particle swarm optimization (PSO) algorithm, the gain of \(\sim\)20 dB is obtained and the DMG is reduced from 4 dB (uniform doping) to 0.14 dB (triple-layer doping). It performs well in gain-flattening and fabrication tolerance over the whole C-band.
# Scalable Block Preconditioners for Linearized Navier-Stokes Equations at High Reynolds Number
## Low-Rank Update of the Block Preconditioner
### Right Preconditioning  
The Broyden preconditioner inherits the tuning condition (21) as a reformulation of the secant equation, which only holds for left preconditioning. However, it can be easily reformulated to handle the case of right preconditioning, as follows  
\[P_{R}=P^{(0)}-\left(P^{(0)}V-A^{-1}V\right)\left(V^{T}P^{(0)}V\right)^{-1}V^{T}P ^{(0)}\]  
satisfying now  
\[AP_{R}V=V.\]  
The unpleasant presence of \(A^{-1}\) in this expression can be eliminated reasoning as before and assuming that now \(AP^{(0)}V=V\Lambda+E_{R}\) and, therefore, \(A^{-1}V\approx P^{(0)}V\Lambda^{-1}\), so that we can define an approximate, but more effective right preconditioner as  
\[P_{R} = P^{(0)}-\left(P^{(0)}V-P^{(0)}V\Lambda^{-1}\right)\left(V^{T}P^{ (0)}V\right)^{-1}V^{T}P^{(0)}\] \[= P^{(0)}-WHV^{T}P^{(0)},\qquad\text{with}\qquad W=P^{(0)}V,\quad H =(I_{p}-\Lambda^{-1})(V^{T}W)^{-1}\]  
Differently from the left preconditioning the preprocessing stage now requires \(p\) applications of the preconditioner \(P^{(0)}\) to form matrix \(W\).
# Hybrid Cooperative Spectrum Sharing Protocols Based on OMA and NOMA in Cognitive Radio Networks
## Appendix C Proof of Theorem 3
 \tag{12}\]  
and \(Q_{3}\) is expressed by:  
\[\begin{array}{l}Q_{3}=Pr\bigg{(}|h_{SP}|^{2}>\frac{\rho_{th}}{\rho_{1}},|h_{SS }|^{2}<\frac{\varphi_{5}(|h_{\text{PR}}|^{2}\rho_{p}+1)|h_{SP}|^{2}}{\rho_{th}} \bigg{)}\\ =\int_{0}^{\infty}\int_{\frac{\rho_{th}}{\rho_{1}}}^{\infty}\int_{0}^{\frac{\rho_{5}( \varphi_{p}+1)}{\rho_{th}}}\frac{1}{\lambda_{SS}}e^{-\frac{x}{\lambda_{SS}}} \frac{1}{\lambda_{SP}}e^{-\frac{y}{\lambda_{SP}}}\frac{1}{\lambda_{PRE}}e^{- \frac{x}{\lambda_{PRE}}}dxdydz\\ =\int_{0}^{\infty}\int_{\frac{\rho_{th}}{\rho_{1}}}^{\infty}\Big{(}1-e^{-\frac{ \varphi_{5}(\varphi_{p}+1)}{\rho_{th}\lambda_{SS}}}\Big{)}\frac{1}{\lambda_{SP}}e ^{-\frac{y}{\lambda_{SP}}}\frac{1}{\lambda_{PRE}}e^{-\frac{x}{\lambda_{PRE}}}dydz \\ =\int_{0}^{\infty}\frac{1}{\lambda_{PRE}}e^{-\frac{x}{\lambda_{PRE}}}e^{- \frac{\rho_{th}}{\lambda_{SP}}}dz-\int_{0}^{\infty}\int_{\frac{\rho_{th}}{\rho_{1}}}^{ \infty}\frac{1}{\lambda_{SP}}e^{-(\frac{\varphi_{5}(\varphi_{p}+1)\lambda_{SP}+ \varphi_{p}\mu_{h}\lambda_{SS}}{\rho_{th}\lambda_{SS}})y}\frac{1}{\lambda_{PRE}}e^{- \frac{x}{\lambda_{PRE}}}dydz\\ =\int_{0}^{\infty}\frac{1}{\lambda_{PRE}}e^{-\frac{x}{\lambda_{PRE}}}e^{- \frac{\rho_{th}}{\lambda_{SP}}}dz-\int_{0}^{\infty}\frac{\rho_{th}}{\rho_{1}} \frac{1}{\lambda_{SP}}e^{-(\frac{\varphi_{5}(\varphi_{p}+1)\lambda_{SP}+ \varphi_{p}\mu_{h}\lambda_{SS}}{\rho_{th}\lambda_{SS}})y}\frac{1}{\lambda_{PRE}}e^where \(C=\frac{g_{SPpA_{2}R}+\lambda_{SSp1}}{\lambda_{SSp1}\lambda_{PR}},D=\frac{g_{S} \lambda_{SP}+p_{pk}\lambda_{SS}}{\overline{g_{SPpA_{SP}}}}\) and \(\mathrm{Ei}(-\mu)=-\int_{1}^{\infty}\frac{e^{-px}}{x}dx\) is exponential integral function from Equation (3.352.4) in [22]. \(\square\)