esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effects of Performance with Variation in Model Factors and Parameters

Table 3: Models selected based on performance and accessibility for binary grouping of leg fatigue levels in low and medium levels along with the values of accuracy (A), precision (P), specificity (S), sensitivity/recall (R), F1 score (F1), and G-index (GI) with classification algorithm (C), categorized according to the type of measures, number of sensors, their location, fatigue region, and grouping method. (Note: All models selected were tested using Grid search. ). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sensors** & **Location** & **A** & **P** & **S** & **R** & **F1** & **GI** \\ \hline
2 & LES, RES & 0.95 & 0.97 & 0.83 & 0.97 & 0.97 & 0.04 \\ \hline
4 & LES, LBF, RES, RBF & 0.94 & 0.96 & 0.78 & 0.97 & 0.97 & 0.05 \\ \hline
1 & RBF & 0.92 & 0.97 & 0.83 & 0.93 & 0.95 & 0.07 \\ \hline
2 & LBF, RES & 0.92 & 0.97 & 0.83 & 0.93 & 0.95 & 0.07 \\ \hline
2 & LBF, RBF & 0.91 & 0.96 & 0.78 & 0.93 & 0.95 & 0.08 \\ \hline
1 & RES & 0.91 & 0.97 & 0.83 & 0.92 & 0.95 & 0.08 \\ \hline
2 & LES, RBF & 0.90 & 0.97 & 0.83 & 0.91 & 0.94 & 0.09 \\ \hline
1 & LBF & 0.88 & 0.94 & 0.67 & 0.92 & 0.93 & 0.10 \\ \hline
1 & LES & 0.85 & 0.92 & 0.56 & 0.90 & 0.91 & 0.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Variation in the values of accuracy (A), precision (P), specificity (S), sensitivity/recall (R), F1 score (F1), and G-index (GI) with number of sensors for XGB model using Grid search with both EMG and Kinematics measures, categorized according to grouping method of FL2G2. Figure 4: Graphs depicting variation of performance with (**a,c**) classification algorithms of Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB); and (**b,d**) model tuning techniques of Baseline, Synthetic Minority Over-sampling Technique (SMOTE), and Grid search (GRID) using the XGBoost algorithm. Higher performance was observed using XGB classification algorithm and using Grid search tuning technique. We investigated the sensitivity of hyperparameters on the metric of accuracy by varying the levels of three parameters: maximum depth (values: 5, 10, 15, 20, 25), learning rate (0.00001, 0.0001, 0.001, 0.1), and subsample rate (0.1, 0.3, 0.5, 0.7, 0.9), as shown in Figure 6. Higher values of model accuracy were obtained with lower values of max depth and higher values of learning rate and subsample rate. Based on the plotted graphs, the results of the experiments are shown in Figure 7.  
Figure 5: Graphs depicting variation of performance across binary and tertiary fatigue level grouping methods for XGBoost algorithm using Grid search (GRID) using data from four wearable sensors on locations of left/right erector spinae (LES/RES) and biceps femoris (LBF/RBF). Higher values were observed for binary vs. tertiary fatigue level grouping methods. a set of levels for each of the three hyperparameters was selected to determine the best combination of these model parameters using the Grid search model tuning technique.
## 7 Experimental Verification
### Signal Feature Extraction  
Although most of the noise has been removed, the denoised vibration signal still cannot directly reflect the trend of bearing performance changes. However, vibration signals are rich in data features and easy to extract, making feature extraction an indispensable step in bearing trend prediction. Based on the needs of reliability prediction, this study uses three methods--time-domain, frequency-domain, and time-frequency-domain--to extract features.  
This study referred to a lot of literature [21] and selected 12-dimensional features on the basis of experiments. Through the operation of calculating Pearson correlation coefficient  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **SNR** \\ \hline LYY-CYCBD & \(-3.24\) \\ CYCBD & \(-4.98\) \\ CEEMDAN & \(-4.82\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of model noise reduction effect.  
Figure 8: Signal after CYCBD processing.  
Figure 9: Signal after LYY-CYCBD processing.  
of all feature pairs (threshold value is 0.85), some features in the literature [21] were deleted. The 12-dimensional features are selected, which have been widely studied and applied in related fields, and are considered to be able to better reflect the characteristics of signals or data. At the same time, considering the computing resources and data processing efficiency in practical applications, selecting the right amount of features helps to avoid excessive computing costs and data dimension disasters. These 12 features can not only guarantee the prediction performance, but also reduce the computational complexity and make the model more operable.  
Regarding feature composition, time-domain features (standard deviation, kurtosis, rectified mean, skewness, peak value, and impulse factor) directly reflect amplitude variations in vibration signals and can effectively capture impact events caused by bearing surface damage (e.g., spalling and cracks); frequency-domain features (spectral skewness, mean square frequency, dominant frequency, and spectral entropy), extracted through Fourier transform, can more effectively characterize signal periodicity, vibration modes, and energy distribution; and time-frequency-domain features (instantaneous frequency and singular value entropy) combine temporal and spectral information, making them suitable for non-stationary signal analysis and capable of capturing time-varying characteristics of fault features. This feature selection approach significantly reduces feature dimensionality while maintaining diagnostic performance.  
Figure 10 shows the 12-dimensional feature parameter set extracted from bearing 3-1.  
The feature parameter initial selection set, composed of the 12 time-domain, frequency-domain, and time-frequency-domain features mentioned above, can more comprehensively reflect the changing trends in the bearing's operating condition.
# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 5 Results and Evaluation
### Different Number of Array Elements  
This experiment investigated the impact of array element count on DOA estimation model performance. This paper considered five scenarios with different element counts--4, 6, 8, 10, and 12--and retrained models under an SNR of 5 dB. A total of 1000 groups of random test data were generated under 5 dB SNR and input them into the three models trained with the same array element count \(M\) and sampling frequency \(f_{s}\). The training results are shown in Figure 10. It can be observed that as the number of array elements increases, the RMSE of all three models decreases. However, as the number of array elements further increases, the downward trend of RMSE slows down, and the impact of array element number on DOA estimation models is far less than that of SNR. The model showed the smallest RMSE in all five cases, indicating its superior performance in this aspect.  
To further assess model stability under varying \(M\) configurations, the standard deviations of RMSE were calculated based on 10 independent runs for each setting. As shown in Table 7, the standard deviation trends with respect to the number of array elements indicate  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & \(f_{s}\) & \(2f_{s}\) & \(3f_{s}\) & \(4f_{s}\) & \(5f_{s}\) \\ \hline TSNetIQ & 0.14 & 0.11 & 0.11 & 0.1 & 0.1 \\ ResNetIQ & 0.43 & 0.28 & 0.23 & 0.18 & 0.13 \\ CNN & 0.73 & 0.54 & 0.42 & 0.38 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Standard deviation of RMSE over 10 predictions under varying sampling frequencies.  
Figure 10: RMSE results of three models under different \(M\) conditions.  
that TSNetIQ maintains the most stable performance across configurations, with standard deviation values decreasing steadily from 0.85 to 0.14. This reflects the strong generalization of model capability and its low sensitivity to variations in array size. ResNetIQ shows moderately higher variability, especially when \(M\leq 6\), stabilizing at 0.43 for \(M=10\) and \(M=12\). In contrast, CNN exhibits the highest fluctuations, with a standard deviation of 2.76 at \(M=4\), which gradually reduces to 0.73 at \(M=12\), indicating a greater dependence on larger array configurations to achieve stable estimates. Overall, TSNetIQ demonstrates superior robustness and consistency across varying array dimensions, making it more reliable for practical applications involving flexible or constrained sensor layouts.
## 5 Research Progress on Membranes for Redox Flow Batteries
### _D_aramic (_Micorporous_ Separator)  
Extensive work with the microporous separator Daramic has been carried out by several research groups. A low cost membrane fabricated from the modification of Daramic with Amberlite 400 CG (an anion exchange resin) has been reported by [PERSON] _et al._ for use in the VRB [65; 69]. The pores of the polyethylene microporous separator were partially blocked with a polyelectrolyte or an ion exchange resin while still imparting ion-selective capability to the membrane. Daramic was selected because of its chemical stability in vanadium electrolytes. Further cross-linking of the modified Daramic using divinyl benzene (DVB) showed a decrease in membrane diffusivity. When this membrane was employed in VRB cells, coulombic, voltage and energy efficiencies of 95%, 85% and 83%, were achieved, the efficiency values being much higher than untreated Daramic. A thin layer of poly (divinyl benzene) was shown to form after cross-linking, as confirmed from the area resistance, diffusivity, FE-SEM, TGA data and pore size distribution characterization [16]. In an attempt to impart cation-exchange capacity, the cross-linked Daramic was modified using poly (sodium 4-styrene-sulfonate) (PSSS) [66]. The composite membrane showed higher conductivity and vanadium ion diffusivity when compared to the anion exchange membranes employed in VRB system. This is because, in the case of  
Figure 3: Schematic showing water transfer test cell (Adapted from [15]).  
cation exchange membranes, the current is carried exclusively by H\({}^{+}\) ions, while the current is carried by both H\({}^{+}\) and SO\({}_{4}\)\({}^{2-}\) ions in anion exchange membranes. Thus, the low resistivity of the cation exchange membrane is due to an increased mobility of H\({}^{+}\) ions.  
Another effort to impart cation-exchange capacity to the cross-linked Darmic by sulfonation using concentrated sulfuric acid showed promising results in terms of ion exchange capacity and net water transfer across the membrane [70]. It was found that the sulfonation reaction was able to incorporate cation exchange groups to the cross-linked Darmic. However, after sulfonation, only slight differences were found in terms of vanadium ion permeability, area resistance and cell performance. Darmic contains mineral oil which acts as a stabilizer, resisting oxidation. When Nafion was directly introduced into the pores of Darmic, it prolonged the open circuit voltage (OCV) decay and extended the time taken for self discharge [71].
## 3 The Deep Learning-Based Approaches
### Network Structure  
This section describes the deep learning-based approaches used for minimum transmit power and PS ratio prediction, and presents a kind of DNN, the FFNN, and three kinds of RNN: the LRN, NARX, and LSTM.  
An FFNN is an artificial neural network where the connections between the units do not form a directed cycle or loops, which is different from an RNN. The information passes through the network follow one direction, from the input layer through the hidden layers and to the output layer [33].  
The RNN is a technology of learning in the deep learning, which has been attracting attention in recent years. RNN performs a memory mechanism with a recurring structure. Through this structure,neurons can use the information of the past in order to affect the output at the current moment, which is suitable for the prediction of time series data. In this paper, three kinds of RNN are used: the LRN, NARX, and LSTM.  
An earlier simplified version of the LRN was introduced by [PERSON] [34]. The LRN is a type of RNN that has a single delay and a feedback loop at each hidden layer, but not at the last layer. The basic LRN architecture is constituted by three layers: input layer, hidden layer, and output layer, as presented in [35].  
NARX is a dynamic RNN whose feedback loop is connected from the last layer to the input layer. The NARX model is based on the linear autoregressive exogenous model, which is used in time-series modeling. The formulation and the basic architecture for NARX are given in [36; 37].  
Unfortunately, the training process of traditional RNNs is influenced by the exploding gradient, which can cause learning to diverge, or the vanishing gradient, where learning either becomes very slow or stops working altogether [38; 39], which prevents complete learning of the time series. One solution is to utilize LSTM networks. Therefore, we have also investigated LSTM RNNs, which introduces a new structure called a memory cell, as presented in [40; 41].
## 3 Performance Analysis of the MPS Beamformer
### Performance of MPS Beamformer with the Number of Iterations 1  
As investigated in the previous section, increasing the number of multibeams in MPS beamformer is very important to speed up the SLL reduction process. On the other hand, for a specific number of multibeams, increasing the number of iterations provides deeper SLL at a decreasing exponential profile due to the increased number of almost near or equal sidelobes in the resulted radiation pattern. Figure 7 demonstrates the convergence profile versus number of iterations for an array of \(16\times 16\) elements with \(\beta=0.1\) using the first two major sidelobe groups (i.e., \(P=2(M+N-4)\)). From this figure, approximately 100 iteration cycles are required to achieve \(-50\) dB while decreasing the required SLL to \(-60\) dB requires more than 500 cycles.  
Figure 6: MPS beamformer convergence profiles at different number of subtracted multibeams.  
Figure 7: MPS beamformer SLL reduction profile with the number of iterations.
## 3 Results and Discussion
### Characterization of the Membrane
\begin{table}
\begin{tabular}{c c} \hline \hline
**NBR/GO with Sulfur Formulation (phr)** & **Crosslink Density (\(\times\)10\({}^{-4}\) mol/cm\({}^{3}\))** \\ \hline
0.5 & 4.7270 \(\pm\) 0.0028 \\
1.0 & 9.0754 \(\pm\) 0.0025 \\
1.5 & 9.6481 \(\pm\) 0.0035 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Crosslink density of the membranes with different sulfur loadings. good structural integrity without any micro cracks are formed at 1.0 phr sulfur loading. The formation of crosslink is too low and too high at 0.5 and 1.5 phr sulfur loading, respectively. Less wrinkle formation in membranes with 1.5 phr sulfur loading is due to the high crosslink density between the NBR rubber chains, which restricts the mobility of the NBR chains, increases viscosity of the compound and prevents the GO nanosheets from emerging to the surface of the membrane. The FTIR spectrum in Figure 2 present the functional groups of NBR latex membranes with and without GO prepared using different loadings of sulfur. The peak around 3400 cm\({}^{-1}\) shows the presence of O-H functional group due to surface-adsorbed water  
Figure 1: SEM micrographs of the membrane surface with different sulfur loadings. (**a**) 0.5 phr sulfur/0 phr GO, (**b**) 0.5 phr sulfur/2.0 phr GO, (**c**) 1.0 phr sulfur/0 phr GO, (**d**) 1.0 phr sulfur/2.0 phr GO, (**e**) 1.5 phr sulfur/0 phr GO, (**f**) 1.5 phr sulfur/2.0 phr GO. molecules [48]. Peak around 1560 cm\({}^{-1}\) represents the N-H bending in NBR. The presence of carboxylic acid O-H stretch can be seen at peak around 1447 cm\({}^{-1}\). Transmission frequencies at, 969 cm\({}^{-1}\) are due to C-O bending [49]. With respect to sulfur crosslinks, peak readings approximately at 921, 721 and 538 cm\({}^{-1}\) indicate the presence of S-OR stretch and S-S disulfide stretch [50]. There are no new peaks other than GO peaks for NBR/GO membranes which indicates there are no new chemical interaction formations between GO and NBR membranes. Thus, the interaction between NBR and GO is purely a physical interaction. XRD analysis was carried out to understand the effect of different sulfur loadings on the dispersion of GO nanosheets in NBR latex membrane, Figure 3. A sharp and intense  
Figure 2: FTIR spectra for NBR and NBR/GO membrane. peak at 20 = 10\({}^{\circ}\)-12\({}^{\circ}\) is the fingerprint diffraction peak for GO [51]. This peak corresponds to the ordered intercalated structure of GO nanosheets with oxygen functional groups such as epoxide and hydroxyl attached to its basal plane while carboxyl and carbonyl attached to the edges. In average 6-7 graphene layers stack together to form the GO nanostructure. In the XRD diffraction spectrum of the membranes, regardless of the sulfur loading, the sharp intense peak at 20 = 10\({}^{\circ}\)-12\({}^{\circ}\) has been replaced with broad peak at 20 = 10\({}^{\circ}\)-25\({}^{\circ}\), which indicates that the number of GO nanosheets stacking into layers had been reduced. It also indicates the exfoliation of GO nanosheets in the membrane [52].
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 8 Applications & Potential Alternatives
### Applications
#### 8.1.4 OPAs for Biological Sensing  
Neural stimulation refers to activation of neurons by an external stimulus to induce an action potential, including electrical, thermal, optical, and mechanical approaches [152]. Electrical simulations have yielded tremendous results and are standardly employed in activating neuronal responses. Electrical stimulations suffer from drawbacks such as electrodes' invasiveness, interference with environment, signal artifacts, and limited resolution [152]. Alternative optical stimulation methods have achieved comparable performance in activating physiological responses, and greatly improved spatial resolution and cell-type  
Figure 15: Applications of OPA structures. (**a**) An integrated OPA chip and the schematic of a ghost imaging system. Reprinted with permission from [54] © The Optical Society. (**b**) An implantable probe based on WGA-based OPA for optogenetic neuromodulation. Reprinted with permission from [56] © The Optical Society.  
specificity [152]. Implantable optical devices with precise stimulation ability to deep brain region neurons and high probe matching speeds are highly needed in neuroscience [56]. OPAs offer a wider range of operation, greater specificity in tissue stimulation, and a lesser need for physical repositioning of the device, which can be used in a variety of biological applications [152].  
A 1 \(\times\) 16 passive Si EF OPA achieved a beamwidth of 7\({}^{\circ}\) for temperature-dependent infrared neural stimulation [152]. A temperature change of up to 1.4 \({}^{\circ}\)C, which was sufficient for triggering biological responses, was observed on a biological phantom [152]. An implantable probe based on OPAs presented in Figure 15b enabled optogenetic neuromodulation [56]. The probe rapidly (<20 \(\upmu\)s) switched and routed multiple optical beams by electrically tuning. The Si\({}_{3}\)N\({}_{4}\) WGA-based probe stimulated the identified sets of cortical layers' neurons and recorded produced spike patterns by co-fabricated electrical recording sites simultaneously [56]. Coherent beam steering was achieved in vitro mouse brain tissue by a wavelength-tunable implantable neural probe employing Si\({}_{3}\)N\({}_{4}\) WGA-based OPAs [153]. An OPA probe immersed in a fluorescein solution generated a continuous FOV of 30\({}^{\circ}\) by wavelength tuning from 485 to 491 nm. A FWHM of <18 \(\upmu\)m over a propagation distance of 400 \(\upmu\)m was achieved. Potential 2D beam forming and steering could be achieved by an additional MEMS mirror-based input scanning system [153]. Another implantable neurophotonic probe based on Si\({}_{3}\)N\({}_{4}\) WGANs realized optogenetic stimulation of rodent brains [55]. The probe's operation wavelength range of 430-645 nm covered the excitation spectra of multiple opsins and fluorophores. A small volume of the brain could be illuminated by a high-resolution optogenetic stimulation based on the probe. The structure could also be used as fluorescence imaging applications [55].
## 2 Previous and Related Works
### Rural Connectivity Technologies  
Recently, there have been many initiatives to connect the unconnected half of the world. These unconnected citizens are, primarily, located in rural areas in developing countries. Main players in the IT world, like Google, Facebook and Intel, are heading these initiatives. In the next paragraphs we will review a couple of their projects and a DTN-Based rural connectivity technology, the DakNet project.  
Google's Loon project [5] is a Google initiative to bring Internet to underdeveloped and remote regions using stratospheric, solar-powered balloons. It uses base stations on Earth, below the balloon's trajectory, and it creates a mesh network between the balloons to transfer messages to these ground base stations with Internet connectivity to transmit replies and requests. Users can connect to the balloons via LTE (Long Term Evolution) devices through a local carrier's infrastructure.  
Facebook's Internet.org [6] uses local infrastructure, along with satellite communications and unmanned autonomous vehicles (UAV), to deliver a limited version of Internet to rural areas not currently covered or where access to Internet is very expensive. Users need an Internet.org account or to be affiliated with Facebook's local mobile partner and they will have access to Facebook, local news, employment, health, and weather websites.  
The main example of DTN use for Rural Connectivity is DakNet [7; 8]. DakNet is a technology based on DTNs that uses buses and local transportation infrastructure to transport messages between small and remote towns and Internet. DakNet has been in use for several years in India and Bangladesh and it uses PROPHET [9] (see next subsection for more details on this protocol) as its routing protocol. DakNet establishes small kiosks in rural towns with no Internet connectivity that allow people to send emails and request information offline, and then, using the transportation infrastructure that comes into town, it transports these requests to Internet using the connection at the bus station in the nearest city and delivers their answers when the transport returns to town.  
There are alternatives to use DTNs in city environments, like the one proposed in [10]. In this proposal, DTNs can be used to collect sensor data for Smart Cities in an Urban Automation Network (UAN). The nodes can be installed in different parts of the city, and a Garbage truck or a bus will eventually (at least once a day, according to the authors) collect the sensor information. In the paper, DTNs are compared with different alternatives and conclude that they are an efficient solution for sporadic transmissions.