esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The deployment of intelligent remote surveillance systems depends on wireless sensor networks (WSNs) composed of various miniature resource-constrained wireless sensor nodes. The development of routing protocols for WSNs is a major challenge because of their severe resource constraints, _ad hoc_ topology and dynamic nature. Among those proposed routing protocols, the biology-inspired self-organized secure autonomous routing protocol (BIOSARP) involves an artificial immune system (AIS) that requires a certain amount of time to build up knowledge of neighboring nodes. The AIS algorithm uses this knowledge to distinguish between self and non-self neighboring nodes. The knowledge-building phase is a critical period in the WSN lifespan and requires active security measures. This paper proposes an enhanced BIOSARP (E-BIOSARP) that incorporates a random key encryption mechanism in a cost-effective manner to provide active security measures in WSNs. A detailed description of E-BIOSARP is presented, followed by an extensive security and performance analysis to demonstrate its efficiency. A scenario with E-BIOSARP is implemented in network simulator 2 (ns-2) and is populated with malicious nodes for analysis. Furthermore, E-BIOSARP is compared with state-of-the-art secure routing protocols in terms of processing time, delivery ratio, energy consumption, and packet overhead. The findings show that the proposed mechanism can efficiently protect WSNs from selective forwarding, brute-force or exhaustive key search, spoofing, eavesdropping, replaying or altering of routing information, cloning, acknowledgment spoofing, HELLO flood attacks, and Sybil attacks.  
autonomous; communication; cost-effective; cryptography; efficient; key management; routing protocol; secure; wireless sensor networks +
Footnote †: journal: Sensors  
0
Footnote 0: _Sensors_**2016**, _16_, 460; doi:10.3390/s16040460
# Sensor Fusion Architecture for Fault Diagnosis with a Predefined-Time Observer
## 2 Sensor Fusion Architecture
### Diagnoser
 \tag{15}\]  
where \(\gamma\) is computed using Equation (13). Thus, by using Theorem 1, every \(e_{i}\) is predefined-time stable, \(i=1,\ldots,d\). Therefore, the origin of the error system (10) is predefined-time stable with \(T_{c}\) as its predefined time. Since the origin of (10) is reached in a predefined time, it implies that \(e(t)=0\), and \(\dot{e}(t)=0\) for \(t\geq T_{c}\). Then, the equivalent observer feedback law \(\Theta_{eq}\) is obtained such that \(\dot{e}=0\) holds. Hence, from Equation (10), \(\Theta_{eq}\) is found as  
\[ \begin{split} 0&=l_{m}(\tilde{x})m(t)-\Theta_{eq}\\ \Theta_{eq}&=l_{m}(\tilde{x})m(t)\end{split}  \tag{16}\]  
So, if \(e=0\), \(\dot{e}=0\), and Assumption 1 holds, then the estimation of the fault magnitudes \(m(t)\) can be computed from Equation (16) as  
\[\hat{m}(t)=l_{m}^{-1}(\tilde{x})\Theta_{eq} \tag{17}\]  
where the pseudoinverse \(l_{m}^{-1}\) exists by Assumption 1. In order to remove the high-frequency components of each estimated fault \(\hat{m_{i}}\), due to the high-frequency signals of \(\Theta_{eq}\), second-order LPFs, such as the ones proposed in Section 2.1.3, are required:  
\[F_{m_{i}}(s)=\frac{\omega_{m_{i}}^{2}}{s^{2}+2\zeta_{m_{i}}\omega_{m_{i}}s+ \omega_{m_{i}}^{2}},\quad i=1,\ldots,d \tag{18}\]where the filter settling time \(t_{sm_{i}}=\frac{4}{\xi_{m_{i}}\omega_{sm_{i}}},\quad i=1,\ldots,d\) should be selected to be approximately equal to \(T_{c}\) to ensure a fast convergence to the real fault \(m_{i}(t)\).
## 4 Experimental Evaluation
### Experiment Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**5-Way Acc.**} & \multicolumn{2}{c}{**20-Way Acc.**} \\ \cline{2-5}  & **5-Shot** & **10-Shot** & **5-Shot** & **10-Shot** \\ \hline Gist + kNN [54] & 59.91 \(\pm\) 2.85 & 69.28 \(\pm\) 2.25 & 44.34 \(\pm\) 1.32 & 52.99 \(\pm\) 1.47 \\ N-Gram + kNN [17] & 54.75 \(\pm\) 2.80 & 57.08 \(\pm\) 2.00 & 47.56 \(\pm\) 2.34 & 47.88 \(\pm\) 2.04 \\ PE + kNN [26] & 70.58 \(\pm\) 1.03 & 78.51 \(\pm\) 0.92 & 56.58 \(\pm\) 1.68 & **64.81 \(\pm\) 1.88** \\ pixel kNN & 51.06 \(\pm\) 1.11 & 53.94 \(\pm\) 1.24 & 33.24 \(\pm\) 0.60 & 38.64 \(\pm\) 0.63 \\ \hline InductionNet [14] & 54.10 \(\pm\) 0.37 & 56.61 \(\pm\) 0.37 & 36.33 \(\pm\) 0.18 & 43.20 \(\pm\) 0.18 \\ SNAIL [38] & 54.60 \(\pm\) 0.37 & 59.11 \(\pm\) 0.35 & - & - \\ Meta-SGD [39] & 65.15 \(\pm\) 0.32 & 67.72 \(\pm\) 0.31 & 41.79 \(\pm\) 0.18 & 44.44 \(\pm\) 0.17 \\ ProtoNet [9] & 69.80 \(\pm\) 0.35 & 76.78 \(\pm\) 0.23 & 58.13 \(\pm\) 0.12 & 63.42 \(\pm\) 0.11 \\ RelationNet [8] & 65.42 \(\pm\) 0.22 & 67.53 \(\pm\) 0.23 & 46.12 \(\pm\) 0.11 & 49.05 \(\pm\) 0.12 \\ HABPN [15] & 67.70 \(\pm\) 0.23 & 70.83 \(\pm\) 0.23 & 49.78 \(\pm\) 0.11 & 54.18 \(\pm\) 0.12 \\ \hline
**ConvProtoNet** & **75.59 \(\pm\) 0.22** & **80.30 \(\pm\) 0.45** & **59.17 \(\pm\) 0.24** & 64.03 \(\pm\) 0.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test accuracy and 95% confidence interval for malware few-shot classification on VirusShare dataset. Note that PE meta-data often fails to extract features for corrupted PE header of many classes. Bold items indicate the highest accuracy among all models under the same experiment setting. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**5-Shot**} \\ \cline{2-3}  & **5-Way Acc.** & **10-Way Acc.** \\ \hline Gist + kNN [54] & 62.06 \(\pm\) 2.00 & 50.71 \(\pm\) 1.48 \\ N-Gram + kNN [17] & 57.82 \(\pm\) 2.07 & 48.88 \(\pm\) 2.12 \\ PE + kNN [26] & - & - \\ pixel kNN & 33.40 \(\pm\) 0.90 & 24.39 \(\pm\) 0.61 \\ \hline InductionNet [14] & 42.87 \(\pm\) 0.32 & 31.10 \(\pm\) 0.18 \\ SNAIL [38] & 47.95 \(\pm\) 0.37 & 25.21 \(\pm\) 0.16 \\ Meta-SGD [39] & 54.01 \(\pm\) 0.34 & 37.94 \(\pm\) 0.14 \\ ProtoNet [9] & 66.14 \(\pm\) 0.24 & 51.05 \(\pm\) 0.10 \\ RelationNet [8] & 52.39 \(\pm\) 0.23 & 40.37 \(\pm\) 0.10 \\ HABPN [15] & 56.90 \(\pm\) 0.24 & 43.58 \(\pm\) 0.09 \\ \hline
**ConvProtoNet** & **68.58 \(\pm\) 0.22** & **57.10 \(\pm\) 0.09** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test accuracy and 95% confidence interval for malware few-shot classification on Drebin dataset. Drebin contains Android applications thus PE meta-data experiments are not reported. Bold items indicate the highest accuracy among all models under the same experiment setting.
## 3 EEG Mechanisms and the Case of North Rhine-Westphalia
### The Case of North Rhine-Westphalia  
This national policy (_EEG_) might have resulted in an uneven impact on a sub-national level, as a result of the uneven distribution of RE sources and installations, conventional energy power plants, mining activities, and their respective supply chains. North Rhine-Westphalia (NRW) represents an illustrative case of a potential \"geographically disruptive\" transition, since the old fossil-fuel based energy system is deeply embedded in its territory and economic structure, whereas renewable energy resources are relatively scarce compared to less densely populated regions.  
In the analyzed year, NRW represented 21.9% of German population and 21.8% of GDP [39]. However, with regard to RET promotion, it received 10.0% of subsidies (i.e., FiTs), yet its energy consumers paid 25.8% of national energy surcharge [40,41].  
Moreover, with regard to national electricity generation (Figure 1), 45.7% of electricity from hard coal and 51.1% from lignite was generated in NRW, in comparison to only 10.6% of electricity from RE [42]. Finally, the energy intensity of the NRW economy is also above the national average, and in fact ca. 32% of overall energy consumption by the industrial and mining sector took place in this federal state [43].  
Given the structure of its economy and energy system, we consider North-Rhine Westphalia an ideal case to study the impact on regional value-added resulting from the national energy transition. Our paper's contribution to the literature is twofold. Firstly, it is one of the few studies, and the only one for Germany, that presents a net impact assessment at a sub-national level. Secondly, to our knowledge, it is the only study (following [PERSON] [8]) that disentangles the regional impact of a national policy, whereas other studies analyze the effect of local deployment of RE on local economies.
Finally, a k-nearest neighbor (kNN) algorithm, Fine kNN, was deployed to perform binary classification of the brain images into MS vs. healthy classes. The ExMPLPQ-based model attained 98.37%, 97.75%, and 98.22% binary classification accuracy rates for axial, sagittal, and hybrid datasets, respectively, using Fine kNN with 10-fold cross-validation. Furthermore, our model outperformed 19 established pre-trained deep learning models that were trained and tested with the same data. Unlike deep models, the ExMPLPQ-based model is computationally lightweight yet highly accurate. It has the potential to be implemented as an automated diagnostic tool to screen brain MRIs for white matter lesions in suspected MS patients. **Keywords: multiple sclerosis; magnetic resonance imaging; feature engineering; local phase quantization**
# Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN
## 8 Performance Evaluation and Simulation
### End-To-End Delay  
This is the time taken by a transmitted packet to go from source to destination. As WBANs are used to monitor important parameters related to human's health, the end-to-end delay must be incredibly small to avoid any mishap. In WBANs the sensed data should move quickly to the target destination. Considering the significance and importance of end-to-end delay in WBANs, this work aimed to decrease it to a minimum level. The results of end-to-end simulation are shown in Table 8 and in Figure 11. The results demonstrate that the EHCRP protocol performs best in terms of end-to-end delay. The dual sink concept plays an important role in minimizing end-to-end delay. At the initial stage, the performance of EHCRP was almost equal to the other counterparts but later on it improved compared to its peers. At the initial stage during initialization the proposed model does a massive calculation by transmitting beacon messages due of which it performed the same as others, but after the 7500 th round, the end-to-end delay decreased distinctly in EHCRP and came down to 130 ms whereas its counterparts have more delay in communication.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Protocol Name**} & \multicolumn{8}{c}{**Packets Received at Different Interval of Time**} \\ \cline{2-10}  & **0** & **2500** & **5000** & **7500** & **10,000** & **12,500** & **15,000** & **17,500** & **20,000** \\ \hline
\begin{tabular}{c} EHCRP \\ (Proposed) \\ \end{tabular} & 0 & 2.5 & 3 & 6.7 & 7.2 & 8.1 & 9.5 & 10.2 & 10.7 \\ E-HARP & 0 & 2.1 & 2.23 & 6.1 & 6.8 & 7.4 & 8.56 & 9.6 & - \\ PCRP & 0 & 0.5 & 1 & 1.4 & 2 & 2.5 & 4 & 4.6 & - \\ EERP & 0 & 0.8 & 1.3 & 1.7 & 1.8 & 2 & 2.8 & 3.9 & - \\ EB-MADM & 0 & 1 & 1.5 & 2.3 & 2.5 & 2.8 & 2.8 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 7: Network throughput vs. time (s).  
Figure 10: Breakdown of network throughput.
## 5 Applications and Services
### Services
#### 5.1.3 Mobile Edge Computing  
UAV-enabled, RIS-assisted communication has been recently applied in the field of MEC [143]. A RISeUAV-assisted MEC is deployed to assist the communication between the  
Figure 6: Aerial backhauling through the RISeUAV to UAV-BSs.  
ground users [97]. The joint UAV trajectory, RIS passive beamforming and MEC resource allocation design are developed to maximize the energy efficiency of the system. To tackle the intractable non-convex problem, this is decomposed into two subproblems and solved iteratively based on SCA and the Dinkelbach method. The same study is conducted for the UAV-BS MEC assisted with ground RIS [51].
## 3 Results and Discussion  
### Laboratory Studies  
After the testing stage, according to the standard procedure, all the UCS values were measured and categorized. Statistical analysis is one of the most important stages for providing appropriate ground data. Although various indexes for multi-indicator analysis for geomaterials [41; 42; 43] exist, in SVM modeling, the predictive analysis is usually based on two directions and two variables. Therefore, underrating the target values is much more important. The main uncertainty [44] regarding the model in this study belonged to the UCS values, which could be modified by considering the distribution function and normalized by the Q-Q chart, as presented in Figures 3 and 4. Table 2 provides the statistical variations in the estimated UCS values for the Maragheh limestone. The quantile-quantile chart (known as the Q-Q chart) was obtained for the UCS results to examine the normality of the data variations, as illustrated in Figure 3. Based on the results of the statistical analysis of the UCS data, it was indicated that the average UCS value measured for the studied limestone was 109.93 MPa, and the calculated standard deviation was 39.754. Regarding the Q-Q chart, the experimental data followed a normal distribution law within the confidence interval of 93.7%. This fact was verified using a normal distribution envelope on the UCS data, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline
**Classifier** & **Hyperparameters** & **Elements** \\ \hline SVM & Kernels & Kernel = ’sigmoid’; Degree = 2 \\ C value & C = 100; Epsilon = 0.1 \\ \hline \end{tabular}
\end{table}
Table 1: The hyperparameters used in utilized models.
# Timbre Preferences in the Context of Mixing Music
###### Abstract  
Mixing music is a highly complex task. This is exacerbated by the fact that timbre perception is still poorly understood. As a result, few studies have been able to pinpoint listeners' preferences in terms of timbre. In order to investigate timbre preference in a music production context, we let participants mix multiple individual parts of musical pieces (bassline, harmony, and arpeggio parts, all sounded with a synthesizer) by adjusting four specific timbral attributes of the synthesizer (lowpass, sawtooth/square wave oscillation blend, distortion, and inharmonicity). After participants mixed all parts of a musical piece, they were asked to rate multiple mixes of the same musical piece. Listeners showed preferences for their own mixes over random, fixed sawtooth, or expert mixes. However, participants were unable to identify their own mixes. Despite not being able to accurately identify their own mixes, participants consistently preferred the mix they thought to be their own, regardless of whether or not this mix was indeed their own. Correlations and cluster analysis of the participants' mixing settings show most participants behaving independently in their mixing approaches and one moderate sized cluster of participants who are actually rather similar. In reference to the starting-settings, participants applied the biggest changes to the sound with the inharmonicity manipulation (measured in the perceptual distance) despite often mentioning that they do not find this manipulation particularly useful. The results show that listeners have a consistent, yet individual timbre preference and are able to reliably evoke changes in timbre towards their own preferences.  
timbre; music perception; music preference; pop music; mixing +
Footnote †: journal: Applied Sciences
###### Abstract  
Although permeation grouting technology has been widely used in engineering practice, there has not been sufficient research on how the distribution of pore sizes in porous media affects the diffusion of grout. In this study, based on the fractal theory of porous media and the Bingham fluid rheological equation, a Bingham fluid permeation grouting mechanism considering the distribution of pore sizes in porous media is proposed. The mechanism is validated through laboratory experiments and numerical simulations using COMSOL 6.0. During the experiments, parallel electrical resistance imaging is employed to monitor the diffusion range of the grout. Furthermore, the effects of grouting pressure, porosity, and water-cement ratio on the diffusion radius of the grout are analyzed. The results show that the Bingham fluid grout diffuses in a semi-spherical shape in the gravel. Additionally, parallel electrical resistance imaging can analyze the diffusion range of the grout in the gravel. The diffusion radius of the Bingham fluid grout in the gravel is smaller than the diffusion radius obtained by considering the particle size distribution theory, with an average difference of 31.8%. Compared to the diffusion radius obtained without considering the particle size distribution theory, the diffusion radius obtained by considering the distribution of pore sizes is closer to the experimental results. The numerically simulated program, which was developed for this study, can effectively simulate the diffusion law of the Bingham fluid in the gravel. So far, the Bingham fluid seepage grouting model considering the different particle size distribution of porous media has been built. The findings of this study can provide theoretical support and technical reference for practical grouting projects.  
Bingham fluid; seepage grouting; porous media; particle size distribution; fractal theory +
Footnote †: journal: Applied Sciences
# Passive Microwave Imagers, Their Applications, and Benefits: A Review
## 2 PMWI Applications and Impact Areas
### Applications and Benefits of PMWIs  
Satellite-based PMWIs have become indispensable tools in environmental monitoring and disaster management. These sensors provide unique insights into various atmospheric and surface processes by operating in the microwave region of the electromagnetic spectrum, typically ranging from 6 GHz to 190 GHz. The ability to penetrate clouds and capture data under all weather conditions makes PMWIs especially valuable in observing and understanding natural phenomena such as TCs and hurricanes, extreme precipitation and floods, fires, and storms. For example, Figure 1 illustrates the precipitation rates from all available microwave sensors in the Integrated Multi-satellite Retrievals for GPM (IMERG) products, captured on 26 September 2024, during Tropical Storm Helene. IMERG data leverage information from a variety of sources including several PMWIs, such as GPM Microwave Imager (GMI), AMSR2, and SSMIS [40].  
The following subsections highlight some applications of PMWI technology as examples, emphasizing the significance of the PMWI frequency bands and citing relevant references to underscore the impact of this technology in advancing the understanding of the Earth's environment.
###### Abstract  
It is expected that in the near future wireless sensor network (WSNs) will be more widely used in the mobile environment, in applications such as Autonomous Underwater Vehicles (AUVs) for marine monitoring and mobile robots for environmental investigation. The sensor nodes' mobility can easily cause changes to the structure of a network topology, and lead to the decline in the amount of transmitted data, excessive energy consumption, and lack of security. To solve these problems, a kind of efficient Topology Control algorithm for node Mobility (TCM) is proposed. In the topology construction stage, an efficient clustering algorithm is adopted, which supports sensor node movement. It can ensure the balance of clustering, and reduce the energy consumption. In the topology maintenance stage, the digital signature authentication based on Error Correction Code (ECC) and the communication mechanism of soft handover are adopted. After verifying the legal identity of the mobile nodes, secure communications can be established, and this can increase the amount of data transmitted. Compared to some existing schemes, the proposed scheme has significant advantages regarding network topology stability, amounts of data transferred, lifetime and safety performance of the network.  
wireless sensor network; nodes mobility; security topology control; identity authentication; soft handover +
Footnote †: journal: Sensors
## 4 Experimental Results
In general, the blocky appearance could be decreased with a smaller value of \(M\) (i.e., a small block size in computing the entropy values). However, this will jeopardize the accuracy of the depth-map, which may lead to blurriness in certain parts on the EDF image. As an example, we apply our proposed method to the 2 sample holograms (with filtering on the depth maps) with \(M=20\), and the EDF images are shown in Figure 8a,b. It can be seen that while the blocky appearance of EDF image corresponding to sample 'B' (Figure 8b) is reduced, certain parts of the 2 EDF images, especially in Figure 8a, are suffering from mild de-focusing. A proper choice of the range of \(M\) is dependent on the setup of the OSH system (such as the size of the hologram and the sampling interval). In our present setup, EDF images of favorable quality can be obtain with values of \(M\) in the range [20; 32]. Figure 6: (**a**) Depth map of hologram “A” obtained with our proposed method (\(M=32\)) without filtering; (**b**) Depth map of hologram “B” obtained with our proposed method (\(M=32\)) without filtering; (**c**) Extended depth of field (EDF) image of hologram “A” reconstructed with our proposed method (\(M=32\)) without filtering on the depth map; (**d**) EDF image of hologram “B” reconstructed with our proposed method (\(M=32\)) without filtering on the depth map. Figure 8: (**a**) EDF image of hologram “A” reconstructed with our proposed method (\(M=20\)) with filtering on the depth map; (**b**) EDF image of hologram “B” reconstructed with our proposed method (\(M=20\)) with filtering on the depth map. Figure 7: (**a**) Depth map of hologram “A” obtained with our proposed method (\(M=32\)) with filtering; (**b**) Depth map of hologram “B” obtained with our proposed method (\(M=32\)) with filtering; (**c**) EDF image of hologram “A” reconstructed with our proposed method (\(M=32\)) with filtering on the depth map; (**d**) EDF image of hologram “B” reconstructed with our proposed method (\(M=32\)) with filtering on the depth map. Finally, we would like to compare our proposed method with the method in [18]. We have applied the method in [18] to reconstructed the EDF images of the 2 holograms, and the results are shown in Figure 9a,b. We observe that the visual quality of the 2 EDF images are generally favorable, but the EDF image of hologram 'B' is more blocky than the one obtained with the proposed method. The computation time based on the same PC is around 356 s, which is about 60 times longer than the proposed method. The correlation score of the EDF images of holograms 'A' and 'B' (with reference with the ones obtained with manual judgement) are 0.982 and 0.741, respectively, which are more or less similar to the proposed method. As for the visual quality, the EDF image of sample 'B' (e.g., around the characters '1' and '2' on the pair of coins) is better in our proposed method.