esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Grad-CAM  
The Grad-CAM [11] method is an improvement of CAM [10] and designed to address the shortcomings of CAM's issues. It is possible to apply Grad-CAM to any convolutional neural network without any architectural modification where the networks' final result map is differentiable with respect to its activation maps. As the output of a typical segmentation network is a 2D array, it is possible to use Grad-CAM to visualize features in the desired layer of a network. The method is highly class-distinctive and localizes different objects in a single input image. It produces regions with inherent smoothing and mapping from a lower resolution convolutional layer to the input layer produces maps that often correspond to intuition, since it is easier for humans to understand regions instead of discrete pixels [7]. The adapted method for semantic segmentation is shown in Figure 3. The selected layer to extract the Grad-CAM from is an additional hyper-parameter. In networks with an encoder-decoder architecture, selecting one of the last layers from the encoder network generates smoother maps. In an encoder-decoder architecture, dimensions of the middle layers' feature maps (from the encoder network) are smallest among the network features. Upscaling (with interpolation) these small maps to match the input map generates smoother maps compared to the layers with larger dimensions, because much more upscaling is required. On the other hand, by selecting layers in the decoder part, the final map shows more detail and may highlight separated regions. This is because the generated map is computed using feature maps of the selected layer as well as the gradients of the output class with respect to the selected layer. Thus changing the layer generates different maps which are dependent on the selected layer. Maps generated with Grad-CAM can be considered as intermediate results for a segmentation network. Although this method is model-specific for CNNs and therefore not applicable to every network, it can help in understanding their internal functionality.  
Figure 2: Applying guided back-propagation to a semantic segmentation network.
## 3 Results and Discussion
### Performance Evaluation
SVM produced a better discrimination output with AUC = 0.993, followed by LR with AUC = 0.961, NB with AUC = 0.923, DT with AUC = 0.892, and KNN with AUC = 0.824. A good classification model has AUC near to 1, which indicates good separability. A poor classification model has AUC near to 0, which indicates the worst measure of separability. On the other hand, an AUC of 0.5 tells us that a model has no capacity whatsoever in separating classes. Figure 9 depicts the ROC curve of the different classifiers using 4096 FC2 features of the VGG16 model. Here, SVM also reported a better separability measure than the other classifiers. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Layer** & **DT** & **SVM** & **NB** & **LR** & **KNN** \\ \hline FC1 & 89.41\% & 95.71\% & 92.43\% & 95.98\% & 78.88\% \\ FC2 & 86.99\% & 96.08\% & 91.96\% & 95.90\% & 85.81\% \\ \hline \end{tabular}
\end{table} 
Table 16: Computed _F1-score_. Figure 8: Receiver operating characteristics (ROC) curve comparing performances of the classifiers using 4096 FC1 features of the VGG16 model. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Layer** & **DT** & **SVM** & **NB** & **LR** & **KNN** \\ \hline FC1 & 88.45\% & 92.98\% & 93.81\% & 93.81\% & 65.60\% \\ FC2 & 84.76\% & 93.33\% & 93.33\% & 93.33\% & 76.31\% \\ \hline \end{tabular}
\end{table} 
Table 15: Computed recall. Furthermore, this study explored the use of the precision-recall curve (PRC) to provide graphical performance estimates using precision and recall performance evaluation measures. PRC has recall on the \(x\)-axis and precision on the \(y\)-axis. Recall gives the proportion of burn samples correctly classified out of the total burn samples, and precision gives the proportion of true positive samples out of the predicted burn samples. PRC has advantages over ROC because the latter tends to provide an overly convincing view of the performance of a classifier with class-imbalanced data. PRC is more informative than ROC and is appropriate for assessing the performance of less represented samples. PRC summarises a trade-off between true positive rate and positive predictive value. Figure 10 provides the PRC of different classifiers using 4096 FC1 features of the VGG16 model. Figure 11 provides the PRC of different classifiers using 4096 FC2 features of the VGG16 model. In both figures, AUC was used to summarise the performances of the classifiers, which a retuned an estimated value. A value near 1 indicates excellent classification accuracy and a value near 0 indicates poor classification outcome. Figure 10: Precision–recall curve (PRC) comparing performances of the classifiers using 4096 FC1 features of the VGG16 model. Figure 9: ROC comparing performances of the classifiers using 4096 FC2 features of the VGG16 model.
## 5 Experiments and Results
### Basic Function Verification  
Figure 9 shows the original swept- frequency oblique backscatter ionograms of six channels. Although the transmitting power is only 500 W, under the effect of the pulse compression gain of m-sequence and multiple coherent accumulation, this system conducted the ionospheric oblique backscatter sounding. The signals between 260 and 300 km are the vertical reflected echoes. The oblique backscatter echoes appeared from 520 to 900 km. It is obvious that the power of vertical echoes is much stronger than that of oblique backscatter echoes. In the vertical echo part, the SNR generally larger than 220 dB, while for the oblique backscatter part, it is difficult to reach 160 dB. The strongest oblique backscatter signals are mainly distributed in the low frequency part, which is most likely due to the superposition of the 2-hops of the vertical-incidence echoes. As shown in Figure 9, the highest reflection frequency of the vertical-incidence signal is 6.2 MHz. This means that it is difficult to judge the mode of the echo signals below 600 km and 6.2 MHz. In essence, it limits the effective range and the operating frequency band of the oblique backscatter sounding.  
Figure 8: Map showing the location of the experiment in Wuhan, Xiantao, Hubei province, China and the antenna pointing direction in azimuth.  
Figure 9: _Cont_.  
Moreover, in addition to the desired signal, for the system operating in the HF band, there are also many strong radio-frequency (RF) interferences entering the receiver simultaneously. Sometimes the intensity of these interference signals can be quite large. At 6 MHz, 9.4 and 9.8 MHz, as examples, the SNR of the interference signals even exceed 205 dB, completely submerging the useful signals, which will also cause great difficulties in the extraction of oblique backscatter leading trace.
## 3 Key Technology of the A2S Integrated MIoT
### Cloud/Edge Computing and Caching
#### 3.2.1 Mobile Edge Computing (MEC)
and storage capabilities capable of handling complex data analysis and processing tasks. It takes data from the mobile edge layer and further processes, analyzes, and stores those data [121]. The cloud computing layer can also provide services such as data management, visual analytics, and decision support. This three-tier architecture based on mobile edge computing brings computing and storage resources closer to offshore devices and sensors, enabling lower latency and faster responses. This architecture can also reduce the dependence on remote cloud computing resources, reduce the DT latency and network congestion, and improve the system reliability [122]. At the same time, this architecture exhibits the good flexibility and scalability needed to adapt to the needs of MIoT applications of different sizes and complexities [123]. Overall, the three-tier architecture based on MEC provides an efficient, flexible, and reliable architecture for the A2S integrated MIoT to support data processing, analysis, and application of the needs of offshore equipment and sensors. By bringing computing and storage resources closer to the edge, more responsive and lower-latency MIoT systems can be achieved.
###### Abstract  
We study the problem of employing a mobile-sink into a large-scale Event-Driven Wireless Sensor Networks (EWSNs) for the purpose of data harvesting from sensor-nodes. Generally, this employment improves the main weakness of WSNs that is about energy-consumption in battery-driven sensor-nodes. The main motivation of our work is to address challenges which are related to a network's topology by adopting a mobile-sink that moves in a predefined trajectory in the environment. Since, in this fashion, it is not possible to gather data from sensor-nodes individually, we adopt the approach of defining some of the sensor-nodes as Rendezvous Points (RPs) in the network. We argue that RP-planning in this case is a tradeoff between minimizing the number of RPs while decreasing the number of hops for a sensor-node that needs data transformation to the related RP which leads to minimizing average energy consumption in the network. We address the problem by formulating the challenges and expectations as a Mixed Integer Linear Programming (MILP). Henceforth, by proving the NP-hardness of the problem, we propose three effective and distributed heuristics for RP-planning, identifying sojourn locations, and constructing routing trees. Finally, experimental results prove the effectiveness of our approach.  
mobile sink; energy efficiency; fuzzy decision system; wireless sensor network; rendezvous point planning +
Footnote †: journal: Sensors
# Situational Awareness Classification Based on EEG Signals and Spiking Neural Network
## 3 Proposed Method
### EEG Feature Mapping  
This section describes a unique mapping of the extracted features using five SCTN-based networks (one for each EEG sub-band). We introduce a novel approach for mapping each of the five EEG sub-bands into a unique spacial map using an SNN. Each of the 14 EEG channels was characterized by five sub-bands. For each sub-band, we constructed a unique spiking neural network which maintained the EEG topographic map, as depicted in Figure 5a. Each network was composed of an \(11\times 11\) matrix of SCTN-type neurons. Every neuron had 14 inputs representing the relevant frequency band derived from each of the 14 channels.  
The contribution of each channel to a specific EEG sub-band (i.e., delta, theta, alpha, beta, and gamma) is represented by a unique spacial topographic map. Within each map, 14 specific neurons were assigned in accordance with the placement of the 14 electrodes on the test subject's head, according to the EEG 10-20 system standard [44]. This representation captured the mutual influences between the channels and their spatial correlation.  
Figure 4: EEG frequency features. (**a,c**) SNN-based spikegram and (**b,d**) FFT spectrogram.  
The network weights were determined without any learning process according to a Gaussian kernel centered around each of the specific neurons (which represented a specific channel). The channels connected to these neurons received a maximal weight for the said channel (i.e., for the specific channel they represented). Therefore, the proposed topographic mapping reflects the spatial correlation and mutual influence among the 14 EEG channels.  
The SCTN network weights are given by  
\[W_{cb}(x,y)=\frac{1}{2\pi\sigma^{2}}e^{-\frac{(x-x_{c})^{2}+(y-y_{c})^{2}}{2 \sigma^{2}}} \tag{4}\]  
where \((x_{c},y_{c})\) represents the coordinates of the specific neuron assigned for a specific electrode and for \(\sigma=1\). Figure 5b depicts the weight distribution for each channel for a given sub-band according to the EEG channel map (Figure 2a).  
Figure 6 shows the five SCTN-based topographic maps (one for each sub-band) compared to the spacial maps created using an FFT. Again, high correlation can be observed between the two feature maps.
# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 2 GNSS Methodologies
### Error Analysis  
Since GNSS meteorology is a rather indirect technique, there can be sources of error in each step of the process. For instance, the presence of patterns of water vapor gradients can induce errors in GNSS-derived IWV to the point that methodologies have been developed to correct for this [27]. Another important feature when computing ZTD from GNSS measurements is the satellite Antenna Phase Center (APC) variations. The APC model changed with the addition of new satellites to the GNSS constellation. [PERSON] et al. [28] studied the influence of not applying proper modeling for APC, obtaining spurious, additional trends in IWV of up 0.15 mm year\({}^{-1}\). On 6 November 2006, IGS changed their processing strategy from using relative antenna phase center calibrations to absolute ones. [PERSON] et al. [29] showed the impact of this change in GNSS IWV products, using data in four locations in Spain from two years before and after this change. With relative calibrations, a 2-3 mm bias of GNSS with respect to other instruments (radiosonde, sun-photometer) was found. However, GNSS IWV after the change to absolute calibrations exhibited a practically zero bias. In addition, RMSE reduced to half and correlations slightly increased.  
Satellite positions and clock corrections are fundamental to the retrieval of tropospheric GNSS products. However, precise data can only be obtained after a few days, so they cannot be used for near real-time applications. A comparison of near real-time water vapor product with final products to compare the effect of rapid/final satellite orbit estimation obtained negligible bias and RMSE below 1 mm [30].  
The mapping functions that transform slant delays into zenith delays are also error-prone. [PERSON] et al. [31] compared GNSS IWV computed with Global Mapping Function (GMT) and Vienna Mapping Functions 1 (VMF1) on a global scale. Both products were compared against radiosonde observations. The authors concluded that there was not a better option. However, both mapping functions must be revisited for climate zones other than mid-latitude regions.  
Another feature in ZTD processing that can affect IWV measurements is cutoff in elevation angle. [PERSON] and [PERSON] [32] computed IWV trend using data-sets obtained with 8 different cutoff elevation angles at seven sites in Finland and Sweden for 14 years (1997-2010). In comparison with radiosonde trends, best RMSE was found for angles of 10\({}^{\circ}\) and 15\({}^{\circ}\). However, the best correlation (0.88) was found for cutoff angles of 15\({}^{\circ}\). Therefore, it was shown that GNSS data contained information about trends, but these were too small to be uniquely detected.  
It is important to notice that, specially when dealing with trend estimation, GNSS-IWV time series data must be retrieved with the same parameters to avoid inhomogeneities in the series.
## 2 Basic Theory
### BUSH Element and the Equivalent Dynamic Model of Normal Contact Stiffness  
The Virtual Spring Element Method is a widely used modeling method for bolted joint interface contact stiffness. This method is simple and has a clear physical interpretation, making it easy to understand, as shown in Figure 3.  
The BUSH element is a special type of spring element with six degrees of freedom at each node, allowing for convenient provision of stiffness in the desired directions. In general, defining a BUSH element requires two nodes, which are connected into a line element.  
The static equilibrium equation of the BUSH element is  
\[\mathbf{R}=\mathbf{Kq} \tag{14}\]  
where \(\mathbf{R}\) and \(\mathbf{q}\) represent the force vector and the displacement vector at the two end nodes of the BUSH element, respectively. The stiffness matrix \(\mathbf{K}\) of the BUSH element can be decomposed into four submatrices corresponding to the two nodes:  
\[\mathbf{K}=\begin{bmatrix}\mathbf{K}_{11}&\mathbf{K}_{12}\\ \mathbf{K}_{21}&\mathbf{K}_{22}\end{bmatrix}. \tag{15}\]  
The six input parameters of the BUSH element are denoted as \(k_{i},(i=1,\dots,6)\) ; the length of the element is denoted as L. The four submatrices are represented as follows:  
\[\mathbf{K}_{11}=\begin{bmatrix}k_{1}&0&0&0&0\\ 0&k_{2}&0&0&0&\frac{k_{2}L}{2}\\ 0&0&k_{3}&0&-\frac{k_{3}L}{2}&0\\ 0&0&0&k_{4}&0&0\\ 0&0&-\frac{k_{3}L}{2}&0&k_{5}+\frac{k_{3}L^{2}}{4}&0\\ 0&\frac{k_{3}L}{2}&0&0&0&k_{6}+\frac{k_{3}L^{2}}{4}\end{bmatrix}, \tag{16}\]  
Figure 3: Equivalent modeling of interface.  
\[\mathbf{K}_{12}=\mathbf{K}_{21}^{T}=\begin{bmatrix}k_{1}&0&0&0&0\\ 0&-k_{2}&0&0&0&\frac{k_{2}L}{2}\\ 0&0&-k_{3}&0&-\frac{k_{3}L}{2}&0\\ 0&0&0&-k_{4}&0&0\\ 0&0&\frac{k_{3}L}{2}&0&-k_{5}+\frac{k_{3}L^{2}}{4}&0\\ 0&-\frac{k_{2}L}{2}&0&0&0&-k_{6}+\frac{k_{2}L^{2}}{4}\end{bmatrix}, \tag{17}\]  
\[\mathbf{K}_{22}=\begin{bmatrix}k_{1}&0&0&0&0\\ 0&k_{2}&0&0&0&-\frac{k_{3}L}{2}\\ 0&0&k_{3}&0&\frac{k_{3}L}{2}&0\\ 0&0&0&k_{4}&0&0\\ 0&0&\frac{k_{3}L}{2}&0&k_{5}+\frac{k_{3}L^{2}}{4}&0\\ 0&-\frac{k_{2}L}{2}&0&0&0&k_{6}+\frac{k_{2}L^{2}}{4}\end{bmatrix}. \tag{18}\]  
Under the experimental system, an acceleration excitation along the normal direction of the interface is applied, as shown in Figure 4. In this case, the interface is subjected to the pressure from the upper structure along the normal direction and the acceleration excitation along the normal direction. The interface mainly exhibits contact and separation along the normal direction, ensuring that the normal contact stiffness plays a major role during the experiment.
## 2 Methods
### CWI of Seismic Interferometry  
Seismic interferometry [33] is a technique used to correlate ambient noise fields to derive Green's functions, which represent wave propagation between two receivers. It can be briefly explained by the convolution of the Green's function \(G\) at points \(A\) and \(B\), with time arguments \(t\) and \(-t\), respectively:  
\[G(x_{B},x_{s},t)*G(x_{A},x_{s},-t)=\int G\big{(}x_{B},x_{S},t+t^{\prime}\big{)} G\big{(}x_{A},x_{S},t^{\prime}\big{)}dt^{\prime}=G(x_{B},x_{A},t). \tag{5}\]  
Green's function represents the system's response to an impulse with \(x_{A}\) and \(x_{B}\) denoting the signals recorded at receivers \(A\) and \(B\) in response to an input signal \(x_{s}\) at the source position \(S\) over time \(t\).  
Equation (5) holds if the receivers \(A\) and \(B\) are surrounded by random noise sources. If there are limitations in the distribution or frequency content of the noise sources, Green's function will not be fully reconstructed. However, it may still be useful for further analysis, as shown in recent laboratory experiments [34].  
The function \(G(x_{B},x_{A},t)\) resulting from the cross-correlation of the signals recorded at two receivers is termed the cross-correlation function (CCF). This function enables the reconstruction of impulse responses, representing the low-frequency wave propagation used in this research. The causal part of the CCF is considered the impulse response for the CWI assessment, correlating the coda parts of the impulsive waves. Consequently, the CWI analysis in low-frequency wave propagation can be described as the \"Correlation of Coda in Causal part of CCF\", referred to as the \"C4 function\" in this study.
# Teleolocation--Remotely Operated Legged Robots
## 1 Introduction
### Teleoperation  
Autonomous robots have proven to be successful in surveillance, assembly, and basic navigation tasks, yet most real-world tasks are beyond the capabilities of state-of-the-art autonomy. Where autonomy is insufficient, human-in-the-loop systems have shown success [1; 2; 3]. Teleoperated systems can employ semi-autonomous robots, reducing the operator's cognitive load. In a reliable system, the robot is tasked with autonomous subtasks, while operator intervention is left for high-level decision making, such as obstacle avoidance or managing a team of robots [4]. In addition, properly incorporated haptic feedback can improve teleoperator performance [5] in the fields of robot-assisted minimally invasive surgery [6], control in cluttered environments [7; 8], obstacle avoidance in aerial navigation [9], and programming welding robots [10] to name a few.  
The benefits of teleoperation are widely associated with manipulation tasks. Examples include robotic maintenance of underwater structures [11; 12], robot-assisted minimally invasive surgery [13; 14], and assembly/welding tasks [15; 16]. Most of these operations rely on robot proxies localized to the task environment and require little if any traversal of terrain.
## 3 Capacitive Sensing with Electronic Detection
### Electronic Detection System  
#### 3.3.1 The Architecture  
The detailed architecture, principle of operation, calculation of Signal to Noise (_S_/_N_) ratio and system level aspects of the electronic detection systems are completely presented in the Supplementary information file. The electronic detection system is based on extremely low noise integrated modified lock-in amplifier architecture and circuit, which is optimised for the array of sensors, and which is able to process signals from four differential sensors that are generally differently functionalised. Each sensor is driven with excitation signal having different frequency, and thus the response from the whole array could be extracted in parallel in \"real time\". Figure 12 shows a simplified block diagram of a signal processing electronics implemented in 0.25 \(\upmu\)m Bipolar CMOS Double diffused Metal Oxide Semiconductor (BCD) process used to extract information from four sensors. It is essential that the electronics and the sensors are carefully optimised to each other because only in that way it is possible to reach the extreme detection level.  
Figure 12: Signal processing block diagram of the low noise electronic measurement system that can process signals from four differently modified sensors.  
The signals at the outputs of both charge amplifiers \(V_{\rm chop}\) and \(V_{\rm chon}\) include information on the capacitance differences of all the sensors connected to the particular amplifier; each sensor response appears at a different frequency. The complete signal is amplified using a fully-differential and programmable low-noise band-pass amplifier. The resultant signal level is well above the thermal noise level of the following blocks. The composed and amplified signal is mixed with a square-wave signal with frequency \(f_{\rm m}\) in a precision passive mixer [24, 25]. The amplitude of the corresponding spectral component is proportional to the excitation signal amplitude (assumed to be constant during the measurements) and corresponding capacitance difference of the sensor, which is caused by the adsorbed target molecules onto the modified capacitor. The signal levels of the spectral components carrying the information are well above the quantization and thermal noise level of the Analog to Digital Converter (ADC), while remaining higher harmonics fall outside the band of interest and are attenuated with a digital decimation filter implemented in the DSP. Further signal processing is digital and consists of digital mixers and LP digital filters. Digital mixers translate the signals from \(f_{\rm av}\left(x=1,2,\ldots,4\right)\) down to DC in a four separate digital channels. The remaining spectral components are further attenuated in digital low-pass filters. The DSP output rate is approximately 14 samples per second with the word-length of 32 bits. The results are transferred to the PC via USB interface, where further signal processing (averaging), storage and presentation are taking place.