esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analyses
### pXRF  
Following [PERSON] et al. [6], we used a Bruker Tracer III-SD portable X-ray Fluorescence (pXRF) analyser (Bruker, Billerica, MA, USA; also see Appendix A). The instrument employs an X-ray tube with an Rh target and a 10 mm\({}^{2}\) silicon drift detector (SDD) with a typical resolution of 145 eV at 100,000 cps. For analysis of copper-based alloys, we found that operating the X-ray tube with a setting of 40 keV at 5.0 \(\upmu\)A in an air-path and through a window composed of 12 mil Al and 1 mil Ti filters ([PERSON]'s Yellow filter) provided a good count rate for the elements of interest. Assays of artefacts were taken for 60 s each, and their locations were recorded on annotated photographs and transcribed to a photogrammetric model of each object. Reference standards were analysed using the same machine and settings outlined above (Appendix A). Each standard was analysed twice for 60 s, and the results were averaged.  
In total, 25 assays were taken on the helmet (Figure 3). The assay locations were selected based on a subjective assessment of the object, which included the visually determined position of different physical features, such as decorative elements, rivets, and areas potentially less affected by patina than others. Assays 1-5 formed a 'cluster assay', which took the average of the results of five assays over a 1 cm diameter area (Figure 4). The aim of the cluster assay was to provide a more representative sample in an area that was considered less affected by patina formation. The order the five assays were recorded in is presented in Figure 4. The cluster assay measures variation over a relatively contained surface area, where both the core metal and the depositional conditions should be largely consistent [6]. This type of approach falls into the category of 'area mapping', as proposed by [PERSON] et al. [23], to ascertain mean values of elemental intensities across an area and address the issue of microscale heterogeneity common on bronze alloys. Assays were targeted at the cleanest areas possible (i.e., those with perceived low surface roughness caused by patina). Calibrated results from the analysis are presented in Table 1.  
In addition to the calibrated assays, the Compton scatter is also useful for analysis. The inelastic Compton scatter of the X-ray target (Rh in this case) is inversely proportional to the mean atomic weight of the sample and therefore provides a measure for the relative density of the sample area (see [24]).
## 2 Materials and Methods
### Computational Domain and Boundary Conditions  
To reduce computational burden, only half of the fluid and solid domain is considered and a symmetry plane is placed at the center of leading edge along the \(x\)-direction. Only half of the 40 cm span length of the plate (i.e., 20 cm for half of the plate) is simulated. The computational domain extends 7 mm and 145 mm in the positive and negative \(x\)-direction from the leading edge, 60 mm in the \(y\)-direction from the symmetry plane of the plate, and 75 mm above and below the plate in the \(z\)-direction. These boundaries are within the range of boundaries in the experimental tank and large enough to have negligible effects on the moving plate. The computational domain with a square-shaped flat plate as a schematic view is shown in Figure 2, with \(x\), \(y\) and \(z\) denoting the direction of thrust, span of the plate and normal to the plane of the plate, respectively. The number of hexahedral cells in the fluid and solid domains are about 237, 860 and 1400. Computations are conducted on a PC equipped with an Intel(r) Core(tm) i5-5257U CPU 2.70 GHz \(\times\) 4 processor and it took about 22 h for the calculation of seven flapping cycles in the lower frequency range.  
The _symmetryPlane_ boundary condition is used for the front planes of the fluid and solid domains. The _movingWall Velocity_ and _zeroGradient_ boundary conditions are specified to both the velocity and pressure on the patch named as _interface_, which is coupled with the plate surface. For the boundary condition on the side, top and bottom patches, the _totalPressure_ and _zeroGradient_ are defined for the pressure and velocity, respectively. For the solid domain, the imposed flapping motion is applied to the leading edge of the plate and the boundary condition on the interface of the plate surface is utilized with _tractionDisplacement_ for the displacement.  
Figure 1: Schematic view of a square flexible plate. The leading edge is indicated by the red line and the trailing edge is indicated by the blue line.  
Figure 2: (**a**) Schematic view of a computational domain. (**b**) Grid generation. The leading edge and symmetry plane of the plate are indicated by red and yellow lines, respectively. The origin of coordinate is at the center of the leading edge of the plate.
# Two-Layer Matrix Factorization and Multi-Layer Perceptron for Online Service Recommendation
## 3 The TMMNN  
The overall structure of the neural network model, based on two-layer matrix factorization and a multi-layer perceptron, is shown in Figure 2, which consists of three layers: the embedding size adjustment layer, the embedding layer, and the recommendation layer. First, in the TMMNN model, for a given sample label pair, the embedding dimension adjustment layer adjusts the embedding dimensions of users and items, and outputs the adjusted sizes; then, the embedding layer will receive user and item numbers, together with the embedding vector dimensions, and output the corresponding embedding vectors; finally, the embeddings output by the embedding layer are sent to the recommendation layer, and the prediction result is obtained after the calculation by matrix factorization and multi-layer perceptron. The details of each layer will be described in the following sections.
## 3 System Architecture and Channel Models
### Noise-Resistant Channel Measurement Model
\(\mathbf{\Phi}^{(\mathbf{m})}\) is defined as \(\mathbf{\Phi}^{(\mathbf{m})}\triangleq\mathbf{X}^{(m)}\mathbf{F}\). Considering the comb-type pilot arrangement, the transmission model of \(K_{p}\times M\) pilot symbols can be expressed as  
\[\mathbf{Y_{P}}=\mathbf{\Phi_{P}H}+\mathbf{W_{P}}, \tag{8}\]  
where \(\mathbf{Y_{P}}=[\mathbf{y}_{\mathbf{P}}^{(1)},\mathbf{y}_{\mathbf{P}}^{(2)},\cdots,\mathbf{y}_{\mathbf{P}}^{(M)}]\) and \(\mathbf{W_{P}}=[\mathbf{w}_{\mathbf{P}}^{(1)},\mathbf{w}_{\mathbf{P}}^{(2)},\cdots,\mathbf{w}_{\mathbf{P}}^{(M)}]\) are the submatrixs of \(\mathbf{Y}\) and \(\mathbf{W}\) at the location of the pilot subcarriers, respectively. \(\mathbf{\Phi_{P}}\triangleq\mathbf{X_{P}F_{P}}\) is the known dictionary matrix where \(\mathbf{X_{P}}\) is the \(K_{p}\times K_{p}\) diagonal matrix with the known pilots along its diagonal and \(\mathbf{F_{P}}\) is the \(K_{p}\times L\) DFT matrix. By vectorizing \(\mathbf{H}\) as \(\mathbf{h}=vec(\mathbf{H}^{T})\), a pilot-based channel measurement model can be constructed according to (8), as  
\[\mathbf{y_{P}}=\mathbf{U_{P}h}+\mathbf{w_{P}}, \tag{9}\]  
where \(\mathbf{y_{P}}=vec(\mathbf{Y_{P}^{T}})\), \(\mathbf{w_{P}}=vec(\mathbf{W_{P}^{T}})\) and \(\mathbf{U_{P}}=\mathbf{\Phi_{P}}\otimes\mathbf{I_{M}}\). AWGN can be adopted to approximate the additive noise \(\mathbf{w_{P}}\). However, the attenuation and noise of the UWA channel may vary over the signal bandwidth. When a small number of pilot symbols are used to estimate the UWA channel with strong colored noise, the accuracy of the pilot-based channel measurement will decrease severely. To improve the performance of channel measurement, a noise-resistant channel measurement model is constructed according to (7) and it can be expressed as  
\[\mathbf{y}=\mathbf{U}\mathbf{h}+\mathbf{w}, \tag{10}\]where \(\mathbf{y}=vec(\mathbf{Y}^{T})\), \(\mathbf{w}=vec(\mathbf{W}^{T})\) and \(\mathbf{U}\) is the \(KM\times LM\) matrix as  
\[\mathbf{U}= \begin{bmatrix}diag\{\mathbf{\Phi}^{(1)}[1,1],\cdots,\mathbf{\Phi}^{ (M)}[1,1]\}&\cdots&diag\{\mathbf{\Phi}^{(1)}[1,L],\cdots,\mathbf{\Phi}^{(M)}[ 1,L]\}\\ &\vdots&\ddots&\vdots\\ diag\{\mathbf{\Phi}^{(1)}[K,1],\cdots,\mathbf{\Phi}^{(M)}[K,1]\}&\cdots&diag\{ \mathbf{\Phi}^{(1)}[K,L],\cdots,\mathbf{\Phi}^{(M)}[K,L]\}\end{bmatrix} , \tag{11}\]  
where \(\mathbf{\Phi}^{(m)}[i,j]\) denotes the entry in the \(i\)th row and the \(j\)th column of \(\mathbf{\Phi}^{(m)}\). All subcarriers are used for channel measurement in (10), as the unknown data symbols can be obtained by data detection or decoding. Assuming that the power distribution over all subcarriers is non-uniform, \(\lambda=[\lambda_{-K/2},\lambda_{-K/2+1},\cdots,\lambda_{K/2-1}]\), \(\mathbf{w}\) in (10) satisfies:  
\[\mathbf{w}\sim\mathcal{CN}(\mathbf{0},\mathbf{\Lambda}), \tag{12}\]  
where \(\mathbf{\Lambda}=diag(\lambda)\otimes\mathbf{I}_{M}\).
## 3 Results
### Synthetic Produced Water
#### 3.4.2 Membrane Distillation Testing  
Figure 9 shows the flux of the PTFE and the PES membrane during a one-day test. The flux using the PTFE membrane was stable up to 4 h of operation. At that moment, water was added to the feed to keep the feed concentration constant. Thereafter, the flux decreases to zero. Figure 10 shows the visual transformation from opaque to transparent of the PTFE membrane, which shows the gradual wetting during the course of the experiment. Remarkably, no increase was observed in permeate conductivity for the PTFE, which remained below 20 \(\upmu\)S/cm. When refilling the feed vessel with water, the emulsion is locally broken (observed visually) and hexadecane is able to penetrate the membrane. Since the hexadecane and PTFE are both apolar molecules, the membrane takes up the hexadecane, which remains in the membrane and blocks the flux. Sodium chloride is not soluble in hexadecane, explaining the unexpected combination of flux decrease due to membrane wetting, without loss of salt retention. In contrast to PTFE, the flux of the PES membrane only steadily decreases, with only 9%. No visual observation of wetting was observed for this membrane, however the steady decrease might indicate that some of the pores are also blocked by the hexadecane, reducing the flux, causing the slight decrease in flux.  
Figure 8: Micelle formation with surfactant in water mixture and emulsification of the oil-in-water mixture.  
Figure 10: Pictures of the PTFE membrane during the experiment with oil/SDS mixture after **(a)** 4 h; **(b)** 5 h; **(c)** 6 h of MD operation.  
Figure 9: Flux as function of time for PTFE and PES with the oil/SDS mixture as feed solution.
# Automated Methodology for Dependability Evaluation of Wireless Visual Sensor Networks
## 4 Proposed Methodology  
The proposed methodology takes advantage of the SHARPE tool to support hierarchical models. These models are structures that provide an overall model solution by composing individual model results, thus avoiding a large overall state space [13; 64]. That way, it is possible to model at a higher level the dependency of events using Fault Trees, while at a lower level the complex behavior can be described by state-based models (Markov Chains, for instance). These formalisms are capable of modeling the required behavior and supporting the extraction of all the required metrics, like availability and reliability [65]. Figure 10 presents an overview of the proposed methodology, which is detailed in the following subsections.  
Figure 9: Fault Tree models of (**a**) network; (**b**) combinations; (**c**) paths; and (**d**) devices.
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 7 Simulations
### Experimental Framework  
We developed a simulator based on SimPy [15], which is a process-based discrete-event simulation framework, implemented in the Python programming language. We modeled the nodes and underwater links. The basic features are low data-rate, large propagation-delay, multipath fading, and high error-probability. The medium access was implemented with TDMA with off-line slot-allocation within the frames.  
Once the system is configured in the simulator, the experiments were run with diverse release-times for the messages in the source nodes. With these variations we can evaluate the network behavior, and the loads in the intermediary nodes for each path. Specifically, we measured the following four parameters:  
1. End-to-End delay.
2. Packet Delivery Ratio (PDR), i.e., the ratio of packets successfully received, compared to the total sent.
3. Network Goodput Ratio, that is, the ratio of packets received before their deadline, compared to the total sent.
4. Number of messages held in the processing queue of nodes.
## 3 Methodology
### A Summary of I2 CG and I2 CL
\(i\in I\), \(u\in\) the set of all clients \(U\)
2:for each round \(t=1,2,\dots\)do
3:\(U_{t}\leftarrow\) (Select a subset of \(U\) to simulate federated clients)
4:\(U_{t}\leftarrow\) (Select a subset of \(U-U_{t}\) to simulate communication-constrained clients)
5:for each client \(u\in(U_{t}\cup U_{t}^{\prime})\) in parallel do
6:for items \(i\in I_{u}\)do
7:\(\delta_{ui}^{t}\gets ClientUpdate(u,\delta_{i}^{t-1})\)
8:endfor
9:endfor
10: Collect updated item model parameters from clients in \(U_{t}\)
11:\(\delta_{i}^{t}\leftarrow\frac{1}{|U_{t}|}\sum_{u\in U_{t}}\delta_{ui}^{t}\)
12:endfor
```  
**Algorithm 1** ServerUpdate  
```
1:Initialize \(\theta_{u}\) and \(score_{u}()\) with the latest update
2:Retrieve user positive feedback \(I_{u}\) according to user index \(u\)
3:Sample negative feedback \(I_{u}^{\prime}\) from unobserved items
4:if\(u\in U_{t}\) or \((u\in U_{t}^{\prime}\) and \(strategy=I2 CG)\)
5:Download \(\delta_{i}\) from the server then initialize \(\delta_{ui}\) with \(\delta_{i}\), \(i\in I_{u}\cup I_{u}^{\prime}\)
6:endif
7:if\(u\in U_{t}^{\prime}\) and \(strategy=I2 CL\)
8: Initialize \(\delta_{ui}\) with locally stored \(\delta_{ui}\), \(i\in I_{u}\cup I_{u}^{\prime}\)
9:endif
10:\(\mathcal{B}\leftarrow(\text{split }I_{u}\cup I_{u}^{\prime}\) into batches of size \(B)\)
11:for\(e=1\) to epoch \(E\)do
12:for batch \(b\in\mathcal{B}\)do
13: Compute \(\
abla_{\theta_{u}}L_{u}\), \(\
abla_{score_{u}}L_{u}\) and \(\
abla_{\delta_{ui}}L_{u}\) with Equation (2)
14:\(\theta_{u}\leftarrow\theta_{u}-\eta\
abla_{\theta_{u}}L_{u},score_{u}\gets score _{u}-\eta\
abla_{score_{u}}L_{u}\), \(\delta_{ui}\leftarrow\delta_{ui}-\eta\
abla_{\delta_{ui}}L_{u}\)
15:endfor
16:endfor
17: Save \(\delta_{ui}\), \(\theta_{u}\) and \(score_{u}()\) locally
18:if\(u\in U_{t}\)
19: Return \(\delta_{ui}\) to server
20:endif
```  
**Algorithm 2** ClientUpdate
## 3 Development of Scabbing Depth Formula
### Derivation of Scabbing Limit Formula  
The scabbing energy (\(E_{sc,z}\)) converges to zero as the distance from the collision point becomes deeper (Figure 5); however, it does not reach zero. At this time, the partially reflected energy would offset \(E_{EP}\) and \(E_{SD}\), because the overall deformed and elastic penetrated slab will return to normal. The remaining thickness (\(h\)-\(x_{pe}\)-\(x_{t}\)) for preventing the scabbing phenomenon can absorb the remaining energy, which is obtained by subtracting \(E_{EP}\) and \(E_{SD}\) from \(E_{SP}\), as shown in Equation (35). However, \(E_{EP}\) and \(E_{SD}\) cannot be calculated because the slab thickness is unknown. As the values of \(E_{EP}\) and  
Figure 4: Definition of the radius of the contact area (\(a_{cl}\)).  
Figure 5: Relation between distance (\(z\)) and penetration depth (\(x_{pe}\)).  
are not large, they are neglected in Equation (36). As mentioned above, the equation converges but does not reach zero; thus, an appropriate lower limit value should be presented. Here, the lower limit assumes 5% of \(E_{SC}\). The 5% of the remaining energy is expected to be consumed by cracks in the rear face of a reinforced concrete panel. Therefore, the value of \(h\) in Equation (37) can be summarized in Equation (38), where \(h\) is equal to the scabbing limit thickness (\(h_{sc}\)).  
\[E_{SP}-E_{EP}-E_{SD}=E_{SC,remain} \tag{35}\]  
\[E_{SP}=E_{SC,remain} \tag{36}\]  
\[E_{sc}\Bigg{(}\frac{\left(h-\left(x_{pe}+x_{t}\right)\right)^{2}}{\left(x_{pe }+x_{t}\right)^{2}}+1\Bigg{)}^{-1}=E_{sc,remain}=0.05E_{SP} \tag{37}\]  
\[h=h_{sc}=5.36(x_{pe}+x_{t}) \tag{38}\]  
where, \(E_{SP}\) is the spalling resistant energy (N-mm); \(E_{EP}\) is the elastic penetrated energy (N-mm); \(E_{SD}\) is the overall deformed energy of panel (N-mm); \(E_{SC,remain}\) is the remaininig energy (N-mm) to be absorbed in the panel; \(E_{SC}\) is the scabbing resistant energy (N-mm); \(h\) is the thickness of the panel (mm); \(x_{pe}\) is the penetration depth (mm); \(h_{sc}\) is the scabbing limit thickness (mm); and \(x_{t}\) is the tunneling depth (mm).
# Community Detection Based on Graph Representation Learning in Evolutionary Networks
## 2 Related Work  
Community detection is one of the most active topics in the field of graph mining and network science. With the continuous expansion of the real-world network scale and the introduction of temporal information, the research on community detection in evolutionary networks can explore community detection algorithms based on the network structure and network information conforming to the real world.  
Network representation learning. In the research of network representation learning, one of the foremost requirements of network representation learning is preserving network topology properties in learned low-dimension representations [16]. Researchers often use methods based on matrix decomposition or random walking to learn the graph representation, such as LLE [17], Laplacian Eigenmaps [14], Deepwalk [10], and LINE [18]. However, these methods typically have high computational complexity and poor generalization ability. In addition, they lack the use of temporal information in evolutionary networks.  
Evolutionary network. The research on the dynamic evolution of networks has been an ongoing hotspot. The dynamic information in networks has proven to be crucial for understanding networks. Many works have attempted to combine the research of evolutionary network representation learning with the basic research of complex networks. To reveal the dynamic mechanism of information dissemination networks, [PERSON] et al. [19] modeled the information propagation process as several discrete networks with continuous-time slices occurring at different rates.  
[PERSON] et al. [20] realized the joint learning of the graph structure information and time dimension information by combining the graph attention mechanism with the convolution of the time dimension, and processing the dynamic graph structure data by using the self-attention layer as time passes. It is of great significance to learn and fuse a network structure, multi-modal information, and temporal information based on effective methods, which can obtain more accurate representations of evolutionary networks.  
Community detection. In recent years, community structure detection has received extensive attention, and communities play an important role in complex systems [21]. However, most of the traditional community detection algorithms for static networks are not suitable for research on evolutionary network structures. [PERSON] et al. [22] proposed a community detection algorithm based on the minimum change granularity for evolutionary networks (DWGD), which can complete dynamic community detection with time slices as the minimum granularity. [PERSON] et al. [23] proposed that Markov chain-based community detection algorithm for evolutionary networks had better computational efficiency and detection performance. In addition, many researchers also studied community detection in evolutionary networks by the Coherent Neighborhood Propinquity of dynamic networks [24], building compressed graphs [25], etc.
## 4 Characterization of Real-Life Signals
### Experiments
#### 4.2.1 Experiment with the Spectrum Analyzer  
An Anritsu MS 2690A spectrum analyzer [36] is used to capture samples of each of the aforementioned signal types. The samples are collected at the rate of 10 MHz for a duration of 1 s. The RSSI is calculated using Equation (1) for \(N=200\). In total, 50 k RSSI values are obtained. The first row of Figure 6 contains the normalized RSSI histograms of the three selected technologies, and the spectrograms are shown at the corresponding position of the second row. The time span of the spectrogram is limited to 30 ms for visualization purposes.  
As anticipated, the Wi-Fi signal appears as short and random bursts in the spectrogram. Since the signals belong to multiple stations and AP's, the histogram contains several peaks between \(-50\) dB and \(-75\) dB, corresponding to signals transmitted by nearby and remote stations, respectively. Note that the histogram varies with the traffic load of the Wi-Fi network and the distance to the stations and AP's. However, due to the nature of contention-based collision avoidance, there is always a significant amount of noise samples present in the time frame of 1 s. Hence, the high peak on the left side of the histogram contributed by noise always exists.  
The LTE signal has the most versatile spectrogram, which is primarily occupied by the frequently occurring reference signal in the idle resource blocks. The 1 MHz-wide signal occurring every 5 ms at the center of the LTE band is the synchronization sequence. The remaining parts of the spectrogram with higher intensity are the resource blocks active for data transmission. The histogram of the LTE signal contains multiple peaks spreading out from \(-80\) dB to \(-55\) dB. Unlike the histogram of Wi-Fi, it is rather difficult to map the peaks to the exact causes for LTE. Moreover, the LTE signal is highly  
Figure 6: The normalized histograms of RSSI (first row) and the spectrograms (second row) for Wi-Fi, LTE and DVB-T signals. All graphs are obtained by post-processing of raw IQ samples collected by the Anritsu 2690A spectrum analyzer at the rate of 10 MHz.  
sensitive to the instant throughput demands of the users; thus, the magnitude of the peaks varies from one moment to another. What remains unchanged is the extremely wide range of variation in the signal strength and the large number of peaks present in the histogram.  
The DVB-T's spectrogram shows that it is highly stable in both the time and frequency domain. This is because the current DVB-T standard only allows continuous transmission and does not contain flexibility in carrier allocation. Therefore, its histogram simply matches the characteristic of Gaussian distribution: only one narrow peak is present. Sometimes, fading may cause some tiny peaks to appear next to the main one. In general, the RSSI's histogram from the DVB-T signal has the minimum number of peaks and the least amount of variation in signal strength among the three technologies.