esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Multiplexing Readout
### MHz-Frequency-Division Multiplexing
An energy resolution of \(2.91\pm 0.03\,\mathrm{eV}\) at 5.9 keV has been achieved in a summed energy spectrum with 20 pixels, from a \(5\times 5\) array of Ti/Au TESs microcalorimeters with an intrinsic single pixel energy resolution of about 2.7 eV. A new experiment has been performed with a larger \(LC\) filters array and using an uniform \(8\times 8\) array of \(80\times 20\,\mathrm{\SIUnitSymbolMicro m}^{2}\) X-IFU-like TESs with a measured single pixel energy resolution of \(\sim\)1.8 eV (see Figure 12). An excellent energy resolution of \(2.35\pm 0.03\,\mathrm{eV}\) at 5.9 keV has been demonstrated with 32 pixels multiplexed [148]. At the time of writing this review, a \(2.22\pm 0.02\,\mathrm{eV}\) at 5.9 keV has been demonstrated with 37 high aspect ratio pixels, with geometry \(80\times 13\,\mathrm{\SIUnitSymbolMicro m}^{2}\), in a k-pixels array multiplexed at frequencies from 1 to 5 MHz [149]. The results fulfill the X-IFU requirements. This is the first convincing demonstration of MHz-frequency division multiplexing with TES microcalorimeters. Improvements are still expected in the coming years, after a fine tuning of the read-out circuit and when employing further optimized detectors currently under fabrication. These recent results show no fundamental limitation yet for this multiplexing scheme. The MHz-FDM is reaching a competitive level of maturity, and has the potential to achieve even higher multiplexing capability per SQUID channel. As a matter of fact, the current implementation of the current MHz-FDM is far from optimal and increasing the the bandwidth to 6 MHz appears to be feasible. Many new ideas have still to be implemented to efficiently use, for example, the limited resources available for space-borne instrumentations [145; 150; 151].
# The Leaky-Wave Perspective for Array-Fed Fabry-Perot Cavity and Bull's-Eye Antennas
## 2 Fabry-Perot Cavity Antennas
### Fabry-Perot Cavities and Far-Field Twisted Beams
Equivalently, such a condition can be explained by the dominance over the entire field of a singular spectral leaky contribution on the antenna aperture. Conversely, from the expression in (3), one may observe that a radiation null is always located at broadside [7; 35]. Assigning to the VEDs a proper phasing scheme, it is possible to obtain a radiated field of a desired orbital angular order, selected from among 0, \(\pm 1\), \(\pm 2\), \(\pm 3\), without affecting the beam angle. It is worthwhile stressing that the latter result was obtained by reducing the TEM mode to a slow-wave regime filling the cavity with a low dielectric-constant material, and exciting the TM\({}_{1}\) mode in a fast-wave regime, with the relevant radial attenuation constant small enough to make the field dominant over the aperture, as shown in the dispersion diagram in Figure 3.  
Figure 3: Dispersion diagram for the dominant TM\({}_{1}\) leaky mode supported by an FPCA. Two different curves for both phase and attenuation constants (represented by solid and dashed lines, respectively) correspond to two different low permittivity dielectrics, obtained respectively with a single layer and through a multilayer, reproduced from [28]. Figure 2: Model, perspective view, and transmission-line model of a circular array-fed Fabry–Perot cavity. Reproduced from [28]. It is demonstrated that, referring to parameters chosen as in [28], a single dominant leaky solution is obtained around 18 GHz, whose relevant far-field pattern in the elevation plane is weakly affected by the chosen OAM order imposed by phasing, as shown in Figure 4.  
In Figure 5, the magnitudes of the scattering parameters are reported, demonstrating values lower than the \(-18\) dB threshold, hence confirming an excellent excitation performance. Figure 4: Normalized radiation pattern for the ideally infinite FPCA extension, at a frequency of 18 GHz, for an elevation angle of 52\({}^{\circ}\), with the filling low-permittivity dielectric obtained with a single layer; further details and effects due to FPCA truncation are available in [28]. Figure 5: Reflection and transmission coefficients of the optimized FPCA feeding network. Due to the circular symmetry of the feeding array, a limited number of scattering parameters have been simulated and reported in [28].
# Dynamic Repositioning of Aerial Base Stations for Enhanced User Experience in 5G and Beyond
## 3 System Model
### Path Loss Model  
The air-to-ground path loss model [13; 22] is used, which gives, in a probabilistic manner, the average path loss:  
\[PL(dB)=20\text{log}\,\frac{4\pi\,f_{c}d}{c}+P(NLoS)\eta_{NLoS} \tag{1}\]  
where \(d\) is the distance between the ABS and receiver, \(P(LoS)\) is the line-of-sight (LoS) probability, and \(P(NLoS)\) is the non-line-of-sight probability. Furthermore, \(\eta_{LoS}\) and \(\eta_{NLoS}\) represent the additional losses in the cases of LoS and NLoS, respectively. The value for \(d\) is given by \(\sqrt{h^{2}+r^{2}}\), where \(h\) and \(r\) are the altitudes of the UAV and its horizontal distance from the receiver, respectively. \(P(LoS)\) is given by  
\[P(LoS)=\frac{1}{1+a\,\,\exp(-b(\theta-a))} \tag{2}\]  
where \(a\) and \(b\) are constants depending on the environment and \(\theta\) is the elevation angle (in degrees) given by \(\frac{180}{\pi}\arctan\left(\frac{h}{r}\right)P(NLoS)=1-P(LoS)\).
# Stable and Low-Spurs Optoelectronic Oscillators: A Review
## 3 Improvement of Optoelectronic Oscillators  
For OEOs, the employment of low-loss, long optical fiber as the ultra-high Q electro-magnetism energy storage element ensures the low phase noise performance. However, it inevitably brings some knotty problems including rock-ribbed spurs and inferior frequency instability at the same time.  
Figure 1: (**a**) The schematic diagram of a fundamental optoelectronic oscillator (OEO). EOM: electro-optical modulator. PD: photodetector. EA: electronic amplifier. PS: phase shifter. PC: power coupler. (**b**) The example spectrum of fundamental optoelectronic oscillator.  
For the wider practical application of OEOs, suppressing spurious mode and improving long-term frequency stability are two burning questions.
## 3 Performance Analysis
### Average Channel Capacity  
The Shannon channel capacity is essentially time-varying relative to the instantaneous value of the received SNR. Therefore, average channel capacity is often used for evaluation. The precise definition of the average channel capacity is obtained by [37], \(\bar{C}=E[B\text{log}_{2}(1+\gamma)]\), where \(E(\cdot)\) refers to the mathematical expectation operator. Usually, we deduce the average channel capacity using the Shannon formula modified by channel CDF:  
\[\bar{C}=\frac{1}{\ln 2}\int_{0}^{\infty}\frac{1-F_{\gamma}(\gamma)}{1+\gamma}d \gamma. \tag{24}\]  
Utilizing \(\frac{1}{1+x}=G_{0,0}^{1,1}\bigg{(}x\begin{array}{c}0\\ 0\end{array}\bigg{)}\), substituting (14) into (24) and using the extended generalized bivariate Meijer's G function (EGBMGF) in [38], we obtain the average channel capacity as:  
\[\bar{C}=\frac{2^{(\phi+\phi)}A_{1}\sqrt{\bar{C}}}{16\pi\ln 2}\gamma_{1}\times G_{0,0,0,0,1,6}^{0,0,1,16,6}\Bigg{[}\begin{array}{c}-\\ -\end{array}\Bigg{|}\begin{array}{c}0\\ 0\end{array}\Bigg{|}\begin{array}{c}\frac{\xi^{2}+1}{2}\\ o_{1}\end{array}\Bigg{|}\begin{array}{c}\frac{\xi^{2}+1}{2}\\ o_{1}\end{array}\Bigg{|}\begin{array}{c}\frac{(\varphi\phi)^{2}C}{16\gamma_{2 }}\end{array}\Bigg{]} \tag{25}\]
# A Two-Stage Routing Protocol for Partitioned Underwater Wireless Sensor Networks
## 1 Introduction
In this phase, we propose a partition resolution strategy to reach and disseminate neighborhood information to the disconnected parts of the network. In phase 2, the neighborhood information collected in phase 1 is used to select appropriate relay nodes for forwarding the collected data to the sink node. A minimum residual energy threshold policy is used to enable busier nodes avoid early death in order to continue their sensing job. We also devise a rerouting strategy to route traffic in case a relay node cannot continue to operate as relay upon reaching its residual energy threshold. In this work, we assume that all sensor nodes are freely mobile with underwater currents. All the sensor nodes transmit with same default transmission power initially. However, the transmission power can be increased when required. We assume that the relative positions of nodes do not change considerably in the short run. This assumption is reasonable because all the sensor nodes move within the same underwater stream. All nodes employ the multipath compensation strategy proposed in [13]  
Figure 1: Commonly used network architecture. to compensate for the multipath effect. Depth levels can be adjusted using pressure sensors; however, we deploy nodes at equal depth, thus forming a two-dimensional (2D) topology. Nevertheless, if we deploy nodes at different depth levels, the three-dimensional problem can be translated to 2D using depth information from pressure sensors. Main contributions of this work are as follows:  
1. Network architecture is proposed that is suitable for mobile UWSNs. 2. A partition resolution strategy is devised which improves network performance by improving the packet delivery ratio. 3. A minimum residual energy threshold is used in order to delay early death of nodes and to shift the traffic load to the less loaded downstream nodes. 4. In order to deal with connectivity holes, a loop-free re-routing scheme is devised to keep the network connected. Rest of the paper is organized as follows: Section 2 details the literature review. Section 3 explains the network architecture and methodology of the proposed protocol. Section 4 contains results and discussion. Finally, Section 5 concludes the paper.
## 2 Methodology
### Failure Mechanism  
The major role of jet-grouted bottom sealing barriers is to prevent or minimize water inflow towards the excavation bottom, so as to ensure that the effect of excavation dewatering on the surrounding environment can be reduced to a lower degree. In general, when considering the design of jet-grouted bottom sealing barriers, on the one hand, sufficient depth \(h_{s}\) is needed to meet the anti-uplift requirement; on the other hand, the thickness of massive horizontal bottom sealing barriers \(h_{g}\) should be kept as small as possible to save on construction cost. When considering the mechanical properties of jet-grouted bottom sealing barriers, the two failure mechanisms that are presented in Figure 2 have been considered in the following:  
(1) Instability failure:  
The whole jet-grouted bottom sealing barriers become unstable and may vertically slip along the underground enclosures when the sum of the self-weight of the untreated soils that are below the excavation bottom and the shear force at the interface between the jet-grouted bottom sealing barriers and the underground enclosures are not enough to resist the uplift action of the groundwater below bottom sealing barriers (Figure 2a).  
(2) Seepage failure:  
By in large, the after soil is improved by conventional high-pressure rotary jet grouting, the strength of the soil increases by several orders of magnitude, and the permeability will also be greatly reduced [24; 25; 26]. Ideally, the jet-grouted bottom sealing barriers can be regarded as almost impermeable materials. However, due to many uncertain factors, such as heterogeneous geology, inappropriate construction equipment, and operation technique, the jet-grouted bottom sealing barriers are still permeable. Thus, groundwater with a high head may induce seepage failure of the jet-grouted bottom sealing barriers (Figure 2b), which results in a large amount of groundwater inflowing towards the excavation. This will bring difficulties to the excavation dewatering, and even cause flooding hazards inside the excavation when it is serious.  
Figure 1: Plan view of jet-grouted bottom sealing barriers.  
Figure 2: Failure mechanisms of jet-grouted bottom sealing barriers: (**a**) Instability failure; (**b**) Seepage failure.
# ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition
## 3 Materials and Methods
### Motivation
#### 3.1.3 Overview of ViT-Cap Module  
Motivated by these factors, this study aimed to propose a vision transformer-based capsule network model for finger vein recognition. A capsule network can be regarded as modelling shared local information. Compared with CNN and traditional neural networks, the capsule network is more adaptable to bad data and can adapt to affine transformation for input data.  
We introduced transformer-based modelling of shared local information and then explored the network structure of the vision transformer based on global and local attention. Our model combined the advantages of capsule networks in processing the underlying vision with the advantages of transformers in processing the relationships between visual elements and objects. Our proposed model solved the problem of the lack of long-range dependence on the encoding image and its inability to selectively focus on important image feature information when the capsule network is used for image classification, thereby achieving better finger vein classification results. In addition, the capsule network can better explore the relationship between features. In the case of a small amount of data, a capsule network has better generalization abilities than CNNs and can resist overfitting better. The model we proposed solves the limitation of a small amount of finger vein data and maximizes the performance of the model.  
The complete architecture of our proposed model is shown in Figure 1. We used the encoder part of the transformer architecture combined with our capsule networks and their dynamic routing to implement finger vein recognition. The model we proposed mainly consisted of three parts. The first part was to process the input finger vein image and linearly embed it into the transformer encoder, the second part was to build the transformer module to process the embedded finger vein image, and the third part was to deploy the capsule network module. The data processed by the transformer is inputted into the capsule network module for further training, and finally, the model results are outputted. A detailed description of the model architecture is expanded in Sections 3.2-3.4.  
Figure 1: The overall architecture of the ViT-Cap model.
# Design and Experimental Study of Potato-Soil Separation Device for Sticky Soils Condition
## 4 Separation Device Performance Test  
### Test Materials and Equipment  
The separation test was performed in the laboratory to examine the effect of the device on potato-soil separation and optimize the run parameters. The various parameters of winter potato-soil mixture in the maturity period were investigated by the research team to resemble the actual field potato-soil mixture closely. As revealed from the result, the soil hardness of the upper potato soil mixture (0-10 cm) was 155.2 KPa, that of the lower potato soil mixture (10-20 cm) in the mature period reached 665.3 KPa, the bulk density of potato-soil mixture was 0.98 g/cm\({}^{3}\), the potato content was 6.30 \(\times\) 10\({}^{-3}\) g/cm\({}^{3}\), and the root content was 1.03 \(\times\) 10\({}^{-4}\) g/cm\({}^{3}\) in dry matter.  
Several fresh potatoes, roots (15.03% moisture content), and soil (18%, 23% and 28% moisture content) were employed as the experimental materials. The samples bin (height 20 cm; width: 30 cm; length: 50 cm; volume: 30,000 cm\({}^{3}\)) was employed to prepare an artificial potato-soil mixture sample. The mixture was loaded into the samples bin, and the sample was compacted to achieve corresponding values of soil hardness of the upper and lower layers. The soil mass, potato tuber, and root in samples with different moisture contents were determined by complying with the field research results, and the result is listed in Table 1. Figure 8 presents the prepared samples of potato-soil mixture.  
The potato-soil separation device is illustrated in Figure 9. The potato-soil separation experiment was performed in situ by driving power machinery. For the respective test, the prepared potato-soil mixture was first placed at the front end of the first-stage lifting chain to run the machine to separate the potato-soil mixture.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Moisture Content** & **Volume** & **Soil Mass** & **Potato Mass** & **Root Mass** \\ _/\%_ & **/cm\({}^{3}\)** & **/kg** & **/g** & **/g** \\ \hline
18 & 30,000 & 35.85 (18\% moisture content) & 613 & 10 \\
23 & 30,000 & 38.18 (23\% moisture content) & 613 & 10 \\
28 & 30,000 & 40.83 (28\% moisture content) & 613 & 10 \\ \hline \end{tabular}
\end{table}
Table 1: Masses of the individual constituents of samples with different initial moisture contents.
# Design and Signal-Decoding Test Verification of Dual-Channel Round Inductsyn Decoding Circuit
## 4 Experimental and Analysis
### Physical Experiment
#### 4.2.1 Excitation-Signal Power-Amplification Circuit Testing Experiment  
The results of amplifying the excitation signal output from the AD2S1210 chip through the _EXC+_ and _EXC_\(-\) pins, and observing the waveforms using an oscilloscope, are shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c} \hline
**Channel** & **Signal Type** & **Amplitude** \\ \hline CH1 & _SIN+_ Original Induced Signal & 4.8 mVp-p \\ CH2 & Amplifier Output Signal & 1.5 Vp-p \\ CH3 & Filtered Output Signal & 1.6 Vp-p \\ CH4 & Phase-shifted Output Signal & 1.6 Vp-p \\ \hline \end{tabular}
\end{table}
Table 2: The signals and amplitudes corresponding to each channel in Figure 7.  
Figure 11: Round inductsyn decoding-circuit experimental testing platform: (**a**) excitation-signal input; (**b**) AD2S1210 excitation-signal power-amplification circuit; (**c**) oscilloscope results.  
CH2 shows the original excitation signal directly output by the AD2S1210 with an amplitude of 6.8 Vp-p and a frequency of 9.96 KHz; CH1 displays the signal after power amplification, with an amplitude of 7.40 Vp-p and a frequency of 10.06 KHz. The two signals have a 180\({}^{\circ}\) phase difference, with a slight amplification in amplitude, and both frequencies are around 10 KHz. These results are in line with theoretical calculations and simulation tests, meeting the circuit's design requirements.
## 4 Experimental Setup
### Riveted Plate  
The riveted plate used in this study is a painted panel from an Airbus A230 aircraft fuselage with a little door for accessing a pressure release valve and was used during regular checks of the aircraft. A full view of the test specimen is shown in Figure 3a. At the inner side, visible in Figure 3c, the door cut-out is surrounded by a supporting frame which provides additional stiffness to the panel. The specimen is made from aluminium alloy AL2024-T3 and is painted with a primer after assembling. The outer surface of the panel is painted with a conventional coating used for civil aircraft.  
Two spots of the joint supporting frame and panel are intentionally damaged to test the proposed inspection methods. Figure 3b,c show a close-up of area I from the outer and inner side respectively. The middle rivet (\"rivet 0\") in area I is hammered from the backside and turned with pliers until the gap between the rivet and the panel is sufficient to move it with the fingers. The neighboring rivets, rivet \"\(-1\) and \"\(1\)\", are not intentionally damaged but have experienced some stress due to the modification of the surrounding region. Additionally, rivet \"\(3\)\" is turned with pliers to loosen the rivet slightly. Figure 3d shows area II with five screws. The nut of the screw \"\(5\)\" is loosened and the screw is slightly hammered (one soft hit) from the back. As visible in Figure 3d, this results in a small gap between the screw and the panel which is visible due to the lifting of the screw. However, the screw is still tight and it is impossible to move or shake it.  
Figure 4 shows a sketch of the cross-section of area I and II. Both, the outer skin and the supporting frame have a thickness of \(d=2.5\,\mathrm{mm}\). The supporting frame and the panel are riveted with countersunk aluminium rives and the hinges are joined with countersunk screws and a screw nut from the inside as depicted in Figure 4.  
Figure 2: A sketch of the three different measurement setups. The setup of the Ultrasound Lock–In Thermography (ULT) (**a**), the setup of the In-plane Heatwave Thermography (IPHWT) (**b**) and the setup of the Scanning Laser Doppler Vibrometer (SLDV) measurements (**c**).  
Figure 3: The two areas of a fuselage panel from an AIRBUS A320 aircraft with modified rivets which are inspected in this study.
# Engineering a Combinatorial Laplacian Solver: Lessons Learned +
## Preliminaries  
### Fundamentals  
We consider undirected simple weighted graphs \(G=(V,E)\) with \(n\) vertices and \(m\) edges. A graph is weighted if we have an additional function \(w\colon E\to\mathbb{R}_{>0}\). Where necessary, we consider unweighted graphs to be weighted with \(w_{e}=1\ \forall e\in E\). We usually write an edge \(\{u,v\}\in E\) as \(uv\) and its weight as \(w_{uv}\). Moreover, we define the set operations \(\cup\), \(\cap\) and \(\setminus\) on graphs by applying them to the set of vertices and the set of edges separately. For every node \(u\in V\), its neighborhood \(N_{G}(u)\) is the set \(N_{G}(u):=\{v\in V:uv\in E\}\) of vertices \(v\) with an edge to \(u\), and its degree \(d_{u}\) is \(d_{u}=\sum_{v\in N_{G}(u)}w_{uv}\). The Laplacian matrix of a graph \(G=(V,E)\) is defined as: \(L_{u,v}:=-w_{uv}\) if \(uv\in E,\sum_{x\in N_{G}(u)}w_{ux}\) if \(u=v\), and \(0\) otherwise for \(u,v\in V\). A Laplacian matrix is always an SDD matrix. Another useful property of the Laplacian is the factorization \(L=B^{T}R^{-1}B\), where \(B\in\mathbb{R}^{E\times V}\) is the incidence matrix and \(R\in\mathbb{R}^{E\times E}\) is the resistance matrix defined by \(B_{ab,c}=1\) if \(a=c\), \(=-1\) if \(b=c\) and zero otherwise; \(R_{e_{1}e_{2}}=1/w_{e_{1}}\) if \(e_{1}=e_{2}\) and zero otherwise. This holds for all \(e_{1},e_{2}\in E\) and \(a,b,c\in V\), where we arbitrarily fix a start and end node for each edge when defining \(B\). With \(x^{T}Lx=(Bx)^{T}R^{-1}(Bx)=\sum_{e\in E}(Bx)_{e}^{2}\cdot w_{e}\geq 0\) (every summand is non-negative), one can see that \(L\) is positive semidefinite. (A matrix \(A\in\mathbb{R}^{n\times n}\) is positive semidefinite if \(x^{T}Ax\geq 0\) for all \(x\in\mathbb{R}^{n}\).)  
Some conventions used throughout the paper: Every function that is parametrized by a single graph will implicitly use \(G\), e.g., \(A=A(G)\). We also assume that \(G\) is connected. This is not a significant restriction, since we can just apply the solver to every component. Of course, in our actual implementation we first decompose the graph into components. Furthermore, whenever we talk about the residual of a vector \(y\) with respect to a linear system \(Ax=b\), we refer to the relative residual \(\|Ay-b\|_{2}/\|b\|_{2}\).