esa-sceva/satcom-chunk-collection huggingface dataset:
# Holographic Silicon Metasurfaces for Total Angular Momentum Demultiplexing Applications in Telecom
## 2 Materials and Methods
### Phase Pattern Calculation
#### 2.1.2 Regular Polygonal Configuration  
We considered the OAM values in the set \(\{-6,-4,-2,0,+2,+4,+6\}\) for a total of 7 OAM channels. We fixed the carrier spatial frequencies in order to arrange the far-field peaks at the vertices of a regular polygon, in the specific case a heptagon. In polar coordinates, the spatial frequencies are given by \(\{(\rho_{j},\)\(\theta_{j})\}=\{(\gamma,j2\pi/7)\},\)\(j=1,\dots\) 7. Therefore, the far-field points appear at equally-spaced angular positions, specified as follows:  
\[\begin{array}{c}r_{j}^{(+)}=r=\gamma\frac{f}{k}\\ \varphi_{j}^{(+)}=j\frac{2\pi}{n}=j\frac{2\pi}{7}\end{array}, \tag{10}\]  
where \(j=1,\dots\) 7, being \(r\) the radius of the circumscribed circle. According to Equation (7), we have:  
\[\tan\Bigl{[}\varphi^{(-)}\Bigl{(}\ell_{j}\Bigr{)}\Bigr{]}=\tan\Bigl{[}\varphi^ {(+)}\Bigl{(}-\ell_{j}\Bigr{)}\Bigr{]} \tag{11}\]  
that is  
\[\varphi^{(-)}\Bigl{(}\ell_{j}\Bigr{)}=\varphi^{(+)}\Bigl{(}-\ell_{j}\Bigr{)}+\pi, \tag{12}\]  
The two orthogonal polarizations are sorted over two overlapping heptagons, as shown in the scheme in Figure 3b. According to Equation (12), the far-field intensity peaks are expected to be at the following angular positions for the left-handed beams:  
\[\varphi_{j}^{(-)}=(7-j+1)\frac{2\pi}{7}+\pi=-(j-1)\frac{2\pi}{7}+\pi, \tag{13}\]  
As shown in Figure 3b, for increasing OAM values, the corresponding spots appear counterclockwise (clockwise) for incident right-handed (left-handed) circular polarization.  
Figure 2: (**a**) Numerical phase pattern for the demultiplexing of optical beams with OAM in the set \(\{-3,-2,-1,0,+1,+2,+3\}\) on a linear array. Pixel size: 6.250 \(\upmu\)m \(\times\) 6.250 \(\upmu\)m. 16 phase levels. Radius length: 180 pixels. (**b**) Far-field channel scheme for the given OAM set and circular polarization states. Right-handed (in blue) and left-handed (in red) circularly-polarized OAM beams are detected in far field on two distinct linear arrays.
# Low-Order Electrochemical State Estimation for Li-Ion Batteries
## 2 Methodology and Models
### Electrolyte phase  
The lithium concentration in the electrolyte satisfies  
\[\frac{\partial C_{e}(x,t)}{\partial t} =\frac{\partial}{\partial x}\Big{(}D_{e}\frac{\partial C_{e}(x,t )}{\partial x}\Big{)}+\frac{\theta_{e}^{0}(x)}{F}\frac{\partial i_{e}(x,t)}{ \partial x}+\frac{i_{e}(x,t)}{F}\frac{\partial\theta_{e}^{0}(x)}{\partial x}, \quad t>0, x\in(0,L)\] (3a) with initial condition \[C_{e}(\cdot,0)=C_{e}^{0}\] and boundary conditions \[\frac{\partial C_{e}(x,t)}{\partial x}=0, t>0, x=0,L, \tag{3b}\]  
where \(i_{e}(x,t)\) is the electrolyte current, \(F\) is [PERSON]'s constant, \(\varepsilon\) is the volume fraction for each electrode, \(C_{e}(x,t)\), \(D_{e}\) and \(t_{e}^{0}\) are the lithium concentration, diffusion coefficient and the transference number of the electrolyte, respectively.  
The electrolyte current in turn is governed by  
\[\frac{\partial i_{e}(x,t)}{\partial x}=\begin{cases}Fa_{s}^{-}j_{n}(x,t)&x\in [0,\delta_{-}]\\ 0&x\in[\delta_{-},\delta_{-}+\delta_{s}]\\ -Fa_{s}^{+}j_{n}(x,t)&x\in[\delta_{-}+\delta_{s},L]\end{cases},\quad t\geq 0 \tag{3c}\]  
with the specific interfacial area of the single sphere-particle  
\[a_{s}^{\pm}=\frac{3(1-\varepsilon)}{R_{\mathrm{s}}^{\pm}}. \tag{3d}\]  
The potential of the electrolyte satisfies  
\[\frac{\partial\Phi_{e}(x,t)}{\partial x}=-\frac{i_{e}(x,t)}{\kappa}+\frac{RT} {F}\Big{(}1-t_{e}^{0}(x)\Big{)}\frac{\partial lnC_{e}(x,t)}{\partial x},\qquad t \geq 0,\quad x\in(0,L)\] (4a) with the boundary condition \[\Phi_{e}(x,t)=0x=0, \tag{4b}\]  
where \(R\) is the universal gas constant, \(T\) the cell temperature, and \(\kappa\) the ionic conductivity of the electrolyte.
## 2 System Design  
### Uav  
The chosen UAV is a Copter 1B of SURVEY Copter (see Figure 1). It has a wingspan of 1.82 m and a length of 1.66 m. Powered by a gasoline engine, the maximal payload capacity of the UAV is 4.1 kg and the endurance is up to 60 min. The nominal flying altitude of the UAV is 150 m and the maximal flight speed is 10 m/s. The UAV possesses a radio communication with its command station. Given a pre-set flight plan registered in the command station, the flight can be performed automatically. Thus, a steady longitudinal/lateral overlap can be assured. An aluminium base mounted on the UAV was adopted for rigid camera installation and cable fixation.
## 3 Results  
### The Experimental Setup and Evaluation Metrics  
The experiment was conducted using Ubuntu 20.04.4 LTS 64-bit as the operating system, with an Intel Xeon Silver 4214 processor (Intel Corporation, Santa Clara, CA, USA) running at [EMAIL_ADDRESS] GHz and 32 GB of RAM. The GPUs used were two NVIDIA Tesla T4 (NVIDIA Corporation, Santa Clara, CA, USA), with a total of 32 GB of video memory. The software environment was built based on the Python 3.8.19 programming language, using the PyTorch 1.13.1 deep learning framework with CUDA 11.7 acceleration. Key dependency libraries included OpenCV 4.10.0 and TorchVision 0.14.1 for computer vision processing, Torch-Pruning 1.5.2 for model compression, NumPy 1.24.3 and SciPy 1.10.1 for scientific computing, as well as Pandas 1.5.3 for data manipulation. The GPU acceleration environment was implemented via CUDA Toolkit 11.7 and cuDNN 8.5.0, with versions strictly compatible with the PyTorch framework to ensure computational stability. The Adam optimizer was employed, with 80 training epochs, and cosine annealing decay was used for the learning rate. Other hyperparameters were set according to those published in the SGCBP paper. The performance of the proposed methods and models was evaluated using three metrics: RMSE, MAE, and MAPE (Mean Absolute Percentage Error). RMSE, MAE, and MAPE were chosen as they are standard and comprehensive metrics for evaluating the accuracy of regression models, reflecting different aspects of prediction error.
## 3 Results and Discussion  
### Quantification of Erinacine A in Rat and Human Liver S9 Analysis  
Erinacine A was examined using rat and human liver S9 fraction and quantified by HPLC-QQQQ/MS at different time points. The remaining verses time profile of erinacineA, in rat and human liver S9 fraction treatment, are shown in Figure 1. Using the 'well-stirred model' and formula calculation [27], the predicted value is shown in Table 1. Rat liver S9 metabolized ~75.4% of erinacine A within 60 min. Human liver S9 metabolized ~49.8% of erinacine A within 120 min. In the presence of NADPH, erinacine A was metabolized more slowly, with a \(t_{1/2}=114.92\) min in human. E\({}_{\text{H}}\) is the fraction of drug being filtered by the liver in one pass, in which the drug is irreversibly removed (extracted). We found that the E\({}_{\text{H}}\) of rat was higher than that of human. This result is reasonable, judging from \(t_{1/2}\) values, because rat liver has been reported to metabolize faster than human liver [28]. Compared with other compound reports using rat liver S9, GKB202 of the _Antrodia cinnanomea_ mycelium (t\({}_{1/2}=3.68\) min) was metabolized more rapidly than erinacine A [29].  
The previous report showed that there was no-observed-adverse-effect of 3 g/kg body weight/day erinacine A-enriched HE in 28-day oral feeding in Sprague-Dawley rats. The current study dosage input for liver cells metabolism gives a reasonable correlation, with a previous in vivo study, of erinacine A compound pharmacokinetics (PK) [30,31]. In one recent in vivo report, the [PERSON] et al. group detected erinacine A in rat plasma after oral administration of HE mycelia (50 mg/kg) at 60 min [32]. This can correlate with the current in vitro finding, in which we demonstrated, theoretically, that there is still ~24.6% remaining of the compound after rat liver S9 metabolism. In addition, approximately half of unmetabolized erinacine A were found in the remaining human liver S9, demonstrating that complete compound structures could enter the bloodstream, which contributes to its effectiveness in neuroprotection. Whether or not the common metabolites provide, the biological function is still under investigation and will be reported in a separate study.
## 2 Materials and Methods
### Experimental Work
#### 2.2.3 Manufacturing Process  
The manufacturing process (schematically presented on Figure 1) was carried out using an LMP200 system (Figure 2a) from Arrow Metal Printing, equipped with a Yb/fibre laser (peak power: 200 W), galvanometer scanner by Raylase and a sealed, inert argon environment to ensure that oxygen levels remained below 10 ppm. This level of environmental control is particularly important when handling pure Mg, a highly reactive substance that is susceptible to oxidation and fire at high temperatures.  
\begin{table}
\begin{tabular}{c c} \hline
**Composition** & **Percentage by Weight [wt\%]** \\ \hline moisture & \textless{}0.08 \\ \hline undissolved HCl substance & \textless{}0.047 \\ \hline Fe & \textless{}0.045 \\ \hline Zn & \textless{}0.008 \\ \hline Cl & \textless{}0.004 \\ \hline Mg & Balance \\ \hline \end{tabular}
\end{table}
Table 2: Powder composition.  
Figure 1: Schematic of the manufacturing process.  
The process began with the supply of metal powder from the powder container, which the recoater distributed in a thin layer on the build platform. A laser source emitted a beam that was directed by the scanner system to selectively melt the powder layer according to the design of the sample. Once melted, the build platform lowered and the recoater applied a new layer of powder, repeating the cycle until the sample was complete. The environmental control system maintained an inert atmosphere to prevent oxidation during melting. The carried-out process was typical of LPBF [31].  
Each design consisted of cubic samples measuring 6 \(\times\) 6 \(\times\) 6 mm\({}^{3}\) (Figure 2b), fabricated on 2 mm thick support pillars to improve adhesion to the substrate and minimise deformation caused by residual stresses. The geometry of the support was chosen to ensure thermal stability throughout the process. For each set of parameters, 3 samples were created. Selected combinations of laser power, scanning speed and layer thickness were used in a grid of experimental settings. The selection was based on initial evaluations and established LPBF process parameters for magnesium.  
The hatch distance and track overlap remained unchanged for all experiments to focus on the effects of the three main variables investigated. The laser beam was concentrated on a spot size of 50 \(\upmu\)m and a bidirectional raster scanning technique was used to ensure uniform heat distribution.  
A precision-controlled mechanism for powder recoating guaranteed the uniform deposition and thickness of the powder layer, which was crucial for maintaining layer continuity and melt pool stability. This setup aimed to identify non-linear responses and interactions between parameters that were theorised to have a significant influence.
## 4 Implementation System and Results  
After completing the hardware design, as well as acquiring the firmware and software needed for our system, we set up the SAM system to test the P/R device. The system model is described in Figure 14a, and the real model of the system is described in Figure 14b. The system test with the P/R equipment was divided into two parts. The first part was setting up and checking the signals that were designed and programmed with the transducer and the class sample. The second part tested the SAM system when scanning samples with the techniques applied above.
## 3 Methodological Developments and Application to the Greater Geneva Area  
Based on the previous work presented in the last section, the methodology has evolved and has been adapted in the context of enlarging the solar cadaster from the canton of Geneva (280 km\({}^{2}\)) to the Greater Geneva area (2000 km\({}^{2}\)) as presented in Figure 2. The region includes three main territories: the canton of Geneva and the region of Nyon, which is part of the canton of Vaud, in Switzerland, and groups of municipalities of the departments of Ain and Haute-Savoie in France.  
Increasing the scale of the zone of interest by over a factor of 7 (from 55 tiles to 294) creates the two following challenges. First of all, a more performant computational system based on GPU should be developed. Section 3.1 presents the workflow of the improved computational structure. Specific aspects have also been improved, as introduced in Section 3.2, related to the shadow casting formula for the integration of distant geometry, and introduction of masks to target a subset of positions on which to perform the analysis. Secondly, a large zone can have local variations in weather patterns, and it must be considered for an accurate analysis (Section 3.3).
# Moving Deep Learning to the Edge
## 4 Deep Learning Inference on Edge Devices
### Computing Devices for Deep Neural Networks at the Edge
Since fully connected layers do not exhibit the same parallelism of convolutional layers, multiple batches of images are stored and computed in parallel. Each batch image is processed by a row of processing elements to keep the performance efficiency, that is to avoid idle processing elements. The 16 bit configuration has an energy efficiency of 580 GOPs/W and consumes 335 mW. DRP [163] is a dynamically reconfigurable engine to accelerate deep learning applications. It has a dynamically reconfigurable array of coarse-grained processing elements. The computing units handle 16 bit fixed-point, 16 bit floating-point, and binary representations of weights and binary representations for both weights and activations. The architecture is reconfigured for each layer. The chip configured for the 16 bit mode has a performance of 960 GOPs. FPGAs are fine-grained reconfigurable architectures that can be reconfigured to implement a particular deep learning model with good performance and energy efficiency [25; 164]. Some solutions implement a general dedicated engine for convolutional layers [165; 121], while others propose a pipelined architecture where all layers are implemented in a pipelined fashion [166; 143]. Initial implementations of DNN with FPGA considered high-density FPGAs. With the migration of deep learning to the edge, low-density FPGAs are also considered as target devices. In [167], a small ZYNQ XC7Z020 FPGA was used to implement a small CNN quantized with a 16 bit fixed-point, achieving a performance of only 13 GOPs (Giga-Operations). In [168], the authors implemented VGG16 in the same FPGA device. With a quantization of 8 bits, the architecture achieved a performance of 84 GOPs. Recently, a configurable accelerator was proposed to implement large CNNs in a ZYNQ XC7Z020 FPGA [169]. With activations and weights represented with an 8 bit fixed-point, the architecture has a peak performance of 408 GOPs and an energy efficiency of 33 GOPs/W. Table 5 summarizes the list of representative chips dedicated to deep learning processing. As expected, the energy efficiency of reconfigurable architectures is lower than that of ASICs. However, coarse-grained architectures permit an optimized datapath configuration, and fine-grained solutions based on FPGA allow the design of dedicated architectures for specific model types and all kinds of model and architecture optimizations.
# Towards Lunar In-Situ Resource Utilization Based Subtractive Manufacturing
## 1 Introduction
### Natural Lunar Resources
Research is currently focused on the form in which the water is present, for example, finely distributed and bound to regolith particles or in the form of chunks of ice. These water deposits could be of enormous importance for space travel as a source for the \begin{table}
\begin{tabular}{l l l} \hline \hline
**Oxide** & **Maria [wgt\%]** & **Terrae [wgt\%]** \\ \hline SiO\({}_{2}\) & 45.4 & 45.5 \\ TiO\({}_{2}\) & 3.9 & 0.6 \\ Al\({}_{2}\)O\({}_{3}\) & 14.9 & 24.0 \\ FeO & 14.1 & 5.9 \\ MgO & 9.2 & 7.5 \\ CaO & 11.8 & 15.9 \\ Na\({}_{2}\)O & 0.6 & 0.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average oxidic composition of the maria (dark plains) and terrae (bright highlands) regolith [34]. production of fuel since the transport of fuel from the Moon into space is possible with significantly less energy and is, therefore, considerably cheaper than transporting it from Earth. Corresponding calculations have shown that delivery of fuel from the Moon up to the geostationary orbit of the Earth is associated with cost advantages [37]. Water is also key to establishing a permanent base on the Moon, as it is required for many uses. The use of the water sources thus simplifies the development of the Moon and, at the same time, enables the establishment of a business model. For this reason, most plans for a lunar base target a south polar crater with ice deposits as a construction site [38] (Figure 3). The construction of the necessary infrastructure must be carried out with local resources, especially regolith, to reduce costs.
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 4 Simulation Environment
### Latency Probing  
After obtaining the average number of decoder iterations for each simulated combination, the obtained values are forwarded to the time-based simulation. A new PPDU is spawned for each available combination. Its length depends on the selected MCS, while the number of decoder iterations, and with the incurred decoding delay, it is set according to the observations made during the bit error simulation. The latter depends on both the MCS and the \(\frac{E_{b}}{N_{0}}\). Furthermore, step 3 also studies RX demapping delays. Figure 8 demonstrates how the first latency probing simulation block changes in accordance with the study step.  
Apart from simulating PPDU delays, the latency probing simulations also provide insight into how individual symbols and other data units propagate through the PHY. The framework is, therefore, capable of identifying individual bottlenecks in the RX DBB. With reference to Figure 3, such conclusions are drawn on the basis of data accumulation in the component input buffers.  
Figure 8: Dependence of the first latency probing simulation block on the study step.  
Figure 7: Difference between the blocks in the bit error simulation process, dependent on the study step.
###### Abstract  
The detection of a moving target using an IR-UWB Radar involves the core task of separating the waves reflected by the static background and by the moving target. This paper investigates the capacity of the low-rank and sparse matrix decomposition approach to separate the background and the foreground in the trend of UWB Radar-based moving target detection. Robust PCA models are criticized for being batched-data-oriented, which makes them inconvenient in realistic environments where frames need to be processed as they are recorded in real time. In this paper, a novel method based on overlapping-windows processing is proposed to cope with online processing. The method consists of processing a small batch of frames which will be continually updated without changing its size as new frames are captured. We prove that RPCA (via its Inexact Augmented Lagrange Multiplier (IALM) model) can successfully separate the two subspaces, which enhances the accuracy of target detection. The overlapping-windows processing method converges on the optimal solution with its batch counterpart (i.e., processing batched data with RPCA), and both methods prove the robustness and efficiency of the RPCA over the classic PCA and the commonly used exponential averaging method.  
UWB; moving target detection; background subtraction; matrix decomposition; low-rank; sparse; RPCA; augmented Lagrange multiplier; online processing; real-time processing +
Footnote †: journal: Sensors  
Article
# The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing
## 2 Preliminaries
### Teacher/Learner  
T/L is a technique in which no training data are required; instead, a previously-trained classifier model (teacher) is introduced to operate simultaneously with a new classifier model to be trained (learner) and provides the labels for the observed data instances; this enables the capabilities of a Learner to perform AR utilising a teacher regardless of the sensor data the teacher was trained with. T/L modelling for AR has shown to have significant potential for improving transfer learning to make systems more robust and versatile [6; 16; 17; 18; 19; 20; 21].  
Some researchers have investigated T/L; for example [PERSON] and [PERSON] [17], [PERSON] et al. [18] and [PERSON] et al. [19] presented alternatives to enable the transference of knowledge, where labelled data are fully available in the source data, but only a fraction of the data are available in the target domain. Overall, the approach consists of incrementally training the model on the target data. The limitation of these studies, however, lies in the assumption that both the source and the target data consist of the same feature-space. [PERSON] and [PERSON] [22] and [PERSON] et al. [23] have proposed alternatives that relax the assumption of the same feature-space by transferring knowledge across different activities and different sensors. They built a mapping between the source and the target domains over a bridge built upon web knowledge. Although their experimentation consisted of different data in highly different-spaces, their extension to mobile, wearable, and environmental (MWE) sensing devices is uncertain. [PERSON] and [PERSON] [24] proposed an uninformed algorithm that leveraged the similarities by constructing a deep neural network (DNN) based feature-level representation of the data in the source and the target domain. The algorithm uses a heuristic classifier-based mapping to assign activity labels to the core observations. Finally, the output of the labelling is fused with the prediction of the source-model to develop a personalised AR algorithm. The limitation of this study, however, lies in the constraint that it relies on the same feature-space. One general limitation of T/L approaches, however, is that the accuracy of the learner's training is bounded by the accuracy of the teacher.