esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 From \(R_{10}(t)\) to \(R(t)\)
### Optimum Filter  
To reduce the noise produced by the simulation steps, \(R_{sim}(t)\) is filtered with a low-pass Butterworth filter of order 10 [14; 15]--whose performance is very similar to that of the ideal filter--therefore producing \(R_{fill}(t)\). Also, \(R_{fill}(t)\) must be scaled to conserve the experimental quantity of the water of the 10 min interval. In conclusion, by filtering and scaling \(R_{sim}(t)\), we obtain the final 1 min rain rate time series \(R(t)\), according to:  
\[R\ =\ \frac{Q_{T}}{Q_{T,R_{fill}}}R_{fill} \tag{12}\]  
With:  
\[Q_{T,R_{fill}}\ =\ \sum_{k\ =\ 1}^{10}R_{fill}(k) \tag{13}\]  
The only parameter that defines the transfer function of the filter is its cut-off frequency \(f_{c}\leq f_{N}\ \left(\min^{-1}\right)\), where \(f_{N}\ =\ 0.5\ \min^{-1}\) is the Nyquist frequency [14; 15].  
To determine the optimum value \(f_{c}\ =\ f_{o}\), we consider the error \(\varepsilon\ (\mathrm{mm/h})\) between the rain rate simulated \(R\) and the rain rate measured \(R_{1}\) at equal probability exceeded:  
\[\varepsilon\ =\ R(P)-R_{1}(P) \tag{14}\]  
Figure 8 shows mean value and standard deviation of \(\varepsilon\) versus \(f_{c}\). It can be noted that (a) the mean error is very small in the entire range (\(\varepsilon\lesssim 0.6\ \mathrm{mm/h}\)) and (b) the standard deviation is minimum at about \(f_{c}\ =\ 1/3\ \mathrm{min^{-1}}\); therefore, in the following, we fix \(f_{o}\ =\ 1/3\ \mathrm{min^{-1}}\) for all simulations.  
Figure 9 show the same time series as Figure 7 after the full simulation process just described. Finally, Figure 10 shows the overall result of this exercise by drawing the probability distribution (PD) that the 1 min rain rate in abscissa is exceeded in the experimental data, i.e., \(P(R_{1})\), and in the simulated 1 min data, \(P(R)\). The two curves are almost indistinguishable, in agreement with the error and standard deviation reported in Figure 8 at \(f_{c}\ =\ 1/3\ \mathrm{min^{-1}}\).  
Figure 7: Experimental \(R_{1}(t)\) (mm/h) (blue, original) and simulated \(R_{1,sim}(t)\) (mm/h) time series (black, simul). Left: low-intensity rain rate event. Right panel: high-intensity rain rate event. The 10 min quantity of water is conserved.  
Figure 8: Mean value (**left panel**, mm/h) and standard deviation (**right panel**, mm/h) of \(\varepsilon\) versus \(f_{c}\).  
Figure 10: Probability distribution (PD) that the 1 min rain rate in abscissa is exceeded in the experimental data \(P(R_{1})\); blue line (original), and in the simulated 1 min data, \(P(R)\), black line (simul).  
Figure 9: Example of 1 min rain rate time series, measured (blue line, original) and simulated (red line, gener), after filtering and water conservation. (**Left panel**): a low rain rate event. (**Right panel**): a high-intensity rain rate event (see also Figure 7).
## 3 Methodology  
### Overview of the Framework  
This study can be divided into six steps, as shown in Figure 1: (1) Extraction of typical block morphology prototype models: a basic data survey was conducted on 340 typical elderly residential buildings and their surrounding blocks in Beijing, gathering fundamental data such as building types and block scales, which will serve as the basis and constraints for the subsequent parametric block morphology intelligent generation. (2) Parametric block morphology intelligent generation: based on the parametric modeling platform Rhino/Grasshopper, and using the survey data from Step 1 as constraints, a parametric block morphology intelligent generation mechanism is proposed, and a parametric block morphology generation model is constructed. (3) Outdoor environmental performance simulation for the block: optimization objectives are determined, and performance evaluation plugins such as Ladybug Tools 0.0.68 [63] and Butterfly 0.0.05 [64] within the Rhino 7/Grasshopper platform are used to perform batch simulations of the block morphology generation model from Step 2. (4) Multi-objective optimization of outdoor environmental performance: the multi-objective optimization tool Wallacei V2.7 [65], based on the Rhino/Grasshopper platform, is used to automatically optimize outdoor environmental performance indicators by controlling the parameters of the block morphology generation model from Step 2. (5) Recording optimization results and morphology indicator calculation: the data-recording plugin TT Toolbox 1.8 [66] is used to record the optimized Pareto front, and the Colibri tool is used to calculate the spatial morphology indicators corresponding to the Pareto front, resulting in an optimized \"morphology-environment\" dataset for the block. (6) Exploration of the \"morphology-environment\" impact mechanism: the foundational dataset obtained in Step 5 is used, and the interpretable machine learning algorithm XGBoost 2.1.3 (eXtreme Gradient Boosting) is introduced to explore the nonlinear relationships between block spatial morphology and outdoor environmental performance.
## 4 Simulation Results  
This section examines R.T.C. France and the Kyocera KC200 GT PV module using the recommended MGA methodology. The first case study focuses on a commercial silicon solar R.T.C. France module that has a sun radiance of 1000 W/m\({}^{2}\) and runs at 33 degrees Celsius. It has an open circuit voltage of 0.5727 V and a short-circuit current of 0.7605 A. Moreover, the maximum point voltage and current of R.T.C. France are 0.4590 V and 0.6755 A, respectively. The second case study concerns the Kyocera KC200 GT PV Module, which has an open-circuit voltage of (32.90 V) and a short-circuit current of (8.21 A) and is made up of 54 multi-crystalline cells connected in series. The maximum power points, voltage, and current for this module are 200 W, 26.30 V, and 7.61 A, respectively. Table 1 displays the acquired characteristics of the RTC France and KC200 GT PV modules along with their upper limit (UL) and lower limit (LL). This section examines and applies the MGA technique to parameter extraction problems for different solar cells/modules of the SD and DD models in order to compare it with other optimization strategies that have been somewhat well-reported. The same 1000 iterations and 200 people are used while applying MGA. Added to that, detailed specifications for both the RTC France PV cell and the Kyocera KC200 GT PV module are displayed in Tabel 1.
# Optimizing Physics-Informed Neural Network in Dynamic System Simulation and Learning of Parameters
## 2 Systems of Ordinary Differential Equation  
Consider a dynamical system characterized by \(n\) ordinary differential equations encompassing \(m\) undetermined parameters (see also, [12]).  
\[\frac{d\theta(t)}{dt}=\Theta(t,\theta(t),\delta(t)),\quad t\in(t_{0},t_{end}), \tag{1}\]  
\[\theta(t_{0})=\theta_{0}\]  
These equations make up the mathematical model that describes the system's behavior being studied, with \(m\) parameters that define the system's characteristics. In this context, \(\theta(t)=[\theta_{1}(t),\theta_{2}(t),\theta_{3}(t),\dots,\theta_{n}(t)]^{T}\) is a vector field composed of \(n\) components. Further, let \(\delta(t)=[\delta_{1}(t),\delta_{2}(t),\dots,\delta_{m}(t)]\in\mathbb{R}^{m}\) be the vector encompassing the unknown parameters, and \(\theta_{0}\in\mathbb{R}^{n}\) be the initial condition. On the right-hand side of (1), we have the vector-valued function  
\[\Theta(t,\theta(t),\delta(t))=\left[\begin{array}{c}\Theta_{1}(t,\theta_{1} (t),\theta_{2}(t),\dots,\theta_{n}(t),\delta_{1}(t),\delta_{2}(t),\dots, \delta_{m}(t))\\ \Theta_{2}(t,\theta_{1}(t),\theta_{2}(t),\dots,\theta_{n}(t),\delta_{1}(t), \delta_{2}(t),\dots,\delta_{m}(t))\\ \cdot\\ \Theta_{\alpha}(t,\theta_{1}(t),\theta_{2}(t),\dots,\theta_{n}(t),\delta_{1}(t ),\delta_{2}(t),\dots,\delta_{m}(t))\end{array}\right]\]  
not necessarily linear, consisting of \(n\) components designated as  
\[\Theta_{k}(t,\theta_{1}(t),\theta_{2}(t),\dots,\theta_{n}(t),\delta_{1}(t), \delta_{2}(t),\dots,\delta_{m}(t))\]  
with \(k=1,\dots,n\). The data for the model equation will be represented by \(\theta^{*}(t)\).  
Using the data measured at \(P\) distinct time points for the given model, \((t_{i},\theta^{*}(t_{i}))\) for \(i=1,2,\dots,P\), our goal is to develop physics-informed neural network (PINN) algorithm that estimates both the unidentified parameters \(\delta(t)\) and the solution \(\theta(t)\) across all time points \(t\). Understanding the value and change of these parameters over time can give useful information about the system's dynamic behavior, which can be used to improve predictive models and solutions.
# Comparison of Volatile Profiles of Meads and Related Uniflorl Honeys: Traceability Markers
###### Abstract  
Volatile profiles of uniflorl honeys and meads prepared in different ways (boiled-saturated, not boiled-unsaturated) were investigated by headspace solid-phase micro extraction (HS-SPME) and dehydration homogeneous liquid-liquid extraction (DHLLE) followed by GC-FID/MS analyses. The obtained data were analyzed by principal component analysis (PCA) to evaluate the differences between the investigated products. The volatile profiles of honey as well as the boiled and the not boiled meads prepared from it showed significant discrepancies. The meads contained more aliphatic acids and esters but fewer monoterenes and aliphatic hydrocarbons than the honey. Significant/substantial differences were found between the boiled (more aliphatic alcohols and acids) and the not boiled meads (more aliphatic hydrocarbons and esters). Some compounds related to yeast metabolism, such as tryptophan, may be considered markers of honey fermentation. This research allowed us to identify chemical markers of botanical origin, retained and detectable in the meads: 4-isopropylclohex-1,3-diene-1-carboxylic acid and 4-(1-hydroxy-2-propanyl)cyclohex-1,3-diene-1-carboxylic acid for linden; valeric acid, \(\gamma\)-valerolactone, \(p\)-hydroxybenzoic acid for buckwheat; 4-hydroxybenzeneacetic acid, homovanillic acid and _trans_-coniferyl alcohol for honeydew; and methyl syringae for canola.  
meads; honey wine; quality control; authenticity; liquid-liquid extraction Article
# A Novel SINS/SRS/CNS Multi-Information Fusion Global Autonomous Navigation Method
## 3 The Multi-Information Fusion Autonomous Navigation System Based on SINS/SRS/CNS
### The Principle of Spectral Redshift Navigation
#### 3.2.2 Measurement Equations  
There are two sets of measurements: one is the SINS/SRS velocity measurement, and the other is the SINS/CNS position measurement.  
1. SINS/SRS velocity measurement  
Given that the velocity provided by SRS is in the \(i\) frame, converting it to the \(g\) frame in the middle-low latitudes is essential:  
\[\mathbf{v}^{g}_{SRS}=\mathbf{C}^{g}_{i}\mathbf{v}_{p}=\mathbf{C}^{g}_{e}\mathbf{C}^{g}_{i}\mathbf{v}_{p} \tag{15}\]  
where \(\mathbf{C}^{g}_{i}\) is the direction cosine matrix from the \(i\) frame to the \(g\) frame; \(\mathbf{C}^{g}_{i}\) is the direction cosine matrix from the \(i\) frame to the \(e\) frame; \(\mathbf{C}^{g}_{e}\) is the direction cosine matrix from the \(e\) frame to the \(g\) frame; and \(\mathbf{v}^{g}_{SRS}\) is the velocity in the \(g\) frame calculated by SRS.  
The velocity differences between SINS and SRS were chosen as one measurement to construct the velocity measurement equation:  
\[\mathbf{Z}^{g}_{v}(t)=\mathbf{v}^{g}_{SINS}-\mathbf{v}^{g}_{SRS}=\mathbf{H}^{g}_{v}(t)\mathbf{X}^{ g}(t)+\mathbf{V}^{g}_{v}(t) \tag{16}\]  
where \(\mathbf{v}^{g}_{SINS}\) is the velocity in the \(g\) frame calculated by SINS; \(\mathbf{H}^{g}_{v}(t)=\begin{bmatrix}0_{3\times 3}&\text{diag}\begin{bmatrix}1&1&1 \end{bmatrix}&0_{3\times 9}\end{bmatrix}\) is the measurement matrix of the velocity; and \(\mathbf{V}^{g}_{v}(t)\) is the velocity noise.  
2. SINS/CNS position measurement  
The position differences between SINS, CNS, and BA were selected as the other measurement to construct the position measurement equation:  
\[\mathbf{Z}^{g}_{p}(t)=\begin{bmatrix}L_{SINS}-L_{CNS}\\ \lambda_{SINS}-\lambda_{CNS}\\ h_{SINS}-h_{BA}\end{bmatrix}=\mathbf{H}^{g}_{p}(t)\mathbf{X}^{g}(t)+\mathbf{V}^{g}_{p}(t) \tag{17}\]  
where \(L_{SINS},\lambda_{SINS},h_{SINS}\) are the latitude, longitude, and altitude obtained by SINS; \(L_{CNS},\lambda_{CNS}\) are the latitude and longitude obtained by CNS; \(h_{\text{BA}}\) is the altitude obtained by BA; \(\mathbf{H}^{g}_{p}(t)=\begin{bmatrix}0_{3\times 6}&\text{diag}\begin{bmatrix}1&1&1 \end{bmatrix}&0_{3\times 6}\end{bmatrix}\) is the measurement matrix of the position; and \(\mathbf{V}^{g}_{p}(t)\) is the position noise.  
The System Equations of the SINS/SRS/CNS Multi-Information Fusion Autonomous Navigation System in the High Latitudes
# Review on Semi-Fragile Watermarking Algorithms for Content Authentication of Digital Images
## 5 Overview on Semi-fragile Watermarking Algorithms  
In recent years, with the attention of the international academic community on semi-fragile watermarking technology, more and more algorithms in this field have been designed. As for the methods of semi-fragile watermark, they can be classified according to different starting points. According to the functions of semi-fragile watermarking algorithms, they can be classified into two categories. One is the unrecoverable semi-fragile watermark that can only be used for tamper detection, and the other is the semi-fragile watermark that can recover the tampered areas. According to the embedding position of the watermark, semi-fragile watermark algorithms can be classified into two categories: watermark in spatial domain, and watermark in transform domain. In the following, we will make an overview on semi-fragile watermarking algorithms for image authentication.
## 4 Taxonomy of MAC Protocols Based on Their Use of WuR Technology
### MAC Protocols without WuR
#### 4.1.4 Adaptive Access Based MAC Protocols for IBSN  
[PERSON] et al. [52] proposed an adaptive access mechanism called QL-MAC based on the Q-learning technique. In QL-MAC, a simple asynchronous CSMA-CA approach is employed over a frame-based structure. It basically divides the time into discrete time units, i.e., the frames, which are further divided into smaller time units, i.e., the slots. Both frame length and slot number are parameters of the algorithm and remain unchanged at the execution time. By means of a Q-Learning-based algorithm, each node independently determines an efficient wake-up schedule in order to limit, as much as possible, the number of slots in which the radio is turned on. Such a non-fixed and adaptive duty-cycle reduces the energy consumption over the time without affecting the other network performances. Authors of [70] compared the performance of QL-MAC with the performance of conventional MAC protocols, such as S-MAC [25] and T-MAC [67] in an OMNETT-based simulation environment. The results show that the adaptive behavior of QL-MAC guarantees better network performances with respect to both the packet delivery ratio and the average energy consumption.  
A similar approach is followed in a reinforced-learning-based medium access control protocol called RL-MAC [53]. The distinct feature of the protocol is that both the frame active time and duty-cycle are dynamically modified in accordance with the node's traffic load as well as its incoming traffic characteristics. The learning algorithm is focused on maximizing energy-efficiency and throughput.
# Unsupervised Pre-Training for Voice Activation
## 3 Results and Discussion
### Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & **Log-Mel Filter Banks** & **wav2 vec** \\ \hline ff & 72.31\% & **78.46\%** \\ res8-narrow & 73.85\% & **84.62\%** \\ res8 & 78.46\% & **90.77\%** \\ res15-narrow & **83.08\%** & 80.00\% \\ res15 & **89.23\%** & 86.15\% \\ res26-narrow & **83.08\%** & 72.31\% \\ res26 & 80.00\% & **83.08\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Test accuracy on the Lithuanian dataset. It can be seen that the use of pre-trained audio features as wav2 vec increases the system accuracy by approximately 10% when up to 10 samples are used per keyword both for the English and Russian language despite the fact that the model was only trained on English audio records. The increase is even bigger if five samples are used. It almost vanishes if up to 20 samples are used.
# 6G to Take the Digital Divide by Storm: Key Technologies and Trends to Bridge the Gap
## 3 The Technologies for Bridging the Service-Delivery Divide  
In this section, we examine the possible implementations of technologies that can be considered powerful tools to face and govern the problem of the service-delivery divide, as they would be able to offer Internet access even where traditional network infrastructures fall short. Some of the technologies that will be mentioned belong to the evolutionary category (see Section 1) as they have been already considered promising for the deployment of 5G but they are not yet suitably widespread.
In particular, if several resonant elements for circular polarization are placed above a microstrip patch antenna that radiates LP waves, the corresponding orthogonal modes would be excited due to the near-field coupling to the bottom radiator and could be tuned individually with subtle changes to the resonant elements. Likewise, these polarizers could be applied in environments demanding temporal polarization transformation, such as for satellite communication [22; 23], antenna arrays and radomes [24; 25]. In this paper, based on single-fed probe structures, dual-layer, low-profile patch antennas with RHCP and LHCP characteristics were designed. A circular polarizer printed with resonant DSSRs as a superstrate is stacked above an elliptical patch antenna to convert the LP field of an elliptical patch antenna into a CP one. Under the capacitive coupling to the elliptical patch antenna below, the DSSRs collaboratively create a pair of orthogonal modes of approximately equal amplitude and of 90\({}^{\circ}\) relative phase shift so that a CP mode is produced. In addition, RHCP and LHCP patch antennas are achieved with identical frequency response by simply changing the offset orientation of the dual bottom splits  
Figure 1: Principle of operation of the target detection based on chip-less tag technology. of the DSSRs. The physical mechanism of the circular polarizer and the antenna configurations are illustrated in Section 2, and the antennas' impedance, axial ratio, and radiation performance are simulated, measured, and analyzed in Section 3. The simulation tool HFSS version 13.0 is used in this paper.
## 2 Materials and Methods
### Characterization of Collected Dust and Soil Samples  
All the collected soil samples were analyzed for their physiochemical characteristics. Initially, soil samples were air-dried, passed through sieve of 2 mm, and stored in airtight plastic containers. Soil pH, electrical conductivity (EC), and soluble cations and anions were analyzed in soil suspension at 1:2 (\(w/v\) ratio) by following the standard procedures [38]. EC and pH were analyzed by EC (YSI Model 35, YSI Inc., Yellow Springs, OH, USA) and pH (WTW-pH-523, WTW GmbH, Troistedt, Germany) meters, respectively. Soluble cations and anions were analyzed by titration, while sodium and potassium were determined by using a flame photometer (Model: AE-EP8501, A and E Lab (UK) Co., Ltd., London, UK). The Walkley and Black [39] method was followed to determine the content of organic matter of soil samples. Soil samples were analyzed by X-ray diffractometer (MAXima X XRD-7000, Shimadzu Corporation, Kyoto, Japan) to observe the mineralogical composition. The mineralogical composition of the dust samples was determined using X-ray diffractometer (MAXima X XRD-7000, Shimadzu, Kyoto, Japan). The dust samples were also weighed to assess the deposition over time.
# A Simple Approach to Dynamic Optimisation of Flexible Optical Networks with Practical Application
## 1 Introduction and Background  
Optimising the location of _opto-electro-optical_ (_OEO_) _ regenerators_ (hereafter referred to as _relays_) in _optical networks (ONs)_ has, for much of the history of ONs, been considered a _static_ problem [1]. Such devices have, until recently, been expensive to both install and operate. In consequence, optimisation of relay placement has been performed offline, with complex algorithms, leading to solutions to be implemented as part of a fixed, long-term network structure [2]. Recent years, however, have seen two significant developments in the field:  
The introduction of _flexible optical network_ (_FON_) technology has led to ONs having the ability to vary transmission parameters to support changing traffic requirements. By modulating across larger bandwidths, FONs can achieve higher bitrates at the expense of shorter transmission distances (whereas, conversely, greater transmission distances can be achieved for lower bitrates) [3].  
The production and installation of OEOs (relays) has fallen sharply due to both improved manufacturing processes and refinements to carrier/switching technologies, although running costs, in terms of power consumption, are still high [4]. Consequently, installing relays in multiple locations but using (powering) them selectively (as, and when, required) may lead to greater efficiencies.  
Describing the engineering principles behind these advances is beyond the scope of this work [5]. However, in essence, the new technology (1) makes the optimisation of relay management a _dynamic_ problem [6] whilst the emphasis on running costs (2) redefines its _objective_[7]. As a result, optimising 'placement' of relays becomes an optimisation of relay 'use'. Higher traffic will mean larger bandwidths,shorter transmission limits and, thus, the need for more frequent regeneration and greater numbers of relays.  
The next section considers comparable optimisation problems, which yield some insight into the task at hand. Section 3 gives the initial formulation and complexity discussion. Useful related approaches are considered in Section 4, and these lead to the proposed algorithm in Section 5, which also provides the proof of NP-completeness. Section 6 tests the algorithm with two known library networks. In Section 7, the problem, and its solution, are generalised and the complexity considered once more. Section 8 suggests future work for other researchers. The paper's overall structure, switching as it does between definition and solution, may appear odd, but this approach is considerably shorter than dealing with each thread independently in turn. For the same reason, the notation is also slightly informal in places.