esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
### Water Borne Pathogen  
Due to the increasing concern for water scarcity, it is expected that by 2025, half of the world's population will endure 'water-stressed' areas [1]. This is due to climate change affecting both the developing and developed world. Already, 1.1 billion people lack access to clean water and 2.7 billion have a water shortage for at least one month of the year, exposing waterborne diseases such as cholera, typhoid fever and hepatitis A [2]. As a result, approximately 525,000 children under the age of five die annually from diarrhea diseases alone. According to the World Health Organization (WHO), lower respiratory infections and diarrheal diseases are the top two causes of deaths in low-income countries in 2016. From this, 11-20 million people get an illness due to typhoid fever, and 128,000-161,000 people die from this annually [2]. Typhoid fever is caused by the Enterobacteriaceaeae Salmonella Typhimurium, which typically is around 2-5 \(\upmu\)m in size. In addition to this, the developed world suffers from pathogens such as Cryptosporidium, which can survive months within waters [3, 4]. This particular pathogen causes gastrointestinal and respiratory illnesses, although the death rate is much lower than typhoid fever. Detection protocols such as the U.S. Environmental Protection Agency (EPA) method 1623.1 [5] stipulate a procedure for _Cryptosporidium_ detection. This method consists of several steps involving filtration, elution, centrifugation, immuno-magnetic separation (IMS) and staining with fluorescent dyes, followed by microscopic examination for the manual identification and enumeration ofocysts.  
[PERSON] [6] has previously established that research has been conducted to identify advances in obtaining results of waterborne pathogen movements. In compliance with WHO, waterbornepathogens have to be carefully monitored and analyzed to implement the safety of water intake. Considering this, microfluidic devices (a set of micro-channels etched into a material e.g., glass [7]) can take a very small sample and conduct a process to analyses the safety of water through an experimental monitoring method.
## 2 Study Sites and Data Sources
### Data Sources  
#### 2.2.1 Satellite Images  
The Sentinel-2 mission has two twin satellites, Sentinel-2A and Sentinel-2B, and each identical satellite is equipped with a multispectral sensor, that covers 13 spectral bands and has a swath width of 290 km. Furthermore, this mission monitors the Earth with a resolution of up to 10 m and a revisit time of 5 d after a dual-satellite network. Compared with other multispectral satellite data, the Sentinel-2 satellite has more time and spatial resolution advantages. It can provide more image data resources for the early identification of crops. Spectral characteristics are the essential characteristics of crop recognition and monitoring. This study used ten common features of original spectral bands, including three visible bands, three red-edge bands, one near-infrared band, and two short-wave infrared waves. The Sentinel data resources we used are obtained through the Google Earth Engine (GEE) cloud platform. The platform's dataset is the Sentinel-2 L2A product, which ESA has pre-processed for radiometric calibration, atmospheric correction, and automatic resampling of the bands to uniform resolution. Hence, the data reflect the reflectance information of the surface. We did the imagery processing to clip each period using the common area SHP to produce an image dataset with a size of 6850 \(\times\) 7767 pixels. GEE platform not only enables users to easily access and process a large number of geospatial data sets but also reduces the time and energy in the image pre-processing stage, providing a new opportunity for rapid and accurate remote sensing crop information monitoring [32; 33; 34].  
In addition, from the analysis of this study's date-by-date crop identification results, it was found that the satisfactory accuracy of early crop identification could be reached (\(\geq\)85%) when used for up to 7 July. Therefore, we collected six available remote sensing images in the early stage, and their information is shown in Table 2.
# A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems
## 2 Takagi-Sugeno Fuzzy Model  
T-S fuzzy models represent complicated multi-input-multi-output (MIMO) systems with fuzzy inference rules and local linear models as follows:  
\[\mathbf{R}^{k}:\ \text{IF}\ z_{1}\ \text{is}\ \mathbf{F_{1}}^{k},\ \ \cdots,\ z_{p}\text{is}\ \mathbf{F_{v}}^{k},\ \text{THEN}\ \dot{\mathbf{x}}=\mathbf{A}_{k}\mathbf{x}(t)+\mathbf{B}_{k} \mathbf{u}(t)+\mathbf{d}_{k},\]  
where \(\mathbf{R}^{k}\) denotes the \(k\)th fuzzy rule, \(k\in\{1,\,2,\,\cdots,\,p\}\), \(p\) is the number of fuzzy rules, \(\mathbf{F_{j}}^{k}\) (\(j=1,\,2,\,\cdots,\,v\)) are the input fuzzy sets, \(\mathbf{x}(t)\in\mathbf{R}^{n}\) is the state variable vector, \(\mathbf{u}(t)\in\mathbf{R}^{m}\) is the input variable vector, \(\mathbf{z}(t)\): \(=[z_{1},\,z_{2},\,\cdots,\,z_{p}]\) are a subset of measurable or observable variables in the state and input vectors that are used for fuzzification, and (\(\mathbf{A}_{k}\), \(\mathbf{B}_{k}\), \(\mathbf{d}_{k}\)) are the matrices of the \(k\)th local model [6]. If the constant bias vector is not null, \(\mathbf{d}_{k}\
eq\emptyset\)for some fuzzy inference rules, the corresponding local models are affine models instead of linear models.  
By using the product fuzzy inference, singleton output membership functions, and centroid defuzzifier, a T-S fuzzy model in continuous-time state-space form can be organized as:  
\[\dot{\mathbf{x}}=\mathbf{A}(\mu)\mathbf{x}(t)+\mathbf{B}(\mu)\mathbf{u}(t)+ \mathbf{d}(\mu), \tag{1}\]  
where \(\mathbf{A}(\mu)=\sum_{k=1}^{p}\mu_{k}\mathbf{A}_{k}\), \(\mathbf{B}(\mu)=\sum_{k=1}^{p}\mu_{k}\mathbf{B}_{k}\), \(\mathbf{d}(\mu)=\sum_{k=1}^{p}\mu_{k}\mathbf{d}_{k}\) and \(\mu_{k}\) denotes the normalized fuzzy membership function \(\mu_{k}=\xi^{k}(\mathbf{z})/\sum_{k=1}^{p}\xi^{k}(\mathbf{z})\) with \(\xi^{k}(\mathbf{z})=\prod_{i=1}^{p}\Phi^{k}_{i}(z_{i})\). \(\Phi^{k}_{i}(z_{i})\) represents the \(i\)-th membership function in fuzzy set \({F_{i}}^{k}\) of the \(k\)-th fuzzy rule. Due to the fact that the membership functions are nonlinear (e.g., triangular or Gaussian), the model in Equation (1), as an aggregation of local linear or affine models, is also a nonlinear model in nature.
## 5 Hybrid Localization Algorithms
### Centralized Localization Algorithms
#### 5.1.1 3D Multi-Power Area Localization Scheme (3D-MALS)  
In [58, 59], ALS has been extended to 3D mobile UWSNs. The authors propose a range-free method based on mobile detachable elevator transceiver (DET) [60, 61] and 3D multi-power area localization scheme (3D-MALS) to localize unknown nodes in deep underwater environment. In hybrid UWSNs, a small number of surface buoys are placed on the water surface. Each buoy is equipped with a DET, which is mainly composed of an elevator and an multi-power level acoustic transceiver. The elevator helps the DET rise or dive in vertical underwater, and the transceiver communicates with unknown nodes. The DET gets coordinate from its buoy when it moves to water surface, then moves down to broadcast position information at some pre-configured depths. At every broadcast position, the DET transmits beacon signals at varying power level as described in [12, 13].  
Unknown nodes listen to beacon signals periodically from the mobile DET. To save energy consumption of unknown nodes, all the measured position information are sent to the sink node to compute the estimation areas. However, the localization accuracy of 3D-MLS is low like that of ALS. Much energy is needed to transmit multi-power level signals. Moreover, localization time depends heavily on the velocity and message sending interval of the DET.
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 4 Studies
### Towards Microsecond Real-Time XPCS at Storage-Ring Sources
#### 4.1.2 Anisotropic Dynamics  
As already introduced in the previous section, anisotropic dynamics are observed frequently in gel or glasses. Besides, anisotropic dynamics are particularly relevant for anisotropic particles. In [118], the dynamics of peanut-shaped magnetic nanoparticles have been studied. In presence of a magnetic field, these particles form chains along the field direction. There the dynamics strongly depends on the length scale probed, showing diffusive and superdiffusive regimes. In the perpendicular direction, diffusion dominates, accompanied by sedimentation due to gravity.  
In presence of such advective motion, the \(g_{2}\) function becomes direction-dependent [119]  
\[g_{2}(\mathbf{q},\Delta t)=g_{2}^{D}(q,\Delta t)\cdot g_{2}^{p}(\mathbf{q}, \Delta t), \tag{23}\]  
where \(g_{2}^{D}\) is the diffusive part from Equation (4) and \(g_{2}^{p}(\mathbf{q},\Delta t)\) describes the contribution from advection. This typically depends on the distribution of velocities [120]. For uniformly distributed velocities, one obtains for the direction of the movement \(q_{\parallel}\)  
\[g_{2}^{p}(q_{\parallel},\Delta t)=\frac{1}{(q_{\parallel}\Delta tv)^{2}}\sin ^{2}(q_{\parallel}\Delta tv), \tag{24}\]  
leading to oscillations in the measured \(g_{2}\) function and thus access to the velocity \(v\). [PERSON] et al. used these models for accessing the dynamics of sedimenting nanoparticles [120]. A well-defined distribution of velocities dominated the early sedimentation stage. At later times, the contribution of advective motion decreased and Brownian motion dominated. Using similar models, [PERSON] and co-workers measured the parabolic velocity distribution of Poiseuille flow in a mm-sized flow channel [121].  
Combining the study of anisotropic dynamics by XPCS and X-ray microscopy, the ion transport in lithium-ion battery electrolytes has recently been revealed [33]. With a heterodyne XPCS approach, oscillations in the \(g_{2}\) functions taken in operando could be related to the ion velocity in the electrolyte providing a concentration-independent transference number of 0.2. This study particularly highlights the applicability of modern XPCS for understanding and solving technological challenges.
## 5 Conclusions  
In this study, we exploited the first available PRISMA time series collected by the hyperspectral satellite over the more densely surveyed agricultural test sites located in Europe and Iran. PRISMA time series were processed to tune and test ML and DL classification approaches for large-scale crop mapping in agriculture, with particular attention paid to early crop mapping. The comparison of the performance of six classification methods--MNB, KNN, RF, SVM, 1D-CNN, and 3D-CNN--on PRISMA and Sentinel-2 time series confirmed the powerfulness of the PRISMA times series when combined with the 1D-CNN algorithm in the production of an accurate crop mapping and in the definition of a precise early crop map product. All common crops from three agricultural sites in Italy and from a region with strong diverse agronomy in South Iran were in fact accurately detected. 1D-CNN showed the highest accuracy in the crop type identification, followed by 3D-CNN, RF, SVM, and KNN as the next accurate methods (89%, 91%, and 92% for winter, summer, and perennial cultivations, respectively). Classification accuracy remained comparable whether a cross-farm or single-farm training scenario was utilized (reduction of 6% in AO). The product accuracy obtained by using the Sentinel-2 time series was substantially lower compared with PRISMA (14%, 15%, 18%, 19%, and 23% for 1D-CNN, 3D-CNN, SVM, RF, and MNB, respectively). The results demonstrate that, when using the PRISMA time series and the 1D-CNN algorithm, early-season crop mapping in the northern Mediterranean agricultural sites is feasible at the appropriate time, which aids with decision making regarding food security and marketing.  
These results show the way to use the hyperspectral time series, even though still timely discontinuous, with agricultural applications on a global scale. Moreover, it is expected that future algorithm development, especially in context of the feature selection in the combined temporal and spectral domains, will boost classification performance.  
Conceptualization, S.P. ([PERSON]), G.L. and R.C.; methodology, M.F.C. and S.M.; software, S.M. and F.R.; validation, R.C.; formal analysis, S.M.; investigation, S.M.; resources, S.P. ([PERSON]); data curation, S.M. and F.R.; writing--original draft preparation, S.M.; writing--review and editing, S.P. ([PERSON]), R.C., F.S. and A.P.; supervision, S.P. ([PERSON]); project administration and funding acquisition, S.P. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Italian Space Agency (ASI) within the PRIS4 VEG project Contract/Agreement n. 2022-5-U.0 CUP n. F43C2200000005 and the SAPP4 VU project Contract/Agreement n. 2022-13-U.0 CUP n. F83C22000550005 5. Data generated by the PRIS4 VEG Consortium under a license from ASI Original PRISMA Product--O Italian Space Agency (ASI)--(2022).  
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflicts of interest.
# An Uplink Channel Estimator Using a Dedicated Instruction Set for 5G Small Cells
## 2 Basic Model
### Pilot Resource Mapping Model  
The main functions of uplink physical channel include correlation demodulation and channel measurement. In PUSCH, DMRS is used for channel estimation. In 5G uplink, the mapping mode of PUSCH is star pilot, which inserts pilots in frequency domain subcarriers and time domain symbols at standard intervals. The work of this section is part of the resource mapping module in Figure 1. After resource demapping, the channel can be estimated according to the distortion of the DMRS between the transmitter and the receiver.  
For the frequency domain DMRS mapping method, 3 GPP TS 38.211 indicates the mapping position of the DMRS in the PUSCH. The research of this paper is based on the channel estimation of 4 \(\times\) 4 MIMO 5G small cells, so the data are divided into four layers for transmission. In addition, small cells serve pedestrians or devices in the Internet of Things, which have low mobility, so the single-symbol DMRS meets the requirements. Combined with the standard 38211, the DMRS frequency domain mapping method used in this paper is shown in Figure 2. The structure of the 12 subcarriers * 14 symbols is called a resource block (RB), which is a basic unit of resource scheduling. A time-frequency resource unit consisting of an OFDM symbol and a subcarrier is called a resource element (RE). The DMRS is mapped on the RE agreed by both the sender and the receiver in the way shown in Figure 2.  
Figure 1: 5G flow chart of uplink channel estimation.  
Figure 2: Schematic diagram of single-symbol DMRS pilot type 1.
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 7 Applications of HRV Analysis
### Sleep Apnea Detection
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author, Year** & **Feature Extraction Methods Used** & **Feature Selection/Reduction Methods Employed** & **Classifiers Used** & **Inference** \\ \hline [PERSON] et al. (2019) & Time and frequency domain methods & The F-score algorithm and PCA & KNN, probabilistic neural network (PNN), SVM, and multilayer & The study proposed a respiration scoring algorithm for OSA patients. The Doubribeophysography (PPC) signal and derived HPV features were used to achieve a classification accuracy up to 95%. \\ [PERSON] et al. (2019) & Time and frequency domain methods & Cohen’s Kappa coefficient of agreement and Pearson correlation coefficient & Linear discriminant analysis (LDA) & A novel approach for the classification of four sleep stages (REM, MEM, combined light, and deep) was proposed. The HRV and motion statistics-based features were employed to achieve an accuracy of up to 85\%. \\ [PERSON] et al. (2019) & Time and frequency domain methods & - & Random forest & A simple OSA screening method was proposed using HRV features and a random forest-based A/N discriminant architecture. The suggested approach showed 76\% sensitivity and 92\%; specificity. \\ \hline [PERSON] et al. (2020) & Time and frequency domain methods & F-score feature selection algorithm & KNN, PNN, MLFNN, SVM & This study proposed AI models based on PPG and HRV features to diagnose GA. The model achieved an accuracy of 91.09\%. \\ \hline [PERSON] et al. (2020) & Time and frequency domain methods & T-test & aggregated Cauchy association test (ACAT) & The study proposed a model using the ACAT algorithm on PPG and HRV data. The model could detect cyclic variation of heart rate (CVHR) with 85\% accuracy. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Studies conducted for sleep apnea diagnoses utilizing HRV analysis in the last 5 years.
# Digital Transformation of Beekeeping through the Use of a Decision Making Architecture
## 2 Context and Related Work
### IoT for Digital & Green Agriculture
Smart and IoT applications entered agriculture with a delay in relation to other fields such as medicine or industry. However, these solutions have started to increase over the years for better monitoring. This paves way for better practices in the form of better quality services and cost reduction. IoT-based approaches in the agriculture and agro-industry have proved to be very useful and have become necessary to propose better adapted services. More precisely, most initiatives in the agriculture and agro-industry focus not only on increasing the production but also on improving services such as sustainability, biodiversity vigilance, etc. IoT is another very important paradigm used in the agro-industrial and environmental field [18]. The authors in Ref. [19] explain that one of the main requirements for devices used in IoT projects is that they must be energy efficient. The main point is to be able to evaluate the energy impact of the proposed architecture. The authors also explain the interest of Edge Computing in the future of agriculture. The IoT driven by AI-based recommendation model is one of the greatest promises of the development [18]. Several research activities have been carried out to study the use of IoT concept and agriculture. The authors of Ref. [20] presented an interesting survey of IoT solutions and demonstrated how IoT can be integrated into the smart agriculture sector. Ref. [21] presented the design of a smart system based on low-cost IoT sensors and popular data storage services and data analytics services on the cloud for crop production. The authors of Ref. [22] presented an overview on the recent trends on sensors and IoT systems for irrigation in Precision Agriculture. On the same scope, Ref. [23] proposed a data acquisition platform that is easily reusable. Other authors [24] have focused on the communication between the sensors and a NoSQL database through the use of an Interoperable Platform. The goal is to support the actors to help them manage their crops. In Ref. [25], the authors present a comprehensive review of emerging technologies for IoT-based smart agriculture. They provide a classification of IoT applications for smart agriculture into several categories. They propose to list the connected smart agriculture sensors that enable the IoT.
## 2 Non-Symmetric Cross-Section
### Symmetrical Channel  
Firstly, the application of this theory will be simplified for a singly \(x\)-symmetric section in Figure 1a where the three standard channels lie. In the case of a channel section with \(x\)-symmetry, the principal axes remain at \(x\) and \(y\). Therefore, \(M_{x}\) and \(M_{y}\) replace equivalent moments \(M_{x}\)' and \(M_{y}\)' and \(F_{x}\) and \(F_{y}\) replace equivalent forces \(F_{x}\)' and \(F_{y}\)'. These reductions to Equations (1)-(7) are sufficient for providing the analyses and numerical calculations that are to follow. In particular Equation (3a) provides the net axial stress, \(\sigma_{z}\), arising from bending and constrained warping combining in an \(x\)-symmetric channel:  
\[\sigma_{z}=\mu Ew_{o}\{\sinh\ [\mu(L-z)]\
i\cosh\ (\mu L)\}\ \sigma_{z}+M_{x}y/I_{x}+M_{y}x/I_{y} \tag{7}\]  
Combining \(q\) in Equation (5b) with Equation (2a), the net shear flow for this channel arises from flexural shear forces and axial torsion as  
\[q=(F_{x}/I_{y})D_{y}+(F_{y}/I_{x})D_{x}+Et\left(\mathrm{d}^{3}\theta/\mathrm{d }z^{3}\right)Q \tag{8}\]It is noteworthy that Equations (7) and (8) provide closed solutions, which will serve as a reliable basis for a comparison with numerical FE analyses and experimental testing. The stress calculations that follow apply to seven points taken anticlockwise along the mean wall perimeter of the symmetric channel in Figure 2a: points 1 and 7 are the flange ends; points 3 and 5 lie at the corners; web point 4 is at the intersection with the \(x\)-axis; and flange points 2 and 6 are at the intersection with the \(y\)-axis.  
Note (1): For a cantilever, the bending moments at a given position, \(z\), along the length are given as \(M_{x}=F_{y}(L-z)\) and \(M_{y}=F_{x}(L-z)\). The moments show their maxima at the fixing where \(z=0\).  
Note (2): The shear flow terms are independent of length \(z\) when transverse shear forces \(F_{x}\) and \(F_{y}\) are applied at the free end of a cantilever beam. Equilibrium requires that every cross-section along the length is subjected to the same shear forces. However, the third term in Equation (7) is dependent upon \(z\) from Equation (6c).  
Note (3): The net shear stress (Equation (7)) has a linear distribution through the thickness. The flexural shear flow, \(q_{F}\), provides the mean shear stress at mid thickness. With \(q_{F}\), the shear flow due to axial torsion \(q_{T}\) is superimposed to give a linear thickness variation in \(q_{net}\) between the free edges.
# Sensor Micro and Nanoparticles for Microfluidic Application
## 2 Construction Principles of Sensor Particles
### Combination of Oxygen and pH Sensing  
Simultaneous determination of oxygen concentration and pH during the enzymatic reaction was presented by [PERSON] et al. (2013) Contactless and inexpensive detection was enabled by using poly(styrene-block-vinylpyrrolidone) (PSPVP) core-shell nanoparticles, whose average diameter was 180 nm. Due to the core-shell structure, the oxygen-sensitive dye PtTPBPF was incorporated into the particle's core and a pH-sensitive dye (aza-BODIPY) was captured into the polyvinylpyrrolidone shell. Modified dual lifetime referencing (m-DLR) was used to sense pH and oxygen in microfluidic system. After the sinusoidally modulated light source excited the indicators, phase shift as well as luminescence emission were recorded. The detection method was based on the overall phase shift measurement of pH and oxygen sensitive luminescence dyes at two different modulation frequencies. It was necessary that luminescent lifetimes of indicators were in different time ranges. In this research, an oxygen-sensitive dye had a relatively long phosphorescence lifetime in the microsecond range when a pH indicator had a shorter fluorescence lifetime in the nanosecond range. The presented system offered the advantage that it used a red-light excitation at 620 nm, which caused decreased background fluorescence and scattering. Additionally, it possessed higher photostability and opens the possibility to use sensor nanoparticles in microfluidic devices without additional integration. In order to monitor pH changes in real-time, Penicillin G acylase catalyzed an enzymatic conversion of Penicillin G into 6-aminopenicillanic acid and phenyl acetic acid was investigated. By comparing results between the simulation and experiment, it was shown that experimental data were in great agreement with the expected simulation results. It was found that oxygen concentrations were determined at a resolution from approximately 0.02 to 0.32 mg/L. The resolution of pH value varied from 0.03 to 0.1 pH units.