esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Smart Feasant Farming System and Data-Mining Process
### Wireless Sensor Network  
The Wireless Sensor Network (WSN) represents the second layer of the architecture of our system, which is based on ultralow powered transceiver modules designed for operation in the worldwide Industrial, Scientific and Medical (ISM) frequency band at 2.4 GHz such as the nRF24L01+ single chip 2.4 GHz transceiver ([[https://www.sparkfun.com/datasheets/Components/SMD/nRF24L01](https://www.sparkfun.com/datasheets/Components/SMD/nRF24L01) Pluss_Preliminary_Product_Specification_v1_0.pdf]([https://www.sparkfun.com/datasheets/Components/SMD/nRF24L01](https://www.sparkfun.com/datasheets/Components/SMD/nRF24L01) Pluss_Preliminary_Product_Specification_v1_0.pdf) (accessed on 15 November 2021)), as well as on a variety of low-power sensors for climate and environmental monitoring. In this regard, the selection of the necessary sensors should favor low-cost sensors that are compatible with hardware and software prototyping platforms such as Arduino and NodeMCU, which are popular and relatively low-cost alternatives for the development of IoT-based systems.  
The choice of this radio-frequency (RF) wireless communication technology over other similar technologies that are equally proprietary, such as Long Range (LoRa), lies in the possibility of exploiting communication protocols that are specially designed to enable high-power data transmission and reception at lower power consumptions. In fact, in the context of peasant and family farming, high bandwidths should be prioritized over long  
Figure 2: System Architecture.  
transmission ranges as constant up links for real-time data streams are frequently deployed over narrow geographic areas.  
Moreover, a typical tree topology in which one of the nodes acts as a base node and the others are central hubs or actual sensor nodes is proposed for the design of the WSN.  
Unlike other network topologies for WSNs, namely, cluster topology and flat topology, tree topology has been demonstrated to save slightly more energy in data-acquisition applications. On the other hand, tree topology can perform worse than cluster topology and chain topology in terms of scalability; similarly, it can perform worse than chain and flat topologies in terms of topology management (overhead) [18]. Nonetheless, a smart farming system such as the one that we pursue in this work, which is aimed at peasant and family farming, is theoretically less likely to suffer from these problems as it would be composed of no more than a few dozen sensors.
## 6 Results, Implementation, and Testing
### Additional Redundancy  
For the purpose of redundancy and enhanced reliability, we take advantage of an existing microphone on the Arduino Nano board by implementing a keyword spotting function on SafeStride as an extra layer of safety measures if the walker user falls. The keyword spotting fall detection technique was based on the Cyberon Speech Recognition Engine, which provided the ability to download a pre-trained machine learning model from its web platform for easy, fast, and effective implementation on microcontrollers such as the Arduino Nano board. This technique is used in speech recognition systems to detect predefined keywords in continuous speech. In the context of fall detection, it can be adapted to detect verbal expressions commonly uttered during or immediately after a fall, such as exclamations of surprise, pain, or calls for help. The use of keyword spotting for fall detection offers the potential advantage of not only detecting a fall event but also capturing the urgency or severity of the fall. For instance, if the system detects phrases like \"Help\" or \"Help me\", it can provide additional evidence of a fall event and potentially trigger more urgent assistance.  
In the initial phase of this project, an attempt was made to create a machine learning model using a publicly available dataset for keyword spotting; however, a dataset that addressed the specific contextual requirements for detecting words or phrases potentially uttered during a fall event could not be identified. Creating such a dataset is very difficult due to the inherent variability observed in pitch, accent, speed, and other speech characteristics. To overcome these challenges, the decision was made to utilize the built-in library of the Cyberon speech recognition engine. The engine-generated model was tested in a lab  
Figure 8: A graphical interface was developed to test the Safestride prototype with continuous data. Recorded data were loaded and sent to the prototype via Serial Communication, utilizing a sliding window (indicated by red vertical lines). The prototype performed machine learning classification using its trained model and promptly returned the result to the application (see the table on the right side).  
environment, where its ability to identify the specific keyword \"help\" was demonstrated. Keyword spotting was included as a supplementary tool in case the motion classification algorithms did not capture a rare fall event.
## 3 Materials and Methods
### The Location Feature Extraction Strategy
#### 3.2.1 Location Feature Extraction Structure
The feature map \(F_{c,context}\) output by the channel attention module was used as its input feature map. First, based on the dimension of the channel, global max pooling and average pooling were carried out to obtain two feature maps (H \(\times\) W \(\times\)1). Then, the two feature maps were concatenated based on the channel dimension, using a \(7\times 7\) convolution kernel to perform channel dimension reduction. The weights of the spatial dimensions \(F_{s,context}\) were generated. At this point, the processing of branch 1 ends. Use spatial attention for \(F_{c,context}\), where \(V_{s}\) is the spatial attention vector obtained by the spatial attention mechanism:  
\[F_{s,cross}=V_{s}\otimes F_{c,context}, \tag{7}\]  
Branch 2 performed a simple convolution on the entered feature map, and adjusted the number of channels to the same number as channels C2 in branch 1. The feature maps of branch 1 and branch 2 were concatenated to obtain the feature map with H \(\times\) W \(\times\) 2C2. The concatenated feature map was sent into the normalization and activation functions of _LeakyReLU_. Finally, the ConvNHS module integrated the features, and the final feature map \(F^{\prime}\) was the output. \[F^{\prime}=\text{ConvNHS}[f_{concate}(F_{s,context};F_{branch2})], \tag{8}\]  
where \(f_{concate}\) refers to concatenation, and \(F_{branch2}\) refers to the feature map obtained after convolution in branch 2. Take a practical example to illustrate the dimensional change of this structure. Supposing the input feature map \(F\) (8 \(\times\) 8 \(\times\) 64. Where 8 \(\times\) 8 was the height and width of the feature map and 64 is the number of channels) entered the ConvNHS module of branch 1 to interact with cross-dimensional information. The number of channels was changed to 32, the size was left the same, and a new feature map \(F_{context}\) (8 \(\times\) 8 \(\times\) 32) was obtained. Then the channel attention module obtained two feature maps (1 \(\times\) 1 \(\times\) 32) through GMP (global max pooling) and GAP (global average pooling). After a series of convolutions, activation functions, additions, and multiplications, the two feature maps were integrated into the channel feature map \(F_{c,context}\)(8 \(\times\) 8 \(\times\) 32). In the spatial attention module, two feature maps were generated after GMP and GAP, and the spatial feature map \(F_{s,context}\) (8 \(\times\) 8 \(\times\) 32) was obtained after concatenation. The original feature map entered branch 2 to obtain \(F_{branch2}\) after a simple convolution (8 \(\times\) 8 \(\times\) 32). After concatenating the feature maps of branches 1 and 2, going through again ConvNHS processing, a final output feature map \(F^{\prime}\) (8 \(\times\) 8 \(\times\) 64) was generated.
## 3 Results
### Fourier Transform Infrared (FTIR) Spectroscopy  
Fourier transform infrared (FTIR) absorption spectroscopy is a technique that provides spectral data on the vibrational frequencies of molecular structures in both inorganic and organic compounds. FTIR spectra act as unique fingerprints for each material studied, as the intrinsic physicochemical properties of materials produce specific wavenumbers corresponding to functional groups (e.g., C-H, O-H, C=O) [36].  
In Figure 1, the FTIR spectra of the food packaging materials--FPC + HNE-PLE (a), FPC (b), and FPC + HNE-MAE (c)--are presented. Bands at wavelengths 3457 cm\({}^{-1}\) (a and c) and 3445 cm\({}^{-1}\) (b) are attributed to O-H stretching, while bands at 1641 cm\({}^{-1}\) (a) and 1653 cm\({}^{-1}\) (b and c) correspond to C=O stretching.
## 3 Experiments
### Remote Sensing Investigations of Large Rivers Deltas
#### 3.5.5 The Ganges-Brahmaputra-Meghna Delta  
The Ganges-Brahmaputra-Meghna delta is especially vulnerable to flooding, ground subsidence, and increased sea salinity intrusion [441]. Salinity intrusion has occurred due to a massive reduction in river inflow caused by dams, barrages, embankments, and SLR. As a result, people have had to travel further upstream to find suitable fresh water in the coastal city of Khulna. Additionally, increased salinity has adversely affected coastal vegetation and animals, such as the death of Sundari trees, the loss of shrimp farming, and the impacts on a wide range of biodiversity. The Sundarban wetland located in the delta is one of the most extensive coastal wetlands globally, covering over 100 km\({}^{2}\). Over the past centuries, the Sundarban mangroves have been modified into paddy lands and shrimp farms, which has accelerated salinity intrusion in the region.
# Vibration Fatigue Life Reliability Cable Trough Assessment by Using Weibull Distribution
## 4 Numerical Application
### Weibull Stress Parameters  
By selecting a reliability \(R(t)=0.95\), from Equation (3) we have, \(n\approx 20\), then by using the \(n\) value in Equation (16), the \(Y_{i}\) elements are obtained, with a mean of \(\mu_{y}=-0.544453\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency (Hz)** & \(\Delta\)**\(A_{res}\) (g)** & \(\sigma_{dyn}\) (Psi/g)** & **Vibration Stress \(\sigma_{vib}\) (Psi)** \\  & **Equation (5)** & **Equation (8)** \\ \hline
2.0 & 60.0 & & 330.00 \\ \hline
8.0 & 203.0 & & 1116.50 \\ \hline
12.0 & 216.0 & & 1188.00 \\ \hline
16.0 & 191.0 & & 1050.50 \\ \hline
26.0 & 138.0 & & 759.00 \\ \hline
38.0 & 120.0 & & 660.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Vibration stress results.  
Figure 5: Acceleration response (three axes).  
Thus, from Equation (12), by using the \(\mu_{y}\), \(\sigma_{1}\), and \(\sigma_{2}\) values, the Weibull parameter \(\beta_{s}\) value is,  
\[\beta_{s}=\frac{(-4)(-0.5445)}{0.995*\ln\left(\frac{1188.00}{330.00}\right)}=1.7087\]  
Following the fitting method represented in Section 4. Equation (48) from [20], the constant to be used in Equation (12) from which the Weibull parameters reproduce the observed \(\mu=759\) was determined (see Equation (12)).  
In the same way, from Equation (14) the logarithm average value is \(\mu_{x}=\ln\sqrt{1188.00*330.00}\) = 6.44, and from Equation (13) the Weibull parameter \(\eta_{s}\) is \(\eta_{s}=\exp\{6.44\}=626.13\). Finally, the Weibull stress family is W(1.7087, 626.13 Psi). Now by using the Weibull stress family results, the strength of random behavior can be determined. Since the Weibull parameters only depend on the principal stress values provoked by the random vibration \(\sigma_{1}\) and \(\sigma_{2}\) values, their random behavior can be obtained by performing the Weibull analysis in the following steps.
# A Multi-Technology Communication Platform for Urban Mobile Sensing
## 3 Architecture Overview
### Network Elements
#### 3.1.1 Data-Collecting Units  
Data-collecting units are stations, without wired connectivity to other entities of the network, composed of a set of sensors with the purpose of collecting environmental information. Each DCU is equipped with a large environmental monitoring sensor set that aims to collect relevant information about the environment condition in a dense urban scenario. This sensor set is able to measure the following environmental variables:  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Processor** & 1.2 GHz 64-bit quad-core ARMv8 CPU \\
**Memory RAM** & 1 GB \\
**WiFi Networking** & 2.4 GHz 802.11n Wireless LAN \\
**Operating System** & 64-bit Raspbian GNU \\ \hline \hline \end{tabular}
\end{table}
Table 4: Raspberry Pi 3 Model B specifications.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Module** & SX1272 \\
**Dual Frequency Band** & 863–870 MHz (Europe); 902-928 MHz (US) \\
**Transmission Power** & 25 mW \\
**Sensitivity** & –134 dBm \\
**Channels** & 8 (868 MHz); 13 (900 MHz) \\
**Range** & Line-of-Sight = 21 km; Non-Line-of-Sight = +2 km \\ \hline \hline \end{tabular}
\end{table}
Table 5: LoRa technology hardware description [35].  
* Temperature;
* Luminosity;
* Wind direction;
* Wind speed;
* Carbon dioxide (CO\({}_{2}\));
* Sound detection;
* Humidity;
* Precipitation;
* Barometric pressure;
* MultiGas (CO, CH\({}_{4}\), NH\({}_{3}\));
* UV index.  
To sense the city, a DCU must deal with the internal communications of the aforementioned environmental sensors, and gather and store the sensed information while handling the multi-technology capabilities. The DCU software architecture is presented in Figure 2. During its development, both hardware and software were designed with the aim of giving versatility and adaptability to the DCU, so that it can integrate new sensors and functionalities such as the inclusion of new communication technologies.
# Pharmacokinetics of Pullulan-Dexamethasone Conjugates in Retinal Drug Delivery
## 3 Results
### Intravitreal Kinetics of Pullulan Conjugates  
Intravitreally administered BDP-pullulan and BDP-pullulan-DEX were monitored by fluorophotometry, fundus camera and OCT in rats. Vitreal elimination of pullulan formulations followed first-order elimination kinetics and the average vitreal half-life of both formulations was about 17 h in the rat eyes (Figure 3, Table 2). Fundus images showed that the formulations retained for about 3-5 days in the rat vitreous (Figure 4). The apparent volumes of distribution of pullulan conjugates (range of 42-84 \(\mu\)L) were close to the anatomical volume of rat vitreous (\(\approx\)50 \(\mu\)L), whereas the vitreal clearance values (range of 1.8-3.5 \(\mu\)L/h) were lower than the average of aqueous humor flow rate in rats (21 \(\mu\)L/h) [48].  
Figure 4: Full color and green fluorescent fundus images of rat eyes before and after intravitreal injection (IVT) of (**A**) BDP-pullulan and (**B**) BDP-pullulan-DEX. After one day, the labeled compounds distribute homogeneously in the vitreous. The length of scale bar is 200 \(\mu\)m.  
Figure 3: Concentrations of fluorescently labelled (**A**) BDP-pullulan (\(n\) = 6 eyes) and (**B**) BDP-pullulan-DEX (\(n\) = 5 eyes) in the vitreous of rats. One compartment model with first-order elimination rate constant was used for curve fitting (lines). The derived kinetic parameters are shown in Table 2. Each line represents the measurement from individual rat eye.  
BDP-pullulan-DEX concentrations in aqueous humor and vitreous were evaluated in rabbits by in vivo fluorophotometry. The vitreal half-life in rabbits (\(\approx\)60 h) was longer than in the rats (\(\approx\)17 h) and the elimination kinetics showed minimal inter-subject variability (Figure 5, Table 2). The average volume of distribution (932 \(\upmu\)L) was close to the anatomical volume of vitreous humor in rabbits (\(\approx\)1.5 mL) (Table 2). Moreover, in rabbits, the vitreal clearance (11 \(\upmu\)L/h) was lower than the average of aqueous humor flow rate in normal albino rabbits (180 \(\upmu\)L/h). The concentrations of BDP-pullulan-DEX in the aqueous humor were consistently about one order of magnitude lower than in the vitreous (Figure 5).
## 6 Conclusions  
A modelling approach has been developed to predict the characteristics of the AE signal due to fiber breaks. The effects of the source position, wave propagation and the transducers are treated numerically and are compared with experiments. The simulated waveforms near the source provide a valuable understanding of the signature of the AE source. In particular, the study of the 2 DFFT can identify the typical modes excited during the signal propagation, which strongly depend on the position of the fiber and on the thickness of the sample. Thus, the analysis of the dispersion modes, which is only accessible by numerical simulations, reveals interesting geometry-dependent features of the waveforms. The results of this work highlight the main limitations of the conventional source identification procedures.  
Figure 21: Evolution of the descriptors vs. distance between epicenter of the source (P2 position) and sensor for the simulated signals detected with nano30 sensors for several thicknesses and positions of the fiber (**a**) amplitude in dB, (**b**) frequency centroid in kHz and (**c**) roll-on frequency and for the picoHF (**d**) and (**e**).  
Modelling approaches that simulate \"primitive\" AE sources can be use in order to: (1) optimize sensor selection and location, (2) quantify the wave propagation effects and (3) generate training datasets for machine learning algorithms. In addition, modelling AE signals with our numerical approach allows us to enlarge the training datasets while avoiding the high costs required by extensive experimental campaigns.  
In spite of its simplicity, the proposed model represents a first step towards a quantitative understanding of the influence of experimental parameters on AE signals generated by fiber breaks. Other damage mechanisms, such as matrix cracking and fiber-matrix debonding, will be considered in a future work.  
Conceptualization (Z.H., N.G., C.F., T.M.), Data curation (Z.H.), Formal analysis (Z.H.), Investigation (Z.H.), Methodology (Z.H., C.F, N.G., T.M.), Supervision (N.G, C.F., T.M.), Writing (N.G., C.F., T.M.).  
This research received no external funding.  
The authors declare no conflict of interest.
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 8 3D Frequency Selective Structures  
Recently, 3D FSSs have been proposed to overcome challenges such as slow roll-offs, sensitivity to oblique incident angles, and poor frequency selectivity faced by conventional 2D FSSs. The 3D FSS structures offer more degrees of freedom in terms of designing new geometries to achieve specifications that are challenging to realize using 2D configurations. A multimode resonator made of 2D array of cavities is proposed in [141; 142; 143]. It offers good control on the position and the numbers of transmission zeroes and poles. For instance, to achieve multiband behavior, one is required to increase the number of poles and zeroes. For traditional designs, an increase in the number of poles and zeros translates to the multi-layer FSS. The 3D FSS shows a stable angular and symmetric filtering response. The 3D FSS is stacked between two dielectric layers covered by the perfect electric conductors, and it has a wall of periodic boundary conditions as shown in Figure 10.  
Though 3D-FSSs have demonstrated an enhancement of filtering characteristics and more degrees of freedom, these structures are complicated to realize and fabricate. In addition, they result in thicker and bulkier profiles compared with their 2D counterparts, which is not desirable in applications requiring a light weight and thin structures. A summary of few recent 3D FSS research studies based on types of elements used, size, and operating frequency is presented in Table 4.  
Figure 10: (**a**) Perspective view of 3D FSS; (**b**) cross-sectional view of FSS (**c**); side-view (Picture is taken from [142]).
## 1 Introduction  
An intensively explored subject [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12], polymer conductive composites are functional composites made by adding conductive fillers to polymers using a specified processing method, yielding steady and sustained electrical conductivity. Their electrical conductivity can also be adjusted with a greater range, and they are easily processed. Due to the aforementioned advantages, polymer conductive composites are commonly used in diverse fields, such as electronics, energy sources, and chemical engineering.  
The electrical conductivity of polymer conductive composites is greatly dependent on the properties of the polymer as well as the conductive fillers' type, content, geometrical shape, and dispersion. The three major categories of conductive fillers are carbon, metal and metallic oxide, the former of which includes carbon black (CB) [5; 13; 14; 15; 16], carbon fibers (CF) [1; 5; 17; 18; 19], graphite [18; 20; 21], carbon nanotubes (CNT) [2; 4; 14; 22; 23], and graphene [4; 24; 25; 26; 27] and is the most frequently used due to its light weight, its easy formation of conductive networks, and its oxidation resistance. Moreover, polymer conductive composites commonly use polypropylene (PP) [1; 5; 16; 20], polyethylene (PE) [1; 18], and polystyrene (PS) [3; 13; 16; 24] as their matrices. PP is a highly commercially available polymer due to its excellent mechanical properties, good heat resistance, low cost, ease of processing, and full recyclability.  
A melt compounding method is more appealing than an in-situ polymerization method or solution mixing method, and its combination of traditional facilities, such as an extruder and a mixer, can give the production a greater diversity of polymers and fillers. Such a method is relatively economical and suitable for mass production [28] and has already been successfully applied in the production of polymer conductive composites with the combination of conductive fillers of CNT, CB, expanded graphite, and grapheme [1; 2; 14; 16; 18]. With a melt compounding method, PP (_i.e._, the matrix) is combined with GNs or CF (_i.e._, conductive fillers) to form PP/GNs and PP/CF conductive composites. The crystallization properties, tensile properties, conductivity and EMI SE of the conductive composites are characterized by DSC, optical microscope observation, tensile test, conductivity test, and EMI SE test.