esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Morphologies of PLLA Nanofibers  
A comparison between tapping mode AFM images of the surface topography of as-spun and annealed nanofibers is given in Figure 3. The effect of the annealing process on the topographical  
Figure 2: Fourier transform infrared spectroscopy (FTIR) spectra in (**a**) the C=O stretching region; and in (**b**) the region of 1000–650 cm\({}^{-1}\) of PLLA nanofibers. X-Ray Diffraction (XRD) patterns presented in (**c**). The numbers 1–4 refer to the as-spun sample, and annealed samples at 90, 100 and 120 \({}^{\circ}\)C, respectively.  
features of the electrospun fibers clearly appears. The as-prepared sample shows a porous structure which most probably results from the solvent's fast evaporation during the electrospinning process [42] (Figure 3a,b). Moreover, under the stretching effect occurring during the electrospinning process, all the polymer chains are expected to orient in the direction of the fiber axis, creating elongated pores at the sample surface. Annealing above the glass transition induces a rearrangement of the surface resulting in a more textured morphology. Although the details of the texture are obscured in the height image, they clearly appear in the phase image (Figure 3b,e) as granules probably corresponding to densely packed crystallites [43]. The diameter of a single nanofiber can also be measured from the tapping height image, although this should be done with caution. The estimation made by measuring the width of the nanofibers is usually inaccurate due to the tip convolution. The average diameter of the nanofibers should instead be evaluated by measuring the distance from the substrate to the highest point of the cross-section profile. It should also be noted that, when operating in tapping mode, the AFM tip gently touches the sample surface and the force between tip and sample is kept at a minimum. Therefore, any damage to the surface can be avoided and the height values measured are the \"true\" heights. As observed in Figure 3c,f, the width of the nanofiber would imply a diameter of approximately 380 nm for the as-prepared sample and 320 nm for the annealed sample, whereas the height cross-section profile, which is more reliable, indicates 285 nm and 175 nm for pristine and heat-treated nanofibers, respectively.  
The average diameter of the fibers as well as the standard deviation were then calculated from 30 different nanofibers and were found to be \(210\pm 88\) nm for the pristine fibers and \(160\pm 44\) nm for the annealed nanofibers (see some representative images in Figures S1-S3 in Supporting Information). The decrease of the diameter of the nanofibers upon annealing probably originates from the collapse of the pores and the growth and merging of crystallites, which are denser than the amorphous phase [43].
# Survey for Soil Sensing with IOT and Traditional Systems
## 4 Inference Method and Evaluation Metrics
### Method for Spectral Information Analysis
#### 4.1.3 Uninformative Variable Elimination Method  
In spectral analysis of soil properties sensing, high spectral resolution spectrometers are utilized, producing a vast number of wavelength variables suitable for the PLSR method. However, not all wavelengths contribute equally to predicting the desired element. Some are either insensitive to the target property or are susceptible to noise. The Uninformative Variable Elimination Method is employed to identify the most pertinent set of wavelengths by evaluating the cross-validation metrics of resulting models [36, 75, 76]. Building on the UVE method, the study by [78] introduced a Monte Carlo (MC) strategy [79] to UVE-PLS, replacing the leave-one-out approach. This novel method, named the ensemble of Monte Carlo uninformative variable elimination (EMC-UVE), refines wavelength selection during data analysis and enhances prediction capabilities for multivariate calibration models. However, the study by [36] found that while UVE can discard irrelevant wavelengths, it might also inadvertently remove valuable ones. Therefore, determining the correct number of wavelengths to retain is crucial.
# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
## 4 Hamming Error-Correcting Code, IIR and FIR Filters
### Filtering Systems
#### 4.2.2 FIR Filter  
The impulse response of the FIR filter has a finite duration, as it converges to zero in a finite time. The FIR filter output \(y(n)\) is simply the sum of the previous, current, and possible future input samples. The FIR filter equation is expressed in Equation (18) [43].  
\[y(n)=\sum\
olimits_{k=0}^{N-1}h(k).x[n-k], \tag{18}\]  
where \(x(n)\), \(y(n)\), and \(h(k)\) represent the input and output samples and filter coefficients, respectively. Each output sample is obtained by summing the most recent input samples, which characterizes the order N of the FIR filter. Figure 6 illustrates an FIR filter:  
Figure 5: IIR filter structure.
## 3 Approach  
### Notations and Definitions  
In this article, we use italic uppercase letters, bold italic lowercase letters, and normal lowercase letters, respectively, to denote matrices, vectors, and scalars. Let \(\textbf{X}\in\mathbb{R}^{n\times d}\) be the incomplete data set with \(n\) instances and \(d\) features. The scalar \(x_{ij}\) is denoted as the \(i\)th row and \(j\)th column of \(\textbf{X}\), where \(i=1,\cdots,n\)\(j=1,\cdots,d\). \(Tr(\textbf{X})\)denotes the trace of matrix **X** if **X** is square, and \(\textbf{X}^{\textbf{T}}\) denotes the transpose of **X**.  
Let **I** be the index matrix indicating whether the information in **X** is complete or not. Specifically, \(I_{ij}\) is defined as  
\[I_{ij}=\left\{\begin{array}{l}1,\text{when }x_{ij}\text{ is observed}\\ 0,\text{when }x_{ij}\text{ is unobserved}\end{array}\right.. \tag{1}\]  
The L\({}_{2,1}\) norm of the matrix **X** is defined as follows:  
\[\left\|\textbf{X}\right\|_{2,1}=\sum\limits_{i}\sqrt{\sum\limits_{j}x_{ij}^{2}}. \tag{2}\]  
Table 1 shows a list of frequently used parameters throughout this paper, along with their short definitions.
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 3 Enhancements and Improvements to the Original Protocol
### Using SRUP for Application Data  
There may be a number of circumstances in which an IoT device would wish to signal application-specific data to a user in a secure and authenticated manner. For situations such as these, the SRUP protocol could be used to carry signed application data messages from the device to the C2 server for dissemination to the user.  
The solution to this would be to create a new SRUP message-type (SRUP_MESSAGE_TYPE_DATA) specifically designed to carry application data. This message type would consist of the following fields (in addition to the base message fields):  
* DATA_ID A variable length string to indicate the data being sent in the message (e.g., \"Sensor_A_Temp\", etc.)
* DATA A variable length byte stream. It is to be assumed that applications (i.e., both device and server software) consuming SRUP are capable of determining the correct data-type to map this byte-stream onto based on the value of DATA_ID. Applications will also be assumed to be able to correctly handle byte-order issues within the application code.
* As each of these is a variable length field, the SRUP message will also need to specify the length of these fields.  
The specific meanings of the DATA and the DATA_ID are system and application specific and, hence, can be used for any purpose by the application or system developer.  
Given that the device originated messages used for application data increase the potential danger of a replay attack, in order to prevent the sort of problems with a replay-attack described previously in this document, it is especially important that the measures described elsewhere within this section be adopted for these messages.
## 2 Experimental System and Scheme
### Experimental Scheme
#### 2.2.1 Factors and Levels of Orthogonal Experiment  
The atomization characteristics of a nozzle typically consist of the flow rate and droplet size. The flow rate of liquid-medium ultrasonic atomizing nozzles includes both the air flow rate and water flow rate. Since the diameter of droplet particles was not uniformly distributed, the average diameter of the droplet group was used to represent the droplet size. At present, there are various methods for calculating the average particle diameter of droplets. The mass median diameter (MMD) and Sauter mean diameter (SMD) were the most commonly used parameters for the droplet size. In this study, SMD was used as an evaluation index for the fineness of droplets.  
In the spraying-based dust reduction system at the engineering sites, both the atomizing medium and the external environment were relatively constant. For certain types of fluid-medium ultrasonic atomizing nozzles, the influencing factors of the atomizing parameters include air pressure (\(p_{\text{air}}\)), water pressure (\(p_{\text{L}}\)), and outlet diameter (\(d\)). Therefore, air pressure, water pressure, and outlet diameter were taken as the three factors of orthogonal design. Based on previous field surveys and actual measurement, the level ranges of these three factors were determined by comprehensive considering of the dust reduction efficiency, water consumption, and site conditions. An orthogonal experiment table was designed using the three factors and the determined levels of the factors. At the same time, in order to highlight the uniformity of the selected values and the gap of the results, the experimental scheme was designed with a fixed interval. Each factor was set to five levels, and the \"three factors, five levels\" L25 (5\({}^{3}\)) orthogonal design method was used in the study. The factors and their levels in the orthogonal experiment are shown in Table 1.
# An Optimized Framework for WSN Routing in the Context of Industry 4.0
###### Abstract
However, OLSR in RFC 3626 confirmed that control nodes, if productive for a particular procedure, send control messages at different stages [8]. The above discussion of Industry 4.0 constraints and use of MANETs for industrial applications has motivated us to propose a new protocol that focuses on the management of interval of control messages, which helps in robust communication in SG Industry 4.0. The proposed approach would provide solutions to issues discussed in the previous subsection by improving the management of control levels in ad hoc sensor networks. This paper has the following contributions:  
1. We propose an optimized routing scheme by updating control interval levels, which reduces the total network cost by lessening the severe flooding and evaluating a head of clusters on the basis of the probability discussed in the proceeding sections. Delay due to control intervals is not tolerable in communication paradigms of SGs, which is solved by the proposed methodology. 2. The novel scheme is based on the selection of the head of the unit responsible for control messages according to the first defined expectation factor. 3. We also selected the optimal path based on multiple parameters such as the mobility of nodes, network scenario as per the connectivity of nodes, energy level of sensor nodes, and missing links between the nodes. 4. With a sequence of rigorous performance evaluations under QoS parameters, the analysis demonstrates that an optimized version of OLSR (OCIR) has improved the QoS metrics. The work ensures the reliable communication for SG applications with low overhead. The rest of the paper is organized as follows: Section 2 shows the highlights of the recent literature of SGs and MANET followed by the proposed work in Section 3. Section 4 presents the result analysis and discussions. Finally, the paper is concluded in Section 5.
## 2 Materials and Methods
### NDVI Classification  
Based on the interpretation of the Normalized Difference Vegetation Index (NDVI), the analysis of Google-Earth-extracted images, RGB (red, green, blue) images, panchromatic bands and ground-based georeferenced information, NDVI thresholds were proposed to classify the area of study into four macro-classes during the analysis period (2015-2021), as shown in Table 2.  
The NDVI thresholds implemented in the proposed classification were selected after an extensive literature review of previous studies where the NDVI was used as the spectral index of diverse land covers [47; 48; 49; 50; 51; 52]. The NDVI values were calculated by the equation proposed in [53; 54]:  
\[NDVI=\frac{\phi_{mir}-\phi_{red}}{\phi_{mir}+\phi_{red}} \tag{3}\]  
where \(\phi_{mir}\) is the near-infrared reflectance and \(\phi_{red}\) is the red-band reflectance. Band 4 (0.64-0.67 um) and band 5 (0.85-0.88 um) represent \(\phi_{mir}\) and \(\phi_{red}\), respectively, in the Landsat products. The NDVI values vary from \(-1\) to 1. Values close to 1 represent vegetation in optimal environmental conditions, whereas low values of NDVI indicate low vegetation density or a different land use. After the macro-class definition, a second classification was performed to refine the LULC characteristics through a more extensive discrimination of pixels. These new classes were defined according to the CORINE land cover inventory proposed by the European Environmental Agency [55]. This classification process required the use of the ancillary data and field data to assign new information to the training set. This assignation was repeated until a suitable spatial distribution was obtained. This new classification consisted of 14 classes, as shown in Table 3.
## 3 Case Studies
### Energy Base Laboratory DCMG  
The Energy Base laboratory at UAS Technikum Wien has equipment capable of simulating an AC microgrid with four houses, denoted as T1, T2, T3 and T4. Among these, house T4 is equipped with a BESS and connected to a PV system installed on the building. Thus, using house T4, it was possible to study a DCMG with the topology shown in Figure 4.  
Figure 5 shows the equipment used in the laboratory.  
The DCMG used is made up of various components, so it is important to understand its technical characteristics. The DCMG has a BYD HVS 5.1 (BYD Company, Shenzhen,  
Figure 4: Topology of the DCMG used in the laboratory.  
Figure 5: DCMGused as a case study in the laboratory.  
China) lithium iron phosphate battery system [21], whose technical characteristics are shown in Table 4.  
The DCMG under study has an interface with the 230 V/50 Hz AC grid, which is provided by the Fronius Symo GEN24 10.0 inverter (Fronius International GmbH, Wells, Austria) [22]. Table 5 shows its technical characteristics. It features MPPT technology and also allows bidirectional energy flow.  
It was not possible to identify the photovoltaic system installed in the system or its power. With regard to the loads, a 4800 W AC load was used.  
A DEWE-800-PA data acquisition device with DEWESOFT software (version 2024.2) was used to monitor the system's behavior.
# Computational Acceleration of Topology Optimization Using Deep Learning
## 1 Introduction  
### General Knowledge  
Topology optimization (TO) is one of the computational tools used for designing lightweight and robust structures and it is well known in many industries. It also indirectly improves the environmental effect of additive manufacturing since less material is used during the 3D printing of structures [1]. Although, when the design of structures is geometrically advanced with multiple initial and boundary conditions, and the computational grid size is fine enough to obtain smoother layouts, the running time of TO significantly increases [2]. Such a computational bottleneck is created mainly due to successive iterations for solving a large system of linear equations obtained from finite element analysis [3].  
There are several methods that can be applied to address this issue and these methods are reviewed in our earlier work [4]. Thus, earlier research works are based on high-performance computing that uses multiple CPU and GPU processors, where a computationally expensive portions of algorithms are run in parallel among the cores [5, 6]. From the reviewed works, it was clear that CPU- and GPU-based computational accelerations are directly related to the number of elements of FEA (i.e., grid size). Moreover, with the increasing number of processors for parallel computing, higher the time for interprocessor communications, which makes CPU- and GPU-based accelerations time-consuming for problems with a higher number of grid elements [4].  
Lately, the application of machine learning (ML) methods for the acceleration of TO run time has gained significant attention. The main reason can be considered as the increasing availability of advanced ML tools, which are also backed up with faster computing hardware [7].
## 2 Simulation Model Setting for Underwater Electric Field Communication
The wavelength of the propagating electric field in seawater can be expressed as follows:  
\[\lambda=\frac{2\pi}{\sqrt{\pi f\mu\sigma}}=13.361\;\mathrm{m} \tag{5}\]  
In the experiment, wave speed \(\upsilon=\lambda f\), in which \(f\) is the frequency measured in Hz, can be configured by the user using the \"frequency\" attribute. \[\upsilon=\lambda f=1.871\times 10^{5}\;\mathrm{m/s} \tag{6}\]  
The following formula implements the LogDistance propagation model, which is used to calculate received power using the so-called LogDistance propagation model. \[L=L_{0}+10n\log\left(\frac{d}{d_{0}}\right) \tag{7}\]  
where \(n\) is the path loss distance exponent, \(d_{0}\) is the reference distance (meters), \(L_{0}\) is the path loss at the reference distance (dB), \(d\) is the distance (meters), and \(L\) is the path loss (dB). The LogDistance model is used in the NS-3.31 simulation. Firstly, the correct reference attenuation value \(L_{0}\) is calculated using the Friis model based on the frequency and propagation speed. Then, the attenuation value at any distance can be computed. In this article, the reference attenuation value \(L_{0}\) is calculated for the underwater electric field transmission frequencies \(f=14\) kHz, \(\lambda=13.361\;\mathrm{m}\), \(d_{0}=1\;\mathrm{m}\), \(n=2\), and \(G_{t}=G_{r}=1\), respectively. \[L_{0}=-10\log\left(\frac{P_{r}}{P_{t}}\right)=-10\log\left(\frac{G_{t}G_{r} \lambda^{2}}{\left(4\pi d_{0}\right)^{2}}\right)=-0.5326\;\mathrm{dB} \tag{8}\]  
According to the LogDistancePropagationLossModel, the total loss \(L\) for a distance of \(d=15\;\mathrm{m}\) is calculated as follows:  
\[\frac{P_{r}}{P_{t}}=\frac{A_{r}A_{t}}{d^{2}\lambda^{2}} \tag{1}\]0  
When the transmission power EnergyDetectionThreshold is set to 16.0206 dB, the threshold for the state is as follows:  
\[\frac{P_{r}}{P_{t}}=\frac{A_{r}A_{t}}{d^{2}\lambda^{2}} \tag{1}\]1  
The threshold for the busy state of CCA (Clear Channel Assessment) in CCAMode-Threshold is \(-6.9686\;\mathrm{dBm}\). In the NS-3.31 simulation parameters based on CSMA/CA underwater electric field multi-node networking, the energy detection threshold is set to \(-96\) dBm, the CCA busy state threshold is set to \(-99\) dBm, and the energy level is set to \(1\). Based on the calculation of underwater electric field communication parameters, the simulation parameters for underwater electric field communication are established as shown in \[\frac{P_{r}}{P_{t}}=\frac{A_{r}A_{t}}{d^{2}\lambda^{2}} \tag{1}\]2
# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
###### Abstract  
The \"Internet of Space Things\" (IoST) is an emerging paradigm to provide Internet and data services around the globe. IoST networks can potentially support the deployment of services in underserved areas, such as monitoring inaccessible areas for early warning applications, open ocean and sea ice monitoring, and surveillance of remote ecosystems such as forests and jungles, among others. To enable the IoST paradigm, designing and developing appropriate routing protocols is crucial. This work presents a methodology based on 2k factorial statistical analysis and an in-house developed space simulator (available upon request) to identify the critical factors affecting the performance of routing protocols in \"Internet of Space Things\" scenarios. The analyzed factors consider reactive and proactive routing approaches, connectivity, and the freshness of routing information. The results provide essential lessons for the research community to design protocols that could adapt under different IoST scenarios. The 2k factorial analysis applied in the study of the routing protocols' performance can be an effective tool for developing specialized routing protocols.  
routing protocols; 2k analysis; IoST +
Footnote †: journal: Applied
## 2 Problem Formulation  
We consider the physical-layer multicasting system, where a single BS equipped with \(N\) antennas and \(N\) RF chains broadcasts a common message to \(M\) single antenna receivers. The received signal at the receiver \(i\) is given as  
\[y_{i}=\sqrt{\beta_{i}}\mathbf{h}_{i}^{H}\mathbf{w}s+n_{i} \tag{1}\]  
where \(s\sim\mathcal{CN}(0,1)\) is the common signal transmitted to \(M\) receivers. \(\sqrt{\beta_{i}}\) and the \(N\times 1\) vector \(\mathbf{h}_{i}\sim\mathcal{CN}(0,\mathbf{1}_{N})\) represent the large-scale and small-scale fading of the \(i\)-th receiver's channel, respectively. \(\mathbf{w}\in\mathbb{C}^{N}\) is the beamforming vector and \(n_{i}\sim\mathcal{CN}(0,\sigma_{i}^{2})\) is the additive white Gaussian noise at the receiver \(i\). The received SNR of the \(i\)-th receiver is  
\[\gamma_{i}=\beta_{i}\Big{|}\mathbf{w}^{H}\mathbf{h}_{i}\Big{|}^{2}/\sigma_{i}^{2} \tag{2}\]Considering the circuit power of the RF chains, the total power minimization problem can be formulated as:  
\[\mathcal{P}_{1}:\ \min_{\mathbf{w}\in\mathbb{C}^{N}} \frac{\|\mathbf{w}\|_{2}^{2}}{\eta}+P_{\text{cir}}\|\mathbf{w}\|_{0}\] s. t. \[|\mathbf{w}^{H}\mathbf{h}_{i}|^{2}\geq\bar{\gamma}_{i},\quad i=1, \ldots,M \tag{3}\]  
where \(\bar{\gamma}_{i}=\bar{\gamma}_{i}\sigma_{i}^{2}/\beta_{i}\) with \(\bar{\gamma}_{i}\) denoting the SNR threshold of the \(i\)-th receiver. In the objective function, the first term represents the transmit power with \(\eta\) denoting the power amplifier efficiency, and the second term represents the circuit power consumption with \(P_{\text{cir}}\) denoting the circuit power cost per RF chain. The \(\ell_{0}\)-norm \(\|\mathbf{w}\|_{0}:=|\left\{n:\mathbf{w}_{n}\
eq 0\right\}|\) represents the number of nonzero entries of the beamforming vector \(\mathbf{w}\), where \(\mathbf{w}_{n}\) represents the \(n\)-th element of \(\mathbf{w}\). We can select the antenna subset corresponding to the nonzero entries of the beamforming vector and the unnecessary RF chains are switched off to reduce the circuit power consumption. The circuit power consumption is proportional to the number of active RF chains \(\|\mathbf{w}\|_{0}\). The total consumed power comprises of both the transmit power and the circuit power of the RF chains.