esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Effect of RF Exposure _in Vivo_: New Insights on Biosystem Functions  
Studies carried out _in vivo_ on animal models are extremely valuable as the protocols can be designed to apply a rigorous and controlled procedure of exposure for longer time, gaining insights on chronic exposure that better predict the degree of risk for population health. Selected _in vivo_ models have been recently used to suit specific scientific questions. For example, _Eisenia Fetida_ (Oligochaeta, heartworm) was selected for standardized assays to evaluate ecological risk in terrestrial ecosystems [52]. This model has been validated for toxicological endpoints of genotoxicity such as DNA damage, anti-oxidative enzyme activities, as well as lipid peroxidation. After only 2 h of exposure to 900 MHz EMF at the power density of 23 Vm-1, damage to protein, lipids and DNA was detected. Another easily amenable _in vivo_ model is _Drosophila melanogaster_ used to study DNA fragmentation after 900 MHz exposure through its reproductive ability [40]. To investigate the effect of RF exposure on morphology, biology and functions of mammalian brain and neuronal network functions [53,54] rodents are intensively used. In 2011, [PERSON] _et al_. [55] carried out a study in order to investigate whether short-term memory is affected by ordinary mobile phone exposure. Likewise for many other perturbing events occurring during memory formation, even for RF exposure, timing is crucial to the final outcome. RF exposure shortly after Object Recognition Task (the ability to judge a previously encountered item) deeply affects memory formation, perturbing trace consolidation. In relation to memory formation and storage, one of the most important subcortical areas of CNS is the hippocampus. Pyramidal cells constitute the architectural and functional landmark of _Cornus Anmonis_ and dentate gyrus. Excitation in these cells is regulated by intracellular and extracellular calcium homeostasis. RF radiation at 835 MHz (SAR value of 1.6 W/kg) affected calbindin and calretinin, inducing a progressive loss of pyramidal cells in mice exposed to three hours of acute treatment [45]. Moreover, [PERSON] _et al_. [56] previously indicated that metabolic functions of cytochrome C oxidase were affected by RF 6 W/kg GSM 900 MHz in the brain and in particular in the perirhinal and entorhinal cortex, impinging on memory formation. In this view, it is striking how relatively few studies were carried out on the effect of RF on neurotransmitters [57]. Neurotransmitters such as serotonin (5 HT) and dopamine orderly regulate acquisition, memory formation and storage in selected districts of the brain. After up to four months of daily RF exposure (1800 MHz, SAR 0.843 W/kg), adult rats increased serotonin and decreased dopamine levels in the hippocampus to affect memory and learning.
## 4 Experimental Study
For instance, when acting as sensors the LVDTs theoretically offer infinite resolution, but in practice, limitations such as electronic noise and non-ideal behavior directly affect precision in trajectory tracking. In addition, unmodeled mechanical friction and delay in signal processing can also contribute to error accumulation. Although each individual factor was not fully isolated and quantified in this study, their combined effects are reflected in the increased average absolute tracking errors compared to the simulation. Nevertheless, the newly developed control scheme has proven to be highly accurate, straightforward to implement, and robust against parameter variations. Furthermore, the incorporation of synchronization errors significantly enhanced the overall performance of the control scheme, ensuring precise and reliable operation.
## Appendix C Cover-Free Families  
In this subsection, we provide some preliminaries about the concept of cover-free families and establish some basic and well-known results. Furthermore, we draw the connection between such concept and the RKI.  
Definition A1 ( r-cover-free family ): Let pair \((X,\mathcal{F})\) be a set system, where \(X\) is a set of points and \(\mathcal{F}\) is a set of subsets (blocks) of \(X\). A set system \((X,\mathcal{F})\) is called r-cover-free family, if for an arbitrary r distinct blocks \(A_{1},\dots,A_{r}\in\mathcal{F}\) and any other block \(A_{0}\in\mathcal{F}\), we have  
\[A_{0}\
ot\subset\bigcup_{i=1}^{r}A_{i}. \tag{105}\]  
The concept of r-cover-free families in the literature was first found in [70]. In the following, we introduce a well-known theorem in the literature, which established a power law decaying the lower and upper bounds on the size of cover-free families.  
Theorem A1 (see [70]): Let \(\mathcal{A}\triangleq\{1,\cdots,|\mathcal{A}|\}\) and \(\mathcal{F}\) be the set of points and subsets, respectively, such that the set system \((\mathcal{A},\mathcal{F})\) constitute a r-cover-free family. Then, let indicate the maximum size of \(\mathcal{F}\) over \(\mathcal{A}\) by \(M(|\mathcal{A}|,r)\). Now, we have  
\[\frac{c_{1}}{r^{2}}\leq\frac{\log M(|\mathcal{A}|,r)}{|\mathcal{A}|}\leq\frac {c_{2}}{r},\]  
for some constants \(c_{1}\) and \(c_{2}\).  
Next, we present a theorem which establish an upper bound on the size of r-cover-free family as follows:  
Theorem A2 (see [71]): Assume that set system \((\mathcal{A},\mathcal{F})\) constitute a r-cover-free family where \(\mathcal{A}\triangleq\{1,\cdots,|\mathcal{A}|\}\). Now, the maximum size of the r-cover-free family, i.e., \(|\mathcal{F}|\), is upper bounded as follows:  
\[\frac{\log M(|\mathcal{A}|,r)}{|\mathcal{A}|}\leq k\cdot\frac{\log r}{r^{2}}, \tag{106}\]_where \(k\) is a constant._  
Next, we explain on the connection between the notion of r-cover-free families in the combinatorics and RKI for noiseless discrete memoryless channel found by [PERSON] in [PERSON] (2016): Let \(a=|\mathcal{X}|\), \(r=a^{\kappa n}\), \(|\mathcal{A}|=a^{n}\), then the RI coding with 0-valued first type error, is upper bounded by:  
\[R_{n}\triangleq\frac{\log\log M(a^{n},a^{\kappa n})}{n}\leq(1-2\kappa)\,\log a +o(1). \tag{100}\]  
Then, for a DMC with input alphabet of size \(|\mathcal{X}|\), we obtain  
\[R\leq(1-2\kappa)\,\log|\mathcal{X}|. \tag{101}\]  
Therefore, for the binary input channel, i.e., where \(|\mathcal{X}|=2\), we obtain \(R\leq 1-2\kappa\).
## 2 Materials and Methods
### Vegetation Sampling and Plant Identification  
The vegetation of pure Deodar forests was surveyed and quantified [35] along the geographic, slope, edaphic, and climatic variables [36]. A line transect ecological technique was used for the vegetation sampling [37; 38; 39; 40; 41]. Sampling was carried out during 2015-2018. The surveyed sites were divided into 23 stands, and three sampling points within each stand were sampled along a 50 meter transect (total = 69 transects). The phytosociological attributes (i.e., density, frequency, cover and their relative values, and importance value) were used for the calculation of the data from each sampled point [42; 43; 44]. The IV was further used to rank each species, and those with the highest IV were considered as the dominant species [42; 43]. Similarly, plant communities were named after three dominant species [45; 46; 47; 48; 49]. The slope angle, aspect (i.e., east, west, south, and north), and exposure of each sampled point were recorded using a clinometer, and altitude, longitude, and latitude were measured by the geographical positioning system (GPS). Plant collection, labeling, pressing, and other herbarium work methodologies were adopted [50; 51; 52; 53; 54; 55; 56]. Their identification was confirmed using Flora of Pakistan [57; 58; 59], and they were submitted to the Hazara University herbarium in Manehra (Pakistan).
## 4 Performance Analysis
### Network Lifetime  
The main challenge in WSNs continues to be energy saving, which extends the network lifetime. An essential efficiency parameter in WSNs is network lifetime, which is measured as the duration until the power of the initial sensor is exhausted. Managing the communication load across sensor nodes is necessary to increase network longevity. Figure 7 and Table 6 show the network lifetime provided by the proposed and existing techniques. The proposed method offers a longer network lifetime for WSNs.  
Figure 8: Communication overload of the proposed and existing methods [41; 42; 43; 44; 45].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Number of Nodes**} & \multicolumn{3}{c}{**Communication**} \\ \multicolumn{2}{c}{**Overload (\%)**} & \\ \hline \multicolumn{2}{c}{OptiGeA [41]} & \multicolumn{1}{c}{RBE-EKM [42]} & \multicolumn{1}{c}{RPL-RPMA [43]} & \multicolumn{1}{c}{ACI-GSO [44]} & \multicolumn{1}{c}{PSL-RH [45]} & \multicolumn{1}{c}{ECC-EERP (Proposed)} \\ \hline
10 & 97 & 53 & 73 & 85 & 62 & 9 \\
20 & 95 & 59 & 75 & 89 & 72 & 12 \\
30 & 99 & 55 & 71 & 81 & 89 & 21 \\
40 & 91 & 51 & 79 & 83 & 91 & 32 \\
50 & 93 & 63 & 83 & 87 & 90 & 41 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Communication overload of the proposed and existing methods.  
Figure 9: Computation time of the proposed and existing methods [41; 42; 43; 44; 45].
# Review of Physical Layer Security in Integrated Satellite-Terrestrial Networks
###### Abstract  
With the success and commercialization of 5G, 3 GPP has started working toward the sixth generation of communication systems. While 5G explored the concept of non-terrestrial networks like satellites and unmanned aerial vehicles working alongside terrestrial networks, 6G is expected to take this integration a step further, aiming to achieve a more coherent network where satellites and terrestrial infrastructure work together seamlessly. However, the complexity and uniqueness of such networks create numerous attack surfaces that make them vulnerable to cyberattacks. The solution to such cyberattacks can be addressed by encryption and other upper-layer authentication methods. However, with the move to higher-frequency bands, such encryption techniques are difficult to scale for low-latency networks. In addition, the recent progress in quantum computing will make networks more vulnerable. To address such challenges, physical layer security (PLS) is proposed as a secure and quantum-resistant way to implement security by taking advantage of the physics of the channel and transceiver. This article reviews the latest trends and progress in PLS in integrated satellite-terrestrial networks (ISTNs) from a signal processing perspective. This work provides a comprehensive survey of the state-of-the-art research conducted, challenges, and future directions in the PLS of ISTNs.  
6G; cyberattacks; physical layer security; integrated satellite-terrestrial network
## 1 Introduction
Furthermore, it did not consider the BESS degradation in future savings. Finally, only two prosumers were considered. For BESS on Madeira Island, the present work extends the work in [10] in several ways. First, two different energy tariffs are considered, a flat and a Time-of-Use (ToU) rate. Second, two battery control strategies are evaluated, greedy and greedy with pre-charge. Third, the BESS size and the amount of pre-charge are selected based on a proposed set of equations that considers the size of the solar PV and the electricity demand. Fourth, storage degradation is taken into consideration when calculating future energy savings. Finally, four additional prosumers are studied. Ultimately, this work contributes to the ongoing European Union (EU) efforts to understand the effects of different regulations schemes on the economic viability of energy storage devices in domestic self-consumption systems [6]. Furthermore, by focusing on an isolated geographical island, we are further contributing to the de-fossilization of non-interconnected island energy systems, which have also been receiving significant attention from the EU (Clean Energy for EU Islands, [[https://euslands.eu/](https://euslands.eu/)]([https://euslands.eu/](https://euslands.eu/)); Smart Islands Initiative, [[http://www.smartslandsinitiative.eu/](http://www.smartslandsinitiative.eu/)]([http://www.smartslandsinitiative.eu/](http://www.smartslandsinitiative.eu/))) due to considerably higher energy production costs compared to the mainland [11]. The remaining of this paper is organized as follows. Section 2 surveys related works. Section 3 provides context and background information for a better understanding of this work. Section 4 thoroughly describes the materials and methods used in this work. Section 5 presents and discuses the obtained results. Finally, Section 6 presents the conclusions and future work directions.
# Geometrical Degrees of Freedom for Cellular Structures Generation: A New Classification Paradigm
## 2 Design and Geometrical Consideration
### CS Generation
#### 2.5.1 CS Generation via Voxel-Based Approaches  
Generally, CSs are built by populating a design space with existing cells, as seen in Figure 1. Voxels are a relatively new concept, but an intuitive manner of representing solids. Each solid is created by adding small cubes in 3D (or voxels). The main disadvantage of this method is the computational cost, discretizing a shape accurately requires a high number of voxels [104].  
As an example, in the work by [PERSON] et al. [104], the authors use voxels to generate CSs. The target geometry is first voxelized, as shown in Figure 15, then a grid was mapped onto the domain and trimmed, as shown in Figure 16. The main advantage of this method is that trimming and filling gaps becomes trivial when using a voxel representation. Unfortunately, the voxel representation cannot exactly represent any curved surface; therefore, staircases arise around such features. This problem can be minimized by using smaller voxels (i.e., higher resolution); however, this increases the computational cost. Another issue is that voxel representation is not well supported in the current CAD software and AM ecosystem. Most CAD software or AM build environments only accept geometry using Boundary REPresentation (BREP) or CSG. Unfortunately, the conversion from voxel to standard CAD file format nulls out the computational cost advantage of using voxels. When converting from voxel to mesh, the resulting STL file is extremely large as well [105].  
Figure 15: Illustration of cellular structure generation via voxel-based approaches. (**a**) Low-resolution pixel image of a circle. (**b**) Similarly low-resolution voxel model of a sphere [104].
## 3 A Prototype System for Pedestrian Positioning
### Particle Filtering
#### 3.9.3 Implementation  
**Ad. 1.** The particles can be initialized, e.g., around a first GPS readout.  
**Ad. 2.** Based on Figure 15, the transition equation for a particle \(i\) reads (55).  
\[\left[\begin{array}{c}x_{i}(k)\\ y_{i}(k)\\ \phi_{i}(k)\end{array}\right]=\left[\begin{array}{c}x_{i}(k-1)\\ y_{i}(k-1)\\ \phi_{i}(k-1)\end{array}\right]+\left[\begin{array}{c}(d(k)+v_{i}(k))\sin( \phi_{i}(k-1)-(\omega_{z}(k)+w_{i}(k))dT)\\ (d(k)+v_{i}(k))\cos(\phi_{i}(k-1)-(\omega_{z}(k)+w_{i}(k))dT)\\ -(\omega_{z}(k)+w_{i}(k))dT\end{array}\right] \tag{55}\]  
\(v_{i}(k)\) and \(w_{i}(k)\) are random realizations of the pedometer and gyroscope noise respectively. Each particle is perturbed individually. \(k\) is the time instant, where \(t=k\cdot dT\).  
**Ad. 3.** The following sources are used to update the particle weights:  
Figure 15: An explanation of the unperturbed state transition: \(d(k)\) is the length of a step estimated by the pedometer. \(\phi(k)\) is the azimuth orientation. \(\omega_{z}(k)\) is the angular velocity around the gravity axis. \(\omega_{z}\) is estimated by the gyroscope and the gravity estimation algorithm. \((x(k-1),y(k-1))\), \((x(k),y(k))\) are the coordinates in the previous and current time instant.  
* GPS. The measurement update for the GPS readouts are given by Equations (56) through (58) based on the Equation (7) \[w_{i}(k) =w_{i}(k-1)\cdot p_{\mathrm{GPS}}(r_{i},\sigma_{\mathrm{GPS}})\] (56) \[r_{i} =\sqrt{((x_{\mathrm{GPS}}(k)-x_{i}(k))^{2}+(y_{\mathrm{GPS}}(k)-y _{i}(k))^{2})}\] (57) \[\sigma_{\mathrm{GPS}} =\beta_{\mathrm{GPS}}\mathrm{HDOP}\] (58) \(\beta_{\mathrm{GPS}}\) is a coefficient which enables to weight GPS readouts with appropriate importance. Larger values of \(\beta_{\mathrm{GPS}}\) makes the \(p_{\mathrm{GPS}}()\) function more tolerant to GPS errors. \[w_{i}(k)=w_{i}(k-1)\cdot w_{\mathrm{map}}(x_{i}(k),y_{i}(k))\] (59)
* comparison between the 3D virtual environment model and depth picture retrieved from the stereo camera. The measurement update equation is given by Equation (60). \[w_{i}(k)=w_{i}(k-1)\cdot f_{L,i}(\mathbf{L_{S}},\mathbf{L_{V,i}})\] (60) For each particle, a virtual environment is generated individually. The coordinates of the virtual camera are set to \([x_{i},y_{i},h,\phi_{i}]\). \(h\) is the height at which the stereo camera is attached. Then the depth map \(L_{V,i}\) is generated. The function \(f_{L}(\mathbf{L_{S}},\mathbf{L_{V,i}})\) is evoked for every particle. This is a computation demanding procedure. Note that, if an unexpected object appears in front of the stereo camera for 10 seconds, given particles' weights will be decreased by \(a^{10/s\cdot 10s}\), _c.f._ Equation (45) (3D matching algorithm is run 10 times a second). This might introduce a large error in the position estimation. Bigger values of \(a\) diminish the benefit of using a stereo camera and 3D model of the urban terrain.
## 2 Materials and Methods
### Experimental Works
#### 2.4.1 Classical Offline Adjustment  
The classical offline adjustment of the total station traverse is performed, having completed all measurements. In our numerical experiment, it has been performed in two variants: by \"including\" and \"excluding\" the merging points 3, 4, 6, 7 (Figure 2). In the first case, the traverse consists of three stations (1, 2, and 5) and four merging points (3, 4, 6, and 7), with coordinates determined using a GNSS receiver, and five measured control points on the high buildings A, B, C, D, and E. In the second case, the traverse consists of only three station points (1, 2, and 5), as determined by the GNSS receiver, and five measured control points (A, B, C, D, and E).  
The differences between the geocentric coordinates of all points obtained from the \"including\" and \"excluding\" computation modes are not greater than 4.2 mm, with a mean value of 1.0 mm and a standard deviation of \(\pm\)1.4 mm. Comparing these with the accuracy of the total station and GNSS measurements, we can conclude that the merging points are not necessary for the spatial orientation of the total station traverse. As we can see, the spatial orientation is very well provided by only the GNSS-measured coordinates of the total station points 1, 2, and 5, and the deflections of the vertical obtained from the EGM model.  
The algorithms and results of the classical offline adjustment obtained via the measured data in the same experiment shown in Figure 2 are presented in the article [28].  
An alternative to the offline adjustment of all observations after making measurements is to adjust the observations online directly during measurements, in real-time, and sequentially.  
In Section 2.4.2, we present the online algorithm for sequential adjustment of the sets of observations performed at successive total station positions.  
In Section 2.4.3, we present the online algorithm for sequential adjustment of the direct observations performed at successive total station positions.  
Online adjustment of observations provides real-time information about the accuracy of the currently measured point, and about improving the accuracy of all previously surveyed points. Thus, it is possible to conduct additional observations in real-time, and select new total station positions to increase the accuracy of the measured points. These are the main practical advantages of the real-time online sequential adjustment method, in comparison with the offline method.
# Progress in Energy Storage Technologies and Methods for Renewable Energy Systems Application
## 3 The Progress of Energy Storage Technologies
### Pumped Hydro Storage  
PHS, also known as pumped storage power generation, is by far the most widely used large-scale, large-capacity energy storage technology in the world. It accounts for more than 99% of the world's total energy storage capacity and is equivalent to about 3% of the world's total installed power generation capacity. PHS is an EES technology with a long history, high technical maturity, and high energy capacity [53]. The construction of pumped storage power plants is an important part of promoting the development of clean energy on a large scale and is of strategic importance for the large-scale absorption of wind, solar, and other clean energy and for improving the stability of grid operation and power quality. A typical PHS plant uses two reservoirs, vertically separated [54]. In terms of the operational characteristics of pumped storage, it can use high water levels for power generation and peak shaving of the grid, or it can use low valley power or wind and photoelectric abandoned energy for pumping, converting electrical energy into water potential energy and storing it for backup [21]. However, some issues affecting the scale of the pumped storage power plant are still to be solved, focusing on the site selection technology, high dam engineering technology, high head large capacity pump turbine technology, new generator technology, intelligent scheduling, operation control technology, etc. The Ikaria Island power station in Greece will be one of the first wind-hydro-storage hybrid stations [55]. Trends in PHS facilities include the construction of faster and larger capacity hydroelectric units, the installation of centralized monitoring, and the use of intelligent control systems. [PERSON] et al. [56] compared to the current state of technology. They studied three approaches for PHES facilities with typical scheduling and found that a 24 h optimized operation strategy was the most beneficial. Under this approach, the charging cycle was about 6 h, obtaining almost 97% of the feasible profit. Furthermore, they pointed out that operators must have accurate price forecasts to maximize profits using electricity price arbitrage.
## 5 A Methodological Approach  
In order to extend results, for example to other climatic conditions or to other kinds of buildings, a method to assess the effectiveness of the integrated system is synthetized by the following steps.  
Step 1--Data collection:  
* Type of building and its geographical site
* Drinking water requirements, in terms of quantity and quality
* Thermal energy requirements, in terms of domestic water heating, quantity and average heating temperature
* Existing heating plant type and efficiency
* Primary air needs
* Existing air conditioning type and efficiency
* Energy consumptions and costs related to vectors and uses
* Water costs, related to water quality  
Step 2--Weather environmental conditions: temperature and relative humidity of the site, preferably at hourly level.  
Step 3--Integrated system behaviour analysis.  
On the basis of thermo-hygrometric environmental conditions, by means of the tailored simulation software, the daily amount of condensed water produced by the machine can be estimated. Moreover, information about its energy consumption and primary air production, in terms of airflow, achieved temperature and relative humidity can be simulated. Furthermore, the heating energy and thermal power which can be employed for domestic water heating can also be determined.  
Step 4--Definition of the covered needs. On the basis of the simulation results and previous data collection, a match between building needs and integrated system potentialities can be performed, in order to understand if the system is capable of fulfilling the requirements (or the percentage of demand that it can meet).  
Step 5--Economic evaluation. The last step of the procedure consists of an effectiveness evaluation of the application. Monetizing all the relevant outputs, the payback time and net present value of the application can be estimated, taking into account some simple economic indexes.