esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The most common parameters in air monitoring are ambient temperature and humidity [14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. The air temperature and humidity are essential factors in the plant growth. The air temperature and humidity can be used to indicate the watering status in the farm automation system [1; 19]. The plant cannot grow when the air temperature and humidity are not in the specific range. For instance, the range of air temperature and humidity of the onion plant are 25-32 \({}^{\circ}\)C and 50-70%, respectively [15]. The plant may die in extreme air temperature and humidity conditions [22]. Several works have considered other additional parameters such as gas pressure, hydrogen (H\({}_{2}\)), liquid petroleum gas (LPG), methane (CH\({}_{4}\)), carbon monoxide (CO), alcohol, smoke [17], carbon dioxide (CO\({}_{2}\)) [18; 22], light [21; 22]. The high concentration of the gases in the air may affect the photosynthesis of the plants [18; 22]. The work in [21] showed that the supplementary LED's lighting affects the rocket plant's growth. In soil monitoring, the most common parameters are soil moisture [14; 15; 16; 18; 19; 20; 22; 24; 25; 26], temperature [16; 18; 20; 22; 23], pH [20; 24; 26], nitrogen, phosphorus, and potassium/kalium (NPK) [23; 24; 25; 26; 27; 28], and electrical conductivity (EC) [26]. The soil moisture represents the water content in the soil. It can be used to manage the watering system of the plant. The soil moisture affects plant growth, leaf health, and photosynthesis process, and indicates the occurrence of plant diseases [16]. The work in [16] also showed that the soil temperature affects grapevine growth, where temperature should be 9 to 50 \({}^{\circ}\)C. The soil pH represents the soil acidity and alkalinity. The typical value is between 5.5 and 7.5. The violation of this range affects plant health and ant leaf discoloration [1]. The concentration of NPK indicates the soil fertility. The NPK are the essential nutrients for plant growth [1; 24; 26]. The information on NPK can be used to control fertilizer usage in the soil. The EC represents the soil dielectric constant related to soil moisture [26]. The sensors for measuring the above parameters are easily interfaced with popular embedded processors such as Arduino or Raspberry Pi and thus suitable for IoT-based monitoring systems. The literature survey of the existing plant monitoring systems de scribed above is summarized in Table 1, where the monitored parameters, sensor hardware, and IoT implementation are shown.
## 3 Numerical Modeling
### Fluid-Structure-Soil Interaction  
#### 3.2.1 Introduction of Interaction  
The mutual influences of fluid, structure, and soil can greatly impact the performance of storage tanks, particularly concerning stability and deformation. Neglecting these interactions can cause discrepancies between the outcomes predicted by the 3D model and the actual behavior of the tank in real-life situations. This oversight could result in inaccurate forecasts of how the tank would react to different loading scenarios, leading to design and construction methods that may need to be revised.  
In contrast, by including the interactions between fluid, structure, and soil in the 3D model, it is possible to accurately depict how a storage tank behaves. This can aid in pinpointing potential issues and informing decisions about the design that enhances the tank's stability and efficiency over time. For instance, when the interplay between fluid, structure, and soil is considered, the model can accurately represent how the soil surrounding the tank behaves and impacts the tank's stability. In short, factoring fluid-structure-soil interactions into the 3D model of storage tanks can result in a more accurate forecast of how the tank will behave, leading to better-informed design choices.  
The interactions between fluid, structure, and soil can significantly affect the behavior of storage tanks, especially in terms of stability and deformation. If these interactions are ignored, the 3D model results may not accurately reflect the behavior of the actual tank in practice. This can lead to incorrect predictions of the tank's response to various loading conditions and suboptimal design and construction practices.  
On the other hand, incorporating the interactions between fluid, structure, and soil into the 3D model can provide a more accurate representation of tank behavior. The authors are grateful for this observation. We have already changed it. This can aid in identifying potential issues and informing design decisions that enhance tank stability and performance over time. For example, if the interactions between fluid, structure, and soil are considered, the behavior of the soil around the tank and its influence on tank stability can be accurately modeled. In summary, incorporating fluid-structure-soil interactions into the 3D model of storage tanks can lead to more accurate predictions of tank behavior and improved design decisions.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Water** \\ \hline Volumetric mass & Bulk modulus \\ \(\rho=1000\) kg/m\({}^{3}\) & K = 2.1 GPa \\ \hline \hline \end{tabular}
\end{table}
Table 4: Water specifications.
## 1 Introduction
The characteristics of remotely-sensed images and lidar data could enable us to classify the vertical structure of a forest. The objective of this study is to classify forest vertical structure from aerial orthophotos and lidar data using an artificial neural network (ANN) approach. A total of five input layers are generated including: a median-filtered index map, a non-local (NL) means filtered index map, and reflectance texture map generated from the aerial orthophotos, and height difference and height texture maps generated from the lidar data. Since it is difficult to determine the presence of a grass layer in aerial images, it is omitted from our study and the forest vertical structure is classified into three groups for the ANN approach. The groups include: (i) single-layered forest that has only the canopy layer; (ii) double-layered forest that possesses the canopy layer and an understory or shrub layer; and (iii) triple-layered forest that is composed of the canopy, understory, and shrub layers. The red-green-blue (RGB) aerial orthophoto is used to obtain optical image information. The digital surface model (DSM) and digital terrain model (DTM) are extracted from the lidar point cloud. The height information is extracted by subtracting the DTM height from the DSM height. The accuracy of the classification is validated using field survey measurements.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
###### Abstract  
With the rapid rollout and growing adoption of 3 GPP 5 thGeneration (5G) cellular services, including in critical infrastructure sectors, it is important to review security mechanisms, risks, and potential vulnerabilities within this vital technology. Numerous security capabilities need to work together to ensure and maintain a sufficiently secure 5G environment that places user privacy and security at the forefront. Confidentiality, integrity, and availability are all pillars of a privacy and security framework that define major aspects of 5G operations. They are incorporated and considered in the design of the 5G standard by the 3 rd Generation Partnership Project (3 GPP) with the goal of providing a highly reliable network operation for all. Through a comprehensive review, we aim to analyze the ever-evolving landscape of 5G, including any potential attack vectors and proposed measures to mitigate or prevent these threats. This paper presents a comprehensive survey of the state-of-the-art research that has been conducted in recent years regarding 5G systems, focusing on the main components in a systematic approach: the Core Network (CN), Radio Access Network (RAN), and User Equipment (UE). Additionally, we investigate the utilization of 5G in time-dependent, ultra-confidential, and private communications built around a Zero Trust approach. In today's world, where everything is more connected than ever, Zero Trust policies and architectures can be highly valuable in operations containing sensitive data. Realizing a Zero Trust Architecture entails continuous verification of all devices, users, and requests, regardless of their location within the network, and grants permission only to authorized entities. Finally, developments and proposed methods of new 5G and future 6G security approaches, such as Blockchain technology, post-quantum cryptography (PQC), and Artificial Intelligence (AI) schemes, are also discussed to understand better the full landscape of current and future research within this telecommunications domain.  
5G; security; survey; Zero Trust architecture; confidentiality; integrity; availability; blockchain; post-quantum cryptography +
Footnote †: journal: Future Internet
## 4 Discussion  
This work has shown a new possibility to significantly increase the output power for a class of electromagnetic vibration energy harvesting devices with its mathematical framework. The term \"class\" refers here to systems employing the coreless electromagnetic minigenerators that to this moment had to be designed with high number of turns detracting from their ability to deliver practical value of the output power. The overall idea of optimal placement of coil turn would also be applicable to other classes of converters, although the model for determination of circuit parameters outlined is Section 2 may not be applicable directly.  
It should be noted that the method was successful due to intentional choice of structure of the electronic converter which has an ability to boost the input voltage from values lower than 0.05 V, and which we selected testing earlier a few other structures. However, for such an excellent performance it requires a very small impedance of the coil connected to the input terminals. Without the boost converter the voltage generated by the optimized system would be even smaller than that of the reference systems mentioned in the preceding section.  
It must be admitted that, generally, an optimization strategy based on the refined surrogate model, which we identified in this work as the best among the three considered strategies, is a known idea [23]. However, it was used here in a different way and validated with respect to its potential to solve the mixed integer nonlinear optimization problem.  
The successful optimization prompts the authors to modify the whole designing procedure of the converter so as to take account for more design criteria such as frequency bandwidth and overall mass. An application of the optimization strategy based on the refined surrogate model in solution of such a multi-objective optimization problem is a very challenging task.  
Conceptualization and methodology, M.K. and M.J.; software, M.K and M.J.; Measurements, M.K.; validation, M.K., M.L.; writing M.K.; supervision, M.J.; project administration, M.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science Centre, Poland, grant number 2016/23/N/ST7/03808.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline  & Calculated & Measured \\ \hline \(R_{c}\) & 5.53 \(\Omega\) & 5.42 \(\Omega\) \\ \(L_{c}\) & 7.05 mH & 6.63 mH \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of calculated and measured coil resistance and inductance.
## 8 Technical Challenges for 6G Development
### Difficulties in Managing Resources for Three-Dimensional Networking  
The expansion of 3D networking into the vertical dimension introduces a new layer, and with this extension, various adversaries may compromise legitimate information, posing a substantial threat to overall system performance. Consequently, innovative approaches to resource management and optimization are imperative, encompassing mobility support, routing protocols, and multiple access. The design of scheduling processes necessitates a re-imagined network structure.
# Performance Assessment of Multi-GNSS PPP Ambiguity Resolution with LEO-Augmentation
## 1 Introduction
The functional model of LEO-enhanced GNSSs PPP and PPP-AR is introduced in Section 3. In Section 4, we introduce the positioning performance of single-system and the four-system combined PPP-AR enhanced using the LEO ambiguity-float solution, and then compare it with LEO-enhanced PPP. A summary of our results is presented in Section 5.
# A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks
## 3 Reinforcement Learning-Based Routing
### Clustering
#### 3.1.1 Clustering-Energy  
In this scheme, we assume that the nodes have no prior information about the energy and distance of other nodes. Initially, a node will check its \(E_{res}\), and based on its level decides about its current status as either CH or CM. If the node energy is 100 times above the energy threshold, then it will immediately declare itself as a CH. Otherwise, it will wait for a small interval equivalent to a single transmission round (for other nodes to declare themselves as CH) and then declares itself as a CH. If the node declares itself as a CH then it will broadcast its CH Identification (ID) and \(E_{res}\) information. This CH ID will be selected randomly between 0 and \(I_{max}\). This broadcast is limited by the \(Tx_{range}=30\) m. The nodes which receive this broadcast will compare their own \(E_{res}\) with the received information. If the \(E_{res}\) of the node is higher compared to that of the current CH, it will declare itself as a CH and broadcast its own \(E_{res}\) and CH ID. On receiving this broadcast, the node that has previously declared itself as a CH will change its status to CM and send an association request to the new CH. All the other nodes except CH which receive this association request will change their CH. On the other hand, if the \(E_{res}\) of the receiving node is lower than the CH, then they will send an association request to the CH and change their status as member nodes.  
Afterwards, the CH will broadcast its cluster ID at \(Range=1.5\times Tx_{range}\). All the CHs that receive this broadcast will forward it until all the CHs receive this ID. If some CH have selected the same ID before, then this will resolve the issue because this broadcast and each CH will now have a unique CH-ID. The process of re-clustering will start when the \(E_{res}\) of the CH reaches the energy threshold value. At that point the CH will broadcast a CH dead message. Nodes that receive this message will start the CH selection process.  
We observe the nodes falling in the overlapping zones of two clusters can act as a GW node. This will provide an added degree of freedom in route discovery. Therefore, based on the above procedure, we derive another scheme called clustering-energy-GW. The only difference is that the GW nodes are formed if a member node receives the broadcast of two or more CHs. The node will set and broadcast its status as a GW node. These schemes are easy to implement and need no central authority, however, this is achieved at a higher cost of cluster formation and CH selection.
# Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
## 3 Proposed SS-FGSM Scheme
### Conventional FGSM Scheme  
In this subsection, we briefly review the modulation and demodulation process of the conventional FGSM scheme. In FGSM modulation, the incoming data bits are divided into two subgroups named the physical-bits subgroup and indexed-bits subgroup. The bits of the physical-bits sub-group are modulated using any digital modulation, while the indexed-bits subgroup are transmitted via the index of active antennas based on a mapping table [17]. The FGSM active antennas might be one or more transmitting the same modulated symbol of the physical-bits subgroup. Therefore, the achievable data rate, \(R_{\text{FGSM}}\) can be explained as in (3). Based on that, if a \(4\times 4\) transmit/receive antennas scenario is considered, the achievable data rate of the FGSM will be 5 bits per channel use (ppcu) using quadrature amplitude modulation (QAM) digital modulation. For example, let the following  
\[\text{data}\left[\begin{array}{cc}\underbrace{10}_{\text{{physical}}}& \underbrace{101}_{\text{{indexed}}}\\ \phantom{\text{bits}}&\phantom{\text{bits}}\\ \end{array}\right]^{T}\text{be the incoming data bits to be transmitted. The first two bits are representing the physical bits will be modulated using QAM modulation resulting constellated modulated symbols \(s\), while the three following bits, representing indexed bits, will be transmitted via the index of active antennas based on a mapping table [17]. Based on the mapping table \(T_{x1}\) and \(T_{x3}\) will be active to transmit the constellated transmitted symbols, \(s\). Therefore, the FGSM \(N_{t}\times 1\) active antenna vector \(x\) will be \(x=[s0s0]^{T}\). Thus, the received FGSM signal on the \(j\)th received antennas in MIMO underwater communication system can be represented as follows:  
\[y_{j}(t)=\sum_{q=1}^{N_{u}}x_{q}(t)*h_{q,j;\mu}(t;\tau)+n_{j}(t,\tau), \tag{11}\]  
where \(x_{q}(t)\) denotes the transmitted data symbols from the \(q\) active antenna and \"\(*\)\" denotes the convolution operation, \(q\) is the index of active antenna out of available activated transmit antennas \(N_{u}\) in each transmission time, and \(N_{u}<N_{t}\) in all SMTs and \(n_{j}(t,\tau)\) is the additive white Gaussian noise over the received signal at antenna \(j\) with \(n\sim\mathcal{CN}(0,N_{o})\), respectively.  
At the receiving end, Maximum likelihood algorithm is employed to estimate \(x\) and \(q\), which can be expressed as follows [17]:  
\[\left[\begin{array}{cc}\hat{x}&\hat{q}\\ \end{array}\right]=arg\underset{q,\;x}{\text{min}}\|y-\;h_{q}x\|^{2}. \tag{12}\]
# Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences
## 2 Literature Review  
### AI NPCs in VR Shopping  
AI has greatly altered NPCs in VR environments, becoming crucial for crafting immersive retail experiences. Non-player characters (NPCs) in virtual reality (VR) shopping applications boost user engagement by offering tailored interactions and recommendations. Multiple studies emphasize the efficacy of artificial intelligence (AI) in enhancing non-player character (NPC) behaviors to closely resemble human interactions, hence creating virtual shopping settings that are more authentic and captivating.  
Researchers have investigated various AI strategies to develop intelligent behavior in non-player characters (NPCs) and have determined that a combination of decision trees, genetic algorithms, and Q-learning is the most successful approach for developing realistic NPC behavior in video games [3]. Utilizing AI-driven non-player characters (NPCs) in virtual reality (VR) can heighten emotional involvement, resulting in a more captivating storytelling experience [4]. A recommendation system has been proposed in the context of VR purchasing to enhance user shopping efficiency [5]. This system utilizes item similarity and tackles the issue of cold start issues.  
This study focused on analyzing the impact of social-aware virtual reality (VR) group shopping on sales. It highlighted the advantages of customizing flexible item displays and promoting social interactions to increase sales [6]. Game characters have become more intricate and realistic due to recent improvements in AI-based game NPCs [7]. An NPC decentralized autonomous model, implemented within a computer ecosystem, produces lifelike movements and behaviors in virtual environments [8].  
A graphical tool has been developed to facilitate the creation of NPC behaviors. This tool enables users to easily author behaviors using a graphical interface [9]. The comparison of AI strategies for NPC behavior in multiplayer online games has resulted in the suggestion of an emotional behavior tree as a proficient system for monitoring NPC emotional states [10]. Extreme AI is a personality engine that improves the interactions between non-player characters (NPCs) and players by using the Five Factor paradigm to generate customizable NPC personalities [11].
## 3 Results
### Assessment Stage
#### 3.1.2 Load Forecasting  
The energy load demand was predicted until 2030 (see Table S3). It was summed up to obtain the yearly projected load demand, as presented in Table 6, followed by the smoothing trend line of the Holt-Winters method plot as shown in Figure 5 with confidence intervals of 80% and 95%. The predicted energy demand in Araceli for the year 2030 is 1612.724 MWh, an increase of 30% from the 2021 data. In Balabac, there is a 131% of forecasted demand from 931.602 to 2147.121 MWh. The current energy demand of 7407.427 MWh in Cuyo is expected to increase by 75% by 2030, which gives a predicted load demand of 12,958.001. With confidence intervals of 80% and 95%, it is expected that the expected value may fall on those intervals.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{3}{c}{**Energy Demand (MWh)**} \\ \cline{2-4}  & **Araceli** & **Balabac** & **Cuyo** \\ \hline
2017 & 790.850 & 499,404 & 5855.638 \\
2018 & 949.633 & 552.387 & 6337.064 \\
2019 & 1054.212 & 705.619 & 6848.156 \\
2020 & 1186.260 & 843.871 & 7406.970 \\
2021 & 1242.335 & 931.602 & 7407.427 \\ \hline \hline \end{tabular} \
## 2 Experimental Data and Evaluation Index
### Evaluation Index  
In this paper, RMSE (Root Mean Square Error), MAPE (Mean Absolute Percentage Error) and \(R^{2}\) (Coefficient of Determination) are used to evaluate the prediction accuracy of the network model quantitatively. The formulas are as follows:  
\[RMSE=\sqrt{\frac{1}{m}\sum\limits_{i=1}^{m}(x_{i}-\hat{x}_{i})^{2}} \tag{4}\] \[MAPE=\frac{1}{m}\sum\limits_{l=1}^{m}\left|\frac{\hat{x}_{l}-x_{i }}{x_{i}}\right|\times 100\%\] \[R^{2}=1-\frac{\sum\limits_{i=1}^{m}(x_{i}-\hat{x}_{i})^{2}}{\sum \limits_{i=1}^{m}(\overline{x}_{i}-x_{i})^{2}}\]  
where \(x_{i}\) is the actual value of the original data, and \(\hat{x}_{i}\) is the predicted value.  
In this paper, the 95% and 96% of the initial voltage are regarded as the judgment standard of the battery's end of life (EOL) [30].
## 2 Preliminaries
### Financial Markets Perspective  
Based on the analysis of prior works in the field, as described by [PERSON] et al. [11], the decision was made to concentrate on market data as the primary source of features for the stock price prediction model. Specifically, the focus is on high-frequency market data characterised by low latency. This low-latency characteristic implies that the time interval between data points in intraday market data can be a fraction of milliseconds. It is also referred to as tick-by-tick data, where a tick denotes any market event for a particular stock. These events can occur with varying frequencies, and as a result, the time interval between two ticks can fluctuate. This represents a significant departure from classic market data, which maintain a constant time span between data points, such as daily intervals.  
The ultimate source of high-frequency market data is a stock exchange. All modern exchanges record trading activity data and provide access to these data for interested parties. These data may include the following:  
* Price data;
* Volume data;
* Time information (timestamp);
* Stock identification information;
* Direction of trade (e.g., buy/sell).  
There are two commonly recognised archetypes of high-frequency market data: limit order book data and order flow data. Each of them is described separately in the following subsections.