esa-sceva/satcom-chunk-collection huggingface dataset:
# Insights into the Issue of Deploying a Private LoRaWAN
## 1 Introduction  
### Internet of Things Connectivity  
During the last decade, wireless communication technologies have advanced significantly [1], and it is expected that the number of connected devices will reach 26.4 billion by 2026 [2]. These technologies became the primary activators and essence for the new paradigm of the Internet of Things (IoT) [3], and we are now witnessing the rise of IoT applications [4]. Nowadays, the IoT covers many different areas, i.e., sensor networks, telemetry systems, and remote metering. These applications have very specific requirements, such as [5] long battery life, long communication range, a high number of nodes per base station, and high density of nodes. The conventional technologies were not prepared for new applications, and therefore they did not offer sufficient solutions. A new type of wireless solution has been introduced to fulfill these new and specific needs, known as low-power wide-area network (LPWAN) [6].  
At present, there are many different technologies recognized as LPWAN, i.e., long-range wireless-area network (LoRaWAN), SigFox, narrowband IoT (NB-IoT), Weightless, Ingenu, Nwave, Waviot, Wi-Fi HaLow, Tlensa, Amber, and many others [7, 8]. Each technology slightly differs from the others, but the main LPWAN parameters, such as long battery life, extended communication range, and relatively low cost, stay the same. However, the SigFox, LoRaWAN, and NB-IoT are the most discussed, with the LoRaWAN being currently the most adopted LPWAN technology for the IoT [9]. The LoRaWAN technology is an open global standard provided by the association of companies known asthe LoRaWAN Alliance(tm) [10]. The LoRaWAN might be deployed as a public or private network, unlike other LPWAN such as SigFox or NB-IoT, which offer only a public variant. This feature offers a new perspective of LPWAN with different applications.  
A major change in the IoT connectivity landscape is expected after the introduction and broad deployment of the 5G (fifth generation) mobile networks. The 5G networks will enable much lower latency, higher capacity, and the higher bandwidth compared to 4G technologies [11]. Notably, the 5G [12] will bring the evolution for NB-IoT and LTE-M. However, as discussed in [13], there is no universal solution or a single technology that fits all applications. Each connectivity option is more or less suitable for a specific application. In the same manner, the 5G NR (new radio) technology is of great interest and may enable a whole plethora of new use cases for tactile IoT in the context of industrial and medical verticals, for example. However, the communication range of high-bandwidth 5G connectivity is rather limited, and, as of today, not so many 5G-enabled locations are present in Czech Republic. For this reason, we expect that LPWAN technologies will still continue playing an important role in the future, while the 5G technology can serve their backbone links and more demanding applications and use cases.
## 2 Method
### Model
#### 2.2.4 Thermo-Economic Model  
The thermo-economic model is necessary to determine the environmental impact and the economic feasibility of the polygeneration system model proposed. The environmental analysis is based on the calculation of the primary energy saving (_PES_). This index is calculated as the percentage difference between the primary energy consumption in the proposed system (PS) with respect to a reference system (RS). In this case, the RS is the one in which the electricity demand is met by the grid and the thermal energy demand is met with a conventional gas boiler. The efficiency of these systems is 46% and 90% (\(\eta_{el,grid}\) and \(\eta_{boiler}\)), respectively.  
\[PES=\frac{PE_{RS}-PE_{PS}}{PE_{RS}}=\frac{\left(\frac{E_{h,boiler}}{\eta_{boiler}} +\frac{E_{el,fromGRID}}{\eta_{boiler}}\right)_{RS}-\left[\frac{E_{el,fromGRID}-E_{ el,toGRID}}{\eta_{del,grid}}\right]_{PS}}{\left(\frac{E_{h,boiler}}{\eta_{boiler}}+\frac{E_{el,fromGRID}}{ \eta_{del,grid}}\right)_{RS}} \tag{11}\]  
Together with primary energy consumption, the CO2 equivalent emissions also need to be calculated. To do so, the electricity withdrawn from the grid and the primary energy consumed by the boiler were multiplied by their respective CO2 emissions factors, shown in Table 4.  
The equivalent CO2 emissions difference was evaluated according to Equation (12):  
\[\Delta CO_{2}=\left(E_{el,fromGRID}f_{el}+\frac{E_{th,boiler}}{\eta_{boiler}}f_ {NG}\right)_{RS}-\left[\left(E_{el,fromGRID}-E_{el,toGRID}\right)f_{el}\right] _{PS} \tag{12}\]  
The economic analysis is instead based on the calculation of the yearly operating cost saving \(\Delta C\) of the PS with respect to the RS. In this analysis, the unit purchasing cost of electricity from the grid \(c_{el,fromGRID}\) and natural gas \(c_{NG}\), for the RS, and the purchasing/selling \(c_{el,toGRID}\) of the electricity from/to the grid for PS, are considered. In the PS, the unit purchasing cost of the biomass \(c_{wood}\) for the auxiliary wood-chip heater is also considered. \(c_{bio,pqr}\) is the unit purchasing cost for the biomass (wood and agricultural crops) which feed the pyrogasifier. Moreover, the maintenance cost \(M\) was considered.  
\[\begin{split}&\Delta C=\left(E_{el,fromGRID}c_{el,fromGRID}+V_{ NG}c_{NG}\right)_{RS}-\left(M+M_{bio,pqr}c_{bio,pqr}+M_{bio,boiler}c_{bio,boiler}\right)_{PS}\\ &-\left(E_{el,fromGRID}c_{el,fromGRID}-E_{el,toGRID}c_{el,toGRID} \right)_{PS}\end{split} \tag{13}\]  
To calculate the payback time of the proposed solution, the capital costs were also considered. More details about this term are shown in the Case Study section.
# Blockchain-Based Multiple Authorities Attribute-Based Encryption for EHR Access Control Scheme
## 4 Scheme Model
### System Model  
As shown in Figure 1, the VO-PH-MAABE system model includes six entities: certificate authorities (CAs), multi-attribute authorities (AAs), cloud servers (CSPs), data owners (DOs), data users (DUs), and Blockchain (BC).  
Attribute authorities (AAs): AAs are responsible for publishing attributes to data users through the blockchain. There is a many-to-many mapping relationship between attributes and attribute management authorities. Each AA manages multiple attributes in an attribute domain, and multiple AAs can manage each attribute across domains. A segmentation of each secret is calculated and sent to the blockchain through secret sharing among AAs.  
Cloud service provider (CSP): CSP provides data storage service for data owners (DO) and pre-decryption service for data users (DU) of cipher text. Because of its honest and curious nature, CSP is not trusted.  
Data owner (DO): DO, also known as the patients themselves being responsible for encrypting data. They define the attribute-based access policy, encrypt the symmetric key by the access policy, and upload the resultant verification hash of the outsourced decryption to the blockchain. Then DO stores the TxID, data cipher, and key cipher returned by the blockchain to the cloud server.  
Data user (DU): Data user wants to view patient cases, such as doctors, medical researchers, and insurance company managers. DU will register their attributes with AAs. In order to access the DO's data, the DU can initiate a pre-decryption request to the CSP. The decryption will be successful only if the user's attributes satisfy the access policy embedded in the cipher text. DU performs a power operation and hash operation on the pre-decryption result to verify the correctness of the outsourced decryption result.  
Blockchain (BC): Blockchain stores public parameters and hashes for outsourced validation. After a user uploads a verification hash, the blockchain returns a transaction identifier TxID to the user. Blockchain also helps entities perform partially trusted computations and allows multiple AAs to collaboratively manage user attributes, which enables distributed management of attributes.
# Real-Time Wave Energy Converter Control Using Instantaneous Frequency
## 1 Introduction
A previous approach [11] leveraged linear wave theory and hydrodynamic datasets generated from Boundary Element Method (BEM) solvers like WAMIT [12] to express hydrodynamic coefficients as functions of instantaneous wave frequency, significantly reducing computational overhead. However, prior methods required complete future water elevation data, limiting practical real-time applicability. This study focuses on the real-time estimation of wave surface elevation's instantaneous frequency without requiring future data. By leveraging linear wave theory assumptions, where hydrodynamic coefficients mainly depend on wave frequency, this method enables the practical adaptation of impedance-matching control strategies for WEC systems in irregular sea conditions. The research aims to develop implementable real-time WEC control strategies that are computationally efficient, robust under realistic conditions, and compatible with standard onboard measurements. The central hypothesis is that dynamically adapting PTO damping based on real-time frequency estimates enhances energy absorption efficiency, with precomputed frequency-dependent hydrodynamic coefficients being accurately adjusted using these estimates. Validation is achieved by demonstrating improved Capture Width Ratio compared to fixed parameter strategies.
# Screening of Plants and Indigenous Bacteria to Improve Arsenic Phytoextraction
## 2 Materials and Methods
### Soil Characterization and Evaluation of As Bioavailability  
Soil samples were analyzed to determine the pH and the total and potentially bioavailable content of arsenic.  
Since the preliminary bioavailability tests revealed a difficult mobilization of arsenic, an adsorption of arsenic to the iron oxides/hydroxides present in the soil was hypothesized; therefore, in addition to the most widely used chemical additive (0.05 M and 1 M KH\({}_{2}\)PO\({}_{4}\)), other extraction solutions were also tested, namely 0.1 M potassium pyrophosphate (K\({}_{4}\)P\({}_{2}\)O\({}_{7}\)), 1% ethylenediaminetetraacetic acid (EDTA), 0.27 M potassium thiosulfate (K\({}_{2}\)S\({}_{2}\)O\({}_{3}\)), and 0.2 M potassium hydrogen oxalate (KHC\({}_{2}\)O\({}_{4}\)). All tests were carried out in duplicate and considering a soil:extracting solution ratio of 1:25 and a contact time of 4 h.
## 3 Optimization of Controllable Rectification Based on Advanced Control Strategies
### MPSMC Control Strategy
#### 3.3.1 Design of Model Predictive Controller
Rewrite the above equation into a matrix expression as follows:  
\[J=(Y-W)^{T}\overline{Q}(Y-W)+U^{T}\overline{R}U \tag{14}\]  
In the formula, \(W\) is the expected input vector of the controller; \(Y\) is the actual input vector of the controller; \(U\) is the output vector of the controller; \(Q\) and \(R\) are control matrices, respectively. The specific components can be represented as follows:  
\[\left\{ \begin{array}{l}W=\Big{(}\begin{array}{ccccc}i_{\textit{dref}}(k+1 )&i_{\textit{dref}}(k+2)&\ldots&i_{\textit{dref}}\big{(}k+L_{p}\big{)}\end{array} \Big{)}^{T}\\ Y=\Big{(}\begin{array}{ccccc}i_{d}(k+1)&i_{d}(k+2)&\ldots&i_{d}\big{(}k+L_{p} \big{)}\end{array}\Big{)}^{T}\\ U=\Big{(}\begin{array}{ccccc}i_{q}(k+1)&i_{q}(k+2)&\ldots&i_{q}\big{(}k+L_{p} \big{)}\end{array}\Big{)}^{T}\end{array} \right. \tag{15}\]  
In model predictive control of the current inner loop, sampling time is chosen based on system requirements and computational capability. For the fast--response system in this paper, the prediction horizon is set to 2-3 sampling periods to balance prediction accuracy and computational complexity. Control gain is adjusted and optimized via simulation to achieve desired overshoot and settling time. The weighting matrix is determined by assigning weights to different terms in the objective function, with reasonable values obtained through simulation analysis to ensure optimal control performance. Based on the aforementioned analysis, the design of the model predictive control current loop can be accomplished. The current loop is designed to regulate the machine-side current, ensuring that the voltage and current on the machine side are in phase. Additionally, it stabilizes the current value at the desired level.
## 1 Introduction
However, to support hand gesture-based interaction, an additional depth sensor attached to the front of smart glasses is required, and coordinate systems between the smart glasses and the depth sensor must be calibrated. On the other hand, the multi-touch interaction uses a touchpad connected to the AR smart glasses (Figure 3(b)). The usage is similar to the touch interface of a smart device, such as the smartphone or tablet. Thus, its interaction is familiar to the worker. However, it cannot support bi-manual operations as the worker must touch the pad with one hand. This study has the following contributions. * We have conducted a comparative and substantial evaluation of user interactions for AR smart glasses concerning visual contexts and gesture interactions, which suggests new directions for task assistance in wearable AR. * We have performed quantitative and qualitative experiments to analyze task performance and usability to provide user-centric visualization and interaction. Figure 4: Two different types of AR interactions: (**a**) hand gesture; (**b**) touch gesture. Figure 3: Three different types of visual contexts for AR smart glasses. * Through the experiment, we have found that FOV and visual registration complement each other so that it is necessary to increase the visual focus of the information by calculating degree-of-interest. * The analysis result was tested by a real use case of industrial workers concerning cognitive load by performing tasks using wearable AR. This paper is organized as follows. Section 2 presents related work. Section 3 defines the visualization and interaction methods in wearable AR. Section 4 describes the first user study to compare the methods for visual contexts and gesture interactions through virtual object manipulations. Experimental results were evaluated through quantitative and qualitative analyses. Section 5 presents the second user study to assess the methods by comparing different FOVs and evaluating a 3D matching operation between physical and virtual objects. Section 6 describes a real use case of industrial workers who have experienced the proposed approach. Section 7 discusses user studies for user-centric task assistance with AR smart glasses and suggests new challenges. Section 8 concludes the paper and suggests future works.
# RBF-Based Monocular Vision Navigation for Small Vehicles in Narrow Space below Maize Canopy
## 2 Materials and Methods
### Navigation Method
#### 2.2.2 Camera Calibration  
Images are captured via a DFK 22 AUC03 camera, the Imaging Source Company, Bremen, Germany, with a resolution of 640 \(\times\) 480 pixels and a shooting frame rate of 87 s\({}^{-1}\). The camera and IPC communication are connected via an Ethernet port. The IPC uses a 2.00 GHz Intel processor, with a memory capacity of 4 GB. The camera is installed on the nose of the vehicle, and the photographing direction coincides with the vehicle moving direction. As depicted in Figure 3, the image coordinates and the world coordinates are different. The acquisition of the original image is shown in Figure 4a. The camera imaging principle is used for the calibration of the camera. Due to the camera's imaging principle, objects on the ground could be mapped to the camera plane. The calibration process of the camera obtains the 3D transformation matrix \(H\). Equation (2) shows the calibrated world coordinates and the corresponding image coordinates of the points on the ground. The normalized transformation matrix can be obtained by solving the linear equations using the least-squares method. Calibration of the camera is calculated by the camera point captured image and positions by LIDAR collection in the world coordinate system, as Figure 4.  
\[\left[\begin{array}{c}x\\ y\\ z\end{array}\right]=H\left[\begin{array}{c}x_{w}\\ y_{w}\\ 1\end{array}\right] \tag{2}\]  
where (\(x\), \(y\)) denote the image coordinates and (\(x_{w}\), \(y_{w}\)) denote the world coordinate system.  
Figure 2: Mobile platform model and simplified model for kinematics. (**a**) Mobile platform model; and (**b**) simplified model for kinematic.
## 6 Conclusions and Future Work  
We show it is possible to determine the touch input location on a touchscreen via acoustic and movement information extracted from a mobile device. Acoustic features proved to be more effective under realistic usage conditions compared with movement data alone; user touch input location can indeed be determined from audio recordings using on-board microphone sensors. Thus, the ensemble machine learning algorithm chosen for this investigation is effective in classifying touch input location with an accuracy of 86.2%. This has wide-ranging implications on user input privacy on mobile communication devices armed with on-board mechano-acoustic sensors.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Counter Measure** \\ \hline Prevention & Limit fine-grained sensor readings \\  & Physical kill-switch \\  & Alternate keyboard layout \\  & Play masking Sounds \\  & Enable haptic feedback \\  & \\ Shielding & Add a heavy rubber phone cover \\  & Dynamically altering the distribution of mass \\ \hline \hline \end{tabular}
\end{table}
Table 3: Counter-measures Against Acoustic Incursions.  
In future work, further investigation of the acoustic signal and physical characteristics of the device will allow us to determine if other acoustic features or features extracted from pre-trained networks will further improve the sensitivity of the model. Neural networks may also be employed to automatically detect touch input. A larger dataset including swipe and long press input across a variety of implements (e.g., stylus) with the device held at different orientation and inclination may also be considered to evaluate the generalisability of the acoustic model. Model transferability across users and devices may be evaluated with the use of pre-trained models created with inputs from different users. This can be further extended with a larger dataset to address dynamic virtual keyboard layouts on different mobile devices such as smartphones and tablets, and collecting additional data with external microphones. This database can be further extended using augmentation techniques. Furthermore, as touchscreen interfaces are adopted by industrial appliances, the approach presented could be used to analyse these devices for potential vulnerability to acoustic side-channels. Finally, the success of incursion mitigation techniques must also be investigated to determine the most effective and practical approaches which can be implemented by users seeking increased data privacy.  
Conceptualization, K.R.T., B.B.T., J.Z. and J.-M.C.; methodology, K.R.T. and B.B.T.; software, K.R.T.; validation, B.B.T.; investigation, K.R.T. and B.B.T.; writing--original draft preparation, K.R.T.; writing--review and editing, B.B.T., J.Z. and J.-M.C.; visualization, K.R.T. and B.B.T.; supervision, B.B.T., J.Z. and J.-M.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData sharing not applicable.  
The authors declare no conflict of interest.
## 4 Evaluation Results
### Sur Thresholds  
First, we discuss the minimum required coding length under different SNRs. Figures 3 and 4 show the theoretical results when the length of URLLC information bits \(k\) and error probability \(\epsilon\) are set to \((256,10^{-5})\) and \((256,10^{-3})\), respectively. It is easy to notice that, when channel quality is good (i.e., SNR \(P\) is large) and modulation  
It is straightforward to calculate the practical coding lengths, denoted by \(\hat{n}\) under different MCS indexes by applying Equation (1) when the information length is given. Table 3 lists the coding length when \(k=256\) bits.  
If we mark the practical coding length on the corresponding curve given in Figures 3 and 4, the minimum required SNR can be found. To be specific, when MCS index 0 is applied, SNR should be no lower than \(-5.751\) dB in order to guarantee error probability of \(10^{-5}\) and SNR no lower than \(-6.275\) dB in order to guarantee error probability of \(10^{-3}\). Another important issue to be noted here is the point \((X=30,Y=35.98)\), which means the required coding length is \(35.98\) when SNR is \(30\) dB. However, the practical coding length is only \(35\). In other words, the error probability of \(10^{-5}\) cannot be guaranteed when applying MCS Index 27 even with high SNR of \(30\) dB (or even upto \(50\) dB which is not shown in this figure). For comparison, if error probability is lowered to \(10^{-3}\), MCS Index 27 can be applied when SNR is higher than \(27.37\) dB as marked in Figure 4.  
Based on the marks given in Figures 3 and 4, SNR thresholds for adaptive MCS selection can be found as listed in Table 4, where the SNR thresholds for dual data duplication are also found. When the channel quality varies, suitable MCS scheme can be selected based on Table 4 dynamically in order to achieve better performance.  
Figure 4: Required coding length with \(k=256\) bits and \(\epsilon=10^{-3}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**MCS Index** & **Coding Length \(\hat{n}\)** \\ \hline
0 & 1093 \\
5 & 174 \\
11 & 94 \\
20 & 48 \\
27 & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Practical coding length in 5G NR (when \(k=256\) bits).
# Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization
## 3 Background
### Grey Wolf Optimization Algorithm  
Grey Wolf Optimization (GWO) is a nature-inspired meta-heuristic algorithm that reflects the social behavior of grey wolves when hunting to solve optimization problems [18]. Particularly, it reflects the hierarchy of leadership and hunting in grey wolves' packs. The hierarchy can be represented as a pyramid: The leader wolf, alpha, making all the decisions, is on top of the pyramid. The second level has the beta, assisting the alpha wolf in their decisions, and it can also substitute the place of the alpha when required. The level after that is called delta, which has the responsibility of protecting the tribe. The lowest level has several wolves, omega, that are dominated by all the other types in the pyramid. The Grey Wolf Optimization (GWO) algorithm consists of these steps:  
* Initialization: Initialize a population of wolves representing potential solutions to the optimization problem. The number of wolves and their positions are generated randomly.
* Fitness evaluation: Evaluate the fitness of each wolf in the population by applying the fitness function of the optimization problem.
* Alpha, beta, delta, and omega determination: Identify the alpha, beta, and delta wolves based on the population fitness values.
* Update positions of the wolves: The beta and delta wolves adjust their positions based on their current position and the position of the alpha wolf, while the omega wolves explore new positions by more random movement in the solution space.  
Figure 1: Self-Organizing Maps’ Structure.  
* Repeat the second step: Evaluate the fitness of the population again after the position updates and determine the new alpha, beta, delta, and omega wolves based on their new fitness values.
* Termination criteria: Check if the termination criteria are met. The termination criteria might include a maximum number of iterations or a fit-enough solution is obtained.  
Once the termination criterion is met, the alpha wolf represents the best solution found by the GWO algorithm, which can be used as the optimal solution to the given optimization problem.