esa-sceva/satcom-chunk-collection huggingface dataset:
# AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context
## 4 Results
### Analysis of Article Volume, Origin, and Access Model
#### 4.1.7 Co-Occurrence Matrix Between Algorithms and Application Areas  
The co-occurrence matrix analysis, as shown in Figure 4, was conducted to research the relationship between specific algorithms and their corresponding areas of application within the agrifood industry. This method provides insights into the frequency with which various AI algorithms, such as deep learning, random forest, and neural networks are used in conjunction with specific agricultural tasks, thereby offering a deeper understanding of the implementation of AI within this domain. The matrix reflects the number of instances where each algorithm is associated with key areas such as crop yield prediction, soil management, pest control, food quality, and digital agriculture.  
The co-occurrence matrix analysis, as shown in Figure 5, was conducted to research the relationship between specific algorithms and their corresponding areas of application within the agrifood industry. This method provides insights into the frequency with which various AI algorithms, such as deep learning, random forest, and neural networks, are used in conjunction with specific agricultural tasks, thereby offering a deeper understanding of the implementation of AI within this domain. The matrix reflects the number of instances where each algorithm is associated with key areas such as crop yield prediction, soil management, pest control, food quality, and digital agriculture.  
The analysis was based on extracting author keywords from the articles, transforming them into a binary matrix, and subsequently calculating the co-occurrence of algorithm-related terms with agricultural applications. This quantitative approach enables an accurate representation of how various AI technologies are used across different research areas.  
The findings reveal that deep learning is the most frequently applied algorithm, with 21 occurrences in crop yield prediction, 28 in digital agriculture, and smaller but notable instances in pest control (3) and food quality (5). By comparison, random forest appears less frequently but is still used across multiple areas, including six occurrences in crop yield prediction and four in digital agriculture. Other algorithms, such as neural networks, demonstrate limited usage, with only two occurrences in food quality and one in digital agriculture. Interestingly, genetic algorithms and support vector machines do not co-occur with any of the identified application areas, indicating either minimal usage or a lack of emphasis on these methods within the current scope of agrifood research.
## 2 Related Work & Background
### Dynamic Data Acquisition  
In Industry 4.0, dynamic data acquisition plays a crucial role as it forms the foundation for automation and data exchange in manufacturing. Real-time data acquisition allows devices, machines, and products to be interconnected via the Internet of Things (IoT), enabling seamless data exchange [55]. Additive manufacturing, artificial intelligence (AI)-driven data analysis, and machine vision are key innovations used to optimize these processes. The quality of research and development in this field is highly dependent on data quality [55].  
The implementation of a Data Distribution Service (DDS) cloud platform demonstrates how the integration of IoT, big data, and cloud computing can address Industry 4.0's dynamic requirements. Sensor data are collected by a microcontroller, aggregated by an edge device (such as a Raspberry Pi), and transmitted via a DDS application to the cloud, where it is stored in a MySQL database [56].  
A cloud-enabled smart data acquisition system at the shop-floor level enables real-time product tracking along the production chain and supports predictive maintenance. This system follows a multi-layer architecture:  
* Sensor layer: Collects data directly from machines or sensors.
* Edge computing layer: Pre-processes data to reduce latency and network congestion.
* Cloud layer: Stores, processes, and analyzes data using big data technologies and AI.
* Application layer: Provides processed data to interfaces and decision-support tools for users.  
This system enables real-time monitoring of conventional production machines while adapting to customer demands [57].  
The integration of Lean Manufacturing and Industry 4.0 technologies, known as Lean 4.0, helps manufacturers optimize production processes and increase efficiency. A digital twin approach for automated cycle time acquisition and Yamazumi analysis provides real-time insights into manufacturing processes, identifying potential bottlenecks [58].  
Data acquisition is a core aspect of Industry 4.0, and processing collected data through cyber-physical systems (CPS), machine learning, virtual environments, IIoT, and augmented reality is essential for production efficiency [59].  
In large-scale smart manufacturing facilities, wireless sensor networks (WSNs) are widely used to meet dynamic production demands. A unified data description and management framework enables the identification of unknown data types and provides interfaces for accessing stored data [60].  
Open-source data acquisition solutions for the Industrial Internet of Things (IIoT) offer cost-effective and flexible alternatives to commercial software, fostering IIoT development. Data can be collected via industrial protocols, converted into OPC-UA or MQTT formats, and stored in time-series databases [61].
## 5 Security Analysis
### Key Sensitivity Analysis  
In the proposed scheme, the most important private key is the proportion matrix \(\mathbf{D}\). Here, the matrix is one-dimensional. To analyze the sensitivity, the message signal \(H\) was transmitted with two close initial values, \(D=0.5+0.1j\) in the sensor node and \(D^{\prime}=0.5+0.11j\) at the sink node. \(A=0\) was selected to remove the effect of noise on the sensitivity. The CMPDFS process is presented in Figure 11. The error between message signal \(H\) and \(H_{g}\) is shown in Figure 12. The error increases with time. Compared with the original signal, the recovered signal is slightly distorted. The results demonstrate that the proposed algorithm has good sensitivity to the private key.
# Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions
## 3 Results
### Validation of the Temporal and Spatial Methods
Figure 4: Comparison of reconstructed LST using the spatial method with LST measured by AWS at 8 sites from 2016 to 2019. in situ (n) indicates the measured LST with cloudy skies at the observation station; in situ (c) indicates the measured LST with clear skies at the observation station. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Methods**} \\ \cline{4-7}
**Satellite** & **Site** & **Statistics** & \multicolumn{1}{c}{**Fitting (NDVI)**} & \multicolumn{1}{c}{**Linear**} & & \\  & & & **(NDVI)** & \multicolumn{1}{c}{**Temporal**} & & \\  & & & & **Interpolation** & & \\ \hline \multirow{5}{*}{Arou} & R\({}^{2}\) & 0.64 & 0.77 & 0.81 & 0.82 \\  & RMSE & 12.71 & 9.27 & 9.01 & 9.00 \\  & NSE & 0.02 & 0.48 & 0.51 & 0.51 \\  & PBias & \(-\)3.39\% & \(-\)2.45\% & \(-\)2.46\% & \(-\)2.44\% \\ \hline \multirow{5}{*}{Daman} & R\({}^{2}\) & 0.88 & 0.89 & 0.88 & 0.88 \\  & RMSE & 10.05 & 8.49 & 8.88 & 9.12 \\  & NSE & 0.41 & 0.58 & 0.53 & 0.50 \\  & PBias & \(-\)0.36\% & \(-\)2.56\% & \(-\)2.67\% & \(-\)2.27\% \\ \hline \multirow{5}{*}{Dashalong} & R\({}^{2}\) & 0.62 & 0.79 & 0.76 & 0.76 \\  & RMSE & 13.74 & 10.74 & 11.27 & 11.35 \\  & NSE & \(-\)0.53 & 0.06 & 0.01 & 0 \\  & PBias & \(-\)4.21\% & \(-\)3.43\% & \(-\)3.55\% & \(-\)3.58\% \\ \hline \multirow{5}{*}{Tera} & R\({}^{2}\) & 0.82 & 0.84 & 0.81 & 0.80 \\  & RMSE & 12.00 & 12.35 & 13.16 & 13.17 \\  & NSE & 0.23 & 0.19 & 0.06 & 0.06 \\  & PBias & \(-\)3.44\% & \(-\)3.71\% & \(-\)3.96\% & \(-\)4.01\% \\ \hline \multirow{5}{*}{Terra} & R\({}^{2}\) & 0.91 & 0.91 & 0.93 & 0.93 \\  & RMSE & 12.41 & 13.47 & 13.47 & 13.64 \\  & NSE & 0.41 & 0.30 & 0.34 & 0.32 \\  & PBias & \(-\)3.84\% & \(-\)4.23\% & \(-\)4.34\% & \(-\)4.47\% \\ \hline \multirow{5}{*}{Sidaoqiao} & R\({}^{2}\) & 0.91 & 0.94 & 0.94 & 0.93 \\  & RMSE & 11.51 & 10.26 & 10.37 & 10.62 \\  & NSE & 0.48 & 0.41 & 0.58 & 0.56 \\  & PBias & \(-\)3.65\% & \(-\)3.30\% & \(-\)3.35\% & \(-\)3.43\% \\ \hline \multirow{5}{*}{Yakou} & R\({}^{2}\) & 0.45 & 0.72 & 0.80 & 0.80 \\  & RMSE & 20.72 & 11.69 & 11.52 & 11.52 \\  & NSE & \(-\)3.1 & \(-\)0.30 & \(-\)0.18 & \(-\)0.19 \\  & PBias & \(-\)6.50\% & \(-\)3.83\% & \(-\)3.87\% & \(-\)3.87\% \\ \hline \multirow{5}{*}{Zhangye} & R\({}^{2}\) & 0.83 & 0.90 & 0.87 & 0.87 \\  & RMSE & 9.45 & 8.12 & 8.64 & 8.83 \\  & NSE & 0.46 & 0.60 & 0.54 & 0.52 \\  & PBias & \(-\)2.68\% & \(-\)2.45\% & \(-\)2.58\% & \(-\)2.63\% \\ \hline \multirow{5}{*}{Overall} & R\({}^{2}\) & 0.76 & 0.87 & 0.87 & 0.86 \\  & RMSE & 13.12 & 10.70 & 10.93 & 11.04 \\ \cline{1-1}  & NSE & 0.22 & 0.48 & 0.45 & 0.44 \\ \cline{1-1}  & PBias & \(-\)3.76\% & \(-\)3.24\% & \(-\)3.34\% & \(-\)3.39\% \\ \hline \end{tabular}
\end{table} 
Table 2: The general performance results of reconstructed LST using the temporal and spatial methods based on Terra data from 2016 to 2019.
## 2 Materials and Methods
### Study Design and Methodology  
For analysis of postural control, the female athletes were tested using a posturographic measurement system (SensoDiaTrain (SDT), Haynl Elektronik, Schonebeck, Germany) based on the interactive balance system (IBS, neuroblastoma GmbH, Vienna, Austria) (Figure 1a,b). This system consists of four independent force plates supporting the heels and forefeet in order to measure postural control at a sampling rate of 32 Hz.  
Testing consisted of four different balance tasks of increasing difficulty (Figure 1a,b). Subjects were instructed to stand as still as possible for a duration of 32 s during each task. General instructions included standing erect and as still as possible, but without rigidity, while looking at a fixed target (eyes open condition only). The sensorimotor subsystems were selectively targeted by the different tasks, including eyes open and closed (visual system) and the use of stable and foam pads (somatosensory system). All tasks were conducted with the subjects head in a neutral alignment (vestibular).  
Detailed information about the calculated parameters and used test positions can be found in previous research [14; 15; 16; 17]. All measurements were conducted at the same time of day and in a quiet room without any distractions.  
The test positions of eyes open, looking forward, and without foam pads (NO), and eyes closed, looking forward, and without foam pads (NC) (Figure 1a), represent the classic Romberg test widely used in medical practice. By closing the eyes in the NC position and thus eliminating the visual information, the information components coming from other sensory organs and processing in the brain substantially increase. This effect is exacerbated by the use of foam pads and the concomitant reduction in somatosensory input, so that the eyes closed, head straight, and foam pads (PC) test position (Figure 1b) overwhelms the vestibular system as the only reliable source of information.
# COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population
## 5 Experiments and Results
### Modeling Process  
The aim of the study was to compare time series methods in regard to predicting the percentage of active COVID-19 cases with respect to the total population.  
To this end, the ten countries with the greatest number of total confirmed cases were selected to experiment with and draw comparisons from, as they account for more than 70% of the confirmed cases globally.  
Each time series model was trained, evaluated and tested using time series representing percentages of active cases with regards to the total population of each of the ten countries.  
For each country, 104 instances were created, each representing the percentage of active coronavirus cases as a fraction of the total population for a single day in the corresponding country. The number of active cases in each country for each day was calculated by subtracting both the recovered patients and the number of deaths from the confirmed cases, while the percentage of the total population that these active correspond to was calculated by dividing each day's active-cases value by the population of the respective country.  
For training and validation purposes, 97 of these instances were used (72 for training and 25 for validation), while a window of seven days was kept aside to evaluate the performance of the predictive models described in Section 3. In terms of the evaluation metric, the root mean squared error (RMSE) was used to assess the predictive power of each of the approaches.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 3 FIR Prototype Filter Design
### Frequency Sampling Methods
#### 3.1.3 Cruz-Roldan's Method I  
A frequency sampling method for arbitrary length FIR filters is proposed in [120]. Define the frequency response of the prototype filter \(H(e^{i\omega})\), let \(H[k]=H(e^{i\omega_{k}})\), where \(\omega_{k}=(k+\alpha)\cdot 2\pi/N\), \(0\leq k\leq(N-1)\) and \(\alpha=0\) or \(1/2\). The authors in [120] assume \(h(n)\) is real and symmetric, thus the filter coefficients are obtained according to [117] when \(\alpha=0\)  
\[h[n]=\frac{1}{N}\left\{P[0]+2\sum_{k=1}^{\lfloor N/2\rfloor-1}P[k]\cos\left( (n+1/2)\cdot\frac{2\pi k}{N}\right)\right\}, \tag{25}\]  
where \(P[k]=H[k]\cdot e^{-j(N-2k-1)\pi/(2N)}\).  
Figure 1: (**a**) Frequency responses of prototype filters implemented based on the least squares (LS) criterion in [51] and the method in [118] (K = 3 and K = 4). (**b**) Frequency responses of prototype filters based on the minimax criterion in [51] and the method in [118] (K = 3 and K = 4).  
* Calculate the optimum values of the frequency response samples \(\mathbf{H}_{opt}[k]\). These values are obtained from (27) and (28), by replacing the initial values of the magnitude response in the transition band in (27a) by the optimised values \(\mathbf{f}_{opt}\) obtained in the previous step \[|\mathbf{H}_{opt}[k]|=\left[\underbrace{1\ldots 1}_{\text{passband}}\ \underbrace{f_{opt}[q+1] \ldots f_{opt}[q+L]}_{\text{transition}}\ \underbrace{0\ldots 0}_{\text{ stopband}}\right],\] (33) \[|\mathbf{H}_{opt}[N-1-k]|=|\mathbf{H}_{opt}[k]|,\] \[\lfloor(N-1)/2\rfloor+1\leq k\leq N-1.\] (34)
* Based on \(\mathbf{H}_{opt}[k]\), the prototype filter coefficients are obtained through (25) or (26). In [65], the objective function \(\psi\) in **step 3** is defined as \[\psi=\max_{\omega}\left\{|H(e^{\mathrm{i}\omega})|^{2}+|H(e^{\mathrm{i}(\omega -\pi/M)})|^{2}-1\right\}.\] (35)  
The minimization algorithms in MATLAB Optimization Toolbox [120] can be used to solve this optimization problem.
# A Novel Covert Channel for IEEE 802.11 Networks Utilizing MAC Address Randomization
## 9 Conclusions
This research was supported by the Polish Ministry of Science and Higher Education with the subvention funds of the Faculty of Computer Science, Electronics and Telecommunications of AGH University of Science and Technology. Not applicable. The data presented in this study are available on request from the corresponding author. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
5G Fifth Generation of Wireless Cellular Technology  
ANQP Access Network Query Protocol  
AP Access Point  
ARQ Automatic Repeat Request  
BI Beacon Interval  
BSS Basic Service Set  
CID Company Identifier  
CRC Cyclic Redundancy Check  
GPS Global Position System  
IE Information Element  
IEEE Institute of Electrical and Electronics Engineers  
IEEE RA IEEE Registration Authority  
IFAT Inter-Frame Arrival Time Analysis  
LSB Least Significant Bit  
LTE Long Term Evolution  
MA-L MAC-Address Block Large  
MAC Medium Access Control  
MCS Modulation and Coding Scheme  
MSB Most Significant Bit  
NIC Network Interface Controller  
NS-3 Network Simulator Version 3  
OFDM Orthogonal Frequency Division Multiplexing  
OS Operating System  
OUI Organizationally Unique Identifier  
PHY Physical Layer  
PR Probe Request  
RA Registration Authority  
RCM TIG Randomized and Changing MAC Addresses Topic Interest  
SSID Service Set Identifier  
STA Client Station  
SC Sequence Control  
SN Sequence Number  
SWP Sliding Window Protocol  
SR Selective Repeat  
TCP Transmission Control Protocol
## 3 Results
### Emission Characteristics
#### 3.3.2 HC Emissions  
Figure 8 shows the results of HC emissions for all test fuels under different engine loads. HC is an important parameter for determining the emission behavior of the engines [27]. From Figure 8, on the whole, HC of most test fuels decreases with the increase of engine load and increases with the increase of ethanol mixture ratio. For example, at 60 Nm, the HC emission of D100, D95E5, D90E10, and D85E15 is reduced by 0.00%, 18.18%, 7.14%, and 14.29% compared with that at 30 Nm, respectively. At 30 Nm, the HC emission of D95E5, D90E10, and D85E15 is increased by 37.5%, 75.00%, and 75.00% compared with that of D100, respectively. Under low load, the diesel engine emits the most HC. This is mainly because the lower cylinder temperature under low load is not conducive to fuel combustion. In addition, ethanol has evaporative cooling effect, which further reduces the temperature in the cylinder and forms more mhifire areas, resulting in more HC generation [34]. [PERSON] et al. [35] also reported that the addition of ethanol leads to an increase in HC compared with the use of neat diesel fuel. In addition, they also pointed out that the HC emissions arise due to wall wetting, insufficient oxygen, and residual fuel in the sac volume.
###### Abstract  
Biodegradable magnesium (Mg) alloys have potential applications in orthopedic implants due to their mechanical and osseointegration properties. However, the surface characteristics, biocompatibility, and toxicity of the released corrosion products in the form of magnesium oxide (MgO) and magnesium hydroxide (Mg(OH)\({}_{2}\)) nanoparticles (NPs) at the junction of implants and in the surrounding tissue are not completely understood. Here, we investigated in vitro cytotoxicity and morphological changes in human fetal osteoblast (hFOB) 1.19 cells in response to various concentrations (1 mM, 5 mM, 10 mM, and 50 mM) of MgO/Mg(OH)\({}_{2}\) NPs by live/dead assay and scanning electron microscopy (SEM). In this study, we performed a surface characterization of MgO/Mg(OH)\({}_{2}\) NPs to evaluate the size of the NPs. Further, an immersion test was performed in Dulbecco's Modified Eagle's Medium (DMEM) with randomly selected various concentrations (1 mM, 5 mM, 10 mM, 50 mM, and 100 mM) of MgO/Mg(OH)\({}_{2}\) NPs to understand the degradation behavior of the NPs, and the change in the pH values from days 1 to 7 was measured. After conducting an immersion test for seven days, the highest concentration (100 mM) of MgO/Mg(OH)\({}_{2}\) NPs was selected to study the element depositions on nanoparticles through scanning electron microscopy-energy-dispersive X-ray spectroscopy (SEM-EDX) mapping. The results from this in vitro cytotoxicity study suggest that less than or equal to 5-mM concentrations of MgO/Mg(OH)\({}_{2}\) NPs are tolerable concentrations for hFOB 1.19 cells. This study provides a foundational knowledge of MgO/Mg(OH)\({}_{2}\) NP cytotoxicity in hFOB 1.19 cells that can help to develop future sustainable biodegradable magnesium-based alloys for orthopedic applications.  
magnesium oxide nanoparticles (MgO NPs); magnesium hydroxide nanoparticles (Mg(OH)\({}_{2}\) NPs); cytotoxicity; human fetal osteoblast (hFOB) 1.19 cells +
Footnote †: journal: Applied
# Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction
## 1 Introduction
Because of the congested traffic conditions and anxious drivers, the effect of honking on the overall traffic noise and its effects on the drivers, pedestrians, cyclists, traffic police personnel, nearby shopkeepers and residents needs to be considered. In this heterogeneous composition of traffic, horn events get to increase noise level (Lden) up to 0.5-13 dBA compared to homogenous traffic conditions [31]. Therefore, the prediction models used for homogenous traffic conditions are not applicable in this heterogeneous context. Thus, to increase the accuracy of noise prediction models, considering honking is required. Some models considering honking have been developed by using regression model and statistical analysis [32], others by introducing a factor for horn correction, with respect of level of service (LOS), in traffic noise models such as CRTN, FHWA, and RLS 90 [31]. In the present work, an attempt has been made to include the honking effect in the traffic noise prediction model developed using machine learning methods. In particular, four different techniques have been selected, calibrated and tested on a case study experimental dataset collected in Patiala city, India. A comparison of these techniques on the basis of criteria of mean square error, correlation coefficient, coefficient of determination and accuracy has also been presented.
# Pressure-Relief Gas Cooperative Drainage Technology in a Short-Distance Coal Seam Group
## 4 Optimization Design of Pressure-Relief Gas Drainage by Mining
### Layers of Pressure-Relief Gas Drainage Holes in the Short-Distance Coal Seam Group  
Reasonable arrangement of drainage holes is the key to achieving the accurate extraction of pressure-reliefed gas in the adjacent layer. The spatial arrangement position of the pressure-relief gas extraction hole determines the effect of pressure-relieved gas extraction in the short-distance seam group mining. From the \"three zones\" of pressure-relieved migration in short-distance seam group mining, the gas-conducting fracture zone is greatly affected by mining; the secondary fractures and gas migration channels are the most developed; and the pressure-relief gas migration is active, which is the main area of pressure-relief gas drainage. Therefore, the arrangement of the pressure-relief gas drainage holes in the short-distance coal seam group is the gas conducting fracture zone area, that is, the space within the maximum development height of the caving zone to the maximum development height of the gas-conducting fracture zone. Therefore, the arrangement horizon of pressure-relief gas drainage holes can be expressed according to Formula (4):  
\[h_{m}<h<h_{g} \tag{4}\]  
\(h_{m}\)--height of the caving zone, m;  
\(h\)--vertical height of the coal seam roof of the pressure-relief gas drainage hole distance mining layer, m;  
\(h_{g}\)--height of gas conducting fracture zone, m.  
The height of caving zone \(h_{m}\) is related to the unidirectional compressive strength of overburden strata \(R_{c}\)[28; 29]. When \(R_{c}<10\) MPa, \(h_{m}\) is calculated according to Formula (5);when 10 MPa \(<R_{c}<\) 20 MPa, \(h_{m}\) is calculated according to Formula (6); when 20 MPa \(<R_{c}<\) 40 MPa, \(h_{m}\) is calculated according to Formula (7); when 40 MPa \(<R_{c}<\) 80 MPa, \(h_{m}\) is calculated according to Formula (8) [30].  
\[h_{m}=\frac{100M}{7.0M+63}+1.2 \tag{5}\]  
\[h_{m}=\frac{100M}{6.2M+32}+1.5 \tag{6}\]  
\[h_{m}=\frac{100M}{4.7M+19}+2.2 \tag{7}\]  
\[h_{m}=\frac{100M}{2.1M+16}+2.5 \tag{8}\]  
\(M\)--height of the mining layer, m.  
The height of the gas-conducting fracture zone \(h_{g}\) is related to the height of the main key strata from the mining layer [31; 32]. When the height of the main key strata from the mining layer is greater than (7\(\sim\)10)\(M\), the mining height \(h_{g}\) is the height of the coal seam of the nearest key strata from the mining layer outside the 7-10 mining height. When the height of the main key stratum is less than (7\(\sim\)10)\(M\), \(h_{g}\) is the top height of the bedrock.