esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Correlation Analysis  
The color element differences in \(\Delta\)E, \(\Delta\)L, \(\Delta\)C, and \(\Delta\)h showed a positive linear correlation with APD, ASA, FED, and TFF (\(p<0.05\)) and a negative linear correlation with FC, TFD, and ACP (\(p<0.05\); Figure 6a).  
To develop an evaluation model for comfortable colors, this study analyzed the correlation between eye-tracking and subjective evaluation metrics with \(\Delta\)E, \(\Delta\)L, \(\Delta\)C, and \(\Delta\)h (Figure 6b). The calculations indicated that APD had a relatively low overall correlation with \(\Delta\)E, \(\Delta\)L, \(\Delta\)C, and \(\Delta\)h (0.01, 0.02, 0.01, and 0.01). ASA showed a strong correlation with \(\Delta\)L (0.31) and a relatively strong correlation with \(\Delta\)E, \(\Delta\)C, and \(\Delta\)h (0.17, 0.18, and 0.16). FFD demonstrated a moderate correlation with \(\Delta\)L and \(\Delta\)C (0.06 and 0.04), with weaker correlations for the other variables. FC showed a strong correlation with \(\Delta\)L (\(-0.33\)) and a relatively strong correlation with \(\Delta\)E, \(\Delta\)C, and \(\Delta\)h (\(-0.15\), \(-0.19\), and \(-0.13\)). TFF had a relatively strong correlation with \(\Delta\)E, \(\Delta\)L, \(\Delta\)C, and \(\Delta\)h (0.1, 0.19, 0.11, and 0.09). TFD exhibited a moderate correlation with \(\Delta\)E, \(\Delta\)L, \(\Delta\)C, and \(\Delta\)h (\(-0.08\), \(-0.15\), \(-0.08\), and \(-0.07\)).  
Figure 5: (**a**) Box plots of participants’ APD, ASA, FFD, FC, TFF, TFD, ACP, and VC across different hue groups (the middle line represents the median, the upper and lower bounds of the box represent the 25 th and 75 th percentiles, respectively, and the upper and lower ends of the whiskers represent the 5 th and 95 th percentiles, respectively). **: \(p\leq 0.01\); ***: \(p\leq 0.0001\). (**b**) The colour combination is sorted by different indicators and their corresponding values.  
ACP showed a strong correlation with \(\Delta\)E, \(\Delta\)L, \(\Delta\)C, and \(\Delta\)h showed a strong correlation with the subjective evaluation metrics ACP and FC, and a relatively strong correlation with the eye-tracking metrics ASA, FC, TIFF, and TFD. They exhibited a moderate correlation with FFD, while APD showed a notable correlation only with \(\Delta\)L.
# Hydrogen Production from Offshore Wind Parks: Current Situation and Future Perspectives
## 2 System Components
### Electrolyzer Technologies
#### 2.2.1 Alkaline Electrolyzers  
AELs are currently the cheapest technology and have the longest lifetime, due in part to being the oldest of the technologies mentioned above [11; 12]. This type of electrolyzer has been used in the industry for roughly 100 years; thus, while further progress is expected, both PEMEL and SOE development will surely be faster [11]. However, they cannot react as fast to changes in production, require complex maintenance of the alkaline fluid, cannot operate below a certain threshold for safety reasons, take longer to start, and present a rather low current density when compared to PEMEL, around 5 times lower [13]. In addition, the output pressure of the hydrogen produced is lower, which requires higher compression for transport and storage, reducing the advantage the lower CAPEX provided initially.  
Historically, this type of electrolyzer has been operated at almost constant power while connected to the grid and recently has seen improvements in the ability to change hydrogen production rate without relevant efficiency losses. One of the highest-powered electrolyzers is a 4 MW module [14], which is claimed to have a dynamic response fast enough to follow the production of a renewable power plant. Furthermore, it is also said that the current density is double compared to the previous generation, that the output pressure is 30 bar, and that it has increased longevity compared to newer technologies [15].
## 3 Results and Discussion
### Essential Oil Compositions
The essential oil compositions for the three essential oils are compiled in Table 1. A perusal of the table reveals that the three _Helianthus_ essential oils are qualitatively similar. The major components for _H. annuus_ \"Chianti\" were \(\alpha\)-pinene (50.65%), camphene (7.26%), limonene (7.20%), bornyl acetate (7.13%), sabinene (6.81%), and \(\beta\)-pinene (5.79%). The essential oil of _H. annuus_ \"Mammoth\" was also dominated by \(\alpha\)-pinene (48.91%), followed by sabinene (17.01%), limonene (7.11%), and germacrene D (6.84%). _H. strumosus_ essential oil was also rich in \(\alpha\)-pinene (58.65%), as well as myrcene (9.79%) and bornyl acetate (4.97%). The compositions of _H. annuus_ essential oils cultivated in north Alabama are very similar to those reported by [PERSON] and co-workers for populations growing in the southern plains of the United States [35]. The essential oils of _H. annuus_ from Pisa, Tuscany, Italy [36]; Lagos, Nigeria [37]; or from western United States [35] had much lower concentrations of \(\alpha\)-pinene and correspondingly higher concentrations of germacrene D. In marked contrast to the essential oils of _Helianthus_, essential oils of _Rudbeckia fulgida_ Aiton and _Rudbeckia hirta_ L. (Asteraceae, Heliantheae) from north Alabama were devoid of \(\alpha\)-pinene, but rich in sesquiterpene hydrocarbons [27].
## 3 EBI Signal Processing and Algorithm Development for HR Estimation
### Preprocessing as Band-Pass Filtering  
In the present experimental context, EBI data were acquired over approximately one minute (with a source frequency of 100 kHz), and the sampling rate is 100 samples per second. PPG was acquired concurrently to establish a congruent ground truth dataset of equal length. The EBI measurements utilize the real part of the complex impedance, encompassing the absolute impedance value, noise, and the inherent cardiac activity embedded within the signal. Different filtering methods are required to obtain the heartbeat from the signal. The noise component of the measured EBI signals is removed using a BPF. The filter's settings were established based on a frequency response range, determined using the upper and lower limits of the HR, assuming that these limits were unattainable. We designed the filter in \(Matlab^{TM}\) filter designer using the parameter band-pass infinite impulse response (IIR).  
The filtered signal is obtained via a zero-phase filtering approach to avoid phase distortion. For this, the Matlab function filt filt(x) is used [40], which processes the input data x in both the forward and reverse directions:  
\[\mathbf{x}_{\text{fit}}(t)=\text{ filt filt filt(H,x(t))}, \tag{3}\]  
where \(\mathbf{x}(t)\) is the input signal as a function of time \(t\), H represents the filter's transfer function, and \(\mathbf{x}_{\text{fit}}(t)\) is the filtered signal.  
* Filter type: IIR filter;
* The first stop-band: lower frequency edge = 0.1 Hz, attenuation = 10 dB;
* Pass-band: frequency edges = [0.5 5.0] Hz;
* The second stop-band: upper frequency edge = 6 Hz, attenuation = 10 dB.  
We employed the elliptic filter design method to create a filter, with the MatchExactly parameter ensuring precise alignment with pass-band frequencies. The signal, set at a constant 100 samples per second, undergoes band-pass filtering for HR estimation, eliminating DC, low, and high-frequency noise.
## 4 Simulation Results and Analysis
### Validation of the BPSK Module  
In the BPSK module, the time-domain waveform and spectrum of the signal obtained after low-depth BPSK modulation are respectively shown in Figures 14 and 15. In the BPSK module, the communication ranging code signal obtained from the DS/SS module needs to first undergo a conversion from unipolar to bipolar signals, i.e., transforming the sequence of 1 and 0 into a sequence of +1 and \(-1\). Then, low-depth BPSK modulation is applied. By modulating the phase of the bipolar signal onto the carrier, the carrier can convey information by either leading or lagging in phase. Additionally, after phase modulation, a main lobe will appear at the carrier frequency, with a bandwidth equal to twice the PRN code rate.  
Figure 11: The simulation model of inter–satellite communication based on Simulink.  
As shown in Figure 14, when the communication ranging code is 1, the carrier phase shifts forward; when the communication ranging code is 0, the carrier phase shifts backward. Figure 15 shows that the spectrum of the signal after low-depth BPSK modulation exhibits sharp peaks at \(\pm\)10 MHz, with the main lobes located in the ranges of \(-\)11 to \(-\)9 MHz and 9 to 11 MHz. This confirms that the communication ranging code, with a chip rate of 1 MHz, is successfully modulated onto a 10 MHz sine wave using low-depth BPSK modulation. Therefore, the phase modulation functionality in the designed BPSK module is correct.  
Figure 12: Time–domain waveforms of the data code, PRN code and communication ranging code.  
Figure 13: Spectrum of the communication ranging code.
# Instance-Based Learning Following Physician Reasoning for Assistance during Medical Consultation
## 5 Medical Consultation Flows
### Starting Attention of a Patient  
Algorithm 2 details the first steps which occur during a medical consultations.  
```
1:showPersonalInfo(P:personalData)
2:showChronicElementDescriptors(P:chronicElems)
3:chronicCTs \(\leftarrow\) getCaseTypesByNames(P.chronicCaseTypes)
4:if chronicCTs \(\
eq\emptyset\)then
5: All case types included in chronicCTs are suggested to physician
6: Physician select CT\({}_{chron_{1}}^{M}\),..., CT\({}_{chron_{t}}^{M}\) to be used as basis
7:CT\({}_{merge}^{M}\) is build by merging CT\({}_{chron_{1}}^{M}\),..., CT\({}_{chron_{t}}^{M}\) (Algorithm 8)
8:applyCaseType(P, CT\({}_{merge}^{M}\)) is called (Algorithm 3)
9:endif
10:Show message agents according its trigger conditions
11:Physician continues with patient attention
```  
**Algorithm 2** Start attention of patient \(P\)  
The physician starts the attention of patient \(P\) by opening a registry editor to record the information of the new medical consultation. Personal information is loaded (line 1) to introduce the patient. All descriptors of chronic elements (line 2) and all chronic case types (line 3) associated with the patient are presented and suggested to the physician, who can select the chronic case types that are appropriate to being applied into the consultation (lines 4-9). Before the physician continues with patient attention, all message agents are evaluated and shown according to its trigger conditions (line 10).
## Materials and Methods
### Data Sources and Preprocessing
#### 2.2.7 Land Use Data  
To accurately capture the vegetation type data in the arid zone of Xinjiang, we selected the 2010 Xinjiang surface cover dataset obtained from the National Earth System Science Data Sharing Service Platform, specifically from the Loess Plateau Science Data Center (available at [[http://loess.geodata.cn](http://loess.geodata.cn)]([http://loess.geodata.cn](http://loess.geodata.cn)), accessed on 20 May 2024). We reclassified the land use types in Xinjiang into forest, cultivated land, grassland, and shrub land.  
Statistical interpolation was used to ensure data completeness and accuracy when dealing with missing data from the flux sites. The mean value of the available data was calculated using R 4.4.0 (available at [[https://cran.r-project.org/bin/windows/base/](https://cran.r-project.org/bin/windows/base/)]([https://cran.r-project.org/bin/windows/base/](https://cran.r-project.org/bin/windows/base/)), accessed on 8 June 2024), and the mean was used to fill in missing values. For raster data processing, we cropped all raster data by masking using ArcGIS 10.8 (available at [[https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview](https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview)]([https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview](https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview)), accessed on 24 April 2024). Subsequently, we resampled all the raster data and unified the row and column numbers. We also unified the spatial reference system (WGS1984 projection) and the spatial resolution (0.05\({}^{\circ}\)). In addition, regarding data quality control, we checked and screened raster data values outside the normal range (e.g., abnormally high or low). We removed or used the average of the neighbouring values for these anomalous values as a replacement. This method preserves the local characteristics of the data and reduces the interference of anomalous values in the overall data analysis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Drought** & **Extreme** & **Severe** & **Moderate** & **Mild** & **Normal** & **Moist** \\
**Type** & **Drought** & **Drought** & **Drought** & **Drought** & **Normal** & **Moist** \\ \hline SPEI & \(\leq\)\(-2.0\) & \(-2.0\)\(-1.5\) & \(-1.5\)\(-1.0\) & \(-1.0\)\(-\)\(-0.5\) & \(-0.5\) & \(\geq\)0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Drought classification.
## 3 Experimental Setup
### Scenario 2: ATSC-Based Scenario at the Signalized Intersection  
The flow chart of the ATSC scenario is shown in part a) of Figure 9. In this model, we used 12 loop detectors to detect all vehicles (CVs and RVs) when passing them. Loop detectors were permanently mounted on the road and measured the traffic flow through them. Based on the collected information, the IMS adjusted the green time of traffic signals according to the set rules. The green time of each phase needed to be greater than the minimum green time (10 s) before switching to another phase. In any green phase, if the loop detector detected vehicles within 5 s, the green time of this phase was extended by \(\Delta_{t}\) (s) to ensure that the platoon had enough time to cross the intersection without waiting. If vehicles were not detected within 5 s, the green phase ended and switched to another phase that had the maximum waiting time. The duration of green time changed continuously depending on the traffic approaching the intersection. The minimum green time for each phase was 10 s, and the maximum was 45 s.  
Based on the communication, the IMS obtained the speed and time interval of all vehicles. The waiting time of each vehicle was recorded when the vehicle speed was below or equal to 0.1 m/s before the intersection. Moreover, the IMS determined the waiting times of all vehicles and the cumulative waiting time of each phase. The selected green phase had the maximum waiting time. After each cycle ends, the IMS updated the waiting time of each direction to select the next green phase.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Simulator & SUMO \\ \hline Num\_layers & 2 \\ (Hidden layers) & 2 \\ \hline Width\_layers & 256 \\ (Neurons) & \\ \hline Loss Function & Mean Squared Error \\ \hline Activation Function & Relu \\ \hline Optimization Function & Adam Optimizer \\ \hline \hline \end{tabular}
\end{table}
Table 4: Structure of neural network.  
Figure 9: Frameworks of ATSC- and MP-based scenarios.
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
## 1 Introduction
### Our Contribution  
We propose a new communication model that can bridge the air gap by utilizing smart plugs. Technically, we can achieve air-gapped host data acquisition by monitoring the energy consumption of computers through smart plugs. Additionally, the switch behavior of smart plugs enables us to send commands and data to infected laptops within the air-gap network. We discuss the required software and hardware environment and operations for connecting the air-gapped networks. Furthermore, we propose detection methods and preventive measures for such threatening behaviors.  
* To the best of our knowledge, this is the first air-gap channel that utilizes smart plugs to achieve bidirectional communication through an air-gapped network. It employs smart plugs for power consumption detection and incorporates manufacturing power switches for bidirectional communication. In comparison to the existing PowerHammer method, which enables only one-way communication, our approach achieves bidirectional communication on the laptop end.
* We conducted extensive experiments on smart plugs and various laptop models to determine the effectiveness and maximum communication speed of PowerBridge.
* The code does not require special permissions (such as root or administrator). Users can operate from ordinary user-level processes or applications with a wide range of security risks.
* PowerBridge utilizes a standard CPU to enable air-gap communication, enabling the establishment of communication channels on nearly any computer or device equipped with a CPU.  
The rest of this article will have the following arrangement: The second part will elaborate on threat scenarios in detail. Next, in the third section, we will introduce the relevant research results of previous scholars. Subsequently, in the fourth section, we provide the technical background for implementing PowerBridge based on smart plugs. Then, in the fifth section, we discuss issues related to data encoding and decoding. The content of signal generation is described in Section 6. Furthermore, Section 7 provides a detailed introduction to evaluation and analysis methods. Finally, an in-depth discussion is conducted in Section 8, and countermeasures are proposed.
## 4 An Understanding of Biomass and Their Characteristics
### Interaction of Nanoparticles with Biomass  
Many nanoparticles have antimicrobial properties, and can therefore play a vital role, with liquid biomass, in water purification [69]. Enhancement of the AD of iron is summarized in (Table 2. Nowadays, NPs are being used for the detection and separation of biological and chemical substances, such as metals (Zn, Cd, Cu, etc.), algae (cyanobacterial toxins, etc.), nutrients (ammonia, nitrate, phosphate, etc.), cyanide, antibiotics, organics, parasites, bacteria and viruses. For water purification, four classes of nanomaterials are being evaluated as functional materials: dendrimers, zeolites, NPscontaining metal, and carbonaceous nanomaterials. Nanofibers and carbon nanotubes (CNTs) also show an affirmative result. Due to a high surface area to volume ratio, better results have been revealed by nanomaterials than other water purification techniques [70]. Monitoring, adsorption, microbial control, photocatalysis, membrane processes, disinfection, monitoring and sensing are potential and current applications of nanotechnology for wastewater and water treatment [71]. But facts and data related to the toxicity of nanomaterials are still not sufficient [72]. There are two main factors responsible for the antibacterial activity of nanoparticles: (i) the physiochemical properties of nanoparticles, and (ii) the nature of bacteria. It has been observed that the antibacterial effect of silver nanoparticles are enhanced by treating coliform bacteria with ultrasonic radiations for a short period prior to nanoparticle treatment. Ag NPs also displayed major activity against biofilms of bacteria. The antibacterial effect, and Ag NPs concentrations, depend upon the class of bacteria to be treated [73].  
Previous research has demonstrated that _Vibrio cholera_ and _Pseudomonas aeruginosa_ showed more resistance than _Salmonella typhi_ and _Escherichia coli_, but bacterial growth was completely abolished at concentrations above 75 mg/L [74; 75]. The antimicrobial activity of silver NPs against _Staph aureus_ and _Escherichia Coli_ has also been studied, and at low concentrations, _Escherichia coli_ was inhibited but _Staph aureus_ was less inhibited [76]. Ag NPs also showed significant adverse effects on filamentous green algae [77]. The biomass to bioenergy conversion by the interaction of nanoparticles has shown the importance of nanomaterials in research. This conversion could be either a biological, chemical or thermal process. The conversion process is affected by inorganic contaminants obtained from organic biomass and molecular size. Functionalized nanoparticles are acquired from both natural and synthetic sources. Within the process, a number of complications can arise because of the existence of multi-faceted interactions, therefore, it needs to be tackled properly. Two types of biomass, i.e., waste sludge and algae, describe the impact of NPs on biomass energy conversion.
# AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems
## 4 Conclusions  
This study presents an AI-driven framework for reducing pilot overhead in 5G mmWave m-MIMO systems, integrating multiple machine learning techniques with hybrid beamforming. The proposed system offers a novel integration of k-clustering, linear regression, random forest regression, and neural network-enhanced singular value decomposition (NN-SVD) to optimize pilot placement and beamforming strategies. Unlike existing approaches, the application of k-clustering directly to pilot signals introduces a unique perspective in optimizing pilot signal allocation by minimizing redundancy without compromising channel estimation accuracy. Another novel approach is the combination of NN and SVD to optimize the beamforming matrix and their implementation in RFR to optimize the system already enhanced with RFR. Moreover, the study demonstrated scalability through simulations with different combinations of Tx and Rx antenna configurations, achieving an \(82\%\) reduction in pilot overhead, a \(250\%\) increase in spectral efficiency, and a tenfold improvement in bit error rate at low SNR conditions, all while achieving a system accuracy of \(98\%\). By bridging traditional pilot management techniques with emerging AI-driven solutions, this research advances the understanding and application of AI in reducing pilot overhead while enhancing system performance in next-generation wireless networks. Future work will explore additional system parameters and alternative AI techniques to further optimize pilot overhead reduction and overall system efficiency.  
Conceptualization, M.R.A.Y., S.A.C. and H.I.; methodology, M.R.A.Y., S.A.C. and H.I.; software, M.R.A.Y.; validation, M.R.A.Y., S.A.C. and H.I.; formal analysis, M.R.A.Y., S.A.C. and H.I.; investigation, M.R.A.Y., S.A.C. and H.I.; resources, M.R.A.Y., S.A.C. and H.I.; writing--original draft preparation, M.R.A.Y.; writing--review and editing, M.R.A.Y., S.A.C. and H.I; supervision, S.A.C. and H.I. All authors have read and agreed to the published version of the manuscript.  
Figure 24: RFR-NNSVD QPSK SE vs. SNR.