esa-sceva/satcom-chunk-collection huggingface dataset:
# Millimeter-Wave Multi-Channel Backscatter Communication and Ranging with an FMCW Radar
## 5 Signal Processing Framework  
### Single Tag Scenario and Range Estimation  
When there is only one tag in the view, the received signal can be represented as (5) after mixing with the local FMCW signal. There is only a fixed frequency offset and a phase offset left. By using fast Fourier transformation (FFT) and a phase-locked loop (PLL), we can derive the offset frequency and offset phase. A pilot signal of long zeros can be placed ahead of the data to assist in frequency offset extraction. The distance between the radar and the tag can be calculated by  
\[\Delta d=\frac{\Delta f\times T\times c}{2B} \tag{17}\]
# Inhibitory Effect of Cold Atmospheric Plasma on Chronic Wound-Related Multispecies Biofilms
## 3 Results
### Genotoxicity  
According to obtained results, all groups of 3T3 cells can be classified as slightly aneuploid (DI \(\geq\) 1.2). Non-treated group (NT) presented DNA index (DI) mean of 1.27. These results suggest that CAP was not genotoxic. Table 1 shows DI median, mean, and standard deviation (SD) of studied groups. Figure 5 represents the distribution histogram of obtained DI \(\times\) number of cells for all analyzed periods of time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **P1** & **P3** & **P5** & **P7** & **NT** \\ \hline Median & 1.24 & 1.33 & 1.30 & 1.27 & 1.24 \\ \hline Mean & 1.25 & 1.32 & 1.31 & 1.29 & 1.27 \\ \hline Standard deviation (SD) & 0.12 & 0.14 & 0.11 & 0.11 & 0.09 \\ \hline \end{tabular}
\end{table}
Table 1: Median, mean and standard deviation (SD) of DNA index (DI) obtained by static cytometry of 3T3 cells when treated with cold atmospheric plasma for 1 (P1), 3 (P3), 5 (P5), and 7 (P7) minutes. NT = non-treated group.  
Figure 4: Cytotoxicity analysis of non-thermal plasma at atmospheric pressure expressed in cell viability. The 3T3 cells were exposed to CAP for 1, 3, 5 and 7 min. The cell viability was determined immediately (gray bar), 24 h (striped bar) and 48 h (black bar) after plasma exposure.
# A Dynamic Service Reconfiguration Method for Satellite-Terrestrial Integrated Networks
## 4 Algorithm Design and Description  
Considering the dynamics and mobility of satellite nodes and users, the solution space of the service migration and reconfiguration optimization problem is large. Many traditional solutions, such as the classical exact solution, heuristic solution, and meta heuristic solution algorithms, have limitations to a certain extent, and are not suitable for large-scale discrete-state space problems [23]. The classical genetic algorithm is a random heuristic search algorithm. Compared with the accurate solution algorithm, the algorithm has higher efficiency and the crossover and mutation steps are random, which can avoid the algorithm falling into local optimization to a certain extent, but a randomness setting that is too high will increase the solution space and reduce the solution efficiency. Based on the classical genetic algorithm, the quantum genetic algorithm (QGA) applies the advantages of quantum superposition states in the quantum algorithm which can be calculated in parallel to accelerate the solution process to the population coding mode and the evolution strategy, and further improves the original solution efficiency and global optimization ability of the genetic algorithm. Moreover, it can make up for the defects of a solution space that is too large and difficult solutions caused by the randomness of chromosome combination crossover and mutation, and it has shown good performance. It can quickly obtain the global optimal solution in a small population size.  
Therefore, we combine quantum machine learning with traditional algorithms, and propose a service migration optimization algorithm based on fuzzy logic (FL) and QGA. Firstly, the basic steps of the proposed algorithm are described in detail, and the pseudo code of the algorithm is given.
## 2 Challenges and Routing in 6G Networks
### Challenges of Routing
#### 2.1.5 Three-Dimensional Networking  
The biggest vision of 6G networks is to provide timeless and spaceless communication. In order to provide communication independent of time and space, many areas of communication must be integrated. It is aimed that 6G networks will cover all of the following areas: space, air, land, and even underwater. Satellites, unmanned aerial vehicles (UAVs), drones, and high-altitude platform stations (HAPSs) will play a crucial role in 6G networks [62]. Owing to the integration of ground, air, space, and underwater areas and 6G networks, this should support communication in three-dimensional space. New methods are needed to combine these areas with different needs into a single platform.
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 4 Materials and Methods
### Ontology-Based Stock Selection
#### 4.2.4 Stock Classification  
With the developed ontology, stocks are sorted into performance categories according to their financial profiles. Classifications are filtered using parameters like dynamically adjusted _min_threshold_ and _min_support_, calculated from quartile ranges of financial metrics, which guarantees strong groupings while adapting to changing market conditions.  
The determination of the _min_threshold_ is achieved dynamically by utilizing quartile ranges of financial metrics, allowing the classification to adapt according to data distribution and market conditions. For example, companies that rank in the top quartiles for liquidity and profitability while managing debt effectively are given priority for further analysis. This method ensures stocks with high relative performance criteria remain prioritized, keeping step with current market dynamics and shifts in financial data distributions.  
Utilizing dynamic thresholds based on quartiles is consistent with advanced machine learning practices and graph-based classification techniques. These data-driven thresholds strike a balance between sensitivity and specificity by leveraging distributions within the dataset. In financial ontologies, this approach improves adaptability and robustness, ensuring that the classification process stays responsive to real-time financial data. This method is supported by previous studies on adaptive and hierarchical frameworks for asset selection.  
The _min_support_ parameter is adaptively set according to the data distribution within the lattice graph structure. By utilizing quartile-based calculations, this framework ensures that only stocks with statistically significant support across financial metrics are selected. This approach reduces overfitting risk while preserving meaningful patterns in the data.
# A Systematic Analysis of Narrowband IoT Quality of Service
## 4 Results
### PHY Measurements: Coupling Loss
As such, the MCL closely matched the 3 GPP specification [4]. Overall, the RSRP and RSSI measurements behaved as expected. There was a slight reduction from the 3 GPP specified MCL of 164 dB, which could be attributed to external interference. The MNO could compensate this by configuring more ECL repetitions, which in turn would increase the latency.
# Invariant Feature Encoding for Contact Handprints Using Delaunay Triangulated Graph
## 2 Related Work
### Palm Prints  
Fingerprints are not the only source of identification information used in forensics, and palm prints have also been used for a long time. They are often left on surfaces at a crime scene as latent and transferred as images. The matching of palm prints is more complicated because palms are larger in area, and they have larger numbers of minutiae obtained within smaller regions. Hence, an automated system for storing and comparing large numbers of palm prints has not been pursued in the same manner as for fingerprints. Lately, palm images have also been used in biometric identification for other purposes, and research has been conducted into their processing, alignment, feature extraction, and matching methods [22]. Fingerprint processing methods cannot be directly used with palm images, and they need modifications to address additional challenges.  
Existing feature extraction methods in palm prints are categorized as structural-based, statistical-based, sub-space-based, and texture-based approaches. Structure-based methods use minutiae points, ridges, singular points, and creases/lines as prominent attributes, while statistical-based methods use entropy, energy, mean, and variance features. Subspace-based methods use linear discriminant analysis, independent component analysis, and principal component analysis. Texture- and transform-based methods adopt Fourier transform, wavelet transform, and Gabor wavelet features [23]. The limitation of this approach is its computational cost.  
The benefits of statistical-based feature extraction are its lower sensitivity to noise and simple process for feature extraction. However, this approach loses a lot of structural discriminative information. Therefore, the structural approach is mostly preferred over statistical methods [24]. Existing palm print feature representations primarily use minutiae, ridges, and creases, followed by fusion of features or scores using palm segments [24]. Matching of image segments is achieved as full- vs. full-print matches, partial- vs. full-print matches, and partial- vs. latent-print matches.
## 4 Experiments and Analyses
### Signal Processing Based on Wavelet Packet Transform  
The received signal always contains a lot of noise and interference. Preprocessing is required before the feature extraction. A trap filter was used to filter the noise of 50 Hz power frequency. An ideal band-pass filter was used to reduce the low-frequency and high-frequency interference. The cutoff frequencies were 50 kHz and 300 kHz, which contain the fundamental frequency, second-order and third-order harmonics' components. The filtered signal was decomposed into a 32-frequency band through a 5-layer WPT with a db8 wavelet packet. The wavelet packet energy of each frequency band was calculated for the decomposed signal. The wavelet packet energy of undamaged pipe is shown in Figure 8.  
The sampling rate is 20 MHz, and the Nyquist sampling frequency is 10 MHz. Each frequency band contains the information of \(10/32=0.3125\) MHz. From Figure 8, the wavelet packet energy of the first frequency band (0-312.5 kHz) is much higher than the other frequency bands. The wavelet packet energy of the first frequency band accounts for 95.79% of the total wavelet packet energy. The main information of a signal is contained in the first frequency band. Therefore, the wavelet packet decomposition coefficient that contains the feature frequency components is used to further the analysis.
## 4 Full State Reconstruction Using the Ae-Dmdc Reduced-Order Model
### Reduced-Order Observer  
The main idea of the reduced order observer is to prevent redundant estimates and reduce model-dependency of the state estimation scheme [1; 2]. For this purpose, the measurements (or parts of them) are directly included unfiltered in the estimator, which is only possible if no significant sensor noise is at place. To introduce the approach, formally consider the state transformation  
\[\mathbf{\zeta}=V\mathbf{z},\quad V=\begin{bmatrix}\tilde{\epsilon}^{\mathrm{T}}\\ T\end{bmatrix}\]  
Figure 6: Output approximation error for the training data for the two different sensor locations \(s_{m}\).  
with \(T\) such that \(V\) is invertible, i.e., \(rank(T)=r_{2}-1\) (as there is only 1 measurement) and \(T\epsilon=0\), meaning that the rows of \(T\) corresponds to basis vectors of the orthogonal complement of the space spanned by the row vector \(\mathbf{c}^{\mathrm{T}}\) (i.e., the measurement subspace). It should be noted that there exist effective numerical algorithms for determining the matrix \(T\), e.g., using null in Matlab.  
Next, consider the partition  
\[\mathbf{\zeta}=\begin{bmatrix}\xi_{m}\\ \xi_{r}\end{bmatrix},\quad\overline{A}=V\bar{A}V^{-1}=\begin{bmatrix}\overline {A}_{m}&\overline{A}_{m\prime}\\ \overline{A}_{r}&\overline{A}_{r}\end{bmatrix},\quad\overline{\mathbf{b}}=V\bar{ \mathbf{b}}=\begin{bmatrix}\overline{\mathbf{b}}_{m}\\ \overline{\mathbf{b}}_{r}\end{bmatrix}\]  
and the associated reduced order observer  
\[\hat{\xi}_{r}(t+1) =\overline{A}_{rm}y(t)+\overline{A}_{r}\hat{\xi}_{r}(t)+\overline {\mathbf{b}}_{r}u(t) \tag{6a}\] \[\hat{\mathbf{z}}(t) =V^{-1}\begin{bmatrix}y(t)\\ \hat{\xi}_{r}(t)\end{bmatrix}\] (6b) \[\hat{\mathbf{x}}(t) =\mathbf{\eta}_{2}(W_{2}\hat{\mathbf{z}}(t)+\mathbf{b}_{2}). \tag{6c}\]  
It is straight forward to verify that the associated observation error \(\tilde{\mathbf{z}}=\hat{\mathbf{z}}-\mathbf{z}\) exponentially converges to zero with time if and only if all eigenvalues of the matrix \(\overline{A}_{r}\) are contained in the open unit disk \(\mathbb{U}_{1}\subset\mathbb{C}\). If some eigenvalues are contained on the unit circle there are estimation error components (in \(\mathbf{\zeta}\) coordinates!) that will remain constant but not grow in time, i.e., for small initial errors, small error evolutions can still be achieved. Given that \(\mathbf{z}=\mathbf{0}\) is mapped onto \(\mathbf{x}=\mathbf{0}\) the convergence in the original state coordinates can be ensured within some accuracy limitation associated to the approximation performance of the autoencoder.
# LoRa Communication Using TVWS Frequencies: Range and Data Rate
## 6 Conclusions  
As LoRa hardware operating at different transmission frequency ranges is available, LoRa technology can also use TVWS frequency bands other than their conventional ISM bands. This enables LPWAN applications in the licensed TVWS bands, avoiding interference and coexistence issues in the high-density ISM band. In this study, we propose a path-loss model for TVWS-based LoRa transmissions and evaluate the communication range and data rate for different scenarios such as free space, outdoor (both urban and rural), and indoor. The range achieved with LoRa increases as the transmissions shift to the lower TVWS frequency ranges compared to the ISM band as per the configuration of \(SF\) and \(BW\). The proposed path-loss model also fares better in terms of uplink delivery rate when compared with the conventional model and other models proposed in the literature. This study can thus provide an estimate to the users about the expected data rate, coverage, and applicable areas for LoRa deployments. Characterizing the radio channel using the proposed path-loss model would be beneficial for designing TVWS-based LoRa networks. Other parameters, such as signal-to-noise ratio, received signal strength indication, and packet delivery rate, can also be investigated for further analysis. Although the approach offers several advantages, a rigorous evaluation of the hardware requirement, the transition and adoption challenges and the cost effectiveness of the same against the backdrop of application diversity is essential. Experimental evaluation of all these parameters can additionally be carried out to support the findings. This approach can further explore precise indoor and outdoor applications with longer-range requirements, such as remote monitoring, asset tracking, and others. The outputs of this study can be useful for effectively utilizing white spaces and the future development of TVWS-based LoRa LPWANs.  
All the authors have contributed equally towards the conceptualization, methodology, analysis, investigation, and resources. The original draft and changes were also performed by all the authors. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2023R97), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2023R97), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. In addition, the authors would like to acknowledge Dr. [PERSON] MIT World Peace University, Pune, India and Deanship of Scientific Research, Taif University, Taif, Saudi Arabia, for funding this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Assessment of _Bacillus pumilus_ Isolated from Fresh Water Milieu for Bioflocculant Production
## 2 Materials and Methods
### Optimization of Culture Conditions for Biofloculant Production
#### 2.5.2 Effect of Nitrogen Sources on Biofloculant Production  
For optimization of the effect of nitrogen sources on production of biofloculant, individual sources of nitrogen (1.2 g/L) were incorporated into production medium contained in separate flasks. The nitrogen source candidates included the organic nitrogen sources such as yeast extract, urea, peptone and tryptone; inorganic nitrogen sources such as ammonium sulfate, and also a mixed nitrogen source composed of yeast extract, ammonium sulfate, and urea. Flocculating activity was determined as previously described.
# Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions
## 2 Materials and Methods
### AWS Measurements  
The AWS measured the land surface temperature (LST) and soil moisture (SM) for the purpose of validation. The surface temperature from AWS was measured using a thermal infrared thermometer. The soil moisture was measured using a soil moisture probe buried underground. The AWS observations were averaged to a frequency of half an hour. The validation data obtained at eight weather stations from 2016 to 2019 were used in this study, namely: Arou, Daman, Dashanglong, Huazhazi, Hunhelin, Zhangye, Yakou, and Sidaoqiao [27; 28]. Detailed information regarding the seven sites is presented in Table 1.
## 3 Results
### Magnetic Properties of the Atomized Fe-N Powders  
Figure 7b-d show the hysteresis loops of the atomized powders prepared at an ejection temperature of 1588 \({}^{\circ}\)C, an atomization pressure of 20 bar, an ejection nozzle diameter of 100 \(\upmu\)m, and different ejection pressures (between 1 and 3 bars). In order to compare the magnetic properties of the Fe-N powders atomized in nitrogen with those of some Fe powders, a reference sample of the Fe powders was atomized in argon gas, both for ejection and for atomization. Figure 7a shows the hysteresis loops corresponding to the reference powders.  
The Fe reference powders (atomized in argon) showed a saturation magnetization of Ms = 214.5 emu/g and a coercivity of Hc = 29 Oe. The saturation magnetization is lower than the theoretical value of 218 emu/g, most likely due to the superficial oxidation of the powders, although the XRD investigations did not reveal the existence of oxides. Both the coercivity and saturation magnetization of the Fe-N powders are superior to those of the Fe reference powders, and they increased with increasing ejection pressure, as well as with increasing Fe-N phase content. Since the \(\gamma\)-F\({}_{4}\)N phase has a lower saturation magnetization than Fe (Ms = 188 emu g [46]) and the \(\alpha^{\prime}\)-Fe\({}_{8}\)N phase has a saturation magnetization comparable to that of Fe alone (Ms = 220 emu/g [47]), the phase responsible for increasing the saturation magnetization of the powders is the \(\alpha^{\prime\prime}\)-Fe\({}_{16}\)N\({}_{2}\) phase. Thus, the powders obtained at an ejection pressure of 1 bar, with a \(\alpha^{\prime\prime}\)-Fe\({}_{16}\)N\({}_{2}\) phase content of 14.3%, show a 9.2 emu/g increase in saturation magnetization compared to the Fe powders, and the coercivity also significantly increases from 29 Oe to 568 Oe. For the powders prepared at 2 bar, for which the phase content was increased to 22.1% (according to Table 1), the saturation magnetization increased to 232.3 emu/g, and the coercivity increased to 675 Oe. However, for the powders obtained at 3 bar, the saturation magnetization decreased to 229.4 emu/g due to the significant increase in the \(\gamma\)-F\({}_{4}\)N phase content. To overcome this impediment, after maintaining a pressure of 3 bar above the melt for 5 min before atomization, the pressure was reduced, and ejection was performed at 1 bar. This approach allowed for the preparation of Fe-N powders with a size of about 30 \(\upmu\)m, an increased \(\alpha^{\prime\prime}\)-Fe\({}_{16}\)N\({}_{2}\) content of 31% (without the \(\gamma\)-F\({}_{4}\)N phase), and a superior saturation magnetization (Ms) of about 234.8 emu/g and a coercivity (Hc) of about 970 Oe.
## 2 Materials and Methods
### Metabolic Activity Analysis  
In order to evaluate the metabolic population during the biodegradation process, expressed as the redox potential of microbial cells, the method of flow cytometry was used. The bacteria samples were taken after 24 h and 168 h of biodegradation, and stained using BacLight(tm) RedoxSensor(tm) Green Vitality Kit (Thermo Fisher Scientific, Waltham, MA, USA). The negative control sample consisted of metabolically inactive, thermally inactivated dead bacteria cells. The analysis protocol was based on the manufacturer instructions, and described in detail in our previous publication [26]. The percentage of the population with high and low metabolic activity was calculated by gating the dot plots of the median fluorescein isothiocyanate fluorescence intensity signals (FITC-A) versus the median signals of the side scatter parameter (SSC-A). The following groups were set: metabolically inactive cells (Q1), metabolically active cells (Q2), and artifacts (Q3 and Q4).
## 3 Application Areas of IoT and UAV in Smart Farming
### Controlling  
In IoT based smart agriculture, controlling is the result of active monitoring in an automated system, where the monitored variables are automatically adjusted to predefined thresholds. Forecasting can also play an important role in controlling. For instance, in smart irrigation systems, irrigation is activated by forecasting drought conditions, hence yield losses can be reduced. Weather forecasts in combination with real-time sensing of soil moisture and soil temperature has been used to control a fully autonomous irrigation scheme in [54]. Sensors installed in tractors and UAVs can also be used to control variable rate of nutrition, fertilisation and site specific weed control technologies [5].
## 4 Holistic Trash Collection System Architecture
### Enhancing QR Code by Integrating Additional Information
#### 4.3.1 QR-Embedded GPS Data to Optimize Waste Collection and Cloud-Based Analytics  
Our system additionally suggests incorporating both the GPS coordinates and a unique ID number into each garbage bin's QR code. This dual inclusion serves as a fail-safe measure, ensuring that even if the QR code becomes damaged or unreadable, the bin's precise location can still be determined using the user's mobile app coordinates. For instance, in the event that the QR code on a garbage bin is compromised, users can resort to their mobile app to access the bin's location by utilizing the GPS coordinates from their own mobile device when in proximity to the bin.  
Waste management personnel can optimize garbage collection routes and schedules by leveraging GPS data acquired from both the bins and sensors. Through a thorough analysis of this GPS data, they can make informed decisions about the most efficient collection routes, resulting in fuel savings and a reduction in greenhouse gas emissions. Furthermore, the data collected by the cloud-based analytics system can be seamlessly integrated with the GPS information obtained from the bins and sensors. This integration offers a holistic view of the waste management system, encompassing vital details such as the precise location and fill level of each bin, the efficiency of garbage collection routes, and the overall impact of waste reduction initiatives.