esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation Procedure
### Implementation of Proposed Model
#### 4.2.1 Database Fragmentation  
The data fragmentation process in DDBMS involves dividing the original dataset into smaller, manageable fragments that can be distributed across multiple nodes. This process is crucial for improving query performance and resource utilization in a distributed environment. The proposed RFO-SVM algorithm enhances this process by optimizing the fragmentation based on various criteria.  
Classification and Fragmentation  
The classified data are divided into fragments. Each fragment contains a subset of the original data, categorized based on a set of predicates (\(\psi\)). These predicates represent conditions or rules used to determine the attributes included in each fragment.  
Predicates: For numerical attributes, predicates can represent conditions such as greater than (bi > value1), less than (bi < value2), or equal to (bi = value3). For categorical attributes, a predicate might represent a specific category (bi = \(\alpha\)).  
Role of Attributes  
Attributes: Attributes (XX1...Xn) are the specific data fields within the dataset. Each attribute is evaluated against the predicates to determine its inclusion in a fragment.  
States: Numerical attributes are assigned states based on the predicates. For example, an attribute might be included in a fragment if its value is greater than a specified threshold.  
Matrices and Frequency Analysis  
Query Frequency Matrix (Qfm): This matrix records the frequency of queries (QS) accessing each attribute. The frequency data are collected from multiple sites, with each site having its own query frequency (QRf, Qlf).  
Attribute Frequency Accumulation (AFAC): Using the query frequencies, the AFAC matrix accumulates the access frequencies of attributes across all queries and sites.  
Distance Costs Matrix (DCM): The Communication Cost Matrix (CCM) is transformed into DCM using a Genetic Algorithm. DCM represents the distance or cost associated with accessing data across different nodes.  
Total Frequencies of Attributes Predicate Matrix (TFAM): By multiplying DCM with AFAC, the TFAM is obtained. The TFAM calculates the overall access costs for each attribute.  
The fragmentation cost model uses the matrices to determine the optimal fragmentation scheme. Attributes are sorted based on their total access costs (TFAM), and the attribute with the highest access cost is selected as the Candidate Attribute (CA) for fragmentation.  
The fragmentation process is then executed based on the CA, ensuring that the data are efficiently divided and allocated across nodes.
# AI-Enabled Compressive Spectrum Classification for Wideband Radios
## 4 Conclusions  
This paper presents a novel algorithm for spectrum characterization in WB radios. The WB spectrum is made up of various NB signals that can be either licit or blocked by a stealthy jammer. The jammer is equipped with the feature detection capability of CR, and, therefore, exploits the optimum pulse-modulated jamming signals to disrupt the communications of targeted licit NB signals. In order to decrease the A/D process complexity, compressive sensing is used to reconstruct the WB signal from the samples acquired at sub-Nyquist rates. Then, in each occupied sub-band, the signals are sent to the CFD detector that computed the SCF oand corresponding \(f\) and \(\alpha\) profiles are extracted and concatenated to form a features vector. The feature vectors are then used to train and test the ANN-based classifier that classified the signals available in each occupied sub-bands either as normal communications signals with particular modulation types or jammed by the stealthy jammer. Moreover, the testing of the ANN using independent test signals revealed that it functioned effectively at low SNRs and high compression settings. The algorithm guaranteed both jammer detection and signal classification rates of 1 at SNRs as low as \(-5\) dB with a compression ratio of 0.50. It is shown that the ANN-based technique greatly increased the classification performances in comparison to plain database-matching methods. Further, it is also shown that our technique outperformed the recently developed feature-based jammer detection methods.  
Our future work will include the collection of a new dataset using software-defined radios and exploring deep learning for anomaly detection and signal classification.  
Conceptualization, T.N.; methodology, T.N.; software, T.N. and R.N.; validation, T.N.; formal analysis, T.N. and R.N.; investigation, T.N. and R.N.; resources, T.N.; data curation, T.N. and R.N.; writing--original draft preparation, T.N. and R.N.; writing--review and editing, T.N. and R.N.; visualization, T.N. and R.N.; supervision, T.N.; project administration, T.N.; funding acquisition, T.N. and R.N. All authors have read and agreed to the published version of the manuscript.  
This work has been carried out under the financial support of grant GRANT2942 of the Deanship of Scientific Research (DSR), Vice Presidency of Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors gratefully recognize the Deanship of Scientific Research (DSR) at King Faisal University for financial assistance provided through grant GRANT2942.  
The authors declare no conflict of interest.
## 2 Delay-Free Phase Noise QRNG Scheme
### Experimental Setup  
We experimentally realized our scheme as Figure 1 shows above, and the setup is described as follows. Two distributed feedback (DFB) laser diodes emit continuous wave (CW) light with center wavelength at around 1550 nm. Specifically, the center wavelength of two DFB laser diodes are originally set at 1549.865 nm and 1549.858 nm respectively, which is 880 MHz separated from each other. The laser output power is set slightly higher than the threshold to obtain highest proportion of quantum noise [31; 34; 36], which is 1.3 mW in our setup. Intensity of signals from two laser diodes are carefully tuned by variable optical attenuator (VOA) to keep the intensity equal. After the signals interfere at a 50:50 beam splitter (BS), optical signals are detected by two homemade photo-detectors with AC coupling (measurement bandwidth 100 MHz). Electric signals are mixed by a frequency mixer, then the signal is filtered by a band-pass filter with 10-1000 MHz passband range to select the frequency component of phase noise, which is the appropriate frequency range in our scheme decided by the implementation. The electric signal after the filter is sampled by an analog-to-digital converter (ADC, ADS5400, sampling frequency 100 MHz, sampling precision 12 bits and input voltage range 1.5 V peak-to-peak). Finally, a field programmable gate array (FPGA, KC705 evaluation board) is adopted to realize randomness extraction and data precision adjustment. The sampling range is 1.5 V, however the peak-to-peak value of the noise signal is only 190 mV, hence there is approximately three unoccupied bits in raw data, which should be eliminated at the beginning in post-processing.  
As mentioned above, we set the difference of center wavelength at around 880 MHz (0.007 nm at 1550 nm wavelength), which is higher than the noise bandwidth from phase fluctuation of laser diodes in our implementation. Noticing that, the difference of center wavelength should not be too large, since the waveform after interfering at the beam splitter may not able to keep stability for heavily mismatched laser diode wavelength, as shown in Figure 2. On the other hand, frequency of fiber jitter noise is usually no greater than 10 MHz. By utilizing a band-pass filter with a 10-1000 MHz passband range at the output port of the mixer, one can substantially eliminate the influence of the fiber jitter term \(\Delta\varphi^{\text{fiber}}\), as well as the difference of center wavelength term \(2\Delta\omega t\). Hence the term of phase noise is distilled and used for further randomness extraction process.
# A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing
## 5 Simulation and Results
### Calculation of Makespan for DRFTSA Using Large Dataset by Using Fixed Number of VMs  
In this subsection, we present the generated makespan for DRFTSA using the large dataset by using fixed number of VMs and 700 to 1000 tasks. In this scenario, we have used a large dataset and a fixed number of VMs. We compared the proposed DRFTSA with state-of-the-art approaches, i.e., PSO, GA, and ACO. Generated makespan for PSO with 700, 800, 900, 1000 tasks are 784.27, 811.26, 943.78, 1048.86, respectively. Generated makespan for GA with 700, 800, 900, 1000 tasks are 767.96, 827.19, 958.14, 1127.36, respectively. Generated makespan for ACO with 700, 800, 900, 1000 tasks are 812.98, 853.36, 876.17, 1176.17, respectively. Generated makespan for DRFTSA with 700, 800, 900, 1000 tasks are 624.78, 645.87, 723.18, 767.99, respectively.  
From Table 6 and Figure 6, it is evident that DRFTSA minimized makespan compared to state-of-the-art approaches while running with a large dataset and using fixed numbers of VMs.  
Figure 5: Calculation of makespan for regular dataset by varying number of VMs.  
Figure 6: Calculation of makespan for large dataset using fixed number of VMs.
# Molecular Basis of Irisin Regulating the Effects of Exercise on Insulin Resistance
## 3 Irisin and Insulin Resistance
### Irisin, GLUT4, and Insulin Resistance  
Glucose transporter 4 (GLUT4) is a high-affinity insulin-responsive glucose transporter that is predominantly expressed in myocytes and adipocytes [55]. The failure of translocation of GLUT4 to the cell membrane in reaction to insulin is recognized as an early step in the development of IR, a pathological condition characterized by tissues showing less responsiveness or resistance to insulin [56]. In myoblast L6 cells, treatment with r-irisin increased the phosphorylation level of p38 MAPK, which consequently stimulated the translocation of GLUT4 towards the membrane of myocytes to promote glucose uptake inskeletal muscle [57]. Meanwhile, glucose uptake in L6 cells was increased in the presence of r-irisin, with the maximal rise occurring at a dose of 62 ng/mL [57]. So irisin can improve glucose uptake and IR by enhancing the intracellular trafficking and action of GLUT4. [PERSON] et al. [58] also found that exogenous irisin boosted glucose and fatty acid metabolism in human skeletal muscle cells, comparable to the insulin-induced response. In addition, r-irisin treatment up-regulated the expression of genes involved in glucose metabolism, such as GLUT4 and hexokinase 2, and the increment of gene expression coincided with enhanced phosphorylation of AMPK [58]. GLUT4 regulates insulin-induced glucose transportation within adipocytes and myocytes [55]. Following a meal, irisin promotes the translocation of GLUT4 from intracellular reserves to the cellular membranes, inhibiting gluconeogenesis in the liver and increasing glucose uptake in skeletal muscle and adipose tissue [56]. Additionally, the insulin-mediated glucose uptake of BAT after browning was 10 times that of WAT, and the expression of GLUT4 was up-regulated remarkably [59, 60]. However, whether this is the same as the increased GLUT4 translocation towards the cellular membrane observed in muscle cells remains unclear [46]. Nevertheless, it is clear that irisin can increase glucose uptake by enhancing the expression of GLUT4 in skeletal muscle and adipose tissue.
## 4 Simulation
### Discussion of Results  
In this section, the effect of a critical parameter in the proposed method on the identification results is analyzed through numerical simulations, and the appropriate parameter selection scheme is established. Further, the effects of source frequency and SNR on the source identification results are investigated, and the VG-SBL method proposed in this paper is compared with the OMP [55], FISTA [56], SBL [49], RPCA-SBL, and off-grid-GL\({}_{1}\)[13] methods. RPCA-SBL is a method that applies RPCA [50] to perform noise reduction preprocessing on SBL.  
Three monopole sources are set up with source locations at (0.36, \(-\)1.24) m, (0.54, \(-\)1.48) m, and (0.86, 1.6) m. The sound is generated as independent and identically distributed complex Gaussian, while 20 dB Gaussian white noise is added to the simulated signal. To quantitatively compare different algorithms, the root mean square error for location (RMSEL) and the root mean square error for magnitude (RMSEM) are used as metrics for evaluating the performance of source identification [43]. These two metrics are defined as:  
\[RMSEL=\sqrt{\frac{1}{S}\sum_{s=1}^{S}\left\|\dot{\mathbf{r}}_{s}^{identified}- \mathbf{r}_{s}^{actual}\right\|_{2}^{2}} \tag{28}\]  
\[RMSEM=\sqrt{\frac{1}{S}\sum_{s=1}^{S}\left(SPL_{s}^{identified}-SPL_{s}^{ actual}\right)^{2}} \tag{29}\]  
where \(\mathbf{r}_{s}^{identified}=[x_{s}^{identified},y_{s}^{identified},z_{s}^{ identified}]^{T}\) and \(\mathbf{r}_{s}^{actual}=[x_{s}^{actual},y_{s}^{actual},z_{s}^{actual}]^{T}\) are the identified location and the real location of the \(s\)-th sound source, respectively, \(SPL_{s}^{identified}\) and \(SPL_{s}^{actual}\) are the identified sound pressure level and the real sound pressure level of the \(s\)-th sound source, respectively, and \(S\) is the number of sources.  
The average results of multiple calculations are given to avoid random errors. If pseudo-sources occur, the first \(S\) sources with the highest amplitude are calculated.
## 1 Introduction
The SAE can reconstruct the upper part of the face in any pose without requiring face detection or splitting steps, overcoming limitations in face detection algorithms, and reducing preprocessing steps, thereby improving the accuracy of pain assessment. The SAFEPA model achieved 98.93% on 10-fold cross-validation and 84.06% on unseen subject data on the UNBC-McMaster dataset, Section 4.1. To the best of our knowledge, sparse autoencoders have not been used in this manner in relevant publications. * Extending the capabilities of the SAFEPA system to recognize seven facial expressions on data with different poses including full left and right profiles. Our results demonstrate SAFEPA's high accuracy in facial expressions recognition, achieving 94.29% on 10-fold cross-validation on the Karolinska Directed Emotional Faces dataset (KDEF) [15] and outperforming state-of-the-art models, highlighting SAFEPA's efficient performance with various head poses including full left/right profiles, Section 4.2. * Investigating the performance of the SAFEPA system to work in real-world situations on a new pain assessment dataset, measuring its accuracy and average processing time. We demonstrate that the SAFEPA system generalizes well, as evidenced by the performance on unseen BioVid Heat Pain datasets [16] processing each video in 17.82 s making the SAFEPA suitable for real-world situations, Section 4.3. The rest of this article is organized as follows. In Section 2, related works are discussed, while Section 3 provides a detailed description of the proposed framework, including the SAE structure and the entire system. Section 4 presents the results of three main experiments and compares them with existing methods in addition to an ablation study to show the positive impact of our autoencoder approach. Finally, Section 5 provides a conclusion and future works.
# Predicting Location of Tweets Using Machine Learning Approaches
## 4 Methodology
### Location Prediction Using Tweet Text and Named Entity  
In this experiment, we aimed to improve the location prediction performance by incorporating another feature: named entities. Named entities such as cities, airport names, and attractions were included in our feature set. We created a list of named entities in Saudi Arabia and, whenever we encountered one of these entities in the tweet text, it was  
Figure 1: Prediction of location for Twitter users flowchart.  
considered as a feature (see Section 4.1). Our assumption was that people from the same city are more likely to mention the name of their city or a famous attraction in their tweets. The same text features from the previous experiment were used in this experiment. We also added the named entities as an additional feature to assess its impact on the prediction performance. The results of this experiment were compared with the results of the previous experiment to determine the effectiveness of adding named entities as a feature.
# Hybrid Multi-Antenna Techniques for V2X Communications--Prototyping and Experimentation
###### Abstract  
The support of the connected vehicle-to-everything (V2X) vision in conjunction with intelligent transportation system applications and services constitute a major 5G objective for modern radio systems and networks. More particularly, 5G deployment will involve multiple radio access network (RAN) technologies and a massive machine-type communication environment, offering a simultaneously supported variety of broadcast, multicast, and unicast applications. In this article, we present an implementation of a diversity engine able to support the multi-objective, multi-RAN, multi-service V2X use cases. The engine is enhanced with the adoption of a hybrid diversity scheme that exploits the beamshaping capabilities of the reconfigurable electronically switched parasitic array radiator (ESPAR) antennas. The hybrid scheme combines conventional maximal ratio combining with beamspace diversity and it improves system performance in terms of reliability and throughput with increased signal-to-noise ratio. It was implemented and demonstrated with integration of novel printed antennas on connected, vehicle-to-vehicle (V2V)-enabled trucks in the context of the Horizon 2020 project ROADART.  
beamspace MIMO; low complexity hybrid diversity; V2V communications experiments; multiple antennas; prototyping; reconfigurable ESPAR antennas +
Footnote †: journal:  
Article
## 5 Discussion  
We discuss scattering details and polarization in this section. The former is to clarify the extinction mechanism in detail and the latter to evaluate performances as a VOA.  
Scattering is usually characterized into Rayleigh and Mie scattering by a size of objects that disturb wave propagation [12]. Rayleigh scattering occurs when the wavelength is even larger than the radius of the object. A Rayleigh-scattering light intensity is inversely proportional to the 4 th power of the wavelength. We cannot completely distinguish the transmission and scattering light fraction, as described in Section 4; however, the low transmission at 0 V may not include much fraction of the transmitted light. Figure 7 shows the extinction ratio dependence on the wavelength. Due to the availability of LDs, the number of plot points is limited to three but the data points are clearly on the -4 th power line. This suggests the extinction mechanism is related to the Rayleigh scattering. The Rayleigh scattering is one of the strong candidates for the explanation of extinction but further investigation is needed to make a judgment.  
We have some data related to polymer structures. It is not easy to characterize the shape and size of a chamber, which is surrounded by the polymer chain, although the chamber shapes and sizes seem dependent on the dopants. This fact is directly connected to the scattering mechanism. We added dyes to enhance the extinction by the absorption but the dyes changed the chamber characteristics as well to affect the light scattering other than the absorption.  
VOAs should not have a transmission dependence on the incident light polarization. Figure 8 shows the 0.635-\(\upmu\)m transmission dependence on the input light polarization. The PLCF does not have an absolute angle definition such as a rubbing angle so that the angle is a relative one. The sample was rotated in the same axis to the light propagation direction. The transmissions change only slightly for all the voltages between 0 and 48 V. The standard deviation of the transmission is lower than 0.1 dB for the  
Figure 6: Extinction ratio dependence on the solid angle. Wavelength: 1.523 \(\upmu\)m.  
LC voltage \(\mathrm{V_{LC}}\) of 20 V or higher. The worst deviation we measured is 7.6% or 0.32 dB, which was observed at 8 V. The PDL is so small that it could be applied to the device in the telecommunication system.
## 2 Optimal Design Consideration and Analytical Modeling  
### Operating Mechanism  
Figure 1 shows the overall schematic of the developed SAW-based NO\({}_{2}\) sensor and interface electronics. The system consists of two SAW devices with identical structure and performance, two oscillators, a mixer, a low-pass filter (LPF), a comparator, a field-programmable gate array (FPGA), and a PC. A microheater and temperature sensor are integrated around the sensor to maintain a stable temperature field. A two-port SAW delay line is formed on a 128\({}^{\circ}\) YX LiNbO\({}_{3}\) substrate. The reason for employing the two-port SAW delay line is to provide a larger sensing area between the two interdigital transducers (IDTs). The NO\({}_{2}\) sensing material (2D graphene/MoS\({}_{2}\)) is applied to the cavity region between the two IDTs.  
The adsorption of NO\({}_{2}\) molecules on the sensing material increases the hole concentration in the p-type MoS\({}_{2}\), leading to an increase in conductivity. Similarly, NO\({}_{2}\) molecules, acting as electron acceptors, enhance the conductivity of the p-type graphene sensing material. A built-in potential barrier is formed between the two monolayers due to the potential energy (P.E.) difference between the two materials, which creates a synergistic effect that promotes carrier separation without the immediate recombination of carriers. Specifically, electrons are transferred from MoS\({}_{2}\) to graphene, and holes are transferred from graphene to MoS\({}_{2}\), minimizing recombination and enhancing charge conductivity. The bonding between the two monolayers is Van der Waals bonding.  
Furthermore, the adsorption of NO\({}_{2}\) molecules induces mass loading on the sensing material, which reduces the propagation velocity of surface acoustic waves. This results in an amplified change in amplitude and a frequency downshift at the output IDT. By calibrating the frequency change with NO\({}_{2}\) concentration, the sensor provides measurable output information.  
Figure 1: Overall schematic of the microheater-integrated SAW sensor and interface electronics.
## 3 Data and Methods  
We used a combination of SAR Sentinel-1 [73], multispectral Sentinel-2 [74], and multispectral PlanetScope [75], covering the period from June to December 2023, together with the U-Net deep learning model, to map the land cover of the CAT. We compared the classification results of various combinations of imagery using the F1 score, which is a measure of the performance of a classification model by balancing precision and recall [76]. A flowchart summarizing the workflow is presented in Figure 3.