esa-sceva/satcom-chunk-collection huggingface dataset:
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 4 Discussion
### Challenges
#### 4.1.4 Environmental and Sensor Limitations  
AIoT-based solar energy monitoring and control systems depend heavily on sensor data for intelligent decision-making, yet environmental conditions and sensor limitations pose persistent challenges. Harsh weather--such as extreme temperatures, dust, humidity, and heavy rainfall--can impair sensor performance, resulting in inaccurate data and unreliable AI predictions. Fluctuations in solar irradiance due to cloud cover or shading further complicate real-time forecasting and energy optimization. Over time, sensor drift, calibration errors, and aging degrade measurement accuracy, necessitating regular maintenance and recalibration, which adds to operational costs. Sensor placement and quality are also critical, as poor positioning can lead to data inconsistencies that undermine AI-driven control strategies. Additionally, power limitations in wireless IoT sensors introduce trade-offs between sampling frequency and battery life, constraining real-time monitoring. To overcome these issues, future systems must adopt self-calibrating sensors, robust signal processing to handle noise and missing data, and energy-efficient sensing protocols. These advancements will improve the reliability, accuracy, and resilience of AIoT-based solar energy solutions.
## 3 Results
### Qualitative Image Analysis  
The results of the visual evaluation are shown in Figures 4 and 5 and Tables 5 and 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**(A) Overall Image Quality**} \\ \hline \hline \multicolumn{6}{c}{Reader 1} & \multicolumn{6}{c}{Reader 2} \\ \hline \hline score & Deeper U-net & RED-CNN & score & Deeper U-net & RED-CNN \\ \hline
1 & 1 & 41 & 1 & 2 & 42 \\ \hline
2 & 5 & 7 & 2 & 0 & 8 \\ \hline
3 & 24 & 2 & 3 & 22 & 0 \\ \hline
4 & 20 & 0 & 4 & 26 & 0 \\ \hline
5 & 0 & 0 & 5 & 0 & 0 \\ \hline \multicolumn{6}{c}{**(B) Noise and Artifacts**} \\ \hline \hline \multicolumn{6}{c}{Reader 1} & \multicolumn{6}{c}{Reader 2} \\ \hline \hline score & Deeper U-net & RED-CNN & score & Deeper U-net & RED-CNN \\ \hline
1 & 1 & 41 & 1 & 1 & 43 \\ \hline
2 & 7 & 8 & 2 & 3 & 7 \\ \hline
3 & 20 & 1 & 3 & 5 & 0 \\ \hline
4 & 21 & 0 & 4 & 39 & 0 \\ \hline
5 & 1 & 0 & 5 & 2 & 0 \\ \hline \end{tabular}
\end{table}
Table 5: Visual evaluation of entire CT image quality by the two readers using different deep learning algorithms.  
Figure 4: Visual evaluation of entire CT image quality by the two readers using different deep learning algorithms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**(A) Normal Local Lesions**} \\ \hline \hline \multicolumn{6}{c}{Reader 1} & \multicolumn{6}{c}{Reader 2} \\ \hline score & Deeper U-net & RED-CNN & score & Deeper U-net & RED-CNN \\ \hline
1 & 0 & 2 & 1 & 0 & 0 \\ \hline
2 & 1 & 21 & 2 & 3 & 42 \\ \hline
3 & 7 & 21 & 3 & 6 & 8 \\ \hline
4 & 12 & 6 & 4 & 31 & 0 \\ \hline
5 & 30 & 0 & 5 & 10 & 0 \\ \hline \multicolumn{6}{c}{**(B) Abnormal Lesions**} \\ \hline \hline \multicolumn{6}{c}{Reader 1} & \multicolumn{6}{c}{Reader 2} \\ \hline score & Deeper U-net & RED-CNN & score & Deeper U-net & RED-CNN \\ \hline
1 & 2 & 16 & 1 & 13 & 0 \\ \hline
2 & 6 & 30 & 2 & 66 & 5 \\ \hline
3 & 26 & 69 & 3 & 44 & 35 \\ \hline
4 & 56 & 9 & 4 & 0 & 68 \\ \hline
5 & 34 & 0 & 5 & 0 & 15 \\ \hline \end{tabular}
\end{table}
Table 6: Visual evaluation of normal local lesions and abnormal lesions by the two readers using different deep learning algorithms.  
Figure 5: Visual evaluation of normal local lesions and abnormal lesions by the two readers using different deep learning algorithms.  
The detectability of abnormal lesions with deeper U-net was significantly better than that with RED-CNN: 95.2% (118/124) vs. 62.9% (78/124) (\(p<0.0001\)) for reader 1 and 97.6% (121/124) vs. 36.3% (45/124) (\(p<0.0001\)) for reader 2. The number of FN with deeper U-net were six and three for readers 1 and 2, respectively. All these abnormal lesions were non-measurable peritoneal dissemination, which were identified as slight subtle soft-tissue infiltration and reticulonodular lesions on the original CT image. The representative images of the FN case are shown in Figure 6.
###### Abstract  
The low-efficiency rate of urban wastewater (UWW) treatment generates tons of discharged water with a high concentration of pollutants, pathogens and antibiotic-resistance genes (ARGs). Microalgal systems may be a green alternative to be implemented as a UWW polishing treatment. This study assessed the ability of _Chorella vulgaris_ and UWW autochronous microalgal species (AMS) to simultaneously remove PO4-P, and reduce the proliferation of cofilorms and ARGs. AMS seems to be more promising due to: (i) the higher specific growth rate, \(\mu_{\text{max}}\) (0.687 \(\pm\) 0.065 d\({}^{-1}\)); (ii) efficient PO4-P removal (92.62 \(\pm\) 0.10%); (iii) faster reduction of coliforms proliferation achieving concentrations below the limits of quantification (6 d); (iv) the reduction of _intl1_ and the ARGs _su1_ and _blat_EM abundance in ca. of 70.4%, 69.2%, and 75.7%, respectively (9 d); and (v) the additional reduction of these genes in ca. of 97.1%, 94.2%, and 99.9%, respectively, after 5 d storage in the dark and at room temperature. Results also revealed that the high pH values in both microalgal systems (due to microalgal growth) were highly correlated with a reduction in the proliferation of coliforms, including _Escherichia coli_. In conclusion, using AMS as a final polishing treatment of UWW seems to be very promising.  
w +
Footnote †: journal: Applied Sciences
## 2 Laboratory Experiments and Regression Analysis  
For the purpose of the research presented herein, a total of 100 granite samples were gathered from the Pahang Selangor Raw Water Tunnel (PSRWT) in Malaysia. Different rock index tests, i.e., the Schmidt hammer test (Rn), the dry density (DD) test, the point load test (Is\({}_{50}\)), as well as BTS were conducted in the laboratory, based on the ISRM standards [6]. In this research, a total of 80 datasets were provided in order to perform modeling and required analyses. Figure 1 summarizes the input and output datasets used. This figure also shows the relationship matrix between all data.  
Simple regression models were utilized for the purpose of identifying the relationship between the model inputs (Is\({}_{50}\), DD, and Rn) and the system output (BTS). A variety of equations, including power, linear, and exponential, were tested for the evaluation and selection of the most appropriate type of equation for the prediction of TS. The equations were evaluated with taking into consideration some prediction intervals (PIs) such as variance accounted for (VAF), root mean square error (RMSE), and R\({}^{2}\), which have been recommended by lots of researchers such as [24; 59; 61; 63]. In addition, the formulas in regard to such PIs were taken from [PERSON] et al.'s [64] research. It is noted that the best fit is the one with a VAF of 100%, RMSE of 0, and R\({}^{2}\) of 1. The equations used for the prediction of BTS, as well as their PIs, are listed in Figure 2. Selection of the equations was on the basis of their PIs results in comparison with other types of equations. For example, as can easily be noticed, R\({}^{2}\) was calculated as 0.698, 0.674, and 0.676, for Rn, Is\({}_{50}\), and DD, respectively. Figure 2 demonstrates the graphs of the equations that were developed for the TS prediction. The obtained results were found significant; however, aiming to achieve TS prediction results of a higher accuracy in real-life situations, it is necessary to develop a number of new models. Accordingly, three intelligent models, i.e., ABC integrated with ANN (ABC-ANN), IWO integrated with ANN (IWO-ANN), and ICA integrated with ANN (ICA-ANN) are constructed for the purpose of predicting the BTS of the granite type rock samples.  
Figure 1: General information related to data.
## 4 Use of Spectroscopic Techniques for Monitoring Changes in Muscle Foods during Processing
### Meat and Poultry Products
#### 4.2.2 Non-Thermal Treatments
The use of ultrasound intensity of 130 kHz was found to reduce the surface temperature of the chicken breast samples by 19.6%, to increase water absorption by 113%, and to produce more uniform cooling rate (around 22.3%) compared to untreated controls. The impact of ultrasound treatment combined with potassium alginate margination on chicken breasts meat samples was also assessed by fluorescence and NMR techniques in a recent study conducted by [PERSON] and others [237]. Changes in fluorescence intensity of tryptophan provided valuable information about the modifications in the environment of aromatic residues of proteins, while NMR spectroscopy provided further information about the water-binding characteristics of meat tissue and protein integrity, occurring under the different applied treatments. Several non-thermal processing techniques that rely on radiolysis (UV and irradiation), electric (e.g., pulsed electric field and high voltage arc discharge), ultrasound, HPP, and plasma treatments can produce radical species. The produced radical species can impact the quality of food and can be measured by electron spin resonance (ESR) [149; 238], although only few studies have been reported in this regard [239; 240; 241]. [PERSON] and coworkers [240] demonstrated the onset of lipid oxidation in meat after HPP treatment and radicals (generated from protein and iron) were found in the myofibrillar and sarcoplasmic protein fraction. The treatment intensity in terms of time, temperature, and HPP all can increase the formation of radicals in meat [239]. Research on free radicals' generation in other emerging technologies is needed to understand their subsequent effects on muscle foods. The effects of PEF on meat has not been widely studied yet. One of the few examples where spectroscopy has been used in this regard is the study conducted by [PERSON] and coauthors who used a colorimeter to investigate color changes after PEF treatment [242]. The authors concluded that the observed differences were not different from those caused by a water bath (at the same \(\Delta\)t). In the same study, also NMR was used to investigate water migration. In agreement with the literature, it has been observed that PEF does not affect \(T_{2}\) times, suggesting this non-thermal treatment does not induce modification in cell-membranes. Another recent NMR-based study has been proposed by [PERSON] and others [243] and it focused on the effects induced by low (2.5 kV, 50 Hz, 20 \(\upmu\)s) and high (10 kV, 50 Hz, 20 \(\upmu\)s) PEF on red deer meat. The results showed that the meat tenderness was improved by the high PEF treatments, while the NMR-based metabolomics showed no effect due to PEF treatment.
## 2 Materials and Methods  
### EQ Data  
We used the available data from the Global Centroid Moment Tensor project [89, 90] (GCMT). Currently, the data covers the global seismicity since the 1 st of January of 1976 until 31 st of January 2022. For the period January 1976 to end of December 2020, we used the 1976-2020 CMT catalog from [[https://www.ldeo.columbia.edu/~gcmt/projects/CMT/catalog/jan76_dec20.ndk](https://www.ldeo.columbia.edu/~gcmt/projects/CMT/catalog/jan76_dec20.ndk)]([https://www.ldeo.columbia.edu/~gcmt/projects/CMT/catalog/jan76_dec20.ndk](https://www.ldeo.columbia.edu/~gcmt/projects/CMT/catalog/jan76_dec20.ndk)) (accessed on 23 May 2022) address while for the period since the 1 st January 2021 to end of January 2022 we used the monthly CMT catalogs from [[https://www.ldeo.columbia.edu/~gcmt/projects/CMT/catalog/NEW_MONTHLY/](https://www.ldeo.columbia.edu/~gcmt/projects/CMT/catalog/NEW_MONTHLY/)]([https://www.ldeo.columbia.edu/~gcmt/projects/CMT/catalog/NEW_MONTHLY/](https://www.ldeo.columbia.edu/~gcmt/projects/CMT/catalog/NEW_MONTHLY/)) (accessed on 23 May 2022) (The home page for all catalogs is the [[http://www.globalcmt.org/CMTfiles.html](http://www.globalcmt.org/CMTfiles.html)]([http://www.globalcmt.org/CMTfiles.html](http://www.globalcmt.org/CMTfiles.html)) (accessed on 23 May 2022)). Following Reference [13], we considered only the EQs with (moment) magnitude \(M_{w}\) (or simply \(M\)) greater or equal than 5.0, i.e., \(M\geq 5.0\), whose epicenters are shown in Figure 1. A frequency-magnitude diagram, see, e.g., Figure 2, provides a means for assessing the approximate level of completeness of the catalog. The data for the whole period examined 1976-2022 suggests linearity of the slope of the frequency-magnitude relation down to a magnitude of 5.0 (Figure 2a), which is the same for the later period 2001-2022 (Figure 2c), while for the earlier period (1976-2000, Figure 2b), the data are more consistent with a break in the slope near \(M_{w}=5.3\). These results are compatible with those of [PERSON] et al. [90], who, when using the same catalog, found a completeness magnitude threshold of 5.0 for the period 2004-2010, while for the earlier period, they reported a break in the slope near \(M_{w}=5.3\) or 5.4, see their Figure 5.  
Figure 1: The map shows global seismicity with all EQs of \(M\geq 5.0\) as reported by GCMT during the period from 1 January 1976 to 31 January 2022 (see References [89, 90] and Section 2.1). The EQs with \(M\geq 8.5\) appear with yellow color, while for \(M<8.5\) with red color. ETOPO1 Global Relief Model [91] was used to integrate the land topography and ocean bathymers. This map was made using Generic Mapping Tools [92].
## 2 Background
### Tensor Operations
* **T-SVD-Based Tensor Nuclear Norm (T-SVD-TNN)**: The SVD of \(\mathcal{A}\) via TNN in the frequency domain and can be calculated as follows: \[\|\mathcal{A}\|_{\otimes}=\sum_{i=1}^{min(n_{1},n_{2})}\sum_{j=1}^{n_{3}}| \mathcal{A}_{f}(i,i,j)|,\] (7) where \(\mathcal{A}_{f}\) is the SVD of the tensor \(\mathcal{A}\) in the frequency domain. * **Tucker Decomposition**: Provides orthogonal subspace decomposition along each mode of the tensor \(\mathcal{A}\)[21]: \[\mathcal{A}\approx\mathcal{B}\times_{1}\mathbf{U}_{(1)}\times_{2}\mathbf{U}_ {(2)}\times_{3}\mathbf{U}_{(3)},\] (8) where \(\mathcal{B}\in\mathbb{R}^{r_{1}\times r_{2}\times r_{3}}\) is the core tensor, \(\{\mathbf{U}_{(i)}\in\mathbb{R}^{n_{i}\times r_{i}}\}_{i=1}^{3}\) are the low-dimensional orthogonal matrices and \(r_{i}\) is the rank of \(\{\mathbf{U}_{(i)}\}\) with \(r_{i}\leq n_{i}\). Tucker decomposition is solved through an iterative optimization procedure known as HOOI, which provides the best projection matrices [22].
###### Abstract  
The relationship between fixed orthodontic treatment and the development of temporomandibular disorders (TMDs) in adolescents has been a topic of considerable debate. This systematic review and meta-analysis aimed to evaluate the impact of fixed orthodontic treatment on the prevalence of TMDs in adolescents. A comprehensive literature search was conducted using PubMed, Web of Science, EMBASE, Google Scholar, and the Cochrane Library, yielding 886 records. After duplicate removal, 665 records were screened, and 8 studies were assessed for eligibility. Following quality assessment using the Joanna Briggs Institute checklist, 4 studies were included in the final analysis. Data were analyzed using a random-effects model in RevMan software. The meta-analysis revealed an overall odds ratio of 0.75 (95% CI: 0.37-1.51, \(p=0.42\)), indicating no statistically significant association between fixed orthodontic treatment and the risk of developing TMDs. Substantial heterogeneity was observed (I\({}^{2}\) = 73%), attributed to variations in study designs, populations, and outcome measures. The risk of bias analysis highlighted concerns in several domains, particularly selection bias and measurement of outcomes. While confounding bias and missing data bias were generally well-controlled, deviations in intervention and inconsistent outcome measurements were noted across the studies. These findings suggest that fixed orthodontic treatment does not significantly alter the risk of developing TMDs in adolescents. However, the substantial heterogeneity and potential biases across the included studies emphasize the need for further high-quality, standardized research to confirm these results and provide clearer clinical guidance.  
adolescent orthodontic treatment; temporomandibular disorders; pre-treatment evaluation; TMJ growth; risk factors; fixed appliances; TMDs management; extraction therapy; long-term outcomes
# Tilted Beam Fabry-Perot Antenna with Enhanced Gain and Broadband Low Backscattering
###### Abstract  
Communication with low radar signature platforms requires antennas with low backscatter, to uphold the low observability attribute of the platforms. In this work, we present the design for a Fabry-Perot (F-P) cavity antenna with low monostatic radar cross section (RCS) and enhanced gain. In addition, peak radiation is tilted inthe elevation plane. This is achieved by incorporating phase gradient metasurface (PGM) with absorptive frequency selective surface (FSS). The periodic surface of metallic square loops with lumped resistors forms the absorptive surface, placed on top of a partially reflecting surface (PRS) with an intervening air gap. The double-sided PRS consists of uniform metallic patches etched in a periodic fashion on its upper side. The bottom surface consists of variable-sized metallic patches, to realize phase gradient. The superstrate assembly is placed at about half free space wavelength above the patch antenna resonating at 6.6 GHz. The antenna's ground plane and PRS together construct the F-P cavity. A peak gain of 11.5 dBi is achieved at 13\({}^{\circ}\) tilt of the elevation plane. Wideband RCS reduction is achieved, spanning 5.6-16 GHz, for x- and y-polarizations of normally incident plane wave. The average RCS reduction is 13 dB. Simulation results with experimental verifications are presented.  
Keywords:phase gradient metasurface; tilted beam; monostatic radar cross section; Fabry-Perot (F-P) cavity antenna; RCS reduction +
Footnote †: journal: Electronics Letters
###### Abstract  
Biological control by beneficial microorganisms is known to significantly reduce the effect of pests on crops yield. Among the biocontrol strategies is the use of entomopathogenic fungi such as _Hirstuella citiriformis_, which has been applied to infect and kill hemipteran insect pests, including _Diaphorina citir_ Kuwayama (Hemiptera: Lividae) and _Bacterica cockrelli_ Sulc. (Hemiptera: Triozidae). These biological agents are applied in the form of conidia that are often combined with other inert materials to facilitate application, protect conidia, and improve their shelf life. The aim of this study was to implement strategies for developing formulations to increase conidia shelf life. We evaluated gum produced from one strain and conidia from two different _H. citiriformis_ strains. Conidia were formulated by evaluating different concentrations of _Acacia_ and _Hirstuella_ gums to enhance conidia viability during storage at 4 \({}^{\circ}\)C or 25 \({}^{\circ}\)C. Results indicated that formulations maintained conidia viability for at least 90 d after storage at 25 \({}^{\circ}\)C (\(\geq\)70% viability) and at least 120 d after storage at 4 \({}^{\circ}\)C, which was significantly (\(p\leq 0.05\)) higher than that of the control, without observing changes in pH values. We also demonstrated 100% formulation purity from days 0 to 120, among all treatments. In conclusion, evaluated formulations maintained _H. citiriformis_ conidia viability for at least three months, when stored at 4 \({}^{\circ}\)C.  
biocontrol; _Acacia_ gum; _Hirstuella_ gum; entomopathogenic fungi; bioinsectides; shelf life; conidia formulation
# A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
## 3 Problem Description
### Assumptions and Constraints
#### 3.2.2 Constraints  
Task Switching Time Constraint  
Due to the strict requirements relating to the attitude of the satellite when the observation task is executed, a certain time interval must be met between two adjacent observation tasks.  
Energy Constraint  
Energy is a renewable resource, but the total battery capacity is limited, so the energy level cannot be higher than the upper limit of the capacity at any time, nor can it be lower than the lower limit of the capacity. The energy level drops when an observation task or a transmission task is performed, and the energy level rises while charging.  
Data Storage Constraint  
Storage constraints are similar to energy constraints. Due to storage capacity limitations, the total amount of data cannot be higher than the upper limit of the capacity at any time, nor can it be lower than the lower limit of the capacity. The difference is that the total amount of data increases when the observation task is performed, and the total amount of data decreases when the transmission task is executed.
# Efficient and Robust Arabic Automotive Speech Command Recognition System
## 3 Methodology
### Dataset Building
#### 3.1.2 Final Dataset Description  
The MASCD dataset was created to develop a robust SR system capable of recog-nizing in-car voice commands in the Moroccan Arabic language. The final dataset consists of 20 classes and 5600 labeled audios. Each audio file is around 2 s in length, containing a single command, sampled at 16 kHz, 16 bit per sample, and in mono channel. Each command was recorded 10 times by 28 contributors. Consequently, we have a total of 280 audio files for each command, resulting in 5600 audio files in total (28 contributors \(\times\) 10 repetitions \(\times\) 20 commands = 5600). The entire process of data collection, processing, labeling, and organizing took us \(\sim\)54 days. The dataset size is \(\sim\)340 MB. The MASCD dataset is publicly available online for research purposes [18].
## 3 Results and Discussion
### Granular Activated Carbon  
#### 3.2.1 GAC Behaviour (Adsorption Determination)  
With the lowest concentration of both GACs, 0.1 g L\({}^{-1}\), 15% of the indigo carmine was adsorbed after 24 h. The capacity to adsorb the model compound increased as the GAC concentration increased. In 9 h, adding 1 g L\({}^{-1}\) of GAC, more than 65% of the indigo carmine was removed when CarboTech DGF 8x30 GL was used, and almost 60% was achieved using NRS Carbon 0.5-2.5. Up to 80% was achieved with both GACs in less than 24 h, being attained approximately after 13 h.  
The behaviours of the two GACs were similar (Figure 5), and the trend for each concentration was analogous. A slight difference between the two materials was observed in favour of fresh GAC; several authors have reported a similar efficiency in fresh and regenerated GAC in the literature [48].