esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Nowadays, in the context of the industrial revolution 4.0, considerable volumes of data are being generated continuously from intelligent sensors and connected objects. The proper understanding and use of these amounts of data are crucial levers of performance and innovation. Machine learning is the technology that allows the full potential of big datasets to be exploited. As a branch of artificial intelligence, it enables us to discover patterns and make predictions from data based on statistics, data mining, and predictive analysis. The key goal of this study was to use machine learning approaches to forecast the hourly power produced by photovoltaic panels. A comparison analysis of various predictive models including elastic net, support vector regression, random forest, and Bayesian regularized neural networks was carried out to identify the models providing the best predicting results. The principal components analysis used to reduce the dimensionality of the input data revealed six main factor components that could explain up to 91.95% of the variation in all variables. Finally, performance metrics demonstrated that Bayesian regularized neural networks achieved the best results, giving an accuracy of R\({}^{2}\) = 99.99% and RMSE = 0.002 kW.  
artificial intelligence; machine learning; solar energy; forecasting; photovoltaic power Article  
Approaches for Photovoltaic Power Prediction:  
A Comparative Study  
on different machine learning techniques for hourly PV power prediction. Moreover, since several factors, namely climatic variables, can affect solar PV output power and add complexity to the prediction process, a principal component analysis (PCA) was conducted to decrease the number of interconnected variables into a smaller number of dominating factors. The prevailing factors were then used as inputs for the predictive models. Finally, the accuracy of the proposed models was assessed using performance metrics, residual analysis, and a diagnostic approach, mainly the regression error characteristic (REC) curve. The main contributions of this study are the following:  
* This study enhances the ability of short-term PV power predictions thanks to the robust and competitive results obtained in terms of R2 and RMSE
* Our approach requires only open data freely available on the web, and anyone with technological skills may create their own customized version.
* The most relevant variables to PV power prediction are identified using PCA.
* Finally, investments in new PV installations will be encouraged thanks to the results of our comparison.
# Effect of Bentonite Content and Hydration Time on Mechanical Properties of Sand-Bentonite Mixture
## 2 Materials and Methods  
### Materials  
The standard silicon sand was used in this study. The standard sand has a uniform particle size grading and was classified as medium-coarse sand. By following the ASTM standards [34; 35; 36; 37], the d10, d30 and d60 of the tested sand were 0.328 mm, 0.468 mm and 0.752 mm, respectively. The C\({}_{\text{u}}\) and C\({}_{\text{c}}\) were 2.28 and 0.89, respectively. The minimum and maximum void ratios of the sand were 0.595 and 0.818, respectively. In the following study, the relative void ratio of the sand specimen was controlled with a constant value of 58% and a void ratio of 0.736.  
The bentonite used in this study was sodium bentonite and calcium bentonite shown in Figure 1. Sodium bentonite and calcium bentonite have a cation of montmorillonite crystal nucleus of Na\({}^{+}\) and Ca\({}^{2+}\), respectively. The calcium bentonite has a dense structure and a large hydrophilic surface. It has a fast water absorption rate and less overall water absorption amount. Thus, it can reach the hydration balance of montmorillonite in a short time. On the other hand, sodium bentonite has a finer structure and a smaller crystal surface area resulting in a lower water absorption rate. The required water absorption is greater than that required for calcium bentonite due to the larger silicon oxide mass ratio. Thus, it requires a longer hydration time and hydration equilibrium time compared to calcium bentonite. Eighty-three percent of the bentonite particles were smaller than 0.08 mm, belonging to high plasticity fine particles with a plasticity index of 324 [38].
## 2 Specimen Preparation and Testing
### Testing System  
The experimental instruments under conditions of uniaxial compression mainly include a Shimadzu I-250 (Japan) testing machine, a PCI-2 acoustic emission monitoring system and a video camera, as shown in Figure 3. The Shimadzu I-250 (Japan) testing machine was used to carry out the uniaxial compression test of sandstone specimens containing en echelon flaws. The loading method and loading speed were displacement control and 0.05 mm/min, respectively. The acoustic emission  
Figure 2: Diagram of selected samples: (**a**) material objects; (**b**) detailed geometries (distance unit: mm).  
signal was preamplified (40 dB) and recorded with a PCI-2 acoustic emission monitoring system (DISP from American Physical Acoustics Company). The threshold for detection was chosen as the signal of an empty experiment (45 dB). To confirm the initiation, propagation and coalescence of cracks between en echelon flaws, a video camera was used throughout the entire period of deformation. Before the experiment, six piezoelectric sensors were fixed on the surface of the specimen, and their positions are shown in Figure 2b. Every piezoelectric sensor was coated with a special coupling agent and fixed with adhesive. At the same time, we evenly coated a layer of butter on the ends of the specimen to reduce the interference of the end effect on the acoustic emission signals. Finally, the loading system, acoustic emission system and photographic system were simultaneously turned on to synchronously collect the mechanical and acoustic emission phenomena during compression failure of the specimen. After the uniaxial compression experiment was completed, the crack patterns of the failure specimens were reconstructed by CT scanning machine.  
To ensure that the mechanical properties of the selected sandstone samples are within a narrow statistical spread, the mechanical properties of three intact sandstone specimens were tested, as shown in Figure 4a. The averages of the uniaxial compressive strength (\(\sigma_{\mathrm{c}}\)), peak strain (\(\varepsilon_{\mathrm{c}}\)) and elastic modulus (E) of the three intact specimens were 58.28 MPa, 1.01% and 7.94 GPa, respectively, and the corresponding coefficients of variation (the ratio between the standard deviation and mean value) were 0.30%, 3.70% and 2.35%, respectively. Therefore, the variability of the mechanical parameters of the selected sandstone is considered small. The selected sandstone specimens can be used to study the mechanical properties, energy dissipation characteristics and acoustic emission parameters during compression failure of sandstone specimens containing en echelon flaws.
## 6 Conclusions  
In this paper, we have proposed an asynchronous formation control scheme based on reinforcement learning and leader-follower structure for multiple USVs in a complex maritime environment. First, a specific USV model and a novel formation shape model have been established, where the formation shape model provides the parameters for formation generation and maintenance. Second, the formation control policies have been proposed for the cooperative USVs to generate the predefined formation shape with minimum moving path while the decentralized formation maintenance policies have been presented to maintain the stability of the geometric formation structure by minimizing the stability error between the real-time relative distances and the expected relative distances for all the USVs. Finally, simulation results have demonstrated that the proposed scheme can generate the required formation shape and maintain the geometry structure of the formation effectively compare with other schemes. In future work, we will take the communication interruption and the disturbance of wind, wave, and current into consideration.  
Conceptualization, J.L. and S.X.; methodology, Y.P. and J.X.; validation, J.X.; formal analysis, H.P.; investigation, Y.L. and R.Z.; writing and editing, J.X.; funding acquisition, S.X. All authors have read and agreed to the published version of the manuscript.  
Figure 8: The mean distance between the leader and the team goal with the proposed scheme and different team sizes during formation generation policies training: (**a**) mean distance for the leader in the team of 3 USVs; (**b**) mean distance for the team of 5 USVs; (**c**) mean distance for the team of 7 USVs.  
Figure 9: The average stability difference with the different number of multiple cooperative USVs and different schemes.  
Funding: Research supported by the National Natural Science Foundation of China (grant no 61625304, 91748116 and 61922053), Project of Shanghai Municipal Science and Technology Commission (grant no 17 DZ1205000), and Shanghai Innovation Action Plan of Science and Technology, China (grant no 19 DZ1207305). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data sharing not applicable. Conflicts of Interest: We declare no conflict of interest.
# Security Implications for Ultra-Low Power Configurable SoC FPAA Embedded Systems
## 2 FPAAs as Physical Computation Devices  
FPAA devices are our vehicle for discussing ultra-low energy computing (Figure 2). FPAA devices allows the user to investigate many physical computing designs within a few weeks of time. The alternative for one design would require years of IC design by potentially multiple individuals. These FPAAs compare favorably against custom designs, and unlike FPGA designs, FPAA architectures are open to the academic community.  
The SoC FPAA [3] ecosystem (Figure 2) represents a device to system user configurable system. An SoC FPAA implemented a command-word acoustic classifier utilized hand-tuned weights demonstrating command-word recognition in less than 23 \(\upmu\)W power utilizing standard digital interfaces (Figure 2) [3]. Multiple analog signal processing functions are a factor of 1000\(\times\) more efficient than digital processing, such as Vector-Matrix Multiplication (VMM), frequency decomposition, adaptive filtering and classification (e.g., [3] and references within). Embedded classifiers have found initial success using this SoC FPAA device towards command-word recognition [3], and accoustic (and biomedical) sensor classification and learning (e.g., [7]) in 10-30 \(\upmu\)W average power consumption. The circuits compute from sensor to classified output in a single structure, handling all of the initial sensor processing and early stage signal processing. This ecosystem scales with newer ICs built to this standard, as expected by all future FPAA devices [8].  
Floating-Gate (FG) devices empower FPAA by providing a ubiquitous, small, dense, non-volatile memory element [9]. A single device can store a weight value, compute signal(s) with that weight value, and program or adapt that weight value, all in a single device available in standard CMOS (e.g., [3]). The circuit components involve FG programmed transconductance amplifiers and transistors (and similar components) with current sources programmable over six orders of magnitude in current  
Figure 2: The SoC large-scale Field Programmable Analog Array (FPAA) IC illustrating command-word speech recognition. We show the high-level block diagram of the SoC FPAA device (**left**), a typical measurement setup and computational block diagram for command-word speech recognition, and measured input and classifier output response classifying the word dark in the TIMIT database phrase. This analog computation (<23 \(\upmu\)W) is radically different than the class of expected analog operations.  
(and therefore time constant) [10]. Devices not used are programmed to require virtually zero power. FG devices enable programming around device mismatch characteristics, enabling each device in a batch of ICs to perform similarly.
## 6 Simulation Result and Analysis
### Deployment Process Simulation Based Transfer Learning  
To demonstrate the adaptability of our deployment mechanism based on transfer learning, we evaluate our method in the transfer learning setting to test the transfer mechanism proposed in Section 5.4. Specifically, the network environment is changed after the 10,000 th terminal request, including the quantity and distribution of terminals, their data transmission parameters, and channel quality.  
As shown in Figure 5, it takes 4000 times to train DRL-QoS-RA from the initial state to converge. As for the transfer stage, only 1000 times or even about 100 times of training are needed for adapting to the new environment with different distribution and to reach acceptable performance. Furthermore, Figure 5 shows the performance of a transferred neural network. Compared with DRL-QoS-RA trained from the initial state, the transferred one achieves an approximate effect on transmission success rate and power efficiency. To sum up, the transfer mechanism can effectively reduce the onboard training time and computing expense in system deployment.
## 3 Proposed Method
### Steps of the Proposed Method
#### 3.2.2 Sentence Feature Extraction  
To extract domain features from sentences, an autoencoder network is employed. The basic principle of the autoencoder is illustrated in Figure 3. An auto-encoder consists of encoders and decoders. Each sentence is encoded to a latent vector, and then the latent vector is decoded to reconstruct the input sentence. The autoencoder is trained for the reconstructed sentence to be consistent with the input. The latent vector is considered a sentence vector that expresses the domain features of the sentence. The output from this step is a list of sentence vectors. The sentence vector of sentence \(i\) is denoted by \(S_{i}\).
## 2 Materials and Methods
### Procedures
#### 2.3.1 Anthropometry and Maturity Status  
Body mass in kilograms was measured on a calibrated physician scale (SECA 799, Hamburg, Germany). Body height was recorded in centimeters on a measuring platform (SECA 799). Sitting height was measured in centimeters. Leg length was calculated as the difference between body height and sitting height. The maturity status (MAT) was then calculated in a non-invasive way using two regression equations (equation 1 for boys and equation 2 for girls), including measurements of age, body mass, body height, sitting height, and leg length taken during the first part of the testing sessions [33]. To take into account the error of approximately 6 months described for the prediction equation in a pediatric population [33], participants with a maturity offset of \(-0.99\) to \(-0.51\) years and \(+0.51\) to \(+0.99\) years were removed from this analysis [34]. Also, participants whose maturity offset was outside of \(-3\) or \(+3\) years were removed to maximize accuracy [34]. This approach allowed for the identification of 3 distinct maturity groups: pre-PHY (maturity offset \(<-1\)), circa-PHY (maturity offset between \(-0.5\) and \(+0.5\)), and post-PHY (maturity offset \(>+1\)).  
\[\begin{array}{l}\text{Boys}=-9.236+0.0002708\times\text{(Leg Length \times Sitting Height)}-0.001663\times\text{(Age \times Leg Length)}+0.007216\times\\ \text{(Age \times Sitting Height)}+0.02292\times\text{(Weight/(Height\times 100))}\end{array} \tag{1}\]  
\[\begin{array}{l}\text{Girls}=-9.376+0.0001882\times\text{(Leg Length \times Sitting Height)}+0.0022\times\text{(Age \times Leg Length)}+0.005841\times\\ \text{(Age \times Sitting Height)}-0.002658\times\text{(Age \times Weight)}+0.07693\times\text{(Weight/(Height\times 100))}\end{array} \tag{2}\]
# A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs
## 1 Introduction
These images obtained by enhancement are added to the original sample database to complete data augmentations, increasing the proportion of low-frequency traffic signs in the dataset, improving the equilibrium of sample distributions, and then improve the overall detection accuracy. (2) It's proposed that a new YOLOv3 architecture to better enable it to detect small targets. The detailed step is deleting the output feature map corresponding to the 32-times subsampling, adding the output feature map of the 4-times subsampling and concatenating it to the original network, which will more fit for small target features and will not reduce the detection effect of medium and big targets in the meanwhile. (3) In order to strengthen object detection and object tracking in real-time, the false detection and missed detection levels caused by the external environment must be reduced. Deep-Sort is applied to object detection, which uses Mahalanobis distance, the smallest cosine distance, and other indicators to associate various targets in the video frames. While stabilizing the actual video bounding box, the error rate and omission rate of video detection are effectively decreased, and the anti-interference performance of the detection algorithm is enhanced too. The remainder of this paper is organized as follows: Section 2 proposes the data enhancement method, detection framework, loss function, and multi-object tracking additionally. Section 3 presents the experimental results, and Section 4 concludes the paper.
## 4 Nanocomposite as Sensors
### Mesoporous-Silica-Cated-QD Composites  
The phrase \"mesoporous materials\" refers to solids based on either ordered or disordered networks with a broad or narrow distribution of pores within the range of 20 to 50 nm, which is good for the incorporation of QDs. These materials have the potential to be used in surface modification for both fluorescence sensing and electrochemical sensing [79; 80]. The porous structure of mesoporous silica is known for its large surface area and volume. These properties make it easy for the QDs to be embedded into the mesoporous silica for the formation of a nanocomposite with enhanced properties of QDs. There have been few reports on the development of mesoporous-silica-coated QDs for the detection of pollutants. For instance, a chiral nematic mesoporous-silica-encapsulated CdS film was used for the detection of trace TNT [81].
## 5 Challenges and Future Directions
### Safety and Privacy  
Further research should explore the use of UAVs in resilient public safety networks. The key areas to address include public safety communications, disaster response in cities for public health, and blockchain integration in UAVs to improve health monitoring [227]. UAVs have great potential to support public safety initiatives and disaster management, especially in urban areas. The future work should focus on enabling UAV-based communications, healthcare delivery, and supply chain tracking for public health emergencies through technologies, such as blockchain. This can enhance the resilience and responsiveness of safety networks using autonomous drone platforms. Implementing security algorithms, such as blockchain, for UAV swarms requires increased computing power and storage on drones, which can reduce the flight time and create latency. However, the resource-constrained nature of UAVs must be considered. Further research is needed to develop secure blockchain implementations tailored to drones without negatively impacting their limited payloads, processing capabilities, and flight endurance times. Security enhancements, such as blockchain, can improve UAV swarm resilience but must balance the processing demands and power consumption [228]. Optimized blockchain architectures can provide security with lower overhead equipment. Advancing UAV swarm security will necessitate co-designing optimized security protocols and hardware to maintain the capabilities within strict weight, processing, and power limits.  
With the further advancement of UAVs in areas, such as standardized regulations, privacy protections, image-processing algorithms, low-cost sensors, extended flight times, and larger payload capacities, there will be more opportunities to integrate drone technology into diverse applications, such as field crop phenotyping. However, image-processing techniques for UAVs still face critical challenges, including varying image orientations, higher overlaps, variable scales, and changing altitudes. Overcoming these issues should be a priority for future UAV research to leverage their potential across different domains fully. As drones become more capable and compliant, researchers will need to develop robust image-processing capabilities that can handle the demands of real-world agricultural, commercial, and scientific applications. Tackling variables, such as image overlaps, scales, and orientations, will be vital to unlocking the full value of UAVs with enhanced flight endurance times, sensors, and payloads.
# Optical Frequency Comb Expansion Using Mutually Injection-Locked Gain-Switched Lasers
## 4 Conclusions  
We have experimentally demonstrated a novel, highly flexible expansion scheme using mutually injection-locked two gain-switched lasers. Moreover, by incorporating a phase modulator with the proposed scheme, we showed that the number of comb lines can be further expanded, by more than 3 times, while achieving an OCNR \(>\) 40 dB. In addition, we also illustrated, via comb densification, the FSR can easily be tuned from 6.25 GHz to 390.625 MHz. Though the comb flatness is compromised, especially operating at low FSRs (in MHzs). In the future work, phase modulator can be replaced with dual-drive MZM modulator for better expansion and flatness. The detailed comb line characterization report highlights the excellent spectral purity (narrow linewidth, low RIN, high degree of phase correlation) of the OFC lines. The proposed scheme addresses one of the main shortcomings of the EI-GSL technique, which is the limited number of comb lines/comb bandwidth and provides a simple and effective technique to generate an OFC with a tunable (from MHz to GHz) channel spacing. Finally, the entire architecture can be photonically integrated, which would enable the realization of such a scheme at reduced cost, size, and energy consumption. In summary, the proposed technique enables simple, play a crucial role in the deployment of OFCs in a wide range of applications.  
Conceptualization, P.D.L. and A.K.-A.; methodology, P.D.L., A.K.-A. and P.M.A.; validation, P.D.L., A.K.-A., P.M.A. and P.L.; formal analysis, P.D.L., A.K.-A. and P.M.A.; investigation, P.D.L., A.K.-A. and P.M.A.; resources, P.D.L., A.K.-A., P.M.A. and P.L.; data curation, P.D.L.; writing--original draft preparation P.D.L., P.L.; writing--review and editing, P.D.L., A.K.-A., P.M.A. and P.L.; visualization, P.D.L., A.K.-A. P.M.A. and P.L.; supervision, P.M.A. and A.K.-A.; project administration, P.M.A., A.K.-A. and P.L.; funding acquisition, P.M.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science Foundation of Ireland's (SFI) career development award (15/CDA/3640), Enterprise Ireland's DTIF (DT20180268), and SFI/RDF (13/RC/2077_P2).  
Not applicable.  
The authors declare no conflict of interest.