esa-sceva/satcom-chunk-collection huggingface dataset:
# Statistical Characterization of Supraharmonics in Low-Voltage Distribution Networks
## 1 Introduction
Several probabilistic distributions are considered for the characterization, and they are compared in order to determine their performance in terms of goodness of fitting (GOF). Data collected at actual low-voltage distribution networks and publicly available at the PQube Live World Map database [22] are considered in the numerical experiments to foster the reproducibility of the results and future contributions to this topic. In summary, the major contributions of this paper to the knowledge are:  
* This is one of the very few contributions on the statistical characterization of supraharmonics, setting a starting point for the development of probabilistic power system analysis tools accounting for supraharmonic emissions and for the convergence toward standardization in the 2-150 kHz range;
* Several distributions are selected and compared to characterize both the overall supraharmonic emissions and the individual supraharmonic components;
* The presentation of numerical experiments based on publicly available datasets that include supraharmonic data collected at multiple actual low-voltage distribution networks. This paper is organized as follows. The methods for the statistical characterizations of supraharmonics are shown in Section 2; these include the techniques for the evaluation of the components from the measured waveforms, the definition of the power quality index that accounts for the global overall supraharmonic emissions, the distributions considered to characterize the supraharmonics and the GOF tests. Section 3 presents the results of the extensive numerical experiments based on actual public data. A discussion of the results and the conclusions are in Section 4.
## 11 Future Directions and Opportunities
### Advanced Functionalization and Hybrid Materials  
The article by [PERSON] et al. [92] explores the potential of using agri-food residues, specifically food losses and waste (FLW), to develop next-generation bioplastics and advanced materials within the context of a circular bioeconomy. It highlights the environmental impacts of food side streams, emphasizing the importance of reducing food loss and waste (FLW). The distinction is made between food loss, which occurs before retail, and food waste, often resulting from consumer behavior. Approximately 14% of food is lost before retail, particularly in cereals and pulses. The valorization of FLW involves transforming these residues into high-value materials, rather than returning them to food, by utilizing methods such as polymerization and biosynthesis to produce bioplastics and other materials.  
Various building blocks derived from food loss and waste (FLW) are explored: (i) Monomers and modified molecules, which include hydroxy acids, epoxides, fatty acids, and lactic acid sourced from fruits, vegetables, and oils, are usable for producing polymers such as polyesters and polyurethane. D-Limonene from citrus peels is a notable monomer, although challenges remain in its polymerization. (ii) Soluble biopolymers (pectin, starch) and insoluble ones (lignin) can be extracted for applications in films, coatings, and medical devices. Lignin enhances cellulose's wet strength, enabling the creation of bioplastics, while proteins like gelatin are functional in medical wearables. Additionally, nanocelluloses from plant waste and chitin nanocrystals from crustacean shells can be utilized, along with biogenic silica microparticles obtained from pineapple peels.  
FLW composition influences film formation, with each component contributing unique properties. Films made from carrots, parsley, and cauliflower show potential but may require reinforcement to achieve optimal strength.  
Manufacturing processes significantly affect material properties. Utilizing FLW can lower production costs through methods such as spinning, casting, and 3D printing. Mycelium-based products are also emerging.
## 4 Applications for IoT Technology in Smart Buildings
### Energy Conservation Through Smart Occupancy Sensor  
The researchers reveal that occupancy sensors for lighting can retain electrical energy up to 30% used in that building. (PIR) sensors detect the movement of living objects such as humans and animals, and they may switch actions automatically to operate the device [89]. As a result, an occupancy sensor-based lighting system ensures that lights are only turned on and off when required, with minimal disruption to routine business operations. The difference in the utilization level of the occupants concerning time in a day can be analyzed by occupancy sensors [90]. Various sensors and actuators are incorporated into the Arduino Mega to connect the building automation system to collect the data from sensors and transfer all the data to the cloud IoT server [91]. The utilization and evaluation of microwave sensors as occupancy sensors give real-time data from the environment.  
Figure 9: IoT energy consumption for smart buildings.
## 3 Results and Discussion
### ShuffleNetV2 Convolutional Neural Network  
ShuffleNetV2 is a deep neural network architecture for efficient and accurate object recognition tasks. The network model was proposed in 2018 and proved to be more accurate than ShuffleNetV1 and MobileNetV1 at the same complexity [22]. The main role of ShuffleNetV2 is to reduce the computational cost of CNNs while maintaining high accuracy, and it achieves this by using a novel channel shuffle operation and a packet convolutional strategy. Channel shuffle operation is designed to exchange information between different channel groups. By mixing channels, ShuffleNet facilitates the flow of information across groups, helping to reduce parameters and computation without losing performance [23]. The packet convolution strategy divides the input channels into multiple groups, and convolution operations are performed separately within each group. This approach further reduces computational costs by reducing the number of connections and computations within the convolution layer.  
The researcher points out that floating point operations per second (FLOPs) can only be used to evaluate the complexity of the model theoretically in current studies; instead of only being used as an evaluation criterion to judge the quality of the model, it should also start from the actual reasoning speed of the model and put forward four criteria for building the model: (1) The output and input channels of the convolutional channel layer should be consistent so that the memory consumption is minimal and the running speed is the fastest. (2) The convolution of the number of convolution groups should not be too large. Otherwise, the performance will be affected. (3) Reducing the number of network branches will improve the running speed of the model. (4) The influence of element-level operations on network speed cannot be ignored. Based on the above four criteria, this researcher designed the ShuffleNetV2 network model.  
The ShuffleNetV2 model has a new and lighter basic unit. The basic block consists of two different structures; as shown in Figure 7, one is a structure with a stride of 1; another is a structure with a stride of 2. The main branches of these two structures are composed of three convolution layers, namely, 1 \(\times\) 1 ordinary convolution, 3 \(\times\) 3 depth separable convolution, and 1 \(\times\) 1 ordinary convolution. But the composition of the input and side branches is different. In the structure with step size 1, the input side performs channel segmentation, and the side branch directly splices the channel segmentation image with the main branch image in dimension. In the structure with step size 2, the image is directly entered into the input end and processed by two branches, whose side branches are composed of 3 \(\times\) 3 depth separable convolution and 1 \(\times\) 1 ordinary convolution. In these two structures, the information communication between the two branches can finally be carried out through channel mixing.
# Performance Comparison of Deep Learning Models for Damage Identification of Aging Bridges
## 3 Proposed Framework
### Swin  
Swin Transformer (SWIN) is a model introduced in the paper \"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows\", developed by researchers at Microsoft Research Asia [25]. As shown in Figure 4, SWIN extends the existing vision transducer architecture to effectively capture the spatial structure of the image, resulting in excellent performance in tasks such as object detection and instance segmentation. The key elements of the SWIN include:  
* Window-Based Self-Attention: SWIN segments the input image into grids and performs self-attention within each, facilitating the integration of local context and global image structure.
* Shifted Window: It employs a hierarchical strategy to enlarge each window incrementally through shifting operations, capturing a variety of resolutions and contextual information.
* Tokenization and Patch Embedding: The model converts the image into a series of patches, which undergo tokenization and embedding processes.
* Hierarchical Feature Fusion: SWIN utilizes a technique for merging features across different scales hierarchically, attaining various resolutions and spatial diversities.  
Experimental results reveal that SWIN markedly outperforms Faster R-CNN and Mask R-CNN in instance-segmentation tasks on the COCO dataset. For example, SWIN has achieved a mean average precision (mAP) \(\sim\)4% higher than the ResNet-50-based Faster R-CNN while maintaining a relatively lightweight model size. By segmenting images into grid-shaped windows, performing hierarchical self-attention, and combining features at multiple scales, SWIN delivers good performance with a streamlined architecture. SWIN and the two preceding models may be compared as follows:  
* Mask R-CNN focuses on object detection and pixel-level segmentation for accurate and reliable results.
* BlendMask builds on Cascade Mask R-CNN, utilizing an ensemble technique to combine multi-stage network outputs and mask predictions.
* SWIN leverages a vision transformer architecture, combining window-based self-attention with hierarchical feature mapping.  
Each model has its unique concepts and architecture; selection among them should be based on specific experimental results and use requirements. In this study, these three models were applied to bridge-damage identification. To compare their performances, the same parameter values were set, and the resulting loss values were comparatively analyzed.
## 4 Results and Discussion
### Performance of Narrowband Bio-Indicators  
Our findings have demonstrated that the narrowband bio-indicators related to chlorophyll, red-edge, and the LUE model can be used to estimate forest productivity and respiration. In general, they have higher correlation coefficients than broadband indices. SPRI*NDVI was the best bio-indicator employed to inverse GPP and Re at the regional scale. The MCARI/OSAVI[750; 750], Vogelmann Index, Chlorophyll Index RedEdge 710, and SPRI*EVI also had high correlation coefficients with GPP and Re.  
The LUE model indices exhibited the best correlation coefficients to facilitate spatially extrapolation of regional scale estimates of C fluxes. Due to the differences of NDVI and EVI in decreasing the LAI saturation effect and atmosphere influence, SPRI*NDVI may be more suitable for monitoring the vegetation productivity with relative low LAI and low atmosphere influence environments. SPRI*EVI may have more potential for high LAI vegetation types and atmosphere influence environments. SPRI*NDVI and SPRI*EVI should be tested in more detail in different ecosystems and environments in future studies.  
MCARI/OSAVI[750; 750] have been proved to have good linearity with chlorophyll content and low sensitivity to non-photosynthetic element effects, mainly at low chlorophyll concentrations and low LAI. The Vogelmann Index was formed by a GPP sensitive band at 740 nm and an insensitive band at 720 nm. The ratio of the two bands can indicate the GPP and Re variations. The Chlorophyll Index RedEdge 710 was also calculated by two relevant bands in the red edge region. All three narrowband bio-indicators showed good performance.
# An Integrated Thermal-Electrical Model for Simulations of Battery Behavior in CubeSats
## 5 Conclusions  
Given one of the CubeSats' main challenges, which is proper energy management in a harsh environment, the platform's limitations must be taken into account by the engineers during the satellite's design. In this context, one of the challenges related to energy management is the need to keep the battery within acceptable temperature ranges, without wasting energy in the process.  
To assist in this process, this paper proposes an integrated simulation model for the energy and temperature of CubeSats, taking into account aspects such as the electrical and thermal characteristics of the battery and the photovoltaic panels and their attitude dynamic in orbit.  
Seven nodes idealize this integration of thermal and electrical models. While the model can support more nodes, such as different payloads, the heat transfer would still be limited to a simplified thermal resistance. Nevertheless, this simplification in the heat transfer by conduction and radiation among the main parts of the satellite is conducted at the expense of computational cost and simplicity for the user, a fundamental feature at the preliminary phases of development of a CubeSat, where the engineers may assess different configurations before proceeding into a much more complex model.  
To illustrate the proposed model, a simulation scenario was generated for a 1U CubeSat in an orbit at 650 km with altitude and dynamics typical of CubeSats with passive attitude control. Tests were performed considering a heater with an on/off controller designed to keep the satellite temperature at an appropriate setpoint. The results presented the model's capacity to report the battery's thermal and electrical aspects, being able to help the designers with testing their own control system.  
For future work, the search for a more accurate thermal model of solar panels is suggested, such as through the finite volumes method. The integrated model should be used for proposing new control systems and task-scheduling algorithms.  
Conceptualization, E.M.F. and S.V.M.; methodology, E.M.F., S.V.M., and L.O.S.; software, E.M.F., S.V.M., and L.O.S. supervision, L.O.S., V.d.P.N., and E.A.B.; writing--original draft preparation, E.M.F., L.O.S., and S.V.M.; writing--review and editing, V.d.P.N., R.G.O., and V.R.Q.L.; funding acquisition, R.G.O. and V.R.Q.L. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by CNPq/Brazil, grant numbers 141196/2019-0 and 141276/2018-5; and by Fundacion M. D. Samuel Solorzano Barruso, Proyecto Solorzano FS/27-2020.  
This work was supported by CNPq, the National Council for Scientific and Technological Development--Brazil, via grant number 141276/2018-5. We would like to thank to Seed Funding ILIND--Instituto Lusofono de Investigacao e Desenvolvimento, COPELABS. Al Proyeto: Uso de algoritmos y protocolos de comunicacion en dispositivos con enfasis en la privacidad de los datos.  
The authors declare no conflict of interest.
## 5 Influence of Consolidation Status  
Three comparative cases are conducted here to show the importance of the separation of overconsolidation and normal consolidation: (1) Class A: complete overconsolidation, the initial preconsolidation pressure is 100 kPa; (2) Class B: overconsolidation and normal consolidation, the initial preconsolidation pressure is 50 kPa; (3) Class C: complete normal consolidation, the initial preconsolidation pressure is 10 kPa.  
Figure 9 is the calculation diagram for all three cases, and the soil is vertically divided into 100 line elements. Assuming that the soil is homogeneous, and top and bottom surfaces are permeable, the total calculation time length is 1200 days. Table 2 lists the other parameters. The load is mathematically described in Equation (16), and the coefficient of consolidation is calculated using Equation (17).  
\[q(t)=\begin{cases}\gamma_{0t}/_{60}\text{kPa}&t\leq 60\text{days}\\ 70\text{kPa}&t>60\text{days}\end{cases} \tag{16}\]  
\[C_{v}=\frac{k(1+e_{0})}{\gamma_{w}a_{v}} \tag{17}\]  
where \(C_{v}\) is the coefficient of consolidation.  
Figure 8: The variation of average degree of consolidation along time.  
Figure 9: The calculation diagram for all three cases.  
The excess pore water pressure of Class A at 2.5 m depth under normal consolidation is apparently larger than that under overconsolidation with the same external loadings (Figure 11). It follows that the compressibility under normal consolidation is larger according to Equation (4). As a result, the settlement under normal consolidation increases faster, being in accordance with the results shown in Figure 12. The results are in agreement and reveal that the separation of overconsolidation and normal consolidation during calculations has a significant influence on the accuracy of results.  
Figure 13 illustrates the evolutions of the average degree of consolidation, which is an important indicator for assessing the development of consolidation. The curves reveal that the average degree of consolidation is significantly influenced by different consolidation statuses, and the trends are consistent with those in Figure 11. In addition, the consolidation rate under normal consolidation is slower than that under overconsolidation.  
Figure 12: Comparisons of settlement at the top surface.  
Figure 13: Comparisons of average degree of consolidation.  
To investigate the errors between the results obtained using nonlinear and constant coefficients, three control cases, using constant compressibility and permeability, were conducted. Table 3 lists the errors of settlement and the excess pore water pressure (EPWP) evaluated at 2.5 m depth at typical time points. The results show the nonignorable influence of nonlinearity of coefficients, and the errors are larger at longer elapsed times. Furthermore, the errors under normal consolidation are lower than those under overconsolidation.
# Frequency Contour-Strip Method for Characterization of Damage in Structures under Noisy Conditions
## 5 Experimental Validation  
### Experimental Description  
Experimental samples are fabricated using a laser-cutting machine to dissect a steel plate into beam segments. The specification parameters for the material and geometry of the steel plate are shown in Table 2. The length of each beam segment is 600 mm, and the clamped part covers 100 mm, leading to a cantilever beam of span 500 mm. A through-width crack is introduced using a computer-controlled wire-cutting machine  
Figure 11: Frequency contour-strip. (**a**) Frequency projection strips. (**b**) Zoomed-in intersection region R.  
Figure 10: Frequency contour-strips. (**a**)Frequency contour-strips of the eigen-frequency panorama. (**b**) Extracted frequency contour-strips.  
to cut the beam along one side. The wire of the machine has a diameter of 0.16 mm, and resulting crack has a width between 0.16 and 0.18 mm. For an arbitrarily chosen sample, Figure 12 presents the setup of the vibration-table dynamic testing. When a hammer is used to impact the beam, the vibration acceleration of the beam is recorded at the sampling frequency 12,800 Hz by an accelerometer mounted at the middle of the beam span. The acquired acceleration responses are preprocessed by ribo4.4 wavelet filter for noise reduction. The type of accelerometer is 1A102E, the type of impact hammer is Modally Tuned (U.S. PAT. NO. 4.799.375), and the data acquisition facility is DH8303N with dynamic signal acquisition and analysis system. These devices are from Jiangsu Donghua Testing Technology Co., Ltd. Figure 13 illustrates the frequency spectra of the beam with a crack of the depth, \(y\), increasing from 10% to 40% at the step of 10% at the location of \(x\) = 3.3%.
###### Abstract  
Advances in machine learning have widened the range of its applications in many fields. In particular, deep learning has attracted much interest for its ability to provide solutions where the derivation of a rigorous mathematical model of the problem is troublesome. Our interest was drawn to the application of deep learning for channel state information feedback reporting, a crucial problem in frequency division duplexing (FDD) 5G networks, where knowledge of the channel characteristics is fundamental to exploiting the full potential of multiple-input multiple-output (MIMO) systems. We designed a framework adopting a 5G New Radio convolutional neural network, called NR-CsiNet, with the aim of compressing the channel matrix experienced by the user at the receiver side and then reconstructing it at the transmitter side. In contrast to similar solutions, our framework is based on a 5G New Radio fully compliant simulator, thus implementing a channel generator based on the latest 3 GPP 3-D channel model. Moreover, realistic 5G scenarios are considered by including multi-receiving antenna schemes and noisy downlink channel estimation. Simulations were carried out to analyze and compare the performance with current feedback reporting schemes, showing promising results for this approach from the point of view of the block error rate and throughput of the 5G data channel.  
5G; New Radio; deep learning; convolutional neural network; CSI reporting Article
# Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks
## 4 Results and Discussion
### Development of the Multilayer Perceptron Model
Figure 6: Loss function and performance measures during the training and validation. Although these works showed substantial success in predicting thermal conductivity, our model upholds higher accuracy and introduces a broader array of concrete constituents into the modeling process.