esa-sceva/satcom-chunk-collection huggingface dataset:
# Composites Containing Felt Wastes from the Automotive Industry
###### Abstract  
(One) Background: Using textile waste materials in composites is a well-known problem and is frequently addressed by various scientific teams. Most of this work concerns textile waste introduced into composites as yarn strands. The present work focuses on adding textile wastes prepared in the form of single filaments of yarn spun to fluff, which was produced from waste felt materials from the automotive industry. (Two) Methods: The material was extracted from the bulkheads of worn-out vehicles, serving as thermal and acoustic insulation. The waste was shredded to form single yarn fibres with a fibre diameter of 0.08-0.3 mm and a 2-8 cm length. The shredded waste was used as a filler and modifier for composites. Four test batches were produced with different recyclate contents. A traditional cementitious composite without additives was used as a comparison material. (Three) Results: Composites filled with 3% felt waste have 23.31% lower density (1.71 g/cm\({}^{3}\)), 71.03% higher absorbability (21.58%), 49.58% lower tensile strength (19.86 MPa), and 53.55% lower compressive strength (3.64 MPa) than traditional composites. Partitions made of these composites had much higher thermal insulation than traditional composites. Composite made of 1% waste was resistant to the phenomena of thermal spalling. Moreover, the spot flame loading did not damage the composite, and there were no scratches or defects. (Four) Conclusions: The tests proved that the waste felt materials could potentially be used as fillers and modifiers in lightweight composites with higher thermal insulation. The addition of felt fibres improves the resistance of the composite to local spalling.  
Keywords:felt wastes; automotive wastes; waste fillers; lightweight composites; thermal insulation; thermal spalling +
Footnote †: journal: Applied Sciences
# Portable Protein and Fat Detector in Milk Based on Multi-Spectral Sensor and Machine Learning
## 1 Introduction
[24] proposed a novel biophoton sensor based on porous silicon ternary photonic crystals for more effective detection of fat concentration in milk. [PERSON] et al. [25] used surface plasmon resonance structural optics based on MXene to detect fat concentration in milk, which was the first study to apply surface plasmon resonance to detect the fat concentration in milk. [PERSON] et al. [26] developed a portable milk composition detector based on an NIR micro-spectrometer, which has driven the development of small spectral instrument equipment. These studies further indicate that although spectral instruments still have a certain cost and complexity, designing a portable milk composition detection instrument with fast detection speed and easy on-site use is of great significance in milk quality control and production. With the continuous development of micro-spectral instruments, it has become possible to achieve detection speeds comparable to laboratory instruments. The aim of this study Is to design a portable instrument that can quickly detect the protein and fat content in milk using new sensor technology, machine learning algorithms, and embedded systems. We designed a broadband near-infrared LED light source driver circuit and a multi-wavelength sensor module, combined with the XGBoost machine learning model, to achieve highly accurate detection, overcoming the problems of traditional methods such as a long measurement cycle, high cost, and high environmental pollution. The development of a portable milk protein and fat detector is of great significance as it provides an efficient, fast, green, and low-cost method for quickly measuring the protein and fat content in milk on-site. This not only helps to improve milk quality control but also provides practical tools for farms, dairy factories, and food testing departments, helping to improve the production and processing processes of the milk industry. In addition, this technology can also be applied to other fields, providing convenient solutions for food analysis and quality control, and has broad application prospects, which can promote the development of portable instruments for milk quality testing.
## 2 Materials and Methods
### Methods  
#### 2.3.1 Statistical Metrics  
We used various statistical measures to evaluate the performance of the CHIRPS product compared to the observed data at each station. Following recommendations from several studies [34; 49; 50; 51], these measures include relative bias (BIAS) to compare the mean  
Figure 2: Percentage of cereal land and production by province in MS region (2003–2018).  
differences, Pearson's linear correlation coefficient (\(r\)) to assess linear relationships, root mean square error (RMSE) to measure mean square deviation, mean absolute error (MAE) to measure mean absolute deviation, and the coefficient of determination (\(R^{2}\)) to indicate the proportion of explained variance (Table 2). We determined the significance of the correlations using \(p\)-values, with a significance level of 0.05. \(p\)-values below this threshold indicate statistically significant correlations [52].  
The \"optimal value\" in Table 2 represents the ideal performance for each metric. A BIAS of 0 signifies no difference between estimates and observations; a correlation coefficient (\(r\)) of 1 or \(-\)1 indicates a perfect linear relationship; an RMSE and MAE of 0 indicate no error; and an \(R^{2}\) of 1 means the model explains all variance in the observed data.  
While stations provide point measurements, satellites average estimates over grid cells. To facilitate comparison, we extracted the CHIRPS precipitation values for the grid cells corresponding to the exact geographical coordinates of the meteorological stations. This process provided paired ground observation and satellite estimation values for each station, allowing us to evaluate the satellite product's performance against in situ measurements.
## 1 Introduction
This article discusses the possibility of using the acoustic emission (AE) method for diagnostics of dissimilar welded joints. For heterogeneous objects with a complex structure, the AE method is more promising in comparison with traditional methods of non-destructive testing. Since the AE method does not imply a probing effect on the test object, the heterogeneity of the structure of a complex object does not interfere with defects detection [15; 16]. Due to its sensitivity to microstructure parameters, the AE method of application makes it possible to detect violations of the structural composition in the material [17; 18; 19]. In a number of experimental research studies [20; 21; 22; 23; 24; 25], it was found that the AE method  
Figure 1: Scheme of a welded joint of pearlitic and austenitic steels. can be effectively applied to detect defects in welded joints, as well as to identify and locate them. The use of the AE method for diagnostics of dissimilar welded joints is insufficiently studied nowadays. Few studies are devoted to the application of AE directly in the process of welding dissimilar welded joints [26; 27; 28]. However, little experience has been successful. In [29], the authors monitored the development of a crack in a dissimilar welded joint between low-alloy pearlitic to austenitic steels. The study was carried out under laboratory conditions for a pre-hydrogenated sample. The AE method showed high sensitivity since it was possible to detect a crack with an opening of no more than 50 \(\upmu\)m.  
The dissimilar welded joints of austenitic steel 12 Kh18N10T to pearlitic steel 09G2S are the subject of research in this paper. The defect-free specimens of dissimilar welded joints as well as specimens with defects and specimens with diffusion interlayers were tested using the AE method. The AE signature specific for different states of dissimilar welded joints were obtained in this study.
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 2 Materials and Methods
### Study Areas and Datasets
#### 2.1.3 Vegetation Indices Extracted from Sentinel-2 Images  
Using most of the Sentinel-2 bands, we derived 5 widely used vegetation indices to construct time series for DTW (Table 1). We resampled the 20 m resolution bands (red-edge 1 and 2, narrow near-infrared and short-wave infrared) to perfectly align with the 10 m spatial resolution bands (blue, green, red, and near-infrared). The Normalized Difference Vegetation Index (NDVI) [45] is a chlorophyll sensitive index sufficiently stable to compare seasonal changes in vegetation growth [46]. The Green Normalized Difference Vegetation Index (GNDVI) was shown to be more sensitive to chlorophyll concentration than NDVI [47] and useful in differentiation in stressed and senescent vegetation [48]. The Normalized Difference Red-Edge (NDRE) is highly sensitive to pigment changes [49] and is correlated with drought induced seasonal variation in leaf photosynthetic rates [50]. The Soil Adjusted Vegetation Index (SAVI) minimizes soil brightness influences accounting for uncertainties resulting from variation in background condition by incorporating a correction factor L in the NDVI formula [48; 51]. The Normalized Difference Water Index (NDWI) is useful to measure vegetation liquid water status and can be a good indicator of irrigation if the time series is sufficiently dense [52; 53] (Table 1).  
For single-band DTW, we used the NDVI time series. For multi-band DTW, we combined the NDVI with GNDVI, NDRE, SAVI, and NDWI to evaluate if integrating multiple time series of vegetation indices into DTW will yield more accurate results for crop classification. The entire workflow of our analysis is shown in Figure 3.
## 5 Conclusions
Additionally, integrating multiple FBGs along a single fiber would enable the simultaneous monitoring of multiple joints, facilitating full-limb or whole-body kinematic analysis. Other future directions include incorporating energy-harvesting systems for self-powered operation, advanced embedded computing for onboard data processing, and novel biosensor technologies such as bacterial nanocellulose-based sensing films for multimodal physiological monitoring [48; 49; 50; 51; 52; 53; 54]. Future work will focus on expanding the quantitative validation of the hybrid FBG-IMU system. This includes the implementation of statistical tools such as correlation coefficients, root mean square error (RMSE), and Bland-Altman analysis to assess the agreement between sensing modalities and address the current version's limitations. Additional efforts will investigate sensor integration into textile substrates to mitigate mechanical slippage and ensure stable readings during high-speed movements. Cost optimization strategies will also be explored, such as multiplexing FBGs on a single fiber and using compact interrogators, to enhance scalability and practical deployment. Moreover, further testing will determine the feasibility of using the FBG sensor independently, depending on the required resolution and clinical context. These directions aim to strengthen the system's robustness, accuracy, and cost-effectiveness for both clinical and sports applications. Conceptualization: R.P.L.d.S.R., M.L.M.A., M.A.M., M.G., O.H.A.J. and J.P.P.d.C. ; methodology: R.P.L.d.S.R., M.L.M.A., M.A.M. and J.P.P.d.C. ; validation: R.P.L.d.S.R., M.L.M.A., M.A.M. and J.P.P.d.C. ; investigation and simulation: M.L.M.A., R.P.L.d.S.R., M.A.M. and J.P.P.d.C. ; writing--original draft preparation: R.P.L.d.S.R., M.L.M.A., M.A.M., M.G., L.O.d.A., O.H.A.J. and J.P.P.d.C. ; writing--review and editing: M.L.M.A., M.G., M.R.C., L.O.d.A., O.H.A.J. and J.P.P.d.C. ; project administration: M.R.C., L.O.d.A., O.H.A.J. and J.P.P.d.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was partially supported by FCT national funds, under the national support to R&D units grant, through the reference projects UIDB/04436/2020 and UIDP/04436/2020. This research was also partially supported by the FAPESP agency (Fundacao de Amparo a Pesquisa do Estado de Sao Paulo) through the project with the reference 2019/05248-7. J.P.P.C. was support by a PQ scholarship with the reference CNPq 304312/2020-7. This research was also partially supported by the FACEPE agency (Fundacao de Amparo a Pesquisa de Pernambuco) through the project with the references APQ-0616-9.25/21 and APQ-0642-9.25/22. O.H.A.J. was funded by the Brazilian National Council for Scientific and Technological Development (CNPq) with the grant numbers 407531/2018-1, 303293/2020-9, 405385/2022-6, 405350/2022-8, and 40666/2022-3. **Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s). **Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Related Works
### Resource Allocation  
An adaptive radio resource allocation scheme for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) based wireless multicast systems was proposed in [13]. A dynamic subcarrier and power control algorithm for the M-D2D group was discussed. The problem of this work is that the power control method introduced in the paper was not optimal. A joint resource allocation and power control scheme for an underlay M-D2D communication was studied in [14]. The paper discussed a maximum weight bipartite matching-based resource allocation scheme and heuristic-based resource allocation scheme. However, in this paper, the fractional frequency reuse (FFR) method was not used in the network. Therefore, the system performance is distorted due to the co-channel interference. In [15], a random linear network coding mechanism for M-D2D communication was presented. The paper discussed the short representation and law dependent probability that reduces the overhead for multicast transmission. However, the paper did not assume the distance dependent multicast transmission technique.  
A multi-objective resource allocation scheme for M-D2D was studied in [16] to guarantee the quality of service (QoS) in a cellular network. In this study, the resource optimization problem was formulated through maximizing system capacity and minimizing power consumption. Regarding the co-channel interference mitigation solution based on the resource allocation, many of the literature works exploit the knowledge of the resource reuse region in terms of its radius to perform interference-aware resource allocation [17]. Additionally, the paper considered the power control technique to maintain the transmit power of cellular users and D2D pairs, which reduces interference into a considerable rate. In [18], an interference-aware resource-sharing scheme was proposed for multiple D2D group communications underlaying cellular networks. In their work, the interference alignment for the D2D communications was analyzed based on the grouping method. Comparing the result of this scheme with that of the random resource sharing scheme demonstrates the improvement in cell throughput. In [19], the authors proposed a resource allocation scheme for M-D2D by considering content sharing in cellular networks based on the social behavior and physical characteristics. The optimization problem was formulated by considering cluster formation by considering the channel allocation method. However, this scheme is not spectrally efficient. In [20], the authors proposed a resource allocation scheme for multiple D2D cluster multicast communications. In their work, an outage probability problem was formulated by maintaining a certain QoS of the conventional cellular users. The results showed that their introduced scheme achieved near optimal sum effective throughput. The disadvantage of this scheme is that the network model considered only an omnidirectional antenna. Therefore, it cannot avoid severe interference.
## 2 Related Works
### MOOS Middleware and Node Adaptivity in Underwater Sensor Networks  
In [21] a FP7 project, Underwater Acoustic Network (UAN), is presented. This project assumes that protection of critical infrastructures off-shore and along the coastline requires a multi sensor network, connected to a command and control (C2) center. The project focuses on underwater communication, using mobile nodes through autonomous underwater vehicles. The aim is to enable a communication channel by moving the autonomous underwater vehicles when some of the nodes in the network are moved around (e.g., nodes located on surface vehicles or other underwater vehicles). A middleware specification, Intervehicle Secure Mission Oriented Operating Suite (IS-MOOS) Publish/Subscribe system, is proposed as well in order to address issues related secure underwater communication. This is a Publish/Subscribe system for inter-process communication (IPC), which supports dynamic, asynchronous, many-to-many distributed communication. This work shows that using mobile nodes, in this case underwater vehicles, and the network can adapt to real-world problems, (for example, oceanic variations).  
This proposal puts forward several ideas that are most appealing for a middleware architecture used for underwater communications, such as the addition of an explicit layer of middleware and a Publish/Subscribe paradigm for communications. Unlike some others that have been included, the focus of research here is the coordination of Autonomous Underwater Vehicles, which is strongly aligned with the kind of hardware expected to be used in the proposal for hardware interoperability that has been presented in this manuscript. However, the model that it seems to follow is still too focused on networking and mobile nodes, rather than hardware interoperability and services to be offered to the application layer.
# Addressing Gaps in Small-Scale Fisheries: A Low-Cost Tracking System +
## 3 Results  
### Data Quality  
About 90.5 h of fishing operations were recorded by the tracker during November 2021, corresponding to a regular time series of 5758 records, corresponding to a total of 29 calendar days.  
No unrealistic speed or course values were found, and no other errors in device transmission (e.g., spikes and gaps) required interpolation between pings.
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 9 Future Directions and Development Trends
### Energy Efficiency and Environmental Impact  
Training LLMs requires tremendous computing power. Minimizing environmental impact through energy efficiency is a paramount concern in advancing LLMs. There are several facets to achieving energy efficiency, as detailed below. Developing energy-efficient algorithms for training LLMs is a coveted goal. Such algorithms will require faster convergence or fewer computational resources through adaptive learning rate schedules, low-precision training, and gradient checkpointing.  
Another promising area of research is designing specialized hardware accelerators optimized for LLM training and inference. Such hardware optimization and accelerators will significantly contribute to efficient computation and thus reduce energy consumption. Related to optimized hardware accelerators is model architecture optimization. Topics to be researched in this direction include model structure optimization, reducing redundant parameters, and developing sparse models. Pruning and sparsity induction through identifying and eliminating redundant or less significant parameters contribute to creating leaner models. Transfer learning and few-shot learning methods reduce the need for extensive training of LLMs on new tasks or domains. Advances in this area can significantly reduce energy requirements via better model generalization with less training. Energy consumption can also be optimized by employing energy-aware training and inference strategies, which include adaptive precision tuning, dynamic pruning, and model scaling.  
Quantization of model weights and compression schemes contributes to the reduced computational overhead of LLMs. For example, knowledge distillation is a technique that helps to decrease the model's memory and computational requirements. Research is needed in lifecycle assessment and environmental impact to inform researchers and provide guidelines and best practices for developing and using LLMs. Such research will document the environmental impact of LLMs by quantifying the carbon footprint and suggestions for footprint reduction. Data center efficiency is pivotal in developing LLMs and deploying downstream applications. Supporting data center efficiency initiatives, including renewable energy sources, is critical. Lastly, collaboration between academia, industry, and policymakers is needed to share best practices, application frameworks, and tools for energy-aware LLMs.
# Removing Land Subsidence Impact from GPS Horizontal Motion in Tianjin, China
## 3 Results
### Model Output  
The model output was inversely normalized in order to obtain the predicted rate of the target horizontal component in the absence of land subsidence.  
The horizontal predicted rates of each station in the subsidence area were converted into the Eurasian frame and synthesized into horizontal velocity vectors. Thereafter, the rates and azimuths of velocity vectors were calculated. The original and model-predicted values of horizontal motion in the Eurasian frame for each station in the subsidence area are presented in Table 1.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline  & \multicolumn{2}{c}{**North Rate**} & \multicolumn{2}{c}{**East Rate**} & \multicolumn{2}{c}{**Horizontal Rate**} & \multicolumn{2}{c}{**Horizontal**} \\
**Station** & \multicolumn{2}{c}{**(mm/yr)**} & \multicolumn{2}{c}{**(mm/yr)**} & \multicolumn{2}{c}{**(mm/yr)**} & \multicolumn{2}{c}{**Azimuth (\({}^{\circ}\))**} \\  & **Raw** & **Pre** & **Raw** & **Pre** & **Raw** & **Pre** & **Raw** & **Pre** \\ \hline NIHE & \(\mathbf{1.41}\) & \(\mathbf{-0.31}\) & 2.78 & 2.23 & 3.12 & 2.25 & **63.11** & **97.91** \\ HECX & \(\mathbf{-0.90}\) & \(\mathbf{-2.13}\) & 3.56 & 3.92 & 3.67 & 4.46 & 104.19 & 118.52 \\ XUZZ & \(\mathbf{-0.05}\) & \(\mathbf{-1.09}\) & 3.94 & 3.39 & 3.94 & 3.56 & 90.73 & 107.82 \\ QING & \(\mathbf{-3.67}\) & \(\mathbf{-2.50}\) & **0.80** & **2.08** & 3.76 & 3.25 & **167.70** & **140.24** \\ WQCG & \(\mathbf{-4.07}\) & \(\mathbf{-3.24}\) & **4.64** & **2.90** & **6.17** & **4.35** & 131.25 & 138.17 \\ GGSL & \(\mathbf{-1.56}\) & \(\mathbf{-1.87}\) & 5.38 & 4.68 & 5.60 & 5.04 & 106.17 & 111.78 \\ TJBH & \(\mathbf{-0.52}\) & \(\mathbf{-0.35}\) & 4.68 & 3.72 & 4.71 & 3.74 & 96.34 & 95.37 \\ PANZ & \(\mathbf{-0.17}\) & \(\mathbf{-0.02}\) & **1.80** & **3.26** & **1.81** & **3.26** & 95.39 & 90.35 \\ JHAI & \(\mathbf{-0.21}\) & \(\mathbf{-0.74}\) & **0.54** & **2.07** & **0.58** & **2.20** & 111.25 & 109.67 \\ XQYY & \(\mathbf{-2.83}\) & \(\mathbf{-2.80}\) & 3.20 & 2.73 & 4.27 & 3.91 & 131.49 & 135.72 \\ \hline \end{tabular} Note: ‘raw’ and ‘pre’ represent the original value and predicted value, respectively. Bolded values are where the difference between raw and predicted rates is more than 1 mm/yr or where the difference between raw and predicted azimuths is more than 20\({}^{\circ}\).  
\end{table}
Table 1: Original and predicted values of horizontal motion in Eurasian frame at each station in subsidence area.  
Figure 6: Vertical data of station NIHE without impact of land subsidence.