esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Ionospheric Data and Methodologies
#### 2.3.4 Monitor the Solar Activity  
In order to properly discriminate the ionospheric anomalies due to external disturbances like solar activity, we provided two representative quantities of the solar conditions  
Figure 3: Yearly mean _TEC_ for the period from 2000 to 2021. The error bar is the standard deviation of the values used to calculate the mean (i.e., the _TEC_ from 23 March to 19 September of each year). A sinusoidal fit has been performed and shown as a green curve with the fit parameter in the embedded table.  
in Figure 4: the solar wind speed and the Interplanetary Magnetic Field (IMF). We retrieved these data from two satellite missions in Lagrangian point L1, i.e., about 1.5 million km from Earth in the line connecting Earth and Sun. Such a place is a special point for the gravitational field (i.e., the Earth and Sun gravitational fields are equipotential at this point) [69] and overall is a point place in the magnetosphere that can record the increase of solar activity with about one hour before the eventual impact on the Earth ionospheric environment. Advanced Composition Explorer (ACE [70]) and Deep Space Climate ObservatoRy (DSCOVR [71]) are two satellites dedicated to monitoring solar activity. The first one was launched in 1997, while the second one was launched in 2015 to replace ACE in case of failures of payloads that are working well beyond the planned mission time of 5 years. Both satellites are equipped with fluxgates magnetometers and electrical cap instruments to measure the plasma properties of the solar wind, including the speed we reported here. In fact, when the solar wind speed and the IMF increase, they indicate a stronger solar activity with a possible perturbation of the ionospheric environment. Therefore, any anomalies identified in the hours following such high solar activity are likely to be due to the impact of the solar wind on the Earth's magnetic field. In the figures, two thresholds are estimated as mean \(\pm\) 2 standard deviations of the same time series, and the solar activity can be considered higher above the upper threshold.
# Adaptive HVAC System Based on Fuzzy Controller Approach
## 4 System Model Implementation
### Humidity Input  
The humidity variable has a significant influence on the change in the desired thermal comfort level. Therefore, incorporating relative humidity into the proposed system is essential to assessing indoor comfort. The ASHRAE standard, based on the guidelines presented in [24; 25], was used to model temperature and humidity to ensure indoor thermal comfort. Therefore, humidity is modeled with three fuzzy sets labeled low (L), normal (N), and high (H) with a value range of 30-100%.
# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 2 Materials and Methods
### Wi-Fi AP Node Data Mining  
Nowadays, Wi-Fi nodes are ubiquitous in society, and the AP (access point) nodes of Wi-Fi refer to wireless access points [24; 25]. Generally, the number of AP nodes available outdoors is greater than those indoors, especially in dense urban areas, commercial streets, office buildings, campuses, and parks. In contrast, residential areas tend to have fewer AP nodes. When entering an urban indoor space, the presence of multiple walls can obstruct signals, leading to a generally lower number of accessible AP nodes compared to outdoor locations.  
Figure 6(a) and Figure 6(b) depict the spectrum maps of indoor and outdoor Wi-Fi channels, respectively. The frequency points are primarily distributed in the 2.4 GHz, 5 GHz, and 6 GHz bands introduced by Wi-Fi 6E. It can be observed that nodes operating in the 2.4 GHz band typically exhibit stronger signal strength. In contrast, the signal strengths in the other frequency bands are generally lower. The 2.4 GHz band offers wider coverage and better penetration but is more susceptible to interference due to its narrower bandwidth.  
The 5 GHz band provides a larger coverage area and more channels, resulting in higher data rates and reduced interference; however, it has smaller coverage and poorer penetration. The 6 GHz band offers even more channels and less interference, making it suitable for environments with high data demands, although its signal strength and coverage may be slightly reduced.  
Figure 7(a) presents the graph showing the number of Wi-Fi AP nodes. A total of 200 experiments were conducted, comprising 100 groups of indoor scanning results and 100 groups of outdoor scanning results across various environments, including office buildings, campuses, playgrounds, residential buildings, and shopping malls. The graph indicates that the number of indoor AP nodes typically ranges from 0 to 30, while the number of outdoor AP nodes generally falls between 25 and 90, demonstrating that outdoor AP nodes are typically more numerous than indoor nodes.  
Figure 7(b) illustrates the Wi-Fi signal strength, which tends to fluctuate significantly indoors, with overall signal strengths ranging from \(-88\) to \(-50\) dBm. In contrast, outdoor signal strengths are lower, generally falling between \(-70\) and \(-95\) dBm. This difference can be attributed to the proximity of indoor Wi-Fi signals to the transmitting point, which experiences less obstruction. Conversely, outdoor signals are generally farther from the transmitting point but can receive a greater number of signals. Therefore, the number of Wi-Fi AP nodes and the average signal strength can also serve as important factors in scene classification.  
Figure 7: Wi-Fi channel spectrum scan: (**a**) indoor, (**b**) outdoor.
# On Nash Equilibria in Non-Cooperative All-Optical Networks +
## 6 Conclusions  
We have considered the problem of determining suitable payment functions for the non-cooperative agents of an all-optical network, in order to induce Nash equilibria using a low number of wavelengths. In particular, we have identified three levels of information, specifying the local knowledge that the agent can exploit when computing their payments. While the complete information level has been fully understood, under the lower levels, the main left open question is the determination of functions that on every topology yield Nash equilibria with a performance better than the worst possible one assigning a different color to each agent. Moreover, still under incomplete information, it would be also nice to improve and extend our results on specific topologies. Furthermore, given that a lot of literature, as discussed in the introduction, focuses on the minimization of the cost due to hardware equipment in optical networks, it would be nice to study the characteristics and performance of games combining our measure (the number of used wavelengths) with other kinds of hardware costs. Moreover, since our findings outline nice connections between payment functions and online algorithms, that allow one to cope with the arbitrary order of the moves of the agents, it would be nice to understand the conditions and eventual systematic methods allowing one to get converging payment functions preserving online algorithms performances under incomplete information. Finally, we would like to remark that this paper constitutes a theoretical contribution to the study of optical networks from a decentralized multi-agent point of view, and it would be interesting to validate the obtained results and proposed approach by some simulation studies to be performed in a future work. To this respect, we believe that many worse lower bounds to the price of anarchy are achieved by exploiting specific and pathological instances, and therefore, the price of anarchy may turn out to be much lower, in practice, in the cases for which the existence of instances with a very large price of anarchy has been theoretically proven.  
Conceptualization, V.B., M.F. and L.M.; methodology, V.B., M.F. and L.M.; formal analysis, V.B., M.F. and L.M.; writing--original draft preparation, V.B., M.F. and L.M.; writing--review and editing, V.B. and L.M.; funding acquisition, V.B., M.F. and L.M. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the PRIN 2008 research project COGENT (Computational and Game-Theoretic Aspects of Uncoordinated Networks), funded by the Italian Ministry of University and Research.
# An FPGA Based Energy Efficient DS-SLAM Accelerator for Mobile Robots in Dynamic Environment
## 5 Experimental Results  
### HERO Platform Description  
The proposed DS-SLAM accelerator is implemented on the HERO platform, which contains an Intel i5-7260U CPU as a host side and an Arria 10 GX 1150 FPGA as an acceleration board. The semantic segmentation thread that needs to be accelerated is deployed on FPGA, and the others run on CPU. We use the OpenCL development process to implement the SegNet network through the Intel FPGA SDK. The clock frequency of the FPGA is 202 MHz and the hardware resource utilization is shown in Table 2. It can be seen that the utilization rate of DSP reaches 100%, almost all of which is used to realize convolution operation.  
Figure 6: Softmax operation diagram.  
The energy-efficient DS-SLAM system implemented on a heterogeneous computing platform is evaluated on the TUM RGB-D dataset [25]. The TUM dataset is mainly for office scenes. Its \"walking\" sequence contains dynamic objects, which are two moving people.
## 4 HSI in Fresh-Cut Product Quality Assessment
### Fresh-Cut Tubers  
Potatoes  
Color, water, and starch content are quality indicators of fresh-cut potatoes [49,52]. Also, in this case, Vis/NIR HSI systems were demonstrated to be effective tools to build prediction maps (Figure 10) for the distribution of such qualitative indicators using different modeling approaches (PLS, SVM, and LS-SVM). Indeed, for color and water content prediction, an R\({}^{2}\) about 0.8 was obtained using any of the modeling methods [49], while [PERSON] et al. (2021) extracted a bulk of characteristic wavelengths (about 10 to 30 wavelengths according to the applied selection method) to build a PLS model for the starch content prediction (R\({}^{2}\) = 0.95 and RMSEP of 1.63 g kg\({}^{-1}\)) [52].  
However, one of the major problems related to fresh-cut potatoes is the foodborne contamination of _Escherichia coli_[53]. [PERSON] et al. (2019) proposed a curcumin-based photodynamic treatment to deactivate the _E. coli_ proliferation on the surface of stored fresh-cut products [74]. In this case, HSI was used by [PERSON] et al. 2021 to explore the illumination time of the photodynamic treatment with the best inactivation effect. A back-propagation neural network approach was used to develop quantitative predictive models for colony quantification using both full spectrum and selected wavelengths (R\({}^{2}\) = 0.97, 0.88; RMSEP = 0.065 log CFU g\({}^{-1}\), 0.142 log CFU g\({}^{-1}\), respectively), identifying 20 min as the optimum treatment time [53].  
Figure 9: PLS vitamin C prediction map from day 0 to day 12 proposed by [PERSON] et al. (2020). For each leaf is indicated the percentage of pixels predicted within the calibration range and the average vitamin C concentration over these pixels. Reprinted with permission from Ref. [48]. 2020, Elsevier.
# Prediction Model for Shield Tunneling Roll Angle and Pitch Angle: A PCA-PSO-LGBM Approach
## 2 Methodology
### Construction of the PCA-PSO-LGBM Model  
The prediction process of the PCA-PSO-LGBM model is shown in Figure 6. First, data preprocessing is performed on the collected shield tunnelling parameters. Then, feature extraction is conducted using PCA and combined with the preprocessed geological parameters to form input-output datasets. Next, LGBM prediction models are constructed separately as prediction targets for the shield roll angle and pitch angle. Finally, PSO is used to optimize the hyperparameters of LGBM, obtaining the optimal prediction models for shield roll angle and pitch angle.
## 4 Data Analysis
### Development of Structural Models
#### 4.3.4 Predictive Relevance Analysis  
The structural model's predictive power is measured using the PLS-SEM technique. Each of the dependent constructs' cross-validated redundancy metrics were determined using a blinding technique. The data showed that the value of the Q\({}^{2}\) numbers of a project's success was 0.264, which is statistically larger than zero. As can be seen in Table 10, it was shown that IVs are a significant predictor of DV.
## 2 Materials and Methods
### Data Processing and Modeling Methods
#### 2.3.2 Feature Wavelengths Selection  
The high-dimensionality of hyperspectral data is reflected in the large amount of spatial and spectral information. However, this high-dimensionality imposes a significant burden on computer hardware, rendering it unsuitable for online data acquisition and rapid processing. Selecting representative characteristic wavelengths is crucial to reducing the dimensionality of hyperspectral data, which not only accelerates processing speed and lowers equipment costs but also enhances model accuracy. In this study, four methods are applied to select representative characteristic wavelengths. Successive projection algorithm (SPA) selects a subset of variables with low multicollinearity through stepwise projection and wavelength elimination, which can identify the characteristic wavelengths and reduce redundancy [40]. Competitive adaptive weighted sampling algorithm (CARS) utilizes Monte Carlo sampling and adaptive weighted sampling combined with Partial Least Squares Regression to identify a subset of important variables and select key wavelengths according to their importance levels [41]. Boruta algorithm is a fully correlated feature selection algorithm based on random forest, which can obtain a minimized optimal feature combination by comparing the importance of initial and shadow features [42]. Interval variable iterative space shrinkage approach (iVISSA) achieves the intelligent optimization of the position, width, and combination of the characteristic wavelength intervals by combining the iterations of global and local searches to obtain the indicative wavelength intervals [43].  
Figure 4: Evolution of the noise level with wavelength evaluated based on spectra of all coal samples. Wavelengths with prominent noise spikes (>0.5%) have been excluded from further analysis.
## 6 Example Analysis  
This paper uses field data from a photovoltaic power generation system in a northern city in China, recorded in 2019, as the experimental dataset for model simulation. The dataset includes historical meteorological data and photovoltaic output data, with a sampling interval of 20 min. The first 70% of the dataset is allocated as the training set, while the remaining 30% is used as the testing set for model training and evaluation.
### Retransmission Rate  
Figure 17 shows the comparison of the retransmission rate of PELCR-TP algorithm with and without BAS, and PLCR algorithm at different bad node rate.  
It can be seen from Figure 17 that with the increasing bad node rate the retransmission rates of the three algorithms increase. The slope of the PELCR-TP algorithm with BAS is much lower than that of both the PELCR-TP algorithm without BAS and PLCR algorithm, while the latter two have no obvious differences between each other. This indicates that in the transmission condition of this simulation, BAS effectively avoids multiple retransmissions to bad nodes and thereby reduces the retransmission rate, but the PELCR-TP algorithm itself does not affect the retransmission rate.
# Eco-Friendly Metadome-Antenna Innovations for Wearable Millimeter Wave Radar Sensing
## 2 Materials and Methods  
This section details the materials utilized in the design and subsequent fabrication of both the antenna and the metasurface. The designs of the antenna and metasurface, conducted through 3D electromagnetic simulation using commercial software, are subsequently presented. The most significant operational parameters of the antenna and metasurface individually, as well as in combination, are shown and confronted with the operational requirements for the intended application.
# Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment
## 2 Related Work
### Model-Based DFL Method  
Model-based DFL methods usually use certain physical principles to calculate the relevant physical characteristics of the target, such as speed, angle, distance, etc., from the  
Figure 1: Fingerprint similarity of different reference points. Both the X-axis and Y-axis indicate the serial numbers of reference points, and the Z-axis depicts the similarity of fingerprints between reference points.  
original signal, and then they calculate the location of the target from the physical characteristics. This kind of method is a process from original signal to physical characteristics to the location of the target.  
[PERSON] et al. [17] proposed to utilize the freedom provided by the Intelligent Reflecting Surface (IRS), composed of a multitude of controllable reflective elements, to modulate the spatial distribution of WiFi signals. This breaks down the spatial resolution limitation of WiFi signals to achieve accurate localization. [PERSON] et al. [18] demonstrated, via simulation, that the integration of information from multiple frequency bands (wideband CSI), such as an increase in subcarrier or virtual antenna, significantly enhanced the performance of MUSIC in separating multipath components and improved localization accuracy. [PERSON] et al. [19] proposed an improved four-dimensional parameter estimation approach based on a two-stage algorithm. They utilized the orthogonal matching pursuit (OMP) for coarse estimation and subsequently employed space-alternating generalized expectation-maximization (SAGE) for parameter refinement, thereby enhancing the accuracy of weak human-induced reflection path parameters. [PERSON] et al. [20] developed a novel DFT system which refines motion-induced phase shifts from highly noisy CSI measurements to detect the Doppler shift. This system achieved joint Doppler velocity and AOA estimation of the target path within the compressive sensing framework, resulting in high accuracy for target velocity estimation and motion tracking. Model-based methods are capable of explaining the propagation of signals and obtaining accurate localization. However, the model-based methods are not practical in complex environments and difficult to adapt to environmental changes.
Vitamin D is a naturally occurring, fat-soluble photoproduct found in both plants and human and animal skin. Vitamin D\({}_{2}\) is present in plants, while vitamin D\({}_{3}\) is traced mainly in the skin of humans and animals. The predominant difference between the two lies in the additional double bond in vitamin D\({}_{3}\)[25]. Both forms of vitamin D exhibit similar effects, and potency, and follow similar metabolic procedures [4]. Vitamin D is synthesized when exposed to UV\({}_{\rm B}\) radiation in fungi, plants, animal skin, and human skin. At this point, it is important to mention that other forms of vitamin D exist, such as D\({}_{1}\), D\({}_{4}\), and D\({}_{5}\), but are of little significance to humans [26]. Chemically, vitamin D shares a structure with cholesterol, consisting of four rings, labeled A, B, C, and D. A broken B ring in cholesterol results in the formation of vitamin D [24]. Since its discovery, vitamin D has been integrated into numerous applications, for several medical, cosmetic, and cosmeceutical purposes [10]. Furthermore, vitamin D has also been implicated in skin aging processes [27; 28]. Many cells, particularly skin cells, can produce both vitamin D and its active form, which exerts distinct biological effects based on the target cell. This observation led to classifying vitamin D as a fat-soluble prohormone and its active form as a hormone [29]. The active complex of vitamin D\({}_{3}\), 1\(\alpha\),25(OH)\({}_{2}\)D\({}_{3}\), plays a significant role as a regulator in a plethora of biochemical cascades and homeostasis systems [30]. The discovery of vitamin D's astonishing properties sparked the development of a wide range of skincare products, cosmetic formulations, and systemic and topical analogs. Vitamin D's anti-inflammatory [25; 30], anti-melanoma [31; 32], anti-cancer [33], anti-UV [25], anti-aging [25; 27; 34], and antioxidant properties [25; 35] have been harnessed against various diseases or conditions, including psoriasis [36; 37; 38], atopic dermatitis [39; 40; 41], vitiligo [42; 43], acne [44; 45; 46], and rosacea [47]. This review explores vitamin D's structural, chemical, biological, and metabolic roles, highlighting its unique activity and supporting its increasing use in the cosmeceutical field. In addition, the review addresses vitamin D's role in different skin-related conditions, the controversies surrounding its impact, and suggestions for future research mainly within the cosmetic domain. A timeline summarizing key milestones in vitamin D research is presented in Figure 1.  
Figure 1: Historical timeline on significant findings in vitamin D research over time. (Parts of this figure were obtained by [[https://thenounproject.com/](https://thenounproject.com/)]([https://thenounproject.com/](https://thenounproject.com/)) (accessed on 29 December 2024)).
## 5 Experimental Results
### RQ2: How Efficient Is LinearTGAN in Improving Wafer Measurement and Inspection Performance?  
#### 5.2.1 Measurement Performance  
A defect detection and segmentation experiment was conducted to evaluate the impact of the proposed SR model on measurement performance. Figure 5 provides an overview of the entire evaluation process. Since the dataset did not contain naturally occurring crack defects, pseudo-defect images were synthesized by superimposing artificial defect masks onto crack-free LR images using OpenCV, enabling a controlled and consistent experimental setup. The SR model was integrated as a preprocessing step and a post-enhancement module. A segmentation network based on the YOLOv8-seg architecture [48] was trained using HR images and evaluated on a test set comprising image patches with dimensions of \(640\times 640\). The evaluation was performed on three input types: original LR images, SR outputs generated by the proposed model, and HR ground-truth images.  
Figure 4: Visual comparisons of the results. Zoom in for a more detailed analysis.  
Figure 6 presents the quantitative evaluation of defect detection and segmentation performance based on four standard metrics: precision, recall, \(mAP_{50}\), and \(mAP_{50-95}\)[49]. The HR inputs yielded the highest performance across all metrics, serving as the upper bound for comparison. The SR images generated by the proposed model closely approximated the HR baseline, exhibiting only minor deviations. In contrast, LR inputs demonstrated significantly reduced performance, particularly in recall (0.573) and \(mAP_{50-95}\) (0.530), underscoring the limitations of LR data for accurate segmentation. These findings confirm that the SR model successfully reconstructs critical structural details, thereby enhancing segmentation accuracy and reinforcing its applicability to non-destructive defect inspection workflows. For a detailed qualitative comparison of segmentation outcomes, refer to Supplementary Figure S4.