esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
## 4 Results  
### Experimental Setup and Results Analysis  
#### 4.1.1 Dataset Introduction  
Part of the dataset is generated by image-downloader, an open-source project that allows users to download images from Google, Bing, and Baidu websites by entering the name of the Image [46]. The other part of the data set mainly comes from Vision China, and this website has video data specifically for aerial photography [47]. The dataset includes six prairie animals, elephants, zebrans, bison, wild horses, giraffes, and hippos, each with about 500 images [48]. Consideration of different time periods, different angles, different distances and occlusions, etc., was achieved by rotating the pictures at different angles, adjusting the contrast, etc. The number of datasets was thus increased to 4 times the original number. The makesense.ai tool was used to label the grassland animals in the picture and divide the dataset into a training set and a test set at a ratio of 9:1. There were  
Figure 6: The network structure of improved SPP module.  
3000 images in the basic dataset, and the resolution of most of the images was 1200 \(\times\) 960. After data amplification, the total dataset contained 12,000 images. Meanwhile, YOLOv5 uses many effective data processing methods to increase the accuracy of the training model and reduce the training time. The main methods of data amplification are Mosaic and Cutout. In addition to these two methods, YOLOv5 also uses image perturbation, changes in brightness, saturation, and hue, the addition of noise, random scaling, random cropping, flipping, rotating, random erasure, etc., to expand the amount of data.
# Pulsed Thermography Applied to the Study of Cultural Heritage
## 1 Introduction
Different thermographic configurations have been used for the analysis of wood-based and canvas-based paintings, providing successful results, for instance, in the detection of delaminations and degraded regions and also in the study of _pentimenti_ and underdrawings by integrating IRT with other imaging techniques [13; 42; 43; 44; 45]. Moreover, in 2012, [PERSON] et al. proposed a new method called Thermal Quasi-Reflectography (TQR), which consists of recording the middle-wave infrared (MWIR) radiation reflected by the investigated sample. Such a method has been successfully applied to the study of rural paintings showing details that are not revealed by conventional infrared reflectography (IRR) [46]. In this review, the results obtained in the past, as well as the new ones, which were considered useful for a more complete description of the state of the art of pulsed IRT applications in the field of cultural heritage are presented. Accordingly, the results are grouped based on the three main categories: the analysis of bronze sculptures, the recovery of hidden or lost texts in ancient books and documents, and the characterization of painted decorations in archival and library supports. First, the application of pulsed IRT to the analysis of ancient bronzes, like the Capitoline She Wolf, which aimed to detect features under the polished and patinated surfaces, are reviewed [47; 48; 49]. Some geometrical characteristics of the casting faults are also investigated by means of a series of quantitative studies [49]. Moreover, the results obtained by IRT in the detection of hidden or invisible book contents are shown. It is described how texts found in many historical bookbindings, generally belonging to scraps of older books and reused to assemble more recent book structures, can be investigated without dismantling the bookbinding [50]. Different kinds of paint decorations on parchment supports are also analyzed by IRT. In particular, IRT is proposed for the investigation of structural, surface, and subsurface features in illuminations [51]. It is shown how IRT allows researchers to characterize the conservation state of the gildings, revealing detachments and inhomogeneities of the gold leaf, and how it can be employed in the detection of subsurface features buried beneath the paint layer, like underdrawings and _pentimenti_[52], which are commonly investigated by other techniques operating in the infrared region [52; 53]. Finally, the integration of infrared thermography and three-dimensional (3D) shape recording methods [54; 55] is discussed, presenting a 3D representation of the IRT results obtained in the case study of the Capitoline Brutus.
###### Abstract  
In the context of the escalating requirement for high-throughput multimedia services, Orthogonal Frequency Division Multiplexing (OFDM) signal systems have become increasingly prevalent in Low Earth Orbit (LEO) satellite communications. In order to promote the judicious, efficient, and cost-effective deployment of satellite spectrum resources, there is a critical need to augment the capabilities of spectrum monitoring technology for uncollaborative LEO satellite OFDM signals. Addressing the complexities inherent in the broad bandwidth, substantial dynamic range, and weak prior knowledge associated with LEO satellite OFDM signal monitoring, this study introduces an innovative methodology. This approach harnesses a broadband parallel flexible filtering variable sampling technique to facilitate the real-time observation of satellite OFDM signals across a wide bandwidth spectrum. Moreover, the research presents a dynamic compensation technique, which utilizes ephemeris information, to mitigate frequency offset and amplitude fading issues within the monitored signals. Post compensation, the study conducts signal identification and parameter analysis, utilizing the intrinsic features of OFDM signals. This technique empowers real-time monitoring and the accurate analysis of satellite broadband OFDM signals, ensuring robust performance in scenarios characterized by weak prior knowledge and significant dynamic variations.  
Keywords:signal monitoring; modulation identification; OFDM signal analysis; dynamic compensation; resampling filters +
Footnote †: journal: Electronics
## 4 Methodology
### Solution Representation and Evaluation
#### 4.1.2 Solution Evaluation  
A fitness function _fit_(\(\cdot\)) is a particular type of an objective function that is used to measure the quality of the solution that increases the chance of finding good solutions and reaches higher coverage for the graph. Thus, there is a direct relationship between the fitness function and the solution quality, so better solutions have a higher fitness function value than worse ones. The fitness function is used to measure the solution effectively.  
\[fit(x)=\alpha\frac{V_{D}}{V_{n}}+\beta\frac{C_{n}}{\sum_{i}^{V_{n}}x_{i}}+ \gamma\frac{V_{n}-\sum_{i}^{V_{n}}x_{i}}{V_{n}}, \tag{1}\]  
where \(V_{D}\) represents number of vertices dominated by the subset of vertices \(D\) in the solution \(x\) and \(V_{n}\) is the total number of vertices in the graph, \(C_{n}\) is the number of vertices in largest connected set in the solution \(x\), and \(\alpha\), \(\beta\) and \(\gamma\) are weights with \(\alpha+\beta+\gamma=1\). The fitness function _fit_ consists of three parts. The first part \(v_{D}/v_{n}\), reflects the size of domination on \(G\) by \(x\). If \(x\) represents a dominating set, then this part is equal to \(1\). The second part, \(\
icefrac{{C_{n}}}{{\sum_{i}^{V_{n}}}}\), reflects the connectivity between the vertices in \(x\). This part is equal to \(1\) if \(x\) represents a connected set. While the third part, \((v_{n}-\sum_{i}^{V_{n}}x_{i})/v_{n}\), distinguishes between two solutions that are equal in the first part and the second part according to the percentage of vertices which are not contained in each of them such that better solutions have a higher percentage than worse ones.
## 5 Modeling  
In the study, a nonlinear analysis method is utilized to describe the relationship between the input parameters and the visual clarity scale. A third-order polynomial regression model is applied, which can be formulated as Equation (1), where \(s\) is the illuminance of the surrounds and \(b\), \(t\), and \(C_{A}\) represent the luminance of the backgrounds and text and absolute lightness contrast (abs(\(t\)-\(b\))) between the backgrounds and text, respectively. They are selected as the inputs for the training procedure. The number of parameters is 20 for the polynomials. The psychophysical experiment for visual clarity collects 240 datasets. A feature scaling method, Min-Max Normalization, is applied to the input data to improve accuracy and faster convergence.  
\[\begin{split} VC(s,b,t,C_{A})&=M_{1\times 1 9}\big{[}s^{3}\ b^{3}\ t^{3}\ C_{A}^{3}\ s^{2}\ b^{2}\ t^{2}\ C_{A}^{2}\ s\ b\ t\ C_{A}\ sb\ st\ sC_{A}\ bt\ bC_{A}\ tC_{A}\ \ 1\big{]}^{T},\\ M=\big{[}107.5\ -2.2\ -0.1\ 1.2\ -135.4\ 21.3\ 18.5\ -23.5\ -7.3\ -0.6\ 1.9\ 6.6\ 31.3\ 6.4\ 46.5\ -38.2\ 1.4\ 0.3\ 1\big{]} \end{split} \tag{1}\]  
Three evaluation metrics, including the coefficient of determination (r\({}^{2}\)), mean absolute error (MAE), and root mean square error (RMSE), are commonly used to evaluate a machine learning model's performance for a regression problem. Figure 7 shows the scatter plot of the predicted values against the ground truth. According to the results, the visual clarity scale obtained from the observers can be well predicted with a high R-squared and low RMSE and MAE, where the values are 0.99, 0.11, and 0.08, respectively. The testing results are listed in Table 5. Additionally, the model shows a high correlation coefficient, and Pearson's r value is 0.99. The models can be further applied to the AR industry and its manufacturer for developing an advanced HUD with real-time enhancements corresponding to the changes in luminance caused by driving conditions.  
\begin{table}
\begin{tabular}{c c
## 2 Materials and Methods
### Environmental Covariates
#### 2.3.3 Organism Covariates  
The organism covariates mainly originated from spectral indices in Landsat 8 OLI and data products from MODIS. For each image, we calculated nine vegetation spectral indices (e.g., CANI: canopy index, EVI: enhanced vegetation index, NDRI: normalized difference red/green redness index, and NDVI: normalized difference vegetation index). The annual average and variance of the nine indices for the growth period of crops (from May to September) and the non-growth period of crops (from October to April in the next year) from 2015 to 2017 were calculated to indicate the average growth of vegetation and the coverage state of vegetation, respectively [37]. A total of four variables (i.e., GPP: gross primary production, NPP: net primary productivity, FPAR: fraction of photosynthesis) active radiation, and LAI: leaf area index) were collected from MOD17A2H and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **Abbreviation** & **Scale** & **Covariate a)** & **Type b)** & **Period** & **Reference** \\ \hline The average of NDRI in the non-crop period & NCRoNDRIa & 30 m & O & Q & 2015–2017 & [25] \\ The average of GPP in the non-crop period & NCRoGPPa & 500 m & O & Q & 2015–2017 & [31] \\ The average of NPP in the non-crop period & NCRoNPPa & 500 m & O & Q & 2015–2017 & [31] \\ The average of FPAR in the non-crop period & NCRoFPARa & 500 m & O & Q & 2015–2017 & [32] \\ The average of LAI in the non-crop period & NCRoLAla & 500 m & O & Q & 2015–2017 & [32] \\ The variance of GPP in the non-crop period & NCRoGPPv & 500 m & O & Q & 2015–2017 & [31] \\ The variance of NPP in the non-crop period & NCRoNPPv & 500 m & O & Q & 2015–2017 & [31] \\ Elevation & ELE & 90 m & R & Q & 2000 & [33] \\ Channel network base level & CNBL & 90 m & R & Q & 2000 & [33] \\ Valley depth & VD & 90 m & R & Q & 2000 & [33] \\ Terrain wetness index & TWI & 90 m & R & Q & 2000 & [33] \\ Oblique geographic coordinate at 30\({}^{\circ}\) & OGC30 & 30 m & N & Q & / & [34] \\ Oblique geographic coordinate at 45\({}^{\circ}\) & OGC45 & 30 m & N & Q & / & [34] \\ Oblique geographic coordinate at 60\({}^{\circ}\) & OGC60 & 30 m & N & Q & / & [34] \\ Oblique geographic coordinate at 105\({}^{\circ}\) & OGC105 & 30 m & N & Q & / & [34] \\ Oblique geographic coordinate at 120\({}^{\circ}\) & OGC120 & 30 m & N & Q & / & [34] \\ Oblique geographic coordinate at 165\({}^{\circ}\) & OGC165 & 30 m & N & Q & / & [34] \\ \hline \hline \end{tabular} a) Soil and parent material covariate (S&P), climate covariate (C), organism covariate (O), relief covariate (R), and position covariate (N). b) Quantitative variable (Q).  
\end{table}
Table 1: _Cont._MOD15A2H [31; 32] from 2002 to 2017 and were aggerated in the same manner as the vegetation indices. Meanwhile, two categorical variables (i.e., CroP: crop types and Crol: crop intensity) were also collected [41; 42].
## 6 Conclusions  
In the current work, an experimental application of an integrated system has been presented. Such a system, which was housed on a hotel roof, was able to extract water from air by means of a reverse compression cycle, and, at the same time, to treat such water to a quality suitable for human consumption, to provide domestic water heating and to deliver a dry and fresh airflow. The system was connected to the existing hotel heating plant, and to the hotel's kitchen and laundry. The test period was approximately one month. During that period, water and energy savings and other machine parameters, including chemical parameters, were monitored. At the end of the experiment, total savings for the month of November were estimated to be USD 7733. Water quality was confirmed to be in compliance with local and other international laws.  
A tailored simulation software tool, calibrated on the basis of the experimental results, was also employed to determine the expected saving for a whole year of the system's operation. On the basis of experimental and simulation results, the payback time of the complete system was estimated. Results were highly satisfactory, not only because they demonstrated that an effective employment of all effects of a reverse cycle was possible, but also from an economic point of view, with a calculated payback time of 2-2.5 years. Furthermore, a procedure aimed at determining the effectiveness of such an integrated system was also developed.  
**Author Contributions:** Sections 1, 5 and 6, A.M.; Sections 2, 3, 4, 5, L.C.; Section 4, P.C.  
**Funding:** This research has been funded by SEAS, Societe de l'Eau Aerienne Suisse.  
**Acknowledgments:** This research has been supported by SEAS, Societe de l'Eau Aerienne Suisse. All the technical contents, contained in the present paper, are patented and copyrighted by SEAS and belong to SEAS.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
**Symbols**  
AHU Air handling unit  
Am average arrestance (ASHRAE) [%]  
c water specific heat [kJ/(kg K)]  
C\({}_{\rm LPG}\) lower calorific value [kJ/dm\({}^{3}\)]  
DHW Domestic Hot Water  
EER Energy Efficiency Ratio [-]  
Em Average Efficiency (ASHRAE) [%]  
E\({}_{\rm s}\) energy consumption of the traditional system [kWh]  
\(\updelta\) maximum number of guests  
G\({}_{\rm da}\) dry air airflow [kg/s]  
G\({}_{\rm na}\) humid air airflow [kg/s]  
h humid air specific enthalpy [kJ/kg]  
LPGc fuel consumption [dm\({}^{3}\)]  
oc occupancy ratio [%]  
RH relative humidity [%]  
T temperature [\({}^{\circ}\)C]  
Wd daily DHW consumption [dm\({}^{3}\)]  
Wg daily DHW for each guest [dm\({}^{3}\)/day]  
x hygrometric degree, or moisture content in air [g vapor/kg dry air]  
\(\Delta\)t temperature difference [\({}^{\circ}\)C]  
\(\eta\) plant efficiency [-]  
\(\rho\) specific mass [kg/dm\({}^{3}\)]  
\(\tau\) time [h]  
\(\tau_{\rm ac}\) air changes time per day [h/day]  
\(\varphi\) heating power [kW]  
\(\varphi_{\rm el}\) electric power [kW]  
**Subscript**  
e outdoor environment  
dp dew point [\({}^{\circ}\)C]  
m mean  
out outlet section of the machine
## 2 Materials and Methods
### Tests in Cement Production  
A series of composite Portland and slag cements were manufactured in laboratory scale by co-grinding 20% and 40% of each slag, respectively with clinker of ordinary Portland cement (OPC). In all cement mixtures 5% gypsum was used and the remaining mass was clinker of OPC. Reference OPC (95% clinker, 5% gypsum) mixtures were also manufactured for comparison purposes. The experimental procedure involved separate crushing of the slag and the clinker, so that 95% is below 5 mm. The crushed materials were mixed with gypsum in the aforementioned ratios and subsequently co-ground in a 5-kg ball mill until targeted specific surface (Blaine) of 4500 cm\({}^{2}\)/g was achieved. Indicatively two cements incorporating 20% slag but targeting to lower Blaine of 3650 cm\({}^{2}\)/g were also comparatively manufactured along with corresponding reference OPC. Once grinding was completed, proper specimens were prepared for the determination of the experimental cement's technological properties according to the corresponding standards specified in EN 197-1 [17], the standard in force for cement products. Initial and final setting time, expansion and early, late and long-term compressive strengths up to 90 days of hydration were measured.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Dust** & **Ore Fines** \\ \hline FeO\({}_{\rm x}\) & 30.44 & 36.65 \\ SiO\({}_{2}\) & 35.00 & 34.85 \\ MnO & 0.50 & 0.87 \\ Cr\({}_{2}\)O\({}_{3}\) & 3.00 & 5.82 \\ Al\({}_{2}\)O\({}_{3}\) & 11.55 & 4.31 \\ CaO & 4.10 & 4.18 \\ MgO & 5.27 & 5.92 \\ NiO & 1.80 & 1.37 \\ S & 0.44 & 0.20 \\ C\({}_{\rm tot}\) & 3.60 & 0.94 \\ Moisture & 0.24 & 1.82 \\ \hline \end{tabular}
\end{table}
Table 1: Typical chemical analysis (wt.%) of Ni-bearing dust and lateritic ore fines [4].  
In the final step of this study, co-evaluation of the results was performed and correlations between the mineralogical analysis and microstructural properties of the produced steel slags with the physical and mechanical characteristics of the produced cements were acknowledged. Since the European Norm in force for the types of cements [17] refers only to the utilization of GGBS in cement production, experimental cements classification was made indicatively following the corresponding ranges described in [17] for blast furnace slags. The obtained results of this research are hereafter presented and discussed.
###### Abstract  
In existing cryptographic key distribution (CKD) protocols based on computational ghost imaging (CGI), the interaction among multiple legitimate users is generally neglected, and the channel noise has a serious impact on the performance. To overcome these shortcomings, we propose a multi-party interactive CKD protocol over a public network, which takes advantage of the cascade ablation of fragment patterns (FPs). The server splits a quick-response (QR) code image into multiple FPs and embeds different \"watermark\" labels into these FPs. By using a CGI setup, the server will acquire a series of bucket value sequences with respect to different FPs and send them to multiple legitimate users through a public network. The users reconstruct the FPs and determine whether there is an attack in the public channel according to the content of the recovered \"watermark\" labels, so as to complete the self-authentication. Finally, these users can extract their cryptographic keys by scanning the QR code (the cascade ablation result of FPs) returned by an intermediary. Both simulation and experimental results have verified the feasibility of this protocol. The impacts of different attacks and the noise robustness have also been investigated.  
cryptographic key distribution; multi-party communication; computational ghost imaging; quick-response code; watermark embedding and extraction; identity authentication +
Footnote †: journal: Sensors  
**2022**, 22, 3994. [[https://doi.org/10.3390/s22113994](https://doi.org/10.3390/s22113994)]([https://doi.org/10.3390/s22113994](https://doi.org/10.3390/s22113994))
## 3 Numerical Analysis
### Findings from Benchmarking  
In this paper, we assumed a scenario with a dominant LoS path, therefore the increase in the average SLL due to tiling and sparse design of STPA and SPA was not detrimental to communication performance, as all apertures demonstrated the same spectral efficiency. The sensing performance of STPA is between CUPA and SPA. The effect of smaller beamwidth of STPA and SPA is reflected in a smaller DLR than the one of CUPA. However, the higher SLLs of SPA and STPA can still result in errors in detection, which results in similar RMSE for these three apertures. The results demonstrate that the SPA has slightly smaller DLR and RMSE than the ones of STPA. This was expected as SPA has a lower average SLL than STPA due to the freedom in the location of its elements, but this freedom makes the manufacturing of SPA more complex. STPA is uniformly distributed in the subarray level and easier to manufacture, although it still has a sparse subarray structure.
## 4 Results and Discussion
### Landslide Susceptibility Maps  
Maps of landslide susceptibility were produced for visual comparison based on the probability of landslide initiation at each raster point, derived from the tuned aforementioned models that provided predictions along with probabilities. The Jenks natural breaks method [51] was employed to reclassify the probability of the original map into five classes: very low, low, medium, high, and severe, representing the susceptibility of 3,093,091 raster points in total, namely the potential of landslide initiation when triggering events happened (Figure 9). By comparing the susceptibility maps of each model, it is evident that while the area proportions of the predicted risk levels vary, the distribution of high-risk and severe-risk zones shows a high degree of consistency. These zones are consistently concentrated along the section of the G3033 Duku Expressway that traverses the Tianshan Mountains. Although the models differ in their underlying mechanisms, they all identified similar high-risk areas along this segment, indicating that topographic and geological factors played a dominant role in model determination. The consistency in the distribution of high-risk zones also suggests that, despite differences in algorithms and non-landslide unit sampling strategies, the models exhibit a certain degree of robustness in landslide susceptibility assessment. Table 2 shows the zonal statistics of landslide susceptibility maps, providing a detailed breakdown of the spatial distribution of landslide susceptibility areas across different models and sampling strategies. The spatial distribution, number of landslide points, and landslide density within each zone are used to assess the effectiveness of each model. Among all models, the SVM-GLHM and SVM-RF-GLHM models exhibit the highest landslide point densities in the \"Severe\" zones, reaching 0.1679 point/km\({}^{2}\) and 0.1791 point/km\({}^{2}\), respectively. This indicates a strong clustering of actual landslide occurrences in areas predicted as high risk, demonstrating strong model performance under the GLHM strategy. Conversely, models utilizing random sampling, such as SVM-Random and SVM-RF-Random, also captured a substantial proportion of landslides in their \"Severe\" zones, achieving point densities of 0.2010 point/km\({}^{2}\) and 0.1525 point/km\({}^{2}\), respectively. While these models showed a more dispersed distribution of landslide points across zones, their high-risk categories still effectively identified landslide locations. Figure 9: _Cont._