esa-sceva/satcom-chunk-collection huggingface dataset:
# Development and Evaluation of Nano-Silica Sustainable Concrete
## 2 Materials and Methods
### Mix Proportions  
Two different cases were studied in this experiment: the influence of nano-silica addition on the durability and strength of concrete mixtures that consist of a 70% dosage of GGBS (M1, M2, and M3) and 30% dosage of GGBS (M4, M5, and M6). M1 and M4 were used as control mixturesm because they contain zero nano-silica dosage. On the other hand, M2 and M3 incorporated 1% and 2% dosages of nano-silica, respectively, and their various test results were compared with their control mixture, M1. Similarly, M5 and M6 incorporated 1% and 2% dosages of nano-silica, respectively, and their various test results were compared with their control mixture M4. Table 1 shows a tabular summary of the mix designs of all the mixtures.
Remote Sens._**2013**, \(5\), 3803-3825; doi:10.3390/rs5083803  
_Remote Sensing_  
ISSN 2072-4292  
www.mdpi.com/journal/remotesensing  
Disentangling the Relationships between Net Primary Production and Precipitation in Southern Africa Savannas Using Satellite Observations from 1982 to 2010  
**[PERSON] * and [PERSON]**  
Department of Geography, University of Florida, Gainesville, FL 32611, USA;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-352-870-9106; Fax: +1-352-392-8855.  
_Received: 18 June 2013; in revised form: 29 July 2013 / Accepted: 29 July 2013 / Published: 2 August 2013_  
**Abstract:** To obtain a better understanding of the variability in net primary production (NPP) in savannas is important for the study of the global carbon cycle and the management of this particular ecosystem. Using satellite and precipitation data sets, we investigated the variations in NPP in southern African savannas from 1982 to 2010, and disentangled the relationships between NPP and precipitation by land cover classes and mean annual precipitation (MAP) gradients. Specifically, we evaluate the utility of the third generation Global Inventory Monitoring and Modeling System (GIMMS3g) normalized difference vegetation index (NDVI) dataset, in comparison with Moderate-resolution Imaging Spectroradiometer (MODIS) derived NPP products, and find strong relationships between the overlapping data periods (2000-2010), such that we can apply our model to derive NPP estimates to the full 29-year NDVI time-series. Generally, the northern portion of the study area is characterized by high NPP and low variability, whereas the southern portion is characteristic of low NPP and high variability. During the period 1982 through 2010, NPP has reduced at a rate of \(-\)2.13 g\(\cdot\)C\(\cdot\)m\({}^{-2}\cdot\)yr\({}^{-1}\) (\(p<0.1\)), corresponding to a decrease of 6.7% over 29 years, and about half of bush and grassland savanna has experienced a decrease in NPP. There is a significant positive relationship between mean annual NPP and MAP in bush and grassland savannas, but no significant relationship is observed in tree savannas. The relationship between mean annual NPP and MAP varies with increases in MAP, characterized as a linear relationship that breaks down when MAP exceeding around 850-900 mm.  
keywords: NPP; precipitation; savanna; NDVI; Southern Africa; AVHRR; MODIS +
Footnote †: journal: Journal of the American Statistical Association
# A Two-Stage Method to Test the Robustness of the Generalized Approximate Message Passing Algorithm
## 3 Fixed Support GAMP Algorithm  
According to the belief propagation theory, if the number of cycles on a factor graph is low, then the robustness of message passing will be enhanced [8]. Therefore, we have an idea that the message passing can be restricted just on the support \(\Lambda\) if \(\Lambda\) has been determined in some way. That is the meaning of FS-GAMP. The steps of FS-GAMP are listed in Algorithm 2. It is very similar to the GAMP algorithm, but the major difference between FS-GAMP and GAMP is that the entries which do not belong to the support \(\Lambda\) are assigned to 0 directly.  
```
Input:\(\mathbf{y},\mathbf{A},\Lambda\) Description:\(\mathbf{x}(t+1),\
u^{\mathbf{x}}(t+1)\)
1:\(\hat{\mathbf{x}}_{\Lambda}(t=1)\leftarrow\mathbb{0}^{|\Lambda|\times 1};\
u^{ \Lambda}(t=1)\leftarrow\mathbb{1}^{|\Lambda|\times 1};\hat{\mathbf{s}}(t=0) \leftarrow\mathbb{0}\)
2:repeat
3:\(\
u^{\mathbf{r}}(t)\leftarrow|\mathbf{A}_{\cdot,\Lambda}|^{2}\
u^{\mathbf{x} }_{\Lambda}(t)\)
4:\(\hat{\mathbf{p}}(t)\leftarrow\mathbf{A}_{\cdot,\Lambda}\hat{\mathbf{x}}_{ \Lambda}(t)-\hat{\mathbf{s}}(t-1)\odot\
u^{\mathbf{r}}(t)\)
5: {(line 5)-(line 8) of GAMP}
6:\(\
u^{\mathbf{x}}_{\Lambda}(t)\gets 1\odot((|(\mathbf{A}_{\cdot,\Lambda}|^{2})^{ 2}\Gamma^{\mathbf{v}}\mathbf{s}(t))\)
7:\(\hat{\mathbf{r}}_{\Lambda}(t)\leftarrow\hat{\mathbf{x}}_{\Lambda}(t)+\
u^{ \mathbf{x}}_{\Lambda}(t)\odot(\mathbf{A}_{\cdot,\Lambda}^{T}\hat{\mathbf{s}}(t))\)
8:\(\hat{\mathbf{x}}_{\Lambda}(t+1)\leftarrow[\hat{\mathbf{x}}_{i}(t+1)]\), where \(i\in\Lambda,\hat{\mathbf{r}}_{i}(t+1)\triangleq\mathbb{E}\{\mathbf{x}_{i}|r _{i}=\hat{r}_{i}(t),\
u^{\mathbf{r}}_{i}(t)\}\)
9:\(\
u^{\mathbf{x}}_{\Lambda}(t+1)\leftarrow[\
u^{\mathbf{x}}_{i}(t+1)]\), where \(i\in\Lambda,\
u^{\mathbf{x}}_{i}(t+1)\triangleq\mathrm{var}\{\mathbf{x}_{i}| r_{i}=\hat{r}_{i}(t),\
u^{\mathbf{r}}_{i}(t)\}\)
10:until
```  
**Algorithm 2**Fixed Support GAMP (FS-GAMP).
## 3 Results
### Effect of DPPH and ABTS Radical Scavenging Activity  
By assessing the capacity of JAUN to scavenge DPPH and ABTS free radicals, its antioxidative proficiency was initially established. As shown in Figure 2, JAUN exhibited a significant dose-dependent manner in effectively reducing the formation of DPPH and ABTS free radicals. The IC\({}_{50}\) values for DPPH and ABTS free radical scavenging were 155.9 and 183.3 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, respectively.
# Multi-UAV Area Coverage Track Planning Based on the Voronoi Graph and Attention Mechanism
## 3 Method
### Region Division Algorithm
It leverages quadtree and interval arithmetic to enhance computational efficiency, discretely calculating the set of pixels with minimal distances and ultimately deriving the Voronoi diagram of the planar polygon. The core idea of this algorithm is to convert an arbitrary polygonal area into multiple triangles or quadrilaterals, thereby providing an effective method for solving complex multi-UAV area coverage problems. Through this transformation, the multi-UAV area coverage problem in natural scenes is subdivided into smaller regions, each of which can be handled by a single drone. As a result, each drone has a clear task area, simplifying the complex multi-UAV trajectory planning problem into more manageable single-drone trajectory planning problems, as shown in Figure 1. This approach not only significantly reduces the complexity of the problem but also improves the efficiency and accuracy of trajectory planning. Make a path covering the entire responsible area to ensure that every point in the area can be effectively covered while minimizing the flight distance as much as possible to save time and energy. To achieve this goal, the trajectory planning algorithm will consider various factors, including selecting take-off and landing points, obstacle avoidance strategies, and adjusting flight altitude and speed. Another important application of the regional decomposition algorithm is the rapid response to emergencies. Since each drone is only responsible for a relatively small area, when an emergency occurs in a specific area, such as natural disasters, environmental pollution, etc., the drone responsible for the area can quickly monitor and evaluate, and other drones can also quickly adjust their tracks as needed to assist in completing emergency tasks. This flexibility and rapid response capability are challenging based on traditional methods. Figure 1: Schematic diagram of regional decomposition. In addition, multi-UAV trajectory planning based on a regional decomposition algorithm is also conducive to collaborative work between UAVs. In actual operation, through the precise division of regions and the transparent allocation of tasks, mutual interference and conflict between UAVs can be effectively avoided, ensuring the smooth progress of the entire regional coverage task. At the same time, this method also facilitates information sharing and collaboration between UAVs. When a UAV completes a task in its area, it can adjust its action plan according to the needs of the overall task and assist other UAVs in completing the task, thereby improving the efficiency and effectiveness of the entire system. Converting the complex multi-UAV regional coverage problem into multiple single-UAV trajectory planning problems not only greatly simplifies the complexity of the problem but also improves the efficiency and accuracy of task execution.
# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 4 Experiments
### Efficiency Analysis
#### 4.7.2 Runtime Test  
To evaluate the efficiency of the proposed method, we tested images of different resolutions on the following platforms. The test environment is as follows:  
* CPU: Intel(R) Xeon(R) Platinum 8368 CPU @ 2.40 GHz;
* GPU: NVIDIA RTX 4090, 24 GB VRAM;
* Operating system: Ubuntu 20.04.  
The experiment uses 10 types of satellite images from the SPARK 2021 dataset. For each type, 200 images are randomly selected. The proposed method is tested at three different resolutions: 256 \(\times\) 256, 512 \(\times\) 512, and 1024 \(\times\) 1024. For each resolution, the average processing time (in milliseconds) and frame rate (Frames Per Second, FPS) are recorded. The detailed results for each satellite image type are presented in Table 5.  
To more intuitively show how the performance changes with image resolution, we plotted a bar chart of the average processing time of different satellite images at three resolutions, as shown in Figure 21.  
The results show that at a lower resolution (256 \(\times\) 256), the average processing time is less than 75 ms, and the frame rate can reach more than 13 FPS, which meets the real-time processing requirements of most space scenarios. At a resolution of 1024 \(\times\) 1024, although the processing time has increased, it can still reach about 5 FPS, showing good scalability and algorithm stability.  
In future actual deployments, target detection can be combined to determine space targets, set a small sliding window, and only enhance and process the target area. This will further reduce the computational burden, improve the real-time response capability of the overall system, and meet the real-time processing requirements of applications in space scenes.
## 3 Results and Discussion
### Raman Spectroscopy of NR-SWCNTs Composites
Figure 8: Raman spectra of (**a**) G-, and (**b**) G’-band of the NR-SWCNTs composites using different cross-linking agents. Additionally, Figure 8b shows a comparison of the G'-band Raman spectra of the pristine SWCNTs and the NR-SWCNTs composites. In most of the NR-SWCNTs composites vulcanized using either sulfur or organic peroxides, the G'-band exhibits decrements in intensity and notable shifts toward higher-frequency compared to the pristine SWCNTs. Shifts in the G' absorption band for the vulcanized materials were calculated from the difference in frequency recorded for the NR-SWCNTs composites and the pristine SWCNTs (2561 cm\({}^{-1}\)) and included in Table 7. As can be observed, the NR-SWCNTs composites vulcanized with organic peroxides show positive shifts from 13 up to 17 cm\({}^{-1}\) concerning the original G'-band frequency, which indicates an interaction between the free radicals generated during the NR vulcanization and the sidewalls of the SWCNTs. According to [PERSON] et al. [18], the extent of the G'-band shift can be correlated to the degree of vulcanization in elastomer composites. Based on this and from the mechanical properties and cross-linking degree obtained in our NR composites, we can confirm that the addition of a small content of SWCNTs (0.7 phr) to NR formulations favorably promotes the vulcanization of NR, mainly when organic peroxides are used as cross-linking agents; this is due to the multiple interactions between SWCNTs and the free radicals generated during the vulcanization process, which leads to considerable enhancement in physical properties of the developed NR-SWCNTs composites.
###### Abstract  
Gyroscopes have been used in previous studies to measure the peak angular velocity of the shoe or foot in the frontal plane (evVel). However, it is not clear whether different test conditions (footwear hardness or locomotion speed) can influence the accuracy of evVel. The purpose of the present study was to compare the accuracy of gyroscopes and electrogoniometers when measuring evVel and the time until evVel (t_evVel) in 12 different conditions using a single axis gyroscope attached to the heel cap. Twenty-four recreational runners were instructed to walk and run on a 15-m indoor track at four locomotion speeds (1.5, 2.5, and 3.5 m/s, and individual running speed) and in three footwear conditions (low to high hardness). The gyroscope data and electrogoniometer data were sampled at a rate of 1000 Hz. Comparisons between both measurement devices showed small mean differences up to 49.8 \(\pm\) 46.9 deg/s for evVel and up to 5.3 \(\pm\) 3.5 ms for t_evVel. Furthermore, strong relationships between gyroscope and electrogoniometer data were found for evVel as well as for t_evVel for all conditions. It can be concluded that gyroscopes can be used to accurately determine evVel and t_evVel under a variety of conditions.  
**Keywords:** peak eversion velocity; gyroscope; electrogoniometer; running speed; footwear; material characteristics
# Popularity-Aware Closeness Based Caching in NDN Edge Networks
## 3 PaC-Based Caching
### A Toy Example  
To better understand the PaC metric, we consider the toy example in Figure 6 that includes eight edge nodes in a hierarchical topology. The nodes \(v_{1}\), \(v_{2}\),...,\(v_{5}\) are ingress nodes tracking the average content request rate. For the sake of simplicity, we assume that three distinct popular contents, namely \(x_{1},x_{2},x_{3}\), should be cached at the edge and their request rate is stable. The available storage space at each node allows for caching only one content.  
It can be observed that the average request rate for \(x_{1}\), the most requested content at the edge, is 22 at \(v_{1}\) and 20 at \(v_{2},v_{3}\), whereas it is 6 at \(v_{5}\). When computing the PaC  
Figure 4: Interest processing in the presence of PaC-based caching.  
Figure 5: Data processing in the presence of PaC-based caching.  
metric for the nodes traversed by the Interests for \(x_{1}\), according to Equation (5), it results in \(PaC(v_{1},x_{1})=22\), \(PaC(v_{2},x_{1})=PaC(v_{3},x_{1})=20\), \(PaC(v_{5},x_{1})=6\), \(PaC(v_{6},x_{1})=\frac{62\cdot 3}{6}=31\), \(PaC(v_{7},x_{1})=\frac{6\cdot 1}{2}=3\), and \(PaC(v_{8},x_{1})=\frac{68\cdot 4}{12}=22.66\). The node with the highest PaC metric for \(x_{1}\) is \(v_{6}\), which is indeed the node closer to the majority of consumers that are attached to the ingress nodes \(v_{1}-v_{3}\). If instead we consider the path \(\{v_{5}\to v_{7}\to v_{8}\}\), the node with the highest PaC is \(v_{8}\). However, caching \(x_{1}\) at \(v_{6}\) would imply that the node at the upper layer, \(v_{8}\), will not receive further Interests for \(x_{1}\) and therefore, its local request rate will decrease. As a consequence, in a subsequent time window, \(x_{1}\) will be cached at \(v_{5}\).  
Vice versa, an approach based on the betweenness centrality, like BEP, would be considered as best cacher for \(x_{1}\) node \(v_{8}\), which has the highest centrality, but it is also the farthest away from the consumers. Therefore, caching \(x_{1}\) at \(v_{8}\) would highly increase the retrieval delay and the intra-domain traffic.  
Content \(x_{2}\) is requested only at \(v_{4}\) and \(v_{5}\) and it results in \(PaC(v_{4},x_{2})=15\), \(PaC(v_{5},x_{2})=3\), \(PaC(v_{7},x_{2})=\frac{18\cdot 2}{4}=9\), \(PaC(v_{8},x_{2})=\frac{18\cdot 2}{6}=6\). The node with the highest PaC metric is \(v_{4}\) which, again, is the one closer to the majority of consumers. Therefore, the majority of requests will be served with minimum delay and with minimum intra-domain traffic. To serve requests coming from path \(\{v_{5}\to v_{7}\to v_{8}\}\), instead, \(v_{7}\) would be selected as cacher.  
By following the same procedure, it can be found that the higher PaC for content \(x_{3}\) is obtained at \(v_{8}\).
# Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks +
## 4 Model Evaluation: Setting the Stage
### Performance Metrics  
Common methods used to assess the performance of binary classifiers include true positive and true negative rates, as well as ROC (Receiver Operating Characteristics) curves, which display the true positive rate against the false positive rate. These methods, however, are uninformative when the classes are severely imbalanced. In this context, \(F_{1}\)-scores and Precision-Recall Curves (PRC) should be used [9; 10]. They are both based on the values of  
\[\text{Precision}(s)=\frac{\text{true positives}}{\text{true positives}+\text{ false positives}}\]  
against the values of  
\[\text{Recall}(s)=\frac{\text{true positives}}{\text{true positives}+\text{ false negatives}}\]  
where \(s\) is a cutoff probability varying between 0 and 1. Precision quantifies the number of correct positive predictions out of all positive predictions made, whereas recall (often also called sensitivity) quantifies the number of correct positive predictions out of all positive predictions that could have been made. Both focus on the positive class (the minority class, anomalies) and disregard the negative (the majority class, normal observations).  
Our \(F_{1}\) score [11; 12] combines these two measures in a single index defined as  
\[F_{1}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{Precision}+\text{ Recall}}. \tag{13}\]  
The closer the \(F_{1}\)-score to 1, the better the prediction model.  
A PRC displays the values of precision and recall as the cutoff \(s\) varies from 0 to 1. The PRC of a skillful model bows towards the point with coordinates \((1,1)\). The curve of a no-skill classifier is a horizontal line at some \(y\)-level proportional to the proportion of positives in the data set. For a balanced data, set this proportion is just 0.5 [9].  
The PRC and \(F_{1}\)-score are complementary in our approach. The \(F_{1}\)-score is used on the anomaly scores outcomes of the models to identify the best configuration and the best model. A PRC is used to select the best cutoff value used in the prediction of the two classes for each model.
###### Abstract  
This study investigated and compared driver behaviors at signalized and non-signalized roundabouts in Qatar, focusing on turn signal usage, lane change behavior, and correct lane usage. The primary objectives were to determine the frequency of turn signal usage, assess correct lane usage, analyze lane change behavior, and compare these behaviors between the two types of roundabouts. Data were collected through a field study at selected roundabouts, where driver behaviors were observed and analyzed. The results revealed significant differences between signalized and non-signalized roundabouts. Turn signal compliance was higher in signalized roundabouts (up to 45%) compared to non-signalized roundabouts (20%). The rate of lane change in signalized roundabouts was observed to be 31%, whereas it was 14% in non-signalized roundabouts, and correct lane usage compliance was higher in signalized roundabouts (60%) compared to non-signalized roundabouts (35%). These findings suggest that traffic signals contribute to safer and more predictable driver behavior, although congestion and long waiting times in signalized roundabouts present challenges. The study recommends improving signage visibility, optimizing signal timings, enhancing road markings, and enforcing traffic regulations to address these issues. The findings can inform traffic engineers and policymakers in enhancing the safety and efficiency of roundabouts.  
Keywords:transportation; driver behavior; road safety; roundabouts; turn signal usage; lane change behavior _Article_
## 2 Heat-to-Power Applications
### Supercritical CO\({}_{2}\) Power Cycles  
#### 2.3.1 Transcritical CO\({}_{2}\) Rankine Cycle  
Carbone dioxide has interesting properties for waste heat recovery with many beneficial features as displayed in Table 3[43; 44; 45]: very low critical point, non-toxic, non-ignitable, widely available, zero ODP, low GWP of 1 by definition, etc. A screening of the archives shows large amounts of studies on this topic, therefore CO\({}_{2}\) Rankine cycles will be discussed separately here.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Small Scale Systems**} \\ \hline
**Application** & **T Range [\({}^{\circ}\)C]** & **Power Production [kWel]** & **Refrigerant** \\ \hline CPV-T ORC with scroll expander and heat exchanger development, [PERSON] et al. [35] & 65–100 & 3 & R-404a \\ \hline Transcritical ORC with scroll expander and four heat exchangers, [PERSON] et al. [37] & 55–120 & 1.5 & R-134a \\ \hline Transcritical ORC with two multistage centrifugal pumps and a twin-screw expander with induction generator, [PERSON] et al. [38] & 90–100 & 20 & R-218 \\ Supercritical ORC with oil-free compressor-turbine unit for a thermally driven heat-pump, [PERSON] et al. [39] & 95–123 & 2 & R-134a \\ \hline \multicolumn{5}{c}{**Large Scale Systems**} \\ \hline Supercritical ORC for geothermal applications, variable operation according to heat available, [PERSON] et al. [41] & 120–180 & 500 & R-134a \\ \hline Supercritical ORC with radial outflow turbine and PP1 working fluid, [PERSON] et al. [42] & 130–170 & 1000 & PP1 \\ \hline Supercritical ORC with two-stage turbocharger for waste-heat recovery from a gas turbine in a natural gas pipeline, Atlas Copco and Mistral Power. [13] & 250 & 7500 & Butane \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental prototypes in small and large-scale systems.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**M (g/mol)** & **BP (\({}^{\circ}\)C)** & **CP (\({}^{\circ}\)C/MPa)** & **ASHRAE** & **ODP** & **GWP 100 Year** & **Atm. Life (Year)** & **LFL \%** \\ \hline
44 & –78.4 & 31/7.38 & A1 & 0 & 1 & \(>\)50 & none \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical properties of the supercritical carbon dioxide.
# Improved YOLOv7 Algorithm for Small Object Detection in Unmanned Aerial Vehicle Image Scenarios
## 4 FPGA Implementation
### Hardware Platform Processing  
The YOLOv7-UAV model will be deployed on the Zynq hardware platform after quantization and compilation into the DPU executable file. The Zynq device includes a Processing System (PS) that is based on the advanced RISC machine (ARM) and programmable logic (PL) that is based on FPGA [41]. First, the DPU IP will be carried in the hardware platform file in the PS part for neural network acceleration. The architecture and configuration of the DPU IP core must take into account the hardware resources of FPGA. Second, the Linux system is built based on the hardware platform file and includes some necessary libraries. Finally, the Linux system will be deployed in the PS part to run the DPU executable file by the Vitis-AI Runtime.  
The hardware platform processing flow is shown in Figure 8. First, the UAV image is sent to the input of the PS part and subjected to a sequence of pre-processing procedures, such as image decoding. Second, the image is sent to the DPU IP core in the PL part for the inference process of YOLOv7-UAV, where the network detects the locations and classes of objects. Finally, the results are output to the PS part for drawing and displaying the prediction.