esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Measurement of the Stress Conversion Coefficient
### Experiment Content
#### 2.2.2 Methods of Measuring the Stress Conversion Coefficient
If the curve of the tensile strain and time is fitted with a straight line through the origin, function y = \(k\)x with a correlation coefficient, R\({}^{2}\), that is greater than 0.95 can be obtained. The radial tensile strain is caused by the tensile stress in this direction. Therefore, it can be considered that the soil sample generates certain tensile stress under the action of compressive stress, and this tensile stress causes the soil to be uniformly stretched at a velocity of \(k\) until the transverse strain of the soil reaches the peak \(\varepsilon_{\text{max}}\) (\(\varepsilon_{\text{max}}\) is 0.065 in Figure 6). By carrying out a uniaxial compression experiment with loading rate \(k_{1}\), the stress-strain curve can be obtained, as shown in Figure 7a. Meanwhile, transverse strain rate \(k_{2}\) can be obtained using the S-type tension sensor, as shown in Figure 6. Therefore, a tensile test with a strain rate of \(k_{2}\) was designed, and the stress-strain curve was obtained, as shown in Figure 7b. In Figure 7a, the integral before the stress-strain curve reaches its peak in the uniaxial compression test and comprises the operation carried out by axial stresses. In the equivalent tensile test, the integral before the strain that reaches \(\varepsilon_{2}\) is the operation carried out by transverse tensile stresses (Figure 7b). Here, \(\varepsilon_{2}\) is the transverse strain when the axial strain reaches \(\varepsilon 1\) in the uniaxial compression test. The operation carried out by internal tensile and axial stresses is substituted into Equation (4) to calculate the stress conversion coefficient. Figure 6: The variation in equivalent strain with time. Figure 7: Operations carried out by principal stress and operations carried out by lateral stress: (**a**) stress–strain curves and operations carried out for uniaxial compression tests; (**b**) stress–strain curve and operations carried out for the equivalent tensile test.
## 1 Introduction
Our solution is based on a reconfigurable fabric, deeply integrated into a processor fabric, aimed at capturing meaningful internal signals by executing a set of detection algorithms and alerting the system in case of an attack. To assess the validity of the approach, i.e., the use of a fully reconfigurable fabric for detection purposes, we evaluated our solution with a frequency gap between the detection module and the processor. We implemented MATANA into the RISC-V Instruction Set Architecture (ISA) [8] softcore processor Rocket [9] with Linux support on the Xilinx ML605 Field Programmable Gate Array (FPGA) platform. We successfully analyzed and designed detection mechanisms for two classes of attacks that exploit different mechanisms: a class of attacks that exploit microarchitecture without altering the control flow of a program (i.e., CSCAs); a class that alters the control flow (i.e., Return-Oriented Programming [ROP] attack). For the first class of attacks, we based our detection on instruction patterns and memory access patterns; we found no false positive for the first approach and a few ones for the second with better sensitivity. For the second class of attacks, we detected alteration on the control flow by defining some metrics and thresholds on attack-free benchmarks and applied them to evaluate attack benchmarks. We also successfully detected attacks with no false positives. The source code of our framework, including MATANA hardware architecture, software stack, and the use cases described in Section 5, is available at [[https://gitlab.laas.fr/matana](https://gitlab.laas.fr/matana)]([https://gitlab.laas.fr/matana](https://gitlab.laas.fr/matana)) (accessed on 10 January 2022). The contributions of the paper are as follows:* A novel microarchitectural signal runtime monitoring and analysis framework, which allows better understanding of interactions between software and microarchitecture at runtime;
* A methodology that enables, from the analysis of purposely chosen microarchitectural signals, the detection of various families of attacks via the characterization of their microarchitectural footprints;
* An open source framework implementation that can run on real hardware with Linux kernel;
* A deep presentation and analysis of two use cases in which we successfully built an appropriate detection logic for CSCAs and ROP attacks. The paper is organized as follows. Section 2 is dedicated to the state of the art and presents some technical solutions dealing with the extraction of microarchitectural information and their relevance for intrusion detection. Section 3 provides an overview of MATANA and presents the considered threat model, the integration process into a target system, and the global detection methodology. Section 4 presents the detailed architecture of MATANA, both hardware and software. Section 5 describes two use cases of attack detection, i.e., CSCAs and ROP attacks on a RISC-V target system. Section 6 discusses the advantages and limitations of our framework, and Section 7 draws some conclusions and perspectives.
## Appendix A  
Defining the common geometry distance \(\rho+c(\delta t_{\rm u}-\delta t^{\rm s})+T\) without considering ionosphere delay as \(\Theta\). The carrier phase measurement \(\varphi\) (in cycles) with wavelength \(\lambda\) and code measurements \(P\) (in meters) are defined as follows:  
\[\left\{\begin{array}{l}\varphi_{1}=\frac{f_{1}}{c}\Theta-\frac{A}{c\bar{f}_{ 1}}+N_{1}+\xi_{\varphi_{1}}\\ \varphi_{2}=\frac{f_{2}}{c}\Theta-\frac{A}{c\bar{f}_{2}}+N_{2}+\xi_{\varphi_{2}} \\ P_{1}=\Theta+\frac{A}{f_{1}^{2}}+\varepsilon_{P_{1}}\\ P_{2}=\Theta+\frac{A}{f_{2}^{2}}+\varepsilon_{P_{2}}\end{array}\right. \tag{1}\]  
where \(f_{1}\) and \(f_{2}\) represent different frequencies, \(t_{\rm u}\) and \(t^{\rm s}\) are the clock errors from the user receiver and satellite and \(\xi\) and \(\varepsilon\) are the unmodelled noise on \(\varphi\) and \(P\). The \(c\) denotes the speed of light. The \(\Theta\) and \(A\) can be expressed by \(P_{1}\) and \(P_{2}\) as follows:  
\[A=\frac{f_{1}^{2}f_{2}^{2}\big{[}(P_{1}-P_{2})-\big{(}\varepsilon_{P_{1}}- \varepsilon_{P_{2}}\big{)}\big{]}}{f_{2}^{2}-f_{1}^{2}}=\frac{f_{1}^{2}f_{2}^{ 2}(P_{1}-P_{2})}{f_{2}^{2}-f_{1}^{2}}+\varepsilon_{A} \tag{2}\]  
\[\Theta=\frac{\big{(}f_{1}^{2}P_{1}-f_{2}^{2}P_{2}\big{)}-\big{(}f_{1}^{2} \varepsilon_{P_{1}}-f_{2}^{2}\varepsilon_{P_{2}}\big{)}}{f_{1}^{2}-f_{2}^{2}}= \frac{f_{1}^{2}P_{1}-f_{2}^{2}P_{2}}{f_{1}^{2}-f_{2}^{2}}+\varepsilon_{\Theta} \tag{3}\]  
where, \(\varepsilon_{A}=\frac{f_{1}^{2}f_{2}^{2}(\varepsilon_{P_{1}}-\varepsilon_{P_{ 2}})}{f_{2}^{2}-f_{1}^{2}}\) and \(\varepsilon_{\Theta}=\frac{\big{(}f_{2}^{2}\varepsilon_{P_{2}}-f_{1}^{2} \varepsilon_{P_{1}}\big{)}}{f_{1}^{2}-f_{2}^{2}}\) are the noise on \(A\) and \(\Theta\), respectively. The wide-lane combination of \(\varphi\) is expressed as:  
\[\varphi_{WL}=\varphi_{1}-\varphi_{2}=\bigg{(}\frac{f_{1}}{c}-\frac{f_{2}}{c} \bigg{)}\Theta-\bigg{(}\frac{f_{2}-f_{1}}{c\bar{f}_{1}f_{2}}\bigg{)}A+N_{w}+ \xi_{w} \tag{4}\]  
where \(N_{w}\) is the wide-lane ambiguity, \(\xi_{w}=(\xi_{1}-\xi_{2})\). Then, the following expression can be obtained:  
\[\begin{array}{ll}\varphi_{WL}&=\frac{f_{1}-f_{2}}{c}\cdot\frac{f_{1}^{2}P_ {1}-f_{2}^{2}P_{2}}{f_{1}^{2}-f_{2}^{2}}-\bigg{(}\frac{f_{2}-f_{1}}{c\bar{f}_{ 1}f_{2}}\bigg{)}\cdot\frac{f_{1}^{2}f_{2}^{2}(P_{1}-P_{2})}{f_{1}^{2}-f_{1}^{2}} +N_{w}+\varepsilon\\ &=\frac{f_{1}^{2}P_{1}-f_{2}^{2}P_{2}}{\lambda_{w}\big{(}f_{1}^{2}-f_{2}^{2} \big{)}}+\bigg{(}\frac{f_{1}-f_{2}}{c\bar{f}_{1}f_{2}}\bigg{)}\cdot\frac{f_{1}^{ 2}f_{2}^{2}(P_{1}-P_{2})}{f_{1}^{2}-f_{1}^{2}}+N_{w}+\varepsilon\\ &=\frac{f_{1}^{2}P_{1}-f_{2}^{2}P_{2}}{\lambda_{w}\big{(}f_{1}^{2}-f_{2}^{2} \big{)}}+\frac{f_{1}f_{2}(P_{1}-P_{2})}{\lambda_{w}\big{(}f_{2}^{2}-f_{1}^{2} \big{)}}+N_{w}  
In addition, the corresponding DD wide-lane ambiguity can be obtained by:  
\[\
abla\Delta N_{w}=(\
abla\Delta\varphi_{1}-\
abla\Delta\varphi_{2})-\frac{(f_{1} \
abla\Delta P_{1}+f_{2}\
abla\Delta P_{2})}{\lambda_{\text{w}}(f_{1}+f_{2})}+ \
abla\Delta\epsilon \tag{10}\]
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
###### Abstract  
The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with spatial science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling.  
**Keywords:** thermal remote sensing; land surface temperature; urban heat island; urbanization; review; impervious surface; MODIS; Landsat; ASTER; heat waves +
Footnote †: journal: Journal of Applied Physics
## 5 Proposed Work
### Computation of the Optimal Number of DCVs
If we set the data collection rate of each DCV from the CH to 40 kbps, then the following scenarios will play out: (1) A single DCV moving at a speed of 2 m/s is used for data collection. The DCV begins its tour from the initial location and visits the closest OP to the CH for data collection from, i.e., OP5. The leader node at OP5 has 96 data packets for transmission, and the stopping time of the DCV to collect all the packets at OP5 is 10.92s according to Equation (7). Similarly, the stopping time of the DCV as it traverses along the other closest points (i.e., OP6, OP7, OP8, OP3, OP2, OP4, and OP1) can be computed using Equation (7) as 19.57 s, 15.87 s, 12.96 s, 9.50 s, 13.28 s, 11.54 s, and 10.78 s, respectively. Thus, the total stopping time is 104.42 s. The trajectory time as predetermined for 500 sensors is 980.96 s, in accordance with Figure 10. Hence, the total time for data collection and wireless charging equals the trajectory time plus the stopping time, i.e., (980.96 + 104.42) = 1085.38 s. Assuming the deadline time for data collection is 400 s, then we can compute the optimal number of MCVs based on Equation (11) as \(V=\left(\frac{1085.38}{400}\right)=2.7\cong 3\). In real-world applications, waste management data need to be retrieved and offloaded as quickly as possible to minimize environmental hazards arising from delays in scheduling and routing of waste collectors by decision makers. The performance of the proposed approach was tested using Equations (7)-(11), which demonstrate how to compute the optimal number of DCVs. Here, we computed \(n=3\) at 400 s.  
We set the deadline time to accomplish data collection to 400 s. Figure 12 shows the result of the maximum time usage with different numbers of MCVs. The figure shows that when \(n>3\) (with more than 3 DCVs), the maximum time usage is less than 400 s. This implies that all DCVs would be able to complete their tasks before the deadline when the number of DCVs is greater than two. Thus, the optimal number of DCVs in this case is three. Figure 13 shows the result of the optimal number of DCVs at specific deadlines. Figure 10: A working example of the proposed model using a single DCV.
# \(k\)-Circle Formation and \(k\)-epf by Asynchronous Robots
## 2 Model and Definitions
Between the two points \(s_{i}\) and \(s_{j}\), \(s_{i}\) is said to have higher configuration rank than \(s_{j}\), if \(y(s_{i})>y(s_{j})\) or \(y(s_{i})=y(s_{j})\) and \(\beta(s_{i})>\beta(s_{j})\). Since the robots have unlimited visibility, they can compute the configuration rank of each point \(s_{i}\in F\cup R(t)\). * **Symmetry about the y-axis. *
* If the robots \(r_{i}\) and \(r_{j}\) for \(i\
eq j\), have the same configuration rank, i.e., \(\gamma(r_{i}(t))=\gamma(r_{j}(t))\), they are said to be symmetric about the \(y\)-axis. Let \(\phi(r)\) denote the symmetric image of \(r\) about the \(y\)-axis. If robots \(r_{i}\) and \(r_{j}\) are symmetric about the \(y\)-axis, then \(r_{i}=\phi(r_{j})\) and \(r_{j}=\phi(r_{i})\). Similarly, two fixed points are said to be symmetric about the \(y\)-axis, if they have the same configuration rank. An active robot in its look phase identifies the set \(R(t)\) to be symmetric about the \(y\)-axis, if each robot position \(r\in R(t)\) has a symmetric image \(\phi(r)\in R(t)\). Similarly, a robot can identify whether the set \(F\) is symmetric about the \(y\)-axis or not. An active robot in its look phase identifies the configuration to be symmetric about the \(y\)-axis if both the sets \(F\) and \(R(t)\) are symmetric about the \(y\)-axis. Since the robots have an agreement on the direction and orientation of the \(y\)-axis, the configuration cannot admit translational symmetry or rotational symmetry. * **Partitioning of configurations:*
* All the configurations can be partitioned into the following disjoint classes: 1. \(\mathcal{I}_{1}-\) All configurations for which the \(y\)-axis is not a line of symmetry for \(F\) (Figure 0(a)). 2. \(\mathcal{I}_{2}-\) All configurations for which the \(y\)-axis is a line of symmetry for \(F\), but it is not a line of symmetry for \(R(t)\) (Figure 0(b)). 3. \(\mathcal{I}_{3}-\) All configurations for which the \(y\)-axis is a line of symmetry for \(F\cup R(t)\) and \(R_{y}(t)\
eq\emptyset\), i.e., there exists a robot position on the \(y\)-axis (Figure 0(c)). 4. \(\mathcal{I}_{4}-\) All configurations for which the \(y\)-axis is a line of symmetry for \(F\cup R(t)\). Furthermore, \(F_{y}=\emptyset\) and \(R_{y}(t)=\emptyset\), i.e., there are no robot positions and fixed points on the \(y\)-axis (Figure 0(a)). \(\mathcal{I}_{5}-\) All configurations for which the \(y\)-axis is a line of symmetry for \(F\cup R(t)\). Furthermore, \(F_{y}\
eq\emptyset\) and \(R_{y}(t)=\emptyset\), i.e., there are no robot positions on the \(y\)-axis, but there are fixed points on the \(y\)-axis (Figure 2b). Note that the classification of the configuration depends only on the \(y\)-axis and \(c\). Since the \(y\)-axis and \(c\) are the same for all the robots, they can easily classify a configuration without conflict.
## 4 Experiments and Results
### Experiments
#### 4.3.2 Dynamic Environment Experiments
Additionally, it significantly enhances safety distance and curvature smoothness, demonstrating its superiority in obstacle avoidance safety and navigation efficiency.
# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
## 2 Materials and Methods
### Data
#### 2.2.2 Water Level  
In this study, the daily water level data (Figure 2) monitored at Poyang gauging station (Figure 1c) were collected from the website of the Hydrological Bureau of Jiangxi Province ([[http://www.jxssw.gov.cn/](http://www.jxssw.gov.cn/)]([http://www.jxssw.gov.cn/](http://www.jxssw.gov.cn/)), accessed on 1 July 2021). The time period selected was from 2014 to 2021, which coincides with the time range of the Sentinel-1 SAR data. The water level was automatically measured using pressure transducers or noncontact transducers based on the reference of elevation systems of Wusong water-level datum [64]. Figure 2 shows the interannual and seasonal fluctuation of the water level from Poyang gauging station. The lake's water level fluctuated regularly from 2014 to 2021, reaching the annual highest water level in the wet season in July every year. There is a secondary water level peak in March or April each year before the highest water level. Furthermore, the highest water level occurred on 12 July 2020, at 22.73 m, when Poyang Lake was suffering from the most severe flood in the study period. The warning water level for Poyang Station is 19.5 m, and the water level exceeded the warning level five times, in 2015, 2016, 2017, 2019, and 2020. The lowest water level occurred on 27 November 2019, at 12.58 m. From Figure 2, we can also see that 50% of the water level values are lower than 15 m.
# Unbiased 3D Semantic Scene Graph Prediction in Point Cloud Using Deep Learning
## 3 Methods
### Loss Function  
Due to the common phenomenon of long-tail distribution in the scene graph dataset, the sample distribution differs significantly between different categories, and the choice of using focal loss [41] is considered as the classification loss function, as follows:  
\[L_{focal}=-\alpha\big{(}1-p\big{)}^{\gamma}\text{log}(p\big{)}\,, \tag{12}\]  
where \(p\) is the logarithm of the object or relation predicate prediction, and \(\alpha\) and \(\gamma\) are hyperparameters, where \(\gamma\) is set to 2. The parameter settings refer to the existing model settings [11; 14], where \(\alpha\in[0,\,1]\) is a category weight, which is the normalized inverse frequency of each category in the object classification.  
In particular, for the predicate classification, inspired by the equalized focal loss (EFL) [42], the idea of using an improved balanced focal loss (BFL) is as follows:  
\[L_{BFL}=-\sum_{i=1}^{C}\alpha\bigg{(}\frac{\gamma_{b}+\gamma_{b}^{i}}{\gamma_{ b}}\bigg{)}(1-p)^{\gamma_{b}+\gamma_{b}^{i}}\text{log}(p), \tag{13}\]  
where the category balance weights are added to the above equation focal loss, and \(\alpha\) is used to distinguish between the edged and unedged connections in the predicate classification, where \(\alpha\) is set to 0.25 and 1, respectively; however, for the partial zero-sample category existing in the dataset, \(\alpha\) is set to 0. Additionally, where \(\gamma_{b}\)= 2, \(\gamma_{b}^{i}\) is the different category balance weights, and the group re-weighting is performed for different predicate relation categories; see Section 4.1 for the detailed group re-weighting details.  
In the scene graph prediction task, the prediction results include object categories \(N_{object}\) with relational triples \(\langle N_{s},p,N_{o}\rangle\). The loss function is defined as follows:  
\[L_{sg}=\lambda L_{focal}^{obj}+L_{EFL}^{pred}, \tag{14}\]  
where according to the triple \(\langle s,p,o\rangle\) properties, set \(\lambda\) = 0.5, the object category predictions are weighted. In addition, the loss function uses the Euclidean distance between the latent vectors of each category and the meta-embedding vectors. The L2-norm is, therefore, the loss in the meta-coding knowledge learning process [14]:  
\[L_{dist}=\sum_{v\in V}d\big{(}h_{v},e_{meta}^{v}\big{)}+\sum_{e\in E}d\left(h _{e},e_{meta}^{E}\right), \tag{15}\]  
where \(e_{meta}^{v}\) and \(e_{meta}^{E}\) are the corresponding meta-embedding vectors of the node, \(V\), corresponding to the entity object and the edge, \(E\), corresponding to the object relation. In the meta-embedding learning task, the loss function can be formulated as follows:  
\[L_{ME}=\lambda L_{focal}^{obj}+L_{EFL}^{pred}+L_{dist}. \tag{16}\]  
Figure 7: Unbiased meta-embeddings as prior knowledge and the corresponding unbiased meta-embeddings of the top-five likelihood prediction categories are selected for fusion.
# A Brief Overview of Energy Efficiency Resources in Emerging Wireless Communication Systems
## 4 Optical Wireless Communication Schemes
Another assumption made is that the transmitters know the channel state information of the real and malicious users. In real applications, the jammers can derive the precoding matrix through the design of an optimization problem, which can maximize the jamming signal reception at the eavesdropper. It does this by aligning it to the null space of the legitimate user's channel [97]. Afterward, the jammers can multiply a random noise signal by the precoding matrix and transmit it to stop information reception at the eavesdropper site [95]. In 2022, an optical HARQ-based LEO-to-ground downlink with power allocation was proposed to enable reliable and energy-efficient transmission under weak turbulence circumstances. With a maximum transmitted power constraint and a target outage probability constraint, the system's energy efficiency and reliability were maximized by minimizing the average power consumption [98]. The authors in [99] contributed to the ongoing development of a next-generation optical wireless communication standard from a European perspective and proposed Li-Fi as a candidate technology for 5G. The IEEE 802.15 working group was deemed the best fit for the bottom-up development of the Li-Fi idea in Europe, America, and the world. The IEEE 802.15.7-2011 standard for visible light communication has been revised and is now known as 802.15.7r1. While the change addresses low data rate communication using imaging sensors, it also allows for establishing high data rate mobile communications using an optical wireless standard. Tables 2 and 3 provide a comprehensive review of renewable energy and optical wireless communication techniques deployed in energy-efficient networks. Figure 6: Diagrammatic illustration of artificial jamming in VLC applications. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**S/N** & **Author** & **County** & **Hybrid System** & **Cases Considered** & \ \begin{tabular}{c} **Simulation** \\ **Software** \\ \end{tabular}  & \begin{tabular}{c} **Achieved Results** \\ \end{tabular}  & **Remarks** \\ \hline
1 & [PERSON] et al., 2014 [62] & Nigeria & PV & \begin{tabular}{c} Comparing powering \\ options of the grid and grid sites \\ \end{tabular}  & PVSYST6.0.7 & - & The cost of the grid/PV system was & A grid system is better for \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 4 Artificial Intelligence in Production, the Fermentation Process, and Bottling Quality Control
### Monitoring Fermentation Variables  
AI technologies facilitate the continuous monitoring of key fermentation variables such as temperature, sugar levels, and yeast activity. These variables are crucial for the successful fermentation of wine, affecting both the process efficiency and the quality of the final product.