esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data
#### 2.2.2 Solar-Induced Chlorophyll Fluorescence (SIF)  
Solar-induced fluorescence (SIF) is mechanistically linked to photosynthesis and is shown to have a near-linear relationship with ecosystem GPP at the ecosystem scale [45; 46]. In this study, the global 'OCO-2' SIF (GOSIF) dataset was used as the indicator of ecosystem productivity (i.e., GPP). The GOSIF was developed by [PERSON] and [PERSON] [13] using discrete OCO-2 SIF soundings, remote sensing data from the moderate resolution imaging spectroradiometer (MODIS), and meteorological reanalysis data through Cubist regression tree model. The GOSIF has a 0.05\({}^{\circ}\) spatial resolution and an 8-day and monthly temporal resolution. This product was available from March 2000 to December 2020.
## 3 Background  
### Cross-Technology Interference Sources  
In this section, we discuss the salient features of the cross-technology sources of interference targeted in the current work, namely IEEE 802.11-based WLAN and MWO.
# Influence of Construction Joint and Bridge Geometry on Integral Abument Bridges
## 2 Parameters and Description
### Uncontrollable Parameters
#### 2.2.2 Thermal Expansion Coefficient  
The thermal expansion coefficient, \(\alpha\), directly influences superstructure strain in response to material temperature changes, and is the primary cause of the difference in response between steel and concrete girder IABs. The range of \(\alpha\) for concrete widely varies owing to variations in concrete mixes that include aggregate properties and proportions, water-to-cement ratio, relative humidity, concrete age, and other factors. When test data are not available, AASHTO LRFD [25] recommends \(\alpha\) for concrete of \(10.8\times 10^{-6}/^{\circ}\)C with a range of 5.4 to \(14.4\times 10^{-6}/^{\circ}\)C. However, the AASHTO recommendation is conservative compared with other research findings [25]. Therefore, the present study has selected \(\alpha\) for concrete equal to \(10.0\times 10^{-6}/^{\circ}\)C. The thermal expansion coefficient of steel is well established and predictable; therefore, an \(\alpha\) equal to \(11.7\times 10^{-6}/^{\circ}\)C was used in the numerical models for the thermal expansion coefficient of steel.
## 6 Conclusions  
This study aimed to prevent attacks on satellites by unauthorized ships in maritime scenarios, as well as activities such as illegal broadcasting in large sports stadiums or illegal leakage of military secrets in critical areas by taking advantage of the high transmission rate and low latency of LEO satellite communication systems, which pose serious threats to the national economy and security. In particular, a LEO satellite downlink distributed jamming optimization method using a non-dominated sorting genetic algorithm was proposed. Field jamming tests were conducted on the LEO constellation of the China Satellite Network in the southwest outskirts of Xi'an, China, and comparative experiments were performed against traditional jamming optimization methods, considering the objective functions, optimal solution sets, jamming coverage, and jamming success rates. The results indicated that under the condition of jamming coverage being no less than 90%, the proposed method outperformed the considered existing jamming optimization methods in terms of  
Figure 10: Variation curves for jamming coverage rates using different optimization methods.  
optimization in the power, time, and frequency domains, yielding a more extensive, scalable, and convergent solution set, thus effectively improving the real-time jamming performance and success rate. The method proposed in this article holds significant application value in both military and civilian security fields. In the future, we plan to conduct further research in order to explore its application in different communication jamming scenarios.  
Conceptualization, L.Z.; Software, L.Z.; Writing--original draft, J.D.; Writing--review & editing, C.T. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China: 62171735, 62271397, 62173276, 62101458, 62001392, 61803310, 61801394; Natural Science Basic Research Program of Shaanxi: 2022 GY-097, 2021 JQ-122, 2021 JQ-693; Shenzhen Science and Technology Program: JCYJ20220530161615033.  
Data are contained within the article.
## 6 Challenges and Future Directions
### Policy Implications and Future Opportunities  
As EVs become more prevalent worldwide, numerous challenges arise, including both technical and policy implications. The first aspect is the mandatory investment cost required to upgrade the power system. These upgrades must encompass both software and hardware improvements. To enable the participation of an EV in the V2G system, a bidirectional battery charger is necessary, which includes a complex controller and high-tension cable. This requirement brings along safety considerations and a potentialenergy loss, which adds to this financial disadvantage [28]. Moreover, implementing systems to mitigate frequency fluctuations and address load-shifting requirements due to increased intermittent power sources is crucial [52]. It is also necessary to tackle the issues surrounding lithium batteries and the impact of V2G systems on them. To achieve this, extensive testing is necessary, alongside the development of improved models of EV battery degradation that consider their composition. This ensures that intelligent systems do not compromise battery lifespan.  
Given the critical need for scalability in V2G systems [53], ensuring interoperability is essential. However, a notable challenge hindering V2G implementation is the presence of interoperability issues, particularly in EV charging stations. Some EVs from major manufacturers may not be compatible with certain charging stations. For instance, Tesla employs a proprietary connector, although they offer adapters to Tesla users to facilitate interoperability with other charging networks. As an example, CHAdeMO [54] has been proposed as a global industry standard, jointly developed by The Tokyo Electric Power Company, Mitsubishi, and Nissan. However, several other connectors exist, such as the SAE J1772 or IEC 61851 standards [55]. Furthermore, interoperability challenges extend to the communication of micro-grids. As communication relies on sensors installed across all network components, each new device integration increases the number of connections, resulting in increasingly complex and slower communication [56]. Hence, it becomes crucial to develop scalable infrastructures, guaranteeing secure and rapid communication among diverse devices.
## 2 Case Study
### Hardware
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **\#Parameters (M)** & **Footprint (MB)** & **\#FLOPs (B)** \\ \hline
1.0 MobileNet-224 [14] & 3.3 & 13.2 & 0.28 \\ EfficientNet-B0 [15] & 5.3 & 21.2 & 0.39 \\ DenseNet-169 [16] & 14 & 56 & 3.5 \\ Inception-v3 [17] & 24 & 96 & 5.7 \\ ResNet-50 [18] & 26 & 104 & 4.1 \\ VGG-16 [19] & 138 & 552 & 16 \\ \hline SSD300-MobileNet [20] & 6.8 & 27.2 & 1.2 \\ EfficientDet-D0 [21] & 3.9 & 15.6 & 2.5 \\ FasterRCNN-MobileNet [22] & 6.1 & 24.4 & 25.2 \\ SSD300-Deeplab [20] & 33.1 & 132.4 & 34.9 \\ FasterRCNN-VGG [22] & 138.5 & 554 & 64.3 \\ YOLOv3 [23] & 40.5 & 122 & 71 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Footprints of available state-of-the-art NNs. The number of parameters of each model is expressed in millions (M). The memory size is expressed in MBs and is computed as four bytes times the number of parameters, since each parameter is a floating point variable. FLOPs are the number of floating points operations required to run a single instance of a given model and are indicated in billions. The upper half refers to models targeting image classification, while the bottom half refers to models targeting object detection. The selected device for our tests reflects the design pipeline of low-end commercial appliances. Indeed, the severe constraints of the device differ from most of the embedded solutions that are leveraged by popular works in the field of constrained AI. To show the significant differences between our solution and previous works, we now compare the ARM Cortex M7 processor with other popular solutions. Table 2 clearly points out that the device we selected for our experiments represents the most constrained device. First of all, from a bare computationally perspective, the board is the only one not leveraging any form of GPU assistance for complex computations. Then, given its focus on minimisation of power consumption, the selected board is the only single-core board, and the only device whose processor runs below the 1 GHz threshold. These factors combined make our board the least powerful, having at most one tenth of the computational power (FLOPS) of other boards. For instance, the most-commonly used device for applying AI solutions on embedded devices has a whopping 2083\(\times\) performance advantage over our board. Furthermore, from the storage perspective, the ARM-based board is 32-times smaller than any other board available on the market which was used in AI-based projects. Overall, given the constrained nature of other devices, it is reasonable to consider the selected board to be a _hyper-constrained_ device.
# Reliability Assessment of Steel-Lined and Prestressed FRC Slabs against Projectile Impact
## 3 Formulation for Reliability Analysis
### Limit State Function  
A normal strike of a hemispherical nose projectile was assumed on prestressed and steel-lined FRC slabs for deriving the desired limit state function. Slab failure was assumed if a given projectile's impact velocity exceeded the ballistic limit of the slab. We can write the limit state function as follows, using \(V_{0}\) and \(V_{p}\) as impact and ballistic velocity, respectively:  
\[g(\underline{x})=V_{p}-V_{0} \tag{2}\]  
If the projectile's impact velocity exceeds the PFRC slab's ballistic limit, the slab is likely to be perforated (i.e., penetrated). For the current study, the ballistic limit expression proposed in Equations (3)-(6) were used for prestressed and steel-lined FRC targets.  
\[V_{p}=V_{a}\text{ for }V_{a}\leq 70\text{ m/s} \tag{3}\]  
\[\text{ and }V_{p}=V_{a}\left[1+\left(\frac{V_{a}}{500}\right)^{2}\right]\text{ for }V_{a}>70\text{ m/s} \tag{4}\]  
\[\text{ where, }V_{a}=3.1\text{ }\rho_{c}{}^{0.14}\text{ }f_{left}{}^{0.39}\left(\frac{dH^{2}}{M}\right)^{0.4}(r+0.3)^{0.5}\left[1.2 -0.6\left(\frac{c_{r}}{H}\right)\right]Exp\left(0.5(0.5 RI)^{5}\right) \tag{5}\]  
\[\text{ and }r=A_{st}\text{ }\%+\left(\frac{100\times T_{l}}{H}\right) \tag{6}\]  
in which \(M\) denotes the mass of the projectile (kg), \(H\) represents the concrete target thickness (mm), \(c_{r}\) denotes the rebar spacing (mm), \(r\) is the percentage of steel reinforcement (rebars + steel lining), \(d\) is the projectile diameter, \(T_{l}\) is the thickness of steel lining (mm), \(RI\) is the reinforcing index, \(\rho_{c}\) is the concrete density (kg/m\({}^{3}\)), and \(f_{left}\) is the effective tensile strength of concrete.  
The effective tensile strength of concrete \(\left(f_{left}\right)\) is calculated by incorporating the effect of prestressing. Thus, it is calculated using the following:  
\[f_{left}=f_{t}+f_{p} \tag{7}\]  
where, \(f_{t}\) denotes the concrete's tensile strength (N/m\({}^{2}\)), \(f_{p}\) is prestress in concrete (N/m\({}^{2}\)).  
The substitution of the expression of \(V_{p}\) in Equation (2), makes the limit state a function of the variables \(\rho_{c},f_{left},H,r,\)\(c_{r},M,\)\(d,RI\), and \(V_{0}\). Due to various factors, these variables are subject to significant uncertainties. As a result, these variables are assumed random in the present study. The variables discussed above can be arrayed as follows:  
\[\underline{x}=\left(V_{0},\text{ }\rho_{c},f_{left},H,r,\text{ }c_{r},M,\text{ }d,RI\right) \tag{8}\]  
The reliability and failure probability of PFRC slabs against projectile impacts are estimated using the derived limit state function. For this purpose, the MCS technique [17] was used.
## 5 Discussion  
The satellite tracking system, called GPS tracking or Global Positioning System, is used to determine the position on the Earth, which we will install on the smart wheelchair or person who needs it to be tracked for high-precision positioning. Depending on the caliber of the receiving equipment, it is possible to track a product or person precisely and with minor deviations of up to 100 m. It is incredibly helpful for locating things. It helps with searching, dictating, and route planning to save time, fuel, and cost, and keep job efficiency under control. It involves ensuring safety in all circumstances.
# A Realistic Hand Image Composition Method for Palmprint ROI Embedding Attack
## 2 Related Works
### Image Composition  
Image composition, a classical task in image processing, encompasses subtasks such as object placement, image harmonization, and image blending [21]. Object placement focuses on placing the foreground to the background with a suitable position, size, and orientation, as shown in [22], in which the authors use two generative modules and a spatial transformation network to decide the position for placement, and [23], which applies the bounding box calculated by the features from the masked convolutions to locate the position. Considering the differences between the color and hue of the foreground and background, image harmonization is used to decrease these differences, as shown in [24], in which the authors designed an image harmonization network with collaborative dual transformation for pixel and RGB transformation to harmonization, and [25], which applied the self-consistent style contrastive learning scheme with background-attentional adaptive instance normalization to harmonization. For seamless composition, image blending is applied to diminish the unnatural edge around the foreground in the composited image, as described in [26], which optimizes the style and content loss with the Poisson blending loss and iteratively updates the pixel of the boundary region for blending, and [27], which proposes using a densely connected multi-stream fusion network to fuse the foreground and the background image. Generally, these tasks have been geared towards achieving realistic visual quality by situating independent objects within a background without explicit precision requirements.  
However, the challenge intensifies when dealing with hand and palmprint ROI, as they constitute an integrated entity with continuous texture connection rather than two distinct objects. Consequently, hand image composition poses a more substantial challenge, demanding a higher level of precision in seamlessly placing an ROI into a palm and ensuring a cohesive appearance of the palmprint as a unified entity. Moreover, the foreground palmprint must preserve most of texture information under harmonization. Conventional image composition methods fall short of meeting the specific requirements of hand composition. Figure 3 illustrates the distinction between image composition and the more intricate task of hand composition.
# Joint Location-Allocation Model for Multi-Level Maintenance Service Network in Agriculture
## 5 Case Study
### Analysis of Contiguity Constraints  
In the MIP-based optimization model, contiguity constraints (13)-(16) are introduced to ensure all service units served by one service station are geographically contiguous. Thus, to analyse the effect of the contiguity constraints in MLFLAPs, optimal solutions without constraints (13)-(16) are obtained in this section. The total service costs without contiguity constraints are 5.760 \(\times\) CNY 10\({}^{5}\), a 1.30% reduction compared with the costs in Section 5.2.  
The location of service stations and distorting results without contiguity constraints are shown in Figure 8. The service districts served by service stations 1, 50, 12, and 122, are not contiguous, and service units 39, 40, 137, and 171 are served by the corresponding service stations across other service districts. In reality, the cross-regional maintenance service shown in Figure 8 is not generally a feasible solution in agriculture, which results in a long service latency and resource waste [5]. In agriculture, the decision-maker may prefer to assign these service units to the nearest service stations, such as allocating service unit 39 to service station 50, service unit 40 to service station 43, service unit 137 to service station 138, and service unit 171 to service station 173.  
Figure 8: The location–allocation without contiguity constraints.  
In all, contiguity constraints should be considered to prohibit cross-region services and ensure a contiguous service area for managers in practical application.
## 2 Experiments and Analysis
### Experimental Results and Range Analysis  
#### 2.2.1 Orthogonal Experimental Design  
The orthogonal experiment design method was proposed by [PERSON] [36], and it is widely used [37; 38; 39]. The core of this method uses the orthogonal table to uniformly select representative conditions from all factors and levels instead of considering all the combinations. Without comprehensive tests, the effects of various factors can be analyzed to find the optimal level combination, which greatly reduces the test cost.  
According to the existing research of [PERSON] et al. [10] and [PERSON] et al. [12], factors such as jet pressure, jet angle, standoff distance, and impact time will have certain effects on the experimental results. They are also the setting parameters of the working conditions that can be controlled by the test equipment during the test, and have a major influence on the control of underwater robots. In order to further understand their effect on test results, a hybrid orthogonal test design L\({}_{16}\)(\(4^{3}\times 2^{1}\)) is selected for range and variance analysis. According to the method of orthogonal test design, three factors, namely jet pressure (\(p\)), jet angle (\(\alpha\)), and impact time (\(t\)), have four levels, respectively. The four levels of jet pressure at low-pressure conditions are 2 MPa, 4 MPa, 6 MPa, and 8 MPa. The four levels of jet angle are 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\). The four levels of impact time are 20 s, 40 s, 60 s, and 80 s, which can be enough to achieve a certain amount of damage. The factor of standoff distance (\(s\)) has two levels, 10 mm and 15 mm. These parameters are determined by considering the practical low-pressure operating conditions and the limitations of the experimental device. The jet pressure is shown on the pressure gauge. The jet angle is a complementary one between the central axis of the jet and the normal of the target surface, and the range is from 0\({}^{\circ}\) to 90\({}^{\circ}\). The standoff distance is the distance from the nozzle outlet to the target surface. The erosion depth (\(H\)) is the maximum distance from the surface of refractory brick to the bottom of erosion pit caused by water jet. The experimental results of erosion depth are used as evaluation indexes to reflect the extent of erosion damage. The deeper the erosion, the stronger the erosion ability of the water jet. The erosion parameters of the water jet test are shown in Figure 4. In Figure 4b), the various variables of working conditions are shown when the water jet impinges on the target surface at a specific angle \(\alpha\). The factors and levels are shown in Table 1, and the orthogonal test results are shown in Table 2.  
Figure 3: Refractory brick surface (black mark indicates erosion target area). (**a**) Before erosion, (**b**) after erosion.
## 4 Conclusions  
In this study, the effect of abnormal process conditions during self-piercing riveting (SPR) on four output parameters such as the cross-sectional characteristics (head height, bottom thickness, and interlock) and the tensile shear strength of the SPR joint were investigated by using parametric investigation. Six types of poor fit-up, setting force with a variation of 5 kN, pre-clamping force with a variation of 3 kN, angular misalignments of \(\pm 20\)\({}^{\circ}\), and an edge offset distance of \(\pm 5\) mm were considered as abnormal process conditions in the experimental design, and the following conclusions were derived.  
Among five abnormal process parameters, the effects of the setting force on all the output parameters were statistically significant at a significance level of \(\alpha=0.05\). Especially for the head height, the bottom thickness, and the tensile shear strength, the setting force was only one significant parameter at a significance level of \(\alpha=0.05\).  
The pre-clamping force has no significant effect on the output parameters at a significance level of \(\alpha=0.05\).  
The bottom thickness was influenced by all input parameters except for the pre-clamping force.  
The tensile shear strength was affected by the setting force at a significance level of \(\alpha=0.05\), and it was also affected by the angular misalignments and the edge offset distance at a significance level of \(\alpha=0.1\). If the consistency of the tensile shear strength is required in process parameter optimization or database construction, these parameters should be considered as control parameters, or the ranges of these parameters should be narrower than those in this experimental design.  
The output parameters are correlated with each other. Among the cross-sectional characteristics, the head height has the highest Pearson correlation coefficient with the tensile shear strength.  
Investigation, T.-E.J.; Methodology, T.-E.J. and D.-H.K.; Supervision, D.-H.K. and C.K.; Writing--Original draft, T.-E.J. and C.K.; Writing--Review and editing, C.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Trade, Industry and Energy, Republic of Korea.  
The authors declare no conflict of interest.
# Trajectory Tracking Outer Loop Regressor-Based Adaptive Controller for a Quadrotor
## 2 Preliminaries  
### Quadrotor Dynamic Model  
The dynamic model of a quadrotor in the inertial reference frame is given as [24; 25]  
\[m\ddot{\mathbf{p}}+mg_{z}+D_{p}(\mathbf{\eta})\dot{\mathbf{p}} = R_{3}(\mathbf{\eta})F, \tag{1}\] \[M(\mathbf{\eta})\ddot{\mathbf{\eta}}+C(\mathbf{\eta},\dot{\mathbf{\eta}})\dot\mathbf{{\eta}}+D_{\eta}(\mathbf{\eta})\dot{\mathbf{\eta}} = W(\mathbf{\eta})^{-\top}\mathbf{\tau}, \tag{2}\]  
where (1) and (2) represent the position and attitude dynamics, respectively; \(\mathbf{p}=[x\;y\;z]^{\top}\in\mathbb{R}^{3}\) is the cartesian position; \(\mathbf{\eta}=[\phi\;\theta\;\psi]^{\top}\in\mathbb{R}^{3}\) is the attitude; \(m\in\mathbb{R}\) is the mass of the vehicle; \(\mathbf{g}_{z}=[0\;0\;g]^{\top}\in\mathbb{R}^{3}\), with \(g\) being the gravitational acceleration constant; \(D_{p}(\mathbf{\eta})\in\mathbb{R}^{3\times 3}\) and \(D_{\eta}(\mathbf{\eta})\in\mathbb{R}^{3\times 3}\) are matrices that model the aerodynamic drag and damping effects, respectively; \(R_{3}(\mathbf{\eta})\in\mathbb{R}^{3}\) is the third column of matrix \(R(\mathbf{\eta})\in\mathbb{R}^{3\times 3}\), which is a rotation matrix; \(M(\mathbf{\eta})\in\mathbb{R}^{3\times 3}\) is the inertia matrix; \(C(\mathbf{\eta},\dot{\mathbf{\eta}})\in\mathbb{R}^{3\times 3}\) is the Coriolis matrix; \(W(\mathbf{\eta})\in\mathbb{R}^{3\times 3}\) is a transformation matrix; and \(F\in\mathbb{R}\) and \(\mathbf{\tau}\in\mathbb{R}^{3}\) are the control inputs in the body reference frame. It is worth mentioning that representation (1)-(2) is valid for \(|\theta|<\tau/2\) and \(\phi\), \(\psi\in\mathbb{R}\).