esa-sceva/satcom-chunk-collection huggingface dataset:
# Engineering a Combinatorial Laplacian Solver: Lessons Learned +
## 3 Implementation
### Flows on Trees  
Since every basis cycle contains exactly one off-tree-edge, the flows on off-tree-edges can simply be stored in a single vector. To be able to efficiently get the potential drop of every basis cycle and to be able to add a constant amount of flow to it, the core problem is to efficiently store and update flows in \(T\). We want to support the following operations for all \(u,v\in V\) and \(\alpha\in\mathbb{R}\) on the flow \(f\):  
* \(\mathrm{query}(u,v)\): return the potential drop \(\sum_{e\in P_{T}(u,v)}f(e)r_{e}\)
* \(\mathrm{update}(u,v,\alpha)\): set \(f(e):=f(e)+\alpha\) for all \(e\in P_{T}(u,v)\)
* We can simplify the operations by fixing \(v\) to be the root \(r\) of \(T\):
* \(\mathrm{query}(u)\): return the potential drop \(\sum_{e\in P_{T}(u,r)}f(e)r_{e}\) and
* \(\mathrm{update}(u,\alpha)\): set \(f(e):=f(e)+\alpha\) for all \(e\in P_{T}(u,r)\).  
The itemized two-node operations can then be supported with \(\mathrm{query}(u,v):=\mathrm{query}(u)-\mathrm{query}(v)\) and \(\mathrm{update}(u,v,\alpha):=\{\mathrm{update}(u,\alpha)\) and \(\mathrm{update}(v,-\alpha)\}\), since the changes on the subpath \(P_{T}(r,\mathrm{LCA}(u,v))\) cancel out. Here, \(\mathrm{LCA}(u,v)\) is the lowest common ancestor of the nodes \(u\) and \(v\) in \(T\), the node farthest from \(r\) that is an ancestor of both \(u\) and \(v\). We provide two approaches for implementing the operations; they are described next in some detail.
# Modelling, Analysis and Performance of a Low Inertia AC-DC Microgrid
## 5 Conclusions  
This paper presented a virtual inertia controller based on a high-pass filter compensator to facilitate providing DC grids with an inertial support to low-inertia AC grids. The control system's dynamic performance is tested using Matlab/Simulink software. The state-space linearized model of the system is developed and validated. It has been proved that the results of the linearized system coincide with those of the non-linear model, verifying the accuracy of the state-space model. The system's frequency response regarding RoCoF and Nadir is compared under different loading scenarios; it has been found that with the motor load connected, the system frequency showed better RoCoF and Nadir compared to the cases where only static loads are introduced. This is because the inertia of the rotating parts of the motors is added to the inertia of the synchronous generator. It has been shown that the virtual inertia controller would enhance the frequency response when activated and that the DC link voltage of the DC microgrid is successfully restored to the nominal value after the contingency. The dynamic performance of the proposed virtual inertia control loop based on the HPF compensator is compared with its LPF counterpart, offering good frequency support but resulting in steady state error in the DC link voltage. In addition, the effects of changing the system's parameters are examined using the state space model and the system's eigenvalues are presented. Future work will include comparing the proposed HPF-based controller with other novel control approaches in the domain of virtual inertia, considering field measurement data captured from the real systems based on the AC-DC microgrid concept.  
Conceptualization, A.M.I.M. and M.I.M.; methodology, A.M.I.M. and M.I.M.; software, A.M.I.M. and M.A.A.; validation, A.M.I.M. and M.A.A.; formal analysis, A.M.I.M. and M.A.A.; investigation, A.M.I.M., M.A.A. and M.I.M.; resources, A.M.I.M. and M.I.M.; data curation, A.M.I.M. and M.A.A.; writing-original draft preparation, M.A.A. and A.M.I.M.; writing-review and editing, A.M.I.M. and M.I.M.; visualization, A.M.I.M. and M.A.A.; supervision, A.M.I.M. and M.I.M.; project administration, A.M.I.M. and M.I.M.; funding acquisition, M.I.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 20: Frequency Response at different generator inertia.
## Nomenclature  
\(N\), M, Q  
Axial force, moment, and shear force of the main beam (see Equation (1))  
\(H\), \(V\)  
Horizontal and vertical forces of the main cable, respectively (see Equation (1))  
\(q\) and \(p\)  
Dead and live loads on the main beam (see Equation (2))  
\(c_{y}\)  
Vertical component of the distributed load of stayed cable (see Equation (2))  
\(EI\)  
Bending stiffness of the main beam (see Equation (5))  
\(c_{y\delta}\&c_{\delta y}\)  
Vertical component force of stay cables generated by dead load and live loads, respectively (see Equation (5)).  
\(H_{\delta}\&H_{p}\)  
Horizontal component of the main cable generated by dead and live loads (see Equation (5))  
\(k\)  
Vertical elastic support stiffness provided by stay cables (see Equation (6))  
\(v_{1}(x,t)\delta v_{2}(x,\xi)\)  
The deflection of elastic foundation beams under concentrated and uniformly distributed loads  
(see Equation (40))  
\(E_{c}\)  
The elastic modulus of the main cable (see Equation (41))  
\(A_{c}\)  
The area of the main cable (see Equation (41))  
\(a_{t}\)  
The thermal expansion coefficient of the main cable (see Equation (41))  
\(\Delta t\)  
Temperature variation of the main cable (see Equation (41))  
\(K_{tx}\)  
Equivalent longitudinal stiffness provided by the longitudinal constraint on the top of the main tower  
\(v_{1}(x,t)\delta v_{1}(x,t)^{\prime}\)  
The vertical deflection and rotation of the main beam at x when the unit concentrated load is at the location t  
(see Equation (44))  
\(M_{1}(x,t)\), \(Q_{1}(x,t)\)  
The bending moment and shear of the beam at x when the unit concentrated load is applied at the position t  
(see Equations (45) and (46))  
\(L_{xd}\)  
Length of the pure suspension section  
\(L_{j\delta}\&L_{xl}\)  
Lengths of the hybrid section and the cable-stayed section at each side  
\(L_{f}\&L_{b}\)  
Auxiliary span length and side span length  
\(q_{c}\)  
Total concentration of main cable and ancillary loads on the cable  
\(q_{h0}\&q_{h1}\)  
Load density bore by hangers of the pure suspension section and hybrid section  
\(q_{0}\&q_{1}\)  
Total load density of the main cable of the pure suspension section and hybrid section
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 2 Image Encryption Algorithms
### Compressive Sensing  
In recent years, there has been a growing interest in compressive sensing-based image encryption algorithms. Compressive sensing-based algorithms have the advantage of simultaneously sampling, compressing, and encrypting images, resulting in a faster encryption time and a reduced image size. In a compressive sensing-based image encryption algorithm, a plaintext image is first transformed into a sparse domain using discrete transform. Then, the transformed image is compressed using a random measurement matrix. The measurement matrix is a matrix used to capture a reduced set of the essential measurements/information of the image's structure, which allows accurate compression and reconstruction from a reduced set of measurements. Finally, the compressed image is encrypted using permutation and diffusion techniques.  
[PERSON] and [PERSON] [43] proposed a novel image encryption algorithm based on compressive sensing, the M sequence, and an improved 1D chaotic map that incorporates elements from logistic, sine, and Chebyshev maps. The proposed algorithm generates a SHA-512 hash from the plaintext image, which is used to derive the control parameters for the improved 1D chaotic map and the M sequence linear-feedback shift registers (LFSRs). The plaintext image is then transformed to a DWT and is scrambled using the M sequence. Next, the improved 1D chaotic system is used to generate a measurement matrix that is used to compress and diffuse the scrambled image to produce the cipher image.  
In [44], [PERSON] et al. proposed an image compression and encryption algorithm based on compressive sensing, Fourier transform, and chaotic maps. The proposed algorithm utilizes a tent-sine chaotic map to generate a measurement matrix, using a SHA-256 hash of the plaintext image as initial parameters. Next, the Arnold transform is used to permute the compressed image, and then a 2D Fourier transform generated by [PERSON]'s hyperchaotic map is used as a mask to produce the cipher image.  
[PERSON] et al. [45] proposed an image encryption based on compressive sensing and DNA encoding. The algorithm utilizes a four-wing hyperchaotic system and Kronecker product (KP). First, a chaotic sequence generated by the chaotic system is used to dynamically control the DNA coding. Afterward, the measurement matrix is obtained by a four-wing chaotic system. Next, the plaintext image is sparsified and permuted by Fisher-Yates random scrambling. Finally, DNA coding and DNA XOR operations are performed to produce the cipher image.
# A Hybrid Strategy Improved SPEA2 Algorithm for Multi-Objective Web Service Composition
## 5 Experiment and Discussion
### Comparison of the IGD and Execution Time  
Table 3 presents the mean IGD values for MSPEA2+, NSGA2, SPEA2+, and SPEA2. It is important to note that the obtained Pareto frontiers from the respective algorithms were directly compared, rather than the final populations. It can been seen from Table 4 that the iteration times are similar across different tasks in the IOM-based WSCP for the four algorithms. The main time complexity lay in the computation of semantic matching and the generation of DAG. Therefore, in this chapter, we focus on discussing the mean IGD values for different algorithms. Notably, MSPEA2+ consistently outperformed the other algorithms, except for a single task (WSC-0806). In contrast, SPEA2+ achieved superior IGD values in only one out of eight tasks, while neither NSGA2 nor SPEA2 exhibited favorable outcomes in any of the eight experiments. SPEA2 and SPEA2+ encountered challenges when faced with the datasets WSC-0801 to WSC-0805. This inherent limitation arises from the use of the archive. SPEA2 and SPEA2+ select individuals with better fitness from the archive and perform crossover and mutation operations. This strategy, however, can lead to local optimization, restricting the algorithm's capacity to further diminish the IGD. For the large datasets of WSC-0806 to WSC-0808, SPEA2 and SPEA2+ leveraged their rapid iteration capabilities, outperforming the NSGA2 algorithm in terms of IGD performance. MSPEA2+ stands out as a promising contender, amalgamating the strengths inherited from  
Figure 7: Mean IGD over generation for non-dominated set: (**a**) WSC-0801; (**b**) WSC-0802.  
NSGA2 and SPEA2+. As anticipated, its performance across various datasets attests to its versatility and effectiveness.  
Yet, when dealing with WSC-0806 dataset which had 8226 services, MSPEA2+ did not outperform SPEA2+. This deviation could be attributed to the algorithm's adeptness at avoiding local optima. Although a preference crossover could bring a better distribution of solutions, it might also delay the iteration speed of MSPEA2+. As a result, MSPEA2+ performed poorly on WSC-0806.
# Potential and Limitations of the New European Ground Motion Service in Landslides at a Local Scale
## 2 Materials and Methods
### Geomorphological Mapping  
The geomorphological mapping approach of the landslide area involved photointerpretation and a comprehensive field survey. Detailed photointerpretation included identifying the landslide boundaries and geomorphological features such as lobe accumulation, main and secondary scarps, minor slope deposits, and movement evidence. This analysis was conducted using the available digital data in a GIS environment. The data, freely obtained from the Instituto Geografico Nacional (IGN) website [55], included orthophotos from 1956, 2003, 2014, 2017, and 2021 with pixel resolutions of 0.25 and 0.5 m, and 2021 LiDAR data with a pixel resolution of 0.25 m. The field campaign, carried out during September 2023, was a critical stage for validating and refining the photointerpretation. During this campaign, indicators of movement (e.g., tension cracks, damage in roads), slope instability deposits, and the shape and boundaries of unstable areas were identified and mapped. Additionally, locations with active EGMS deformations were visited and studied in order to validate the EGMS results. In addition, a damage inventory map was carried out (see below). Thus, a detailed geomorphological map of the landslide at 1:5000 was made to illustrate the landslide features (see Section 3.2).
## 4 Experimental Results
### Propeller Measurement Results  
Following validation of the standard platform, the measurement system was applied to actual marine propeller blades. During the measurement process, the measurement axis was parallel to the surface normal of the target measurement plane. The system was programmed to perform 100 consecutive measurements under both the continuous  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Mean** & **Variance** & **Levene Statistic** \\ \hline Second decimal place & 0.29 & 0.213 & 1779.26 *** \\ \hline Third decimal place & 4.47 & 7.081 & \\ \hline \end{tabular}
\end{table}
Table 2: Homogeneity of variance test for decimal precision on the standard platform measurements (N = 1200) (*** \(p<0.001\)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Sum of Squares** & **df** & **Mean Square** & **\(F\)** & **Significance** \\ \hline Between-group & 0.016 & 3 & 0.005 & & \\ \hline Within-group & 254.943 & 1196 & 0.213 & 0.025 & 0.995 \\ \hline Total & 254.959 & 1199 & & & \\ \hline \end{tabular}
\end{table}
Table 3: Stability analysis of measurement values collected from the standard platform (N = 1200).  
measurement mode and single-point measurement mode. Each data set comprised measurements at the same points on three propeller blades, with four repeated measurements per point, yielding a total of 1200 data points. The precision and stability of the two measurement modes were compared. Homogeneity testing was conducted to determine the level of decimal precision reliably achieved, and ANOVA was used to assess whether repeated measurements introduced systematic bias. Finally, the independent-sample \(t\) test was conducted to determine whether the two measurement modes differed significantly in terms of measurement accuracy, repeatability, and measurement time. In this manner, the most efficient mode for this measurement platform was identified.
# Research on Real-Time Control Strategy for HVAC Systems in University Libraries
## 3 Experimental Cases
### Device Model and Objective Function  
We built chiller and pump models to accurately model the HVAC systems. The data required for modeling were collected from a university library project in Hubei Province. In total, 80% of the data were used for modeling, and the remaining 20% were used for validation.
## 2 Advantages of On-Site Hospital Facilities  
Having GMP facilities located directly within academic hospitals provides major advantages for producing autologous/allogenic ATMPs. Proximity allows cell-based therapies to be rapidly manufactured on-site and delivered directly to patients. This avoids risks associated with transporting fragile cell products long distances between external manufacturing sites and hospitals [2]. This closeness not only preserves the integrity of cellular products but also simplifies logistics, reducing both the costs and risks associated with transporting products over long distances. Furthermore, the alignment of manufacturing with clinical schedules allows for flexible batch planning, accommodating rapid changes due to production dynamics or patient needs [3; 4]. On-site facilities also enable the smooth translation of therapies from laboratory discovery to clinical trials. Once safety has been established in early phase studies, hospital facilities can continue providing patient access through expanded access programs.  
One of the major disadvantages faced by hospital facilities is limited space. Hospitals must integrate cleanrooms and manufacturing equipment into existing infrastructure not designed for production. Facilities must be strategically designed to make optimal use of available space. The use of isolators and closed systems can limit the need for larger cleanrooms [5; 6]. Careful planning allows the creation of multi-product facilities that segregate therapies like viral vectors or gene-modified cells. Despite space restrictions, on-site locations remain ideal for the frequent manufacturing of personalized cell therapies requiring rapid delivery in small batches [3].
## 2 Results
### Effect of Ammonium on Amino Acid Content in Leaves  
Changes among amino acids in response to concentrations of ammonium were quantified, and the results were shown in Figure 1. The increased ammonium in the solution also significantly induced most kinds of amino acid content in _M. aquaticum_ leaves. The dominant amino acids were asparagine (Asn), glutamine (Gln), and serine (Ser), which accounted for more than 80% of the total amino acids. Asn content significantly (\(p\) < 0.05) increased under 1 (L2 group) and 12 mmol/L (L3 group) ammonium, and the content in the L4 group reached 16 times of the LNC group. Meanwhile, the Gln content also increased significantly, reaching 231 ug g\({}^{-1}\) FW in the L3 group, and 468 ug g\({}^{-1}\) FW in the L4 group, respectively. The aromatic amino acids, phenylalanine (Phe) and tyrosine (Tyr)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Groups**} & **Ammonium Concentration** & \multirow{2}{*}{**RGR (g d\({}^{-1}\) FW)**} & \multirow{2}{*}{**Fv/Fm**} & **Root Length (cm)** & **Root Number** \\  & **(ug g\({}^{-1}\) FW)** & & & \\ \hline LNC & 44.752 \(\pm\) 8.037 d & 0.044 \(\pm\) 0.008 \({}^{\circ}\) & 0.805 \(\pm\) 0.006 b  & 17.12 \(\pm\) 1.343 & 29 \(\pm\) 2 a \\ \hline L1 & 57.655 \(\pm\) 7.134 d & 0.08 \(\pm\) 0.017 ab & 0.818 \(\pm\) 0.006 ab  & 18.243 \(\pm\) 1.136 & 30 \(\pm\) 3 a \\ \hline L2 & 100.94 \(\pm\) 8.316 c & 0.113 \(\pm\) 0.034 a & 0.833 \(\pm\) 0.004 a  & 12.183 \(\pm\) 0.949 b  & 20 \(\pm\) 3 b  \\ \hline L3 & 212.258 \(\pm\) 13.871 b & 0.088 \(\pm\) 0.004 ab  & 0.832 \(\pm\) 0.004 a  & 7.397 \(\pm\) 0.728 c  & 8 \(\pm\) 4 c  \\ \hline L4 & 464.647 \(\pm\) 44.995 a & 0.056 \(\pm\) 0.031 bc & 0.779 \(\pm\) 0.018 c  & 5.693 \(\pm\) 1.696 c  & 4 \(\pm\) 3 c  \\ \hline \hline \end{tabular}
\end{table}
Table 1: Phenotype character analysis of _Myriophyllum aquaticum_ under different concentrations of ammonium. RGR: relative growth rate. Fv/Fm: maximum quantum yield of PSII. FW: fresh weight. DW: dry weight. LNC: group treated with 0 mM ammonium. L1: group treated with 0.1 mM ammonium. L2: group treated with 1 mM ammonium. L3: group treated with 12 mM ammonium. L4: group treated with 36 mM ammonium. The values are the means \(\pm\) SDs (\(n\) = 3). Different letters indicate a significant difference at 0.05.  
that were substrates for phenol and flavonoid biosynthesis, were also significantly increased with greater ammonium content. Proline (Pro), which is an osmoticum and ROS scavenger often found to increase in response to abiotic stress, also showed a significant increase under high concentrations of ammonium.
## 6 Experiment
### Results in the Black-Box Setting
#### 6.3.1 Effectiveness of Poisoning Attack
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Attack Method**} & \multicolumn{4}{c}{**RDMA Method** Not Deployed**} & \multicolumn{4}{c}{**Deploying the RDMA Method**} \\ \cline{3-10}  & & **1\%** & **3\%** & **5\%** & **10\%** & **1\%** & **3\%** & **5\%** & **10\%** \\ \hline \multirow{6}{*}{ML-100K} & Random & 0.002 & 0.003 & 0.004 & 0.005 & 0.002 & 0.002 & 0.004 & 0.004 \\  & Random–Similarity & 0.003 & 0.005 & 0.007 & 0.008 & 0.002 & 0.004 & 0.006 & 0.005 \\  & Popular & 0.002 & 0.004 & 0.005 & 0.006 & 0.001 & 0.001 & 0.002 & 0.004 \\  & DRLAttack & **0.009** & **0.011** & **0.013** & **0.02** & **0.007** & **0.008** & **0.011** & **0.014** \\ \hline \multirow{6}{*}{ML-1M} & Random & 0.001 & 0.002 & 0.003 & 0.004 & 0.001 & 0.001 & 0.002 & 0.003 \\  & Random–Similarity & 0.003 & 0.005 & 0.006 & 0.007 & 0.002 & 0.004 & 0.005 & 0.002 \\ \cline{1-1}  & Popular & 0.002 & 0.003 & 0.005 & 0.006 & 0.001 & 0.001 & 0.002 & 0.002 \\ \cline{1-1}  & DRLAttack & **0.006** & **0.009** & **0.011** & **0.017** & **0.004** & **0.007** & **0.008** & **0.01** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Attack effects before and after deployment of RDMA defense methods (number of false users).
# Differences in Body Fat in Athletes Categorized by Resting Metabolic Rate
## 4 Results
### Female Athletes  
There were significant differences (\(p<0.05\)) in BF% across RMR groups, as summarized in Table 3. Female athletes in the low relative RMR group had significantly higher BF% values than those in the high relative RMR group (mean difference, [95% confidence intervals]) (5.8, [2.4, 9.2] kcal/kg; \(p<0.01\)). Female athletes in the moderate relative RMR group had higher BF% values than those in the high relative RMR group (3.3, [\(-0.1\), 6.7] kcal/kg; \(p=0.049\)).  
A weak relationship between BF% and body weight (r = 0.162; \(p=0.026\)) was observed (Figure 1A). A very strong relationship existed between FFM and body weight (r = 0.828; \(<\)0.001) (Figure 1B).  
A moderate relationship between RMR/kg of FFM and BF% (r = 0.366; \(p<0.001\)) was observed (Figure 2A). Lastly, a strong inverse relationship between RMR/kg and BF% (r = \(-0.510;p<0.001\)) was observed (Figure 2B).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Group** & \(n\) & **Body Fat (\%)** & **Body Weight (kg)** & **BMI (kg/m\({}^{2}\))** \\ \cline{2-6} \multirow{3}{*}{Males} & Low (\(<\)26 kcal/kg) & 32 & 20.7 \(\pm\) 9.1 * & 103.6 \(\pm\) 18.8 * & 30.9 \(\pm\) 4.8 * \\ \cline{2-6}  & Moderate (26.1–29 kcal/kg) & 32 & 13.5 \(\pm\) 5.8 & 88.7 \(\pm\) 14.4 & 26.9 \(\pm\) 4.2 \\ \cline{2-6}  & High (\(>\)29.1 kcal/kg) & 34 & 13.0 \(\pm\) 9.0 & 86.1 \(\pm\) 14.1 & 26.2 \(\pm\) 3.6 \\ \hline \multirow{3}{*}{Females} & \multirow{3}{*}{\(p\) value} & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 \\ \cline{2-6}  & Low (\(<\)24 kcal.kg) & 30 & 25.5 \(\pm\) 7.4 * & 71.6 \(\pm\) 12.8 * & 25.2 \(\pm\) 4.7 * \\ \cline{2-6}  & Moderate (24.1–27 kcal/kg) & 31 & 23.0 \(\pm\) 4.3 \# & 63.3 \(\pm\) 8.9 \# & 22.7 \(\pm\) 2.4 \\ \cline{2-6}  & High (\(>\)27.1 kcal/kg) & 31 & 19.7 \(\pm\) 4.5 & 60.8 \(\pm\) 8.1 & 21.3 \(\pm\) 1.8 \\ \hline \hline \end{tabular} Data presented as mean \(\pm\) standard deviation. BMI = body mass index; RMR = resting metabolic rate; kg = kilograms. * Denotes significant difference between low and high relative RMR groups (\(p<0.05\)). # Denotes significant difference between moderate and high relative RMR groups (\(p<0.05\)).  
\end{table}
Table 3: Summary of body fat percentage and body weight across groups.  
Figure 1: (**A**) Association between body fat percentage and body mass; (**B**) Association between fat-free mass and body mass.  
Body mass (\(\beta=0.162\)), FFM (\(\beta=-0.257\)), RMR/kg (\(\beta=-1.017\)), and RMR/kg of FFM (\(\beta=0.934\)) were significant predictors of BF%. Table 4 presents a summary of linear regression analysis.