esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The present work is aimed at comparing the efficiency of different cleaning methods, some traditional methods and a recently developed cleaning system, that are applied in the restoration and maintenance of tapestries, a topic that to our knowledge has been little treated in the scientific literature. The analytical set-up developed was applied to a precious tapestry from the Quirinale Palace (Rome). Because of the fragility, in particular the areas where metallic yarns are present on the textile artefact and the need to clean these at the same time preserving the integrity of the metallic yarns, the new recently developed innovative system based on the hydro-aspiration was employed together with more traditional methods.  
The analytical techniques applied (SEM-EDX, IC, TOT) allowed us to compare the effectiveness of the different cleaning methods and evaluate their effect on the textile artefact. Although SEM-EDX has been previously employed in some studies to evaluate the state of preservation of textile fibres, IC and TOT, together with SEM-EDX, were applied here for the first time to test and compare the effectiveness of cleaning methods by analysing the deposited dirt, i.e., the carbonaceous fraction present in the particulate matter or the soluble species present on the surface of the tapestry.  
The hydro-aspiration method turned out to be the least invasive one towards the metal yarns, allowing removal of dirt but at the same time preserving the yarns and avoiding detaching the fragile corrosion patinas. In conclusion, the added value of the present work with respect to the state-of-the-art in the field is to have a less invasive and at the same time effective cleaning method available.  
Conceptualization, P.F. and E.M.; methodology, P.F., E.M., R.B. and E.B.; formal analysis, V.C., L.S. and E.M.; investigation, V.C., L.S. and E.M.; data curation, V.C., L.S., A.B. and C.A.L.; writing--original draft preparation, P.F. and V.G.; writing--review and editing, P.F., V.C., C.A.L., A.B. and M.B.; supervision, P.F.; project administration, P.F., R.B. and E.B.; funding acquisition, P.F., R.B. and E.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** Data will be made available upon request.  
**Acknowledgments:** The authors would like to thank [PERSON] for some of the images taken of textile fibres and the corresponding analyses. They would also like to thank [PERSON] and the Palazzo del Quirinale for the collaboration.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Monitoring Scheme
### Vertical Displacement Monitoring at Wall Top  
The vertical and lateral displacements at the top of the diaphragm wall are the primary components of its deformation. The magnitude of vertical displacement at the wall top directly reflects foundation pit settlement, making it a critical focus in pit engineering monitoring. Vertical displacement measurements are conducted using a total station and ancillary equipment. The total station instrument employed in this study was the TM50 high-precision smart total station (Leica, Switzerland), featuring a distance measurement accuracy of 0.6 mm + 1 ppm. This precision specification satisfies the rigorous requirements for large-scale structural displacement monitoring applications. Eight monitoring points (numbered QDC-1 to QDC-8) are uniformly distributed along the central axis of the diaphragm wall. These points are installed at the intersection between the capping beam top surface and the central axis plane of the diaphragm wall, as illustrated in Figure 9.  
Figure 8: Layout of monitoring points for deep horizontal displacement of diaphragm wall: (**a**) plan view; (**b**) elevation view (cm).  
Figure 9: Layout of monitoring points for vertical displacement at wall top: (**a**) plan view; (**b**) elevation view.
# SPL Features Quantification and Selection Based on Multiple Multi-Level Objectives
## 4 Feature Selection Algorithms
### Falcon Selection Algorithm  
The Falcon selection algorithm, is a variant of the greedy algorithmic family, takes an input the list of quantified features, sorted by added value, and product/category level objectives. Initially, the algorithm will attempt to satisfy the objective (if present) that requires each product category to contain a minimum number of features. This is achieved by first attempting to add the features that belong to these categories. In more detail, for each category with a minimum number of features requirement, the algorithm traverses through the sorted list, identify the features of the same category, and attempt to add them to the product. The algorithm validates using the constraint violation approach, described below.  
1. The added value of the feature must be positive.
2. The number of features under the product's category must be less than the maximum number of features for this category.
3. The number of occurrences of the feature in the product must be less than the maximum permitted.  
Additionally, the following product/category property objectives are checked for each numeric quantified property.
4. The total value of all features currently participating in the product and category for this property is less than the defined value.  
After the algorithm ensures that each category has the minimum number of features required by the user-defined category objectives, it will attempt to satisfy (if required) the total property value of a product and/or category to be more than a certain value. To attempt this, the algorithm will sort the features based on maximum property value, giving priority to features with high property values. It will then attempt to add them to the product one-by-one until the constraint is validated. Upon completion of this task, it will attempt to add more features in order to increase the value of the product. The algorithm will traverse through the sorted list, adding all the features that do not violate any constrain. This is achieved by reusing the constrain violation mechanism described above. The above algorithm is illustrated in the Figure 2.  
Falcon is guaranteed to execute in minimum time, but it is not guaranteed to produce a product with the best combination of features or even to produce a product due to the known limitations of the greedy algorithms. For example, the algorithm may fall in a dead-end due to miss-selection of features that have higher value but prevent other features with less individual added value, causing higher combined added value to be selected.
## 3 Results
### Mineralogy-Geochemistry and Elemental and Structural Analysis  
The geochemical composition of serpentinite samples, as summarized in Table 2, reflects their ferromagnesian nature with significant amounts of SiO\({}_{2}\) (35.1-41.9 wt%), MgO (22.8-38.8 wt%), and Fe\({}_{2}\)O\({}_{3}\) (5.0-7.4 wt%). These heavy-metal oxides, along with trace elements such as MnO and minor oxides (Na\({}_{2}\)O, K\({}_{2}\)O, CaO, and P\({}_{2}\)O\({}_{5}\)), contribute to the intrinsic density and elemental makeup of serpentinites. Importantly, as the thickness of serpentinite samples increases, their capacity to attenuate ionizing radiation also improves. This is due to the longer path length within the material, allowing greater interaction probability between incident radiation and heavy elements present in the rock matrix. Consequently, thicker serpentinite slabs exhibit enhanced radiation-shielding performance, making them promising candidates for applications requiring effective gamma and neutron attenuation.  
The SEM images of the serpentinites are provided in Figure 5. In Figure 5B, the Mg content is higher compared to Figure 5A. The elevated atomic percentages of both O and Mg relative to other elements in the samples are indicative of the serpentine type and suggest the presence of antigorite-composed serpentinites. As seen from Figure 6, the color distribution ranging from light gray to gray is related to the chemical composition of the serpentinites. The morphology of serpentine crystals, with sizes ranging from 0.1 to 0.3 \(\upmu\)m, is shown as antigorite in Figure 6A,B, and as lizardite in Figure 6C,D. Lizardite exhibits a smooth, platy surface, while antigorite displays a wavy surface morphology.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \multirow{2}{*}{**Sample**} & \multicolumn{8}{c}{**LREE**} & \multicolumn{8}{c}{**HREE**} \\ \cline{2-13}  & **La** & **Ce** & **Pr** & **Nd** & **Sm** & **Eu** & **Gd** & **Tb** & **Dy** & **Ho** & **Er** & **Tm** & **Yb** & **Lu** & **CRE** & **License** & **License** & **License** \\ \hline Sr-1 & 0.90 & 0.48 & 0.16 & 0.34 & 0.22 & 0.11 & 0.18 & 0.18 & 0.08 & 0.19 & 0.05 & 0.18 & 0.04 & 3.29 & 2.10 & 1.19 \\ \hline Sr-2 & 0.35 & 0.27 & 0.12 & 0.38 & 0.18 & 0.14 & 0.24 & 0.22 & 0.16 & 0.09 & 0.18 & 0.04 & 0.17 & 0.05 & 2.59 & 1.30 & 1.29 \\ \hline Sr-3 & 0.44 & 0.42 & 0.10 & 0.36 & 0.21 & 0.11 & 0.20 & 0.18 & 0.19 & 0.10 & 0.18 & 0.04 & 0.17 & 0.04 & 2.74 & 1.53 & 1.21 \\ \hline Sr-4 & 0.90 & 0.34 & 0.14 & 0.37 & 0.15 & 0.13 & 0.19 & 0.21 & 0.17 & 0.09 & 0.20 & 0.05 & 0.19 & 0.04 & 3.10 & 1.90 & 1.20 \\ \hline Sr-5 & 0.66 & 0.44 & 0.13 & 0.42 & 0.16 & 0.14 & 0.21 & 0.16 & 0.18 & 0.08 & 0.19 & 0.04 & 0.18 & 0.05 & 3.04 & 1.81 & 1.23 \\ \hline \end{tabular}
\end{table}
Table 3: Rare earth element (ppm) analysis results of serpentinite samples.  
Figure 6: SEM images of serpentinite rocks: (**A**) Sr-1, (**B**) Sr-2, (**C**) Sr-3, (**D**) Sr-4.
# Evaluation of Fire Blight Resistance of Eleven Apple Rootstocks Grown in Kazakhstan Fields
## 4 Discussion
The susceptibility of B.9 might be explained on the basis of non-grafted rootstocks [59], considering that resistance in the field was only confirmed for grafted B.9 in an early study [30]. Further investigations should be carried out in fields with both grafted and non-grafted B.9 rootstock. A recent study on the genetic background of resistance of rootstock '62-396' revealed the presence of QTL FB_F7 and an additional resistant allele tested by SSR marker CH-F7-FB1 [60]. However, information about the resistance of rootstock to fire blight in greenhouses or in fields was not provided. In the current study, as a result of our two years of field monitoring, we demonstrated for the first time that '62-396' carries four resistant alleles of five SNP markers and is resistant to fire blight. We assume that Mr5 was included in the breeding process during the development of '62-396' [61]. Resistant loci on LG3 and LG7 cause the '62-396' rootstock to be of interest to current breeding programs and grafting because these loci explain up to 80% [61] and 46% [10; 18] of the phenotyping variation, respectively. Despite the enormous potential of wild apples, the local rootstocks Zh.5 and _M. sieversii_ were found to be susceptible and to lack the investigated markers. Because _M. sieversii_ is represented by different genotypes, the evaluation of resistance to fire blight of the species is not possible, particularly when genotype by environment interactions significantly contribute to the resistance [62; 63]. Diverse levels of fire blight resistance among _M. sieversii_ accessions have also been previously reported [13; 25; 26; 27; 28; 44]. Two years of monitoring enabled us to distinguish whether rootstocks were susceptible or resistant to fire blight when grown in the climate conditions of the Kazakhstan southern region. Among the eleven studied rootstocks, we found G.41, G.16, and '62-396' to be the most promising for propagation and grafting because they were not infected during the monitoring period despite the weather conditions in 2022 being favorable for the development and spreading of _E. amylovora_. All other rootstocks showed higher levels of infection in 2022 compared with 2021. The present study is the first to evaluate the fire blight resistance of foreign and local rootstocks in the conditions of the Kazakhstan southern region, considering their genetic background and climate conditions. Previous works have mostly focused on testing M9 and MM106, which were introduced to the fields of Kazakhstan many years ago.
# Integrated Motion Planning for Assembly Task with Part Manipulation Using Re-Grasping
## 2 Overview of Algorithm  
The objective of this work is to develop an integrated planning algorithm which can simultaneously plan the feasible grasp, part manipulation and collision free C-space path for robot to assemble multiple parts. The algorithm is based on RRT, which grows a tree of robot configurations that can cover the C-space. Let \(K\) be the total number of parts to be assembled. The parts are assumed to be represented as polytopes. A body frame is attached to each part to define its position and orientation with reference to the inertial frame. Let \(P=(p,R)\) be the pose of a part where \(p\in\mathbb{R}^{3}\) be the position and \(R\in\mathbb{SO}(3)\) be the rotation matrix representing the orientation of the part. Let \(P_{i,init}\), and \(P_{i,final}\) be the initial, and final poses of parts, respectively where \(i=\{1,\dots,K\}\). It is assumed that the initial, and final poses of the parts along with the part sequence for the assembly are known. Given the set of grasps \(G\), which are the hand poses to grasp a part, the planner will be able to choose a feasible grasp such that robot could grasp the part to be assemble it without interfering the other parts in sub-assembly. A grasp can be represented by the position and orientation of the hand frame in grasping pose and the contact points of hand on grasped object with respect to hand frame. If it is not possible to find a feasible grasp for a part in its initial pose, the planner will find the solution that how to manipulate or re-grasp the part to find a feasible grasp solution.  
The outline of the algorithm is as follows: First the sequence of poses for each part are identified using orientation graph search. Then a set of feasible grasp poses of hand are identified to pick and place the part without having collision with environment. Later, the motion of the manipulator is planned using an RRT based algorithm. By taking inspiration from Grasp-RRT [12], while growing the tree using RRT, attempts are made to connect the tree to configurations whose hand poses are the chosen feasible grasp. After successfully grasping a part, robot motion is planned to move the part to is final pose. The detail of different parts of the algorithm are presented in next section.
## 2 Description of the Systems Under Investigation and Experimental Setups
\begin{table}
\begin{tabular}{c|c c c c c c c c c c c} \hline  & \multicolumn{2}{c}{**0.5\({}^{\prime\prime}\) Cut**} & \multicolumn{2}{c}{*** 0.375\({}^{\prime\prime}\) Cut**} & \multicolumn{2}{c}{**0.25\({}^{\prime\prime}\) Cut**} & \multicolumn{2}{c}{*** 0.125\({}^{\prime\prime}\) Cut**} & \multicolumn{2}{c}{**0.1\({}^{\prime\prime}\) Cut**} & \multicolumn{2}{c}{**No Cut**} \\
**Accel** & _w/o_ & _w/_ & _w/o_ & _w/_ & _w/o_ & _w/_ & _w/o_ & _w/_ & _w/o_ & _w/_ & _w/o_ & _w/_ \\ \hline
**Mode 1** & 109.31 & 106.89 & 108.58 & 106.37 & 107.94 & 105.63 & 107.20 & 104.93 & 106.97 & 104.7 & 168.94 & 165.29 \\ \hline
**Mode 2** & 297.51 & 290.87 & 295.69 & 289.34 & 293.97 & 287.58 & 292.06 & 285.71 & 291.54 & 284.38 & 444.69 & 395.39 \\ \hline
**Mode 3** & 348.08 & 331.63 & 342.44 & 327.22 & 337.04 & 322.34 & 331.35 & 317.34 & 330.0 & 314.27 & 670.77 & 649.25 \\ \hline
**Mode 4** & 387.36 & 379.11 & 379.19 & 371.78 & 371.34 & 364.30 & 363.58 & 356.97 & 361.91 & 353.82 & 749.49 & 728.92 \\ \hline
**Mode 5** & 500.98 & 450.47 & 498.27 & 446.98 & 495.72 & 443.71 & 493.36 & 440.83 & 492.70 & 438.09 & 865.83 & 846.72 \\ \hline
**Mode 6** & 525.11 & 511.62 & 516.03 & 502.85 & 507.03 & 493.46 & 497.57 & 483.86 & 495.45 & 480.92 & 1103.0 & 1073.2 \\ \hline
**Avg. \%** & \multirow{2}{*}{4.79\%} & \multirow{2}{*}{4.69\%} & \multirow{2}{*}{4.75\%} & \multirow{2}{*}{4.75\%} & \multirow{2}{*}{4.75\%} & \multirow{2}{*}{4.75\%} & \multirow{2}{*}{5.12\%} & \multirow{2}{*}{4.82\%} \\
**decrease** & & & & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: Natural frequencies of BARC systems without accelerometers and with accelerometers found through FEA. \({}^{*}\) Only computationally determined, no experimentation performed. the results of each study, discussing the possible presence of nonlinearities found in each system and how the central cut affects the BARC system behaviors. These experimental investigations will allow researchers in the future to qualitatively and quantitatively understand how changing the central-cut width may alter the expected results.
## 4 Case Studies
### An Example of Network Security Risk Assessment
#### 4.1.7 The Analysis of the Sensitivity of the Proposed Method  
In this part, to examine the robustness of the proposed approach, the sensitivity analysis of the proposed approach is done by changing the BPAs of some criteria.  
For example, the evidence of \(a_{15}\) and \(a_{22}\) (abbreviated as \(m_{15}\) and \(m_{22}\)) is changed, respectively, by assigning all the probability mass to the complete set \(\Theta\), which means maximizing the uncertainty and minimizing the useful information content. Then the evaluation result is calculated. The corresponding results are shown in Figures 4 and 5.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **VL** & **L** & **ML** & **M** & **MH** & **H** & **VH** & \(\Theta\) \\ \hline \(m_{1}\) & 0.0023 & 0.0886 & 0.0932 & 0.2082 & 0.2439 & 0.2321 & 0.0743 & 0.0575 \\ \(m_{2}\) & 0.0474 & 0.0516 & 0.1151 & 0.1577 & 0.3033 & 0.1656 & 0.1452 & 0.014 \\ \(m_{3}\) & 0 & 0.0825 & 0.0971 & 0.0971 & 0.3058 & 0.2088 & 0.1263 & 0.0825 \\ \hline \end{tabular}
\end{table}
Table 11: The BPAs of bottom criteria after combination in [25].  
Figure 3: Comparison of evaluation results of different methods.  
From Figures 4 and 5, it can be seen that although the uncertainty of the evidence increases and the useful information content reduces, the approach proposed in this paper can still make the correct evaluation, which proves that the proposed assessment approach is robust. Besides, changes in the evidence of the criterion with the larger weight will have a greater influence on the assessment result, which accords with this fact.
# Capability Analysis of Earth Observation Data for Integrated Emergency Management
## 2 Conceptual Foundations
### Data Platforms and Providers of Satellite-Derived Information for Emergency Management
Sile [32], for example, used satellite imagery to monitor and analyse ground subsidence and the movement of objects over time to prevent substantial damage to buildings. Everbridge [33] used Planet Satellite imagery to identify power outages during critical events and notify emergency teams, and implemented AI to recommend remedial action based on historical data. Other similar providers focusing on emergency management include ICEYE [34], which operates a radar imaging satellite constellation for natural catastrophe mapping; MAXAR [35], which provides radar imagery and optical imagery at 15 cm and 30 cm resolution for wildfire and disaster response; and Telespazio Vega [36], which performs similar operations for natural disaster management.
# Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks
## 4 Proposed MAC for LLDN
### Proposed CSMA/CA Mechanism for LLDN
The cross-correlation value is greater than the known signal threshold \((KS_{threshold})\), when \(KS^{j}\) is aligned to the same features in the received signal. The node makes the channel free for others in the vicinity, if the correlation value is above \(KS_{threshold}\). A node goes for the transmission if it finds the cross-correlation value to be below \(KS_{threshold}\), i.e., if it does not receive any signal during the CCA from other nodes present within the carrier sense range of the node. As shown in Figure 2a, let data packets be arrived at nodes \(A\), \(B\) and \(C\) at time \(t_{0}\), \(t_{1}\) and \(t_{2}\), respectively, where nodes \(A\) and \(B\) perform the channel access at the same slot after their random backoff. Both nodes find the channel busy due to performing CCA simultaneously. Hence, both of them should go for the random backoff. On the contrary, in IEEE 802.15.4e LLDN MAC, both nodes find the channel idle and transmit there data, which leads to a collision. Again, as shown in Figure 2b, node \(C\) finds the channel idle and transmits its data successfully. However, node \(C\) finds the channel busy in IEEE 802.15.4e LLDN MAC. Therefore, by avoiding simultaneous transmissions, our proposed channel access mechanism saves energy for nodes \(A\) and \(B\) and enhances throughput for node \(C\). The detailed procedure of our proposed carrier sensing mechanism is given in Algorithm 1. ```
0: Known signal \(KS\) and received signal \(Y\). 0: Channel access success/failure. 1: Locate backoff period boundary, and perform CCA for 128 us;
2:if channel is found idle then
3:\(j=1\);
4: Enable transmitter and transmit one random signal \(KS^{k}\) from \(KS\);
5: Receive the signal \(Y\);
6:while\(j\leq m\)do
7:if\(j\
eq k\)then
8: Calculate \(C(KS^{j},Y,I)\);
9:if\(C(KS^{j},Y,I)>KS_{threshold}\)then
10: Channel access failure and stop;
11:endif
12:else
13: j = j + 1;
14:endif
15:endwhile
16: Channel access success;
17:endif
```  
**Algorithm 1** New carrier sensing mechanism  
Figure 2: Channel access mechanism.
# Machine Learning for Network Intrusion Detection--A Comparative Study
## 4 Background
### Algorithms and Techniques
#### 4.2.2 Linear Support Vector Machine  
Linear support vector machine (LSVM) is one of the most used supervised ML algorithms. It can be used for regression, binary classification, and multi-class classification problems. An LSVM algorithm works by using the proximity between samples. In a high-dimensional space, LSVM divides the samples by class and then creates a line between them (hyperplane). The algorithm determines how far each sample is from the line; these points are called support vectors. They help to create a model of the line, which is the prediction. The distance between the samples and the hyperplane is called the margin. The goal is to obtain the best hyperbola with the largest margin between the hyperplane and the support vectors. LSVM is a good algorithm in terms of memory efficiency, but it has some drawbacks. One of its disadvantages is that it does not perform well in large and noisy datasets; only small and clean data frames benefit from this algorithm.