esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 ML and the Security of Blockchain
### Anomaly Detection and Intrusion Prevention  
One of the primary applications of ML in blockchain security is anomaly detection. Given the decentralized nature of blockchain, detecting malicious behavior or anomalies can be particularly challenging. Supervised ML models, such as decision trees, random forests, and support vector machines (SVMs), can be trained to detect abnormal activities on the blockchain network. These anomalies may include fraudulent transactions, irregular mining patterns, or unusual block generation times, which could signal attempts at double-spending or other forms of fraud [108].  
In addition to supervised techniques, unsupervised learning methods, such as clustering algorithms, can learn normal behavior patterns and subsequently flag deviations. These approaches are particularly useful for detecting novel attacks that have not been previously encountered [109; 110]. Together, these ML models help bolster blockchain security by providing real-time intrusion prevention and early warning systems.
# 2D Phase-Based RFID Localization for On-Site Landslide Monitoring
## 4 Hamaliere Landslide Monitoring
### Surface Displacement Monitoring Results
#### 4.3.2 2D Localization for All Tags  
Figure 9 shows an overview of the xOy displacement norm measured by the RFID-phase for all available tags during the measurement period. The total displacement is also shown for every tag in Table 3. All RFID localization results fit with reference measurements, notably for displacements greater than 1 m. The steep displacement increase in January 2022 concerning tags 51, 4e and A, was confirmed by tacheometer measurement. This rapid and localized deformation generated cracks and a landslide retrogression of about two meters in this area. A south-east tendency is clearly validated and corresponds to the landslide  
Figure 8: RFID localization in the xOy plane, using phase data for tag A (Figure 6). The total displacement is about 1.6 m. The color plot represents the time evolution of the RFID relative localization. The red crosses represent the reference measurements using a tacheometer, with an estimated error of about 4 cm. The tacheometer measurement of March 2021 is set as an absolute reference for relative localization. The black crosses correspond to the estimated random error bars for TD-phase localization (calculated via the model developed in Section 4.2).  
main direction, as can be seen in the qualitative vector mapping in Figure 10, with various displacement amplitudes depending on tag location. This opens the way to 2D spatio-temporal monitoring of the landslide surface, offering the possibility to better understand the physical mechanisms at the origin of the landslide activation and propagation, and to build new early warning monitoring systems.  
Figure 10: Vector mapping of the total 2D displacement for all available tags from January 2021 to September 2021. The scale is modified for clarity with a 1 m displacement reference (black arrow). The red arrows represent the displacement estimated from the RFID measurements, and the black arrows represent the displacement computed from reference measurements. The blue points numbered 1 to 4 correspond to the reader antennas.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Tag** & **51** & **A** & **4e** & **26** & **55** & **sf** & **2d** & **5c** & **59** & **5b** \\ \hline Total disp (m) & 1.54 & 1.37 & 1.20 & 0.81 & 0.75 & 0.85 & 0.69 & 0.67 & 0.74 & 0.56 \\ \hline Reference (m) & 1.57 & 1.45 & 1.28 & 0.81 & 0.79 & 0.74 & 0.77 & 0.74 & 0.72 & 0.59 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Total 2D displacement norm for all presented tags computed from the RFID phase, from January 2021 to February 2022. The reference is computed from the tracheometry measurements, with an estimated error of \(\pm\)4 cm.  
Figure 9: Cumulative 2D displacement norm for each tag, with reference measurements performed via tracheometer (black crosses). An offset was added to every plot to increase readability. The total displacement values are given in Table 3.
# Optimization of Submodularity and BBO-Based Routing Protocol for Wireless Sensor Deployment
## 3 Routing Protocols for Wireless Sensor Networks
### Routing Protocol Based on BBO Algorithm  
BBO algorithm is an information intelligence heuristic algorithm first proposed by [PERSON] in 2008. The habitats of biological populations have their corresponding Habitat Suitability Index (HSI), which is used to describe the quality of the habitat environment, and the factors that affect the fitness index are called Suitable Index Variables (SIVs). The BBO algorithm has the advantages of simple operation, fast convergence, and fewer parameters [28]. The standard BBO algorithm uses a simple linear migration model, but in the real biogeographic environment, species migration often occurs randomly and does not follow this rule. Complex and natural migration models are much better than simple migration models [23; 29]. In this paper, a cosine migration model is used. When the number of species in the habitat is either large or small, the change in the immigration rate \(\lambda\) and the emigration rate \(\mu\) is relatively stable. When the number of species in the habitat is neither large nor small, the immigration rate \(\lambda\) and the emigration rate \(\mu\) changes quickly. The expression of the cosine migration model is shown in Equations (26) and (27). \[\lambda_{k}=\frac{I}{2}(\cos(\frac{k\pi}{n})+1) \tag{26}\]  
\[\mu_{k}=\frac{E}{2}(-\cos(\frac{k\pi}{n})+1). \tag{27}\]  
In Equations (26) and (27), \(I\) is the maximum value of the immigration rate, \(E\) is the maximum value of the emigration rate, \(k\) is population number and \(n\) is the maximum population number. The mutation operator provides a certain global search capability for the algorithm through the mutation of the habitat's own information. \[m_{i}=m_{\max}(1-\frac{p_{s}}{p_{\max}}). \tag{28}\]  
In Equation (28), \(m_{max}\) is the maximum mutation rate, \(p_{s}\) is the probability that habitat \(i\) has \(s\) species, and \(p_{max}=max(p_{s})\). The steps of optimizing wireless sensor network routing protocol based on the BBO algorithm are as follows. Lines 3-7 select CH with complexity \(O(n^{2})\). Lines 9-31 reach the minimum fitness value with complexity \(O(qn^{2})\) where \(q\) is the number of iterations. Lines 10-20 calculate the migration rate with complexity \(O(n^{2})\). Lines 21-28 calculate the mutation rate with complexity \(O(n)\) and lines 32-35 calculate with complexity \(O(m_{2})\) where \(m_{2}\) is the number of nodes (\(m_{2}\geq n\)). The computational complexity of Algorithms 2 is \(O(qn^{2})\) per round approximately.
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 3 Proposed Architecture for Real-Time Flood Monitoring
### Phase 2: Postprocessing Filters  
The depth pipeline captured frames which were logged into a frameset required for postprocessing. The postprocessing flow implemented in this study followed the recommended sequence provided by Intel in the RealSense documentation [44]. The filters applied in this context were to clean up the image, smoothen the depth readings and to recover missing depth pixels as much as possible. The general flow of applying the filters is shown in Figure 3.
## 3 Experiments and Analysis
### Compared Methods  
To address the problem that traditional enhancement methods have an insufficient generalization ability and that deep-learning-based methods, especially fusion-based methods, are unable to balance performance enhancements and real-time performance, we compared the method proposed in this paper with the following seven methods: UDCP [7], Fusion-based [11], UGAN [18], Water-Net [17], CWR [20], and two feature-based algorithms, MSBDN-DFF [43] and FFA-Net [44], for image dehazing. Among them, UDCP and Fusion-based algorithms are traditional methods for underwater image processing, while the others are deep-learning-based methods that require the measurement of model parameters and the number of GFLOPs. Water-Net, MSBDN-DFF, and FFA-Net are based on image fusion or multi-scale feature fusion, while the other methods are non-fusion methods.
## 2 Methodology
### The Multi-Scale Project  
#### 2.2.1 Visualisation of LoD 0-1 in GIS Environment  
In order to obtain an integrated and multi-scale model, the GIS project with the lower levels of detail (0 and 1) has been created. Thanks to the cartographic data available in the regional geoportal it  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{DJI SPARK} & \multicolumn{2}{c}{CANON EOS 5 DSR} \\ \cline{2-4} _Photogrammetric Acquisition_ & & & \\ \hline \multirow{5}{*}{**ACQUISITION DATA**} & Sensor [CMOS] & 1/2.3 & 1, 7 \\ \cline{2-4}  & Focal Length [mm] & 4 & 20 \\ \cline{2-4}  & Flight height [\(\approx\)m] & 30 & \(\backslash\) \\ \cline{2-4}  & Total n\({}^{\circ}\) of images & 469 & 142 \\ \cline{2-4}  & GSD (Ground Sample & & \\ \cline{2-4}  & Distance) [mm] & 10 & 0, 7 \\ \hline \multirow{5}{*}{**POST-PROCE Seed DATA**} & _Dense Point Cloud_ & & \multicolumn{2}{c}{_Merged Point Cloud_} \\ \cline{2-4}  & n. of points & 620.049.832 & 234.545.525 & 295.147.711 \\ \cline{1-1} \cline{2-4}  & RMSE [m] & 0,024 & 0,0148 & 0,0188 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Photogrammetric data of acquisition and post-processing phase.  
Figure 3: Regions of point cloud segmentation (roof, security elements and walls).  
has been possible to design the GIS project using both commercial and open-source software (ArcGIS Pro by ESRI and QGIS).  
From the geoportal of the Umbria Region several dataset have been downloaded thanks to the WebGIS application. These are:  
* the CTR (technical regional map), 1:10 k-1:5 k (number 337024_G e 325143_G) with building and hydrography layers;
* the DTM (digital terrain model), 1:5 k;
* the geological regional dataset, 1:10 k for structural elements such as faults;
* the seismic risk map, 1:10 k with linear and areal elements.  
Moreover, the products derived from the 3D metric survey were considered as well as the orthophoto from UAV photogrammetry with a GSD (ground sample distance) of 3 cm/px and the DSM (digital surface model).  
In the GIS project, firstly, it is possible to visualise the LoDs of San Lorenzo Church and its surroundings, starting from a general map overview of the urban context of the city of Norcia. The LoD 0 shows the regional DTM, roads, seismic risk elements and buildings (in 2D) (Figure 4). In the LoD 1 (Figure 5) the St Lorenzo Church and its context, are represented in 2.5D; these data derive from the DSM, elaborated after the aforementioned survey campaign of data acquisition. For this LoD, the UAV orthophoto is also considered.
# Comparative Study of Physics Engines for Robot Simulation with Mechanical Interaction
## 4 Simulation and Results
### Simple Scenario  
In this scenario, we measured the average acceleration during the sliding while maintaining contact with the lower box for all of the upper boxes, multiplied this by mass to obtain the average friction force, and then calculated the error with an analytic solution. Then, we calculated the average of errors for all the upper boxes and used this as a performance index for each physics engine.  
This performance index was tested for all the physics engines by varying the time step, and the results can be seen on the top of Figure 5. In addition, we obtained the penetration depths occurring at all the vertices of all the upper boxes and measured the maximal penetration depths as shown at the bottom of Figure 5 to observe how well the contact force prevented the penetration.  
Figure 4: Snapshot of peg-in-hole scenario: the peg is first pressed down to insert the peg into the hole (**1**,**2**), and once the peg is in, it is pressed down and pushed in \(x\) direction to generate contact (**3**,**4**).  
From the results, for a simple scenario, ODE and Bullet showed good performance on the basis of the contact force, and Bullet showed the best performance when penetration was also considered. In the case of MuJoCo, a relatively large contact force error was observed, and a large penetration occurred, since it ignored the nonpenetration constraint, as stated in Section 2.4. However, it improved the simulation speed compared to other physics engines not only in the simple scenario, but also in all other scenarios that we had implemented.
# Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
## 5 Methodology
### Convolutional Neural Networks (CNNs)
#### 5.2.2 Pooling Layers  
The pooling layer has two primary jobs. The first is reducing the number of computations performed in the network and reducing the spatial dimensions of the representation. The second is controlling the overfitting issue. There are three common types of pooling layers. The first type is average pooling, which applies the average operation on a selected window. The second type is max pooling, which takes the maximum value of the selected window. The third type is the global average pooling layer, which reduces the whole input into one value. It helps reduce the spatial dimensions of a three-dimensional tensor to a one-dimensional tensor. Average and max-pooling layers use a slidingwindow (such as \(2\times 2\); \(3\times 3\)) to reduce the size. However, the global average pooling layer performs a more extreme dimensionality reduction by turning the whole size to one dimension [75] as illustrated in Figure 5. This layer is more robust to spatial translations and helps avoid overfitting.
## 3 Results and Discussion
### Destructive Characteristics and Crack Development Mechanism Analysis  
The propagation of new cracks at the tips of prefabricated cracks mainly includes the following two forms: wing cracks and secondary cracks (Figure 12). Wing cracks are initiated by tension and are classified as tensile cracks. Secondary cracks are formed by coplanar shear of prefabricated cracks and are classified as shear cracks. Anti-wing cracks often form under combined tensile-shear or compression-shear stresses. As the rock sample approaches its peak strength, anti-wing cracks appear at or near the tip of the prefabricated macro-crack and rapidly expand. The failure mode is dominated by anti-wing cracks [51; 52; 53; 54].  
Figure 13a shows the specimen after zero freeze-thaw cycles. Cracks WC-1 and AWC-1 first form at the tip of the prefabricated crack, followed by connected WC-2 cracks. With the increasing number of cyclic loads, downward crack AWC-2 and crack SC-1 along the direction of the prefabricated crack form. Eventually, the specimen becomes destabilized and fails. Figure 13b shows the specimen after 40 freeze-thaw cycles. First, connected crack WC-1 forms at the tip of the two prefabricated cracks, followed by cracks WC-2 and AWC-1. Crack SC-1 forms along the upper crack tip, extending to the top of the crack. With the increasing number of cyclic loads, crack SC-2 forms from top to bottom, and  
Figure 11: Conceptual model of freeze–thaw and fatigue load deterioration processes.  
eventually, the specimen becomes destabilized and fails. Figure 13c shows the specimen after 80 freeze-thaw cycles. First, connected crack WC-1, crack AWC-1, and crack SC-1 simultaneously form at the tips of the two prefabricated cracks. Next, crack AWC-2 forms at the tip of the upper prefabricated crack, and a new crack, SC-2 develops in the middle, continuing to extend. With the increasing number of cyclic loads, AWC-3 cracks form at the tips of the upper prefabricated cracks, continuously extending and developing to produce SC-3 cracks. Eventually, the specimen becomes destabilized and fails.  
Figure 13 shows that the fractured granite is suddenly destroyed under periodic loading, accompanied by particle and block ejections, exhibiting characteristics of sudden and burst failure. As the number of freeze-thaw cycles increases, the extent of specimen failure gradually diminishes, and the rock block ejection weakens. This indicates that freeze-thaw cycles weaken the internal structure of the specimen, reduce the bearing capacity and energy storage capacity of the granite, and make its fracture surface more complex.  
Damage Constitutive Model of Fractured Granite under the Combined Action of Freeze-Thaw and Fatigue Load
# Remote Servo Tuning System for Multi-Axis CNC Machine Tools Using a Virtual Machine Tool Approach
###### Abstract  
Servo systems affect the performances of machining in accuracy and surface quality for high speed and precision machine tools. This study introduces an efficient servo tuning technique for Computer Numerical Control (CNC) feed drive systems using particle swarm optimization (PSO) algorithm by virtual machine tool approach. The proposed approach contained a system identification phase and a servo tuning phase based on the same bandwidth for all axes feed drive systems. The PSO algorithm was adopted to obtain the system parameters and maximize the corresponding bandwidth. An efficient two-step servo tuning method based on gain and phase margins was proposed for high speed and precision requirements. All feed drive systems controller gains were optimized simultaneously for synchronization. A remote system called Machine Dr. was established for servo tuning and monitoring. Simulation and experimental results were introduced to illustrate the effectiveness of the proposed approach.  
feed drive system; five axes; machine tools; servo tuning; particle swarm optimization Article
# How Biological and Environmental Factors Affect the Quality of Lavender Essential Oils
## 3 Biological and Environmental Factors Affecting EO Production
### Developmental Factors and Plant Age
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Environmental Factors**} & \multicolumn{3}{c}{**Plant Development**} \\ \cline{2-7}  & **Rain** & **Temperature** & **Diurnal** &
\begin{tabular}{c} **Ontogeny** \\ **Inflorescence** \\ **Development** \\ \end{tabular} & **Plant Age** & **Genotype** \\ \hline \multirow{3}{*}{Linalool} & \(-\) & & & + Middle & \\  & [39; 41] & + & ZT4 & [58] & + up to 4y & \\  & [41] & [13] & + Late & [46] & \\  & [43] & & & [26; 37; 52] & \\ \hline \multirow{2}{*}{Linalyl acetate} & \(-\) & + & ZT12 & + Late & \\  & [41] & [41] & [13] & [26; 58] & [38; 45] \\ \hline \multirow{2}{*}{Camphor} & \(+\)[41] & + & Max at 10 h & + & \\  & \(-\)[43] & [41] & [13] & [41] & \\ \hline \multirow{3}{*}{1–8 Cineole} & & + & & + & \\  & [41] & \(-\)[41] & Max at 10 h & [26; 41] & \\  & [41] & \(-\)[13] & [13] & \(-\) Late & \\  & & [38] & & [41] & \\ \hline \multirow{2}{*}{Borneol} & & & Max at 10 h & + & \\  & & & [13] & [41] & \\ \hline \multirow{2}{*}{Limonene} & & & Max at six h & + Late & \\  & & & [13] & [26] & \\ \hline \multirow{2}{*}{3-Octanone} & & & Max at 10 h & + up to 4y & \\  & & & [13] & & [46] \\ \hline \multirow{2}{*}{\(\alpha\)-Terpineol} & & & Max at 10 h & + Late & + up to 4y & \\  & & [13] & [26; 41; 52] & [46] & \\ \hline \multirow{3}{*}{Terpinen-4-ol} & \(+\) & + (temperature & & + & \\  & [41] & drop) & & [26] & \\ \cline{1-1}  & & [41] & & + Late & \\ \hline \multirow{2}{*}{Lavandulyl acetate} & & & Max at 18 h & + & \\ \cline{1-1}  & & & [13] & [26] & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Factors affecting the specific compounds of the EO produced by lavender flowers. (\(-\) means decrease and + means increased amount).
## 1 Introduction  
### Background and Motivation  
De-commissioning the Fukushima Daiichi Nuclear Power Plant (NPP) is one of the most important challenges we must overcome, an effort that will take decades. In general, structures deteriorate over time; a de-commissioning site is no exception, making safe and continuous work difficult. The earthquake and subsequent tsunami damaged the site's reinforced concrete structures, while seawater injection damaged those inside the Primary Containment Vessel. Such structures are not only the targets to be dismantled but also comprise an important basis for future de-commissioning work, meaning it is imperative that these structures do not collapse during work. The current status of the Fukushima Daiichi NPP can be observed on the Tokyo Electric Power Company website [1; 2]. To guarantee workability hereafter, we have begun developing non-destructive testing and structural health monitoring techniques for the Fukushima Daiichi NPP [3; 4].  
[PERSON] et al. [5; 6; 7; 8] have reported a testing method for the interface condition between concrete and rebar with an ultrasonic guided wave. Their work considered the arrangements of the ultrasonic sensor and transmitter, as well as its effect on the ultrasonic signals. Meanwhile, [PERSON] et al. [9] investigated the Time of Flight change in a propagating ultrasonic guided wave due to corrosion and mass load. Most recently, [PERSON] [10] conducted research on the guided wave on the rod of reinforced concrete from theoretical, numerical, and experimental perspectives. These research papers provide useful information on and insight into testing methods for the Fukushima Daiichi NPP. However, these previous endeavors employed Piezoelectric Transducer (PZT) or Electromagnetic Transducer (EMAT) in their work, neither of which accounted for remote operations.  
To accommodate the aging of the reinforced concrete structure, many researchers have addressed remote non-destructive testing. For example, [PERSON] et al. [11] reported on the Laser-hammering method, which irradiates the concrete surface using a pulsed-laser. The excited ultrasonic waves generated by the pulsed-laser are measured using a remote Laser Doppler Vibrometer (LDV). [PERSON] et al. [12], on the other hand, presented a testing method using Long Range Acoustic Devices and LDV.  
These approaches target remote and rapid operations; however, they focus on the cracks or voids near the concrete surface, which is not suitable for detection of inner corrosion. Non-destructive testing for the de-commissioning site must have the integration potential for a remotely controllable robot system, in order to avoid radiation exposure. Hence, in this paper, we focus our interest on laser technology. A pulsed-laser can excite ultrasonic waves [13] and a continuous wave laser can be applied to dig holes in the concrete surface [14] to expose the rebar. Flexible optical fiber cable can guide the laser in narrow or complicated spaces, and various LDVs are commercially available.
## 5 Discussion and Conclusions
### Methodological Contributions  
The generic method discussed in this paper could be used to predict any subsystem/equipment-level mass budget of a satellite, which is essential for calculating midpoint indicators with the help of FCC data and ESA's LCI database. ESA has released an updated LCI space-specific database to the public [69]. This will be primarily beneficial for users with readily available component-level mass budget data. The results will be accurate if users use primary data (component level) to calculate the environmental effects during any of the phases for their respective satellite. However, for users without primary data and who want to predict the footprint of any satellite or constellation of satellites, the method presented in this paper would be beneficial.