esa-sceva/satcom-chunk-collection huggingface dataset:
# Industrial Cyber-Physical System Evolution Detection and Alert Generation
## 2 Use Case: Press Line Domain
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Press Machine Characteristics**} & \multicolumn{3}{c}{**Product Scale**} \\ \cline{2-4}  & **Small** & **Medium** & **Large** \\ \hline Average number of devices per Press Machine & 20 to 49 & 50 to 99 & \textgreater{}500 \\ \hline Percentage of affected devices when the Press Machine & 40\% to 59\% & 40\% to 59\% & 40\% to 59\% \\ evolves (added, removed, or modified)? & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The characteristics of a Press Machine. monitored due to customer requirements, so new devices must be inserted), remove existing ones (e.g., due to an anomaly, the device is damaged and must be removed). or modifying them (e.g., a device is updated and is now able to send more data that was not previously considered). Additionally, note that every device can send different attributes. These ones can also vary depending on the state of the ICPS, i.e., some of the devices located in the ICPS are intelligent; they are able to change the data architecture depending on the status of the machine that is being monitored. Device information is then sent to the users, so they can make decisions, e.g., repairing a device, since it is not working properly, or do predictive maintenance because the Remaining Useful Lifetime (RUL) is approaching to zero (predictive maintenance [20]). In spite of this, note that the ICPS is composed of different machines with different characteristics. In the same manner that devices inside the machines can evolve, the press line itself can evolve due to customers requirements, i.e., new machines can be introduced inside the press line. At the same time, it is necessary that none of these machines stop, since that would bring negative consequences to the production (e.g., loss of money). In addition, in this particular use case explained above, between 30 and 50 people are needed to support the proper functioning of the press line. Notice that the quantity of people would depend on each press line to supervise. Therefore, being aware of what is happening in the industrial domain is crucial. When an evolution happens, many people need to be alerted in order to detect anomalies to reduce system downtime. In Table 2 is shown the different roles that the users have in order to support a press line. Thus, depending on the user role, the interest of the users in terms of data is different. In spite of that, all of them need to be aware of what is happening in the ICPS that is being monitored. This results in the following conclusions: (1) a solution able to automatically detect ICPS evolution is necessary. (2) Alerting each user about the evolution is necessary to be aware of the status of ICPS, as it helps in making decisions. (3) The presented solution needs to be scalable in order to give responses to different ICPS sizes.
## 3 Results
### The Non-Linear Thermal Buckling Modal Response  
To simulate the initial disturbance of the blade, the first order linear buckling deformation is reduced 500 times to small deformation. The geometric position of blade nodes are updated as the initial state of iteration. Meanwhile, the maximum deformation of linear buckling is set as the critical displacement of blade instability, which is beneficial to control the termination of calculation. In the solution, the effects of initial defects and large deformation response are considered. Then, the step-by-step iteration method is implemented to calculate the non-linear thermal buckling modal response, with deformations of first four orders shown in Figure 11 [1].  
As exhibited in Figure 11, the first mode buckling mainly occurs at the edge of the blade tip, presenting as a twill. At the same time, there is large bending and torsional deformation, with the largest displacement change. Meanwhile, compared with the experimental analysis in Ref. [32], it is clearly found that the characteristics of nonlinear thermal buckling modes are consistent with blade tip cracking in a thermal power plant, given in Figure 12 [1].  
For the second mode buckling, the deformation of the blade is a semi concentric ring with slight bending, mainly appearing at the edge of the blade root. Different from the above modes, in the third and fourth order modal buckling, buckling surface area gradually become larger, with the significant deformations appearing at the top edge. It is worth noticing that the range of buckling response is directly proportional to the increase of modal order, majorly distributed at the edge and tip of the blade, with a variety of deformation forms.  
Figure 11: The nonlinear buckling modes of the blades [1]. (**a**) The first-order; (**b**) the second-order; (**c**) the third-order mode; and (**d**) the fourth-order mode.
## 5 Results
The rapid flood map produced by means of Sentinel-1 radar image acquired on 11 March 2018 confirms this spatial extension, since the binary water mask shows many water surface pixels (coloured in red) detected inland the shoreline of the lake, identifying the flooded zones (Figure 6c). From the NDWI map we can also extract the current water surface area along the lake, which reveals alike results (Figure 6d). Figure 7 shows images referred to 14 August 2017, chosen as date in summer period when no flood or critical lake fluctuation event occurred. The RGB colour composites true colors and SWIR/NIR/Red derived from Sentinel-2 optical imagery acquired on 15 August 2017 (Figure 7a,b) reveal dry pattern images and the water level of the lake appears extensively below the one detected in previous images acquired on flood event dates. In this case, the boundary between lake and land is well defined and easily recognizable. There was no influence of water on land, both over agricultural territories or over informal areas. The rapid flood map produced by means of Sentinel-1 radar image acquired on 14 August 2017 as well as the NDWI map produced by means of Sentinel-2 optical image shows an identical reduced spatial extension of Shkoder lake, confirming the water level dropped down during the dry season (Figure 7c,d). Finally, the validation step was performed by combining the obtained results based on satellite Sentinel-1 radar image acquired on 11 March 2018 (shown in Figure 5c) with the official delineation maps available through the Copernicus EMS (activation ID EMS273). The EMS maps were produced for the Shkoder area situation on 18 March 2018 by employing RADARSAT-2 scene acquired on 14 March 2018 and Sentinel-1 scene acquired on 18 March 2018. These official data were achieved by using ESR World Imagery as background ortophoto and by rapid processing of satellite SAR imagery. Results comparison (Figure 8) shows a successful spatial agreement between the EMS map and the rapid flood map produced throughout our Sentinel-1 radar-based procedure. Good spatial overlapping between the two maps is obtained over the flooded areas along Shkoder lake, while slightly dissimilar results are obtained along rivers, possibly due to the later date (18 March 2018) of the EMS delineation map.
# A Low-Ambiguity Signal Waveform for Pseudolite Positioning Systems Based on Chirp
## 2 Mathematical Models
### Auto-Correlation Function  
The ACF of the ChPN signal, \(R(t+\tau,t)\), can be expressed as:  
\[R(t+\tau,t)=E\big{[}s(t)^{*}s(t+\tau)\big{]}=\sum_{k=-\infty}^{+\infty}\sum_{n =-\infty}^{+\infty}\left\{\begin{array}{l}E({c_{n}}^{*}c_{n+k})[p(t-nT_{c}) ]^{*}\\ \times p(t-nT_{c}+\tau-kT_{c})\end{array}\right\} \tag{4}\]  
According to (4), \"\(\star\star\)\" represents complex conjugate, \(R(t+\tau,t)\) is a periodic function with respect to the variable \(t\) with a period of \(T_{c}\); (1) indicates that the mean value of \(s(t)\) is also a periodic function with the same period \(T_{c}\). So the ChPN signal is cyclostationary, and its ACF, \(R(\tau)\) with \(t\) eliminated, can be obtained by the time average of \(R(t+\tau,t)\):  
\[R(\tau)=\frac{1}{T_{c}}\sum_{k=-\infty}^{+\infty}R_{c}(k)R_{p}(\tau-kT_{c}) \tag{5}\]  
Figure 1: Illustration of waveforms of (**a**) PRN code signal; (**b**) Chirp signal; (**c**) ChPN signal.  
\(R_{p}(\tau)\) is the ACF of \(p(t)\), \(R_{c}(k)\) is the ACF of \(c_{n}\). Assuming that the PRN code sequence is random, non-periodic, identically distributed, equiprobable and independent, we have:  
\[R_{c}(k)=E({c_{n}}^{*}{c_{n+k}})\approx\left\{\begin{array}{ll}1,&k=0\\ 0,&k\
eq 0\end{array}\right. \tag{6}\]  
Thus, (5) can be simplified as:  
\[R(\tau)=R_{p}(\tau)/T_{c} \tag{7}\]  
where the \(R_{p}(\tau)\) is as follows [23]:  
\[R_{p}(\tau)=T_{c}\frac{\sin\Bigl{(}\pi B_{s}\tau\Bigl{(}1-\frac{|\tau|}{T_{c} }\Bigr{)}\Bigr{)}}{\pi B_{s}\tau}\cos(2\pi f_{0}\tau),-T_{c}<\tau<T_{c} \tag{8}\]  
Based on Equations (7) and (8), the ACF is given by:  
\[R(\tau)=\frac{\sin\Bigl{(}\pi B_{s}\tau\Bigl{(}1-\frac{|\tau|}{T_{c}}\Bigr{)} \Bigr{)}}{\pi B_{s}\tau}\cos(2\pi f_{c}\tau),~{}-T_{c}<\tau<T_{c} \tag{9}\]  
Based on the Equation (9), the computer numerical results show that the ACF curve reaches its maximum at \(\tau=0\) and its first zero at \(\tau\approx\pm 1/2B_{s}\), and the ACF main peak width is about \(1/B_{s}\). Therefore, the sharpness of the ACF main peak is proportional to \(B_{s}\); a wide \(B_{s}\) results in a sharp correlation peak, and thus a fine multipath resolution as well as good acquisition and tracking performance. What's more, the signal-to-noise ratio (SNR) of the output of the match filter in the receiver is amplified by the following two factors: (1) the compression gain of chirp, \(G_{c}=10\log(B_{s}T_{c})\) dB [24]; (2) the dispreading gain \(10\log(N_{c})\) dB, where \(N_{c}\) is the PRN code length.
## 4 Results and Discussions
### Visual Results of the Segmentation
This tool has polygon-based markings, making it difficult to annotate the regions of the parasites. Figure 9 illustrates some examples of imprecise annotations by the tool. Figure 8: Examples of false positives recognized by the model. Original clipping on the (**left**); Ground truth in the (**center**); Predicted label on the (**right**). Figure 7: Results for amastigotes segmentation. Original clipping on the (**left**); Ground truth in the (**center**); Predicted label on the (**right**). Analyzing the results obtained by the proposed methodology, it is possible to list the following main limitations of this work: (1) It detects only human visceral leishmaniasis parasites; (2) The noise in the images can cause an erroneous detection; (3) The used dataset contains few VL image samples.
## 4 Training and Simulation
### Comparative Analysis of the Control Effects in the MBS Simulations  
To validate the effectiveness of the proposed control strategy, a cosimulation involving the integration of SIMPACK and Python was conducted on the prototype vehicle. The Hoo control algorithm and the PER-MADDPG controller were tested for use in evaluating the dynamic response of the IRWs. The Hoo algorithm involved in the comparison is a model-based state feedback controller based on the \(\mu\)-synthesis theory. This algorithm designs the controller \(K_{\textit{H}\textit{i}\textit{i}\textit{i}\textit{i}}\) to minimize the robust Hoo performance established on IRW dynamic equations that include structural uncertainties, particularly at the wheel-rail contact interface. The Hoo controller ensures that the closed-loop system remains quadratically stable for all permissible uncertainties, thus ensuring IRW system stability. Due to the varying bounds of external disturbances in different tracks, the robust Hoo algorithm requires particular optimization for each condition. In contrast, the proposed algorithm does not require parameter adjustments for specific operating conditions.  
We focused on two specific operating conditions for our analysis: the straight-line condition at 120 km/h and the 1500 m radius curve at 150 km/h, as detailed in cases 1 and 4 of Table 2.
# Path Planning for Localization of Radiation Sources Based on Principal Component Analysis
## 2 Proposed Method
### Localization of Multiple Radiation Sources  
After the final radiation source estimation, the k-means algorithm is applied to the 3D voxel values obtained through simple back-projection for localizing multiple radiation sources. In this study, the number of clusters in k-means clustering is manually determined based on the observation of the final radiation source estimation. However, the number of clusters can be automatically estimated using an information criterion, such as the Bayesian information criterion [27].
# A Mathematical Model for Efficient and Fair Resource Assignment in Multipath Transport
## 2 Terminology
### Participants  
**Definition 4**.: _Flow: The set of all data packets belonging to the same communication, e.g., an individual file transfer between two nodes, regardless of the interfaces used at the sender and receiver side and the path(s) between the sender and receiver, is denoted as a flow._  
In Figure 1, all data transported from the sending node \"a\" to the receiving node \"e\" is a flow. The set of all traffic demands resp. data flows (single or multipath) is denoted as \(K\).  
**Definition 5**.: _Subflow: A subflow occurs when a flow is split into multiple paths; it denotes the packets of a flow that use a certain interface at the sender and receiver and a certain path inside the network._  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & **a1** & **a2** & **b1** & **b2** & **c1** & **c2** & **d1** & **d2** & **d3** & **e1** \\ \hline a1 & \(\  
In Figure 1, there are two subflows that are equivalent to the available paths identified in Definition 2.
# Stress Dispersion Design in Continuum Compliant Structure toward Multi-DOF Endoluminal Forceps
## 1 Introduction
However, in these studies [24; 25; 26; 27], a problem arose regarding the high computational cost due to the nonlinear analysis with fine mesh and the exploratory thickness determination of elastic hinges. In this study, we propose a novel method to efficiently determine the thickness of the elastic hinges by modeling the stress distribution mathematically using the theory of the beam of uniform strength [28]. In particular, the technical contributions of this paper are: (1) A novel design method was proposed to determine the thickness of continuum hinges based on the beam of uniform strength theory. (2) Endoluminal forceps were developed with a wide range of motion and a small radius of curvature even in a narrow lumen due to the stress dispersion design method. This paper is organized as follows: Section 2.1 presents the mechanical structure of the designed forceps. Section 2.2 describes the design method for obtaining the new hinge parameters. The section is followed by the stress evaluation through FEA presented in Section 3.1. In Section 3.2, a prototype is used to compare the differences in motion generation between the variable thickness of the hinge and the constant thickness of the hinge. Section 4 discusses the results, as well as the study's contributions and limitations. Finally, Section 5 provides the conclusions.
## 2 Materials and Methods
### Model Overview
#### 2.1.1 The Vegetation Photosynthesis Model (VPM)  
VPM [25; 26] assumes that the leaf and forest canopies consist of photosynthetically active vegetation and nonphotosynthetic vegetation that has been successfully used to simulate GPP in different ecosystems, including tropical evergreen forest [67], temperate deciduous forest [26], evergreen needleleaf forest [25], and cropland [68] at a site scale. The GPP in VPM is estimated as follows:  
\[GPP~{}=~{}PAR\times fPAR\times\varepsilon_{g} \tag{1a}\] \[fPAR~{}=~{}EVI\] (1b) \[\varepsilon_{g}~{}=~{}\varepsilon_{max}\times ft\times ftv\] (1c) \[ft~{}=~{}\left\{ \begin{array}{cc}\frac{(T_{a}-T_{min})(T_{a}-T_{max})}{(T_{a}-T_ {max})(T_{a}-T_{max})-(T_{a}-T_{opt})^{2}}&(T_{min}<T_{a}<T_{max})\\ 0&(T_{a}<T_{min}\text{ or }T_{a}>T_{max})\end{array} \right.\] (1d) \[fw~{}=~{}\frac{1+\text{LSWI}}{1+\text{LSWI}_{max}}\times P_{s}\] (1e) \[P_{s}~{}=~{}\left\{ \begin{array}{cc}\frac{1+\text{LSWI}}{2}&(\text{before leaf full expansion})\\ 1&(\text{after leaf full expansion})\end{array} \right. \tag{1f}\]  
where \(\varepsilon_{g}\) represents the actual LUE, \(ft\) represents the effect of air temperature (\(T_{a}\)) on \(\varepsilon_{\max}\), and \(fw\) illustrates the water stress on \(\varepsilon_{\max}\). \(T_{\min}\), \(T_{\max}\), and \(T_{\text{opt}}\) represent the minimum, maximum, and optimum air temperatures for photosynthetic activities, respectively. EVI is the enhanced vegetation index, LSWI is the satellite-derived land surface water index, \(\text{LSWI}_{\max}\) is the maximum LSWI during
## 3 Preliminaries
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Meaning** \\ \hline \(u_{0}\) & The user vehicle. \\ \(sv_{j}\) & The \(j^{th}\) service vehicle. \\ \(T_{0}\) & The computation task of \(u_{0}\). \\ \(T_{up}^{max}\) & The maximum acceptable completion delay of \(T_{0}\). \\ \(T_{agv}^{max}\) & The achievable communication time between \(u_{0}\) and \(sv_{j}\). \\ \(O_{j}\) & The proportion of tasks offloaded from \(u_{0}\) to \(sv_{j}\). \\ \(O_{j}^{max}\) & The maximum achievable task proportion of \(sv_{j}\) to \(u_{0}\). \\ \(v_{i}(t)\) & The speed of vehicle \(v_{i}\) at time \(t\). \\ \(R\) & The V2V data transmission rate. \\ \(f_{j}\) & The amount of computing resources that \(sv_{j}\) can lend. \\ \(t_{j}\) & The completion delay on \(sv_{j}\). \\ \(t_{total}\) & The total completion time of the task. \\ \(p\) & The total price that \(u_{0}\) should pay for asking SVs. \\ \(C\) & The base price of unit size data of task. \\ \(a_{j}\) & The \(sv_{j}^{*}\)s dynamic adjustment parameters of price. \\ \(w_{j}\) & The financial condition of \(sv_{j}\). \\ \(d_{uj}\) & The distance between \(u_{0}\) and \(sv_{j}\). \\ \(v_{uj}\) & The relative speed between \(u_{0}\) and \(sv_{j}\). \\ \(r_{uj}\) & The link reliability of the communication channel between \(u_{0}\) and \(sv_{j}\). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of important symbols.
###### Abstract  
This study investigates the development of high-performance asphalt binders modified with Trinidad Lake Asphalt (TLA) and styrene-butadiene-styrene (SBS) polymers to enhance pavement durability under extreme climate conditions. A comprehensive evaluation of physical, rheological, and mechanical properties was conducted using Superpave performance tests, Multiple Stress Creep Recovery (MSCR), and a Dynamic Shear Rheometer (DSR). The results indicate that integrating 20% TLA significantly increases stiffness and rutting resistance by 51.7% compared to unmodified PG 64-22 asphalt, while 10% SBS improves elasticity and enhances elastic recovery by 85.3%. However, at 15% SBS, excessive viscosity was observed, reaching 13,000 cP at 135 \({}^{\circ}\)C, posing workability challenges and sampling challenges in the lab environment. The MSCR test confirmed that binders modified with 20% TLA and 15% SBS exhibited over 88% recovery and reduced non-recoverable creep compliance (J\({}_{\text{nr}}<0.01\) kPa\({}^{-1}\)), demonstrating superior resistance to permanent deformation. Additionally, low-temperature rheological testing (BBR at \(-12\)\({}^{\circ}\)C) revealed that SBS incorporation mitigates excessive stiffness caused by TLA, improving the binder's flexibility. These findings underscore the potential of TLA-SBS modified binders in achieving long-lasting, traffic-resilient pavements for extreme climatic conditions. Field validation is recommended to assess long-term feasibility in real-world applications.  
extreme climate performance; Superpave performance criteria; high-performance binders; sustainable pavement engineering Article
# Maximizing the Capacity of Edge Networks with Multicasting
## 5 Multicast Transmission Distance  
From the aforementioned analysis, the system capacity can be represented if the average transmission distance of the multicast tree can be formulated. However, it is difficult to calculate the average transmission distance since the content may be duplicated at any branch, and the distribution of branches is uncertain. To make the study more tractable, the upper and lower bound on the transmission distance are analyzed by employing the multicast tree feature. The first is the last-hop multicasting, i.e., the transmission adopts the unicast mode in the former hops and the multicast mode only occurs in the last hop as shown in Figure 2a. The second is the complete k-ary multicast tree, i.e., each branch node has \(k\) children.  
We here employ Figure 2 to illustrate the difference in the two communication models. In Figure 2a, the root node needs to transmit two times for the same content if the last-hop multicasting is adopted, the number of transmitting time is only one if the complete \(k\)-arymulticasting technology is used in Figure 2b, whereas the number is four if we use the unicast communication.  
The average transmission distance in the two cases can now be analyzed.
## 3 Data Analysis and Results
### The Samos Aftershock Sequence  
A strong and shallow earthquake of \(M_{w}=7.0\) struck Samos Island on the Aegean Sea (Figure 1), on 30 October 2020. Its aftershocks area covers the region between longitudes 26.10\({}^{\circ}\) E-26.99\({}^{\circ}\) E and latitudes 37.64\({}^{\circ}\) N-37.98\({}^{\circ}\) N. The catalogue, for a completeness magnitude of \(M_{c}=2.5\), includes 1158 aftershocks (Table 1).  
Using the same methodology as previously, fitting the \(Q\)-exponential function to the noticed data up to a value near to \(T_{c}\) yields \(q=1.63\) (Figure 5a). We estimate \(T_{c}\) to be 9761 s, based on the deviation from predicted linearity (Figure 5b). In the early time aftershock part, most of the interevent times, with \(T\) values less than \(T_{c}\) exist, which forces us to conclude that the Tsallis entropy mechanism is dominant in this early part of the aftershock  
Figure 4: **(a)** The interevent times CDF of the 2021 \(M_{w}6.3\) Elassona Earthquake, for the aftershocks with \(M>M_{c}\). The scarlet stroke is the \(Q\)-exponential fitting with \(q=1.62\). The change of colors indicates the crossover between the NESP (blue circles) and BG statistics (green triangles). **(b)** The \(ln_{Q}P\)(\(>\)\(T\)) as a function of \(T\), where the scarlet line presents the fitting with \(q=1.62\) and correlation coefficient 0.9936 up to \(T_{c}\). \(T_{c}\) value close to 4587 s is suggested by the deviation from linearity. **(c)** Interevent time \(T\) evolution with time \(t\) since the main shock. The red line illustrates the \(T_{c}\) value.  
evolution. With the progress of time, the pattern of the aftershock sequence, such as finite degrees of freedom and long-range memory, are notpredominant anymore and the BG statistical physics controls the aftershocks evolution (i.e., \(q=1\)) (Figure 5c) [14].