esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Network Access Control Security Framework
### Access Control Algorithm Description  
The access control algorithm of our proposal assumes that each node is always in one of seven states: new node, pending, assessment, image update, update assessment, authorized and malicious. Figure 4 illustrates the decision process from the initial 'new node' state until one of the final states is reached, which is either 'authorized' or'malicious'.  
The 6 LoWPAN nodes can use link layer detection mechanisms and/or router advertisement messages to detect new networks. If a new network is detected, the 6 LoP  
Figure 4: Access control decision process.  
LSEND protocol to perform the address registration process, as described in section 3.2. The address registration process is used to detect new nodes presence. In the listening mode, the automatic approval is in use and, therefore, the new node moves to the authorized state and its address is added to the border router as a registered entry [6]. This is the less secure operation mode and is used by the administrator to determine which nodes are connected.  
In the active mode, the automatic approval is not set and, therefore, the new node moves to the pending state and its address is stored in the border router neighbor cache as a tentative entry [6]. Before the authorized state is reached, only messages related to the assessment, image deployment and authorization are allowed. In the pending state, the administrator can classify the new node as malicious if: (i) additional nodes are not expected, (ii) the administrator does not rely on the received information or (iii) the new node was considered malicious in the past. If the administrator does not consider the new node as malicious, the next step is to verify if the assessment agent is installed. If yes, the node moves to the assessment state and its security compliance is evaluated. Then, the node transits to the authorized state if the evaluation is successful or to the malicious state, otherwise. If the assessment agent is not installed, the node moves to the image update state, where the border router tries to install on the node a new image using Deluge (the new image includes the operating system, the assessment agent and all required modules). If the configuration of the new image is refused by the node, if moves to the malicious state. Otherwise, the node moves to the update assessment state and its security compliance is evaluated. Then, the node transits to the authorized state if the evaluation is successful or to the malicious state, otherwise.  
When a node is moved to the malicious state, its address is marked as garbage-collectible in the border router neighbor cache, the address is propagated by the RPL messages to all authorized nodes as a restriction, and the address is pruned from all nodes RPL candidate neighbor list and cannot belong to the DODAG instance in use. So, in the malicious state, no messages from these nodes are processed or forwarded by any other LoWPAN node.
# Temperature Load Mode of Bridge in Permafrost Region of Qinghai-Tibet Railway
###### Abstract  
The Qinghai-Tibet railway is the plateau permafrost railway with the highest altitude and the longest line in the world. The natural conditions along the line are harsh, with many unfavorable factors such as low temperature, strong ultraviolet radiation, and large changes in daily temperature, resulting in frequent bridge damage. In order to study the bridge temperature field and its effect in the permafrost region of the Qinghai-Tibet railway, a long-term field test was carried out, and a calculation model of sunshine temperature field of concrete two-piece T-beam was established based on the principle of meteorology and heat transfer. On this basis, the beam temperature difference load mode, beam section temperature distribution law, and temperature effect were obtained. As revealed, the daily temperature difference of the Qinghai-Tibet railway bridge is large in winter, which is related to the ground's effective radiation and surface reflectivity, and the maximum value has exceeded the current codes. The beam section temperature field shows \"internal heat and external cold\" in the morning and \"internal cold and external heat\" from noon to evening. Under the action of strong radiation and large temperature difference, bridge displacement occurs frequently, by which it is easy to cause damage to rail fasteners and bridge bearings. Based on the field test and finite element analysis, the bridge temperature difference load mode was proposed, which makes up for the deficiency that the relevant codes do not consider the plateau's special climatic conditions and can provide a reference for the construction of plateau railways such as the Sichuan-Tibet railway.  
Qinghai-Tibet railway; concrete bridge; temperature field; finite element; heat transfer; experimental study +
Footnote †: journal: Applied Sciences
## 3 Results
### Prediction of plaque Progression  
Overall plaque progression was defined for each 3 mm segment according to the presence of at least two of three conditions: reduction of lumen area by >20%, increase of plaque area by >20%, increase of plaque burden by >20%. Over the 4000 coronary segments analyzed, 1557 (39%) showed plaque progression. CCTA imaging and computational modeling data in segments without or with evidence of plaque progression are compared in Table 3.  
Figure 2: Example of a patient case as analyzed using the developed software. Panels A and B present the segmentation results, panel C shows the calculated SmartFFR for one branch, as well as metrics of lumen and wall area and plaque burden among the vessel’s length. Panel D presents the reconstructed geometry back-projected on the coronary computed tomography angiography (CCTA) volume rendering and panel E shows the ESS distribution on the analyzed vessel. At univariate linear regression analysis, older age, higher blood concentrations of triglycerides, and some specific lipid species (CE_18_3, CE_20_3, CE_20_4, PC_36_0, PC_36_3, TG_54_2 TG_18_18_18_0, PS_38_6, PS_40_6, SM_42_1) were associated with plaque progression (Table 4). Among the baseline morphological and computational CCTA variables, lower plaque burden, lower minimum ESS, higher maximal LDL concentration, and higher SmartFFR were all associated with plaque progression. However, at multivariate linear regression analysis only age (\(p\) = 0.0067) and lower baseline plaque burden (\(p\) < 0.0001) remained independently associated with plaque progression. The univariate and multivariate logistic regression analysis for the prediction of plaque progression confirmed the previous results (Table 5). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Baseline**} & \multicolumn{2}{c}{**Follow-Up**} & \multicolumn{2}{c}{**Delta**} & \\ \hline \multirow{3}{*}{**Mean**} & \multirow{3}{*}{**Std. Dev.**} & \multirow{3}{*}{**Mean**} & \multirow{3}{*}{**SD**} & \multirow{3}{*}{**\(p\)-Value Baseline vs. Follow-Up**} & \multirow{3}{*}{**Mean**} & \multirow{3}{*}{**SD**} & \multirow{3}{*}{**\(p\)-Value Baseline vs. Change between Follow-Up and Baseline**} \\  & & & & & & & & & \\ \hline
**Lumen area** (mm\({}^{2}\)) & 6.86 & 3.89 & 6.34 & 3.90 & \textless{}0.0001 & \(-\)0.56 & 2.95 & \textless{}0.0001 \\
**Plaque area** (mm\({}^{2}\)) & 7.16 & 4.30 & 7.29 & 4.62 & \textless{}0.0001 & 0.14 & 4.41 & \textless{}0.0001 \\
**Plaque burden** & 51.02 & 14.66 & 52.96 & 17.85 & \textless{}0.0001 & 1.79 & 19.27 & \textless{}0.0001 \\
**Minimum ESS (Pa)** & 1.78 & 2.36 & & & & & & \\
**Normalized max.** & 0.24 & 0.12 & & & & & & \\
**LDL concentration** & 0.89 & 0.12 & & & & & & \\
**SmartFFR** & 0.89 & 0.12 & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CCTA imaging variables at baseline and follow-up and results of computational modeling of endothelial shear stress (ESS), LDL concentration and calculation of SmartFFR at baseline.
# The Influence of Grain Size on the Abrasive Wear Resistance of Hardox 500 Steel
###### Abstract  
High-strength martensitic steels with boron are among the leading materials widely recognized for their exceptional resistance to abrasive wear. These steels exhibit some of the highest strength indices among bulk steels, a result of their specific chemical composition, thermomechanical rolling processes at the steel mill, and the use of pure, high-quality ones. With hardness values ranging from 400 to 650 HBW, they are ideal for demanding applications such as excavator buckets, plow blades, shafts, wear-resistant bars, and container liners. One critical microstructural property contributing to their high mechanical performance is the prior austenite grain size (PAG). A finer grain structure is associated with enhanced plasticity, and plastic deformation plays a significant role in abrasive wear mechanisms. However, this relationship between grain size and wear resistance is not well-documented in the literature, with few studies providing specific quantitative data. To address this gap, the authors conducted a study to examine the effect of prior austenite grain size on wear resistance when exposed to loose abrasive electrofused alumina no. 90. The findings indicate that applying targeted heat treatment can increase hardness by 58 Brinell units compared to the as-delivered condition. Moreover, as grain size increases from 18 um to 130 um, the relative abrasive wear resistance coefficient \(K_{b}\) decreases from 1.00 (for Hardox 500 steel in its as-delivered state) to 0.80 for austenitized material treated at 1200 \({}^{\circ}\)C.  
marts: steel; heat treatment; abrasive wear; prior austenite grain size +
Footnote †: journal: Applied sciences  
0000-0001-6500-5500]B. [PERSON]
## 1 Introduction
Experiments were conducted exploiting the case study of Cortes de Pallas [2], where three-year period monitoring surveys have been undertaken with sub-millimetric electronic distance meter (EDM) techniques and CRP. The result of the survey consists of a 3D model where the combination of these techniques ensures a complete mapping of the site, avoiding the creation of gaps in the point clouds thanks to the compensation of one technique on the other and vice versa. This 3D model provides a basis for the analysis of the rocky landslide deformation monitoring. The main contribution of this manuscript is to demonstrate how MMS can complement the 3D mapping of a challenging environment, by integrating multi-source data in a unique reference frame and with an accuracy comparable with other state of art methods. Several tests are presented, providing the research community with guidelines that will be useful for other similar settings, presenting the MMS as an alternative approach when other geomatic methods fail. This article is structured as follows: after a first state-of-the-art review in using MLS for mapping and monitoring purposes, Section 3 describes the geomatics techniques used for the data acquisition in the selected case study. Section 4 focuses on the data processing and the integration of georeferenced point clouds from MMS with the CRP. In Section 5 the results of this comparison are evaluated to assess the accuracy achieved in the combination of these data. The discussion and conclusion are finally presented in Sections 6 and 7.
# Virtualized MME Design for IoT Support in 5G Systems
## 5 Virtualized MME Design for M2M
### Baseline Schemes
#### 5.2.2 Overdimensioned Scheme  
The second baseline scheme considered is the same as the baseline scheme, but in this case, it is overdimensioned to satisfy \(\overline{D}_{j}\) at each tier of the vMME at all times, including during the traffic peaks caused by M2M devices.  
Hence, this scheme is designed with a target arrival rate equal to the sum of the mean arrival rate for MBB and the mean arrival rate during the largest event of mM2M and IM2M devices. Since we assume there is no coordination of mM2M and IM2M events (see Section 3.2), the resources do not need to be designed for simultaneous mM2M and IM2M events. Peaks from mM2M devices are expected to have a bigger impact on the vMME because of the massive number of mM2M devices. Then, we focus the arrival rate on mM2M peaks, using the mean arrival rate during the largest mM2M traffic peak, \(\overline{\lambda}_{mM2M}^{maxvert}\). Therefore, the target arrival rate used in this scheme, which is the same for all tiers, can be computed as:  
\[\lambda_{FE}=\lambda_{SL}=\lambda_{SDB}=\overline{\lambda}_{MBB}+\lambda_{mM2M }^{peak}+\lambda_{IM2M}^{intsmooth} \tag{10}\]
# Enhancing Weather Forecasting Integrating LSTM and GA
## 4 Results and Discussion  
This section presents the results obtained in relation to the estimated values and the forecast weather variables. Regression models were used to estimate and fill the missing data for Quinta de Santa Barbara. Subsequently, LSTM networks were employed to forecast weather variables for 3 to 15 days.  
The models used in this study were built using the Python 3.11 programming language [37]. The LSTM networks were implemented using the TensorFlow tool [38], a widely used framework for deep learning applications in Python, and the regression models were developed using the Scikit-learn library [39].  
Figure 2: Applied methodology, considering the datasets and methods used for short- to medium-term weather forecasting.
## 6 Discussion
### M2M Information and Services  
When looking at the reviewed M2M information and service technologies, several subcategories can be found. ETSI M2M defines horizontal M2M service capabilities, which are agnostic for the information content. There are several technologies defining data models (e.g., oBix), ontologies (e.g., OWL) and device management (e.g., OMA DM, UPnP). Open service frameworks (e.g., OSGi) and specification of overlay architectures for integrating sensor networks and applications on the Web (e.g., OGC SWE). The review shows that this area contains a huge amount of technologies which are not necessarily interoperable, and mix at least in the subcategory level. In addition, there are multiple projects working in the area and have defined their own proposal for the architecture and technologies. For example, Hydra [139], Runes [140], IoT-A [141], iCore [142], Sofia [143] and Fi-Ware [144]. It is seen here that there are some essential starting points, such as e.g., having horizontal M2M service capabilities which could be applied with multiple domain M2M applications. However, more detailed analysis and synthesis are needed to establish solid basis for subcategories to enable multiple stakeholder system within M2M information and service level. It is clear that technologies in this area are not ready for this, because for example, ETSI M2M assumes that M2M applications know all details of the device installation and data interpretation, which is challenging for application developers. It is also important to enable means for information level interaction with multiple stakeholders, and enabling autonomic decision making with applications which have not initially been designed for such a purpose. This kind of system is expected to be able to monitor the system ininformation level, analyze the situation, plan the required actions, and execute the control events towards the system automatically or at least semi-automatically. In addition, interacting systems need to understand the meaning of the received events, and be able to create actions in such a way that the other player understands it accordingly. This area is still mostly open for future research, and solutions are needed to make _autonomic_ M2M service networks a reality. There is a need to develop self-active decision engines enabling autonomic monitoring, analyzing, planning and executing responses to M2M systems. This includes need for smart and adaptive technologies for reasoning based on the complex events and state changes happening in the system. In addition, the autonomic decision engines need to be connected with generic service capabilities in a horizontal way to enable basis for interoperation in a services level.
# Contingent Task and Motion Planning under Uncertainty for Human-Robot Interactions
## 3 A Proposal for Contingent Task and Motion Planning
### Contingent-FF Overview  
The _Contingent-FF_ task planner [1] handles uncertainty in the initial state and in the result of actions. The task planner has two main components which are heuristic computation and search space. For the heuristic computation, the planner uses a modified version of the _Relaxed Planning Graph (RPG)_ used in the _Fast-Forward (FF)_ planner [27]. The relaxed plan including a number of relaxed actions is computed from the _RPG_, and the heuristic value is the length of this relaxed plan. Also, promising actions (called helpful actions in _FF_) are extracted from the relaxed plan as a pruning technique in the search space, as discussed in _FF_. The _Contingent-FF_ planner extends the _RPG_ process, called _CRPG_, by adding unknown facts in an additional layer in the heuristic phase. Known facts are basically those which do not have uncertainty and unknown facts are the ones which could be the result of nondeterministic actions or uncertain in the initial state. It introduces reasoning about unknown facts that allows such facts to become known in the _RPG_ process. Once _CRPG_ is successfully built, the relaxed plan is extracted.  
In _Contingent-FF_, belief states including known and unknown facts are considered. The search space starts from the initial belief state and applies an _And-Or_ search. The search space progress is guided by the heuristic value and helpful actions. The result of planning provides conditional plans that may involve a variety of sensing actions whose outcome causes different plan branches.
## 4 Feature Vector Generation  
For generating a feature vector for the logistic regression model, the DFT is applied to the matched filtering output of the sonar, the magnitude of this DFT result is used as a feature. The target strength is shown in Figure 3 according to the rotation angle of the target with respect to the incident signal. A significant change in target strength can be observed depending on the angle. Figure 4 shows examples of the magnitude spectrum for the received signal for rotation angles of 10 degrees, 60 degrees, and 90 degrees, respectively. It is observed that the spectrum varies depending on the angle. The feature vector reflecting these spectral characteristics are utilized in the logistic regression model. Note that the target response is depending on the grazing angle from the sonar. However, since the water depth is  
Figure 4: Magnitude spectra of the target response for various rotation angles for an linearly frequency modulated (LFM) signal of 13 kHz center frequency and 14 kHz bandwidth.  
In this study, 22,000 feature vectors are generated; 11,000 feature vectors are obtained from the signals representing that a target is in the detection range while the remaining 11,000 vectors are from the clutter signals without a target in the region.  
For the case that a target is present, the rotation angle of the target is changed by 10 degrees from 0 degrees to 90 degrees. For each rotation angle, TCR is increased by 1 dB from \(-5\) dB to 5 dB. 100 signals, \(x_{t}(t)+c_{b}(t)\), are generated for each TCR and each rotation angle, where \(x_{t}(t)\) is the target response and \(c_{b}(t)\) is the clutter signal.  
Whenever one feature vector \(x_{i}\) with \(y_{i}=1\) is generated, the same condition is applied to the generation of the corresponding clutter signal \(c_{o}(t)\) and another clutter signal \(c_{t}(t)\) with the same power of the target signal for a given TCR is generated and added to the clutter signal \(c_{o}(t)\). Note that each signal consists of 1501 samples and DFT is performed to obtain a magnitude of each frequency band to construct a feature vector. Figure 6 shows an example of the time domain waveform pair with \(y_{i}=1\) (Figure 5(a)) and \(y_{i}=0\) (Figure 5(b)). The two waveforms are very similar and the magnitude spectra of the two waveforms shown in Figure 6 are shown in Figure 7. It is difficult to discriminate one from another. However, the magnitude spectrum in Figure 6(a) has a specific spectrum structure as shown in Figure 4.  
Figure 5: Magnitude spectra of the target responses for two grazing angles and two rotation angles.  
Note that the magnitude spectrum is an even function for a real signal. Since the feature vector is generated from the magnitude spectrum of the DFT of the signal, the size of the feature vector is half of the length of the DFT, which can reduce the number of learning parameters. Since the advantage of using the magnitude spectrum is that delay or time synchronization is not critical, the energy of the signal of interest should be just in the DFT window interval.
## 3 Spatial Characterization Methods  
### 3D Convex Hull Algorithm  
Consider a subset S contains \(n\) points, where the line segment connecting any two points is contained. The convex hull is defined as the smallest convex set. To find the smallest convex hull in 3D, an initial hull with four facets is formed. Before new points are added to the hull gradually to form a new hull, the point farthest away from the facet should be added to the hull first. Points inside the hull are ignored and added to a visible set \(V\). For the point outside the hull, the unassigned point that above the facet is added to an outside set FO. Then, calculate the point in the outside set FO, and put visible points into set \(V\), ensuring that no other points are visible in the outside set. The boundary of \(V\) is the set of horizon ridges. Then, a new facet is created from each ridge and point \(p\). The new hull is formed after deleting the old facets in \(V\).  
In this research, the quickhull algorithm was used to find the convex hull [39]. Delaunay triangulation was the method used to implement the triangulation function.