esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 SOM and WSN
### Underlying Data Model  
Bearing in mind that in the case of sensor networks the final objective of an adversary is to compromise the system, and that the easiest way to do it is to send false sensor outputs to the base station in order to give wrong impression of the phenomenon that is being observed, we establish our model of a sensor on sequences of sensor measurements.  
Our model is based on two important assumptions:  
1. The adversary can capture only a limited number of nodes, which means that most of the output data produced by the sensor nodes is normal. If this is not the case, it means that the adversary can subvert any protocol in the network, which would require for the network re-initialization.
2. Output data produced under the influence of an adversary are statistically different from the output produced during the normal operation of the network. For this reason, we establish the detection of anomalies in data as outlier detection. The definition of outliers is rather fuzzy, but it is considered that an outlier is an observation that lies an \"abnormal\" distance from other values in a random sample from a population, in other words extreme points in the data cloud.  
If any of these assumptions is not fulfilled, our model is not able to work properly.  
Our model of sensor outputs relies on sequences of data. In other words, we establish sequential features of data in order to capture the temporal coherence in sensor outputs. In this way, by detecting anomalies in sequences of sensor outputs, we detect the presence of an attacker, including previously unseen attacks, which is the main advantage of our approach. If we had decided to use numerical features, e.g., number of connections between two sensors that occur within a certain period of time, it would have been impossible to detect unknown attacks as we cannot define the numerical features of unknown attacks.  
The temporal model proposed here consists of sub-sequences of measurements of a certain size n (called n-grams) and either its frequency or its occurrence in the given sequence (\(\phi(x)\) in the text). For example, the sequence:  
\[1.00,1.20,1.23,1.22,1.10,1.00,1.20,1.23\]  
can be characterized with the following model (\(n=3\)):  
Figure 1: Visualization Property of SOM Clustering.  
\[1.00 1.20 1.23 \text{occurrence }2\text{, frequency }0.33\] \[1.20 1.23 1.22 \text{occurrence }1\text{, frequency }0.17\] \[1.23 1.22 1.10 \text{occurrence }1\text{, frequency }0.17\] \[1.22 1.10 1.00 \text{occurrence }1\text{, frequency }0.17\] \[1.10 1.00 1.20 \text{occurrence }1\text{, frequency }0.17\]  
The proposed model offers many advantages. Above all, the time to calculate the similarity between two sequences is linear, which guarantees high speed of the process. Furthermore, it permits the pairwise calculation of similarity, which is essential for its application in anomaly detection based on clustering techniques.
# Application of SSIR Method for the Design of Fungicides
## 4 Materials and Methods
It should be noted that a simple SSIR calculation including the number of molecules, sites and residues appearing in this work takes only a few seconds. Many ranking parameters can be given [18; 19; 20; 21; 22]. For example, when defining P as the total number of active (positives) compounds in the set and N as the total number of inactive (negative) ones (the total number of elements is n = P + N), a classifier response hits with a number of true positives, TP, true negatives, TN (correct rejections), false positives, FP (false alarm or type I error), and false negatives FN (failures or type II errors), and from these parameters, other figures of merit can be defined for the classifier:  
* Sensitivity or true positive rate (TPR), also called hit or recall = TP/P = TP/(TP + FN);
* Precision or positive predictive value (PPV) = TP/(TP + FP);
* PPV = FP/(TP + FP);
* F\({}_{1}\) score or harmonic mean of precision and sensitivity = 2 \(\cdot\)TP/(2 \(\cdot\)TP + FN + FP);
* Specificity (SPC) or true negative rate (TNR) = TN/N = TN/(FP + TN);
* SPC = FP/N = FP/(TN + FP);
* Negative predictive value (NPV) = TN/(TN + FN);
* Accuracy (ACC) or concordance = (TP + TN)/(P + N);
* Matthews correlation coefficient (MCC) =  
\[\text{(TP}\cdot\text{TN}-\text{FP}\cdot\text{FN)}/\text{SQRT}[\text{(TP}+ \text{FP)}\cdot\text{(TP}+\text{FN)}\cdot\text{(TN+FP)}\cdot\text{(TN+FN)}]\]  
Once the classification is stablished, one can travel along it and the classification parameters can be calculated at each point. A Receiver Operating Characteristic (ROC) curve shows the sensitivity versus the false positive rate (FPR, i.e., 1-specificity) values at each point [23; 24; 25; 26]. Throughout the classification, it is desired to reproduce a high sensitivity (i.e., good performance at detecting positive cases) against a small false positive rate. The better the classifier, the faster the curve rises. This feature, together with the fact that these parameters are limited to values between 0 and 1, leads to an area under the ROC (AU-ROC) curve, sometimes also called area under the curve (AUC). The AU-ROC is limited to the interval of values from 0 to 1. An AU-ROC value of 0.5 denotes a neutral (non operative or random) classifier. The closer the AU-ROC is to the unit value, the better the classifier. It should be noted that the AU-ROC value can be interpreted as the probability to correctly classifying a pair of molecules, one being active and the other non-active. Another relevant parameter to take into account in classifications is the F\({}_{1}\) score. This value is the harmonic mean between precision and sensitivity (recall). The merit of the F\({}_{1}\) score relies on the fact that it balances both concepts and reaches small values if any of both measures decreases. It is a good parameter that blends the concepts of what fraction of positives are actually positive (precision) and what fraction of true positives have been detected (sensitivity). The score has proven to be a good parameter even when dealing with unbalanced classes.
## 5 Questionnaire Survey
### Data Analysis and Discussion
#### 5.2.2. Higher Cost (High Capital Cost, High Construction Cost, and Additional Transportation Cost)  
Secondly, \"higher capital cost\" is another significant obstacle to expanding the market of modular buildings. It is worth noting that \"high construction cost\" and \"additional transportation cost\" are taken into account as other two significant constraints related to \"higher cost\", ranking at the first half of 27 factors. Some previous works have demonstrated that modular construction is a cost-saving method concerning the entire life cycle of buildings [77]. The initial investment in equipment and land for the production of modules has a direct bearing on soaring capital cost. In addition to the cost of fixed assets, additional transportation cost including shipping and road transport contributes to approximately 20% of the total cost [47]. Numerous investigations manifested that, aligned with the increasing degree of prefabrication, the cost of modular buildings is higher than that of conventional buildings, due to the existing immature market and industry of modular construction [46]. Therefore,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Code** & **Mean** & **Standard Deviation** & **Ranking** \\ \hline C9 & 3.569 & 1.076 & 1 \\ C4 & 3.558 & 1.052 & 2 \\ C2 & 3.483 & 1.324 & 3 \\ C1 & 3.471 & 0.976 & 4 \\ C10 & 3.466 & 1.061 & 5 \\ C25 & 3.459 & 1.250 & 6 \\ C6 & 3.451 & 1.248 & 7 \\ C22 & 3.442 & 1.060 & 8 \\ C5 & 3.434 & 1.342 & 9 \\ \hline C11 & 3.428 & 0.987 & 10 \\ C16 & 3.414 & 1.117 & 11 \\ C15 & 3.403 & 1.184 & 12 \\ C17 & 3.382 & 1.012 & 13 \\ C27 & 3.367 & 0.994 & 14 \\ C12 & 3.343 & 1.049 & 15 \\ C3 & 3.324 & 1.063 & 16 \\ C7 & 3.309 & 1.044 & 17 \\ C24 & 3.285 & 1.031 & 18 \\ \hline C19 & 3.274 & 1.106 & 19 \\ C26 & 3.266 & 0.983 & 20 \\ C14 & 3.197 & 0.994 & 21 \\ C23 & 3.144 & 0.979 & 22 \\ C18 & 3.125 & 0.936 & 23 \\ C20 & 3.114 & 1.142 & 24 \\ C13 & 3.097 & 1.028 & 25 \\ C8 & 3.075 & 0.966 & 26 \\ C21 & 3.028 & 1.151 & 27 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ranking of constraints hindering modular buildings.  
taking into consideration the existing profitable traditional construction method, the perceived higher cost becomes another significant constraint hampering application of modular buildings.
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 3 Background in Programmable Data Plane and 5G and Beyond
### Software Defined Networking, Programmable Data Plane, and Devices
#### 3.1.4 P4  
Programming Protocol-Independent Packet Processors, P4 [6], is a high-level domain-specific programming language designed for programming network data planes using the PISA processing pipeline. This enables the development of adaptable, customizable devices that can be precisely adapted to meet the demands of specific applications and network configurations. The main principles of P4 are:  
* Programmability: Network operators define how packets are parsed and processed in a way that is both adaptable and extensible.
* Protocol Independence: Packets are processed independently of the underlying protocols or technologies used in the network.
* Match-Action Pipeline: A match-action pipeline model (i.e., PISA) is used to process packets. In this model, incoming packets are matched against a set of rules that define how they should be processed.
* Target-Independent: Code can be compiled to run on various network devices, such as switches, routers, and programmable network interface cards (NICs) regardless of the specific target.  
The P4 Language Consortium has undertaken the development and standardization of P4 as a programming language [19]. It has undergone several revisions since its initial specification in 2014, with P414 being the first standardized version [20]. Introduced in 2016, the P416 specification [21] builds upon its predecessors and offers a broader range of capabilities. It extends the applicability of P4 to cover a diverse range of targets, including application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), and network interface cards (NICs).
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c} \hline
**Name** & **Type** & **Petrographic Description** \\ \hline Azul Valverde (AV) & Limestone & Fine-grained microcrystalline calciclastic and bioclastic limestone, bluish gray, with rounded and elongated micritic calcite (pellets) and sparitic calcite. \\ \hline Arrábida Breccia (B) & Breccia & Breccia composed of multicolored carbonate clasts cemented by a red, ferruginous clay-carbonate matrix. \\ \hline Beige Grande (BG) & Limestone & Microsparitic limestone with a beige tone, containing peloids, fossils, and oolites. \\ \hline Beige Medium (BM) & Limestone & Fine-grained beige limestone composed mainly of micritic calcite with scattered peloids and fossil fragments. \\ \hline Beige Medium Light (BML) & Limestone & Beige micritic limestone with intermediate grain size, containing peloids, sparitic calcite, and sparse fossil remains. \\ \hline Encarnadaño (EN) & Limestone & Reddish-pink microcrystalline limestone. Biopelmicrosparite with stylolitic structures filled with carbonate minerals. \\ \hline Hardblue (HB) & Limestone & Fine-grained grayish-blue limestone, composed of micritic calcite and sparitic cement, with peloids and fossil remains. \\ \hline Lioz (LZ) & Limestone & Microcrystalline fossiliferous limestone with various fossil content, including rudist fossils. \\ \hline Estremoz Branco (MB) & Marble & White marble with a granoblastic texture, composed of equidimensional calcite crystals. \\ \hline Moca Creme (MC) & Limestone & Light beige bioclastic limestone, composed of micritic calcite and sparitic cement, with peloids, fossils, and occasionalolites. \\ \hline Estremoz Rosa (MR) & Marble & Pink-colored marble with a fine to medium grain size. \\ \hline Ruivina (RV) & Marble & Dark gray to black marble with a fine grain size, occasionally displaying foliated textures due to biotite and chlorite minerals. \\ \hline Condeixa Travertine (TV) & Travertine & Tufa limestone with a beige-brown color and a concretionary texture. \\ \hline \end{tabular}
\end{table} 
Table 1: Petrographic description of the stones studied. direction. In total, 11,700 measurements were performed across all samples, resulting in 1950 average values used in the correlation analysis. The extensive repetition for each specimen enhanced repeatability and minimized measurement uncertainty. The minimal variation among repeated impacts on each face further demonstrates the reliability of HL as a consistent NDT parameter in this study and strengthens confidence in the observed correlations. To assess the relationships between NDT results and capillary coefficients, multiple fitting models were tested, including linear, logarithmic, polynomial, and exponential models. For each lithotype and parameter pair, the model yielding the highest coefficient of determination (R\({}^{2}\)) was selected and used to represent the correlation.
# Geometrical Degrees of Freedom for Cellular Structures Generation: A New Classification Paradigm
## 3 Highlights of Manufacturability Challenges of Cellular Structures
For SLS and BJ, support structures are generally not necessary for overhangs, as the powder provides enough gravity support [115; 117]. Furthermore, such processes do not experience excessive thermal gradients. For SLA, although not robustly documented in literature, design guidelines by 3D Hubs and Materialise specify that support structures are only needed to anchor the object to the build plate and ensure print object continuity in respect to the build plate [119; 120]. Another design limitation stems from the material-process-design relationship, where the minimum feature size of an RVE correlates with the resulting porous defects in the CS. For LPBF and EBM, the dimension of the energy source, the powder size and the minimum desired feature size in an RVE, as well as the surrounding RVE neighbors result in a complex set of melt pool and heat transfer phenomena. Such interplay can often result in pore defects; the mechanism of pore formation, the classification of pores, and strategies to mitigate such defects in powder bed fusion processes have been reviewed and summarized by [PERSON] et al. [121] and [PERSON] and [PERSON] [122]. For BJ technologies, the scale of the interaction between the layer resolution, the liquid binder droplets, and the powder morphology and size can pose limitations in terms of the lattice structure geometric and green density quality [123]; such qualities are also impacted by the sintering and densification process. For SLA, pore defects are only mentioned when printing ceramic-loaded polymers into complex-shaped objects, where the pores are mainly created by the sintering post-processing steps rather than the AM process itself [124]. As for FDM, the minimum feature size of an RVE and the nozzle diameter play a role in pore defect generation. The RVE inner features may be inadequately filled due to discontinuity in the toolpath and gaps between the extruded plastic filament [125]. For MJ, pore defects are not mentioned [126] except in the context of polymer-loaded ceramic production [127].
## 3 Results
### Selection of Node Importance Evaluation Indicators
#### 3.1.4 Clustering Coefficient  
In complex networks, the clustering coefficient is defined as the ratio of connections between neighboring nodes of a given node in the network and is used to describe the degree of aggregation of nodes in the network. If node \(v_{i}\) is directly connected to nodes \(v_{s}\) and \(v_{t}\), then nodes \(v_{s}\) and \(v_{t}\) are likely to be directly connected. This phenomenon reflects the dense connectivity characteristic between certain nodes, i.e., the clustering characteristic of complex networks. The larger the clustering coefficient, the greater the influence of the node on network connectivity. The clustering coefficient can vividly describe the closeness of the pivotal nodes of the network, which can better portray the aggregation characteristics of the RWCTN. It can be written as follows:  
\[CL(v_{i})=\frac{2E_{i}}{k_{i}(k_{i}-1)}, \tag{5}\]  
where \(CL(v_{i})\) denotes the clustering coefficient of node \(v_{i}\). \(k_{i}\) denotes the number of nodes adjacent to node \(v_{i}\). \(E_{i}\) denotes the actual number of edges connected to nodes \(k_{i}\). If these nodes \(k_{i}\) are all interconnected, there are up to \(k_{i}(k_{i}-1)/2\) edges between them.
## 2 Theoretical Behavior of Distributed versus Concentrated Volt/var Local Control Strategy
### Distributed versus Concentrated Var-Sinks
#### 2.5.2 Voltage Drop Behavior  
Figure 7 presents the theoretical voltage drop amplitude in every feeder segment as a function of the feeder segment number for the distributed and concentrated var-sink cases. As expected, in the distributed case, the largest voltage drop falls at the feeder begin on the feeder segment fs20, because \(n\cdot Q\) flows there. Meanwhile, the smallest one drops on the last feeder segment, fs1, because only \(Q\) flows there. In contrast, the voltage drop in the concentrated case falls evenly on all of the feeder segments.  
Figure 6: The theoretical exchanged reactive power as a function of the bus number for the distributed and concentrated var-sink cases.
## 2 Methodology  
As reported in [14], most secondary studies in software engineering are qualitative in nature. In contrast, PRISMA, one of the most widely known frameworks for conducting systematic reviews, is primarily oriented toward quantitative reviews, mixed-methods syntheses, and meta-analyses, particularly in health and clinical research domains. To address the unique needs of engineering-focused reviews, this study adopts the SEGRESS methodology (Software Engineering Guidelines for REporting Secondary Studies) [15], a framework specifically developed for systematic reviews in software engineering and technology-oriented disciplines. SEGRESS builds upon and adapts many of the core principles of PRISMA, including transparent study selection, clearly defined inclusion and exclusion criteria, and the use of the PRISMA-style flow diagram to report the different stages of the literature search process. SEGRESS provides tailored guidance for analyzing studies that focus on tools, platforms, architectures, and integration strategies, making it particularly well-suited for domains such as robotics simulation, where traditional meta-analytic approaches are often infeasible due to the diversity of study designs, evaluation methods, and reporting practices.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Year** & **Reference** & **Scope** & **Simulator Description or Comparison** & **Research Question Focus** & **Methodology** \\ \hline
2021 & [PERSON] et al. [12] & Physics-based simulation & Dynamics engines and platforms described & Limitation of physics-based simulation in robotic systems (implicit) & Narrative review \\
2021 & [PERSON] et al. [13] & Field, soft, and medical robotics, manipulation and learning & Comparison of sensors, actuators, fluid/soft-body dynamics, IK, ROS, rendering, VR & Capabilities, limitations, and application areas of physics simulators in robotics (implicit) & Narrative review \\
2024 & [PERSON] et al. [11] & AI-driven perception/control for wheeled mobile robots & Comparison of physics engines, languages, open-source availability & Identifies WMR applications, tasks, commonly used ROS-compatible simulators & PRISMA (systematic) \\
2024 & [PERSON] et al. [10] & Digital-twin in manufacturing & Simulation factors: human operator, robot agent, ergonomics, timing, interoperability & Identifies digital twin applications, tools, barriers in HRC & PRISMA (systematic) \\
2025 & This study & Industry 4.0/5.0 technologies & Industry 4.0/5.0 cultures, scalability, advantages/limitations of simulators & Identifies ROS-compatible simulators, applications, SEGRESS (systematic) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of related surveys on robotic simulation platforms.
## 5 Discussion
### Future Work  
The validation results indicate a significant decrease in the accuracy of the ROD extraction when C-band microwave signals are influenced by vegetation. In contrast, the L-band, with its stronger penetration capability, is more suitable for snowmelt monitoring in vegetated areas. Currently available L-band satellite data include ALOS-2 with a 14-day revisit cycle and LT-1 with an 8-day single satellite and 4-day dual satellite revisit cycle. Additionally, the upcoming NASA-ISRO SAR (NISAR) Mission plans a revisit cycle of 12 days. We plan to use L-band data to extract the ROD and compare the results with those obtained from the C-band.  
We explored the potential of Sentinel-1 data for the ROD extraction under different land cover types in this study. However, we only analyzed the ROD in the Sierra Nevada Mountains of the western United States, leaving the annual ROD variations in other regions unclear. In future research, we will further utilize the GEE platform to analyze the differences in ROD variations among different regions.
# PALANTIR: An NFV-Based Security-as-a-Service Approach for Automating Threat Mitigation
## 5 Data Modelling
### Constraint Satisfaction Problem for Enabler Selection  
In this section, we expose and justify the modelling of the constraint satisfaction problem by blueprinting the design of the SM component.  
We define \(\mathbb{B}\) as the Boolean set and \(\mathcal{M}\) as the set of the \(n\) available deployment configurations for a security mechanism. In practice, a deployment configuration refers to the option to deploy a mechanism implementing an SC to a specific infrastructure. We can state that  
\[\#(\mathcal{M})=n\]  
This set can be enumerated, as follows:  
\[\mathcal{M}=(m_{i})_{0\leq i<n}\]  
To generalise this problem to multiple security mechanisms, we consider  
\[\mathcal{M}^{\prime}=(m_{i})_{0\leq i<n^{\prime}}\]  
with \(\#(\mathcal{M}^{\prime})=n^{\prime}\) and \(\mathcal{M}\subseteq\mathcal{M}^{\prime}\), where \(M^{\prime}\) represents all the deployment configurations of all security mechanisms.  
Figure 5: Examples of a personalised policy presented as FSMs.  
To define a valid constraint satisfaction problem, we must define three main parameters: (i) a set of variables to be affected as a solution of the CSP; (ii) a set of interval domains for the variables requiring affectation; and (iii) a set of constraints that the affected variable should satisfy to evaluate a solution as acceptable. As the core of the problem is to determine which deployment configuration should be enabled, we define the set of variables to be affected as  
\[X=\left(x_{i}\right)_{0\leq i<n^{\prime}} \tag{1}\]  
where \(x_{i}\) denotes whether the deployment configuration \(m_{i}\) is to be enabled or kept disabled. We can therefore infer the definition domain of those variables: they can either be affected by a positive value (1) to express a requested deployment, or by (0) to express its rejection. We formalise this assertion as follows:  
\[D=\left(D_{i}\right)_{0\leq i<n^{\prime}}=\mathbb{B}^{n^{\prime}} \tag{2}\]  
We now have to express the different requirements of the SM component as a set of constraints to be satisfied by each affectation of \(X\), and construct, accordingly, a cost function to order all the valid affectations. The following sections outline how infrastructure-issued and SCs-issued constraints and the cost of capability are evaluated.