esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Critical Gap for Pedestrian Crossing Using Fuzzy Logic System
## 2 Methodology
### Fuzzy Logic Analysis  
The membership function is the fundamental component of a fuzzy logic system and the most important step in fuzzy set building; i.e., the fuzziness feature in a fuzzy set is controlled by its membership function. There are a number of possible shapes for the membership functions that include Gaussian, triangular, trapezoidal, as well as other forms. The one condition a membership function must actually satisfy is that it needs to vary between 0 and 1. The triangular and trapezoidal membership functions are often adopted in many applications because of their simplicity and ease of calculations over other function types, such as bell, sigmoidal, asymmetric, LR, and Gaussian, which may require more complicated arithmetic operations. In addition, a relatively smaller amount of data is needed to define a triangular and trapezoidal membership function. It is hence prudent to start with a triangular form in our case.  
In this analysis, a triangular membership function was specified by three input parameters {a, b, c} to represent the number and lengths of accepted gaps and length of rejected gaps. These are the x coordinates of the three vertices of u(x) in the fuzzy set A. The MATLAB software was used to build the fuzzy logic systems [37; 40]. Each of the input variables was defined by three parameters (low, medium and high). Therefore, the three input variables are the number and lengths of rejected gaps and length of accepted gaps, and the output variable of the fuzzy logic system is the critical gap. Table 1 below  
Figure 1: Architecture of Fuzzy System.  
shows membership functions used, their names, and input variables with the parameters' ranges. These values are calculated from real data collected at the selected site in Edinburgh.
## 5 Discussion
### Research Questions  
Based on the analyzed literature and the results, the research questions can be assessed as follows:  
RQ1: The results suggest that variations in modeling assumptions across different techniques can influence the predictive performance in significant ways. Linear models are sensitive to data points that deviate substantially from the expected pattern, which can shift the overall model fit. Tree-based methods divide the data into segments, but the positions of these divisions depend on the initial splits, leading to potential instability in predictions. Support vector machines, in contrast, rely on the choice of kernel functions, which can either support discovering complex nonlinear patterns or restrict the model to simpler boundaries. Together, these findings indicate that the assumptions and choices made by each method have important consequences for predictive accuracy and stability in transitional contexts.  
RQ2: The findings also indicate that adapting data mining approaches to the conditions in transitional economies can support improved reliability and interpretability of the results. Adjusting methods to address irregular data distributions, changing market conditions, and evolving institutional frameworks can lead to better alignment between the model assumptions and observed data. This alignment supports more consistent predictions that reflect the unique challenges faced by enterprises in these environments. As a result, an approach that considers local conditions, tuning procedures, and data characteristics can improve the practical usefulness of predictive analytics in transitional economies.
# Smooth Information Criterion for Regularized Estimation of Item Response Models
## 5 Simulation Study 3: Mixed Rasch/2 PL Model  
Recently, a mixed Rasch/2 PL model [52] (see also [53]) received some attention. The idea of this unidimensional IRT model is to find items that conform to the Rasch model [54], while there can be a subset of items that follow the more complex 2 PL model [9]. The IRF of this model is given by  
\[\mathsf{P}(X_{i}=1|\theta)=\Psi(\exp(\alpha_{i})\theta-b_{i}). \tag{20}\]  
Note that the IRF in (20) is just a reparametrized 2 PL model with item discriminations \(a_{i}=\exp(\alpha_{i})\). Hence, \(\alpha_{i}=\log(a_{i})\) are the logarithms of item discriminations \(a_{i}\). The case  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Type-I Error Rate**} & \multicolumn{4}{c}{**Power Rate**} \\ \cline{3-10}
**CL** & \(N\) & **AIC** & **SAIC** & **BIC** & **SBIC** & **AIC** & **SAIC** & **BIC** & **SBIC** \\ \hline \multirow{3}{*}{small} & 500 & **16.7** & 5.1 & 2.1 & 2.5 & **41.2** & **30.6** & **9.4** & **22.4** \\  & 1000 & **18.7** & 8.8 & 2.0 & 3.2 & **58.0** & **46.5** & **17.9** & **24.3** \\  & 2000 & **15.1** & 6.2 & 1.7 & 0.7 & 86.5 & 82.4 & **44.0** & **38.3** \\  & 500 & **18.5** & 4.8 & 3.1 & 2.8 & **68.5** & **59.5** & **27.3** & **52.0** \\  & 1000 & **17.0** & 10.0 & 2.5 & 3.2 & 87.4 & 81.0 & **59.2** & **70.6** \\  & 2000 & **13.3** & 7.8 & 1.3 & 0.7 & 98.6 & 98.7 & 92.3 & 92.6 \\ \hline \hline \end{tabular} Note. CL = size of cross-loadings; Type-I error rates larger than 10.0 and power rates smaller than 80.0 are printed in bold font.  
\end{table}
Table 4: Simulation Study 2: Type-I error rate and power rate for cross-loadings as a function of the size of cross-loadings and sample size \(N\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Absolute Bias**} & \multicolumn{4}{c}{**RMSE**} \\ \cline{3-10}
**Par** & **CL** & \(N\) & **AIC** & **SAIC** & **BIC** & **SBIC** & **AIC** & **SAIC** & **BIC** & **SBIC** \\ \hline \multirow{9}{*}{\(\rho\)} & \multirow{3}{*}{small} & 500 & **0.064** & **0.054** & **0.104** & **0.070** & 0.151 & 0.102 & 0.134 & 0.110 \\  & & 1000 & **0.040** & **0.070** & **0.088** & **0.085** & 0.157 & 0.096 & 0.124 & 0.1  
\(\alpha_{i}=0\) corresponds to the Rasch model because \(a_{i}=\exp(\alpha_{i})=1\), while \(\alpha_{i}\
eq 0\) results in item discriminations \(a_{i}\) different from 1. The mean of the factor variable \(\theta\) is fixed to 0, while the standard deviation \(\sigma\) should be estimated.  
In order to achieve identifiability of the model parameters, a sparsity structure of the logarithms of item discriminations \(\alpha_{i}\) is imposed. Hence, the majority of items is assumed to follow the Rasch model. Again, the sparsity structure is directly implemented in a regularized estimation of the mixed Rasch/2 PL model.
# Microwave Liquid Crystal Technology
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
5G & System for Mobile Communication of the 5 th generation \\
**D** & Displacement field \\ DC & Direct current \\ DW & Dielectric waveguide \\ E & Electric field \\ \(\epsilon_{\rm eff}\) & effective permittivity \\ \(\epsilon_{\parallel}\) & effective permittivity of an LC volume for parallel alignment \\ \(\epsilon_{\perp}\) & effective permittivity of an LC volume for perpendicular alignment \\ FoM & Figure of Merit \\ GEO & geostationary orbit (satellite) \\ HPBW & Half-power beam width \\ IL & Insertion loss (\(\rm{IL}=-|S_{21}|\)) \\ IP3 & Third-order intercept point \\ ITU & International Telecommunication Union \\ K\({}_{\rm a}\)-band & Communications band from 26.5 GHz to 40 GHz \\ K\({}_{\rm u}\)-band & Communications band from 12 GHz to 18 GHz \\ LC & Liquid Crystal \\ LCD & Liquid crystal display \\ LEO & low earth orbit (satellite) \\ MMI & Multimode interference \\ mmWave & Millimeter wave \\ OLED & Organic LED \\ PIM & Passive intermodulation \\ PET & Polyethylenterephthalat \\ PTFE & Polytetrafluoroethylene (Teflon) \\ RF & Radio frequency \\ SIW & Substrate integrated waveguide \\ TDS & time-domain spectroscopy \\ TN & twisted nematic \\ UV & Ultra violet \\ VNA & Vector network analyzer \\ W-band & Communications band from 75 GHz to 110 GHz \\ WRC & World Radio Conference \\ \end{tabular}
# Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) Systems
## 4 Multi-Agent Communication and Networking
### Clustering Protocols  
The concept of clustering is the basis for a class of protocols designed specifically for multi-agent systems. A \"cluster\" is a functionally centralized collection of nodes (or agents) that operates according to a common transmission schedule. The purpose of a cluster is to ensure reliable communication between all of its members [44].  
At start-up, agents are not organized into clusters and instead go through a cluster-formation phase depicted in Figure 5. During this phase, they broadcast \"probe\" packets to discover their neighbors and any potential candidates for cluster membership. After forming their respective clusters, each cluster elects a designated head responsible for organizing all cluster activity. Over the course of normal operation, the agent trajectories within a cluster might diverge, so clusters periodically go through maintenance phases to evict some members and elect new heads.  
Each of the phases described above has been studied extensively in the literature. Ideally, clusters should be stable (as far as possible), so the criteria for cluster-membership must favor candidates with close trajectories. This raises the problem of measuring \"closeness\" and the associated trade-offs between accuracy and responsiveness [45; 46; 47]. Leader election is a research area in its own right. All distributed agreement is subject to the [PERSON]'s dilemma which states that perfect consensus is generally impossible. The literature includes several protocols for electing cluster-heads with varying degrees of consensus [48].  
Every form of communication requires some level of synchronicity between agents. Clock synchronization protocols have been studied together with impossibility results for different models of clock behavior [49; 50]. There are also communication protocols for unsynchronized systems with bounded clock skew [51].
# Efficient Secure Routing Mechanisms for the Low-Powered IoT Network: A Literature Review
## 12 Conclusions and Findings  
The WSN-IoT is recognized as a technology that has already had economic consequences and generated the hope that different sectors' competitive domains will change dramatically. In the manufacturing industry, sensor-equipped machines can collect data from the production system in real-time and use it to store and synchronize real-world data on the cyber side. It is possible to continually develop and synchronize with the natural world by changing data in real-time and contrasting cyberspace with physical space simultaneously. IIoT, which acts as an inevitable IR 4.0 infrastructure, imposes high reliability, low latency, scalability, energy consumption, and security requirements. However, many low-power wireless specifications and infrastructure protocols aim to meet and conform to those criteria. The emphasis is on addressing the current problems and pursuing future exploration avenues to discover potential solutions. In particular, the \"smart\" side of IR 4.0 is focused on the availability of self-awareness, self-management, and self-healing intelligent networks. That includes technical concerns such as real-time cellular networking, 5G cellular networks, shallow power usage, and industrial cyber-security. The most trending WSN-IoT security mechanism techniques are blockchain, cryptography algorithms, and AI techniques. These can be an excellent solution to this problem and provide accuracy, autonomy, security, and efficient results.  
This article aims to provide a detailed overview of WSN-IoT's characteristics and aspects for low-powered IoT mechanisms; the paper throws light on the research gaps, including WSN-IoT issues and challenges. According to our findings, researchers have proposed WSN-IoT security mechanisms. However, the significant limitations are that these solutions are still under study and in progress. As previously stated, most of the work conducted so far is in its early stages, is only for research or academic purposes, and does not involve working in real-time scenarios. Considerable research has been for educational purposes and lacks proper deployment on a large scale and testing in the natural environment. Furthermore, frameworks' functional and large-scale deployment are significant limitations in the context of current research and contributions.
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 2 Background
### Indoor Search and Rescue (SAR) Operations for Swarms of Drones
#### 2.1.2 UAVs in SAR Operations  
Especially with regard to disaster response, deployment of assets in the immediate aftermath of an event can be extremely dangerous (for the assets) due to the unstable nature of the situation, as well as simply because the condition thereof is unknown. It has become increasingly common to deploy autonomous technology prior to allowing human personnel into an area, as human intervention might be inefficient or come at a risk of harm to personnel [30]. With this in mind, ref. [31] highlights the ability to sense as an integral basic functionality of drones. The literature makes frequent reference to the potential [32] of using UAVs as autonomous or semi-autonomous operating data acquisition platforms: for example, refs. [33; 34] identify a number of different application areas, with SAR operations being among them.  
Figure 1: Examples of the drones available in the Intelligent Autonomous Systems group at TNO: (**left**) a small device for proof-of-concept testing; shoe for scale. (**Center**) a heavy-duty AceCore NEO drone capable of carrying payloads in excess of 8 kg outside and in normal weather conditions. (**Right**) a bottom-mounted gimbal for visual data collection, possibly to be analyzed onboard for fast victim recognition. The presented work in this article has not been deployed under real-world conditions, but TNO and VTT are planning to conduct real-world tests in the near future; cf. [9; 24].  
Specifically, the deployment of a UAVs, be it as single units or as a swarm, to act as a node, or to form an entire cell network themselves [35], has been evaluated in the literature. The ability to dynamically adapt the network's topology in reaction to link loss increases resilience against outages and other connectivity problems [36]. With regard to data protection and the security aspects of a network, ref. [37] propose a secure communication protocol for inter-UAV/UAV-sensor communication.  
One important application category of such operation are indoor operations, in which assets may have to operate in the absence of--or under the assumption of unreliable or compromised--communication infrastructures [38]. As the safety of the dispatched personnel depends on the ability to keep them apprised of changes in the situation, establishing a reliable means to communicate is often one of the first steps in deployment. When considering the use of drones, a number of challenges have been identified. Timely and efficient creation of infrastructure is often crucial, but hampered by the uncertainty of the condition of the environment. Under the assumption that this is known, establishing the optimal locations for the nodes is relatively straightforward and could be calculated in advance/offline. As this assumption does often not hold, even the collision-free deployment process into an indoor environment [39] (as a prerequisite to surveillance and reconnaissance to establish situational awareness) can be complicated.
# Joint Deployment Optimization of Parallelized SFCs and BVNFs in Multi-Access Edge Computing
## 2 Recent Works
### Our Research  
Due to the flexibility of traffic scheduling and the advantages of parallelism, this approach of splitting user requests into multiple sub-flows can flexibly schedule traffic and balance load, which is suitable for satisfying the requirements of delay-sensitive applications in the MEC network. However, the work above only considers that the reliability decreases significantly as the number of parallel VNFs increases or only that the parallel VNFs can directly replace failure VNFs. The former ignores the situation where parallel VNFs can improve reliability, and the latter does not consider whether the delay requirements are satisfied after replacing the failed VNFs. Based on our discussions above, we propose a new scheme to reduce delay and enhance the reliability of P-SFCs and formulate the problem of maximizing throughput.
## 5 Conclusions  
In this study, detailed theoretical modeling and experimental measurements were shown for novel silicon-based WG geometry. SiN multilayer structures were fabricated based on finite element modeling. At first, we fixed a set of parameters (wavelength and polarization) and performed simulations by varying interlayer height and WG width. The simulations show efficient single-mode waveguiding at wavelengths in the IR range (1.5-3 um) for a WG made of 125-nm-thick SiN layers and 90-nm-thick SiO\({}_{2}\) layers. Furthermore, a stress simulation was performed, showing a stress gradient in the center of every WG. We characterized the waveguiding behavior in a wide range of wavelengths (532, 635, 1550, and 2640 nm). The WGs showed good waveguiding behavior at NIR-MIR wavelengths (both 1550 and 2640 nm, with loss values close to 4 dB), while losses increased at wavelengths in the visible range (about 20 dB). The losses measured for multilayer WGs were similar to those reported by other studies for Si or Si\({}_{3}\)N\({}_{4}\) WGs [27; 28]. Instead, from calculations, we did not observe any variation in loss values for different WG widths, because of minimal variations in the stress profile.  
In addition, ZBLAN and chalcogenide glass are emerging as materials for MIR, particularly for fiber components. However, silicon technologies enable the realization of planar photonic circuits and devices (i.e., filters or resonators). The coupling, not simple in such a spectral region, could be improved by using precisely designed chalcogenide optical components.  
In summary, we can conclude that Si\({}_{\text{x}}\)N\({}_{\text{y}}\)/SiO\({}_{2}\) multilayer WGs are good candidates for simultaneous MIR/NIR low-cost integrated optical components, providing broadband operation for spectroscopy and quantum technologies.  
For research articles [PERSON] performed the measurements, performed the numerical simulations, analyzed the data, and wrote the manuscript; [PERSON] contributed to the conception of the experiment, to the development of the experimental setup, and to simulation analysis; [PERSON] performed experimental measurements; [PERSON] and [PERSON] contributed to the conception of the experiment, to the simulations, and to the data interpretation; and [PERSON] contributed to the conception of the experiment and coordinated the experiment. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Provincia Autonoma di Trento, Grandi Progetti 2012 call, within the SiQuro project.  
Not applicable.  
The data presented in this study are available on request from the authors.  
The authors wish to thank [PERSON] and [PERSON] for the WG design and fabrication, and [PERSON], [PERSON], and [PERSON] for useful discussion.  
The authors declare no conflict of interest.
## 5 Deployment
### Particle Resampling  
The _Particle Resampling_ is parallelized based on the number of particles. Therefore, a monodimensional parallelization is performed. Algorithm 6 shows a general template for the resampling step. Each tested resampling variation contains a different selection strategy, as reported in Line 4. The particle weights are preliminarily copied onto the shared memory to optimize memory access. Our implementation iterates over each particle with a stride corresponding to \(blockDim\cdot gridDim\). Therefore, it is possible to launch the kernel with the following:  
* Sequential configuration (\(1B1T\)): one block and one thread.
* Thread-based configuration (\(1 BNT\)): one block and \(min(N_{p},1024)\) threads.
* Block-based configuration (\(MB1T\)): \(N_{p}\) blocks and one thread.
* Two-dimension configuration (\(MBNT\)): Upper Bound (\(\frac{N_{p}}{1024}\)) blocks and 1024 threads.  
```
1: Retrieve \(\mathbf{W}\)\(\triangleright\) vector of weights, Shared Memory
2:\(\mathbf{W}=\sum_{j=0}^{N}w_{j},j=0,\ldots,N_{p}\)\(\triangleright\) traditional methods only, Shared Memory
3:for\(m=gridId\) to \(N_{p}\), step = \(blockDim\cdot gridDim\)do
4: Choose \(selectedIdx\), \(selectedIdx\), \(\mathbf{\rho}_{r}^{selectedIdx}\), \(\mathbf{\rho}_{r}^{selectedIdx}\)\(\triangleright\) to Thread
5: Retrieve \((x_{r}^{selectedIdx},y_{r}^{selectedIdx},\mathbf{\rho}_{r}^{selectedIdx})\)\(\triangleright\) to Global Memory
6: Retrieve map features for \(selectedIdx\)-th particle\(\triangleright\) to Thread
7: Transfer map features for \(selectedIdx\)-th particle\(\triangleright\) to Global Memory
8:endfor
```  
**Algorithm 6** Importance Resampling  
The cumulative prefix sum is computed with parallel reduction as in Section 5.4 for the traditional methods. As for rejection resampling, \(w_{max}\) is computed as \(max\{w_{0},\ dotsw_{N}\}\), meaning that one collective operation is introduced. This is because we do not assume that the particle weights are normalized, which makes the estimation of an upper bound hard to formulate. On the other hand, a work-efficient parallel reduction based on [(69; 70)] is implemented for the maximum computation. Unlike the inclusive prefix sum, which requires iterating over the particles twice, such an implementation requires iterating only once, increasing the efficiency of our implementation of rejection resampling in comparison with traditional methods.  
In the case of resampling, the memory limitation is determined by the amount of shared memory available in the GPU.