esa-sceva/satcom-chunk-collection huggingface dataset:
# Topology Optimization of Functionally Graded Structure for Thermal Management of Cooling Plate
## 3 Evaluation and Analysis
### Discussion  
Different ranges of local filter radius will affect the material distribution of the solid phase and fluid flow. Therefore, two cases have been introduced to evaluate the effect of the range on the structural design. The local volume constraint was set to 0.5. The design domain and boundary condition are the same as Figure 5.  
Figure 6(a) shows that the shape and width of the flow channels were similar when the local filter range was \([r,2r]\), while subplot (b) indicates that the slender runners arose when the influence radius was the range \([r,4r]\). When compared with subplot (c) in Figure 6, we can observe that enlarging the search range of the filter radius enabled the slender flow channel of the cooling plate, and a relatively small range of the filter radius ensured structural homogeneity. Therefore, the utilization of the filtering range should follow the demand of the practical application.  
Secondly, a laminar flow model requires a Reynolds number \(\text{Re}=\rho uL/\
u\) less than the critical value, where \(\rho\), \(u\), \(\
u\) are the density, velocity, and viscosity, respectively, and parameter L is feather size of the passage way. Note that the proposed method assumes the stable laminar flow in the channel as the velocity field, and the size of the runner are both small. Turbulence may occur if the boundary conditions or the feature size of the runner for the numerical simulation violates the standard of the laminar flow model.
# Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips
###### Abstract  
Millions of individuals rely on urban transportation every day to travel inside cities. However, it is not clear how route parameters (e.g., traffic conditions, waiting times) influence users when selecting a particular route option for their trips. These parameters play an important role in route recommendation systems, and most of the currently available applications omit them. This work introduces a new hybrid-multimodal routing algorithm that evaluates different routes that combine different transportation modes. Hybrid-multimodal routes are route options that might consist of more than one transportation mode. The motivation to use different transportation modes is to avoid unpleasant trip segments (e.g., traffic jams, long walks) by switching to another mode. We show that the possibility of planning a trip with different transportation modes can lead to improvement of cost, duration, and quality of experience urban trips. We outline the main research contributions of this work, as (i) an user experience model that considers time, price, active transportation (i.e., non-motorized transport) acceptability, and traffic conditions to evaluate the hybrid routes; and, (ii) a flow clustering technique to identify relevant mobility flows in low-sampled datasets for reducing the data volume and allow the execution of the analytical evaluation. (i) uses a Discrete Choice Analyses framework to model different variables and estimate a value for user experience in the trip. (ii) is a methodology to aggregate mobility flows by using Spatio-temporal Clustering and identify the most relevant of these flows using Curvature Analysis. We evaluate the proposed hybrid-multimodal routing algorithm with data from the Green and Yellow Taxis of New York, Citi Bike NYC data, and other publicly available datasets; and, different APIs, such as Uber and Google Directions. The results reveal that selecting hybrid routes can benefit passengers by saving time or reducing costs, and sometimes both, when compared to routes using a single transportation mode.  
big data; flow clustering; intelligent transportation systems; multi-source data analyses; spatio-temporal data analyses; user experience
# A Statistical Approach to Discovering Process Regime Shifts and Their Determinants
## 2 Literature Review
As per [PERSON]'s Value Chain, the literature coverage in this context is divided into primary processes, including logistics, service, marketing and sales, and operations, and into secondary processes, such as infrastructure, procurement, research and development (R&D), and human resource management (HIRM) [15]. In business services, [PERSON] et al. [47] redesigned the customer reservation process of the largest travel agencies in Korea. Moreover, [PERSON] et al. [48] compared form handling process variants within Xerox Services for actionable insights on the most common process behaviors. Similarly, marketing and sales have previously been analyzed using data mining techniques such as clustering, profiling and predictive modeling [49]. The latest research in this area was reported by [PERSON] and [PERSON] [50], who redesigned the booking process of a utility company considering process and time perspectives. In operations, [PERSON] et al. [51] exploited process, time and organizational perspectives in an emergency context to detect bottlenecks and inefficiencies in multi-robot missions. [PERSON] et al. [52] integrated process mining and Bayesian networks for predicting maintenance intervals for manufacturing equipment. In logistics, [PERSON] [53] investigated a warehouse management system of a manufacturing company with conformance checking, and [PERSON] et al. [54] assessed lateness probability in container handling. In another work, [PERSON] et al. [55] analyzed event data of a warehouse to reconstruct a process model using Global Positioning System devices and Radio Frequency Identification readers. Finally, in the procurement process, [PERSON] et al. [56], [PERSON] and [PERSON] [57] and [PERSON] et al. [58] performed the detection of internal control violations or workarounds in the procurement process, and [PERSON] et al. [59] streamlined and standardized the IS-supported procurement process of three manufacturing companies with a process-mining-enabled decision support system. It is clear from the above discussion that the bulk of the process mining literature has primarily focused on problems that may be conceived as technological systems in the sense that their expected behavior is mechanistic due to their expectedly well-defined structure, and they do not have considerable convoluted social and economic determinants. Therefore, a major gap exists in mining socio-economic or socio-technical systems that are prevalent in the wider technological, social, political and economic spheres. [PERSON], [PERSON] and [PERSON] [15] have recently highlighted the same issue of ignoring the social side in business problems. Consequently, a general approach is needed, which may be used to mine complex process behaviors of such systems. We thus propose a methodology that seeks to capture behavioral changes (regime shifts) in such complex systems, whose knowledge can be employed in process conformance and enhancement.
## 3 Nonlinear Mechanical Properties for the PRC with Interfacial Debonding
### Extrinsic Parameter  
Figure 4 shows the stress-strain curves of the PRC with different loading strain rates. The volume fraction of the SiC particles is 15%. The interfacial debonding and thermal residual stress are both considered in the cases. The reinforced phase is also assumed to involve spherical particles. Three different strain rates, namely 0.0005/s, 0.001/s, and 0.01/s, are considered. For continuous fiber-reinforced composites with perfect interfacial bonding [40], the strain rate affects the nonlinear responses, and it can be discerned but to a limit extent only in the nonlinear stage. Meanwhile, the higher strain rate tends to stiffen the constitutive behaviors. Similarly, the PRC shows obvious rate-dependency of the deformation. It is difficult to discern the effect of the different strain rates on the mechanical properties of the PRC with interfacial debonding in the linear elastic region. While the strain rate effects on the stress-strain behaviors can be easily discerned when the longitudinal strain \(\varepsilon_{\mathrm{xx}}\) increases to 2.4% (the point C), as shown in the figure, it should be noted that the mechanical properties for the PRC with interfacial debonding show evident nonlinear characteristics, and plastic features tend to be strengthened with increasing strain rates. Compared with the loading strain rate  
Figure 3: Stress-strain responses of composites with different particle shapes.  
0.0005/s, the strain rates 0.001/s and 0.01/s provide 10.8% and 34.5% respective increases for the stiffness behaviors of the PRC when the true strain increases to 5%.
## 3 Experimental Setup  
To study the feasibility of the proposed multi-sensing EMI approach for grouting compactness monitoring, a post-tensioning tendon duct with a 100 mm outer diameter and a prestressed steel with 12 mm outer diameter were prepared in the laboratory. Three PZT wafers, PZT1, PZT2, and PZT3, which are connected in series, are mounted onto the top, middle, and bottom of the same section of the duct surface and then connected to the impedance analyzer (Keysight E4990A). During the experiments, the grout was poured into the post-tensioning tendon duct through the grout hole.  
Figure 4: The impedance signatures for the PZT patches connected in series.  
The experimental setup of grouting compactness monitoring based on the multi-sensing EMI method is shown in Figure 5.  
To avoid the overlapping of different peak frequencies, three PZT wafers with different diameters were used. The materials used for PZT wafers are PZT-5H. The dimension and material properties of PZT wafers are shown in Tables 1 and 2, respectively.  
In this experiment, five different levels (empty, 25%, 50%, 75%, and 100%) of grouting compactness, which is represented by case Gi (i = 1, 2, 3, 4, 5) for simplicity, were studied and shown in Figure 6. According to the literature [72], the frequency range of the EMI technique is selected by manually finding the resonance peak and setting a specific range for analysis. Prior to the test, the resonant frequencies of the PZT wafers used in this experiment were measured as 80-90 kHz, 110-120 kHz, and 160-190 kHz, respectively. Therefore, a sweep frequency signal was generated from 20 to 200 kHz to monitor the impedance signature corresponding to different grouting compactness levels. These impedance signatures consist of 1601 data points within the defined frequency range. The impedance signature for the empty grouting compactness was measured and defined as a baseline.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Diameter (mm)** & **Thickness (mm)** \\ \hline PZT1 & 20 & 0.3 \\ PZT2 & 16 & 0.3 \\ PZT3 & 12 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimension of PZT wafers.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Density & 7500 kg\(\cdot\)m\({}^{-3}\) \\ Young’s modulus & 127.2 GPa \\ Poisson ratio & 0.29 \\ Piezoelectric coefficients & \\ d\({}_{31}\)d\({}_{32}\) & 0.274 nC\(\cdot\)N\({}^{-1}\) \\ d\({}_{33}\) & 0.593 nC\(\cdot\)N\({}^{-1}\) \\ d\({}_{24}\)d\({}_{15}\) & 0.741 nC\(\cdot\)N\({}^{-1}\) \\ Dielectric coefficients & \\ \(\varepsilon_{11,22}\) & 27.7 nF\(\cdot\)m\({}^{-1}\) \\ \(\varepsilon_{33}\) & 30.1 nF\(\cdot\)m\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of PZT-5H.  
Figure 5: Experimental setup.
## 3 Results
### Control Specim Analysis and Experimental Data Processing  
A photographic record was created for the control specimen and for the test specimens after each of the aging cycles, in order to enable comparison and analyze the morphology of the initial specimens and any changes resulting from the thermo-hygrometric cycles, such as textural alterations, chromatic, formal, and dimensional modifications, adhesion to the surface, and material interaction. This provided an initial general overview for the subsequent analyses of **efficiency, stability, and compatibility**, which are detailed in the following sections.
# Managing Emergency Situations in VANET Through Heterogeneous Technologies Cooperation
## 4 Vehicle On-Board Equipment
### On-Board Unit (OBU)  
OBU consists of the control unit, the communication subsystem and the sensor network. Control and communication unit is based on the single board industrial PC of _Embedded Platform for Industrial Computing_ (EPIC) form. Type of EPIC module is NANO-9452 and it is a product of IEI Technology Corp., Taiwan. The _Controller Area Network_ (CAN) interface is interconnected with EPIC module by internal PC/104+ bus and it creates interface between OBU and sensor network. The sensors represent all those devices used for measuring/sensing of temperature, gas concentration, pressure, vehicle inclination and glass break. These sensors have the task of monitoring status of dangerous and, then, sending detected data to the monitoring centre in order to take the opportune countermeasures to rescue system in case of damage or accident [45]. In [46] the authors describe a device able to connect vehicles to RSUs: the _Single Board Computer_ (SBC), based on a prototype of the Institute for Infocomm  
Figure 9: _Map Update Message_ (MapUpMsg) and _Rerouting Order Message_ (ROMsg) messages format.  
Research in Singapore. The SBC has the capability to host a CAN interface, while WLAN-based communications have been enabled by means of an installed wireless chip, supporting the IEEE802.11b standard. The SBC device represents an interesting solution for OBUs, also because it adopts some interfaces provided by the _Communications Access for Land Mobiles_ (CALM) architecture [47]; the main problem is represented by the absence of an integrated component approach, since each sub-system is interconnected in different boards.
# A Comprehensive Review and Assessment of Cybersecurity Vulnerability Detection Methodologies
## 5 Challenges and Potential Solutions for Automating Vulnerability Detection
### Infrastructure Challenges  
This category of challenges is multifaceted owing to the diversity, complexity, and scale of modern environments. The target environments and protected systems face multiple issues related to scalability and performance (cloud and distributed systems), diversity of components (not limited to legacy systems, IoT devices, edge computing), resource constraints (computational, storage, and battery resources), real-time detection and response (financial and industrial control systems (ICS)), lack of standardization (CPE, inconsistency vendor names, varying protocols), limited visibility in multi-tenant (cloud environments), among others [147,148].  
Potential Solutions. To bypass the previous issues, it is recommended to adopt real-time monitoring solutions (like cloud-native monitoring, lightweight detection systems, behavioral anomaly analysis), lightweight models and offloading computational tasks for IoT devices (edge computing, firmware updates and patch automation, and protocol-aware detection), integrate AI models of vulnerability detection associated with the continuous training process, and federated learning for IoT devices and edge computing, among others, to ease the automation of vulnerability detection and reduce its impact [149, 150, 151, 152].
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
## 5 Application of Silica Mesopores in Drug Delivery  
In recent decades, significant advancements in pharmaceutical industry and medicinal chemistry have led to deep understanding of physico-chemical properties of medical compounds and provided the opportunity for recognition of cellular absorption strategies for effective treatments [160; 161; 162; 163; 164]. Initially, in some cases, such as chemotherapy for cancer, therapeutic methods relied on the use of toxic drugs with undesirable complications and limited beneficial effects [165]. To overcome this problem,targeted drug delivery carriers (TDDCs) were designed to deliver effective doses of drugs to targeted tissue cells [166]. The success of these drugs depends on the fabrication of bioavailability carriers with high drug loading capabilities and no early release [165]. The drug delivery agent should have a compatibility with the drug [166], be capable of encapsulation [167], feature high drug loading [168], and have the appropriate release rate [169]. Due to their importance, changeless drug delivery systems are focused structurally so that drug materials are transmitted without immediate release to the target site [165]. Among the many sustained carriers, mesoporous silica materials with a specific surface structure are capable of gradually releasing various drugs and controlling this process [170]. Mesoporous silica materials are attractive due to their resistance to heat [171], pH [172], resistance to mechanical stress [173], very small pore sizes [174], and have more potential compared with other _Nano_-carriers in terms of greater accuracy in the selective loading of various drug molecules and their large pore volumes available for drug loading [175]. The use of a silica mesostructure for the purpose of drug delivery was reported in early 1983. Since that time, silica nanoparticles have been used widely as drug carriers due to their high biocompatibility and easy formulation with medicines. A wide range of drugs is used, such as small molecules, photosensized molecules, proteins, deoxyribonucleic acids, and ribonucleic acids with silica carriers, in the treatment of diseases such as cancer, Parkinson's disease, and heart problems. The use of silica nanoparticles to deliver bioactive molecules could protect these molecules from degradation while allowing controlled release, prolonged circulation in the blood, improved disease targeting, and reduced side effects [176].  
In this section, drug deliveries induced by endogenous and exogenous stimuli, including pH, redox, enzymatic, glucose, H\({}_{2}\)O\({}_{2}\), adenosine triphosphate (ATP), temperature, light, magnetic field, and ultrasound stimuli, will be discussed.
## 2 Designed NISC-Based Architectures
### Demapper Architecture
#### 2.2.2 _DemaNisc_ Module  
The _DemaNisc_ module is considered the main core of the architecture design tailored to implement the Max-Log-MAP demapping algorithm. From a hierarchical scope, it can be viewed as a concatenation of five units:  
1. Euclidean distance unit (EDU)
2. A priori LLR summation unit (ASU)
3. Inter-subtraction unit (ISU)
4. Minimum finders unit (MFU)
5. Output unit (OU)  
Euclidean distance unit (EDU):  
This unit incorporates all hardware resources that are involved in computing the Euclidean distance. It is provided by the in-phase (\(I\)) and the quadrature (\(Q\)) components of the received symbols \(y^{I}\) and \(y^{Q}\), constellation symbols \(x^{I}\) and \(x^{Q}\) and fading factors \(\rho^{I}\) and \(\rho^{Q}\), in addition to the noise variance \(\sigma^{2}\). At each computation, the Euclidean distance unit can deliver one two-dimensional distance or two one-dimensional distances.  
Figure 4: Proposed NlSC-based demapper architecture.  
A priori LLRs summation unit (ASU):  
The hardware resources located in ASU generate the a priori LLRs summation of input LLRs, which are required in the case of turbo demodulation. The LLR values stored in _AprMem_ memory and the vector \(v\) representing the binary mapping \(\mu\) of symbols from the _Constellation LUT_ are the inputs to this unit.  
Inter-subtraction unit (ISU):  
The a priori LLRs summation values generated by ASU are delivered to ISU, which subtracts them in parallel from the value of two-dimensional Euclidean distance calculated by EDU. To perform this functionality, ISU includes a subtractor set, which is made of eight real subtractors and sufficient registers to store the output results.  
Minimum finders unit (MFU):  
Minimum finders are established to compute the minimum functions required in the Max-Log-MAP algorithm. This unit integrates eight minimum finder blocks, the architecture of which is presented in Figure 5. Each block is concerned with finding the minimums associated with a bit location \(v^{i}\) along all constellation symbols. For the additional details about the structures of each unit, the reader can refer to [14].  
Output unit (OU):  
The output unit is responsible for delivering the final LLR values corresponding to each bit. The inputs of this unit are the minimum values available in the registers of the minimum finders unit. Once the minimums of all constellation points are determined, this unit produces the difference between minimum pairs, which correspond to each bit location. The resultant differences are then stored in output registers.
## 4 Hardware Implementation Design
### Power Dissipation  
For 1 Kbps requirement, 1 kHz clock will be sufficient. However due to XPower Analyzer limitations [23] we can get a power estimate for frequencies as low as 1 MHz (for worst case 100% toggling rates). Specifically, the total power usage of an FPGA device can be broken down to total static power and total dynamic power. Static power is associated with DC current while dynamic power is associated with AC current. Static power is power consumed while there is no circuit activity. Dynamic power is power consumed while there is inputs/circuit activity (Table 4).
## 3 Results and Discussion
### Results of Parametric Study  
#### 3.2.1 Results in Terms of Training Function  
As reliable results were obtained as shown in Figure 5, this section presents the influence of using different training algorithms on the performance of the ANN model. To this end, other parameters, such as activation function, was fixed as tansig, number of neurons was fixed as 20 and cost function was fixed as mean square error. Figure 6a-d present the evaluation of R\({}^{2}\), Slope, RMSE and MAE, respectively, as a function of the training algorithms. It should be noted that, as shown in Figure 6, the box plot was adopted to represent the probability distribution of error criteria over 1000 random sampling runs, including median, mean, 25%-75% and 9%-91% percentiles. The performance was ranked based on the median value of the distribution. Results showed that the BFG and LM algorithms exhibited the best performance, with respect to R\({}^{2}\), Slope, RMSE and MAE, respectively. Furthermore,  
Figure 5: Statistical convergence for (**a**) R\({}^{2}\) and Slope, (**b**) root mean square error (RMSE) and mean absolute error (MAE), over 1000 random sampling runs. Various curves are associated with the configurations of the parametric study.  
regarding R2, RMSE and MAE, BFG and LM also produced a lower level of fluctuation than other algorithms, as can be seen in terms of the 25%-75% percentiles of the distribution. Finally, the LM algorithm was selected as it provided the highest median value with respect to R2, and the lowest median value with respect to RMSE and MAE.  
These observations of superior performance of the LM algorithm were in accordance with the literature. This algorithm was developed, in combining the conventional gradient descent technique and Gauss-Newton algorithm, in order to enhance the efficiency of the training process [54]. As a result, the LM technique has been widely used in various investigations in different areas of research [24; 37; 43; 63].