esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 VN Allocation and Enforcement Methodology
### Virtual Network Classification  
Using the SCG model, the VN is constructed. The customized VN is classified as either blocking or non-blocking network. Let \(vm_{i}^{r}\) and \(vm_{i}^{s}\) denote the maximum receive rate and send rate of the \(i\)-th VM in the \(\overline{VM}\) set. We say that the VN is non-blocking, including two aspects: endpoint link is non-blocking and network fabric is non-blocking. To VN, the endpoint link is the access link that  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notations** & **Description** \\ \hline \(S_{i,j}\) & The _\(jth\)_ port of _\(ith\)_ physical switch S \\ \(VS_{i,j}\) & The _\(jth\)_ port of _\(ith\)_ virtual switch VS \\ \(interface(S_{i})\) & The port number of \(S_{i}\) \\ \(interface(VS_{i})\) & The port number of \(VS_{i}\) \\ \(ib(S_{i,j})\) & Physical ingress bandwidth of \(S_{i,j}\) \\ \(eb(S_{i,j})\) & Physical egress bandwidth of \(S_{i,j}\) \\ \(rib(S_{i,j})\) & Residual ingress bandwidth of \(S_{i,j}\) \\ \(reb(S_{i,j})\) & Residual egress bandwidth of \(S_{i,j}\) \\ \(vib(VS_{i,j})\) & Ingress bandwidth requirement of \(VS_{i,j}\) \\ \(veb(VS_{i,j})\) & Egress bandwidth requirement of \(VS_{i,j}\) \\ \(vib(VS_{i,j})\) & Residual ingress bandwidth of \(VS_{i,j}\) \\ \(reb(VS_{i,j})\) & Residual egress bandwidth of \(VS_{i,j}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.  
connects VM to the logic switch, and the network fabric is the network apart from the part that the access link connects. Otherwise this is the blocking network (blocking VN).  
1. The endpoint link non-blocking: For arbitrary VM in the VN, it can accept the traffic from all other VMs at their permitted maximum sending rate simultaneously. We describe as follows: \[\sum_{i\
eq j}vm_{j}^{s}\leq vm_{i}^{r},\forall vm_{i}\in\overline{VM}\] (1)
2. The network fabric non-blocking: \[\left\{\begin{array}{l}\frac{\overline{VM}}{\sum\limits_{im_{i}}}\mathit{ iveb}(p_{vm_{j}\in\overline{VM},vm_{i}},ve_{1})[i\
eq j]\leq\mathit{iveb}(ve_{1})\\ \frac{\overline{VM}}{\sum\limits_{vm_{i}}}\mathit{iveb}(p_{vm_{j}\in\overline{VM},vm_{i}},ve_{2})[i\
eq j]\leq\mathit{iveb}(ve_{2})\\ \ldots\\ \frac{\overline{VM}}{\sum\limits_{vm_{i}}}\mathit{iveb}(p_{vm_{j}\in\overline{VM},vm_{i}},ve_{l})[i\
eq j]\leq\mathit{iveb}(ve_{l})\end{array}\right.\] (2)
# Cybersecurity Comparison of Brain-Based Automotive Electrical and Electronic Architectures
## 4 Architectures
### Three-Brain E/E Architecture  
The three-brain architecture (see Figure 16) is the approach where modern vehicle functions (estimated at approximately 800) are distributed in three HPCs. Other names for this architecture are \"three HPCs\" and \"Zonal Architecture\".  
The architecture consists of three HPCs, where the functions of the vehicle are distributed in these HPCs in order to increase performance and reduce the overall load on each HPC. As shown in Figure 17, the three-brain architecture has three brains which are  
Figure 16: Representation of Three-Brain E/E Architecture.  
Brain1 HPC, Brain2 HPC, and Brain3 HPC. Each of these brains host several functions where the interators (sensors/actuators) are either directly connected to these brains or connected via ECUs (in this case, ECUs are responsible for managing the interactors), to be able to perform those functions. As with One-Brain and Two-Brain architectures, the communication between the Brain HPCs and the Telematics is accomplished using two-ways Ethernet channels and the communication between the Brain HPCs and the interactors (sensors/actuators) is performed using bus systems.  
* **Use Case:**  
Assuming 800 functions are hosted by the whole vehicle, then approximately 266 functions are hosted on each HPC. The use case diagram of the three-brain architecture is shown in Figure 17, where only one function for each HPC (Domain) is illustrated. The three HPCs are assigned for three domains, such as Brain1 HPC is allocated for Infotainment domain, Brain2 HPC is allocated for Body domain, and Brain3 HPC is allocated for Powertrain domain in the use case diagram. Brain1 HPC receives the TPMS signal though a bus system, processes the signal, and sends the received value to the Head Unit Display (HUD). When the KeyFob is pressed or in close proximity, Brain2 HPC receives the signal from the KeyFob and checks whether the KeyFob is authentic and then opens/closes the door of the vehicle. Brain3 HPC receives the images taken by the 360 camera, recognizes the surrounding objects and drives the car autonomously based on the logic implemented in Brain3 HPC.  
The distribution of the functions does not necessarily have to be even. The functions, which required high computational power, can be assigned to a brain with a higher processing power. In this case, autonomous driving (AD) can be connected to the brain with the highest processing power. Another way of distributing the functions can be based on the critical and safety function, such that these functions can be assigned to the brain with higher safety and redundant features. Figure 18 shows the realization of the three-brain architecture in the Ansys Medini Analyze software.  
Figure 17: Use Case Representation of Three-Brain Architecture.
# Virtual Replication of IoT Hubs in the Cloud:
## 3 Architecture
### Synchronization Protocol
At this point, the IoT Hub updates its RR by adding \(id_{i}\) and then starts publishing to the broker all the resources (R) using the topic \(id_{i}\) (\(pub(id_{i},R)\)), which guarantees that the new replica will receive the resources. When the synchronization procedure has ended, the replica will be automatically kept synchronized with the IoT Hub during the normal system lifecycle. Figure 6: Sequence Diagram illustrating the operation and message exchange between all actors involved and the communication protocols used at each phase  
When resources are polled for (as will be described in Section 3.2.1), the IoT Hub might find out that a resource targeted by some requests has changed. A request targeting a resource that either has not been cached or is not considered fresh must be forwarded by the IoT Hub to the smart object. Upon receiving the response from the smart object, after updating its cache and forwarding the response to the requesting replica, the IoT Hub uses the synchronization protocol to publish the updated information to all of its replicas. When observing resources (as described in Section 3.2.2), the synchronization procedure resembles the same of the polling case. When resources are pushed by smart objects to the IoT Hub (as described in Section 3.2.3), the IoT Hub uses the synchronization protocol to publish the updated information at all replicas. Note that this synchronization strategy is needed only for those replicas that are part of the request/response loop: in fact, all resources are automatically synchronized with the request-issuing replica by design, since the replica also perfectly reproduces the behavior of the actual IoT Hub. Figure 7: The broker-based message flow between the IoT Hub and its replicas is shown in (**a**), while the internal structure of the Replica Registry module of the IoT Hub is shown (**b**). Pub/Sub approaches for data synchronization may cause inconsistencies between the state of the IoT Hub and Virtual Replicas at some moments. This may be caused by the following:  
* A lack of Internet connectivity with Cloud Virtual replicas: in this case there is no way to keep the states synchronized until the connection resumes;
* In a Pub/Sub architecture messages may be delivered to consumers at different times so that a Client requesting the state of a resource may receive different responses depending on the Virtual Replica serving the request. However, it is import to point out that all replicas are typically being executed in Cloud Environments/Carrier-grade Data Centers which guarantee high availability and reliable and low latency connectivity, thus minimizing the inconsistency time windows. Our work relies on an Optimistic Locking approach which solves the inconsistency issue by versioning the state of resources and leaving clients responsible for managing race conditions. Consensus Algorithms (e.g., Raft or Paxos [28, 29]) may introduce additional overhead whose trade offs should be carefully evaluated according to application requirements.
# Exploiting Lunar Navigation Constellation for GNC Enhancement in Landing Missions
## 3 GNC Algorithms  
The Guidance Navigation and Control strategy exploited in this landing scenario relies on the architecture shown in Figure 1.  
From the real world simulation, the dynamics of the lander and of the servicers is propagated in a high-fidelity environment, and the sensor readings are generated. These are fed to the navigation block, whose output is the absolute state estimation. The guidance and control block employs this state, together with the targeted trajectory, to generate a control action, \(\mathbf{u}_{k}\).
## 2 The Proposed Method  
In this section, we introduce the main phases of our proposed method for detecting defective and abnormal die patterns from a target wafer. For a simpler description, we consider the SAT image demonstrated in Figure 1 as an example for presenting the proposed method, assuming that the original SAT image has a pixel resolution of \(w_{\text{Orig}}\times h_{\text{Orig}}\). It is evident that there are a large number of similar dies that regularly repeat on the wafer. In this study, every die is a minimum unit that needs to be analyzed. In general, the wafer is well aligned during the SAT imaging process. Template-matching methods can be used to find all dies if a reliable template is obtained in advance. Consequently, we first introduce an algorithm for automatically extracting a standard template. Thereafter, the die patterns need to be detected and classified successively. Therefore, the proposed method is divided into three main phases: (1) automatic template extraction, (2) die pattern detection and clustering, and (3) die pattern classification.  
Figure 1: Original scanning acoustic tomography (SAT) image: example wafer.
# A Methodology to Improve Energy Efficiency and Sustainability in Urban Environments
## 2 Case Study Identification  
It was necessary to analyze, firstly, the main characteristics of the selected case study area, where the methodology was developed and tested. It is, specifically, the Piovego North University area (Figure 3). It is located in Padua, a city in the Veneto region, in the northeast part of Italy.  
Due to the phenomenon of global warming and climate change, temperatures are changing, altering climate maps, and bringing local temperatures and conditions closer to a climate characterized by heavy autumn rains and moderate summer drought, with very hot summers and warm winters [19]. The selected area belongs to the University of Padua and covers a significant surface area of approximately 50.000 m\({}^{2}\). The area mainly consists of buildings used for student classrooms, laboratories, offices, and the university canteen,  
Figure 3: (**a**) Padova localization (Google Maps); (**b**) study area, Piovego North University campus (Google Maps).  
Figure 2: Urban heat island mitigation strategies.  
with outdoor spaces. To ensure reliable simulations of the initial condition, it is crucial to have a clear idea of the condition of the existing materials, collecting data regarding their composition, their wear degree, and their mutations compared to new material, as these are fundamental input data. Therefore, we directly conducted in situ extraction of eight core samples to later subject the existing materials to laboratory tests, analyzing their chemical composition and physical behavior. The core samples were extracted according to the different types of materials identified during a survey.  
This area was chosen because it is representative and can reflect many characteristics of urban environments. This is a complex that has structural conformations that are quite common in the area, and in Italy in general, for agglomerates that serve the same function. This allows for comparable results for similar structures, and not only the study area.  
Within the area, 76% of the surfaces resulted to be impermeable; 33% belonged to the building roofs, and 43% had conventional materials known for their heat retention properties, such as asphalt and concrete. The remaining 24% of the area was covered by green surfaces (Figure 4). For the subsequent analyses, it is essential to highlight data on the extension of these green areas, including the specific location of trees and information about their main characteristics.
# An Adaptive Symmetrical Load Balancing Scheme for Next Generation Wireless Networks
## 4 Simulation Setup  
We have executed the simulations of the SD-Wi-Fi architecture using OMNeT++ as shown in Figure 4. A high density SD-Wi-Fi is illustrated where the purple circles denote the controllers and applications installed. The green messages are the flow requests generated by the wireless devices/mobile nodes. The format for the flow request is the same used by physical layer and medium access control (MAC) layer of OMNeT++. Where MN denotes the mobile nodes. The APs are connected to OpenFlow enabled switches. OMNeT++ is a modular, open-source, and extensible simulation framework used for building discrete event simulations of various network protocols, architectures, and algorithms. It is written in C++ and is based on the event-driven simulation paradigm, where events are processed in sequential order. The IDE allows users to configure various simulation parameters, such as simulation time, packet generation rate, and routing protocol parameters. It provides real-time visualization of the simulation progress, including network traffic, node behavior, and packet flow. It also allows users to analyze simulation results using built-in tools.  
For the analysis of the proposed ASLB, we have analyzed delay, throughput, packet loss rate, average number of handoffs and workload optimization parameters. Our network  
Figure 4: The zoomed simulation topology of high density SD-Wi-Fi in OMNET++ environment  
topology is formed using one UC and three RCs with OpenFlow switches and APs. We have used 100 WDs in simulation. The simulation topology is depicted in Figure 5. All simulations are performed on a HP PC with windows 11, 16 GB of RAM and a AMD Ryzen 5 5600U with Radeon Graphics @ 2.30 GHz processor.  
Table 4 shows the important parameters taken into consideration during simulation of the proposed ASLB.
## 3 Results and Discussion
### Solar System Performance Depending on Alumina Concentration
#### 3.4.2 Temperature Distribution of Nanofluids According to Solar Radiation on September 24  
According to the analysis of solar radiation and temperature distribution shown in Figure 6, DW increased by 18.13 \({}^{\circ}\)C, and the 0.1, 0.3, and 0.5 wt% alumina nanofluid temperatures increased by 18.21, 19.59, and 20.23 \({}^{\circ}\)C corresponding to percentage increases of 0.4%, 8.1%, and 11.6%, respectively, compared with DW. The results are shown in Table 2. Thus, it can be concluded that an increase in nanofluid concentration leads to increased nanofluid heat absorption. The experimental results showed that the 0.3 wt% nanofluid exhibited the highest efficiency when considering the efficiency of the alumina nanofluid.
# Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services
## 5 Performance Evaluation and Results
### Characteristics of the Proposed Methodology  
In this paper, we present a resource-aware cloud admission control scheme for cloud services which ensures that a minimal resource level is maintained before the grant of admission control services to cloud applications. Below we gave a short discussion on the uniqueness, advantages, and shortcomings of the presented approach.  
The uniqueness of the proposed scheme is that uses application-awareness as benchmark for the application acceptance of resources. Looking at the current state of affairs in the cloud and data center industry, the use of proprietary hardware and switching equipment discourages the free and open access to hardware resources of the data center. Therefore, the application-awareness is required to achieve transparency for performing network statistics.  
By offloading the control plane to computing platforms running on centrally-controlled, software-controlled commodity hardware, SDN unleashes the potential to operate computation-intensive machine learning tools and solve complex optimization problems in a centralized fashion. Therefore, we use application-awareness concepts and employ those using software-defined networking devices in the experimentation process.  
The application-awareness concepts when introduced to the field of cloud services admission control not only help in revisiting the traditional cloud services admission control concepts but also helps in exploring new avenues for managing service level deliverance requirements under varying network traffic conditions. The presented approach will not only facilitate the management of data center resources but will also facilitate in achieving the desired level of service for end-users.  
In terms of the limitations for the proposed technique, the services admission controller (actually the SDN controller) might face bottleneck issues when confronting numerous service requests simultaneously. Apart from the bottleneck issue is the security challenges for the services. These challenges can be resolved by limiting the number of services or by employing multiple scalable SDN controllers. For the security issues, third party security services and restriction-based solutions can be implemented.
## 2 Molecular Mechanisms of Antimicrobial Agents Recalcitrance in Clinically Important Biofilms
### Mutational Resistance and Gene Transfer  
So far, we discussed the triggering factors and the molecular mechanisms leading to changes in gene expression responsible for the phenotypic shift that occurs in biofilm bacteria and renders them more recalcitrant to antimicrobials than their genetically identic free-floating counterparts. However, we should not forget that independently of biofilm formation, planktonic antibiotic susceptible bacteria may select antibiotic-resistant mutants when exposed to subinhibitory antibiotic concentrations. This type of mutational resistance addresses the antibiotic mode of action through one of the following mechanisms:  
1. modifications of the antibiotic target for instance by decreasing its drug affinity [85];
2. modification of the drug itself by the expression of drug-inactivating enzymes, such as \(\beta\)-lactamases [86];
3. decrease in the drug uptake through reduced porin expression [87; 88; 89];
4. over-expression of bacterial drug efflux pumps thereby extruding the antibiotic out of the bacterial cell [12; 90; 91; 92];
5. development of alternative metabolic routes that bypass those pathways targeted by the antibiotic [93].  
Bacteria that have previously undergone mutations can also associate to form biofilms, and biofilm association enhances the mutability rate [94]. The dense and packed structure of biofilms promotes horizontal gene transfer--that is, the transfer of genetic material between cells except for transfer from parent to offspring. HGT can be accomplished through a transformation in which bacteria uptake naked DNA from their environment through conjugation, which requires cell-to-cell contact presumably via pilus, or through transduction when DNA is carried from the donor to the receiver cell by a viral vector or bacteriophage.
## 4 Materials and Methods
### Volatiles Collection and GCMS Analysis using HS-SPME  
4.3.1 VOCs Extraction with HS-SPME  
The analysis of volatiles was focused on cabbage for infested and uninfested plants with the green peach aphid. Cabbages were placed individually into 4 L glass jars, and one plant in each jar was analyzed. For each glass jar, a 5 mm port was drilled into the side, into which a septa (20633 Thermogreen(r) LB-2 Septa, plug) was placed and used for the collection of infested and uninfested plant VOCs. Aluminum foil of 100 m \(\times\) 44 cm (Vital Packaging Company) was used to carefully cover and wrap the surface of the top of the plant pot, and the glass jar placed upside down on the plant. The reason for selecting glass jars is that it is easy to capture the VOCs emitted and also easy to wash, clean and oven-dry them at 100 \({}^{\circ}\)C for a minimum of 30 min to sterilize. VOCs were extracted from samples, which were infested and uninfested cabbage plants with _M. persicae_. For extracting VOCs from samples, headspace technique analyses were used with three replicates in all experiments, for profiling and characterization of VOCs from both plants. The identification of VOCs was conducted with the SPME fiber by extracting the compound from the headspace of treatments. Three phase fibers 50/30 \(\upmu\)m divinylbenzene/carboxen/polydimethyl siloxane (PDMS/CAR/DVB; Sigma-Aldrich, Australia, catalogue number 57347-U) coating was selected for volatiles released from infested and uninfested plants. The SPME fiber is commonly used and this three phase fiber was selected because it was being used for the analysis of a wide range of analysts. The fibers were first conditioned at the range of operating temperature recommended by the manufacturer, before analyses were conducted. For optimizing various conditions, the sealing time was optimized to 2.30 h under laboratory temperature 25 \(\pm\) 1 \({}^{\circ}\)C, and the SPME fiber was exposed to the headspace of the samples by inserting the SPME into the jar through the septum for two hours to extract the VOCs, which characterized the optimum extraction time. The desorption time of SPME fiber was 5 min in the GC injection port. The SPME was used because it is a fast, simple and modern tool for GC-MS analysis.
# Guaranteeing QoS for NOMA-Enabled URLLC Based on \(\kappa\)-\(\mu\) Shadowed Fading Model
## 1 Introduction
### Organization  
The remainder of this paper is organized as follows. In Section 2, the system model for a CF mMIMO system under the \(\kappa-\mu\) shadowed fading model is described. Section 3 introduces SNC and presents the Mellin transform over both arrival and service processes as well as the UB-QDVP. Simulation results and an analysis are given in Section 4, followed by the conclusions in Section 5.  
_Notations_ : \(\mathbb{C}^{K\times N}\) is a complex matrix with \(K\) rows and \(N\) columns. The modulus and expectation operators are denoted by \(|\bullet|\) and \(\mathbb{E}(\bullet)\), respectively. \((\bullet)^{T}\) and \((\bullet)^{H}\) denote the transpose and conjugate transpose, respectively, of their arguments. \((\bullet)_{lk}\) denotes the element in the \(l\)th row and \(k\)th column of a matrix. \(\mathcal{CN}(0,1)\) denotes a complex Gaussian distribution with a mean of zero and unit variance.