esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market
## 6 Results
### Comparative Evaluation
#### 6.2.2 Different Offloading Mechanisms  
Towards evaluating the significance of the game theoretic data offloading mechanism proposed by our DO-MECS framework, a comparison between our mechanism and a computationally simplistic mechanism where each end-user offloads a fixed portion (i.e., percentage) of its data, was performed, while for fairness purposes the rest of our proposed framework (i.e., server selection and optimal pricing mechanisms) was kept intact in all strategies. Specifically, with respect to the alternative data offloading mechanism, three different variations were examined, where the end-users send 25%, 58.6% and 100% of their total data \(I_{u}^{(t)}\), respectively, to the selected MEC servers. It should be noted here that the alternative with fixed portion (i.e., percentage) of 58.6% data offloading of user's maximum amount of data was selected because it corresponds to the same average end-user data offloading, as the one produced by our proposed framework in the considered experiment.  
The corresponding comparative results are depicted in Figures 7 and 8, where the average MEC servers' profit and the average end-users' utility, respectively, as a function of the time for the different offloading mechanisms were obtained. In particular, it was evident that as expected the more data the end-users offloaded to the MEC servers, the higher profit the MEC servers experienced. However, this happens at the cost of very low average utility experienced by the end-users, as clearly demonstrated from the curves corresponding to the 100% offloading alternative. Moreover, it was observed that by allowing the end-users to send a constant amount of data without enabling them to dynamically adapt their offloading amount of data based on the system's conditions (as our framework evangelizes), always resulted to significantly lower average end-users' utility. As a result, the proposed DO-MECS framework offered incentives to the end-users to participate in the non-cooperative data offloading game in order to dynamically and autonomously determine the optimal amount of data, while the MEC servers experienced the best levels of profit that they could achieve based on the decisions of their customers, i.e., end-users.  
Figure 6: Average end-users’ utility vs. time slots for different learning rates.
## 4 In-Depth Analysis and Results
### Qualitative Content Analysis
#### 4.1.1 Group 1: Production Planning and Control Supported by BIM  
Group 1 refers to studies concerned with the management of construction processes according to Lean principles that are also supported by BIM technology. Thanks to the a-priori BIM-LPS interaction matrix filter and the rigorous focalization of this review procedure, most of such comprise BIM-LPS co-applications in construction execution. Several authors developed so-called \"Lean IT-tools\" which support one or many aspects of the LPS and provide a link to BIM models [22; 23; 49; 55; 84; 86; 89]. However, none of them supports the Last Planner(r) System to its full extent, since the tools either support only collaborative phase planning [55], even neglect the collaborative aspects such as hand-offs discussion among last planners [22; 23; 49] or systematic task-breakdown [84; 86; 90] as stipulated in the current LPS process benchmark [91].  
Besides methodological insufficiencies with respect to LPS, this literature sample reveals that the BIM integrations are also not fully developed up to the data processing level: Either, only 3D-representations of BIM models are considered [84; 86; 90] in case of some integrated IT-systems, LPS elements were included in a non-digital fashion in regular BIM coordination meetings [83], or lean production philosophy was applied in parallel with BIM models but not truly integrated [27; 50; 70].  
This literature sample is also characterized by case studies in the construction execution phase of different project types to find and demonstrate evidence of the described BIM-Lean synergies [54; 70; 73; 74]. Examples here are engineer-to-order (EtO) projects [50; 73] or areas of mechanical, electrical, and plumbing (MEP) works [70; 80; 83].  
Another aspect of this literature sample deals with BIM models made available at the construction site to support the implementation of lean principles, which are referred to as BIM-stations [29].
## 4 Results and Discussion
### Average of the Mean Distance around All Solutions  
Figures 9 and 10 as well as Table 5 show the quantification of diversity using the average of the mean distance around all solutions (ADAA) and its proposed variants, i.e., modified ADAA (mADAA) and niche ADAA (nADAA).  
Figure 9: Quantification of diversity using the average of the mean distance around all solutions.  
Figure 10: Quantification of diversity using the average of the mean distance around all solutions (_Cont._).  
The ADAA computes the sum of the average pairwise distances of each particle to all other particles in the swarm and then finds the average. In essence, the ADAA computes the average spread of the entire swarm. The effect is that, as the particles converge towards optima, the diversity will decrease and then stagnate at a relatively high diversity value.  
The results obtained by the standard ADAA correlate with the expectation, i.e., for the diversity to decrease gradually. However, for some of the problems, e.g., \(f_{2}\) and \(f_{8}\), this was not the case. Analysis of the positions of the particles in various iterations showed that, for these two problems, convergence started to occur early in the search. As such, particles were only exploiting near their points of convergence, leading to larger distances from other particles. This too should be expected of this measure as niching algorithms are expected to explore widely over the entire search space.  
For the mADAA, the results are as expected. The pairwise average distances was calculated with respect to each niche and then averaged. As such, it was expected that the small intra-niche distances would lead to small diversity.  
The behaviour of the nADAA was somewhat similar to that of the standard ADAA. Since the pairwise distances are calculated using only unique solutions, it is therefore correct to posit that the quantified diversity will be high in both the exploitation and the exploration phase.  
In summary, the nADAA is a suitable measure to quantify the spread of the solutions of niching algorithms, while the mADAA is a suitable measure to quantify diversity within the candidate niches.
## 7 Ground-Segment Optimization Aspects
### Applicable Optimization Methods for Ground Segment
#### 7.2.1 Non-Dominated Sorting Genetic Algorithm II (NSGA-II)  
NSGA-II, originally introduced in [53], is an improvement over MOGA [52], which has three characteristics; i) it uses an elitist principle; ii) it uses an explicit diversity-preserving mechanism (called crowding distance); iii) it emphasizes non-dominating solutions. We provide the steps of NSGA-II in Algorithm 4. It is to be noted that the individuals that form the population are the possible solutions that we are interested in. For example, for the GS planning problem, a possible implementation is to define a GS network distributed randomly in the area of interest as an _individual_; such _individual_ will be made up of a set of four vector parameters, namely the 3D position, the elevation angle, the number of GS, and the overall manufacturing cost. The parameters of an _individual_ are thus not limited to scalars only, but they can be vectors or a set of vectors themselves. Furthermore, as NSGA-II is an advanced variation of GA, it has identical overhead properties with it.
# Characterization of Inter-System Biases in GPS + BDS Precise Point Positioning
## 4 Characterization of ISB
### ISB Characterization Affected by the Satellite Clock Datum  
In this experiment, randomly, we collect data from three IGS stations including PNGM, HKSL and GAMG from day of year (DOY) 034-036, day of year (DOY) 128-130, day of year (DOY) 227-229, 2018, respectively. The observation data are available over FTP ([[ftp://cddis.gsfc.nasa.gov](ftp://cddis.gsfc.nasa.gov)]([ftp://cddis.gsfc.nasa.gov](ftp://cddis.gsfc.nasa.gov))) for free.  
The absolute ISBs estimated with different precise products differ a lot in magnitude. To show the results clearly, all estimates are subtracted by the ISB estimated in first epoch.  
Figure 2 shows the ISB time series of three stations using WUH, CODE and GFZ precise products, respectively. The ISBs suddenly jump between two adjacent days, and the amplitude of the fluctuation is different-with-different precise products. This fluctuation is caused by the change of satellite clock datum [31]. Moreover, the daily ISBs based on CODE and WHU products are stable, while the variation of daily ISBs corresponding to GFZ products is remarkable. As we can see, the time series of ISBs from three stations using same products are consistent. It further proves that the change of satellite clock datum affects the ISBs.  
Figure 1: Geographic distribution of stations used in the global positioning system (GPS)/ BeiDou navigation satellite system (BDS) precise-point positioning (PPP) experiment to analyze the characteristic of inter-system bias (ISB).
# Actuating Performance Analysis of a New Smart Aggregate Using Piezoceramic Stack
## 3 Comparison and Discussion
### Influence of the Outer Diameter of Protective Shield  
Figure 9 shows the influence of the outer diameter \(d_{o}\) of the protective shell on the first resonance frequency and the first electromechanical factor, and Figure 10 illustrates the relationship between the displacement at \(z=h_{l1}\) and the outer diameter \(d_{o}\) of the protective shell at the frequency \(f=20\) kHz. Figure 9 demonstrates that the first resonance frequency increases 23.4-23.86% as the outer diameter of the protective shell increases from 12.4 mm to 16 mm, while the first electromechanical coupling factor decreases 32.66-33.5%. Figure 10 indicates that the displacement decreases 64.41-64.76% as the outer diameter of the protective shell increases from 12.4 mm to 16 mm.  
Figure 8: The influence of the thickness of the PZT disc \(h_{\mathrm{p}}\) on the displacement at \(z=h_{l1}\).  
Figure 9: The influence of the outer diameter of the protective shell \(d_{\mathrm{o}}\) on the first resonance frequency and electromechanical factor.
# Network Performance Optimization for Low-Voltage Power Line Communications
## 5 Simulation Results
### Network Performance Simulation
#### 5.2.2 Stations Numbers Estimation Simulation  
Let us use 20 adjacent time slots as an example. Each time slot is 50 ms, and the channel states do not change for each time slot. The detailed parameters are as follows: assuming that the channel is initially idle, the Markov transition probability of the channel is \(p=\left[\begin{array}{cc}0.9&0.1\\ 0.15&0.85\end{array}\right]\), and the steady-state probability is \(\pi=[0.6,0.4]\). The channel state of the 20 adjacent time slots was calculated using the Markov chain model.  
Figure 16: Comparison of access delay.  
Figure 15: 512 B payload bandwidth utilization.  
Matrix \(B_{1},B_{2},\ldots,B_{30}\) for stations 1 to 30 is calculated as follows:  
\[B_{1}=\left[\begin{array}{cc}0.92&0.08\\ 0.1&0.9\end{array}\right],\,B_{2}=\left[\begin{array}{cc}0.92&0.08\\ 0.07&0.93\end{array}\right],\ldots,\ B_{30}=\left[\begin{array}{cc}0.95&0. 05\\ 0.07&0.93\end{array}\right] \tag{57}\]  
Matrix A was obtained by calculation. The number of competitive stations for each station was estimated using the HMM. We performed simulations for 5, 10, 15, and 20 stations. The stations were peer-to-peer, and the number of competitive stations was estimated. We used a single station as an example to explain the results of the game between stations. The game mechanisms for the other stations were the same and not repeated herein.  
The comparison results for the MAP algorithm and the proposed method were provided from the abovementioned parameters.  
For asymmetric channels and noise interference in an LVPLC environment, the average bandwidth utilization of the time slots was relative to the results of the MAP algorithm. For 5, 10, 15, and 20 stations, the average bandwidth utilization in the time slots by the proposed method increased by 89.2% and 51.3% (Figure 17). The MAP algorithm only used the current time to estimate the number of stations. The results were not modified, and the accuracy was relatively low. The proposed method used the emission probabilities to estimate the number of stations. It produced more accurate information regarding competitive stations, guaranteeing network saturation performance at a cost of space and time complexity.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 2 Related Works
Interestingly, they highlighted some reasons why IoT requires the incorporation of CR, including the case for efficient spectrum utilization, intelligent decision making, efficient bandwidth allocation for IoT objects, and the alleviation of interference situations. [PERSON] et al., in [20], surveyed the challenges and design requirements for developing software-defined wireless sensor networks (SDWSN) with focus on their application to IoT. They discussed the use of CR in SDWSN and emphasized the importance of CR and the need for further research with regards to improving the performance of CR-based SDWSN. In a separate survey, authors in [21] discussed issues surrounding the use of LPWAN as a possible communication access technology for cognitive radio sensor network (CRSN) with special use in smart grid infrastructures. They focused on the benefits of LPWAN in CR-SG networks. They mentioned a number of research gaps including challenges in the implementation design model and issues surrounding the use of LPWAN in CRSN-based SG systems. Further, a smart unified communication solution was proposed to improve smart grid systems while mitigating existing challenges. Different from the above-mentioned articles, our present article focuses on providing a background pertaining to the integration of CR in LPWANs. Noting that our survey may be one of the earliest overviews in this regard, and indicating that the concept of CR-LPWAN systems in IoT is still at its infancy, this article will be beneficial to the budding researcher who seeks to explore possible research challenges within the CR-LPWAN field.
# Multipoint Thermal Sensing System for Power Semiconductor Devices Utilizing Fiber Bragg Gratings
###### Abstract  
This study investigates the feasibility of using fiber Bragg grating (FBG) sensors for multipoint thermal monitoring of several power semiconductor devices (PSDs), such as insulated gate bipolar transistors (IGBTs), and rectifiers assembled on a common heatsink in a three-phase inverter. A novel approach is proposed to integrate FBG sensors beneath the baselines of the IGBT modules, avoiding the need for invasive modifications to the device structure. By strategically positioning multiple FBG sensors, accurate temperature profiles of critical components can be obtained. The experimental results demonstrate the effectiveness of the proposed method, with the temperature measurements from FBG sensors closely matching those obtained using thermal infrared (IR) cameras within \(\pm 1.1\)\({}^{\circ}\)C. This research highlights the potential of FBG sensors for reliable and precise thermal management in power electronic systems, contributing to improved performance and reliability.  
fiber optic sensors; power electronics; IGBT; junction temperature; multipoint sensing
## 5 Autonomous Vehicle Vulnerabilities
### Hardware Vulnerabilities: Engine Control Unit (ECU)  
The engine control unit (ECU) plays a vital role in the orchestration of a vehicle's functionalities, acting as the central processing entity for a range of control functions within an automotive system. By interpreting, analyzing, and managing a myriad of electronic signals, the ECU oversees critical operational aspects of vehicles, including fundamental features such as the core braking system.  
Various studies have underscored the vulnerability of ECUs to sophisticated infiltration strategies. The work of [PERSON] [81] is particularly instructive, revealing how intruders can exploit the onboard digital audio broadcasting radio as an entry point to gain unauthorized access to ECUs. Upon breaching this boundary, malicious attackers have the capacity to manipulate the CAN (controller area network), a critical communication highway that interconnects different vehicular subsystems. Such disruptions can have profound implications, potentially compromising the core functionalities of the vehicle and thereby posing significant safety risks.  
The potential gravity of such security breaches is further underscored by the observations of [PERSON] et al. [82]. Their findings indicate that the security measures currently implemented in ECUs are often insufficient in thwarting attempts at unauthorized firmware access or modification. Given that firmware alterations have the potential to completely reprogram a vehicle's behavior, this vulnerability is of significant concern in the context of public safety. However, their research is not only diagnostic but also offers a path toward remediation. They propose the adoption of an asymmetric cryptographic framework rooted in the use of public-private key pairings. This approach helps to ensure that any firmware introduced to the system originates from a verified and trusted source. In this way, the risk of unauthorized firmware modifications can be considerably mitigated, helping to safeguard against malicious intent.  
Despite this, guaranteeing the robustness of ECU security is a difficult task, given the complexity and evolution of cyber security threats. Not only is the scope of possible infiltration techniques a challenge, but so is the depth of possible exploits once access has been gained. The discovery of these latent vulnerabilities requires exhaustive testing and evaluation, utilizing both proven cryptographic techniques and emergent cyber security methodologies.
## 3 Experiments and Results
### Performance Analysis
#### 3.2.5 Test Scenario-5

Table 7: Confusion Matrix for Discriminant Analysis for \(p=1\), \(m=130\), \(i_{max}=15\). \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Device** & **Drill** & **Fan** & **Grinder** & **Hair Dryer** & **Hedge Trimmer** & **Lamp** & **Paint Stripper** & **Planer** & **Router** & **Sander** & **Saw** & **Vacuum Cleaner** \\ \hline Drill & 0 & 0 & 16.67 & 0 & 0 & 0 & 3.33 & 0 & 46.67 & 16.67 & 0 & 16.67 \\ \hline Fan & 0 & 100 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline Grinder & 0 & 0 & 90.00 & 0 & 10.00 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Confusion Matrix for Naive Bayes for \(p=1\), \(m=130\), \(i_{max}=15\). \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Device** & **Drill** & **Fan** & **Grinder** & **Hair Dryer** & **Hedge Trimmer** & **Lamp** & **Paint Stripper** & **Planner** & **Router** & **Sander** & **Saw** & **Vacuum Cleaner** \\ \hline Hair Dryer & 0 & 0 & 0 & 100 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline Hedge Trimmer & 0 & 0 & 0 & 0 & 100 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline Lamp & 0 & 0 & 0 & 70.00 & 0 & 30.00 & 0 & 0 & 0 & 0 & 0 \\ \hline Paint Stripper & 0 & 0 & 60.00 & 0 & 0 & 0 & 40.00 & 0 & 0 & 0 & 0 \\ \hline Planet & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 100 & 0 & 0 & 0 \\ \hline Router & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 100 & 0 & 0 \\ \hline Sander & 0 & 0 & 13.33 & 0 & 0 & 0 & 0 & 0 & 0 & 86.67 & 0 & 0 \\ \hline Saw & 2.50 & 0 & 15.00 & 0 & 50.00 & 0 & 0 & 0 & 20.00 & 2.50 & 0 & 10.00 \\ \hline Vacuum Cleaner & 2.86 & 0 & 37.14 & 0 & 0 & 0 & 0 & 0 & 0 & 20.00 & 0 & 40.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._ \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Device** & **Drill** & **Fan** & **Grinder** & **Hair Dryer** & **Hedge Trimmer** & **Lamp** & **Paint Stripper** & **Planner** & **Router** & **Sander** & **Saw** & **Vacuum Cleaner** \\ \hline Drill & 83.33 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 10.00 & 0 & 6.67 & 0 \\ \hline Fan & 0 & 90.00 & 0 & 0 & 0 & 10.00 & 0 & 0 & 0 & 0 & 0 \\ \hline Grinder & 10.00 & 0 & 70.00 & 0 & 0 & 0 & 0 & 0 & 0 & 20.00 & 0 \\ \hline Hair Dryer & 0 & 0 & 0 & 95.00 & 0 & 5.00 & 0 & 0 & 0 & 0 & 0 \\ \hline Hedge Trimmer & 0 & 0 & 0 & 0 & 60.00 & 0 & 0 & 0 & 0 & 40.00 & 0 \\ \hline Lamp & 0 & 0 & 0 & 5.00 & 0 & 95.00 & 0 & 0 & 0 & 0 & 0 \\ \hline Paint Stripper & 0 & 0 & 0 & 0 & 0 & 0 & 100 & 0 & 0 & 0 & 0 \\ \hline Planet & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 100 & 0 & 0 & 0 \\ \hline Router & 40.00 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 60.00 & 0 & 0 \\ \hline Sander & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 100 & 0 & 0 \\ \hline Saw & 0 & 0 & 10.00 & 0 & 12.50 & 0 & 0 & 0 & 10.00 & 2.50 & 65.00 & 0 \\ \hline Vacuum Cleaner & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Confusion Matrix for \(k\)-NN for \(p=1\), \(m=130\), \(i_{max}=15\).
## 3 Proposed Methodology
### Scenario Generation  
The objective of the analysis is to generate acceptable executions that can reveal the evolution of the SoS from a safe to a hazardous state. These executions represent corner case scenarios that expose a possible hazardous evolution of the system that the designers should evaluate to compute the effective probability of occurrence of the generated hazardous event chain and design the safety control architecture of the SoS.  
Formally, an SoS is modeled as a state transition system. A state transition system is a tuple \(M=(S,\Init,\Trans)\) where \(S\) is a set of states (possibly infinite), \(Init\) is a subset of \(S\) (initial states), and \(\Trans\) is the state transition relation that maps a state to a set of target states. A run of the transition system is a sequence of states \(\pi=s_{0}s_{1}\ldots s_{n}\ldots\) starting from an initial state \(s_{0}\) s.t. \(Init(s_{0})\) and \(\forall k\geq 0\Trans(s_{k},s_{k+1})\) hold.  
The hazardous states are modeled as a linear temporal logic (LTL) formula \(\varnothing=G[\
eg H(s)]\) where \(H(s)\) is a Boolean formula that encodes the hazardous states.  
The scenario generation process produces a set of finite traces \(\Gamma=[\pi_{1},\pi_{2},\ldots,\pi_{N}]\). Each generated trace \(\pi_{k}=\langle s^{0},s^{1},\ldots,s^{M}\rangle\) satisfy the hazard LTL formula \(\pi_{k}\vDash\varnothing\) following the common semantics of the \(\vDash_{M}\) operator in the context of LTL logic.  
Each generated trace reveals an executable scenario that leads to a hazard condition, which can be used further to diagnose the design flaws in the SoS models and operational hierarchy. The design flaws can be classified into three categories: (i) single design fault of an individual CS model or the interaction of multiple CSs; (ii) combination of design faults in CSs; (iii) unsafe SoS emergent behavior after CSs' integration.  
It is the responsibility of the ATG tool to generate a meaningful set of scenarios to identify such unsafe emergent behavior, enabling designers to reason about the SoS and how individual CSs impact the safety of other systems.