esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 IoT Service Orchestration via NDN
### Centralized Mechanisms  
In Reference [42], a centralized solution for service orchestration in NFN is provided that is called Computation Service Management (CS-Man). The authors consider a static IoT network where a service manager assigns tasks to other nodes according to their capabilities, as recorded in a database called Service Repository. Despite the advantage of leveraging a centralized knowledge of the nodes' capabilities, in such a design, the service manager represents a single point of failure and a potential bottleneck for the provisioning of services, as it is queried at every service request.  
In References [44; 45], a 3-level framework called ICN-isapiens is presented, where a fog layer, consisting of smart home servers (HSs), is introduced between the physical world and the remote cloud to support real-time services and to hide the heterogeneity of IoT devices. In ICN-isapiens, HSs are installed in houses and provided with a multi-agent software application to manage predefined low-latency monitoring and actuation services. Long-term storage and complex analysis are, instead, performed in the cloud. Each HS leverages NDN primitives to control a predefined set of IoT devices, while communication with the cloud is performed via IP. The application developers define the association between HS, IoT devices, and agents. Then, a system component called deployer is in charge to load the agents in the designed HSs and to start the system. As a result, ICN-isapiens depend on a centralized component that assigns the agents and, therefore, the services to specific nodes and in a static fashion. Hence, it may lack the flexibility required to properly manage a dynamic fog environment.
# Enhancement of Yaw Moment Control for Drivers with Excessive Steering in Emergency Lane Changes
## 4 Yaw Moment Control Design  
### Yaw Moment Limit and Wheel Torque Distribution  
During emergency lane changes, vehicles under safety control often engage in combined cornering, traction, and braking maneuvers, which result in significant coupling between lateral and longitudinal wheel slips and friction forces. By considering combined slip, we can effectively account for these coupling effects and ensure that the resulting friction forces remain within the friction circle (see [27; 28; 29] and references therein). In this section, our focus is on addressing the combined slip effect and specifically on generating a constrained yaw moment as outlined in (32) and (2), while adhering to restricted combined wheel slip. This restriction is crucial as it prevents the generation of excessive longitudinal friction forces, which could otherwise reduce the necessary cornering forces during emergency lane changes and potentially compromise stability and safety. Let \(\mathbf{S}=\left[s_{L},s_{S}\right]^{T}\) represent the combined wheel slip and \(\left\|\mathbf{S}\right\|=\sqrt{s_{L}^{2}+s_{S}^{2}}\) stand for its magnitude, where \(s_{L}\) and \(s_{S}\) denote the longitudinal and lateral wheel slips, respectively. When operating in the linear region of tire/road friction, the traction/brake force of each wheel can be expressed as  
\[\frac{T_{j}}{r_{ej}}=f_{xj}=f_{zj}k_{j}s_{Lj},~{}~{}j=1,2,\ldots,4 \tag{38}\]  
where \(T_{j}\) and \(r_{ej}\) are the wheel torque and effective radius, respectively; \(k_{j}\) and \(s_{Lj}\) are the slope of the tire/road friction coefficient and longitudinal wheel slip in that order; and notation \(f_{zj}\) is the normal load of each wheel estimated by (see [29] and references therein)  
\[f_{z1} =\frac{1}{2}\frac{l_{r}}{l_{f}+l_{r}}mg-\frac{1}{2}\frac{h}{l_{f}+l_{r}}ma _{x}-\frac{h}{2l_{d}}\frac{k_{f\phi}}{k_{f\phi}+k_{r\phi}}ma_{y} \tag{39a}\] \[f_{z2} =\frac{1}{2}\frac{l_{r}}{l_{f}+l_{r}}mg-\frac{1}{2}\frac{h}{l_{f}+l_{r}}ma _{x}+\frac{h}{2l_{d}}\frac{k_{f\phi}}{k_{f\phi}+k_{r\phi}}ma_{y} \tag{39b}\]\[f_{z3} =\frac{1}{2}\frac{l_{f}}{l_{f}+l_{r}}mg+\frac{1}{2}\frac{h}{l_{f}+l_{r} }ma_{x}-\frac{h}{2l_{d}}\frac{k_{r\phi}}{k_{f\phi}+k_{r\phi}}ma_{y} \tag{39c}\] \[f_{z4} =\frac{1}{2}\frac{l_{f}}{l_{f}+l_{r}}mg+\frac{1}{2}\frac{h}{l_{f}+l_{r}}ma_{x} +\frac{h}{2l_{d}}\frac{k_{r\phi}}{k_{f\phi}+k_{r\phi}}ma_{y} \tag{39d}\]  
In (39), notations \(a_{x}\) and \(a_{y}\) are the longitudinal and lateral accelerations and \(k_{f\phi}\) and \(k_{r\phi}\) are the front and the rear roll stiffness, respectively. This study assumes, in emergency situations, the proposed wheel torque control engages all wheels with the same amount of longitudinal wheel slip, i.e., \(|s_{L}|=\big{|}s_{L}\big{|}\), \(j=1,2,\ldots,4\), and the tire/road friction characteristics and the effective radius are the same for the four wheels, i.e., \(k=k_{j}\), \(r_{e}=r_{ej}\), \(j=1,2,\ldots,4\).
# Structural Damage Detection Based on Real-Time Vibration Signal and Convolutional Neural Network
## 2 Methods  
In this paper, the acceleration that was obtained from measuring the steel frame model and used as the CNN input to detect structural damages. The CNN could automatically extract damage features from these signals without analyzing the indicators like traditional detection methods. The overview of the proposed method was organized as follows: (1) got the structural response data through the FEA and vibration experiments; (2) trained the CNN by using the samples obtained from the FEA; (3) tested the trained CNN using single damage, multiple damages, and combined datasets.  
This paper used a steel frame beam as the research object (Figure 1a). The steel frame had a length, width, and height of 9.912 m, 0.354 m, and 0.354 m, respectively. The steel frame consisted of 355 rods; each rod had a hollow circular cross section with an external radius of 0.005 m and thickness of 0.002 m. The two ends of the steel frame were fixed. Damages were introduced in 9 rods (numbered 1 to 9 in Figure 1b) in the steel frame. The response signals of the 13 measurement points (labelled as A1 to A13) on the bottom chords were used as the inputs of the CNN samples. The 4 excitation points (F1-F4) were on the top chords (Figure 1b). In this paper, the acquisition frequency of the response signals in the numerical simulations and vibration experiments was 100 Hz, and the collection time was 8 s for each excitation.
# An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems
## 4 Proposed Trust Model
### Direct Trust
In [24], they believe that weight is related to interactive times and frequency. [PERSON] et al. hold that \(\mu_{ij}(t)\) should be dependent on the accumulated deviation [32]. From our perspective, we suppose that \(\mu_{ij}(t)\) should mainly be determined by the current performance deviation. That is to say, we pay much attention to its previous performance if agents act stably. If agents perform differently with their actual property frequently to hide their performance, the evaluation should play a more important role in the final estimation. Usually, \(\mu_{ij}(t)\) is close to 0, for instance, \(\mu_{ij}(t)=0.1\), indicating that most agents consistently react, which is rational and intuitive in practical life. In this paper, we define \(\mu_{ij}(t)\) as follows:  
\[\mu_{ij}(t)=\frac{exp(1)}{exp(|S_{ij}(t)-Scur_{ij}(t)|-1)+exp(1)} \tag{10}\]  
Much uncertainty is involved when generating the evidence due to incomplete information, the interactive frequency, and randomness. A frequency factor \(fq_{ij}(t)\) is defined to show the uncertainty of \(c_{i}^{\prime}s\) direct experience in the last period, and it is defined as  
\[fq_{ij}(t)=1-[\frac{2*\lambda}{\pi}*atan(\kappa_{(i,j)})]^{2} \tag{11}\]  
where \(\kappa_{(i,j)}\) shows the amount of interaction between \(c_{i}\) and \(s_{j}\) in the last period considered. The value of \(\lambda\) is influenced by the interaction type and the willingness to trust others; it is usually close to 1. As displayed in Equation (11), uncertainty caused by the amount of interaction, incompleteness, and randomness is described, and the uncertainty decreases to 0.2 when two agents have at least eight interactions. As a result, the generated BPAs can be represented as follows:  
\[\left\{ \begin{array}{ll}m_{ij}^{d}(T)=S_{ij}(t)*fq_{ij}(t)\\ m_{ij}^{d}(nT)=(1-S_{ij}(t))*fq_{ij}(t)\end{array} \right. \tag{12}\]  
Then \(m_{ij}^{d}(T,nT)=1-(m_{ij}^{d}(T)+m_{ij}^{d}(nT))\), the direct experience can also be rewritten as \(bpa_{ij}^{d}=(m_{ij}^{d}(T)\), \(m_{ij}^{d}(nT)\), \(m_{ij}^{d}(T,nT))\).
# Optical Cable Lifespan Prediction Method Based on Autoformer
## 3 Related Work
### Optical Cable Length Calculation Method  
Throughout the service life of optical cables, changes in environmental temperature and varying loads prompt stress, deformation, and fluctuations in cable length [14; 15]. This situation arises from two principal causes:  
1. Temperature-Induced Length Alterations: Changes in ambient temperature prompt thermal expansion or contraction in the optical cable materials, thereby inducing fluctuations in their length.
2. Load-Related Deformation: Variations in stress or load can lead to deformation in the optical cable. While the optical fibers themselves primarily exhibit elastic behavior, other components may experience different types of deformation, consequently leading to corresponding alterations in length.  
Assuming the optical cable transitions from state \(m\) (temperature \(t_{m}\), loading \(g_{m}\), and stress \(\sigma_{m}\)) to state \(n\) (temperature \(t_{n}\), loading \(g_{n}\), and stress \(\sigma_{n}\)), the length changes due to thermal variations and stress variations can be expressed by Equations (2) and (3), respectively, where \(\alpha\) and \(E\) represent the thermal expansion coefficient and elastic modulus of the optical cable sheath, respectively.  
\[L_{t}=[1+\alpha(t_{n}-t_{m})]L_{m}, \tag{2}\]  
\[L_{n}=\bigg{[}1+\frac{1}{E}(\sigma_{n}-\sigma_{m})\bigg{]}L_{t}, \tag{3}\]  
Substituting (2) into (3) yields the following:  
\[L_{n}=L_{m}[1+\alpha(t_{n}-t_{m})]\bigg{[}1+\frac{1}{E}(\sigma_{n}-\sigma_{m} )\bigg{]}, \tag{4}\]  
Further expanding (4) results in the term \(\big{[}\frac{\alpha}{E}(t_{n}-t_{n})(\sigma_{n}-\sigma_{m})\big{]}\), which due to its minute magnitude, can be disregarded within an acceptable margin of error, simplifying (4) to  
\[L_{n}=L_{m}\bigg{[}1+\alpha(t_{n}-t_{m})+\frac{1}{E}(\sigma_{n}-\sigma_{m}) \bigg{]}, \tag{5}\]  
It is important to note that this calculation simplifies the thermal expansion of the optical cable by using a single value (\(\alpha\)) for the thermal expansion coefficient. In reality, different components of the cable, such as the optical fibers, buffer tubes, and sheath, may have different thermal expansion coefficients. However, for the scope of this study, we simplified this aspect by using the thermal expansion coefficient of the sheath as a representative value. This simplification is common in preliminary cable length change calculations as the sheath's properties often dominate the overall thermal behavior. Future work could explore incorporating the individual thermal expansion coefficients of different cable components for a more precise calculation.
# Natural Computing Applied to the Underground System: A Synergistic Approach for Smart Cities
## 5 Algorithms Involved
### Fireworks Algorithm
These systems reduce storage and requirements at runtime, as well as the noise regarding data representation when it comes to essential associations. The Non-negative Matrix Factorization, whose parameters are widely explained at (NMF, [36]) arrives to a low-rank approximation that verifies that non-negativity constraints are satisfied. NMF approximates a data matrix by where and are the NMF factors. NMF requires all entries in, and to be zero or positive. The following figure shows the FWA-based optimization algorithm pseudo code for NMF--see Algorithm 2-, showing explicitly the high level of configurability associated with FWA, where SIO stands for Swarm Intelligence Optimization. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Function**} & **FA’s Mean** & **CPSO’s Mean** & **SPSO’s Mean** \\  & **(StD)** & **(StD)** & **(StD)** \\ \hline \multirow{2}{*}{Sphere} & 0.000000 & 11,857.425781 & 24,919.099609 \\  & (0.000000) & (3305.973067) & (3383.241523) \\ \hline \multirow{2}{*}{Rosenbrock} & 19.38330 & 2,750,997,504.000000 & 5,571,942,400.000000 \\  & (11.94373) & (1,741,747,548.420642) & (960,421,617.568024) \\ \hline \multirow{2}{*}{Rastrigin} & 0.000000 & 10,940.148438 & 24,013.001953 \\  & (0.000000) & (3663.484331) & (4246.961530) \\ \hline \multirow{2}{*}{Griewank} & 0.000000 & 3.457273 & 7.125976 \\  & (0.000000) & (0.911027) & (0.965788) \\ \hline \multirow{2}{*}{Ellipse} & 0.000000 & 2,493,945.500000 & 5,305,106.500000 \\  & (0.000000) & (1,199,024.648305) & (1,117,954.409340) \\ \hline \multirow{2}{*}{Cigar} & 0.000000 & 122,527,168.000000 & 149,600,864.000000 \\  & (0.000000) & (28,596,381.089661) & (13,093,322.778560) \\ \hline \multirow{2}{*}{Tablet} & 0.000000 & 15,595.107422 & 42,547.488281 \\  & (0.000000) & (8086.792234) & (8232.221882) \\ \hline \multirow{2}{*}{Schwefel} & 4.353733 & 8,775,860.000000 & 6,743,699.000000 \\  & (1.479332) & (1,217,609.288290) & (597,770.084232) \\ \hline \multirow{2}{*}{Ackley} & 0.000000 & 15.907665 & 18.423347 \\  & (0.000000) & (1.196082) & (0.503372) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical mean and standard deviation of solutions found by the FA, the CPSO and the SPSO on nine benchmark functions over 20 independent runs of 10000 function evaluations [34]. Copyright © IGI Global 1988-2018. ```
Given matrix \(A\in R^{m\times n}\) \(H_{0}=rand(k,n)\) % Compute in parallel for\(i=1\) to \(m\)do  Use SIO to find \(w_{i}^{r}\) that minimizes \(||a_{i}^{r}-w_{i}^{r}H_{0}||_{F}\), \((min||.||_{F}\) of row \(i\) of \(D\)) endfor % Gather \(W=[w_{1}^{r},\cdots,w_{m}^{r}]\) % Compute in parallel for\(j=1\) to \(m\)do  Use SIO to find \(h_{j}^{c}\) that minimizes \(||a_{j}^{c}-Wlr_{j}^{c}||_{F}\), \((min||.||_{F}\) of col \(j\) of \(D\)) endfor % Gather \(H=[h_{1}^{c},\cdots,h_{n}^{c}]\)
```  
**Algorithm 2**Pseudo code for the initialization procedure for NMF factors \(W\) and \(H\). The two for-loops in lines 4 and 10 can be executed concurrently [33].
## 3 Materials and Methods
### Flux Tower Measurements  
Each site has two flux towers. The first tower continuously monitored meteorological data. The second monitored only ecological properties. Meteorological data, including air temperature, precipitation, global solar radiation, humidity, wind speed, and soil temperature were collected every 30 min. The 3-PG model requires data on air temperature, precipitation, and solar radiation. Figure 1 shows monthly meteorological data, which were used as input parameters, for Hitsujigaoka and Takayama from 2001-2006. Vapor pressure deficit was calculated based on maximum and minimum temperatures. Days when the temperature was below 2 \({}^{\circ}\)C were counted as frost days. CO\({}_{2}\) flux was measured by the eddy covariance method [21]. These data were collected in 30-min increments and used to validate the results. CO\({}_{2}\) flux was used to estimate net ecosystem exchange (NEE). Then, GPP was estimated by NEE and ecosystem respiration. Canopy respiration was not observed in the study, therefore NPP is not available.
# Situational Awareness Classification Based on EEG Signals and Spiking Neural Network
## 4 Experimental Results
### Simulation Results
Applying only the three dominant sub-bands (delta, theta, and alpha) for mental classification resulted in 94.1% accuracy compared with 96.8% with all five available sub-bands while reducing the number of required neurons by about 9%. Using all of the frequency sub-bands with only five EEG electrodes resulted in a slight accuracy loss of 1.9% but saved about 50% of the required neurons.
# Resource Profiling and Performance Modeling for Distributed Scientific Computing Environments
## 1 Introduction
Our evaluation results demonstrate that the proposed prediction schemes outperform well-known common approaches in terms of accuracy, and actually can help users in a shared resource environment to run their large-scale HTC/MTC applications by effectively forecasting various computing resource capacity and performance. To summarize, the contributions of our paper can be as follows:  
* Design of novel resource profiling and prediction models including **Adaptive Filter-based Online Linear Regression (AFOLR)** and **Adaptive Filter-based Moving Average (AFMV)** by effectively employing linear combination of past predicted values and recent profiled data
* Implementation of our proposed schemes on top of SCOUT system which can periodically profile and manage information of distributed scientific computing environments
* Application of the proposed scheme and policies to a _real_ large-scale international and interdisciplinary computing environment for running many-tasks
* Comprehensive evaluation results of AFOLR and AFMV models including quantitative analysis and microbenchmark experiments for Many-Task Computing (MTC) workloads  
The rest of this paper is structured as follows. In Section 2, we briefly describe motivation & background of this study, and present some of related research work. Section 3 presents our resource profiling and forecasting system along with various prediction methodologies including proposed adaptive profiling models. Section 4 presents comprehensive evaluation results of our proposed schemes and Section 5 summarizes and throws light on areas for further investigation. Finally, Section 6 highlights our conclusions and future work directions.
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 6 The SDN/NFV Domain  
The need to deploy different services, each with their quality requirements on the same physical/logical infrastructure has fueled the introduction of concepts such as \"Network slicing\". It basically involves splitting the physical and logical resources of the network to  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Prototype** & **Consensus Algorithm** & **Chain size** & **Number of prefixes** & **Throughput** & **Nodes** \\ \hline Public v1 & PoS—centralised random beacon & 1 GB & 150k & 6 tps & 9 \\ \hline Public v2 & PoS—decentralised random number generation & 2.5 GB & 350k & 10 tps & 2 \\ \hline Private & Hyperledger endorse-all and SOLO ordering & 40 MB & 5k & \(\sim\)625 tps & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the different blockchain prototypes and experiment results. _tps_ stands for transactions per second.  
isolate the resources used to support a specific service, while guaranteeing the provision of said services. In this sense, the progress of novel technologies such as SDN and NFV has opened the possibility of providing such services. Indeed, the underlying physical network of an infrastructure operator/owner can now be abstracted, combining the resulting elements into complete and self-contained virtual infrastructures (slices).  
In this section, we discuss the investigations carried out in ALLIANCE related to SDN/NFV-controlled optical networks. In this domain, we have considered both the packet and the optical layers, meaning that we have proposed an overall solution where an SDN/NFV orchestrator controls an ultra-low-latency and ultra-high-capacity spatially and spectrally flexible all-optical network infrastructure. In particular, our research activities to improve the optical layer are first introduced in Sections 6.1 and 6.2, with special emphasis on advanced modulation formats for both access and transport networks. Therefore, we present the proposed control layer based on an SDN/NFV orchestrator enhanced with our KDN vision and fed with the data collected by the monitoring element in Section 6.3. The overall solution enables the automatic and autonomous deliver of network slices customised to the requirements of the vertical services and applications while optimising the use of both the IT and the optical layer resources.
## 2 The Baseline ECU Design and Numerical Models
### Simulation Tests Performed  
As previously explained in Section 2.3, the Beta model charging and discharging models were split due to computational restrictions, particularly within the charging model. Since a full charging cycle from 80 to 200 bar was not possible due to the high computational resources required, the charging cycle was divided into six 10 bar increments across the pressure range between 80 and 190 bar. Tables 4 and 5 summarise all the simulation runs  
Figure 7: A mathematical model of the [PERSON] turbine system in Simulink®, focusing on the PCS and Needle Valve SimscapeTM model.  
performed using the Alpha and Beta models. The runs were selected based on input powers which were within the operating capabilities of the pump, in accordance with Figure 2 in Section 2.1. For example, charging the system at 1.5 MW at a pressure of 160 bar would not be possible according to the pump curves, even when applying variable speeds, since the pump reaching such high pressures would require greater speeds as well as a greater power input. Similarly, if a power of 5.0 MW were to be inputted to charge the system when the PCS is at a pressure of 80 bar, the flowrate limit would be reached, thus also reaching a pump power output limit and not satisfying the requirement of charging the system at the desired 5.0 MW. The Root Mean Square Error (RMSE) and Normalised RMSE (RMSE\({}_{N}\)) were used as the statistical tools to evaluate the accuracy in results obtained between the Alpha and Beta models. Equations (17) and (18) present the equations applied for the different RMSE evaluations.  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(X_{Alpha}-X_{Beta})^{2}}{n}} \tag{17}\]  
\[RMSE_{N}=\frac{RMSE}{(X_{Max}-X_{Min})} \tag{18}\]  
where \(n\) is the number of observations, \(X_{Alpha}\) and \(X_{Beta}\) are the Alpha and Beta model recorded values and \(X_{Max}\) and \(X_{Min}\) are the maximum and minimum recorded values of the dataset being analysed.
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 3 Faults in WSNs  
As discussed in the previous section, the scenario in this paper includes six faults [1; 8]. These faults lie in the data-centric perspective of WSNs, while calibration fault and battery failure are grouped into the system-centric perspective. Since WSNs are deployed in harsh and hostile circumstances, e.g., thunderstorm, snowstorm, rain, etc., they are likely to have frequent and unexpected errors. The occurrence of faults [2] during normal operation can result in harsh consequences involving the loss of human life, as well as economic and environmental losses. To prevent these calamitous situations, faults are categorized into multiple classes. The core reason to categorize faults is to detect them at an early stage so that appropriate measures can be taken to resolve them or to opt for an alternative solution to prevent any critical situation. A description of fault categories is outlined below according to the collected data.  
The gathered data are modeled by the triplet _d(n, t, f(t))_; where _f(t)_ represents the sensed value at time \(t\) of node \(n\). This equation is represented in Equation (1):  
\[f(t)=\alpha+\beta x+\eta \tag{1}\]  
In the above equation, sensed data \(f(t)\) are modeled as \(\alpha\), which is offset, \(\beta\) represents gain, while \(\eta\) is the noise present in the data [1].