esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Personalized medicine is a hot topic to develop a medical procedure for healthcare. Motivated by molecular dynamics simulation-based personalized medicine, we propose a novel numerical simulation-based personalized diagnosis methodology and explain the fundamental procedures. As an example, a personalized fault diagnosis method is developed using the finite element method (FEM), wavelet packet transform (WPT) and support vector machine (SVM) to detect faults in a shaft. The shaft unbalance, misalignment, rub-impact and the combination of rub-impact and unbalance are investigated using the present method. The method includes three steps. In the first step, Theil's inequality coefficient (TIC)-based FE model updating technique is employed to determine the boundary conditions, and the fault-induced FE model of the faulty shaft is constructed. Further, the vibration signals of the faulty shaft are obtained using numerical simulation. In the second step, WPT is employed to decompose the vibration signal into several signal components. Specific time-domain feature parameters of all of the signal components are calculated to generate the training samples to train the SVM. Finally, the measured vibration signal and its components decomposed by WPT serve as a test sample to the trained SVM. The fault types are finally determined. In the simulation of a simple shaft, the classification accuracy rates of unbalance, misalignment, rub-impact and the combination of rub-impact and unbalance are 93%, 95%, 89% and 91%, respectively, whereas in the experimental investigations, these decreased to 82%, 87%, 73% and 79%. In order to increase the fault diagnosis precision and general applicability, further works are continuously improving the personalized diagnosis methodology and the corresponding specific methods.  
Keywords:personalized diagnosis; shaft; numerical simulation; wavelet packet transform; support vector machine +
Footnote †: journal: Applied Sciences
###### Abstract
In [19], a heuristic algorithm based on power loss and voltage indices was described for the sizing and placement of D-STATCOM in radial distribution systems. Based on the literature review of the above-mentioned state-of-the-art studies, this study makes the following contributions:  
* Formulation of the mixed-integer nonlinear programming (i.e., MINLP) problem of the optimal location and sizing of D-STATCOMs in distribution networks into a mixed-integer convex optimization model using the branch power flow representation of the grid, which generates a new conic optimization model. * Implementation of a new optimization strategy called the genetic-convex optimizer that works in a master-slave connection, where the master stage is guided by a classical [PERSON] and Beasley genetic algorithm (CBGA) to determine the location of  
Figure 1: Schematic diagram of a distribution static compensator (D-STATCOM). the D-STATCOMs, and the slave stage is entrusted with the optimal sizing of the D-STATCOMs via second-order cone programming (SOCP). * Inclusion in the optimization model of the curves associated with residential, industrial, and commercial loads under an economic multi-period operation environment for radial distribution networks. * Comparison of the proposed master-slave approach with the GAMS optimization package, where the results show that the proposed approach achieves a better reduction of the total operating costs in the test system. The scope of this research is circumscribed to medium-voltage distribution grids with strictly radial configuration, and the convex reformulation of the power flow problem is based on the branch method reported in [20], which works under the radiality assumption. In addition, D-STATCOMs are modeled for optimization purposes as constant reactive power sources since the power electronic interface presented in Figure 1 makes it possible to control the amount of reactive power provided to the electrical distribution system. The rest of this study has the following structure: Section 2 describes the general MINLP model that represents the problem of the optimal siting and sizing of D-STATCOMs in electrical grids, considering a time-varying formulation that minimizes the annual operative costs regarding energy losses added with the annualized investments in D-STATCOMs. Section 3 presents the convex reformulation of the MINLP model that converts it into a mixed-integer second-order cone programming equivalent by using the branch power flow representation of the distribution networks with radial configuration and the main characteristics of the CBGA, to determine the location of the D-STATCOMs using an integer codification. Section 4 describes the distribution test feeder.
## 4 Experiments
### Parameter Analysis  
This section focuses on analyzing the parameters in the model on the Yelp dataset and finding the most suitable numerical settings for the model in order to achieve the optimal experimental results and improve the debiasing ability of the model.  
The more critical parameters are as follows: sentiment weight \(\varepsilon\), discard threshold \(\mu\), and \(\iota\). The final results of the parameter analysis are shown in Figure 5.  
The tuning experiments reveal that the model reaches the optimal equilibrium on the Yelp dataset when \(\varepsilon\) = 0.6. A \(\varepsilon\) value that is too high leads to excessive dominance of sentiment scores over the fusion results, weakening the credibility of the original scores; while \(\varepsilon\) that is too low fails to fully utilize the fine-grained information in the reviews, leading to a decrease in the model's generalization ability under sparse data.  
The settings of \(\mu\) and \(\iota\), on the other hand, directly affect the balance between data quality and integrity. When \(\mu\) = 2.0, the system is able to effectively filter out noisy samples with a rating-sentiment deviation of more than 2 points and avoid over-deleting normal users. The setting of user-level discard threshold \(\iota\) = 0.3 ensures that only when more than 30% of a user's reviews are judged to be of low quality will they be removed as a whole. This strategy retains the majority of users' data while successfully removing about 2% of malicious review-scrubbing accounts, which improves the signal-to-noise ratio of the overall dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **MSE** & **MAE** & **ACC** \\ \hline MF-StaticIPS & 0.1916 & 0.3428 & 0.4437 \\ TMF-StaticIPS & 0.1275 & 0.3124 & 0.5642 \\ MF-DANCER & 0.1629 & 0.2979 & 0.4926 \\ TMF-DANCER & 0.1178 & 0.2974 & 0.5831 \\ CISD & 0.1848 & 0.3326 & 0.5449 \\ \hline SCTD & 0.1109 & 0.2831 & 0.5992 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with different methods on the Jingdong dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **SCTD Mean** & \begin{tabular}{c} **TMF-DANCER** \\ **Mean** \\ \end{tabular} & \
\begin{tabular}{c} **Mean** \\ **Difference** \\ \end{tabular} &
\begin{tabular}{c} **95\%** \\ **Confidence** \\ **Interval** \\ \end{tabular} & \(p\)**-Value** \\ \hline MSE & 0.1032 & 0.1045 & \(-\)0.0013 & \([-\)0.0021, \\ MAE & 0.2527 & 0.2444 & +0.0083 & \([-\)0.0001, \\  & 0.6202 & 0.6151 & +0.0051 & \([+\)0.0089] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical significance test on the Yelp dataset (SCTD vs. TMF-DANCER).  
Figure 5: Parametric analysis of the SCTD model (Yelp).
## 5 Conclusions  
In this paper, we proposed an improved LEACH protocol for cluster-based UASNs where the cluster number is optimized with an underwater energy propagation model to reduce the network's energy consumption, and a transmission scheduling algorithm was also proposed to achieve conflict-free parallel data transmission. Furthermore, the integration of replication computing into the proposed protocol allows the network nodes to autonomously execute a deterministic algorithm that determines their clusters and conflict-free transmission times for each communication round. This approach significantly reduces the energy consumption of the network during signaling interactions, further increasing its lifetime. The proposed protocol was simulated and compared with the existing methods in NS3, and the experimental results proved the viability of the proposed protocol.  
However, this paper focused exclusively on stationary, two-dimensional underwater acoustic sensor networks, leaving the dynamics of mobile nodes and three-dimensional node distribution unexplored. In our focused study, we focused on the path length in acoustic energy transfer, recognizing its significant impact on the efficiency within these specific network parameters. The path length was found to be a critical factor in the effective energy transfer for the considered scenarios. Nevertheless, a comprehensive understanding requires the consideration of additional factors such as medium properties, signal frequency, and environmental disturbances, which, while recognized, were not the primary focus of our current research. Future studies should extend both the proposed clustering algorithm and the parallel transmission scheduling algorithm to scenarios involving mobile nodes and nodes arranged in a three-dimensional space, incorporating these broader aspects for a more thorough exploration of acoustic energy transfer. Additionally, while this paper examined UASNs within a two-layer centralized framework, future research could delve  
Figure 6: Performance of proposed protocol under different node densities. (**a**) Average residual energy; (**b**) average number of surviving nodes; (**c**) the number of packets received by the base station; (**d**) average round when the first death node occurs.  
into more complex network structures, such as distributed or multi-hop configurations, to provide a comprehensive analysis and understanding of these specific network scenarios.  
Conceptualization and methodology, J.Z.; software and validation, K.T. and C.Z.; formal analysis and data curation, K.T., J.Z. and C.Z.; writing--original draft preparation, C.Z. and J.Z.; supervision, J.Z. All authors have read and agreed to the published version of the manuscript.  
The National Natural Science Foundation of China, grant number 61372083.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 5 Performance Evaluation
### Simulation Settings
#### 5.1.3 Comparison Methods  
* Random: The random algorithm stochastically chooses cloudlets to place/traverse VNF instances and routing paths to connect two adjacent VNF instances for every incoming IoT-SR;
* DLG [43]: For each arrived IoT-SR, the delay-least-greedy (DLG) method is commonly employed to select cloudlets and the paths that exhibit the minimum delay. Simultaneously, an enhancement is made to this method by assigning a high cost to cloudlets and links with high loads, thereby preventing their selection;
* DDQN-SP: This algorithm utilizes the DDQN algorithm to select cloudlets and the Dijkstra algorithm to search the routing path for each IoT-SR;
* DLA-DVPRP [2]: This algorithm utilizes the shortest path algorithm based on the Lagrange relaxation method to solve the delay and packet loss aware dynamical VNF placement and routing problem (DLA-DVPRP) for SFC requests. We modify it to adapt to IoT-SR in the dynamic IoT-MEC network.
## 3 Data Processing
### Thermodynamic Investigation  
The study of the ORC installation, based on experimental data, was carried out using a zero-dimensional (0D) model, by solving energy balances on components and the global system, developed with EES software [77]. A coupling was carried out with the REFPROP v9.1 software [78] to allow for the implementation of thermodynamic properties in the mixture and the pure fluid. These properties result from [PERSON]'s model adapted for the considered fluids [79; 80].  
According to the theory of uncertainties propagation, the measurement uncertainties for the calculated output variables of the model were calculated using the root-sum-square method. The uncertainty \(U_{Y}\) of the variable \(Y\) was calculated as a function of the uncertainties \(U_{Xi}\) for each measured variable \(X_{i}\), as defined in Equation (1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Device** & **Range** & **Uncertainty** \\ \hline Electrical power & Wattmeter & 0–3250 W & \(\pm\)0.3\% \\ Volumetric flow rate (heat source) & EFM & 0–3500 L/h & \(\pm\)0.23\% \\ Volumetric flow rate (cooling source) & EFM & 0–2500 L/h & \(\pm\)0.33\% \\ Temperature & Type-K thermocouples & 0–1100 \({}^{\circ}\)C & \(\pm\)0.1 \({}^{\circ}\)C \\ Pressure & APS & 0–7 bar & \(\pm\)1\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measurement devices’ characteristics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**T\({}_{\text{in,hf,evap}}\)** & **T\({}_{\text{in,cf,cond}}\)** & **q\({}_{\text{v,hf}}\)** & **q\({}_{\text{v,cf}}\)** & **\(\dot{\text{\text{\bf i}}}_{\text{\bf wf}}\)** \\ \hline (\({}^{\circ}\)C) & (\({}^{\circ}\)C) & (L/h) & (L/h) & (kg/s) \\
110 & 13.5 & 300–3500 & 250–2500 & 0.055–0.069 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main parameter settings.  
\[U_{Y}=\sqrt{\sum_{i}\left(\frac{\partial\gamma}{\partial X_{i}}\right)^{2}\cdot{U_ {X_{i}}}^{2}} \tag{1}\]
## 3 Turbo Equalization Concept
### The Proposed System Model
In contrast to the MAP algorithm used by the SISO decoder from the classic turbo equalization scheme, which requires a large memory as well as many multiplication and exponentiation operations [51], the min-sum algorithm used by the LDPC decoder of our proposed system simplifies the computational complexity by using simple arithmetic operations according to [50].
## 2 Materials and Methods
### Hyper-Parameter Optimization
#### 2.3.2 Bayesian Search  
Bayesian Optimization (BO), more commonly known as Bayesian Search (BS), builds a surrogate model for optimizing the models [25]. Gaussian Process (GP) is commonly used because of its flexibility and strength. BO uses a surrogate model to approximate the objective function based on the observed data points [29]. Unlike GS and RS, BO uses an iteration approach to evaluate previously obtained results for future evaluation. This is carried out with an acquisition function that determines the next parameter to be evaluated. The Bayesian Optimization procedure is as follows:  
1. Sampling the objective function at a few initial points.
2. Using the initial samples to build a surrogate model of the objective function.
3. Defining an acquisition function that uses the surrogate model to determine the next point to evaluate.
4. Evaluating the objective function at the point suggested by the acquisition function.
5. Updating the surrogate model with the new data point.
6. Repeating steps 3-5 until a stopping criterion is met.  
Figure 2 below visualizes how Bayesian Optimization works through two main components: a surrogate model and an acquisition function.  
Figure 1: Grid Search and Random Search.
# Nanovised Control Flow Attestation
## 5 Control Flow Attestation for Linux
### The Hyplet Nanovisor
#### 5.3.2 The Hyplet Security  
As noted, VHE hardware is not available at the time of this writing, and, as such, we are forced to use software measures to protect the hypervisor. On older ARM boards, it can be argued that a security bug at hypervisor privilege levels may cause greater damages compared to a bug at the user process or kernel levels, thus poising system risk.  
The hyplet also escalates privilege levels, from exception level 0 (user mode) or 1 (OS mode) to exception level 2 (hypervisor mode). Since the hyplet executes in EL2, it has access to EL2 and EL1 special registers. For example, the hyplet has access to the level 1 exception vector. Therefore, it can be argued that the hyplet comes with security costs on processors that do not include ARM VHE.  
The hyplet uses multiple exception levels and escalates privilege levels. So, it can be argued that using hyplets may damage application security. Against this claim, we have the following arguments.  
We claim that this risk is superficial and acceptable, for processors without VHE support. Most embedded systems and mobile phones do not include a hypervisor and do not run multiple operating system.  
In the case where no hypervisor is installed, code in EL1 (OS) has complete control of the machine. It does not have a lesser access code running in EL2, since no EL2 hypervisor is present. Likewise, code running in EL2 can affect all operating systems running under the hypervisor. Code running in EL1 can only affect the current operating system. When only one OS is running, the two are identical.  
Therefore, from the machine standpoint, code running in EL1 when EL2 is not present has similar access privileges to code running in EL2 with only one OS running, as in the hyplet use case.  
The hyplet changes the system from a system that includes only EL0 and EL1 to a system that includes EL0, EL1, and EL2. The hyplet system moves a code that was running on EL1 without a hypervisor to EL2 with only one OS. Many real-time implementations move user code from EL0 to EL1. The hyplet moves it to EL2; however, this gains no extra permissions; running rogue code in EL1 with no EL2 is just as dangerous as moving code to EL2 within the hyplet system. Additionally, it is expected that the hyplet would be a signed code; otherwise, the hypervisor would not execute it.  
The hypervisor can maintain a key to verify the signature and ensure that the lower privilege level code cannot access the key.  
Furthermore, real-time systems may eliminate even user and kernel mode separation for minor performance gains. We argue that escalating privileges for real performance and real-time capabilities is acceptable on older hardware without VHE where hyplets might consist of a security risk. On current ARM architecture with VHE support, the hyplet do not add extra risk.
## 6 Network Setup
### Principle of 3D-Ocdma-Pon
Additionally, each unit contains the components specified for spectral and temporal encrypting techniques, which are FBGs and delay lines, respectively. Thus, the upstream data generated from the OLT are divided by the star couplers (SCs), and thereafter they are accumulated by combiners to ensure the delivery of the summed signals to the ONU over SMFs. Figure 14: 3D-SWZCC/OCDMA-PON architecture. The combiner works to convey the combined signals of different users over an SMF. For this section of the ONU, a power splitter is connected to an FBG, which in turn is connected to a delay line. The connection between the OLT and ONU sections can be achieved with the assistance of an SMF. The modulated pulses are handed over to the spectral encoder from the MZM; in our proposed network, we utilize a single set of FBGs, since the code weight is always equal to one. The FBG reflects a coincided wavelength to 1s of the \(X_{g}\) code word. The spectrally encrypted signals are re-conveyed over a specified temporal moment with the help of delay lines. Finally, for spatial encrypting, the SCs accumulate between two different incoming data by two users who previously encrypted spectrally and timing and re-convey them into the combiner after passing them over optical switches. After the data are received, the combined signals are divided and decrypted spectrally using the FBG and then temporally using the delay line. A single PD is placed in order to fulfill two targets: the first for the spectral direct detection (SDD) technique and the second for returning the data to its original nature (electrical signal). Finally, a low-pass Bessel filter and BER analyzer complete the remaining components for each ONU. Table 5 elucidates how to encode and decode in three dimensions, spectral, temporal, and spatial, when there are eight users and an appropriate code word for each of them. Let us take the third user as an example: it needs an encoder or, in other words, one filter, for (\(\lambda_{1}\)) spectral encoding. Later, a filter should be linked to the second delay line (\(t_{2}\)). Finally, the delay line should be linked to the first SC (\(C_{1}\)). Using these links, the three-dimensional encoding is obtained; it is shown in more detail in Figure 15 (system schemes). The three-dimensional decoding operation is the opposite of the encoding operation.
## 3 Modeling Framework
### Delivery Mission Scenarios  
To assess how real-world constraints impact UAV energy consumption, we define four mission scenarios of increasing complexity. Each scenario corresponds to a modification of one or more parameters in the physical model described in Section 3.2. Table 1 summarizes the characteristics of each configuration.  
* **Scenario 1--Ideal Mission:** Baseline configuration with constant speed, no external disturbances, and maximum time available for delivery. The UAV consumes energy based solely on the physical flight profile (Equation (6)), without any additional constraints or corrections.
* **Scenario 2--Delivery Time Constraint:** A delivery time is enforced based on the bus's travel time between two stops. The UAV must complete its delivery and return to the bus within this fixed time window, potentially requiring an increase in horizontal speed \(v_{h}\) on the return phase. This leads to increased drag and power, hence higher energy consumption.
* **Scenario 3--Aerodynamic Disturbance:** This scenario includes thrust variations due to proximity effects, such as urban obstacles and bus motion. Correction factors  
Figure 2: Delivery Profile.  
derived from CFD simulations (Section 3.4) are applied to the thrust computation, altering \(F_{T}\) and thereby modifying \(P\) and \(E\).
* **Scenario 4--Early Return Strategy:** In order to avoid impacting the bus schedule, the UAV is required to return before the bus reaches the next stop. This constraint reduces the available time \(t_{h}\) for delivery and return, requiring higher cruise speed, which further increases power and energy consumption.  
This scenario-based structure enables a controlled and progressive evaluation of energy overconsumption factors. Each configuration is simulated with varying payloads, as detailed in Section 4.2, to assess the system's sensitivity to operational constraints.