esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Evaluation and Discussion
### Case Study
#### 7.3.2 Effectiveness of Anti-Debug Instructions  
Assuming that Tamper-Proofing Instructions prevent the program itself from modifying, we applied Anti-Debug Instructions to guarantee that the execution environment of the program  
Figure 11: Sample of tamper-proofing instructions.  
is harmless. As described in Section 6, we designed seven anti-debug security instructions. That is to say, we designed seven different Handlers to achieve the goal of all these anti-debug instructions.  
Experimentally, _VMGuards_ randomly chooses the Execution_Time_Difference monitor Handler to detect whether the program is being debugged during protection procedure, as we can see from Figure 12.  
Processing Handler will be chosen to respond to the monitored debugging behavior, seen from Figure 13. The MessageBox Handler was chosen to respond to the Execution_Time_Difference Handler.  
In every execution, the program will randomly choose one of the five monitor ADI Handlers to detect--for example, whether the program is under attack by the debugger or the difference in execution time is very different. If the chosen executed monitor ADI happens to be detected, then the program will randomly choose one of the two processing ADIs to alert users that the program is being debugged with a MessageBox or just terminates the program roughly.  
Figure 14 shows 100 times the execution results of the protected program. As mentioned before, which monitor will be executed by the Handler and Processing Handlers depends on the random number. As a result, the statistical results are not an inexorable law to follow.  
Figure 12: Sample of Monitoring ADHandler.  
Figure 13: Sample of Processing ADHandler.
# Green Public Procurement for Accelerating the Transition towards Sustainable Freight Transport
## 2 Literature Review
### Green Vehicles in Freight  
When choosing a vehicle, transporters consider criteria such as the size of the vehicle, its capacity, the range, fuel usage, etc. such that they match with their requirements. With the new technological advances, use of battery electric vehicles is supported as a measure to decarbonise freight transport, especially in urban areas in which transport is characterised by short distances, many stops and low driving speed [33].  
Studies show that despite the high investment cost of battery electric freight vehicles, they are cost competitive with conventional diesel vehicles [34]. However, in some cases, transporters need to implement measures such as reducing the scope of service, mixing the fleet with conventional vehicles, using opportunity charging, etc., to increase the profitability of EVs as a compensation to high investment costs [35; 36]. Moreover, external factors such as the availability of charging stations on public spaces [37] or exemption from paying road-toll or parking fees [38] are also recognised as influential factors in promoting the use of BEVs in urban freight transport.  
Norway has been successful in introducing policies for boosting the uptake of EVs in passenger transport. However, the incentives such as financial support schemes administered by the public agency Enova are less comprehensive for the BEVs in freight. Exemption from value added tax and discounts for registration taxes are already offered for fossil fuel vehicles [39] and support for purchasing the vehicles of establishing charging infrastructure covers a subtle part of the investment costs [40].  
Considering the limited options to offer as financial support for freight transporters in Norway, the role of Green Public Procurement is highlighted as a tool to accelerate the transition towards more sustainable freight transport. However, the barriers for effectively using the tool to promote zero emission vehicles into freight system is not well studied in the literature.  
This paper aims to fill this research gap by investigating the potentials of green public procurement from both procurers and transporters points of view. It highlights the important factors to succeed with the GPP as a tool for transitioning towards more environmentally sustainable freight transport and how it is perceived both by public officials and transporters by providing empirical data.
## 2 Materials and Methods
### Experimental Configuration  
A triaxial testing apparatus was utilized in this experiment, as shown in Figure 2. The maximum axial load of the test system is 3000 kN and the maximum confining pressure is 200 MPa with a triaxial cell. The pore systems have a capacity of 200 MPa, and the temperature can reach 200 \({}^{\circ}\)C, which meets the experimental requirements of injection-driven shear tests under triaxial conditions in this study. The axial stress \(\sigma_{1}\) (MPa) and confining pressure \(\sigma_{3}\) (MPa) can be monitored and obtained in real time within the GCTS. The deformation of a sample under stress is shown in Figure 2b. The normal stress \(\sigma_{n}\) (MPa), effective normal stress \(\sigma_{n}\)/ (MPa), and shear stress \(\tau\) (MPa) on the fault are as follows:  
\[\sigma_{n}=\sigma_{3}+(\sigma_{1}-\sigma_{3})sin^{2}\psi \tag{2}\]  
\[\sigma_{n}t=(\sigma_{3}-p)+(\sigma_{1}-\sigma_{3})sin^{2}\psi \tag{3}\]  
\[\tau=(\sigma_{1}-\sigma_{3})\sin\psi cos\psi \tag{4}\]  
where \(\sigma_{1}\) (MPa) and \(\sigma_{3}\) (MPa) are the axial stress and confining pressure, respectively; \(p\) (MPa) is injection fluid pressure; and \(\psi\) (\({}^{\circ}\)) is the fault inclination angle with respect to the core axis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Density (g/cm\({}^{3}\))** & **Young’s Modulus (GPa)** & **Poisson’s Ratio** & **UCS (MPa)** \\ \hline
2.80 & 92.85 & 0.26 & 152.84 \\ \hline \hline \end{tabular} UCS represents the uniaxial compressive strength.  
\end{table}
Table 1: Average measurements of physical and mechanical properties of granite.  
Figure 1: (**a**) A sketch of the granite sample size design. (**b**) A well-prepared granite sample.  
In this experiment, the axial deformation and radial deformation can be measured using two linear variable differential transformer (LVDT) position sensors, as shown in Figure 2a. Thus, slip distance \(d_{s}\) (\(\mu\)m) and slip rate \(v_{s}\) (\(\mu\)m/s) along the fault can be calculated as follows:  
\[d_{S}=\Delta\,zcos\psi+\Delta\,xsin\sin\psi \tag{5}\]  
\[v_{s}=\frac{d_{s}}{t} \tag{6}\]  
where \(\Delta z\) (\(\mu\)m) is the axial deformation of the sample minus the compression deformation of the rock matrix, \(\Delta x\) (\(\mu\)m) is the total radial deformation of the sample minus rock matrix, and \(t\) (s) represents time.
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 3 System Model
### Two Nodes Reliable Model (2n-Rm)
#### 3.2.2 Expected Transmission Count  
Another important factor to measure link reliability is ETX. It is a probability of successful packet delivery ratio and its acknowledgment over a link. ETX is computed through equation that is given below:  
\[ETX=\frac{1}{Sr*Ar} \tag{23}\]  
\(Sr\) and \(Ar\) represent the packet sending ratio and packet acknowledgment ratioge limit is set to 70 m and computes Euclidean distance between source to, respectively. ETX depends upon the delivery ratio, which directly relates to the throughput of network. It also determines the path loss ratio and selects the optimal route for the packet.
# An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering +
## 2 Related Work
### Multiobjective Clustering  
MOEA is popularly applied in data clustering. [PERSON] et al. discussed extensively MOEA clustering as well as the application of metaheuristics, such as differential evolution, particle swarm optimization and genetic programming, for data clustering. In [21], [PERSON] and [PERSON] developed a multiobjective particle swarm optimization algorithm for automatic data clustering, using connectivity and cohesion objective functions and a locus-based adjacency genetic scheme. They reported their results using ARI, but ARI was not used in this study, making it not comparable. [PERSON] et al. [22] proposed two ensemble MOEA clustering algorithms, NSGA-II-ECFE and MOEA/D-ECFE, using four objective functions, DB, Dunn, cohesion, and stability, for patient stratification. Their ensemble construction involved using K-means to generate base clustering. They tested on 55 synthetic and 35 real patient stratification datasets. Unlike [PERSON] et al. [22] and [PERSON] et al. [20], we did not use ensemble techniques but chose to use a simple swarm intelligence technique BBPSO to maintain our population of solutions. Instead of PSO, as in [21], we used BBPSO, which is parameter-free. It is interesting to note that despite its simplicity, there are not as many works applying BBPSO or MOO BBPSO to data clustering. To name a few, [PERSON] et al. applied BBPSO clustering for unsupervised image segmentation [23], using a quantization-error-based objective function. [PERSON] and [PERSON] [24] proposed cooperative bare-bone particle swarm optimization (CBPSO) for data clustering, using a new centroid-based encoding scheme for each particle and the Chernoff bounds on a suitable population size.  
Other MOC techniques apply other nature-inspired approaches. [PERSON] et al. [25] proposed a multiobjective fuzzy clustering framework, MOF-TMS, with a tissue-like membrane system, which has a special cell structure for integrating a non-dominated sorting technique with a modified differential evolution mechanism. The proposed technique was developed to optimize three objective functions and was demonstrated to produce good results on many benchmark datasets. [PERSON] et al. [26] proposed a magnetic optimization algorithm (MOA) inspired by the magnetic force for data clustering. The algorithm is not sensitive to initialization, and the particle update is based on the magnetic resultant force. [PERSON] et al. [27] proposed the evolutionary state-based novel multiobjective periodic bacterial foraging optimization algorithm (ES-NMPBFO). This is a novel multiobjective periodic bacterial foraging optimization (BFO) algorithm for data clustering, incorporating PSO mechanisms into the chemotaxis operation.
# Resilience Quantification of Smart Distribution Networks--A Bird's Eye View Perspective
## 3 Taxonomy of Resilience Evaluation Methods
### Resilience Metric Computational Method
#### 3.3.1 Service and Assets Performance Only  
Resilience computation is solely based on performance measures obtained from the operational services and infrastructural assets of the network. Metrics can be calculated from a curve describing the evolution of performance with time [41], using a justified empirical formula [114], following an analytical derivation, or taken directly as the consequences observed from the event [80].
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 6 Discussion, Limitations, and Future Work
### Ethical Implications  
Integrating the outcomes of NIDSs and other security devices can enhance the effectiveness of the proposed SIEM scheme by reducing irrelevant and duplicate alerts, leading to improved incident handling performance. However, the use of an NIDS raises ethical concerns that must be considered. Below we list some ethical implications of conducting integration and analysis on security alerts using SIEMs [74; 75].  
* Privacy: Security alerts contain sensitive data, e.g., personal information, usernames, and passwords. Thus, analyzing security alerts can raise privacy concerns if these alerts are stored without appropriate safeguards.
* Data security: NIDS-collected data integrated in the SIEM can be of great value to attackers. Thus, the SIEM must have robust security measures in place to protect the collected data.
* False positives: The proposed SIEM scheme attempts to reduce the number of FPs; however, some may remain. This can result in unnecessary investigations and harm innocent users. To ensure accountability, the proposed visualization technique can summarize the security situation associated with the alert.
* Legal compliance: Similar to NIDSs, the SIEM must comply with applicable laws and regulations, particularly those related to data protection and privacy.  
Organizations can mitigate these ethical concerns by implementing appropriate policies and procedures for NIDS and SIEM usage. These policies should include comprehensive data collection and retention guidelines as well as procedures to handle FPs and protect user privacy. In addition, organizations must ensure that NIDSs and SIEMs comply with legal and regulatory requirements, and that these systems are audited and reviewed regularly.
## 3 Mathematical Modeling of CCPP
### CCPP Modeling
#### 3.1.1 Gas Turbine  
A gas turbine consists mainly of a compressor, combustion, and turbine. The same modular modeling approach was used to create modules for each component separately. In this case, the compressor and turbine were modeled using a table look-up approach and the equations required in the modeling process are shown below [20; 21].  
\[\left(\frac{g\sqrt{T_{in}}}{p_{in}},\eta\right)=f\left(\pi,n/\sqrt{T_{in}} \right), \tag{1}\]  
\[T_{out}=T_{in}\left(1+\frac{1}{\eta}(\pi^{(k-1)/k}-1)\right) \tag{2}\]  
\[P_{c}=gc_{p}(T_{out}-T_{in}) \tag{3}\]  
\[\frac{dP_{3}}{dt}=\frac{R_{S}T_{g}}{V}(g_{in}+g_{b}-g_{out}) \tag{4}\]  
\[\frac{dT_{3}}{dt}=\frac{\left[g_{in}h_{in}+g_{b}(h_{b}+\eta_{b}LHV)-g_{out}h_{ out}\right]}{\tau_{cc}g_{out}c_{post}} \tag{5}\]  
\[\frac{dn}{dt}=\left[\frac{30}{\pi}\right]^{2}\frac{1}{h_{1}N}\left(P_{t}-P_{c} -P_{f}-P_{E}\right) \tag{6}\]  
Figure 3: Schematic of the single-shaft CCPP. HP—high-pressure turbine, IP—intmediate-pressure turbine, LP—low-pressure turbine, SH—superheater, RH—reheater, EV—evaporator, FP—feed water pump, RP—recycle pump, CP—condensate pump.  
where \(g\) is air flow; \(T\) is temperature; \(n\) is rotational speed; \(k\) is specific heat ratio; \(\pi\) is pressure ratio of per sub-module; \(\eta\) is efficiency; \(LHV\) is low calorific value of fuel; \(h\) is enthalpy; \(t\) is time; \(P\) is power output; \(c_{p}\) is constant-pressure specific heat capacity; \(\tau_{cc}\) is time constants; \(\omega\) is angular velocity; \(J\) is rotational inertia; \(P_{E}\) is load power; \(P_{f}\) is the power consumed owing to mechanical losses, drive assist systems, etc. The subscript of each parameter can be summarized as follows: \(in\) is the inlet parameter of the component, \(out\) is the outlet parameter of the component, \(c\) is the compressor, \(t\) is the turbine, and \(b\) means combustion.
## 5 Performance Evaluation
### Mobility Patterns
#### 5.4.4 Truncated Levy Walk (TLW)  
It has been claimed that human mobility has the same characteristics as Levy Walks that follows the heavy-tailed Levy distribution [51]. This leads to a number of short flights followed by a long flight and fewer harsh turns as depicted in Figure 15(b). Therefore it is expected for the filter to perform better under this mobility pattern.  
Figure 17 compares the evaluation metrics for different mobility patterns. The distribution of velocity is different but we kept the same mean velocity for fairness. The PDR reached a peak during the simulation with TLW since the harsh turns are minimized in this mobility pattern and there are a lot of short flights that reduce the number of required handoffs. In RDM, the data traffic is not as well-received as the control traffic. This is caused by the fact that RDM challenges the localization algorithm with unpredictable churns. For RWP and RDM, the localization error is higher than TLW and GMM. This is due to the higher randomness in direction and velocity in these patterns. E2E delay for RDM is shown to be higher because of the MN residing in the edges of the simulation area.
# Recent Advances in Myoelectric Control for Finger Prostheses for Multiple Finger Loss
## 3 Signals and Finger Motor Function
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Developments** & **Study** \\ \hline
1922 & Passive hooks and shoulder harness were designed by [PERSON]. & [34] \\ \hline
1948 & The concept of the prosthesis ideally should replace not only mechanical function but & [35] \\  & also cutaneous and kinesthetic sensation. & \\ \hline
1948 & Myoelectric control was first implemented by [PERSON], a physics student at & \\  & Munich University and patent application. & [36] \\ \hline
1968 & The first commercial myoelectric hands were available from the middle of the 1960s. & [37] \\ \hline
1968 & The concept of natural appearance during function “dynamic cosss” which & \\  & contributes to the complexity of design both in terms of segmental trajectories and in & [38] \\  & terms of mechanical noise is highlighted. & \\ \hline
1970 onwards & Various advancements in the Myoelectric control prostheses through research and & \\  & training systems. & \\  & Availability of advanced commercial limb prostheses, such as the i-Limb from Touch & [11; 14; 19; 20; 21; 24; 23; 24; 22] \\  & Bionics, BeBionic from RSL Steeper, and Michelangelo by Ottobock. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary on the developments in myoelectric finger prostheses. Figure 3: The results of root mean square (RMS) of the electromyogram (EMG) signals from six channels through flexion of the wrist at three different levels [16]. and Michelangelo [43; 44; 45; 46; 47]. Table 2 shows the details of the myoelectric hand/finger prostheses.
## 3 Results and Discussion
### Analysis of Flavor Precursors for Coffee Beans Grown at Various Altitudes  
#### 3.2.1 Organic Acids  
Organic acids are related to coffee acidity, and the structural composition and content differences have a direct effect on the cupping quality [10]. Five organic acids were detected  
Figure 2: The composition and content of alkaloids and chlorogenic acids at different altitudes. (Different letters in the same line indicate significantly differences (\(p\) < 0.05) from each other using [PERSON]’s multiple comparison test).  
in the raw coffee beans, as shown in Table 1. Quinic acid was the main organic acid, followed by malic acid, oxalic acid, acetic acid, and citric acid. There was no obvious change trend in organic acid content with increasing growing altitudes. Quinic acid forms chlorogenolactone with CGAs during the roasting process, increasing the bitterness of the beverage [28].  
Malic acid and citric acid are ideal for enhancing the perception of sweetness, flavor freshness, and fruitiness [29]. At an altitude of 1100 m, the concentration of malic acid rose dramatically, reaching 3.77 times that at 930 m and 4.21 times that at 1250 m. The amount of malic acid decreased during the roasting process [3]. When its residual concentration was too high, a distinct bitterness was perceived in the cupping test [30]. Citric acid was also significantly affected by altitude, with its content decreasing sharply beyond 1100 m. Furthermore, citric acid was decreased during roasting and might not be present in high-altitude coffee.  
Acetic acid exhibited a trend similar to that of citric acid. However, its concentration increased during roasting [3], imparting fruity, wine-like, and fermented aromas to the coffee beverage [31]. Oxalic acid behaved similarly to quinic acid, showing no clear trend with growing altitudes. It is a relatively mild acid that enhances flavor and has a minimal impact on coffee [27].
# Design Strategies for Mobile Click-and-Load Waiting Scenarios
## 5 Discussion  
### Waiting Interface Strategies in Page Hierarchy Transitions  
Experimental results revealed significant differences in time perception across various interface strategies, which is consistent with existing research findings, suggesting that moderate visual fillers can effectively divert attention, thus reducing users' subjective time estimation during waiting periods [28]. In all interface strategies, subjective ratings of time reproduction in the three transition scenarios were consistently lower than the 4 s objective time, supporting the applicability of the attention-gating mechanism [42]. This indicates that appropriate visual stimuli not only alleviate users' waiting anxiety but also systematically alters their perception of the passage of time.  
Further analysis showed that two-stage interface strategies (e.g., 4 and 5) outperformed single-stage strategies (e.g., 1, 2, and 3) in terms of the perceived loading speed. Notably, Strategy 5 (blank page + spinning circle) achieved the highest mean scores for time reproduction, satisfaction, and emotional valence, demonstrating good generalizability across multiple transition scenarios. This may be attributed to the balance between its segmented feedback format and lower visual complexity, which not only aligns with users' psychological expectations of the loading process but also effectively mitigates the negative impacts of information overload [43]. In contrast, interfaces with complex visual elements, such as Strategies 2 and 4, performed poorly in time reproduction ratings, further confirming the positive correlation between visual complexity and time overestimation [44]. On the other hand, Strategy 1 (blank page), which provided no feedback, performed the worst across all dimensions. The lack of loading feedback created a sense of uncertainty and  
Figure 19: Results of layout strategy for the half-screen page scenario (Experiment 3). Vertical lines: standard deviations (SDs). *: \(p\) < 0.05.  
anxiety, significantly lowering satisfaction and emotional valence. This finding underscores the importance of introducing dynamic feedback in transition loading scenarios.  
Among the single-stage strategies, Strategy 2 (spinning page) performed the best overall, particularly in the satisfaction dimension, where it achieved the highest mean score. Designers may selectively use Strategy 2 (moderate loading) or Strategy 3 (skeleton screen, suitable for more content loading) based on the complexity of its content to better meet user expectations.  
It is noteworthy that although Strategy 5 demonstrated the best overall performance in the content page scenario, Strategies 3 and 4 (skeleton screens) scored higher in emotional valence. Skeleton screens can establish reasonable expectations for the loading amount through a structural pre-presentation, thereby enhancing users' emotional positivity. This result suggests that designers should consider guiding users' emotional expectations through structure in scenarios with large data loads to optimize their waiting experience.