esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Prospects for Further Research  
#### 5.3.1 Selection of predisposing Factors  
The selection of predisposing factors is one of the main steps in establishing a landslide hazard model and has a significant impact on the model's predictive ability [3]. This process requires the comprehensive consideration of the geological environment and the formation mechanism of the landslide to generate the model spatial datasets [11]. In practice, it is difficult to fully reflect the impact of related conditions in a certain area on landslide formation through the selected predisposing factors [63]. On the one hand, the predisposing factors of landslide evolution do not act in isolation, and it is difficult to fully quantify them. For example, in this study, the earthquake, lithology, and precipitation factors were only subjected to preliminary vectorization due to limitations in data acquisition and the method of quantitative analysis. This often leads to varying degrees of distortion of the predisposing factors. On the other hand, there are numerous predisposing factors for landslide occurrence, and regional differences in them are common. Existing studies indicate that the most frequently utilized predisposing factors encompass geology, geomorphology, hydrology, and land cover, with over 40 specific types [3; 11]. In general, the LHR model is established by selecting the main controlling factors through field investigations and statistical analysis. This leads to the neglect of some landslide predisposing factors, and the regional differences in the development of landslides cannot be completely reflected in the LHR model. Especially in LHR studies at provincial or larger scales, the established model can only embody the general laws of the overall area and often has high uncertainty [64]. Therefore, it is necessary to explore a systematic and practical method through extensive practice for the selection of predisposing factors. This method should follow the principles of the adaptation of the physical mechanism and the optimization of the regional characteristics.
## 3 Results and Discussion
### FGD Through Zeolite 13X  
#### 3.3.1 Fixed-Bed FGD Experiments  
In order to investigate the impact of different properties of adsorbent materials on FGD in mild settings, namely, at low reactor temperatures (T < 100 \({}^{\circ}\)C) and low SO\({}_{2}\) concentrations, the SO\({}_{2}\) uptake capacity of Ca(OH)\({}_{2}\) was compared with that achieved through the deployment of zeolite 13X under analogous operational conditions. Specifically, FGD experiments were conducted at 75 \({}^{\circ}\)C, relative humidities of 30 RH% and 45 RH%, and 600 ppm of SO\({}_{2}\). The samples were labeled based on FGD temperature and RH%, as indicated in Table 3. The discussion that follows will also include the parameters \(\lambda\) and \(\upmu\)max and the breakthrough sulfur capacity (mg/g) presented in Table 4.  
The breakthrough curves of 13X in Figure 5 are markedly different from those of PC in Figure 3, indicating that 13X exhibits an enhanced SO\({}_{2}\) uptake capacity. Such an improved FGD performance can be attributed to the greater surface area available for SO  
Figure 4: High-resolution spectra of S 2p core levels for: (**a**) pristine PC; (**b**) PC exposed to SO\({}_{2}\) at 75 \({}^{\circ}\)C and 30 RH%; (**c**) PC exposed to SO\({}_{2}\) at 160 \({}^{\circ}\)C and 30 RH%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**adsorbent Material** & **Temperature** & **RH\%** & **SO\({}_{2}\) Concentration** & **Label** \\ \hline
13X Zeolite & 75 \({}^{\circ}\)C & 30\% & 600 ppm & 13X [75 \({}^{\circ}\)C\({}_{\_}\)30 RH%] \\
13X Zeolite & 75 \({}^{\circ}\)C & 45\% & 580 ppm & 13X [75 \({}^{\circ}\)C\({}_{\_}\)45 RH%] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental conditions and labels for zeolite 13X. The values in the “Temperature” column refer to the temperatures of the adsorbent during the FGD experiments, which correspond to position 5 in Table S1.  
adsorption of the zeolite in comparison to PC (i.e., 791 m\({}^{2}\)g\({}^{-1}\) vs. 35.9 m\({}^{2}\)g\({}^{-1}\), respectively). Moreover, the zeolite surface sites exhibited notable catalytic activity, which may have boosted the adsorbent properties of this material. As the RH% increased, both the \(\mathfrak{t}_{\mathfrak{b}}\) and \(\mathbf{\lambda}\) values increased, yet the breakthrough sulfur capacity was significantly reduced. It can be observed that the \(\mathbf{\mu}_{max}\) value of 13X [75 \({}^{\circ}\)C\(\_\)30 RH%] is approximately half that of 13X [75 \({}^{\circ}\)C\(\_\)45 RH%], indicating a slower decay in removal efficiency over time. This results in SO\({}_{2}\) uptakes of 51 mg/g for 13X [75 \({}^{\circ}\)C\(\_\)30 RH%] and 41 mg/g for 13X [75 \({}^{\circ}\)C\(\_\)45 RH%] (see Table 4). 13X characterizations before and after the FGD experiments were used to explain the influence of humidity on the breakthrough curve results.  
The uptake capacities of zeolite 13X are compared to those of other zeolites in Table S4.
## 4 SLA Specification for the Fog Storage Services
### SLA Life Cycle  
SLA life cycle (see Figure 2) is composed of several phases: Negotiation, Deployment, Monitoring, Violation detection, Reporting and SLA termination [37]. Each phase of the SLA life cycle is explained as follows.  
**Negotiation:**: Parties involved in the SLA specify service terms and levels of the provided service on which to agree and may contain also monetary elements. If negotiation adapts to changing QoS demands of the user, the negotiation is dynamic. For example, the user might request for more Fog storage instances. SLA terms might be formalised either by standardised application-agnostic templates, such as WSLA [38] and WS-Agreement [39], or in ad hoc manner that is understood by the enclosed parties. This paper contributes to formalisation of the SLA negotiation by defining custom SLA templates, as described in Section 4.3.
**Deployment (Establishment):**: Service requests from users are provisioned in respective Fog storage nodes. The SLA specification and evaluation define the allocation capacity of resource nodes. It is common to classify SLAs in various classes, such as gold, silver and bronze, to which users are assigned. In our work, SLA is established by applying user-specified QoS requirements and cost constraints in the SLA template, and then invoking the Multi-objective optimisation framework to enforce them.
**Monitoring:**: The deployed services, as well as the resource nodes where the services are run, are being monitored periodically for their health status. In case of substantial service disruptions, the terms of the SLA might get violated. Monitoring can span many dimensions, such as FRs and NFRs of the job, status of resource nodes and network conditions. We use monitoring to estimate performance-related metrics to detect SLA violations.  
Figure 1: Introducing dynamic Fog Storage Services.  
**Violation:**: The likelihood of a job failing or not meeting its defined service levels represents a violation alert and may be sometimes reported as part of monitored data. Our work focuses on SLA control mechanisms in order to maintain QoS as agreed by the SLA.
**Reporting:**: Provisioning of services and audits is reported in log files that can be securely stored on trusted storage infrastructures, immutable ledgers or similar.
**Termination:**: An SLA may end either when the service is finished or as a response to the violation of one of the parties.  
This work is mainly focused on the negotiation and deployment stages, whereas the implementation of the monitoring, violation detection, reporting and termination stages in the context of SLA management are elaborated in our previous work [40].
# Design and Implementation of Virtual Security Function Based on Multiple Enclaves
###### Abstract  
Network function virtualization (NFV) provides flexible and scalable network function for the emerging platform, such as the cloud computing, edge computing, and IoT platforms, while it faces more security challenges, such as tampering with network policies and leaking sensitive processing states, due to running in a shared open environment and lacking the protection of proprietary hardware. Currently, Intel(r) Software Guard Extensions (SGX) provides a promising way to build a secure and trusted VNF (virtual network function) by isolating VNF or sensitive data into an enclave. However, directly placing multiple VNFs in a single enclave will lose the scalability advantage of NFV. This paper combines SGX and click technology to design the virtual security function architecture based on multiple enclaves. In our design, the sensitive modules of a VNF are put into different enclaves and communicate by local attestation. The system can freely combine these modules according to user requirements, and increase the scalability of the system while protecting its running state security. In addition, we design a new hot-swapping scheme to enable the system to dynamically modify the configuration function at runtime, so that the original VNFs do not need to stop when the function of VNFs is modified. We implement an IDS (intrusion detection system) based on our architecture to verify the feasibility of our system and evaluate its performance. The results show that the overhead introduced by the system architecture is within an acceptable range.  
NFV; SGX; enclave; hot swapping click 10.3390/613010012
## 4 Discussion
### Study Limitations  
The main limitation of this study was its cross-sectional design, which prevented causal inference. The demographic composition of the participants may affect the generalizability of the results, although this is also an advantage, as the majority of participants were young and healthy, minimizing the influence of confounding factors such as comorbidities or menopause in women, which could affect the quantity and quality of bone tissue in older individuals. Self-reported environmental factors, such as physical activity, alcohol consumption, calcium intake, and vitamin D, are subjective and may not be adequately accounted for. This study also lacked comprehensive dietary data, detailed information on dietary supplementation, and blood levels of calcium and vitamin D, which could shed light on whether the observed associations were mediated by diet quality and specific nutrients. In addition, the sample size, particularly among men, was a limitation. However, further studies are required to confirm these findings. Finally, measuring BMD, FM, and LBM at a single time point could not account for long-term changes; therefore, a longitudinal study is required to better understand these interactions over time.
## 4 Discussion
### Limitations and Future Work  
The presented study has several limitations at different levels. First, the available data from the CRT-EPiggy19 challenge errt useful to identify and better understand key aspects of different CRT models. However, several factors associated with EAM acquisition and processing induced a non-negligible data uncertainty that can limit the conclusions from the study. As well, hemodynamic descriptors, e.g., based on Doppler-derived measurements, were not available from the experimental study in [PERSON] et al. [24], preventing the optimization of important CRT parameters such as the AV delay, which has been found a potential non-responder factor [54]. Moreover, even in the case of better animal experiments, models should also be tested on in vivo human data to investigate its added value in the CRT clinical pipeline. Furthermore, the processing and modeling of each case, including a large number of simulations for parameter optimization, is very time consuming. The consequence is that only a few cases could be processed in our study and by the CRT-EPiggy19 participants, limiting the impact and generalizability of the benchmark analysis. A more comprehensive comparison with other FEM-based and meshless models in common data would be beneficial.  
The proposed SPH-based modeling pipeline provided simulation results comparable to the state-of-the-art alternatives, but several improvements could be incorporated. Firstly, the inclusion of the anisotropic ratio and the myocardial layer for each ventricle in the optimization pipeline could give more degrees of freedom to match EAM data. Additionally, the parameter optimization schemes used by all participants of the CRT-Epiggy19 challenge were not taking advantage of recent technological advances such as the use of deep learning algorithms [55; 56], variational approaches [57], reduced-order models [58; 59] or GPU-based architectures [60], which allows for the exploration of a larger space of parameter solutions at reduced computational times. Moreover, cardiac multi-physical models should provide more realistic simulations, allowing for the inclusion of hemodynamic factors and improving the adjustment of CRT configuration through flow ratios [61], perfusion models [17], lumped models of the whole cardiovascular circulation [18] or with a complete torso [20].
## 2 Literature Review  
### Artificial Neural Networks  
Artificial Neural networks (ANNs) are well suited for classification and prediction [20]. ANNs enable problem-solving by changing the structure of interconnected components [21]. The reason for the popularity of ANNs is that is they are non-parametric statistical models, which do not need any assumptions between input and output variables [22], and that they have the ability to learn from experience and enhance their functions to improve classification and prediction accuracy [23]. Nonlinear modeling machine learning (such as ANNs) is used to extract information from noisy data, and can avoid over-fitting, making it generally more robust [24; 25]. The ANNs are composed of nodes connected by directed links, and each link has a numeric weight [26]. Preparation of the ANNs includes data cleaning and processing, and choosing the number of hidden layers, number of nodes, type of activation function and learning algorithm rate. In this research, two types of ANNs are applied: multilayer perceptron (MLP) and radial basis function (RBF) neural networks. Both MLP and RBF are used in predictive modeling as'supervised' methods, in the sense that the model-predicted results can be compared against the known values of the target variable [27].  
The first algorithm is the MLP neural networks as in Figure 1, which consist of three kinds of layers: Atablien input layers, a hidden layer(s), and an output layer. MLP can have one or many hidden layers, and the number of hidden layers can be determined by trial and error, or intelligently. There are some neurons in each layer. Artificial neurons, as processing units, constitute ANNs, which are parallel distributed systems. The amount of input and output data determines the number of neurons in the input and output layers. The input layer contains the predictors. The hidden layer contains unobservable nodes, or units. The value of each hidden unit is some function of the predictors; the exact form of the function depends [28], in part, upon the network type and in part upon user-controllable specifications. The output layer contains the responses. Each output unit is some function of the hidden units. The second type are RBF neural networks as in Figure 2, with the same three layers, which have the ability to generalize the results with high tolerance of input noise [23]. In other words, the RBF network consists of a layer of units, performing linear or non-linear functions of the attributes, followed by a layer of weighted connections to nodes, whose outputs have the same form as the target vectors. As seen from Figure 2, RFB networks have only three layers, which makes them superior to the MLP for easy design [29]. More details about the structure and specifications of MLP and RBF can be found in [27; 28; 29; 30; 31; 32; 33].  
Figure 1: Schematics of multilayer perceptron neural networks [30].
## 4 Results and Discussions
### Effects of Surface Crack Propagation on Meshing Stiffness and Vibration Characteristics
#### 4.2.2 Effects of the Surface Crack Propagation Progress on DTE  
As presented in Figure 12, the DTE value of the spalling fault is obviously greater than that of the crack fault, which is due to the meshing stiffness of the spalling fault on the gear tooth being smaller than that of the crack fault. Meanwhile, the effects of crack fault cases on the time domain of DTE are not obvious before the crack evolves into a spalling fault.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Failure Degree**} & \multirow{2}{*}{**Crack Position**} & \multicolumn{6}{c}{**Crack Parameters**} \\ \cline{3-6}  & & & \(L_{1}\) & \(L_{2}\) & \(D\) & \(x_{1}\) \\ \hline Case \#0 & Healthy & \(P\) & 0 & 0 & 0 & 5 \\ Case \#1 & 33.3\% crack & \(S_{1}\) & 1 & 30 & 0.73 & 4 \\ Case \#2 & 66.7\% crack & \(S_{2}\) & 2 & 30 & 1.39 & 3 \\ Case \#3 & 100\% crack & \(S_{3}\) & 3 & 30 & 2 & 2 \\ Case \#4 & Spalling & \(S_{3}\) & 3 & 30 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of surface crack propagation case.  
Figure 11: Effects of surface crack propagation on meshing stiffness: (**a**) single-tooth pair, (**b**) double-tooth pairs.  
Figure 10: Schematic of surface crack propagation.  
The effects of surface crack propagation on DTE spectra are also studied and presented in Figure 13. It is observed that the side frequency components appear near the mesh frequency \(f_{m}\) and its harmonic frequencies (\(2f_{m}\), \(3f_{m}\),... ) in the spectrum diagrams of four fault cases, and the amplitudes of side frequency go up gradually with the surface crack propagation. In addition, the interval of the two adjacent side frequency components equals to the rotation frequency \(f_{n}\) of the driving gear. It can be concluded that the surface crack propagation has a small influence on the time domain of DTE, but an obvious influence on the sideband components of the DTE spectrum, which is an important feature for the diagnosis of early spalling failure.  
Figure 12: Effects of surface crack propagation on time domain of DTE.  
Figure 13: _Cont._
# Training-Free Acoustic-Based Hand Gesture Tracking on Smart Speakers
## 4 Results
### Impact of Ambient Noise  
Since our TaGesture relies on acoustic signals, we evaluated the impact of ambient sound on it. The volunteers were at 2 m in front of the transceiver and we set two types of noises at 0.5 m away from the transceiver. The first type of noise is human voice. We asked another volunteer to read an article with a normal speech volume. The second type of noise is external music played by an external mobile phone, which played music at 40% and 80% of its maximum volume. We used the Decibel X app on iPhone 13 Pro (Apple Inc., Cupertino, CA, USA) to measure the sound pressure level at the position of the transceiver. The sound pressure level of four cases are quiet (37.9 dB), speak (55.1 dB), music at 40% maximum volume (63.1 dB), and music at 80% maximum volume (69.2 dB), respectively. As shown in Figure 9, the performance of hand gesture recognition under different noises  
Figure 8: Impact of different distance.  
Figure 7: Evaluation of IMM Kalman Filter.  
are similar. The reason is that the frequency of the ambient noise is below 14 kHz, and thus they cannot affect our TaGesture with the signal in the frequency range of 17-23 kHz.
## 3 Results and Discussion
### MIMO Performance Analysis
#### 3.4.2 Diversity Gain  
Diversity gain is a MIMO performance parameter that is used to capture the effect of spatial diversity, especially in a MIMO system. The more the received signals are uncorrelated, the higher is the value of the parameter. The diversity gain for a MIMO system can be calculated using Equation (5).  
\[\text{Diversity Gain}=\left[\frac{\gamma_{c}}{SNR_{c}}-\frac{\gamma_{1}}{SNR_{1}} \right]_{P(\gamma_{c}<\gamma_{s}/SNR)} \tag{5}\]  
The plots for the simulation- and measurement-based DG for the mid-band and high-band MIMO configurations are reported in Figures 18 and 19. Here, it can be noted that the values of the DG for both simulation and measurement achieve the typical range of the DG (reported in the literature).  
Figure 19: The measured and simulated DG for mmWave MIMO configuration: (**a**) simulated DG; (**b**) measured DG.  
Figure 18: The measured and simulated DG for mid-band MIMO configuration.
# My Smartphone tattles: Considering Popularity of Messages in Opportunistic Data Dissemination
## 7 Performance Evaluation Setup
### Metrics  
We used the following metrics in our evaluations, all of them computed for both popular and unpopular messages.  
**Delivery Ratio**: This metric shows how many messages were delivered at all nodes compared to how many messages were produced. The Delivery ratio \(\eta\) is calculated according to Equation (2).  
\[\eta=\frac{1}{N}\sum_{i=1}^{N}\frac{M_{rx,i}}{M} \tag{2}\]  
where \(M\) is the total number of messages created in this network and \(M_{rx,i}\) is the number of messages received at node \(i\) (destination-less scenario).  
**Delivery Delay**: This shows the time between the receipt of a message and the time that message was created, for each first reception of messages at each user, averaged over all messages and all users. The network-wide _Mean Delivery Delay_\(\delta\) is computed as in Equation (3) where \(N\) is the number of nodes in the network.  
\[\delta=\frac{1}{N}\sum_{i=1}^{N}\delta_{i} \tag{3}\]  
where \(\delta_{i}\) refers to the _Node Delivery Delay_ and is computed according to Equation (4).  
\[\delta_{i}=\frac{1}{M}\sum_{j=1}^{M}\left(\tau_{rx,i,j}-\tau_{tx,j}\right) \tag{4}\]  
where \(M\) is again the total number of messages created in this network, \(\tau_{tx,j}\) is the time the \(j\)th message was generated and \(\tau_{rx,j,j}\) is the time node \(i\) received it.  
**Network Overhead**: This shows the total number of bytes sent out from the nodes in the network. This includes everything: data bytes (for Epidemic and Keetchi) and summary vectors (for Epidemic only). We used network overhead to evaluate the scalability and the energy consumption of the protocols, since the most energy expensive operation in the protocols presented is sending of messages.