esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimization Methodology for Adapting Legacy SGX Applications to Use Switchless Calls
###### Abstract  
A recent innovation in the trusted execution environment (TEE) technologies enables the delegation of privacy-preserving computation to the cloud system. In particular, Intel SGX, an extension of x86 instruction set architecture (ISA), accelerates this trend by offering hardware-protected isolation with near-native performance. However, SGX inherently suffers from performance degradation depending on the workload characteristics due to the hardware restriction and design decisions that primarily concern the security guarantee. The system-level optimizations on SGX runtime and kernel module have been proposed to resolve this, but they cannot effectively reflect application-specific characteristics that largely impact the performance of legacy SGX applications. This work presents an optimization strategy to achieve application-level optimization by utilizing asynchronous switchless calls to reduce enclave transition, one of the dominant overheads of using SGX. Based on the systematic analysis, our methodology examines the performance benefit for each enclave transition wrapper and selectively applies switchless calls without modifying the legacy codebases. The evaluation shows that our optimization strategy successfully improves the end-to-end performance of our showcasing application, an SGX-enabled network middlebox.  
intel SGX; enclave switches; benchmarking +
Footnote †: journal: Applied Sci.
# Remote Sensing and Machine Learning for Safer Railways: A Review
## 3 Descriptive Analysis
### Distribution by Journal  
Figure 3 provides a distribution view of the relevant publications across various journals. The set of 55 reviewed publications were distributed across 40 different journals.  
IEEE Access and IEEE Sensors Journal contained most of the reviewed publications, with five appearing in each. Following closely were the journals Measurement and Sensors, each containing three of the reviewed publications. Additionally, Applied Sciences, IEEE Transactions on Intelligent Transportation Systems, and Neurocomputing each contained two of the reviewed publications. The remaining journals, collectively labeled \"Others\", contained one of the reviewed publications each. In summary, the diverse array of journals approached the topic from various perspectives, including computer science, engineering, and transportation.
## 5 Computational Experiments
### Results
Consumptions according to standard WLTP. Source: [[https://www.km77.com/](https://www.km77.com/)]([https://www.km77.com/](https://www.km77.com/)) (accessed on 15 August 2022)) and a fuel cost of 1.78 EUR/L (the average fuel cost in Barcelona (Spain) \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Trips Completed with LL**} & \multicolumn{4}{c}{**with Reservation**} & \multicolumn{4}{c}{**w/o Reservation**} \\  & & \# & \# & \(t_{C\to D}\) & \(t_{D\to VR}\) & \(t_{VR\to LL}\) & \# & \(t_{C\to D}\) & \(t_{D\to LL}\) \\ \hline \hline
**Exp. 1** & **Here and Now** & 0 & 0 & - & - & - & 0 & - & - & - \\
**Booking** & 0 & 0 & - & - & - & - & 0 & - & - \\
**All** & 0 & 0 & - & - & - & 0 & - & - & - \\ \hline \hline
**Exp. 2** & **Here and Now** & 696 & 696 & 0.1 min & 3.4 min & 7.9 min & 0 & - & - \\
**Booking** & 348 & 348 & 0.1 min & 7.3 min & 15.8 min & 0 & - & - \\
**All** & 1044 & 1044 & 0.1 min & 4.7 min & 10.6 min & 0 & - & - \\ \hline \hline
**Exp. 3** & **Here and Now** & 736 & 735 & 0.1 min & 3.4 min & 7.6 min & 1 & 0.1 min & 6.2 min \\
**Booking** & 352 & 352 & 0.1 min & 8.0 min & 15.8 min & 0 & - & - \\
**All** & 1088 & 1087 & 0.1 min & 4.9 min & 10.3 min & 1 & 0.1 min & 6.2 min \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Summary of time delays in the system decisions for trips with a last leg (PT + RP or 3-leg trips). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**RP Trips**} & \multicolumn{2}{c}{**PT Trips**} & \multicolumn{2}{c}{**RP + PT Trips**} & \multicolumn{2}{c}{**PT + RP Trips**} & \multicolumn{2}{c}{**3-Leg Trips**} \\  & & **Dist.** & **Fare** & **Dist.** & **Fare** & **Dist.** & **Fare** & **Dist.** & **Fare** & **Dist.** & **Fare** \\ \hline \hline
**Exp. 1** & **Direct** & 7.14 km & 9.64 EUR & - & - & - & - & - & - & - & - \\  & **Expected** & 8.16 km & 8.99 EUR & - & - & - & - & - & - & - & - \\  & **Actual** & 9.43 km & 8.58 EUR & - & - & - & - & - & - & - & - \\ \hline
**Exp. 2** & **Direct** & 7.67 km & 10.32 EUR & 5.19 km & 7.15 EUR & 7.83 km & 10.52 EUR & 9.49 km & 12.65 EUR & 11.47 km & 15.18 EUR \\  & **Expected** & 8.59 km & 10.00 EUR & 3.81 km & 1.14 EUR & 8.12 km & 4.39 EUR & 9.44 km & 4.35 EUR & 13.32 km & 7.92 EUR \\  & **Actual** & 9.67 km & 9.88 EUR & 3.81 km & 1.14 EUR & 8.16 km & 4.35 EUR & 10.15 km & 5.32 EUR & 14.22 km & 8.77 EUR \\ \hline
**Exp. 3** & **Direct** & 7.64 km & 10.28 EUR & 5.23 km & 7.19 EUR & 8.03 km & 10.78 EUR & 9.42 km & 12.55 EUR & 11.77 km & 15.56 EUR \\  & **Expected** & 8.51 km & 9.81 EUR & 3.84 km & 1.14 EUR & 8.11 km & 4.41 EUR & 9.30 km & 4.35 EUR & 13.51 km & 8.02 EUR \\  & **Actual** & 9.54 km & 9.54 EUR & 3.84 km & 1.14 EUR & 8.14 km & 4.38 EUR & 10.00 km & 5.33 EUR & 14.38 km & 8.91 EUR \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Summary of mean experienced distances and user fares (no sharing) for RP, PT, RP + PT, PT + RP and 3-leg trips.
## 4 Experimental Results and Discussion
### Evaluation Metrics  
To evaluate the performance of the proposed IDS, we have used eight evaluation metrics including:  
* The confusion matrix that is used to report the number of correctly predicted samples (represented in two factors TP and TN) and the number of incorrectly predicted samples (represented as FP and FN). The predicted values can be described as positive and negative values, whereas the actual values can be described as true and false values. The two-class confusion matrix is shown in Figure 5.
* The accuracy indicates the model's power to classify the result of benign instances correctly, as shown in Equation (9): \[ACC=\frac{TP+TN}{TP+TN+FP+FN}\] (9)  
Figure 4: NSL-KDD dataset description.  
Figure 3: IoTID20 dataset taxonomy.  
* The recall is known as sensitivity or detection rate and indicates the model's power to correctly identify attacks (the actual values) as shown in Equation (10): \[Recall=\frac{TP}{TP+FN}\] (10)
* The precision indicates the model's power to be correctly predictive, which means how many positive predictions (attacks) are predicted correctly, as shown in Equation (11): \[Precision=\frac{TP}{TP+FP}\] (11)
* The F1-measure plays a trade-off between recall and precision in all instances to improve contradiction of recall and precision, as shown in Equation (12): \[F1-Measure=\frac{2*Recall*Precision}{Recall+Precision}\] (12)
* The False Positive Rate (FPR) indicates the model's power to calculate the percentage of misclassified attack instances as normal. This is represented as follows in Equation (13): \[FPR=\frac{FP}{FP+TN}\] (13)
* Receiver operating characteristics area under the curve (ROC AUC) which is used as a measure of the usefulness of a test in general at various threshold settings. The greater area the more useful test (ranges from 0.0% to 100%).
* Training Time is the amount of time duration measured in seconds (s) that the ML model takes to train the model using a specific dataset.
## 2 Materials and Methods
### VOC Metabolite Analysis
#### 2.4.2 GC-MS Instrumentation  
After sampling of plant volatiles from an FEP bag, the SPME fibre was manually inserted into the inlet of an Agilent 7890A GC coupled to a 5975C inert XL MS (Agilent Technologies, Santa Clara, CA, USA). Separation was performed with a J&W DB-WAXetr column (30 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m film thickness; Agilent Technologies, Santa Clara, CA, USA) using ultrapure helium (BOC Ltd., Mile End South, SA, Australia) as the carrier gas at a constant flow of 1 mL min\({}^{-1}\). The GC oven temperature began at 40 \({}^{\circ}\)C for 3 min, then increased at 5 \({}^{\circ}\)C min\({}^{-1}\) to 240 \({}^{\circ}\)C, and was held for 10 min at 240 \({}^{\circ}\)C. The Gerstel CIS inlet was fitted with an ultra-inert SPME liner (0.75 mm i.d., Agilent Technologies) and held at 240 \({}^{\circ}\)C throughout the run. Electron ionisation mass spectra were recorded at 70 eV in full scan mode in the range of \(m/z\) 35-350. Instrument control and data analysis were performed using Agilent ChemStation software (ver. E.02.02.143).
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 1 Introduction
The study mainly concentrated on: (a) investigating the feature selection from quad- and compact polarimetric SAR data; (b) testing the performance of these features using several supervised classification algorithms, and (c) comparing SAR data from these modes to achieve marine oil spill classifications.
###### Abstract  
Wireless sensor networks (WSNs) face a fundamental challenge: their sensors run on batteries, making energy efficiency crucial. While researchers have tried to extend network lifespans by improving routing and access control protocols across different layers, this remains a complex issue. One promising solution is opportunistic routing (OR), which uses multiple nodes to relay data. This approach reduces how long senders must wait for a specific next-hop node and helps prevent data loss from collisions. That said, choosing which nodes should act as forwarders can greatly affect how well the network performs. To tackle this problem, we developed a new approach called FCM-OR, a local density-based forwarder selection algorithm for opportunistic routing in WSNs. Our algorithm is particularly effective in networks where sensors are not evenly spread out and are densely packed. It uses fuzzy c-means (FCM) clustering to smartly pick the best forwarders based on how many nodes are nearby. By focusing on the sender's immediate surroundings, FCM-OR helps solve the problems that arise when different parts of the network have varying densities of nodes. We also created a new way to measure routing effectiveness called \"forwarding rank\". To test how well our protocol works, we ran extensive simulations comparing it to existing methods, including opportunistic routing with duty-cycled WSNs and load-balanced opportunistic routing. The results are clear: FCM-OR significantly improves both network performance and energy efficiency, especially in networks where nodes are not uniformly distributed.  
wireless sensor networks; opportunistic routing protocol; local density; fuzzy c-means clustering; forwarder selection algorithm; non-uniform density Article
## 2 An Overview of Recent Advances in Design Optimization of Electromagnetic Devices
### Optimization Methods
#### 2.3.5 Topology Optimization  
The optimization discussed above is mainly about the structure size or dimension optimization of the electromagnetic devices, which is one of the three main optimizations in engineering, structural size, shape, and topology optimizations. The topology optimization aims to obtain the optimal layout of components in the design domain for the best objective performance. Compared with the former two optimization methods, the topology optimization is more adept at innovative concept design with superior performance. Moreover, it can shorten the design cycle with less expertise to the optimal design [112]. Topology optimization has been an important research topic in computational electromagnetics for a significant time. It has attracted much attention nowadays due to the requirements of some modern electromagnetic devices, like the in-wheel motor drive systems for (hybrid) EVs, and the development of some advanced AI techniques like deep learning (this will be discussed in the next section) [113; 114; 115; 116]. As mentioned in Section 2, unsprung weight is a major challenge for in-wheel-motors [40; 41]. The weight of in-wheel motors must be minimized for smooth driving performance and better vehicle reliability and durability. Topology optimization is an effective method to achieve this goal. In many situations, some holes can be designed to the ferromagnetic cores of the motors, such as the stator cores of SPMSMs (Figure 1 ef) [112].  
There are some challenges for topology optimization as well. To ensure good manufacturability of the obtained design, some constraints, like rounded corners, should be considered in the optimization. Alternatively, this aim can be achieved by robust topology optimization (a combination of robust optimization and topology optimization).
# Study of Global Navigation Satellite System Receivers' Accuracy for Unmanned Vehicles
## 4 Discussion
* If the GNSS receiver used only one constellation (for example, MS32 SN1), then the number of processed satellite signals did not exceed 10. This significantly increases the HDOP values to [0.9, 1.0] and PDOP values to [1.3, 1.4]. The same situation appears if the GNSS receiver uses only a fraction of the received signals to calculate the position,  
Figure 17: U-blox NEO F10T receiver (patch and helix antenna, respectively). as in the case of the MAX M8Q module and multi-constellation reception of GPS and GLONASS signals. * The comparison of the results from the MAX M8Q and MAX M10S receivers shows significant improvement in the number of used constellations from 2 to 4 and the number of the processed satellite signals from 8 \(\div\) 10 to 16 \(\div\) 20, which reduced the HDOP value down to 0.6. The comparison of the position accuracy of L1/L5 GNSS receivers in our previous research [34] shows that the obtained HDOP and PDOP values are improved compared with other GNSS receivers, such as Quectel LC79D and LC29. The significant improvement of dilution-of-precision parameters is also obtained according to other publications [35; 36], where the best PDOP values reported were from 1.8 to 2.0 for L1 and L1/L5 GNSS receivers, but the number of processed satellites was limited to 10 [V]. Nevertheless, although the results were measured in Eastern Europe (in Sofia, Bulgaria), similar PDOP values are obtained for the polar regions [37], where minimum, mean and maximum PDOP values for all GNSS satellites are reported as 0.7, less than 1.2 and less than 1.8, respectively. Future research is to be directed to the study of the position accuracy of IMU/GNSS systems in urban environments where the presence of buildings and trees induce signal reflections and attenuations when the number of visible satellites is not sufficient and GNSS receivers are unable to achieve the required accuracy of unmanned vehicles. The practical realization of such integrated IMU/GNSS systems requires data fusion from both navigation systems, which is accomplished by the Kalman filter because its optimality in fusing data has been proved.
## 3 Signal Spatial Reconstruction Method for Relay Cooperative Networks
### Extended to \(K\) Users
For the common signals, \(\mathbf{Z}_{c}\) is defined as an equivalent matrix that does not contain \(\phi(K,i)=\left[\pi c(s,t),\ldots,\pi c(m,n)\right]_{1\times K/2}\cdot\mathbf{ Z}_{c}\) indicates that \(K/2\) common vectors satisfying \(\mathbf{z}_{c}^{\pi c(s,t)},\cdots,\mathbf{z}_{c}^{\pi c(m,n)}\), \(ands\
eq t\
eq\cdots\
eq m\
eq n\) are removed from all \(K\times(K-1)/2\) common signal vectors. It can define \(\mathbf{T}_{c}^{i}\in null\left( \begin{array}{c}\text{-}\phi(K,i)\\ \mathbf{Z}_{c}\end{array} \right)\). The received signals at user \(i\) can be expressed as  
\[\mathbf{y}^{[i]}=\mathbf{Q}^{[i]}\mathbf{H}^{[i,r]}\left(\sum_{m=1}^{K(K-1)/2 }\mathbf{T}_{p}^{m}\mathbf{z}_{R,p}^{m}+\sum_{n=1}^{K-1}\mathbf{T}_{c}^{n} \mathbf{z}_{R,c}^{n}\right)+\mathbf{n}^{[i]} \tag{30}\]  
where \(\mathbf{Q}^{[i]}=\left[\mathbf{q}_{p}^{[i,1]}\quad\mathbf{q}_{p}^{[i,2]} \cdots\quad\mathbf{q}_{p}^{[i,K]}\quad\mathbf{q}_{c}^{[i,1]}\quad\mathbf{q}_{ c}^{[i,2]}\cdots\quad\mathbf{q}_{c}^{[i,K]}\right]^{T}\). The equivalent matrix \(\mathbf{\hat{H}}^{[i,r]}=\mathbf{Q}^{[i]}\mathbf{H}^{[i,r]}\) of relay to user \(i\) is a diagonalization matrix. In order to further improve system capacity, the private signals degrees of freedom sent by each user can be increased from \(1\) to \(d_{p}\). Similarly, the degrees of freedom of the common signals can be increased to \(d_{c}\). \(d_{p}K(K-1)\) private signals and \(2d_{c}(K-1)\) common signals will be sent to the relay at the same time. If the number of user antennas and the number of relay antennas meet \(M=\left[\frac{1}{2}(N+d_{p}+d_{c})\right]\) and \(N\geq\frac{1}{2}d_{p}K(K-1)+\frac{1}{2}d_{c}K(K-1)\) respectively, the DoF of \(\eta_{sum}(K)=d_{p}K(K-1)+d_{c}K\) can be achieved.
# A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior
## 3 Traffic Status Prediction
### Challenges in Traffic Prediction  
There are a number of challenges in the field of traffic prediction modeling concerning time intervals. Employing the traffic prediction modeling for long-term time intervals faces several issues [19]. First, the uncertainties of the prediction increase due to the lack of data associated with short time intervals. Second, aggregating traffic data will lead to a high rate of errors in the prediction outcome. On the other hand, modeling short-term traffic prediction requires high computations and is highly sensitive to outliers. Therefore, using data analysis in traffic modeling plays an important role in reducing the drawbacks of predicting short-term or long-term traffic status, where it provides tools and functions that help in cleansing and transforming data into useful information before applying prediction models [20].
## 3 Results and Discussion
### Exam Dyes Behavior
#### 3.2.4 Role of Other Parameters Like pH or Pigment Concentration  
Some publications have studied the influence of diverse parameters on dye extraction, absorption, stability, etc. [64; 86]. [PERSON] studied the effects of extracting temperature and solvent and also the effect of pH on DSSC efficiency [76]. Anthocyanin extracts from five fruits were studied by [PERSON] and coworkers [54]. Different pH values were also tested. UV-Vis spectra of the extracts showed an increase in the wavelength of the maximum absorbance peak with increasing pH (also found in Refs. [80; 83]), and the highest conversion efficiency was obtained using a pH 4 dye solution. [PERSON] and col. studied papaya-leaf dye and the influence of pH, finding the most efficient and stable cell for pH 3.5 [70].  
In our group, we have not detected a strong pH influence on spectral data. Extracts of eggplant gave different peak positions even having similar pH values [37]. Bougainvillea extracts all have similar acid pH values (-5). For beetroot extracts, the lower the pH, the lower the wavelength (around 520 nm) for the main betalin pigment peak, as Solutions 1 to 3 with pH 5.6, 1.7 and 1.4 presented the peak for wavelengths 523, 519 and 516 nm, respectively. Similar pH dependence for betalains has been previously found in the literature [84].  
Scarce authors concentrate the dyes [28], and in the case that they do, it is usually a previous step to refinement or purification [63]. In our works, simply concentrating dye solutions does not apparently change the spectral data, nor cell performance, although FF increases about 20%, as can be checked in Table 4.
## 3 Results and Discussions
### The Dynamic Response of the Rotor System  
The previous section analyzed the dynamic response of the fan position where the bird strike force acts directly, while the bird strike affects the whole rotor system. Figure 13 shows the displacement of other mass points vs. the time. Looking back at the time history of displacement diagrams of the fan in Figure 12b, the displacement amplitude of the bearings and the turbine is significantly smaller. When the fan is not hit by a bird, the dimensionless displacement amplitude is 0.87. After bird strike, the displacement rises to 14.1, an increase of 12.23. In the other mass points of the rotor, the displacements are, respectively 0.23, 0.01, 0.21, and 0.07 from the position close to the fan hub to the end of the blade before bird strike. After bird strike, the maximum displacements increase significantly to 3.97, 1.41, 1.13, and 0.38, respectively, for the noted positions. As the bird strike force directly acts on the fan hub, the closer the bird impact point to the fan hub is, the larger the maximum displacements would be.  
To study the response of the rotor system at different times, the position displacement of the rotor system at five typical instances is demonstrated in Figure 14. The effect of bird strikes on the horizontal displacement of the rotor system is obvious from the graph. At \(t=2\) s, the bird strike has not yet occurred, and the displacement of the rotor is small. When the bird strike occurs at \(t=3\) s, in a very short time, i.e., at \(t=3.01\) s, the overall displacement level of the rotor increases significantly. Similar to the previous conclusion, the closer the impact position to the fan position is, the greater the magnitude of the displacements increase would be. At bearing 3, which is far from the fan, the variation of displacement is very small, and the maximum displacement does not exceed 0.38. In practical studies, special attention should be paid to the response at the bearing close to the fan. At \(t=3.5\) s, the vibration amplitude starts to drop rapidly due to damping effect, reaching 75% at the fan. Finally, at \(t=4.5\) s, the displacement curve is almost identical to that at the initial steady state. Therefore, bird strike is very likely to cause damage to the engine in an instant.  
Figure 13: The time history of displacements of different mass point positions of the rotor system. Time history of (**a**) bearing 1, (**b**) bearing 2, (**c**) rotor 2, and (**d**) bearing 3.
## 4 Experimental Results
### Computational Complexity
While the ranking module is quadratic in number of samples from the background and foreground i.e., \(O(\mathsf{F}*fg(j)*bg(j))\), nevertheless, it is only triggered when the re-sampling step is required. Since the re-sampling step is not carried in every step, most of the time the tracking is performed using only the novel optimized MSPF. Therefore, asymptotically speaking, this makes the overall complexity of our proposed AMF-MSPF framework equal to \(O(n)\) with high probability.
# A Review of Reactor Designs for Hydrogen Storage in Clathrate Hydrates
## 2 Hydrate-Formation Technologies
### Fixed-Bed Reactor  
The fixed-bed reactor (FBR) configuration is known for its ability in significantly enhancing the kinetics of hydrate formation in hydrate-based gas-separation applications [45; 46; 47; 48; 49]. It has to be mentioned that the experimental apparatus for FBR experiments is the same as unstirred/stirred reactor, except for the fact that in the FBR setup, a porous medium is located at the bottom of the reactor (Figure 7). FBR enhance the gas/liquid contact, provides more surface for hydrate nucleation and has the advantage of eliminating agitation as an energy-intensive and high-cost hydrate-formation step in stirred tank reactors. Extra surfaces can be provided by employing different porous media like silica gel, silica sand, lightweight and highly porous foam, metallic packing, and glass beads in FBR. The bed materials can be used repeatedly in many hydrate-formation cycles [45; 46; 47; 50; 51]. In a study published by [PERSON] et al. [52], the diverse effects of different bed materials, such as sand, gel, and foam in a FBR scheme were compared with hydrate-formations rate using the standard stirred tank reactor configuration (Figure 7). It this work, the importance of the role of available surface and microstructure of the bed materials is discussed [52].  
It is worth mentioning that, although using a porous materials as the bed can enhance the kinetics of hydrate formation, the presence of added materials will cause some challenges in the final application of gas hydrates in gas-storage applications.