esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results Comparison and Characteristics Analysis
### The Linear Part  
The analysis method of the linear part is the same as that of the rotary part. The analysis diagrams and expressions of the linear part can be obtained by interchanging the correlation quantities of the rotating part and that of the linear part in diagrams and expressions in Section 3, e.g., \(Z_{A}\leftrightarrow Z_{C}\), \(\dot{I}_{Am}\leftrightarrow\dot{I}_{Cm}\), \(I_{A}\leftrightarrow\dot{I}_{C}\), \(R_{A}\leftrightarrow\dot{R}_{C}\), \(X_{A}\leftrightarrow X_{C}\), etc. The characteristics of the linear part are calculated and compared with simulation results, as shown in Figure 13, Tables 5 and 6.  
The comparison between calculation and simulation shows that the calculation results are in good agreement with the simulation results and all of the errors are less than 8.2%. The errors are accepted. Hence, the proposed ECM is applicable to the linear part of the 2 DoFDDIM with SSR. The values of stator phase current, efficiency and power factor calculated by ECM are bigger than the ones obtained by FEM. This phenomenon is caused by ignoring the influence of the edge effect and the impedance of the rotor core. For the force curves in Figure 13c, the curves obtained by calculation and simulation have the same trend. The calculation result is less than the simulation one when the slip is small (0.2 \(<\)\(s\)\(<\) 0.5). However, the calculation value is the bigger one when the slip is large (0.6 \(<\)\(s\)\(<\) 0.7).  
Figure 13: Characteristics of the linear part: (**a**) Stator phase current; (**b**) Force; (**c**) Efficiency; (**d**) Power factor.
# DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks
## 5 Performance Evaluation
### Evaluation Results
#### 5.3.3 Effects of the Event Rate  
The effects of the event rate on network performance are shown in Figure 6. The event rate was varied from 0.1 packets/s to 2.0 packets/s. Note that the duty cycle interval is constant in this case. The results indicate that the event rate significantly affects the achieved DSR and total energy consumption, whereas the average E2E delay is not affected by the event rate.  
Figure 6a demonstrates the achieved DSR as the event rate increases from 0.1 packets/s to 2.0 packets/s. DDS still obtains the best performance and satisfies the required DSR. On the other hand, SROD-MM and OGP meet the requirement, while OAPF does not satisfy it with an event rate higher than 0.1 packets/s. In particular, SROD-WA cannot fulfill the required DSR, even at the lowest event rate (i.e., 0.1 packets/s).  
In addition, the achieved DSR of the schemes tends to decrease as the event rate increases. For example, the achieved DSR under DDS goes from 99.4% to 98.6%. Similarly, OAPF achieves DSRs that decrease from 96.1% to 60.9% while the achieved DSRs of OGP decrease from 99.5% to 96.2%. This is because of a large number of collisions with the high network traffic.  
In terms of power consumption, as observed in Figure 6b, DDS outperforms other schemes when the event rate is greater than 0.5 packets/s. For instance, with an event rate of 1.0 packets/s, the energy consumption of DDS, SROD-MM, OAPF, and OGP is 994.5 J, 1055.8 J, 1985.4 J, and 1970.2 J, respectively. In addition, the energy consumption of the schemes dramatically increases when the event rate rises because, with a larger number of generated packets, nodes have to be active longer to transmit packets. Recall that a longer active period leads to higher power consumption. In addition, OAPF and OGP  
Figure 5: Effects of the DSR on the achieved DSR, total energy consumption in the network, and average E2E delay with delay bound \(\theta=30\) s and an event rate of 0.5 packet/s.  
consume more energy than DDS since the average hop count of packets under OAPF and OGP is larger than under DDS.  
Figure 6c shows the effect of the event rate on the average E2E delay. The trend for the average E2E delay of the five schemes is relatively consistent. Since the average E2E delay is calculated for packets that are successfully transmitted to the sink, and the duty cycle interval is constant, the average E2E delay does not significantly change. DDS achieves a lower average E2E delay than SROD-MM and SROD-WA, because the duty cycle interval obtained under DDS is smaller than that with SROD-MM and SROD-WA. Since the transmission period in OAPF is short, OAPF achieves a lower average E2E delay than DDS. However, OAPF consumes the most energy and does not satisfy the required DSR when the event rate is higher than 0.1 packets/s.
## 2 Materials and Methods
### Creation of a Proactive System Based on Predictions
The whole process is known as _stacked generalization_ and can be further enhanced by adding multiple stacking levels. Again, and to enable comparisons of this method with grading, the classifiers used are naive Bayes, a Bayesian network taught with the TAN algorithm and, finally, kNN with \(k\) ranging from 1 to 5. * Multi scheme: This is a meta-classification method implemented by [PERSON] [67] which allows for the combination of classifiers in a simple manner. This method employs a combination rule based on the results obtained via cross validation and the error rate measured as the mean square error from several classifiers. * Testing the model: For each fold, first, we measured the accuracy of the model; in other words, how well the classifier performs in terms of correctly classified instances. Moreover, we also evaluated the error rate between the predicted value set X and the real value set Y (both having the size of the testing dataset m) with mean absolute error (MAE) (shown in Equation (1)). \[\text{MAE}=\frac{\sum_{i=1}^{n}\lvert y_{i}-x_{i}\rvert}{n}=\frac{\sum_{i=1}^{n }\lvert e_{i}\rvert}{n}.\] (1) Similarly, we used Root Mean Square Error (RMSE) (shown in Equation (2)). \[RMSE=\frac{1}{n}\cdot\sqrt{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}\] (2)Finally, we also tested each model measuring the Area Under the ROC Curve (AUC) [68]. This ranges from 0 to 1, where 0.5 indicates random guessing and 1.0 indicates perfect classification. Higher AUC values indicate better model performance.
# A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression
## 4 Simulation and Results: Comprehensive Analysis
### Simulation of Novel EEG-B3V3S/C1 Circuit Design
#### 4.1.2 Simulation in an Environment with an Electrical Grid with Frequency 60 Hz  
The same simulation, as previously analytically described, was performed in a 60 Hz power grid environment. It is obvious that this simulation produced the corresponding time and frequency domain simulation graphs, which are not presented in this subsection, for reasons of space conservation. The overall results were deemed satisfactory. Specifically, the CMRR of the circuit was assessed by short-circuiting the two input channels with a voltage source of 0 V DC offset and 1 V AC amplitude across all frequencies. While performing AC analysis, the CMRR yielded a frequency response function dependent on the frequency variable. In the band of interest of alpha, beta and theta waves, CMRR was approximately 98 dB, which is a slightly better performance than in the 50 Hz case. In frequencies greater than 110 Hz, CMRR dropped below 90 dB and at 3 kHz reached 62 dB. Meanwhile, in the area below 1 Hz, CMRR reached levels up to 163.5 dB at 10 mHz, similar to the 50 Hz case. The simulated notch depth was measured at 40.05 dB, which is acceptable, based on [56], which defines realistic expectations for maximum realizable notch depth between 40 dB and 50 dB but is not as good as in the 50 Hz scenario.  
Noise analysis was performed on the circuit for the 60 Hz configuration. The result of 1.4887 \(\mu\)Vpp noise from 0.1 Hz to 10 Hz before amplification was identified in a 50 Hz environment. The total power consumption was measured at approximately 20.5 mWatt. All the measurements were conducted in a similar manner as the one described in Section 4.1.1 for the 50 Hz experiments.
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 3 Proposed Framework
### Enhanced Counter Synchronization and Session Management  
Our framework implements a robust counter synchronization protocol that handles network anomalies while maintaining cryptographic security. This addresses a critical vulnerability in our initial approach, where session termination upon any verification failure was overly simplistic and prone to denial-of-service conditions.  
```
0: Encrypted image vector [], brightness factor \(\alpha\in\mathbb{R}\)
0: Encrypted adjusted image []
1: Input Validation:
2:if\(\alpha\leq 0\) or \(\alpha>5.0\)then
3:return\(\bot\) (Invalid brightness factor)
4:endif
5: Scalar Multiplication:
6:[] \(\llbracket\textbf{I}^{\prime}\rrbracket\leftarrow\llbracket\textbf{I}\rrbracket\odot\alpha\) {SIMD ciphertext-plaintext multiplication}
7: Ciphertext Management:
8:[] \(\llbracket\textbf{I}^{\prime}\rrbracket\leftarrow\textsc{Relinearize}( \llbracket\textbf{I}^{\prime}\rrbracket)\) {Maintain ciphertext size}
9:[] \(\llbracket\textbf{I}^{\prime}\rrbracket\leftarrow\textsc{Rescale}( \llbracket\textbf{I}^{\prime}\rrbracket)\) {Scale down for noise management}
10: Noise Budget Check:
11:if\(\textsc{NoiseBudget}(\llbracket\textbf{I}^{\prime}\rrbracket)<\) threshold then
12:return\(\textsc{NOISE\_BUDGET\_LOW}\) {Warning for potential decryption issues}
13:endif
14:return\(\llbracket\textbf{I}^{\prime}\rrbracket\)
```  
**Algorithm 5** Homomorphic SIMD Brightness Adjustment
# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 3 A Framework of 5G Transportation
### A Proposed Framework  
A framework for 5G transportation services should be able to take new limitations such as EE. According to this research, the proposed framework provides the necessary features to handle the present and future limitations with 5G+ requirements. Multiple Access (MA) allows the data to be distributed according to the users' preferences/priorities and transmitted concurrently between the users and service providers. In some situations, users also can be called potential service providers. For instance, broadcasting services such as audio and video provide multiple programs to different users according to their preferences.  
Data traffic is increasing with some users, and devices need an intelligent traffic management system that provides data traffic according to priority through the control system of the SDN technology. According to [19], softwarization handles the data traffic in a programmable approach, allowing users to manage their data whenever they want. In addition to this, the flexibility and reliability of the system's operation simplify users' potential challenges, such as the speed of browsing during peak times. Network data management and softwarization increase end-to-end reliability, which improves traffic performance such as speed. Emerging 5G systems and applications can handle maximum data traffic when the transportation system employs multiple radio access technologies.  
The novelty of the proposed framework depended on the intelligent approach of the security solutions to improve the intelligent features of ITS in the connected vehicles. An enhanced version of the future transportation services should monitor the safety and deliver the appropriate security solutions dynamically. Although sensors dominate the controllability of the features in transportation services, connected and intelligent systems based on AI configure the features dynamically. As an extra feature, AI-based security solutions, ITS of autonomous driving in connected vehicles, and intelligent navigation dynamically provide proactive services. Additionally, the ITS allows transportation users to reduce the waste of time and energy by optimizing routes, reducing congestion, improving vehicle and driver performance, and promoting better management of the transportation system.
## 3 Results and Discussion
### Time Consumed by Imaging Reconstruction  
Imaging frame rates are paramount in long-range imaging LiDAR systems, particularly concerning real-time imaging capabilities. A thorough investigation into the system's real  
Figure 5: Imaging at night. (**a**) Building at 9000 m. (**b**) TV tower head at 13,000 m. (**c**) Building at 14,000 m. (**d**) Building at 192,000 m.  
Figure 6: Imaging on a rainy day. (**a**) Microwave radar antenna at 150 m. (**b**) Clock situated atop the building at 250 m. (**c**) Building under construction located at 1500 m. (**d**) Attic within the building observed at 2000 m.  
time imaging performance was conducted, as depicted in Figure 7, which illustrates the time consumed by imaging reconstruction across various imaging parameters, including frame rates. The experimental findings underscore the intricate correlation between imaging frame rates and sampling rates with the image resolution, alongside the computational power of the computing hardware. Remarkably, employing a standard computer setup facilitates the consistent achievement of imaging frame rates at 24 Hz across diverse sampling settings, while upholding a resolution of 24 \(\times\) 24 pixels.
# Unsupervised Predominant Sense Detection and Its Application to Text Classification
## 5 Experiments
### Text Classification
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Category** & **CNN** & **WSD** & **DSS** & **SFC** \\ \hline Law & 0.846 & 0.853(+0.007) & 0.899(+0.046) & 0.908(+0.009) \\ Finance & 0.904 & 0.906(+0.002) & 0.939(+0.033) & 0.923(\(-\)0.016) \\ Industry & 0.798 & 0.796(\(-\)0.002) & 0.893(+0.097) & 0.873(\(-\)0.020) \\ Publishing & 0.738 & 0.736(\(-\)0.002) & 0.753(+0.017) & 0.739(\(-\)0.014) \\ Admin. & 0.875 & 0.875(0.000) & 0.918(+0.043) & 0.933(+0.015) \\ Economy & 0.924 & 0.930(+0.006) & 0.968(+0.038) & 0.972(+0.004) \\ Art & 0.734 & 0.741(+0.007) & 0.753(+0.012) & 0.817(+0.064) \\ Fashion & 0.608 & 0.609(+0.001) & 0.628(+0.019) & 0.775(+0.147) \\ Politics & 0.817 & 0.813(\(-\)0.004) & 0.900(+0.087) & 0.964(+0.064) \\ Religion & 0.710 & 0.639(\(-\)0.071) & 0.855(+0.216) & 0.804(-0.051) \\ Sports & 0.987 & 0.987(0.000) & 0.993(+0.006) & 0.995(+0.002) \\ Tourism & 0.342 & 0.298(\(-\)0.044) & 0.348(+0.050) & 0.469(+0.121) \\ Military & 0.873 & 0.873(0.000) & 0.917(+0.044) & 0.943(+0.026) \\ Meteorology & 0.853 & 0.848(\(-\)0.005) & 0.875(+0.027) & 0.863(\(-\)0.012) \\ \hline Micro F-score & 0.887 & 0.889(+0.002) & 0.937(+0.048) & 0.948(+0.011) \\ \hline Macro F-score & 0.786 & 0.779(\(-\)0.007) & 0.832(+0.053) & 0.855(+0.023) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification performance (Topmost 10%). 18 words with 22 senses in all, each of which appears in SFC. In contrast, in our DSS, we used the topmost 10% of the target words that have 442 senses in all. Overall, the results show that domain-specific senses improved text classification performance. The best improvement is \"Religion\" (+0.216), and the worst is \"Art\" (+0.012). In contrast, SFC is the best improvement (+0.147) for \"Fashion\" and the worst \"Religion\" (\(-\)0.051). One reason is that \"Religion\" is frequently associated with \"Politics\" and \"Military\" and its document size is smaller than the other two categories according to Table 1, however, it betters than WSD significantly at 0.165. The text classification used here is very simple, that is, CNN with single channel. There are lots of text categorization techniques applicable to the small number of training documents [58; 69], and it will be worthwhile examining these with our model.
# Study on Two-Phase Mixing Inside Flow Focusing/Blurring Nozzle by Gray Distribution Analysis Method
## 2 Materials and Methods
### Experimental Platform  
A schematic diagram of the experimental platform is shown in Figure 2. When the gas-liquid mixing inside the nozzle is studied, the properties of water and common liquid working fluids (such as gasoline) are similar. Therefore, on the basis of experimental safety and environmental protection, water and air were selected as the working fluid and atomizing gas, respectively. The liquid was injected into the nozzle from a water tank through a rotameter and check valve. The gas path is similar to the liquid path but the power source is an air compressor. The water and air flow rates were measured by rotameters and the parameters and adjustment range of the experimental equipments are labeled in Figure 2. The adjustment range of gas and liquid flow rates is determined by pre experiment. In the flow rate range selected in the Figure 2, the flow focusing/blurring experimental nozzle designed by us can work in different modes according to the needs.  
A white LED plane light was used as the experimental light source to obtain two-phase flow images. The plane light is a surface light source, this kind of light source has soft light, uniform illumination, small contrast between light and dark, and shadows do not easily appear in the image. The use of a plane light can effectively reduce the impact of plexiglass and fluid refraction on visualization. The gray distribution analysis method requires high definition of the experimental images. In the flow blurring mode, the gas-liquid flow in the nozzle changes rapidly with time and a relatively clear image can be obtained by choosing a smaller shutter speed. Therefore, the images of the two-phase flow were captured using a SLR camera (Canon 80D), which supported images with 24 million pixels and had a shutter speed of 1/8000s.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value/mm** \\ \hline Nozzle inner tube diameter & \(D\) & 5 \\ Tube hole distance & \(H\) & 1/2/3/4/5 \\ Orifice diameter & \(D_{h}\) & 3/5/7 \\ Hole length & \(l_{h}\) & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural parameters of the experimental flow focusing/blurring nozzle.  
Figure 2: Schematic diagram of experimental platform.  
When we do each set of experiments, the air injection and liquid supply flow rates were adjusted to form different two-phase flow conditions. The water pump and air compressor were turned on such that the two fluids were mixed. The pipelines were checked to ensure that no leakage occurred and then the gas and liquid flow rates were adjusted slowly and smoothly to the experimentally specified value. When the experimental conditions reached a steady state for about 1 min, the experimental data, including the air and liquid flow rates, and two-phase flow images, were recorded using the facilities and instruments described previously.
# Extending the Performance of Hybrid NoCs beyond the Limitations of Network Heterogeneity
## 3 On the Performance Improvement of Hybrid NoCs
### Adaptive Routing
#### 3.2.2 Virtual Channel Allocation  
In SlideAcross, VA is performed non-speculatively after SA in the same cycle according to the pipeline design. VA is hence required to be very lightweight so as to prevent increasing the critical path delay of the router dramatically.  
NoC uses VCs to implement the virtual network (VN) for CMP to isolate different types of messages. Each VN can also contain multiple VCs. In this work, we require at least two VCs (VC0 and VC1) in each VN to prevent routing deadlock. To make VA simple, we require a packet to retain its original VC inside its VN. For example, the packet of VC0 will still be VC0 after successful VA. Therefore, the VC of a packet is determined at injection and is not changed during its lifetime in the network. Such a simple VA rule can be appended to the SA process; the winner of an output port also owns the corresponding VC of the output port. The SVC tag (if idle) is also assigned to the winner of SA and performed in parallel with VA. This VA procedure is simpler than what has been done in [51], where VA picks up a VC from the idle VC pool and is done after SA in the same cycle. The high-performance router in [51] demonstrates the efficiency of such pipeline design.  
Proposed VA scheme allows VA to be performed efficiently after SA in the same cycle, reducing the router pipeline without speculation. Due to this deterministic VC assignment scheme, a head flit requests for SA only when its VC at the output port is idle. Therefore, when a head flit wins SA, it will surely obtain a VC, increasing crossbar utilization. A potential drawback for this simple VA scheme is that the buffer utilization of different VCs can be imbalanced in asymmetric traffic patterns. However, this problem can also be solved by sharing the buffer between VCs [49].  
Figure 7: Bypassing and adaptive routing pipeline stages.  
Figure 8 illustrates the adaptive routing datapath and the bypass datapath by detailing west input and east output. At each input port, there is an SVC buffer reserved for SVC packets. The crossbar is composed of input multiplexers and output multiplexers to be cost effective [17; 50]. The red bold arrow in the figure is a bypass datapath that connects the west input link directly to the east output multiplexer. Mux2 connects the red arrow with the east output port when there is no request for the east output port, forming one of the pre-setup intra-dimension bypass datapaths.  
Control modules are coloured with blue in this figure, including the bypassing control, input multiplexer arbiter, output multiplexer arbiter, VC allocator and SVC allocator. VC and SVC allocator absorb the arbitration results of the 5:1 arbiter (SA-II) and allocate the VC and SVC tag to the winning packet accordingly. Selection units automatically select the less congested path for buffered packets by masking the congested output port in the output port request vector.
## 3 Results and Discussion
### Enzymatic Activity Variation during Desalting Process  
#### 3.3.1 Cathepsins Activity Variation during Desalting Process  
In fresh fish, the autolytic activity of major muscle endogenous proteases induces the hydrolysis of key myofibrillar proteins, and thus contributes to muscle textural deterioration. The main proteolytic systems involved in the hydrolysis of myofibrillar proteins during the post-mortem storage of fish muscles are the lysosomal cathepsins [31]. Although cathepsin' activity has been found to decrease with the increasing salt concentration during the salting process [32], no information was found in the literature concerning cathepsins' activity during the desalting of salted cod.  
Salted cod showed cathepsins' B and D activities of 1.53 \(\pm\) 0.19 FU/min/g IS and 26.3 \(\pm\) 0.7 \(\upmu\)g tyrosine/min/g IS, respectively, at day 0 in Figure 3A,B. During desalting, cathepsins' activities significantly decreased (\(p<0.05\)) in cod fish muscle to an activity of \(\approx\)0.48 FU/min/g IS (for cathepsin B at both temperatures) and 9.3 \(\upmu\)g tyrosine/min/g IS (for cathepsin D after 8 h of desalting) with no further changes. This decrease corresponds to an activity reduction around 70 and 65% of the initial activity for cathepsin B and D activities, respectively. This reduction in cathepsin B and D activities might be due to the loss of soluble protein to the desalting water. The temperature of desalting showed statistically significant effects in the lysosomal cathepsins' activity (\(p<0.05\)) during the first hour, in the case of cathepsin B, and during 4 h in the case of cathepsin D, with no effect afterwards.