esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 NFV and SFC Frameworks Taxonomy
### Resource Allocation and Service Orchestration Frameworks
#### 4.1.4 SNF Framework  
SNF is a SFC framework proposed by [PERSON] et al. [16], which synthesises SFCs with the main goal of performance optimisation by eliminating redundancy in packet processing across the service chain. Typical causes of redundancy are eliminated by the SNF framework by (i) creating a logical processing entity to handle all chain-wide operations on received packets, rather than handling network functions as separate processes, (ii) discarding packets that need to be discarded very early in the service chain; (iii) reducing multiple read operations by collecting read operations and constructing classes of traffic as a directed acyclic graph, which is synthesised into a classifier, and (iv) reducing the number of write operations by modifying traffic classes in a single operation.  
The concept of set theory and graphs is employed for traffic classification to achieve the synthesis of similar network functions to improve the overall performance of the service chain. Another key feature of the SNF framework is the management of statesacross multiple network functions in a service chain, which enables the synthesis of stateful service chains. At any given time, there is a processing core that actively classifies all the received frames into the required traffic class units. The ingress traffic is hashed using RSS, which helps to serve bi-directional flows by the same processor and re-writer [16]. Figure 9 depicts the SNF framework on a device with two network cards, where each NIC is tied to a CPU core.
## 1 Introduction  
Since the first report by [PERSON] et al. [1], transarterial chemoembolization (TACE) has been widely performed for inoperable hepatocellular carcinoma (HCC). In 2002, two randomized controlled trials (RCT) demonstrated that TACE for inoperable HCC showed a survival benefit compared with best supportive care (BSC) [2; 3]. Two meta-analyses also proved the clinical usefulness of TACE in prolonging HCC patients' survival [4; 5]. Therefore, TACE has been recognized as an effective treatment option for inoperable HCC worldwide, and as a first-line treatment for HCC in Barcelona Clinic Liver Cancer stage B (BCLC-B) (Figure 1) [6].  
Now, two major techniques of TACE have been performed: conventional TACE (cTACE) with iodized oil (Lipidolol 480, Guerbet Japan, Tokyo, Japan) and gelatin sponge (GS) particles; and TACE with drug-eluting beads (DEB-TACE). cTACE has been mainly developed in Asian regions, whereas DEB-TACE has been focused in Western countries. The concepts and rationales for both techniques, as well as adverse effects, are quite different; therefore, we should use them appropriately according to the patient and tumor condition. Thus, the purpose of this article is to describe the TACE strategy for BCLC-B HCC, mainly based on the conceptualistic position and techniques of TACE treatment in Japan.
## 2 Results and Discussion
### Fingerprinting of Cinnamon Drugs Using UV/Vis
#### 2.5.1 Unsupervised Multivariate Data Analyses of Cinnamon UV/Vis Dataset  
Considering that the resulted UV spectra did not show characteristic features for the analyzed samples, the dataset of all investigated samples was subjected to supervised and unsupervised MVDA to aid in samples discrimination and markers identification based on characteristic \(\lambda_{\text{max}}\) and to further compare to modeling results derived from GC/MS dataset.  
Compared with SPME models, HCA (Figure 6A) and PCA score plot (Figure 6B) failed to classify all samples into distinct groups, except for CP-1. CP-1 replicates were clustered away from other samples on the negative side of the PCA score plot along PC1. In general, UV models were found rather weak, with low prediction powder and failing to predict neither authenticated the sample's geographical origin nor product origin compared to GC-MS. Nevertheless, the PCA score plot model showed a high total variance (86.4%), with PC1 accounting for 74.3%.  
Similarly, modeling of authenticated samples alone showed scattered plots without a clear pattern with no segregation of biological replicates for each accession (Figure 6C). CA samples are the only clustered samples along PC2 with negative score values, though with no tight clustering of its biological replicates compared to GC/MS results (Figure 3B). Therefore, supervised MVDA, including OPLS-DA, was further attempted to extract more information from the UV dataset, failing to show clear segregation of cinnamon drugs.
# Wasserstein Generative Adversarial Network Based De-Blurring Using Perceptual Similarity
###### Abstract  
The de-blurring of blurred images is one of the most important image processing methods and it can be used for the preprocessing step in many multimedia and computer vision applications. Recently, de-blurring methods have been performed by neural network methods, such as the generative adversarial network (GAN), which is a powerful generative network. Among many different types of GAN, the proposed method is performed using the Wasserstein generative adversarial network with gradient penalty (WGANGP). Since edge information is the most important factor in an image, the style loss function is applied to represent the perceptual information of the edge in order to preserve small edge information and capture its perceptual similarity. As a result, the proposed method improves the similarity between sharp and blurred images by minimizing the Wasserstein distance, and it captures well the perceptual similarity using the style loss function, considering the correlation of features in the convolutional neural network (CNN). To confirm the performance of the proposed method, three experiments are conducted using two datasets: the GOPRO Large and Kohler dataset. The optimal solutions are found by changing the parameter values experimentally. Consequently, the experiments depict that the proposed method achieves 0.98 higher performance in structural similarity (SSIM) and outperforms other de-blurring methods in the case of both datasets.  
deblurring; generative adversarial network; perceptual similarity; style information; Wasserstein distance +
Footnote †: journal: Applied
# Enhanced Trajectory Tracking via Disturbance-Observer-Based Modified Sliding Mode Control
## 3 Simulation and Discussion
### Simulations on Second-Order System
#### 3.1.1 Case 1 (Faster Error Convergence)  
In first case, there was no disturbance in the system (\(d=0\)). The constant (1 rad/s) was set as the desired trajectory. The output error of the system can be observed in Figure 7. The proposed control's logic converged faster than that of the ESO-based SMC. The faster convergence was due to the addition of integral term in sliding surface. The convergence depends upon the value of the integral gain (\(c_{2}\)). But it should be selected optimally. Larger  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ESO Based SMC** & **ESO Based PIDSMC** \\ \hline a = 15 & a = 15 \\ \(K\) = 50 & \(K^{\prime}\) = 50 \\ \(c_{o}=1\) & \(c_{o}=1\) \\ \(c_{c}=1\) & \(c_{c}=1\) \\ \(c_{1}=5\) & \(c_{1}=5\) \\  & \(c_{2}=2.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Controllers’ parameters for simulations.  
Figure 6: MATLAB/Simulink block diagram.  
values of integral gain can reduce the increase time, but this creates overshoot in the system, which is not desirable. In this research, the value of the integral gain (\(c_{2}\)) was selected by hit and trail. The control input can be observed in Figure 8. Figure 9 depicts the estimation accuracy of the ESO. The absolute average error of the ESO-based PIDSMC was less than that of the ESO-based SMC (Table 2).
## 5 Optimisation of a Wind Farm--Kinetic Energy Storage System
### Discussion
Before establishing the parameter value, one should analyse the actual wind farm operating conditions, especially its mean working power. Increasing the power \(P_{3 min}\) above the value results in a significant increment in the required energy storage capacity and may mean that the storage unit cannot be fully charged. Consequently, the system is not able to eliminate all assumed energy deficits lasting up to \(T_{max}\) and does not meet a certain group of constraints \(T_{sumTmax}\) (e.g., Table 1--results for \(T_{max}\) = 30 min and constraints \(T_{sumTmax}\) = 75 h). A lack of solutions for the cases analysed in Table 1 applies to a farm with low mean annual power of ca. 1 MW. In cases where solutions were found, the optimum mean annual power values of the wind farms ranged from about 1.5 MW to 3 MW. The optimisation of the kinetic energy storage capacity helps to achieve high percentage values of the coefficient of elimination of power generating periods when the power value is below \(P_{3 min}\) (\(\Delta T_{sumTmax\%}-\) Equation (9)) which amount to 40-80% (mean 53%) of the analysed cases depending on the value of time T\({}_{max}\) and constraint value \(T_{sumTmax}\) (Figure 10). The value of the indicator \(\Delta T_{sumTmax\%}\) increased as the system operation constraints become more stringent (shorter times \(T_{sumTmax}\)), entailing a significant increase in the required storage capacity. When the value of the constraint time is reduced twice (from \(T_{sumTmax}\) = 150 h to \(T_{sumTmax}\) = 75 h), the optimised values of the energy storage capacity were 5 to 14 times higher, depending on time \(T_{max}\). Values of power not supplied to the power grid \(\Delta A\) (Equation (10)) in the system operation periods with power below \(P_{3 min}\) and time up to \(T_{max}\), analysed in a one-year period (Table 1), were established for the optimised structures of a farm with a kinetic energy storage system. The power value increased as time \(T_{max}\) and constraint time \(T_{sumTmax}\) increase. In the case of the analysed farm with power \(P_{WFn}\) = 10 MW, the values were low (Table 1) compared to the total energy generated in a one-year period and they did not exceed 0.5%. It should be emphasised that it was possible to achieve the presented results of a wind farm--kinetic energy storage system optimisation due to a short (ca. 47 s) period of wind speed averaging. For a typical value of 10 min, it was not possible to precisely simulate the system cooperation with the power grid due to an approximated analysis of the energy storage operation. In such cases, the results for the minimum energy storage capacity \(A_{FES\mathit{min}}\) suffered from a much higher error. Based on the authors' experience in the modelling of energy storage using RES, they suggest that the averaging period of wind speed measurements and other parameters related to power generation in RES--irradiation in particular--should be shorter than the period used currently and should amount to one or a few seconds.
## Appendix A Range of motion
### Maximum voluntary isometric torque  
Study**Effect size**Lower limit**Upper limit** **p-value**Weight**  
[PERSON] et al. (2002) - [PERSON] functional - A & -0.37 - 1.18 & 0.44 & 0.37 & 7.18 &  
[PERSON] et al. (2021) - A & 0.00 & -0.64 & 0.64 & 1.00 & 11.56 &  
[PERSON] et al. (2021) - H protocol - A & 0.05 & -0.70 & 0.79 & 0.90 & 8.52 &  
[PERSON] et al. (2021) - L protocol - A & 0.08 & 0.69 & 0.86 & 0.84 & 7.90 &  
[PERSON] et al. (2012) - 0.00 & -0.71 & 0.89 & 0.83 & 7.29 &  
[PERSON] and [PERSON] (2014) - A & 0.10 & -0.51 & 0.70 & 0.76 & 12.76 &  
[PERSON] et al. (2021) - A & 0.10 & -0.62 & 0.82 & 0.79 & 9.12 &  
[PERSON] et al. (2014) - A & 0.11 & -0.69 & 0.91 & 0.79 & 7.29 &  
[PERSON] and [PERSON] (2014) - A & 0.13 & -0.50 & 0.77 & 0.68 & 11.53 &  
[PERSON] et al. (2021) - A & 0.28 & -0.42 & 0.97 & 0.43 & 9.64 &  
[PERSON] et al. (2022) - Newest protocol - A & 0.33 & -0.48 & 1.13 & 0.42 & 7.20 &  
Overall**  
Model functions-default model  
Four studies reported tendon stiffness [72; 74; 85] or tangent modulus [83], and all the studies did not show a significant change. Of these studies, two studies reported two values that were obtained from two different sites (i.e., free/whole Achilles tendon [83]) or from the middle and final third parts of the stress-strain curve [85], leading to a total of six data points from the four studies. A meta-analysis revealed no significant overall small effect with no heterogeneity (\(d\) = 0.05; 95% CI = \(-\)0.22 to 0.33; \(p\) = 0.70; \(I^{2}\) = 0%; Figure 6E).  
markers indicate a significant overall effect size. Abbreviations: low-intensity (LI); high-intensity (HI); muscle-directed (MD); hip-neutral (HN); low-intensity group (GLow); high-intensity group (GHigh); soleus (SOL); proximal region of SOL (SOLp); distal region of SOL (SOLd); medial gastrocnemius (MG); proximal region of MG (MGp); intermediate region of MG (MGI); distal region of MG (MGd); lateral gastrocnemius (LG); proximal region of LG (LGP); intermediate region of LG (LGI); distal region of LG (LGM); Achilles tendon tangent modulus considering the middle (ATm) and end (ATe) third parts of curves; whole Achilles tendon stiffness (ATw); free Achilles tendon stiffness (ATf); semitendinosus (ST); seminembranosus (SM); biceps femoris (BF) [45; 46; 47; 72; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86].  
Three studies reported shear elastic modulus [45; 46] or shear wave speed [47]; one study examined two muscles (i.e., medial/lateral gastrocnemius [45]), one study examined three muscles (i.e., semitendinosus, semimembranosus, and biceps femoris [46]), and one study measured the shear wave speed from two different regions (i.e., proximal/distal parts) for soleus and from three different regions (i.e., proximal/intermediate/distal parts) for medial/lateral gastrocnemius [47]. This led to a total of 13 data points being available from the three studies, and 12 out of the 13 data points showed a significant decrease. A meta-analysis revealed a significant overall moderate to large effect with no heterogeneity (\(d=-0.58\); 95% CI \(=-0.76\) to \(-0.40\); \(p<0.001\); \(I^{2}=0\%\); Figure 6F).
# Analysis of 5G and LTE Signals for Opportunistic Navigation and Time Holdover
## 4 Discussion
### Further Work  
This study was initially started as part of a larger effort looking into cellular SoP-based navigation. The results presented here are directly useful for any application considering use of LTE or 5G signals for time holdover or SoP navigation exploitation. One way in which to interpret the information presented in this paper is that a system tracking the signals with a setup comparable to the one presented and measuring the relative ranges to a tower can achieve a time error rate in the order of 0.03 parts per billion (ppb). This can provide additional constraints on existing navigation platforms, particularly for drones operating at altitude, which will be minimally affected by multipath technology, thereby significantly improving the quality of the navigation solution in degraded environments (such as in situations where the GNSS is jammed or otherwise not available). For example, the results obtained from the ICE tower showed that, over longer time frames, the range measurement can drift significantly with some towers, but a short-term range rate provides high-quality results for all observed towers. This is important information for when range or velocity constraints on navigation platforms are considered.  
Another way in which this work can be improved is by trying to relax the requirements on the local oscillator. For practical applications, it would be desirable to have a receiver system that does not depend on a GPS-disciplined Rb oscillator. This work provides valuable insight for further steps where it could be investigated if differential measurements from multiple towers can be used to estimate the local clock error in a manner similar to how GNSS state estimation is performed.
## 2 Materials and Methods
### Method
#### 2.3.1 Preprocessing Operations  
SAR  
_(a) Delineation of Urban Areas_  
Currently, the main urban areas are delineated manually, as this is a preprocessing operation that is not time-critical. Alternatively, the World Settlement Footprint 2015 10 m resolution dataset could be used to identify urban areas [57]. This is an open access dataset available on the European Space Agency Urban Thematic Exploitation Platform.  
_(b) Identification of Training Areas for Water and High Land_  
Training areas for the water and high-land classes are used to determine a SAR backscatter threshold to discriminate between flooded and unflooded areas. Unassigned heights in the LiDAR data, where the water has acted as a specular reflector reflecting too large a signal directly back to the LiDAR sensor, are used as the water training area. Unassigned heights may be present in unflooded river channels or permanent water bodies. The high land, which is not likely to be flooded, is taken as the highest 10% of pixels in the area. These pixels must not contain unassigned heights so that they are not water. Figure 9 of [42] shows an example of the training areas for the Wraysbury test site.
## 2 Materials and Methods
### Volatile Free Aroma Compounds  
Grape berries randomly selected from each group were ground using an electric blade grinder and the grape juice was obtained by centrifugation and filtration. Grape juice (10 mL) was transferred to a 20 mL capacity headspace vial containing 10 \(\upmu\)L of acetonitrile and 0.3 g of NaCl. Acetonitrile was used as an internal standard to quantify aroma compounds, and NaCl was used to enhance the volatility of aroma compounds. The sample vial was incubated at 50 \({}^{\circ}\)C with constant stirring for 1 h. SPME fiber (50/30 \(\upmu\)m DVB/CAR/PDMS; Supelco, Bellefonte, PA, USA) was introduced into the headspace for 20 min to adsorb volatile aroma compounds. The SPME fiber was injected into the injection port (250 \({}^{\circ}\)C) of a gas chromatograph (6890N, Agilent Technologies, Santa Clara, CA, USA) equipped with an Agilent 5975 Series Mass Selective Detector operated in positive ion electron impact ionization mode at 70 eV with a scan range of 50-700 \(m/z\)The volatile aroma compounds were separated in an HP-INOWAX capillary column (30 mm \(\times\) 0.32 mm \(\times\) 0.25 \(\upmu\)m; Agilent Technologies) with purified helium gas at a constant flow rate of 2 mL/min. The purge flow to the split vent was set to 144.9 mL/min for 1 min. The oven temperature was programmed to initially hold at 40 \({}^{\circ}\)C for 5 min, increase to 250 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min, and finally held at 250 \({}^{\circ}\)C for 5 min. Volatile aroma compounds were identified by comparing their retention times with authentic standards and their mass spectra with those from the NIST 11 (National Institute of Standards and Technology) mass spectral library. The concentrations of volatile aroma compounds were estimated using the following equation [11]:  
\[RF_{SC}=\frac{A_{IS}\times C_{SSC}}{C_{IS}\times A_{SC}}\]  
\[C_{SC}=\frac{C_{IS}\times A_{SC}\times RF_{SC}}{A_{IS}}\]  
where \(RF_{SC}\) is the response factor for a specific compound, \(A_{IS}\) is the peak area of an internal standard, \(C_{SSC}\) is the concentration of the standard of a specific compound, \(C_{IS}\) is the concentration of the internal standard, \(A_{SC}\) is the peak area of a specific compound, and \(C_{SC}\) is the concentration of a specific compound.  
The odor activity value (OAV) of the identified volatile aroma compounds was calculated from the concentration of each compound divided by its perception threshold in water, and values higher than one were considered active odorants. Perception threshold data were obtained from the literature [12; 13; 14; 15; 16; 17].
# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 6 Energy Efficiency and Resource Allocation Algorithms  
The objective of energy efficiency is defined as maximizing the amount of data transferred per unit energy consumed by the system [76]. In Section 6.1, we present the selected papers given in Table 5, which provide EE solutions to converged optical and radio networks with AI-based techniques. A discussion on the future challenges and potential research directions are provided in Section 6.2.
# Revisiting the Detection of Lateral Movement through Sysmon
## 7 Python_Evtx_Analyzer
### Dataset  
PeX was evaluated over a 10-day dataset regarding the analyzer's detection and alert rates. Those are processed as attack-related fractions based on the detected rule-based logs, along with the False Positive (FP) and False Negative (FN) alert messages it generates. The collected data contain normal and malicious logs generated from the continuous interaction with the constituent systems of the SOHO network in Figure 1. Specifically, the nine LM attacks of Section 6 were re-executed multiple times over the 10-day period and mixed with legitimate user activity upon the targeted SOHO network. The legitimate traffic includes the captured logs of the first day, consisting basically of user logins and system logouts, web browser usage and Internet surfing, file sharing among the various stations of the network, email traffic and various social media account logins and user interactions with each of the six client stations included in Table 2. For reasons of reproducibility, but also for advancing research efforts in this area, the resulting dataset is publicly offered at [2].  
Regarding the malicious traffic generated, the three variants of the _Exploitation of Remote Services_ LM methodology were executed continuously for the first three days in a row, simultaneously with the normal utilization network traffic. The next four days were devoted to the execution of the _Credential Exploitation Hashing Attacks_. Finally, the last two days of the 10-day testing period were concentrated on a mixed repetition of the aforesaid attacks, leading to the creation of an \(\approx\)870,000 log dataset of Sysmon _evtx_ files. It should be noted that each day represents 3 to 6 h of continuous capturing of network logs activity and not a whole 24 h.
## 3 Types of Supplementary Dampers and Their Characteristics
### Tuned Mass Dampers
#### 3.5.6 Applications  
Tuned Mass Dampers find applications in a wide range of structures due to their effectiveness in controlling specific vibration modes. They are commonly used in tall buildings for both wind and seismic vibration control, where they can significantly improve occupant comfort and structural integrity. TMDs are also effective for slender structures such as towers, masts, and chimneys, where wind-induced vibrations can be particularly problematic. In civil infrastructure, they are employed in footbridges and long-span bridges to mitigate vibrations caused by pedestrian traffic or wind. Offshore structures also benefit from TMD technology, which helps control wave-induced motions.  
A characteristic application of tuned mass dampers, examining the TAIPEI 101 skyscraper, appears in Figure 13. This example illustrates how TMDs can be effectively integrated into the design of super-tall buildings to control wind-induced vibrations and improve structural performance.
## 2 The Surface Energy Balance: A Cornerstone in Understanding ET and RS Derived Variables
### The Latent Heat Flux (\(Le\))  
The energy required for the ET process is commonly referred to as the latent heat flux. It represents the rate at which heat is lost from the surface due to evapotranspiration. \(LE\) is typically obtained as a residual from the surface energy balance equation and is linearly related to the ET rate. It can be calculated using the formula provided in [22].  
\[LE=\lambda ET \tag{6}\]  
The value of \(\lambda\), representing the latent heat of vaporization of water, is temperature-dependent and is usually assumed to be \(2.45\times 106\) J/kg at a temperature of 20 \({}^{\circ}\)C [5].