esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Schematics and Frequency Regulation Structure
### Structure of Frequency Regulation  
Figure 2 demonstrate the structure of frequency regulation based on the conventional frequency regulation model, where the behaviors of the speed governor, reheat turbine, synchronous generator, and electric load are included [5]. In Figure 2, \(R\) designates the droop coefficient, whose physical meaning is to properly allocate the output power of synchronous generator \(P_{m}\) according to the change of system frequency \(\omega_{r}\) so as to accurately share the load among various synchronous generators. \(T_{G}\) refers to the time constant of the speed governor, and it is introduced by the time delay of the actuator. \(F_{HP}\), \(T_{RH}\), and \(T_{CH}\) are the coefficients of the reheat turbine. Among these coefficients, \(T_{RH}\) representing the time constant of the reheat turbine normally plays a decisive role. With a typical value of 7 s, \(T_{RH}\) is far greater than the other coefficients [5]. \(H\) denotes the combined inertia coefficient of synchronous generator and turbine, which is defined as the ratio of rotational kinetic energy to the rated power (normally ranges from 2-10 s [5]). Its significance was emphasized many times by the previous references [21; 22; 23; 24; 25; 26; 27; 28; 29]. Generally, it is believed that the increased penetration of renewable energy resources leads to reduced system inertia, which will significantly deteriorate the frequency and angle stability of power electronics-based power systems. In order to address this issue, various approaches were proposed to increase the inertia coefficient \(H\)[7; 9]. \(D\) stands for the damping factor of loads, which  
Figure 1: Schematic diagram of a grid-tied EV charger operating as a VSM.  
indicates that the power consumption of frequency-dependent loads will change when the system frequency deviates from its nominal value.  
The structure of frequency regulation based on VSMs is shown in Figure 3. Being different from Figure 2, the model of the turbine is excluded from this control structure so that the dynamics of frequency regulation can be improved while the other control blocks remain unchanged.  
In Figure 3, the transfer function from the variation of load power \(\Delta P_{L}\) to the frequency variation \(\Delta\omega_{r}\) can be derived as:  
\[G_{P_{L}\rightarrow\omega_{r}}(s)=\frac{\Delta\omega_{r}}{\Delta P_{L}}= \frac{-R(1+sT_{G})}{(2 Hs+D)(1+sT_{G})R+1}=G_{0}\cdot\frac{s+z_{1}}{s^{2}+2 \zeta\omega_{n}s+\omega_{n}{}^{2}} \tag{1}\]  
where \(z_{1}\) denotes the zero of \(G_{PL\rightarrow\omega r}(s)\), \(\omega_{n}\) represents the undamped natural frequency, and \(\zeta\) stands for the damping ratio. These parameters can be further represented as  
\[G_{0}=-\frac{1}{2H},z_{1}=\frac{1}{T_{G}},\omega_{n}=\sqrt{\frac{DR+1}{2 HRT_{G}}},\zeta=\frac{(2H+DT_{G})}{4 HT_{G}}\sqrt{\frac{2 HRT_{G}}{DR+1}} \tag{2}\]
# Lightweight and Robust Malware Detection Using Dictionaries of API Calls
###### Abstract  
Malware in today's business world has become a powerful tool used by cyber attackers. It has become more advanced, spreading quickly and causing significant harm. Modern malware is particularly dangerous because it can go undetected, making it difficult to investigate and stop in real time. For businesses, it is vital to ensure that the computer systems are free from malware. To effectively address this problem, the most responsive solution is to operate in real time at the system's edge. Although machine learning and deep learning have given promising performance for malware detection, the significant challenge is the required processing power and resources for implementation at the system's edge. Therefore, it is important to prioritize a lightweight approach at the system's edge. Equally important, the robustness of the model against the concept drift at the system's edge is crucial to detecting the evolved zero-day malware attacks. Application programming interface (API) calls emerge as the most promising candidate to provide such a solution. However, it is quite challenging to create API call features to achieve a lightweight implementation, high malware detection rate, robustness, and fast execution. This study seeks to investigate and analyze the reuse rate of API calls in both malware and goodware, shedding light on the limitations of API call dictionaries for each class using different datasets. By leveraging these dictionaries, a statistical classifier (STC) is introduced to detect malware samples. Furthermore, the study derives into the investigation of model drift in the STC model, employing entirely distinct datasets for training and testing purposes. The results show the outstanding performance of the STC model in accurately detecting malware, achieving a recall value of one, and exhibiting robustness against model drift. Furthermore, the proposed STC model shows comparable performance to deep learning algorithms, which makes it a strong competitor for performing real-time inference on edge devices.  
API call sequence; statistical classifier; model drift; malware detection Article
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 5 Experimental Results and Discussion
### Results and Performance Analysis
#### 5.2.2 Overall Performance Analysis  
It can be observed from the Tables 3 and 4, that the proposed method at \(\delta=100\) demonstrates a significant encoding time complexity reduction with a lower impact on the measured objective and subjective quality, compared to the implementation presented in [10]. The algorithm proposed by [PERSON] et al. [34] shows a slightly higher encoding time gain at a higher visual quality impact. The proposed method with \(\delta=20\) records the highest encoding complexity reduction, with an average \(\Delta T\) of 67.34%. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{**Proposed (\(\delta\) =**} & \multicolumn{2}{c}{**Proposed (\(\delta\) =**} & \multicolumn{2}{c}{**[PERSON] et al. [10]**} & \multicolumn{2}{c}{**[PERSON] et al. [34] vs.**} \\  & **100) vs. HM** & \multicolumn{2}{c}{**20) vs. HM**} & \multicolumn{2}{c}{**vs. HM**} & \multicolumn{2}{c}{**HM**} \\ \hline  & \(\Delta\) **T (\%)** & \begin{tabular}{c} **BD-Rate \(\dagger\)** \\ (\%)** \\ \end{tabular} & \(\Delta\)**T (\%)** & \begin{tabular}{c} **BD-Rate \(\dagger\)** \\ (\%)** \\ \end{tabular} & \(\Delta\)**T (\%)** & \
\begin{tabular}{c} **BD-Rate \(\dagger\)** \\ (\%)** \\ \end{tabular} & \(\Delta\)**T (\%)** &
\begin{tabular}{c} **BD-Rate \(\dagger\)** \\ (\%)** \\ \end{tabular} \\ \hline Kimono & 72.60 & 1.53 & 81.06 & 4.27 & 80.74 & 4.24 & 70.50 & 2.60 \\ Basketball Pass & 52.68 & 0.45 & 72.15 & 4.01 & 51.84 & -0.73 & 54.55 & 0.03 \\ BQTerrace & 56.17 & 2.58 & 72.94 & 7.28 & 52.03 & 1.68 & 56.78 & 2.03 \\ Traffic & 61.71 & 0.72 & 78.59 & 7.08 & 49.48 & 1.91 & 59.02 & 2.39 \\ RaceHorses & 40.92 & 0.76 & 62.75 & 5.34 & 49.07 & 0.78 & 53.98 & 1.75 \\ BlowingBubbles & 31.78 & 2.20 & 40.21 & 3.79 & 31.33 & 3.83 & 31.59 & 1.68 \\ Johnny & 56.61 & 2.09 & 70.46 & 3.71 & 71.99 & 3.08 & 71.35 & 4.57 \\ KristenAndSara & 59.41 & 1.94 & 66.74 & 6.87 & 62.14 & 2.33 & 68.78 & 3.40 \\ PeopleOnStreet & 49.59 & 2.13 & 75.61 & 10.46 & 44.42 & 1.29 & 56.49 & 1.25 \\ PartyScene & 42.37 & 0.89 & 52.91 & 3.61 & 29.68 & 0.08 & 44.72 & 1.65 \\ \hline Average & 52.38 & 1.52 & 67.34 & 5.64 & 52.27 & 1.84 & 56.78 & 2.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Coding efficiency and complexity reduction performance (All Intra main).
# Attosecond Streaking Time Delays: Finite-Range Interpretation and Applications
## 2 Finite-Range Interpretation
### Analysis of Streaking Time Delays Using Classical Electron Dynamics
It has been found [20; 21; 22; 23] that the classical estimates for the streaking time delays remain approximately constant while varying \(t_{i}\) over one field cycle. However, we point out that, independent of the representation of the streaking time delay as integral (Equation (7)) or in its finite-difference approximation (Equation (8)), these classical predictions diverge for \(E_{\rm s}=0\) and, hence, are not applicable at the corresponding times \(t_{i}\). For further discussion of the derivation, please see Refs. [20; 21]. In order to apply the classical estimates, the corresponding predictions have to be validated against results of ab initio numerical calculations. It has been shown [11; 12; 20; 21] that the classical results, based on Equations (7) and (8), are in good agreement with ab initio TDSE results for the streaking time delay. This is exemplified in Figure 2 for (a) hydrogen atoms, (b) helium atoms and (c) neon atoms, where the classical estimates (solid lines) are compared with the results of ab initio calculations from Refs. [24; 25]. Please note that Figure 2 also includes a comparison with predictions from an analytical formula (dashed lines), which will be discussed in Section 2.3. The level of agreement within the range of a few attoseconds provides the basis to use the classical analysis to gain insights into an interpretation and various aspects of the streaking time delay.
# Technical Interoperability for Machine Connectivity on the Shop Floor
## 5 Discussion  
The described artifacts have been implemented and tested in a real-life production system at SKF and have been proven to enable several steps towards CPPS. When 5G networks become available in a few years they will enable fast and scalable connectivity for the shop floor. The question for the 5G-Enabled Manufacturing project was therefore how that connectivity can be utilized. The dedicated LTE network that was deployed during the project was more than capable of handling the presented solution (128 kbps < 17 Mbps), which means that it fulfills the requirement of fast and scalable connectivity. Data is collected from several different systems mounted on the grinding machine, which increase the generalizability of the described solution.  
By exploiting Calvin as a common connectivity platform together with open industry standards for data communication and storage, relevant information is made visible in a cloud environment. Kafka and MongoDB are used to stream aggregated data to the analytics center that can apply algorithms and increase system transparency. Since Calvin also aligns the different types of connected systems, both locally in the factory and centrally in the private cloud, it would also simplify the process of connecting more machines and equipment, a prerequisite for more predictive capacity. Furthermore, the mobile operator support system allows manufacturing operators to access real-time data and to share important information.  
There are many ways to collect data from machines, and some of the described enabling technologies and their application have been tested before [46; 47]. However, when practically collecting data from a machine on the shop floor there seems to be no obvious solution that fits a generic machine setup. Machine suppliers are adding their own support for data collection, but this gives rise to the  
Figure 5: Example data entry in JSON format.  
issue of data ownership and supporting a multitude of different monitoring systems. Manufacturing companies that collect their own data can be more certain of their own access to it and how it is used. However, some data should perhaps also be centrally collected by equipment suppliers, given the benefit of volume. The solution described here is one generic way to acquire data from machines or equipment for storage in a private cloud and for further distribution.
# A Partitioned Rigid-Element and Interface-Element Method for Rock-Slope-Stability Analysis
## 3 Slope Stability Analysis
### Criteria of Slope Failure  
The following three criteria are often used in slope stability analysis when using the SRM [35].  
1. Non-convergence of the solution. When slope failure occurs, it is impossible to find a solution from the governing equations that can not only satisfy the static equilibrium but also satisfy the stress-strain relationship and strength criterion, so in this case, neither the displacement solution nor the force solution converges. Therefore, many numerical methods take non-convergence as the criterion of slope failure. However, when the slope does not fail but has large deformation, the solution will also not converge, so it is necessary to combine other criteria to judge the stability of the slope.
2. A plastic zone going through the slope from the bottom to the top. This is only a necessary condition for slope instability, but not a sufficient condition. Because the plastic zone penetration does not necessarily represent the failure of the structure, the slope will be unstable only when there is infinitely developing plastic deformation and large displacement on the slip surface.
3. Displacement mutation [PERSON] used it as a failure criterion when he proposed the SRM. By focusing on the nodal displacement on the slope, with the extension of the plastic zone and the increase in the number of iterations, the displacement will mutate somewhere and the slope will become unstable. However, when the slope failure occurs, not all the nodal displacements will change dramatically, so it is necessary to select and observe the node with the obvious displacement mutation artificially; as a result, the judgment may be subjective.  
According to the characteristics of the above criteria, a slope failure criterion suitable for the PRE-IE method can be obtained through the following example.
## 4 Analysis of Simulation Results
### Model Mismatch  
The above prediction compensator is based on the accurate mathematical model of the controlled object, however, in the actual process of pH regulation of liquid fertilizer, the accurate mathematical model of the system was difficult to obtain because of the complexity inside the system, which led to the actual model's inability to completely match with the theoretical controlled object model, thus seriously affecting the control effect of the controller and making the controller unable to operate stably. In this paper, three controllers were simulated under the conditions of model mismatch to examine the performance of the controllers.  
Figure 11: Step response curves of the three controllers under model matching.  
Figure 12: Training error curve of BP-PID-Smith controller under model matching.  
In this paper, the actual model was set to \(K=1.5\), \(T=1.5\), \(\tau=12\), and a perturbation of amplitude 0.1 was added. The step response curves of the three controllers under the model mismatch condition are shown in Figure 13, the training error curves of the BP-PID-Smith controller are shown in Figure 14, and the dynamic performance comparison is shown in Table 2.  
Figure 14: Training error curve of BP-PID-Smith controller under model mismatch.  
Figure 13: Step response curves of the three controllers under model mismatch.  
According to the data in Table 2, even though the Smith controller had a smaller maximum overshoot of 0.28%, its response speed was the slowest, the PID-Smith controller had a great improvement in response speed and stability compared to the Smith controller but there was still a higher overshoot, and the BP-PID-Smith controller had a better performance than the other two controllers both in terms of response speed and stability. The performance of the BP-PID-Smith controller was better than the other two controllers in terms of response speed and stability, and it had a smaller overshoot. Therefore, the BP-PID-Smith controller can meet the control requirements in the actual regulation process.
## 3 Results and Discussion
### Effects of Surfactant in Directing the Size and Morphology of Alanate Nanostructures
For TDA-stabilized LiAlH\({}_{4}\) and NaAlH\({}_{4}\), the sphere-like particles evolved into a rod-like structure at a concentration above 5 mM. The same phenomenon has been observed with TDA-stabilized NaBH\({}_{4}\) where spherical nanoparticles transformed into bar-like morphologies at increasing TDA concentrations [37]. Indeed, TDA has been proposed to have two CMCs and this would lead to variations in morphologies [41; 62]. Considering this, we propose that the self-assembly of TDA at its first CMC value would lead to a spherical micelle, and the nanorod is possibly the result of a side-to-side attachment of alanate nucleic or the aggregation of surfactants into larger rod-like micelles at the second CMC value [62; 63; 64]. Figure 5: The change in the average particle size of (**a**) LiAlH\({}_{4}\) stabilized with -RNH\({}_{2}\) (HXA, DDA, and ODA), (**b**) LiAlH\({}_{4}\) stabilized with -RSH (HTT, DDT, and ODT), (**c**) NaAlH\({}_{4}\) stabilized with -RNH\({}_{2}\) (HXA, DDA, and ODA), and (**d**) NaAlH\({}_{4}\) stabilized with -RSH (DDT and ODT) with an increase in surfactant concentration. Error bars represent standard deviation of the mean particle size. A morphological evolution upon increasing the surfactant concentration to 10 mM was also observed in NaAlH\({}_{4}\)-HXA, NaAlH\({}_{4}\)-DDA, and NaAlH\({}_{4}\)-ODA. In this case, the nanoparticles severely agglomerated into a thread-like network (Figures S9-S11). This may be due to the entanglements and interaction among the various micelles structure at high concentration. Indeed, extremely small nanoparticles possess a high surface free energy, therefore the particles may aggregate into a thread-like network to minimize the excess free energy by reducing the number of end caps [65; 66; 67]. As observed from TEM analysis (Table S1 and Figures S3-S8), the optimum surfactant concentration to produce the smallest LiAlH\({}_{4}\) nanoparticles is \(\sim\)10 mM. Therefore, LiAlH\({}_{4}\) stabilized with 10 mM of surfactants was further investigated. On the other hand, NaAlH\({}_{4}\) particles stabilized by HXA, DDA, and ODA were severely agglomerated at a surfactant concentration of 10 mM. Hence, NaAlH\({}_{4}\) stabilized with 1 mM of surfactants was further investigated as well-defined nanoparticles were produced at this concentration.
## 3 Methodology
### Data Processing
#### 3.2.3 Screening for High-Risk Interaction Scenarios  
In previous studies, the scenario with a TTC smaller than 3 s was considered as hazardous. Therefore, in this study, vehicle pairs where the MTTC between the MSV and NRV at the most dangerous moment was smaller than 3 s were defined as dangerous vehicle pairs. In order to standardize the scenario length and adapt to the input dimensions of the GAN model, it was necessary to unify the time steps. The trajectory data of dangerous vehicle pairs for the 2 s before and 1 s after the most dangerous moment (a total of 3 s) were combined to form a dangerous scenario. This three-second window, capturing the motion states of both vehicles, collectively formed what we defined as a roundabout merging high-risk scenario in this study.  
The original RounD trajectory data contain redundant information, such as speed, in the global coordinate system, and excessive features can affect the convergence and performance of the TimeGAN model. In this study, only the coordinates were selected as basic features because other features, such as speed and acceleration, can be derived from coordinates. Therefore, the horizontal and vertical coordinates of the two vehicles forming the vehicle pair (MSV and NRV) were used as model inputs. In cases where data for certain vehicles were missing, leading to incomplete data, such scenarios were excluded. After excluding scenarios where vehicle trajectory data were missing, a total of 1747 dangerous scenarios were screened out.
# A New Bit Repair Fast Reroute Mechanism for Smart Sensors IoT Network Infrastructure
###### Abstract  
Today's IP networks are experiencing a high increase in used and connected Internet of Things (IoT) devices and related deployed critical services. This puts increased demands on the reliability of underlayer transport networks. Therefore, modern networks must meet specific qualitative and quantitative parameters to satisfy customer service demands in line with the most common requirements of network fault tolerance and minimal packet loss. After a router or link failure within the transport network, the network convergence process begins. This process can take an unpredictable amount of time, usually depending on the size, the design of the network and the routing protocol used. Several solutions have been developed to address these issues, where one of which is the group of so-called Fast ReRoute (FRR) mechanisms. A general feature of these mechanisms is the fact that the resilience to network connectivity failures is addressed by calculating a pre-prepared alternative path. The path serves as a backup in the event of a network failure. This paper presents a new Bit Repair (B-REP) FRR mechanism that uses a special BIER header field (Bit-String) to explicitly indicate an alternative path used to route the packet. B-REP calculates an alternative path in advance as a majority of existing FRR solutions. The advantage of B-REP is the ability to define an alternative hop-by-hop path with full repair coverage throughout the network, where, unlike other solutions, we propose the use of a standardized solution for this purpose. The area of the B-REP application is communication networks working on the principle of packet switching, which use some link-state routing protocol. Therefore, B-REP can be successfully used in the IoT solutions especially in the field of ensuring communication from sensors in order to guarantee a minimum packet loss during data transmission.  
Keywords:internet of things (IoT); Fast Reroute; bit repair (B-REP); failure repair +
Footnote †: journal:  
_Article_  
A New Bit Repair Fast Reroute Mechanism for Smart Sensors IoT Network Infrastructure
## 2 Wireless Communication Technologies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tech/References** & **Frequency (GHz)** & **Range (m)** & **Power (mW)** & **Data Rate (kbps)** \\ \hline
**RFID**[35; 36] & 0.1356, 0.86–0.96 & 0–3 & 200 & 640 \\ \hline
**Bluetooth**[37; 38; 39; 40] & 2.4–2.5 & 1–100 & 2.5–100 & 1–3 \\ \hline
**Bluetooth low energy**[41; 42] & 2.4–2.5 & 1–100 & 10 & 1 \\ \hline
**ZigBee**[43; 44; 45] & 2.4–2.5 & 10–100 & 35 & 250 \\ \hline
**Wi-Fi**[46; 47] & 2.4–2.5 & 150–200 & 1 W & 54 \\ \hline
**UWB**[48; 49] & 3.1–10.6 & 3–10 & 250 & 53–480 \\ \hline
**Adaptive network topology** & 2.4–2.5 & 30 & 0.01–1 & 20–60 \\ \hline
**Medical implant** & & & & \\
**communication system** & & & & \\
**(MICS)**[51] & & & & \\ \hline
**Infrared data association** & & & & \\
**(IrDA)**[52] & 0.00038 & 0.1 & 4–10 & \(1\times 10^{6}\) \\ \hline
**Near field communication** & & & & \\
**(NFC)**[53] & 0.1356 & 0.05 & 0.15 mW & 424 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of available wireless wearable technologies. LPWANs offer low data rates and energy consumption, starting from 2G and extending to 5G networks [46; 47; 48]. The principle of free hardware has recently been implemented in various wireless applications to control and analyse data. However, these technologies have not yet been fully utilised in healthcare, even though they are gaining traction due to their compatibility with Bluetooth, Wi-Fi, ethernet, LoRa (long-range), and Lora WANs (long-range wide-area networks). One example is ESP8266 EX, which boasts a combination of affordability, energy efficiency, compactness, and dependable performance [54].