esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion
## 2 Methods for Ground-Motion Simulation
### Control Conditions of the Synthesis Coefficient  
A well-matched ground motion is defined as a ground motion with a minimum error in the response spectrum constraint and a minimum error in the duration constraint. The mean absolute error (MAE) is used to match the response spectra of the synthesized ground motions with the target spectrum. We used MAE as the error because it is a more common and straightforward error compared to other errors [43]. Equation (4) introduces the constraint equation for the response spectrum.  
\[E_{1}\ =\ \sum_{j\ =\ 1}^{m}\Big{[}\Big{|}S_{a}\Big{(}\sum_{i\ =\ 1}^{n}k_{i}a_{i},T_{j}\Big{)}-S_{a}^{*}\big{(}T_{j}\big{)}\Big{|}\Big{]}, \tag{4}\]  
where \(E_{1}\) refers to the MAE between the response spectra of the synthesized ground motions and the target spectrum, \(S_{a}^{*}\big{(}T_{j}\big{)}\) represents the target response spectrum obtained from the ground-motion prediction equation in the target area, and \(T_{j}\) indicates the selected self-oscillation period control point in the spectral matching process. m is the number of response spectrum period points. Furthermore, \(S_{a}\left(\sum_{j=1}^{n}k_{i}a_{i},T_{i}\right)\) represents the response spectrum calculated from the new time-history records obtained by a linear combination of the extracted characteristic ground motions, where \(\sum_{i=1}^{n}k_{i}a_{i}\) represents the new time-history record obtained by multiplying the extracted N characteristic ground motions by their respective coefficients.  
The simulating method applied by the software considers not only the amplitude and frequency spectrum reflected by the ground-motion response spectrum, but also the parametric characteristics of the duration, which is an important parameter of ground motions. As the structure undergoes nonlinearization, the probability of permanent deformation increases with longer durations due to the cumulative effect of earthquakes. Therefore, ground-motion duration is a very important constraint. It is necessary to obtain a reasonable prediction of the ground-motion duration and select the ground-motion duration prediction equation suitable for the target area. The constraint equation for the duration is introduced as Equation (5).  
\[E_{2}\ =\ [|D_{5-95}-d_{5-95}|], \tag{5}\]  
where \(d_{5-95}\) denotes the target ground-motion duration time (different types of duration definitions can be selected, and this paper considers a 5-95% significant duration as an example to introduce the method). The new synthetic ground-motion duration is recorded as \(D_{5-95}\).
## 3 Security Issues And Challenges
### Challenges on CC
#### 3.2.2 The Security Challenges of the CC  
Undoubtedly the most challenging aspect for CC is security. Despite deploying different security measures to secure CC, there are still many challenges for CSP to tackle [37], as follows:  
* _The threats from inside and outside:_ Malicious threats from outside the firm are dangerous to CSPs because the attacks are unpredictable and could cause severe damage to CSPs and CCCs. However, insider threats are considered the most dangerous since the adversaries emerge from within the company that provides CC service.
* _The superlative between public CC and on-premises CC (private):_ The best protection to dramatically reduce attacks is to utilize on-premises CC, which is less complex than public CC. At the same time, they are costly and disregard the principal motivation behind CC.
* _The multi-tenant:_ In the greater generality of the owners of CSP, a single hypervisor includes numerous VM that belong to multiple CCCs. These are called hosted VM and can be the contender of one another or followed by adversities to attack another VM. It expands the chance of an attack and, subsequently, inserts the challenge of innovating a secured approach.
* _The access from the web:_ CC is powerless against a wide scope of application-layer attacks, like injection attacks of the structured query language (SQL), and HTTP flood attacks. There ought to be appropriate significant knowledge about safety efforts for software developers. The advancement life cycle of software should incorporate the security control technique into SaaS improvement.
* _The guarantee hypervisor:_ The hypervisor or virtual machine manager (VMM) oversees and manages various OS running on a single metal server that is participated by the CCCs. The primary mission of VMM is to allot the resources to the OS or VM linked to a CCC. A VMM must protect the VM boundaries. Any security issues or attacks on VMM will compromise the security of the VM hosted on the physical servers.
* _The network scope dynamics and protocols:_ Services provided by the CC are assorted, complicated, and elastic, which could potentially create numerous security issues.  
Therefore, security mechanisms, such as IDS and intrusion prevention systems (IPS) for detecting and preventing attacks on CC are required [38].
## 4 Experimental Setup
### Measurement Procedures
A 0.3 nm tunable band-pass filter (TBPF) was also inserted in both setups to suppress the ASE-ASE (amplified spontaneous emission) beat noise in the photo-detection. These additional elements are highlighted in violet in Figures 7 and 8. EDFA gains and laser powers were tuned in order to have large OSNR values at the receiver and enable a system-level characterization even in the presence of this IL impairment. Large OSNR levels were necessary to attribute any transmission penalty to the transmitter functionality. Figure 8: RM characterization setup. \(P_{\text{opt}}\) and \(P_{\text{pd}}\) stand for the total power in the respective locations down the optical signal path. Dashed lines indicate connections that were done at some stages of the measurement sessions. OSA, optical spectrum analyzer. Figure 7: MZM characterization setup. \(P_{\text{opt}}\) and \(P_{\text{pd}}\) stand for the total power in the respective locations down the optical signal path. Dashed lines indicate connections that were done at some stages of the measurement sessions. TLS, tunable light source; EDFA, erbium-doped fiber amplifier; TBPF, tunable band-pass filter; PC, polarization controller; SM, single mode; VOA, variable optical attenuator; PM, power meter; PIC, photonic integrated circuit; EIC, electronic integrated circuit; PPG, pulse pattern generator; ED, error detector; TIA, trans-impedance amplifier; PD, photo-detector.
# Gyrotrons as High-Frequency Drivers for Undulators and High-Gradient Accelerators
## 4 Gyrotron-Driven Ultra-Compact Particle Accelerators
In each of them, the Bragg ring grooves and the channels of electrons with a diameter of 0.3 mm are ablated by using a femtosecond laser. The first variety is a 300 GHz CW millimeter-wave diamond structure. It is appropriate for 1 MeV range CW linear accelerators used for food irradiation, sterilization, industrial radiography, and cargo inspection. To obtain 1 MeV acceleration in a 10 cm long structure with an accelerating field of 10 MV/m a gyrotron with CW power of 3 kW is considered. The second variety is a C-band CW structure made of TiO\({}_{2}\)- doped Al\({}_{2}\)O\({}_{3}\) ceramic. According to the computer-aided design, the 1 MeV dielectric-loaded accelerator with a 1 MV/m accelerating gradient has only 1.53 kW thermal losses (of which 46% is the dielectric loss). In the framework of the Ultra-Compact XFEL project which is being pursued at the University of California, Los Angeles [66], a high-gradient 34 GHz standing-wave RF accelerating structure for the longitudinal phase-space linearization with an integrated voltage of at least 15 MV working on the 6 th harmonic of the main Linac frequency has been investigated recently. Based on analytical and numerical studies of the possible designs, an in-depth analysis of the requirements imposed on the different components has been presented. Additionally, it is reported that estimations on the gyro-klystrons provide an RF output of about 3 MW that, in combination with the pulse compressor system, can generate an RF output power of 12 MW in order to feed the Ka-band linearizer.
## 9 Results and Discussion
### Tail Latency Analysis  
Figure 9 presents the evaluation of tail latency (99 th percentile) and latency-sensitive performance across each workload. The analysis reveals two behavioral patterns of our model. For short-lived applications, the hybrid model shows the second-highest latency, up to 2\(\times\) that of LC (best) and 1.5\(\times\) that of HotC. However, for resource-intensive workloads, the hybrid model improves significantly, becoming the second-best performer. In both cases, the higher median latency compared to other methods is expected due to the queuing overhead introduced by the buffer-aware scheduler. This overhead is more noticeable in short-lived applications because of their very short execution time.  
For a deeper analysis and understanding of these results, we break down tail latency (99 th percentile) into three components: queuing time, cold start overhead, and execution time. We then compare tail latency (P99) for both short-lived and resource-intensive functions to assess the contribution of each component. The results (Figure 10) show that OW suffers from frequent cold starts in both cases, resulting in the highest overall latency despite having the shortest queuing delay. In contrast, the hybrid model exhibits the longest queuing time among all baselines. This queuing overhead--up to 60% of total latency in short-lived functions--is due to the buffer-aware scheduler, which holds requests until warm containers become available. This strategy minimizes cold starts across all cases--7% in short-lived workloads and 14% in resource-intensive functions. However, in the short-lived case, it noticeably increases the waiting time for these brief tasks compared  
Figure 8: (**a**) The overall latency of each method. (**b**) The latency distribution of the tested applications.  
to HotC, which experiences more cold starts. While avoiding cold starts ensures more consistent latency for most users, requests that fall within the 99 th percentile experience significant queuing delay (132 ms), which can be problematic for latency-sensitive short tasks. In contrast, under compute-intensive workloads, the hybrid model shows improved tail performance (second only to LC). Although it continues to incur the highest queuing time (nearly twice that of HotC), it significantly reduces cold start occurrences, which is critical for handling resource-intensive functions efficiently.  
That said, despite the increased median latency, SLA violations remain within acceptable limits, as previously demonstrated. Moreover, the number of spawned containers is significantly lower than in methods with shorter median latency. For instance, the hybrid model spawns up to 60% fewer containers than LC, the method with the lowest median latency.
# A Survey on Penetration Path Planning in Automated Penetration Testing
## 4 Planning Models in Penetration Path Planning
### Graphical Security Model
#### 4.1.1 Attack Tree  
[PERSON] [31] first introduced the concept of attack trees, a hierarchical model designed for visualizing attack paths by breaking down top-level goals into specific steps and conditions, providing a structured framework for security experts to analyze penetration paths. Building upon this foundation, [PERSON] et al. [32] proposed an enhanced attack tree that constructs the model by analyzing user behavior to ascertain attack probabilities. Subsequently, [PERSON] et al. [33] introduced the ordered weighted averaging attack tree, offering a comprehensive attack path analysis that integrates both attack cost and probability. While the attack tree model is intuitive, concise, and logical, offering the advantages of structure and reusability, it faces scalability challenges and cannot model the cyclic attack process when modeling complex, multi-target attack scenarios due to the inherent limitations of its tree structure.
## 4 Results
### Forward Motion
So, by considering the number of nodes in the input layer = 2, _ANN_s with 3-10 neurons in the hidden layer (as a \"black box\") were used to estimate the resistance coefficient of the submarine for the speeds of 0.1, 0.45, 1.15, and 1.7 m/s, and Sum Square Error (\(SSE=\left(\left(C_{R-f}\right)_{CFD}-\left(C_{R-f}\right)_{pre}\right)^{2}\)) of the models were estimated and shown in Figure 9. \(\left(C_{R-f}\right)_{CFD}\) and \(\left(C_{R-f}\right)_{pre}\) are the predicted resistance coefficient by using _CFD_ and _ANN_, respectively. Figure 9 shows that _ANN_ with six neurons in the hidden layer (_NON_ = 6) has the best performance for predicting resistance coefficients. Equations (17) to (19) and the trained _ANN_ with _NON_ = 6 were used to predict the resistance coefficient of the submarine for the speeds 0.1, 0.45, 1.15, and 1.7 m/s, and the results were shown in Figure 10. The error estimated as \(Error=100*\frac{\left(C_{R-f}\right)_{CFD}-\left(C_{R-f}\right)_{pre}}{\left( C_{R-f}\right)_{CFD}}\) was also calculated for different methods and shown in Figure 11.  
Figure 8: _ANN_ structures with large _NON_ in the hidden layer. Figure 9: The _SSE_ of _ANN_ versus the _NON_ for forward motion. According to Figure 11a, Equations (18) and (19) are unable to estimate accurately the resistance coefficient for speeds lower than those used in training the artificial neural network. The model error varies from 15 to 30 percent depending on the submarine's length and diameter. \(NILS\) (Equation (17)) estimates the resistance coefficient with a maximum error of 15%. \(ANN\) with \(NON=6\) estimates the resistance coefficient with a maximum error of 3% and 10% for the submarines with \(L/D_{S}>7.5\) and \(L/D_{S}<7.5\), respectively. Figure 11: Comparison of the estimation error of the resistance coefficient of the different cases of the submarines for different forward speeds, (**a**) \(V_{f}=0.1\) m/s, (**b**) \(V_{f}=0.45\) m/s, (**c**) \(V_{f}=1.15\) m/s, (**d**) \(V_{f}=1.7\) m/s. Figure 10: Comparison of the resistance coefficient of the different cases of the submarines for some forward speeds, (**a**) \(V_{f}=0.1\) m/s, (**b**) \(V_{f}=0.45\) m/s, (**c**) \(V_{f}=1.15\) m/s, (**d**) \(V_{f}=1.7\) m/s. As shown in Figure 11b,c, Equations (17) and (19) and \(ANN\) with \(NON=6\) provide high accuracy for estimating resistance coefficients at speeds used for training \(ANN\)s and \(NLLS\). The results for the forward speed 1.7 m/s (Figure 11d) which was higher than the range of speeds used in the extraction of relationships indicates that Equations (17) and (19) estimate the resistance coefficient with a maximum 10% and 7% error, respectively. \(ANN\) with \(NON=6\) shows a better performance than other methods, with maximum error of 3%.
# A New Coding Paradigm for the Primitive Relay Channel +
## 2 Existing Upper and Lower Bounds
### Direct Transmission Lower Bound  
In the direct transmission, the source communicates with the destination by using an optimal point-to-point code. The relay transmission is fixed at the most favorable symbol for the channel from the source to the destination.  
For the _general relay channel_, direct transmission allows for achieving the following rate ([17] Section 16.3):  
\[R_{\rm DT}=\max_{p_{X_{\rm S}},p_{\rm R}}I(X_{\rm S};Y_{\rm SD}|X_{\rm R}=x_{ \rm R}). \tag{11}\]  
For the case of the _primitive relay channel_, the direct transmission lower bound specializes to  
\[R_{\rm DT}=\max_{p_{X_{\rm S}}}I(X_{\rm S};Y_{\rm SD}). \tag{12}\]  
Note that the direct transmission lower bound (12) meets the cut-set upper bound (2), and it equals the capacity of the primitive relay channel when either of the following two conditions holds:  
1. the primitive relay channel is reversely degraded, which implies that \(I(X_{\rm S};Y_{\rm SD})=I(X_{\rm S};Y_{\rm SD},Y_{\rm SD})\);
2. \(C_{\rm RD}=0\).  
For the special case of the _erasure relay channel_, the direct transmission lower bound can be rewritten as  
\[R_{\rm DT}=1-\varepsilon_{\rm SD}. \tag{13}\]  
The direct transmission lower bound (13) meets the cut-set upper bound (3), and it equals the capacity of the erasure relay channel when either \(1-\varepsilon_{\rm SD}=1-\varepsilon_{\rm SR}\varepsilon_{\rm SD}\) or \(C_{\rm RD}=0\).
# Design, Challenges and Developments for 5G Massive MIMO Antenna Systems at Sub 6-GHz Band: A Review
## 2 5G Massive MIMO at Sub-6 GHz
### 5G Massive MIMO Antennas for BS Applications  
Many techniques are used in the architecture and configuration of antennas to design mMIMO systems for 5G sub-6 GHz applications. Many base station antenna designs attempt to cover dual band/multiband in compact volume and support mMIMO systems in base station applications.
# A Survey of UV Filters Used in Sunscreen Cosmetics
## 2 Results
### Composition Complexity of the Analyzed Preparations
Among those for adults (n = 10), the most prevalent were ZnO (40.0%) and TiO\({}_{2}\) (30.0%), followed by nanometric scale forms of ZnO and TiO\({}_{2}\), and the chemical filters phenylbenzimidazole sulfonic acid, butyl methoxydibenzoylmethane, ethylhexyl salicylate, diethylamino hydroxybenzoylacyl hexyl benzoate, and octocylene, all of which were present in 10% to 20% of samples. Hence, it can be seen that most single- and two-component preparations tended to use physical filters as sunscreen ingredients, and among these, the non-nano form is more common than the nano form. The complexity of the composition of sunscreen preparations is related to the scope of UV protection of individual filters. When properly selected, the UV filters in a given preparation should fully protect skin against both UVA and UVB radiation. A commonly used combinations of filters that absorb a wide range of UV rays are, e.g., the mixture of Avobenzene (BMBM) with octocrylene (OC) and/or ethylhexyl salicate (EHS) or the fusion of Avobenzene (BMBM) with Tinosrob S (BEMT), and/or ethylhexyl salicate (EHS) and/or diethylamino hydroxybenzoylacyl hexyl benzoate (DHHB). Avobenzene (BMBM) and DHHB provide protection against UVA radiation. Tinosrob S (BEMT) is broad-spectrum filter (UVA and UVB). In turn, the most popular UVB filters include EHT, EHS, OC, EHMC, and PBSA [10; 11; 19].
## 2 Numerical Simulation Method of Riser Vortex Shock Vibration
### Dynamic Model of Riser Structure  
The riser model is simplified into Euler-Bernoulli beams, assuming the same material and section shape along the tube length. The forward-flow direction is the x axis, with the cross-flow direction being the y axis, and the length direction of the riser is the z axis. The riser model is shown in Figure 1, while the riser unit is shown in Figure 2.  
Taking the coordinate system shown in the above figure as an example, the differential control equation for the motion of a three-dimensional riser per unit length is shown in Equation (1) below, without considering the influence of waves on its motion.  
\[\frac{\partial^{2}}{\partial z^{2}}\bigg{[}\text{EI}\frac{\partial^{2}u_{i}(z, t)}{\partial z^{2}}\bigg{]}-\frac{\partial}{\partial z}\bigg{[}T(z)\frac{ \partial u_{i}(z,t)}{\partial z}\bigg{]}+m\frac{\partial^{2}u_{i}(z,t)}{ \partial t^{2}}+c=(z,t) \tag{1}\]  
where EI is flexural stiffness and EA is tensile stiffness. \(m\) is riser mass per unit length, and c is structural damping. \(\text{u}_{i}(\text{z},\text{t})\) indicates the displacement of cross-flow and down-flow. \(\text{F}_{i}(\text{z},t)\) indicates the fluid force of cross-flow and down-flow (i = 1 is down-flow, i = 2 is cross-flow) and the \(\text{T}(\text{z})\) is tension force inside the riser.
# Optimization of Machining Parameters for Corner Accuracy Improvement for WEDM Processing
## 3 Experiment and Discussion
### Effect of Parameter on Corner Error
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & **F-Value** & _p-Value (Prob \textgreater{} F)_ & **Remarks** \\ \hline Model & 0.001396 & 27 & 0.000052 & 7.95 & \textless{}0.0001 & Significant \\ ON & 0.000647 & 1 & 0.000647 & 99.50 & \textless{}0.0001 & Significant \\ OFF & 0.000028 & 1 & 0.000028 & 4.27 & 0.0490 & Significant \\ OV & 0.000036 & 1 & 0.000036 & 5.53 & 0.0266 & Significant \\ SV & 0.000022 & 1 & 0.000022 & 3.39 & 0.0770 & \\ WT & 0.000196 & 1 & 0.000196 & 30.22 & \textless{}0.0001 & Significant \\ WA & 0.000010 & 1 & 0.000010 & 1.50 & 0.2316 & \\ ON OFF & 0.000001 & 1 & 0.000001 & 0.17 & 0.6808 & \\ ON OV & 0.000029 & 1 & 0.000029 & 4.48 & 0.0440 & Significant \\ ON SV & 0.000000 & 1 & 0.000000 & 0.01 & 0.9226 & \\ ON WT & 0.000005 & 1 & 0.000005 & 0.69 & 0.4130 & \\ ON WA & 0.000001 & 1 & 0.000001 & 0.21 & 0.6478 & \\ OFF OV & 0.000013 & 1 & 0.000013 & 1.94 & 0.1760 & \\ OFF SV & 0.000003 & 1 & 0.000003 & 0.48 & 0.4943 & \\ OFF WT & 0.000002 & 1 & 0.000002 & 0.29 & 0.5965 & \\ OFF WA & 0.000002 & 1 & 0.000002 & 0.34 & 0.5623 & \\ OV SV & 0.000002 & 1 & 0.000002 & 0.31 & 0.5839 & \\ OV WT & 0.000000 & 1 & 0.000000 & 0.01 & 0.9307 & \\ OV WA & 0.000000 & 1 & 0.000000 & 0.08 & 0.7858 & \\ SV WT & 0.000000 & 1 & 0.000000 & 0.08 & 0.7837 & \\ SV WA & 0.000000 & 1 & 0.000000 & 0.08 & 0.7837 & \\ WT WA & 0.000000 & 1 & 0.000000 & 0.05 & 0.8297 & \\ ON\({}^{2}\) & 0.000005 & 1 & 0.000005 & 0.72 & 0.4024 & \\ OFF\({}^{2}\) & 0.000051 & 1 & 0.000051 & 7.88 & 0.0094 & Significant \\ OV\({}^{2}\) & 0.000008 & 1 & 0.000008 & 1.22 & 0.2799 & \\ SV\({}^{2}\) & 0.000219 & 1 & 0.000219 & 33.69 & \textless{}0.0000 & Significant \\ WT\({}^{2}\) & 0.000102 & 1 & 0.000102 & 15.63 & 0.0005 & Significant \\ WA\({}^{2}\) & 0.000000 & 1 & 0.000000 & 0.06 & 0.8056 & \\ Residual & 0.000169 & 26 & 0.000007 & & & \\ Lack-of-Fit & 0.000151 & 21 & 0.000007 & 2.04 & 0.2197 & \\ Pure Error & 0.000018 & 5 & 0.000004 & & & \\ Cor. Total & 0.001565 & 53 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ANOVA results of quadratic response surface model for 60\({}^{\circ}\) corner. Figure 3: Pareto chart response surface model for 30\({}^{\circ}\) corner.