esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model of the Digital Beamforming Architecture in System-Level CAD
### TX Chain Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Nominal State** & **Ideal State** \\ \hline EVM (\(\%\) RMS) & 1.54 & 1.54 \\ EVM (peak) & 3.36 & 2.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Characteristics of the transmitted signal, versus nominal and ideal transmitting chain. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cases** & \multicolumn{2}{c}{\(\theta_{0}=0^{\circ}\)} & \multicolumn{2}{c}{\(\theta_{0}=30^{\circ}\)} & \multicolumn{2}{c}{\(\theta_{0}=52^{\circ}\)} \\ \hline  & **G (dB)** & **SLL (dB)** & **G (dB)** & **SLL (dB)** & **G (dB)** & **SLL (dB)** \\ \hline Nominal conditions & 28.86 & 21.11 & 29.17 & 18.17 & 26.63 & 15.63 \\ Numerical coupling & 28.55 & 22.25 & 29.28 & 21.42 & 27.15 & 19.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Antenna gain (G) and side-lobe level (SLL) for the full TX chain, considering all components with nominal parameters, compared with the previous results given in Table 6 (pertinent row is duplicated for easier comparison). The dynamic range of the transmit chain was analyzed to evaluate the impact of PA compression on the EVM. The results of the analysis, maintaining the PA compression at the nominal state, namely the output 1 dB compression point of 27 dBm, and increasing the total channel power, are reported in Figure 13. The effect of increasing the transmitted signal power is evident in terms of a significant increase of EVM versus the PA output power level, starting from an output power of about 14 dBm. It is worth noticing that this level corresponds to about 38 dBm of total power delivered from the system to the entire 319-element array (not including the antenna gain). In order to evaluate the overall performance degrading with respect to subsystems degradation, we analyzed the EVM for subsystem parameters worse than the nominal ones. This could occur for different causes, for instance aging and temperature stress. For this purpose, we considered a PN of \(-108\) dBc/Hz @ 1 MHz and ENOB = 4; in this case the EVM versus transmitted power is 0.1% worse than in nominal conditions until the PA power is below the compression point. Once the PA enters the compression region, the EVM is dominated by the PA nonlinearity itself and no variations are observed between the two cases; cf. Figure 13.  
As an example of constellation degradation, Figure 14 shows the effect of compression for 22 dBm output power (Figure 14a), as well as for 30 dBm output power (Figure 14b), in nominal conditions. Figure 11: Influence of the EVM of phase noise and ENOB. (**a**) Influence of the phase noise—nominal condition considers phase noise \(=-120\) dBc/Hz at 1 MHz offset from the carrier. (**b**) Influence of ENOB—nominal value is ENOB = 6. Figure 12: EVM, expressed as a percentage, obtained by varying both the imbalances of the upconverter.
# Design of Optical Tunnel Switching Networks for Big Data Applications
## 4 Torus Optical Tunnel Switch Network with 49 Nodes  
To increase the capacity of the network, Figure 7 shows a three-plane Torus topology optical tunnel switching network with 49 nodes. Figure 8 shows the module of the three-plane Torus topology optical tunnel switching network with 49 nodes. Each module is composed of 32 servers, 3 MUXs, 3 CDMUXs, an SDN switch and 3 nodes, with a node in each plane, as shown in Figure 8, where each node is a \(5\times 5\) WSS. Each module consists of three planes corresponding nodes, so 12 \(\lambda\)s can establish 6 direct tunnels for the X-axis and Y-axis, respectively. In addition, there are 36 relay tunnel nodes for each module to be established. Similarly, if each plane is responsible for transferring a relay tunnel node, the three direct tunnels are responsible for the traffic of 4 modules, so the average throughput is 75% (3/4). Each module has 36 servers that average to send to 48 modules with an average throughput of only 75% (36/48). Therefore, the three-plane Torus topology optical switching network is sufficient to provide 100% of the average traffic of the server.  
Figure 6: Tunnel creation of first plane.  
Figure 7: Three-plane Torus topology optical tunnel switching network with 49 nodes.  
Figure 9 shows tunnel creation of a three-plane Torus topology optical tunnel switching network with 49 nodes for the first plane. Let X = Y = 7. Therefore, node N (1, 7) and node N (6, 7) can establish a direct tunnel, and node N (1, 7) and node N (7, 2) need to establish a relay tunnel through node N (7, 7). Similarly, node N (1, 3) and node N (7, 3) can establish a direct tunnel, and node N (1, 3) and node N (6, 1) need to establish a relay tunnel through node N (6, 3). Other nodes can also establish direct tunnels and relay tunnels in sequence.  
The three-plane Torus topology optical switching network can greatly increase the capacity of the network. This architecture utilizes 84 \(\lambda\)s to provide 1764 servers and establish 2352 tunnels. The architecture in [11] utilizes 40 \(\lambda\)s to provide 400 servers and establish 600 tunnels. So, while the number of wavelengths has only doubled, the number of servers that it provides can be increased by more than four times. In the future, if the number of wavelengths can be increased, the number of servers provided can be increased.
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 3 Background  
### 5G System Architecture  
The 5G system architecture (5 GS) includes a 5G access network (AN), 5G core network (5 GC), and UE [26] (Figure 1) and is defined as service-based. The 5G (R)AN provides a wireless interface for the UE. The 5G base station (gNB) of the (R)AN provides the GTP to the UE. GTP is a tunneling protocol that is defined to carry data within mobile networks. The network packet generated from the UE is transmitted to the 5 GC via GPRS tunneling through the (R)AN. GTP consists of a control plane (GTP-C), user plane (GTP-U), and charging (GTP' derived from GTP-C) traffic [27].  
Several NFs in the 5 GC provide services through the serviced-based interface (SBI) of the SBA. The 5 GC network is responsible for functions such as session management, mobility, authentication, and security [28]. The control plane is responsible for control, and the user plane is responsible for data packet transmission [29]. In the control plane, mobility is mostly managed through AMF and SMF in 5 GC. The AMF manages the access and mobility of UEs through location service messages. The SMF allocates IP addresses to the UEs and manages user plane services [30]. PCF defines policies of all types in the network and provides them to NFs (e.g., AMF, SMF, et al.) in other control planes [31; 32]. Each NF of the 5 GC constituting the SBA controls data transmission [33]. The 5 GS was enhanced to support network data analysis services through the NWDAF [34]. NWDAF collects and analyzes information on various network domains. It is a core functional entity that provides analysis-based statistical and predictive insights into 5 GC. To perform tasks such as mobility prediction and optimization, DDoS attack detection, and predictive QoS and data correlation, ML algorithms can utilize multiple network information collected through the NWDAF [35]. The user plane function (UPF) of the 5 GC user plane is connected to the data network (DN) and is responsible for packet routing and forwarding.
# Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments
## 4 The Proposed Algorithm
### Attention Mechanism
#### 4.1.2 Feed-Forward Attention  
In the traditional attention mechanism introduced in Section 4.1.1, \(\varphi(\cdot)\) in Equation (2) is a learnable function, which depends on the newly generated output, \(s_{t-1}\), and the current state, \(h_{j}\). In for feed-forward attention, \(\varphi(\cdot)\) only depends on the current state \(h_{t}\), so Equations (2) and (3) become,  
\[e_{t}=\varphi(h_{t}), \tag{4}\]  
and  
\[\alpha_{t}=\frac{\exp\!\left(e_{t}\right)}{\sum_{k=1}^{T}\exp\!\left(e_{k} \right)}, \tag{5}\]  
respectively, where \(\alpha_{t}\) represent the value of the weight vectors, \(e_{t}\) and \(e_{j}\) represent the intermediate vectors. Thus, the attention mechanism is actually modeled by a super vector, \(c\), of the input sequence by computing an adaptive weighted average of the state sequence \(h\),  
\[\mathrm{c}=\sum\
olimits_{t=1}^{T}\alpha_{t}h_{t}, \tag{6}\]  
The feed-forward attention mechanism is shown in Figure 2. Intuitively, an attention mechanism will put emphasis on the important steps to yield high values in the vector \(c\) when modeling sequences.  
Figure 2: The structure of the “feed-forward” attention mechanism [13; 19]. Vectors in the state sequence \(h_{i}\) are fed into the learnable function \(\varphi(h_{i})\) to produce a weight vector \(\alpha\). The vector \(c\) is computed as a weighted average of \(h_{i}\) with the weighting given by the weight vector \(\alpha\).
## 3 Results and Discussions
### Membrane Characterization  
This section presents the analysis of Fourier Transform Infrared Spectroscopy with Attenuated Total Reflectance (FTIR-ATR), X-ray Photoelectron Spectroscopy (XPS), and Atomic Force Microscopy (AFM) to elucidate the structural and surface differences between the PES and sPES membranes.  
The FTIR-ATR spectra of PES and sPES membranes are shown in Figure 6. The absorption peak at 1010 cm\({}^{-1}\) is characteristic of the aromatic -SO\({}_{3}\)H symmetric stretching vibrations [29]. The normalized intensity of sPES is slightly stronger than that of PES, suggesting the introduction of sulfonic acid groups into the polymer chains. Additional characterization techniques are necessary to confirm this observation.  
XPS is essential for providing detailed information on the surface chemistry of membranes, particularly for identifying specific functional groups. Figure 7 presents the XPS spectra of PES and sPES membranes. The peak at 168 eV is characteristic of S2p, which indicates the existence of -SO\({}_{3}\)H [17], providing evidence of the sulfonation process. From the XPS spectra, the peak of the sulfonic acid group of sPES is slightly stronger than that of PES. This enhancement can be attributed to the sulfonation process of sPES membranes, which incorporate more sulfonic acid groups into the polymer chains. Although the differences in peak intensities between PES and sPES are subtle, they align with the FTIR results,  
Figure 6: FTIR-ATR spectra of selected PES and sPES membranes.  
collectively demonstrating the successful incorporation of sulfonic acid groups into the sPES polymer matrix.  
The surface morphologies of the PES and sPES characterized by AFM are shown in Figure 8. Surface changes reflect the influence that sulfonation has on the membrane morphology and can be observed by difference in the mean roughness (Ra). Consistent with previous research findings, the Ra parameters increased from PES (0.996 nm) to sPES (3.149 nm) with an increase in the sulfonation in the casting solution [30]. For PES, a relatively flatter smooth-phase morphology can be observed, while the sPES membranes exhibit more cluster-like features.
## 3 Results and Discussion
### Analysis of the Temperature Distribution along the Depth Direction  
Figure 5 shows the temperature distributions along the depth direction for the simulation conditions in tests 1 and 2. The temperature distributions are obtained at the points marked in the inset of Figure 5. The temperature distribution at point 2 was obtained when the laser beam reached the midpoint of the second scan track. As shown in the figure, temperature decreases significantly near the surface; however, the decrease of the temperature is alleviated as the depth increases. It was also found that at a depth of approximately 400 \(\upmu\)m, the temperature becomes closer to initial condition (400 \({}^{\circ}\)C). The temperature distributions at the first (point 1) and third (point 3) scan tracks are nearly identical for both the 0% and 50% overlap ratios. This indicates that the temperature distribution along the depth direction in the scan tracks is nearly uniform regardless of the number of scanning paths or the overlap ratio. The temperature gradients between the surface and at a depth of 100 \(\upmu\)m depth for the first and third scan tracks at an overlap ratio of 0% are approximately 6.07 \({}^{\circ}\)C/\(\upmu\)m. For the overlap ratio of 50%, the temperature gradient is also similar for the first and third scan tracks. In the region where the beam overlap (point 2), the temperature gradient is relatively low compared to that obtained in the scan tracks due to the low surface temperature in the overlapped region. For the 0% overlap ratio, it is ~1.3 \({}^{\circ}\)C/\(\upmu\)m, which is very gentle, but it increases to ~4.07 \({}^{\circ}\)C/\(\upmu\)m at an overlap 50%. According to the simulation results, the temperature changes greatly along the y and depth (or z) direction. Because dopant activation and diffusion are highly dependent on the temperature, it is  
Figure 4: Temporal temperature profile obtained at midpoints between two adjacent scanned lines: (**a**) test 1, (**b**) test 2, (**c**) test 3 and (**d**) test 4 (cross and circle mark in the figure indicate point 1 and point 2, respectively).  
expected that the dopant activation and diffusion outcomes will vary considerably along the y and z direction.
## 3 Computation of the principal components
### Distributed estimation of the covariance matrix  
This section presents an alternative methodology to distribute the computation and storage of the covariance matrix within the network. The methodology relies on the observation that sensors that are geographically distant often present low correlation values. This hypothesis often holds in real-world situations as shown in [32, 33]. This leads us to make the simplifying hypothesis that covariances between sensors which are out of reach is equal to zero. Note that the radio range can usually be tuned on wireless sensor platforms by increasing or reducing the power transmission level [25]. If available, this feature could be used to set the radio range such that nearby correlated sensors are within radio range.  
Let \(\mathcal{N}_{i}\) be the neighborhood of the sensor \(i\), i.e., the set of sensors which are in the communication range of the \(i\)-th node. An instance of neighborhood is illustrated in Figure 1. Our simplifying hypothesis assumes that \(c_{ij}[t]=0\) for all \(j\
otin\mathcal{N}_{i}\). This makes the computation of the covariance matrix scalable to large networks. The covariances between sensors \(i\) and \(j\) can indeed be computed recursively by Equation (10) once the sensor \(i\) keeps over time a record of \(t\), \(S_{j}\) and \(S_{ij}\) for \(j\in\mathcal{N}_{i}\). This hypothesis will be referred to in the following as the _local covariance hypothesis_.
## 3 Methodology for Precise Code Positioning  
The functional models for precise code positioning are outlined in this section, described based on the general observation model. To simplify, we designated the three frequencies as numbers 1, 2, and 3. The specific frequencies indicated by the numbers are shown in Table 4, and the values in parentheses are in MHz. It is important to note that, in the case of BDS-3, the IF combination of B1C/B1I introduces a significantly higher noise amplification factor compared to that of B2a/B1I [38]. Therefore, the IF13 combination in the experiment employs the IF combination of B2a/B1I. In the context of BDS-3, this means that the IF combination used is IF1223, but for simplicity, it is still referred to as IF1213 in the model derivation below.
# A Laser Irradiation Method for Controlling _Pieris rapae_ Larvae
###### Abstract  
At present, chemical pesticides remain the main approach for controlling _Pieris rapae_ (L.) (Lepidoptera: Pieridae). This research proposes a novel laser irradiation method for managing _P. rapae_ larvae as an alternative to chemical control. The effectiveness of controlling larvae and the influencing factors of lasers were studied to estimate optimal parameter combinations. Tests using the antifeedant effect and mortality of the larvae as dependent variables showed that the laser power, irradiation area, laser opening time and irradiation position were positively correlated with the _P. rapae_ controlling effect. The optimal parameters for each factor were the following: laser power, 7.5 W; irradiation area, 6.189 mm\({}^{2}\); laser opening time, 1.177 s; and irradiation position, middle of the abdomen. Based on these observations, a validation experiment was performed using the optimal combination of parameters, and the results showed that the antifeedant percentage of _P. rapae_ larvae within 24 h posttreatment was 98.49%, whereas the mortality rate was 100%. The optimal parameter combination identified in the study was suitable for _P. rapae_ larvae from the first- to fifth-instar stages, and a more effective controlling effect was observed with the younger larvae. These results can provide a theoretical basis for future pest control using laser pest-killing robots.  
chemical pesticide; laser; _Pieris rapae_; mortality; pest control
## 3 Length Measuring Capability and Three-Dimensional Measurement Capability  
### Statistical Characterisation and MPE Statements  
The MPE statement says that the difference between estimated distance \(\hat{d}\) derived from CMM measurement and the true distance \(d\) is bounded by a linear function of distance:  
\[|\hat{d}-d|\leq A+d/B.\]  
The MPE statement for a particular CMM is usually estimated from multiple measurements of calibrated length artefacts [28]. The MPE statement characterises CMM (length measuring) behaviour using two parameters \(A\) and \(B\). The MPE statement can be re-interpreted in terms of uncertainty \(u(d)\) associated with distance measurement,  
\[Ku(d)\leq A+d/B, \tag{6}\]  
where \(K\) (typically \(K=2\) or \(K=3\)) ensures that the probability of exceeding an MPE statement is suitably small. This paper allows us to quantify how the various influence factors contribute to the maximum permissible error. Conversely, an MPE statement can also be used to put bounds on the statistical parameters associated with the modelledinfluence factors. For example, a valid MPE statement can be used to bound the size of scale errors associated with a CMM. These issues are considered in detail in Section 6.8. Regarding the MPE statement as a quasi-statistical characterisation of the length measuring capability of a CMM, the MPE statement can also be used to give uncertainty statements for features derived from distances. Such an approach was also considered in the EUCoM project [29].
# Analytical and Graphical Profiling of Thread-Milling Cutters for Forming Internal Threads
## 2 Literature Review  
### Thread-Forming Methods  
Thread forming is a complex and laborious technological process. Nevertheless, the formation of threads and other helical surfaces is one of the most common technological tasks in mechanical engineering. Threads are widely used in various designs of machines, mechanisms, devices and many differently shaped parts and fasteners.  
The wide variety of machine parts with different shapes, sizes and accuracies of helical surfaces, as well as the variety of materials from which they are made, also determines the variety of methods for forming threads, which are reduced to the following three groups:  
* Methods for thread forming by chip removal;
* Methods for forming threads by plastic deformation of the processed material in a non-heated state;
* Methods for forming threads during the primary forming of parts.  
The first two methods are the main and most common ones. Their choice, in addition to the accuracy, productivity and efficiency provided, mainly depends on the values of the mechanical characteristics of the material being processed--Brinell hardness (HB), tensile strength (R\({}_{\text{m}}\)) and relative elongation (A%).  
Cutting processes provide higher quality characteristics of the machined surfaces, high accuracy, low roughness and higher strength, yield and fatigue limits of the machined parts.  
The third group of methods forms threaded surfaces or parts of threaded surfaces in hard alloy and plastic parts. The methods used are powder metallurgy, pressing or pressure casting, etc.  
Figure 1 shows a flowchart with the main characteristics determining the applicability of the methods of forming threads [1,2].
# Study on Surface Damage Induced by High Heavy Layer Movement and Mining-Induced Earthquakes
## 3 Results
### Comparative Analysis of Subsidence Patterns with and Without Mining-Induced Earthquakes
The 600-650 m stage showed greater relative increases (26.45% top stratum; 18.51% heavy layer) before fracturing, while post-fracture stages exhibited smaller but persistent increases, revealing seismicity's dual-phase impact: amplifying bending deformation in intact strata, and accelerating compaction in fractured zones. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Simulation Scenario** & \begin{tabular}{c} **Subsidence Displacement** \\ **Without Mining-Induced** \\ **Earthquake (m)** \\ \end{tabular} & \
\begin{tabular}{c} **Subsidence Displacement** \\ **with Mining-Induced** \\ **Earthquake (m)** \\ \end{tabular} &
\begin{tabular}{c} **Increment Percentage** \\ **Incernical Percentage** \\ \end{tabular} \\ \hline top stratum during & & & \\
600–650 m excavation & & & \\ top stratum during & & & \\
650–700 m excavation & & & \\ top stratum during & & & \\
700–750 m excavation & & & \\ heavy layer during & & & \\
600–650 m excavation & & & \\ heavy layer during & & & \\
650–700 m excavation & & & \\ heavy layer during & & & \\ heavy layer during & & & \\
700–750 m excavation & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Subsidence displacement comparison with and without mining-induced earthquakes. Figure 12: _Cont_. From Figure 12, the following patterns can be observed:  
1. In the process of surface damage induced by the movement of the heavy layer, the primary factor is the bending and fracturing of the heavy layer itself, while the heavy layer-type mining-induced earthquakes triggered by its movement play a secondary role. 2. Before the heavy layer undergoes fracturing, applying a single seismic event (defined as a simulated test event within our experimental setup) significantly increases the subsidence of both the heavy layer and the top stratum, with increases of 18.51% and 26.45% during excavation from 600 m to 650 m. After fracturing, applying a single seismic event causes a smaller increase in subsidence, with increases of 2.29% and 3.18% during excavation from 650 m to 700 m. This is because mining-induced earthquakes intensify movement of the heavy rock layer. During the heavy layer's subsidence, rocks break and expand, increasing porosity. Mining-induced earthquakes compress these pores, increasing the density of broken rocks and leading to greater subsidence. 3. The subsidence of different positions in the heavy layer and the top stratum is affected differently by mining-induced earthquakes. After a seismic event, subsidence decreases progressively from directly above the epicenter to both sides, indicating that closer proximity to the epicenter results in higher compaction of loose rocks and more significant changes in subsidence.