esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Carrier Phase Recovery Approaches
### Block-Wise Average Carrier Phase Recovery  
As an \(n\)-th power feed-forward carrier phase recovery approach, which is schematically shown in Figure 3, the block-wise average algorithm calculates the \(n\)-th power of the received symbols to remove the information of the modulated phase in the \(n\)-PSK coherent transmission systems, and the computed phase (\(n\)-th power) data are summed and averaged over a certain block. The averaged phase value is then divided over \(n\), and the final result is regarded as the estimated phase for the received symbols within the entire block [29; 30]. For the \(n\)-PSK coherent optical communication systems, the estimated carrier phase for each process block using the block-wise average algorithm can be expressed as:  
\[\varphi_{BWA}\left(k\right)=\frac{1}{n}\text{arg}\left\{\sum\limits_{p=1+ \left(q-1\right)\cdot N_{BWA}}^{q\cdot N_{BWA}}x^{n}\left(p\right)\right\}, \tag{10}\]  
\[q=\left\lceil k/N_{BWA}\right\rceil, \tag{11}\]  
where \(k\) is the index of the received symbol, \(N_{BWA}\) is the block length in the block-wise average algorithm, and \(\left\lceil x\right\rceil\) means the closest integer later than \(x\).  
The BER floor of the block-wise average carrier phase recovery in the \(n\)-PSK coherent optical communication systems can be derived using the Taylor series expansion, and can be approximately described using the following equation:  
\[BER_{floor}^{BWA}\approx\frac{1}{N_{BWA}\text{log}_{2}n}\cdot\sum\limits_{p=1 }^{N_{BWA}}erfc\left(\frac{\pi}{n\sqrt{2}\sigma_{BWA}\left(p\right)}\right), \tag{12}\]  
\[\sigma_{BWA}^{2}\left(p\right)=\frac{\sigma_{T}^{2}\left[2\left(p-1\right)^{ 3}+3\left(p-1\right)^{2}+2\left(N_{BWA}-p\right)^{3}+3\left(N_{BWA}-p\right)^ {2}+N_{BWA}-1\right]}{6N_{BWA}^{2}}, \tag{13}\]  
Figure 2: Schematic of one-tap normalized LMS carrier phase recovery algorithm.  
where \(\sigma_{T}^{2}\) is the total phase noise variance (or effective phase noise variance) in the long-haul high-speed \(n\)-PSK optical transmission systems.
# NFV/SDN as an Enabler for Dynamic Placement Method of mmWave Embedded UAV Access Base Stations
## 3 Architecture and System Models
### Architecture  
Figure 1 shows the overall architecture [4]. UAVs can be placed in the sky without being affected by ground restrictions, thus ensuring a higher probability of a line-of-sight environment, which is important in the millimeter-wave band. In addition, since the base station itself is mobile, it is possible to quickly construct a network in a disaster area. Moreover, for the selection of the types of UAVs, this study assumes the use of a multirotor UAV rather than a fixed-wing UAV for our temporary base stations. Unlike fixed-wing UAVs, multirotor UAVs have a higher degree of freedom of movement, enabling the optimization of not only the placement but also the UAV's trajectories [35].  
As shown in Figure 1, the system consists of two types of UAVs: access UAVs and backhaul UAVs. The backhaul UAV is responsible for relaying the traffic sent from the base station on the ground and other backhaul UAVs to other UAVs. If the distance between the access UAV and the neighboring base station is fixed, by using backhaul UAVs as relays in between base stations and access UAVs, the transmission distances between these links are effectively shortened. The shorter communication distance between UAVs alleviates the effects of distance attenuation and rain attenuation, which are problems in the millimeter-wave band. This allows for a longer communication distance between the access UAV and the ground base station, which can be used for various use cases. The access UAV provides the traffic sent from the backhaul UAV to the user on the ground. Since the access UAV provides data directly to the user, its placement has a significant impact on the data rate provided, which is the main focus of this paper.  
Figure 1: System architecture.
# A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
## 6 Attacks that Require Advanced Detection
### AS Path Forgery  
AS path forgery is neither direct nor simple, as it requires careful planning and execution to be successful and to remain undetected. AS path forgery, by altering the AS path attribute in BGP announcements, can create discrepancies in routing information that are observable from multiple vantage points. The technique of leveraging data from these diverse points can enhance the detection of such forgeries, as inconsistencies in AS path information across different parts of the Internet might indicate a forgery attempt. This criterion supports the inclusion of AS path forgery for its potential to benefit from early detection through a comprehensive global routing table analysis.  
AS path forgery does not inherently require collaboration between multiple ASs to be executed. However, the distributed nature of the Internet means that the effects of such forgeries can propagate widely, affecting routing decisions across numerous ASs. While this criterion focuses on collaborative attacks, the widespread impact of AS path forgery suggests that its detection could benefit from group-level analyses, albeit indirectly.  
This attack directly involves complex interactions across ASs, as the forged AS path can mislead routers about the best path for traffic, affecting routing decisions globally. Understanding these interactions and the dynamics of AS relationships is crucial for detecting AS path forgery, aligning well with this inclusion criterion. AS path forgery exemplifies the sophisticated manipulation of routing information. Attackers must have a deep understanding of BGP operations and the trust relationships between ASs to craft believable, yet false, AS paths. This level of sophistication in manipulating routing information strongly meets the criteria for inclusion. The nature of AS path forgery allows it to be relatively stealthy, as it can be designed to appear as legitimate routing information. This stealthiness, coupled with the potential for such attacks to evade detection by conventional monitoring systems that may not closely scrutinize AS path attributes, aligns with the criteria for stealthiness and evasive techniques.
# Multi-Connectivity-Based Adaptive Fractional Packet Duplication in Cellular Networks
## 3 Multi-Connectivity in Cellular Networks
### Limitations of Multi-Connectivity
#### 3.5.1 Delay and Packet Reordering  
Since UE is connected to two different RATs, the radio resource management (RRM) procedures can be different and radio link conditions can also add up to the transmission delay. The packets might very well arrive out of order at the UE. A proper packet-reordering mechanism is needed to solve this problem and avoid excessive buffering, which leads to degraded services for time-sensitive applications.
# Face Keypoint Detection Method Based on Blaze_ghost Network
## 4 Experiment and Result Analysis
### Result and Discussion  
#### 4.3.1 Comparative Experiment  
This paper uses the NME (Normal Mean Error), FR (Failure Rate), Inference Time, and model size as evaluation indicators. Among them, the NME is the normalized average error, which is the value obtained by averaging the normalized estimated errors of all keypoints on a face. The FR is another standard for evaluating the accuracy of facial keypoint positioning algorithms. The Inference Time is the time it takes to locate the keypoints. This paper uses 2500 testing data from WFLW for testing and compares the experimental data of the proposed model with the data of the original network model, as shown in Table 5.  
From the experimental data comparison in Table 5, it can be seen that the improved backbone network proposed in this paper, Blaze_ghost, has reduced the NME and FR to varying degrees in the evaluation indicators. At the same time, although the Inference Time and model size have not decreased, the model still maintains its lightweight structure. These data indicate that the overall performance of the improved facial keypoint detection algorithm has been significantly improved.
## 5 Results and Discussion
### Sentiment Classification  
#### 5.1.1 Results Based on IMDB Dataset  
Table 1 represents a comparative analysis of the Sentiment Classification model for several performance metrics through altering training data. The analysis of TD-Spider TaylorChoA-GRU for the precision, recall and F-measure is listed below. In 80% of training data, the precision of the developed TD-Spider TaylorChoA-GRU is 0.935. The recall of TD-Spider TaylorChoA-GRU is 0.941 for 80% of training data. In addition, considering 80% of training data, the F-measure of the developed model is 0.938.  
Figure 4 represents the accuracy and loss for the Sentiment Classification model. Accuracy means the degree to which a measurement is accurate in relation to its true value. Here, when the iteration increases, the accuracy also increases. Similarly, the consequence of a poor prediction is loss. When we increase the iterations, the loss is decreased. Hence, the proposed method provides a better performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metrics** & **Data (\%)** & **HSACN** & **MCNN** & **DCBVN** & [3] & [34] & [17] & **Proposed** \\ \hline \multirow{4}{*}{Precision} & 60\% & 0.785 & 0.825 & 0.855 & 0.869 & 0.895 & 0.900 & 0.904 \\  & 70\% & 0.805 & 0.835 & 0.865 & 0.888 & 0.903 & 0.908 & 0.914 \\  & 80\% & 0.824 & 0.854 & 0.885 & 0.898 & 0.925 & 0.929 & 0.935 \\  & 90\% & 0.852 & 0.874 & 0.905 & 0.927 & 0.941 & 0.943 & 0.949 \\ \hline \multirow{4}{*}{Recall} & 60\% & 0.795 & 0.835 & 0.865 & 0.877 & 0.905 & 0.908 & 0.914 \\  & 70\% & 0.814 & 0.841 & 0.885 & 0.898 & 0.914 & 0.919 & 0.925 \\  & 80\% & 0.841 & 0.865 & 0.895 & 0.908 & 0.937 & 0.939 & 0.941 \\  & 90\% & 0.865 & 0.885 & 0.914 & 0.937 & 0.965 & 0.968 & 0.970 \\ \hline \multirow{4}{*}{F-measure} & 60\% & 0.790 & 0.830 & 0.860 & 0.873 & 0.900 & 0.904 & 0.909 \\  & 70\% & 0.809 & 0.838 & 0.875 & 0.893 & 0.908 & 0.913 & 0.920 \\ \cline{1-1}  & 80\% & 0.833 & 0.860 & 0.890 & 0.903 & 0.931 & 0.934 & 0.938 \\ \cline{1-1}  & 90\% & 0.859 & 0.880 & 0.910 & 0.932 & 0.953 & 0.955 & 0.959 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of Sentiment Classification for IMDB dataset in terms of precision, recall and F-measure.
# Research on Residual Life Estimation Method for KMN Steel Based on Nonlinear Ultrasonic Testing
## 4 Discussion
### Verify  
Next, estimate the residual life of another set of KMN specimens to verify the effectiveness of the method. The nonlinear ultrasonic test is performed on the KMN fatigue specimen and the nonlinear coefficient is measured. The equivalent crack length at the early fatigue stage (before 10\({}^{6}\) cycles) is obtained using the empirical formula Equation (13). The fatigue life \(N_{\rm p}\) and residual life \(N_{\rm r}\) of the specimen at this time are calculated by  
Figure 10: The relationship curve between normalized nonlinear coefficient and equivalent crack length under bending fatigue.  
Equations (14)-(16). The very high cycle fatigue initiation life model of KMN steel established by our research group in the early stage is shown in Equation (18) [31]. The prediction results are compared with the actual fatigue test results, as shown in Table 6.  
\[N_{\text{i}}=\frac{18 GW_{\text{s}}}{(\Delta\sigma-\Delta\sigma_{-1})^{2}\pi(1- \
u)!}, \tag{18}\]  
where \(N_{i}\) is the initiation life of the crack, \(W_{\text{s}}\) is the specific fracture energy, \(G\) is the shear modulus of the KMN-I matrix, \(l\) is the semilength of the slip band and \(\
u\) is Poisson's ratio. \(l\) is about half the grain size (10 \(\upmu\)m). The specific fracture energy of low carbon steel (\(W_{\text{s}}\approx\) 3.8 \(\times\) 10\({}^{5}\) N\(\cdot\)m\({}^{-1}\)) was used in this study.  
It can be seen from Table 6 that there is a certain error between the KMN fatigue life estimated by the nonlinear ultrasonic testing method and the test result, because the dispersion of the fatigue test is relatively large. Especially in the high cycle fatigue stage, the fatigue life under the same stress level may vary several times or even ten times. The safety factor of fatigue life is usually greater than 2, so we believe that the relative error is within the allowable range of engineering errors. The error is particularly low when the number of fatigue cycles is high. The effectiveness of this method is proven, so nonlinear ultrasonic testing can be used to estimate the residual life of KMN steel.
## 2 EPIC Earthquake Early Warning System  
EPIC is an algorithm that provides a warning for ground shaking, produced by earthquake waves. In this approach, a seismic station network is used to detect the first incident energy at the surface and convert the information in these low-amplitude waves into an estimate of the peak ground shaking that follows. The closest stations to the earthquake epicenter are the first to detect seismic energy [30]. The EPIC methodology identifies P-wave arrivals transmit from the sensors, determines the location of the earthquake using arrival times, and estimate its magnitude based on the epicentral distance and the peak displacement (\(Pd\)) [2]. Additionally, the maximum predominant period (\(T_{p}^{max}\)) in the 4 s following the P arrival is scaled with the magnitude of the earthquake. It then tries to estimate ground shaking using attenuation relations. All data is continuously collected, and as additional data is available from the first stations and additional stations, the hazard map can be updated every time. Since the most extensive ground-shaking observations are also made close to the epicenter, they can be integrated into the hazard assessment.
# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 3 Experimental Setup
### Evaluation Framework
#### 3.3.4 Speaker Turn Detection  
The ability to accurately detect speaker transitions represents a fundamental aspect of diarization quality that might be masked in the overall DER. We evaluate turn detection performance using precision (P), recall (R), and F1-score, providing detailed insight into the system's capability to identify speaker changes accurately. These metrics are particularly relevant when analyzing the impact of different feature sets and adaptation strategies on temporal resolution and speaker transition detection.  
This comprehensive evaluation framework enables a more nuanced understanding of system performance across different aspects of the diarization task. By examining these complementary metrics, we can better assess the relative strengths and weaknesses ofdifferent feature configurations and adaptation strategies, providing insights that might be obscured by relying solely on DER.
# TSCH and RPL Joining Time Model for Industrial Wireless Sensor Networks
## 2 TSCH and RPL Network Deployment
### RPL Tree Network Topology Construction
The Trickle Timer state can only be reset if a DIS message is received, starting the algorithm again with its minimum value, as seen in Figure 2. In implementations such as Contiki, it is proposed to use the current state of the Trickle Timer to modify the value of the EB transmission period, since it allows speeding up the synchronization process when the value of the Trickle Timer is low, in addition to avoiding congesting the network with EB messages when the Trickle Timer value is high. This behavior directly affects the process of deploying a new node, since if the nodes that make up the network have a very high Trickle Timer value, the EB period will also be high and the new nodes will take longer to obtain the synchronization to the TSCH network. In this situation, it is not possible to reset the Trickle Timer value using DIS messages, since the new node waiting to join the network is not yet synchronized. Figure 2: RPL Trickle Timer evolution in time for \(I_{min}=4\) s and _Doublings times_ = 6.
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 3 FLMPC: Proposed Protocols
### Problem Description  
The intensity of noise signals is high in shallow water as compared to deep water [21]. In LMPC, the source node establishes binary tree as shown in Figure 1. This establishment increases the number of duplicate packets in dense network and results in collision. Random distribution and energy depletion of sensor nodes create void holes. LMPC forwards data packet on the link which has minimum \(PER\) and acceptable \(DR\) in the presence of errors without considering the probability of void hole occurrence. As shown in Figure 4a, node \(C\) is a current forwarder. \(A\) and \(E\) are low depth nodes and lies within the transmission range of \(C\). Link to \(A\) might have less errors than \(E\) and is suitable to become forwarder, however, \(A\) leads to void hole because it has no node in its transmission range. If \(C\) directs data packet to \(E\), error chances might be higher. However, the probability of the data packet to reach the destination is very high in this situation. LMPC divides the network filed into  
Figure 3: Absorption and Frequency Relation.  
equal sized layers irrespective of considering noise intensity in shallow water. High noise makes the data packets erroneous which effects the reliability of the data packet.
## 3 Materials and Methods
### Case Study  
To achieve the objectives of this paper, we selected two large cities in Pakistan, Lahore, and Rawalpindi, as case studies to get a clear understanding of urban sprawl in major South Asian cities and its relationship to socioeconomic and mobility characteristics.  
Figure 1: The flow chart of the steps of the methodology.  
Lahore is the second-largest city in Pakistan [38], located in the northeast area of Punjab Province; it is also the provincial capital. Lahore experienced rapid urbanization over the past few decades. The urban population increased from 1.12 million in 1951 to 11 million in 2017 [39]. The metropolitan area of Lahore has a very varied physical structure, including urban, peri-urban, and rural areas. One evaluation of land use and land cover change in Lahore indicated that agricultural areas and vegetation land were transformed into built-up areas in a peri-urban pattern in the outer areas of Lahore between 1999 and 2011. Correspondingly, 84% of the increase in built-up areas occurred in outer areas compared to 22% in the central urban region [40].  
Rawalpindi is the fourth-largest city of Pakistan by population [38], and is located in the northern part of Punjab Province. It is located adjacent to Islamabad, the capital of Pakistan. As such, it is a part of the Islamabad-Rawalpindi metropolitan area. Rawalpindi is situated on the southern slopes of the northwestern Himalayas, known as the Margalla Hills, and the Indus River flows through the city [41]. Rawalpindi experienced rapid urbanization between 1800 and 1940 when the urban population tripled. The city has experienced a great deal of in-migration as well as rapid urban development. Most of the growth has occurred outside of Rawalpindi, in a spider-web structure away from the main city [41].  
In addition to urbanization and an increasing growth footprint, Pakistani cities, particularly Lahore and Rawalpindi, have suffered from a lack of comprehensive urban development, an absence of integrated and consolidated urban administration, and inefficiency in the implementation of urban plans; these aspects have contributed to unplanned urban development and urban sprawl.
## 5 Simulation Results and Discussion
### Case 2--SOC Restoration  
This case shows that the proposed controller can be used to restore both the frequency and SOC without using communication. In this case, it is assumed that the SOCs of all grid-forming units are exactly the same.  
As shown in Figure 7, the SOC drops below \(SOC_{min}\) (set as 20%) at 0.8 s. Subsequently, the low SOC signal (see Figure 1) is turned on and the grid-forming units change their \(P_{ref}\) to \(P_{res}\) (see Figure 1). Note that the active power of grid-forming units in Figure 7 did not change at 0.8 s. This means that there is no load change and the frequency is changed only to notify other grid-feeding units of the SOC shortage. After less than 0.2 s, the \(\Delta f_{int}\) of the grid-feeding units is turned on and the grid-feeding units start to restore the frequency by increasing their output. Simultaneously, by increasing the output of the grid-feeding units, the output of the grid-forming units becomes \(P_{res}\) (set as -0.5 MW) and the SOC is restored back to above \(SOC_{min}\).  
Figure 7: SOC restoration results when the SOCs are same.
# Research on an Evaluation Method for the Adaptability of TBM Tunnelling
## 2 TBM Adaptability Evaluation Method and Project Overview
### Project Overview  
A hydraulic engineering project in Xinjiang, with a total length of 540 km, consists mainly of the XE tunnel (139.04 km), the KS tunnel (283.27 km) and the SS tunnel (92.15 km). The tunnels account for 95.6% of the total length and are all deep-buried superlong tunnels. The tunnels are constructed mainly by the TBM method and supplemented by the mining method. According to a geological investigation, the TBM passes through eight regional faults and 129 secondary fault fracture zones. In general, the faults and fissures near the tunnel body are not well-developed, and the fissure faces are mostly filled with quartz veins. The basic regional earthquake intensity is the VII degree zone. The lithology of the tunnel is mainly late Variscan intrusive rocks with Permian, Triassic, Jurassic, Cretaceous mudstone, and sandstone.  
Due to the limitation of site conditions, it is difficult to obtain all of the geological conditions and TBM tunnelling conditions of the XE tunnel, KS tunnel and SS tunnel, so the typical interval K112+000-K114+000 in the XE tunnel, the typical interval K2+310-K4+310 in the KS tunnel and the typical interval K18+500-K20+500 in the SS tunnel are selected to study the TBM tunnelling conditions. The geological profiles of the interval tunnels K112+000-K114+000, K2+310-K4+310, and K18+500-K20+500 are shown in Figure 2. The specific parameters of the interval tunnels are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**\(n\)** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline \(RI\) & 0 & 0 & 0.58 & 0.90 & 1.12 & 1.24 & 1.32 & 1.41 & 1.45 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification standard of TBM tunnelling adaptability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Degree of** & **\textgreater{}0.9** & **0.9–0.7** & **0.7–0.4** & **\textless{}0.4** \\
**Adaptability** & & & & & & \\ \hline Adaptation criteria & Completely adaptable & Adaptable & Slightly adaptable & Inadaptable \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification standard of TBM tunnelling adaptability.