esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Case Background  
### Engineering Overview  
The key tunnel projects of the Tongwei-Dingxi Expressway mainly include the Jinping Tunnel, Mayingxia Tunnel, Huajialing Tunnel, and Tuanjie Tunnel. Among them, the Jinping Tunnel, Tuanjie Tunnel, and Baijiachuan Tunnel all traverse shallow-buried water-rich strata, presenting extremely complex geological conditions. In particular, the entrance and exit strata of the Tuanjie Tunnel have high water content and poor stability of the surrounding rock, making it highly prone to engineering issues such as water seepage, slope cracking, and collapse of the arch crown and sidewalls. Figure 1 is a schematic diagram showing the location of the Tuanjie Tunnel.  
The Tuanjie Tunnel is located in the loess ridge area and is a long, large-scale loess tunnel with separate left and right tunnels. It is currently known as the loess tunnel with the highest proportion of VI-grade surrounding rock in China, with VI-grade surrounding rock accounting for 49.8% and V-grade surrounding rock accounting for 50.2%. The tunnel traverses mainly through the Upper Pleistocene loess strata, with aeolian loess (Q3\({}^{\text{e}\text{o}\text{l}}\)) in the upper part and alluvial-proluvial loess (Q3\({}^{\text{a}\text{l}+\text{p}}\)) in the lower part. The entrance and exit sections of the tunnel pass through soft plastic intercalations. The groundwater level is within the design elevation of 20.0 m and is locally 25.0-40.0 m above the tunnel roof. The surrounding rock in the tunnel section is plastic to soft plastic loess. The tunnel is shallowly buried and water-rich, with large deformations and high susceptibility to collapse in the surrounding rock. It is highly prone to stripping and deformation after excavation, posing high construction risks and technical challenges. It serves as the pilot test section for tunnel construction in water-rich strata along the entire route. Figure 2 is the longitudinal geological profile of the left entrance of the tunnel using the Leading Software.  
The tunnel excavation has revealed that the Q3\({}^{\text{a}\text{l}+\text{p}}\) soft plastic surrounding rock and soil have an average moisture content of 26%, an average void ratio of 0.75, and an average saturation degree of 104.5%. The average liquid limit is 25.2%, the average plastic limit is 17.1%, the average plasticity index is 9.74, and the liquid index is 0.87, with an average liquid index of 0.84. It is classified as a silt interlayer and appears in a soft plastic state. The average compression coefficient is 0.59 MPa\({}^{-1}\), and the average compression modulus is 5.23 MPa, indicating high compressibility soil. It has a cohesion of 10.76 kPa and an internal friction angle of 20.83\({}^{\circ}\).  
Figure 1: Schematic diagram of the location of Tuanjie Tunnel.
## 2 Preliminaries
### Polar Codes  
Polar codes, belonging to the category of Forward Error Correction (FEC) codes, are founded upon the theory of channel polarization. This theory involves the processing of channels, specifically channel combining and splitting, to select reliable channels with capacities near 1 for the transmission of information bits. Conversely, the less reliable channels, with capacities closer to 0, are designated for the transmission of \"frozen\" bits, which are uniformly set to zero [21].  
Let \(u_{1}^{N}\) denote the source sequence, consisting of \(u_{1},u_{2},\cdots,u_{N}\) bits. Among these \(N\) bits, \(K\) are information bits, while the remainder are \"frozen\" bits. The positions of both the information and \"frozen\" bits are predefined and known to both the transmitter and receiver. The code rate is given by \(R=K/N\). Following the polar encoding process, the codeword \(x_{1}^{N}=(x_{1},x_{2},\cdots,x_{N})\) is produced as  
\[x_{1}^{N}=u_{1}^{N}G_{N}=u_{1}^{N}B_{N}F^{\otimes n},F=\left[\begin{array}{ cc}1&0\\ 1&1\end{array}\right], \tag{3}\]  
where \(G_{N}\) represents the generation matrix, \(B_{N}\) represents the bit reversal matrix, and \(\otimes\) denotes the Kronecker Product.  
In [22], a scheme employing polar codes tailored for the VLC channel is introduced. This scheme demonstrated a notable reduction in errors attributed to noise interference during light signal transmission. It led to an improvement in coding gain and an expansion of the reliable communication coverage. The proper selection of parameters, such as code length and rate, can further lower the BER of the VLC system. Thus, to enhance the reliability of the VLC system, we proceed with the polar code construction method described in the previously mentioned scheme. Furthermore, the decoding process is carried out using the successive cancellation algorithm.
# Cooperative Power-Domain NOMA Systems: An Overview
## 4 Open Issues and Challenges
### Receiver's Complexity  
The core philosophy of NOMA is to offer all users the entire available bandwidth in order to maximise bandwidth utilization. However, this solution involves a rise in receiver complexity because superposition coding is necessary to generate the adequate signal, and then, the original information (data) is retrieved using successive interferencecancellation and decryption techniques on that signal [204]. Even if they have the worst channel gains, every user in the cluster must decode data from every other user. Due to the receiver's increased complexity, there has been an increase in energy consumption as well. The performance gain of NOMA can be enhanced by using SIC at cell-edge users, as demonstrated by the authors of [205]. The development of an easy-to-use, effective SIC receiver is essential to NOMA. Multi-stage SIC lowers multi-path fading and bit error rate. Also, performance of the system is impacted by the signal's decoding order. High-SNR signals are initially deciphered. The performance of the SIC receiver is enhanced by a low-complexity, highly effective power allocation algorithm [206]. A practical implementation of this is its use to decode and detect desirable signals when non-idealities and imperfections cause error propagation in SIC. Due to the signal processing required for SIC, receiver complexity increases as the number of UEs rises.  
A user's performance is impaired by the SIC receiver's complexity and error propagation [207; 208; 209]. As a result, non-linear detection that is more efficient with better performance is necessary for addressing this problem and decreasing the impact of error propagation. To increase signal detection performance, the data symbols are decoded, demodulated, and exchanged by receivers more effectively. However, concerns including the propagation of errors, effective design of the receiver, and signal identification accuracy must be alleviated to improve receiver performance.
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 2 Proposed Frequency-Based Haze and Rain Removal Network (FHRR-Net) Framework
### Image Restoration Using Atmospheric Scattering Model  
In this section, we describe the final process of the framework. After estimating the transmission map from the low-frequency network and removing the rain streaks with the high-frequency network, using these information, degraded images are restored based on physical methods by rewriting the atmospheric model \(1\) into following equation:  
\[J(x)=\frac{I(x)-A}{t(x)}+A. \tag{10}\]  
Figure 6: A visual example of dilated convolution. (**a**) Traditional convolution. (**b**) Dilated convolution.  
Throughout proposed networks, estimated transmission maps and estimated rain-removed images are obtained. Specifically, we can obtain \(t\) and \(I\) using the low-frequency high-frequency encoder-decoder networks, respectively. The value of \(t\) can be zero value in an image, such as bright light and sky regions. In these pixels, the value in (1) can have the infinite value. Therefore, a lower limit of \(t(x)\) was set to 0.1 to prevent the denominator from zero. Additionally, we need to estimate the atmospheric light \(A\) based on the following equation:  
\[I(x)=A,\ if,\ t(x)<t_{th}. \tag{11}\]  
In (11), we choose the darkest 0.1% of pixels in a transmission map \(t(x)\). This part means the highest intensity haze in the hazy image \(I(x)\), as the atmospheric light \(A\). In order to set the threshold for \(A\), a threshold value experiment is needed, which we perform in the next section. As above, \(I\), \(t\), and \(A\) are determined. We assign values to the image degradation model to get a clean image.
## 6 Experiment
### Results in the Black-Box Setting
#### 6.3.2 Spy User Selection  
During experiments on poisoning attacks in a black-box setting, the number of spy users and their composition are two critical factors that can significantly influence the effectiveness of the attacks. We conducted experiments on the ML-100K and Last.fm datasets across three recommendation algorithms. Initially, we varied the number of spy users (maintaining an equal ratio of active to inactive users), and the results are illustrated in Figure 5.  
DRLAttack consistently achieved higher hit rates across all spy user settings compared to DRLAttack-White and DRLAttack-DNN, and the hit rates for all three attack methods increased as the number of spy users grew. With fewer spy users (such as 20 to 30), the increase in hit rate was more moderate, while with a larger number (such as 50 to 100), the improvement was more pronounced.  
After verifying the impact of the number of spy users on the effectiveness of the attacks, we explored the influence of the type of ordinary user behaviors simulated by spy users on attack performance. With the number of spy users fixed at 50, the experimental results, as shown in Figure 6, indicate that on the ML-100K and Last.fm datasets, the hit rate of DRLAttack initially increased as the proportion of active users rose, albeit modestly. However, after reaching a peak (at an equal ratio of 25 active to 25 inactive users), the hit rate began to decline as the proportion of active users continued to increase. A similar trend was observed in the attack methods of DRLAttack-White and DRLAttack-DNN. These results suggest that considering the diversity and complexity of user behavior patterns is crucial when designing attack strategies against recommendation systems.  
Figure 5: Impact of the number of spy users on the effectiveness of the attack.
###### Abstract  
This paper proposes the development of a tuning methodology which can set the proper values of the Computer Numerical Control (CNC) parameters to achieve the required machining performance. For the conventional operators of machine tools, the CNC parameters were hard to be adjusted to optimal settings, which was a complicated and time-consuming task. To save time in finding optimal CNC parameters, the objective of this research was to develop a practical methodology to tune the CNC parameters effectively for easy implementation in the commercial CNC controller. Firstly, the effect of the CNC parameters in the CNC controller on the tool-path planning was analyzed via experiments. The machining performance was defined in the high-speed (HS) mode, the high-accuracy (HP) mode, and the high-surface-quality (HQ) mode, according to the dynamic errors of several specified paths. Due to the CNC parameters that have a particularly critical effect on the dynamic errors, the relationship between the CNC parameters and the dynamic errors was validated by the measured data. Finally, the tuning procedure defined the anticipated dynamic errors for the three machining modes with the actual machine. The CNC parameters will correspond with anticipated dynamics errors based on several specified paths. The experimental results showed that the HS mode was the fastest to complete the path, and the completion time of the HP and HQ modes were increased by 37% and 6%, respectively. The HP mode had the smallest dynamic errors than other modes, and the dynamic errors of the HS and HQ modes are increased by 66% and 16%. In the HQ mode, the motion oscillation was reduce significantly, and the tracking error of the HS and HP modes were increased by 85% and 28%. The advantage of the methodology is that it simplifies set-up steps of the CNC parameters, making it suitable for practical machine applications.  
CNC parameters; machining mode; high speed; high accuracy; high surface quality +
Footnote †: journal: Applied Sciences
# Statistical Analysis and Modeling for Optical Networks
## 5 Statistical Analysis and Modeling for Optical Networks
### Characterization of Optical Networks
#### 5.1.3 Small-World Network  
Small-world (SW) networks are characterized by a logarithmic relationship between the average path length and the number of nodes in the network (i.e., network size), N [13; 78]. Specifically, the typical number of steps required to connect two random nodes scales as log (N), meaning even large networks can be traversed with few links. Optical networks that exhibit this property are referred to as small-world-like (SW-like). Most optical networks exhibit SW-like characteristics. In Figure 4, we show an SW-like optical network (i.e., Via Network) which has nodes at London, Paris, Amsterdam, Frankfurt, Berlin, Munich, Lisbon, Madrid and Barcelona. Just below it, we show its SW traits.  
Figure 4: The small-world characteristics are shown graphically for Via Network. In this figure, we show the topology (in blue) of Via Network and its ring structure just below it. The ‘SW-like’ characteristic of this network is clearly visible.  
SW networks exhibit a unique structure characterized by high clustering and short path lengths, striking a balance between local cohesion and global reachability. In these networks, most nodes are connected to nearby nodes, forming tightly knit clusters, while a few long-range connections significantly reduce the average shortest path between any two nodes. This structure enhances information flow, robustness, and efficiency, making SW networks common in social interactions, biological systems, and technological infrastructures. The defining properties of SW networks are high clustering coefficient and small average shortest path, which enable rapid communication and resilience against random failures. SW networks emerge naturally in systems like neural networks, power grids, and the Internet, where efficiency and redundancy are crucial. The Watts-Strogatz model mathematically captures these characteristics, demonstrating how rewiring a small fraction of links in a regular network can transform it into a small-world structure [PERSON] and [PERSON] (1998). Their prevalence highlights their fundamental role in complex systems across disciplines.
# A Comprehensive Study on EDFA Characteristics: Temperature Impact
## 3 Results and Discussion
### Maximum Gain Variation with Temperature  
The EDFA gain variation with input signal wavelength that is represented in Figure 3 showed two different peaks. One narrow peak is located around 1530 nm, while another wider peak is located at around 1550 nm. In case of a 3-m Al/P-Silica EDFA with the input and pump powers of 10 \(\upmu\)W and 80 mW, respectively, the maximum of the two peaks is the narrow one located at exactly 1532 nm. Its variation with the operating temperature is depicted in Figure 4.  
Figure 2: Emission cross-sections change wavelengths at different temperatures.  
Figure 4: Maximum gain variation with temperature compared to a linear best fit (dotted red).  
Figure 3: Gain variation with signal wavelength at different temperatures when pumped with (**a**) 980 nm and (**b**) 1480 nm.  
From Figure 4, it can be noted that the maximum gain value at the 1532-nm input signal wavelength shows a nearly linear decrease in its value with the increase in the surrounding temperature while working at an environmental temperature higher than \(-20\)\({}^{\circ}\)C. This linear behavior is also proved through the linear best fitting simulated in the figure by the dotted red line. It is worthy to mention that changing any of the EDFA operating parameters will affect the maximum gain variation with temperature.
## The Methods of Establishing RSP Model and RSHARQ
### A Reliability System Performance Model (RSP)  
Bit errors can be corrected or detected by a reliability system. ECC is used to correct n-bit errors and detect n + 1-bit errors; ARQ is used to detect n-bit errors without the ability to correct errors, where n is the length of the error bits. If errors detected could not be corrected, the system will send data again. HARQ is a scheme combining ECC and ARQ. Considering ECC and ARQ integration, a reliability system performance model with relationship evaluation between error rates, reliability method cost and bandwidth has been presented. Details of the RSP model are shown below.  
Most parameters mentioned in Section 2.1 are decided by chip fabrication technology. The designer can only decide the spacing and length of interconnects. For a given length of an interconnect, crosstalk error rates e can be expressed as function g, as Equation (9) shows:  
\[\mathrm{e}=\mathrm{g}(\mathrm{s},\mathrm{f_{m}}) \tag{9}\]  
In digital circuit design, the spacing between interconnects determines the maximum amount of route lines and the max bandwidth B. Equation (9) can be modified into Equation (10):  
\[\mathrm{e}=\mathrm{g}\bigg{(}\frac{1}{\mathrm{B}},\mathrm{f_{m}}\bigg{)} \tag{10}\]  
Assuming that a reliability system with HARQ could correct a bit errors and detect b bit errors in a code of length n bits, where b is larger than a, the possibilities of single transmission success \(\mathrm{P_{a}}\) and system accurate judgment \(\mathrm{P_{b}}\) can be calculated with Equations (10) and (11).  
\[\mathrm{P_{a}}=\sum_{\mathrm{m}=0}^{\mathrm{a}}\frac{\mathrm{n!}}{\mathrm{n! }(\mathrm{n-m})!}\cdot\mathrm{e^{m}}(1-\mathrm{e})^{\mathrm{n-m}} \tag{11}\]  
\[\mathrm{P_{b}}=\sum_{\mathrm{m}=0}^{\mathrm{b}}\frac{\mathrm{n!}}{\mathrm{m! }(\mathrm{n-m})!}\cdot\mathrm{e^{m}}(1-\mathrm{e})^{\mathrm{n-m}} \tag{12}\]  
The possibility of resending data in single transmission is P:  
\[\mathrm{P}=\mathrm{P_{b}}-\mathrm{P_{a}} \tag{13}\]The possibility of reliability scheme failure is \(\mathrm{P_{f}}\):  
\[\mathrm{P_{f}}=1-\mathrm{P_{b}} \tag{14}\]  
The expected number of one-packet transmission times is \(\mathrm{N_{t}}\):  
\[\mathrm{N_{t}}=1\cdot\mathrm{P}+2\cdot\mathrm{P}(1-\mathrm{P})+3\cdot\mathrm{P} (1-\mathrm{P})^{2}+\cdots+1\cdot\mathrm{P}(1-\mathrm{P})^{1-1}+\cdots=\frac{1} {\mathrm{P}} \tag{15}\]  
The effective throughput of the HARQ reliability system is \(\mathrm{\eta_{SR}}\):  
\[\mathrm{\eta_{SR}}=\frac{1}{\mathrm{N_{t}}}\bigg{(}\frac{\mathrm{k}}{\mathrm{n }}\bigg{)}=\bigg{(}\frac{\mathrm{k}}{\mathrm{n}}\bigg{)}\mathrm{P} \tag{16}\]  
where k is the original code length without ECC encoding.  
As a result, the effective bandwidth \(\mathrm{B_{w}}\) can be calculated as follows:  
\[\mathrm{B_{w}}=\mathrm{\eta_{SR}}\cdot\mathrm{B} \tag{17}\]  
With the formula mentioned above, the RSP model has been built to explain the relationship between error rates, reliability method cost and bandwidth.  
Summarizing the SSTC model and RSP model, a reliability system evaluation model (BFRS) is established from backend to frontend.
# Intelligent Planning and Research on Urban Traffic Congestion
## 2 Related Work  
### Intelligent Transportation  
The intelligent traffic system (ITS), also known as the Intelligent transportation system, includes the advanced fields of science and technology (information technology [32], computer technology [33], data communication technology [34], sensor technology [35], electronic control technology [36], automatic control theory [37], computer simulation [38],Artificial Intelligence [30], etc.) that are effectively integrated into transportation [39], service control, and vehicle manufacturing, in order to strengthen the relationship between vehicles, roads, and users, thus forming a comprehensive transportation system that plays a large and all-round role.  
The intelligent passenger flow guidance system [40] is applied in airports and other places with large passenger flow. In urban traffic networks/expressways, the automatic motor vehicle control system, operation vehicle scheduling management system, non-motor vehicle intelligent management system, intelligent scheduling system, and so on can be used. Their function is to rationally plan the road for people and vehicles through the intelligent system [37], improve the transportation efficiency and capacity of the road network, alleviate traffic jams, reduce the occurrence of traffic accidents, and reduce energy consumption and environmental pollution. According to one regional forecast, using ITS can reduce traffic jams by 30%, save energy by 10%, reduce air pollution by 25%, and reduce traffic accidents by 50%.  
ITS consists of three parts: information collection, information processing, and information release. In China, ITS systems have begun to take shape in traffic management, electronic toll collection, traffic information services, traffic safety, operation management, traffic infrastructure management, and ITS data management. An intelligent transportation system is a complex comprehensive system, which can be composed of the traffic management system, public transportation system, vehicle control system [41], electronic toll collection system, park and ride facility [42], traffic information service system [43], and emergency rescue system, as well as other subsystems.  
Many scholars at home and abroad have proposed many corresponding measures and solutions for various outstanding problems at different intersections. One is prevention-oriented, studying the causes of congestion, mainly starting from the characteristics of traffic flow, through construction and planning road routes to alleviate road congestion problems, such as: building viaducts, building public transport lanes, broadening main roads, building roundabouts, etc.; the other is based on control, by controlling signal lights, controlling vehicle directions, etc., to alleviate traffic congestion problems, such as implementation of the prohibition of left, \"U-shaped\" turns, the harbor-style left turn lane method, shift left, and change to left, etc.