esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 OTFS Channel Estimation
### DDIPIC Algorithm  
The algorithm proceeds iteratively for a maximum of \(P_{\max}\) iterations and the CDDPM matrix is initialized as \(\mathbf{R}\big{(}\mathbf{\tau},\mathbf{\
u}\big{)}=\mathbf{0}\). The details of the DDIPIC algorithm are provided in Algorithm 1. At each iteration, the parameters of one path are estimated as follows:  
1. _Coarse estimation (line 2)_: The cost function in (19) is maximized over the DD grid of integer multiples of delay and Doppler resolutions, providing a coarse estimate of delay and Doppler shift. The search area is limited to \([0,L_{\text{res}}]\times[-K_{\text{res}},K_{\text{res}}]\), where \(L=\big{\lceil}\frac{\max}{\max}\big{\rceil}\) and \(K=\big{\lceil}\frac{v_{\max}}{\max}\big{\rceil}\). 2. _Fine estimation (lines 5-11)_: An iterative procedure runs for a maximum of \(s_{max}\) iterations to provide a better estimate of the off-grid DDs. In each iteration, the cost function in (19) is maximized over a search area comprising fractional delays and Dopplers around, for \(s=1\), the coarse estimate obtained in step 1 or, for \(s>1\), the fine estimate obtained in the previous iteration. The search space is narrowed down as the iteration progresses, reducing the delay and Doppler search widths (lines 6-7). The fine estimation procedure ends when the stopping criteria for \(\tau\) and \(\
u\) are met. The search space for the fine estimation procedure is called \(F_{DD}^{(s)}\), and we define \(\Gamma=\{-\lfloor\frac{n_{\tau}}{2}\rfloor,...,0,...,\lfloor\frac{n_{\tau}}{2} \rfloor\}\) and \(\Lambda=\{-\lfloor\frac{n_{\tau}}{2}\rfloor,...,0,...,\lfloor\frac{n_{\tau}}{2} \rfloor\}\) where \(m_{\tau}\) and \(n_{\
u}\) are design parameters. Notice that after the Cartesian product (denoted \(\times\)) of the two sets, only positive delays need to be considered. The parameters \(\epsilon_{\tau}\) and \(\epsilon_{\
u}\) are chosen to achieve the desired accuracy compatibly with \(s_{\max}\). 3. _Check stopping criterion (line 15-19)_: A residue vector is computed as \(\mathcal{E}^{(i)}=\mathbf{y}-\mathbf{R}(\hat{\tau},\hat{\mathbf{v}})\hat{\mathbf{g}}\) and if \(\|\mathcal{E}^{(i)}\|<\epsilon\) is satisfied, the algorithm stops and a number of \(\hat{P}\) paths are detected; otherwise, before starting the search for a new path, all previous estimates are refined as described below. The convergence tolerance parameter \(\epsilon\) for the stopping criterion must be chosen not to underestimate the number of propagation paths, not to miss those with smaller amplitudes, and not to overestimate the number of propagation paths due to residual IPI or noise. Since the noise is Gaussian, a reasonable value could be set taking into account the fact that the number of symbols per frame is \(MN\), and the noise variance is \(N_{0}\), so that we have set its value to \(\epsilon=3\sqrt{MNN_{0}}\). 4. _Refinement procedure (line 20-21)_: All coarse and fine estimates are performed again for all the previously detected paths until the current one. In particular, at the \(i\)-th iteration, lines 2-13 are run again iteratively for \(i_{\text{ref}}=1,...,i\).
## 3 Numerical Simulation
### Reliability Verification  
The CHN-T1 standard model [48] was selected to verify the reliability of the numerical method. The CHN-T1 standard model was a 2m-scale transonic large aircraft designed by the CARDC. The model was tested in ETW and the 2.4 m \(\times\) 2.4 m transonic wind tunnel of the CARDC for comparative verification, and the reliability of the test results was quite high.  
The comparison between the calculation results and test results is shown in Figure 4. The research models of CFD and experimental fluid dynamics (EFD) are almost the same. The tests were conducted in two different wind tunnels with the same tail support. It was shown that the numerical results of the CHN-T1 model was in good agreement with the experimental results in the two wind tunnels. In particular, the simulation accuracy of aerodynamic coefficients at \(\alpha\) = 0\({}^{\circ}\) and in cruise conditions was quite high (the deviation of \(C_{L}\) was less than 0.0055 and the deviation of \(C_{D}\) was no more than 0.0005, as shown in Table 3). The results show that the numerical method of this study was reliable and met the simulation requirements for support interference.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Grids** & \(\mathbf{2\times 10^{6}}\) & \(\mathbf{4\times 10^{6}}\) & \(\mathbf{11\times 10^{6}}\) & \(\mathbf{19\times 10^{6}}\) \\ \hline \(\Delta C_{L}\) & \(-0.0008\) & \(-0.0009\) & \(-0.0004\) & \(-0.0003\) \\ \(\Delta C_{D}\) & \(0.0031\) & \(0.0016\) & \(0.0006\) & \(0.0004\) \\ \(\Delta C_{m}\) & \(-0.0004\) & \(-0.0005\) & \(-0.0003\) & \(-0.0002\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of grid independence study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **D/mm** & **L/mm** & **H/mm** & **P/\({}^{\circ}\)** \\ \hline original & 52 & 300 & L2 & 15 \\ variable & 44, 55 & 200, 400 & L2 \(-100\), L2 + 100 & 13, 17, 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different geometric parameters of the V-shaped support.  
Figure 3: The grid of V-shaped support with different parameters (the straight-section diameter D = 44 mm/52 mm/55 mm, the straight-section length L = 200 mm/300 mm/400 mm, the first-section length H = L2 - 100 mm/L2/L2 + 100 mm, the inflection angle P = 13\({}^{\circ}\)/15\({}^{\circ}\)/17\({}^{\circ}\)/20\({}^{\circ}\), and multiple variable parameters L = 400 mm/H = L2 + 100 mm).
## 5 Results and Discussion
### Experimental Discussion  
The experimental results demonstrate the efficiency and accuracy of the proposed method based on the 3D scanner. The point cloud data obtained by the detection method can truly show the information of the surface of the measured object, and can effectively extract the plane point cloud of the supporting block plane. The standard deviation of the measured points and flatness are small, the precision of extraction is high. The method can accurately measure the detection indicators related to the supporting block plane, which proves that the detection method used in this paper is reliable, which lays a good foundation for the subsequent extraction of the track slab detection index parameters.
# A Carrier-Based Gardner Timing Synchronization Algorithm for BPSK Signal in Maritime Communication
## 3 Materials and Methods
### Timing Error Detection  
Unlike (5), the CB-Gardner performs error detection by utilizing three sampled values on the passband instead of determined symbols. When \(\varphi_{y_{1}}\in[0,\pi]\), for example \(\varphi_{y_{1}}=0\), as shown in Figure 5, and assuming that the position of \(y_{1}\) is relatively accurate, the signal may broaden due to the effect of Doppler (i.e., \(\lambda<1\)), and the detection points \(y_{2}\) and \(y_{3}\) may shift.  
Referring to the error detection Equation (5) of the Gardner algorithm, we define the detection error as  
\[err_{n}=y_{2}*(y_{1}-y_{3}) \tag{11}\]  
It can be inferred that \(err_{n}>0\), which leads to a decrease in the control word in the NCO. Consequently, the NCO estimates that \(\lambda_{n}<1\). Conversely, when \(\lambda>1\), the NCO estimates that \(\lambda_{n}>1\). Thus, \(err_{n}\) accurately indicates the directional information of Doppler and can be used as timing synchronization error. But when \(\varphi_{y_{1}}\in[\frac{\pi}{2},\frac{3\pi}{2}]\), for example, \(\varphi_{y_{1}}=\frac{\pi}{2}\), if \(\lambda<1\), as shown in Figure 6, according to (11), we can see that \(err_{n}<0\) at this point. This will give the NCO incorrect information of \(\lambda_{n}>1\).  
Therefore, we need to correct (11). To differentiate between the two cases of \(\varphi_{y_{1}}\in[0,\pi]\) and \(\varphi_{y_{1}}\in[\frac{\pi}{2},\frac{3\pi}{2}]\), a simple method is to compare the absolute values of \(y_{1}\) and \(y_{2}\) when the signal-to-noise ratio (SNR) is not very low. Thus, the final equation for CB-Gardner's error detection is  
\[err_{n}=\left\{\begin{array}{l}y_{2}*(y_{1}-y_{3})\,\left|y_{1}\right|> \left|y_{2}\right|\\ y_{2}*(y_{3}-y_{1})\,\left|y_{1}\right|<\left|y_{2}\right|\end{array}\right. \tag{12}\]  
In this case, the sign of \(err_{n}\) will correctly indicate the directional information of Doppler. In addition, \(err_{n}\) also contains information about the magnitude of the Doppler, enabling the NCO to accurately detect both the magnitude and direction of the Doppler. Since the error detection is carrier-based and independent of the specific symbol, the CB-Gardner does not fail even when there are a large number of consecutive identical symbols.  
For BPSK signals, (11) is used for error detection, while for quadrature phase shift keying (QPSK) and higher-order modulation methods, (12) is required.
## 5 Case Study  
The building chosen as case study hosts the Department of Architecture, Built Environment and Construction Engineering (DABC) of Politecnico di Milano, and is located in Milan (Italy). It is a four-story building, hosting administrative offices, research spaces, and university staff offices, for a total of 4300 square meters of gross floor area. Rooms have variable dimensions depending on their use. The building has a symmetrical layout, with a common space in the center and two side corridors. The offices and workspaces are located on either sides of the corridors. Each floor houses at least one bathroom. Before the current study, the building has never been monitored. Therefore, neither data regarding the actual occupancy patterns, nor information about actual cleaning and maintenance activities are currently analyzed and optimized. Furthermore, no space optimization has been performed in relation to the use of available rooms and the actual occupancy indexes at room-level in the building. This case study building acts as prototype for a future application of the proposed method to other university's buildings.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System Fault** & **Data Error in Collected Data** & **Data Error during Real-Time Test Campaign** \\ \hline Missing data: data are not collected & O = 0 p & O = 0 p \\ \hline \multirow{3}{*}{Outliers: one or more consecutive anomalous values} & O \textless{} 0 p & \\  & Values of O unacceptable for room & \\  & dimensions, e.g., O = 50 p in a & O \textless{} 0 \\  & 10-square-meter office & \\ \hline \multirow{3}{*}{Stuck values: those values occur when a sensor fails in detecting and a previously-detected value remains fixed} & \multirow{3}{*}{O \textless{} 0 outside the working hours} & No correspondence between detected O and actual occupancy values, e.g., O \textgreater{} 0 in an empty room \\  & & \\ \hline \multirow{3}{*}{Noise: it represents corrupted values} & \multirow{3}{*}{Not detectable in collected data} & No correspondence between the virtual agent movements detected by the system and displayed in the platform, and the actual movements of operator B \\  & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: System faults [22] and related data errors. Data errors are divided into errors observed in collected data and errors detected during real-time test campaigns.  
The case study section is divided in two subsections: the first one describes the application and results of preliminary analyses on the building that supported the planning and installation of the IoT sensor system; the second subsection describes the two test campaigns with specific focus on the detected system faults and related proposed adjustments.
## 2 Data and Methodology
### Methodology
#### 2.3.1 Water Cloud Model  
To distinguish the backscattering of soil and vegetation in the vegetated areas, the WCM developed by [PERSON] and [PERSON] [22] was adopted in our research. The WCM is based on the radiative transfer model, assuming that the vegetation canopy is a uniform horizontal cloud layer, ignoring multiple scattering between the vegetation and the surface. In the WCM, the scattering of the vegetated areas can be divided into two parts: one is the backscattering reflected directly by the vegetation, and the other is the scattering from the soil [17]. The model is simple and practical in describing radar scattering mechanisms in  
Figure 4: Workflow of this research.  
vegetated areas. Thus, this model has often been used for relevant information estimation in vegetated areas. The WCM is expressed as:  
\[\sigma^{0}=\sigma^{0}_{veg}+T^{2}\sigma^{0}_{soil} \tag{2}\]  
\[\sigma^{0}_{veg}=AM_{v}\cos(\theta)\Big{(}1-T^{2}\Big{)} \tag{3}\]  
\[T^{2}=\exp(-2 BM_{v}\sec(\theta)) \tag{4}\]  
In addition, the term \(\exp(-2 BM_{v}\sec(\theta))\) was described with the Taylor formula:  
\[\exp(-2 BM_{v}\sec(\theta))=1-2 BM_{v}\sec(\theta)+2B^{2}{M_{v}}^{2}\sec^{2}( \theta)+\cdots \tag{5}\]  
where \(\sigma^{0}\) is the total backscattering coefficient, dB; \(\sigma^{0}_{veg}\) is the vegetation backscattering coefficient, dB; \(\sigma^{0}_{soil}\) is the soil backscattering coefficient, dB; \(M_{v}\) is the VWC, kg/m\({}^{2}\); \(T^{2}\) is the double attenuation factor; \(\theta\) is the incident angle, \(\sigma^{0}\); \(A\) is the vegetation reflectance; and \(B\) is the attenuation factor.
# Research on Visual Positioning of a Roadheader and Construction of an Environment Map
## 4 Map Construction and Test
### Construction of 3D Map and Test
#### 4.3.2 Mapping Test Based on Vision SLAM  
Based on RTAB-MAP framework, the image data collected by RGB-D camera are input, the dense 3D point cloud map is constructed by mapping the test of the simulated roadway (shown in Figure 14), and the octree map is constructed via octree compression and is shown in Figure 15.  
Figure 14: Dense 3D point cloud map of simulated roadway.  
Figure 13: Principle of octree compression.  
Octree map is convenient for navigation of the roadheader because of its portable characteristics. Based on the octree map shown in Figure 15, the multi-layer cost map for navigation is constructed by introducing parameters, such as the expansion radius of obstacles and the frequency of map updates, as shown in Figure 16. The red arrow in the figure represents the body pose, the darkly colored expansion area around the body is a local cost map, which is used for local path planning; the rest of the area is a global cost map, which is used for global path planning. In this test, the Dijskra algorithm is used for global path planning, and the red lines in the figure are the trajectory of the global path planning. The TEB (timed-elastic-band) algorithm is used for local path planning, and the blue line is the trajectory of local path planning.  
Figure 16: Navigation and obstacle avoidance based on the octree map and cost map.  
Figure 15: Octree map of simulated roadway.  
As can be seen from Figure 16, when an obstacle is temporarily added in the navigation test, the dynamic obstacle expansion region can be marked by updating the cost map, and the obstacle avoidance based on local path planning is successfully realized.
# Experimental Study on Freezing Front Model of Alpine Tunnel under Wind Field
## 3 Experimental Results and Analysis
### Characteristics of Radial and Longitudinal Air Temperature Distribution in Tunnels
#### 3.1.3 Characteristics of Longitudinal Overall Temperature Distribution in Tunnels  
Refer to the research method of [PERSON] et al. (2014) [PERSON] et al. (2014). Using the Surfer software, the cloud charts of the longitudinal overall temperature change of the tunnel are simulated when the temperature is \(-1.6\)\({}^{\circ}\)C, \(-10.6\)\({}^{\circ}\)C, and the wind speed is 0 m/s, 1.8 m/s.  
It can be seen from Figure 10 that there is no freezing zone in the surrounding rock of the mountain, and only a small negative temperature zone exists at the tunnel entrance. At the wind speed of 1.8 m/s, we can definitively see that the cold air mass at the tunnel entrance extends to the inside of the tunnel.  
It can be seen from Figure 11 that the top of the surrounding rock of the mountain, the surrounding rock of the portal lining and the surrounding rock of the inverted arch base of the portal all have different degrees of freezing areas. Under the wind speed of 3.6 m/s, the longitudinal increase distance of the 0 \({}^{\circ}\)C isotherm at the tunnel entrance reaches 35 cm, the longitudinal freezing length of the surrounding rock of the arch top reaches 85 cm, and the longitudinal freezing length of the surrounding rock of the inverted arch base reaches 135 cm.  
Figure 9: Temperature variation curve in tunnel under different test air temperature and different wind speed: (**a**) The temperature is \(-1.6\)\({}^{\circ}\)C; (**b**) The temperature is \(-4.8\)\({}^{\circ}\)C; (**c**) The temperature is \(-8.4\)\({}^{\circ}\)C (**d**) The temperature is \(-10.6\)\({}^{\circ}\)C.  
It can be seen from the overall temperature distribution vector diagram of the tunnel that when the test air temperature is low, there is an obvious thermonuclear region in the surrounding rock of the mountain. As the test air temperature decreases, the thermonuclear region gradually shrinks, the surrounding-rock freezing area at the arch bottom gradually increases, and the surrounding-rock freezing area at the arch crown gradually expands to the inside of the mountain. Compared with no ventilation, the influence of ventilation on the longitudinal and radial temperature distribution of the tunnel is more obvious, and the greater the wind speed is the greater the influence is. In particular, the longitudinal temperature distribution of the tunnel is more widely affected by ventilation [32].  
Figure 10: Vector Diagram of Tunnel Longitudinal Temperature Distribution at Test Air Temperature \(-1.6\,^{\circ}\)C: (**a**) 0 m/s; (**b**) 1.8 m/s.
# FANT: Flexible Attention-Shifting Network Telemetry
## 3 FANT System
### Architecture  
As shown in Figure 2, the FANT architecture consists of a data plane (forwarding devices) and a control plane (INT controller and probe generator/collector).  
**Forwarding devices** are switches or routers equipped with Segment Routing (SR) and inband telemetry functionalities. Their responsibilities include:  
1. Forwarding business traffic;
2. Forwarding INT-Probes based on SR instructions;
3. Embedding the in-device status into the probe packets' INT header as per the instructions in the INT header.  
**Probe generators/collectors** are virtual machines or servers mounted at specified locations of the topology. For example, in a data center, they are generally allowed to be mounted on certain Top-of-Rack (TOR) switches according to the network operator's network planning. This is because mounting them on higher-layer switches (i.e., core switches or aggregation switches) could impact network throughput. Their duties involve:  
1. Constructing and sending probe packets;
2. Receiving probe packets and extracting monitoring results from the INT header;
3. Reporting the monitoring results to the INT controller.  
The **INT controller** is a cluster of servers with the following tasks: (i.) Analyzing measurement results from the current measurement cycle and determining the measurement requirements for various locations in the next cycle and (ii.) utilizing the attention-switching strategy to control the probe generators/collectors for focus adjustment  
Figure 2: Architecture of the FANT system.
# RCA-PixelCNN: Residual Causal Attention PixelCNN for Pulsar Candidate Image Lossless Compression
## 1 Introduction
Especially in the time-phase subfigures and frequency-phase subfigures, we can visually observe this vertical correlation. This is because the pulse signals from the same point source radiation share the same phase, resulting in a prominent stripe in these two subfigures. The subfigures of pulsar candidate bodies have a size of 32 \(\times\) 32, and traditional convolution operations may easily overlook distant information. To effectively model pixel density, our model structure must fully leverage the spatial structural correlation of these images, capturing both local feature dependencies and distant correlated feature information. We propose a causal residual attention module that employs self-attention to overcome local limitations. The causal constraint of the self-attention module ensures autoregression, and the use of residual connections guarantees that deeper network layers do not degrade performance. The overall model architecture is similar to the PixelCNN model and is referred to as the RCA-PixelCNN model. The model is trained and validated on the HTRU1 dataset and fine-tuned on FAST's pulsar candidate image data for practical compression tasks. The Guizhou Normal University FAST Early Data Center participated in all pulsar search work for CRAFITS observations with FAST, including the first pulsar discovered by FAST, PSR J1900-0134 [21], and the first millisecond pulsar, PSR J0318+0253 [22]. Our methods were employed in the data processing stages of these significant scientific discoveries. The main innovations are as follows:  
1. Introducing the causal residual self-attention module to address the shortcomings of PixelCNN. 2. In experiments, the proposed model is trained and validated using the HTRU1 dataset. The average negative log-likelihood values are compared with baseline models such as the GMM, STM, and PixelCNN. The results indicate that the proposed model outperforms the others. 3. Describing the practical compression encoding process by combining the proposed model with arithmetic coding.
## 5 Real-World Application: Government Debt Analysis
### Methodology Adaptation to Real-World Data  
The transition from our synthetic demonstration dataset to real-world economic data required several methodological adaptations while preserving the core theoretical framework. Unlike the controlled synthetic environment, real-world data presented several challenges:  
* **Missing Values**: Handled through forward-fill imputation using available debt values;
* **Temporal Alignment**: Standardized annual observations across all countries;
* **Scale Normalization**: Maintained original debt-to-GDP percentages for interpretability.  
We adapted our feature construction approach by focusing exclusively on the primary debt indicator (\(f_{1}\)), eliminating the complexity of multi-feature weighted averages used in the synthetic example. This simplification allows for  
* Direct interpretation of categorical transitions in terms of fiscal policy changes.  
* Clear identification of debt crisis periods and fiscal consolidation efforts.
* Transparent comparison across countries with different economic structures.  
The categorical transformation employed global quartile boundaries calculated across all countries and time periods:  
\[\begin{split}\text{Lower Bound}&=Q_{1}-1.5\times IQR =-21.15\%\\ Q_{1}&=22.73\%\\ Q_{2}\text{ (Median)}&=44.16\%\\ Q_{3}&=66.80\%\\ \text{Upper Bound}&=Q_{3}+1.5\times IQR=132.92\% \end{split} \tag{14}\]  
This global approach ensures consistent categorical interpretation across all entities, enabling meaningful cross-country comparisons while preserving the relative position of each country within the broader fiscal landscape.  
Each country's fiscal trajectory was constructed as a sequence of categorical assignments over the 63-year observation period. The trajectory \(T_{i}\) for country \(i\) is defined as follows:  
\[T_{i}=(c_{i,1960},c_{i,1961},\dots,c_{i,2022}) \tag{15}\]  
where \(c_{i,t}\in\{0,1,2,3,4,5\}\) represents the categorical assignment for country \(i\) in year \(t\).