esa-sceva/satcom-chunk-collection huggingface dataset:
# Modulation Characteristics of High-Speed Transistor Lasers  
[PERSON]  
1 State Key Laboratory of Luminescence and Application, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; [EMAIL_ADDRESS] (L.F.); [EMAIL_ADDRESS] (PJ.); [EMAIL_ADDRESS] (Q.C.); [EMAIL_ADDRESS] (L.Q.); [EMAIL_ADDRESS] (L.L.); [EMAIL_ADDRESS] (C.Q.); [EMAIL_ADDRESS] (Y.S.); [EMAIL_ADDRESS] (Y.W.); [EMAIL_ADDRESS] (Y.W.);
[PERSON]  
[PERSON]  
1 State Key Laboratory of Luminescence and Application, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; [EMAIL_ADDRESS] (L.F.); [EMAIL_ADDRESS] (PJ.); [EMAIL_ADDRESS] (Q.C.); [EMAIL_ADDRESS] (L.Q.); [EMAIL_ADDRESS] (L.L.); [EMAIL_ADDRESS] (C.Q.); [EMAIL_ADDRESS] (Y.S.); [EMAIL_ADDRESS] (Y.W.);  
[PERSON]  
1 State Key Laboratory of Luminescence and Application, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; [EMAIL_ADDRESS] (L.F.); [EMAIL_ADDRESS] (PJ.); [EMAIL_ADDRESS] (Q.C.); [EMAIL_ADDRESS] (L.Q.); [EMAIL_ADDRESS] (L.L.); [EMAIL_ADDRESS] (C.Q.); [EMAIL_ADDRESS] (Y.S.); [EMAIL_ADDRESS] (Y.W.);  
[PERSON]  
1 State Key Laboratory of Luminescence and Application, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; [EMAIL_ADDRESS] (L.F.); [EMAIL_ADDRESS] (PJ.); [EMAIL_ADDRESS] (Q.C.); [EMAIL_ADDRESS] (L.Q.); [EMAIL_ADDRESS] (L.L.); [EMAIL_ADDRESS] (C.Q.); [EMAIL_ADDRESS] (Y.S.); [EMAIL_ADDRESS] (Y.W.);  
However, compared with DML, substantially less research has been conducted on TL. More research is needed on TL to address various issues. Among these is the fact that the power of TL is less than the commercially required level, which also limits its modulation capability (see Section 2.2). Further, numerous problems relating to long-wavelength InP- based TL need to be addressed before it is commercially used in optical fiber communication; these problems include low mode gain [17] and strong inter-valence band absorption in the base region [18]. Although these shortcomings limit the application of TL, it has been reported that the modulation characteristics (including bandwidth, power, and transmission rate) of TL can be improved by optimizing the base structures (e.g., quantum well (QW)) and introducing intracavity photon-assisted tunneling and external auxiliary circuits. This paper introduces and discusses methods that can improve the modulation performance of TL based on the abovementioned schemes.
## 2 Materials and Methods
### Dataset and Imaging Techniques  
Patras is the third-biggest city in Greece, accounting for approximately 200,000 inhabitants. Medical services of our institution expand to a broader geographical area, including a population of almost 1,000,000. The retrospective study involves 648 confirmed participants with pHPT, 535 females and 113 males, aged 58.1 \(\pm\) 12.5 years, who underwent parathyroid scintigraphy in the Department of Nuclear Medicine of the University Hospital of Patras, Greece. The period of the study ranges from January 2010 to December 2020. The planar dual-phase technique with \({}^{99\text{m}}\)Tc-Sestamibi was used in all participants.  
Moreover, whenever judged necessary by medical experts, the thyroid subtraction technique was also used. The latter refers to 529 cases. \({}^{99\text{m}}\)Tc-pertechnetate (TcO4) for thyroid delineation was administered either after the conclusion of the dual-phase study or on another day.  
The prospective external data set comprised 100 consecutive patients (87 females and 13 males), who also underwent scintigraphy in our department. This set differs from the retrospective group because the patients were subsequently subjected to parathyroidectomy in 2021-2022 in our or other surgical units. The results were available for the evaluation of the model on this external dataset. Table 2 summarises the dataset's characteristics.  
We used a pinhole collimator for planar imaging and placed it 10 cm over the neck. A SPECT/CT imaging session focused on the neck and the mediastinum using a high-sensitivity parallel-hole collimator took place approximately 30 min post-tracer injection. However, only planar imaging data have been included in the present study. Regarding the acquisition device technology, planar and SPECT/CT imaging were performed by the Hawkeye-4 system (General Electric Healthcare, Chicago, IL, USA).  
The planar scintigraphic studies were evaluated retrospectively by three experienced Nuclear Medicine physicians (D.J.A, more than 20 years; N.D.P, more than 8 years; and T.I.S., more than 15 years). In a few ambiguous cases, the final decision was reached by consensus.
## 3 Results
### Comparison with Numerical Calculations
Figure 6d shows the comparison of the measured benzene concentrations with the numerical results. Since the Aachen and San Diego mechanisms do not include these species, they are omitted from the plot. The HyChem mechanism predicts the highest maximum concentration, followed by DLR and Creck. There is approximately a factor of six between HyChem and Creck. The location of the maximum concentration is about 1.5 mm for all mechanisms and, as with the species profiles considered previously, is about 2 mm ahead of the experimentally observed values. The peak concentration of the Creck mechanism agrees very well with that of the surrogate. Figure 6e shows the comparison for the toluene concentrations. Only the DLR and Creck mechanisms include these species. Since the DLR mechanism was calculated with a surrogate mixture consisting partly of toluene, it shows very high concentrations at the beginning and a meaningful comparison with the measured data is not possible. The maximum concentration of the Creck mechanism fits well with the concentrations measured experimentally for the Surrogate. The location of the calculated maximum is again about 2 mm further upstream of the experimental values. Finally, the comparison with the naphthalene concentrations is shown in Figure 6f. Both the Creck and DLR mechanisms underestimate the concentrations by about an order of magnitude, and, compared to the surrogate, the calculated maximum is again further upstream of that observed experimentally. As expected, the high concentrations at the burner exit in the Jet A-1 flames could not be predicted because they appear due to the composition of the kerosene.
###### Abstract  
Shallow-water deltas are a subject of sedimentary research and represent a significant target for oil and gas exploration. The Yanchang Formation of the Triassic in the Ordos Basin comprises numerous shallow-water delta blocks. This paper addresses the core issues pertaining to the sedimentary facies, sedimentary characteristics and sand body distribution of the Chang 9 oil layer formation of the Upper Triassic in the Ordos Basin. Guided by the relevant theories and methods of contemporary sedimentology and sedimentary geology, the reservoir characteristics are described and studied in detail through ordinary thin sections, cast thin sections, graphical representations of particle size and scanning electron microscopy experiments. The experimental results indicate that the porosity in the study area ranges from 3% to 12% and that the permeability is between 0 and \(1.5\times 10^{-3}\)\(\upmu\)m\({}^{2}\), which is consistent with classification as an ultra-low-porosity and ultra-low-permeability reservoir. The Chang 9 sandstone is composed of feldspar sandstone and lithic feldspar sandstone. The average content of quartz is low, at less than 31%, while the average content of feldspar is high, at more than 34%. The average content of rock debris is between 10% and 20%. Therefore, the compositional maturity of the Chang 9 sand body is generally low. The particle size distribution exhibits a positive deviation, indicating that the sediments in the sand body are primarily coarse-grained components. The kurtosis of the particle size-frequency curve is observed to vary from flat to very sharp. The Chang 91 lake is classified as a shore shallow lake with basin subsidence and lake transgression. The Chang 9 period saw the development of the Chang 91 sedimentary facies into a semi-deep lake-deep lake environment. The vertical structural style of the Chang 9 oil layer formation in the basin can be roughly summarized into three basic structural types: the sedimentary structures observed in the area include box-shaped upward thinning, bell-shaped upward thinning and funnel-shaped upward thickening. The delta front area in Chang 9 is notable for its size and the prevalence of underwater distributary channel microfacies. The sand body distribution is stable, with sand layer thicknesses ranging from 15 to 30 m. The evaluation and summary of the sedimentary characteristics of the Chang 9 oil layer formation provide a geological basis for future exploration and development in the study area.  
Ordos Basin; Chang 9 oil layer formation; sedimentary facies; sand body characteristics +
Footnote †: journal: Applied Sciences
# Effect of Control Measures on Wheel/Rail Noise When the Vehicle Curves
## 3 Results and Discussions
### Wheel/Rail Noise Prediction and Preliminary Validation
It can be seen that the acceleration of the leading inner wheel with squeal is much larger than that of trailing inner wheel without squeal. In addition, highest peaks occur at modal frequencies corresponding to axial modes with zero-nodal-circle, showing that these modes are easy to be excited during curving. The axial mode shapes of wheel are expressed as (m, n). m represents number of nodal circle. n represents number of nodal diameter. For example, (0, 1) represents zero-nodal-circle with one-nodal-diameter. Thus, the frequencies and corresponding axle mode shapes are 265 Hz (0, 1), 353 Hz (0, 0), 468 Hz (0, 2), 1167 Hz (0, 3), and 2008 Hz (0, 4). These frequencies by simulation in this paper have a good agreement with the FRF test in the semi-anechoic room [8]. Figure 10a,b give the sound pressure level (SPL) (at the point which is 30 cm away from the outside of wheel tyre) of leading wheel and trailing wheel in time domain and frequency domain. It can be seen that the SPL of the leading wheel with squeal is much higher than that of trailing wheel at frequencies of axial modes. Figure 8: Lateral creep force of leading wheel and trailing wheel (simulation results). Figure 9: _Cont._  
Figure 10: SPLs at a distance of 30 cm from the leading and trailing wheels in (**a**) time domain and (**b**) frequency domain (simulation results). Figure 9: Acceleration of leading wheel and trailing wheel in (**a**) time domain and (**b**) frequency domain (simulation results). Wheel/rail noise is also tested near a wheel (860 mm diameter) used in metro when the vehicle passes a sharp curve (radius = 250 m) at the speed of 45 km/h. The microphone is arranged in the outside of the leading wheel with the distance of 30 cm, which is shown in Figure 11. In the process of field test, squealing noise can be heard clearly. Figure 12 gives comparison between simulation and experimental results of SPL of the leading wheel with the same curve radius and running speed. Figure 12 shows that the SPL at the frequencies of 265-468, 1167, 2008 Hz are significant for both simulation and experimental results. These frequencies have a good agreement with the zero-nodal-circle and n-nodal-diameter axial wheel modes for simulation and experimental results. Amplitude difference percentages between simulation and experiment of 265-468, 1167, and 2008 Hz are 3%, 7%, and 6.5% of their corresponding peaks, respectively. The differences mainly come from the simplification and assumption of the actual situation from the simulation model. In other words, the current model can predict all squealing frequencies and give relatively accurate amplitudes of them. Figure 11: Microphone arrangement. Figure 12: Comparison between simulation and experimental results of SPL of the leading wheel. Thus, this simplified model can be used for regular investigation on wheel/rail noise when the vehicle curves.
# A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend
## 3 Establishment of Tower Tilt Calculation Model
### Establishment and Analysis Model of the Distance from the Center of the Tower  
Once the tower tilt monitoring model and algorithm have been established, it is necessary to find out the tilt distance of the tower pixels in every location. The specific process is as follows:  
1. According to the standard \"Operation Regulations of Overhead territorial Lines (DS/CLC/TR 50412-1)\", set the tilt range of the pole and tower, and divide the dangerous image area;
2. Convert the pole and tower images to grayscale and binary image transformation;
3. Define the upper left corner of the binary image as the coordinate origin (0, 0), go through the fixed interval from left to right and from top to bottom, determine the pixel coordinates of the tower, and judge the top and bottom positions of the tower according to the \(y\)-axis pixel coordinates:
4. Perverse the outline of the tower, take any point on it as the high point and the bottom of the tower as the lowest point, and calculate the tower outline's pixel height at any point from the bottom;
5. Calculate the actual height of the outline of the tower, the distance between the camera and the tower, and the distance from the known horizontal parts;  
Figure 8: Top view of the schematic position of the camera relative to the opposite pole tower.  
* Compare the desired distance with the set distance threshold, determine the danger point, and mark it. The tower traversal process is shown in Figure 9.
# Variance-Triggered Two-Step GPS Acquisition
## 3 Variance-Triggered Two-Step Acquisition
### The Coarse Search Step
#### 3.1.2 Reduced Number of Frequency Bins  
To further reduce the search complexity in the CS step, the number of frequency bins used to correlate the signal across the whole Doppler frequency range is reduced, i.e., \(\delta_{CS}>\delta_{FS}\). As a result, the computational complexity of the frequency loop of the CS step decreases with the ratio of \(\frac{\delta_{CS}}{\delta_{FS}}\).  
Despite the advantages of computational complexity, decreasing the number of bin reduces precision in the estimation of the Doppler frequency shift. Nevertheless, the lost precision is reestablished in the FS step with the use of narrower frequency bins. Figure 5 shows the relationship between the frequency bin width in the CS step and in the FS step. The additional memory complexity added due to this second matrix is very small. In Section 4, we detail the memory usage.  
Although GPS theory identifies that the typical bin width should be 667 Hz for the L1 C/A code, in the example of Figure 6, we intentionally used a larger bin width to show that even for those large bins, it is possible to identify a promising frequency region. The case with fine bins has 40 bins of 500 Hz and the case with coarse bins has 10 bins of 2 kHz. Observe that the peak can still be noticed even though with rough precision.  
Figure 4: Illustration of the values of a correlation matrix for a satellite search after 4 consecutive rounds of 1023 samples, with the number of samples increasing from (**a–d**).
## 3 Implementation of the OAC-DES
### Evolutionary Algorithm in OAC-DES
#### 3.2.2 The Proposed (1+1)-DES
0:\(\mathbf{Local}\):\(\mathbf{x}_{orig}^{(g)}=(x_{orig}^{(g)},\sigma_{orig}^{(g)})^{T}\), \(\mathbf{x}_{init}^{(g)}=(x_{init}^{(g)},\sigma_{init}^{(g)})^{T}\), and \(\mathbf{x}_{best}^{(g)}=(x_{best}^{(g)},\sigma_{best}^{(g)})^{T}\)
0: The best reference \(i_{r}^{best}(k+1)\), \(\sigma\) and \(\hat{\omega}(k)\). 1:\(\mathbf{x}_{orig}^{(0)}=\langle i_{r}(k),\sigma\rangle\); [initialization of an original solution]
2:\(\mathbf{x}_{init}^{(0)}=\langle i_{r}(k),\sigma_{0}\rangle\); [initialization of an initial solution]
3:\(\mathbf{x}_{best}^{(0)}=\mathbf{x}_{orig}^{(0)}\); [initialization of the best solution]
4:\(\langle f_{best}^{(0)},\hat{\omega}_{best}^{(0)}(k)\rangle=\text{evaluate}( \omega_{r}(k),\omega_{a}(k-1),\mathbf{x}_{best}^{(0)})\); [current fitness]
5:for\(g=0\)to\(\textit{MAX\_GEN}-1\)do
6:\(\mathbf{x}_{orig}^{(g+1)}=\text{mutate}(\mathbf{x}_{orig}^{(g)})\); {uncorrelated mutation according to Equation (3)}
7:\(\langle f_{orig}^{(g+1)},\hat{\omega}_{orig}^{(g+1)}(k)\rangle=\text{evaluate}( \omega_{r}(k),\omega_{a}(k-1),\mathbf{x}_{orig}^{(g+1)})\);
8:\(\mathbf{x}_{init}^{(g+1)}=\text{mutate}(\mathbf{x}_{init}^{(g)})\); {uncorrelated mutation according to Equation (3)}
9:\(\langle f_{init}^{(g+1)},\hat{\omega}_{init}^{(g+1)}(k)\rangle=\text{evaluate}( \omega_{r}(k),\omega_{a}(k-1),\mathbf{x}_{init}^{(g+1)})\);
10:if\(f_{init}^{(g+1)}<f_{orig}^{(g+1)}\)then
11:\(\mathbf{x}_{orig}^{(g+1)}=\mathbf{x}_{init}^{(g+1)}\); \(f_{orig}^{(g+1)}=f_{init}^{(g+1)}\); \(\hat{\omega}_{orig}^{(g+1)}(k)=\hat{\omega}_{init}^{(g+1)}(k)\);
12:endif {long-step search prevailed?} 13:if\(f_{orig}^{(g+1)}<f_{best}^{(g+1)}\)then
14:\(\mathbf{x}_{best}^{(g+1)}=\mathbf{x}_{orig}^{(g+1)}\); \(f_{best}^{(g+1)}=f_{orig}^{(g+1)}\); \(\hat{\omega}_{best}^{(g+1)}(k)=\hat{\omega}_{orig}^{(g+1)}(k)\);
15:endif {best solution found}
16:endfor
17:return\(\langle\mathbf{x}_{best}^{(g+1)},\hat{\omega}_{best}^{(g+1)}(k)\rangle\); {the final solution: \(\mathbf{x}_{best}^{(g+1)}=\langle i_{r}^{\text{best}}(k+1),\sigma\rangle\)}
```  
**Algorithm 1** Pseudocode of (1+1)-DES
# A New Method of GPS Water Vapor Tomography for Maximizing the Use of Signal Rays
## 2 Methods  
### GPS Water Vapor Estimation  
In this study, the SWV derived from GPS-based receivers is exploited to obtain the water vapor distribution information. The SWV can be obtained by the following formula [24; 25]:  
\[\text{SWV}=\frac{10^{6}}{\rho_{water}R_{\omega}\big{[}(k_{3}/T_{m})+k_{2}^{ \prime}\big{]}}\times\text{SWD} \tag{1}\]  
where \(\rho_{water}\) is the density of liquid water; \(R_{\omega}=461/(kg\cdot K)\) is the specific gas constants for water vapor; \(k_{2}^{\prime}=16.48 KhPa^{-1}\) and \(k_{3}=3.776\times 10^{5}K^{2}hPa^{-1}\) are constants; and \(T_{m}\) is the weighted mean tropospheric temperature, which is calculated by an empirical formula [26]. In order to obtain the SWD, the ZWD and the wet delay gradient parameters should be mapped into the elevation direction and the unmodeled residuals should be taken into account [27; 28]. The specific calculation formula is as follows:  
\[\text{SWD}=m_{w}(e)*[\text{ZWD}+\cot(e)(G_{NS}^{w}*\cos\varphi+G_{WE}^{w}*\sin \varphi)]+R \tag{2}\]  
where \(e\) and \(\varphi\) are the satellite elevation and azimuth, respectively; \(m_{w}\) is the wet mapping function; \(R\) denotes the unmolded residuals; \(G_{NS}^{w}\) and \(G_{WE}^{w}\) are wet delay gradient parameters in the north-south and east-west directions, respectively; and ZWD is the zenith wet delay, which can be calculated by subtracting the zenith hydrostatic delay from the zenith total delay.
## 7 Conclusions  
In this paper, we presented the design of a proposed customised IDS for a heterogeneous RPL-IIoT network with the assistance of a deep FTL model. First, the proposed FT-CID model removes  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Training Data Size**} & \multicolumn{4}{c}{**Intrusion Detection Accuracy (\%)**} \\ \cline{2-6}  & \multicolumn{2}{c}{**Centralised**} & \multicolumn{2}{c}{**Federated**} \\ \cline{2-6}  & \multicolumn{2}{c}{**CNN**} & \multicolumn{1}{c}{**MLP**} & \multicolumn{1}{c}{**LR**} & \multicolumn{1}{c}{**Proposed**} & \multicolumn{1}{c}{**Ft-CID**} \\ \cline{2-6}  & \multicolumn{2}{c}{**CNN**} & \multicolumn{1}{c}{**MLP**} & \multicolumn{1}{c}{**LR**} & \multicolumn{1}{c}{**FT-CID**} & \multicolumn{1}{c}{**(without FS)**} \\ \hline
40 & 67.16 & 55.86 & 53.25 & 82.83 & 73.24 \\ \hline
60 & 71.23 & 56.95 & 54.96 & 84.17 & 79.65 \\ \hline
80 & 74.89 & 58.04 & 56.12 & 85.52 & 80.03 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative experimental results of intrusion detection accuracy.  
Figure 8: False-positive rate performance for FT-CID, CNN, and MLP.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Training Data Size**} & \multicolumn{2}{c}{**False-Positive Rate (\%)**} \\ \cline{2-4}  & **Proposed FT-CID** & **CNN** & **MLP** \\ \hline
40 & 16.57 & 31.251 & 43.74 \\ \hline
60 & 15.01 & 27.36 & 42.17 \\ \hline
80 & 13.29 & 24.98 & 41.76 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative experimental results of false-positive rate.  
the noise and filters irrelevant features from the simulated RPL-IIoT dataset by modelling the IGWO algorithm. In the FT-CID model, the deep FTL algorithm relies heavily on the CNN algorithm to build local models based on the IIoT network, i.e., global model updating and local model optimisation. The logit output of each local model greatly influences the updating of the global model in the federated server. Thus, the logits in the global model assist in the optimisation of the local models in the sensors or the IIoT networks. In the FT-CID model, the TL model involves parameter transfer and relational knowledge transfer during global model updating and local model optimisation with the SGD model. Thus, the experimental results illustrate that the proposed FT-CID model yields 10.63% higher intrusion detection accuracy than the centralised CNN model in a heterogeneous IIoT network or AMI applications.  
In future work, the data collected in this study will be analysed and processed to analyse the attack intention of intruders or correctly predict the intruder's intrusion behaviour.  
[PERSON] was responsible for content, conceptualization, methodology, software, and writing--original draft preparation. [PERSON]-R. was responsible for reviewing--discussion and feedback--co-authored the manuscript, and supervised the research. [PERSON] was responsible for reviewing--discussion and feedback--and co-supervised the research. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 5 Framework Evaluation
### Autonomous Operation  
The CMS framework, along with the integrated and coordinated operations architectures, is designed to operate autonomously with minimal human intervention. This autonomy is essential for ensuring the scalability of operational scenarios, reducing the costs associated with NTN management, and improving overall system efficiency. The adaptive architecture, however, cannot be considered fully autonomous, as operational control is shared with the NTN MANO, whose level of autonomy falls outside the framework's scope. From the perspective of infrastructure and architecture challenges, autonomy enables the implementation of a hybrid architecture, as distributed operations fundamentally require autonomous capabilities for effective execution. Regarding interoperability and standardization challenges, the framework addresses the reliance of existing satellite and network management tools on semi-manual operations and human supervision. By integrating both domains within a single autonomous system, the CMS enhances the automation of operations. For integrated management challenges, the framework addresses the mixed scheduling challenge by enabling a combination of distributed scheduling onboard satellites and centralized master scheduling from the ground. Additionally, it mitigates scenario uncertainty by enabling real-time reactions to dynamic conditions, as continuous monitoring and autonomous response are key to handling unpredictable events. This is closely related to the adaptive scheduling challenge, where an autonomous system can continuously integrate real-time telemetry and system updates for ongoing in-loop optimization. Finally, regarding the operational dynamics challenge, this autonomy directly tackles traffic offloading and unstable link management challenges by reacting to real-time system changes, ensuring efficient resource management, and maintaining system stability under dynamic conditions.