esa-sceva/satcom-chunk-collection huggingface dataset:
# Mitigation of Nonlinear Distortions for a 100 Gb/s Radio-Over-Fiber-Based WDM Network
## 3 Analytical Modeling
The general power consumption of optical transmitter, 'Tx', is calculated as  
\[P_{T}=\Omega_{D}^{-1}C(P_{DML}+P_{map}+P_{QM}+P_{M-ary}+P_{QAM}+n_{MZM}.P_{MZM}) \tag{22}\]  
where \(\Omega\) denotes power conversion efficiency, \(P_{map}\) is the signal mapping of power consumption, \(P_{QM}\) means power consumption due to QM, \(P_{M-ary}\) explains M-ary power consumption, \(P_{QAM}\) shows the power consumption by reason of QAM, \(n_{M}ZM\) means the quantity of MZMs required for WDM-RoF transmission. Similarly, the power consumption at the Rx side is measured as  
\[P_{R}=\Omega_{D}^{-1}C(P_{FBG}+P_{PD}+P_{QM}+P_{M-ary}+P_{amp}+P_{QAM}) \tag{23}\]  
The parameters \(P_{FBG}\), \(P_{PD}\), \(P_{QM}\), \(P_{M-ary}\), \(P_{QAM}\) and \(P_{amp}\) define the power consumption because of FBG, PD, QM encoder, M-ary pulse generator, amplification and QAM demodulator. The attained signal is explained as  
\[A_{x}=E|\Gamma_{n}|^{4}/E^{2}[|\Gamma_{n}|^{2}] \tag{24}\]  
where \(A_{n}\) is the amplitude of the received signal and \(\Gamma_{n}\) is amplitude of the transmitted signal. Furthermore, the sequence of recovered signal with compensated NLIs, LDs and PAPR is determined as  
\[Z_{n}=z_{n}/\sqrt{\Sigma_{n}}=1^{\forall}|z_{n}|^{2}/\forall \tag{25}\]  
In Equation (25), factor of power noise is described by \(\forall\). The BER is used for the analysis of NLIs, LDs and PAPR parameters. If these issues exist then how many bit can be transferred including low error size per bit [9]? BER is given as  
\[f_{1}=1-r_{p}f_{c}(0\leq r_{p}\leq 1) \tag{17}\]0  
\[f_{1}=1-r_{p}f_{c}(0\leq r_{p}\leq 1) \tag{17}\]1where \(N\) denotes symbols for estimating error vector magnitude (EVM), \(E_{r}\) is the vector of received signal, \(E_{t}\) is the transmitted vector signal, \(p_{a}\) is the average transmitted power of the used modulation. In Equation (26), the complimentary error function (erfc) is used for the evaluation of probability functions that involve a Gaussian process. Moreover, parameter G denotes the QAM level. These equations are employed for the analyzing the properties of the proposed model and how the WDM-RoF generates its outcomes. The upcoming section includes the simulation results based on the analytical model using a modified version of the equalizer presented in [38].
# GNSS Interference Monitoring and Detection (GIMAD) System +
## 4 Results
### C-SAM Test Results  
The out-band CRPA and the in-band CRPA of the GIMAD antenna set underwent calibration in an anechoic chamber, as is illustrated in Figure 9. During this calibration process, angular calibration parameters (relative amplitude and relative phase) were obtained for each carrier frequency and certain array orientations. The obtained calibration corrections had to be applied for the estimation of the AoA at the carrier frequency under analysis.  
Figure 8: GIMAD CAMM (O-SAM) real-time detection at 876 MHz.  
Figure 7: GIMAD CAMM (MAM) real-time detection on L1.  
Figure 9: AOA calibration in an anechoic chamber.  
In order to obtain the calibration parameters, the CRPAs were rotated with a moving platform rather than a fixed transmitting antenna.  
In a second round, once the calibration parameters were already computed, the CSAM algorithms were tested first in the anechoic chamber.  
Two cases are presented: the Out-of-Band (OOB) RFI AoA estimation and the In-Band RFI AoA estimation.  
In order to address the OOB AoA estimation, an RFI of 827.5 MHz is broadcast inside the anechoic chamber. This OOB RFI is active from snapshot 200 to 700, as shown in Figure 10. The estimated AoA error is given for the different AoA techniques. Beamformer is depicted in blue, Capon in orange, and MUSIC in yellow. We observe in Figure 11 that the maximum AoA error is for Beamformer, which is about 10\({}^{\circ}\). For Capon and MUSIC, the AoA error is below 4\({}^{\circ}\).  
The technique that provides better AoA estimation without filtering and is considered the primary localization technique is MUSIC. In order to address the in-band AoA estimation, an RFI of 1575.42 MHz (L1) is broadcast inside the anechoic chamber. It shows an accurate estimation of the angle of arrival of the L1 RFI using MUSIC and Capon techniques.
## 5 Conclusions  
This paper proposes a sealing method wherein the downhole casing is processed with threaded grooves and then plugged by a low-melting-point alloy plug. In this paper,  
Figure 14: Variation of gas sealing performance of casing alloy plugs with different roughness at 30 \({}^{\circ}\)C with increasing length-to-diameter ratio.  
the effect of surface roughness and threaded groove inside the casing on the gas sealing performance of alloy plugs was analyzed through experiments. And the effect of ambient temperature and length-to-diameter ratio on the gas sealing performance of alloy plugs was researched, which provided theoretical support for the application of the method of CCUS using Sn58 Bi inside the casing. We obtained the following conclusions:  
1. The sealing performance of the alloy plug is relatively affected by the ambient temperature. With the increase in ambient temperature, the radial stress inside the alloy plug-casing interface rises, the alloy combines more tightly with the inner surface of the casing, and the gas sealing performance of the alloy plug appears to be significantly increased.
2. The threaded groove on the inner surface of the casing has a significant effect on the gas sealing performance of the alloy plug. Under the condition of the same length-to-diameter ratio of the alloy plugs, the alloy plugs of the threaded casing have a significant increase in gas sealing performance due to the increase in the area of the alloy-casing interface. As the pitch decreases, the degree of gas flow path zigzaging increases, and the gas sealing performance of the alloy plug increases.
3. The gas sealing performance of alloy plugs showed an obvious linear positive correlation with their length-to-diameter ratio. The gas sealing performance of alloy plugs is less affected by the roughness of the inner surface of the casing. Relying only on the radial force generated by the micro-expansion during the molding process of the alloy plug, the alloy is not sufficient to fully fill the micron-sized grooves.
4. When the casing was processed through internal threaded grooves, the threaded grooves should be designed at the lower section position of the target area. The gravity force generated by the alloy above the threaded grooves can further increase the degree of filling of the molten alloy with the threaded portion, and this design method can further improve the gas sealing performance of the threaded alloy plug.  
C.Z.: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Formal Analysis, Project Administration. T.C.: Visualization, Writing--Original Draft, Writing--Review and Editing, Data Curation. W.W.: Resources, Software, Supervision, Writing--Review and Editing. G.L.: Project Administration, Validation, Funding Acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China, contract No. U23B2081.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.
## 1 Introduction  
Currently, GIS vector maps are used in applications in many fields, such as science, navigation, and online or offline digital map services. GIS vector maps are created and developed by the merging of cartography, statistical analysis, and database technology based on vector models [1]. Due to the fact that GIS vector maps may have significant value, GIS vector map data may be stolen or easily purchased and redistributed or resold several times without obtaining any permission from the original data providers. In addition, some applications of vector maps by the military or some personal applications of vector maps must be secured from unauthorized users. Consequently, GIS vector map data protection is necessary to prevent illegal duplication and distribution.  
GIS vector map security includes copyright protection [2; 3; 4], access control for users, and vector map data encryption in order to prevent attacks or illegal distribution during storage and transmission, and damage in the integration process of geographical information [5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. However, access control on the Web is unable to prevent attacks or illegal duplication and distribution, and watermarking techniques are only useful in identifying ownership and for copyright protection, while unauthorized users should not be able to see, attack, or extract GIS vector map content. Therefore, GIS vector map data encryption is necessary and suitable for protection. Moreover, conversion between data formats is vulnerable to attack and security techniques based on database management systems are not responsive to the various data formats of vector maps [10; 11]. Thus, the encryption techniques for GIS vector map should be responsive to the various data formats of GIS vector maps and the security requirements.  
In order to meet the issues above, we propose an encryption algorithm for GIS vector map data in this paper. Our algorithm solves the current issues of GIS vector map security by encrypting the geometric objects of GIS vector maps. The main intent of the proposed algorithm is to extract geometric objects (polylines and polygons) from GIS vector maps in order to encrypt them. Geometric objects are used to compute the backbone object of polyline/polygon. The backbone object is then selectively simplified by the multi-scale simplification algorithm in order to determine the feature vertices of the backbone object. The feature vertices of the backbone object is encrypted by the AES (Advanced Encryption Standard) algorithm and the secret key. Finally, all vertices of the backbone object are randomized by the random Gaussian distribution in order to generate the encrypted vector map. To clarify the detailed contents of the proposed algorithm, this paper is organized as follows: In Section 2, we discuss vector map data security techniques and explain the relationship between vector map data and the proposed algorithm. In Section 3, we explain the proposed algorithm in detail. We then perform experiments, discuss the experimental results, and evaluate the performance of the proposed algorithm in Section 4. The conclusion is provided in Section 5.
# Research on Decision-Making Methods for Autonomous Navigation in Inland Tribitary Waterways
## 2 Analysis of the Autonomous Navigation Process
### Autonomous Collision Avoidance Under the Risk of Collision
#### 2.2.2 The Principle of Collision Avoidance Action  
Du proposed a three-tiered protective framework for maritime conflict resolution, established to address the inherent disparity in collision risk perception between give-way and stand-on vessels. As a give-way ship, the vessel is obligated to proactively initiate evasive maneuvers at the earliest stage to mitigate collision risks. Conversely, when acting as a stand-on ship, its role shifts to supporting the give-way ship in collision avoidance and addressing emergencies caused by the give-way ship's failure to act or insufficient maneuvers. This framework is particularly critical in inland tributary environments, where vessels exiting tributaries typically face two options: (1) continuing navigation along the original waterway side, or (2) crossing to the opposite side. Consequently, the action choices of tributary-exiting vessels and the resulting shift in avoidance obligations between the target ship (TS) and the own ship (OS) must be rigorously analyzed.  
Figure 4 illustrates the systematic conflict resolution procedure. The process begins with acquiring and integrating navigation data from the OS, surrounding TSs, and the environment. The system then performs trajectory prediction within a defined timeframe. Using a collision risk identification model, it identifies potential conflicts between the OS and specific TSs. In compliance with the RPRCIRCA, the OS's collision avoidance actions depend on its assigned role:  
1. As the give-way ship: The OS must execute appropriate evasive maneuvers.
2. As the stand-on ship: The OS evaluates the timeliness and adequacy of the TS's actions. If the TS's maneuvers are sufficient and timely, the OS maintains course and speed. If the TS's actions are inadequate, the OS implements corrective measures to ensure the TS passes safely outside the OS's ship domain.  
Figure 3: Analysis of navigation rules for inland tributary waterways.
## 3 Results
### Validation of the Cylindrical Electrode Pair  
In Figure 9 the analytical and numerical solutions of electric potential within a central grid are presented for the voxel sizes 0.5 \(\times\) 10\({}^{-3}\) m, 1.0 \(\times\) 10\({}^{-3}\) m, and 3.0 \(\times\) 10\({}^{-3}\) m. A comparison between both solutions was illustrated as a relative error with respect to the analytical solution in Figure 10. According to the results, the datapoints of the numerical solutions again lined up with the analytical solution after the inclusion of EPE for all voxel sizes. Specifically, the inclusion of EPE significantly reduced the maximal errors from 9.3%, 4.0%, and 71.8% for the voxel sizes 0.5 \(\times\) 10\({}^{-3}\) m, 1.0 \(\times\) 10\({}^{-3}\) m, and 3.  
m, 1.0 \(\times\) 10\({}^{-3}\) m and 3.0 \(\times\) 10\({}^{-3}\) m, respectively, which also significantly reduced to values equal to or smaller than 0.4% after inclusion of EPE.  
Figure 10: Relative error of electric-potential distributions of the cylindrical electrode pair model (central grid) with respect to the analytical solution along the x-axis for the voxel sizes (**A**) 0.5 \(\times\) 10\({}^{-3}\) m, (**B**) 1.0 \(\times\) 10\({}^{-3}\) m, and (**C**) 3.0 \(\times\) 10\({}^{-3}\) m. The grey bars represent the electrodes of the cylindrical electrode pair. The discontinuity in the blue and red lines reflects the local relative error approaching 0%.  
Figure 9: The absolute electric-potential distributions of a cylindrical electrode pair model (central grid) along the x-axis excluding/including the Electric-Potential Estimation (EPE). The electric-potential distributions were calculated analytically (black line), and numerically with voxel sizes 0.5 \(\times\) 10\({}^{-3}\) m, 1.0 \(\times\) 10\({}^{-3}\) m, and 3.0 \(\times\) 10\({}^{-3}\) m (colored symbols). In (**A**) EPE was excluded, while in (**B**) EPE was included. The grey bars illustrate the electrodes of cylindrical electrode pair.  
Furthermore, in Figure 11 the relative error of the numerically calculated resistance values with respect to the analytical value were shown as a function of voxel sizes and grid types. EPE significantly reduced the maximal errors in the region in between the electrodes from 7.1%, 3.2%, and 29.6% for the voxel sizes 0.5 \(\times\) 10\({}^{-3}\) m, 1.0 \(\times\) 10\({}^{-3}\) m and 3.0 \(\times\) 10\({}^{-3}\) m, to 0.5%, 0.8% and 1.0%, respectively, for the central grid. For the shifted grid, EPE also significantly reduced the maximal errors from 2.3% and 25.5% for the voxel sizes 0.5 \(\times\) 10\({}^{-3}\) m and 3.0 \(\times\) 10\({}^{-3}\) m, to 0.6% and 1.3%, respectively. For the voxel size 1.0 \(\times\) 10\({}^{-3}\) m the error slightly increased from 1.8% to 2.5%.
## 3 System Model and Security Goals
### IoT-Based System Model
#### 3.1.2 Gateway/Server  
The server behaves as a gateway among sensor nodes and remote users. The server is a powerful machine that verifies the legitimacy of the remote users and grants access to the resources available in the healthcare environment. The server also plays a role as a registration authority (RA). We assume that the server contains a secure database and that the attacker cannot access data stored in the database.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Components** & [38] & [39] & [40] & [42] & [44] & [45] \\ \hline Computation Cost (ms) & 0.1236 & 0.0853 & 0.1101 & 0.0656 & 0.0749 & 0.0762 \\ Communication Cost (bits) & 2976 & 2048 & 3296 & 1600 & 4822 & 1792 \\ Number of Messages Exchanged & 4 & 4 & 2 & 3 & 6 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Qualitative comparisons between existing schemes.
# Low-Complexity Convolutional Neural Network for Channel Estimation
## 4 DL-Channel Estimation
### Proposed Convolutional Neural Network
In order to robustly estimate the channel and adjust the weights of the network for this application, the CNNs are trained offline using an image-to-image regression. At each iteration, the training data are generated varying the channel profile, Doppler effect, delay spread, and SNR value, in order to consider a wide range of environmental 5G scenarios. Each value associated to each parameter is randomly selected from the predefined valid range, as shown in Table 1.  
For each iteration of training, the predicted image is compared to the perfect channel estimation using the loss function reported in Equation (8). \[loss=\frac{1}{2}\sum_{i=1}^{O_{H}\times O_{W}\times O_{C}}(\mathbf{H_{i}}-\mathbf{H}\mathbf{p _{i}})^{2} \tag{8}\]  
where \(O_{H}\), \(O_{W}\), and \(O_{C}\), represent the height, width, and depth of the channel response, respectively. The target image \(\mathbf{H}\) is the actual channel, while the prediction \(\mathbf{H}\mathbf{p}\) is the estimated channel. The aim of the training is to minimize such loss by progressively updating the parameters of the CNN. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Range** \\ \hline Channel profiles & TDL-A, TDL-B, TDL-C, TDL-D, TDL-E \\ Doppler effect & 20–200 Hz \\ Delay spread & 100–300 ns \\ SNR levels & \(-5\) to 15 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Configurable parameters in the neural network training. Figure 6: Comparison of the implemented CNNs: (**a**) the model of CNN that enhances the estimation provided by a preliminary interpolation stage; (**b**) the model of the proposed neural network that completely estimates the channel.
# A Novel Sucrose Isomerase Producing Isomaltulose from _Raoultella terrigena_
## 3 Results and Discussion  
### Sequence Analysis of Pal-2  
The Pal-2 from _Raoultella terrigena_ was predicted to encode 599 amino acids and has a theoretical isoelectric point of 5.88 and a molecular mass of 69.8 kDa. The phylogenetic  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Primer** & **Sequence (5/-3/)** \\ \hline N498P-F & CGTGAACTGACCGAACCCAAATCTGTGTCAAC \\ N498P-R & GTTCACACAGATTTGGGTTCGGTCAG \\ Q275R-F & GGGCTCCAACATCCACCGTTATATACCAGAAAAC \\ Q275R-R & GTTCATTTTCCTGGATATAAGGGTGTGTTTGGACC \\ V355A-F & CAAATGGATACCACCGCGGTGAATACGGTGTGAAC \\ V355A-R & GTTCCAACCGTATTACCCGCGGTGTATCCATTTG \\ A492E-F & CTACTAGATGATGATGATGATGATGACGGAAACTGAACCGAAAAC \\ A492E-R & GTTTTGGCTACGTGTACCGTGTGTATTACTGTGTAG \\ Y246L-F & GTTTCGAATACCTTCCGACCGTCCAAAATCCAGTTTC \\ Y246L-R & GAAAACCTGGGATTTTGGACAGGGTGTCCAACGGATTCCAAAAC \\ H481P-F & GTAACCCGGTGTTCCCGTAAACCGAACTAC \\ H481P-R & GTAAC  
tree of Slases from various microorganisms was constructed, including _Gluconobacter thailandicus_ sp. NBRC 3257, _Pectobacterium atroseticum_, _Rhizobium_ sp. MX-45, _Pantoea dispersa_, _Enterobacter_ sp. FMB-1, _Klebsiella_ sp. LX3, _Klebsiella pneumonia_, _Erwinia rhapontici_, and _Serratia plymuthica_ sp. A30. As shown in Table 2, all of these Slases shared more than 45% of identity with Pal-2, where Pal-2 is closer to Slases from _Klebsiella_ sp. LX3 and _Klebsiella_ Pneumoniae. Amino acid sequence alignments of Slases from different microorganisms in Figure 1 shows that most amino acids are strictly conserved in Slase. Similar to the glycoside hydrolase family 13 (GH13) enzymes, Slases also contained a catalytic triad (Asp 199, Glu 255, and Asp 329) and two conserved histidine residues (His 145 and His 368), which are located in the catalytic pocket and involved in the catalysis of the substrate [31]. Most Slase amino acid sequences [32] harbor a 325 RLDRD329 motif that determines product specificity and this motif also exists in Pal-2.  
Figure 1: Amino acid sequence alignments of Slases: the strictly conserved areas are shown with a red background and the highly conserved residues are shown using red words surrounded by a blue box.
# In-Situ Nanoparticles: A New Strengthening Method for Metallic Structural Material
## 3 Materials Fabrication
### In-Situ Nanoparticle Reinforced Cu Alloys
#### 3.2.2 DSC (Differential Scanner Calorimetry) Analysis  
Precipitation behavior of iron-rich nanoparticles during solidification was investigated by DSC thermal analysis, as shown in Figure 6[64]. The as-cast Cu-10 Sn-2 Zn-1.5 Fe-0.5 Co alloy was melted and heated until 1573 K, and then cool down at a rate of about 20 K/min. Obviously, there are three exothermic peaks between 1273 K and 1573 K during the cooling process. Peaks at 1291 K and 1283 K correspond to the solidification of primary \(\alpha\)-Cu phase and \(\alpha\)-Cu + L \(\rightarrow\beta\) peritectic transformation respectively, which is consistent with previous DSC result of binary Cu-Sn alloy [72].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Temperature/K**} & \multirow{2}{*}{**Phase**} & \multicolumn{5}{c}{**Phase Compositions/wt. \%**} \\ \cline{3-6}  & & **Fe** & **Co** & **Cu** & **Zn** & **Sn** \\ \hline
1300 & FCC\_L12 & 68.389 & 23.671 & 7.782 & 0.089 & 0.070 \\
1200 & FCC\_L12 & 72.802 & 21.575 & 5.398 & 0.141 & 0.084 \\
1000 & BCC\_B2 & 76.113 & 23.545 & 1.258 & 0.196 & 0.023 \\
600 & BCC\_B2 & 74.953 & 24.975 & 0.067 & 0.004 & \(7.17\times 10^{-6}\) \\
298 & / & 62.01 & 17.03 & 19.69 & 0.32 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of iron-rich phase in Cu-10 Sn-2 Zn-1.5 Fe-0.5 Co alloy at different temperatures [71].  
Figure 5: (a) The equilibrium mole fraction of phases calculated using the TCCU2 database in Thermo-Calc package as a function of temperature for Cu-10 Sn-2 Zn-1.5 Fe-0.5 Co alloy; (b) is an enlarged view of the dotted box in (a) [71].  
In the upper-left inset, there is a relatively gentle exothermic peak at 1365 K which is consistent in precipitation temperature shown in Figure 5, revealing that iron-rich nanoparticles can indeed in-situ precipitate before Cu matrix solidifies.