esa-sceva/satcom-chunk-collection huggingface dataset:
# Condition Rating Prediction Using an Interactive Deterioration Model Development Package
## 6 Comparison of Deterioration Models for Wyoming Bridges
### Stochastic Deterioration Models  
Four stochastic deterioration algorithms were coded in the DMDP: three were based on the Markov chain process (percentage prediction, logistic regression, and optimization-based) and one used the Weibull distribution to estimate the sojourn time. The deterioration models for the deck elements based on all of the inspection data are plotted in Figure 10. The deterioration curves for the percentage prediction and logistic regression algorithms were found to be similar. The optimization-based algorithm exhibited a rapid decrease during the first years, following which the deterioration process was almost flat, regardless of the initial condition ratings. The proportion of condition ratings between \"4 (poor)\" and \"7 (good)\" from the historical inspection data resulted in a small decrease in the entire age range for the optimization-based algorithm. The sojourn times for the condition ratings, indicated by the bullets in the deterioration models, were estimated using the Weibull distribution, and polynomial curve-fitting was applied to plot the deterioration model illustrated in Figure 10d. The deterioration curves starting from \"7 (good)\" and \"5 (fair)\" were shifted compared to that starting from \"9 (excellent)\". The curve-fitting algorithm thus significantly affects the deterioration shape. In fact, a rapid decrease is generally observed in the other methods, but differences exist between the deterioration curve and bullets.  
Further analysis was carried out to investigate the effect of the classification tree level applied in the stochastic methods. Figure 11 presents an example of the superstructure deterioration models for a subset associated with the three explanatory variables \"DST 1\" (cast-in-place concrete), \"BRW 4\"  
Figure 10: Deck deterioration models for all Wyoming bridges using (**a**) percentage prediction, (**b**) logistic regression, (**c**) optimization algorithm, and (**d**) Weibull distribution.  
(range between 4 and 6 m), and \"DL 5\" (MS 18). The quantity of inspection data and bridges were slightly higher than required for these variables. Similar to the deterioration models for all of the inspection data, the models for the percentage prediction and logistic regression algorithms were almost identical. The condition ratings decreased rapidly in early age and became almost flat for the remaining service life. The decrease in the condition ratings under the optimization-based algorithm was mainly concentrated in the first two decades. The number of components corresponding to the high-level of condition ratings used for the optimization process nearly flattened this curve. The deterioration model for the Weibull distribution was similar to those for the percentage prediction and logistic regression algorithms, but differences were evident between the bullets representing the estimated sojourn time and the deterioration curve resulting from the curve-fitting process.
## 7 Robustness
### Security Analysis
#### 7.5.4 Security Features  
The implementation incorporates several security measures that work together. Random selection through PRNG use in 8 \(\times\) 8 block selection prevents the easy detection or removal of watermarks. The consistent PRNG seed applies uniform randomisation across block locations without adding computational overhead. Recipient-specific watermarks assign unique identification to improve traceability and accountability. Moreover, using a consistent seed reduces computational complexity critical when processing large volumes of forensic data. This structure supports secure watermarking on multiple images or full-disk images at scale.
# Experimental Study of a Signal Modulation Method to Improve eLORAN Data Channel Communications
## 1 Introduction  
The positioning, navigation, and timing (PNT) systems are important to the national economy and security of any country [1; 2; 3]. One of the high-precision ground-based PNT systems is Enhanced LOng-RAnge Navigation (eLORAN). It is the most widely used service among the long-standing and proven series of low-frequency LORAN systems [4; 5; 6].  
The important difference between eLORAN and traditional LORAN-C is that eLORAN adds a data channel to the transmitted signal channel [7; 8; 9]. This data channel can convey correction information, warnings, and signal integrity information to the user's receiver. The use of the data channel allows the eLORAN system to use nonprecision instruments to meet the very demanding requirements of landing aircraft, and to safely navigate the ship to the port under low visibility conditions. eLORAN can also provide differential data transmission and can be used as a global navigation satellite system (GNSS) redundant system [10; 11; 12].  
eLORAN data communication has become an important direction in extending the application of eLORAN systems. There are mainly two types of data modulation methods used for eLORAN systems: tri-state pulse modulation and ninth pulse modulation. Tri-State Pulse Position Modulation (3S-PPM), also known as Eurofix, is standardized by Radio Technical Commission for Maritime services (RTCM) and International Telecommunication Union (ITU) [13; 14; 15; 16]. Eurofix is adopted in China's BPL long wave time service system and Changhe 2 navigation system. The signal-to-noise ratio (SNR) required for eLORAN data demodulation is higher than that for signal acquisition and detection. Both of Eurofix and ninth pulse modulation modes are phase modulation, which tends to cause phase blur and decrease the demodulation success rate when the propagation distance is far away [17; 18; 19; 20].  
According to China's High Accuracy Ground-based Time Service System plan, the system plans to build three eLORAN time service broadcasting stations in western China, in order to adapt to the complex signal propagation environment in the inland areas of western China. In this paper, a modulation mode similar to On-Off Keying (OOK) is proposed. In additional pulse on-off modulation (APM), the success rate of demodulation is higher in the same environment and is more suitable for applications in complex propagation environments, and the performance of the APM modulation method is verified by modification of the BPL timing service system.
# Performance Analysis of BDS-5G Combined Precise Point Positioning
###### Abstract  
Precise point positioning (PPP) technology is one of the core technologies in the field of GNSS high-precision positioning. It is used widely because it can realize centimeter-level positioning in outdoor environments by using only a single receiver. However, its convergence is time-consuming, particularly in urban areas where satellite occlusion is more severe. A combined BeiDou Navigation Satellite System (BDS) and fifth generation mobile communication technology (5G) PPP observation model is proposed, in which the two kinds of observations are combined and solved at the original observation level. The impact of different numbers and geometries of 5G base stations on the convergence time of PPP is analyzed from both static and dynamic perspectives. The results confirm that PPP technology combining BDS and 5G can effectively accelerate convergence while improving the accuracy of positioning.  
BeiDou Navigation Satellite System; fifth generation mobile communication technology; precise point positioning; carrier phase observation; time of arrival Article
## 2 Materials and Methods
### Analyzing NCMs for Decision Support  
#### 2.2.1 Static Analysis of NCMs  
The static analysis of an NCM includes some features that can give us important information about the map model. According to [40], centrality is a measure of the importance of nodes in an FCM. Centrality shows how much the node is linked to other nodes and what the cumulative strength of these links is. The centrality of a node (\(C_{j}\)) in an NCM is the sum of (the number of nodes that directly or indirectly cause \(C_{j}\)) and the number of nodes that cause or induce \(C_{j}\). These numbers are calculated from the number of nodes on the pathways that flow into (and out) of \(C_{j}\). The number and strength of nodes in an FCM are as follows.  
Concept centrality (\(C_{j}\)) = IN(\(C_{i}\)) + OUT(\(C_{i}\)), where IN(\(C_{i}\)) is the total of weights of causal connections comprising all pathways linking nodes \(C_{j}\), \(i\
eq j\), to \(C_{i}\) and OUT(\(C_{i}\)) is the sum of weights of causal links constituting all paths connecting node \(C_{i}\) to all nodes \(C_{j}\), \(i\
eq j\).  
Absolute values are used in summing causal weights to give the same weight to positive and negative causes. Concepts with a high centrality value deserve special consideration in any analysis to support decision-making.  
The suggested framework employs the measurements mentioned above, which are based on the absolute values of the neutrosophic adjacency matrix [41]:  
* _Outdegree_ is the sum of the row elements in the neutrosophic adjacency matrix and represents the strength of the variable's outward connections (\(c_{ij}\)). It actually indicates the cumulative strengths of connections (\(C_{ij}\)). It is a measure of how much a given variable influences other variables. \[od(vi)=\sum_{i=1}^{n}c_{ij}\] (2)
* _Indegree_ is the sum of the column components in the neutrosophic adjacency matrix, and it represents the strength of the connections (\(c_{ij}\)) that stem from the variable and shows the cumulative strength of variables entering the unit. \[id(vi)=\sum_{i=1}^{n}c_{ji}\] (3)
* _Total centrality_ is calculated by summing the variable's indegree and outdegree. \[td(vi)=id(vi)+od(vi)\] (4)  
The adjacency matrix is used for static analysis, with the weights' absolute values taken into account [42]. Static analysis in NCMs includes the neutrosophic number (a + bI, where I represents indetermination) [43]. Then, it applies a procedure known as de-neutrophication, in which I \(\in[0,1]\) is replaced by their lowest and maximum values [44].  
Finally, we will require the following equations to deal with the average of the extreme values that are beneficial in our research to generate a single result, as described in [44]. This number is used to select which parameters will be included in our case study.  
\[\lambda[(\text{a}_{1},\text{ a}_{2}])=\frac{a_{1}+a_{2}}{2} \tag{5}\]Then,  
\[\text{A}>\text{B}\Leftrightarrow\frac{a_{1}+a_{2}}{2}>\frac{b_{1}+b_{2}}{2} \tag{6}\]
## 2 FSO Communication System
### Receiver Side
#### 2.3.1 Non-Coherent FSO Systems  
Non-coherent FSO systems, also known as IM/DD systems, rely on the intensity of light to transmit information. At the Rx, changes in light intensity are directly detected by a photodetector without the need for a local oscillator (LO) to mix the optical fields. The Rxfront-end in IM/DD FSO systems, depicted in Figure 6b, comprises optical filters and a lens. The primary function of the lens is to gather and focus the incoming light beam onto the photodiode (PD). Commercial FSO systems predominantly use solid-state PDs made from materials such as Indium Gallium Arsenide (InGaAs), Silicon (Si), or Germanium (Ge). These materials are chosen due to their high quantum efficiency and their sensitivity to the commonly utilized wavelengths in FSO systems. Furthermore, these PDs possess very short transit times, resulting in high bandwidth and fast response times, making them ideal for FSO applications [1, 18].  
Solid-state PDs can either be Positive-Intrinsic-Negative (PIN) diodes or avalanche PDs (APDs), each offering distinct advantages and disadvantages. PIN diodes are known for their simple structure and reliability, while APDs provide internal gain, enhancing their sensitivity but also introducing higher noise and requiring higher operating voltages. The current output from the PD is converted into a voltage using a Trans-Impedance Amplifier (TIA), typically a low-noise Operational Amplifier (Op-Amp) with a load resistor. The selection of this load resistor is crucial and depends on factors such as the transmission rate, the dynamic range of the converted electrical signal, the thermal noise generated by the Rx, and the need to match impedance with other components in the Rx. Finally, the voltage signal from the TIA is passed through a Low-Pass Filter to reduce thermal and background noise, ensuring a cleaner signal for further processing [1].
## 3 Results
### Characteristics of the Aeroelastic Model Tree
#### 3.3.3 Damping  
The same method for identifying the frequency of the model was used to identify the damping of the model. The free vibration decay time history atop the trunk was recorded by two displacement transducers oriented in the streamwise and across-wind directions. The logarithmic decrement formula is used to calculate damping, i.e.,  
\[\delta=\ln\frac{D_{i}}{D_{i+1}}=\frac{2\pi\xi}{\sqrt{1-\xi^{2}}} \tag{8}\]  
where \(\delta\) is logarithmic decrement, \(D_{i}\) and \(D_{i+1}\) are two successive peaks of amplitude, and \(\xi\) is damping. The decaying free vibration time history shown in Figure 13a was measured on top of the trunk, which includes the crown and the trunk frequency components, as can be seen in the PSD function shown in Figure 13b. To obtain the decaying free vibration of the crown, the spectral energy associated with the crown is first isolated, as shown in Figure 13d, and used to reconstruct the time history (Figure 13c) by the inverse Fourier transform. The damping associated with the trunk was identified in the same manner, and the associated decaying displacement and spectral energy are shown in Figure 13e,f, respectively. The damping associated with the trunk and the crown frequencies are obtained by fitting the peaks with envelope curves using Equation (8).  
This process was repeated for different initial trunk displacements, \(\overline{d}_{t}\), to see the effects on damping. The results for crown and trunk are shown in Figure 13g,h, respectively. As can be seen, the damping is generally higher when the initial displacement is small. As the imposed displacement becomes larger but less than \(\overline{d}_{t}/H\leq 0.1\%\), the identified damping decreases. Further increasing the initial displacement (i.e., \(\overline{d}_{t}/H>0.1\%\)) does not alter the damping significantly. As compared to the crown damping shown in Figure 13g, the trunk damping in Figure 13h diverges more, possibly due to the direct connection to the ground, which is more sensitive to the details of the connection. The maximum crown damping is about 8.5% observed in C7, which is close to the reference (6-8%) [34]: The maximum trunk damping is about 3.3% observed in C7, which is smaller than the target trunk damping of the prototype (5.4%). This gap about the trunk damping between the model and the prototype would cause discrepancy on swaying behaviour of the trunk under wind load.  
Figure 12: Frequencies of the aeroelastic model in the along-wind and across-wind directions.  
The parameters of dynamic characteristics of the model at full scale are summarized in Table 3. In summary, the aeroelastic model properly simulates the aerodynamic shape, branch streamlining effects, crown damping, mass and bending stiffness distributions of the prototype. However, because of excess bending stiffness of the leaf cluster models, the aeroelastic model has incorrect leaf streamlining effects and trunk damping compared with the prototype.
# Numerical Analysis of the Kinematic Accuracy of the Hermetic Harmonic Drive in Space Vehicles
## 4 Results
This section is longer in the case of a disk generator, which means that this type of hermetic harmonic drive will have greater inertia, and therefore lower kinematic accuracy. Figure 16: The kinematic error of circular spline in reversing motion relative to the angle of rotation of a double-disc generator. The measured backlash for the harmonic drive with double disc generator was equal to 1.16 [arc\(\cdot\)min], and for the harmonic drive with cam generator, 0.98 [arc\(\cdot\)min]. The values recommended by the producer for harmonic drives in standard execution equal 2.5 [arc\(\cdot\)min], whereas the value of the backslash for the special harmonic drive was equal to 1.5 [arc\(\cdot\)min] [19; 20]. Both are based on Figures 16 and 17 and values of delay, it can be concluded that the recurring error for a harmonic drive with a cam generator is lower than the case of using a double disc generator. The obtained results clearly indicate that the cam generator allows one to obtain the highest of all the tested kinematic accuracy of the toothed wave hermetic transmission, also in the presence of reversing motion.
# Globally Scheduling Volunteer Computing
## 2 The Architecture of BOINC
### The BOINC Client  
The BOINC client runs on volunteer devices. Versions of the BOINC client are available for all major computing platforms.  
The volunteer can specify a set of \"computing preferences\" that control when computing and file transfers can be carried out, how many CPUs can be used, how much RAM and storage can be used, and so on.  
Each device is modeled as a set of \"processing resources\", each possibly with multiple instances. The types of processing resources currently recognized by BOINC are CPUs, NVIDIA GPUs, AMD GPUs, and Intel GPUs. The client maintains a queue of jobs, each of which uses a subset of the processing resources. The client runs jobs in a way that (a) tries to fully utilize the available processing resources; (b) tries to meet the deadlines of jobs; and (c) respects limits on RAM usage based on the working-set size of jobs. It runs jobs at low process priority to minimize impact on system performance.  
A client can be \"attached\" to one or more projects. It maintains estimates of the duration of the remaining jobs for each processing resources. When this falls below a threshold, it selects one of the attached projects and issues a \"scheduler RPC\" to its server, requesting jobs for one or more resources. The reply to this RPC includes, for each job, URLs of the app version's files and the job's input files. The client fetches these files and adds the jobs to the queue. The scheduler RPC and file transfers all use HTTP so that the client can function behind firewalls.  
When the client is attached to a project, it caches the app version files so that these in general are downloaded only once. There is also a provision for caching job input files that are used by multiple jobs. Thus, each project has a \"disk footprint\" on the device; this may be many GB.
## 4 Results and Discussion
### Determination of the Design Compaction Level
#### 4.1.2 Variation of Compacted Specim Height  
The mixture was compacted under three different moisture contents. The middle moisture content was slightly higher than the optimal moisture content achieved in the vibration compaction test. It should be noted that the optimal moisture content was determined by performing the vibration compaction test according to the specified compaction level in reference [15]. The relationships between the compaction number and specimen height under different moisture contents are shown in Figure 7.  
Figure 4: Diagram of the Superpave gyratory compactor used in this paper.  
Figure 5: Compaction curves of (**a**) Aggregate A and (**b**) Aggregate B of different mixture weights.  
Figure 6: Variation of ultimate specimen height with mixture weight: (**a**) Aggregate A; (**b**) Aggregate B.  
The compaction curves in Figure 7 were fitted, as shown in Table 3.  
It was found that the relationships between the compaction number and specimen height for both Aggregate A and B fit the logarithmic curves well. These fitting formulas were used to back calculate the specimen height under each compaction number.
# Remote-Sensing Image Encryption Algorithm Using the Advanced Encryption Standard
## Theoretical Principle
### Chaotic System  
The Piece-Wise Linear Chaotic Map (PWLCM) is defined by:  
\[x_{\mathrm{i}+1}=F_{\mathrm{q}}(x_{i})=\left\{\begin{array}{ll}\frac{x_{ \mathrm{i}}}{q}&0\leq x_{i}<q\\ \frac{x_{\mathrm{i}}-q}{05-q}&q\leq x_{i}<0.5\\ F_{\mathrm{q}}(1-x_{\mathrm{i}})&0.5\leq x_{i}<1\end{array}\right. \tag{1}\]  
where \(x_{\mathrm{i}}\in[0,\,1)\) and \(q\) is the control parameter [17]. When \(q\in(0,\,0.5)\), this chaotic system can provide an excellent and random chaotic sequence, which is suitable for image encryption.
# Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks
## 5 Experiment
### Experimental Analysis
#### 5.4.3 Node Consistency  
The Jaccard coefficient was used to measure the consistency of the ranking of the Top-k nodes, and the results are shown in Figure 7. The LBIA algorithm consistently achieved high Jaccard values across all nine networks, ranking first or second in each network. With the increase in the number of nodes, the Jaccard values of the LBIA algorithm were all above 0.6, and its recognition ability was stable and did not fluctuate significantly. The LBIA algorithm outperformed the other algorithms in terms of Jaccard similarity values across the four tightly interconnected networks and demonstrated a superior capability in identifying key nodes within general networks. These findings indicate that the LBIA algorithm is more effective at accurately identifying critical nodes in networks with dense interconnections.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **BC** & **CC** & **DC** & **PageRank** & **KSGC** & **LCH** & **NTPR** & **IDME** & **LBIA** \\ \hline Dolphins & 0.5970 & 0.5600 & 0.8483 & 0.7940 & 0.8597 & 0.0459 & 0.0467 & 0.8506 & 0.8633 \\ USAir97 & 0.5264 & 0.7872 & 0.7470 & 0.5686 & 0.8412 & 0.1422 & 0.0759 & 0.8922 & 0.9067 \\ Bio-celegans & 0.4832 & 0.6230 & 0.6805 & 0.5486 & 0.8444 & \(-\)0.0025 & 0.0005 & 0.8304 & 0.8459 \\ Eurroad & 0.4978 & 0.3234 & 0.6607 & 0.5154 & 0.7259 & 0.1630 & 0.1382 & 0.6401 & 0.7050 \\ Rt-assad & \(-\)0.0024 & 0.0052 & \(-\)0.0014 & 0.0093 & 0.0122 & 0.0089 & 0.0035 & 0.4280 & 0.4374 \\ Soc-hamster & 0.5530 & 0.7933 & 0.7529 & 0.2995 & 0.8333 & 0.2066 & 0.2603 & 0.8168 & 0.8355 \\ CiteSeer & 0.5470 & 0.4990 & 0.7170 & 0.3648 & 0.7790 & 0.0041 & 0.0261 & 0.6811 & 0.7458 \\ CA-GrQc & 0.1329 & 0.2679 & 0.1599 & 0.0326 & 0.2101 & 0.2269 & 0.1943 & 0.7311 & 0.7532 \\ CA-Erdos992 & 0.0459 & 0.0948 & 0.0502 & 0.0209 & 0.0657 & 0.0794 & 0.0633 & 0.4674 & 0.5037 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The average Kendall \(\tau\) value of nine algorithms under different propagation probabilities.  
Figure 7: The Jaccard value between the Top-k nodes for different algorithms: The different colored lines represent the Jaccard values obtained by the different algorithms on the corresponding networks as the probability of propagation changes. The higher the Jaccard value, the higher the number of nodes with the same ordering.
# Analysis of the Impact of Multipath on Galileo System Measurements
## 3 Methodology
### Code Multipath
Assuming that the dispersion of these combinations in addition to the measurement noise describe the time correlation (in the case of TD) and that the correlation resulting from the use of the same antenna by both receivers (in the case of DD and TD), the time correlation can be determined according to the following formula [35]:  
\[\mathcal{CM}_{a,f}^{i}=M_{a,f}^{i}+E_{a,f}=P_{a,f}^{i}-L_{a,f}^{i}-2\delta I_{ a,f}^{i}+\lambda_{f}N_{a,f}^{i} \tag{4}\]7  
where the symbol \(\text{D}\{\cdot\}\) denotes the dispersion operator. In the measurement experiment described in Section 4, time correlations determined on the basis of weekly observations of the zero-length baseline at the WUT1 station were used. For the receivers used in the experiment, these coefficients for code observations with an interval of 1 s range from 0.75 to 0.97 for individual GPS observations and from 0.64 to 0.78 for Galileo observations.