esa-sceva/satcom-chunk-collection huggingface dataset:
# Regional Adaptability Analysis of Solar Roof Utilization Technologies in China
## 2 Methodology
### Evaluation of the Economic Benefit  
The economic benefit is obtained by economic analysis of a specific technology, which considers the investment cost and operation and maintenance cost of such technology, together with governmental subsidy and the cost saving due to energy saving. Once the investment payback period is calculated, the economic gain of different technologies will be determined for different buildings and different cities. The work flow of the economic analysis is shown in Figure 9. The annual income is calculated based on the calculated energy savings of the systems.  
Table 8 shows the investment cost, the operation or management expense, and the governmental subsidy for the two solar technologies. The data are determined based on the national guidelines and recommendations from the leading manufacturers.  
With PVs being more efficient and cost-effective, the operation and maintenance (O&M) cost of PVs might gradually decrease in the coming years. However, it is difficult to foresee and quantify the O&M cost of PVs in the future; therefore, the decrease in O&M  
Figure 8: CO2 emission reduction by the solar energy technologies. (**a**) Solar thermal. (**b**) Solar PV.  
Figure 9: Work flow of the economic analysis.  
cost due to technology improvement is not considered in the economic prediction. This limitation of the study means that the analysis is conservative and might underestimate the potential of PV technologies.  
Based on the work flow shown in Figure 9, the payback period of roof utilization technologies is calculated. The typical payback periods are listed in Table 9.  
The average payback time of a solar PV system for all the investigated buildings is 12 years, while the payback time decreases to 4.5 years if a flat plate solar collector is used to provide domestic hot water service. However, the payback time for a PV system could be as lower as 5 years and as high as 26. The longer investment payback period is seen for Chongqing and Xingyi city because of poor solar radiation resource. Since the lifetime of the solar thermal system is about 10-15 years, most systems can be paid back. Solar PV technology has a longer investment payback period (on average 12 years), but it is still within the lifetime of a PV system.
## 3 Research Framework  
A hybrid model represented as EPIJaya-SVR is proposed for grouting power prediction in this study. It combines the merits of the EWT, the PACF, SVR, and the IJaya algorithm. The overall framework used is intuitively depicted in Figure 2. It mainly includes five steps:  
1. Collect the original grouting power series based on the grouting real-time monitoring system [39]. Utilize the EWT to decompose the obtained time series into a specific number of subseries and one residual series adaptively to extract meaningful information from them and reduce modelling complexity. Denoise the original grouting power series by discarding the uncorrelated residual series.
2. Apply the PACF to analyze the correlation between the data in each subseries and determine the optimal input variables objectively.
3. Utilize the SVR model to forecast the decomposed subseries; employ the IJaya algorithm to optimize the hyperparameters of the built SVR models. The constructed IJaya-SVR model can thus achieve better prediction accuracy.
4. Sum the prediction results of the subseries to formulate an ensemble forecasting result for the original series.
5. Apply the hybrid model established by using the methodologies mentioned in steps 1-4 to an actual project. The grouting power variation range and trend can be predicted and analyzed. The applicability and advantages of the proposed hybrid model are discussed and verified in comparison with other prediction models.
###### Abstract  
The atmosphere exhibits variability across different time scales. Currently, in the field of atmospheric science, statistical filtering is one of the most widely used methods for extracting signals on certain time scales. However, signal extraction based on traditional statistical filters may be sensitive to missing data points, which are particularly common in meteorological data. To address this issue, this study applies a new type of temporal filters based on a one-dimensional convolution neural network (1D-CNN) and examines its performance on reducing such uncertainties. As an example, we investigate the advantages of a 1D-CNN bandpass filter in extracting quasi-biweekly-to-intrasasonal signals (10-60 days) from temperature data provided by the Hong Kong Observatory. The results show that the 1D-CNN achieves accuracies similar to a 121-point Lanczos filter. In addition, the 1D-CNN filter allows a maximum of 10 missing data points within the 60-point window length, while keeping its accuracy higher than 80% (\(R^{2}>0.8\)). This indicates that the 1D-CNN model works well even when missing data points exist in the time series. This study highlights another potential for applying machine learning algorithms in atmospheric and climate research, which will be useful for future research involving incomplete time series and real-time filtering.  
climate variability; temporal filter; 1D convolution neural network; quasi-biweekly-to-intraseasonal oscillation; machine learning; Hong Kong 2024  
for Atmospheric Variability: Reducing the Sensitivity of Filtering Accuracy to Missing Data Points
# Investigation of Salt and Water Diffusion During Dry Salting, Wet Curing, and Ultrasonic Wet Curing
## 3 Results and Discussion
### Salt Content  
The effect of both factors on salt content was confirmed (for the curing method F(2;42) = 34.252; \(p\) < 0.001 and for the curing duration F(6;42) = 72.368; \(p\) < 0.001), and then the separability of the groups was examined with post hoc Tukey tests. The average salt content values and standard deviations of the concentrations from independent measurements supplemented with the results of the Tukey test are shown in Figure 2.  
It can be seen that the average salt concentration measured in the meat samples increases most slowly during dry salting and most quickly during wet curing supplemented with active ultrasound treatment. Given that a saturated salt solution was used for wet curing, it can be assumed that it approaches saturation on the surface of the meat in both processes. However, there is a significant difference between the two processes, the explanation for which is that, during dry salting, the salt is present in crystalline form and thus is unable to diffuse until water diffusing from the inside of the meat to the outside appears in its environment and dissolves it. The diffusion of salt towards the inside of the meat is therefore limited by the diffusion of water from the outside of the meat.  
There is also a striking difference between wet curing and wet curing supplemented with active ultrasound treatment. Ultrasonic treatment loosens the tissue structure due to cavitation phenomena acting on the surface of the meat, and the \"sponge effect\" inside the meat helps the flow of the salt introduced into the solution. Our study therefore confirms the previous research results [5], and it can be clearly stated that the change in salt concentration corresponds to the following order: DRY SALTING < WET PICKLING < WET PICKLING + US.
## 3 Background and Related Works
### HIP DEX Packet's Format Analysis and Proposed Compression
#### 3.4.2 HIP DEX-6 LoWPAN Header's Compressibility
The standard HIP DEX state machine is presented in Figure 5.  
Since it is not feasible for IoT devices (like is the case in [22]), we assume that the CTTP (see Section 4.1, for more details), sends a repeatable CLOSE message for the responder, immediately after receiving the CLOSE message from the initiator. We consider the following parameters:  
* \(MSL\_lp\): The MSL time required to the last packet sent by the sender's HIP to attend the receiver's HIP successfully;
* \(MSL\_cttp=\frac{MSL}{2}\): The time spent by the CLOSE packet sent by the initiator to attend the CTTP. We consider that it is the same interval time spent by the CTTP to send the CLOSE packet to the responder;
* \(UAL\_r\): The UAL waiting time of the responder for the reception of a message, such that \(UAL\_r<UAL\). If \(UAL\_r=UAL\), the responder may tear down the HIP association;
* \(P\_close\): The time interval that the CTTP does not exceed to send repeated CLOSE messages to the responder. The CTTP iterates in sending the CLOSE messages to the responder during a maximum period P_close as \(P\_close<MSL\_cttp+UAL\_r\). In the HIP standard, the responder waits for \((MSL)\times 2+UAL\) period (in minutes) before tearing down the active association even if no CLOSE packet was received. At the worst case, in LC-DEX, the responder is waiting for \(MSL\_lp+(MSL\_cttp)+P\_close\) and at the best case, the responder waits only for \(MSL\_lp+(MSL\_cttp)\times 2\). Within this solution, the responder terminates, certainly, the connection with a minimized waiting period. We can assume that we minimize until zero the probability of losing the CLOSE message. If a CLOSE message reaches the responder when the connection is already terminated (because CTTP still iterates sending CLOSE packets), it has no effect. Figure 6 describes the proposed HIP DEX connection termination. The minimization of the number of HIP DEX messages (**from eight messages to six**) reduces the computation resources for their processing by the device and also, some bandwidth of the communication channel. Furthermore, the benefits of the proposed association termination solution are that it is suitable for constrained devices in IoT with lower capabilities and is applicable in both HIP DEX and HIP DEX. Figure 5: HIP state machine diagram [5].
###### Abstract  
Internet of things (IoT) for precision agriculture or Smart Farming (SF) is an emerging area of application. It consists essentially of deploying wireless sensor networks (WSNs), composed of IP-enabled sensor nodes, in a partitioned farmland area. When the surface, diversity, and complexity of the farm increases, the number of sensing nodes increases, generating heavy exchange of data and messages, and thus leading to network congestion, radio interference, and high energy consumption. In this work, we propose a novel routing algorithm extending the well known IPv6 Routing Protocol for Low power and Lossy Networks (RPL), the standard routing protocol used for IPv6 over Low-Power Wireless Personal Area Networks (6 LoWPAN). It is referred to as the Partition Aware-RPL (PA-RPL) and improves the performance of the standard RPL. In contrast to RPL, the proposed technique builds a routing topology enabling efficient in-network data aggregation, hence dramatically reducing data traffic through the network. Performance analysis of a typical/realistic precision agriculture case, considering the potato pest prevention from the well-known late blight disease, shows that PA-RPL improves energy saving up to 40% compared to standard RPL.  
Keywords:RPL; PA-RPL; WSN; 6 LoWPAN; Smart Farming; In-network data aggregation; IoT +
Footnote †: 2020, 2760; doi:10.3390/s20102760
## 3 Simulation Design of Proposed PDM-256-QAM Modulated OAM-OSC System
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Values** \\ \hline System capacity & 4.8 Tbps \\ \hline Wavelengths and channel spacing & 5 (1552.52, 1553.32, 1554.12, 1554.92, 1555.72) \\  & and 0.8 nm \\ \hline Total channels & 15 \\ \hline Laser Input Power and Extinction ratio (ER) & 30 and 30 dB \\ \hline Modulation & PDM-256-QAM \\ \hline OSC length & 10,000 km to 24,000 km \\ \hline OSC wavelength window, transmitter, and & C band, 1–5 15 cm \\   receiver antenna diameter & 0.8 and 1 \(\mu\)rad \\ \hline Transmitter and receiver optical efficiency and & 0.8 and 1 \(\mu\)rad \\   Pointing errors & 20 Gsymbol/s and 8 \\ \hline Symbol rate and sample per bit & 20 Gsymbol/s and 8 \\ \hline OAM beams & LG0,0, LG14,0, and LG40,0 \\ \hline EDFA gain and noise figure in pre/post & 20, 4 dB \\ \hline Photodetector and responsivity & PIN and 0.8 A/W \\ \hline DSP algorithms for dispersion, phase, and & BPS, CMA, VPE \\   nonlinear effects compensation & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters of proposed OAM-OSC system.
## 3 Modeling and Analysis Method
### Generating Fiber Network of Unit-Cell for Different Compression Ratios  
The fiber network was generated by the commercial software GeoDict's Fiber-guess module through the random modeling process. The randomly distributed GDL fiber network was generated using the voxel mesh technique. The star length distribution (SLD) method was used to obtain the overall fiber orientation tensor for the unit-cell. The SLD approach analyzes a voxel's length for predefined directions. The components of the fiber orientation unit vector \(n=(n_{x},n_{y},\,n_{z})\) are calculated by SLD [54; 55; 56; 57]. The tensor was averaged over all voxels contained in the unit-cell. The overall average tensor is derived to overcome the difficulty of considering the direction for each distributed discontinuous carbon fiber. The fibers were modeled as circular rods with a length of \(l=200\)\(\upmu\)m and a diameter of \(d=10\)\(\upmu\)m. The resolution is 100, corresponding to a voxel length of \(2\upmu\)m. Initial fiber volume fraction is 17.89%. The domain size is \(100\times 100\times 100\) (\(200\)\(\upmu\)m\(\times 200\)\(\upmu\)m\(\times 200\)\(\upmu\)m). Additionally, the [PERSON]'s moduli and [PERSON]'s ratios of the matrix and of the fibers are set to \(E_{m}=3.12\) GPa, \(v_{m}=0.38\), \(E_{f}=230\) GPa and \(v_{f}=0.2\), respectively. The phase contrast of this combination amounts to \(\varsigma=73.7\).  
When a fiber network was created with various compression ratios under the above conditions, the carbon fibers were drained out of the unit-cell domain during the compression process. As a result, a procedure was performed to exclude fibers and resin that deviated from the domain area. Figure 2 shows the variation of fiber volume fraction along the \(x\)-direction and \(y\)-direction according to the GDL compression ratio. There are rapid changes in the fiber content at the two opposite parallel sides, which are the boundaries of the domains, and this is due to the out-of-domain fibers and matrix.  
Figure 3 shows the GDL fiber network according to the compression ratio, which allows visual identification of changes in porosity, carbon fiber and epoxy content. Tables 2 and 3 indicate the results for the overall orientation tensor for carbon fiber as reinforcement, and geometric properties with the compression ratios, respectively. Figure 3 and Table 2 are the input values for calculating the equivalent properties in the commercial program ABAQUS.  
Figure 2: Generated fiber volume fraction for different compression ratios: (**a**) Fiber volume fraction (\(x\)-dir); (**b**) fiber volume fraction (\(y\)-dir).  
Figure 3: Generated fiber network of unit-cell: (**a**) 10% compressed gas diffusion layer (GDL) model; (**b**) 20% compressed GDL model; (**c**) 30% compressed GDL model; (**d**) 40% compressed GDL model; (**e**) 50% compressed GDL model.
## 2 Methods
### Wireless bandwidth allocation
#### 2.2.4 Route Establishment and Data Transmission
The controller group \(C\) sets an interval Time \(T\)
6. for each controller \(c_{i}\)\(\in\)\(C\)\(c_{j}\) unicasts RREPi message to Node\({}_{i}\) within its coverage AND Node\({}_{i}\)\(\in\) Pathset with fields: Rsu_ID, Vehicle_ID, Package_ID, Last_Hop, Next_Hop, SendTime, DelayTime, ExpiredTime, CMC
7. end for
8. for each Node\({}_{i}\)\(\in\) Pathset
9. when Node\({}_{i}\) receives RREPi; then immediately updates the Routing table AND connects with Next_Hop marked in RREP\({}_{i}\)
10. end for
11. if link establishment fails OR link damages
12. go to 4
13. else
14. link maintains Until \(T\) time out then go to 4 OR data transmission finishes then go to 15
15. end if
16. END Algorithm 2
```  
**Algorithm 2** the whole session process.
## 5 Flight Results
### Link Budget Verification  
In order to verify the link budget given in Table 10, in-orbit measurements of SNRs for various distance and directional alignment were performed. Due to regulatory constraints, the ISL sessions were only established while in contact with the ground station. The results are depicted in Figure 16. The measurements were done for distances of 10 km (45\({}^{\circ}\) pointing offset), 25 km (no attitude control, thus free-tumbling), 65 km (45\({}^{\circ}\) pointing offset), and 148 km (30\({}^{\circ}\) pointing offset). The x-axis shows the duration for the corresponding ISL session. The antenna pairs remained unchanged during a session. The black horizontal lines indicate the lower limit for each modulation scheme. For BPSK (\(r=0.5\)), a SNR of 4 dB is required; for 8-PSK (\(r=0.75\)), a SNR of 14 dB is required.  
For 65 km distance and a constant pointing offset of 45 deg, the SNR remained stable, at an average of 4.6 dB, which aligns with the theoretical margin of 4.69 dB from Table 10. For 148 km distance, the measured average SNR margin of 7 dB corresponded well to the theoretical value of 7.56 dB. One source of discrepancy between measured and analyzed SNR might the attitude determination of the ADCS, which induces biased values for the RX and TX gains. The strong SNR variation in the 25 km free tumbling case resulted from the pointing loss due to uncontrolled rotational motion. The limit of 400 km will be tested as soon as this distance is reached.  
Figure 14: Relative distance development of satellites since launch.  
Figure 15: Graph representation of S-NET nodes (August 2018, not to scale) with numbers in km.
# Smooth Optimised A*-Guided DWA for Mobile Robot Path Planning
## 2 Global Path Planning
### Improved A* Algorithm
#### 2.3.2 Path Smoothing  
Although the pruning strategy eliminates redundant nodes in the path, the straight-line connections between nodes create abrupt directional changes that fail to satisfy the kinematic constraints of the robot. Therefore, further path optimisation is required. This paper employs the quasi-uniform cubic B-spline curve (with curve order \(k=4\), degree \(k-1=3\)) to optimise the path, ensuring a high degree of consistency between the optimised path and the original trajectory.  
The expression for the quasi-uniform B-spline curve is:  
\[p(u)=\sum_{i=0}^{n}p_{i}\cdot B_{i,k}(u),u\in[u_{k},u_{k+1}] \tag{3}\]  
where \(p(u)\) is the smoothed path point, \(u\) is the curve parameter; \(p_{i}\) is the initial path point set, \(n\) is the number of path points; \(B_{i,k}(u)\) is the \(k\)th-order (\(k-1\)) B-spline basis function,  
Figure 2: A* algorithmic planning path.  
is the curve order, and \(i\) is the basis function index. The B-spline basis functions are defined via the Cox-deBoor recurrence formula:  
\[B_{i,0}(u)=\left\{\begin{array}{ll}1,&u_{i}\leq u\leq u_{i+1}\\ 0,&else\end{array}\right. \tag{4}\]  
\[B_{i,k}(u)=\frac{u-u_{i}}{u_{i+k}-u_{i}}.B_{i,k-1}(u)+\frac{u_{i+k+1}-u}{u_{i+ k+1}-u_{i+1}}.B_{i+1,k-1}(u) \tag{5}\]  
The knot vector \(U=\{u_{0},u_{1},u_{2},....,u_{n+k}\}\) is constructed as a quasi-uniform clamped vector, in which the start and end knots are repeated k times to ensure that the spline curve interpolates the first and last control points. Specifically, the knot vector is defined as:  
\[U=0,0,0,0,1,2....,n-k+1,n-k+2,n-k+2,n-k+2 \tag{6}\]  
To generate the final smoothed trajectory, the curve is sampled over the domain \(u\in[u_{i},u_{i+1})\) with a fixed parameter interval: \(\Delta u=0.01\), \(u_{j}=u_{k}+j\cdot\Delta u\), \(j=0,1,....,N\), where:  
\[N=\left\lfloor\frac{u_{n+1}-u_{k}}{\Delta u}\right\rfloor \tag{7}\]  
Through the analysis of the above formula, it can be concluded that the characteristics of quasi uniform B-spline curves can be summarised as follows. On any node interval, there are at most \(k+1\) non-zero k-order basis functions. In the interval \([u_{i},u_{i+1})\), only the \((i-k)\) th path point to the \(i\) Path points are used for computation to give B-spline curves local variability. Based on these characteristics, this paper uses quasi uniform cubic B-spline curves for path smoothing, effectively eliminating abrupt points in the path and ensuring the continuity and smoothness of the trajectory.
## 3 Results
### Validation with Different Testing Specimens  
The transfer function and simulation model were established for a specific experimental setup including a sensor type as well as a testing specimen with set thickness of 60 mm. However, in addition to the coded excitation shape, the sample size, material and shape also have distinct influence on the resulting echoes. Especially thinner substrates exhibit more edge effects and, therefore, also contribute to the distortion of ultrasonic signals. In this section, specimens with a thickness of 15 mm and 30 mm were tested in addition to the 60 mm specimen to validate that the concept is applicable to a larger parameter space.  
A summary of the results is shown in Table 2. Measurements on the 30 mm specimen are comparable to previous results on 60 mm samples with a variation of 1 percentage points for the sine wave and 3 percentage points for the five-pulse sequence (ccf. Figure 5c). The shape of the CCF is almost identical throughout the whole signal and also for subsequent lower order minima. The measurement on the 15 mm steel object shows variations of 8 percentage points for the sine wave and a variation of 4 percentage points for the five  
Figure 5: Empiric optimization of ultrasound echoes, (**a–c**) pulse sequences (normalized to 1 V) (orange) and related ultrasound echoes (blue), (**d–f**) Normalized CCF of the ultrasound echoes for pulse sequences. The * markers highlight the extrema of the CCF.  
pulse sequence compared to the 60 mm sample. Even for a sample with 25% thickness the overall signal shape of the CCF is still comparable.
## 4 Discussion on the Effective Stress Principle for Saturated Claystone
### Discussion on the Influencing Factors of Effective Stress Coefficient  
The effective stress coefficient of claystone was probably related to the testing conditions.  
The results of Section 3.2.2 showed that the deformations of the samples were not obvious when \(\Delta\sigma_{c}=\Delta\sigma_{p}=0.5\) MPa under different loading rates (0.01 kPa/s, 0.02 kPa/s, 0.04 kPa/s), indicating that the effective stress coefficient of claystone was still close to 1 even if different pressure increments and loading rates were taken. Therefore, the following conclusion can be drawn:  
1. Whether the testing condition was \(\Delta\sigma_{c}=\Delta\sigma_{p}=0.2\) MPa or \(\Delta\sigma_{c}=\Delta\sigma_{p}=0.5\) MPa, the results were the same. It means that the neutral stresses may have little effect on the determination of the effective stress coefficient for claystone.
2. The results with different loading rates showed an inconspicuous deformation. We may conclude that as long as the loading rates are low enough so that the pore pressure can be dissipated time-efficiently, it will not affect the effective stress coefficient of claystone.
# Power System Voltage Correction Scheme Based on Adaptive Horizon Model Predictive Control
## 3 Adaptive Horizon Parameters  
MPC is a receding-horizon multi-step optimization method. The computation time and control performance are closely related to the horizon parameter selection, which determines when and how the system variables will reach their target states. This section presents the determination of adaptive horizon parameters, which are periodically updated according to the real-time measurements and system trajectory prediction during the entire optimization process of MPC.