esa-sceva/satcom-chunk-collection huggingface dataset:
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
## 3 Problem Overview and Motivation
### Feasibility of Compressive Sensing  
After verifying the network dynamics of the two systems and with the above key findings, we now check the prerequisite for compressive sensing--the data should be sparse, or can be transformed to become sparse.  
There are various methods to estimate the sparsity of a matrix. \(L0\) norm counts the number of zeros in a matrix. However, \(L0\) norm is not convex and does not have a close form [41]. Rank computation [42] is easy but it is sensitive to measurement noise [43]. Therefore, as used in [44], we use the portion of large eigenvalues to estimate the number of principal components which are required to accurately reconstruct the matrix [45]. Figure 3 reveals that the top 50% of the Eigenvalues account for 80% (George Town) and 90% (Zhoushan) \"energy\", which indicates the data share low-rank property to some extent. However, the link quality matrix still should be transformed to a sparser form to achieve a better performance.  
To validate the above hypothesis, we compare the performance of data recovery with different algorithms. Figure 4 reveals the heat maps of link quality matrix with different reconstruction algorithms. The values of link quality are within the range of 0 to 255, which are represented from blue to yellow in Figure 4. A higher value of the link quality means a better condition of the link. Figure 4a is a part of link quality matrix we collected in the George Town system, y-axis represents indexes of links while x-axis represents the timeline. Due to the asymmetry of the link quality between two nodes, there are 36 links in the network. Figure 4a is the original data matrix. Figure 4b,c shows the recovered link quality matrices by using standard CS tool in MATLAB and LESS, respectively, under a 50% sampling rate. The estimation errors are 20.69 using CS tool and 4.89 with LESS respectively. The details of LESS is introduced in the next sections.  
Note that the estimation error is the average of all the errors for the recovered elements in the data matrix. The result demonstrates that using CS tool straightly does not perform well without elaborate design. The behind reason is that the MATLAB CS tool fails to capture the data property such as the periodic pattern in the two intertidal systems. In Section 4, we talk about how to tailor CS for link quality estimation.  
In summary, the intertidal WSN systems demonstrate promising features--highly dynamic, sparse and temporal correlated, which make sparse sampling a good candidate for achieving low communication overhead. We first look into the detailed design challenges and solutions for utilizing compressive sensing in Section 4 and propose the protocol design equipped with compressive sensing in Section 5.  
Figure 3: Top k Eigenvalues in George Town and Zhoushan.
# Real-Time State Evaluation System of Antenna Structures in Radio Telescopes Based on a Digital Twin
## 6 Example Analysis and Results
### Operating Result
#### 6.2.3 Interface Operation Results  
The real-time rotation angle of the antenna of the prototype system is obtained by the MPU6050 acceleration sensor, and the real-time data are sent to the Unity3D client by serial communication. The client assigns the received rotation Euler angle to the rotation attribute of the twin model and realizes the motion state interaction between the twin model of the radio telescope and the actual model. For mechanical state monitoring, Figure 17 shows the visual interface of panel structure deformation under the current twin model at 6 m/s wind speed, 0\({}^{\circ}\) wind direction, and 32.6\({}^{\circ}\) elevation state. The lower right corner is the output cloud diagram of finite element analysis under this working condition. By comparing the two, it can be seen that the change trend is consistent, indicating that the DTs can show the more real structural state of the antenna in the current environment.  
From the data point of view, as shown in Table 3, the relative error between the maximum deformation prediction value and the simulation value is within 5%, the \(R^{2}\) is close to 1, and the \(RMSE\) is at a lower value, which shows that the RF regression model has high prediction accuracy.  
Figure 16: RF surrogate model prediction effect.  
Figure 17: Interface operation results.
# Sage-Husa Algorithm Based on Adaptive Double Forgetting Factors
## 2 Adaptive Sage-Husa Filtering Algorithm
### Improvement of Sage-Husa Algorithm
Judging the size of the prior residual and the theoretical residual, if the prior residual is less than or equal to the theoretical residual, there is no need to re-update the measurement noise; if not, the measurement noise needs to be re-updated as follows: \(R_{k}=\alpha_{k}*[(1-d_{1,k})R_{k-1}+d_{1,k}(\delta_{k-1}\delta_{k-1}^{T}+H_{ k-1}P_{k-1|k-1}H_{k-1}^{T})]\). 6. Compute the Kalman gain \(K_{k}=P_{k|k-1}H_{k}{}^{T}\Big{(}H_{k}P_{k|k-1}H_{k}{}^{T}+R_{k}\Big{)}^{-1}\), estimation error covariance matrix \(P_{k}=(1-K_{k}H_{k})P_{k|k-1}(1-K_{k}H_{k}){}^{T}+K_{k}R_{k}K_{k}{}^{T}\), state estimation \(\hat{X}_{k}=\hat{X}_{k|k-1}+K_{k}e_{k}\), and posterior residual \(\delta_{k}=Z_{k}-h\big{(}\hat{X}_{k}\big{)}\). (7) Update the second adaptive forgetting factor with the posterior residual as follows: \(b_{2}=\lambda_{\text{min}}+(1-\lambda_{\text{min}})*2^{L_{2}}\), \(L_{2}=-\frac{\sum\limits_{i=-5}^{L-1}\delta_{i}\delta_{i}^{T}}{S}\). (8) Calculate the weighted coefficient according to the adaptive forgetting factor as follows: \(d_{2,k}=\frac{(1-b_{2})}{(1-b_{2}^{2})}\). (9) Update the process noise as follows: \(Q_{k}=(1-d_{2,k})Q_{k-1}+d_{2,k}(K_{k}e_{k}e_{k}{}^{T}K_{k}{}^{T}+P_{k|k})\). The flow chart of the improved Sage-Husa algorithm is presented in \[R_{k}=(1-d_{k})R_{k-1}+d_{k}\Big{(}e_{k}e_{k}{}^{T}\Big{)} \tag{13}\]4
## 2 Overview of the Power Ramp-Rate Control Methods
### First Order Low-Pass Filter (FOLPF) Method  
The FOLPF is used to suppress high frequencies from the input signal with an attenuation factor of \(-20\) dB/dec after the cutoff frequency of the oscillations. In the case of PRRC, the cutoff frequency should be selected to attenuate oscillations in the PV power. Thus, it is important to analyze the worst-case power oscillation that can be observed during the day. The output equation for the FOLPF is given by [24; 25; 26; 27]:  
\[\mathrm{P_{ess}[k]=Z^{-1}\big{\{}G(z)P_{pv}(z)\big{\}}-P_{pv}[k]} \tag{6}\]  
where  
\[\mathrm{G_{folpf}(z)=\frac{(T_{s}/T_{f})z^{-1}}{1+(T_{s}/T_{f}-1)z^{-1}}} \tag{7}\]  
and \(Z^{-1}\{\cdot\}\) is the inverse z-transform operator. The terms \(T_{s}\) and \(T_{f}\) are the sampling period and the time constant, respectively. As the ratio \(T_{s}/T_{f}\) becomes larger, the bandwidth of the FOLPF increases. It is important to note that, if \(T_{s}/T_{f}\) become larger than 2, the eigenvalues of \(\mathrm{G_{folpf}(z)}\) become located outside the unitary circle and the FOLPF will become unstable. Thus, the desired transient response must be selected in terms of the sampling period.
# Cooperation-Assisted Spectrum Handover Mechanism in Vehicular Ad Hoc Networks
## 3 Proposed Scheme
### Assumptions  
In this paper, the proposed scheme is based on the above two assumptions.  
1. Assumption 1: In this study, we assume that adjacent spectrums will not interfere with each other. In fact, many technologies can mitigate the interference problem, such as inter-cell interference coordination (ICIC) and interference cancellation technology.
2. Assumption 2: According to the IEEE 802.22, the BS will regularly broadcast to inform the local area of the spectrum usage status. Therefore, we assume that all SUs can get the immediate spectrum usage status. Our partial scanning method is based on this information to select the suitable channels for scanning. If the SU has not entered the signal range of the new BS, it is still possible to obtain spectrum information through the multi-hop V2V communication mode.  
Figure 4: The concept of the cooperation-assisted spectrum handover (CASH).  
Figure 3: Cooperative transmission: (**a**) relay mode and (**b**) co-transmission mode.
## 4 Results
### A Co-Occurrence Map from a Bibliographic Database
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cluster** & **No. of Terms** & **Most Occurrences** & **Fewest Occurrences** \\ \hline \multirow{4}{*}{Cluster 1} & \multirow{4}{*}{22} & Industrial Internet of Things & Arduino \\  & & IoT & Automation \\  & & & Sensor & Crowdsourcing \\  & & & Design & Data fusion \\  & & & Web & Taxonomy \\ \hline \multirow{4}{*}{Cluster 2} & \multirow{4}{*}{19} & Challenges & Devices \\  & & Management & Adoption \\  & & Communication & Classification \\  & & Big data & Issues \\  & & Future & Opportunities \\ \hline \multirow{4}{*}{Cluster 3} & \multirow{4}{*}{17} & Authentication & Anonymous \\  & & Security & Confidentiality \\  & & Wireless sensor networks & Consensus \\  & & Protocol & Cryptanalysis \\  & & Scheme & Performance evaluation \\ \hline \multirow{4}{*}{Cluster 4} & \multirow{4}{*}{16} & Cloud computing & Validation \\  & & Cryptography & Standards \\  & & Encryption & Software \\  & & Protocols & Integration \\  & & Smart cities & Informatics \\ \hline \multirow{4}{*}{Cluster 5} & \multirow{4}{*}{15} & Blockchain & Social networks \\  & & Framework & Smart contract \\  & & Internet of Things (IoT) & Reputation \\  & & Model & Health \\  & & Social Internet of Things & Bitcoin \\ \hline \multirow{4}{*}{Cluster 6} & \multirow{4}{*}{13} & Cloud & Clustering \\  & & Networks & CoAP \\  & & Optimization & Congestion control \\  & & System & Routing \\  & & Wireless & Sensor networks \\ \hline \multirow{4}{*}{Cluster 7} & \multirow{4}{*}{13} & Cybersecurity & Analytics \\  & & Internet of Things & Attack detection \\  & & Implementation & Industrial internet \\  & & Privacy & Malware \\  & & Intrusion detection & Network security \\ \hline \multirow{4}{*}{Cluster 8} & \multirow{4}{*}{12} & Internet & Lora \\  & & Things & Scalability \\  & & RFID & Supply chain \\  & & Energy & Agriculture \\ \hline \multirow{4}{*}{Cluster 9} & \multirow{4}{*}{11} & Architecture & Fog \\  & & Edge computing & Gateway \\  & & Edge & Smart devices \\ \hline \multirow{4}{*}{Cluster 10} & \multirow{4}{*}{9} & Algorithm & Indoor navigation \\  & & Information & Location \\  & & Network & Mobile \\  & & System & Selection \\ \hline \multirow{4}{*}{Cluster 11} & \multirow{4}{*}{9} & Fog computing & 5G \\  & & Healthcare & Prediction \\ \cline{1-1}  & & Machine learning & Sdn \\ \cline{1-1}  & & Smart home & Resource-allocation \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Synthesis of VOSviewer keyword occurrences on the economic influence of IoT. Regarding the identified keywords related to IoT, a total of 118 expressions and revealing terms were found for the Internet of Things. It is about terms such as automation, data fusion, design, monitoring, sensors, web, big data, devices, wireless, attacks, cloud computing, software, smart contracts, machine learning, smart homes, etc. In addition, regarding the terms related to the \"IoT economy\", 38 terms were identified, such as smart cities, industry 4.0, innovation, management, opportunities, blockchain, trust management, agriculture, environment, scalability, etc.
# Low Cost Circularly Polarized Antenna for IoT Space Applications
## 3 Prototyping and Measurements
### Antenna Measurements  
The reflection coefficient of the prototype was measured. A comparison with the simulation in Figure 9 shows a small disagreement. Because of the fabrication issue, the bandwidth of the measurement results was much smaller than the simulation. However, considering a \(-10\) dB S11 criteria, a 27 MHz frequency bandwidth from 860 to 887 MHz was obtained that is suitable for the application. The 3D radiation pattern was measured using near-field StarLab station from Satimo. The antenna had a total efficiency of \(-1.7\) dB at 868 MHz, as presented in Figure 10. The measurement shows a peak RHCP gain of 3 dBic at 868 MHz and a 50 MHz (844-894 MHz) \(-3\) dB RHCP Gain frequency bandwidth. Moreover, a 14 MHz \(-3\) dB axial ratio bandwidth was obtained in the broadside direction. Finally, the radiation pattern at 868 MHz presented in Figure 11 confirms that a \(-3\) dB beamwidth of \(117^{\circ}\) was obtained.  
Figure 8: Picture of the prototype.  
Figure 10: Total efficiency, peak RHCP Gain, and broadside axial ratio versus frequency.  
Figure 9: Simulated and measured reflection coefficient of the complete system.
## 3 Results
### Artifactual Regions  
In Figure 7, the vertical spacing between the upper and lower layers of receivers was 10 cm. At this distance, the signals received by the two layers were approximately similar, which led to the emergence of artifacts. Based on the analysis in the previous section, this study concluded that to achieve z-axis resolution capability, at least three rows of transmitters or receivers should be arranged along the z-axis, similar to the arrangement in the x and y directions. Consequently, the measurable area for the layout method shown in Figure 6e was limited to the region within the red box.  
To enhance the signal difference between the two receiver layers, this experiment increased the vertical distance between them. The test results, as shown in Figure 8, revealed that while symmetrical artifacts were not observed, the z-axis resolution capability  
Figure 5: Results using 19.0% moisture content grain. (**a**) The “fan-shaped” layout configuration; (**b**) Hotspot localization results near the transmitter (x-axis); (**c**) Hotspot localization results far from the transmitter (x-axis). Near the x-axis, the layout demonstrated resolution capability in both x and y directions. However, at greater distances from the x-axis, while x-axis resolution was maintained, y-axis resolution decreased significantly. The predicted hotspot regions appeared “elongated” and were inaccurately positioned along the y-axis, showing a shift towards the x-axis.  
remained poor due to insufficient z-axis sampling data. This outcome aligned with the analysis from the previous section. Furthermore, the increased electromagnetic wave propagation distance enhanced the differentiation between the data received by the two antenna layers. However, the lower layer of receivers still detected the anomalous region above, resulting in a small number of anomalous points at lower positions.
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## 3 Implementation of MBSS Technique Using SDR Technology
### Impulsive Noise Reduction
#### 3.3.5 Impulsive Noise Reduction Algorithm  
This section presents the pseudocode used for the impulsive noise reduction block.  
1. Function noise_reduction (\(R_{i}(k)\)):
2. \(L_{1}=0.01\)
3. \(L_{2}=0.4\)
4. \(a_{Li}\); \(d_{ji}=\textbf{pywt.waveedec}\) (\(R_{i}(k)\), '\(db1\)', \(L=1\))
5. \(a_{Li}^{t}\gets a_{Li}\)
6. \(d_{ji}^{t}\gets d_{ji}\)
7. \(dd_{ji}\) = **reescale** (\(d_{ji}\))  
Figure 11: Difference between original PSD and modified PSD.  
Figure 12: Difference between original PSD and modified PSD using multiple SDR devices.  
\(ind=\textbf{find}\) (\(d_{ji}>L_{1}\) & \(d_{ji}>L_{2}\))
* **for p in range (len(\(ind\))-1,2,\(-\)1)**:
* \(aux=\)_ind[\(p\)\(-\)1]_
* **if** \(ind\)[\(p\)\(]\) == \(aux+1\)
* \(ind\) = **delete** (\(ind\),[\(p\)\(-\)1])
* \(d^{\prime}_{ji}(ind-1)=1\times 10^{-15}\)
* \(d^{\prime}_{ji}(ind)=1\times 10^{-15}\)
* \(d^{\prime}_{ji}(ind+1)=1\times 10^{-15}\)
* \(d^{\prime}_{Li}(ind-1)=a_{L}(ind+1)\)
* \(d^{\prime}_{Li}(ind)=a_{L}(ind+2)\)
* \(d^{\prime}_{Li}(ind+1)=a_{L}(ind+3)\)
* \(R^{\prime}_{i}(k)\)= **pywt.xwerec** (\(d^{\prime}_{Li}\); \(d^{\prime}_{ji}\); \(db1\)','symmetric')
* **return** ( \(R^{\prime}_{i}(k)\) )
## 6 Conclusions  
In this paper, a comparative study was carried out on the ratcheting behavior of a pipe with and without assembly parts under internal pressure and cyclic bending loading through a validated finite element model. The effects of the assembly contact and the clearance between the sleeve and the pipe on the ratcheting response were also considered and discussed. The conclusions can be drawn as follows:  
1. Assembly contact between the sleeve and the pipe suppressed the ratcheting response of the pipe with assembly parts and changed the monotonic trend of the ratcheting response in the pipe without assembly parts along the pipe axis.
2. The hoop ratcheting strain of the pipe was much larger than the axial ratcheting strain under internal pressure and cyclic bending loading, and the hoop ratcheting strain played a leading role in cumulative plastic deformation of the pipe.
3. The hoop ratcheting strain increased with an increase in internal pressure, leading to an increase of the diameter of the steel pipe and the average ratcheting strain rate.
4. There were peak and valley values of the hoop ratcheting strain in the pipe with assembly parts along the pipe axis. The hoop ratcheting response between the peak value and valley value was controlled and suppressed by assembly contact between the sleeve and pipe, while in other areas it was dominated by the bending moment and increased with an increase of the bending moment. The valley and peak values were the transition points between the two control modes.
5. The clearance between the sleeve and the pipe had a different effect on the ratcheting response at positions near the sleeve, such as Position 1 and Position 2, and far away from the sleeve, such as Position 3. Near the sleeve, the ratcheting response increased with an increase of the clearance, whereas it decreased with an increase of the clearance when distant from the sleeve (where the bending moment decreased). When the clearance was large enough, the ratcheting response was no longer affected by the clearance.  
In this work, failure of the pipe could not be simulated due to limitations of the constitutive model. Thus, a fatigue life predictive model considering the ratcheting effect will be carried out in future work.  
**Author Contributions:** Formal analysis, H.Y.; Methodology, H.Y.; Software, H.Y.; Supervision, Y.D. and P.H.; Validation, H.Y.; Writing--original draft preparation, H.Y.; Writing--review & editing, Y.D. and P.H.  
**Funding:** This research was funded by the National Water Pollution Control and Treatment Science and Technology Major Project (2017 ZX07201001).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities
## 3 State of the Art of Practice and Research
### Current Practice and Research
#### 3.1.6 Future Intelligent Transportation Systems (ITS)
The communication network between UAVs and vehicles needs to be kept functioning all the time to ensure the operation of ITS. This requires UAV communications to be energy-efficient so that the UAV could fly for a long enough time. [PERSON] et al. designed a UAV trajectory path for establishing energy-efficient UAV communication [86]. Enhancing the communication networks could improve the data routing through UAVs. For this purpose, researchers have worked on considering UAVs as mobile aerial RSUs ad-hoc vehicular networks (VANET) in recent years. [PERSON] et al. proposed a routing technique for VANET named UVAR (UAV-Assisted VANET Routing Protocol) to improve the reliability of data delivery [87]. The data routing process becomes challenging due to rapid topology changes, high vehicle mobility, and connectivity issues [88]. To overcome the routing issues, [PERSON] et al. proposed a routing protocol that includes two ways of routing [89]. Data collection using UAVs is also challenging due to the limited resources of UAVs. As direct data collection from all storage devices is not possible, this could result in unfair data collection. [PERSON] et al. proposed a UAV speed control-based fairness data collection (USCFDC) scheme to address this issue [90].
## 5 Discussion
### Specific Advantages of EUS in Diagnosis of Pancreas Divisum  
EUS provides real-time, high-resolution images, which allow for dynamic assessment and immediate therapeutic interventions if needed. EUS can simultaneously evaluate other potential causes of pancreatitis or abdominal pain, such as pancreatic masses, cysts, or inflammatory changes and has several advantages over other imaging modalities in the diagnosis of pancreas divisum. Compared to ERCP, EUS is significantly less invasive and avoids the risk of post-procedural pancreatitis. Unlike MRCP and secretin-enhanced MRCP, EUS provides real-time, high-resolution images with the ability to assess both the pancreatic ductal anatomy and the surrounding pancreatic parenchyma. Additionally, EUS allows for fine-needle aspiration (FNA) or fine-needle biopsy (FNB) when needed, which is not possible with MRCP or ERCP. One of the key strengths of EUS is its ability to dynamically evaluate the minor papilla and detect subtle changes in pancreatic morphology that may not be apparent on MRCP alone. Furthermore, EUS can simultaneously assess other possible aetiologies of pancreatitis or abdominal pain, such as small pancreatic tumours, cystic lesions, or chronic inflammatory changes, which could mimic or coexist with pancreas divisum. In cases where pancreas divisum is suspected but MRCP is inconclusive, EUS serves as an essential complementary tool, particularly in patients with recurrent pancreatitis of unclear aetiology. While MRCP and secretin-enhanced MRCP provide non-invasive visual  
Figure 7: EUS imaging of pancreas divisum, showing the main pancreatic duct at the pancreatic body (white arrow) without communication with [PERSON]’s duct at the isthmus of the pancreas.  
ization of the ductal system, they may have limitations in identifying incomplete pancreas divicum or subtle ductal anomalies. Secretin-enhanced MRCP, though improving ductal visualization, has variable sensitivity in detecting minor papilla dysfunction, which can be more precisely assessed through EUS with secretin administration. Moreover, EUS-guided manometry is an emerging technique that may provide future insights into the functional assessment of pancreatic drainage in patients with pancreas divicum.
###### Abstract  
Supercritical Water Gasification (SCWG) has the capacity to generate fuel gas effluent from wet biomass without previously having to dry the biomass. However, substantial efforts are still required to make it a feasible and competitive technology for hydrogen production. Biomass contains cellulose, hemicellulose and lignin, so it is essential to understand their behavior in high-pressure systems in order to optimize hydrogen production. As the main component of biomass, cellulose has been extensively studied, and its decomposition has been carried out at both subcritical and supercritical conditions. Most previous works of this model compound were carried out in batch reactors, where reaction times normally take place in a few minutes. However, the present study demonstrates that gasification reactions can achieve efficiency levels of up to 100% in less than ten seconds. The effect of temperature (450-560 \({}^{\circ}\)C), the amount of oxidant (from no addition of oxidant to an excess over stoichiometric of 10%, n = 1.1), the initial concentration of organic matter (0.25-2 wt.%) and the addition of a catalyst on the SCWG of cellulose in a continuous tubular reactor at short residence times (from 6 to 10 s) have been studied in this work. Hydrogen yields close to 100% in the gas phase were obtained when operating under optimal conditions. Moreover, a validation of the experimental data has been conducted based on the theoretical data obtained from its kinetics.  
supercritical water gasification; cellulose; hydrogen; continuous reactor; process parameters; kinetics +
Footnote †: journal: Applied Sciences