esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Field Trial with Traps and Encapsulated Batic  
The traps were installed at the Maquehue Experimental Field, located 17 km south of Temuco, in the La Araucania Region, which belongs to the Faculty of Agricultural and Environmental Sciences at the University of La Frontera. The evaluated orchard is divided into three continuous zones, containing Stella, Regina, and Lapins cherry varieties, with planting distances of 4 \(\times\) 3 m. The trees are approximately 25 years old and are trained in a vase or multi-axis system, which is a common training system in Chile. Drip irrigation is employed, utilizing irrigation tapes and drippers for each unit, and each tree is watered daily. No cherry orchard was subjected to the application of agrochemicals (pesticides or fertilizers) during the season related to the experiment.  
The control bait (pure alginate) was alternately placed with the WVM attractive bait across the three cherry varieties mentioned previously. To minimize edge effects potentially caused by adjacent factors, the first planting rows were discarded during installation. The traps were hung on the cherry trees with a wire and with a distance of 4 m between trees. This was repeated in the three varieties of cherry trees mentioned above [47]. Trap density was adjusted following the guidelines of Groupe de travall Baies and SAG, with three rows of six traps each, totaling eighteen units (six per variety). The height of trap placement followed SAG's recommendations, specifying that traps be placed within the plant/tree foliage, near the presence of fruit, and should not be directly exposed to the sunlight [24; 48]. Once the traps were installed, the bait and control were placed. The microencapsulates of each treatment were placed in a 30 mL plastic container in quantities of 12 g/container. Small perforations were made in the lid of each container to allow the dispersion of the bait and control odor. Each container was placed in the central area of the trap to allow individual SWD to enter through any of its entrances (Figure 1).  
The installation period began in August 2022, and the traps remained in place until the end of the orchard's productive season (December 2022). This month was selected because it coincides with the increase in average temperatures to 10 \({}^{\circ}\)C, the threshold at which SWD has been reported to initiate its activity [1; 43]. Starting at this zero-visit point allowed for the analysis of the insect's peak population.  
The microencapsulated baits were replaced every 25 days, and the traps were inspected weekly. Insects captured in the traps were collected every 7 days and transferred to the Applied Entomology Laboratory at the University of La Frontera for identification using entomological keys [49].
## 6 Conclusions  
Due to the influence of the complex environment at the tunnel portal, the blasting vibration signal of the slope in tunnel portal sections contains a lot of high-frequency noise, which will cover the characteristics of the real signal and is not conducive to further analysis  
Figure 6: Comparison between original signals and denoised signals.  
of its propagation and attenuation law. Therefore, the WOA-VMD signal noise reduction method is proposed for the blasting vibration signal processing of tunnel portals.  
(1) The measured signals were decomposed by VMD to obtain IMFs with different dominant frequencies, which avoided the problems of endpoint distortion and mode confusion. The WOA algorithm adaptively determines the VMD parameters \([K,\alpha]\), realizes the optimal decomposition, and avoids the randomness of subjective decision.  
(2) The noise reduction analysis of the blasting signal at the tunnel portal shows that the accurate decomposition of the WOA-VMD algorithm and the noise recognition of MPE effectively improve the noise reduction effect. The AOK time-frequency energy spectra of vibration signals before and after noise reduction were compared and analyzed. The WOA-VMD noise reduction method removed the high-frequency noise energy and effectively retained the main energy of the signal. The denoised signal can be used to judge whether the vibration caused by blasting is under or over the accepted limits, which can determine the stability of the portal.  
(3) The denoising methods of CEEMDAN, VMD, and WOA-VMD all have good fidelity and a certain denoising effect. The noise reduction signal waveform and evaluation index of the WOA-VMD algorithm are better than that of the CEEMDAN and VMD algorithms, which verifies the effectiveness of the novel method, and provides technical support for a detailed analysis of the blasting vibration propagation law and harmful effects at tunnel entrances.  
Conceptualization, Z.L.; Methodology, Y.P. All authors have read and agreed to the published version of the manuscript.  
Transportation Science and Technology Project of Zhejiang Province (Grant no. 2020004); Zhejiang Construction Technology Innovation Association Project (Grant no. 2021B55); State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering, Jianghan University (Grant no. PBSKL2022D08); Scientific Research Project of Hunan Education Department (Grant no. 22B0486).  
Not applicable.  
The authors are grateful to [PERSON] and [PERSON] for their help with the acquisition of signals for this paper.  
The authors declare no conflict of interest.
# Power System State Estimation Approach Considering Transmission Line Temperature
## 6 Discussion  
The proposed approach is to estimate the power system states considering electro-thermal coordination, and the calculation results include the power system states and transmission line temperature. The performance of the proposed approach in terms of efficiency, effectiveness, and accuracy can be verified by the comparison analysis in the above case study.  
From Figure 5, it is clear that the PDFs with the proposed ETC-SE approaches are nearly identical and more concentrated compared with the WLS-SE method. Additionally, compared to the WLS-SE method, the results in Table 2 and Figure 6 show that the proposed AJ- and ITS-ETC-SE approach is effective to decrease the errors. Moreover, the iteration numbers and computation time are compared in Table 4. The results show that the proposed ITS-ETC-SE approach is effective in accelerating the solving process. Since the dimension of the measurement vector, state vector, and Jacobian matrix increases to a larger extent after integrating line temperature into the SE model, the computation time of the AJ-ETC-SE approach is much longer than the ITS-ETC-SE approach and WLS-SE method.  
\begin{table}
\begin{tabular}{c c c} \hline \(T_{a}\) (\({}^{\circ}\)C) & ITS-ETC-SE & AJ-ETC-SE \\ \hline
25 & 7 & 8 \\
35 & 7 & 8.04 \\
40 & 7.01 & 8.21 \\
45 & 7.02 & 8.43 \\
50 & 7.02 & 8.69 \\ \hline \end{tabular}
\end{table}
Table 6: Iteration numbers of different algorithms under different ambient temperatures.  
\begin{table}
\begin{tabular}{c c c} \hline \(V_{w}\) **(m/s)** & ITS-ETC-SE** & **AJ-ETC-SE** \\ \hline
1 & 7 & 8 \\
2 & 6.98 & 7.38 \\
5 & 6 & 6.47 \\
10 & 6 & 5.59 \\
20 & 5.2 & 5.01 \\ \hline \end{tabular}
\end{table}
Table 7: Iteration numbers of different algorithms under different wind velocities.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of III-Conditioned Lines** & ITS-ETC-SE** & **AJ-ETC-SE** \\ \hline
1 & 7 & 8 \\
3 & 7 & 8.53 \\
5 & 7 & 8.60 \\
8 & 7.03 & 8.61 \\
10 & 7.05 & 8.63 \\ \hline \end{tabular}
\end{table}
Table 8: Iteration numbers with ill-conditioned branches.  
Figure 9 verifies the superior computational performance of the ITS-ETC-SE approach when compared to the AJ-ETC-SE approach. Furthermore, the sensitivity analysis is conducted by considering different scenarios in terms of ambient temperature, wind velocity, and ill-conditioned branches. The iteration numbers of different methods are summarized in Tables 6-8, and the results show that the proposed ETC-SE approaches show good convergence performance with environmental condition variation and ill-conditioned branches. The iteration numbers increase as the ambient temperature increases and wind velocity declines. Moreover, reliable convergence can also be achieved when ill-conditioned branches exist, and there are merely tiny increases in iteration numbers, which will facilitate the utilization of the proposed ETC-SE approaches in practical application.
# Knowledge Graph Double Interaction Graph Neural Network for Recommendation Algorithm
## 5 Experiments
### Experimental Settings
#### 5.2.3 Parameter Settings  
The hyper-parameter settings on the datasets are shown in Table 2.  
We implemented the KGDI model in python3.6, tensorflow1.13.1, and numpy1.16.6. We optimized all models with Adam optimizer. We used the default Xavier initializer to initialize the model parameters. For deep learning, the regularization coefficient \(\lambda\) and learning rate \(\eta\) generally belong to empirical science and were set according to the empirical values of the baseline. The hyper-parameter settings for baselines were as follows. For LFM, in the MovieLens-1M dataset, the hyper-parameter was \(d=8\), \(\lambda=10^{-7}\) and in the Book-Crossing dataset, the hyper-parameter was \(d=64\), \(\lambda=2\times 10^{-5}\). For RippleNet, in the MovieLens-1M dataset, the hyper-parameter was \(d=8,H=2,\lambda_{1}=10^{-6},\lambda_{2}=0.01\), \(\eta=0.01\) and in the Book-Crossing dataset, the hyper-parameter was \(d=4,H=3\), \(\lambda_{1}=10^{-5}\), \(\lambda_{2}=0.05,\eta=0.005\). For KGNN-LS, in the MovieLens-1M dataset, the hyper-parameter was \(d=8,K=16,H=1,\lambda=10^{-4},\eta=5\times 10^{-4}\) and in the Book-Crossing dataset, the hyper-parameter was \(d=64,K=8,H=2,\lambda=2\times 10^{-5},\eta=2\times 10^{-4}\). The hyper-parameter settings not specified in the baseline model were the same as the default values in the experiments. For the MoviesLens-1M dataset, the embeddings size was [4096] and for the Book-Crossing dataset the embeddings size was [256].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(K\) & \(d\) & \(H\) & \(\lambda\) & \(\eta\) \\ \hline MovieLens-1M & 2 & 64 & \(1\) & \(10^{-7}\) & \(2\times 10^{-2}\) \\ Book-Crossing & 2 & 32 & \(1\) & \(2\times 10^{-6}\) & 0.005 \\ \hline \hline \end{tabular} Where \(K\) represents the number of neighbors to sample; \(d\) represents the dimensions of user and entity embedding; \(H\) represents the number of iterations when calculating entity representation; \(\lambda\) represents the weight of \(L_{2}\) regularization; and \(\eta\) represents the learning rate.  
\end{table}
Table 2: Hyper-parameter settings for the two datasets.
## 4 Results
### Forest Aboveground Biomass Map
#### 4.2.2 ALS-Based AGB of 2012  
* We built the stepwise regression model with the field-based forest AGB as a dependent variable by inputting various ALS-based canopy metrics including nine canopy percentile heights, mean height and plot density (Table 4) as independent variables. It was found that the canopy mean height did a good job in predicting the variations in field-based forest AGB with the linear regression model as \(AGB=0.826\times h_{\mathrm{m}}-3.208\) (R\({}^{2}=0.83\), \(n=26\), \(p<0.01\), RMSE \(=1.09\) kg/m\({}^{2}\)).
* After getting the ALS-based forest AGB map, we found that it had a heterogeneous spatial distribution pattern with maximum, minimum, and average values of 16.27 kg/m\({}^{2}\), 0.06 kg/m\({}^{2}\), and 6.07 kg/m\({}^{2}\), respectively. By comparing the forest AGB of 2012 between the ALS- and field-based methods, high correlation (R\({}^{2}=0.81\), \(n=26\), \(p<0.01\)) was observed (Figure 4), which showed that the ALS-based forest AGB could serve as a validation data for the BEPS results.
# A Method for Adapting Stereo Matching Algorithms to Real Environments
## 5 Conclusions  
The research presented in this paper shows that using the ACS module is most suitable for reducing decalibration caused when a side image is moved in the y-axis with respect to the reference image. If a side image is moved less than two pixels, then using the ACS causes a quality deterioration in the resulting disparity maps, remaining below 1%. It needs to be emphasized that without this module, the results contain over 38% more errors when the extent of decalibration in the y-axis is equal to two pixels. Decalibration caused by rotation is more problematic. The ACS module also limits the increase in the error rate; when the rotation is below \(\pi/180\), the increase in the error rate does not exceed 13% when the ACS is used. Nevertheless, this is a good result in comparison to cases where the ACS is not used; without using the ACS, the results contain over 66% more errors when a rotation is equal to \(\pi/180\).  
The problem with developing technology based on stereo cameras is that research has either focused on camera calibration or stereo camera algorithms; each of these aspects assumes perfect results from the other, which cannot be achieved in real applications. This study presents a hybrid approach of combining bundle adjustment with pattern-based calibration and stereo matching. Working under the assumption that there are imperfections in camera calibration can start an entire branch of research focused on designing stereo matching algorithms intended for use with imperfectly calibrated cameras. This branch can include both inventing these algorithms and preparing methods for testing them.  
This work was supported, in part, by a ministry subsidy for research to Gdansk University of Technology, Poland.  
Figure 6: The influence of parameter \(H\) on estimated translation in the y-axis (**a**) and estimated rotation (**b**).
## 2 Methods
### Bench Tests  
#### 2.3.1 Equipment Specification  
Choosing the insertion levels for the ports to install injection lances later was a key moment in the research. This decision was based on the modeling results presented in [7; 21]. As a result, 12 injection lances were installed symmetrically on the boiler sidewalls in the target boiler. The application ports were installed three at each of two levels, i.e., approximately 1.5 m and 4 m above the grate. The distribution scheme and application port photography are shown in Figure 6.  
The work was led using the SNCR test facility, consisting of the following modules:  
* Reagent preparation unit;
* Feed and distribution module;
* Injection lances.  
The reagent preparation unit consisted of urea solution, demineralized water tanks, and a module to produce the reagent at the required concentration. Commercially available AdBlue(r) was used as the urea solution. Tests investigated the effects of solution concentrations of 32.5% and 12%.  
The feed and distribution module of the test installation is shown in Figure 7. Its task was to supply selected lances with reagent and compressed air with the parameters desired by the operator. Each lance was supplied individually with urea solution, demineralized water, and compressed air.  
This research utilized injection lances operating based on the two-factor atomizer principle with internal mixing. In two-factor atomizers, the energy to atomize the liquid is supplied via compressed air. This study analyzed the effect of three nozzle tip types on the nitrogen oxide reduction in the exhaust gas. One-, two-, and three-hole nozzles were tested (Figure 8). Each nozzle allowed a different shape of the reactant droplet stream to be obtained inside the boiler combustion chamber.  
During the test runs, exhaust gas emissions had to be measured to assess the performance of the SNCR system. The exhaust gas components whose content was measured during each measurement series were NO, NO2, N2O, SO2, CO, CO2, H2O, and CH4. A Gasmet DX4000 analyzer (Gasmet Technologies Oy, Helsinki, Finland) was used for the measurement. This analyzer uses the FT-IR (fast Fourier transform infrared) measurement method. The insertion point for the sampling probe was the fl  
Figure 6: Lances’ distribution scheme with locations marked in blue (**left**) and built-in application ports for injection lances (**right**).  
stream of all heat transfer surfaces in the boiler, upstream of the dust extraction system. The insertion point of the analyzer probe is marked on the boiler diagram shown in Figure 9.  
Figure 8: Selected nozzle tips tested: single- and three-hole.  
Figure 7: Feed and distribution module.  
Figure 9: Boiler profile with an indication of the sampling point for flue gas composition analysis.
## 3 Results and Discussion  
### Operational Performance of the DWTS  
The detailed performance of the DWTS is summarized in Table S1. The DWTS demonstrated good performance in terms of COD (52% on the average) and total N destruction (44%). The SO4 in the effluent of the system was significantly lower (2 mg/L) than that in the effluent (34.1 mg/L). A summary of the chemical characteristics of the PSD and AnMBR in the DWTS is provided in Table 1. There was a large difference in the organic loading rate between the AnMBR and PSD, in which 0.71 g of COD and 0.16 g of VSS per L wastewater were loaded into these two bioreactors every day. The DWTS functioned efficiently in regard to biogas production. Approximately 122 L per kg COD in the AnMBR and 374 L of biogas per kg volatile suspended solids (VSS) in the PSD were observed, and the average methane composition accounted for approximately 80%-85% and 60-65% of the biogas in the AnMBR and PSD, respectively. These results implied that a considerable amount of organic matter was converted into methane in both bioreactors. Similar pH, TSS, and VSS values were monitored in both reactors (Table 1). The temperature was kept at approximately 20 \({}^{\circ}\)C and 35 \({}^{\circ}\)C for the AnMBR and PSD, respectively. In terms of methane and biogas recovery, the PSD offers potential advantages over the AnMBR.
# SALMA: A Novel Middlebox Infrastructure System Based on Integrated Subnets
## 6 Implementation
### Consolidation Challenge
Middlebox equivalence checking: to examine the new integrated policy rules that are installed into the unified middlebox having all the previous individual middlebox rules. 2. Middlebox rule redundancy checking: to eliminate redundant rules after integration. 3. Middlebox rule conflict checking: to detect and eliminate conflict rules after integration. To solve the non-integrable middlebox challenge, we used both hardware and software features of middleboxes. From our investigation, we found that most firewalls are supported by several configuration management features that facilitate the partitioning of a single large ACL list into multiple small lists. These lists are installed into different tables. A configurable classifier in the first table is responsible for classifying incoming traffic and resubmitting it to the right list. This feature is similar to the multiple tables feature in OpenFlow switches, where the first table, table = 0, is the classifier, and the other tables contain the ACL lists. In this work, we utilized this feature to configure each table with the needed context. For example, when the ACLs of firewall-1 and firewall-2 cannot be integrated with each other, the ACLs of these two firewalls are configured in table = 1 and table = 2, respectively. The lists of other firewalls can then be integrated in table = 3. Additionally, the integrated ACLlists need to pass the validation and verification test.This feature has an additional advantage: the reading of the ACL list is faster in cases of small lists.
## 4 Signal Processing  
An overall signal processing flowchart is shown in Figure 7. At the beginning, the raw signals are digitized and an ellipse fitting algorithm is used to compensate for non-idealities. Afterwards, arctangent demodulation is employed to retrieve the relative distance signal from the phase information. This distance signal is then filtered using different bandpass filters to retrieve the according components of the radar signal. Specific algorithms are finally utilized for further analysis. All steps will be explained in detail in the following.
# Development of Digital Training Twins in the Aircraft Maintenance Ecosystem
## 4 Discussion
### Ethical and Regulatory Considerations  
The implementation of a digital training twin ecosystem that collects, processes, and analyzes learner data must adhere to strict ethical standards and regulatory requirements. The present system design incorporates safeguards focused on data privacy, security, informed consent, and compliance with regional and international regulations, most notably the General Data Protection Regulation (GDPR).  
To ensure data privacy, all learner-related data--including competence vectors, session logs, and behavioral metrics--is processed locally on edge devices. No raw personal data is transmitted to the cloud. Instead, federated learning enables only encrypted model updates to be shared, preventing central aggregation of sensitive data. All communications use TLS-encrypted channels over MQTT and Kafka protocols, ensuring confidentiality and integrity during transfer.  
Informed consent is a foundational element of system deployment. Learners must be provided with clear, accessible information outlining what data is being collected, for what purpose, how long it will be stored, and who has access to it. Consent must be explicit, recorded, and revocable, with procedures in place to fulfill right-to-erasure or data access requests, in compliance with Articles 7 and 17 of the GDPR.  
Regarding data minimization and purpose limitation (Articles 5 and 6 GDPR), the system collects only the minimum data necessary for competence assessment and orchestration. No biometric, location-based, or unrelated personal identifiers are stored. All analytical processes are bound to a clearly defined educational purpose.  
To support fairness and transparency, all orchestration decisions can be traced via audit logs, and learners are entitled to explanations of how their training paths are determined (Article 22 GDPR). The orchestration engine is regularly reviewed for potential algorithmic bias across demographic or skill-based learner segments.  
Additionally, institutions deploying the system are encouraged to conduct Data Protection Impact Assessments (DPIAs) prior to launch, especially in scenarios involving sensitive learner groups, high-volume data collection, or cross-border model sharing. Integration with federated unlearning modules in future versions will further empower users with control over their data footprint.