esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Approach
### Imaging System  
The high-speed imaging setup layout is depicted in Figure 3, described in detail in our previous work [61]. The flame behavior for every case was evaluated through OH*-CL CH\({}_{2}\)O- and SO\({}_{2}\)-PLIF.  
The detection system was identical for both PLIF and chemiluminescence measurements. A UV band-pass filter (FF01-315/15-48-D, Semrock, New York, NY, USA) was fitted to the camera lens for OH*-CL measurements. About 100,000 images were recorded for every condition while the repetition rate of the camera was kept at 10 kHz, and the intensifier was gated at 50 us.  
For PLIF measurements, the third and fourth harmonics at 355 nm and 266 nm from a commercial burst-mode laser (Quasimodo, Spectral Energies, Dayton, OH, USA) ran at 20 kHz within a 10 ms burst duration. The output power at 355 nm was about 100 mJ/pulse, and the value for 266 nm was about 20 mJ/pulse, respectively. The PLIF signal was captured perpendicular to the laser sheet using a HiCATT intensifier (Lambert, Groningen, The Netherlands) and a high-speed 12-bit camera (Phantom v2012, Vision Research, Wayne, NJ, USA) equipped with a Cerco 2178 UV lens (100 mm / 2.8). The gate of the image intensifier was set for 200 ns, fully opening immediately after the arrival of the laser pulse. An 11-band-pass filter (FF01-CH\({}_{2}\)O-48, Semrock, New York, NY, USA, covering the 380-480 nm wavelength range) was placed in front of the camera lens to capture the  
Figure 3: High-speed PLIF/chemiluminescence imaging system.  
Figure 2: (**a**) Axial velocity fluctuation of the fuel at the jet nozzle as a function of the loudspeaker voltage for 5 different mean fuel velocities and (**b**) time-resolved velocity under a loudspeaker voltage of 0.6 V (FMFRF amplitude \(\sim\)80%) along the jet centerline at the downstream height of 1 mm above the nozzle exit in three acoustic periods with a time interval of 0.1 ms.  
CH\({}_{2}\)O emissions, while a short band-pass filter (FF01-292/27-50-D, Semrock, New York, NY, USA) was utilized for SO\({}_{2}\)-PLIF detection. The frame rate of two high-speed cameras was synchronized with the laser system via a programmable timing unit (PTU, LaVision, Ypsilanti, MI, USA). The time delay of the applied acoustic sine wave relative to the laser pulse was also controlled by PTU and a digital delay generator (DG645, Stanford Research, Sunnyvale, CA, USA), permitting phase-specific measurements. Five data sets of the flame were acquired for every test condition, and every set consisted of 200 images sampled at 20 kHz for image post-processing.
# Distributed Uniform Streaming Framework:
## 6 Platform Transparency Using Dust: A Connected Driving Use Case
### Middleware Transparency  
In the previous section, we described the importance of hardware transparency in the development of the OBU architecture. Next to the hardware transparency, we are also able to create the middleware transparency by using the DUST-Core as a middleware platform. The middleware interface within DUST allows us to change the middleware without the need to change the application components in the application graph. This is also a key requirement within the development of the OBU because when an original equipment manufacturer (OEM) will implement software from the Smart Highway testbed to a real vehicle, they will not use a middleware such as ZeroMQ or MQTT but will send event message on a standard from the automotive industry (e.g., Controller Area Network (CAN), Flexray or Automotive Ethernet). This change in middleware is possible because of the middleware interface in the DUST-Core. In the case that we want to send message events over a CAN-interface, we create a new middleware object in the DUST-Core which is able to send and receive message with a CAN-interface (see Figure 8). The application components do not need to be changed although there is a large change in communication middleware between the application components.
# A Robust \(H_{\infty}\) Application for Motor-Link Control Systems of Industrial Manipulators
## 2 \(H_{\infty}\) Control Approach Basics  
### General Outlines  
\(H_{\infty}\) represents one of the most known robust control techniques. It minimizes the effects of high disturbances and model uncertainties and it ensures system stability in the presence of variations in parameters and operating conditions.  
The actors that play a fundamental role in the control structure (Figure 1) are the _generalized plant_\(M\) and the _controller_\(G_{c}\), whose derivation depicts the purpose of the design.  
The generalized plant \(M\) includes the nominal plant, the actuators, the sensors, and two weighting functions taking into account all the design requirements. Its inputs, \(w\), represent references, disturbances, sensor noises, while its outputs, \(z\), represent the signals that have to be controlled and the plant outputs. Both the plant and the controller are assumed to be rational and proper functions. Furthermore, the state-space models of \(M\) and \(G_{c}\) are assumed to be available and their realizations are assumed to be stabilizable and detectable [20].  
From a theoretical point of view, developing an \(H_{\infty}\) control system means designing a controller \(G_{c}(s)\) that minimizes the \(H_{\infty}\) norm of the closed-loop transfer matrix \(T_{wz}(s)\):  
\[G_{c}(s)=arg\min_{G_{c}\in G_{c}^{\text{Grid}}}||T_{wz}(s)||_{\infty} \tag{1}\]  
Figure 1: Feedback Control System.  
The controller is chosen among a set of controllers that make the system internally stable (\(G_{c}^{stab}\)) as the one that produces a signal \(u\) that counteracts the influence of \(w\) and \(z\)[20], minimizing the norm \(||T_{wz}(s)||_{\infty}\).  
It must be underlined that the solution of (1) is not unique in general. In addition, the computation of an optimal \(H_{\infty}\) controller is not easy, as well as not always requested. Rather, it is preferable to find controllers that are _very close_ to the optimal ones.  
This leads to the class of sub-optimal \(H_{\infty}\) control problems: the controller is chosen as the one that stabilizes the closed-loop system \(||T_{wz}(s)||_{\infty}\) and achieves \(||T_{wz}(s)||_{\infty}<\gamma\), a non-negative number which represents the best \(H_{\infty}\) performance [21].  
There are many ways to solve the optimization problem in (1) and to derive the controller transfer function. Here, the synthesis will be accomplished by means of an LMI-based technique, exploiting in particular the LMI Matlab Toolbox.
## 3 Results
### With Source Term Results  
With-source test results can be found in Table 5 and show that the all-Tanh model performed best, with a R2 score of 0.9905 \(\pm\) 0.0086 and an average relative error of 0.49% \(\pm\) 0.27%. Its prediction plot can be found in Figure 9 and can be compared with the hybrid model, the second-best performer, whose representative plot is also found in Figure 9. The hybrid model had a R2 score of 0.9600 \(\pm\) 0.0317 and an average relative error of 1.41% \(\pm\) 0.071%, and, while competitive, its differences with the all-Tanh model can be seen graphically when comparing the plots. The all-ELU model scored notably worse in performance values.  
Figure 8: Representative depictions of the convergence plots for the without-source experiments, mapping iteration to loss reduction. _Loss U_ represents the initial and boundary condition term, and _Loss F_ represents the PDE residual term, in the loss function. The all-Tanh model (**top left**), hybrid model (**top right**), and all-ELU model (**bottom**) show their respective training behavior.  
Figure 7: Representative non-source predictions for convective boundary conditions using hybrid activation function combination (**left**) and all-Tanh activation function combination (**right**).  
Convergence rates for the three models are depicted in Figure 10. Average iterations for the all-Tanh model were consistently higher than the hybrid model and the hybrid model's average iterations were consistently higher than the all-ELU model. Run failures were observed to be 0% for both the all-ELU and all-Tanh models, while the hybrid model had a failed run about 33% of the time.  
Figure 10: Representative depictions of the convergence plots for the with-source experiment, mapping iteration to loss reduction. _Loss U_ represents the initial and boundary condition term, and _Loss F_ represents the PDE residual term, in the loss function. The all-Tanh model (**top left**), hybrid model (**top right**), and all-ELU model (**bottom**) show their respective training behavior.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **R2 Score** & **Avg. Relative Error** & **Iterations** \\ \hline All-Tanh & 0.9905 \(\pm\) 0.0086 & 0.49\% \(\pm\) 0.27\% & 8580 \(\pm\) 3330 \\ Hybrid & 0.9600 \(\pm\) 0.0317 & 1.41\% \(\pm\) 0.71\% & 3933 \(\pm\) 2223 \\ All-ELU & 0.7381 \(\pm\) 0.2769 & 3.98\% \(\pm\) 2.53\% & 2642 \(\pm\) 2454 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results for insulated boundary condition test with source term.  
Figure 9: Representative predictions of insulated boundary conditions including source term with all-Tanh combination (**left**) and hybrid combination (**right**).  
In addition to the source experiment, a side test was conducted to determine the effect of the magnitude of the scaling constant for the S&T method on the performance of the model. For the experiment, the same all-Tanh model was tested in succession while gradually increasing the scaling constant per test. It was observed that, as the scaling constant grew in magnitude, the R2 score declined.
## 3 Method Implementation
### Algorithm Design  
Summarize the overall algorithm based on the approach presented in this paper. After preparing the dataset, use feature extraction networks to extract feature vectors of different modalities. These feature vectors are then fused into a multimodal feature vector using a contrastive learning strategy. GraphSAGE is subsequently used for inference, and the inference result must exceed a set threshold to be considered reliable data. In this paper, the threshold setting is discussed. By reviewing relevant literature, we initially chose a lower threshold (e.g., 50%) for experimentation and found that the model generated too many false positives during fault diagnosis, leading to poor performance in accuracy, recall, and F1 score. When we selected a higher threshold (e.g., 70%), it missed some real faults in consumer electronics, resulting in false negatives and affecting diagnostic efficiency. We then adjusted the threshold and found that setting it at 60% achieved optimal results in terms of accuracy, recall, and F1 score. Based on experimental observations and a review of relevant literature, we consider inference results with a probability greater than 60% as reliable data, while results with a probability below 60% are regarded as unreliable data. The fault results obtained by GraphSAGE are compared with entities in the multimodal knowledge graph based on similarity, yielding the final fault description entity. Through reasoning based on relationships between entities, the fault diagnosis result is derived. This diagnosis result is then input into the fine-tuned CPT model to generate the final fault diagnosis text, which is subsequently presented to the user. The algorithm design of the model is shown in Algorithm 1.  
```
0: Multiple modal data Result: Knowledge graph inference results
1: initialization;
2:while ResNet and Bert obtain multiple modal information \(M_{m}\in M\)do
3: multimodal vector \(=Softmax(sim(f_{x},f_{y}))\)
4:endwhile
5: GraphSAGE get multimodal vector;
6:\(\mathcal{J}_{\theta}\) get multimodal vector;
7:get failure results;
8:if failure probability \(\geq\) 60% then
9: knowledge graph get failure result;
10:else
11: no failure
12:endif
13:if failure results text feature \(=\) head entity then
14: unrecognizable;
15:else
16: semantic matching templates;
17:endif
18: get knowledge graph inference results \(R\);
19:\(CPT\) get \(R\);
20: fault description and solution;
```  
**Algorithm 1** Modal Fusion and Reasoning Process
# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 3 Components of AIoT in Aquaculture
### AI Algorithms in Aquaculture
#### 3.2.1 Machine Learning (ML) Approaches in Aquaculture
Value-based learning and policy gradient techniques are combined in actor-critic frameworks to improve aquaculture RL applications by balancing short-term operational efficiency with long-term sustainability. In such frameworks, the actor selects actions, while the critic evaluates their effectiveness, guiding improvements over time. Furthermore, RL can be extended to multi-agent systems where several agents cooperate or compete within shared environments. In aquaculture, where a multi-tank setup is used, RL agents can coordinate feeding and aeration across multiple tanks to optimise resource allocation and ensure consistent fish growth. Advanced RL techniques such as proximal policy optimisation (PPO) and soft actor-critic (SAC) address stability and efficiency challenges, making them highly promising for real-world aquaculture applications.
# Examining the Optimization of Spray Cleaning Performance for LiDAR Sensor
## 3 Results and Discussion
### Optimized Cleaning Model for the Entire Target Area
#### 3.1.3 Results of the Third Spray  
To calculate the cleaning rate of the third spray, the quadratic model was most suitable, and it showed high explanatory power with an R-squared value of 0.9026 and adj. R-squared value of 0.8655. The adeq. Precision of the third spray model (14.173) was appropriate as it was higher than 4. Variance analysis results of the third spray model are presented in Table 5.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **R-Squared** & \
\begin{tabular}{c} **Adj.** \\ **R-Squared** \\ \end{tabular} &
\begin{tabular}{c} **Adeq.** \\ **Precision** \\ \end{tabular} & **F-Value** & \(p\)**-Value** \\ \hline Second spray & 0.9205 & 0.8902 & 16.6642 & 30.39 & \textless{}0.0001 ** \\ \hline \hline \end{tabular}  
* * \(p<0.05\).  
\end{table}
Table 4: Variance analysis results of the second spray model.  
Figure 9: Three-dimensional graphs of the cleaning rate of the first spray to (**a**) washer pressure and spray time, (**b**) spray angle and spray time, (**c**) target point and spray time, (**d**) spray angle and washer pressure, (**e**) target point and washer pressure, and (**f**) target point and spray angle.  
In the third spray model, the primary terms of the spray time and washer pressure were significant at a significance level of 5%, and the quadratic term of the washer pressure was significant at a significance level of 5%. When the regression coefficients of the factors were estimated based on the method of least squares, the quadratic equation was expressed as  
\[\begin{split}\text{\emph{Cleaning Rate (Third spray)}}=-4.0314+13.4966\times\text{\emph{ spray time}}+15.3039\times\text{\emph{washer}}\\ \text{\emph{pressure }}-1.0362\times\text{\emph{washer pressure}}^{2} \end{split} \tag{3}\]  
The 3D surface and 2D contour graphs drawn based on the quadratic equation of the third spray model are shown in Figure 11.  
For the third spray model, tendencies similar to the results of the second spray model were observed, but the influence of the quadratic term of the washer pressure on the cleaning rate was larger. The cleaning rate significantly increased with the washer pressure, and it tended to increase with the spray time. Initially, the cleaning rate increased alongside the target point, but it decreased as the target point further increased. Conversely, the cleaning rate barely changed as the spray angle increased.
#### 3.1.2 Root Mean Square Error (RMSE) Average of Major Elements of Mixed Coals  
The average RMSE (%) can be obtained from the RMSE values of the PLSR models for the major elements in mixed coal. The average RMSE (%) can be defined by the following equation:  
\[\text{RMSEC(V)(avg.)}(\%)=\frac{\text{RMSEC(V)}}{\text{Average of Property}}\times 100, \tag{3}\]  
where the average of property is the mean value of each elemental concentration used as the reference value. As shown in Table 1, the RMSEC (avg.) and RMSECV (avg.) were 0.7570 and 0.9538 in the original data for carbon. For the carbon data pre-processed by SG smoothing, the RMSEC (avg.) and RMSECV (avg.) were 0.7089 and 0.9080, respectively. The RMSEC (avg.) and RMSECV (avg.) (%) were 0.6229 and 1.0064 for the carbon data pre-processed by the SG derivative, respectively. The RMSECV (avg.) value was the lowest with SG smoothing. The RMSEC (avg.) and RMSECV (avg.) in the original data for hydrogen are 0.8652 and 1.3489, respectively. The RMSEC (avg.) and RMSECV (avg.) in the SG-smoothing data are 0.8461 and 1.3914, respectively. The values of the RMSEC (avg.) and RMSECV (avg.) were 0.6258 and 1.0519 with the SG derivative method, respectively. The RMSEC (avg.) and RMSECV (avg.) with SG derivate method could be improved. In the case of oxygen, the RMSEC (avg.) and RMSECV (avg.) are 6.7308 and 9.0414 in the original data, respectively. The RMSEC (avg.) and RMSECV (avg.) with the SG-smoothing method are 6.7563 and 9.0984, respectively. The RMSEC (avg.) (%) and RMSECV (avg.) were 5.8870 and 9.3883 with the SG derivative method, respectively. The RMSECV (avg.) was the lowest for the original data. In the case of sulfur, the RMSEC (avg.) and RMSECV (avg.) were 7.6019 and 9.1453, respectively. The RMSEC (avg.) and RMSECV (avg.) with the SG-smoothing method were 7.5097 and 9.0301, respectively. The RMSEC (avg.) and  
Figure 15: Comparison of relative errors between measured and predicted sulfur concentrations for the PLSR models using the original data and pre-processed data.  
RMSECV (avg.) were 5.8513 and 8.6710 with the SG derivative method, respectively. The RMSEC (avg.) and RMSECV (avg.) with the SG derivate method were slightly improved.
## 4 HMBAC Design and Implementation
### Janus Implementation
#### 4.3.4 Smart Contracts
In addition, it allows for the efficient revocation of user access through attribute certificate revocation lists issued by the relevant authorities, instead of applying costly attribute key revocation techniques. Finally, it communicates with the LSC in order to record the transaction on the blockchain. To allow for interoperability of credentials issued by different stakeholders, all root certificates of all stakeholders are stored in the PBC. For the addition or removal of CAs, the functions \(addCA()\) and \(removeCA()\) are triggered accordingly. Note that both functions require agreement between current stakeholders through the voting mechanism. Only after agreement has been achieved through the voting mechanism, the function \(updateTrustAnchors()\) will update the stakeholders' certificates in the PBC. The \(majorityUpdate()\), invoked by the PSC, is called when an election ends (either by majority agreement or by timeout) to inform the TMSC. **Access Control Smart Contract (ACSC)** main function is to enforce the predefined access policy when users request access to data stored within the domain. The Inter-BC API forwards the request and triggers the \(policyEnf()\) function. Using the \(data\_ID\) and the \(roles\) provided in the payload, it determines whether or not to grant access and forward the request to the KSSC. **Logging Smart Contract (LSC),** enforces \(single\)\(source\)\(truth\) in our system. For each data access request, the \(requestLog()\) function is automatically triggered by the \(requestAccess()\) function of the PSC. Two main processes are supported by the LSC, \(registration\) and \(retrieval\) of the logs. Log registration is utilized with the functions: \(updateLog()\), for updating the details of uploaded stakeholders' certificates and temporal ACLs; \(updateRequestLog()\) for updating existing request records; and \(majorityUpdate()\) for updating election records. The \(getUserRequestLog()\) function implements log retrieval for users who want to access their request record. The \(retrieveLogInit()\) and \(retrieveLog()\) functions are utilized for starting an access-granting Election, when an auditor requests access to the logs stored on PBC and the log retrieval accordingly. **Key Store Smart Contract (KSSC),** enforces the \(single\)\(point\)\(of\)\(access\) property in our system in the following way. As described in Section 4.2.3, data are MA-CP-ABE encrypted, based on predefined access policies, which are modified to additionally require decryptionwith the domain attribute key. The KSSC is the only component that may access the domain's attribute private key. The \(requestData()\) function is invoked by the ACSC to initiate the process, only after the user's roles have been verified and connects to the Database API to forward the \(data\_1D\) of requested data.
Secondly, a beam search algorithm is applied to obtain a rapid and robust solution for a coexistence scenario consisting of the NGSO and GSO satellites systems that can create a large number of beams. The structure of this paper is organized as follows. The interference scenario, combined with interference evaluation index and antenna radiation patterns, interference parameters analysis, and the channel allocation method are presented in Section 2. In Section 3, a co-frequency compatibility simulation between the NGSO and GSO satellite communication systems is performed to verify the effectiveness of the channel allocation method, and then the simulation results are analyzed. In Section 4, the conclusions are deduced.
## 2 Regions and Methods
### Model Building
#### 2.2.2 Geometric Model and Physical Parameters  
According to the geological survey report, the main slide surface was selected as the calculation profile, and a two-dimensional geometric model of 46 m in length and 19 m in height was established (Figure 3). It can be separated into three sections, including the sliding surface (mudstone softened soil), sliding mass (weathered sandstone, silty clay), and sliding bed (mudstone). The landslide model uses PLANE82 element from ANSYS Library, with 6379 nodes and 6218 finite elements.  
In order to study the failure mechanism of the cutting slope clearly, it is very necessary to conduct indoor tests on the soil properties of undisturbed soil samples in the study area. The laboratory tests include the natural moisture content  
Figure 3: Finite element mesh schematic diagram of the main slide’s cross-section.  
test, proportion test, grain composition measurement, direct shear test, and variable head permeability test, etc. As a result, the parameters of the sliding zone soil model that were fitted based on engineering geological survey data and laboratory test results are shown in Table 1.
## 3 The Proposed Method  
In this section, an improved ground moving-target parameter estimation and imaging method for multichannel high-resolution SAR is proposed. Firstly, the Hough and SOKT are used to correct the range migration to achieve target coherent accumulation. Secondly, DCCF is constructed to reduce the order of the range migration phase response in the slow time domain. Then, the parameter estimation is converted into peak detection with the SAC algorithm and 2D-FFT, which simplifies the parameter estimation process. Finally, the moving-target focusing imaging and relocation are performed based on the estimated parameter.