esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Wind Turbine Model Validation and Disturbances  
To investigate FOWT dynamics and implement new structural control elements, the work begins with the use of a simulation model that captures the system's behavior and allows for easy modifications and efficient simulations. OpenFAST, a high-fidelity software tool, provides reliable results but requires substantial computational resources and programming effort for model modifications. Therefore, this study utilizes a simplified FOWT model, validated against realistic non-linear OpenFAST simulations.  
The model under study has three degrees of freedom (DoF), allowing pitch motion in the tower plane (fore-aft direction). The masses represent the turbine tower, platform, and TMD mounted in the nacelle. It has been shown that the dynamics of the blades and other components have minimal impact compared to the effects of the three primary DoF considered, thus the blades and mooring lines have not been included. The main objective of this work is to reduce vibrations and stress in the main structure. Initially, the model does not account for external wind or wave forces, focusing instead on demonstrating the effectiveness of the semi-active control in reducing tower displacement through adaptive TMD parameter tuning.  
A floating barge-type wind turbine is selected for a more challenging case. While vibration reduction is a common issue for all wind turbines, it is particularly critical for offshore wind energy converters. In this case, the FOWT considered has a horizontal axis with a 5 MW nominal power output, three blades measuring 63 m in length, and a hub height of 90 m. The floating platform measures 40 \(\times\) 40 \(\times\) 10 m and is anchored to the seabed with eight mooring lines [5].
## 4 Performance and Simulation Results  
This section demonstrates the performance and simulation results of the original BA protocol and the MBA. Then, MBA performance is compared with the existing protocols in the same field, which are GQT1, GQT2, GBT, and TBT, under various influences such as capture effect probability, interference probability, total number of tags, and tag IDs length (for query tree protocols GQT1 and GQT2). Moreover, to evaluate the performance of tags identification, two metrics are considered:  
The total number of cycles (Identification delay)  
The number of the cycles consumed for identifying all the tags in reader range. These cycles are divided into the following categories:  
* Readable/success cycles (\(s\)): the reader can decode one tag signal.
* Collision cycles (\(c\)): multiple tags respond simultaneously, and the reader detects collision tags signal. Or, one tag responds with interference influence and no distinction is made between collision and interference responses.
* Interference cycles (\(i\)): one tag responds to the reader request, and the reader cannot decode the tag signal due to the interference influence, but a distinction is made between collision and interference responses.
* Idle/empty cycles (\(d\)): No tag responds to the reader request.
* Identification efficiency  
The number of the success cycles to the total number of cycles consumed for identifying all the tags in reader range.  
\[\eta\ =\ \frac{s}{s\ +\ c\ +\ d\ +\ \mathbf{\xi}} \tag{18}\]  
in which \(s\), \(c\), and \(d\) are success, collision, and idle cycles without the cycles that resulted from interference responses. Additionally, \(\mathbf{\xi}\) is the number of additional cycles (\(c\) and \(d\)) for non-blocking protocols, or (\(i\), \(c\), and \(d\)) for blocking MBA protocol, produced due to interference responses.
## 4 Results and Discussion
### Spatial TDR Tracing during Water Table Decreasing  
When this experimental platform was firstly set up, before loading the sample into the column, some previous tests were conducted to validate the signal variation of TDR traces. Thus, a pre-test was completed by dropping the water table for a single column from an originally saturated condition in order to characterize the decrease in the reflection coefficient along TDR waveforms. An illustration of this pre-test is shown in Figure 5(a) and the TDR traces for different water tables are shown in Figure 5(b). With the water table dropping downward, the TDR traces logged from 0 to 3 min clearly show an increase in the reflection coefficient along the flat ribbon cable measured from the top. After four min, the water table is stable since no variation in the TDR waveform is visible.
## 4 Self-Healing Approaches
### Network Architecture
#### 4.2.7 Agent Architecture  
Agent architecture is the design and implementation of intelligent software agents that can autonomously detect and respond to faults or errors in a system. These agents are designed to operate in a distributed system, with each agent responsible for monitoring a specific aspect of the system and taking action to resolve issues when they arise.  
Agent architecture is described in [31] as a rule-based architecture that uses an \"if-then\" rationalising scheme, in which the consequent result relies on prior experience. Then, appropriate agent architecture influences how well the system's agents handle their operating environment through inference reasoning. The scope for simultaneous internal and external monitoring of the system is limited due to what is referred to as a threading obstacle with JADE/JESS integration, as noted in [PERSON] [31]. The alternative and viable option is to structure agent communication from JADE agents to the JESS inference by granting JESS access privileges to the agent communication language (ACL) message or allowing JESS to add ACL message objects to the JESS working memory. Agent architecture in a self-healing system typically consists of the following components:  
* Sensing: Agents can monitor the system and gather data about its current state. The data may include system performance metrics, error logs, and other relevant information;
* Diagnosis: Agents use data gathered during the sensing phase to analyse the system's current state and identify any faults or errors. Various techniques can be employed to achieve this, such as comparing current data to historical trends or utilising machine-learning algorithms to detect and pinpoint abnormal behaviours;
* Decision-making: Once a fault or error has been detected, agents must decide how to respond. In such scenarios, the process may involve selecting from a predetermined set of response options, such as restarting a process or diverting traffic to a backup system;
* Action: Agents take action to resolve the fault or error using pre-define or adaptive responses. This process may involve coordinating with other agents to initiate a synchronised response or adjusting the system configuration to prevent future occurrences;
* Learning: Agents continually learn from their experiences and adapt their behaviour over time to improve effectiveness. To adapt effectively, agents may need to adjust their response strategies based on the outcomes of past responses and update their system models using new data.
# Evaluating Progressive Web App Accessibility for People with Disabilities
## 3 Progressive Web Apps (PWAs)
### Caching Storage Application Programming Interface (API) and Offline Functionality  
The service worker kernel is equipped with a caching interface; the cache can store static content that users can access without an internet connection. Depending on the caching settings that have been set, the service worker undertakes caching of the content that the user has already visited. If the user's device does not have an internet connection, all cached information is temporarily displayed until the connection is restored [16]. In that way, PWAs achieve the fastest loading of apps, making the apps usable even without an internet connection. The only limitation is the browser's storage limit. The process of creating and retrieving caching content is presented in Figure 3.  
To set up a caching mechanism, an \"addEventListener\" must be installed. Through the installation, event items are added on the cache's property as an array, and these properties can be used instead of the server's content when there is no internet connection. Moreover, even if an internet connection exists, the service worker sends the cached content back to the app, and only the missing content is requested by the webserver. To keep the cached content up to date, the service worker must regularly update its cached contents by deleting old caches and creating a new one. If the page is dynamic and its content changes frequently, refreshing the dynamic cached elements is possible with periodic JSON fetch calls.  
Adopting techniques from native apps, PWAs suggest creating an offline fallback page with a custom service worker. On this page, the user is notified of the lack of internet connection and, with an automatic reload based on the online event, is informed that a new reconnection attempt will be performed in x seconds [2].
# An Overview on Deep Learning Techniques for Video Compressive Sensing
## 2 Compressive Sensing
### Reconstruction Algorithms
#### 2.3.2 Greedy Algorithms  
Greedy algorithms are commonly used in CS applications because of their low complexity and their fast reconstruction. Currently, the most exploited greedy algorithms are classified into sequential and parallel greedy pursuit techniques. Sequential methods count gradient pursuit [22], matching pursuit (MP) [23; 24], orthogonal matching pursuits (OMP) [25], regularized OMP (ROMP) and stagewise OMP (StOMP) [26; 27; 28]. Although OMP allows a faster signal reconstruction than convex relaxation approaches, it deteriorates the recovery quality for signals with low sparsity. Therefore, improved versions of OMP have been proposed to avoid these drawbacks such as compressive sampling matching pursuit (CoSaMP) [29], subspace pursuit (SP) [30], Regularized OMP [27], Stagewise OMP [26], and orthogonal multiple matching pursuit [31]. Those techniques are considered as parallel greedy pursuit methods.  
Obviously, the performance of the reconstitution algorithms depends on the applications and there is no obvious metric to determine the best reconstruction algorithm. However, for some algorithms, we can compare their complexity and the minimum measurements required for the CS recovery.
###### Abstract  
The operational reliability of rail vehicle pantograph systems is evaluated by transforming T-S multistate fault trees into dynamic Bayesian networks (DBNs), which take into account system multistability, long-lasting operation, dynamic failure, and maintenance recovery. The T-S multistate fault tree structure is constructed by the content validity ratio and content validity index; the T-S gate rule expressing causal uncertainty is constructed by using fuzzy theory and dependent uncertain ordered weighted averaging expert scoring, and finally, the pantograph T-S multistate fault tree is transformed into a DBN model characterizing the dynamic interaction and time dependence of the system. The dynamic evolution laws of reliability of a pantograph system in maintenance and maintenance-free states over time are inferred, compared and analyzed. The results show that the system availability of a pantograph system decreases continuously during 720 days of operation. The system availability without maintenance decreases to 0.881, and the system availability with maintenance is 0.952. The reliability of a pantograph system can be effectively ensured with maintenance during the operation period; the sensitivity analysis is performed by changing the failure rate of the equipment to 120% or 80%; the fall indicator, the electrical control box, and the elevating bow motor are the weak links in the system, and the impact of fault escalation on the reliability of a pantograph system is analyzed. It is then verified that the system reliability can be further improved by using a preventive maintenance strategy, and the steady-state reliability can be gradually reached, which is about 0.9968, providing a reference for the maintenance of a pantograph system.  
reliability analysis; T-S multistate fault trees; dynamic Bayesian network; fuzzy theory; pantograph systems +
Footnote †: journal: Applied sciences
## 4 Experimental Validation
### Experimental Results
#### 4.1.2 Glass Microspheres  
The glass microspheres were shifted over a range of displacement distances \(\rm\Delta z_{o}\) from \(-10\) mm to \(+7\) mm, at increments of 1 mm. Figure 13a shows the experimental values of \(\rm M_{L}\) obtained for different object displacements. In the calculation of the transverse magnification, \(x_{0}~{}=~{}\varnothing_{sph}/2\) represents the radius of the microspheres, with \(\varnothing_{sph}~{}=~{}14.5~{}\upmu\)m and \(x_{r}~{}=~{}\varnothing_{img}/2\) corresponding to the diameter of a microsphere in the reconstructed image. The experimental values of \(\rm M_{T}\) as a function of \(\rm\Delta z_{o}\) are plotted in Figure 13b. In this case, the variation in the experimental values of the transverse magnification was greater than the calculated in the simulation. This was attributed to the fact that the magnified image of the diffracted wavefront by the microsphere was very small, and it was recorded  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Theoretical** & **Min.** & **Max.** \\ \hline \(\rm M_{L}\) & \(-9.480\) & \(-9.670\) & \(-9.290\) \\ \(\rm M_{T}\) & \(-3.079\) & \(-3.090\) & \(-3.067\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Maximum and minimum values of longitudinal magnification \(\rm M_{L}\) and transverse magnification \(\rm M_{T}\) attributed to experimental errors.  
Figure 12: (**a**) Experimental longitudinal magnification \(\rm M_{L}\) as a function of displacement distance \(\rm\Delta z_{o}\) and (**b**) experimental transverse magnification \(\rm M_{T}\) as a function of \(\rm\Delta z_{o}\). The error bars in (**a**) are associated with the micrometer resolution of the linear translation stage, and the error bars in (**b**) are associated with the resolution of the precision rules used for measuring the image distance.  
in just a few pixels of the CCD. In such a form, tiny variations in the number of pixels of the reconstructed image represent a significant change in the calculated size of the image.  
The average and standard deviation of the transverse and longitudinal magnification values were calculated for the same two microscopic objects studied. The corresponding results are summarized in Table 3. In almost all cases, the average values fall within the maximum and minimum boundaries of the expected magnifications, considering experimental errors. Note that the values with the least standard deviation for each object are shaded in Table 3. The Tamura coefficient and the entropy exhibit the smallest standard deviation, indicating that they are the most effective evaluation criteria for obtaining the best focused images. In the case of the image reconstruction of the glass microsphere, the standard deviation of the transverse magnification was higher, as observed in Figure 13b.
# Asymptotic Performance Analysis of the MUSIC Algorithm for Direction-of-Arrival Estimation
## 9 Applications of Derived Analytic Expression of MSE of the MUSIC Algorithm
### Localization Accuracy in the MUSIC Algorithm for Use With Super Resolution Fluorescence Microscopy  
The authors in [32] propose a new algorithm called MUSICAL which is a modified version of the classical MUSIC algorithm. The MUSICAL is proposed to solve a few problems in the classical MUSIC algorithm. Although the authors show that the MUSICAL algorithm outperforms the conventional MUSIC algorithm, they do not derive an explicit expression of the MSE of the distance estimate for the MUSICAL algorithm. The derivation leading to (40) in this paper can be modified to derive the expression of the MSE of the MUSICAL algorithm. The modification should reflect the difference between the MUSICAL algorithm and the MUSIC algorithm.
# An Accelerated Maximum Flow Algorithm with Prediction Enhancement in Dynamic LEO Networks
## 3 System Model and Problem Formulation
### Construction of the e-TEG Satellite Network
As data processing progresses, this capacity is dynamically updated to reflect energy consumption, ensuring that the total data processed across all time slots does not exceed the satellite's energy budget:  
\[\sum_{h=1}^{k}C_{v_{i}}^{h}\leq C_{v_{i}},\quad\forall i\in[1,N] \tag{12}\]By constructing the e-TEG with temporal replicas, virtual nodes, and resource-constrained arcs, the framework accurately captures the dynamic and resource-constrained nature of LEO satellite networks. This representation enables efficient computation of maximum flow solutions while adapting to frequent topology changes and operational limitations. Figure 3 gives a demo example of the e-TEG model for a LEO with one source, two relays, and one destination ground station in three time slots.
# Unsupervised Vehicle Re-Identification Method Based on Source-Free Knowledge Transfer
## 3 Experiment
### Experimental Results and Analysis
#### 3.3.2 Experimental Results and Analysis on VeRi776\(\rightarrow\)VehicleID  
The experimental results of this method on VeRi776\(\rightarrow\)VehicleID are shown in Tables 2 and 3. It can be seen that the proposed method also achieved the best experimental results on the VehicleID datasets. When the test size is 800, 1600, 2400, and 3200, the values of Rank-1 are 52.76%, 47.65%, 43.87%, and 41.77%, respectively, the values of Rank-5 are 67.29%, 63.83%, 62.43%, and 60.42%, and the values of mAP are 58.33%, 53.72%, 50.42%, and 47.29%. It is worth noting that the method proposed in this paper has a huge improvement compared with the direct application of the target domain on the source model (direct transfer). Taking test size = 800 as an example, Rank-1 and mAP are increased by 13.2% and 15.32%, respectively. In addition, since the scale of VeRi776 is much smaller than that of VehicleID, there are very few generalization studies on the realization of vehicle re-identification tasks from small datasets to large datasets, so we can only select some from a small number of research works. Relatively prominent methods are compared with the model in this paper. It is not difficult to see from Tables 2 and 3 that compared with PAL, which is most similar to the method in this paper, it shows a stable improvement in different test sizes.  
To this end, from the experimental results in the Tables 1-3, it is evident that our proposed method demonstrated remarkable performance in both the VehicleID \(\rightarrow\) VeRi776 and VeRi776 \(\rightarrow\) VehicleID experiments. This validates the effectiveness of the proposed approach, indicating its ability to enhance the performance of unsupervised domain-adaptive vehicle re-identification methods even in a source-free domain setting. However, when  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Test Size = 2400**} & \multicolumn{3}{c}{**Test Size = 3200**} \\ \hline
**Methods** & **Rank-1** & **Rank-5** & **mAP (\%)** & **Rank-1** & **Rank-5** & **mAP (\%)** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **mAP (\%)** \\ \hline PUL [30] & 30.90 & 47.18 & 34.71 & 28.86 & 43.41 & 32.44 \\ CycleGAN [20] & 27.15 & 46.52 & 31.86 & 24.83 & 42.17 & 29.17 \\ PAL [13] & 41.08 & 59.12 & 45.14 & 38.19 & 55.32 & 42.13 \\ \hline Direct Transfer & 31.05 & 48.52 & 34.72 & 28.12 & 42.98 & 31.99 \\ ours & **43.87** & **62.43** & **50.42** & **41.77** & **60.42** & **47.29** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative experimental results of the method in this paper on VehicleID (part 2).  
compared to some supervised learning methods, there is still a noticeable performance gap in our experimental results. This implies the need for further exploration and refinement to bridge this gap, particularly in the domain adaptation methods.
## 4 The Design of the Proposed Filter  
After predicting the dimension values of the proposed preliminary dual polygonal resonator from the validation sample of the proposed ANN model, its frequency response is shown in Figure 8. As can be seen from the frequency response, this resonator has a transition zero with more than a 60 dB attenuation level. Additionally, the dual polygonal resonator has an insertion loss of about 0.5 dB in the pass band. The dimensions of the proposed preliminary dual polygonal resonator, predicted by the ANN, are listed as follows: L\({}_{1}\) = 1.2 mm, W\({}_{1}\) = 1.56 mm, L\({}_{7}\) = 0.8 mm, W\({}_{7}\) = 9.9 mm, L\({}_{8}\) = 0.8 mm, W\({}_{8}\) = 2.2 mm, W\({}_{9}\) = 0.1 mm, W\({}_{10}\) = 0.2 mm, S = 0.2 mm, L\({}_{T}\) = 19.3 mm, W\({}_{T}\) = 9.9 mm.
## 3 Materials and Methods
### Cardiovascular Event Transfer Learning Model  
#### 3.7.1 Model Architecture  
In pursuit of enhancing the model's classification capabilities to encompass CEs, a transfer learning strategy was implemented. The pre-existing model, initially designed for AF classification, served as the foundation. To facilitate the adaptation to CEs, the layers beyond the first dense layer, including those responsible for AF classification, were frozen. This retention of foundational aspects allowed the model to retain its learned features specific to AF, while accommodating the incorporation of new knowledge related to CEs. For model extension, additional layers were introduced and fine-tuned to the distinctive characteristics of CE data. A new dense layer, comprising 32 units and employing ReLU activation, was introduced, followed by a 0.5 dropout layer to enhance the generalization. The output layer, a single dense layer, utilized a Sigmoid activation function to tailor the model's output for the binary classification of CEs. This strategic combination of frozen and extended layers ensured a synergistic transfer of knowledge from the original AF-focused model, promoting robust classification capabilities across diverse cardiac events; see the CE model architecture in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**Model: “Keras Sequential”**} \\ \hline
**Layer (type)** & **Output Shape** \\ Input layer (Dense) & (, 64) \\ Activation (Activation) & (, 64) \\ Hidden layer 1 (Dense) & (, 64) \\ Activation (Activation) & (, 64) \\ Dropout 1 (Dropout) & (, 64) \\ Hidden layer 2 (Dense) & (, 64) \\ Activation (Activation) & (, 64) \\ Dropout 2 (Dropout) & (, 64) \\ Output layer (Dense) & (, 1) \\ \hline \multicolumn{3}{c}{**Trainable params:** 12,545} \\ \hline \hline \multicolumn{3}{c}{**Non-trainable params:** 0} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Atrial fibrillation (AF) model architecture summary. Computational complexity analysis of the proposed deep learning network architecture: For a given number of epochs \(E\), training samples \(N\), input dimensionality \(D\), and hidden layer size \(H\), the complexity per epoch is on the order of \(\mathcal{O}(N\cdot D\cdot H)\). Over \(E\) epochs, it scales to \(\mathcal{O}(E\cdot N\cdot D\cdot H)\).
# A Review of the Influence of Steel Furnace Slag Type on the Properties of Cementitious Composites
## 5 Requirements for SFS Characterization
### Microstructural Assessment  
Microstructural analysis of polished SFS sections can be effective to better assess the phase distribution, such as with scanning electron microscopy (SEM) with backscattered electron (BSE) imaging and mapping with energy dispersive X-ray (EDX) spectroscopy [10, 24, 66, 251, 264, 281, 313, 314, 315]. This technique has also proven to be effective in evaluating that the surface of SFS particles may be different from the particle interior [10, 251], as exampled in Figure 1, which could be a contributing factor in how concrete with SFS aggregates behaves, as discussed in Section 4. Optical microscopy of polished sections has also been demonstrated to be effective at characterizing the phases present in SFS samples [264, 292, 313]. Coupling SEM/EDX with XRD data allows for a more detailed compositional and corroborative analysis.