esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Quantitative Evaluation of Real-Time Data Transmission  
Verifying whether the real-time data transmitted from multiple patient monitors and ventilators is displayed on a central monitor and remote mobile applications without loss of information is one of the most important evaluations in the proposed VCMS.  
Figure 5 shows the comparison results of the error between the ideal and measured DTC according to the number of patient monitors and ventilators. The wave parameters sent from the patient monitor and ventilator to the VCMS server were 14 and 5, respectively, and the numerical parameters were 57 and 41, respectively. The wave parameters of the patient monitor included seven electrocardiogram channels: respiratory rate, oxygen saturation (SpO\({}_{2}\)), four IBP channels, and carbon dioxide. The ventilator measured pressure, flow, volume, end-tidal carbon dioxide, and SpO\({}_{2}\). The ideal DTC for the number of patient monitors and ventilators was calculated using Equation (1). The ideal DTC, when each patient monitor and ventilator are one, is as follows: the DTC of a patient monitor = 21,424 bit/s and that of a ventilator = 5512 bit/s. For clinical evaluation, up to 18 units of maximum patient monitoring and ventilator use were measured in the ICU over a measurement period of 3 months. The measured DTC error was less than 10\({}^{-7}\), ranging from 1/1 to 18/18 (number of patient monitors/number of ventilators).  
Figure 6 shows the results of the DTR difference according to the DTC in a scatter plot. A total of 36 patient monitors and ventilators were measured up to 484,848 bit/s; the mean of the measured DTR difference was 1.99 \(\times\) 10\({}^{-7}\), and the standard deviation was 9.97 \(\times\) 10\({}^{-6}\). The 95% confidence interval (CI) was measured from \(-\)1.16 \(\times\) 10\({}^{-7}\) to 5.13 \(\times\) 10\({}^{-7}\). This result indicates that the measurement data were delivered in real time without information loss, except for essential losses that can occur through errors in delivering the bits received during communication [50].  
Figure 5: Bar graph showing the error rate results of the ideal data transfer capacity (DTC) and measured DTC according to the number of patient monitors and ventilators.
## Appendix B Sensor Accuracy  
Here, sensor accuracy will be discussed.  
For this analyses, track is taken into account only if both sources (AIS and HFSWRs) provide stable data. AIS data is taken as more accurate one and HFSWR data is compared to it. Methodology is following:  
1. When both HFSWR and AIS are stable there is more data points originating from AIS than MRFTPs. So, only the AIS data point which is the closest to corresponding observed MFRTP will be taken into consideration.
2. Since AIS data point is considered more accurate, range error is calculated for MRFTP. Same is true for angle and speed errors.
3. For this analyses Hamilton class cutter ([[https://en.wikipedia.org/wiki/Hamilton-class_cutter](https://en.wikipedia.org/wiki/Hamilton-class_cutter)]([https://en.wikipedia.org/wiki/Hamilton-class_cutter](https://en.wikipedia.org/wiki/Hamilton-class_cutter))) sailing approximately 40 to 60 nautical miles of shore is observed during 4 h period.
4. Data obtained during observation (417 data MRFTP-AIS pairs) are presented below.  
The first range accuracy will be discussed.  
Majority of tracks provided by HFSWR network have insignificant range error, since majority of range errors are lesser than 0.6 km (see Figure 14). This can be expected since HFSWR declared range error is less than 0.5 km. Next, angular error will be presented.  
From Figure 15 it can be noted that angular error is mostly less than 1.2 degrees, which is in accordance with declared values (up to \(\pm\)1.2\({}^{\circ}\)). At the end speed error will be discussed.  
Since precision of vessel's radial speed measurement is declared to \(\pm\) 1 m/s, data present in Figure 16 confirm declared values.  
Figure A4: Range accuracy.
## 4 Review and Comparative Evaluation of VQA Models
### Review of VQA Models
This model is based on an FIS that uses the same input parameters as the current model but relies on a Mamdani rule-based framework with membership functions generated via FCM (Fuzzy C-Means) clustering. In FLAME-VQA, the rules were manually defined and subsequently adjusted through domain expertise and rule induction, resulting in a relatively complex rule base of 36 rules. Moreover, to increase the model accuracy, the weights were used on each rule to further fine-tune and define its contribution to the aggregated and defuzzified result. In contrast, the current model employs the ANFIS framework to automatically generate fuzzy rules and optimize membership functions, yielding a more compact and efficient model with fewer rules while maintaining prediction accuracy. In the next subsection, the discussed VQA models are evaluated by comparing their predicted MOS ratings against user MOS ratings from the LIVE-YT-HFR database for 480 video sequences, using RMSE, PCC, and SROCC as performance metrics. Due to their inherent simplicity and reliance on basic pixel-level or similarity measures, traditional metrics like PSNR and SSIM are expected to yield higher prediction errors when estimating MOS, as they often fail to capture the nuanced distortions in HFR content. In contrast, advanced metrics that incorporate multi-scale analysis, feature preservation, and/or spatio-temporal dynamics (such as FSIM, ST-RRED, SpEED, GSTI, or VMAF) are anticipated to more accurately reflect the perceptual quality. While conventional methods offer computational efficiency, their limited sensitivity to the temporal and structural complexities of modern video signals suggests that they may not perform as well under conditions demanding fine-grained quality prediction.
# IGBT Overvoltage Protection Based on Dynamic Voltage Feedback and Active Clamping
## 4 Parameter Influence of the Dynamic Voltage Feedback Circuit
### \(R_{4}\) Impact Mechanism Analysis  
\(R_{4}\) mainly affects the charging and discharging time of the capacitor and the feedback current in the dynamic voltage feedback circuit, wherein the charging and discharging expression of the capacitor is as follows:  
\[\begin{cases}\tau=RC\\ U_{CC}=U\times[1-e^{(-t/\tau)}]\\ U_{CD}=U_{0}\times e^{(-t/\tau)}\end{cases} \tag{9}\]  
where \(\tau\) is the charge time constant, U is the power supply voltage, \(U_{0}\) is the voltage on the capacitor before discharge, \(U_{CC}\) is the charge voltage value at time t on capacitor \(C_{1}\), and \(U_{CD}\) is the discharge voltage value of the capacitor.  
The discharge current can be expressed as as follows:  
\[I=U_{CD}/R \tag{10}\]  
According to Formulas (9) and (10), \(R_{4}\) affects the off-voltage spike. Changing the size of \(R_{4}\) will affect the discharge voltage value of capacitor \(C_{1}\) at any time and change the size of the feedback current, thus causing the rise rate of \(V_{ge}\) to change and affecting the voltage spike of \(V_{ce}\). Figures 7 and 8 show the simulation waveform images of \(V_{ce}\) and \(V_{ge}\), respectively, when the size of \(R_{4}\) is changed under the condition of constant \(C_{1}\) and \(U_{Z51}\).  
Figure 6: Curve of \(V_{ce(max)}\) changing with \(C_{1}\).  
Figure 7: \(V_{ce}\) comparison diagram under different \(R_{4}\) values.  
The also figure shows that with an increase in the value of \(R_{4}\), the rising rate of \(V_{ce}\) will be larger, resulting in a larger voltage spike of \(V_{ce}\), which will trigger the active clamp circuit earlier, and at the same time, the falling rate of \(V_{ge}\) will also be larger, resulting in \(V_{ge}\) at time \(t_{3}\), a larger voltage boost appears to suppress the voltage spike.  
Change the size of \(R_{4}\) for many times, keep \(C_{1}\) and \(U_{Z51}\) unchanged, and get the curve of \(V_{ce(max)}\) changing with \(R_{4}\) as shown in Figure 9. It can be seen that \(R_{4}\) has the same effect on \(V_{ce(max)}\) as \(C_{1}\), but the change rate of \(V_{ce(max)}\) is small.
# Deep Convolutional Neural Network with KNN Regression for Automatic Image Annotation
## 3 Our Proposal
### Image Segmentation Using JSEG Algorithm  
According to [36], the best way to recognize objects from an image is to segment them and then extract features from those segmented regions. However, object segmentation, both using supervised and unsupervised approaches, is itself a complex task. Despite the difficulty of achieving precise and accurate semantic segmentation, it has been proven on many occasions that segmented areas hold valuable annotation cues regardless of the quality of segmentation [16; 34].  
JSEG is a powerful unsupervised segmentation algorithm for color images that proved its effectiveness and robustness in a variety of applications [37; 38]. JSEG has recently witnessed various improvements to improve its performances, such as in the problem of oversegmentation [16; 39]. In our study, the JSEG proposed in [18] has been employed to segment the image into a set of semantic regions, as illustrated in Figure 2.  
Figure 2: A general scheme of texture-enhanced JSEG (T-JSEG) segmentation method. At the preprocessing level, the HSV color space is firstly quantized and all pixels of the image are then mapped to their corresponding bins. At the segmentation level, the J-image and a class map for each windowed color region are calculated, and then a clustering/growing algorithm is applied to obtain distinct regions.
###### Abstract  
Inherited retinal dystrophies (IRDs) are a clinically and genetically heterogeneous group of diseases with more than 250 causative genes. The most common form is retinitis pigmentosa. IRDs lead to vision impairment for which there is no universal cure. Encouragingly, a first gene supplementation therapy has been approved for an autosomal recessive IRD. However, for autosomal dominant IRDs, gene supplementation therapy is not always pertinent because haploinsufficiency is not the only cause. Disease-causing mechanisms are often gain-of-function or dominant-negative, which usually require alternative therapeutic approaches. In such cases, genome-editing technology has raised hopes for treatment. Genome editing could be used to (i) invalidate both alleles, followed by supplementation of the wild type gene, (ii) specifically invalidate the mutant allele, with or without gene supplementation, or (iii) to correct the mutant allele. We review here the most prevalent genes causing autosomal dominant retinitis pigmentosa and the most appropriate genome-editing strategy that could be used to target their different causative mutations.  
Inherited retinal dystrophies; autosomal dominant retinitis pigmentosa; photoreceptors; loss-of-function; gain-of-function; dominant-negative; CRISPR/Cas; gene supplementation; genome-editing; AAV vector
## 3 Materials and Methods
### Reasoning Engine and Rules Based on SWRL  
Dynamically controlling the WSN configuration is a complex task that requires intelligent inference based on specific working state and requirements. Owing to this requirement, with ontology used in memory, additional rules have been developed to extend the information inferred by the semantic framework.  
As an alliance to OWL, SWRL can be used to write rules to reason about OWL individuals and to infer new knowledge about those individuals. SWRL is an expressive OWL-based rule language, which can be used to write rules to reason about OWL individuals and to infer new knowledge about those individuals. A SWRL rule contains an antecedent part, which is referred to as the body, and a consequent part, which is referred to as the head. The body and head consist of positive conjunctions of atoms. Informally, a SWRL rule may be read as meaning that if all the atoms in the antecedent are true, then the consequent must also be true. In SWRL rules, the symbol \"\(\wedge\)\" represents conjunction, \"?x\" represents a variable, and \"!\" represents implication. If there is no \"?\" in the variable, then it is an individual. A SWRL editor is available as part of Protege-OWL [24].  
Using the previously proposed ontology, our objective was to dynamically control a WSN's energy consumption and design a set of SWRL reasoning rules. The complexity of dynamic control methods for WSN energy consumption means that a variety of control strategies have been proposed. In this paper, we only considered controlling the sample and communication frequencies, by dynamically switching the WSN between low frequencies, high frequencies, or sleep to save energy. Our rules are extendable, which means that we can conveniently add more complex or more reasonable inference rules.
# Proposal of an Adaptive Fault Tolerance Mechanism to Tolerate Intermittent Faults in RAM
## 3 Design of an Adaptive Fault Tolerance Mechanism
### Case Example: Intermittent Fault Detection
Note that the actual number of different encoders and decoders (i.e., \(m\) in the figure) is 40, one for the SEC-DED code and 39 for all the EPB3932 codes. This synthesis approach has the advantage of reducing the reconfiguration latency of the system because the change of the \"ECC Encoder\" module takes just one clock cycle, and the change of the \"ECC Decoder\" and the \"Syndrome Decoder\" takes another clock cycle. Indeed, as the multiplexers and demultiplexers in Figure 4 are controlled by signals generated by the logic circuitry in the \"Adaptive Memory Controller\" from \"ERROR PRONE BIT\" and \"ENCODING MODE\" signals (see Figure 2), to activate a different ECC, it is only necessary to change those signals, which is made in one single clock cycle. Thus, the reconfiguration latency required for this approach is two clock cycles plus the scrubbing latency, which depends on the memory size only. However, it has a notable drawback: all possible versions of the reconfigurable modules must be present in the device. This provokes high area overhead and (apparently) greater power consumption (see Section 4.5). Figure 5: “Adaptive memory controller”. Detail of “ECC Encoder” and “ECC Decoder” modules in the static version (synthesized in a non-reconfigurable FPGA). For simplicity, the other elements have been omitted. Second, we have synthesized the system in a reconfigurable FPGA (see description in Section 4.5). In this case, we have adapted the Plasma_ADAPTIVE model to allow this feature, adding an external module to manage the FPGA reconfiguration. We will refer to this version as \"dynamic\". Figure 6 shows the block diagram for this synthesis approach. The \"Reconfiguration Manager\" module in the figure is external to the microprocessor (although synthesized in the same FPGA), and the \"Configuration ROM\" module is a permanent memory (a flash card, for example) external to the FPGA that stores the bitstreams of all the modules that should be reconfigured in case of swap (i.e., \"ECC Encoder\", \"ECC Decoder\" and \"Syndrome Decoder\"). The mission of the \"Reconfiguration Manager\" is to read a bitstream file from the \"Configuration ROM\" and reconfigure a module in the FPGA, as indicated by the \"Adaptive Memory Controller\". This approach has the important advantage of reducing the complexity of the system considerably in terms of hardware usage, as only one version of the reconfigurable circuits is physically present in  
Figure 6: Block diagram of a dynamic version of Plasma_ADAPTIVE model (synthesized in a reconfigurable FPGA). the device. This greatly diminishes the area overhead. Its main drawback is the time consumed to reconfigure the modules, increasing the reconfiguration latency. In Section 4.5, we will discuss the overhead introduced by each of these synthesis approaches.
# Photonic-Assisted Scanning Receivers for Microwave Frequency Measurement
In this letter, we adopted the concept of deconvolution, which is commonly used in areas such as optical imaging and spectroscopy to break the Rayleigh criterion for point spread functions [22; 23], for scanning receiver systems for RF frequency measurement. Specifically, the matrix pencil method, which enables a precise recovery of frequency components via solving a generalized eigenvalue problem, was adopted for deconvolution due to its high robustness to noise [24]. The information of the input microwave signals is directly obtained from its reconstructed spectrum by means of deconvolving the system's output using matrix pencil method, thus improving the resolution and accuracy of the scanning system. As a proof of concept, a frequency measurement of both single-tone and multiple-tone microwave signals was conducted, and the results show that this method is effective in recovering the frequency information with high resolution and accuracy.
# A Dynamic, Cost-Aware, Optimized Maintenance Policy for Interactive Exploration of Linked Data
## 3 Proposed Methodology
### Scheduling Updates of Linked Data  
To update the view, for each transaction a maintenance task is created and accumulated in the task queue. The traditional policy is to update each job one by one regardless of the time consumption. However, in our approach we combine each small tasks into a larger maintenance job. The maintenance job will be executed only once. Our maintenance manager eliminates redundant update tasks. Therefore, reducing the size of the update stream. The maintenance job begins when the system has a freecycle. We hide the maintenance process from incoming queries to improve the performance of response time. We use CAMP [12; 33] to determine when a local view needs to be updated. CAMP identifies the highly dynamic sources in the Linked Data that are frequently updated and captures these changes to updates the local views. CAMP visits the data sources based on a preference score. Highly dynamic sources should be fetched at a high priority. The preference score is based on the history of the sources. The preference score is computed as shown in Equation (3):  
\[f_{PASU}(X)=\sum_{i=0}\frac{\Delta(\mathrm{X_{t_{lastupdate-1}}},\mathrm{X_{ t_{lastupdate}}})}{\mathrm{t_{lastupdate(X)-1}}}. \tag{3}\]  
In Equation (3), \(t_{lastupdate}\) is a function that returns the time when Linked Data was last updated. This function is recursive, as \(t_{lastupdate-1}\) returns the time before the last update and \(\Delta\) quantifies the amount of the changes such as addition and deletion of items. The maintenance policy indicates when the Linked Data source should be fetched. However, it is possible to retrieve the sources in the order of their assigned preference. The overall flow of the proposed algorithm is shown in Algorithm 1. The CAMP algorithm first generates the view for the incoming queries as shown in Line 1. As an input parameter, CAMP takes a query model \(Q\) and job scheduler, which indicates the time to schedule the resources. CAMP also chooses the appropriate policy to facilitate the performance of the maintenance policy \(P\). The maintenance manager releases a policy \(J^{r}\) to update the local views before the query evaluation in Line 6 and 7 in a maximum execution time \(K_{max}\).  
```
Input :Query \(Q\), Job Scheduler \(H\) Output : The entire maintenance process \(P\) // Creation of the maintenance process
1\(V^{q}=createView\left(Q\right)\) for\((i=0;i\leq Maintenance;i++)\)do
2\(P=MaintenancePolicy\left(Q,Views(V^{q})\right)\) // Update from the most dynamic sources
3\(updatecache\gets J^{r}\);
4\(J^{r}=P.release\left(JobScheduler\right)\) // Check that the recent changes occur in the LOD sources
5 if\(f(X_{i},t_{i})\) recent changes then
6\(updatecache\gets t_{i+1}\) ;
7
8 else
9 return\(f(X_{i},K_{max})=update(X_{i})\);
```  
**Algorithm 1**Change-Aware Maintenance Policy (CAMP).
## 2 Materials and Methods
### Feature Reduction and Main Clustering
#### 2.2.3 Umap  
The remaining dimensions are further reduced and transformed to a two-dimensional representation using the uniform manifold approximation and projection (UMAP) [41] method. Besides reducing computational effort with regard to a subsequent clustering, such a two-dimensional representation also allows for visualizing the corresponding results. In this context, UMAP is an efficient, non-linear manifold algorithm that preserves the local structure of the data, while trying to maintain their global structure as well. Alternative approaches, such as the widely used t-SNE [52; 53] or the LargeVis [54] algorithm, that are also capable of preserving locality, often fail to retain global information. In addition, they perform rather poorly on high-dimensional data, like the set of light spectra considered in this work, in terms of run time and scalability. For these reasons, UMAP was selected as the method of choice. Its most important parts, in particular those that ensure diversity of feature representations with regard to a subsequent clustering, will be presented in the following. For further details on the background and implementation of the UMAP algorithm, the interested reader is referred to the original paper of [PERSON] et al. [41].  
The cost function of the UMAP approach is given by  
\[C_{\text{UMAP}}=\sum_{i\
eq j}\underbrace{\
u_{ij}\log\left(\frac{\
u_{ij}}{ \omega_{ij}}\right)}_{\text{preserve locality}}+\underbrace{(1-\
u_{ij})\log \left(\frac{1-\
u_{ij}}{1-\omega_{ij}}\right)}_{\text{preserve globality}}, \tag{1}\]  
where \(v_{ij}\) denotes a metric for assessing the similarity between two light spectra \(i\) and \(j\) in the high dimensional space \(X\) given by  
\[\
u_{ij}=(\
u_{|i}+\
u_{|ij})-\
u_{|i}\
u_{|ij|}, \tag{2}\]  
with  
\[\
u_{|ji}=\text{e}^{\frac{-d(\
u_{i}x_{j})-\
u_{i}}{\sigma_{i}}}, \tag{3}\]  
and \(d(p,q)=\parallel q-p\parallel_{2}\) being the Euclidean distance, whereas \(w_{ij}\) evaluates their similarity in the lower dimensional embedded space \(Y\) and reads  
\[\omega_{ij}=(1+a\parallel y_{i}-y_{j}\parallel_{2}^{2b})^{-1}. \tag{4}\]  
Here, the first term of Equation (1), the so-called cross-entropy, preserves the local connectivity of the original data and brings similar light spectra closer together. The second term of the cost function, on the other hand, is intended to preserve the global structure of the data and moves dissimilar spectra further away from each other. This specific behavior of the UMAP cost function basically ensures that more diverse light spectra exhibit larger differences in the embedded space, even though, as stated previously, their absolute distance can no longer be used as a meaningful measure of true difference.