esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Characteristic Deformation and Associated Physical Processes at Piton de la Fournaise volcano
### Displacements of the Central Volcanic Cone  
Long- to intermediate-term deformation occurs over the course of days to months and affects the entire central volcanic cone. This type of deformation signal is thought to reflect pressure changes within the shallow magma reservoir, or changes of the hydrothermal system of the volcano [1; 15; 22; 31]. Long-term inflation is thought to reflect a pressurization of the shallow reservoir, indicating feeding from depth and accumulation of magma in the reservoir. This type of inflation, therefore, serves as an early eruption precursor alongside the occurrence of seismicity [21]. As the central volcanic cone of Piton de la Fournaise is equipped with a dense network of permanent GNSS stations in addition to more than 75 campaign GNSS benchmarks that are re-measured by the observatory staff upon each eruption (Figure 2), these longer-term pressure changes within the shallow reservoir are well observed and modelled by means of and based on ground instrumentation and measurements [1; 22; 31; 32; 33].  
Long-term surface deformation signals in InSAR data were observed, described, and modelled by [PERSON] [34] over a period between 2003 and 2007, who suggested that displacements of the volcano's central cone might be related to magma accumulation and withdrawal from the shallow reservoir. Long-term subsidence affecting the central cone was detected over a long period following the major 2007 eruption from SAR interferometry (Figure 5). This time the signals were interpreted as related to changes within the volcano's hydrothermal system [18]. However, surface subsidence patterns that affect the central cone and that are thought to reflect compression of the shallow reservoir or hydrothermal system are difficult to discriminate from topography-correlated atmospheric phase delays, especially in single time step interferograms [35; 36; 37]. MT-InSAR approaches potentially overcome these limitations and can help to better estimate and discriminate the atmospheric phase component, allowing for the retrieval of long-term deformation trends (Figure 5). However, compared to the dense ground-based deformation network operated by OVPF, SAR interferometry does not add much crucial information on the long- to intermediate-term displacements that only affect Piton de la Fournaise's well-equipped central volcanic cone.  
InSAR signals of small spatial extent, either of magmatic or hydrothermal origin, are linked to a shallow source underneath the central volcanic cone. At Piton de la Fournaise, large-wavelength signals that extent beyond the Enclos Fouque caldera and that could be related to the changes within a deeper magmatic reservoir or storage zone have never been observed using SAR interferometry. This does not mean that such large-wavelength displacements do not exist, but as most InSAR data are incoherent outside the Enclos Fouque caldera (Figure 4), this technique is not sufficient to pick up on any large signals that extent beyond the caldera, should they exist.
# Post-Quantum Delegated Proof of Luck for Blockchain Consensus Algorithm
## 4 Experiments
### Compression Efficiency Analysis  
In order to address the increased signature sizes associated with post-quantum cryptography algorithms such as Falcon and Dilithium, we incorporated a block compression algorithm to optimize the storage and transmission efficiency. The graph in Figure 2 illustrates the compression ratio achieved as the size of the transaction data increases. The compression algorithm was tested with varying data sizes ranging from 10,000 to 100,000 transactions. The corresponding compression ratios (%) were measured. As seen in Figure 2, the compression ratio increases as the data size grows, reaching a peak of 42.12% at 80,000 transactions. Beyond this point, the compression ratio stabilizes between 40% and 42%. This suggests that the compression algorithm is particularly effective for larger data sets, reducing storage requirements by over 40% in scenarios with large transaction volumes. This improvement in data compression allows for better scalability of the blockchain network by accommodating the larger signature sizes introduced by post-quantum algorithms. Moreover, the compression helps to offset the increase in storage space required for post-quantum signatures, ultimately enhancing the blockchain's efficiency without compromising security.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Sign/s** & **Verify/s** \\ \hline ECC 256 & 46,900.2 & 16,063.7 \\ \hline ECC 384 & 1106.3 & 1370.7 \\ \hline Falcon-512 & 4107.6 & 24,078.9 \\ \hline Falcon-1024 & 2046.4 & 11,867.8 \\ \hline Dilithium2 & 15,197.0 & 41,114.4 \\ \hline Dilithium5 & 7583.7 & 15,479.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of ECDSA, Falcon, and Dilithium for signing and verification.
## 2 The Proposed Methodology
### Experimental Setting  
Segmentation experiments were conducted on Wake Forest Baptist Medical Center's high-performance computer cluster. We took advantage of 16 GB Nvidia Tesla P100 PCI-E GPU (Nvidia, Santa Clara, CA, USA). We used the Deep Learning Toolbox of MATLAB R2018b (MATLAB 9.5, MathWorks, Natick, MA, USA, 2018) to implement the U-Net architecture, and Dice coefficient [26; 27] to evaluate the performance of the segmentation.  
Figure 5: The image stitching result for three of the selected frames. The stitching process expands the original field-of-view of the input frames. (**a–c**) Input images, (**d**) stitching result.  
Figure 6: Example result of post-processing steps (**a**) original image generated by the composite image generator; (**b**) image cropped by applying converting to gray level, blurring with a Gaussian filter, gray level thresholding and identification of the coordinates of the area that is needed for subsequent processing; (**c**) image enhanced by a de-hazing technique.  
Minimization of the pixel-wise cross-entropy loss is achieved via stochastic gradient descent using the Adam optimizer [28] and learning rate of 0.001. We used mini-batches of size 16 for U-Net. Early stopping was employed to avoid over-fitting [29; 30].  
In this study, we generated 100 composite images from the corresponding videos in Dataset 2. The videos in Dataset 2 were categorized into different eardrum abnormalities by four expert physicians (three ENT specialists and one pediatrician) based on physical examination and evaluation of the patient at the time of the encounter.  
Base process: In the base-process, we stitch all the frames of the video data regardless of their content to create composite images. The base process does not incorporate neither a human intervention nor a computerized technique to process or analyze video frames before stitching step, i.e., every frame is used in the resulting composite image. Unlike SelectStitch, the resulting composite images do not benefit from segmentation-based frame selection nor post-processing.  
Two rounds of evaluation: The composite images produced by SelectStitch were compared by three ENT specialists against the base processes in terms of their diagnostic capabilities. This reader study provides an evaluation of a new measurement or analysis method [31]. In order to do this, three ENT specialists graded each composite image using one of the three categories: 1 (Poor) refers to a situation that he/she cannot diagnose well or the image has serious problems; 2 (Moderate) refers to an image which is poor quality but still usable for diagnosis; and finally 3 (Good) refers to a good quality composite image which can be used for diagnosis. Doing these measurements with three ENTs provided us a measure of inter-reader variability, and repeating the study in a second round provided us an estimate of intra-reader variability.
# A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks
## 5 Performance Evaluation
### Metrics and Simulation Parameters  
We first define some metrics to measure the performance of NCHARQ.  
1. Transmission Redundancy : the metric is defined as in Equation (14): \[\eta=\frac{number\ of\ sent\ packets\ in\ all\ nodes}{number\ of\ original\ data\ packets}\] (14) This metric indicates the average sending packets for transmitting a data packet successfully and \(\eta\geq 1\). A large \(\eta\) means the system is inefficient and thus more energy consumption is needed. The main source of energy consumption for underwater applications comes from the sending data process, and we mainly measure the energy consumption by calculating the number of sending packets under the same transmitting sound level. The larger the number of average sending packets for transmitting one data packet successfully, the more energy that the system consumes.
2. Average Delay: the metric is defined as in Equation (15): \[\phi=\frac{time\ delay\ of\ sending\ all\ the\ packets}{number\ of\ original\ data\ packets}\] (15)This metric indicates the average end-to-end delay from the sender to the destination node for a single data packet transmission. A small \(\phi\) means the system has a high throughput and better channel utilization.  
The underwater acoustic channel is modelled by Matlab as a binary erasure channel (BEC). The relevant specifications and parameters are presented in Table 2.  
We assume the nodes are distributed in a cubic box of 2500 \(\times\) 2500 \(\times\) 2500 m\({}^{3}\) as shown in Figure 4. At the initial state (time 0), Node 0 (source node) is located at (0, 0, 0) and Node \(N\) (destination node) is located at (2500, 2500, 2500). Thus, the total transmission range is about 4.33 km. The relay nodes are distributed uniformly in a straight line between Node 0 and Node \(N\). All the nodes in the network can move randomly within their mobility range. Thus, the packet error rate \(P_{pac}\) changes randomly with time passing by. We choose a relatively small data packet size (50 Bytes) and original data packet number (100) in one block here, so our parameters can adapt to a high-dynamic underwater environment.
## 3 Experiments and Results
### Drift-Elimination Experiment
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **MAPE (\%)** & **RMSE (V)** & **MAE (V)** \\ \hline RNN & 1.3477\% & 0.0421 & 0.0351 \\ GRU & 1.0650\% & 0.0358 & 0.0286 \\ AGRU & 0.3234\% & 0.0109 & 0.0085 \\ Transformer & 0.2634\% & 0.0085 & 0.0069 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of different algorithms on simulated signals with Gaussian noise. Figure 6: Drift-elimination results of different algorithms for simulated signals with Gaussian noise. (**a**) RNN. (**b**) GRU. (**c**) AGRU. (**d**) Transformer. This study evaluated the model's efficacy in eliminating drift under stress-induced strain conditions. Before the experiment was conducted, parameters were configured in the control software of the electronic universal testing machine, and the parameters included specimen shape, test speed, control mode, and target strain value. These parameter settings enabled the electronic universal testing machine to adhere to a predefined program, gradually increasing the tensile force until the specimen reached the predetermined strain value. Once the high-temperature extensometer detected that the strain reached the initial set value, the electronic universal testing machine temporarily maintained the force and stabilized the strain at this value for a duration before incrementally increasing the tensile force to the subsequent strain value. After the test was started, the voltage was recorded without applying force. Then, the electronic universal testing machine progressively increased the tensile force, stretching the nickel-based high-temperature alloy substrate to 500 \(\mathrm{\mu\epsilon}\), 1000 \(\mathrm{\mu\epsilon}\), and, finally, 1500 \(\mathrm{\mu\epsilon}\). Each time the strain reached a predetermined value, the electronic universal testing machine kept the current force and maintained the strain for 90 s. When the strain value reached 1500 \(\mathrm{\mu\epsilon}\), the machine kept the force for a period of time, and the test was ended. In Figure 8, the black curve represents the recorded voltage, whereas the red curve depicts the voltage after drift elimination. The experimental results indicated that drift was effectively mitigated under the application of force. Figure 7: Signals at different temperatures and drift elimination. (**a**) Ambient temperature of 23 \({}^{\circ}\)C. (**b**) Ambient temperature of 100 \({}^{\circ}\)C. (**c**) Ambient temperature of 170 \({}^{\circ}\)C. (**d**) Ambient temperature of 210 \({}^{\circ}\)C.  
The Transformer model was trained using the deep learning framework PyTorch 1.13.1 in Python 3.7 on a personal computer equipped with an AMD Ryzen7 5800H CPU 3.20 GHZ (Advanced Micro Devices, Inc., Santa Clara, CA, USA), an NVIDIA GeForce RTX 3060 GPU (NVIDIA Corporation, Santa Clara, CA, USA), and 16 GB RAM (Micron Technology, Inc., Boise, ID, USA).
## 4 Results and Discussion
### Actual Detection of Debonding Defect Based on Air-Coupled Ultrasonic Testing
Through previous research [18], it is clear that when P3 polyphase coded signals are applied to actual ACUT, in order to obtain higher SNR and amplitude of transmitted signal, polyphase coded signal with higher bit number and 3-cycles per sub-pulse should be selected, and the length of polyphase coded signals need to meet the hardware requirement of ACUT system (Section 3.2) (that is, when the length of polyphase coded signal exceeds 100 bits, the pulse compression signal will be distorted and the effect of pulse compression will be poor). Therefore, when 33-bits P3 polyphase coded signal with 3-cycles per sub-pulse is selected, good processing effect can be obtained, as shown in Figure 11. The compressed signal of Figure 11a is collected in intact area, and the compressed signal of Figure 11b is collected in defect area. As can be seen from comparison between Figures 10 and 11a, the amplitude level and SNR of transmission signal are greatly improved (the amplitude of transmission signal increased from 0.07 V to 2.08 V, and the SNR (Signal-to-noise ratio) of transmission increased from 1.0 dB to 9.2 dB). Comparing Figure 11a,b, it can be seen that the  
Figure 10: The original signal for detecting honeycomb sandwich composite material. transmission initial wave of P3 polyphase coded pulse compression signal significantly attenuate in the defect area, which is consistent with the simulation result as shown in Figure 9. In actual detection, the distance between transducer and sample is much larger than that of simulation model, so the transmission initial wave arrives later, but the law and result are consistent, that is, P3 polyphase coded pulse compression signal has a good response to the debonding defect of honeycomb sandwich composite. Therefore, the characterization of debonding defect of honeycomb sandwich composite based on P3 polyphase coded pulse compression signal is reasonable in the actual ACUT detection.
###### Abstract  
Biofilms pose a major challenge to the food industry, as they develop on both biotic and abiotic surfaces and contribute to the persistence of antibiotic-resistant pathogens. This study evaluated the antimicrobial and antib Biofilm potential of _Satureja thymbra_, _Thymus capitatus_, and _Origanum hirthum_ essential oils (EOs), their main components (thymol, carvacrol, \(p\)-cymene, and \(\gamma\)-terpinene), and ethanolic and ethyl acetate extracts of the water-steam distillation residue of _T. capitatus_ (WSTRTc). Minimum Inhibition Concentration (MIC) and Minimum Bactericidal Concentration (MBC) values of EOs and WSTRTc extracts ranged from 0.6 to 56.8 mg/mL. The corresponding MIC values of the main components ranged in lower values (0.5-2.4 mg/mL). Minimum Biofilm Inhibition Concentration (MBIC) and Minimum Biofilm Eradication Concentration (MBEC) were also determined. MBIC values for the EOs ranged from 0.6 to 4.7 mg/mL against biofilms of _Escherichia coli_, _Listeria monocytogenes_, _Proteus mirabilis_, and _Salmonella_ Enteritidis. Significant antibiotic/im activity at concentrations \(>\) 61 mg/mL and \(>\) 20 mg/mL was recorded by the ethanolic and ethyl acetate WSTRTc extracts, respectively, but no activity against _L. monocytogenes_ and _P. mirabilis_ biofilms was documented. These findings highlighted the efficacy of EOs and extracts derived from Greek herbs, suggesting their application in the food and pharmaceutical industry as natural antimicrobials and biofilm inhibitors.  
antibifilm activity; pathogenic bacteria; _S. Hymbra_; _T. capitatus_; _O. hirthum_
## 4 Results Analysis
### Lift Force Variation  
The lift generated by the blade is a key parameter of a lift-type VAWT, because it determines the magnitude of the tangential force that causes the turbine rotation. One of the effects of the new VP-control changes the lift of the blade. The variation of the local lift coefficient during a complete rotation in four TSRs is illustrated in Figure 10, which reveals that, when the TSR = 4, the lift coefficient fluctuates intensively during a whole range of azimuthal angles, whether in the VP-VAWT or FP-VAWT, and a much lower value is observed at around 90\({}^{\circ}\) and 270\({}^{\circ}\). This is because the AoA values in the large region around 90\({}^{\circ}\) and 270\({}^{\circ}\) are still larger than the stall static angles, which leads to the occurrence of the stall and then a decreased lift coefficient. As can be observed in Figure 10, the VP-blade does not create a larger maximum lift coefficient than the FP-blade, but it does have a wider stall zone.  
Figure 9: Angle of attack (AoA) as a function of azimuthal angle in variable pitch (VP) and fixed pitch (FP) VANTs. (**a**) TSR = 4; (**b**) TSR = 4.5; (**c**) TSR = 5; (**d**) TSR = 5.5.  
The reason is that the new VP-approach increases the AoA and creates a wider azimuthal zone having a larger AoA than the stall value, but the maximum effective AoA is not changed. All of these phenomena indicate that the VP-approach designed for the rated TSR is not suitable to be applied to improve the performance of the VAWT at much lower TSRs.  
Figure 10: Lift coefficient as a function of azimuthal angle in VP-VAWTs and FP-VAWTs. **(a)** TSR = 4; **(b)** TSR = 4.5; **(c)** TSR = 5; **(d)** TSR = 5.5.  
At TSRs larger than 4.5, stall does not occur; the curve of the lift coefficient changes smoothly, and shows an arched shape in the upwind or downwind half-cycles in the FP-VAWT. In the same conditions, the stall does not occur in the VP-VAWT either; larger lift coefficients are obtained almost in the whole rotation zone except four at positions, namely: 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\). Finally, the novel approach achieves a larger zone with the largest lift coefficients in the upwind half-cycle and two new larger maximum coefficients in the downwind half-cycle. A comparison of the increase among the TSRs reveals that the increase of the lift is in the downstream region as the TSR increases. Even when the TSR = 5.5, the lift is affected by the AoA; as shown in Figure 9, the VP-blade produces lower lift than the FP-blade in an extensive zone of the downwind region.
## 5 Results and Discussion
### Full-Band Modeling and Quasi-Harmonicity
The complexity of SM is \(\mathcal{O}\left(N\log N\right)\), which is the complexity of the FFT algorithm for size \(N\) inputs. ESPRIT estimates the parameters of EDS with singular value decomposition (SVD), whose algorithmic complexity is \(\mathcal{O}(L^{2}+K^{3})\) for an \(L\) by \(K\) matrix (frame size _versus_ the number of sinusoids). Adaptation in eaQHM is an iterative fit where each iteration \(i\) requires running the model again as described in Section 3. For each iteration \(i\), eaQHM estimates the parameters with least squares (LS) via calculation of the pseudoinverse matrix using QR decomposition. The algorithmic complexity of QR decomposition is \(\mathcal{O}\left(K^{3}\right)\) for a square matrix of size \(K\) (the number of sinusoids). Adaptation of the sinusoids in eaQHM can result in over-fitting. The amplitude and frequency modulations capture temporal variations inside the frame such as transients and instrumental noise around the partials. However, adaptation must not capture noise resulting from sources such as quantization, which is extraneous to the sound. Ideally, the residual should contain only external additive noise without any perceptually important information from the sound [17]. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Algorithmic Complexity**} \\ \hline  & **SM** & **EDS** & **eaQHM** \\
**Complexity** & \(\mathcal{O}\left(N\log N\right)\) & \(\mathcal{O}(L^{2}+K^{3})\) & \(\mathcal{O}\left(ik^{3}\right)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of algorithmic complexity in Big-O notation. The table presents the complexity as a function of the size of the input \(N\), \(L\), and \(K\) and the number of iterations \(i\). See text for details. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Number of Real Parameters \(p\) Per Sinusoid \(k\) Per Frame \(m\)**} \\ \hline  & **SM** & **EDS** & **eaQHM** \\
**Analysis** & \(p=3\) & \(p=4\) & \(p=4\) \\
**Synthesis** & \(p=3\) & \(p=4\) & \(p=3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the number of real parameters \(p\) per sinusoid \(k\) per frame \(m\) for the analysis and synthesis stages of the SM, EDS, and eaQHM. The table presents the number of real parameters \(p\) to estimate and to resynthesize each sinusoid inside a frame.
## 3 Requirement Analysis and System Overview
### System Pipeline  
According to the literature analysis related to the quality of education, the questionnaire can be designed from two aspects: students and supervisors. By putting forward various attributes related to students' scientific research, the data mining methods are used to find out which attributes are related to whether students can publish academic achievements, so as to obtain the main factors affecting the quality of students' academic ability training. The workflow of this paper is shown in Figure 1. Firstly, according to the established research objectives, we designed a questionnaire with 20 influencing factors in five modules: basic information of students, academic participation of students, basic information of supervisors, and academic guidance of supervisors and academic achievements (evaluation basis) (**R1**). After pre-processing the questionnaire data, the algorithms of Lasso, Elastic Net, and Orthogonal Matching Pursuit are used to select the features of the original data of the second-year and third-year graduate students, to find out the most important factors affecting the publication of paper (**R2**). Then, we use the classification algorithms such as support vector machine, naive Bayes, logistic regression, random forest, and multi-layer perceptron to train the classifier to verify the effectiveness of feature selection while predicting the future academic publications of first-year graduate students (**R4**). In addition, we provide a set of visual and interactive interfaces that allow users to visually compare algorithm results and interactively conduct analysis to assess suggestions for improving graduate academic ability (**R3,R4**).