esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Tortuosity Estimation of Nerve Fibers and Retinal Vessels in Ophthalmic Images
## 4 Experimental Results
### The Effectiveness of Curvilinear Structure Enhancement for Tortuosity Grading  
Figure 6 shows intuitively why the proposed enhancement approach can improve the tortuosity estimation. Where Figure 6 is the raw image, Figure 6, are the 3D image after exponential curvature estimation and the 2D curvature image map after projection, respectively. Similarly, Figure 6 is an enhanced image obtained by our curvature enhancement method from the original image. Figure 6, are the results of exponential curvature estimation based on the enhanced image. The traditional method of tortuosity metrics of the original image is to calculate on the pre-segmented image of the original image. In contrast, our proposed tortuosity estimation is based on the curvature map. The tortuous structure has a higher value in the curvature map, while the background pixel is close to zero. Compared with Figure 6, we can see that Figure 6 is greatly affected by the background noise of the original image. Figure 6 has the clearer structure and background.  
\begin{table}
\begin{tabular}{l r r r r} \hline \hline
**Metrics** & **Healthy** & **Dry Eye** & **Diabetes** & **Dry Eye and Diabetes** \\ \hline AC & 5.73 \(\pm\) 4.89 & 5.56 \(\pm\) 3.43 & 4.12 \(\pm\) 2.23 & 4.72 \(\pm\) 3.69 \\ WAC (\(\times 10^{3}\)) & 0.47 \(\pm\) 0.18 & 0.48 \(\pm\) 0.17 & 0.50 \(\pm\) 0.20 & 0.52 \(\pm\) 0.26 \\ AAC & 14.93 \(\pm\) 10.18 & 12.74 \(\pm\) 7.95 & 13.51 \(\pm\) 8.23 & 13.36 \(\pm\) 9.75 \\ ACR & 0.98 \(\pm\) 0.01 & 0.97 \(\pm\) 0.01 & 0.97 \(\pm\) 0.01 & 0.97 \(\pm\) 0.01 \\ CHD & 73.31 \(\pm\) 20.63 & 69.16 \(\pm\) 19.06 & 62.02 \(\pm\) 16.80 & 63.74 \(\pm\) 21.72 \\ CUR & 74.85 \(\pm\) 21.03 & 70.92 \(\pm\) 19.48 & 64.11 \(\pm\) 17.09 & 65.47 \(\pm\) 21.63 \\ DCI (\(\times 10^{4}\)) & 0.33 \(\pm\) 0.26 & 0.37 \(\pm\) 0.35 & 0.44 \(\pm\) 0.42 & 0.42 \(\pm\) 0.32 \\ ICM & 5.82 \(\pm\) 1.64 & 5.60 \(\pm\) 1.60 & 5.13 \(\pm\) 1.50 & 5.23 \(\pm\) 1.68 \\ MAC & 0.64 \(\pm\) 4.16 & 0.80 \(\pm\) 3.29 & 0.05 \(\pm\) 3.67 & 0.36 \(\pm\) 3.90 \\ SQC (\(\times 10^{3}\)) & 0.30 \(\pm\) 0.14 & 0.32 \(\pm\) 0.14 & 0.36 \(\pm\) 0.15 & 0.36 \(\pm\) 0.17 \\ SCC (\(\times 10\)) & 0.36 \(\pm\) 0.09 & 0.36 \(\pm\) 0.07 & 0.35 \(\pm\) 0.08 & 0.34 \(\pm\) 0.07 \\ TC (\(\times 10^{2}\)) & 0.42 \(\pm\) 0.22 & 0.41 \(\pm\) 0.13 & 0.43 \(\pm\) 0.17 & 0.45 \(\pm\) 0.22 \\ TD & 0.09 \(\pm\) 0.03 & 0.10 \(\pm\) 0.01 & 0.10 \(\pm\) 0.01 & 0.10 \(\pm\) 0.01 \\ Kexp & 0.17 \(\pm\) 0.06 & 0.21 \(\pm\) 0.04 & 0.24 \(\pm\) 0.07 & 0.25 \(\pm\) 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Different metrics in control subjects and other diseases cases.  
We also obtained the tortuosity grading results after removing the enhancement step by the proposed method, referred as Kexp in Table 2. It can be observed that the tortuosity grading results of Kexp+ are better than the Kexp one. The proposed enhancement method is helpful to boost the final results with 0.023 and 0.014 in accuracy on both two fiber datasets.
## 3 Proposed Cost-Effective Auto-Scaling (CEAS) Framework
### Monitoring and Provisioning Amazon EMR Clusters for Auto-Scaling
#### 3.3.3 Auto-Scaling EMR Clusters  
This subsection details how the proposed CEAS framework saves processing time for the jobs based on real-time workloads in the AWS.  
To process jobs or to interact with the software deployed over the EMR cluster, a secure connection must be established to the master node, then the interfaces and tools can be accessed. First, the input data is processed by using the MapReduce utility on the Hadoop cluster. The data is then stored in an HDFS file system or Amazon S3. Finally, the stored data is passed for processing in a sequential manner and the processed output is written to a specific HDFS or S3 location.  
To successfully run the allocated jobs, Amazon EMR cluster follows the following two steps:  
* _Step 1 (STARTING)_: The applications deployed on a Hadoop cluster is provisioned by using EMR. The cluster state is at STARTING point.
* _Step 2 (BOOTSTRAPPING)_: Additional applications, if required, can be installed on the cluster. This is known as BOOTSTRAPPING process.
* _Step 3 (RUNNING)_: The cluster runs all the steps sequentially after the above two steps have completed. The cluster state is thus called RUNNING.  
Upon completion, the cluster goes to a wait or shutdown state. In case of a cluster failure, it terminates all the EC2 nodes and deletes the data saved on the slave nodes.
# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 3 Physical Basis of Multi-Sensor Precipitation Estimation
### Cloud-Motion-Based Method
The forecast at the analysis time is then determined by averaging the forward- and backward-propagated PMW estimates, weighted inversely according to their error variances. However, unlike the GSMaP_MVK algorithm, this KF model relies solely on propagating PMW estimates for precipitation forecasting without considering the changes in the pattern or intensity of precipitating systems. The GPM IMERG algorithm also utilizes the KF model [30,112]. Additionally, [PERSON] et al. [113] proposed the REFAME method, which incorporates a forward-adjusted advection of microwave estimates to construct high-resolution precipitation analyses. In this algorithm, PMW estimates are propagated along motion vectors defined by 2D cloud tracking while accounting for intensity changes during the propagation process.
## 4 Discussion
The only technological platform whose name has been mentioned by more than one paper (in fact, 10 of them) was Unity. [RQ7]
9. The analyzed literature brought a considerable scientific contribution, primarily consisting in the introduction of new applications of machine learning and gamification, and their evaluation, but also in the introduction of new methods, techniques, or procedures, enhancing machine learning and/or gamification. Nonetheless, further progress in the development of the field requires standardization of evaluation dimensions and scales as proposed by the GATUGU framework [40]. [RQ8]
10. The combination of machine learning and gamification was found to be effective in multiple applications, as indicated by the vast majority of papers that reported positive evaluation results. This, however, is not enough to confirm that the vast majority of machine learning and gamification applications are effective, as the reported results may be affected by publication bias, with the researchers and journals more inclined to publish positive results than negative ones and statistically significant ones over non-significant ones [41]. [RQ9]
11. The scientific discussion regarding the combination of machine learning and gamification still seems to be in an early stage of its development, as only one publication of the analyzed set ([32]) received almost 100 citations (according to Web of Science), and only nine others surpassed the threshold of 10 citations. This is also indicated (and, to some extent, caused at the same time) by the small number of papers dedicated to comparing and reviewing existing applications--this, however, is naturally limited by the wide spread of areas at which the described applications were addressed. [21]
## 3 Result Analysis of the Proposed MPA
### RLC Lumped Element Extraction and Equivalent Circuit of the Proposed MPA Using ADS  
The equivalent RLC model of the proposed MPA using Agilent ADS software is presented in Figure 8. The values of R, L, and C have been chosen so that the equivalent impedance of the designed antenna is reasonably matched with the characteristic impedance (50 \(\Omega\)) of the transmission line. An optimal impedance match between the antenna and the transmission line is required to transfer maximum power from the input port of the antenna to its antenna structure [35]. For the resonance frequencies of 9 GHz and approximately 13 GHz, resistance \(R1=52.56\)\(\Omega\), inductance \(L1=49.8\) pH, and capacitance \(C1=6.369\) pF are assigned to the parallel RLC circuit, along with resistance \(R2=51.88\)\(\Omega\), inductance \(L2=135.8\) pH, and capacitance \(C2=1.10\) pF as presented in Figure 8. As the antenna input port, the input terminal source in ADS is configured as a terminal block (Term is G) with a 50 \(\Omega\) characteristic impedance and acts as an input port for the antenna. The values of R, L, and C are considered in such a manner that the equivalent circuit impedance is equal to the antenna input port impedance. The impedance matching performance is observed in ADS using the S-parameter block. In the S-parameter block, the intended frequency range is swept from 7 GHz to 17 GHz in 10 KHz increments. The plot of the return loss response (dB (S (1,1)) is chosen for plotting the resonant circuit's output result. The resonant circuit yields a resonant frequency of 9.0 GHz with a return loss of \(-33\) dB, and at 13 GHz with a return loss of -35 dB, as shown in Figure 9.  
Figure 8: Circuit diagram of a parallel RLC circuit for resonant frequency (refer to CST simulation) using ADS Agilent simulation.  
Figure 9: Simulated result using ADS Agilent equivalent circuit.
## 3 Results and Discussion
### Human Health Risk Assessment (HHRA)
In this study, the uncertainty of the human health risk assessment could relate primarily to the concentrations of CLHCs in groundwater and the resulting uncertainties in the model evaluation of their outdoor and indoor air concentrations. Health risks were calculated from the maximum concentrations of CLHCs in the groundwater, determined in one sampling campaign, which corresponded to the worst-case scenario. However, it is known that the concentrations of CLHCs in groundwater fluctuate seasonally, spatially and vertically, making an impact on the exposure concentration [134, 8]. As stated above, a significant exposure route was indoor breathing, with a modelled indoor air concentration of individual CLHCs using the Johnson-Ettinger transport model incorporated in the RISC software, version 5.0. Research has documented large spatial variability in soil gas concentrations of VOCs under buildings, which is less observable in indoor air concentrations due to efficient air mixing [50]. However, on the other hand, there are significant temporal fluctuations of the indoor air concentrations of VOCs over time periods of hours to months [135]. These temporal fluctuations are dependent on several factors, e.g., building differential pressure, temporal changes of soil moisture, temperature and groundwater table, building ventilation, meteorological conditions, etc., [136]. Therefore, it is almost impossible to consider a single value of the indoor air concentration of VOCs when assessing their intake via the inhalation route. It should be also emphasised that the values of some toxicity parameters for _cis_-DCE are not available, so its contribution to the overall health risk might be underestimated.
###### Abstract  
The proliferation of battery-operated end-user electronic devices due to technological advancements, especially in medical image processing applications, demands low power consumption, high-speed operation, and efficient coding. The design of these devices is centered on the Application-Specific Integrated Circuits (ASIC), General Purpose Processors (GPP), and Field Programmable Gate Array (FPGA) frameworks. The need for low-power functional blocks arises from the growing demand for high-performance computational units that are part of high-speed processors operating at high clock frequencies. The operational speed of the processor is determined by the computational unit, which is the workhorse of high-speed processors. A novel approach to integrating Very Large-Scale Integration (VLSI) ASIC design and the concepts of low-power VLSI compatible with medical image compression was embraced in this research. The focus of this study was the design, development, and implementation of a Power Delay Product (PDP) optimized computational unit targeted for medical image compression using ASIC design flow. This stimulates the research community's quest to develop an ideal architecture, emphasizing on minimizing power consumption and enhancing device performance for medical image processing applications. The study uses area, delay, power, PDP, and Peak Signal-to-Noise Ratio (PSNR) as performance metrics. The research work takes inspiration from this and aims to enhance the efficiency of the computational unit through minor design modifications that significantly impact performance. This research proposes to explore the trade-off of high-performance adder and multiplier designs to design an ASIC-based computational unit using low-power techniques to enhance the efficiency in power and delay. The computational unit utilized for the digital image compression process was synthesized and implemented using gpdk 45 nm standard libraries with the Genus tool of Cadence. A reduced PDP of 46.87% was observed when the image compression was performed on a medical image, along with an improved PSNR of 5.89% for the reconstructed image.  
application-specific integrated circuits; low-power VLSI; error-tolerant adder; medical image compression; peak-signal-to-noise-ratio; vedic arithmetic; Iterative carry save adder; power delay product optimized multiplier
# A Method to Optimize Routing Paths for City-Pair Airlines on Three-Layer Air Transport Networks
## 4 Experiments
### Analysis of Aviation Networks
#### 4.1.1 Air Route Network  
In this paper, we focus on the Chinese air route network \(G_{ARN}\), where there are \(W=1547\) waypoints and \(R=2632\) air route segments. In \(G_{ARN}\), the average degree is merely 3.38, and the largest degree is only 13.
# Emotion Classification Algorithm for Audiovisual Scenes Based on Low-Frequency Signals
## 2 Materials and Methods
### LFS-Based Sentiment Classification Algorithm for Audovisual Scenes
#### 2.2.3 Audio Signal Feature Extraction  
Feature extraction from audio signals is an essential module in audio scene classification, and the selection of audio features is often analyzed from the time domain, frequency domain, or cepstral domain. As shown in Table 3, the 5.1 audio format contains unique content and characteristics in each channel, including movie dialogues and background music. Therefore, the initially selected parameters include 50 time-domain, frequency-domain, and cepstral-domain eigenvalues that are relevant to speech emotion recognition and music emotion recognition fields. Due to the application environment for the 5.1 sound format movie scene emotion classification being based on online real-time processing, the audio signal to be processed needs to be windowed with the calculated movie clip length, i.e., how long the film clip is, and a determination of the type of emotion is given. Based on the statistical results of movie clip lengths and considering the clip length that can best represent a certain emotion type, a total of 11 window lengths, ranging from 0.5 s to 10 s, were determined for segmenting the movie scene clips. Then, the librosa, AudioSegment, and pyAudioAnalysis audio feature extraction toolkits were used to calculate the values of various audio eigenvalues for each clip, and the mean value, standard deviation (STD), and maximum-minimum difference of each feature parameter were calculated. With a full set of six channels, each sample in the dataset contains 900 features, but if only the SW channel features are retained, there are only 150 features per sample.  
Mel-frequency cepstral coefficients (MFCCs) are an important feature parameter in the field of audio signal processing and the focus of the experiments in this paper, and have good robustness in classifying the emotions of audio scenes [30]. An MFCC is an inverse spectral parameter extracted in the frequency domain of the Mel scale, which describes the nonlinear auditory characteristics of the human ear and has good recognition performance, even when the signal-to-noise ratio is reduced [31]. The main steps in the feature extraction process are preprocessing of the audio signal, fast Fourier transform (FFT), passing through a Mel filter bank, taking logarithms, and discrete cosine transform (DCT) [32]. After these steps, the M-order cepstrum parameters are obtained, from which the n-dimensional parameters are taken as the MFCC features of the audio signal. This paper used the librosa package to extract the 24-dimensional MFCC, where the sampling rate (SR) was set to 22,050 and n_mfcc was set to 24.
## 2 Motor Fault Diagnosis and Prediction
### Motor Current Signature Analysis  
Motor current signature analysis (MCSA) is a widely used condition monitoring technique that evaluates the frequency components of stator current signals to infer motor health. It is particularly attractive due to its non-intrusive nature, requiring no mechanical disassembly or additional instrumentation beyond current sensors [17].  
The presence of faults, electrical or mechanical, introduces characteristic frequency components into the motor current. These frequencies arise from phenomena such as rotor asymmetry, load unbalances, or mechanical misalignment, which disturb the magnetic field and alter the current waveform. By applying spectral analysis, often via Fast Fourier Transform (FFT), these fault signatures can be isolated and monitored over time [18].  
The effectiveness of MCSA lies in its ability to detect both localized defects and systemic issues. However, its accuracy depends on consistent operating conditions, as variations in load or supply can affect signal interpretation. Despite this, it remains a foundational method in motor diagnostics and is particularly effective when combined with data-driven analysis techniques [9; 14].
# Bounce: A High Performance Satellite-Based Blockchain System
## 3 Bounce Protocol  
### Components  
The _Bounce_ system Figure 1 consists of (i) several satellites each carrying a _Bounce Unit_ consisting of a Read-Only memory and hardware that performs communication, cryptographic primitives, and basic computation, preferably embedded in a tamper-resistant package having the functionality of a hardware security module [33]; (ii) a set of terrestrial Sending Stations that package user transactions into blocks and send blocks to satellite Bounce Units (iii) a set of Broadcast Ground Stations that communicate with Sending Stations and satellites; (iv) a communications infrastructure allowing the Broadcast Ground Stations to communicate among themselves and with the other terrestrial components and to send information to the worldwide users of the blockchain; and (v) a Mission Control component, which assigns slots to satellites and assigns roles to the terrestrial components.
# Effect of Fireproof Coatings on the Post-Fire Behavior of CFRP Composite Sheets
###### Abstract  
This study examines the effect of two self-developed novel fireproof coatings on the fire resistance of carbon fiber-reinforced polymer (CFRP) composite sheets. The post-fire flexural properties were chosen as important indicators of fire resistance. This research involves two distinct fire conditions, the standard fire condition and the large-space fire condition, to simulate general interior fires and large-space fires. The post-fire flexural performance of CFRP sheets coated with two different self-developed fireproof coatings was evaluated through three-point bending tests after fire exposure. The experimental findings demonstrated a significant reduction of up to 80% in the post-fire flexural properties of CFRP sheets without fireproof coatings. However, CFRP sheets coated with fireproof coatings exhibited a substantial enhancement in their post-fire flexural properties. Under the large-space fire condition, the fire-resistance duration of CFRP sheets coated with fireproof coatings of at least 25 mm thickness exceeded 2 h, satisfying the requirements of GB50016-2014 fire-resistance class III (columns), class I (beams), and class I (floor slabs), respectively. Under the standard fire condition, CFRP sheets covered by fireproof coatings of 35 mm thickness exhibited a fire-resistance duration of 0.75 h, meeting the requirements of GB50016-2014 fire-resistance class IV (columns), class IV (beams), and class III (floor slabs), respectively.  
fireproof coatings; carbon fiber-reinforced polymer; fire resistance; post-fire flexural properties; large-space fire condition; standard fire condition
## 2 System Structure and Coupling Mechanisms
### Coupling Mechanism  
The control strategy of a parallel-series system is crucial to maintaining the stability of the system during steady-state operation and fault periods. The coupling mechanism for series and parallel connections is analyzed separately.  
In the series-connected part, that is the HVDR, if the network operates under a constant DC voltage (CDV) mode, the sum DC voltage of HVDR in the string is \(V_{dc}\), which is determined by the onshore converter and is a fixed value. The output voltage is shown as follows:  
\[\begin{cases}V_{out\_N}=\dfrac{V_{dc}}{n}\\ P_{avg}=\dfrac{\sum\limits_{j=1}^{n}P_{j}}{n}\Rightarrow V_{outj}=\dfrac{P_{j} }{P_{avg}}V_{out\_N}\end{cases} \tag{1}\]  
The nominal output voltage of DR \(V_{out\_N}\) is obtained by dividing \(V_{dc}\) by N. The average output power \(P_{avg}\) is obtained by dividing the sum of the output power of all wind turbine generators in the branch by the number of wind turbines \(n\).  
When a power imbalance occurs, the unit's output voltage with the higher power will exceed its rated value. In addition, once a HVDR unit is out of operation, its power command is 0, and the remaining units will operate in an overvoltage state even if their power remains balanced. However, considering system safety and insulation voltage costs, the output voltage of the HVDR should be limited. The output voltage curves for two units are shown in Figure 1(a).  
If the network operates under a constant DC current (CDC) mode, the current flow through the string is equal. Each HVDR's output voltage will vary with its output power, which decouples the unit's output voltage coupling and will not be disturbed by the adjacent HVDRs. The output voltage curves are shown in Figure 1(b).  
\[V_{outj}=\dfrac{P_{j}}{l_{dc}} \tag{2}\]  
Figure 2: A comparison of the CDV and CDC modes.  
In the parallel-connected part, that is the MVDR, if the network operates under the CDV mode, each unit's output current will vary with active power.  
\[I_{dc(i,j)}=\frac{P_{(i,j)}}{V_{dci}} \tag{3}\]  
If the parallel-connected MVDR operates under the CDC mode, the output voltage of each unit will vary with output power. When the MVDR output powers are different from each other, the output current with the higher power will exceed its rated current value.  
\[\left\{\begin{array}{l}I_{dc,N}=\frac{P_{avg}}{V_{dci}}\\ P_{avg}=\frac{\sum\limits_{j=1}^{n}P_{(i,j)}}{n}\end{array}\right.\Rightarrow I _{dcj}=\frac{P_{(i,j)}}{P_{avg}}I_{dc,N} \tag{4}\]  
According to the analysis above, the CDC control mode is recommended for a series-connected topology, and the CDV control mode is recommended for a parallel-connected topology.  
The MMC can work in the current control mode, CDC, or voltage control mode, CDV. With MMC's higher freedom of control capability, the coordinate control strategy for parallel-series topology is possible.