esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
It can be installed in an underwater environment to provide 360\({}^{\circ}\) \"omni-directional\" uniform light emission around its circumference. This large FOV can fascinate the optical detection in underwater turbulent environments. The proposed LSTM-NN has the time-memorizing characteristics to enhance the OCC signal decoding. The proposed LSTM-NN is also compared with other decoding methods in the literature, such as the PPB-NN. The experimental results demonstrated that the proposed LSTM-NN outperforms the PPS-NN in the UWOCC system. A data rate of 2.7 kbit/s can be achieved in UWOCC, satisfying the pre-forward error correction (FEC) condition (i.e., bit error rate, BER \(\leq\) 3.8 \(\times\) 10\({}^{-3}\)). We also found that thin fiber also allows spatial multiplexing to enhance the transmission capacity. The main contributions of this work are summarized as follows:  
* Underwater VLC systems reported in the studies usually employ LD or LED Txs. Here, we employ SGOF Tx, which is lightweight, flexibly bendable, and water resistant. It can also provide 360\({}^{\circ}\) \"omni-directional\" uniform light emission around its circumference. * Underwater VLC systems reported in the studies usually employ PD or APD Rxs. Here, we employ a CMOS image sensor-based OCC, which allows reliable and larger FOV detection. * In this demonstration, both SGOF Tx and the CMOS image sensor are immersed in water to achieve more accurate experimental results. * Here, RS decoding is realized by the proposed LSTM-NN, which has the time-memorizing characteristics to enhance the decoding of the OCC signal. * Parallel SGOF Txs are also studied to achieve spatial multiplexing, enhancing the total transmission capacity.
## 6 Experiments and Results
### Effect of the Data Reduction with Clustering  
The distribution of the intrusion and normal activity instances for each cluster is presented by Figure 6. As observed in the figure, the training dataset is clustered into three clusters. The first cluster contains a majority of the instances as intrusion which dominates the normal ones. The same observation is concluded for the other clusters having a domination of the intrusion instances for the second cluster and an absence of the normal instances for the third cluster.  
In contrast, the distribution of the intrusion and normal activities for each cluster after the reduction process is presented in Figure 7. As intended, to provide representative dataset, a similar distribution can be observed in Figure 7 compared to Figure 6. The only difference is the number of instances, which are reduced by 10% for each cluster, as discussed in Section 5.1. Note that the reduction is not performed equally for each label of the same cluster but rather performed at the cluster level, producing a reduced number of cluster instances.  
Figure 6: Distribution of instances before reduction.  
Figure 7: Distribution of instances after reduction.
## 3 Results and Discussion
### Color Measurement of Edible Fixed Oils
Chlorophyll is also undesirable because it gives the oil an unpleasant green or brown color. The chlorophyll content is a measure of seed maturity and quality but also indicates the use of improper pressing conditions [37; 38]. The chlorophyll content in the examined oils varied from 0.0 (almond, pumpkin seed, coffee bean, pine cone, mustard seed, chia seed, apricot, macadamia, and virgin coconut oils) to 30.0 mg/kg (sea buckthorn oil), followed by avocado oil (17.6 mg/kg). Black cumin, evening primrose, grape seed, pomegranate, canola, soybean, and poppy seed oils had chlorophyll contents greater than 1000 ppb. In the other oils, the level of chlorophyll did not exceed 800 ppb. Chlorophyll levels in edible oils should not exceed 50 mg/kg to prevent its rapid oxidation in the presence of light [39]. Contrary to chlorophyll, b-carotene, belonging to the carotenoid group, is desirable due to its antioxidant and coloring properties. Soybean oil contained the highest content of b-carotene (61,176 ppb), followed by linseed (60,421 ppb) and canola (60,121 ppb) oils. In the other oils, the content of b-carotene ranged from 12.9 to 36.7 mg/kg, with the exception of avocado oil (55.0 mg/kg). The content of b-carotene in almond, pine cone, pomegranate, mustard seed, chia seed, peanut, apricot, macadamia, sesame, and virgin coconut oils did not exceed 9.0 mg/kg.
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 3 Applications of FFD in Early Fault Diagnosis of Rotating Machinery
### Adaptive Decomposition Methods
#### 3.1.5 Vmd  
VMD decomposes a multi-component signal into an ensemble of band-limited intrinsic mode functions (IMFs). These IMFs can then be used to diagnose early rotating machinery faults. [PERSON] et al. [84] used VMD to decompose the signal into IMFs, and the number of IMFs was determined by adaptive scale space spectrum segmentation. Finally, the Teager energy operator of the effective IMF components was applied to realize the early fault identification. [PERSON] et al. [85] used improved autoregressive-minimum entropy deconvolution and VMD to extract the periodic transient impulses of single-fault or multiple-faults in rotating machinery. [PERSON] et al. [86] developed an early online chatter identification method for milling processes. In this method, an optimized VMD was used to decompose the signal, and the sub-components containing chatter information were extracted using a simulated annealing algorithm. Finally, the approximate entropy and the sample entropy were used to detect five types of operating conditions. [PERSON] et al. [87] proposed a parameter optimization algorithm to select the crucial VMD parameters. Then, the weak features were extracted by using the VMD with the optimized parameters. In [88], rescaling subsampling compression is used to preprocess the signal, and analytical mode decomposition was used to enhance and select signal components. Then the processed signal was decomposed into IMF by VMD to analyze weak multi-frequency signals. In [89], EMD was used to decompose the signal into IMFs selected by relatedness and kurtosis. Then VMD was used to decompose the reconstructed signal using the selected IMFs. Finally, envelope analysis was used for incipient fault diagnosis of rolling bearing. The methods mentioned above method have been summarized in Table 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Authors** & **Methodologies** \\ \hline [PERSON] et al. [81] & Wavelet spatial neighboring coefficient + EWT \\ [PERSON] et al. [82] & EWT + Hilbert Transform \\ [PERSON] et al. [80] & Bistable stochastic resonance + EWT \\ [PERSON] et al. [83] & Kurtogram + EWT + sparse regression \\ \hline \hline \end{tabular}
\end{table}
Table 4: Applications of empirical wavelet transform (EWT) method in early fault diagnosis of rotating machinery.
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 2 Ionosphere Characteristics
### Technical Procedures and Metrics to Analyze the Ionospheric Variations
#### 2.4.1 Oblique and Vertical Sounding  
In telecommunication systems, a ionospheric sounding is a technique that can provide real-time data about radio propagation conditions over an HF link, by means of a basic system consisting on having a synchronized transmitter and receiver [22]. In this sense, the time delay between the transmission and the reception can be translated to the identification of the effective ionospheric layer altitude.  
Vertical incidence sounding normally uses a collocated transmitter and receiver and involves a certain number of preselected frequencies, which are directed vertically to the ionosphere. The values of the reflected returned signals are used to determine the ionospheric layer altitude in an effective term [26]; this is also the technique used to determine the critical frequency. Oblique incidence sounding uses a transmitter in one end and a receiver on the opposite; the distance between those two devices is the effective channel to be evaluated (in terms of ionosphere). The receiver has to be synchronized to the transmitter, and usually deploys a ionogram as a result (see Figure 1). The transmitter uses either a stepped frequency signal or a swept frequency signal over the ionospheric channel, which is measured at the receiver. The ionogram, as depicted in Figure 1, pretends to display the real altitude as a function of frequency by means of the virtual altitude measurement using the time delay.
# GCS-YOLO: A Lightweight Detection Algorithm for Grape Leaf Diseases Based on Improved YOLOv8
## 4 Experiments and Analysis
### Comparison of Results  
To validate the performance differences between GCS-YOLO and the baseline model in detecting three types of grape leaf diseases, we compared the results of the two algorithms using Precision-Recall (P-R) curves. The P-R curve is a critical performance evaluation tool that visually illustrates the trade-off between precision and recall across varying detection confidence thresholds. The area under the P-R curve, known as Average Precision (AP), is a core metric in object detection. A higher AP value signifies a superior balance between precision and recall, reflecting stronger overall model performance. The P-R curves are shown in Figure 10. Notably, leaf blight exhibits the most significant improvement in the AP, with a 2.7% increase. The lesions of leaf blight are not only numerous but also small, making their detection more challenging compared to other categories. This substantial improvement primarily stems from the model's enhanced ability to precisely focus on lesions of varying sizes and locations, thereby boosting detection performance. The improved model also demonstrates superior detection capabilities in other disease categories. Overall, the enhanced model achieves a 1.3% increase in mAP, outperforming the baseline model.  
Figure 11 shows that in the detection results of YOLOv8, partial tiny lesions were missed in columns 1, 2, 5, and 6. Detecting these small lesions with sparse features is critical for the early identification of grape leaf diseases. In columns 3, 4, and 6, grape leaf diseases  
Figure 10: P-R curve comparison chart.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **[EMAIL_ADDRESS]\%** & **Parameters/M** & **GFLOPs/G** &
\begin{tabular}{c} **Model** \\ **Size/MB** \\ \end{tabular} & **FPS/s** \\ \hline Faster-RCNN & 85.5 & 28.29 & 940.9 & 109.0 & 25.3 \\ YOLOv8 & 94.9 & 3.01 & 8.2 & 6.2 & 153.8 \\ YOLOv10 & 93.5 & 2.27 & 6.7 & 5.6 & 149.5 \\ YOLOv5 & 94.4 & 2.51 & 7.2 & 5.2 & 134.2 \\ YOLOv6-lite & 90.3 & 1.06 & 0.8 & 2.5 & 80.2 \\ YOLOv7-tiny & 94.2 & 6.01 & 13.0 & 12.0 & 113.2 \\ GCS-YOLO & 96.2 & 1.63 & 4.5 & 3.6 & 136.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Different algorithm comparison experiment.  
were misclassified as another disease with similar visual characteristics. After optimization, the improved model achieves precise detection of various grape leaf diseases. This improvement stems from the enhanced feature extraction capability and multi-scale feature representation of the model, which significantly improves its adaptability. Experimental results demonstrate that GCS-YOLO not only delivers superior detection performance but also achieves model lightweighting, making it highly suitable for real-time grape leaf disease detection.
# GIS-Based Multi-Objective Routing Approach for Street-Based Sporting-Event Routing
## 1 Introduction
### Literature Review
#### 1.2.3 Route-Selection Factors: Route Variables and Environmental Variables  
For the routing in this study, we used route and environmental variables. We fed the route variables into a modified Floyd-Warshall algorithm to generate multiple routes. We entered the environmental variables into NSGA-II's generic crossover and mutation operators to produce offspring. The NSGA-II applies the genetic algorithm (GA) to create new offspring to find optimized solutions between route and environmental variables [27].  
A route variable is the physical condition of a road required for a vehicle's progression at a sporting event. The variables that we used consisted of the road's width, the number of turns, the turning radius required of the vehicle, and the total length of the track. The physical conditions of the Formula E racetrack and the selection factors for the route can be confirmed through FIA's event specifications and interviews with professional track designers; the FIA has a relatively flexible index that responds to different environmental contexts for each host city. In this study, we defined the index as a quantitative parameter for implementing the optimization algorithm.  
Environmental variables minimize an event's impact on a city and help to reduce inconvenience for citizens by identifying facilities that are blocked due to the event and setting weights according to their impact on citizens' lives. For example, planning committees should avoid holding events on roads adjacent to educational facilities, such as kindergarten and elementary schools. In addition, the roads used should not block access for vehicles from adjacent disaster-prevention facilities, such as police and fire departments. In this way, we listed the facilities necessary for citizens' daily lives and set the avoidance weight for each facility. We directly fed this information into the optimal criterion for the path extracted through the algorithm.  
Table 1 defines the race-route and environmental variables. The index is composed of three levels based on the order of the algorithm development and data processing. The data consisted of parameters and values.  
The data in Level 1 encompass parameters for selectively extracting roads drawn from the city's road network that can be used in the routes for sporting events. The Level 2 data involved parameter inputs for the path-search algorithm. The data in Level 3 included environmental variables that are inputs for routes optimization. We defined the environmental variables as a list of facilities to be avoided to prevent inconvenience for citizens. Each indicator had a weight set to reach the optimal value according to priority and was parameterized so that the user can enter the desired value. Section 3 outlines the definitions of the weights set.  
For this study, we configured the city where the event is to be held and the city's central point (the main bases equipped with all the infrastructure for the event) for the user to select.
# WhistleGAN for Biomimetic Underwater Acoustic Covert Communication
## 3 Analysis of Experimental Results
### Communication Performance Assessment  
In this subsection, the BER of the biomimetic UWA communication using the real dolphin whistles and the generated whistles are compared. The DCGAN (256) and the proposed WhistleGAN were used to generate the whistles. DCGAN (128) was not used for the comparison because the DCGAN (128) has low DoM. The modulation methods used for the communication were CV-CFM, orthogonal frequency division multiplexing (OFDM), and hybrid orthogonal whistle modulation (HOWM). The data rates of the three techniques were set to 50 bps [16; 17]. The modulation parameters of all methods were the same as in previous studies and a symbol bandwidth was 200 Hz and the forward error correction was a 1/3 turbo code [16; 17]. The computer simulations were performed on three UWA channels attained from the West Sea, South Sea, and East Sea of Korea. The channel impulse responses (CIRs) and BER results of each UWA channel are shown in Figures 7-9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Score** & **5** & **4** & **3** & **2** & **1** \\ \hline
**Opinion** & Same & Very similar & Similar &
\begin{tabular}{c} Slightly \\ different \\ \end{tabular} & Different \\ \hline \end{tabular}
\end{table}
Table 6: The MOS test scoring criteria.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Scheme** & **Real dolphin** & **DCGAN (256)** & **DCGAN (128)** & **WhistleGAN** \\ \hline
**Score** & 4.3 & 4.2 & 4.0 & 4.3 \\ \hline \end{tabular}
\end{table}
Table 7: The MOS test results.  
Figure 7: _Cont_.  
Figure 8: (**a**) The CIR of the South Sea of South Korea; and the BER results of (**b**) CV-CFM; (**c**) OFDM; (**d**) HOWM.  
Figure 7: (**a**) The CIR of the West Sea of South Korea; the BER results of (**b**) CV-CFM; (**c**) OFDM; and (**d**) HOWM.  
Figures 7a-d, 8a-d and 9a-d depict the CIRs, the BERs of the CV-CFM, BERs of the OFDM, and the BERs of the HOWM, respectively. The black line, the blue line, and the red line denote the BERs with the real whistles, with the whistles by the DCGAN, and with the proposed WhistleGAN. The solid lines denote the uncoded BER and the dashed lines denote the coded BER.  
Figures 7-9 demonstrate the similar BER performances for the the real dolphine and the generated whistles by the proposed WhistleGAN through the computer simulations of the three conventional biomimetic UWA covert communication methods and the three different UWA channel models. Therefore, We verified that the proposed WhistleGAN generates a variety of dolphin whistles with a high DoM and similar BER performance to real dolphin sounds using a lower complexity than the conventional DCGAN (256).
## 3 Experimental Campaigns at FLAME
### Auxiliary Beamline Target Area at FLAME
#### STARK Broadening Plasma Density Measurements  
Both transverse and longitudinal plasma density measurements are performed by exploiting the Stark effect. In addition, an optical imaging system of the entire plasma target able to collect the plasma fluorescence is set.  
Figure 5: Examples of targets designed and manufactured at the FLAME facility. In detail, the design and 3D printing realization of a 3 cm long capillary with a diameter of 500 \(\upmu\)m on the **top row**, the design of a 6 cm long curved capillary (**bottom left**), and a nozzle with a 2 mm aperture (**bottom right**) are shown.  
Diagnostics based on the Stark broadening exploit plasma recombination light, namely plasma fluorescence, which is emitted after the discharge during the recombination of the adopted gas (hydrogen, H\({}_{2}\), in the case under study). A spectrometer (Fully Automated Imaging Spectrometer iHR320, HORIBA Ltd, Kyoto, Japan) with a resolution of a few angstroms is adopted for its capability of resolving the chosen emission line (\(H_{\alpha}=656\) nm or \(H_{\beta}=486\) nm) coupled with a camera equipped with an intensifier (IStar 720 Andor Intensified CCD camera, Andor Technology, Belfast, UK) to increase the signal-to-noise ratio.  
The optical setup, as shown in Figure 6, is set as follows: the two-lens system images the emitted light onto the spectrometer's slit. The light dispersed by the gratings inside the spectrometer is collected onto a CCD camera, which requires an intensifier to increase the signal-to-noise ratio. By delaying the acquisition time, it is possible to perform a temporal scan of the longitudinal distribution of the plasma electron density profile.  
The duration of the plasma recombination process is about tens of ns, while the plasma discharge generally lasts for 1-2 us, because electrons slowly exchange energy with the nuclei. Therefore, the recombination light of the emitted plasma is detectable. The ICCD gate width can be set down to 5 ns with an available scanning delay with respect to the laser pulse arrival time. In this configuration, each row of the ICCD detector image corresponds to a specific longitudinal slice of the capillary. The plasma density can be retrieved by fitting the spectral profiles with a Cauchy distribution. In turn, the plasma density [36] is determined by \(\Delta\lambda_{1/2}\) corresponding to the Full Width at Half Maximum (FWHM) of the fitted profiles:  
\[n_{e}=8.02\times 10^{12}\bigg{(}\frac{\Delta\lambda_{1/2}}{\alpha_{1/2}} \bigg{)}^{3/2}\ \text{cm}^{-3}, \tag{3}\]  
where the coefficient \(\alpha\) is tabulated in Ref. [37] with values of \(2.23\times 10^{-2}\) for the \(H_{\alpha}\) line and \(9.77\times 10^{-2}\) for the \(H_{\beta}\) line when assuming a 3.5 eV plasma temperature and \(10^{18}\) cm\({}^{-3}\). These adopted plasma properties are consistent with our system parameters of volume, pressure, and current [38; 39].
## 2 Materials and Methods
### Principle of Planetary Boundary Layer Height (PBLH) Retrieval  
In this study, the potential temperature (\(\theta\)) curve is used to retrieve the planetary boundary layer height (PBLH). From the temperature profile, the potential temperature \(\theta\) is calculated for each height using Equation (1), where T is air temperature, P\({}_{0}\) is the reference pressure, P\({}_{(2)}\) is air pressure, R is the ideal gas constant, and C\({}_{\text{P}}\) is the specific heat capacity at constant pressure.  
\[\theta=\text{T}\cdot\left(\frac{\text{P}_{0}}{\text{P}_{(2)}}\right)^{\frac{ \text{R}}{\text{C}_{\text{P}}}} \tag{1}\]  
The thermal planetary boundary layer height (PBLH), derived from microwave radiometer (MWR) observations, is represented as H\({}_{0}\). The retrieval algorithm for H\({}_{0}\) is outlined as follows:  
1. [label=1., ref=1]
2. **Parcel Condition Check:** First, assess whether there exists a height at which the potential temperature \(\theta_{(2)}\) is lower than \(\theta_{(0)}\). This parcel condition is applicable only under convective conditions [40; 41]. When the condition is met, H\({}_{0}\) is defined as the height where \(\theta_{(2)}=\theta_{(0)}\), representing the Mixing Layer (ML) height.
3. **Stable Condition for PBLH:** If the parcel condition is not satisfied, the PBLH is calculated under stable conditions. In this scenario, H\({}_{0}\) is defined as the top of the stable boundary layer (SBL), where the potential temperature gradient \(\theta^{\prime}_{(2)}\) shows a decreasing trend [20; 41]. When the minimum of \(\theta^{\prime}_{(2)}\) is greater than or equal to zero, H\({}_{0}\) is the altitude corresponding to the minimum \(\theta^{\prime}_{(2)}\). Otherwise, H\({}_{0}\) is determined as the height at which \(\theta^{\prime}_{(2)}=0\)[11; 22; 25].  
To calculate the pressure at different altitudes, we use the hyposometric equation (also known as the barometric formula), which relates the pressure at a given height to the surface pressure and the temperature profile. The hyposometric equation is given by  
\[\text{P}_{(2)}=\text{P}_{0}\cdot\exp\left(-\frac{\text{g}\text{z}}{\text{RT} }\right) \tag{2}\]  
where P\({}_{(2)}\) is the pressure at the height, P\({}_{0}\) is the surface pressure, g is the acceleration due to gravity, R is the ideal gas constant, T is the temperature at the height, and z is the altitude.  
By applying this equation, we can compute the atmospheric pressure at each measurement level from the ground up. This allows us to calculate the potential temperature (\(\theta\)) for each altitude, which is required for retrieving the planetary boundary layer height (PBLH) as described in the methodology.