esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Classification Algorithms  
Several classifiers were tested and comparatively evaluated, including K-Nearest Neighbors (KNN), Naive Bayes, Random Forest (RF), Support Vector Machine (SVM), Logistic Regression, Extreme Gradient Boosting (XGBoost), and a Multi-Layer Perceptron (MLP). All computations and model development steps were implemented using Python (version 3.13) and related open-source libraries (in particular, the scikit-learn library).  
To address the class imbalance between in situ (\(n=24\)) and invasive (\(n=47\)) lesions, class weighting strategies were applied to selected models. Specifically, the SVM was configured with class_weight = 'balanced', and the XGBoost classifier was adjusted usingthe scale_pos_weight parameter to enhance learning from the minority class. The other classifiers were trained without explicit rebalancing.  
Model validation was conducted using Leave-One-Out Cross-Validation (LOOCV), where each individual sample is used once as a test case while the rest serve as the training set. This process is repeated for every sample in the dataset. LOOCV is particularly well-suited for small and heterogeneous datasets. At each iteration of the LOOCV scheme, feature standardisation was performed using the training set only. Specifically, z-score normalisation was applied by fitting the scaler on the training data and subsequently applying the same transformation parameters to the test instance.  
To prevent data leakage arising from the use of data augmentation techniques within the LOOCV process, the following strategy was adopted. Each original sample was identified by a unique suffix, distinguishing it from its corresponding augmented versions. During each LOOCV fold, the original sample of the current patient was selected as the test set, and all its augmented versions were completely excluded from the training data. The training set was composed exclusively of samples (original and augmented) belonging to the other patients, with the exclusion of any data related to the patient under test. Also, data standardisation (scaling) was performed only on the training data and then applied to the test sample.  
For each classifier, the area under the ROC curve (AUC) was computed to comprehensively evaluate discriminative performance. In our analysis, we defined in situ lesions (the minority class) as the positive class. The decision threshold corresponding to the highest overall classification accuracy was then selected to calculate the final evaluation metrics, including precision, recall (sensitivity for in situ), F1-score, and to construct the confusion matrix. A flow chart is provided in Figure 2.
# Characterization of Light-To-Frequency Converter for Visible Light Communication Systems
## 3 Characterization of the LTF for a VLC System
### LTF Response to an Optical Periodic Signal  
For this evaluation process, the objective is to observe the response of the LTF converter when it is excited by a periodic signal. Using this type of signal is helpful to observe the advantages and disadvantages of using the LTF as a receiver in a VLC system. For this case, the arbitrary waveform generator (AWG) RLGOL DG4162 was used to generate the modulated signal, applied to the base of the 2N3904 NPN transistor, configured in saturation mode, and acting as the driver of the LED. A frequency sweep is then carried out for the modulating signal \(f_{OOK}\) from 1 kHz until reaching the saturation frequency of the LTF. For each frequency, the separation distance of the link between the transmitter LED and the LTF was changed, from 0 cm up to the distance where the LTF output frequency was greater than or equal to the frequency of the modulating signal, that is, \(f_{o}\geq f_{OOK}\). This limit makes sense from the viewpoint of the frequency generated by the LTF, that is, \(f_{o}\) reaches its maximum value during the half-period in which the modulated signal is in a high state (presence of the optical signal).
## 4 Detector Prototype and Readout Electronics  
### SiPM Array  
In this study, SiPMs of the J30035 series from SensL (Cork, Ireland) were selected as the photon sensors [25]. The SiPM is a pixel-type photon detector with many advantages, such as compact dimensions, easy integration into arrays, even gain response, and low cost. To achieve a large area of imaging targets, four arrays with a size of \(8\times 8\) were arranged to form a complete array with an effective area of about 50 mm \(\times\) 50 mm, as shown in Figure 6. The size of a single SiPM unit was 3.14 mm \(\times\) 3.14 mm, and the gap between adjacent SiPMs was 0.36 mm.  
Figure 6a shows a complete array containing 16 \(\times\) 16 arrays. Figure 6b presents a microcell forming a basic SiPM unit. This microcell consisted of a photodiode connected in series to a burst resistor operating in the avalanche mode.  
\begin{table}
\begin{tabular}{c c} \hline \hline EJ200 Physical and Scintillation Constants & \\ \hline Light output in \% relative to anthracene & 64 \\ Density, g/cm\({}^{3}\) & 1.023 \\ Polymer base & Polyvinyl toluene \\ Wavelength of max. emission, nm & 425 \\ Scintillation efficiency, photons per 1 MeV \(e^{-}\) & 10,000 \\ Rise time, ns & 0.9 \\ Decay time, ns & 2.1 \\ Pulse width, FWHM, ns & 2.5 \\ Organic flours, \% & 3 \\ Number of H atoms per cm\({}^{3}\) & \(5.17\times 10^{22}\) \\ Number of C atoms per cm\({}^{3}\) & \(4.69\times 10^{22}\) \\ Number of electrons per cm\({}^{3}\) & \(3.33\times 10^{23}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specification of the EJ200 [24].  
Figure 5: The light attenuation length for the EJ200 with a section of 1 mm \(\times\) 1 mm.
# A Proposal of a Troposphere Model in a GNSS Simulator for VANET Applications
## 4 GNSS Application in VANET
In addition to the RINEX file, a National Marine Electronics Association (NMEA) file (a text file describing the coordinates of the receiver in the form of strings) for the simulated vehicle coordinates has to be provided. These coordinates must have a frequency of 10 Hz, which means that 10 NMEA strings correspond to one second of simulation. A particular type of NMEA string specifies the position of the receiver; some of the most relevant parameters are--latitude, longitude, GPS signal quality, and so on. The data is sent through messages (sentences) which differ in their content. Each sentence is composed of a starting character \"$\", an ending character \"*\", the checksum formed by two characters and finally by <CR> <LF>. After the $ character, two letters identify the device. In our satellite navigation case we have \"GP\" code that identifies GPS. The next three letters identify the type of message. Each message, in turn, has a predefined content divided into various fields separated by commas. The most common messages are: Global Positioning System Fixed Data (GGA), Recommended Minimum Specific GNSS Data (RMC), GNSS DOP and Active Satellites (GSA), GNSS Satellites in View (GSV), Geographic Position-Latitude/Longitude (GLL), Course Over Ground and Ground Speed (VTG). In particular, the NMEA string type for our experiments is GGA, Global Positioning System Fix Data. In the following, an example of NMEA string extract from our path in middle latitude and in Table 2 an explanation of the different terms in the string are provided:  
$GPGGA,090000.00,3921.28520559,N,01613.51936448,E,1,05,2.87,160.00,M,  
\(-\)21.3213,M,\"7F
# An Improved Artificial Bee Colony for Feature Selection in QSAR
## 6 Experimental Results and Analysis
the proposed ABC-PLS-1 has no edge over time consumption, it boosts the accuracy of FS in QSAR, which is exactly what QSAR modeling needs. According to the above experimental results, we come to the conclusion that the proposed ABC-PLS-1 performs well in QSAR. In addition, to investigate whether the scout bee phase is redundant when dealing with the feature selection for low-dimensional and medium-dimensional regression prediction problem, we do further experiments on the ABC-PLS-1 by setting different values of _Limit_. Table 6 shows the experimental results of three performance metrics when the scout bee operator takes different _Limit_ values on the three datasets. The best results are identified in boldface. In the case of no scout bee phase, i.e., \(Limit=\infty\), the \(Q^{2}\) on the Artemisinin dataset, BZR dataset, and Selwood dataset are, respectively, 0.7731, 0.5757, and 0.9338, which are respectively 0.15%, 0.33% and 0.12% larger than that in the case of setting the _Limit_ to 100; The root mean square error are respectively 0.7468, 0.7153, and 0.1906, which are, respectively, 0.25%, 0.34%, and 0.19% smaller than that in the case of setting the _Limit_ to 100. The number of the selected features on the Artemisinin dataset is 0.3 smaller than that in the case of setting the _Limit_ value to 100. Therefore, the scout bee operator is redundant in dealing with the feature selection for low-dimensional and medium-dimensional datasets in regression. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Artemisinin** & **BZR** & **Selwood** \\ \hline PSO-PLS & \(\mathbf{86.15\pm 7.6716}\) & \(\mathbf{44.99\pm 8.4498}\) & \(\mathbf{32.25\pm 6.5508}\) \\ WS-PSO-PLS & \(92.84\pm 6.6510\) & \(66.89\pm 5.0317\) & \(46.83\pm 4.3476\) \\ BFDE-PLS & \(273.09\pm 11.0566\) & \(224.29\pm 15.8253\) & \(158.28\pm 6.6698\) \\ ABC-PLS & \(103.68\pm 4.2085\) & \(99.50\pm 2.2932\) & \(80.68\pm 4.5513\) \\ ABC-PLS-1 & \(224.20\pm 36.1620\) & \(185.83\pm 5.3332\) & \(113.30\pm 2.9640\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The execution time (in seconds) of five SI-based algorithms for FS in QSAR. Figure 7: Box-plots of \(RMSE\) for different methods on three datasets.
# Dual Partial Reversible Data Hiding Using Enhanced Hamming Code
## 3 Proposed Scheme
### Numerical Examples
#### Data Embedding Phase  
Assume that the secret bits to be embedded are  
\[m=[1\;0\;1].\]  
The two cover pixels prepared for embedding are  
\[x_{j}^{1}=[1\;0\;0\;1\;1\;1\;1],\quad x_{j}^{2}=[1\;0\;0\;1\;1\;1\;1].\]  
The embedding process proceeds as follows:  
1. Virtual Pixel Reconstruction: A virtual pixel is formed: \[y=[1\;1\;1\;1\;1\;1].\]
2. Target Coset Leader Mapping: The secret bits \(m=[1\;0\;1]\) correspond to a coset leader index of 5.  
3. Optimal Codeword Identification: Among the codewords associated with coset leader 5, the optimal codeword minimizing the error is selected: \[\varepsilon=\min\Bigl{(}(14-15)^{2}+(7-7)^{2}\Bigr{)}=1,\] \[h=[1\;1\;1\;0\;1\;1\;1].\] (4) Stego Pixel Reconstruction: The cover pixels are updated as follows: \[x_{j}^{1} =[1\;0\;0\;1\;1\;1\;0],\] \[x_{j}^{2} =[1\;0\;0\;1\;1\;1\;1].\] These updated pixels are stored in the stego images \(\Delta\mathbf{I}_{1}\) and \(\Delta\mathbf{I}_{2}\) respectively.
## 3 Results  
### Antibiotics and Proton Pump Inhibitors  
In a collection of eight articles [37; 38; 39; 40; 41; 42; 43; 44] showing a morphological effect of antibiotics and other substances classically used in _H. pylori_ therapies, microscopic and culture methods were used. In four of them [38; 39; 42; 43], different staining techniques and fluorescence analysis were additionally included (Table 1 and Figure 1).  
In the first article [37], dedicated to an activity of amoxicillin (AMX) and \(\beta\)-lactamase inhibitors, the ability of these substances to stimulate production of coccoid forms and separation of outer and inner membranes during 3-6 h of exposure was noticed.  
In the next five analyzed articles [38; 39; 40; 41; 43], observations regarding a morphstructural effect of antibiotics come from publications primarily focusing on new compounds with a potential anti-_H. pylori_ activity. [PERSON] et al. [38; 39] showed that AMX within 24 h reduced the viability of _H. pylori_ by 3 log when treated with MIC. This phenomenon was accompanied by an appearance of spherical forms and an increased permeability of cell membranes. In subsequent articles [40; 41], it was observed that 72-h exposure of _H. pylori_ to MICs of tested antibiotics (AMX, CLR, MTZ, and TET) resulted in a presence of numerous coccoid forms (>85%). The morphological effect of these antibiotics was significantly lower, although still present, when sub-MICs of CLR, TET, and MTZ were used. An amount of spherical forms after 72 h counted for <15% and 25-30% for \(\frac{1}{4}\times\) MIC and \(\frac{1}{2}\times\) MIC, respectively. Formation of coccoid forms was the most intense for AMX and after 72 h was equal to 30% and 70% for \(\frac{1}{4}\times\) MIC and \(\frac{1}{2}\times\) MIC, respectively. These observations are in line with the reports of [PERSON] et al. [42], who noticed that sublethal CLR and MTZ concentrations after 3 days of exposure have a relatively low potential to stimulate transformation of _H. pylori_ into spherical forms (30-40%). This was in opposition to the very high pro-transformative potential of AMX (>95%). Furthermore, a one-day incubation of spherical _H. pylori_ forms with 2\(\times\) MIC of these antibiotics reduced the viability by 99.8% (CLR) and 62.6% (MTZ), while only 0.03% when exposed to AMX. The decreased sensitivity of coccoid forms to AMX was also recognized by [PERSON] et al. [43]. Here, a 24-h treatment with MIC of this antibiotic completely lowered the viability of spiral forms, while the use of very high concentrations of AMX (20\(\times\) MIC) reduced an amount of viable spherical forms only by 20%.  
The last included article [44] determined an activity of omerazole (a proton pump inhibitor) and found a direct relationship between the viability and number of coccoid forms. Exposure to \(\frac{1}{2}\times\) MIC for 3, 6, and 9 days contributed to the formation of 10%, 40%, and 90% of these morphological forms, respectively. In addition, an increase in the viability and transformation into spiral forms was obtained by removing omeprazole from the culture medium.
## 1 Introduction
(2014) [17] used SG smoothing to predict paddy soil properties, and the goodness of fit of the calibration model for VIS and NIR showed a coefficient of determination R\({}^{2}\) > 0.78 for all the physical properties of soil. The analysis utilizing DRS could substitute conventional methods of soil properties analysis which consume time and cost and proceed with rapid and accurate soil properties. [PERSON] et al. (2017) [18] predicted soil properties of total nitrogen (TN), pH, and soil texture using PLSR analysis and demonstrated the soil property prediction model using DRS. [PERSON] et al. (2022) [19] analyzed soil properties using various preprocessing methods and optimized the prediction model according to each preprocessing selection. The purpose of this study is to analyze the soil properties of saltwater paddies in real time in Korean fields and develop soil property prediction regression models for wet soil and dry soil by applying Savitzky-Golay smoothing (SG smoothing) and Standard Normal Variate (SNV) preprocessing methods. We collected 120 samples of saltwater hazelnut soil in Korea and analyzed the soil properties. The specific objectives were as follows:  
1. Collect spectral data of soil in a wet state (wet soil) and soil in a dry state (dry soil) and develop a predictive regression model using PLSR analysis;
2. Perform comparative analysis between the soil property prediction regression models using the preprocessing techniques of SG smoothing and SNV.
# The Status of Space Weather Infrastructure and Research in Africa
## 3 Human Capital Development
### URSI Commission G Working group: \"Capacity building and training\"  
During the International Radio Science Union (URSI) General Assembly 2021, held in Rome from 28 August to 4 September, a new working group (WG) in the framework of Commission G (Ionospheric Radio and Propagation) has been established. This WG, named \"Capacity Building and Training,\" is chaired by [PERSON] (INGV, Italy) and co-chaired by [PERSON] (PU, Kenya), [PERSON] (ICTP, Italy), and [PERSON] (now deceased; BC, USA). The \"Capacity Building and Training\" working group deals with activities related to the training of students and young scientists mainly from developing countries. The main objectives of the working group are: to organize international workshops, especially for young scientists from developing countries; to facilitate visits/exchanges for young scientists by putting in place actions for fundraising; and to organize periodical webinars for sharing new research among the commission G community members. This group is championing the aspirations of the African space weather research community.
## 4 Stability Analysis of the Slope of Roadbed Supported by Timber-Frame Beam-Bamboo Anchor Rod
### Maximum Shear Strain of the Slope  
The maximum shear strain of the slope is an important index to measure the force condition of the soil body of the slope, and the dynamic response of the soil body of the slope under train loading can be inferred from the maximum shear strain of the slope to infer the potential slip surface or damage location of the slope. After calculation by Midas GTS NX 2016 finite element software, the cloud diagrams of the maximum shear strains of the embankment slopes under train loads were extracted for different arrangements of bamboo anchors and wooden frame beam support arrangements. Figure 25a shows that the maximum shear strain of the slope is distributed at the foot of the slope when there is no wood frame beam-bamboo anchor support, and the potential slip surface is formed with the bed surface at the top of the slope, and the slope is most prone to shear damage at the foot of the slope. Figure 25b shows that when the slope is supported by bamboo anchors and wooden frame beams, the maximum shear strain of the slope appears below the loading place at the top of the slope, and the maximum shear strain of the slope gradually spreads and expands to the slope body from the loading place at the top of the slope, and the shear strain decreases greatly. The maximum shear strain at the foot of the slope is very small, and a potential slip surface is not formed in the body of the slope.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Side Slopes** & **Safety Factor K** & **Safety Factor Improvement Rate** \\ \hline Unsupported slope & 1.103 & - \\ Three rows and three rows of & 1.325 & 20.13\% \\ bamboo anchor slope & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Coefficient of safety of slopes with different number of rows.  
Figure 24: A 3D numerical model of bamboo anchor–wood frame beam roadbed slope.  
In summary, compared with the maximum shear strain cloud diagram of the unsupported embankment slope, the maximum shear strain is obviously smaller when the slope is supported, and the maximum shear strain is also very small in the area of the slope supported by bamboo anchors and wooden frame beams; the maximum shear strain at the foot of the slope decreases significantly, and there is no through maximum shear strain slip surface formed in the slope, and the range of the distribution is also reduced. This shows that the maximum shear strain distribution range and size of the slope can be greatly reduced in the wooden frame beam-bamboo anchor support, reducing the probability of shear damage at the foot of the slope, effectively supporting the slope, and improving the stability of the slope.
# Remote Sensing in Urban Forestry:
## 2 Remote Sensing Applications in Urban Forests
### Theme 1: Multi-Source Input
#### 2.1.3 Satellite Imagery and Aerial Imagery  
In practice, the combination of satellite imagery and aerial imagery has seldom been adopted, given that aerial imagery can replace satellite imagery in terms of optical capability. Only when extra data, such as infrared and thermal infrared data, are needed would such a combination become necessary. The majority of recent publications utilising both satellite and aerial imagery (three of five) have studied the mitigation effect of urban forests on the urban heat island. For example, [PERSON] [66] estimated how the woodlands' tree protection policy adopted by The Woodlands Township, Texas, USA, affected the urban heat island at the neighbourhood level. The author compared the land surface temperature of two neighbourhoods in the woodlands and five control neighbourhoods in other urban areas. The land surface temperature was obtained from the thermal bands of Landsat-5 (TM) imagery. Unlike most urban cooling studies that used HSR satellite imagery to extract land use information, this study exploited the US National Agricultural Imagery Program's (NAIP) aerial imagery to classify land cover at a fine scale. An object-based method was coupled with a support vector machine (SVM) classifier to identify trees. When dealing with high-resolution imagery, object-based methods have been reported to be superior to pixel-based methods for their ability to reduce noise and increase accuracy [67; 68]. Aerial imagery is superior to satellite imagery in terms of concurrent high spatial and spectral resolution. However, prohibitive flight costs limit its use to small/moderate areas. In the US, NAIP has been offering aerial imagery with continental coverage during the agricultural growing seasons to the public since 2003. NAIP imagery with four spectral bands is acquired at a 1-m ground sample distance with horizontal accuracy that matches within 6 m of ground control points, which can be used during image inspection and processing. A number of recent urban forest studies have benefitted from free NAIP images.
# Application of the Segmented Correlation Technology in Seismic Communication with Morse Code
## 2 Principles and Methods
### Communication Mode  
The establishment of a communication system first needs to determine the method of signal encoding. In different applications, Morse code is one of the most commonly used methods to realize information transmission about short signal [17; 18; 19]. Compared with other methods, Morse code relies on a stable and unchanging wireless signal so that its wireless communication equipment is simpler and can be used in high noise and weak signal environments [20]. These features will contribute to the realization of seismic communication, so Morse code is adopted in this paper. By encoding information into dot, dash, symbol spaces, character spaces and word spaces, the information can be designed in various orders to deliver different punctuation marks, numbers and letters. Based on the principle of Morse code, a communication network can be set up with seismic transceivers, which can encode, decode, receive and emit seismic signals [21; 22; 23], as described in Figure 1.  
A complete signal, which is compiled by transceivers, consists of a beginning signal, a Morse signal and an end signal. When transceiver 2 receives the beginning signal from transceiver 1, transceiver 2 is activated and decodes the seismic signal. When transceiver 2 receives the end signal from transceiver 1, transceiver 2 interrupts the communication with transceiver 1 and transmits the decoded signal in the form of a complete signal. Then  
Figure 1: A long-distance transmission scheme about seismic signals.  
the compiled signals received by other transceivers can be transmitted again so that seismic signals travel a long distance. In this system, the communication distance is the length between the first transceiver and the last transceiver, and the distance between the two adjacent transceivers is the relay distance. This paper will discuss how to take advantage of the SCTMC to extend them. Although the integrated seismic transceiver has not been manufactured yet, it can be taken over by some instruments like vibroseis sources, hammer sources and geophones.