esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on Improving the Machining Performance of Scrolls
## 2 Adaptive Feed Rate Planning  
In the production of scrolls, a chunk of aluminum alloy, which is the material most widely used, is sequentially machined in rough, semi-fine, and fine machining processes. As about 65% of the whole material is removed during the rough machining process, it is worth increasing the material removal rate to shorten the overall machining time. Conventionally, increasing the material removal rate means directly increasing the feed rate during the machining processes. However, the overall stiffness of the milling machine, the surface smoothness of the workpiece, and the cutter lifetime are diminished if the feed rate is too high. In this section, in contrast to conventional solutions, adaptive feed rate planning, which can be used to adjust the feed rate during machining processes where cutting loads are kept close to a given maximum acceptable value, is proposed to increase machining efficiency.  
Earlier research has shown that the real tooth path of a milling cutter is a trochoid [24; 25]. A trochoidal tooth path is often used to simulate and compute the chip thickness for machining conditions involving a fixed cutting width and depth. In the analysis of scroll manufacturing, however, the simulation of a trochoidal tooth path is cumbersome and not easy to compute. Therefore, we used circular arcs to approximately simulate the tooth path and then determine the cutting area, which is the intersection of the tooth path and the workpiece's shape. From the cutting area per cutter tooth, we also determined the cutting amount per cutter tooth, since machining scrolls is also a kind of plane-milling process.  
As shown in Figure 1, the cutting area per cutter tooth can be obtained from the calculation of the area intersected by two adjacent tooth paths; i.e., the circular arc segments depicted by red curves and the workpiece's shape depicted by the blue block. The feed rate per cutter tooth, denoted \(F_{z}\), and the average cutting area per cutter tooth for each path segment, denoted \(A_{z}\), can be obtained with the following equations:  
\[F_{z}=\frac{F}{S\cdot Z} \tag{1}\]  
\[A_{z}=\frac{A\cdot F_{z}}{l} \tag{2}\]  
where \(F\) is the feed rate, \(S\) is the spindle speed, \(Z\) is the number of teeth for a cutter, \(A\) is the cutting area for each path segment, and \(l\) is the length of each path segment.  
Figure 1: Cutting area intersected by tooth paths and scroll workpiece: (**a**) full view, (**b**) zoomed-in view.  
Combining Equations (1) and (2) and eliminating \(F_{z}\) yields:  
\[F_{adp}=\frac{A_{z}\cdot l\cdot S\cdot Z}{A} \tag{3}\]  
where \(F_{adp}\) is the adaptive feed rate, which can be deduced from the cutting area per cutter tooth. By inputting the adaptive feed rate obtained with Equation (3) into the controller of the milling machine, we were able to accomplish adaptive feed rate planning.
# Dual-Modality Transformer with Time Series Imaging for Robust Epileptic Seizure Prediction
###### Abstract  
Automated EEG classification algorithms for seizures can facilitate the clinical diagnosis of epilepsy, enabling more expedient and precise classification. However, existing EEG signal preprocessing methods oriented towards artifact removal and signal enhancement have demonstrated suboptimal accuracy and robustness. In response to this challenge, we propose an Adaptive Dual-Modality Learning Model (ADML) for epileptic seizure prediction by combining time series imaging with Transformer-based architecture. Our approach effectively captures both temporal dependencies and spatial relationships in EEG signals through a specialized attention mechanism. Evaluated on the CHB-MIT and Bonn datasets, our method achieves 98.7% and 99.2% accuracy, respectively, significantly outperforming existing approaches. The model demonstrates strong generalization capability across datasets while maintaining computational efficiency. Cross-dataset validation confirms the robustness of our approach, with consistent performance above 96% accuracy. These results suggest that our dual-modality approach provides a reliable and practical solution for clinical epileptic seizure prediction.  
Keywords:dual-modality Transformer; time series imaging; robust epileptic seizure prediction; chronic neurological disorder; EEG; deep learning; computational perception +
Footnote †: journal: Applied sciences
# Identification of Ratholes in Desert Steppe Based on UAV Hyperspectral Remote Sensing
## 3 Data Processing
### Principal Component Analysis  
Hyperspectral data have many continuous bands and are high-dimensional data, and the spectral information contains a large amount of useless information. In the process of data analysis, redundant data information will increase the difficulty and time cost of data processing, and reduce its efficiency; therefore, it is necessary to reduce the dimensionality of the spectral data to extract the main spectral characteristic information. Principal component analysis can reduce the dimensionality of the data and minimize the redundant information among different variables by diagonalizing the covariance matrix, so as to compress the multi-band image information to a preset number of bands and achieve the effect of feature extraction. In this study, principal component analysis was performed on 50 spectral samples of each of the three types of features in the 400-700 nm and 400-1000 nm bands.  
The corresponding contribution rates and cumulative contribution rates of the first six principal components are presented in Table 1. For the 400-1000 nm band range, the contribution of the first principal component was 77.35%, the cumulative contribution of the first three principal components was 95.81%, and the cumulative contribution of the first six principal components reached 98.16%. For the 400-700 nm band, the contribution of the first principal component was 76.02%, the cumulative contribution of the first three principal components reached 99.07%, and the cumulative contribution of the first six principal components reached 99.59%. From the data in the table, it can be seen that, under the premise of a certain number of principal components, the contribution rate of the spectral data in the range of 400-1000 nm was higher compared with that in the range of 400-700 nm when the spectral data were selected for analysis. Meanwhile, the cumulative contribution rate of the first three principal components reached more than 95%, indicating that the first three principal components could be used to describe most of the features.
## 4 Results  
### Features of Time-Frequency and Wavenumber Spectrum  
The shear spectra in the time-frequency and wavenumber domain have some significant features, which are essential to corroborate the turbulence data quality and also to dominate the turbulent energy transfer. For the collected original data of shear velocity, strong fluctuations at small scales are visible (Figure 4). Naturally, when the measured shear time series is decomposed by the VMD method, eight significant IMF modal components with different time scales and mean frequencies are generated to analyze the original signal here. Subsequently, the VMD decomposition results of turbulence shear in the time domain are extracted to their respective IMF modes, as shown in Figure 4 (left panels). Additionally, the features of the frequency spectrum for each extracted IMF mode in the frequency domain are transformed using FFT transform, as shown in Figure 4 (right panels). All the IMF modal components have different frequency components and significant differences in frequency bands. The VMD decomposition results of turbulent shear are effective to extract the multiscale and multi-frequency IMF components due to the contribution of the active turbulent eddies.  
Shear spectra in the wavenumber domain are commonly used to evaluate the order of magnitude of dissipation rates at which energy flows are irreversibly converted to heat by molecular friction from the largest scales to the smallest scales in the integrated dissipation process. Additionally, the Nasmyth spectrum, as a classical standard spectrum, is used to evaluate the measured shear wavenumber spectra. As shown in Figure 5, the shear wavenumber spectra density and Nasmyth spectra calculated using Equation (6) are given for four profiles. The order of cleaned TKE dissipation rates is resolved as low  
Figure 4: VMD decomposition results of shear signals in time-frequency domain.  
as \(10^{-9}\) Wkg\({}^{-1}\). The raw shear spectra and the cleaned spectra using the coherent noise removal method [23] conform well with the standard Nasmyth spectrum both in shape and level, which further proved that the platform is reasonably stable in the deep sea and the measured shear data are quietly effective.
## 3 Results and Discussion
### Experiment 3: Upscaling Starch-Rich Biomass Production  
Very few studies have tested starch production from C. _vulgaris_ at a larger scale, and most experiments done so far, including our studies in flat panel reactors, have adopted a protocol where the cultures were centrifuged and resuspended in a starvation medium. Such protocols cannot be applied on large scales.  
Thus, two further separate experiments were performed with C. _vulgaris_ in 25 L tubular photobioreactors inoculated at a low density (<0.1 g L\({}^{-1}\)) in a modified M8a medium containing 20% of the original nitrogen content. Giving a limited amount of nitrogen has allowed the algae to proliferate during the first cultivation days before exhausting the nitrogen and beginning the transition to starvation metabolism.  
The difficulty when limiting the amount of nutrients supplied, rather than completely removing it at a known time point by centrifugation lays in identifying the exact moment of nutrient exhaustion. With a species like C. _vulgaris_, which in the present experiments has again been shown to accumulate maximum starch between 12-24 h from starvation, monitoring nitrogen consumption is essential to both (i) increase the light supply at the correct moment and (ii) harvest the algae before starch degradation begins.  
To monitor consumption nitrogen was consequently checked daily with rapid test strips, and the results were subsequently confirmed with ion chromatography. In Figure 6a we see that the 20% of N source in these conditions was exhausted within three to four days from inoculation, allowing for the culture to acclimate and grow while the light was daily increased until it was finally set to 1300 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) on t0 of starvation to support starch production. Starch follow up showed an increase over the 12 h after starvation up to 44% in both experiments performed.  
Figure 6: Time sequence of two experiments growing C. _vulgaris_ in limiting medium containing 20% nitrogen in experiment 3. Experiment A was harvested after 24 h from starvation (x = 24), experiment B was harvested 36–48 h after starvation. (**a**) Nitrate content in the medium measured with ion chromatography (solid line) and starch accumulation in the cells (dotted line) (**b**) Absorbance at 750 nm.  
Experiment A was followed up further to 48 h after starvation, and notably the starch content remained above 40% even after the 12 h peak. In experiment B, where starch-rich biomass was harvested between 12 and 24 h after starvation, the growth was altogether faster than in the former trial (Figure 6b) achieving higher biomass productivity and a final starch production upon the harvesting of 1 g L\({}^{-1}\). Harvesting 12-24 h after the onset of stress implies that nitrogen starvation mainly impacted starch accumulation, not other components of the biomass since other known effects of starvation usually require longer incubation times. Additionally, through a short-term stress phase, the supply of high light intensity, which is a factor that may greatly influence the cost of production, is limited to a brief time.
# Hybrid Graph Models for Traffic Prediction
## 6 Experimental Results
### Ablation Studies
#### 6.2.2 Impact of DGCN  
In order to verify the importance of DGCN, some experiments were conducted to analyze the impact of different variants of DGCN on predictive performance. The HGM-V6 uses temporal attention to model temporal information in DGCN and the HGM-V7 removes the entire DGCN branch. The experimental results are recorded in Table 3. Compared with the HGM, the HGM-V6 uses temporal attention to extract information. Since the proposed method continuously updates the dynamic adjacency matrix during the model training process, the dynamic adjacency matrix cannot effectively represent the topological information of the network at the initial stage, so temporal attention does not perform well on the dynamic adjacency matrix. Furthermore, if the proposed method removes the DGCN branch, the predictive performance can also drop. On the METR-LA dataset, MAE, RMSE, and MAPE decreased by 0.22, 0.46, and 0.89% over the next 12 time steps, respectively.
## 5 Food Security in the Face of Increasing Food Demands
Given their low crop productivity rates, Sub-Saharan Africa's fertilizer application rates are still far too low, averaging 10 kg of nutrients per hectare (ha) arable land (with huge differences between Sub-Saharan countries and with South Africa as the only significant African importer), in comparison to 86 kg/Ha in South Asia, 118 kg/Ha in Latin America, 198 kg/Ha in an average middle-income country and 288 kg/Ha in a high-income country [77]. On the other hand, with its own maize yields decreasing as mentioned above, Morocco is simultaneously the largest exporter of phosphate rock (PR) worldwide, with 2,575 kilotons in 2010 [76]. Another critical dimension of food security relates to food waste. A study by the Swedish Institute for Food and Biotechnology (SIK) and the FAO in 2011 confirmed that approximately 30% of edible food for human consumption will be wasted (or lost), equalling approximately 1.3 billion tons per year [78]. Although a global phenomenon, big regional differences exist with respect to food waste occurring along the food supply chain (FSC). Food losses occur at the beginning of the FSC in production post-harvest and processing stages, whereas food waste occurs during retail and final consumption [79]. This distinction is important in order to highlight regional peculiarities in consumption patterns. The study further outlines that the per capita food waste and losses of 170 kg/year in Sub-Saharan Africa can mostly be attributed to losses in the early stages of the FSC, whereas only 6 kg/year can be attributed to food wasted by consumers. In comparison, per capita food wasted by consumers in Europe accounts for 95 kg/year in Europe and 95-115 kg/year in North-America, as part of a total of 280 kg/year in Europe and 300 kg/year in North America per capita food losses and waste. At a global level it shows that 222 mega tons of food waste at the consumer level in industrialized countries equal almost the total net food production of 230 mega tons of Sub-Saharan Africa. In addition, food losses and waste become a growing global problem with respect to their deposition at landfills, leading to pests, toxic liquids, odours and methane gas as a tremendous risk for climate change. While recent policies are beginning to tackle the issue of food waste (e.g., in France, a law was introduced to prohibit supermarkets from discarding food, requiring it to be donated) [80], the success of such pocket-efforts remains to be seen.
## 1 Introduction
GNS3, the selected network simulator in this study, contains Ubuntu NFV solutions for data transmission. Ubuntu virtual machines work as clients and servers. Wireshark, an open-source packet analyzer, has been selected to monitor the data transmission between client and server. In addition, cybersecurity encryption can be implemented in Ubuntu devices to avoid security risks. Digital twin simulation allows basic testing of 5G technologies, and radio coverage graphics visualize the interaction between 5G radio and its surrounding environment. The combination of NFV technologies and additional modules allows end-to-end connection between client and server. Implementing this set of tools can help speed up the adoption process of 5G technologies for non-familiar adopters.
# Detection of SQL Injection Attack Using Machine Learning Techniques: A Systematic Literature Review
## 5 Discussion  
Machine Learning and Deep Learning Techniques for Detection of SQL Injection Attacks (Related to Q1)  
In this section, the results reported in Section 4 are discussed. In related studies, various algorithms and techniques can be used for detecting SQL injection attacks. Table 1 summarizes the algorithms under review, in addition to the employed datasets and evaluation methods.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Dataset Size**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**FFR**} & \multirow{2}{*}{**FNR**} & \multirow{2}{*}{**TP**} & \multirow{2}{*}{**FN**} & \multirow{2}{*}{**FP**} & \multirow{2}{*}{**TN**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multicolumn{1}{c}{\multirow{2}{*}{**F1**}} & \multirow{2}{*}{**Score**} \\ \cline{3-3} \cline{8-16}  & & & & & & & & & & & & & & & & \\ \hline \multirow{16}{*}{[13]} & Naive Expression & & & - & 10.9\% & 16.7\% & 34.9\% & 18.2\% & - & - & - & - & - & - & - & - \\ \cline{2-16}  & SVM & & & - & 4.1\% & 8.3\% & 41.4\% & 18.2\% & - & - & - & - & - & - & - & - \\ \cline{2-16}  & LID & & & - & 0.0\% & 0.0\% & 41.8\% & 18.2\% & - & - & - & - & - & - & - & - \\ \cline{2-16}  & RF & & & - & 0.0\% & 0.0\% & 37.9\% & 9.1\% & - & - & - & - & - & - & - & - \\ \cline{2-16}  & K-means & & & - & 0.0\% & 0.0\% & 37.9\% & 9.1\% & - & - & - & - & - & - & - & - \\ \hline \multirow{16}{*}{[14]} & CNN-BLSTM &
\begin{tabular}{c} Collected from \\ variation technique \\ \end{tabular} & \begin{  
Table 1 shows that most of the studies focused on using supervised machine learning to detect and classify SQL injection attacks; 89% of the studies used supervised learning, and 4% used unsupervised learning and mixed learning, whereas 3% used other types of learning, as shown in Figure 2.
###### Abstract  
Orthogonal frequency division multiplexing (OFDM) is an efficient multicarrier scheme that uses different types of guard intervals such as cyclic prefix (CP) and known symbol padding (KSP) (zero padding (ZP), unique word (UW), etc.) in block formation. Among these guard intervals, CP varies for each block, while other guard intervals remain fixed from block to block. These guard intervals efficiently perform channel estimation, synchronization and remove inter-block interference (IBI); nevertheless, none of the existing schemes develop any relationship between the guard interval (sequence) and the data symbols on different subcarriers of the OFDM block. We present a new idea of selecting the guard interval based on the data symbols of a subset of subcarriers in the block and exploit the high auto-correlation of the selected guard sequence to improve the bit error rate (BER) performance of the system. The results based on a fair comparison show that our enhanced orthogonal frequency division multiplexing (eOFDM) scheme inherits significant improvements in BER and the capacity of a multicarrier system as compared to the existing techniques.  
guard intervals; OFDM; known symbol padding; cyclic prefix; zero padding 2022  
[PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] BER Reduction and Capacity Enhancement with Novel Guard Sequence Selection for Multi-Carrier Communication. _Sensors_**2023**, _23_, 217. [[https://doi.org/10.3390/s23010217](https://doi.org/10.3390/s23010217)]([https://doi.org/10.3390/s23010217](https://doi.org/10.3390/s23010217))  
Academic Editor: [PERSON]  
Received: 25 November 2022  
Revised: 16 December 2022  
Accepted: 20 December 2022  
Published: 25 December 2022
## Materials and Methods
### Analysis of Oil Volatile Compounds  
Extraction and analysis of oil volatile compounds were performed by means of HS-SPME/GC-MS-FID according to [PERSON] et al. [30]. Samples for volatile analyses (0.5 g) were prepared in 10-mL vials and placed in a vial heater at 40 \({}^{\circ}\)C for a 10 min equilibration time. Headspace volatile compounds were adsorbed onto a headspace-solid-phase microextraction (SPME) fiber divinylbenzene/carboxoen/polydimethylsiloxane (DVB/Carboxen/PDMS) 50/30 \(\upmu\)m (Supelco Co., Bellefonte, PA, USA). Sampling time was carried out at 40 \({}^{\circ}\)C for 50 min. Desorption of volatile compounds was completed into the GC injector. Compound identification was carried out by means of a 7820A/GC-5975/MSD system (Agilent Technologies, Santa Clara, CA, USA), equipped with a DB-Wax capillary column (60 m \(\times\) 0.25 mm i.d., film thickness, 0.25 \(\upmu\)m: J and W Scientific, Folsom, CA, USA). Operating conditions were as follows: He as carrier gas at 1 mL/min flow rate; injector in splitless mode at 250 \({}^{\circ}\)C; column held for 6 min at 40 \({}^{\circ}\)C and then programmed at 2 \({}^{\circ}\)C/min to 168 \({}^{\circ}\)C; the mass detector ran in the electronic impact mode at 70 eV, the source temperature was set at 230 \({}^{\circ}\)C and the mass spectra were scanned at 2.86 scans/s in the \(m/z\) 40-550 amu range. Compound identification was performed by matching against the Wiley/NBS Library, and by GC retention time against standards. For quantitative purposes, the volatile fraction was analyzed three times on a HP-6890 GC equipped with a FID detector (Agilent Technologies, Santa Clara, CA, USA) using also a DB-Wax capillary column operated under the following conditions to reproduce the same retention times for volatile compounds than those of the 7820A/GC-5975/MSD system: N\({}_{2}\) as carrier gas at 17 psi constant pressure; injector and detector at 250 \({}^{\circ}\)C; column held for 6 min at 40 \({}^{\circ}\)C and then ramped up at 2 \({}^{\circ}\)C/min to 168 \({}^{\circ}\)C. For quantification, calibration curves for each compound were made in re-deodorized high-oleic sunflower oil. Limits of quantitation (\(\geq\)three times LOD) and concentration ranges were established according to typical contents in virgin olive oil volatile fractions. Linear regression curves were found for all the compounds with regression coefficients higher than 0.989. A blank containing no oil and a mixture of volatile standards, used at the beginning and regularly during the sample analyses, were run as controls. The volatile compounds were clustered into different groups according to the number of carbons (C6 and C5 compounds) and esters originated through the LOX pathway and the branched-chain (BC) volatile compounds coming from the amino acid metabolism.