esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Profile Dual-Polarized Magneto-Electric Dipole Antenna for 5G Applications
## 2 Materials and Methods
### Magnetic Dipole Structure  
In the design of many MEDs, magnetic dipoles and electric dipoles are integrated, which leads to complicated or unstable structures of MEDs. In the proposed antenna, the magnetic dipoles, that is, the vertical metal walls, were separately processed into a stable structure, as shown in Figure 1c. The magnetic dipoles were designed as four three-sided structure units, in addition to the vertical metal wall of each unit as a magnetic dipole; another metal wall was added at the bottom to make the structure of the magnetic dipole more stable.  
According to the principles of antenna theory, the antenna usually resonates at about half a wavelength, which is applicable to electric dipole antennas and magnetic dipole antennas. It can be inferred that the magnetic dipole antenna can resonate at the original frequency, as long as the total current path is similar to that of the prototypical antenna, thus realizing the design of a low-profile antenna. The methods of reducing the MED profile were described in Section 1. Here, the meandering slots method adopted by the proposed antenna is introduced in detail.  
Figure 3 exhibits the theoretical current distributions on the magnetic dipole, i.e., the vertical metal wall. The total length of the magnetic dipole was close to half a wavelength; that is, the total length of the current path on the magnetic dipole was close to half wavelength. Slots were added to the magnetic dipole, as slots prevent the conductive current from passing through; the conductive current bypasses the slots and continues to flow after encountering the metal slots, as shown in Figure 3b. The path length of the current increases, which is equivalent to prolonging the length of the magnetic dipole, and the purpose of meandering can be achieved by etching the slots. That is, the slots can reduce the profile of the antenna when the center frequency is constant. Next, it is necessary to explain how and where the slots were etched.  
First of all, the current distribution must be analyzed when selecting the slot position, and the best meandering effect can be obtained when the slot is opened at the position with the maximum current. As shown in Figure 4a, the maximum current distribution of the slotless magnetic dipole tended to be distributed closer to the vertical part of the feeding structure. Therefore, the location of the slots was preferentially chosen according to the specific current distribution in the actual design. Taking the proposed low-profile MED obtained after etching slots as an example, the current distribution on the vertical wall is shown in Figure 4b. It can be seen that when the currents flowed from bottom to top after the slots were etched, the currents of the vertical wall flowed along the meandering slots,  
Figure 3: Theoretical current distributions on the metal wall (**a**) without meandering slots and (**b**) with meandering slots.  
thus realizing meandering. The purpose of extending the current path with the meandering current was eventually achieved.
## 2 Related Works
While the model demonstrated the ability to identify malicious patterns, its performance was hindered by data scarcity, limiting its generalization to a wider range of malware variants. In this extended work, we enhance feature representation by pre-training Code2 Vec, \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Sample** & **Data** & **Method** & **Algorithm** \\ \hline
[15] (2011) & PDF & Source codes & Lexical analysis of JavaScript code and obfuscation in benign JavaScript & Machine learning \\ \hline
[16] (2012) & JavaScript & Source codes & Investigation and detection of obfuscated JavaScript codes & Bayesian method \\ \hline
[21] (2014) & PDF & Source codes & Reference chain5, compression, redundant information in JavaScript & Machine learning \\ \hline
[17] (2019) & JavaScript & Bytes & Propose a novel model combining LSTM and CNN for sequence learning & LSTM+CNN \\ \hline
[18] (2019) & PDF & Bytes & Manual labeling of the byte sequences, investigating the structure of the data & CNN \\ \hline
[19] (2020) & HWP & Bytes & Malware detection, propose a padding method to incorporate the length of byte streams & CNN (SPAP) \\ \hline
[22] (2021) & JavaScript & Source codes & Oversampling, feature extraction using Doc2 Vec and attention & Doc2 Vec, attention \\ \hline
[20] (2023) & MS Office & Bytes & Malware detection, propose a method that aggregates the stream-level results to obtain file-level results & CNN (MalCov, SPAP) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of previous works on document-type malware detection. which captures both character and word-level semantics more effectively. Additionally, we employ SeqGAN for data augmentation, addressing data scarcity and improving the model's robustness. This combination leads to superior classification performance and better adaptability to emerging malware threats. Through the literature review, several research gaps were identified as follows: First, many studies have developed detection models primarily focused on executable files. There is a noticeable absence of research on document-based malware, despite the increasing prevalence of document malware threats. Second, although bytes are commonly used as input for deep learning-based language models, script codes remain underexplored, despite their potential to benefit significantly from such models. Some studies use an n-gram language model to extract n-grams from bytes and apply machine learning models. Third, although deep learning models such as CNN and recurrent neural networks have been widely used, the state-of-the-art model known as Transformer has not yet been applied to a scripting code. Attention-based language models, which effectively focus on the malicious part in sequential input, have shown promise in malware detection. To overcome these drawbacks, we propose the development of a language model specifically tailored for malware detection.
# A Novel FFT_YOLOX Model for Underwater Precious Marine Product Detection
## 4 Conclusions  
In this paper, a novel detection model FFT_YOLOX based on a modified YOLOX is proposed. Firstly, a unique FFT_Filter was presented, which is a computational efficient and conceptually simple architecture to capture global information of images. Then, a novel FFT_YOLOX model was introduced with fewer model parameters and FLOPs by replacing the standard 3 \(\times\) 3 kernel in the original backbone of YOLOX model with a FFT_Filter for an underwater object detection vision task. This method could quickly and automatically locate the position of a precious marine product, e.g., a sea cucumber, a sea urchin, or a scallop, in this study. Experiments conducted the merged URPC in the context of underwater object detection, demonstrating the effectiveness and generalization of the visual representation of our proposed FFT_YOLOX model. The results of the comparison with the original YOLOX method demonstrated the superiority of the proposed method. The proposed efficient algorithm will help in the image manipulation detection domain and also paves the way for future research in detecting further types of marine precious products.  
Conceptualization, P.W. and K.C.; methodology, K.C.; software, P.W.; validation, Z.Y. and H.P.; formal analysis, Z.Y.; investigation, P.W.; resources, K.C.; data curation, T.Z.; writing--original draft preparation, P.W.; writing--review and editing, K.C.; visualization, K.C.; supervision, K.C.; project administration, Z.Y.; funding acquisition, K.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by The Foundation of Liaoning Province Education Administration under Grant JL202015, The key R & D projects in Liaoning Province under Grant  
Figure 6: The enlarged important areas of the detection results of different models.  
2020 JH2/101000043 & The Science and Technology Major Special Plan Project of Liaoning Province under Grant 2020 JH1/10200002.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author, [EMAIL_ADDRESS], upon reasonable request.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.
# Tip Estimation Method in Phantoms for Curved Needle Using 2D Transverse Ultrasound Images
## 2 Materials and Methods  
The proposed needle tip estimation method in successive transverse US images can be divided into three stages: needle detection, needle shape tracking, and tip estimation. The processing diagram are shown in the Figure 3. The needle location will be manually selected as an initial region of interest (ROI) by the binary method in the first US image. After that, the prediction of needle position from a Kalman filter can be transformed in the transverse US images. At the same time, the next needle position in this ROI can be found through the histogram method. The KF is then updated for the current precise needle position and prediction of the next position. After all the cross-sections of the needle have been collected,  
Figure 2: The proposed tip estimation method. STEP 1: 2D transverse US images with needle cross-sections are collected by using RUS; STEP 2: Needle cross-sections are detected and tracked in the successive US images; STEP 3: Needle shape is constructed and its tip is estimated.  
the needle shape can be fitted by the curve fitting method (part C in Figure 3). Finally, the position of the needle tip can be estimated from the curve fitting result based on the length of the needle. In this study, the ROI is set as a square window with a length of three times larger than the needle diameter and its center represents the needle position. Needle detection (part B in Figure 3) is mainly about image processing, while the Kalman filter is used for needle shape tracking (part A in Figure 3).
## 5 Computational Experiments
### Comparison with Existing Distributed Algorithms  
In this subsection, Algorithm PTO is compared with three competitors (i.e., CNP [24], DETS [26], and ADMM [27]), as described in Section 5.1. The comparison results for small-scale, medium-scale, and large-scale instances are grouped in Tables 6-8, respectively. The performances of the four algorithms vary markedly in terms of \(aRV\), \(aCT\), and \(aRT\), and optimal values are marked in bold. These tables show that PTO obtains the optimal \(aRV\) and \(aCT\) in almost all instances, and only in a few cases does DETS perform the best. This \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Instance Type** & **Constellations** & **Task Number** & **Task Density** & **Deadline** & **Combination Number** \\ \hline Small & A, B & 3, 5 & low, high & emergency, & \(2\times 2\times 2\times 2=16\) \\ Medium & C, D & 10, 20 & low, high & emergency, & \(2\times 2\times 2\times 2=16\) \\ Large & E, F & 50, 100 & low, high & emergency, & \(2\times 2\times 2\times 2=16\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameter Size for Each Instance Type. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Default Values** \\ \hline Data size of tasks \(\lambda_{i}\) & 10–30 Mbit \\ Workload of task \(\mu_{i}\) & 1–1.5 Kcycle/bit \\ Computing capacity of satellites \(C_{s}\) & 5 GHz \\ Memory space of satellites \(M_{s}\) & 500 Mbit \\ Energy consumption limitation of satellites \(H_{s}\) & 5000 J \\ Rate of LISL \(r^{LISL}\) & 100 Mbps \\ Transmission power of RISL \(\varepsilon_{1}\) & 2 w \\ Transmission power of LISL \(\varepsilon_{2}\) & 1 w \\ Effective capacitance coefficient \(\kappa\) & \(10^{-28}\) \\ Weight factors \(\alpha\) and \(\beta\) & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulation Parameters. is because DETS uses a directed diffusion algorithm that considers communication and computation resources, yielding good solutions for small-scale instances. However, it tends to become stuck in local optima for large-scale instances. Figure 6 further illustrates the line charts of \(aRV\), \(aCT\), and \(aRT\) for these algorithms across different instances.
## 3 Results and Discussion
### Simulation of Titration Curves  
It was possible to simulate the curves of FOS/TAC titration with Scilab, which then allowed finding the relationships to distinctly identify bicarbonate and acetic acid contents.  
The lines in different colors in Figures 2 and 3A,B represent the simulated concentrations of two compounds (sodium bicarbonate and acetic acid) using the FOS/TAC titration of Nordmann. Comparing them with the experimental results, it was shown that the simulation of the titration from the acid-base equilibria allowed a good prediction of the concentrations of bicarbonate and acetic acid when the Nordmann method was used. The R\({}^{2}\) of 0.991 and 0.997 was, respectively, obtained. The mean absolute percentage errors (MAPE) were found at 5% and 7%, respectively. This indicated that by using the numerical tool, we could safely simulate the titration of the two compounds by avoiding the material and experimental constraints (e.g., detection limit of the titrator, uncertainties related to the preparation of solutions at low concentrations of FOS and TAC).  
Consequently, the results of the simulation instead of the experiment were used to calibrate the Nordmann FOS/TAC method and, accordingly propose new formulas to calculate the FOS and TAC more precisely.  
Figure 3: (**A**) Experimental (points) and simulated (lines) acetic acid concentrations versus true acetic acid concentrations at different sodium bicarbonate concentrations; (**B**) Figure 3A zoomed for [HAc] between 0 and 5000 mg\(\cdot\)L\({}^{-1}\).
# Adaptive Virtual RSU Scheduling for Scalable Coverage under Bidirectional Vehicle Traffic Flow
## 6 Performance Evaluation
### Cache Replication
#### 6.3.1 Special Case Scenario  
In this section, we will evaluate the system performance in a special case scenario, in which the popularity distribution of content replica can be known in advance, and the request demand can be redirected to any RSU if possible. Accordingly, we can have the number of minimal RSUs deployed in the optimal solution as the benchmark, and compute the relevance of other three strategies in Figure 11. When \(s=0.2\), we can have a uniform distribution with an almost equal popularity for each replica. In this case, the proposed adaptive greedy strategy and uniform distribution can have an approximate optimal solution. This is because each replica may have similar request demands. Meanwhile, the Top-K solution has to deploy more RSUs for the coverage of the whole service, as only the most popular content is replicated and the popularity difference is minor. As the skewness becomes higher, we can see that the Top-K strategy will have a better performance and the uniform distribution strategy will have a worse performance. On the other hand, our proposed adaptive-greedy strategy can have a close-to-optimal performance, and can be scalable to the change of request popularity skewness.  
Figure 11: Performance comparison with different skewness.  
Figure 10: Connectivity with vehicle density variation.
# Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize
## 2 Materials and Methods
### Data Processing and Modeling Methods
#### 2.3.4 K-Nearest Neighbor Algorithm  
K-nearest neighbor algorithm (KNN) is a classification method based on the closest training examples in the feature space. If the majority of an unknown sample's K-nearest neighbors in the training set belong to a certain class, then this unknown sample is classified as this class. KNN is suitable for multi-classification problems, and the parameter K influences the performance of the KNN model [29]. In this paper, the value of K is 4 throughout.
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 3 Reliability of Tunnel Lighting System
### Main Problems of Existing Tunnel Lighting Modes  
According to the relevant literature and engineering practices, tunnel lighting modes have gone through the fixed lighting mode, time-series-based lighting mode, initially intelligent lighting mode with adjustable luminance, and intelligent lighting mode with variable color temperature and luminance, and these are featured in Table 11.  
In practice, each of the above modes has some issues to varying degrees, such as:  
* Some modes pay too much attention to saving energy.
* Some modes take less care in handling equipment failures.
* In most cases, tunnel operation relies heavily on the field experience of the operation manager without sufficient theoretical support.
* In most cases, inadequate attention is paid to monitoring whether the inside lighting adjustment is accurately performing as the preset dimming strategy expects.
* There is still insufficient emphasis on the reliability of lighting systems.
## 3 Results and Discussion
### Milk and Yoghurt Proximate Composition
[PERSON] et al. [68] determined the composition of commercially available full-fat sheep milk yoghurt produced by small dairy companies in Greece. Their findings were as follows: moisture content ranged from 77.00 to 86.76 g per 100 g, ash content from 0.87 to 0.98 g per 100 g, protein content from 4.41 to 5.03 g per 100 g, and fat content from 2.31 to 6.89 g per 100 g. Yoghurt proximate composition differed significantly from the composition reported by [PERSON] et al. [69], that examined commercially available yoghurt of varying types, including traditional and strained yoghurt, as well as low-fat and full-fat varieties. Finally, the composition of commercially available traditional type full-fat yoghurt (12 samples), based on packaging nutrient information, as derived from the websites of main supermarkets that trade in Greece is as follows: protein content ranges from 5.2 to 5.9 g per 100 g, fat content from 6.6 to 6.7 g per 100 g, and carbohydrates 3.2 to 5.0 g per 100 g.  
The yoghurt in this study shows notable differences compared to the reported data. Moisture content (82.32-83.39%) is consistent with the findings of [PERSON] et al. [68] (77.00-86.76%), while yoghurt from the UOP and POP treatments were denser. Protein content in the UOP treatment yoghurt (5.93%) exceeded the upper range reported by [PERSON] et al. [68] (5.03%) and matched the supermarket data for traditional full-fat yoghurt (5.2-5.9%). Fat content (6.06-6.79%) matches the information provided on supermarket websites (6.6-6.7%) and falls at the higher end of the range reported by [PERSON] et al. [68] (2.31-6.89%). Ash and carbohydrate content align with previously reported ranges, indicating that the yoghurt from this study, particularly UOP, provide enhanced nutritional profiles compared to traditional options. Notably, the protein content in yoghurt from ewes on the UOP and POP diets exceeded the 5.5% minimum required by Greek food legislation [28], ensuring compliance with national standards. By contrast, samples from the Control diet had slightly lower protein content. However, protein levels below the 5.5% minimum have also been observed in commercially available yoghurt, both in laboratory-tested samples (4.27-5.03%) [69] and in values reported on product labels (5.2-5.9%). This indicates that deviations from national standards are not uncommon. Furthermore, the Codex Alimentarius [70] sets a minimum protein requirement of 2.7% and a maximum fat limit of 15% for fermented milk products, confirming that yoghurt from all treatment groups comply with these standards.
# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 4 Clutter Statistics and Detection  
As mentioned in the introduction section the ship detection method used in this paper is based on a CFAR. CFAR algorithms are pixel-based. Therefore, for a given \(i\)th complex amplitude pixel \(Z_{\text{i}}\), where \(i=1,\ 2,\ldots T_{\text{P}}\) and \(T_{\text{P}}\) is the total number of pixels, there exist two hypotheses \(H_{0}\) and \(H_{1}\)[17]  
\[H_{0}:\ Z_{\text{i}} =C_{\text{i}}+N_{\text{i}},\] Moving target is absent (11) \[H_{1}:\ Z_{\text{i}} =C_{\text{i}}+N_{\text{i}}+S_{\text{i}},\] Moving target is present (12)  
where \(C_{\text{i}}\), \(N_{\text{i}}\) and \(S_{\text{i}}\) are the clutter, noise and target signals, respectively. The sea clutter models discussed in this section are formulated based on the backscatter intensity in Doppler domain, therefore for simplicity, \(\left|Z\right|^{2}=I\) is written. Once a distribution function \(f(I)\) is chosen to model the ocean backscatter, and its parameters are estimated from the ocean samples, then the PFA for a given threshold \(\eta\) is given by  
\[P_{\text{fa}}=\ \int_{\eta}^{\infty}f(I)\ dl. \tag{13}\]  
In the following sections, different sea clutter models and the methods used to determine the unknown sea clutter statistics are discussed.
## 3 Results and Discussion
### Effect of F-T Cycles on SWCC for Three Dry Densities  
Table 2 shows that the saturation-moisture content of the low-density sample is higher than that of the high-density sample. The low-density sample reaches the AEV before the high-density sample. At this time, the water begins to flow in the liquid phase and quickly dissipates as the suction increases, and the SWCC shows a turning point. In the transition section, the slope \(s_{1}\) of the low-density sample SWCC in the semi-logarithmic coordinate system is larger than that of the high-density sample. This is due to the fact that the low-density soil sample has a looser particle structure with a larger pore size and a total pore volume [35]. The smaller pores of high-density soil samples make the pore water more difficult to dissipate, so it has a higher moisture- holding capacity.  
After the soil sample undergoes the F-T cycles, the characteristic parameters show similar trends as when the density decreases. Table 2 shows that as the number of F-T cycles increases, the saturated water content of the sample increases slightly by 0.22% to 0.45% compared to the unfrozen sample. Samples subjected to F-T cycles will begin to dissipate moisture at lower inhalation locations, that is, the AEV of this kind of samples will become lower. After seven F-T cycles, the AEV of the three dry density samples will reduce to 76%, 68%, and 59% of the initial value, respectively. The \(s_{1}\) increases as the number of F-T cycles increases. The trend of the dehumidification rate of the transition section is characterized by \(s_{1}\) and \(s_{1}\) increased with the increase in the number of F-T cycles. Compared with the original curve, the \(s_{1}\) calculated by the SWCC of three dry density samples increased by about 0.8% after seven F-T cycles. With an increase to the suction force, the unfrozen sample first reaches the residual water content, which makes the dehydration difficult, and the pore-water-dissipation rate is significantly reduced. When the samples under different F-T cycles reached this residual state, the pores of the unfrozen sample held more water.