esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Chirp Transform Spectrometer  
### Principle of the CTS System  
Based on the radar pulse compression technology and the special device of SAW filter with dispersion characteristics, the classical CTS can realize spectrum measurement by physical transform. The measured signal \(f\left(t\right)\) is firstly mixed with a chirp modulation signal to expand its frequency bandwidth. The chirp modulation signal is a linear frequency modulated signal having a certain dispersion constant \(\left(\mu_{e}\right)\) and time-bandwidth product \(\left(T_{e}B_{e}\right)\), where \(B_{e}\) represents the bandwidth of the chirp signal. This chirp signal can be expressed as a quadratic phase modulated signal:  
\[f_{chirp}\left(t\right)=e^{-j\pi\mu_{e}t^{2}/T_{e}}\qquad 0<t<T_{e}. \tag{1}\]  
The modulated signal with expanded bandwidth resulting from the mixer can be expressed as  
\[f_{mix}\left(t\right)=f\left(t\right)\cdot f_{chirp}\left(t\right)=f\left(t \right)\cdot e^{-j\pi\mu_{e}t^{2}/T_{e}}. \tag{2}\]  
This process is known as the signal expansion, which substantially expands the bandwidth of the measured signals. Subsequently, the expanded modulated signal carrying the information of the measured signal will be amplified, filtered, and compressed into pulses by a SAW filter, which is called the compressor. Considering a perfect match between the expander and compressor, the SAW filter has a certain dispersion constant \(\left(\mu_{c}\right)\), which is a complex conjugate with that of the chirp signal \(\left(\mu_{c}=-\mu_{e}\right)\) and time-bandwidth product \(\left(T_{e}B_{c}\right)\), where \(T_{c}\) and \(B_{c}\) represent the time duration and working bandwidth of the compressor, respectively. The output signal from the compressor is the convolution of the system impulse response \(h\left(t\right)\) and the modulated signal \(f_{mix}\left(t\right)\):  
\[f_{compress}\left(\tau\right)=\int_{-\infty}^{+\infty}f_{mix}\cdot h\left(\tau -t\right)dt. \tag{3}\]  
Considering \(\left(\mu_{c}\right)=\left(-\mu_{e}\right)\) and ignoring the time length of the expander and compressor:  
\[\begin{split} f_{compress}\left(\tau\right)&=\int_ {-\infty}^{+\infty}f\left(t\right)\cdot e^{-j\pi\mu_{e}t^{2}}\cdot e^{j\pi\mu_ {e}\left(\tau-t\right)^{2}}dt\\ &=e^{j\pi\mu_{e}\tau^{2}}\int_{-\infty}^{+\infty}f\left(t\right) \cdot e^{-j2\pi\mu_{e}\tau t}dt\\ &=e^{j\pi\mu_{e}\tau^{2}}\int_{-\infty}^{+\infty}f\left(t\right) \cdot e^{-j2\pi ft}dt.\end{split} \tag{4}\]  
Considering a phase factor \(e^{-j\pi\mu_{e}\tau^{2}}\) added to (4), the compressed signal is just the Fourier transform of the input measured signal. This procedure is called pulse compression of the modulated signal. The spectral information can therefore be found by detecting the time distribution and amplitude envelope of the output pulses in the time domain [15].
## 2 Research Methods and Content
### Geometric Model and Grid Division  
The three-dimensional model created can be saved as an STL file, which can be imported directly into the AVL-Fire preprocessor for automatic grid partitioning. Grid partitioning mainly utilizes Fire's built-in grid generation tool FEP, which includes several main steps such as defining selections, generating volume meshes, FEP settings, and partitioning moving meshes. The geometric structure of the engine encompasses the intake port, exhaust port, intake and exhaust valves, cylinder, and hydrogen injection nozzle, thereby constituting a fully enclosed model, as depicted in Figure 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Bore (mm) & 93 \\ Stroke (mm) & 102 \\ Connecting rod length (mm) & 152 \\ Compression ratio & 10 \\ Maximum power (kW) & 37 \\ Maximum power speed (r/min) & 2500 \\ IVC (\({}^{\circ}\)CA) & \(-\)147 \\ EVO (\({}^{\circ}\)CA) & 140 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic technical parameters of the H\({}_{2}\)ICE.  
In order to further guarantee the calculation accuracy, the grid independence of this model was verified by jet penetration length and cone angle, which can be seen in Figure 2a,b, respectively. It can be observed that the accuracy of the computational results with 1.2 million grids is relatively slightly lower. In the meantime, the results are very close to those of the 1.2 million and 1.8 million grids for both jet penetration length and cone angle. To ensure both computational precision and efficiency, the number of grids during the calculation process was set to approximately 1.2 million. Moreover, the time-step independence was assessed for the model. The refinement levels differed across the various computational domains, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Refinement Region** & **Refinement Region** \\ \hline Cylinder & Fix embedding/2, AMR/3 \\ Intake/exhaust valve angle & Fix embedding/4 \\ Nozzles & Fix embedding/4 \\ Injector exit & Fix embedding/4 \\ Intake port & AMR/3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grid control strategy.  
Figure 1: H2 ICE model.  
Figure 2: Grid independence verification. (**a**) Jet penetration length. (**b**) Jet cone angle.
_Appl. Sci._**2015**, \(5\), 1033-1049; doi:10.3390/app5041033  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Multi-Bit Data Hiding Scheme for Compressing Secret Messages \({}^{\dagger}\)**  
**[PERSON] \({}^{1,\dagger,\ast}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Department of Computer Science and Information Engineering, National Yunlin University of Science \(\&\) Technology, No.123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Graduate School of Engineering Science and Technology Doctoral Program, National Yunlin University of Science \(\&\) Technology, No.123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan; E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) This paper is an extended version of our paper published in The International Multi-Conference on Engineering and Technology Innovation 2015, Kaohsiung, Taiwan, 30 October-3 November 2015.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +886-5-534-2601 (ext. 4515); Fax: +886-5-531-2170.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 12 August 2015 / Accepted: 23 October 2015 / Published: 4 November 2015_  
**Abstract:** The goal of data hiding techniques usually considers two issues, embedding capacity and image quality. Consequently, in order to achieve high embedding capacity and good image quality, a data hiding scheme combining run-length encoding (RLE) with multi-bit embedding is proposed in this paper. This work has three major contributions. First, the embedding capacity is increased \(62\%\) because the secret message is compressed before embedding into the cover image. Secondly, the proposed scheme keeps the multi-bit generalized exploiting modification direction (MGEMD) characteristics, which are effective to reduce modified pixels in the cover image and to maintain good stego image quality. Finally, the proposed scheme can prevent modern steganalysis methods, such as RS steganalysis and SPAM (subtractive pixel adjacency matrix), and is compared to MiPOD (minimizing the power of the optimal detector) scheme. From our simulation results and security discussions, we have the following results: First, there are no perceivable differences between the cover images and stego images from human inspection. For example, the average PSNR of stego images is about 44.61 dB when the secret message (80,000 bits) is embedded for test cover images (such as airplane, baboon, Lena) of size \(512\times 512\). Secondly,on average, 222,087 pixels were not modified after embedding for the cover image. That is to say, \(12\%\) less pixels are modified as compared to the MGEMD method. From the performance discussions, the proposed scheme achieves high embedding capacity and good image quality, but also maintains stego image security.  
**Keywords:** RLE; MGEMD; RS steganalysis; embedding capacity
## 4 Multi-Threaded Control with C/S Architecture Incorporating AES Encryption and TLS Protocol
### Security Analysis
#### 4.3.1 Network Layer Security Analysis  
For Distributed Denial of Service (DDoS) attacks, attackers flood the target server with a large number of requests, making it unable to provide services normally. By using TLS encrypted communication, the server is able to distinguish between legitimate and attacking requests, thus better coping with DDoS attacks.
## 3 Mechanical Overview
### Estimated Production Cost  
The estimated production cost for the joint module is 250 USD, including the following elements:  
* 3D-printing cost;
* cost of COTS mechanical parts (e.g., springs, nuts, bolts, bearings); and
* electrical components (e.g., micro-controller, sensors, and actuator).  
As shown in Figure 3, _Serpens_ allows for realising different connections, such as pitch connection, yaw connection and pitch-yaw connection.  
The pitch connection allows _Serpens_ to move only in 1D, forward or backward. The yaw-connecting configuration makes it possible to move _Serpens_ similar to real snakes with all the joints rotate around the yaw axis. The pitch-yaw-connecting configuration enables _Serpens_ to have some modules that rotate around the pitch axis and others around the yaw axis, respectively. This makes it possible to achieve new locomotion capabilities, such as sidewinding, rotating and rolling [26].  
An exploded view of the joint module design is shown in Figure 4. To strengthen the modular attributes of _Serpens_ through a generic and reusable module-design, each joint module is fitted with a screw-less assembly mechanism, a micro-controller, an actuator, an elastic gear, a rotary encoder, a bearing mechanism and a battery-pack. The proposed design enables the assembly process to be performed in an uncritical manner with respect to ordering and rotation of joints, while contributing to a better weight distribution throughout the body.  
Figure 3: Two different connections can be achieved with _Serpens_: (**a**) pitch connection; (**b**) yaw connection; and (**c**) pitch–yaw connection.  
Figure 4: An exploded view of a joint module: (1) screw-less assembly mechanism; (2) micro-controller; (3) actuator; (4) elastic gear; (5) rotary encoder; (6) bearing; and (7) battery-pack.
## 4 Simulation Results and Analysis
### Analysis of Simulation Results
#### 4.1.2 Analysis of Model Parameters Change on System Performance
As a result, during the second stage, a larger set of residual subcarriers is assigned to VUEs, resulting in an overall enhancement of the system throughput for VUEs. Figure 8: The relationship between the number of subcarriers V and the VUEs throughput. Figure 7: Relationship between vehicle speed and VUEs’ throughput.
## 2 Problem Modeling
### Problem Notations  
As shown in Figure 2, communication parties (denoted by \(r\)) need to find a reasonable power allocation strategy to resist power interference from jammer (denoted by \(m\)), so as to maximize information transmission. Assuming that there are \(n\) (\(n\geq 3\)) available information transmission channels at time \(t\), \(r\) has power budgets of \(S_{r}\), \(S_{r}\subseteq R\), the power allocated by the \(r\) to channel \(k\) is \(R_{k}\), \(\sum\limits_{k=1}^{n}R_{k}\leq S_{r}\), \(k=1,2,3,\ldots,n\), the vector of power allocation is denoted as \(\widetilde{R}=[R_{k}]\), \(1\leq k\leq n\). Similarly, \(m\) has a power budget of \(S_{m}\), \(S_{m}\subseteq R\), the power allocated by \(m\) to channel \(k\) is \(M_{k}\), \(\sum\limits_{k=1}^{n}M_{k}\leq S_{m}\), \(k=1,2,3,\ldots,n\), the vector of power allocation is denoted as \(\widetilde{M}=[M_{k}]\), \(1\leq k\leq n\). As the receiver, if the received SINR exceeds the minimum requirement \(\beta\) (\(\beta\) is determined by the type of service), i.e.,  
\[\frac{R_{k}}{M_{k}+N_{k}}\geq\beta \tag{1}\]  
then information can be transmitted successfully; otherwise, the link is too poor to be useful, \(N_{k}\) is the variance of white noise when the receiver receives signal from channel \(k\).  
Since the BG is a zero-sum game, the utility functions of \(r\) and \(m\) can be defined as  
\[U_{r}(\tilde{R},\tilde{M}) = -U_{m}(\tilde{R},\tilde{M}) \tag{2}\] \[= \sum\limits_{k=1}^{n}W_{k}l(R_{k}-(M_{k}+N_{k})\beta)\]  
Figure 2: Schematic diagram of power allocation confrontation between communication parties and jammer. The communication parties on the left adopt a suitable power allocation strategy to maximize the number of information transmission channels, and the jammer on the right transmits a certain interference power to prevent the information transmission of the communication parties.  
where \(W_{k}\) represents the value of available channel \(k\), \(R_{k}\geq N_{k}\beta\), and  
\[l(R_{k}-(M_{k}+N_{k})\beta)=\left\{\begin{array}{cc}1,&R_{k}-(M_{k}+N_{k})\beta \geq 0\\ -1,&R_{k}-(M_{k}+N_{k})\beta<0\end{array}\right. \tag{3}\]
## 3 Approaches to Building a Parallel Algorithm and Its Software Implementation
### Parallel Algorithm for Scalar Multiplication of Vectors  
We will assume that the storage structure of the elements of vectors \(a\) and \(b\) (which each contain \(N\) elements) is the same as that of vector \(x\) (see Figure 2). We will denote such parts of vectors stored on different processes as \(a_{part(n)}\) and \(b_{part(n)}\). Each such part will consist of \(N_{part(n)}\) elements. To simplify the presentation of the material, consider indexing for vectors stored in the first row of the process grid (with index \(m=0\)). Therefore, \(\texttt{n}\in\overline{0,\texttt{num\_col}-1}\).  
The terms that arise when calculating the scalar product of vectors can be divided into groups as follows (we will also give an example in which only nine MPI processes are involved in the calculations):  
\[(a,b) =\big{(}\big{[}a_{part(0)}\quad a_{part(1)}\quad a_{part(2)}\big{]},\big{[}b_{part(0)}\quad b_{part(1)}\quad b_{part(2)}\big{]}\big{)}=\] \[=\big{(}a_{part(0)^{\prime}}b_{part(0)}\big{)}+\big{(}a_{part(1)}, b_{part(1)}\big{)}+\big{(}a_{part(2)},b_{part(2)}\big{)}=\] \[=\texttt{scalar\_product}_{temp(0)}+\texttt{scalar\_product}_{ temp(1)}+\texttt{scalar\_product}_{temp(2)}=\] \[=\texttt{scalar\_product}.\]  
Then, each process involved in the calculation will perform the operation \((a_{part(\texttt{rank})^{\prime}},\)\(b_{part(\texttt{rank})})\). As a result of this operation, one of the terms that make up the final scalar product will be calculated. Each process participating in the calculation can perform this action independently of other processes while operating only with the data that are located in the memory of this process. We implement these calculations in parallel. Then, the corresponding terms along each line of the process grid must be collected (due to the exchange of messages between processes) on at least one process and summed up.  
Thus, the parallel software implementation of the scalar multiplication of vectors can be formalized in the following form:  
scalar_product_temp = array(dot(r_part, r_part), dtype=float64)  scalar_product = array(0,dtype=float64)  comm_row.Allreduce([scalar_product_temp, 1, MPI.DOUBLE], op=MPI.SUM)  
Note that the result of such a software implementation of scalar multiplication will be stored on all processes of the communicator comm.  
**Remark 5**.: _The downside of this algorithm is that different groups of processes, each of which forms its own communicator comm_rou, perform the same actions. However, this minus is insignificant in the context of the parallel implementation of the conjugate gradient method, in which this algorithm accounts for a negligibly small part of the calculations in the case of solving \"large\" problems._
# Adsorption Separation of Various Polar Dyes in Water by Oil Sludge-Based Porous Carbon
## 3 Results
### The Adsorption Properties of OSC for Organic Dyes
Under these conditions, electrostatic attraction may not be the dominant factor governing the adsorption of MB and CV. Instead, other types of interactions, such as van der Waals forces or hydrophobic interactions [53], may play a more significant role in enhancing the removal of MB and CV by OSC700. As the pH continues to rise, the surface of OSC700 becomes increasingly negatively charged, leading to competitive adsorption and enhanced hydrophobic interactions, which consequently reduce the removal rates of MB and CV. The acidity coefficient of malachite green is 13.78 \(\pm\) 0.29. When the pH exceeds this value, malachite green remains positively charged in the solution. Although this may induce some electrostatic repulsion, Figure 6e demonstrates that the protonation of malachite green does not significantly affect its adsorption by OSC700. For methylene blue, the acidity coefficients are 2.6 and 11.2. As the pH increases, the concentration of hydrogen ions in the solution decreases, reducing electrostatic repulsion. Consequently, the removal efficiency of methylene blue gradually increases, reaching its peak at a pH of approximately 6, where charge equilibrium is established. At this point, electrostatic forces are no longer the primary factor influencing removal efficiency. However, with a further increase in pH, the solution becomes enriched with hydroxide ions, which compete with methylene blue for adsorption sites on OSC700, decreasing removal efficiency. Figure 8: Langmuir (**a**), Freundlich (**b**), PFO kinetic curves (**c**), PSO kinetic curves (**d**), particle diffusion adsorption kinetic curves (**e**), thermodynamic curves (**f**) models for OSC700 adsorption of MG, MB, CV. The acidity coefficient of crystal violet is 0.8 [54]. As the pH increases from 2 to 10, the concentration of hydrogen ions decreases, while that of hydroxide ions increases. The removal efficiency of crystal violet reaches its maximum at a pH of 6, where equilibrium is attained. During the pH increase from 2 to 6, electrostatic repulsion reduces the removal efficiency. As the pH rises from 6 to 10, the competitive adsorption of ions in the solution further affects the removal efficiency, resulting in an initial increase followed by a decrease.
# High-Resolution Mapping of Subsurface Sedimentary Facies and Reservoirs Using Seismic Sedimentology
## 2 Current Understanding of Seismic Resolution  
The concept of seismic resolution evolves with time. Three definitions have been established over the past 70 years based on different criteria [13]. [PERSON] [14] argued that the constant \"peak-to-trough\" propagation time at \(\lambda/8\) (\(\lambda\) = dominant wavelength typically between 15 and 150 m) is the seismic resolvable limit. [PERSON] [15] believed that the \"flat spot\" of the composite waveform at \(<\!\!\lambda/4\) should be defined as the limit of resolvability. [PERSON] and [PERSON]'s [16] \"peak-to-trough separation\" or [PERSON]'s criteria have become widely accepted. They convinced most interpreters that, for a wedge with a step-wise impedance profile (e.g., a moderate-impedance sandstone encased in lower impedance shale above and a higher impedance shale below, see Figure 1), the resolution limit is most practically defined by the \"peak-to-trough separation\" of the composite waveform at \(\lambda/4\), where two peaks (point to right) can be identified with a center shallow trough (point to left). For a wedge with a box impedance profile (e.g., a sandstone encased in higher impedance shale), this corresponds to the amplitude \"tuning point\", where the maximum composite amplitude is measured (see Figure 2). The concept of resolution is closely tied to our cognitive understanding of the geologic subject (e.g., a feature, a 2D bed, or a 3D body). New concepts may emerge in the future to accommodate new interpretational demands. In addition, the definition of seismic resolution is derived from an idealized earth model and represents the best theoretical resolution. However, the actual resolution is not always as high as the theoretical resolution, partly due to imperfect interpretation. We aim to find more efficient techniques and workflows to improve the actual resolution.  
Figure 1: The seismic resolution as defined in a synthetic model that depicts a step-wise impedance profile of a sand wedge surrounded by lower impedance shale above and higher impedance shale below. A zero-phase Ricker wavelet is used. R refers to the reflection coefficient. In this case, [PERSON]’s [PERSON]’s (2016) “peak-to-trough separation” at \(\lambda/4\) is accepted as a seismic resolvable limit.  
Figure 2: At \(\lambda/4\) thickness, maximum amplitude (tuning point) is observed in a synthetic model of a low-impedance wedge (sand) encased in high-impedance rock (shale). A zero-phase wavelet is applied. R refers to the reflection coefficient. The thickness at the tuning point is equivalent to the seismic resolvable limit.
# Diagnosis by SAM Linked to Machine Vision Systems in Olive Fitting Machines
## 1 Introduction
### State-of-the-Art Segmentation Models for Industrial and Agri-Food Applications  
Image segmentation under variable illumination, background clutter and occlusion is challenging [1]. Traditional algorithms (thresholding, active contours, region growing) have gradually been replaced by deep-learning approaches that are more robust to such variability [1].  
Four families dominate current practice:  
* U-Net [12]--a symmetric encoder-decoder that yields accurate semantic masks even with medium-sized datasets but does not separate individual instances.
* Mask R-CNN [10]--adds an instance-mask branch to Faster R-CNN, producing highly precise masks at the cost of heavy computation and dense supervision.
* DeepLab v3+ [21]--leverages atrous (dilated) convolutions and ASPP to achieve state-of-the-art IoU, yet relies on deep backbones that increase latency.
* SAM [16]--a foundation model trained on SA-1B; delivers zero-shot masks but uses a ViT-H encoder (~632 M parameters) that is computation-intensive. MobileSAM [22] shrinks SAM \(\approx 60\times\), reaching \(\sim\)10 ms image\({}^{-1}\).  
Agri-food studies have begun to adapt SAM to crop imagery [17], employ it as an annotation accelerator [1], or benchmark it against task-specific CNNs [20]. UAV trials confirm MobileSAM's ability to delineate fruits at video rate [19]. Table 1 summarizes the main trade-offs of these standalone models.