esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Design and Results
### Results
#### 4.5.1 Compression Ratio (CR)  
The Compression Ratio (CR) was employed in this study as a key performance metric. CR values were calculated separately for HCA, LPHEA, HBLIES, and the DEA across all images in the datasets. Similarly, CR values were computed for the S+DEA compression algorithm, and its performance was compared against BPG, JPEG-LS, and JPEG2000. All CR values are summarized in Table 4, demonstrating that the proposed DEA consistently outperformed the benchmark encoding algorithms across all images in the datasets. Furthermore, the integration of the segmentation preprocessing step into the DEA, as introduced in the S+DEA framework, resulted in further improvements in compression performance. The improvements are highlighted by explicitly presenting the relative gains of the DEA and S+DEA over other encoding and compression algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Image** \\ **Name Tag** \\ \end{tabular} } & \multicolumn{5}{c}{**Encoding Algorithms**} & \multicolumn{5}{c}{**Compression Algorithms**} \\ \cline{2-9}  & \multirow{2}{*}{**HCA**[23]} & \multirow{2}{*}{**LPHEA**} & \multirow{2}{*}{**HBLIES** [29]} & \multirow{2}{*}{**DEA**} & \multirow{2}{*}{**BPG** [55]} & \multirow{2}{*}{**JSEG** [56]} & \multirow{2}{*}{**JPEG** [57]} & \multirow{2}{*}{**S+DEA**} \\  & & & & & & [56] & & \\ \hline
1.1 & 1.27 & 1.26 & 1.84 & 1.96 & 1.81 & 1.88 & 1.87 & 2.08 \\
1.2 & 1.05 & 1.02 & 1.18 & 1.33 &
## 1 Introduction
More explicitly, this study evaluates the global performance of an SFT taking into account hydrodynamic and seismic excitations. The formulation of the problem includes the modeling of cable element, hydrodynamics, ground motions and the SFT structure itself. A numerical model of the SFT prototype to be built in Qindao Lake is used to evaluate the global performance under different loadings, and some useful conclusions are drawn that can provide an initial step towards the realization of this innovative structural solution for waterway crossings.
# Discrete-Time Asymptotic Tracking Control System for a Satellite with a Solar Panel
## 5 Numerical Simulations
\right].\]  
The results of the numerical simulations are shown below, in Figures 4-8. Figure 4 shows parts of the exact reference attitude angle \(\alpha_{r}(t)\), which is sampled in the digital control system shown in Figure 3.  
These parameters led to the following state space matrix of the plant \(\Sigma_{\mathcal{G}}\):  
\[\left[ \begin{array}{c|cc|cc|c}\bar{A}&\bar{B}\\ \hline\bar{\mathcal{C}}&0\end{array} \right]=\left[ \begin{array}{cccc|cc|cc}0&0&1& 0&0\\ 0&0&0&1&0\\ -882.4&882.4&-0.0144&0.0144&1.3072\\ 6136.4&-6136.4&0.1&-0.1&0\\ \hline 1&0&0&0&0\end{array} \right]. \tag{154}\]  
The obtained results of the numerical simulations are presented in Figures 9 and 10, which indicate satisfactory and rapid decay of the tracking error \(e(t)\). A more elaborate robustness analysis of the developed discrete-time controller (similar to that performed in the study by [20] for the continuous-time case) could be carried out based on the structured singular values; however, this would require further research.
# A Privacy-Preserving Routing Protocol Using Mix Networks in Opportunistic Networks
## 4 Evaluation
### Anonymity Analysis  
Our mixnet-based proposal can ensure sender and receiver anonymity, and sender-receiver unlinkability.  
Sender anonymity: message \(M\) cannot be linked to any sender \(S\). In our mixnet proposal, receiver \(R\) cannot trace back sender \(S\), as \(R\) can only monitor its previous node. A single mix node only knows the previous and successor node. Even if the attacker compromises a number of mix nodes, it is still impossible to connect the sender and receiver if there is at least one honest mix node.  
Receiver anonymity: message \(M\) cannot be linked to any receiver \(R\). When message \(M\) is forwarded in our OppNet schema, it goes through a number of mix nodes, since there is at least one mix node that cannot be compromised. The attacker cannot obtain any information from message \(M\). Thus, our proposed mixnet method can provide receiver anonymity.  
Sender-receiver unlinkability: It is essential to trace the routing path in the OppNet to link sender and receiver. However, this was not the case in our scenarios. For any honest sender \(S\) and receiver \(R\), message \(M\) could be encrypted and rerandomized with mix nodes.
# Developments, Applications, and Innovations in Agricultural Sciences and Biotechnologies
## 3 Sustainable Vegetable Cultivation
### The Benefits of Multiched Ridge Cultivation  
The study on multiched ridge cultivation combined with double-row planting systems revealed multiple benefits, as follows:  
Yield improvement: the system increased fresh fruit yields by 40.8% for eggplant and 35.3% for capsicum, demonstrating its potential to enhance food production.  
Water conservation: by reducing evaporation and improving soil moisture retention, multiched ridge cultivation is considered a sustainable water management practice, which reduces irrigation water use by up to 32%.  
Mechanized planting: the use of advanced transplanters ensures uniform planting depth and spacing, reducing labor requirements and improving planting efficiency.  
The study also highlights the role of mechanization in modern agriculture. The newly designed vegetable transplanter integrates multiple functions, including ridging, mulching, and transplanting, in a single pass. This innovation not only reduces labor costs but also ensures consistency in planting, which is critical for optimizing crop performance.
## 3 The Proposed Features Extraction Method
### Description of the Test Cardan Shaft System  
To validate the proposed model, an experiment was conducted on an RK4 rotor test rig (RK4 Rotor Kit) benchmark, as shown in Figure 5. Measurement of the displacement due to misalignment of a Cardan shaft are discussed in this section. For this reason, specially designed Hooke's joints mimicking the misalignment of the Cardan shaft were constructed and equipped in the experimental set-up shown in Figure 5c. The set mainly consisted of the driveshaft with a created breathing crack and a driven shaft connected by a miniaturized Hooke's joint, which consisted of three essential parts: two yokes (1a and 1b), a cross, and two central and identical discs (Figure 5e). Self-aligning bearings were used to ensure that the bearing would take up the bending mode shape of the shafts at the supports, including a motor and rotor. There were displacement sensors (Bently 3300 XL 8 mm with sensitivity 7.87 V/mm) and a tachometer that produced one pulse per revolution to provide a reference for the angle position of the unbalance. Four eddy current sensors were connected to the data acquisition device to capture and display the experimental time histories for the whirl twin-rotor. To measure the relative displacement of the cracked driveshaft, two mutually perpendicular proximity probes were mounted  
Figure 4: Flowchart of the proposed method.  
at \(\pm\)90\({}^{\circ}\) from the true vertical and oriented in two planes perpendicular to the shaft. The probes were staggered in the probe block probe (Figure 5d) at the vicinity of the crack location. An asset condition monitoring Scout 100 from the Ascent 2011 Comtest was used to collect the vibration data of the input cracked rotor system and to improve transient response performance. The input voltage of the Bently speed controller varied from 0 to 5 V, corresponding to the input motion to the driveshaft of a rotational speed from 0 to 10,000 RPM.  
The problem of joint flexibility when the driveshaft is damaged is sometimes a limiting factor in achieving excellent performance. The critical part of preparation for the experiment was the extraction of the breathing crack mechanism on the driveshaft by two perpendicular probes connected to a data acquisition system. For this purpose, the sampling frequency was 12,800 Hz, and the nominal rotational frequency of the rotor was 20 Hz. An unbalanced excitation force, rotating with each disc-shaft at a frequency equal to the rotors spin speed, was considered. Then the real breathing crack was generated by adding cyclic loading discs to the driveshaft at the slit location. The detailed parameters of the completed rotors system used for both simulation and experiment are given in Table 1.  
Figure 5: The experimental setup consisting of (**a**) a connected shaft with a crack and Hooke’s joint; (**b–d**) modified Rotor-Kit 4 components: 1–3—data acquisition control; 4—motor; 5—probe sensors; 6—driveshaft-crack; 7—disc 1; 8—flexible coupling; 9—self-aligning bearing; 10—tachometer; 11—disc 2; 12—output shaft 2; (**e**) 1a–1b yoke; 2a cross shaft; 3a sleeve bearing.
## 3 Experiments and Discussions
### Semi-Supervised Classification for Facial Images
Samples from the AT&T and ORL datasets exhibit variations in multiple angles, leading to challenges in accurate categorization. As can be seen, the conventional dimensionality reduction techniques fail to recognize the same object at different angles, resulting in very low accuracy. On the other hand, compared to RPCA and LE, FMDR achieves superiority of 46.79% and 45.46% in Acc, 54.5% and 51.89% in Pre, respectively. For the ORL dataset, FMDR gains 64.44% Acc and 62.76% Pre, which are 32.11% and 16.05% higher than RPCA, respectively. This phenomenon indicates that FMDR can accurately describe the manifold relationship between samples according to the high-frequency kernel function, which leads to increased similarity between different angle samples of the same object, enabling correct classification. Furthermore, the standard deviations of 100 repeated trials in th AR dataset are recorded in Table 2. The units are the same as the indicators, presented as percentages, with bold highlighting indicating optimal performance. As the results indicate, compared to the baseline method and SOTA method, the FMDR algorithm achieves the lowest standard deviation of results across multiple iterations, indicating the higher stability of the algorithm and better performance obtained in repeated experiments.
# A New Hybrid Prime Code for OCDMA Network Multimedia Applications
## 3 Code Construction  
The proposed HPC code is based on using two different prime numbers \(P_{1}\) and \(P_{2}\); \(P_{2}\) is the greater prime number than \(P_{1}\). Each prime number can generate a number of code words equal to the same prime number. Each code word contains a number of chips equal to the corresponding prime number. Each code word contains one active chip \"HIGH\" and the remaining chips are \"ZEROs\". The position of the active chip is \(d_{i\;or\;j}\), where \(d_{i\;or\;j}\in\left\{0,\;1,\;2,\ldots,\;P_{i\;or\;j}-1\right\}\). For the first prime number \(P_{i}\), the code words can be generated as \(\left\{X_{oi},\;X_{1i},\;X_{2i,},\;\cdots\;X_{(P_{i}-1)i}\right\}\) and for the second prime number \(P_{j}\), the code words can be generated as \(\left\{X_{oj},\;X_{1j},\;X_{2j,}\;\cdots\;\cdots\;X_{(P_{i}-1)j}\right\}\). For example, when \(P_{1}=5\) and \(P_{2}=7\), the generated code words are listed in the following two groups:  
\[\begin{array}{l}X_{01}=\left[1\;0\;0\;0\;0\right]-0\;\text{shift}\\ X_{11}=\left[0\;1\;0\;0\;0\right]-1\;\text{shift}\\ X_{21}=\left[0\;0\;1\;0\;0\right]-2\;\text{shift}\\ X_{31}=\left[0\;0\;0\;1\;0\right]-3\;\text{shift}\\ X_{41}=\left[0\;0\;0\;0\;1\right]-4\;\text{shift}\end{array}\text{ for }P_{1}=5\]  
\[\begin{array}{l}X_{02}=\left[1\;0\;0\;0\;0\;0\right]-0\;\text{shift}\\ X_{12}=\left[0\;1\;0\;0\;0\;0\;0\right]-1\;\text{shift}\\ X_{22}=\left[0\;0\;1\;0\;0\;0\;0\right]-2\;\text{shift}\\ X_{32}=\left[0\;0\;0\;1\;0\;0\;0\right]-3\;\text{shift}\\ X_{42}=\left[0\;0\;0\;0\;1\;0\right]-4\;\text{shift}\\ X_{52}=\left[0\;0\;0\;0\;0\;1\;0\right]-5\;\text{shift}\\ X_{62}=\left[0\;0\;0\;0\;0\;0\;1\right]-6\;\text{shift}\end{array}\text{ for }P_{2}=7\]The code words listed in the above two groups are then utilized to construct the two code sequence trees as shown in Tables 1 and 2 based on the branching operation.  
The number of code sequences in the first and second trees in Tables 1 and 2 are \(K_{1}=P_{1}(P_{1}-1)\) and \(K_{2}=P_{2}(P_{2}-1)\), respectively. However, it is also shown in the two-code sequence trees that \(K_{1}=\frac{K_{2}}{2}-1\). Thus, the number after the minus sign, such as 1, represents the number of code sequences that are not used in coding construction (the bold faced and highlighted code sequences in Table 2).
## 3 Experiments and Results
### The Experiments on Different Radii in the Context-Aware Strategy  
We conducted experiments to analyze the performance of FENet using different radii in the context-aware strategy. As shown in Table 4, the proposed method achieves best accuracy when the radius is set to 2. The last column represents the detection result by using all pixels in the ground-truth box. When the radius is set to less than 2, the detection accuracy is lower, which indicates that the positive sample information will be lost if the effective area is set too small. When the radius is set to too high, the detection accuracy decreases. It means that a larger region will lead to interference caused by the background. Considering the target size and experimental results, we set the radius to 2.0.
## 2 Background and Related Studies  
### Isarn Language and Tone  
Isarn is a tonal language spoken in Northeastern Thailand. Locals speak Isarn in different dialects depending on the region. Some Isarn dialects have five tones but others have six [30]. In this research, we focus on the dialect spoken in Central-Northeast Thailand (covering Khon Kaen, Kalasin, Udonthani, and Mahasarakham provinces). There are six tones in the central Isarn dialect: Mid (M), low (L), mid-falling (MF), high falling (HF), high (H), and rising (R). The following examples show a word consisting of the same phonemes in different tones: M #n/kha:/ (\"cost\"), L #n /kha:/ (\"kill\"), MF #n /kha:/ (\"trade\"), HF #n /kha:/ (\"stick\"), H #n /kha:/ (\"galangal\"), and R #n /kha:/ (\"leg\"). Figure 1 shows the typical pattern of each tone analyzed using the speech of a native male speaker.  
In the past, the Isarn was written using the Isarn Dharma script. The difficulty of representing the Isarn by the Isarn Dharma script is its written style which lacks tone markers to identify the tones [31]. Nowadays, people mostly use Thai script, which is the official script of Thailand, to write the Isarn language. Although Thai script has tone markers, there is no formal standard rule for written Isarn language [32]. The written depends on the personal style. The same grapheme words may pronounce in different sounds with different meanings depending on surrounding words. This leads to ambiguity problems in pronunciation and text processing [33]. For example, the Isarn word \"\(\mathfrak{w}\mathfrak{w}\)\" can be pronounced as /ja:m/ (\"visit\") in the sentence \"\(\mathfrak{w}\mathfrak{w}\mathfrak{w}\mathfrak{w}\mathfrak{w}\)\" /k\({}^{\text{h}}\mathfrak{w}\mathfrak{w}\mathfrak{w}\)\" /k\({}^{\text{h}}\mathfrak{w}\mathfrak{w}\mathfrak{w}\)\" (means \"I visit you at home\" in English) or \"\(\mathfrak{w}\mathfrak{w}\mathfrak{w}\mathfrak{w}\mathfrak{w}\)\" (\"time\") in the sentence \"\(\mathfrak{w}\mathfrak{w}\mathfrak{w}\mathfrak{w}\mathfrak{w}\mathfrak{w} \mathfrak{w}\mathfrak{w}\mathfrak{w}\)\" /nam daj caw si paj lio\(\mathfrak{n}\mathfrak{n}\mathfrak{n}\mathfrak{n}\)/ (means \"What time will you go to school?\" in English). In this study, the conversion of text into a linguistic specification is achieved using the front-end module [33] and corrected manually using audio recordings for reference.
## 4 Materials and Methods
### Quantitative Real-Time PCR  
The total RNAs were extracted from three organs (leaf, stem, and root) of _A. paniculata_ using Trizol reagent. The quality of the RNA was assessed by analyzing the A260/280 ratio and resolving on 1.2% (\(w/v\)) agarose gel. The expression of the representative terpenoid genes in _A. paniculata_ was detected by qRT-PCR. Four candidate gens (_DXS_ Cluster-13593.10332, _HDS_ Cluster-11337, _HDR_ Cluster-11370, and _Ent_-cpp Cluster-13593.11310) were selected, and qRT-PCR primers were designed based on nucleotide sequences (Table S13). A QuantStudio 1 Real-Time PCR (Applied Biosystems, Carlsbad, USA) performed the qRT-PCR reactions with three replicates for each sample. The 10 uL qRT-PCR reaction mixture contained 5 uL of 2\(\times\) SYBR Green I mix, 0.3 uL each forward and reverse primers, 2 uL cDNA template, and 2.4 uL ddH2O. The qRT-PCR parameters were as follows: 94 \({}^{\circ}\)C for 10 min (initial denaturation); 40 cycles at 95 \({}^{\circ}\)C for 5 s, annealing at 60 \({}^{\circ}\)C for 20 s, and extension at 72 \({}^{\circ}\)C for 10 s. The melting curve conditions were 60 \({}^{\circ}\)C for 8 s and 95 \({}^{\circ}\)C for 8 s, followed by 1 cycle. The results were analyzed using 2\({}^{-\Delta\Delta\Delta\text{Ct}}\) method with _actin_ (KJ494921.1) as an endogenous control [67].
## 3 System Modeling  
Figure 2 describes the gas flow mechanism in the valve-controlled actuator system. The mass flow rates of the gas flowing through the two valve ports are \(\dot{M}_{\mathrm{a}}\) and \(\dot{M}_{\mathrm{b}}\), the gas pressures in the two chambers are \(p_{\mathrm{a}}\) and \(p_{\mathrm{b}}\), the gas volumes are \(V_{\mathrm{a}}\) and \(V_{\mathrm{b}}\), and the gas temperatures are \(T_{\mathrm{a}}\) and \(T_{\mathrm{b}}\). In addition, \(p_{\mathrm{s}}\) represents the supply pressure, \(x_{\mathrm{v}}\) and \(y\) stand for the valve spool displacement and the actuator piston displacement respectively, and \(\theta\) is the rotation angle of the actuator.  
As shown in Figure 2, the two valve ports of the proportional valve are connected with the two chambers of the actuator respectively. When the valve spool moves to the right, chamber **a** is charged, and chamber **b** is discharged. The two-chamber pressure difference drives the piston to move and converts the linear motion into the rotational motion of the intermediate gear. Before the system modeling, we need to make the following assumptions [28; 29; 30]:  
1. The working medium is ideal gas, which follows the ideal gas law.
2. Supply pressure \(p_{\mathrm{s}}\) and supply temperature \(T_{\mathrm{s}}\) are constant, and the parameter of each point in the chamber is equal at each instant.
3. There is no leakage between the valve-controlled actuator system and the outside and between the two chambers.
4. The flow state of the gas flowing through the orifice is an isentropic adiabatic process.
5. The change of each parameter in the dynamic process is only a small amount (small disturbance hypothesis).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Model** & **Parameters** \\ \hline Compressor & PANDA 750-30L & Maximum supply pressure: 0.8 MPa \\ Air service unit & AC3000-03 & Maximum working pressure: 1.0 MPa \\ Proportional directional control valve & FESTO MPTE-5-M5-010-B & 5/3-way valve, 0–10 V driving voltage \\ Pneumatic rotary actuator & SMC MSQ30A3 & Bore: 30 mm stroke: 190\({}^{\circ}\) \\ Rotary encoder & ES8510 & Resolution: 36000 P/R \\ Data acquisition card & NI PCI-6229 & 32-bit counter; \(-\)10–10 V voltage output \\ IPC & IPC-610H & Standard configuration \\ \hline \hline \end{tabular}
\end{table}
Table 1: The specific models of the main components.  
Figure 2: Gas flow mechanism in the valve-controlled actuator system.  
The derivation of the system model begins with the basic equations of the valve-controlled actuator, including the orifice equation of the mass flow rate, the continuity equation of the mass flow rate, and the dynamic equation of the pneumatic rotary actuator [31; 32].
## 4 Experiment and Results
### Evaluation Metrics  
We utilize two evaluation metrics to assess the quality of SR images: PSNR and Structural Similarity (SSIM). Additionally, for evaluating the naturalness of restored face images on two real-world test sets, we employ the widely used Natural Image Quality Evaluator (NIQE). PSNR measures the difference between the SR image and the HR image by calculating the pixel mean square error between the two images. A higher PSNR value, expressed in dB, indicates less distortion in the reconstructed image. The PSNR is calculated as follows:  
\[PSNR=10 log_{10}\bigg{(}\frac{(2^{8}-1)^{2}}{f_{MSE}(y,y^{\prime})}\bigg{)} \tag{13}\]  
\[f_{MSE}=\frac{1}{HW}\sum\limits_{i=0}^{H-1}\sum\limits_{j=0}^{W-1}\Big{(}y_{i, j}-y^{\prime}_{i,j}\Big{)} \tag{14}\]  
where \(y\) indicates the HR image, \(y^{\prime}\) indicates the SR image, and \(H\) and \(W\) are the corresponding height and width of the image, respectively.  
SSIM evaluates the similarity between the SR image and the HR image based on various factors such as brightness, contrast, and structure. It provides a value within the range of [0, 1], where a higher value indicates a higher similarity and better reconstruction effect between the two images. The calculation process of SSIM can be expressed as follows:  
\[SSIM=\frac{\big{(}2\mu_{x}\mu_{y}+c_{1}\big{)}\big{(}\sigma_{xy}+c_{2}\big{)} }{\big{(}\mu_{x}^{2}+\mu_{y}^{2}+c_{1}\big{)}\big{(}\sigma_{x}^{2}+\sigma_{y} ^{2}+c_{2}\big{)}} \tag{15}\]  
where \(c_{1}\) and \(c_{2}\) are constants, \(\mu_{x}\) and \(\mu_{y}\) are the means of HR images and SR images, \(\mu_{x}^{2}\) and \(\mu_{x}^{2}\) are the standard deviations of HR images and SR images, respectively, and \(\sigma_{xy}\) is the covariance.  
NIQE is a no-reference metric that measures the difference between two multivariate Gaussian models: one for natural images and the other for evaluated images without ground truth. It utilizes quality-aware features from the natural scene statistic model. A smaller NIQE value indicates higher visual quality. The calculation process is as follows:  
\[D(v_{1},v_{2},\epsilon_{1},\epsilon_{2})=\sqrt{((v_{1}-v_{2})^{T}\big{(} \frac{\epsilon_{1}+\epsilon_{2}}{2}\big{)}^{-1}(v_{1}-v_{2}))} \tag{16}\]  
where \(v_{1}\), \(v_{2}\), \(\epsilon_{1}\) and \(\epsilon_{2}\) represent the mean vectors and covariance matrices of the natural images and the evaluated images.
## 2 Signal Processing Principles  
This section focuses on the decoding algorithm developed in this paper. Given that the algorithm is formulated based on the Poisson distribution probability density function, we first provided a detailed mathematical formulation of the Poisson statistics in ultraviolet communication systems to establish the theoretical foundation for subsequent algorithm design. Sections 2.2-2.5 then present a step-by-step analysis of the decoding process, sequentially detailing the signal processing flow: pulse photon counting, frequency cross-correlation calculation, CDR clock synchronization implementation, and threshold decision-making. Section 2.6 introduces the encoding and decoding of LDPC and RS codes.