esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### FTIR Spectroscopy  
FTIR spectroscopy was performed to identify the biomolecules that were responsible for the capping and efficient stabilization of AgNPs. The key peaks, wavenumbers, and interpretation of the probable functional groups are displayed in Figure 2 and Table 1 of the FTIR spectra of seed extract and Aw-AgNPs. The intensity of the banding is decreased due to silver ion reduction in biosynthesized nanoparticles (Figure 2B). The band at 1019.06 cm\({}^{-1}\) indicates the N-H bonding vibration of amides. The bands between 1019 and 668 cm\({}^{-1}\) in the seed extract illustrates the C=C stretching mode in aromatic compounds and indicates the occurrence of certain aromatic compounds such as flavonoids. Ajwa seed powder consists of various bioactive compounds like flavonoids, terpenoids, alkaloids,  
Figure 1: UV spectrum of the AgNPs synthesized from Ajwa date seed extract. Values expressed as mean \(\pm\) SE of the mean (\(n=3\)). (Inset: change in color from pale yellow to tan-brownish color).  
glycosides etc. Researchers also demonstrated the presence of hydroxyl, carboxyl, and carbonyl functional groups in carbohydrates, flavonoids, tannins, and phenolic acids of Ajwa date fruit and seed extracts which may be accountable for the reduction of the Ag\({}^{+}\) ions and stabilization of AgNPs [2; 10]. The role of flavonoids and phenolic compounds in the reaction mechanism during formation of AgNPs could be derived from their electron or hydrogen donor properties, with the keto form present on the backbone of flavonoid compounds being responsible for the reduction of Ag\({}^{+}\) to Ag.  
Figure 2: FTIR spectrum of Ajwa dates. (**A**) Seed extract; (**B**) AgNPs in the range of 4000 to 400 cm\({}^{-1}\).
# From the Martian Surface to Its Low Orbit in a Reusable Single-Stage Vehicle--[PERSON]
## 4 Discussion  
Ease of maintenance and refurbishment instilled in the design philosophy the idea of modular systems, so that the vehicle could be more readily maintained, especially given the arduous nature of maintenance and refurbishment on Mars. In addition, the consumables of the vehicle will all be producible on Mars, allowing for continued operation without relying on Earth for launch-critical supplies. Furthermore, non-complex materials and additive manufacturing methods have been chosen such that component refurbishment is as easy and cheap as possible for the colony.  
Moreover, a cargo-only version of Charon could be developed in more detail. This removes the need for the life support and abort system, shaving roughly 5500 kg off Charon, which could be used as payload mass instead.  
The concept and technology development, as well as the preliminary design of Charon are all completed. This places the project at the start of Phase B. Still to be done in this phase are the completion of the technology, mock-ups delivery, specification and interface documentation, testing, and prototyping.  
Relative to other concepts that could achieve the same goals, Charon is deemed relatively more safe and more robust. For instance, compared to Starship from SpaceX [56], Charon possesses an abort system, enabling the capsule to quickly separate from the vehicle if need arises. While SpaceX claims that their vehicle will be safe enough not to have an abort system, it is deemed that the extra safety of having an abort system is of high  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **LOM** & **LOV** & **LOC** \\ \hline \(\mu\) & 9.75E-3 & 1.2E-3 & 7.9E-4 \\ \(\sigma\) & 1.737E-3 & 2.702E-4 & 2.184E-4 \\ \(\mu+3\sigma\) & 1.496E-2 & 2.010E-3 & 1.445E-3 \\ \(\mu-3\sigma\) & 4.539E-3 & 3.897E-4 & 1.348E-4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: LOM, LOV and LOC probabilities and their sensitivity analysis.  
importance. This becomes clear given the high number of flights that Charon will have to make between the Martian orbit and surface.  
Because of this, it is recommended to use Starship for launches from Earth to Low Mars Orbit, and from there, use Charon to transport the crew and cargo in multiple flights between the orbiting stations to the Martian surface. This way, Charon would make the highest number of flights, while keeping the crew safe, and Starship would fly a large number of crew members from Earth to Mars.  
Furthermore, compared to NASA's similar Hercules vehicle concept [1], Charon is capable of supporting six crew for up to seven days, higher than Hercules' capability of four crew for three days. However, Charon's payload capacity is only 1200 kg compared to Hercules' 5750 kg for crew missions. Charon's engines are similar to Hercules', both using oxygen and methane, however Charon both has more engines (9 to 5), and thus a higher engine-out capability, and also has a higher thrust per engine. This necessitates approximately 10% more propellant per mission.
## 4 Experimental Results
### Human Ear Datasets
#### 4.1.1 Ccu-De  
CCU-DE [36] is a dynamic and small sample human ear dataset established by the Human Ear Laboratory of Changchun University. The database takes into account all possible situations and influencing factors of human ears in practice, such as illumination, angle, movement, occlusion, etc., including pictures and videos. There are five human ear databases in total, as shown in Table 5.  
In order to diversify the dataset and better verify the effectiveness of the ear recognition algorithm proposed in this paper, we selected Eardata3 and Eardata4 ear video samples for experiments. We selected No. 25 and No. 26 in Eardata3 and No. 1, No. 27 to No. 33 ear videos in Eardata4.  
In the training of the human ear recognition model, feature extraction and training are carried out on one picture after another. Therefore, dynamic video is captured in this paper at the rate of 2 frames per second, and the CCU-DE human ear dataset is selected as shown in Table 6. There are 3274 pictures. According to the ratio of training set: test set:  
\begin{table}
\begin{tabular}{c c} \hline
**Database** & **Shooting Situation** \\ \hline Eardata1 & Static human ear fixed-point shooting \\ Eardata2 & Video of human ears moving in translation with the human body \\  & when the human body is walking normally at different angles \\  & Video of the photographed standing in the shooting center doing a \\  & 90\({}^{\circ}\) uniform rotation motion \\  & Video of the photographed standing in the shooting center doing a \\  & 180\({}^{\circ}\) uniform rotation motion \\  & Dyanamic human ear video with interference information \\ \hline \end{tabular}
\end{table}
Table 5: Human ear database information of CCU-DE.  
verification set = 3:1:1, the training set has 1964 pictures, and the test set and verification set each have 655 pictures, respectively.
## 3 Multi-Model Modeling of the CO\({}_{2}\) Capture Process
### Identification of the Local Linear Models
The identified local model at the \(j\)-th operating point has the following state-space form:  
\[\left\{ \begin{array}{c}x_{j}(k+1)=A_{j}x_{j}(k)+B_{j}u(k)\\ y_{j}(k)=C_{j}x_{j}(k)+D_{j}u(k)\end{array} \right. \tag{10}\]  
where \(A_{j}\), \(B_{j}\), \(C_{j}\), \(D_{j}\) are constant matrixes identified via the SID method. \(u\) and \(y_{j}\) are the model inputs and outputs, respectively. \(x_{j}\) is the state vector. \(k\) represents the present sample time. Note that, different from the transfer function methods that have to identify four models for the \(2\times 2\) system, the state-space model can use only one model to represent the relationship between the multiple inputs and outputs. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Groups**} & \multicolumn{2}{c}{**CO\({}_{2}\)**} & **Flee Gas** & **Lean MEA** & **Steam Mass** & **CO\({}_{2}\) Mass** & **MEA Mass** \\  & **Capture** & **Mass Flow** & **Mass Flow** & **Flow Rate** & **Fraction in Outlet** & **Fraction in Lean** \\  & **Rate (\%)** & **Rate (kg/s)** & **Rate (kg/s)** & **(kg/s)** & **Flee Gas (kg/kg)** & **Solvent (kg/kg)** \\ \hline Group I & 50 & 0.13 & 0.4468 & 0.02498 & 0.1350 & 0.3078 \\ Group I & 60 & 0.13 & 0.5572 & 0.03223 & 0.1121 & 0.3071 \\ Group I & 70 & 0.13 & 0.6687 & 0.03987 & 0.0873 & 0.3079 \\ Group I & 80 & 0.13 & 0.7765 & 0.04764 & 0.0603 & 0.3079 \\ Group I & 90 & 0.13 & 0.8841 & 0.05571 & 0.0314 & 0.3078 \\ Group I & 95 & 0.13 & 0.9428 & 0.06012 & 0.0160 & 0.3073 \\ Group II & 80 & 0.07 & 0.4064 & 0.02127 & 0.0602 & 0.3073 \\ Group II & 80 & 0.09 & 0.5257 & 0.03032 & 0.0598 & 0.3076 \\ Group II & 80 & 0.11 & 0.6501 & 0.03876 & 0.0603 & 0.3080 \\ Group II & 80 & 0.13 & 0.7765 & 0.04764 & 0.0601 & 0.3075 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Selected operating points.
## 4 Sum-Rate Performance with SQ-Based CSI Feedback  
In RVQ, if the channel matrix size is large or the required quantization error is quite small, increased quantization bit number \(B\) results in an unaffordable codebook size. The exponentially increasing codebook size increases both the memory size and the codeword searching complexity. Under the circumstances, the traditional SQ method shows up as an alternative. For complex matrix \(\mathbf{H}\in\mathbb{C}^{N\times M}\), each entry is quantized with \(2B\) bits, and the real and imaginary parts of each entry are separately quantized with \(B\) bits.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 3 Ground Resource Scheduling Model
### Optimization Objective  
The objective of researching the hardware resource scheduling problem in satellite data transmission ground stations is to maximize the reception of critical satellite data transmission with limited resources. Therefore, the following optimization goals are primarily designed to address this issue.
## 3 Results and Discussion
### Experiments on Numerical Datasets  
Then, we further investigated the performance of the proposed similarity measurement on pure numerical datasets. Table 4 shows the information of six numerical datasets, including the number of samples, attributes and classes, and class distribution. To evaluate the performance of the proposed similarity measurement applied to k-means on numerical datasets, we also conducted experiments compared to the most classical numerical data clustering algorithms, k-means algorithms. These two clustering algorithms were applied to different numerical datasets; Table 5 shows the mean and variance of the clustering error of clustering by applying different algorithms. In addition, before using the k-means method to cluster, the dataset was normalized to between 0 and 1.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Instance** & **Attribute** & **Class** & **Class Distribution** \\ \hline Waveform & 5000 & 40 & 3 & 33.84\%, 33.06\%, 33.10\% \\ Wine & 178 & 13 & 3 & 33.15\%, 39.89\%, 26.97\% \\ Mass & 961 & 5 & 2 & 53.69\%, 46.31\% \\ Seeds & 210 & 7 & 3 & 33.33\%, 33.33\%, 33.33\% \\ Iris & 150 & 4 & 3 & 33.33\%, 33.33\%, 33.33\% \\ Fertility & 100 & 9 & 2 & 88.00\%, 12.00\% \\ \hline \hline \end{tabular} Waveform denotes waveform+noise Dataset in Waveform Database Generator (Version 1)  
Mass denotes Mammographic Mass Dataset.  
\end{table}
Table 4: Description of numerical datasets.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Dataset** & **Correlation Coefficient for** & **Correlation Coefficient for** \\  & **Categorical Attributes** & **Numerical Attributes** \\ \hline Statlog (Heart) & \(-0.2602\) & \(0.3885\) \\ Hepatitis & \(-0.0151\) & \(0.7953\) \\ Cylinder Bands & \(0.3648\) & \(0.3955\) \\ Australian & \(0.9314\) & \(0.7281\) \\ Dermatology & \(0.7822\) & \(\backslash\) \\ Zoo & \(-0.2878\) & \(\backslash\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The correlation coefficient between dependencies and weights in each mixed dataset.  
It can be seen that except for the Wine dataset and the Fertility dataset, the proposed similarity measurement applied to k-means outperforms the k-means method on other datasets. Normally, the clustering accuracy of the iterative clustering algorithm based on the proposed similarity measurement is 6.09% higher than that of k-means, especially for the Mass dataset, with a similarity that is 26.69% higher than that of k-means.  
Similarly, the correlation coefficient between dependencies and weights was calculated to analyze why the iterative clustering algorithm outperforms the k-means method, and the correlation coefficient of each dataset is shown in Table 6. It can be found that the Mass dataset with the best clustering result has the highest correlation coefficient. Perhaps the weights of this dataset are well allocated according to the contribution of each clustering attribute.
## 5 Analysis Techniques for WebAssembly
### Detecting Vulnerabilities in WebAssembly Binaries
#### 5.2.1 Static Analysis  
**Wassail**. Wassail [53] was the first static analysis method for detecting vulnerabilities in WebAssembly binaries. It uses a compositional, summary-based analysis approach that strictly focuses on information flow. For each WebAssembly function, it computes a summary that describes how information flows within that function, and these summaries are then used during the subsequent analysis of function calls. The information flow analysis is expressed as a data flow analysis on a Control Flow Graph (CFG), and the information flow of the entire program is approximated by composing the function summaries. The authors claim that similar approaches have been shown to scale well [68; 69], but the scalability of Wassail has not been evaluated.  
**Wasmati**. Wasmati [56] detects vulnerabilities in WebAssembly binaries by constructing a Code Property Graph (CPG). Vulnerabilities are detected by searching for specific patterns in the CPG sub-graphs, which include the execution order, execution path, data dependencies, and control flows. An issue with this approach is that the number of nodes in the CPG grows rapidly because the target of indirect calls cannot be determined statically. To address this, Wasmati optimizes the CPG generation process by adding additional annotations, caching intermediate results, and using efficient graph traversal algorithms. As a result, the authors found that constructing the CPG only took an average of 58 s per binary. They also found that Wasmati was able to effectively find vulnerabilities in WebAssembly binaries while providing a low false positive rate.  
**WASP\({}_{1}\)**. WASP\({}_{1}\)[58] is a concolic execution engine for WebAssembly modules that can be used for uncovering vulnerabilities and bugs. Concolic execution, which combines concrete execution with symbolic execution and explores one execution path at a time, is employed to explore all the feasible paths of the program. Specifically, symbolic execution is used to generate the concrete inputs for exploring multiple execution paths, to maximize code coverage. To demonstrate the feasibility of uncovering vulnerabilities, the authors constructed WASP-C, a symbolic execution framework for testing C programs using WASP\({}_{1}\). WASP-C takes a C program as input, annotates it, compiles it to WebAssembly, and analyzes it using WASP\({}_{1}\). They found that WASP-C was effective at uncovering bugs and vulnerabilities. However, a limitation of WASP-C is that it requires a high-level source code to uncover vulnerabilities, meaning it can only be used to analyze open source programs.
## 3 Properties of Multifunctional Cemertitious Composites
### Conductive Fillers
#### 3.1.1 Carbon Fibers (CFs)  
Carbon fibers (CFs) have been extensively utilized in the fabrication of multifunctional cementitious composites (e.g., [24; 40]). These conductive fillers significantly enhance the electrical conductivity of the composites while also improving their flexural and tensile strength [41]. In addition to their multifunctional properties, the improved mechanical strengths are particularly beneficial, as road pavements are often subjected to flexural and tensile stresses under traffic loads [12]. Incorporating CFs into cementitious composites can therefore improve crack resistance and fracture toughness [42]. However, it is important to note that dispersion quality plays a crucial role in their effectiveness, as the performance of CFs decreases under poor dispersion conditions [43; 44]. The optimal CF content for multifunctional cementitious composites varies, depending on several factors. For instance, the aspect ratio (i.e., the ratio of fiber length to diameter) plays a crucial role in determining the performance of these composites [45]. An increase in the aspect ratio typically results in enhanced mechanical properties and electrical conductivity. However, it is important to note that an excessively high aspect ratio, often associated with fiber lengths greater than 12 mm, may negatively impact the performance of the composites due to issues such as fiber entanglement and agglomeration [22].
## 3 Experiments and Results
### Experiment Setup
#### 3.1.2 Robotic Leg Prosthesis  
The robotic leg prosthesis consists of a mechanical structure, a control system, and a sensor system. The mechanical structure was constructed using an aluminum alloy and nylon fibers. As shown in Figure 1, the total mass of the leg prosthesis is 4.8 kg. The range of motion of the active knee joint was approximately 0\({}^{\circ}\) to 120\({}^{\circ}\). The active ankle joint was about -45\({}^{\circ}\)-45\({}^{\circ}\). The motion of the joint was driven using a Maxon DC motor (Maxon Group, Sachseln, Switzerland). The encoder of the DC motor collects the rotation angle data. The length of the leg prosthesis can be changed by regulating the position between the ball nut of the coupler and pyramid connector. The parameters of leg prosthesis were designed as \(m_{1}=1.5\) kg, \(m_{2}=3\) kg, \(d_{1}=0.15\) m, \(d_{2}=0.15\) m, \(I_{1}=1.3\) kg \(\cdot\) m\({}^{2}\), \(I_{2}=1.9\) kg \(\cdot\) m\({}^{2}\), \(l_{1}=0.222\) m, and \(l_{2}=0.173\) m.  
Data recordings acquired from the sEMG and motor encoder are used in the analysis during experiments. The Trigno wireless wearable sensors of Delsys Co., Ltd., Natick, MA, USA are mounted on the user's contralateral leg in the experiments to measure the sEMG signal. The joints of the prosthesis are driven by a Maxon dc flat brushless motor EC45. The servo driver Elmo connects with the computer via a CAN bus to control the Maxon EC 45 Power Max brushless motors. The interface was designed on the computer with Visual Studio 2010 to monitor the change in the signals of the sensors mounted on the leg prosthesis, sampled at 1 kH. Using this interface, information such as the position, velocity, and torque can be stored and analyzed. The mechanical limit for the joints of the prosthesis can avoid excessive movement in the experiments.
## 3 Proposed Method
### Data Pre-Processing  
Biological signals often harbor motion-induced artifacts, causing distortions in the signal. To mitigate signal distortion in the PPG data, a Butterworth filter technique for waveform smoothing, using the zero-phase filter FiltFilt, was employed. This filtering technique ensures the preservation of the signal trend behavior by facilitating low-pass and high-pass frequency cutoffs. Specifically, a fourth-order Butterworth filter was employed, with low-pass and high-pass cutoffs set at 0.5 Hz and 8 Hz, respectively. Conversely, due to the ABP signal's invasive and sensitive waveform nature, the raw data were retained to facilitate systolic and diastolic point extraction.  
Following signal filtering, the PPG signals underwent zero-mean and standard deviation scaling normalization (Equation (1)). This normalization strategy aims to minimize potential signal distortions within the segments by adjusting their scale and highlighting possible deviations from patterns observed in normal blood pressure, particularly in cases of hypertension. In Equation (1), \(X_{i}\) represents the current segment, \(x_{i}\) stands for the mean value, \(n\) denotes the number of samples, \(\sigma\) is the standard deviation, and \((Xn_{(i)})\) represents the adjusted segment. This pre-processing step ensures that the input signals are appropriately scaled and free from motion-induced artifacts, enhancing the robustness and effectiveness of the subsequent stages in NeuBP.  
\[\begin{split}\sigma&=\sqrt{\frac{\sum(x_{i}-x_{i})^{2 }}{n}}\\ Xn_{(i)}&=\frac{X_{i}-x_{i}}{\sigma}\end{split} \tag{1}\]  
An important task to undertake when utilizing machine learning algorithms is to determine the input dimension for the intended model. Biosignal data commonly possess vector dimensions, and the length of the vector determines the number of data that feed into the model. Therefore, since datasets often contain long samples that can impede model learning, it is common to use subsamples of these data derived with sliding window techniques. These techniques may incorporate an overlapping element applied to preserve information between windows and increase the number of observations, as depicted in Figure 2.  
Both the ABP and PPG signals undergo the segmentation process illustrated in Figure 2. However, the systole (peaks from a part of a time series) and diastole (valley from  
Figure 2: Segmentation with sliding window and 50% overlap of two time series. The windows are the non-grayed areas.  
a part of a time series) extraction step is applied exclusively to the ABP signal. During each segmentation of the ABP, tuples containing values extracted from the systole and diastole of the segmented time series are stored. This procedure facilitates the computation of mean systole and diastole values, establishing a connection with the corresponding PPG segment. Consequently, this process is crucial to associating a PPG segment with the target values of systole and diastole.
# Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM
## 2 Materials and Methods  
### Study Areas  
The main study area is in Youyi County, Heilongjiang Province. Youyi County is situated in the northeastern part of Heilongjiang Province, in the hinterland of the Sanjiang Plain. The overall terrain is relatively flat, with hills in the southwest and plains in the northeast. The region features a temperate continental climate. The unique geographical and climatic conditions make Youyi County one of the important commodity grain bases and the demonstration window of modern agriculture in China. The cultivated area of Youyi County accounts for about 78% of the county, and the main crops are corn, soybean, and rice. The large cultivated area, diverse agricultural scenes, and different parcel sizes make it a primary choice for our study area.  
To demonstrate the generalizability of our method, we identified two additional study areas within Heilongjiang Province, chosen for their distinct topographical features and crop cultivation patterns in contrast to those of Youyi County. They are located in the southern part of Suihua City (A1) and the western part of Jixi City (A2), Heilongjiang Province, where the topography of A2 is dominated by hilly and mountainous terrain, with large undulations and parcels of varying sizes. The main crops planted are maize and rice, while the topography of A1 is flatter, with larger parcels, and the main crop planted is maize (the region of study areas is shown in Figure 1).  
Figure 1: Study areas: (**a**) Heilongjiang Province; (**b**) Youyi County; (**c**) southern part of Suihua City (A1); (**d**) western part of Jixi City (A2).