esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Trajectory Tracking Control of a Magnetorheological Prosthetic Knee Joint
## 2 Design of MR Prosthetic Knee Joint
### Kinetic Modeling of MR Prosthetic Knee Joint  
To realize the controllable damping force of the MR prosthetic knee joint and reasonably design the MR damper structure, it is necessary to determine the required damping force of the MR prosthetic knee joint. Since the control object of the MR prosthetic is the knee joint, the main research is to control the knee joint in the swing phase. According to the Lagrangian equation, the kinetic equation of the MR prosthetic knee joint in the swing phase can be expressed as:  
\[\left\{\begin{array}{c}\tau_{1}-\tau_{2}=M_{11}\bar{\theta}_{1}+M_{12}\bar{ \theta}_{2}+C_{1}+G_{1}\\ \tau_{2}=M_{21}\bar{\theta}_{1}+M_{22}\bar{\theta}_{2}+C_{2}+G_{2}\end{array}\right. \tag{4}\]  
where \(\tau_{1}\) and \(\tau_{2}\) are the moments applied to hinge E and hinge C, respectively; \(\theta_{1}\) and \(\theta_{2}\) are the angles between EC, CA and the vertical direction respectively; \(M_{11}\), \(M_{12}\), \(M_{21}\) and \(M_{22}\) are the moments of inertia matrix components respectively; \(C_{1}\) and \(C_{2}\) represent the centripetal force and Coriolis force of EC and CA respectively; \(G_{1}\) and \(G_{2}\) express the gravity of EC and CA, respectively.  
The moment of inertia matrix components \(M_{11}\), \(M_{12}\), \(M_{21}\) and \(M_{22}\) can be expressed as:  
\[M_{11}=I_{1}+m_{1}{I_{\rm{p1}}}^{2}+m_{2}{I_{\rm{p2}}}^{2} \tag{5}\]  
\[M_{12}=M_{21}=I_{1}+m_{1}{I_{\rm{p1}}}^{2}+m_{2}{I_{\rm{p2}}}^{2} \tag{6}\]  
\[M_{22}=I_{2}+m_{2}{I_{\rm{p2}}}^{2} \tag{7}\]  
where \(I_{1}\) and \(I_{2}\) are the moments of inertia of EC and CA, respectively; \(m_{1}\) and \(m_{2}\) are their masses, respectively; \(l_{\rm{p1}}\) and \(l_{\rm{p2}}\) are the lengths to their center of mass, respectively.  
Coriolis force \(C_{2}\) can be expressed as:  
\[C_{2} =-m_{2}{I_{1}}l_{\rm{p2}}{\rm{sin}}(\theta_{1}-\theta_{2})\dot{ \theta}_{1} \tag{8}\]  
where \(l_{1}\) is the length of EC.  
The gravity \(G_{2}\) can be expressed as:  
\[G_{2}=m_{2}l_{\rm{p2}}g\sin\theta_{2} \tag{9}\]  
The control torque \(\tau_{2}\) of MR damper acting on hinge C is:  
\[\tau_{2}=Fd \tag{10}\]  
Figure 5: Length of the MR damper.  
where \(F\) is the output damping force of the MR damper; \(d\) is the distance from hinge C to BD, which can be expressed as:  
\[d=l_{1}l_{2}\sin(\theta_{1}-\theta_{2})/l_{\text{MRD}} \tag{11}\]  
where \(l_{2}\) is the AC length, and \(l_{\text{MRD}}\) is the BD length.  
Substituting Equations (5)-(11) into (4), it can be deduced as:  
\[Fd=M_{21}\bar{\theta}_{1}+M_{22}\bar{\theta}_{2}+C_{2}+G_{2} \tag{12}\]  
It can be seen that the MR prosthetic knee joint is a highly nonlinear system. Nonlinear terms such as moment arm \(d\), Coriolis force \(C_{2}\) and self-weight \(G_{2}\) will affect the knee angle control of the MR prosthetic knee joint.
###### Abstract  
In object-oriented remote sensing image classification experiments, the dimension of the feature space is often high, leading to the \"dimension disaster\". If a reasonable feature selection method is adopted, the classification efficiency and accuracy of the classifier can be improved. In this study, we took GF-2 remote sensing imagery as the research object and proposed a feature dimension reduction algorithm combining the Fisher Score and the minimum redundancy maximum relevance (mRMR) feature selection method. First, the Fisher Score was used to construct a feature index importance ranking, following which the mRMR algorithm was used to select the features with the maximum correlation and minimum redundancy between categories. The feature set was optimized using this method, and remote sensing images were automatically classified based on the optimized feature subset. Experimental analysis demonstrates that, compared with the traditional mRMR, Fisher Score, and Reliefer methods, the proposed Fisher Score-mRMR (Fm) method provides higher accuracy in remote sensing image classification. In terms of classification accuracy, the accuracy of the Fm feature selection method with RT and KNN classifiers is improved compared with that of single feature selection method, reaching 95.18% and 96.14%, respectively, and the kappa coefficient reaches 0.939 and 0.951, respectively.  
object-oriented; feature selection; Fisher Score; mRMR
## 5 Conclusions  
We have explored the impact of physical snow properties on TOA spectral radiance and how machine learning neural networks could be used to form a snow parameter retrieval algorithm utilizing multi-band and continuous (hyperspectral) coverage. We have determined that employing continuous spectral coverage, such as that offered by the planned SBG VSWIR instrument, seems to offer some improvements to the quality of snow parameter retrievals when compared with multi-band approaches, used by sensors such as MODIS and SGLI, by lowering the APD and MAE for snow grain size and snow impurity concentration. While our study focused on snow parameter retrieval in a single homogeneous snow layer, continuous (hyper) spectral coverage will likely show even more improvements for a vertically inhomogeneous snow-pack consisting of multiple dissimilar layers. An area of future research could be exploring how the improvements observed in our sensitivity study offered by continuous spectral coverage affects parameter retrieval in more diverse systems, such as systems with vertically inhomogeneous snow grain size and impurity profiles, as well as snow systems containing \"contaminants\" such as algae and mineral particles. Another avenue of research could be exploring benefits offered to cloud screening of optical imagery involving snow. Continuous spectral coverage offers increased degrees of freedom for machine learning algorithms to understand and quantify complex systems, which makes sensors with continuous spectral coverage a good choice for future remote sensing endeavors. The hyperspectral SPR algorithm developed and tested in this study showed promising results when used with simulated data. However, it must be developed further and tested against real snow spectral data. The JPL AVIRIS, AVIRIS-NG, and PRISM airborne imaging spectrometers have collected such data. It would be a logical next step to use such data to test the capabilities of our SPR algorithm.  
Individual research contributions: Conceptualization, K.S. and C.G.; methodology, W.L. and E.P.; validation, E.P. and W.L.; writing--original draft preparation, E.P.; writing--review and editing, K.S. and T.T.; visualization, E.P.; supervision, W.L and K.S.; project administration, K.S.; funding acquisition, C.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by NASA/AMES Research Cooperative Agreement No. NASA-NNX12 AD05A, CFDA No. 43.001 through Bay Area Environmental Research Institute.  
Data Availability Statement: Data used in this paper are available upon request, please contact the corresponding author with any inquiries.  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflict of interest.
###### Abstract  
The effects of modified atmosphere packaging (MAP) in combination with superchilling (-1.3 \({}^{\circ}\)C) on the physicochemical properties, flavor retention, and organoleptic evaluation of turbid samples were investigated during 27 days storage. Results showed that high-CO\({}_{2}\) packaging (70% or 60% CO\({}_{2}\)) combined with superchilling could reduce the productions of off-flavor compounds, including total volatile basic nitrogen (TVB-N) and ATP-related compounds. Twenty-four volatile organic compounds were determined by gas chromatography-mass spectrometry (GC/MS) during storage, including eight alcohols, 11 aldehydes, and five ketones. The relative content of off-odor volatiles, such as 1-octen-3-ol, 1-penten-3-ol, (E)-2-octenal, octanal, and 2,3-octanedione, was also reduced by high-CO\({}_{2}\) packaging during superchilling storage. Further, 60% CO\({}_{2}\)/10% O\({}_{2}\)/30% N\({}_{2}\) with superchilling (-1.3 \({}^{\circ}\)C) could retard the water migration on the basis of the water holding capacity, low field NMR, and MRI results, and maintain the quality of turbid according to organoleptic evaluation results during storage  
turbot; modified atmosphere packaging; superchilling; shelf life; quality +
Footnote †: journal: Medical  
Considering that psychotropic microorganisms can survive and proliferate at cold temperatures, the microbiological safety of fresh turbot should be controlled through inhibiting or delaying of microbiological growth.  
Superchilling is the process of lowering the temperature of a product just below its initial freezing temperature, and thereby approximately 5-30% of water is preserved frozen within the food products [6; 7]. Superchilling has been used in fish processing to increase the shelf life and has been successfully applied in the preservation of Atlantic mackerel [8], hairtail [9], olive flounder [10], seabream [11], as well as other seafood products. Besides the temperature control, MAP could also inhibit the growth of spoilage bacteria on fish. [PERSON] et al. [12] reported that superchilling (\(-0.7\)\({}^{\circ}\)C) with high-CO\({}_{2}\) packaging (60% CO\({}_{2}\)/40% N\({}_{2}\)) could substantially inhibit the biochemical and microbiological deterioration of catfish (_Clarias gariepinus_) muscle during storage. [PERSON] et al. [13] reported that 2 \({}^{\circ}\)C combined with 60% CO\({}_{2}\)/10% O\({}_{2}\)/30% N\({}_{2}\) could slow down the TVB-N and TMA-N increase and extend the shelf life by about four days.  
The objective of this research was to assess the potential benefits of superchilling at \(-1.3\)\({}^{\circ}\)C combined with different high-CO\({}_{2}\) MAP systems by monitoring the biochemical changes, determining the volatile compounds using GC/MS, and evaluating the organoleptic properties during storage.
# Optimal Task Offloading Strategy for Vehicular Networks in Mixed Coverage Scenarios
## 5 Simulation and Performance Evaluation
### PPO for Task Offloading Strategy
#### 5.2.1 Experiments Setting  
First, we need to simulate a congested traffic environment and obtain relevant traffic data. We use SUMO to create a 2 km long road in the east-west direction with seven intersections. The north-south direction of each intersection has a 100 m road, and there is a 300 m gap between each intersection in the east-west direction. Vehicles from all directions are present, but this study focuses on the vehicles traveling in the east-west direction (marked in green). We use a large traffic flow and a combination of traffic lights to create a congested environment.  
Starting from the beginning of the road, there are 11 RSUs and six BSs in this scenario. Each RSU has a coverage range of 200 m, and each BS has a coverage range of 400 m. Vehicles generate tasks to be unloaded randomly at certain locations, with the position, speed, task size, acceleration, and the load condition of nearby RSUs/BSs at the time of task generation all randomly selected within a certain range. The relevant parameter settings of mixed coverage scenarios, such as coverage and computing power, are referred to [30; 31]. These device parameters are reasonable, and the simulation experiment scene is close to the real mixed coverage traffic scenario.  
Table 3 shows the traffic-related parameters, and Table 4 shows the relevant parameters of the PPO-Clip approach.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Learning rate (lr) & \(5\times 10^{-6}\) \\ Discount factor & 0.93 \\ Clip & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PPO-Clip parameters.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Vehicle computing power & 0.4 & \(\mathrm{GHz}\) \\ RSU computing power (zero load) & 4.8 & \(\mathrm{GHz}\) \\ BS computing power (zero load) & 8 & \(\mathrm{GHz}\) \\ RSU transmission rate (zero load) & 30 & Mbps \\ BS transmission rate (zero load) & 100 & Mbps \\ Task complexity \(k\) & 20–40 & - \\ Task size \(D\) & 0.1–2.5 & Mbit \\ RSU coverage range & 400 & m \\ Coverage of BS & 200 & m \\ RSU calculation load & 80–100\% & - \\ BS calculation load & 80–100\% & - \\ Number of vehicles & 3 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation road environments parameters.
## 2 Results and Discussion
### Purification of the Antioxidant Peptides from SHCH  
#### 2.2.1 Ultrafiltration  
Protein hydrolysate is a complex mixture of active and inactive peptides (of various sizes) and amino acid compositions, and ultrafiltration membrane technology is an important method for the fractionation of protein hydrolysate and the enrichment of peptides with specific MW ranges [1; 5]. SHCH was fractionated by ultrafiltration using two molecular weight cut-off (MWCO) membranes (10 and 3 kDa), and three fractions, SHCH-I (MW \(<\) 3 kDa), SHCH-II (3 kDa \(<\) MW \(<\) 10 kDa), and SHCH-III (MW \(>\) 10 kDa), were prepared. As shown in Figure 1, the HO\(\bullet\) scavenging activity of SHCH-I was 79.10% \(\pm\) 2.38% at 15 mg protein/mL, which was significantly stronger than those of SHCH, SHCH-II, and SHCH-III (\(p<\) 0.05). The MW of peptides plays a critical role in bioactivity, and protein hydrolysates with smaller MW usually exhibited higher antioxidant activity than larger MW hydrolysates [4; 5]. SHCH-I, which is abundant in smaller MW peptides, showed high HO\(\bullet\) scavenging activity, and the result was in agreement with other reports that the ultrafiltration fractions of protein hydrolysates with lower MW could more effectively interact with the free radicals interfering in oxidative processes [6; 9].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protease** & **Enzymolysis Condition** & **Yields (g/100 g** & **Degree of** & **HO\(\bullet\) Scavenging** \\  & **Cartilage**) & **Hydrolysis (DH\%)** & **Rate (\%)** \\ \hline Papain & pH 7.0, 60 \({}^{\circ}\)C, 4 h, total enzyme dose 2.5\% & 1.93 \(\pm\) 0.08  & 34.85 \(\pm\) 1.05  & 21.37 \(\pm\) 0.35  \\ \hline Trypsin & pH 8.0, 40 \({}^{\circ}\)C, 4 h, total enzyme dose 2.5\% & 2.11 \(\pm\) 0.11  & 62.38 \(\pm\) 1.67  & 21.58 \(\pm\) 0.26  \\ \hline Neutrase & pH 6.0, 50 \({}^{\circ}\)C, 4 h, total enzyme dose 2.5\% & 1.85 \(\pm\) 0.06  & 50.67 \(\pm\) 1.85 [FOOTNOTE:]Footnote : \({}^{\circ}\)-d Values with different letters indicate significant differences at the same concentration (\(p<\) 0.05).[ENDFOOTNOTE] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hydroxyl radical (HO\(\bullet\)) scavenging activity of the protein hydrolysate of scalloped hammerhead cartilage using different proteases (\(c=\) 15 mg protein/mL).
## 3 Methodology
### Building of Adaptive Classification Model
#### 3.2.1 Initializing the Size of Ensemble Model  
Although Online Bagging is used for ensemble model construction, the size of the model, that is, the number of base classifiers is a user-defined parameter whose value is required before the model generation operation starts. The importance of this parameter comes from the fact that it represents the stopping condition of each learning step, which is related to the complexity of the required computing resources. It is recommended that the value of this parameter in the model tends to be in the low range value (about ten base classifiers). With the continuous arrival of stream elements, mining stream classifiers are expected to have a fast response in the learning and classification process.  
In online bagging, any new data sample is selected according to the Poisson(1) distribution. The classification decision of the ensemble bagging model is based on voting for all K base classifiers with the same weight. It assigns an initial weight \(\mathrm{w}=1\) to each new data example and then passes it to the first weak learner. If this data example is misclassified, its weight will increase before passing it to the next weak learner. The base learner in the proposed model is a VFDT classifier, and the ensemble size used is 10 learners. The ARF classifier is presented in [54]; it uses the resampling method of Online Bagging with a different adaptive method. The parameters of ARF can be categorized into two groups, the first one related to the bagging model and the second related to the base learner. The first group includes:  
* The ensemble size, which is the total number of base learners in the model;
* Maximum number of features that can be tested in each split;
* Warning threshold, which detects when the background tree will be built as a response for distribution change;
* Drift threshold, which detects when the current tree will be replaced by the background tree.  
The second group includes the parameters for building VFDT, which are:
* The tree size that can be controlled by its depth or/and the maximum number of terminal nodes;
* Minimum number of data instances in a terminal node to make the splitting;
* The type of criterion that should be used for splitting.  
All the seven parameters in both groups are user-defined, and they should be chosen carefully. The reason for that is because their values have a direct effect on the model performance in terms of efficiency and accuracy.
## 1 Introduction  
Precipitation infiltration recharge is a significant supply source of groundwater under normal circumstances. In China, a compacted clay layer (CCL) with saturated hydraulic conductivity of less than 1.0 \(\times\) 10\({}^{-7}\) cm/s and a thickness of no less than 2 m can be set as an impervious lining of a landfill [1, 2]. Compacted clay layers (CCLs) with certain hydraulic conductivity and thickness can be used as impervious lining in waste disposal technologies abroad (USA (K \(\leq\) 10\({}^{-7}\) cm/s, d \(\geq\) 0.60 m) (40 CFR258), Germany (K \(\leq\) 10\({}^{-8}\) cm/s, d \(\geq\) 0.75 m), EU (K \(\leq\) 10\({}^{-7}\) cm/s, d \(\geq\) 1.00 m), Japan (K \(\leq\) 10\({}^{-6}\) cm/s, d \(\geq\) 0.50 m)) [3, 4, 5, 6, 7, 8, 9, 10]. These layers listed above show great impermeability of the cohesive soil. Cohesive soil is regarded as a relatively impermeable layer because of its extremely low permeability in traditional hydrogeological studies.  
Cohesive soil water and solute transport studies not only consume a great deal of time but are also hard to do [11]. Various studies have attempted to use technologies such as hydrological geochemistry, stable isotopes, in situ monitoring, and numerical simulations to reveal the migration of soil moisture and the conversion relationships among rainfall,soil moisture, and groundwater [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. But almost all of these studies were located in arid and semi-arid areas with unconfined groundwater aquifers. The moisture migration and recharge pattern of thick cohesive soil with low hydraulic conductivity covering a micro-confined groundwater aquifer in a humid area is still unclear.  
The Quaternary stratum is widely distributed in the Jianghan Plain and is covered with thick cohesive soil at a thickness of more than 10 m. Based on stable isotopes (D, \({}^{18}\)O) technology, using in situ monitoring technology, we found the moisture migration and recharge pattern of thick cohesive soil in the field test site with a micro-confined groundwater level change. Finally, we obtained the precipitation infiltration recharge amount and soil water-groundwater exchange amount through soil moisture transport numerical simulation by Hydrus 1D. This research is of great significance for groundwater resources evaluation and development and groundwater environmental protection in humid climate plain areas such as the Jianghan Plain.
## 2 Materials and Methods
### One-Dimensional Convolutional Neural Network (I-D CNN)
#### 2.4.1 Network Architecture of 1-D CNN Model  
The architecture of the 1-D CNN under consideration was meticulously designed for sequential data processing. The network comprises the following layers (Table 3):  
* Input layer: A sequence input layer that accepts data with a specified number of channels, which was tailored to the dimensions of the input sequences;  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \# & **Name** & **Type** & **Activations** & **Learnable Sizes** \\ \hline \multirow{3}{*}{1} & sequenceinput & \multirow{3}{*}{Sequence Input} & \multirow{3}{*}{308 (C) \(\times\) 1 (B) \(\times\) 1 (T)} & \multirow{3}{*}{-} \\  & 308 dimensions) & & & \\ \hline \multirow{3}{*}{2} & lstm (LSTM with & \multirow{3}{*}{LSTM} & \multirow{3}{*}{750 (C) \(\times\) 1 (B)} & InputWeights: \\  & 750 hidden units) & & & 3000 \(\times\) 308 \\  & & & & RecurrentWeights: \\  & & & & 3000 \(\times\) 750 \\  & & & & Bias: 3000 \(\times\) 1 \\ \hline \multirow{3}{*}{3} & fc (3 fully connected & \multirow{3}{*}{Fully Connected} & \multirow{3}{*}{3 (C) \(\times\) 1 (B)} & Weights: 3 \(\times\) 750 \\  & layer) & & & Bias: 3 \(\times\) 1 \\ \hline \multirow{3}{*}{4} & softmax (softmax) & Softmax & \multirow{3}{*}{3 (C) \(\times\) 1 (B)} & \multirow{3}{*}{-} \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Layer information of LSTM architecture.  
* Convolutional layers: Two 1-D convolutional layers, each followed by a rectified linear unit (ReLU) activation function. The first convolutional layer utilizes a filter size of \(filterSize=5\) and \(numFilters=128\) filters. To preserve temporal causality, padding is set to \"causal\". The second convolutional layer doubles the number of filters to \(2\times numFilters\), maintaining the same filter size and padding strategy;
* Normalization layers: Layer normalization is applied following each ReLU activation to stabilize and accelerate the training process;
* Pooling layer: A global average pooling layer is applied to reduce the dimensionality of the feature maps, summarizing the most salient features;
* Output layers: The network concludes with a fully connected layer that maps the pooled features to the desired number of output classes, followed by a softmax layer to facilitate a probabilistic interpretation of the class predictions.
## 7 Robustness
### Security Analysis
#### 7.5.3 Key Management  
The PRNG seed is the system's primary lock. This method uses a consistent seed across investigation batches to simplify key management and restricts seed access to authorised users through secure storage protocols. The design allows for periodic seed rotation between major investigations. This strategy balances robust security with the demands of a forensic procedure where consistency and efficiency are paramount.  
Unlike zero-knowledge approaches [33] that enable verification without revealing the watermark itself, this system prioritises forensic workflow compatibility, where transparency and straightforward extraction remain essential for evidence-handling procedures.