esa-sceva/satcom-chunk-collection huggingface dataset:
# An Integrated Capacity Allocation and Dynamic Pricing Model Designed for Air Cargo Transportation
## 3 Results, Discussion, and Conclusions
### Future Studies  
Our suggestions for future studies are separate for capacity allocation and dynamic pricing. For capacity allocation, overbooking, no-shows, and cancelations can be considered in the model. A hybrid model can be built to solve these problems. Overbooking, no-shows, cancelations, and discounts can be added as inputs to the dynamic pricing model. In addition, product type inputs can be added to the model to provide pricing for different product types. To improve the performance of the model, wider ranges of hyperparameters can be used, and different hyperparameter optimization techniques, such as Bayesian optimization, can be tried, compared with other models, and different ANN architectures can be used as well.  
Conceptualization, D.I.A. and S.E.A.; methodology, D.I.A. and S.E.A.; software, D.I.A. and Alptekin. S.E; validation, D.I.A.; formal analysis, D.I.A.; investigation, D.I.A. and S.E.A.; resources, D.I.A.; data curation, D.I.A.; writing--original draft preparation, D.I.A.; writing--review and editing, S.E.A.; visualization, D.I.A.; supervision, S.E.A.; project administration, D.I.A.; funding acquisition, D.I.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
This research is supported under the \"100/2000 YOK Ph.D. scholarship program\".  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ANN & Artificial Neural Network \\ CVaR & Conditional Value at Risk \\ GMAE & Geometric Mean Absolute Error \\ MAD & Mean Absolute Deviation \\ MARE & Mean Absolute Relative Error \\ MaxAE & Maximum Absolute Relative Error \\ \end{tabular}
###### Abstract  
This work reports both numerical and experimental studies of the reconditioning of a microwave plasma jet chemical vapor deposition (MPJCVD) system for the growth of diamond film. A three-dimensional plasma fluid model is constructed for investigating and conditioning the MPJCVD system and optimizing its operating conditions. The methodology solves electromagnetic wave and plasma dynamics self-consistently using an adaptive finite element method as implemented in COMSOL Multiphysics. The whole system has been modeled under varying parameters, including the reactor geometry, microwave power, and working gas pressure. Using an operating condition identical to the optimized simulation results, the MPJCVD system successfully fabricates a diamond-thin film on a graphite substrate. The SEM image reveals the presence of a diamond film uniformly distributed with particles of a size of \(\sim\)1 \(\upmu\)m. The field emission from the diamond film grown from our homemade MPJCVD system on the graphite substrate presents extraordinary properties, i.e., extremely high current density and relatively low turn-on voltage. The turn-on electric field observed could be as low as \(\sim\)4 V / \(\upmu\)m. This developed model provides valuable physical insights into the MPJCVD system, which guided performance improvements. The work may find applications in surface hardening and provide a better cold cathode for field electron emission.  
MPJCVD; diamond film fabrication; plasma; field emission +
Footnote †: journal: Applied Sciences  
Article
# IMPA versus Cloud Analysis and IDA: Different Methods to Evaluate Structural Seismic Fragility
## 2 Methodology
### Performed Nonlinear Analysis
#### 2.3.1 Cloud-Based Analysis
* Convert the pushover curve of the \(n\)th mode shape to a capacity curve in the ADRS format by (Equations (6)-(9)): \[a_{C,n}=\frac{V_{b,n}}{M_{tot}\cdot a_{n}}\] (6) \[d_{C,n}=\frac{u_{r,n}}{\Gamma_{n}\cdot\phi_{n,r}}\] (7) \[\Gamma_{n}=\frac{\phi_{n}^{T}MI}{\phi_{n}^{T}M\phi_{n}}\] (8) \[\alpha_{n}=\Gamma_{n}\frac{\phi_{n}^{T}MI}{M_{tot}}\] (9) where \(M_{tot}\) is the total mass of the structure, \(\phi_{n}\) is the \(n\)th natural vibration mode, \(\phi_{n,r}\) is the amplitude of the \(n\)th natural vibration mode at the roof of the structure, and \(\Gamma_{n}\) and \(\alpha_{n}\) are the modal participation factor and modal mass of the nth mode, respectively;
* Convert 5% damped response spectrum from the standard pseudo-acceleration \(S_{a}\) versus the period of vibration \(T\) format to the ADRS format by (Equation (10)): \[S_{De}(T)=S_{a}(T)\bigg{(}\frac{T}{2\pi}\bigg{)}^{2}\] (10) where \(S_{D}(T)\) is the displacement spectrum;
* Plot demand and capacity diagrams together in the ADRS space. Determine the bilinear capacity curve. Iteratively determine the displacement demand for the \(n\)th mode shape. In this step, the dynamic analyses of a sequence of equivalent linear systems with successively updated values of equivalent viscous damping are involved;
* Reduce the elastic spectrum by the equivalent viscous damping (Equation (11)); \[\eta=\sqrt{\frac{10}{5+v_{eq}}}\] (11)
* Determinate the performance point or the maximum expected demand in terms of top displacement;
* Convert displacement demand found in step 3 to global top displacement and individual component of local deformation (i.e., interstory drift) for the \(n\)th mode shape;
* Evaluate maximum demand to capacity ratio values according to Equation (1). If two or more modes of vibration are considered, combine the local modal responses according to the square-root-of-sum-of-squares (\(SRSS\));
* Estimate parameters of the linear regression model in the logarithm scale to fit the pairs of demand to capacity ratio (\(DCR_{LS}\)) and \(IM\);
* Draw structural fragility curve according to Equation (5).
However, the model's accuracy significantly decreases at higher altitudes, making it unsuitable for detecting livestock groups that are easily startled [22]. [PERSON] and [PERSON] et al. integrated a Dyhead module with the detecting head of the YOLOV7 model. They improved the model via knowledge distillation, enhancing accuracy while decreasing identification time, furthering the application of sheep facial recognition technology in real-world applications [23]. YOLO series models can accurately identify and locate animals and can distinguish them from the background. Remote sensing using UAVs can gather target imagery rapidly and comprehensively [24]. The combination of UAV-acquired image data and YOLO target detection provides a new solution for livestock detection in large-scale pasture grasslands. This study's goal is to gather and build an image collection of popular livestock species (cattle, sheep, and yak) in the Qinghai region while also promoting relevant computer vision research and applications. Building upon the original YOLOv8n model, this study proposes improvements and optimizations, resulting in the CCS-YOLOv8 model, which is aimed at enhancing the detection performance for livestock targets. The following are the particular optimization techniques:  
1. A CBAM is integrated into the C2f module of YOLOv8n to analyze image information more effectively and emphasize salient features. By enhancing the representational capacity of the output feature information, the model's detection accuracy is consequently improved. 2. The lightweight up-sampling operator CARAFE is introduced to solve the shortcomings of conventional up-sampling operators, which have small receptive fields and disregard the semantic content of feature maps. 3. To mitigate the loss of small target feature information, an additional small object detection layer is incorporated into the YOLOv8n neck structure. This layer facilitates the extraction of livestock characteristics and details across multiple receptive fields.
## 4 Discussion
### Different Sensitivities of SIF and GPP to Air Temperature
In addition, SIF is directly linked with electron transport, and electron transport (linear, cyclic, pseudo-cyclic) still operates under cold conditions if there is light available, which is another important photoprotective mechanism [24; 73]. The uncertain energy distribution under cold conditions and the potential response of PSI and PSII fluorescence to environmental stress might result in the dynamic range of \(\Phi_{F}\) being smaller than that of \(LUE_{p}\) in response to low temperature. In addition, some studies have reported that clear and cloudy conditions can affect the relationship between SIF and GPP [36]. We further evaluated the clear and cloudy conditions on the \(tSIF\)-GPP relationship by considering \(T_{a}\) (Figures S11 and S12), and the results also showed that the \(tSIF\)-GPP relationship was affected by \(T_{a}\) during both clear and cloudy days. By removing the negative \(tSIF\) values, an improvement in the GPP estimation based on \(tSIF\) by considering \(T_{a}\) was also found (Figures S16 and S17). In our study, we found that there were certain different sensitivities in response to air temperature between \(LUE_{p}\) and \(\Phi_{F}\)which indicated that the influence of temperature should be considered for a more accurate SIF-GPP model.
## 3 Materials and Methods
### Feature Selection  
For high-dimensional datasets, feature selection plays a crucial role, improving performance and estimators accuracy scores [33]. Current radiomic studies use several feature selection methods for model building. In this study, seven of the most used methods found in the literature were selected to build a classification pipeline for radiomics in CBCT.  
The variance threshold provides a baseline approach by removing all zero-variance features. Univariate methods were also used by several authors such as [PERSON] et al. [18], [PERSON] et al. [19], [PERSON] et al. [21], [PERSON] et al. [34]. [PERSON] et al. [24], [PERSON] et al. [35], who also used Analysis Of Variance (ANOVA) method with good results. Another one is the Recursive Feature Elimination (RFE) with an SVM estimator [16; 36]. The goal of RFE is to select features by recursively considering smaller and smaller sets [33]. An approach also evaluated is the introduction of cross-validation in the RFE. A different approach is to select the features that maximize an estimator's metric (selected from the model). [PERSON] et al. [37] considered an SVM and [PERSON] et al. [38] a Linear Regression (LR) model. The selected feature selection methods and used parameters during training are shown in Table 4.  
To implement these methods, scikit-learn library was used [33] and the chosen parameters are the default ones. Each of the feature selection methods was evaluated with different classifiers (see Section 3.5) in multiple pipelines. The idea is not to evaluate the best feature selection method or classifier but the pipeline or pipelines that best suit this particular dataset and the task of distinguishing favourable/unfavourable prognoses for EBRT PCa patients from CBCT images. Furthermore, no parameter optimization was performed but that will be considered for future work.
## 2 Materials and Methods
### Heterologous Expression of RgGES  
The full-length coding sequence of the _RgGES_ candidate was amplified with primer 27-28 and then subcloned into the expression vector pET28a at the _Bam_H I/_Sal_ I site to generate the plasmid pET28a-_RgGES_. The expression of _RgGES_ in _Escherichia coli_ (_E. coli_) BL21(DE3) cells was induced with 0.3 mM isopropyl-\(\beta\)-D-thiogalactopyranoside (IPTG) at 16 \({}^{\circ}\)C, 160 rpm for 16 h. After the cultivation, the transgenic cells were collected by centrifugation and broken by ultrasonication in the lysis buffer (20 mM sodium phosphate, 300 mM sodium chloride with 10 mM imidazole; pH 7.5). The crude protein extracts were incubated with a HisPur Ni-NTA resin to purify the recombined RgGES protein following the manufacturer's instructions. The purified RgGES protein was desalted into the assay buffer (100 mM HEPES-KOH, 1 mM MgCl\({}_{2}\), pH 7.0). The concentration of RgGES protein was evaluated by the Nanodrop spectrophotometer and its purity was analyzed using SDS-PAGE.
# Efficient Opportunistic Routing Protocol for Sensor Network in Emergency Applications
## 3 Proposed Opportunistic Routing Protocol
### Terminologies  
Minimum Period of Connectivity--in opportunistic networks, the minimum period of connectivity (MPC) represents the connectivity strength between two nodes while they are moving. This property also represents successful data packet transmission by a node. The MPC (represented in time) between two nodes \(a\) and \(b\) is denoted as  
\[MPC_{ab}=\frac{R_{c}-d}{V_{a}+V_{b}} \tag{1}\]  
where \(Rc\) is the communication range of nodes \(a\) and \(b\), \(d\) is the distance between \(a\) and \(b\). \(V_{a}\) is the velocity of node \(a\) and \(V_{b}\) is the velocity of node \(b\).  
Figure 2: Flowchart of proposed approach.  
The Global Minimum Period of Connectivity (\(MPC_{g}\)) is a predefined parameter given to the proposed protocol in order to set up a network having clusters with the same \(MPC_{g}\) between its nodes.  
Weight--the weight of a node represents whether the node is stable or not. Equations (2)-(4) calculate the weight of a node as follows.  
\[Y=\text{Total number of neighbors of node }a=\left|neighbors(a)\right| \tag{2}\]  
\[X=\sum\
olimits_{bneti\text{\scriptsize{\it{belongs}}}(a)}\frac{MPC_{ab}}{MPC_{g}} \tag{3}\]  
\[Weight=X-Y. \tag{4}\]
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 2 Diagnosability Architecture for Control Systems Based on Structural Analysis
### Fault Diagnosis Based on Structural Analysis
#### 2.2.2 Principles of Fault Diagnosis Based on Structural Analysis
The blank part of the diagram is \"0\", the shaded part is \"1 or 0\", and a light-colored dotted line indicates matching. By DM decomposition, the incidence matrix is decomposed into three parts: the structurally underdetermined part \(G^{-}(E^{-},V^{-})\), structurally just-determined part \(G^{0}\big{(}E^{0},V^{0}\big{)}\), and structurally overdetermined part \(G^{+}(E^{+},V^{+})\). Where the structurally just-determined part can be further divided into \(G_{1},G_{2},\ldots G_{n}\). It can be seen that the DM decomposition of the system is not unique, but \(G^{-},G_{1},G_{2},\ldots G_{n},G^{+}\) is definite. From Figure 1, it can be seen that there exists a column complete matching for \(G^{+}(E^{+},V^{+})\), a perfect matching for \(G^{0}\big{(}E^{0},V^{0}\big{)}\), and a row complete matching for \(G^{-}(E^{-},V^{-})\). By removing any of the constraints, the structurally overdetermined part still has a column complete matching. The design of a diagnostic system is mainly through structural decomposition, based on the structurally overdetermined part to design the residuals. The specific steps are as follows:  
Firstly, establish the system structural model and determine the MSOs;  
Secondly, this redundant equation and the matched constraints associated with it are synthesized to form a collection of MSOs for generating residuals;Thirdly, a fault detection matrix is built based on the sensitive relationship between MSOs and the faults to analyze the fault detectability and isolability.
## 3 PCS Framework for Designing RL Algorithms for Digital Intervention Development
### Computability (C)  
Computability has to do with the efficiency and scalability of algorithms, decisions, and processes. While the original PCS framework focused on the computability of training and evaluating models, we also consider computability to include the ability to implement the algorithm within the constraints of the study. In the online RL setting, computability encompasses all issues related to ensuring that the RL algorithm can select actions and update in a timely manner while running online. The performance of the online RL algorithm must be evaluated under the constraints of the study; key RL algorithm design constraints that could arise include:  
* **Timely Access to Reward and State Information:** The investigators may have an ideal definition of the reward or state features for the algorithm; however, due to delays in communication between sensors, the digital application, and the cloud storage, the investigators' first choice may not be reliably available. Since RL algorithms for digital interventions must make decisions online, the development team must choose state features that will be reliably available to the algorithm at each decision time. Additionally, the team must also choose rewards that are reliably available to the algorithm at update times.
* **Engineering Budget:** One should consider the engineering budget, supporting software needed, and time available to deliver a production-ready algorithm. If there are significant constraints, a simpler algorithm may be preferred over a sophisticated one because it is easier to implement, test, and set up monitoring systems for.
* **Off-Policy Evaluation and Causal Inference Considerations:** The investigative team often not only cares about the RL algorithm's ability to learn but also about being able to use data collected by the RL algorithm to answer scientific questions after the study is over. These scientific questions can include topics such as off-policy evaluation [15; 16] and causal inference [17; 18]. Thus, the algorithm may be constrained to select actions probabilistically with probabilities that are bounded away from zero and one. This enhances the ability of investigators to use the resulting data to address scientific questions with sufficient power [19].
# A New Method for Calculating Prediction Parameters of Surface Deformation in the Mining Area
## 3 Methodology
### Construction of Combined Model
#### 3.3.2 GA-Optimized ELM Neural Network  
The ELM neural network has a strong nonlinear fitting ability, but due to the random generation of the weight matrix between the input layer and hidden layer and that of the threshold matrix of hidden layer neurons, the prediction fitting ability and prediction stability of the network model are insufficient. To select a proper ELM simulation model,  
Figure 7: Topological structure of ELM neural network.  
this paper proposed a GA-optimized ELM neural network. The specific modeling process is as follows:  
1. Determine the relevant parameters of the GA algorithm;
2. Carry out coding and population generation, randomly generate weights and thresholds of the ELM neural network, and generate the original population by binary code;
3. Calculate the fitness function value of each individual, calculate RMSE of each individual's test set, and take it as the individual fitness value;
4. Population evolution;
5. Training and prediction of ELM network: Decode the final population after iterative optimization to obtain the optimized weights and thresholds, and assign to the ELM; train the ELM network by training samples, and calculate output layer weight \(\hat{B}^{\prime}=H^{\prime+}T^{\prime}\) by the least squares method; finally, bring the test samples into the ELM model for prediction.
# Seismic Random Noise Attenuation via Low-Rank Tensor Network
## 3 Results
### Experimental Results on Synthetic Data  
On the synthetic dataset, the average SNR on the validation set computed by our proposed LRTNet, Pyseistr, and TNN-SSTV are 9.37 dB, 6.46 dB, and 6.10 dB, respectively. The SNRs computed for each data sample are given in Table 1. From the quantitative comparison, it can be observed that after tuning with deep learning, LRTNet outperforms Pyseistr and TNN-SSTV in seismic denosing tasks.  
In addition, we present the denoising results of each method on validation volume No. 8 in Figures 2-4 for qualitative analysis. As shown in the fourth row (subfigures m-p), LRTNet achieves the best denoising performance, as it produces smoother results with sharper fault details compared to baseline methods. This demonstrates its superior ability to preserve structural information while effectively removing noise. In the fifth row (subfigures q-t), the noise removed by LRTNet retains more noticeable signal patterns than Pyseistr, similar to TNN-SSTV. This indicates that LRTNet performs slightly less optimally than Pyseistr, which exhibits the least signal leakage. To further validate these observations, we provide statistical information on the local similarity tensor in Table 2. The local similarity results in the last row (subfigures u-x) align with the conclusions drawn from the removed noise, showing consistency across both qualitative and quantitative evaluations. While Pyseistr yields the best local similarity, LRTNet does not show a significant disadvantage in this regard. Overall, the combined quantitative and qualitative analyses highlight the exceptional performance of our proposed LRTNet on the synthetic dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Data** & **LRTNet** & **Pyseistr** & **TNN-SSTV** \\ \hline No. 0 & 8.6201 & 5.6587 & 5.9185 \\ No. 1 & 10.3855 & 6.9493 & 6.3235 \\ No. 2 & 11.1391 & 7.0777 & 6.4930 \\ No. 3 & 11.1522 & 7.
## 4 Experiments and Results
### Implementation Details  
Our experiments were divided into two groups. In one group, experiments were conducted on BrainWeb and NAMIC, where we built our training set and testing set as in [13] for fair comparison. In the other group, the experiments were evaluated on the IXI dataset, where we built the training and testing sets, and then trained DGGRN from the scratch.  
**Training set:**: LR images were generated according to the following steps:  
1. The original image \(x\) were convolved by \(3\times 3\) Gaussian kernel with standard deviation of 1.
2. The results of convolution were down-sampled with factors of \(2,3\) and \(4\), respectively.  
The same degradation was applied in [9; 13]. It aims to simulate the generation of a LR MRI image in the spatial domain.  
For BrainWeb and NAMIC, we built the training sets with the same data as in [13]. For IXI dataset, 300 2D T2w images from 10 people were used for network training. By flipping and rotation, 16 augmented images were generated from each training image for data augmentation. In addition to these affine augmentations, we extended the training dataset via elastic deformation [38].  
**Test set:** For testing, we randomly selected samples from persons excluding the training data. Specifically, we selected 56 samples from persons in IXI. The test sets of BrainWeb and NAMIC were produced as in [13].  
**Network structure:** The SR subnet is composed of eight gradient-guided Resblocks. Each block consists of two convolutional layers. The gradient modeling subnet consists of four convolutional layers with dense connections. Following CNN-based SR studies [18; 21], we set the convolutional filter 64 to \(3\times 3\). We used the method of Xavier to initialize the weights, and the biases were initialized to zero.  
**Training details:** Our network was implemented based on Tensorflow [39]. To minimize the overhead and to fully utilize the GPU memory, the batch size was set to 64 and the training stopped after 65 epochs when no improvement was observed. The LR T2w 12 \(\times\) 12 patches and their gradient magnitude maps were fed into network as the inputs. According to the report of [PERSON] _et al._[11], the Adam method [40] provides fast convergence and better reconstruction results than SGD. Thus, we trained the network with the Adam optimization with \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\). The initial learning rate was \(1\times 10^{-4}\) which was decreased by 10% every 20 epochs.  
**Hardware:** All experiments were conduct on a PC with a 2.1 GHz Intel Xeon E5-2620 CPU and an NVIDIA Titan X GPU (12G Memory). All compared approaches were run on the same machine.