esa-sceva/satcom-chunk-collection huggingface dataset:
# Wettability of Carbonate Reservoir Rocks:
## 4 Results and Discussion
Figure 6 displays the quantitative wettability indices \(I_{AH}\) (Amott-Harvey), \(I_{USBM*}\) (USBM*), \(I_{L}\) (Lak), and \(I_{ML}\) (modified Lak) against the reservoir quality indicators \(FZI\), \(RQI\), and Winland \(R_{35}\). In general, as rock quality indicators increase (or rock quality improves), the wettability indices decrease, meaning that the rocks become more oil-wet. Results showed that the USBM* and Amott-Harvey indices, \(I_{USBM*}\) and \(I_{AH}\), were weakly correlated to the rock quality indicators (0.03 \(<\) R2 \(<\) 0.2). Although the Lak and modified Lak indices, \(I_{L}\) and \(I_{ML}\), were intermediately correlated to the rock quality indicators \(RQI\) and Winland \(R_{35}\) (0.41 \(<\) R2 \(<\) 0.50), they were weakly correlated to \(FZI\) (R2 = 0.27 for \(I_{L}\) and 0.14 for \(I_{ML}\)). As shown by [PERSON] et al. [74], while \(FZI\) is a function of grain diameter, the value of \(RQI\) is mainly controlled by pore-throats and their sizes. Winland \(R_{35}\) is also a function of pore-throat radii [76]. Since the Lak and modified  
Figure 5: Correlations among different wettability indices calculated on core samples studied here. (a) USBM* vs. Amott–Harvey, (b) Lak vs. modified Lak, (c) USBM* vs. modified Lak, (d) USBM* vs. Lak, (e) Amott–Harvey vs. Lak, and (f) Amott–Harvey vs. modified Lak. Lak indices are based on the relative permeability measurements, they are strongly affected by wetting characteristics of larger pore throats, which control fluid flow through rocks. The network of larger pore throats also controls rock quality. However, the USBM* and Amott-Harvey indices take into account the effect of both small and large pore throats. This could be the reason for relatively high correlations between Lak and modified Lak indices and rock quality indicators _RQI_ and Winland _R35_. However, the number of samples used in this study is limited to 19 core plugs and the dataset is rather homogeneous (samples were collected along one well in a carbonate reservoir). Adding more samples from different formations may result in more scatter in the data. Further investigations are required to address the trend between wettability indices and rock quality indicators. Figure 6: Different wettability indices versus rock quality indicators for the carbonate rock samples used in this study. (**a**) Amott-Harvey vs. _FZI_, (**b**) Amott-Harvey vs. _RQI_, (**c**) Amott-Harvey vs. Winland _R35_, (**d**) USBM* vs. _FZI_, (**e**) USBM* vs. _RQI_, (**f**) USBM* vs. _RXi_, (**g**) Lak vs. _FZI_, (**h**) Lak vs. _RQI_, (**i**) Lak vs. Winland _R35_, (**j**) modified Lak vs. _FZI_, (**k**) modified Lak vs. _RQI_, (**l**) modified Lak vs. Winland _R35_.
# Anodic Oxidation of 3D Printed Ti6 Al4V Scaffold Surfaces: In Vitro Studies
## 2 Materials and Methods  
### Scaffolds' 3D Modeling and Manufacturing  
The unit cell can be defined as the fundamental building block of cellular structures, which, when replicated in various directions, creates a pattern. The samples were designed and manufactured with a unit cell that had a diamond-based architecture, which was based on previous studies [18]. The design of the scaffolds and the final geometries of the cell structures were created using Materialise Mimics(r) software. The resulting file was exported in an STL format for production, as shown in Figure 1. Scaffolds measuring 8 mm in diameter and 3 mm in height were produced.  
A total of 237 samples were produced using the Powder-Bed Fusion Electron-Beam melting (PBF-EB) process with a commercial Ti\({}_{6}\)Al\({}_{4}\)V alloy powder, Grade 5, with a particle size distribution of 45-106 \(\upmu\)m sourced from Arcam-GE Additive. The Arcam EBM Q10 Plus equipment was used for this purpose, employing the Additive Model. Out of the total number of samples, 115 (48.53%) were successfully produced with interconnected pores and exhibited no significant irregularities in their structure compared to the CAD design. The Ti\({}_{6}\)Al\({}_{4}\)V scaffolds, after being removed from the PBF-EB machine and cleaned, can be seen in Figure 2.  
After the manufacturing process, the samples were chemically polished for five minutes at room temperature under conditions of agitation at 400 rpm in the solution proposed by [PERSON] et al. [19] (1:4:5 volume ratio of 40% HF, 70% HNO\({}_{3}\), and H\({}_{2}\)O). They were then rinsed in distilled water and dried with cold air. It was used to clean and remove unmelted particles without compromising the structure of the scaffolds. After cleaning, the samples underwent an anodic oxidation treatment.  
Figure 1: Computer-aided design of the scaffolds used in this study with a cell featuring a diamond-shaped lattice. (**a**) An isometric view and (**b**) a top view of the scaffolds.  
Figure 2: Ti\({}_{6}\)Al\({}_{4}\)V scaffolds processed using Powder-Bed Fusion Electron-Beam Melting (PBF-EB): (**a**) just finished, after removal from the machine; (**b**) after cleaning.
### Motivation and Main Contribution  
The motivation and main contribution of this paper can be summarized as follows:  
* We propose the TAS/SC technique for the primary network to enhance the OP performance for the primary network as well as to increase the spectrum access possibility of the secondary network.
* Based on the exact closed-form expression of the OP of the primary network, we derived the closed-form expressions of the transmit power for the secondary source and relay nodes under the condition that the minimum QoS of the primary network must be guaranteed.
* The PRS method is applied to the secondary network to enhance the OP performance at the secondary sources.
* We derive new exact closed-form formulas of OP at the source nodes over the block Rayleigh fading channel, which are checked and corrected with Monte Carlo simulations.  
We organize this paper as follows: the introduction is in Section 1. The proposed system model is illustrated in Section 2. In Section 3, we analyzed the performance of the primary and secondary networks. The results and discussion are given in Section 4. Finally, the conclusions and useful recommendations are provided in Section 5.
# Volatiles in Berries: Biosynthesis, Composition, Bioavailability, and Health Benefits
## 2 Biosynthesis of Volatiles in Plants
### Biosynthesis of Other Plant Volatiles  
2.3.1. Phenylpropanoids/Benzenoids  
Phenylpropanoids/benzenoids are synthesized from the shikimate pathway [31]. The shikimate pathway starts with the condensation of phosphoenolpyruvate and erythrose 4-phosphate [51]. Chorismic acid is formed by the elimination of ring alcohol from shikimic acid, and this forms the phenylpropionic acid skeleton [29]. Cinnamic acid and p-hydroxycinnamic acid undergo many enzymatic reactions to produce volatile compounds, including eugenol and benzyl benzoate [52].
## 3 Methods
### Established Dataset
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment Number** & **N** & _S/D_ & _H/D_ & \(D_{\text{r}}\) (\%)** & \(\upsilon_{\text{r}}\) / **D** & \(Q_{\text{u}}\) (**kN**) \\ \hline T1 & 1 & 0 & 3 & 85.8 & 0.050 & 22.9 \\ T2 & 1 & 0 & 6 & 85.8 & 0.140 & 108.7 \\ T3 & 1 & 0 & 9 & 85.8 & 0.204 & 236.2 \\ T4 & 1 & 0 & 85.8 & 0.242 & 357.5 \\ T5 & 1 & 0 & 12 & 85.4 & 0.238 & 313.4 \\ T6 & 1 & 0 & 2 & 86.7 & 0.032 & 9.9 \\ T7 & 1 & 0 & 3 & 86.4 & 0.055 & 22.1 \\ T8 & 1 & 0 & 96.2 & 0.067 & 22.9 \\ T9 & 1 & 0 & 4 & 86.7 & 0.091 & 42.9 \\ T10 & 1 & 0 & 6 & 86.4 & 0.128 & 108.1 \\ T11 & 1 & 0 & 6 & 96.2 & 0.146 & 121.7 \\ T12 & 1 & 0 & 7.5 & 90.0 & 0.180 & 161.6 \\ T13 & 1 & 0 & 86.4 & 0.170 & 176.4 \\ T14 & 1 & 0 & 8 & 96.4 & 0.188 & 217.6 \\ T15 & 1 & 0 & & 88.8 & 0.201 & 249.9 \\ T16 & 1 & 0 & 9 & 96.1 & 0.192 & 270.3 \\ T17 & 1 & 0 & 96.2 & 0.166 & 260.0 \\ T18 & 1 & 0 & 10 & 96.4 & 0.190 & 309.6 \\ T19 & 1 & 0 & 10.5 & 90.0 & 0.201 & 271.8 \\ T20 & 1 & 0 & & 85.4 & 0.227 & 322.1 \\ T21 & 1 & 0 & 12 & 91.7 & 0.209 & 364.9 \\ T22 & 2 & 1.5 & 7.5 & 88.7 & 0.153 & 158.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental program and key results used in this paper [18]. Figure 5: The test setup: (**a**) for the details about model anchors; (**b**) for the loading equipment. After the normalization and binarization of the dataset, we divided the whole dataset into two parts: a training set comprising 80% of the data and a testing set comprising the remaining 20%. The data in the training set were used for training the GBDT-PSO model and hyperparameters tuning, while the model performance was evaluated using the validation set [48]. The authors note here that, although the dataset size seems relatively small, it is still by far the largest available dataset regarding helical anchors with multiple helices. Laboratory experiments involving helical anchors with multiple helices are cumbersome; thus, collecting validated data is extremely challenging. Considering the number of input features, which was four in the current study, the dataset size was feasible (as shown in the modelling results section).
# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 2 Types of Smart Sensors in Precision Agriculture
Optimal results were obtained for Ca, K, Mg, and Fe, while lower Mn concentrations and limited lines with low intensities affected the accuracy of N and P predictions. The study also explores the prediction of non-element soil parameters such as pH, with Lasso and GPR outperforming PLSR, and examines various methods of data pre-processing. Another study [75] introduces AgriTalk, an affordable IoT platform designed for precise soil cultivation in agriculture. Turmeric cultivation experiments conducted with AgriTalk demonstrate a significant improvement in turmeric quality, particularly achieving curcumin concentrations ranging from 4500 to 5500 mg/100 g--five times higher than existing products. The paper illustrates the intuitive configuration of connections between sensors and actuators to achieve desired farming intelligence and the effective maintenance of AgriTalk for precision farming applications. Through measurement, analytic analysis, and simulation experiments, the research delves into the IoT message delays of AgriTalk. In particular, the findings reveal that the delays for automatic control and automatic-manual control switching, even over long distances (more than 30 km), are very brief (less than 0.2 s), demonstrating AgriTalk's ability to promptly respond to quick and dynamic changes in the field environment during soil cultivation. In another context, the authors of [76] address the need for predicting path loss in Wireless Underground Sensor Networks (WUSN) to optimize network performance. The proposed model, WUSN-PLM, relies on an accurate forecast of the Complex Dielectric Constant (CDC) to assess path loss in various communication scenarios (Underground-to-Underground, Underground Aboveground, and Aboveground Underground). WUSN-PLM considers reflective and refractive wave attenuation based on the burial depth of sensor nodes. Evaluation of WUSN-PLM involves extensive real-world measurements using different transceiver pairs in the botanic garden of the University Cheikh Anta Diop in Senegal. The results demonstrate that WUSN-PLM surpasses existing path loss models across various communication types, providing a precise and balanced accuracy of 87.13% and 85%, respectively, for deployment on cost-effective sensors.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 4 Ecosystem
### Challenges
#### 4.1.2 Attack Protection  
There are several attacks that can be conducted against chaotic image encryption. Research works focusing on these attacks are studied below.  
Plaintext-Related Image Encryption (Protection against Plaintext Attacks)  
Good encryption algorithms should possess resistance to common types of attacks including plaintext attacks. Many resilient algorithms have been proposed such as the one by [PERSON] et al. [156], where they used a random DNA sequence to initialize chaos maps. [PERSON] and [PERSON] [157] proposed another resilient algorithm utilizing pixel permutation and Josephus traversing. Yet another algorithm based on [PERSON]'s chaotic system was proposed by [PERSON] et al. [158], aiming at improving resilience against known/chosen plaintext attacks. In the diffusion stage of this algorithm, the key stream elements created by [PERSON]'s chaotic system are rotated in such a way that the rotation is dependent on the value of the plain pixel. This way, the key stream is a function not only of the key, but also of the plain image. This notably strengthens the cryptosystem against known/chosen plaintext attack. Critiques based on the attack resilience of existing proposed algorithms have also been made. One such critique was made by [PERSON] et al. [159], where vulnerabilities related to diffusion and permutation in a hyper-chaos algorithm were discovered and improved upon.
# Analysis of MTF in TDI-CCD Subpixel Dynamic Super-Resolution Imaging by Beam Splitter
## 3 Experiments
### Simulation Analysis
#### 3.1.2 Simulation of Subpixel Imaging with Linear TDI-CCD  
As using TDI will increase the effect of a mismatch in speed, the actual drift angle \(\theta\) is very small (0-0.3\({}^{\circ}\)) when \(M\cdot d\leq 2\). In order to facilitate simulation, we set \(M\cdot d=2\) and \(\theta=0.15^{\circ}\). Some other parameters are set according to ZY-3 satellite nadir camera in Table 1. The MTF can draw the surface with changes in \(u\) and \(v\) according to results in Section 2.3, as shown in Figure 10.  
Figure 10a shows the MTF of single TDI-CCD, Figure 10b shows the TDI-CCD sampling points in the best position when \(a=b=\frac{p}{2}\), Figure 10c shows the result with the serious offset of \(a=b=\frac{14p}{15}\). Figure 10d shows the result with a minor offset of \(a=b=\frac{2p}{3}\). It can be seen that the MTF at Nyquist frequency in the best position is significantly better than the other two graphs from these results. To highlight their differences, Figure 11a,b give the differences between MTF in Figure 10b,c, MTF in  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Index** \\ \hline Ground resolution & 2.1 m \\ Width & 51 km \\ MTF of Lab & 0.23 (71.5 lp/mm) \\ SNR & \(\geq\)25 \\ Pixel size & 7 \(\upmu\)m \\ TDI stages & 24 \\ CCD duty cple & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key technical parameters of the ZY-3 satellite nadir camera.  
Figure 10: Modulation transfer function (MTF) of different sampling positions in TDI–CCD. (**a**) MTF of single TDI-CCD; (**b**) MTF of sampling points in the best position; (**c**) MTF of sampling points with the serious offset; (**d**) MTF of sampling points with the minor offset.  
Figure 10b,d respectively. We can suppose that if subpixel imaging technology were applied in ZY-3, the resolution will be increased by almost 2 times at best under the condition of accurate imaging position. Although this is only a theoretical simulation, the resolution should also be clearly improved in the application of remote sensing cameras.
# Image Deblurring under Impulse Noise via Total Generalized Variation and Non-Convex Shrinkage
###### Abstract  
Image deblurring under the background of impulse noise is a typically ill-posed inverse problem which attracted great attention in the fields of image processing and computer vision. The fast total variation deconvolution (FTvd) algorithm proved to be an effective way to solve this problem. However, it only considers sparsity of the first-order total variation, resulting in staircase artefacts. The L1 norm is adopted in the FTvd model to depict the sparsity of the impulse noise, while the L1 norm has limited capacity of depicting it. To overcome this limitation, we present a new algorithm based on the Lp-pseudo-norm and total generalized variation (TGV) regularization. The TGV regularization puts sparse constraints on both the first-order and second-order gradients of the image, effectively preserving the image edge while relieving undesirable artefacts. The Lp-pseudo-norm constraint is employed to replace the L1 norm constraint to depict the sparsity of the impulse noise more precisely. The alternating direction method of multipliers is adopted to solve the proposed model. In the numerical experiments, the proposed algorithm is compared with some state-of-the-art algorithms in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), signal-to-noise ratio (SNR), operation time, and visual effects to verify its superiority.  
fast total variation deconvolution; total generalized variation; non-convex shrinkage; Lp shrinkage; direction method of multipliers; image restoration +
Footnote †: journal:  
_Article_  
Image Deblurring under Impulse Noise via Total Generalized Variation and Non-Convex Shrinkage
# Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
## 4 Methods and Models
### BiLSTM Model  
This paper employs a Bidirectional Long Short-Term Memory Network (_BiLSTM_). The _BiLSTM_ deep learning model, compared to other deep learning models, can simultaneously learn from both past and future temporal features, making better use of the time series characteristics of traffic flow on road segments. As a result, its prediction performance is significantly superior to other deep learning models, allowing for more accurate predictions of future road segment traffic flow. Therefore, it can be used as a method for short-term traffic flow prediction. In the experimental setup for road segment traffic flow prediction in this section, an analysis from a temporal perspective shows that the predictive accuracy of all models decreases as the number of forecast days increases. However, the _BiLSTM_ deep learning model is able to predict road segment traffic flow more accurately [33, 34]. The _BiLSTM_ network structure consists of a forward _LSTM_ and a backward _LSTM_, allowing the feature data of the dataset to simultaneously have both past and future feature information. The network structure of the model is shown in Figure 5.  
\[\overset{\rightarrow}{h_{t}}=LSTM(h_{t-1},x_{t}) \tag{4}\]  
\[\overset{\leftarrow}{h_{t}}=LSTM(h_{t+1},x_{t}) \tag{5}\]  
\[h=\overset{\rightarrow}{\alpha}\overset{\rightarrow}{h_{t}}+\overset{ \rightarrow}{\beta}\overset{\leftarrow}{h_{t}} \tag{6}\]  
where \(x_{t}\) is the input data at the moment of \(t\), \(\overset{\leftarrow}{h_{t}}\) is the forward _LSTM_ implicit layer output information, \(\overset{\leftarrow}{h_{t}}\) is the reverse _LSTM_ implicit layer output information, and \(\alpha\) is the constant coefficient of \(\overset{\rightarrow}{h_{t}}\), which denotes the weight; similarly, \(\beta\) is the constant coefficient of \(\overset{\leftarrow}{h_{t}}\), which denotes the weight.  
Based on the temporal characteristics of passenger pick-up probability, this paper utilizes the _BiLSTM_ model, which is commonly used for time series prediction tasks, to improve the accuracy of passenger pick-up probability prediction. The _BiLSTM_ model, as shown in Figure 6, follows the main process outlined below:  
Figure 5: _BiLSTM_ model network structure.  
Step 1: Use historical passenger pick-up data from taxis as input to the _LSTM_ module to extract time-related features.  
Step 2: Combine bidirectional search to enhance the _LSTM_ model's ability to capture bidirectional information, effectively addressing the issue of long-term dependency and gradient vanishing.  
Step 3: Use a fully connected layer to construct the number of passengers on taxi roads. This can predict the passenger volume on a particular road at a specific time period (5 min) on a certain day. Finally, calculate the passenger pick-up probability for each road according to the road passenger pick-up probability calculation method.
## 3 Materials and Methods
### Preliminaries and Notations
#### 3.1.5 Forwarding Orientation  
In order to prolong the longevity of UWSNs, it is significant to adopt an energy-efficient forwarding method. Inspired by the murmuration of a swarm of swallows, [PERSON] et al. [14] proposed a biotic model, the Hybrid Projection Model, which defines the murmuration via two metrics: the opacity and the orientation.  
Figure 2: Movement model of sensor nodes.  
As can be seen in Figure 3, the orientation is mathematically defined as average accumulation of vectors created by the neighbors of a node, which can be calculated by Equation (4),  
\[\mathbf{e}_{ori}(i,t)=\frac{1}{|H(i,t)|}\underbrace{(\mathbf{v}_{1}(i,t)+ \mathbf{v}_{2}(i,t)+...+\mathbf{v}_{j}(i,t)+...+\mathbf{v}_{|H(i,t)|}(i,t))}_{|H (i,t)|\text{ neighbor vectors}},\;\eta_{j}(i,t)\in H(i,t) \tag{4}\]  
where \(\mathbf{e}_{ori}(i,t)\in\mathbb{R}^{3}\) denotes the vector of orientation, \(|H(i,t)|\) is the number of neighbors of \(v_{i}\) within its communication range and \(\mathbf{v}_{j}(i,t)\in\mathbb{R}^{3}\) denotes the vector from \(v_{i}\) to its \(j\)-th neighbor \(v_{j}\) at time slot \(t\). The orientation can be acquired locally via the Received Signal Strength (RSS) and Arrival of Angle (AoA) of the broadcasting packets from neighborhood.  
The length of \(\mathbf{e}_{ori}(i,t)\) denotes the absolute value of the orientation and the orientation direction is denoted by the direction of \(\mathbf{e}_{ori}(i,t)\). Nodes with large orientation values are generally located on the edge of a neighborhood. Otherwise, they are near the centers of their neighborhoods and nodes with lower orientation values are more likely to be the relay node. It has been proved that determining the forwarding direction via orientation metric is energy-efficient [12]. Moreover, there is no requirement for localization when using the orientation metric, which is very suitable for underwater sensors due to their inaccessibility to GPS signals. Therefore, we adopt the orientation metric to determine data forwarding direction.
# Cyber Resilience Limitations in Space Systems Design Process: Insights from Space Designers
## 4 Results and Findings
### Engineering Methodologies and Practices
They also highlighted embedding cybersecurity into the end-to-end engineering process, which, in return, can foster a culture of security awareness and build systems.