esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Amphiphilic Polymer/Nano-Silica Composite on Shale Stability for Water-Based Muds
## 3 Results and Discussion
### Pressure Transmission Test  
Figure 9 presents the downstream pressure change with time when the shale cores interacted with different test fluids. As shown in Figure 9, the pressure transmission rate of the shale cores was so fast that the downstream and upstream pressures were basically the same after 40 min. For the base fluid, a lower growth rate of the downstream pressure was observed, and it took about 128 min to achieve the equilibrium for the downstream and upstream pressures. For the base fluid containing 1% SiO\({}_{2}\), it took about 183 min to achieve the equilibrium for the downstream and upstream pressures. For the base fluid containing 1% SMA/SiO\({}_{2}\), it took about 255 min to achieve the equilibrium, which was significantly longer than the penetration times of the shale cores and the base fluid containing 1% SiO\({}_{2}\). Thus, SMA/SiO\({}_{2}\) played a notable role in slowing down the shale pressure transmission rate. The plugging capacity of SMA/SiO\({}_{2}\) was further evaluated through analyzing the shale permeability  
Figure 8: TGA (thermogravimetric analysis) curves of SMA/SiO\({}_{2}\).  
Figure 7: PSD curves of SMA/SiO\({}_{2}\).  
change. The permeability of the shale cores before being plugged was 1.68 \(\times\) 10\({}^{-6}\) um\({}^{2}\). After being treated by the base fluid and the base fluid containing 1% SiO\({}_{2}\), the permeability was decreased to 1.09 \(\times\) 10\({}^{-7}\) um\({}^{2}\) and 8.00 \(\times\) 10\({}^{-8}\) um\({}^{2}\) respectively. Most noteworthy, after being plugged by the base fluid containing 1% SMA/SiO\({}_{2}\), the permeability was significantly reduced to 6.45 \(\times\) 10\({}^{-8}\) um\({}^{2}\). Therefore, SMA/SiO\({}_{2}\) possessed the capacity to seal the nano-scale pores and fractures, and a lower pressure transmission rate and shale permeability were observed after interact with SMA/SiO\({}_{2}\).  
The SEM images of the shale cores after the pressure transmission experiments are shown in Figure 10. As shown in Figure 10a, a lot of pores and fractures developed in the shale cores and it can be inferred that the diameter of shale pores and the width of shale fractures were mainly distributed between 50-500 nm. These pores and fractures were effectively plugged after interaction with SMA/SiO\({}_{2}\) in the base fluid (Figure 10b). After being plugged, the core surface changed to be smooth and dense, and an isolation layer was formed on it. As a result, the shale permeability was significantly reduced and the plugging capacity of SMA/SiO\({}_{2}\) was excellent.
## 1 Introduction  
This study is a brief overview and synthesis of key points relating to the dynamics of alpine grasslands on the high-altitude and generally arid Qinghai-Tibet Plateau (QTP). It presents a synthesis of what is known about the key issues relating to alpine grasslands and the people, both locally and in distant countries, who depend on the ecosystem goods and services the grasslands provide. Many people use the grassland daily for their lives and livelihoods in this remote and harsh environment. Its purpose is to introduce the region and highlight aspects of the biogeography, ecology, climatology and demography that make this region on the \"Roof of the World\" or the \"Third Pole\" such an important one that provides water to about two billion people. In China itself, hundreds of millions rely on the rivers that arise on the QTP to provide water for irrigation, industrial uses, navigation and domestic water supply. Many hundreds of millions of people who live in countries in South Asia and South-east Asia are utterly dependent on flows along the Mekong, Brahmaputra and the other major rivers that arise on the QTP. The QTP supports millions of semi-nomadic herders and their livestock in Qinghai and in Tibet where livestock such as yak, hardy Tibetan sheep and Bactrian camels provide the sole source of sustenance and income.  
Regrettably, anthropogenic environmental degradation (Figure 1)--exacerbated by changing climate [1], is accelerating as populations of both people and livestock rise [2], and policy measuresdesigned to protect biodiversity and watersheds are implemented. Climate change, overgrazing and other reasons caused continuous degradation of alpine grasslands (see below). Several categories of alpine grassland are recognized based on the botanical composition and species diversity (see below). The management regimes under which they are used has changed a lot in the past 80 years (see below) and land degradation is now a major concern.
## 2 Dispersion-Managed Link and WDM System Modelling
### WDM transmitters, Recivers and Midway-OPC  
The WDM transmitters and receivers located at both ends of the link in Figure 1 are based on intensity modulation (IM) and direct detection (DD), respectively. First, in each transmitter, data bits generated by 127 (=\(2^{7}-1\)) pseudo-random bit sequence (PRBS) intensity-modulate light from distributed feedback laser diode (DFB-LD) in modulator. The output electric field of intensity-modulated 127-bit signals is modeled in return-to-zero (RZ) format with 10-dB extinction ratio (ER) and duty cycle of 0.5. It is also assumed that these RZ pulses are frequency chirp-free. We assume that the center wavelength of each channel is 1550 nm to 1568.4 nm with 100 GHz (0.8 nm) spacing, based on ITU-T Recommendation G.694.1.  
The signals of these 24 channels with different wavelengths are multiplexed in the AWG-MUX and propagate through the first half-link consisting of 30 fiber spans. The chromatic dispersion is somewhat compensated by the dispersion management applied to each fiber span, but distortion due to the interaction between the Kerr nonlinearity and the residual chromatic dispersion is unavoidable. The multiplexed signals distorted while propagating through the first half-link are converted into phase-conjugated signals at the midway-OPC and propagated through the remaining second half-link. The received signals can be restored similarly to the original signals, as the conjugated signals effectively experience the effects of the interaction of Kerr nonlinearity and chromatic dispersion in the second half-link.  
Phase-conjugation in midway-OPC is generated through the process of four-wave mixing (FWM) of the input multiplexed signal and the pump light in a highly nonlinear dispersion-shifted fiber (HNL-DSF) as a nonlinear medium for phase-conjugation. The power and wavelength of the pump light for generating the phase-conjugated waves are assumed to be 18.5 dBm and 1549.75 nm, respectively. Consequently, each wavelength of the conjugated 24 channels in the midway-OPC is then arranged from 1549.5 nm to 1528.5 nm (\(-0.8\) nm interval).  
Each channel demultiplexed through DeMUX is finally received after optical and electrical processing based on the DD method. We assume that the receiver bandwidth is 0.65 times 40 Gb/s. The WDM receiver is comprised of an EDFA with a noise figure of 5 dB, optical filter, PIN diode as photodetector, pulse-shaping Butterworth filter, and decision circuit.
# Deep Model Poisoning Attack on Federated Learning
## 2 Background and Related Works
### Collaborative Learning  
With the rapid growth of datasets, the machine learning model becomes increasingly complex, and training a deep neural network on a large dataset can be time and resource consuming. In traditional centralized framework, multiple participants are forced to upload their private datasets into a trust central server on which it is possible to learn a model (see Figure 1a for illustration). With the concern of privacy leakage, confidentiality organizations, like the government and bank, may not willing to participate in centralized learning [23]. Another elastic approach to mitigate this drawback is to partition the training dataset, concurrently train separated models on each subset, and then aggregate model parameters through a parameter server [24]. However, this approach may suffer from great efficiency degrading. Regarding privacy and efficiency preservation, the authors of [1] propose the federated learning framework to collaboratively train a deep learning model with many participants.  
Figure 1b shows the training procedure of federated learning. At each round \(t\), the central server releases shared global model \(\mathbf{G}^{t}\) to all participants, and randomly selects a subset \(S^{t}\) of \(n\) participants from a total of \(N\) clients. Each selected client \(i\) optimizes global model \(\mathbf{G}^{t}\) to obtain local model (\(\mathbf{L}_{i}^{t+1}\)) for \(t+1\) round while using its private dataset \(\mathcal{D}_{i}\). Subsequently, client \(i\) submits model update \(\mathbf{\Delta}_{i}^{t+1}=\left(\mathbf{L}_{i}^{t+1}-\mathbf{G}^{t}\right)\) back to to the central server. Finally, the central server averages the received updates in order to obtain the new joint model:  
\[\mathbf{G}^{t+1}=\mathbf{G}^{t}+\frac{\eta}{n}\sum_{i\in S^{t}}\mathbf{\Delta}_{ i}^{t+1} \tag{1}\]  
where \(\eta\) is the learning rate and \(\mathbf{\Delta}_{i}^{t+1}\) denotes model updates.  
As shown in Figure 1, the main differences between centralized learning and federated learning can be summarized as: (1) in centralized learning system, clients fully control a subset of training data, but have no access to change neural network directly. In this scenario, a malicious client may inject poisoning data to launch a data poisoning attack; and, (2) in collaborative learning system, as demonstrated in Equation (1), malicious clients can directly modify model parameters and inject adversarial features in a model update to launch a model poisoning attack. Owing to the aforementioned advantages, the federated learning paradigm has recently been transferred into many deep learning scenarios [25; 26; 27; 28]. In the following paragraphs, we will review some of the strategies that are related to data poisoning attack and model poisoning attack, and then revisit their differences.
## 1 Introduction
### Related Work  
In the literature, GPs have been constructed with multi-resolution structures in different ways. In [22], a set of smooth GPs is defined, each over a nested set of training points. A Voronoi tessellation was implemented in [23]. A kd-tree to approximate the training points in conjunction with conjugate gradient for fast inference is studied in [24]. This idea has been extended in [25] by using product trees. In [26], large scale terrain data are modeled with a kd-tree and neural network kernel to account for the non-stationary data. Distributed Gaussian processes [27] provide a divide and conquer approach by recursively distributing computations to independent computational units and then recombining them. In practice, in real time field applications, these distributed units are often not available. As for the methods in [14], all of these focus on the training points in the dataset, not on the set of test points where predictions are being made.  
There exists a myriad of tree data structures such as kd-trees, octrees [28], ball trees [29] and cover trees [30]. Each have their own advantages and disadvantages. Since this work focusses on points representable by three dimensions (situated on a triangular meshes or in a point cloud), an octree or kd-tree is most applicable. In addition, locally, points on a polygon mesh that define the shape of a polyhedral object are generally not very well spread out in all three dimensions, leaving a kd-tree as the more reasonable choice.
## 3 Main Findings from the Project and Technology Analyses
If only one dot is shown, it means that all projects are in the research phase. Finally, the length of line shows the average level of maturity including all four development phases, with longer lines indicating higher maturity level. Some technologies clearly have many projects in the research phase. This is the case for advanced driver assistance systems (ADAS) platforms and connected and automated vehicles (CAV) controllers and sensor fusion; both of which, however, have a large number of projects in more mature development phases. Some other technologies (in particular the collaborative logistics ecosystem technologies) are researched by projects in more mature development phases. CAV controllers and sensor fusion technologies are researched only in the latest FP (H2020). Some caution is necessary in interpreting the results, since they may reflect developments from a specific project that researches the technology from a certain perspective and not the technology as a whole. However, the aggregated outcome provides an indication on the overall development of the technology. Figure 4: Top 20 technologies for the smart mobility and services (SMO), connected and automated transport (CAT), and network and traffic management systems (NTM) STRIA roadmaps. SMO-related technologies in yellow, CAT-related technologies in blue and NTM-related technologies in red. Abbreviations: ADAS: advanced driver assistance systems; CAV: connected and automated vehicles; ATM: air traffic management; ATFCM: air traffic flow and capacity management; ICT: information and communication technologies.
# Prediction of Cell-Penetrating Peptides Using a Novel HSIC-Based Multiview TSK Fuzzy System
## 1 Introduction
These problems include the following: (1) many proposed predictors adopt multiple feature descriptors, but they simply splice each feature vector and directly input the hybrid feature into the prediction model. The disadvantage of doing so is that the interaction from different views and the statistical characteristics of the data are ignored. The predictive performance is also compromised as a result. (2) Most machine learning models are actually black boxes; however, a fuzzy system with a knowledge base based on fuzzy rules has good interpretability and can provide insightful suggestions to study the underlying rules. To solve the above problems, a CPP predictor based on a multiview TSK fuzzy system is proposed, and the main workflow of the process is shown in Figure 1. First, two feature descriptors were employed, namely, soft symmetric alignment and pseudo-amino acid composition. Then, the correlation-based feature selection algorithm was adopted to remove redundant features and noise. The resulting feature subset was input into the multiview TSK fuzzy system. Finally, the multiview decision result was obtained. The main contributions of this study are as follows: (1) We introduce a multiview TSK fuzzy system based on HSIC. Compared with other machine learning methods, TSK fuzzy systems have advantages in interpretability. The introduction of multiview mechanisms allows for comprehensive insight into the intrinsic laws of the data. HSIC was utilized to measure the independence and enhance the complementarity between different views. (2) The proposed method is competitive or better than the state-of-the-art CPP predictors. The empirical results show that our method has broad application prospects.
## 3 Results and Discussion
### Na+/K+ Ratio in A. yydowii  
Eukaryotes also compensate osmotic imbalance through intracellular regulation of potassium and sodium ion levels [116], which has been as well observed in halophilic and halotolerant fungi [117, 118, 119, 28, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129]. Intracellular sodium ions are potentially toxic for eukaryotic cells, as they can inhibit numerous metabolic reactions and change cell membrane potential and transport systems [120, 121]. Therefore, as the extracellular amount of sodium increases, the cells of halotolerant and halophile microorganisms increase the number of cation transporters to maintain a high K+/Na+ ratio [122, 117]. Intracellular potassium is required in these conditions to sustain the potential across the plasma membrane, compensating for osmotic imbalance and the negative charges of macromolecules. Potassium is also involved in the regulation of protein synthesis and function [123, 124].  
In the halotolerant yeast _Debaryomyces hansenii_, used here as a control, the increase of extracellular sodium triggered the accumulation of potassium inside the cell. At 0.5 M NaCl, the K+/Na+ ratio was ten times smaller than in the media without NaCl (Figure 7). Increasing the extracellular concentration to 2.0 M NaCl, a four-fold increase with respect to the condition with 0.5 M NaCl, reduced the K+/Na+ ratio ten-fold. In contrast, _A. yydowii_ does not seem to have the same compensatory mechanisms to regulate intracellular cation concentrations when growing in the medium with 0.5 M NaCl. In this condition the K+/Na+ ratio decreased almost twenty times compared to the medium without salt. A higher concentration of NaCl apparently triggered the accumulation of potassium or the removal of sodium, as the K+/Na+ ratio rose by two- to four-fold in the medium with 2.0 M NaCl compared to 0.5 M NaCl (Figure 7). This indicates that, as a consequence of salinity stress, the transport systems that regulate K+ uptake and Na+ extrusion were upregulated or activated [119], whereas this was not a requirement for the proper functioning of _A. yydowii_ cells under optimal salinity. In this regard, this moderate halophile fungus has a different K+/Na+ accumulation pattern as compared to _S. cerevisiae_ or other halotolerant fungi such as _D. hansenii_ or _Debaryomyces nepalensis_ when exposed to increasing concentrations of sodium [118].
# A Noniterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissues +
## 3 Noniterative Simultaneous Rigid Registration Method (NSRR)
### Algorithm Workflow  
We summarize our noniterative simultaneous rigid registration algorithm (NSRR) in Algorithm 1.  
```
Input: original serial section images \(\mathbf{I}_{i}\quad(i=1,\ldots,n)\)
1:\(\mathbf{X}_{i,i+j}\longleftarrow\) landmarks of \(\mathbf{I}_{i}\quad(i=1,\ldots,n;j=-1,1)\)
2:\(\widehat{\mathbf{X}}_{i,i+j}\longleftarrow\) centralized \(\mathbf{X}_{i,i+j}\)\((i=1,\ldots,n;j=-1,1)\)
3:\(\mathbf{A}_{i}\longleftarrow\widehat{\mathbf{X}}_{i+1,i}\widehat{\mathbf{X}}_{ i,j+1}^{T}\quad(i=1,\ldots,n-1)\)
4:\(\mathbf{V}_{i}\mathbf{S}_{i}\),\(\mathbf{U}_{i}\longleftarrow\) SVD of \(\mathbf{A}_{i}\quad(i=1,\ldots,n-1)\)
5:\(\mathbf{C}_{i}\longleftarrow diag(1,\det(\mathbf{V}_{i}\mathbf{U}_{i}^{T})) \quad(i=1,\ldots,n-1)\)
6:\(\theta_{i}\longleftarrow\) solution of Equation (5)\((i=1,\ldots,n-1)\)
7:\(\mathbf{H}_{i}\longleftarrow\left[\begin{array}{cc}\cos\theta_{i}&-\sin \theta_{i}\\ \sin\theta_{i}&\cos\theta_{i}\end{array}\right]\quad(i=1,\ldots,n-1)\)
8:\(\mathbf{W}_{i}\longleftarrow\mathbf{H}_{i}\mathbf{V}_{i}\mathbf{C}_{i}\mathbf{ U}_{i}^{T}\quad(i=1,\ldots,n-1)\)
9:\(\mathbf{R}_{1}\longleftarrow\)\(\mathbf{I},\mathbf{R}_{i}\longleftarrow\prod\limits_{k=1}^{i-1}\mathbf{W}_{k}\quad(i=2, \ldots,n)\)
10:\(\mathbf{Z}_{i}\longleftarrow\mathbf{R}_{i}\mathbf{X}_{i,i+1}-\mathbf{R}_{i+1 }\mathbf{X}_{i+1,i}\quad(i=1,\ldots,n-1)\)
11:\(\mathbf{Th}_{i}\longleftarrow-m_{i}^{-1}\mathbf{Z}_{i}\mathbf{e}_{i}^{T}+\frac {m_{i}^{-1}}{m_{1}^{-1}+\cdots+m_{n-1}^{-1}}\sum\limits_{j=1}^{n-1}-m_{i}^{-1} \mathbf{Z}_{i}\mathbf{e}_{i}^{T}\quad(i=1,\ldots,n-1)\)
12:\(\mathbf{T}_{1}\longleftarrow\mathbf{0}_{2\times 1}\), \(\mathbf{T}_{i}\longleftarrow\sum\limits_{j=2}^{i}\mathbf{Th}_{j-1}\quad(i=2, \ldots,n)\)
13:\(\widehat{\mathbf{T}}_{i}\longleftarrow\mathbf{I}_{i}\) transformed by \(\mathbf{R}_{i}\) and \(\mathbf{T}_{i}\quad(i=1,\ldots,n)\) Output: registered serial section images \(\widehat{\mathbf{T}}_{i}\quad(i=1,\ldots,n)\)
```  
**Algorithm 1**Non-iterative Simultaneous Rigid Registration (NSRR).
# A Solar-Thermal-Assisted Adiabatic Compressed Air Energy Storage System and Its Efficiency Analysis
## 4 Results and Discussion
### Parameter Analysis
#### 4.2.2 Air Turbine Inlet Temperature  
Figure 7 illustrates the effect of air turbine inlet temperature on \(W_{\text{COM}}\), \(W_{\text{TUR}}\), and mass flow of VP-1. As revealed in Figure 7, the increasing inlet air temperature of the turbine increases the power generation of the turbine system owing to a greater decrease in the enthalpy of air, which can be calculated by Equation (10). As inlet temperature of the air turbine increases, the VP-1 mass flow also increases because the TUR subsystem needs more heat energy to enhance inlet air temperature. When the inlet temperature of the air turbine reaches 280 \({}^{\circ}\)C, the maximum \(W_{\text{TUR}}\) and mass flow of Therminol VP-1 reaches 956 kWh and 2.7 kg/s, respectively. No compressor parameters were changed, so \(W_{\text{COM}}\) remained the same.  
Figure 8 shows that the ESE, RTE, and \(\eta_{\text{ex}}\) increased with air turbine inlet temperature. As shown in Figure 7, \(W_{\text{TUR}}\) increased while \(W_{\text{COM}}\) remained unchanged, which resulted in greater ESE according to Equation (25), while \(Q_{\text{tant}}\) was constant due to the parameter being keep constant during the compression stage. Finally, by a comprehensive calculation using Equations (26) and (27), RTE and \(\eta_{\text{ex}}\) increase with the air turbine inlet temperature.  
For example, when air turbine inlet temperature increases from 240 \({}^{\circ}\)C to 280 \({}^{\circ}\)C under design inlet air pressure, ESE, RTE, and \(\eta_{\text{ex}}\) increase by 5.21%, 4.43%, and 4.38%, respectively. Therefore, all the efficiencies increase with the increasing air turbine inlet temperature. Moreover, the increments in the ESE, RTE, and \(\eta_{\text{ex}}\) are linear; the increasing rate of total output energy also increases with the increasing inlet air turbine pressure.  
Figure 8: Effect of inlet air turbine temperature on ESE, RTE, and \(\eta_{\text{ex}}\).  
Figure 7: Effect of air turbine inlet temperature on \(W_{\text{COM}}\), \(W_{\text{TUR}}\), and VP-1 mass flow.
## 4 Experiments and Results of the Algorithm
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Function** & **Indicator** & **IHO** & **HO** & **ZOA** & **BWO** & **BES** & **CSO** \\ \hline \multirow{3}{*}{F1} & Best & 0 & 0 & 6.79 \(\times\) 10\({}^{-257}\) & 0.0012855 & 3.5607 \(\times\) 10\({}^{-49}\) & 2.0899 \(\times\) 10\({}^{-24}\) \\  & Mean & 0 & 0 & 5.7194 \(\times\) 10\({}^{-248}\) & 0.0085168 & 1.4081 \(\times\) 10\({}^{-40}\) & 3.3275 \(\times\) 10\({}^{-20}\) \\  & Std & 0 & 0 & 0 & 0.009002 & 4.4204 \(\times\) 10\({}^{-40}\) & 6.2509 \(\times\) 10\({}^{-20}\) \\ \hline \multirow{3}{*}{F2} & Best & 3.8064 \(\times\) 10\({}^{-237}\) & 4.0835 \(\times\) 10\({}^{-196}\) & 3.9486 \(\times\) 10\({}^{-136}\) & 0.021046 & 5.265 \(\times\) 10\({}^{-28}\) & 4.5561 \(\times\) 10\({}^{-21}\) \\  & Mean & 3.2568 \(\times\) 10\({}^{-227}\) & 4.0886 \(\times\) 10\({}^{-184}\) & 3.3091 \(\times\) 10\({}^{-131}\) & 0.062712 & 1.526 \(\times\) 10\({}^{-26}\) & 1.1554 \(\times\) 10\({}^{-19}\) \\  & Std & 0 & 0 & 7.3205 \(\times\) 10\({}^{-131}\) & 0.027747 & 1.4162 \(\times\) 10\({}^{-26}\) & 1.0822 \(\times\) 10\({}^{-19}\) \\ \hline \multirow{3}{*}{F3} & Best & 0 & 0 & 5.8723 \(\times\) 10\({}^{-10}\) & 0.17059 & 3.2083 \(\times\) 10\({}^{-18}\) & 789.3893 \\  & Mean & 0 & 0 & 5.6762 \(\times\) 10\({}^{-157}\) & 1.9486 & 3.5527 \(\times\) 10\({}^{-9}\) & 5458.957 \\  & Std & 0 & 0 & 1.7211 \(\times\) 10\({}^{-156}\) & 2.1075 & 1.0771 \(\times\) 10\({}^{-8}\) & 3255.1196 \\ \hline \multirow{3}{*}{F10} & Best & 4.4410 \(\times\) 10\({}^{-16}\) & 4.4410 \(\times\) 10\({}^{-16}\) & 4.4410 \(\times\) 10\({}^{-16}\) & 0.0013155 & 3.9968 \(\times\) 10\({}^{-15}\) & 9.3157 \(\times\) 10\({}^{-12}\) \\  & Mean & 4.4410 \(\times\) 10\({}^{-16}\) & 4.4410 \(\times\) 10\({}^{-16}\) & 4.4410 \(\times\) 10\({}^{-16}\) & 0.0098595 & 0.066907 & 3.5656 \(\times\) 10\({}^{-11}\) \\  & Std & 0 & 0 & 0 & 0 & 0.0