esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Models  
### Preparation of Test Materials and SCB Specimens  
#### 2.1.1 Asphalt  
The penetration degree of matrix asphalt used in this study was 90, and the modifier was 0.25 mm rubber powder particles. The rubber powder particle content was 20% by weight of asphalt. The quantitative matrix asphalt was weighed, the quality accuracy was controlled at 0.01 g, the temperature of the matrix asphalt was controlled at 140 \({}^{\circ}\)C, and the rubber powder particles preheated and dried in advance were slowly added to the matrix asphalt many times, and gradually heated up and stirred. Finally, the temperature of asphalt and rubber powder mixture was controlled at 180 \({}^{\circ}\)C, and the mixture was stirred for 30 min. The evenly mixed mixture of asphalt and rubber powder was put into the high-speed shear emulsifier to maintain the shear rate of 6000-7000 r/min and the temperature of 160 \({}^{\circ}\)C for 30 min, and then it was kept in the oven at 120 \({}^{\circ}\)C for 2 h to prepare the CR. The technical matrix asphalt and CR indices indices are shown in Table 1.  
Asphalt is a complex mixture of macromolecules. In the study of the chemical composition of asphalt, the asphalt was divided into several parts according to different solubility and physicochemical properties by using the different solubilities of various organic compounds in asphalt. According to the four-component analysis method stipulated in _Standard Test Methods of Bitumen and Bituminous Mixtures for Highway Engineering_ (JTG E20-2011) [32],  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technical Index** & **CR** & **Matrix Asphalt** \\ \hline Penetration (25,100 g, 5 s) (0.1 mm) & 68.2 & 91.5 \\ Softening Point/\({}^{\circ}\)C (Ring and ball test) & 57.3 & 51.5 \\ Ductility (5 cm/min,10 \({}^{\circ}\)C)/cm & 17.2 & 9.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical indices of CR and matrix asphalt.  
the four-component analysis test of matrix asphalt was carried out. The mass percentages of the four components of matrix asphalt are shown in Table 2.
## 3 System Model
### Virtual Networking
#### 4.2.1 Overlay Network  
This section introduces overlay network based on DHT (Distributed Hash Table) implementation. Overlay network is an application layer network, which is application layer oriented with no or little consideration of network layer and data link layer. Overlay networks allow routing messages based on logical addresses to destination hosts that are not identified by IP addresses. Without any central server, each client is responsible for a small range of routes and serves a small number of unreachable nodes, thus enabling the routing of the entire DHT network.  
Figure 8: The workflow of confidential message transmission.  
We define a series of nodes \(n_{1},n_{2},n_{3},\ldots,n_{n}\in\mathcal{N}\) in the overlay network plane of the application layer, as shown in Figure 9. They all have IP address-independent logical addresses generated by the same rules as defined before, with an address space of 160 bit, but their address space may become smaller due to the different definitions of \(D_{min}\), and the actual address space is \((160-D_{min})\) bits. The unique logical address generated by each node according to the algorithm is the identification of each node in the DHT network and is the basis for routing messages to be sent. We calculate the distance between two nodes using their logical distance instead of the physical distance. For example, to calculate the distance between node A and node B, we simply calculate the exclusive OR distance \(A\oplus B\) between them.  
In the lower layer of the physical bearer network, we do not care about the way they access the WAN, the access location. They can use any kind of network layer, data link layer to access the network at any location, but when they access the network, they are divided into reachable nodes \(\mathcal{R}=\{1,2,3,\ldots,n\}\) and unreachable nodes \(\mathcal{U}=\{1,2,3,\ldots,n\}\) according to whether the node is reachable or not, \(\mathcal{R}\cup\mathcal{U}=\mathcal{N}\). The node behind the red firewall in the Figure 9 is an unreachable node, but in the actual network access, we assume that the nodes do not know the way they access the network. They do not know the node type themselves. The node type needs to be determined with the help of nodes in the network that assist in accessing the network.  
At this time, there are a series of reachable nodes \(r_{1},r_{2},r_{3},\ldots,r_{n}\in\mathcal{R}\subseteq\mathcal{N}\) and a series of unreachable nodes \(u_{1},u_{2},u_{3},\ldots,u_{n}\in\mathcal{U}\subseteq\mathcal{N}\) in the network. The type \(n_{i}\) of nodes may change when different access methods and different locations are used to access the WAN, i.e., whether \(n_{i}\in\mathcal{R}\) or \(n_{i}\in\mathcal{U}\) can be determined only when a node is connected to the network. In different access methods, \(r_{i}\) and \(u_{i}\) may be transformed into each other.
## 3 Dual-Site Micro-Scale Diversity Scheme
### Capacity Evaluation  
In this section, the capacity of a dual MISO-SIMO system is evaluated for a dual-site micro-scale diversity scenario. Using the rain attenuation time series generated using the SST, the capacity time series were obtained using (4) for the SIMO systems and (7) for the MISO ones.  
For the SIMO scenario, results are obtained for 19.701 GHz (Ka-band downlink) as well as 39.402 GHz (Q-band downlink). The clear-sky SNR is considered equal to 30 dB.  
Figure 5: CCDF of rain attenuation for the two locations along with the CCDF of the joint-minimum rain attenuation at 19.701 GHz, Ka-band downlink.  
Figure 6: Emulated rain attenuation time series obtained by applying the SST for the two locations at 39.402 GHz, Q-band downlink.  
Figure 7: CCDF of rain attenuation for the two locations along with the CCDF of the joint-minimum rain attenuation at 39.402 GHz, Q-band downlink.  
In Figure 8, the resulting outage capacity is shown for the SISO and the SIMO system for both Ka-band and Q-band links. It is observed that in clear-sky conditions the micro-scale system can effectively deliver 11 b/s/Hz, while the SISO one 10 b/s/Hz. This gain arises from the use of multiple antennas at the receiver, thus proportionally increasing the SNR (also referred to as array gain). As the attenuation increases (lower exceedance probabilities) the micro-scale scheme still offers a gain, e.g., at 0.1% of the time the capacity for the SISO link is 9 b/s/Hz while for the SIMO one is 10 b/s/Hz regarding the Ka-band downlink case.  
For the uplink, i.e., the MISO case, the outage capacity is shown in Figure 9 for various values of \(SNR_{CS}\). The operating frequency is assumed at 29.881 GHz, i.e., Ka-band uplink. The gain, also evident in this case, increases with increasing attenuation, i.e., in low probabilities; this is attributed to the precoding vector used in the MISO system. As a matter of fact, in clear-sky conditions all the signals are transmitted with the same power and equal to the power of a SISO link divided by the number of transmitting antennas; it is therefore apparent why there is no gain in clear sky conditions. On the other hand, when attenuation increases, from (5), the highest power ratio will occur at the link experiencing the least attenuation. Therefore, a gain of up to 50% could be achieved for low probabilities as observed in Figure 9.
# A Charging and Discharging Data Privacy Protection Scheme for V2G Networks Based on Cloud-Fog-End
## 3 Implementation
### Entity Key Generation  
\(CP_{i}\) selects a random number \(x_{i}\in Z_{q}^{*}\) as the partial private key, calculates the partial public key \(X_{i}=x_{i}P\), and then sends its identity \(ID_{CP_{i}}\) and partial public key \(X_{i}\) to the KGC.  
After verifying the validity of \(CP_{i}\)'s identity, the KGC selects a random number \(y_{i}\in Z_{q}^{*}\), generates \(CP_{i}\)'s partial public key \(Y_{i}=y_{i}P\), calculates \(h_{1i}=H_{1}(ID_{CP_{i}},\ X_{i},\ Y_{i})\), calculates \(d_{i}=y_{i}+s\cdot h_{1i}\) as \(CP_{i}\)'s partial private key, and then sends \((Y_{i},\ d_{i})\) to \(CP_{i}\) through a secure channel.  
After receiving the partial public and private keys, \(CP_{i}\) verifies the validity of the partial public and private keys through equation \(d_{i}P=Y_{i}+H_{1}(ID_{CP_{i}},\ X_{i},\ Y_{i})\cdot P_{pub}\) to prevent malicious KGC attacks. If the verification fails, it will be discarded directly, and part of the public and private keys will be requested again. Otherwise, \(CP_{i}\)'s private key \(SK_{i}=(x_{i},\ d_{i})\) and public key \(PK_{i}=(X_{i},\ Y_{i})\).  
The key generation process of \(FN_{w}\) and the CSO is similar to \(CP_{i}\), after authentication using identity identifiers \(ID_{FN_{w}}\) and \(ID_{CSO}\), and the generated key pairs are \(SK_{w}=(x_{w},\ d_{w})\), \(PK_{w}=(X_{w},\ Y_{w})\) and \(SK_{\text{c}}=(x_{\text{c}},\ d_{\text{c}})\), \(PK_{\text{c}}=(X_{\text{c}},\ Y_{\text{c}})\), respectively.
## 3 Results
### Data from and Results of the Subjective Questionnaire  
The statistical results of the questionnaire were obtained on a 1 to 3 scoring scale. For the scene favorites survey (see Figure 8), the scores for unoptimized and optimized S1 were 16 and 113, a 6.06-times improvement. For unoptimized and optimized S2, the scores were 24 and 154, a 5.42-times improvement. For unoptimized and optimized S3, the scores were 80 and 183, a 1.29-times improvement. Thus, the optimized scenes were more recognized and favored by the subjects.
# Synthesizing Explainability Across Multiple ML Models for Structured Data
## 3 Proposed Method: Weighted Importance Score and Frequency Count (WISFC)
### Overview and Rationale  
WISFC addresses two central questions: \"Can using many models yield a more reliable set of predictors?\" and \"How does WISFC improve the identification of stable predictors?\" Instead of relying on visual comparisons of overlaps, WISFC quantifies two complementary metrics for each feature:  
* WIS: its mean normalized importance (e.g., inverse-rank or scaled score) over all models
* FC: the proportion of models ranking it in their top-k.  
By combining these, WISFC highlights features that are both strongly and consistently important. It is model-agnostic (i.e., any numeric importance output can be normalized and fed into WISFC) and particularly useful when data are scarce, trading brute-force modeling diversity for feature-selection robustness.
# Phosphorus Mobilizing Enzymes of _Alnus_-Associated Etomycorrhizal Fungi in an Alaskan
## 3 Results
### EMF Specific Activities within and Among Hosts  
Low sample sizes and minimal overlap of EMF species among hosts limited our ability to compare enzyme activities of specific EMF species among hosts or among successional stages on a particular host. However, the two dominant alder-associated EMF taxa, _Alnicola luteolofibrillosa_ 2 and _Tomentella_ sp. 6, which constituted 43% of all alder root tips, showed significant differences in their enzymatic capacity to break down different sources of organic P. Compared to _Alnicola luteolofibrillosa_ 2, _Tomentella_ sp. 6 showed a greater potential acid phosphatase activity at both the low and high substrate concentrations, whereas _Alnicola luteolofibrillosa_ 2 had higher potential phosphodiesterase activity at the higher substrate concentration (Figure 5). Nearly all (96%) of _Alnicola luteolofibrillosa_ 2 found on alder root tips occurred in early succession, while _Tomentella_ sp. 6, also an alder specialist, was more evenly distributed on alder tips between early (44%) and mid (50%) succession.  
Figure 3: Comparison of mean \(\pm\) S.E. activities of acid phosphatase, phosphodiesterase and phytase of alder root tips dominated by Ascomycete versus Basidiomycete ectomycorrhizal fungi (EMF) averaged across all horizons and stages. Significant differences between phylum are indicated above bars at \(p<0.10\) (*), \(0.05\) (**), \(0.01\) (***), and \(0.001\) (***). Marginal (\(R^{2}_{m}\)) and conditional (\(R^{2}_{c}\)) values generated for linear mixed effects models evaluate variance explained by fixed and fixed + random effects, respectively (see Statistics section above).
## 4 Results and Accuracy Assessment  
### Results  
This study employs both the information volume and evidence weight models, using grid units as the evaluation units. By performing grid calculations on 11 influencing factors, the information volume model and the evidence weight model were applied to assign weights and overlay them, ultimately resulting in the geological hazard risk zoning for Xiaojin County, as shown in Figure 5. A total of 152 disaster points were used for hazard zoning validation, including 82 landslides, 23 debris flows, 2 collapses, and 45 unstable slopes.  
The hazard assessment results and the statistical distribution of geological disaster points are shown in Figure 5. As shown in Figure 5, the areas of medium and low hazard zones are the largest, while the disaster point density is highest in the high and very high hazard zones. Additionally, the high and very high-hazard zones in Xiaojin County are mainly concentrated along rivers and valleys. Furthermore, as indicated in Table 3, cross-validation with existing geological disaster points shows that 90.79% and 85.53% of the geological disaster points are located within the high and very high hazard zones defined by the information volume model and the evidence weight model, respectively. Therefore, the geological hazard risk assessment results obtained in this study show a good consistency with the distribution of existing geological disasters. In the areas of high and  
Figure 5: Geological hazard risk zoning map.  
very high hazard zones of geological disasters, the information on ground objects such as roads, houses, and water systems is densely distributed, which will cause great losses once a disaster occurs. Therefore, disaster prevention and mitigation measures are essential.
# Online Database for Retrieval Information about Prebiotics and Their Activity
## 1 Introduction
The prebiotic index has been used, for example, to study wheat food grade curdlan (1->3)-beta-D-glucan) from _Alcaligenes faecalis_[25] or oligo-glucomannan from _Amorphophilus konjac_[26]. The \"useful\" bacteria (typically, _Bifidobacterium_ and _Lactobacillus_ genera) from faeces are took into account in the mentioned techniques, not pure cultures of probiotics. The \"harmful\" _Clostridium_ and _Bacteroides_ are commonly recognized as antagonists, means that their specific growth rates are included in the equation with \"minuses\". This approach is related to the prebiotic concept. Moreover, as [PERSON] et al. pointed [23], PI allows to compare the results of different researches on prebiotics. Methods for prebiotic activity evaluation which apply pure cultures are not utterly informative, as it is not possible to take into account the interaction of bacteria. However, [PERSON] et al. [27] proposed a quantitative measure of prebiotic activity, the prebiotic activity score. Its dimension is based on the comparison of specific growth rates. The prebiotic activity score is less common, although it has been used in a number of studies [28; 29; 30; 31]. The well known probiotic bacteria such as _Bifidobacterium lactis_ subsp. _animals_ Bb-12 or _Lactobacillus rhamns_ GG, are commonly used. So called enteric mixture (a lot of _Escherichia coli_ strains) is supposed as antagonists. One of the most common models of the intestine is a three-stage continuous culture, as proposed by [PERSON] et al. [32]. Using this method, the prebiotic effect, which has also been associated with an increase in the number of probiotics and production of SCFA, has been evaluated, for example, for mixture of galactooligosaccharides, lactose, glucose and galactose (B-GOS) [33] and fructans with differing in degree of polymerization [34]. 'A mixture of synergistically acting probiotics and prebiotics' has been defined as synbiotics [7]. Synbiotics incorporate the properties of probiotic and prebiotic ingredients and provide a greater effect than that of the probiotics or prebiotics separately. One of approaches to assessing such synergism _in vitro_ is the co-culturing of probiotic bacteria with selected pathogens in prebiotic contained media [35, 36, 37, 38]. The criteria obtained from a quantitative model of co-culture has been suggested for the comparison of symbiotic activity against pathogens in different conditions [39]. Analysis of existing databases on prebiotics and probiotics has shown that there are no such databases containing information about prebiotic activities and the methods for their evaluation. In this paper, a developed online database (ODRAP) is presented, which includes unique content concerning different probiotics and prebiotics, along with their activities.
# Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor
## 3 Multi-Channel Recurrent Predictor
### Multi-Channel Recurrent Unit  
In this sub-section, we present a novel multi-channel recurrent unit (MCRU), which is specially designed as a deep-learning predictor for sensor signals. The predictor resolution \(R_{p}\) is set to the ADC resolution \(R_{a}\) in order to keep the integrity of each value sampled by ADC. Specifically, every \(R_{a}\) bit is re-grouped into \(C\) channels, each of which corresponds to \(R_{c}=\frac{R_{a}}{C}\) bits and represents a specific precision scale of the sample value. Each channel is assigned an independent \(\mathcal{H}_{\text{GRU}}\); thus, the context information of the corresponding precision scale is retained individually.  
At each time step \(t\), \(C\) predictions, namely, \(\mathbf{p}_{t}^{1},\cdots,\mathbf{p}_{t}^{C}\), are made in order from less-significant-bit (LSB) channels to more-significant-bit (MSB) channels, as is illustrated in Figure 2. Specifically, for the \(j\)-th channel, the corresponding state vector is updated first:  
\[\mathbf{s}_{t}^{j}=\left\{\begin{array}{ll}\mathcal{H}_{\text{GRU}}^{1}( \mathbf{x}_{t}^{1},\mathbf{s}_{t-1}^{1}),&\text{if}\quad j=1,\\ \mathcal{H}_{\text{GRU}}^{j}(\mathbf{x}_{t}^{j},\mathbf{s}_{t+1}^{j},\mathbf{ s}_{t-1}^{j}),&\text{otherwise.}\end{array}\right. \tag{11}\]  
Here, \(\mathbf{x}_{t}^{j}\) represents the \(j\)-th re-grouped input with one-hot encoding. Note that an LSB channel has a lower value of \(j\). After the state vector has been updated, a prediction of \(\mathbf{x}_{t+1}^{j}\) is made based on the new state vector \(\mathbf{s}_{t}^{j}\):  
\[\mathbf{p}_{t}^{j}=\mathcal{P}^{j}(\mathbf{s}_{t}^{j};\text{softmax}). \tag{12}\]  
Note that the prediction of each channel \(\mathbf{p}_{t}^{j}\) is calculated independently by the corresponding perceptron operator \(\mathcal{P}^{j}\). After the prediction has been made, it will be either sent to the encoder with raw data to produce a compressed data point, or sent to the decoder with a compressed data point to retrieve the original data. In whichever case, the true value \(\mathbf{x}_{t+1}^{j}\) corresponding to the prediction \(\mathbf{p}_{t}^{j}\) will be sent to the state transition function in the next channel (if has), as in Equation (11).  
In this paper, we refer to the context-based compressor that uses the proposed MCRU as a dynamic predictor as a _multi-channel deep recurrent compressor_ (MCDRC), which uses arithmetic coding as the coding module by default.
## 2 Related Work
### Fundamental AI
#### 2.1.2 Optimization Algorithm  
Currently, in machine learning, there are various optimization algorithms for supervised learning. In [25], a theoretical explanation and the implementation of the results of optimization algorithms for comparing several models are presented for reference and development. A conclusion is also reached that there is no such universal optimization algorithm for all problems. However, for the problem of building a decoder for the DS8-PSK scheme using an NN, which could be assumed to be a softmax classifier, AdaGrad [26] is the most suitable solution method for such a straightforward problem. The reason is that Adagrad performs much larger updates for infrequent parameters than frequent parameters. Moreover, the need to manually tune the learning rate of stochastic gradient descent methods is eliminated in AdaGrad [27], and the robustness of the stochastic gradient descent method is greatly improved by this method [28].  
From [26] and [27], the AdaGrad algorithm could be expressed as the following pseudo-code.  
\begin{tabular}{l l} \hline \hline \multicolumn{2}{l}{**The Adagrad algorithm**} \\ \hline
1. & **Require:** Global learning rate \(\epsilon\), initial parameter \(\theta\) \\
2. & **Require:** Small constant \(\delta\), perhaps \(10^{-7}\), for numerical stability \\
3. & Initialize gradient accumulation variable \(\mathbf{r}=0\) \\
4. & **while** stopping criterion not met **do** \\
5. & Sample a minibatch of \(m\) examples from a training set \\
6. & \(\big{\{}x^{(1)},\dots,x^{(m)}\big{\}}\) with corresponding targets \(\mathbf{y}^{(i)}\): \\
7. & Compute gradient: \(g\leftarrow\frac{1}{m}\
abla_{\theta}\sum_{i}L\big{(}\big{\{}x^{(i)};\theta \big{)},\mathbf{y}^{(i)}\big{\}}\) \\
8. & Accumulate squared gradient: \(\mathbf{r}\gets\mathbf{r}+\mathbf{g}\odot\mathbf{g}\) \\
9. & Compute update: \(\Delta\mathbf{\theta}\leftarrow-\frac{\epsilon}{\delta+\sqrt{\mathbf{g}}}\odot\mathbf{g}\). (Division and \\
10. & square root applied element-wise) \\
11. & Apply update: \(\mathbf{\theta}\leftarrow\mathbf{\theta}+\Delta\mathbf{\theta}\). \\
12. & **end while** \\ \hline \hline \end{tabular}  
From [29], the update rule of the Adagrad algorithm could also be simplified by (3) and (4):  
\[\mathbf{\theta}_{t+1,i}=\mathbf{\theta}_{t,i}-\frac{\mathbf{\epsilon}}{\sqrt{\mathbf{G}_{t,i} }+\mathbf{\delta}}\cdot\
abla_{\mathbf{\theta}_{t}}L(\mathbf{\theta}) \tag{3}\]  
\[\mathbf{G}_{t,i}=\sum^{t}\
abla_{i}L(\mathbf{\theta})^{2} \tag{4}\]  
where \(\delta\) is the smoothing term that avoids division by zero (usually on the order of \(1e\)-\(8\)). One can see that the parameter \(\mathbf{\theta}_{t+1,i}\) is modified based on a ratio that involves its current gradient and the sum over its past gradients.
## 2 Materials and Methods
### Calculation of Combination Index  
HCC827 GR cells were treated with gefitinib alone at concentrations of 4, 6, 8, 10 uM, or in combination with glycyrol in a fixed ratio of 1:1 for 48 h. The changes of cell viability were evaluated by crystal violet staining, and the data generated were used to calculate the combination index (CI) using the following Chou-Talalalay equation [9]:  
\[\text{CI}=\frac{\text{CA},x}{\text{IC}x,\text{A}}+\frac{\text{CB},x}{\text{ IC}x,\text{B}}\]  
where CA, \(x\) and CB, \(x\) are the concentrations of agent A and agent B used in combination to achieve \(x\%\) combinatory effect; IC\(x\), A and IC\(x\), B are the concentrations for single agents to achieve the same effect. CI \(<\) 1 indicates synergism, CI \(=\) 1 indicates additive effect, and CI \(>\) 1 indicates antagonism.