esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Sources
#### 2.2.4 Site Verification  
Three flux sites were used to validate the GPP simulated by the BEPS_O\({}_{3}\) model. The three stations are HaiBei Station (shrub-HBG), Changbaishan Station (conifer-CBS) and Dinghushan Station (hardleaf-DHS). GPP data for all three sites came from ChinaFlux. Figure 1 shows the locations of the three sites, and Table 1 shows the basic information about the three sites. These site data were downloaded from China Flux ([[https://www.chinaflux.org/](https://www.chinaflux.org/)]([https://www.chinaflux.org/](https://www.chinaflux.org/)) (accessed on 28 September 2019)), and they were obtained at the site scale through the use of eddy covariance equipment for observation. The obtained data were cleaned by the site manager and then uploaded. It can be directly applied to the conduct of research work.  
Figure 1: Land cover map. Seven vegetation types are described in the figure: evergreen needleleaf forest (ENF), deciduous coniferous forest (DNF), deciduous broad-leaved forest (DBF), evergreen broad-leaved forest (EBF), mixed forest (Mix), and shrub (Shrub). Unlike the differences in climate zones, the vegetation types in the temperate zone use (N), while those in the subtropical zone use (S). The three symbols represent the names and locations of three flux observation stations. The circular symbol represents the Haibei Shrub Observation Station (HBG), the triangular symbol represents the Changbaishan Coniferous Forest Observation Station (CBS), and the square symbol represents the Dinghushan Broadleaf Forest Observation Station (DHS).
## 2 Materials and Methods
### Adsorption Experiment  
An adsorption experiment was conducted by preparing initial concentrations of used oil (C\({}_{0}\)) at 50%, 25%, 12.5%, and 6.25% \(v/v\) through dilution with hexane at room temperature. Additionally, undiluted used oil (100% \(v/v\)) was included as a control for comparison. Subsequently, 50 mL of each used oil concentration was mixed with 1 g of A-BP adsorbent under ambient conditions (308.15 K). The reaction was conducted as a batch experiment with continuous stirring for 60 min to achieve equilibrium. At 10 min intervals, aliquots of the suspension were filtered using Whatman No. 1 filter paper. The resulting supernatant was analyzed to determine the percentage of free fatty acids (%FFA) in the used oil via titration with a standardized base solution. This calculation assumes that oleic acid is the predominant free fatty acid present in the oil. The result reflects the extent of hydrolysis or degradation occurring in the used oil.  
After the adsorption process was completed, a 5 g sample of the used oil solution was dissolved in a solvent mixture consisting of 25 mL ethanol and 25 mL diethyl ether. Phenolphthalein (1 mL) was introduced as an indicator into the solution, followed by titration with a standardized 0.5 M potassium hydroxide (KOH) solution until the appearance of a stable pink endpoint. The free fatty acid content was quantified based on the oleic acid equivalent, as described in Equation (1) [26; 27].  
\[FFA\ (\%)=\frac{V_{KOH}\times C_{KOH}\times MW_{\text{oleic acid}}}{W\times 10} \tag{1}\]  
where V\({}_{\text{KOH}}\) represents the volume of the KOH solution used (titrant), C\({}_{\text{KOH}}\) denotes the concentration of the KOH solution (0.5 M), MW\({}_{\text{oleic acid}}\) is the molecular weight of oleic acid (282 g/mol), and W indicates the weight of the used oil (5 g).
# Knowledge Interpolated Conditional Variational Auto-Encoder for Knowledge Grounded Dialogues
## 6 Experiments and Analysis
### Human Evaluation  
In addition to the automated evaluation, we also carried out a human evaluation on the WoW dataset to gauge the quality of responses produced by our model and the baseline models. We hired three professional annotators to assess the generated responses based on four criteria, which fall into two categories: (1) _Coherence (C)/Fluency (F)_: This assesses whether the response is coherent with the dialogue context and fluent to read. (2) _Knowledge Correctness (KC)/Knowledge Diversity (KD)_: This assesses whether the response is consistent with the provided knowledge and presents relevant knowledge diversely and engagingly. For both the _Test Seen_ and _Test Unseen_ settings, each annotator rated 100 randomly sampled responses generated by each model using a five-point scale (1-5).  
Human evaluation results are listed in Table 3. We observe that GTTP is a robust baseline method, except for the metric _Knowledge Diversity(KD)_, since GTTP tends to copy the whole sentence of the given knowledge. The proposed KIVd,p achieve the best performance in terms of most metrics, in which KIVp performs slightly lower on _Knowledge Diversity_ than SKT, but outperforms by a substantial margin compared with the rest of the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test Seen**} & \multicolumn{8}{c}{**Single Reference**} & \multicolumn{8}{c}{**Multiple References**} \\ \cline{2-13}  & **PPL** & **AVE** & **EXT** & **GRY** & **Dist1** & **Dist2** & \(\Delta\)Bk & **PPL** & **AVE** & **EXT** & **GRY** & \(\Delta\)Bk \\ \hline SKT & 46.3 & 88.1 & **90.7** & 70.6 & **6.76** & 28.5 & - & 52.1 & 94.5 & **96.1** & 77.2 & - \\ \hline KIVc & 9.251 & 84.9 & 42.0 & 77.3 & 0.4 & 2.4 & 3.6 & 7.204 & 87.5 & 41.4 & 68.1 & 10.3 \\ KIVd & 3.141 & **97.0** & 88.1 & 96.1 & 6.5 & **34.8** & 3.5 & 0.502 & 98.3 & 94.0 & 97.3 & 8.0 \\ KIVp & **2.665** & **97.0** & 88.7 & **96.2** & 6.6 & **34.8** & **0.04** & **0.415** & **98.9** & 94.2 & **97.5** & **3.2** \\ \hline \hline \end{tabular}  
* The numbers in Bold represent the best results for the corresponding measure.  
\end{table}
Table 2: Automatic evaluation results on HOLL-E Dataset. \(\Delta\)Bk in table denotes \(\Delta\)BLEUk.  
baselines. This result verifies that the proposed interpolation-based models, especially KIV\({}_{\text{P}}\), can present relevant knowledge excitingly and engagingly while keeping the response coherent with the context.
###### Abstract  
The sum throughput of a cellular network can be improved when nearby devices employ direct communications using a resource sharing technique. Multicast device-to-device (M-D2D) communication is a promising solution to accommodate higher transmission rates. In an M-D2D communication, a multicast group is formed by considering a transmitter that can transmit the same information to multiple receivers by considering the transmission link conditions. In this paper, we focus on the uplink interference generated due to the non-orthogonal sharing of resources between the cellular users and M-D2D groups. To mitigate the interference, we propose a spectrum reuse-based resource allocation and power control scheme for M-D2D communication underlaying an uplink cellular network. We formulate the throughput optimization problem by considering the fractional frequency reuse (FFR) method within a multicell cellular network. In addition, a metaheuristic-tabu search algorithm is developed that maximizes the probability of finding optimal solutions by minimizing uplink interference. To analyze fairness resource distribution among users, we finally consider [PERSON]'s fairness index. Simulation results show that the proposed scheme can improve the coverage probability, success rate, spectral efficiency, and sum throughput of the network, compared with a random resource allocation scheme without a metaheuristic-tabu search algorithm.  
Keywords:device-to-device communication; resource reuse; multicasting; metaheuristic-tabu search algorithm; coverage probability; throughput; spectral efficiency +
Footnote †: journal:
## 3 Wavefront Sensorless AO-RL Environment  
The RL environment is implemented according to the standards of the Open AI Gymnasium framework [45]. The HCIPy: High Contrast Imaging for Python package [46] serves as the foundation of the RL environment. HCIPy offers a comprehensive set of libraries related to AO, including wavefront generation, atmospheric turbulence modeling, propagation simulation, fiber coupling, implementation of deformable mirrors, and wavefront sensors. A simulated AO-RL environment is a critical first step in the process of developing RL-based wavefront sensorless satellite communications downlink systems. It enables one to assess and refine the RL to meet the strict requirements of this domain prior to costly evaluation in physical simulations and the real world.  
The AO system simulated in this environment couples 1550 nm light into a single-mode fiber under various turbulence conditions which are characterized by the parameter \(D/r_{0}\) (as shown in Figure 2). This parameter represents the ratio of the telescope's diameter (\(D\)) to the Fried parameter (\(r_{0}\)) and it serves as a measure of the quality of optical transmission through the atmosphere. A lower Fried parameter leads to more pronounced wavefront distortions. In this work, we maintain a constant \(D\) value of 0.5 m, while the \(r_{0}\) value is adjusted to evaluate the system's performance under varying atmospheric turbulence levels. It is assumed that the atmospheric turbulence is either quasi-static or semi-dynamic, and the satellite remains in a fixed position throughout the experiments.  
A graphical presentation of the RL environment is displayed in Figure 1. The simulation environment updates in discrete time steps for practical purposes and to maintain consistency with the standard RL framework. At each time step \(t\), the RL agent receives an observation of the system's current observation (\(o\)) and a reward (\(r\)). The observation encodes the power after the focal plane in the AO system, while the reward is computed by utilizing the power distribution after the focal plane. Based on the agent's parameterized policy, \(\pi_{\theta}\), and the current observation, the agent selects the next action \(\pi_{\theta}:o\to a\). The agent's actions control the deformable mirror in the AO system. When controlled optimally, the incoming optical beam becomes concentrated and centered on the single-mode fiber.
## 2 Related Research Work
Meanwhile, the Entropy-Based Clustering Scheme (EBCS) also considers cluster load in terms of supported sensor nodes as a key parameter of criteria for the CH election procedure. [39]. The cluster load can be defined as the number of CMs that can be efficiently handled and supported by the current CH. EBCS includes remaining energy, distance to the BS, and the sum of distances to neighboring sensor nodes as other parameters of criteria besides cluster load. In EBCS, a new method is introduced to predict the residual energy at the start of the next round of communication based on consumed energy. This new method is used to select the CH for next round of communication. Energy heterogeneity is characterized using sensor nodes labeled as normal, intermediate and advanced in EBCS. In previous studies, several researchers have adopted entropy weight coefficient method for making decision in clustering environment [14; 15; 16; 18]. Entropy weight-based multi-criteria decision routing is a routing technique in which the next hop decision is based on Multi-Criteria Decision Analysis (MCDA) and Multi-AttributeDecision Modeling (MADM) using entropy weight coefficients [14; 15; 17; 18]. [PERSON] et al. [16] presented an optimization of objective and subjective weights based on fuzzy MADM routing for selection of next best hop in a heterogeneous WSN. Authors in [16] utilized the entropy weight coefficient method to avoid excessive deviation from the objective weights. [PERSON] et al. [14] developed an index system for capacity assessment using entropy weight coefficient method. [PERSON] et al. [40] discussed hesitant fuzzy information aggregation in decision making problems. [PERSON] et al. [41] discussed the hesitant fuzzy entropy, cross entropy, and their usage in MADM applications. [PERSON] et al. [42] merged the fuzzy logic method with clustering in order to propose a new data fusion method based on a fault tolerant WSN for improving the availability of communication bandwidth. In order to reduce the data processing load on BS and efficiently distinguish the authenticity of archived data, [PERSON] et al. [43] proposed a wireless sensor data fusion method based on fuzzy theory to improve the service quality in WSNs. [PERSON] et al. [44] presented an adaptive fuzzy logic algorithm to address the inaccuracies in data fusion. [PERSON] et al. [45] developed Hesitant Language Preference Relationships (HLPR) to improve the credibility of WSNs by fusing uncertain information and putting forward exact opinion about different WSN schemes. [PERSON] et al. [19] proposed a novel data fusion algorithm inspired by hesitant fuzzy entropy to reduce the redundant sensory data transfer in WSN clustering. In this paper, we have utilized some ideas from state-of-the-art research and provided a detailed solution for optimally handling problems like energy consumption and network lifetime using a novel hesitant fuzzy entropy-based opportunistic clustering and data fusion scheme in WSN.
## 2 Theory
### Advantages of RBFCCN  
The RBFCCN offers several advantages. The cascade-correlation architecture has the ability of incremental learning. The term incremental learning means that the network builds its topology during training by adding and training one hidden unit at a time. The first advantage is that the incremental learning ability avoids the moving target problem in the BNN and the network converges rapidly. Second, by training to a threshold of residual error, the cascade-correlation architecture does not require to determine the amount of hidden units in the network before training. Third, multiple networks can be obtained by training only once. These trained networks are networks with hidden units ranging from one to the total number of hidden units added to the cascade-correlation network. Fourth, by using RBF transfer functions, RBFCCNs are suitable for performing novel class evaluation, i.e., the ability to identify unknown data or outliers in a data set.
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 6 Evaluation and Results
### Non-Static Scenarios  
In real life, user terminals can move and the number of UAVs in the fleet can change. Thus, the positioning strategy has to be able to adapt to these changes. In this subsection, the strategy is implemented in three different non-static scenarios to evaluate this adaptation. Specifically, in all of them, the initial scenario is the same as in the previous subsections, but after 2500 iterations, when the algorithm has converged as shown in Figure 4, the scenario starts to change.
# Transcriptional Analysis of Masson Pine
## 4 Discussion
### Effects of Elevated CO\({}_{2}\) on Masson Pine Carbohydrate and Cell Wall Component Synthesis  
In photosynthetic cells, fixed carbon eventually transforms into sucrose and starch. These compounds are the main form of carbohydrate transport and storage in advanced plants. _SPS_, _SUS_ and _INV_ are considered to be the key genes in the sucrose synthesis pathway [44]. The results of our experiment suggest that there were no significant differences in _SPS_ and _SUS_ levels between the CK and different treatments, while _INV_ showed an increased difference. This result suggested that, sucrose produced by photosynthesis was more likely to be decomposed into glucose-6-phosphate (G-6-P) and fructose-6-phosphate (F-6-P), and then participated in subsequent reactions is the main metabolic direction of sucrose under high CO\({}_{2}\) stress. [PERSON] et al. [45] found that INV was significantly up-regulated and SUS was relatively inhibited in mature leaves of in vitro tomato plantlets with high CO\({}_{2}\) stress under the condition of providing exogenous sucrose (3%). In addition, other studies also showed that the expression of INV and the contents of glucose and fructose in leaves increased under cold, NaCl, PEG and other stress conditions, which showed similar phenomena to CO\({}_{2}\) stress conditions [46; 47].  
In the starch synthesis pathway, three different starch-formed synthetases (GBSS, SSS and SBE) are the key regulatory enzymes. In this study, the expression level of _GBSS_ were significantly higher than that of _SBE_ and _SSS_ under high CO\({}_{2}\) stress. _GBSS_ generates a branchless linear glucan starch chain (amylose) by specific binding to starch granules, which is necessary for the production of amylose [48]. Due to the high expression of _GBSS_, we suspected that, amylose was more inclined to synthesize under high CO\({}_{2}\) stress in Masson pine. The similar results were confirmed in [PERSON] [49] study. He found that the CO\({}_{2}\) stress could significantly improve the activity of GBSS in winter wheat, resulting in a large amount of amylose production, and the expression level increased first and then decreased, which was similar to this experiment.
## 2 Methodology
### Fault Signal Detector Design
In this paper, we use the following variable to define the detector statistic:  
\[\beta_{GPS,k}=e_{i,k}-e_{GPS,k}=\hat{X}_{GPS,k}-\hat{X}_{i,k} \tag{29}\]  
The variance of this variable can be written as:  
\[T_{GPS,k}=E\big{[}\beta_{GPS,k}\beta_{GPS,k}^{T}\big{]}=E\big{[}\epsilon_{GPS,k}e_{GPS,k}^{T}-\epsilon_{GPS,k}e_{i,k}^{T}-\epsilon_{i,k}e_{GPS,k}^{T}+ \epsilon_{i,k}e_{i,k}^{T}\big{]}=P_{GPS,k}-P_{GPS,i,k}-P_{i,GPS,k}+P_{i,k} \tag{30}\]  
where \(P_{GPS,i,k}\) and \(P_{i,GPS,k}\) are the cross covariance between the Sub_Kalman_Filter_1 and the state propagator \(i\). We set the same initial conditions for the Sub_Kalman_Filter_1 and the state propagator \(i\); then, \(P_{GPS\_i,k}=P_{i,GPS,k}=P_{i,k}\), thus the variance can be written as:  
\[T_{GPS,k}=P_{GPS,k}-P_{i,k} \tag{31}\]  
Define the fault detection function:  
\[\lambda_{GPS,k}=\beta_{GPS}^{T}T_{GPS,k}^{-1}\beta_{GPS} \tag{32}\]  
Define the fault detection criteria:  
\[\left\{ \begin{array}{l}\lambda_{GPS,k}\geq\epsilon_{\beta},\ fault\\ \lambda_{GPS,k}<\epsilon_{\beta},\ no\ fault\end{array} \right. \tag{33}\]  
where the threshold \(\epsilon_{\beta}\) is determined by the function of false alarm rate. Figure 4: The principle of fault signal detector. Detector \(\chi^{2}\) residual error of Sub_Kalman_Filter_1 can be written as:  
\[d_{GPS,k}=\Delta\varphi_{GPS,k}-\Delta\hat{\varphi}_{ijk} \tag{34}\]  
Covariance residual error:  
\[S_{GPS,k}=H_{GPS,k}P_{GPS,k}H_{GPS,k}^{T}+R_{GPS,k} \tag{35}\]  
Define the fault detection function:  
\[\gamma_{GPS,k}=d_{GPS,k}^{T}S_{GPS,k}^{-1}d_{GPS,k} \tag{36}\]  
Define the fault detection criteria:  
\[\left\{ \begin{array}{ll}\gamma_{GPS,k}\geq\varepsilon_{d},\ fault\\ \gamma_{GPS,k}<\varepsilon_{d},\ no\ fault\end{array} \right. \tag{37}\]  
where the threshold \(\varepsilon_{d}\) is determined by the function of false alarm rate. If the state \(\chi^{2}\) detector or the residual \(\chi^{2}\) detector detects a fault signal of GPS system, then \(m_{g}=0\), otherwise \(m_{g}=1\). Thus, we can generate a fault detection matrix, which will be used for fault signal isolation in the process of data fusion.
## 3 The Proposed Method
### NeuroTIS  
NeuroTIS considers the label dependencies among codon labels, and, between codon labels and TISs, it receives the input of mRNA sequences and outputs the probability of a codon and translation initiation site; hence, the problem of Equation (1) can be reduced to solve the following MAP estimation problem:  
\[(z_{\kappa}^{*},\mathbf{y}^{*})=\arg\max_{z_{\kappa},\mathbf{y}}p(z_{\kappa},\mathbf{y}| \mathbf{s}) \tag{2}\]  
where \(\mathbf{y}=y_{1}y_{2}\ldots y_{n}\) represents the codon labels of \(\mathbf{s}\) and \(y_{i}\); \(\in\{1,0\}\) denotes whether the position \(i\) in \(\mathbf{s}\) is the first nucleotide of a codon (\(y_{i}=1\)) or not (\(y_{i}=0\)).  
NeuroTIS introduces a dependency network to represent and simplify the dependency relationships among variables. As shown in Figure 2, it is assumed that there exists weak interdependency among codon labels. TIS is dependent on codon labels and mRNA sequence. Then, according to the chain rule of probability [34], Equation (2) can be reduced to the problem as follows:  
\[(z_{\kappa}^{*},\mathbf{y}^{*})=\arg\max_{z_{\kappa},\mathbf{y}}p(z_{\kappa}|\mathbf{y}, \mathbf{s})p(\mathbf{y}|\mathbf{s}) \tag{3}\]  
Despite the above reduction, Equation (3) is still an NP-hard problem; NeuroTIS adopts a greedy inference and decomposes the TIS prediction problem into two following subproblems:  
\[\hat{\mathbf{y}}=\arg\max_{\mathbf{y}}\prod_{i=1}^{n}p(y_{i}|\mathbf{s},y_{i- \
u},y_{i+\
u}) \tag{4}\] \[\hat{z}_{\kappa}=\arg\max_{z_{\kappa}}p(z_{\kappa}|\mathbf{s},\hat{ \mathbf{y}}) \tag{5}\]  
where NeuroTIS efficiently infers variables \((z_{\kappa},\mathbf{y})\) in two stages: the first stage outputs CDS probabilities \(\hat{\mathbf{y}}\), which are then processed along with TIS sequence data through a second inference stage to infer TIS \(\hat{z}_{\kappa}\). Note that NeuroTIS assumes that there exists weak interdependency among codon labels and models the codon labels' consistency among three positions with a step interval \(v\) to avoid the information redundancy among neighboring positions. In practice, NeuroTIS employs a skipBRNN to estimate conditional probability distributions \(p(y_{i}|\mathbf{s},y_{i-\
u},y_{i+\
u})\). With regard to conditional probability distribution \(p(z_{\kappa}|\mathbf{s},\hat{\mathbf{y}})\), NeuroTIS employs a CNN for the fact that it can effectively capture local patterns and model non-linearity in data.
## Abbreviations  
The main variables appearing in this paper are summarized below:  
\[\begin{array}{ll}h_{k}^{N_{k}}&\text{channel gain from the source node to the $N_{k}$-th relay node}\\ g_{k}^{N_{k}}&\text{channel gain from the $N_{k}$-th relay node to the destination node}\\ \mathbb{C}_{k}&\text{channel capacity of the $k$-th TS}\\ \mathbb{G}_{C}&\text{capacity gain by our proposed algorithm over the greedy algorithm}\\ \tau&\text{switching loss when switching between neighboring relay nodes}\\ \tau_{i,j}&\text{switching loss $\tau_{i,j}$ between $i$-th relay node and $j$-th relay node}\\ N&\text{number of relay nodes}\\ \omega,\,\theta,\zeta&\text{parameters of the convolutional layers and two streams of fully connected layers in the online network}\\ \omega^{\prime},\,\zeta^{\prime}\,\theta^{\prime}&\text{parameters of the convolutional layers and two streams of fully connected layers in the target network}\\ \mathbf{S}_{k},\,\mathbf{A}_{k},\,R_{k}&\text{current state space, action space, reward of the whole system at $k$-th TS}\\ \tau&\text{discount factor of the DQN-RS algorithm}\\ \zeta,\lambda&\text{learning rate of the online network and target network}\end{array}\]
# A Novel Dual Authenticated Encryption Scheme Suitable for Social Networking Services
## 2 Preliminaries
### Algorithms  
We describe the constituted algorithms of the proposed DAE scheme as follows:  
**Setup:** Taking a security parameter \(k\) as input, a system authority runs the algorithm to generate necessary public parameters _params_.  
**Keygen:** The algorithm takes as input an index \(i\), and then outputs a corresponding key-pair (\(x_{i}\), \(Y_{i}\)) along with a public key certificate \(\mathit{Cert}_{i}\). Note that a valid certificate \(\mathit{Cert}_{i}\) should be issued by a Certificate Authority who also maintains a certificate revocation list (CRL) to store revoked public key certificates. Anyone obtaining a public key first requests its corresponding certificate to verify the public key validity.  
**AEncrypt:** The algorithm accepts input of a message \(m\), two public keys of designated recipients and the private key of sender. The output is a corresponding authenticated ciphertext \(\delta\).  
**ADecrypt:** The algorithm takes three parameters as input including an authenticated ciphertext \(\delta\), one private key of designated recipients and the public key of sender. If the ciphertext \(\delta\) is valid, it outputs the decrypted message \(m\) and its signature \(\Omega\). Otherwise, an error symbol \(\bot\) is returned as a result.