esa-sceva/satcom-chunk-collection huggingface dataset:
# Contribution of Climate Change and Grazing on Carbon Dynamics in Central Asian Pasturelands
## 2 Materials and Methods
### Model Description  
In this study, we used the Biome-BGC grazing model which was developed in our previous studies to analyze the NEE dynamics of pasturelands of Central Asia under grazing effects and multi climate dataset. This model was developed to study the hydrological processes and carbon and nitrogen dynamics for grassland ecosystems by incorporating the grazing process into the original Biome-BGC model [15; 16]. In the new model, gross primary productivity (GPP) followed the Farquhar photosynthesis function [17], considering both shaded and sunlit foliage. Respiration processes, including autotrophic growth (\(g_{r}\)) and maintenance (\(m_{r}\)) respiration, and soil heterotrophic (\(h_{r}\)) respiration, were mainly regulated by temperature and water availability. Grazing loss of plant biomass carbon (\(D_{r}\), g C ha\({}^{-1}\) d\({}^{-1}\)) is calculated according to grazing efficiency (\(G_{e}\), ha/d/sheep unit) and grazing intensity (\(GI\), head/ha).  
\[D_{r}=G_{e}\times GI\times\Big{(}C_{leaf}-\Big{(}C_{leaf}\Big{)}_{r}\Big{)} \big{(}0<D_{r}<D_{x}\times G_{i}\big{)} \tag{1}\]  
where \(C_{leaf}\) is the leaf biomass, carbon (g C m\({}^{-2}\)), \(\Big{(}C_{leaf}\Big{)}_{r}\) is the residual leaf carbon, which is unavailable to be grazed (g C m\({}^{-2}\)), and \(D_{x}\) is the livestock's maximum ingestion rate (g C d\({}^{-1}\) head\({}^{-1}\)) [18]. In the model, we further divided \(D_{r}\) into five components: the respiration of livestock, methane release from livestock, carbon in meat production, carbon transfer from feces, and urine into soil.  
As a result, \(NEE\) is calculated as the difference between \(GPP\) and ecosystem total respiration and grazing consumption.  
\[NEE=GPP-m_{r}-g_{r}-h_{r}-D_{r} \tag{2}\]  
where \(m_{r}\) is maintenance respiration, \(g_{r}\) is autotrophic growth respiration, \(h_{r}\). is soil heterotrophic respiration, and \(D_{r}\) is grazing loss of plant biomass carbon.  
In our previous studies, the model has been systematically parameterized and calibrated according to field observations [14; 15; 16; 19]. Specifically, the simulated net primary productivity (NPP), both under grazing and protected conditions, were validated against experiment data, and the modeling NEE, as well as water cycle, were validated using eddy covariance flux measurements. In this study, a negative NEE value indicates a carbon loss from the ecosystem.
## 4 Tracking Control Based on EKF
### EKF with Acoustic Localization Information  
Kalman filter is a widely used correct-predict formulation to deal with velocity estimation and stochastic environmental disturbances [15]. EKF further extends the system dynamics to nonlinear types by linearizations to propagate an approximation of the state estimation and covariance. With the models Equations (18), (20) and (22), we are ready for the EKF design.  
Assuming that \(T_{\mathrm{s}}^{\mathrm{t}}=nT_{\mathrm{s}}^{\mathrm{c}}\) and \(n\geq 2\) is an integer. Then, at control time step \(k\) while the new acoustic localization information is available, i.e., the localization step \(k/n\), the AUV predicted state is corrected by  
\[\mathbf{\hat{x}}(k)=\mathbf{\bar{x}}(k)+\mathbf{K}(k)[\mathbf{\hat{y}}  
where \(f_{\rm d}\) is the discrete function that generalizes Equation (20). Note the the parameters with a horizontal bar overhead such as \(\bar{b}\) and \(\bar{x}\) all indicate the predicted system information. The Kalman gain matrix \(\mathbf{K}(k)\) is derived by  
\[\mathbf{K}(k)=\begin{cases}\bar{\mathbf{P}}(k)\mathbf{C}^{\rm T}\big{(}\mathbf{C}\bar{\mathbf{P}}(k )\mathbf{C}^{\rm T}+\mathbf{\Sigma}\big{)}^{-1},&\text{if }\ k/n\ \text{is integer},\\ [0,0,0]^{\rm T},&\text{otherwise}.\end{cases}\]  
The covariance matrix \(\bar{\mathbf{P}}(k)\) for the prediction error \(\mathbf{x}(k)-\bar{\mathbf{x}}(k)\) is computed by the recursion  
\[\mathbf{P}(k) =(\mathbf{I}-\mathbf{K}(k)\mathbf{C})\bar{\mathbf{P}}(k), \tag{29}\] \[\bar{\mathbf{P}}(k+1) =\mathbf{A}(0|k)\mathbf{P}(k)\mathbf{A}^{\rm T}(0|k)+\mathbf{E}(0|k)\Sigma_{b} \mathbf{E}^{\rm T}(0|k), \tag{30}\]  
where \(\mathbf{P}(k)\) is the covariance matrix of the estimation error \(\mathbf{x}(k)-\bar{\mathbf{x}}(k)\).  
Through this standard Kalman filter procedure, the predicted system state \(\bar{\mathbf{x}}(k)\) can be corrected by the acoustically \"measured\" outputs \(\mathbf{y}(k)\) whenever this information is available. The corrected states \(\bar{\mathbf{x}}(k)\) can then be used for feedback design in MPC. However, as mentioned earlier, the control cycle is generally shorter than the localization cycle, i.e., \(T_{\rm S}^{\rm c}\leq T_{\rm S}^{\rm I}\), which means \(\mathbf{y}(k)\) is not guaranteed to be there for each control time step \(k\). When the acoustic measurement is not available, the predicted state \(\bar{\mathbf{x}}(k)\) from Equation (28) is used for feedback, which is commonly known as the dead-reckoning technique [14].
## 5 Security Analysis and Comparison
### Cropping Attack  
The cropping attack is a key difficulty in image transmission [8]. The encrypted image has some pixels missing. Moreover, because the suggested image encryption is based on chaotic hybrid maps, it is resistant to data loss (6.25%) and can successfully retrieve the original image. The suggested approach's performance for cropping masks is examined, and the results are displayed in Figure 15. The encrypted images and their PSNR indicate the suggested method's resilience in the face of a cropping attack. The PSNR values of the cropped encrypted image vs. the noisy encrypted Lena image are shown in Table 3.  
Figure 14: Noise attack.  
Figure 13: Key sensitivity analysis on the application of wrong keys.
## 3 Results and Discussion
### Weighted Comprehensive Index Evaluation Method
#### 3.7.3. Standardization Results  
Before the standardization, the whole set of index data (such as Mw and WBC) with an order of magnitude difference need to be performed with the same power root to make them all become values between 0 and 10, so as to get rid of the huge ratio difference caused by the different digital factor magnitude. If the data in the same column have no order of magnitude difference but are all greater than 10, which should be divided by the same multiple until all converted into values between 0 and 10. The result of the data processed by the root operation is shown in Table 7.  
All the original indicator data were standardized and converted to 0-10 interval values. Then the average value of each index of the evaluation object was taken as the standard value to index the raw data. The normalization results of each index standard are shown in Table 8. It can be observed that the M\({}_{W}\), DD, WBC, FBC and SS values of three kinds of chitosan after the standard normalization treatment are significantly different, while the TS and PS values are less different.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimension** & **Indicator** & **Impact on \(GQI\)** & **Weight** \\ \hline Molecular structural & M\({}_{W}\) & – & 0.20 \\ (0.4) & DD & + & 0.20 \\ \hline \multirow{2}{*}{Physiochemical (0.3)} & WBC & + & 0.12 \\  & FBC & + & 0.12 \\  & TS & + & 0.06 \\ \hline Bio-functional & PS & + & 0.12 \\  & SS & + & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Content and weight coefficient of chitosan chemistry performance indexes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Molecular Structural**} & \multicolumn{2}{c}{**Physiochemical**} & \multicolumn{2}{c}{**Bio-Functional**} \\ \cline{2-7}  & **M\({}_{w}\)** & **DD** & **WBC** & **FBC** & **TS** & **PS** & **SS** \\ \hline CS & 7.2 & 0.83 & 1.8 & 2.7 & 5.25 & 0.28 & 1.03 \\ CSC & 6.2 & 0.93 & 2.3 & 2.9 & 4.70 & 0.30 & 1.07 \\ CSS & 2.8 & 0.98 & 3.3 & 3.9 & 4.56 & 0.31 & 1.46 \\ \hline M & 5.4 & 0.91 & 2.5 & 3.2 & 4.84 & 0.30 & 1.19 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Data of chitosan biochemistry performance indexes processed by root operation.
## 5 Experiments
### Performance Evaluation
#### 5.1.2 Scenario-2  
For this scenario, we perform three simulations with different parameters, namely, Case 4, Case 5, and Case 6. Table 5 presents the parameters utilized in the Monte Carlo simulation, which is based on the original distribution.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Case** & \(\mathbf{\lambda_{0}}\) & \(\mathbf{\lambda_{1}}\) & \(\mathbf{r_{01}}\) & \(\mathbf{r_{10}}\) & \(\mathbf{\theta_{0}^{*}}\) & \(\mathbf{\mu^{*}}\) \\ \hline
1 & 0.7 & 0.7 & 0.1 & 0.4 & 0.68 & 0.51 \\
2 & 0.5 & 0.5 & 0.1 & 0.4 & 1.26 & 0.28 \\
3 & 0.3 & 0.3 & 0.1 & 0.4 & 2.06 & 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Scenario-1: Parameters for the proposed IS simulation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Case** & \(\mathbf{\lambda_{0}}\) & \(\mathbf{\lambda_{1}}\) & \(\mathbf{r_{01}}\) & \(\mathbf{r_{10}}\) & \(\mathbf{\mu}\) & \(\mathbf{\lambda_{mean}}\) \\ \hline
1 & 0.7 & 0.7 & 0.1 & 0.4 & 1.0 & 0.7 \\
2 & 0.5 & 0.5 & 0.7 & 0.4 & 1.0 & 0.5 \\
3 & 0.3 & 0.3 & 0.1 & 0.4 & 1.0 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scenario-1: parameters for MC simulation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline Case & \(\mathbf{\lambda_{0}}\) & \(\mathbf{\lambda_{1}}\) & \(\mathbf{r_{01}}\) & \(\mathbf{r_{10}}\) & \(\mathbf{\mu}\) & \(\mathbf{\lambda_{mean}}\) \\ \hline
1 & 0.7 & 0.7 & 0.1 & 0.4 & 1.0 & 0.7 \\
2 & 0.5 & 0.5 & 0.7 & 0.4 & 1.0 & 0.5 \\
3 & 0.3 & 0.3 & 0.1 & 0.4 & 1.0 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scenario-1: IS simulation’s parameters.  
Table 6 displays the parameters used in the optimal distribution simulation of the Importance Sampling (IS) scenario.  
The parameters for the proposed technique are displayed in Table 7. In contrast with the parameters used in Monte Carlo simulation, the suggested technique preserves the values of \(\lambda_{0},\lambda_{1},r_{01},r_{10}\) and only modifies the service rate parameter \(\mu\). The service rate \(\mu^{*}\) of the proposed IS simulation is determined using Equation (15).
# Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation
## 5 Analysis and Discussion
### Effectiveness of the Utterance Encoder Layer  
We try to replace BERT with LSTM as the classification utterance encoder. The encoder parameters of the utterance of the domain adversarial model are transferred to the encoder of the classification model, and the results are shown in Table 8.  
When we replace the encoder, results demonstrate that BERT provides better representations of utterances than LSTM. When Cornell is the source domain, the gap is 3.16% on  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{8}{c}{**DailyDialog**} \\ \cline{2-10}  & **Happy** & **Disgust** & **Surprise** & **Angry** & **Fear** & **Sad** & **Micro F1** & **Macro F1** & **Average (w) F1** \\ \hline bc-LSTM & 55.99 & 25.17 & 53.54 & 22.78 & 0.00 & 14.81 & 50.37 & 28.72 & 48.78 \\ DialogueRNN & 61.75 & 0.00 & 33.06 & 37.34 & 0.00 & 29.44 & 55.65 & 26.95 & 53.78 \\ DialogueGCN & 57.67 & 0.00 & 8.89 & 5.19 & 0.00 & 1.68 & 50.91 & 12.24 & 42.69 \\ \hline \(\text{DAN}_{\text{Cornell}}\) & 61.52 & 29.38 & 48.65 & 41.79 & 20.00 & 36.62 & 56.01 & 39.66 & 55.20 \\ \(\text{DAN}_{\text{Ubuntu}}\) & 60.95 & 27.63 & 48.15 & 41.41 & 19.05 & 37.33 & 55.61 & 39.09 & 54.60 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with different methods on DailyDialog. Micro F1, Macro F1 and Weighted F1 metrics are used to evaluate classification performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Avg. Dialogue** & **Avg. Utterance** \\ \hline IEMOCAP & 49.23 & 15.81 \\ MELD & 9.57 & 8.07 \\ DailyDialog & 7.81 & 14.08 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Statistics of the datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Source** & **Uterance Encoder** & **IEMOCAP** & **MELD** & **DailyDialog** \\ \hline Cornell & LSTM & 61.24 & 58.51 & 53.08 \\ BERT & 64.40 & 59.44 & 55.20 \\ Uburtu & LSTM & 62.83 & 58.15 & 53.05 \\ BERT & 63.94 & 59.23 & 54.60 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Impact of the Different Utterance Encoder Layer. Weighted F1 metrics are used to evaluate classification performance.  
IEMOCAP and 2.12% on DailyDialog. The other gaps are all around 1%. However, the effect of LSTM as an utterance encoder also exceeds the performance of using BERT as the utterance encoder without the adversarial network. This indicates that a suitable utterance encoder and domain adversarial network can jointly promote performance improvement.  
Moreover, we try to employ the utterance encoder parameters of the domain adversarial network to initialize the utterance encoder parameters of the emotion recognition model. However, we find that this method is not helpful for performance improvement. The possible reason for this phenomenon is that the representations of utterances differ between generative and emotion recognition tasks in different domains.
# Sentence Compression Using BERT and Graph Convolutional Networks
## 3 Sentence Compression Using BERT and GCNs
### Sentence Compression Using Directed GCNs  
As a dependency tree inherently has directional information between a node and its parent, GCNs with undirected edges cannot fully represent syntactic tree information. We addressed this shortcoming by extending the GCNs to include directional information on edges--referred to as _directed_ GCNs(\(d\)-GCNs) in this study.  
To fully utilize the directed edges in a directed graph, the propagation steps of parent and child nodes should be treated differently. Therefore, two types of weight matrixes are incorporated in Equation (5) to glean more precise structural information [12]:  
\[T^{h}=\sigma(D_{p}^{-1}A_{p}\sigma(D_{c}^{-1}A_{c}T^{h-1}\theta_{c}^{h})\theta _{p}^{h}), \tag{5}\]  
where \(D_{p}^{-1}A_{p}\) and \(D_{c}^{-1}A_{c}\) are the normalized adjacency matrixes for the parent and child nodes, respectively, and \(\theta_{p}^{h}\) and \(\theta_{c}^{h}\) are learnable weight matrixes at the \(h\)-th layer to linearly transform parent and child nodes, respectively.  
Figure 2: Sentence compression model with a GNN.  
We modify the propagation rule in Equation (5) slightly for this study. Equation (6) is a node propagation rule of the \(d\)-GCN proposed in this study. Figure 4 further explains Equation (6). The \(k\)-th input node \(T_{k}^{h-1}\) to the layer h can be propagated to \(T_{k}^{h}\) by Equation (6):  
\[T_{k}^{h}=\sigma((\sigma(T_{k}^{h-1}\theta_{I}^{h}) \tag{6}\] \[\qquad\qquad\oplus\sigma(T_{P(k)}^{h-1}\theta_{p}^{h})\] \[\qquad\qquad\oplus maxpooling_{\forall c\in C(k)}(\{\sigma(T_{c}^{ h-1}\theta_{c}^{h})\}))\theta_{I}^{h}),\]  
where the functions \(P(k)\) and \(C(k)\) return a parent index and child indexes of the \(k\)-th word in a dependency tree.  
As a node can have several child nodes in a dependency tree, an aggregator is necessary to combine all child nodes' information. In Equation (5), all child nodes are aggregated by a simple element-wise mean. In this study, we adopt an element-wise max-pooling aggregator to combine child information because not all child nodes are equally important to their parent. In the final step of node propagation, another linear transformation is performed by multiplying the weight matrix \(\theta_{I}^{h}\). This step is then followed by the non-linearity ReLU function.
## 1 Introduction
### Methodology: Test Setup, Load Application, and Measurement Setup
This displacement was made possible by introducing a sliding surface (PTFE) between the steel plates and the press, in order to avoid undesirable stresses. The displacement was kept low by using a wedge plate positioned between the press and the upper steel beam. Figure 5: Illustration of the test setup: (**a**) view of the test body from the side onto the web. (**b**) Section A–A: torsional load application area; Section B–B: prestressing against the strong floor using hydraulic presses to achieve the restraint; Section C–C: additional restraint to avoid tipping of the specimen. The measuring equipment consisted of load cells (Lorenz K18 1000 kN and HBM RTN100T/C3 1000 kN (Lorenz Mestechnik GmbH, Alford, Germany), maximum relative error of indication \(<\pm 1\%\), measurement frequency 5 Hz) (Figure 6a) used to document the reaction forces in Sections B-B and C-C, as well as all forces in the used hydraulic presses. To measure displacements, transducers (Solartrton BS50 100 mm (Solartrton Metrology, West Sussex, UK), maximum relative error of indication \(<\pm 1\%\), measurement frequency 5 Hz) were attached to the undersides of both webs (Figure 6b). An optical measuring system (Aramis 4M, resolution 2352/1728 pixels, GOM, measurement frequency 1/4 Hz) was used for crack documentation (Figure 6a). As the area of and around the vertical joint of TSjoint was of particular interest, this area was also recorded using the optical measuring system. The lateral displacement of the lower press was determined using transducers (Schreiber SM222.20.2 SX79 20 mm (Schreiber Mestechnik GmbH, Oberhaching, Germany), maximum relative error of indication \(<\pm 1\%\), measurement frequency 5 Hz). For the test sequence itself, the force in the two presses of Section A-A was increased until torsional failure of the beam occurred. After documentation, the body was then rotated and reinstalled for the next test.
# Machine Learning Methods with Noisy, Incomplete or Small Datasets
## 2 Medical Applications  
Interestingly, the majority of the contributions are related to specific applications in medicine. Three papers addressed different problems or diseases in Neuroscience. For example, in [1], [PERSON] et al. (Argentina-Spain-Japan) reviewed recent approaches to deal with incomplete or noisy measurements by applying signal decomposition methods and showed their usefulness in epileptic intracranial electroencephalogram (iEEG) signals classification, among other applications. Finding epileptic focus with iEEG is usually difficult mainly because available datasets labeled by expert medical doctors are scarce. In [2], [PERSON] et al. (China-South Africa) proposed a few-shot learning method for the severity assessment of [PERSON]'s disease based on a small gait dataset. The proposed algorithm solves the small-data problem by using permutation-variable importance (PVI) and persistent entropy of topological imprints; as well as applying a support vector machine (SVM) classifier to achieve the severity classification of Parkinson disease patients. In [3], [PERSON] et al. (China) addressed the problem of small and unbalanced datasets in functional magnetic resonance imaging (fMRI) for neuroscience studies. Their technique combines Independent Component Analysis (ICA) for dimensionality reduction, data augmentation to balance data and a convolution-gated recurrent unit (GRU) network. Results on episodic memory evaluation are reported.  
The other papers that addressed medical applications are described as follows. In [4], [PERSON] et al. (Japan) introduced a deep learning method based on an auto-encoder architecture to detect and remove shadow artifacts in ultrasound images. The model can be trained on unlabeled data (unsupervised) or with few pixel labels available (semi supervised). The method has been applied to fetal heart diagnosis. In [5], [PERSON] et al. (Saudi Arabia) investigated a machine learning approach to predict diabetes mellitus based on a handful set of features obtained by simple laboratory tests, allowing a cost-effective and rapid screening tool. They compared different machine learning classifiers and provided a set of recommendations based on those analyses. In [6], [PERSON] et al. (China) proposed a method to measure the length of the root canal length, which is crucial for an effective treatment of endodontics and periapicalitis. The authors employed a neural network on multifrequency impedance measurements.
## 3 Experimental
### Filter Development, Test Methods, and Results
#### 3.1.2 Proof of Principle for Coated Samples
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Nitrite and Nitrate Concentrations Determined by Ion Chromatography (mg\(\cdot\)dm\({}^{-3}\))** \\ \hline
**Sample** & **Nitrite (NO\({}_{2}\))\({}^{-}\)** & **Nitrate (NO\({}_{3}\))\({}^{-}\)** \\ \hline UK\({}^{\dagger}\) blank\({}^{*}\) & 0.0 & 2.0 \\ \hline UK blank repeat \({}^{*}\) & 0.0 & 1.8 \\ \hline NP \({}^{\dagger\dagger}\) blank \({}^{*}\) & 0.0 & 2.3 \\ \hline NP blank repeat \({}^{*}\) & 0.0 & 0.8 \\ \hline UK exposed \({}^{**}\) & 1.5 & 3.6 \\ \hline UK exposed repeat \({}^{**}\) & 1.3 & 4.1 \\ \hline NP exposed \({}^{**}\) & 1.0 & 4.0 \\ \hline NP exposed repeat \({}^{**}\) & 1.1 & 7.0 \\ \hline \hline \end{tabular} \({}^{\dagger}\) UK = unbleached Kraft substrate. \({}^{\dagger\dagger}\) NP = recycled newspaper substrate. \({}^{*}\) Non-exposed to synthetic NO\({}_{2}\) \({}^{**}\) Exposed to synthetic NO\({}_{2}\) at 10 ppm concentration for 72 h.  
\end{table} 
Table 6: Nitrite and nitrate concentrations comparing non-exposed and exposed smooth coated substrate samples. Figure 13: Static exposure procedure for coated samples, which included the gas-loaded super syringe, removed samples in storage vessels and aqueous extraction for ion chromatographic analysis. The samples had already reacted partly to produce nitrate simply by pre-exposure to ambient air (Table 6). This was expected, but it indicated that, when designing such filters, a simple surface adsorption does not suffice, as a stored filter under ambient conditions would eventually have its surface completely saturated by absorbed gas, whereas the shelf life of a reactive material is far longer since the material itself becomes gradually consumed. In addition, the reaction could be damped unless the reaction product(s) are removed. Given the sealed nature of this experiment, reaction products were not removed during exposure.