esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Results of Image Classification  
Models such as OBIA ANN and patch CNN, as well as their integration by the four common frameworks, have been utilized to classify the image data of the training and test areas. Figures 7 and 8 show the classification maps obtained for the training and test areas using the assessed methods. The classification maps were produced according to six land cover classes, including grassland, road, building, dense vegetation or trees, and water. The training and test areas are mostly urban/built-up areas. The areas also consist of relatively complex road networks due to parking lots. Water bodies in the areas are mostly artificial small lakes. Both the training and test areas contain dense vegetation (or small trees) and grasslands.  
Figure 8: Classification maps obtained for the test area with the pixel ANN, patch CNN, and the four OBIA-CNN integration frameworks assessed in the current research.  
Some classification maps present salt-and-pepper-like noise, while others contain less random noise, and the maps appear as vector maps. The maps of the pixel ANN contain random noise and misclassification between building and grassland and dense vegetation or tree classes. The maps of the patch CNN have less random noise and misclassifications. It also can be observed that the patch CNN improves the detection of the roads compared to the pixel ANN. The maps of the OBIA ANN contain very little random noise. However, they contain significant misclassification between building and road classes. It seems that the ANN could not learn useful contextual features from the OBIA features, or the latter is not enough to separate the building and road classes. The maps obtained by the patch filtering appear to have less random noise and misclassification compared to the patch CNN. The maps of the decision fusion are better than the OBIA ANN and with less misclassification between building and road classes.
## 4 Evaluation of BER for Dynamic Directional Modulation
### Zero Mean Artificial Non-Gaussian Noise: Results and Discussion  
In this subsection, we continue to use the synthesis method of [19], but we set the artificial noise to constant magnitude and random phase, as proposed in [16; 23; 24], so that \(PE_{DDM}\) can be adjusted easily based on (8). In that case, the injected artificial noise is non-Gaussian and the constellation symbol cluster is constrained inside a circle (Figure 5). Therefore, there may be some observation angles where the total noise (\(rp_{av\_W}(\phi)+N_{AWGN}\)) does not follow a Gaussian distribution. Figure 6 shows the results of BER for a DDM configuration where the use of (12) and expressions in Table 2 fails to predict accurately BER in the side lobes and the edges of the information beam. Those observation angles satisfy  
\[rp_{av\_conv}(\phi)\gg rp_{av\_W}(\phi) \tag{13}\]  
and  
\[rp_{av\_W}(\phi)>N_{AWGN}. \tag{14}\]  
Nevertheless, it must be noted that these conditions are very particular (low \(rp_{av\_W}(\phi)\) and low \(N_{AWGN}\)) and do not apply in most practical scenarios. Indeed, if we test again all the configurations of Figure 2 using zero mean artificial non-Gaussian noise, we obtain the same results of Figure 3 because (13) and (14) are not satisfied at any observation angle. However, we are still spreading the artificial noise uniformly in all the observation angles out of the main beam, so the synthesis method is still inefficient in terms of \(PE_{DDM}\).  
Figure 4: Array of four omnidirectional antennas along x-axis, spacing \(\lambda/2\). BER in XY plane for DDM and different number of available radiation patterns per symbol (\(P\)). QPSK modulation, \(\phi_{0}=90^{\circ}\), SNR(\(\phi_{0}\)) = 12 dB, \(PE_{DDM}\) = 75%. Zero mean artificial Gaussian noise. Only half-plane is plotted due to symmetry. (**a**) Stream of \(10^{6}\) symbols. (**b**) Our approach.
# Automatic Age and Gender Recognition Using Ensemble Learning
## 4 Experimental Evaluation
### Experimental Results
#### 4.3.1 Performances of Base-Level Classifiers  
In this study, five different classifiers, namely KNN, SVM, RF, E-TREE, and LR, were examined as base-level classifiers. First, parameter optimization was performed for each of these classifiers with the cross-validation method. Then, the models were created with the determined optimum parameters and training of each was carried out. Finally, the process was completed by testing the models with a dataset that was not used in the training phase. The same dataset was used in the training and testing phases of all models. This dataset was selected from the Turkish subsection of the MCV dataset according to the criteria specified in Section 3.1, and 75% of the dataset was used for model training and the remaining 25% for model testing. The accuracy, precision, recall, and F1 scores obtained inthese experiments are given in Table 3. The best results for each metric are highlighted in bold in the table.  
As seen in Table 3, SVM is the best classifier in terms of all evaluation metrics among the base-level classifiers. The SVM classifier reached 96.37% accuracy by correctly classifying 1937 of 2010 speeches in the test dataset, followed by E-TREE with 96.02% accuracy, RF with 95.57% accuracy, and KNN classifiers with 91.94% accuracy. Among the base-level classifiers, the classifier with the lowest accuracy is LR with 91.59%. The confusion matrix of the SVM classifier is given in Table 4. The confusion matrix represents the classifier's predictions in detail and is widely used in performance evaluation. From Table 4, it can be seen that the M50s and F80s are the classes with the highest accuracy, with 100% and 98.80%, respectively. On the other hand, F20s and F50s are the classes classified with the lowest accuracy, with 86.34% and 94.35%, respectively.
# Liquid-Crystal-on-Silicon for Augmented Reality Displays
## 1 Introduction  
Dating from the 1970s, Hughes research labs creatively combined liquid crystal (LC) with semiconductor substrates, enabling optically addressed liquid crystal light valves [1; 2], which paved the way for the invention of liquid-crystal-on-silicon (LCoS) display technology [3; 4; 5]. Through decades of extensive efforts, LCoS technology has proven its success in a wide range of fields like displays [6; 7; 8; 9; 10; 11; 12; 13], adaptive optics [14; 15], metrology [16; 17], telecommunications [18], quantum physics [19; 20], etc. The emergence of LCoS is jointly contributed by the application of liquid crystal (LC) electro-optic response behaviors and the development of the silicon complementary metal oxide semiconductor (CMOS) backplane technique. The structure of a typical reflective LCoS panel is shown in Figure 1: an LC layer is sandwiched between an indium tin oxide (ITO)-coated glass and a CMOS backplane. Two thin polyimide (PI) alignment layers determine the LC alignment direction. The Aluminum (Al) electrodes on the Silicon CMOS backplane are pixelated to provide independent voltage control. When an incident linearly polarized light traversing through the LC layer twice, its polarization state or phase is modulated depending on the LC alignment mode.  
Recently, LCoS has been widely implemented in augmented reality (AR) displays because of its high luminance (>30,000 nits), compactness, high-resolution density (>4000 pixels per inch), and potential of holography generation [12; 21; 22; 23]. However, the cutting edge LCoS still needs improvement to meet the stringent AR requirements. In this paper, we review the LCoS technology intended for AR displays. In Section 2, we briefly describe the basic light modulation principles ofLCoS. In Section 3, we address the fringing field effect issue impairing the LCoS display performance, and propose a new device structure to mitigate this problem. High-resolution and small-pitch LCoS panels are especially desirable for augmented reality displays, but how to suppress the fringing field effect remains a technical challenge. In the last section, the LCoS-integrated AR head-mounted display (HMD) and head-up display (HUD) systems are demonstrated. The key parameters determining the display performance are discussed in detail.
## 3 Theory: Problem Statement and Mathematical Formulation
### Model B2 Formulation: Worker Assignment  
Model B2 has two objectives--that is, minimising the total inventory level and minimising the cell workers' idle time variation in each cell. In addition to the notations summarised in Section 3.1, two additional notations are introduced in model B2. The formulation of Model B2 can be expressed as follows:  
\[\begin{array}{c}\text{Input Parameter}\\ a_{uc}\\ \text{Decision variable}\\ \text{$x_{up}$}\end{array}\]  
If worker \(w\) belongs to cell \(c\), \(1\); otherwise, \(0\)  
If worker \(w\) is assigned to part \(p\), \(1\); otherwise, \(0\)  
The objective functions and penalty functions of Model B2 can be expressed as follows:  
\[\min Z_{2}=\sum_{p=1}^{P}\left(\sum_{w=1}^{W}\frac{3600\cdot q_{pt}\cdot x_{up }\cdot\alpha_{wt}}{ST_{p}}-D_{p}\right)+\sum_{k=1}^{3}pen_{k} \tag{32}\]  
\[pen_{1}=\sum_{p=1}^{P}\max\left(j\cdot\left(D_{p}-\sum_{w=1}^{W}\frac{3600 \cdot q_{pt}\cdot x_{up}\cdot\alpha_{wt}}{ST_{p}}\right),0\right) \tag{33}\]  
\[pen_{2}=\max(j\cdot\left(IVR_{c}-\epsilon\right),0) \tag{34}\]  
\[pen_{3}=\max\left(\sum_{w=1}^{W}\left(j\cdot\left(\sum_{p=1}^{P}x_{up}-A \right)\right),0\right) \tag{35}\]  
subject to,  
\[IVR_{c}=\frac{1}{\bar{I}_{c}}\sqrt{\frac{\sum_{w=1}^{W}\left(I_{uc}-\bar{I}_{ c}\right)^{2}}{\sum_{w=1}^{W}a_{uc}}}\ \ \ \forall c \tag{36}\]  
\[\bar{I}_{c}=\frac{1}{\sum_{w=1}^{W}a_{uc}}\sum_{w=1}^{W}I_{uc}\ \ \ \forall c \tag{37}\]  
\[I_{uc}=A-\sum_{P=1}^{P}x_{up}\cdot h_{pc}\cdot s_{wt}\cdot q_{pt}\ \ \ \forall c,t,w \tag{38}\]  
\[x_{up}\in 0,1 \tag{39}\]\[I_{ux}\in INT^{+} \tag{40}\] \[IVR_{c},\overline{I}_{c}\in\ R^{+} \tag{41}\]  
Equation (32) represents the objective function of Model B2, which is the total inventory level. Equation (33) represents the unsatisfactory demand penalty function. Equation (34) represents the epsilon constraint to minimise the cell workers' idle time variation of cell \(c\). Equation (35) represents the overtime working penalty of worker \(w\). Equations (36)-(38) can be used to calculate Equation (34). Equation (39) represents the binary decision variable. Equation (40) represents the positive integer variable. Finally, Equation (41) represents the positive real-number variables.
# The Role of Earth Observation, with a Focus on SAR Interferometry, for Sinkhole Hazard Assessment
## 4 The role of Earth Observation in Sinkhole Hazard Assessment
### Compilation of sinkhole Inventories
Ultimately, the mitigation of sinkhole risks based on susceptibility mapping and sinkhole management plans derived from probabilistic models depends on the reliability of the predictive models on which they are based [15]. Although significant reductions in the number of occurrences of sinkholes have been observed based on risk management plans derived from sinkhole inventories [13], the reliability of the predictive capability remains unknown in a large number of instances [15]. For this reason, several investigations have been devoted to the search of small scale vertical movements that may be indicative of impending sinkhole collapse. The detection of small scale precursors to sinkhole formation may contribute to the provision of sinkhole early warning, therefore further minimising the probability of catastrophic events. The detection of vertical movement of the surface is measureable, in ideal conditions, by geodetic or remote sensing techniques as outlined in Section 4.2.
# IECI: A Pipeline Framework for Iterative Event Causal Identification with Dynamic Inference Chains
## 3 Methodology
### Semantic-Role Guided Causal Inference Graph
#### 3.2.1 Contextual Representation with Semantic Role Attention  
The SRCIG module is jointly modeled by BERT and BiLSTM. At the text input stage, it incorporates a semantic role attention mechanism to enhance the expressiveness of the semantic representation of events.  
Specifically, the input document is first represented as a sequence of n words \(\mathrm{x}_{1},\mathrm{x}_{2},\ldots,\mathrm{x}_{n}\). This sequence is then fed into the pre-trained language model BERT to obtain a sequence of semantic representation vectors \(\mathrm{E}_{1},\mathrm{E}_{2},\ldots,\mathrm{E}_{n}\) corresponding to each word. Subsequently, BiLSTM is introduced to capture global semantic dependency information further. The BERT output is used as the input of BiLSTM to obtain the hidden state representations \(\mathrm{H}_{1},\mathrm{H}_{2},\ldots,\mathrm{H}_{n}\).  
We employ semantic role labeling (SRL) as a guiding signal to incorporate prior syntactic-semantic knowledge, increasing the weight of event-detection-related words. A Semantic Role Attention module is proposed to weight the BiLSTM output, computed as follows.  
First, obtain the semantic role embedding vectors \(\mathrm{S}_{1},\mathrm{S}_{2},\ldots,\mathrm{S}_{n}\) for each word, which is generated by SRL and corresponds to different role categories. For each time step t, calculate its corresponding semantic attention score \(\alpha_{t}\):  
\[\alpha_{t}=\frac{\exp\left(\mathrm{H}_{t}^{\top}\mathrm{W}_{a}\mathrm{S}_{t} \right)}{\sum_{j=1}^{n}\exp\left(\mathrm{H}_{j}^{\top}\mathrm{W}_{a}\mathrm{S }_{j}\right)}, \tag{20}\]  
Figure 2: SRCIG module diagram.  
where \(W_{a}\) is the learnable attention weight matrix.  
Ultimately, the contextual representation of each location is obtained through attentional weighting:  
\[y_{t}=\alpha_{t}\cdot H_{t}, \tag{21}\]  
The contextual representation of the whole text can be denoted as \(y_{1},y_{2},\dots,y_{n}\), which serves as input to the subsequent modules.
# Pediatric Endoscopy, Update 2020
### Sedation
#### 1.5.3 Special Aspects of Sedation  
Sedation of children should follow guidelines especially made from the pediatric and anesthesiological society [14; 15; 16]. It should be noted that painful procedures require a deeper sedation compared to adult patients. Therefore, special knowledge in sedation of children is recommended.  
Before sedation, any kind of mobile orthodontic appliances and jewelry such as nose ring, tongue, and cheek piercing must be removed to avoid the risk of aspiration or swallowing.  
The physician who is responsible of the sedation must not be the person who is performing the endoscopy. Most of the children can be sedated by using the principles of analogsedation performed by a pediatrician, only in case of severe problems by an anesthesiologist. The necessity of general anesthesia should be made dependent from the age of the child, comorbidity and kind of procedure [15; 17; 18]. The indication for performing the procedure under general anesthesia is also influenced by local individual factors like availability and technique. Special rules should be applied for newborns and infants. This age group does not necessarily need general anesthesia. However, long procedures with the risk of prolonged air insufflation should be performed under general anesthesia (per example ERCP). Rectal biopsies for the diagnosis of Hirschsprung disease can be taken under analogsedation; on the other hand, dilatation (bougienage) of the esophagus in case of esophageal atresia should be performed under general anesthesia. It is also recommended to perform a general anesthesia in children with a physical handicap (per example for placing of a transabdominal feeding tube). However, even this is performed differently in different hospitals [12; 15; 17; 19].  
Nurse administration of propofol sedation should not be used in children. Pre-oxygenation is recommended in all sedation procedures. In contrast to adult sedation, the reaction of children to the medication can be sudden and unexpectedly strong. Therefore, experience is necessary to be able to response immediately and appropriately. The most common sedative is propofol in children, and it can be safely administrated by specifically trained non-anesthesiologists. The addition of midazolam, fentanyl, remifentanil, and/or ketamine to propofol may increase effectiveness without creating more adverse events [15]. One study shows the sedation protocol with propofol was safe and efficient. The administration of midazolam provided no additional benefit in propofol-based sedation [20].  
The selection of the sedation method should account for the possibility of a psychological trauma for the child. In any case, the physician should be prepared for an unexpected emergency during the procedure. A common complication is the laryngospasm. The ESGE/ESPGHAN guidelines suggest to perform OGD under general anesthesia, and only if general anesthesia is not available, under deep sedation in a carefully monitored environment. However, they state that this presents a weak recommendation based on low quality evidence [7; 8].
## 3 Background
### Communication Principle of Snowflake Protocol  
The Pluggable Transport (PT) technology can obfuscate Tor traffic into the back-ground traffic by cryptographic padding, domain fronting, or tunneling. Snowflake is a state-of-the-art Tor PT based on WebRTC, built on the base of Flashproxy [30]. It also includes the domain fronting in its communication process. Snowflake is more like a hybrid of previous PT technologies, providing easy-to-use access to Tor for Tor users in the censorship region. Snowflake is based on establishing WebRTC connections between peers and sending access requests to the Tor network via browsers volunteering outside the censorship area. WebRTC, the core technology that Snowflake relies on, is a web framework and suite of protocols that support peer-to-peer connections between browsers. In the following section, we will introduce the communication principle of Snowflake from two aspects: the communication process of Snowflake and the UDP-based encrypted data transmission protocol DTLS (Datagram Transport Layer Security).  
Figure 1: Threat model, we assume the opponent is an ISP or major gateway.
###### Abstract  
Thermoelectric (TE) materials are a kind of energy material which can directly convert waste heat into electricity based on TE effects. Ternary Cu\({}_{2}\)SnSe\({}_{3}\) material with diamond-like structure has become one of the potential TE materials due to its low thermal conductivity and adjustable electrical conductivity. In this study, the Cu\({}_{2}\)SnSe\({}_{3}\) powder was prepared by vacuum melting-quenching-annealing-grinding process. The nano-TiO\({}_{2}\) particles were introduced into the Cu\({}_{2}\)SnSe\({}_{3}\) matrix by ball milling. Spark plasma sintering (SPS) was employed to fabricate the TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) composites. The X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), and transmission electron microscopy (TEM) were used to study the phase and microstructure of TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) composites. Electrical resistivity, Seebeck coefficient, and thermal conductivity measurement were applied to analyze the thermoelectric properties. For the 1.4%TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) composite, the electrical conductivity was improved whereas the Seebeck coefficient was lower than that of pure Cu\({}_{2}\)SnSe\({}_{3}\). For other TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) samples, the Seebeck coefficient was improved while the electrical conductivity was reduced. The thermal conductivity of TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) composites was lower than that of Cu\({}_{2}\)SnSe\({}_{3}\) matrix, which is attributed to the lower carrier conductivity. A maximum \(ZT\) of 0.30 at 700 K for the 1.0%TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) composite was obtained, which was 17% higher than that of the pure Cu\({}_{2}\)SnSe\({}_{3}\) at 700 K.  
thermoelectric; Cu\({}_{2}\)SnSe\({}_{3}\); nano-TiO\({}_{2}\); composites +
Footnote †: journal: Applied Physics Letters
## 3 A New Approach for Bias Detection Combining Rule Mining and Regression Analysis
### Model Comparison and Selection
Consider the model hierarchy in Figure 2. By applying our model selection algorithm, at the beginning, we retrieve the two parent nodes, i.e., \(M_{5}\) and \(M_{2}\). Supposing that the ANOVA test indicates that \(M_{1}\), \(M_{3}\) are the only children scoring better than \(M_{5}\), \(M_{2}\), respectively, \(M_{1}\) and \(M_{3}\) are added to the stack \(\mathcal{M}_{p}\). Since \(M_{1}\) does not have any other child, it is added to \(\mathcal{M}_{sel}\). Instead, \(M_{3}\) has to be compared against \(M_{6}\). If the ANOVA test determines that \(M_{6}\) is better than \(M_{3}\), \(M_{6}\) is added to \(\mathcal{M}_{p}\) and, since it has no children, in the next iteration, it is added to \(\mathcal{M}_{sel}\). At this point the procedure terminates, since there are no more models to compare, returning \(\mathcal{M}_{sel}=\{M_{1},M_{6}\}\). It is worth noting that the proposed pruning procedure can theoretically lead to miss some interesting models. As the comparison only accounts for directly nested models, it is possible that models that score better than their ancestors but not than their direct parents are discarded. For example, since \(M_{4}\) does not score better than \(M_{5}\), it is not selected for the next iterations and, therefore, \(M_{6}\) would have not been considered for the ANOVA test. However, it is reasonable to expect such loss to be limited. If a child model includes a variable providing a strong explanation of the observed effects, one can reasonably expect such variable(s) to have been picked up by other rules (step 1) and therefore to occur in other regression models. This leads to the otherwise discarded model being tested against different parents. This is the case, in our example, for \(M_{6}\). While the procedure would have discarded \(M_{6}\) if it had only \(M_{4}\) as a parent, this model is still considered in the comparison against \(M_{3}\) and, hence, it gets a chance to be selected.
# Two Low-Complexity Efficient Beamformers for an IRS- and UAV-Aided Directional Modulation Network
## 4 Simulation and Analysis
### Discussion of the Results  
As shown in Figures 2-9, the advantages and disadvantages of the proposed methods in this paper compared with existing methods were analyzed by several performance indicators. The gap in the SR performance between the Max-SR-SLNR method and the SDR method in [37] was small, and the computational complexity of the Max-SR-SLNR was much lower than the SDR in [37]. To further improve the SR performance of the MRT-NSP-PA method, the relationship between the orientation of the CM beamforming and \(N_{s}\) was explored. By changing the orientation of the CM beamforming, the MRT-NSP-PA method not only had the advantage of a high SR performance but also low complexity.
## 2 Materials and Methods
### MSC Injection Via the Tail Vein  
GFP-MSCs isolated from the bone marrow of transgenic mice were cultured and passaged three times before injection. Mice (n = 5 per group) were anesthetized by combined anesthesia (0.3 mg/kg medetomidine, 4.0 mg/kg midazolam, 5.0 mg/kg butorphanol) and ex vivo-expanded MSCs (\(1\times 10^{6}\) cells in 2 mL of PBS) were administered via the tail vein at 48 h before tooth extraction. Control mice (n = 5) were injected with PBS via the tail vein.