esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Mirror-Less Graphene-Based Perfect Absorbers
## 2 Graphene Perfect Absorbers with External Mirror
### 'Single-Resonance/Mirror Absorber' Scheme  
As previously mentioned, perfect absorption in this absorption scheme can be attained when critical coupling condition is satisfied at resonance wavelength. Key parameters in Equation (1) (that is, leakage rate, loss rate, and resonance wavelength) are controlled by incident angle [18; 19; 41; 42; 43; 44] or periodically patterned structures [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40], as well as graphene doping level.
## 5 Validation of RPAL SCAOF in Simulations
### Validation of Energy-Aware Routing Metrics and SCAOF Performance
#### 5.3.1 Network Simulation Scenarios: 20 and 30 LLN Nodes
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Performance**} & \multicolumn{8}{c}{**Performance Metrics (+: Increase, –: Decrease)**} \\ \cline{2-7}  & & \multicolumn{4}{c}{**Lifetime**} \\ \cline{2-7}  & & \% of living & \% of active & \% of living & \% of active & \% of living & \% of active \\
**by Using** & First dead & \begin{tabular}{c} nodes = 50\% \\ (min) \\ \end{tabular} & \begin{tabular}{c} nodes = 50\% \\ (min) \\ \end{tabular} & \begin{tabular}{c} nodes = 50\% \\ (min) \\ \end{tabular} & \begin{tabular}{c} nodes = 30\% \\ (min) \\ \end{tabular} & \begin{tabular}{c} nodes = 30\% \\ (min) \\ \end{tabular} & \
\begin{tabular}{c} nodes = 0\% \\ (min) \\ \end{tabular} &
\begin{tabular}{c} nodes = 0\% \\ (min) \\ \end{tabular} \\ \hline
**20 nodes** & +3.4 & +1.25 & +7.63 & +2.25 & +12.17 & –4.75 & –3.53 \\ \hline
**30 nodes** & +3.03 & \(-\)6.75 & +1.58 & –7.51 & +1.81 & –6 & +4.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Network lifetime of 20 and 30 LLN nodes scenarios. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Performance**} & \multicolumn{2}{c}{**Average Data**} & \multicolumn{2}{c}{**Average**} & \multirow{2}{*}{\begin{tabular}{c} **Control Plane** \\ **Overhead** \\ **(bytes)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Average Path** \\ **(ms)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Average** \\ **CoAP RTT** \\ **(ms)** \\ \end{tabular} } \\ \hline
**20 nodes** & +34 & –3.62 & +0.87 & \(\approx\) +2541 & +0.61 & –124.37 \\
**30 nodes** & +38 & –9.18 & +0.88 & \(\approx\) +3724 & +1.71 & –110.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: General performance metrics of 20 and 30 LLN nodes scenarios. simulation, and it proved that our Link Color metric is able to improve the communication of the nodes running CoAP applications by impacting the DODAG topology rank. The costs due to SCAOF become bigger as the number of nodes is increasing, but the results would be better if the nodes are assigned as normal energy level since the small volume of energy introduces very sensitive calculation of routing metrics that might lead to frequent parent reselections. Although the network churns only happen in local areas, RPAL model will certainly trigger local repair mechanisms more times than the original RPL protocol. Furthermore, SCAOF still can achieve lower packet loss rate as its composite routing metric approach could balance the traffic (_i.e._, the routing metric brings RDC information) as well as avoid energy depletion (_i.e._, RE routing metric). Furthermore, a certain level of delay for the common periodical application packet transmission is not a serious issue compared with the network lifetime performance as A-LLNs are delay-tolerant networks.
# Automated Classification of the Tympanic Membrane Using a Convolutional Neural Network
## 3 Results
### Class Activation Map  
The presence or absence of the heat map of the CAM image was calculated using a new dataset (right TM: 844; left TM: 974) of the test phase. The overall activation rate of the CAM was 8.7% for the left normal TM and 52.5% for the right normal TM. The overall analysis results are summarized in Table 3. The top three activation sites were the EAC anterior-inferior portion (52.2%), cone of light (46.1%) and malleolar short process (45.2%) in the left normal TM. For the side detection of the left normal TM, TM posterior-superior (OR: 17.56) and TM posterior-inferior (OR: 37.87) were statistically significant. However, there is no statistically significant subsite for the presence or absence of perforation. For the right normal TM, malleolar short process (89.9%), EAC posterior-superior (87.4%) and EAC anterior-inferior (86.8%) were frequent activation sites. There is no statistically significant site for the side detection and the detection of presence or absence of perforation. The overall activation rate for the TMs with perforation was 94.8% for the left and 60.3% for the right. EAC posterior-superior (74.3%), EAC posterior-inferior (63.3%) and middle ear (61.8%) were the top three activation sites for the left TM with perforation. Perforation margin (OR: 9.03) and middle ear (OR: 3.55) were statistically significant only in the detection of presence or absence of perforation for the left TM with perforation. For the right TM with perforation, middle ear (75.9%) was first, EAC anterior-inferior (59.8%) was second and perforation margin (53.9%) was third. For the right TM with perforation, middle ear (OR: 37.88) was statistically significant in the detection of the presence or absence of perforation. There was no statistically significant site in the detection of the side for the TM with perforation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Label** & **Side** & **Perforation** \\ \hline Left normal (663) & 96.5\% & 89.1\% \\ Right normal (773) & 99.2\% & 91.7\% \\ Left perforated TM (181) & 95.6\% & 91.1\% \\ Right perforated TM (201) & 99.5\% & 94.5\% \\ Overall (1818) & 97.9\% & 91.0\% \\ \hline \hline \end{tabular}  
* TM: tympanic membrane.  
\end{table} 
Table 1: Test phase accuracy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **AUROC** & **Accuracy** & **Sensitivity** & **Specificity** & **PPV** & **NPV** \\ \hline Side & 0.98 & 97.9\% & 99.3\% & 96.3\% & 96.9\% & 99.1\% \\ Perforation & 0.92 & 91.0\% & 90.5\% & 92.9\% & 98.0\% & 72.3\% \\ \hline \hline \end{tabular}  
* AUROC = area under the receiver operating characteristic curve; PPV = positive predictive value; NPV = negative predictive value.  
\end{table} 
Table 2: Diagnostic performance of the CNN.
## 2 Materials and Methods
### Methods
#### 2.4.4 Fuzzy Logic Fusion Method and Decision Making  
The proposed fusion method provides the AHI risk score as a percentage for each moving window by combining the ACP and SDA indexes. All ACP and SDA values are mapped to the percentage scale from 0 to 1. The center values for ACP and SDA indexes are 0.55 and Threshold(n), and the ranges are (0.4, 0.7) and (0.75*Threshold(n), 1.25*Threshold(n)), respectively. The fuzzy logic of the mapping relationship is illustrated in Figure 2.  
Figure 2: The black line represents fuzzy sets (Fc) for weighing amplitude changes of peaks (ACP), and the blue line represents Fc for standard deviation of airflow (SDA) values for risk evaluation.  
After mapping, the AHI risk score can be computed using the following formula, which changes over time:  
\[\text{Risk score(t)}=F_{c}*Risk_{ACP}(t)+(1-F_{c})*Risk_{SDA}(t),\ 0\leq F_{c}\leq 1 \tag{8}\]  
The optimal values of the \(F_{c}\) parameters are addressed in a subsequent section. If the maximum risk score reaches 80% in the observation window, the window is marked as an AHI event. Figure 3 represents a signal channel airflow waveform with ACP and SDA values/ranges and details how the AHI-related event is identified.  
Notably, our method is independent from signal DC shift and its absolute values of amplitudes. The dynamic threshold is designed to adapt to different breathing patterns without prior knowledge, so the algorithm is robust for variant patterns and peak amplitudes and can be applied on sensors with different voltage levels.
## 3 Results and Discussion
### Physicochemical Properties of Chicken Meat  
#### 3.2.1 pH of the Meat  
The results presented in Table 3 demonstrate the effect of AITC on the physicochemical properties of chicken meat. After the chicken meat was treated with AITC, the pH values decreased significantly (_p_ \(<\) 0.05). The different concentrations of AITC exhibited a significant difference in pH reduction compared to the control group at day 14 and day 1 of storage. As the AITC concentration increased, the pH decline intensified, with a slight decrease observed in the control group. The pH values of the control and MIC-4 were 6.02 and 6.04, respectively, at the beginning of the storage. Both declined to 5.82 and 5.72 on the last day of storage. Similarly to this study, [PERSON] [28] also recorded a decrease in pH of chicken meat after coating it with AITC-rich mustard extract solution. However, [PERSON] et al. [31] found no effect of AITC on reducing the pH value of meat, thus rendering the current study's results inconsistent with their findings. It has been reported that a decrease in pH by one unit increases protein denaturation rate by twelvefold; therefore, the enzyme activity can determine the rate of pH decline during postmortem glycolysis [57].
# Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues +
## 2 Background and Related Work
### Text-Based Approaches to MI Analysis  
In terms of text-based approaches to automatic coding, [PERSON] et al. [5] used N-grams and similarity features to develop the first model for identifying reflection, while [PERSON] et al. [7] used a labelled topic model to generate MI codes. More recently, deep-learning-based models have been utilised. For example, [PERSON] et al. [10] and [PERSON] et al. [19] studied the use of RNNs for behaviour prediction, followed by [PERSON] et al. [11], who did so under a multi-label multi-task setting to improve the performance, as well as [PERSON] et al. [12], who also investigated the forecasting of the codes of upcoming utterances.  
For therapist empathy modelling, an early approach was from [PERSON] et al. [6] with an n-gram language model. [PERSON] et al. [8] leveraged language features inspired by psycholinguistic norms, while [PERSON] et al. [9] used LSTMs to produce turn-level behavioural acts further processed to predict empathy. Separately, [PERSON] et al. [20; 21] explored leveraging links between therapeutic and general conversation empathy to tackle therapist empathy prediction in low-resource scenarios.
# Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
## 4 DoA Estimation
### Beamforming  
For many decades, beamforming has been a well-known technique for the estimation of direction [30]. PWD is a frequently used beamformer [37]. PWD has a maximum rejection characteristic of isotropic spatial noise and is expressed over frequencies \(k=1,\,\ldots,\,K\) as  
\[\hat{y}(\Omega)=\frac{1}{K}\sum_{k=1}^{K}\mathbf{W}(k,\Omega)\hat{p}_{nm}(k). \tag{38}\]  
where \(\hat{p}_{nm}(k)\) is the noisy form of Equation (18), and the weighting is  
\[\mathbf{W}(k,\Omega)=\frac{4\pi}{\left(N_{h}+1\right)^{2}}\mathbf{Y}(\mathbf{\Omega})\mathbf{ B}^{-1}(kr),\]  
and the unity beamformer in the look direction is ensured by the first term. This given PWD does not whiten the noise and hence exhibits poor performance in the wideband scenario. However, noise whitening can be implemented as given in [19].  
PWD can be implemented here using time-domain smoothing as  
\[\hat{y}(\Omega)=\frac{1}{N}\sum_{i=1}^{N}\mathbf{Y}^{H}(\Omega)\overline{q}_{nm}( t_{i}), \tag{39}\]  
The output energy of the time-domain beamformer at \(\Omega\) is  
\[P_{PWD}(\Omega)=\ \ ||\hat{y}\left(\Omega\right)||^{2}=\mathbf{Y}(\Omega)\hat{R}_{aa }\mathbf{Y}^{H}(\Omega), \tag{40}\]  
where \(\hat{R}_{aa}\) is the estimated covariance matrix of the array defined in Equation (34).
# A GIS-Based Hot and Cold Spots Detection Method by Extracting Emotions from Social Streams
## 1 Introduction
If all the documents corresponding to a subzone's time frames are classified with pleasant ER linguistic labels over a prefixed threshold, this subzone is labeled as a hot spot. Likewise, if those documents are classified with unpleasant ER linguistic labels over that threshold, the subzone is labeled as a cold spot. Finally, a map of the hot and cold spots of the study area is built. Figure 2 shows a schematized architecture of FESC. Figure 1: Example of Pleasant/Unpleasant ER fuzzy partition  
Inputs for the framework are the study area divided into subzones, the data stream with the posts entered by citizens in the analyzed period, the Dictionary of the Emotional Word containing the dictionary of terms relating to the single emotional categories, and the ER fuzzy partition. The output of the process is the hot and cold spots map which highlights the subzones classified as hot and cold spots, corresponding to the zones with unpleasant/pleasant emotions relevance classified above the threshold for all time frames. A detailed description of the components of the framework is shown in Section 3. The main benefits of our method can be summarized as follows:  
* Unlike other hot and cold spot detection methods, which take into consideration only localized events, FESC recognizes hot and cold spots by analyzing the moods of citizens extracted from social networks; this makes it possible to use hidden knowledge to evaluate, on the basis of citizens' emotions, which subzones of the study area are more critical and which, on the other hand, are less affected by the phenomenon being investigated;
* FESC is computationally faster than cluster-based hot and cold spot detection methods; in fact, FESC does not need to detect the exact geometric shape of a spot but evaluates the prevailing emotional states of citizens to determine which subzones can be classified as hot or cold spots; this mode significantly reduces the CPU times of the algorithm;
* to overcome the critical issues of the cluster-based approaches adopted for the classification of documents based on the prevailing emotional category, which needs to fix the number of clusters equal to the number of emotional categories and to establish a one-to-one correspondence between clusters and emotional categories, FESC adopts  
Figure 2: Schema of the proposed GIS-based framework. an approach based on the construction of a fuzzy partition of the relevance of pleasant and unpleasant emotional categories. The remaining portions of the essay are structured as follows: The introduction to formal concepts and ideas on hot (or cold) spots are provided in Section 2, along with a synthetic description of the emotion-based document categorization approach [6]. The logical overview described in Section 3 serves as an introduction to the suggested framework. The findings of the tests and case studies that demonstrate the viability of the suggested strategy are presented in Section 4. We discuss the paper's closing findings in Section 5.
## 2 Materials and Methods
### Proposed Methodology
#### 2.1.5 Model Evaluation  
In order to evaluate the performance of the fitted MLP, spatial kriging and spatiotemporal kriging models, mean absolute error (\(MAE\)), root mean squared error (\(RMSE\)), and coefficient of determination (\(R^{2}\)) metrics were used.  
The \(MAE\), \(RMSE\), and \(R^{2}\) are calculated as below.  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{ai}-\hat{y}_{fi}| \tag{16}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{ai}-\hat{y}_{fi})^{2}} \tag{17}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{ai}-\hat{y}_{fi})^{2}}{\sum_{i=1}^{n}(y_{ai}- \overline{y}_{ai})^{2}} \tag{18}\]  
where \(\hat{y}_{fi}\) is the predicted value, \(y_{ai}\) is the actual value, and \(\overline{y}_{ai}\) is the mean value of \(y_{ai}\). The smaller the values of MAE and RMSE and larger the \(R^{2}\) value, the better the forecasting is.  
The hybrid model, which integrates MLP and spatial kriging, was implemented in python and the spatiotemporal kriging model was fitted using R software (version 4.3) and the related packages. In the spatiotemporal model, the data were represented as Space Time Full Data Frame (STFDF) object before fitting the variogram since data considered in this study were with complete space time grid with s substations and t time intervals of their observations.
# Tethered Balloon Cluster Deployments and Optimization for Emergency Communication Networks
## 3 System Model and Problem Formulation
### Objective Function  
The objectives of the deployment are to maximize the target coverage \(f_{1}\), defined as the ratio of the number of covered users to the total number of users, and the system energy efficiency \(f_{2}\), defined as the sum of the reception rates of all covered users divided by the sum of the transmit power of all balloons. Thus, the fitness function for the balloon deployment problem can be formulated as Equation (18). \(\mathbb{X}\) represents an assignable deployment scenario that satisfies the constraints (C1-C12).  
In Equation (18), it can be seen that as the balloon deployment height increases, the coverage increases, while the LoS probability and path loss increase and the user reception rate decreases accordingly when the balloon transmit power is determined, which can lead to a reduction in energy efficiency, and thus the two objective functions are in conflict with each other. The problem belongs to mixed integer nonlinear programming; improving one of them must lead to the degradation of the other. It is an NP-hard problem, which is difficult to solve by using traditional methods. Therefore, it is considered to be solved by using MOPSO in Section 4.  
\[F(\mathbb{X})=\underset{\{P_{n}\},\mathcal{N},\mathcal{M},\{P_{n}\}}{\max}[f_ {1}(\mathbb{X}),f_{2}(\mathbb{X})] \tag{18}\]* \[C1: \mathcal{P}_{n}\in\mathcal{P},\quad\forall n\in\mathcal{N},\] (18a) \[C2: \mathcal{D}_{m}\in\mathcal{D},\quad\forall m\in\mathcal{M},\] (18b) \[C3: \mathcal{H}_{\min}\leq h_{n}\leq h_{\max},\] (18c) \[C4: \mathcal{D}_{n,i}\geq d_{\min},\quad\forall n,i\in\mathcal{N},n \
eq i,\] (18d) \[C5: \mathcal{P}_{\min}\leq P_{n}\leq P_{\max},\] (18e) \[C6: \sum_{n\in\mathcal{N}}M_{n}\leq M,\] (18f) \[C7: \mathcal{M}_{n}\cap\mathcal{M}_{i}=\varnothing,\quad\forall n,i \in\mathcal{N},n\
eq i,\] (18g) \[C8: \bigcup_{b\in\mathcal{B}}\mathcal{N}_{b}=\mathcal{N},\] (18h) \[C9: \mathcal{N}_{b}\bigcap\mathcal{N}_{e}=\varnothing,\quad\forall b,e\in\mathcal{B},b\
eq e,\] (18i) \[C10: \sum_{n\in\mathcal{N}}B_{n}\leq B,\] (18j) \[C11: \mathcal{C}_{n,m}\in\left\{0,1\right\},\quad\forall n\in \mathcal{N},m\in\mathcal{M},\] (18k) \[C12: \mathcal{D}_{n,m}\geq C_{n,m}\cdot\Lambda,\quad\forall n\in \mathcal{N},m\in\mathcal{M}.\] (18l)
## 2 Provisioning MB Optical Connections
### Lightpath Provisioning  
Armed with the ability of estimating the QoT for different channel assignments in an efficient way, as well as with the possibility to improve the QoT of the signal by mitigating NLI noise, a DT-assisted MB-RSA algorithm that selects the path and the channel during lightpath provisioning can be devised. A simple flowchart is presented in Figure 4.  
The procedure starts after receiving a request specifying the source and destination nodes and the required capacity. An MF and SR are selected to provide the required capacity, which determines the maximum pre-FEC BER that needs to be ensured. At this point, the procedure can be extended to consider multiple combinations of MF/SR, as well as multiple parallel lightpaths that provide the required capacity. Nonetheless, for the sake of simplicity, let us assume that just one lightpath with the best combination of MF/SR is considered, so \(k\) shortest paths between source and destination nodes with available channels are computed.  
Figure 3: Example of \(\Phi^{i}_{out}\) feature and regular (**a**) (reproduced from [20]) and optimized (**b**) detection area for CP \(i\) = [20].  
The procedure now iterates until a feasible solution is found, or otherwise the request is blocked. A set of available channels in the given route is computed. Then, the algorithm (_i_) determines whether there are available channels that can provide the required QoT given the characteristics of the route, specifically the total number of spans and their length; (_ii_) if some channel can meet the required QoT, then it finds the optimal channel assignment; (_iii_) otherwise, it selects the best channel assignment among the available channels, computes the optimal areas to mitigate the NLI noise as much as possible, and determines whether that solution would provide the required QoT.  
The next section elaborates on the sketched OCATA-MB methodology and proposes algorithmic approaches.
## 3 Results
### Ablation Study  
This section compares the E-EFNet performance of different lightweight models on the FGG and YR datasets. The compared models include ResNet50, MobileNet, Inception, NASNet, EfficientNet, and E-EFNet, where each model's results are reported in Table 1 over both datasets. The results shown in Table 1 show that EfficientNet surpassed other methods by achieving higher accuracy and lower false alarms. However, the performance of E-EFNet was significantly better than all the other models, achieving an FPR of 0, FNR of 0.22, and accuracy of 99.91% on FGG, while achieving 98.74% precision, 98.70% recall, 98.40% accuracy, and 98.74% F1-score on the YR dataset. The E-EFNet achieved superior performance compared to other state-of-the-art deep learning models on FGG and YR datasets. E-EFNet achieved high accuracy, precision, recall, and F1-score with a low false-positive and false-negative rate, indicating its effectiveness for effective fire detection. The ablation study also demonstrated the impact of the proposed modifications to the EfficientNet architecture, resulting in improved performance. Therefore, E-EFNet can be considered a promising model for fire detection. The effectiveness of the proposed E-EFNet model is further enhanced by accurately classifying challenging samples, such as fire-like objects and distant fires, which is demonstrated through the visualized results shown in Figures 3 and 4 for the FGG and YR datasets, respectively. These figures provide clear evidence that the model is highly capable of successfully categorizing difficult instances, which is a significant achievement in fire detection.  
\begin{table}
\begin{tabular}{c c c c|c c c c} \hline \hline \multicolumn{3}{c|}{**FGG Dataset**} & \multicolumn{5}{c}{**YR Dataset**} \\ \hline
**Method** & **FNR** & **Accuracy** & **FPR** & **Precision** & **Recall** & **Accuracy** & **F1-Score** \\ \hline ResNet50 & 0.68 & 97.85 & 0.81 & 84.85 & 80.21 & 83.92 & 81.74 \\ MobileNet & 2.18 & 91.87 & 1.85 & 92.31 & 85.71 & 88.46 & 88.89 \\ Inception & 0.54 & 97.45 & 0.83 & 90.91 & 94.34 & 92.59 & 92.84 \\ NASNet & 1.03 & 96.82 & 0.91 & 90.45 & 89.99 & 90.91 & 90.91 \\ EfficientNet & 0.45 & 98.21 & 0.17 & 94.34 & 96.15 & 95.24 & 95.23 \\ E-EFNet & 0.22 & 99.91 & 0 & 98.74 & 98.70 & 98.40 & 98.74 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of E-EFNet with state-of-the-art model over FGG and YR dataset.