esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 3 Results
### Model Classification Results and Performance Comparison
#### 3.2.3 Five-Element Classification  
The target variable values for the five classifications are \"\(-\)2, \(-\)1, 0, 1, 2\", with all valid sample data utilized for model construction, incorporating SMOTE and Bayesian optimization. After evaluating these models, we obtained the classification accuracy, precision, recall, and F1 scores for each category, as presented in Table 6 and Figure 11.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Emotional Category** & **Classifier** & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline \multirow{4}{*}{Valence} & XGBoost & 64.0\% & 0.640 & 0.643 & 0.634 \\  & RF & 47.8\% & 0.478 & 0.487 & 0.476 \\  & DT & 26.5\% & 0.265 & 0.301 & 0.268 \\  & LR-GD & 26.5\% & 0.265 & 0.335 & 0.278 \\ \hline \multirow{4}{*}{Arousal} & XGBoost & 59.6\% & 0.596 & 0.598 & 0.590 \\  & RF & 44.1\% & 0.441 & 0.475 & 0.452 \\ \cline{1-1}  & DT & 30.9\% & 0.309 & 0.329 & 0.313 \\ \cline{1-1}  & LR-GD & 24.3\% & 0.243 & 0.349 & 0.265 \\ \hline \end{tabular}
\end{table}
Table 6: Performance comparison of valence and arousal five-element classification models based on different classifiers.  
Figure 11: Comparison of confusion matrix between valence and arousal five-element classification model. The classifiers used are as follows: **(a)** XGBoost; **(b)** RF; **(c)** DT; **(d)** LR-GD.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Emotional Category** & **Classifier** & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline \multirow{4}{*}{Valence} & XGBoost & 77.2\% & 0.772 & 0.765 & 0.766 \\  & RF & 64.0\% & 0.640 & 0.646 & 0.642 \\  & DT & 46.3\% & 0.463 & 0.549 & 0.490 \\  & LR-GD & 44.1\% & 0.441 & 0.574 & 0.468 \\ \hline \multirow{4}{*}{Arousal} & XGBoost & 74.3\% & 0.743 & 0.740 & 0.738 \\  & RF & 58.1\% & 0.581 & 0.578 & 0.579 \\ \cline{1-1}  & DT & 56.6\% & 0.566 & 0.604 & 0.572 \\ \cline{1-1}  & LR-GD & 42.7\% & 0.427 & 0.485 & 0.445 \\ \hline \end{tabular}
\end{table}
Table 5: Performance comparison of ternary classification models based on different classifiers for valence and arousal.  
The results of the five classifications indicate that the XGBoost model exhibits the best classification performance in emotional pleasure, although its accuracy is only 64.0%. In terms of emotional arousal, the XGBoost model demonstrates superior classification performance; however, the accuracy of all four models remains below 60%. Therefore, in practical terms, none of these four models can adequately address the emotional quality assessment of rural visual landscapes in five-element classification.
# Facial Expression Recognition Based on Discrete Separable Shearlet Transform and Feature Selection
## 3 The Proposed Algorithm
### Dimension Reduction Based on Linear Discriminant Analysis  
Dimension reduction can not only reduce data dimensions, but also extract effective information and discard useless information. Some well-known methods used for dimension reduction of a feature space are the principal component analysis (PCA) [20] and the linear discriminant analysis (LDA) [21]. In this study, LDA is used for data dimension reduction.  
The within-class scatter matrix \(\mathbf{U}_{\text{A}}\) and between-class scatter matrix \(\mathbf{U}_{\text{B}}\) are defined as follows:  
\[\mathbf{U}_{\text{A}}=\sum_{j=1}^{C}V_{j}\big{(}\overline{m}_{j}-\overline{ \overline{m}}\big{)}\big{(}\overline{m}_{j}-\overline{\overline{m}}\big{)}^{T}, \tag{19}\]\[\mathbf{U}_{\text{B}}=\sum_{j=1}^{C}\sum_{m_{k}\in c_{j}}\left(m_{k}-\overline{m}_ {j}\right)\left(m_{k}-\overline{m}_{j}\right)^{T}, \tag{20}\]  
where \(V_{j}(j=1,2,\cdots,C)\) is the number of vectors in the \(j\)th class \(c_{j}\), \(C\) is the number of classes (here, \(C\) represents 6 facial expressions), \(\overline{m}\) is the mean of all the vectors, \(\overline{m}_{j}\) is the mean of the class \(c_{j}\), and \(m_{k}\) is the vector of a specific class. Therefore, the optimal discrimination projection matrix of LDA can be written as:  
\[\mathbf{D}_{\text{opt}}=\arg\max_{\text{D}}\frac{\left|\mathbf{D}^{T}\mathbf{ U}_{\text{B}}\mathbf{D}\right|}{\left|\mathbf{D}^{T}\mathbf{U}_{\text{A}} \mathbf{D}\right|}=[d_{1},d_{2},\cdots,d_{o}]. \tag{21}\]  
The size of \(\mathbf{D}_{\text{opt}}\) is \(o\times r,o\leq C-1\), and \(r\) is the number of elements in a vector. When the within-class scatter matrix \(\mathbf{U}_{\text{A}}\) is nonsingular, according to Lagrange multiplier method, the column vector of the optimal projection matrix \(\mathbf{D}_{\text{opt}}\) satisfies the characteristic equation \(\mathbf{U}_{B}d_{i}=\lambda\mathbf{U}_{A}d_{i},i=1,2,\cdots,o\); put the characteristic equation into Equation (21) and we get \(\mathbf{D}_{\text{opt}}=\arg\max\lvert\lambda\rvert\). Therefore, we only need to reserve the eigenvectors that correspond to \(o\) eigenvalues with larger absolute values, and the low-dimensional space composed of \(o\) eigenvectors is the low-dimensional space we are looking for. Because the rank of each \(\left(m_{k}-\overline{m}_{j}\right)\) in \(\mathbf{U}_{B}\) is 1, the maximum rank of \(\mathbf{U}_{B}\) is \(C\). However, if we know the first \(C-1\)\(\overline{m}_{j}\), the last \(\overline{m}_{C}\) can be linearly represented by the first \(C-1\)\(\overline{m}_{j}\). Thus, the maximum rank of \(\mathbf{U}_{B}\) is \(C-1\), so there's at most \(C-1\) eigenvectors, thus \(o\leq C-1\). Thus, LDA maximizes the total scattering of the data while minimizing the within scattering of the classes.
## 6 Experimental Evaluation
### The Distributed Mapping Service
#### 6.2.1 Energy Consumption  
Energy consumption is classified as follows: energy needed to distribute a query, energy needed to transmit readings, and energy needed to receive readings. Figure 9 plots mapping traffic against the number of nodes in the network. Overall, the distributed mapping service saves significant amount of energy compared to the centralised mapping service. Evidently, delivering all readings back to the sink incurs heavy traffic, which depletes the energy of sensor nodes. The traffic overhead incurred by centralised mapping grows rapidly and scales proportional to the number of nodes in the network, \(O(n)\). Suppression-based mapping reduces the traffic exchange through spatial and temporal suppression techniques. Although the utilisation of in-network suppression techniques implements mapping with reduced traffic cost, it does not reduce the scale of the generated traffic. This is because of the additional cost introduced by the broadcast of the hash functions created by the sink to every data sources to enable multi-hop suppression. When data exhibits low spatial correlation, the suppression technique  
Figure 8: A mineral map derived from AVIRIS data.  
Figure 9: The cost of map generation against node density.  
becomes less effective resulting in higher traffic returned to the sink. Moreover, energy for overhearing neighbours turns out to be the dominant factor in the overall energy consumption. In the distributed scheme, the global map is built from locally computed partial maps without relying on communications with the centralised sink. To avoid excessive communication, partial maps are continuously updated locally avoiding complete re-computation of the global map. Unlike suppression-based mapping, the distributed mapping service scales fairly well with respect to the network size and achieves these energy saving without requiring any extra set-up messaging (e.g., hash functions). Based on data in Figure 9, the distributed mapping scheme reduces the mapping traffic by up to \(70\%\) and \(30\%\) compared to the centralised mapping and the suppression-based mapping schemes respectively.
## 4 IoT Adoption Benefits
### Direct Benefits
#### 4.1.4 Assets Management Efficiency  
Effective resource utilization requires communication and information exchange between assets and the organization [1; 3; 24; 52]. The connected sensors embedded in all equipment and tools allow the direct tracking of assets directly [39; 42; 79], whether localized or geographically distributed [76], as well as the acquisition of more precise information regarding the equipment's health [63], and improve the speed and accuracy of the equipment [36], through a set of algorithms for investigating the equipment's performance status over time [34]. Such a process provides visibility into asset status [63], allows remote diagnostics, easily locates issues [45], and improves asset utilization [46]. IoT data can also help companies track products condition \"from the floor to store\" and beyond [41], including the time spent in cargo and the time it took to fly off the shelf, and even the temperature at which it was stored [43]. Furthermore, IoT allows for more accessible information sharing for live inventory levels and captures internal and external movements to help manage stock [43, 59]. This process leads to inventory and raw material reductions and better purchasing decisions [11].
## 1 Introduction
For that, we used a database, previously collected by members of our research team, containing electrocardiogram (ECG) and facial electromyography (EMG) signals of adults with ASD and TD, while they were being exposed to positively, negatively and neutrally valenced CO and BO [30]. The ECG is the electrical response of the heart muscles to its movement. It presents a cyclic pattern with an almost standard representation which is composed of three types of waves: the P wave, responsible for atrial depolarization; the QRS complex, responsible for ventricular depolarization; and the T wave, responsible for ventricular repolarization [31]. The shape of the ECG can reveal not only heart problems but also variations in the heartbeat induced by distinct emotional states [32]. There is substantial evidence that depending on how an emotional stimulus is presented, cardiovascular activity can differ significantly based on both the valence and intensity of the stimulus [31]. EMG, on the other hand, is a technique for evaluating and recording the electrical activity generated by muscle movement [33]. Here, EMG was used to monitor facial expressions based on the activation of facial muscles associated with different emotional states [34], specifically the zygomaticus major muscle, associated with happiness, and the frontalis and corrugator supercilii muscles, associated with fear [35]. To explore physiological responses among ASD and TD during olfactory stimulation, several features were extracted from ECG and facial EMG signals, and their potential to differentiate between both groups was analysed. Moreover, although the main focus of our work is concerned with the characterisation of physiological responses to olfactory stimuli, we also attained a first approach to predict ADS based on those. Thus, two main research questions were raised: (1) Are there significant physiological differences between the the ASD and TD groups? (2) Is it possible to predict adults with ASD based on ECG and facial EMG signals collected during olfactory stimulation?
## 4 Analysis of Classification Results
### Influence of Different Stimulators on WT-DBN  
To improve the accuracy of training and testing and reduce the model loss function, this study explores the impact of the sigmoid and tanh activation functions on the WT-DBN model. Half of the dataset is used to train model, while rest of the dataset is used for validating the model's performance. The training accuracy and loss of the WT-DBN classification model based on the two activation functions during the iteration process are shown in Figure 9. The precision and loss metrics for the training sample, along with the forecasting accuracy, are displayed in Table 1.  
Based on Figure 9, the training precision and loss arc of the WT-DBN sorting method founded on two types of activation functions can be observed during 500 iterations. The WT-DBN classification method founded on the sigmoid activation function exhibits higher classification accuracy and lower loss compared to the tanh activation function; although there is some minor random fluctuation during training, the overall classification accuracy exceeds 90%. As illustrated in Table 1, the average loss value for sig  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Sigmoid** & **Tanh** \\ \hline Average Loss & 0.1671 & 0.2847 \\ Training Average Precision & 98.63\% & 78.01\% \\ Training Precision & 100\% & 78\% \\ Classification Precision & 97.17\% & 74.01\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Assessment of training and classification founded on two activation functions.  
Figure 9: Training precision and loss.  
during the training process, the average accuracy for sigmoid is greater than tanh, and the classification accuracy for sigmoid is also greater than tanh. The WT-DBN classification model based on the sigmoid activation function demonstrates better learning performance and training effects, attributed to the sigmoid function's output ranging from 0 to 1, rendering it appropriate for binary classification problems. While the tanh function's output range is between \(-1\) and \(1\), its derivative approaches zero when the input is close to positive or negative limits, potentially resulting in the disappearing of gradient value. Taking the convergence of loss arc and training accuracy into consideration, this research selects sigmoid as the activator for WT-DBN.
## 3 Algorithm
### Denoising Algorithm  
Because the pixel structure and readout circuit of DVS are very different from traditional image sensors (CCD, CMOS), the output data form is completely different. The data of the frame image can be expressed mathematically as a matrix with the same size as the sensor pixels. The DVS event stream is asynchronous output, that is, when the pixels of the sensor are activated, the events generated by these pixels are output, and if they are not activated, they are not output. The data generated in this way is sparse and cannot form a matrix like a frame image.  
Figure 2: Schematic of the spatiotemporal neighborhood. The red-point indicates a newly arrived event at \(t1\), the surrounding yellow part indicates the spatial neighborhood of the new-arrival event, and delta, t, which is the time period before \(t1\) is the time neighborhood of the event. The time neighborhood and spatial neighborhood form a spatiotemporal neighborhood.  
In DVS, events are delivered in pulses, so, events can be expressed as pulse functions mathematically:  
\[e_{0}(x_{0},y_{0},t_{0})\Rightarrow\delta(x-x_{0},y-y_{0},t-t_{0}). \tag{7}\]  
The DVS event steam can be expressed as the accumulation of event pulses, that is:  
\[\text{ES}(x,y,t)=\sum_{i=1}^{\text{N}}\delta(x-x_{i},y-y_{i},t-t_{i}), \tag{8}\]  
where N is the number of events contained in the event stream and \(\delta(x,y,t)\) is a pulse function.  
The noise model Equation (1) shows that the more BAs generated by a non-hot pixel, the lower the probability within a fixed time interval. However, real events are generated by the movement of objects or the change in light intensity. The activated pixels are generally adjacent. According to the handshake circuit structure, it can be known that the event timestamps in the same row are the same, and the time difference between events in adjacent rows is in nanoseconds. Therefore, in a certain space-time, the number of BAs is less than a certain threshold with a high probability, and the number of events generated by the real target will be greater than this threshold, which can be expressed as:  
\[\underset{x_{i},y_{i},t_{i}\in\Gamma_{\Delta t}^{L}}{\iint\!\!\!\!\!\!\!\!
# A Strain Hardening and Softening Constitutive Model for Hard Brittle Rocks
## 3 Mechanical Behaviour of Brittle Hard Rock
### Normalised Hardening and Softening Factors  
To obtain the stress value with the same yield state under each confining pressure and then discuss the relationship between the strength parameters and the axial plastic strain, the normalised hardening factor and softening factor are proposed to characterise a certain yield state corresponding to a certain stress value in the strain hardening and softening stages under different confining pressures. The initial yield stress, peak stress and residual stress are regarded as the stress values corresponding to the initial yield state, peak yield state and residual yield state, respectively. Then, a certain stress value of one yield point under each confining pressure can also be regarded as the stress value corresponding to a certain yield state. According to reference [11; 39], the normalised hardening factor and softening factor are defined to describe the yield state.  
The hardening factor \(D_{h}\) is defined as the ratio of the difference between certain axial stress and the initial yield stress to the difference between the peak stress and the initial yield stress in the strain hardening process, i.e.,  
\[D_{h}=\frac{\sigma_{h}-\sigma_{s}}{\sigma_{p}-\sigma_{s}} \tag{14}\]  
where \(\sigma_{p}\) is the peak stress and \(\sigma_{h}\) is a certain axial stress of the strain hardening stage, \(\sigma_{s}<\sigma_{h}<\sigma_{p}\).  
The softening factor \(D_{ss}\) is defined as the ratio of the difference between the peak stress and a certain axial stress and the difference between the peak stress and residual stress in the strain softening process, i.e.,  
\[D_{ss}=\frac{\sigma_{p}-\sigma_{ss}}{\sigma_{p}-\sigma_{r}} \tag{15}\]  
where \(\sigma_{r}\) is the residual stress and \(\sigma_{ss}\) is a certain axial stress of the strain softening stage, \(\sigma_{r}<\sigma_{ss}<\sigma_{p}\).  
When \(D_{h}=0\), the rock reaches the initial yield stress \(\sigma_{s}\), plastic yielding occurs, and subsequently, the rock enters the strain hardening stage. The hardening factor increases with increasing axial stress. When the rock reaches the peak stress, the strain hardening stage ends and then enters the strain softening stage. The peak stress point is both the endpoint of the strain hardening stage and the starting point of the strain softening stage, that is, \(D_{h}=1\) and \(D_{ss}=0\). As the post-peak stress gradually decreases, the softening factor gradually increases until it reaches 1, that is, \(D_{ss}=1\). This indicates the end of the strain softening stage, and then the rock enters the residual stress stage, as shown in Figure 2. If the initial yield, peak and residual stress under each confining pressure are determined, the stress value corresponding to any hardening and softening factor under each confining pressure can be calculated accurately by Equations (14) and (15).  
Figure 2: Evolution schematic of normalised strain hardening and softening factors.
## Open Issues in DTN Multicasting  
The DTN paradigm successfully addresses some of the problems related to intermittent connectivity in the opportunistic network. However, there are indeed some prominent open issues worthy of researchers' attention. The redefinition of multicast routing in DTN also gives rise to new issues in different aspects, such as security, node behavior, and the performance of DTN. This section intends to give some insight into open issues in DTN and to further discuss them in terms of multicast routing.
## 3 Integrated Quantum Photonics Systems
At a distance of 151.5 km of standard single-mode fiber, a secure key rate of 1.3 k/s was obtained, as well as a very low quantum-bit error rate, 0.9 %, at a distance of 202 km. The device consisted of an integrated silicon photonics chip, comprising a photonic integrated circuit of dimension \(4.50\times 1.10\) mm\({}^{2}\) with an adjacent electronic-driver integrated circuit of \(4.50\times 0.75\) mm\({}^{2}\). The integrated circuits were then bounded and integrated into a small printed circuit board combined with a second, larger printed circuit board connected to all the electrical signals needed and to a computer-controlled FPGA (field-programmable gate array).
## 3 Building Load Type
### Classify Based on Energy Type
#### 3.3.2 Thermal Load  
Thermal load refers to the thermal energy that needs to be added or removed from a space or system in order to achieve a specific temperature condition. It can encompass both the need for heating load and cooling load. Accurate thermal load prediction plays a pivotal role in the optimal planning and efficient operation of energy systems [117]. It not only effectively reduces energy waste and improves the comfort of occupants but also significantly extends the service life of building equipment [118]. Accurate prediction of thermal load serves as a crucial foundation for efficient energy planning. Given the significant and complex time delay effects between building heat load and various influencing factors, it is imperative to ensure the synchronization between characteristic variables and load time variations during the prediction process. It is worth noting that in different buildings, the data acquisition and control intervals of the control systems often vary [119]. Therefore, accurately predicting load demand and energy consumption is essential for achieving precise matching between the supply and demand sides of heating, ventilation, and air conditioning systems [120]. As shown in Figure 11, a thermal load forecasting method based on real-time data from smart meters is proposed, utilizing an online correction mechanism to ensure the contextual adaptability of the thermal load prediction model.  
Data-driven modeling for heat load prediction is an important strategy in response to energy conservation, emission reduction, and environmental protection needs [121]. By fully integrating data science and information technology, it can significantly enhance the operational efficiency of urban heating systems. Currently, there are various modelsavailable for heat load prediction, including but not limited to statistical regression models [122], SVR [123], neural network models [51], and models based on decision trees [124]. These models provide powerful technical support for accurate prediction and efficient management of building heat loads.