esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid InceptionV3-SVM-Based Approach for Human Posture Detection in Health Monitoring Systems
## 3 Materials and Methods
### Model Selection  
Through the classification process, we selected the right deep convolutional neural network and machine learning methods from the available choices. The performance of different DCNN models was examined during the model selection process and the DCNN technique that performed the best was chosen. On a separate test set, the effectiveness of the chosen DCNN model (model evaluation) was examined. The weights of the target model were initialized with a transfer data set to perform transfer learning [41]. Consequently, the target model had previously received object recognition training. However, these objects were not those of the intended task (bending, lying, sitting, and standing human postures). As a result, our training set of annotated human images was used to fine-tune the baseline techniques.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Posture Class** & **Number of Instances** & **Training** & **Validation** & **Testing** \\ \hline Bending & 1200 & 768 & 192 & 240 \\ \hline Lying & 1200 & 768 & 192 & 240 \\ \hline Sitting & 1200 & 768 & 192 & 240 \\ \hline Standing & 1200 & 768 & 192 & 240 \\ \hline Total & 4800 & 3072 & 768 & 960 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution over the posture of the Data Set.  
Figure 1: Proposed system block diagram.  
SVM has been tested in a variety of computer vision applications, including image identification and handwritten digit identification, with positive outcomes (support vector machines for remote sensing image classification). Since SVM can manage both semi-structured and structured data, as well as advanced functions if the right kernel function can be generated, it was also applied. The adoption of generalization in SVM reduces the likelihood of overfitting and allows scaling with high-dimensional data. It is not trapped in a local optimum [42].  
Deep convolutional neural networks serve as the foundation networks for deriving abstract feature maps from input data. Baseline networks are common architectural building blocks that can be used with different data sets for image categorization [41; 43]. LASSO FS was selected due to its automatic selection of features and its ability to reduce overfitting in models [44].  
In this study, we evaluate the base networks InceptionV3 [45], DenseNet121 [46], and ResNet50 [47].
# Density Self-Adaptive Hybrid Clustering Routing Protocol for Wireless Sensor Networks
## 1 Introduction  
Wireless sensor networks (WSNs) are composed of a large number of low-cost micro sensor nodes with processing, storing, and communicating capabilities [1]. With the development of technology, WSNs have been applied to many fields, such as environmental monitoring [2; 3], object tracking [4], healthcare system [5], and some others.  
In wireless sensor networks, the main task of the routing protocol is to establish the route from source node to sink node, and transmit data reliably. In routing protocols, the efficiency of energy, the reliability of data transmission [6], authentication [7], and the low latency of the network construction [8], are all research hotspots. Among them, the problem of energy consumption is of major concern. Clustering is a basic method to design the energy efficient, robust, and scalable sensor networks [9]. It uses the rotation of cluster heads (CHs) to balance the energy consumption to all nodes, so as to improve the energy efficiency. In terms of the CH generation mode, there are distributed and centralized strategies. In the distributed way, there is no control center in the network, and the node itself decides whether to become a CH or not. This algorithm has good scalability, but it can easily lead to uneven distribution of CHs, so the load balance of the cluster is poor. When the centralized strategy is adopted, all of the nodes transmit the energy and location information to the base station (BS). Then, the BS selects the suitable CHs. As the selection is based on global information, the distribution of CHsis more reasonable and the energy load is relatively balanced. However, the communication with the BS each round wastes a lot of energy and increases the network delay.  
Thus, our work is to study the problem of clustering in hierarchical routing protocols. In order to evenly distribute the energy to each node and prolong the network lifetime, we propose a new algorithm to select the CHs. In our proposed algorithm, the distributed and centralized methods are used in a mixed way to select CHs. In this way, it combines the advantages of both in order to save energy and ensure the rationality of CH distribution. In addition, we propose a scheme for the cluster density adjusting to achieve a balanced energy load.  
The remainder of this paper is organized as follows: Section 2 briefly reviews some clustering protocols. The DAHC protocol is described in Section 3. Section 4 evaluates the performance through simulation results, and finally Section 5 will draw our conclusions.
## 1 Introduction
The aim of our study is to exploit the vision transformer's ability to learn more discriminative and informative features, which is the most important factor considered for the fine-grained recognition problem, as well as to utilize the inference speed of the one-stage object detector simultaneously. Our method integrates transformer encoder blocks with CSP-Darknet53 [20], which results in expanding the receptive field to forecast various scale features through considering the object's local and global information. We swapped several CSP bottleneck blocks with transformer encoder blocks, and after comparing the bottleneck block with the transformer encoder block, we anticipated that the transformer encoder block accumulates both global and local contextual details. Our model learned more discriminative and informative features, leading to improved performance in downstream tasks, and also enhanced feature representations, which are beneficial for fine-grained image classification, where capturing detailed visual patterns is crucial. We utilized some recent computer vision methods, such as the transformer encoder block, multi-stage feature fusion, and various training approaches. To summarize, we have contributed some important and notable improvements to fine-grained image recognition:  
* To the best of our understanding, our study is the first to demonstrate the capability of transformer encoder blocks using a one-stage object detector with respect to the fine-grained image recognition task. * We proposed a single-stage fine-grained model to improve efficiency and minimize the level of complexity, compared to existing two-stage models for the fine-grained recognition problem. * We introduced the transformer encoder blocks in the backbone of Yolov5 to capture detailed visual patterns and feature representations. * We replaced the Yolov5 detection heads with three transformer heads at the output to detect discriminative fused features extracted using the transformer encoder blocks. To validate the algorithm's performance, comparison studies were conducted, and the empirical results indicate that the model is capable of recognizing sub-classes with high precision and accuracy. The rest of the article is structured as follows: Section 2 discusses related work in detail, providing an overview of the existing two-stage and one-stage methods to overcome the fine-grained recognition problem. Section 3 first introduces the existing Yolov5 backbone structure, subsequently modifies the backbone using transformer encoder blocks, and finally compares the proposed model to the existing model. Section 4 summarizes the experimental setup and presents the training and validation results from a fine-grained dataset using evaluation metrics, demonstrates the proposed model's results on the test set, and finally compare the results with the state-of-the-art fine-grained recognition methods. Discussion of these results is covered in Section 5, and lastly, Section 6 concludes the proposed work.
# Incorporating New Technologies in EEIO Models
###### Abstract  
We propose a methodology to add new technologies into Environmentally Extended Input-Output (EEIO) models based on a Supply and Use framework. The methodology provides for adding new industries (new technologies) and a new commodity under the assumption that the new commodity will partially substitute for a functionally-similar existing commodity of the baseline economy. The level of substitution is controlled by a percentage (%) as a variable of the model. In the Use table, a percentage of the current use of the existing commodity is transferred to the new commodity. The Supply or Make table is modified assuming that the new industries are the only ones producing the new commodity. We illustrate the method for the USEEIO model, for the addition of second generation biofuels, including napthna, jet fuel and diesel fuel. The new industries' inputs, outputs and value-added components needed to produce the new commodity are drawn from process-based life cycle inventories (LCIs). Process-based LCI inputs and outputs per physical functional unit are transformed to prices and assigned to commodities and environmental flow categories for the EEIO model. This methodology is designed to evaluate the environmental impacts of substituting products in the current US economy with bio-versions, produced by new technologies, that are intended to reduce negative environmental impacts. However, it can be applied for any new commodity for which the substitution assumption is reasonable.  
eurmentally extended input-output (EEIO); life cycle assessment (LCA); new technologies; biofuels +
Footnote †: journal: Applied sciences
## 1 Introduction
Since a strict selective detection targeting only a single compound is in most cases not possible [15], QCM sensors are often applied for versatile detection of substances belonging to specific groups in a total analysis. Although there are no QCM sensing applications for LCFAs and to AD processes, similar compounds, such as volatile fatty acid (VFA) [16] and olive oil [17; 18], have been successfully detected using QCM in the gas phase. QCM sensors have mainly been used for detecting trace compounds in air. In recent decades, advances in QCM methodology have enabled liquid applications and some researchers have used QCM measurements to analyze wastewater samples [19; 20]. [PERSON] and [PERSON] [19] showed the selective detection of Cu\({}^{2+}\) among various metal ions in real wastewater. In addition, our previous studies have demonstrated that QCM can sense the crystallization of lauric acid on the surface of a cooled electrode as a gradual decrease in resonance frequency [21]. These studies indicated that a QCM sensor can quantitatively detect LCFAs in anaerobic digestate. In this study, the performance of a QCM sensor in a selective and quantitative detection of LCFAs is evaluated, and the feasibility for its practical use in monitoring an increasing trend in LCFA accumulation during the operation of an anaerobic digester is investigated. Since the major LCFA species accumulated in digesters treating de-oiled grease trap waste (GTW) is palmitate [7], followed by oleate, palmitate detection is emphasized in the measurements in this study.
# Some Results on Shop Scheduling with S-Precedence Constraints among Job Tasks
## 3 Flow Shop
### Flow Shop with Two Jobs and \(m\) Machines (\(Fm|n=2,s-prec|C_{\max}\))
Hence, we write  
\[F_{A}(v)=\min_{0\leq u\leq v}\{F_{B}(u)+L_{A}(u+1,v)+\delta(F_{B}(u)+S_{A}(u+1,v)-K)\}. \tag{5}\]  
The first terms accounts for the fact that in the previous block the leader is B, while the rightmost term (\(\delta(\cdot)\)) rules out solutions in which the sum of the start time of the last block and the span of the block itself exceeds \(K\). Symmetrically, one has:  
\[F_{B}(v)=\min_{0\leq u\leq v}\{F_{A}(u)+L_{B}(u+1,v)+\delta(F_{A}(u)+S_{B}(u+1,v)-K)\}. \tag{6}\]  
Expressions (5) and (6) are computed for \(v=1,\ldots,m\). If at least one of the values \(F_{A}(m)\) and \(F_{B}(m)\) has a finite value, a schedule of makespan not exceeding \(K\) exists. The values of machine index for which each minimum in (5) and (6) is attained define the blocks of the schedule, which can, therefore, be easily backtracked. Equations (5) and (6) must be initialized, simply letting \(F_{A}(0)=F_{B}(0)=0\). Notice that in general one cannot infer the value of the minimum makespan schedule directly from this procedure. If the minimum in the computation of \(F_{A}(m)\) has been attained for, say, machine \(M_{u}\), it does not imply that \(F_{B}(u)+S_{A}(u+1,m)\) is indeed the minimum makespan. This is because the overall makespan may be due to a previous machine, and the algorithm has no control on this aspect. For instance, in the sample schedule of Figure 7 the makespan is attained on machine \(M_{6}\). However, its actual value has no relevance, so long as it does not exceed \(K\), since it does not affect the values \(F_{A}(v)\) and \(F_{B}(v)\) subsequently computed. Concerning complexity, each computation of (5) and (6) requires \(O(m)\) comparisons. Since the whole procedure is repeated at each step of a binary search over \([LB,UB]\), the following result holds. Problem \(Fm|n=2,s-prec|C_{max}\) can be solved in \(O(m^{2}log(UB-LB))\).
# Design of a Multi-Condition Emotional Speech Synthesizer
## 5 Experiments and Results
### Discussion of Attention Value Sequences  
Attention plays an important role in the synthesis of speech by the Tacotron. To accurately learn and synthesize the Attention module of the Tacotron requires about four hours of speech data for each speaker. This study complemented the lack of data by  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Anger** & **Happiness** & **Neutrality** & **Sadness** \\ \hline \multirow{2}{*}{Real data} & Clarity & 5 \(\pm\) 0 & 4.8 \(\pm\) 0.33 & 5 \(\pm\) 0 & 5 \(\pm\) 0 \\  & Emotion score & 5 \(\pm\) 0 & 5 \(\pm\) 0 & 4.71 \(\pm\) 0.2 & 4.75 \(\pm\) 0.3 \\ \hline \multirow{2}{*}{Proposed} & Clarity & 4.11 \(\pm\) 0.36 & 3.95 \(\pm\) 0.42 & 4.11 \(\pm\) 0.45 & 3.95 \(\pm\) 0.46 \\  & Emotion score & 4.1 \(\pm\) 0.43 & 3.8 \(\pm\) 0.55 & 4.21 \(\pm\) 0.41 & 4.11 \(\pm\) 0.45 \\ \hline \multirow{2}{*}{Emotional ETE Speech synthesizer} & Clarity & 3.75 \(\pm\) 0.44 & 2.78 \(\pm\) 0.51 & 4.01 \(\pm\) 0.59 & 3.81 \(\pm\) 0.52 \\  & Emotion score & 3.88 \(\pm\) 0.45 & 2.8 \(\pm\) 0.53 & 4.5 \(\pm\) 0.38 & 4.08 \(\pm\) 0.49 \\ \hline \multirow{2}{*}{GST-Tacotron} & Clarity & 3.57 \(\pm\) 0.33 & 3.51 \(\pm\) 0.26 & 3.53 \(\pm\) 0.4 & 3.55 \(\pm\) 0.33 \\  & Emotion score & 3.53 \(\pm\) 0.26 & 2.51 \(\pm\) 0.34 & 3.6 \(\pm\) 0.32 & 3.55 \(\pm\) 0.39 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the MOS test.  
extending the embedding vector to multi-conditions, to enable the model to train on the constructed data with the additional TTS database.  
Figure 7 shows the Attention value sequences when the model was trained using only the constructed data without additional training data from TTS. In Figure 7, the Attention value sequences have blurred parts in the middle of the sequence, within the red circle. As a result, the model was confused about which part of the output of the encoder it should focus on, causing broken generated speech. In the case of the GST Tacotron, it was unable to learn by adding data, and it was difficult for the Attention module to learn when the data are insufficient, because it relies on reference audio.  
Figure 8 shows the Attention value sequences of speech generated by the GST Tacotron. The sequences have gaps or blurred parts in the middle of the sequence, within the red circle.  
Figure 9 shows the Attention value sequences of speech generated by the proposed model. We observed clearer and sharper Attention value sequences, unlike previous studies. This facilitated the learning of Attention using the database together with the database for TTS. Since the Attention value sequences were clear and sharp, clearer and cleaner emotional speech could be synthesized. Therefore, the result of the proposed model was superior to the results of previous studies with regards to the MOS test described in Section 5.1.  
Figure 7: Attention value sequences when the model was trained using only the constructed data without additional training with the text-to-speech data.
## 4 The Calculation of Reliability Based on Fuzzy Mathematics
### The Calculation Method of Structural Reliability  
When calculating the reliability of the structure through a direct analytical method, the joint density function of the basic random variable must be obtained and multiple integrations must be calculated, but it is often difficult to achieve a satisfactory result. However, as a method to calculate structural reliability, the Monte Carlo method has certain advantages: the computed results will not be influenced by the function form; any distribution can be considered; and the calculation process can be conducted by a computer program with good stability. Therefore, this method was chosen to calculate the reliability of structures by programming [28].  
The membership function of fuzzy performance functions \(\widetilde{Z}\) was adopted to represent the gradual process from reliability to the failure of the structure. According to fuzzy random probabilistic theory, the fuzzy failure probability of SRHPC frame structure is shown in Equation (20):  
\[\widetilde{P}_{f}=P\Big{(}\widetilde{\Omega}\Big{)}=\int\limits_{-\infty}^{+ \infty}f(z)\mu(z)dz, \tag{20}\]  
where \(\widetilde{\Omega}\) is the fuzzy failure event of frame structure; \(f(z)\) is the probability density function used to show the randomness of a structural failure event; and \(\mu(z)\) is the membership function used to show the fuzzy of structural failure event.  
To describe the fuzzy transition area of structural failure, the indicator function \(I[g(X_{i})]\) in the traditional Monte Carlo method was replaced by the membership function of fuzzy function \(\widetilde{Z}\), as shown in Equation (21):  
\[I[g(X_{i})]=\begin{cases}1&z_{i}\leq r_{1}\\ -\frac{z_{i}-r_{2}}{r_{2}-r_{1}}&r_{1}<z_{i}<r_{2}\\ 0&z_{i}\geq r_{2}\end{cases}, \tag{21}\]where \(X_{i}=(x_{i1},x_{i2}\ldots,x_{in})\) is the \(i\)th sample vector of the random vector \(X=(x_{1},x_{2}\ldots,x_{n})\). The moment estimation value of structural failure probability can be calculated through Equation (22):  
\[\hat{P}_{f}=\frac{1}{N}\sum_{i=1}^{N}I[g(X_{i})]=\frac{n_{f}}{N}, \tag{22}\]  
where \(N\) is the total sample number.  
The sample variance of moment estimation is shown in Equation (23):  
\[\hat{\sigma}^{2}=\frac{1}{N}\hat{P}_{f}\Big{(}1-\hat{P}_{f}\Big{)}. \tag{23}\]
# Preparation of an Asymmetric Membrane from
## 3 Results and Discussion
### Extraction of Cellulose from Sugarcane Bagasse
#### 3.1.2 Fourier Transform Infrared Spectroscopy (FT-IR)  
The efficiency of cellulose isolation is also expressed by the FT-IR spectra of raw SB and of extracted cellulose. Figure 1 shows the FT-IR spectra of the initial SB and extracted cellulose. The significant solubilization of lignin is represented by the absence of the peak at 1512 cm\({}^{-1}\), which is associated with C-C stretching of the aromatic skeletal vibration in lignin [5; 26]. Furthermore, the intensity decrease of the peak at 1246 cm\({}^{-1}\) is ascribed to the characteristic C-O stretching of guiaiaacyl rings in lignin [27; 28].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Samples a**} & \multicolumn{3}{c}{**H\({}_{2}\)O\({}_{2}\) Treatment**} & \multicolumn{3}{c}{**Result**} \\ \cline{2-9}  & **Concentration** & & & & & & \\ \cline{2-9}  & **of H\({}_{2}\)O\({}_{2}\) (\%, \(\%\)/\(v\))** & **Time (h)** & **Weight b** & **Cellulose wt (\%)** & **Hemicellulose wt (\%)** & **Lignin wt (\%)** & **Yield (\%)** \\ \hline CE-1 & 5 & 4 & 6.0 & 83.05 & 8.25 & 7.94 & 60\% \\ CE-2 & 2 & 4 for once & 6.6 & 89.09 & 7.11 & 3.07 & 66\% \\ \hline \hline \end{tabular}  
* The samples obtained after dewax, alkali, and EDTA treatment. b Weight (g) of the extracted cellulose. Initial weight of raw sugarcane bagasse = 10 g.  
\end{table}
Table 1: Chemical composition of extracted cellulose.  
The disappearance of peak at 1743 cm\({}^{-1}\) assigned either to the acetyl and uronic ester groups of the hemicelluloses or to the ester linkage of carboxylic group of the ferulic and p-coumeric acids of lignin and/or hernicelluloses [29; 30]. Simultaneously, the increase in the bands at 3412 cm\({}^{-1}\) and 2902 cm\({}^{-1}\), respectively, is assigned to the presence of the stretching of O-H and C-H bonds, which are typical bands of cellulose molecules [7]. The peak at 897 cm\({}^{-1}\) is associated to the \(\beta\) (1-4) linkages between the glucose monomers [31]. The intensity of the mentioned peaks increases as a function of the purity of cellulose in the obtained materials.
## 2 Finite Element Modeling and Verification
### Verification of Finite Element Models
#### 2.2.2 Laboratory Test of a Quarter Scale Model Bridge  
Laboratory testing of a quarter-scale model for simple-span right bridge with a span of 18.2 m and a roadway width of 7.3 m was carried out by [PERSON] et al. [31]. The model bridge (referred to as C15) was a quarter-scale model with a span length of 4.5 m and deck width of 1.8 m supported by five I-girders at a spacing of 0.45 m. The cross section and longitudinal view of bridge are shown in Figure 5. The steel I-girders had a flange thickness of 4.80 mm, flange width of 58 mm, web thickness of 3.5 mm, and total height of 203 mm. Channels with dimensions of 25 mm \(\times\) 9.5 mm \(\times\) 0.30 mm and a spacing of 15.8 cm were installed as shear connectors and the intermediate diaphragms applied at the abutments and spaced at one-third intervals along each span consisted of 76 mm \(\times\) 50 mm \(\times\) 52 mm angles; these were welded to the webs of the beam about 13 mm below the top of the beam. Four 20 kN concentrated loads were applied at the mid-span symmetrically about the longitudinal axis of the bridge, as shown in Figure 5. As the figure shows, the laboratory and FE results compared fairly well, although the FE results were slightly higher than those obtained in the laboratory, varying by up to 1.5%, 4.6% and 3.4% for the external, intermediate and central girders, respectively. This discrepancy is likely because the experimental loads were not localized at an exact position but instead distributed over the entire steel supporting plate [32]. This suggests that the proposed FE technique accurately predicts the response of I-girder bridges to static loading.
###### Abstract  
Background: to analyze the dento-alveolar effects induced by two treatment appliances (i.e., RME and Clear Aligners) in growing subjects presenting with early mixed dentition and mild maxillary deficiency. Methods: digital casts of 32 children treated with RME (RG: 17 subjects) or with Clear aligners (CAG: 15 subjects) were collected. Linear and angular values were measured in both groups on the upper arch for both pre- (T1) and post-treatment (T2) models. An unpaired \(t\)-test was used to test significant differences between groups (\(p<0.05\)). Results: the comparison of T2-T1 changes for linear measurements between groups showed a greater increase in the inter-canine width (+1.5 mm \(\pm\) 0.5 mm) and in the first inter-deciduous molar width (+1.4 \(\pm\) 0.4 mm), also at the trans-palatal level in the CAG group. Conversely, in the posterior region of the upper arch, a greater increase in the first inter-molar distal width was found in the RG group (+1.2 \(\pm\) 0.4 mm) when compared with the CAG group. A significant increase in the crown angulation in the CAG group was found for all the teeth except for the first molars. Conclusions: the rapid palatal expander widened the palate, tipping the first upper molars bucucally to a greater extent, whereas the Clear aligners caused a greater increase in the canine width.  
rapid maxillary expansion; Clear aligner; early mixed dentition; growing subjects