esa-sceva/satcom-chunk-collection huggingface dataset:
# An IoT Surveillance System Based on a Decentralised Architecture
## 3 Proposed Architecture
### Architecture Elements
#### 3.4.1 Edge Computing
\[P_{Q}=\frac{\left(c\rho\right)^{c}}{c!}\times\frac{1}{1-\rho}p_{0} \tag{4}\]  
\[p_{0}=\left[\sum_{k=0}^{c}\frac{\left(c\rho\right)^{k}}{k!}+\frac{\left(c\rho \right)^{c}}{c!}\times\frac{1}{1-\rho}\right]^{-1} \tag{5}\]  
Finally, we can achieve the latency of the systems by assuming that the overall latency is composed of several terms such as propagation delay, transmission delay, routing node delays and processing delays on the computational nodes. For a cloud only model we can reuse the equation already proposed in [41]  
\[L_{cloud}=\alpha\times min_{i=0}^{m}\left(d_{ue,AP_{i}}\right)+\beta\times d_{ AP,cloud}+d_{node} \tag{6}\]  
At the same manner we can achieve the Edge based system latency:  
\[L_{edge}=\alpha\times min_{i=0}^{m}\left(d_{ue,AP_{i}}\right)+d_{node}+d_{s} \tag{7}\]  
To better understand the next step we have to specify that with the term smart device we mean both UGV and smart camera. Thus, let us to define the \(\alpha\) term, it is a proportional constant for the uplink and for the downlink bandwidth from the smart device to the access point (AP). At the same way the \(\beta\) term is the proportional constant for the uplink and for the downlink bandwidth from the AP to the cloud. The \(d_{ue,AP_{i}}\) is the distance from the \(i\)-th AP to the smart device. The \(d_{AP_{i},cloud}\) term is the distance between the \(i\)-th AP to the cloud. Moreover, the \(m\) term is the number of the available AP. Regarding the Equation (7), the term \(d_{s}\) is the delay that a task can found due to an overload on the chosen edge node. We assume that this terms is equal to 5 ms as suggested in [41]. The list of the symbols used in the mathematical formulation are shown in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(\lambda\) & arrival rate \\ \(\mu\) & service rate \\ \(c\) & number of available servers \\ \(V\) & computing latency per task \\ \(d_{comp}\) & computation delay \\ \(f\) & instructions per seconds \\ \(K\) & number of instructions required per task \\ \(d_{node}\) & delay in a computation node \\ \(W\) & queuing delay \\ \(\rho\) & utilization factor \\ \(P_{Q}\) & queue probability \\ \(L_{cloud}\) & cloud based latency \\ \(L_{edge}\) & edge based latency \\ \(\alpha\) & proportional constant for uplink and downlink from the smart device to AP \\ \(\beta\) & proportional constant for uplink and downlink from AP to the cloud \\ \(d_{ue,AP_{i}}\) & distance from \(i\)-th AP to smart device \\ \(d_{AP,cloud}\) & distance between \(i\)-th AP to the cloud \\ \(m\) & number of available AP \\ \(d_{s}\) & delay for overload on the edge node \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Symbols in the mathematical formulation.
# A Design Method for Gammachirp Filterbank for Loudness Compensation in Hearing Aids
## 4 Experiments and Discussion
### Loudness Compensation Experiment
#### 4.3.2 Subjective Assessment  
Ten patients with hearing loss were invited to the following experimental tests and all of them had had the experience of wearing hearing aids for more than three months. Due to personal reasons, four of them withdrew from the trial. The subjective assessment experiments were performed on six hearing-impaired patients to prove the increase in intelligibility. The hearing threshold measurements of the patients who did not wear hearing aids was implemented based on simplified pure tone audiometry, and the measured tones were 125, 250, 500, 750, 1000, 1500, 2000, 3000, 4000, 6000, and 8000 Hz. The audiogram results are shown in Figure 9 for both left and right ears.  
Figure 8: Index comparison of 4-channel and 16-channel loudness compensation system based on the gammachirp filterbank.  
Figure 7: Index comparison of 4-channel loudness compensation system based on the gammachirp filterbank and the gammatone filterbank.  
The patients were required to listen to test speech signals and to reconstruct speech signals after multichannel loudness compensation. The sound pressure level of the test signals was adjusted to 10 dB, 20 dB, 30 dB, 40 dB, 50 dB, 60 dB, and 70 dB, and for every level, 50 original sentences and 50 compensated sentences were tested. The patients were asked to repeat the sentences. When their reproduction was completely correct, the patients were deemed to have correctly identified the sentence.  
The average speech-signal recognition rates before and after loudness compensation based on the 4-channel gammachirp and gammatone filterbanks are shown in Table 3. The  
Figure 9: Pure tone audiograms for the subjects: (**a**) S1; (**b**) S2; (**c**) S3; (**d**) S4; (**e**) S5; (**f**) S6.  
experiments show that after loudness compensation, the patients' speech recognition rate of speech signals at various sound pressure levels had improved. Additionally, the hearing threshold of the patients decreased, and some speech files with low sound pressure levels could be identified by the patients. When the sound pressure level of the testing speech increased, the identified rate was remarkably improved through loudness compensation. Moreover, the average recognition rate of the output speech signals after the loudness compensation based on the gammachirp filterbank was better than the system output based on the gammatone filterbank, which verifies the effectiveness of the method.
# Ontology with Deep Learning for Forest Image Classification
###### Abstract  
Most existing approaches to image classification neglect the concept of semantics, resulting in two major shortcomings. Firstly, categories are treated as independent even when they have a strong semantic overlap. Secondly, the features used to classify images into different categories can be the same. It has been demonstrated that the integration of ontologies and semantic relationships greatly improves image classification accuracy. In this study, a hybrid ontological bagging algorithm and an ensemble technique of convolutional neural network (CNN) models have been developed to improve forest image classification accuracy. The ontological bagging approach learns discriminative weak attributes over multiple learning instances, and the bagging concept is adopted to minimize the error propagation of the classifiers. An ensemble of ResNet50, VGG16, and Xception models is used to generate a set of features for the classifiers trained through an ontology to perform the image classification process. To the authors' best knowledge, there are no publicly available datasets for forest-type images; hence, the images used in this study were obtained from the internet. Obtained images were put into eight categories, namely: orchards, bare land, grassland, woodland, sea, buildings, shrubs, and logged forest. Each category comprised 100 images for training and 19 images for testing; thus, in total, the dataset contained 800 images for training and 152 images for testing. Our ensemble deep learning approach with an ontology model was successfully used to classify forest images into their respective categories. The classification was based on the semantic relationship between image categories. The experimental results show that our proposed model with ontology outperformed other baseline classifiers without ontology with 96% accuracy and the lowest root-mean-square error (RMSE) of 0.532 compared to 88.8%, 86.2%, 81.6%, 64.5%, and 63.8% accuracy and 1.048, 1.094, 1.530, 1.678, and 2.090 RMSE for support-vector machines, random forest, k-nearest neighbours, Gaussian naive Bayes, and decision trees, respectively.  
ontology; feature extraction; convolutional neural networks; image classification Article
# Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images
## 1 Introduction
This paper proposes research on an accurate multi-target tracking algorithm for infrared images based on optical flow estimation, which improves the accuracy and recognition rate of infrared image multi-target tracking. The algorithm is suitable for rapid multi-target identification, detection, and tracking. Infrared target detection and tracking systems are passive detection systems with the advantages of good concealment, strong anti-interference capability, high accuracy, and good performance [9; 10]. They have become some of the indispensable key technologies in modern warfare. The algorithm can be applied to target detection and tracking of threatening infrared targets (UAVs, missiles, aircraft, etc.) in the air [11], at sea, and on land, and to provide target-related information for defensive weapons systems [12], which has certain research significance and value. This paper is divided into five parts. Firstly, in this introduction, the current state of research on related technologies has been analyzed. An infrared multi-target precision tracking algorithm has been proposed to address current technical problems, such as the poor accuracy and low recognition rate of infrared multi-target tracking, and its application environment and related research significance and value have been clearly defined. This is followed by a brief introduction to the underlying theory of this research in the Section 2. The theoretical derivation and algorithmic structure of the algorithm proposed in this paper are discussed in detail in the Section 3. The experimental validation and detailed data analysis are then carried out in the Section 4. The strengths and weaknesses of the algorithm proposed in this paper are discussed in light of the data analysis, and future directions for continued research and the application of the technique are explored. Finally, clear conclusions and the significance and value of the research are outlined in the Section 5.
## 2 Computational Model
### Grid Independency  
The grid independency tests are illustrated in Figure 6. In essence, the mesh was progressively refined in the part of the domain surrounding the heat exchangers (there was practically no need to do this in an empty resonator). The description of mesh density is given as cell counts in Figure 6a for five cases. Here, the velocity values at point \"m\" for phases 6 and 16 are taken as test variables (+ve and -ve values due to the change of direction). Similarly, the resulting axial velocity profiles (from the wall to channel centre), for selected phases in the cycle are further illustrated in Figure 6b. For clarity, velocity profiles are shown only for three cell counts. At low mesh density, the velocity profile is slightly over-predicted at all 6 phases shown. The velocity within the boundary layer appears to be the same for all cases because the mesh is always designed to be denser in that area. It is found that the model with a total of 45,910 cells is sufficient to provide a grid-independent solution.
# Upper Limb Orthoses: Integrating Topology Optimization and 3D Printing for Custom Fit and Function
## 4 Design Process for Custom Limb Orthoses  
The design of custom limb orthoses is fundamentally driven by the topology optimization (TO) process, which involves two critical phases: formulation and solution. Formulation Phase: This initial phase focuses on defining the mathematical framework for the optimization problem. It involves specifying the objective function and the constraints that guide the design process. The objective function typically aims to minimize strain energy or weighted compliance, which is inversely related to the stiffness of the orthosis. By minimizing this function, the design process seeks to maximize the structural performance and stability of the orthosis. Solution Phase: In this phase, the TO algorithm applies the defined objective and constraints to iteratively refine the design. The process adjusts the material distribution within the orthosis to find an optimal configuration that adheres to the constraints while achieving the minimal strain energy. The constraints usually include limitations on material volume or density, ensuring that the final design is both practical to manufacture and effective in performance. The culmination of these phases results in a custom limb orthosis that is optimized for both structural efficiency and functionality, meeting the specific needs of the user while adhering to the practical constraints of manufacturing and material use. This approach ensures that the final product not only provides the necessary support and comfort but also utilizes materials in an efficient and cost-effective manner.
# Thermoelectric Generator-Powered Long-Range Wireless Real-Time Steam Leak Detection in Steam Traps
## 3 Present Technology Scenario  
### Detection of Steam Losses in Steam Traps  
After evaluating the criticality of steam leaks, it is essential to determine when a steam trap is malfunctioning. Detection methods range from non-intrusive approaches, which do not interfere with the steam process, to intrusive methods that require installing sensors inside the steam trap. Non-intrusive methods include visual inspections, where the continuous venting of live steam, rather than intermittent condensate discharge, signals a malfunction. Temperature profiling using infrared thermography or surface sensors can also help detect leaks by comparing inlet and outlet temperatures; if both are similar, it may indicate steam escape. Additionally, ultrasonic detection devices identify the high-frequency sounds of steam leaks, distinguishing them from the lower frequencies associated with normal condensate discharge. Intrusive methods involve installing flow meters or differential pressure gauges at the discharge line to quantitatively detect abnormalities, such as continuous or excessive flow, which indicate steam leakage.  
Figure 3: [PERSON]’s equation representing steam loss in Kg per hour.
###### Abstract
These issues thus limit the applicability of roots findings, especially for a large number of mode shapes and its corresponding root values. On the other hand, deep neural networks such as the long short-term memory recurrent neural network, show superior capability in terms of performance and their extensive dataset-handling capability in predicting the system parameters. Therefore, this study proposes the LSTM-RNN model for approximating the transcendental equation roots (Eigenvalues) for the first time. [PERSON] and [PERSON] were the first to develop this network model in 1997 [26]. LSTM-RNN deep neural networks are implemented in numerous research topics, including neural computing, estimating transmission quality, time series forecasting, textual and image recognition, etc. [27; 28; 29; 30; 31]. In general, LSTM-RNN contains a collection of layers that are connected to the blocks known as memory blocks [32; 33; 34; 35]. All the information is stored in these memory blocks, which are connected with the memory cells and three input, output, and forget multiplicative units. For the structural monitoring of cantilever beams, [PERSON] et al. use Bayesian and deep learning LSTM and convolutional neural network (CNN) techniques [36]. [PERSON] et al. use LSTM-RNN neural networks for damage detection of the structure incorporated into the cantilever beam [37]. [PERSON] et al. use LSTM-RNN neural networks to approximate the polynomial roots [38]. The following are the objectives and sequence of this paper: (1) Throughout the analysis, the first ten natural frequencies and corresponding transcendental roots (Eigenvalues) of prismatic cantilever steel beam (with specifications in Table 1) subjected to clampedfree (CF) boundary condition based on the conventional Euler-Bernoulli beam theory are evaluated as an example; (2) datasets for natural frequencies and roots are generated through FEM package ANSYS; (3) roots are then evaluated numerically with the help of MATLAB programming for cross-validation of the generated dataset results; (4) the proposed LSTM-RNN model is constructed to approximate the roots of the transcendental equation for the transverse vibration of the prismatic cantilever beam with CF case; (5) finally, for the validation of transcendental root values, the results are compared with the FEM ANSYS (datasets), MATLAB (numerical) and LSTM-RNN model (predicted) values. Moreover, network outputs are obtained by adjusting the input data and parameters of the neurons and then further comparing these outputs to the predicted analytically obtained results. Finally, a comparative analysis has been performed between ANSYS, numerical analysis and the proposed network model for ten modes of the natural frequencies and their corresponding transcendental root values. The remaining paper is assembled as follows: Section 2 presents the mathematical modelling, Section 3 elaborates on the fundamental concepts behind the LSTM-RNN model, Section 4 compares analytical and experimental results with a brief discussion, and, Section 5 includes several results and directions for future research.
## 3 Results
### Relationships between Physical, Sensory, and Proximate Composition of GFB  
Figure 2 shows the relationships between the physical, sensory, and proximate composition properties of the bread samples, whereby the two MFA factors explained 95.07% and 87.89% of the total variation of the GFB made with WS and BS, respectively. For the WS-based GFB, Factor 1 was correlated with sensory properties when blended with potato starch, describing the 50 WS:100W and 75 WS:120W samples. By contrast, the loaf specific volume, crumb firmness, protein, lipid, and fiber contents described the 100 WS:100W sample, which had the highest WS concentration. Factor 2 was correlated with the moisture content due to the to the fact that it used the highest W-level when referring to sample 50 WS:140W (Figure 2a,b).  
For the BS-based GFB, Factor 1 was positively correlated with flavor and overall acceptance and crumb moisture when BS was blended with potato starch and when the highest W level was added, i.e., the 50 BS:140W sample. By contrast, the loaf specific volume, crumb firmness, protein, lipid, and fiber contents explained the 100 BS:100W sample. Factor 2 was explained by the moisture content due to the 100 BS:140W sample (Figure 2c,d).  
Figure 1: Representative images of gluten-free bread formulations prepared using a 22-factorial design with two center points to study the effects of white sorghum (WS) and bronze sorghum (BS) flour and water (W) levels on a flour weight basis. A cm measuring scale was used.  
Figure 3 shows the dendrogram, which illustrates the existence of two groups of GFB made with WS. The first group contains three samples: 50 WS:100W, 50 WS:140W, and 75 WS:120W, while the second group contains two products made with 100 WS:100W and 100 WS:140W. In addition, a moderate clustering was seen since the WS model presented a cophenetic correlation coefficient value of 0.612. On the other hand, Figure 3b identified the existence of three groups between the GFBs based on BS. The first one only identified the 100 BS:100W sample, the second one contained two samples: 50 BS:100W and 75 BS:120W, while the third one contained two samples with the highest W-levels: 50 BS:140W and 100 BS:140W. In addition, there was a high degree of clustering since the BS model presented a cophenetic correlation coefficient value of 0.734.  
Figure 2: Multiple factor analysis correlating the physical (in brown), sensorial (in red), and proximate composition (in green) the properties of gluten-free bread with different water (W) levels and based on white sorghum (WS in sub-figures **a**,**b**) or bronze sorghum (BS in sub-figures **c**,**d**) flours. Bread ID: WS, white sorghum; BS, bronze sorghum; W, water. The letters and numbers indicate the ingredient proportions in the flour weight basis (g/100 g).
## 4 Methodology
### School Context  
Twenty teachers from the primary and junior departments of an international private K-12 school in mainland China were selected as samples depending on their accessibility and availability. Although more participants would have given greater variation and generalizability, it is vital to highlight that \"sample size may also be restricted by cost in terms of time, money, stress, administrative support, the number of researchers, and available resources\" [36].  
The school utilizes instructional materials in English. Among all the subjects, mathematics, science, and physical education have been selected as CLIL subjects taught in English. Under the CLIL program described above, when the foreign teacher teaches in English online on Zoom (an online delivery platform), the Chinese teacher of each class also functions as a subject assistant and translates part of the academic vocabulary during the course. This is because, although students' English ability is relatively high in this school, they are limited to proficiency in daily conversation. Their accumulation of academic vocabulary still needs to improve, necessitating the occasional need for a teacher's translation explanation. Although some scholars argue that it appears to be an established normalizing mission to keep L1 (the first language) out of the classroom environment when L2 (the second language) is being learned, other researchers have also presented diverse perspectives. There are numerous advantages to utilizing L1 strategically in CLIL. For instance, [PERSON] [37] notes that students automatically draw on their L1 resources when they meet second-language activities and concepts. Rather than avoiding L1, it is productive to encourage teachers to value learners' L1 resources and to make conscious judgments about using L1 in the classroom. Hence, this school does not abandon the role of the first language entirely within the CLIL curriculum. The Chinese teachers occasionally translate and clarify the areas of confusion for the students in their native tongue while assisting the lecturers in maintaining classroom discipline or facilitating classroom activities.  
In addition, the online course was taught using a PowerPoint generated by the teachers based on the textbook and the software provided along with the textbook, which includes videos, music, and games. Despite having some resources already suitable for online learning, the content of the software supplied in the textbook needs to be revised to support online classroom activities; hence, teachers frequently need to seek out extra online resources on their own.
## 3 Results and Discussion
### Model Performance Results  
The results show that GBR model performance was highly effective in predicting SMC in both soil textures (Figure 4). The model performance was evaluated using test data and validated by using 5-fold cross-validation. In silt loam soil, the model achieved the highest accuracy, with an R\({}^{2}\) of 0.98, MSE of 0.73, RMSE of 0.85, MAE of 0.45, and NSE of 0.95 (Figure 5), reflecting the model's consistency and generalization capability. In loam soil, the model performance was slightly lower, likely due to the higher variation in SMC values compared with silt loam, with an R\({}^{2}\) of 0.94, MSE of 0.94, RMSE of 0.97, MAE of 0.73, and NSE of 0.94. These results highlight the impact of soil texture on predictive model performance, where less prediction accuracy was shown in loam soil due to its higher variability in SMC compared with silt loam soil where its balanced structure and high water retention had a significant role in enhancing model accuracy. These results emphasize the need for soil-specific modeling to enhance SMC prediction [46] and the necessity for integrating more physical soil properties into models for improving prediction reliability across different soil textures.  
Figure 4: Model performance and comparison between SMC predicted and measured values in (**A**) loam and (**B**) silt loam soil.  
Figure 3: Variations in soil moisture content (SMC%) across five depths and two soil textures during the studied vegetation period, based on repeated measures ANOVA.