esa-sceva/satcom-chunk-collection huggingface dataset:
# End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks
## 6 The Inter-Cluster Communication  
### The Traffic Load and the Stability Conditions  
Here we assume that buffer capacity or queue size in each CH is infinite. As has been pointed out in a previous section, data packets will flow downstream from external rings towards to the sink. Let us consider a total number of \(r_{max}\) rings. For any given CH located at ring \(k\) and based on the principle of load balancing pointed out in the previous section, the normalized average number of data packets transmitted per TDMA slot in the TDMA sub-frame, \(\rho_{k}\), is,  
\[\rho_{k}=c_{k}\frac{L^{\prime}(1)}{N_{ms\mathcal{T}}},\quad\text{for }1\leq k \leq r_{max}. \tag{10}\]with  
\[c_{r_{max}}=1;\quad c_{k}=1+\frac{\#\text{CH}_{k+1}}{\#\text{CH}_{k}}c_{k+1}, \text{for}\ k<r_{max}. \tag{11}\]  
being \(\#\text{CH}_{k}\) the number of CHs at ring \(k\). Table 5 shows the values of \(c_{k}\) for several sizes of the WSN (\(r_{max}=0,1,2,\ldots,7\). and \(\#\text{CH}_{k}=1,6,12,\ldots,42\)). Clearly we must have \(1>\rho_{1}>\rho_{2}>\ldots>\rho_{r_{max}}=L^{\prime}(1)/N_{msT}\) where the first inequality gives the stability condition of the WSN. Please note that the sink also acts as a CH; although we do not pay attention to the traffic load supported by the sink, \(\rho_{0}\), since we assume that it is connected to the data center through a high bandwidth link and with no power limitation. Also, observe that the term \"1\" in \(c_{k}\), Equation (11), reflects the local traffic while the other term takes into account the exogenous traffic offered to a CH from external rings.
# Web Mapping for Farm Management Information Systems: A Review and Australian Orchard Case Study
## 2 Background
### A Primer on Web Mapping
The image scale and regularity of image updating of these resources varies by location and is related to population density. Satellite-based imagery is typically used, although input from other platforms is used, e.g., aerial imagery, when available, e.g., from local national mapping agencies [44]. When higher resolution imagery is not available, Landsat imagery (spatial resolution of 15 m) is used [47]. Under their fair use policy, Google Earth (GE) imagery is free for use for websites with less than a certain number of tiles/visits per day. Local map resources may also be available. For example, the Queensland Government maintains Queensland imagery as an online map service [48]. The resource contains imagery collected on multiple dates from 1930 to 2019 and is a mosaic of ortho-rectified aerial imagery of high spatial resolution (0.5-50 cm) from remotely piloted aircraft, piloted aircraft, and satellites [48]. UAV imagery can also be locally acquired [5; 49].
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 2 Background
### Communication Primitives for Distributed Deep Learning  
Collective communication encompasses a range of operations, and Figure 3 illustrates the most commonly employed methods: broadcast, gather, allgather, reduce, and all-reduce.  
* Broadcast: The broadcast operation involves transmitting data originating from one node to all other nodes. Essentially, one node sends out the data, and all remaining nodes receive those identical data. Broadcast is utilized when the same data are required across all nodes.
* Gather: The gather operation consolidates data gathered from all nodes into a single designated node. Each node forwards its data to the central node, which accumulates all received data. Gather is used when you need to collect distributed data into a single dataset.
* Allgather: Similar to the gather operation, allgather also involves transferring data collected from all nodes, but in this case, it disseminates the data to all nodes. Every node transmits its data to all other nodes, leading to each node possessing a comprehensive set of data.
* Reduce: The reduce operation focuses on transmitting results derived from computations performed across multiple nodes to a single designated node, where these results are combined. Each node shares its data with other nodes, executes computations, and transmits the computed results to one designated node. Reduce is instrumental in aggregating and summarizing outcomes obtained from multiple nodes.
* All-reduce: The all-reduce operation extends the concept of reduce by transferring the results of operations performed by several nodes to all nodes, and subsequently, combining these results. Each node communicates its data with all other nodes, and following computations, all nodes collect and integrate the computation results. All-reduce is crucial in distributed learning for gradient updates, parameter synchronization, and comprehensive model updates across all nodes.  
These collective communication operations play a pivotal role in distributed deep learning, facilitating the efficient exchange and coordination of models and data. Each operation plays a crucial role in transmitting and combining data according to specific communication patterns. In distributed learning, these operations are essential for efficient model training and communication.
# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
###### Abstract  
The accuracy of dam deformation prediction is a key issue that needs to be addressed due to the many factors that influence dam deformation. In this paper, a dam deformation prediction model based on IeALL (IGWO-EEMD-ARIMA-LSTM-LSTM) is proposed for a single-point scenario. The IeALL model is based on the idea of a combination model. Firstly, EEMD is used to decompose the dam deformation data, and then the ARIMA and LSTM models are selected for prediction. To address the problem of low prediction accuracy caused by simple linear addition of prediction results from different models in traditional combination models, the LSTM model is used to learn the combination relationship of different model prediction results. The problem of neural network parameters falling into local optima due to random initialization is addressed by using the improved gray wolf optimization (IGWO) to optimize multiple parameters in the IeALL combination model to obtain the optimal parameters. For the multi-point scenario of dam deformation, based on the IeALL model, a dam deformation prediction model based on spatio-temporal correlation and IeALL (STAGCN-IeALL) is proposed. This model introduces graph convolutional neural networks (GCN) to extract spatial features from multi-point sequences, increasing the model's ability to express spatial dimensions. To address the dynamic correlation between different points in the deformation sequence at any time and the dynamic dependence on different points at any given time, spatio-temporal attention mechanisms are introduced to capture dynamic correlation from both spatial and temporal dimensions. Experimental results showed that compared to ST-GCN, IeALL reduced the RMSE, MAE, and MAPE by 16.06%, 14.72%, and 21.19%. Therefore, the proposed model effectively reduces the prediction error and can more accurately predict the trend of dam deformation changes.  
model dam deformation prediction; combined model; neural network; spatio-temporal correlation +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Construction Method of Combined Model
#### 2.2.4 Combined Model  
Modification models, combined with the basic model, constitute the combined model. When predicting new data points, the basic model is used to generate the initial predicted value \(\overline{y}\). The working condition of the data point is then determined. Based on the judgement of the working condition, the corresponding modification model is selected to obtain the deviation prediction value \(\Delta y\), and the sum of both, \(\overline{y}+\Delta y\), yields the final prediction value \(\hat{y}\).
## Materials and Methods
### VOCs Extraction and Analysis  
VOCs that were released from the samples to the atmosphere headspace solid-phase microextraction Arrow (HS-SPME Arrow) were used. As sorbent, 1.10 mm DVB/C-WR/PDMS SPME Arrow fiber (Shimadzu, Kyoto, Japan) was chosen. Briefly, 0.5 g \(\pm\) 0.005 of fresh samples or 0.25 g \(\pm\) 0.005 of dried samples was weighed. Before the extraction, 0.5 ug of undecane-2-one (Sigma-Aldrich, Steinheim, Germany) was used as an internal standard. The extraction was performed in 20 mL headspace vials, which were pre-conditioned at 45 \({}^{\circ}\)C for 5 min; then the VOCs were extracted for 30 min at the same temperature, while the sample was shaken at 250 rpm. Thereafter, the analytes were thermally desorbed for 3 min at the injection port temperature. The analyses were run in triplicate.  
The separation, identification, and quantification of analytes were performed on the Shimadzu QP 2020 Plus apparatus (Shimadzu, Kyoto, Japan) equipped with a ZB-5 Msi (Phenomenex, Torrance, CA, USA) column (30 m \(\times\) 0.25 mm \(\times\) 0.25 um). Injector conditions: Temperature 220 \({}^{\circ}\)C; helium as carrier gas with linear velocity 35.0 cm\(\cdot\)s\({}^{-1}\); split 10. The analyte separation was carried out with the following temperature program: 40 \({}^{\circ}\)C, then 130 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\); then 280 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\), held for 5.5 min. The MS mode was scanned (40-400 \(m/z\)) with an ion source temperature of 250 \({}^{\circ}\)C and an interface temperature of 250 \({}^{\circ}\)C.  
The analyte identification was based on the comparison of linear retention indices (LRIs) and mass spectra. As a reference database, the Flavours and Fragrances of Natural and Synthetic Compounds 3.0 (FFNSC 3.0) library was used. The experimental LRIs were calculated on the basis of simultaneous analysis of the n-alkanes c 7-c 40 mixture (Sigma-Aldrich, Steinheim, Germany). The identification limits were as follows: LRIs \(\pm\)15; mass spectra similarity \(\geq\)90%. Area normalization with an internal standard peak area was used as the quantification method.
## 3 Results
### Numerical Scheme and Simulations
The following data are considered for the numerical test: \(d_{1}=0.1\), \(d_{2}=1.0\), \(u_{c}=0.25\), \(\overline{u}=1.0\), \(\mu=0.5\), \(\
u=10.0\), \(\delta=10.0\), \(\chi=20\), and \(\gamma=2\). Further, our experimental simulation considers a small time step \(\Delta t=0.05\), and the numerical flux \(G\) is defined by Equation (19); we consider \(\Phi_{\uparrow}=\Phi(\min\{z,u_{m}\})\) and \(\Phi_{\downarrow}=\Phi(\max\{z,u_{m}\})-\Phi(u_{m})\), where \(u_{m}=\frac{\overline{u}}{\sqrt{\gamma+1}}\). The aforementioned parameters yield to the homogeneous steady state, \((u_{s},\ v_{s})\ =\ (0.25,\ 0.25)\), and to the range of unstable wave numbers \(0.11<k^{2}<426.36\). Initially the cell density is located with as a small random perturbation around the homogeneous steady state; in our test, we take \(u\ (0,\ x,\ y)\ =u_{s}+\mathrm{rand}(0,\ 1)\). Meanwhile, for the chemoattractant, we take \(v(0,\ x,\ y)\ =v_{s}\). Figure 2 shows the plot of the initial condition for the cell density distribution; the red regions correspond to the highest cell density so the cells are aggregated into a \"random\" distribution. A zero-flux boundary condition is imposed for the boundary conditions. Figures 3 and 4 show the evolution of the cell density in space for different time moments. It shows that the random distribution of the cell density leads to a merging process in all directions of the space at \(t=0.05\) s. Then after \(t=0.85\) s, the spatial patterns begin to form, and the aggregations start to merge and emerge and then to form uniform spot patterns as at time \(t=14\) s. Next, after time \(t=30\) s, these spot patterns stabilize (with a stationary \(L^{2}\)-norm of solutions less than \(10^{-14}\)) to finally form 33 spatially inhomogeneous stable patterns as shown in the snapshot to the bottom of Figure 4.  
Figure 2: Initial condition of the cell density with a random small perturbation around the steady state \(0.25\leq u\leq\)0.2599.
## 2 Methods
### Task Scheduling Model  
#### 2.1.1 Problem Statement  
It is assumed that the task set is \(T=\{t_{1},t_{2},\ldots,t_{m}\}\) and the node set is \(W=\{w_{1},w_{2},\ldots,w_{n}\}\). Each task in set \(T\) is expressed as \(t_{i}(1\leq i\leq m)\), and the task scheduling algorithm will match task \(t_{i}\) with node \(w_{j}(1\leq j\leq n)\) according to the collection ability of each collection node in collection node set \(W\). The result of static task scheduling can be expressed by \(\{<t_{1},\ w_{1}>,<t_{2},w_{2}>,\ldots,<t_{i},w_{j}>\}\). Dynamic task scheduling aims to dynamically adjust the matching sequence of tasks and nodes, of which the scheduler dynamically reschedules tasks that have not yet been executed according to the current task execution speed of each node.
## 4 Evaluation of Results
### Public Transport Priority  
Signal control management in public transport priority assumes the allocation of the green phase to public city transport vehicles when encountering traffic lights. Giving priority to public transport is a measure implemented to limit individual motor traffic as one of the most significant positive effects of sustainable transport development in cities, which is reflected in the reduction in greenhouse gases and other positive effects related to environmental protection and energy efficiency.
# Energy Behaviour of Coal Failure under Uniaxial Cyclic Loading/Unloading
## 5 Discussion
### Energy Behaviour Criterion of the Coal Specim
#### 5.1.2 Energy Consumption Growth Factor Criterion  
The main reason for coal failure was that the dissipative energy and the elastic strain energy reached a certain equilibrium mechanism. In fact, the elastic strain energy of the coal specimen reached the limitation of its energy storage, and the accumulation of the dissipation energy also reached the maximum state. Therefore, the energy consumption growth factor can better reflect the energy state of the energy storage mechanisms to a large extent. Then, it also can accurately reflect the energy state and stability of the coal specimen. Therefore, the energy consumption growth factor criterion was proposed in this paper.  
As shown in Figure 14, when the energy consumption growth factor (\(k_{HF}\)) \(\leq\) 0.22 \(\pm\) 0.003, the ratio of the dissipative energy to the elastic energy is at a low value and a small slow decline occurs. The coal specimen is in a stable state at this stage. When 0.22 \(\pm\) 0.003 \(\leq\)\(k_{HF}\)\(\leq\) 0.40 \(\pm\) 0.002, the ratio of the dissipative energy to the elastic energy changes to a large extent. The dissipative energy ratio has a sudden growth, and the energy consumption growth factor also has a sudden growth in a short period, the coal specimen is in a critical state of failure. When the energy consumption growth factor \(k_{HF}\)\(\geq\) 0.40 \(\pm\) 0.002, the coal specimen has been completely destroyed. It can be known that the energy consumption growth factor criterion of coal specimen can well describe the energy dissipation state and stability state.
## 1 Introduction
However, fewer studies have been reported on reducing the tonal noise from airfoil instabilities using structured porous edges. [PERSON] and [PERSON] [17] were some of the first to perform such experimental investigations on an asymmetric, highly cambered NACA 65(12)-10 airfoil at an \(\alpha=0^{\circ}\) angle of attack. They showed that 3D-printed porous trailing edges could suppress the tonal noise from laminar instabilities, especially in the case of a pore diameter \(d=1\) mm and porosity \(\sigma=30\%\) at a low free-stream velocity of \(V_{\infty}=20\) m/s. On the other hand, at high velocities of \(V_{\infty}\geq 40\) m/s, additional bluntness-related tonal peaks appeared to jeopardize the overall noise reduction performance. Since then, as far as the authors know, no detailed parametric study on reducing the tonal noise from airfoil instabilities by using structured porous trailing edges has been presented in the literature, and this study therefore aims to answer the following questions: (1) Can structured porous trailing edges reduce the tonal noise of a symmetrical airfoil, such as a NACA 0012 airfoil? (2) How do the pore diameter, pore coverage, pore position, and pore arrangement order affect tonal noise reduction? (3) How do the chordwise spacing and spanwise spacing between adjacent pores affect tonal noise reduction? (4) Can structured porous trailing edges still suppress tonal noise at a non-zero angle of attack, and is there any difference from a zero angle of attack? These motivate the present paper, which aims to fill this knowledge gap. Detailed experimental measurements in an open-jet wind tunnel were performed on a set of NACA 0012 airfoils with different sub-millimeter-scale porous settings. Experimental results revealed that a noticeable tonal noise reduction can be obtained for the symmetrical NACA 0012 airfoil. Moreover, design parameters for structured porous edges, such as pore position and pore coverage, affect noise reduction performance, and the influence is different between a zero angle of attack and a non-zero angle of attack. These experimental results will be used to guide parameter designs in numerical methods (e.g., using a large eddy simulation and the Ffowcs Williams-Hawkings acoustic analogy) to reveal the underlying noise reduction mechanisms, which is part of our future work and will be presented in other papers. The remainder of the paper is organized as follows: Section 2 reviews the experimental setup and data processing in detail. In Section 3, the noise measurement results of the different experiments are presented and discussed. Finally, Section 4 concludes the findings of the present experimental study.
## 1 Introduction
First, the system captures a scan of the patient's 3D point cloud, registers a model of the human body, estimates the location of key anatomical landmarks, and produces an initial map of high-quality auscultation locations. It then adopts informative path planning, using auditory feedback to adaptively search the region of interest for high-quality auscultation. The auditory feedback is based on sound quality estimators trained on a database of cardiac and pulmonary stethoscope recordings. To determine the optimal auscultation site, a Bayesian optimization (BO) problem was formulated where the unknown sound quality field was estimated as a semi-parametric residual Gaussian process model with a prior map that depends on a latent translation offset and sound quality scaling parameters. However, this proposed system configuration was based on remote control and did not focus on autonomous auscultation. From these studies, and our literature search, it can be concluded that there is still little work on platforms for autonomous auscultation. It can also be concluded that the robots used in these studies are collaborative robotic arms, due to the possibility of safety interaction with humans. Allied to the robotic arm there is always a sensor/camera to visualize the patient and the location to where the robotic arm should move. Given the above ideas, this paper presents a robotic system that allows the autonomous placement of a stethoscope based on external body information, while satisfying patient safety in terms of stethoscope surface contact with the body. This platform performs the autonomous auscultation on patients' back, since it is easier to acquire volunteers to provide images of the back than of the chest to create the dataset, differentiating itself from the work performed by [PERSON] et al. [23]. The auscultation points shown in Figure 1 were used in the present work, bearing in mind the shoulder blades positions not to auscultate on top of them, because they make auscultation difficult (block the sound transmission). These points are obtained through the knowledge of human anatomy and from reference points whose location is estimated through a red, green, blue depth (RGB-D) vision system supported on AI, which are the main novelties in this work.
## 3 Test Results and Observations
### Accumulation of Carbon Powder and Ash Powder
#### 3.1.3 Results from Images of Substrate with Accumulated Ash Powder  
X-ray images of the substrate with ash powder in four different quantities--12.5 g, 25 g, 100 g and 125 g--were taken to compare their pixel values with those from the images of carbon powder accumulation, and they are shown in Table 7. As for Table 7a, depicting the substrate with 12.5 g of ash powder, the plug and the upper part of the plug are shown in white more than those in Table 5a, displaying the new substrate due to the accumulation of ash powder. In the case of Table 7b, with 25 g of ash powder, the area accumulated with ash powder on the outlet of the substrate appeared white clearly and was easily observable. Even in Table 7c, with 100 g of ash powder, and Table 7d, with 125 g of ash powder, both of which were excessive in quantity, the accumulation status was easily visually identifiable.
# Securing the Future of Web-Enabled IoT: A Critical Analysis of Web of Things Security
## 8 Directions for Research
### Machine Learning and Artificial Intelligence for WoT Security  
Machine learning (ML) and artificial intelligence (AI) offer significant potential for enhancing WoT security through predictive analytics, anomaly detection, and automated response systems. Future research should investigate the development of ML and AI models that can analyze vast amounts of data generated by WoT devices to identify patterns indicative of security threats. These models could be used to predict potential attacks, automatically adapt security measures, and improve incident response times [68; 95].
## 5 Experiment and Result
### Word Encoder Architectures  
In this section, we compare the performance of three common architectures for the word encoder. (1) None: the baseline that not uses the word encoder, as is described in Section 3. (2) BLSTM: a layer of 512 dimensional bi-directional LSTM. (3) Transformer: six layers of 512 dimensional Transformer blocks. (4) Conv+Transformer: a layer of 1D CNN with kernel size 3 followed by six layers of 512 dimensional Transformer blocks.  
We first investigated the model size and the inference speed of the TTS systems. We synthesized the test set on an Intel Xeon E5-2680 CPU. As is shown in Table 1, when the model size grows, the inference speed becomes slow. The largest model with Conv+Transformer has an inference speed 39% slower than the baseline that not uses word encoder. If we use BLSTM as the word encoder, both the model size and the inference speed are very close to the baseline.  
Figure 3: Model architecture with acoustic word embeddings.  
Figure 4 illustrates the log-likelihood curves of phone-level prosodies with various word encoder architectures. In both the training and validation sets, we can observe that all the systems with acoustic word encoders outperform the baseline system that does not use word information. Moreover, the Conv+Transformer achieves the best performance out of the three common architectures. This is reasonable because it is already known that the Transformer has better capabilities than a simple LSTM [15] in sequence modeling, and the convolutional layer directly considers the adjacent word contexts. Therefore, we used Conv+Transformer in all the following experiments for the best performance.