esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
## 3 Model Design
### Extended ELM Model Structure Design
The average accuracy of the model's five training sessions is output, and the k-fold cross-verification technique (5-fold) is used in the training process. The tanh function is used as an activation function in each layer, which can be expressed by the formula  
\[\rm f(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{8}\]  
Then, the output of the full connection layer is input into the ELM model, the ELM model categorizes the data, and finally the vector classification results are output. The loss function of the model uses categorical cross-entropy, which is used to describe the distance between two distributions. The formula is  
\[\rm J=-\sum_{i=1}^{K}y_{i}\log(p_{i}) \tag{9}\]  
where K is the number of categories, y represents the label, and p is the output of the neural network [22]. In the process of iteration, the root mean square prop (RMSProp) optimization algorithm is used to optimize the model. It has the advantages of small swing amplitude in update and fast convergence speed of loss function. Each formula is as follows:  
\[\rm s_{dw}=\beta s_{dw}+(1-\beta)dW^{2} \tag{10}\]  
\[\rm s_{db}=\beta s_{db}+(1-\beta)db^{2} \tag{11}\]  
\[\rm W=W-\alpha\frac{dW}{\sqrt{s_{dw}}+\epsilon} \tag{12}\]  
\[\rm b=b-\alpha\frac{db}{\sqrt{s_{db}}+\epsilon} \tag{13}\]  
In the above formulas, \(\rm s_{dw}\) and \(\rm s_{db}\) are the accumulated gradient momentum of the loss function in the previous (\(t-1\)) iteration, and \(\beta\) is an index of gradient accumulation. The RMSProp algorithm corrects the swing amplitude by calculating the weighted average value of the square of the gradient differential, which makes the swing amplitude smaller, so the network converges very fast. The above is the introduction of the algorithm theory and formulas applied in the experimental process. The next section will discuss the simulation results of the extended ELM model in detail.
## 4 Discussion
### Quality of Studies  
The quality of studies was found to be predominantly good, scoring in the 60-79% quantile of the Crowe Quality Assessment bracket. However, most studies scored poorly in their results section, averaging 2/5, demanding further research with stronger methodological rigour. Studies tended to lack statistical analysis to corroborate their findings. For example, most studies claimed radiation equivalence of their phantoms to patients; however, they did not conduct any tests to confirm equivalence [18; 30; 32; 33; 34; 35; 36; 38; 39; 40; 41; 42; 44; 45; 48]. Studies were additionally biased by evaluating their phantom attenuations using different CT scanners and protocols to their patient counterparts [34; 36; 37; 39; 42; 45; 46; 47]. Controlling these parameters is paramount as HU values are influenced by different scanners and different voltages [51]. X-ray attenuation not only depends on the physical density and effective atomic number of the material but also on the energy of the X-ray photons [52]. Materials with a low effective atomic number, such as adipose tissue, exhibit increased Hounsfield Units (HU) with higher energy photons. Conversely, materials with a higher effective atomic number, such as bone and calcium, exhibit lower HU with higher energy photons due to the greater ease with which the X-ray beam penetrates them, diminishing photoelectric absorption [48; 51; 52]. Appreciably, sourcing the exact scanner poses a practical challenge, given the diverse brands and types available.  
Studies were also limited by not detailing phantom costs and printing times. Only six studies reported costs, ranging from AUD 64-5500 [18; 30; 32; 37; 40; 41] and seven studies reported manufacturing time, ranging from 7 h to 12 days [18; 30; 32; 35; 40; 41; 44]. Future studies should prioritise transparency by thoroughly documenting their research methodologies, allowing for replication and validation. Although there is limited transparency regarding costs, the reported expenses are notably more affordable than commercial anthropomorphic phantoms, which can reach exorbitant prices upwards of AUD 40,000 [53]. This, coupled with the growing accessibility of 3D printers and printing materials to the general public, makes 3D-printed phantoms an attractive option [8].
## 3 Experimental Results and Analysis
### Experimental Analysis  
The PyTorch deep learning framework and PyCharm (Python 3.8) within Anaconda 4.12.0 were used as the simulation platform. For hardware configuration, we utilized an i9-11900K CPU operating at a frequency of 3.50 GHz with 64 GB of memory, accompanied by an NVIDIA RTX3090 GPU with a memory capacity of 24 GB. We used the NVIDIA RTX3090 for inference evaluation.  
In this section, relevant experiments are carried out to analyze the influence of the number of transformer components on the precision and speed of pose inference in the translation transformer and orientation transformer subnetworks. The influence of different activation functions as the output layer of orientation quantity on its precision is analyzed. Finally, the advantages and disadvantages of this method are compared with other methods through comparative experiments.
## 3 Evaluation  
In this section, we present the experimental results of the active contour methods defined in the previous section for comparison purposes. We also explain the implementation of the methods. To evaluate the effectiveness of the active contour methods, seven suitable measures applied, namely: Dice, Jaccard, Bfscore, Precision, Recall, Iteration and Time. If the first five measures score better (closer to one or closer to 100%), then the implemented disease investigation scheme is confirmed as a better procedure. If \(I_{O}\) and \(I_{GT}\) represent the binary output image of the segmentation result and the binary image of the ground truth, respectively, the measures are defined as follows:  
\[\text{Precision}=\frac{TP}{TP+FP}, \tag{18}\] \[\text{Recall}=\frac{TP}{TP+FN},\] (19) \[\text{Jaccard}=\frac{\left|I_{o}\cap I_{g}\right|}{\left|I_{o} \cup I_{g}\right|},\] (20) \[\text{Dice}=\frac{2\left|I_{o}\cap I_{g}\right|}{\left|I_{o} \right|+\left|I_{g}\right|},\] (21) \[\text{Bfscore}=\frac{2\times Precision\times Recall}{Recall+ Precision}, \tag{22}\]  
where TP, FP and FN are true positive, false positive and false negative rates.  
Figure 6: Samples of images of COVID-19 database and their ground truth (a binary image where the white parts represent the area of infection).
## 3 Materials and Methods
### Research Methodology  
#### 3.2.1 Convolutional Neural Networks: Main Aspects  
CNN refers to a computational method that mimics the functionality of the brain's neurons. CNNs include input, hidden, and output layers, where each layer consists of nodes connected by edges. The term deep neural network applies to a CNN with at least two hidden layers. CNNs have demonstrated great accuracy and efficiency and are trustworthy for use in image recognition tasks. One of their main advantages is that they can operate effectively with only images as input and do not need visual extraction of features [31]. CNNs have established their position in medical image analysis based on their fascinating results [32]. Each layer is described in detail below.  
The first layer is the convolutional layer, for which the name denotes the type of neural network. The primary block is a convolutional layer comprising filters that use the convolution operation to build activation maps. Activation maps are made up of extracted patterns based on the input images and are in charge of classifying unseen data.  
After that is the pooling layer, inserted after each convolutional layer and downsamples the picture while discarding pixel values categorized as noise. As a result, the computational time decreases, and the pixel values that are relevant to the structure of the CNN are forwarded according to the dataset.  
Following that, a dropout layer is added to avoid overfitting. Dropout sets random pixel values to zero so that they are not included in the training method, reducing computing time.  
Following that, we have the flatten layer, which turns multi-dimensional data into vectors.  
Finally, fully connected layers are applied, connecting each node to the preceding one and calculating the prediction using activation functions. RELU (rectified linear unit) was used for the convolutional layers and softmax was used for the output activation functions [5].
# Photogrammetric Measurement and Analysis of the Shape Profile of Pneumatic Artificial Muscles
###### Abstract  
Inaccuracies in modeling of the geometric shape of PAMs has long been cited as a probable source of error in modeling and design efforts. The geometric shape and volume of PAMs is commonly approximated using a cylindrical shape profile, even though its shape is non-cylindrical. Correction factors--based on qualitative observations of the PAM's general shape--are often implemented to compensate for error in this cylindrical shape approximation. However, there is little evidence or consensus on the accuracy and form of these correction factors. Approximations of the shape profile are also used to calculate the internal volume of PAMs, as experimental measurements of the internal volume require intrusive testing methods and specialized equipment. This research presents a photogrammetric method for measuring the shape profile and internal volume of PAMs. A test setup, method of image data acquisition, and a preliminary analysis of the image data, is presented in this research. A 22.2 mm (7/8 in) diameter PAM is used to demonstrate the photogrammetric procedure and test its accuracy. Analysis of the tested PAM characterizes trends of the shape profile with respect to pressure and contraction. The common method of estimating the diameter--through the use of the cylindrical approximation and initial geometry of the PAM--is tested by comparison to the measured shape profile data. Finally, a simple method of calculating the internal volume using the measured shape profile data is developed. The presented method of acquiring photogrammetric measurements of PAM shape produces an accurate characterization of its shape profile, thereby mitigating uncertainty in PAM shape in analysis and other efforts.  
Keywords:McKibben actuators; pneumatic; hydraulic; photogrammetry +
Footnote †: journal: _Actautators_
## 2 Shaking Table Test of LNG Storage Tank Model
### Test Model  
This paper takes an actual large-scale LNG storage tank as a reference. Considering the complexity of its structure, a simplification was made when designing the structure model storage tank. It is necessary to ensure that the prototype structure is similar to the experimental structure during the design, but it is difficult to meet this requirement in most cases. Therefore, the storage tank structure model only retains the main structure of large LNG storage tank, such as the outer tank, pile foundation, dome and inner tank, ignoring the structures that do not bear weight, such as the aluminum ceiling, steel dome and auxiliary pipelines.  
The geometric dimensions and material parameters of the tank model are as follows. The height of the outer tank wall is 2 m, and the sagittal height of the dome is 0.2 m. The inner diameter of the outer tank wall is 2.5 m, and the thickness of the outer tank wall is 0.2 m. The distance between the outer tank and the inner tank is 0.35 m, the diameter of the inner tank is 0.9 m, the height of the inner tank is 1.5 m, and the wall thickness of the inner tank is 5 mm. There are 13 pile foundations in the model tank. For the seismic storage tank, the pile foundation and the bearing platform of the storage tank are directly connected. For the seismic storage tank, lead -core rubber bearings are arranged between the pile foundation and the bearing platform of the storage tank, as shown in Figure 1a,b. The diameter of the pile is 0.4 m and the length of the pile is 0.3 m. The plans of the seismic storage tank and isolation storage tank are shown in Figure 1. The outer tank is made of C50 concrete with an elastic modulus, Poisson's ratio and density of 34.5 GPa, 0.167 and 2500 kg/m\({}^{3}\), respectively. The inner tank is made of steel with and elastic modulus, Poisson's ratio and density of 210 GPa, 0.3 and 7800 kg/m\({}^{3}\), respectively.  
The earthquake shaking table is a three-dimensional horizontal excitation hydraulic drive device. The specific parameters of this device are as follows: the size of the table is 6 m \(\times\) 6 m; the maximum load capacity is 60 t; the maximum anti-overturning moment is 1800 kN\(\cdot\)m; the limit displacement of the table is \(\pm\) 250 mm. The measurement point layout of the storage tank model is shown in Figure 1. Acceleration sensors and displacement sensors were arranged in the experiment. According to the structural characteristics of the storage tank, acceleration sensors were arranged at the pile foundation, the bearing platform, the height of the center of mass, and the dome, and the displacement sensor was arranged at the bottom of the dome. Figure 2a is the test model of the storage tank, and Figure 2b is the actual layout of the acceleration sensor. The acceleration sensor selected for the test was Endevco 7290E (Endevco Corporation, Irvine, CA, USA); the acceleration range that can be tested was \(\pm\)10 g. The Endevco 7290E is a rugged, variable capacitance accelerometer with integral electronics for voltage regulation, filtering, and signal amplification.
## 3 Results
These findings highlight physiotherapy as having a positive impact on several of the most disturbing symptoms in patients suffering from COVID-19. The second factor represents 13.9% of the total variation of the data set and is represented by the parameter pulse at admission/at discharge. Pulse values at admission are one of the clinical parameters that show a statistical difference between patients diagnosed with COVID-19 who participated in mild/active therapy (\(p\) = 0.024). \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Variables** & **Rank Sum** & **Rank Sum** & **U** & **Z** & **_p_ & **Z** & **_p_ \\  & **Mild PT** & **Active PT** & & & & **Adjusted** & **Adjusted** \\ \hline Ferritin at admission & 502.500 & 532.500 & 177.500 & \(-\)1.645 & 0.100 & \(-\)1.645 & 0.100 \\ \hline Ferritin at discharge & 544.000 & 491.000 & 219.000 & \(-\)0.697 & 0.486 & \(-\)0.697 & 0.486 \\ \hline IL-6 at admission & 459.500 & 575.500 & 134.500 & \(-\)2.627 & 0.009 & \(-\)2.627 & 0.009 \\ \hline IL-6 at discharge & 471.500 & 563.500 & 146.500 & \(-\)2.353 & 0.019 & \(-\)2.354 & 0.019 \\ \hline CRP at admission & 496.500 & 538.500 & 171.500 & \(-\)1.782 & 0.075 & \(-\)1.782 & 0.075 \\ \hline CRP at discharge & 488.500 & 546.500 & 163.500 & \(-\)1.964 & 0.049 & \(-\)1.964 & 0.049 \\ \hline Dyspnea at admission & 517.000 & 518.000 & 192.000 & \(-\)1.313 & 0.189 & \(-\)1.600 & 0.110 \\ \hline Dyspnea at discharge & 512.500 & 522.500 & 187.500 & \(-\)1.416 & 0.157 & \(-\)2.601 & 0.009 \\ \hline Pulse at admission & 476.000 & 559.000 & 151.000 & \(-\)2.250 & 0.024 & \(-\)2.253 & 0.024 \\ \hline Pulse at discharge & 554.000 & 481.000 & 229.000 & \(-\)0.468 & 0.640 & \(-\)0.470 & 0.639 \\ \hline Oxygen saturation on admission & 666.000 & 369.000 & 159.000 & 2.067 & 0.039 & 2.093 & 0.036 \\ \hline Oxygen saturation on discharge & 655.500 & 379.500 & 169.500 & 1.827 & 0.068 & 1.905 & 0.057 \\ \hline Dry cough at admission & 549.000 & 486.000 & 224.000 & \(-\)0.582 & 0.560 & \(-\)0.668 & 0.504 \\ \hline Dry cough at discharge & 500.000 & 535.000 & 175.000 & \(-\)1.702 & 0.089 & \(-\)2.889 & 0.003 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Biological and clinical variables (mild vs. active physiotherapy). Figure 3: Graphical representation of factor weights in the case of patients who performed mild and active physiotherapy. The third factor represents 12.5% of the total variation of the data set and is represented by the oxygen saturation values at admission/at discharge. The results of the Man-Whitney U test showed that the parameter oxygen saturation at hospitalization has a statistical difference between the groups of patients who participated in mild/active physiotherapy (\(p=0.036\)). In fact, oxygen saturation values are a significant parameter in the diagnosis of patients with COVID-19, especially in establishing the severity of the disease.
## 3 Results and Discussion
### Comparative LCA of Hydrogen Production: Biogas Reforming vs. Agricultural Residue
#### 3.2.3 Human and Ecosystem Toxicity  
This study also assessed the toxicity impacts on human health and ecosystems. The human non-carcinogenic toxicity (HNCT) impact from biogas reforming (23.88 kg 1,4-DCB eq) is substantially higher compared to gasification (1.506 kg 1,4-DCB eq). Similarly,  
\begin{table}
\begin{tabular}{l c c} \hline
**Impact Category** & **H\({}_{2}\) Production by** & **H\({}_{2}\) Production from** \\  & **Gasification (\textgreater{}99\%)** & **Biogas Reforming (\textgreater{}99\%)** \\ \hline GWP (kg CO\({}_{2}\) eq) & 1.298778438 & 5.04713142 \\ SOD (kg CFC11 eq) & 2.14344 \(\times\) 10\({}^{-5}\) & 2.72936 \(\times\) 10\({}^{-5}\) \\ IR (kBq Co-60 eq) & \(-\)0.035526092 & 0.37295624 \\ OFHH (kg NOx eq) & 0.002332391 & 0.01257/7232 \\ FPMF (kg PM2.5 eq) & 0.001513217 & 0.02432786 \\ OFTES (kg NOx eq) & 0.002376209 & 0.012821774 \\ TA (kg SO\({}_{2}\) eq) & 0.004978886 & 0.121065517 \\ FEW (kg P eq) & 0.000315479 & 0.00413299 \\ ME (kg N eq) & 6.98639 \(\times\) 10\({}^{-6}\) & 0.006735414 \\ TET (kg 1,4-DCB) & 4.148159856 & 33.44363458 \\ FWET (kg 1,4-DCB) & 0.03321721 & 0.413201296 \\ MET (kg 1,4-DCB) & 0.049880356 & 0.562196103 \\ HCT (kg 1,4-DCB) & 0.018535482 & 0.323963195 \\ HNCT (kg 1,4-DCB) & 1.508073488 & 2.28455044 \\ LU (m \(\simeq\) a crop eq) & 0.015720069 & 3.683536534 \\ MRS (kg Cu eq) & 0.0106641388 & 0.048885568 \\ FRS (kg oil eq) & 0.330117896 & 1.10519594 \\ WC (m\({}^{3}\)) & 5.373767301 & 0.040951105 \\ NRE (MJ eq) & 0.457 & 1.05 \\ CED (MJ eq) & 0.685 & 1.95 \\ \hline \end{tabular}
\end{table}
Table 5: LCA comparative results of H\({}_{2}\) production processes.  
terrestrial ecotoxicity (TET) is much more pronounced in biogas reforming (33.44 kg 1.4-DCB eq) than in gasification (4.15 kg 1,4-DCB eq), underscoring the lower environmental impact of gasification in terms of chemical toxicity. Figures 4 and 5 visually illustrate these differences, emphasizing the higher environmental burdens associated with biogas reforming across multiple impact categories. Table 5 shows that biogas reforming generates higher toxic emissions due to residual contaminants in feedstocks and the combustion of fossil-based gas. On the other hand, biomass gasification benefits from cleaner combustion pathways, reducing hazardous emissions.
# Improving Energy Efficiency on SDN Control-Plane Using Multi-Core Controllers
## 1 Introduction
### SDN on Data Center Networks (DCN)
Many works have used network programmability to enhance energy efficiency [9; 10; 11; 12; 13; 14]. Most works use already known or new routing algorithms that optimize for energy-consumption by turning off links or entire switches [9; 13; 14; 15]. Another common strategy is to distribute the load of switches among many controllers [10; 12]. [PERSON] et al. [11] defined the best controller location to minimize energy consumption, while [PERSON] et al. [16] used the northbound interface to migrate applications and virtual machines to better meet Service Level Agreements (SLA) while minimizes link usage. Despite the existence of many solutions for energy-efficiency on SDN, only a few of those consider the control-plane consumption. The justification for this is that, in the early days of SDN, it was common to have one controller tied to many switches so the data-plane energy consumption was much higher than the control-plane, thus deserving more attention; however, now, as many works have demonstrated [17; 18; 19], the recommendation is to have many controllers load-balancing the control of switches. Furthermore, many solutions add tasks to the controller without accounting for the impact on energy consumption of the now much more busy components. Most of the research is focused on data-plane efficiency and neglects the energy cost of high demanding algorithms on the controller such as genetic algorithms [12] or new routing algorithms [13]. In addition, many of the control-plane solutions consider a single controller [9; 13; 14] or for multi-controller all the controllers must have full and synchronized knowledge of the network topology. Since the controller's role is crucial to the network operation, it must be protected by redundancy. The parallel implementation is capable of dealing with many instances of the controller running including a physically distributed manner. An important innovation presented here is a deeper look at controllers' energy consumption beyond the traffic and network interfaces considering the processor power consumption. The novel parallel implementation allows leveraging the energy efficiency on current off-the-shelf processors already present on DCN.
# In-flight Wind Field Identification and Prediction of Parafoil Systems
## 1 Introduction
Therefore, it is desirable to develop a novel online wind field estimation method for parafoil systems using only the GPS information during the flight, which can estimate the wind field at different altitudes and can be achieved with reduced cost and complexity compared to traditional methods. In this paper, we study a combination method of in-flight wind field identification and prediction for parafoil systems. Firstly, a wind field identification method is derived for online wind field identification. Then, we derive a wind field prediction model based on the atmospheric motion during an airdrop. Combined with the wind field identification results, the proposed prediction model is used to predict the altitude dependent wind fields in lower altitudes. Finally, the effectiveness of the proposed methods is verified by different simulation cases. The remainder of this paper is organized as the following. In Section 2, a dynamic model of parafoil systems is given briefly. A wind field identification method using only GPS information is discussed in Section 3. In Section 4, a wind field prediction model and an average wind field identification-prediction method are presented. Section 5 discusses the simulation results before we conclude the paper by Section 6.