esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor
###### Abstract
When adopted, IIoT enables intelligent data acquisition and smart decision making. Furthermore, it enables efficient, reliable, and scalable mission critical infrastructures which can be easily maintained [11]. ML has extensively been applied in the development of smart factories. Such application includes object detection, which is widely used for automatic surface inspection [12]. Contrary to popular belief, the concept of smart factory does not exclude the human factor, as humans will continue to play a key role in all operations [13], despite the influence of process automation and ML. In light of this, it is important to ensure the safety of factory workers. To achieve this, there is the need to deploy solutions, such as emergency detection systems, that can respond to the safety and health needs of persons working at the smart factory floor especially those working in isolation. Moreover, the use of ML for the enablement of such applications, is still an issue that is open for exploration. In this regard, we have explored the use of multiple sensors for the acquisition of data in the development of an emergency detection system. We also investigated different ML candidates for the most efficient and accurate emergency detection system in the smart factory. Recent research have proved that ML would not only drastically reduce the rate of false alarms drastically but would also reduce the computational cost of the system when a small dataset is used [14]. Moreover, given that the data generated from smart homes and smart factories are heterogeneous, the use of efficient ML approaches for the detection and classification of various scenarios is a massive research problem [15]. Consequently, we present a simple test-bed comprising three different sensors for the purpose of gathering the data used in the development of our emergency detection system. Our contributions can be summarized as follows:  
1. We proposed a human-centered test-bed for an emergency detection system. The test-bed is made up of sensors that measure vibration, breathing, movement, and distance location. 2. We investigated various ML candidates using the state of the art evaluation metrics. This is critical to identifying the most efficient ML solution for the development of emergency detection systems. 3. We designed a convolutional neural network (CNN) algorithm for the classification and detection of anomalous situations in the smart factory. We provided a novel dataset, which is available on demand to assist in research pertaining to emergency detection systems. The rest of this paper is organized as follows: Section 2 presents the basic concepts of ML, smart factory, and IIoT in the form of related works and establishes the research gap. Thereafter, a concise but informative explanation of the materials and methods of the testbed is presented in Section 3. Section 4 describes the performance evaluation where the various ML candidates are compared to determine the most efficient; then, Section 5 concludes the paper.
## 3 Results and Discussion  
### Morphology and Surface Roughness  
Figure 5a,b shows the laser confocal scanning microscopy images of samples _a1_, _a2_ and _b1_, _b2_ in air with calculated RMS and _Rz_ roughness, respectively. A 128 um \(\times\) 128 um scan size was used to present data. For both the superoleophilic and superoleophobic surfaces, the SiO\({}_{2}\) nanoparticles coalesced together to form a random morphology with certain roughness. It can be noted that the _Rsm_ roughness of sample _a2_ was larger than that of _a1_. This result can be explained by the SiO\({}_{2}\) nanoparticles with increasing concentration coalescing together into a larger group, which enhanced the spacing parameters of the surfaces. Therefore, the _Rsm_ roughness increased from _a1_ to _a5_ and _b1_ to _b5_, as shown in Table 1, and the _Rz_ roughness kept consistent at \(\sim\)1100 \(\pm\) 100 nm for all samples.  
Figure 5: Confocal laser scanning images in the air, and measured _Rsm_, _Rz_ roughness of (**a**) superoleophilic surface samples _a1_ and _a2_, and (**b**) superoleophobic surface samples _b1_ and _b2_.
## 3 Experiments and Analyses
### Analysis of Improved Model Results  
The core of lightweight network design is to reduce the computational and spatial complexity of the model while ensuring accuracy as much as possible and to improve the  
Figure 8: Loss curve of the bounding box.  
detection speed of the model. In order to verify the effectiveness of the model improvement, this paper further analyzed the performance of precision, recall, mAP, and F\({}_{1}\) score under three different influencing factors, such as the backbone improvement, loss function improvement, and PAN improvement, and the results are shown in Table 3.  
From Table 3, we can see that the final improved model YOLOv5s-Camellia in this paper has the highest precision, [EMAIL_ADDRESS], and F\({}_{1}\) scores of 98.6%, 98.8%, and 97.99%, respectively, which are 0.4%, 0.3% and 0.05% higher than the original YOLOv5s, verifying that the improvement measures in this paper have positive impact. However, the recall of the model has decreased by 0.3%, which is not much different from the original YOLOv5s network. The reason is that after the improvement of the lightweight of the backbone network, the depth of the network becomes shallow, which leads to the reduction of the model's ability to distinguish between positive and negative samples. In conclusion, the final improved model of this paper has better overall performance.
## 3 Results and Discussion
### Results for Time-Frequency-Domain  
The numerical search algorithm has been used to numerically solve the transcendental equations to yield the roots of the dispersion equation [40]. In the layered structure with porous medium, the dispersion function of the guided wave is a complex function. For any given frequency, the corresponding wavenumber is also complex. The real part corresponds to the phase velocity. In this subsection, we validate the formulation developed in this study with results from the elastic medium model. The purpose of the comparison is to demonstrate the accuracy and efficiency of the proposed approach. Two models are chosen: (i) a water-filled pipe embedded in infinite medium, and (ii) a water-filled pipe embedded in finite porous medium.
## 4 Discussion
### Stretching versus Stretching  
The sleeper stretch and the cross-body stretch are the most popular stretching techniques for overhead athletes and are mainly described as self-performed stretching. The sleeper stretch is performed in a side-lying position maintaining the scapula stabilized against the treatment table. However, the cross-body stretch is normally performed without any type of stabilization, allowing the scapula to move and reducing the stretching of the soft tissues [56]. The manual stabilization of the scapula seems to produce a better stretching of the tissues, showing greater benefits in internal rotation ROM than cross-body stretch without manual stabilization. Both techniques are commonly included as a standard stretching protocol, achieving both statistically significant improvements in shoulder ROM without differences between them. Thus, both techniques can be considered for the treatment of overhead athletes with GIRD as long as the scapula is stabilized.  
From a clinical perspective, the current study found that stretching is an effective intervention to improve internal rotation and horizontal adduction ROM in athletes with GIRD, and its combination with glenohumeral dorsal gliding seems to produce better benefits in internal rotation ROM compared to stretching in isolation. Each stretch should be performed for between three and five repetitions for at least 30 s, and immediate effects may be noted after the first session. The combination of the sleeper and the cross-body stretches is the most common protocol, and no differences have been found when both stretching techniques have been compared. However, the cross-body stretch has been shown to be more beneficial for improving shoulder internal rotation ROM when manual stabilization is applied. Despite that, the results should be interpreted with caution because the certainty of the evidence was rated as very low for all the outcome variables.  
Three limitations must be considered in this study. First, some studies presented a small sample size, which could be a source of bias. Second, several subgroups were considered for statistical analysis, which complicates the interpretation of the results. Third, some quantitative analysis could not be performed using mean change scores due to the lack of variability data. Future studies should assess the shoulder range of motion in the three planes, assess longer periods of follow-up, and assess the best dose.
## 4 Discussion
Nonetheless, the operator who performed the statistical analysis was not informed about which group the models or measurements belonged to. Another limitation was that inter-examiner reliability was not analyzed, but a more expert operator did review measurements before statistical analysis. In addition, only transverse measurements were assessed, and no superimpositions were performed. This decision rested on the particular importance of measuring transverse dimensions during orthodontic treatment. Making such measurements with \"Ortho Viewer\" 3 Shape Software (3 Shape, Copenhagen, Denmark) is immediate and easy for the clinician. In future, it would be of interest to analyze sagittal and vertical measurements on both conventional and AI-generated intra-oral models. A further avenue of research would be to analyze the fit of clear aligners made for AI-generated intra-oral models as compared to conventional digital intra-oral models taken from intra-oral scans. In summary, while AI-generated models demonstrated overall reliability, further prospective and controlled studies are warranted to confirm their accuracy across all spatial dimensions and in various clinical scenarios, including with and without the presence of attachments or resin grip points. Such investigations will be essential to support the broader integration of AI tools into orthodontic diagnostics and appliance fabrication workflows.
## 3 Numerical Computing Model
### Model Establishment
#### 3.1.1 Fundamental Model and Boundary Conditions  
With an extensive library of material models, Abaqus can simulate stress and deformation at low and high strain rates and at small and large strains. It especially has a huge advantage for dealing with the problems of interaction, large deformation, and nonlinearity. Consequently, it is used in our work. A numerical model established with this finite element software is shown in Figure 4. The overall size of the model is 180 m \(\times\) 40 m \(\times\) 80 m \((x\times y\times z)\), where x represents the axis of the transverse tunnel, \(y\) represents the direction  
Figure 3: The cross section of the lining structure for vehicle passage.  
Figure 2: The cross section for lining the structure of the widened tunnel.  
along the tunnel axis, and z represents the vertical direction. The actual engineering tunnel is buried at a depth of 180 m. In the numerical model, the thickness of the soil covering the top of the tunnel is approximately 35 m. The gravity load is applied directly on the top of the model. In the numerical model, the \"Tie constraint\" is utilized to describe the soil-tunnel interaction, and thereby, the deformation compatibility condition between the displacements of the tunnel and the surrounding rock is realized.  
To increase the accuracy of the model, artificial viscoelastic boundaries were established around the numerical model. Based on existing research results, the parameters of the spring-damping system at the artificial boundary node can be calculated as follows:  
\[\begin{array}{l}K_{n}=\alpha_{n}\frac{G}{c}A,C_{n}=\rho c_{p}A\\ K_{T}=\alpha_{t}\frac{c}{r}A,C_{T}=\rho c_{s}A\end{array} \tag{1}\]  
The normal stiffness coefficient and tangential stiffness coefficient of the spring are denoted as \(K_{n}\) and \(K_{T}\) respectively, while \(C_{n}\) and \(C_{T}\) represent the normal and tangential damping coefficients. The existing research results can be used to calculate these parameters. The correction coefficients for the normal and tangential viscoelastic artificial boundaries are denoted as \(\alpha_{n}\) and \(\alpha_{t}\), respectively. For three-dimensional problems, the typical range for \(\alpha_{n}\) is 1.0 to 2.0, while the range for \(\alpha_{t}\) is 0.5 to 1.0, based on existing research results. It is recommended by [PERSON] et al. (2012) to use correction coefficients \(\alpha_{n}\) = 1.33 and \(\alpha_{t}\) = 0.67. \(G\) represents the shear modulus, while \(C_{s}\) and \(C_{p}\) are the velocities of the S wave and P wave, respectively. \(\rho\) is the density of the medium, \(r\) is the distance from the wave source to the artificial boundary, and \(A\) represents the control area of a cell node. A dynamic and implicit analysis step is created in Abaqus/Standard to acquire the dynamic responses of the tunnel, which involves a direct-integration dynamic analysis method.  
Figure 4: (**a**) Overall model. (**b**) Tunnel section.
# DPMF: Decentralized Probabilistic Matrix Factorization for Privacy-Preserving Recommendation
## 4 Evaluation
### Setting
#### 4.1.2 Implicit Data  
Since DPMD is a model investigating both explicit and implicit feedback, and both _FilmTrust_ and _Epinions_ contain only explicit ratings, we need to make modifications to the original dataset. Specifically, we randomly sample user \(i\) and its rated item \(j\) from the observed data at a certain percentage \(\alpha\) to obtain the positive implicit feedback \(M_{ij}\), and the rest remains for representing the explicit feedback \(R_{ij}\). For the sake of simplicity, we set \(\alpha\) as 50% in the following experiment. Moreover, 70% of the dataset is used for training and the rest is for testing.
Sensors_**2010**, _10_, 4497-4520; doi:10.3390/s100504497  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Adaptive Square-Shaped Trajectory-Based Service Location Protocol in Wireless Sensor Networks**  
**[PERSON], [PERSON] and [PERSON] ***  
Dept. of Computer Science and Engineering, Kangwon National University, Chuncheon Gangwondo, 200-701, Korea; E-Mails: [EMAIL_ADDRESS] (J.-H.L.); [EMAIL_ADDRESS] (H.-J.L.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-01-4914-0107; Fax: +82-33-252-6390.  
_Received: 22 March 2010; in revised form: 8 April 2010 / Accepted: 14 April 2010 / Published: 30 April 2010_  
**Abstract:** In this paper we propose an adaptive square-shaped trajectory (ASST)-based service location method to ensure load scalability in wireless sensor networks. This first establishes a square-shaped trajectory over the nodes that surround a target point computed by the hash function and any user can access it, using the hash. Both the width and the size of the trajectory are dynamically adjustable, depending on the number of queries made to the service information on the trajectory. The number of sensor nodes on the trajectory varies in proportion to the changing trajectory shape, allowing high loads to be distributed around the hot spot area.  
**Keywords:** service location; trajectory; data replication; load scalability; robust
# Target Localization Based on Bistatic T/R Pair Selection in GNSS-Based Multistatic Radar System
## 1 Introduction
GNSS itself has the accurate positioning capability, so the design of the satellite constellation orbit is a better geometric configuration for multistatic positioning. On this basis, after adopting our algorithms, it can achieve more effective passive target positioning. The remaining arrangements of this paper are as follows: Section 2 briefly explains positioning method of multistatic radar and analyzes the positioning performance. Section 3 introduces the two proposed optimization algorithms in detail. The experimental simulation results are provided in Section 4. Finally, a conclusion is given in Section 5.
# A Thermodynamic Model for Cryogenic Liquid Hydrogen Fuel Tanks
## 3 Validation of Thermodynamic Model
### Validation of Evaporation Model  
Validation of the proposed model for liquid hydrogen vaporization was performed with the tank model and analysis results of [PERSON] et al. (2018). The tank had a cylindrical shape with a radius of 0.5 m and a height of 2.0 m. Liquid hydrogen was contained at a height of 1.0 m in the tank, and gaseous hydrogen filled in the remaining area of the tank. The temperature of the liquid hydrogen was 21 K, the temperature of the gaseous hydrogen was 24 K, and the pressure was 150 kPa. A heat flux of 10 W/m\({}^{2}\) was transferred uniformly through the tank body.  
To verify the prediction accuracy of the proposed model, the temperature and pressure analysis results of the gas area were compared. Figure 2 displays the results of the temperature and the pressure. The black solid line is the result of the present model, and the red open circle is the data from the reference. Figure 1(a) shows that the temperature increased as the heat inflow occurred. The gas temperature of the present model was slightly lower than that of the reference data at the end of the measured period, and the maximum relative error was 0.73%. The present analysis result for pressure represents a very similar value to that of the reference; as shown in Figure 1(b), the relative error was calculated as within 0.44%. Thus, both results produced a very slight difference of less than 0.8% relative error. The developed model predicted temperature and pressure changes well due to heat inflow and boil-off, even though it did not consider the convection coefficient inside the tank. Therefore, it can be said that the present model produced very good results.  
Figure 2: Comparison of image temperature and tank pressure for evaporation model validation.
## 3 Examples
### A nonlinear example (pure Stratonovich diffusions)  
Pure Stratonovich diffusions are governed by Stratonovich SDEs  
\[dX(t) = \sigma(X(t))\circ dW(t) \tag{11}\]  
with differentiable coefficients \(\sigma(x)\geq 0\). For our analysis, we suggest to transfer this equation to its equivalent Ito SDE (see [2])  
\[dX(t) = \frac{1}{2}\sigma(X(t))\sigma^{\prime}(X(t))dt+\sigma(X(t))dW(t) \tag{12}\]  
Now, we apply our algorithm. For this purpose, it is interesting to observe that the transform  
\[F(x) = \int^{x}\frac{dz}{\sigma(z)} \tag{13}\]  
has vanishing operator image for \(\mathcal{L}^{0}\). By chain rule, we find that  
\[\mathcal{L}^{0}F(x)=\frac{1}{2}\sigma(x)\sigma^{\prime}(x)\frac{dF(x)}{dx}+ \frac{1}{2}[\sigma(x)]^{2}\frac{d^{2}F(x)}{dx^{2}}=\frac{1}{2}\sigma^{\prime} (x)-\frac{1}{2}\sigma^{\prime}(x)=0\]Moreover, one encounters  
\[\mathcal{L}^{1}F(x)=\sigma(x)\frac{dF(x)}{dx}=\sigma(x)\frac{1}{\sigma(x)}=1\]  
Therefore, steps [1], [2] and [3] with constants \(a_{0}=a_{1}=b_{1}^{1}=0\) and \(b_{0}^{1}=1\) can be successfully conducted, and its common solution is of the form (13) with existing inverse \(F^{-1}\) (invertible since \(\sigma\geq 0\)). The trivial solution of step [4] is \(U(t)=W(t)\) with \(U(0)=F(X(0))\). Eventually, step [5] yields the solution  
\[X(t)=F^{-1}(U(t))\]  
As a fairly simple sub-example, one can treat  
\[dX(t)=\frac{m^{2}}{4}dt+m\sqrt{X(t)}dW(t)=m\sqrt{X(t)}\circ dW(t)\]  
with \(X(0)\geq 0\), which is also known as Bessel-type diffusion with dimension parameter \(m>0\). Here,  
\[F(x)=\frac{1}{m}\int^{x}\frac{dz}{\sqrt{z}}=\frac{2}{m}\sqrt{x}\]  
Hence, its inverse is \(F^{-1}(x)=\left(\frac{m}{2}x\right)^{2}\). Therefore, the algorithm gives the positive solution  
\[X(t)=\left(\frac{m}{2}W(t)+\sqrt{X(0)}\right)^{2}\]
## 3 Conventional Detection Scheme
### Reduced-Complexity Sequential Detection (RCSD) Scheme  
Based on the symmetric property of the PSS, a sequential IFO and SID detection algorithm is presented in [18]. To mitigate the effect of the fading distortion, the channel-compensated metric is computed using normalized PSS [18]  
\[\Phi_{b}(m)=\frac{1}{N_{p}/2-1}\sum_{k\in\mathcal{D}^{+}}\hat{Y} _{l}(k+m)\hat{Y}_{l}(-k+m+1), \tag{10}\]  
where \(\hat{Y}_{l}(k)=\hat{Y}_{l}(k)/|\hat{Y}_{l}(k)|\) and \(\Phi_{b}(m)\) represent the metric to obtain the IFO in the RCSD scheme. The estimated IFO \(\hat{v}\) is considered as the argument \(m\) that minimizes the cost function \(|\Phi_{b}(m)-1|\)  
\[\hat{v}= \underset{|m|\leq M}{\arg\min}\left|\Phi_{b}(m)-1\right|. \tag{11}\]  
The SID detection is sequentially performed with one-dimensional, search space  
\[\hat{n}= \underset{n\in\{0,1,2\}}{\arg\max}\Phi_{c}^{I}(n), \tag{12}\]  
where \(\Phi_{c}(n)\) means the metric to obtain the SID in the RCSD scheme given by  
\[\Phi_{c}(n)=\sum_{k\in\mathcal{D}^{+}}\{\tilde{Y}_{l}(k+\hat{v}) +\tilde{Y}_{l}^{*}(-k+\hat{v}+1)\}D_{n}^{*}(k). \tag{13}\]
# NewApproach to Predict the Motion Characteristics of Single Bubbles in Still Water
## Materials and Methods
### Machine Learning Approaches
#### 2.3.1 Regression Problem-Solving  
Feed forward back propagation neural network (FBNN) has great applicability to deal with nonlinear regression problems. For this technique, two processes are involved, which are forward propagation of input data from the input layer through the hidden layer to the output layer and back propagation of error in each layer and constant adjustment of the weight and bias to obtain the optimised outcomes. The mathematical expression of the general concept for feed forward back propagation neural network is described as follows:  
Assume \(Y_{i}^{n}\) refers to the output of the \(i\)-th neuron in the \(n\)-th layer of the neural network, then  
\[Y_{i}^{n}=f(w_{i1}x_{1}+w_{i2}x_{2}+\cdots+w_{in}x_{n}+b_{i}) \tag{1}\]  
where x is the input, \(Y\) is the output, \(w\) is the weight, and \(b\) is the bias.  
It is an important step to activate the neurons using the transfer function, where the commonly used transfer function is tansig function, as shown in Equation (2):  
\[f(x)=\tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{2}\]  
In this study, FBNN was selected to develop models for the final velocity and drag coefficient prediction of single bubbles, where Re, Eo, and We numbers were chosen as the input features.  
Re number is an important dimensionless quantity in fluid mechanics used to predict the flow patterns in different fluid flow situations. It can be defined as the ratio of inertial force to viscous force. The formula of Re number is given as:  
\[\mathrm{Re}=\frac{\rho_{l}V_{T}d}{\mu_{l}} \tag{3}\]  
where Re is Reynolds number, \(\rho_{l}\) is density of liquid, \(V_{T}\) is final velocity of bubble, \(d\) is diameter of bubble, and \(\mu_{l}\) is viscous force of bubble. In this case, such a number was fixed in the range of 100 to 3200.  
Eo number is another key element in fluid mechanics, particularly while measuring the importance of gravitational forces compared to surface tension and characterizing the single bubble motion. The Eo number can be obtained via:  
\[\mathrm{Eo}=\frac{gd^{2}\Big{(}\rho_{l}-\rho_{g}\Big{)}}{\sigma} \tag{4}\]  
where Eo is Eotvos number, \(g\) is gravitational acceleration, \(\rho_{l}\) is density of liquid, \(\rho_{g}\) is density of gas phase, and \(\sigma\) is surface tension. In this study, Eo number ranged from 0.5 to 18.  
Meanwhile, We number also plays an important role in fluid mechanics, especially for studying the single bubble motion. It is a measure of the relative importance of the fluid inertia compared to its surface tension. The formula for We number calculation is:  
\[\mathrm{We}=\frac{\rho_{l}V_{T}^{2}d}{\sigma} \tag{5}\]  
where We is Weber number, \(\rho_{l}\) is density of liquid, \(V_{T}\) is final velocity of bubble, d is diameter of bubble, and \(\sigma\) is surface tension. Throughout the experiments, such a value was set within a range of 0 to 16.  
The general architecture of the prediction models was shown in Figures 3 and 4.