esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Cloud Approach for Connected Vehicles
## 5 Evaluation
### Simulation Results  
#### 5.1.1 Simulator Description  
In order to validate the proposed models, we developed a simulator from scratch and Figure 17 is an example of a screenshot.  
This time-discrete simulator (with a step of 100 ms) takes into account the communication range of the vehicles and the RSUs. The mobility of the vehicles is also handled. Each vehicle emulates the full ETSI stack (geonetworking, security layer, and cooperative awareness message (CAM)) and simulates NDN, RENE, and our proposed approaches. According to the already discussed working hypothesis, it uses a Manhattan city topology and the vehicles are homogeneously spread. At the start, RSUs are randomly placed on the junctions and the vehicles are stationary. After the setting up, the core of the simulator is awakened, making the vehicles move at a constant speed and proceeding to the ICN exchanges between the ITS stations. After a threshold, the RSUs start transmitting at a regular frequency for 10 s. At the end of these 10 s, we save the results on the network overhead and the data freshness. Then, the process is repeated 20 times from the RSU placement step. Finally, we average the results.  
Table 8 shows the specifications of the computer that the simulations have been run on. Due to these performances, we were limited to small-scale default values for every variable, which are given in Table 9. However, despite these limitations, we manage to distinguish the curve's trend. Let us note that the values are realistic and based on our observations in European C-ITS pre-deployment projects Scoop, InterCor, and C-Roads.  
Figure 17: Simulation Extract.
# Drone and Controller Detection and Localization: Trends and Challenges
###### Abstract  
Unmanned aerial vehicles (UAVs) have emerged as a rapidly growing technology seeing unprecedented adoption in various application sectors due to their viability and low cost. However, UAVs have also been used to perform illegal and malicious actions, which have recently increased. This creates a need for technologies capable of detecting, classifying, and deactivating malicious and unauthorized drones. This paper reviews the trends and challenges of the most recent UAV detection methods, i.e., radio frequency-based (RF), radar, acoustic, and electro-optical, and localization methods. Our research covers different kinds of drones with a major focus on multirotors. The paper also highlights the features and limitations of the UAV detection systems and briefly surveys the UAV remote controller detection methods.  
Unmanned aerial vehicles (UAVs); detection technologies; radio frequency-based (RF); radar; acoustic; electro optical; hybrid fusion; controller detection
# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 2 Methodology  
### Data Collection  
The dataset from LUCIA Valladolid includes all variations in energy demand for HVAC systems over a year and corresponds to 8760 h as shown in Figure 1a. This dataset will be very useful in showing the variation in energy consumption due to seasonal changes, daily patterns, and other operational conditions. With this broad dataset, researchers and energy managers will be able to find peak demand periods, evaluate the efficiency of systems, and create strategies for energy optimization in HVAC systems. This could also be helpful in predictive modeling and implementation of demand response strategies. The FUHEM building dataset, containing 100 data points collected randomly, refers to heating energy demand in Madrid as shown in Figure 1b. This dataset gives insight into how various factors influence energy consumption for heating, such as building design, occupancy patterns, and external weather conditions. With the analysis of these data points, stakeholders will have a better understanding of the thermal performance of buildings in urban environments--something quite critical to enhancing energy efficiency. The findings deduced from this dataset will provide insightful suggestions for retrofitting strategies and designing more sustainable buildings. Complementing the analysis of the FUHEM building, the EII dataset includes 260 random data points related to both heating and cooling demands as shown in Figure 1c. This dataset provides a broader perspective on energy consumption across different types of buildings and usage scenarios. By examining both heating and cooling demands, researchers can identify trends and correlations that are essential for developing integrated energy management solutions. The insights gained from the EII dataset can support the design of heating and cooling systems that are more adaptable to varying climate conditions and user requirements [33].  
Figure 1: Scheme for the buildings in Spain: (**a**) LUCIA building (nZEB), (**b**) FUHEM building and (**c**) EII building.
# Efficient Low-Complexity Turbo-Hadamard Code in UAV Anti-Jamming Communications
## 5 Conclusions  
In this paper, an improved turbo-Hadamard code based on tail-biting is proposed for anti-jamming in a UAV FH system. For the encoder side, the proposed overall turbo-Hadamard codes are constructed by concatenating several tail-biting convolutional-Hadamard codes in parallel. For the decoder side, we proposed a low-complexity decoding algorithm for tail-biting turbo-Hadamard codes. Simulation results showed that the proposed encoder scheme can achieve a significant performance in transmission efficiency over the canonical turbo-Hadamard codes, and the corresponding decoder can achieve an outstanding performance in terms of the hardware implementation complexity over the existing low-rate codes. Furthermore, based on the simulation results, our proposed tail-biting turbo-Hadamard codes can help to combat malicious jamming in the UAV communication scenario. In our future work, we will continue to improve the tail-biting turbo-Hadamard codes, making them suitable for more complex UAV communication scenarios with strong anti-jamming ability. It would also be interesting to design a more efficient algorithm of even lower complexity which could take advantage of prior knowledge regarding the channel state information by employing machine learning.  
**Author Contributions:** Formal analysis, X.D., M.H., X.Y. and X.M.; writing--original draft, X.D.; writing--review and editing, X.D., M.H., X.Y. and X.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Scientific Foundation of China under grants 62101050, 62001022, U1836201, and National Key Research and Development Program of China under grants 2019 YFB1803200 and 2021 YFC3320200.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images
## 2 Filtering Methods
### PDE-Based Filter
#### 2.4.4 Analysis of PDE Filtering Methods  
As the PDE filtering methods have achieved good performance in de-speckling for ultrasound images [7], it is necessary to discuss their details here. By observing the three algorithms of the PDE methods, a diffusion model can be divided into isotropic diffusion and anisotropic diffusion, and simply expressed as: \(I_{\int\limits_{f}}(t)=I_{g}(t)+\varphi\), where \(\varphi\) is the diffusion function, \(I_{g}(t)\) is the contaminated image, and \(I_{\int\limits_{f}}(t)\) is the result of diffusion. In isotropic diffusion, \(\varphi\) is the function of \(\big{|}\
abla I_{g}(t)\big{|}\). While in anisotropic diffusion, \(\varphi\) is the function of \(\
abla I_{g}(t)\). Then the characteristics of PDE can be drawn as follows.  
1. Either isotropic or anisotropic diffusion can improve the clarity of the edge detail. It can make a blurred region be sharper after diffusion.
2. In the case of ignoring the influence of additive noise, the diffusion process can be expressed as the process of convergence from blurred edges to ideal ones.  
Additive noise in medical ultrasound images, however, has brought greatly negative effects to the diffusion process. For instance, in the WJD model, additive noise can be mistaken as the edge part. Moreover, in the TV model, additive noise leads to serious influence on the total variation minimization process. That is to say, the diffusion process tends to be regarded as an inverse degradation process, but little related to the removal of additive noise. Therefore, we usually conduct image de-noising for additive noise before diffusion in medical ultrasound images. From the histogram of image in smoothing area, we can easily find that additive noise is similar to a GWN. As a result, many filters based on the diffusion method choose the Gaussian low-pass filter to restrain noise.  
Furthermore, for the process of inverse degradation, we can use the property of the diffusion equation to make the blurred edge converge to the ideal edge. In practical applications, the diffusion cannot be completed at once, but as an iterative process:  
\[I_{\int\limits_{f}}(t)=I_{g_{n}}(t)=I_{g_{0}}(t)+\varphi(\
abla I_{g_{0}}(t)  
For the same diffusion results, we can increase \(\varphi_{i}\) to reduce the iterative time. However, in fact, the greater \(\varphi_{i}\) enhances the negative impact of noise. Assuming that there is a pulse as noise, if \(\varphi_{i}{}^{\prime}>\varphi_{i}\), then  
\[I_{g_{n}}(t)-I_{g_{0}}(t)=\sum_{i}^{n}\varphi_{i}{}^{\prime}>\sum_{i}^{n} \varphi_{i} \tag{33}\]  
Obviously, we need to set \(\varphi_{i}\) rationally to reach the balance between iterative time and noise-sensitiveness.
## 4 Results and Discussion
### Correlation for Orifice Coefficient  
Engineers usually pay more attention to the correlation between the orifice coefficient and the geometrical and operational parameters in application. Though the orifice coefficient was usually expressed as the function of Reynolds number [26], which is not easy to obtain in the leakage process of RAPH, a dimensionless correlation was proposed to evaluate effects of the gap size, seal sheets number, spacing of adjacent sealing sheets, and the total pressure difference on the orifice coefficient.  
\[C_{d}=\left(\frac{x}{h_{2}}\right)^{A}\left(\frac{\Delta p_{tot}}{p_{0}}\right) ^{B}\left(Z\right)^{C}\left(\frac{d}{W}\right)^{D} \tag{7}\]  
where \(p_{0}\) (101.325 kPa) is the ambient pressure; \(h_{2}\) (150 mm) and \(W\) (400 mm) are the height and weight of the seal sheet, respectively; and the power values of \(A\), \(B\), \(C\), and \(D\) are assumed constant. These values were determined by using nonlinear fitting according to the data obtained. Table 2 presents the power values in Equation (7) for the most common cases of \(\alpha=\pm 25^{\circ}\) in application, which would  
Figure 10: Contours of velocity at different bending angles.  
Figure 9: Effect of different bending angle on leakage.  
provide theoretical reference for the design and improvement of seal system in the rotary regenerative air preheater.
###### Abstract  
This study aimed to investigate the post-activation performance enhancement effects of drop jumps (DJs) on lower limb stiffness [leg stiffness (K\({}_{\text{leg}}\)); vertical stiffness (K\({}_{\text{vert}}\))] and the related mechanical [maximal ground reaction force (F\({}_{\text{max}}\)), vertical displacement of the centre of mass (\(\Delta\)y), and lower limb length (\(\Delta\)L)] and kinematic parameters [step length (SL), step frequency (SF), flight time (FT), and contact time (CT)] during high-speed treadmill running. For this purpose, 18 male physical education students performed 10 s running bouts on a treadmill at a speed of 6.67 m\(\cdot\)s\({}^{-1}\) in an experimental condition (EC) or in a control condition (CC). During the EC, following a 5 min treadmill running warm-up at 2.22 m\(\cdot\)s\({}^{-1}\), the participants were tested pre and 3 min post five DJs, while during the CC, the participants did not perform DJs. The study results revealed that EC significantly increased F\({}_{\text{max}}\), \(\Delta\)y, SL, and FT, while SF decreased after performing drop jumps (mean differences: 0.026 \(\pm\) 0.007 kN, \(p\) = 0.003; 0.001 \(\pm\) 0.001 m, \(p\) = 0.025; 0.034 \(\pm\) 0.013 m, \(p\) = 0.019; 0.05 \(\pm\) 0.001 s, \(p\) = 0.005 and \(-\)0.057 \(\pm\) 0.023 Hz, \(p\) = 0.026, respectively). K\({}_{\text{leg}}\) and K\({}_{\text{vert}}\) showed no significant differences (\(p\) > 0.05) following the preconditioning exercise. The findings suggest that a pylemetric stimulus of low volume and a short recovery period prior to high-speed treadmill running may be beneficial to acutely improve running mechanical and kinematic parameters without affecting lower limb stiffness.  
acute effect; running mechanics; running kinematics; stiffness Article
# A Status Evaluation of Rock Instability in Metal Mines Based on the SPA-IAHP-PCN Model
## 1 Introduction
Compared to previous methods, the IAHP-SPA model further mitigates the impact of expert judgment biases, enabling more reliable parameter weight values. Due to the prolonged duration of metal mining operations, solely performing a static evaluation of the rock instability status in metal mines is inadequate. In view of that, the partial connection number, including the first PCN, the second PCN, the third PCN and the fourth PCN, is introduced to analyze the development trend. The partial connection number, an adjoint function of the connection number, was proposed by [PERSON] in 2005 [29]. Because of the superiority of analyzing the development trend of the evaluation system, the PCN has been widely used in various fields. [PERSON] et al. [30] predicted the risk development trend of each factor of human factors by using the partial connection number. In the study of [PERSON] et al. [31], the development of dam seepage behavior was forecasted using the partial connection number. Based on the partial connection number, the risk of a developing trend and a type of metro tunnel crossing an underground pipeline was analyzed by [PERSON] et al. [32]. [PERSON] et al. [33] forecast the risk development trend of aviation maintenance by utilizing the partial connection number. In addition, the partial connection number is widely utilized in the subway construction field [34], the spread of internet public opinion [35], the student education field [36], and so on. Therefore, the dynamic evaluation of rock instability in metal mines can be performed using the PCN. In the end, the results of both static and dynamic evaluations for metal mines are obtained. For the weight of the index, the Interval Analytic Hierarchy Process (IAHP) is used to calculate the weight interval [37; 38]. However, if the interval number is not a specific weight value, then it is calculated by introducing the three-element connection number in the SPA theory. Then, the contributions of static and dynamic parameters are analyzed [39]. Following this, the model is validated using a real-world case study in Shaanxi. Meanwhile, we provide practical recommendations for mine safety. Lastly, we evaluate the effectiveness of the SPA-IAHP-PCN model in assessing rock instability risks in metal mines under dynamic conditions. The process of the SPA-IAHP-PCN model includes four parts: construction of the index evaluation system and calculation of index weights (Section 2.1), establishment of the five-element identical-discrepancy-contrary model (Section 2.2), conformation of PCN (Section 2.3), and constructing the SPA-IAHP-PCN model (Section 2.4).
###### Abstract  
Carbon nanotubes are materials that have been investigated for diverse applications including the adsorption of metals. However, scarce literature has described their behavior in the case of the adsorption of precious metals. Thus, this work reports the efficient adsorption of gold from cyanide or chloride media on multiwalled carbon nanotubes (MWCNTs). In a cyanide medium, gold was adsorbed from alkaline pH values decreasing the adsorption as the pH values were increased to more acidic values. In a chloride medium, the MWCNTs were able to load the precious metal and an increased HCl concentration (0.1-10 M), in the aqueous solution, had no effect on the gold uptake onto the nanotubes. From both aqueous media, the metal adsorption was well represented by the pseudo-second order kinetic model. In the cyanide medium, the film-diffusion controlled process best fitted the rate law governing the adsorption of gold onto the nanotubes, whereas in the chloride medium, the adsorption of the metal onto the nanotubes is best represented, both at 20 \({}^{\circ}\)C and 60 \({}^{\circ}\)C, by the particle-diffusion controlled process. With respect to the elution step, in cyanide medium gold loaded onto the nanotubes can be eluted with acidic thiourea solutions, whereas in the chloride medium, and due to that the adsorption process involved the precipitation of zero valent gold onto the multiwalled carbon nanotubes, the elution has been considered as a leaching step with aqua regia. From the eluates, dissolved gold can be conveniently precipitated as zero valent gold nanoparticles.  
multiwalled carbon nanotubes; gold(I); gold(III); adsorption; elution; gold nanoparticles _Article_
## 4 Numerical Results and Discussion
### Convergence Study  
The convergence for displacements, \(w^{*}\), and stresses, \(\sigma_{x}{}^{*}\), of a moderately thick (\(a/h=20\)) and symmetric cross-ply (\(0^{\circ}/90^{\circ}/0^{\circ}\)) spherical panel (R\({}_{1}/a=\) R\({}_{2}/b=10\)) is shown in Figure 2. A rapid and monotonic convergence is observed for the Fourier series of the terms \(m\), \(n>10\) for displacements and \(m\), \(n>\)20 for stresses within the boundary discontinues method. The same convergence characteristics are observed for grid numbers \(m\), \(n=13\) within the generalized differential quadrature method.
## 1 Introduction
The materials were characterized using various instrumental techniques such as scanning electron microscopy (SEM), X-ray diffraction (XRD), nitrogen adsorption, differential thermal analysis (DTA), and thermogravimetry (TGA), and the adsorption mechanism was studied using kinetic and adsorption models through both linear and nonlinear approaches, as well as by means of Fourier-transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). Experiments with real contaminated water were conducted with the material that demonstrated the highest adsorption capacity. Desorption experiments and reusability were also performed.
## 3 Empirical Gramians
### State-Space Empirical Gramians
#### 3.1.3. Empirical Linear Cross Gramian  
The (linear) cross Gramian [56; 57] quantifies the controllability and observability, and thus minimality, of an underlying square, \(M:=\dim(u(t))=\dim(y(t))=:Q\), linear system and is defined as:  
\[W_{X}:=\int_{0}^{\infty}\mathrm{e}^{At}\,BC\,\mathrm{e}^{At}\, \mathrm{d}t=\int_{0}^{\infty}(\mathrm{e}^{At}\,B)(\mathrm{e}^{A^{T}t}\,C^{ \intercal})^{\intercal}\,\mathrm{d}t.\]  
Augmenting the linear system's dynamical system component with its transposed system (The transposed system is equivalent to the negative adjoint system), induces an associated controllability Gramian of which the upper right block corresponds to the cross Gramian [58; 59]:  
\[\begin{pmatrix}\dot{x}(t)\\ \dot{z}(t)\end{pmatrix}=\begin{pmatrix}A&0\\ 0&A^{\intercal}\end{pmatrix}\begin{pmatrix}x(t)\\ z(t)\end{pmatrix}+\begin{pmatrix}B\\ C^{\intercal}\end{pmatrix}u(t)\Rightarrow\overline{W}_{C}=\begin{pmatrix}W_{ C}&W_{X}\\ W_{X}^{\intercal}&W_{O}\end{pmatrix}. \tag{4}\]  
The empirical variant restricted to the upper right block of this augmented controllability Gramian (4) is given by the following definition based on [60].  
**Definition 3** (Empirical Linear Cross Gramian).: _Given non-empty sets \(E_{u}\) and \(S_{u}\), the **empirical linear cross Gramian**\(\widehat{W}_{Y}\in\mathbb{R}^{N\times N}\) is defined as:_  
\[\widehat{W}_{Y} =\frac{1}{|S_{u}|}\sum_{k=1}^{|S_{u}|}\sum_{m=1}^{M}\frac{1}{c_{ k}^{2}}\int_{0}^{T}\Psi^{km}(t)\,\mathrm{d}t,\] \[\Psi^{km}(t) =(x^{km}(t)-x^{km})(z^{km}(t)-z^{km})^{\intercal}\in\mathbb{R}^{ N\times N},\]  
_with the state trajectories \(x^{km}(t)\) and adjoint state trajectories \(z^{km}(t)\) for the input configurations \(\dot{u}^{km}(t)=c_{k}e^{m}\circ u(t)+\bar{u}\), and the offsets \(\bar{u}\), \(x^{km}\), \(z^{km}\)._  
For an asymptotically stable linear system, delta impulse input \(u_{i}(t)=\delta(t)\) and an arithmetic average over time as offset \(\bar{x}=\frac{1}{T}\int_{0}^{T}x(t)\,\mathrm{d}t\), \(z=\frac{1}{T}\int_{0}^{T}z(t)\,\mathrm{d}t\), the empirical linear cross Gramian is equal to the cross Gramian due to the result of the empirical controllability Gramian. The numerical computation of this empirical Gramian requires \((2\cdot|S_{u}|\cdot M)\) trajectories and is related to balanced POD [61].
## 3 Related Work
### Underwater Sensor Nodes  
U-sensor nodes collect data using equipped sensors and periodically send the data to the sink. U-sensors determine the next hop to forward the data packet based on the information in the _PHE_ messages that they have received from the sink or GWs. More specifically, when a U-sensor receives a _PHE_ message from a GW or the sink, it stores the message in \(DET\_NEXT\_HOP\_QUEUE\). The U-sensor also maintains a timer, \(DNH\_TIMER\). When the timer expires, the U-sensor examines the travel time of the _PHE_ message and the number of hops that the _PHE_ message passed from GWs or the sink. Based on the information, the U-sensor determines the next hop that will forward the sensed data to a GW or the sink, such that the data can be delivered to GWs/sink with the minimum path length and delay.  
Other routing metrics such as ETX (Expected Transmission Count) and ETT (Expected Transmission Time) can be also applied for path selection.