esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Reality Training Application for the Condition-Based Maintenance of Induction Motors
The application is based on two training modules. The first is focused on knowledge associated with induction motors and its parts, the assembly of workbenches and familiarization with the elements of induction motors. The second module employs MCSA to detect induction motor failures such as broken rotor bars, misalignments, unbalances, and gradual wear on gear case teeth. Indeed, the MCSA module is used to analyze the fault-related frequency components that can be detected over the current spectra estimated by applying fast Fourier transform; additionally, the module aims to show the relationship between the theoretical basis and the practice. Additionally, this VR tool allows us to obtain the experimental raw data from the virtual environment for such failures. This application is intended to be implemented as a complementary experimental tool within the educational programs for engineering degrees in those courses in which experimental tests are mandatory to train the students, as well as in the training of operators working with induction motors in industry. Likewise, this application represents a practical solution in e-learning situations or when facing critical situations, such as the current SARS-CoV-2 pandemic (COVID-19), which do not allow the implementation of experimental laboratory tests. The design of this tool includes the development and modeling of the 3D virtual environments, the experience, and the interface programming that allows the incorporation of data from external simulation programs or by integrating acquired real-world data. It is designed to be used with virtual reality devices for greater immersion and interactivity, but it can also be used as a desktop tool, thereby increasing its final dissemination. The remaining sections of this article are organized as follows: in Section 2, the most recent works on immersive virtual reality applications to enhance training in engineering domains are presented. In Section 3, the theoretical underpinnings of the topics in the study are explained, as are the case studies to be addressed. In Section 4, the design and development of the immersive VR training application are described. In Section 5, the validation with final users of the VR-tool application is presented. In Section 6, the results of the validation stage are discussed and compared with the related work. Finally, In Section 6, the main conclusions of this research are highlighted, and future lines of work are established.
# Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images
## 2 Filtering Methods
### Local Statistical-Based Filter
#### 2.2.2 Lee Filter  
The LF is based on local statistics [12] proposed by [PERSON] [38], and dealing with the multiplicative noise signal. Many researchers have used the LF for de-speckling in Synthetic Aperture Radar (SAR) images [39; 40]. [PERSON] [41] indicates that the LF can also perform well for the medical ultrasound breast image. Based on the properties of LF, images can be converted into a linear expression regardless of whether it contains additive or multiplicative noise.  
Therefore, this is appropriate for the BUS image in which multiplicative noise and additive noise coexist. The LF function for this compounded noise can be expressed as  
\[I_{\underset{f}{J}}=I_{\underset{f}{J}}+k(I_{g}-\overline{n_{m}}\cdot I_{ \underset{f}{J}}-\overline{n_{a}}) \tag{3}\]Notation \(n_{m}\) is the multiplicative noise and \(n_{a}\) is the additive noise. \(I_{\overline{f}}\), \(I_{\overline{g}}\), \(\overline{n_{m}}\) and \(\overline{n_{a}}\) stand for the mean of \(I_{f}\), \(I_{g}\), \(n_{m}\) and \(n_{a}\), respectively. In addition,  
\[k=\frac{\overline{n_{m}}\cdot Q}{I_{\overline{f}}^{2}\cdot\sigma_{2}{}^{2}+ \overline{n_{m}}^{2}\cdot Q+\sigma_{1}{}^{2}}\text{ and }Q=\frac{\text{var}(I_{g})+I_{\overline{g}}^{2}}{ \sigma_{2}{}^{2}+\overline{n_{m}}^{2}}-I_{\overline{f}}-\sigma_{1}{}^{2} \tag{4}\]  
where \(\sigma_{1}^{2}\) is the variance of \(n_{a}\), \(\sigma_{2}^{2}\) is the variance of \(n_{m}\), and \(\text{var}(I_{g})\) is the variance of \(I_{g}\).  
As the speckled medical ultrasound image can be described as \(I_{g}=I_{f}\cdot n_{m}+n_{a}\), the Lee filtering algorithm seems to be effective. However, in fact, when it is used in BUS images, failure arise in accurately estimate the mean and variance of \(n_{m}\), which may affect the performance of the suppression of multiplicative noise.
## Abbreviations  
The following abbreviations are used in this manuscript:  
4G fourth-generation  
5G fifth-generation  
ACI adjacent channel interference  
AOR accelerated over-relaxation  
AWGNadditive white Gaussian noise  
B5G beyond 5G  
BERbit error rate  
CAORChebyshev accelerated over-relaxation  
CGconjugate gradient  
CHSORhybrid SOR-based Chebyshev acceleration  
CMAscomplex multiplications and additions  
CPcyclic prefix  
CSIchannel state information  
CSORChebyshev successive over-relaxation  
eMBBenhanced mobile broadband  
FBMCfilter bank multi-carrier  
FFTfast Fourier transform  
FIRfinite impulse response  
GSGauss-Seidel  
IB-DFEiterative block decision-feedback equalizer  
ICIinter-carrier interference  
IFFTinverse fast Fourier transform  
i.i.d.independent and identically distributed  
IMTInternational mobile telecommunications  
IoTInternet of things  
ISIinter-symbol interference  
IFIC-LRAiterative parallel interference cancellation based on the lattice reduction-aided  
LoSline of sight  
LRAlattice reduction-aided  
LSleast-square  
M-MIMOmassive multiple input-multiple output  
MMSEminimum mean square error  
mMTCmassive machine-type communications  
MSORmodified successive over-relaxation  
MUDmulti-user detector  
NRIDAnew randomized iterative detection algorithm  
OFDMorthogonal frequency-division multiplexing  
OOBout-of-band  
P/Sparallel-to-serial  
PICparallel interference cancellation  
PSDpower spectral density  
QAMquadrature amplitude modulation  
RIRichardson  
RIDArandomized iterative detection algorithm
# Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks
## 3 Methods
### Description of the Transmission Process
The global network uses the above formula to calculate the advantage function of each time step, namely  
\[Adv_{t}=\sum_{y>t}\phi^{y-t}v^{t}_{\lambda}-Q_{\sigma}\big{(}E^{t}_{\lambda}\big{)} \tag{25}\]  
Among them, \(Q\) is the state value function, and \(\sigma\) is the Critic network parameter. Step 2. Use \(D(I)=-\sum_{t=1}^{t_{n}}\left(\sum_{y>t}\phi^{y-t}v^{t}_{\lambda}-Q_{\sigma} \big{(}E^{t}_{\lambda}\big{)}\right)^{2}\) to calculate the loss function of the Critic network, and back-propagate to update the parameter \(\sigma\) of the Critic network. Step 3. Use \(D^{CUT}(\pi)\) and the advantage function to update the parameters of the Actor1 network. Step 4. After the circle step, use the network parameters in Actor1 to update the parameters of Actor2. Step 5. Repeat steps 1-4 until the model converges. After the global network model converges, it can guide the agent to obtain corresponding actions in different environmental states, and then select a reasonable set of nodes to participate in the federated learning aggregation. The detailed FABD Algorithm 1 is as follows:  
Input: The initial state of the network, federated learning task information  
Output: Node selection scheme  
1. Initialize network, equipment, and task information, randomly initialize system status and global network parameters  
2. FOR move \(\epsilon\) [1,2,..., MO]  
3. FOR sub_move \(\epsilon\) [1,2,..., MO\({}_{s}\)]  
4. Each agent executes the node selection action \(z_{\lambda,t}\) according to the global PPO strategy \(z_{\lambda,t}=\theta(E_{t})\)  
5. Each agent obtains the reward \(v_{t}\) and the next state \(E_{t+1}\) according to Equation (16) and saves the current state, state, action, and reward as a sample  
6. Update current network and device status information  
7. END FOR  
8. Each agent will synchronously upload the collected data to the global network  
9. Update Actor1 network parameter \(\pi\) according to Formula (25) advantage function and  
Formula (24)  
10. Update the parameter \(\sigma\) of the Critic network according to \(D(I)\) backpropagation  
11. IF sub_move%circle==0  
12. Use the function in Actor1 to update Actor2  
13. END IF  
14. END FOR  
**Algorithm 1** FABD, FL node selection algorithm based on DPPO in IOT algorithm
## 4 Experimental Results
### Experimental Results Analysis  
During the model construction process, comparative experiments were conducted to evaluate the impact of incorporating weather features on the predictive performance of the models. The experiments were carried out using the Informer (INF), SVR, and MLR models, comparing the performance of models with and without weather features added to the baseline features.  
As shown in Table 5, the inclusion of weather features led to a significant improvement in the \(R^{2}\) values of the models, increasing them by 5 to 10 percentage points. This improvement can be attributed to the strong influence of weather on natural gas demand. The correlations between minimum and maximum temperatures and gas purchase volume were notably negative (\(r\ =\ -\ 0.67\) and \(r\ =\ -\ 0.69\), respectively), indicating that lower temperatures drive higher heating demand. These patterns cannot be fully explained by time-based features (e.g., year and month) alone. Therefore, weather features were included by default in all subsequent experiments.  
For each model (Informer, MLR, SVR), four experiments were conducted: one without using regional and usage features (INF), one with regional features (INF-RB), one with usage features (INF-UB), and one with both regional and usage features (INF-RB-UB). The approach was applied consistently across MLR and SVR. This objective aims to evaluate the impact of different feature combinations on the model's predictive performance and identify the optimal feature combination scheme.  
Next, the prediction results of the Informer, MLR, and SVR models will be presented and analyzed in detail.
# Cryptographic Key Management in Delay Tolerant Networks: A Survey
## 2 Background
### Preliminaries  
Cryptographic key management is the process by which cryptographic keys are generated, stored, protected, transferred, loaded, used, and destroyed [8] and is one of the most difficult problems in DTN security [9]. The reason is that cryptographic key management generally requires multiple round trips in order to securely exchange or establish keys. This is problematic because of the long delays and possible connectivity disruptions in such restricted networks. As further discussed in Section 4, there are currently no key management schemes that appear to suit DTNs. Naturally, poor or weak cryptographic key management will have an adverse effect on the cryptographic techniques, which risk of being rendered insecure or inefficient [10].  
Security initialization or bootstrapping, as the name suggests, is how to initially establish security associations between the communicating nodes. Key establishment is one of the basic concepts in this context, which is defined as a method that two or more parties adopt with the aim of sharing a secret value for secure communication. Key establishment is divided into (a) key transport or key distribution and (b) key agreement. In key transport, one party creates or receives a secret value and securely transfers it to the other party. In key agreement, a shared secret value is derived jointly by two (or more) parties. As already mentioned, the bulk of the research so far has been focusing on two main approaches. More specifically, the two main up-to-date proposed ideas are IBC and PKI. Both approaches are based on asymmetric or public-key cryptography (PKC).  
* _Identity Based Cryptography (IBC)_--[PERSON] first introduced IBC in 1985 [7]. In this cryptographic approach, user identifier information such as email address, IP address, and so forth are used as a public key for encryption and verification of digital signatures instead of certificates. In addition, in IBC, the Private Key Generator (PKG) is the central authority (similar to a Certificate Authority, CA in PKIs) which generates the private keys for participants.
* _Public Key Infrastructure (PKI)_--Traditional asymmetric or public key cryptography widely and effectively used in the Internet and a plethora of business realms relies on a PKI. The latter depends on the availability and security of a CA, a central control point that everyone trusts.
## 3 Dynamic Calculation of Bridge and Train Vibrations
### Comparison with Additional Considered Fictitious Bridge Structures  
The existing considered bridge structure whose properties were used in setting up the bridge model features a natural frequency \(n_{d,1}\) that is very close to the natural frequencies of the primary suspension stage, i.e., the bogie frames themselves. This could also explain the clear detectability of the frequency components in this frequency range in the results of the FFT analysis. In order to check whether the bridge's natural frequency can generally be identified in the frequency components of the acceleration response of the bogie frame and to check the applicability and limits of the proposed vehicle-based iSHM method, calculations were also carried out for two further fictitious bridge structures in analogy to what was described above. Their spans \(L\), mass distributions \(\mu\), and natural frequencies \(n_{1}\) are in the range of the limit values for realistic combinations of structural properties determined based on existing bridge structures and can be seen in Table 2.  
Analogous to Figure 9, the results of the FFT analysis of the train-induced acceleration responses of the bridge structure and the bogie frame, normalized to the respective maximum of a velocity step, are shown in Figures 10 and 11 for these two structures, with the ordinate limited to a frequency of 30 Hz in both plots. The respective computed natural frequencies \(n_{1}\) are plotted at 11.51 Hz in Figure 10 and 23.17 Hz in Figure 11, respectively,  
Figure 9: Considered steel bridge: planar illustrations of normalized FFT analysis results of bridge structure accelerations (**top**) and bogie frame accelerations (**bottom**) for train speed range \(\Delta v_{2}=100\) km/h \(\dots\) 200 km/h.  
the modal masses of the bridge due to the wheelset masses of the multi-body model of the train).  
In addition, the natural frequencies of the considered fictitious bridge structures can also be clearly seen in the bottom diagram of Figures 10 and 11, showing the FFT results of the accelerations of bogie frame \(b2\) during the bridge crossing event. In the case of the fictitious bridge no. 1 with a first bending frequency of \(n_{1}=11.51\) Hz, this is mainly the case in the range from \(v_{\text{crit},5}\) to \(v_{\text{crit},7}\), while the natural frequency of the bogie frames themselves also account for more significant portions of the acceleration responses. The natural frequency of the bogie frame is also clearly pronounced in the results of the second fictitious bridge no. 2. Here, particularly at the lower train speeds, larger shares of the acceleration responses also occur in the vicinity of the first bending frequency of the bridge structure at 23.17 Hz. However, there is a substantial overlap with the excitation frequencies, and, as a result, a wide dispersion of the frequencies that make up the maximum acceleration peak is discernible, which makes it challenging to identify the bridge's natural frequency at some speeds clearly.
# Centimeter-Level Precise Orbit Determination for the Luojia-1A Satellite Using BeiDou Observations
## 3 BeiDou Multipath Modeling with Luojia-1A Observations
### Modeling the Multipath of BeiDou Observations
The results from [PERSON] and [PERSON] [24] are also presented in the figure for comparison. The upper panels of the figure represent the model for the B1 signal. It is obvious that the MP1 models of the Luojia-1A satellite are elevation-dependent, which is marginal below the 40\({}^{\circ}\) and gradually decreased as the elevation increase after 40\({}^{\circ}\). According to the panels (b) and (c), the MP1 models of the Luojia-1A satellite are similar to that of the Wanninger model above 40\({}^{\circ}\), especially the model of the MEO satellites. However, since the results of the Luojia-1A satellite also include the satellite-induced MP effects, there are still minor differences. On the other hand, as shown in the lower panels, there is no elevation dependence for the MP2 models of the Luojia-1A satellite, which shows a slow decrease after 60\({}^{\circ}\). The obvious differences between MP1 and MP2 models may be attributed to the Luojia-1A receiver that can smooth the BeiDou B2 signal. The MP models built from the Luojia-1A onboard observations are not as smooth as the Wanninger model, which may be because the Wanninger model adopts much longer observed time. The MP models can be used to correct the pseudorange measurements directly for the single-frequency orbit determination of LEO satellites. Figure 5: The relationship between multipath and antenna elevation angle for different BDS satellites: (a) C02, (b) C07, (c) C11, (d) C05, (e) C10, (f) C14.
# Review on Wearable Technology in Sports: Concepts, Challenges and Opportunities
## 3 Hardware and Software of WT in Sport
### Sensors Layer
#### 3.1.4 Environmental Condition Sensors  
Environmental condition sensors on the WT are sensors that measure environmental effects such as air quality, humidity, temperature, air pressure, and UV light level [80; 81].  
These sensors are used to monitor the condition of the environment where athletes are present. Air quality sensors assess the impact of factors such as air pollution and allergens by monitoring athletes' respiratory conditions during exercise. Humidity sensors track the humidity levels in the surroundings, while temperature sensors monitor environmental temperature changes. Air pressure sensors track atmospheric pressure variations and are used for altitude calculations. UV light sensors measure athletes' exposure levels to sunlight.
## 3 Results and Discussion
### Concerning the Mechanical Test and GOM ARAMIS Measurement Series
#### 3.1.1 Observations During and After the Casting  
After the specimens were stripped from the casting mold, their surfaces were smooth and evenly covered with concrete (cement grout), so painting of the specimens was easily performed. They were then stored in water to ensure their internal drying and good surface quality.  
Shrinkage (contraction) measurements were carried out using GOM ATOS Triple Scan, where the shrinkage was determined by comparing the scanned CAD (computer-aided design) models; the results are presented in Section 3.2.  
In the current section, the surface changes and observations of the test specimens are presented. During underwater storage, the concrete in most of the specimens, where the layer was thinnest, partially or entirely flaked off, revealing the reinforcing element.  
Figure 12 shows the test specimens stored under water for 21 days prior to the DIC measurements (performed by the GOM ATOS instrument), where the reinforcing element is visible due to the flaking.  
The positioning of the reinforcement materials impacts the structural performance and bending behavior of the samples. If the amplification were closer to the neutral axis, it would reduce the amplification effect. On closer inspection of the concrete elements, it was observed that white precipitation occurred, and it was most prominent on the GYP and HI  
Figure 11: Separation of the ABS reinforcement from the concrete without adhering to white precipitation.  
Figure 12: Surface of concrete specimens before DIC measurements (executed by GOM ATOS and GOM ARAMIS instruments).  
specimens' surfaces, but minimal signs of precipitation can also be seen on ENG and ESD specimens. Only in the case of ABS specimens was there no precipitation on the surface. The white, powdery precipitate is likely to be lime from the concrete.  
Another observation related to Figure 8 is the flaking of the thin concrete layer surrounding the reinforcing element. Initially, the specimens, when placed under water, had a smooth, even surface free of cracks. On the 14 th day, tiny visible cracks appeared in the regions where partial flaking occurred by the 21 st day. The detachment of the thin concrete layer from the reinforcing layer could have been caused by the white substance that formed as a solid deposit, resembling powder or even the presence of water.  
Flaking can damage the surface of the specimens, which could cause errors during the DIC measurement series. The precipitation phenomenon was already observed in the previous publication of the authors of the current paper, where amorphous PLA waste was used as a reinforcing element [66]. Figure 13 shows the white material precipitated on the surfaces of the HI and GYP specimens.  
In the case of HI specimens, it was further observed that the 3D-printed fibers on the longer side of the test specimen started to split and separate from each other layer by layer. This observation foreshadows the possibility of the entire printed structure disintegrating.
# Temperature Dynamics in Early Pregnancy: Implications for Improving In Vitro Fertilization Outcomes
###### Abstract  
In assisted reproductive technology, in vitro fertilization involves cultivating embryos in an artificial environment, often yielding lower-quality embryos compared to in vivo conditions. This study investigated core body temperature (CBT) fluctuations in mice during early pregnancy. Their CBT was measured with a high temporal resolution to identify the optimal thermal conditions during the first five days post-fertilization, aiming to improve in vitro culture conditions. Data were collected from 12 female mice, with 8 becoming pregnant, using temperature loggers every minute for 11 days. Data analysis focused on trends, circadian rhythms, frequency components, and complexity using multiscale entropy (MSE). The results for the pregnant mice showed a mean CBT increase from 37.23 \({}^{\circ}\)C to 37.56 \({}^{\circ}\)C post-mating, primarily during the light phase, with a significant average rise of 0.58 \({}^{\circ}\)C. A Fourier analysis identified dominant 24, 12, 8, and 6 h components, with the 24 h component decreasing by 57%. Irregular fluctuations decreased, and MSE indicated increased complexity in the CBT time series post-mating. These results suggest that reducing diurnal temperature variations and maintaining a slightly elevated mean CBT of approximately 37.5 \({}^{\circ}\)C, with controlled minor fluctuations, may enhance embryo quality in pregnant mice. This study provides a reference for temperature regulation in embryo culture, improving embryo quality by aligning in vitro conditions with the natural thermal environment of the fallopian tubes.  
core body temperature (CBT); embryo quality; in vitro fertilization (IVF); assisted reproductive technology (ART) +
Footnote †: journal: Medical Physics