esa-sceva/satcom-chunk-collection huggingface dataset:
# Rapid Estimation of TVWS: A Probabilistic Approach Based on Sensed Signal Parameters
###### Abstract  
The current demand for a wireless electromagnetic spectrum is higher than ever before due to rapid technological development in the field of information and communication technologies that has resulted in monumental growth in data-centric services. The usage of idle TV channels in the Television Ultra High Frequencies (TV-UHF) band (500-698 MHz), also known as Television White Spaces (TVWS), is a relatively new and promising concept for wireless connectivity that can be used to cater to the demand. A challenge in this setting is to figure out a fast and cost-effective method of TVWS presence estimation, such as the use of open hardware and software tools, reducing sensing time. This article proposes a Rapid Estimation Method (REM) for TVWS estimation that uses the statistical information of the sensed signals. Our probabilistic approach analyzes the collected parameters of more than eight million data samples taken by scanning the TV-UHF spectrum in the city of Windsor, ON, Canada. The calculated statistical parameters and a group of auxiliary parameters were combined to estimate rapidly the amount of TVWS available in the sensed locations. By applying the proposed rapid estimation method, the presence of TVWS was identified and verified with an accuracy of about 76% according to the results obtained, the average variation when comparing the calculated and detected probabilities of TVWS was in a range of 15%, and the method could be a viable solution to the spectrum need.  
TVWS; dynamic spectrum access; false alarm probability; detection probability; rapid estimation method. +
Footnote †: journal:
## 2 Experiments  
### Experimental Setup  
As shown in Figure 1, the LIBS system is composed of two main parts, which are the laser generation part, Figure 1a, and the signal collection part, Figure 1b [18]. In the laser generation part, the laser pulse was focused to ablate the target sample, and the plasma was then induced from the ablated sample. The laser source was an Nd-YAG (Litron Lasers Inc., Rugby, UK) operating at a wavelength of 1064 nm and an energy of 30 mJ over a pulse duration of 8 ns with a repetition rate of 1 Hz in Q-switch mode. A 100 mm focal length lens (Thorlabs Inc., Newton, NJ, USA) was used to focus the beam onto the sample surface. In the signal collection part, the spectrometer collected the emitted signals when the excited electrons from atoms and ions came back to the ground state. The light from the induced plasma was focused through a plano-convex lens with a 40 mm focal length lens to a fiber-optic bundle. A fiber-optic cable with a 1 mm diameter was installed at an angle of 45\({}^{\circ}\) with respect to the normal direction of the sample surface. The optical fiber was directly connected to the slit entrance of a spectrometer with four-channel charge-coupled devices (J200, Applied Spectra, Fremont, CA, USA). The spectrometer had a spectral range of 180-884, a spectral resolution of 0.1 nm, and a signal-to-noise ratio of about 250:1. A digital delay/pulse generator (Stanford Research Systems, model DG535) was used to control the delay times when the data were recorded. The software program Aurora was used for data acquisition. The integration time was set at 1.05 ms [19].
## 4 Integration of Biosensors
### Node Configuration  
The configuration of nodes in multi-hop networks is a critical aspect that significantly influences the efficiency and performance of the entire network. In the context of patient monitoring systems utilizing multi-hop communications, configuring nodes appropriately is paramount to ensuring seamless data transmission, minimal latency, and optimal resource utilization. In the realm of multi-hop IoT networks, the configuration of nodes holds paramount importance, and the ESP32-WROVER-E microcontroller shown in Figure 8 stands out as a notable example due to its widespread adoption and versatility among microcontrollers [55]. In the context of patient monitoring systems leveraging multi-hop communication, the configuration of ESP32 nodes plays a critical role in shaping the efficiency and performance of the entire network.  
Figure 7: The five-layer IoT structure.  
In the context of multi-hop networks, ESP32's configuration requires careful consideration of numerous specifications. The following details outline key parameters and settings crucial for optimal ESP32 node configuration, ensuring seamless integration into the patient monitoring ecosystem.  
* Wireless communications setup: The ESP32 supports various wireless communications protocols like Wi-Fi and Bluetooth, making it suitable for multi-hop networks. Configuring these communication interfaces ensures seamless connectivity between nodes.
* Network topology design: Designing an effective network topology is crucial. ESP32 nodes can be configured in a mesh topology, allowing each node to communicate with nearby nodes to form a multi-hop network.
* Power management: Efficient power management is vital for patient monitoring devices, especially those relying on batteries. The ESP32's deep-sleep and low-power modes can be configured strategically to optimize power consumption, extending the node's operational life.
* Data packet handling: Configuring how data packets are handled during transmission is key. ESP32 nodes can be programmed to efficiently manage packet routing, error handling, and acknowledgment mechanisms, ensuring reliable data delivery in patient monitoring scenarios.
* Dynamic routing algorithms: ESP32 nodes can implement dynamic routing algorithms such as AODV or DSR. These algorithms adapt to changes in network conditions, automatically adjusting the routing paths based on factors like node mobility or signal strength.
* Security measures: Implementing security measures is essential in patient monitoring systems. ESP32 nodes can be configured to use encryption and authentication protocols, safeguarding sensitive health data as they traverse the multi-hop network.
* Quality of service: Configuring QoS parameters ensures that critical health data receive priority treatment. ESP32 nodes can be set to prioritize specific types of data, guaranteeing timely delivery and minimizing delays in transmitting vital health information.  
Figure 8: The ESP32-WROVER-E is an enhanced version of the ESP32 microcontroller.
## 3 Results  
Figures, showing the LMF-HRV-correlations at individual level across a six-day period are displayed in Appendix A. A repeated measures analysis of variance (ANOVA) was performed in order to determine the statistical significance of the differences between the correlations. The repeated measures ANOVA examines the mean difference within subjects across 10 days of the experiment (28 February-11 March). Knowing that there were regional differences in the LMF we first tested LMF-HRV-correlation coefficients for all participants to see if they differed by location over the 11-day study period. Using Repeated Measures ANOVA significant differences were found between participants at the five different location groups over the 11-day study period (Day * Location, \(p<0.001\)). Given this finding the initial desire to test all participants together as one group became inappropriate and was abandoned. Next, we performed a repeated measures ANOVA separately for each of the groups at each location. Significant differences in the mean LMF-HRV-correlations were found between days for each group as shown in Table 2. Note that since one of the objectives of the study was to observe the effects of the applied Heart Lock-In procedure, individuals whose HRV data corresponding to the Heart Lock-In time period was either missing or too noisy were excluded from the further study. Statistical significance of the mean synchronization between participants' HRV and LMF activity was analyzed using the Pearson correlation significance test. \(p\) Values yielded by the test are displayed in Table 3. It can be noted that mean LMF-HRV-synchronization on day 6 (HLI day) was significantly different from zero for all five groups. Post hoc tests for all pairwise comparisons between days for each group location are shown in Figures 2 and 3 and Tables 4-8. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(N\) & **Mean Sq.** & **df** & **F** & \(p\)**\textless{}** & **Partial \(\eta^{2}\)** \\ \hline California & 17 & 0.50 & 10 & 8.46 & 0.001 & 0.35 \\ Lithuania & 19 & 0.44 & 10 & 9.26 & 0.001 & 0.34 \\ New Zealand & 21 & 0.40 & 10 & 6.22 & 0.001 & 0.24 \\ Saudi Arabia & 19 & 0.18 & 10 & 2.62 & 0.01 & 0.13 \\ United Kingdom & 11 & 0.18 & 10 & 2.88 & 0.01 & 0.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the repeated measures ANOVA test (assessing the differences in LMF-HRV-correlation) performed separately for each of the groups at each location. Note that this table shows significant differences in the mean LMF-HRV-correlation between days for each group.
# Robust Cross-Validation of Predictive Models Used in Credit Default Risk
## 4 The Partitioning Problem and Cross-Validation Variants
### Other Cross-Validation Variants  
The flexibility of cross-validation methodology has allowed the introduction of even more variants than the ones discussed above; however, they were finally discarded as potentially useful validators in this study for different reasons. Some of them will be briefly mentioned here.  
Leave-p-out cross-validation (LpO-CV) is an exhaustive method where all possible test sets of \(p\) observations are used as hold-out test samples. Given the huge number of possible test sets of \(p\) elements taken from a population of \(N\) elements, where \(N\) has a minimum order of magnitude of hundreds or thousands, this variant has been discarded as it is computationally infeasible in real-life applications. Only the special case of \(p=1\) is considered, as it is the LOOCV technique.  
Relevant to mention is Importance Weighted Cross-Validation (IWCV), which was especially designed to address the problem of mitigating the a priori existence of a covariate shift rather than trying to avoid such an induced shift during the data partition [29]. IWCV measures the importance of the covariate deviation and considers it when calculating the model performance to decrease the bias of the estimations as much as possible. For this reason, this technique does not try to reduce the existing deviation in the covariates but rather to correct the deviation afterwards in the calculations. Therefore, this is a complementary technique that could be combined with other CV variants and is, therefore, not a benchmark for direct comparison against any other.  
Nested cross-validation is a methodology that chains an additional cross-validation when building the \(k\) models within the training datasets. When a regular k-fold cross-validation is carried out to perform a model validation, each of the \(k\) times a model is trained in one of the corresponding train sets, another cross-validation with a different \(k\) parameter (usually lower) is conducted to make that model training. Then, this additional k-fold cross-validation has the objective of building a better model by means of selecting the most convenient model complexity to avoid overfitting [27]. In the present research, model complexity is already predefined in a set of given models to be compared; therefore, nested cross-validation cannot be used in this experimental design.
## 3 Segmentation Platform  
Together with the proposed algorithm, we have developed a plugin extension for 3D Slicer [26] that (i) provides a remote AI-Assisted Annotation service (AIAA) to medical doctors from a High-Performance Computing (HPC) cluster, allowing them to use state-of-the-art methods to perform the automatic segmentation of desired tissue from medical images, and (ii) provides a mechanism to collect the segmented and validated data generated in step (i). We can use the stored data to fine-tune the existing neural network models. These enhanced models can then be employed for automatic tissue segmentation in step (i).  
Since the current state-of-the-art medical image processing methods are based on Deep Learning (DL), and typically, DL algorithms are trained with a large amount of data, we allow for the use of multiple GPUs during the training phase to produce models of the required quality in a reasonable amount of time. We use the multi-GPU nodes of an HPC cluster to train models from scratch, as well as single GPU nodes to provide model inference using AIAA. All of the connections and data are encrypted using Secure Shell (SSH).  
The entire concept is depicted in Figure 1. Two main sections can be distinguished: one runs at a medical doctor's site in a local hospital (frontend), and the other operates at an HPC cluster facility (backend). The frontend allows the doctor to load, view, and perform automatic segmentation, and improve annotation on the medical data using HPC resources. The backend part provides the computational power and other required features.  
The provided concept adopts the Clara Train SDK [27] from NVIDIA. It contains several APIs, such as those for AIAA, and a training framework for DL-based model training. We use Clara Train SDK, version 4.0, which is solely PyTorch-based. The Clara Training Framework builds on the open-source MONAI framework [28], which is specifically devoted to deep learning in healthcare imaging.  
Clara's AIAA is a client-server-based architecture that delivers a C++ or Python client API. Many medical image viewers, such as 3D Slicer (see Figure 2) can be interfaced as clients [29] to obtain the AIAA services offered by the server running on the network. Based on the pre-loaded models on the server, the AIAA can perform the automatic segmentation of specific tissues and display the results. Doctors can edit and postprocess the resulting tasks. A modified version of this extension, accommodated for HPC cluster usage, has been used by medical doctors to provide improved annotations of the 3D-IRCADb dataset.  
Figure 1: The main concept of the tool for medical image processing and analysis.
# A Review of Thermal Error Modeling Methods for Machine Tools
## 2 Thermal Error Modeling Method for Machine Tools
### Grey System  
In 1982, Professor [PERSON], a Chinese scholar, established the theory of \"grey system\". It is a mathematical method that can extract useful information from data with limited quantity and accuracy to obtain the characteristics of the research object [36]. The general grey system model (GM(n,m)) [37], is shown in Equation (3) below.  
\[\frac{d^{n}X_{1}{}^{(\xi)}(k)}{dt^{n}}+a_{1}\frac{d^{n-1}X_{1}{}^{(\xi)}(k)}{ dt^{n-1}}+\cdots+a_{n}X_{1}{}^{(\xi)}(k)=b_{1}X_{2}{}^{(\xi)}(k)+b_{2}X_{3}{}^{( \xi)}(k)+\cdots+b_{m-1}X_{m}{}^{(\xi)}(k) \tag{3}\]  
It is an \(n\)-order gray model with \(m\) variables where \(X_{1}\) is the thermal error; \(X_{2}\)\(\dots\)\(X_{m}\) are the temperature data; \(N\) is the order of the differential equation; \(M\) is the number of data types; and \(\zeta\) is the number of transformations. Notably, by changing the number  
Figure 2: Fitting performance of the MRA model.  
of variables and order of equation, different GM models will be obtained. Among these models, the first-order univariate model GM(1,1) is the most commonly used model.  
Modeling based on the grey system theory is simpler, more convenient, and does not depend on massive and complete data information [38]. Based on the standard gray system model GM(1,1), a genetic algorithm was used by [PERSON] and [PERSON] [39] to optimize the dimensions and variable weights of the new gray system model to minimize the residual value of the optimized model. This optimized model can not only reflect the systematic trend of thermal errors, but can also weaken the influence of the random change of thermal error, thereby the prediction accuracy of the thermal error model was improved. However, considering that the solution of the existing gray prediction model GM(1,N) was not accurate enough, [PERSON] [40] used gray control parameters to develop an improved model GMC(1,N) on the basis of GM(1,N). The improved model was more accurate because its grey differential equation was linear and its solution was based on the superposition principle. In order to further improve the prediction accuracy of grey GMC(1,N) model, [PERSON] et al. [41] proposed a new gray GMC(1,N) machine tool thermal error prediction method based on the CS algorithm named as CS-GMC(1,N) model. This model was compared with the gray model based on particle swarm optimization (PSO) named as the PSO-GMC(1,N) model. It was found that the prediction accuracy of the CS-GMC(1,N) model was higher than that of the PSO-GMC(1,N) model.
## 2 Materials and Methods
### Feature Extraction of Surface EMG  
The selection of the characteristic value usually determines the classification effect of the classifier. In the surface EMG signal analysis, the main feature analysis methods are the time domain analysis and the frequency domain analysis. Time domain features include absolute average, root mean square, variance, and zero-crossing points. Frequency domain features include average power frequency and median frequency.
# Numerical Analysis of the Breakdown Process of CF\({}_{3}\)I at Low Pressure
## 4 Discussions
### Power Balance
Because the secondary electron emission power is too small compared to the other powers, it was displayed separately with a smaller Y-axis to avoid coinciding with the X-axis in the conventional coordinate range display. In the before-breakdown stage (0-6.4 \(\upmu\)s), the number of particles is too low and the reaction is not intense enough, resulting in low power terms for each particle as shown in Figure 15, with values close to 0. Consequently, further discussion of this stage is unnecessary. During the breakdown stage (6.4-15 \(\upmu\)s), the power related to electrons and positive ions increases sharply. The electron density is smaller than the positive ion density, resulting in a lower growth rate for the electron-related power compared to the positive ion-related power, as shown in Table 1. The secondary electron emission power is observed to have a negligible effect, as seen in Figure 15a,b. The exponential growth of positive ion density and the violent ionization reaction lead to a significant increase in the heating power of positive ions, as shown in Figure 15d. The heating power associated with negative ions remains relatively constant, as the number of particles, energy, and current associated with negative ions is comparatively small, resulting in a correspondingly small heating power, as seen in Figures 11, 12b,c and 13. In the after-breakdown stage (15-150 \(\upmu\)s), the plasma enters a stable state, and the power associated with each particle gradually decreases, reaching a dynamic equilibrium state, consistent with previous observations. Positive ions contribute the most to the heating power, accounting for approximately 62% of the total. Negative ions exhibit a small proportion of heating power, around 1.5%. The power loss of electrons is mainly due to boundary losses, accounting for approximately 85% of the total losses. The remaining losses come from collision losses. On the other hand, for ions, the ratio of the boundary loss power drops below 40%, and most of the power absorbed by the ions is dissipated through the elastic collision and recombination process due to their larger mass and lower mobility, leading to a smaller number of ions bombarding the plate.
# Optomechanical Analysis and Design of Polygon Mirror-Based Laser Scanners
## 4 Linearization of the Scanning Function
### The Returning Incidence Point  
From Figure 9a, considering Equation (A4), the following conditions can be made:  
\[\left\{\begin{array}{ll}C\hat{I}_{k}^{\prime}O>\pi/2\\ CI_{k+1}^{\prime}O\leq\pi/2\end{array}\right.=>\ \frac{\xi}{2\psi}\leq k<\frac{\xi}{2\psi}+1,k= \left[\frac{\xi}{2\psi}+1\right]. \tag{38}\]  
therefore the reflection \"\(k\)\" for which the beam returns at the \"\(k+1\)\" reflection is  
\[k=\left[\frac{\xi}{2\psi}+1\right]. \tag{39}\]  
In the two examples in Figure 9, \(k=2\) in Figure 9a (as the returning point is \(\mathrm{I}_{3}\) on mirror \(\mathrm{M}_{1}\)) and \(k=3\) in Figure 9b (as the returning point is, in this case, \(\mathrm{I}_{4}\) on mirror \(\mathrm{M}_{2}\)).
## 3 Methodology
### System Optimization
#### 3.4.1 Variance Penalty for Dialogue Rounds  
Reducing the number of dialogue rounds is essential for optimizing the efficiency of the system, particularly in real-time applications where response time is critical. To achieve this, we introduce a mathematical approach that minimizes unnecessary dialogue rounds while maintaining accuracy in the responses.  
The core idea is to penalize response variance across dialogue rounds, thereby encouraging the LLMs to converge on consistent answers more quickly. Let \(R_{i}\) represent the response generated by the LLM in the \(i\)-th round, and \(\mu\) represent the mean response across all rounds. The variance \(\sigma^{2}\) of the responses can be calculated as follows:  
\[\sigma^{2}=\frac{1}{N}\sum_{i=1}^{N}(R_{i}-\mu)^{2}, \tag{2}\]  
where \(N\) is the total number of dialogue rounds. A higher variance indicates a greater inconsistency in the LLM's responses, which is a potential sign of hallucination. To reduce the variance and, consequently, the number of rounds, we introduce a penalty function \(P\) based on the variance:  
\[P=\alpha\cdot\sigma^{2}, \tag{3}\]  
where \(\alpha\) is a scaling factor that adjusts the weight of the penalty. This penalty is added to the system's loss function during training, encouraging the LLM to generate consistent responses with minimal rounds.
### Summary  
MyFlex-\(\delta\) was developed and optimized following a design methodology presented by [PERSON] et al. in [74]. Five prototypes of MyFlex-\(\delta\) were produced optimizing five stiffnesses for as many weight categories of users: 60 kg, 70 kg, 80 kg, 90 kg and 100 kg. The final stiffnesses of the five samples, to confirm also the results of the finite element analyses during the optimization, were subsequently determined with static tests which were equivalent to the static tests proposed both in the ISO 10328 standard and AOPA guidelines. Static and fatigue tests at critical loads were subsequently carried out to certify these prostheses from a structural point of view, following the ISO 10328 test procedure. Once the certification was obtained for functional tests with users, human subjects testing with three participants with transfemoral amputation were performed, with all participants having different weights. Results of the MyFlex-\(\delta\) were compared to the participants daily prosthesis. Being compared with a device that is used every day by the participants, from these tests, the authors did not expect a great improvement from the point of view of gait symmetry with MyFlex-\(\delta\), but at least a maximum difference of 10% in the evaluation parameters was the goal. A possible improvement was expected by the authors on the perceptions of the participants regarding the walks on uneven terrain and changes of direction, thanks to the spherical ankle joint. Therefore, the aim of the human subject testing was to evaluate gait symmetry and ankle movements during level walking with the MyFlex-\(\delta\) in persons with a transfemoral amputation compared to participant's current prosthetic foot. In addition, participants' experience with the MyFlex-\(\delta\) on uneven and even terrain was subjectively evaluated. In the continuation of the reading of this paper, the reader will find in sequence the brief presentation of the mechanical structure of MyFlex-\(\delta\) (Section 2.1.1), its working principle (Section 2.1.2), a very short description of the design methodology followed (Section 2.2) and then a more accurate description of the human subjects testing performed with the participants (Section 1.5.2). Finally, the results of human subjects testing are reported (Section 3) and discussed (Section 4).