esa-sceva/satcom-chunk-collection huggingface dataset:
# The Importance of Gram-Negative Rods in Chronic Rhinosinusitis
## Future Perspectives  
A detailed analysis of the impact of individual microorganisms or groups of bacteria on histopathology, immunology, the presence of disease-related protein components in the mucus, and the clinical picture of CRS may allow the determination of the endotype activated in an individual patient. These data will improve the diagnosis, prognosis, and management strategies.  
Furthermore, the integration of microbiological analyses with proteomic investigations (e.g., protein profiling of nasal mucus) and immunological studies (e.g., characterization of the inflammatory response) holds the potential to enhance our understanding of CRS significantly and to lay the groundwork for the development of personalized therapeutic approaches.  
Furthermore, given the confirmed presence of bacterial biofilms in CRS, as demonstrated in recent studies, the implementation of novel combination therapies specifically targeting biofilm structures--such as probiotic/prebiotic formulations, biologic agents, and photodynamic therapy--should be considered as promising avenues for treatment.  
Traditional culture-based methods may fail to detect the full spectrum of microbial species involved; thus, the application of metagenomic sequencing techniques to characterize the complete microbial profile of the sinonasal environment may offer critical insights into the role of the sinus microbiota in the pathogenesis of CRS.  
**Limitations**  
1. The samples in our study were obtained from patients with CRS during ESS. The patients with acute rhinosinusitis or exacerbations of CRS were excluded.
2. The sampling site for CRS and control patients was not the same. The anterior ethmoid sinus mucosa was collected from CRS patients, whereas the interior turbinate mucosa was collected from control patients.
3. The study protocol did not include molecular methods, so the results refer only to cultivable bacteria.
## 2 Materials and Methods  
### Low-Cost Sensor Module  
The spectral sensors for the low-cost module were selected based on criteria intended to circumvent the high cost and frequent maintenance requirements typically associated with high-precision instrumentation, thereby being suitable for scalable and dense spatial deployment. These criteria included long-term operational stability, extended sensor lifetime, reduced hardware cost, ease of integration with mobile platforms, and a simplified, robust design. The ensemble includes: an AS7265x Smart Spectral Sensor manufactured by ams Osram [33,34](from SparkFun Electronics, CO, USA), an ILTR390 UV Sensor manufactured by Adafruit Industries [35] (Adafruit Industries, NY, USA), and an Analog GUVA-S12 SD UV Sensor also manufactured by Adafruit Industries [36] (Adafruit Industries, NY, USA), at a combined cost of approximately USD 80. Figure 2 depicts the components of the low-cost sensor ensemble.  
The constituent sensors were also selected to ensure broad spectral coverage across the solar spectrum. The AS7265x, a smart 18-channel sensor that combines three sensors: the AS72651, the AS72652, and the AS72653, gives raw counts corresponding to the number of photons in each of 18 channels from 400 nm to 1000 nm. The LTR390, one of the few low-cost ultraviolet (UV) sensors available [35], offers UVA sensing with a peak spectral response between 300 nm and 350 nm, and gives unitless digital output values corresponding to both ambient light and UVA intensity. The GUVA-S12 SD is capable of detecting the 240 nm to 370 nm range of light, covering the UVB, and most of the UVA spectrum. The output is  
Figure 2: Ensemble of low cost sensors. (**a**) AS7265x Smart Spectral Sensor. Source: [34]. (**b**) LTR390 UV Light Sensor. Source: [35]. (**c**) GUVA-S12 SD UV Sensor. Source: [36].  
an analog voltage signal that varies in proportion to the intensity of incident UV radiation, serving as a proxy for the incident UV abundance. The sensors are temporally synced and operate at a temporal frequency of 10 s. In addition to the main body of spectral sensors, the module also hosts a GPS sensor that records the longitude, latitude, and the altitude of the location being observed.  
While the low-cost sensor ensemble was curated to span a broad spectral range, and albeit being sufficient for capturing key spectral features relevant to SSI estimation, low-cost sensors are inherently not equivalent in precision to research-grade instruments. Therefore, since the objective is to be able to reproduce NIST-traceable irradiance estimates from these low-cost measurements, the input feature set was augmented with additional key physical determinants of SSI. This included the solar zenith angle, solar azimuth angle, and the surface reflectance. The availability of solar radiation is primarily governed by the Sun's position relative to the observer [37]. The solar zenith angle and solar azimuth angle determine the position of the Sun in the sky relative to a point on Earth's surface, and hence play a critical role in the magnitude and distribution of SSI.
# Analysis of Behavioral Characteristics of Smartphone Addiction Using Data Mining
## 4 Data Analysis Based on Data Mining
### Data Mining for Self-Report Questionnaires
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Attribute (Item)** & **Coefficient** & **Std. Error** & **Std. Coefficient** & **Tolerance** & **t\_Stat** & \(p\)**-Value** & **Code** \\ \hline Salience 1 & \(-\)0.1037 & 0.0363 & \(-\)0.1496 & 0.8617 & \(-\)2.8553 & 0.0047 & *** \\ Salience 2 & 0.0888 & 0.0321 & 0.1532 & 0.9643 & 2.7628 & 0.0062 & *** \\ Salience 3 & 0.1165 & 0.0375 & 0.1678 & 0.9712 & 3.1030 & 0.0021 & *** \\ Mood control 1 & \(-\)0.0976 & 0.0281 & \(-\)0.1985 & 0.8856 & \(-\)3.4719 & 0.0006 & *** \\ Mood control 3 & 0.0790 & 0.0324 & 0.1439 & 0.9547 & 2.4404 & 0.0154 & ** \\ Mood control 4 & 0.1182 & 0.0290 & 0.2153 & 0.9026 & 4.0829 & 0.0001 & *** \\ Tolerance 1 & \(-\)0.1155 & 0.0368 & \(-\)0.2103 & 0.7292 & \(-\)3.1424 & 0.0019 & *** \\ Tolerance 2 & 0.0837 & 0.0347 & 0.1374 & 0.9771 & 2.4146 & 0.0165 & ** \\ Withdrawal 1 & 0.1807 & 0.0335 & 0.3217 & 0.8618 & 5.3859 & 0.0000 & *** \\ Conflict 1 & 0.1542 & 0.0348 & 0.2849 & 0.8174 & 4.4341 & 0.0000 & *** \\ Conflict 3 & 0.0902 & 0.0280 & 0.1868 & 0.8316 & 3.2227 & 0.0014 & *** \\ Recurrence 3 & 0.1024 & 0.0349 & 0.1834 & 0.7577 & 2.9323 & 0.0037 & *** \\ Recurrence 4 & \(-\)0.0988 & 0.0444 & \(-\)0.1554 & 0.5207 & \(-\)2.2232 & 0.0271 & ** \\ (Intercept) & 0.0250 & 0.1464 & & & 0.1705 & 0.8648 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Multiple linear regression results of the SAS survey.
# Vibration Damage Analysis of Bottom Hole Assembly Under Axial Impact Based on Dynamic Analysis
## 2 Impact Simulation Model
### Drill String Fatigue Solving Strategy  
According to the axial impact force equation of the drill bit:  
\[\frac{m_{b}}{K}\frac{d^{2}}{dt^{2}}u(t)+\frac{\rho cA_{b}}{K}\frac{d}{dt}u(t)= \frac{pcA_{b}}{K}v_{0} \tag{11}\]  
Figure 6: Stress–strain relationship of rock.  
where \(m_{b}\) is the mass of the drill bit, \(A_{b}\) is the cross-sectional area of the impact hammer, \(v_{0}\) is the impact velocity, \(u(t)\) is the footage depth of the drill bit, \(\rho\) is the density of the hammer material, \(c\) is the longitudinal wave velocity of the material, m/s, and \(K\) represents the axial stiffness of the drill string system, N/m.  
Considering the small deformation of the drill string system [24; 25; 26; 27], the Mizers equivalent stress is used to calculate the synthesis of stress:  
\[\sigma^{\prime}_{o\text{max}}=\sqrt{\frac{1}{2}}\Big{[}\Big{(}\sigma_{z}+ \sigma_{\text{max}}-{\sigma_{r}}^{2}\Big{)}+(\sigma_{r}-\sigma_{t})^{2}+( \sigma_{t}-\sigma_{z}-\sigma_{\text{max}})^{2}\Big{]} \tag{12}\]  
\[\sigma^{\prime}_{o\text{min}}=\sqrt{\frac{1}{2}}\Big{[}\Big{(}\sigma_{z}+ \sigma_{\text{max}}-{\sigma_{r}}^{2}\Big{)}+(\sigma_{r}-\sigma_{t})^{2}+( \sigma_{t}-\sigma_{z}+\sigma_{\text{max}})^{2}\Big{]} \tag{13}\]  
The formula for calculating the fatigue damage under a given stress is as follows:  
\[N=\frac{N_{1}}{\sum_{i=1}^{l}\alpha_{i}(\frac{\sigma_{i}}{\sigma_{L}})^{d}} \tag{14}\]  
\(N\) is the total life; \(\sigma_{i}\) is the stress amplitude at the highest stress level, MPa; \(N_{1}\) is the fatigue life under stress; \(\alpha_{i}\) is the \(\sigma_{i}\) ratio of the number of cycles at a given stress level to the total number of cycles; \(d\) represents the material damage index; and \(l\) is the index of the stress level.
## 1 Introduction
### Central Bank Digital Currency
The International Monetary Fund (IMF) defined CBDC as a digital representation of a sovereign currency issued by and a liability of a jurisdiction's central bank or other monetary authority [17]. Under the CBDC system, the central bank directly manages the whole currency circulation, which is the biggest difference from the current electronic financial systems operated by various commercial entities and brings many advantages. First, the central bank's intervention in the payment system will minimize many steps in money circulation and promote competition between entities operating the electronic financial systems. As JP Morgan predicted, this could save charges for the system use that are spent in the present electronic financial systems [18]. Second, users will not need to use various commercial payment systems, and thus, the possibility of personal data breaches will be reduced. Unlike electronic financial systems that leave data trails through a number of commercial entities, under the CBDC system, the central bank directly manages personal data [19]. Third, the CBDC system can solve the problem of financial exclusion. Unlike electronic financial systems that do not provide financial services to unbanked people, estimated to be almost 1.7 billion people worldwide, under the CBDC system, CBDCs can be directly provided to users without using traditional bank accounts [20; 21]. Blockchain technology is regarded as the option that can strengthen the merits of the CBDC system, as it is based on distributed ledger technology (DLT), which does not need intermediaries and is well known for its strong security.
# The Role of Electron Trajectories in XUV-Initiated High-Harmonic Generation
## Appendix A Harmonic Phase in the Coulomb-Corrected Three-Step Model  
Here we describe the calculation of the trajectories, the harmonic phase, and the delay-dependent sub-cycle oscillations in the CCTSM. For convenience, we use atomic (Hartree) units throughout this paragraph (\(e=\hbar=m=1\)). In the CCTSM, trajectories are determined by numerical integration of [PERSON]'s equation of motion with a combined Coulomb-laser potential in one dimension. Helium is modeled by a soft core potential [43]:  
\[V(z)=-\frac{Z_{\text{eff}}}{\sqrt{a^{2}+z^{2}}}, \tag{1}\]  
where \(z\) is the radial coordinate, \(Z_{\text{eff}}=1.353\) is the effective charge, and \(a^{2}=0.001\) is the soft core parameter. We also tested other model potentials, e.g., [36], but did not find a significant difference. Each incident harmonic \(n\) of the IR driver gives rise to an ionization channel. For each ionization channel, electrons are released at the origin (\(z=0\)) with initial velocities \(v_{0}=\pm\sqrt{2(-I_{\text{p}}-V(0)+n\omega)}\), with the sign reflecting the ambiguity of downhill and uphill trajectory branches (\(I_{\text{p}}\): atomic ionization potential; \(\omega\): angular frequency of the IR field). Numerical integration then gives rise to long and short trajectories, with ionization times \(t_{0}^{(i)}\) and recollison times \(t_{1}^{(i)}\). The phase of an outgoing harmonic \(N\) is dictated by a specific ionization channel associated with the incident harmonic \(n\) and a specific trajectory (index \(i\)):  
\[\phi^{(i)}(N\omega)=N\omega t_{1}^{(i)}-S(t_{1}^{(i)},t_{0}^{(i)})-I_{\text{ p}}\left(t_{1}^{(i)}-t_{0}^{(i)}\right)-n\omega t_{0}^{(i)}, \tag{2}\]  
where \(S(t_{1}^{(i)},t_{0}^{(i)})\) is the classical action accumulated by the electron along its trajectory. The sub-cycle oscillations with XUV-IR delay follow from a coherent summation of all trajectories producing the outgoing harmonic \(N\). For the comparison of long and short trajectories (see Section 4.4), we sum downhill and uphill trajectories corresponding to all four ionization channels, weighted by their quantum diffusion. We employ the reconstructed ionization channel transition amplitudes from Reference [24].
## 3 Results
### Experimental Measurements
#### 3.3.1 Tightening of Clamp Screws  
The first analyses were focused on the effect of the tightening of clamp screws (using torque \(M_{c}\)) on the deformation of the holder. These bolts had to be tensioned to press the clamps into the magnet to ensure a stable position of the magnet. So, it is obvious that this torque \(M_{c}\) cannot be too small. On the other hand, too large of \(M_{c}\) torque causes internal forces in the holder and leads to unfavorable deformation of the inner U-shaped frame (Figure 15).  
The five series of measurements with different clamp tightening torques \(M_{c}\) were performed. In each case, the torque \(M_{r}\) on the adjustment screws was increased from 0 Nm to 5 Nm or 10 Nm. To assess the possibilities of the calibration using the adjustment screws, in this experiment, the dial indicators were zeroed after a set of the clamp-tightening torques \(M_{c}\). Each result is presented on curves in Figure 16 showing the trend and range of the magnet position calibration. The displacement of the magnet was measured in points indicated in Figure 10b. It can be seen that for a small torque \(M_{c}\) (i.e., \(M_{c}=1\) Nm), the deformation of the holder does not increase linearly with the screw torque. This may result in an uneven magnet movement, which can be observed based on different displacements of the magnet on both sides. For a larger \(M_{c}\) (i.e., \(M_{c}\geq 2\) Nm), handle deformation showed a repeatable linear trend. It is obvious that the increase in torque \(M_{r}\) results in the lowering of the magnet position (pushing into the holder). It is also observed that the increase in torque \(M_{c}\) results in a slightly larger displacement of the magnet.  
Figure 16: Displacement of magnet for different clamp-tightening \(M_{c}\) and adjustment screw \(M_{r}\) torques.  
Figure 15: Deformation of inner U-shaped frame due to large tightening of clamp screws.
# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Guidelines and Prerequisites
#### 4.6.3. _E_radication of Multiple Sensors and Expanding of Storage of Data  
As established in Section 4.1, image-based SHM, like any type of SHM, is developed and applied: (1) to identify and discover; (2) to measure and monitor; (3) to automate and increase efficiency; and (4) to promote development and create 3D models. Data fusion methods bring together numerous sensor responses with information from a connected data bank to draw more assumptions about potential danger than a single sensor would and increase accuracy and reliability [156]. Any civil infrastructure may be monitored in different and many areas. In line with the study of [157], there is a need to provide multiple structural decline detection at local points. These include concrete cracks, pavement cracks, steel cracks, spalling, delamination, crack propagation, rust detection, loose bolt detection, and damage construction via 3D reconstruction. Multiple sensors for human loading and vehicular loading detection were used. In both CVs-SHM for local level CV-SHM-LL and CV-SHM for global level CV-SHM-GL, the object in the physical world is projected into an image through a camera and lens. By evaluating the changes in the images such as their motion or an irregular phenomenon, those events in the real world can be estimated. Damages, on the other hand, are measured by their influence on certain structural characteristics such as material type and connection. The best selection of one or more applicable SHM evaluation techniques and sensors involves an understanding of the structural attributes most substantially altered by the damages of interest. Future research must focus on presenting the prospective use of fewer sensors that is multi-purpose [158], highly sensitive [159], and strategically placed on critical points, usually connections, of the structures resulting in more economical conduct of SHM. Accordingly, a faster region-based convolutional neural network can deliver human-like inspection of structures [69]. This is accomplished by developing a database with 2366 pictures (500,375 pixels) identified for five categories of damages: concrete fracture; steel rust with two levels (middle or upper); bolt rusting; and metal delamination. Collectively, images require more data storage [69]. If piled up, big data is expected and so is data analysis and exploration entails. From here, we can start to consider the following questions when performing a classification task when dealing with big image files:  
1. How are the features generated?
2. What is the best number of features to use?
3. Having adopted the appropriate features, for the specific task, how does one design the classifier?
4. How can one assess the performance of the designed classifier?
5. What is the classification error rate? This is the task of the system evaluation stage.
## 3 Modulation Techniques  
Digital modulation techniques impress the digital signal onto a carrier signal for data transmission. A sequence of digital data is used to alter the parameter of a high frequency called carrier signal. Thus, signal transmission takes place by modulating different parameters like amplitude, phase and frequency of the signal. Modulation techniques provide high data rate transmission, data security, quality signal, simple architecture, low power consumption, good performance over a fading communication channel, increased channel capability, greater accuracy in the presence of noise and distortion, _etc._[22]. However, there are a number of tradeoffs in the digital modulation like hard to design complex structured, disadvantages of analog counterparts and bandwidth size. The main criteria for choosing the kind of modulation schemes are based on power, bandwidth and system efficiencies [23,24]. Figure 3 shows the most common modulation used in biomedical devices.  
Modulation schemes are chosen or designed according to the channel characteristics in order to optimize their performance. Channels can be classified as additive white Gaussian noise channels (AWGN), band limited channels and fading channels. The AWGN channel is a universal channel model for analyzing modulation schemes by adding the white Gaussian noise to the signal passing through the channel. The band limited channel is limited when it is smaller than the signal bandwidth. The fading channel happens when the amplitude and phase change rapidly over a short period of time.
## 5 Challenges and Future Research Directions
### Future Research Directions
#### 5.2.2 Blockchain-Based Security Strategies  
Blockchain technology offers a decentralized data management platform, enabling data sharing and synchronization between nodes in SAGINs. Through encryption technology and consensus mechanisms, blockchain ensures security and privacy during data transmission [166].  
_IoT Security Communication_: In SAGINs, blockchain can ensure secure communication between IoT devices by preventing unauthorized access or tampering with device data through its immutability.
2. _Traffic Offloading and Management_: Traffic in SAGINs can be offloaded and managed using blockchain technology, optimizing traffic distribution and improving network performance through blockchain's transparency and immutability. Enhanced Practical Byzantine Fault Tolerance algorithms can be used to evaluate node security, ensuring the safety of traffic offloading [167].
3. _Digital Identity Authentication_: Blockchain can provide a secure, immutable identity authentication mechanism, enhancing the security and privacy of digital identities. For example, [PERSON] et al. [168] introduced a blockchain-based collaborative credential management scheme for anonymous authentication in vehicular SAGINs. This scheme ensures efficient and secure credential issuance and management while reducing on-chain overhead.
## 4 Datasets and Experimental Results
### Analyzing the Impact of the Training Sample Ratios  
This section studies the sensitivity of the SF-ICNN technique to the number of training samples (#training). To accomplish this, the CNN classifier is trained using a different #training in the suggested SF-ICNN technique. The classification accuracy results for both HSI datasets are shown in Figure 8. This figure shows that, as expected, the classification accuracy of SF-ICNN increases with an increasing sample size. Furthermore, even with limited #training, the suggested approach continues to function well.  
Figure 8: Variation of the OAs of the proposed SF-ICNN method in relation to the #training: (**a**) IP, and (**b**) PU.  
Figure 7: Variation of the suggested method’s OAs according to the network’s iteration number on validation samples.
## 4 OWS Q-Learning Algorithm
### Speedy Q-Learning Algorithm  
Although the Q-learning algorithm is a classical algorithm of reinforcement learning, the algorithm has certain limitations [56]. When the number of state actions of the agent is limited, the Q-learning algorithm can converge to the optimal strategy, but when the discount factor \(\gamma\) is close to 1, the convergence speed of the Q-learning algorithm will become very slow [56]. The speedy Q-learning algorithm was first proposed in 2011 by [PERSON] et al. [57]. The speedy Q-learning algorithm improves the Q-learning algorithm by using an aggressive learning rate, replacing the learning rate of an updated criterion in the Q-learning algorithm with a larger learning rate, which improves the convergence speed of the algorithm [57]. In essence, the speedy Q-learning algorithm replaces the estimation function of the historical Q value with that of the current Q value and selects a more efficient estimator, thus improving the convergence speed of the Q-learning algorithm [53].  
Assuming that \(M(s^{\prime},a^{\prime})=\underset{a^{\prime}}{max}Q(s^{\prime},a^{\prime})\), \(M_{-}(s^{\prime},a^{\prime})\) represents the maximum value function value of the next state when the Q value of state action \((s,a)\) is updated in the previous iteration step. The Q value update process of the speedy Q-learning algorithm [53] is:  
\[Q(s,a)=(1-\alpha)Q(s,a)+\alpha\left[r+\gamma M_{-}\left(s^{\prime},a^{\prime} \right)\right]+(1-\alpha)\gamma\left[M\left(s^{\prime},a^{\prime}\right)-M_{- }\left(s^{\prime},a^{\prime}\right)\right] \tag{15}\]  
The pseudocode for the speedy Q-learning Algorithm 2 is as follows:  
```
Input: state set \(S\), action set \(A\), reward function \(R\) Output: optimal state-action value function \(Q_{s}(s,a)\), from the optimal state-action value function to obtain the optimal strategy \(\pi_{*}(S)\) Process:
1. Initialize \(Q(s,a)=M(s,a),s\), and have a Q value of 0 for all state-action pairs for \(i=1\) to episodes
2. Repeat:
3. Based on the value function Q, use a strategy (such as strategy \(\varepsilon-greedy\)) to select action \(a\) in state \(s\);
4. Execute action a to obtain instant reward \(r\) and obtain the next state \(s^{\prime}\);
5. \(a^{*}\gets argmaxQ(s^{\prime},a^{\prime})\)
6. \(\sigma\leftarrow(2\alpha-1)\gamma M(s,a)\)
7. \(M(s,a)\leftarrow\text{Q}(s^{\prime},a^{*})\)
8. \(Q(s,a)\leftarrow(1-\alpha)[Q(s,a)+\gamma M(s,a)+ar+\sigma]\)
9. \(s\gets s^{\prime}\)  Until \(s\) is terminated  End for
```  
**Algorithm 2** Speedy Q-learning algorithm