esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Decision Dynamic Intelligent Routing Protocol for Delay-Tolerant Networks
## 2 Routing Decision in Double Q-Learning Environment  
In this section, we will explore the application of Q-learning algorithms for routing decisions in DTNs. Our main focus will be on two key components: the dual Q-learning algorithm and the Q-table update.
## 3 Results and Discussion
### Thermal Conductivity Results

Table 4: ANOVA analysis for thermal conductivity of the biocomposite. Figure 4: Effect of CPP and HSO content on the thermal conductivity coefficient. The model's low error rate (residual SS = 1.071 \(\times\) 10\({}^{-7}\)) and the non-significant \"Lack of Fit\" test (\(p\) > 0.05) demonstrate that the model represents the data accurately and provides reliable results. In conclusion, Table 4 highlights the critical roles of CPP, HSO, and MDI factors in biocomposite production. This underscores the necessity of exercising caution and precision when controlling these factors to achieve the optimal thermal conductivity properties.
## 2 Related Work  
### DC Voltage Overshoots  
The problem of a fault is investigated and the FTC scheme is applied to stabilize the DC voltage overshoots during the short-circuit condition of WECS. The DC overshoot value is measured as severe and active (\(P_{\text{S}}\)), and reactive (\(Q_{\text{S}}\)) power values are similarly observed to erupt at grid side converter (GSC) of WECS. An FTC scheme is developed to suppress the DC overshoots under the short-circuit conditions of the power system.  
While faults occur in the system at the grid side, for the line side converter (LSC), the power transformation will be interrupted. Hence, the wind farms' production ought to be imperfect to confirm the protection of the DC link voltage. The proposed technique establishes communication among both sides to prevent the DC voltage from increasing to its rated rates. The method of this procedure relies on the stability of the DC voltage. Unambiguously, in healthy operational environments, the DC voltage is observed to be constant and close to its rated value. However, under the faulty conditions, it is abrupt and rises toward an undesirable value. For this objective, a block is developed to calculate the DC voltage and determine its rate of variation. Once the fault happens at the AC grid side in the system, the DC voltage increased owing to the uninterrupted energy transfer from the wind farms' side. The authors of [15; 31; 32] studied other different kinds of controllers and investigated the improvement of the performance of the DFIG system and synchronization with the grid.  
It is worth noting that AC-DC grid converters deteriorate from two frequency ripples in the active power and the DC-link voltage during faults. In this paper, the active component current (i.e., the d-axis current \(i_{\text{d}}\)) is appointed to control the DC-link voltage according to the following control loop in Equation (1):  
\[i_{d}^{\star}=k_{\text{p1}}\big{(}V_{dc}^{\star}-V_{dc}\big{)}+\frac{k_{\text{ i1}}}{s}\big{(}V_{dc}^{\star}-V_{dc}\big{)} \tag{1}\]  
where \(V_{\text{dc}}\) is the DC-link voltage and \(k_{\text{p1}}\) and \(k_{\text{i1}}\) are the proportional and integral gains for the DC-link voltage control loop, respectively. Meanwhile, the reactive component current (i.e., the q-axis current \(i_{\text{q}}\)) provides a rapid reactive injection current during faults. The control loop of the reactive injection current is represented as follows in Equation (2):  
\[i_{q}^{\star}=k_{\text{p2}}\big{(}V_{g}^{\star}-V_{g}\big{)}+\frac{k_{\text{ i2}}}{s}\big{(}V_{g}^{\star}-V_{g}\big{)}+i_{q\_inj} \tag{2}\]  
where \(V_{\text{g}}\) is the RMS grid voltage; \(k_{\text{p2}}\) and \(k_{\text{i2}}\) are the proportional and integral gains for the reactive injection current control loop, respectively; and \(i_{\text{q\_inj}}\) is the amount of reactive injection current to the grid during short circuits. The latter control loop is activated when the grid voltage drops to lower than the 0.5 p.u. threshold.
# Analysis of 3D-Printed Zirconia Implant Overdenture Bars
###### Abstract
Additive manufacturing, or 3D printing, is having a significant impact on dentistry, due to a surge in efficiency and cost-effectiveness [16]. However, the process has been primarily limited to plastics and resins. Recently, additive manufacturing in zirconia has been introduced due to advances in 3D printing technology [17]. Despite this, dental implant components, including implant superstructures, are still currently fabricated through subtractive manufacturing via milling. In summary, AM is increasingly used to fabricate surgical guides, dentures, and implant components, offering enhanced fit, reduced waste, and shorter production times [16; 18; 19]. Studies have shown that 3D-printed overdentures can achieve comparable masticatory performance and patient satisfaction to conventional analog methods [18; 19]. However, challenges remain, particularly with ceramic materials like zirconia, which require precise shrinkage compensation and thermal processing. Despite the promising mechanical and biological properties of zirconia, its application in 3D-printed implant superstructures is still unknown. This preliminary investigation explored the fabrication of solid dental implant bars through 3D printing using 3Y-TZP zirconia. The primary objective of this investigation is to establish a practical workflow. Additionally, a lattice-structured overdenture bar will be designed and evaluated, as this novel geometric configuration has demonstrated adequate physical properties in the field of orthopedics [20]. Lastly, an assessment of fit (using a simulated mandible and a denture) and the flexure and stress results, when subjected to a load, will be investigated.
## Materials and Methods
### Ranking of Features Based on Expert Opinions
#### 3.1.5. Generating Preference Rates of Each Expert for Each Attribute  
The preference rates \(p\) of the experts were calculated according to Equation (3). Figure 4 contains the preference indices grouped according to the attributes (lower subplot), for better visibility.  
Figure 3. The \(a^{2}\) results, the number of inconsistent triples, as well as the consistency index derived from the questionnaire for the 28 experts and 6 attributes. The different experts are denoted by numbers 1 to 28 on the horizontal axis, the attributes by the color of the columns, according to the legend at the bottom of the plot. The number of inconsistent triples is shown with a solid dark blue line and the consistency rate is given by a solid dark red line, but the scale corresponding to the consistency index is on the right hand side of the plot.  
Figure 2. The results of the questionnaire for the 28 experts and 6 attributes. The different experts are denoted by the numbers 1 to 28 on the horizontal axis and the attributes by the colors of the columns, according to the legend at the bottom of the plot.  
3.1.6. Determining the Ranks of the Attributes in Decision Making  
The preference rates were converted to rankings, according to Section 2.6.6. The rankings of the consistently answering experts are shown in Figure 5, together with the cumulative ranking of the attributes. The standard deviation of the rankings were \(\sigma_{Size}=0.57\), \(\sigma_{Shape}=1.04\), \(\sigma_{Other}=1.02\), \(\sigma_{Contour}=0.95\), \(\sigma_{Density}=0.37\), and \(\sigma_{Homogeneity}=1.07\). It can be seen that the ranking of density as the most important attribute has a very small deviation. In addition, the ranking of size as the least important attribute had a rather low standard deviation. The ranking of the other attributes was not that clear and sharp, they all had a standard deviation of about 1.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 6 TWT in Wi-Fi 6
### Target Wake Time (TWT) Mechanism in Wi-Fi 6
#### 6.2.3 Agreements  
Individual agreements are negotiated between a single station and its access point. Both can cancel the agreement. Figure 13(a) illustrates the use of an individual agreement. For the negotiation, the station sends a TWT request and the access point replies with a TWT response. The schedule is defined by the TWT parameters that are provided within the next beacon frame. The station must be awake to receive this beacon frame but it can sleep as soon as the TWT schedule is decoded from the beacon. The station is awake within the SPs to transmit or receive frames to or from its access point. This procedure may repeat for a longer time than a beacon interval depending on the agreement or terminate within the same beacon interval.  
Wi-Fi 6 has enhanced the original TWT mechanism to also consider broadcast agreements. A broadcast TWT corresponds to an SP for a group of stations. These agreements avoid duplicating broadcast or multicast packets that have to be delivered to several stations. Indeed, all the stations have to stay awake to receive broadcast frames.  
Figure 13: Individual and broadcast TWT agreements. (**a**) Implicit, non-trigger-enabled, and unannounced individual agreement. Three Service Periods (SPs) are scheduled periodically within one beacon interval. (**b**) Trigger-enabled, unannounced broadcast agreement. Two SPs are scheduled on two different listen intervals.  
In contrast to the individual agreements, the parameters for the TWT sessions are not provided in normal beacon frames but in so-called target beacons. After having transmitted their request, the corresponding stations can sleep until the next target beacon. Target beacons are sent periodically every listen interval and contain the schedule for all the TWT sessions. A listen interval may contain multiple SPs corresponding to different groups of stations. The stations participating in the TWT agreements must wake up for the target beacons as there might be updates for the parameters.  
Figure 14b illustrates the operation with a broadcast agreement. All the stations can send their requests to their access point just after the beacon. Thus, the access point collects the TWT requests and sends a TWT response, announcing when the next target beacon will be transmitted. All the stations decode it and can sleep until the next target beacon. The stations decode the different SPs assigned by the access point in the target beacon. Then, they have to wake up for the service period (SP) they have been assigned to. Here, both stations 1 and 2 participate in the first SP, whereas only station 1 is involved in the second one.
## 3 Results and Discussion
### Analytical Method and Application of MMISB in Apple Samples  
The efficiency of the obtained polymer was evaluated with a very simple extraction protocol for the isolation of PAT from apple samples prior to analysis. The protocol consisted simply of pouring in, stirring and pouring out solvents from a beaker. To carry out the extraction process, it was only necessary to have a magnetic stirrer and an external magnet. Satisfactory recoveries of PAT were obtained using this molecularly imprinted glass-covered stir-bar, with 60%-70% of recovery in a minimum loading time of 45 min under stirring. These results were in the range of the recoveries obtained by [PERSON] et al. using a graphene-based magnetic material to extract PAT from apple juice [8]. In this context, [PERSON] et al. applied commercial MIP-SPE columns for detection of patulin in apple products, obtaining recoveries of \(>\)77% [12]. Figure 3 shows a chromatogram of a blank apple sample (_a_) and a sample spiked with patulin (_b_) at 50 ng\(\cdot\)g\({}^{-1}\), both extracted with the MMISB protocol and analyzed by HPLC-MS/MS. The magnetic NIP stir-bar showed a mean recovery difference of 20% less.  
On the other hand, the deterioration of the polymeric coat of the stir-bar was imperceptible after several hours of use, resisting the whole set of experiments, thus indicating that the adhesion of the polymer was strong and stable. The glass layer as a cohesion agent between the bar and the polymeric coat can be assumed. Imprinted polymers, especially the ones cross-linked with DVB, are stable over a long time and can be reused [17]. The analytical limits were calculated from the signal-to-noise ratio. Using this method, the limits of detection (LOD) and quantification (LOQ) correspond to the concentrations of the analyte that would yield a signal equal to three and 10 times the noise level, respectively [18]. The calculated LOD (S/N = 3) was 10 ng\(\cdot\)g\({}^{-1}\) and the LOQ (S/N = 10) was 50 ng\(\cdot\)g\({}^{-1}\).
## 5 What Is the Benefit of SWE in Children?
### Scores in Fatty Liver Disease and Fibrosis: Are They Better than SWE?  
Another alternative for the non-invasive assessment of liver fibrosis are the serologic tests such as FibroTest, APRI, Forns Index, Fib-4, NAFLD test, PNFI, pediatric NAFLD fibrosis score (PNFS), and so on. FibroTest-ActiTest has been validated in children with chronic hepatitis C [101; 102]. Only a few studies have been published regarding the comparative value of ultrasound-based elastographic techniques and serologic tests. In a pilot study, it was found that, for the diagnosis of cirrhosis, the AUROCs for TE, FibroTest, and APRI were 0.88, 0.73, and 0.73, respectively [49]. In an Egyptian cohort, the AUROCs of TE and APRI score for discriminating significant fibrosis (F2, Metavir score) were 0.883 and 0.746, while the correlations with liver biopsy were 0.58 and 0.53, respectively [21].  
The advantages of serological tests are that they do not require any specialized equipment, however patented tests are expensive and not readily available. FibroTest-ActiTest, even though expensive, has the advantage of giving information regarding the severity of inflammation.  
Considering the scarce comparative data between serology and elastography tests in children, a definite conclusion regarding which one is the best cannot be drawn.
# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 4 System Architecture
### Hardware Architecture and System Integration
#### 4.1.2 Noise Source Analysis and Mitigation  
Our comprehensive analysis identifies three primary noise sources:  
Motor Electromagnetic Interference (EMI): DC motor operation generates electromagnetic noise with dominant peaks at 120 Hz and 250 Hz. The EMI intensity is as follows: \(I_{EMI}(f,d)=P_{motor}(f)\times G_{coupling}\times d^{-2}\).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Component** & **Specifications** & **Function** \\ \hline ESP32 (Espressif Systems, Shanghai, China) & Dual-core 240 MHz, 520 KB SRAM, & Processing, communication, audio I/O \\  & Bluetooth 4.2 & \\ INMP441 (InvenSense, San Jose, CA, USA) & 24-bit I2S MEMS microphone, 61 dB & Environmental audio capture \\  & SNR, 20 Hz-20 kHz & \\ L298N & Dual H-bridge, 2 A per channel, 5–35 V & Motor direction and speed control \\ DC motors & 6 V, 200 RPM, 300 mA nominal & Rover movement \\ LiPo battery & 7.4 V, 2200 mAh, 25 C & Power supply \\ Rover Chassis & Tank-style tracked platform, dual motors & Physical platform \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primary hardware components.  
Figure 3: Integrated hardware architecture and physical layout.  
Bluetooth Communication Interference: The 2.4 GHz transmission creates broadband interference (\(-\)45 dBm to \(-\)60 dBm during active periods).  
Mechanical vibration coupling: Platform vibrations create low-frequency noise (5-50 Hz) masking environmental signals.  
Three-Tier Mitigation Strategy:  
1. Physical Isolation: Strategic component placement, mechanical decoupling (15-20 dB isolation), dedicated ground planes.
2. Electronic Filtering: Active power supply filtering (>30 dB noise rejection above 1 kHz), differential I2S signal routing (25 dB cross-talk reduction).
3. Digital signal processing: Real-time adaptive filtering achieving 12-18 dB motor harmonic suppression.
## 2 System Model  
### Construction and Assumption  
We consider a D2D-enabled cellular network hosting \(M\) EUs in the set \(m\in\{1,2,3,\ldots,M\}\) and \(N\) D2D transmission pairs in the set \((DT_{n},DR_{n})\in\{(DT_{1},DR_{1}),(DT_{2},DR_{2}),(DT_{3},DR_{3}),\ldots,(DT _{N},DR_{N})\}\) as well as one BS that assigns the spectrum allocation to the cellular users, as seen in Figure 1. The BS assigns the spectrum band into \(C=M\) orthogonal channel with equal bandwidth and each EU is preassigned an orthogonal channel for its data transmission. Hence, the interference among EUs is avoided [36; 37]. Based on the considered D2D-enabledd cellular network, the DTs are capable of bypassing the BS and directly communicating with the proximity device, namely DRs. The overlay spectrum-sharing scheme is employed for the sake of sharing the spectrum between D2D links and cellular links. More explicitly, the EU with unfavorable channel condition may share part of its spectral resources to a D2D transmission pair in exchange for cooperative transmission assistance. A D2D transmission pair \((DT_{n},DR_{n})\) intends to provide cooperative assistance for a EU \(EU_{m}\) in order to convey its own tele-traffic by using the cellular spectrum assigned to the \(EU_{m}\). The EUs cooperative with D2D pairs in overlay mode which cellular link and D2D link occupy different cellular spectrum [38]. Hence, the interference of EUs and D2D pairs is avoided as well.  
Both the EUs and DTs are assumed to be limited by the same maximum transmit power \(P_{MAX}\). The channel gain is assumed to be modeled by [39]:  
\[h_{ij}=LH^{2}d_{ij}^{-\eta} \tag{1}\]  
where \(L\) denotes the pathloss constant, while \(H\) represents the fast Rayleigh fading channel. Furthermore, \(\eta\) represents pathloss exponent and \(d_{ij}\) denotes the distance between node \(i\) and node \(j\). The notations of the system are listed in Table 1.  
Figure 1: The system model.
## 2 Simulation and Experimental Procedure
### Theoretical Model
\[\frac{dP_{p}{}^{\pm}(z)}{dz}=\pm\Gamma(v_{p})[N_{3}\sigma_{e}(v_{p})-N_{1} \sigma_{a}(v_{p})-\alpha(v_{p})]P_{p}^{\pm}(z) \tag{6}\]  
\[\frac{dP_{s}{}^{\pm}(z)}{dz}=\pm\Gamma(v_{s})[N_{2}\sigma_{e}(v_{s})-N_{1} \sigma_{a}(v_{s})-\alpha(v_{s})]P_{s}^{\pm}(z) \tag{7}\]  
\[\frac{dP_{ASE}{}^{\pm}(z,v_{k})}{dz}=\pm\Gamma(v_{k})[N_{2}\sigma_{e}(v_{k})-N _{1}\sigma_{a}(v_{k})-\alpha(v_{s})]P_{ASE}^{\pm}(z,v_{k})\pm 2N_{2}\Gamma(v_{k}) hw_{k}\Delta v_{k}\sigma_{e}(v_{k}) \tag{8}\]  
In (6)-(8), \(v_{p}\) and \(v_{s}\) correspond to the frequency of the pump and signal, respectively; \(\sigma_{e}\) and \(\sigma_{a}\) are the emission and absorption cross sections of Er\({}^{3+}\) ions, respectively; and \(\alpha\) is the total attenuation of active fiber, which also includes RIA. The forward and backward components of ASE noise spread over a continuous frequency range. Thus, in order to perform the numerical computations, the whole Er\({}^{3+}\) spectrum is sampled into \(K\) frequency slots centered at the frequency \(v_{k}\) with bandwidth \(\Delta v_{k}\), \(k=1,2,\ldots,K\). The overlap factor \(\Gamma(v)\)  
Figure 1: Schematic of the erbium energy levels system used in the model, adapted from [35]. The green arrows correspond to the energetic transitions involving \({}^{4}\)I\({}_{15/2}\), \({}^{4}\)I\({}_{13/2}\) and \({}^{4}\)I\({}_{11/2}\) energy levels. The dotted arrows indicate non-radiative decays, and the dashed arrows correspond to the ion–ion energy transfer mechanisms. quantifying the transverse optical mode profile overlapping with erbium ion distribution is defined as follows  
\[\Gamma(v)=\frac{\int_{0}^{2\pi}\int_{0}^{Rc}|E(r,\phi,v)|^{2}rdrd\phi}{\int_{0}^ {2\pi}\int_{0}^{\infty}|E(r,\phi,v)|^{2}rdrd\phi} \tag{9}\]  
with  
\[\frac{dN_{2}}{dt}=W_{12}N_{1}-W_{21}N_{2}-\frac{N_{2}}{\tau_{21}}-2C_{up}N_{2}^{2 }+C_{14}N_{1}N_{4} \tag{2}\]0  
In (9), we consider that the actual erbium ion distribution is constant on the optical fiber core with radius \(R_{c}\). Moreover, \(E(r,\phi,v)\) is the transverse electric field envelope normalized using Equation (10). The overlap factor is a very important parameter, since it quantifies the percentage of the guided optical mode that interacts with the RE doping the fiber core. Therefore, an accurate calculation of the overlap factor results in an accurate estimate of the REDFA performance [33]. The computational code used for the prediction of the EDFA performance needs various input parameters: fiber parameters (fiber length, core radius, overlapping factor); spectroscopic parameters (radiative and non-radiative lifetimes, upconversion and cross-relaxation coefficients, erbium ion concentration, emission and absorption cross sections spectra); operative parameters (pump and signal wavelengths, pump and signal power, pumping scheme) and irradiation parameters (RIA at pump and signal wavelengths, which depend on dose rate, dose and temperature).
## 3 Dependable Wireless System
### Shortened Code
Define \(q\) as the number of copies of a bit in the repeater and \(r\) as the number of additions of bits in the combiner. The parameters \(q\) and \(r\) are important factors that determine the characteristics of the code. The parity check matrix of systematic RA code is shown in Figure 4. The number of 1's in the column of information bits is called the column weight, and its number is equal to \(q\). The number of 1's in the row of information bits is called the row weight, and its number is equal to \(r\). When designing RA codes, the number and position of 1's are important parameters which affect the characteristics. As  
Figure 2: Encoding procedure of \((n-m,k-m)\) shortened code. matrix, it can also be considered as a low-density generator matrix (LDGM) code [27] which is a kind of LDPC code [28]. Therefore, RA code can be decoded by an algorithm such as message passing as in LDPC code, and its characteristics are close to the Shannon limit.