esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analysis of Measured and Simulated Bifacial Energy Gain (BG)  
We make our results comparable to previous studies by including modeled versus measured bifacial energy gain per Equation (1, where \(E_{BF}\) and \(E_{MF}\) are the energy produced by the bifacial and monofacial systems, respectively, and \(P_{STC,BF}\) and \(P_{STC,MF}\) are the front side power ratings of the bifacial and monofacial systems, respectively. The \(P_{STC}\) values are obtained from I-V measurements made at DTU:  
\[BG\;[\%]=\;\left(\frac{E_{BF}/P_{STC,BF}}{E_{MF}/P_{STC,MF}}\;-1\right)\times 100 \tag{1}\]  
Note that bifacialvf and pvfactors (Table 1) do not incorporate cell temperature models or electrical models. Therefore, we also present bifacial gain in terms of the rear to frontside irradiance ratios, in order to compare results from all software:  
\[BG\;[\%]\;=\;\frac{G_{POA,Rarr}}{G_{POA,Front}}\times BF\times(1-Bifi_{loss}) \times 100 \tag{2}\]  
where, in Equation (2), \(BF\) is the bifaciality factor defined as the ratio of rear to frontside efficiency at standard test conditions (STC), which is 0.67 according to the DTU indoor flash I-V measurements. The \(Bifi_{loss}\) term here accounts for two separate parameters: The electrical losses induced by non-uniform backside illumination and the losses due to structural profiles that shade the backside of the array. The electrical mismatch caused by non-uniform backside irradiance is not a constant value but varies with the prevailing environmental conditions [19; 57; 58]. In our previous work [19], we determined that on a clear day at solar noon, the irradiance nonuniformity-induced mismatch for the bifacial arrays at our site is roughly 0.025 (without considering the front-side irradiance contribution), and is used to calculate \(Bifi_{loss}\). The aforementioned Soltec white paper [52] that recommends 0.007 as a value for structural shading loss, which is subsequently used here. The \(Bifi_{loss}\) value used in this work therefore amounts to 0.032. Note that the performance models tested here sometimes consider additional parameters, such as the transparency of the PV array and the reflectivity of the PV array's back and front side (see Table 10).  
Validation of bifacial performance models requires measurements of the rear plane-of-array irradiance (\(G_{POA,Rarr}\)). Figure 2d shows a photograph of two pyranometers that are used for this purpose. The instruments shown are mounted on the backside of the fixed-tilt system, 17 panels east of the western array edge. The rear facing pyranometers on the HSAT are located 12 panels north of the southern array edge. Ray trace simulations made by [17] have shown that this distance into the 45-m-long array should be sufficient to remove edge-brightening effects and to be representative of the semi-infinite assumption that is common among 2-D VF models.
## 4 Signal Preprocessing  
The raw IF signals were collected in the form of a 3D cube data (time, chirp, and antenna). Fast Fourier transformations were performed on the IF signals to estimate the range, velocity, and angle of arrival (AoA) of the moving target [14,37]. A cloud point is a 3D model composed of a set of points used in the literature to describe a list of detected targets provided by radar signal processing [38]. Figure 3 illustrates a four-step signal preprocessing workflow to generate a series of cloud points, each comprising different features, such as the 3D spatial position (x, y, and z), velocity, and AoA [39].
## 6 Interpretation of the Adsorption Energy  
The multilayer model supplied two energies (\(-\varepsilon_{1}\)) and (\(-\varepsilon_{2}\)) during the adsorption process, which are utilized to describe the kind of interactions between the CO\({}_{2}\) molecules and also between the zeolite surface and the CO\({}_{2}\) molecules. The two energies are determined by the next expressions [34; 35; 36]:  
\[-\varepsilon_{1}=-\text{RTLn}\frac{\text{P}_{\text{vs}}}{\text{P}_{1}} \tag{5}\]  
\[-\varepsilon_{2}=-\text{RTLn}\frac{\text{P}_{\text{vs}}}{\text{P}_{2}} \tag{6}\]  
where P\({}_{\text{vs}}\) represents the vapor pressure at saturation of CO\({}_{2}\).  
The adsorption energies are determined based on the pressures at half-saturation (P\({}_{1}\) and P\({}_{2}\)) evaluated by model application. These pressures are principally related to the first layers and those created by N\({}_{2}\) during the adsorption mechanism. The values of the determined energies listed in Table 2 are inferior to 30 kJ/mol, indicating that the CO\({}_{2}\) adsorption on ZSM-5 can be related to a physisorption phenomenon for which the van der Waals interactions are responsible. In addition, the values of the adsorption energies are negative confirming the exothermic nature of the CO\({}_{2}\) adsorption on the tested samples.  
The temperature effect on the adsorption energies are illustrated in Figure 9. It is evident that the adsorption energies reduce with temperature, which is clarified by the decrement in the accessible binding sites and consequently the decrement in the average active site energy [37]. In fact, the molecules of carbon dioxide are initially bonded on the sites presenting intense affinity with a high adsorption energy. After the occupation of these sites, the molecules of carbon dioxide are bonded through sites characterized by lower adsorption energy that is actually decreased.
## 10 Knowledge Gaps and Future Directions  
### Research Gaps  
Despite significant advances in understanding plant-microbe interactions in the rhizosphere, several critical knowledge gaps remain. The precise mechanisms by which the rhizosphere microbiome modulates root metabolism and exudation, and how plants fine-tune these complex belowground interactions, are still largely unexplored, particularly regarding long-distance and systemic signaling processes such as systemically induced root exudation of metabolites (SIREM), a transport process that describes how microbial colonization at one plant site (e.g., leaves or roots) triggers systemic changes that result in the transport and exudation of specific metabolites [101]. The chemical communication that leads to defense priming and induced systemic resistance is not yet fully understood, especially the linkage between below- and aboveground plant physiological processes and the specific metabolites involved in these signaling events [192]. While root exudates are known to play a pivotal role in shaping the rhizosphere microbiome and mediating plant-microbe associations, the diversity and functional specificity of exudate compounds, as well as their roles in microbial recruitment, nutrition, and signaling, require further elucidation [100,193]. Additionally, the initial steps of the chemotactic recruitment of beneficial rhizobacteria to root exudates and the identification of key chemoeffectors and their cognate chemoreceptors remain incomplete, limiting our ability to engineer or manipulate these interactions for improved plant health [123].
# Advances in Three Dimensional Bioprinting for Wound Healing: A Comprehensive Review
## 4 3D Bioprinting Techniques
### Other Techniques
#### 4.4.2 Freeform Reversible Embedding of Suspended Hydrogels (FRESH)  
Bioprinting Techniques  
One of the most difficult obstacles facing effective bioprinting is the breakdown of soft and liquid-like bioinks due to gravity, and the resultant decrease in print quality. It might be difficult to create a robust structure without physical support when printing multiple bioinks layer by layer. Another issue is not curing the structure quickly enough and with the right hardness throughout the printing process. To solve this issue, the Freeform Reversible Embedding of Suspended Hydrogels (FRESH) approach was created. The bioinks are extruded in efficient stress-support water, allowing them to remain in the appropriate position until cured. This successfully solves the issue of structural stability while printing [211; 212; 213; 214; 215].  
[PERSON] et al. have successfully recreated the tissue and organ functions of the human heart, from capillary to full organ scale, using the FRESH 3D bioprinting technique. As a result of the measurements performed, they evaluated the conformity of the print with the original model. For the mechanical functionality analysis, they integrated the valve into an arterial pumped flow system to mimic physiological pressures and observed the valve leaflets cyclically opening and closing. During this observation, the flow rate through the leaflets was measured and showed \(<\)15% backflow (regurgitation) with a maximum open area of 19.5% [211].
# From Bitcoin to Central Bank Digital Currencies: Making Sense of the Digital Money Revolution
## 4 Benefits and Risks of CBDCs
### Benefits of CBDCs
However, the BIS also cautions that the impact on fighting illegal activities may not be significant, since a traceable CBDC \"would not necessarily be the main conduit for illicit transactions and informal economic activities\" [43] (p. 9), and with the most likely CDBC architecture proposals, the \"game of cats and mice\" between regulatory agencies and money launderers is likely to continue with little change [79]. **More inclusion of the unbanked or underbanked. ** Financial inclusion is one of the important motivations for the introduction of CBDCs mentioned by central banks [80]. According to the Bank of England [54] (p. 19), \"the provision of basic accounts and an electronic payment system by the central bank could make a significant difference to financial inclusion\". The institution considers this relevant in \"developing countries where the banking and payments system are underdeveloped\". However, the problem may be significant even in advanced economies, especially for the individuals most in need in times of crisis. In May 2020, 14 million American adults did not have a bank account and were waiting weeks or months to receive their coronavirus disease 2019 (COVID-19) stimulus checks. Additionally, they tend to lose between 1% and 10% of the check's value to the cashers [81]. The use of CBDC digital wallets would enable higher efficiency and justice in the allocation of relief funds. However, the BIS also cautions that \"for some segments of the population, barriers to the use of any digital currency may be large\" [43] (p. 9). An example discussing CBDC acceptance in a region of Spain, exploring various sociodemographic variables, is presented in [82]. **Positive overall macroeconomic impact. ** The wide adoption of a CBDC will reduce the costs of running the payment system (namely by reducing the frictions and costs associated with the storage, transport, and management of cash), increase its resilience to operational risks (cyberattacks, operational failures, and hardware faults), reduce tax evasion, corruption, and illicit activities, increase financial stability, reduce the costs of private monopolistic control, especially in the situation of a structural decrease in cash usage, and increase financial inclusion, especially in underbanked economies [83; 84]. Additionally, it will reduce the regulatory costs of the banking sector by removing the need for a fractional reserve system [85], imposing a better discipline on commercial banks [86], reducing the vulnerability of banks, and reducing the political and economic incentives for governments to bail out the \"too big to fail\" institutions [87]. All in all, the benefits of the introduction of a CBDC in the payment system may spill over to the overall economy with a significant impact on the GDP, depending on the adoption rate of the CBDC by the general public.
# UCMAC: A Cooperative MAC Protocol for Underwater Wireless Sensor Networks
## 4 Operation of Proposed Protocol
### Waiting Times  
The source, the cooperators, and the destination each manages a waiting-mechanism to escape undesirable collisions. In this section, we calculate the waiting times that must be very carefully set to make the proposed scheme work properly.
## 4 Discussion  
Since the widespread adoption of smartphones in 2009, numerous studies over the past 15 y have explored their effects on human posture and gait. However, recent systematic reviews directly aligned with this study's objectives--specifically, those examining smartphone-induced spinal posture and gait changes among healthy individuals--  
Figure 3: Alterations in cognitive function and gait biomechanics during smartphone use.  
Figure 2: Alterations in cervical, thoracic, and lumbar spine biomechanics during and after smartphone use.  
remain limited to reviews published in 2020 and 2023 [27; 37]. [PERSON]'s 2020 review comprehensively assessed 46 individual studies related to smartphone-induced changes in posture and gait [27]. The current review builds on this by incorporating recent individual studies published after October 2020, which were not included in earlier reviews [5; 9; 10; 15; 18; 26; 30; 33; 34; 36; 38]. [PERSON]'s more recent 2023 review focused on smartphone-related gait alterations and included data from healthy participants, older adults, and individuals with cognitive impairments [27]. The current review extends this work [25] by incorporating additional studies on smartphone-induced gait alterations [27; 34; 35; 36; 38; 39; 40; 41; 42; 43] and exclusively includes research involving healthy individuals, excluding studies on populations with specific clinical disorders. Smartphones have become indispensable in modern life; yet, while offering convenience, they also negatively affect physical functioning, underscoring their dual impact. Therefore, this study aims to provide a comprehensive synthesis of the literature on the effects of smartphone use on posture and gait. Through a narrative analysis, this review systematically evaluates how increased smartphone usage--both in frequency and duration--affects spinal postural alignment and key gait parameters, including walking speed [5; 14; 15; 20; 25].
## 4 Indoor Environment Modeling and Optimization of the Received Power
### Formulation of the Optimization Problem  
The goal is to maximize the total received power \(P_{r}\) at the receiver. This power is obtained by summing the contributions of all reflections from the mirrors in the IRS. Therefore, the defined optimization function is presented as follows:  
\[\max_{\mathbf{X},\mathbf{Y},\mathbf{Z}}P_{r}^{\text{flat}}(t)=\max_{\mathbf{X},\mathbf{Y},\mathbf{Z}}\sum_{i=1}^{M}\sum_{j=1}^{N}\Bigg{(}P_{s}\frac{A_{e}}{ d_{ij}^{2}}\cos^{m}(\theta_{ij})R(\theta_{ij})T_{ij}(\lambda)H_{ij}(t)\cos( \psi_{r})\Bigg{)}, \tag{24}\]  
where \(\mathbf{X},\mathbf{Y},\mathbf{Z}\) are the positions \((x_{ij},y_{ij},z_{ij})\) of the flat mirrors.  
For convex mirrors, the objective function maximizes the received power over time, considering the signal dispersion due to convex reflections.  
\[\max_{\mathbf{X},\mathbf{Y},\mathbf{Z}}P_{r}^{\text{convex}}(t)=\max_{ \mathbf{X},\mathbf{Y},\mathbf{Z}}\sum_{i=1}^{M}\sum_{j=1}^{N}\Bigg{(}P_{s} \frac{A_{e}}{d_{ij}^{2}}\cos^{m}(\theta_{ij})F_{\text{conv}}(R_{ij})R(\theta_ {ij})T_{ij}(\lambda)H_{ij}(t)\cos(\psi_{r})\Bigg{)}. \tag{25}\]
# Working Length Transfer in the Endodontic Clinical Practice: A Comparative Study
## 1 Introduction  
The determination and the maintenance of a correct working length (WL) are the most important factors for the success of root canal treatment [1]. WL is defined as the distance from a coronal reference to the point at which canal preparation and obturation should terminate [2]. A major concern in the prevention of technical errors and endodontic failures is the maintenance of appropriate WL throughout clinical practice [3].  
Electronic apex locators are precise and reliable in more than 90% of cases [4, 5]. They are ergonomically advantageous, as they allow the WL to be checked several times during the clinical procedures without increasing the overall treatment time [6]. WL should be checked three times during treatment: first after the initial canal scouting with manual K-files (KF), then after glide path and immediately after root canal coronal enlargement, because WL changes are expected due to straighteningof the canal curvatures (CC) [7; 8; 9]. Therefore, WL transfer between the KF and the subsequent mechanical shaping instruments should be extremely precise in order to equally prevent over- and under-instrumentation. Over-instrumentation with nickel-titanium (NiTi) rotary or reciprocating files of augmented taper may have clinical implications, including apical transportation [10] and overfilling [11] with defective apical seal control, especially in curved canals, and greater incidence of post-operative pain [12; 13]. On the other hand, under-instrumentation may predispose the creation of blocks and ledges, which can negatively influence the long-term prognosis of the endodontic treatment, leaving a residual bacterial load inside the root canal [3]. Therefore, during the clinical practice, WL is usually checked electronically and radiographically with a manual K-File (KF) and determined by the position of a rubber stop at the coronal reference point. Afterwards, the WL is measured with a manual endodontic gauge and transferred to the NiTi mechanical instrument selected for shaping [5; 6]. A careful WL transfer procedure is required to ensure measurement reliability [14], but this clinical procedure may be prone to errors. The objective of this study was to evaluate the accuracy of two different clinical methods of transferring WL between endodontic instruments. This aspect could be crucial for the optimization of clinical practice and for improvements in treatment outcomes. The null hypothesis is that both methods are comparable in transferring WL measurements.
## 4 Results  
Applying the determined correlation, the model component was adapted so that the calculation from the fluid to the solid side could be simulated at various operating points. As shown in Figure 14, a very high accuracy can be achieved in the prediction of the temperature in the gap between the outer gear and the housing. The precise temperature prediction at both negative and positive temperatures (from \(-40\)\({}^{\circ}\)C to \(+20\)\({}^{\circ}\)C) is also crucial for the selected objective of this study. Figure 14 shows the measurement (blue bars) and the simulation from the viscous friction heating model (orange bars). The 5% error bars symbolize the variance of the measurement in relation to the absolute value at the operating point. Possible effects for the variance are discussed in Section 5.  
Following the successful temperature prediction, the next step was to integrate the viscous friction model into the overall pump simulation. For this purpose, the previously simplified models of the pressure and leakage simulation were replaced by input variables from the pump model. The more complex model with the highlighted sub-models is shown in Figure 15 below. On the left side of Figure 15 is the previously explained model for the viscous friction heating. On the right-hand side, arranged from bottom to top, the gray box contains the vector component of the pump and the overall efficiency calculation, the green box contains the leakage flow calculation, the yellow box above contains the pressure calculation and the blue box shows the convergence criteria calculation.  
Figure 14: Simulated vs. measured oil temperatures in gap between outer gear and pump housing.  
Figure 16 shows the measured torque (blue curve) and the two simulated torque values for the pump shaft of the models with and without viscous friction heating at \(-20\)\({}^{\circ}\)C, 1500 rpm pump speed and 6 bar system pressure. The calculated torque of the model without viscous friction heating (dotted red line) is very close to the initial torque after hydrodynamic lubrication has been established (approx. after 2 s). The model with viscous friction heating (dotted green line), on the other hand, is very close to the mean value of the measured steady-state torque after approx. 30 s. This shows that with the developed method not only the gap temperatures (Figure 14) can be predicted very accurately, but also that the resulting viscosity change leads to a very accurate simulation of the pump shaft torque by using the integrated model approach of Figure 15.  
Figure 16: Simulated vs. measured torque with and without viscous heating model at \(-20\)\({}^{\circ}\)C.  
Figure 15: Pump simulation with integrated viscous friction heating model.
# Model-Free Identification of Nonlinear Restoring Force with Modified Observation Equation
## 1 Introduction  
### Background  
Civil engineering structures are prone to deterioration or damage due to certain factors, such as strong dynamic loadings, material fatigue accumulation and environmental corrosion. To date, a variety of vibration-based damage detection and system identification methods have been developed [1; 2]. Most of them are applicable when the structure of interest is linear. However, nonlinearity is generic in nature, and linear behavior is an exception [3]. The occurrence of a fault in an initially linear structure will, in many cases, result in nonlinear behavior. Therefore, it is highly desirable to develop nonlinear identification algorithms to facilitate the evaluation of the structural performance and condition for the purpose of prevention of potentially catastrophic events.  
It is known that typical sources of nonlinearities include geometric nonlinearity, inertia nonlinearity, material nonlinearity and boundary nonlinearity. It is of importance to have an accurate model for thedescription of the nonlinear behavior of the physical structure prior to parameter estimation. Nonlinear modeling is a challenging step, because nonlinearity may be caused by many different mechanisms and may result in plethora of dynamic phenomena. Without a precise understanding of the nonlinear mechanisms involved, the identification process is bound to fail. A priori knowledge and nonlinearity classification may help to select a reasonably accurate model of the nonlinearity. If little is known about the form of the model before starting the identification process, one may resort to nonparametric approximations or expressions.
# Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
## 4 Proposed Model  
In this study, the researchers have focused on normalizing the data for the LTE downlink scheduling algorithms, which could improve their resource allocation performance concerning fairness. This was performed with the assistance of the proposed ANN model. This model included some initial phases, including:  
1. Determining the data in the LTE downlink system that required treatment using the ANN models;
2. Pre-processing processes used for excluding the conflicting data;
3. Data partitioning, wherein the data was separated into two parts (i.e., training and testing);
4. Assigning these data a number of input-output nodes, hidden nodes, and the transfer function for the topology of the ANN model, which explains the association between nodes and the choice of the ANN architecture;
5. Network training that considers experimentation to validate the proposed network model using the proposed algorithm and data classification; and
6. Finally, the model selection, which includes a selection technique based on overall performance.  
The technique's primary goal was to determine the diagnostic accuracy network model. Figure 1 demonstrates the framework of the proposed ANN normalized model used for the LTE downlink scheduling algorithm.