esa-sceva/satcom-chunk-collection huggingface dataset:
# Narrowband Internet of Things (NB-IoT):
## 5 Open Research Questions and Discussion
### Latency  
NB-IoT latency tolerance is set to 10 ms. This is due to its support for use cases of UEs that are in environments with bad channel conditions [95; 96; 97]. Initial cell acquisition, frequency, and timing requirements, RACH transmission, half duplex mode of transmission and several repetitions that are performed during transmission are some of the features that play part in the overall data transmission delay. Several works are trying to reduce the timing requirement so as to reduce transmission latency of devices; however, most of the works have not addressed delay by taking into consideration the massive congestion that is expected for the IoT networks, processing delays due to low complex devices, queuing delays, propagation delays especially with long-range feature, as well as errors and error recovery.  
However, early data transmission schemes and the second NB-IoT HARQ process for devices that have good channel conditions are among the features that can be used to reduce the transmission latency and improving the transmission link performance. However, only a handful of research articles have discussed the effectiveness of these processes when applied in NB-IoT.
# Native Smartphone Single- and Dual-Frequency GNSS-PPP/IMU Solution in Real-World Driving Scenarios
## 2 Mathematical Models and Data Processing Strategies
### Single-Frequency and Dual-Frequency PPP-IC Model  
Ionospheric delay is a critical error source for PPP processing. The traditional ionospheric-free (IF) model is capable of eliminating the first-order ionospheric delay through the combination of dual-frequency observations [38; 39]. Unlike the IF PPP, uncombined PPP is able to use external ionospheric delay information to benefit positioning performance. Recently, refs. [40; 41] rewrote the uncombined PPP observation equation by decoupling the receiver DCB with estimated slant ionospheric delay. In this context, ref. [27] suggests that ionospheric constraints (IC) bring significant benefits to low-cost devices, and this PPP-IC equation can be written as:  
\[\begin{split}& P_{r,1}^{s}=\rho_{r,1}^{s}+(cdt_{r}+b_{r,P_{IF}})+T_{r}^ {s}+I_{r,1}^{s,constrained}-\frac{f_{2}^{2}}{f_{1}^{2}-f_{2}^{2}}DCB_{r}+ \varepsilon_{P_{1}}\\ &\Phi_{r,1}^{s}=\rho_{r,1}^{s}+(cdt_{r}+b_{r,P_{IF}})+T_{r}^{s}-I_ {r,1}^{s,constrained}+\frac{f_{2}^{2}}{f_{1}^{2}-f_{2}^{2}}DCB_{r}+\tilde{N}_{r,1 }^{s}+\varepsilon_{L_{1}}\\ & P_{r,2}^{s}=\rho_{r,2}^{s}+(cdt_{r}+b_{r,P_{IF}})+T_{r}^{s}+\frac {f_{1}^{2}}{f_{2}^{2}}\frac{I_{r,1}^{s,constrained}}{f_{1}^{2}-f_{2}^{2}}DCB_{r} +\varepsilon_{P_{i}}\\ &\Phi_{r,2}^{s}=\rho_{r,2}^{s}+(cdt_{r}+b_{r,P_{IF}})+T_{r}^{s}- \frac{f_{2}^{2}}{f_{2}^{2}}I_{r,1}^{s,constrained}+\frac{f_{1}^{2}}{f_{1}^{2}-f _{2}^{2}}DCB_{r}+\tilde{N}_{r,2}^{s}+\varepsilon_{L_{2}}\\ &\tilde{N}_{r,1}^{s}=N_{r,1}^{s}+b_{r,\[PERSON]_{1}}-b_{\[PERSON]_{1}}^{s}- b_{r,P_{IF}}+b_{P_{IF}}^{s}-\frac{f_{2}^{2}}{f_{1}^{2}-f_{2}^{2}}DCB_{r}\\ &\tilde{N}_{r,2}^{s}=N_{r,2}^{s}+b_{r,\[PERSON]_{2}}-b_{\[PERSON]_{2}}^{s}- b_{r,P_{IF}}+b_{P_{IF}}^{s}-\frac{f_{1}^{2}}{f_{1}^{2}-f_{2}^{2}}DCB_{r} \end{split} \tag{1}\]  
where \(P_{r,i}^{s}\) and \(\Phi_{r,i}^{s}\) are the pseudorange and carrier-phase measurements on frequency \(i\) (\(i\in\{1,2\}\)); \(\rho_{r}^{s}\) is the geometric distance between satellite \(s\) and GNSS receiver \(r\); \(c\) is speed-of-light in vacuum, and \(dt_{r}\) refer to receiver clock offsets; \(b_{r,P_{IF}}\) and \(b_{P_{IF}}^{s}\) represents the receiver and satellite code biases in IF combination, respectively; likewise, \(b_{r,\Phi}\) and \(b_{\Phi}^{s}\) represent, respectively, the receiver and satellite phase biases; \(T_{r}^{s}\) is the slant troposphere delay; \(f\) refers to the signal frequency, and \(I_{r,1}^{s,constrained}\) is estimated ionospheric delay constrained by external GIM (Global Ionosphere Map) information on first frequency. \(\hat{N}_{r,i}\) is the estimated carrier-phase ambiguity, containing code and phase biases; \(DCB_{r}\) is the estimated receiver DCB; \(\varepsilon_{P}\) and \(\varepsilon_{\Phi}\) are, respectively, pseudorange and carrier-phase unmodelled errors including measurement noise and multipath error.
## 3 Results and Discussion
### Characterization of Basil Pesto Samples
#### 3.2.3 Oxidative Stability  
Fresh green pesto is an oil-in-water emulsion (small lipid droplets dispersed in an aqueous phase) characterized by a lipid content of about 48 g 100 g\({}^{-1}\) of product. Generally, the oxidation process has a negative impact on products with high-fat levels. The oxidative stability of this product takes place in the interface of the oil-water phases due to the interaction between lipid and water-soluble pro-oxidant agents [43]. Moreover, some metals typically occurring in basil leaves could speed up phenolic degradation by oxidative reactions [1]. To evaluate the resistance of fat degradation, the samples were subjected to a high oxidative stress environment using the OXITEST reactor. As reported in Table 7, the fresh pesto sauce samples \"F and F+\" (without and with PE) showed the highest values of induction period (IP), corresponding to about 12 h. The sample enriched and subjected to thermal treatment (P+) revealed an IP slightly lower (11:85 h). The lowest result was determined in the P sample with an IP of 10:4 h. Considering the end of the evaluated storage period (90 days), F was the pesto sample that showed the greatest susceptibility to oxidative deterioration (IP of 8:15 h).  
The high oxidative stability observed in the pesto sauce s fortified with the AE suggests that the incorporation of phenolic compounds delays the propagation phase of lipid oxidation. These results are comparable with [PERSON] et al. [36], who observed how the chemical structure of the phenolic compounds in the olive oil byproducts enables them to interact with the active O\({}_{2}\), reducing the primary and secondary compounds generated during oxidation and significantly increasing the oxidative stability of the lipid matrix. The resistance of oxidation did not show a significant variation over time regardless of the sample's treatment.
# Characterization of Ductile Crack Propagation by Fractal Energy Dissipation Rate
## 1 Introduction  
The crack resistance curve is widely applied to characterize irreversible energy dissipation. The ductile crack growth resistance curve is generally presented in the form of fracture mechanics parameters, i.e., path-independent \(J\)-integral or crack opening displacement \(\delta\) versus crack extension \(\Delta a\) -- that is, a crack starts to propagate at \(J=J_{0}\) or \(\delta=\delta_{0}\), then grows stably, and eventually reaches its steady-state value. Typically, the steady-state value is much larger than the initiation value, and primarily comprises the additional irreversible contribution from remote and local plastic energy dissipation in the bulk [1]. Therefore, ductile fracture in metals generally exhibits a rising resistance curve, in contrast to the flat curve of brittle materials.  
Extensive research has focused on this problem. [PERSON] et al. [2] argued that progressive dilatation of the fracture process zone leads to an increase in crack resistance during crack growth until a specific value corresponding to the fully developed process zone is reached. Size effects may be caused by the deviation of the actual stress from the first singular-term solution, i.e., Hutchinson-Rice-Rosengren (HRR) solution, at a finite distance from the crack front. The higher accuracy of stress with higher-order series expansion as well as the first term of the stress solution can capture a better crack initiation toughness and resistance curve [3; 4; 5; 6]. However, for large plastic energy dissipation and crack extension, the two or three-parameters fracture parameters cannot describe the accumulated energy at a given crack increment and the true \"driving force\" [7; 8]. Despite these drawbacks, the fracture resistance curve remains the most conventional method for the ductile fracture toughness characterization. Moreover, the classical fracture mechanics must suppose that the resistance curve eventually reaches steady values if the cracked body is relatively large compared with the fully developed process zone at the crack tip.  
Obviously, this postulate does not sufficiently explain some phenomena, particularly regarding crack propagation and its topology.
## 3 High-Energy Few-Optical Cycle Parametric Sources in the IR Spectral Region
### Optical Parametric Chirped Pulse Amplification  
In order to scale the OPA energy above the mJ-level, extremely intense pump lasers are required, above the terawatt scale. However, it is technologically challenging to scale the energy of femtosecond pump lasers. On the other hand, energetic picosecond pulses are available based on well-established gain media, such as Nd- or Yb-doped crystals or fibers that can be exploited for pumping parametric amplifiers.  
In this sense, a scheme has been developed known as Optical Parametric Chirped Pulse Amplification (OPCPA) [36; 37], which is very similar to the Chirped Pulse Amplification (CPA) occurring in laser gain media. In OPCPA systems, a femtosecond laser system is typically used for generating the seed pulses, while a high peak power picosecond laser is used for generating the pump pulses. Therefore, an energy transfer takes place from ps-pump pulses to fs-signal pulses. In order to optimize energy transfer efficiency, a good temporal overlap is required between these two pulses. Overlap is obtained by first stretching the seed pulse to the picosecond time scale and then, after the amplification step, compressing it back to its nearly Transform-Limited (TL) duration. Besides duration matching, also synchronization of the pump and seed pulses inside the amplification media is required. Synchronization can be achieved either electronically, by using properly-delayed electronic signals for triggering the emission of the pump and seed lasers, or optically, by using a portion of the seed pulse for triggering the pump laser emission. In both cases, pulses synchronization is one of the main issue in OPCPA schemes, contributing to a significant enhancement of the system complexity.  
In OPCPAs, parametric amplification is usually implemented in a non-collinear configuration so as to exploit the benefits of a broadband amplification.  
Thus, OPCPA systems provide the possibility to amplify extremely broadband optical pulses up to energy levels that are inaccessible by OPA systems because of limitations due to optical damage thresholds. On the other hand, an OPA design presents some advantages with respect to an OPCPA design. In particular, the problem of synchronization is avoided since both pump and seed pulses come from the same laser source. Moreover, there is no need of proper stretching-compression stages on the seed beam. As a result, the experimental setup of an OPA system is considerably simpler than that of an OPCPA system.
## 3 Positioning Model Establishment and Solution
### Processing of Abnormal Data  
As shown in Figure 9, when the signal is disturbed in the two-dimensional space, the anchor points A0, A1, and A2 are the centers of the three circles, and the intersection of the two forms six intersection points in the plane. By defining an initial value of X to screen out the three points A, B, and C that are far away, the initial value of X is based on the scene given by task 2, and the center point of the anchor points A0, A1, A2, and A3 is (2500, 2500, 1500). By solving the norm of the difference between the theoretical distance and the real distance from these 6 points to A0, A1, and A2, 4 values can be obtained, and the norm of the difference between the theoretical distance and the actual distance between the real target point and A0, A1, and A2 is obvious, which is smaller than the values obtained by  
Figure 7: Target position obtained from normal data.  
the other three points, so the point with the smallest norm of the distance difference needs to be screened out by the \"fmincon\" function in Matlab, and the position of this point is the position of the target point to be positioned.  
For the three-dimensional space, the anchor points A0, A1, A2, and A3 are the centers of the four spheres in Figure 8. Similarly, the norm of the difference with the real distance is screened from the point set obtained by the intersection of them. The coordinates of the target points can be accurately positioned by weighting the coordinates of the smallest points.  
Although it can be determined by calculation that one of the four data is disturbed during the signal transmission process, it cannot be clearly determined which data is disturbed. Simplified into two dimensions, if one of A0, A1, and A2 is disturbed, the circle formed by these three anchor points will form a small area DD1D2, and the specific position of the target point in this area cannot be determined, as shown in Figure 10.  
When selecting three random spheres to meet at a point, only one of the four intersection points is the real target point. Since we cannot determine which of the four points is the real target point, we take the average by weighting. To reduce the error caused by interference, the purpose is to reduce the interference caused by abnormal. A schematic diagram of abnormal data error is shown in Figure 11.  
Figure 8: Schematic diagram of normal data measurement error.  
Figure 9: Schematic diagram of abnormal data position (The letters on the figure indicate the centers of the three circles or the points of intersection on the circles).  
improve the \(z\)-axis positioning accuracy, this paper is based on BP algorithms, and calculations obtain coordinate values and generate \(z\)-axis compensation terms. The neural network after normal data training is shown in Figure 12[25; 26].
## 5 Conclusions  
In this study, GLVs were identified from _Sabal palmetto_ that were infected with the lethal bronzing phytoplasma, nearby palms that were healthy but at risk of infection, and healthy palms outside of the disease area. The GLVs with the highest release were 1-hexanol, 3-hexenal, hexanal, E-2-hexenal, and Z-3-hexen-1-ol. Infected palms generated higher levels of hexanal and 2-hexenal than non-infected palms and palms that were at risk of infection generated 3-hexenal, whereas infected plants and healthy plants outside of the disease area did not have this compound.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/plants12112164/s1](https://www.mdpi.com/article/10.3390/plants12112164/s1)]([https://www.mdpi.com/article/10.3390/plants12112164/s1](https://www.mdpi.com/article/10.3390/plants12112164/s1)). Figure S1: GC-MS chromatograms of elevated GLVs in _S. palmetto_ leaf samples; Table S1: Volatiles released from _S. palmetto_ leaves; Table S2: Green leaf volatile average (\(\pm\)SE) concentrations (GC/MassSpec Counts) from infected (_I_) palms; Table S3: GLVs concentrations of cabbage palms in close proximity (_NIT_) to lethal bronzing-infected palms; Table S4: GLVs concentrations of non-infected and non-threatened (_NINT_) cabbage palms.  
Conceptualization, B.W.B.; methodology, J.A.F.; software, J.A.F. and J.A.R.; validation, J.A.F., J.A.R., S.C.N.Q. and E.E.H.; formal analysis, J.A.F., J.A.R. and B.W.B.; investigation, J.A.F., B.D.L., E.E.H. and J.A.R.; resources, S.C.N.Q., D.R.C.S.D. and B.W.B.; data curation, J.A.F. and J.A.R.; writing--original draft preparation, J.A.F., J.A.R., B.W.B.; writing--review and editing, J.A.F., J.A.R., S.C.N.Q. and B.W.B.; visualization, J.A.F. and J.A.R.; supervision, S.C.N.Q. and B.W.B.; project administration, D.R.C.S.D. and S.C.N.Q.; funding acquisition, J.A.F., D.R.C.S.D. and S.C.N.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by FAPESP, grant number 2020/01977-1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Rate** & **T** & **Hold Time** & **Run Time** \\  & (\({}^{\circ}\)**C/min**) & (\({}^{\circ}\)**C**) & **(min)** & **(min)** \\ \hline Initial & & 40 & 2 & 2 \\ \hline Ramp 1 & 5 & 150 & 1 & 25 \\ \hline Ramp 2 & 10 & 250 & 5 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: GC-MS oven temperature programming.  
**Acknowledgments:** J.A.F. thanks the Sao Paulo Research Foundation-FAPESP (2020/01977-1) for providing the post-doctoral scholarship. We also thank [PERSON] at UERJ for his suggestions regarding data analysis, [PERSON] at UNICAMP for his initial suggestions regarding the GC-MS, and [PERSON] at UF/IFAS for providing GC-MS support.
# Evaluation of Coupling PV and Air Conditioning vs. Solar Cooling Systems--Case Study from Jordan
## 2 Materials and Methods  
Sustainable cooling technologies that are powered by renewable sources of energy and environmentally friendly cooling liquids are becoming increasingly popular. Refrigeration and air conditioning that is driven by solar energy is specifically advantageous for countrieswith increased ambient temperatures and solar radiation rates. Compared with fossil fuels, solar radiation is both sustainable and renewable and is available when the demand for cooling is at its highest. Sorption technologies are generally employed by solar thermal systems within a hermetic or open thermodynamic cycle. Moreover, it is possible to convert the surplus electricity produced by PV modules into cooling via the use of vapour compression cycles. The primary obstacle preventing the use of solar thermal systems within developing countries is the absence of available systems and the necessary parts. To penetrate markets in countries like Jordan, it is necessary for solar cooling systems to satisfy various criteria, including that they should be robust, affordable and available.  
This study investigated two kinds of cooling systems powered by photovoltaic and solar thermal cooling systems, respectively, where both were situated on the German Jordan University (GJU) campus located in Amman, Jordan.
# Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens
## 1 Introduction
### Computational Methods for Tumor T-Cell Antigen Prediction
#### 1.2.2 Other Prediction Tools
Initially, the baseline models are trained using these diverse approaches, generating a broad spectrum of predictive outputs. These outputsare then aggregated to form a new probabilistic feature vector. Through an optimization process that incorporates feature selection strategies, this vector serves as the foundation for constructing the stacked model, which aims to leverage the collective strengths of the individual models for improved performance in identifying tumor T-cell antigens. In the years following the inception of Vaxijen, an abundance of new data concerning immunogenic proteins of tumor origin have accumulated in the scientific literature, necessitating an update to its dataset. Novel data expand the repertoire of immunogenic proteins, thereby facilitating the derivation of new models with heightened predictive power. In this study, a diverse array of machine learning (ML) methods, including \(k\)-nearest neighbor (\(k\)NN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), were applied to an updated set of known tumor antigens. The resulting ML models underwent rigorous validation through receiver operating characteristic (ROC) statistics on an external test set. The three top-performing models are to be subsequently integrated into the latest version of the Vaxijen server.
# Victim Detection and Localization in Emergencies
## 3 Overview of Location Technologies
### WiFi  
WiFi is another ubiquitous wireless technology (although mainly in indoor scenarios and with small deployments) used for communications. It is made up of different versions of the IEEE802.11 protocol family, which are supported by a very wide base of consumer devices for communications. Two different approaches of WiFi are widely implemented for location services.
# Broadening of Light Coupling to Waveguide Modes in Solar Cells by Disordered Grating Textures
## 2 Structure and Method  
### Device Structure  
In this work, hydrogenated amorphous silicon (a-Si:H) thin-film solar cells are simulated as test devices. The advantage of these devices is given by the soft band edge which results in a wide spectral region of weak absorptance. Here, any prolongation of the effective light path, in particular the excitation of waveguide modes, significantly improves the generated photocurrent. Furthermore, a-Si:H solar cells can be fabricated on a large variety of textured substrates with comparable material properties [9]. This is due to the amorphous growth, which results in a relaxed constraint between the absorber material and the substrate.  
The investigated solar cell structure is the widely used superstrate configuration, where the light illuminates through a transparent superstrate, i.e. glass. On top of the glass, a transparent conductive oxide (TCO)--in our case an 80-nm thick aluminum-doped zinc oxide--is placed, which forms the front contact layer of the solar cell. Afterwards, a 250-nm thick a-Si:H slab is placed, representing the layer stack of p-doped, intrinsic, and n-doped a-Si:H of the solar cell. The layer stack is finalized by the back-reflecting contact consisting of an 80-nm thick TCO and a perfect electric conductor (PEC) half space. The nanophotonic light trapping texture is assumed at the interface between the glass superstrate and the front TCO layer. The textures are two-dimensional gratings consisting of nanopillars capped by half-spheres. A sketch of the device for a period \(p\) of 500 nm is shown in Figure 1a.  
Figure 1: **(a)** Schematic illustration of the nanophotonic thin-film solar cell. The frontside is textured with a periodic array of nanopillars (gray) in the glass superstrate (white) capped by half-spheres and a transparent conductive oxide layer (TCO) as the front contact (blue). The a-Si:H layer (orange) forms the solar cell absorber which is finalized with a back reflector consisting of a TCO (blue) and a perfect electric conductor (black). A cross-section image of the simulated electric field energy for a wavelength of 700 nm is shown on the right side. **(b)** Illustration of different ways of disorder which are introduced to the grating structures.