esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Section  
In the literature it was pointed out that the generation of X waves in a linear system \"requires non-trivial input shaping techniques that involve the use of dispersive elements such as the axicon\" ([4]). Our experiments were performed with a Ti:sapphire laser oscillator (Venteon PULSE: ONE PE, pulse duration around 6.5 fs, FWHM bandwidth 300 nm, center wavelength 800 nm, repetition rate 80 MHz, pulse energy \(>\) 7 nJ, linearly polarized) as light source and a programmable liquid-crystal-on-silicon spatial light modulator (LCoS-SLM, HoloEye, 1920 \(\times\) 1200 pixels, pixel size 8.1 \(\upmu\)m) as flexible beam shaper. It was found that few-cycle pulses of Ti:sapphire laser oscillators are transferred by the low-dispersion SLMs with only marginal temporal distortions [50]. The SLM can flexibly emulate the phase maps of axicons via calibrated grayscale patterns and works in reflection. We demonstrated that an adaptive correction of tilt-induced spatial aberrations can be obtained by programming linear transformations. In this way, tilt-induced beam ellipticity was compensated at angles of incidence up to 50\({}^{\circ}\). The setup is schematically drawn in Figure 4. Single and multiple light bullets were generated with the SLM. For the spatial and temporal characterization of the light bullets, the combination of a Shack-Hartmann geometry (based on the division of the wavefront into separated channels) and a collinear nonlinear autocorrelator is used. In this case, a primary light bullet can be decomposed into an array of sub-bullets to ensure a clear separation of local parameters by avoiding an overlap during the propagation. The basic approach of such a time-wavefront sensor was recently described in detail in [51].  
The light bullets were analyzed by combining the shaper with a 2D second order nonlinear autocorrelator consisting of a balanced interferometer, a nonlinear crystal and a matrix camera with zoom lens. The spectral phase was measured with few-cycle spectral phase interferometry for direct electric field reconstruction (FC-SPIDER, APE). To enable for working at low intensities down to the single-photon level, intensity profiles were analyzed with a highly sensitive electron-multiplying charge coupled device (EMCCD) camera (iXon, Andor). Second harmonic generation (SHG) was performed in thin BBO crystals (thickness 10-20 \(\upmu\)m). The long effective lever arms formed by the extended needle-shaped nondiffracting zones lead to a high angular sensitivity of the system. Therefore, the setup enables not only to extract _spatially resolved pulse duration_ via image autocorrelation but also _wavefront curvature_ and _vortex characterization_[52]. Furthermore it was demonstrated that a _time delay mapping_ with sub-fs resolution is possible by a relative measurement of the arrival times of light bullets in spatially separated channels [39].
## 3 Antenna Performance Enhancement Techniques
### Effect of Dielectric Resonator on Gain and Bandwidth Enhancement  
How to achieve a high gain and wide bandwidth a dielectric resonator antenna (DRA) for 5G wireless communication is proposed in [18]. In a sequence, to achieve a high value of gain, the DRA antenna is operational at higher order mode TEx \(\delta_{15}\), while to improve the bandwidth, the quality factor is reduced by employing a hollow cylinder at the center of the DRA, as shown in Figure 9. A 50 \(\Omega\) micro strip line with a narrow aperture slot was used for excitation of DRA. For the TEx \(\delta\)mm mode, the resonance frequencies can be extracted from the dielectric waveguide model (DWM); the three waves \(\mathrm{K_{X}}\), \(\mathrm{K_{Y}}\), and \(\mathrm{K_{Z}}\) could be extracted by solving the transcendental equation as follows.  
\[\mathrm{k_{x}}\tan\left(\frac{\mathrm{k_{x}a}}{2}\right)=\sqrt{(\epsilon_{\mathrm{ r}}-1)\mathrm{k_{0}^{2}-k_{x}^{2}}} \tag{1}\]  
\[\mathrm{k_{x}^{2}+k_{y}^{2}+k_{z}^{2}=\epsilon_{\mathrm{r}}k_{0}^{2}} \tag{2}\]  
\[\mathrm{k_{0}}=\frac{2\pi\mathrm{f_{0}}}{\mathrm{a}},\mathrm{k_{y}}=\frac{m \pi}{\mathrm{b}},\mathrm{k_{z}}=\frac{n\pi}{\mathrm{d}} \tag{3}\]  
\[\mathrm{f_{0}}=\frac{\mathrm{c}}{2\pi\sqrt{\epsilon_{\mathrm{r}}}}\sqrt{ \mathrm{k_{x}^{2}+k_{y}^{2}+k_{z}^{2}}} \tag{4}\]
## 3 Methodology Section
### Description of the Location of the System  
Tehran City is the location considered for both systems. The center of Tehran, Iran, is on latitude 35.689\({}^{\circ}\) N, and longitude 51.5\({}^{\circ}\) E. The city has an arid and hot climate, long summers, and cold winters. For the yearly average, the average precipitation is about 230 mm and less than two months of frost. The annual average of sunshine hours and daylight hours of Tehran City are 11 and 13 hours for a day [68,69]. For the 15 July, May, and January, the hourly changes of temperature are illustrated in Figure 3. Generally, the average temperature of July is higher than May followed by January. For July, the maximum temperature is 36.9 \({}^{\circ}\)C, and the minimum temperature is 23 \({}^{\circ}\)C. The highest temperature of the fifteenth day of May is 21.6 \({}^{\circ}\)C, and the lowest temperature is 9.9 \({}^{\circ}\)C. For January, the maximum temperature is 7.8 \({}^{\circ}\)C, and the minimum temperature is \(-\)5 \({}^{\circ}\)C. It should be mentioned that the highest temperature of the fifteenth day occurs at 15 o'clock, while the minimum happens at 5 o'clock [65].  
Figure 3: Average temperature changes of 15 January, May, and July.  
Figure 2: Block diagram of main computer program and different subroutines connections.  
As shown in Figure 4, the average temperature and wind speed of each month during a year for Tehran City is presented. This statistical data was based on the measurement for a period of 20 years. January and February had the lowest temperature among all with a temperature of 2.8 \({}^{\circ}\)C and 5.1 \({}^{\circ}\)C. Additionally, the highest temperature was for July with a temperature of 29.35 \({}^{\circ}\)C, followed by August and June with a temperature of 28.65 \({}^{\circ}\)C and 26.2 \({}^{\circ}\)C [65]. The lowest wind average velocities were in December and September with an amount of 3.6 m/s and 3.8 m/s. May with 5.9 m/s wind average velocity, April with 5.6 m/s, and June with 5.4 m/s respectively have the highest values [65].  
Monthly solar radiation for during a year was exhibited in Figure 5. The variations ranged from 152.9 up to 320.8 W/m\({}^{2}\). As can be seen, the lowest solar radiation was about 152.9 followed by 160.4 W/m\({}^{2}\), which occur respectively in December and January. June has the highest solar radiation of about 320.8 W/m\({}^{2}\), followed by July and May that had 316.7 W/m\({}^{2}\) and 311.5 W/m\({}^{2}\) solar radiation [65].  
Figure 4: Monthly average air temperature and wind speed in different months of a year.  
Figure 5: Monthly average solar radiation in different months of a year.  
In Table 9, \(r_{comp}\) and \(r_{GT}\) define the pressure ratio of the compressor and the gas turbine.
# Efficient Data Exchange between WebAssembly Modules
## 4 Karmem Approach
### Data Layout
#### 4.2.2 Arrays  
Arrays represent a common data structure that allows for the storage of multiple items and iterations through them. In this document, we will consider \"arrays\" as dynamical arrays. Each programming language can implement this feature differently, but, usually, we can distinguish between three types of implementations: Arrays-of-Struct (AoS), Struct-of-Arrays (SoA), and Arrays-of-Pointers (AoP).  
Usually, OOD tends to use AoP or AoS, which makes it harder to use SIMD instructions [46; 47] and also introduces additional performance penalties in handling scheme evolutions. KarmemIDL allows the programmer to specify the layout type. The default one is SoA, which is a method of representing homogeneous data structures in vectors instead of representing a vector of heterogeneous data structures. This technique offers several advantages, particularly by enabling the use of SIMD and always having a fixed step size. Due to the data-access pattern, SoA can be beneficial by allowing partial data reads and avoiding cache pollution with unnecessary data. Furthermore, it can save space by eliminating paddings between each field. This method is particularly useful in scenarios that benefit from sequential data access and efficient cache usage, as was highlighted in a study by [PERSON] [45]. While DOD has received limited research attention, we hypothesize that Karmem can benefit from DOD. This approach simplifies implementation across multiple languages and leverages compiler optimizations such as auto-vectorization. This advantage is due to the usage of a stable homogeneous array representation, which is native in most programming languages, unlike the abstracted representations offered by other serializers such as FlatBuffers and Cap'n'Proto.  
Karmem also uses consistent alignment across all languages and architectures. Arrays are aligned by 512 bits, which is a recommendation from Intel to take advantage of AVX512 [49]. It is important to note that WASM is currently limited to 128-bit SIMD lanes.  
Considering the shared-memory aspect of Karmem and the potential reusability of such arrays, they include not only the size but also the capacity. This design helps to avoid reallocations and reduces the performance cost associated with copying and shifting data.  
Figure 2 illustrates the binary representation of an array of 2D Point (X, Y), ignoring padding and additional data (such as sizes) for simplification. The AoS method (A) is used in C, Go, Zig, and many other languages, whereas the AoP method (C) is commonly found in languages based on OOD, such as Java. Karmem uses the SoA (B) by default.
# Collision Avoidance Adaptive Data Rate Algorithm for LoRaWAN
## 7 Conclusions  
In this work, we introduced a novel adaptive data rate algorithm termed SSFIR-ADR for LoRaWAN networks, designed to improve the packet delivery ratio and reduce energy utilisation in LoRaWAN networks. The study addresses the detrimental effects of interference, which is increasingly prominent in rapidly growing IoT technologies and leads to higher packet collision rates within the LoRaWAN network. Through extensive simulations and analysis, we have evaluated the SSFIR-ADR algorithm in comparison to other ADR implementations, including the standard ADR algorithm, ns3-ADR algorithm, and FL-ADR scheme. Our findings indicate that the SSFIR-ADR algorithm achieves significant improvements in key performance metrics The obtained results highlight the superiority of our proposed solution over the standard ADR and ns3-ADR algorithms pertaining to energy consumption, packet delivery ratio, and interference rate. While the FL-ADR algorithm demonstrates superior energy consumption optimisation and interference reduction, it falls short of achieving a comparable packet delivery rate. In contrast, the SSFIR-ADR algorithm strikes a balance by achieving significant energy savings without compromising the uplink packet delivery ratio and confirmed packet success rate. The SSFIR-ADR performance is attributed to the algorithm's ability to mitigate the adverse effect of interference, resulting in fewer collisions and retransmissions. This was achieved without compromising the UL-PDR and CPSR. Our approach presents a promising option for practical LoRaWAN implementations.  
R.K.: Conceptualisation, methodology, writing--original draft. G.P.H.: Conceptualisation, writing--review and editing. A.M.A.-M.: Conceptualisation, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
This work is based on the research supported in part by our industry partner Telkom. The grant holder acknowledges that the opinions, findings, and conclusions or recommenda  
Figure 11: Number of EDs vs. Interference/collision rate.  
tions expressed in any publication generated by this research are those of the authors, and that our industry partners accept no liability in this regard.  
The authors declare no conflicts of interest.
# SROR: A Secure and Reliable Opportunistic Routing for VANETs
## 2 Related Work
### Problems That Need to Be Solved  
Although the above-mentioned work achieve high performance in transmitting packets, there is still much room for performance improvement.  
Firstly, selecting the relay node in VANETs is a challenge task due to dynamic topology. Deep reinforcement learning algorithm has strong learning ability and can quickly capture the knowledge of dynamic environment. Therefore, the proposed SROR algorithm adopts a deep reinforcement learning to select the relay node.  
Secondly, V2V communication is vulnerable to packet drop attacks due to open communication environments in VANETs. However, most of existing routing protocols have not taken the packet drop attacks into account. While these attacks reduce the success rate of transmitting packets, resulting in a large number of packet losses. For this purpose, the proposed SROR algorithm takes the packet delivery ratio into account when selecting the relay node. The facts behind this strategy are that packet drop attacks result in a low packet delivery ratio. To this end, the node is prevented from participating in routing if a node has a low packet delivery ratio. Thus, the attackers are not considered to be a relay node, and this is helpful to ensure the security of VANETs.  
Finally, although some existing work has also applied DRL to the routing protocols, the sensitivity of DRL to learning rate and exploratory factor are not analyzed. Conversely, the proposed SROR algorithm fully analyzes the sensitivity.  
Therefore, the proposed SROR algorithm addresses the performance in transmitting packet as well as defence against packet drop attacks. In addition, a DRL is employed in SROR algorithm to obtain the optimal route path as well as to prevent attackers from forwarding packets.
###### Abstract  
We review and synthesize key findings and limitations of mathematical models for prostate cancer, both from theoretical work and data-validated approaches, especially concerning clinical applications. Our focus is on models of prostate cancer dynamics under treatment, particularly with a view toward optimizing hormone-based treatment schedules and estimating the onset of treatment resistance under various assumptions. Population models suggest that intermittent or adaptive therapy is more beneficial to delay cancer relapse as compared to the standard continuous therapy if treatment resistance comes at a competitive cost for cancer cells. Another consensus among existing work is that the standard biomarker for cancer growth, prostate-specific antigen, may not always correlate well with cancer progression. Instead, its doubling rate appears to be a better indicator of tumor growth. Much of the existing work utilizes simple ordinary differential equations due to difficulty in collecting spatial data and due to the early success of using prostate-specific antigen in mathematical modeling. However, a shift toward more complex and realistic models is taking place, which leaves many of the theoretical and mathematical questions unexplored. Furthermore, as adaptive therapy displays better potential than existing treatment protocols, an increasing number of studies incorporate this treatment into modeling efforts. Although existing modeling work has explored and yielded useful insights on the treatment of prostate cancer, the road to clinical application is still elusive. Among the pertinent issues needed to be addressed to bridge the gap from modeling work to clinical application are (1) real-time data validation and model identification, (2) sensitivity analysis and uncertainty quantification for model prediction, and (3) optimal treatment/schedule while considering drug properties, interactions, and toxicity. To address these issues, we suggest in-depth studies on various aspects of the parameters in dynamical models such as the evolution of parameters over time. We hope this review will assist future attempts at studying prostate cancer.  
prostate cancer; mathematical models; clinical application; mathematical oncology; parameter evolution; personalized medicine; adaptive therapy; optimal schedule; parameter identification; uncertainty quantification
# Reinforcement Learning Approach for Adaptive C-V2X Resource Management
## 2 C-V2X Mode 4
### C-V2X Resource Management
According to [PERSON]'s capacity formula, it is observed that the impact of a rising interference level (resulting in a lower signal-to-interference-plus-noise ratio (SINR)) on capacity reduction is more significant than what may be anticipated originally. It is worth noting that doubling the interference level does not result in a halving of the capacity, contrary to expectations. This conceptual understanding justifies the implementation of densely populated cells and frequency reuse. The subsequent rise in interference and its mitigated impact on capacity is counterbalanced by the presence of a more significant number of cells and the ability to accommodate a more extensive user base [35]. Presently, the existing systems function with SINR that are in close proximity to the maximum threshold, with just a marginal difference of a few dB. Various modulation and radio schemes exhibit varying degrees of proximity to the limit, prompting the introduction of two parameters to delineate the performance of these distinct schemes. The performance evaluation of modulation schemes may be effectively quantified by using a significant measure derived from the capacity formula. Hence, the parameter denoted as modulation efficiency (alternatively known as channel spectral efficiency) is defined as follows: The radio system is required to compute the least SINR for the chosen \(N_{PRB}\) in order to ensure effective execution of data transmission. The \(I_{MCS}\) and TB sizes have been found to meet the requirements specified in the 3 GPP reference table. The minimum SINR or SINR threshold (\(SINR_{min}\)) might be stated differently depending on the size of the TB. The minimum SINR can be expressed as,  
\[SINR_{min}=10\text{log}_{10}\left(2^{\frac{\eta_{c}}{\sigma}}-1\right), \tag{3}\]  
where \(\eta_{c}\) is the channel spectral efficiency, \(a=0.4\), is a constant taken from the 3 GPP standard [34]. As the value of \(I_{MCS}\) increases, there is a corresponding increase in the \(SINR_{min}\). This may be attributed to the increasing significance of the beacon (TB) capacity, resulting in a decrease in \(N_{PRB}\)[34]. The increased capacity of the TB may result in a higher signal power required for effective transmission of data. Consequently, the minimum SINR (\(SINR_{min}\)) exhibits an upward trend.
## 4 Results and Discussion
### Detection of Crops Fertilization Treatment at Landscape Level
As previously observed, Millet shows a weak response to fertilization treatment. This is partly related to more millet cultivation in the most constraining areas, reducing the fertilization effect in plots and increasing the contribution of the field effect. In addition, the fraction of variance explained by fertilization treatments would have been higher if farmers would not have fertilized some cotton and maize fields before the protocol had been put in place. Although total fertilizer application levels were adjusted proportionally to correct for that pre-existing situation, the effects of application on the control plots could not be corrected for. Figure 11: Proportion of the total NDVI variance explained by the three components of spatial variation (stratum, field and plot) for five crops. The sowing and fertilization application windows are shown for each crop as green and grey shade, respectively.
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 3 Digital Twin Technology in Smart Grids
### Core Concepts and Implementation Requirements
#### 3.2.1 Digital Twin Integration Framework  
Digital Twin technology represents a transformative approach for monitoring and regulating smart grids, where it facilitates a dynamic and real-time simulation environment that mirrors the physical aspects of electrical grids. This integration promises to enhance the performance related to strategic planning, predictive maintenance, and monitoring within the grid infrastructure.  
Figure 7 illustrates the key aspects of Digital Twin implementation in smart grids, encompassing the following:  
* Real-Time Monitoring and Control: DT allows for the real-time monitoring and control of grid components, providing operators with up-to-date information to make informed decisions. For instance, the work by [PERSON] et al. in [40] explores the use of Digital Twins integrated with software-defined networking to enhance the cybersecurity of smart grids through real-time data exchange and analysis.
* Predictive Maintenance: By simulating various grid scenarios, Digital Twins help predict potential failures before they occur, thus minimizing downtime and extending the lifespan of grid components. This application is detailed in the study by [PERSON] et al. in [74], where a Digital Twin-enabled system facilitates predictive maintenance strategies for energy distribution.
* Enhanced Cybersecurity: With the increasing connectivity of smart grids, cybersecurity becomes crucial. Digital Twins can simulate cyber-attack scenarios to help develop robust defense mechanisms. An example of this is described by [PERSON] et al. [75], where Digital Twins are used to forecast demand and enhance cybersecurity measures within the grid.
* Optimization of Grid Operations: Digital Twins contribute to the optimization of grid operations by modeling and analyzing the flow of electricity. This ensures efficient energy distribution and helps in managing demand response strategies effectively.
## Congestion Based Mechanism for Route Discovery in a V2I-V2V System Applying Smart Devices and IoT  
**[PERSON] and [PERSON] ***  
Grupo de Comunicaciones y Tecnologias de Informacion (COMIT), Departamento de Ingenieria de Sistemas y Computacion, Universidad de Los Andes, Carrera 1 Este 19*-40, Bogota 111711, Colombia; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +57-339-4949 (ext. 1723).  
Academic Editors: [PERSON] and [PERSON]  
_Received: 30 November 2014 / Accepted: 19 March 2015 / Published: 31 March 2015_  
**Abstract:** The Internet of Things is a new paradigm in which objects in a specific context can be integrated into traditional communication networks to actively participate in solving a determined problem. The Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) technologies are specific cases of IoT and key enablers for Intelligent Transportation Systems (ITS). V2V and V2I have been widely used to solve different problems associated with transportation in cities, in which the most important is traffic congestion. A high percentage of congestion is usually presented by the inappropriate use of resources in vehicular infrastructure. In addition, the integration of traffic congestion in decision making for vehicular traffic is a challenge due to its high dynamic behavior. In this paper, an optimization model over the load balancing in the congestion percentage of the streets is formulated. Later, we explore a fully congestion-oriented route discovery mechanism and we make a proposal on the communication infrastructure that should support it based on V2I and V2V communication. The mechanism is also compared with a modified [PERSON]'s approach that reacts at congestion states. Finally, we compare the results of the efficiency of the vehicle's trip with the efficiency in the use of the capacity of the vehicular network.  
**Keywords:** IoT; traffic congestion; vehicular routing; load balancing optimization; fairness; congestion; V2I; V2I; ITS