esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning Techniques for Non-Terrestrial Networks
## 6 Machine Learning Techniques in NTN
### ML for Protocol Stack Layers
#### 6.2.1 Physical Layer  
In the physical layer, ML can improve performance in many ways. As an example, in [45], a CNN is used to classify outputs of the receiver among possible constellations of modulation schemes, such as 16 QAM and 64 QAM. The authors used AlexNet, which is a DNN made of 650 thousand neurons and 60 million parameters. Results show that AlexNet can be more accurate than the traditional support vector machine (SVM) based techniques. More specifically, the authors measured the accuracy of the classifier as a function of the SNR (signal-to-noise ratio) of four modulation types: QPSK, 8 PSK, 16 QAM and 64 QAM. Simulation results show that in the low SNR region, QPSK and 8 PSK identification obtained an accuracy greater than 90%, and about 60% for 16 QAM and 64 QAM. Nevertheless, on average, this technique showed to be sufficiently (\(>\)80%) accurate at an SNR \(\geq\) 4 dB. Propagation loss prediction is another field where ML can help. Statistical models such as Okumura-Hata require less computational power, but at the same time are less accurate than the deterministic ones, such as those based on ray tracing methods. However, these models require more computational power and more detailed input data. In [46], the authors presented a NN-based procedure to predict the path loss in an urban environment. The main goal is to approximate the ray tracing performance while reducing the computation cost. Results showed an accuracy of \(\pm\)2.5 dB for uniformly built-up environments and 4.9 dB for non-uniform environments.
# Conductive Ink Printed Fabric Antenna with Aperture Feeding Technique
## 5 Measurement and Result Discussion
values obtained in the measured results of the BLE model. The measured x-pol. values towards the back-end of the model were rather high as seen in Figure 11a,b. A \(-\)5.1 dB x-pol. value in the _xz_ plane and \(-\)7.7 dB in _yz_. Similar to the back radiation, this could be attributed to the smaller ground size which resulted in spurious fringing currents not being properly grounded. The occurrence is not observed in the LoRa model with a 150 \(\times\) 145 ground size, achieving x-pol. values better than \(-\)13.9 dB and \(-\)17.7 dB in the _xz_ and _yz_ planes, respectively. Although lower, the radiation efficiency of the aperture-coupled fabric antenna is comparable to the reference copper sheet-based felt patch antenna at 84% radiation efficiency (see Table 1) with the former having a bulk conductivity of 1.97 MS/m and copper being 58.7 MS/m. (97% difference). The measured antennas performed well in the measurement with good conformity with the simulation in general. Recent fabric-based antennas with aperture/proximity feeding techniques are shown in Table 3 and compared with the proposed design. As shown, the aperture-coupled antenna with coaxial cable feed performs comparatively well to the referenced works with respect to the parameters shown. Reference [22] showed a PCB-backed solar cell placed on a thick 11.95 mm fabric that operated in the North American LoRa frequency band of 915 MHz. Although it obtained a good single element gain value of 3.0 dBi at a 2.84% fractional bandwidth, the front-to-back ratio was very poor owing mainly to the aperture slot method used. An improved concept is observed in [19; 20; 21] with radiation efficiencies close to the proposed designs. The SMA probe-fed design in [24] produced a very good radiation efficiency value of 65% from an initial low of 10% due to the inclusion of the artificial magnetic conductor (AMC) backed reflector. However, as mentioned earlier, the PCB components in these feeding structures make the antenna generally bulky with little comfort when worn due to the inflexible PCB back structure. In addition, the back reflectors are added primarily to mitigate the large back radiations in the aperture feeding structures [21]. The proposed design achieves comparable front-to-back ratios and radiation efficiencies as shown in Table 3 without the need for back reflectors and inflexible PCB structures. The objectives set out in this manuscript in finding a suitable and simple solution to feed fabric antennas were thus achieved.
## 4 Frequency Estimation of Midspan Deflection Signal Acquisition
### Estimation Method Based on the Power Spectrum
When the cutoff frequency \(f_{1}=0.62\) Hz and the acquisition frequency \(f_{s}=1.24\) Hz, the error of each digital characteristic of the signal is close to or less than 5%, and the reliability of the acquisition frequency is more than 95%, which shows that it meets the requirements of practical engineering.
## 3 The CPN Model
### The CPN Model
#### 3.2.5 Deadlock Analysis  
Generally, deadlocks occur when processes stay blocked for ever (waiting for an event caused by another process that never occurs) and in such cases, probably the whole system needs to be restarted. To show that a model is deadlock-free, we will show that an initial marking will appear again after a number of transition firings [35]. In the CPN context, the deadlocks are analyzed as in the simple PN context. We start with an initial desirable for the model purposes marking and we analyze all the possible firings. If the system does not have blocked processes, then it is deadlock free. One indication for the non-existence of deadlocks is the ability of the model to start from a marking and, after a series of firings, to return to the same marking.  
**Proposition 1**: _The model of Figure 3 is deadlock-free._  
**Proof.** Assume that initially there is one token in places \(p_{1}\) and \(p_{2}\), that is, there is one job that requests its dominant resource and there is an initial input rate regulation. Then \(t_{1}\) is active and one token will move to \(p_{4}\). This enables \(t_{4}\) and when \(t_{4}\) fires, one token moves to \(p_{1}\) and one to \(p_{5}\). This enables \(t_{2}\), which places one token to \(p_{2}\) and with one token to \(p_{1}\), \(t_{1}\) is reactivated. This circle can repeat itself forever, indicating that the model is network-free for the part that involves the overall processing for the DSQ (places \(p_{1}\), \(p_{2}\), \(p_{4}\), and \(p_{5}\)). The other two parts can be analyzed in a similar manner and also be proven to be deadlock-free.
## 1 Introduction  
Due to its excellent castability and mechanical properties, ADC12 alloy has attracted the attention of the automotive and aviation industry [1]. However, the coarse or lamellar shape of eutectic Si and long needle-like iron-rich phases exists in the alloy and reduces the mechanical properties and elongation [2]. Thus, ADC12 aluminum alloy applications are also severely limited.  
The microstructure of the alloy has been improved by the addition of some special elements. Currently, some reports indicate that the distribution of eutectic silicon, or other phases in the matrix, were improved effectively by adding rare earth [3] or alkali metal [4] to the melt. The coarse or lamellar shape eutectic Si was changed into fine or short rod-like particles and the acicular \(\beta\)-Al5 FeSi was modified into short rods. Among them, rare earth Y has attracted much attention from scholars due to its low price and remarkable effect. [PERSON] et al. [5] studied the effect of rare earth Y on the hot-tearing behavior of Al-Cu alloy. They revealed that Y has a distinct grain refining effect on the alloy, which decreased the liquidus temperature and shortened the crystallization range. [PERSON] et al. [6] found that the grains of an Al-Zn-Mg-Cu-Zr alloy were significantly refined by adding a mix of rare earth elements (Gd and Y). Several researchers [7; 8] demonstrated that the rare earth Y has an excellent refining effect on Si and iron-rich phases while also reducing alloy defects.  
However, the microstructure of the alloy can still be further optimized, even if an appropriate amount of rare earth has been added. Recently, some researchers found that a heat treatment process could further improve the microstructure of aluminum alloy containing rare earths. [PERSON] et al. [9] demonstratedthat an excellent microstructure could be obtained by heat treatment on an AlSi\({}_{10}\)Cu\({}_{3}\) alloy with the rare earths La and Yb. The heat treatment process promotes solid solution and precipitation strengthening. Furthermore, the La\({}_{3}\)Al\({}_{11}\) and Si phases acted as strengthening phases in the alloy and were spheroidized and dispersed.  
In this study, the effect of Y addition and solid solution, and Y addition on the microstructure of ADC12 alloy was investigated. The purpose was to find the optimum Y content, study the modification mechanism of rare earth, and study the effects of heat treatment on the various phases of the ADC12 alloy.
# Comparing Blockchain Standards and Recommendations
## 4 Selected Standards
### ANSI Accredited Standards Committee X9  
ASC X9 released the final version of the _Distributed Ledger and Blockchain Technology Study Group Report_[19] in 2018. In their study they worked together with experts of various fields and assessed what types of standardization effort would be both needed and beneficial especially for the financial sector but also other industries, as well as to increase the adoption of DLT. However, the study only focuses on permissioned blockchains, as it is deemed necessary for compliance with existing regulations for the market. Throughout their report, each section is marked with either _high_, _medium_ or _low_. The authors use this indicator to express the urgency on how much standard is needed for each specific part, each supplemented with a recommendation on how to proceed. The majority of the document focuses on and explains security needs and issues of blockchains, especially for finance. Their overall recommendations are for developers to be cautious and for the industry to take a three staged approach consisting of assessing whether there are existing non-blockchain standards that cover the same topic, using incremental improvements for blockchain specific implementations and as a third, stirring discussion in areas where there is immediate need for standardization. To provide better understanding of the key components of blockchain systems, there is a high-level reference architecture included in the appendix which is used to generally explain how a DLT system works.  
Pros: ANSI provides a substantial overview of possible and needed standardization directions that can be of immense value for bodies of standardization and companies trying to fill the gap.  
Cons: As this report is technically just a list of suggestions for possible standardization, there is limited value for regular end users and organizations which are looking for guidance.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 1 Introduction
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Item** & **Descriptions** & **Item** & **Descriptions** \\ \hline
2D & Two Dimensional & OWC & Optical Wireless Communication \\
3D & Three-Dimensional & RRC & Real-Time Control \\
3G & Third Generation & mm-wave & Millimeter Wave \\
4G & Fourth Generation & MR & Mixed Reality \\
5G & Fifth Generation & m-Wave & Micrometer-Wave \\
6G & Sixth Generation & NCHO & Network-Controlled Handoff \\ ABSs & Aerial Base Stations & NEMO & Network Mobility \\ AI & Artificial Intelligence & NLoS & Non-Line-Of-Sight \\ AMF & Access And Mobility Management & PCI & Physical Cell Identity \\ API & Application Programming Interface & PGW & Packet Data Network Gateway \\ AR & Augmented Reality & PMIP & Proxy Mobile IP \\ AuC & Authentication Center & PPP & Poisson Point Process \\ BSs & Base Stations & PPs & Ping-Pongs \\ CoA & Centroid Of Area & QoS & Quality Of Service \\ CoMP & Coordinated Multi-Point & REHO & Reduced Early \\ D2D & Device-to-Device & RL & Reinforcement-Learning \\ DBS & Drone Base Stations & RLFs & Radio Link Failures \\ DRL & Deep Reinforcement Learning & RNN & Recurrent Neural Network \\ DSM & Different Speed Model & RSRP & Reference Signal Received Power \\ FAA & Federal Aviation Administration & RSS & Received Signal Strength \\ FMIPv6 & Fast Mobile IPv6 & RSSI & Received Signal Strength Indicator \\ GCS & Ground Control Station & RWP & Random Waypoint \\ GPS & Global Positioning System & S-BS & Serving Base Station \\ GPUs & Graphical Processing Units & SDN & Software-Defined Network \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of abbreviations.
# Game Theory Meets Wireless Sensor Networks Security Requirements and Threats Mitigation: A Survey
## 6 Applicable Future Trends
### Attractive WSNs Applications
#### 6.3.2 Wireless Underground or Underwater Sensor Networks  
The applications wireless underground sensor networks include acoustic signals such as Earthquake disaster observation, underwater sensor networks (UWSNs), agriculture applications, military uses, etc [129; 130; 131]. In [52], a Bayesian zero-sum game approach is proposed to confront the jammers intervention in order to disrupt the acoustic signals of UWSN. The model aims at maximizing the channel underwater capacity in the presence of different types of noise and jammer. In [59], a jamming game is proposed to model the interactions between jammers and underwater sensors. More specifically, the model concentrates on the reactive jammers that determine their jamming power based on the ongoing sensors' traffic stream. This jammer type is more severe than responsive jammer (i.e., constant jammers). In other words, the model proposes a learning-based anti-jamming enhancement method whereas every sensor decides its own transmit power regardless of the channel gain of the jammers.
## 2 Material and Method  
### Plant Materials  
The experiments were performed with table grape cultivar 'Hanxiangmi' between May 2021 and June 2021 in an irrigated vineyard at Cixi, Zhejiang, China (30\({}^{\circ}\)16\({}^{\prime}\)6.59\({}^{\prime\prime}\) N, 121\({}^{\circ}\)25\({}^{\prime}\)2.18\({}^{\prime\prime}\) E). The physical and chemical properties of the soils were as follows: soil type, clayey soil; pH, 6.4; soil depth, 102 cm; total organic matter content, 25 g/kg; basic nitrogen, 202 mg/kg; available phosphorus, 35 mg/kg; available potassium, 114 mg/kg; soil bulk density, 112.3 g/cm; groundwater level, 55-60 cm. Thirty-six uniform vines planted in 2010 were cultivated under the rain-shelter cultivation greenhouse, were randomly selected and grouped into four treatments: control (CK), girdling treatment (G), foliar fertilization treatment with K (F), and the combination treatment of girdling and foliar fertilization with K (GF). At 42 days after anthesis (DAA) (one week before veraison, 27 May), the trunk of each vine in the G and GF groups were girdled by removing a 7 mm ring of back using a girdling knife. The foliar fertilization treatment with K was applied to the grapevine twice in F and GF groups at 42 DAA and 49 DAA (veraison). For each application, 200 mL of 7.35 mM K\({}_{2}\)O was sprayed over the leaves of each plant. During the experimentation period, other nutrients have not been applied in grape plants, and the meteorological conditions were listed in Table S1. Field management and plantation protected, including irrigation and pest control, followed the normal agricultural practice. Berries were harvested weekly from 49 DAA to 77 DAA. For each biological replicate, 100 berries were randomly collected at each sampling date and half the berries were randomly chosen for physicochemical analysis. The remaining berries were frozen in liquid nitrogen and stored at \(-80\)\({}^{\circ}\)C for subsequent analysis of aroma composition and RNA extraction.
## 2 Materials and Methods  
In the present study, the biocomposite films were prepared by using _Manihot esculent_ (or tapioca/cassava starch (TS)) as the base polymer, _Chromolaena odorata_ (Siam weed (SW)) as an inhibitor, and flax seed gel (FS) as a binder. The process began by preparing the base polymer, adding 20 g of TS to 200 mL of water, and thoroughly mixing it for 30 min using a magnetic stirrer. The leaves of SW were collected, cleaned, and ground in an agate motor and pestle using water as the solvent for the preparation of the inhibitor. In this way, 500 mL of leaf extract was prepared from 100 g of leaves. Next, the leaf extract was added to the starch solution in three different concentrations, 40 mL, 80 mL, and 120 mL, and stirred for 30 min. To this mixture containing the TS and SW, binder FS was added and stirred for 1 h till a slurry solution was obtained. Then, it was poured into a mould to form sheets and allowed to sun dry for 24 h, followed by an air-drying period of 24 h, forming thin sheets of biocomposite material. The biocomposite samples with 40 mL, 80 mL, and 120 mL SW extracts, labelled as BC1, BC2, and BC3, respectively, were subjected to various characterisations to find their applications in day-to-day life and industries.  
The investigation of the biocomposites involved a thorough exploration of their physical, optical, and antibacterial properties. The samples' morphologies and structures are understood from the field-emission scanning electron microscopic (FESEM--Nova Nano, FEI, Hillsboro, OR, USA) images and X-ray diffraction (Bruker D8 ADVANCE --XRD, Billerica, MA, USA) patterns. The composites' thermal stabilities and yields were studied through thermogravimetric analysis (TGA) employing the PerkinElmer (Waltham, MA, USA) simultaneous thermal analyser 6000, and vibrational mode analysis within the 4000-400 cm\({}^{-1}\) range was conducted using a PerkinElmer Fourier Transform Infrared (FTIR) spectrometer. The composites' optical characteristics, like the absorption and optical emissions, were obtained through the Jasco (Oklahoma City, OK, USA) V550 Ultraviolet-visible (UV-Vis) absorption spectrometer and the Horiba (Kyoto City, Japan) Fluorolog TCSPC photoluminescence spectrometer, respectively. The antibacterial properties of all the biocomposites were identified using the agar-well diffusion method. The goal of this multifaceted analysis was to offer a thorough comprehension of the varied properties displayed by the composite material.
## 3 Results and Discussion
### Numerical Analysis of Spatiotemporal Effect
#### 3.2.4 Internal Support Axial Force  
From the initiation of the second steel support to the completion of the pit base slab, the axial force changes in the first and third (concrete supports) and second, fourth, and fifth (steel supports) supports are shown in Figures 13 and 14. Regarding the first support within the pit plane, it was under pressure when the second support was installed, and during the installation of the third support to the completion of the base slab, except for the end well supports, which were under pressure, the standard section supports were under tension. The axial force of the end well supports was less than that of the standard section supports, primarily due to the three-dimensional effects of the excavation and additional corner bracing. The spatial effect resulted in stronger lateral constraints at the end wells, reducing horizontal displacement and consequently lowering the axial force. Additionally, extra corner bracing was implemented at the end wells, further restricting deformation and distributing the loads more effectively, leading to reduced axial forces in the end well supports. The support axial forces continuously increased until the fourth support was installed. After the installation of the fifth support, the axial forces decreased until the base slab was completed, where they still showed a reduction.  
Figure 13: Change in the axial force of the first concrete support.  
support remained under compression from installation to the completion of the base slab, and its axial force increased. Among the five supports, the third support experienced the highest axial force. This may be mainly attributed to its position and concrete material. The third support was located at the middle of the excavation, which subjected it to the most significant stress redistribution in the excavation process. The higher stiffness of the third concrete support contributed to its ability to resist displacement more effectively, which resulted in a greater concentration of axial force. The fifth support was present for a short duration during pit excavation. Except for the fifth support, the other supports showed a pattern of initially increasing and then decreasing axial forces. As the excavation depth increased, the axial forces in the upper supports decreased, showing signs of stress relaxation.
# Turbo Decoder Design based on an LUT-Normalized Log-MAP Algorithm
## 4 The SISO Design of the Turbo Decoder
As the degree of parallelism increases, the number of LUTs increase with the same multiple. Therefore, in hardware implementation, the proposed LUT-Nor-Log-MAP algorithm can transform the required 14 LUTs operation per bit into addition operations during turbo code decoding, and thus reduce logic resource consumption. Table 3 shows the logic resource consumption of the turbo decoder and its SISO under different algorithms. Simulation results show that decoder logic resource consumption based on the LUT-Nor-Log-MAP algorithm is 74.5 K. Compared with the Max-Log-MAP algorithm, our design consumes more about 8.4% logic resources, but our design has advantages in decoding performance. Compared with the LUT-Log-MAP algorithm, our design saves about 2.1% of logic resources with a similar decoding performance. Figure 11: Function module structure of the NFU. Figure 12: The rule of state trellis transition for the TD-LTE turbo codes. Figure 6:0 Structure of the modified SISO unit.
## 4 Analysis and Discussion
### Approximation Based on Envelope Comparison
From the above analysis, the approximation is based on the inverse comparison with the simulation envelope in the non-detection zone. As shown in Figure 10, the main steps are as follows:  
Data pre-processing: we prepared the envelope data in the experiment and the simulation without the flaw. The experimental data can be replaced by the experimental data of a similar square steel bar without flaws in practical measurements. For the simulation data, we can use \(D0\) = 0 mm. * Step 1: The experimental data with the flaw obtained in the non-detection zone are processed using the envelope calculation and we subtract the envelope of the experimental data without the flaw to reduce the effect of the signal trailing. Then the result of the envelope is extracted in the effective comparison interval (from 7.649 \(\times\) 10\({}^{-4}\) s to 4.653 \(\times\) 10\({}^{-3}\) s). * Step 2: Based on the obtained experimental envelope (Envelopment 2-Envelopment 1), the simulation range (\(L\in[L_{1}\), \(L_{2}]\)) is selected according to the rough classification described in Section 4.3 to reduce the computation cost. * Step 3: According to the comparison requirements, the variable value of L is selected in the appropriate range (\(L_{1}\) mm \(\leq L_{1}\) + \(k\Delta x\leq L_{2}\) mm, \(\Delta