esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Lower-Layer Protocols for VANET  
VANET applications can be supported through V2R (Vehicle-TO-Roadside) and V2V (Vehicle-to-Vehicle) communications. IEEE 802.11 (WiFi) [31], IEEE 802.16 (WiMAX) [32] and DSRC (Dedicated Short Range Communications) [33] technologies can be used in this model of communication.  
With the DSRC standard, OBU (OnBoard Units) placed at each vehicle can send or receive data to or from roadside units (RSUs), as well as communicating directly with each other. There are thus many potential applications in urban areas (or in populated countries in Europe or Southeast Asia) such as forward collision warning, electronic parking payments, and approaching emergency vehicle warning [34]. Unfortunately, it is limited in its distance coverage and may not cover rural areas in countries like Canada and USA. Furthermore, DSRC is based on 802.11a and cannot handle high-speed mobility while maintaining an acceptable data rate. It also does not have the ability to hand over connections at high speed, which is essential for high-speed motion and cannot support high data rates. DSRC would probably be integrated with other technologies for use in the VANET if it is to be universally accepted. For example, we can use dynamic spectrum sharing for both DSRC and WiMax to improve the communication efficiency as well as the spectrum utilization to cover both the rural and urban areas [35].  
WiMAX (Worldwide Interoperability for Microwave Access) [14] is a 4G equivalent technology that enables the delivery of last mile wireless broadband access. WiMAX technology [36] can eliminate the use of cables and can save costs when used in remote and rural areas. The technology is scalable and has a flexible frequency re-use scheme because it uses OFDMA (Orthogonal Frequency Division Multiple Access) technology. Unlike DSRC, WiMAX was designed to handle high data rates. It implements full MIMO (Multiple-Input and Multiple-Output) which is good for mobile and car applications by enhancing timely information delivery to save lives and improve quality of life. Many laptops in USA already support such interface even though its present price is still high. With the above observations on and comparison work with DSRC, one sees that WiMAX is the most cost effective approach by providing a data rate that can satisfy the needs of our vehicle camera users (low latency and high coverage) at high speed and at an affordable cost.
# Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm
## 3 Machine Learning Algorithms
### Proposed Machine Learning Algorithms  
In this research, we suggest a model to estimate the raster angle, which is one of the printing parameters in FDM technology, from data obtained from models with different geometric structures. A block diagram of the proposed system is shown in Figure 5. In the first stage, normalization between 0 and 1 was applied to the data to make the classification process more efficient. The min-max method given in Equation (9) below was used as the normalization method.  
\[x^{\prime}=\frac{x_{i}-x_{min}}{x_{max}-x_{min}} \tag{9}\]  
Figure 4: Structure of the RFR prediction model.  
In this equation, \(x^{\prime}\) represents the normalized data, \(x_{i}\) is the input value, \(x_{min}\) is the smallest number in the input set, and \(x_{max}\) refers to the largest number in the input set. After the normalization phase, the data selection phase was initiated. During the data selection phase, training-test data were selected. At this stage, the cross-validation method was used 10 times. In this study, 80% of all data were used for training and 20% for testing. In the next stage, the ML algorithms were applied. Five different regression algorithms were used. These algorithms are SVM, GPR, ANN, DTR, and RFR. Finally, the performance analysis of the predictive models was conducted.
# Influence of Region on Sensory and Chemical Profiles of Pennsylvania Gruner Veltliner Wines
## 2 Materials and Methods
### Chemical Analysis
#### 2.4.2 Volatile Wine Analysis  
Headspace-solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) was used to determine aroma composition of wines using a method from previous work, with some modifications [24; 25]. An Agilent Technologies 7890B GC 5977B MS (Santa Clara, CA) with an MPS autosampler (Gerstel, Inc., Linthicum, MD, USA) and Rtx-Wax 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m GC column (Restek, Bellefonte, PA, USA) was used for sample analysis. Agilent MassHunter GC/MS software and Gerstel MAESTRO software was used to control the GC and autosampler, respectively. For sample preparation, 2 mL of wine and 50 \(\upmu\)L of internal standard mix (9.9 mg/L d8-napthalene and 13.7 mg/L 2-octanol in 100% HPLC grade methanol) were added to 20 mL crimp cap headspace vials (Restek) containing 2 g of sodium chloride (Dot Scientific, Burton, MI, USA). Samples were incubated at 30 \({}^{\circ}\)C for 5 min and then extracted for 30 min at the same temperature using a 2 cm DVB/CAR/PDMS SPME fiber (Supelco, Bellefonte, PA, USA). The fiber was desorbed at 250 \({}^{\circ}\)C for 10 min in the splitless mode, using a constant helium flow of 1 mL/min for analyte separation throughout the run and an oven program starting at 30 \({}^{\circ}\)C for 1 min, followed by a 10 \({}^{\circ}\)C/min ramp up to 250 \({}^{\circ}\)C and a hold for 5 min at this temperature. The analytes were detected in the scan mode from 33 to 350 amu with 8.1 spectra collected per second.  
Sampling occurred during the DA evaluation sessions so analysis could be completed on sensory samples and multiple bottles. For wines made in 2018 (Year 1), samples for aroma analysis were prepared in 6 replicates (2 replicates from each sample evaluation day), and for wines made in 2019 (Year 2) samples were prepared in 5 replicates (1 replicate from each evaluation day). Compounds were identified via the National Institute of Standards and Technology (NIST) Mass Spectral Library (version 17). Compounds were reported in internal standard (IS) equivalents (\(\upmu\)g/L).
## 3 Examples
### The Effects of Calculated Parameters on Results  
#### 3.2.1 The Effects of Ground Displacement \(\delta\) on the Pipeline Settlement  
In the analytical calculation, the ground displacement \(\delta\) is an important parameter for determining the pipelines' settlement. For Case #1 and Case#2, the results have been evaluated by comparing a set of calculated data, which were attained by setting different parameters. The range and values of parameters involved in Case#1 and Case#2 are \(\delta\) = 20-40 mm and \(\delta\) = 23-33 mm, respectively. The settlement curve of the pipelines of two cases is shown in Figure 5. It can be observed from Figure 5 that the ground displacement \(\delta\) plays an important role in the settlement displacement of pipelines. The \(\delta\) is closely related to the settlement displacement curve shape; the results show that the bigger input \(\delta\) causes bigger settlement displacement, and the peaks of settlement displacement distribution become sharper. As for an illustration in Case#1, when \(\delta\) increases from 20 mm to 40 mm, the maximum settlement displacement of pipelines increases by 102.94% (from 16.35 mm to 33.18 mm). Similarly, in Case#2, when \(\delta\) increases from 23 mm to 33 mm, the maximum settlement displacement of pipelines increases by 49.71% (from 22.07 mm to 33.04 mm).  
Figure 6 shows the relationship between \(\delta\) and the maximum settlement displacement of pipelines. It can be seen that the maximum settlement displacement of pipelines increases linearly with increasing \(\delta\), and it also indicates the deformation that the two possess in concert and harmony. This positively proportional relationship will help engineers to grasp the settlement displacement of pipelines in theory.  
Figure 5: Effect of ground displacement on the pipeline settlement.  
Figure 6: Relationship between ground displacement and maximum settlement displacement.
## 2 Materials and Methods
### Movement Task  
For this study, we performed an experiment to evaluate 3D visuomotor control quantitatively, using circular tracking movements for the frontal and sagittal planes relative to the subject in the VR space (see Figure 1). The subjects were seated on a chair built for the experiment and wore an HMD. Prior to the experiment, we orally confirmed that each subject could properly perceive stereoscopic vision. Subjects were asked to hold the physical controller in their dominant hand. We ran a calibration to locate the target's initial position optimized for the subject's arm length and height to minimize the different anthropometric parameter impacts on experimental results. The target rotated at 0.125,  
Figure 1: Experimental procedure. (**A**) The circular tracking experiment for the body’s frontal plane (\(ROT(0)\)). (**B**) The circular tracking experiment for the body’s sagittal plane (\(ROT(90)\)). For both (**A**) and (**B**), the top inset illustrates the circular tracking movement on each plane of the body in VR 3D space.  
0.25, 0.5, or 0.75 Hz along the orbit after a 3 s countdown with a sound effect. The subjects were asked to move the tracer to the target position during the countdown and then perform a circular tracking movement. The target stopped after three loops. At the end of each trial, the target stopped for 1 s and played a sound. Four trials each were performed with the target rotating in the frontal (\(ROT(0)\) in Figure 1A) and sagittal planes (\(ROT(90)\) in Figure 1B). The first trial for each plane was a practice run and was excluded from the analysis. Therefore, 32 experimental trials were performed (4 trials \(\times\) 4 speeds \(\times\) 2 planes) for each subject. To avoid a subject learning effect, the experiment was performed with random counterbalance.
## 3 System Design
### System Scenarios  
#### 3.2.1 Registration on IPFS Private Network  
Figure 4 illustrates the scenario of registering on the IPFS private network. An archivist is assigned as an administrator, who has access as Boot Node. All other users are granted access as Client Nodes. All nodes have to set a bootstrap node configuration, using a boot node peer ID and IP address. Hereafter, the boot node generates and copies a single swarm key for all client nodes. A swarm key allows each IPFS node to connect with other nodes. All nodes in the private network must have the exact same swarm key. The IPFS private network is restricted from the public IPFS network.
## 1 Introduction
For these reasons, a conservative surgery sparing a toe, a metatarsal bone, or a ray would be less traumatic and more acceptable by patients when feasible [26]. Currently, minimally invasive surgery (MIS) represents an innovative approach and is becoming more popular because of lower complication rates [27]. Different minimally invasive and percutaneous osteotomies, at different levels of the distal metatarsal bones, generally by a dorsal approach and without head resection, have been proposed including osteoclasts, a V-shaped cut, a Gauthier osteotomy or oblique cuts such as in a Weil osteotomy or its variants [28; 29]. The common goals of these minimally invasive or percutaneous procedures is not only to favor the reduction of bone-induced pressure on the PDFU and consequently promote its healing, but also to restore the metatarsal parabola of the forefoot, preventing recurrent, transfer skin lesions and possible future wound and bone infection [6]. Due to the preserved soft tissue covering and its characteristic stiffness in diabetic foot, the primary stability of these osteectomies is so high that successive osteosynthesis not only is not necessary, but it would also be harmful in preventing the dorsal elevation of the metatarsal bone with respect to the ulcer level. In fact, the patient is asked to walk on the operated foot in the immediate postoperative period to elevate the head dorsally and to release plantar pressure. Technically, the bone cut is usually perpendicular to the metatarsal neck [26]. Recently, some authors reported the outcomes of perpendicular or oblique osteectomies using minimally invasive techniques located at different levels of the metatarsal neck or at the distal diaphragys [27; 30]. These include a variant of the most widespread Distal Metatarsal Metaphyseal Osteotomy (DMMO) [31], a percutaneous technique used routinely in the last decade for the treatment of metatarsalgia [27; 32]. A recent meta-analysis about the contemporary management of diabetic neuropathic foot ulcerations revealed disappointing functional outcomes [33]. Moreover, the meta-analysis showed that minimally invasive surgery for PDFUs by metatarsal osteectomies is a poorly investigated topic [33], contrary to the several traditional surgical techniques explored in the literature [20; 21; 22; 33]. The goals of this systematic review with meta-analysis and meta-regressions were to critically evaluate the available literature to establish if there is sufficient evidence demonstrating that minimally invasive metatarsal osteectomies (MIMOs) proposed for PDFU treatment are (1) effective in achieving ulcer healing and (2) effective in preventing recurrence of ulcers, and (3) to summarize their rates of post-operative complications.
# Estimation of Stability Parameters for Wide Body Aircraft Using Computational Techniques
## 3 Xflr  
The stability and control analysis of an aircraft has been an exclusive subject in the field of aeronautics. Software solutions such as XFLR have been designed to study stability and control dynamics. This evolution has not only aided in the dynamic modeling of the aircraft but also contributed to the study of aerodynamics. XFLR is a piece of stability analysis software in which there is an easy means of drawing up the aircraft according to the physical measurements and performing aerodynamic analysis to obtain the necessary aerodynamic data for the evaluation of stability derivatives. It is a tool used to analyze a wing, airfoil, or complete airplane with various Reynolds number operations [53]. It is also compatible with Windows as well as LINUX and is easily downloadable. Originally, XFoil was written in FORTRAN, but now the code has been translated into C/C++ for XFLR. The latest version of the software introduces the stability analysis of the planes. It also has inverse and direct analysis capabilities for XFoil [45]. XFLR uses forward differentiation for the determination of the stability and control derivatives. Hence, the derivatives are computed through Taylor's series approximations in this software solution.
# A Component-Based Approach to Early Warning Systems: A Theoretical Model
## 3 Results
### Requirements for Early Warning Systems
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Natural Hazard** & **Primary Cause** & **Group** & **Secondary Cause** & **Group** \\ \hline \multirow{4}{*}{Floods} & Rainfall & Meteorological & Soil moisture & Hydrological \\ \cline{2-5}  & Snowmelts & Hydrological & Temperature & Meteorological \\ \cline{2-5}  & Not applicable & Not applicable & Pressure & Meteorological \\ \cline{2-5}  & Not applicable & Not applicable & Humidity & Meteorological \\ \hline \multirow{4}{*}{Droughts} & Temperature & Meteorological & Groundwater & Hydrological \\ \cline{2-5}  & Evapotranspiration & Hydrological & Humidity & Meteorological \\ \cline{2-5}  & Rainfall & Meteorological & Not applicable & Not applicable \\ \hline Extreme temperatures & Temperature & Meteorological & Humidity & Meteorological \\ \hline \multirow{2}{*}{Wildfires} & Temperature & Meteorological & Soil moisture & Hydrological \\ \cline{2-5}  & Humidity & Meteorological & Wind & Meteorological \\ \hline Wind & Wind & Meteorological & Pressure & Meteorological \\ \hline \multirow{2}{*}{Landslides} & Rainfall & Meteorological & Soil moisture & Hydrological \\ \cline{2-5}  & Earthques & Geophysical & Not applicable & Not applicable \\ \hline Earthques & Ground motion & Geophysical & Not applicable & Not applicable \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Primary and secondary causes of natural hazards. ration [62; 63; 64]. A lack of groundwater further accelerates evapotranspiration, as does low humidity. Wildfires, while a natural hazard, are strongly influenced by anthropogenic activity. The key causes therefore include physical parameters that significantly increase the risk of ignition. In this regard, temperature and air humidity play a crucial role [65; 66; 67]. For landslides, from a climatic perspective, precipitation levels have a major impact. During rainfall events, the soil becomes saturated with water, leading to a rise in groundwater levels and soil moisture. Rainfall therefore reduces embankment stability [68]. As pointed out by [PERSON] et al. [69], in addition to rainfall, internal dynamic factors, such as earthquakes, play a significant role. As noted by [PERSON] [70], ground shaking and ground failure lead to ground motion, which contributes to the occurrence of earthquakes. Beyond identifying primary and secondary causes, it is also important to consider the final effect, manifestation. This effect reflects the destructive impact of various hazards, and in the development of an EWS, its monitoring is crucial. Observing these parameters plays the most important role in the response phase of the crisis management cycle. Manifestation refers to the direct impact of a natural hazard, representing its primary consequence upon occurrence. Essentially, it marks the transition of the hazard from a potential threat to an active force with measurable consequences. Table 3 illustrates the manifestations of natural hazards, which represent the primary danger or sources of risk to the population.
## 5 Results and Discussions
### Proposed LQR-FIC Controller against Pole Placement Controller
Figures 9 and 10 show comparisons of the Bode diagram of an open-loop response frequency response according to the values of \(\lambda\) and \(\mu\) with and without the use of FI and FD pre-compensators, respectively. It can be observed from Figure 9 that the fractional integral pre\(-\)compensator makes the augmented system more phase lagging. In contrast to this observation, the fractional derivative pre\(-\)compensator increases the phase margin, as shown in Figure 10, that is it increases the phase margin of the feedback system. Figure 8: Closed-loop Bode plots of pole placement. Figure 9: Open-loop Bode plots of the plant with and without FI compensator. For a visual comparison of the time responses of the closed-loop systems, Figure 4 presents the fault-free condition, Figure 5a,b presents the 50% and 80% faulty cases. For a visual comparison of the frequency responses that reveal the relative stability of the closed-loop system, the Bode plots are presented for the same fault-free and faulty cases in Figure 11a,b respectively. In addition, the Bode plots in the case of fractional-order derivative (FD) for the fault-free and under 50% faulty cases are depicted in Figure 12a,b, respectively. Accordingly, the associated frequency response performance values are presented in Table 3. All these simulation tests reveal that the closed-loop system is more robust in the case of having the fractional-order integral (FIC) pre-compensator than having the fractional-order derivative compensator in terms of gain, phase, and delay margins as shown in Figure 11a,b, and Table 3. This is achieved owing to simultaneously optimized feedback controller gains and the pre-compensator parameters. Figure 11: (**a**) Closed-loop Bode plots of LQR-FI compensator for \(\lambda=0.1\) : fault—free, and (**b**) closed-loop Bode plots of LQR-FI compensator with 50% loss of actuator effectiveness for \(\lambda=0.1\). Figure 12: (**a**) Closed-loop Bode plots of LQR-FD compensator for \(\lambda=0.6\): fault-free, and (**b**) closed-loop Bode plots of LQR-FD compensator with 50% loss of actuator effectiveness for \(\lambda=0.6\).
## 3 SDMob Architecture
### Basic SDMob Architecture  
In this subsection, we review the basic SDMob architecture that was presented in a previous work [12]. Figure 2 illustrates and compares the basic SDMob against the enhanced version. Inside the WSN network, SDMob uses Contiki's protocol stack including RPL/6 LoWPAN networking with a 64-bit network prefix and IEEE 802.15.4. The WSN is composed of one MN in the basic SDMob (multiple MNs in the enhanced SDMob), and Static Nodes (SN). SNs are also known as anchors and provide multi-hop access for the MNs, and provide the controller with the required information for localization. The SDN controller is placed outside of the constrained network, separately as a Linux-server process. It admits to the control information collected via the sink node in the WSN simulated network. The SDN controller is bundled with the border router, which is responsible to decode messages from the low-power domain to be used by the controller and data server. The border router is also a Linux process that is connected to the WSN with the help of a node called Serial Line Internet Protocol (SLIP) radio. The Contiki's implementation of the SLIP protocol prepares the wireless IP packets to be transmitted over a serial line. The particle filter runs the location estimation algorithm to accurately locate future positions of the mobile node. The implementation of the border router and the particle filter are interfaced using Linux pipes (shared memory in the enhanced SDMob due to its fast inter-process communication).  
In this paper, we assume the MNs are equipped with Inertial Measurement Unit (IMU) sensors, but the architecture can be generalized to nodes that do not benefit from IMU sensors by estimating the velocity based on previous RSSI measurements. However, measuring the velocity is more accurate compared to the estimation techniques, since it exploits the extra information from the sensors. On the other hand, many applications nodes are already using such sensors, so the overhead could be negligible.  
This design is highly inspired by the ideas in an IETF draft titled \"root-initiated routing state\" [33] that allows a centralized entity to manipulate the routing states in the distributed operation of RPL. The routing rules that are installed by the controller are of a higher priority compared to the routes learned by receiving DIO packets. The SDN controller's traffic relies on RPL for its basic connectivity. It is only after the convergence of the standard RPL protocol that the MN can utilize the upward/downward routes to access the controller and the centralized tracking algorithm can notify the anchors to relay MN's traffic.  
For the smooth operation of SDMob, certain mechanisms are required. Below we describe some of the challenges and the mechanisms adopted in basic SDMob to address them.