esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 M2M-Based Division of MAC Layer Protocols
### Code-Expanded Random Access (CERA)  
CERA is the modification of an already-developed long-term evolution (LTE) protocol called dynamic random-access channel (RACH) resource allocation. The objective of CERA is only to support more devices, as compared to LTE, without increasing resources [33]. The approach utilizes the same orthogonal preambles as those of LTE, but a single phantom codeword is used for multiple frames, causing the preambles to be actuating in a virtually combined frame. When two nodes want an information exchange or channel access, they select sub-frames from a singular preamble with a random probability. Collision occurs only when two nodes select the same sub-frame in same preamble. The approach is further enhanced by embedding a code work inside the virtual frame.  
M2M is not bound to the above-mentioned random access techniques. Enhancement of IEEE 802.11 ab for M2M communications [34], fast adaptive slotted ALOHA (FASA) [35], M2M using cellular networks [6], cognitive radio-based M2M communications in OFDMA [36], and polling-based channel access [37] are also implemented for efficient channel access and accomplishing application-constrained channel utilization.
## 6 Results and Discussions
### Limitations and Challenges  
The biggest challenge we faced during our research was sorting and filtering the data of rescue cases. The rescue database was full of null values and sometimes meaningless data. This is quite understandable as it is very difficult to collect data in emergency cases. Based on our findings, in more than 80% of the cases, the patients or related people cannot provide enough information. Sometimes, the patients do not even want to disclose health information or are uncomfortable answering health-related questions to rescuers. The health vitals taken are often manually recorded, as the sensors don't work well when a patient is unstable. During our data analysis, we considered those data limitations and used extensive data mining and pre-processing steps as explained in Section 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** &
\begin{tabular}{c} **Inference Time** \\ **(Seconds)** \\ \end{tabular} \\ \hline XGB & 0.002992 \\ \hline ANN & 0.048838 \\ \hline RF & 0.006981 \\ \hline SVC & 0.001994 \\ \hline KNN & 0.001987 \\ \hline LR & 0.001006 \\ \hline NB & 0.000960 \\ \hline TVM-VTA & 0.013271 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Inference time for ML models.  
We also predicted potential challenges in the hardware part. The wearable we wanted to develop by deploying an AI model in FPGA-based hardware may face some limitations in the future as below:  
* Battery life and reliability can be considered as the main challenge for stand-alone FPGA-based hardware. Further research is needed to find a reliable solution, e.g., exploring Bluetooth technology instead of WiFi for patient data collection, use of a buck-boost converter on expanding wearable battery life, etc. Further, in rescue contexts, the reliability of a healthcare device is quite important. In a massive accidental situation, where the rescue environment gets quite harsh, it is quite challenging for a hardware device to function properly unless it has high robustness.
* Interfaces to medical devices manufactured by different companies for collecting data are often a great challenge. Generally, the manufacturers have different protocols for data transmission and sharing as no universal standard exists. Moreover, the transmitted data format is not unique and depends on the compatibility of the device. Hence, in the future, the wearable may face challenges to integration with different medical devices and generation of test data for the AI model.
## 3 Results and Discussion
### Electromagnetic Field on Different European Roads
#### 3.1.1 Austria
In the first road section (P1 AT-P2 AT), _Ex_, _Ey_, and _Ez_ components have almost the same mean (_Ex_ = 0.22 V/m, _Ey_ = 0.24 V/m, and _Ez_ = 0.23 V/m) and median (_Ex_ = 0.17 V/m, _Ey_ = 0.19 V/m, and _Ez_ = 0.17 V/m), meaning that each of the components contributes equally to _Etotal_ due to the multipath environment of the electromagnetic wave propagation in the car's cabin. From Figure 3, we can see that the electric field on the probe _y_-axis has a slightly higher mean and median in the second road section (P2 AT-P3 AT). The distribution of the electromagnetic field in the car depends on the intensity and polarization of the incident and reflected electromagnetic waves. In the road section where the measurements were made, electromagnetic waves probably penetrated through the car side windows. We suppose that the higher measured values at the _y_-axis (placed parallel to the side windows) of the LSProbe variant E are caused by the arrival of the direct signal from the side windows.
# Design and Depth Control of a Buoyancy-Driven Profiling Float
## 1 Introduction
ADRC has excellent anti-interference ability, but the ADRC controller has many control parameters and cannot be calibrated fast and effectively, thus requiring long time and high costs in practical applications. In this study, a small depth linear actuator piston-type float was built to continuously observe and track the thermocline. The float was equipped with a temperature-depth sensor, a 4G or Beidou communication system, a GPS positioning system, and a variable buoyancy system (VBS). All modules necessary to build the float were off-the-shelf and low-cost, which provides the possibility of mass production and deployment. In addition, the electronic bin and the buoyancy bin of the float adopted a detachable installation method, which was small in size and light in weight, easy to disassemble and assemble, and easy to transport. Furthermore, in order to fulfill the requirements of depth keeping and speed control, a segmented PD control method is proposed in this paper. This method enabled the float to reach the target depth at the desired speed and remain at the target depth. In addition, to reach different depths in the same experimental environment, the method does not need to change the parameters, which greatly limits time and cost of the experiment. The float was tested at depths between 5 and 60 m in Qiandao Lake, confirming its speed tracking capability and depth retention capability. Finally, the distribution characteristics of the thermocline in the Qiandao Lake were determined by using the temperature data of the float during the depth control process at three different positions.
## 5 Conclusions  
Our current study is the first to examine cortical activity in response to auditory speech in quiet and in noise in prelingually deaf CI adults; results were compared to NH adults, who listened to natural and vocoded speech, using fNIRS measurements. Our findings demonstrated a clear relationship between SIN understanding and the degree of cortical response to speech in the right and left temporal cortices of the MTG. At the group level, a significantly smaller cortical response to speech in quiet and noisy conditions was shown, compared to NH controls, in CI individuals with poor SIN understanding. At a more general level, a significant correlation was shown between SRTn and cortical response to SIN across all participants (CI, NHV, and NH). These findings support the notion that neuroimaging data from the right and left MTG can serve as neural markers for behavioral speech understanding. They may also suggest that prelingually and postlingually deafened CI users activate similar neural regions during processing of auditory speech. This conclusion must be reassessed in future studies including prelingual and post-lingual deafened CI users.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app122312063/s1](https://www.mdpi.com/article/10.3390/app122312063/s1)]([https://www.mdpi.com/article/10.3390/app122312063/s1](https://www.mdpi.com/article/10.3390/app122312063/s1)), Table S1: fNIRS studies from the last decade that examined cortical activation related to auditory speech in (a) NH participants listening via acoustic simulations of CI hearing (i.e., vocoded stimuli), (b) CI users, and (c) both NH participants listening via CI simulation, and CI users. GLM = general linear model, PCA = principal component analysis, SSC = short-distance separation channel, LML = linear mixed model, RM-ANOVA = repeated-measures analysis of variance, SPM = statistical parametric mapping, ROI = region of interest, includes a cluster of channels. HBC = \(\Delta\)HbO - \(\Delta\)HbR.  
Conceptualization, Y.Z.; Formal analysis, M.L. and Y.Z.; Investigation, M.L.; Methodology, Y.Z.; Project administration, Y.Z.; Resources, Y.Z.; Software, M.L.; Supervision, M.B. and Y.Z.; Visualization, M.L. and Y.Z.; Writing--original draft, M.L. and Y.Z.; Writing--review & editing, M.B. and Y.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Tel Aviv University (approval code 0003676-1 on 8 January 2021).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author.  
We thank [PERSON] for helping with the statistical analyses. We also wish to thank all the participants for participating in the study.  
The authors declare no conflict of interest.
# Continuously Beam-Steered Phased Array Antenna Using GaN Varactors for Millimeter-Wave Applications
###### Abstract  
A continuously steerable beam patch antenna array employing a classical phase shifter based on GaN HEMTs is presented. Here, the GaN HEMTs are used as varactor diodes to achieve the tunability purpose. By controlling the DC bias of these varactors from \(-2\) V to \(2\) V, the proposed array antenna can provide continuous beam steering from \(0^{\circ}\) to \(+25^{\circ}\) in the azimuth plane at \(41.20\) GHz, while achieving a low side-lobe level and good impedance matching performances. Using GaN HEMTs as varactors to achieve beam steering capability has never been tried before to the best of our knowledge. The measurement results agree well with the simulation results and validate the effectiveness of the proposed beam steering based on GaN technology. This proposed phased array antenna will find numerous applications within future wireless communications systems, especially for millimeter-wave applications.  
phase shifter; antenna array; GaN HEMTs; mm wave applications Article
## 2 Theory
### Traditional Cam-Shift Algorithm
#### 2.2.1 Back Projection  
As the RGB (red, green and blue) color space is sensitive to changes of light intensity, a Cam-Shift algorithm converts RGB color space to HSV color space, first to decrease the impact of changes in light intensity. In the HSV color space, H, S, and V represents hue, saturation, and value respectively. Second, the H channel is spilt out as a single grayscale image to obtain its normalized histogram. Third, the value of each pixel in the image is replaced with the corresponding probability value according to the histogram of the H component. As shown in Figure 3, specific steps for calculating the back-projection are as follows:  
1. Select the LED signal source area as the tracking target and calculate its color histogram of the H component in HSV color space.
2. Obtain the hue value (H component) \(h_{i,j}\) of pixel \((i,\ j)\) in the image and find its corresponding interval \(u\) according to \(bin\big{(}h_{i,j}\big{)}\).
3. Find the probability value of the corresponding interval \(u\) in the histogram.
4. Use this probability value as the value of pixel \((i,\ j)\) of the back-projection.
5. Replace the value of each pixel in the image with its corresponding probability value.  
The pixel value of each point in the original image is replaced by its color histogram according to the color histogram of the target area, which is the area of the target LED signal source in the VLC system. Then, the final projection of the image is obtained from the probability value such that the back-projection image is a color probability distribution map. In the Cam-Shift algorithm, the back projection is calculated based on the H component of the LED signal source area (the tracking target area), and the color histogram of the H component and the back-projection image are shown in Figure 4. As can be seen from Figure 4, the color of the LED signal source is different from that of the background emitting-tubes. Therefore, the target area can be located based on the back-projection image after obtaining the color histogram of the H component.  
Figure 3: The process of obtaining the back-projection image.
# Upgrading _In Vitro_ Digestion Protocols with Absorption Models
## 2 Technological Approaches of Intestinal Epithelium Models
### Cell Cultures Used in Epithelium Models
#### 2.3.3 Utilization of Scaffolds  
The standardization of 3D cell cultures is a challenge [53]. The reproduction of the 3D structure of the tissue improves the physiological behavior of intestinal cells [24].  
Some protocols use scaffolds for the preparation of 3D models. In most cases, the scaffolds reproduce (or induce) the structure of the intestinal villi/crypts or the tube-like shape of the gut [29]. The scaffold can be made of synthetic or semi-synthetic substances (such as polyethylene glycol or hyaluron hydrogels) or extracts of animal origin (collagen, extracellular matrix (ECM)) [60]. The determination of the most important characteristics of synthetic scaffold matrixes and the standardization of the scaffolds are important research goals because the matrix properties (biochemical characteristics, rigidity, architecture) influence cell behavior [8; 17]. Using micro-engineered surfaces may also be an option [54].  
[PERSON] et al. [61] built a bioreactor by inserting porous villous scaffolds made of poly-ethylene-co-vinyl-acetate (PEVA) in a fluidic device. They cultured Caco-2 cells on these scaffolds. The Caco-2 cells actively transported more glucose in the presence of flow. Compared to membrane inserts plates, culturing Caco-2 on villous scaffold reduces TEER and increases brush border enzyme expression [61].  
In cases of shaped 3D intestinal systems, it is a challenge to synchronize the proper environment for the complex necessities (e.g., surface mechanics, adhesion, porosity) of primary cells with the growth and differentiation factors in a manner that will facilitate a cell-type distribution that is similar to the _in vivo_ condition [8].
## 1 Introduction
### Solutions for Idle Listening
#### 1.1.2 Active and Passive Wake-Up Radio  
Another solution to idle listening and overhearing in a network system is by using the wake-up radio, which a low power radio is attached to the main radio in charge of waking it up when an incoming signal is sensed. Having this device, the main radio can stay in sleep mode all the time without having to bother waking up and listening for incoming signals from time to time. This completely solves the problem of idle listening in the sensor node with no time synchronization overhead. The wake-up message and the data can be communicated in different channels, which makes it possible for the two message types to be exchanged simultaneously, hence reduce the possibility of collision. Several hardware designs and protocols have been developed to improve the performance and efficiency of the wake-up radios. Authors have extensively reviewed and analyzed the concept on many occasions [11; 12].  
There are two types of wake-up radios based on the power usage, namely; active and passive wake-up radios. The active wake-up radio is a second radio with low power that receives a continuous, external power supply such as batteries. Compared with active wake-up radios, passive wake-up radios do not require energy from the battery or any other power supply source connected physically. However, they harvest energy from the transmitted wake-up signal. Although passive wake-up radios require minimal energy through this process, the receiver sensitivity for passive wake-up radios is relatively lower, hence the wake-up range becomes shorter.  
There is also ultralow power wake-up solution, where few components (such as the microcontroller, comparator, etc.) in the wake-up radio circuitry are battery powered, while the remaining components are powered with the energy from ambient energy or incoming signal. These designs [13; 14; 15] operate within the nanowatt power range and reduce power consumption but operate within a limited communication range. Passive wake-up radios, however, require no battery to operate, as their circuit is entirely powered passively from the ambient energy or incoming RF signal.
## 2 The Principle and Kinematics Analysis of the Drive Mechanism of
### Kinematics Analysis of Drive Mechanism of Die-Cutting Device
The angular velocity change information of the drive rod can be calculated from the Formula (3) (Figure 4):  
The angular acceleration equation of the drive rod can be obtained by calculating the first derivative of Formula (3) to time. \[ \begin{array}{l}-l_{1}\omega_{1}^{2}sin\varphi_{1}+l_{2}a_{2}cos\varphi_{2} -l_{2}\omega_{2}^{2}sin\varphi_{2}=l_{3}a_{3}cos\varphi_{3}-l_{3}\omega_{3}^{2 }sin\varphi_{3}\\ l_{1}\omega_{1}^{2}cos\varphi_{1}+l_{2}a_{2}sin\varphi_{2}+l_{2}\omega_{2}^{2 }cos\varphi_{2}=l_{3}a_{3}sin\varphi_{3}+l_{3}\omega_{3}^{2}cos\varphi_{3} \end{array}  \tag{4}\]  
The solution can be obtained:  
\[a_{2}=\frac{l_{4}\omega_{4}^{2}-l_{1}\omega_{1}^{2}cos(\varphi_{1}-\varphi_{3 })-l_{2}\omega_{2}^{2}cos(\varphi_{2}-\varphi_{3})}{l_{2}sin(\varphi_{2}- \varphi_{3})}\]  
\[a_{3}=\frac{l_{2}\omega_{2}^{2}+l_{1}\omega_{1}^{2}cos(\varphi_{1}-\varphi_{2 })-l_{3}\omega_{3}^{2}cos(\varphi_{3}-\varphi_{2})}{l_{3}sin(\varphi_{3}- \varphi_{2})}\]  
In the formula: \(a_{2}\) and \(a_{3}\) are angular acceleration of \(AB\) and \(BC\). The angular acceleration change information of the drive rod can be calculated from Formula (4) (Figure 5):  
Figure 4: Angular velocity of drive rod. After calculating the angular acceleration of the driving rod, the inertia force of the driving rod can be obtained. Since the subsequent calculation in this paper only needs the strength of inertia force and moment, the inertia force and moment of inertia are calculated in scalar. Therefore, the strength of the inertia force and torque of the drive rod can be calculated by Formulas (5) and (6) [25]. \[F^{\prime}=ma_{i} \tag{5}\]  
\[J=mr_{T}^{2} \tag{6}\]  
In the formula: \"\(m\)\" is the weight of the drive rod, \"\(r_{T}\)\" is the rotation radius of the drive rod. From the above analysis results, the drive rod has three times of rapid change of angular velocity and direction in one operation cycle, and the speed is large (Figure 4). In Figure 5, the acceleration of the drive rod is very large and the acceleration time is very short. Therefore, it can be concluded that the impact of the drive rod is very large, so the drive mechanism has high requirements for the rigidity and strength of the drive rod. From Formulas (5) and (6), it can be seen that the mass of the drive rod is positively related to its inertial force and moment of inertia. Therefore, the increase of the weight of the drive rod will inevitably lead to a significant increase in its inertial force and moment of inertia, which will add more additional loads to the drive rod. Therefore, the necessary condition to improve the performance of the drive mechanism is to increase the rigidity and strength of the drive rod and reduce its weight.
# Free Monoterpene Isomer Profiles of _Vitis Vinifera_ L. cv. White Wines
## 2 Materials and Methods
### Classification of the Grape Variety and Wine Style Based on Enantiomer Fractions  
The classification of varietal wines using EF calculation was different than by isomer concentrations (Figure 5A,B). The 86% of variability was explained by two statistically significant discriminant functions, with F1 and F2 contributing 55% and 31%, respectively. Muscat, Torrontes, and Riesling wines had similar EF profiles that are found in the lower positive or negative F1 direction with _S_-(\(-\))-limonene and (_2R_,4S)_-(+)_-cis_-rose oxide EFs as the important variables. Gewurztraminer and Viognier wines were separated from Muscat and Torrontes wines along the F2 axis, with profiles that were slightly different and characterized by (_2R_,5S)_-(\(-\))-_cis_-linalo oxide EF. Chardonnay and Pinot gris wines were clustered in the positive F1 and F2 direction with (_2R_, _4R_)-(\(-\))-_trans_-rose oxide, _S_-(\(-\))-nerol oxide, _S_-(\(-\))-\(\alpha\)-terpineol, and _R_-(\(-\))-linalo enantiomer fraction as important variables (Figure 5A,B). Sauvignon blanc wines located in the positive F1 direction with high positive scores, therefore (_2R_,5R)_-(+)_-trans_-linalo oxide EF could be important variable to separate this varietal wine from others (Figure 5 B).  
Style classification based on EFs for sweet and medium sweet wines were mainly distributed in the F1 function (75% of the variability) with high negative scores (Figure 5C). _S_-(\(-\))-limonene EF were the separating variables (Figure 5D). Dry wines were located in the opposite direction along the  
Figure 5: **Discriminant plot of grape variety and wine style on monoterpene enantiomer fractions**. Panel **A**, varietal wine scores were represented by centroids surrounded by 95% confident regions (the solid circle). Panel **B**, all enantiomer fractions vectors were shown based on varietal wines. Panel **C**, wine styles scores were represented by centroids surrounded by 95% confident regions. Panel **D**, all enantiomer fractions vectors were shown based on wine style.  
positive F1 axis, with most of the EFs as the important variables. Medium dry wines were separated in the middle of F1 axis from other styles by (2\(R\),5S)-(--)_cis_-linaloid oxide EF (Figure 5D).
## 5 Experimental Setup
### Dealing with the Class Imbalance Problem  
A common phenomenon in real-world applications, such as the one at hand, class imbalance problems often deteriorate the performance of traditional classifiers. By studying the current state of the art in the field, we have followed a hybrid approach that performs oversampling of the minority class based on the SMOTE (Synthetic Minority Oversampling TEchnique) [10] algorithm and under-sampling of the majority class using the Tomek links score to identify noisy and borderline examples. SMOTE is based on the idea that each example of the minority class is synthetically re-generated using its k-NN graph instead of a randomized sampling with replacement. The Tomek link [51] method can be viewed as the means for guided under-sampling, where the observations from the majority class are removed.
# Estimation of Thermal Diffusivity for Greenhouse Soil Temperature Simulation
## 3 Results and Discussion  
The results of greenhouse data collection, soil diffusivity determination, and evaluation of the soil temperature prediction are presented in this section. Greenhouse data were collected and separated into cooling and heating seasons. The soil diffusivities were calculated by using one set of the collected data following the five methods described in the soil thermal diffusivity estimation methods section and were then used as input value for the soil temperature prediction model. Then, the performances of the soil temperature prediction model at different depths using the calculated thermal diffusivities were evaluated against another set of collected greenhouse data.