esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 MIMO Equalization for Mode-Dependent Loss  
The mode-dependent loss (MDL) arises from imperfections in optical components such as optical amplifiers, couplers, and multiplexers, as well as from non-unitary cross-talk in the fiber and at fiber splices and connectors, where the modes experience differential losses or gains when propagating through the optical link, which leads to signal-to-noise ratio disparities along with a loss of orthogonality between modes. The MDL is a capacity-limiting effect reducing the multiplexing benefit of SDM communication systems, which cannot be completely removed at the receiver by simply inserting mode scramblers. The probability distribution functions (PDF) of the accumulated MDL for the different coupling levels, with and without mode scrambling, are shown in Figure 20 as an example [41].  
Furthermore, as there are up to three spatial modes being transmitted in our modeling, Figure 21 illustrates the equalization performance of the BER curves at different OSNRs by means of the STBC-RLS algorithm for a PDM-QPSK signal, from which we can see that the BERs of all spatial modes fall after MIMO equalization.  
Figure 19: The summary of BER vs. OSNR for various modulation formats with or without using STBC-RLS compensation [29].  
Figure 18: The BER vs. OSNR comparison for PDM-64 QAM signal in LP\({}_{11\text{a}}\) mode with or without using the STBC-RLS scheme [31].  
The LP\({}_{01}\) mode is able to realize a lower required OSNR for a \(10^{-3}\) BER than the LP\({}_{11}\) mode groups, predominantly because its more centralized mode pattern makes the LP\({}_{01}\) mode suffer a lesser level of impact from mode coupling and cross-talk. Al though the LP\({}_{11a}\) and LP\({}_{11b}\) modes share an identical mode pattern as a pair of degenerate modes, their BER performance appears a bit dissimilar triggered by spatial mismatching and rotation. Since we found that the transmitted signals on both \(x\)- and \(y\)-polarizations provide a comparable performance, here, in these plots, the LP\({}_{11b}\) mode is adopted as the average of two orthogonal polarizations for an easier analysis.  
Additionally, the BER versus OSNR relationships based on the STBC-RLS algorithm for PDM-16 QAM and PDM-64 QAM signal compensation for LP\({}_{01}\), LP\({}_{11a}\), and LP\({}_{11b}\) modes are presented in Figures 22 and 23, respectively. To reach a BER below \(10^{-3}\), the LP\({}_{01}\) mode requires a 16.5-dB OSNR, while the LP\({}_{11b}\) and LP\({}_{11a}\) modes need about 17.7-dB and 18.1-dB OSNRs for the PDM-16 QAM signal, while, for the PDM-64 QAM signal, the LP\({}_{01}\) mode requires a 21.3-dB OSNR, while the LP\({}_{11b}\) and LP\({}_{11a}\) modes need about 22.8-dB and 23.6-dB OSNRs, respectively.  
Figure 21: The BER vs. OSNR for three different modes for a PDM-QPSK signal using the STBC-RLS scheme [27].  
Figure 20: Mode-dependent loss (MDL) probability distribution functions under different coupling levels [41].
# Upscaling Customer Access Network Using Spectrum Conversion-Slicing-Duplication Technique
## 4 Conclusions  
This research explains how to increase the scalability of the existing network without installing new cable while ensuring the signals that reaches the end users are good and meet all the requirements that have been set (for Q factor and BER). There are a few ways of network scaling, such as XPM and XGM, or a hybrid technique like the OCDMA hybrid system. But, in this study, we choose the spectrum slicing and duplication technique as it is expandable. The simulation was conducted using Optisystem 18.0, where there are a few parameters that are observed, such as the eye diagram, Q factor and BER. Using this technique, the separation ratio can be controlled through the number of output ports of the components added to the network. The analysis was focused on this spectrum slicing and duplication technique and the results obtained. Through our study using this technique and network design, the total user access achieved was 196,608 users, as calculated through the output port of AWG \(\times\) splitter \(\times\) WDM Demux \(\times\) splitter (6 \(\times\) 32 \(\times\) 32 \(\times\) 32). The Q factor for 196,608 users is 6.43617 and the BER is 4.52 \(\times\) 10\({}^{-11}\), and it meets the standard for an optical communication system. This is the first reported simulation of the design to date.  
Conceptualization, N.F.M.A.; investigation, J.A.R.; supervision, M.S.A.-R.; review, I.H.K.; and editing, I-S.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science, Technology and Innovation (MOSTI), Malaysia, through the National Science Fund (NSF; 01-01-01-SF0493).  
Not applicable.  
This research was conducted in the Broadband, Network & Security Laboratory, Universiti Ke-bangsaan Malaysia (UKM). The presented work was conducted in the Computer and Network Security Laboratory, Universiti Kebangsaan Malaysia (UKM), funded by the Ministry of Science, Technology and Innovation (MOSTI), Malaysia, through the National Science Fund (NSF; 01-01-01-SF0493).  
The authors declare no conflict of interest.  
Figure 10: Max. Q factor (average) versus number of users.
## 5 The Density of the Longitudinal Orbital Angular Momentum at the Focus  
To analyze the effect of spin-orbit conversion at the focus, we find the density of the longitudinal component of the orbital angular momentum (OAM) of the field (1). To do this, we use the definition of the OAM vector [37]:  
\[\mathbf{L}=\frac{1}{8\pi\omega}\sum\limits_{p=x,y,z}\text{Im}\left(E_{p}^{*}( \mathbf{r}\times\
abla)E_{p}\right). \tag{14}\]  
We consider only the longitudinal projection of the OAM vector in order to compare it with the longitudinal projection of the SAM. Substituting (3) into (14), we obtain:\[\begin{array}{l}L_{z,\alpha}=\text{Im}\Big{(}E_{x}^{*}\frac{\partial}{\partial \varphi}E_{x}+E_{y}^{*}\frac{\partial}{\partial\varphi}E_{y}+E_{z}^{*}\frac{ \partial}{\partial\varphi}E_{z}\Big{)}=\\ \quad=\frac{1}{1+\alpha^{2}}\Big{[}n(1+\alpha^{2})I_{0,n}^{2}+\alpha^{2}(n-2 )I_{2,n-2}^{2}+(n+2)I_{2,n+2}^{2}+2(n+1)I_{1,n+1}^{2}\\ \quad+2\alpha^{2}(n-1)I_{1,n-1}^{2}+2\alpha\cos(2\varphi)((n-1)I_{0,n}I_{2,n-2 }+(n+1)I_{0,n}I_{2,n+2}-2 nI_{1,n-1}I_{1,n+1})\Big{]}.\end{array} \tag{15}\]  
From (15) at \(\alpha=1\), we obtain the longitudinal projection of the OAM at the focus of an optical vortex with linear polarization, previously derived in [23]:  
\[\begin{array}{l}L_{z,\alpha=1}=\Big{[}nI_{0,n}^{2}+\frac{1}{2}(n-2)I_{2,n-2}^ {2}+\frac{1}{2}(n+2)I_{2,n+2}^{2}+(n+1)I_{1,n+1}^{2}\\ \quad+(n-1)I_{1,n-1}^{2}+\cos(2\varphi)((n-1)I_{0,n}I_{2,n-2}+(n+1)I_{0,n}I_{2,n+2}-2 nI_{1,n-1}I_{1,n+1})\Big{]}.\end{array} \tag{16}\]  
And if we suppose the parameter \(\alpha=0\) in (15), then we obtain the longitudinal projection of the OAM at the focus of the optical vortex with right-handed circular polarization, which was previously derived in [23]:  
\[\begin{array}{l}L_{z,\alpha=0}=nI_{0,n}^{2}+(n+2)I_{2,n+2}^{2}+2(n+1)I_{1,n+ 1}^{2}.\end{array} \tag{17}\]  
Equation (15) by varying the parameter \(\alpha\) makes it possible to continuously change the OAM of the beam at the focus from (17) to (16). In the initial field plane (1), the density of the longitudinal component of the OAM can be found from (1):  
\[\begin{array}{l}L_{z,0,\alpha}=nA^{2}(\theta).\end{array} \tag{18}\]
## 5 Emerging Trends in FSO Communication
### Incorporation of Emerging Technologies
#### 5.1.3 Hybrid FSO/THz System  
THz frequencies (0.1-10 THz) offer even higher data rates than mmWave, potentially supporting tens to hundreds of Gbps. The THz spectrum provides ample bandwidth, which is crucial for future high-capacity communication systems. FSO can complement THz communication by providing high-capacity links in scenarios where THz propagation is limited due to atmospheric absorption and scattering. Integrating FSO with THz technologies can leverage advanced modulation techniques to maximize data throughput and SE. AI-driven adaptive systems can dynamically allocate FSO and THz resources based on real-time performance metrics, optimizing the communication link's quality and capacity. So, the scheme is a key enabler for wireless terabit networks, which are essential to enable future data-intensive applications, including holographic communications and immersive virtual reality. Hybrid FSO/THz systems can enhance space communication links, providing high-speed data transfer between satellites and ground stations or inter-satellite links [2].  
In general, the integration of FSO communication with RF, mmWave, and THz technologies represents a notable trend in the evolution of wireless communication systems. These hybrid systems leverage the strengths of each technology to overcome their individual limitations, resulting in enhanced performance, reliability, and capacity. As research and development continue, these integrated systems are expected to be instrumental in the deployment of next-generation networks, supporting a broad spectrum of applications from urban connectivity to space communications. This convergence of technologies represents a significant advancement in realizing the vision of ubiquitous, high-speed wireless communication [2; 365].
## 2 Background
### Motivation and Contributions  
Many of the MRAM PUF proposals discussed in Section 2.4 have many shortcomings. First, most designs have CRPs that scale linearly with size, making them weak PUFs. Furthermore, many of these designs have only been simulated in software or rely on wafer-level reads, limiting their practical viability. To the best of our knowledge, our design is the first of its type to be implemented on a low-power client device to emulate real use cases.  
Our work builds upon the work in [38], which used MRAM devices to generate random sequences. This work differs from other MRAM PUF proposals, providing a novel MRAM TAPUF design with CRPs that scale quadratically with size, categorizing it as a strong PUF. The main contributions to this approach are as follows:  
* A novel design is introduced that uses two different MRAM devices, increasing the number of CRPs;
* The PUF is implemented on a low-power device, better simulating actual use cases. This approach allowed us to evaluate the PUF's performance under realistic conditions and assess its suitability for practical applications.
# A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
###### Abstract  
Machine learning algorithms are increasingly used in various remote sensing applications due to their ability to identify nonlinear correlations. Ensemble algorithms have been included in many practical applications to improve prediction accuracy. We provide an overview of three widely used ensemble techniques: bagging, boosting, and stacking. We first identify the underlying principles of the algorithms and present an analysis of current literature. We summarize some typical applications of ensemble algorithms, which include predicting crop yield, estimating forest structure parameters, mapping natural hazards, and spatial downscaling of climate parameters and land surface temperature. Finally, we suggest future directions for using ensemble algorithms in practical applications.  
bagging; boosting; stacking; remote sensing applications; ensemble
## 3 Results and Discussion
### Post-Disgorging at 15 Weeks  
At 15 weeks there was little difference between the ZD and BS for any compounds studied likely due to sugar hydrolysis (Table 5). It is interesting to note that by 15 weeks the concentrations of VOCs in ZD and BS were similar to the Brut wine prior to sugar addition.  
Ethyl hexanoate, ethyl octanoate and ethyl octanoate was found to be at the highest concentration in B and lowest in PN. Although there was not a statistically significant difference between wines for ethyl butanoate, Brandy has high ethyl ester concentrations and ethyl esters decline during sparkling wine lees aging. The Pinot noir wine used in the _dosage_ was from the 2009 vintage so had spent 5 years aging on yeast lees which may account for the different amounts of ethyl hexanoate in the final sparkling wines at this stage. In ZD, BS and PN, all _dosage_ solutions made from sparkling wines were found to have statistically similar concentrations of ethyl hexanoate.  
Branched acid derivatives were at their highest in PN and all 3 VOCs were statistically significant (\(p<0.05\)). IW had the were lowest ethyl isobutyrate and the lowest ethyl-2-methylbutyrate was found in OC. UC had the lowest 1-hexanol and B was found to have the highest concentration just as it was at 5 and 10 weeks post-disgorging. Additionally, B had the lowest concentration of 2-phenylethanol while OC had the highest in 2-phenylethanol. There were greater changes in VOCs between 5 and 10 weeks than between 10 and 15 weeks post-disgorging.  
The PCA (Figure 2) at 15 weeks' post-disgorging shows that the 3 treatment wines that had _dosage_ solutions made from sparkling wines (second fermentation in bottle) are placed in the positive sector. The BS and ZD wines are extremely similar due to their close proximity. Likewise, OC and UC are also close together but near to IW, and these 3 wines used still wines in the _dosage_ solutions. The ethyl esters that dominated B wine are responsible for its position on the peripheral of the PCA. These results suggest that by 15 weeks post-disgorging, the production methods used to produce the wines in the _dosage_ treatments influenced the differences in the VOC results.  
Figure 2: Principal Components Analysis (PCA) of the volatile aroma compounds (VOCs) in wines analysed at 15 weeks post-disgorging. NV sparkling wine without sugar addition = zero _dosage_ (ZD), NV sparkling wine + sugar (BS), uncoaked still Chardonnay wine + sugar (UC), Pinot noir 2009 sparkling wine + sugar (PN), Brandy + sugar (B), kewine (IW) + sugar and oaked still Chardonnay wine + sugar (OC).
## 2 Materials and Methods
### Data Sources  
In the survey, the participants were questioned about the following:  
* Gender: man/woman/others.
* Age, grouped in generations: Generation Z (born 1997-2012); Millennials (born 1981-1996); Generation X (born 1965-1980); Boomers (born 1946-1964) [49].
* Height, measured in feet and inches, and weight, measured in pounds. BMI was determined: BMI = 703 \(\times\) weight (pounds)/[height (inches)]2. BMI is considered an index with very high specificity (97%) to detect obesity [50]. Self-reported weight and height online have shown to be a valid method, with moderate to good agreement between measured anthropometric data and those self-reported [51].  
Do you smoke? Yes/No/Occasionally. It has been stated that traditional epidemiological risk factors can be collected with equivalent or superior reliability online compared with conventional methods [52]. -Highest academic degree attained, classified by a doctorate degree; master's degree; bachelor's degree; associate degree; trade/technical/vocational training; some college credit, no degree; high school graduate or the equivalent. -How long the participants have been regularly on Instagram, classified as less than 1 year, between 1-2.5 years, and more than 2.5 years. -How many hours per week on Instagram looking over nutrition or physical activity. -The physical activity executed by the participants was registered with the short form \"last 7 days\" of the International Physical Activity Questionnaire (IPAQ) [53]. It was self-administered, and vigorous physical activity (minutes per week), moderate physical activity (minutes per week), time spent walking (minutes per week), and time spent sitting (hours per day) were recorded. This questionnaire is considered reliable and valid for noting physical activity information [54].  
In order to test the influence of the usage of any meal tracker platform to record food intake regarding BMIs, the participants answered about the usage over the last month of any meal tracker platforms to record their food intake. The answer was classified as: No/Yes.
## 3 Experimental Study of Squeal Noise
### Characterisation of Squeal Frequencies
The maximum level is close to that observed for the first frequency \(f_{1}\) and the directivity is characterized by a level maximum which remains at half \(x<0\). The squeal noise related to \(f_{4}\) is characterized by a high level around 100 dB. Contrary to the results previously discussed for the first three frequencies, the evolution of the acoustic radiation per revolution is relatively stable for \(f_{4}\) and the directivity reveals a high level located in the centre of the microphone antenna and mainly oriented along a diagonal at 45 degrees to the x-axis. Finally, the highest acoustic level is obtained for \(f_{5}\). Although the acoustic level seems fairly homogeneous and located at the center of the antenna over the two revolutions of the disc, the directivity seems to be linked to the periodicity of a revolution of the disc and mainly oriented along a diagonal at 45 degrees to the x-axis. In conclusion, this analysis illustrates that evolutions per revolution of the radiated acoustic fields for the contribution of the five main frequencies during a braking test can be complex and very different for each frequency. They are further characterized by three main criteria (cf. Table 1): the maximum sound level and its localization, the directivity of the acoustic radiation and the periodic or non-periodic evolution of the acoustic pattern and level per revolution of the disc. It is worth noting that in the proposed study, the analysis is carried out by examining the directivity of the squeal noise and its evolution over time relative to the fixed brake reference frame. There are two reasons for this choice: the first one is that some elements of the disc (caliper, pads) do not rotate and the friction zone that generates the brake squeal and the associated radiated noise is therefore immobile in the fixed frame of reference. The second reason relates to the experimental set-up itself, since the system for measuring radiated noise is non-rotating. Instead of examining the time variation of squeal noise in the fixed brake reference frame it would also be possible to interpret the time variation that the pictures show as a sound emitted at the frequency of the brake rotation. However, in the latter case, it would not be possible to easily dissociate the acoustic filed emitted which are non-rotating and rotating over time in the fixed frame of the brake system, knowing that such results then make it possible to offer first analyses on the fact that the squeal noise comes from fixed parts, rotating parts or a combination of both.
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 4 Materials and Methods for Offline Fire Detection
### Data Collection, Integration, and Preparation  
The effectiveness of deep learning models, such as convolutional neural network (CNN) models for forest fire detection, relies on the quality and size of the datasets utilized. A high-quality dataset allows for deep learning models to capture a wider range of characteristics and achieve enhanced generalization capabilities. To accomplish this, a dataset surpassing 12,000 images was gathered from various publicly available fire datasets, such as BowFire [12], FiSmo [13], Flame [14], and newly acquired images. The collected dataset comprised aerial images of fires taken by UAVs in diverse scenarios and with different equipment configurations. Some representative samples from this dataset are shown in Figure 2. Before utilization, a data preparation process was performed during which the images devoid of fires and those where fires were not discernible were discarded. After the data collection and integration, three main tasks were performed to prepare the data.  
Figure 2: Representative samples from the compiled dataset with original labels.  
* Data augmentation: In order to increase the size of the dataset, amplify the dataset's diversity, and subject the machine learning model to an array of visual modifications, data augmentation was performed. This was conducted by applying geometric transformations such as scaling, rotations, and various affine adjustments to the images in the dataset. This approach enhanced the probability of the model in identifying objects in diverse configurations and contours. We ultimately assembled a dataset containing 12,000 images. Upon assembling the dataset, the images were uniformly resized to dimensions of 416 \(\times\) 416.
* Data labeling: The crucial labeling task was carried out manually, facilitated by the MATLAB R2021b Image Labeler app. This task was time and effort consuming as each of the images in the dataset underwent meticulous labeling, classifying the regions in them into one of the three categories: fire, non-fire, or smoke. Completing this labeling procedure entailed creating ground truth data that included details about the image filenames and their respective bounding box coordinates.
* Data splitting: The dataset containing all the incorporated features was primed for integration into the machine learning algorithms. Yet, prior to embarking on the algorithmic application, it was recommended to undertake data partitioning. A hold-out sampling technique was used for this purpose where 80% of the images were for training and 20% for testing.
# Design and Performance Evaluation of eLoran Monitoring System
## 5 Integrity Monitoring Analysis
### Thresholds for Integrity Judgments  
The actual value and the predicted value should be very close to each other when there is no fault, and when there is a fault in the time service system or a drastic change in the propagation path, the difference between the two values will become larger than the normal error range, which can be used as the basis for the judgment of fault detection.  
Before the integrity judgment, a threshold value needs to be determined as the upper limit of error to measure the system without fault. The threshold value is generally obtained empirically, and in practice, it is generally taken as five times the actual value of the STD value.
## 2 Material and Methods
### Data  
#### 2.2.1 Vegetation and Chlorophyll Fluorescence Data  
We acquired annual vegetation index data, including from the Normalized Difference Vegetation Index (NDVI) and MODIS-Enhanced Vegetation Index (EVI), for China from 2000 to 2019. NDVI data were sourced from the Resource Environment Science Data Registration and Publication System, and EVI data were obtained from the GEE cloud platform. Both datasets were preprocessed and classified based on their respective index values. Additionally, Solar-Induced Chlorophyll Fluorescence (SIF) data were provided by the National Tibetan Plateau Science Data Center. We selected 4-day resolution SIF data from 2000 to 2019 and synthesized the maximum values of the annual SIF datasets.  
Figure 1: Location of the study area and its basic geomorphology. BJ: Beijing; TI: Tianjing; HE: HeiBei; SX: ShanXi; NM: NeiMengGu; LN: LiaoNing; JL: JiLin; HL: HeiLongliang; SH: ShangHai; JS: JiangSu; ZJ: Zhejiang; AH: AnHui; FJ: Fujian; JX: JiangXi; SD: ShanDong; HA: HeiNan; HB: HuBei; HN: HuNan; GD: GuangDong; GX: GuangXi; HI: HaiNan; CQ: ChongQing; SC: SiChuan; GZ: GuiZhou; YN: YunNan; XZ: XiZang; SN: ShanXi; GS: GanSu; QH: QingHai; NC: NingXia; XJ: Xinjiang; TW: TaiWan.