esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Multicast Group Formation
The proposed incremental multicast grouping (IMG) scheme provides the opportunity to create sessions with many tiles of different quality, therefore, the number of sessions is equal to the number of user groups. However, at the same time, as users can request several multicast sessions, so the number of multicast sessions and multicast groups can be different. The work [30] presents an optimization algorithm for combinatorial clustering, in which multicast groups are divided into smaller subgroups, and subcarriers are allocated to each subgroup. The authors propose a new multicast grouping scheme based on generalized gradient approximation (GGA) and a genetic grouping algorithm to maximize the aggregate data rate. GGA is an evolutionary clustering technique in which the corresponding structures of the clustering problem are encoded as genes on the chromosomes. An initial group of chromosomes is first created by matching the multicast clustering solution to the chromosome structure. And then, according to the fitness values, all chromosomes are ranked from best to worst, and they are assigned the probability of selection based on the normalized geometric distribution.
# Future Wireless Networking Experiments Escaping Simulations
## 3 Steps to Set Up Experiments
### US Testbeds--POWDER and COSMOS  
In this paper, we are mostly focusing on IoT experiments with/without using SDN. For the POWDER testbed, such experiments have not been extensively performed to date, to the best of our knowledge, as POWDER is mostly suited for cellular and MIMO types of networks [19]. For COSMOS, there have been some wireless IoT and wireless edge cloud experiments, reported in [7; 26]. The authors of [7; 26] used bird's eye view cameras which are a part of the COSMOS testbed to measure the social distancing between pedestrians during the COVID-19 period. The camera devices, which form the edge layer of the COSMOS IoT infrastructure, gathered these video data and advanced computer vision and data-processing algorithms were run in the cloud layer to draw important inferences about social distancing.  
In [27], an experimental framework is developed using the COSMOS testbed to study dynamic spectrum access. This work is again more suited for radio networks and does not exactly match the type of experiments we plan to do which are more ad hoc in nature. An SDN-controlled dynamic provisioning of light paths is reported in [28] for an optical x-haul (front/back/cross-haul) network. They used mininet to emulate the software part and the COSMOS infrastructure for the hardware part. The work in [28] is somewhat relevant to our project as we also plan to run SDN as a means to control our inter-testbed experiments. Again, in [29], the authors use SDN in a cloud-RAN to control the handover of mobile equipment from one Remote Radio Head (RRH) to another. When mobile equipment is at the verge of a handover and receiving signals from two RRHs, the SDN controller performs dynamical optical switching and wavelength re-allocation between the RRHs. The optical wavelengths are used as a front-haul and the entire setting up is performed over the COSMOS testbed. The work in [29] is also highly relevant to our work in terms of dynamic switching and resource allocations using SDN.
# Robust Estimators in Geodetic Networks Based on a New Metaheuristic: Independent Vortices Search
## 2 Classical and Robust Approaches
### Iteratively Reweighted Least Squares  
The Iteratively Reweighted Least Squares (IRLS) method represents a class of REs that works on the reweighting of \(\mathbf{W}\) to estimate the parameters. This type of estimator has its own iterative methods to obtain a robust solution, thus not needing any external numerical method (such as MH).  
The iterative method chosen was proposed in [37] and has presented good results [20; 37; 41].  
In [37], the author presented two weight adjustment strategies. Strategy I held better results and, therefore, is adopted in this study:  
\[w_{i}=\begin{cases}1,&\text{if }\left|\tau_{i}\right|\leq C,\\ C/\left|\tau_{i}\right|,&\text{if }\left|\tau_{i}\right|>C,\end{cases} \tag{15}\]  
where \(\tau_{i}\) is given by  
\[\tau_{i}=\frac{\mathbf{c}_{i}^{T}\mathbf{W}\mathbf{v}}{\hat{\sigma}\sqrt{\mathbf{c}_{i}^{T} \mathbf{W}\mathbf{R}\mathbf{c}_{i}}}, \tag{16}\]  
with \(\mathbf{c}_{i}\) being a unit vector, only filled with a 1 in the \(i\)th position, and with \(\hat{\sigma}\) given by Equation (11). The quantity \(\mathbf{R}\) is the redundancy matrix [42]  
\[\mathbf{R}=\mathbf{I}-\mathbf{A}\left(\mathbf{A}^{T}\mathbf{W}\mathbf{A}\right)^{-1}\mathbf{A}^{T}\mathbf{W}, \tag{17}\]  
where \(\mathbf{I}\) is an identity matrix. Strategy I is, then, computed as follows [37]:  
1. Initialize the counter \(i=0\) and calculate the initial solution of the parameters \(\mathbf{\hat{x}}^{(0)}\) using Equation (3).
2. Increment \(i=i+1\) and implement the weight adjustment strategy I using Equation (15).
3. Construct the equivalent weight matrix \(\mathbf{W}_{R}\) using Equation (12) by applying the calculated values to \(w_{i}\).
4. Stop the iteration if the difference \(\left\|\mathbf{\hat{x}}^{(i)}-\mathbf{\hat{x}}^{(i-1)}\right\|\) is less than a threshold value, or if a maximum number of iterations has been reached. Otherwise, return to step 2 and start a new iteration.
## 1 Introduction  
Fourier transform infrared (FTIR) spectroscopy is a kind of microscope to view sample images using the infrared region light source and was developed in the late 1970s [1]. Unlike other optical microscopes with absorbing glass optics, it uses reflex optics to cover the entire infrared spectral region [2]. FTIR spectroscopy that is made of both a FTIR spectrometer and a microscope provides optical visualization and infrared spectral data. FTIR spectroscopy has been applied in various areas, including polymer science, forensic science, semiconductors, and (bio) chemical analyses.  
Infrared spectral data, which are mostly used in chemical and physical sciences, provide information on molecular vibrations absorbing particular energy when infrared light is irradiated on a molecular bond. They were originally employed to obtain information on molecule structure, but are now used to describe any chemical changes in chemical processes. Since the infrared spectra of materials are based on molecular structures, they may be used in both qualitative and quantitative assessments to inspect raw materials in the pharmaceutical and chemical industries [3]. In the biomedical area, FTIR spectroscopy is applied to identify bacterial and fungal infection [4] and to find collagen and proteoglycan in bovine cartilage [5] as a tool for tissue diagnosis [6].  
Obesity is a state in which adipocytes in the body are abnormally accumulated. Obesity generates various social, economic, and medical issues and is possibly related to the development of various human diseases such as cardiovascular disease, type II diabetes,infertility, osteoarthritisritis, and certain cancers [7; 8]. Obesity involves both white and brown adipocytes. White adipocytes have a unilocular lipid droplet shape, have low mitochondrial density, lack the thermogenic marker mitochondrial proton pump uncoupling protein 1 (UCP1), and have function as energy storage and endocrine regulation. Brown adipocytes contain multivocal lipid droplets, have a high mitochondrial density and UCP1, and their role is thermogenesis and endocrine regulation. Despite the severity of obesity, numerous fundamental studies have been conducted, but few of them used FTIR spectroscopy [9; 10; 11; 12; 13]. Thus, this study was conducted to characterize and distinguish brown and white adipocytes by using FTIR spectroscopy.
# Joint Iterative Decoding Design of Cooperative Downlink SCMA Systems
## 6 Simulation Results  
In this section, numerical simulation results were conducted to evaluate the BER performance of the proposed cooperative downlink SCMA system under the additive white Gaussian noise (AWGN) and Rayleigh channels. The SCMA system configurations in Equations (1) and (2) were considered, where the loading factors are \(\lambda=150\%\) and \(\lambda=200\%\), respectively. The channel gain \(\mathbf{h}\) was modeled as a independently and identically distributed circularly symmetric complex-valued Gaussian random variable with zero mean and covariance \(\sigma^{2}\mathbf{I}\). The codebook in [30] was adopted in the base station and relay.
## 2 Data Acquisition and System Design
### Data Enhancement  
To strengthen the richness of training samples' richness and increase model robustness, three data enhancement methods are used in this paper, namely Mixup, Cutout, and Cutmix. Mixup randomly selects two images from the training samples and mixes them in proportion but does not affect the labeling results. Cutout randomly cuts out a particular area in the chosen image and fills in a specific pixel value, which does not affect the labeled result. Cutmix randomly cuts the samples in the training set, but does not fill in fixed pixel values and uses another randomly selected sample area to fill in the image quality. In the citrus dataset, there are severe problems of mutual occlusion and leaf occlusion between citrus. Through data enhancement, the model is more inclined to recognize objects from local images, enhance the model's positioning ability, and improve the robustness.
## 3 The Hierarchical Steel Surface Detection Methodology
### Defect Image Classification
In the top-down pathway, a feature pyramid network (FPN) [48] generates the multi-scale feature maps from semantic-rich layers in a top-down hierarchy. The hierarchy of the feature maps generated by the ResNet50 is laterally connected with the feature maps of FPN on the same spatial size with strong semantics. The levels in FPN mainly focus on providing the hard features that are difficult to detect. The output feature maps of FPN are used in the prediction of objects and their classes. The block diagram representation of FPN is shown in Figure 2. The classification subnet section consists of a fully convolutional network connected to \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Feature Extractor** & **VGG16** & **VGG19** & **ResNet50** \\ \hline Accuracy & 98.6 & 97.2 & 92.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between the three feature extractor models. each level of FPN. The feature maps generated at each pyramid level serve as input to the classification subnet. These feature maps go through four sets of convolutional layers of size 3 \(\times\) 3 with 256 filters each, followed by rectified linear unit (ReLU) activation, again with a convolutional layer of size 3 \(\times\) 3 with C \(\times\) A filters, where C is the number of classes and A is the number of anchor box, followed by sigmoid function for classification of objects. The regression subnet is attached to FPN and aligned parallel to the classification subnet. The subnet has identical convolutional layers to the classification subnet, except for the last convolutional layer with 4 \(\times\) A filters. This subnet detects the bounding box of an object present in an image. The final image output is predicted with the bounding box and respective class. The reason to choose this model is that it performs better than the one-stage detectors. Approximately, there is a gap of 6 points in the average precision (AP) of RetinaNet with the nearest competitor model, deconvolutional single shot detector (DSSD). The two-stage detectors are well-known for good accuracy, Faster R-CNN is the top-performing model among them; regardless of that, RetinaNet surpasses the model with a gap of 2.3 points. This model uses focal loss as its loss function, which is very good for class imbalance.
# Time-Series Forecasting of Seasonal Data Using Machine Learning Methods
## 3 Development of the Models
### Est  
The ETS models are a family of time series models with an underlying state space model consisting of a level component, a trend component (T), a seasonal component (S), and an error term (E). Point forecasts can be obtained from the models by iterating the equations for \(t=T+1,\ldots\), \(T+h\) and setting all \(=0\) for \(t>T\). For example, for the model, ETS (M, A, N) \(=(1+\varepsilon_{T+1})\). Therefore \(\hat{y}_{T+1|T}=\ell_{T}+b_{T}\). Similarly,  
\[y_{T+2}=(\ell_{T+1}+b_{T+1})(1+\varepsilon_{T+2})=[(\ell_{T}+b_{T})(1+ \alpha\varepsilon_{T+1})+b_{T}+\beta(\ell_{T}+b_{T})\varepsilon_{T+1}](1+ \varepsilon_{T+2}). \tag{5}\]  
Therefore, \(\hat{y}_{T+2|T}=\ell_{T}+2b_{T}\), and so on (see Equation (5)). These forecasts are identical to the forecasts from [PERSON]'s linear method and also to those from model ETS (A, A, N). Thus, the point forecasts obtained from the method and from the two models that underlie the method are identical (assuming that the same parameter values are used). The ETS point forecasts constructed in this way are equal to the means of the forecast distributions, except for the models with multiplicative seasonality [42]. As with the Holt-Winters model, the ETS model can be obtained using the statmodels library. The _ETSModel_ function takes as input parameters an array of data with time series counts, a flag defining seasonal component type add/mul, and seasonal period value (for the example under consideration seasonal_periods = 24). The _get_prediction_ method of the model object, which receives as input parameters two timestamp values corresponding to the start and end time for the desired prediction horizon, is used to obtain the prediction. When using pandas, these values can be found as start and end values of the pandas dataframe object index, where the test selection of the time series is stored. The result for the ETS model is shown in Figure A3 (see Appendix A).
## 6 Development Trends and Prospects
### Integration  
Integration refers to the fusion of multi-source data and multimodal detection. Traditional single detection methods may be limited in certain operating conditions and have difficulty providing comprehensive fault information. With the advancement of data fusion technology, more and more research focuses on combining multi-source data such as vibration signals, thermal imaging, electromagnetic signals, and acoustic signals for multimodal detection. For example, by combining vibration data with thermal imaging images, researchers can more comprehensively identify fault features. This trend enhances the robustness of detection and is suitable for complex industrial scenarios.  
To understand how multimodal technology is transforming bearing fault detection, the development of bearing fault detection can be divided into three stages.  
The first stage of the single-modal method may fail under dynamic conditions due to environmental changes.  
In the second stage, with the advancement of sensor technology and signal processing techniques, the limitations of a single sensor can be overcome by integrating data from multiple sensors, leveraging the advantages of various modes, or ensuring the reliability of diagnostics through cross-modal redundant data. For instance, by combining infrared imaging and acoustic emission signals, it is possible to stably monitor the temperature rise in bearings and structural microcracks in high-speed operating equipment under harsh conditions.  
In the third stage, with the gradual maturity of multimodal data processing methods based on artificial intelligence and machine learning, deep correlations in multi-source data can be efficiently explored. Convolutional neural networks, for example, are used to integrate vibration, image, and thermal signals to form an end-to-end diagnostic framework.  
Recently, [PERSON] and others proposed a method for constructing a multimodal knowledge graph based on multimodal data, designing a rolling bearing fault diagnosis method that utilizes a multimodal knowledge graph completion model based on relational cascade graph attention networks to capture the relationships between various faults. The multimodal data include time series vibration signals, spectra, and dataset description texts. Experimental results on the multimodal knowledge graph constructed from seven bearing datasets demonstrate the robustness of the proposed method [102].
## 5 Experimental Results
### Experimental Setup  
The experiments were conducted on a power inverter module (PIM) with an apparent power of 150 kVA. The specifications of the PIM are available in Table A1 of Appendix A. DC power supplies were employed to ensure power for the PIM system basis chip (SBC) and the PIM DC bus. For the sensorless control algorithm's implementation and machine control, a microcontroller from the NXP Semiconductors, specifically MPC5775E, was used. The MPC5775E microcontrollers target automotive and industrial battery management and inverter applications. The communication between the microcontroller MPC5775E and PC was secured through the CAN interface. The application code was created in S32 Design Studio for Power Architecture. The USB Multilink Universal was utilized for debugging the application code from the PC to the target processor. FreeMASTER Run-Time Debugging tool observes the control variables and allows application users to control the machine current, speed, etc. A traction PMSM developed for golf cart applications with a rated power of 1.41 kW was used to determine experimental results. The parameters of the PMSM are provided in Table A2 of Appendix A. A DC dynamometer was used to provide load conditions for experimental PMSM. A torque sensor with a measuring range \(\pm\)20 Nm was placed between the PMSM machine and DC dynamometer to provide accurate information about the actual motor torque. Photos of the experimental setup are shown in Figures 19 and 20.
## 3 Results and Discussion
### Zeta Potential Analysis  
Zeta potential (ZP) values reveal information regarding the surface charge and stability of biosynthesized AuNPs. As can be seen from Figure 6, the average ZP value of \(-\)17.4 mV indicated that the capping molecules present on the surface of AuNPs are mainly comprised of negatively charged groups and also responsible for moderate stability of the nanoparticles. The sample did not precipitate for a period of one month, indicating that the AuNPs are stable for a reasonable period of time. Many reports have proposed that surface-active molecules can stabilize the nanoparticles and a reaction of the metal ions is possibly facilitated by reducing sugars and/or terpenoids.  
Figure 5: **(a)** High-resolution transmission electron microscopy (HRTEM) micrograph of synthesized AuNPs; **(b)** selected area of electron diffraction pattern (SAED) of the synthesized AuNPs showing the rings.  
Figure 6: Stability of AuNPs at \(-\)17.4 mV in zeta potential analysis.
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 5 Targeted Safety Interventions
### ATMSs: Advanced Traffic Management Systems  
[PERSON] et al. [96] divided modern monitoring and management into four phases--data gathering, transmission, analysis, and application--surveying sensor technologies, 4-6 G and LPWAN communications, and AI-driven analytics for congestion and safety.  
[PERSON] et al. [97] cataloged challenges for traffic management systems, including heterogeneous data, real-time hazard representation, route-choice side effects, and security/privacy in vehicular ad hoc networks.  
[PERSON] et al. [98] presented a deep learning traffic-surveillance suite (Mask R-CNN, YOLO, and Faster R-CNN) that detects queues with 90.5% accuracy and stationary vehicles with an F1 of 0.83, outperforming manual methods.  
[PERSON] et al. [99] prototyped a V2I-based fuzzy-logic controller that dynamically manages headways and speed, prioritizing safety in complex urban layouts; IEEE 802.11p field tests confirm feasibility.  
Advanced traffic management system research demonstrates the progression from conceptual frameworks to practical implementation, with significant performance achievements but persistent challenges. [PERSON] et al.'s deep learning systems achieve impressive detection accuracy (90.5% for queues), while [PERSON] et al.'s V2I controllers demonstrate real-world feasibility through field testing. However, the challenges cataloged by [PERSON] et al.--heterogeneous data integration, real-time processing, and security concerns--highlight the gap between laboratory performance and system-wide deployment. The four-phase framework proposed by [PERSON] et al. provides structure for understanding system complexity, but the practical challenges suggest that each phase presents implementation barriers that may limit overall system effectiveness. The contrast between high-performance individual components and systemic integration challenges indicates that ATMS success depends as much on architectural design and standardization as on component-level performance.
## 3 Manufacturing Techniques for Wearable Antennas  
The design and moreover the manufacture process of a wearable antenna is of crucial importance in terms of antenna performance and manufacturing time. The manufacture process of wearable antennas should guarantee good agreement with the design and simulation results and should lead to robustness and repeatability of the wearable antenna. This arises from the fact that the wearable antenna is supposed to be embedded on the garments and it will operate under different conditions (movement of the wearer, weather conditions, temperature, bending and crumpling conditions). In addition, washability is a requirement in the case of embroidered textile antennas where the goal is that these antennas should be part of the clothes. Embroidered antennas are a great opportunity to connect wearable antenna technology with the industry of textiles. In this section, the manufacturing techniques used so  
Figure 1: **(a)** Q-meter measurement setup measuring a piece of felt by using the 1.9 GHz split post resonator; **(b)** Resonator-Vector Network Analyzer (VNA) setup measuring a piece of felt by using the 1.1 GHz split post resonator.  
far on wearable antennas are described and there are two categories: (1) rigid and (2) flexible (often textile) antennas.
## 3 Performance Analysis  
### Outage Probability  
Outage probability is defined as the probability that the instantaneous SNR of the system, \(\gamma\), is lower than a certain threshold value \(\gamma_{th}\); therefore, the outage probability of a hybrid RF/FSO system is deduced by the definition \(P_{out}(\gamma_{th})=\Pr(\gamma<\gamma_{th})\):  
\[P_{out}(\gamma_{th})=F_{\gamma}(\gamma_{th})= 1-A_{1}\sqrt{\gamma_{th}}\exp\left(-\frac{\gamma_{th}}{\gamma 1}\right)\times G_{1,6}^{6}\left[B\gamma\right]\left.\begin{array}{c}\frac{ \xi^{2}+1}{2}\\ 0_{1}\end{array}\right]. \tag{17}\]