esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In the past few years, it has become increasingly important to automatically detect falls and provide feedback in emergency situations. To meet these demands, fall detection studies have been undertaken using various methods ranging from wearable devices to vision-based methods. However, each method has its own limitations and one common limitation that is prevalent in almost all fall detection studies is that they are restricted to indoor environments. Therefore, we focused on a more dynamic and complex outdoor environment. We used two-dimensional features and Rao-Blackwellized Particle Filtering for human detection and tracking, and extracted three-dimensional features from depth images estimated by the supervised learning method from single input images. As we used the methods in combination, we could distinguish a series of states in which a person falls more precisely and then successfully perform fall detection under dynamic and complex scenes. In this study, we solved the initialization problem, the main constraint of existing tracking studies, by applying the particle swarm optimization method to the human detection system. In addition, we avoided using the background reference image feature for image segmentation due to its vulnerability towards dynamic outdoor changes. The experimental results show a reliable and robust performance for the proposed method and suggest the possibility of effective application to the pre-existing surveillance systems.  
Keywords:fall detection; 3D human tracking; on-street surveillance; depth with single camera; surveillance system +
Footnote †: journal: Medical Image Analysis
###### Abstract  
The bearingless concept is a plausible alternative to the magnetic bearing drives. It provides numerous advantages like minimal maintenance, low cost, compactness and no requirement of high-performance power amplifiers. Controlling the rotor position and its displacements under parameter variations during acceleration and deceleration phases was not effective with the use of conventional controllers like proportional-integral-derivative (PID) and fuzzy-type controllers. Hence, to get the robust and stable operation of a bearingless switched reluctance motor (BSRM), a new robust dynamic sliding mode controller has been proposed in this paper, along with a sensorless operation using a sliding ode observer. The rotor displacement tracking error functions and speed tracking error functions are used in the designing of both proposed methods of the sliding mode switching functions. To get a healthy and stable operation of the BSRM, the proposed controller's tasks are divided into three steps. As a first step, the displaced rotor in any one of the four quadrants in the air gap has to pull back to the centre position successfully. The second step is to run the motor at a rated speed by exciting torque phase currents, and finally, the third step is to maintain the stable and robust operation of the BSRM even under the application of different loads and changes of the motor parameters. Simulation studies were conducted and analysed under different testing conditions. The suspension forces, rotor displacements, are robust and stable, and the rotor is pulled back quickly to the centre position due to the proposed controller's actions. The improved performance characteristics of the dynamic sliding mode controller (DSMC)-based sliding mode observer (SMO) was compared with the conventional sliding mode controller (SMC)-based SMO.  
asymmetric converter; bearingless; dynamic sliding mode control; sensorless; sliding mode observer +
Footnote †: journal: Applied Soft. 2020, 10, 4070; doi:10.3390/app10124070
# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 4 Experiment Implementation
### Environment and Metrics  
The experimental setup was conducted in a Linux environment, utilizing both PyTorch 1.7.1 and TensorFlow 2.10 frameworks. The batch size was set to 8, and the ADAM  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Database** & **Healthy (Normal)** & **Glaucoma (Abnormal)** & **Resolution** \\ \hline REFUGE & 1440 & 160 & 2124 \(\times\) 2056 (train), 1634 \(\times\) 1634 (test) \\ \hline ORIGA & 482 & 165 & 3072 \(\times\) 2048 \\ \hline G1020 & 625 & 296 & 3004 \(\times\) 2423 \\ \hline AI-HUB 1  & 1566 & 1806 & 2796 \(\times\) 2848 (Healthy), 1964 \(\times\) 2000 (Glaucoma) \\ \hline Private 2  & 299 & 284 & 1270 \(\times\) 793 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets used in this work.  
optimizer was used. The learning rate was adjusted according to the specific requirements of the experiments, ranging from 0.01 to 0.0001. This flexibility in adjusting the learning rate helped optimize the training process depending on the model's performance and convergence rates during different phases of the experiment.  
When assessing the system's effectiveness, accuracy is a commonly used metric. It measures the proportion of correct predictions made by the classifier. The accuracy of a classifier is calculated by considering the total number of correct predictions divided by the total number of predictions made. Accuracy is evaluated using the following equation.  
\[\text{Accuracy(ACC)}=\frac{TP+TN}{TP+TN+FP+FN} \tag{3}\]  
Sensitivity, also known as recall or the true positive rate, quantifies the ability of a classifier to correctly identify positive instances. It is calculated as the ratio of the number of true positive predictions to the total number of actual positive instances. Sensitivity is given as follows:  
\[\text{Sensitivity(Recall)}=\frac{TP}{TP+FN} \tag{4}\]  
Specificity, also referred to as the true negative rate, measures the ability of a classifier to correctly identify negative instances. It is calculated as the ratio of true negative predictions to the total number of actual negative instances. Specificity is given as follows:  
\[\text{Specificity}=\frac{TN}{FP+TN} \tag{5}\]  
Precision is the number of instances that are truly positive out of the total instances predicted as positive. Precision is given as follows:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{6}\]  
The AUC curve, or Area Under the Curve, is a graphical representation commonly used in binary classification tasks to evaluate the performance of a model. It plots the true positive rate (TPR, Sensitivity) against the false positive rate (FPR, 1-Specificity) across different threshold values. A higher AUC value (closer to 1) indicates a better discrimination ability of the model, where it effectively distinguishes between positive and negative instances.  
\[\text{AUC}=\int_{0}^{1}\text{TPR}(x)\,dx,x=\text{FPR} \tag{7}\]
## 3 Beamforming System  
Beamformers can be divided into their analog and digital counterparts [23; 24]. Analog beamformers modify phase and gain through phase shifters and variable gain amplifiers, while digital beamformers change them by multiplying the sampled signals by complex weights. However, the control platform used is very similar for both. Both require a control platform used to calculate the phases or complex weights usually using very similar algorithms. In this work, we employed the ABDP platform [19] to test digital beamforming approaches resorting to a Zynq UltraScale+ RFSoC ZCU216 evaluation board from Xilinx, which utilizes the XCZU49 DR RFSoC. The objective of implementing this architecture is to expedite the development of arrays and beamforming algorithms through the design, iteration, test, and validation stages. This is achieved by providing a software-defined system that can test a multitude of configurations (array size, geometry, number of elements, frequency, etc.) and beamforming algorithms (classic and adaptive) without requiring hardware modifications. We believe that this will ultimately drive the cost of final products down by reducing development time and cost.  
The block diagram of the proposed system is presented in Figure 12, and it is composed of four major components: the ADCs, the processing system (which includes the beamforming algorithm), the programmable logic, and the DAC.  
The digital samples from each antenna's signal acquired by the ADCs are supplied to the programmable logic (PL) via the PL interface, where weights calculated by the beamforming algorithm are applied to the samples. The output of the PL contains the improved signal containing information of every satellite. Before converting the signal back to its analog form, the signal must be modulated back to the 1.57542 GHz frequency. The result is a similar signal to the one sampled, but now with improved satellite signal quality. It can then be converted to its analog form using the DAC. This analog output can then be supplied to a traditional GNSS receiver.  
The calibration process for the different channels and the applied compensations to guarantee phase balance and group delay are detailed in [19], along with a detailed description and validation. Furthermore, the configuration of the FPGA's parameters such as sampling rate are also presented in [19].  
Figure 12: Block diagram of the employed ZCU216 digital beamforming system.
## 3 Coverage Control Using Memetic Algorithm  
### The Set Covering Problem (SCP)  
The SCP, one of the NP-Complete problems, was first defined in [14]. We apply this definition to the node scheduling optimization here. In this study, the SCP involves the issue of managing coverage with energy efficiency. SCP is to find a set of nodes with the minimum cost so that each POI is covered by at least one node. Thus, the SCP considered in the CoCMA can be formulated mathematically as follows:  
_Optimization Model_:  
\[\min\ \sum_{i}g_{i}\cdot x_{i}\ \ \ \ i\in[1,\ N] \tag{4}\]  
_Subject to_:  
\[\begin{array}{l}\sum_{i}\Re_{i,j}\cdot x_{i}\geq 1\,\ \ \ j\in[1,\ M]\\ x_{i}=1\ \ \mathrm{or}\ \ 0\,\ \ i\in[1,\ N],\end{array} \tag{5}\]  
where \(g_{i}\) is the cost of activating each node; \(x_{i}\)'s are the key decision variables that need to be determined by the CoCMA (e.g. one is for activation, and zero is for inactivation). The objective function in Equation (4) minimizes the total number of nodes needed to be activated. In this paper, we suppose that the cost of activating each node is identical (i.e., \(g_{i}=1\)). The constraint described in Equation (5) stipulates that every \(p_{j}\) must be covered by at least one node. When the network is activated, using the optimization model depicted in Equations (4) and (5) is critical, since the model improves energy efficiency of the network.  
Meta-heuristics are a group of approximate methods, designed to cope with optimization problems. For example, genetic algorithms, memetic algorithm (MA), problem-space search, neural networks and simulated annealing are all members of the family of meta-heuristics. In order to efficiently find the optimal solutions, we develop a meta-heuristic method that employs an iterative generation process to explore the search spaces based on learning strategies [32]. Based on these reasons, we propose the CoCMA to solve the SCP using a MA and an optimization strategy, named the wake-up scheme. The proposed CoCMA will be described in detail as follows.
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 3 Calculation Model and Method of the Main Circuit Parameters
### Calculation Formulas of Main Circuit Parameters
#### 3.2.7 Calculation of the Rated Power of the Converter Transformer  
The total rated three-phase power of the six-pulse converter transformer is:  
\[S_{\text{transN}}=\sqrt{3}U_{\text{vN}}I_{\text{vN}}=\frac{\pi}{3}U_{\text{dioN}}I _{\text{dN}} \tag{41}\]  
where \(I_{\text{vN}}\) and \(U_{\text{vN}}\) are the rated AC current and voltage at the valve side, respectively.
## 3 Integrated Radio Coverage and Device Capacity Analysis Framework
### Methodology  
The overall coverage and capacity dimensioning consists of the following steps:  
**Radio Coverage Planning**: This is related to link budget calculations to determine the maximum achievable range of the IoT Access Network, considering transceiver characteristics, maximum transmission power levels, antenna gains, path loss models, receiver sensitivity, and minimum SNR threshold values for achieving the required Quality of Service.  
**Traffic Assessment**: This is related to the determination of the required PHY layer bits and symbols that have to be transmitted over the air in order to convey the required application layer packet bytes. It involves the determination of the Time over the Air (ToA) of each device transmission, the consideration of the transmission activity and traffic models for the assumed application per user, the calculation of the maximum number of available time and frequency resources for data, and control channels, as well as the consideration of different transmission modes employed for different ranges within the coverage area.  
**Network Dimensioning**: This involves the actual calculation of the maximum number of devices that can be covered by an IoT base station or a gateway, the level of the achieved throughput per device or per base station, and the actual spare capacity that can be secured for future network expansion steps.  
The methodology outputs can be directly used or can be fed back for further modification of the model inputs in order to refine the expected application requirements and network coverage targets. Figure 1 depicts the overall methodology procedure.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Maximum Range (km)** & **Number of Uplink Repetitions (\(N_{rep}^{U}\))** \\ \hline
6.097 & 1 \\ \hline
10.459 & 2 \\ \hline
14.989 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: NB-IoT range-based uplink repetitions.
# Area-Efficient Universal Code Generator for Multi-GNSS Receivers
## 3 Previous Multi-Frequency Universal Code Generator
As shown in Figure 4, in [6], the maximum length shift register was implemented, and ROM, MUX, and XOR circuits were configured so that feedback polynomials, output registers, and initial values different by code generator can be selectively determined. As many multiplexers as the number of bits of the shift register, as many XOR logics as (the number of bits of the shift register \(-\) 1), and Feedback ROMs1/2 and Output ROMs1/2 that store the multiplexers' control signals are used. Initial ROMs1/2 store the initial values determined by code, and the initial values are set in the shift registers connected according to the code types and satellite numbers. Therefore, each ROM needs as much width as the number of shift registers and as much depth as the supported (code types \(\times\) the number of satellite numbers). The code generators shown in Figure 4 can universally generate various types of codes with a single LFSR structure. The code type and the satellite number are received as inputs, the feedback polynomials, output registers and initial values of SR1 and SR2 are determined, and the generated PRN codes are output as codes. For example, in the case where the PRN code for satellite number 2 of the GPS L1C/A signals is generated using the code generators shown in Figure 4a, in order to set the feedback polynomials, Feedback Taps 1/2 receive the code type as an input, and \(\left\{\text{F}_{1,13},\text{F}_{1,12},\text{F}_{1,11},\ldots,\text{F}_{1,1}\right\}\) is set to \(\left\{0001000000100\right\}\), and \(\left\{\text{F}_{1,14},\text{F}_{1,13},\text{F}_{1,12},\ldots,\text{F}_{1,1}\right\}\) is set to \(\left\{00001110100110\right\}\). In addition, to set the output registers, Output Tap1 receives the code type and \(\left\{\text{O}_{1,13},\text{O}_{1,12},\text{O}_{1,11},\ldots,\text{O}_{1,1}\right\}\) is set to \(\left\{0001000000000\right\}\). Since Output Tap2 is affected by the satellite number, it receives code type and satellite number as inputs, and \(\left\{\text{O}_{1,14},\text{O}_{1,13},\text{O}_{1,12},\ldots,\text{O}_{1,1}\right\}\) is set to \(\left\{0000001000100\right\}\). Additionally, since Initial ROMs1/2 are affected by the satellite number, they receive the code type and satellite number as inputs, and \(\left\{\text{I}_{1,13},\text{I}_{1,12},\text{I}_{1,11},\ldots,\text{I}_{1,1}\right\}\) is set to \(\left\{000111111111111\right\}\), and \(\left\{\text{I}_{1,14},\text{I}_{1,13},\text{I}_{1,12},\ldots,\text{I}_{1,1}\right\}\) is set to \(\left\{0000111111111111\right\}\). Therefore, the universal code generator structure presented in [6] can reduce hardware complexity because it can selectively generate various codes according to the given type and satellite number inputs.
# Context-Aware Trust and Reputation Routing Protocol for Opportunistic IoT Networks
## 3 Proposed CATR Routing Protocol for OppIoT Networks
### Beta Distribution and Trust Modeling
Another possibility is that node \(N_{2}\) may present itself as a genuine node; however, upon receiving the data packet, it may drop it. Hence, the considered trust mechanism should not only take into account the behavior of \(N_{2}\) to determine whether it is trustworthy or not, but it should also consider the recommendations for node \(N_{2}\) that have been received from other nodes. These recommendations are taken into account in the design of our CATR model illustrated in \[E(x)=\frac{\alpha}{\alpha+\beta} \tag{2}\]2  
Using the above-discussed preliminaries, we now focus on the details of the design of the aforementioned modules/sub-modules. The considered notations and hashmap data structure used in this design are given in Table 1 and Table 2, respectively. \begin{table}
\begin{tabular}{l l} \hline \hline
**Notations** & **Meaning** \\ \hline \(N\) & Number of nodes in the network \\ \(n\) & Neighbor node \\ \(S\) & A message’s source node \\ \(D\) & Node to which a message is destined \\ \(s\) & Sender node \\ \(r\) & Receiver node \\ \(m\) & Node that computes the trust value of a node (Trustor node) \\ \(M\) & Messages generated by a node \\ \(\zeta\) & Number of successful encounters between two nodes \\ \(\omega_{k}\) & Weights for different constants, used in Equation (8), where k = 1, 2, 3.. \\ \(\lambda\) & Number of unsuccessful encounters between two nodes \\ \(BT_{mn}\) & Beta Trust of node \(n\) as computed as node \(m\) \\ \(EF_{nD}\) & Encounter Frequency Degree of node \(n\) with \(D\) \\ \(PF_{mn}\) & Packet Forwarding Degree of \(n\) as computed by \(m\) \\ \(RC_{mn}\) & Recent Contact Degree of \(n\) as computed by \(m\) \\ \(CD_{mn}\) & Contact Durability Degree of \(n\) as computed by \(m\) \\ \(DT_{mn}\) & Direct Trust of \(n\) as computed by \(m\) \\ \(IT_{mn}\) & Indirect Trust of \(n\) as computed by \(m\) \\ \(OT_{mn}\) & Overall Trust of \(n\) as computed by \(m\) \\ \(K\) & Total number of nodes encountered by node \(n\) till that instance of time. \\ \(p\) & Probability of receiving an untrusted signal from an untrusted node \\ \(q\) & Probability of receiving a trusted signal from a trusted node are considered \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations.
# Speaker Diarization: A Review of Objectives and Methods
## 5 Models to Apply for Speaker Diarization
### Neural (Machine Learning) Methods
#### 5.5.6 Neural Variants of Speaker Embeddings  
An approach called the _d-vector_ (for \"deep vector\") has an ANN process for each data frame and its context is to achieve frame-level embedding. The output activations from an ANN's final layer trained by speech from a given speaker are accumulated to form a compact speaker representation [54]. A low-dimensional speaker embedding model may be the average of activations from that last ANN layer (viewed as a bottleneck layer). These embedding d-vectors represent utterances in a space of fixed dimensions, thus allowing simple classification.  
_X-vector_ is another speaker embedding variant that uses a sliding window of frames as input, with a TDNN [55]. It has frame-level and segment-level sub-networks, merged with a _statistics pooling_ layer, to find means and variances of frame-level embeddings. This layer projects variable-length input into a fixed-length model by accumulating statistics of hidden node activations. Pooling over frame-level representations concatenates means and standard deviations over each set of speaker data. The x-vector usually has PLDA to calculate scores, while the d-vector uses cosine similarity [56]. X-vector clustering may also be used with variational Bayesian re-segmentation [17, 37]. This refines the positioning of the time stamps that were roughly estimated during clustering.
# Low-Thrust Transfer to Quasi-Synchronous Martian Elliptic Orbit via Nonlinear Feedback Control
## 2 Orbit Dynamics
### Operational Orbit and Coverage Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Latitude (Deg)** & **50** & **60** & **70** & **80** & **90** \\ \hline \(\min\{\varepsilon_{min}\}\) (deg) & 0.4 & 9.7 & 19.2 & 29.0 & 38.7 \\ \hline \(\max\{\varepsilon_{min}\}\) (deg) & 1.0 & 10.3 & 19.8 & 29.4 & 38.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Extremals of the minimum elevation angle (deg), depending on latitude. Figure 3: Nominal conditions: semimajor axis (**left**) and eccentricity (**right**).
# 3D Ear Normalization and Recognition Based on Local Surface Variation
## Appendix A Parameter Selection  
The parameters used in ear detection, LSV extraction, and ICP matching algorithms in this paper are listed in this section. We also provide a description of the result of the changes in parameters.  
Detection Related Parameters:  
The threshold value of the objectness score (\(T_{\text{s}}\)): We used the default value \(T_{\text{s}}\) = 0.8. It was found that almost every ear region detected in the three databases obtained a objectness score above 0.95. Therefore, the default value was selected in this paper. Increasing its value to over 0.95 would increase the false negative rate.  
LSV Feature Extraction Related Parameters:  
1. The neighborhood sizes and thresholds of LSV value (\(N\)-ring and \(T_{l}\)): We chose 3-ring and \(T_{l}\) = 0.01. Although the variation of rigid local surface is an inherent feature, the LSV value of a vertex changes with the scope of its neighborhood. We found that the neighborhood size of 3-ring was appropriate for the 3D ear feature expression. As shown in Figure 12, the matching time and performance were reduced along with the increase of threshold value in every group since masses of useful ear data were eliminated. Therefore, the threshold of 0.01 was adopted in the system.
2. The smoothness value of the gridfit function (\(S\)): It determines the eventual smoothness of the estimated surface. A larger value means the surface will be smoother. We utilized \(S\) = 2. On the one hand, using a higher value may result in a loss in details of the ear surface. On the other hand, the data may contain some noise on the surface when a lower value is used.  
Ear Normalization Related Parameters:  
The iteration times of ICP for normalization (\(t_{i}\)): It defines the iteration times of ICP algorithm between the coarsely segmented ear data and the ear template. As shown in Figure 13, we compared the performance of the proposed method on the UND-J2 database with different iteration times; five iterations were determined according to the recognition performance.  
Matching Related Parameters:  
1. The registration error threshold (\(\tau\)): We chose \(\tau=1\times e^{-4}\) mm, which means that the ICP algorithm will stop if the improvement in mean square difference between iterations drops below 0.0001. If a higher value of \(\tau\) is used, the iteration of ICP will stop before the two data point clouds matched perfectly. However, the computation time cost will increase when a lower value is selected.
2. The upper limit of ICP iteration times (\(k\)): It determines when the iteration of ICP stops if the improvement in mean square difference between iterations cannot drop below \(\tau\). We found that most of the iterations stop within 20 iterations. Therefore, to strike a balance between computational complexity and recognition accuracy, we set the upper limit to 30.