esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders
## 2 Related Work
### Deep Learning Based Method  
In recent years, deep learning approaches have been successfully applied to many computer vision tasks [14; 15], as well as in the field of anomaly detection [38; 39].  
In some works, convolutional auto-encoders or some fully convolutional networks were used to reconstruct or generate a new set of frames or feature maps [6; 20; 40]. For a sequence video frames without anomalies, [PERSON] et al. [6] trained a fully convolutional network (FCN) model that resembled the U-Net to predict the next frame. Then the deviations between the predicted frame and its groundtruth frame were used to predict the anomalies in the detection phase. Instead of the latent codes from the middle of auto-encoders, [PERSON] et al. [20] used the output of a convolutional auto-encoder, which could be considered the reconstruction of the input frame sequences. As the auto-encoder was trained from the normal video sequences, the reconstruction error was applied as an anomaly score. However, due to the good capacity and generalization of the deep neural network, the assumption that abnormal events would trigger larger reconstruction errors or generation deviations does not necessarily hold. Therefore, reconstruction errors or generation deviations of normal and abnormal patterns will be unstable and have no fixed measurement range.  
Therefore, we focus on the approaches of extracting features by the auto-encoder and detecting anomalies by estimating probabilities of the features [21; 41; 42]. [PERSON] et al. [42] structured a deep convolutional neural network with the kernels trained by a sparse auto-encoder. Taking the cubic patches captured from the original images as inputs, the feature maps from three intermediate and the last layers were pushed into their corresponding Gaussian classifiers. In the work of [21], three stacked denoising auto-encoders were proposed to learn spatial features, temporal features and their fusion. Then three one-class SVM models were used to evaluate the learned features and predict the anomaly score of each patch. Apart from the preprocessing of cropping the input frames into patches, the main problem of these methods is that all the features share the same Gaussian classifiers or one-class SVM models, even though the features are extracted from different regions of the input.  
Furthermore, in the work of [22], part of a pre-trained convolutional neural network (CNN) was intercepted as the feature extractor in the form of fully convolutional network (FCN), which can extract the features of each receptive field without cropping the input frames into patches. However, the pre-trained CNN was trained as a classification from other databases that consist of static natural images and the default number of the input channels was set as three according to RGB images, thus we are skeptical of the reasonableness of using the pre-training networks.
# Estimation of Inter-System Biases between BDS-3/GPS/Galileo and Its Application in RTK Positioning
## 3 Datasets and Experiments
### Performance of RTK Positioning
#### 3.4.1 Single System vs. Multi System  
Figure 7 displays the positioning errors of SS-DF and TS-DF RTK of the GODE-USN7 baseline on DOY 120, 2021 with a cutoff elevation angle of 10\({}^{\circ}\). To evaluate the SR and positioning accuracy of different RTK modes, the statistics of the positioning errors are shown at the top of the time series of vertical errors through the RMS in centimeters. For the SS RTK, despite having the most satellites in GPS, only 12 BLOCK IIF and 4 III-A satellites can transmit the L5 signal. Thus, the SR and accuracy of the float solution of GPS-DF RTK are slightly lower than those of BDS-DF. However, demonstrated by the accuracy of the correctly fixed solution, the GPS performs the best in SS-DF RTK. Galileo has a similar positioning accuracy to BDS-3 but a lower SR. With the TS applied, the SR and the positioning accuracy improve considerably. The SR of TS-DF RTK is to 100%, whereas the SRs of SS-DF remain lower than 80%. Compared with SS-DF, the positioning accuracy of TS-DF improves over 40% in the float solution and 9% in the correctly fixed solution. The results of RTK positioning in different modes indicate that when more systems are applied, a sufficient number of satellites ensures correctly fixed ambiguity resolution, and the better distribution of satellites increases the positioning accuracy of both the fixed and float solutions.
## 2 Results and Discussion  
### Characterization of the Homemade Needle Coated with PDMS  
The objective of characterization in this study was to evaluate the properties of the prepared needles coated with PDMS in order to optimize their efficiency and range of application. This investigation was achieved via the utilization of FTIR, TGA, and SEM.  
The infrared absorption spectra characteristics of the main functional groups of the prepared PDMS coating were recorded using an FT-IR spectrometer and were compared to those of another PDMS sample from a study carried out by [PERSON] et al. [46]. The main absorption signals corresponding to the different functional groups in PDMS were identified at the following wavenumbers: from 2963 to 2857 cm\({}^{-1}\) (C-H stretching in CH\({}_{3}\)), 1261 cm\({}^{-1}\) (CH\({}_{3}\) symmetric bending in Si-CH\({}_{3}\)), 1096 cm\({}^{-1}\) and 1021 cm\({}^{-1}\) (Si-O-Si), and at 800 cm\({}^{-1}\) (CH\({}_{3}\) rocking in Si-CH\({}_{3}\)), as shown in Figure 1A.  
TGA analysis was performed to further study the thermal stability of the prepared PDMS layer, the decomposition process could be separated into two stages. The first stage occurs in the temperature range from 100 to 300 \({}^{\circ}\)C with a weight loss of 1.31%; it mainly corresponds to the elimination of remnant organic matter and humidity. Afterward, the second decomposition stage is observed in the interval from 300 to 600 \({}^{\circ}\)C with about 50.58% weight loss; it could be explained by the disintegration of PDMS chains. The results showed that the PDMS polymeric crosslinked material is thermally stable below 300 \({}^{\circ}\)C. This is a very important parameter to confirm the thermal stability of the sorbent material (PDMS) for extraction in highly efficient and reproducible applications in microextraction techniques. This can be seen in Figure 1B.  
The morphology of the homemade needle coated with PDMS was evaluated by SEM. As shown in Figure 2A, the PDMS surface appears as a rough and strongly uneven aspect; this morphology increases the possibility of diffusion of the analytes and their transfer from the aqueous phase. Additionally, the presence of wrinkles with pores contributes to increasing the surface area of the sorbent and enhances its extraction capability. In addition, in Figure 2B, the layer thickness on the needle coated with PDMS is unequal, and it was evaluated to be in the range between 10 and 20 \(\upmu\)m.  
Figure 1: Characterization of needle coated with PDMS by (**A**) FT-IR of the PDMS polymer and (**B**) TGA of the PDMS polymer.  
Figure 2: Scanning electron microscopy of the PDMS homemade needle; (**A**) internal surface of the PDMS layer; (**B**) thickness of the PDMS layer.
# Selecting Workers Wisely for Crowdsourcing When Copiers and Domain Experts Co-exist
## 4 Task Assignment
### Copier Detection and Removal
Thus, we have:  
\[P(t_{j}\in T^{t}|w_{i}\to w_{i^{\prime}})=q_{i^{\prime}}^{j}\cdot r+P_{t}^{j} \cdot(1-r) \tag{8}\]\[P(t_{j}\in T^{f}|w_{i}\to w_{i^{\prime}})=(1-q_{i^{\prime}}^{j})\cdot r+P_{f}^{j} \cdot(1-r) \tag{9}\]  
Finally, the probability that \(w_{i}\) and \(w_{i^{\prime}}\) provide different values on task \(t_{j}\) is the probability that \(w_{i}\) provides a value independently, and the value differs from that provided by \(w_{i^{\prime}}\):  
\[P(t_{j}\in T^{d}|w_{i}\to w_{i^{\prime}})=P_{d}^{j}\cdot(1-r) \tag{10}\]  
Thus, the conditional probability of \(\phi\) is:  
\[P(\phi|w_{i}\to w_{i^{\prime}})=\prod_{t_{j}\in T^{t}}\Big{[}q_{i^{\prime}}^{ j}\cdot r+P_{t}^{j}\cdot(1-r)\Big{]}\]  
\[\cdot\prod_{t_{j}\in T^{f}}\Big{[}(1-q_{i^{\prime}}^{j})\cdot r+P_{f}^{j} \cdot(1-r)\Big{]}\cdot\prod_{t_{j}\in T^{d}}\Big{[}P_{d}^{j}\cdot(1-r)\Big{]} \tag{11}\]  
We compute \(P(w_{i}\to w_{i^{\prime}}|\phi)\) accordingly:  
\[P(w_{i}\to w_{i^{\prime}}|\phi)=[1+\frac{1-\alpha}{\alpha}\cdot\prod_{t_{j}\in T ^{t}}\frac{p_{t}^{j}}{q_{i^{\prime}}^{j}\cdot r+P_{t}^{j}\cdot(1-r)}\]  
\[P_{f}^{j}=P(t_{j}\in T^{f}|w_{i}\perp w_{i^{\prime}})=\frac{(1-q_{i}^{j}) \cdot(1-q_{i^{\prime}}^{j})}{|a|^{j}} \tag{5}\]0  
Here, \(\alpha=P(w_{i}\sim w_{i^{\prime}})\) (\(0<\alpha<1\)) is the a priori probability that two workers are dependent. By applying the Bayesian method, we can compute the dependent probability for every pair of workers \(w_{i}\) and \(w_{i^{\prime}}\). If the probability \(P(w_{i}\to w_{i^{\prime}})\) is larger than threshold \(\beta\), then we say there is a dependency between \(w_{i}\) and \(w_{i^{\prime}}\). If both \(P(w_{i}\to w_{i^{\prime}})\) and \(P(w_{i}\to w_{i^{\prime}})\) are larger than threshold \(\beta\), then we should identify the direction of copying. We regard the worker with lower accuracy as the copier between them. To punish the copiers, we not only avoid assigning the current task to them, but also halve their credibility to facilitate copying detection for the upcoming tasks. The algorithm pseudo code of the copier detection and removal part is shown in Algorithm 3. The time complexity for all tasks to complete copier detection and removal is \(O(C_{K}^{2}|H|)\). If Step 2 and Step 3 cycle \(v\) times, the time complexity of task assignment is \(O(|T|\big{(}|W||D|+|W|^{2}+v(K|T|+C_{K}^{2}|H|)\big{)})\). In practice, \(v\leq 5\). ```
1:functioncopierdisposal(iscopier, \(\big{\{}\hat{W_{t_{j}}}\big{\}}\), \(\{q\}\))
2:iscopier = false
3:copiernum = 0
4:for\(w_{i}\in\hat{W_{t_{j}}},w_{i^{\prime}}\in\hat{W_{t_{j}}};w_{i}\
eq w_{i^{ \prime}}\)do
5:compute \(P(w_{i}\sim w_{i^{\prime}}|\phi)\) by (4-12)
6:if\(P(w_{i}\sim w_{i^{\prime}}|\phi)>\beta\)then
7:if\(q_{i}^{j}<q_{i^{\prime}}^{j}\)then copier = \(w_{i}\)
8:elsecopier = \(w_{i^{\prime}}\)
9:endif
10:iscopier = true, copiernum++
11:remove copier from \(\hat{W_{t_{j}}}\)
12:halve the credibility of the copier
13:endif
14:K = copiernum
15:endfor
16:return iscopier, K
17:endfunction
```  
**Algorithm 3** Copier disposal.
## 4 Simulated Results of the Overall DFE Architecture
Relative to the signal only scenario, IBB, ACS, and NBB scenarios would lead to 11-36 dB degradation but all the mean SNR values were still above 40 dB. To summarize, the mean SNR values met the 40 dB specification of LTE-A for all the ten cases or frequency modes with our proposed and implemented DFE. After taking the hardware behavior into account, the C++ cycle-accurate model is developed from the Matlab and C++ functional models. Figure 18 shows how a bit-accurate model was derived from the cycle-accurate model by using a class fixedDT. x(6,2) denotes the number of bits above the decimal point is 6 and the number of bits below the decimal point is 2. In order to evaluate cycle-accurate and bit-accurate performances, each hardware block was modeled both cycle accurately and bit accurately. The cycle-accurate model updates all the registers every cycle so that the content of each register varied in a cycle-accurate manner, as exemplified in the left part of Figure 18. Moreover, in order to evaluate the performance of the proposed DFE, the DFE, and the DFE, the DFE, and the DFE, the DFE, and the DFE, the DFE, and the DFE, the DFE, and the D  
to facilitate the bitwidth optimization, a new C++ class called fixedDT was introduced. Once a fixedDT class was instantiated with the number of integer bits and the number of fractional bits, it took into account all the fixed-point effects such as quantization and overflow, by making use of the C++ operator overloading. This helped to make the relevant bitwidth optimization more efficient. Figure 16: Specifications at the analog filter output (**middle**) and at the DFE filter output (**bottom**) in the case of 4G contiguous CA (bandwidth = 40 MHz). Figure 17: Mean signal-to-noise ratio (SNR) values obtained from the receiver model that includes our proposed and implemented DFE. Figure 19 shows the mean SNR values for the Matlab model (small circles connected by solid lines) and the C++ bit-accurate model (solid lines). Blue, black, pink, and red represent signal only, IBB, ACS, and NBB coexisting, respectively. The bit-accurate model attained a slightly less mean SNR than the Matlab model but still met the 40 dB required specification of the LTE-A standard. The ten cases in the \(x\) axis corresponded to the ten frequency modes. The discrepancy between the C++ model and the Matlab model lay in the range of 0.5-15 dB and was the largest in the signal only scenario but all the scenarios (signal only, IBB, ACS, and NBB) met the 40 dB mean SNR requirement (which corresponded to an error vector magnitude or EVM value well below 1%, assuming 64 QAM or quadrature amplitude modulation).
# Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm
###### Abstract  
The bacterial foraging optimization (BFO) algorithm successfully searches for an optimal path from start to finish in the presence of obstacles over a flat surface map. However, the algorithm suffers from getting stuck in the local minima whenever non-circular obstacles are encountered. The retrieval from the local minima is crucial, as otherwise, it can cause the failure of the whole task. This research proposes an improved version of BFO called robust bacterial foraging (RBF), which can effectively avoid obstacles, both of circular and non-circular shape, without falling into the local minima. The virtual obstacles are generated in the local minima, causing the robot to retract and regenerate a safe path. The proposed method is easily extendable to multiple robots that can coordinate with each other. The information related to the virtual obstacles is shared with the whole swarm, so that they can escape the same local minima to save time and energy. To test the effectiveness of the proposed algorithm, a comparison is made against the existing BFO algorithm. Through the results, it was witnessed that the proposed approach successfully recovered from the local minima, whereas the BFO got stuck.  
mobile robots; path planning; bacterial foraging optimization; local minima; information sharing; swarm robots; dynamic environment; static environment Article
## 3 Methodology
### Room Segmentation
#### 3.1.1 Detection of Openings  
Since openings are generally attached to wall surfaces and as holes in the point clouds of wall surfaces, the extraction of doors and windows is based on the hierarchical relationship of plane-contour. The surfaces are first extracted based on the previous plane segmentation [31] (see wall surfaces illustrated in Figure 2a). The 3D point clouds of wall surfaces are projected into a 2D plane using the following conversion:  
\[\begin{split}& X_{v}=\frac{(0,0,1)\times(n_{x},n_{y},n_{z})}{[(0,0,1)\times(n_{x},n_{y},n_{z})]}\ Y_{v}=\frac{X_{v}\times(n_{x},n_{y},n_{z})}{[X_{ v}^{2}\times(n_{x},n_{y},n_{z})]}\ Z_{v}=(n_{x},n_{y},n_{z})^{T}\\ & T=\left(X_{v},Y_{v},Z_{v}\right)(x,y,z)\cdot T=(x_{2},y_{2},z_ {2})\end{split} \tag{1}\]  
where \((n_{x},n_{y},n_{z})\) are normal vectors of 3D plane; \(X_{v},Y_{v},Z_{v}\) are vectors of the 3D plane coordinate axis that constituted the transformation matrix \(T\); \((x,y,z)\) are coordinates of 3D plane; \((x_{2},y_{2})\) are the 2D coordinates of the transformed plane; and, \(z_{2}\) is depth.  
Figure 2: Detection of openings. (**a**) Extracted wall surfaces. (**b**) Wall surfaces converted into binary image. (**c**) Template match. (**d**) Detected doors.  
The 2D projected points are converted into a binary image, as shown in Figure 2b. For every binary image, morphological corrosion transformation is used to remove noise. The find-contour method [41] is then applied during the extraction of plane outline to get sets of contour points, such that every contour is independent. Afterward, the bounding boxes of contours are calculated. Based on the size of the bounding box, the contours are categorized into doors, windows, invalid. The invalid consists of holes resulting from occlusion and undetected openings. In our study, the template match method, which is implemented in the OpenCV library [42], is applied to extract undetected openings. (In Figure 2c, an extracted opening as the template that encircled in red; in Figure 2d, the identified doors are shaded in green). Compared with the previous method [31], the extracted opening boundaries are visually more defined. As for the extraction of pillars, the technique is similar to extracting openings.
# Experimental Validation of Peer-to-Peer Distributed Voltage Control System
## 5 Device-to-Device Communication
### D2D Communication Modules  
The D2D communication modules are implemented based on a modular WSAN/IoT platform (wireless sensor and actuator network/Internet of Things) [36]. Each module is composed of three submodules stacked on top of each other, as shown in Figure 6. The radio submodule (the top submodule) hosts the RN2483 LoRaWAN radio transceiver (Microchip Technology, Chandler, AZ, USA). The main submodule (the middle submodule) includes the microcontroller (ST32F217, STMicroelectronics, Geneva, Switzerland), the power circuitry, and other peripherals. The USB submodule (the lower submodule) hosts an FTDI USB-UART chip (FT8U232 AM, FTDI, Glasgow, UK) (FTDI: future technology devices international (semiconductor device company), USB: universal serial bus, UART: universal asynchronous receiver-transmitter). Additionally, each D2D module needs to have an 868 MHz SMA (SubMiniature version A) antenna. Also a mini or micro USB cable (any of these two, but only one at a time) should be connected to the USB submodule to interface with the agents (actuators and observers). The power required for the module's operation is also provided via the USB interface (maximum consumption is in the order of 200-300 mW).
## 1 Introduction
A boost converter placed in front of the three-phase full bridge inverter was employed in order to realize the maximum power point tracking (MPPT) of the photovoltaic (PV) system in [16; 17; 18]. However, the method could not be directly applied on BLDC machine drives and dynamic characteristics optimization was not in the study range. The research in [19] concentrated on the design of a four-quadrant operation simulation model of a BLDC machine to reduce torque ripples, but the speed range expansion and fault protection were not examined. This paper proposes a model-based optimized permanent magnet (PM) BLDC machine topology for dynamic response enhancement and fault protection improvement. The topology is composed of a boost converter, two power switches for protection, and a three-phase full bridge inverter. The static and dynamic characteristics of the closed-loop control system are obtained under normal and faulty operating conditions. The response time is shortened significantly during the start-up, speed adjustment and abrupt load change processes compared with conventional topology. The fault protection scheme involving control factors of \(S\), \(K_{1}\), \(K_{2}\) and \(K_{3}\) is extracted for faulty situations to prevent secondary malfunction. The protection circuit mainly deals with over-voltage and over-current fault isolation, while the boost converter handles situations under over-load and temporary under-voltage conditions. Only two voltage output levels of the converter are allowed in order to avoid frequent switching operations. The voltage and current limits under different situations prove that the proposed strategy could increase operation reliability and that the mean time between failures (MTBF) of the whole system will be decreased. A simulation model and experimental prototype are constructed and manufactured to validate the proposed topology and control method for an aeronautical fuel pump system.
# Layout Model of Bike-Sharing Facilities in the Transfer-Influenced Area of a Subway Station
## 2 Methodology
### Transfer-Cost Function Establishment
#### 2.2.1 Travel-Time Cost  
Travel-time cost refers to the value of the time used by travelers to choose a certain travel mode from the origin to the destination. This paper utilizes the unit time value to quantify the travel time cost. The accuracy of this parameter is related to the transfer mode division. In addition, the travel time includes not only the running time of the travel mode, such as bike-riding and bus operating time, but must also consider the bike-locking time and the waiting time for the bus in the whole transfer process. Based on the above analysis, this paper establishes the travel-time cost model, including the bike-sharing lock time and the average waiting time of bus.  
The unit time value is estimated mainly on the basis of the traveler's annual income [23]. The calculation method is shown in Equation (1).  
\[\lambda=\frac{Income}{T} \tag{1}\]  
where \(Income\) is the average income of travelers, and \(T\) is the average working time of travelers.  
The travel time cost model includes the total time spent by the traveler from the origin to the subway station. The travel time cost model of walking, bike-sharing and bus is shown as following:  
1. Walking. When the traveler chooses to walk to the subway station to transfer to the rail transit, travel time \(T_{walk}\) indicates the time for the traveler to walk to the station. The walking travel-time cost \(C^{T}_{walk-R}\) is shown in Equation (2). \[C^{T}_{walk-R}=\lambda\times T_{walk}\] (2)
2. Bike-sharing. When the traveler chooses to ride a shared bike to the rail station, the travel time includes the time for the traveler to walk to the bike-sharing facility \(T_{walk}\), the time for opening and locking the bicycle \(T_{lock}\), and the time for the traveler to cycle to the station \(T_{bike}\). The bike-sharing travel-time cost \(C^{T}_{bike-R}\) is shown in Equation (3). \[C^{T}_{bike-R}=\lambda\times[T_{walk}+T_{lock}+T_{bike}]\] (3)
3. Bus. When the traveler chooses to take the bus to the rail station, the travel time includes the time for the traveler to walk to the bus stop \(T_{walk}\), the waiting time at the bus stop \(T_{wait}\), and the time to take the bus to the station \(T_{bus}\). The bus travel time cost \(C^{T}_{bus-R}\) is shown in Equation (4). \[C^{T}_{bus-R}=\lambda\times[T_{walk}+T_{wait}+T_{bus}]\] (4)