esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Robot Formation Platform based on an Indoor Global Positioning System
## 2 Indoor Multi-Robot Formation Platform
### Wheeled Mobile Robot Hardware  
In our multi-robot formation platform, for more practicality, lower costs, and better compatibility, we chose to design and manufacture our custom mobile robots. Mobile robot hardware including eight components is shown in Figure 4, and each module installed on the mobile robot is listed in Table 1.  
The mobile robot with differential drive serves as the general platform for multi-robot formation control experimentation. It is equipped with a microcontroller Unit-STM32, a mobile beacon, a six-degree-of-freedom gyroscope MPU-6050, tow motors, and power equipment.  
Figure 4: Wheeled mobile robot hardware.  
The robot is controlled by a low-cost and high-performance microcontroller Unit-STM32. The CPU core is an ARM 32-bit Cortex-M4 CPU with FPU running at 168 MHz with 1 Mbyte of Flash memory and 192 KB of SRAM [32]. The microcontroller Unit-STM32 is selected as the core control cell of the mobile robot, which is used to execute the formation controller algorithm. In our multi-robot formation platform, the control is distributed so that each robot has a microcontroller by itself. This distributed method will not cause the whole operation of multi-robot system paralysis or confusion in the case of robot problems or central processor faults. In addition, it is easy to add or reduce the number of robots in the process of operation.  
STM32 is equipped with rich communication interfaces. Through its serial port, it can interact with the indoor global-positioning system. At the same time, it drives and controls the other sensors such as the communication sensors, motion tracking sensor, and so on.  
The platform adopts a distributed framework, and each robot is equipped with a microprocessor. Thus, the control is distributed in the sense that each robot makes, by itself, the decision of when to transmit its state, and the control law is computed locally.  
The stepper motors mount directly to the bottom circuit board and are controlled with PWM generated by the microcontroller. The 41 mm wheels give the robots a maximum speed of 0.3 m/s, while the minimum controllable speed is around 0.03 m/s.
## 3 MIMO Detection  
Due to the interference among the different UEs, signal detectors are used to estimate the transmitted symbol \(\mathbf{x}\) from the quantized received signal vector \(\mathbf{y}_{q}\) modeled in (8). This is to solve an  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(b\) & 1 & 2 & 3 & 4 & 5 \\ \hline \(\beta\) & 0.3634 & 0.1175 & 0.03454 & 0.009497 & 0.002499 \\ \hline \end{tabular}
\end{table}
Table 2: The value of \(\beta\) for \(b\leq 5\).  
Figure 1: Large-scale MIMO system with \(K\) UEs and \(N\) receive antennas. Each received signal \(y_{n}\) is quantized by two \(\text{ADC}_{s}\) for its real and imaginary values.  
inverse problem and tremendous solutions have been studied, as surveyed in [16]. We assume that the knowledge of the channel matrix \(\mathbf{H}\) and the power of the AWGN are known at the receiver side and thus a signal detector can be constructed and reused for each coherent block. We here consider three types of detectors, i.e., the MMSE detector, the LR-SIC detector, and a modified multi-branch LR-SIC detector (MMB-LR-SIC), as example detectors of different performance and complexity. Among them, the MMSE detector is a representative linear option; the MMB-LR-SIC is adapted from the variable list detector (VLD) of [41], representing a sophisticated detector with good BER performance; and LR-SIC has complexity and performance in between MMB-LR-SIC and MMSE. The study of this paper can also be extended to other signal detectors.
# Formation Control of Nonlinear Multi-Agent Systems with Nested Input Saturation
## Main Results
### Extension to Multi-Input Multi-Output MASs  
The M-dimensional agent is described by:  
\[\begin{split}\dot{\zeta}_{i}&=f_{i}(\zeta_{i})+G_{ i}(\zeta_{i})u_{i}+d_{i}(t)\\ \dot{u}_{i}&=\text{sat}_{i}\big{(}a_{i}(t)\big{)},\ i =1,\ldots,N\end{split} \tag{17}\]  
where \(\zeta_{i}\in\mathbb{R}^{M}\) denote the sate of each agent, \(u_{i}\in\mathbb{R}^{M}\) are the control inputs, the functions \(f_{i}:\mathbb{R}^{M}\rightarrow\mathbb{R}^{M}\), \(G_{i}:\mathbb{R}^{M}\rightarrow\mathbb{R}^{M\times M}\), \(d_{i}:\mathbb{R}^{+}\rightarrow\mathbb{R}^{M}\) for \(i=1,\ldots,N\) denotes the nonlinear terms of the system and the external disturbances, respectively, and \(\zeta_{0}:\mathbb{R}^{+}\rightarrow\mathbb{R}^{M}\) corresponds to the state of the leader. To solve the input-constrained formation control problem with adaptive performance we follow a similar approach as in Section 3.2, given a controllability assumption on the positive (or negative) definiteness of the matrices \(G_{i}(\cdot)\) for \(i=1,\ldots,N\).  
Specifically, the neighborhood error feedback is defined as:  
\[e_{i}=\sum_{j\in\mathcal{N}_{i}}a_{ij}(\zeta_{i}-\zeta_{j}+d_{ij})+b_{i}(\zeta _{i}-\zeta_{0}+d_{i0})\in\mathbb{R}^{M}\]  
for \(i=1,\ldots,N\). Using the Kronecker product \(\otimes\), we derive the overall neighborhood error vector:  
\[\mathbf{e}=\left[e_{1}^{T},\ldots,e_{N}^{T}\right]^{T}=\left((L+B)\otimes I_{ M}\right)(\boldsymbol{\zeta}-\boldsymbol{\zeta}_{0}+\mathbf{d})\]  
where \(\boldsymbol{\zeta}=\left[\zeta_{1}^{T},\ldots,\zeta_{N}^{T}\right]^{T}\in \mathbb{R}^{NM}\) represents the overall state vector, \(\boldsymbol{\zeta}_{0}=\left[\zeta_{0}^{T},\ldots,\zeta_{0}^{T}\right]^{T} \in\mathbb{R}^{NM}\), and \(\mathbf{d}\) denotes relative offsets with respect to the leader, as dictated by the desired formation. Adopting the proposed control protocol (6)-(10), element-wise for each neighborhood error \(e_{i}(t)\) it can be easily verified that the decentralized control scheme presented in Table 1 ensures \(|e_{ij}(t)|<\rho_{ij}^{1}(t)\) for all \(t\geq 0\), \(i=1,\ldots,N\), and \(j=1,\ldots,M\), as well as boundedness of all closed-loop signals.
# Edge AI for Real-Time Anomaly Detection in Smart Homes
## 3 Proposed System Architecture
### Anomaly Detection Approach  
The system integrates two complementary machine learning models to enable accurate and efficient anomaly detection at the edge, balancing computational efficiency and detection performance.  
The first model, Isolation Forest (IF), is a lightweight, unsupervised machine learning algorithm designed for efficient anomaly detection in high-dimensional datasets. Unlike traditional classification models requiring labelled training data, IF identifies outliers by isolating data points that significantly deviate from normal patterns. Its low computational overhead makes it well suited for real-time processing on resource-constrained edge devices. IF is especially effective for detecting point-based anomalies in temperature, motion, and power consumption, making it ideal for fast, low-power deployments in smart home environments. IF offers superior scalability and computational efficiency compared to one-class SVM, making it an ideal choice for anomaly detection in large-scale, high-dimensional IoT environments [18].  
To complement the IF model, the system incorporates a Long Short-Term Memory Autoencoder (LSTM-AE), which is particularly adept at analyzing sequential data. Since smart home sensor readings often follow predictable temporal patterns, LSTM-AE models can learn these behavioral trends and detect deviations that indicate potential anomalies.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Layer** & **Function** & **Key Components** \\ \hline IoT Sensor Layer & Collects temperature, motion, and energy usage data & DHT22, PIR motion sensor, smart plugs \\ Edge Processing Layer & Detects anomalies in real time at the edge & Isolation Forest, LSTM Autoencoder \\ Cloud Layer (Optional) & Stores long-term data, retrains models if needed & Cloud ML APIs, federated learning \\ User and Alerting System & Notifies users and enables real-time monitoring & Mobile app, web dashboard \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the Edge AI-based anomaly detection framework, outlining each system layer’s core functionalities and key components, from data collection to anomaly detection, cloud processing, and user interaction.  
Unlike IF, which primarily identifies isolated outliers, LSTM-AE effectively recognizes long-term irregularities, such as gradual energy consumption spikes or repetitive but anomalous activity patterns [19].
# Effects of Outlet Shrinkage on Hydraulics in Hyper-Concentrated Sediment-Laden Flow
## 1 Introduction
[21] examined the energy dissipation of the sediment-laden water flow in a ski jump, and found that the flow discharge is similar to that of fresh water; the turbulent vortex becomes larger and the turbulent energy is reduced; the fluctuating energy and vortex scale both increase with the increasing sediment concentration due to the increased viscosity. The flaring gate pier widens the tail part of the traditional flat tail pier, so that the water flow forms a narrow and long flow regime at the outlet of the overflow, increasing dispersion and air entrainment of the water nappe in the longitudinal direction and improving the energy dissipation efficiency. As early as the 1970s, [PERSON] [22] proposed the idea of setting a flaring gate pier on the top of the arch dam to make the water nappe fully disperse in the longitudinal direction and promote energy dissipation by air entrainment. In 1974, [PERSON] [23] proved that energy dissipation can be significantly improved in hydraulic model tests with flaring gate piers. In recent years, flaring gate piers of various types have been used in many projects, such as Y-shaped, X-shaped, new X-shaped, and T-shaped flaring gate piers [24]. In the design for energy dissipation, the change in overflow shape was also affected by sediment concentration. However, the research on shape optimization of overflow was almost all about fresh water. The flaring gate pier was a common optimization for energy dissipation. In earlier research in the laboratory, the effect of sediment concentration on the deep outlet was noted [21]. In this paper, the effect of the shape of surface outlet was considered. Model experiments were conducted using a recirculating flume with three overflow shapes (contraction ratio: 0.5, 0.4, 0.3) at four sediment concentrations (0 kg/m\({}^{3}\), 50 kg/m\({}^{3}\), 150 kg/m\({}^{3}\), and 250 kg/m\({}^{3}\)). The influence of sediment concentrations and outlet shrinkages on the water nappe were studied, along with the flow regime and pressure fluctuations in the plunge pool.
## 2 Materials and Methods
### Lipidic Fraction Extraction  
The lipidic fraction of baobab fruit pulp was extracted according to the procedure validated by [PERSON] et al. [29], with a few modifications. The sample (about 150 mg) was combined with 75 \(\mu\)L of 0.1% BHT (\(m/v\)), 50 \(\mu\)L of tool (internal standard, 0.1 mg/mL), and 1 mL absolute ethanol. The solution was homogenized for 30 min using an orbital vortex mixer (VV3, VWR International, Darmstadt, Germany). After that, 2 mL of n-hexane (HPLC grade) was added, and the solution was homogenized for 30 min. Next, 1 mL of 1% NaCl (\(m/v\)) was added. The supernatant was collected and preserved following centrifugation (5000 rpm for 5 min) with a Heraeus Labofuge A (Hanau, Germany). The residue was re-extracted with 2 mL of n-hexane for 30 min before centrifugation (5000 rpmfor 5 min). The resultant supernatant was collected and mixed with the previous extract. An appropriate amount of anhydrous sodium sulphate (Na\({}_{2}\)SO\({}_{4}\)) was added to the solution, which was then centrifuged (5000 rpm for 5 min). After being evaporated to dryness, the supernatant was mixed with 1 mL of solvent and injected into an HPLC-DAD-FLD (high-performance liquid chromatography connected to a diode array detector and fluorescence detector) system for assessment of the vitamin E profile.  
Vitamin E Profile by HPLC-DAD-FLD  
Vitamin E quantification was carried out through HPLC-DAD-FLD, employing an integrated HPLC system (Jasco, Tokyo, Japan) that included an AS-950 automated injector, a PU-980 pump, and an MD-2015 multiwavelength diode array detector (DAD) coupled to an FP-2020 fluorescence detector (Jasco, Tokyo, Japan). The system was programmed for excitation at 290 nm and emission at 330 nm. Each compound was chromatographically separated on a normal phase SupelcosiITM LC-SI column (75 mm \(\times\) 3.0 mm, 3.0 \(\upmu\)m) (Supelco, Bellefonte, PA, USA), following the method described by [PERSON] et al. [29]. Chromatographic data analysis was conducted using Borwin-PDA Controller Software (Jasco ChromNAV, version 2.02.08, Tokyo, Japan).  
Vitamin E vitamers in samples were identified by comparing the retention times with the respective standards (\(\alpha\)-, \(\beta\)-, \(\gamma\)-, and \(\delta\)-tocopherols and \(\alpha\)-, \(\beta\)-, \(\gamma\)-, and \(\delta\)-tocotrienols), using UV spectra as a reference. Quantification was made through the analysis of fluorescent signals, and subsequently converted to concentration units using calibration curves obtained from commercial standards for each compound. Tool was used as the internal standard. The results were expressed as mg/100 g of sample dry weight (dw).
## 2 Materials and Methods
### Isolation and Culture of Human Chorionic Mesenchymal Stem Cells  
Human chorionic mesenchymal stem cells (CMSCs) were isolated from the part of the extraembryonic membranes, chorion. Human term placentas by section (38-40-week gestation) were obtained from healthy donor mothers. Further use of placental tissues was performed with the donor's agreement. The amnion and chorion were then manually separated and chorion was washed in phosphate-buffered saline (PBS) (Thermo Fisher Scientific, New York, NY, USA) containing penicillin 100 IU/mL, streptomycin 100 ug and amphotericin 0.25 ug (Thermo Fisher Scientific). After isolating a 10 \(\times\) 10 cm part of chorion from amnion, the tissue was washed and cut into small pieces of 0.5 \(\times\) 0.5 cm. Tissue was digested of 15 min at 37 \({}^{\circ}\)C in dispase II 2.4 U/mL (Sigma-Aldrich, St. Louis, MO, USA) in [PERSON]'s Modified Eagle's Medium (DMEM) (Thermo Fisher Scientific). Small pieces were washed and then centrifuged at 150 g-force for 15 min. Chorion pieces were incubated for approximately 90 min at 37 \({}^{\circ}\)C in DMEM (Thermo Fisher Scientific) containing 1 mg/mL collagenase type II (Thermo Fisher Scientific). Cells were collected by centrifugation at 150 g for 15 min. Dispersed chorion cells were filtered through a sterile Falcon(r) cell strainer 40 \(\upmu\)m (Thermo Fisher Scientific). Chorionic cells were cultured in vitro at a density of 4000 cells/cm\({}^{2}\) in alpha Minimal Essential Medium (alpha MEM) GlutaMAX supplement (Thermo Fisher Scientific) supplemented with 10% fetal calf serum (FCS) (Thermo Fisher Scientific) and antibiotic-antimycotic solution (ATB) contained penicillin 100 IU/mL, streptomycin 100 ug and amphotericin 0.25 ug (Thermo Fisher Scientific). Isolated chorionic mesenchymal cells, called CMSCs were maintained at 37 \({}^{\circ}\)C in a humidified atmosphere with a 5% CO\({}_{2}\) atmosphere. During the first week all the medium and no adherent cells were removed and replaced with fresh medium. When the cells were more than 80% confluent, they were recovered with 0.25% trypsin/ethylene diamine tetra acetic acid (EDTA) (Thermo Fisher Scientific) and detached by trypsinization. The cells were released and trypsin ETDA was neutralized with 10% FCS. In the next passages, the cell suspension was used for testing immunomodulatory properties and phenotype characterization cells.
## 2 Proposed Method  
In the image processing process, in order to acquire the detailed information of the mammograms, wavelet transform and contourlet transform are generally used. For detecting AD, it is necessary to better display the boundary and contour information in the mammograms, but the traditional wavelet transform has a limited number of directions, and lack translation invariance; although the contourlet transform is effective in representing the anisotropy of the image, it may cause aliasing of the image when performing directional filtering, and does not have translation invariance. In order to compensate for this limitation, the NSCT [23] was proposed. The NSCT has the translation invariance that is not available in contourlet transform and wavelet transform, and has the characteristics of multiple scales and multiple directions, which avoid the generation of pseudo Gibbs effects.  
The PCNN simulates the synchronous oscillation phenomenon in the visual cortex of animals such as cats and monkeys [24]. PCNN is a third-generation artificial neural network model with deep biological basis. Compared with the traditional neural network model, PCNN could generate synchronous oscillation phenomenon, which is closer to the human visual system. It can realize the segmentation of image and the identification of targets without training, and it is widely used in the field of image processing [25].  
In recent years, NSCT and PCNN have penetrated into the field of image processing, but they have not yet been applied to the field of detecting AD. NSCT can better obtain the detailed information of the mammograms to get the high-frequency information of AD. PCNN could complete AD detection accurately without a large of images, which exactly satisfied a little AD data condition. Therefore, this work proposes a method that combines NSCT and Otsu-PCNN to detect AD. The proposed method is shown in Figure 1.  
This paper selects the \"B_3412_1.LEFT_CC\" mammogram in the DDSM database for a comprehensive description of the proposed detection method.  
Figure 1: The flow chart of the proposed algorithm. NCST: non-subsampled contourlet transform; AD: architectural distortion; PCNN: pulse coupled neural network.
# Photoacoustic Imaging of Tattoo Inks: Phantom and Clinical Evaluation
## 1 Introduction  
Non-invasive in vivo imaging has been an integral part of the armamentarium in dermatology for decades [1, 2]. In addition to traditional dermoscopy and ultrasound (US), optical imaging methods such as Raman spectroscopy, confocal microscopy, and optical coherence tomography have demonstrated utility in the diagnosis of skin diseases [3, 4, 5]. Photoacoustic imaging (PAI) or optoacoustic imaging is a relatively new imaging method that provides excellent optical contrast with the spatial resolution of US [6, 7]. Although administration of exogenous agents can enhance contrast on PAI, this is not essential given the abundance of endogenous chromophores such as melanin and hemoglobin in tissue. Clinical studies have demonstrated the usefulness of PAI as a label-free imaging tool for assessment of melanomas and non-melanoma skin cancers [8, 9, 10] and to visualize microvascular and inflammatory changes in the skin [11]. However, the ability of PAI to visualize tattoos has not been previously reported. Since tattoo dyes are known to absorb and reflect light, we hypothesized thatPAI can visualize and determine the three-dimensional localization of tattoo pigments. To test this hypothesis, we first examined the optoacoustic properties of commercially available tattoo inks in a tissue-mimicking phantom. Subsequently, we examined the ability of PAI to visualize an intradermal tattoo in a healthy volunteer.
# Analysis of Surface EMG Parameters in the Overhead Deep Squat Performance
## 1 Introduction
Three exercises, monopodal squat, forward lunge, and lateral step-up exercises, were analyzed in [17]. To process the sEMG signal, the authors applied the RMS algorithm and normalized the smoothed signal by MVIC value. The overhead deep squat is also one of the exercises that is very often used as a fitness test, as it requires flexibility and mobility in the hips, knees, ankles, and lower back strength to drop into the squat, hold it, and rise [18]. The squat is one of the most commonly prescribed exercises in the rehabilitation process [10]. Muscle activation patterns based on the sEMG signal during different squat techniques are studied in [19]. The authors draw attention to the fact that although many sports medicine and performance professionals encourage patients to execute squats, there is rather little information regarding differences in muscle activation patterns in the lower limbs during squats. Meanwhile, a poorly performed squat may expose the lower limb joints to excessive torques, which may require adaptive muscle activation strategies to stabilize the lower limb joints. Since the assessment of the correctness of exercise performance by physiotherapists may be subjective, we proposed some parameters based on the sEMG signal to support the assessment of the chosen muscles' work in the overhead deep squat. In this paper, we focus on the statistical analysis of the excitation of selected muscles and parameters of sEMG signal recorded for athletes with different levels of correctness of overhead deep squat performance.
## 2 Materials and Methods
### X-CT Three-Dimensional Reconstruction  
The imaging principle of the XRM system is shown in Figure 4. The coal sample is positioned between the X-ray source and detector. A conical X-ray beam irradiates the sample, projecting onto a scintillator screen after passing through it. This process results in an initial geometric magnification. The scintillator converts the X-rays into visible light, which is further optically magnified by high-resolution detector optics. The visible light is then projected onto the CCD array detector.  
During scanning, the sample rotates continuously in a circular motion at a defined angular velocity, pausing at specific angles. Projection data are collected at each angle, and 3D tomographic images are reconstructed using cone-beam algorithms.  
After XRM testing of the coal sample (Figure 5a), X-CT tomographic images are exported (Figure 5b). ImageJ software 1.52p is used for image processing, 3D reconstruction, and measurement through the following steps:  
1. Preprocessing of images, including enhancement and filtering;
2. Three-dimensional reconstruction using volume rendering to produce 3D grayscale images (Figure 5c). Grayscale values correlate with the atomic number and density of the measured substances. These values are concentrated in three intervals, corresponding to mineral particles, clay and organic matter intrusions, and the carbon matrix. Pseudo-colors are assigned to these three substances to generate a color image (Figure 5d);
3. Extraction of regions of interest (ROI) using segmentation algorithms, enabling the generation of three-dimensional spatial distribution maps of mineral particles (Figure 5e);  
Figure 4: Schematic diagram of the XRM system imaging principle.  
* Extraction of mineral particles. As shown in Figure 5d,e, mineral particles are randomly distributed in three-dimensional space, while clay and organic matter intrusions form strip-like patterns aligned parallel to the Z-axis of the coal sample.
## 5 Connected Vehicles
### Benefits and Business Opportunities
#### 5.2.5 Driving Behavior Analysis  
Big Data algorithms can comprehensively analyze and evaluate vehicle condition and driving behavior based on the vehicle and driver information tracked and recorded by the Telematics system [96]. Further, the system will alarm drivers of bad driving behavior and recommend targeted vehicle insurance items and active safety applications to reduce driving risks and traffic accidents [97].