esa-sceva/satcom-chunk-collection huggingface dataset:
# An Analysis of MPTCP Congestion Control
## 7 MPTCP Congestion Control  
It terms of congestion control, using multiple subflows for one connection can be complex. With regular TCP, congestion occurs on a single path between two end-hosts. MPTCP uses different paths to transfer data. Generally, two paths tend to experience different levels of congestion and delay [2,19]. For instance, nearly all smartphones are equipped with at least WLAN and 4G/5G interfaces which each experience different RTIs.  
A simple solution is to use a separate congestion control algorithm on each subflow (uncoupled CC). Alternatively, new CC algorithms have been designed in which the total congestion window is shared between all subflows (coupled CC). Figure 5 shows different examples of a single-path TCP and multi-path CC algorithms.  
Whether coupled or uncoupled, MPTCP CC algorithms should aim to achieve three main goals [6]:  
* **Improve Throughput:** Total throughput of subflows should be at least as good as a regular TCP flow on the best available path.
* **Do No Harm:** Subflows should not use more capacity on a shared bottleneck than if they were using a regular TCP flow over any of the paths.
* **Balance Congestion:** Subflows should aim to avoid congested paths while achieving the first two goals.  
The first two goals together ensure fairness at the shared bottleneck. Goal three is based on the concept of resource pooling. When each subflow sends more data through the least congested path, the traffic in the network will move away from congested areas [1,6,32,33].  
To achieve fairness (goals one and two), a CC algorithm needs to detect when there is a shared bottleneck or when the paths are separate. In case of a shared bottleneck, the algorithm needs to estimate what a single TCP would get over the same path and split the link's capacity to that of a regular TCP flow. Similarly, when paths are disjoint, an algorithm needs to ensure subflows achieve higher throughput than TCP.
# A Dual Stage Low Power Converter Driving for Piezoelectric Actuator Applied in Micro Mobile Robot
###### Abstract  
Piezoelectric actuators are widely utilized to convert electrical energy into mechanical strain with considerable potential in micro mobile robot applications. However, the use of Pb-based Lanthanumdoped Zirconate Titanates (PZTs) leads to two difficulties in drive circuit design, namely, high voltage step-up ratio and high energy conversion efficiency. When some devices driven by piezoelectric actuators are used in emerging technologies, such as micro mobile robot, to perform special tasks, low mass, high energy density, and high conversion efficiency are strategically important. When these demands are considered, conventional drive circuits exhibit the disadvantages of being too bulky and inefficient for low mass applications. To overcome the aforementioned drawbacks, and to address the need for a piezoelectric bimorph actuator, this work proposed a high step-up ratio flyback converter cascaded with a bidirectional half-bridge stage controlled, via a pulse width modulation strategy, and a novel control method. Simulations and experiments were conducted to verify the ability of the proposed converter to drive a 100 V-input piezoelectric bimorph actuator using a prototype 108 mg (excluding printed circuit board mass), 169 (13 \(\times\) 13) mm\({}^{2}\), and 500 mW converter.  
piezoelectric actuator; high step-up ratio; high efficiency; small size; micro mobile robot
# An Improved Lightweight Model for Defect Detection on Paths in Images
## 2 Materials and Methods
### ADown Module: Lightweight Down-Sampling Operation  
To maintain the algorithm model's lightweight design and ensure its detection efficiency, this paper introduces the ADown module for down-sampling operations, integrated with the CCFM structure. ADown is a lightweight convolutional technique proposed by [PERSON] et al. [21] that significantly reduces the parameter count while improving accuracy. The ADown module performs down-sampling using convolution, average pooling, and max pooling. The input feature map undergoes down-sampling via an average pooling layer, which reduces its width and height using a defined kernel size and stride. After average pooling, the feature map is divided into two parts using a chunk operation, and the divided feature maps are further processed through separate convolutional layers to extract and refine features. For the second part of the feature split, it first goes through a max pooling layer for further down-sampling to enhance robustness to spatial features. The feature map obtained from max pooling is subsequently passed through a convolutional layer to refine and extract features (as shown in Figure 4). The model concatenates the processed feature maps along the channel dimension and outputs them.  
The ADown module can be employed in YOLOv8 to replace traditional down-sampling operations, such as the Conv module. After integrating the CCFM to improve the conventional PAN-FPN structure of YOLOv8, the ADown module can be seamlessly integrated into the backbone and head sections of YOLOv8, further reducing the number of parameters to achieve a lightweight algorithm model. Within the backbone, it can be utilized for down-sampling between different layers of the feature map, while in the head  
Figure 3: The fusion block in the CCFM module.  
section, it can assist in further refining the resolution of the feature map for more precise object detection. In this study's improvement of YOLOv8s, the ADown module is incorporated into both the backbone and head sections. The ADown module replaces all four Conv modules in the backbone section. Similarly, the Conv modules in two down-sampling operations are also replaced by it, in the head section.
# WaveConv-sLSTM-KET: A Novel Framework for the Multi-Task Analysis of Oil Spill Fluorescence Spectra
## 3 Results and Discussion
### Spectral Analysis
This behavior is likely due to interfacial effects between the oil and seawater, which influence the fluorescence characteristics as well as light propagation effects in the oil film and seawater. These findings indicate that the quantity of spilled oil alters the fluorescence spectral distribution. Therefore, oil-type identification and oil film thickness prediction should be analyzed concurrently to capture their synergistic effects. Separating these tasks with traditional methods may undermine the practical utility of LIF technology in field applications. Figure 6: Fluorescence spectra of oil spills under different thicknesses and laser powers: (**a–c**) show the fluorescence spectra of Shell HELIX 0W-40 at 60 mW, 70 mW, and 80 mW laser powers, respectively; (**d–f**) correspond to Shell HELIX 5W-20; (**g–i**) correspond to Mobil Super 4T 20W-50; (**j–l**) correspond to Mobil Super 1000 x1 Diesel 15W-40; and (**m–o**) correspond to Mobil 2000 5W-40. Figure 7: Fitting curves of the relationship between thickness and fluorescence intensity under static conditions for HELIX 0W-40 and Mobil 2000 5W-40: (**a–c**) show the fitting curve of Shell HELIX 0W-40 at 60 mW, 70 mW, and 80 mW laser powers, respectively; (**d–f**) correspond to Mobil 2000 5W-40. The spectral acquisition results further indicate that the oil type, detection mode, and laser power significantly influence the fluorescence spectra of oil spills. Traditional methods often overlook the interrelations among these factors, raising concerns about their accuracy and practicality. In contrast, our data acquisition system effectively simulates real-world oil spill scenarios. Combined with a multi-task deep learning framework that simultaneously predicts oil type and film thickness, the system enhances the model's comprehensiveness, practicality, and reliability.
# Recent Advances in Additive Manufacturing of Fibre-Reinforced Materials: A Comprehensive Review
## 2 Techniques and Materials
### In Situ Consolidation  
Automated fibre placement (AFP) is the backbone method of in situ consolidation. With the addition of the AM's method of fused filament fabrication (FFF), there can be a plethora of building options. A strip of pre-impregnated fibre is either laid on a building plate and can take normal forms, or on a mould, which could also be printed with FFF [38; 39]. The strip of fibre is pre-impregnated with a thermosetting material like resin epoxy. Its deposition takes place through a nozzle; a drum presses the strip of fibre onto the matrix and is heated on the spot using an external heat source to cure. Electroimpact is one of the companies that adopts this technology on a large industrial scale with robotics of six-axis freedom of motion. Due to the method of the heat treatment, the matrix can be made from the PAEK group of thermoplastics (PEK, PEEK, PEKK, etc.), with low-temperature thermoplastics (nylon, ABS, etc.), or with the use of water-soluble thermoplastics for the construction of matrices. This technique ensures that each layer is fully bonded and solidified during the deposition and before the construction of the next one; thus, the final part has excellent mechanical and dimensional properties, while the curing time is reduced substantially. It is particularly useful for producing high-performance components with complex geometries and tight tolerances, where precision and strength are critical. Carbon and glass fibres are mainly used for reinforcing elements. Each of the previously referred technologies utilizes almost the same materials as the reinforcing and matrix elements. In Table 1, an in-depth possible combination of these materials for each technology is shown.
## 5 Evaluation Results
### Detection Accuracy of the ML Classifier Depending on the Number of Selected Features  
The result of the test demonstrates that the detection accuracy of individual ML classifiers also varies depending on the number of features selected when using the feature  
Figure 18: Detection accuracy by individual ML Classifiers.  
selection technique. As described in Section 4.3, features were selected using three statistical models. They were 1 chi square test, 2 analysis of variance (Anova) F test, and 3 mutual information test, whereby the accuracy of each ML classifier was measured with the application of these statistical models. Figure 19 shows the detection accuracy depending on the number of selected features when the chi square test is used to select features.  
Figure 20 shows the detection accuracy depending on the number of selected features when the analysis-of-variance (Anova) F test is used to select features.  
Figure 21 shows the detection accuracy depending on the number of selected features when the mutual information test is used to select features.  
Figure 20: Detection accuracy when analysis-of-variance (Anova) F test is applied.  
Figure 21: Detection accuracy when mutual information test is applied.  
For all the cases above, it can be verified that the detection accuracy of the Naive Bayes classifier goes down with the increasing number of selected features. Table 10 is drawn by adjusting/tuning the parameters of the model to extract the optimal result, using four features (_t_mean_time_, _t_var_time_, _t_imsi_count_, and _total_count_) selected from the feature selection.
# Drive on a Greener Way: A Case Study on Navigating Cross-Regional Traffic Networks in South China
###### Abstract  
Green navigation presents a challenge to sustainable mobility. Carbon emissions are a key indicator for evaluating the sustainability of a route. Some methods of green navigation consider the fastest and shortest route to be the least carbon-emitting option, solely from the driver's perspective. To address this issue, various studies have incorporated road factors into the sustainability evaluation model and designed static route planning algorithms to minimize carbon emissions. However, there has been no comprehensive analysis of carbon emissions from both the road and the driver perspectives, and the influence of a sustainability evaluation using dynamic traffic states has not been considered. In this paper, we propose a carbon emission evaluation model based on the life cycle assessment (LCA) and a dynamic route planning algorithm that focuses on near-real-time traffic states. First, we develop an evaluation model for carbon emissions from both the road and the driver perspectives using a carbon footprint measurement method. Next, we propose a route planning method with both a static calculation and near-real-time adjustment to minimize carbon emissions. Finally, we select three cases from South China with different characteristics to verify the effectiveness of our model and algorithm. This evaluation model calculates the carbon emissions caused by all parties involved, providing a comprehensive assessment of the total carbon emissions generated by traffic. This approach addresses the problem of traditional route planning, which often fails to account for the influences of variable traffic conditions on the greenest route. We verify the effectiveness of the greenest navigation algorithm and near-real-time green navigation and apply these two aspects to cases where the greenest route is compared with other common navigation results from different dimensions. We compare carbon emissions from vehicles and roads from electric vehicles and gasoline-powered vehicles. If an electric vehicle is equipped with a wind power generator, the proportion of the vehicle's carbon emissions will be very small. For other vehicle types, the carbon emissions will be more than 1000 times the road emissions.  
sustainable mobility; green navigation; route planning; life cycle assessment Article
## 2 System Overview  
Referring to the schematic overview shown in Figure 1, the proposed FESS is assumed installed into a house, together with a PV power plant. The latter is equipped with its own power conversion system and three-phase (or single-phase) connected to the main electrical cabinet. The house is assumed characterized by some single-phase loads, such as domestic lighting systems and various household appliances (washing machine, dishwasher, vacuum cleaner, etc.), which are grouped together and generically denoted by \"loads\". Both PV and loads concur to determine the overall domestic power demand, which can be either positive or negative depending on PV generation and load consumption values. In this scenario, the role of the proposed FESS is to increase the self-consumption, namely to store the excess energy coming from PV during daytime hours, releasing it in accordance with load consumption. Consequently, FESS acts as an energy buffer, by time-deferring PV generation to better match the load profile, thus minimizing the energy exchange of the house with the main grid.
## 3 Vibration Measurement and Analysis  
In this study, we first monitored the support frame and stack of a petrochemical heater before reinforcement, in situ. The heater was 62 m high. The stack of the heater was 47 m long, and its average diameter was 1.35 m. The steel grade of the heater was ASTM A36. We measured the strains of the support frame and stack at selected positions, as shown in Figure 1. These monitoring positions were determined by stress analysis for the original heater shown in Figure 2b using ANSYS.  
Figure 1: \(Cont.\)  
Figure 1: Stress-monitoring positions for the support frame and stack of a petrochemical heater before reinforcement: (**a**) Stress-monitoring positions of the stack (marked by red ellipse); (**b**) Orientation of the stress-monitoring positions of the stack; (**c**) Position G1; (**d**) Position G3; (**e**) Position G5; (**f**) Stress-monitoring positions of the support frame; (**g**) Position V4; (**h**) Position V14; (**i**) Position H11; (**j**) Position H12.  
Figure 2: In Situ photograph and model of the heater. (**a**) in situ photograph, (**b**) model of original heater, (**c**) model of present heater (with reinforcement marked by circle and rectangle).  
From the monitoring results, it was found that the stress of the stack did not exceed the fatigue limit (10.87 ksi or 75 MPa). However, the maximum stress amplitude at location V14 on the support frame was 14.3 ksi (or 98.6 MPa, consistent with 497.2754 \(\mu\) strain, as shown in Figure 3), which exceeded the fatigue limit.  
In the following section, the finite element method was applied to analyze the stress distribution of the heater. We attempted to exert a force in the resonant vibration (northwest/southeast) direction on the heater until the stress at location V14 on the support frame reached 14.3 ksi (or 98.6 MPa). This force was then applied on both the heater before reinforcement and the heater with reinforced support frame. The results were analyzed and compared, and improvement suggestions for the support frame are proposed according to the analysis results so that the operating security of the heater can be improved.
## 3 Methods
### Detecting Anomalies in Real-Time Telemetry Data  
#### 3.3.1 Data Matching Based on a Knowledge Graph  
This study is based on a constructed knowledge graph with feature data information, which allows the generation of feature data subsets representing different spacecraft working states. When real-time telemetry data are received, a reference dataset with a similar working state can be selected from the knowledge graph for comparison to determine whether the spacecraft is in an abnormal condition.  
Figure 7: Visualization of the data processing workflow.  
First, perform matching based on the static attribute data of sensors. According to the spacecraft's mission state and working mode, select the sensor feature datasets with the same mission state and working mode from the spacecraft equipment knowledge graph for matching and assign the corresponding sensor feature data to each sensor's real-time data.  
Next, perform matching based on the dynamic attribute data of sensors. At each anomaly detection interval, group the real-time sensor data according to the sampling time to form a detection sequence and compute the similarity between this detection sequence and the sensor feature data corresponding to the static attribute data matching process. If the similarity obtained from the matching exceeds a predefined threshold, the detection sequence is considered normal. If the similarity is below the threshold, the detection sequence is deemed abnormal, and the abnormal sensor information is used to retrieve the corresponding spacecraft equipment from the spacecraft equipment knowledge graph, outputting the abnormal equipment information.
## 3 Experiment
### AE Monitoring  
After acoustic emission signals arrived at 24 PZTs, they were firstly pre-amplified by 45 dB and then digitized at a sampling rate of 25 MHz. To count the AE events, AE signals from two selected sensors were amplified by 20 dB and then fed to peak detectors with a dynamic range of 100 dB. There were only 1720 AE event waveforms recorded in our test. The P-wave's first motions of AE were selected using the automatic method based on the Akaike information criterion (AIC) [58]. Then, manual checks were performed to prevent the wrong picks. To obtain better location precision, only the source locations of AE events with more than eight P-wave arrival times were determined. The location errors for most AEs were better than 2 mm. To highlight the spatial distribution of AE events, AE density at each voxel was calculated as the reciprocal of the sum of squared distances from each AE location.  
We determined the magnitude \(M\) of AE events following [59]:  
\[M=\log(A_{\text{max}}/A_{0})+M_{0} \tag{9}\]  
where \(A_{\text{max}}\) is the maximum amplitude of the AE signal, \(A_{0}\) is the threshold for detection, and \(M_{0}=-11\) is a calibration parameter to roughly estimate the absolute AE magnitude. Based on the AE data recorded, the maximum likelihood method was used to estimate the frequency magnitude distribution. The temporal changes in b-values were calculated for a running window of a constant AE number \(n=500\). According to the overall AE activity in a specific time window, we calculated the b-values for the \(n\) (\(\geq\)50) closest AEs to each voxel within a maximum radius of R\({}_{\text{max}}\) = 25 mm. The b-map is beneficial for explaining stress redistribution during the fracture.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies  
Designing any system involves various aspects, from the preliminary conceptual design to the final hardware implementation [51]. Energy-efficient design requires a holistic approach which considers all aspects of the system. This includes selecting energy-efficient hardware, designing energy-conscious algorithms which minimize computational overhead, minimizing transmission energy cost, scheduling tasks to optimize resource usage, and leveraging the energy-saving capabilities of the hardware platform. By integrating these elements, one can ensure that the system operates efficiently, reducing overall energy consumption. Neglecting any of these aspects can compromise the system's energy efficiency, making a holistic approach essential.  
In this work, we systematically present the power optimization strategies explored during our review of various FPGA-based WMDs in the related research papers. These strategies were categorized into _\"Designing Energy-Conscious Algorithms\"_, _\"Minimizing Transmission Energy Costs\"_ \"Energy-Efficient Task Scheduling\"_ and _\"Exploiting FPGA Features\"_, as summarized in Figure 3.
###### Abstract  
Chalcogenide glasses have attracted growing interest for their potential to meet the demands of photonic applications in the Mid-Wavelength InfraRed (MWIR) and Long-Wavelength InfraRed (LWIR) transmission windows. In this work, we investigated the photosensitivity to femtosecond laser irradiation of a dedicated chalcogenide glass, along with its possible applications in micro-optics. In order to address the SWAP problem (Size, Weight and Power), this work took advantage of recent techniques in femtosecond laser direct writing to imprint flat and integrated optical systems. Here, we wanted to simplify an infrared multispectral imaging system which combines a lens array and a filter array. Each channel has a focal length of 7 mm and an f-number of 4. We show in this paper that the chosen GeS\({}_{2}\)-based chalcogenide glass is very promising for the fabrication of graded index optics by fs-laser writing, and particularly for the fabrication of Fresnel lenses. We note a very important phase variation capacity in this infrared material corresponding to refractive index variations up to +0.055. A prototype of Fresnel GRIN lens with a refractive index gradient was fabricated and optically characterized in the Vis range.  
optical glasses; chalcogenide; optical components; femtosecond laser writing; IR optics Article