esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries  
### System Dynamics  
The dynamic model of a lower limb rehabilitation exoskeleton joint is described as  
\[M(\zeta)\zeta+C(\zeta,\zeta)\zeta+G(\zeta)+\tau^{dis}=\tau+f \tag{1}\]  
where \(\zeta\in\Re^{n}\) is the joint position vector, the positive definite symmetric matrix \(M(\zeta)\in\Re^{n\times n}\) represents the bounded inertia matrix, \(C(\zeta,\zeta)\in\Re^{n}\) denotes the bounded centripetal and Coriolis matrix, and \(G(\zeta)\in\Re^{n}\) is the bounded gravitational vector. \(\tau^{dis}\in\Re^{n}\) is the unmodeled disturbances of the exoskeleton, and \(f\in\Re^{n}\) represents the contact torque between the patient and the exoskeleton. \(\tau\in\Re^{n}\) denotes the input torque.  
**Property 1** ([1]).: \(\dot{M}(\zeta)-2C(\zeta,\zeta)\) _is a skew-symmetric matrix, that is, \(\Im^{T}(\dot{M}(\zeta)-2C(\zeta,\zeta))\Im=0\), \(\forall\Im\in\Re^{n}\)._  
**Assumption 1**.: _The unmodeled disturbances of the exoskeleton \(\tau^{dis}\) are bounded, and there exists a constant \(\tau_{dis}\) that satisfies_  
\[||\tau^{dis}||<\tau_{dis} \tag{2}\]  
The aim of this paper is to devise an adaptive impedance control law, which is based on motion intention estimation, for lower limb exoskeletons during the later stage of rehabilitation. This control law is intended to enable \(\zeta\) to track the desired joint angles \(\zeta_{d}\) while ensuring the safety of the system.  
The \(\zeta_{d}\) satisfies the following expected impedance model:  
\[M^{d}(\zeta-\zeta_{d})+C^{d}(\zeta-\zeta_{d})+G^{d}(\zeta-\zeta_{d})=f \tag{3}\]  
where \(f\) is a known quantity, and the positive definite diagonal matrices \(M^{d},C^{d},G^{d}\in\Re^{n\times n}\) denote the desired inertia, the damping, and the stiffness matrices, respectively.  
In the predefined task, the desired trajectory of the rehabilitation exoskeleton is available for the controller design. However, in this paper, the \(\zeta_{d}\) is unknown and is determined by the consciousness of human autonomous motion.  
Thus, the impedance model (3) is rewritten as  
\[M^{d}(\hat{\zeta}-\hat{\zeta}_{H})+C^{d}(\hat{\zeta}-\hat{\zeta}_{H})+G^{d}(\zeta -\hat{\zeta}_{H})=f \tag{4}\]  
where \(\hat{\zeta}_{H}\) is an estimate of the human motion intention \(\zeta_{H}\), which will be given later.
# E2-VINS: An Event-Enhanced Visual-Inertial SLAM Scheme for Dynamic Environments
###### Abstract  
Simultaneous Localization and Mapping (SLAM) technology has garnered significant interest in the robotic vision community over the past few decades. The rapid development of SLAM technology has resulted in its widespread application across various fields, including autonomous driving, robot navigation, and virtual reality. Although SLAM, especially Visual-Inertial SLAM (VI-SLAM), has made substantial progress, most classic algorithms in this field are designed based on the assumption that the observed scene is static. In complex real-world environments, the presence of dynamic objects such as pedestrians and vehicles can seriously affect the robustness and accuracy of such systems. Event cameras, which use recently introduced motion-sensitive biomimetic sensors, efficiently capture scene changes (referred to as \"events\") with high temporal resolution, offering new opportunities to enhance VI-SLAM performance in dynamic environments. Integrating this kind of innovative sensor, we propose the first event-enhanced Visual-Inertial SLAM framework specifically designed for dynamic environments, termed E2-VINS. Specifically, the system uses visual-inertial alignment strategy to estimate IMU biases and correct IMU measurements. The calibrated IMU measurements are used to assist in motion compensation, achieving spatiotemporal alignment of events. The event-based dynamicity metrics, which measure the dynamicity of each pixel, are then generated on these aligned events. Based on these metrics, the visual residual terms of different pixels are adaptively assigned weights, namely, dynamicity weights. Subsequently, E2-VINS jointly and alternately optimizes the system state (camera poses and map points) and dynamicity weights, effectively filtering out dynamic features through a soft-threshold mechanism. Our scheme enhances the robustness of classic VI-SLAM against dynamic features, which significantly enhances VI-SLAM performance in dynamic environments, resulting in an average improvement of 1.884% in the mean position error compared to state-of-the-art methods. The superior performance of E2-VINS is validated through both qualitative and quantitative experimental results. To ensure that our results are fully reproducible, all the relevant data and codes have been released.  
robot vision; dynamic SLAM; event camera; robust bundle adjustment +
Footnote †: journal: Applied sciences
###### Abstract  
Human space exploration presents an unparalleled opportunity to study life in extreme environments--but it also exposes astronauts to physiological stressors that jeopardize key systems like vision. Corneal health, essential for maintaining precise visual acuity, is threatened by microgravity-induced fluid shifts, cosmic radiation, and the confined nature of spacecraft living environments. These conditions elevate the risk of corneal abrasions, infections, and structural damage. In addition, Spaceflight-Associated Neuro-Ocular Syndrome (SANS)--while primarily affecting the posterior segment--has also been potentially linked to anterior segment alterations such as corneal edema and tear film instability. This review examines these ocular challenges and assesses current mitigation strategies. Traditional approaches, such as terrestrial eye banking and corneal transplantation, are impractical for spaceflight due to the limited viability of preserved tissues, surgical complexities, anesthetic risks, infection potential, and logistical constraints. The paper explores emerging technologies like 3D bioprinting and stem cell-based tissue engineering, which offer promising solutions by enabling the on-demand production of personalized corneal constructs. Complementary advancements, including adaptive protective eyewear, bioengineered tear substitutes, telemedicine, and AI-driven diagnostic tools, also show potential in autonomously managing ocular health during long-duration missions. By addressing the complex interplay of environmental stressors and biological vulnerabilities, these innovations not only safeguard astronaut vision and mission performance but also catalyze new pathways for regenerative medicine on Earth. The evolution of space-based ophthalmic care underscores the dual impact of space medicine investments across planetary exploration and terrestrial health systems.  
microgravity; corneal injury; ocular protection; bioprinting; corneal edema
## 3 Our Proposed Object Detection Algorithm Combined with Varyblock
### Preset Bounding Box  
The author of YOLOv3 employs the K-means approach to obtain the predefined bounding box on the COCO dataset during the training stage. The YOLOv3 k-means approach uses K pairs of width and height values at random as initial cluster centers, making the K-means method susceptible to the initial cluster [55]. The initial clustering center must be artificially determined, and different beginning clustering centers can result in drastically different clustering results. Because the beginning point selection affects the classification results of the K-means method, the K-means++ clustering algorithm can greatly reduce the final error of the classification results. As a result, the scale of the preset bounding box is calculated using the K-means++ clustering technique in this study. It should be noted that the YOLOv3-Tiny network utilized in this paper only requires six anchors; hence, the k-means++ algorithm only uses six cluster centers instead of nine.  
The k-means++ clustering technique enhances the initial point selection, although the rest of the processes are identical to the k-means algorithm. The key notion is that during the initial cluster center selection, the distance between cluster centers should be as large as possible. The preset bounding box is generated using the k-means ++ clustering algorithm in this paper. Algorithm 1 depicts the algorithm procedure. We may considerably minimize the time it takes to locate an object in a picture by creating a preset bounding box.  
```
Input: The bounding box set X of the object in the dataset. Output: Randomly select a sample from the set X as the initial cluster center C1; For each sample x in set X, calculate the distance between it and the initial cluster center C1, denoted by D(x); Calculate the probability that each sample is selected as the center of the next cluster; According to the roulette method, select the next cluster center; Repeat Step 2 to Step 4 until a total of six cluster centers are selected; Use the standard k-means algorithm for set X; Output the bounding box set Y.
```  
**Algorithm 1**The process by which the K-means++ clustering algorithm generates the preset bounding box.  
Before the training, the k-means++ is first applied to the dataset, which will generate six preset bounding boxes. They are utilized in the prediction of objects at various scales, and the location information of the object in the image can be acquired by using constant correction and regression. Taking a set of preset bounding boxes generated during the  
Figure 5: Result of WGAN; (**a**) poor performance; (**b**) good performance.  
experiment as an example, Table 3 shows the preset bounding boxes, which are essentially a set of datasets with known height and width.  
Depending on the size of the object we wish to identify in the image, the size of the predefined bounding box can be changed. It can also be customized for a single category of objects if necessary.
# Mapping a Guided Image Filter on the HARP Reconfigurable Architecture Using OpenCL
## 5 Guided Image Filtering
### OpenCL Code
#### 5.2.3 Channels  
An OpenCL channel behaves as a FIFO, with a producer and a consumer. An OpenCL channel has a limited capacity, and can therefore be saturated. If this occurs, the producer stalls until data can be placed in the channel. In the OpenCL implementation of the guided image filter algorithm, arrays \(a\) and \(b\) are directly sent from kernel 0 to kernel 1 through an OpenCL channel. Moreover, as kernel 1 also needs the guidance image for its computations, kernel 0 sends this array to kernel 2 through an OpenCL channel as well. This is indicated in Listing 3. The use of channels is activated by enabling the pragma cl_altera_channels.  
```
1#pragmaOPENCL EXTENSIONcl_altera_channels:enable
2 channelstructstruct_a_bch_0 attribute((depth(128)));
3 channelstructguidech_1 attribute((depth(128)));
4...
5__kernelvoidguided_filter_0(globalstructimage*restrictinput,
6 globalstructguide*restrictguide)
7{
8...
9 write_channel_altera(ch_0,output_buf);
10 write_channel_altera(ch_1,guide_buf_out);
11}
12
13__kernelvoidguided_filter_1(globalstructimage*restrictoutput)
14{
15 structimage_filteredinput_buf-read_channel_altera(ch_0);
16 structguideguide_buf-read_channel_altera(ch_1)
17...
18}
```  
Listing 3: Use of OpenCL channels between two kernels.  
The keyword restrict in lines 5, 6, and 13 indicates that the memory addressed by input, guide, and output does not overlap. This prevents the compiler from assuming nonexistent memory dependencies which may limit optimizations. In line 2 and line 3, the channels 0 and 1 are declared with the structs for sending the respective data values of each channel and a buffer size of 128 data elements. In line 5, kernel 1 is declared taking a pointer to the filtering input and the guide input to be read from global memory. After applying the required computations, the data, contained by variables output_buf and guide_buf_out are sent through the channels ch 0 and ch 1 to kernel 2. Kernel 2 in line 13 reads data from the channels, performs its computations, and writes its output to the memory location pointed to by parameter output.
# ICARUS--Very Low Power Satellite-Based IoT
###### Abstract
The ICARUS system can selectively change the behavior (e.g., tag sensor duty cycle) of single tags by using a dedicated downlink control channel from the ISS to ground. The paper is structured as follows. In Section 2, we explain the ICARUS system concept, and we list all ICARUS signal parameters for the uplink (UL) and the downlink (DL). In Section 3, the properties of the ICARUS radio wave propagation channel are  
Figure 1: Taxonomy overview of satellite and non-satellite IoT systems. explained, which are important for the digital receiver design. Section 4 provides overview information on the ICARUS tags, where Section 5 will detail link budget parameters and will explain the UL ISS antenna concept. This article closes with Section 6, where first animal behavioral research results are summarized. The main contribution of the paper is twofold: (I) we introduce major technical parameters and design aspects of the ICARUS system; (II) we explicitly explain how the joint time and frequency signal synchronization is realized by means of conjugate complex CAZAC preamble sequences.
## 3 Experimental Results and Analysis
### Droplet Size Distribution Analysis  
The D\({}_{[3,2]}\) of the circular-hole rotating core micro-fog nozzle was almost below 90 \(\upmu\)m when the axial distance was 70 cm, and the droplet size data were very ideal. Therefore, in this section, we selected the droplet size distribution with an axial distance of 70 cm for analysis. There is the droplet size distribution of the circular-hole rotating core micro-fog nozzle under different Ps and different Ds at an S of 70 cm in Figure 4. As can be seen from the cumulative volume fraction curve in Figure 4, the P is fixed, the nozzle outlet pipe diameter is increased, the D\({}_{[3,2]}\) has a little fluctuation, and the overall performance is gradually increasing; when the D was constant, the D\({}_{[3,2]}\) decreased with the increase of the P. As can be seen from Figure 4, the P is fixed, and with the increase of D, the droplet volume frequency peak moves to the right, that is, toward the direction of the increasing droplet particle content; at the same time, it can be seen from the histogram of the capacity frequency spectrum that with the increase of the P, the peak value of the capacity frequency spectrum showed a trend of increasing continuously and then decreasing slowly. Figure 4 is a volume frequency histogram, from which we can also see that when the D was constant, as the pressure of the water supply increased, the droplet size distribution range was smaller than before, and the peak value increased first and then decreased. When the P was constant, with the D increased, the droplet size distribution range became larger, and the peak gradually increased. As we can see, the SMD generally conforms to the normal distribution from Figure 4. When the D remains unchanged, the atomization ability will gradually increase with the enlargement of the P. The enhancement of the atomization ability will help to prevent the breakup of the droplets, resulting in a decrease in droplet size. Keeping the nozzle water supply pressure unchanged and reducing the nozzle outlet diameter, the Weber number of the liquid flow will become larger, which will lead to the increase of the surface disturbance amplitude of the water jet emitted by the nozzle, making the atomization effect of the nozzle more perfect and showing the regularity that the droplet size decreases with the decrease of the diameter of the outlet pipe. Similarly, the turbulence and cavitation generated by the internal structure of the nozzle affect the primary atomization of the jet ejected from the nozzle. When the outlet diameter was set to be small, the turbulence of the liquid in the nozzle would increase, and the local friction of the liquid would increase, resulting in more complete jet damage [35].
# Research on Micro-Vibration Analysis of Segmented Telescope Based on Opto-Mechanical Integration
## 5 Discussion  
The effects of translation and tilt of the primary mirror on the WFE of the segmented primary mirror are comprehensively analyzed by theory and simulation. Piston error and Tip & Tilt error have the most significant effect on wavefront error. The results show that, for the decenter of the X-axis (Y-axis) direction of the sub-mirror, the farther away from the coordinate axis the position of the sub-mirror is, the more obvious the influence of the WFE is. The Piston error in the \(Z\) direction is more serious than the decenter, and the RMS of the wavefront of the system is increased to 0.45\(\lambda\) when the Piston error is 2 \(\upmu\)m. The  
Figure 15: X- and Y-axis cross-sections of PSF at different exposure times (**a**) X-axis cross-section; (**b**) Localized magnification of the secondary peak of X-axis cross-section; (**c**) Y-axis cross-section; (**d**) Localized enlargement of the secondary peak of Y-axis cross-section.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Exposure Time/s** & **X-Axis Ratio** & **Y-Axis Ratio** \\ \hline
0.25 & 0.4490 & 0.6077 \\
0.5 & 0.4572 & 0.6160 \\
0.75 & 0.4546 & 0.6197 \\
1 & 0.4565 & 0.6201 \\
2 & 0.4628 & 0.6207 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ratio of PSF sub-peak to main peak value.  
WFE caused by the tilting of the sub-mirror is not obviously related to the position of the sub-mirror in the range of 1 \(\upmu\)rad, and when the tilting of the sub-mirror is 1 \(\upmu\)rad, the RMS of the system is 0.15\(\uplambda\). The sub-mirror Clock error has a smaller effect on the wavefront, and the error tolerance is 10\({}^{3}\) times larger than that of the tip & tilt error.  
The opto-mechanical integration analysis of the micro-vibration of the optical system is carried out by using the established dynamic data connection. With an acceleration amplitude of 10 \({}^{-3}\)m/s\({}^{-2}\), the system's WFE RMS over a 4 s time horizon was better than 1/14\(\uplambda\) at most moments. The shape of PSF is blurred and the center of PSF is shifted by micro-vibration. The ratio of secondary peak to main peak at different exposure time is used as the evaluation parameter. When the exposure time is 2 s, the ratio of X-axis to Y-axis reaches the maximum of 0.4628 and 0.6207, and the imaging effect is the worst at this time. The simulation process can do micro-vibration simulation test on the ground for segmented telescope, which can provide ideas for the optimization of micro-vibration isolation structure and active optical control.
# Multi-Input Attention Network for Dehazing of Remote Sensing Images
## 4 Experiment and Results
### Metrics  
We used peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and feature similarity (FSIM) as the metrics to evaluate the performance of our models. PSNR is commonly used in image fusion tasks. It measures the ratio between the effective information of the image and the noise, which can reflect whether the image is distorted. The larger the value, the better the quality of the final dehazed image. SSIM describes structural similarity. The closer it is to 1, the higher the similarity with the haze-free image, which indicates a better dehazing effect. FSIM is a variant of SSIM that uses phase coherence to focus more on the contribution of different local features to the overall structure of the image. Still, the closer the value is to 1, the higher the similarity is.
# Performance Analysis of a Novel TCP Protocol Algorithm Adapted to Wireless Networks
## 2 Materials and Methods
### TCP Sockets  
In Linux, the different communication protocols are implemented by sockets, which works as a common interface between the user and the different systems files and device systems. Three data structures for the sockets handling are used, the first is called \"socket buffer\", which store the packet information, the second is called \"socket\", which register the open connections, and the last is called \"sock\", which maintains the open connections state [17].
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 2 Materials and Methods
### Overall Methodology
#### 2.2.6 Study Site Portion Delineation and Extraction  
The orthomosaics and DSMs produced generally have some artifacts and blurred pixels, mostly along image boundaries. To guarantee the good quality of the image intended for processing, it is necessary to delimit and extract portions of the different study sites; this is to get rid of the poor quality (blurring) due to edge effects (Figure 5). Thus, from an initial area varying between 130 and 132 ha for each site, the areas retained at this step for Sites 1, 6, 8, 9, and 11 are 87, 94, 97, 102, and 121 ha, respectively.  
Figure 4: Overview of the flight plan (Site 1) in the Bossématé classified forest.
# A State-of-the-Art Review on the Study of the Diffusion Mechanism of Fissure Grouting
## 2 Current Status of Research on Grouting Materials
### Current Status of Research on the Percolation Effect on Slurry Diffusion in Fissured Media
Current Status of Research on the Self-Expansion Effect of Slurry on the Diffusion of Slurry in Fissure Media  
Most existing diffusion theories are based on chemical slurries such as cement slurry or sodium silicate slurry, often without considering the expansion of the slurry. However, polymer slurries have self-expansion characteristics, so the diffusion theory of polymers in the fissure medium needs to be studied separately. [PERSON] et al. [68] used the finite volume method to discretize two-phase flow control equations, the Youngs algorithm to track the two-phase flow interface, and the Simple algorithm to solve the modified equations. They used the results to establish a diffusion model of high-polymer fissures and verified the correctness of the model through numerical examples. [PERSON] et al. [69] designed a plate fissure-grouting model test device to explore the influence of temperature on the diffusion law of self-expanding high-polymer in a fissure. The experimental results showed that there is a significant influence on the law of temperature on the diffusion of self-expanding high polymers, which helps to study the grouting mechanism and the development of grouting technology for high polymers in fissures. [PERSON] et al. [70] established a diffusion theory formula for high polymers in the expansion stage by solving the force balance equation of the high-polymer fissure expansion diffusion stage microelement and verifying the correctness of the theoretical formula using numerical simulation.
# Thermoelectric Generator-Powered Long-Range Wireless Real-Time Steam Leak Detection in Steam Traps
## 5 Main Implementation and Results
### Device Characterization in an Uncontrolled Environment  
In this section, the device was characterized in an uncontrolled environment (Figure 16). The system was installed on a buoy steam trap, also known as a mechanical steam trap (Figure 16a). To monitor the steam and condensate temperatures, two PT100 temperature probes were placed at the inlet and outlet of the steam trap. Additionally, an ultrasonic microphone with a 50 kHz bandwidth was installed on the steam trap, and data acquisitions were performed at a sampling rate of 100 kbps.  
To protect the microphone membrane from the heat generated by the steam trap, a capsule was developed to maintain a 30 cm separation between the microphone and the  
Figure 16: (**a**) Test of buoy steam trap. (**b**) The gateway. (**c**) Test of the thermodynamic steam trap.  
Figure 15: Power generation characteristics with a set of hot and cold surface temperatures.  
trap. To transmit the sound between the steam trap and the microphone, an aluminum waveguide was designed, effectively reducing the temperature by nearly half. Also, it is possible to show the LoRaWAN Wirnet Kerling gateway (b).  
The same results were obtained in a second set of tests monitoring a thermodynamic steam trap (Figure 16c). In Figure 16, the following are detailed:  
1. The heat-powered all-in-one IIoT.
2. The ultrasound microphone.
3. The inlet and outlet PT100.