esa-sceva/satcom-chunk-collection huggingface dataset:
# Simple Ultrasonic-Based Localization System for Mobile Robots
## 1 Introduction
From office spaces to industrial labs, this summary highlights the evolution and challenges in achieving precise indoor positioning [18]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**System**} & \multirow{2}{*}{**Room Size m\({}^{2}\)/ Environment**} & \multicolumn{2}{c}{**LOS/**} & \multirow{2}{*}{**2D Accuracy [m]**} \\  & & & & **NLOS/** & **Mean \(\pm\) Std** \\ \hline \multirow{4}{*}{2014} & 802.15.4a & \multirow{4}{*}{\begin{tabular}{c} compliant \\ UWB System \\ \end{tabular} } & 5.3 \(\times\) 11.5/ & \multirow{4}{*}{\
\begin{tabular}{c} LOS (Static) \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} \textless{}0.4 \(\pm\) 0.04 \\ \end{tabular} } \\  & & & LOS (Dynamic) & 0.89 \(\pm\) 0.08 \\  & & & NLOS (Dynamic) & 0.88 \(\pm\) 0.1 \\
2016 & ATLAS & Laboratory & LOS & 0.21 \\
2017 & BeSpoon & 12 \(\times\) 12/Industrial & Mix & 0.71 \\  &
\begin{tabular}{c} Ubisse \\ DecaWave \\ \end{tabular} & Laboratory & & 1.10 \\
2019 & Pozyx & Industrial & LOS (1.5 m) & 1.5 \(\pm\) 0.03 \\  & & Laboratory & NLOS (1.5 m) & 1.75 \(\pm\) 0.03 \\  & & & LOS (10.9 m) & 11.6 \(\pm\) 1.7 \\  & & & NLOS (10.9 m) & 11.6 \(\pm\) 4.4 \\
2019 & TimeDomain & Galvanic & LOS (Static) & 0.38 \\  & PulsON440 & Industry & NLOS (Static) & 0.22 \\
2019 & Pozyx & Industrial & LOS & 0.22 \\  & & Laboratory & NLOS & >1 \\
2020 & DecaWave & Industrial & LOS (Static) & 0.01 \(\pm\) 0.01 \\
1997 & Active BAT & Office & LOS & 0.03 \\
1998 & Prototype & 0.5 \(\times\) 0.4 & LOS (Static) & 0.04 \(\pm\) 0.01 \\
2000 & MIT Cricket & Office & LOS & 0.1 \\
2003 & DOLPHIN & Office & LOS & \\
2010 & LOSNUS & Office & LOS (Static) & 0.001 \\
2011 & Prototype & 1.2 \(\times\) 1.8 m & LOS (Static) & 0.03 \\
2016 & Prototype & Laboratory & LOS (Static) & 0.02 \\
2017 & Prototype & Laboratory & LOS (Dynamic) & 0.012 \\  & Decawave TREK1000 & 24 \(\times\) 14/ & \multirow{4}{*}{
\begin{tabular}{c} -0.2 \\ (UWB and US) \\ \end{tabular} } \\
2019 & (UWB) & Industrial & Mix (Static) & (UWB and US) \\  & Locate-US (US) & Laboratory & & \textless{}0.2 (US) \\  & & & robot) & \textless{}0.12 (UWB) \\  & & & Mix (Dynamic- & \textless{}0.65 (US) \\  & & & moving person) & \textgreater{}0.5 (UWB) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons between different technologies [18]. The system we are developing is based on trilateration to determine the position of the robot in the room relative to at least three tags. It is a system that assumes very low costs and easy implementation into the selected environment, as well as simple configuration of the entire system in that environment. The disadvantage may be that the system consists of multiple tags that must be installed with sufficient density above the space in which the mobile robot will move. Distances from the robot to the tags are measured using ultrasonic waves in one direction from the tag to the robot. Here, reliable and, as far as possible, accurate time synchronization of tags with the receiver is also necessary [14; 15].
# Reducing System Load of Effective Video Using a Network Model
## 2 Related Work
### Bicubic interpolation  
By using bicubic interpolation--a traditional upscaling method--the size of LR is the same as that of HR. Bicubic interpolation is a method of estimating the value of a new pixel as the pixel is increased by using the values of 16 adjacent pixels and weights according to distance.
## 1 Introduction
### The Luminescence Mechanism of AIEgens  
Common organic fluorescent dyes used in chemical sensor detection, such as rhodamine, fluorescein, and indocyanine green, are known for their high fluorescence quantum yields and sensitivity. However, they also exhibit limitations, including short Stokes shifts, poor photostability, and insufficient water solubility, which hinder their practical applications. The introduction of AIE molecules has transformed the understanding of photoluminescence properties in materials.  
The luminescence of AIE materials is widely attributed to the restriction of intramolecular motion (RIM), which encompasses both intramolecular rotation (RIR) and intramolecular vibration (RIV) [47; 48; 49; 50; 51; 52; 53; 54]. In a good solvent, AIEgens typically experience nonradiative decay as energy is dissipated through these intramolecular motions. However, when a poor solvent is introduced, AIEgens aggregate, which suppresses intramolecular motion and shifts the energy dissipation pathway towards radiative decay. Unlike conventional organic fluorescent molecules, the aggregated state of AIEgens inhibits intermolecular stacking, resulting in high fluorescence quantum yields and exceptional photostability in both aggregated and solid states.  
Currently, AIEgens based on the classical RIM mechanism include tetraphenylethylene (TPE), hexaphenylislole, cyanostilbene, and 9,10-distyrylanthracene, all of which exhibit remarkable AIE characteristics and excellent luminescence controllability. [55; 56; 57] These materials are easy to synthesize and functionalize, making them valuable for applications in pesticide detection. Furthermore, \"irregular AIEgens\" [58] and natural AIEgens [59] have emerged, broadening the scope of AIE materials. Irregular AIEgens, which include nanodots, metal nanoparticles, and non-conjugated polymers, lack rotating aromatic moieties but still demonstrate superior optical properties, multifunctionality in surface modification, unique surface plasmon resonance, and electrical catalysis. These materials hold significant potential for detecting pesticide residues [60; 61]. Natural AIEgens, derived from sustainable biomass materials, offer advantages such as sustainability, low cost, biocompatibility, and ease of preparation. They particularly excel in pest and disease management [62]. Overall, advancements in molecular engineering have resulted in diverse structural forms of AIE molecules, providing a robust foundation for designing innovative AIE probes.
# Surface Plasmon Resonance-Based Fiber Optic Sensors Utilizing Molecular Imprinting
## 3 Fiber Optic Surface Plasmon Resonance Sensors  
The use of optical fiber for implementing SPR started in 1990 [38; 39]. After that the fiber optic SPR (FO-SPR) sensors have been reported for gas, chemistry and biology-related samples [40; 41; 42; 43]. For the fabrication of a FO-SPR sensor, the cladding of the fiber is removed from the fiber structure leaving the bare core exposed for the length of sensing region selected. The metal layer is coated directly on the bare core of the fiber on top of which dielectric and other coatings are used if needed. The length of the sensing region needed is decided in accordance with the metal and dielectric layer chosen for a particular application. The sensing length influences the number of reflections of the ray in the core which further affects the width of the SPR curve [31]. The spectral interrogation scheme is used in fiber, instead of angular interrogation, since a range of guided rays are launched into the fiber [32]. The guided rays generate evanescent waves by total internal reflection which excites surface plasmons at the metal-dielectric interface. The excitation of surface plasmons through the evanescent wave is generally affected by three important factors: the fiber geometry (such as core diameter, probe geometry, V number, sensing length), the incident light properties (like angle of incidence and wavelength) and the metal thin film parameters (choice of metal, film thickness, film roughness) [31; 32]. The sensing application involves the calibration curve of analytes by plotting the transmitted power from the fiber as a function of wavelength. The wavelength at which the dip occurs in the SPR curve gives the resonance wavelength, which changes with the change in the refractive index of the sensing medium.  
The fiber-based sensor can be used in two configurations namely: transmissive and reflective. In transmissive configuration, the sensing region is fabricated somewhere in the middle of the fiber of chosen length, with a metal coating over its core followed by the dielectric region. The light is coupled from source to the fiber through one end and the reflected light from the interfaces of the sensing region reaching the other end of the fiber is coupled to the analyzer [44]. The reflective configuration involves a thick metal coating on one of the end face of the fiber and the sensing probe is also prepared at this end of the fiber after the removal of cladding. The incident light gets reflected from the mirror-like thick metal coating [40]. Earlier it was reported that implementation of the transmissive concept was difficult with less reproducibility and affecting sensor performance [38], but from the late 90 s till now, the transmissive method-based FO-SPRs have reported high recovery and reproducibility with easy implementation, biocompatibility and best results for a variety of applications.
# Digital Twin for Human-Robot Collaboration in Manufacturing: Review and Outlook
## 4 Challenges
Various safety challenges need to be considered to enable a safe human-robot interaction environment [153,199]. DT prototypes can assist with the safe planning of HRC scenarios, while the DT instance can monitor and plan the movement of the robots to ensure real-time safety feedback. However, the degree of fidelity of these simulation models in HRC still lacks clear definition and clarity. A number of reports suggest how DT can add value to a manufacturing operation and how DT can help solve the problems faced in the manufacturing environment [200, 201, 202]. However, DT is a very long way from achieving what it is fully capable of doing. The research reported in the literature either describes examples of different architectures that can be used to build the DT, or demonstrates a DT which captures only a small part of the physical process. Full-scale DTs, incorporating all the essential aspects of a system from control to production management to the collaboration of an HRC employed in a shopfloor, are yet to be developed. The dynamic updating of the DT model relies on information from various points, such as sensor data, controller data and management tools. 5G wireless network systems that provide low latency and higher bandwidth for faster communication can potentially overcome the challenges faced within CPS and IoT [203]. Therefore, it can contribute to the advancement in DT-HRC technology for manufacturing processes [204,205]. Furthermore, a 5G network coupled with high-performance computing (HPC) and simulation software can potentially improve the fidelity of the DT model, thereby providing a more realistic representation of the real-world model [206]. Additionally, this can enable access to the smart factory paradigm to remotely monitor and control aspects of the real-world model.
## 2 Materials and Methods
### Pigments Sample Preparation  
The microalgae carotenoid extract was prepared by mixing 1 g of finely powdered, dried microalgal biomass with 2.5 mL of 2 M ethanolic KOH and 10 mL of DCM. The sample was then sonicated for 1 h in a sonicator bath ([PERSON], LMUC-3), followed by 2 h of temperature-controlled orbital shaking at 300 rpm using a shaker (REMI, CIS 24 BL). Then, 2.5 mL of DCM was added, vortexed briefly, and centrifuged (Centrifuge 5810R, Eppendorf, Germany) at 4000 rpm for 10 min to obtain the supernatant. The aforementioned procedure was repeated using the residue pellet and DCM until a colorless solvent was visible. The cumulative supernatant portion was dried using a rotary evaporator (BUCHI, R-210) operating at 40 \({}^{\circ}\)C. Subsequently, 7 mL of 50% aqueous ethanol (\(v/v\)) was added to the dried residue, followed by 10 min of centrifugation at 4000 rpm, and the supernatant was collected as the microalgal carotenoid extract [29].  
Ultra-High Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry (UPLC/Q-ToF-MS) Analysis  
The extracts containing microalgal carotenoids were analyzed using the Acquity H class series UPLC system (Waters, Synapt XS HDMS) coupled with a quadrupole time-of-flight mass spectrometry (QToF). A 5 \(\mu\)L sample was injected into the Acquity UPLC C18 BEH column (2.1 \(\times\) 50 mm, 1.7 \(\upmu\)m particle size, Waters) using electrospray E5 with a 5 \(\upmu\)L injection volume and a flow rate of 0.25 mL min\({}^{-1}\). The mobile phase consisted of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B) solvent. The initial condition was 90% A--10% B (0-2 min), followed by 80% A--20% B (2-5 min), 70% A--30% B (5-10 min), 50% A--50% B (10-12 min), 90% A--10% B (12-14 min), and thereafter. The mobile phase and column were then allowed to return to their initial states of equilibrium. Electrospray ionization (ESI) acquisition was performed with the Auto MS/MS method in positive mode. For the mass spectrometer, the following parameters were used: desolvation gas at 950 L/h, cone gas at 30 L/h, desolvation temperature at 450 \({}^{\circ}\)C, source temperature at 120 \({}^{\circ}\)C, capillary voltage at 3.22 keV, cone voltage at 50 V, and collision energy at 4 ev. The gases used were N\({}_{2}\) and Argon, with a pressure of 6-7 bar and 5-6 bar, respectively.
## 3 Application of Integrated Transducer Arrays for Generation of Sound Waves
### Experimental Investigations
#### 3.3.2 Microphone Measurements  
Due to the excitation of the integrated actuator-array mechanical flexural plate waves were generated. In regard to the achievement of a directed radiation of plate waves, the excitations of the transducers were delayed 24 us relatively to the previous transducer. In regard to the directional characteristic of the acoustic wave and the defined radiation angle, the spatial distribution of sound pressure was recorded. For this purpose a 4'' free-field microphone (MK301, Microtech Gefell, Gefell, Germany) was used and the sound pressure was determined at different heights (10 mm, 30 mm, 70 mm) over the plate. The raster of the discrete measuring points in each height was set to 20 mm. Figure 13 shows the planar coordinate system in the plate plane, whereupon the origin was set to the end of the first transducer element.  
The recorded time series of out-of-plane velocities and sound pressures obtained using the laser Doppler velocimetry technique and microphone provide a basis for the subsequent analysis regarding the mechanical and acoustic wave propagation. The results of this analysis are presented in this section.
# Curvature Sensing and Control of Soft Continuum Robots Using e-Textile Sensors
## 5 Results and Discussion
### Performance of the Jacobian-Based Shape Control  
An experiment was conducted to implement feedback curvature control using the proposed Jacobian-based controller. The trained shape sensing CNN model was integrated into the control scheme commanding the robot to reach the desired curvature and angle of curvature plane. The Arduino Uno was used to control the servo motors according to the obtained driving angles.  
In Figure 12, the Jacobian-based shape control scheme is assessed for accurately reaching the desired \(\kappa_{d}\) and \(\phi_{d}\). By selecting the gain \(K=0.25\), the system initially exhibited instability with significant oscillations and prolonged response times, as shown in Figure 12a,b. As a result, the system needed tuning to improve performance. By adjusting the gain parameter, \(K\), the system's behavior was significantly enhanced, with setting \(K=0.5\) resulting in a notable improvement in system response, as evidenced in Figure 12c,d. The system demonstrated stability and a more desirable response, indicating successful optimization of the system through gain tuning. In conclusion, meticulous design and tuning of the system parameters played a crucial role in achieving stable and efficient system performance. The iterative process of modeling, testing, and tuning the system parameters proved instrumental, highlighting the importance of the proposed control scheme.  
Figure 12: Results of the Jacobian-based control used for reaching the reference desired shape parameter (\(\kappa\)) at (**a**) \(K=0.25\) and (**c**) \(K=0.5\) gains, while controlling the shape parameter (\(\phi\)) at (**b**) \(K=0.25\) and (**d**) \(K=0.5\) gain values.
## 1 Introduction
The innovative aspect of this work lies in the synthesis of ZIF-8 via a scalable, rapid synthesis route and its incorporation into cellulose filters using the kraft pulping method, which enhances the filter's structural integrity and adsorption efficiency. We systematically investigate the effects of synthesis parameters, including stirring time and pH, on the particle size and morphology of ZIF-8 and evaluate the CO\({}_{2}\) adsorption capacity of both ZIF-8 and ZCAF relative to the conventional monoethanolamine (MEA) reference. The findings demonstrate that the ZIF-8-based cellulose filters exhibit significantly improved CO\({}_{2}\) loading, with up to a 41% increase in adsorption capacity for ZIF-8 and a 22% increase for ZCAF compared with MEA. This study not only highlights the potential of ZIF-8 as a high-performance adsorbent but also introduces a sustainable and scalable approach to fabricating air filters with enhanced CO\({}_{2}\) capture capabilities. Our work contributes to the ongoing development of advanced materials for environmental remediation and offers a pathway toward more efficient and eco-friendly CO\({}_{2}\) capture technologies.
# Graph-based Method for App Usage Prediction with Attributed Heterogeneous Network Embedding
## 1 Introduction
Then, our proposed framework transforms the historical App usage records in physical spaces to a large attributed heterogeneous network and the representation learning process is conducted, which can reduce the number of feature dimensions while making full use of various information to mine potential features. Eventually, the App usage prediction is formulated as a link prediction problem based on a Neural Network. More concretely, our main contributions in this paper to App usage prediction are three-pronged:  
* First, we introduce network embedding to App prediction problem. By converting application, location, and temporal information into different types of nodes in a heterogeneous network and mapping related attribute information into the same potential space, we can capture the relationship between Apps and time or location or the previous App. * Second, we propose a novel App usage prediction framework named AHNEAP based on representation learning on the attributed heterogeneous network. The framework contains three major components: data pre-processing, representation learning, and link prediction. The data pre-processing generates an attributed heterogeneous network from historical App usage records. Then, representation learning produces universal embedding features for elements related to the App usage e.g., App name and timestamp. Finally, the link prediction process completes the fusion of embedding of the time, location, and the previous application obtained from the previous step firstly, then constructs a neural network to decide the importance of the three types to predict the most likely used App within a certain period of time. * Finally, we conduct extensive experiment evaluations with the _LiveLab_ App usage dataset, and demonstrate and analyze the prediction performance about the top-k candidate applications. Experiments show that AHNEAP outperforms the baseline methods: Most Frequently Used (MFU), Most Recently Used (MRU), and traditional Bayes. The rest of the paper is organized as follows: Section 2 provides a coarse introduction of related work. Section 3 shows our graph-based App usage prediction framework. In Section 4, extensive experiments to evaluate the performance of the new model are conducted and analyzed. Finally, we summarize and conclude this paper in Section 5.
## 4 Results and Discussion
### Summary
#### 4.3.2 Permeability  
The same samples previously used to determine the loading curves were used to perform permeability tests, as summarized in Figure 15. An additional scaffold sample of intermediate pore size (medium 130 \(\upmu\)m capillary radius) was used.  
Permeability increased with pore size, regardless of the technology used to manufacture the porous medium. In Figure 16, a zoom in on Figure 15 for the pore size 90, a permeability of \(1.16\times 10^{-10}\) m\({}^{2}\) for a similar pore size (90 \(\upmu\)m), is shown for the SLM sample, 2.276% higher when compared with the result of \(4.9\times 10^{-12}\) m\({}^{2}\) for copper sintering.  
Another key aspect is the pore arrangement inside the porous media that was revealed through these comparative tests. Intuitively, it can be deduced that the more complex this pathway is, the lower  
Figure 16: Comparative test on permeabilities between sintering and scaffold for a similar pore size of 90 \(\upmu\)m.  
Figure 15: Comparative of permeability.  
the permeability results, which may be clearly observed from the previous figure when comparing the permeability of the powder sintering and the scaffold. A permeability value that was two orders of magnitude higher than the value for the powder sintering method with similar pore sizes demonstrates one of the great advantages of the SLM technique.
# SMR-RS: An Improved Mask R-CNN Specialized for Rolled Rice Stubble Row Segmentation
## 2 Materials and Methods  
### Dataset  
All experimental data were collected during the daytime on 9 August and 10 August 2022 in Cailing Township, Duchang County, Jiujiang City, Jiangxi Province, China, within 48 h after the first season's harvest, in sunny weather. They were photographed with the handheld collection equipment at a height of approximately 1.5 m above the ground and with the camera facing in the direction of the extension of the rolled rice stubble rows. The collection equipment was a Canon m6 camera; 856 images were collected, each containing 1-2 complete rolled rice stubble rows. The rolled rice stubble rows in the field can be divided into three main categories:  
1. Heavily covered by straw discharged from the harvester.
2. When just after harvesting, with a straw-green color.
3. When the harvest is finished a few hours later, yellowish in color.  
These three categories are displayed as shown in Figure 1.  
All the collected images were cropped, and the resolution was standardized to \(1080\times 1920\), and LabelMe software was used to label the images. Each target object in each image is labeled with segmentation and localization information, and there is only one object category, i.e., the rolled rice stubble row, and the labeled image is shown in Figure 1. The labeled images were enhanced by randomly flipping them horizontally to double their number, and the training and test sets were divided in a ratio of 8:2 to obtain 1317 images for the training set and 397 images for the test set.  
Figure 1: Dataset samples. Three different rolled rice stubble rows: (**a**) heavily straw-covered; (**b**) yellowish; (**c**) greenish.