esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
We present a directly competitive fluorescence assay for highly sensitive detection of melamine in milk using magnetic dummy molecularly imprinted polymers (MDMIPs). The detection principle is based on competitive binding between the fluorescent label and melamine on the MDMIPs. The fluorescent label was obtained by combining fluorescein isothiocyanate (FITC) with melamine in ethanol and water. MDMIPs were prepared on the surface of Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) nanoparticles using 2,4-diamino-6-methyl-1,3,5-triazine as dummy template. The MDMIPs were characterized and their adsorption capacity was evaluated based on their static adsorption and Scatchard analysis. Results suggest that MDMIPs were successfully coated on the Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) surface and had a core-shell structure. Adsorption experiments suggested that the MDMIPs had higher specific recognition capacities for melamine and FITC-melamine (FITC-Mel) than did magnetic dummy molecularly non-imprinted polymers. Competitive binding between FITC-Mel and melamine was performed under the optimum conditions to determine melamine quantitatively. The linear range of this fluorescence assay was 0.1-20 mg/L for melamine detection. The detection limit was 0.05 mg/L in negative milk samples. The assay was also successfully employed to detect melamine in spiked milk samples, with satisfactory recoveries, i.e., between 70.2% and 92.7%.  
ELISA-like fluorescence assay; magnetic dummy molecularly imprinted polymers; 2,4-diamino-6-methyl-1,3,5-triazine; fluorescein isothiamate; melamine
## 2 Experimental Setup and Mission Architecture
### Tracking Payload  
The tracking payload is composed of several sub-systems, listed below. Most of them act in a redundant way to maximize the possibility to recover the payload:  
1. The APRS transmitter (StratoTrack model by StratoGear [15]) transmits, every few minutes (three available TX periods of 1.5 to 15 min), the position of the HAB collected by the GPS modules embedded in the module, as well as a few additional pieces of information such as the external temperature, GPS lock quality and voltage of its battery pack (Figure 5a). Data are transmitted using the dedicated Very High Frequency (VHF) channel (144.80 MHz) and collected by one or more of the many existing amateur-operated APRS receiving stations and can be accessed using the APRS public website ([[https://aprs.fi](https://aprs.fi)]([https://aprs.fi](https://aprs.fi))).
2. The SPOT Trace Modem [16] transmits the position of the balloon using the Globalstar LEO constellation every few minutes (Figure 5b). The service is very reliable, with the modem capable to successfully transmit the position even if the payload lands deep in the woodlands (as happened 4 times out of 5 in this project).
3. The on-board computer, namely the Eagle Flight model by High-Altitude Science (HAS) [17], that collects data with a periodicity of 6 s from its embedded GPS receivers as well as from its own two temperature and pressure sensors (Figure 5c). One sensor is installed outdoors in order to record the external temperature and pressure while the second one is located within the Styrofoam box.
4. The UHF Beacon, namely a BeeLine Transmitter, that is used to transmit a beacon on 433 MHz used to locate the landed HAB using a UHF Fox Hunter. As the signal  
Figure 4: Schematics of the radio frequency (RF) payload and tracking payload arrangement.  
cannot be emitted while flying, the UHF beacon is only activated at landing, using a relay controlled by a Raspberry Pi 3 microcomputer.
* The 433 MHz controlled buzzer that is used to emit a sound to help recovery of the landed payload during the night or within woodlands.
* A video camera used to record video during the flight. The position of the video camera was changed before every mission in order to adjust the view or to image in different directions.
# Using Modeling to Select the Type of Microwave Field Emitter for Dense-Layer Grain Dryers
## 2 Materials and Methods
### Features of Electromagnetic Field Transmission by Semicircular Waveguides  
Selection of a semicircle waveguide is, first of all, defined by the specific technological features of the convective-microwave processing. Technical implementation of this method requires the drying agent supply into the area of material processing. With regard to the design convenience, waveguides with a semicircle cross-section are an optimal choice.  
The critical wavelength in a circular waveguide, for type \(E_{nm}\) wave, can be found from the following equation [27]:  
\[\lambda_{\text{cr}}=\frac{2\pi r}{u_{mn}}, \tag{12}\]  
or, in application to type \(H_{mn}\) wave:  
\[\lambda_{\text{cr}}=\frac{2\pi r}{u_{mn}^{\prime}}, \tag{13}\]  
where \(r\) is waveguide radius,  
\(u_{mn}\) are roots of Bessel functions and of their derivatives, for type \(E\) wave, and  
\(u_{mn}^{\prime}\) are roots of Bessel functions and of their derivatives, for type \(H\) wave.  
Wave types transferred in circular waveguide, depending on its radius, are presented in Figure 9.  
In a waveguide with a semicircle cross-section, as well as in a circular waveguide, the critical wavelength, for type \(H\) wave, is \(\lambda_{cr}=3.41\) r. For type \(E\) wave, \(\lambda_{cr}=1.64\) r. It means that, for an inner radius of 50 mm of the waveguide, the critical wavelength, for type \(H\) wave, is \(\lambda_{cr}=170\) mm, while for type \(E\) wave, \(\lambda_{cr}=82\) mm. Based on the values of critical wavelengths for a waveguide radius of 50 mm, the propagation of electromagnetic waves of \(E_{11}\) type in such waveguides is problematic. Therefore, mainly waves of type \(H_{11}\) will go through this waveguide.  
Figure 9: Wave types transferred in circular waveguide.
## 2 State of the Art
### Examples of Framework Implementations with Dynamic Configurations
#### 2.4.2 Ewings Framework
In addition to its core services, the Ewings Framework includes several built-in features that enhance its overall functionality:  
* NAT (Network Address Translation): This feature enables IP address mapping, extending the range of networks with active internet connections;
* Mesh Networking: Mesh networking capabilities facilitate the creation of mesh networks using the ESPNOW feature, promoting efficient inter-device communication;
* Background Operation Tools: A collection of tools supporting background operations such as task scheduling, logging, and data-type conversion;
* Device IoT (currently in beta): This feature manages data transmission to an MQTT server, with configurations managed via the local server [24]. Figure 6: Ewings ESP Framework structure (adapted from [24]). The Ewing's Framework can also be suitable for edge computing, especially when considering its compatibility with lightweight, flexible, and distributed applications that need to run close to data sources.
# Low-Complexity Convolutional Neural Network for Channel Estimation
## 6 Numerical Simulation and Analysis
Increasing the number of layers appears especially beneficial at lower SNR levels, though its impact diminishes at higher SNR values. Despite these hardware-oriented design choices impacting the MSE analysis, the proposed CNN outperforms the LS technique by an order of magnitude at low SNR levels and maintains competitive performance at higher SNRs. The largest deviation from MMSE is 0.018 at 5 dB, which is minimal, concerning the ability of the transmission system to recover the information using up-sampling and error correction post-channel estimation [3]. Therefore, with its hardware implementation benefits, this CNN-based channel estimation approach thus proves to be a viable option for 5G NR systems, showing that effective results can be achieved through neural networks without requiring highly complex designs.
# Numerical Modeling of Engineering-Scale Jointed Coal Mass and Confining Pressure Effect
## 2 Model Construction Approach for Engineering-Scale Jointed Coal Mass  
Intact coal and the joint network are the basic components that make up the coal mass [37; 38]. As the size of the coal mass increases, the number of internal joints increases. It is exponentially more difficult to obtain the mechanical parameters of the coal mass using experiments, whereas the use of a discrete element numerical method can fill in the gaps in the laboratory studies of coal specimens in terms of the scale effect and discontinuous mechanical property. PFC2D simulates the mechanical behaviors of coal rock by a large number of particles and contact between particles. In this study, the parallel bond model (PBM) was used for intact coal to achieve translation, rotation, and separation of particles, while the Smooth-Joint Model (SJM) was adopted for joints to achieve the behaviors such as the rupture and large deformation of the material [39].  
The PFC2D 6.0 software (version 6.0) does not preset the material constitutive relations, so it is necessary to assign different contact models to the matrix and joints, respectively, and to calibrate the mechanical parameters for coal mass model construction. The construction of an engineering-scale coal mass model can be accomplished through the following steps.  
1. Carry out the statistics of joint distribution patterns in the coal mass and establish the initial models under the uncalibrated parameters.
2. Calibrate the parameters of the laboratory-scale coal sample model based on the laboratory uniaxial compression results.
3. Establish an engineering-scale model (without joints) based on the laboratory-scale model.
4. Calibrate the joint model parameters based on the direct shear test results.
5. Embed the joints into the engineering model to complete the modeling.
# Routing Schemes in FANETs: A Survey
## 2 Background and Motivating the Need for FANETs
### What Are the Requirements of Routing in FANETs?  
Routing in FANETs has four requirements as follows:  
* High adaptability since UAVs must adapt to the highly dynamic network topology with low node density and cater to link disconnections and network partitions [7]. Hence, route discovery (i.e., establishing routes for data dissemination) and maintenance (i.e., reestablishing routes) must be sufficiently adaptive to improve route reliability (or robustness) in FANETs. This means that the routing table, which maintains the routes and their route costs, must be constantly updated and that reliable routes must be identified.
* High scalability since UAVs must cater to large-scale applications that require multiple UAVs [82] with high or low node density. Hence, route discovery and maintenance must be supported by collaboration and coordination among UAVs to improve network scalability.
* High residual energy since UAVs, which are powered by battery, must establish routes with sufficiently high residual energy to reduce link disconnections and network partitions as a result of node failures [83] in order to prolong route lifetime.
* Low latency since UAVs must cater to real-time (or delay-sensitive) applications, such as collision prevention in multi-UAV swarm, as well as disaster relief and rescue operation. Hence, route discovery and maintenance must reduce latency as investigated in References [84; 85].
* High bandwidth since UAVs must gather data or sensing outcomes from a single or multiple locations and send them to radio access infrastructure for processing and decision making. Hence, route discovery and maintenance must establish routes with high (or sufficiently high) bandwidth.
## 4 Evaluation
### Loose Synchronization  
This VLC system is not strictly synchronized. Thus, if the transmitter and receiver systems are not perfectly in sync, errors might occur. The sampling frequency of the receiving system is hard to adjust, hence the modulation frequency was set to a perfect multiple to get a loose synchronization. The impact of synchronization offsets or phase slipping was evaluated by analyzing the BER of the communication system with slightly different modulation frequencies over a long period of 15 min of communication. With such a long time span, even the best adjusted loose synchronization runs into errors caused by phase slipping some time. The results depicted in Figure 17 show the resulting BER over the chosen modulation frequency. As the camera on the receiving side has a frame sampling rate of 30 Hz, the perfect modulation frequency for the transmitter is 120 Hz. A close-up setup was used to see the whole stripe pattern in the captured image and the modulation frequency was tuned, so that no movement of the stripe pattern could be perceived. This is what was considered the best possible loose synchronization. For the other measurements, the modulation frequency was slightly changed to purposely get a synchronization offset. The results show how such a synchronization offset in this not strictly synchronized system affects the BER.  
For the measurements, the exposure time was set to 1/4000 s and the pulse width ratio was chosen according to the best result of the previous experiment, i.e., 47.6%. As expected, the best BER was achieved with the best possible synchronization. In this case the BER was 0.48%. The bigger the offset, the worse the BER. The worst synchronized modulation frequency that was tested was 119.91 Hz, i.e., the modulation of one bit lasts 25 us too. With this synchronization offset, the BER increased to 1.44%. From this experiment it can be concluded that the better the loose synchronization between transmitter and receiver, the better the BER. However, even if there is a synchronization offset of up to 25 us per modulated bit, the BER is still in an acceptable range. The sign of the synchronization offset, i.e., the direction of the stripe pattern movement, does not matter in this system. In both cases errors are caused by transitions of the stripe pattern moving through the area of the modulated light source. The only difference the sign or direction of the offset causes, is that in the long term for a too slow modulation frequency, one bit from each modulated taillight will be duplicated by the receiver. For a modulation frequency that is too fast, one bit from each modulated taillight will be missing in the received bit stream. However, both cases affect the BER in the same way.
## 3 Methods
### Experimental Setup and Evaluation  
#### 3.4.1 Virtual Image Generation  
The prediction performance of the CGAN-based virtual image generation was evaluated in relation to the different input combinations. Since the virtual Sentinel-2 imagery is predicted using the SAR-based features as inputs after training the CGAN model, the effect of using different SAR-based features was investigated by comparing the case using RVI with the case of only using the dual-polarization backscattering coefficients. Moreover, due to the fact that the CGAN\({}_{\text{2S}}\) presented in this study utilizes the features extracted from all spectral bands of the Sentinel-2 imagery (i.e., VNIR, red-edge, and SWIR bands), two CGAN models using different input spectral bands were additionally considered for comparison purposes. The first CGAN model only utilized four 10 m VNIR bands as inputs for generating virtual VNIR bands (hereafter referred to as CGAN\({}_{\text{VNIR}}\)). As for the second CGAN model (hereafter referred to as CGAN\({}_{\text{RESH}}\)), five resampled red-edge/SWIR band images were utilized to generate virtual red-edge and SWIR bands. Thus, the prediction performance of CGAN\({}_{\text{2S}}\) was compared with CGAN\({}_{\text{VNIR}}\) and CGAN\({}_{\text{RESH}}\) to generate virtual VNIR and red-edge/SWIR bands, respectively (Table 6).  
The prediction accuracy of the generated virtual images was quantitatively evaluated using an actual Sentinel-2 image on 30 July. Two accuracy statistics, including the root-mean-square error (RMSE) and the correlation coefficient (CC), were used as the quantitative measures of prediction accuracy. The structural similarity (SSIM) and the peak signal-to-noise ratio (PSNR) [70] were also used to measure the spatial similarity between the actual and the virtual images. The larger CC, PSNR, and SSIM values and the smaller RMSE value indicate that the virtual image is more similar to the actual image.
## 5 Characterization
### CW Results and Discussion
Thus, high loss means the RSOA moves to  
Figure 10: Measured RIN at 60, 100 and 140 mA RSOA bias currents: (**a**) For the RSOA ASE spectrum-sliced; (**b**) self-seeded RSOA-FCL; and (**c**) externally seeded RSOA colorless transmitters. For all cases, the RIN increases for lower bias current. The externally seeded RSOA shows the lower RIN followed by the RSOA-FCL; (**d**) shows the zoomed in section of (**b**) where the RIN shows peaks corresponding to the longitudinal modes of a 15 m SMF cavity RSOA-FCL. the linear region and the noise is no more suppressed, resulting in a high RIN. Next, Figure 11b shows the measured RIN when using a 0.6, 2, and 3 nm bandwidth OBPFs in the cavity. The 0.6 nm bandwidth gives relatively higher RIN. This can be related to the higher power loss with narrow band filtering. The 2 and 3 nm bandwidths have similar RIN. Another interesting point about the RSOA-FCL is the large number of longitudinal modes due to the extra-long cavity. The length of the cavity is determined by the distribution fiber of the PON, which ranges from tens of meters to kilometers. We can resolve these modes in the electrical domain, and the results are plotted in Figure 12. The plots show the exemplary section of an electrical spectrum for 100 m and 1 km SMF cavity RSOA-FCLs. As previously stated, the FSR of the modes is \(c/2 nL\). The FSRs for the three cavities are 1 and 0.1 MHz, respectively. For a cavity of 1 km length with a FSR of 0.1 MHz and an OBPF filter bandwidth of 2 nm, millions of modes could exist in this source. This highly multimode nature makes it a low-coherence source that is quite susceptible to chromatic dispersion and mode partition noise [21]. Figure 11: RIN characterization of the RSOA-FCL: (**a**) The RIN increases when introducing additional loss in the cavity; (**b**) The impact of the optical bandpass filter (OBPF) bandwidth on the RIN is shown. Figure 12: Resolved longitudinal modes of the self-seeded RSOA-FCL for: (**a**) 100 m and (**b**) 1 km SMF cavities. FSR = \(c/2 nL\), where \(c\), \(n\), \(L\) are the speed of light in vacuum, the refractive index and the length of the SMF cavity.
## 4 Experiments
### Comparison with the State-of-the-Arts
From Table 7 and Figure 10, we can observe that:  
(a) There are varying degrees of reduction in reconstructing face images using different methods. The Uformer, which proves its effectiveness in denoising, requests a particular denoising dataset for training, or else it will be unable to construct reasonable images. On the contrary, the proposed method, which introduces the guiding blocks and PRUM to mine and preserve face structures, has successfully reconstructed face images with noises. (b) Noises like Gaussian and salt-pepper influence face structures (i.e., SSIM) much more than others. However, they can easily be recovered using the proposed method. This is because the natural face images taken from image sensors always contain Gaussian noises, which makes the proposed method familiar with this kind of noise. The salt-pepper noise influences only a few pixels, while the /8 times degradation further weakens its impact. Therefore, it can be overcome by the face structure preserving modules of the proposed method. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **Gaussian** & **Poisson** & **Rayleigh** & **Gamma** & **Exponential** & **Uniform** & **Salt Pepper** \\ \hline Bicubic & 23.96/0.6486 & 21.95/0.6400 & 21.95/0.6398 & 22.02/0.6404 & 22.43/0.6417 & 21.95/0.6400 & 23.98/0.6489 \\ Uformer [15] & 23.77/0.6303 & 21.97/0.6260 & 21.98/0.6253 & 22.05/0.6262 & 22.44/0.6274 & 21.98/0.6258 & 23.81/0.6320 \\ Ours & **27.38/0.7837** & 24.08/0.7817 & 24.09/0.7808 & 24.19/0.7818 & 24.84/0.7818 & 24.10/0.7817 & **27.36/0.7823** \\ Noise HR & 26.49/0.6175 & **26.34/0.9818** & **26.10/0.9322** & **26.43/0.9602** & **26.51/0.8309** & **26.29/0.9638** & 26.40/0.6959 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparisons of different \(\times\)8 SR on face images with noises (PSNR\(\uparrow\)/SSIM\(\uparrow\)). Figure 9: Visual noise images and their HR outputs. Please zoom in for better comparison.
## 2 Related Work
### Depth Estimation  
Monocular depth estimation tackles the problem of estimating scene depth via a single RGB image. Estimating depth/distance from a single RGB image is an ill-posed problem, physically, in the viewpoint of computer vision. However, numerous deep learning approaches have been developed to address this challenge in terms of the maximum probability. Most of these methods are based on the autoencoder architecture. The encoder extracts the embedding features from RGB images and then the decoder uses learnable upsampling layers to progressively recover the depth predictions. Depth estimation networks can be categorized into unsupervised, supervised and self-supervised approaches. Supervised learning networks [8; 9; 10; 11] use GT depths to perform per-pixel regression of depth values, unsupervised networks [13] do not need GT depth data for the training process and self-supervised networks can exploit the support of low-dimensional (LD) depth sensors or pose network [14] to promote the accuracy of monocular depth estimation. For training a self-supervised network, stereo images can be emulated from monocular sequences by training a second pose estimation network to predict the relative poses applied in the projection function [14]. In general, pure supervised learning networks need precise high-dimensional GT depth maps for deepening the learning and the pure unsupervised learning demands of the sufficient RGB stereo image datasets. Thus, they appear unsuitable for estimating high-precision real-world depths under the constraints of only using low-power equipment and anticipating practical perspective distances. Our proposed network, which belongs to the self-supervised approaches, is a very lightweight model for quickly inferring high-precision monocular depths.