esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Proposed Method
### Enhanced Anomaly Discriminator  
In rail fastener anomaly detection, the successive downsampling operations in encoder architectures induce progressive loss of high-resolution image features, significantly compromising the detectability of small-scale anomalies. To address this limitation, the proposed method integrates an enhanced joint pyramid upsampling (E-JPU) module within the discriminator network, as illustrated in Figure 5. The module applies convolution operations on the acquired Conv2, Conv3, and Conv4 feature maps to extract additional feature information. An upsampling operation is then applied to recover the spatial size of the lower-resolution feature maps for subsequent fusion. Next, a \(1\times 1\) convolutional layer is added to capture finer details of the fastener. The feature maps \(y_{a}\) at different resolutions are concatenated and fed into branches with dilation rates (\(D_{r}\)) of 1, 2, 3, and 4, respectively, to capture contextual information at different scales. These concatenated feature maps \(y_{b}\) are then passed through a convolutional layer, where the number of channels is adjusted before feeding the output feature maps \(y_{c}\), containing enhanced feature information, into the enhanced anomaly discriminator.  
In the inference stage, both the teacher generator and the student generator reconstruct the input images. The student generator uses enhanced feature maps for reconstruction; if an image contains anomalies, significant differences occur between the reconstructed and input images. The anomaly score \(A\), calculated based on the enhanced anomaly discriminator output, quantifies the anomaly level of the images. The formula for calculating the anomaly score is in Equation (10):  
\[A=\Phi\left(\frac{\mathrm{D}(\mathrm{G_{s}}(\mathrm{E}(I)))-\mu}{\sigma}\right) \tag{10}\]  
where \(\phi(.)\) is the sigmoid function, \(\mathrm{E}\), \(\mathrm{G_{s}}\), and \(\mathrm{D}\) are the encoder, student generator, and enhanced anomaly discriminator, respectively, and \(\mu\) and \(\sigma\) are the mean and standard deviation computed over a normal sample distribution.  
The threshold \(\tau\) for the anomaly score \(A\) is set based on the distribution of normal samples. First, the mean \(\mu\) and standard deviation \(\sigma\) of the normal samples are computed. Then, the threshold is defined as \(\mu+\mathrm{k}\sigma\), where \(\mathrm{k}\) is a hyperparameter. An anomaly score  
Figure 5: Enhanced joint pyramid upsampling module model structure diagram.  
is typically considered normal if it is less than or equal to the threshold, while scores exceeding the threshold are classified as anomalies.
## 5 Modelling Rationale
### Modelling Assumptions
#### 5.3.2 Failure Model and Fault-Tolerance Coverages of the Modelled System
If this happens the intended listeners do not receive the frame and, thus, they cannot carry out their operation properly. & Yes \\ Frame replication counter corrupted, creating \(n\
eq k\) replicas. & \(n>k\). If a device creates more replicas than expected their transmission may affect the transmission of correct scheduled frames. & No \\  
manifest as stated in the failure model. Second, we find the error containment coverage, which refers to the probability with which an error containment mechanism effectively contains faults. In our models, the assumption coverage is a parameter that can be modified, but in this article we have established an assumption coverage of 100% for three reasons. First, the assumption coverage of fault-tolerant systems is usually high, as these systems are conscientiously designed to ensure that they exhibit the failure semantics that it is assumed for them. Second, we tend to assume Byzantine failure modes for most devices, which is the least restricted mode and thus corresponds to 100% assumption coverage. Third, we assume that most failure modes cause the failure of the stream, which is a sufficiently pessimistic assumption. Regarding the coverage of the error containment mechanisms, we must note that the error containment mechanism used to drop erroneous frames is Ethernet's CRC. As we have discussed in Section 4, the CRC can detect virtually all erroneous frames and, thus, we assume a coverage of 100%. We must highlight that assuming a lower coverage would result in an unfair evaluation of the reliability achievable not only by PTRF but also by TSN.
# Catalytic Degradation of 4-Ethylpyridine in Water by Heterogeneous Photo-Fenton Process
## 1 Introduction
The decomposition of H\({}_{2}\)O\({}_{2}\) on the surface Fe\({}_{3}\)O\({}_{4}\) produces radicals species (HO\({}_{2}\)\({}^{\bullet}\) and HO\({}^{\bullet}\)) that are capable of degrading any type of organic pollutant according to the following equations (Equations (3) and (4)):  
\[\parallel\mathrm{Fe}_{\mathrm{Surface}}^{3+}+\mathrm{H}_{2}\mathrm{O}_{2} \rightarrow\parallel\mathrm{Fe}_{\mathrm{Surface}}^{2+}+\mathrm{HOO}^{ \bullet}+\mathrm{H}^{+} \tag{3}\]  
\[\parallel\mathrm{Fe}_{\mathrm{Surface}}^{2+}+\mathrm{H}_{2}\mathrm{O}_{2} \rightarrow\parallel\mathrm{Fe}_{\mathrm{Surface}}^{3+}+\mathrm{HO}^{ \bullet}+\mathrm{H}^{+} \tag{4}\]  
The catalyst can be easily collected by magnet or by filtration and be regenerated and recycled for additional experiments. The efficiency of natural and synthetic Fe\({}_{3}\)O\({}_{4}\) heterogeneous catalysts in the production of HO\({}^{\bullet}\) radicals by decomposition of H\({}_{2}\)O\({}_{2}\) was demonstrated by several authors [35; 36; 37; 38; 39]. Furthermore, it was also reported that the combination of Fenton-like oxidation using Fe\({}_{3}\)O\({}_{4}\) as a catalyst with UV irradiation enhanced the efficiency of the whole process [35; 36; 34]. The hydroxyl radicals produced by photo-decomposition of H\({}_{2}\)O\({}_{2}\) (Equation (2)) attack the molecules adsorbed on the surface of the catalyst degrading them and make more catalytic sites available. The formation of stable iron (III)-carboxylate complexes would be minimized and higher mineralization yield would be obtained. The aim of this present work was to investigate the degradation of 4 EP in water by heterogeneous photo-Fenton oxidation using Fe\({}_{3}\)O\({}_{4}\) as a catalyst and to demonstrate the reuse of the heterogeneous catalyst several times in photo-Fenton oxidation. Different methods of activation of the catalyst were used, including acid treatment, drying at room temperature, and heat treatment at 100 \({}^{\circ}\)C, 250 \({}^{\circ}\)C, and 500 \({}^{\circ}\)C. The effects of principal experimental parameters, such as initial pH, Fe\({}_{3}\)Odose, H\({}_{2}\)O\({}_{2}\), and 4 EP concentrations on the performance of 4 EP degradation and mineralization by heterogeneous photo-Fenton process were evaluated. 4 EP and the formation of organic intermediates during degradation experiments were monitored by high-performance liquid chromatography (HPLC). The formation of inorganic nitrogen species (NO\({}_{3}\)-, NO\({}_{2}\)-, NH\({}_{4}\)+) as mineralization end-products were monitored by ion chromatography. Accordingly, a simple 4 EP degradation mechanism by the heterogeneous photo-Fenton process was proposed.
## 4 Discussion
Moreover, our simulation results suggested that the maximum von Mises strain in the ICP occlusal mode was 4.6-times higher than that in the complete mandible model without ICP occlusion. Therefore, the occlusal force encountered in daily life had a considerable effect. However, during orthodontic treatment, the jawbone was not constantly subjected to occlusal force. This study only demonstrated that occlusal force could affect simulation results. Therefore, we recommend that the analysis of tooth movement caused by orthodontic force should incorporate occlusal force. Numerous studies have assumed that the PDL is a linear elastic material [10; 11; 17; 18; 19; 20]. However, the PDL is mainly composed of collagen fibers [34], and its material properties are more similar to those of a nonlinear elastic material. Numerous studies have directly measured PDL material properties, but relevant studies have suggested that the material properties of soft tissues are easily affected by freeze-thaw cycles at temperatures of \(>\)20 \({}^{\circ}\)C [35]. Unfrozen human cadaver jawbone specimens are unavailable, and experiments on most human cadaver bones or tooth specimens can only be conducted after a freeze-thaw cycle. [PERSON] et al. [22] and [PERSON] et al. [24] have conducted mechanical experiments on mandibular fragments of human corpses to directly measure the elastic modulus of the PDL. Experimental results have suggested that the PDL is a bilinear elastic material. [PERSON] et al. [21] and [PERSON] et al. [23] have measured the PDL elastic modulus in rat and miniature pig specimens and have also demonstrated that the PDL has bilinear elastic properties. Our simulation results for the bilinear elastic PDL model differed from those for the linear elastic PDL model, which is in agreement with the findings of other studies [36; 31]. When the PDL was assigned a low linear, moderate linear, high linear, and bilinear elastic modulus, the maximum von Mises strains on the canine PDL were 6.047, 2.594, 0.887, and 1.811, respectively. However, because of the limitations of the different finite-element models and methods of exerting orthodontic force, we were unable to conduct an absolute-value comparison between our simulation results and those of other studies. However, the simulation results suggested that strain was more concentrated in the PDL when the PDL was assumed to have a linear elastic modulus, and it was more evenly distributed when the PDL was assumed to have a bilinear elastic modulus. Although we established a complete mandible model and simulated the ICP occlusal mode, this study had several limitations. First, orthodontic wire was not used in the finite-element model. Thus, friction between brackets and orthodontic wire was not considered. Second, bone is an inhomogeneous anisotropic tissue with a trabecular structure. In this study, bone had linear elastic, isotropic, and homogenous material properties, in accordance with those adopted in most relevant studies. Third, we simulated only the ICP occlusal mode; other occlusal modes may have different effects on the PDL.
## 3 Results and Discussion
### Tightness Monitoring of Prototype Cooling Chambers  
The tightness control of cooling and freezing modules filled with food products packed in collective packaging was carried out using the FLIR T1020 thermal imaging camera (Figures 10 and 11). The maximum time the products remained in the chambers until they were picked up by customers was set at 24 h. During the tests, the recorded ambient temperature was 23 \({}^{\circ}\)C.  
In the prototype of the cooling and freezing module (Figures 10 and 11), minimum temperatures in certain areas were identified, which indicated the presence of thermal bridges and insufficient insulation. These were due to improperly placed gaskets, both on the hinge and lock sides. Thermal bridges were found at the edge of contact between the shelves and the plane of the chamber door. The identified irregularities were eliminated in the prototype device. The places requiring elaboration in the construction of the cooling module have been shown in the thermograms:  
* Structure leakage--chamber door side wall of the module, causing thermal bridges and insulation leakage between the module and the monoblock (see minimum point Bx1 on Figure 12),
* Thermal bridges on the side wall of the module and insulation leakage between the module and the monoblock (see minimum point Bx1 on Figure 13).  
Figure 10: Thermogram for the loaded chamber 2 of the cooling module.  
The temperatures of the stored food in the prototype module, after 24 h of storage, were acceptable (Figures 10 and 11). Thermovision made it possible to show the places where modules were modified (leaks removed), the energy losses were lower, which translated into financial savings during operation and benefited for the natural environment (lower amount of carbon dioxide emissions). Such action made the developed innovative devices more attractive for food distributors, as appropriate storage conditions were ensured with lower operating costs.  
The structural imperfections shown on the example of the cooling module were also identified in the freezing module, where the leakage phenomenon was even more noticeable, which was visible in the exemplary thermograms of the freezing module cabinet in Figure 13.  
The MainBox device has been implemented. The first device was tested in Poznan, Poland. The manufacturer of the device ensures that the tests were successful, hence the decision to go with an offer of cooperation to grocery store chains, drugstores, or catering companies.  
Figure 11: Thermogram for the loaded chamber 2 of the freezing module.  
Figure 12: Thermograms of the cooling module—structure leakage: chamber door side wall of the module, caused thermal bridges; insulation leakage between the module and the monoblock.
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
###### Abstract  
Motion artefacts from involuntary changes in eye fixation remain a major imaging issue in optical coherence tomography (OCT). This paper reviews the state-of-the-art of retrospective procedures to correct retinal motion and axial eye motion artefacts in OCT imaging. Following an overview of motion induced artefacts and correction strategies, a chronological survey of retrospective approaches since the introduction of OCT until the current days is presented. Pre-processing, registration, and validation techniques are described. The review finishes by discussing the limitations of the current techniques and the challenges to be tackled in future developments.  
optical coherence tomography; retinal motion artefacts; image registration 2019
## 2 Methods
### Deep Dictionary Learning and Encoding Networks  
The reconstruction ability of the signal can be reconstructed by a good dictionary, so sparse representation is a key step in dictionary learning, and it has been studied since the 1990s. Dictionary learning was first proposed by [PERSON] et al. [50] in 1993. Their over-complete dictionary theory laid a solid foundation for dictionary learning. [PERSON] et al. [51,52] proposed a form of dictionary learning based on sparsity, which promoted the development of sparse representation and aroused more scholars' longing for dictionary learning. Due to the fact that some dictionaries are manually designed under certain mathematical constraints and are not flexible enough to represent complex natural image structures, in recent years, researchers have shifted to learning dictionaries directly from image data and developed many dictionary learning methods [53, 54, 55, 56]. The purpose of dictionary learning is to obtain a sparse representation of the original signal, and the resulting sparse representation has strong representation ability for the original data [57]. Dictionary learning can compress the vast majority of redundant information in existing data, thereby obtaining information with a certain utilization value. Dictionary training is the process of reconstructing raw data, that is, obtaining the corresponding dictionary matrix through training. Such dictionaries can be divided into two types. The first type is fixed dictionaries, such as the Discrete Cosine Transform (DCT) dictionary [58], Curve let dictionary [59], Wavelets dictionary [60], etc. The second type is an adaptive dictionary, which learns the features of the specified image to better achieve sparsity and is suitable for special signals.  
Dictionary learning is widely used in image processing. It has been applied in many aspects in practice and can solve problems very well, such as removing noise from images, restoring images through various transformations [61], analyzing the pixel composition of images [62], distinguishing sounds [63], and classifying images according to different situations [64]. The content of this paper is crack detection to identify and analyze the crack image. We adopt a method that combines dictionary learning and deep learning, which has good representation ability and is called DDLCN [65]. DDLCN combines the advantages of both and uses a new learning encoding layer to replace the original convolution layer. The core of DDLCN is to replace the traditional convolution layer with a dictionary learning encoding layer for efficient computation.  
DDLCN has better function calculation ability, which mainly stems from its ability to deal with nonlinear problems linearly. See Figure 2. It takes into account the basic vector in the coding layer and carries out a deeper layout, and the extraction of each feature is more explicit. It is layered in multiple directions, and the data representation is more optimized.
###### Abstract  
Rice is the staple crop for more than half the world's population, but there is a lack of high-resolution maps outlining rice areas and their growth stages. Most remote sensing studies map the rice extent; however, in tropical regions, rice is grown throughout the year with variable planting dates and cropping frequency. Thus, mapping rice growth stages is more useful than mapping only the extent. This study addressed this challenge by developing a phenology-based method. The hypothesis was that the unsupervised classification (k-means clustering) of Sentinel-1 and 2 time-series data could identify rice fields and growth stages, because (1) the presence of flooding during transplanting can be identified by Sentinel-1 VH backscatter; and (2) changes in the canopy of rice fields during growth stages (vegetative, generative, and ripening phases) up to the point of harvesting can be identified by Normalized Difference Vegetation Index (NDVI) time series. Using the proposed method, this study mapped rice field extent and cropping calendars across Peninsular Malaysia (131,598 km\({}^{2}\)) on the Google Earth Engine (GEE) platform. The Sentinel-1 and 2 monthly time series data from January 2019 to December 2020 were classified using k-means clustering to identify areas with similar phenological patterns. This approach resulted in 10-meter resolution maps of rice field extent, intensity, and cropping calendars. Validation using very high-resolution street view images from Google Earth showed that the predicted map had an overall accuracy of 95.95%, with a kappa coefficient of 0.92. In addition, the predicted crop calendars agreed well with the local government's granary data. The results show that the proposed phenology-based method is cost-effective and can accurately map rice fields and growth stages over large areas. The information will be helpful in measuring the achievement of self-sufficiency in rice production and estimates of methane emissions from rice cultivation.  
paddy fields; cropping pattern; phenology; Sentinel-1; Sentinel-2; Google Earth Engine; machine learning; deep learning Article
# A Total Crop-Diagnosis Platform Based on Deep Learning Models in a Natural Nutrient Environment
## 2 Related Works
### A Design of an Optimized Farm Sensor Gateway (OFSG) Based on Wireless Sensor Network  
The proposed OFSG consists of an application layer, a network layer, an adaptation layer, and a media access control/physical (MAC/PHY) layer as shown in Figure 3. Because the application layer, the network layer, and the MAC/PHY layer among these are based on IPv6 protocol, this paper covers just the adaptation layer. A farm central server requests data collection to the application layer. The application layer delivers the request to a network layer and the network layer delivers the request to an adaptation layer by turns. Finally, the adaptation layer delivers the request to a MAC/PHY layer. The MAC/PHY layer collects the sensor data from the sensors of diverse environments and delivers the collected data to the farm central server through the adaptation layer by using compression and fragmentation.  
Here, the adaptation layer is composed of two sub-modules as follows. First, an interconnection protocol sub-module (IPS) collects data by interconnecting sensor nodes which uses Zigbee, Wi-Fi and Bluetooth in diverse communication environments. Second, because the maximum data size that sensor nodes can transmit is 127 bytes, they have to transfer the fragments by data fragmentation. A sensor data fragment sub-module (SDFS) reduces the number of sensor data fragmentation times through the compression of this fragment header. The fragment's payload size is increased to a maximum of 101 bytes.
## 2 Model Development
### Propagation Filtering
#### 2.3.2 Ground Effect  
In Equation (20), the ground effect is modeled in a physical way as the interference between direct and ground reflected sound. A flat topography is assumed, _i.e._, only one ground-reflected path is modeled, which is implemented by adding a second signal path. The ground-reflected sound differs from direct sound by scaling with its propagation distance and a complex reflection factor, as well as an additional delay. The complex reflection factor depends on frequency, geometry and ground surface type and is realized by the filter \(h_{\text{gr},t^{\prime}}\). \(h_{\text{gr},t^{\prime}}\) is the impulse response of the spherical wave reflection coefficient at an infinite locally-reacting surface. The ground surface is acoustically described by a frequency-depending surface impedance, for which the widely-used empirical model of [PERSON] and [PERSON] [53] was used.  
In [46], the additional delay of the ground-reflected sound was modeled by a digital delay of integer length. However, in this application, due to the higher relative source speed and short delays, audible artifacts (\"zipper noise\") occur. Therefore, a separate resampling is performed in Equation (22) for the ground-reflected sound. Furthermore, this type of processing eliminates the spectrally-fluctuating errors (see Figure 1 in [46]).  
The spherical wave reflection coefficient filter \(h_{\text{gr},t^{\prime}}\) is implemented by an FIR filter designed using the inverse FFT, as described in [46]. It has to be made sure that the filter lag is compensated. However, compared to [46] for this application, substantially more filter taps are required to reproduce the correct interference pattern. Figure 7 shows simulation results for the standard configuration of road traffic noise emission measurements (a) and a receiver point at distance \(D=100\) m and height 2 m with sound propagation over grassy ground (b). For the former case a filter with 40 taps is sufficient, as the difference to the simulation with a filter with 400 taps stays well below 1 dB for all frequencies (nearly perfect coincidence of curves in Figure 7a). For the latter case, however, Figure 7b shows that such a short filter is not able to correctly reproduce the interference pattern and creates large errors at mid and low frequencies. A filter length of 400 taps allows simulations that are in good agreement with the exact solution for both cases. Large errors only occur near the Nyquist frequency. An update interval of the filter coefficients of 200 ms is used.