esa-sceva/satcom-chunk-collection huggingface dataset:
# Changes in Quality Features of Pork Burgers Prepared with Chokeberry Pomace During Storage
## 2 Materials and Methods
### Preparation of Black Chokeberry Pomace and Determination of Dry Matter in Fresh Pomace  
The black chokeberry fruits (_Aronia melanocarpa_) of the Nero variety originated from a conventional plantation in eastern Poland (Lublin Vivodeship), with a high degree of fertilization, and were harvested in mid-September 2024. The pomace was delivered to the laboratory the next day after harvesting and transported in a refrigerated vehicle. Before production, fresh chokeberries were washed and pressed using a laboratory hydraulic press (BUCHER Unipektin AG HPL 14; Bucher Unipektin AG, Niederweningen, Switzerland) equipped with a drainage filter recommended for pressing berries and pome fruits. Approximately 10 kg of chokeberries underwent a three-cycle juice pressing process at a pressure of 5 bar. Between cycles, the material was loosened to improve yield and reduce water content in the pomace. The appearance of fresh black chokeberry pomace is shown in Figure S1. After production, the pomace was vacuum-packed in 0.070 mm thick PA/PE bags using a Multivac C200 packaging machine (Multivac, Natalin, Poland) in two portions, each weighing approximately 0.5 kg, then frozen and stored at \(-60\pm 1\)\({}^{\circ}\)C. On the day of burger production, the required amount of frozen pomace was defrosted in a microwave oven (4 min, 900 W) and shredded into a \"paste\" consistency using a Thermomic(r) device (Vorwerk, Warsaw, Poland) at a knife speed of 4000/min for 5 min at a maximum temperature of 37 \({}^{\circ}\)C.  
The dry matter content of the pomace was determined gravimetrically. A 0.05 g (\(\pm\)0.001 g) sample was dried stepwise at 70 \({}^{\circ}\)C for 40 min, 90 \({}^{\circ}\)C for 40 min, and 95 \({}^{\circ}\)C for 30 min, with cooling intervals in a desiccator. Drying continued until weight differences between successive measurements were no greater than 0.001 g.
## 4 Discussion
### Importance of Urban Forests for Regional Carbon Budget  
In the land process-based model, as well as the remote sensing model, urban built areas are usually masked according to land cover data based on the hypothesis that the photosynthesis of urban forest is weak [45; 47]. However, our study illustrated that the photosynthetic capacity of urban forest was also considerable (Table 3). Meanwhile, in Suzhou, the coverage of urban forest was 2253.1 km\({}^{2}\), which was significantly higher than the 371.9 km\({}^{2}\) of vegetation in non-built area. As a result, about 44.43% of the total GPP in Suzhou was contributed by urban forests (Figure 3). Therefore, the GPP of urban forest is an important part of the regional carbon budget and should not be ignored.  
The average GPP of urban forests was significantly lower (about 36.3%) than the average GPP of vegetation in the non-built area (Table 3). This reduction of GPP can primarily be attributed to many factors related to human activities in built areas [61; 62]. From an ecological perspective, urban forests are highly fragmented and, as a result, they are vulnerable to human activities [63]. Additionally, in view of the atmosphere environment, urban forests are usually exposed to a high concentration of PM2.5 and O\({}_{3}\), which damages leaf tissue and affects photosynthesis [64]. However, the urban environment also includes some positive factors for carbon assimilation, for example, the increment in temperature due to the heat island effect [61; 65], high CO\({}_{2}\) concentration from greenhouse gas emission [45; 66], and enhanced nitrogen deposition from fossil fuel [67]. If we can improve the management of urban forests to maximize positive factors that promote carbon assimilation, urban forests will be able to play a more significant role in regional carbon budgets, which will help China achieve the target of carbon neutrality.
## 2 Wireless Communication for Maritime Applications
### Communication between Sensors and Relays
#### 2.1.3 ZigBee  
ZigBee is based on the IEEE 802.15.4 standard with additional routing and networking functionality [13], such as meshing. Mesh networking is particularly useful in applications where the range between two devices may be too large but an intermediate device in the range of both could forward the message [13].  
In Europe, it uses the frequency bands of 868 MHz and 2.4 GHz (ISM). In the 2.4 GHz band, the theoretical maximum data rate is around 125 kbps [13]. In the sub-GHz band, the data rate drops to 20 kbps [14].  
The ZigBee protocol is designed so that the radios automatically form a network after deployment without user intervention. The protocol also manages retries, acknowledgements, and message routing. Additionally, it can also self-heal the network [13].  
ZigBee was developed keeping battery life in mind, and consequently devices can operate on a battery for years. Additionally, ranges of over 100 m can be achieved [13], with some manufacturers claiming theoretical rural and urban line-of-sight ranges up to 14.5 km and 2.5 km, respectively, with data rates of 10 kbps [15].
# Generative Adversarial Networks in Retinal Image Classification
###### Abstract
The classification performance by exploiting only classic data augmentation obtained 78.6% sensitivity and 88.4% specificity, while, with the images generated by the GAN, the results increased to 85.7% sensitivity and 92.4% specificity. Similarly to the method proposed by the authors of [5], considering that data augmentation techniques can be used to create synthetic datasets sufficiently large to train machine learning models, [PERSON] et al. [6] resort to GANs to perform a data augmentation from patient data obtained through Internet of Medical Things sensors for chronic obstructive pulmonary disease monitoring. Their results show that synthetic datasets created through a GAN are comparable with a real-world dataset. Also, the authors of [7] consider the idea of performing data augmentation in the medical domain considering a GAN; as a matter of fact, they simulate the distribution of real data and sample new data from the distribution of limited data to populate the training set, and exploit a GAN for the augmentation and segmentation of magnetic resonance images. The researchers of [8] propose a data augmentation method for generating synthetic medical images using GANs, for the generation of cancerous and normal images. Moreover, they demonstrate that generated images can be exploited to improve the performance of the ResNet18 deep learning model for biomedical image classification [9; 10; 11; 12]. The authors of [13] resort to unsupervised anomaly detection by exploiting the images generated by a GAN, with the aim of detecting brain anomalies, with a focus on [PERSON]'s disease diagnosis. The idea of this paper is to demonstrate that GANs can be a tool used to generate biomedical fake images (in particular related to retina images) which, in addition to not being distinguishable by the human eye, are not distinguishable by a dedicated trained classifier. To generate fake retina images a DCGAN is exploited. In the proposed experiment, we consider eight different machine learning classifiers, including four convolutional neural network-based classifiers, demonstrating that, although a good number of images are correctly recognized as fake, some images, however, manage to evade the detection of the various classifiers. In particular, we note that, as the number of epochs increases, the fake images, becoming more and more realistic, are better able to evade detection by the classifiers. The paper proceeds as follows: in the next section preliminary background notions about GAN are provided; in Section 3 we describe the method we designed and implemented to understand whether a DCGAN is able to generate images related to the retina that are indistinguishable from the real ones; the results of the experimental analysis are shown in Section 4; a discussion about the adoption of GAN in the biomedical field, with a specific focus on the retinal images, is provided in Section 5; and, finally, conclusions and future works are described in the last section.
## 3 Results
### Tidal Pattern  
The snap rate pattern followed the tidal cycle and was higher at high tide and lower at low tide, as shown in Figure 5. The line plot of Figure 5a depicts the per-hour change in the snap rate for 90 days and the difference at low and high tides. The means and standard deviations of the snap rate at high tide were 2205 \(\pm\) 320 and 1957 \(\pm\) 372 in the first- and second-half periods, respectively, and those at low tides were 2100 \(\pm\) 320 and 1800 \(\pm\) 320 (Figure 5b). The mean snap rate was 13% higher at high tide (\(p\) < 0.001) than at low tide. As a result of period analysis using fast Fourier transforms, the solar and half-diurnal casting cycles were dominant. Tidal signals show strong semidiurnal and diurnal components  
Figure 4: Circadian pattern of the snap rate of snapping shrimp. (**a**) Snap rate by day on the x-axis (9/11–12/15) and hour on the y-axis: (00:00–24:00). Color bar: snap rate per minute. Arrowheads are pointing to the sunrise and sunset times. (**b**) Mean and standard deviation of the snap rates in a day. (c) The percentage excess snap rate in a day.  
with some other components, such as four and six times a day. Based on this, the snap rate explains that the diurnal component is dominant, followed by the semi-diurnal component among the components of the tidal cycle (Figure 5c).  
Figure 6 presents the wavelet analysis to show the coherence between the snap rate of snapping shrimp and the tidal level. They have a strong coherence with semidiurnal (0.5 day, K2 = 0.50) and diurnal (1 day, K1 = 1.00) periods. Therefore, the semidiurnal tide from the principal lunar and solar components and the diurnal tide from lunisolar and principal lunar periods affect the snap rate [31; 36]. Longer periods, such as 15 days (lunar cycle: Mf = 13.65, Mm = 27.5), have relatively weak coherence because of the shorter period pattern.  
Figure 5: The tidal pattern of the snap rate of snapping shrimp. (**a**) Snap rate by day on the x-axis (9/11-12/15) and hour on the y-axis: (00:00–24:00). Color bar: snap rate per minute. The blue circles are the times of high tide, and the red marks of x represent the low tide. The snap rate was lower at low tide than at high tide. (**b**) Mean snap rates and standard deviations at high and low tides show significant differences at the 1 st- and 2 nd-half periods. (**c**) Fast Fourier transform (FFT) of snap rate and tidal level.
# Accounting for Training Data Error in Machine Learning Applied to Earth Observations
## 1 Introduction
### Characterizing Training Data Error
#### 1.2.1 Map Accuracy Assessment Procedures
The first of these is the design of the sample itself (i.e., the placement of sample units), which should be probability-based but can follow several different designs (e.g., simple random, stratified, cluster, systematic) depending on the application and a priori knowledge of the study area [39; 57]. The second aspect is the response design, which governs the procedures for assigning values to the map reference samples [39; 57]. These include the choice of the sample's spatial and temporal units, the source of the data that the sample extracts from (e.g., high resolution imagery), and the procedure for converting reference data values into map-relevant values [39; 57]. For a categorical map in which the reference data source is high-resolution imagery, the map reference sample is assigned labels corresponding to the map legend (e.g., a land-cover scheme) based on a human supervisor's interpretation of the imagery [57]. A key aspect of response design is that map reference data should be substantially more accurate than the map being assessed, even though they are always likely to have some uncertainty [30; 39; 46; 47; 57]. This uncertainty should be measured and factored into the accuracy assessment [39; 46]. However, in practice this accounting is rarely done, while map reference data uncertainty is also rarely examined [34; 38; 57]. This tendency is illustrated by [PERSON] et al. [65], who reviewed 209 journal articles focused on object-based image analysis, finding that one third gave incomplete information about the sample design and size of their map reference data, let alone any mention of error within the sample. Errors in map reference data can bias the map accuracy assessment [47; 99], as well as estimates derived from the confusion matrix, such as land cover class proportions and their standard errors [46]. To correct for such impacts to map accuracy assessment, one can use published accuracy assessment procedures, including variance estimators, that account for map reference error [38; 46; 47]. These approaches depend on quantifying errors in the map reference data.
# New Approach for Brain Tumor Segmentation Based on Gabor Convolution and Attention Mechanism
###### Abstract  
In the treatment process of brain tumors, it is of great importance to develop a set of MRI image segmentation methods with high accuracy and low cost. In order to extract the feature information for each region of the brain tumor more effectively, this paper proposes a new model Ga-U-Net based on Gabor convolution and an attention mechanism. Based on 3D U-Net, Gabor convolution is added at the shallow layer of the encoder, which is able to learn the local structure and texture information of the tumor better. After that, the CBAM attention mechanism is added after the output of each layer of the encoder, which not only enhances the network's ability to perceive the brain tumor boundary information but also reduces some redundant information by allocating the attention to the two dimensions of space and channel. Experimental results show that the model performs well for multiple tumor regions (WT, TC, ET) on the brain tumor dataset BraTS 2021, with Dice coefficients of 0.910, 0.897, and 0.856, respectively, which are improved by 0.3%, 2%, and 1.7% compared to the base network, the U-Net network, with an average Dice of 0.887 and an average Hausdorff distance of 9.12, all of which are better than a few other state-of-the-art deep models for biomedical image segmentation.  
brain tumor; Gabor convolution; CBAM attention mechanism; medical image segmentation
## 2 Material and Methods
### The Group Key Agreement  
The following protocol describes the A-GDH2 Key Agreement protocol introduced in [12]. When the keys \(K_{i,n}\) shared by \(\mathcal{U}_{i}\) and \(\mathcal{U}_{n}\) equal 1, then we obtain the Initial Key Agreement of CLIQUES, named IKA.2 in [9].  
Suppose we have \(n\) users \(\mathcal{U}_{1},\ldots,\mathcal{U}_{n}\) who wish to agree upon a common key. Let \(p\) be a prime and \(q\) a prime divisor of \(p-1\). Let \(G\) be the unique cyclic subgroup of \(\mathbb{Z}_{p}^{*}\) of order \(q\) and let \(\alpha\) be a generator of \(G\). Then, for each \(i=1,\ldots,n\), the user \(\mathcal{U}_{i}\) selects a secret element \(r_{i}\in\mathbb{Z}_{q}^{*}\). Let \(K_{i,n}\in\mathbb{Z}_{q}^{*}\) be a common key shared in advanced by \(U_{i}\) and \(U_{n}\) for every \(i=1,\ldots n\).  
The protocol proceeds as follows:  
1. For \(i=1,\ldots,n-2,\ \mathcal{U}_{i}\) sends to \(\mathcal{U}_{i+1}\) the message \(C_{i}=C_{i-1}^{r_{i}}\), where \(C_{0}=\alpha\).
2. \(\mathcal{U}_{n-1}\) broadcasts \(C_{n-1}=C_{n-2}^{r_{n-1}}\) to the other users \(\mathcal{U}_{1},\ldots,\mathcal{U}_{n-2},\mathcal{U}_{n}\).
3. \(\mathcal{U}_{n}\) computes the shared key \(K=C_{n-1}^{r_{n}}\).
4. For \(i=1,\ldots,n-1,\mathcal{U}_{i}\) sends \(D_{i}=C_{n-1}^{r_{i}-1}\) to \(\mathcal{U}_{n}\).
5. \(\mathcal{U}_{n}\) broadcasts \(\{D_{1}^{r_{n}\cdot K_{1}},D_{2}^{r_{n}\cdot K_{2n}},\ldots,D_{n-1}^{r_{n}\cdot K _{n-1}},C_{n-1}\}\) to \(\mathcal{U}_{i}\), \(i=1,\ldots,n-1\).
6. For \(i=1,\ldots,n-1,\ \mathcal{U}_{i}\) computes the shared key \(K=D_{i}^{r_{n}\cdot K_{i}\cdot r_{i}\cdot K_{i,n}^{-1}}\).
# Time-Evolving Graph Convolutional Recurrent Network for Traffic Prediction
## 3 Proposed Method
### Method Overview  
Figure 3 illustrates the overview of our proposed method, which is referred to as Time-Evolving Graph Convolutional Recurrent Network (TEGCRN). The model mainly consists of three parts. First, the time-evolving adjacency graphs are generated through a tensor-composing method using the adaptive embeddings of traffic nodes and time slots. Then, the resulting adaptive graphs are combined with predefined static distance-based graphs and employed in graph convolution module to capture the inter-node information. Finally, the graph convolution modules with time-evolving graphs are used to replace the fully-connected layers in GRU to form an integrated RNN encoder-decoder predicting model. In each time step of the encoder-decoder, a different adaptive graph is selected depending on the time of day. The key thought of this method is to learn the implicit dynamics of spatial adjacency together with the traffic sequential features so as to capture more detailed spatiotemporal information. In the following subsections, we will describe each part in detail.
## 3 Results
### Microscopic Soil-Structure Analysis Using SEM-EDX  
The surface morphology and the elemental composition of treated soil particles were analysed using SEM-EDX. The images were used to observe the micro-scale interaction (i.e., visualisation of interactive effects) between the soil and the best performing fraction.  
Figure 7a-d shows the results of the SEM testing on treated weathered granite (WG), and the selected fraction. The untreated WG block/sample exhibited an arranged book- and cluster-like micro-structure [23]. The surface morphology of the WG soil sample, treated with 2.3% of the fraction (Figure 7a-d), highlights a region of compacted soil versus the pores present on the block surface. Based on the results, it was found that the stabilisation process modifies the porous network of this soil type. In addition, as shown in Figure 7d, new white layers of reaction products were formed on the surface of WG soil particles, with more structured and rigid soil particle stacking morphology was observed, as shown in Figure 7c, which is indicative of the bio-coating stabilisation effect. This micro-structure also shows closer bonding of particles of the treated soil, thereby increasing its stability.  
As an approach to further understand the mechanism of the soil stabiliser, EDX was used. This approach is important for two reasons, (1) assess any changes in the soil elemental composition and (2) further understand the bio-stabilisers mechanism of action. A combination of silicon (Si) and aluminium (Al) was picked up in several EDX micrographs (see Figure 8). This is typical for expansive soils. After treatment of the soil, silica and alumina sheets may combine in various ways, however these combinations may  
Figure 7: SEM micrographs showing (**a–d**) weathered granite soil treated with the best prototype (**a**) (2.3% dosage). Key: (**a1**)–covered with a pellicle of fine material also traces of the much smaller particles on the surfaces of these grains; (**b1**)–showing white surface coat, or surface polymeric coating; (**c1**)–soil inter-particle stacking; (**d1**)–aggregates of soil material, area compacted particles with minimal pore spaces, the visible however pore space and edges (black)—induced by breakage on the blocks.  
be indicative of weak van der Waals forces and may retain more water. The presence and extent of the pellicle of fine material covering the grains of the samples can be related to the mineralogical composition of this soil type which play a role in the performance of the soil after treatment (Figure 8b).
## 3 The Hybrid PSOCS Algorithm
### Particle Swarm Optimization  
Particle Swarm Optimization (PSO) is an evolutionary algorithm that was inspired by the social behavior of bird flocking [17]. Like other evolutionary algorithms, the PSO starts with initial solutions and finds the best global optimum. In particular, it considers a number of particles to be candidate solutions. Each particle flies around in the search space with a velocity to the best solution. The best solution found so far by a particle is called _pbest_. The best _pbest_ among all the particles is called _gbest_. In order to modify its position, each particle must consider the current position, the current velocity, the distance to _pbest_, and the distance to _gbest_. The equation for this modification was given as follows:  
\\[v_{i}^{t+1}=w\\times v_{i}^{t}+c_{1}\\times rand\\times\\left(\\textit{pbest}_{i}-x _{i}^{t}\\right)+c_{2}\\times rand\\times\\left(\\textit{gbest}-x_{i}^{t}\\right) \\tag{1}\\]  
\\[x_{i}^{t+1}=x_{i}^{t}+v_{i}^{t+1} \\tag{2}\\]  
where \\(v_{i}^{t}\\) is the velocity of particle \\(i\\) at iteration \\(t\\), \\(w\\) is inertia weight, \\(c_{1}\\) and \\(c_{2}\\) are acceleration coefficients, \\(rand\\) is a random number within 0 and 1, \\(x_{i}^{t}\\) denotes the current position of particle \\(i\\) at iteration \\(t\\), \\(\\textit{pbest}_{i}\\) represents the _pbest_ of agent \\(i\\) at iteration \\(t\\), and \\(\\textit{gbest}\\) is the best solution so far.  
In each iteration, the velocities of particles are obtained by Equation (1). Then, the positions of particles are calculated by Equation (2). The particle positions will be changed until a stopping condition is met.