esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
During the last decade, advances in the remote sensing of greenhouse gas (GHG) concentrations by the Greenhouse Gases Observing Satellite-1 (GOSAT-1), GOSAT-2, and Orbiting Carbon Observatory-2 (OCO-2) have produced finer-resolution atmospheric carbon dioxide (CO\({}_{2}\)) datasets. These data are applicable for a top-down approach towards the verification of anthropogenic CO\({}_{2}\) emissions from megacities and updating of the inventory. However, great uncertainties regarding natural CO\({}_{2}\) flux estimates remain when back-casting CO\({}_{2}\) emissions from concentration data, making accurate disaggregation of urban CO\({}_{2}\) sources difficult. For this study, we used Moderate Resolution Imaging Spectroradiometer (MODIS) land products, meso-scale meteorological data, SoilGrids250 m soil profile data, and sub-daily soil moisture datasets to calculate hourly photosynthetic CO\({}_{2}\) uptake and biogenic CO\({}_{2}\) emissions with 500 m resolution for the Kanto Plain, Japan, at the center of which is the Tokyo metropolis. Our hourly integrated modeling results obtained for the period 2010-2018 suggest that, collectively, the vegetated land within the Greater Tokyo Area served as a daytime carbon sink year-round, where the hourly integrated net atmospheric CO\({}_{2}\) removal was up to 14.15 \(\pm\) 4.24% of hourly integrated anthropogenic emissions in winter and up to 55.42 \(\pm\) 10.39% in summer. At night, plants and soil in the Greater Tokyo Area were natural carbon sources, with hourly integrated biogenic CO\({}_{2}\) emissions equivalent to 2.27 \(\pm\) 0.11%-4.97 \(\pm\) 1.17% of the anthropogenic emissions in winter and 13.71 \(\pm\) 2.44%-23.62 \(\pm\) 3.13% in summer. Between January and July, the hourly integrated biogenic CO\({}_{2}\) emissions of the Greater Tokyo Area increased sixfold, whereas the amplitude of the midday hourly integrated photosynthetic CO\({}_{2}\) uptake was enhanced by nearly five times and could offset up to 79.04 \(\pm\) 12.31% of the hourly integrated anthropogenic CO\({}_{2}\) emissions in summer. The gridded hourly photosynthetic CO\({}_{2}\) uptake and biogenic respiration estimates not only provide reference data for the estimation of total natural CO\({}_{2}\) removal in our study area, but also supply prior input values for the disaggregation of anthropogenic CO\({}_{2}\) emissions and biogenic CO\({}_{2}\) fluxes when applying top-down approaches to update the megacity's CO\({}_{2}\) emissions inventory. The latter contribution allows unprecedented amounts of GOSAT and ground measurement data regarding CO\({}_{2}\) concentration to be analyzed in inverse modeling of anthropogenic CO\({}_{2}\) emissions from Tokyo and the Kanto Plain.  
anthropogenic CO\({}_{2}\) emissions; biogenic CO\({}_{2}\) flux; diurnal variation; gross primary production; ecosystem respiration; net ecosystem exchange; megacity; top-down approach +
Footnote †: journal: Remote Sensing
## 3 Sliding-Mode Controller
### Reachability Analysis  
The reachability conditions evaluate the capability of the controller to reach the desired surface (i.e., desired behavior) from any arbitrary operation condition. Figure 7 shows a graphical representation of the reachability conditions: when the switching function \(\Psi\) is operating above the desired surface (i.e., \(\Psi>0\)), the switching function derivative must be negative (i.e., \(\frac{d\Psi}{dt}<0\)) to reach the desired surface \(\Psi=0\); similarly, when the switching function is operating below the desired surface (i.e., \(\Psi<0\)), the switching function derivative must be positive (i.e., \(\frac{d\Psi}{dt}>0\)) to reach the surface.  
The analysis of the reachability conditions also requires defining the conditions of the control signal \(u\) to produce the desired sign on the switching function derivative. Such an analysis is performed using the sign of the transversality value (44): in this case, the transversality value is positive, which means that a positive value of the control signal (\(u=1\)) produces a positive sign on the switching function derivative, while \(u=0\) produces a negative sign on the same derivative. Therefore, those reachability conditions can be mathematically expressed as follows:  
\[\lim_{\Psi\to 0^{+}}\left.\frac{d\Psi}{dt}\right|_{u=0}<0 \tag{45}\] \[\lim_{\Psi\to 0^{-}}\left.\frac{d\Psi}{dt}\right|_{u=1}>0 \tag{46}\]  
Evaluating the theoretical reachability conditions (45) and (46) using the expression for the switching function derivative (42) leads to the following dynamic restrictions for the reference signal:  
\[\frac{di_{r}}{dt}<(2-d)\cdot\frac{v_{pv}}{L_{1}}+(1-d)\cdot\frac {v_{pv}}{L_{2}}-\frac{di_{pv}}{dt} \tag{47}\] \[\frac{di_{r}}{dt}>(2-d)\cdot\frac{v_{pv}-v_{b}}{L_{1}}+(1-d)\cdot \frac{v_{pv}-v_{b}}{L_{2}}-\frac{di_{pv}}{dt} \tag{48}\]  
Therefore, those dynamic restrictions must be fulfilled to ensure the stability of both the proposed SMC and PV system.  
Figure 7: Derivation of the reachability conditions.
## 3 Results and Discussion  
### Training Time Comparison  
The computational efficiency of machine learning models is a crucial consideration, particularly when dealing with large datasets or complex architectures. We measured and compared the training times for CNN-1, CNN-2, and the three pre-trained models: Xception, DenseNet201, and VGG16. Each model is trained with 300 epochs, with the model with the best loss function results saved for use in the evaluation process. Table 4 shows the time required for each model to be trained.  
As shown in Table 4, the custom CNN models, CNN-1 and CNN-2, required significantly less training time compared to the pre-trained models. Specifically, CNN-1 was the most efficient, completing training in just 20 min, while CNN-2 took 42 min. In comparison, all three pre-trained models require a considerably longer time to train.  
The training time is also dependent on the hardware used. This was achieved using an NVIDIA Tesla T4 GPU, a high-end hardware device designed for machine learning tasks. However, the use of high-end hardware may not be accessible in all practical scenarios when the models are deployed. The comparison shows that CNN-1 and CNN-2, having simpler model architecture, can achieve faster training times, making them suitable for applications where the computational efficiency is a critical factor.
## Materials and Methods
### CMF Generation  
The experiments were performed using relatively weak magnetic fields of magnitudes less or comparable to the magnitudes of the Earth's magnetic field or geomagnetic field (GMF) (which normally fall within the range of 30 to 70 uT) and those within frequency ranges close to the industrial frequency present in laboratory facilities (50 Hz). Therefore, in order to standardize the measurement conditions, which should be minimally dependent on ambient conditions, specialized research equipment was used, i.e., a hypomagnetic environment device (Figure 1).  
This device provided a high level of reduction in the external GMF--up to 10,000 times (with the residual static field not exceeding 10 nT), and substantially lowered the alternating technogenic noise (to a few nT, as confirmed by measuring). The device consisted of three cylindrical permalloy magnetic shields (with the 1 mm thick walls) fitted coaxially one inside the other and having closures with inlet ports to connect the device to measuring or temperature stabilizing equipment (the inner shield being 22 cm in diameter and 42 cm in length) (Figure 1). The residual fields inside the device were directly measured with a Mag-03 MS100 fluxgate magnetometer (Bartington, Witney, UK). To experimentally create a uniform standard weak combined static and alternating magnetic field (CMF), a special electromagnetic induction coil (solenoid) was placed inside the device. The solenoid was connected to a source of direct current to generate a static field and to a source of low-frequency alternating signals to generate the alternating field component. The coil had the following characteristics: 18 cm diameter, 36 cm length (a 720-loop copper wire of 1 mm diameter), and 7.5 Ohm resistance. The solenoid was used to create an area within the shielding unit of a uniform weak CMF, in which test samples were incubated.  
Two sets of experiments were performed using the magnetic field induction device described above. The first experiment was aimed at selecting the exact frequency of the CMF alternating component between 12.6 Hz or 48.5 Hz. The second experiment included exposure to a wide range of frequencies. As wide-range signal, an electroencephalogram (EEG) recording was used, which is a typical example of an electrophysiological signal with a power spectrum containing a large number of different frequencies, but most importantly, it contains the required frequency of 12.6 Hz.  
Figure 1: Hypomagnetic environment device (magnetic field induction device): (**a**) cross-section diagram; (**b**) unit photograph. 1—magnetic shields; 2—solenoid; 3—thermostated rack; 4—experimental samples.
## 4 Experiments and Results
### Dataset  
In this paper, we implement the proposed methods on the popular BreaKHis dataset [5]. It is a publicly available breast cancer image dataset consisting of 7,909 microscopic images divided into two main classes (benign and malignant) obtained from 82 anonymous patients at various magnification factors. The original images are captured in the standard RGB format with a total of \(752\times 582\) pixels at different magnifying factors: 40\(\times\), 100\(\times\), 200\(\times\) and 400\(\times\) (see Figure 8). Without compression or normalization, all images are cropped to 700 \(\times\) 460 pixels to remove black border areas and saved in the portable network graphics format.  
The BreaKHis dataset was gathered during a clinical study in 2014 with approval from the relevant authorities; the patients were invited to participate and were then referred to the P&D Laboratory (Brazil) to collect samples. Hematoxylin and eosin (HE) stained breast tissue biopsy slides were used to create these samples. Surgical open biopsy (SOB) or a surgical procedure via a small incision of human skin was used to collect these samples, which were then labeled by pathologists at the P&D Lab. Additionally, expert pathologists and additional tests, such as immunohistochemistry diagnosis, were used to confirm all sample cases. The dataset's picture distribution is shown in Table 1.
# Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images
## 1 Introduction
After a careful study of the fusion problem between WV and Planet, it turns out that pansharpening algorithms, such as the algorithms mentioned in [16; 17], cannot be applied. The main reason is that most existing pansharpening algorithms require low resolution multi-spectral images and a high resolution pan image at the time of prediction. In the fusion of WV and Plant images, this is not possible. We have actually applied several pansharpening algorithms such as Gram-Schmidt Adaptive (GSA), Principal Component Analysis (PCA), etc. in [16] to enhance a MODIS image at time \(t_{2}\) by using a better resolution Landsat image at an earlier time \(t_{1}\). The results are not satisfactory due to severe changes in image contents between the two images. In this paper, we summarize the results of applying STARFM, FSDAF, and HCM approaches to fusing WV and Planet images for forward prediction. To the best of our knowledge, no one has carried out such a study before for WV and Planet images. The first two approaches, STARFM and FSDAF, are well-known in fusing the MODIS and Landsat community. The third approach (HCM) was motivated by our recent pansharpening work for synthesizing a high resolution hyperspectral image by fusing a high resolution color image with a low resolution hyperspectral image cube [18]. Similar to STARFM and FSDAF, only one pair of Planet and WV images is needed for prediction. Experiments using actual Planet and WV images were used to quantify the performance of the three approaches. Our paper is organized as follows. Section 2 briefly reviews the three fusion methods. Section 3 presents our experimental results. Finally, conclusions and future research directions will be presented in Section 4.
# Masked Convolutions Within Skip Connections for Video Anomaly Detection
## 6 Ablation Studies
### Comparative Analysis of Baseline and Skip Connection Variants  
We explored three foundational variants of our MaskedSkipUNet architecture to understand their individual contributions to anomaly detection. The AutoEncoder, which uses only the encoder and decoder without any skip connections or MaskedConv3D layers, served as our baseline and achieved an AUC score of 87.1%. Adding skip connections  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Ped2** \\ \hline AutoEncoder & 87.1 \\ UNet & 75.3 \\ Conv3D SkipUNet & 95.2 \\ \hline
2D MaskedSkipUNet & 89.0 \\ E/D Masks MaskedSkipUNet & 81.5 \\ \hline One Mask MaskedSkipUNet & 85.2 \\ Two Mask MaskedSkipUNet & 94.3 \\ Three Mask MaskedSkipUNet & 96.5 \\
**MaskedSkipUNet** & **98.4** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study results summarising the AUC scores attained by each model variant. The table underscores the influence of distinct architectural components on the anomaly detection performance, revealing the incremental benefits of MaskedConv3D layers and skip connections. The bold marked figures indicate the best performing score.  
Figure 8: Visual representations of the architectures evaluated in our ablation studies (Section 6). From the baseline AutoEncoder to our primary contribution, the MaskedSkipUNet, each variant elucidates the impact of incorporating or excluding specific components like skip connections, MaskedConv3D layers, and their interplay.  
to the AutoEncoder resulted in the UNet model, which surprisingly showed a decreased AUC score of 75.3%. This suggests that UNets may be more effective at reconstructing anomalies, thereby reducing their anomaly detection capabilities. Lastly, the Conv3D SkipUNet variant, which replaces MaskedConv3D layers with standard Conv3D layers, yielded a notable AUC score of 95.2%.
# Elimination of the Solid Graininess Issue with Different Micro-Pattern Structures at Flexo Printing
## 1 Introduction
Flexography is challenging with gravure printing-related ink transfer achievement. In principle, flexo printing ink transfer directly connects anilox volume; it can make low-volume cell aniloxes printing solids worse than gravure. Micropatterns significantly increase solid ink density in flexo with plastic film substrate. Micro-patterns transferring ink to the solid area better, increasing SID, the characters, lines, and the edge of the solid area can easily result in better ink laydown. Micro-pattern application on the flexographic plate has a higher SID than the non-pattern part, which has also become a block to the quality of the flexographic printing [13]. Micro-patterns also increase ink coverage for white color with transparent plastic film substrates [14]. Mechanical printing settings, press speed, and surface tension of ink also affect ink transfer [15]. The ability of a printing system to reproduce a sharp image with clear details is vital for high-quality reproduction. The geometry of the printed elements, sharpness, and noise of the edges, together with a uniform ink layer, are important indicators of quality production that need to be analyzed. They are directly linked to the reproduction of lines, texts, and dots that are part of every image. Quality parameters of thin elements can be evaluated by measuring line wicking, which makes lines and text blurry or thick [16]. The goal of this research is to print at higher solid ink density and obtain less grainy solids with generated various micro-patterns. Printing conditions were stable, and only micro-patterns were changed. The obtained result will show differences between pattern and non-pattern surface print results. The significance of this study is to improve the ink laydown and to provide a problem-free structure for the solid areas of the objects, such as text, graphics, barcodes, etc., with narrow web flexography. For instance, if the ink laydown is not sufficient in barcode printing, it may cause problems in use as a result of packaging.
## 2 System Model
### Phase II: Relay-to-Destination Point-to-Point Channel  
In phase II, the relay transmits the linear combination estimated in Equation (7) to the destination, \(D\). The channel output observed at the destination, \(\mathbf{y}_{D}\in\mathbb{R}^{n}\), is given by:  
\[\mathbf{y}_{D}=h_{RD}\hat{\mathbf{v}}_{R}+\mathbf{z}_{D} \tag{8}\]  
where \(h_{RD}\in\mathbb{R}\) is the channel coefficient between the relay node and the destination. We assume that \(h_{RD}\) is a uniformly distributed Gaussian variable with mean, \(\mu_{h_{RD}}\), and variance, \(\sigma_{h_{RD}}^{2}\), such that \(h_{RD}{\sim}\mathcal{N}(\mu_{h_{RD}},\sigma_{h_{RD}}^{2})\). The additive i.i.d. Gaussian noise vector is given by \(\mathbf{z}_{D}=\left[\begin{array}{cccc}z_{D1}&z_{D2}&\ldots&z_{Dn}\end{array} \right]^{T}\), \(z_{Di}\sim\mathcal{N}(0,\sigma_{2}^{2})\). Consequently, the estimate of \(\hat{\mathbf{v}}_{R}\) obtained at the destination using the decoder, \(\mathcal{D}_{D}:\mathbb{R}^{n}\rightarrow\Lambda^{\prime}\), is given by:  
\[\hat{\mathbf{v}}_{D}=\mathcal{D}_{D1}(\mathbf{y}_{D}) \tag{9}\]  
The destination obtains a linear combination of the original source signals at the end of two-phase transmission. In order to decode the original source signals, the destination node collects \(L\) such linear combinations. After \(L\) repeated transmissions of their respective message from the \(L\) sources, the destination obtains \(L\) linear combinations of \(L\) source messages. Using these linear combinations, the destination decodes the original source messages using the decoder:  
\[\hat{\mathbf{s}}=\mathcal{D}_{D2}(\mathbf{v}) \tag{10}\]where \(\mathbf{v}=\left[\begin{array}{cccc}\hat{\mathbf{v}}_{D}^{1}&\ldots&\hat{ \mathbf{v}}_{D}^{L}\end{array}\right]\) is the matrix containing \(L\) linear combinations obtained at the destination. \(\hat{\mathbf{v}}_{D}^{l}\) denotes the \(l\)th linear combination obtained at the destination. The original message matrix estimated at the destination is given by \(\hat{\mathbf{s}}=\left[\begin{array}{cccc}\hat{\mathbf{s}}_{1}&\ldots&\hat{ \mathbf{s}}_{L}\end{array}\right]\). Note that in this paper, we focus on a real-valued system model for simplicity; however, we assert that the work can be extended to a complex-valued system.
# Communication Transport Protocol Strategies for Rail Applications
## 1 Introduction
Moreover, a modified version of SIP has been adopted for service discovery and user registration along with new emulator for railway signalling protocols, which has been used to generate new and more realistic test-cases. To investigate the most suitable strategies in selecting transport protocols, two main rail applications have been considered: (i.) signaling and (ii.) generic data transfer (i.e., via file transfer). The first rail application is fundamental for the normal operations of the train movement and it is based on the train transmitting its position report (PR) to the remote control center, aiming at managing train traffic along the overall railways. After receiving the PR of each train, the control center evaluates the train position and the positions of the other trains, thus responding with the authorization of the movement (or movement authorization, MA), not allowing the train to reduce its speed or stop. The second rail application we considered is related to the transmission of critical files. In this case, we evaluate the available data rate and the latency required by a file transfer in case multiple bearers are adopted the ACS. The paper is organized as follows. In Section 2, the rail context is described and the main rail applications and their requirements are recalled. Furthermore, in Section 3, the ACS architecture and its characteristics are detailed. In Section 4, the proposal of registration of the rail applications in the whole network to make them available to clients is described. In Section 5, the testbed implemented is presented in order to evaluate the potentials of ACS. In Section 6, results of the emulator and the testbed are reported. Finally, conclusions are drawn in Section 7.
# Review of V2X-IoT Standards and Frameworks for ITS Applications
## 1 Introduction
In addition, taking into account that in 2022, 82% of the total bandwidth will be used for video sharing data, this problem will be even more acute and sensitive [11]. Due to the limitations of the latest DSRC technologies, great attention is paid to cellular V2X communication technologies. The C-V2X connection allows for increased coverage of streams of data transmitted, however, each cellular station broadcasts information to all devices within its coverage, and those devices communicate with the station using unicast. A growing number of vehicles is increasing the number of unicast streams, thereby straining network resources, which, in turn, results in greater messaging latency. In the case of traffic safety, data transmission latency is one of the key, critical and minimizable parameters. Being aware that the number of mobile users and data transfer amounts since 2017 have been increasing and will still be increasing until 2022 several times [11], it remains unclear whether the current mobile, cellular network infrastructure will be capable of transmitting data via C-V2X communication technology within the permissible latency. For the latter reasons, it is necessary to look for DSRC and C-V2X architectural solutions that support reliable and efficient V2X connections. In order to carry out research on DSRC and C-V2X communication technologies, it is necessary to develop integrated circuits [12; 13; 14; 15; 16; 17; 18; 19; 20; 21] and, on their basis, development platforms supporting all possible V2X communication technologies. This article consists of four chapters. In the first chapter, the main standards of DSRC and C-V2X technology are reviewed, including their regulated spectrum bands and the intended use. Essential shortcomings and limitations of DSRC and C-V2X technologies by revealing the need for the conjuncture of these technologies and the creation of hybrid architectures are also presented in this chapter. Subsequently, the second chapter provides a comprehensive review of commercially available systems, R&D platforms, and transceivers, that can be used for V2X system development, deployment and testing. An analysis of commercial products features and functions is carried out, which needs to be taken into account when a versatile V2X-IoT framework for ITS applications is being developed. The final chapter concludes the presentation of the essential V2X and IoT progress that has been spotted over the recent years and summarizes suggestions for further research and technology development.
## 6 Experimental Results and Discussion
### Performance Analysis  
The performance of all methods is summarized in Table 10. The models using SVM and GLRLM or all features with any selection method give the highest accuracy, approximately 88%. For example, SVM tried all cases of feature sets based on F-score for approximately 40 s. Finally, the highest accuracy obtained by the model, which contains SVM and all features selected by SFS, is 89.25%. Nevertheless, in case of reducing the processing load, the threshold of F-score for SVM could be considered because it also gives a good performance. It took only 1.8 s to achieve the classification rate of 87.375%, compared with 40 s for SFS. These differences will have significant meaning for processing a large dataset.