esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dual-Branch Self-Boosting Network Based on Noise2 Noise for Unsupervised Image Denoising
## 3 Methodology
### Network Architecture  
This work introduces a novel approach to image denoising by utilizing a dual-branch network architecture. The schematic representation of the entire network is depicted in Figure 3. The model consists of two main components: the clean image generator and the noise generator. To reconstruct the clean output image \(I_{content}\) from a given noisy image, the clean generator employs the DnCNN architecture [15] without skip connections and batch normalization layers (BN). The DnCNN architecture has been widely used for image denoising tasks and has demonstrated excellent performance. By omitting skip connections and batch normalization layers, we aimed to simplify the network architecture while maintaining its effectiveness in denoising. The noise generator, on the other hand, is responsible for generating the noise component of the image. It comprises three types of layers, as depicted in Figure 3. The initial layer utilizes 64 filters of size \(3\times 3\times c\) to generate 64 feature maps. Rectified linear units (ReLU) are then applied for nonlinearity. Here, 'c' represents the number of image channels, where 'c = 1' for grayscale images, and 'c = 3' for color images. For layers 2 to \((D-1)\), we continued to employ 64 filters of size \(3\times 3\times 64\). Additionally, batch normalization was incorporated between the convolution and ReLU layers in order to accelerate the training process and enhance the network's generalization ability. Finally, the last layer of the noise generator utilizes 'c' filters of size \(3\times 3\times 64\) to reconstruct the denoised output image. By combining the clean image generator and the noise generator within the dual-branch network, we aimed to exploit the complementary information captured by these two components. The clean image generator focuses on recovering the underlying content, while the noise generator aims to estimate and remove the noise component. This joint learning process enables the network to effectively denoise the input images and produce high-quality output results.  
Figure 3: Overview of our proposed DBSNet framework. (a) The given noisy image during training passes through the content generator and the noise generator. The decomposed image content and noise component will then be fed back to the DBSNet to establish more powerful constraints for training. (b) When inferencing, the given noisy image only passes through the content generator to obtain the denoised image.
## 3 Materials and Methods
### Data Preprocessing
#### 3.2.1 Data Cleaning  
The role of data cleaning is to remove unnecessary data which are insignificant for the analysis. The numbers and punctuation are a type of data that do not have an impact on the overall performance of ML classifiers, and they are irrelevant for the sentiment classification of text. Stop words are also a type of unnecessary data that add unnecessary complexity to the text analysis. Thus, we removed numbers, punctuation, non-Arabic characters, and stop words from the reviews.  
Handling Words with Repeating Characters  
We also noted that some users add some words with repeating characters. Although this type of repetition can be considered as a sign of emphasizing users' emotions, it also needs to be fixed before passing the data to the next preprocessing steps. Spelling check tools can be very useful for fixing such problems. However, we should be careful when the repeating characters are removed. The spelling checker not only has to handle the repeating characters in a word, but it also has to produce a word that is consistent with the overall sentence context, e.g., the word \"_j_\" (repeat) in the following sentence: \"_j_\" (the teacher repeats the lesson) contains two \"_\" characters. Therefore, if the spelling checker removes the repeated character (in this case the letter \"_\"), a new word \"_\" will be produced, which is absolutely a different word and it is irrelevant to the sentence context.  
Dialect Identification  
The Arabic users may write their review content freely using either the modern standard Arabic (MSA) or their \"spoken\" forms, each of which is a regional dialect (allahjah, \"accent\"). There are several regional dialects, such as Egyptian, Levantine, Gulf, Iraqi, Maghrebi, and Yemeni. Identifying to which region a user belongs improves the accuracy of the ASA systems by avoiding wrongly classifying a review content. Table 5 shows a review of content with different regional dialects.
# Cyclic Elasto-Plastic Behaviour of 51 CrV4 Steel for Railway Parabolic Leaf Spring Design
## 3 Material and Analysis Procedure
### Computational Approach  
A computational approach using finite element analysis (FEA) in ANSYS software 2023 R2 was adopted to verify the cyclic material properties derived from the statistical analysis of the experimental data. A pure-displacement finite element formulation was considered for a cyclic small elasto-plastic deformation analysis of the material. The system of differential equations was solved incrementally by applying a displacement increment, \(\Delta\mathbf{u}\), in a single quadratic 20-node isoparametric finite element (\(1\times 1\times 1\)) following full integration Gauss scheme, which simulates the material behaviour under a uniformly distributed strain condition by considering isostatic boundary conditions. The matrix equation of the structural problem is represented in Equation (17) such that  
\[\mathbf{K}_{t}\Delta\mathbf{u}=\mathbf{F}_{ext}-\mathbf{F}_{int}, \tag{17}\]  
was solved according to the Newton-Raphson procedure (with the \(L2\)-Norm), using the direct sparse method with LDLT factorisation. In Equation (17), \(\mathbf{F}_{ext}\) denotes the external forces applied at nodes, \(\mathbf{F}_{int}\) denotes the restoring loading vector, and \(\mathbf{K}_{t}\) denotes the symmetric and positive-definite global tangent matrix. The cyclic elasto-plasticity modelling was made considering the von Mises plasticity criterion combined with the Chaboche (kinematic plus isotropic) hardening model [41]. The integration of the plasticity law was made via the return mapping algorithm for small deformations according to the Euler backward implicit scheme [96; 97; 98]. According to this scheme, a discretisation of the constitutive equation is made for each increment of deformation, \(\Delta\mathbf{e}\), for each time step, \(n\). In each increment, the [PERSON] plasticity criterion is computed such that  
\[f_{tr}^{(n)}=\sqrt{\frac{3}{2}}||\mathbf{S}_{tr}^{(n)}-\mathbf{a}_{tr}^{(n)}||-R_{tr}^ {(n)}-\sigma_{y}, \tag{18}\]  
where \(\mathbf{S}_{tr}^{(n)}\) denotes the trial deviatoric stress, \(\mathbf{a}_{tr}^{(n)}\) denotes the trial back stress, and \(R_{tr}^{(n)}\) denotes the trial expansion stress. If \(f_{tr}\leq 0\), then the process is elastic; however, if \(f_{tr}>0\)the process is plastic. In the case of elastic processes (\(f_{tr}\leq 0\)), the state for step \(n+1\) is the same as the trial state computed to \(n\) such that  
\[\mathbf{\sigma}^{(n+1)}=\mathbf{\sigma}^{(n)}_{tr}=\mathbf{\sigma}^{(n)}+\mathbf{C}\Big{(} \mathbf{\varepsilon}^{(n+1)}-\mathbf{\varepsilon}^{(n)}\Big{)}, \tag{19}\]  
with \(\mathbf{C}\) denoting the constitutive matrix for the linear elastic material. On the other hand, in the case of plasticity occurrence, \(f_{tr}>0\), the algorithm computes the plastic multiplier for \(n+1\), where \(\Delta\lambda^{(n+1)}\), considering the Newton-Raphson root-finding algorithm.
## Materials and Methods  
### Study Site  
This study was conducted in Naples Bay (26\({}^{\circ}\)5\({}^{\prime}\) N, 81\({}^{\circ}\)47\({}^{\prime}\) W), Naples, FL, USA. Naples Bay is a semi-diurnal, micro-tidal estuarine system [24], located in Southwest Florida (Figure 1). The mangroves around the southern part of Naples Bay are a small fraction of an estimated 2000 to 5000 km\({}^{2}\) of mangrove forests in Florida. The climate of Southwest Florida is subtropical, with an average annual temperature of 23.6 \({}^{\circ}\)C. Precipitation is seasonal, with 60% to 65% of the annual 1346 mm\(\cdot\) year\({}^{-1}\) precipitation occurring from June through September [35].  
The greater Naples Bay is primarily sand-dominated, though remnants of oyster beds supply the estuary with calcareous shell material. In comparison with other adjacent estuarine systems, Naples Bay is shallow (<7 m) [36]. Prior to urbanization, seagrass beds and oyster bars were found throughout the bay. However, as a result of dredging, channelization, and compartmentalization of water flows, and now polluted stormwater from an urban landscape, the system has lost much of its natural estuarine characteristic [36]. Around Naples Bay, 70% of the coastal fringing mangrove communities had been converted to human-dominated residential developments by 2005 [36]. The northern portion  
Figure 1: Map detailing study locations in south Naples, Florida near the Gulf of Mexico, of the two mangrove tidal creeks used in this study: disturbed Hamilton Avenue Creek and reference Susan’s Creek. Also shown are the specific locations of the three hydrogeomorphic settings for mangrove soil sampling near, or on, each of the two tidal creeks. Locations of Naples/Dollar Bays, Gulf of Mexico, the Naples Botanical Garden, and the Everglades Wetland Research Park are also shown.  
of Naples Bay is highly urbanized, while the southern portion and its connection to Dollar Bay are relatively unaltered due to protection afforded through the Rookery Bay National Estuarine Research Reserve (RBNER). The hydrologically disturbed Hamilton Avenue Creek is located 600 m south of Naples Bay proper and 1.9 km from the Gulf of Mexico. Susan's Creek is 1.3 km south of Naples Bay proper and 2.6 km away from the Gulf of Mexico.  
This study focuses on three comparable mangrove functional landscapes (basin, fringe, and riverine) in two distinct tidal creeks adjacent to Naples and Dollar Bays in Naples, Florida (Figure 1). Susan's Creek (SC) provided a reference mangrove hydrologic setting with no obvious hydrologic alteration. Hamilton Avenue (HA) Creek is a hydrologically disturbed mangrove Creek; human development, dredge spoils, and upstream land-use changes have altered the natural flow and sediment deposition regime of this location. An additional dwarf mangrove site in Susan's Creek was sampled to compare carbon sequestration rates and carbon pool with the other study sites.
# A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression
## 4 Simulation and Results: Comprehensive Analysis  
The initial goal of the simulation is to quantify the difference between theoretical value predictions for the different sub-circuits and real-world simulated implementation values of the proposed novel EEG-B3V3S/C1 circuit design. In particular, this simulation of the circuit's electronic behavior can assess the design's efficiency because it provides accurate voltage and current waveform representations, verifying that the authors' pre-defined requirements for the circuit are met at a satisfactory percentage. Moreover, another goal is to visualize the human body and environment model signal generator's output, assessing how realistic it is and its alignment with laboratory experiment results. Furthermore, emphasis is given to examining the EEG output in the following ways:  
* Time domain to make a qualitative comment with regard to CMRR, power consumption, gain, and noise level before the first amplification.
* Fourier frequency spectrum to validate if the information in the alpha, beta and theta wave bands is preserved.
* Fourier frequency spectrum and time domain to validate that the compound muscle movement artifacts are sufficiently suppressed.  
The simulation is carried out both in a 50 Hz and 60 Hz power grid environment, because these frequencies cover the most common EEG signal measurement cases that are not performed in a shielded environment of electromagnetic interference from the power grid [58]. Subsequently, a comprehensive comparison is conducted between the proposed novel EEG-B3V3S/C1 circuit design's simulated results and those of the Case \"1\" [47], Case \"2\" [48] and Case \"3\" [49] that were presented previously in this work.  
This section is divided into two segments: (A) the simulation of the circuit in the time and Fourier analysis frequency domain, and (B) the comparison of the proposed circuit (EEG-B3V3S/C1) with the Case \"1\" [47], Case \"2\" [48] and Case \"3\" [49].
# Event-Triggered Robust Fusion Estimation for Multi-Sensor Time-Delay Systems with Packet Drops
## 2 Problem Formulation and Some Preliminaries
Suppose, \(f_{\phi}(x)=N(x;\tilde{x},\Omega_{x})\), \(f_{G}(g)=N\big{(}g;0,\Omega_{g}\big{)}\), \(f_{Y}(y)=N\big{(}y;H\tilde{x},\Omega_{y}\big{)}\) where \(\Omega_{y}=\Omega_{g}+H\Omega_{x}H^{T}\). Thus, the optimal transmission strategy is derived as  
\[\|Y-H\tilde{x}\|_{\Omega^{-1}}^{2}\geq\theta,\]  
where \(\theta=\gamma_{m}^{-1}(1-a)\). The random variable \(\|Y-H\tilde{x}\|_{\Omega^{-1}}^{2}\) obeys the chi-square distribution with a degree of freedom m where \(\gamma_{m}\) is the chi-square distribution function with a degree of freedom \(m\). **Remark 1**: _The considered multi-sensor system is shown in Figure 1. Each sensor node has state estimation performance with a state delay. Each sensor sends its local state estimate to the fusion center through an unreliable communication channel. All local estimates are fused with data at the fusion center through the best linear unbiased estimation criterion._
# Angular Trajectory Design for MR-SPS Using Bezier Shaping Approach
## 2 Model Description  
### MR-SPS Structure  
In terms of structure, MR-SPS can be divided into three parts, as shown in Figure 1. For a MW-level MR-SPS, there are two solar arrays in each side in the north and the south direction, each solar array is composed of 10 solar subarrays, the size of each subarray is 30 m \(\times\) 100 m, and the distance between two subarrays is 2 m. The main structure is a truss structure, which consists of the upper and lower north-south main truss structure and the longitudinally connected main truss structure, with a total length of 800 m and width of 51 m. The upper north-south main truss structure is used to support the microwave transmitting antenna that is fixed to the main structure. The longitudinally connected main truss structure connects the north-south main structures together. The main structure and the antenna form the central body structure. The diameter of the microwave transmitting antenna is 140 m.  
It is assumed that the structural flexibility is not considered. Since the attitude of the 10 subarrays in each side is consistent, they can be considered to be fixed to each and form a big array. Torque actuators mounted on the central body generate three-dimensional control torque, and motors between the solar arrays and the main structure generate control torque only along the \(y_{a}\)-axis.
# Green-Dyeing Processes of Plant and Animal Fibers Using Folium, an Ancient Natural Dye
## 3 Results
### Evaluation of Color Fastness Through Fiber Optics Reflectance Spectroscopy (FORS)  
One of the best ways to evaluate color strength on the fibers is using fiber optic reflectance spectroscopy (FORS) and, in particular, the K/S ratio. An increase in K/S leads to an increase in absorption of the dye [22; 23; 24; 25].  
The color fastness could also be evaluated in samples treated with chitosan by comparing the spectrum of dyed fibers with that of pure folium.  
The spectrum reported in Figure 4 shows the two characteristic peaks of folium that could be observed also in the spectra of wool and cotton treated with plasma and chitosan after the dyeing process, reported in Figure 5. The presence of chitosan on the fibers guarantees the adhesion of the dye after washing step. The presence of the folium dye after the washing process is confirmed by the presence of the two characteristic bands of folium at 545 e 575 nm in each of the samples analyzed.  
The two characteristic peaks of folium are present in the samples of cotton and wool after the dyeing and wash phases. Samples were treated with He or He-O\({}_{2}\) plasma.  
According to the FORS spectra reported in Figure 5, it is possible to notice how, for the samples of wool treated with plasma, the peaks of dye folium (at 545 and 575 nm) are more evident than those present in the samples of cotton treated with plasma. These data indicate a greater adhesion of the dye on the wool fiber treated with plasma and coated with chitosan compared to the cotton fiber under the same conditions.  
Figure 4: FORS spectrum of folium in Kubelka-Munk coordinates.
## 3 Methodology
### Decomposition Modules
#### 3.1.2 Series Decomposition Block  
Real-world sequential signals (e.g., PV power sequence) are often entangled with temporal patterns that are informative for forecasting. Time series decomposition is an effective strategy to decouple knotted patterns from sequences. Among decomposition methods, seasonal-trend decomposition [33] has been widely employed as a feature engineering technique, which separates sequences into seasonal and trend parts. Inspired by Autoformer [34], we apply this decomposition idea as a series decomposition block (SDBlock) to enhance the pattern extraction ability of DualET. Given an input sequence \(\mathbf{X}\), the procedure is  
\[\mathbf{S},\mathbf{T}=\text{SDBlock}(\mathbf{X}), \tag{3}\]  
where \(\mathbf{T}\) is the moving average result of \(\mathbf{X}\) and is considered as the trend part; \(\mathbf{S}\) is the residual part (i.e., the detrend part), which is regarded as the seasonal part. To keep the sequence length unchanged, a padding operation is performed on the input sequence.
## 2 Method
### Fixed Point Stability Analysis
#### 2.2.3 Discussion of Fixed Point Stability Results  
The analytical results presented above, and notably conditions (54) and (64), suggest the following considerations:* An adequately balanced mix of direct and contrarian subjects within the traveling population is conducive to fixed point stability, whereas strongly homogeneous route choice behaviors tend to trigger the occurrence of instabilities;
* The range of \(\varphi\) values ensuring fixed point stability depends on the inertia (\(\alpha\)) and memory depth (\(\beta\)) parameters, as well as on the degree of sensitivity of costs to flows (\(\gamma\)) and of route choices to costs (\(\mu\));
* The stability regions defined by conditions (54) and (64) are not affected by a swap of the values of \(\alpha\) and \(\beta\) or by a swap of the values of \(\gamma\) and \(\mu\);
* The width of the stability regions decreases as \(\gamma\) and/or \(\mu\) increase. Unlike the latter parameters, which appear on both sides of expressions (54) and (64), \(\alpha\) and \(\beta\) affect only the lower bound of the stability region. More specifically, it is easy to check that the derivatives of the left-hand sides of (54) and (64) with respect to \(\alpha\) and \(\beta\) have a positive sign. In light of expressions (1)-(3), this suggests that appropriately reduced day-to-day cost and flow updating rates have the potential to offset the destabilizing effects of steeply increasing cost-flow functions and highly cost-sensitive route choice behaviors;
* Conditions (55), (56) and (65), (66) indicate that the composition of the traveling population in terms of direct/contrarian choice behaviors may become irrelevant when stability of the fixed point is already ensured by sufficiently small values of the other model parameters;
* Comparison of expressions (54) and (64) suggests that the stability region for fourth-power cost functions is twice as large as for linear cost functions. This circumstance can be explained by observing that, at the \(\overset{\sim}{F}=0.5\) fixed point, the derivative of the fourth-power function is equal to \(\gamma/2\), which is exactly half of the derivative of the linear cost function appearing in (54).
## 5 Results
### Functional Experiments of the FloatingBlue Architecture  
For the functionality tests of the \"FloatingBlue\" proposal, the SNs were deployed in a citrus plantation in a remote area in the Amazonian jungle. Figure 11 illustrates one of the three SNs placed in the ground and two DMs installed in the test environment. DM 1 was mounted on a DJI Mavic 2 Zoom drone, and DM 2 was installed on a tractor.  
Parameters were configured for the BLE and dtn7-go protocols in the DM node. For BLE, the scanner window time was adjusted to 5 s, which is longer than the advertising interval of the SN. The parameters for dtn7 are detailed in Table 3.  
Figure 11: SN and DM installed in the test scenario.  
Figure 10: Relationship between battery autonomy of the SN transmission power.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Parameter** \\ \hline Discovery Protocol & IPV4 e IPV6 \\ Websocket & ws://0.0.0.0:8080/ws \\ CLA protocol & TCPCL \\ Routing Algorithm & Epidemic \\ Node-id & dtn://node-name/ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Configuration of dtn7.  
During the experiments, a Wi-Fi access point was used to collect results via SSH and establish a WebSocket channel with the DM 2 device. The access point is an optional element in the \"FloatingBlue\" architecture.  
According to Figure 12, the test began with the takeoff of DM 1 from the point labeled \"Start\". DM 2 was 219.5 m from the Start point and outside the communication range of DM 1. During the test, the tractor with DM 2 remained stationary, while the UAV carrying DM 1 flew at a constant altitude of 30 m to avoid interference with local vegetation.  
After takeoff, DM 1 followed GPS coordinates, sequentially flying over SN 1, SN 2, and SN 3. At each SN, it remained stationary for 3 to 5 min to collect a significant volume of data. It flew over the tractor with DM 2 and returned to the \"Start\" point. Figure 12 depicts this trajectory. The communication transceiver of the DM 2 was deactivated to ensure disconnection despite the distance of DMs. Later, it was reactivated only when DM 1 directly flew over DM 2. In the end, the data are collected by the processing station, which interfaces with the user once the DM 2 comes within the communication range of the station.  
The _DTN Handler_ and _BLE Handler_ services were analyzed using the 'dtn show' tool, which reports the bundle contents, allowing for the analysis of bundle integrity. DM 1 and DM 2 were disconnected, without contact, for 51 min. DM 1 produced 437 bundles collected from the three SNs during this period. When the connection between DM 1 and DM 2 was restored, all bundles from DM 1 were transferred to DM 2 with a Packet Delivery Ratio of 100%.