esa-sceva/satcom-chunk-collection huggingface dataset:
# Electromagnetic Imaging of Uniaxial Objects by Two-Step Neural Network
## 4 Numerical Results
### Relative Permittivity between 1.5 and 2  
In this scenario, we establish the permittivity distributions ranging from 1.5 to 2. We examine 10 scatterers with unique permittivity distributions, capable of freely moving across 50 positions within the measurement area. We proceed to estimate the dielectric coefficient distribution via the received scattered field information from the DCNN with 5% Gaussian noise added. This information is then inputted into the CNN for electromagnetic image reconstruction. Figures 7 and 8 illustrate the respective TM and TE results. Figures 7a and 8a display the ground truth of the \(\varepsilon_{z}\) and \(\varepsilon_{x}\) scatterers, respectively, while Figures 7
# Computationally Efficient Robust Color Image Watermarking Using Fast Walsh Hadamard Transform
## 4 Performance Evaluation and Simulation Results
### Computational Complexity and Execution Time  
A 512 \(\times\) 512 sized Lena image is watermarked with a 64 \(\times\) 64 sized watermark and the time taken for embedding and decoding using the proposed algorithm is measured on HP Laptop EliteBook 8470p with Windows 7 Operating System, 8 GB RAM, clock speed of 2.8 GHz with Intel i5 core CPU. It is found that the total time consumed by the algorithm is 0.665 s.  
Compared to DCT based algorithms [3] and FWHT based algorithms, the proposed algorithm takes much less time. The time taken by different algorithms for embedding the same sized watermark is compared in the Table 1 given below.
# Research on Vehicle Re-Identification Algorithm Based on Fusion Attention Method
## 2 Our Model Architecture
### Local Feature and Channel Attention Module Fusion  
The vehicle feature map in the local feature branch is evenly divided into three parts horizontally. Then, these three parts are input into the channel attention mechanism module to obtain the local features of each part. It is well known that high-level convolutional channels have semantic dependencies and class selectivity in trained CNN classifiers. In the Vehicle Re-ID case, it is assumed that the high-level channels are also \"grouped\", i.e., some channels share similar semantic context, such as the foreground is a vehicle, occlusion, or background, and the correlation between them becomes stronger. CAM [16] is designed to group and aggregate these semantically similar channels.  
Given an input feature map \(A\in R^{C\times H\times W}\), where \(C\) is the total number of channels, H\(\times\)W is the feature map size, and the channel attention matrix \(X\) is calculated as \(X\in R^{C\times C}\).  
\[x_{ij}=\frac{exp(A_{i}\cdot A_{j})}{\sum_{j=1}^{C}exp(A_{i}\cdot A_{j})},i,j\in \{1,\cdots,C\} \tag{8}\]  
Here, \(x_{ij}\) represents the influence of channel \(i\) on channel \(j\). The final output feature map \(E\) is calculated by Equation (9).  
\[E_{i}=\gamma\sum_{j=1}^{C}\left(x_{ij}A_{j}\right)+A_{i},i,j\in\{1,\cdots,C\} \tag{9}\]  
\(\gamma\) is a hyper-parameter that tunes the influence of CAM.  
In the channel attention mechanism, each part undergoes the same operation. The obtained result is multiplied by the elements of the original feature image, and finally the two results are added to obtain a three-part channel-attention-weighted feature image.
# Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots
## 2 Trajectory Planning Based on MINCO Trajectory Classes
### Differential Flatness-Based Control Optimization Trajectories
#### 2.1.3 Multisegment Trajectory Optimization Problem in the Unconstrained Case  
For costly optimization of single-segment trajectory control given only a set of initial and termination boundary conditions, commonly used methods include polynomial curve-based and Bessel curve-based trajectory optimization. If the environmental obstacles are complex, the planning results obtained may be more costly or have a smaller safety threshold. If multiple polynomial trajectories are considered, each of which is represented by a polynomial curve, and each of which has a set of boundary conditions, a trajectory with a large safety threshold at a small cost can be obtained as long as the intermediate points are chosen appropriately.  
The \(M\) segment polynomial trajectory control cost optimization problem for the unconstrained case can be described as Equation (17):  
\[\min_{z(t)}\int_{0}^{T}v(t)^{T}Wv(t)dt (a)\] \[z^{(s)}(t)=v(t),\forall t\in[t_{0},t_{M}] (b)\] \[z^{[s-1]}(t_{0})=\overline{z}_{0},\;z^{[s-1]}(t_{M})=\overline{z} _{f} (c) \tag{17}\] \[z^{[d_{i}-1]}(t_{i})=\overline{z}_{i},\;1\leq i\leq M (d)\] \[t_{i-1}<t_{i},\;1\leq i\leq M (e)\]  
where the time region from \(t_{0}\) to \(t_{M}\) is separated into \(M\) time segments by \(M+1\) fixed time points. Each time segment corresponds to a section of the trajectory. The initial and termination conditions of the entire trajectory are represented, respectively, by \(\overline{z}_{0},\overline{z}_{f}\in R^{ms}\). \(\overline{z}_{i}\in R^{md_{i}}\) is the intermediate condition and \(d_{i}\leq s\), indicating that the intermediate condition of the derivatives of the 0 th to the \(d_{i}-1\) order derivatives needs to be satisfied for \(z(t)\) at the time \(t_{i}\), i.e., it must satisfy \(z(t_{i}),\dot{z}(t_{i}),\ldots,z^{d_{i}-1}(t_{i})\). For example, when \(d_{i}=1\), only the 0 th order derivative, i.e., the position, is included, which is reflected in the trajectory that the trajectory needs to pass through for this waypoint \(z(t_{i})\), but there is no restriction on the velocity, acceleration, etc., at this point.
## 4 Methodology
### Multi-Source Domain Adaptation
Thus, using standard backpropagation and gradient descent (or variants like AdamW) optimizes the entire network such that \(\theta_{d}\) receives signals to minimize \(\mathcal{L}_{d}\), while \(\theta_{f}\) receives combined gradient signals to minimize \(\mathcal{L}_{y}\) and maximize \(\lambda_{\text{dann}}\mathcal{L}_{d}\), achieving the desired adversarial training objective. In our implementation, we add a special domain token at the beginning of each input sequence to indicate its source (e.g., '<FOMC>', '<ECB>', '<QE_PERIOD>', '<TIGHTENING_PERIOD>'). The domain classifier \(G_{d}\) (e.g., an MLP with several fully connected layers) takes feature representations from an intermediate layer of Hyena (e.g., the 4 th layer) and outputs a probability distribution predicting the input's domain class. The domain adversarial loss \(\mathcal{L}_{\text{dann}}\) typically uses cross-entropy:  
\[\mathcal{L}_{\text{dann}}=-\frac{1}{N}\sum_{i=1}^{N}\sum_{k=1}^{K_{d}}d_{ik} \log G_{d}(G_{f}(x_{i}))_{k} \tag{9}\]  
where \(N\) is the batch size, \(K_{d}\) is the number of domain classes, \(d_{ik}\) is a one-hot vector indicating the true domain of sample \(x_{i}\), and \(G_{d}(\cdot)_{k}\) is the probability predicted by the domain classifier that \(x_{i}\) belongs to the \(k\)-th domain. By minimizing an upper bound on the source domain risk and the H-divergence between domains [41], DANN encourages the model to learn more generalizable, domain-invariant feature representations, thereby improving prediction performance on unseen target domains (like ECB texts or texts from future economic cycles).
## 3 Discussion
### Combination of Space-Borne and Ground-Based InSAR Systems
The best approach is to promote international collaboration between space agencies and heritage administrations, encouraging stakeholders of satellite SAR systems to lay more stress on heritage conservation and support the operation and popularization of heritage monitoring by differential radar interferometry.
## 5 Results and Discussion
### Magnetic Field Distribution Analysis  
Magnetic flux density is a direct indicator of the electromagnetic fields generated by current flow within conductors. Figure 11a-c illustrate the magnetic flux line distributions for single-segment, three-segment, and five-segment 2D models at 50 Hz, respectively.  
The magnetic flux distributions indicate that in single-segment models, flux lines are densely concentrated near the conductor's boundaries, reflecting high magnetic flux density due to pronounced skin effects. As segmentation increases, the flux lines become more dispersed, indicating a reduction in localized magnetic field intensities. The five-segment model showcases a more uniform flux distribution, corroborating the effectiveness of segmentation in mitigating skin and proximity effects.  
Figure 11: Magnetic flux line representation at 50 Hz for 1-segment (**a**), 3-segment (**b**) and 5-segment (**c**) 2D models.
## 3 Results
### Spatial Visualization of Fresh Tongue by SMAP Products  
Figure 6 shows the L3 salinity data centered on 17 February 2020 from the SMAP and HYCOM products in the background. Overlaid are the salinity measurements recorded by the saildrone between the 16-20 February 2020. The SMAP data in Figure 6 shows less variability than the saildrone measurements, due in part to differences in spatial and temporal resolution. The JPL product is smoothly varying with some potentially erroneous values in several pixels directly adjacent to South America that could be due to issues with the land mask correction or could also potentially reflect real coastal variability.  
Near-land retrievals: The RSS product masks regions near land, and generally matches the JPL retrievals while also appearing to be marginally more saline. There appears to be a fresh 'halo' around the land in both RSS products that is not present in any other data. The RSS40 salinity has large biases near land that are not present in the JPL product as well as additional fluctuations, without any clear indication of improved spatial resolution. Near land, the JPL additional retrievals match the HYCOM data and appear to add value.  
Figure 5: Sea surface salinity difference versus sea surface salinity between (**a**) RSS70, (**b**) JPL and (**c**) RSS40 (orbital L2 no repeat data) and (**d**) HYCOM and the saildrone instrument SBE37 as recorded by saildrones 1026, 1060 and 1061.  
Fresh Tongue: All versions of the SMAP satellite observations identified the fresh tongue but recorded moderately more saline waters across the fresh tongue when compared with the measurements of the saildrone, measuring a 33.33 psu minimum (18 February 2020 08:59:00) in contrast to the 33.49 psu, 34.03 psu and 33.52 psu of the JPL, RSS70 and RSS40, respectively. HYCOM fails to reproduce the fresh tongue event in its prediction as seen in Figure 6.
## 3 Composite Matrices and Applications
### Polymers
The smoke release rate (SRR) and total smoke release (TSR) were reduced, especially in PA12/3C/1W, suggesting better smoke suppression. Additionally, the limiting oxygen index (LOI) tests show increased LOI values, thus confirming enhanced flame retardancy with the addition of H-CNTs and P-IF-WS2. This was attributed to the formation of a hydrogen-bonded network structure, which inhibits thermal movement and reduces plasticizing effects during combustion [88]. The ongoing advancements in polymer composites through the integration of nanomaterials, like CNTs, are leading to materials that not only meet but exceed the traditional performance metrics of polymers. The use of solid-phase molding, functionalization of fillers, and innovative manufacturing techniques, like SLS, are critical in realizing composites that are mechanically superior and thermally conductive while also being flame resistant. This holistic improvement in polymer properties opens new doors for their application across various high-demand industries, enhancing their functionality and safety profiles. The conclusions of the composites investigated in this section are summarized in Tables 1-4. \begin{table}
\begin{tabular}{l l l} \hline
**Composites** & **Formulation** & **Output** \\ \hline CNT/PP-I & Solid-phase molding with & Highest electrical conductivity \\ CNT-coated PP granules & (70 S/m at 2 wt\% CNT) \\ \hline CNT/PP-II & Regular solid-phase molding & High electrical conductivity but \\  & & lower than CNT/PP-I \\ \hline CNT/PP-III & Regular molding & Lower conductivity than \\  & & CNT/PP-I and CNT/PP-II \\ \hline CNT/PP-IV & Solution mixing & Conductivity is lower by seven \\  & & orders of magnitude than \\  & & CNT/PP-I \\ \hline CNT/PP-V & Melt mixing & Conductivity is significantly \\  & & lower than CNT/PP-I and \\  & & CNT/PP-II \\ \hline \end{tabular}
\end{table} 
Table 1: Electrical conductivity performance. (Summary of the work of [PERSON] et al. [69]).
# Wake-Up Receiver-Based Routing for Clustered Multihop Wireless Sensor Networks
## 4 Proposed Approach
### Node Description  
The wireless nodes used in this work are based on commercial off-the-shelf (COTS) components that are usually cheaper in setup, maintenance, expansion, and development. In order to save energy, the used WuRx equipped with a 3.0 V battery uses passive components for continuous listening to the radio channel. Figure 3 shows a photo of the implemented sensor node. The MCU utilised on the boards is a 16-bit MSP430G2553 [31] microcontroller operating at 8 MHz and manufactured by Texas Instruments. The MSP430 can enter multiple low-power modes; when in Low-Power Mode 3 it consumes 2.55 \(\upmu\)W. The communication radio is a SPIRIT1 [32] radio module from STMicroelectronics. It has a current consumption of 21 mA when transmitting at +12 dBm output power at 868 MHz and about 9 mA when receiving with an approximated sensitivity of \(-118\) dBm. The LF WuRx chip AS3933 is a low power, 3-channel ASK receiver with a current consumption of around 3 \(\upmu\)A in listening mode. The AS3933 [33] is designed for carrier frequencies of 15 kHz to 150 kHz using On-Off-Keying (OOK) modulation. The modulated OOK signal of 18.7 kHz is converted to the carrier signal of 868 MHz which, when received by a sensor node, is converted back to the kHz band by the passive envelope detector. The AS3933 correlates the incoming signal with the node-specific address and generates an interrupt if both addresses match.
## 1 Introduction
[24] studied the adsorption of a reactive blue 4 dye from an aqueous mixture by carbon nanotubes. The maximum sorption capacity was observed to be 502.5 mg/g. Moreover, [PERSON] et al. [18] studied the adsorption of anionic and cationic dyes from a liquid mixture using gelatin-based magnetic nanocomposite beads, including carboxylic acid functionalized carbon nanotubes. The results showed the percentage removal to be 76.3% of methylene blue. [PERSON] et al. [25] reported that the treatment of organic chemicals compounds by MWCNTs may include more mechanisms, such as hydrophobic interactions, \(\pi\)-\(\pi\) interactions, electrostatic interactions, Lewis acid-base interactions, and hydrogen bonding, which reveals that this requires essential additional investigation. Ismate violet 2R was selected as a model compound in this research due to its wide application range, which includes coloring paper, and dyeing cottons, woods, and coating for paper stock and medical purposes, in addition to its potential dangerous effects. Accordingly, the aims of this work were the preparation, functionalization, and purification of multi-walled carbon nanotubes (MWCNTs), as a novel approach, and the application of MWCNTs as an adsorption substance to remove Ismate violet 2R (IV2R) dye from aquatic effluents under various conditions using the batch adsorption method. The main parameters evaluated were the initial dye concentration, pH, contact time, temperature, and adsorbent dose. Moreover, the changes in the physical, chemical, and morphological structure owing to imitation reactions were studied through Raman, XRD,BET surface, FTIR, and SEM characterizations. Additionally, thermodynamic and kinetics studies were conducted, and the experimental equilibrium was investigated using various sorption isotherm models to assess the adsorption mechanism. In addition, to estimate the regenerative capacity of MWCNTs, the interference of wastewater on the adsorption of IV2R dye was also examined. The novelty of this study can be summarized as the first case, in the oxidized MWCNT literature, of MWCNTs modified with such functionalities and applied to the elimination of IV2R dye. Furthermore, previous studies on dye removal by a single functional group indicated lower elimination than that presented in this work. Moreover, the highest suitable model was achieved by using error functions.
## 4 Results and Discussion
### Binder Fraction Characterization  
The selected samples were treated in order to obtain the fine binder fractions (SGs) via wet gravimetric preparation, which were then analyzed using XRPD (Table 3 and Figures S6-S10 in the Supplementary Materials). The results show the presence of calcite (ranging from 19 to 82%), magnesite (from 1 to 15%), abundant amorphous phases (up to 59%), hydrotalcite (LDH, abundant in sample MV_SS_07), aragonite (3% in sample MV_SS_01 only), phyllosilicates (mica and magnesium-rich clays), and hydromagnesite (abundant in samples MV_SS_08 and MV_SS_10). The aggregate fraction, primarily composed of quartz and feldspars (albite), was completely removed during the binder extraction procedure. The presence of aragonite (a metastable polymorph of calcium carbonate) in sample MV_SS_01 could be associated with the alteration phenomena of paracrystalline phases formed during pozzolanic reaction and/or may be due to the carbonation process of calcium oxide [43]. The indication of the presence of biogenic aragonite is not reliable, since no evidence of shells or similar aggregates was found in the macroscopic and microscopic investigations.  
The identification of LDH phases such as hydrotalcite is crucial for radiocarbon dating purposes; LDH minerals form as a result of the pozzolanic reaction and have a high capacity to fix carbonate anions (CO3\({}^{-2}\)) into their crystalline structure, introducing contamination from younger carbon into the system, which may shift the radiometric dates towards later dates.  
To address these issues, in order to eliminate these radiocarbon contaminants, purification methods for the mortar matrix were developed, utilizing a thermal treatment at 550 \({}^{\circ}\)C (a temperature at which the decomposition of LDH phases occurs but not that of the calcium carbonate in the binder fraction, which decomposes at higher temperatures, approximately 800-850 \({}^{\circ}\)C [44]).  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Samples** & & **Calcite** & **Arag.** & **Magnesite** & **Micas** & **Mg-Clays** & **HydroMg** & **LDH** & **Amorp.** \\ \hline MV\_SS\_01 & SG & 30.7 & 3.0 & 0.0 & 7.4 & 0.0 & 0.0 & 0.1 & 58.9 \\ \hline MV\_SS\_02 & SG & 82.1 & 0.0 & 1.4 & 3.3 & 0.0 & 0.0 & 0.0 & 13.2 \\ \hline MV\_SS\_07 & SG & 25.6 & 0.0 & 14.2 & 5.0 & 2.1 & 0.0 & 4.8 & 48.3 \\ \hline MV\_SS\_08 & SG & 19.0 & 0.0 & 2.7 & 0.0 & 8.4 & 16.9 & 0.0 & 53.0 \\ \hline MV\_SS\_10 & SG & 22.4 & 0.0 & 15.4 & 0.0 & 15.7 & 9.9 & 2.1 & 34.6 \\ \hline \end{tabular}
\end{table}
Table 3: Quantitative XRPD analyses, expressed as %wt of the mineral phases identified in the analyzed samples.
## 3 Methodology
### Stage II: GAN-Based Model Training Using the SentiGAN Model
#### 3.2.1 Generator  
The present study uses a modified version of the SentiGAN model ([PERSON] and [PERSON], 2018). The generator of the modified SentiGAN model is built using an LSTM model, namely, \(G_{i}(X_{t+1}\big{|}S_{t};\theta_{g}^{i})\). At time step \(t\), the generator generates the word sequence \(X_{1:t}=\{X_{1},\dots,X_{t}\}\), and it generates the next word based on the words generated before \(S_{t}=\{X_{1},\dots,X_{t}\}\). Each category in the classification task corresponds to a unique generator. At the beginning of model training, each generator is initialized by vector z, which is a vector generated from a normal distribution, and the generator then generates the next word \(X_{t}\) based on the previously generated word sequence \(S_{t}\).  
\[G_{i}=\left\{\begin{array}{ll}G_{i}\Big{(}z;\theta_{g}^{i}\Big{)}&\text{ initialized generator's input}\\ G_{i}\Big{(}X_{t+1}\big{|}S_{t};\theta_{g}^{i}\Big{)}&t\geqq 1\end{array}\right. \tag{1}\]  
The generated data \(X_{1:t+1}\) are passed into the discriminator and are assigned the penalty probability \(V_{Di}^{Gi}\), and the generator then updates the model parameters based on the penalty probability. The penalty-based loss function is as follows:  
\[L(X)=G_{i}\Big{(}X_{t+1}\big{|}S_{t};\theta_{g}^{i}\Big{)}\cdot V_{Di}^{Gi}(S _{t},X_{t+1}) \tag{2}\]  
The total loss that the generator must minimize is as follows.  
\[J_{G_{i}}\Big{(}\theta_{g}^{i}\Big{)}=\mathbb{E}_{X\sim P_{S_{t}}}[L(X)]=\sum \limits_{t=0}^{t=|X|-1}G_{i}\Big{(}X_{t+1}\Big{|}S_{t};\theta_{g}^{i}\Big{)} \cdot V_{Di}^{Gi}(S_{t},X_{t+1}) \tag{3}\]  
Because the discriminator can only judge a full sentence, the Monte Carlo search algorithm and roll-out policy are used to sample the final \(|X|-t\) unknown tokens in incomplete sentences. Subsequently, the penalty-based loss is calculated as follows:  
\[V_{Di}^{Gi}(S_{t-1},X_{t})=\begin{cases}\frac{1}{N}\sum\
olimits_{n=1}^{N}(1 -D_{i}(X_{1:t}^{n};\theta_{d}))&t<|X|\\ 1-D_{i}\big{(}X_{1:t}^{n};\theta_{d}\big{)}&t=|X|\end{cases} \tag{4}\]  
where \(X_{1:t}^{n}\) is a sample that is obtained through \(N\)-time Monte Carlo search and is based on \(G_{i}\) and the current state \(S_{t}\). \(X_{1:t}^{n}\) is passed into the discriminator and achieves a probability pertaining to whether the semantic similar \(i\)th category original sentences. The present study uses an LSTM model as the generator, and each generated word is determined through the Softmax function.  
\[p(X_{t})=softmax\Big{(}LSTM_{\theta_{g}}(h_{t-1},X_{t-1})\Big{)} \tag{5}\]
## 2 Internet of Vehicles and Smart Cities
### Main Challenges
#### 2.4.5 Scalability  
It is also extremely important that the IoV can have numerous amounts of devices connected at a given time without a significant decrease in performance, ensuring high scalability. Otherwise, it will not serve its purpose of achieving large-scale coverage and guaranteeing that services reach the users.
## 2 Background Information for Method Development
### Image Transformation
Required Position and Orientation Sensors  
From these different tracking sensors, the most accurate positioning system could be the system that works with ultra-wideband (UWB) [54]. The accuracy of this system claims to be (\(\pm\))10 cm [54]. According to [54], \"the accuracy achieved with this technology is several times better than traditional positioning systems based on WIFI, Bluetooth, RFID or GPS signals.\" [54]. Some companies are developing UWB positioning sensors. One of them is called Pozyx. The sensors produced by this company include a tag and some anchors (at least four anchors are required). The tag sends and receives signals to anchor modules through a wireless radio technology called ultra-wideband (UWB) [54]. These signals can penetrate walls in an indoor environment. The anchor modules play the role of reference points for the tag. In this system, to calculate the position, the distance of one tag module to each anchor module is calculated based on time-of-flight (TOF) of the waves between the tag and anchors, where [54]:  
Distance = time of flight \(\times\) speed of light  
Speed of light = 299,792,458 m/s  
Then, through a method called multilateration [55], the position of the tag module with regard to anchor modules is calculated. For 3D orientation purposes, some sensors such as acceleration, magnetic field, and angular velocity are embedded in the tag module, which handles orientation responsibility. According to the sensor manual [54], each of these sensors has its own limitations, but through combining the outputs from different types of sensors, 3D orientation is computed accurately.