esa-sceva/satcom-chunk-collection huggingface dataset:
# Photocatalytic Cementitious Material for Eco-Efficient Construction--A Systematic Literature Review
## 1 Introduction  
In 2017, the World Health Organization (WHO) reported that environmental risks-for example air pollution, contaminated water, lack of sanitation, and poor hygiene-take the lives of 1.7 million children under the age of five [1]. A polluted environment is a deadly one, and hence, reducing environmental risks could save millions of lives. Technological innovations may address such environmental problems and create an environmentally more sustainable future. Titanium dioxide (TiO\({}_{2}\)) has emerged as an excellent photocatalyst for reducing environmental risks. Early works focused on treatment of polluted water using TiO\({}_{2}\) in powder suspension forms [2; 3]. This method is efficient due to the large surface area of the catalyst and the absence of mass transfer limitations. However, the widespread application is limited as it has an additional separation problem when used as a suspension: recycling it from the treated water after use, which makes it time-consuming and cost ineffective [4; 5; 6].  
In order to overcome these problems, many techniques have been proposed for immobilization of TiO\({}_{2}\) on solid support [6; 7]. In recent years, there has been increasing interest in using cementitious materials as catalyst supporting media [8; 9; 10; 11; 12]. The main strategy is to exploit facades of buildings in urban areas as catalyst supporting media. The cementitious materials used in building facades offer enormous surface area for photocatalytic reactions [13]. This strategy has a good potential in urban pollution controland at the same time maintaining the aesthetic of the building's appearance [10, 14, 15, 16]. Furthermore, incorporating TiO\({}_{2}\) photocatalyst into cementitious materials may be able to keep the cement-based surface sterile by inhibiting the growth of microorganisms through photocatalytic degradation [10, 17, 18]. Thus, utilizing cement-based materials as catalyst supporting media potentially transform ordinary cementitious materials into environmentally friendlier products, such as air-purifying pavement, self-cleaning external wall, and antibacterial concrete, as shown in Figure 1.  
This paper presents a systematic review of the cementitious composite materials containing TiO\({}_{2}\) photocatalyst. The review aims to present the key findings in photocatalytic cement-based composite materials and identify the areas in which future improvement is needed. This review will start with the basic mechanism of photocatalysis, bandgap and energy level position, and improvement of pristine TiO\({}_{2}\) with doping and sensitization, as presented in Figure 2. This will be followed by a discussion on the applications of photocatalytic cementitious materials, such as air purification, self-cleaning, and self-sterilization function. The influence of incorporating photocatalyst into cementitious materials will be summarized and discussed. The efficiency of photocatalytic cementitious materials and the current drawback will also be discussed.
# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
## 3 Proposed Method
### FDNN Model
#### 3.2.2 Detection Module  
The detection module encompasses a fully connected deep neural network structure featuring nonlinear layers. This neural network consists of \(K\) fully connected layers with the initial \(K-1\) fully connected layers directly succeeded by an activation layer. The ReLu function is selected as the activation function, which is written as  
\[f_{ReLu}(x)=\max(0,x)\,. \tag{18}\]  
Figure 2: FDNN receiver model.  
Additionally, we employ the Softmax classification function at the end of the FDNN network, where the output represents the probability of the estimated symbol. The Softmax function can be represented as  
\[f_{Soyfmax}(x_{n})=\frac{e^{x_{n}}}{\sum_{m}e^{x_{m}}}\,. \tag{19}\]  
The total detection module can be expressed as  
\[\hat{\mathbf{x}}_{i} =\mathrm{F}(\mathbf{I}_{i}) \tag{20}\] \[=f_{Softmax}(\mathbf{W}_{K}f_{ReLU}(\mathbf{W}_{K-1}\cdots\mathbf{W}_{1}f_{ReLU }(\mathbf{I}_{i}+\mathbf{b}_{1})\cdots+\mathbf{b}_{K-1})+\mathbf{b}_{K})\,,\]  
in which \(\mathbf{W}_{K}\) and \(\mathbf{b}_{K}\) represent the weight matrix and bias vector of the \(k\)-th fully connected layer. \(\hat{\mathbf{x}}_{i}\) signifies the estimated value of the one-hot code output by the detection module. This estimated value can be transformed into the estimated modulation symbols \(\hat{\mathbf{s}}_{i}\) by  
\[\hat{\mathbf{s}}_{i}=f_{OH}^{-1}(\hat{\mathbf{x}}_{i}),\hat{\mathbf{s}}_{i}\in S_{i}\,. \tag{21}\]
# In Situ Assessment of Uplink Duty Cycles for 4G and 5G Wireless Communications
## 3 Results
### Impact of RSRP on the PUSCH Transmit Power per RB and Duty Cycle  
Figure 10 shows the PUSCH Tx power per RB as a function of the RSRP. We can clearly observe that the PUSCH Tx power per RB decreases with increasing RSRP values irrespective of the technology used, i.e., 4G LTE or 5G NR, or the use case (application). We observe that the 5G NR PUSCH Tx power per RB was lower than that for 4G LTE (see Figure 10a). We remark that the number of bits per RB was also lower for 5G NR compared with 4G LTE. We also observe large variations in the PUSCH Tx power per RB for constant RSRP values (up to 20-30 dB), and vice versa. Figure 10b shows the PUSCH Tx power per RB as a function of the RSRP for each of the considered use cases: the PUSCH Tx power per RB decreases with increasing RSRP values, except for when the maximum transmit power is reached (for RSRP values between \(-95\) and \(-105\) dBm and for the FTP and iPerf use cases).  
Figure 10: _Cont._
## 2 Cepstrum Analysis for Wheel Flat Detection
### Detecting the Harmonic Component of Flat Signals from the Vibration Signal Including Noise Signals  
In order to verify that the cepstrum analysis is effective in the case including noise signals by the rail irregularities and the vibrations of bogie components, such as axle bearings and reduction gears, the vibration signals of a normal wheel were measured using acceleration sensors during operation of the Seoul subway Line 7, as shown in Figure 12. The non-contact type speed sensor that generates 90 pulses per a revolution of the wheel was innately installed in the bogie. Using this speed sensor signal, the measured vibration signal can be converted into an angular domain signal. The measured data is shown in Figure 13.  
The vibration signal of the flat wheel was obtained superimposing the simulation result at the same speed condition on the measured vibration signal of the normal wheel, and then it was analyzed by the cepstrum.  
The cepstrum analysis results are shown in Figure 14. The peak factor of the flat wheel is approximately two times that of the normal wheel including only the noise signal. The peak value is detected in the harmonic components of the wheel rotation. The flat signal was detected by the cepstrum analysis, even though the flat signal is not visible in the vibration signal of the flat wheel because the magnitude of the noise signal is about four times that of the flat signal.
# A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification
## 3 Experiment
### Model Evaluation and Analysis
#### 3.3.3 Classification Performance on Different Ship Types
Overall, the classification performance is best for towing vessel and oil tanker trajectories, while fishing and sailing vessel trajectories exhibit relatively poorer classification results. To improve the model's ability to distinguish between the trajectories of fishing and sailing vessels, we plan to add more training data for these two types in future work in order to assist the model to better differentiate them. Additionally, we intend to introduce more discriminative, manually defined features, such as operational type and weather conditions, as contextual information, and optimize the model by refining its architecture and loss function to enhance overall performance.
## 3 Results  
### Non-Contact Ultrasonic Damage Inspection  
Figure 4 depicts the visual results of the ultrasonic wave propagation for both specimens. The ellipses shown in the top figures demonstrate the approximate location and shape of the damage in each specimen. The ultrasonic waves propagate as Lamb waves in these thin CFRP laminates, and it was confirmed that only the first symmetric (S\({}_{0}\)) and first anti-symmetric (A\({}_{0}\)) modes exist in the low frequency range between 50 and 400 kHz by using the dispersion curve analysis program Disperse [23]. The faster S\({}_{0}\) mode is almost invisible in the figure since the amplitude detected by the LDV was very low. On the other hand, the detected amplitude of the slower A\({}_{0}\) mode was high enough for its propagation behavior with a pulsed shape to be clearly visualized. In both specimens, when the A\({}_{0}\) mode reaches the damage, a phase delay is observed. Furthermore, following the phase delay, reflected waves from the damage are distinctly observed; the shape of the reflected wave strongly depends on that of the damage shown in Figure 1. It is very important that the detailed interactions between the Lamb wave and the impact-induced damage were clearly visualized, and that the damage was easily detected using the proposed non-contact laser imaging method for ultrasonic-wave propagation. These results demonstrate the drastic improvement in inspection time without losing the quality in the ultrasonic images [20,21].  
Figure 3: Schematic of the pulsed laser scanning area and the retroreflective tape position on the impacted specimen.
# A Recursive Least-Squares with a Time-Varying Regularization Parameter
## 2 The Least-Squares Estimation Problem  
This paper deals with estimating a vector \(\mathbf{x}\in\mathbb{R}^{n\times 1}\) from a linear model of the form  
\[\mathbf{y}=\mathbf{A}\mathbf{x}+\mathbf{z}, \tag{1}\]  
where \(\mathbf{y}\in\mathbb{R}^{m\times 1}\) is the observation vector, \(\mathbf{A}\in\mathbb{R}^{m\times n}\) is a full-rank data matrix, (i.e., rank \((\mathbf{A})=m\) if \(m\leq n\) or rank \((\mathbf{A})=n\) if \(n\leq m\)), and \(\mathbf{z}\in\mathbb{R}^{m\times 1}\) is a Gaussian noise vector, (i.e., each element of \(\mathbf{z}\) follows a Gaussian distribution). The LS estimation solves the following unconstrained minimization problem [20]:  
\[\min_{\mathbf{\hat{x}}}\|\mathbf{y}-\mathbf{A}\mathbf{\hat{x}}\|_{2}^{2}, \tag{2}\]  
where \(\|.\|\) is the Euclidean norm. That is, we seek an estimate of \(\mathbf{x}\), (i.e., \(\mathbf{\hat{x}}\)) that minimizes the \(l_{2}\)-norm of the residual error. Two cases can be identified based on dimensions \(m\) and \(n\). The first case is the over-determined least-squares, where the number of rows of data matrix \(\mathbf{A}\) is at least equal to the number of its columns, i.e., \(m\geq n\); therefore, Equation (2) either has a unique solution or an infinite number of solutions [20]. In this case, the solution of Equation (2) is given by  
\[\mathbf{\hat{x}}_{\text{LS}}=(\mathbf{A}^{T}\mathbf{A})^{-1}\mathbf{A}^{T} \mathbf{y}. \tag{3}\]  
The second case is the under-determined least-squares, where the number of columns of \(\mathbf{A}\) exceeds the number of rows, i.e., \(m<n\). In this case, there is an infinite number of solutions [20].  
A variation of the standard least-squares optimization problem involves a regularization parameter which servers two purposes. First, it allows the incorporation of some a priori information about the solution into the problem formulation. Second, it alleviates problems associated with rank-deficiency or ill-conditioning of the data matrix \(\mathbf{A}\)[20].  
Different regularization methods have been presented in the literature which include Tikhonov regularization, the shrunken estimator, the covariance shaping LS estimator [21] and ridge regression [22]. In Tikhonov regularization methods [6], a penalty term is added to Equation (2) to shrink the elements of \(\hat{\mathbf{x}}\) towards zero. A variety of penalty terms have been proposed; a popular form is the \(l_{2}\) penalization defined as follows [23]:  
\[\min_{\hat{\mathbf{x}}}\|\mathbf{y}-\mathbf{A}\hat{\mathbf{x}}\|_{2}^{2}+ \gamma\|\hat{\mathbf{x}}\|_{2}^{2}, \tag{4}\]  
where \(\gamma\in\mathbb{R}^{+}\) is a regularization parameter. The solution of (4) is given by  
\[\hat{\mathbf{x}}_{\text{RLS}}=(\mathbf{A}^{T}\mathbf{A}+\gamma\mathbf{I}_{n}) ^{-1}\mathbf{A}^{T}\mathbf{y}, \tag{5}\]  
where \(\mathbf{I}_{n}\) is the identity matrix of dimension \(n\). It is shown that there always exists \(\gamma>0\) for which (5) produces lower mean squared-error (MSE) than the LS estimator in (3) [23].
# Antipodal Vivaldi Antenna Arrays Fed by Substrate Integrated Waveguide Right-Angled Power Dividers
## 3 Results
### SiW Four-Way Power Divider as Feed System
This is caused by the fact that all right-angled power dividers are designed to provide nearly identical amplitude in the outputs to provide a uniform aperture amplitude which consequently affects side lobes in the radiation pattern. The side lobe levels can be reduced by using unequal-amplitude dividers, thus tapering the aperture amplitude. However, the beamwidth will be increased by this measure. Table 4 presents a comparison between six recent antenna systems and four proposed designs in this paper. As shown in the table, most antenna systems use inline power dividers as feed systems and obtain a single directive beam while by using right-angled power dividers, we are able to provide dual beam, single beam, and also patterns in any desired directions by controlling the output phase. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Antenna System** & **Directivity (dBi)** & \begin{tabular}{c} **Max Side Lobe** \\ **Level (dBi)** \\ \end{tabular} & \
\begin{tabular}{c} **Bandwidth (GHz)** \\ \end{tabular} &
\begin{tabular}{c} **HPBW (Degree)** \\ \end{tabular} \\ \hline System A & 12.9 & 3.26 & 21.6–23.8 & 15.84 \\ System B & 9.3 & \(-\)6.8 & 26.11–28.33 & 27.36 \\ System C & 10.7 & \(-\)5.7 & 25.7–29 & 24.5 \\ System D & 13.7 & 8.3 & 20–24.12 & 12.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Directivity, maximum side lobe level, bandwidth, and HPBW of the four antenna systems in their mid-band frequency ranges. System A. Vivaldi antenna array with three-way SIW power divider. System B: Vivaldi antenna array with two-way 180 degrees SIW power divider. System C: Vivaldi antenna array with two-way 120 degrees SIW power divider. System D: Vivaldi antenna array with four-way SIW power divider. Figure 24: Co- and cross-pol performances of Vivaldi antenna array with four-way SIW power divider at 23 GHz, (**a**) H-plane, (**b**) E-plane.
# Improving Spaceborne GNSS-R Algal Bloom Meteorological Data
## 4 Experiments and Evaluation  
To prove the effectiveness of GNSS-R data and meteorological data in cyanobacteria bloom detection, as well the superiority of the RUSBoost method, three assessment stages are involved in this section, which are the manual threshold value method classified by the single GNSS-R observable, and the results with/without meteorological factors considered through the ML method and error analysis. Meanwhile, the training and testing process of the RUSBoost algorithm and the performance indexes are also illustrated.
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 2 Overview of Feature Detectors and Descriptors
### Mser  
The authors of [36] presented the Maximally Stable Extremal Regions (MSER) algorithm, which is used to detect blobs in images. [PERSON]. [37] presented its updated version, which is computationally faster than the original algorithm. During the procedure, regions are initially recognized using connectivity analysis, and then connected maximum and minimum intensity regions are calculated. Extremal regions are closed by affine or projective transformations as well as changes in illumination. As a result, they are also scale- and rotation-invariant.
## 5 Result and Discussion
### Influence of Track Dynamics  
The three different track structures mentioned in Section 3.2 are compared here under the influence of the track dynamics. Figure 12 shows the rail wheel contact force vs. frequency response. It can be seen that the contact force was larger than the others between 50 and 150 Hz for the normal slab track with a fastener stiffness of 60 MN/m, and the peak was between 30 and 60 Hz for others. In Figure 6, P2 frequency is considered when the wheel and rail receptance were equal. This was true at nearly 63 Hz, 54 Hz and 40 Hz for three different fasteners valued at 60, 45 and 20 MN/m for a single-layer track, respectively, and 45 Hz for the ballastless track. It can be observed from Figure 12 that the peaks were in the vicinity of these frequencies.  
The dynamic stresses of three different track structures are shown in Figure 13. The peak stress for all the tracks was found to be around 62 Hz but the peak stress of a normal slab track with a fastener stiffness of 60 MN/m was 1.5, 1.7, and 2.0 times greater than the ballastless track, at 45 and 20 MN/m for the single-layer track, respectively. It can be concluded that the P2 resonance for this fastener stiffness is close to 62 Hz, leading to a peak in the wheel-rail contact force (Figure 12).  
Figure 11: Dynamics stress at the 1.5-circle position of inner and outer springs: (**a**) lower and (**b**) upper positions.  
Figure 12: Power spectral density (PSD) of the wheel–rail contact force for a normal slab—i.e., 20, 40 and 60 MN/n—and ballastless track.
# Patch-Font: Enhancing Few-Shot Font Generation with Patch-Based Attention and Multitask Encoding
## 5 Experimental Results
### Comparison with SOTA Methods
#### 5.2.1 Korean Results
Qualitative Results: Figure 3 illustrates the qualitative analysis of font generation methods on Korean printed and handwritten datasets. The rows labeled \"Content\" represent the input source font, while the rows labeled \"Target\" display the ground truth. The outputs of six methods--FUNIT, DM-Font, LF-Font, MX-Font, FM-GAN, and Patch-Font (ours)-- are compared, with red boxes highlighting failure cases. FUNIT struggles significantly with content preservation, introducing noise and breakages in characters like '8]' and 'A]'. It demonstrates poor style transfer, failing to replicate key stylistic elements. DM-Font performs better in preserving global style but introduces artifacts in specific glyphs, such as inconsistencies in '8]' and '8]', particularly for printed fonts. Moreover, while it retains the overall structure, local stylistic fidelity suffers. LF-Font shows instability in generating consistent strokes, leading to structural breakages in characters like '8]' and 'A]'. This issue is more prominent in handwritten fonts, with higher  
Figure 3: Visual comparison of font generation methods applied to the Korean phrase ‘8]’, ‘8]’, ‘meaning ‘Have a good day’, with each row representing a different method for Korean printed and Korean handwritten fonts. The content row displays the content images used as the source, while the target row presents the ground truth target images. Red boxes indicate failure cases in other methods (e.g., losing curvature or introducing noise), highlighting challenges in preserving character integrity and style consistency across different. Our approach preserves local stroke details while matching the overall style. structural complexity. MX-Font captures the general style but fails to replicate fine-grained details, leading to inaccuracies in characters like '\(\lambda\)l' for handwritten fonts. Noise and inconsistencies are observed in printed fonts. FM-GAN demonstrates a reasonable ability to replicate style, especially for printed font, but struggles with content fidelity. Some distortion appears in characters like '\(\mathbb{R}\)', compromising the overall visual quality. Patch-Font (ours) excels in preserving both global style and local structural details. It produces high-quality outputs that closely match the ground truth, even with challenging handwritten fonts. Fine-grained details like curvature and stroke thickness are faithfully reproduced without noticeable artifacts. Patch-Font's qualitative performance aligns with the quantitative results in Table 1. It consistently outperforms baseline methods in both content and style accuracy across printed and handwritten datasets, demonstrating its ability to generalize effectively to unseen characters and styles.
## 5 Experiment
### Datasets
#### 5.1.2 UNSW-NB15 Dataset  
The UNSW-NB15 dataset [37], released by the Cyber Range Lab of the Australian Centre for Cyber Security (ACCS), combines real-world normal network activities with contemporary synthesized attack behaviors. Captured in a private environment, the dataset includes 2,218,761 benign flows (87.35%) and 321,283 attack flows (12.65%), categorized into ten classes: benign, analysis, backdoor, DoS, exploits, fuzzers, generic, reconnaissance, shellcode, and worms. The significant imbalance between normal and malicious flows necessitates preprocessing before use. This preprocessing involves removing excess normal flows from the dataset before experimenting. From the processed dataset, we used 50,000 flows, including 30,614 (61.22%) normal flows and 19,386 (38.78%) malicious flows, for the experiment. Each flow in the dataset is characterized by 49 features, including the class label. In the experiments of this paper, we examined these features using feature selection techniques before incorporating the data into the training model.
# Smart Irrigation Systems from Cyber-Physical Perspective: State of Art and Future Directions
## 3 Problem Space
### Environment Factors Dimension
#### 3.2.3 Crops  
IoT-driven systems are increasingly being used to monitor crop growth stages and modulate irrigation practices. For instance, [PERSON] et al. [57] combined soil moisture sensors to obtain the soil moisture value, a temperature sensor to obtain the air temperature and light sensors, and leaf wetness sensors to obtain the crop growth status. The system adopts an Arduino board as the central control unit, which is used to recognize the growth rate of crops and extract the insights from the data, leading to the crops' health and yield. Once the water exceeds the volume, it will activate the pump to suck out the water.  
[PERSON] and [PERSON] [40] used real-time data via sensors like soil moisture, temperature, humidity sensors with historical crop data, and weather API information. These integrated data empower the system to recommend suitable crops for specific growth stages. [PERSON] et al. [42] used machine learning models, including Decision Tree, K-Neighbor, and Random Forest, to predict crop yield. They leveraged measured data from sensors like soil moisture, temperature, and humidity sensors (DHT11) and a Random Forest classifier for this purpose. Also, [PERSON] et al. [48] designed a mobile app-driven system based on crop type and growth stage, which divides five types of crops (rice, sugar cane, wheat, potato, and corn) into five stages: 1-30 days, 31-60 days, 61-90 days, 91-120 days, and 121-150 days. Since different crops require different volumes of water, the system predetermines the water usage for each crop and then distributes the water based on predefined water usage. Once the irrigation system is activated, real-time data from sensors are compared with a predefined threshold to ensure the proper volume of water will be provided.  
These examples underscore the importance of IoT technologies in crop growth stage monitoring and adaptive irrigation. By collecting data from various types of sensors, leveraging machine learning, and integrating historical and real-time information, these systems contribute to precise and efficient irrigation practices throughout different growth phases.