esa-sceva/satcom-chunk-collection huggingface dataset:
# Bending Performance of a Prestressed Concrete Composite Girder Bridge with Steel Truss Webs
## 2 Experiment Design
### Loading and Measuring Points Layout  
In this experiment, a two-point symmetric loading method was employed, with the loading points positioned at the two middle diaphragms in the longitudinal direction and along the central axis in the transverse direction. Two 50 t hydraulic jacks were used to load synchronously. The top of the jacks was locked on the reaction beam, and the loading value was controlled by the force sensor. During the loading process, the load increased by 5 kN  
Figure 3: Manufacture of the test specimen: (**a**) Assembling of bottom reinforcements and installation of the steel truss webs; (**b**) Assembling the formwork and reinforcement of the top plate; (**c**) Concrete pouring; (**d**) Completed test specimen; (**e**) Test specimen loading.  
Figure 2: Dimensions of the test specimen (unit: cm): (**a**) Elevation view; (**b**) A-A section; (**c**) B-B section; (**d**) C-C section; (**e**) Steel truss web joint along the longitudinal direction; (**f**) Steel truss web joint along the transverse direction.  
in different stages until the test specimen was damaged. In the elastic stage, the load at all steps remained unchanged, and the strain as well as the deflection were measured after loading for 3 min. During the elastoplastic stage, the displacement meter was monitored, and measurements were taken once the displacement value stabilized. Simultaneously, the initiation and propagation of cracks on the concrete top and bottom plates were observed.  
Figure 4a shows the strain measuring section, displacement meter, and sectional strain measuring point layout in the test specimen. A total of five strain measuring sections were arranged in this test, namely, mid-span sections (I-I and II-II), \(L/4\) sections (III-III and IV-IV), and \(3\)\(L/4\) sections (V-V). Among them, section V-V was arranged for comparative analysis with section III-III, and the measuring points of each cross-section were arranged as shown in Figure 4b. Seven vertical displacement measuring sections were arranged at equal intervals. A longitudinal displacement measuring point was arranged at the right girder end.
## 4 Research and Analysis  
Before evaluating the performance of our proximity detection, we explain data processing methods to reduce fluctuation in RSSI data. RSSI data without pre-processing are called raw data. The raw data are shown in Figure 5a. As seen in Figure 5a, the fluctuation is shown in the raw data. The fluctuation can affect the accuracy of proximity classification. So, we use pre-processing models to reduce the fluctuation of RSSI data. We use three types of pre-processing: Kalman filter (KF), Kalman filter implemented using an autoencoder (KFAE), and Denoising autoencoder (DAE). These pre-processings can be applied up to two times. Notations about overall pre-processing are represented in Table 3. The results of pre-processing are represented in Figure 5. The following introduces how to implement pre-processing methods.
## 3 Compensator Design  
### Battery Module Voltage Control Loop Compensator Design  
The WEDES system design parameters are shown in Table 1. Based on the small-signal model in Figure 3, the uncompensated discrete-time transfer function of the battery module voltage loop for the \(r^{\text{th}}\) battery module \(G_{\text{bust}}\) (Z) consists of \(G_{\text{PWM}}\), \(G_{\text{vdr}}\), \(G_{\text{vTRr}}\), \(k_{\text{divr}}\), \(ZOH_{\text{vr}}\), and digital computation delay \(Delay_{\text{dr}}\). \(G_{\text{busN}}\) (Z) is calculated as given by Equation (21), and its bode plot is shown as the dashed curve in Figure 6.  
\[L_{MXr_{uncomp}}(z)=Z[G_{PWM}(s)\cdot G_{\text{vdr}}(s)\cdot G_{vTRr}(s)\cdot ZOH _{vN}(s)\cdot Delay_{dN}]=\frac{-0.0001999\text{z}-0.000272}{\text{z}^{3}-1.977 \text{z}^{2}+0.9789\text{z}}, \tag{21}\]  
Figure 5: Circuit diagram of the wireless power transfer (WPT) stage in the WEDES system (**a**) equivalent circuit and (**b**) T-model.  
where \(ZOH_{vr}(s)=\frac{1-e^{-sT_{s}}}{s}\) ; \(Delay_{dr}(s)=e^{-sT_{delay}}\); \(T_{delay}\) is the digital controller computation delay and it is equal to 10 \(\upmu\)s in the experimental implementation; \(G_{PWM}=~{}1/1024\) ; \(K_{div}=11\) with 1 k\(\Omega\) and 10 k\(\Omega\) resistors as the voltage divider.  
In the battery module voltage compensator design, there is a right-hand-plane-zero (RHPZ), which is located at 4.86 kHz. This RHPZ is introduced due to the existing boost converter. To guarantee the stability of the system, the compensated control bandwidth should be smaller than RHPZ. With a compensator G\({}_{v\text{-bus}}\)(z) given by (22), the compensated battery module output voltage control loop gain (\(G_{MXr\_comp}=G_{MXr\_uncomp}\)(z)-\(G_{MXr}\)(z)) achieves a control bandwidth of 1.62 kHz and a phase margin of 45\({}^{\circ}\), as shown on the solid curve in Figure 6.  
\[\text{G}_{MXr}(\text{z})=\frac{950.8\text{z}^{2}-1850\text{z}+899.7}{\text{z} ^{2}-\text{z}} \tag{22}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(V_{MXr}\) & 0–20 V & \(L_{T}\), \(L_{R}\) & 24 \(\upmu\)H \\ \(V_{bus-ref}\) & 30 V & \(C_{T}\), \(C_{R}\) & 0.4 \(\upmu\)F \\ \(I_{out}\) & 2 A & \(k_{TR}\) & 0.3 \\ \(L_{MXr}\) & 47 \(\upmu\)H & R\({}_{\text{PT}}\), R\({}_{\text{RT}}\) @ 50 kHz & 0.1 \(\Omega\) \\ \(C_{MXr}\) & 220 \(\upmu\)F & \(f_{sw\_bridge}\) & 50 kHz \\ \(f_{sw\_converter}\) & 100 kHz & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters of the wireless distributed and enabled battery energy storage (WEDES) system.  
Figure 6: The bode plot of the uncompensated (red-dashed curve) and compensated (blue-solid curve) battery module voltage control loop gain.
## 1 Introduction
### Contribution  
To the best of our knowledge, no prior work has applied explainable AI techniques such as SHAP to shaft power estimation, making our study the first to offer model-level transparency and interpretability in this context. Second, our dataset includes nine vessels--more than any reviewed study--whereas the largest prior dataset covers only five vessels. Third, we perform a comparative analysis of seven machine learning models, whereas most studies evaluate no more than two or three models; only one study benchmarks five. Finally, our dataset spans 36 months of continuous operational data, providing unmatched temporal coverage. These contributions address key research gaps related to explainability, generalisability, algorithmic diversity, and dataset scalability in the maritime ML literature. To summarise, the main contributions are as follows:  
1. Development of a data-driven framework: Leveraging 36 months of sensor data from nine (9) Very Large Crude Carriers (VLCCs), the study develops and evaluates a comprehensive machine learning framework for shaft power prediction.
2. Comparison of diverse ML models: Multiple models--including k-NN, SVM, Decision Trees, Random Forest, XGBoost, LightGBM, and neural networks--are rigorously compared using \(R^{2}\), standard deviation, and confidence intervals.
3. Integration of Explainable AI: SHapley Additive exPlanations (SHAP) are employed to interpret model predictions, identifying the key features.
## 3 Results and Discussion
### Static Property Analysis Based on VSM  
The magnetic properties of the samples with the Ta and W buffer and capping layers were examined to compare the effects of temperature changes on the magnetostatic properties using a magnetic hysteresis loop (\(M\)-\(H\) loop). Magnetic fields (with strengths ranging from +12.5 to -12.5 kOe) were applied in the in-plane direction at four temperatures: room temperature (293 K), 400, 500, and 550 K (Figure 3). Figure 3a,b show the \(M\)-\(H\) loops of the Ta/CoFeB/MgO/Ta and W/CoFeB/MgO/W samples, respectively. The low-scale loops are depicted in the insets in Figure 3. The value of \(M_{\text{s}}\) was derived from these loops. Both samples exhibited a decrease in \(M_{\text{s}}\) with increasing temperature. The \(M_{\text{s}}\) values for the samples with the Ta buffer and capping layers were 1133, 1089, 1045, and 1005 emu/cc at 293, 400, 500, and 550 K, respectively, whereas those for samples with W buffer and capping layers were higher at the same temperature (1213, 1163, 1111, and 1065 emu/cc at 293, 400, 500, and 550 K, respectively). This decrease in \(M_{\text{s}}\) value with an increase in temperature can be attributed to the increase in thermal fluctuations with temperature [54]. The difference in \(M_{\text{s}}\) values between samples with Ta and W buffer and capping layers was imputed to the intermixing between the CoFeB and buffer layers, leading to the formation of a magnetic dead layer and considering the potential influence of intermixing between the Si atoms and layers [55; 56; 57].  
The \(T^{1/3}\) power law was used to fit the \(M_{\text{s}}\) values at different temperatures. The results are shown in Figure 4; the data points denote the experimental values and the line represents the fitted values. The choice of the \(T^{1/3}\) power law was driven by the large error between the fitting values of [PERSON]'s law and experimental values at high temperatures [43; 52; 58]. Differences in magnetization values between the samples with Ta and W buffer and capping layers indicated the presence of a magnetic dead layer within the CoFeB layers, attributed to intermixing between the CoFeB and buffer layers [55]. The sample with Ta buffer and capping layers exhibited a lower \(M_{\text{s}}\) value than that of the sample with W buffer and capping layers, owing to the greater intermixing between CoFeB and buffer layers in the former sample. Depending on the buffer layer materials, the degree of diffusion of the buffer layer and the influence of the orbital and lattice by the interface  
Figure 2: (**a**) X-ray diffractometer (XRD) scans of the samples with Ta and W buffer and capping layers, (**b**) X-ray photoelectron spectrometer (XPS) depth profile of the sample with Ta buffer and capping layers, and (**c**) XPS depth profile of the sample with W buffer and capping layers.  
vary, and these affect the magnetic dead layer and magnetization change. As a result, the magnetic dead layer in the W/CoFeB sample was thinner compared to that in the Ta/CoFeB sample, resulting in a higher \(M_{\text{s}}\) for the W/CoFeB sample [59].
# Novel MIMO Antenna System for Ultra Wideband Applications
## 2 Proposed MIMO Configuration  
### Mimo Single Element  
The design of a single antenna element is depicted in Figure 1. The front side of the designed antenna consists of a fractal circular ring radiator [23] fed using a 50 \(\Omega\) microstrip feed line (see Figure 1a), while the back side consists of a notch loaded trapezoidal partial ground plane, as illustrated in Figure 1b. The radius \(R_{1}\) of the outer circular ring is calculated using the design expressions mentioned in [24], and the width of the feeding line is calculated using the procedure described in [25]. From Figure 1a, one can observe that six circular rings of radius \(R_{3}\) are embedded in the main radiator to realize a fractal geometry. These rings are rotated at an angle of 60\({}^{\circ}\). Furthermore, the trapezoidal ground plane is utilized (see Figure 1b) to create wideband multi-resonance characteristics of the input impedance, which leads to achieving enhanced bandwidth [26], while the square notch etched from the ground plane offers maximum impedance matching in the operating bandwidth. The proposed antenna is designed using a low-cost FR-4 laminate with \(\epsilon_{r}\) = 4.3 and \(h\) = 1.6 mm. The overall design dimensions of the antenna are: \(W_{S}\) = 30, \(L_{S}\) = 30, \(R_{1}\) = 9.5, \(R_{2}\) = 7.5, \(R_{3}\) = 3.5, \(R_{4}\) = 2.4, \(W_{f}\) = 3, \(L_{f}\) = 10, \(W_{g}\) = 30, \(L_{g}\) = 9.5, \(L_{g1}\) = 5.5, and \(S\) = 3 (all dimensions are in mm).  
The construction of the proposed antenna starts with the design of a circular ring patch radiator backed by a conventional partial ground plane, shown in Figure 2a, and its respective reflection coefficient (S\({}_{11}\)) is depicted in Figure 3. From the results, it is observed that the circular ring provides a dual-band response around 3 GHz and 10 GHz. To excite more resonant modes in the band of interest, six circular rings of radius \(R_{3}\) are embedded in the main radiator (see Figure 2b), which ultimately leads to a fractal geometry. This configuration increased the impedance bandwidth, but there is still a mismatch between 7.58 GHz and 9.38 GHz (see Figure 3). To improve the impedance matching, a trapezoidal shaped ground plane with a square notch is utilized, as shown in Figure 2c. The modification in the ground structure leads to achieving an UWB frequency response ranging from 2.69 GHz to 12 GHz, as shown in Figure 3.  
Figure 1: Schematic of the proposed single antenna element (**a**) front side (**b**) back side.  
Figure 2: Design evolution of single antenna element. (**a**) Step-1. (**b**) Step-2. (**c**) Step-3 (Proposed).  
Figure 4 depicts the presented antenna element's realized gain and radiation efficiency. The antenna gain varies in the range of 0.55-5 dB in the operating bandwidth, while the radiation efficiency is noted to be \(>\)75% in the band of interest (see Figure 4).
## 4 EOFM Experiment on the Diode and Discussion  
### Experimental Setup  
To verify the calculation data, an EOFM experiment was performed on the diode. The equipment we used was the Laser Scanning and Mapping System at the National Space Science Center, Chinese Academy of Sciences. As shown in Figure 7, the experimental setup was composed of an optical system and electrical system. The laser source provided a continuous beam at a wavelength of 1319 nm. After the laser passed through the objective lens, the spot diameter inside the DUT was about 1 um. The laser was focused on the active region of the DUT using a visible light camera. The incident light power on the DUT was about 11.48 mW. A photodiode was used to measure the reflected light power. The photodiode was plugged into the spectrum analyzer to analyze the characteristics of the frequency domain signal from the DUT. The spectrum analyzer received the modulated electrical signal and output the EOFM signal strength at the frequency of interest [17]. The stepper motor controller controlled the XYZ translation stage to move the DUT. A waveform generator was utilized to drive the DUT. The computer controlled the spectrum analyzer, waveform generator, and stepper motor controller via the general-purpose interface bus (GPIB) to automate the experiment. A detailed description of the experimental setup can be found in [28].  
Figure 5: Relative reflectivity variation \(|\Delta R/R_{0}|\) for \(V_{p}=4V\), considering the bandwidth of the laser \(\Delta\lambda=2\) nm.  
Figure 6: EOFM signal \(\Delta R\) calculated for different voltages applied.
## 3 Security Implications and Fundamental Approaches of Blockchain-Based Data Storage Methods
### The Role of Cryptographic Methods in Blockchain-Based Data Storage Systems
#### 3.2.1 Asymmetric Encryption  
Asymmetric encryption secures data using a public-private key pair, where information encrypted with the public key can only be decrypted by the corresponding private key. This mechanism ensures data confidentiality and integrity, allowing only authorized entities to access the encrypted data [58; 110; 117].  
In blockchain-based systems, asymmetric encryption plays a critical role in authentication, data integrity, and access control [102]. It is particularly essential for securing smart contracts, verifying digital signatures, and managing authorized access in on-chain environments [118]. High-security sectors such as finance and healthcare rely on asymmetric encryption to ensure data accuracy and confidentiality. However, processing large datasets with this method can lead to increased computational overhead and extended processing times [114]. To address these performance limitations, asymmetric encryption is often integrated with off-chain solutions for more efficient large-scale data management. Digital signatures serve as a fundamental cryptographic tool for verifying the authenticity of blockchain transactions [53]. Algorithms such as ECDSA and Ed25519 provide fast and secure transaction verification, making them widely adopted in networks like Bitcoin and Ethereum [119]. These methods enhance data security and transaction transparency across various industries, including finance, healthcare, and IoT [120]. While the high computational cost and performance constraints are notable drawbacks, asymmetric encryption remains a cornerstone of blockchain security [112; 121].
## 4 Conclusions  
In this paper, the change of the resistivity of the CNFPUC material with the bending strain at different ambient temperatures was tested, and the four-point bending fatigue life tests of different stress levels were conducted at the corresponding temperatures; the relationship between the fatigue cumulative damage and the change of the resistivity was analyzed. The following conclusions can be drawn:  
Figure 13: SEM diagram. (**a**) CNF lap conduction; (**b**) CNF pull out voids.  
1. As an intelligent flexural reinforcement material, the resistivity increased with flexural tensile strain at various ambient temperatures, presenting a two-stage trend of small increase and sharp increase.
2. Under periodic loading, the fatigue life of CNFPUC was in accordance with the Weibull distribution of two parameters. The resistivity increased with the increase of stress level and cycle times in the bending state, showing a three-stage variation rule. When the damage amount was up to 90%, the resistivity increased sharply. With the increase of stress level, the effect of temperature on resistivity decreased gradually.
3. When the bond between CNFPUC and the bottom of the concrete beam was used as flexural reinforcement, the fatigue cumulative damage of flexural tension could be predicted by using the first two stages of CNFPUC fatigue resistivity evolution curve. Damage threshold resistivity or ultimate resistivity could be used as a reference standard for starting damage or failure, which provides a theoretical basis for practical engineering application.  
[PERSON] and [PERSON] conceived the study; [PERSON] performed the experiments, formal analysis and wrote the main manuscript; [PERSON] reviewed and edited this manuscript; and all authors approved the final manuscript.  
This research was funded by Scientific Research Project of Qiqihar University, No. 135309357,135309358.  
The authors declare no conflicts of interest.
# MLKD-Net: Lightweight Single Image Dehazing via Multi-Head Large Kernel Attention
## 5 Ablation Study
### Evaluation on the Number of Heads in MLKA
#### 5.1.1 Evaluation on Channel Splitting Strategy  
To assess the influence of the channel splitting strategy, we varied the number of heads in the MLKA module, which corresponds to how many groups the input channels are divided into during the channel split step (Equation (2)). Specifically, the configurations Split-H1, Split-H2, and Split-H4 correspond to one, two, and four heads, respectively.  
As illustrated in Figure 5, both variants achieved optimal performance on the SOTS-Outdoor dataset when the number of heads was set to two. In particular, the Split-H2 configuration achieved the highest PSNR in both model sizes, recording 36.03 in the Tiny version and 37.24 in the Small version. The performance improvement from Split-H1 to Split-H2 highlights the advantage of adopting a multi-head structure within the MLKA module. By employing multiple heads, MLKA enables each head to focus on distinct spatial patterns or receptive field regions. This allows the model to extract diverse and complementary features in parallel, enriching the overall representation without increasing the parameter count significantly. The resulting feature diversity enhances the model's ability to capture fine-grained haze distributions and structural details, thereby improving the perceptual quality of the restored image.  
Conversely, the four-head configuration (Split-H4) yielded lower performance, which we attribute to the reduced number of channels assigned to each head. In our model, where the embedding dimensions are relatively compact ([24, 48, 96, 48, 24]), this constraint limits each head's ability to learn expressive spatial features. As a result, excessive fragmentation weakens the overall effectiveness of the attention mechanism and ultimately leads to the observed performance degradation.  
To further investigate whether this degradation was primarily caused by reduced channel capacity per head, we conducted a channel expansion experiment in which the feature map was first expanded before splitting, allowing each head to receive the same number of channels as in the original input.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Device**} & \multicolumn{2}{c}{**FP32**} & \multicolumn{2}{c}{**FP16**} & \multicolumn{2}{c}{**INT8**} \\  & **PSNR** & **Time** & **PSNR** & **Time** & **PSNR** & **Time** \\ \hline A100 GPU & 26.16 & 8.8 & 26.16 & 7.1 & 25.87 & 6.2 \\ Jetson Orin Nano & 26.16 & 74.97 & 26.16 & 74.97 & 24.92 & 55.24 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation of MLKD-Net-S on the NVIDIA Jetson Orin Nano board. All time values were measured in milliseconds (ms).
## 2 Related Work  
Many studies have been made in order to compare relational and non-relational databases based on different metrics. The main metrics in this research were storage space, command syntax, the latency of queries, database connection time, and schema design.  
[PERSON] et al. [10] presented a comparative study and evaluated the performance of two types of databases: MySQL as a relational database and MongoDB as a non-relational database.  
This study of performance metrics included latency and database size. The comparison was based on the performance evaluation of inserting and retrieving a huge amount of IoT (Internet of Things) data, while assessing the performance of these two types of databases to work on resources with different specifications in cloud computing.  
The challenge to find a balance between characteristics of classical relational databases management systems and opportunities offered by NoSQL database management systems (MongoDB) were investigated in [11; 12], which proposes an integration approach to support hybrid database architecture (MySQL, MongoDB, and Redis).  
In [13], the authors presented a study between MongoDB as a non-relational database and MySQL as a relational database describing the advantages of using a non-relational database compared to a relational database integrated into a web-based application, which needs to manipulate large amounts of data.  
The authors of [3] conduct a performance evaluation for CRUD operations for several non-relational and relational databases (MongoDB, CouchDB, Couchbase, Microsoft SQL Server, MySQL, and PostgreSQL); the performance was evaluated based on the CRUD operations in terms of the time of queries and the size of data with and without replication; however, in this case, the analysis was oriented only on simple queries over a simple database structure. In [14], a comparison between Elasticsearch and MySQL via searching values in larger key-value datasets was made.  
In this idea, our paper makes a detailed analysis of all CRUD operations and shows how the performance of the application can be influenced by increasing the complexity of the queries and the amount of data on which those operations were applied. Besides the well-known MySQL database, the paper also approaches CouchDB, a non-relational database that was not so much studied in the literature. The analysis considers a complex database with multiple joins and considers different data structure approaches: two structures for MySQL, one for relational and one for document support, that was added more recently in MySQL, and two structures for CouchDB.