esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Accelerated Corrosion Test for Supporting Devices in a Floating Photovoltaic System
###### Abstract  
Recently, countries from around the globe have been actively developing a new solar power system, namely, the floating photovoltaic (FPV) system. FPV is advantageous in terms of efficiency and cost effectiveness; however, environmental conditions on the surface of water are harsher than on the ground, and the regulations and standards for the long-term durability of supporting devices are insufficient. As a result, this study aims to investigate the durability of supporting devices through a novel type of accelerated corrosion test, copper-accelerated acetic acid salt spray (CASS). After an eight-day CASS test, the results demonstrated that only a small area of white protective layer on the SUPERDYMA shape steel was fully corroded and rusted. Moreover, five types of screw, fastened solidy on the SUPERDYMA shape steel, namely a galvanized steel screw capped with a type 316 stainless steel (SS) nut, a type 304 SS screw, a type 410 SS screw, a monte-passivated galvanized steel screw, and a XP zinc-tin alloy coated steel screw, achieved varying degrees of rust. In general, the corrosion degree of the eight-day CASS test was more serious than that of the 136-day neutral salt spray (NSS) test. Therefore, the CASS test is faster and more efficient for the evaluation of the durability of supporting devices.  
floating photovoltaic (FPV); supporting device; durability; copper-accelerated acetic acid salt spray (CASS) +
Footnote †: journal: Applied Sciences  
1
# Research on the Settlement Patterns of Tunnel-Surrounding Rock Under Groundwater Conditions
###### Abstract  
In the current excavation of water diversion tunnels, significant challenges such as water inrush, rockburst, and large deformations continue to persist. Consequently, research on the stability of the surrounding rock after tunnel excavation is of great scientific importance. To address the impact of groundwater on tunnel-surrounding rock deformation under complex geological conditions, this study utilizes a combination of field monitoring and numerical simulation. Specifically, the research simulates the excavation process under both groundwater and non-groundwater conditions. Systematically, it analyzes the deformation patterns of tunnel-surrounding rock under groundwater conditions. The findings reveal the following: (1) Settlement and excavation mileage exhibit a clear trend of \"steep decline, gradual decline, stable,\" both stabilizing around 35 m after the excavation reaches the monitoring cross-section. Changes in groundwater levels, whether rising or falling, can either accelerate or delay the point at which settlement stability is achieved. (2) The numerical simulation settlement curves closely align with the field monitoring curves, with simulated settlement values slightly exceeding the monitored results. However, the error rate between the two remains below 20%, indicating the reliability of the method. (3) Groundwater significantly impacts water-sensitive strata such as Ioess and sandstone, with maximum settlement values at the tunnel vault and ground surface under groundwater conditions being 2 to 3 times those observed in the absence of groundwater. This study provides a scientific basis for optimizing tunnel design and construction processes. Future research should focus on refining the numerical simulation model, by incorporating additional monitoring data for validation and enhancing the safety of tunnel construction.  
groundwater; tunnel engineering; numerical simulation; field monitoring; settlement +
Footnote †: journal: Applied sciences  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], T. Research on the Settlement Patterns of Tunnel-Surrounding Rock Under Groundwater Conditions. _Appl. Sci._**2025**, 15, 3796. [[https://doi.org/10.3390/appl15073796](https://doi.org/10.3390/appl15073796)]([https://doi.org/10.3390/appl15073796](https://doi.org/10.3390/appl15073796))
## 4 Experimental Results and Analysis
### Comparison with Existing Methods  
The comparison algorithms include PCA [16], LPP [23], MMDA [35], NSPE [36], MFA [37], MLE [38], SPCA [39], and SSMRPE [40]. PCA adopts the principle of maximizing the global data variance to find the orthogonal projection. The main purpose of LPP is to find a linear transformation of the data that minimizes the distortion of the pairwise relationships between neighboring data points. In other words, LPP tries to map similar data points close to each other in the reduced space. MMDA is a novel discriminative multi-manifold analysis method that learns discriminative features from image patches. NSPE can maintain non-negative sparse reconstruction relations for data in low-dimensional subspaces. MFA based on an extreme learning machine is recommended to improve spectral regression and kernel marginal Fisher analysis. MLE further improves the multi-streamer LE algorithm by adding spatial and data tagging information. SPCA considers the diversity of different homogeneous regions, which means that different regions should have different projections. SPCA is able to make full use of the spatial information through super-pixel segmentation. SSMRPE utilizes a new spatial-spectral combined distance (SSCD) to fuse the spatial structure and spectral information to select effective spatial-spectral neighbors of HSI pixels. Among all of the compared algorithms, PCA, LPP, MFA, NSPE, MLE, and MMDA are dimensionality reduction methods based on spectral information, which consider only the spectral information of HSIs for the dimensionality reduction task. In addition, among all of the downscaling methods based on spectral information, LPP, MFA, and NSPE are single-manifold learning methods, while MLE and MMDA are multi-manifold learning methods. The SPCA, SSMRPE, and SSAMMA algorithms consider both the spectral information and spatial structure of an HSI. To ensure the accuracy of the comparison experiments, the SVM classifier was used to classify the dimensionality reduction data obtained via the different algorithms.  
For better analysis of experimental parameters, the optimal values of their parameters are chosen for all comparison algorithms in the paper. Based on the IP dataset, the number of near neighbors in the LPP, MFA, NSPE, and MLE algorithms is set to 8. The MMDA inter-manifold near neighbor is set at seven and the intra-manifold near neighbor is set at nine. The spatial window size of SPCA is set at eight, and the number of near neighbors is set at nine. The spatial window size of SSMRPE is set at five, and the number of near neighbors is set at nine. Using the PU data set, the number of near neighbors in the LPP, MFA, NSPE, and MLE MFA, NSPE, and MLE is set at nine. The number of inter-manifold near neighbors of MMDA and SPCA is set at 8, and the number of intra-manifold near neighbors is set at 10. The spatial window size of SSMRPE is set at 13, and the number of near neighbors is set at 20. The way that we calculate these optimal numbers is identical to the proposed SSAMMA method. The details can be found in the parameter analysis of this paper.
## Theory Description  
### TF Plane Denoising Using Sparse Low-rank Matrix Estimation  
Commonly, multicomponent signal is defined as a superimposition of AM-FM components. The raw TF plane can be obtained by classical STFT and the TF transformation coefficient is expressed as a matrix. In this paper, the problem of estimating a sparse low-rank matrix from a noisy TF transformation coefficient of STFT is firstly addressed. The mathematical model of low-rank matrix approximation can be illustrated as follows:  
\[Y=V+W \tag{1}\]  
where \(Y\) is the noise observation matrix computed by STFT, \(W\) represents additive white Gaussian noise (AWGN) matrix and \(V\) is corresponding to sparse low-rank matrix estimation of raw STFT. In order to estimate the sparse low-rank matrix \(V\), the model has been transformed into a typical mathematical optimization problem, which is composed of a data-fidelity term and two parameterized non-convex penalty functions [31]. The specific formula is expressed as follows:  
\[F(V)=\frac{1}{2}\|Y-V\|_{F}^{2}+\lambda_{0}\sum_{i=1}^{k}\phi(\sigma_{i}(V);a _{0})+\lambda_{1}\sum_{i=1}^{m}\sum_{j=1}^{n}\phi(V_{i,j};a_{1}) \tag{2}\]  
where \(\|Y\|_{F}^{2}=\sum\limits_{i,j}\abs{Y_{i,j}}^{2}\), \(\phi(v)\) is the non-convex penalty function, \(\sigma_{i}(V)\) represents the singular values of the matrix \(V\), regularization parameters \(\lambda_{0}\) and \(\lambda_{1}\) are used to adjust weights between data-fidelity term and constraint term. The most commonly used penalty functions can be defined as:  
\[\phi(v;a)=\frac{|v|}{1+a|v|/2},a\geq 0 \tag{3}\]  
The proximity operator of penalty function is defined as follows:  
\[prox_{\phi}(y,\lambda,a)=\text{argmin}\Big{(}\frac{1}{2}(y-v)^{2}+\lambda\phi (v;a)\Big{)} \tag{4}\]  
To ensure that the target function is strictly convex, the selected parameters need to satisfy the following conditions:  
\[0\leq a_{0}\lambda_{0}+a_{1}\lambda_{1}<1 \tag{5}\]  
The objective function in formula (2) can be solved by alternating direction method of multipliers (ADMM) to achieve the separation of variables [32]. Then, the sparse low-rank matrix estimation \(V\) of STTF transformation coefficient can be obtained for the following TF signature identification and signal adaptive decomposition.
## 3 System Model and Proposed Technique
### DS-PIM-Based SIM OFDM  
We propose a novel variant of SIM OFDM for visible light communication. Instead of the fixed sub-block size of conventional SIM OFDM, the proposed approach suggests the implementation of variable block size, using a double-sided PIM (DS-PIM) to modulate B1 before subcarrier selection, thereby improving bandwidth utilization. In the proposed modification, the DPIM symbol shown in Table 2 shall have an added 1 at the end of the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Frame** & **PPM** & **DPIM** \\ \hline
000 & 1000 & 1 \\ \hline
001 & 0100 & 10 \\ \hline
010 & 0010 & 100 \\ \hline
011 & 0001 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Modulation Data Frame PPM, DPIM.  
Figure 4: Waveforms of (**a**) 2-bit data (**b**) DPIM-modulated waveform.  
symbol as well, ensuring that two subcarriers per sub-block are always turned on. Table 3 shows the DPIM and DS-PIM symbols for two-bit data.  
The DS-PIM symbol is then used for carrier selection out of a total 5 available sub-blocks from 5 subcarriers, instead of the 4 used for conventional SIM OFDM. The block diagram of the SIM OFDM sub-block creator, based on the proposed approach, is presented in Figure 5.  
The use of DS-PIM ensures that all 5 subcarriers are not always occupied and may dynamically be assigned to the next sub-block. The ending 1 serves a secondary purpose of indicating the last subcarrier needed by the current sub-block, marking the end of the sub-block and enabling the dynamic assignment of subcarriers. Table 4 shows the possible sub-block sizes for the proposed scheme. The unoccupied subcarriers are represented by F in Table 4 and may be assigned to the next sub-block.  
A total of \(10^{5}\) bits (\(8\times 10^{4}\) on B2 and \(2\times 10^{4}\) on B1) were modulated using DS-PIM-SIM OFDM. The average sub-block size was calculated to be 3.5 instead of 4. This translates to 12.5% less bandwidth usage.  
Figure 6 shows all 4 possible locations of ON subcarriers for a 2-bit index word. In a 5-subcarrier block, there is only 1 possible instance when all 5 subcarriers are in use.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index Bits** & **DS-PIM** & **Subcarriers** \\ \hline
00 & 11 & S\({}_{1}\),\(S_{2}\),F,F,F \\ \hline
01 & 101 & S\({}_{1}\),0,\(S_{2}\),F,F \\ \hline
10 & 1001 & S\({}_{1}\),0,0,\(S_{2}\),F \\ \hline
11 & 10001 & S\({}_{1}\),0,0,\(S_{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Subcarrier selection for a 2-bit index data word on B1, using the proposed scheme.  
Figure 5: Block diagram of SIM OFDM sub-block creator, using the proposed technique.
# Power Management Strategy for an Autonomous DC Microgrid
## 4 Experimental Tests
### Test 1
#### 4.1.1 DG Duty-Cycle Mode  
In duty-cycle mode, DG power was set to be the sum of the maximal battery bank recharging power and the difference between PV power and load power when the DG was started.  
Figure 6a shows the DC microgrid power evolutions during the nine hours. During the first interval (\(9:00\leq t<11:01\)), the electrochemical battery bank and PV source are the active suppliers (case 8). As time goes on, the energy stored in the SC decreases through self-discharging until reaching the constraint. Thus, during the second interval (\(11:01\leq t<12:05\)), the electrochemical battery bank not only supplies the load, but also gives some power to the SC to maintain the energy level stored in the SC. This represents the case 6 given in Figure 3. Then, in the third interval (\(12:05\leq t<13:52\)), there is excess PV power, which is preferentially recharged into the SC. This represents the case 4 given in Figure 3. At around \(12:22\), the SC is full. In the next interval (\(13:52\leq t<14:52\)), the energy of the battery bank is first used up. \(SOC_{B}\) reaches its lower limit and then the SC supplies the load during DG start-up. Once the DG can offer stable power, the excess power first recharges the SC. Afterwards, the electrochemical battery bank begins to absorb power when the SC is fully recharged.  
Figure 5: (a) Solar irradiance and photovoltaic (PV) cell temperature and (b) Load power demand and PV maximum power point tracking (MPPT) power.  
The DG is then stopped after one hour. This represents the case 9 given in Figure 3. In the fifth interval (\(14:52\leq t<15:21\)), the deficient power is greater than the electrochemical battery bank's maximal discharging power and so load-shedding takes place. This represents the case 7 given in Figure 3. In the sixth interval (\(15:21\leq t<17:38\)), around \(15:21\) and \(16:38\), the DG is started up twice because load-shedding demand is more than 20% of total power demand. This represents the case 9 given in Figure 3. In the last interval (\(17:38\leq t<18:00\)), only the battery bank supplies the deficient power. This represents the case 8 given in Figure 3.  
Figure 6b,c show \(SOC_{B}\) and \(SOC_{SC}\) evolutions, reflecting the fact that the electrochemical battery bank and SC run well for providing and absorbing energy within their respective restrictions. Considering that the goal of this nine-hour experiment was to verify the feasibility of the proposed strategy with a real PV power profile, the experimental results showed that the power was well balanced and the DC bus voltage was stable with acceptable fluctuations (the maximum fluctuation amplitude is within \(\pm 1.5\%\) of the rated voltage equal to 400 V), as shown in Figure 6d.
# Magneto-Acoustic Imaging in Biology
## 1 Introduction  
Measurement of electrical conductivity has many potential applications in biology and medicine. For instance, tumors often have different electrical properties than the surrounding tissue, so imaging conductivity might be suitable for cancer diagnosis and screening. The distribution of conductivity has been determined using several techniques, including electrical impedance tomography, magnetic resonance electrical impedance tomography, and magnetic induction tomography. This review considers relatively new methods to image conductivity based on magnetic forces, often known as Lorentz forces [1]. Two of these techniques--Magneto-Acoustic Tomography with Magnetic Induction (MAT-MI) and Magneto-Acousto-Electrical Tomography (MAET)--will be analyzed in detail.
## 4 V-DPAP: Vertex-Constrained Directed Path Allocation Problems
### Framework Definition  
**Definition 6**.: _A Vertex-Constrained Directed Path Allocation Problem (V-DPAP) is a DPAP \(\langle\mathcal{A},\mathcal{G},\mu,\phi\rangle\) where function \(\phi\) is defined by a set of conflicts \(\mathcal{C}\) between vertices of the graph. Each conflict \(\sigma\in\mathcal{C}\) is a non-empty set of vertices \(V_{\sigma}\) that cannot all be selected by an allocation. Moreover, we assume that the vertices in \(V_{\sigma}\) all belong to distinct graphs._  
_From this, function \(\phi\) returns a value of \(0\) for a selection of paths \((p_{1},\ldots,p_{m})\) if and only if there exists a conflict \(\sigma\in\mathcal{C}\) such that all vertices in \(V_{\sigma}\) are traversed by one path in \((p_{1},\ldots,p_{m})\). Formally, \(\phi(p_{1},\ldots,p_{m})=0\) if there exists \(\sigma\in\mathcal{C}\) such that \(V_{\sigma}\subseteq\bigcup_{i=1}^{m}V_{p_{i}}\), where \(V_{p_{i}}\) denotes the set of vertices in path \(p_{i}\)._  
The previous definition covers both binary conflicts holding on two vertices and n-ary conflicts holding on any set of vertices. This differs from our initial framework, called PADAG, where only binary vertex conflicts were considered [4]. We will sometimes define a V-DPAP as a tuple \(\langle\mathcal{A},\mathcal{G},\mu,\mathcal{C}\rangle\) equivalent to \(\langle\mathcal{A},\mathcal{G},\mu,\phi\rangle\) since \(\phi\) is non-ambiguously defined by the set of conflicts \(\mathcal{C}\).  
**Example 3**.: _Figure 3 illustrates a V-DPAP that contains two conflicts, namely, conflict \(\sigma_{1}=\{\mathrm{a}_{2},\mathrm{b}_{2}\}\) that invalidates any combination of paths traversing both \(\mathrm{a}_{2}\) and \(\mathrm{b}_{2}\), and conflict \(\sigma_{2}=\{\mathrm{a}_{3},\mathrm{b}_{3}\}\) that invalidates any combination of paths traversing both \(\mathrm{a}_{3}\) and \(\mathrm{b}_{3}\). It can be shown that these conflicts lead to the same valid allocations as the ones provided in the DPAP of Figure 1._
## 4 Results and Discussion
### Hardware in the Loop Analysis
#### 4.3.1 Islanding Operation  
Figure 20 shows the islanded mode of the grid with experimental results of DC bus voltage regulation and comparison can be observed with BFASMC simulations. There can be observed some spikes at \(t\) = 32 s, 34 s, 36 s, 38 s. 40 s, 42 s, 44 s, 46 s, and 48 s, due to DC load current variation under islanded mode. However, the controller shows a fast convergence to stabilize the DC bus. Moreover, a comparison of the closed-loop HIL results with the MATLAB/Simulink results depicts the satisfactory performance of the controller. In Figure 21, HIL BFASMC DC bus regulation comparison is presented with HIL-SMC during the islanded mode. It can be observed clearly that SMC has more chattering and overshoot than the BFASMC which shows the effectiveness of the proposed BFASMC controller.  
Figure 19: HIL setup.
## 3 Security Aspects Regarding Wireless Body Area Framework
### Authentication and Key Agreement  
As mentioned above, certificate-based authentication, whereas effective in traditional networks, is not well-suited for WBANs due to the limited computational capabilities and memory resources of WBAN devices. The overhead associated with certificates, including their creation, distribution, and storage, can significantly impact the performance and efficiency of WBANs [12]. Authentication is essential in ensuring that only authorized devices are being connected to the WBAN. To address these limitations, alternative authentication mechanisms that are more lightweight and resource-efficient are explored.
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 7 Implementation and Real-World Performance Analysis
### Model Training and Optimization  
The detection model was trained on a dataset of 1,500 medical vial images spanning 11 different classes, ensuring robustness in various real-world conditions, such as lighting variations, occlusions, and background clutter. The dataset was augmented with random rotations, brightness adjustments, and contrast variations to enhance the generalizability of the model.
## 1 Introduction  
Regular inspection and maintenance operations are crucial to meet safety regulations and to ensure efficient functioning of machines and structures, reporting issues before problems become critical. This is particularly true in power plants, Oil & Gas industries and civil infrastructures, where unexpected failures or breakages may lead to catastrophic events and potential loss of life. In addition, such assets may require an extraordinary monitoring in response to unexpected natural events or when malfunctions and alarms occur. In this perspective, the spread of inspection robots represents an important technological breakthrough. These robots are designed with the primary goal of minimizing the risks related to the involvement of humans in such dangerous and repetitive tasks. Ground inspection robots can be roughly divided into two families according to their main purpose: general inspection and the monitoring and inspection of specific equipment.  
During general inspection, the robots span extensive areas in order to reach the target, such as pipes, pumps, valves, tanks, etc. Often, such systems consist of wheeled [1] or tracked [2] platforms equipped with sensors and robotic arms. Narrow spaces, slopes and stairs are still open challenges that require more advanced devices [3, 4].  
Within industrial plants, some assets require thorough inspections, i.e., external and internal. Therefore, many specialized robots, [5; 6], have been developed to cover these critical components. To accomplish the task, these devices exhibit small-sized and target-oriented designs [7; 8; 9].  
Pipe inspection robots have to traverse long distances in highly constrained environments. For locomotion, mainly wheels or tracks are used, [10; 11]. However, pipelines result from the combination of many segments; therefore, snake robots are gaining momentum in this context, see [12; 13; 14].  
Although the robots discussed so far display interesting design solutions, these systems still present some important limitations. For instance, general inspection robots are often big platforms, and this also represents their main weakness. The robotic arms equipped may help in accessing difficult-to-reach targets; however, the inspection workspace is constrained by the platform dimensions, since the arms cannot detach. On the other hand, most of the specialized devices rely on human operators for the deployment phase. However, requiring the presence of workers near the inspection site conflicts with one of the primary goal of inspection robots, i.e., to limit/reduce the presence of human operators in situ. An interesting technology is represented by the so-called marsupial robots, where the main platform houses and releases smaller devices for inspecting the hardest-to-reach places [15; 16; 17].  
In the wake of marsupial robots, we started to develop a new self-reconfiguring Hybrid Platform, which combines different robots in order to maximize the application range of our system. In particular, we focused on the design and testing of the Crawler Unit.