esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Definition of Optimization Problem
### Design Variables  
In Figure 4, the mapped values of cable pre-tension forces for cable numbers \(i,j\) and \(k\) are exemplified on the B-spline curve, representing their respective pre-tension forces. The coordinates of the control point consist of the horizontal coordinate \(x\) and the vertical coordinate \(y\). From the side span to the mid tower, the ranges of the horizontal coordinates \(x\) corresponding to the three B-splines are (\(0\leq x\leq L_{s}\)), (\(0\leq x\leq L_{m}/2\)), and (\(0\leq x\leq L_{m}/2\) + \(L_{c}\)), where \(L_{c}\) is the length of 5 pairs of crossing cable suspension deck, which is 75 m. The number of crossing cables remains unchanged during the optimization process. The range of the vertical coordinate \(y\) is defined by the control function \(f(x)\), i.e., \(f(x)_{\min}\leq y\)\(\leq f(x)_{\max}\)), as shown in Figure 4. This cable pre-tension force feasible region is associated with the deck' dead load and the horizontal coordinate in order to reduce the solutions space and accelerate the convergence of the optimization problem. The function of the cable pre-tension force feasible region is calculated as follows:  
\[\left\{\begin{array}{l}f(x)_{\max}=(1+\beta)\cdot\frac{\lambda_{c}G\lambda_{ x}}{h_{x}}\\ f(x)_{\min}=(1-\beta)\cdot\frac{\lambda_{c}G\lambda_{x}}{h_{x}}\end{array}\right. \tag{4}\]  
where \(\beta\) represents the amplification factor, which determines the size of the cable pre-tension force feasible region, and its value range is (0,1). \(\lambda_{c}\) is the cable distance on the deck. In the ordinary zone \(G\) is the deck' dead load, and in the crossing zone \(G\) is half of the deck' dead load. \(l_{x}\) and \(h_{x}\) are the length of the cable at \(x\) coordinate and the vertical projection length of the cable respectively, which can be determined by the structural geometric parameters. If the force distribution of the cable system is less understood, a  
Figure 4: Pre-tension forces represented by conic quasi-uniform B-spline curves.  
larger amplification factor can be set. The amplification factor in the two function curves can take different values.
# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 1 Introduction
These ranges reflect realistic constraints in domains where data privacy, limited smart meter deployment, or short observation periods restrict data availability. Moreover, despite the presence of privacy-preserving GAN variants (e.g., PATE-GAN and CTGAN), few works have emphasized architectures that are both compact enough to work well with small data and capable of maintaining key statistical and behavioral properties like daily cycles, peaks, and variance. Therefore, there is a need for developing a lightweight, convolutional GAN model that operates reliably across various dataset domains and balances training stability, temporal fidelity, and data privacy. This study aims to address the abovementioned gap by proposing and benchmarking a Conv1D-WGAN-GP architecture specifically tailored for 24 h load profile generation. It combines the stability of Wasserstein loss with gradient penalty and the pattern-detection capabilities of convolutional layers, offering a novel solution for robust synthetic data generation in smart grid applications where training data are limited, sensitive, or heterogeneous. Moreover, the proposed model is optimized for operating with small-scale datasets, which is a common limitation in many practical situations.
# Pitting Corrosion of Steel Pipes in Water Supply Systems: An Influence of Shell-like Layer
## 2 Materials and Methods  
Dried scale samples from old corroded steel pipes were examined in the current research. The study was based on the investigation of the effect of electrolyte concentration on pitting corrosion. It was supposed that a shell-like layer provides an exchange between internal and external media [9; 42], and consequently, the liquid fraction of the scale inside the tubercles can be extracted by repeated dissolution and analyzed.  
The experiment consisted of extracting tubercles from defective pipes and analyzing soluble scale components of tubercles after the pipelines were taken out of service. At the same time, the anionic composition was focused on because during drying, iron cations are oxidized to form insoluble substances deposited in the structure of the tubercles. It allows for analyzing the processes inside the pipe even if the study was carried out after a long period from the pipe extraction from the distribution network.  
Studies of the structural features of the tubercles were carried out on the pipes after being in operation. The inner surface of a fragment of an uncoated steel pipe DN 300, extracted from the water supply system in Moscow, is shown in Figure 3. Corrosion deposits on the surface of the pipe are solid, dense, and durable, with oval tubercles measuring from 5 \(\times\) 5 to 10 \(\times\) 10 mm and with a height of 5-8 mm. The pipe service life was 36 years. The tubercles were divided into three groups according to the following criteria. Tubercles with a full set of structural differences were assigned to Group 1: surface layer, shell layer, core, and base. The second group of tubercules was without a base, and the third group included the most deformed tubercules removed from the pipe. Figures 3 and 4 show photographs of the inner surface of the pipe.  
The surface of the pipe under the layer of corrosive deposits has traces of entire ulcerative corrosion and spot damage on the background of flat traces. The depth of spot damage was from 1.0 to 3.0 mm, and the diameter was from 2 to 5 mm. There are also traces of extensive corrosion; the thickness of the corroded metal can be estimated from the remaining sections at 0.5-1.0 mm.  
The content of chlorides, calcium, and magnesium was determined by titration with standard solutions. Iron, sulfates, and nitrates were measured on a Lange 5000 spectrophotometer (Hach, Loveland, CO, USA), and pH was measured with a Hanna HI 2215 pH meter (Hanna Instruments, Woonsocket, RI, USA). Micrographs of the scale were made on a Quanta FEI 250 electron microscope (FEI Company, Hillsboro, OR, USA) in the mode of secondary electrons without coating at an accelerating voltage of 10-25 kV.
## 1 Introduction  
The incrementing growth rate of the world population and the ever-growing need for food alongside the pressure to achieve sustainable goals have driven changes from conventional farming approaches to sustainable agricultural ones. Aquaponics is an agricultural approach that links fish and soilless plant production in a recirculating ecosystem where natural bacteria convert fish waste into plant nutrients [1]. The plants take up the nutrients from the water to grow whilst purifying the water, which returns to the fish tanks. A coupled aquaponics system contains aquatic organisms, bacteria and plants that benefit from each other in a closed recirculated water body. A standard aquaponics system has a growbed where the plants are grown and a fishtank is placed under the growbed tray where the fish are kept. Fishtank water is recirculated between the growbed and fishtank. Vermiponics is an aquaponics system with red tape earthworms, scientifically known as _Eisenia fetida_, added onto the growbed media. The earthworms added onto the growbed media help to break down any solid waste from the fish and any detritus from the plants, and also provides additional nutrients for the plants. Earthworms help convert organic waste to fertilizer [2]. They contain hormone-like substances, which encourage the health and growth of plants [3]. They are rich in protein and recommended as fish feed to aquaponics systems, making them environmentally sustainable [4]. This helps to reduce the negative environmental impact of the conventional aquaponics systems. The amino acid content in the earthworms is higher than the standard fish meal [5; 6]. Therefore, it is hypothesized that the vermiponics testbed with earthworms tend to be more efficient and sustainable. This article further describes the related work in Section 2 and the materials and methods in Section 3. Later, the results are illustrated in Section 4, followed by the discussion of the results in Section 5.
## 3 Mathematical Model
### Passenger Dynamic Demand Analysis
#### 3.2.2 Passenger Dynamic Demand and Ticket Prices  
We use the exponential demand function to describe the relationship between passengers' ticket-purchasing demand and ticket price, mainly based on the following two main points. (1) The value of the exponential demand function is always greater than zero. There is no need for non-negative processing in the model construction process, which fits the actual situation wherein demand cannot be negative. (2) The exponential demand function includes linear functions. Due to the limited data available in this paper, if the number of data is large enough, a linear regression analysis of demand and price can be undertaken, and the parameters can be valued.  
The exponential demand function is expressed as in Equation (2), where a and b are parameters.  
\[\lambda(\rm p)=ae^{-bp} \tag{2}\]  
\(\rm p^{a}\) is the actual ticket price and Equation (3) represents the relationship between the intensity and the actual ticket price.  
\[\lambda^{a}(\rm p^{a})=ae^{-bp^{a}} \tag{3}\]  
When the characteristics of passenger ticket-purchasing demand fit the non-homogeneous Poisson process, the relationship between dynamic demand and ticket price at the th period of the pre-sale period in section (i,j) is derived as in Equation (4).  
\[\rm d_{ij}^{t}=X_{ij}\Big{(}t_{ij}^{k}\Big{)}\cdot e^{-bp_{i}^{a}(\frac{\rm r_{ ij}^{t}}{\rm r_{ij}^{t}}-1)} \tag{4}\]  
The flexibility coefficient of the price \(\rm E_{p}^{t}\) describes the degree to which demand is affected by price and is an important parameter in the demand function, which is calculated in Equation (5).  
\[\rm E_{p}^{t}=\lim_{\rm\Delta p\to 0}\left(\frac{\rm\Delta d_{ij}^{t}}{\rm \Delta p_{ij}^{t}}\times\frac{\rm P_{ij}^{t}}{d_{ij}^{t}}\right)=\frac{\rm d _{ij}^{t}}{\rm dP_{ij}^{t}}\times\frac{\rm P_{ij}^{t}}{\rm d_{ij}^{t}} \tag{5}\]  
The value of \(\rm E_{p}^{t}\) reflects the sensitivity of passenger demand to ticket price changes. \(\rm E_{p}^{t}\) varies at different times of the pre-sale period. At the beginning of the pre-sale period, passengers have enough time to choose their travel time and route; at this moment the sensitivity is relatively high, so the flexibility coefficient of the price is relatively large. Near the end of the pre-sale period, passengers lack sufficient time to re-plan their itineraries and are less sensitive to changes in ticket price than at the beginning of the pre-sale period, so the coefficient is relatively small. Equation (4) can be transformed as follows.  
\[\rm d_{ij}^{t}=X_{ij}(t_{ij}^{k})\cdot e^{-E_{p}^{t}(\frac{\rm P_{ij}^{t}}{ \rm r_{ij}^{t}}-1)} \tag{6}\]  
The negative sign in Equation (6) represents that there is a negative correlation between demand and price. The ticket-purchasing demand decreases with the increase in the ticket price.
## 4 Proposed Algorithm
### Wavelet Transform of SDPPG Signals  
With the selected mother wavelet, the wavelet transform is performed at each channel. As described in Section 3, signals are iteratively decomposed up to level \(N\), which depends on the sampling rate, as in Figure 10. For example, if the sampling rate of an SDPPG signal is 256 Hz, then the level \(N\) is set to 6 so that the frequency band of the decomposed approximation components becomes between 0 and 4 Hz [14]. Table 1 shows the frequency band according to the level of the wavelet transform at a 256 Hz sampling rate.  
A signal period is detected using approximation components, \(A_{N}\) in level \(N\), for which the frequency range is between 0 and 4 Hz, whereas movement noise is detected using detail components \(D_{N\text{-}3}\) in a level N-3, for which the frequency range is between 32 and 64 Hz. Since measured signals are low-pass filtered out with a cutoff frequency of 20 Hz, as explained in Sections 2 and 4.2, signals higher than the cutoff frequency are mainly related to movement noise at the sampling rate of 256 Hz. In addition, the heart rate is obtained from \(A_{N}\) by computing PPIs.  
\begin{table}
\begin{tabular}{c c c} \hline
**N** & \(A_{N}\) (**Hz**) & \(D_{N}\) (**Hz**) \\ \hline
1 & 0–128 & 128–256 \\
2 & 0–64 & 64–128 \\
3 & 0–32 & 32–64 \\
4 & 0–16 & 16–32 \\
5 & 0–8 & 8–16 \\
6 & 0–4 & 4–8 \\ \hline \end{tabular}
\end{table}
Table 1: Frequency Distribution when the Sampling Rate is 256 Hz.  
Figure 10: Decomposition level decision procedure.  
Figure 9: Cross-correlation between two channels.
## 3 Factors Influencing and Mechanistic Pathways in Egg Yolk Emulsification
### Enzymatic Treatment
This enhancement was attributed to the presence of lyso-phospholipids and increased protein solubility. Emulsions with enzyme-treated egg yolk were less affected by pH and ionic strength, maintaining their stability even in high-salt environments. Although flocculation was higher in these conditions, the overall rheological and creaming behavior of the emulsions remained unaffected, indicating that the forces driving flocculation are relatively weak in emulsions made with enzyme-treated egg yolk. The increased solubility of the modified proteins in the egg yolk caused by the enzyme treatment was determined to be the primary reason for the considerable improvement in emulsion characteristics at a low pH. However, this benefit was less pronounced at pH 6.5 and 0.52 M NaCl, where egg yolk proteins are already highly soluble [44]. A recent study by the same group showed that emulsions prepared using phospholipase A\({}_{2}\)-modified egg yolk and granules had better stability than those with modified plasma. Additionally, emulsions stabilized with plasma and egg yolk modified by enzymes had greater freeze-thaw stabilities than emulsions stabilized with granules [45]. As mentioned earlier, the type of enzyme influences the degree of hydrolysis and emulsifying properties. Alkaline protease had higher hydrolysis activity than neutral protease and trypsin during egg yolk HDL hydrolysis. The final degrees of hydrolysis were 9.35%, 9.98%, and 10.61% for neutral protease, trypsin, and alkaline protease, respectively. All enzymes enhanced the emulsifying activity and emulsion stability of egg yolk HDL. The optimal hydrolysis times were 1.5, 3.5, and 3.5 h for alkaline protease, neutral protease, and trypsin, respectively [84]. Similarly, subtilisin-assisted enzymatic hydrolysis enhanced the emulsifying qualities of egg yolk granules [89]. All things considered, it may be said that enzymatic treatment can change the emulsifying qualities of egg yolks and be utilized to create food products for different target audiences. To improve the enzymatic hydrolysis procedure and processing environment, more research is necessary. Furthermore, studies should look into how freezing, heat treatment, and pH affect the effectiveness of enzymatic hydrolysis.
# YOLO-SE: Improved YOLOv8 for Remote Sensing Object Detection and Recognition
## 4 Experimental and Results Analysis  
### Experimental Setup and Evaluation Metrics  
#### Experimental Environment  
The experimental platform used in this study is shown in Table 1.  
The SIMD dataset is a multi-class, open-source, high-resolution, and fine-grained remote sensing object-detection dataset. It consists of 5000 images with a total of 45,096 objects distributed across 15 different classes such as cars, airplanes, and helicopters. The distribution of classes and the sizes of objects in the training set are shown in Figure 8. SIMD is characterized by class imbalance and the presence of small objects. As Figure 8a illustrates, the \"car\" class has more than 16,000 objects, while some classes like \"helicopter\"  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Version** \\ \hline CPU & Intel(R) Xeon(R) CPU E5-2696 v4 @ 2.20 GHz \\ GPU & NVIDIA GeForce RTX 3090, 24 GB \\ Memory & 64 GB \\ Operating system & Ubuntu 22.04 \\ Deep learning framework & Pytorch 1.13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental platform.  
Figure 7: The values of \(w\), \(h\): (**a**) CIoU; (**b**) WIoU.  
and \"fighter\" have fewer than 500 objects. We partitioned the images into 80% for training and 20% for testing.  
In this experiment, YOLOv8s' pretrained weights were used, and the training was conducted for 200 epochs with a batch size of 16 and an input image size of 1024 \(\times\) 1024 pixels.
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 7 Open Source Approaches
### Interoperability and Security Testing  
As we have mentioned in Section 6, interoperability testing and security testing of Open RAN are important issues to be addressed. Currently, the only available solution to ensure an interoperable and secure Open RAN system is the O-RAN Alliance's standardized architecture description and testing procedure. There is no concrete testing system yet for vendors to ensure that their Open RAN products are interoperable and secure. Open-source testing software could solve the Open RAN interoperability and security testing problem. The cybersecurity domain has implemented open-source testing tool solutions, such as OSSEC, Kali Linux, and OpenVAS. We think the future direction of Open RAN security testing can also be directed into open-source solutions, following the example of the cybersecurity testing trend. These open-source security testing tools can provide the basic security testing requirements to meet the test case procedure requirements provided by O-RAN Alliance's TIFG in [113]. Companies can still sell advanced testing services or products that provide extra features compared to their open-source counterparts.  
The same concept could be applied for interoperability testing. Open-source interoperability testing tools can be developed to provide the minimum testing requirement for Open RAN products. Vendors can use this solution to ensure that their Open RAN products are interoperable with Open RAN other products. However, the system integrator role will remain the same due to the existence of open-source interoperability testing tools.  
The Open RAN multivendor ecosystem is still complex, whether open-source interoperability testing tools exist. Although MNOs can use open-source interoperability testing tools, using the services from system integrators might provide ease rather than forming an internal interoperability-focused team. System integrators can also provide advanced services for interoperability testing.
## 2 Materials and Methods
### Experimental Measurements  
All canopy spectra were measured with an ASD FieldSpec Pro spectrometer (Analytical Spectral Devices, Boulder, CO, USA). This spectrometer is fitted with a 25\({}^{\circ}\) field of view, which operates in the 350-2500 nm spectral range, with sampling intervals of 1.4 nm between 350 nm and 1050 nm, and 2 nm between 1050 nm and 2500 nm. The spectrometer was calibrated using the whiteboard before the measurement. The measurements were made from a height of 0.6 m above the rice canopy under clear sky conditions between 9:00 a.m. and 11:00 a.m. on the 14 th, 21 st, and 28 th day after rice heading (DAH 14d, DAH 21d, and DAH 28d; DAH means \"days after heading\".).  
Meanwhile, three samples from each plot were selected for measuring LAI and SPAD. During the measurement, the instruments LI-3000C and SPAD-502 Plus were employed, and flag leaves were selected for the SPAD measurement. Three flag leaves of rice were randomly selected to measure SPAD in each plot, and there were nine SPAD measurement data in the same period for each treatment. Three rice plants were also randomly selected to measure the LAI of rice in each plot.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Treatment** & **Shading Material** & \
\begin{tabular}{c} **Radiation Transmittance** \\ **Rate (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Diffuse Radiation** \\ **Fraction (\%)** \\ \end{tabular} \\ \hline CK & None & 100.00 a & 19.13 a \\ T1 & Nylon mesh & 85.90 b & 31.09 b \\ T2 & Plastic film & 85.58 b & 39.98 c \\ \hline \hline \end{tabular} Different lower case letters (a,b,c) between treatments indicate significant differences at \(p<0.05\).  
\end{table}
Table 1: Data of shading treatments.  
Figure 1: The temporal variations in meteorological elements (radiation, precipitation, and temperature) at the station in 2016 (**a**) and 2017 (**b**), respectively.
# Beam Training for Millimeter-Wave Communication Based on Tabu Table Enhanced Rosenbrock Algorithm
## 5 Tabu-Search-Based Enhanced Rosenbrock Algorithm
### Boundary Problems
#### 5.3.2 Index Boundary Description  
Since the beam search is performed in the 2-D discrete integer plane, we should ensure that the beam index is always within the index range during the probe moving. As shown in Figure 2, the beam index exhibits intrinsic structure at the border of the search region. Considering that the RA is essentially a local search, in order to solve the boundary problem of the search area, we surround the beam indexes when the index range is exceeded. Taking the beam index \(I\) as an example, the beam index beyond the range (i.e., \(I<1\) or \(I>N\)) is given by  
\[I=\left\{\begin{array}{ll}\text{mod}(I,\;N),&if\;\text{mod}(I,\;N)\
eq 0\\ N,&otherwise\end{array}\right. \tag{18}\]  
Based on the earlier discussions on the four important aspects of TS, we apply the TS algorithm to the probe moving of the RA in Algorithm 1. The flow of the probe moving of the proposed beam training algorithm is summarized in Algorithm 2. Notice that: (1) Algorithm 2 is corresponding to subprogram of Step1 in Algorithm 1; (2) the SA algorithm is combined with Rosenbrock, so temperature parameter \(T_{g}\) is updated in the pattern moving in Algorithm 1; (3) the complete algorithm flow of the suggested beam search scheme which is based on the newly designed numerical search has been provided in Algorithms 1 and 2.  
Figure 2: Beam diagram.  
```
1:Initial solution \(x^{(1)}\); aspiration function \(A(x)\); initial move steps \(\xi_{1}\), \(\xi_{2}\); initial search directions \(d^{(1)}\), \(d^{(2)}\);
2:tabu table \(T(x)\); initial iteration counter iter and the maximum iterations max_iter.
3:The probed solution \(x^{(n+1)}\).
4:for\(i=1-2\)do
5:if the beam index beyond the range then
6: Calculate the beam index according to (18).
7:endif
8:if\(surt\big{(}x^{(i)}+round\big{(}\xi_{i}d^{(i)}\big{)}\big{)}>surt\big{(}x^{(i) }\big{)}\)then
9:\(x^{(i+1)}=x^{(i)}+round\big{(}\xi_{i}d^{(i)}\big{)}\)
10:if\(x^{(i+1)}>A(x)\)then
11:\(A(x)=x^{(i+1)}\)
12:endif
13: Update \(T(x)\).
14:else
15:whileiter \(<\) max_iterdo
16: Update \(x^{(i+1)}\) and \(T_{l}\) according to SA.
17: Update \(T(x)\) and \(A(x)\) according to TS.
18:endwhile
19:endif
20:endfor
21:Output \(x^{(n+1)}\).
```  
**Algorithm 2** The probe moving of the proposed algorithm (Subprogram of Step1 in Algorithm1).
## 5 Experimental Verification
### Camera Calibration and Stereo Correction Experiment  
Considering factors such as calibration cost, accuracy, and robustness, the \"Zhang's Calibration\" method was selected to calibrate the binocular camera used in the project. First, a checkerboard is printed and stuck on the plane as a calibrator. Second, the orientation of the calibrator or camera is adjusted to obtain six pairs of calibration board pictures taken by left and right lenses respectively. Finally, the pictures are sent to the camera calibrator calibration tool in MATLAB, from which the checkerboard corner points are extracted to estimate five internal parameters and six external parameters in the ideal distortion-free situation. Then, the actual distortion coefficient is further estimated using the least squares method for distortion correction (Figure 4) and stereo correction (Figure 5). After calibration and stereo correction of the camera, relevant experiments such as image feature extraction based on SIFT and feature matching can be carried out.