esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Assumptions  
### Network Model  
In the section, we present a three-dimensional network model, which is shown in Figure 1. Underwater acoustic sensors are distributed at random within the marine environment, and other details are:  
1. There are two kinds of nodes: underwater sensor nodes, which are immobile and divided into CHNs and CMNs after cluster formation, and an SN, which is located on the surface of the monitoring area.
2. There is only one SN in the network, which is the destination node and has energy supplies. Nevertheless, underwater sensor nodes have limited energy and they do not have energy supplies.
3. The ordinary underwater nodes have the equal initial energy and the unique IDs.
4. The locations of nodes could be acquired through the localization algorithm [51].
5. We could control transmitting power based on the different distances to receiving nodes.
6. CMNs gather data and transmit them to CHNs through a single hop. Once the CHN receives the data, the CHN fuses them and forward them towards the SN through multiple hops. If one CHN is close to the SN, it sends data towards the SN through one hop.  
Figure 1: The schematic diagram of the network model.
## Appendix B Detailed Description and Background Data of the Calculation Model and Hourly Simulation
### Wind Hydrogen Production and Seasonal Hydrogen Storage Balance  
Wind hydrogen production and the seasonal hydrogen storage balance is shown in the green rectangle in Figure 3. As the amount of solar electricity consumption variation is limited due to the limited amount of suitable roof area, the amount of installed wind capacity, together with energy storage, closes both the hourly and the annual energy balance. The large-scale wind turbine park shared with other smart urban areas produces electricity (E\({}_{\text{W}}\)) and is directly connected to a water electrolysis and compression system (W2-W6) and has no connection with any other electricity grid. The wind energy production is sized such that the net amount of consumed hydrogen from the seasonal hydrogen storage in underground salt caverns is zero on a yearly basis. There is no curtailment of wind electricity (E\({}_{\text{W}}\)), and all electricity produced is used for the production and compression of \"wind\" hydrogen (H\({}_{\text{W}}\)) from water (H\({}_{\text{2}}\)O\({}_{\text{W}}\)).  
The wind turbine park performance is based on the 4.2 MW land-based Enercon E-141 EP4 [335] for the Near Future scenario, and, for the Mid Century, it includes future power curve improvements based on [336]. In both scenarios, the hub height is 159 m, and the rotor diameter 141 m. The wind electricity production (E\({}_{\text{W}}\)) is calculated using the hourly wind speed values from both Almeria (see Table A1, footnote 1) and Hamburg [179; 180; 181; 182]. The wind speeds are assumed to be equal in both scenarios and are scaled [337] to the aforementioned hub height with a roughness factor z\({}_{0}\) of 0.13 m [338].
# An Enhanced System Architecture for Optimized Demand Side Management in Smart Grid
## 3 Proposed CHEMA and its Implementation
### Energy Cost Optimization Model  
In light of the above discussion, the scheduling problem has been formulated for a set of appliances \(APP=\{1,2,...,N\}\) in a horizon of 24 equal time slots, which are represented by a set of \(T=\{T_{1},T_{2},...,T_{N}\}\). Appliances' power rating and per unit energy cost are used for calculating the cost for each category of the load. If \(A_{ij}\), \(R_{ij}\) and \(B_{ij}\) represent the burst, regular and partial baseline loads and \(a_{ij}\), \(r_{ij}\) and \(b_{ij}\) represent the ON and OFF states of the corresponding category with 1 and 0 values, respectively. The optimization problem will be as follows:  
\[C_{t}=\sum_{i,j}A_{i,j}*a_{ij}*c_{j}+\sum_{i,j}R_{i,j}*r_{ij}*c_{j}+\sum_{i,j }\chi_{i,j}*b_{ij}*c_{j}. \tag{3}\]  
The final objective function can be written as:  
\[Minimize\ (C_{t}) \tag{4}\]  
subject to:  
\[\bigg{(}\sum_{i}C_{A}+\sum_{i}C_{R}+\sum_{i}C_{X}\bigg{)}\leq C_ {j},\ \ \forall j\in T, \tag{4a}\] \[a_{ij},r_{ij},b_{ij}\in\{0,1\}, \tag{4b}\]  
where  
\(c_{j}\) electricity cost in time slot \(j\)  
\(C_{A}\) capacity of burst load \(C_{R}\) capacity of regular load  
\(C_{\chi}\) capacity of partial baseline load  
\(j\) time slot  
\(T\) total time horizon  
\(a_{ij}\) appliance ON/OFF status  
\(C_{t}\) total electricity cost.  
The binary optimization problem described in Equations (4), (4a), and (4b) is solved for different scheduling options using the single Knapsack technique. Implementation of various Simulink modules is described in subsequent sections.
## 3 Results and Discussion
### Compressive Strength and Drying Shrinkage of AASC
#### 3.1.3 Effect of Alkaline Activator
In previous studies [19; 47], it has been reported that the drying shrinkage of slag mortar activated by liquid sodium silicate increases with the increase in sodium concentration at a constant SiO\({}_{2}\)/Na\({}_{2}\)O ratio. This indicates that the quantity of alkaline activator in a mix is a key factor influencing the drying shrinkage of AASC.
## 3 Method  
UMFNet is a frequency-guided enhancement framework specifically designed for object detection in low-light conditions, aiming to solve the \"spectral trilemma\" described above: (1) the loss of high-frequency structural details during the denoising process; (2) the amplification of low-frequency illumination distortion; and (3) the cross-band interference in multi-scale features. As shown in Figure 1, UMFNet is connected as a pre-enhancement module before the YOLOv8 detector to form an end-to-end trainable system.  
Figure 1: The proposed network generates feature maps \(p_{1},p_{2},p_{3},p_{4}\), where each \(p_{i}\) (with \(i\in\{1,2,3,4\}\)) has twice the channels and half the resolution of \(p_{i-1}\). Additionally, \(p_{1}^{*}\) has the same number of channels and resolution as \(p_{1}\).  
Given a low-light input image \(I\in\mathbb{R}^{3\times H\times W}\), our goal is to learn the mapping \(F_{\theta}:I\mapsto I_{enhanced}\) to generate an enhanced image, which is then fed into YOLOv8 for detection. UMFNet mainly consists of the following parts.  
**Encoder**: It receives the low-light input image and generates an initial feature representation.  
**Residual Blocks (\(\times\)2)**: They extract and refine multi-level features.  
**Frequency-Adaptive Fusion (FAF) Module**: It processes different frequency components to achieve illumination-adaptive frequency-domain decomposition and fusion.  
**Spatial-Channel Coordinated Attention (SCCA) Mechanism**: It coordinates the interactions between frequency-domain features, reduces high-frequency hallucinations, and enhances key structural information.  
**Decoder**: It reconstructs the processed features into an enhanced image for further processing by YOLOv8.
# Improved Training of CAE-Based Defect Detectors Using Structural Noise
## 3 Improvement of Defect Detection Accuracy Using Structural Noise
### Defect Detection Using CAEs  
As introduced in the previous section, a CAE is first trained on a dataset made of pictures of flawless pieces. Once ready, this model is fed defective images of which it must spot irregularities. Once the reconstruction is done, the areas that differ between the input and the output are considered \"defective\", as in Figure 4 (where the output is shown as perfect for explanation purposes).  
Figure 3: Hypothetical restorations performed by an advanced CAE.  
Figure 2: Succession of layers within our CAE.  
As the neural network is trained on pieces that are intact, it does not know how to handle a faulty piece, thus, it attempts to encode and decode it as a regular piece. Since the faulty pieces are processed indifferently, the \"code\" is decoded as usual, which leads to output an approximation of what the piece would look like without flaws. For example, in Figure 5, an image of a damaged piece is fed to an autoencoder.  
It should be noted that this works only for the defective pieces of which similar counterparts were used to train the model. If the shapes, sizes, positions or rotations of the pieces tested contrast too much with those of training data, the model will not be able to handle them correctly. Adding noise to the training dataset can improve the reconstruction phase. In this way, the autoencoders become more resilient to slight variations in the data they handle.
## 4 Experimental Validation  
### Experimental Setup and Data Description  
The experimental data were acquired on the test bench in Figure 4, which features a two-stage planetary gearbox. In addition, a parallel shaft gearbox supported by bearings, a variable speed drive motor, and a magnetic powder brake can be found. The driving power of the whole experimental bench is provided by a variable speed motor, and its maximum speed can reach 5000 rpm. Then, the torque output by the motor is connected to the input shaft of the first-stage planetary gearbox through the torque sensor and the encoder. The components are connected to each other so that the whole system can work smoothly. At the end of the test, the programmable magnetic brake provides resistance to the system and gradually stops the entire system.  
In order to verify the ability of the proposed method to identify data from different health conditions, vibration signals of the first-stage planetary gearbox were collected under four conditions: normal condition, planetary gearbox bearing outer-race fault, planetary gearbox bearing inner-race fault, and planetary gearbox bearing roller fault. The acquisitions were performed at a sampling frequency of 15,360 Hz and a motor speed of 2100 rpm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Operation** & **Number of Filters** & **Filter Size** & **Activation Function** & **Output Size** \\ \hline Input & / & / & / & (100, 100) \\ Conv & Kernels & 20 & \(3\times 3\) & ReLu & \((98,98,20)\) \\ Pooling & Pooling size & / & \(2\times 2\) & Ave & \((49,49,20)\) \\ Conv & Kernels & 20 & \(2\times 2\) & ReLu & \((48,48,20)\) \\ Pooling & Pooling size & / & \(2\times 2\) & Ave & \((24,24,20)\) \\ FC & / & 120 & / & ReLu & (120, 1) \\ FC & / & C & / & Softmax & (C, 1) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters set of each layer.  
Figure 3: The flowchart of the novel proposed algorithm.
# Interpretation of Bridge Health Monitoring Data from Satellite InSAR Technology
## 3 Method
### Correlation between Time Series  
Displacements of isostatic structures are typically linearly correlated to the loads applied (e.g., dead loads, live loads, and temperature variations). Many studies investigate the correlation between time series of environmental loads and measurements from long-term monitoring of civil structures [30]. Correlation analysis is a method of statistical evaluation used to study the strength of a relationship between continuous variables. Given two random variables, X and Y, the Pearson correlation coefficient \(\rho_{XY}\) expresses their linear correlation [31]. The Pearson coefficient ranges between \(-1\) and \(+1\): \(+1\) means perfect direct correlation, and \(-1\) means perfect inverse correlation. Investigating a potential correlation between variables employing the Pearson coefficient involves considering the hypothesis of a linear relationship between them. However, it should be acknowledged that variables can exhibit more complex relationships than simple linear ones. Remarkably, two datasets with low or no linear correlation may demonstrate a robust nonlinear relationship. Nevertheless, assessing linear correlation as a preliminary step is valuable for identifying variables that exhibit a relationship before performing further correlation analyses.
# Axial and Bending Bearing Capacity of Double-Steel-Concrete Composite Shear Walls
## 1 Introduction
[11] proposed simplified formulas used to evaluate the flexure strength of the SC walls. However, all the calculation methods were derived using plastic stress distributions at the cross sections, which was the same as [22], and only suitable for calculating the compression members with large eccentricity. When applying to a small eccentric load, the methods would cause larger calculation errors, because the steel plates in tension did not reach the yield strength. [PERSON] et al. [23] developed a model for the elastic and plastic analysis of the axial force-moment capacity of SC walls and analyzed the M-\(\varphi\) curve and the axial force and moment curve. They derived the formula for axial force-moment based on the key factors gained by numerical calculation and parameter analysis. However, the factors were complex and did not take into account the effects of structural measures, so the formula cannot be used widely in practical engineering. [PERSON] et al. [10] used the strip method to deduce the calculation formula of normal section bearing capacity, which applied to both small and large eccentric load. However, the strip method was complex and the ultimate compression strain of the concrete was taken as 0.0033, which was inconsistent with the confinement effect of steel plates on the concrete. [PERSON] et al [24] proposed a confinement-sensitive plasticity constitutive model for concrete in triaxial compression. It incorporates a three-parameter loading surface, uncoupled hardening and softening functions, following the accumulation of plastic volumetric strain and a nonlinear Lode-angle-dependent plastic potential function. [PERSON] et al. [25] created an accurate nonlinear finite element model with the ATENA software, and the influences of different parameters were studied. Due to the excellent strength and ductility, SC Walls have already been applied to a lot of structures requiring high resistance against severe loads since the 1980s, such as nuclear facilities, or marine environment structures. In China, SC walls are mainly used in high-rise buildings, including Yancheng TV Tower [26] and Guangzhou TV Tower. Due to the lack of detailed design codes, the above structureswere mainly carried out based on the overall concept of the structure regarding other composite structures, so a reasonable and correct design method for SC Walls is urgently needed. In this paper, based on the experimental results of 49 SC walls and the results of numerical analysis, the bending failure characteristic of SC walls was studied and summarized, and a primary assumption of SC walls was put forward. Then according to the ultimate equilibrium theory of the cross section, the axial and bending capacity formula was deduced and compared with the existing experimental results.
## 2 Materials and Methods
### In Vitro Cytotoxic Assay  
The in vitro cytotoxicity of the finger-fabricated AuNPs was evaluated against HepG2 (hepatocellular carcinoma), Mcf7 (adenocarcinoma), and OEC (normal oral epithelial) cell lines by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl trazolium bromide) assay method. Each cell line was seeded in 96-well tissue culture plates (1 \(\times\) 10\({}^{5}\) cells/100 \(\upmu\)L/well) and incubated under a 5% CO\({}_{2}\) atmosphere for 24 h at 37 \({}^{\circ}\)C. Then, the monolayer cell sheet was washed twice by washing media before adding the maintenance media (RPMI medium with 2% serum). Thereafter, cells were treated with various concentrations of the biogenic AuNPs (31.25, 62.5, 125, 250, 500, and 1000 \(\upmu\)g mL\({}^{-1}\)) and incubated for 48 h, with three wells free of NPs used as controls. After completion of the incubation, the media were poured from the plates; each well received 50 \(\upmu\)L of fresh MMT solution in phosphate-buffered saline solution (PBS, 5 mg mL\({}^{-1}\)) and was mixed thoroughly for 5 min; then, plates enveloped with aluminum foil were further incubated for 4 h at 37 \({}^{\circ}\)C. MTT solutions were dumped off, and 100 \(\upmu\)L of DMSO (10%) was added to solubilize the formed formazan crystals. Plates were kept in dark and shaken for 30 min. Ultimately, absorbance was measured at 570 nm using an ELISA reader [45]. Cellular morphological changes were disclosed by inverted microscopy (ECLIPSE Ts2, Nikon, Shinjuku, Tokyo, Japan), whereas the percentages of cell viability were calculated by the following equation:Cell viability \((\%)=\dfrac{\text{Absorbance of treated sample}}{\text{Absorbance of control}}\times 100\) (3)  
The IC\({}_{50}\) values were calculated using curve fitting (cubic spline) with Prism software (GraphPad Software, San Diego, CA, USA) by an algorithm described by [PERSON] et al. [46].  
Results were statistically analyzed using SPSS v18 (SPSS Inc., Chicago, IL, USA). An analysis of variance (ANOVA) test was used for multiple sample comparison, followed by [PERSON]'s multiple comparison test.
# Adjacent Vehicle Number-Triggered Adaptive Transmission for V2V Communications
## 1 Introduction
* Data traffic jam in traffic jams or platoons  
In a platoon or a traffic jam, the quality of wireless service is difficult to be guaranteed if the service is only delivered through the V2I. We can expect that lots of passengers in platooning vehicles want to enjoy the internet services such as multimedia streaming with high data traffic demand [15]. Particularly, when the location of the vehicles in the traffic jam is not well covered by the service area, the users will suffer from the low service quality. In Figure 2, where many cars under the traffic jam belong to only one AP, huge service demand is requested by the vehicles which may exceed the maximum capability for the single AP. Therefore, some vehicles may not acquire the service from the AP. The solution can be that the vehicles which can acquire the data from the AP by connecting to the cloud server will store it and disseminate it to other vehicles. In [16], a similar case was examined, where the V2V was exploited to assist the V2I to deliver advertisements from the network to the  
Figure 1: The vehicles in the gap area between the APs’ coverage. vehicles. The results indicated that the exploitation of the V2V enhanced the information dissemination in the vehicular network when RSU deployment was sparse or dense [16]. From the examples, it is obvious that the enhancement of the unreliable service delivery in the V2V networks is required for the future. Furthermore, the V2V still faces some challenges such as a harsh communication environment due to the severe scattering without line-of-sight and the frequent topology changes of the VANET due to the high mobility of vehicles [17]. Therefore, in this paper, we propose an adaptive data rate selection scheme according to the changes of physical network topology in order to improve the reliability and continuity of data transmission in V2V communications. Focusing on the physical layer aspects, a new algorithm is suggested, and its performance is investigated in terms of error probability and throughput. The paper is organized as follows: In Section 2, we review the related literature. Our proposed method is presented in Section 3. In Section 4, the simulation results are analyzed. Finally, our findings and further work are described in Section 5 of the conclusion.
# A Specification-Based IDS for Detecting Attacks on RPL-Based Network Topology
## 2 Topology Attacks on RPL  
Our works [2; 3; 4; 5] have discussed some of the particular topology attacks on RPL including the Rank, the Local repair, the Neighbor, and the IDS attack. Attacks with the same nature in wireless sensor or ad hoc network can also apply for RPL, as discussed in [2; 6]. We will summarize some of the prominent attacks from those that work as the targets for our IDS to detect as follow.  
* **The Rank attack:** after the attack is triggered, the malicious node changes the way it processes the DIO messages from other neighbors so that it will get the node with the worst rank as the preferred parent. This kind of attack will create un-optimized route for all the packets that go through the malicious nodes, and it also creates more traffic to the victim.
* **The Sinkhole attack:** the malicious node will propagate its rank with a good value, normally the same rank of the sink. As a result, its neighbors will select it as their preferred parent and send traffic to that node. The Sinkhole attack is often combined with the Selective Forwarding attack (a performance attack which is out of scope of this paper) to drop all the traffic attracted.
* **The Local repair attack:** after the attack is triggered, the malicious node starts broadcasting local repair messages periodically, though there is no problem with the link quality around the node. Other node upon receiving the local repair messages will need to recalculate the route that is related to the malicious nodes. This kind of attack creates more control overhead messages, as well as some packet dropping because of temporarily unavailable route.  
* **The Neighbor attack:** after the attack is triggered, the malicious node will replicate any DIO messages that it receives and broadcast them again. The victims who receive this type of messages may think that it has a new neighbor, which is not in range. Moreover, if the new neighbor advertises a good rank then the victims may request it as the preferred parent and change the route to the out range neighbors.
* **The DIS attack:** after the attack is triggered, the malicious nodes will send the DIS messages periodically to its neighbors. The DIS messages can be sent two ways, which will lead to a different response from the receivers. The first way is to broadcast DIS, the receivers upon receive will have to reset the DIO timer as they realize that there is something with the topology around. The second way is to unicast this DIS message to all nodes in the neighbor list, the receivers upon receive will unicast DIO message to the sender. Both of these ways add more control overhead on the network.  
All of these presented attacks are practically shown to affect significantly RPL performance [2; 3; 5]. The attacks can be summarized in Table 1 below.
# Research Progress of Enhanced Thermal Evacuation and Cooling Technology for High-Speed Motors
## 2 Different Application and Cooling Requirements for High-Speed Motors
### Air Gap Convection  
Exhausting hot gases inside a motor is another common route for cooling the motor. In order to improve cooling efficiency, [PERSON] et al. [38] designed an air inlet and outlet to achieve internal air cooling and to reduce thermal resistance to heat evacuation. [PERSON] et al. [39] added a circulating air-cooled cooling structure for the application of external air supply to the motor of a high-speed centrifugal blower, as shown in Figure 12. The results show that forced ventilation-cooling of the motor gap can improve motor cooling efficiency.  
Figure 11: Schematics of (**a**) air-cooled permanent magnet synchronous motor; (**b**) motor; (**c**) a single heat pipe; (**d**) heat pipe structure [36].  
Figure 12: Schematic diagram of open-type air cooling for motor [39].  
For convective heat transfer in the motor gap, improving the gas flow state in the gap is a very important parameter, which is affected by the flow rate, gap size, etc. [PERSON] et al. [40] exploited the homogeneity of a motor and its driven compressor rotor to enhance axial convection heat evacuation. [PERSON] et al. [41] studied the effect of gap size for air convection heat transfer efficiency and explored the cooling effect of a 0.18-0.24 mm gap. It was found that as the air-cooled motor gap decreased, the flow velocity of the fluid on the stator and rotor surfaces increased for more effective heat dissipation. [PERSON] et al. [42] enlarged the internal slots of a stator core for ventilation ducts to improve stator convective heat evacuation efficiency. [PERSON] et al. [43] found that a single U-shaped arrangement of permanent magnets was more effective in cooling the rotor. For improving the convection transfer efficiency of a stator and rotor, [PERSON] et al. [44] added a fan to the main shaft and enlarged the grille area, as illustrated in Figure 13.