esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In the present study, it was evidenced that crude glycerol and effluent from anaerobic digestate, which are waste products derived from the biofuel industry, can serve as alternative nutrient source inputs in _S. limacinum_ cultivation. The optimal effluent concentration in order to maximize DHA production was 48% _v/v_, yielding 49.7 g L\({}^{-1}\) biomass and 0.8 g L\({}^{-1}\) DHA with intracellular lipids containing primarily 18:0 saturated fatty acids. The use of _S. limacinum_ can be an effective tool for the waste stream processing of the biofuel industry, since this species can efficiently utilize crude glycerol as a carbon source for growth and development. Moreover, it was shown that yeast extract, trace elements and artificial sea salts can be effectively replaced by inorganic fertilizers and effluent for the heterotrophic cultivation of _S. limacinum_. The importance of trace elements in the culture of _S. limacinum_ is highlighted by this study, while future research is needed to enlighten their effects on biomass productivity and DHA yield of the species. In order for the implementation of digested effluent to be applicable in the fermentation of _S. limacinum_ at an industrial scale, and simultaneously, to produce a product that could be used in aquafeeds for fish oil replacement, it is necessary to improve the DHA yields through optimization of the cultivation conditions.  
The above process of the use of biofuel industrial waste streams as resource input in microalgae cultivation, which in turn can be used for the production of aquaculture feeds with high nutritional value, is in line with the circular economy concept.  
Conceptualization, N.K. and I.T.K.; Data curation, S.B.; Funding acquisition, N.K.; Investigation, S.B. and D.A.; Methodology, S.B. and D.A.; Project administration, N.K.; Supervision, N.K.; Writing--original draft, S.B.; Writing--review and editing, N.K., D.A. and I.T.K. All authors have read and agreed to the published version of the manuscript.  
This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH-CREATE-INNOVATE (project code: T1 EDK-01580).  
The authors declare no conflict of interest.
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 5 Wire-Grid Sparse Antennas
### Occa Method
#### 5.4.2 Connected OCGA Approach  
The structure of the sparse antenna obtained using OCGA might add technical difficulty in manufacturing such antennas, which might differ according to the structure. For example, in the case of sparse printed antennas using OCGA, the antenna's physical geometry can be manufactured easily, but for reflector antennas, as shown in Figure 9, using OCGA complicates their manufacturing. Since OCGA can be easily optimized and adapted, any condition might be applied depending on the requirements. To demonstrate this capabilities of OCGA, we proposed another approach of obtaining sparse antennas using OCGA. We assumed that the above sparse antenna needs to be manufactured and in order to construct the structure prototype, for example using a 3D printer before metallization, we modified the algorithm of OCGA to give a structure of physically connected wires. In other words, the wires are used only where the current flows. The sparse antennas obtained using this approach and their gain results are compared with those obtained using the wire grid and presented in Figure 10. The results show good consistency, and the structures seem to be easier to manufacture.  
Figure 8: Antenna gain obtained using the wire grid comparing to those measured in [301] in E-plane at 5.1 GHz (**a**) and 5.9 GHz (**b**); in H-plane at 5.1 GHz (**c**) and 5.9 GHz (**d**).  
Figure 9: Sparse antenna structures obtained using the general OCGA (sparse) at 5.1 GHz **(a)** and 5.9 GHz **(b)** with GEET = 10%; and their gain results compared to those obtained using the wire grid in E-plane at 5.1 GHz **(c)** and 5.9 GHz **(d)**; and in H-plane at 5.1 GHz **(e)** and 5.9 GHz **(f)**.  
Figure 10: Sparse antenna structures obtained using the connected OCGA (sparse) at 5.1 GHz (**a**) and 5.9 GHz (**b**) with GEET = 10%; and their gain results compared to those obtained using the wire grid in E-plane at 5.1 GHz (**c**) and 5.9 GHz (**d**); and in H-plane at 5.1 GHz (**e**) and 5.9 GHz (**f**).
# Preparation and Properties of Epoxy Composites with Multi-Scale BN Sheets
## 3 Results and Discussion
### Thermal Conductivity  
BN is a softer filler, which can attain a higher packing density at a low filler content and provide a larger surface contact between micro and nano particles than other ceramic thermally conductive fillers. Figure 3 shows the thermal conductivity of the BN/EP composite. It can be seen that the addition of N-BN platelets firstly elevates the thermal conductivity of BN/EP composites, and then a tendency of declining happens. The thermal conductivity of the composites increases with the increase in the proportion of boron nitride. The thermal conductivity of N-BN/EP composites increased from 0.9 W/m-K to 1.52 W/m-K, as the N-BN added. Compared with M-BN/EP composite, the thermal conductivity of 20% N-BN/EP is increased to 1.6 times than that of it. Nanoparticles filled the gap between micron BN particles. Nano-sized BN particles played a connecting role between micron-sized BN particles, increased thermal conductivity channels, and improved thermal conductivity.  
Figure 1: FT-IR spectrum of the modified-BN By KH550.  
Figure 2: The SEM diagram of the cross-section of the N-BN/EP composite material. (**a**) 10% N-BN/EP; (**b**) 20% N-BN/EP.  
As the content of nano-BN increased to 30%, the thermal conductivity of 30% N-BN/EP decreases significantly. This may be caused by the agglomeration of nano BN. Aggregated BN reduces the formation of thermal conduction pathways to some extent. Local heat accumulation can also cause other performance degradation.
## 3 Proposed Multiple Stationary Human Targets Detection Method
### Step B: TPOI Extraction  
Convolutional neural network is a classic method in deep learning. It is inquired from the idea that neuron connection from the animal's brain. CNN have been shown to produce state-of-the art results in areas such as speech recognition [48], visual object detection [49], and image classification [50]. The proposed convolutional neural network architecture for TPOI extraction is shown in Figure 2. The range-slow-time matrix obtained after the pre-processing step is used as the input of the model. The size of the matrix is 800 \(\times\) 600, that is, it contains 600 impulse responses, and each impulse response contains 800 sampling points. The goal of this model is to extract the TPOIs in the range dimension from the data matrix, using the characteristic difference between the respiration signal and other signals in the slow-time dimension.  
The CNN architecture has a stack of six blocks. Each block is composed of three layers: a convolutional layer, a batch normalization (BN) layer, and a rectified linear unit (ReLU) layer. For a 2D convolutional (Conv2d) layer, let \(Z_{i}\) be the \(i\)th 2D input, and \(K_{j}\) be the \(j\)th filter kernel of size \(\sigma_{k}\times\sigma_{k}\). The \(j\)th output feature map \(Y_{j}\) is the sum of convolutions of the 2D inputs \(Z_{i}\) with the filter kernel \(K_{j}\), then added with a trainable bias \(b\). Mathematically, the output feature map can be computed as:  
\[Y_{j}=\sum_{i}K_{j}\otimes Z_{i}+b, \tag{7}\]  
where \(\otimes\) is the 2D convolution operation.  
The distribution of each layer's inputs changes during training, as the parameters of the previous layers change [51]. We introduce a BN layer to perform the normalization for each training mini-batch, which allows us to set a higher learning rate and be less careful about parameter initialization. To make the gradient backpropagate further in the deeper network, a shortcut is introduced after the BN layer. The activation function can introduce  
Figure 2: Pipeline of the proposed CNN architecture for TPOI extraction.  
non-linearity and enhance the feature learning ability of the network. We adopt ReLU as our activation function, which is mathematically expressed as [52]:  
\[f_{\text{ReLU}}(x)=\max(0,x). \tag{8}\]  
The CNN architecture ends with a max-pooling operation. The output of CNN is an 800-sized vector, where a value of 1 means this range bin has a TPOI and 0 means no TPOI. The detailed overall CNN architecture parameters are shown in Table 1.
# Chloride Resistance of Assembled Bridge Piers Reinforced with Epoxy-Coated Steel Bars
## 1 Introduction
The mechanical properties and durability of the interface between old and new concrete can be improved by adding expansion agents to the concrete in the backing zone. [PERSON] et al. [20] showed that appropriate compressive stress can improve the chloride resistance of concrete. Therefore, for an assembled concrete bridge in the chloride environment, the durability of the prefabricated joint should be addressed. From existing studies, the following shortcomings can be concluded: (1) Almost all of the studies used specimens containing the interface between the old and new concrete to simulate the assembled connection joint and did not restore the actual assembled joint type or the influence of the construction process; (2) The methods for introducing damage to the epoxy coating on the steel bar surface were too simplistic, mostly involving scratching, peeling, or drilling to remove a regular area, resulting in concentrated surface damage the did not accurately replicate actual damage in engineering practice; (3) Few studies have linked the corrosion of epoxy-coated steel bars with chloride ion penetration processes, thereby failing to reflect the variety of corrosion behavior with the spatial and temporal distribution of chloride ions. Therefore, to understand the corrosion behavior of epoxy-coated steel bars with surface damage in assembled concrete bridge pairs in a chloride environment, the steel bars were embedded into two typical connection joints of the assembled pier of a sea-crossing railway bridge. To simulate the frequent damage types that may exist in the construction process, the epoxy-coated steel bars were treated in three different ways: bending, scratching, and knocking. By testing the electrochemical indexes of steel bars and the chloride ion content in concrete, the critical chloride ion contents of the surface-damaged epoxy-coated steel bars were determined for both types of joints. Finally, based on the obtained parameters, the service life of the assembled concrete bridge pier joints was predicted using a numerical method. The results can provide valuable insight into the application of epoxy-coated steel bars in such prefabricated sea-crossing bridges.
## 4 Proposed Approach
### Algorithm Execution Overview  
The overall execution of our HGA-based system consists of three phases: initialization, iterative optimization, and termination. In the initialization phase, a population of random routes is generated. During optimization, the algorithm evolves the population via selection, crossover, adaptive mutation, and local search with Tabu Search. At each iteration, the LSTM model is queried to provide travel time predictions, allowing the fitness function to adapt to real-world conditions dynamically. The algorithm terminates when a fixed number of generations is reached or when the population converges.  
By combining global exploration from GAs, fine-grained local refinement from TS, and real-time adaptation from the LSTM model, our hybrid algorithm achieves significantly improved route optimization performance over traditional single-method approaches.
# Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning
## 3 Experiments and Results
### Steering and Speed Control PID Parameter Selection  
Steering and speed control play essential roles in autonomous driving, and good trajectory tracking can only be achieved if the steering and speed of the vehicle can be accurately controlled. The entire vehicle is a complex control system and obtaining the steering wheel and speed control parameters by purely theoretical analysis is incredibly difficult, so this paper used repeated practical tests to select the appropriate PID control parameters.  
Figure 9 shows the control performance of different PID parameters when the steering wheel and speed (5 km/h) are given a step signal and from the experimental curve, it can be seen that the PID parameters represented by the light blue curve are better regardless of the steering wheel or speed. Following repeated tests, a set of steering wheel PID parameters was obtained that we believe to be optimal, and three sets of speed PID parameters at 5 km/h, 10 km/h, and 15 km/h were also obtained, as can be seen in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Kp** & **Ki** & **Kd** \\ \hline Steering & & 0.0113 & 0.021 & 0.00005 \\  & 5 km/h & 0.094 & 0.038 & 0.000022 \\ Speed & 10 km/h & 0.09 & 0.04 & 0.000025 \\  & 15 km/h & 0.043 & 0.013 & 0.000065 \\ \hline \hline \end{tabular}
\end{table}
Table 1: PID parameters for steering and speed.  
Figure 8: Vehicle used in the experiments.  
Figure 9: PID control experiments of steering and speed.
# Virtual Reality or Videoconferencing for Online Learning? Evidence from Comparative Meta-Analyses
## 4 Results and Discussion
### Moderator Analysis for VC Platform-Supported Online Learning
#### 4.3.3 Instructional Design in VC Studies  
Direct instruction was used in the majority of studies (54.3%), followed by inquiry learning (17.1%), experiential learning (11.4%), collaborative learning (11.4%), and game-based learning (5.7%). The various pedagogies did not exhibit significant differences in effect size (\(Q_{B}\) = 6.510, \(p\) = 0.164). This finding suggests that the choice of pedagogy may not have a decisive impact on learning outcomes in VC-supported online learning. Surprisingly, despite the lack of significant variance, game-based learning produced the largest effect size (\(g\) = 1.270). We believe this may be attributable to the greater autonomy studentshad in the game [85], which increased student engagement and satisfaction. This finding challenges the assumption that direct instruction is the most appropriate pedagogical method and opens up new possibilities for innovative applications of the VC environment. Although there are relatively few studies on this aspect, existing research has shown its potential and value.  
Regarding learning outcome types, knowledge was the most commonly used learning outcome measure (51.4%), with only 2.9% of studies using behavior. There was no significant moderating effect found for learning outcome type (\(Q_{B}=1.480\), \(p=0.687\)). However, it is worth noting that the effect size of behavior was the greatest (\(g=0.657\)). One possible reason for this phenomenon is that learners' behaviors--such as speaking, asking questions, and participating in discussions--are more readily observable by teachers and other students on VC platforms. This emphasis may further amplify the impact of behavior on learning outcomes.
###### Abstract  
A non-proportional reduction in strength parameters is widely used in slope stability assessment, but the current asynchronous reduction in strength parameters only considers the cohesion c and internal friction angle \(\rho\), which is suitable for slope stability assessment under static loads. Under seismic loads, however, tension at the rear edge of the slope often accompanies the appearance of ground cracks. In order to consider the relationship between tensile strength, cohesion, and the internal friction angle reduction coefficient, starting with the linear softening attenuation law of soil material strength parameters, a functional relationship between cohesion and internal friction angle is obtained. Then, considering that the failure of microelements in the tensile and shear zones conforms to the tension and shear of joint failure, the relationship between tensile strength, cohesion, and the internal friction angle reduction coefficient is derived. By establishing a homogeneous slope model and comparing and analyzing the progressive instability failure modes of slopes under static and seismic conditions, the stability and potential slip surface differences of slopes under two different working conditions are explored. The research results indicate that slope instability is a gradual, cumulative failure process under both static and dynamic conditions. The instability mode of the slope under static conditions is shear failure. In contrast, under dynamic loads, the instability failure of the slope is manifested as shear failure upward at the foot of the slope and tensile failure downward at the top of the slope. The stability coefficient of slopes under earthquake conditions is reduced by 17.3% compared to that under static conditions. Under earthquake conditions, the potential sliding surface under an asynchronous reduction in strength parameters is shallower than that under static conditions and deeper than that without an asynchronous reduction in strength parameters. Overall, the research results provide a reference for slope stability analysis and support design optimization under earthquake loads.  
Keywords:asynchronous discounting; soil softening laws; stability coefficients; slip surface
The temperature data obtained on-site at the Xuefeng Lake Bridge reflects the vertical temperature distribution of the bridge's main girder, with the distribution of measurement points shown in Figure 1. This study offers predictive data for analyzing the temperature distribution of the main girder, which can then be used to assess the impact of this temperature distribution on the vertical deformation of the main girder. By considering these impacts during construction, the elevation of the main girder can be adjusted to better meet the design requirements for the bridge's alignment. The main work of this paper is as follows:  
1. We proposed a short-term bridge temperature prediction model based on the CPO-CNN-BiLSTM-Attention model, which effectively captures the long-term dependence relationship of temperature sequences;
2. We used the CPO optimization algorithm to optimize the key parameters of the model to improve the accuracy of the model;
3. We used a large amount of real temperature data from Xuefeng Lake Bridge for extensive analysis, proving the generalization ability and stability of the model. The rest of this paper is structured as follows: Section 2 discusses the processing and utilization methods of the data materials and the model. Section 3 outlines the experimental process and results, as well as the comparison of models. Finally, we present our thoughts and conclusions.
# Femtosecond-Laser-Assisted Fabrication of Radiation-Resistant Fiber Bragg Grating Sensors
## 3 Materials and Methods
### Measurements and Analysis  
To investigate the performance of the FBG sensors, the output from a super-luminescent light-emitting diode (SLED) (Inphenix; IPEVM1020, Livermore, CA, USA) was linked with Port 1 of an optical circulator that passed through Port 2 and reached the FBGs. The signals transmitted through the FBGs were examined using an optical spectrum analyzer (OSA) (Yokogawa; AQ6375, Tokyo, Japan), as illustrated in Figure 2d. In contrast, the reflected signals from the FBGs came from Port 2 to Port 3 and were investigated using an interrogator (Micron Optics; SM125, Atlanta, GA, USA). To study the radiation resistance of the FBG sensors, we placed the FBG sensors inside a Gamma chamber and irradiated them with a Co\({}^{60}\) Gamma-ray (\(\upgamma\)-ray) at a radiation dose rate of 6000 gray/hour until the accumulated radiation dose reached 10\({}^{5}\) gray (Gy) (Figure 2e). For radiation resistance measurement, we followed the TIA-455-64 standard. A thermocouple temperature sensor (TC Temp. sensor) was placed adjacent to the FBG sensors to monitor the temperature inside the Gamma chamber. The Bragg wavelength shift and the amplitude of the reflected FBG signal were continuously monitored for a wide temperature range (25 \({}^{\circ}\)C to 335 \({}^{\circ}\)C).
# Flexural Performance of Novel Nail-Cross-Laminated Timber Composite Panels
## 3 Experimental Results and Analysis
### Ultimate Bending Capacity  
FP Innovation theory system refers to the simplified theoretical analysis of conventional CLT panels of Canada's FP Innovation: mechanical connection theory and combined K-factor theory [20]. Mechanical connection theory and European standard \"Eurocode 5\" [21] were combined, thus the maximum bending stress in the CLT panels can be considered as:  
\[\sigma_{max}=\ \sigma_{global}+\ \sigma_{local} \tag{1}\]  
\(\sigma_{global}\) is the overall stress and can be calculated by Equation (2):  
\[\sigma_{global}=\frac{\gamma_{1}E_{1}a_{1}M}{(EI)_{eff}} \tag{2}\]  
Figure 13: Effect of number of nail layers on load-deflection and load-strain relationship. (**a**) Load-deflection curve; (**b**) Load-strain curve.  
\(\sigma_{local}\) is the local stress and can be calculated by Equation (3):  
\[\sigma_{local}=\frac{0.5E_{1}h_{1}M}{\left(EI\right)_{eff}} \tag{3}\]  
In Equations (2) and (3): \(\gamma\) is the connection coefficient (\(0<\gamma\leq 1\)); \(a_{1}\) is the distance between the center of the first layer and the neutral axis of the CLT panels; and \(h_{1}\) is the thickness of the first layer of slatted wood. From Equations (1) to (3), we can get:  
\[\sigma=\frac{ME_{1}}{\left(EI\right)_{eff}}\left(\gamma_{1}a_{1}+0.5h_{1} \right)_{max} \tag{4}\]  
If the material of each layer is the same, in other words, \(E_{1}=E_{2}=E_{3}=E\), then:  
\[\sigma=\frac{M}{I_{eff}}\left(\gamma_{1}a_{1}+0.5h_{1}\right)_{max} \tag{5}\]  
The following assumption has been made per the Canadian Standards CSA-O86:  
\[\sigma_{max}=\ \sigma_{global}+\ \sigma_{local}\leq\varphi f_{b} \tag{6}\]  
where, \(\varphi\) is the resistance coefficient, and \(f_{b}\) is the flexural strength of the wood.  
Then the theoretical bending moment of the CLT panels can be calculated by:  
\[M_{r}=\varphi f_{b}\frac{I_{eff}}{\left(\gamma_{1}a_{1}+0.5h_{1}\right)} \tag{7}\]  
The effective bending stiffness (\(EI\))\({}_{eff}\) of the CLT panels using the mechanical connection theory is calculated as:  
\[\left(EI\right)_{eff}=\sum_{i=1}^{n}\left(E_{i}l_{i}+r_{i}E_{i}A_{i}l_{i}a_{i} ^{2}\right) \tag{8}\]  
where, \(E_{i}\) is the modulus of elasticity of the \(i\)-th layer; \(I_{i}\) is the moment of inertia of the \(i\)-th layer; \(A_{i}\) is the area of the \(i\)-th layer; \(a_{i}\) is the distance between the center of the \(i\)-th layer and the neutral axis of the CLT panels; \(r_{i}\) is the combination coefficient of the \(i\)-th layer. According to the general combination structure theory, the size of the combination degree between the combination plates defines the combination coefficient \(r\), where (a) \(r=0\) indicates no combination; (b) \(0<r<1\) indicates partial combination; and (c) \(r=1\) represents complete binding.