esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Tie Cable Replacement Method of Half-through Tied-Arch Bridge
## 2 Bridge Constitution  
The third Lingjiang bridge is located on the Lingjiang River in Linhai City, China. The bridge was completed and opened to traffic in 2009, and its structural form is a three-span half-through tied-arch bridge with a span arrangement of 36 m + 110 m + 36 m. The main arch-rib section adopts a truss concrete-filled steel tubular structure, with a rising height of 27.5 m and a rising span ratio of 1/4. The side arch rib adopts a reinforced concrete structure, with a rise height of 10 m and a rise span ratio of 1/7.2. The make-up steel strand tie cable is anchored at the ends of the side arches. The model of the tie cable is OVMXG15-19-B high-strength low relaxation steel strand, with a strength of 1860 MPa. Six tie cables are arranged on one side, with a total of 12 tie cables on the whole bridge. The substructure of this bridge adopts a pile-cap foundation, and the arch toes of the main arch and side arch are consolidated in the arch seat. According to the principle that the direction from the small station to the large station is positive, the left side is upstream and the right side is downstream. The layout of this type of bridge is shown in Figure 1.  
After ten years of operation, and due to the continuous friction between the tie cable and the main arch rib, three tie cables were damaged at the position where they passed through the main arch rib, among which the damage to tie cables #11 and #12 was the most serious. Through on-site inspection, it was found that three steel strands in tie cable #11 were broken, and eight steel strands were worn, as shown in Figure 2a. The PE protective sleeve of the tie cable #12 was damaged, as shown in Figure 2b.  
The damage to the tie cables seriously affects the safety of the bridge. To prevent its subsequent development into tie cable fracture, which could cause more serious structural  
Figure 1: The layout of the third Lingjiang bridge.  
Figure 2: Damage to tie cables: (**a**) Steel wire abrasion of tie cable #11 and (**b**) damage of PE protective sleeve of tie cable #12.  
damage, this study focuses on the replacement methods of the tie cable of such bridges, hoping to provide technical guidance for the subsequent tie cable replacement.
# Underestimates of Grassland Gross Primary Production in MODIS Standard Products
## 3 Results  
### Comparison of \(\mathrm{GPP_{MOD}}\) and \(\mathrm{GPP_{EC}}\)  
Figure 2 illustrates the seasonal variation of \(\mathrm{GPP_{MOD}}\) and \(\mathrm{GPP_{EC}}\) at all sites. \(\mathrm{GPP_{MOD}}\) and \(\mathrm{GPP_{EC}}\) exhibit consistency in the magnitudes and seasonal variations at most of the grassland sites. GPP values started near zero in the winter, began to increase in the spring, reached its peak during the summer, and decreased quickly after its peak (Figure 2). The \(\mathrm{GPP_{EC}}\) in most sites were underestimated by the \(\mathrm{GPP_{MOD}}\). For example, the \(\mathrm{GPP_{EC}}\) were underestimated by 58.48%, 56.24%, and 53.25% in AT-Neu, RU-Sam, and IT-Tor, respectively (Figure 2a), ((l), and (j)). The coefficients of determination (\(R^{2}\)) between \(\mathrm{GPP_{MOD}}\) and \(\mathrm{GPP_{EC}}\) varied from 0.17 (CI, 0.08-0.29) at the RU-Sam site to 0.83 (0.77-0.89) at the DE-Gri site with all being statistically significant at \(p\) < 0.05 (Figure 3) and all these analyses were tested with a 95% confidence interval (Table 2). The linear regression between \(\mathrm{GPP_{MOD}}\) and \(\mathrm{GPP_{EC}}\) showed that \(\mathrm{GPP_{MOD}}\) performed well at DE-Gri (\(R^{2}\) = 0.83 (0.77-0.89), \(RMSE\) = 1.90 (1.61-2.18) g Cm\({}^{-2}\) day\({}^{-1}\)), CZ-Bk2 (\(R^{2}\) = 0.82 (0.74-0.88), \(RMSE\) = 1.31 (1.05-1.54) g Cm\({}^{-2}\) day\({}^{-1}\)), CH-Cha (\(R^{2}\) = 0.79 (0.70-0.86), \(RMSE\) = 3.64 (3.29-4.00) g Cm\({}^{-2}\) day\({}^{-1}\)), CH-Fru (\(R^{2}\) = 0.78 (0.71-0.84), \(RMSE\) = 3.19 (2.78-3.57) g Cm\({}^{-2}\) day\({}^{-1}\)), and AT-Neu (\(R^{2}\) = 0.78 (0.72-0.83), \(RMSE\) = 4.05 (3.52-4.55) g Cm\({}^{-2}\) day\({}^{-1}\)), but it performed poor at RU-Sam (\(R^{2}\) = 0.17 (0.08-0.29), \(RMSE\)= 0.89 (0.74-1.03) g Cm\({}^{-2}\) day\({}^{-1}\)), AU-Dap (\(R^{2}\) = 0.28 (0.14-0.45), \(RMSE\) = 3.31 (2.88-3.70) g Cm\({}^{-2}\) day\({}^{-1}\)), US-SRG (\(R^{2}\) = 0.40 (0.18-0.59), \(RMSE\) = 0.68 (0.58-0.77) g Cm\({}^{-2}\) day\({}^{-1}\)), and IT-Tor (\(R^{2}\) = 0.50 (0.33-0.67), \(RMSE\) = 2.81 (2.23-3.34) g Cm\({}^{-2}\) day\({}^{-1}\)) (Figure 3). Moreover, GPP\({}_{\text{MOD}}\) had a \(R^{2}\) of 0.66 (0.63-0
## 1 Introduction
In this study, we propose a novel joint DBN (J-DBN) equalizer for the combination of two loss functions based on blind CMA and DD-LMS equalizers. The J-DBN nonlinear equalizer not only retains an excellent capability of mitigating nonlinear impairment but also reduces the computational complexity. By using our proposed J-DNN equalizer, we experimentally off-line demonstrated a 144 Gbit/s dual polarization QPSK (DP-QPSK) signal generation over a 20-km standard single-mode fiber (SSMF) and 3-m wireless 2 \(\times\) 2 multiple-input multiple-output (MIMO) link at 500 GHz with BER below 3.8 \(\times\) 10\({}^{-3}\). The results show that a J-DBN equalizer can significantly improve the receiver sensitivity performance. In addition, the computational complexity of the J-DBN equalizer can be approximately 46% lower than that of traditional equalizers with similar BER performance. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Frequency** & **Line Rate** & **Format** & **Distance** & **Pol. Mux** & **AI** & **Year** & **Ref.** \\
**(GHz)** & **(Gb/s)** & **Format** & **(m)** & **Pol. Mux** & **Algorithm** & & \\ \hline
340 & 53.5 & 16 QAM & 54.6 & Single & CNN & 2022 & [21] \\
120 & 40/55 & 16/64 QAM & 200 & Single & 2D-CNN & 2022 & [22] \\
135 & 60 & PAM-8 & 3 & Single & DNN & 2020 & [23] \\
140 & 90 & PAM-4 & 3 & Single & DNN/LSTM & 2021 & [24] \\
80 & 60 & 64 QAM & 1.2 & Single & Dual-GRU & 2022 & [25] \\
500 & 144 & QPSK & 3 & Dual & DBN & 2022 & This work \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of photonics-aided wireless transmission systems based on AI algorithms.
## 1 Introduction  
Weeds are plants capable of causing qualitative and quantitative damage in terms of the yield and commercial value of agricultural products. These plants have characteristics that can increase the damage they cause, such as germination in adverse conditions, production of very long-lived and easily dispersible seeds, high competitiveness and rapid growth [1]. To have successful and sustainable crop production, it is necessary to develop alternative control strategies against weeds.  
The chemical approach of using synthetic herbicides, often chosen for their speed and ease of use, is becoming increasingly problematic in terms of environmental pollution due to their low biodegradability and high persistence and percolation of the active compounds into soil and water [2]. Furthermore, due to the use of pesticides with similar mechanisms of action, resistance phenomena are also increasing [3]. Because of these issues, various formulations and active ingredients potentially capable of exerting an allelopathic action have been studied to be integrated with traditional weed control methods [4].  
Alleochemicals must be released from the donor plant in a variety of ways, such as leaching from leaves and stems, volatilization from plant parts, and release by decomposition of organic residues or as root exudates. The phytotoxicity of these compounds are attributed to their ability to interfere with the biological mechanisms of the target plant at a biochemical (e.g., synthesis of DNA and protein alteration), physiological (e.g., respiration alteration) or structural (e.g., cuticle and cell membrane modification) level. The global effect of these mechanisms can be translated into reduced germination, a lower growth of shoots and roots, a lower absorption of nutrients and a decrease of the photosynthetic rate [5].  
Essential oils (Eos) are among the products with allelopathic activity. When effective, their action causes rapid drying of the green parts of the plant by destroying the leaf cuticle and cell membranes [6]. EO-based herbicides have already been patented as natural and non-toxic products that can be used as a safe alternative for weed control in organic farming systems [7]. According to [PERSON] et al. [1], five commercial organic herbicides derived from EOs were available on the US market prior to 2020.  
In this work, we focus on the phytotoxic potential of EOs obtained from four Pinaceae species, such as _Abies alba_ [PERSON], _Picea abies_ (L.) [PERSON], _Pinus cembra_ [PERSON] and _Pinus mugo_ [PERSON]. To this end, _Lolium multiformum_ [PERSON] and _Sinapis alba_ L., weeds of wheat in central-southern Italy and characterized by phenomena of resistance to conventional herbicides, including glyphosate [8; 9], were used as receiver plants both in pre- and post-emergence tests. The volatile chemical profiles of the four EOs, both in vapor and liquid phase, were characterized by SPME-GC-MS and GC-MS techniques, respectively.
## 3 Experiments and Results
### Result Comparisons
the average time of the fine-tuning method to train 50 epochs is 10,106 s while that of our method is only 151 s. In general, our method demonstrates competitive performance in classification accuracy and reduces the training time in comparison to the fine-tuning methods. **Experiment 5**: Effectiveness of different architectures  
Here, we conduct an experiment on the Beijing clinic dataset to prove the effectiveness of different architectures. The dataset is divided as in experiment 4. The experiment is repeated 10 times with 50 epochs for each time. The accuracy of each epoch is recorded for calculating the mean accuracy of the 10 experiments. We build two classification models for comparison. The first model (model 1) extracts features from the bottleneck instead of middle layer and the features are sent to the CNN to extract advanced semantic features. Another model (model 2) extracts features from the \"mixed8\" layer (the output of InceptionD) but the features are used directly as input to the fully connected layer. The mean sensitivity of each model was shown in Figure 5. After 50 epochs for training, the sensitivity for model 1, model 2, and the proposed method are 95.52%, 95.13%, and 98.61%, respectively. From the result, we can conclude that: (1) Extracting the features from the middle layer is better than from the bottleneck in our case. (2) The CNN can effectively improve the classification accuracy. Figure 4: The classification sensitivity of different methods. Figure 5: The mean accuracy of different architectures.
# Prediction of Oleic Acid Content of Rapeseed Using Hyperspectral Technique
## 2 Materials and Methods
### Evaluate Indicator  
In order to evaluate the model performance, three indicators, namely the coefficient of determination(\(R^{2}\)), root mean square error (RMSE), and relative error (RE), are employed in this work and shown as follows:  
\[R^{2}=\frac{\sum_{i=1}^{n}\left(\hat{Y}_{i}-\overline{Y_{p}}\right)^{2}}{\sum _{i=1}^{n}\left(Y_{i}-\overline{Y}\right)^{2}} \tag{11}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(\hat{Y}_{i}-Y_{i}\right)^{2}} \tag{12}\]  
\[\text{RE}=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{\hat{Y}_{i}-Y_{i}}{Y_{i}} \right|\times 100\% \tag{13}\]where \(\dot{Y}_{i}\) is the observed value, \(\dot{Y}_{i}\) is the predicted value, \(\overline{Y}\) is the average observed value, \(\overline{Y_{p}}\) is the average predicted value, and \(n\) is the total number of samples. The three evaluators describe the model's interpretation ability, model error, and model relative error, respectively.
# A Two-Stage Local Path Planning Algorithm Based on Sampling and Optimization Methods
## 2 Related Work
### Multi-Path Generation and Evaluation Method  
The primary idea behind the multi-path generation and evaluation method is to generate a series of possible candidate paths in the configuration space surrounding the mobile robot. Subsequently, for each candidate path, collision risk with the environment,  
Figure 1: The framework of PSF local path planning method.  
proximity to the goal, smoothness relative to the current velocity, and other metrics are evaluated based on predefined objective or cost functions. Finally, the optimal path is selected to fulfill the local path planning task.  
Dynamic window approach (DWA) is one of the most widely used algorithms in local path planning for mobile robots. While DWA can address path planning problems in complex and dynamic environments, its performance in planning local paths is closely related to the number of candidate paths. Therefore, some scholars have proposed improvement methods. For instance, parameter adaptation through fuzzy logic [7; 8; 9] and learning methods [10; 11] has been suggested. Additionally, deep learning methods have been proposed to generate feasible trajectory sequences omnidirectionally as auxiliary candidate trajectories [12]. Semantic maps were generated by semantic simultaneous localization and mapping algorithms for robot planning under uncertainties [13]. However, approaches based on deep learning or planning with semantic maps require a lot of computing resources, resulting in problems such as high computational complexity and weak generalization ability. There have also been improvements in sampling methods to make them applicable to systems with uncertainty [14]. In the field of autonomous driving, the frenet optimal trajectory (FOT) algorithm proposed by [PERSON] et al. [15] adopts a similar approach. This algorithm samples a series of candidate terminal states and fits smooth curves connecting the current state to these candidate states to plan the vehicle's trajectory. Subsequently, an evaluation function considering factors such as safety and comfort is used to select the optimal trajectory [16].
## 6 Conclusions  
In the performed study, 41 distributions of equivalent load \(q_{av}\) (from inverse triangular, through uniform to triangular and intermediate distributions) were analysed for each case. In each analysed case, the only variable was load distribution (parameter \(\delta\)). The obtained results of the deflection curve mapping error allowed for the formulation of the following conclusions:  
* In many of the analysed cases, the best representation of the geosynthetic deflection curve (the smallest mapping error) was obtained for a distribution represented by \(\delta=0\) or close to it.
* Inverse triangular distribution usually allows to obtain a fairly accurate representation of the real deflection curve of the load transfer platform geosynthetic reinforcement of embankments on soft soils improved with columns.
* If the bearing resistance of soft soil is significant, the load distribution that allows for a better representation of the LTP reinforcement deflection in the final phase of the structure operation, depending on load ratio \(q_{av}\) and stiffness of the reinforcement, and with the same value of the subgrade reaction, may be different. In the considered case 3R for measurements after 138 days, the smallest mapping error was obtained for the distribution with \(\delta=0\), and for case 4R--for the distribution with \(\delta=0.275\).
* The analyses have shown that among the considered load distributions, it is not possible to identify one that ensures the most accurate forecasting of the LTP reinforcement behaviour in every situation.
* Large values of the mapping error obtained for measurements made during the embankment construction phases and at a small distance from its implementation in the case of sections 3R and 4R indicate that the analytical methods do not allow for a reliable prediction of geosynthetic behaviour in these phases in the case with soil characterized by a significant coefficient of resistance \(k\). When soft soil between the columns takes part in the load transfer (soils with significant value of subgrade reaction \(k\)), there is no justification for calculating the behaviour of the reinforcement in the embankment construction phases.  
B.G. conceptualization, investigation, methodology, numerical calculations, visualization, writing--original draft, writing--review and editing. M.G. conceptualization, investigation, methodology, numerical calculations, visualization, writing--original draft, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# Application of Thermoelectric Generators for Low-Temperature-Gradient Energy Harvesting
###### Abstract  
Thermoelectric generators (TEGs) convert a temperature difference into useful direct current (DC) power. TEGs are solid-state semiconductor devices that are generating a lot of interest for energy harvesting purposes in Internet of Things (IoT) applications. This paper analyzes the behavior of state-of-the-art TEGs designed for low temperature gradient operation, with special emphasis on IoT systems for health monitoring for high-voltage alternating current (AC) and DC applications. In such applications, the energy harvesting unit plays a leading role in supplying wireless sensors (WSs). An application example is also presented with the aim to monitor the health condition of devices installed in the tubular busbars found in electrical substations. Since substation busbars heat up due to the Joule effect, there is a small thermal gradient between the busbar and the ambient, so the TEG can convert this heat flow into useful DC energy to supply low-power WSs. This paper assesses the performance of different TEG devices for this application, where very low temperature gradients are expected. The results presented show that with temperature gradients as low as 5 degC it is possible to supply WSs.  
energy harvesting; power systems; solid-state thermoelectric generator; wireless sensors Article
## 10 Emerging Topics and New Trends
### Nonlinear Equalization  
Sound reproduction systems can exhibit an undesirable behavior not only due to the room acoustics, but also due to loudspeaker and amplifier systems that can produce linear and nonlinear distortions. In order to remove the nonlinear effects, in [277; 278; 279] equalizers that involve Volterra filters to model the amplifier-loudspeaker-enclosure are used before driving the output signal through the loudspeakers. In this way it is possible to equalize not only the linear behavior of the system, but also its nonlinear behavior considering adaptive procedures.
# Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory
## 3 Recognition Model
### Network Structure
#### 3.2.1 Group Convolution and Channel Shuffle  
In order to reduce the parameters, complexity, and overfitting, and further improve the recognition accuracy, we introduced the ideas of group convolution and channel shuffle derived from ShuffleNet [38], as shown in Figure 3.  
Figure 3: Schematic of group convolution and channel shuffle.  
Given that there are two channels of I/Q signals, two convolutional groups extract features for I and Q signals, respectively, in the first convolutional layer. This means that the input and output channels are divided into two groups. Then, the two sets of convolutional kernels are used to perform convolutional operations on the two input channels, respectively, after which their outputs are inputted into the next convolutional layer. The output channels of the first convolutional layer number 32, so there are 16 channels to recognize the I and Q signals, respectively. However, if the next convolutional layer continues to use group convolution, it separates the feature information of the I and Q signals, and the output channels only contain part of the information of the input channels, which subsequently affects the recognition accuracy.  
Therefore, we introduced the method of channel shuffle. In the first convolution layer, the output channels with I and Q information are combined in turn to ensure that they are arranged at intervals. In the second convolution layer, group convolution is also used. It is ensured that each group contains I and Q information, leading to 16 groups in this layer. Because the output channels number 64, each group has four output channels. After the convolution operation, the same method of channel recombination is used on the output channels, thus forming four new groups containing information about each previous group. In the third convolution layer, the number of convolution groups is set to four, and the four groups are convolved separately.
## 2 Results and Discussion
### Texture of CRMP
#### 2.3.2 The Effect of Starter Cultures  
Differences were found in the mean values of textural variables (Figure 2). The use of _S. thermophilus_ TM11 generally resulted in cheeses with the lowest hardness, cohesiveness, resilience and the highest adhesiveness, whereas DAC showed the opposite results, regardless of the storage time. However, the changes in hardness are not significant (\(p>0.05\)) among the different CRMP, regardless of the storage time within the three cheese trials.  
Figure 2: Changes in textural properties of the direct-acidified cheese (DAC) and the cultured cheeses with _S. thermophilus_ (TM11 and SP1.1) during storage.  
No significant differences in adhesiveness were observed among the different CRMP in the first day, but the cheese made with _S. thermophilus_ TM11 showed significantly increased adhesiveness in the other days (\(p<0.05\)) compared to DAC. Moreover, _S. thermophilus_ TM11 led to cheeses with lower cohesiveness and resilience than DAC, regardless of the storage time. The cheese made with _S. thermophilus_ SP1.1 did not display textural variable changes, except that resilience in days 7 to 45 showed a slightly decrease, in contrast to DAC.  
If a food has lower adhesiveness compared with cohesiveness, then the probe is likely to remain clean [46], _i.e._, the food has the ability to hold together. The ratios of adhesiveness to cohesiveness ranged in a diminishing sequence from _S. thermophilus_ TM11 (50.7-64.0, 95% confidence interval for mean) to SP1.1 (32.8-39.0) to DAC (20.6-26.6), and showed a significant difference (\(p<0.01\)) regardless of the storage time.  
Texture is one of the most important characteristics of cheese that determines identity and acceptability [44]. Lowfat cheese has a rubbery, dry and hard body and texture [15]. The use of some special cultures can improve the body and texture of the low fat cheese due to the high proteolysis levels [8], but proteolysis may lead to strong and exciting flavors which further result in low acceptability for some consumers. In the research, EPS-producing _S. thermophilus_ TM11 improved the texture properties by increasing the moisture retention as well as by producing the proper flavors in cheese.