esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Step Prestressing with Hybrid SMA Wires
## 1 Introduction
[19] developed band strips with ribs by using an Fe-based SMA for prestressing concrete. Fe-based SMAs are relatively cheap and have higher strength and elastic modulus than NiTi SMAs. [PERSON] et al. [20] and [PERSON] et al. [21] studied the applications of Fe-based SMAs. The highly developed SME, high stiffness, high strength, and low production cost of Fe-SMAs have prompted international research on their application in such civil engineering fields as damping, active control, and pre- or post-stressing tensioning of structures [22; 23]. [PERSON] et al. [6] proposed near-surface mounted (NSM) strengthening techniques that significantly reduce the probability of harm resulting from the corrosion of prestressing members. They aimed to estimate the applicability of Fe-SMA to NSM strengthening techniques. [PERSON] and [PERSON] [24] analyzed fatigue crack growth in steel elements reinforced with SMA and fiber-reinforced polymer composite patches. [PERSON] and [PERSON] [25] studied the compressive behavior of high-strength concrete columns confined by SMA spirals. Moreover, an attempt has been made to enhance the compressive behavior of concrete by using a SMA [26]. The pullout resistance of SMAs have been evaluated to compare the difference of the pullout resistance caused by SME and wire shape [27; 28]. In this study, we conceptualize a prestressing method that considers hybrid SMA wires. This concept facilitates multi-step application of prestressing force to reinforced concrete (RC) members. \"Hybrid\" means that two types of SMA wires are mechanically combined. If an SMA tendon has two different austenite temperatures, its SME can be controlled in steps, which helps optimize the prestressing operation. As a preliminary study, experiments are conducted in two stages to observe the multi-step behavior of the RC member after it is prestressed using hybrid SMA wires. The first set of experiments involves multi-step shape recovery tests to evaluate the ranges of austenite temperatures of the SMA wires and to confirm the multi-step shape recovery behavior of the hybrid SMA wires. The second stage involves multi-step prestressing tests to confirm whether shape recovery is feasible under multi-step prestressing. The experimental parameters are determined by combinations of the SMA wires and heating temperatures.
# LSH Models in Federated Recommendation
## 4 Comparative Analysis of Experiment
### The Choice of Sample Number  
In the context of the NCF federated recommendation framework, the choice of sample number plays a crucial role in the speed of federated learning. The experiment results showing in Table 3 involves traditional MF federated recommendation with varying sample  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**RMSE of Model**} \\ \cline{2-3}  & **No Federated Learning** & **Federated Learning** \\ \hline MF & 0.8956 & 0.9433 \\ GMF & 0.7744 & 0.8773 \\ MLP & 0.8333 & 0.9014 \\ NeuMF & 0.8011 & 0.8457 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The impact of federated learning on different models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**No Federated Learning**} & \multicolumn{2}{c}{**Federated Learning**} \\ \cline{2-5}  & **HR** & **NDCG** & **HR** & **NDCG** \\ \hline GMF & 0.4788 & 0.2827 & 0.4331 & 0.2216 \\ MLP & 0.4093 & 0.2414 & 0.4029 & 0.2408 \\ NeuMF & 0.4887 & 0.3030 & 0.4411 & 0.2529 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Three models in NCF federated recommendation.  
numbers randomly selected for each round of federated learning. As the number of samples per round increases, the duration of each round also lengthens, leading to potential delays in client model uploads in real-world scenarios. To address this, the product of the sample number per round and the number of federated learning training rounds are kept constant to maintain consistency in the total number of samples used for training.  
The experimental findings demonstrate that, with a consistent total dataset size for training, lower sample numbers per round correspond to reduced RMSE values and improved recommendation accuracy. While fewer samples enhance accuracy, they also increase communication frequency between clients and the server in practical applications. Therefore, in a federated recommendation model, striking a balance between communication costs and recommendation effectiveness is essential. Opting for a smaller sample size in a conducive communication environment can enhance recommendation accuracy.
# UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health
## 2 Methods
### Experimental Design  
The existing trial layout and plots were used to distribute the experimental treatments throughout the study area. These were used because they were clearly demarcated and accurately mapped, making design and navigation simple. Of the 35 plots five plots were randomly assigned to each of 6 treatments (Table 1), with the remaining 5 plots were excluded from the trial. All plots were rectangular and contained 35 trees in seven rows planted with a spacing of 3.5 \(\times\) 4.5 m. Trees that were dead or severely suppressed were not included in the trial. The mean number of dead, severely suppressed, or missing trees per plot was 5.79 (range = 2-11). The experimental treatment defined the size of the cluster (groups of treated trees) that were poisoned to cause physiological stress and simulate the symptoms that might be expected during a disease outbreak. The objective of using different sized clusters was to investigate the sensitivity of different remotely sensed datasets to different levels of physiological stress as might be associated with certain diseases. Study trees were treated with Metsulfuron methyl in the form of water-dispersible granules at a rate of 200 g/L. This mixture was injected through holes drilled into the tree stem. Holes were drilled 25 cm apart around the circumference of each stem and 15 mL of herbicide was deposited into each hole. In total, 13.32 L of Metsulfuron mixture was applied to the study site. Administration by injection was used as it allowed for carefully targeted application and because expert advice indicated it would invoke changes in foliar colour and retention within the duration of the study. Herbicide application was completed for all trees in a single day on the 3 December 2015 [12].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Treatment** & **Description** & **n** & **Area (m2)** \\ \hline
0 & Control, no trees poisoned & 0 & 575 \\
1 & Single tree closest to plot centre is poisoned & 1 & 18 \\
2 & Two trees closest to the plot centre poisoned & 2 & 30 \\
3 & Four trees closest to the plot centre poisoned & 4 & 77 \\
4 & Eight trees closest to the plot centre poisoned & 8 & 126 \\
5 & Sixteen trees closest to the plot centre poisoned & 16 & 279 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental treatments used in the study. The number of trees subjected to herbicide application in the treatment and the mean cross-sectional area occupied by the canopy of the treated trees is shown. This table is modified from [12].  
Figure 1: Location of the study site within New Zealand with examples of the broadband multispectral imagery from RapidEye (**a**) and the UAV MicaSense Red Edge camera (**c**) with both images showing the trial area at a 1:5000 scale. The normalised spectral response curves for the RapidEye (**b**) and the MicaSense RedEdge (**d**) sensors are also shown based on data provided by the sensor developers.
## 2 Existing Leak Detection and Localization Methods
### Methods Based on Signal Processing
#### Acoustic Correlation Analysis  
This method adopts the sound generated by pipeline leak as signal source, installs acoustic sensors along the pipeline to detect sound in leak point and uses signal processing technology for leak detection and localization [20,21]. Due to the limitations of object detection and the application environment, it is absolutely necessary to deploy many acoustic sensors along the pipeline for long distance detection. This method has the advantages of high detection and localization accuracy. Especially, this method does not need to set up complex mathematical model and could detect small leaks.
# High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5
## 3 Experiment and Dataset
### Experimental Datasets  
The dataset used in this experiment is based on the open-source Chinese Power Line Insulator Dataset (CBLID), and the dataset is expanded. The open-source Labellmg data annotation tool is used to annotate the dataset. The dataset is divided into two categories: N-insulator (normal insulator) and D-insulator (defective insulator), using YOLO label format file to save labels. The dataset is divided into a training set and a test set at a ratio of 8:2. In the network training, the original insulator image size of the input network is 1152 \(\times\) 864. The insulator data are first scaled to the standard size of 640 \(\times\) 640, and then input into the backbone network to process the image.
# Surface Defect Detection of Rolled Steel Based on Lightweight Model
###### Abstract  
A lightweight rolled steel strip surface defect detection model, YOLOv5s-GCE, is proposed to improve the efficiency and accuracy of industrialized rolled steel strip defect detection. The Ghost module is used to replace the CBS structure in a part of the original YOLOv5s model, and the Ghost bottleneck is employed to replace the bottleneck structure in C3 to minimize the model's size and make the network lightweight. The EIoU function is added to improve the accuracy of the regression of the prediction frame and accelerate its convergence. The CA (Coordinate Attention) attention method is implemented to reinforce critical feature channels and their position information, enabling the model to identify and find targets correctly. The experimental results demonstrate that the accuracy of YOLOv5s-GCE is 85.7%, which is 3.5% higher than that of the original network; the model size is 7.6 MB, which is 44.9% smaller than that of the original network; the number of model parameters and calculations are reduced by 47.1% and 48.8%, respectively; and the detection speed reached 58.8 fps. YOLOv5s-GCE meets the necessity for real-time identification of rolled steel flaws in industrial production compared to other common algorithms.  
defect detection; ghost; attention mechanism; EIoU
# Power Allocation and User Grouping for NOMA Downlink Systems
###### Abstract  
Non-orthogonal multiple access (NOMA) technology allows multiple users to use the same time-frequency resource to send signals, which can improve spectral efficiency and throughput. We study the problems of user grouping and power allocation in the downlink of a multi-carrier NOMA system. The sum rate is the optimization goal. A step-by-step optimization is adopted. Users are grouped first and then power is allocated. For user grouping, the user grouping method based on the maximum channel gain difference is improved to prevent users with similar channel gains from being grouped together. For power allocation, the deep learning power allocation algorithm is used among subcarriers. Then, the closed-form expressions of power allocation between multiplexed users are derived according to the minimum transmission rate constraint. The simulation results show that compared with the fractional transmit power allocation method and fixed power allocation method, the deep learning power allocation method improves the system sum rate by about 2.2% and 19%, respectively. The power allocation methods we propose improve the system sum rate by about 10% compared to the fractional transmit power allocation method used among subcarriers and between multiplexed users.  
deep learning; non-orthogonal multiple access; user grouping; power allocation; resource allocation +
Footnote †: journal: Applied
## 4 Surface-Immobilized Peptides on Medical Implants
### Antimicrobial Peptides Released from Titanium
Titanium surface coating using an LBL assembly involving Tet213, chitosan and hyaluronic acid demonstrated excellent and sustained long-term antimicrobial activity against _S. aureus_ and _P. gingialis_[117]. Moreover, the surface promoted the cellular attachment, accompanied by low levels of cytotoxicity and erythrocyte haemolysis. Another technology developed for the sustained release of antimicrobial from coatings for 3-4 weeks includes multi-alternating layers of polymer and lipid biodegradable coating, called the Polymer Lipid Encapsulation Matrix (PLEX), containing polylactic-co-glycolic acid, dipalmitoyl phosphatidylcholine] choline and distearoyl phosphatidylcholine] choline [167,168]. The PLEX was used in combination with OP-145 to coat intramedullary nails, which were then placed in rabbit bone, and resulted in improved clearance of _S. aureus_ infection, compared to uncoated implants, both on the implant and at the peri-implant tissue [115]. Moreover, the PLEX method has been recently used to capture two AMPs at the same time, SAAP-145 and SAAP-276, both derivatives of LL-37, with antimicrobial and anti-biofilm properties. When SAAP-145 and SAAP-276 were incorporated in a PLEX coating, a constant release was achieved for up to 30 days, after an initial burst release of >50% of the loaded peptides. In a murine model for biomaterial-associated infection, SAAP-145-PLEX and SAAP-276-PLEX coatings significantly reduced the number of culture positive implants, in an _S. aureus_ infection model [116]. In situ formation of the coating on the implant is facile for clinical applications, for example via spraying the antimicrobial matrix on the implant in the surgical room. A gelatin-based hydrogel, functionalised with catechol motifs to promote its binding efficiency on titanium and loaded with HHC-36 has been reported, and, to our knowledge, it is the only study in which the AMP coating is applied on the implant via spraying [130]. A burst release of the AMP was observed, with a cumulative release of 37% within the first 24 h after incubation and a relatively steady release for the next 20 days of incubation, resulting in a final cumulative release to roughly 90%.
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 4 Data Rate Optimization
### **P5** Optimization  
Now that the proper slices of RB have been found out, the task is to change the transmitting power accordingly to get the result.  
\[\textbf{P5:}\textbf{maximize}\ (\textbf{R}_{k})=\sum_{b\in B} \boldsymbol{\varTheta}\textbf{B}_{kb}\frac{\sum_{b\in B}a_{kb}(t)}{M}\times \log_{2}\left(1+\frac{\textbf{P}_{kb}(t)\textbf{h}}{\sigma_{k}^{2}(t)}\right) \tag{29}\]\[s.t.\sum_{b\in B}P_{kb}\leq P_{b} \tag{30}\]  
Since we assume that the data of the same service request from the same MS will all be mapped to the same type of physical channel (for example, language information from user A in remote areas can all be mapped to satellite channels), the channel matrix here can be set to be the same when the transmission power is optimized, that is, the transmission power of each resource block is the same. Thus, we change the restriction of the \(P3\) as  
\[s.t.P_{kb}=\frac{P_{b}}{N} \tag{31}\]
## 4 Discussion  
The method of impedance (or admittance) measurements is undoubtedly a promising, fast and simple method. The biggest advantage is that the analyzed milk does not require any treatment before measurement. Furthermore, the availability of portable impedance analyzers would make it easy to carry out checks in the field (for example, in bars, restaurants and refreshment points, where the consumption of milk is frequent and therefore the correctness of its conservation is important). However, there is no doubt that, in order to use this technique, there is still a lot of work to be completed. First, we pointed out that the measurements strongly depend on the temperature. Although in the laboratory it is quite simple to perform temperature control, to perform measurements on the field it would not be possible, unless making the measurement system more complex and risking going against the logic of \"portability\". Therefore, it is important to perform a characterization as the temperature varies, in order to derive a relationship that allows for the comparison of the data obtained under different measurement conditions.  
Figure 13: Comparison among not expired milk, milk expired from 2 months and milk expired from 7 months. The samples belonged to the same type and brand of milk. In (**a**) the Nyquist plots are shown, in (**b**) the Imaginary parts of the admittance, to highlight the modification of the electrical characteristics of expired milk even for frequencies lower than the relaxation frequency of the electrode polarization.  
Another point to highlight is that the measurements strongly depend on the type of electrode used, both in terms of material and geometry. It would therefore be important to standardize the electrodes to be used.  
Another point to explore is the difference between each brand. The method used seems to highlight that within each brand it is possible to distinguish between the different types of milk, both by comparing the measurement curves and by the values of the components of the equivalent circuit. However, it is important to note that the samples were tested with the same expiration period and at the same temperature. For this reason, therefore, it is important to carry out a large-scale characterization. Furthermore, the investigated method certainly seems reliable for the study of different types of milk within the same brand, while variations in the electrical parameters suggest prudence in wanting to use the method for different brands together.
## 3 Results
The highest difference in the water content between Case A and Case B was less than 4%. In addition, the impact was limited in space (the affected region was up to few cm from the bentonite-granite boundary) and were not influenced the transport of non-reactive anion Cl\({}^{-}\) (compare Figures 5a and 8a)). Figure 5: Concentration profiles at different times (1 y, 5 y, 18 y of heating) for Case A: (**a**) Cl\({}^{-}\) A; (**b**) Na\({}^{+}\). Figure 8: Concentration profiles at different times (1 y, 5 y, 18 y of heating) for Case B: (**a**) Cl\({}^{-}\); (**b**) Na\({}^{+}\). Figure 6: Impact of consideration of mineral dissolution, cation exchange and surface complexation (Case B) on mineral volume fraction and porosity: (**a**) volume fraction profile of separate minerals; (**b**) porosity profile (after 1 year, 5 years, 18 years of heating). Figure 7: Modelled profile of water content evolution in the bentonite due to hydration under nonisothermal conditions. Figure 8b presents the modelling results of the solute (Na+) transport for Case B. The Na+ concentration in porewater was directly affected by ion exchange with K+, Ca2+, and Mg2+. However, it was also indirectly affected by the mineral dissolution/precipitation because these processes led to changes in the concentration of calcium and magnesium in the porewater. Comparison of the Na+ concentration between Case A (see Figure 5b) and Case B (see Figure 5b) indicates that in Case B, a decreased concentration of Na+ in the zone near the heater could be seen after 1 or 5 y of heating, but for the longer time, the concentration near the heater was predicted to be higher. As demonstrated, the consideration of additional chemical processes taking place in the bentonite (Case B) had no significant influence on the non-reactive Cl- transport, but it could be important for other radionuclides whose chemical interactions (sorption) with porous media depend on porewater pH (for example, Cs [3], Ni [2], etc.). For pH evolution, surface protonation and mineral dissolution/precipitation reactions are relevant. The simulated pH profile across the bentonite at different time steps is presented in Figure 9.  
Based on the modelling results, the pH evolution in the bentonite was quite different between Cases A and B. In Case B, the pH increase was observed near the boundary with the granite and then equilibrated at \(\sim\)pH = 7, while the peak was not observed in Case A, and a gradual decrease in the pH was recorded. This clearly indicates that omitting and not considering some relevant chemical processes in the bentonite could affect indirectly the transport of radionuclides which sorption is pH sensitive.
# Factors Influencing the Effects of Triticale on Laying Hens' Performance: A Meta-Analysis
## 3 Results  
### Dataset  
In Figure 1, the PRISMA flow diagram outlines the steps of our search strategy. In short, 3741 citations were found across various databases (Google Scholar: 3460; PubMed: 7; Science Direct: 274). Through screening, duplicates and unsuitable papers were removed. Ultimately, six articles were selected for data extraction and meta-analysis. These articles yielded 16 experiments, as some contained multiple treatments, particularly various percentages of triticale inclusion. Each article's triticale inclusion percentages determined the experiment divisions. An experiment was constituted by a control diet linked with a specific triticale inclusion percentage. Table 1 showcases the datasets and experimental specifics of these six studies, published between 1987 and 2021. The experiments involved laying hens from various strains including Leghorn, Babcock B-380, Dekalb XL, Lohmann, and Hy-Line Brown. Triticale inclusion levels in these studies ranged from 0% to 70%.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Author (Year)** & **Hens’ Strains** & \
\begin{tabular}{l} **Hens’** \\ **Numbers** \\ \end{tabular} & **Triticale Strains** &
\begin{tabular}{l} **Triticale** \\ **Percentges (\%)** \\ \end{tabular} & **Factors of Analysis \({}^{1}\)** \\ \hline
**[PERSON] et al. (2020)** & _[PERSON]_ & 180 & _[PERSON]_ & 30; 45; 60; 75; 90 & EP, EW, EXC, FI, FCR \\
**[PERSON] et al. (2026)** & _Babcock B-380_ & 126 & _[PERSON]_ & 30; 60 & EP, EW, FI, FCR, \\
**[PERSON] (2027)** & _Dekalb XL_ & 192 & _[PERSON]_ & 30 & EW, EXC \\
**[PERSON] et al. (2028)** & _[PERSON]_ & 72 & _[PERSON]_ & 70 & EP, EXC, FI, FCR \\
**[PERSON] and [PERSON] (2015)** & _[PERSON]_ & 80 & _[PERSON]_ & 70 & EP, EW, EXC, FI \\
**[PERSON] et al. (2028)** & _[PERSON]_ & 360 & _[PERSON]_ & 5; 10; 15; 20 & EP, EW, EXC, FI, FCR \\ \hline \hline \end{tabular}  
\({}^{1}\) EP: egg production; EW: egg weight; FI: feed intake; EXC: egg yolk color.  
\end{table}
Table 1: Dataset overview and meta-analysis details.
## 4 Discussion
### Limitations and Future Work  
One of the limitations of our study is that we used retrospective data; a prospective dataset comparing our methods to standard radiological review would lend further validity to our model. The other limitation is that the sample size is very small. As the strength of deep learning methods often improves with an increase in the number of subjects, having a higher sample size would improve our model. The limited number of patients relative to the number of features utilized in deep learning methods may increase the risk of overfitting. We addressed this potential pitfall by cross-validation of all steps. Prospective validation of our signature on a larger cohort is necessary to establish further reproducibility.  
In the future, we aim to develop a deep learning-based method that can tailor the segmentation methodology depending upon the lesions of different brain regions. We also believe that a systematic analysis of the characteristics of lesions of different brain regions via an automatic image analysis framework could lead to a better understanding of the relevant underlying biology, and thus help in precisely and accurately assessing the prediction of the outcomes of lesions.