esa-sceva/satcom-chunk-collection huggingface dataset:
# Fine Characterization Method of Concrete Internal Cracks Based on Borehole Optical Imaging
## 2 Crack Image Acquisition
### Measuring Project  
The measurement project is a highway tunnel with open excavation and concealed burial construction. After backfilling with soil and stone, a long and deep crack appeared on the bottom plate along the axis of the tunnel, as shown in Figure 4. In order to quantitatively evaluate the cracks, several drilling and coring operations were carried out at the crack location. However, the drilled concrete core cannot accurately describe the characteristics of cracks. Because some concrete cores are broken and damaged, it is impossible to determine whether the damage is caused by drilling factors or crack expansion. More importantly, it is difficult to determine the width of cracks and the extension direction inside the concrete, and it is impossible to evaluate the development of cracks with time through the concrete core.  
Therefore, in order to quantitatively characterize the morphological characteristics of internal cracks in concrete, the cracks on the borehole wall were quantitatively measured using borehole camera technology in the borehole after coring. Due to the large number of on-site boreholes, we selected nine representative boreholes for measurement. The borehole number and measurement depth are shown in Table 1. The diameter of all boreholes was 57 mm, and the diameter of the optical probe used in this paper was 51 mm. Figure 5 shows the measurement site and measurement equipment.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Borehole** & **zk15 + 025** & **zk14 + 896** & **zk14 + 931** & **zk15 + 018** & **zk14 + 994** & **zk14 + 819** & **zk14 + 858** & **zk14 + 870** & **zk14 + 903.5** \\ \hline
**Depth/m** & 1.02 & 1.70 & 1.06 & 1.85 & 1.80 & 1.78 & 1.84 & 1.50 & 1.69 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Borehole number and measurement depth.  
Figure 4: Concrete crack and concrete cores.  
After measurement, high-resolution borehole wall images of nine measurement holes were obtained, as shown in Figure 6. In the borehole wall image, structural information, such as aggregate, damage, and cracks, can be clearly observed. For the convenience of viewing, the shape of the crack is depicted with red lines in Figure 6. It should be reminded that the wide black vertical defects in the figure are not cracks, but borehole wall defects caused by the overlapping of adjacent boreholes. There are two types of cracks in the nine boreholes, the cracks that expand from the orifice (zk15 + 025, zk14 + 896, zk15 + 018, zk14 + 994, zk14 + 858, and zk14 + 870) and the cracks inside the concrete (not extending to the orifice; zk15 + 025, zk14 + 931, zk14 + 819, and zk14 + 903.5).  
Figure 5: Measurement site and measurement equipment.  
Figure 6: borehole wall images of 9 measuring boreholes.
# Polymer-Stabilized Silver (Gold)-Zinc Oxide Nanoheterodimer Structures as Antimicrobials
## 1 Introduction
These factors pose serious limitations for the wide application of such composites in biomedicine, laboratory settings, and at industrial scales especially. For practical applications as bactericidal and antibiotic drugs, the simplicity of their preparation and their ability to dissolve in water are important properties. Thus, taking into account the potential use of Ag(Au)/Zn complexes as bactericidals, a facile efficient and inexpensive route for obtaining such colloidal water-dispersed nanoheterostructures is still required. Nanoparticles of metals, their oxides, and complexes have a natural tendency to agglomerate in colloidal solutions. Surface protection of nanoparticles using various stabilizers increases their kinetic stability [72]. Polymers are most often used to stabilize nanoparticles. In particular, organic polymers can act as stabilizers of bioactive nanoparticles, preventing their aggregation by reducing their surface energy [59]. Water-soluble polymers can promote the binding of metal nanoparticles to the pathogen cell due to highly active potential interactions between repeating polymer units and multivalent surface properties of bacteria [73]. Previously, polyvinylpyrrolidone was used to obtain heterostructures, but through a two-step process [74]. This polymer was used to promote the deposition of Zn oxide onto AuNPs. Growing oxide shells on seed nanoparticles requires the control of several processes: (a) the nucleation and growth of the shell material; (b) the \"wetting\" of the shell material on the seeds; and (c) the aggregation of nanoparticles [75]. Synthesis of a ZnO-Ag heterostructure was carried out by a precipitation method with cellulose nanofibers as a stabilizer (with the formation of Ag nanoparticles on ZnO particles) [76]. Using dextran as a reducing agent and stabilizer, ZnO-Au NPs composites with petal-like morphology were synthesized [77]. In comparison with a number of natural and synthetic polymers, maleic acid (anhydride) copolymers have a complex of positive properties. Maleic acid residues contained copolymers have a regular structure; they are water soluble, amphiphilic, available, and nontoxic. Previously, such copolymers were used to stabilize silver nanoparticles [78]. It was shown that such nanocomplexes can be used as antimicrobial agents in combination with traditional antibiotics [57] or silver cations [79]. AgNPs have also been incorporated in a phenol-containing polymer [60] and into preparations containing specific lectin markers on the surface of pathogen cells [80]. The purpose of this study was to develop a convenient synthesis of silver (gold)/ZnO colloidal nanostructure which are stabilized by maleic acid copolymers, and to dope these nanostructures with zinc ions for use as antimicrobials.
# High-Throughput Online Visual Detection Method of Cracked Preserved Eggs Based on Deep Learning
## 5 Optimization and Discussion of the Detection Model
### Discussion  
A plethora of studies has been conducted on egg crack detection. At present, the detection methods of egg crack are mainly machine vision detection and acoustic detection. These two methods have their advantages and disadvantages. Acoustic detection has better sensitivity to cracks, which captures small cracks based on the difference between the acoustic signals. This method can be applied to detect small cracks that are not easy to be detected by the naked eye. However, the acquisition of acoustic signals is greatly affected by the surrounding environment, so a special acquisition device is often needed. Therefore, this method cannot achieve a suitable detection effect in a noisy environment, but in practice, the online detection equipment will produce large noise. As a result, acoustic detection is difficult to be applied to online equipment. Literatures [28,29] adopt acoustic detection methods to realize crack detection. However, in recent years, the successful application of online detection of egg cracks has become more and more urgent. The machine vision detection method has gradually become mainstream. This kind of method has fast detection speed, low requirements for the working environment, and low equipment cost, but it has poor detection effect for cracks that are difficult to be observed by human eyes. The works of [10,17] have adopted machine vision to realize the online detection of eggshell crack. The work of [17] is the closest to our study. In recent years, with the rapid development of deep learning, deep learning models have achieved great progress in image processing. Based on the learning ability, deep learning models often achieve higher accuracy than traditional image processing methods with a simpler programming process. At present, there are few studies on the application of deep learning models to the online detection of eggshell cracks. Therefore, the method proposed in this paper provides a reference for implementing online detection based on deep learning models. Compared with the work of [17], the model constructed by this method is simpler, so it has higher detection efficiency, fewer parameters, less calculation amount, and lower computing power of online detection equipment. The image acquisition equipment used in this paper also has higher efficiency and better stability, so it has more application prospects. This study also has the problem of insufficient detection ability of microcracks on the preserved egg surface that are difficult to be observed by human eyes. In the later stage, we will further increase the sample size, improve the application scope of the model, and increase the samples of microcracked preserved eggs for the next research.
## 5 Conclusions and Future Work  
The traditional filter method in underground metal detection typically receives a fixed or random initial value, resulting in unsatisfactory convergence performance. To improve the convergence performance of the filter algorithm in underground metal detection, the initial value estimation algorithm for the KF and EKF is studied in this paper. To verify the performance of the IVE-KF algorithm, a simulation platform is built. The simulation results show that, in a simulation environment where the detection area is 4 m \(\times\) 4 m and \(SNR\) is 20 dB, the accuracy of the horizontal initial value estimation can be stabilized within 0.5 cm, and the accuracy of depth initial value estimation is stable within 7 cm. The performances of the filter algorithm with a random initial value and the IVE-KF algorithm are compared in this paper. The results show that the convergence of IVE-KF is far better than the convergence of the filter algorithm with a random initial value. The performances of the LM algorithm and IVE-KF algorithm are also compared, and the results show that, when \(SNR\) is  
Figure 17: RMSE comparison of the position error between the inversion algorithm and filter algorithm.  
Figure 18: RMSE comparison of the magnetic polarizability tensor error of the inversion algorithm and filter algorithm.  
greater than 30 dB, the detection accuracy of IVE-KF is close to the LM algorithm accuracy. Our method provides a relatively accurate initial value for the filter algorithm in underground metal detection. In future work, we plan to focus on improving the accuracy of the filtering algorithm under low-\(SNR\) conditions. On the other hand, our research about underground metal detection is based on the assumption of Gaussian noise distribution. The analysis of the underground metal detection method in non-Gaussian noise is very helpful for practical application. Our next step is to improve the algorithm's performance in the non-Gaussian noise environment.  
Conceptualization, Y.W. and P.W.; Data curation, Z.W. and Z.L.; Formal analysis, P.W., N.L. and C.Z.; Funding acquisition, Y.W.; Investigation, Z.W. and P.W.; Methodology, P.W., N.L. and C.Z.; Project administration, P.W.; Resources, Y.W.; Software, Z.W. and Z.L.; Supervision, Y.W. and P.W.; Validation, Y.W. and Z.W.; Writing--original draft, Z.W.; Writing--review and editing, P.W.  
This research was funded by the China Postdoctoral Science Foundation under Grant No. 2019M650778, Fundamental Research Funds for the Central Universities under Grant No. 2302018 FRF-GF-18-016B and the National Natural Science Foundation of China under Grant No. 61971031.  
The authors would like to thank all other members of the research team for their contributions to this research.  
The authors declare no conflict of interest.
## 3 Results
### Description of Studies  
One study was a randomized controlled clinical trial [30] (Supplementary Table S1), two studies were cohort prospective studies [31; 32; 30] (Supplementary Table S1), ten were cross-sectional studies [11; 12; 23; 24; 26; 27; 28; 29; 34; 35] and two studies presented cross-sectional data on patients followed for 10 years [25] and 4 years [33] (Supplementary Table S2).  
The total sample population consisted of 1325 patients and 4005 implants. The follow-up of the included cohort prospective studies ranged from 6 months to 4 years.  
Two studies [24; 25] analyzed the presence or absence of KM, while one study analyzed KM \(\geq\) 1 and no KM [28]. Eleven studies [11; 12; 23; 26; 27; 29; 30; 32; 33; 34; 35] were stratified into two groups KM \(\geq\) 2 and KM \(<\) 2 mm, while one study included both KM \(>\) 2 and KM \(\leq\) 2 [31].  
Implant failure and complications were not presented as outcome measures.  
Only four studies analyzed patient-reported outcomes [23; 25; 32; 34].  
All the included studies reported the values of parameters related to peri-implant soft tissue health. PPD and PI (or mPI) were reported in all studies. BoP was reported in seven studies [24; 25; 27; 30; 31; 32; 34], while mBI was reported in seven studies [11; 23; 24; 26; 28; 33; 35], GI in was reported in seven studies [11; 12; 29; 30; 31; 33; 34], and mGI was reported in one study [26].  
Radiographic bone level was reported in all included studies except one [26].  
Mucosal recession was reported in six studies [25; 26; 28; 31; 33; 35].  
Smoking habits were reported in all included studies except one [24]. Smokers were excluded in three studies [12; 30; 31], and heavy smokers were excluded in other three studies [23; 32; 33].  
History of periodontal disease was reported in eight studies [23; 25; 27; 28; 30; 32; 34; 35].  
In the cross-sectional studies, time point assessment ranged from more than 6 months after loading to 15 years. The cohort prospective studies of [PERSON] et al. [31] had a follow-up of six months, while those of [PERSON] et al. [32] had a follow-up of 4 years. In the RCT study [30], the reported follow-ups were at 6, 12 and 18 months after the free gingival graft.
## 2 System Architecture  
An EDS can have tens of thousands of pole transformers ranging widely over hundreds of square kilometers. A monitoring center in a residential division of a city is a data collecting point which gathers the power quality information from scattered pole transformers deployed over the city. The distribution network for an EDS consists of three subsystems, as shown in Figure 1; a collection subsystem, a relay subsystem, and a monitoring subsystem. The collection subsystem is composed of several distribution substations (hereafter, the term'substation' is exchangeable with 'distribution substation'). Each substation is connected to several feeders. Each feeder collects the power quality data from hundreds of pole transformers and delivers them to the substation. Since pole transformers have been deployed sparsely at distances of hundred meters, a WSN using the IEEE 802.11b standard [10] is employed to construct the collection subsystem in order to reduce the deployment and management costs. The relay subsystem is responsible for delivering the data gathered by the substations to the monitoring subsystem via wired infrastructure due to the long distance between the relay subsystem and the monitoring subsystem. The monitoring center in the monitoring subsystem processes the power quality data to recognize the current status of situations and takes appropriate actions based on the assessed situation [11]. Since substations in the relay subsystem are connected to the monitoring center through a high-speed wired network, the communication between them is highly reliable. Thus the problem of data delivery in EDS is the same as the data delivery problem at the collection subsystem.
# An FPGA Design with High Memory Efficiency and Decoding Performance for 5G LDPC Decoder
## 4 Results and Discussion  
In this work, an LDPC decoder was developed in Verilog Hardware Description Language (HDL) instead of using intellectual property (IP) modules. The results of FPGA synthesized and implemented after place and route are achieved by using Xilinx Vivado 2021.2. This section additionally reports on the decoding performance of several 5G LDPC codes.
## 3 Results and Discussions  
### Robocasting of Lattice, Dense and Bi-Layer Parts  
#### 3.1.1 Paste Formulations  
Table 1 presents the constituents of paste formulations used to print the three types of scaffolds. In this table, the percentage of the dispersant is calculated in relation to the solid content, and the method and PEI values are calculated in relation to the water content of the pastes. P1 was the first formulation that led to the successful printing of the lattice parts, at 49.2 vol% solid along with 6.5 and 16% of method and PEI, respectively. However, the high organic matter content in this formulation could lead to residual pores during sintering, and thus to incomplete consolidation [60]. Therefore, in order to reduce the organic matter, P2 was prepared at the same solid loading as P1 but at reduced method and PEI amounts (they were decreased from 6.5 to 3.5 and 16 to 8%, respectively). However, as can be seen in Figure 2, the samples made from P2 showed several surface cracks once they were dried. The problem was overcome by restoring the method concentration to 6.5% (P3). It seemed that at the reduced amount of method as a viscosifying agent, the evaporation of water took place at higher speeds inducing local drying stresses. The new formulation (P3) was prepared at a higher solid loading (51 vol%) than the previous ones (49 vol%), still showing a good printability. Therefore, this P3 formulation was selected for the printing of all structures of which the obtained results are presented hereinafter.  
Although higher speeds are more desirable to lower the printing time, the printings of dense and especially bi-layer samples were more problematic. Because of the sharp corners and also different raster patterns containing circular travels, the printing speeds were decreased from 290 to 226 mm/min  
Figure 2: Stereomicroscope observation of a dried porous sample fabricated from P2 indicating drying cracks on the filaments’ surfaces.  
for the dense samples and to 129 mm/min for the bi-layer samples as indicated in Table 1. In fact, at higher printing speeds, the tool paths were less accurate, and changing direction at corners reduced their sharpness to rounded edges. However, it must be considered that the printability of any structure is a function of several factors such as paste formulation and rheology, the shape of the part, the printing speed, nozzle sizes, temperature and relative humidity of the printing chamber, etc. and a successful printing is achieved when these parameters are controlled concurrently.
# Green Communication in IoT for Enabling Next-Generation Wireless Systems
## 4 Numerical Results and Discussion
### System Evaluation
#### 4.3.1 Explainability of the Proposed Protocol  
Figure 9 shows the convergence of EFRP, and how the optimal path between the source and destination was finally determined, taking into consideration the proposed objective function and the system constraints, i.e., minimizing the total amount of energy consumption for all IoT devices that participated in the results path. The X-axis shows the number of iterations, and the Y-axis shows the value of best values of the proposed objective function in each iteration.
# Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks
## 2 Survey of Energy Optimization Approaches in WSNs
### Energy Harvesting Solutions  
The emerging trend whereby demand for energy is satisfied, is deploying energy harvesting solutions [40; 41]. The motes provide extra energy by gathering, kinetic (wind, waves, gravity, vibration), piezo, electromagnetic (radio frequencies, photovoltaic), or thermal energy (solar, temperature gradients) for an unlimited amount of time. Harvested energy from irregular and fluctuating sources such as wind power, photovoltaics, capacitive methods, etc., are low and not continuously available to meet the demand of a power system [41; 42; 43; 44]. Indeed, they are often produced away from the consumer places, where local infrastructure is less robust. In smart health applications, recovering energy brings risks and skin infections [43]. Consequently, it would be wise not to equip the network more but to implement an efficient energy management strategy allowing reliable control of the nodes.  
The contributions of [42] focus on an energy manager based on a multiple converter of energy sources. The energy storage system consists of two capacitors to provide a rapid initialization capability. To solve this issue, many researchers have proposed various methods that are classified according to two antagonistic approaches. First, some studies proposed adapting the performance to meet the requirements of the lifetime or the availability of energy. Although promising, these efforts have been examined rather roughly through a high level abstraction, by adapting the duty cycle or the sampling frequency [45; 46], except that they do not provide an independent energy-based configuration. Second, high-level power management mechanisms drive the hardware directly without any isolation, resulting in a time-consuming and hard to debug configuration. Additionally, this solution suffers from high losses due to variable workloads.  
The strong impact of weather conditions, such as changing from night to day or vice versa, limits the performance of the energy harvesting. Typically, the relay node maintains a high QoS throughout the day, but extremely low QoS at night. The problems associated to those techniques range from erroneous hypotheses to unforeseen difficulties and unpredictable environmental dynamics. In conclusion, the non-stability of these systems dependent on climate and on the environment. The importance of wake-up time interval from \(T_{EI}\) to \(T_{NEI}\) and the degradation of performance and QoS affected negatively the expansion of this type of system.
## 2 Method
### MultiSPIDeePONet Architecture
Furthermore, the network output embeds physics-informed constraints through automatic differentiation technology, with these physical constraints forming part of the loss function. Therefore, the proposed method not only achieves data-independent training but also explains the output results at the physical level. MulSPIDeepONet uses automatic differentiation to formulate an appropriate regularization mechanism for the output of the network. This guarantees that the target output function satisfies any given differential constraint. Thus, the loss function is defined as follows:  
\[L(\theta)=L_{operator}(\theta)+L_{physics}(\theta), \tag{11}\]  
where  
\[L_{physics}(\theta)=\frac{1}{NQ}\sum_{i=1}^{N}\sum_{j=1}^{Q}\Big{|}\mathcal{N }(u^{i},F_{\theta}(u^{i})(y^{i}_{u,j})(G^{i}_{u,y,j}))\Big{|}^{2}, \tag{12}\]  
and  
\[L_{operator}(\theta)=\frac{1}{NP}\sum_{i=1}^{N}\sum_{j=1}^{P}\Big{|}F_{\theta}( u^{i})(y^{i}_{u,j})(G^{i}_{u,y,j})-F(u^{i})(y^{i}_{u,j})(G^{i}_{u,y,j})\Big{|}^{2}, \tag{13}\]  
Figure 3: MulSPIDeepONet architecture. Different colors represent different parts of the network, and the asterisk (*) represents the set of trainable parameters in the network. in which \(\theta\) represents the set of all trainable weights and bias parameters in the trunk network, branch network, and transition subnetwork. The variables \(\left\{u^{i}\right\}_{i=1}^{N}\), \(\left\{y_{u,j}^{i}\right\}_{j=1}^{P}\), and \(\left\{y_{u,j}^{i}\right\}_{j=1}^{Q}\) are the same as in Equations (6) and (7). \(L_{operator}(\theta)\) is used to fit the available solution measurements, while \(L_{physics}(\theta)\) imposes the basic PDE constraints.