esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability-Enhanced Camera Lens Module Classification Using Semi-Supervised Regression Method
## 1 Introduction
Though no strict guideline is established for the right amount of training data; since it varies  
Figure 1: Exploded view of camera lens module. depending on the type of problem to be solved, a general rule of thumb is to have the number of data at least ten times the feature dimension [3]. However, obtaining big data is often limited due to the lack of available infrastructure, which raises demand for a method to create a similar training set and provide it with a reasonable label. In the manufacturing industry, the labeling of acquired data is often very restricted since it is only possible after the actual manufacturing of products, which often requires much resource and time. Such restriction led to the development of various methods that artificially provide labels to those data without the need for an actual experiment. Most previous studies, however, focus on the pseudo-labeling of categorical data. Semi-supervised regression (SSR), a method to pseudo-label a continuous variable has so far been limited in real-world applications due to the complexity of understanding the existing algorithms that provide a suitable regression output. In this study, a simple but effective SSR coupled with simple random sampling (SRS) for the generation of synthetic data is proposed to boost the overall regression performance of DNN. Then, camera lens module classification is performed based on the improved regression model enhancing the reliability of the classification result. To the best of our knowledge, no prior work has ever attempted a deep learning-based approach to the classification of the camera lens module. The module is classified as either'satisfactory' or 'unsatisfactory' based on the regression result that is improved using SSR. The rest of the paper is broken down as follows. Section 2 provides an overview of related work, whilst Section 3 details the proposed methodology. The experimental result is discussed in Section 4.1, and finally, the paper is concluded in Section 5.
# Hailstorm Detection by Satellite Microwave Radiometers
## 1 Introduction
The method has been validated with surface observations derived from the National Oceanic and Atmospheric Administration (NOAA) Storm Official Reports using the same kind of analysis described in [29] showing high statistical scores in hail detection. Such high skills in retrieving hail patterns suggested further experiments exploiting the MHS-like frequency channels. The applications to ATMS, GMI and the Special Sensor Microwave/Imager (SSM/I) revealed very promising results and lead the way to extending the method to all sensors presently in orbit and to future satellite microwave radiometers with operational frequencies in the 150-170 GHz range.
## 4 Results and Discussion
Moreover, for PEN, HOPG and soot particle films the area indented by an indenter of 50-100 nm radius may be considered as a semi-infinite space and so the hardness and Young's modulus values may be assumed to be correct. On the other hand, this is not possible for a single particle with a dimension in the same order of magnitude of the tip radius, or even lower [21]. Moreover, the imaging of a nano-indent performed on a single nanoparticle is quite hard to perform. It is worth underlining that nanoindentation measurements on single soot particles have been performed and reported, but for the reasons listed above they could not be taken with full confidence on an absolute scale [21]. Figure 4 reports typical force-distance curves measured on Sample #1 and on Sample #2. It is possible to observe that at a given deformation the maximum load applied to Sample #2 is higher than that of Sample #1. The adhesion forces measured for both samples are in the order of 0.2 \(\pm\) 0.01 \(\mu\)N.  
Figure 3: Semi-contact AFM image of an indent on HOPG surface (**a**); same image as in the upper panel after cropping and analysis with the software Gwyddion (**b**). Both Sample #1 and Sample #2 films showed lower hardness and Young's modulus compared to HOPG. This could be explained looking at the structure of particle films. Soot nanostructured films are composed by nanoscale grains which can be considered as the basic units of the film and they are packed together in a porous structure with a void fraction in the order of 80% [52]. So, the deformation of particle films measured by indentation is likely governed by inter-grain mechanisms, such as the motion and the slippage of particles and grains between them or on the substrate, rather than intra-grain slippage and motion along the basal plane of the graphitic nano crystallites that compose soot particles. The difference in hardness and Young's modulus measured between the two samples can be related to the different properties of the nanograins that compose the two films in terms of internal nanostructure, dimension and aggregation degree of pristine particle, as well as agglomeration of particles in the nanograins during the deposition process.
## 6 Simulation and Results  
A simulation approach has been employed, in order to justify both the detection capability of the proposed system and the robustness of the protocol against malicious or accidental submission of invalid information.
## 3 Results and Discussion
### Results of Microwave-Assisted Extraction
#### 3.3.2 Predictive ability of the models applied to MAE  
The values predicted by the models for TPC and AA are reported in Table 3. The predicted values are in agreement with the experimental results since the variation between the observed and predicted values was lower than 20%. The correlation coefficients R between the observed and predicted values were the following: 0.9808 and 0.8155 for TPC and AA of chicory extracts; 0.7734 and 0.8811 for TPC and AA of fennel extracts. According to these data, the models were able to satisfactorily describe the experimental data concerning chicory by-products.  
3.3 Final Consideration on the Influence of the Independent Variables on the Phenolic Content and Antioxidant Activity of the Extracts  
At the end of the presentation of the results, it emerges that the solute-to-water ratio was the parameter that most influenced the TPC and AA of the extracts, sometimes positively (TPC and AA of chicory submitted to UAE; TPC of fennel submitted to MAE), sometimes decreasing them (TPC of chicory submitted to MAE; AA of chicory and fennel submitted to MAE). This behaviour is due to the fact that the volume of the solvent must be sufficient to permit good hydration and swelling of the matrix without running into excessive dilution that also would affect the cost-efficiency of the operation [21]. Concerning UAE extraction, temperature, time and their interactions contributed to the increase in TPC and AA by increasing solubility and mass transfer, in agreement with the findings of [PERSON] et al. [40] and [PERSON] et al. [41].
## 1 Introduction  
### Background  
Daily life in modern society is closely linked to transportation, and many problems arise as a result. Foremost among these is traffic congestion, which is classified as recurring and non-recurring, and recurring congestion can often be more easily predicted and managed on the basis of historical data. However, infrequent congestion is typically triggered by random events, such as various types of traffic crashes. Indeed, approximately 25 to 60 percent of the total delay experienced in the United States is due to road traffic accidents [1]. Therefore, it is important to prevent such accidents or reduce the harm caused by them. These goals can be addressed by predicting potential traffic accidents. Traffic accident research focuses on two aspects, i.e., pre-and post-crash. The likelihood and risk of the accident are often predicted prior to the crash, and the impact, cause and duration of the crash are often studied post-crash [2]. Currently, the focus of traffic accident research is primarily on expressways. Due to the difficulty in obtaining traffic flow and accident data, there is a paucity of research on long tunnel traffic accidents. To conclude, based on the actual traffic flow and accident data from the Yingtian Street tunnel in Nanjing, this paper will investigate the traffic situation on the road and the duration of traffic accidents in the city's special tunnel environment and assist traffic operators in formulating reasonable traffic policies to reduce the harm caused by traffic accidents.
# FDCNet: Frontend-Backend Fusion Dilated Network Through Channel-Attention Mechanism
## 4 Experiments
### Performance on Common Datasets
#### 4.4.2 The UCF_CC_50 Dataset  
The UCF_CC_50 dataset proposed by [PERSON] et al. [2] includes 50 images with different perspective and resolutions. It is an extremely congested dataset. Due to its limited images and various crowd counts and scenes, it is an extraordinary challenging dataset training for estimation. Hence, we perform five-fold-cross validation following the standard setting in Reference [2], making the utmost of the few samples.  
Result comparisons of MAE and MSE are listed in Table 3. The errors of our model are the smallest among all the models both in MAE and MSE, which indicates our method outperforms all other  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Part A**} & \multicolumn{2}{c}{**Part B**} \\ \cline{2-5}  & **MAE** & **MSE** & **MAE** & **MSE** \\ \hline MCNN [16] & 110.2 & 173.2 & 26.4 & 41.3 \\ SwitchCNN [8] & 90.4 & 135.0 & 21.6 & 33.4 \\ SaCNN [12] & 86.8 & 139.2 & 16.2 & 25.8 \\ CSRNet [11] & 68.2 & 115.0 & 10.6 & 16.0 \\ FDCNet & 75.1 & 118.5 & 10.3 & 15.8 \\ \hline \end{tabular}
\end{table}
Table 2: The estimation errors on ShanghaiTech dataset.  
Figure 3: Some output density map of our FDCNet and the VGG-16 backbone. The first row are the original images in dataset, and the second row are the estimation density maps of the VGG-16 backbone. The third row are the estimation density maps of FDCNet. The images are selected from all datasets. They are selected from (**a**) ShanghaiTech Part A dataset; (**b**) ShanghaiTech Part B dataset; (**c**) UCF_CC_50 dataset; (**d**) UCSD dataset; (**e**) the small dataset.  
methods for crowd counting on the UCF_CC_50 dataset. Compared to CSRNet [11], the MAE was improved by 7.25%, and MSE was improved by 18.9%, which demonstrates its excellence for extremely dense crowds.
# In-Band OSNR Measurement Method for All-Optical Regenerators in Optical Domain
###### Abstract  
We propose an in-band measurement method of optical signal-to-noise ratio (OSNR) output from an all-optical regeneration system with a nonlinear power transfer function (PTF) according to the fact that there are different average gains of signal and noise. For the all-optical quadrature phase-shift keying (QPSK) regenerator as an example, the output OSNR is derived from the input OSNR and the total gain of the degraded QPSK signal. Our simulation shows that the OSNR results obtained by this method are in agreement with those calculated from the error vector magnitude (EVM) formula. The method presented here has good applicability for different data rates but is also useful for analyzing the OSNR degradation of other nonlinear devices in optical communication links.  
nonlinear optics; optical information processing; all-optical regeneration; four-wave mixing +
Footnote †: journal: Applied Physics Letters
## 5 Simulation Results and Analysis
### Validation Effectiveness of the DRFTM
#### 5.2.3 Validation of the Trust Update Mechanism  
In this context, node trustworthiness is represented by a trust value on a scale from 0 to 1, where 0 signifies a complete lack of trustworthiness and 1 represents absolute trustworthiness. Initially, during the network's inception, all nodes are assumed to possess a trust value of 1. To evaluate the effectiveness of the DRFTM in discerning between normal and malicious nodes, we monitor the evolution of trust values over time. For this experimental configuration, we introduce an initial 30% of malicious nodes to initiate a hybrid attack (Section 3.2). As depicted in Figure 6, normal nodes consistently maintain a high trust value, whereas malicious nodes undergo a swift decrease in trust, eventually stabilizing at a low trust value of 0.1. This finding highlights the DRFTM's proficiency in effectively distinguishing between normal and malicious nodes.  
Figure 5: Comparison between random forest and C4.5: (**a**) detection accuracy over time; (**b**) error rate over time.  
Figure 6: Change in trust value between normal and malicious nodes.
# Layout Model of Bike-Sharing Facilities in the Transfer-Influenced Area of a Subway Station
## 3 Results and Discussion
### Model Pareneters  
1. The unit time value. According to the relevant statistical data, in 2019, the per capita disposable income of Nanjing residents was 64,372 yuan. According to Equation (1), the unit time value of Nanjing residents is 0.51 yuan/min (40-h week).
2. Walking speed. Generally speaking, the walking speed of travelers is between 1-1.5 m/s. In this paper, the walking speed was 1.25 m/s.
3. Single ride cost. There are two common billing rules for bike-sharing: the first is the normal single payment, and the second is the monthly card payment. The normal payment for a single ride is based on the time spent on each bike. According to the latest Mobike pricing rules in November 2019, a single ride costs 1.5 yuan within 15 min, and if it exceeds 15 min, the duration fee is 0.5 yuan/15 min. The price increase trend makes it more cost-effective to pay with monthly cards. With the purchase of monthly cards, there is no limit to the number of rides users can take. Travelers who use shared bikes to transfer to rail transit use shared bikes frequently, so the monthly card payment method is cheaper. In this paper, it was assumed that all users who transferred to transportation used monthly cards to pay. As an example, with Mobike's latest monthly card of 25 yuan the cost of each riding is 0.5 yuan (22-day month, twice per day).
4. Riding speed. According to the riding report published by Mobike, the average riding speed of bike-sharing is 8 km/h.
5. Opening and locking time. Considering that travelers need to spend time opening and locking the bike in addition to actual traveling time, this paper estimates that the opening and locking time of a single trip is 2 min.
6. Bus fare. The regular bus lines currently operating in Nanjing all adopt the one-ticket system, and the fare is 2 yuan. Therefore, the value of a single bus fare is 2 yuan.
7. Bus speed. The operating speed of a bus is affected by road traffic conditions and the time it takes to pick up and drop off passengers. Bus operating speed was checked in real time through the Baidu map API interface, and the operating speed of the bus in Nanjing was found to be 20 km/h.  
Figure 5: Clustering results of alternative location point distribution.  
Bus waiting time. There are four bus lines in the affected area of the Xinglongdajie subway station, and the average waiting time for passengers is 8 min.  
In summary, the values of the parameters required by the model are shown in Table 2.
# Enhancement of Ultrasonic Guided Wave Signals Using a Split-Spectrum Processing Method
## 4 Experimental Validation
### Experiment #2: Two Saw Cut Defects  
The aim of this test was to investigate the distance limitation of SSP where a small feature (i.e., defect) is close to a dominant feature (e.g., weld). The initial investigation started with the optimum parameters utilised for a single saw cut defect to evaluate the outcome of SSP distance limitation.  
An experiment was carried out on the same eight-inch pipe that was utilised for the previous experiment. In this experiment, another saw cut defect was added to the pipe at a location that was 48 cm from the far pipe end, as shown in Figure 10. The size of the defect was incrementally increased from 1% CSA to 8% CSA. Note that another 8% CSA saw cut defect already existed 1.5 m from the far pipe end. All of the setups, the tool location, and the frequency test region were exactly the same as the previous exercise. However, only the results relating to 44 kHz were presented here because, according to the previous experiment, this frequency gave the best SNR enhancement.  
The proposed method was applied to the collected data gathered from this experiment. It was observed that the initial result obtained with these parameters was not as successful, and it was only able to identify defects down to 4% CSA. Hence, the brute force search algorithm was applied to find the optimum parameter values for this scenario, again in order to improve the performance of the defects, and the capability to find smaller defects. As a result, optimum values were discovered that gave the chance to detect defects down to 2% CSA for a second defect. The unprocessed signal, and the signal after applying SSP with the new optimum parameters are presented in Figure 11. This figure clearly demonstrates that the data after applying SSP are tidier in general, and in terms of defect detection, down to 2% CSA is noticeable. However, it can be seen that the coherent noise is hardly reduced by the new parameters, and SNR is slightly improved. Therefore, it is confirmed that there is a trade-off between detecting small features next to a dominant signal and improving the SNR. In  
Figure 10: Experimental setup for the same eight inch pipe (Figure 7) with two saw cut defects.  
Figure 9: SNR calculation–peak amplitude of the defect (S) to the root mean square (RMS) value of the noise region (N) for 44 kHz.  
addition, this result confirms the result of the synthesised analysis where it was stated that the distance limitation of SSP to identify adjacent features is around 50 cm when using a 10-cycle Hann windowed at 44 kHz.
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 5 Challenges and Open Research Topics
### Open Research Topics
#### 5.2.2 Hybrid Precoding for RIS-Assisted Networks  
Designing hybrid precoding algorithms under EMF exposure constraints presents a challenging optimization problem. Research should focus on formulating joint optimization strategies for precoding matrices and power allocation, incorporating machine learning and understanding radio propagation dynamics.
# Static Bearing Capacity Investigation of a Novel Prefabricated Light-Steel Beam-Column Connection
## 3 Experimental Results
### Effect of the Truss-Beam Type on the Proposed Beam-Column Connection  
The effect of the truss-beam type, including double-truss and single-truss beams, on the static bearing capacity of the proposed light-steel beam-column connection is investigated in this section. Figure 16a-d shows a comparison between specimens using double-truss and single-truss beams. For specimens without a sleeve tube to reinforce the column, the comparison shown in Figure 16a,c reveals that the specimen consisting of double-truss beams have a higher load-bearing capacity and a lower global deflection compared to the specimen composed of single-truss beams; however, the comparison shown in Figure 16b,d reveals that the specimens consisting of single-truss beams have a higher load-bearing capacity and a lower deflection compared to the specimens composed of double-truss beams, where the column is reinforced by a sleeve tube. For the specimens without a sleeve tube, a specimen with double-truss beams may have a 22.3-29.  
tube, a specimen with single-truss beams may have a 70.5-124% increment of the loading capacity compared to specimens using double-truss beams.
## 3 Results and Discussion
### Solubility Studies
Additionally, from Table 1 it can be immediately seen that a particular trend for the solubility of the tested oligomers seems to exist. In fact, besides THF and DMF, that offered slight solubility for some of the oligomers, strong halogenated (fluorinated or chlorinated) solvents are required to achieve at least some dissolution degree for most of the PET cyclic oligomers, with the former presenting the best results. These observations seem to be in accordance with what has already been reported by [PERSON] et al. for the solubility of the PBT cyclic oligomers [18]. However, it can also be observed that both the PET cyclic tetramer and hexamer (even-numbered cyclic oligomers) presented lower solubilization rates than their odd-numbered counterparts, even when using halogenated solvents. A specific solubility behavior of the tetramer/hexamer seems to exist, in contrast with the trimer/pentamer/heptamer cyclic oligomers. In fact, both the tetramer and hexamer were practically insoluble, except when using HFIP and the mixture TFA/CHCl\({}_{3}\) 8:1 _v/v_, very different from what has been observed for the odd-numbered oligomers. The exception seems to be the PET cyclic dimer, the smallest even-numbered cyclic oligomer: this molecule was soluble/freely soluble in almost all the tested solvents. It must be considered that the PET cyclic dimer is a relatively small molecule (M = 385 Da) and that the extent of the ring is considerably smaller than in case of tetramer (M = 769 Da) or hexamer (1153 Da). As expected, the size and the ring extension of the cyclic oligomers seems to affect their solubility. Both the tetramer and the hexamer are considered as large, almost macrocyclic molecules, when compared with typical small organic molecules. However, this may not be the only factor in place, as both the PET cyclic pentamer and heptamer, despite being even larger molecules/cycles, present slightly better solubility results than the tetramer/hexamer. The spatial configuration of the rings (symmetrical for the even-numbered oligomers, non-symmetrical for the odd-ones) may also play a role (see Figure 1). All these observations show that the number of repeating units may play an important role when studying the solubility of cyclic oligomers that can be related to structural differences. Additionally, the solubility properties of the polymer itself (PET) are very similar with both the cyclic hexamer and heptamer. Only the unusual organic solvent HFIP, along with the mixture of TFA/CHCl\({}_{3}\) 8:1 _v/v_, seemed to be sufficiently efficient in solubilizing these molecules. The latter was finally selected to be used in the preparation of solutions for NMR and HR-MS analysis.
###### Abstract  
This paper solves the leader-following consensus problem for a class of second-order multi-agent systems with input quantized by a newly proposed adaptive dynamic quantizer. The novel dynamic quantizer is an adaptive quantizer that combines the logarithmic quantizer and the uniform quantizer by introducing dynamic gain parameters to achieve quantizer adaptive adjustment. It has advantages of logarithmic, uniform, and adaptive dynamic quantizers in ensuring reducible communication expenses and acceptable quantizer errors for better system performance. On this basis, we transform the guide way climbing frame (GWCF) under ideal conditions into a second-order multi-agent system and solve the motion synchronization problem of GWCF. Finally, we illustrate our approach by numerical examples.  
second-order multi-agent systems; adaptive dynamic quantizer; consensus; motion synchronization; distributed control law