esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Localization and High Accuracy Recognition of Tire Surface Embossed Characters Based on CNN
## 4 Character Recognition
### Improved LeNet-5 Network
#### 4.3.5 GAP Replaces the Flatten Layer  
The traditional LeNet-5 network has been classified into SoftMax with a Flatten full connection, resulting in problems such as an excessive number of full connection parameters, low training speed, and easy overfitting. To achieve the purpose of reducing parameters and reducing training duration, global average pooling is used instead of the Flatten layer. The average pooling of the full picture of each feature diagram means that each picture only gets one output, which can have much fewer network parameters.
## 4 Scenarios
### SCHC Compression  
As explained earlier, SCHC is based on using a set of rules that are defined in advance for communication. The exact content of the compression rules is left to the implementer; however, the standards provide some indications and examples of compression rules for IPv6, the UDP and DLMS. In this work, the SCHC compression of a UDP/IPv6 packet follows the example of RFC 8724 [37] with perfect header compression. DLMS header compression follows [39], while DLMS payload compression is based on the analysis of the structure of the real traffic used in the measurements. The compression of the DTLS header is rather basic and relies on the custom configuration used in [PERSON]'s implementation.
## 3 Discussion
### Hemolysis  
To measure the hemolytic activity of peptides, 2% human red blood cells were added to various dilutions of peptide reconstituted in PBS in a sterile U-bottom polypropylene 96-well plate following a protocol developed in our laboratory Briefly, defibrinated human blood was obtained from de-identified healthy donors from BioIVT (Westbury, NY, USA). An aliquot (1 mL) of blood was centrifuged at 1600 \(\times\) \(g\) for 10 min and plasma was discarded, and then 1 mL of sterile 1\(\times\) PBS (HyClone) was used to wash red blood cells at least three times. In the last wash step, the supernatant was discarded, and the pallet was resuspended with 750 \(\upmu\)L of sterile PBS. RBC suspension (2% RBC) was prepared by adding 200 \(\upmu\)L of washed RBCs to 9.8 mL of sterile 1\(\times\) PBS. A total of 50 \(\upmu\)L of 2% RBCs were added to each well (\(n\) = 3) containing diluted peptides, resulting in a final peptide concentration of 100, 10, and 1 \(\upmu\)g/mL. A total of 2% RBCs with 1\(\times\) PBS alone served as the negative control (no peptide), and 2% RBC in Triton X-100 served as the positive control. The plate was incubated for 1 h at 37 \({}^{\circ}\)C and then centrifuged at 1000 rpm for 2 min. The supernatant was transferred to a fresh 96-well plate (tissue culture-treated Falcon 353072) and read at OD540. The percentage of hemolysis was calculated based on the 100% hemolysis control (Triton X-100). The experiment was repeated twice.
# Experimental Measurements of a Joint 5G-VLC Communication for Future Vehicular Networks
## 3 5G Field Trials  
### Field-Trials Organization  
The 5G field-trial project has been organized in phases and follows the standardization process. The 3 rd Generation Partnership Project (3 GPP) has completed the first version of the 5G standard ready for deployment in 2017. It is named non-standalone 5G NR (NSA 5G NR) and it requires a fall back to long term evolution (LTE) networks for partial operation. Currently, the stand alone (SA) 5G network architecture is under definition (to be completed in June 2020).  
Project organization:  
1. Set-up phase: The ZTE Research and Innovation Lab has investigated and tested new 5G technologies.
2. Roll-out phase: Several NR base stations (BSs) have been deployed to test all network elements and basic network functions. Initially, the deployment relays on the existing LTE-Core Network (CN) following the NSA network architecture. At the end of the project the SA network architecture (with a 5G-CN) will be deployed and evaluated.
3. Service phase: Extensive field trials to validate the KPIs and to test innovative services provided on the 5G network infrastructure.  
Figure 1: VLC-5G network topology.
# Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression
## 2 Background and Related Works  
### Background  
Customer churn occurs when customers stop using a company's service or product within a certain timeframe [8], whether they switch providers or terminate the service [9]. Businesses measure churn as a percentage of customers lost relative to the total number of customers over a specific period [10]. On average, each lost customer costs a business 243 USD [11], which negatively impacts the profitability and growth of any company. This underscores the importance of minimizing churn, as it helps maintain profit margins and foster progress. It is often said that retaining an existing customer is easier and more cost-effective than acquiring a new one [12]; in fact, it is about five times more expensive to acquire new customers than to retain existing ones [9]. Customer retention is generally cheaper because existing customers already trust the brand [13]. Additionally, 86% of consumers recommend the service or product to people they know, while 66% give positive reviews online [11].  
Several factors can lead to customer churn, including poor customer service, an inadequate onboarding process, a lack of ongoing customer support, insufficient value, low-quality communications, and a lack of brand loyalty [13]. Addressing these issuescan help reduce churn rates and improve customer retention, especially since even a 5% increase in customer retention can elevate company revenue by 25-95% [11].  
Customer churn is measured using the customer churn rate [12]. To calculate it, we divide the number of customers lost during a specific period (such as a year, a month, or a quarter) by the number of customers we had at the beginning of that period. Then, multiply the result by 100 to express the churn rate as a percentage [8]. The churn rate formula is shown in the equation below [8]:  
\[\text{(Lost Customers $\div$ Total Customers at the Start of the period)}\times 100 \tag{1}\]  
Calculating the churn rate is important as it indicates the rate at which a company is losing customers or subscribers. A high churn rate can negatively impact profits [1]. Therefore, the company should aim for a churn rate as close to 0% as possible [8]. Identifying customers likely to churn is crucial in this context. This can be achieved using statistical models and machine learning algorithms that predict risk scores for each client, allowing for proactive churn detection. This process involves collecting data, including customer reviews, demographics, and purchasing patterns. Machine learning techniques like support vector machines (SVM) and logistic regression (LR) are commonly used in this context [14].
## 1 Introduction  
Regular inspection of aircraft engines is essential for safe flight operations. Yet maintenance is a major contributor to aircraft accidents and incidents [1; 2; 3; 4]. According to the Federal Aviation Authority (FAA) and the International Air Transport Association (IATA), incorrect maintenance has started the event chain of every third accident and of every fourth fatality [2; 3]. Human factors are the dominant contributor to maintenance errors and account for approximately 80% of them [2; 3; 5]. Within those, search and judgement errors are the primary issue [6; 7]. Furthermore, it is known that structural failures are the main reason for maintenance-related incidents and that those are likely to occur on the engine, e.g., feature of a blade [2]. Thus, engine inspection is arguable one of the most important maintenance, repair and overhaul (MRO) activities and is responsible for ensuring that all engine parts conform to the standard and meet all safety critical requirements. Any defect must be detected at the earliest stage to avoid propagation and any negative outcome.  
The visual inspection of engine blades, which is the area under examination here, is a highly repetitive, tedious and time-consuming task [8], performed by human operators, who are unreliable, inconsistent, subjective, prone to error and have different personal judgements based on the individual's risk appetite [9; 10; 11]. Furthermore, inspectors must balance two conflicting aspects of visual inspection, namely safety and performance [12]. On one hand, no critical defect can be missed to ensure safe flight operation, while keeping the false-positive rate as low as possible. On the other hand, inspectors do not have a great deal of time and must meet the takt time to avoid causing a bottle neck to the operation. Beside the human element, there are other factors influencing the visual inspection of engine blades, including operational, environmental, procedural and part and equipment related factors [13,14].  
Hence, there was an interest to assess whether there is a technical system equivalent to the human that might overcome those limitations of human error and improve visual inspection quality and reliability, thus contributing to flight safety. The purpose of this research was to compare the human operators' inspection abilities and performance in engine blade inspection with those of advanced technologies including image processing (IP), artificial intelligence (AI) and 3D scanning.
## 3 The Proposed Operation of the LVC Converter
In this operating condition, the simultaneous switching of signals does not coincide at any time. Figure 7c shows the PWM generator in which the duty cycle is \(D=0.6\), whilst the corresponding waveforms of this operation point are shown in Figure 7f. This example corresponds to the situation in which \(D>0.5\), and in this situation, sometimes \(s_{a}=1\) while \(s_{b}=0\), and other times we have the opposite situation, \(s_{a}=0\) while \(s_{b}=1\), and may have both signals as high (\(s_{a}=1\) while \(s_{b}=1\)), which resembles overlapping in the firing signals. We can also see that both firing signals (\(s_{a}\) and \(s_{b}\)) are not low at the same time (at any time). Despite the combinations of firing signals, the interleaved PWM does not change the duty cycle, and as we can see from Figure 6, as the voltage in the inductors only depends on their transistor's state. Similarly, the current through the capacitors only depends on their respective transistors' states. This means that the previously derived mathematical model still represents the converter behavior, which is at least the dynamic and the steady state value of the state equations, but there is a difference, which is the output voltage ripple calculation. Figure 7: PWM generator with different duty cycles: (**a**) \(D=0.4\); (**b**) \(D=0.5\); (**c**) \(D=0.6\), and important waveforms of the PWM generator with the aforementioned duty cycles (**d**) \(D=0.4\); (**e**) \(D=0.5\); and (**f**) \(D=0.6\). _Voltage Ripple at the Output Port with the Proposed Operation_  
The proposed operation has the advantage of having a lower voltage ripple at the output port of the converter compared to the traditional operation. In the traditional operation, both transistors switch on and off at the same time, causing both capacitors to charge or discharge at the same time, resulting in the voltage ripples adding up. However, the proposed operation has states in which one capacitor is being charged by its respective inductor while the other is being discharged by the output current, leading to a slower change in output voltage. This happens when one transistor is closed while the other one is open (see Figure 6). The voltage ripple at the output port can be calculated in different equivalent circuits; let us calculate it during the time when one capacitor is being charged, and the other is being discharged, which occurs for a certain amount of time depending on the duty cycle. The voltage ripple at the output port can be expressed as (20), taking into account that both capacitors are equal and both inductors have the same current (see (9) and (10)). In Equation (20), there is a component (negative) which makes the voltage decrease, while the other component (positive) makes the voltage increase, a difference which results in a kind of cancellation of the derivative of the output voltage. \[\Delta v_{outN}= \begin{cases}\frac{(1-D)T_{S}}{2}\left(\frac{I_{1}-2I_{out}}{C} \right)&if\quad D>0.5\\ \frac{DT_{S}}{2}\left(\frac{I_{1}-2I_{out}}{C}\right)&if\quad D<0.5\end{cases}  \tag{20}\]
## 2 Materials and Methods
### Accuracy Evaluation Method
#### 2.3.2 Prediction Error  
The time scale is set to improve the accuracy of satellite broadcast clock offset parameters, so as to ultimately serve users well. The clock offset parameter is calculated by the whole-network adjustment method and used for clock offset prediction. The difference between prediction result and actual clock offset is calculated to obtain the prediction error of clock offset.  
\[\Delta T_{error}(t)=\Delta T_{pred}(t)-\Delta T_{actual}(t) \tag{8}\]  
where \(\Delta T_{actual}(t)\) refers to the actual clock offset, \(\Delta T_{pred}(t)\) denotes the predicted clock offset, and \(a_{0},a_{1},a_{2}\) indicate the time difference, frequency difference and frequency drift, respectively, which are the clock offset parameters calculated through the whole-network adjustment.  
The satellite clock offset prediction is divided into short-term prediction and long-term prediction. Specifically, first-order polynomial fitting is performed by 2-h clock offset data to predict 1-h result, and statistics the RMSs of 1-h prediction error. In contrast, second-order polynomial fitting is performed by 48-h clock offset data to predict 24-h result, and statistics the RMSs of 24-h prediction error. This paper adopts the short-term prediction method for evaluation.
# Focus on Disaster Risk Reduction by ResNet-CDMV Model After Natural Disasters
###### Abstract
To investigate the correlation between the types of secondary disasters and common everyday items, in this study, we establish six secondary disaster factor datasets, namely, fire, flammable objects, explosive objects, toxic substances, trapped persons, and dangerous buildings, of which fire is the first type of secondary disaster factor, and the other five are the second type. The formation rules of disaster events after natural disasters occur are part of the analysis that uses the computer vision method. A CDMV (Class Decision making by Models Vote) decision method for improving the AP value is constructed. Based on this, the ResNet-CDMV is formed by adding the single ResNet model [12] training method, which realizes the classification and recognition of multiple objects in an image. The monitoring and early warning of disaster events are of great significance. The rest of this article is arranged as follows: Section 2 introduces the relevant research of secondary disaster monitoring and early warning. The proposed framework is explained in Section 3. The experimental results are presented in Section 4. Section 5 discusses the scientific nature of the framework proposed in this article. Finally, the research is summarized in Section 6.
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 3 Results and Discussion
### Analysis of Results  
In this study, the feasibility and accuracy of extracting maize plant height at the field scale from point cloud data were verified through a correlation analysis with the manually measured height data.
## 3 Experimental Results and Analysis
### Failure Pattern  
The shape of cracks is an essential tool in identifying the failure pattern of non-persistent joints [11]. Secondary cracks and wing cracks are the dominant categories [30]. Wing cracks, which follow a curved path and diverge from the rock bridge, are classified as tension cracks. In contrast, secondary shear cracks, which are approximately parallel to the shear direction or have a certain inclination angle, are classified as shear cracks [9]. The failure patterns can be determined from the type of cracks in the rock bridge. Tensile, mixed, and shear failure refer to rock bridge failure due to tensile crack propagation, tensile and shear crack interconnection, and shear crack propagation, respectively [14].  
Figure 6a shows the failure of non-persistent joints with different joint apertures. Specimens 30-0.5-2 and 30-1.0-2 showed mixed failure patterns, and 30-2.0-2 showed a shear failure pattern. As the joint aperture increases from 0.5 to 2.0 mm, the width of the shear failure zone decreases by 29.09% and 50.91%. Figure 6b shows the failure of non-persistent joints under different joint persistency. Specim 20-1.0-2 exhibited tensile failure, specimen 30-1.0-2 demonstrated mixed failure, and specimen 40-1-2 displayed shear failure. As the joint persistency increases from 20% to 40%, the width of the shear failure zone decreases by 5.13% and 29.27%. Figure 6c shows the failure of non-persistent joints under different normal stresses. Specimens 30-1.0-1 and 30-1.0-2 showed mixed failure patterns, while specimens 30-1.0-3 and 30-1.0-5 showed shear failure patterns. As the normal stress increases from 1 MPa to 5 MPa, the width of the shear failure zone decreases by 7.14%, 19.05%, and 66.67%.  
Figure 5: Histograms of peak normal displacement and final normal displacement: (**a**) joint aperture; (**b**) joint persistency; (**c**) normal stress.  
A comprehensive analysis of the effects of the three variables joint aperture, joint persistence, and normal stress on the shear failure pattern of non-persistent joints shows a trend: as these variables increase, the propagation of tensile cracks in the specimen is inhibited, and the failure pattern gradually changes from tensile or mixed failure to shear failure. At the same time, as these factors increase, the width of the failure zone gradually decreases, and the extent of damage becomes more concentrated on the rock bridge. The cracks of the specimen reflect the distribution of internal stress [31]. A variation in the stress distribution during shear is the cause of the variations in failure patterns. An analysis of the stress evolution process is required to further understand the shearing behavior of non-persistent joints.
## 2 Materials and Methods
### General Features of the System
#### 2.4.2 Evaluations Performed and Location of Catch Cans  
To determine irrigation uniformity and losses due to interception, measurements were taken in a corn crop (_Zea mays_, Nidera 734) cultivated under no tillage (6 October 2016) on soybean residue with a 0.52 m row spacing and a density of 4.2 plants per linear meter. Catch cans were distributed following the standards of the American National Standards Institute and American Society of Agricultural Engineers [19]. Catch cans (0.11 m in diameter and 0.22 m in height) were placed at the center of the crop interrow, 0.5 m above the ground and spaced 3 m (Figure 4a). Catch cans were radially arranged along the irrigation equipment, with the first one being at a distance of 20 m to the center of the pivot; thus, more than 99% of the irrigated area was evaluated. Between 8 and 15 catch cans were used per segment, depending on the segment length. A no-crop condition (control) was also evaluated by distributing radially an equal number of catch cans (Figure 4b). To evaluate losses due to direct evaporation, three catch cans were placed 100 m from the end of the equipment to measure the volumes evaporated during the time of application of the different depths used.  
Figure 3: Irrigation system used and segment arrangement along the system.
# Uplink Transmit Power Control for Single-Carrier Grouped FDMA with Iterative Multiuser Detection
## 2 System Model
### Transmitter and Receiver Principles for Data Plane
With MUIC, the one-tap frequency-domain weight of the minimum-mean-square error (MMSE)-FDE and the corresponding equalized symbol were derived in [6]. Specifically, the one-tap frequency-domain weight can be represented as  
\[G_{u,m}(k)=\frac{\sqrt{L_{u}P_{u,m}}\Lambda_{u,m}(k)}{\sum_{u^{\prime}\in\mathcal{ U}_{m}}L_{u^{\prime}}P_{u^{\prime},m}v_{u^{\prime},m}|\Lambda_{u^{\prime},m}(k )|^{2}+\sigma_{N}^{2}}\, \tag{7}\]  
and the frequency-domain equalized symbol is obtained as  
\[\hat{X}_{u,m}(k)=G_{u,m}^{*}(k)Z_{u,m}(k)+(\mu_{u,m}-G_{u,m}^{*}(k)\sqrt{L_{u}P _{u,m}}\Lambda_{u,m}(k))\hat{X}_{u,m}(k), \tag{8}\]  
for each \(k=1,\cdots,K\), where \(Z_{u,m}(k)=[\mathbf{Z}_{u,m}]_{k}\) and the mean \(\mu_{u,m}\) is defined as  
\[\mu_{u,m}\triangleq\frac{1}{K}\sum_{k=1}^{K}G_{u,m}^{*}(k)\sqrt{L_{u}P_{u,m}} \Lambda_{u,m}(k). \tag{9}\]  
The average variance \(\
u_{u,m}\) is obtained as  
\[\
u_{u,m}\triangleq\frac{1}{K}\sum_{n=1}^{K}\
u_{u,m}(n), \tag{10}\]  
where  
\[L_{u}=\left(\frac{c}{4\pi d_{u}f_{c}}\right)^{2}, \tag{2}\]0 \[L_{u}=\left(\frac{c}{4\pi d_{u}f_{c}}\right)^{2}, \tag{2}\]1  
where \(\bar{x}_{u,m}(n)\) is the \(n\)th time-domain mean sample from the decoder, and \(Q\) is the modulation level and \(\alpha_{q}\) is symbol alphabet. The fundamental principles of SC-GFDMA are well presented in [5; 7]. For the entire transmitter and receiver block diagrams of SC-GFDMA with a user-specific interleaver and a subcarrier mapper, readers are referred to Figure 2 in [7].
## 3 Experimental Verification
Two types of equivalent frequencies were used to yield the theoretical value of diffraction: one was the 500 Hz frequency, and the other was the varied equivalent frequencies computed in this paper. The actual value and the theoretical values are shown in Figure 5. The figure reveals that the error of diffraction calculated with the variety of noise equivalent frequencies is less than that calculated with 500 Hz frequency alone. Under the same experimental conditions, the average error using the 500 Hz frequency is approximately 2.3 dB, whereas the average error with the variety of noise equivalent frequencies is approximately 0.9 dB. With a difference of approximately 1.4 dB, the accuracy of the proposed method in calculating the noise barrier diffraction is undoubtedly more justified. Figure 4: Noise attenuation experiment with distance. \begin{table}
\begin{tabular}{c c c c} \hline
**Vehicle Type** & \(\boldsymbol{L_{1}}^{\prime}\) & \(\boldsymbol{L_{2}}^{\prime}\) & \(\boldsymbol{\Delta L}\) \\ \hline Light Car & 82.02 & 70.86 & 11.16 \\ Middle Car & 84.13 & 73.10 & 11.03 \\ Heavy Car & 84.02 & 72.89 & 11.13 \\ \hline \end{tabular}
\end{table} 
Table 3: Data collected in the noise attenuation experiment with distance (dB).