esa-sceva/satcom-chunk-collection huggingface dataset:
# Wet Snow Flashover Characteristics of 500-kV AC Insulator Strings with Different Arrangements
###### Abstract  
In order to study the wet snow flashover characteristics of 500-kV AC insulator strings under different arrangements, wet snow flashover tests were carried out in the large climate chamber of China Electric Power Research Institute (CEPRI). The wet snow flashover voltages were obtained by the even-rising method and the flashovers were filmed by a camera. The test results showed that the installation of an anti-icing shed of large diameter could increase the wet snow flashover voltage. The distance between the two insulators was a key parameter that influenced the discharge process and the flashover voltage. Under \(\Lambda\)-string arrangement, for common insulators, the flashover performance of iced insulators increased with the connection angle; for anti-icing insulators, the flashover performance increased at first and then decreased with the connection angle. In wet snow conditions, when the connection angle was at the commonly adopted angle of 60\({}^{\circ}\), the flashover performance of the common insulators under the V-string and \(\Lambda\)-string arrangements was almost the same. For anti-icing insulators, however, the V-string arrangement was recommended according to the tests. The results obtained in this study can provide a reference for external insulation design in wet snow conditions.  
wet snow flashover; \(\Lambda\)-string; II-string; V-string; shed type +
Footnote †: journal: Applied Sciences
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 3 Applications in Food Microbiology
### Authentication and Traceability
In conclusion, MALDI-TOF MS holds significant potential in enhancing the authentication and traceability of food products by providing rapid, reproducible, and high-resolution microbial fingerprints. When integrated into food-quality management systems, it offersa proactive approach to verifying product integrity, combating fraud, and reinforcing consumer confidence in the global food supply.
# A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network
## 4 Simulation Results and Discussion
### Results Discussion
#### 4.4.1 Number of Alive Nodes (NAN)  
One of the main benefits of hierarchical clustering is that it improves network lifetime by efficient utilization of energy. The number of alive nodes (NAN) is an important performance parameter for calculating network life time [36]. Figures 4a-d and 5a-d refer to RPGM, RWP, GM and MH datasets with two different speeds, respectively. They show the results comparison of our proposed algorithm with existing routing protocols LEACH-MAE, Mobile-LEACH and LEACHDistance-M based on number of alive nodes. In Figure 4, LEACH-MEEC using RPGM mobility model outperformed LEACH-MAE, Mobile-LEACH and LEACHDistance-M using four mobility models. Our proposed algorithm's first node die (FND) and last node die (LND) performance is better than all other algorithms. This is primarily because of communication cost of LEACH-MAE, Mobile-LEACH and LEACHDistance-M is much higher as compared to our proposed algorithm. Since LEACH-MAE, Mobile-LEACH selects CL only based on remaining energy, whereas LEACHDistance-M considers distance from base station (base station is located outside area of sensor distribution) and energy to select the CL [40]. In LEACHDistance-M, sensor nodes have high overhead, which reduces network lifetime. In Figure 5, increasing the speed of mobile sensor nodes, the proposed algorithm network lifetime is better than LEACH-MAE, Mobile-LEACH and LEACHDistance-M. The proposed algorithm introduces connectivity feature among the sensor nodes, therefore CL requires less energy consumption to communicate with member nodes, which results in maximum number of remaining alive nodes. RPGM outperforms other mobility models, which can be seen graphically in Figure 5.  
Figure 4: Number of alive nodes versus time for 100 nodes, maximum speed 1.5 m/s, (**a**) RPGM Dataset; (**b**) RWP Dataset; (**c**) GM Dataset; (**d**) MH Dataset.  
Figure 5: Number of alive nodes versus time for 100 nodes, maximum speed 7.5 m/s, (**a**) RPGM Dataset; (**b**) RWP Dataset; (**c**) GM Dataset; (**d**) MH Dataset.
## 4 Literature Review on DAS+PRS
### Pattern Classification
#### 4.2.3 Generative Model-Based Methods  
A GMM-based pattern classification algorithm was employed in [22; 23; 24; 25]. First, each class was modeled by a single-component GMM in the training stage. Then, the testing stage assigns each feature vector the class with the highest probability given the set of GMMs. The strategy in [25] added two post-processing methods to the output of the GMM-based pattern classification system, which classifies machine+activity pairs and also aimed to threat detection: (1) a majority voting decision, for which each acoustic trace is classified as the class to which more frames are assigned, and (2) a temporal and spatial analysis procedure, in which acoustic traces corresponding to activities that do not spread more than 80 s around 40 m are considered spurious and are removed from the system. In the same way, acoustic traces classified as threat in the threat detection mode of the system are grouped in a same threat in case these are separated less than 80 s in time and 40 m in position to avoid generating many threat decisions. The authors of [24] also added a post-processing method to combine the outputs of the pattern classification system from the feature vectors that spread different temporal contexts (short, medium, and long). This combination was done at the likelihood level and consists in computing a new likelihood for each original feature vector to classify this as the class with the highest likelihood. Three methods were employed to conduct the likelihood combination: (1) The sum method added the likelihoods obtained from the contextual feature vectors and normalized this sum by the number of temporal contexts (3 in this case), (2) the product method multiplied the likelihoods obtained from the contextual feature vectors and normalized the result by the number of temporal contexts (3 in this case), and (3) the maximum method assigned the original feature vector the class with the highest likelihood given the contextual feature vectors.
# Combination of LiCs and EDLCs with Batteries:
## 2 Proposed Hybrid Energy Storage System  
The descriptive block diagram presented in Figure 1 highlights the focus of this research, which is the combination of the battery, SC, and HC for use by an EV. From this figure, it can be seen that the main components of this research include; the energy storages, the multiple input converter, the control system, the electronic load, and the various sensors (voltage and current sensors) that enable the controller to make intelligent decisions about the utilization of energy.  
The system controller is the brain box of the entire system; it is responsible for processing measurements and calculating the duty ratios required by the switches in the multiple input DC-DC converter. The system controller measures the state of charge of the energy storages, measures the current consumption of the motor drive train, and the voltage at the DC bus, which is also the output of the DC-DC converter and controls the DC-DC converter output to attain the required voltage level through the duty PWM signals. The system controller also decides which of the energy storages should get charged when there is energy available for storage through regenerative braking; it does this using a set of predetermined rules.
## 3 Results and Discussion
### Burr Height  
The burr defect forms most pronounced at the exit of the hole and, therefore, it is in that area where the measurement is made.  
With the help of an optical microscope, a visual inspection of the holes has been carried out, identifying the areas where the burr defect has appeared most clearly.  
Figure 14 shows the values obtained in the three repetitions of the tests for each combination of parameters. In contrast to the roughness and diameter, the burr formation presents important variations between its repetitions, the biggest difference being found in test 8 with a standard deviation value of 58.21 \(\upmu\)m. These deviations increase notably in the tests with higher cutting speed (Vc), finding that the variations between repetitions for low values of cutting speed are very small.  
\begin{table}
\begin{tabular}{c c c} \hline
**Source** & \(f\)**-Value** & \(p\)**-Value** \\ \hline Model & 15.55 & 0.000 \\ Linear & 23.36 & 0.000 \\ Vc & 0.14 & 0.715 \\ f\({}_{\rm zt}\) & 5.51 & 0.032 \\ f\({}_{\rm za}\) & 3.80 & 0.069 \\ Strategy & 68.15 & 0.000 \\
2-Way Interactions & 5.63 & 0.008 \\ Vc * f\({}_{\rm zt}\) & 13.87 & 0.002 \\ Vc * f\({}_{\rm za}\) & 5.54 & 0.032 \\ Vc * Strategy & 2.16 & 0.161 \\ \hline \end{tabular}
\end{table}
Table 10: Variance analysis for Ra.  
Figure 13: Effects plot for diameter.  
In [14], the burr is classified in three differentiates stages. In the first place, stage I is where there are burrs heights between 0 and 0.1, stage II is where the burr heights between 0.1 and 0.3, and a last stage defines that the burr would be excessive. According to this classification, the results obtained would fit in stage I with the worst results remaining in stage II, except the case where tool broke (not represented).  
Figure 15 shows the Pareto chart of the standardized diagram of effects, where no parameter appears as significant. However, in Figure 16 there is a marked slope that indicates the trend of the term. In this way, it can be observed that there is a tendency to increase when there are greater values of cutting speed, tangential advance and up-milling strategy, although the increase in the height of the burr is related to the increase in temperature and the axial forces, there is not sufficient evidence to statistically assure their significance.  
Burr formation is a complex process that can involve many uncontrolled variables, including tool wear and temperature. This could explain the variability between repetitions and, therefore, makes it difficult to obtain a significant parameter from the tests carried out.  
Table 12 shows the analysis of variance where it is observed that none of the terms have a \(p\)-value below 0.05, indicating that there is no evidence that any term is significant. In spite of this, the terms' cutting speed and tangential advance are the closest and have a greater \(f\) value, being reflected in the slope of the effect's graphs.  
Figure 14: Measured burr height values.  
Figure 15: Pareto chart of the standardized effects for burr height.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 4 Flexible Antennas for a Sub-6 GHz 5G Band
### Flexible MIMO Antennas for Sub-6 GHz 5G Band
#### 4.3.4. DRA-Based Flexible Antenna  
In [80], a circularly polarized rectangular dielectric resonator antenna with a ceramic material is proposed for mid-band 5G applications, as shown in Figure 22a. The antenna is compact but lacks the desired flexibility, due to the low flexibility of the ceramic material. However, a conformal metal strip is used to achieve the circular polarization. The antenna has a bandwidth of 3.57-4.48 GHz which makes it suitable for a specific application, as shown in Figure 22b. The axial bandwidth of almost 28.33% is achieved, as shown in Figure 22c. A better isolation of \(-\)28 dB between the elements is achieved using an \"S\" shaped defective ground plane structure. An average gain of 6 dBic is achieved.  
Figure 22: DRA MIMO antenna for the sub-6 GHz 5G application: (**a**) Antenna prototype; (**b**) Simulated and measured S-parameter; (**c**) Axial ratio of the proposed antenna. Reprinted from ref. [80].
## 2 Literature Study
### Existing Track Quality Indices
#### 2.2.11 Overall Track Geometry Index  
The overall track geometry index OTGI considers standard deviations and mean values of the track gauge signal, which is split into a positive and negative fraction because of differing tolerances, the alignment, the longitudinal level (profile), and the twist (14)-(18). These individual indices are aggregated using line-class-depending importance coefficients \(a\), \(a^{\prime}\), \(b\), \(c\), \(d\) (13) (test section: \(a\) = 0.25, \(a^{\prime}\) = 1.0, \(b\) = 0.25, \(c\) = 0.13, \(d\) = 0.28). [18]  
\[OTGI=\frac{\frac{a}{2}*GI^{+}+\frac{a^{\prime}}{2}*GI^{-}+b*AI+c*PI+d*TI}{\frac {a+a^{\prime}}{2}+b+c+d} \tag{13}\]  
With:  
\[\text{Positive gauge index}:\ GI^{+}=\left|\overline{x}_{ Gauge}+3*SD_{Gauge}\right| \tag{14}\]  
\[\text{Negative gauge index}:\ GI^{-}=\left|\overline{x}_{ Gauge}-3*SD_{Gauge}\right| \tag{15}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **SD New Track** & \
\begin{tabular}{c} **SD Maintenance** \\ **(V\({}_{\text{max}}\) \textgreater{} 105 [kph])** \\ \end{tabular} &
\begin{tabular}{c} **SD Maintenance** \\ **(V\({}_{\text{max}}\) \textless{} 105 [kph])** \\ \end{tabular} \\ \hline Longitudinal level & 2.5 & 6.2 & 7.2 \\ Alignment & 1.5 & 3.0 & 3.0 \\ Gauge & 1.0 & 3.6 & 3.6 \\ Twist & 1.75 & 3.8 & 4.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Threshold levels for the track geometry index (SD in [mm]). [12].  
\[\text{Alignment index}:\ AI=\Big{(}\big{|}\overline{x}_{AlignmentLeft}\big{|}+3*SD_{ AlignmentLeft}+\big{|}\overline{x}_{AlignmentRight}\big{|}+3*SD_{AlignmentRight}\big{)}/2 \tag{16}\]  
\[\text{Profile index}:\ PI=\Big{(}\big{|}\overline{x}_{ProfileLeft}\big{|}+3*SD_{ ProfileLeft}+\big{|}\overline{x}_{ProfileRight}\big{|}+3*SD_{ProfileRight}\big{|} /2 \tag{17}\]  
\[\text{Twist index}:\ TI=\big{|}\overline{x}_{Twist}\big{|}+3*SD_{Twist} \tag{18}\]
## 1 Introduction
- The presence of common-mode voltage (CMV) resulting from the high-frequency switching of inverter devices [70; 71; 72]: CMV creates common-mode currents between the stator phase windings and ground, which can lead to accelerated bearing wear, reducing their service life [73]. Additionally, these currents do not contribute to electromechanical energy conversion and add to the motor's heat losses [74]. - An increase in power losses, due to the greater number of power switches when compared to two-level converters. It should be noted that each DTLI configuration has a direct influence on performance, complexity, and field of application. Table 1 summarizes the main applications, along with the advantages and disadvantages associated with each type of configuration. In order to fully exploit the potential of these structures and optimize their design, this paper proposes an in-depth state-of-the-art review of the three DTLI configurations and analyzes their impact on system quality and efficiency. To this end, this paper is organized as follows: Section 2 presents the principle of operation of the system under study; Section 3 explores in detail the three DTLI configurations and their specific features; Section 4 analyzes the effect of the DC bus voltage ratio on DTLI performance; Section 5 deals with the control principles applied to this structure; Section 6 discusses the evaluation criteria in terms of quality and efficiency; and, finally, Section 8 summarizes the conclusions of this study.
## 6 FWA Transport Network Solutions
### Lessons Learned  
In general, WDM-based systems like coarse WDM (CWDM) and dense WDM (DWDM) show encouraging features that are very attractive for transport network applications. For example, besides the presented low-latency and high-throughput, CWDM is an economical solution considering fiber resources utilization and uncooled/low-cost transceivers. Moreover, DWDM is generally renowned for the offered vaster channel counts which can be supported effectively. Based on this, an additional amount of densely deployed small cells and the related DUs can be effectively supported. Additionally, it aids in enhancing the efficiency of fiber resources. Note that WDM-based systems can be employed in combination with the PON scheme to improve the system performance further. Consequently,this solution is very suitable for the envisaged ultra-dense small cells and massive DUs deployment as explained in Section 7. It is remarkable that, for effective deployment of RAN and to meet certain network demands, particularly in the urban scenarios, the network entities should be as numerous as possible and in close proximity to the subscribers. Therefore, the DUs can be deployed on locations like street lamp poles and building walls. Consequently, WDM scheme implementation can effectively support the random nature of the remote element placement.  
In addition, as previously mentioned, there are several approaches by which the C-RAN transport network can be achieved; however, the stringent requirements imposed by the C-RAN transport network make the fiber-based scheme the extensively implemented option. Nonetheless, it should be noted that optical fiber deployment for ultra-dense networks is highly time-consuming. Besides, it could make the implementation of the C-RAN scheme ineffective and relatively inflexible. Furthermore, as aforementioned in Section 6.1, wireless transport network profers flexible and appealing solutions for effective communication between the CU and DU. This is due mainly to the proffered merits like lower cost, higher flexibility, and unchallenging deployment compared with the fixed wired solutions. Consequently, scalable and advanced optical wireless schemes with operational simplicity and ease of deployment are certainly required [47].  
Moreover, besides the discussed physical fiber-based methods, another appealing and practical optical wireless solution presented in Section 6.1 is the FSO communication system. The FSO scheme offers a range of advantages like high-capacity and low-latency that make it feasible for attending to the network demands in an economical way [164; 165; 368; 369; 389]. The possibilities for an FSO transport network and some advanced schemes that are suitable for FSO system performance improvement, while alleviating the severe system demands, are considered in [166; 167]. A number of potential 5G fronthaul schemes are illustrated in Figure 21. Also, the transport network technology classification is depicted in Figure 22.  
Figure 21: Potential 5G transport solutions: (**a**) WDM-PON, (**b**) microwave, (**c**) point-to-point, (**d**) OTN, and (**e**) Ethernet.
# Improved Performance of Li-ion Polymer Batteries Through Improved Pulse Charging Algorithm
## 1 Introduction  
In the age of ubiquitous use of lithium-ion polymer (LiPo) batteries in many battery-powered portable electronic devices [1], battery charging has become an imperative topic of research. Inappropriate and ineffective battery charging can have adverse effects on the battery and hence result in limited battery cycle life or even thermal runaway. Electronic device consumers are increasingly desiring prolonged battery usage and faster battery charge times. This desire has driven researchers to investigate energy storage devices with increased energy density, lower discharge rate, and sustained safer fast charging [2, 3]. Researchers are also exploring different charging algorithms that increase battery charge and energy efficiencies, decrease charge time, and ensure longer battery cycle life [4, 5, 6]. The energy storage device of choice for most consumer electronics are lithium-ion (Li-ion) batteries [1]. Market research project li-ion batteries to reach USD 67.7 billion by 2022 and USD 92 billion by 2024 [7, 8]. This is due to their high energy density and low self-discharge rate. However, drawbacks includestringent charging requirements and high cost. LiPo batteries use gel-like polymer electrolytes that aids in a more flexible form factor and lighter weight [9]. The difference between LiPo batteries and Li-ion batteries is the electrolyte. In the regular Li-ion batteries, liquid electrolytes based on lithium salts in organic solvents are used. However, Li-ion polymer batteries use a high-conductivity polymeric semisolid to support the electrolyte and improve ion movement compared to Li-ion batteries, which use a liquid electrolyte [9]. The polymeric semisolid allows the battery to be formed in different form factors with the additional advantages of improved safety and good mechanical strength. LiPo batteries are therefore used in many portable electronic devices because of these favorable characteristics.  
These batteries have stringent charging algorithms and such algorithms can go a long way to increase battery cycle life, decrease charging time, and help improve charge and energy efficiencies [10]. One of the most popular and acknowledged gold standard charging algorithms for LiPo batteries is the constant current-constant voltage (CC-CV) charging algorithm. This algorithm is also considered the benchmark against which all other proposed charging methods are compared.
# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 3 Background
### Self Optimization Network in HetNets
#### 3.3.2 Mobility Robustness Optimization  
Inefficient HCP settings in traditional schemes become even more challenging in HetNets and dynamic scenarios when networks become dense and conditions change dynamically and often. These suboptimal settings result in more RLF and a higher number of HOs. MRO, also known as handover optimization, is a Self-ptimization function that improves the condition of handovers in the network to enable continual mobility for UEs. It focuses on minimizing RLFs, unnecessary handovers, ping-pongs, and call dropouts resulting from poor handover parameter settings. The serving and target cells' RSRPs are continually monitored, and handovers are initiated based on the triggering point defined by the handover parameters. There are several mobility characteristics such as HOM, TTT, CIO, and so on, and a lot of the research has focused on optimizing a triggering point specified by HOM and TTT.  
To maximize the HOM and TTT for both MRO and LBO, a variety of algorithms have previously been suggested and implemented. The research community is very interested in MRO. The multiple HO optimization strategies used for various radio access technologies reported in the literature illustrate this. To improve HO performance, all of these studies [34; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83] employed various combinations of HCPs and optimization techniques. And the algorithms' performance has been mainly evaluated using KPIs such as HOF, HOR, HOPP, UHO, CDR, RLF, CBR, IT, and throughput. Table 3 lists the various key performance indicators and the articles in which they were used for performance evaluation.