esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Transport Layer in WMSN
### Open Research Issues  
?From the above discussion, we can conclude that the existing transport protocols in sensor networks can be classified into two groups, as shown in Figure 3: Standard protocols (which include TCP, UDP, and their variants and modifications), and Non-standard or Application-specific WMSN transport protocols (which can focus on Congestion control only, Reliability only, or both Congestion control and Reliability).  
In many real-time applications, if a standard transport protocol is to be used, UDP is preferred over TCP especially when timeliness is concerned than reliability. However, because of the unique characteristics of WMSNs that we mentioned in the beginning of this section, we believe that TCP with some modifications can better handle multimedia content over WMSNs. Some work is done in this direction such as Sensor Transmission Control Protocol (STCP) [56] that proposes some modifications in the TCP header fields in order to support video transmission and differentiated service model.  
Most of the proposed application-specific transport protocols do not take into consideration the multimedia requirements in WMSN and none of them addresses its diverse concerns. This can be seen clearly in the performance evaluation conducted in [64], where it was shown that many of the proposed transport protocols cannot provide acceptable video transmission and do not support real-time communication in WMSN. Therefore, we believe that designing a transport protocol with appropriate performance metrics for both reliability and congestion control and based on the application layer source coding techniques will be a promising direction in this research area.
# An Empirical Assessment of Performance of Data Balancing Techniques in Classification Task
###### Abstract  
Many real-world classification problems such as fraud detection, intrusion detection, churn prediction, and anomaly detection suffer from the problem of imbalanced datasets. Therefore, in all such classification tasks, we need to balance the imbalanced datasets before building classifiers for prediction purposes. Several data-balancing techniques (DBT) have been discussed in the literature to address this issue. However, not much work is conducted to assess the performance of DBT. Therefore, in this research paper we empirically assess the performance of the data-preprocessing-level data-balancing techniques, namely: Under Sampling (OS), Over Sampling (OS), Hybrid Sampling (HS), Random Over Sampling Examples (ROSE), Synthetic Minority Over Sampling (SMOTE), and Clustering-Based Under Sampling (CBUS) techniques. We have used six different classifiers and twenty-five different datasets, that have varying levels of imbalance ratio (IR), to assess the performance of DBT. The experimental results indicate that DBT helps to improve the performance of the classifiers. However, no significant difference was observed in the performance of the US, OS, HS, SMOTE, and CBUS. It was also observed that performance of DBT was not consistent across varying levels of IR in the dataset and different classifiers.  
c 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
classification; machine learning; data balancing techniques; imbalanced data; performance assessment
# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
###### Abstract  
Vehicular edge networks (VENs) connect vehicles to share data and infotainment content collaboratively to improve network performance. Due to technological advancements, data growth is accelerating, making it difficult to always connect mobile devices and locations. For vehicle-to-vehicle (V2V) communication, vehicles are equipped with onboard units (OBU) and roadside units (RSU). Through back-haul, all user-uploaded data is cached in the cloud server's main database. Caching stores and delivers database data on demand. Pre-caching the data on the upcoming predicted server, closest to the user, before receiving the request will improve the system's performance. OBUs, RSUs, and base stations (BS) cache data in VENs to fulfill user requests rapidly. Pre-caching reduces data retrieval costs and times. Due to storage and computing expenses, complete data cannot be stored on a single device for vehicle caching. We reduce content delivery delays by using the cuckoo search optimization algorithm with cooperative content caching. Cooperation among end users in terms of data sharing with neighbors will positively affect delivery delays. The proposed model considers cooperative content caching based on popularity and accurate vehicle position prediction using K-means clustering. Performance is measured by caching cost, delivery cost, response time, and cache hit ratio. Regarding parameters, the new algorithm outperforms the alternative.  
content caching; content popularity; vehicular edge network; cuckoo search optimization 202313780. [[https://doi.org/10.3390/app13020780](https://doi.org/10.3390/app13020780)]([https://doi.org/10.3390/app13020780](https://doi.org/10.3390/app13020780))
###### Abstract  
Recently, the use of unmanned aerial vehicles (UAVs) and LPWANs (low-power wide-area networks) has been a good solution to the problem of data collection for environmental monitoring in remote areas without infrastructure, and there are many valuable research works in this field. UAV data collection for sensor nodes is becoming a challenge, that is, the amount of data will affect the UAV's communication time and flight status, especially in LPWAN systems. In this paper, the optimization schemes are proposed to improve the efficiency of UAV for collecting data in LoRa network monitoring systems. Firstly, an improved clustering algorithm for the LoRa network is proposed, which considers the influence of distance between the cluster heads and the UAV take-off point. Secondly, we present an improved Genetic Algorithm for path planning to reduce the UAV flight distance, which introduces the Teaching-Learning-based Optimization (TLBO) and local search optimization algorithms to improve convergence speed and the path solution. Then, a LoRa 2.4 GHz adaptive data rate strategy with a dual channel is designed based on distance and link quality, to reduce the data transmitting time between the UAV and the cluster head nodes. Finally, we carry out the simulations and experiments. The results show the performance of the proposed schemes, which means that these can improve the efficiency of UAV data collection with low cost LoRa networks in remote areas without infrastructure.  
data collection; UAV; path planning; LoRa; adaptive data rate 2022 6, 173. [[https://doi.org/10.3390/drones6070173](https://doi.org/10.3390/drones6070173)]([https://doi.org/10.3390/drones6070173](https://doi.org/10.3390/drones6070173))
## 3 Proposed Work Methodology
### Methods of Optimization  
Optimization means searching for the best global solution using an objective function with a given (or predefined) range of search space by maximizing or minimizing its value [60]. In operation research, optimization is crucial in making educated decisions and solving project challenges that occur before and during the execution of a project. The main purpose of optimization is to minimize or maximize a predesigned objective function so as to ensure the best solution or collection of possible alternatives under the conditions [61]. For such problems, several advanced algorithms exist for obtaining the optimal solutions, which are widely used in optimization processes [62]. Among these are genetic algorithms (GAs), which are particularly effective tools for addressing complex optimization problems, including those involving multiple objectives. GA was chosen due to its extensive use and proven success in solving optimization challenges across various fields. First, the genetic algorithm encodes parental solutions as chromosomes to create the first population. The population structure of the genetic algorithm in the optimization technique is shown in Figure 4.  
The main aim of the optimization technique used in this research was to enhance the balance between time and cost, presented as a multi-objective optimization problem. The approach aims to determine the optimal schedules that reduce both the total project duration and cost while promoting the effective use of available resources. The elitist non-dominated sorting genetic algorithm (NSGA-II) was utilized for multi-objective optimization and searching for optimal non-dominated solutions due to its effectiveness in handling all possible solutions and obtaining optimal solutions in each simulation run based on the Pareto concept. NSGA-II was selected from among the multi-objective evolutionary optimization algorithms (MOEAs) because it has been widely used in numerous applications and is known for its reliability [63]. The algorithm utilizes a crossover operator to exchange genes between parent chromosomes and a mutation operator to introduce genetic diversity. In the developed methodology, the partial mapped crossover (PMX) is used to maintain the order of elements during crossover, while the simple reciprocal mutation operator swaps genes randomly.  
Figure 4: Genetic algorithm population framework. Xij = activity i under performance mode j; where j \(\in M_{j}\); \(M_{j}\) = number of performance mode for activity i; n = number of activities.
## 4 Modeling and Optimization Concerns  
The machine performance index is well known to be highly affected by Acc/Dec planning. A data-driven model is presented in this section for predicting the machine performance index. The MCoE and MT were computed from the command and position feedback, and the CVib was computed from the accelerometer. The Z-score was applied to normalize the performance index, and the BPNN was applied to establish the model and used to evaluate the testing trajectory.
## 2 Methodology
### Statistical Features  
The acoustic emissions obtained from the spherical tank were time-domain measurements of the activity within the tank. These measurements consisted of voltage amplitude values taken at specific time intervals. Analyzing these measurements directly did not yield satisfactory results. Moreover, the sampling frequency of the measured signals was 1 MHz, and thus, each sample contained a huge number of data points. Therefore, quantification of the statistical features of the AE signals was needed to reduce the dimensions of the original data. Using time-domain statistical features, not only greatly reduces computational complexity, but also discriminated between the signals associated with the different structural integrity conditions in the feature space. The time-domain statistical features extracted from AE signals in this study consist of the peak (P), root mean square (RMS), kurtosis (K), crest factor (C), impulse factor (IF), shape factor (SF), skewness (S), square root mean (SRM), margin factor (MF), peak to peak (PP), kurtosis factor (KF), energy contained in the signals (E), and clearance factor (CF) values. The mathematical representation of these statistical features is given in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Features** & **Expression** & **Features** & **Expression** & **Features** & **Expression** \\ \hline Peak (P) & \(\max(|x|)\) & Root mean square (RMS) & \(RMS=\left(\frac{1}{N}\frac{N}{\sum x_{i}^{2}}\right)^{\frac{1}{2}}\) & Crest factor (Cf) & \(MF=\frac{\max(|x_{i}|)}{(\frac{1}{N}\sum x_{i}^{2})^{\frac{1}{2}}}\) \\ Impulse factor (IF) & \(IF=\frac{\max(|x_{i}|)}{\frac{1}{N}\sum x_{i}^{2}}\) & Kurtosis (K) & \(KV=\frac{1}{N}\frac{N}{\sum x_{i}^{2}}\left(\frac{x_{i}-x_{i}}{N}\right)^{4}\) & Skewness(S) & \(SV=\frac{1}{N}\sum\limits_{i=1}^{N}(\frac{x_{i}-x_{i}}{N})^{3}\) \\ Square root Me an (SRM) & \(SRM=(\frac{1}{N}\frac{N}{\sum x_{i}^{2}}|\sqrt{|x_{i}|})^{2}\) & Margin factor (MF) & \(MF=\frac{\max(|x_{i}|)}{(\frac{1}{N}\sum\limits_{i=1}^{N}\sqrt{|x_{i}|})}\) & Peak-to-peak value (PP) & \(PPV=\max(x_{i})-\min(x_{i})\) \\ Kurtosis factor (KF) & \(KF=\frac{\frac{1}{N}\sum\limits_{i=1}^{N}(\frac{x_{i}-x_{i}}{N})^{4}}{(\frac{1 }{N}\sum\limits_{i=1}^{N}x_{i}^{2})}\) & Energy (E) & \(E=\sum\limits_{i=1}^{N}x^{2}\) & Shape factor (SF) & \(SF=\frac{RMS}{\frac{1}{N}\sum\limits_{i=1}^{N}|x_{i}|}\) \\ Clearance Factor (CF) & \(\frac{\frac{P}{N}}{\sum\limits_{i=1}^{N}\sqrt{|x_{i}|}}\) & & & \\ \hline \end{tabular}
\end{table}
Table 1: Statistical time domain features (x is the acoustic emission signal).  
Figure 1: The proposed spherical tank crack-detection model. AE: acoustic emissions.
## 4 Experimental Results and Analysis
### Tendermint, Results and Analysis  
As shown in Figure 4, the transaction throughput of Tendermint is impacted significantly by the size of the transactions exemplified in the differences in the bottom graph from the 2 KB test (blue and orange lines) and the 10 KB test (green and red lines). However, the throughput (measured in bytes) is unaffected by the block and transaction size, see top graph in Figure 4. Further evident in the graphs, there is an initial spike in throughput across tests, this is due to a commonly known Tendermint performance bottleneck related to indexing transactions when using larger block sizes [44]. When compared to the Quorum tests, Tendermint resembles the Quorum State runs in that writes all data into the State. Nevertheless, we posit that Tendermint outperforms Quorum in cases where transactions are more computationally intensive, due to its smart contracts utilising GOLANG, and running on a single node rather than being executed simultaneously across all nodes on the Ethereum Virtual Machine in Quorum. When comparing the Tendermint test results with the performance claims from Table 1, it is clear that our results and those from Table 1 are widely divergent. This is explained by the transaction size used in the tests carried out by [PERSON] [5], as tests were never conducted using transactions larger than 250 bytes. To confirm that Buchman's performance claims hold, we tested our Tendermint setup using a transaction size of 32 bytes and obtained a throughout peak of over 10,000 tps. The results from the Tendermint tests can be seen in Table 4 and confirm that the level of performance delivered by Tendermint would satisfy the national B2B/B2G use-case for a country the size of Denmark of 130 to 135 tps, when compressed 2 KB transactions are used. As with Quorum, the requirement for the entire Euro-zone of 9000 to 10,000 tps is not satisfied with Tendermint.
###### Abstract  
Observing calves at birth may help to identify risk factors for, and reduce, calf loss in extensive beef systems. The objectives of this study were to: (1) evaluate two commercial satellite birth alert systems to enable the observation of newborn calves and (2) assess behavioral changes of cows around calving. Vaginal Implant Transmitters (VIT) paired with Global Navigation Satellite System (GNSS) collars were worn by 20 cows in Trial 1 and 10 cows in Trial 2 to identify birthing events. The VIT and GNSS collars contained a temperature sensor, accelerometer, and very high frequency (VHF) to communicate with a handheld tracker, and ultra-high frequency (UHF) for communication between the VIT and GNSS collar, which had two-way communication using Iridium satellites. A change (Brand 1) or drop (Brand 2) in temperature of more than 3 \({}^{\circ}\)C and inactivity triggered the VIT to communicate an expelled alert to the collar, which transmitted the birth alert information via Iridium (device ID, date, time and geolocation of the GNSS collar at expulsion). Cows and calves were tracked in the paddock following a birth alert to assess their health and status. Overall, true birth alerts occurred in only 27.6% of devices. Cows remained active on the day of calving travelling \(5.54\pm 4.11\) and \(5.00\pm 2.80\) km/day compared to \(6.45\pm 2.79\) and \(6.12\pm 2.30\) km/d on days when calving did not occur for Trial 1 and 2, respectively (mean \(\pm\) SD). Average activity of the accelerometer X- and Y-axis on calving day was reduced by 15%-20% compared to other days in Trial 1 (\(p<0.05\)) but not in Trial 2 (\(p>0.05\)). Results suggest that these two birth alert systems are not suitable for use in extensive systems and the further development of the technology is required. Cows in the current trials remained active on the day of, and after, calving, indicating that a faster, real-time alert system and communication protocol would be required to achieve the aim of finding newborn calves.  
vaginal implant tag; birth site; beef cattle; calving; remote sensing technology +
Footnote †: journal: Remote Sensing, 2020, 12, 3963; doi:10.3390/rs12233963
# Theoretical Approach to Predicting the Diffusion Radius of Fracture Grouting in Soil-Rock Mixtures
## 4 Results and Discussion
### The Fracture Diffusion Formula of Bingham Fluid in Clay  
Due to the thick slurry, low grouting pressure, and small particle size of the soil, its pores were relatively smaller, and the slurry had less groutability. The grouting process proceeded more slowly, and the grouting time was up to 12 min. The slurry flow rate was low, and the final grouting volume was 1.8 L.  
The slurry was a Bingham fluid, and the Group #10 grouting test conformed to the Bingham fluid fracture grouting model, so its grouting pressure, diffusion radius, and grouting time relationship should conform to Equation (14).  
Therefore, the theoretical diffusion radius \(R\) can be deduced from the parameter values and the relationship equation and compared with the slurry diffusion radius measured in the test to verify the correctness and applicability of the diffusion equation. After the measurement and calculation of the shear test, the calculation parameters of the #10 group of test soils are given, as shown in Table 5.  
The calculated parameters in Table 5 were obtained from the experimental mechanical properties of the soil-rock mixture in the state of natural water content (10%), and the natural water content data were obtained from the survey report of the relying project (Wangjiacheng Station to Shengjiabao Station of Chongqing Railway Line 4 Phase II Project).  
When the initial water content of the soil changes, the corresponding calculated parameter values (peak strength [39; 40]), elastic modulus [41], Poisson's ratio, etc.) will change, and the corresponding calculated parameter values can be obtained from the mechanical property tests of soil-rock mixture at different water contents. They can be calculated by substituting into Equations (14) and (15).  
The initiation fracture pressure was calculated and brought into the Bingham fluid fracture grouting diffusion formula (Equation (14)). The Squeeze Theorem was used to approach the unique real value, and the calculated result of the diffusion radius was 22.744 cm, while the test result of the grouting test concretion was 23.05 cm, with an error of 1.33%. The calculation result of the Bingham fluid fracture grouting diffusion formula was considered accurate. It is suitable for analyzing Bingham fluid fracture grouting in clay.
# Optimal Energy Integration and Off-Design Analysis of an Amine-Based Natural Gas Sweetening Unit
## 4 Results and Discussion
### Selection of ORC Fluid  
The selection of the proper working fluid is of utmost importance for maximal energy output as well as the conversion efficiency of the ORC loop. Moreover, it also impacts the size of the equipment and the working conditions such as the pressure ratio of the turbine, working fluid flow rate, evaporation pressure and temperature, and heat exchanger area. In this study, three different subcritical linear hydrocarbons were screened across the conditions investigated, and the most promising working fluid was identified for each scenario. Figure 10 depicts the behavior of ORC net energy output for the three investigated fluids at different inlet CO\({}_{2}\) content and a compression ratio of 2.66.  
In the case of linear hydrocarbons, as the carbon number increases, the results show that the averaged ORC net power between all the scenarios drops by \(\sim\)8% per carbon number. R290 (propane) was found to outperform other fluids from an energy production standpoint. However, our results also indicate that the required evaporator pressure and flow rate decrease drastically if higher carbon number fluids are used. Moreover, the expansion of propane is wet, meaning that propane droplets can appear at the final stage of expansion, which could be a source of damage if certain types of compressors are used.  
In conclusion, even if R290 leads to a slightly improves net power production, higher carbon number fluids are associated with fewer technical challenges, leading to a better trade-off between power production and working conditions. Regarding ORC fluid circula  
Figure 10: ORC net power output of the different fluids at comp. ratio = 2.66.  
Figure 9: Contribution of individual waste heat sources, Comp. ratio = 2.66, working fluid: R600a.  
tion, the average results of the three working fluids are presented in Table 4. The results indicate that R290 requires a higher flow rate and more pumping power. It is therefore the most sensitive to pump efficiency. The maximum ORC power output associated with the best working fluid suitable for each individual scenario is presented in Figure 11.
# Comprehensive Energy Demand Response Optimization Dispatch Method Based on Carbon Trading
## 4 Integrated Energy Demand Response Model
### Electric Load Demand Response  
In this paper, the load of electric power users is divided into the rigid electric load, translatable electric load, and reducible electric load, and the user is compensated according to the change in the electric load. The rigid electrical load is an unchangeable part of the electrical load and does not participate in the demand response. Translatable load can shift a portion of the load from one time period to another within a specific range, and reducible load is the load that can be directly reduced. Translatable electrical load and reducible electrical load can participate in demand response.  
The electric load demand response model is as follows:  
\[L_{e,t}^{load}=L_{e,t}^{base}+L_{e,t}^{shift}+L_{e,t}^{cut}-L_{e,t}^{2 th} \tag{11}\]  
\[L_{e,t}^{min}\leqslant L_{e,t}^{load}\leqslant L_{e,t}^{max} \tag{12}\]  
where \(L_{e,t}^{base}\), \(L_{e,t}^{shift}\), \(L_{e,t}^{cut}\), and \(L_{e,t}^{load}\) are the rigid load, the translatable load, the reducible load, and the load after the demand response of the electrical load at time \(t\), respectively. \(L_{e,t}^{2 th}\) is the power consumption of the electric heating at time \(t\). \(L_{e,t}^{min}\) and \(L_{e,t}^{max}\) are the minimum electrical load and the maximum electrical load that the power user can bear at time \(t\), respectively.  
The translatable electrical load model is as follows:  
\[0\leq L_{e,t}^{shift}\leq L_{e,t}^{shift,max} \tag{13}\]  
\[\sum_{t=1}^{T}L_{e,t}^{shift}\Delta t=0 \tag{14}\]  
where \(L_{e,t}^{shift}\) is the displacement of the electrical load at the time \(t\). \(L_{e,t}^{shift,max}\) is the upper limit of the electrical load shift in the t period. The upper limit of the shiftable load in this paper is set to 20% of the electrical load before the demand response.  
The load reduction model is as follows:  
\[0\leqslant L_{e,t}^{cut}\leqslant L_{e,t}^{cut,max} \tag{15}\]  
where \(L_{e,t}^{cut,max}\) is the maximum value that can reduce the electrical load. One temporarily sets the maximum value of the electric load that can be reduced to 10% of the electric load before the demand response.