esa-sceva/satcom-chunk-collection huggingface dataset:
The optimization problem of the FLP is known to be NP-hard, making it challenging to determine the optimality of the solutions obtained from these algorithms. Thus, there is a need for alternative methods that can reasonably approximate the solutions obtained from various sources, thereby reducing the computational resources and time required for problem-solving. In addition, we aim to obtain approximate solutions for the Pareto front, where gaps exist in the solution space explored by genetic algorithms. To achieve this, various predictive models were employed. The calculation of the Pareto front through genetic algorithms is a computationally intensive task, which can result in the intractability of combinatorial optimization problems. This is due to the need to search through all possible combinations to find the solution with the minimum value. However, genetic algorithmsmay often become trapped in a local optimum, which may also be dominated by a previous solution on the Pareto front. The obtained experimental results demonstrate that reasonable approximations of the Pareto front generated by genetic algorithms can be obtained. However, despite being sophisticated solutions, they may not cover all points in some cases. To address this limitation, it is necessary to provide decision makers with tools or methods that present a complete set of solutions for the Pareto front for a given dataset of interest. To achieve this, we complemented the incomplete solutions generated by genetic algorithms by utilizing machine learning principles, specifically by applying various predictive models. The results indicate that reasonable solutions can be obtained, but the precision of the approximations depends on the performance of the predictive model used. This study utilized cubic splines, exponential regressions, K-nearest neighbors (KNN), and multiple imputations by chained equations (MICE) as machine learning principles. This paper makes the following contributions:  
1. A video game through which the general public can learn about a multi-objective optimization problem, namely the FLP. While playing, people learn about multi-objective optimization. 2. The use of crowd computing as a means to collect solutions to complex problems. These crowdsourced solutions complement the coverage of popular methods, such as genetic algorithms. 3. Fast and efficient machine learning principles based on predictive models complementing the genetic algorithm, even for large instance problems. Decision makers who require immediate solutions can benefit from these approximations. The organization of this work is structured as follows: Section 2 provides the necessary background information relevant to the problem being addressed, as well as the datasets utilized in the experiments. Section 3 discusses the relevant previous work. Section 4 outlines the implementation of the proposed video game. Section 5 presents the use of machine-learning-based predictive models. The analysis of the results is presented in Section 6. Finally, the conclusion and recommendations for future research are included in Section 7.
###### Abstract  
Power converters in the smart grid systems are essential to link renewable energy sources with all grid appliances and equipment. However, this raises the possibility of electromagnetic interference (EMI) between the smart grid elements. Hence, spread spectrum (SS) modulation techniques have been used to mitigate the EMI peaks generated from the power converters. Consequently, the performance of the nearby communication systems is affected under the presence of EMI, which is not covered in many situations. In this paper, the behavior of the G3 Power Line Communication (PLC) channel is evaluated in terms of the Shannon-Hartley equation in the presence of SS-modulated EMI from a buck converter. The SS-modulation technique used is the Random Carrier Frequency Modulation with Constant Duty cycle (RCFMFD). Moreover, The analysis is validated by experimental results obtained with a test setup reproducing the parasitic coupling between the PLC system and the power converter.  
power line communication; spread spectrum modulation; electromagnetic interference; Shannon-Hartley; power converters +
Footnote †: journal:
# Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor
## 2 Context-Based Lossless Compression for Sensor Signals
### Digital Signals and Sequence Predictor  
A digital signal \(\{s_{i}\}\) of length \(L\) sampled by an \(R_{a}\)-bit ADC is stored as a sequence of bits \(\{b_{j}\}\) in hardware:  
\[\underbrace{b_{1}^{s_{1}},\cdots,b_{R_{a}}^{s_{1}}}_{s_{1}},\underbrace{b_{1}^{ s_{2}},\cdots,b_{R_{a}}^{s_{2}}}_{s_{2}},\cdots,\underbrace{b_{1}^{s_{L}}, \cdots,b_{R_{a}}^{s_{L}}}_{s_{L}}, \tag{1}\]  
where \(R_{a}\) is the \(ADC\)_resolution_, and \(b_{j}^{s_{i}}\in\{0,1\}(1\leq i\leq L,1\leq j\leq R_{a})\) denotes the \(j\)-th bit in the \(i\)-th sampled value. In this paper, we assume that the order of bits in each sampled value is ascending, i.e., \(b_{1}^{s_{i}}\) and \(b_{R_{a}}^{s_{i}}\) represent the lowest- and highest-order bits in \(s_{i}\), respectively.  
On the other hand, a sequence predictor \(\mathcal{Q}\) predicts future values based on previously observed values by modeling relationships between data points in a data series \(\{x_{t}\}\). At time step \(t\), \(\mathcal{Q}\) regards the value to be predicted as a random variable \(\xi_{t}\), and gives a probability distribution over all its possible values \(\{v_{1},\cdots,v_{N}\}\):  
\[\Pr(\xi_{t}=v_{i}|x_{1},\cdots,x_{t-1}),\quad i=1,\cdots,N, \tag{2}\]  
where \(x_{1},\cdots,x_{t-1}\) are previous observations. Given a digital signal \(\{s_{i}\}\), a sequence predictor usually predicts \(R_{p}\) bits at a time by re-grouping the bit sequence in Equation (1) to \(\{x_{t}\}\). In this way, each \(x_{t}\) has a total of \(2^{R_{p}}\) possible values, i.e., in Equation (2), \(N=2^{R_{p}}\). In the following discussion, we refer to \(R_{p}\) as the _predictor resolution_.  
In this paper, we use _bits per character_ (BPC, [21]) to measure the performance of a sequence predictor. Note that for digital-signal predictors, the BPC on a re-grouped sequence \(\{x_{t}\}\) of length \(T\) is calculated as  
\[-\frac{8}{TR_{p}}\sum_{t=1}^{T}\log_{2}\Pr(\xi_{t}=x_{t}|x_{1},\cdots,x_{t-1}). \tag{3}\]
## 2 Review of Literature
### Logistics Process Reliability Assessment
In terms of the quality of information, by improving the quality of the messages transmitted after analyzing the occurring errors, it is possible to improve communication by indicating the errors that occur to a specific sender or recipient. Identification and assessment of bottlenecks at each level of the process will allow for maintaining the process continuity, keeping the timely execution of the operation, and will also allow for increasing the efficiency and effectiveness of the process through continuous improvement and introduction of changes. 3 The Proposed Method for Assessing the Logistics Process in Terms of Information Flow Unreliability  
The scientific literature on the reliability assessment covers mainly the systemic approach, and the reliability assessment is not carried out for individual components of the system. Information quality is also assessed in the context of the entire engineering system, and individual messages for a specific sender and recipient are not considered. Therefore, in the further portion of the article, a method is presented that will assess the reliability of the process as well as the quality of information flow at the same time. The process has been divided into elements, where at the lowest level is the physical execution of operations based on the received message, and the reliability of the process refers to the timeliness of the operation performance. The literature specifies the delay thresholds without specifying what their impact will be, depending on the value of the delay. In turn, the quality of information is determined on the basis of the results of surveys carried out for a given engineering system. The evaluation of the quality of information is determined on the basis of the selected aspects; however, there are no indications as to which specific data in the message being transferred is incorrect and what effect this may have on the further execution of the process in the analyzed engineering system.
## 3 Three-Layer Cloud Dispatching Architecture
### Cluster Selection Layer
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**Cluster 1**} & \multicolumn{2}{c}{**Cluster 2**} & \multicolumn{2}{c}{**Cluster 3**} \\ \cline{2-6}  & \(R_{k}\) & \(C_{k}\) & \(M_{k}\) & \(R_{k}\) & \(C_{k}\) & \(M_{k}\) & \(R_{k}\) \\  & **(22,25,30)** & **(24,28,36)** & **(25,32,15)** & & \\ \hline \(A_{1}\) & 5 & & 2 & 3 & (23.3, 242, 26.7) \\ \(A_{2}\) & 8 & & 2 & 0 & (22.4, 256, 31.2) \\ \(A_{3}\) & 4 & & 4 & 2 & (23.4, 276, 29.4) \\ \(A_{4}\) & 3 & 6 & 1 & (23.5, 275, 32.1) \\ \(A_{5}\) & 5 & 1 & 4 & (23.4, 281, 24.6) \\ \(A_{6}\) & 10 & 0 & 0 & (22, 250, 30) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Example of Cluster Selection Algorithm. According to Table 2, assignment A\({}_{1}\) assigns five tasks to Cluster 1, two tasks to Cluster 2 and three tasks to Cluster 3. We used Equations (1)-(3) to calculate the average of \(R_{i}\), \(C_{i}\), and \(M_{i}\), and the results are \(R_{1}=\frac{22\times 5+24\times 2+25\times 3}{10}=23.3\); \(C_{1}=25\times 5+28\times 2+32\times 3=242\); \(M_{1}=\frac{30\times 5+36\times 2+15\times 3}{10}=26.7\). The same procedure is followed for the other assignments, too. Step 3: Select the schedule that meets the condition of \(R_{i}\geq 22\) and \(C_{i}<260\); Here, \(A_{1}\), \(A_{2}\), and \(A_{6}\) are selected. Step 4: According to the conditions of Step 3, we choose \(A_{1}\) with the highest reliability and smallest MakeSpan. Since the result of assignment \(A_{1}\) is better than others, assignment \(A_{1}\) is elected as the combination to dispatch in this example. The above procedure is the most suitable solution when the MakeSpan is the main concern. However, when the reliability is the main concern, the MakeSpan and Cost become the masking factors to filter out the schedule with the best reliability. After finished the CSA layer, the tasks can be dispatched to the corresponding clusters in the cloud cluster section layer. Subsequently, the appropriate server nodes need to be elected to complete the task in the next layer.
# Analysis of Volatile Components in Different _Ophiocordyceps sinensis_ and Insect Host Products
###### Abstract  
The artificial production of _Ophiocordyceps sinensis_ mycelia and fruiting bodies and the Chinese cordyceps has been established. However, the volatile components from these _O. sinensis_ products are not fully identified. An efficient, convenient, and widely used approach based on headspace solid-phase microextraction (HS-SPME) combined with comprehensive two-dimensional gas chromatography and quadrupole time-of-flight mass spectrometry (GC\(\times\)GC-QTOFMS) was developed for the extraction and the analysis of volatile compounds from three categories of 16 products, including _O. sinensis_ fungus, _Thitarodes_ hosts of _O. sinensis_, and the Chinese cordyceps. A total of 120 volatile components including 36 alkanes, 25 terpenes, 17 aromatic hydrocarbons, 10 ketones, 5 olefines, 5 alcohols, 3 phenols, and 19 other compounds were identified. The contents of these components varied greatly among the products but alkanes, especially 2,5,6-trimethyldecane, 2,3-dimethylundecane and 2,2,4,4-tetramethylocane, are the dominant compounds in general. Three categories of volatile compounds were confirmed by partial least squares-discriminant analysis (PLS-DA). This study provided an ideal method for characterizing and distinguishing different _O. sinensis_ and insect hosts-based products.  
Ophiocordyceps sinensis; Thitarodes hosts; Chinese cordyceps; volatile compounds; HS-SPME GC\(\times\)GC-QTOFMS; partial least squares-discriminant analysis Article
# A Road Crack Detection Method Based on Residual and Attention Mechanism
## 2 Proposed Method
### Our Method  
Existing road crack detection algorithms often have the problem of low accuracy in detecting small cracks. In order to solve these problems, this article introduces the ResNet model's excellent residual structure in the encoding stage of UNet's model to improve the network's ability to extract crack detail features. In addition, we have improved the UNet structure's skip connections by introducing attention mechanisms in the skip connections section, aiming to enhance the network's crack localization and anti-interference capabilities. Figure 6 shows the network structure of our model. We evaluated the network model proposed in this article using publicly available datasets. The experimental results on public datasets showed that our model outperforms state-of-the-art crack segmentation models in terms of overall performance in road crack segmentation.
## 3 Results and Discussion
### In Vitro Tubulin Inhibition Assay
In the tubulin assembly assay such a type of curve is not a typical pattern, and it is usually interpreted as a sign of more complex or multi-phase interaction with the tubulin or the microtubule polymerization machinery. Similar kinetic behavior was reported for paclitaxel at specific concentrations, epothilones and some synthetic tubulin polymerization stabilizers. The tubulin polymerization inhibition effect of compound 6c is exerted through two lag phases--between 0 and 10 min and between 45 and 55 min. Figure 3: Tubulin (final 54 \(\upmu\)M) in the presence of PB-GTP buffer (**a**), 10 \(\upmu\)M paclitaxel (**b**), 10 \(\upmu\)M albendazole (**c**), and 10 \(\upmu\)M of compounds **4a** (**d**), **4b** (**e**) and **6c** (**f**). Reaction was conducted in thermostated spectrophotometric chamber at 37 \({}^{\circ}\)C for 120 min. Turbidity was measured at 340 nm every 66 s.  
The slope factor of the Boltzmann fit (d\(\chi\)) describes the rate of the aggregation growth, i.e., the smaller the d\(\chi\), the faster the aggregation growth. In the studied series of compounds, slope factors between 7 and 10.4 min were observed, which is only slightly faster than the spontaneous polymerization of tubulin. The observed modulation effect of the studied compounds shows a different trend from those established for the larvicidal effect against _T. Spiralis_ ML. Notably, compound **6c**, showing the most pronounced tubulin polymerization inhibition, did not manifested marked anti-trichinella efficacy, suggesting that alternative mechanistic properties are likely to be involved in the antiparasitic effect. One possible route to be considered is modulation of mitochondrial function through ultrastructural alterations in mitochondria [26] or inhibition of certain mitochondria membrane enzymes [27].
## 5 Results and Discussion
### Packet Delivery Ratio  
Figure 16c shows that the PDR of the malicious network is less than the healthy and NTRUEncrypt-secured network of 20 nodes. This is because the packets received are less; the network drops many packets during the routing due to an attacking node. In addition, network congestion occurs due to packet loss, hindering the proper packet delivery. In the NTRUEncrypt-secured case, the performance is not far behind the healthy one because of the decrease in packet drop value.
###### Abstract  
Intelligent scheduling plays a crucial role in minimizing transportation expenses and enhancing overall efficiency. However, most of the existing scheduling models fail to comprehensively account for the requirements of urban development, as exemplified by the vehicle routing problem with time windows (VRPTW), which merely specifies the minimization of path length. This paper introduces a new model of the heterogeneous green city vehicle routing problem with time windows (HGCVRPTW), addressing challenges in urban logistics. The HGCVRPTW model considers carriers with diverse attributes, recipients with varying tolerance for delays, and fluctuating road congestion levels impacting carbon emissions. To better deal with the HGCVRPTW, an adaptive multi-objective genetic algorithm based on the greedy initialization strategy (AMoGA-GIS) is proposed, which includes the following three advantages. Firstly, considering the impact of initial information on the search process, a greedy initialization strategy (GIS) is proposed to guide the overall evolution during the initialization phase. Secondly, the adaptive multiple mutation operators (AMMO) are introduced to improve the diversity of the population at different evolutionary stages according to their success rate of mutation. Moreover, we built a more tailored testing dataset that better aligns with the challenges faced by the HGCVRPTW. Our extensive experiments affirm the competitive performance of the AMoGA-GIS by comparing it with other state-of-the-art algorithms and prove that the GIS and AMMO play a pivotal role in advancing algorithmic capabilities tailored to the HGCVRPTW.  
vehicle routing problem with time windows; multi-objective optimization; adaptive strategy +
Footnote †: journal: Applied  
1
Footnote 1: School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China; [EMAIL_ADDRESS] (W.Z.); [EMAIL_ADDRESS] (X.B.)
## 3 Proposed Method
### Improved Geographic Routing Protocol  
The outer area uses an improved geographic routing protocol which increases the reliability of the network compared to the standard geographic routing. Geographic routing has a drawback in that it may cause infinite loops of packets, which happens when all its neighboring nodes are farther away than it is from the sink; Figure 2 shows the schematic diagram of a node caught in an infinite loop. According to the standard geographic routing protocol, when node B generates a packet, since node C is closer to the sink than node A, node B transmits data to node C, and node C will only transmit data to node B. In this way, the data of node B and node C are trapped in an infinite loop. For this reason, the improved geographic routing defines a hopeful node, when a node exists at least one neighboring node closer than it is to the sink, such a node is called a hopeful node, and if it does not exist, such a node is a hopeless node, according to Figure 2, node A and node B are hopeful nodes and C is a hopeless node, so in the improved geographic routing protocol the node B will choose node A as the next hop, so the infinite loop is avoided. Secondly, to maintain the energy balance in the outer area, we will select the node that satisfies the energy threshold as the next hop. The initial energy threshold of each node is half of the initial energy, the node in the protocol prioritizes the nearest hopeful node that meets the energy threshold as the next hop, if there is no such a hopeful node, this energy threshold will be multiplied by \(\frac{1}{2}\), and then continue to search for the nearest hopeful node as the next hop. When the energy threshold is less than the energy of sending the data once, the node will choose the next hop among the hopeless nodes. This improves the data reliability in the outer area and also balances the energy consumption. The pseudocode for the improved geographic routing is displayed inside Algorithm 1.  
Figure 1: The network division of ERG.
## 2 Materials and Methods
### Drought Indices  
In this study, Vapor Pressure Deficit (VPD) is a key variable, representing the difference between the saturation vapor pressure, which is determined by near-surface temperature, and the actual vapor pressure. It is a function of both the saturation vapor pressure and relative humidity. The VPD data used here are derived from the TerraClimate dataset. This dataset employs climate-aided interpolation to integrate the high spatial resolution climate normal from the WorldClim dataset with temporally varying data of coarser spatial resolution from CRU Ts4.0 and the Japanese 55-year Reanalysis (JRA55) [25]. In our study the spatial resolution of VPD data is 0.01\({}^{\circ}\). Additionally, soil moisture data are extracted from the GLEAM V3 dataset. This dataset estimates terrestrial evaporation and root zone soil moisture based on satellite observations, providing valuable information for large-scale hydrological applications, climate research, and land-atmosphere feedback studies on global and regional scales [26; 27]. Here, all data are processed as monthly averages and resampled to a 0.05\({}^{\circ}\) spatial resolution using bilinear interpolation to facilitate subsequent analysis.  
Moreover, the Standardized Precipitation-Evapotranspiration Index (SPEI) is utilized as a novel drought index in this research. SPEI quantifies drought severity based on its intensity and duration and can identify the onset and end of drought events. Its multi-scalar feature, which distinguishes it from other widely used drought indices, allows for the identification of different types of droughts and their impacts in the context of global warming. In this study, the Thornthwaite equation is used to simulate Potential Evapotranspiration (PET) using mean temperature data from the NOAA, NCEP, CPC, GHCN_CAMS gridded dataset. Monthly precipitation sums data are obtained from the 'first guess' Global Precipitation Climatology Centre (GPCC). The CPC data, originally at a resolution of 0.5\({}^{\circ}\), is interpolated to 1\({}^{\circ}\). Following the calculation procedure, a 3-month SPEI (SPEI3) dataset for 2001 to 2020 with a spatial resolution of 0.1\({}^{\circ}\) is generated for analysis. The 3-month aggregation period of SPEI is particularly relevant for vegetation monitoring. This time frame aligns well with the growth cycles of many plants and crops, allowing for a more accurate assessment of drought impacts on vegetation growth [28]. Furthermore, the aggregation of SPEI over a 3-month period mitigates the influence of isolated anomalous drought months, making it widely relevant in studies related to vegetation drought [29].
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 2 Methodology
### Integrated Interference Model
#### 2.1.3 Path Loss  
In actual operational scenarios, path loss (\(P_{loss}\)) should include both Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions [30]. LOS indicates that there is a direct line of sight between the interference source and the affected object, without any obstacles. NLOS indicates that there is no direct line of sight due to obstacles.The computed results for the path loss in free-space, line-of-sight (LOS), and non-line-of-sight (NLOS) scenarios are presented in Figure 7.  
Figure 6: RA antenna pattern. (**a**) 3D. (**b**) Elevation angle \(\theta=0^{\circ}\).  
Figure 7: Path loss of free space, LOS, and NLOS.  
The conditions for calculating the path loss in Figure 7 include a frequency of 3.93 GHz, a RMa base station height of 35 m, a building height of 7 m, a street width of 25 m, and a height of 1 m for the target object. At a distance of 5 km, the path loss value obtained in the LOS scenario can be approximately 15 dB higher than that in free space, while the path loss value obtained in the NLOS scenario can be approximately 23 dB higher than that in free space. Figure 7 also illustrates the changing trends of the three types of losses with distance. At shorter distances, the attenuation increases more rapidly, while at longer distances, the increase in attenuation becomes more gradual. In the deployment protection method, we assume that the path loss between the RA antenna and the base station is random, with a 50% probability of LOS loss and a 50% probability of NLOS loss.