esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Multi-Objective Optimization for Edible Oil Transportation Scheduling Method
### A* Path Planning Algorithm  
Prior to executing the multi-objective scheduling optimization, a dedicated shortest path must be determined for each individual task. Once the shortest path information is obtained, it enables further evaluation of the required pipeline resources and total transportation time for task execution. The system iteratively runs the path planning algorithm to identify the next task for the multi-objective scheduling until no feasible paths remain for any outstanding tasks.  
In this study, all edges in the factory map are considered directed and non-negative. Due to the specific production requirements of edible oil transportation pipelines, certain nodes impose directional constraints to prevent backflow, such as unidirectional designs in specific tanks, valves, and pipeline sections. Based on these characteristics and constraints, the A* algorithm was selected as the core path planning method. The A* algorithm employs a heuristic search strategy to efficiently identify the optimal path by combining the actual cost from the start node to the current node with the estimated cost from the current node to the goal [29].  
The value for \(f(\text{n})\) represents the estimated total cost from the start node A to node n, combining both the actual cost incurred and the estimated cost to reach the goal.  
\[f(\text{n})=g(\text{n})+h(\text{n}), \tag{3}\]  
The value for \(g(\text{n})\) denotes the actual cost from the start node A to node n, where \(cost(\text{A, n})\) is the actual movement cost along the path from A to n.  
\[g(\text{n})=cost(\text{A, n}), \tag{4}\]The value for \(h\)(n) is the heuristic estimate of the cost from node n to the goal node, often computed as the Euclidean distance and serving as the heuristic function value.  
\[h(n)=\sqrt{\left(x_{goal}-x_{n}\right)^{2}+\left(y_{goal}-y_{n}\right)^{2}}, \tag{5}\]  
For each neighboring node n of the current starting node A, perform the following node expansion rules:  
1. Compute the actual cost from the starting node A to node n, denoted as Equation (4);
2. Estimate the heuristic cost from node n to the target node, denoted as Equation (5);
3. Calculate the total cost of node n as Equation (3).  
If node n is not yet included in the open list, it is added. If node n is already present in the open list, its f-value is updated accordingly to reflect the improved path cost.
# Holographic Silicon Metasurfaces for Total Angular Momentum Demultiplexing Applications in Telecom
## 2 Materials and Methods
### Phase Pattern Calculation
#### 2.1.3 Equally-Spaced Hemi-Circular Configuration  
In order to arrange the far-field channel at equally-spaced angular positions without overlap, a semicircle configuration appears to be the best choice. In this case we considered the set \(\{-9,-6,-3,0,+3,+6,+9\}\) and we fixed the carrier spatial frequencies in such a way that the far-field peaks were arranged over a semicircle of constant radius \(r\) at equally-spaced angular positions (see Figure 4b), specified as follows:  
\[\begin{array}{c}r_{j}^{(+)}=r=\gamma^{\frac{f}{k}}\\ \varphi_{j}^{(+)}=j\frac{2\pi}{2n}=j\frac{2\pi}{14}\end{array}, \tag{14}\]  
where \(j=1,\,\dots\,7\). According to Equation (7), the far-field intensity peaks are expected to be at the following angular positions:  
\[\varphi_{j}^{(-)}=(7-j+1)\frac{2\pi}{14}+\pi=-(j-1)\frac{2\pi}{14}, \tag{15}\]  
The two orthogonal polarizations are therefore sorted over two complementary semicircles without overlapping, as shown in the scheme in Figure 4b.  
Figure 3: (**a**) Numerical phase pattern for the demultiplexing of optical beams with OAM in the set \(\{-6,-4,-2,0,+2,+4,+6\}\) on a heptagonal configuration. Pixel size: \(6.250\)\(\upmu\)m \(\times\)\(6.250\)\(\upmu\)m. \(16\) phase levels. Radius length: \(180\) pixels. (**b**) Far-field channel scheme for the given OAM set and circular polarization states. Right-handed (in blue) and left-handed (in red) circularly-polarized OAM beams are detected in far field on two distinct heptagons.
# Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models
## 3 Statistical Models for Forecasting Traffic
### Holt-Winters Model  
The Holt-Winters model, also known as triple exponential smoothing, is used to predict time series data that exhibit both trend and seasonal variations [18; 46; 47]. There are different types of trends and seasonality: additive and multiplicative in nature, meaning linear and exponential, respectively.  
Given a time series of data \(x_{i}\), \(i=0,\ldots,t-1\), let us denote the forecast value of \(x_{t}\) at time \(t\) as \(y_{t}\), the smoothed value of \(x_{t}\) at time \(t\) as \(s_{t}\), the estimate of the trend at time \(t\) as \(b_{t}\), and the seasonal change factor at time \(t\) as \(c_{t}\). Depending on the types of trend and seasonality, the following formulas represent the Holt-Winters model [48]:  
* additive (linear) trend and additive (linear) seasonality \[y_{t+h} =s_{t}+hb_{t}+c_{t+h-m\left(\lfloor\frac{h-1}{m}\rfloor+1\right)},\] \[s_{t} =\alpha(x_{t}-c_{t-m})+(1-\alpha)(s_{t-1}+b_{t-1}),\] \[b_{t} =\beta(s_{t}-s_{t-1})+(1-\beta)b_{t-1},\] \[c_{t} =\gamma(x_{t}-s_{t})+(1-\gamma)c_{t-m};\]
* multiplicative (exponential) trend and additive (linear) seasonality \[y_{t+h} =s_{t}\cdot hb_{t}+c_{t+h-m\left(\lfloor\frac{h-1}{m}\rfloor+1 \right)},\] \[s_{t} =\alpha(x_{t}-c_{t-m})+(1-\alpha)s_{t-1}\cdot b_{t-1},\] \[b_{t} =\beta\frac{s_{t}}{s_{t-1}}+(1-\beta)b_{t-1},\] \[c_{t} =\gamma(x_{t}-s_{t})+(1-\gamma)c_{t-m};\]* additive (linear) trend and multiplicative (exponential) seasonality \[y_{t+h} =(s_{t}+hb_{t})\cdot c_{t+h-m\left(\lfloor\frac{b-1}{m}\rfloor+1 \right)},\] \[s_{t} =\alpha\frac{x_{t}}{c_{t-m}}+(1-\alpha)(s_{t-1}+b_{t-1}),\] \[b_{t} =\beta(s_{t}-s_{t-1})+(1-\beta)b_{t-1},\] \[c_{t} =\gamma\frac{x_{t}}{s_{t}}+(1-\gamma)c_{t-m};\]
* multiplicative (exponential) trend and multiplicative (exponential) seasonality \[y_{t+h} =s_{t}\cdot hb_{t}\cdot c_{t+h-m\left(\lfloor\frac{b-1}{m} \rfloor+1\right)},\] \[s_{t} =\alpha\frac{x_{t}}{c_{t-m}}+(1-\alpha)s_{t-1}\cdot b_{t-1},\] \[b_{t} =\beta\frac{s_{t}}{s_{t-1}}+(1-\beta)b_{t-1},\] \[c_{t} =\gamma\frac{x_{t}}{s_{t}}+(1-\gamma)c_{t-m};\]  
where \(\alpha\) is the data smoothing factor, \(\beta\) is the trend smoothing factor, and \(\gamma\) is the seasonal change smoothing factor.
## 3 Results and Discussions
### Analysis of Solid Waste Landfill  
According to the risk analysis of excess sludge, the residual sludge from the refinery in this study is general solid waste. If it is landfilled, it needs to be landfilled to a general solid waste landfill. The excess sludge production is large, the water content of which is high, remaining 80-85% after mechanical dewatering, thus taking up large spaces. In addition, sludge with a high water content lacks the geotechnical stability necessary for landfill disposal, which causes certain difficulties in landfill operations [54; 55]. Germany first specified the geotechnical properties of sludge landfill disposal [56]. The sludge should be filled with a shear strength of not less than 25 kPa, or an unconfined compressive strength of not less than 50 kPa. In China's \"Technical Specification for Domestic Waste Sanitary Landfill Treatment\" (GB 50869-2013) [57], the geotechnical indices for sludge landfill must fulfill the following conditions: the shear strength of the cross plate is not less than 25 kPa, and the unconfined compressive strength is not less than 50 kPa. [PERSON] et al. [54] found that the sludge moisture content can be reduced to about 64% to meet the rockfill soil mechanics index. When the moisture content of Cq residual sludge drops to about 62%, it can meet the rockfill soil mechanics index. However, China does not give specific provisions on geotechnical indicators for industrial solid waste landfill disposal. In China's \"General Industrial Solid Waste Storage and Disposal Site Pollution Control Standards\" (GB18599-2001) [58], when solid waste with high humidity is required to be landfill, pretreatment, or adsorption desiccant is required.  
The excess sludge has a high content of organic components and a high content of C, H, O and N. For example, the contents of C, H, O, and N in Cq sludge are shown in Figure 2. Landfill disposal is easy to cause anaerobic degradation of sludge. It produces gases, such as CH4 and CO2, which have certain negative effects on the environment.
## 6 Distributed LoWMob (DLoWMob) Mobility Support Scheme  
In this section, we propose the DLoWMob scheme, a distributed version of LoWMob. DLoWMob enhances the performance of LoWMob by introducing the concept of MSP. It reduces the signaling traffic at the GWs and enables the route optimization in 6 LoWPAN. While LoWMob enables 6 LoWPAN mobility without having MNs involved in most of the mobility-related signaling, it causes the GWs to be involved in all of the mobility-related signaling and data packet handling. That is, whenever a MN associates with a new SN, the SN has to send an LU messages to the GW that could negatively affect the lifetime of the SNs and ultimately the whole PAN. Moreover, the MN's data packets are routed through the GW; since it has the binding information of MN's ID with its current associated SN, thereby increasing the traffic concentration at the GW.  
In DLoWMob, the PAN is assumed to be partitioned into multiple regions, as shown in Figure 10. Each region has one MSP and its associated SNs which are managed by the MSP. Hence there exists a hierarchy in the PAN. The GW manages MSPs only, while each MSP manages its associated SNs in its region.  
The main functions of the MSP are two-fold; firstly, it limits the number of LUs to the GW by managing the binding information between MNs and SNs in its region locally. That is, it reports LUs to the GW only when a MN enters in its own region. While a MN moves in its region, from a SN to another SN in the region, no LU is required to be sent to the GW. Consequently, MSPs could reduce significant network bandwidth and the energy consumption. Secondly, it enables the data packets between MNs and its CNs in a PAN to be routed between their corresponding MSPs. That is, it removes the need of having every data packets passing through the GW, thus making route optimization possible. The next two subsections describe each function in detail.
# A Component-Based Approach to Early Warning Systems: A Theoretical Model
## 4 Discussion
### Data Management and Data Quality
Some types are used more frequently than others. Since it was not possible to comprehensively assess all types of sensors, ten types were selected for each manifestation of a natural crisis event based on the studied literature. As the number of publications on sensors runs into the thousands, it was not possible to set precise conditions for a systematic analysis. Nevertheless, such a summary of sensor types is useful, as it highlights the need for in-depth analyses and comparisons of individual sensor types. Some sensor types are used more frequently than others for various reasons. For example, resistance temperature detectors (RTDs), thermocouples, and thermistors are the most common types of temperature sensors [104]. Despite efforts to find more comprehensive reviews, it was found that most are limited to only 2-3 types of sensors and do not provide a more thorough comparison. Another limitation was the definitions of sensor types by different authors. Within optical temperature sensors, there is another division into a greater number of types, based on the material used or technological differences. In the case of fires, the sensors for air quality must focus directly on the chemicals that may be present in the air. These include, for example, carbon monoxide, nitrogen dioxide, and other hydrocarbons [110]. In the case of earthquakes, the majority of sensors used are micro-electromechanical system (MEMS)-based sensors [147]. Other types of sensors were rarely found, which is why their number is significantly lower compared to others. These are just examples of specifics, of which there is an indeterminable number. However, such a grouping of sensor types, focusing on several parameters, is quite unique, and no similar grouping was found. The researched area requires further attention, particularly regarding the integration of modern sensors into EWSs. Climate change and the increasing occurrence of extreme weather events have amplified both the intensity and frequency of natural hazards. As a result, studying natural hazards and disaster risks is crucial for protecting livelihoods and economic stability. Effective forecasting and EWSs rely on precise hazard-related data, with forecasting accuracy improving through the integration of multiple models. Additionally, advanced numerical weather prediction technologies, such as the Weather Research and Forecasting (WRF) model, have been developed based on a deep understanding of atmospheric dynamics. However, challenges remain in accurately simulating and predicting hazard and disaster evolution under varying meteorological and complex terrain conditions. Therefore, enhancing our understanding of natural hazard processes is essential for improving EWSs, which play a vital role in saving lives [148]. The use and necessity of implementing EWSs are also part of the smart cities concept, where the focus is on ensuring the safety of residents [149].
## 2 Selection Method of Wavelet Parameters
### CMOR Wavelet Parameters of Target Frequency  
Assuming that the duration of one discrete signal is \(T_{0}\) and the frequency nearest to the target frequency \(f_{k}\) is \(f_{n}\), then the difference between \(f_{k}\) and \(f_{n}\) is \(\delta f=|f_{k}-f_{n}|\), and the frequency resolution should satisfy the condition: \(\Delta f<\delta f\). According to Equation (12), the wavelet parameters \(f_{b}\) and \(f_{c}\) that satisfy this condition are:  
\[f_{c}\sqrt{f_{b}}\geq\frac{2\sqrt{\ln 2}f_{k}}{\pi\delta f^{\prime}}>\frac{2 \sqrt{\ln 2}f_{k}}{\pi\delta f} \tag{14}\]  
where \(\delta f^{\prime}\) is a constant and \(0<\delta f^{\prime}<\delta f\). As for the edge effect, available studies have shown that the limit of \(\delta t\) is \(\delta t\leq 0.2T_{0}\)[20; 23]. According to Equation (13), the \(f_{b}\) and \(f_{c}\) that satisfy the limit of \(\delta t\) are:  
\[f_{c}\sqrt{f_{b}}\leq\frac{T_{0}f_{k}}{15\sqrt{2}} \tag{15}\]Therefore, the optimal \(f_{b}\) and \(f_{c}\) for the target frequency \(f_{k}\) need to satisfy both Equations (14) and (15). Moreover, \(T_{0}\) needs to meet the following condition:  
\[\frac{30\sqrt{2\ln 2}}{\pi\delta f^{\prime}}\leq T_{0} \tag{16}\]  
When \(T_{0}\) cannot satisfy Equation (16), the signal duration should be extended by padding with an equal number of zeros before the signal occurs and after its disappearance [29].  
From Equations (10), (12), (13), we can find that as the value of \(f_{c}\sqrt{f_{b}}\) increases, \(\Delta t\) and \(\delta t\) synchronously increase, while \(\Delta f\) reversely decreases. The optimal \(f_{b}\) and \(f_{c}\) for the target frequency need to meet the requirements of \(\Delta f\), and the \(\delta t\) should be as small as possible. According to Equations (14) and (15), we therefore let \(f_{c}\sqrt{f_{b}}=\frac{2\sqrt{\ln 2}f_{k}}{\pi\delta f^{\prime}}\). Theoretically, the \(f_{b}\) and \(f_{c}\) that meet this equation can not only satisfy the requirement of frequency resolution but also satisfy the limit of edge effect.
# A Self-Supervised Tree-Structured Framework for Fine-Grained Classification
## 4 Experiment
### Conducted Experiments and Results
#### 4.3.1 Experiment 1  
Experiment 1 aims to examine the influence of child clusters on the training time and accuracy of the bird dataset. A tree-structured classifier with varying numbers of child clusters (i.e., 2, 3, 4, and 5) is employed. The accuracy and training time of the classifier are being evaluated to assess the impact of child clusters on the classification performance while holding other variables constant. Specifically, a Support Vector Machine (SVM) with a linear kernel function is used as the machine-learning matcher, ResNet [26] is utilized as the CNN model, and a k-means++ algorithm with Euclidean distance is implemented for clustering. Table 4 presents the impact of the number of child nodes on both classification accuracy and training time. To begin with, in order to showcase the effect of training the proposed model, Figure 9 is provided, which displays partial images of the training and validation loss.  
Figure 8: The diagram of the training phase of the tree-structured classifier.  
Table 4 presents the classification accuracy of the bird dataset using the ResNet model without the proposed method, which is 68.63%. However, Table 4 shows that the proposed method enhances the accuracy of classification trees with child clusters 2, 3, 4, and 5 by 1.17%, 1.52%, 1.31%, and \(-\)0.13%, respectively. These results suggest that the tree-structured classifier with three child clusters performs better in classification than those with other child clusters. Conversely, the accuracy of classification decreases when the number of child clusters is 4 or 5. Even when the number of child clusters is 5, there is a negative growth in classification accuracy. These results indicate that the accuracy of a tree classifier reaches a certain threshold and then declines as the number of clusters increases. Furthermore, Table 4 displays the training time obtained using ResNet, which amounts to 1480.41 seconds. The present study compares the performance of a proposed method, which consists of 2, 3, 4, and 5 child clusters, with the conventional ResNet architecture in terms of training time. According to the results, the proposed method outperforms the conventional ResNet architecture, with corresponding reductions in training time of 5.76%, 32.91%, 37.34%, and 41.01%, respectively. Furthermore, an increasing trend in the reduction of training time with an increase in the number of child clusters is observed, indicating the effectiveness of the proposed approach.
###### Abstract  
Conventional farming practices can provide higher agricultural yields through the extensive use of fertilizers, pesticides, and other inputs. These practices have been associated with severe environmental effects, such as eutrophication, acidification, etc. Laser technology, among many other techniques, could be a viable option for environmental reduction if incorporated into agricultural production systems. However, the environmental performance of using lasers in agriculture practices needs to be investigated in order to provide sustainable management of the agriculture sector. Therefore, in this study, the life cycle assessment (LCA) of laser-induced maize production in bio-stimulated seeds was compared to conventional farming practices using the software SimaPro ver. 9.0. The study emphasized human toxicity, freshwater ecotoxicity, and marine ecotoxicity due to their significant contributions. The results demonstrated that laser technology was an environmentally friendly system for treating maize seeds before sowing. The study further identified the mineral fertilization process as the most critical threat to the environment. Based on normalization, maize production process-related toxicity accounts for the highest environmental impacts of 8.2 and 7.3 kg 1,4-DCB/ton of grain produced by conventional practices and laser maize production, respectively, on the general environmental profile. At the endpoint level, the irradiated maize seeds performed better than their non-irradiated counterpart impacting human health at 5.46 \(\times\) 10\({}^{-3}\) DALY, ecosystems at 1.86 \(\times\) 10\({}^{-5}\) species-year, and resources at 60.74 USD 2013. Soil management was also identified as the conventional maize production process with the most significant environmental impacts. The greatest observed impacts were on marine ecotoxicity (19.23 kg 1,4-DCB) and freshwater ecotoxicity (12.94 kg 1,4-DCB) per ton of grain produced. The evaluation of potential human toxicity concluded that zinc contributed more than 90% in ReCiPe 2016 Midpoint (H) and benzene contributed approximately 22% in CML 2000. The study concluded that improved environmental performance was obtained for laser-induced maize production compared to conventional farming practices. The LCA can provide information to policymakers and government agencies about shifting to more sustainable agricultural practices in arid regions, such as Iraq, prone to drought linked to water availability and soil salinity.  
graff maize; LCA; sustainability; environmental performance; laser technology
## 6 Multi-Service Dependency
### Using Both Network and Application-Level Data  
Macroscope [79] takes advantage of both network and application-level data to extract dependencies between networked services/components. It automatically extracts the dependencies of networked applications using application-level data such as application ID, process ID, along with network-level data, to construct application-service dependency graphs at different levels of granularity. It separates dependencies into static dependencies that are more frequent and transient dependencies that are less frequent and uses a two-step algorithm to identify them. At the first step, it classifies applications into one of two types: it merely generates connections produced from static dependencies, or it generates connections from both static dependencies and transient relations. The second step takes data from the applications with connections appearing from both static dependencies and transient relations, and makes use of usage frequency information to distinguish them. In addition, it extracts different levels of dependencies between application and services such as application-level dependency, application instance and service instance dependencies, using different collections of data.
# A Comprehensive Survey on Knowledge-Defined Networking
## 2 Overview of Knowledge-Defined Networking (KDN)
### KDN vs. SDN vs. Conventional Network
#### 2.3.8 Operational Cost  
Traditional networking is expensive to operate, as it requires skilled programmers to configure and manage the network. Using a logically centralized control plane, the operational cost of SDN can be drastically reduced compared to traditional networks. The operational cost savings in KDN are even higher than in SDN, as it uses AI and machine learning techniques to generate and compose knowledge that can be used to automate network management tasks with the least involvement of humans (network administrators).