esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy-Based Multi-Modal Query-Forwarding in Mini-Datacenters
## 6 Results and Discussion
### Query Forwarding Behavior Within Mini-Datacenter
#### 6.1.2 Buffer Occupancy with Medium Occupancy  
In this experimental setup, the gateways were equipped with medium-occupied buffers, where the buffer occupancies ranged from 35 to 65%, as illustrated in Figure 10, and the buffer occupancies were found to be below 50% around 80% of the simulation time. The variation in gateway availability, overloading, gamma factor, and reliability is demonstrated in Figure 11. Since most of the buffer occupancies were less than 50%, the gamma factor was primarily influenced by the query backlog at the command center (CC), varying from 17% to a maximum of 100%. However, when the buffer occupancy reached 60%, the proportion of overloaded gateways increased towards the end of the simulation, and the gamma factor dropped to a lower value, which negatively impacted the number of redundant gateways involved in query forwarding. Gateway overloading was found to be minimal (zero) during 80% of the simulation, but it started to increase, reaching a maximum of 60% near the end. Here, the reliability of the query flow fluctuated between 96% and a maximum of 99%.  
Figure 10: Gateways buffer occupancies during query forwarding with medium initial buffer levels.
# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
###### Abstract  
The accurate prediction of scallion prices can not only optimize supply chain management and help related practitioners and consumers to make more reasonable purchasing decisions, but also provide guidance for farmers' planting choices, thus enhancing market efficiency and promoting the sustainable development of the whole industry. This study adopts the idea of decomposition-denoising-aggregation, using three decomposition and denoising techniques combined with three single prediction models to form a base model. Various base models are divided into different combinations based on whether the computational structure is the same or not, and the optimal weights of the combinations are determined by using the improved particle swarm optimization-genetic algorithm (PSO-GA) optimization algorithm in different combinations. The experimental results show that the scallion price in Shandong Province from 2014 to 2023 shows an overall upward trend, and there is a cyclical and seasonal fluctuation pattern of \"high in winter and low in summer\"; the semi-heterogeneous-PSO-GA model reduces the MAPE by 49.03% and improves the directional accuracy by 41.52%, compared to the optimal single prediction model, ARIMA. In summary, the combined model has the most accurate prediction and strong robustness, which can provide ideas and references for the difficult problem of determining the optimal weights of the combined model in the field of predicting the prices of agricultural products.  
optimization; PSO-GA; forecast combination; semi-heterogeneous combination; agricultural commodity price; Shandong scallion +
Footnote †: journal: Applied sciences
# Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm
## 5 Experimental Assessment
### Summary of Findings  
QSA outperforms other algorithms in minimizing hop count, making it highly effective for delay-sensitive applications.  
QSA requires more computational time due to its extensive search operations, but this trade-off is justified in scenarios demanding high deployment quality.  
QSA maintains stable performance as the number of users, base stations, and ESs increases, showcasing strong scalability.  
An optimal population size exists (~40) to balance performance and efficiency.  
These results validate QSA's effectiveness in optimizing edge computing deployment, providing robust, scalable, and high-performance solutions for dynamic network environments.  
Figure 14: Performance variation trend with increasing population size.  
Figure 15: Time consumption trend with increasing population size.
## 4 Results and Discussion  
### Abundance and Types of Organic Matter in Source Rocks  
TOC is an important indicator and, when combined with S\({}_{1}\) + S\({}_{2}\), can evaluate the abundance of organic matter in source rocks. The TOC of the coal sample is 43.9 wt.%; the TOC distribution of the carbonaceous mudstone samples is 13.9-33.1 wt.%, with an average of 21.4 wt.%, and the S\({}_{1}\) + S\({}_{2}\) value is 7.77-76.38 mgHC/g rock, with an average of 42.66 mgHC/g rock. The TOC distribution of dark mudstone samples is 0.53-4.2 wt.%, with an average of 1.71 wt.%, and the S\({}_{1}\) + S\({}_{2}\) value is 0.53-21.51 mgHC/g rock, with an average of 4.90 mgHC/g rock. Generally speaking, the rock display is predominantly good to excellent (Figure 3).  
Pyrolysis is a typical technique used in Rock Eval to determine the type and maturity of organic matter [16; 17]. The HI of carbonaceous mudstone samples is distributed between 55 mgHC/gTOC and 250 mgHC/gTOC, with an average of 167.7 mgHC/gTOC; the HI of dark mudstone samples ranges from 27 mgHC/gTOC to 228 mgHC/gTOC, with an average of 102.2 mgHC/gTOC. The type of organic matter is predominantly type III, occasionally type II, as shown in Figure 4.  
Figure 3: Organic matter abundance of coal-measure source rocks.
## Materials and Methods
### Net Primary Productivity Assessment and Validation
\(Qi\) refers to the experimental value meant for the \(i\)th experiment dataset. \(n\) refers to the sample dimension. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site Name** & **Site ID** & **Coordinates** & **Network** & **Biome Type** \\ \hline \hline Trang Khao Chong & th\_trang\_khao\_chong & 99.7980 E, 7.5435 N & FORESTGEO & EBF \\ \hline Songkhla & th\_songklla\_songklla\_met\_sta & 100.6046 E, 7.1844 N & AERONET & EBF \\ \hline Sukhothani & th\_sukothai\_mixed\_vegetation\_site & 99.4299 E, 16.9399 N & ASIAFLUX & DLC \\ \hline Ubon Ratchathani & th\_ubonratchathani\_ubon\_ratchathani & 104.8710 E, 15.2455 N & AERONET & DLC \\ \hline Ratchaburi & th\_ratchaburi\_dry\_dipterocarp\_forest\_flux & 99.5000 E, 13.5833 N & ASIAFLUX & DDF \\ \hline Nakhon Pathom & th\_nakhonpathom\_silpakorn\_univ & 100.0412 E, 13.8193 N & AERONET & EBF \\ \hline Nakhon Nayok Mo & th\_nakhonnayok\_mo\_singto & 101.3500 E, 14.4333 N & FORESTGEO & EBF \\ \hline Kanchanaburi Mae & th\_kanchanaburi\_mae\_klong & 98.8333 E, 14.5667 N & ASIAFLUX & MDF \\ \hline Chiang Mai Omkoi & th\_chiangmai\_omkoi & 98.4317 E, 17.7983 N & AERONET & EBF \\ \hline Chachoensao Rubber & th\_chachoensao\_rubber\_flux\_chachoensao & 101.4414 E, 13.6656 N & ASIAFLUX & RFC \\ \hline Singapore Bukit Timah & sg\_bukit\_timah & 103.7800 E, 1.3500 N & FORESTGEO & EBF \\ \hline Negeri Sembilan Pasoh & my\_negerisembilan\_pasoh\_forest\_reserve & 102.3062 E, 2.9730 N & ASIAFLUX & EBF \\ \hline Nong Khai Rubber Flux & th\_nongkhai\_rubber\_flux\_bueng\_kan & 103.3201 E, 18.2391 N & ASIAFLUX & RFC \\ \hline Lampang Mae Moh & th\_lampang\_mae\_moh & 99.7167 E, 18.4167 N & ASIAFLUX & DBF \\ \hline Mandalay MTU & mm\_mandalay\_mtu & 96.1861 E, 21.9729 N & AERONET & DLC \\ \hline Bac Lieu Bac Lieu & vn\_baclieu\_bac\_lieu & 105.7300 E, 9.2800 N & AERONET & DLC \\ \hline \hline \end{tabular}  
* The meaning of abbreviations is given as follows: Evergreen Broadleaf Forest (EBF), Rubber Forest Cover (RFC), Mixed Deiduous Forest (MDF), Dry Dipterocarp Forest (DDF), Diverse Land Cover (DLC), Deciduous Broadleaf Forest (DBF).  
\end{table} 
Table 2: Profile and description of FLUXNETS used.
# Many-Objective Optimization for Anomaly Detection on Multi-Layer Complex Interaction Networks
## 3 Method
### Algorithmic Overview  
In this paper, our objective is to detect the anomalous behavior of a set of nodes in multi-layer networks. For this, we start by identifying a set of nodes that exhibit some unusual behavior in terms of their interaction with other nodes. In summary, we focus on the network structure and target nodes with little connectivity or a deviating network structure, i.e., nodes having a low centrality in the network and its respective layers. This is formalized in the novel ACE score. Utilizing many-objective optimization and statistical network analysis methods using the ACE score, we apply the following steps for identifying the anomalous behavior of a set of _candidate nodes_ in the multi-layer network.  
1. First, we obtain general network statistics: We estimate the centrality of all nodes in each layer of the multiplex network as well as the network density of each layer.
2. Second, we apply our many-objective optimization approach: we identify a set of less important nodes through minimization using node centrality as a criterion; the number of layers becomes the number of objective functions to be optimized simultaneously for obtaining the Pareto Front.
3. Finally, we apply a semiautomatic human-centered approach: we start with the Pareto Front, where we select a _candidate node_ from that Pareto Front, guided by the ACE score, if 1. it is weakly connected, i.e., with no connection in at least one network layer; 2. it has a very low centrality in a rather dense layer - assessed by the ACE score; or 3. its centrality is almost zero. After that, we interactively inspect the further ranked levels in order to determine other candidate nodes in a human-in-the-loop approach. In this way, we directly utilize the ranking provided by the proposed many-objective optimization approach.  
Figure 1 depicts the presented approach. It is important to note that, during the semiautomatic step, different candidate nodes are proposed in close collaboration with the human-in-the-loop. Furthermore, during inspection and selection, all previous steps are transparently available in order to enable, e.g., \"drill-down\", \"zoom and filter\", and \"details on demand\" techniques, as proposed in the _visual information seeking mantra_ by [PERSON] [55]. For this, specific visualizations of the different layers can also be utilized, as also discussed in our case studies. In this way, the candidate nodes as well as their embedding in the network and the respective layers can be transparently assessed.
###### Abstract  
In wireless sensor and actor networks, when an event is detected, the sensor node needs to transmit an event report to inform the actor. Since the actor moves in the network to execute missions, its location is always unavailable to the sensor nodes. A popular solution is the search strategy that can forward the data to a node without its location information. However, most existing works have not considered the mobility of the node, and thus generate significant energy consumption or transmission delay. In this paper, we propose the trail-based search (TS) strategy that takes advantage of actor's mobility to improve the search efficiency. The main idea of TS is that, when the actor moves in the network, it can leave its _trail_ composed of continuous footprints. The search packet with the event report is transmitted in the network to search the actor or its footprints. Once an _effective footprint_ is discovered, the packet will be forwarded along the trail until it is received by the actor. Moreover, we derive the condition to guarantee the trail connectivity, and propose the redundancy reduction scheme based on TS (TS-R) to reduce nontrivial transmission redundancy that is generated by the trail. The theoretical and numerical analysis is provided to prove the efficiency of TS. Compared with the well-known expanding ring search (ERS), TS significantly reduces the energy consumption and search delay.  
trail-based search; event report transmission; mobility management; routing; wireless sensor and actor networks +
Footnote †: _Sensors_**2017**, 17, 2468; doi:10.3390/s17112468
## 2 Data and Methodology
### Data  
#### 2.2.1 Field Survey Data  
Between 27 January 2023 and 3 February 2023, researchers conducted a week-long survey in the study area to comprehend garlic and winter wheat cultivation practices. The survey route commenced in Tai'an, continuing to Jinxiang, then to Qixian and Zhongmu, followed by Pizhou and Lanling, and concluding in Laiwu. Before departure, points of interest within each county of the study area were selected based on satellite remote sensing imagery and time series data, with the sampling route planned according to the distribution of these points. The counties in the study area are major garlic-producing regions in China, where garlic and winter wheat cover nearly all cultivated areas, occupying vast expanses of the landscape, with minimal field weeds. Small patches of vegetables and other crops adjacent to garlic fields present challenges to classification accuracy. Trees are mainly concentrated near mountains, towns, villages, and along the roads between fields. Vegetables and other vegetation are predominantly found around villages, with much of it grown in greenhouses.  
Figure 1: Diagram of garlic fertility period.  
Figure 2: Overview of the study area.  
During the survey, samples of various vegetation types were collected, with a particular focus on garlic and winter wheat. Given the vast expanse of the study area and the intricacy of its terrain, achieving uniform sample collection was challenging. Consequently, in addition to field sample points, visual samples were also selected. To ensure the accuracy of visual samples, high-resolution remote sensing images were initially employed to classify the sample categories, followed by integration with Sentinel-2 NDVI time series remote sensing images for final categorization. Subsequently, the accuracy of the data was verified via meticulous comparison of all samples with high-resolution remote sensing images. Ultimately, the samples utilized in this study were compiled from ground-truthing samples and visual samples.
## 1 Introduction
investigated the hyporheic exchange process in a sand-gravel mixed riverbed and pointed it out that the pore water flowed preferentially through the sediment of high-permeability, which affected nitrogen transport pathways [23]. The sediment heterogeneity could result in the existence of tiny anoxic blocks within the aerobic zone near the shore, thus affecting nitrogen processing [24]. The retention time of river water is usually extended in the low-permeability riverbed sediment, which is beneficial to the full reaction between nitrogen and dissolved oxygen (DO) [25]. In addition, the migration ability of NH\({}_{4}^{+}\) in the hyperheic zone may be limited to a certain extent due to the electrostatic attraction by soil particles [26]. Eco-environmental factors mainly include the reactant concentration, redox potential (Eh), temperature (T), and pH [27; 28; 29]. The nitrogen-related biogeochemical reactions such as nitrification and denitrification are greatly governed by DO concentration and Eh in hyperheic zones [30]. Dissolved organic carbon (DOC) is another key factor controlling the nitrogen cycle, and increasing DOC concentration could promote denitrification [31]. [PERSON] et al. found that DOC and DO could be preferentially consumed in the near-shore hyporheic zone [32]. Additionally, temperature could affect the microbial activity, reaction rate, and DO concentration, consequently influencing the process of nitrogen cycle in the hyporheic zone [33; 34]. Some studies demonstrated that pH showed a positive correlation with NO\({}_{3}^{-}\) concentration and a negative one with NH\({}_{4}^{+}\) concentration [35]. Although the nitrogen cycle in hyperheic zones has been extensively studied, there is little work aiming at the case of low-permeability riparian hyporheic zones of tidal rivers. At present, the mechanism underlying how tides drive nitrogen migration and transformation in riparian hyporheic zones with low permeability sediment is not very clear. The main objective of the study was to characterize the spatiotemporal variations of various inorganic nitrogen concentrations in groundwater in poorly permeable hyporheiczones of tidal rivers and discuss the potential reason why such variations existed. A representative transect was chosen to carry out the drilling, monitoring, and sampling. By means of onsite monitoring and lab analysis, the response of the riparian groundwater level to the tide-driven fluctuation of the river stage was studied, the spatiotemporal variations of inorganic nitrogen concentrations in the groundwater in the riparian hyporheic zone were analyzed, and the potential influencing factors were discussed. The results are expected to enrich the theory of river hyporheic zones and provide a scientific basis for nitrogen pollution treatment of tidal rivers.
# GA-Based Optimization Method for Mobile Crane Repositioning Route Planning
## 4 Method Verification
### Comparison with Heuristic Method and Discussion  
To validate the optimal solution search performance of RPOS, the RPOS result was compared to the results obtained by applying a conventional heuristic-based method to the same test case. The mobile crane reposition route suggested by the heuristic-based method was [124, 124, 124, 124, 124, 231, 231, 231, 231, 136, 136, 136, 50] ((i.e., 124\(\rightarrow\)231\(\rightarrow\)136\(\rightarrow\)50), and the total required operating time was 4919 s. The mobile crane was relocated thrice, which was similar to the result of RPOS, but the total operating time required by this plan was 13.7% longer. This means that the optimal solution search performance of RPOS was higher. The search procedure of the heuristic-based method is as follows. First, the feasible points of the mobile crane for the \(i\)-th demand point were identified. Second, the feasible points for the \(i\)+1 th demand point were identified. Third, coincident points between \(i\) and \(i\)+1 th feasible points were identified. Fourth, the mobile crane was placed at a coincident point. Fifth, if the location point of the mobile crane belonged to the set of \(i\) + 2 feasible points, the crane remained at the current location point; otherwise, it moved to one of the \(i\) + 2 feasible points. This series of processes were  
Figure 5: Graphical representation of the results.  
repeated for every demand point. The solution derived based on this approach minimized the relocation of the mobile crane. However, the optimal solution was not guaranteed. RPOS, on the other hand, found the near-global optimal solution that required less total operating time while minimizing the need to relocate the mobile crane. The difference in the solution search between the heuristic method and RPOS in this test case study was the decision made for the placement point of the mobile crane at the 11 th demand point. The heuristic method decided to move the mobile crane to 136 because the point 136 was a feasible point for the 11 th--14 th demand points. In other words, the mobile crane was placed at a coincident point of four demand points. RPOS decided to move to 176, a feasible point for the 11 th--13 th demand points, which minimized the total required operating time. The difference in the total required operating time between the solutions suggested by the heuristic method and RPOS was 593 s, which may not be substantial in real world scenarios. However, this test case was performed at a relatively simple site. If there are more loads and the on-site situation is more complex, the difference in the optimal solution search performance will be much greater.
# Insights into the Issue of Deploying a Private LoRaWAN
## 3 Experimental Environment
### Public Lonavan  
To represent the public network, the LoRaWAN network of the Czech national operator CRa (Ceske Radiokomunikace) was selected (the coverage is displayed in Figure 6). The estimated coverage is based on the theoretical range of 8 km per gateway [58] (the availability was verified in a calibration test in front of the campus building). Therefore, our selected location should be decently covered by the national LoRaWAN operator with multiple nearby gateways. A total of 10 CRa gateways are located within a radius of 16 km from the university campus. Even though there are several public LoRaWAN providers in the Czech Republic, the selected national operator is the only one covering the whole Czech Republic with hundreds of deployed base stations (WAN area). The other operators mostly provide only local services and their network covers only selected locations (LAN/MAN areas).
# Frost Resistance of Coal Gangue Aggregate Concrete Modified by Steel Fiber and Slag Powder
## 3 Results and Discussion
### Air-Void Characteristics before F-T Test
#### 3.3.2 Effect of Slag Powder Content on Air-Void Structure  
Figure 16 illustrates the air-void distribution and cross-sectional morphology of CGAC with different substitution rates of slag powder. After substituting cement with slag powder, the frequency of air-voids with chord lengths of 0.02-0.03 mm decreased, however, the frequency of air-voids with chord lengths of 0.03-0.04 mm and 0.06-0.08 mm increased. For the air-void content, the 0.03-0.04 mm chord length air-voids decreased after incorporating the slag powder. It can also be seen from the cross-sectional morphology that after substituting cement with slag powder, the number of coarse pores reduced and fine isolated closed pores formed due to the filling effect of slag powder. This is beneficial to the frost resistance of CGAC to some extent, but due to the slow pozzolanic reaction of slag at an early age, the improving effect of slag powder on the frost resistance of CGAC is limited. Generally, considering the heterogeneity of CGAC, slag powder cannot effectively optimize its air-void structure, bringing about limited effects on improving its frost resistance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Quantity of** & **Mean Chord** & **Air Content** & **Specific Surface** & **Spacing Factor** \\  & **Voids** & **Length (mm)** & **(\%)** & **Area (mm\({}^{-1}\))** & **(mm)** \\ \hline CGAC & 769 & 0.232 & 3.19 & 17.23 & 0.367 \\
1 vol.\% CPSF & 678 & 0.213 & 2.54 & 17.34 & 0.403 \\
1 vol.\% HESF & 983 & 0.191 & 3.35 & 18.94 & 0.295 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Air-void characteristic parameters of CGAC with different steel fiber types.  
Figure 16: Air-void distribution and cross-sectional morphology of CGAC with (**a**) 10% cement substitution rate; (**b**) 40% cement substitution rate (1: Air-void distribution; 2: Cross-sectional morphology).  
The characteristic parameters of air-voids in CGAC with different cement substitution rates are shown in Table 8. It can be seen that compared with that of the ordinary CGAC, the quantity of air-voids and air content of CGAC with 40% slag powder decreased by 13.9% and 30.7%, respectively, and the air-void spacing factor increases by 15.8%, which is entirely consistent with the change of the frost resistance of CGAC with the increase of slag powder content.