esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries and Definitions
### Network Architecture  
In this paper, we consider a simple three-layer edge cloud computing model for multi-user MEC, as shown in Figure 1. The first layer is the IoT layer, which consists of a set of mobile devices. After the user decides the request offloading and computational resource scheduling, the IoT layer sends the request from mobile devices to the second edge layer or the third cloud layer. The edge layer is closer to the IoT layer and consists of a set of miniature base stations with edge servers. The cloud layer is further away from the IoT layer and consists of a macro base station with one deep cloud for processing large amounts of data and storing them for future use.  
We assume that there is one macro base station with a cloud server, n micro base stations (BSs) with edge servers, and that the total number of mobile users is n. The locations and heights of all BSs are fixed. The average power consumption Pma and computational power Rma of the macro base station are two times the average power consumption Pmi and computational power Rmi of the micro base station, respectively. The maximum transmission power of a mobile user is Pmax. Each mobile user u generates one computational request at a time, and the request includes Qu = <Wq, Sg, Prg, Tgq, Tbq>, where Wq represents the workload of request q, i.e., the amount of computation required to fulfill the request, and Sg represents the size of the request input data. We use Prg to denote the request priority that represents the importance of different requests. Tgq and Tbq are the desirable latency threshold and tolerable latency threshold, respectively. The average delay Tavg = (Tgq + Tbq)/2 for request q. The location of the mobile user is denoted by put and the location of the base station is denoted by pnt.
## 3 RS Applications in Ocean
### Ocean Tide (OT)
Tidal currents' power intensifies when flowing through the narrow and shallow channels in the islands or over the shallow ridges, although they are weak in most parts of the strait [268]. Furthermore, tidal current power is renewable and predictable energy [269], mostly occurring at narrow tidal channels. Tidal current generators not only cause fewer environmental concerns than barrages, but can also be installed incrementally to meet the increase in demand over time [270]. Therefore, mapping the tidal energy density is crucial to consider the economic possibility, while tidal currents vary regionally. In this regard, a higher level of measurements, including spring and neap tidal cycles and higher temporal and spatial measurements, are required [269]. OTL displacements are the elastic response of the Earth to the redistribution of water mass from OT, which can cause deformation gradients of several millimeters to centimeters near coastal regions [271]. One of the most productive inland and coastal ecosystems is tidal wetlands. Tidal wetlands are important for trapping sediment and pollution, recreational purposes, and flood control. They are also natural barriers against saltwater intrusion into freshwater aquifers [272, 273]. The tidal regime, relative SL rise, land cover change, and sedimentation are driving factors influencing tidal wetlands [274]. Tides increase rates of relative SL rise, resulting in brackish water and a shift to becoming nontidal wetlands in freshwater tidal areas. So far, different RS systems have been successfully employed for OT studies. Table 5 summarizes the advantages and limitations of the RS systems that have been frequently used for OT measurement. Figure 8: Global distribution of tidal flats during 2014–2016. The figure is directly adopted from Reference [9].
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 2 Preliminaries
### IoT Technology and Smart Agriculture and Animal Husbandry
The integration of visual data with other sensor information offers comprehensive situational awareness, enabling early detection of anomalies and timely interventions. (6) System Outcomes  
Automatic irrigation: Leveraging soil moisture data and preset thresholds stored within the server's analytics modules, the system can autonomously trigger irrigation pumps. This optimizes water usage, ensuring that crops receive the necessary hydration without wastage. Livestock management: By analyzing data from sensors and camera feeds, the server can detect anomalies such as abrupt temperature changes or reduced animal activity. These detections prompt alerts to users or direct commands to actuators, facilitating prompt corrective actions to maintain livestock health and welfare. Through these interconnected modules and clear data flows, the proposed architecture demonstrates how IoT devices, analytics, and blockchain integration can enhance transparency, trust, and efficiency in both agriculture and animal husbandry.
## 5 Machine Learning Models
### RF Prediction Model and Its Performance  
After determining the import characteristics, the RF prediction model is developed using the same dataset as the ANN model. The total dataset is divided into the training set (70% of the dataset) and testing set (30% of the dataset). The RF model is trained using the training set, whereas the test set is used to assess the model's performance on unseen data. Two RF prediction models are developed, one for the dataset from the five-story building models and other for the dataset from the ten-story building models. Data normalization is not required for RF model adjustment [90; 91]. Figure 16 shows the performance of the RF models for the five- and ten-story building models. The predicted CDAF levels are shown against the simulated values in Figure 17. In cases of accurate predictions, all data points align with the black dash-dotted line, indicating identical predicted and actual values. Both images show data points that are consistently close to this line, indicating satisfactory predictions from the two random forest (RF) models. Based on Figures 13 and 16, it can be inferred that the RF (random forest) outperforms the ANN (artificial neural network) in terms of predictive performance, as indicated by its slightly higher \(R^{2}\) value. This suggests the superiority of ensemble learning with 250 tree estimators over a single ANN estimator, despite the universal approximation ability of the ANN model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Hyperparameter** & **Search Range** & **Optimal Value** \\ \hline \multirow{4}{*}{Random Forest (RF)} & Number of estimators & [50 100 150 200...450 500] & 250 \\  & Maximum depth & [5 10 15 20 25 30] & 20 \\  & Minimum sample split & [2 5 8 11 14 17 20] & 2 \\  & Minimum sample leaf & [1 2 3...8 9 10] & 1 \\  & Random state & [0] & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Optimal hyperparameter values for the RF model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Five-Story**} & \multicolumn{3}{c}{**Ten-Story**} \\ \cline{2-7}  & \(\mathbf{R^{2}}\) & _RMSE_ & _MAPE_ & \(\mathbf{R^{2}}\) & _RMSE_ & _MAPE_ \\ \hline Training & 0.996 & 0.014 & 2.279 & 0.996 & 0.013 & 2.654 \\ Testing & 0.983 & 0.032 & 5.961 & 0.985 & 0.036 & 6.853 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Performance of the RF model.  
Figure 16: Simulated and RF’s predicted CDAFs for (**a**) 5-story models and (**b**) 10-story models.  
Figure 15: Architecture of the RF model.
## 4 Conclusions  
In summary, the proposed KTP-based FBSs have been successfully demonstrated for the measurements of liquid concentration. The FBSs' designs were systematically evaluated to discuss the measurement performance and optimize the incident angle of the probe light. The repeatable and stable phase measurements were obtained with the simple sensors. The achievable resolutions of concentration were 0.0092 wt. % and 0.0088 wt. % for the SW and HCL solutions, respectively. The RI is around 1.425 RIU for the HCL concentration of 40 wt. % [20]. Although the only low concentration of 2.5 wt. % of HCL solution was used for the first trial experiments, the simulations in Figure 4 offer the possibility that the wide RI (1.30-1.50 RIU) can also be measured in the proposed FBS. Finally, the experimental evidence suggests that with the chemical resistance property, the KTP sensor has the potential for dynamic concentration monitoring of chemical solutions in harsh environments.  
The authors gratefully acknowledge the financial support provided by Ministry of Science and Technology in Taiwan under grant number MOST 104-2221-E-218-027 for this work.  
[PERSON] designed the measurement principles and analyzed the experimental results. [PERSON], [PERSON], and [PERSON] conducted the experimental tests and the simulations. [PERSON] wrote the paper.  
There are no conflict of interest.
## 2 Scheduling Requirements and Modeling Foundations of Roadheaders  
Roadways are tunneled by a series of basic movements, such as direction and speed adjustment, drilling and cutting of the coal wall, section shaping and fixing, coal and rock transportation, and so on. Bolt support is a temporary support used before the arch hydraulic support is implemented when a section is completed; the roadheader needs to withdraw appropriately to provide working space for bolting. When the pose of the roadheader is unpredictable, path planning and tracking must be executed, which is referred to as \"roadheader scheduling\" in this paper.  
For many wheeled robots or vehicles, path planning and tracking can be carried out synchronously by optimal control. Since the expected roadway for coal mining is usually a straight line inside the coal bed, it is not necessary to replan a path for the roadheader after each section is formed. Instead, it is better to pre-plan a path for tracking than to use dynamic path planning. In other words, this paper only focuses on the tracking strategy under a planned path.  
A pre-planned path for the roadheader to travel back onto the target roadway includes well-planned turning speed (\(\dot{\varphi}\)) and forward velocity (\(\dot{x}\)) for the roadheader according to certain operational requirements, as introduced in the literature [23].  
To achieve certain turning speed and forward velocity, the motion of the tracked vehicle is considered similar to that of wheeled ones: when the driving wheels on both sides rotate at the same angular speed and in the same direction, the vehicle moves forward or backward in a straight line; when they rotate at same angular speed but in opposite directions, the vehicle would turn in place; when they rotate at different angular speeds that are well designed, the vehicle would turn along a certain expected curve.  
Nevertheless, the roadheader scheduling is different from that of wheeled vehicles or small tracked vehicles on the ground, based on the following considerations. For example, the turning speed, forward velocity, and their gradients for the roadheader are usually limited for safety or by a restricted working space; the track skidding between the tracks and ground is difficult to formulate exactly; the tolerable position derivation is small compared with the size of the vehicle, and therefore a slight movement might result in over adjustment; with a certain level of pose detection rate and accuracy, the accumulated measurement errors should be taken into account.
## 3 Results and Discussion
### Effect of Parameters on Pore-Filling Percentage
Figure 5: Top-view of CNPs filled AAO film interposer with different temperatures in 8 h: **(a)** 20 \({}^{\circ}\)C; **(b)** 40 \({}^{\circ}\)C; **(c)** 60 \({}^{\circ}\)C; and **(d)** the relationship of the pore-filling percentage versus electrodeposition temperature; **(e)** top-view of the corroded area at 60 \({}^{\circ}\)C; and **(f)** the whole area at 60 \({}^{\circ}\)C.  
The effect of electrodeposition temperature can be explained by diffusion. Equation (2) shows the relationship between diffusion coefficient and temperature:  
\[\mathrm{D}=\mathrm{D}_{0}*\mathrm{exp}((-\mathrm{Q})/\mathrm{RT}) \tag{2}\]  
where D is diffusion coefficient, D\({}_{0}\) is diffusion constant, Q is activation energy, R is gas constant, and T is absolute temperature. It can be seen that the diffusion velocity is positively correlated with the temperature. The Cu\({}^{2+}\) needs to diffuse from the electrolyte to the bottom of the pores. Cu\({}^{2+}\) at high temperature would have a high diffusion velocity and enter the nanoscale pores more easily than that at low temperature, leading to a high pore-filling percentage. AAO film provided the dielectric performance and supporting frame for vertical interconnection, and its integrity is very important. Although high pore-filling percentage (Figure 5c) could be observed at 60 \({}^{\circ}\)C, serious corrosion of the AAO film was also observed at 60 \({}^{\circ}\)C. As shown in Figure 5e, the dissolution of AAO films in some area resulted in the exposure of CNPs. The dissolution was caused by the excessive hydrogen ions. It was mentioned above that sulfuric acid was used in the electrolyte and thus lots of hydrogen ions were contained in the electrolyte and the chemical reaction between acid and metallic oxide would occur with the following way:  
\[6\mathrm{H}^{+}+\mathrm{Al}_{2}\mathrm{O}_{3}\to 2\mathrm{Al}^{3+}+3 \mathrm{H}_{2}\mathrm{O} \tag{3}\]  
At low temperature, the chemical reaction rate is slow, which has a small influence on the corrosion of AAO. As the temperature increases, the chemical reaction rate increases gradually. Hence, the corrosion of AAO is serious at high temperature. It is necessary to balance the conflict between the damage and the high pore-filling percentage. In summary, the electrodeposition temperature of 40 \({}^{\circ}\)C was expected to be optimum, with the electrodeposition current density of 5 A/dm\({}^{2}\) in this work.
# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 4 Localization Algorithms  
Once the distances to the fixed nodes have been determined, the target can determine its position, which is performed in the so-called positioning phase.  
In this section, the range-based and Anchor-based algorithms are used, so it is necessary to have the presence of reference nodes whose position is known in advance. Also, as mentioned earlier, the RSSI is used as the distance estimation method, i.e., the target node converts the RSSI value ascribed to each Anchor into a distance, thus estimating its own distance from the reference nodes. The problem will be tackled with a two-dimensional approach, i.e., the position of the localizing node will be understood as completely known once two of its three spatial components are known. This simplification is legitimated by the type of domain that a localization system can cover. In fact, not only is the two-dimensional position compatible with all the cases of practical interest that the new frontier of indoor localization presents, but it is also relatively simple to extend most of the results to an actual three-dimensional case.
# ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion
## 2 The Proposed Method
### Rotation-Equivariant Bidirectional FPN
A \(3\times 3\) convolution is added before all the final output feature maps \(\{P_{2},P_{3},P_{4},P_{5}\}\) to reduce the aliasing effect of upsampling. Our proposed ReBiFPN achieves the following objectives:  
(1) Multi-scale information fusion: By leveraging the structure of the feature pyramid, we extract features at different scales. The fusion of features from various scales enhances the network's receptive field, enabling it to detect ships of different scales. This capability allows the network to better adapt to variations in ship size and shape, thereby improving the robustness of ship detection. (2) Bi-directional feature propagation: The use of a bi-directional feature propagation mechanism facilitates the exchange and interaction of information between different levels of the feature pyramid. This bidirectional propagation enables high-level semantic information to propagate to lower levels, enriching the semantic representation of lower-level features and enhancing their expressive capacity. Simultaneously, lower-level features can also propagate to higher levels, providing more accurate positional information, which aids in precise target localization. (3) Multi-level feature fusion: We adopt a multi-level feature fusion strategy that progressively merges features from different layers. This approach allows the network to fuse semantic and positional information at different levels, resulting in more comprehensive  
Figure 4: Structure diagram of rotation-equivariant bidirectional feature fusion feature pyramid network (ReBiFPN), where all convolution and interpolation operations are based on E(2)-equivariant steerable CNNs [22] (E2-CNNs). Figure 3: Traditional feature pyramid network (FPN) structure schematic. and accurate feature representations. Through multi-level feature fusion, the finer details and contextual information of ships can be better captured, thereby improving the accuracy of ship detection. From a theoretical standpoint, our proposed ReBiFPN demonstrates superior adaptability to scale variations compared to FPN. It effectively extracts richer semantic information and accurately localizes targets, ultimately leading to improved precision in object detection. Subsequent experiments provide further validation of these findings.
## 1 Introduction
As reported in many studies, the concentration of carotenoids in ripe grapes depends on cultivars [21], ripening stage, climate region [22], altitude [23], soil water retention capacity [21], and degree of bunch exposure to sunlight [15]. The highest carotenoid concentration occurred in the hot regions, likely due to the higher amount and intensity of the received solar radiation [22; 24]. In warm climates, the level of \(\beta\)-carotene at harvest resulted as three-six-fold higher than that of lutein [18; 19; 25]. In particular, light is the main factor responsible for the changes in the biosynthesis of carotenoids [4] promoting both their accumulation before veraison and causing their degradation during ripening [26]. The degree of the bunch exposure to sunlight appeared to influence the ratio epoxyxanthophylls: non-epoxyxanthophylls whereas high UV-B levels favored carotenoid degradation [23; 27], actually, higher rates of degradation emerged during the hotter period of grape ripening [28]. Furthermore, vineyard topographic features, such as slope gradient and aspect or altitude, along with the heterogeneity of vineyard vigor and different vine management may generate a great variability in microclimatic conditions (light, temperature, and humidity) within vineyards, canopy and bunch zone, likely influencing grape ripening and quality. The impact of the vineyard microclimatic characteristics on Nebbiolo grape development, ripening and anthocyanin accumulation, as well as on the evolution of grape norisoprenoid precursors has already been investigated [29; 30; 31]. Until now, only one study regarding the evolution of the carotenoid compounds in Nebbiolo grapes has been carried out [28]. Many studies explored the impact of artificial regulation of the bunch exposure to sunlight, by leaf removal or other canopy manipulation, on grape metabolic composition [15; 22; 23; 32]. Nevertheless,integrated studies focusing on the impact of vineyard aspect and natural vine vigor on bunch microclimate and grape aroma precursors are lacking. Therefore, the aim of this study was to complete a previous research by assessing the concentration and seasonal accumulation pattern of the most relevant carotenoids in Nebbiolo grapes as well as to study the link between bunch microclimate and carotenoid evolution during ripening.
## 3 Methodology
### Correlated Multi-Objective Energy-Efficient Routing Scheme for SDWSN-IoT
We define energy efficiency of the network as the ability of the network to perform its intended functions and tasks (e.g., data transmission and processing) while minimizing the use of energy resources. This objective has been expanded to include several other correlated objectives that necessitate simultaneous consideration, such as load balancing and reliability. Optimizing load balancing is crucial because balancing data traffic loads across the network can help prevent some nodes from depleting their energy quickly, prolonging the network's overall operation. Reliability, on the other hand, pertains to the ability of the communication link to consistently deliver data packets without errors or failures. To optimize the reliability objective, we focus on selecting links with good quality since a higher link quality usually leads to increased reliability. When the link quality is strong and stable, the chances of data packets being transmitted accurately and successfully are higher. Given these insights, optimizing both load balancing and the link quality is pivotal in enhancing the energy efficiency and overall functionality of SDWSNs-IoT. Therefore, optimizing any of the objectives mentioned above is expected to have a positive impact on the network's energy efficiency. As such, our proposed scheme revolves around the concept of dynamic objective selection, which takes into consideration that certain objectives may hold greater relevance or reliability in specific states compared to others, tailoring the decision-making process accordingly. Further details on the proposed dynamic objective selection scheme are provided in the next subsection below.
# Effects of Glyphosate and Its Metabolite AMPA on Aquatic Organisms
## 2 Glyphosate (N-(phosphonomethyl)glycine)  
Glyphosate (GLY) belongs to the phosphonoamino acid class of pesticides. Glyphosate is an acid that can be associated with different counter cations to form salts [15]. This herbicide is a crop desiccant, broad-spectrum, nonselective, postemergency herbicide that affects all annual and multiannual plants and aquatic weed control in ponds, lakes, canals, etc. [34; 35].  
Unlike GLY, whose small molecule consists of a linear chain with weak bonds, the molecules of other herbicides are usually arranged in aromatic circular structures. This difference reduces the persistence of glyphosate in the environment [36]. For higher water solubility, GLY is formulated as potassium salts or isopropylamine salts and a surfactant, poly-oxyethylene amine (POEA), is added to enhance the efficacy of the herbicide. Another formulation, Rodeo, contains the isopropylamine salt (IPA) of GLY without the surfactant and is primarily used for controlling aquatic breeds [35; 37] or Roundup Transorb, which contains a mix of 15% POEA and additional surfactants [38]. Roundup includes 48% of active agent IPA [34] or potassium salts in the range 167-480 g L\({}^{-1}\). The exact amount depends on the type of area where the Roundup is applied [39].