esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Analysis
### Radiation Pattern  
#### 4.3.1 E-Plane  
The main lobe is narrow and directive, centered at 0\({}^{\circ}\), confirming forward-focused radiation, as shown in Figure 7. An excellent front-to-back ratio and minimal side lobes improve spatial filtering. Strong E-plane directivity is achieved due to uniform current distribution from the Wilkinson feed and symmetrical patch arrangement. This ensures efficient beamforming along the boresight, with minimal back radiation, desirable in on-device or vehicular environments.  
Figure 7: \(Cont.\)  
In Figure 7, a broad lobe centered at \(0^{\circ}\) confirms that the antenna radiates forward effectively. Peak gain is shown around 11.5 dBi, consistent with measured results. No strong side lobes or back radiation are shown. This is also typical for a well-matched microstrip-fed array. The energy is concentrated in the forward direction, and vertically, the spread is less focused than horizontally, as expected. This reinforces that the antenna is meant for planar targeting, not elevation scanning.
## 2 Frequency Characteristic by Exchange of Frequency Containment Reserves Control
### Exchange of Frequency Containment Reserve (E-FCR)  
The exchange of FCR control is another common way of managing HVDC to sustain a frequency comparable to the droop concept on HVDC. This study [18] presented the HVDC link between GB and CE utilizing multiple parameter gains values (Kp) as an exchange of the FCR control. The remarkable differences of exchange frequency containment reserves (FCRs), i.e., the input signal for E-FCR control only uses the frequency difference from one specific region [9; 18]. As shown in Figure 3, a signal from System B, also known as Jeju Island, is used for the E-FCR control operation. Moreover, the frequency variation of Jeju Island (System B) is maintained at a value of 60 Hz during normal operation. However, the exchange of FCR control via HVDC is initiated when a contingency is applied.  
As shown in Figure 3, the topology control for the E-FCR control is uncommon because an LFC system network will only be able to adjust MIDC power based on the frequency deviation of one area. This characteristic makes the E-FCR method susceptible to frequency data sharing since it enforces just one broad area measurement, which is often left unsecured and makes the LFC system more open to cyber attacks. Additionally, the actual frequency droop response, i.e., 1%, of the Korean power system was discussed [19] to describe the various aspects of balancing arrangement schemes in the event of a cyberattack operation. On the other hand, in this study, the author approached similar droop values for each HVDC system. The main reason similar droop values are enforced is to increase awareness of power system operators in terms of cyber attack detection in the MIDC system. The gain value of \(\text{Kp}_{\text{(LCC/VSC)}}\) is applied based on the following equation:  
\[\text{Kp}_{\text{(lcc/VSC)}}=\frac{\text{Prating}_{\text{(lcc/VSC)}}}{\text{f}_ {0}\times\text{ Droop}_{\text{(lcc/VSC)}}\left(\%\right)} \tag{3}\]  
where \(\text{Prating}_{\text{(lcc/VSC)}}\) is the maximum capacity of each HVDC line, i.e., 400 and 200 MW for LCC and VSC, and \(\text{Droop}_{\text{(lcc/VSC)}}\) is the real percentage value that is usually applied in the real HVDC frequency control in Korea's power systems. A specific droop value for each HVDC system is enforced, e.g., 1% in LCC and VSC HVDC. The remarkable difference in \(\text{Prating}_{\text{(lcc/VSC)}}\) value will allow power system operators to adjust different gain value operations between two types of HVDC. In addition, through this situation, power system operators could maintain network security, especially when blind data and low-cost cyber attacks occur, i.e., attacks which only focus on one rectifier station.  
Figure 3: E-FCR control through HVDC operation.
# Routing Protocols for Delay Tolerant Networks:
## 3 Performance Evaluation
### Impact of the Mobility Model on Performance
One should follow a different approach, for example using the approach of [5], where the mobility patterns of a very small subset of the nodes is dictated by the communication protocol in order to overcome cases of network partitions.
# Recommendation Algorithm for Multi-Task Learning with Directed Graph Convolutional Networks
## 3 Multi-Task Learning Recommendation Model for Directed Graph Convolutional Networks  
This section takes advantage of the respective advantages of the directed graph convolutional network, the TransH algorithm, and multi-task learning to propose a multi-task learning recommendation model for directed graph convolutional network (MTL-DGCNR), the overall framework of which is shown in Figure 1, including the DGCNR layer, the knowledge graph embedding layer, and the MTL learning layer. The DGCNR layer is further divided into an input layer, a preprocessing layer, a DGCN layer, and an output layer. In the input layer, the user's micro-behavior \(m_{i}\) and feature matrix \(X\) are defined through the interaction information between the user and the product in the user session; the data source of the preprocessing layer is \(m_{i}\) and \(X\), and the first-order proximity and second-order proximity of each node are defined according to the generated directed graph. The first-order proximity, the second in-degree proximity, and the second-order out-degree proximity are used as the input of the DGCN layer; the DGCN layer includes the proximity convolution module, the mapping \(Z\) module, and the attention mechanism module. The proximity convolution module performs the convolution operation on the output of the preprocessing layer. The local preference \(Z_{L}\) of each node is obtained by \(Z\) module's aggregating, and the attention mechanism is used to assign appropriate weights to each node, and finally the global session s is generated; the output layer is composed of a multi-layer perceptron (MLP) and a softmax classifier. The global session \(s\) and the user candidate item set \(i_{j}\) are embedded into a multi-layer perceptron (MLP), which calculates the final score through the softmax classifier to obtain the loss value of the DGCNR model. DGCNR completes the training of commodity information and operation behaviors involved in the training samples. In the knowledge graph embedding layer, the knowledge graph of the product is defined as a triplet \(<\)entity, relationship, entity\(>\) or \(<\)entity, attribute, attribute value\(>\), and the TransH algorithm is used for learning them. In the MTL learning layer, the knowledge graph embedding is introduced into the MTL paradigm as an auxiliary task to assist the recommendation task of DGCNR. Each part is described in detail below.
## 5 Conclusions  
Mapping allergenic tree species in highly urbanized areas is an important task as it helps for an understanding of their distribution and prevalence in urban areas. Still, most  
Figure 4: Allergenic heatmap derived from ground reference points (**first row**); classification scenario 3 (**second row**); subtraction of the ground reference points heatmap and CS3 heatmap (**third row**).  
studies concentrate on allerggenic pollen vegetation mapping on a large scale, and accurate detection of individual allergenic tree species is challenging due to their smaller site and patchiness in urban green spaces. Therefore, this research tried to assess the feasibility of mapping allergenic tree species in highly urbanized areas using high-resolution PlanetScope imagery. Secondly, to evaluate and compare the RF, XGB, and MLP classifiers and feature selection methods for the accurate detection of individual allergenic tree species.  
Therefore, this research showed the feasibility of using multitemporal eight-band PlanetScope imagery to detect allergenic tree species in highly urbanized areas. Furthermore, for the supervised classification of the allergenic tree species in urban areas, the XGB method, with an OA of 73.13%, outperformed the RF and MLP methods by using multitemporal PS imagery with vegetation indices and texture features. In terms of OA, classification accuracy increased by 22.88% and 2.86% from the classification on single-date imagery and multitemporal imagery with VI, respectively. In the multitemporal classification scenarios (i.e., CS2 and CS3), the plane trees were detected with the lowest number of false positives and false negatives.  
The PS imagery turned out to be highly useful for tree mapping in urban green areas. By identifying and mapping allergenic tree species, city planners and public health officials can make informed decisions about tree planting and management. Future research should combine satellite imagery with very high-resolution (e.g., Pleiades, GeoEye) and LiDAR datasets with deep learning methods because it has the capability to identify and classify dominant tree species in highly complex urban environments with increased accuracy.  
Conceptualization, M.G. and I.P.; methodology, M.G. and D.D.; software, D.D.; validation, M.G. and I.P.; formal analysis, D.D.; investigation, D.D.; resources, M.G. and I.P.; data curation, M.G. and D.D.; writing--original draft preparation, D.D.; writing--review and editing, M.G., D.D. and I.P.; visualization, M.G.; supervision, M.G.; project administration, M.G.; funding acquisition, M.G. and I.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by the European Space Agency (ESA) as part of the scientific project: \"Automatic monitoring of narrow-leaved ash (Fraxinus angustifolia Vahl) forests by remote sensing methods and Copernicus data (Grant No. RS4 EST)\".  
The authors declare no conflict of interest.
## 3 Link Level Parameters and Channel Coefficient Generation
### Small Scale Parameters
#### 3.3.2 Cluster Powers  
The second set of parameters is the cluster power, denoted by \(P_{n}\). For each cluster, we first compute  
\[P_{n}^{\prime}=\exp\biggl{(}-\tau_{n}\frac{r_{\tau}-1}{r_{\tau}DS}\biggr{)}10^ {-Z_{n}/10} \tag{40}\]  
where \(Z_{n}\sim\mathcal{N}(0,\zeta^{2})\), \(\zeta\) represents the per-cluster shadowing and can be found in Table 7.5-6 of [21]. In order to have the sum of the cluster powers equal to one, we divide the previous power set \(P_{n}^{\prime}\) by the total power and so we obtain the normalized set \(P_{n}\)  
\[P_{n}=\frac{P_{n}^{\prime}}{\sum_{n=1}^{N_{cl}}P_{n}^{\prime}}. \tag{41}\]  
In the case of \(LOS\) condition, an additional specular component is added to the first cluster \(P_{1,LOS}=K_{R}/(K_{R}+1)\), where \(K_{R}\) is the Ricean K-factor converted to linear scale. Therefore, in the case of \(LOS\), we need to change the normalization strategy as follows:  
\[P_{n}=\frac{1}{K_{R}+1}\frac{P_{n}^{\prime}}{\sum_{n=1}^{N_{cl}}P_{n}^{\prime }}+\delta(n-1)P_{1,LOS}. \tag{42}\]  
After the normalization, we remove the clusters with less than \(-25\) dB power compared to the maximum cluster power. The scaling factor does not need to be changed after cluster elimination. Once we have the cluster powers \(P_{n}\), we assign the power to each ray by splitting \(P_{n}\) equally over the total number of rays, so \(P_{n,m}=P_{n}/M_{ray}\), where \(n=1,\dots,N_{cl}\) is the cluster index and \(m=1,\dots,M_{ray}\) is the ray index.
## 2 Materials and Methods
### Excitation Modelling
#### 2.1.3 The Proposed Spatiotemporal Mining-Induced Kinematic Excitation (SMIKE) Model
Based on thousands of recordings, the empirical formulas for tremor peak accelerations have been proposed for strong seismic events [41]:  
* The relation between the peak epicentral acceleration \(a_{0}\) and the seismic energy within the range \(2\times 10^{5}\leq E\leq 10^{9}\) J for tremors of local magnitude \(1.8\leq M_{L}\leq 4\) \[a_{0}=1.33\times 10^{-3}\cdot(logE)^{2.66}-0.089\] (13) where: \(a_{0}\) and \(E\) are expressed in [m/s\({}^{2}\)] and [J], respectively. * The function describing the decrease in bedrock peak acceleration \(a_{R}\) outside the epicentral zone (which is assumed to be 1 km in radius) for tremors of local magnitude \(1.8\leq M_{L}\leq 3\) with increasing epicentral distance (up to 10 km) \[a_{R}=a_{0}\cdot H(R)\] (14)where: \(H(R)=1.53\cdot R^{0.155}\cdot e^{-0.65\cdot R}+0.014\ R=\sqrt{r^{2}+0.52}\), \(r\) is the epicentral distance in [km], and 0.5 km is the mean depth of tremors in the USCB. The effects of subsurface soil layers on the value of ground accelerations depend on the layer thickness, type, shock energy, and seismic wave frequency. The maximal surface acceleration \(a_{SR}\) is obtained by multiplying the bedrock acceleration by the amplification factor \(w_{a}\)  
\[a_{SR}=a_{R}\cdot w_{a} \tag{15}\]  
The values of the amplification factor for the USCB range from 0.5 to 4.5. Exemplary values are collated in Table 1[41]. As one can see, the local site conditions play a crucial role in amplifying the bedrock accelerations, and the amplifying factor should be determined based on local data.
## 1 Introduction
To simulate a turbulent flow, the RANS(Reynolds-Averaged Navier-Stokes) modeling approach has been widely employed leading to the Reynolds-averaged N-S, where the instantaneous values of velocity and pressure are defined as the sum of a mean and fluctuating component [49]. The simplest RANS models are the eddy viscosity models (based on the Boussinesq hypothesis) where the Reynolds stresses are modeled using an eddy (or turbulent) viscosity \(\
u_{\rm t}\). The eddy viscosity can be determined from a turbulent time-scale (or velocity scale) and a turbulent length-scale using the turbulent kinetic energy (\(k\)) and the turbulence dissipation rate (\(\varepsilon\)). The most commonly-applied turbulence model for its reasonable accuracy to estimate \(\
u_{\rm t}\) for a wide range of flows, is the two-equation \(k\)-\(\varepsilon\) model [50]. The two-equation \(k\)-\(\varepsilon\) model has been utilized in [PERSON] [51; 52] and [PERSON] et al. [53], showing a good comparison between the numerical and experimental turbulent quantities. Another more recent approach is the Large Eddy Simulation (LES). The main difference between the RANS and LES approaches is that the N-S Equations are, in the former, time-averaged and, in the latter, space filtered. Within Eulerian methods, the RANS approach is the most widely used, followed by LES due to its lower computational cost. Recently, hybrid RANS-LES methods, such as detached eddy simulation (DES), are growing in popularity [54]. The purpose of this paper is to show the versatility of a grid-based and meshless method to simulate free surface hydrodynamic problems. In Section 2, focus is put on giving the basic concepts of both numerical formulations; in Section 3, as an application, both a weakly compressible smoothed particle (WCSPH) model, coupled with a sub-particle scale (SPS) turbulence model and a non-hydrostatic discontinuous/continuous Galerkin model (non-hydrostatic D/C Galerkin) are used to study the physics of regular and solitary waves propagation, breaking and run-up.
## 4 Theoretical Derivation
### Influence of Thickness of the Frozen Curtain on Pipeline Settlement  
To delve deeper into the complex relationship between the thickness of the frozen curtain and pipe settlement presented in Figure 13, we can explore the underlying physical mechanisms and engineering effects. With the increase in the thickness of the frozen curtain, its performance as a support structure is significantly enhanced, which is comprehensive in nature. Firstly, the thicker frozen curtain acts as a more solid barrier, and its enhanced support capacity is directly reflected in the effective resistance to the surrounding soil pressure and deformation. This enhanced resistance not only limits the scope and intensity of soil disturbance during tunnel excavation, but also significantly reduces the resulting soil settlement, providing important assurances for the safety and stability of tunnel construction.  
During another critical stage of tunnel construction--the thawing process--the thicker frozen curtains also exhibit their unique advantages. With higher strength and stability, these thick frozen soil walls can release internal stress more slowly and contentedly in response to the challenge of melting, thus reducing the risk of sudden subsidence caused by rapid melting. Smaller melting volume and slower melting speed mean that the stability of the subsurface structure is better maintained, which is conducive to the smooth progress of construction and the improvement of final engineering quality.  
It is particularly noteworthy that when the thickness of the frozen curtain is increased from 0.5 m to 1 m, the subsidence phenomenon above the tunnel shows a significant reduction. This transition indicates that the 1 m thick frozen curtain has been able to exert a strong support role during excavation, effectively alleviating the negative impact of ground disturbance. However, as the thickness of the frozen curtain is further increased to 2 m, although the subsidence volume continues to decrease, the rate of decrease is not as significant as before. This phenomenon can be explained from the perspective of mechanics and the saturation effect of materials: when the frozen curtain reaches a certain thickness, the growth of its bearing capacity will gradually become flat, i.e., reaching a \"saturated\"  
Figure 12: Influence of tunnel freezing temperature on pipeline settlement.  
state. At this point, the marginal benefits of further increasing the thickness for subsidence control will gradually decrease.  
In summary, the rational design of tunnel freezing curtain thickness is one of the key factors in controlling pipe settlement, ensuring construction safety and engineering quality. By scientific calculation and engineering practice, determining the optimal freezing curtain thickness can effectively reduce ground disturbance and settlement while optimizing construction costs and time, achieving a win-win situation of economic benefits and social benefits.
# Optimal Routing for Time-Driven EH-WSN under Regular Energy Sources
###### Abstract  
The recent provision of energy-harvesting capabilities to wireless sensor networks (WSN) has entailed the redefinition of design objectives. Specifically, the traditional goal of maximizing network lifetime has been replaced by optimizing network performance, namely delay and throughput. The present paper contributes to this reformulation by considering the routing problem for the class of time-driven energy-harvesting WSN (EH-WSN) under regular or quasi-periodic energy sources. In particular, this paper shows that the minimum hop count (MHC) criterion maximizes the average duty cycle that can be sustained by nodes in this type of scenarios. This is a primary objective in EH-WSN, since large duty cycles lead to enhanced performance. Based on a previous result, a general expression is first obtained that gives mathematical form to the relationship between duty cycle and traffic load for any node in a time-driven EH-WSN fed by a regular energy source. This expression reveals that the duty cycle achievable by a node decreases as its traffic load increases. Then, it is shown that MHC minimizes the average traffic load over the network, and thus it maximizes the average duty cycle of nodes. This result is numerically validated via simulation by comparison with other well-known routing strategies. Accordingly, this paper suggests assigning top priority to the MHC criterion in the development of routing protocols for time-driven EH-WSN under regular energy sources.  
energy-harvesting wireless sensor network; solar radiation; energy consumption model; duty cycle; throughput; medium access control layer; routing; minimum hop count; shortest-path routing +
Footnote †: journal: Sensors
# Structure Control Using Bioderived Solvents in Electrochemical Metal-Organic Framework Synthesis
## 3 Results
### Synthesis with CyreneTM  
Using the synthesis conditions described in the previous literature for use with traditional solvents as a starting point [6], the quality of the crystalline MOF coatings obtained when using CyreneTM as the solvent were optimized by varying linker concentration, solvent ratios, reaction temperature and reaction duration. The conditions detailed in Table 1 are the result of this optimization process.  
The XRD patterns of the MOF coatings obtained when using CyreneTM for Cu and BTC, Zn and mIM, Zn and bIM, Co and mIM, Zn and eIM and Zn and IM are shown in Figure 1. In three cases, Cu and BTC, Zn and mIM and Co and mIM, the crystalline MOF coatings were the same phase as obtained using traditional solvents as confirmed by comparison with the predicted peak positions [27, 28]. It is worth noting that the pattern obtained, of HKUST-1, ZIF-8 and ZIF-67, respectively, displayed comparatively low peak intensities at low angle (Figure 1A,B,D) in comparison to that obtained with the traditional solvents (Figure 1A,B,D). This is attributed to difficulties encountered with background correcting the data caused by the greater influence of the underlying metal electrode on some of the samples.  
In the other three cases the use of CyreneTM as the solvent has led to significant changes in the phase of MOF obtained as the coating. In the case of Zn and eIM (Figure 1E) the presence of the bio-solvent has led to the formation of a purer coating composed entirely of ZIF-14 with no trace of the RHO phase previously observed detected [27, 28]. Conversely with Zn and IM (Figure 1F) changing the solvent has led to the formation of a coating that appears to be composed of a mixture of three different phases [27, 28, 30]. This ability to apparently select which phase is formed simply as a function of the solvent used is of interest as much research has been devoted the use of additives for achieving this [31]. Zn and bIM also form a completely different phase than expected in the presence of CyreneTM (Figure 1C) but unfortunately due in part to the small amount of material obtained identification of this material has not yet been possible.  
The SEM images of these coatings (Figure 2) corroborate much of what was observed in the XRD data. The images generally show sparser coatings, with more bare metal electrode visible, which explains the issue with backgrounding and Figure 2C clearly shows little material was synthesized in the case of Zn and bIM. It is also clear that the crystalline quality of the coating is significantly improved when using CyreneTM, with this particularly apparent when comparing Figure 2D-F to Figure 2D-F.  
Figure 1: XRD patterns of the MOF coatings obtained using CyreneTM as the solvent for: (**A**) Cu and BTC; (**B**) Zn and mIM; (**C**) Zn and bIM; (**D**) Co and mIM; (**E**) Zn and eIM; (**F**) Zn and IM compared against predicted peak position values [27; 28; 30; 31].