esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential of a SAR Small-Satellite Constellation for Rapid Monitoring of Flood Extent
## Appendix B  
Appendix B describes how the orbital elements of each satellite were defined in our method. We applied two-line element (TLE) sets, which are data formats comprising listed orbital elements. Because a TLE specifies an orbit's size and shape and how the orbit is oriented with respect to the Earth, we can simulate each satellite's movement and compute its location at a specific time. For existing satellites, public information may be available, but for a virtual constellation that includes non-existent satellites, it is necessary to first define their TLE. To overcome this issue, we selected an existing satellite and adjusted some aspects of its TLE in accordance with the structure of the WC method. Here, ICEYE-X2--one of the SAR-equipped small satellites--was selected as a reference for TLE. This satellite offers global coverage as it is in SSO. It orbits the Earth 15 times per day, and the inclination of its orbit is 97.69\({}^{\circ}\) with reference to the equator [17].  
Supposing the reference satellite's RAAN is \(RAAN_{o}\) [\({}^{\circ}\)] and its mean anomaly (MA) --the position of satellites on each orbit plane--is \(MA_{o}\) [\({}^{\circ}\)]. Following the WC method, as shown in Figure A1 (right), \(RAAN_{i}\) [\({}^{\circ}\)]--the RAAN of the \(i\)-th orbit--and \(MA_{i,j}\) [\({}^{\circ}\)]--the MA of the \(j\)-th satellite on the \(i\)-th orbit--can be calculated as follows [24]:  
\[RAAN_{i}=RAAN_{o}+i\frac{180}{P}\] (A1)  
\[MA_{i,j}=MA_{o}+iF^{\prime}+j\frac{360}{S}\] (A2)  
With regard to the other orbital elements in TLE, values identical to those of the reference satellite were used for this computation.
## Appendix A Some Properties of the Fourier Coefficients  
In this Appendix A, we study analytical properties of the Fourier coefficients \(f_{mn}\) and \(g_{mn}\) for \(m=1,2,3,4,5\) that appear in (27). Based on the definition, \(\Psi_{(+)}(x,\alpha)\) is regular in \(\tau>-k_{2}\) except for a simple pole at \(\alpha=k\cos\theta_{0}\), whereas \(\Phi_{-}(x,\alpha)\) is regular in \(\tau<k_{2}\cos\theta_{0}\). In addition, \(\Phi_{m}^{r}(x,\alpha)\) with \(m=2,\ 3,\ 4,\ 5\), and \(\Phi_{1}^{0}(x,\alpha)\) are all entire functions. Therefore, it follows that  
\[\lim_{\alpha\to i\gamma_{n}}(\alpha-i\gamma_{n})\Big{[}\Phi_{1}^{0}(x,\alpha)+ \Psi_{(+)}(x,\alpha)\Big{]}=0, \tag{101}\]  
\[\lim_{\alpha\to i\pm 1\gamma_{mn}}(\alpha\mp i\Gamma_{mn})\Phi_{m-1}^{r}(x, \alpha)=0,m=2,3,4,5. \tag{102}\]  
Substituting (29) and (30) into (101), (102), respectively and carrying out some manipulations, we obtain that  
\[\begin{array}{l}C_{5n}^{+}(i\gamma_{n})=\big{(}\frac{n\pi}{2b}\big{)}U_{(+)} (i\gamma_{n})\text{ for odd }n,\\ \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad =-\big{(}\frac{n\pi}{2b}\big{)}V_{(+)}(i\gamma_{n})\text{ for even }n,\end{array} \tag{103}\]  
and  
\[C_{mn}(\pm i\Gamma_{mn})=0,\ n=1,2,3,\dots. \tag{104}\]  
with \(m=\ 2,\ 3,\ 4,\ 5\), Equations (38) and (39) define the unknown functions \(U_{(+)}(\alpha)\) and \(V_{(+)}(\alpha)\). The set of Equations (103) and (104) provide a system of simultaneous algebraic equations that establish a connection between the unknown functions \(U_{(+)}(\alpha)\) and \(U_{(+)}(\alpha)\) and the Fourier coefficients \(f_{mn}\) and \(g_{mn}\), for \(m=1,2,3,4,5\). By solving these equations for \(f_{nm}\) and \(g_{mn}\), we derive that  
\[\begin{array}{l}f_{mn}=\frac{n\pi}{b}P_{mn}U_{(+)}(i\gamma_{n})\text{ \ for odd }n,\\ \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad =-\frac{n\pi}{b}P_{mn}V_{(+)}(i\gamma_{n})\text{ \ for even }n.\end{array} \tag{105}\]  
\[\begin{array}{l}g_{mn}=\frac{n\pi}{b}Q_{mn}U_{(+)}(i\gamma_{n})\text{ \ for odd }n,\\ \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad =-\frac{n\pi}{b}Q_{mn}V_{(+)}(i\gamma_{n})\text{ \ for even }n.\end{array} \tag{106}\]  
where  
\[\delta_{n}=\frac{\left[\rho_{4n}e^{-2\Gamma_{5n}(L_{4}-L_{5})}-\rho_{5n}\right]e^{ -2\gamma_{5n}L_{5}}}{1-\rho_{4n}\rho_{5n}e^{-2\Gamma_{5n}(L_{4}-L_{5})}}.\] (A22)  
Equations (A3)-(A6) and (A21) can be conveniently used to decompose the Wiener-Hopf Equations (38) and (39).
## 5 More on Seismic Attenuation  
### Vertical Attenuation Filters  
The attenuation performance in the horizontal plane is spoiled if not accompanied, at every stage, by matching vertical attenuation. Because of unavoidable mechanical imperfections and of the effects of Earth's sphericity, vertical noise leaks into the horizontal plane at each step of an attenuation chain [2]. Suitable vertical attenuation can be achieved using modular Geometric Anti-Spring filters in appropriate number and size to match the requirement of each individual optical element. The Geometric Anti-Spring filter is a mature technology used in Virgo upgrades [35], HAM-SAS [36], TAMA [37], KAGRA and scientific and commercial platforms. They are sophisticated but simple mechanical oscillators that can be tuned to low resonances but were never required to operate at or above 0.15 Hz. Examples of two kinds of filters that can be re-sized to satisfy all the Einstein Telescope requirements are illustrated in Figure 5. The low-frequency operation (0.12 Hz) shown in Figure 6 was achievedfor the top Geometric Anti-Spring filters of KAGRA. This tuning already matches the resonant frequency of the longest pendula envisaged in this paper.  
A caveat is that low resonant frequency is achievable only over a very narrow dynamical range, as evident in the right panel of Figure 6. If the resonant frequency is tuned to lower values, the frequency vs. height curve becomes progressively narrower. This tuning requires matching the spring strength to the payload weight at \(<\)10\({}^{-4}\) level, which can be achieved with thermal control of individual filters. The Young modulus of steel changes by \(\sim\)3 \(\times\) 10\({}^{-4}\)/K with temperature. The required load matching can be achieved with a thermal control of a tenth of a degree, which is easily achievable in a vacuum tank. A range of several degrees is needed while up to 100 K of heating is allowable without inducing creep on properly treated m  
Figure 5: A pre-isolator (**top**) and of a standard filter (**bottom**) designed and built for KAGRA. The pre-isolator is composed by a short, inverted pendulum footed directly on the bedrock in an alcove at the top. It supports a large Geometric Anti-Spring filter that in its turn suspends a chain of standard filters. The standard filter is similar but smaller. Both can be scaled to the heavier payloads of the Einstein Telescope.  
Figure 6: Tuning of a KAGRA pre-isolator Geometric Anti-Spring filter. (**Left**) Frequency tuning of the resonant frequency obtained by radial compression of the cantilever blades forming the Geometric Anti-Spring mechanism [38]. (**Right**) Dependence of the resonant frequency from the working point. The working point can be fine-tuned in situ via controls of the temperature of the vacuum tank.
# High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks
## 3 Design of the HECRA
### Data Transmission Phase  
After the cluster formation, the CH sends a control message, which causes the CMs to enter a sleeping state, sense the environment, and collect the data. During the intracluster communication phase, the CH sends a wake-up message, which is received by the CMs, and sends the collected data to the CH, which conducts operations such as fusion.  
In the intercluster communication phase, the communication between CHs involves multihop transmission; specifically, the CH broadcasts a pathfinding message, including its node ID and depth information; if another CH receives a pathfinding message, the CH that receives the message determines whether its own depth is shallower than that of the node that sent the message, replies with a confirmation message if it meets the condition, and if it does not meet the condition, it ignores the message and replies with a confirmation message after it receives a pathfinding message sent from the same node for the second time. As shown in Figure 2, the \(CH_{1}\) broadcasts a pathfinding message within its transmission distance, \(CH_{2}\), \(CH_{3}\), and \(CH_{4}\) receive the pathfinding message because the depth of \(CH_{2}\) is greater than \(CH_{1}\); thus, it does not reply to node \(CH_{1}\), while \(CH_{3}\) and \(CH_{4}\) are all smaller than \(CH_{1}\); thus, \(CH_{3}\) and \(CH_{4}\) reply to the \(CH_{1}\) confirmation message, and the content of the message includes its node ID and the forwarding weights. The formula for the forwarding weights is as follows:  
\[\omega_{i}=\alpha\frac{E_{i}}{E_{Init}}+\beta\bigg{(}1-\frac{D_{i}}{\text{ max\_D}}\bigg{)} \tag{10}\]  
where \(i\) is the number of current CHs, \(E_{i}\) is the residual energy of the node, \(E_{init}\) is the initial energy of the node, \(D_{i}\) is the depth of the node, max\(\_D\) is the maximum depth of the ocean, and \(\alpha\) and \(\beta\) are weighting coefficients that are used to balance the effects of two factors, namely, energy and depth, respectively, and \(\alpha+\beta=1\).  
After the \(CH_{1}\) receives the confirmation message replied to by node \(CH_{3}\) and node \(CH_{4}\), it compares the forwarding weights of \(CH_{3}\) and \(CH_{4}\) and selects the CH with the largest weight as the message forwarding node.
## 3 Case Study  
### Case Description  
To verify the effectiveness and applicability of the multi-objective optimization method, a simulation was conducted on an ice hockey arena in a cold region of China. The arena features an ice surface area of 1800 square meters, measuring 30 m in width and 60 m in length, and is equipped with 6 rows of bleachers. It serves as a training facility and includes 205 spectator seats. Parameters are given in Table 1.
# Diffusion Model as a Base for Cold Item Recommendation
## 1 Introduction
DMs are also applied to augment data for the next-item [41; 42; 43] and cross-domain recommendation settings [44]. DMs are inherently well suited for improving the start-time accuracy of cold items, as they control the generation process based on given conditions. By conditioning on side information, DMs can generate more \"natural\" collaborative embeddings for cold items. However, only a few studies [44; 45] have applied DMs to cold item recommendation, and these are not directly focused on improving start-time recommendation accuracy. Further investigation is needed to explore how DMs can be effectively applied in this domain. To this end, we propose a method that adopts a DM as the foundation for cold item recommendation, leveraging its generative capability without modifying existing CF-based recommender systems. Our proposed method consists of two modules. In the first module, we use a DM [46] to directly generate items' collaborative embeddings, conditioned on their side information. To more accurately generate collaborative embeddings of cold items, we adopt guidance information calculated from a mixture-of-expert architecture in the predictor of the DM. Then, the generated item collaborative embeddings, along with the user embeddings calculated by the base recommender, are passed to the second module, which refines the embeddings to enable more accurate recommendations. We train the second module using the same interaction data from the training set that was used to train the base recommender. Our contributions are three-fold:  
* To the best of our knowledge, our work is the first to evaluate the potential of DMs in improving the start-time recommendation accuracy of cold item recommendations. * We propose a simple add-on method that is easy to implement compared to more complex models, and easily applicable to existing base recommender systems without modifications. * Through extensive evaluations on real-world datasets, we demonstrated that the proposed method outperforms state-of-the-art approaches. In addition, a hyperparameter sensitivity analysis and an ablation study show how DM hyperparameters affect recommendation performance and highlight the relationship between the DM module and the refinement module. We believe these findings offer valuable insights for future research. The rest of this paper consists of five sections. Section 2 provides a comprehensive overview of existing research relevant to this study, and contrasts the differences between prior work and the proposed method. Section 3 describes the proposed method in detail, outlining the underlying concepts and explain details for implementation. Section 4 gives the evaluation details including the dataset, evaluation metrics, and comparison methods. The evaluation and analysis of the proposed method, along with a discussion of the results, are presented in Section 5. Finally, Section 6 concludes the paper by summarizing the key findings and suggesting directions for future work.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 3 Water Station Infrastructure  
The water station consists of two water tanks each surrounded by several wells at a distance. The main tank is supplied by ten wells, and the secondary by four wells, as shown in Figure 1. However, one of the wells supplying the secondary tank is merely a few meters away from the tank.  
According to [2], the automation system design features two PLCs assigned to each tank, which are responsible for processing control signals to op  
Figure 1: Water Well and Pipeline Network of Sof-Algeen Station—Archived Map from the Water and Sanitation Branch in Zintan City (Drawn by Eng. [PERSON], 1 January 2017).  
throughout the network. This operation is guided by feedback from level sensors and differential pressure readings that monitor water levels within tanks and flow within the pipelines, a process made complex by the widespread distribution of the wells. Instruments located near the PLCs, such as the level sensors in the water tanks and the pressure switches of the centrifugal pumps, are directly wired, simplifying their integration.  
Based on the automation system and the structure of the water station, determining the locations of the nodes in each well and using the water tanks as the base station (BS) where the PLCs are to be located, several network topologies can be proposed. These topologies are configurations for the wireless network, chosen depending on the wireless technology used (e.g., IEEE 802.11 or Trusted Wireless). They enable the wireless network to provide reliable and scalable communication, which in turn supports the automation system. The proposed network topologies are as follows:  
Star topology: This configuration allows for easy setup and straightforward fault detection, as each device communicates through the central node. Its downfall is its reliance on the central node, as its failure can disable the entire network [17];  
Mesh topology: in a mesh topology, nodes are not only connected to the central node but also to other nodes in the network, allowing data to take multiple paths from source to destination [17];  
Cluster-tree topology: This topology combines elements of multiple star topologies into a larger, tree-like network [18]. In the water network, this topology could position the BS as the root, with clusters of wells as intermediary nodes extending to individual wells.
## 2 Materials and Methods  
According to [48], the case study methodology allows an understanding and explanation of more complex problems. Given the still limited amount of research that concerns VRPs with delivery and furniture assembly, the case study methodology was used since it allowed to adapt to the problem, as it had a high level of flexibility compared to other qualitative methods [49]; this study was also considered an exploratory investigation [50]. Similar to [51] or [52], a single case study was chosen, considering its potential to be used as a base for future multiple case study approaches. In this case study, the unit of analysis considered is the process of route planning for customers that request the delivery, assembly, and installation of furniture at their homes.  
The company under study delivers, assembles, and installs furniture and other products for the IKEA brand. Operating in mainland Portugal, this study includes the company's routes carried out within the area of Lisbon and the Tagus Valley.  
At the time of this research, in the company under study, the route creation process was not automated. It was performed manually by a company worker. This may result in routes with very long service times or, on the other hand, in routes that may have reduced service times. Consequently, there is a need to reschedule the team allocated to a specific route. From the end customer side, the customers may refuse to receive the items outside the established time window. In this sense, there is a need to find alternative solutions that support the process of route planning. Thus, the main aim of this case study is to develop a heuristic approach that supports the process of route planning, considering the delivery, assembly, and installation of furniture.  
All routes' starting point is the company's warehouse, and the ending point is the last customer inserted in the route.  
Each route is executed by a single team, selected from a set of available teams with different skills:  
* Each team has a vehicle with a specific transport capacity;
* The customers time window must be respected;
* Constraints must be respected.  
The research comprised four main phases that are presented and described in Figure 1:  
(1) Problem characterization; (2) Data collection; (3) Development of the heuristic approach;  
(4) Validation of the heuristic approach. The following subsections describe in detail each one of these phases.
## 3 Results
### Simulation of GFET  
To simulate the band structure of honeycomb graphene lattice, a proper unit cell, Figure 1, with the nearest neighbor approximation is chosen to compute the Hamiltonian matrix, \(h(k)\). Then, \(h(k)\) eigenvalues are extracted as energy for different values of \(k\). The simulated results show the energy dispersion of Bulk Graphene has no bandgap, and the conduction and valence bands touch each other. The metallic nature and ambipolar conduction of the bulk graphene are due to its zero bandgap. There are six points of high symmetry in the band structure of bulk graphene at \((k_{x}a,k_{y}b)=(0,\,\pm 2\pi/3)\), \((\pm\pi,\pm\pi/3)\) as observed from Figure 4, so the bandgap is zero at these points. The E-k relationship around of the high symmetry points is almost linear, that shows the 'nearly massless' nature of electron in graphene. Additionally, the simple tight-binding model predicts that Zigzag GNR (_ZGNR_) has no bandgap, regardless of its width. The band structure of wide ZGNR looks similar to that of bulk graphene quantized along the \(k_{x}\) axis, Figure 5a. Narrow Armchair GNR (AGNR) has a bandgap similar to semiconductors for particular values of the _number of armchairs_, _Na_, so it can be used as a semiconductor to create graphene-based FET, Figure 6.  
Figure 4: Band structure of graphene. (**a**) E–K surface plot for bulk graphene; (**b**) contour plot of conduction band.  
Figure 5: Energy dispersion. (**a**) Energy versus \(k_{y}(k_{x}=0)\); (**b**) energy versus \(k_{x}(k_{y}=0)\).  
Figure 6: _Cont._  
The bandgap of AGNR for armchair number, \(N_{a}=3l;+2;l\) integer, loses its semiconducting properties and becomes metallic. Additionally, the bandgap of AGNR decreases with increasing width. The band structure of wide AGNR looks similar to that of bulk graphene sampled along the \(k_{y}\). axis (Figure 5b).
# Seismic Response of Shield Tunnel with Double-Layer Lining
## 4 Result Analysis  
### Structural Acceleration  
Taking the acceleration at the crown as an example, here analyzes the acceleration response characteristics of the shield tunnel with double-layer lining (Figure 5).  
From Figure 4, it can be observed that the seismic acceleration curve shapes of the segmental rings and secondary lining are largely consistent, indicating good transfer of internal forces between layers. The initial seismic response of the tunnel structure is relatively small, with both the segmental lining and secondary lining reaching peak acceleration at approximately 6.4 s. The maximum acceleration on the segmental lining is 0.37 g, which exceeds the maximum acceleration of 0.36 g on the secondary lining. This indicates that the segmental lining directly experiences forced displacement transmitted from the stratum, resulting in a more pronounced seismic response.  
By extracting the maximum accelerations of the tunnel structure and the stratum at the same height, the seismic acceleration amplification factor can be obtained (Figure 6).  
From the above figure, it is evident that at the invert, the acceleration amplification factor of the segmental lining and stratum is relatively small, around 1.9, while that of the secondary lining is approximately 2.02. As the burial depth decreases, the acceleration amplification factor in the structure and the stratum gradually increases. At the crown, the acceleration amplification factor of the segmental lining, secondary lining, and stratum are more consistent, with a value of approximately 2.45. The acceleration amplification law on the segmental lining is generally consistent with that of the surrounding stratum due to the constraining effect of the stratum on the segmental lining, resulting in seismic responses consistent with the adjacent stratum. However, the acceleration amplification factor on the secondary lining is generally greater than that on the segmental lining. This is because the secondary lining has one side facing the tunnel clearance, experiencing less constraint, thus exhibiting a more pronounced acceleration response.  
Figure 5: Acceleration time–history curve: (**a**) Segmental lining; (**b**) Secondary lining.  
In conclusion, there is a certain degree of fluctuation in the acceleration amplification effect between the tunnel structure and stratum, but overall, the acceleration amplification factors of the tunnel structure gradually increase from the invert to the crown. Due to the strong interaction between the stratum and the segmental lining, the acceleration amplification factors of these two at the same location are generally consistent.
# Compact Beam-Scanning Reflectarray Antenna with SLL Reduction Using In-Plane Panel Translations
## 3 Multi-Stage Design
### SLL Reduction
The illumination taper contours at \(-9\) dB for panel positions \(d_{\pm 11}\) are overlaid in all three cases.