esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Joint Resource Allocation for Multibeam Satellite Based on Metaheuristic Algorithms
## 3 Constraint Handling Method
### Complexity Analysis and General Application Architecture  
Assume that \(M\) denotes the total number of beams, \(G_{e}\) represents the number of generations, and \(N_{pop}\) is the algorithm's population size. For the UBA method, the computational complexities associated with eliminating overlapping bandwidth and allocating unused bandwidth are \(O\big{(}G_{e}N_{pop}M\big{)}\) and \(O\big{(}G_{e}N_{pop}M\big{)}\), respectively. The total computational complexity is as follows:  
\[O\big{(}G_{e}N_{pop}M+G_{e}N_{pop}M\big{)}=O\big{(}2G_{e}N_{pop}M\big{)}. \tag{21}\]  
Figure 4: Principles of coding methods and algorithm evolution (NDBC). A column of adjacent beams is set up one by one as a dominant beam column and a non-dominant beam column, respectively. The two beam columns are denoted as \(BS_{D}=\{2,4,\cdots\}\) and \(BS_{ND}=\{1,3,\cdots,N_{b}\}\), with \(N^{d}\) and \(N^{nd}\) beams, respectively.  
Figure 5: Calculation of the bandwidth of the dominant beam. To maximize bandwidth utilization, we set bandwidth of the dominant beam to its maximum value (subject to the constraints). Although unused bandwidth remains, this bandwidth cannot be reallocated in the current scenario.  
For the NDBC method, the computational complexity of the method is primarily attributable to the computation of the dominant beam's bandwidth. We assume \(M^{nd}\) as the total number of non-dominated beams. The computational complexity is as follows:  
\[O\Big{(}G_{e}N_{pop}M^{nd}\Big{)}\approx O\bigg{(}\frac{1}{2}G_{e}N_{pop}M \bigg{)}. \tag{22}\]  
Therefore, the computational complexity of NDBC is approximately one-fourth that of UBA. As shown in Table 1, we investigated the degree of beam dominance for UBA and NDBC in order to determine the effect of the bandwidth constraint handling method on the search flexibility of the algorithm. As the order is randomly reversed in step 1 for UBA, the first and last beams are actually dominated by their neighboring beams. The non-dominated beams in NDBC are not dominated by one another, whereas the first and last beams are dominated by only one of their neighboring beams. We quantify the number of neighboring beams that can exert influence on a beam as its degree of dominance; the greater the degree of dominance, the less flexible the beam's bandwidth.  
As shown in Table 1, assuming a column of adjacent beams with \(M\) beams, the UBA method is dominated by two adjacent beams for almost all beams. On the other hand, NDBC only needs to encode and optimize the bandwidth of the non-dominated beams, thereby practically achieving complete search flexibility. This modification effectively enhances the algorithm's global optimization performance. The general architecture of the NDBC method applied to the metaheuristic algorithm is depicted in Table 2.
# Find You: Multi-View-Based Location Inference for Twitter Users
## 5 Experimental Results  
In Section 5, we present the evaluation results of the location inference model proposed in this work on the public dataset. Specifically, we conduct extensive experiments to answer the following research questions:  
* How well does the MVGeo model proposed in this work perform on location inference compared to current state-of-the-art baseline models?
* How well does the model proposed in this work perform when trained on a small amount of data using only the tweet view?
* How do the tweet view and the network view affect the inference effectiveness of the model?
* What is a good choice for the number of subdistributions for the Gaussian mixture model in the network view?
# Enabling Autonomous Agents for Mobile Wireless Sensor Networks
## 6 Tests and Results
### Test Methodology  
The tests are carried out using the Stage simulator. The simulated environment consists of a wall-delimited obstacle-free 150 by 150 m area. In this area, 100 differential-driven robots are placed, as a direct comparison with our previous work [43], although different scenarios can be set up in terms of robot density or robot models, as explained in Section 5.6.  
Robots solely use LiDAR signals within a range of 3.5 m and the required sensors for odometry estimation. The characteristics present in these robots make them comparable with actual robotic platforms like Turtlebot 3. This fact, combined with the usage of ROS 2, can enable large-scale deployments of multirobot systems like this one in real scenarios.  
The set of tests performed is stated in Table 3. With respect to SPF-only tests, our baseline values, an analysis of the \(\alpha\) parameter is carried out by testing different values such as 4, 5, 6, 7, and 10, with lower numerical values representing weaker cohesion forces. Regarding the agents' strategy, the three previously described algorithms (Section 5.5) are tested. In addition, we examine whether the agents' negotiation algorithm could replace the cohesion forces involved in the readjustment operation. Avoiding the need to set a specific value is a complex and time-consuming process that can significantly impact the results. To assess this, tests without cohesion forces are conducted using the latest algorithm, and each algorithm is labelled accordingly as 'alg0', 'alg1', 'alg2', and 'alg2 sc' for Algorithms 0, 1, 2, and 2 without cohesion force, respectively. As in the SPF-only test, several alpha values are considered, although for clarity's sake, only the results for the optimal alpha value (1.6) are presented. To ensure statistical significance of the results, 30 runs for every test configuration are performed.
# Research on Path and Sequence Planning for Multi-Cabin Onboard Transportation of Large Cruise Ships
## 2 Mathematical Model of Multi-Cabin Transportation Path Planning
### Design of Model Variables and Constraints  
Cabin module transportation path planning can be divided into two primary components: the optimization of the cabin transportation sequence and the optimization of the transportation path for each cabin. The optimization of the cabin transportation sequence is a Project Scheduling Problem (PSP), where the design variable is an ordered sequence that includes all the cabins to be transported. The optimization of the transportation path for each cabin is a Traveling Salesman Problem (TSP), typically modeled using a grid method to describe the environment. Obstacles are represented as obstacle grids, and the starting point, target point, and constraint points are defined by their Cartesian coordinates. The design variable in this case is the turning direction of the cabins at each grid. Figure 2 illustrates the grid division of the cabin module transportation environment. A two-dimensional Cartesian coordinate system is established within the area, with the origin, x-axis, and y-axis defined, and the positive directions of the axes determined. Each grid is sequentially numbered from the origin along the positive directions of the x-axis and y-axis. The \(\textsl{g}_{\text{begin}}\) indicates the starting grid. Each cabin module's starting point and destination can be defined on the grid. Obstacles include non-prefabricated cabin obstacles and cabin wall obstacles; non-prefabricated cabin obstacles are represented by single grid points, while cabin wall obstacles are represented by the grid boundaries. The goal is to determine an optimal path from the starting point (A9) to the target point (J5), ensuring that the cabin module can safely navigate through all obstacles and reach its target position in the shortest possible time, without any collisions.  
During the cabin module transportation process, there are numerous constraints, such as the maximum allowable number of cabins to be transported, the maximum allowable number of cabins to be installed, and the specified installation time for each cabin. Therefore, before planning the transportation paths, these constraints should be accurately defined by the shipyard's construction personnel to ensure the path planning aligns with the actual production requirements.  
Additionally, the transportation area where the cabin modules are located contains many non-prefabricated cabins and bulkheads, which must be considered in the grid map. In this study, non-prefabricated cabins are treated as grid obstacles, while bulkheads are treated as line obstacles. It is important to note that, compared to the cabin modules, the thickness of bulkheads is relatively thin, so the time spent passing through line obstacles is negligible during the transportation of the cabin modules.  
Figure 2: Grid division diagram.
## 3 Proposed Watermarking Framework
### Structure of Watermarking Network to Be Trained
#### 3.2.3 WM Embedding Network  
The WM embedding network concatenates the results of the 64 channels of the pre-processed host information and one channel of the pre-processed WM information and uses them as the input to output the watermarked image information. The network consists of CL-BN-AF (ReLU) for the front four blocks, and the last block consists of CL-AF (tanh). The tanh activation maintains the positive and negative values to meet the data range of \([-1,1]\) to the input host information. All CL strides are set to 1 to maintain the resolution of the host image for invisibility. All blocks, except the last one, have 64 CL filters; the last block has the same number of filters as the number of channels in the host image, which is one here. Because we are aiming for invisible watermarking, we use the mean square error (MSE) between the watermarked image (\(I_{WMed}\)) and the host image (\(I_{host}\)) as a loss function (\(L_{1}\)) of the pre-processing network and the embedded network. This is shown in Equation (1).  
Figure 2: Structure of proposed network for training.  
\[L_{1}=\frac{1}{MN}\sum_{i,j}^{MN}\left[I_{host}(i,j)-I_{WMed}(i,j)\right]^{2} \tag{1}\]  
Here, \(M\times N\) is the resolution of the host image.
# A Novel QoS Provisioning Algorithm for Optimal Multicast Routing in WMNs
## 3 The Proposed NMHA Algorithm
### Analysis of the NMHA Algorithm
 \tag{14}\]  
According to Equations (11), (12) and (14), it has  
\[\frac{w_{i}(\mathbf{T}^{\mathrm{opt}})}{\mathrm{L}_{i}}\cdot\beta-(n-1)\leq \theta^{*}\cdot\beta \tag{15}\]  
It implies that  
\[\frac{w_{i}(\mathbf{T}^{\mathrm{opt}})}{\mathrm{L}_{i}}\leq\theta^{*}+\frac{n- 1}{\beta} \tag{16}\]  
Let \(\frac{n-1}{\beta}=\varepsilon\cdot\theta^{*},\varepsilon>0\), then  
\[w_{i}(\mathbf{T}^{\mathrm{opt}})\leq(1+\varepsilon)\cdot\theta^{*}\cdot \mathrm{L}_{i},1\leq i\leq\mathrm{K} \tag{17}\]  
[PERSON] and [PERSON] have proved in literature [14] that there is corresponding relationship between each j and nodes pair \((t_{j-1},t_{j})\), in which \(i,j=2,3,\ldots,q\) and \(v_{1},v_{2},\ldots,v_{\mathrm{k}}\) are from \(1\) to k. Let \(j\) and number pair \(\left[t_{q(j)-1},t_{q(j)}\right]\) be one-to-one relationship:  
\[ \begin{array}{ll}w_{i}(\mathbf{T}^{\mathrm{opt}}_{\mathrm{MPH}})&=\sum \limits_{i=2}^{q}c(v_{i},\mathbf{V}_{i-1})\leq\sum\limits_{i=2}^{q}c(v_{t_{q(i) -1}},v_{t_{q(i)}})\\ &=\sum\limits_{j=2}^{q}c(v_{t_{j-1}},v_{t_{j}})\leq 2(1-1/q)\cdot w_{i}( \mathbf{T}^{*})\end{array}  \tag{18}\]  
According to Equations (17) and (18), it has  
\[w_{i}(\mathbf{T}^{\mathrm{opt}}_{NMHA})=w_{i}(\mathbf{T}^{\mathrm{opt}}_{MPH} )\leq 2(1+\varepsilon)(1-1/q)\cdot\theta^{*}\cdot\mathrm{L}_{i},1\leq i \leq\mathrm{K} \tag{19}\]  
Hence, the NMHA algorithm can finally find a \(2(1+\varepsilon)\left(1-1/q\right)\)-approximation multicast routing tree to MCMR, and the time complexity of the algorithm is \(O(qnm^{2}\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{missing  
routing tree to MCOMR. Thus, the upper bound of approximation ration should be discussed to evaluate the performance of NMHA. To the optimal multicast routing tree \(\mathbf{T}^{*}\) in \(\mathbf{G}\), it has  
\[w_{i}(\mathbf{T}^{*})\leq\theta^{*}\cdot\mathrm{L}_{i},1\leq i\leq K \tag{20}\]  
According to Equations (19) and (20), it has  
\[w_{i}(\mathbf{T}^{opt}_{NMHA})\leq 2(1+\varepsilon)(1-1/q)\cdot w_{i}^{*},\ \text{s.t.}\ w_{i}^{*}=\theta^{*}\cdot\mathrm{L}_{i} \tag{21}\]  
The multicast routing is discussed in this paper, which means that number of destination nodes are more than two, it has  
\[2(1+\varepsilon)(1-1/q)\leq 2(1+\varepsilon)\cdot\frac{1}{2}=1+\varepsilon \tag{22}\]  
Hence, the upper bound of approximation ration of the NMHA algorithm is \(1+\varepsilon\), and thus the theorem is proven.
## 4 Discussion
### UAV-Based Mangrove Forest Inventory  
The derived tree locations were in close correspondence with field inventory data and expert analyses at an RMSEr in the order of 20 cm. However, since the low-cost UAV is equipped with an RGB camera system, the estimated tree location is limited to the description of the canopy top inherent in the CHM. It is different from the LiDAR system where the beam is able to penetrate the dense canopy cover. The UAV-based SfM photogrammetry method tended to underestimate the number of individual trees, even though it has a higher accuracy in the sparse mangrove forest. Based on the result, the low accuracy in the dense forest of the northern delta lobe is likely due to the mangrove plantation programme that has created a more homogenous canopy and an almost flat CHM. This prevented complete detection of all treetop positions. However, despite the underestimated tree location, considering that colonising mangrove species during primary succession is shade-intolerant, therefore, the loss in detection might be marginal, in particular when the canopy is still sparse. The estimated individual positions of the trees can give us more information regarding the height distribution of the mangrove's trees and  
Figure 14: Relationship of the mangrove height dependent on stand age on Porong Delta Lobes.  
density (Supplementary Materials S1). As shown in Figure 9 we can observe the influence of mangrove planting on the north delta to the height variation indicated by the bimodal distribution (Figure 9a).  
Despite its limitation, the UAV-based tree detection offers the advantages of lower technical requirements and lower costs when compared to a LiDAR system [39]. With miniaturisation, increasing proliferation, and advancement in sensor technology, the possibility arises of adding multispectral sensors to accuracy of the UAV-based method while keeping it lightweight and low-cost. The image acquisition was conducted for five days or equal to 8.8 hectares/day since it was limited by the time frame of the optimum sunlight and battery capacity. The UAV survey in this study was conducted by two people. In comparison, traditional mangrove forest inventory with three experienced surveyors needs two complete days (seven hours/ day) to sample 0.05 hectares of the forest [15]. Thus, UAV-based forest inventory is likely to increase the possibility for frequent and rapid mangrove monitoring. Moreover, this method can be further developed as citizen science monitoring. With an off-the-shelf drone, pre-planned flight, open tutorial, and validation procedure, the public can be involved in this kind of mangrove monitoring programme. Depending on the needs, the UAV-based SfM photogrammetry is useful to estimate mangrove biophysical properties from the plot level up to the landscape-level. Moreover, another product generated in the workflow, for instance, the DTM is useful in studying the biogeography to understand the feedback loop of vegetation and the environment forcing.
## 3 Results and Discussion
### Morphology and Surface Analysis
XPS data, as summarized in Table 1, suggest similar TeO\({}_{2}\) content for the samples Bi\({}_{2}\)Te\({}_{3}\) and Sb\({}_{2}\)Te\({}_{3}\), while the other oxide phases (Bi\({}_{2}\)O\({}_{3}\) and Sb\({}_{2}\)O\({}_{3}\)) vary in quantity depending on the **x**. The ternary phases revealed _iep_ values in between those of samples where 2 \(>\)**x** \(>\) 0. This agrees with the trend that would be expected in parallel to the changes in Bi\({}_{2}\)O\({}_{3}\) or Sb\({}_{2}\)O\({}_{3}\) content. When **x** = 0, the surface shows a closer character to Bi\({}_{2}\)O\({}_{3}\) with an _iep_ of around 6.5, which is lower than pure Bi\({}_{2}\)O\({}_{3}\) due to the co-existence of TeO\({}_{2}\). This value changes towards more acidic region with the increasing **x** (Sb content), reaching the iep of 3.3 at **x** = 2, a bit higher than Sb\({}_{2}\)O\({}_{3}\) due to the co-presence of TeO\({}_{2}\). Although these data are not deterministic, they help to get a better insight into the nanoplatelets' surface in connection with the XPS results.
# Experimental Validation of GNSS Interferometric Radio Occultation
## 4 Data Processing, Analysis and Discussion
### Interferometric RO Processing  
Here, we present the results obtained using two variants of the iRO processing. The conventional iRO method (Section 4.3.1) obtains the Doppler estimate from computing the autocorrelation function of the occulting signal at a 1 ms delay (the period of the C/A signal). The Zenith iRO method (Section 4.3.4) records a segment of the received signal when the transmitter is in a near-Zenith position, and uses later this signal to compute the delay and Doppler compensated cross-correlation with the occulting signal.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 7 Future Research Directions
### Interaction between Different Segments  
Using new methods to provide continuous integration between space-based networks, air-based networks, and the ground cellular network is a key challenge for the integrated space-air-ground network. It is crucial to incorporate several key factors into various cases. Cross-layer convention plans are required to ensure interface consistency. It is also essential to provide a flexible and adaptive interface that allows various parts to interact to achieve various advantages. An example would be the performance of consistent data exchangeand information transfer between various systems. Because of the features provided by UAV mobility as they move in 3D space, the expanding range of services may necessitate the use of UAVs as gateways to numerous systems. It is critical to prepare the interacting components in such a complex system to ensure consistent interface quality.
# Distance Protection for Coexistence of 5G Base Station and Satellite Earth Station
## 3 Distance Protection Methods  
In this section, we present investigations of the functions of the distance protection method, including the I/N protection threshold, exclusion zone, restriction zone, and BS Tx power control scheme in the restriction zone.  
Figure 6: Antenna gains of a 16 \(\times\) 16 antenna array.
# Optimal Tests for Combining \(p\)-Values
## 2 Methods
### New Tests Based on Gamma Distribution
#### 2.2.2 Connections between \(T_{G(\alpha)}\) and Existing Popular Tests  
Although the existing popular tests described in Section 2.1 were proposed a long time ago, and their theoretical properties and empirical performances have been extensively studied and compared [6; 7; 25; 28; 29], surprisingly, their relationships have not been fully investigated, and the theoretical explanation on the differences of their performances is lacking. In this subsection, we show that they are connected to the aforementioned gamma distribution-based tests \(T_{G(\alpha)}\) with different \(\alpha\) values. In fact, we have the following results:  
**Theorem 1**.: _The class of gamma distribution-based tests \(T_{G(\alpha)}\) include special cases that are equivalent to the aforementioned existing popular methods. More specifically,\[\begin{array}{l}T_{G(0)}\triangleq\lim_{a\to 0+}T_{G(a)}\equiv T_{p};\\ T_{G(0.5)}\equiv\chi_{n}^{2};\\ T_{G(1)}\equiv F_{p};\text{ and }\\ T_{G(\infty)}\triangleq\lim_{a\to\infty}T_{G(a)}\equiv Z_{p}.\end{array}\]  
The proof of Theorem 1 is given in the Appendix A.
## 6 Lab Experimental Validation
### Signal Analysis
#### 6.2.2 Planet Gear Fault  
Figure 9 shows the planet gear fault signal and its Fourier spectrum. For this case, we choose IMF5 for demodulation analysis based on the sensitive IMF selection criterion, because its center frequency 1422 Hz is the highest and its instantaneous frequency ripples around the fifth harmonic of gear meshing frequency.  
Figure 10 shows the amplitude and frequency demodulation analysis result. In the envelope spectrum (Figure 10a), the planet carrier rotating frequency and its second harmonic (1-2)\(f_{c}\) are dominant, the planet gear fault characteristic frequency harmonics \((1\)-3)\(f_{p}\), as well as their difference combinations with the planet carrier rotating frequency \(mf_{p}-nf_{c}\), are also prominent. These findings accord with theoretical expectations on the planet gear fault symptoms, and indicate the planet gear fault. The planet gear fault will result in an uneven load distribution among planet gears and thereby will magnify the AM effect of planet carrier rotation on gear meshing vibrations. Moreover, the AM effect of planet gear fault is modulated by the time-varying transfer path due to planet carrier rotation. These factors lead to presence of the planet gear fault characteristic frequency harmonics, the planet carrier rotating frequency harmonics, and their sum and difference combinations, in the envelope spectrum of planet gear fault case.  
In the Fourier spectrum of instantaneous frequency (Figure 10b), peaks also appear at the planet gear fault characteristic frequency harmonics \(mf_{p}\), the planet carrier rotating frequency harmonics \(nf_{c}\), and their difference combinations \(mf_{p}\pm nf_{c}\). Although it has a more complex structure than theoretical expectations, considering the features revealed in the envelope spectrum, a comprehensive analysis of amplitude and frequency demodulated spectra can still confirm the planet gear fault.  
Figure 8: Demodulation analysis of IMF3: (**a**) Envelope spectrum; and (**b**) Fourier spectrum of instantaneous frequency.  
Figure 9: Planet gear fault signal: (**a**) Waveform; and (**b**) Fourier spectrum.
# OPC-UA Agent for Legacy Programmable Logic Controllers +
###### Abstract  
Open platform communications (OPC) unified architecture (UA) is a communication standard increasingly used in industrial automation systems to enable the exchanging of control and management data between distributed entities. This paper proposes the design of an OPC-UA agent to enable UA information service and client functionalities in legacy programmable logic controllers (PLCs). The agent runs on a separate machine connected to the PLC using a dedicated link and maintains shared memory for certain variables in the PLC. Based on the periodically synchronized variables, the agent services the OPC-UA information model and executes client function blocks on behalf of the PLC. One important design feature is the remote procedure call of IEC 61131-3-based function blocks using synchronized variables. This allows the standard OPC-UA client functions to be used in existing PLCs which only support numeric types and do not support strings or complex structures. To validate the proposed design, we implement an agent prototype and demonstrate the successful monitoring and control of an industrial robot controller via OPC-UA. Through experiments, we evaluate the performance of UA functions in terms of the latency of read services for an increasing number of items. The evaluation results are believed to provide useful insights into agent-based approaches for integrating legacy PLCs into the OPC-UA framework.  
open platform communications; unified architecture; programmable logic controller; remote procedure call; agent +
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