esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 MAPPO-Based Joint Caching and Computation Offloading Scheme
### Scalability Analysis and the Role of the Attention Mechanism  
The multi-agent joint optimization strategy based on MAPPO proposed in this paper, under the CTDE paradigm, effectively enhances the algorithm's scalability when dealing with high-dimensional observation and action spaces through a carefully designed neural network architecture. Specifically, a state encoder transforms high-dimensional raw local observations into compact feature representations, thereby reducing the input dimension for the core network. Building upon this, the centralized Critic network creatively integrates a multi-head attention mechanism, enabling it to dynamically focus on and selectively weight the most relevant and critical information within the observation space. This mechanism efficiently captures complex correlations and interaction dynamics between agents, as well as between agents and the environment. Regardless of changes in the number of agents in the system, it avoids the inefficiencies that traditional fully connected networks might suffer due to dimensionality explosion, significantly enhancing the algorithm's learning efficiency and evaluation accuracy when dealing with increasing dimensions.
## 2 Methodologies
### Mathematical Model of Real-Time Single-/Dual-Frequency PPP
#### 2.4.2 Single-Frequency Model  
For single-frequency PPP, Equations (10) and (11) form the observation equations. Here, the B1C and B2b measurements will be used in this paper. Thus, the satellite DCB corrections should be considered,  
\[\left\{\begin{array}{l}t_{B1C}^{s}=t^{s}-b_{B1C}^{s}\\ t_{B2b}^{s}=t^{s}-b_{B2b}^{s}\end{array}\right. \tag{16}\]  
Compared with the IF model, the ionospheric delay of each satellite and the receiver DCB cannot be eliminated. Therefore, they are estimated as parameters [33; 34; 35]. The corresponding state vector can be expressed as,  
\[\mathbf{x}=\left[\delta x_{r}\quad\delta y_{r}\quad\delta z_{r}\quad ct_{r}\quad d _{wet}\quad\mathbf{N}_{j}\quad b_{r}\quad\mathbf{I}_{r}\quad\right]^{T} \tag{17}\]  
where \(b_{r}\) and \(\mathbf{I}_{r}\) are the receiver DCB and the ionospheric delay vector.  
Due to the correlations between the ionosphere and receiver DCB, we introduce the following pseudo-observations to improve the estimation accuracy of single-frequency PPP [33].  
\[I_{r,j}^{s}=40.28 STEC/f_{j}^{2}+\omega_{r_{s,j}^{s}},\omega_{r_{s,j}^{s}} \sim(0,\sigma_{r_{s,j}^{s}}^{2}) \tag{18}\]  
where \(STEC\) denotes the vertical electronic content obtained from the GIM model; \(\sigma_{r_{s,j}^{s}}^{2}\) is the variance of the priori ionospheric model errors (\(\omega_{r_{s,j}^{s}}\)).
## 2 Development of Ultra High-Strength Concrete  
In the constituents of the mixture of the developed ultra high-strength concrete, sand of 0-5 mm, fine aggregate of 5-12 mm, coarse aggregate obtained from limestone of 12-19 mm and 19-25 mm, Portland cement, ground granulated blast furnace slag (GGBF slag) and super plasticizer were used. During the production process, the concrete became workable after adding water and super plasticizer to the mixture of cement, GGBF slag and aggregates, which are manufactured in dry form and blended for a time period of 3-4 min in the mixer. The composition of the developed concrete mixture is shown in Table 1.  
The workability of the concrete obtained in the concrete mixer was very low. The measured slump values were observed to be between 0-1. Therefore, the mixture was placed within cylindrical sample containers having dimensions of \(\mathcal{O}150\times 300\) mm with the help of 3-stage Farming on the vibrating plate, in order to avoid segregation and to obtain a compact and homogeneous structure. The hardened concrete samples have been cured in a cure room at a temperature of 24 \({}^{\circ}\)C and a humidity of 97% until the quasi-static tests. The quasi-static tests were carried out at a strain rate of \(10^{-5}\) s\({}^{-1}\) in a compression test device, as shown in Figure 2a. A photo of the specimen used in these compression tests is shown in Figure 2b.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Materials**} & \multirow{2}{*}{**Quantity (kg/m\({}^{3}\))**} & **Ratio (\%)** \\  & & **(Volume)** \\ \hline Cement & 450 & \\ GGBF slag & 45 & \\ Sand (0–5 mm) & 39 & \\ Limestone (5–12 mm) & 33 & \\ Limestone (12–19 mm) & 16 & \\ Limestone (19–25 mm) & 12 & \\ Water/Cement & & 13 \\ Super Plasticizer & & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the ultra-high-strength concrete.  
Figure 2: **(a)** Quasi-static test setup. **(b)** A cylindrical specimen after testing.  
In order to observe the results of the strengthening process, the samples were tested for failure on the 1 st, 7 th, 28 th and 56 th days following production. Three samples were tested on each test day. The average strength of the three samples was calculated for the compressive strength values of the daily testing samples. The variation of the quasi-static compression test results are given in Table 2.
We perform a detailed performance comparison of the horizontal and vertical communication systems. Additionally, we compare the performance of horizontal cooperative UWVLC systems operating at different depths. To the authors' knowledge, a performance comparison of horizontal and vertical UWVLC systems using multiple parallel relays and a best relay selection strategy is not available in the existing literature. We demonstrate the effect of depth on the performance and presented results, showing the need for an extra relay in vertical transmission compared to horizontal transmission to achieve similar performance. It is shown that adopting the optimal relay selection resulted in substantial performance benefits compared to a no-relay scenario. Furthermore, it is proven that the outage performance of the horizontal system improves with increasing depth. The rest of the paper is organized as follows: underwater dual-hop horizontal and vertical channel models are described in Section 2. The closed-form expressions of the outage probability and ergodic capacity are derived in Section 3. The numerical and simulation results are presented in Section 4, with conclusions drawn in Section 5.
## 2 Materials and Methods
### Materials
#### 2.1.2 NCEP/DOE Reanalysis Data  
The monthly average reanalysis data from 1988 to 2017 was collected from the National Centers for Environmental Prediction/Department of Energy (NCEP/DOE) Reanalysis II. The data included geopotential height, ice concentration, meridional wind, zonal wind, air temperature, and outgoing long-wave radiation (OLR). They were 17 layers of data sets (i.e., 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, and 10 hpa). As moisture flux was concentrated in the lower atmosphere below 850 hPa, the dataset at 850 hPa was used in the study. The grid resolution was \(2.5^{\circ}\times 2.5^{\circ}\).
# Local Community Detection Based on Small Cliques
## 3 Local Community Detection Algorithms Based on Small Cliques
### Implementation Details  
As for LTE and Local T, we build and maintain a local graph data structure for counting triangles. For TCE, we only need the subgraph \(H\) induced by the community \(C\) and its shell \(S(C)\), as we are only interested in triangles where at least one node is in \(C\). Like for LTE, we use a 4-ary (max-)heap for storing \(S\) that supports increasing keys.  
When a new node \(u\) is added to the community, only scores of neighbors of \(u\) are affected. Therefore, when a node \(u\) is added to the community, we only need to either add \(u\)'s neighbors that are not in \(C\) to \(S\) or update their scores (Algorithm 1 (line 8)). The former means that the score of node \(v\) only consists of \(\omega(u,v)/\deg_{w}(v)\). The latter implies that we need an increaseKey operation with the updated score: \(\textsc{score}_{\text{new}}(v)=\textsc{SCORE}_{\text{old}}(v)+\omega(u,v)/ \deg_{w}(v)\). The total running time of all priority queue operations are therefore \(\mathcal{O}(\operatorname{vol}(C)\log(\operatorname{vol}(C))\).  
The total running time is composed of building the local graph \(H\), computing the scores by listing triangles and updating the priority queue. In total, this is \(\mathcal{O}(\operatorname{vol}(C\cup S(C))+m_{H}\cdot\alpha(H)+\operatorname {vol}(C)\log(\operatorname{vol}(C)))\). Note that compared to LTE, the subgraph \(H\) is smaller as it does not contain the neighbors of the shell.
# Investigation of Zero Moment Point in a Partially Filled Liquid Vessel Subjected to Roll Motion
## 3 Results and Discussion
Next, we summarize the movement of the CoM along with the fluid bulk and the air-liquid interface in Figure 10. The position of the CoM at different time steps can be visually observed in the figure at different angles corresponding to previously described range of roll angles. The results of the present study highlight the close relation between liquid slosh and CoM, which has hitherto received little attention in the literature due to experimental limitations in determining the CoM. Figure 11 shows the change in behavior of the CoM across the different fill levels of the tanker. This indicates that with the decrease in fill level the CoM moves outwards from the center of domain. Similar to the pressure curves in Figure 8, fill levels greater than 50% exhibited minor changes in the displacement of the CoM, while, contrary to the pressure curve, the maximum displacement was shown by a 18% fill level (instead of 30%). Overall, the two characteristics exhibited similar trends and complemented each other. Using the CoM information and estimating the ZMP according to Equation (1), we plotted the ZMP, the CoM, and the left pressure probe with respect to time for 18% fill level (Figure 11). This showed alternation of the ZMP around the center of domain corresponding to the movement of the fluid bulk. In later roll motions, oscillations were observed in the ZMP location which correspond to sloshing frequencies, while, overall, the Zero Moment Point curve follows the CoM curve (Figure 12). Figure 11: Zero Moment Point (ZMP), Center of Mass, and pressure comparison. Figure 10: Center of Mass vs. fill level of container. The evolution of the ZMP across different fill levels is summarized in Figure 7.0 We observed oscillations in 20%, 30%, and 40% fill levels, while the onset of these oscillations was earliest in the smallest fill level. It can be seen in the case of a 30% fill level that after the onset of oscillations there was slight dampening before it peaked again, while at fill levels of 50% or greater there was no oscillation in the ZMP curve. Hence, we suggest that the ZMP of a dynamic liquid domain can be calculated to estimate its stability, determined by retaining the ZMP within the support polygon as in the present study cases.
## 4 Results and Discussion
### Mass-Center Vertical Fluctuation  
The mass-center vertical fluctuation is defined as the difference between the maximum with the minimum mass-center height of quadruped robot in the whole period of quadruped bounding. The smaller mass-center vertical fluctuation leads to more stable vertical motion, which represents a better resistance to vertical jitter.  
The relation between MCPSS with mass-center vertical fluctuation is shown in Figure 9. It can be seen that the effect of MCPSS on mass-center vertical fluctuation is obvious at high speed, but implicit at low and medium speeds on the whole. Moreover, the mass-center vertical fluctuation presents the single valley variation as MCPSS moves from spinal joint to hip joint. The valley value of mass-center vertical fluctuation corresponds to MCPSS being near the geometric center of spinal segment at all speeds. In more detail, MCPSS with respect to the smallest mass-center vertical fluctuation corresponds to \(\lambda=0.4\) at low speed, and \(\lambda=0.6\) at medium and high speeds. Significantly, when the quadruped bounding is very fast horizontally, MCPSS being at the hip joint will cause considerable mass-center vertical fluctuations at all speeds. On the whole, MCPSS near the geometric center of spinal joint is an attractive choice with respect to mass-center vertical fluctuation, which can maintain a better resistance to the vertical jitter.
# MEC Server Sleep Strategy for Energy Efficient Operation of an MEC System
## 5 Performance Evaluation
### Combined Effect
To compare the time taken for sleep control, we examine the end-to-end inference time, starting from predicting the task input rates to determining the sleep control vector. In Figure 9, we show the distribution of the end-to-end inference time as the box plots. When we inspect the \(Q\)-values, LSTM-GA obtains \(Q_{1}=440\) ms, \(Q_{2}=449\) ms, and \(Q_{3}=463\) ms. The proposed method reduces the \(Q\)-values significantly. When our method is used, \(Q_{1}=18\) ms, \(Q_{2}=21\) ms, and \(Q_{3}=22\) ms. In other words, compared to LSTM-GA, the proposed method decreases the median end-to-end inference time from 449 ms to 21 ms, which is a 95.32% reduction. With respect to the IQR, our method reduces the IQR of the end-to-end inference time from 23 ms to 4 ms, which corresponds to an 82.16% decrease.
# Location-Based Lattice Mobility Model for Wireless Sensor Networks
## 4 Simulation Setup and Analysis of Results  
We simulate LMM using OMNet++[24]. The network contains 500 sensor nodes that are randomly deployed over a 1600 \(\times\) 1600 square meter field. Initially, the border node remains in a corner of each region and movable sensor nodes are placed at the corners of the regions. When the simulation begins, the mobile sensor nodes move back and forth in the network regions. Each simulation run lasts for 14 min, and three mobility models are tested: CMM, WMM, and RWMM. We use the pheromone termite routing protocol to handle the packet generation rate and pheromone sensitivity used in [25].  
At the transport layer, packet re-ordering is a major issue, particularly for multimedia contents [26], so we use Reliable Multi Segment Transport protocol (RMST) [27] to help re-order the packet, which guarantees consistent pre-packet delivery for different packet types and enables faster recovery in case of packet loss. The packet size is set to 128 bytes for file transfer and 512 bytes for video, audio, and image transfer. We also consider a sensor application module with a constant bit-rate source that maintains QoS requirements. The contending algorithms CMM, WMM, and RWMM have comparable accuracy and tune our proposed LMM algorithm to have the similar accuracy in order to compare the performance. The detailed simulation parameters are presented in Table 2.  
We obtained several results, but used the following metrics to demonstrate the performance of the LMM in the WSN:  
Average delay and average power consumption  
The times taken by the sensor nodes to reach different positions using the LMM, CMM, RWMM, and WMM at fixed and variable rates of motion.  
The drop rates using the LMM, CMM, RWMM, and WMM at different rates of motion.  
The residual energy of the nodes and border node after changing the locations using the LMM, CMM, RWMM, and WMM.
## 3 Fault Estimation Using an Extended State Observer  
In this section, an extended state observer of uncertainties/disturbances and faults/failures is presented.  
The dynamic model of the robot manipulator of Equation (5) can be rewritten in state space as:  
\[\ddot{q}=\mathbf{M}^{-1}(q)(\tau-\mathbf{H}(q,\dot{q}))-\mathbf{M}^{-1}(q) \zeta(q,\dot{q},\ddot{q},\tau,t) \tag{6}\]  
where \(\mathbf{H}(q,\dot{q})=\mathbf{C}(q,\dot{q})q+\mathbf{G}(q)+\mathbf{F}_{f}( \dot{q})\). \(\zeta(q,\dot{q},\ddot{q},\tau,t)=\rho(t)\tau+\psi(q,\dot{q},\ddot{q},t)-\mathbf{ f}(t)\) represents uncertainties/disturbances and faults/failures.  
In the state space, the dynamic model of Equation (6) becomes:  
\[\left\{\begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=f(x_{1},x_{2},\tau)+\phi(x_{2},x_{2},\tau,t)\end{array}\right. \tag{7}\]  
where \(x_{1}=q\in\Re^{n}\), \(x_{2}=\dot{q}\in\Re^{n}\), \(f(x_{1},x_{2},\tau)=\mathbf{M}^{-1}(q)(\tau-\mathbf{H}(q,\dot{q}))\), and \(\phi(x_{1},x_{2},\tau,t)=-\mathbf{M}^{-1}(q)\zeta(q,\dot{q},\ddot{q},\tau,t)\).  
An extended state observer [22] is given as:  
\[\left\{\begin{array}{l}\dot{x}_{1}=\hat{x}_{2}+\frac{\alpha_{1}}{\varepsilon }(x_{1}-\hat{x}_{1})\\ \dot{x}_{2}=\hat{f}(x_{1},\hat{x}_{2},\tau)+\frac{\alpha_{2}}{\varepsilon^{2}} (x_{1}-\hat{x}_{1})+\hat{\phi}\\ \dot{\phi}=\frac{\alpha_{3}}{\varepsilon^{3}}(x_{1}-\hat{x}_{1})\end{array}\right. \tag{8}\]  
where \(\hat{x}_{1},\hat{x}_{2},\hat{f}\), and \(\hat{\phi}\) are estimates of \(x_{1},x_{2},f\), and \(\phi\), respectively,\(\alpha_{1},\alpha_{2}\), and \(\alpha_{3}\) are positive constants, polynomial \(s^{3}+\alpha_{1}s^{2}+\alpha_{2}s+\alpha_{3}\) is Hurwitz, and \(0<\varepsilon<1\).  
The stability of system Equation (7) with observer Equation (8) is shown in [6] with the conditions \(0<\varepsilon<1\) and \(\left|\dot{\phi}\right|\leq L\). The observer error convergence is given as:  
\[\left|\left|\overline{c}\right|\right|\leq\frac{2\varepsilon L\left|\left|PB \right|\right|}{\lambda_{\min}(Q)} \tag{9}\]  
where \(A=\left[\begin{array}{ccc}-\alpha_{1}&1&0\\ -\alpha_{2}&0&1\\ -\alpha_{3}&0&0\end{array}\right]\)_and_\(B=\left[\begin{array}{c}0\\ 0\\ 1\end{array}\right]\), and there is a symmetric positive definite matrix Q satisfying the Lyapunov equation:  
\[A^{T}P+PA=-Q \tag{10}\]
## 2 Advanced Metering Infrastructure (AMI) Features
### Interconnection Messages in AMI System  
Over AMI communication networks, interactive messages are sent and received between smart grid devices. These messages include topics such as electricity pricing, remote load management, meter data, notifications for power outages and returns, publishing DR projects, subscribing to or canceling DR projects, and more. There are two components involved in the sending and receiving of these communications. The first one is user-level  
Figure 1: AMI framework.  
devices (SMs, UGs, etc.) which are the sending components. To keep things short, we refer to these devices as NXs. The other component is the data management system (DMS) which aids in the transmission and reception of AMI data.  
Messages may be categorized as either unicast, broadcast, or multicast. The unicast message is sent from DMS to NX, but broadcast messages are sent to all NXs, whereas multicast messages are sent to DMS and a subset of NXs participating in the same DR project. Table 1 details the types of messages, along with their respective senders, recipients, and modes of transmission [19]. This table also includes the time requirements for various message types based on the requirements of the State Grid Corporation of China. Even though these time estimates are not identical in every country, their variances are negligible when considering the global smart grid's overall development goal.
# Exploring the Spatiotemporal Alterations in China's GPP Based on the DTEC Model
## 1 Introduction
Since the conclusion of the 20 th century, China has initiated a range of environmental recovery and conservation measures, including China's afforestation program (CAP) and the Conversion of Cropland to Forest Program (CCFP). The main objective of these initiatives is to restore the natural environment through a variety of human endeavours, such as afforestation and the protection of natural forests [23]. Therefore, understanding how anthropogenic influences and climate change affect carbon sinks in detail is essential for understanding China's terrestrial ecosystems [24]. In this research, the GPP of Chinese areas from 2001 to 2020 was calculated using the DTEC model, and we analysed the spatial-temporal patterns and their influencing factors. The main research objectives were as follows: (1) to evaluate the applicability of the DTEC model in the Chinese region and create a new GPP dataset, (2) to analyse the spatial and temporal distribution characteristics of the new GPP data, (3) to explore the correlations with different climatic variables, and (4) to evaluate the relative impacts and contributions of anthropogenic activities and the climate to GPP at the regional and national levels.
# Reliable Application Layer Routing Using Decentralized Identifiers
## 5 Implementation and Evaluation
### Evaluation Scenario  
The proposed solution was evaluated through a smart city emulation scenario. In this scenario, the owner of the domain \"smart-city.iot\" delegates to 100 building owners the \(Resource_{\textit{URL}}\) \"smart-city.iot/building-X\", where \(X\) is a number between 1 and 100. Then, each building owner can configure the IoT devices of each smart building to advertise resources using the delegated URL, e.g., the owner of \"building-1\" could configure a light controller to advertise the URL \"smart-city.iot/building-1/lights\".  
Figure 2: Assertion key resolution.  
In our evaluation scenario, we consider the topology depicted in Figure 3. In particular, each building is attached to a different edge node, and each edge node is attached to a different SDN switch. We group SDN switches in two groups, and we connect all SDN switches of the same group to another SDN switch. Finally, the latter two SDN switches are connected using a _backbone_ link. In our evaluation scenario, each building advertises 50 different \(Resource_{URL}\)s every 10 s and a DID document every 1 min. Advertisements take place almost simultaneously. Table 1 summarizes the parameters used in our evaluation scenario.  
As a baseline for comparison of our system, we consider the trust schema defined in [36]. This trust schema is used by a popular ICN architecture known as named data networking (NDN) [37], and it resembles WebPKI. In particular, this schema defines \"trust anchors\", which are public keys used to sign digital certificates. Additionally, each (edge) router is configured with rules that map prefixes of \(Resource_{URL}\) to identifiers which are resolved to a digital certificate: this certificate includes a public key that can be used for validating the digital signature included in the corresponding \(Resource_{URL}\) advertisement. Therefore, compared to our solution, a trust anchor has the same role as the registrar, the identifier of a digital certificate is equivalent to a DID, and a digital certificate is equivalent to a DID document. Similarly to our solution, the solution of [36] can be used for creating \"trust chains\". In the considered use case, a trust anchor would sign a digital certificate for the domain \"smart-city.iot\", and the private key that corresponds to the key included in the latter certificate would be used to sign the digital certificates of each building.