esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Since the CCF propagates between the PN and CN alternatively and the existing data-driven methods for PCPs construction only collect the observation data from the PN, which are not applicable for preventing the CCF due to insufficient data. In order to prevent the propagation of CCF in an ADN, not only the measured data from the PN but also the measured data from the CN should be collected. It is difficult to establish a collaborative simulation of the physical process and the computational process in an ADN, such as digital twin. However, reinforcement learning techniques can analyze and summarize the interaction between the physical process and the computational process of an ADN by using the existing empirical data and play the role of simulation and experiment. Using reinforcement learning techniques, there are three advantages for constructing a PCP: less time, lower cost, and higher accuracy. Therefore, a PCP construction method based on deep deterministic policy gradient idea (shorted as PCMD) is proposed to prevent the propagation of CCF in an ADN, and this method needs to collect the data about voltage stability, frequency stability, current overload prevention, robustness for CCF, and control cost. However, there are some challenges during the construction process. That is, how to choose the concrete objectives and weight them into the reward function; how to adjust parameters so as to ensure that the construction process of PCMD converges and a PCP against CCF exists; how to ensure that the construction process converges to a local optimal solution; how to verify the effectiveness of the proposed PCMD. Some contributions are concluded as follows. (1) A modeling method is proposed for the preventive control using the finite automaton theory. The preventive control model describes the effect of CCF in an ADN after the intervention of control actions and can guide the gather of required trajectory dataset and the construction of a PCP. (2) PCMD for PCP construction is presented, and it guarantees the voltage stability, the frequency stability, the current overload prevention, and the improved robustness against CCF of an ADN. The constructed PCP can generate control actions with low cost based on the principle of shortening a CCF propagation sequence as far as possible. (3) An experimental China Electric Power Research-Cyber-Physical System (CEPR-CPS) case from China Electric Power Research Institute is carried out to validate the performance of the proposed method. The result shows that the effectiveness of the PCP constructed from the PCMD is better than that with others, such as a multi-agent method in terms of reducing the number of failure nodes and avoiding the state space explosion. The paper is organized as follows. A preventive control model is given in Section 2. In Section 3, the specific construction method is presented. Section 4 gives a case provided by China Electric Power Research Institute to validate the effectiveness of the construction method. Section 5 outlines the discussion.
## 3 System Model and Problem Formulation
### System Model
\right\}\rho_{in}^{min}\leq\ \rho_{in}<\ r. \tag{4}\]  
Similarly, the PDF of users in edge zone in terms of polar coordinates (\(\rho_{out},\ \varphi\))is:  
\[f(\rho_{out})=\ \frac{2\big{(}\rho_{out}-\rho_{out}^{min}\big{)}}{\big{(}R- \rho_{out}^{min}\big{)}^{2}} \tag{5}\]  
Figure 2: Uplink frequency partitioning using the FFR scheme. \[f(\varphi)=\frac{3}{2\pi}\,\ \forall\ 0\leq\varphi\leq\frac{2\pi}{3}. \tag{6}\]  
Therefore, the corresponding cumulative distribution function (CDF) is:  
\[ \begin{array}{l}P(\rho_{out}) =\int_{\rho_{out}^{in}}^{R}f(\rho_{out})d\rho_{out}\\ =\int_{\rho_{out}^{in}}^{R}\frac{2(\rho_{out}-\rho_{out}^{in})}{(R-\rho_{out}^{ in})^{2}}d\rho_{out}\\ =\frac{1}{(R-\frac{2\pi^{min}}{R})^{2}}[2(\rho_{out})^{2}-2\rho_{out}\cdot\rho_{ out}^{min}]_{\rho_{out}^{min}}^{R}\\ =\frac{2\pi^{min}}{(R-\rho_{out}^{min})}\end{array} \,\ \rho_{out}^{min}\leq\ \rho_{out}<\ R. \tag{7}\]  
where \(\rho_{in}^{min}\) and \(\rho_{out}^{min}\) are the minimum allowable distance from the eNB to center zone and edge zone users.
# Probabilistically-Shaped DMT for IM-DD Systems with Low-Complexity Fast WHT-Based PDSP
## 5 Conclusions  
In this paper, we proposed and experimentally investigated a low-complexity fast WHT-based PDSP scheme to combat the unbalanced impairments and reduce the implementation complexity of PS-enabled DMT IMDD transmission systems. After 50 km SMF transmission, the results show that pre-equalization, WHT-based FDSP and fast WHT-based PDSP schemes have almost the same BER and GMI performance. Compared with the traditional PS-enabled scheme, there is about 1 dB gain for receiver sensitivity by employing the three pre-processing schemes. However, the proposed fast WHT-based PDSP scheme is a lower-complexity option for the implementation of the PS-DMT systems with large SC SNR fluctuation.  
Conceptualization, Y.L. and H.L.; methodology, Y.L., H.L. and M.C.; software and validation, Y.L. and H.L.; writing--original draft preparation, Y.L. and H.L.; review and editing, M.C. and T.Z.; supervision, M.C.; project administration, M.C. and Y.C.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Scientific Research Fund of Hunan Provincial Education Department of China under grants 20B330 and 21A0562, and the Construct Program of the Key Discipline in Hunan Province, China.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 6 Conclusions
The frequency at which data is collected may not adequately capture the temporal dynamics and changes occurring within peatlands;
* In an operational context, where large volumes of data are involved, automated algorithms for peatland analysis become necessary. However, the existing algorithms are primarily designed for optical data, and there is a scarcity of studies focusing on radar data for peatland analysis;
* There is limited availability of field validation data, particularly in inaccessible areas. This limitation hampers the validation and verification of remote sensing-based peatland analysis methods, impeding the reliability of the results;
* Another limitation is the lack of quantitative error assessment methods for manual and automated peatland analysis using remote sensing. Establishing robust measures to assess the accuracy and uncertainty of remote sensing data-derived peatland information is crucial for ensuring the reliability and validity of the findings.
## 3 Predict and Forward Scheme Based on Node Profile
### Predict Phase: Studying the State of the Network
#### 3.2.1 Prelearn Message Collects and Updates the Statistics about the State of the Network  
For many years, the researchers have argued that the mobility of wireless devices is unpredictable in the network. However, in the literature [9], an experiment, which focuses on exploring the human mobility in a real-word scene, shows that the distributions of both contact and inter-contact times in the network presents a power law distribution. According to the theory of the experiment, the movement of delay tolerant network (DTN) nodes may be based on the predictable mobility models. If a node encounters another node frequently, they are more likely to meet each other in the future. Therefore, we adopt the \(tanh\) function to convert the average number of historical encounters between nodes into the probability of the future meeting between them. Firstly, we define a routing metric \(M_{(node_{1},node_{2})}\), which represents the number of encounters between \(node_{1}\) and \(node_{2}\) in the past period of time \(t\)Moreover, the forwarding metric (the possibility of the future meeting) between \(node_{1}\) and \(node_{2}\) can be defined as  
\[P_{(node_{1},node_{2})}=tanh(\frac{M_{(node_{1},node_{2})}}{t})=\frac{e^{\frac{M }{t}}-e^{-(\frac{M}{t})}}{e^{\frac{M}{t}}+e^{-(\frac{M}{t})}} \tag{3}\]  
where \(\frac{M}{t}\) represents the average number of encounters between between \(node_{1}\) and \(node_{2}\) during the previous period of time \(t\). After the Prelearn message obtains the attribute characteristics of a certain node, it may never route the node again, so it is important for nodes to update the forwarding metrics. Moreover, PRoPHET has provided an efficient scheme to update the forwarding metrics in opportunistic networks, so the updated forwarding metric \(P_{(node_{1},node_{2})}\)\({}_{new}\) between \(node_{1}\) and \(node_{2}\) can be shown as  
\[P_{(node_{1},node_{2})}=P_{(node_{1},node_{2}),old}+[1-P_{(node_{1},node_{ 2}),old}]\times\alpha_{init} \tag{4}\]  
where \(\alpha_{init}\) is defined as an initialization constant and \(P_{(node_{1},node_{2})}\)\({}_{old}\) is the meeting probability before the update. If they do not contact with each other for a long time, the forwarding metric between them is gradually getting aged, so we define \(\beta\in[0,1]\) as a constant, which is used to control the aging change of forwarding metrics. Additionally, the updated forwarding metric between \(node_{1}\) and \(node_{2}\) can be calculated as  
\[P_{(node_{1},node_{2})}=P_{(node_{1},node_{2}),old}\times\beta^{t} \tag{5}\]  
where \(t\) is the previous period of time before the update. In opportunistic networks, the geographic location of nodes changes over time and the forwarding metrics are the important basis for a successful data transmission, so it is necessary for Prelearn messages to execute the updating operation for the meeting probability between nodes.  
Figure 2: The routing process diagram of Predict phase.
## 5 Discussion and Future Work  
In our study, RK-SLAM demonstrated an excellent performance across both synthetic scenes with accurate and complete information and real-world scenes with perturbations and missing data. And they had different resolutions (1200 \(\times\) 680 RGB-D images for Replica and 640 \(\times\) 480 RGB-D images for TUM-RGBD and ScanNet). Our proposed SLAM system can run effectively across these datasets with varying resolutions and still achieve good results. This highlights the robustness of our proposed SLAM system to varying data. Particularly in the ablation experiments, we compared the related keyframe window with the sliding window approach proposed in [28]. The sliding window in [28] updates the window based on the sequence order, disregarding inter-frame correlations. In contrast,  
Figure 4: Comparison of mapping results in the ablation study. The first row shows the full images, while the second row provides zoomed-in views of the regions with the red box from the first row.  
Figure 5: The extent of fine-tuning of the keyframe pose in the sliding window \(W_{s}\) with the number of iterations.  
the related keyframe window considers the content of the scene, breaking the original sequential order. Even if the orders of keyframes are far apart in the sequence, they are selected due to their relation and fine-tune the scene map and the poses of the keyframes together. The experimental results clearly demonstrate that this method can achieve better localization and mapping. Moreover, through the exploration of the sliding window, we discovered changes in the amount of keyframe fine-tuning during the iteration process. Leveraging this insight, we set the sliding window size to maximize the effectiveness of the sub-Gaussians.  
However, RK-SLAM still has shortcomings.  
1. In terms of mapping performance, RK-SLAM excels in PSNR and LPIPS metrics but shows a significant gap in the SSIM metric.
2. The datasets used in the experiments mostly consist of static scenes, whereas the real world is dynamic. Our SLAM system has no account for dynamic scenes and may, therefore, struggle to handle rapidly changing scenes.
3. Although we constrained the maximum sizes of the sliding window and the related window, the iterations still require a significant amount of computation, preventing real-time performance.  
Therefore, in future work, we will address these issues. First, we need to investigate the reasons behind the significant discrepancy in the SSIM metric to improve our algorithm. Additionally, to enable an efficient algorithm deployment, we aim to design a new Gaussian mapping method that avoids the computational bottlenecks of the current approach. Furthermore, for dynamic scenes, we need to develop a new method to mitigate the impact of dynamic disturbances.
## 5 Challenges and Open Issues
### Technology Demands
#### 5.1.3 Data Security and Privacy  
In the metaverse, as a digital social community, the users can experience multiple privacy and security risks. The amount of data will be increasing exponentially in the metaverse with more frequent data collection, retrieval and use. This data includes sensitive personal information (e.g., user's demographics, physical culture, and economics); and personal communications and behaviors (i.e., user's interactions, habits, and choices in the metaverse). The designers and developers of the metaverse will be required to provide solutions to control data use and to protect users from privacy and security threats according to established polices.
# CMS: A Continuous Machine-Learning and Serving Platform for Industrial Big Data
## 5 Case Study and Experimental Results
### Model Evolution  
This platform has been deployed into the power plant for 6 months. During this period, this power plant has under a major overhaul, including equipment inspection, cleaning and optimization (from 17 December 2017 to 9 March 2018). The model evolution is clearly observed during this process.  
**Impacts from major renovation:** Before the renovation, eight different predictive models are trained with the historical data of recent 1 month. Figure 4a shows the changes in model accuracy in terms of Mean Absolute Percentage Error (MAPE) which is used to indicate the feasibility of the generated model. Before the renovation, all four models can achieve a prediction accuracy under 0.1%, which is rather accurate. However, after the renovation, the continuous usage of existing models might have significant jumps of MAPE, from 4 to 10 times. With the continuous equipment operation, almost all the MAPEs have a rising trend. This clearly indicates that the major renovation has a very important influence on the model. This observation clearly shows the importance of having a model that is periodically updated.  
Figure 4: Model evolution and effects of model update. (**a**) Model evolution before/after the renovation; (**b**) Mean Absolute Percentage Error (MAPE) without sustainable model update; (**c**) MAPE with sustainable model update.  
**Impacts from system adjustment:** The other important observation related to the model evaluation is from the system adjustment. Right after the plant resumed normal operation, the model needs to be updated each day. Otherwise, the model MAPE might also have a significant increase, which is shown in Figure 4b. When normal operation of the plant resumes, a model trained with 5 days' data is used to calculate MAPE for the rest of the 9 days. Figure 4c shows the MAPE trends of the model with a sustainable model update from the sixth days of normal operation after renovation. This fact against our common sense, as the model of the power plant would normally not change at this rate (the original model update cycle is set to 7 days, which is rather enough based on our modeling experience). The plant operators told us that they had been adjusting some important system control parameters during these days. This might explain such fast changes. With our introduced daily model updates (at 1 a.m., a new model is generated with the last 5 days' data), all the MAPEs of the eight models gradually decrease. Thus, it is also important to validate the current execution model in an appropriate way and trigger a model update if the model fails the validation. However, the online validation is challenging as the data distribution of the test data might deviate from train data. How to distinguish the impacts whether from the model deviation or the data deviation remains an area for investigation.
## 4 Results
### Evaluation Result of Design Concept Scalability  
In addition to the above evaluations performed on the 3U CubeSat, the scalability of the concept down to the lower CubeSat form factor was verified. The BIRDS-3 satellite (shown in Figure 17) is used for this assessment. BIRDS-3 is a 1U CubeSat with a structure based on the conventional design concept. Similar evaluation methods were employed to check its suitability for highly efficiency-demanding mass production applications. For the purpose of this evaluation, a slot-based 1U STM was designed (shown in Figure 18) and manufactured. Then, it was compared with an existing conventional-type BIRDS-3 platform. A similar liaison representation is used to show the relationship between components in the assembly in Figures 19 and 20.  
Figure 16: Complexity evaluation of 3U conventional and slot-type structures.  
Figure 17: Conventional-type BIRDS-3 1U model.
## 2 Materials and Methods
### Methodology
#### 2.1.2 Covariance Matrices  
After the preprocessing phase, the principal clustering procedure begins with the calculation of the sample covariance matrix (SCM) between two epochs, as depicted in Figure 1. Initially, a random \(i\)th epoch is selected from a set of e-epochs; subsequently, the covariance matrix is computed relative to the remaining e-epochs. The method to determine the covariance between a randomly selected epoch \(e_{i}\) and \(e_{j}\) is presented in Equation (1).  
\[Cov\big{(}e_{i},e_{j}\big{)}=\frac{1}{N-1}\sum_{t=1}^{N}(e_{it}-\bar{e}_{i}) \big{(}e_{jt}-\bar{e}_{j}\big{)} \tag{1}\]  
where:  
\(e_{it}\) and \(e_{jt}\) = Individual data points of \(e_{i}\) and \(e_{j}\) at time t;  
\(\bar{e}_{i}\) and \(\bar{e}_{j}\) = Sample mean of \(e_{i}\) and \(e_{j}\).  
The SCM is expressed as follows:  
\[C=\begin{bmatrix}Cov(e_{i},e_{i})&Cov\big{(}e_{i},e_{j}\big{)}\\ Cov(e_{i},e_{j})&Cov(e_{j},e_{j})\end{bmatrix}\]  
where:  
\(Cov(e_{i},e_{i})=Var(e_{i})\);  
\(Cov\big{(}e_{j},e_{j}\big{)}=Var(e_{j})\).
## 4 Discussion
Additionally, we should highlight that the method proposed in this paper is based on the usage of lightweight CNN models following the objective of creating models capable of operating on edge devices. Therefore, an additional discussion about the tradeoff between classification accuracy and computational complexity, as well as the time required for pre-processing in the case of the graph convolutional network from [18] would be required for making any precise conclusions. Also, as the method we propose exploits transfer learning in the case of the video modality, the application of the federated learning approach in the case of C-GCN network might be more complex. Such considerations could be a part of our future research.
# Accelerated Inference of Face Detection under Edge-Cloud Collaboration
## 2 Related Work
### Model Partition  
To make full use of computing resources at the edge and cloud, some recent works offload DNN inference tasks from local to cloud servers [12; 21; 22; 23]. Neurosurgeon [12] firstly determined a DNN partition point and placed the partition point on the edge side in the shallow layer, and the partition point in the deep layer of the cloud, to achieve the purpose of joint reasoning. DDNN [22] applied a similar principle to map DNNs into a distributed computing hierarchy. DDNN is capable of accommodating DNN inference in the cloud, while also allowing fast, localized inference using shallow parts of neural networks at the edge and end devices. Branchy-GNN [23] utilized an edge computing platform for efficient graph neural network (GNN)-based point cloud processing and added branches to the network structure for an early exit at the edge. In addition, they adopted learning-based joint source-channel coding (JSCC) for intermediate feature compression to reduce communication overhead.  
To complete face detection tasks on resource-constrained devices, some works introduced model pruning strategies into face detection models, but they are all based on traditional cloud-based inference or edge-based inference. For example, PruneFaceDet [24] used a network pruning method to prune the EagleEye [25] model to reduce the model size. However, this method was based on the traditional reasoning method. In contrast, our method decouples the model in multiple steps, makes full use of the mutual influence of model compression and model partition, and combines the computing resources of the edge and the cloud to achieve the purpose of accelerating the model inference speed.
###### Abstract  
The efficiency of a metro station is determined by the transfer capacity it has on the platform. This is the critical area and the primary motivation for this research. This study analyzed the impact of platform typology on the efficiency and capacity of metro stations. Through the study, the simulation of different typologies, the design logic of the station was analyzed from the ground up, examining each of its components from both a physical and operational perspective. To evaluate the efficiency and capacity of a platform configuration, Fruin's level of service or LOS is used to compare the efficiency across different platform typologies, allowing for the quantification of constraints within the platform configuration. The platform configuration, access points, and connectors impact the station's transfer capacity. This configuration must align with the environmental conditions and the station's role within the system as a whole. The mixed-platform station configuration is twice as efficient as a central platform station and slightly more efficient than a side-platform station, with variations depending on station usage, environment, and position within the network. Under symmetric flow conditions, the side platform is more efficient, reaching a LOS D (density between 1.54 and 3.57 passengers/m\({}^{2}\)). Under asymmetric flow conditions, the central platform is more efficient, reaching a LOS D. However, under both symmetric and asymmetric flow conditions, the mixed platform is more efficient than the two previous configurations, and this design is proposed as the most suitable for transfer station designs, reaching a LOS D. A modular station design is proposed, where a mixed station is built with the capacity to expand based on increased passenger demand. This means constructing a central-platform station initially, and when the capacity is reached (LOS = E or density of 5.26 passengers/m\({}^{2}\)), the second phase is built, adding lateral platforms, thus converting it into a mixed station and doubling its capacity.  
Keywords:infrastructure; platform; metro station; simulation; passenger; capacity; efficiency +
Footnote †: journal: Applied sciences