esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D-STARNET: Spatial-Temporal Attention Residual Network for Robust Action Recognition
## 4 Experiments
### Ablation Experiments  
In this section, we conducted sufficient ablation experiments to verify the effectiveness of various improvements in 3D-STARNET. The results of the ablation experiments are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Acc/Top1 (NTU-RGBD120)** & **Acc/Top1 (UCF101)** \\ \hline Posec3d & 93.03\% & 93.10\% \\ Posec3d + ST-Module & 96.15\% + **3.12\%** & 93.12\% + **0.02\%** \\ Posec3d + MS-Residual & 95.51\% + **2.48\%** & 93.60\% + **0.5\%** \\
3D-STARNET & 96.74\% + **3.71\%** & 93.96\% + **0.86\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiment results.  
Figure 8: Confusion matrix.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Acc/Top1 (NTU-RGBD120)** & **Acc/Top1 (UCF101)** \\ \hline Posec3d & 93.03\% & 93.10\% \\ Posec3d + ST-Module & 96.15\% + **3.12\%** & 93.12\% + **0.02\%** \\ Posec3d + MS-Residual & 95.51\% + **2.48\%** & 93.60\% + **0.5\%** \\
3D-STARNET & 96.74\% + **3.71\%** & 93.96\% + **0.86\%** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different methods on NTU-RGBD120 and UCF101 from acc/top1.  
First, the original ResNet Bottleneck backbone network was improved by changing it to a staged residual structure. With the number of parameters unchanged, the accuracy was improved by 2.48% on the NTU-RGBD120 dataset and by 0.5% on the UCF01 dataset. Another experiment introduced the spatiotemporal attention module into the ResNet network model, which achieved a 3.12% accuracy improvement on the NTU-RGBD120 dataset and kept the accuracy basically the same on the NCF101 dataset. Finally, the above two improvements were introduced on the basis of ResNet, which improved the overall network in terms of information transmission efficiency and model feature focusing ability, achieving 3.71% and 0.86% accuracy improvements in the two datasets, respectively.
# Efficient Actor Recovery Paradigm for Wireless Sensor and Actor Networks
## 5 Simulation Setup and Experimental Results
### Time Complexity  
The quality of the running applications depends on the time complexity of algorithm. The time complexity is normally measured by calculating the number of basic operations and time consumed for those operations performed by the algorithm. The algorithm that takes less time improves the performance of the running applications. In Figure 15, we show the average time consumed for input data processing by EAR algorithm in comparison with RNF, ACRA, ACR and DPCRA. Based on the experimental results, we observed that EAR sent more input data in minimum time as compared with other competing algorithms. EAR sent maximum 54 KB input data within 0.065 s, whereas other protocols took 0.067-0.094 s in sending the same amount of data. EAR achieves minimum time because using the single operation for either pre-failure or post-failure recovery processes help reducing the time complexity.  
To analyze the time complexity of EAR, lets determine the processes involved in the pre-failure and post failure process. In EAR, each critical actor node has a pre-assigned backup actor node which monitors its critical node. If consecutive heartbeat messages are not received from the critical node, backup actor node handles the recovery process. Let's assume the critical actor is designated as (AC) and its backup node is designated (AB), the following instructions illustrate the pre-failure process:  
{If (AB.HeartbeatMonitor(AC) == false);  Ab.Recover(AC);  
Figure 14: Data loss vs. Data recovery during 20 events.  
While post failure process is handled by the backup node (AB). Thus, AB moves towards the failed actor (AC) location in order to recover the network partition. Also, the neighbors of the critical node will use the stored information to communicate with the backup node (AB) in order to restore connectivity.  
PostFailure(AC, AB)  
Move(AB, c);  
Connect(AB, Neighbors(AC))  
To calculate the time complexity for the previous operation, we assume that each process takes a time \(T(n)\) which is illustrated in Table 4. As shown in Table 4, each statement takes O(1).  
Figure 16 is used to represent the time complexity analysis of different algorithms. It is used to illustrate the complexity description of the algorithms. The time complexity of EAR and other competing algorithms is obtained using Big O notation is given in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Process** & **No. of Statements** & **Statement** & **Running Time** & **Time Complexity** \\ \hline \multirow{2}{*}{**Pre-failure process**} & 1 & If (AB.HearbeatMonitor(AC) = false); & \(T(n)=1\) & O(1) \\  & 2 & Ab.Recover(AC) & \(T(n)=1\) & O(1) \\ \hline \multirow{2}{*}{**Post-Failure**} & 1 & move(ab, ac) & \(T(n)=1\) & O(1) \\  & 2 & Connect(AB, Neighbors(AC)) & \(T(n)=1\) & O(1) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Pre-failure and post-failure time analysis of EAR using O Big operation.  
Figure 15: Execution Time of EAR, RNF, DPCRA, ACR, and SCRA in terms of seconds.
# DM-SLAM: Monocular SLAM in Dynamic Environments
## 2 Materials and Methods.
### System Review
#### 2.3.1 DLRSAC Implementation in DM-SLAM  
DLRSAC performs static semantic extraction to create a static initial map with respect to ORB-SLAM2. This algorithm doesn't require any prior information, yet it introduces distribution degree evaluation to break the problem of \"chicken-egg\" circulation in dynamic scenes. The camera pose estimation can be completed more confidentially and more accurately even if static feature points in the scene don't take precedence.  
Static initial maps are constantly used in camera tracking, which affect subsequent map expansion. Therefore, the accuracy of DLRSAC is much more important than the recall rate. The accuracy of the algorithm is improved by analyzing the reason why the algorithm fails, introducing detection methods and resetting the system when failure occurs. Analysis of the reasons for failure is reviewed below:  
1. The parallax between the two frames is so small that solving F matrix at this time is pathological problem.
2. The motion in the scene is too complicated to affect the model selection in each grid, so the motion model is finally selected.
3. The scene appears motion blur (low-textured area), leading to few matches and few sufficient Grid models. Detection methods are reviewed below:  
1. Verify the parallax of map points. If the mean value is too small, the algorithm is considered to fail. In order to call a large parallax, we use two frames separated by several frames instead of two consecutive frames at the time of initialization. An additional benefit of doing this is that it makes the motion and noise separable.
2. Verify that the distribution of selected Grid model. If the value is too small, the algorithm is considered to fail.
3. Check the two frames used for initialization, reset system if there are too few features matching.  
We delay the initialization until this method passes the failure test. Figure 2 shows the static feature extraction effect of the algorithm in different motion scenarios in TUM fr3_walking_rpy.  
Figure 1: The framework of DM-SLAM. DM-SLAM share same loop closing thread with ORB-SLAM2. There are three process including tracking, local mapping and loop closing. Tracking is mainly responsible for extracting the initial pose of the camera utilizing the input images, where we integrate distribution and local-based RANSAC algorithm (DLRSAC) into the initialization in tracking process. Local mapping is responsible for saving, filtering and optimizing key frames and map points, where we integrate algorithms 1 into candidate point decision. The closed-loop detection mainly detects whether there is a closed-loop, and if so, global optimization is performed.
## 3 Results
### NEP Simulations under All Sky Conditions during 2013-2016
#### 3.3.3. NEP Simulations Using EMGPP and Re (LDR) Models  
Thirdly, the hourly NEPs were calculated using EMGPP and Re (LDR) (with the 3-factor and 2-factor methods). The ratios of the mean calculated to the observed Re in 2013, 2014, 2015, 2016, and 2013-2016 were 1.94, 2.32, 2.97, 3.89, and 2.68 for using the 3-factor EMGPP and Re (LDR) models, and 2.04, 2.36, 3.01, 3.92, and 2.73 for using the 2-factor models. Thus, the hourly NEPs overestimated the observations by a factor of 2.7 (ranged from 1.94 to 3.92). Similar (but a little smaller) ratios of mean daily, monthly, and annual sums were also obtained.
## 3 Analysis of Three-Stage Incremental Cooperative Communication
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technique** & **Feature** & **Domain** & **Flaws/Deficiencies** & **Results Achieved** \\ \hline Received Signal & Cooperative routing, Consideration & WSNs, Wildfire & Greater percentage of delayed packets, More & Better energy consumption, More \\ Strength & of QoS and energy consumption, & monitoring, Shadowing & average delay to sink, Restricted to a & Network lifetime \\ Indication & Use of Motivated Reinforcement (RSSI) [8] & Learning (MRL) algorithm & effect of trees & single sink \\ \hline Cloud-assisted & Utilization of cloud computing to & WBANs, Cloud assisted & Latency in cloud communication and bad & Enhanced throughput and energy \\ Random & enhance the performance of the & channel conditions decrease throughput & efficiency in error-prone channels \\ Network Coding & cooperative scheme based on & & \\ (CRNC-MAC) [9] & network coding & & \\ \hline Human Energy & Human energy harvesting protocol, & WBANs, Hybrid polling & No comparison with other MAC protocols, no & improved energy efficiency \\ Harvesting & polling and probabilistic contention & MAC protocol & analytical performance evaluation & and delay \\ (HEH-MAC) [10] & & & \\ \hline Random Linear & compressed sensing and distributed & WBANs, cooperative & Less throughput in error-prone channels & Increased energy efficiency of \\ Network & cooperation for reliable data & compressed sensing & & sensor nodes \\ Coded-Aided & transmission & & & \\ Cooperative & & & \\ Compressed & Sensing & & & \\ (RLNC-ACCS) [11] & & & & \\ \hline CEH-MAC [13] & Exploits energy harvesting & WBANs, Cooperative & No network channel coding and analytical & Improved energy efficiency and \\ information for communication & MAC scheme that exploits energy & performance evaluation & network throughput \\ \hline HTC [15] & Scheme for High SNR radio, Energy-harvesting & WSNs, Cooperation-based networks & SNRs of the source-AP link and all source-relay-AP links mutually correlated, essentially different from conventional cooperative networks with independent link SNRs & Impacts of time allocation, relay number and relay position, on the throughput \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the state-of-the-art work. RLNC, Random Linear Network Coding; CEH, cooperative energy harvesting; HTC, harvest-then-cooperate; MDTED, modified double-threshold energy detection.
## 2 Materials and Methods
### Results of the Second Detailed Landcover Map Update Process  
In the second update process, field surveys were performed to check areas that could not be classified based on aerial photos or maps, or were suspected of temporal changes. Figure 4 shows a typical correction based on a field survey in an area where the completion of a building site was not clearly marked on the map.  
Figure 4: Example results of the second detailed landcover map update process, (**a**) Aerial photo; (**b**) After first update; (**c**) Field survey photo; (**d**) After second update.  
Figure 3: Example results of the first detailed landcover map update process, (**a**) Existing landcover map; (**b**) Landcover map after the first update.  
Through the field survey, most areas that were occluded by buildings and shadows on the map and could not be classified were changed to \"roads\" or \"other grass,\" and the existing parks within apartment complexes were classified as \"other grass\" after the completion of the landscing work. As a result, all such items showed small changes in areas, such that \"other grass\" showed the largest change rate with 0.11%.
# Design and Implementation of a Passive Optical Network for a Small Town +
## 1 Introduction  
In recent years, the demand for data rates for consumer communication needs has increased significantly. Fifth-generation (5G) mobile radio access networks provide a solution to these high bandwidth requirements, and are expected to provide users with low latency, high data rates, and ubiquitous access [1]. However, 5G requires network densification to meet ambitious capacity targets [2].  
Broadband client access, supporting data rates of tens of Mbit/s, is expected to be based on optical transmission. Generally, most customers connect to the nearest central office (CO) with twisted copper wires. In order to achieve broadband connectivity, the wires are replaced or supplemented with fiber optic cables.  
Nowadays, research is constantly being conducted on various options for providing optical transmission, because the process of building a new infrastructure is expensive. Therefore, optimal solutions are being sought to ensure efficient optical transmission [3].  
The popular point-to-point concept is a communication approach used in systems such as Synchronous Digital Hierarchy (SDH) and Ethernet. Passive optical networks (PON), including Ethernet PON (E-PON) and broadband PON (B-PON), are technologies that enable significant streamlining of the physical network infrastructure, leading to a reduction in maintenance costs [4]. PON standardization is still evolving and the terminology may vary depending on the context. PONs can be considered to be optical access networks/optical feeder/optical distribution networks depending on the optical signal's transmission [5].  
The purpose of this article is to present the design, implementation, and evaluation of a fiber-to-the-home (FTTH) access network based on a gigabit-capable passive optical network (GPON). The purpose of this study is to present a way to build optical connectivity to any private home, commercial or industrial facility, with minimal costs, and to provide high-speed internet and IP Television.  
An FITH network is a cost-effective and profitable solution based on passive technology. Compared to copper wires, optical fiber has almost infinite bandwidth and, at the same time, increases network performance [6,7].  
The remainder of this paper is organized as follows. Section 2 reviews the fundamental technologies and standards relevant to PON and GPON systems. Section 3 presents the design methodology, including service analysis, traffic modeling, topology selection, and optical budget calculations, which form the basis for network planning. Section 4 discusses the implementation results, highlighting technical procedures, infrastructure layout, and installation practices. Finally, Section 5 concludes this study and outlines directions for future development and expansion of the network.
# An Overview of Emergency Communication Networks
## 3 Review of Emergency Communication Networks  
### Network Classification  
Emergency communication networks provide timely and effective communication support for all kinds of emergencies, and are an essential part of the comprehensive emergency support system. For any country, the establishment of effective emergency communication based on the disaster communication backbone is an important component of disaster relief. Emergency communication networks are divided into different categories according to different standards. The commonly used classification standards are as follows:  
* According to the transmission medium, networks can be divided into wired communication networks and wireless communication networks.  
Wired communication networks use wired transmission media in the form of communication. The common forms of communication are fixed telephone communication, telephone line/network cable access to the Internet, and optical fiber communication. Wireless communication networks use wireless transmission media. Common communication forms include microwave communication, mobile communication, and satellite communication.
* According to the attributes, networks can be divided into public networks and private networks.  
A public network is a network operated and managed by operators, and connects each private network and user terminal through a public user network interface. A private network refers to a network within a unit or department. At present, private networks mainly use ethernet, Asynchronous Transfer Mode (ATM), and various wireless technologies.
* According to geographical classification, networks can be divided into local area networks (LANs), metropolitan area network (MANs), and wide area network (WANs).  
A LAN is a communication network having a distribution ranging from hundreds of meters to several kilometers. The transmission coverage area of a MAN is mainly within cities, with a distribution area ranging from hundreds to dozens of kilometers. The transmission coverage area of a WAN includes provinces, countries, and even the world, and ranges from hundreds to tens of thousands of kilometers.  
This section analyzes the existing emergency communication network technologies that are mainly used at present: satellite communication [47; 48; 49], wireless ad hoc networks [50; 51], cellular communication [26; 52], and wireless private networks [53; 54; 55].
## 3 Results and Discussion
### Catalytic Application in the Solvent-Free Friedel-Crafts Alkylation Reaction
#### 3.2.3 Effects of Catalyst Amounts on the Friedel-Crafts Alkylation Reaction of p-Xylene with BnCl  
In this section, the results for a study of the influence of catalyst amounts are shown in Figure 2 and Table 1 (in entries 5 to 9, the reaction temperature condition is 80 \({}^{\circ}\)C for 30 min). It can be seen that when the 5c dosage is above 0.3 mole %, the BnCl conversion reaches approximately 95%. However, the BnCl conversion does not increase significantly when the 5c amount is higher than 0.3 mole %, indicating that the BnCl conversion has reached a limiting value when 5c is higher than 0.3 mol %. The DPM selectivity decreases from 93.0% at about 0.3 mole % of 5c to 91.0% at 1.0 mole 0% of 5c. From the point of view of high reaction efficiency and economic green chemistry, the optimal catalyst amount of 1.0 mole % was chosen. It appears that 5c is the predominate catalyst for the Friedel-Crafts alkylation of p-xylene with BnCl under appropriate conditions.  
.2.4 Effects of Reaction Temperature on the Friedel-Crafts Alkylation Reaction of p-Xylene with BnCl  
The reaction temperature is an important key factor affecting the BnCl conversion and DPM selectivity in this Friedel-Crafts alkylation. Therefore, the influence of the reaction temperature was studied at various temperatures ranging from room temperature to 100 \({}^{\circ}\)C while keeping the amount of 5c at 1.0 mole % and the reaction time at 30 min. The results are presented in Figure 3. This figure clearly shows that the BnCl conversion increases rapidly to approximately 99.9% when the temperature is increased from 50 to 80 \({}^{\circ}\)C and remains above 90% at 100 \({}^{\circ}\)C Similarly, DPM selectivity increases rapidly from 0 to 99.9% as the temperature is increased from 25 to 50 \({}^{\circ}\)C. Increasing the reaction temperature has the most significant influence on the BnCl conversion and DPM selectivity due to the more vigorous molecular collisions at higher reaction temperatures [56]. Another possible reason for the dramatic changes in BnCl conversion and DPM selectivity may be due to the melting point of B-L MIL 5c (the melting point is about 83 \({}^{\circ}\)C). When the reaction temperature is gradually elevated to 80 \({}^{\circ}\)C, 5c has been melted to contribute to the homogeneous state with p-xylene and BnCl. However, as temperature is raised to 100 \({}^{\circ}\)C, the BnCl conversion and the DPM selectivity do not increase further. This may have been due to the limitations of the chemical reaction equilibrium, given the energy consumption of the reaction. Therefore, 80 \({}^{\circ}\)C was selected as the proper reaction temperature.  
Figure 2: The effects of the amount of catalyst on the Friedel–Crafts alkylation reaction of p-xylene with BnCl (at a reaction temperature of 80 \({}^{\circ}\)C, a reaction time = 0.5 h, and a molar ratio of p-xylene: BnCl = 2:1).
# A Carbon Benefits-Based Signal Control Method in a Connected Environment
## 5 Evaluation
### Results
#### 5.2.4 Carbon Benefits  
Figure 11 illustrates the carbon benefits of CBCVs. The carbon reduction rewards are benchmarked against the current price in China's carbon trading market, which is valued at RMB 90 per ton of carbon dioxide reduced. At this pricing, the average reward per CBCV ranges between RMB 0.0015 and 0.0165, exhibiting a certain regularity.  
As the CR of CBCVs increases, the average carbon revenue per CBCV declines. This is because when the proportion of CBCVs in the traffic flow is relatively low, the actions of each CBCV can influence more non-CBCVs, thus improving the carbon benefits from the perspective of individual vehicles. Conversely, as the proportion of CBCVs in the traffic flow increases, fewer non-CBCVs can be influenced. For instance, at a 70% CR, only 30% of the vehicles are non-CBCVs, meaning that on average, only one out of every two CBCVs will influence a non-CBCV. In high CR environments, the guidance role of CBCVs is shared among many vehicles, reducing the number of non-CBCVs that each CBCV can influence, lowering the carbon reward per CBCV. Although the collective carbon reduction capacity of CBCVs remains, the contribution of each CBCV is diluted, leading to reduced individual carbon rewards.  
With increasing traffic demand, the average carbon revenue per CBCV shows an upward trend, indicating that CBCVs can achieve higher carbon reduction rewards under busy traffic conditions. When the v/c is 0.2, the average rewards per vehicle range from RMB 0.0015 to 0.0021. When the v/c ratio is 0.5, the average rewards per vehicle range from RMB 0.0047 to 0.0057. When the v/c ratio is 0.8, the average rewards per vehicle range from RMB 0.0085 to 0.0165. This is because increased traffic demand leads to denser traffic flow, allowing CBCVs to influence more non-CBCVs through their speed strategies. Additionally, the increase in traffic volume generally results in higher per-vehicle carbon emissions, providing more room for emission reduction through the guiding effectiveness of each CBCV.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Traffic** & **Fixed Signal** & **Adaptive** & **The** & **Compared to** & **Compared to** \\
**Demand** & **Control** & **Signal** & **Proposed** & **Signal** & **Signal** \\
**Structure** & **Method (s)** & **Method (s)** & **Method (s)** & **Control** & **Control** \\ \hline I & 49.6 & 33.6 & 29.1 & 41.33\% & 13.39\% \\ II & 52.1 & 33.9 & 29.5 & 43.38\% & 12.98\% \\ III & 51.1 & 34.1 & 30.1 & 41.10\% & 11.73\% \\ IV & 52.9 & 35.1 & 30.2 & 42.91\% & 13.96\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average delay (s) under different transportation demand structures.