esa-sceva/satcom-chunk-collection huggingface dataset:
# Natural Language Description of Videos for Smart Surveillance
## 2 Literature Survey
In the first approach, a video is viewed as a series of 2D images, from which frame-level features are drawn out using the GoogleNet (CNN) model that is trained on ImageNet. In the second approach, a video is considered as 3D data which are a series of video segments, and it treats variation with respect to time for feature extraction. Here, segment-level features are mined using dense trajectories and a 3D CNN (C3D) model pretrained on the Sports 1M dataset. The pooling method used for these two approaches merges various frame or segment features into one video feature vector. The decoder generates captions using these feature vectors. The LSTM network based on the language model is trained with some tweaks. This architecture can generate multiple captions which pick the best candidate from the group. The authors concluded that this model is appropriate for the MSR-VTT (MSR-Video to Text) dataset, which consists of various types of videos. The results demonstrate that the captions generated from this model closely relate to human evaluation. Real-world images cannot be explained by just one label due to rich information such as the attributes, scenes, objects and parts of objects that are contained in it. The proposed combined CNN-RNN [20] approach for multi-label image classification learns not only semantic but also co-occurrence dependency. This approach was tested on three benchmark multi-label datasets--MS COCO, NUS-WIDE and VOC PASCAL 2007--with promising results. Many classifiers are unable to perform when the training set is small. Building large, labeled data requires a lot of time and effort. Generative models can produce realistic data samples to bridge this gap. A new model, the category sentence generative adversarial network (CS-GAN) was proposed in [21]. This model combines reinforcement learning, a GAN and an RNN to learn and reproduce the sentence structure. CNN models tend to lose spatial information and might misclassify objects in different orientations and proportions. To solve this problem, capsule networks are introduced which not only work for images, but also for natural language processing (NLP) problems. A capsule network to solve the multi-label text classification problem was proposed in [22]. The experiments showed promising results using low-capacity resources. In this work, we have proposed a CNN- and LSTM-based multi-line video caption generation system. However, we have focused on learning task-specific features for the scene, human (age, gender and emotion), object, dress and action descriptions. We have tested this on TRECVid, an agriculture and university surveillance dataset. Such a framework is helpful for many applications such as surveillance, intrusion detection and alert systems, person re-identification and video-to-text summarization.
## 2 UAV Communication
### Characterizing the UAV Network  
It is necessary to characterize a network to comprehend its nature, authentication-related limitations and opportunities [35]. How does the topology of the UAV network affect the authentication requirements?  
As nodes fail or migrate away, how often does the network become partitioned? What can be done to extend the network's life? Is it necessary to have self-organizing and self-healing abilities? What kind of structure would be more appropriate? Which authentication protocols can be executed at various layers? Is it possible to dynamically add and remove nodes with authentication? Are the connections intermittent and how good are they? In this section, we present a comprehensive view of the characteristics that run across all of the works, as well as the research trajectory in UAV networks. This section presents the emerging UAV network requirements to comprehend the security-related work in subsequent sections.
## 4 Results and Discussion
### Rheological Properties  
The rheological properties of asphalt binders, as determined by the Dynamic Shear Rheometer (DSR) test, provide essential insights into the binder's resistance to deformation and fatigue life. This series of tests, presented in Figures 9-11, assess the performance in terms of the rutting factor (G*/sin \(\delta\)) for the original, RTFO-aged, and PAV-aged samples, along with the fatigue factor (G* sin \(\delta\)) post-PAV conditioning.  
Figure 10: Results of DSR after RTFO.  
Figure 9: Results of DSR of original binder.  
For the original binders, Figure 9 shows that the inclusion of nanomaterials such as NT, NA, and NS leads to an increase in the G*/sin \(\delta\) values across various temperatures, signifying an improved resistance to permanent deformation. Specifically, the NT-modified binder demonstrates a significant increase in the rutting parameter, indicating an enhanced capacity to withstand rutting at elevated temperatures.  
Upon RTFO aging, as depicted in Figure 10, all nanomaterial-modified binders exhibit increased rutting resistance. However, the degree of improvement varies with each nanomaterial type, with NS showing the highest rutting factor, followed by NA and NT. This trend suggests that NS is particularly effective in enhancing high-temperature performance, potentially extending the service life of pavements subjected to heavy traffic loads.  
The fatigue resistance of the binders, reflected in the fatigue factor (G* sin \(\delta\)) after PAV aging, is illustrated in Figure 11. Here, the data indicate a trade-off between increased stiffness and fatigue life. While nanomaterials contribute to higher stiffness, as indicated by the elevated G* sin \(\delta\) values, this also implies a slight increase in the risk of fatigue cracking. Despite this, the enhancements in rutting resistance may offset the marginal reduction in fatigue life, especially in regions with high temperature variations.  
When ranking the nanomaterials based on their effects on the rutting index (G*/sin \(\delta\)) and fatigue index (G* sin \(\delta\)), SiO\({}_{2}\) emerges as the most effective in improving rutting resistance, with the recommended optimal dosage being 6%. For fatigue resistance, NT at a dosage of 4% is preferable, as it presents a balanced modification, increasing the stiffness without unduly compromising the binder's fatigue life.  
In conclusion, the strategic selection and concentration of nanomaterials are pivotal in modifying asphalt binders, as evidenced by the DSR results. NS stands out for its rutting resistance, while NT offers a balanced enhancement in both rutting and fatigue indices. Optimal dosages should be carefully considered to maximize performance benefits and ensure the durability and longevity of asphalt pavements.
# Resource Allocation on Blockchain Enabled Mobile Edge Computing System
###### Abstract  
Currently, the concept of Mobile Edge Computing (MEC) has been applied as a solution against the plethora of demands for high-quality computing services. It comprises several essential processes, such as resource allocation, data transmission, and task processing. Furthermore, researchers applied blockchain technology, aiming to enhance the robustness of the MEC system. At present, resource allocation in the MEC system is a very hot field, but there are still some problems in the resource allocation process under the traditional MEC architecture, such as privacy disclosure and so on. Moreover, the resource allocation problem in a blockchain-enabled MEC system will be more complicated, while the mining process may have an impact on resource allocation policy. To address this issue, this paper investigates the resource allocation problem with blockchain-based MEC system architecture. A brand new consensus mechanism: proof of learning (PoL), is applied to the system, which does not waste the computing resources of edge computing servers. Based on this, we modeled the system mathematically, focusing on server processing latency, mining latency, rewards under the new consensus, and total cost. The asynchronous advantage Actor-Critic (A3C) algorithm is used to optimize resource allocation policy. To better capture the long-time trend of the system, the temporal convolutional network (TCN) is implemented to represent the policy function and state-value function in the reinforcement learning model. The results show that the A3C algorithm based on TCN not only converges faster but also is more stable.  
mobile edge computing; blockchain; proof of learning; resource allocation; asynchronous advantage Actor-Critic; temporal convolutional network +
Footnote †: journal: Electronics Letters
# DC-DC Buck Converters with Quasi-Online Estimation of Filter Capacitor Equivalent Parameters
## 2 Materials and Methods
### Scheme Based on the Implementation of the Principle from Figure 7d
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Capacitor**} & \multirow{2}{*}{**Parameter (P)**} & – & \multirow{2}{*}{\(\sigma_{\text{P}}\)} & \multirow{2}{*}{\(\sigma_{\text{P}}\)[\%]} \\  & & \(P\) & & \\ \hline \multirow{4}{*}{C21} & \(\hat{T}_{1\_1}\)[ms] & 1.68772 & 0.00051816 & 0.030702 \\  & \(\hat{T}_{2\_1}\)[ms] & 41.20848 & 0.03851804 & 0.066143 \\  & \(\hat{C}_{e\_1}\)[\(\mu\)F] & 460.4057 & 0.452717 & 0.09833 \\  & \(\hat{R}_{se\_1}\)[\(\,\)] & 0.345 & 0.004538 & 1.315316 \\ \hline \multirow{4}{*}{C22} & \(\hat{T}_{1\_2}\)[ms] & 0.8836596 & 0.000506425 & 0.05731 \\  & \(\hat{T}_{2\_2}\)[ms] & 18.98359 & 0.005894927 & 0.031053 \\ \cline{1-1}  & \(\hat{C}_{e\_2}\)[\(\mu\)F] & 210.8587 & 0.067257 & 0.0319 \\ \cline{1-1}  & \(\hat{R}_{se\_2}\)[\(\,\)] & 0.869533 & 0.00267 & 0.307099 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters estimated with PO for the buck converter in Figure 12 (average values, average squared deviations in absolute values, and in percentages compared to the average value) for the case C21—470 \(\mu\)F/25 V, C22—220 \(\mu\)F/25 V.
# Virtual Scene Construction for Seismic Damage of Building Ceilings and Furniture
## 4 Case Study
### Technical Evaluation  
To evaluate the reasonableness of the proposed method, three earthquake scenes are constructed and compared as demonstrated as Figure 13. According to the seismic design code of China [40], earthquakes can be classified into three levels (i.e., minor, moderate, and severe), and the constructed three scenes correspond to such three levels. For the THA of the building of case study in Beijing, the El Centro ground motion is adopted and the PGAs of the minor, moderate and severe earthquake are 0.07 g, 0.2 g, and 0.4 g, respectively [40]. Through the proposed method, the original scene and three indoor earthquake scenes are shown in Figure 13. In the minor earthquake scene, only the bookcase  
Figure 12: Integrated indoor earthquake scene, (**a**) original scene; (**b**) furniture overturns; (**c**) ceiling titles fall; (**d**) ceiling titles fall to the floor.  
overturns, whereas both bookcase and television overturn in the moderate and severe earthquake scenes. Furthermore, ceiling tiles also fall in the severe earthquake scene. Therefore, the constructed indoor earthquake scenes exhibit more severe damage with the development of earthquake levels, which indicates the reasonableness of the proposed method.
# Development of Traffic Scheduling Based on TSN in Smart Substation Devices
## 2 Smart Substation Delay Analysis and TSN Technology Analysis  
In the smart substation network architecture, Ethernet infrastructure is used to facilitate information interaction across three layers of devices. Because various types of service traffic are intertwined within two networks, the traffic scheduling technology of TSN is used to optimize QoS for different types of service traffic.
## 2 Methodology
### Validating the Developed Approach in Wettability Prediction  
To test the reliability of this mathematical model on the field, water saturation and field NMR T\({}_{2}\)LM log data were used. The integration of water saturation helps to analyse the depth where hydrocarbon and water coexist, leading to the wettability phenomenon. The field cut-off for water saturation was increased from 70% to 80%. The increment was to accommodate enhanced and chemical oil recovery mechanisms and varying field cut-offs for net pay zones based on the field development plans and production forecasts. Based on several chemical enhanced oil recovery at field scale, residual oil saturation at 20% is optimal [40]. The overall reservoir water saturation and NMR T\({}_{2}\)LM relationship were established using cross plots. The reservoir interval was subsequently divided into either increasing or decreasing water saturation zones, based on water saturation cut-off. That is, where saturation increases up to the point it starts to decrease is delineated and vice versa. This subdivision created several zones where a cross plot of saturation and T\({}_{2}\)LM was employed to help analyse the effect of hydrocarbon on T\({}_{2}\)LM. The average T\({}_{2}\)LM for the observation interval is computed to establish wettability.
# Future Industrial Networks in Process Automation: Goals, Challenges, and Future Directions
## 4 Evolution Challenges
### Distributed Real-Time System Challenges  
Distributed real-time systems are commonly deployed in process automation, where the whole process is carried out with multiple control networks, each consisting of a local set of sensors and actuators [17]. The process conditions are monitored periodically by the sensors, which can be denoted as a snapshot of the whole process line. The controllers are responsible for making computations based on the received snapshot and sending out action commands to the actuators within predefined time bounds. To guarantee correct operation, the periodic process snapshot, as well as the functionality of all the controllers, should be available at every controller that needs the data before executing the control application. For instance, all the controllers belonging to the distributed control system need to act on the same set of information in order to guarantee the correct output. This is a general requirement that the system should be in a consistent state at all times, independent of if there are changes in process data, applications, or in the network. Changes have to be coordinated in time as well in order to preserve the real-time properties during run-time changes and avoid bringing the ongoing process down.
# Strategies for Post-Translational Control of Protein Expression and Their Applications
## 5 Applications
### Gene and Cell Therapies  
Several research groups and pharmaceutical companies are working on the application of not only PROTAC, but also degrons, for human therapeutic use. Presently, a strategy for the therapy of diseases using artificial therapeutic cells is attracting attention. Chimeric antigen receptor T cell (CART) therapy is an advancing immunotherapy in the adoptive cell transfer field. In this therapy, T cells collected from a patient's peripheral blood are genetically engineered ex vivo to express chimeric antigen receptor (CAR) proteins on their cell membranes, which are then transferred into the patient. The T cells are genetically engineered ex vivo to recognize tumor-specific antigens expressed on the surfaces of tumor cells. CART is activated through binding to the tumor-specific antigen, and then kills the tumor cells by releasing cytokines (Figure 7a). CAR is a synthetic membrane protein consisting of an extracellular antigen-binding domain, an extracellular spacer, a transmembrane domain, a costimulation domain, and a T cell activation domain [88; 89]. A strategy that allows reversible and variable control of CAR expression would be therapeutically beneficial to avoid toxicities caused by excessive immune responses, such as cytokine release syndrome (CRS). [PERSON] et al. showed that the use of the SMASH system allows the regulation of CAR expression and function in cells [90]. [PERSON] et al. showed that the LID system enables the regulation of CAR expression and function in vitro and in vivo [91]. [PERSON] et al. generated T cells expressing a fusion protein of the IKZF3-derived degron attached to the C terminus of CAR19, which recognizes the cancer-specific antigen CD19 [92]. They demonstrated that CAR expression and function could be regulated in a tunable and reversible manner at the cellular and animal levels using CAR19-degron T cells and lenalidomide, which is a ligand of this degron (Figure 7b). In addition to its side effects, another concern of the CART therapy is the limitation of CART efficacy because of \"T cell exhaustion\". CART is continuously stimulated by the high antigen load of cancer cells, which triggers CAR tonic signaling. Excessive CAR signaling leads to the overexpression of immune inhibitory proteins because of global transcriptional and epigenetic alterations, resulting in the loss of CART function. This dysfunctional state is described as T cell exhaustion. The [PERSON] group showed that transient inhibition of CAR signaling by the DD system reverses the phenotypic and transcriptional features of T cell exhaustion, remodels the exhausted epigenome, and restores the anti-tumor function [93].
# Influence of Incident Shock on Fuel Mixing in Scramjet
## 4 Results and Discussion
### Mixing Efficiency
As the fuel spread downstream in case 5, a further deflection of the fuel occurs due to the separation shock wave, as shown in Figure 30, leading to a greater penetration depth of the fuel and an increase in mixing efficiency by nearly 50% compared to cases 4 and 6. In case 2, it was observed that the weaker separation shock wave formed before the cavity led to a less pronounced secondary deflection of the fuel plume. Additionally, the relatively thinner boundary layer limited the fuel transport capacity in the spanwise direction, significantly inferior to that of case 1 and case 3. As the fuel propagated downstream, these factors collectively resulted in a gradual decrease in the mixing efficiency of case 2, ultimately falling below that of case 1 and case 3. Figure 30: X = 2.244 m fuel distribution with streamlines for all cases. Figure 29: X = 2.22 m fuel distribution with streamlines for all cases.
# Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks
## 3 VGRQ: The Proposed Routing Mechanism
### Phases of VGRQ
#### 3.2.2. Cell Header Election and Chain Formation  
Once the virtual grid is constructed in the sensor field, cell headers are elected for each grid cell using Algorithm 1. Every sensor node computes the distance between itself and the center of its corresponding grid cell (Step 1). Sensor nodes whose distance is less than the defined threshold (Step 2) broadcast a header election message containing their location, grid cell ID, energy, and distance (Step 3). Sensor nodes receiving this message that belong to the same grid cell and whose distance is less than the one mentioned in the message reply with a header candidate message. This message contains the sensor node's ID, distance from the grid cell's center, and energy (Steps 4, 5, and 6). The node closest to the midpoint of the grid cell having maximum energy is elected as the cell header (Step 9). To notify sensor nodes of its grid, the cell header broadcasts an I am header message consisting of location and ID (Step 10). This process is executed in every grid cell.  
```
0:\(C(x,y)\), \(ID\), \(Energy\), \(d\)
0: Cell header election
1: Sensor node \(i\) calculates its distance \(d\) from the mid-point of grid cells
2:if\(d_{i}<threshold\)then
3: Broadcast \(Header\_Election\) message \(\langle C_{i}(x,y)\rangle,ID\), \(Energy\), \(d_{i}\rangle\)
4:for each node \(j\) receiving \(Header\_Election\) message do
5:if\((C_{i}(x,y)==C_{j}(x,y)\)&&\(d_{j}<d_{i})\)then
6: send \(Header\_Candidate\) message \(\langle ID,Energy,d_{j}\rangle\)
7:endif
8:endfor
9: Node \(i\) gives up &&elects node \(k\) having smallest distance to mid-point of grid cell &&e highest energy
10: Node \(k\) broadcasts \(I\_am\_Header\)\(\langle C(x,y),ID\rangle\) message
11:endif
```  
**Algorithm 1** Cell header election  
In VGRQ, cell headers take charge of keeping sink location information, sending queries to AoI, collecting data from cell members, and aggregating it and forwarding it to the sink. Since cell headers carry these many responsibilities, they lose their energy faster than the other nodes and become hotspot nodes. To prevent this situation, cell headers progressively exchange their responsibilities with their respective cell members to maintain uniform energy distribution in the cluster.  
Once cell header election is completed, chain(s) of four cell headers are constructed by following Algorithm 2. Chain construction is accomplished on the basis of the \(x\)-coordinate and \(y\)-coordinate of CHs. The grid cells whose CHs participate in chain formation are said to belong to a _sector_. As shown in Figure 3, there are five total sectors and each sector constitutes four grid cells.  
Figure 2: Assigning grid ID.
# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
###### Abstract  
Mobile devices are a common target for augmented reality applications, especially for showing contextual information in buildings or construction sites. A prerequisite of contextual information display is the localization of objects and the device in the real world. In this paper, we present our approach to the problem of mobile indoor localization with a given building model. The approach does not use external sensors or input. Accurate external sensors such as stationary cameras may be expensive and difficult to set up and maintain. Relying on already existing external sources may also prove to be difficult, as especially inside buildings, Internet connections can be unreliable and GPS signals can be inaccurate. Therefore, we try to find a localization solution for augmented reality devices that can accurately localize itself only with data from internal sensors and preexisting information about the building. If a building has an accurate model of its geometry, we can use modern spatial mapping techniques and point-cloud matching to find a mapping between local device coordinates and global model coordinates. We use normal analysis and 2D template matching on an inverse distance map to determine this mapping. The proposed algorithm is designed to have a high speed and efficiency, as mobile devices are constrained by hardware limitations. We show an implementation of the algorithm on the Microsoft HoloLens, test the localization accuracy, and offer use cases for the technology.  
Augmented reality; BIM; localization; indoor localization; pose estimation; point cloud; indoor navigation Article