esa-sceva/satcom-chunk-collection huggingface dataset:
# PSON: A Serialization Format for IoT Sensor Networks
## 5 Research Results
### Serialization/Deserialization Speed
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Protocol** & **Test01** & **Test02** & **Test03** & **Test04** & **Test05** & **Test06** & **Test07** & **Test08** & **Test09** & **Test10** & **Average** \\ \hline JSON S & 28,767 & 116,705 & 188,648 & 33,814 & 7602 & 6865 & 6708 & 10,350 & 11,082 & 51,759 & 46,230 \\ JSON DS & 36,577 & 90,513 & 115,070 & 41,774 & 9132 & 8481 & 8033 & 11,032 & 10,809 & 74,032 & 40,545.3 \\ BSON S & 123,806 & 140,927 & 427,762 & 256,979 & 31,812 & 31,846 & 31,837 & 32,081 & 32,020 & 250,959 & 136,002.9 \\ BSON DS & 76,403 & 86,572 & 264,767 & 159,726 & 19,020 & 19,092 & 19,083 & 19,114 & 19,090 & 153,429 & 83,629.6 \\ MsgPack S & 15,875 & 31,524 & 47,345 & 20,153 & 4714 & 4819 & 5085 & 5597 & 5737 & 26,417 & 16,726.6 \\ MsgPack DS & 16,677 & 68,870 & 100,840 & 14,366 & 4394 & 4047 & 4034 & 5427 & 7330 & 33,787 & 25,977.2 \\ Protocol Buffers S & 16,487 & 45,835 & 117,739 & 11,449 & 4931 & 6347 & 5093 & 5403 & 5986 & 27,574 & 24,684.4 \\ Protocol Buffers DS & 24,162 & 32,412 & 59,527 & 13,952 & 6286 & 10,642 & 6216 & 6360 & 7152 & 41,606 & 20,831.5 \\ PSON S & 8854 & 23,372 & 40,624 & 9364 & 2627 & 2632 & 2617 & 3387 & 3676 & 14,637 & 11,179 \\ PSON DS & 13,011 & 13,618 & 29,044 & 15,432 & 3578 & 5726 & 4318 & 4225 & 4500 & 23,119 & 11,657.1 \\ XML S & 127,529 & 119,323 & 240,721 & 193,044 & 55,073 & 54,538 & 54,506 & 55,618 & 55,504 & 184,497 & 114,035.3 \\ XML S & 163,530 & 177,467 & 313,182 & 257,228 & 80,087 & 79,212 & 79,159 & 81,217 & 81,216 & 242,851 & 155,514.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ESP32 Serialization (S)/Deserialization (DS) speed.
# Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction
## 2 Methodology
### Construction of Relationship Graphs between Metro Stations
#### 2.2.1 Network Structure Correlation Graph  
When two stations are situated in close proximity, their passenger flow interactions tend to exhibit stronger correlations. That means the topology association between adjacent stations is a critical spatial relationship within the metro network. This spatial relationship exerts a notable influence on the trajectory and speed of passenger traffic, thereby shaping the dynamic behavior of the metro system. In order to effectively capture the relationship among stations, we establish a network structure correlation graph denoted as \(G_{c}\) referring to the real-world network map. The value of item \(a_{c}(i,j)\) of adjacency matrix \(A_{c}\) is assigned to 1 if two stations \(i\) and \(j\) are adjacent, and 0 otherwise. Additionally, diagonal entries of the matrix are set to 0 to avoid self-loop connections between stations and themselves, thus reducing the repeated redundant information and computational burden.  
\[a_{c}(i,j)=\left\{\begin{array}{cc}1,&\text{when stations $v_{i}$ and $v_{j}$ are adjacent},\\ 0,&\text{otherwise}.\end{array}\right. \tag{1}\]
## 4 Formal Model  
In this section, the problem is formalized using an Integer Linear Programming ILP model. For this, time is discretized. Each unit of time or slot is atomic and events are considered to be synchronized with the beginning of the slot. Time is counted with natural numbers \(1,2,\ldots,H\), where \(H\) is the least common multiple of the flows' periods. The set of flows, \(F\), is known from the beginning. However, this condition can be later relaxed to deal with dynamic flows of arrivals and departures. As has been defined before, flows have real-time constraints and are periodic. The network, \(R=(V,L)\), is described by a set of switches and servers \(V\), together with the set of links that interconnect them, \(L\). In hard real-time systems, all deadlines should be met. In the case where a single deadline is missed, the system is said to be not feasible [12]. In such situations, the quality of the applications involved may be diminished, for example, by reducing the bandwidth because the size of the flow is dwindled or by enlarging the periods [20; 37]. However, in this paper, it is assumed that flows are not hard real-time, and thus some activation deadlines may be missed or even a flow that is not schedulable may be left out. Thus, in the context of this paper, an optimal feasible schedule is one in which as many flows as possible are scheduled with as minimum energy demand as possible. It may happen that the flows are not schedulable from a real-time point of view, that is independently of the energy demand, they can not be scheduled in such a way that all satisfy the deadlines. In these cases, the scheduler will try to send as many flows or flow activations as possible. In these cases, two approaches were followed. In one, an activation is considered as the entity to be scheduled. That is, it is possible to schedule one or more activations of a flow, while other activations of the same flow may be unscheduled. In the other criterion, all activations of a flow must be completely scheduled or the flow is not scheduled at all reducing with this the demand. To describe the model, two sets are introduced, and the variables used are presented in Table 2.  
* Set of flow activations \(f\): \(I_{f}=\{1,\ldots,H/p_{f}\}\). * Set of valid slots for the activation \(k\) of flow \(f\): \(H_{f}^{k}=\{r_{f}^{k}\leq h<v_{f}^{k}\}\quad\forall k\in I_{f}\). Figure 2: Priority inversion in Server 1. A red, B green, C yellow and D blue. A hierarchical two objective model is considered. The main objective is to maximize the number of activations or flows transmitted, according to the approach followed. Then, the secondary objective is to transmit all these while minimizing the energy consumption. Note that the \(w^{\prime}s\) variables (and the constraints that involve them) are only necessary in the version that takes into account flow as the unit to be scheduled.
## 7 Big Data and Blockchain in Decision-Making
### Regulatory and Compliance Issues  
The legislative framework for blockchain and big data is still evolving, as many countries have challenges keeping up with the fast progress of these technologies. Adhering to data protection regulations like GDPR in Europe and CCPA in California presents further difficulties. The unchangeable nature of blockchain poses challenges in adhering to legislation that mandates the modification or deletion of data upon request. Furthermore, decentralized decision-making sometimes lacks well-defined regulatory frameworks, resulting in uncertainty and possible legal liabilities. Organizations must effectively manage complex regulatory frameworks to guarantee that their utilization of blockchain and big data adheres to both local and international legal requirements, a process that may be demanding in terms of resources and complexity [117].  
To address regulatory compliance issues, hybrid blockchain models are being explored. These models combine the benefits of public and private blockchains, allowing for controlled access and compliance with regulatory requirements. Moreover, regulatory sandboxes are being used to test and refine blockchain applications within a controlled environment before full-scale deployment. These sandboxes enable developers to understand and navigate regulatory frameworks better, ensuring that their solutions are compliant.
# Improving Landslide Recognition on UAV Data through Transfer Learning
## 4 Experimental Results and Discussion
### Recognition Result and Evaluation
#### 4.3.3 P-R Curve (Precision-Recall Curve)  
The P-R curve is a curve composed of precision and recall, with recall as the horizontal axis and precision as the ordinate axis. When the P-R curve is closer to the top right, the model performance is better. As shown in Figure 13, the model of the transfer learning is better than the SSD model.  
Figure 11: The precision of the SSD model (**left**) and the transfer learning model (**right**).  
Figure 12: The recall of the SSD model (**left**) and the transfer learning model (**right**).
## 3 Methods
### Implicit Representation  
We want to learn a generalized representation of 3D eyeglass frames of different shapes. In this section, we learn a Signed Distance Function (SDF), which is a continuous function that maps \(P\in\mathbb{R}^{3}\) to a signed distance \(s\),  
\[SDF:\mathbb{R}^{3}\rightarrow\mathbb{R},P\in\mathbb{R}^{3},s\in\mathbb{R}\,. \tag{3}\]  
The sign represents whether the point is inside (negative) or outside (positive) the watertight surface and the amplitude represents the distance from the watertight surface. Therefore, we further express SDF as follows:  
\[SDF:\mathbb{R}^{3}\times\mathcal{X}\rightarrow\mathbb{R}. \tag{4}\]  
We implicitly defined the underlying surface of the object as the 0-level set of the neural function, denoted as \(SDF(\cdot)=0\). This implicit surface can be rendered through raycasting or rasterization of a mesh obtained with, for example, marching cubes.  
Inspired by [PERSON] et al. [45], we represent the object geometry as an SDF field and directly regress the continuous SDF from point samples using deep neural networks. The re sulting trained network can predict the SDF value of a given query position, from which we can extract the zero level-set surface by evaluating spatial samples. In practice, we use a Multi-Layer Perceptron (MLP) neural network \(f_{g}\) as the decoder. It takes a 3D point \(P\in\mathbb{R}^{3}\) and latent vector \(\mathcal{Z}\) as the input and generates its distance to the closest surface. We train the parameters \(\theta_{g}\) of \(f_{g}\) to obtain a good approximator of the given SDF:  
\[SDF(P)\approx f_{g}(P,\mathcal{Z},\theta_{g}), \tag{5}\]  
The training is performed by minimizing the sum over losses between the predicted \(f_{g}(P)\) and real SDF values of points \(s\) under the following \(L_{1}\) loss function:  
\[\mathcal{L}(f_{g}(P),s)=|\operatorname{clamp}(f_{g}(P),\delta)-\operatorname {clamp}(s,\delta)\ |, \tag{6}\]  
where \(\operatorname{clamp}(P,\delta):=\min(\delta,\max(-\delta,P))\) introduces the parameter \(\delta\) to control the distance from the surface over which we expect to maintain a metric SDF. Larger values of \(\delta\) allow for fast ray tracing, since each sample gives information about safe step sizes. Smaller values of \(\delta\) can concentrate network capacity on details near the surface.  
Once trained, the network implicitly represents the surface as the zero iso-surface of \(f_{g}(P)\), which we can visualize using raycasting or marching cubes.
## 2 Evaluation Indices and Data Acquisition  
### Evaluation Indexes  
This paper mainly takes a certain brigade mobile communication system as the research object and selects eight representative indexes in order to evaluate the system's operation efficiency. The specific evaluation indexes and definitions are shown in Table 1.
# Turning Video Resource Management into Cloud Computing
## 2 Related Work  
Web systems based on traditional storage servers are mainly used for video resources management. Low efficiency is the bottleneck of these systems. For improving the management efficiency, clusters are used in the systems. With the increasing volumes, data types, and complex application scenarios of video data (especially single video with a large size), current systems cannot meet their management needs. At present, Hadoop-based cloud-computing technologies are extensively used in various fields, such as video transcoding systems [4], data processing cross-platforms [5], and multimedia conferences [6]. Scalable and fault-tolerant architecture that supports the parallel processing of large volumes of video data is becoming increasingly necessary for flexible, robust, and efficient processing of large volumes of data [2]. However, current video resource management systems make it difficult to manage videos in big volumes. Additionally, these applications lack uniform architecture and layer models to conveniently share resources and efficiently communicate each other. This paper focuses on building a video resource management system serving various users in a cloud-computing environment based on Hadoop Distributed File System (HDFS).  
This paper tries to design a system that utilizes HDFS based on a cloud server. Thereby, HDFS and the Map/Reduce framework are briefly introduced. Hadoop inspired by Google's Map/Reduce and Google File System (GFS) [7] is a software framework that supports data-intensive distributed applications handling thousands of nodes and petabytes of data [8]. It can perform scalable and timely analytical processing of large data sets to extract useful information. Hadoop consists of two important frameworks: (1) (HDFS) (Figure 1), like GFS, is a distributed, scalable and portable file system written in the Java programming language; (2) Map/Reduce (Figure 2) which is the first framework developed by Google for processing large volumes of data sets [9].  
The Map/Reduce paradigm is a framework for processing huge datasets of certain kinds of distributable problems using a large number of computers (nodes), collectively referred to as a cluster [10]. It consists of an initial Map stage, where a master node takes the input, chops it into smaller or sub-problems, and distributes the parts to worker nodes, which process the information; next is the Reduce stage, where the master node collects the answers to all the sub-problems and combines them to produce the job output. A popular Map/Reduce implementation is Apache's Hadoop, which consists of one Job Tracker, to which client applications submit Map/Reduce jobs. The Job Tracker pushes work out to available Task Tracker nodes in the cluster, which execute the Map and Reduce tasks [11]. This paper proposes architecture for video resource processing and online management in a HDFS-based cloud-computing environment, which is sufficiently flexible to be deployed in either a private or public cloud environment.
## 3 LCOE Results and Discussion
HJT technology has the highest \(LCOE_{2}\) values, with 6.51, 7.10 and 7.94 c\(\cels\)/kWh for 3 kW, 5 kW and 7 kW PV systems, reflecting the high capital investment. It is followed by PERC technology with \(LCOE_{2}\) ranging from 6.32 to 7.52 c\(\cels\)/kWh for the three PV systems, due to its shorter lifetime of 25 years, compared to around 30 years, specific to the other PV technologies. The most feasible PV technology depends on the installed power of the system. For the 3 kW, the lowest \(LCOE_{2}\) is obtained by bifacial TOPCon technology, 7.33 c\(\cels\)/kWh, due to its medium capital investment and high energy production. For the 5 kW system, the cheapest PV technology is N-type with a value of 6.68 c\(\cels\)/kWh, because of its advancements in increasing the conversion efficiency, achieving 23.1%. Finally, for the 7 kW system, the  
Figure 4: The average \(LCOE_{1}\) values for each analyzed PV technology. Figure 5: The average \(LCOE_{2}\) values for the five PV technologies studied. most efficient technology is TOPCon, with an average value of 6.05 c/kWh for \(LCOE_{2}\), considering it has the lowest capital investment. The \(LCOE\) results clearly demonstrate that the capacity of the PV system can influence its feasibility. The systems with higher installed power are more efficient, even if the initial capital investment is greater. Moreover, the incentives granted by the government can substantially reduce the capital investment and lower the \(LCOE\) values. HJT and PERC technologies have the highest LCOE values regardless of the installed power of the system, while the other PV technologies have the lowest LCOE, but only for some systems: N-type for the 5 KW system, TOPCon for the 3 kW and bifacial TOPCon for 7 kW system. The \(LCOE\) values are compared in Table 4 with other results from similar studies presented in the Introduction section. As can be observed, the lowest LCOE values are obtained with the proposed LCOE model when considering the incentives granted by the Romanian government. Without the subsidies, the LCOE values of the five PV technologies are similar with the ones obtained in [13] in Southern California (United States), in [11] where the analyzed projects are in China and with the solar farms from India, analyzed in [10]. The values obtained in [9] and [12] and are higher, due to high capital investment costs.
## 4 Results and Discussions
### The Effect of Emissivity  
When surface radiation among the walls is taken into consideration, three additional parameters (the wall emissivity \(\varepsilon\), radiation to conduction number \(Nr\) and temperature ratio \(G\)) are used to characterize the effect of radiation. Figure 7 shows the isothermals (on the left) and streamlines (on the right) for \(Ra=10^{5}\), \(Kr=10\), \(G=1.2\), \(Nr=10\), \(\phi=0.5\) and \(Pr=0.71\), with \(\varepsilon=0.2\), \(0.4\), \(0.6\), \(0.8\) and \(1\). For \(\varepsilon=0.2\), the isothermals are horizontally distributed in the greatest amount of space of the cavity, but have an intense vertical distribution near the hot interface. As \(\varepsilon\) increases, the isothermals in the cavity turn sparse and there is a general tendency of the levelness of the isothermals to reduce slowly. This indicates that natural convection is weakened as \(\varepsilon\) increases; this is because the increasing \(\varepsilon\) is able to strengthen surface radiation. From the streamlines, two vortexes formed for \(\varepsilon=0.2\). As \(\varepsilon\) increased, the vortexes become smaller and even has a tendency for disappearance for \(\varepsilon=1\). This means natural convection in the cavity is inhibited by surface radiation, and the decreasing maximum velocity also proves this conclusion.  
Figure 8 shows the evolution of the local convective Nusselt number of the interface as a function of \(\varepsilon\). It is easy to find that \(Nu_{\rm conv}\) decreases with increasing \(\varepsilon\) and that the change of \(Nu_{\rm conv}\) has a similar trend from \(\varepsilon=0.1\) to \(\varepsilon=0.9\). In addition, the maximum \(Nu_{\rm conv}\) occurs in the lower half of the cavity and the minimum \(Nu_{\rm conv}\) occurs in the higher half of the cavity. Figure 9 shows the evolution of the average convective, radiative and total Nusselt numbers of the hot interface as a function of \(\varepsilon\). It can be seen that \(\overline{Nu}_{\rm rad}\) increases with increasing \(\varepsilon\), but \(\overline{Nu}_{\rm conv}\) decreases as \(\varepsilon\) increases. It indicates that the surface radiation of the solid walls is enhanced by an increase in \(\varepsilon\), which leads to a more uniform temperature distribution in the cavity. Therefore, the natural convection of the air is weakened due to the decrease of the temperature difference in the cavity. Nevertheless, the increase in \(\overline{Nu}_{\rm t}\) means that the total heat transfer is strengthened in the cavity. The reason for this is that in this case, surface radiation has a greater effect on the total heat transfer compared to natural convection.  
Figure 6: Variations of the average Nusselt number as a function of \(Kr\).  
Figure 7: _Cont._  
Figure 8: Variations of the local Nusselt number at different \(\varepsilon\).
## 7 Conclusions  
This research shows that people can experience and perform actions in remote places, through a robotic agent having the illusion of being physically there. The sensation can be compelled through immersive interfaces; however, technological contingencies can affect human perception. Considering the results from studies on human factors, we provide a set of recommendations for the design of immersive teleoperation systems aiming to improve the sense of telepresence for typical tasks (ex. Table 11). The mitigation of issues like system latency, field of view, frame of reference, or frame rate contribute to enhancing the sense of telepresence. The presented example of the evaluation methodology allows analyzing how perceptual issues affect task performance. By decoupling the flows of an immersive teleoperation system, we start to understood how vision and interaction fidelity affects spatial cognition.  
Task experiments with participants using traditional vs. immersive interfaces allowed quantifying the disturbance introduced by each component on the system. For example, taking as a reference a simple manual pick-and-place task, the introduction of a visual see-through HMD increased the time to perform it by 78%; the introduction of a manual gripper tool increased that time by 252%; and the combination of visual and tool mediation increased the overall time by 372%. Decoupling the flowsof an immersive teleoperation system allowed a separate analysis of visual feedback disturbances (e.g., limited FOV) without the influence of other factors that affect the frame of reference for motor-action. Our findings show that misalignment between the frame of reference for vision and motor-action or the use of tools that affect the sense of body position or sense of body movement leads to higher mental workload and has a higher effect on spatial cognition. Misalignment between kinesthetic and visual feedback increases the mental workload and compromises the sense of telepresence and the embodiment feeling. The mental workload to control the suggested video feedback component is considerably lower (in the immersive interface); however, the combination of both requires a higher effort (i.e., active visual mediation plus tools). Thus, a recommendation is to keep activities at skill-based behavior levels, where familiar perceptual signals are essential to lower the cognitive effort. Future work includes the evaluation of the traditional interface setup, considering the control of the remote camera orientation with a joystick, and the evaluation of the proposed immersive interface to control a robotic arm with haptic feedback.  
All authors contributed equally to this work. All authors read and agreed to the published version of the manuscript.  
This research has been partially supported by Fundacao para a Ciencia e a Tecnologia (FCT), project UIDB/00048/2020.  
This publication acknowledges the support of the University of Coimbra, Institute of Systems and Robotics, Coimbra, Portugal and the Polytechnic Institute of Tomar, Portugal.  
The authors declare no conflict of interest.