esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### RAP Mortars Mixed with RTFO Binder
#### 3.2.2 Low Temperature Determinations of RAP Mortar  
The previous data indicated that the cracking resistance at a low temperature of a modified binder is based on the stiffness and m-values at various test temperatures. In this section, the minimum low temperature determinations of the RAP mortars mixed with RAPs (A-F) and RTFO binders are summarized.  
In Figure 5, it can be found that the RAP mortar with a higher aged binder content has a higher low temperature when its stiffness value is 300 MPa. In other words, the aged binder results in a higher stiffness regardless of RAP type. However, various RAP mortars generally have different low temperature values, dependent on RAP type.  
Figure 4: Stiffness values and m-values of RAP sources A–F modified with reclaimed asphalt pavement (RTFO) binder PG 58-28 at \(-6\)\({}^{\circ}\)C, (**a–f**) RAP sources A–F.  
Figure 5: _Cont._  
In accordance with m-values of the RAP mortars, it can be noted that, in Figure 6, in some cases, m-values are greater than 0.300 when the test temperature is lower than \(-18\)\({}^{\circ}\)C. Therefore, these RAP mortars can resist a low temperature of \(-18\)\({}^{\circ}\)C or even lower. In addition, it can be noted that a higher aged binder content results in a higher low temperature regardless of RAP type.  
Figure 5: Low temperature determinations of RAP sources A–F with RTFO binder PG 58-22 in terms of stiffness, (**a–f**) RAP sources A–F.  
Figure 6: _Cont._  
The minimum low temperatures of various RAP mortars mixed with various RAP sources and aged binder contents are shown in Table 3. It can be observed that the low temperatures derived from the conducted regression analysis were summarized from stiffness values and m-values. As before, a higher temperature was selected as a minimum low temperature in this study because this could satisfy the demand of the asphalt binder to resist the pavement cracking.
# Fractal Metasurfaces and Antennas: An Overview for Advanced Applications in Wireless Communications
## 4 Fractal Metasurfaces  
Metasurfaces are two-dimensional structures composed of subwavelength unit cells arranged in a precise pattern. These structures are engineered to manipulate and control the propagation of electromagnetic waves. Metasurfaces offer unique properties and functionalities not found in traditional surfaces or bulk materials. The design of metasurfaces involves tailoring the shape, size, and arrangement of the subwavelength elements to achieve wavefront manipulation. By controlling the interaction between the incident electromagnetic waves and the metasurface, various effects can be achieved, including focusing, polarization conversion, beam steering, and absorption. This versatility makes them useful in various applications, such as wireless communications, electromagnetic compatibility, cloaking, energy harvesting, sensing, and beyond. However, metasurfaces also exhibit a number of limitations, such as the following:  
Figure 13: Dual-band/dual-polarized unit cell [50]: (**a**) top view and 3D view; (**b**) reflection-phase measurement setup and measured phase curves for different polarizations; (**c**) measured and simulated radiation patterns for X-polarization at \(f_{1}=28.7\) GHz and \(f_{2}=32.8\) GHz, and prototype photo.  
* sensitivity to the impinging wave incident angle which may lead to performance degradation or altered functionalities;
* fabrication challenges related to the precision and the costs of the adopted manufacturing process in realizing subwavelength elements.  
In the last decade, fractal metasurfaces have been proposed and studied as an exciting alternative to traditional electromagnetic metasurfaces [10; 51; 52; 53; 54; 55; 56; 57; 58; 59]. In fact, the auto-similarity and miniaturization capabilities of fractals not only allow for the realization of very compact devices, but can enable technologies for multiband and broadband applications. Moreover, fractal metasurfaces have been of interest in various industrial applications such as radiation transfer, power control, antenna gain enhancement, and cloaking systems, resulting in several patents [51; 52].  
Below are examples of fractal metasurfaces designed for various frequency bands and applications. Specifically, this section is divided into subsections that examine the following: fractal metamaterial absorbers, including a brief study of how fractal shapes can affect absorber performance; fractal metamaterial polarizers; and metamaterial surfaces for antenna applications. This section concludes by outlining the current challenges of and potential future developments for fractal metasurfaces.
# Internet of Things Platforms for Academic Research and Development: A Critical Review
## 2 Internet of Things Platforms
### Closed Source Platforms
Cloud and Edge stacks cooperate in optimizing the platform for each specific application. In terms of communication with IoT devices, Predix supports the open platform communications-unified architecture (OPC-UA), MQTT and Modbus transport control protocol (TCP), among others. The platform is particularly well suited for data analytics, thanks to the availability of machine learning models, and to the possibility of deploying analytics algorithms both at the edge or in the cloud of the platform. Cisco is another major IT player with a strong interest in IoT, as witnessed by their Edge Intelligence platform, formerly known as Kinetic [22], that also supports edge and fog computing for distributed networks, as well a traditional device-to-cloud communication scenario. Devices send data using messages, in formats including text, binary or JSON, ensuring thus a wide device support. The platform also supports integration with external databases for data storage and processing. Finally, Google also have their proposal for IoT, named Google Cloud IoT [23], that is organized in three basic components: device, gateway, and cloud. The device component includes every hardware or software that directly interacts with the world. A gateway is a component that allows devices that do not have internet access to reach the cloud, and which may also perform data processing on the data collected from connected devices. Finally, the cloud component is the centralized software platform where data received from gateways or devices is collected, combined and analyzed, and transferred for storage or publishing. MQTT and HTTP communication protocols are supported for data uploading by devices, and end-to-end security is provided by means of the transport layer security (TLS) security protocol. The review above shows that the above closed source platforms from major IT players share a common approach, focused on business customers interested in a SaaS/PaaS solution, rather than in doing in-house development of new features, although such development is sometimes possible in the platforms. Many other platforms not described above share the SaaS/PaaS approach; a comprehensive list is provided in Table 1, analyzing the platforms described above and several others in terms of the set of criteria defined in Section 2.
# Domain Knowledge Graph Question Answering Based on Semantic Analysis and Data Augmentation
## 5 Experiment and Analysis  
### Data Augmentation  
Currently, the training of the model relies heavily on manually annotated corpora, which may not achieve good results when the dataset is small in size [49; 50; 51]. Data augmentation refers to the use of various techniques and methods to expand the training  
Figure 8: Cypher query statement template.  
Figure 7: Flowchart of answer query process.  
dataset, thus improving the model's performance and robustness. [PERSON] et al. [52] proposed four simple operations, including synonym substitution, random deletion, random swapping, and random insertion, to prevent overfitting and enhance model generalization. [PERSON] et al. [53] used a generative language model GPT-2 for text data augmentation and achieved excellent augmentation results in few-shot scenarios.  
As there are currently no open-source question corpus data in the household registration field, all the data used to train the model in this paper come from the question corpus collected from Wuhan Municipal Government Service Center's inquiry system. After removing a small number of invalid and abandoned question corpus due to household registration policy changes, a total of 1427 authentic questions related to household registration were obtained. We labeled all these questions and obtained the question classification dataset required for the experiment. As shown in Fig 10, through visualization and analysis of the dataset, we found that the dataset is extremely unbalanced: the number of data samples for class 5 questions is more than three times that of class 3 questions. Since the smaller class size in the dataset can lead to overfitting during model training, we used the GPT-3.5-turbo generative language model to expand the total amount of the dataset by two methods: synonym substitution and random insertion of irrelevant words.  
To enable LLM to rewrite questions according to the requirements, we need to design prompts given to GPT. Currently, there are two main types of prompt templates: cloze prompts [54] and prefix prompts [55]. Since question rewriting belongs to the sentence generation task, the prefix prompt method is often more beneficial because such tasks align well with the model's left-to-right property [56]. We designed prompts based on the CRISPE prompt framework [57] provided by [PERSON], and the specific steps are shown in Table 4.  
The final prompt is as follows: 'As a user who is going to handle household registration business, you have questions about the handling information of some household registration matters. Please rewrite the given question, provide a similar question, and ensure that the original meaning of the sentence is preserved. The rewriting method includes synonym substitution and random insertion of irrelevant words. The sentences that need to be rewritten are:'. We rewrote the 1427 questions multiple times and finally obtained a dataset of 7055 questions.
# Elemental Abundances of Moon Samples Based on Statistical Distributions of Analytical Data
## 4 Discussion
### Chang'E-5 Samples
#### 4.2.2 Geochemical Signatures  
The content of CaO of CE5 is 11.36%, which is lower than the boundary value of 13.5% for low-Ca and high-Ca moon samples. Therefore, the CE5 sample is the low-Ca type of moon sample. In terms of Ti content, moon samples can be divided into three types: high Ti (TiO\({}_{2}\geq\) 6%), low Ti (1% \(\leq\) TiO\({}_{2}<\) 6%), and very low Ti (TiO\({}_{2}<\) 1%) [11]. The content of TiO\({}_{2}\) of CE5 is 5.43%, which indicates that the CE5 sample is the low Ti type of moon sample.  
According to the illustrations of moon samples, the geochemical signatures of the CE sample are also illustrated in Figure 3 to Figure 6.  
With respect to the MORB, the CE5 sample is clearly rich in Cr\({}_{2}\)O\({}_{3}\), TiO\({}_{2}\), and FeO and poor in Na\({}_{2}\)O (Figure 3) in major oxides in terms of their concentrations, and is also enriched in Cr\({}_{2}\)O\({}_{3}\), TiO\({}_{2}\), and FeO and depleted in Na\({}_{2}\)O in the diagrams. The REE pattern of CE5 is tilted right slowly, except for the clear negative Eu anomaly, and its REE concentrations are clearly higher than those of the MORB (Figure 4). In the spider diagrams (Figures 5 and 6), the CE5 sample is clearly rich in Cs, Ba, W, Th, U, Nb, Ta, Pb, Zr, Hf, Ti, Li, Sr, Cr, and Be and poor in V, Cu, and Zn in terms of their concentrations, and is enriched in Cr and depleted in Rb, K, P, V, Cu, and Zn in the spider diagrams. Among the total moon sample, low-Ca sample, and high-Ca sample, the CE5 sample is closer to the low-Ca sample in concentrations or patterns (or diagrams), which are illustrated in Figure 3 to Figure 6. This is consistent with the low-Ca type of moon samples discriminated on CaO content, as mentioned previously.  
With respect to the total moon sample, the CE5 sample is clearly rich in Ti, Fe, Mn, P, Sc, K, REEs, Th, U, Nb, Ta, Zr, Hf, Sr, Ba, W, and Be and poor in Mg, Al, Cr, and Ni in terms of their concentrations. From this view, the CE5 sample is the KREEP type of moon sample [49; 50] because it is rich in K, REEs, and P relative to the moon sample. However, the contents of K\({}_{2}\)O in the CE5 sample and the low-Ca sample are 0.175% and 0.171%,respectively, which are almost the same within the relative error. From this view, the CE5 sample is the non-KREEP type of moon sample [1] because of the non-enrichment of K relative to the low-Ca moon sample.  
Except for the aforementioned studies on the CE5, more and more articles are being published [51; 52; 53; 54] on hot and interesting topics, which are very helpful in promoting the research of the Moon. The elemental abundances of the CE5 sample in this paper will be improved on with more studies in the near future.
## 2 Related Work
The communication between unreachable nodes is indirectly achieved in some applications of decentralized data transactions through blockchain, cloud storage or IPFS, which can guarantee the data interaction between any peers [13; 14; 15]. However, waste of resources, blockchain to all peers to reach consensus, cloud storage or IPFS decentralized storage to the whole network presence, not peer-to-peer transmission mode. Several studies have proposed the use of decentralized address mapping [46; 47]. The goal is to implement a decentralized end-to-end communication solution without a central authority. Addresses can be mapped by pre-known names, relying on a blockchain decentralized ledger. However, the connectivity of mobile nodes cannot be guaranteed and no solution is proposed for nodes that are behind NAT. [PERSON] et al. [48], [PERSON] et al. [49], and [PERSON] et al. [50] consider the problem of unreachable nodes and propose the use of a fixed relay server to relay these data, which can realize that communication can be carried out between any nodes through the network. Although end-to-end direct transmission can be achieved, this will affect the degree of decentralization of the network, and the relay server may have a single point of failure, etc. Close to our work, [PERSON] et al. [51] proposed to solve the communication problem between unreachable nodes by decentralized relaying, but their approach of finding nodes using broadcast will cause flooding bottleneck in case of large number of nodes in the network. Moreover, the use of relaying alone for end-to-end communication can cause waste of network resources to some extent. [PERSON] et al. [52] used a structured P2P network to organize the nodes which does not causes flooding bottlenecks due to large number of nodes. However, these nodes are composed of cloud services and still have a centralized nature. Moreover, the end-to-end communication in the proposed scheme uses messaging, which relies on the active message discovery mechanism of the receiver. Furthermore, there is no mechanism to establish an end-to-end transmission channel, so a large amount of data cannot be delivered continuously. The proposed scheme in this paper does not require additional centralized servers and achieves IP address-independent routing between nodes including reachable and unreachable nodes through a structured overlay network that stores node indexes distributed across the network rather than unstructured flooding. The end-to-end transmission channel is established according to the type of nodes at both ends during data flow transmission. Once established, they can continuously transmit data without relying on other facilities, which saves network resources to some extent. Furthermore, in terms of security assurance, the proposed scheme in this paper does not depend on centralized trusted institutions and ensures not only the security of data transmission but also the security of structured overlay networks through the implicit relationship of node addresses and asymmetric encryption system.
# Mobility-Multihoming Duality
## 7 Discussion  
The issue of naming and address semantic overloading is a long-standing problem within the Internet community [57; 58]. Indeed, over time, the over-use of IP addresses for various different purposes has caused confusion with address usage. There is now additional complexity in the IP addressing architecture when considering functionality such as mobility and multihoming [59].  
ILNP follows the end-to-end principle [60] in considering the naming and addressing issue. The clean, crisp separation of semantics for addressing in ILNP, with clear distinction in the usage between NID values (relevant only at the host for identity and not topologically significant) and L64 values (used for routing, as they are topologically significant). With the use of dynamic bindings for NID and L64 values within the network stack, this allows ILNP to avoid much of the complexity introduced by other proposals, as summarised in Section 2 and Table 1.  
However, it is instructive to have a critical discussion of the issues that arise from using the Identifier Locator approach proposed. This is especially instructive in light of the established assumptions for the use of IP addressing and naming that exists today.
## 4 Algorithm for Energy and Fuel Consumption Minimization
### Influence of Traffic Conditions  
There are systems like the one presented in [30] that take into account the speed limits and recommend the driver the speed in order to reach the destination on time. One characteristic that the proposed system includes is the recalculation process of the optimal speed profile considering the traffic conditions. Data from the traffic conditions are considered according to the following two situations:  
* Traffic jams that appear periodically, so they are well-known and predictable and can be considered as stretches with a variable speed limit.
* Unforeseen traffic jams that are transmitted to the vehicle via V2V or V2I communications.  
In the second case, the traffic jam progression is not known a priori. For this reason, the system uses the Greenhields linear traffic model [68; 69]. Hence, as Figure 3 shows, initial traffic conditions are denoted by point 1 (speed \(v_{1}\), density \(k_{1}\), intensity \(i_{1}\)) and traffic perturbation forces to move to point 2 (\(v_{2}\), \(k_{2}\), \(i_{2}\)). When the perturbation disappears, the traffic state point moves to the maximum intensity conditions 3 (\(v_{3}\), \(k_{3}\), \(i_{3}\)) and then the initial conditions are recovered.  
Figure 3: Operation traffic states when a perturbation appears.  
According to the traffic shock waves theory, the perturbation will propagate downstream at a speed of \(v_{2}\) and upstream at a speed of:  
\[v_{01}=\frac{i_{1}-i_{2}}{k_{1}-k_{2}} \tag{5}\]  
so traffic between both waves is characterized by condition 2.  
When the perturbation disappears, a new shock wave appears at the head of the traffic jam that propagates upstream at a speed of:  
\[v_{02}=\frac{i_{2}-i_{3}}{k_{2}-k_{3}} \tag{6}\]  
So traffic between the sections defined by the head of the jam and that wave will have the characteristics defined by state 3.  
Finally, when the second shock wave reaches the first one, the initial conditions 1 will be recovered by means of a new wave that moves downstream from the tail of the traffic jam at a speed of:  
\[v_{03}=\frac{i_{3}-i_{1}}{k_{3}-k_{1}} \tag{7}\]  
Hence, from the knowledge of the initial traffic conditions 1 and the properties of the perturbation (state 2), it is possible to evaluate the traffic jam evolution and obtain the maximum length \(L\) of the traffic jam and the time \(T\) that initial conditions take to be reset.  
\[L=v_{2}\cdot T_{p}-v_{01}\cdot\left(\frac{\left(v_{2}-v_{01}\right)\cdot T_{p }}{v_{01}-v_{02}}+T_{p}\right) \tag{8}\]  
\[T=T_{p}+\frac{\left(v_{2}-v_{01}\right)\cdot T_{p}}{v_{01}-v_{02}}+\frac{v_{2 }\cdot T_{p}-v_{01}\cdot\left(\frac{\left(v_{2}-v_{01}\right)\cdot T_{p}}{v_{ 01}-v_{02}}+T_{p}\right)}{v_{03}} \tag{9}\]  
where \(T_{p}\) is the perturbation duration.
# Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment
## 2 Related Work
### VBL Methods  
A comprehensive comparison of different VBL methods is given in Table 1. Compared with matching only and pixel-based localization methods (top two rows of Table 1), ArUco, ArPico and PicPose can produce more precise locations with a real scale output. Among planar object methods, ArUco markers are pre-coded black-and-white cells, ArPico detects pictures that have a clear rectangular border, while PicPose can handle all planar objects without any border or shape restriction. Moreover, PicPose is able to use partially visible objects for localization while the other planar object based methods cannot. Due to the neural-network based object detection complexity, the total processing time of PicPose is longer than ArPico, but still reasonably short (\(<\)100 ms in our project) to support real-time localization of moving devices. We will present detailed performance comparisons in this paper.
# Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
## 2 Data-Driven Traffic Estimation and Prediction
### License-Plate Recognition Data  
License plate recognition (LPR) data are emerging data sources for vehicle re-identification as well as traffic estimation and prediction. Large-scale LPR data collection systems are being developed all over the world, e.g., the automatic number-plate recognition (ANPR) systems. However, due to data privacy concerns, there are few open datasets and few relevant studies.  
In [74], a six-day LPR dataset from a small road network in Langfang, China, was used to estimate and predict link-based traffic states, and the feasibility was validated by using a more comprehensive link-level field experiment dataset. A similar type of data is electronic registration identification (ERI), which was also used in traffic flow prediction [75]. The pros of LPR or ERI data are their precise tracking abilities of individual vehicles. However, this feature also causes data privacy concerns. Another disadvantage is that there are no publicly available LPR or ERI datasets.