esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Work  
Firstly, we propose a new CIDAE model based on DAE. The advantage of CIDAE is that it alleviates the problem of data sparsity by using the idea of imputation. Additionally, our experimental results show that CIDAE is more accurate than the AE algorithm and the DAE algorithm.  
Then, we optimize the advanced NeuMF model based on the training process. O-NeuMF-p is more focused on the learning of counterexamples than NeuMF-p, and is more robust. Furthermore, the experimental results confirm that the idea of introducing random numbers into the label \(y_{uv}\) in \(y^{-}\) is beneficial and improves the accuracy of NeuMF-p.  
Finally, this paper fuses CIDAE and O-NeuMF-p with reference to the idea of ensemble learning. I-NMF can not only alleviate the problem of data sparsity, but also fully exploit the ability of deep neural networks to learn potential features, and I-NMF is also robust. It can be seen from the experimental results that CIDAE and O-NeuMF-p can complement each other to achieve a higher accuracy. Moreover, our experimental results prove that I-NMF performs better than current advanced recommendation algorithms on both datasets.  
It is worth emphasizing that this paper provides a new fusion idea; not only the combination of CIDAE and O-NeuMF-p, but also the combination of different types of recommendation algorithms.  
In the future, there are two directions to extend our work. On the one hand, we can try more ways to combine different kinds of recommendation algorithms to achieve a better accuracy. On the other hand, we can try to incorporate auxiliary extra data into recommender systems, such as social relations, review text, type of items, personal information of users, and so on. We can further improve the accuracy of the model by adding extra valuable data.  
Conceptualization, X.W. and B.Z.; Data curation, X.W.; Formal analysis, X.W.; Funding acquisition, B.Z., G.Z., and F.C.; Investigation, X.W. and F.C.; Methodology, X.W.; Project administration, B.Z. and G.Z.; Resources, B.Z. and G.Z.; Software, X.W.; Supervision, B.Z.; Validation, X.W.; Visualization, X.W. and F.C.; Writing--original draft, X.W.; Writing--review & editing, X.W. and F.C.  
This work was partially supported by the National Key Research and Development Program of China (No. 2017 YFC0907505), National Natural Science Foundation of China (No. 61772128, 61303096), Shanghai Natural Science Foundation (No. 18 ZR1414400, 17 ZR1400200), Xinjiang Social Science Foundation (No. 2015 BGL100), and Fundamental Research Funds for the Central Universities (No. 16D111208).  
Thanks for experimental equipment support provided by Kanwei Zheng.  
The authors declare no conflict of interest.
# Comparison of Land Cover Categorical Data Stored in OSM and Authoritative Topographic Data
## 2 Related Works
### OpenStreetMap Data Quality  
With the advent of OSM, due to the lack of control over the VGI data that characterize most user-provided web resources, many questions have been raised about the quality and reliability of the data. However, as summarized in [PERSON] et al.'s [28] study on OpenStreetMap, in some regions, OSM geodata are more complete and geometrically and semantically more accurate than the corresponding proprietary datasets. [PERSON] [29] was the first who compared the quality of OpenStreetMap data with the Meridian2 data maintained by the Ordnance Survey (OS). The research results published in the fourth year of the project indicate the comparable quality of OS and OSM in terms of accuracy and completeness. The most comprehensive assessment of OSM quality was presented by [PERSON] and [PERSON] [30], providing such elements of spatial data quality as geometric, attribute, semantic and temporal accuracy, logical consistency, completeness, lineage and usage. The outcome of their research shows heterogeneous quality across regions and land use elements, indicating relatively high positioning accuracy and very different completeness depending on the density of OSM volunteers. [PERSON] and [PERSON] [31] analyzed OpenStreetMap, focusing on determining the optimal routes for pedestrian traffic. Nevertheless, as demonstrated by [PERSON] et al. [32] and [PERSON] and [PERSON] [33], other topographic data with which OSM data are compared are not always more accurate.  
Contrary to the extensive analysis of buildings and roads in OSM [31; 34], the first attempt at analyzing land use features was carried out by [PERSON] and [PERSON] [35]. The authors compared the land use polygons and land use patterns of OSM and Urban Atlas data, and found that the location agreements expressed by kappa were 91, 79 and 76% across the three classification levels, while the attributes of both datasets matched at 81, 67 and 65%. Significantly worse completeness and accuracy results were observed by [PERSON] et al. [36] when comparing OSM land cover with global CCI-LC (Climate Change Initiative Land Cover) data in 168 countries. In 129 countries, completeness did not exceed 40%, and in only 17 European countries was it higher than 60%. Much better results were obtained for accuracy, which was higher than 60% in 149 countries.
## 2 Fundamental Principles of Quasi-Zero Stiffness
### Several Types of Negative Stiffness Actuator
#### 2.3.2 Disc Spring as Negative Stiffness Actuator  
The QZS vibration isolation system, incorporating disc springs as negative stiffness actuators, is particularly well-suited for deployment in environments characterized by constrained axial space and demanding load conditions. [PERSON] [53] accomplished the conversion between positive and negative stiffness by leveraging the shape memory characteristics of epoxy shape memory polymers, significant return deformation properties, thin-shell theory, and viscoelasticity theory, exploiting the negative stiffness attributes of disc springs. Figure 10a illustrates the study conducted by [PERSON] [54] on the QZS isolator model employing disc springs of uniform and varying thicknesses. The authors resolved the system's response under varying excitations, scrutinized the impacts of parameters such as the excitation amplitude and damping ratio on the system's transmissibility, and confirmed that the QZS isolator design employing disc springs surpasses the linear system in terms of the low-frequency line spectrum isolation capability. The issue of the pronounced longitudinal vibration in ship propulsion shaft systems was mitigated through the deployment of the disc spring QZS vibration isolator. The system's steady-state response was computed utilizing the harmonic balance method, facilitating the determination of the nonlinear stiffness and other system parameters. This ensured that the designed vibration isolator could satisfy the requirement for low-frequency vibration isolation even under substantial loads, achieving a minimum vibration isolation frequency of 10 Hz. However, its applicability is confined to particular operational scenarios and may prove ineffective under fluctuating conditions.  
Figure 9: Air spring structures of a QZS isolator: (**a**) the seat suspension schematic diagram proposed by [PERSON] [50]; and (**b**) schematic diagram of the QZSPVI composed by the load-bearing mechanism and stiffness correction mechanism proposed by [PERSON] [51].  
A pioneering compact stiffness-modulated anti-vibration structure was devised, employing high-energy-density disc springs and volute springs as proposed by [PERSON] [55] in Figure 10b. By incorporating advantageous nonlinear stiffness characteristics into these springs, the compact size, substantial loading capacity, and exceptional vibration isolation performance of the proposed anti-vibration structure can be achieved.  
Disc springs provide numerous advantages, such as robust resistance to deformation and load impact, along with effective vibration isolation capabilities. Nonetheless, their uniform design structure may lead to significant size discrepancies, posing challenges in terms of ensuring design precision. Moreover, their limited elasticity constrains their utility in various projects.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 4 ITGA Design
### Selection Operator  
We adopt the tournament selection (TS) method for selection operations. The calculation process of the TS method is shown in Algorithm 1.  
```
Input: Pop, \(n\) and fits :The population to be selected, the number of individuals selected each time, and the corresponding fitness value list of the population to be selected. Output: Popnew :The new population generated by Pops.
1:while PopLen\(>\) Popnew.Len do :Len indicates the size of the population.
2: Popn\(\leftarrow\)Select \(n\) individuals from Pop
3: fitn\(\leftarrow\)Select \(n\) fitness value from fits that correspond one-to-one with Popn
4: Sort Popn in descending order based on the values in fitn
5: Add the Popn with the highest fitn value to Popnew
6:endwhile
7:returnPopnew
```  
Figure 6: Flow chart of the initial population generation.  
After applying the selection operator, a new population is generated. This new population competes for survival with the initial population using a one-on-one survivor competition (OSC) Method (Algorithm 2). Specifically, each chromosome from the two populations under consideration is evaluated and the fitter ones are selected. Finally, the resulting population is output.  
```
Input: Pop\({}_{\text{parent}}\), Pop\({}_{\text{child}}\), fits\({}_{\text{child}}\) and fits\({}_{\text{parent}}\) \(\cdot\) Parent population waiting for OSC, Offspring population waiting for OSC, Fitness value list corresponding to the offspring population, and Fitness value list corresponding to the Parent population. Output: Pop\({}_{\text{parent}}\) and fits\({}_{\text{parent}}\) \(\cdot\)The new parent population generated by OSC and the new parent fits generated by OSC.
1:for\(i=1\): Pop\({}_{\text{parent}}\).Len do \(\cdot\)Len indicates the size of the population.
2:if\(\text{fits}_{\text{parent}}[i]\)\(<\) fits\({}_{\text{child}}[i]\)then
3:\(\text{fits}_{\text{parent}}[i]\)\(=\) fits\({}_{\text{child}}[i]\)
4:\(\text{Pop}_{\text{parent}}[i]\)\(=\) Pop\({}_{\text{child}}[i]\)
5:endif
6:endfor
7:returnPop\({}_{\text{parent}}\)
```  
**Algorithm 2**OSC Method
# Two-Level-Composite-Hashing Facilitating Highly Efficient Anonymous IoT and D2D Authentication
## 2 Related Work
First, we propose a novel hashing and identification mechanism called 2 LCH, which could efficiently generate and synchronize pseudonyms. Second, we add two new anonymous protocols called the server-assist-once-a-while D2S2D authentication protocols and the direct D2D authentication protocol. Third, compared to the previous work [35; 36], the new protocols are much more efficient; and we facilitate direct D2D anonymous authentication. Fourth, we introduce how to implement our protocols using the MQTT 5.0 AUTH API. Fifth, we verify the security properties in AVISPA. Both AVISPA and the Burrows-Abadi-Needham (BAN) logic [37] are two popular tools for verifying security protocols; however, there are several reports like [20] showing the limitations of the BAN logic and the failure in verifying the claimed goals. Contrarily, AVISPA, even though it has some limitations like its limited algebra expressions, provides several tools like OFMC (On-the-Fly-Model-Checker) [38] to plot possible attacks on the specification to rigidly check the safety of the specification. Here, we adopt AVISPA for verification. Users specify their security protocols using the High-Level Protocol Specification Language (HLPSL) [39]. AVISPA can simulate the execution flow of a protocol or attack a specified protocol. Embedded tools like OFMC try to plot possible attacks on the specifications. If it successfully plots any attacks, then it will report the weaknesses and the attack process; otherwise, it reports \"SAFE\". In addition to the above discussed features and security properties. One less-discussed feature in the existent publications is whether a proposed IoT authentication scheme could be easily applied on the standard IoT communication protocols. MQTT is one popular IoT communication protocol; it is lightweight, very easy to use, and widely deployed globally. To gain its lightweight, MQTT avoids the supporting of integrity protection and confidentiality; instead, it assumes a secure sockets layer (SSL) is being supported underlying a MQTT deployment. There exist many MQTT security-enhanced proposals. Some of them designed the specialized hardware to reduce the SSL computation overhead on IoT devices. Some proposals like [40] propose authenticated key agreement schemes for MQTT systems. [PERSON] et al. [41] proposed a two-phase authentication framework in which any secure key agreement scheme can be performed in the first phase, and then the hash value of the session key is supplied as a password in the second phase MQTT CONNECT API. [PERSON] [35] proposed an anonymous IoT protocol in the MQTT 3.11 CONNECT API. In this paper, we will show how to implement our D2S protocol, our D2S2D protocol using the MQTT 5.0 Auth API [5].
# A Survey of LoRaWAN for IoT: From Technology to Application
## 8 Extensions
### IPv6 over LoRaWAN  
In the current implementation of LoRaWAN protocol stack the application layer resides on top of MAC layer. However, if the end nodes need to be accessed from the Internet, then the LoRaWAN end nodes should become IP compatible. Several standards for IPv6 communication over constrained devices and networks exists, such as adaptation layers (6 LowPAN [113], 6 Lo [114], and 6 TiSCH [115]), header compression techniques (LOWPAN-IPHC [116], LOWPAN-NHC [116], and SCHC [117]) and application layer protocols for constrained devices (CoAP [118]).  
Recently, solutions for IPv6 communication over LoRa and LoRaWAN are being studied [119; 120; 121; 122]. In [119], feasibility usage of different existing techniques for enabling IPv6 over constrained devices in LoRaWAN have been studied. The Static Context Header Compression (SCHC) is presented as one of the most feasible candidate for LoRaWAN. It provides header compression for CoAP, UDP and IP as well as fragmentation possibilities. In LPWAN networks, the communication context is quite static, being that end node will communicate with a foreknown server and will use certain services. Thus, in SCHC, the static context is composed of a set of rules that defines how to compress different headers: CoAP, UDP and IPv6. In [120], SCHC was used to compress headers for IPv6 over LoRaWAN for an Internet-to-Vehicle (IoV) application. In [121], the 6 LowPAN adaptation layer and IP Header Compression (IPHC) together with fragmentation possibility were used to enable IPv6 communication for LoRaWAN devices. To this end, in static context (where end devices are registered by personalization) the source IP can be removed as there is a unique mapping between DevAddr and source IPv6. In the case of dynamic context (activation over the air), none of the IP addresses (source and destination) can be removed. No UDP header compression is used too.  
In [122], Contiki protocol stack was ported to LoRa based node and the IPv6 communication was enabled. The 6 LoWPAN adaptation layer as well as radio duty cycle layer are used to comply with duty cycling rules.
## 3 Results and Discussion
### Attention Module Comparison Experiments  
In the comparative experiments, various attention mechanisms were incorporated into the UNet baseline model. SE [32] employs 1D attention weights, Coordinate and CBAM employ 2D attention weights [33], and Biformer [34] employs 3D attention weights. Comparatively speaking, the Coordinate structure is more intricate, as it necessitates constructing an attention module and integrating it into the network, along with introducing additional parameters such as convolutional kernels and fully connected layers. Similar to Coordinate, both SE and CBAM introduce extra parameters and possess relatively complex structures. Although Biformer can capture more intricate feature interactions through self-attention mechanisms, it requires a substantial number of parameters, has a complex structure, and incurs high computational costs. Overall, SWS does not introduce additional parameters but still achieves comparable performance in terms of inference speed and accuracy to other attention mechanisms [35].  
In Table 1, The UNet baseline model, serving as the baseline, has a P of 68.4%, an R of 51.0%, and a D of 58.4%. SE and CBAM offer comparable performance, with the P for both being approximately 69.3%, R being approximately 51.6%, and D being approximately 59.0%. The increase in their parameters is approximately 590 K. In contrast to the original model, they augment more parameters, yet the improvement in accuracy is rather limited. For the current task, they are not favorable choices. Biformer has a P of 65.0%, an R of 48.3%, and a D of merely 55.4%. Nevertheless, it possesses the largest number of parameters, increasing by 4.67 M. Comparatively speaking, Biformer is the least effective, introducing the greatest number of parameters but experiencing a notable decline in accuracy. Therefore, Biformer is the poorest choice. Coordinate achieves a P of 69.9%. The recall rate is similar to that of the baseline model, and D improves to 59.4%. The increase in parameters is 560 K. Among all the attention modules, the increase in parameters of Biformer is the second smallest, but the accuracy improves significantly. The P and R reach 70.8% and 53.4% respectively, and D increases to 60.9%. On the whole, SWS proves to be an outstanding choice for this task.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **P** & **R** & **D** & **Params** \\ \hline Base & 68.4 & 51.0 & 58.4 & – \\ SWS & 70.8 & 53.4 & 60.9 & +460 K \\ Coordinate & 69.9 & 51.7 & 59.4 & +560 K \\ SE & 69.3 & 51.9 & 59.3 & +590 K \\ CBAM & 68.2 & 51.6 & 58.7 & +590 K \\ Biformer & 65.0 & 48.3 & 55.4 & +4.67 M \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of different attention performance.
## 2 General State-of-the-Art
### Point Cloud Processing
#### 2.1.1 Machine Learning and Deep Learning Approaches  
In general, machine learning and its subset deep learning solutions have seen a surge in popularity in these recent years since the advent of big data [32]. Machine learning approaches are robust against  
Figure 1: The overall 3D pipeline for the 3D reconstruction of historical edifices, from a point cloud up to HBIM (Heritage Building Information Model)-compatible 3D models. This paper will focus on the manual bottlenecks of the pipeline (red inverted trapeziums) up to before the 3D modeling process (long-dashed elements), although a preliminary result of automatic 3D modeling of beam structures will also be briefly presented.  
noise and occlusions, and generally reliable. Its main disadvantages, however, is the necessity of a large amount of training data and the computing power needed to train the algorithm. The usual method to create training data is to segment and classify point clouds manually [33], although synthetic training data can also be generated in some cases [34]. It also remains a largely black-box solution and therefore leaves very little room for user intervention [26].  
Various types of machine learning and deep learning techniques are available, as described in [32]. In [35], a comparison on several machine learning and deep learning techniques were performed. The authors in [36] described a deep learning approach to classify outdoor point clouds in the case of heritage sites, while the authors in [37] proposed the use of a multi-scalar approach for classifying multi-resolution TLS data. As deep learning is a well-established technique in the realm of 2D image recognition, one way to perform point cloud classification is to apply deep learning on 2D images created from point cloud color (orthophotos, UV textures, etc.) [38]. The technique is also often used to perform the segmentation and classification of point cloud generated by aerial platforms (aerial photogrammetry, ALS) as it enables the reduction of the (usually more complex) 3D point cloud into a 2.5D problem [39].  
While the appeal of machine learning is strong for performing point cloud processing in the case of complex geometries as encountered in heritage objects, the main bottleneck remains the generation of the training dataset [25]. In this paper, an algorithmic approach is considered in order to provide a fast result which may eventually be used to help generate training data for future machine learning techniques. Indeed, manual labeling of heritage objects also present a particular difficulty since objects in the same class may have many variations.
## Governing Equation and Simulation Method
### Governing Equation
\begin{split}\frac{\mathrm{d}}{\mathrm{d}t}\int_{z_{1}}^{z_{2}} \int_{y_{1}}^{y_{2}}\int_{x_{1}}^{x_{2}}H\mathbf{Q}\mathrm{d}x\mathrm{d}y \mathrm{d}z&+\left[\int_{z_{1}}^{z_{2}}\int_{y_{1}}^{y_{2}}H( \mathbf{E}_{\mathrm{a}}-\mathbf{E}_{\mathrm{d}})\mathrm{d}y\mathrm{d}z \right]_{x_{1}}^{x_{2}}\\ &+\left[\int_{z_{1}}^{z_{2}}\int_{x_{1}}^{x_{2}}H(\mathbf{F}_{ \mathrm{a}}-\mathbf{F}_{\mathrm{d}})\mathrm{d}x\mathrm{d}z\right]_{y_{1}}^{y_{2} }\\ &+\left[\int_{y_{1}}^{y_{2}}\int_{x_{1}}^{x_{2}}H(\mathbf{G}_{ \mathrm{a}}-\mathbf{G}_{\mathrm{d}})\mathrm{d}x\mathrm{d}y\right]_{z_{1}}^{z_{ 2}}\\ &-\iint_{\text{cut-plane}}\left\{(\mathbf{E}_{\mathrm{a}}-\mathbf{E }_{\mathrm{d}})n_{x}+(\mathbf{F}_{\mathrm{a}}-\mathbf{F}_{\mathrm{d}})n_{y}+( \mathbf{G}_{\mathrm{a}}-\mathbf{G}_{\mathrm{d}})n_{z}\right\}\mathrm{d}S=0, \end{split}  \tag{5}\]  
where the Heaviside function \(H(x,y,z)\) gives \(H=1\) in the air and \(H=0\) in the object; and the vector \(\mathbf{n}\) represents the unit normal vector directed outward form the object into the air. Considering the boundary condition \(u\cdot n=0\) at the object surface, Equation (5) is reduced to the following form:  
\[ \begin{split}\frac{\mathrm{d}}{\mathrm{d}t}\int_{z_{1}}^{z_{2}} \int_{y_{1}}^{y_{2}}\int_{x_{1}}^{x_{2}}H\mathbf{Q}\mathrm{d}x\mathrm{d}y \mathrm{d}z&+\left[\int_{z_{1}}^{z_{2}}\int_{y_{1}}^{y_{2}}H( \mathbf{E}_{\mathrm{a}}-\mathbf{E}_{\mathrm{d}})\mathrm{d}y\mathrm{d}z \right]_{x_{1}}^{x_{2}}\\ &+\left[\int_{z_{1}}^{z_{2}}\int_{x_{1}}^{x_{2}}H(\mathbf{F}_{ \mathrm{a}}-\mathbf{F}_{\mathrm{d}})\mathrm{d}x\mathrm{d}z\right]_{y_{1}}^{y_{2} }\\ &+\left[\int_{y_{1}}^{y_{2}}\int_{x_{1}}^{x_{2}}H(\mathbf{G}_{ \mathrm{a}}-\mathbf{G}_{\mathrm{d}})\mathrm{d}x\mathrm{d}y\right]_{z_{1}}^{z_{ 2}}=\iint_{\text{cut-plane}}\mathbf{\sigma}\mathrm{d}S,\end{split}  \tag{6}\]where the variable \(\mathbf{\sigma}\) on the right-hand side of Equation (6) represents the wall flux vector (the interaction between the object and the air), which is directed from the object into the air:  
\[\mathbf{\sigma}=\left( \begin{array}{c}0\\ pn_{x}-\tau_{xx}n_{x}-\tau_{xy}n_{y}-\tau_{xz}n_{z}\\ pn_{y}-\tau_{yx}n_{x}-\tau_{yy}n_{y}-\tau_{yz}n_{z}\\ pn_{z}-\tau_{xz}n_{x}-\tau_{zy}n_{y}-\tau_{zz}n_{z}\\ 0\end{array} \right)\simeq\left( \begin{array}{c}0\\ pn_{x}-\tau_{w}b_{x}\\ pn_{y}-\tau_{w}b_{y}\\ pn_{z}-\tau_{w}b_{z}\\ 0\end{array} \right). \tag{7}\]  
The vector \(b\) is the stream-wise unit vector parallel to the wall. The scalar value \(\tau_{w}\) of the wall shear stress is obtained by the wall-stress model. Wall flux of mentum through the pressure and the shear stress are included, while wall flux of mass and energy do not exist in the problems considered here. The governing Equation (6) is solved using a cell-centered finite volume method.