esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Allocation to Massive Internet of Things in LoRaWANs
## 1 Introduction
Hence, in an environment of heavy traffic load, the network becomes more congested owing to the bi-directional communications and retransmissions, leading to significant packet loss. Thus, the ring-based SF allocation approaches exhibit a less efficient SF allocation, resulting in a lower packet success ratio in situations of heavy bi-directional traffic load. To improve the packet success ratio, we propose to allocate SFs to EDs based on the ED sensitivity by measuring the received power that a GW would receive from the ED. The proposed SF allocation is different from the ring-based approaches, where the proposed scheme assigns different SFs to EDs having the same distance from the GW. The contributions of this paper are threefold: (1) This paper provides an in-depth survey and analysis of inter-SF and intra-SF interferences. Through simulation results, we show the collision overlap time occurred due to the collision of packets with intra- or/and inter-SF interferences. Additionally, we show the impact of both intra- or inter-SF interferences on average end-to-end and ACK delays using our proposed schemes under path loss, shadowing, and building penetration losses in the LoRaWAN network. (2) When a packet loss occurs due to interference, each ED steps up its SF, resulting in a higher SF. It is shown that higher SF is highly vulnerable to interference, thus the proposed scheme based on the channel status decrements and increments SF to lower the chances of collisions. (3) Unlike fixed-width SF rings-based approaches, our proposed ED sensitivity-based scheme assigns different SFs in the same region based on the signal strength received at the GW. This behavior makes it different from the existing ring-based approaches and helps to lower the impact of interference. The rest of this paper is organized as follows: Section 2 surveys the recent research work in the area of resource allocation. The LoRaWAN network model, comprising link measurement and link performance, is presented in Section 3. Section 4 presents the proposed SF allocation schemes. The experimental results and analysis of the proposed SF allocation schemes are presented in Section 5, while Section 6 concludes this paper.
###### Abstract  
Micro-milling has found extensive applications in machining components with thin-walled microstructures, such as terahertz slow-wave structures, microfluidic chips, and micro-molds. Due to the influence of size effects, micro-milling exhibits higher specific energy consumption compared with traditional milling, implying that more energy is consumed to remove a unit volume of material, particularly in challenging-to-machine materials like Ti-6 Al-4V. Historically, research on parameter optimization for micro-milling has predominantly focused on enhancing machining quality and efficiency, with limited attention given to energy efficiency. However, in the context of the \"double carbon\" strategy, energy conservation and emissions reduction have garnered significant attention in the manufacturing industry. Therefore, this paper proposes a micro-milling parameter-based power consumption model. Based on this, a specific energy consumption model can be obtained. Moreover, evolutionary algorithms are utilized for the optimization of micro-milling parameters, which aims to achieve comprehensive enhancements in both machinability and sustainability. The optimization objectives encompass improving surface quality, dimensional accuracy, material removal rate, and specific energy consumption during the micro-milling process for thin-walled micro-structures. Among them, NSGA-III achieves the best optimization results. Under conditions in which cutting energy consumption and processing efficiency are very close, the optimization outcomes based on NSGA-III lead to the best machining quality, including the minimum surface roughness and dimensional errors, and the largest surface fractal dimension. The optimal combination of micro-milling parameters is \(n=\) 28,800 rpm,\(f_{2}=\) 2.6 \(\upmu\)m/t, and \(a_{p}=\) 62 \(\upmu\)m.  
Keywords:thin-walled micro parts; micro-milling; machinability; sustainability; multi-objective optimization +
Footnote †: journal: Applied
## 2 Materials and Methods
### Materials  
This study was approved by the Institutional Review Board of Severance Hospital, Yonsei University College of Medicine, Seoul, Korea. For the evaluation of the effects of surgery, 78 pairs of head CT data from patients who underwent craniosynostosis surgery were used, consisting of 20 cases of severe sagittal synostosis, 7 of mild sagittal synostosis, 10 of severe bilateral coronal synostosis, 5 of mild bilateral coronal synostosis, 17 of unilateral comorbid synostosis, and 19 of unilateral lambboid synostosis. The levels of severity of sagittal and bilateral coronal synostoses cases were classified into cases with a CI of 0.75 or less and 1.0 or more, respectively. The mean ages of the subjects before and after surgery were 11.19 months (SD: 7.16 months, range: 2-36 months) and 27.94 months (SD: 10.44 months, range: 12-60 months), respectively, and the mean age interval before and after surgery was 17.16 months (SD: 7.64 months, range: 3-45 months). The generation of the mean normal model used CT data from 27 normal subjects, with an average age of 9.81 months (SD: 5.52 months, range: 3-22 months). CT images were acquired with six different high-speed multidetector systems (Siemens SOMATOM Sensation 64, Siemens SOMATOM Definition Flash and AS+, Munich, Germany, GE Healthcare LightSpeed VCT, GE Healthcare Revolution, GE Healthcare Discovery CT750 HD, Chicago, IL, USA). Each image had a matrix size of 512 \(\times\) 512 pixels with an in-plane resolution of 0.25-0.46 mm, a slice thickness range of 0.63-3.0 mm, and 65-313 images per scan.
## 4 Main Security and Privacy Challenges
### Edge Paradigm Challenges  
The Edge paradigm is considered to offer huge benefits to Edge customers such as storage, data processing, just to name a few. However, unlike the Cloud paradigm, Edge computing still faces big security and privacy challenges, which we will explore despite these many gains in this subsection.  
* Data Injection: When a machine is vulnerable, an attacker can push harmful information to share negative information. The act of injecting dangerous data by a malicious attacker into a device is known as poisoning. Data can be faked, then used to create fraudulent messages to render the nodes of the target compromised, and it is called an external forgery, for example, in a modern digital industrial production line where the adversary happens to give false machine readings, therefore causing severe functional changes with the bad aim to harm the devices [65].
* Eavesdropping: In this scenario, an attacker can mask itself and observe network traffic during transmission and capture data illegally. It is quite hard to point out this type of attack because the attacker happens to hide inside the platform [111].
* Privacy Leakage: The absence of strict access control to the node of Edge can easily lead to data privacy being tampered with. However, the attack strength is very low. The information generated from devices situated at Edge proximity is stored and processed in the Edge data building. Customers classified these Edge data buildings can leak information since the content is known [112].
* Distributed DoS: Attackers usually take advantage of network protocol vulnerabilities to launch attacks on Edge nodes, causing network damage and restricting resource access and provision of services. Attackers carry out these attacks by loading the server with many data packets to shut down the channel by jamming the server's bandwidth. Another option is where the Cloud data server or the Edge systems are being flooded with data packets to massively take out resources [65].
* Permission and Access Control: Unauthorized access is a major challenge in the Edge paradigm. It is important to know an individual or employee before authorizing them to access any sensitive information in the system. It can be achieved by establishing access control protocols. Connectivity between several pieces of equipment and other services can be considered secured when access control measures and permission are implemented [113].
# Effect of Environmental Parameters on Structural Health Status Assessment Using OMA Techniques
## 8 Correlation of Natural Frequencies with Temperature and Axial Force
### Evaluation of the Effect of Simulated Damage
A frequency variation on Mode 3 of 0.0% \(\div-\)0.2% on the sound beam (Tables 4 and 5) and of \(-\)2.1% \(\div-\)2.6% on the damaged beam (Tables 8 and 9) can be appreciated by Cov-SSI and PolyMAX methods using the correction with axial force. Therefore, it can be estimated that the additional 5% mass at L/10 causes a variation of the frequency of the third mode of \(-\)1.9% \(\div-\)2.6%. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **PP** & \multicolumn{3}{c}{**Cov-SSI**} & \multicolumn{3}{c}{**PolyMAX**} \\ \cline{2-8}  & **Mode 1** & **Mode 1** & **Mode 2** & **Mode 3** & **Mode 1** & **Mode 2** & **Mode 3** \\ \hline
**Mass 1\%** & \(-\)1.4\% & \(-\)1.4\% & 0.0\% & \(-\)0.6\% & \(-\)1.3\% & +0.3\% & \(-\)0.6\% \\
**Mass 3\% a** & \(-\)4.2\% & \(-\)4.2\% & [-] & \(-\)2.5\% & \(-\)4.1\% & [-] & \(-\)2.5\% \\
**Mass 5\%** & \(-\)0.7\% & \(-\)0.7\% & \(-\)1.1\% & \(-\)2.1\% & \(-\)0.6\% & \(-\)0.6\% & \(-\)2.6\% \\ \hline \end{tabular}
\end{table} 
Table 8: Beam 1: frequency variation, 5-min intervals discretization, correction with axial force. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **PP** & \multicolumn{3}{c}{**Cov-SSI**} & \multicolumn{3}{c}{**PolyMAX**} \\ \cline{2-8}  & **Mode 1** & **Mode 1** & **Mode 2** & **Mode 3** & **Mode 1** & **Mode 2** & **Mode 3** \\ \hline
**Mass 1\%** & \(-\)1.4\% & \(-\)1.4\% & \(-\)0.1\% & \(-\)0.6\% & \(-\)1.3\% & +0.3\% & \(-\)0.6\% \\
**Mass 3\% a** & \(-\)4.2\% & \(-\)4.2\% & [-] & \(-\)2.5\% & \(-\)4.1\% & [-] & \(-\)2.5\% \\
**Mass 3\% b** & \(-\)4.0\% & \(-\)4.1\% & \(-\)0.2\% & \(-\)2.5\% & \(-\)3.9\% & +0.1\% & \(-\)2.7\% \\
**Mass 5\%** & \(-\)0.7\% & \(-\)0.7\% & \(-\)1.1\% & \(-\)2.2\% & \(-\)0.6\% & \(-\)0.6\% & \(-\)2.6\% \\ \hline \end{tabular}
\end{table} 
Table 9: Beam 1: frequency variation, 10-min intervals discretization, correction with axial force. When the correction is performed using ambient temperature, the difference can rise to \(-1.3\%\div-1.6\%\) on the sound beam (Tables 6 and 7) and to \(-3.1\%\div-3.3\%\) on the damaged beam (Tables 10 and 11). The compensation with ambient temperature provides an initial frequency spread of about \(-1.45\%\); this is large if compared with the effect of the mass \(-1.5\%\div-2.0\%\). Therefore, the introduction of this mass cannot be clearly appreciated if frequency correction is done using ambient temperature.
# Discussions on the Processing of the Multi-Component Seismic Vector Field
## 4 Conclusions and Perspectives  
The multi-component seismic wavefield is a physical vector field. To fully demonstrate the advantages of multi-component seismic data, we first have to retain the seismic vector field information during pre-processing, at least without interrupting the relative spatial direction and amplitude relationship between the multiple components. Secondly, we need an advanced technique to convert the kinematic and dynamic information hidden in the vector wavefield into the elastic parameters of the underground medium, such as velocity anisotropy, stress distribution, pore pressure, fluid saturation, and fracture size, which directly relates to oil and gas reservoirs. In this paper, we introduced several pre-processing methods for a multi-component seismic data vector field. The examples of real multi-component seismic data indicated that the vector processing technique could better avoid the distortion of vector field information and give more accurate predictions than the scalar processing technique.  
Simply because studies on the extraction and utilization of seismic vector information are still in their infancy, vector processing techniques are currently outside of mainstream industrial interests. However, we believe this field will continue to develop, taking a key role in the future of fossil energy exploration, earthquake research, and micro-seismic monitoring.  
Conceptualization and formal analysis--Y.W. and PS.; Investigation and methodology--C.W.; Reference collection--Y.L., The paper was written by all the authors. This research was funded by the National Natural Science Foundation of China (Grant Nos. 41874166, 41504107, and U183208), the National Science and Technology Major Project (No. 2017 ZX05018005), and the Research and Development Fund of Sinopce (No. P18070-5). The authors declare no conflict of interest.
# Multilevel Regularization Method for Building Outlines
## 1 Introduction
However, due to the diverse morphology of buildings in high-resolution remote sensing images, the building outlines extracted from them have complex positional relationships between nodes, more redundant and noisy points, zigzagging and corner deformation of the outline lines, and so on. The existing building outline regularization algorithms also have the following problems: First, these algorithms lack robustness and universality as most are only effective for certain types of buildings and cannot simultaneously handle multiple types of building outlines. Second, they heavily depend on the initial extraction results; misclassifications or omissions in the initial results directly lead to deviations from the true shapes in the regularization results. Third, these algorithms are insufficiently automated as they rely excessively on data sources, prior knowledge, and manual assistance. To address these problems, this article proposes a fast, efficient, and accurate method for building outline regularization, dividing the regularization process into three steps: overall, local, and detailed regularization. First, using the MBR of the building outline as a reference, the building outline is optimized by the Hausdorff distance algorithm. Then, by combining the convex hull of the building outline and the distribution characteristics of nodes, the unregularized portions of the building outline are split into multiple line chains and classified for regularization. Finally, through operations such as removing redundant points, completing missing points, and orthogonalizing corners, the building outline is deeply optimized to obtain regularization results that conform to the actual features of the building. The proposed method provides the following three contributions:  
* The calculation of the Hausdorff distance is improved by considering the distribution characteristics of nodes in the building outline. * An MBR algorithm is presented based on the improved Hausdorff distance method, addressing the issue of directional deviation present in commonly used MBR methods. * A new \"overall-local-detail\" multilevel regularization method was designed for building outlines.
## 2 Materials and Methods
### Methods
#### 2.3.1 Analysis of Conditioning Factors
tendency of water to accumulate and move down the slopes [46]. TPI is relevant because it characterizes the topographic position of a sector with its surroundings and because of its association with the physical processes of slopes [47]. TRI quantifies the roughness of the terrain, which makes it possible to obtain a better topographic characterization that influences the instability of slopes, as well as the runoff rate [48]. STI provides an understanding of the dynamics of sediment transport, which is crucial for understanding landslide hazards and their consequences at specific sites such as dams [49]. Finally, the solar radiation index is important because it influences aspects such as evapotranspiration, which has an impact on soil moisture levels and, therefore, on soil stability [50]. Not DEM Dependent Factors  
These factors also play a crucial role in LSM. Land cover is related to the occurrence and distribution of landslides since land use transition processes, such as urbanization, increase the danger of their occurrence [51]. These changes have a significant influence on the susceptibility to landslides, with, for example, abandoned cultivated land being very susceptible to landslides [52]. Distance to roads is important because roads generate disturbances that cause landslides due to the cutting of slopes to build them, which also produces geological disturbances that increase the probability of failures and, therefore, landslides [53]. Distance to rivers and streams is essential because the proximity of these streams to slopes increases water infiltration and soil saturation, leading to changes in slope stability [54]. Furthermore, according to [PERSON] and [PERSON] [55], there is a higher density of landslides along rivers, which decreases as the distance to the rivers increases. Another critical factor is NDVI, as it provides information on vegetation growth and its spatial distribution. In addition, with the changes reflected by this index, it is possible to identify alterations in the vegetation cover since it indicates areas potentially prone to landslides [56]. Last but not least, lithology is widely used in landslide susceptibility analyses, as it is considered a critical factor in the mechanical and hydraulic properties of bedrock and soil cover [46]. Because the conditioning factors are the same as those previously applied [7; 12], they have not been included in this study. The corresponding maps can be found in Section 2.3.1 (p. 8-11) [7] and Section 3.2 (p. 8-9) [12].
# Enhancement of Question Answering System Accuracy via Transfer Learning and BERT
## 7 Conclusions  
In this paper, by fusing CWS in the NER task, the accuracy of candidate entities is improved without introducing too much noise. A multi-channel entity disambiguation model is proposed to enhance the features of candidate entities to bridge the semantic gap between the question and the knowledge base. The model makes full use of the problem encoding and candidate information features extracted by the BERT model and has strong generalization for application in multi-domain knowledge bases. The current method is evaluated from experiments on the NLPCC-ICCPOL-2016 KBQA Chinese open question and answering dataset, with an average F1 score of 87.74%.  
The results of experiments indicate that we have developed a satisfactory question answering system for the Chinese dataset. Our method performs better on the NLPCC-ICCPOL 2016 KBQA dataset. The system we developed may fill the gap in the technical research of the tutor selection service system. Later, we will use the BAT-KBQA framework on the Chinese Academy of Sciences tutor dataset to develop a tutor KBQA for students, so that students can quickly select tutors.  
In future work, different subtasks will be trained in combination, or more knowledge graph representation learning methods can be introduced to obtain richer features.  
**Author Contributions:** Conceptualization, S.D.; Data curation, Q.Z. and H.W.; formal analysis, Y.L. and Q.Z.; investigation, S.D. and Y.L.; Funding acquisition, S.D.; Supervision, S.D.; resources, K.D., S.D. and Y.Z.; software, K.D.; writing--original draft preparation, K.D. and S.D.; writing--review and editing, S.D. and Y.Z.; visualization, H.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Key R&D Program of China (Grant No. 2021 YFB3500100), the National Natural Science Foundations of China (Grant Nos. 52250005, 21875271, U20B2021, 21707147, 51372046, 51479037, 91226202, and 91426304), the Key R&D Projects of Zhejiang Province (No. 2022C01236, 2019C01060), the Entrepreneurship Program of Foshan National Hi-tech Industrial Development Zone, the Major Project of the Ministry of Science and Technology of China (Grant No. 2015 ZX06004-001), Ningbo Natural Science Foundations (Grant Nos. 2014A610006, 2016A610273, and 2019A610106).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Quality Assessment of the BIC/B2a Signals from BDS-3 Operational Satellites
### Differential ISB estimation between BDS-3/GPS/Galileo BIC-L1-E1 and B2a-L5-E5a signals
#### 4.3.1 Evaluation Statistics  
ADOP is an easy-to-compute scalar diagnostic to measure the intrinsic precision of the ambiguities, which is defined as [39]:  
\[ADOP=\sqrt{|Q_{\Delta d}|^{\frac{1}{n}}}(cycle) \tag{7}\]  
where \(n\) the dimension of the ambiguity vector, \(|Q_{\Delta d}|\) the determinant of the variance-covariance (vc)-matrix of float ambiguities. A lower ADOP corresponds to a higher precision of the ambiguities, and an ADOP smaller than 0.12 cycles corresponds to an ambiguity success rate larger than 99.9% [40].  
Besides ADOP, the empirical success rate can also be used to verify the performance of ambiguity resolution, which is defined as the percentage of accepted and correctly-fixed epochs in total number of epochs [41]. The ambiguities are confirmed to be correct by comparing them with the true ambiguities, which are estimated based on all the available GPS/Galileo/BDS observations over the observation period and resolved using LAMBDA method [42]. The ratio, defined as the quadratic norm of a suboptimal solution divided by that of the optimal solution, is utilized to validate the correctness of fixed ambiguity solution. If the ratio value is larger than a given threshold, e.g., 2.0 or 3.0, then the fixed ambiguity solution is accepted. The Fixed Failure Rate Ratio Test with user defined failure rate (P\({}_{\text{f}}\)) of 1% and 0.1% are used as well [43; 44].