esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed LOADng Enhancement for IoT Networks
### Proposal Overview  
The proposed LOADng-IoT is composed of three components: the Internet route discovery process, the Internet Route Cache (IRC) system, and a new error code for RERR messages. The first component is responsible for finding IN nodes without the requirement of previous knowledge of its address in the local network. Thus, a node that wants to send a message to the Internet should start an Internet route discovery process by broadcasting a special RREQ, named RREQ-IoT. The message has the objective of seeking an IN that can be used as a gateway for the RREQ-IoT originator. To reduce the number of broadcast transmissions, an intermediate node that knows a route for an IN can forward the RREQ-IoT message to it using unicast transmission (in the same way as SmartRREQ). When an IN receives an RREQ-IoT message, it should generate a special RREP to answer the request. This message, named the RREP-IoT, is forwarded in unicast via the opposite route created by the RREQ-IoT. Each node that receives an RREP-IoT should create an entry on its Routing Set with the information that the message originator has an Internet connection. When the RREP-IoT reaches its destination, the node should immediately start to send the Internet data messages. The proposed Internet route discovery process of LOADng-IoT is fully described in Section 4.4.  
The second component is responsible for storing the Internet routes (routes to Internet-connected nodes) removed from the Routing Set. During the Internet route discovery process, the nodes create entries on the Routing Set to the INs. These entries, which have a valid time, can expire and be removed from the Routing Set when not used. Thus, to reduce the number of transmissions in a new Internet route discovery and to allow the nodes to follow a previously known Internet route, these entries, when removed, have some of its information inserted in a new data structure, the IRC. The IRC should always be consulted when a new Internet route discovery process is started and should, when possible, indicate a previously known Internet route to direct the discovery process. The IRC is optional and should be adopted according to the hardware capacity of the network devices. A complete description of the proposed IRC is presented in Section 4.5.  
The third component is a new error code for RERR messages. The INTERNET_CONN_LOST error code should be used to indicate to an Internet data message originator that it was not possible to forward the message to the Internet. Thus, the receiver of the RERR should start a new Internet route discovery process to find a new IN to transmit its Internet messages. The generation, processing, and functioning of the proposed new error code for RERR messages are detailed in Section 4.6.
## 3 Research Method  
This study used the conceptual method to develop a comprehensive model capable of identifying the antecedents and impact of perceived value on organizational IoT adoption using theories and frameworks of information systems [68]. This method was chosen to investigate unexplored constructs in previous studies and introduce new constructs to bridge the gap in the existing literature. In contrast to theory studies, the conceptual study does not need to deliver a new theory but instead bridge existing theories and frameworks in exciting ways to create different insights, expand the field of exploration, and link findings across disciplines [68; 69; 70]. [PERSON] [70] identified four types of conceptual studies: theory adaptation, theory synthesis, model development, and typology. The model development type has been used in this study to construct a theoretical framework that predicts relationships between concepts [70]; thus, this conceptual paper proposes new relationships between constructs rather than testing them empirically and develops an integrated model that links these constructs to a number of propositions [69; 70; 71].  
To conduct this conceptual study, a systematic literature review (SLR) was used following PRISMA guidelines [72]. Five academic databases have been identified to select articles: the IEEE, the Web of Science, Emerald, Science Direct, and Google Scholar, from the period 2015 to August 2022. The following search query was used to review and analyze literature: (\"Internet of things\" OR IoT) AND (adoption OR diffusion) AND (model OR framework) AND (\"manufacturing sector\"), to establish the foundation required to envision possible constructs that enable IoT adoption in the MS.  
The articles selection procedure followed the screening and filtering iterations by reading each article's title, abstract, and keywords. Then, the articles from the first filter were filtered further by carefully reading them in full text to select the ones related to the study only. In addition, the eligibility criteria were applied to all articles. The eligibility criteria included that the article should be in the English language, published in a journal or at a conference, dedicated to the organizational level, studied IoT technology, and not a technical article. Finally, a quality assessment was conducted to assess the risk of bias on the basis of 8 criteria [3] for all the included articles which are 42 articles to ensure the validity and reliability of the selection process. The quality assessment criteria and results can be found in Appendix A, Tables A1 and A2.  
From the final set of selected articles, the relevant influential factors have been analyzed and incorporated into suitable IS theories to propose the conceptual model. The following section presents the theoretical rationale and constructs used in the study.
## 3 Results
### Recording Distance: 0.6 m
 \\ \hline M0 & 58.52 & 38.18 & 89.74 & 65.15 \\ \hline M1 & 44.16 & 25.24 & 84.41 & 60.15 \\ \hline M2 & 38.19 & 30.77 & 75.00 & 42.26 \\ \hline M3 & 53.82 & 37.09 & 85.16 & 48.53 \\ \hline M4 & 44.95 & 20.71 & 75.96 & 55.28 \\ \hline M5 & 62.50 &  
was 9.4 pp less than that in the M0 condition (H = 45.470, \(p\) < 0.023), in the M2 condition it was 15.5 pp less than that for M0(H = 48.890, \(p\) = 0.015), in the M3 condition it was 15.7 pp less than that for M0 (H = 60.230, \(p\) = 0.003), and in the M4 condition it was 15.0 pp less than that for M0 (H = 58.600, \(p\) = 0.003). Despite the lack of significance, the ACC of ASRT under speech-shaped noise for female speakers in the M5 condition was 11.1 pp less than that in the M0 condition (H = 29.580, \(p\) = 0.139). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mask Condition**} & \multicolumn{2}{c}{**ASR\({}_{\text{D}}\)**} & \multicolumn{2}{c}{**ASR\({}_{\text{T}}\)**} \\ \cline{2-5}  & **Restaurant Noise** & **Speech-Shaped Noise** & **Restaurant Noise** & **Speech-Shaped Noise** \\ \hline M0 & 78.95 & 50.00 & 100.00 & 90.00 \\ \hline M1 & 66.67 & 42.50 & 91.67 & 80.65 \\ \hline M2 & 66.67 & 30.00 & 92.12 & 74.47 \\ \hline M3 & 67.54 & 40.06 & 97.50 & 74.34 \\ \hline M4 & 66.67 & 41.43 & 89.18 & 75.00 \\ \hline M5 & 63.96 & 45.56 & 100.00 & 78.89 \\ \hline M6 & 82.35 & 46.06 & 90.45 & 83.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Median ACC (%) for female speakers under noises at a recording distance of 0.6 m.  
Figure 6: The ACC for female speakers at a recording distance of 0.6 m. (**a**) ASRD, no noise; (**b**) ASRT, no noise; (**c**) ASRD, restaurant noise; (**d**) ASRT, restaurant noise; (**e**) ASRD, speech-shaped noise; (**f**) ASRT, speech-shaped noise. Kruskal–Wallis tests and post-pairwise comparisons: *, \(p\) < 0.05; **, \(p\) < 0.01.
# Research on Lateral Deformation Control Criteria of Metro Shield Tunnels with Excessive Ellipticity
## 2 Excessive Transverse Deformation Cases in Ghuangzhou Metro  
### Introduction to Tunnel Defect Cases  
The subway tunnel in Guangzhou is located in a typical upper soft and lower hard stratum, as shown in Figure 1. Most of the tunnel body is positioned within silt and silty soil, while the base of the tunnel rests in a comparatively hard residual soil layer. Above the tunnel, an intercity expressway has undergone multiple reclamation processes due to uneven settlement of the soft soil. This has caused continuous consolidation and settlement of the silt layer and an amplified soil weight over the tunnel, inducing significant lateral deformation. The soft silt layer struggles to provide sufficient lateral support for the shield tunnel structure, which leads to excessive lateral deformation. Such distortion in shield tunnels frequently results in joint leakage and even structural issues such as tunnel concrete segment cracking and collapse, compromising the structural integrity and safety of passing trains.  
Figure 1: Cross-section of the defected tunnel and the stratum in which it is located.
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 2 Literature Review
### Application of Deep Learning in Speaker Recognition  
GANs have seen extensive application in various fields, especially in tasks involving data generation and enhancement. Research in deep learning and speech processing has demonstrated that GANs can address significant challenges in areas like speaker recognition, speech enhancement, and data augmentation. In this section, we explore the role of GANs in conjunction with other deep learning techniques and spectral feature extraction methods, as well as their application in generating and enhancing speech data.  
[PERSON] et al. proposed a method for extracting speaker features using deep neural networks, called Deep Speaker Embeddings. These embeddings effectively distinguish between different speakers, significantly enhancing the accuracy and stability of speech recognition [1]. [PERSON] et al. introduced a CNN-based end-to-end speaker verification method for text-independent vocal verification by learning speech feature representations. This approach demonstrates good adaptability and robustness when processing different speech datasets, while also showcasing the application of CNNs in speech feature extraction, which could serve as a reference for combining MFCC and a GAN to generate samples [2]. [PERSON] et al. presented a method for speaker recognition using deep neural network (DNN) embeddings. This method extracts features through a DNN for vocal recognition, leading to a significant performance boost [3].  
[PERSON] and [PERSON] introduced the VoxCeleb2 dataset to demonstrate its use in deep voice recognition, showing a notable improvement in voice recognition performance. VoxCeleb2 contains a large variety of speech data, which contribute to advancing deep learning research in this field [4]. [PERSON] et al. proposed a voice recognition model based on a phoneme perception deep network. By incorporating phonemic information, this model improves its ability to differentiate vocal features, thus enhancing recognition accuracy [5]. [PERSON] and [PERSON] introduced SincNet, a deep network model for speaker recognition directly from raw waveforms. SincNet shows strong performance by extracting features directly from the original audio waveform, avoiding complex pre-processing steps [6]. [PERSON] et al. developed an end-to-end text-dependent speaker verification method that extracts features from speech data using a DNN. This approach simplifies traditional feature extraction and matching steps, improving verification accuracy through end-to-end learning [7].  
[PERSON] et al. also proposed another text-independent speaker verification method using DNN embeddings, significantly enhancing verification performance. This approach emphasizes the robustness and consistency of DNN embeddings in handling different textual content [3].
# Long-Term Forecasting Using MAMTF: A Matrix Attention Model Based on the Time and Frequency Domains
## 3 A Matrix Attention Model Based on the Time and Frequency Domains  
### Problem Definition  
We are given a set of multivariate time series data \(X_{n}=\left[\begin{array}{c}x_{1}^{1},x_{2}^{1},\cdots,x_{n}^{1}\\ x_{1}^{1},x_{2}^{2},\cdots,x_{n}^{2}\\ \cdots,\cdots,\cdots,\cdots,\cdots\\ x_{1}^{m},x_{2}^{m},\cdots x_{n}^{m}\end{array}\right]\) of length n. In this, m represents the mth multivariate variable, n represents the length of the data, and \([\cdot]\) represents a set of data. A multivariate time series can be viewed as a combination of multiple univariate time series. Multivariate time series forecasting refers to the use of previously known multivariate time series data \(X_{c}\) to predict unknown future multivariate time series data \(Y_{o}\). In this, c represents the input length, and o represents the predicted length. Long-term multivariate time series forecasting is encouraged to have a longer output length o than in previous work [14; 22; 29]. This can be expressed using Equation (1).  
\[Y_{o}=F(X_{c}), \tag{1}\]  
\(F(\cdot)\) represents the mapping relationship between \(X_{c}\) and \(Y_{o}\) learned by the model.
# Quality of Experience in 6G Networks: Outlook and Challenges
## 3 Enabling Technologies of Sixth Generation Networks
### Physical Layer  
In general, 6G networks are expected to deliver above \(\sim\)100 Gbps over the air. Achieving this will require exceeding the capabilities of mmWave and venturing into the terahertz band, i.e., 0.1-10 THz [43]. In this range, the network will rely on wide-band allocations. Such access is readily considered for backhauling 6G networks. However, expecting terahertz communication to the handset will require overcoming considerable challenges that include channel integrity and antenna design.  
Massive and dense MIMO have a crucial role in 6G networks, especially if THz is enabled. As noted in [7], the use of specific technologies such as metamaterial-based antennae,reconfigurable intelligent surfaces (RISs), and orbital angular momentum communication would all allow for holistic resolution to the physical layer limitations and may act as the cornerstone technologies for the 6G radio interface.  
RISs (also called intelligent reflective surfaces, IRSs) comprise the evolution of amplify-and-forward relay [44]. They introduce an agent of change into reflective surfaces to control the signal's quality at the receiver. Ideally, these surfaces (or lenses) have high resolution and accommodate prompt and optimal control. However, both resolution and reconfiguration parameters have upper limits, whether in fabrication or operation. It is expected that these surfaces will nevertheless allow for the gains needed for 6G's THz communication.  
To realize further gains, the 6G radio interface is expected to allow for true full-duplex wireless [45]. Given recent advances in self-interference cancellation, full-duplex wireless is set to offer substantial gains in terms of spectral efficiency. It will also provide further advantages when combined with the recently emerging nonorthogonal multiple access [46]. However, cross-layer interactions must be made to realize the full potential of these technologies.
# Cloud Detection and Tracking Based on Object Detection with Convolutional Neural Networks
## 4 Conclusions and Future Work
Future work on this methodology includes the improvement of the model and the dataset, new real tests, cross validation with other methods, the implementation of new previously mentioned techniques, tests to find the best camera configuration, model hyperparameter optimization, improving the calculation of the speed of the clouds and the time remaining, and studying how to reduce the net cost even more. **Author Contributions:** Conceptualization, J.A.C., J.B. and J.F.-R.; methodology, J.A.C. and J.B.; software, J.A.C. and J.B; validation, J.A.C., J.B., B.N. and Y.F. ; formal analysis, J.A.C. and J.B.; investigation, J.A.C. and J.B.; resources, J.F.-R., A.A.-M. and D.-C.-A.-P.; data curation, J.A.C., J.B., B.N. and Y.F. ; writing--original draft preparation, all authors; writing--review and editing, all authors; visualization, J.A.C., J.B., B.N. and Y.F. ; supervision, J.F.-R., A.A.-M. and D.-C.-A.-P.; project administration, J.F.-R., A.A.-M. and D.-C.-A.-P.; funding acquisition, J.F.-R., A.A.-M. and D.-C.A.-P. All authors have read and agreed to the published version of the manuscript. **Funding:** This work is part of the HELIOSUN project (More efficient Heliostat Fields for Solar Tower Plants) with reference PID2021-126805 OB-100, funded by the Spanish MCIN/AEI/10. 13039/5011000011033/FEDER UE, and Plan Andaluz de Investigacion, Desarrollo e Innovacion (PAIDI 2020) of Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades de la Junta de Andalucia funds. **Acknowledgments:** This work was partially supported by the computing facilities of Extremadura Research Centre for Advanced Technologies (CETA-CIEMAT), funded by the European Regional Development Fund (ERDF). CETA-CIEMAT belongs to CIEMAT and the Government of Spain. **Conflicts of Interest:** The authors declare no conflict of interest.
# Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency
## 2 Related Research
Therefore, the impact of dependencies between tasks on the effect of resource allocation needs to be considered. However, this has not been considered in existing studies. Therefore, considering the satellite edge environment, this paper proposes a resource scheduling algorithm for joint computing communication resource allocation while considering task dependencies. A summary of the above literature studies is summarized in Table 1, in which \(\surd\) indicates that there are studies in this area.
# An Optimization Design of Bi-log Hybrid Antenna with Taguchi's Method for EMI Measurements
## 5 Conclusions  
A novel broadband Bi-log hybrid antenna using [PERSON]'s method has been presented as a global optimization design. The antenna has a linear, continuous, and stable AF, exhibiting a good flatness in the frequency range of 30 MHz to 6 GHz. The FBW is as large as 198%. The optimization of antenna performance is achieved with a VSWR of <2, a 5 dB attenuator, and a symmetry of <\(\pm\)0.5 dB. This antenna consists of a bowtie and an LPDA structure that are fed with a taper placement on each boom side and a quasi-air coils wideband Balun feeding is achieved. The design concepts of the proposed antenna and its feeding structure have been well introduced. The antenna with the least number of elements, i.e., 21, is used to cover the specified bandwidth. The antenna with compact size and is lightweight results in the directive patterns of constant gain, while occupying a dimension that is reduced by more than 12%. This can also lead to reduced costs of fabrication and materials used, especially in mass production. It also highly satisfies the specifications of regulatory standards proposed by ANSI C 63.4, especially for EMI measurement antenna applications. Since most electromagnetic interference problems are highly complex, the proposed antenna with optimal design is used to quickly obtain accurate test results for EMI measurement.  
Figure 10: Measured cross-polarization rejection.  
Figure 9: Measured result of the (**a**) antenna factor with and without 5 dB attenuator and (**b**) antenna efficiency.  
**Author Contributions:** Conceptualization, C.-H.L. and D.-B.L.; methodology, C.-H.L. and D.-B.L.; software, C.-H.L.; validation, C.-H.L.; formal analysis, C.-H.L.; investigation, C.-H.L.; resources, D.-B.L.; data curation, C.-H.L.; writing--original draft preparation, C.-H.L.; writing--review and editing, C.-H.L.; visualization, C.-H.L.; supervision, D.-B.L.; project administration, D.-B.L.; funding acquisition, D.-B.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.  
**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.
# Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra
## 4 Materials and Methods
### Impacted Buildings  
Each of the buildings that have their own property are grouped in an empty Unity GameObject called \"Buildings affected\" to easily apply the change to each of them.  
Figure 6: Selected area after running the script (Blender).  
To detect the contact between the water plane and each building, the collider pipeline from Unity was used. The buildings are static objects and it is the water plane that moves. The trigger \"collided pipeline\" was used, as it detects collision without causing them. It allows the water plane to pass through the buildings.  
The first process is to add a mesh collider and set it to convex. It allows Unity to automatically apply a collider based on the geometries. The object that contains this mesh then needs to turn on the collider component's elements called \"trigger\". A Rigidbody needs to be added to make the trigger work and is added to the water plane as it is the object that moves and passes through the object.  
The change in colour makes it easier for the user to see which buildings are impacted by the water height. In Unity, two callback functions, OnTriggerEnter and OnTriggerExit, are utilised to detect collisions. These callbacks determine if the colliding object is a building, using \"tags\" as identifiers.  
In the conditional diagram in Figure 7, these two functions are called individually and the use of the variable \"affected\" avoids some unnecessary repetition by playing with them. The height comparison is performed between the building's centroid and the water plane's centroid using Unity Transform. Three colours, \(C_{d}\), \(C_{y}\), and \(C_{r}\), are used to display the status of a building, defined as follows:  
* \(C_{d}\) (default colour): indicates no contact with water.
* \(C_{y}\) (yellow colour): indicates that the building is in contact with water.
* \(C_{r}\) (red colour): indicates that the building is below water.  
\(B_{i}\) is the status of the building \(i\) and \(h_{w}\) is the water height. The status can be determined by the following conditions:  
\[B_{i}=\begin{cases}C_{d}&\text{if }h_{i}>h_{w}\\ C_{y}&\text{if }h_{i}=h_{w}\\ C_{r}&\text{if }h_{i}<h_{w}\end{cases}\]  
where \(h_{i}\) is the height of building \(i\).  
The change in colour \(C_{i}\) is linked by these conditions, providing a visual indication of the buildings impacted by the water height \(h_{w}\).  
Figure 7: Conditional diagram of each trigger collision callback.