esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Backup Beams for Instantaneous Link Recovery in mmWave Communications
## 5 Performance Evaluation
### Received Power Delay Profile  
Figure 5 shows the received power profile in time domain for the proposed scheme as opposed to conventional schemes (i.e., testing adjacent beam directions) shown in Figure 6. The proposed recovery scheme yields a rich scattering profile due to the wide spatial coverage achieved by the instantaneous back-up beams, with HPBW that exceeds the obstacle's dimensions, as well as enriching the reflections in the Rician path gains in the channel settings. For example, the proposed scheme exhibits 4-5 rays in 2-3 clusters when blockage is triggered, as opposed to 2-3 rays in 1-2 clusters for the neighboring beams testing and conventional codebook schemes. Furthermore, the recorded clusters here are received with power levels of \(-60\) dBm, which relaxes the receiver sensitivity requirements. This is compared to \(-120\) dBm and \(-140\) dBm signal levels for the other schemes, which results in significant challenges in acquiring the signal, limiting coverage ranges, as well as providing poor low channel capacity and impeding high channelization services for SA mmWave networks.  
Figure 4: Spectral efficiency for various blockage parameters.  
Figure 5: Power delay profile for conventional schemes (adjacent beam and aggregation).
## 4 Results
#### ANOVA Test

Table 3: The RR estimation results were obtained using the SVR, LSTM, and GBA models, which were calculated as the difference from the reference RR method to express it as the MAE and SD evaluation method, where the MF denotes the multiphase feature extraction and CA is the autocorrelation function-based power spectral feature extraction. using the MAE results of the CASVR, CALSTM, and CAGBA models. We then see the \(p\)-value (0.0022) obtained from the MAE results of all six models using the BIDMC dataset. Figure 4a well displays the MAE results of the SVRMF and CASVR models; we see the MAE results of the LSTMMF and CALSTM models in Figure 4b; In Figure 4c shows the MAE results of the GBAMF and CAGBA models. We observe the MAE results of the SVRMF, LSTMMF, and GBAMF models in Figure 4d. Then, we show the MAE results of the CASVR, CALSTM, and CAGBA models in Figure 4e. We can see the MAE results of all six models based on the RRSYNTH in Figure 4f. Figure 5 shows the MAE results in the same order as Figure 4. Here, the results of Figure 4 are using the RRSYNTH dataset, and the results of Figure 5 are the results of experiments with the BIDMC dataset. Figure 4: The box below represents the MAE and SD compared to the reference RR method obtained using the BRSYNTH dataset; where (**a**) is the MAE results of SVRMF and CASVR, (**b**) denote the MAE results of LSTMMF and CALSTM, (**c**) is the MAE results of GBAMF and CAGBA, and (**d**) denote the MAE results of SVRMF, LSTMMF, and GBAMF, (**e**) is the the MAE results of CASVR, CALSTM, and CAGBA, and (**f**) is the MAE results of all models. Figure 5: The box below represents the MAE and SD compared to the reference RR method obtained using the BIDMC dataset; where (**a**) is the MAE results of SVRMF and CASVR, (**b**) denote the MAE results of LSTMMF and CALSTM, (**c**) is the MAE results of GBAMF and CAGBA, and (**d**) denote the MAE results of SVRMF, LSTMMF, and GBAMF, (**e**) is the the MAE results of CASVR, CALSTM, and CAGBA, and (**f**) is the MAE results of all models. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Source** & **SS** & **df** & **MS** & **F** & **\(p\) Value** & **SS** & **df** & **MS** & **F** & **\(p\) Value** \\ \hline BG & 4.12 & 2 & 2.06 & 6.01 & 0.0043 & 6.10 & 5 & 1.22 & 4 & 0.0022 \\ WG & 19.52 & 57 & 0.34 & - & - & 34.72 & 114 & 0.304 & & \\ Total & 23.64 & 59 & - & - & - & 40.82 & 119 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Using the BIDMC dataset, the ANOVA results of the left side columns were obtained from the CASVR, CALSTM, and CAGBA; the right side columns were obtained from SVRMF, CASVR, LSTMMF, CALSTM, GBAMF, and CAGBA models.
###### Abstract  
The global navigation satellite system (GNSS)-derived height determination technique is applied in the field of surveying owing to the broad use of GNSS and the development of precise local geoid models. In Korea, this technique was officially adopted in 2020 for public surveying, such as urban facility mapping; it is also treated as an efficient way to unify the vertical datum of the inland and island areas of Korea. Here, GNSS surveying was conducted on 19 stations located in Korea's coastal regions and islands, and GNSS-derived elevations were determined. When each GNSS-derived elevation was compared with elevations from spirit leveling, all stations showed differences of less than 3 cm when GNSS surveying was conducted for 4 h/day over two days; they were smaller than 5 cm with 2 h of surveying. These differences meet the standards of GNSS-derived elevations in Korea. In addition, GNSS-derived elevations were compared with those obtained via sea-crossing leveling in two regions, showing differences smaller than 1 cm. Sea-crossing leveling takes longer than GNSS-derived height determination, and its accuracy can be significantly affected by various environments, such as sea fog. Thus, GNSS-derived height determination represents a practical and useful technique.  
GNSS-derived height determination; GNSS; geoid; sea-crossing leveling +
Footnote †: journal: Applied Sciences
## 2 Proposed Method
### Model Specification  
We start by recalling here the finite mixture of \(K\) multivariate generalized Normal distributions (MGND).  
**Definition 1** (Multivariate generalized Normal distribution).: _We say that \(\vec{Y}\), D-dimensional vector, has a multivariate generalized Normal distribution with parameters \((\vec{\mu};\Sigma;\beta)\) and noted as \(\vec{Y}\sim MGN^{D}(\vec{\mu};\Sigma;\beta)\), if its density function is defined as [10]:_  
\[p(\vec{Y}|\theta)=p(\vec{Y}|\vec{\mu};\Sigma;\beta)=\frac{\Gamma(\frac{d}{2B}) }{\pi^{\frac{d}{2}}\Gamma(\frac{d}{2B})2^{\frac{d}{2B}}}\frac{\beta}{|\Sigma|^ {\frac{1}{2}}}\exp\Big{[}-\frac{1}{2}((\vec{Y}-\vec{\mu})^{T}\Sigma^{-1}(\vec {Y}-\vec{\mu}))^{\beta}\Big{]}. \tag{1}\]  
For the case of finite mixtures, we suppose that we have \(K\) components following a multivariate generalized Normal distribution. Let \(\mathcal{Y}\) set of vectors, such as \(\mathcal{Y}=\{\vec{Y}_{1},\vec{Y}_{2},\dots,\vec{Y}_{N}\}\), be a realization from \(K\) mixture components. Here the \(\vec{Y}_{n}=(x_{n1},\dots,x_{nD})\) is D-dimensional. Thus, the likelihood is defined as follows [4]:  
\[p(\mathcal{Y}|\Theta)=\prod_{n=1}^{N}\sum_{k=1}^{K}w_{k}p(\vec{Y}_{n}|\theta_{ k}), \tag{2}\]  
where \(\Theta=(\vec{w},\theta)\). \(p(\vec{Y}_{n}|\theta_{k})\) is the \(k^{th}\) component of the mixture defined by its parameters \(\theta_{k}=(\vec{\mu}_{k},\Sigma_{k},\beta_{k})\). Here, \(\{w_{k}\}\) is a mixing weight parameter that satisfies \(0\leq w_{k}\leq 1\) together with \(\sum_{k=1}^{K}w_{k}=1\).  
* \(\vec{\mu}_{k}\) is the mean vector of the MGGD distribution.
* \(\beta_{k}\) represents its shape parameter. It inspects the peakedness and the spread of the distribution.
* \(\Sigma_{k}\) is its covariance matrix, also called a scatter matrix.  
It is noted that each \(\vec{Y}_{n}\) is generated from one component.
###### Abstract
[PERSON] et al. [32] extracted acoustic emission damage signals from composite plates and then generated time-frequency maps by continuous wavelet transform, which were fed into CNN to identify damage regions. This method can exploit the time-frequency information in the raw signal, but it is almost unable to detect any classes. [PERSON] et al. [26] employed the number of frames of full wavefield propagation as input to a ConvLSTM model and the damage map as output to identify delamination damage. Although these methods are capable of accurately identifying damage, their computational complexity is rarely considered in the aforementioned methods. Their networks have high computational and memory costs, which are not conducive to industrial applications. There are two main reasons for this problem: (1) The sample size is large. The input data to the model is obtained by sampling from multiple sensors, with each sample usually consisting of several thousand elements. (2) These aforementioned SHM methods usually adopt conventional convolutional neural networks, which usually have a large number of model parameters and huge computational complexity in practice. To address the above problem, we propose an efficient lightweight deep-learning-based damage-detection approach. In the proposed approach, first, the raw signal data are reshaped by transforming multiple one-dimensional sequences contained in one sample into a two-dimensional matrix, which can effectively reduce the size of the input data while retaining valid information and providing a new perspective for feature extraction. Then, we further propose a novel lightweight convolution and attention network (LCANet) for damage detection. In the proposed LCANet, a lightweight convolution module is designed for feature extraction using group convolution, depthwise convolution, and channel shuffle. Considering that the use of lightweight special convolution can reduce the feature-extraction capability of the model slightly and that the convolutional model can only extract local features, a multi-head self-attention mechanism (MHSA) is further adopted in front of the fully connected layer classifier of the model to improve the model performance. Experimental results demonstrate that the proposed LCANet method can efficiently and accurately detect the damaged region with outstanding performance. The rest of this paper is organized as follows: Section 2 introduces conventional convolutional networks for damage detection, including a brief flow of detection and the parts of a convolutional network. Our proposed approach is presented in Section 3, including the introduction of the detection system, data processing, and description of LCANet. Then, we describe the experimental setup and perform experiments on damage region recognition in Section 4. The advantages of the proposed method are demonstrated by comparison with existing methods. Finally, we conclude Section 5.
# From Security Frameworks to Sustainable Municipal Cybersecurity Capabilities
## 4 Results
### Technology
#### 4.5.1 Evolving Municipal ICT Landscape and Cybersecurity Technology Needs  
Municipal ICT infrastructures are undergoing major transformations. This includes the increasing adoption of cloud services, encompassing both SaaS applications and Infrastructure-as-a-Service from international hyperscalers. Simultaneously, the increasing digitalization of municipal services involves developing, introducing, or improving digital solutions for citizen communication, welfare technology in healthcare, and IoT and industrial control systems for technical services. This architectural shift increases the potential external attack surface, as municipal systems become more exposed through web applications, APIs, and cloud service interfaces. Concerns reported include vulnerabilities in these new application layers and the complexities of managing security configurations across diverse cloud platforms. To mitigate these expanding attack surfaces and manage the complexities of cloud security, municipalities require new categories of security tools. The contributors reported concerns related to solutions for enhanced information classification to manage data in diverse cloud environments, tools to enforce evolving compliance rules across hybrid infrastructures, and modern data protection platforms adapted to cloud-native architectures. Furthermore, capabilities for effectively scanning and managing vulnerabilities in these expanded and more dynamic environments were seen as crucial.
# Integrated Fiber-FSO WDM Access System with Fiber Fault Protection
## 3 Experiment and Results
Thus, the achieved power budgets of downstream and upstream signals are 29 and 29.5 dB, respectively. The measured sensitivity of L-band FSO wavelength is inferior to that of the C-band signal, as shown in Figures 5 and 6, due to fiber dispersion effect at the longer wavelengths. Here, the insets (i) to (iv) of Figure 7a,b are the measured eye diagrams of 1560.61 (\(\lambda_{\text{1L}}\)) and 1563.86 nm (\(\lambda_{\text{SL}}\)) at the received powers of \(-21.5\) and \(-15\) dBm and \(-22\) and \(-14.5\) dBm, respectively. These measured eyes also seem clear below the FEC limit. Furthermore, the FSO downstream and upstream wavelengths will induce the same total loss of 15.5 dB, including the AWG\({}_{\text{4}}\) (6 dB), WCP (1 dB), 25 km SMF (0.2 \(\times\) 25 = 5 dB), OC (0.5 dB) and two FCs (3 dB), respectively. Here, the redundant budgets of 13.5 and 14 dB can be achieved to determine the wireless lengths of the downstream and upstream FSO transmissions, respectively. According to the previous FSO study [25], the obtained power budget of 13.5 dB can support 800 m free space transmission link based on the prior designed optical system without signal amplification. If we want to extend the FSO transmission distance, an optical amplifier can be added in the ONU side for signal amplification. To improve the signal performances of fiber and FSO traffics, the simplified PON architecture can improve the power budget for providing a longer optical fiber and FSO transmission distance. In addition, to clearly express the signal performance obtained in the PON system, the corresponding results of fiber and FSO signal is listed in Table 1.  
Figure 7: Measured FSO BER performances of (**a**) \(\lambda_{\text{1L}}\) to \(\lambda_{\text{4L}}\) and (**b**) \(\lambda_{\text{SL}}\) to \(\lambda_{\text{SL}}\) through 25 km SMF and 2 m FSO links, respectively. Insets (i) to (iv) are the observed corresponding eye diagrams under different received powers.
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 6 Performance Analysis  
In this section, the computational and communication cost of our protocol is compared to that of existing related protocols [18; 19; 20; 21].
# A Comprehensive Study of the Use of LoRa in the Development of Smart Cities
## 5 Discussion  
### Smart City Strategies Using IoT-LoRa  
To become a smart city, more sustainable and socially inclusive, a city should take advantage of IoT technologies. Several cities around the world are using LoRa to transform and improve their services in different areas such as health, education, and transportation. Amsterdam was the first city covered by LoRa using only 10 gateways. Other cities in other continents followed. Figure 17 shows some cities covered by LoRa, which include North American cities such as Boston, Ottawa, and Mexico City, South American cities such as Sao Paulo, Montevideo, and Buenos Aires, and Asian and European cities such as Pekin, Manchester, and Zurich.  
Manchester has invested in transportation, social healthcare, culture, energy, and environment, considering that the data deliver information to allow the definition of better policies and create a more dynamic city infrastructure. Through sensors, it is possible to improve different types of services for visitors and citizens travelling in a city. For example, cyclists can detect when they are approaching a dangerous road; they can also detect road conditions and receive alerts in case of accidents. Manchester used six LoRa servers and 25 base stations to cover the city. Montevideo is another city using LoRa. This city has focused on the management of public lighting, water meters, noise measurement, agricultural services, and optimization of garbage containers. On the other hand, Zurich uses IoT to determine places with forgotten bikes, and zones with distribution grid power failures. Table 13 shows a short description of IoT applications used in some smart city strategies.  
Figure 17: Smart cities according to ITU in 2019 [79].
## 4 Deep RL-Assisted Resource Allocation
### Proposed DQL-Based Approach
#### 4.2.2 Action Space  
The action space is the set of all available actions the agent takes from the current state, denoted as \(a^{(t)}\in A\). In this paper, we assumed that the agent is in charge of the RB allocation and the DQN algorithm comes down to optimizing transmit power control for each user in a multi-cell network. More specifically, the total number of possible actions relies on the number of power levels the BS can distribute to the corresponding receivers. Different actions in the set of all available action spaces denote different power controls that the BS can schedule to mobile users in the multi-cell system. Even though the transmission system mostly adopts continuous power values for minimizing complexity, in this paper, we use discrete power levels between 0 and \(P_{max}\). We denote by \(A_{m}\) the (non-empty) set of action spaces assigned to cell \(m\) and defined \(a_{\text{m}}\in A_{m}\) is the random selected action for cell \(m\). For each cell, we have \(n\) number of actions, and then the number of actions for \(M\) number of cells will be \(M\times n\). Each action \(a_{\text{m}}\in A_{m}\) corresponds to the power levels we are using. The possible emitting power is quantized exponentially in \(|A|-1\) levels along with a zero-power level, which indicates that there is no signal transmission. For each cell \(m\), we assume that the action space has \(|A_{m}|>1\) discrete power levels. Therefore, the action space can be expressed as  
\[A_{m}=0,\frac{P_{max}}{|A_{m}|-1},\ \frac{2P_{max}}{|A_{m}|-1},\ \ldots,P_{ max} \tag{18}\]  
Here, if there is no other described information regarding the wireless environment, the DQN agent performs action formation \(a_{m}=\underset{a}{\text{argmax}}Q^{k}(s_{t},a_{t};\theta)\) with probability \(\epsilon\).
# Formation Control of Dual Auto Guided Vehicles Based on Compensation Method in 5G Networks
###### Abstract  
With commercial application of 5G networks, many researchers have started paying attention to real-time control in 5G networks. This paper focuses on dual auto guided vehicles collaborative transport scenarios and designs a formation control system in current commercial 5G networks. Firstly, the structure of the 5G network researched in this paper is introduced. Then the round-trip time of 5G networks is measured and analyzed. The result shows that although the 5G round-trip time has randomness, it is mainly concentrated in 19 \(\pm\) 3 ms, and the jitter mainly in 0 \(\pm\) 3 ms. The Kalman filter is applied to estimate the transmission delay and experiment result shows the effectiveness of the estimation. Furthermore, the total delay including transmission delay and execution delay in control system is discussed. After establishing the AGV kinematic and formation model, complete control system based on compensation method is proposed. Finally, an experiment is carried out. Compared to the result without formation control, maximum distance error is reduced by 82.61% on average, while maximum angle error 45.91% on average. The result shows the effectiveness of the control system in formation maintaining in 5G network.  
5G; formation control; auto guided vehicle; Kalman filter +
Footnote †: journal: _machines_  
0000-0001-9123-318
## 3 Theoretical Framework
### Application of Language Learning Theories
#### 3.1.4 Detailed Description of AI Algorithms  
In this study, we employed several advanced AI algorithms to ensure the effectiveness and precision of our language learning system. Specifically, we utilized machine learning techniques including supervised learning models such as support vector machines (SVMs) and neural networks to analyze language usage patterns and predict learning outcomes. Natural language processing (NLP) algorithms were applied to assess linguistic structures and provide immediate, contextually appropriate feedback. These NLP techniques included tokenization, part-of-speech tagging, and named entity recognition, which were crucial in understanding and interacting with learners' inputs. To enhance the adaptability of our system, we implemented adaptive learning algorithms that dynamically adjusted the learning content based on the learner's progress and feedback. This section outlines each algorithm's functionality, its integration into our system, and how it contributes to the personalized learning experiences we aim to provide.
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 5 O-RAN Alliance Architecture
### O-Dui
Connecting this O-DU and O-RU functions split is the O-FHI. There are four planes inside the O-FHI, which are the Control, User, Synchronization, and the Management Plane. As we have known, the CP is a protocol for passing control signals, including scheduling and beamforming commands, configuration parameters, configuration requests, status, and responses. UP transfers the user data, such as the DL, UL, and PRACH In-phase/Quadrature (IQ) data. The Synchronization Plane (S-Plane) is used for achieving timing synchronization between multiple units of equipment. For S-Plane, there are several clock models and synchronization topologies that are extensively explained in [63]. Finally, the Management Plane (M-Plane) handles maintenance and monitoring signals. The data flows in the O-FHI are shown in Figure 18.  
W68 is in charge of developing the O-DU specifications. O-FHI specifications are developed by WG4. O-DU high is developed by Radisys while O-DU low is developed by Intel. In OSC H release, O-DU High introduces multi-scheduler algorithm support. Other features added in this release also include Inter CU Handover, and E2 enhancements that support E2 AP V30. Currently, integration testing with O-DU Low is ongoing while waiting for the availability of the RU simulator. For the O-FHI, Intel has finished implementing the C, U, and S planes into the O-FHI library in the OSC repository. O-RAN adopters can directly use this open-source library in developing their O-RAN compliant DU and RU. However, the M-plane has not yet been developed. In fact, the M-Plane specifications are still under development.