esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation of Proposed Architecture
### Centralized MEC/Cloud Orchestrator  
#### 4.2.1 Logical Implementation  
First, we propose a centralized MEC/Cloud Orchestrator on the Cloud Owner that collectively manages multiple Private/Local Telecom Operators' MEC and integrates with cloud services. Figure 6 shows a proposal overview. The MEC/Cloud Orchestrator consists of GUI and Orchestrator functions and has northbound/southbound interfaces. For the northbound interface, GUI functions are provided to the end user. For the southbound interface, the Orchestration function is used for application deployment, setup, information collection/monitoring to each cloud, and multiple MECs. Here, managing multiple MECs collectively has three main advantages:  
1. The usage log information of the application that the end user has used in the cloud can be used as input information to AI/ML. Then, the cached content can be deployed by AI/ML on MEC as a usage prediction or made known to end users as recommendation information and selected.
2. When the end user has contracts with multiple Private/Local Telecom Operators, it is possible to track the movement of the application used by MEC when the end user moves, because multiple MECs are managed collectively.
3. Because the MEC/Cloud Orchestrator monitors each resource, the awareness of the physical location means that visualization management of the entire network can be performed, allowing network route change in the event of a disaster, etc., to be taken into consideration.  
Thanks to the above-mentioned advantages, the proposed centralized scheme can collectively manage and operate multiple MECs.
# A Forecast Model Applied to Monitor Crops Dynamics Using Vegetation Indices (NDVI)
## 3 Results and Discussion
### Olive
Figure 13: Representation for olive, estimation model and the residuals given by Equation (26). As an example, we divided the sample into a training time series, comprising the available data from January 2013 to January 2015, and a tested (validation) time series from February 2015 to December 2017. Data from January 2018 to December 2018 were forecast out of the given sample. Figure 15 and Table 1 show the coefficients applied for the 2018 monthly prediction of the different types of crops. Finally, we display the associated forecast confidence intervals for \(\alpha=0.05\) in Figure 16. These are the confidence intervals for the predictions shown in Table 1 for each of the crops: maize varieties, aggregated maize and olive. In addition, it would be interesting to incorporate NDVI data obtained on the ground to check if the accuracy of these models can be improved. The NDVI derived from satellite sources and on the ground are not directly comparable; the NDVI data sets produced from these sources are frequently similar [45]. In this sense, the technological advances in unmanned vehicle systems (UAVS) that acquire NDVI data at low altitude [46] are of great help to monitor vegetation and crops with high spatial and radiometric precision, because they eliminate the effects of the atmosphere in the acquisition of reflectivity values in red and infrared. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time** & **Maize** & **Maize** & **Maize** & **Maize** & **Maize** & **Olive** \\
**Period** & **Type 1** & **Type 2** & **Type 3** & **Type 4** & **Aggregate** & **Olive** \\ \hline
01/2018 & 0.418990 & 0.533135 & 0.451573 & 0.439748 & 0.449730 & 0.496241 \\
02/2018 & 0.422720 & 0.591989 & 0.483806 & 0.426376 & 0.442843 & 0.461002 \\
03/2018 & 0.402277 & 0.678137 & 0.412611 & 0.388669 & 0.462257 & 0.410330 \\
04/2018 & 0.456171 & 0.811104 & 0.485727 & 0.402678 & 0.537400 & 0.522606 \\
05/2018 & 0.529588 & 0.621669 & 0.530057 & 0.499155 & 0.580861 & 0.537193 \\
06/2018 & 0.609008 & 0.638714 & 0.659564 & 0.686565 & 0.609717 & 0.504093 \\
07/2018 & 0.747731 & 0.721217 & 0.742334 & 0.849985 & 0.652915 & 0.504024 \\
08/2018 & 0.767648 & 0.893007 & 0.766180 & 0.827189 & 0.645175 & 0.473098 \\
09/2018 & 0.562713 & 0.700946 & 0.647841 & 0.662895 & 0.608800 & 0.507662 \\
10/2018 & 0.478441 & 0.623085 & 0.562679 & 0.508142 & 0.526868 & 0.496602 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Forecast for type 1–4 maize crops and olive crops from January 2018 to October 2018. Figure 15: Forecast out of sample for the first ten months of 2018 for four types of maize crops, olive and aggregated maize.
## 2 Materials and Methods  
### Literature Search  
This section shows the process for obtaining data by searching for information in different databases and a metasearch engine where it was necessary to look at and introduce keywords and variables to find the required information. The websites or databases chosen for the study were IEEExplore, ScienceDirect, Taylor & Francis, Springer, and a metasearch engine, Google Scholar. Those websites were considered because they contained scientific, academic, and technical articles of great prestige with the possibility of finding open access studies.  
Additionally, we applied the PICOS [19] approach in constructing the research questions for our article: People (P), Intervention (I), Comparison (C), and Outcome (O).  
Considering the objective of our study, only articles related to education, industry, marketing, medicine, and entertainment (P) where AR is applied (I) were selected and analyzed to obtain current AR applications, benefits, challenges, and problems that will orient and update researchers for future AR studies and developments (O).  
The questions designed for our study were created to identify the main aspects to fulfill the aim of our research:  
1. Which aspects link AR with the five chosen areas of study?
2. What are the benefits, challenges, and problems that AR mostly faces in all areas?  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**AR and VR Fields** & **Estimated Users** & **Market Disrupted** & **Investment in Dollars** \\ \hline Videogames & 216 mn & Videogames & $11.6 bn \\ Live events & 95 mn & Live ticket sales & $4.1 bn \\ Video Entertainment & 79 mn & Online streaming & $32 bn \\ Retail & 32 mn & E-commerce & $1.6 bn \\ Real Estate & 0.3 mn & Commissions & $2.6 bn \\ Education & 15 mn & K-12 and higher-ed software & $0.7 bn \\ Healthcare & 3.4 mn & Patient monitoring & $5.1 bn \\ Military & 0.7 mn & Defense training and simulation & $1.4 bn \\ Engineering & 3.2 mn & CAD/CAM software & $4.7 bn \\ \hline \hline \end{tabular}
\end{table}
Table 1: Goldman Sachs Global Investment Research [7].
# ResADM: A Transfer-Learning-Based Attack Detection Method for Cyber-Physical Systems
## 2 Background  
### Attack Detection  
Network attack detection is a crucial task in maintaining CPS security, as it aims to identify and prevent unauthorized network activities. Traditional statistical, learning-based methods have had certain limitations in this regard, including vulnerability to detection by attackers and sensitivity to normality assumptions. As machine-learning technology continues to advance, these conventional methods are gradually being replaced. In recent years, extensive research has focused on utilizing machine-learning models,such as Bayesian, support vector machine, and decision tree, for network-attack detection. However, these methods rely heavily on expert knowledge and struggle to uncover the underlying and concealed features of network attack behaviors. Furthermore, with the increasing number of devices and the expanding network data in network scenarios, this problem becomes even more pronounced, necessitating the use of more self-learning and adaptive methods to address this challenge.  
Deep-learning technology, with its ability to construct nonlinear network structures with multiple hidden layers, offers the advantage of deeply learning sample data and adapting to high-dimensional learning and prediction requirements. It also saves a significant amount of time during feature extraction. As a result, deep learning has achieved remarkable success in various fields, including image recognition, natural language processing, and speech recognition. Within the context of CPS attack detection, some studies [9] have utilized a multi-scale CNN structure to explore the spatial features of network attacks while employing LSTM to extract temporal features, resulting in improved accuracy of attack detection. Furthermore, previous research has proposed the use of BiLSTM networks to analyze bidirectional, temporal features of data streams, enabling the discovery of hidden temporal features of network attacks and significantly reducing false alarm rates [10]. However, it is important to acknowledge the challenges associated with deep-learning methods, such as gradient-vanishing and -exploding, which has led to unstable weight updates in the model and resulted in under-fitting or over-fitting situations.
## 2 Experimental Section
### In Situ Measurements  
Continuous measurements of some physical, chemical, and biological parameters were performed at two moored buoys (JS and SSC). Parameters including water temperature, turbidity (Turb), pH, DO, oxidation-reduction potential (ORP), and Chl \(a\) were measured using a continuously recording multiparameter underwater sensor (YSI 6600, Yellow Springs Instruments, Yellow Springs, OH, USA). These parameters were collected at 2-h intervals from 28 October 2008 to 10 November 2010 in SSC station and from 30 July 2010 to 10 November 2010 in JS station. Interruption of measurements occasionally occurred for the malfunction of instruments. The probes were hung from the buoy and took measurements sequentially at 0.5, 1, and 1.5 m depth. The DO probes were first calibrated in air-saturated water prior to deployment, while after that they were calibrated in air-saturated water weekly. The monitoring accuracy of water temperature, Turb, pH, DO, ORP, and Chl \(a\) are 0.15 \({}^{\circ}\)C, 0.3 NTU, 0.2, 0.1 mg\(\cdot\)L\({}^{-1}\), 20 mV, and 0.1 \(\mu\)g\(\cdot\)L\({}^{-1}\), respectively. In addition, a HOBO H21 micro meteorological station (Onset Computer Corporation, Bourne, MA, USA) was mounted on each of the buoys. The following meteorological data were collected at 5-min intervals and recorded as one hour averages: Air temperature, relative humidity, wind speed, and wind direction. The monitoring accuracy of these parameters are 0.2 \({}^{\circ}\)C, 2.5%, 0.5 m\(\cdot\)s\({}^{-1}\), and 5\({}^{\circ}\), respectively.  
Figure 1: The map of the study area, and the location of the two sampling sites—Shangshancun (SSC) and Jinshu (JS) buoy stations. The downwelling shortwave radiation was collected at the Taihu Laboratory for Lake Ecosystem Research (TLLER).
###### Abstract  
Short and medium-span bridges account for more than 90% of the bridges in China, and it is not suitable to use conventional health monitoring technology to monitor them. Based on an analysis of the proportion of live load, displacement under load, the action time of live load and other factors affecting short and medium-span bridges, this paper determines a feasible technical solution using mid-span deflection to monitor bridges with requirements including measuring range,  
China, had reached 51,106, including 46,000 short and medium-span bridges, accounting for 90% of the total [7]. Among them, hollow slab girder bridges, T-beam bridges and arch bridges accounted for the vast majority of short and medium-span bridges. Short and medium-span bridges have relatively short spans and small deflections compared with long-span bridges such as cross-sea and river-crossing bridges because the monitoring of dynamic deflection for long-span bridges has higher requirements on the hardware and software of the system [8]. At present, the prices of the existing products on the market are relatively expensive. On the other hand, the installation always takes a long time [9; 10; 11]. As a result, these health monitoring systems for short and medium-span bridges have not been widely promoted and applied [12]. Due to the great differences in material, span, environment, etc., a variety of detection principles, measurement methods and instruments for bridge deflection detection have been researched and developed at home and abroad. Bridge deflection measurement methods mainly include the dial indicator method, level gauge measurement method, theodolite measurement method, total station measurement method, inclinometer measurement method, digital imaging or photoelectric imaging measurement method, connecting pipe measurement method, radar interferometry and GPS measurement method, etc. [8; 10; 13; 14; 15]. The application range and measurement accuracy of the above displacement sensors are shown in Table 1.  
The commonly used deflection monitoring equipment for bridge health monitoring mainly includes connecting pipes, satellite positioning systems and inclinometers. Judging from the existing bridge deflection measurement equipment, there are mainly the following deficiencies in the application of short and medium-span bridge structure monitoring [3; 9; 13; 16]. First, monitoring is a long-term, real-time, and uninterrupted process, and some measurement equipment cannot work under all weather and in real time. Second, there are a large number of hollow slabs and some T-shape beam structures in short-span or medium-span bridges, and measurement equipment is not easy to install and arrange, especially for bridges across rivers and lines. Third, the general span of short and medium-span bridges and the deformation is small, and the measurement accuracy and sampling frequency of deflection are relatively small. Fourth, the cost of some sensors is relatively high, which is not suitable for short and medium-span bridge
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 3 Cognitive HSTN
### Key Performance Parameters  
The ergodic capacity of a multiple-antenna cognitive HSTN was analyzed in [143], where both the secondary terrestrial and primary satellite system had interference power constraints. The Meijer-G function-based analytical expression for the ergodic capacity of the secondary network was derived. The ergodic capacity and OP of an underlay-cognitive HSTN with secondary satellite and primary terrestrial network were also investigated in [132] by analyzing the impact of imperfect CSI with both proportional and peak interference constraints.  
The outage performance of a cognitive HSTN with an interference constraint was investigated in [129] by using the Meijer-G function, and a novel closed-form expression of OP for the considered cognitive network was derived. The asymptotic results at a high SNR were also presented for both the proportional interference constraint and the peak interference constraint, revealing the important performance advantages, the achievable diversity order, and the coding gain of the cognitive HSTN.  
In [130], the authors investigated the performance of a cognitive fixed satellite services (FSS) and mmWave terrestrial network to provide a general framework for the coexistence of an FSS and terrestrial network based on ITU recommendations and state-of-the-art channel models. Closed-form analytical OP expressions were also derived to understand the impact of various network parameters.  
The outage performance of a multi-user spectrum-sharing HSTN was investigated and opportunistic scheduling of multiple users was proposed in [144]. Both direct and relay links from a primary satellite source to multiple terrestrial users and a secondary transmitter-receiver pair on the ground were considered.  
To maximize the achievable rate of the terrestrial user by jointly optimizing the base station, UAV transmit power and UAV trajectory subject to the interference temperature threshold imposed on satellite networks and the UAV's mobility constraints, ref. [145] presented a coordinated multi-point transmission (CoMP) architecture in a cognitive HSTN associated with UAVs.  
The outage analysis of an overlay-cognitive HSTN in which an opportunistically selected secondary IoT network enables primary satellite-terrestrial communications in the presence of terrestrial and extraterrestrial combined interference was presented in [102].  
The comprehensive outage performance of an overlay-cognitive multi-user satellite-terrestrial network was presented in [134], in which a primary satellite communicates with a selected terrestrial receiver via a secondary IoT network in the presence of interference from extraterrestrial sources (ETSs) and terrestrial sources (TSs).  
Considering practical channel assumptions, including small-scale fading, the effect of interference due to spectrum sharing between the terrestrial and satellite systems in a cognitive HSTN was discussed in [109].
# Optical Code Construction of 2D Spectral/Spatial BIBD Codes for SAC-OCDMA Systems
## 5 Results and Analysis
### Numerical Analysis
The BER varied due to variations in the received power. In addition, the 2D-BIBD code provides the desired optical transmission requirements with minimum transmitted adequate power (\(P_{sr}=-27.5\) dBm). Therefore, improvement in BER was obtained when compared to other codes such as DEU (\(P_{sr}=-26.5\) dBm) and DPD (\(P_{sr}=-25.5\) dBm). It is also observed that PINN was the dominating noise in the system and occurred due to a change in the amount of received power. Further, PINN is directly proportional to the number of interfering users, and therefore the power received is inversely proportional to the quantity of interfering users. However, interference noise is reduced due to the excellent cross-correlation properties of the proposed code and resulted in the suppression of intensity noise (Figure 5). Figure 6 shows the variations between BER and broadband source (BBS) effective width for 20 active number of users, a data rate at 622 Mbps and \(P_{sr}\) at \(-10\) dBm. It is observed that the spectral width is inversely proportional to the BER for all the systems. The BER performance of the 2D-BIBD code is improved due to its low in-phase cross-correlation properties when compared to the 2D-DPD and 2D-DEU codes. Moreover, the  
Figure 5: Relationship between BER and effective source power for specific 20 users at a data rate of 622 Mbps. BER performance could be improved by enhancing the spatial code length. The 2D-BIBD code outperforms the 2D-DPD and 2D-DEU codes at a spectral width of 7 THz for error-free transmission.
## 2 System Overview
### Automotive Remote Sensors
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Mobileye 630** \\ \hline Frame size & 640 \(\times\) 480 pixels \\ Dynamic range & 55 dB linear \\ \multicolumn{2}{c}{100 dB in HDR} \\ Range accuracy (longitudinal) & \textless{}10\% (in general) \\ Width accuracy & \textless{}10\% \\ Horizontal field-of-view (FOV) & 38 deg \\ Data update & 66–100 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Automotive camera system specifications. Lidar stands for light detection and ranging, and as the name suggests, it measures the relative distance and angle to a target within its FOV by transmitting laser light and measuring reflected light. Near-infrared light with a wavelength of 905 nm is typically used for automotive lidars, and they are capable of higher localization performance compared with other automotive ranging sensors. Lidar sensors attracted much attention of the automated driving research community since they were used by many groups who participated in the DARPA Grand Challenges [39; 40; 41]. Despite the advantage of lidar sensors, most automakers are yet to incorporate them into production vehicles largely due to their drawbacks such as the high manufacturing cost and bulky form factor. Moreover, high-performance lidars generate millions of data points per second [42]. Processing lidar data points is often computationally expensive and the perception and tracking performance based on lidar measurements can vary depending on the algorithm used [43; 44; 45]. The use of lidar sensors is outside the scope of this paper, and perception approaches based on multiple remote sensors including lidars will be investigated in future work.
# Information and Future Internet Security, Trust and Privacy
## 1 Introduction  
According to the ITU-T Technology Watch Reports [1], the internet is evolving steadily and rapidly in order to meet the requirements of new applications, services, and users. The future internet will be an important part of international and national infrastructure. The evolutionary trend has enabled the internet to address challenges and problems over the past decade. Currently, the internet of things (IoT) enables billions of internet-connected devices, e.g., smart sensors, to communicate and interact with each other over the network/internet worldwide. It offers the capacity for remote monitoring and control and continues to be adopted in many domains. For example, it is the basis of smart cities, helping to achieve a better quality of life and lower consumption of resources [2]. In addition, smartphones are the most commonly used IoT devices, which can help to control washing machines, refrigerators, or cars.  
However, the IoT also faces many challenges with respect to information and internet security. For example, attackers can impersonate a relay node to compromise the information integrity during communications. When they control or infect several internal nodes in an IoT network, the security of the whole distributed environment will be greatly threatened. There are many internal threats in such distributed environments, e.g., passive message fingerprint attacks [3], a kind of collusion attack in which malicious nodes can work together to exchange the required messages and send false information to degrade the alarm aggregation process. Hence, there is a need to safeguard information and the internet environment against the plethora of modern external and internal threats.  
This Special Issue focuses on information and internet security with the aim of soliciting the latest technologies, solutions, case studies, and prototypes on this topic.