esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing the State of the Fog Computing to Enable 5G Network Technologies
## 1 Introduction
In addition, this study will contribute to providing the knowledge that helps to emerge the modern technologies to deliver efficient data dissemination services. For example, the concept of Network Function Virtualization (NFV) in heterogeneous networks using FC as an enabler to 5G is not clear in present times since there is a need for improvement in implementing NFV for mobile communication networks as explored in earlier studies [9; 10; 11]. Moreover, a large amount of energy is required for the processing of more substantial applications [12]. Minimizing energy consumption is key for commercializing FC technology in 5G systems. Another challenge is the pricing policies because different parties require different prices of resources for the use of FC services [13]. Thus, finely granulated accounting management and price policy are needed in resource management of the FC and 5G ecosystem. Finally, the issue of visibility of data to the third party by 5G networks makes privacy and security a pertinent point in FC architectures [14]. These issues and challenges can be addressed using FC in 5G networks. It is evident that both technologies, i.e., FC and 5G, are compatible with each other. However, combining both concepts is envisaged to be the future of Internet services. The present study aims to find a better solution and a more sustainable future for humanity. This paper is structured as; Section 1 gives a brief introduction about the requirements of present study. Section 2 provides a brief review of related study. Section 3 presents a review related to 5G, importance of IoT, applicability (on-going projects) and a brief SWOT analysis on the existing computing network paradigms. Section 4 explains why Fog is envisaged to be a better enabler to 5G networks by giving examples of its existing use cases. Section 5 gives an overview of FC. In Section 6, several use cases of FC are explained. Section 7 elaborates how the convergence of Fog and 5G network can be actualized. In Section 8, some of the future research directions are described. At the end, in Section 9 the study is concluded.
## 4 Our Proposed 5 GAKA-LCCO Protocol
To verify \(T_{SN}\), time synchronization between the SN and the HN needs to be maintained. * Remove the synchronization failure procedure. Earlier, SQNs were used in the 5G AKA protocol because strong random number generation was not possible in the USIM, but in the current generation, this is not an issue anymore [21]. Additionally, the SQN concealment mechanism is not sufficiently protected, leading to leakage of SQNs and thus allowing activity monitoring attacks [21]. Hence, we remove \(SQN\) from \(AUTN\) and use \(RAND\) alone. * Replace the MAC failure procedure with a timeout mechanism on the HN. If \(XMAC\) and \(MAC\) are different, then the UE directly discards the second received message without responding to a \"MAC failure\" indication, so the HN will initiate a new authentication procedure towards the UE when the HN does not receive an authentication response message or a synchronization failure message within a certain period of time. * Reduce the communication and computation overhead of the authentication process. Firstly, the first sent message of the SEAF (including \(RAND_{SN}\) and \(T_{SN}\)) is added, and the authentication of the UE is advanced to the verification of \(SUCI\). Secondly, \(K_{SEAF}\) and \(SUPI\) are moved to the second sent message of the AUSF. Finally, after receiving the \(AUTN\), the UE will no longer respond to the SEAF. This reduces the number of messages in the authentication process, as well as the communication and computation overhead. Note that the timestamp mechanism for the SN and the HN is added to our proposed 5 GAKA-LCCO protocol for the following reasons:  
* The timestamp mechanism for the SN and the HN can overcome DoS attacks against the HN. This is because the first received message of the HN in the proposed 5 GAKA-LCCO protocol cannot be replayed because \(T_{SN}\) is included in this message. * According to [2; 3; 4], the SEAF initiates an authentication with the UE during any procedure for establishing a signaling connection with the UE, according to SEAF's policy. If the random number mechanism is used to overcome DoS attacks against the HN, then the first received message of the HN must be sent from the SEAF, and the HN responds to the SEAF with a random number. However, the first received message of the HN can be replayed, which means that DoS attacks against the HN still cannot be overcome.
# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
## 2 Methods
### Initial Matching Point Detection
#### 2.1.2 Estimation of Variation in Translation  
OPC is a nonlinear frequency-domain correlation technique that is based on the Fourier shift property, which is the shift in the spatial coordinate frame of two functions that results in a linear phase difference in the frequency domain of the Fourier transforms (FTs) of the two functions, as shown in the following equations:  
\[g_{2}(x,y)=g_{1}(x-x_{0},y-y_{0}) \tag{3}\]  
\[F_{2}(u,v)=F_{1}(u,v)e^{-j(ux_{0}+vy_{0})} \tag{4}\]  
where \(g_{1}\) and \(g_{2}\) are the two image functions of the spatial coordinate frames, \(F_{1}\) and \(F_{2}\) are the corresponding image functions of the frequency domain of the FTs, and \(x_{0}\) and \(y_{0}\) are the parameters of the shift [25].  
Between the resampled ZY-1-02C image and the DOM of ETM+, the OPC results produce only the overall translation parameters, i.e., \((x_{0},y_{0})\). A high level of accuracy is not required. Considering the complex matching conditions, the 20-pixel accuracy of OPC can meet the image-matching requirements for NCC. Given the large size of the scene images, (the size of a ZY-1-02C image scene is approximately 12,000 \(\times\) 12,000 pixels), OPC is conducted in several image blocks of one image scene to improve its efficiency. Each block has a group of translation parameters for image matching.
## Cases Studies  
### Introductions of a Practical Complex Distribution Network and Scenarios  
To fully demonstrate the effectiveness and advantages of the proposed strategy, lots of tests verified by simulations via ETAP power system simulation software have been done via several examples with different operation status. Due to limited space, a practical complex distribution network in China, which is shown as Figure 2, is used to give detailed descriptions.  
The practical complex distribution network contains 94 nodes, 93 branches, 4 tie lines (10-87, 25-37, 54-55, and 76-85), and 76-85), and 5 DGs. In the case of normal power supply, total capacity of all the loads is 13,379.29 kW + 4519.12 kvar. Two reactive power compensations are installed on node 11 and node 59, respectively. A fluctuant harmonic source and a negative sequence current source, which fit normal distribution, are set at node 51 and node 61, respectively. Limited by space, more detailed instructions of the distribution network are listed in Tables A1 and A2 of Appendix A.  
For the distribution network, the following benchmark scenario is selected as the typical situation to give detailed descriptions:  
Benchmark scenario: disconnected with network and DG1, DG2, and DG3 are out of the running.  
It is worth mentioning that the methods proposed in this paper can be applied to the distribution network benchmarks with different scales and topologies as well as to different scenarios of the same benchmarks under electrical faults at different locations.
## 2 Materials and Methods  
### Data  
#### 2.1.1 Merka-2  
MERRA-2 is a global atmospheric reanalysis produced by the NASA Global Modeling and Assimilation Office (GMAO) in 2017 [75]. It provides data beginning in 1980, with continuous updates, having high spatial (0.5\({}^{\circ}\) latitude \(\times\) 0.625\({}^{\circ}\) longitude and 72 hybrid sigma-pressure layers from the surface to 0.01 hPa) and temporal (hourly) resolution, that is freely available at [76]. The goals of MERRA-2 are to provide a regularly gridded, homogeneous record of the global atmosphere, climate system and improved land surface representation and cryospheric processes. MERRA-2 is the first long-term satellite-era global reanalysis to assimilate space-based observations of aerosols, representing their interactions with other physical parameters in the climate system. It was introduced to replace the original MERRA product and reflects recent advances in atmospheric modeling and data assimilation, namely assimilation of modern hyperspectral radiance and microwave observations, along with GPS-Radio Occultation datasets, use of NASA's ozone profile observations that began in late 2004, as well as additional advances in both the GEOS model and the GSI assimilation system [77]. More specifically, the MERRA-2 SSR fluxes are utilized in the present study, computed by the shortwave radiation parameterization described by [78]. This parameterization scheme resolves the absorption by water vapor, oxygen, ozone, carbon dioxide, and aerosols, while the SSR fluxes are integrated virtually over the entire solar spectrum, from 0.175 \(\upmu\)m to 10 \(\upmu\)m. In the ultraviolet and visible regions, the spectrum is divided into 8 bands, and single ozone absorption coefficient and Rayleigh scattering coefficient are used for each band. In the infrared, the spectrum is divided into 3 bands, and the k-distribution method is applied for water vapor absorption. For each one of the 72 atmospheric layers, transmissivity and reflectivity are first calculated using the \(\delta\)-Eddington approximation [79], while spectral fluxes are then calculated using the two-stream adding method for the composite layers. The total SSR flux at each pressure level and at the Earth's surface is the weighted (by the fraction of extraterrestrial solar flux contained in the above-mentioned bands) sum of these fluxes. For the aim of this study monthly MERRA-2 SSR fluxes, called \"SWGDN\", are utilized.
## 3 Methodology
### In-Line Dimensional Inspection
#### 3.2.1 3D Scanning System  
Figure 4 presents the setup for the 3D scan of the warm workpiece.  
Laser triangulation is used to reconstruct the surface. Two independent laser line projectors impact the workpiece inner (punch zone) and outer (forming matrix zone) sides, respectively. Since the surface emits light in the red spectrum due to the high temperatures of the workpiece, the two lasers are chosen to work in the blue spectrum.  
The first laser is placed above the workpiece, with an elevation near 45 degrees with respect to the plane that supports the workpiece. This laser allows to scan the inner (punch zone) surface of the axle. This laser is observed by 2 cameras since the geometry of such surface is self-occluding. The cameras must be as close to the workpiece as the heat emitted by it permits (approx. 500 mm), in accordance to the operating temperature prescribed for them.  
The second laser is positioned underneath the plane supporting the workpiece (elevation near \(-45\) degrees). The external surface (forming matrix zone) of the workpiece is scanned from below, through slots of the supporting table. The laser projection on such surface is captured by a single camera since no occlusions occur.  
Since the laser projections do not lie on a plane parallel to their respective camera plane, Scheimpflug adapters [29] are incorporated in all the cameras to fix their plane of focus. In addition, each camera has an interference filter which allows it to only see light near the laser wavelength (450 nm \(\pm\) 25 nm, see Table 2).  
A 3-grip system holds the workpiece from the shaft. The grip system is made of steel with ceramic coating to stand the high temperatures. The 3-grip system is mounted on a rotating disk such that the workpiece is rotated 360 degrees around its revolution axis during the scanning. The cameras capture a static image of the workpiece at each pulse of the encoder. Thus, the reconstruction is performed with 360 images per camera.  
Figure 3: Cylindrical \(\mathcal{C}_{bore}\) (blue) and conical \(\mathcal{C}_{cone}\) (red) features on the CAD reference used to compute the revolution axis \(A\) (green). (**a**) CAD features \(\mathcal{C}_{bore}\), \(\mathcal{C}_{cone}\) and \(A\); (**b**) Reference axis point \(a_{0}\) defined where \(\mathcal{C}_{cone}\) achieves an specific diameter \(d\).  
The 3D mesh reconstruction from the acquired images is executed with HALCON [30]. The acquisition and reconstruction of the warm workpiece takes about 5 s (average). Finally, a pyrometer is used to track the average temperature of the workpiece during the 3D reconstruction.  
The full setup has been installed on a foam cushion layer to isolate the sensors form the vibrations induced by the forging presses.
## 3 Materials and Methods
### Biological Data Collection
#### 3.1.2 Urease Activity at a Cellular Level  
The MICP method is based on the process of microbially released urease catalysing the hydrolysis of urea, and the activity of the urease is critical in determining the rate of reaction, i.e., the stabilising effects of the MICP method; therefore, an understanding of the urease activity is required at the laboratory stage, as introduced afterwards. The cultures of _S. pasteurii_ were grown to the early stationary phase. The cultures were centrifuged at 18,516\(\times\)\(g\) for 15 min at 4 \({}^{\circ}\)C and the supernatant was discarded. Cell pellets were washed 3 times in phosphate-buffered saline and final cell pellets were resuspended in 3 g L of NB and 187 mM of NH\({}_{4}\)Cl to give an OD\({}_{600}\) of \(\sim\)1.0. Cells were then diluted 1:10 into solutions containing 3 g L\({}^{-1}\) of NB, 187 mM of NH\({}_{4}\)Cl and different concentrations of urea (0-1 M). Solutions were incubated at 30 \({}^{\circ}\)C and 150 RPM. Conductivity measurements were taken at 0 h, 1 h, 2 h and \(\sim\)18 h. It was assumed that the reaction rate was linear during the first hour based on the results of [35]. Two biological and two technical replicates were carried out for each solution.
## 3 Calibration Methodologies & Dataset Used
### Crossover Analysis  
Multi-mission crossover analysis provides relative but global calibration results and supports the discovery of geographically distributed error patterns [31]. This analysis was initially applied in [32], while a detailed description of this methodology is given in [33].  
Additionally, a crossover analysis has been carried out with nearby cross-over points in the vicinity of the PFAC Cal/Val sites. The Sentinel-3A Pass No.14, for example, intersects with Jason-3 Pass No.109 about 20 km south of Gavdos (Figure 13). The analysis involves the examination of the sea-surface heights, as estimated by Sentinel-3A and Jason-3, when they overpass that crossover location within a window of \(\pm 3\) days [34].  
Dates of the crossovers as of April 2016 for both missions are briefly shown in Figure 14. An analysis of the sea-surface heights as estimated by the Sentinel-3A and Jason-3 altimeters at this crossover location indicates that Sentinel-3A measures heights higher th  
Figure 12: Timeline of Sentinel-3A Pass No.335 sea-surface calibrations at the Gavdos Cal/Val site in west Crete, Greece. The green boxes illustrate days of satellite overpass and availability of sea-surface heights by the in-situ scientific instruments.  
by about +4 cm with a standard deviation for the estimated mean as \(\pm\)8 cm. Results for this crossover analysis have also been reported in [35].
# Predictive Maintenance on the Machining Process and Machine Tool
## 5 Case Study
### Acquisition  
During the turning process, from December 2017 until May 2019, several measurement were recorded, which provided information about how the machine was working. These Condition Monitoring (CM) signals were obtained with a 5 Hz frequency. Although several signals were acquired, the final state of the tool (piece quality) was not measured. The acquired signals, such as spindle load, piece number, and tool position, were recorded as a collection of observations made sequentially through time, which are known as time series. In this case, experts in the process suggest the spindle load signal to be studied, while other signals are useful for segmentation and characterization. The spindle load represents machine effort and is the more critical signal over all the sensorized ones. As a result of this acquisition step, around 1100 series and 550,000 pieces were stored for further analysis. The technology applied for monitoring data is shown in Figure 2. The monitoring system (data recorder) was connected to the machine tool, which recorded a database accessible via the Internet and had different interfaces. The machine tool data recorder worked on the same principle as the black box on a flight deck. It communicated with the control unit of the machine and some independent sensors, in order to retrieve targeted data. The data recorder was able to communicate with Computer Numerical Control (CNC). Its design only permitted the recording device to communicate with the interface of the control unit, in order to access the control memory in read-only mode. At no point would it therefore disturb the execution of the program. All monitored data were configured by default to be stored in a secured remotely accessible database. The connection between the database and the machine tool data recorder was done through an Ethernet connection.
## 1 Introduction
The Deep Adversarial Subdomain Adaptation Network (DASAN) method was proposed [14], and the distribution of relevant subdomains is aligned by minimizing the LMMD of the same category between domains, as shown in Figure 2. Reconstruction-based DA methods typically use shared encoders to capture domain-invariant representations while minimizing reconstruction losses in source and target domains to maintain domain-specific information [15]. [PERSON] proposed a Sparse Stacked Denoising Autoencoder (SSDAE), which extracts features from target domain samples by directly inheriting the mapping learned on source domain samples, using only a small number of target domain data pairs [16]. The mapping is fine-tuned to achieve good diagnostic results. However, in the domain adaptation problem discussed above, the primary research focus lies in optimizing the alignment of subdomain distributions among relevant categories. Surprisingly, the quantitative relationship between the sample distributions of the source and target domains in the domain discriminator is often overlooked. In fact, this relationship plays an extremely critical role in domain adaptation. Figure 1: Blue and yellow geometric shapes represent samples in the source domain (**a**) and target domain (**b**), with different shapes representing sample categories in different domains and different dashed colors representing different decision boundaries. Addressing the aforementioned challenges, this article proposes a Residual Adversarial Subdomain Adaptation Network based on the Wasserstein metric (RASAN-W). More precisely, this article introduces the Wasserstein metric into subdomain adaptation, facilitating a more accurate quantification of distribution dissimilarity between domains. Moreover, it captures fine-grained domain information and effectively aligns subdomain distributions within the same category. It achieves global domain distribution alignment and empowers this approach to dynamically adjust to diverse data distributions and task demands. The main contributions of our work are as follows:  
1. A deep transfer neural network method based on residual subdomain adversarial adaptation of the Wasserstein metric is proposed and used for bearing fault detection. 2. The domain-invariant distribution features between relevant subdomains are aligned through LMMD, and the Wasserstein metric is introduced as the optimization target of RASAN-W, which effectively quantifies the discrepancy between the sample distributions of the source domain and the target domain. 3. A large number of experiments and ablation studies were conducted on multiple bearing fault datasets, and comparative analysis with several mainstream methods verified the effectiveness and superior generalization ability of this method.
## 3 Path-Following Control Strategy with Deep Deterministic Policy Gradient
### Deep Deterministic Policy Gradient for Path-Following  
The path-following controller based on the DDPG algorithm makes use of a pair of neural networks, one for learning the policy (actor) and the other for learning the value function (critic) and introduces target networks to reduce bias in estimation through delayed updates. Additionally, it employs an experience replay buffer to store and replay samples, which reduces sample correlation, increases sample efficiency and enhances the learning capability of the algorithm. As used in the original paper [15], an Ornstein-Uhlenbeck (OU) process [33], which generates temporally correlated noise, is introduced to explore the action space. An overall architecture is depicted in Figure 4.  
Figure 4: DDPG-based controller for path following. The optimization process of the two network pairs is illustrated, with the parameters used to optimize the critic and actor networks distinguished by the colors blue and red, respectively. The exploration noise is only added in the training phase. The apostrophe implies that the variable pertains to onward steps.