esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
At the end of both pipelines, the extracted features in the shape of fully connected layers are concatenated and then become the input for the generator, where it is used to reconstruct the original input image. The generator has partial-skip connections in a UNet-fashion [17] from both the CNN and the DCN pipeline, which allows the information from the shallow layers to propagate more efficiently to deeper layers [18] to alleviate the vanishing gradient problem, and the mode collapse [19]. Afterward, both the input image and the reconstructed output image are sent to a discriminator to be distinguished as real or fake. The discriminator's latent vector also learns the reconstructed normal input's latent space in the proposed scheme. The motivation to combine two sub-networks comes from the dilated convolution's capability to extract global features without increasing the computational cost [20; 21], and the combination of both the local and the global features improves the performance of the model as it was previously applied in the field of machine learning by concatenating the extracted local and global featureswhich are then subsequently trained with traditional machine learning classifiers such as SVM [22; 23]. In other words, the proposed scheme applies the same philosophy into a deep learning model by having both sub-networks extract these features automatically and then concatenate them as a result of the training. Moreover, as a part of the ablation study of the parallel encoder, supervised training is performed with a Softmax layer with three nodes added to the end of the concatenated feature vector to perform supervised classification. To sum up, in this paper, an unsupervised anomaly detection model for age classes (child, adult, elderly) using surveillance image data is introduced. There are no unsupervised anomaly detection models for age detection with only full-body images to the authors' best knowledge. The majority of the models are supervised and are based on facial features [24]. The deep learning model proposed in this paper is the extended and improved version of the previous work introduced in [25]. The multi-class normality scheme is applied where a single class in the dataset is designated to be the abnormal class, and the remaining classes are designated to be the normal class. The proposed model performs better than the authors' previous work and all the benchmark models qualitatively and quantitatively.
# Spatiotemporal Image Fusion in Remote Sensing
## 3 Synthesis: Challenges and Opportunities
### Other Advanced Methods for Spatiotemporal Image Fusion  
Deep learning has gained the attention of the remote sensing community in the last years for various image understanding and image classification problems ([PERSON] et al., 2019). It has for example been successfully used for pan-sharpening ([PERSON] et al., 2019; [PERSON] et al., 2020; [PERSON] et al., 2021), feature and decision-level fusion ([PERSON] et al., 2019; [PERSON] et al., 2021) and spatial-spectral image fusion ([PERSON] et al., 2019; [PERSON] et al., 2021; [PERSON] et al., 2021). An overview of deep learning for data fusion is provided by [PERSON] et al. (2019) and [PERSON] et al. (2019). Despite its proven efficiency in different image fusion scenarios, this method has rarely been used for spatiotemporal image fusion. [PERSON] et al. (2019) proposed a CNN model for fusing Landsat and MODIS data by considering both spatial heterogeneity of the landscape and temporal changes occurring during the observation period. Our remote sensing community could further benefit from advances in deep learning occurring in computer vision where efficient convolutional networks for learning spatiotemporal features from video dataset have been successfully developed ([PERSON] et al., 2021).  
Bayesian methods have been successfully applied for fusing images in the spatial and spectral domain ([PERSON] et al., 2021). Despite their ability to handle uncertainties in input images, a rather reduced number of Bayesian methods for spatiotemporal fusion of satellite images have been developed ([PERSON] et al., 2021; [PERSON] et al., 2021).  
Another example of spatiotemporal image fusion methods includes those based on physical models. [PERSON] et al. (2019), for example, proposed a semi-physical fusion method that uses MODIS Bi-directional Reflectance Distribution Function (BRDF)/Albedo to predict BRDF at the spatial resolution of the ETM+ images.
# Deep Learning for Detecting Verticillium Fungus in Olive Trees: Using YOLO in UAV Imagery
## 5 Results  
### Data Collection and Dataset Processing  
The three fields that were the subject of our research were photographed at different times between the 5 th of October and the 4 th of November of 2022, shown in detail below, in Figure 5.  
In total, 429 images were captured out of which 137 were in Field A, 152 were in Field E, and 140 where in Field K. The images had a high level of overlap and were captured in sequence. These images created three orthomosaics, one for each field, and then cut into tiles. The tiles created were 160 with almost every field having the same number of them (Field A: 50, Field E: 55, Field K: 55).  
The total number of annotation instances (trees or parts of trees) that were created originally was 3038. However, the three fields varied greatly in terms of percentage of damage inflicted by the verticillium wilt (Table 3).  
To tackle this problem, stratified shuffling, available through the Scikit-Learn Python library [35] was employed during the train-test-validation split and its use resulted in datasets of almost equal percentage of damaged trees (Table 4). The stratified shuffling method creates datasets by sampling without replacement from the original dataset while also taking into account the class of the samples so that every resulting dataset contains the same distribution of classes. The created train, validation and test datasets contained 60%, 20% and 20% of the total images (train: 96 images, validation: 32 images, test: 32 images), respectively.
# Performance of Tilting Pad Journal Bearings with the Same Sommerfeld Number
## 3 Simulation Results and Discussions
### Bearing Static and Dynamic Characteristics  
This section presents the static and dynamic characteristic of TPJBs for Scale No. 1 (\(\phi\) = 35 mm) through No.7 (\(\phi\) = 245 mm) with three sets of Sommerfeld numbers: S = 0.065 (500 rpm), 0.129 (1000 rpm) and 0.466 (3600 rpm).  
Figure 5 presents (a) eccentricity ratio, (b) attitude angle, (c) tilt angle of Pad No.5. and (d) non-dimensional pivot deformation (\(p_{pet}\)/\(Cb\)) for both the rigid pad TPJB and the flexible pad TPJB. Tilting angles for the flexible pad model is not provided for the difficulties measuring the tilt angle of a flexible FE pad model. It is important to emphasize that at low Sommerfeld number S = 0.065, the difference of static characteristic between small (Scale No. 1, \(\phi\) = 35 mm) to large (Scale No. 7, \(\phi\) = 245 mm) TPJBs is minute. However, for high Sommerfeld number S = 0.466, the difference of static performance of TPJB is noticeable, in particular, from Scale No. 1 (\(\phi\) = 35 mm) to Scale No. 4 (\(\phi\) = 140 mm). The reason behind this change can be explained by Figure 5,d, i.e., the pivot deforms more at higher Sommerfeld number. The nondimensional pivot deformation is defined as a change in pivot deformation in radial direction divide by the bearing clearance (\(C_{b}\)). Also, there are other thermal characteristics that affect the bearing performance which will be discussed in the later section. Both the rigid pad TPJB and the flexible pad TPJB show similar static characteristic trends. The current numerical cases present LBP (load-between-pad) condition; the name indicates that two bearing pads support the vertical bearing load. The LBP bearing configuration generally has less pivot deformation than the LOP (load-on-pad) configuration.  
Figure 5: Bearing static characteristics.  
Figure 6 presents the dynamic performance of TPJB in terms of nondimensional stiffness and damping coefficients that are explained in Equations (16) and (17), respectively. Similar to static characteristics of TPJB, the dynamic performance of TPJB shows large variation from Scale No. 1 (\(\phi\) = 35 mm) through Scale No. 7 (\(\phi\) = 245 mm) for high Sommerfeld number S = 0.466. This is due to pivot deformation, change in viscosity, and thermal deformation (see later in Figures 7-9). Interestingly enough, the numerical analysis for TPJB with a flexible pad tends to show a lower damping coefficient than the TPJB with a rigid pad assumption. This analysis again confirms the past findings [10; 11] that assuming the pad is rigid tends to over-predict the damping coefficients compared to test data. It is obvious that, in reality, the pad has a certain degree of flexibility. The pad flexibility affects less on TPJB stiffness coefficients.  
Figure 6: Nondimensionalized dynamic characteristics.  
Figure 7: Cont.  
Figure 8: _Cont_.  
Figure 7: Thermal characteristics.
# Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis
## 6 Conclusions and Future Work  
In this paper, we first proposed an SK-Type FBL coding scheme for the GRC with noisy feedback by introducing the lattice-based strategy to reduce the impact of the feedback channel noise. Numerical results show that when the power of the feedback channel is sufficiently large, the achievable rate almost approaches the rate of the GRC with noiseless feedback, and the required blocklength to achieve a desired decoding error probability is significantly short. Then, based on this scheme, we extended it to the MISO FRC with noisy feedback. The essence of this proposed scheme is to further extend the original real number scheme to the complex number. Simulation results show that even if there is noise in the feedback channel, this proposed scheme can still significantly improve the achievable rate of the MISO FRC without feedback. Finally, for the above MISO FRC with the noisy feedback model, we further consider multi-antenna Eavesdropper and derive a lower bound of the secrecy level in different cases by calculating the equivocation rate when Eavesdropper knows the complex dither signal sequence or not. We show that though perfect secrecy cannot be achieved in the noisy feedback model, it almost approaches perfect secrecy for some special cases. One possible future work is to extend this scheme to the fading MIMO case.  
**Author Contributions:** Formal analysis, H.Y.; writing--original draft, R.H.; writing--review and editing, H.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author due to (specify the reason for the restriction).  
**Acknowledgments:** Authors would like to thank anonymous reviewers for careful reading of the manuscript and providing constructive comments and suggestions, which have helped them to improve the quality of the paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Case Studies and Co-Simulation Validation
### Response of the Wind Turbine to a Fault in an Islanded Operation  
The last experiment is the evaluation of the performance of the grid-forming control in the co-simulation approach in an islanded operation. This is the most severe condition to test the controller and, hence, the operation of its numerical model in a co-simulation setting. First, both the external grid and the synchronous generator are put out of service. In this situation, the wind turbine generator with the grid-forming controller is the only generating unit in the grid to supply power to the load and modulate the grid voltage. Next, the reference fault is applied at t = 1 s on the grid terminal. The dynamic response of the wind turbine is shown in Figure 22. The low-frequency oscillations are larger here, compared to the weak-grid condition, and the effect of the auxiliary damping signal by the PSS to eliminate these oscillations is shown in Figure 22.
## 4 Media Access Control
### Cooperative Sensing
#### 4.2.1 Cooperation Architectures
The non-CH nodes accordingly choose their preferred CHs and the resulting cluster arrangement first ensures network uptime before the first power depletion of the CHs is maximized. However, the study has multiple limitations, such as depending on prior knowledge, including the locations and quantity of nodes, as well as adjustable transmission powers. The most important challenge is that all CHs are fixed in advance and it is only the allocation of cluster members that the algorithm is able to update. The weight-based distributed clustering algorithm proposed in [68] takes into account multiple parameters to improve the battery life of nodes. For example, the sum of the distances with all neighbors is considered since communication with distant entities consumes more power. The update of clustering is only triggered when changes of the distances are detected. The mobility of nodes is also considered and less mobile nodes are more likely to be made CHs. Furthermore, to address the problem of unbalanced workload, the cumulative time that nodes have acted as CHs is also computed as one of the metrics to gauge the extra power consumed for being CHs. The above algorithm infers the power consumption via the measurement of time. However, it does not consider the fact that nodes may have different energy levels at the commencement of the clustering algorithm. The algorithm proposed in [65] determines CHs based on not only the uptime of CHs but also their transmission activities. By calculating the remaining power level of the node by taking into account the number of sent and received packets, a metrics of remaining power and capability of acting as CHs is generated. The algorithm then selects the nodes with the highest metrics value to be the CHs. A similar scheme is presented in [62] as the energy efficient cluster header selection (ECS) algorithm. Although it also elects the CH based on power level, the algorithm is designed to use only local information of each node to curb controlling overhead. Consequently, power information of neighbors are not explicitly exchanged but inferred based on the presumption that the initial power levels of all nodes are known in advance. An adaptive topology formation algorithm based on spectral environment is devised in [72]. A given node starts listening in to all usable channels one by one and: (a) becomes a CH if it hears nothing, or (b) joins an existing cluster as a regular member if it receives a beacon from the CH of that cluster, or (c) goes on to listen in the next available channel if it is more than one hop to the existing CH.
## 1 Introduction
### Contribution  
SOA principles have been adopted in the past for the rapid and flexible development and management of value-added telecom services. However, a literature review on SOA models for Next Generation Networks [9] shows that, while many works focused on the integration of distributed service components at the service stratum, the benefits of applying the SOA paradigm to the transport functionalities have been scarcely investigated. Furthermore, recent works on network and service virtualization [10] show that the opportunity to adopt the SOA approach in telecom cloud environments remains quite unexplored. We argue that the adoption of SOA principles in the NFV and SDN technological landscape can ease the provision of network services that can be dynamically discovered, negotiated, and composed also from different providers to meet specific user and service requirements [7; 11].  
In this work, we propose a service-oriented approach for the orchestration of network services deployed as a cloud of Virtual Network Functions (VNF) on top of a NFV infrastructure that also takes full advantage of granular traffic steering capabilities provided by SDN. To this purpose, we refer to the SOA technology-agnostic architectural guidelines for organizing, (re)using, and integrating distributed networking capabilities provided by different systems [8]. First, we provide a survey of reference service scenarios for virtualized networks and, then, we elaborate how SOA, NFV, and SDN provide complementary features toward adaptive composite network service delivery, and organize these features in a set of tasks required for realizing dynamic network service chaining: service composition specification, service selection, service delivery, and placement. Then, we describe the architectural design of a SOA-inspired NFV orchestrator with SDN network control capabilities, also providing references to related standardization initiatives and illustrating its main workflows. We also present preliminary results of our ongoing activities for the implementation and testing of a prototype to validate the proposed approach. Finally, we conclude the paper with a discussion of benefits for NSPs along with a description of research challenges posed by the proposed approach.
# User-BS Selection Strategy Optimization with RSSI-Based Reliability in 5G Wireless Networks
## 6 Performance Evaluation
### Comparative Simulations between RSSI-Based and Distance-Based Selection Strategy

Table 5: Simulation setup for BSs with given user distribution. Simulation results show that the reliability of the selection strategy based on RSSI was 3.6% higher on average than that of the selection strategy based on distance in the UMi, SMa, and UMa scenarios. This demonstrates that RSSI could reflect more information with the user-BS selection strategy under both user-known and user-unknown cases, which means that our algorithm is more effective than traditional distance-based algorithms in common scenarios due to the better fulfilment of the high requirements for reliability in 5G wireless networks.
# Mobility-Enabled Edge Server Selection for Multi-User Composite Services
## 3 System Model
### Prerequisite Definitions
\(Sp\) _denotes the initial location of the user in the mobile path;_
2. \(T=\{t_{i}\}_{i=0}^{i=n}\) _is a set of discrete time points, with_ \(t_{0}\) _as the start time and_ \(t_{n}\) _as the stop time;_
3. \(L\) _is a set of discrete location points of the user;_
4. \(F\) _is a mapping function between time and location:_ \(\forall t_{i}\in T,F(t_{i})\to L\)_._  
In Definition 4, when the user is in initial position \(Sp\), the time is set as \(t_{0}\); when the user's invoking services are finished, the time is set as \(t_{n}\). In addition, function \(F\) indicates the speed while the user moves. If the user moves with a high speed in a certain region, the time consumption is small; if the user moves with a low speed in a certain region, the time consumption is big.
## 4 Catalyst Ink: Effect of Its Formulation on Its Microstructure and Macroscopic Properties
### Solvent
\begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Solvent**} & \multicolumn{1}{c}{**Structure**} & \multicolumn{1}{c}{\(\epsilon\) **@ 20 \({}^{\circ}\)C [144]**} & \multicolumn{1}{c}{**Viscosity**} & \multicolumn{1}{c}{**Boiling Point**} & \multicolumn{1}{c}{**Surface Tension**} & \multicolumn{1}{c}{\(\delta\) **and \(\delta_{total}\)***} & \multicolumn{1}{c}{\(\delta_{D}\) **= [145]**} & \multicolumn{1}{c}{\(\delta_{D}\) **= [145]**} & \multicolumn{1}{c}{\(\delta_{H}\) **= [145]**} \\  & & **@ 25 \({}^{\circ}\)C [144]** & \multicolumn{1}{c}{**(\({}^{\circ}\)C)**} & \multicolumn{1}{c}{**25 \({}^{\circ}\)C [144]**} & \multicolumn{1}{c}{**25 \({}^{\circ}\)C [145]**} & \multicolumn{1}{c}{**25 \({}^{\circ}\)C [145]**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} \\ \hline n-Butyl acetate & CH\({}_{3}\)-CO-O(CH\({}_{2}\))\({}_{3}\)CH\({}_{3}\) & 5,07 & 0.685 & 126 & 24.88 & 17.4 & 15.8 & 3.7 & 6.3 \\ \hline Isomany alcohol & (H\({}_{3}\))\({}_{2}\)CHCH\({}_{2}\)CH\({}_{3}\)OH & 15.63 & 3.69 & 131 & 23.73 & 21.3 & 15.8 & 5.2 & 13.3 \\ \hline P-Systeme & C\({}_{4}\)H\({}_{4}\)(CH\({}_{3}\))\({}_{2}\) & 2.2735 & 0.603 & 138 & 27.80 & 17.9 & 17.6 & 1.0 & 3.1 \\ \hline N-Adimethylformamide & (CH\({}_{3}\))\({}_{2}\)NCHO & 38.25 & 0.794 & 153 & 38.74 & 24.9 & 17.4 & 13.7 & 11.3 \\ \hline CyclohexID & HOCH(CH\({}_{2}\))\({}_{3}\) & 16.4 & 57.5 & 162 & 33.25 & 22.4 & 17.4 & 4.1 & 13.5 \\ \hline Diacetone alcohol (DA) & CH\({}_{3}\)CO(CH\({}_{2}\)COH)(CH\({}_{3}\))\({}_{2}\) & 18.2 & 2.8 & 168 & 31 [14] & 20.8 & 15.8 & 8.2 & 10.8 \\ \hline Aniline & CH\({}_{3}\)-NH\({}_{2}\) & 7.06 & 3.85 & 184 & 42.12 & 22.6 & 19.4 & 5.1 & 10.2 \\ \hline
1,2-Proylene glycol (PG) & HOCH\({}_{3}\)-CHOH-CH\({}_{3}\) & 27.5 (0 \({}^{\circ}\)C) & 40.4 & 187 & 35.99 & 30.2 & 16.8 & 9.4 & 23.3 \\ \hline Dimethyl sulfoxide & (CH\({}_{3}\))\({}_{2}\)SO & 47.24 & 1.987 & 189 & 42.92 & 26.7 & 18.4 & 16.4 & 10.2 \\ \hline Octanol & CH\({}_{3}\)(CH\({}_{2}\))/OH & 10.3 & 7.29 & 195 & 26.96 & 20.6 & 16.0 & 5.0 & 11.9 \\ \hline Enzyme glycol (EG) & HOCH\({}_{2}\)CH\({}_{2}\)OH & 41.4 & 16.06 & 197 & 48.02 & 32.9 & 17.0 & 11.0 & 26.0 \\ \hline N-Methyl-2-pyrrolidone & C\({}_{3}\)H\({}_{3}\)NO & 32.55 & 1.65 [148] & 203 & 40.21 & 23.0 & 18.0 & 12.3 & 7.2 \\ \hline Benzyl alcohol & C\({}_{4}\)H\({}_{2}\)CH\({}_{3}\)OH & 11.92 (0 \({}^{\circ}\)C) & 5.47 & 205 & 36.8 [149] & 23.8 & 18.4 & 6.3 & 13.7 \\ \hline Formamide & HOCN\({}_{2}\) & 111 & 3.34 & 210 & 57.03 & 16.6 & 17.2 & 26.2 & 19.0 \\ \hline
1,3-propanediol (PD) & HOCH\({}_{3}\)OH & 35.1 & 42.3 [125] & 214 & 53.125
## 1 Introduction
Based on previous research, the combination of deep learning and pitting damage determination will greatly stimulate pitting damage research and is of high practical value. This paper proposes a pitting damage determination method based on the convolutional neural network for steel column components, which uses convolutional neural networks to determine the degree of pitting damage to steel columns by extracting the vibration characteristics of the members. First, a multi-parameter localized random pitting model is proposed to describe random pitting in combination with time-varying features and spatial distribution. Then, the mechanical properties of the pitting steel column members are analyzed, and the relationship between the ultimate strength of the specimens and the corrosion rate is investigated. Finally, a convolutional neural network framework based on component vibration modes is developed. The critical corrosion rate is used as the criterion for component damage determination training; in addition, the effectiveness and accuracy of the network are analyzed.
## 3 Multi-Sensors Spatio-Temporal Multi-Correlation Fusion Analysis
### Time Deviation Correlation  
Multi-sensor data fusion requires timely synchronization. According to the above spatial coordinate relation, the influence of spatial position between the two sensors is eliminated and the data synchronization is realized. Spatial position correlation is used to estimate the time deviation between two sensors [35]. Taking sensor 1 as reference, the time deviation between sensor 1 and sensor 2, and the time deviation between sensor 1 and sensor 3 are,= respectively expressed as follows:  
\[\tau_{1}=\frac{1}{3}\Big{(}\frac{X_{2}-X_{1}}{X_{1}}+\frac{Y_{2}-Y_{1}}{Y_{1}}+ \frac{Z_{2}-Z_{1}}{Z_{1}}\Big{)},\ \tau_{2}=\frac{1}{3}\Big{(}\frac{X_{3}-X_{1}}{X_{1}}+\frac{Y_{3}-Y_{1}}{Y_{1}}+ \frac{Z_{3}-Z_{1}}{Z_{1}}\Big{)} \tag{9}\]  
In the above equations, \(\tau_{1}\) and \(\tau_{2}\) were, respectively, the time deviation between sensor 1 and sensor 2, and the time deviation between sensor 1 and sensor 3. Substitute Equation (8) into Equation (9) to obtain:  
\[\tau_{1}=\frac{1}{3}\Big{(}\frac{a_{2}}{X_{1}}+\frac{-b_{1}}{Y_{1}}+\frac{c_{2} -c_{1}}{Z_{1}}\Big{)},\tau_{2}=\frac{1}{3}\bigg{(}\frac{a_{3}}{X_{1}}+\frac{b_ {3}-b_{1}}{Y_{1}}+\frac{c_{3}-c_{1}}{Z_{1}}\Big{)} \tag{10}\]