esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Energy Optimization Techniques in UAV-Based Cellular Networks  
Energy optimization is very important in UAV-based cellular networks because it prolongs the battery lifetime of the UAV-BSs, thereby reducing the OPEX; it minimizes greenhouse gas emissions; and it also reduces the CAPEX as it enables the deployment of fewer UAVs [13; 19]. Hence, as the next generation of cellular networks envisions the adoption of both airborne and ground-based BSs in a three-dimensional deployment scenario, there is a need to more carefully consider the energy consumption of UAV-BSs in order to prevent their energy consumption from escalating. This would not only result in reduced electricity bills for mobile network operators but, more importantly, it would lead to a reduction in greenhouse gas emissions to enable both government and corporate organizations to achieve the net-zero emission drive [18].  
In Section 6, we considered the four major categories of energy optimization in UAV-based cellular networks, while in Section 7, we presented an overview of some of the popular algorithms, both conventional and ML, that can be applied for energy optimization in UAV-based cellular networks. This section reviews the specific approaches proposed in the literature for energy optimization in UAV-based cellular networks and the specific algorithms used in implementing such approaches. Hence, for each deployment type, standalone UAV networks and UAV-assisted cellular networks, we first identify various approaches for energy optimization. Then, under each approach, we review the specific conventional and ML algorithms used to minimize the network's energy consumption.
## 2 Literature Review
### Indirect Measurement Techniques
#### 2.2.3 Conductivity Method  
Conductivity measurement is a common method to identify pure and salty water. This method is known by its simple structure, easy implementation, fast response, and low cost, which makes it widely employed in various industrial applications [18; 19; 20]. Typically, conductivity sensors are classified into two types: electromagnetic induction conductivity and electrode conductivity. Usually, the probe of an electromagnetic induction conductivity sensor contains two metal coils, which are the excitation coil and the induction coil. First, an alternating current excitation signal is applied to the excitation coil of the sensor probe in order to induce an electric field. The induced electric field is then transmitted to the induction coil through the water/liquid to be determined. Therefore, an alternating current output voltage forms on the induction coil. The output voltage and conductivity of the solution form a nonlinear function that can be used to identify different type of solutions, particularly salty water and pure water. [PERSON] et al. [21] proposed a four-electrode conductivity sensor. It includes two electrodes which are placed in a uniform electric field, and the other two are measured. This configuration is proven to help avoid the measurement errors caused by the polarization effect. [PERSON] et al. [22] employed four electrodes in a measuring board with a conductivity accuracy of 0.03 mS/cm to classify different liquid solutions including seawater and pure water. Some other commercial salinity sensors such as the famous Sea-Bird CTD (Conductivity, Temperature, Depth) profiler are based on conductivity ratio measurement [23]. These sensors need a highly reliable pump to flush water through the conductivity cell at a constant rate and can then be applied to the underway monitoring. It is sensitive to the water velocity, and makes the system complicated and difficult to miniaturize [24]. Furthermore, the non-conductive part of dissolved material in seawater cannot be taken into account by the conductivity CTD [25].
# SDN Based Collaborative Scheme for Mitigation of DDoS Attacks
## 3 System Design and Architecture
### Controller-to-Controller (C-to-C) Protocol
#### 3.1.2 Certificate Section  
The certificate section is comprised of a certificate attached by the communicating system to authenticate its legitimacy.  
The idea of certificate chaining is not new and it is heavily used in day to day communications, e.g., in authenticating the DNS records, in client to server communication and server to server communication. There are two types of certification authorities (CAs): root CAs and intermediate CAs. In order for a certificate to be trusted, that certificate must have been issued by a CA that is included in the trusted store. In our system, a trusted store is a directory containing root or intermediate certificates and other private keys of the user. There is no particular directory specified in Linux for trusted store. We have created our own in the POX controller folder.  
If a certificate presented by a neighboring controller is not issued by a trusted CA then the certificate of the issuing CA is checked to see if the certificate of the issuing CA was issued by a trusted  
Figure 3: Payload of C-to-C protocol with attack definitions.  
Figure 2: Payload structure of C-to-C protocol.  
CA and so on until either a trusted CA is found (at which point signature is verified and flows are installed) or no trusted CA can be found (at which point the whole payload is dismissed).
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 6 Conclusions and Future Scope  
This survey explored the evaluation of mobile networks from 1G to 5G, its requirements, and the key parameters of 5G, including coverage definitions and coverage requirements. It addressed the coverage enhancement techniques in 5G such as small cells, CA, D2D, NOMA, MIMO, and optimization in 5G. Each technique's advantages, limitations, and future challenges are understandably illustrated. Afterwards, we explored the new requirements of coverage enhancement techniques such as interference management, spectrum management, device discovery, mode selection, CSI requirements, security and privacy, network deployments, and testing issues. Further, we have provided the pros and cons of existing techniques and their future challenges for enhanced coverage in 5G networks. Our paper shows the accurate path for coverage enhancement solutions and its future requirements.  
One of the main goals of this paper was to provide readers with the most recent and relevant information on coverage enhancement techniques for 5G networks and beyond. To this end, we focused on the most recent studies in the field and aimed to demonstrate the current research focus within the community. We believe that the information and insights presented in this paper will be valuable to researchers working in the field of 5G wireless communications and beyond.  
The use of AI in cellular networks towards SDN, effective deployment of edge computing, implementation of RAN, network slicing management, network visualization, THz communication, visible light communication, quantum communication, virtual reality, and security and privacy issues are the future research challenges of 5G networks and beyond 5G.  
This work is supported and funded by Telekom Malaysia Research & Development Grant, RDTC: 211034, MMUE/220006, TM, Malaysia.  
**Conflicts of Interest:** The authors declared no conflict of interest.
# Modernising Receiver Operating Characteristic (ROC) Curves +
## 3 Materials and Methods
### Item Response Theory (IRT) and the Rasch Model  
Apart from MSA, a second part of renovating ROCs is the deployment of modern measurement theory which can address the special character of categorical-based measurements in a way not traditional in CTT. An observed response in a performance test is typically scored binarily in one of two categories: as \(c=1\) for correct and \(c=0\) for incorrect and presented as \(P_{success,i,j}\) for each instrument, \(i\), to a specific object, \(j\). These observed responses constitute the raw data, \(x_{i,j}\), which cannot be analysed as regular quantitative data, but are characterised by ordinality, as explained in Appendix A. The responses are also not measures of the instrument's ability, nor the task difficulty, but depend on both (see Appendix A.3).  
A compensation for the ordinality of counted fractions (Appendix A.1) [4; 5] and the provision of separate estimates of the quantities' difficulty, \(\delta\), and ability, \(\theta\), attributed, respectively, to the object and instrument (Appendix A.2), are carried out in modern analyses by making a logistic regression with the Rasch modelling of the log-odds ratio to the raw binary response data, \(P_{success}\) over all instruments, \(i\), and objects, \(j\)[18]:  
\[ln\left(\frac{P_{success,j,}}{1-P_{success,i,j}}\right)=\left(\theta_{i}-\delta _{j}\right) \tag{1}\]  
A detailed summary of the Rasch model and IRT is given in Appendix A.1, including comparing and contrasting with non-parametric and ranking tests still widely used in the field. A logistic regression of Equation (1) to the data can be readily performed with conventional software, such as WINSTEPS(r)3.80.1, RUMM2030, and different R-packages (e.g., _eRM, fam, mirt_). In this work, we calculate \(P_{success,i,j}\) in two separate ways--see Section 3.4.2.
# Research on Wellbore Trajectory Prediction Based on a Pi-GRU Model
## 3 Experiments and Analysis
### Multimodel Comparison
Especially in engineering applications, the predictive performance of a model is often more crucial than its training efficiency. Therefore, Pi-GRU provides better prediction results while ensuring efficient training.
## 1 Introduction
Simulations have been validated with a fabricated prototype. Off-broadside peak gain of 9.4 dB was achieved at \(-38^{\circ}\) offset in the elevation plane, along the axis of the gradient. The wideband (in-band + out-of-band) monostatic RCS reduction was achieved over a frequency range of 4-16 GHz (120%), with average RCS reduction exceeding 8.5 dB for the two orthogonal (x/y) polarizations. The low scattering property of the proposed antenna makes it suitable to be integrated with stealth type platforms. That is because the platform's low observability would still remain low despite mounting the antenna onto it for communication, and this paves the way for its multiple applications in the military and defense realm. One example is a side looking air borne radar (SLAR) [30], where the antenna points to a sideward direction and requires physical tilting of its structure. The proposed antenna can potentially be used in this scenario. Similarly, for the aerial security, surveillance, and reconnaissance applications, it can be used on unmanned aerial vehicles (UAVs) and drones where downward pointing high gain beam is more pragmatic for communication with the ground targets [31]. The development of low RCS multiple-input multiple-output (MIMO) antennas is also becoming popular due to their technological advantages, and as such, the proposed technique can be further developed to realize pattern decorrelated low observable MIMO antennas [32; 33]. In addition, the antenna can be utilized for any military communication application where fix tilt-angled communication is required [34].
## 4 Vehicle-Bridge Coupling Numerical Simulation
### Vehicle-Bridge Coupled Model  
The substructure analysis and modal analysis in ABAQUS were used to obtain the.sim and.inp files that contained information such as the mass, stiffness, damping matrices, geometry, and node coordinates. These files were then converted into flexible body information (.fbi) files using the conversion interface in SIMPACK. The track was modeled with 60 kg/m steel rails, and the track centerline distance was 4.6 m. The flexible track was imported using the Flextrack module provided in SIMPACK. The wheel-rail contact relationship was modeled using the equivalent elasticity method, with the tangential and normal contact forces calculated using the simplified Kalker contact nonlinear theory and the Hertz nonlinear elastic contact theory, respectively [26; 27; 28].  
Under the condition of high-speed operation, the German track spectrum is regarded as one of the most representative standards [29; 30]. The research object of this study was the long-span steel truss arch bridge of a high-speed railway, so the German low-interference track spectrum was adopted as the basis of the track irregularity analysis. The specific expressions of the orbital spectral function are as follows:  
Track irregularity:  
\[S_{v}(\Omega)=\frac{A_{v}\Omega_{c}^{2}}{(\Omega^{2}+\Omega_{r}^{2})(\Omega^{2 }+\Omega_{c}^{2})} \tag{1}\]  
Track direction irregularity:  
\[S_{c}(\Omega)=\frac{A_{v}b^{-2}\Omega_{c}^{2}\Omega^{2}}{(\Omega^{2}+\Omega_{ r}^{2})(\Omega^{2}+\Omega_{c}^{2})(\Omega^{2}+\Omega_{s}^{2})} \tag{2}\]  
Track level and gauge irregularity:  
\[S_{a}(\Omega)=\frac{A_{a}\Omega_{c}^{2}}{(\Omega^{2}+\Omega_{r}^{2})(\Omega^{ 2}+\Omega_{c}^{2})} \tag{3}\]  
\(\Omega\) is the spatial frequency of the track irregularity;  
\(\Omega_{c}\), \(\Omega_{s}\), and \(\Omega_{r}\) are the truncation frequencies;  
\(A_{v}\) and \(A_{a}\) are the roughness constants;  
\(b\) is half the distance between the left and right rolling circles, and the value was 0.75 m.  
The bridge and flexible track were connected through fasteners, with the fasteners simulated using force elements in SIMPACK. The vehicle-bridge coupling model is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Modal Order** & & & & & \\
**of Natural** & **Characteristics** & **Calculated** & **Result/Hz** & **Value/Hz** & **Ratio** & **Damping** \\
**Frequencies** & & & & & & \\ \hline
1 & Transverse & & & & & \\  & symmetry & & & & & \\
2 & Vertical & & & & & \\  & antisymmetry & & & & & \\
3 & First-order & & & & & \\  & torsion & & & & & \\  & Transverse & & & & & \\
4 & antisymmetry & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main bridge self-oscillation frequency.
## 3 Results
### Experimental Results  
Figure 19 reports the results of experimental measurements conducted on the plant in which the architecture with DCP was implemented. More specifically, Figure 19a reports the profile of the absorbed current by phase; Figure 19b reports the electrical active power absorbed by the frequency converter. The average value of the experimentally measured active electric power is 14.85 kW.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Standard** & **Regenerative** & **High–Low** & **VDP** & **DCP Static** \\ \hline Regenerative & \(-47.1\%\) & & & & \\ High–Low & \(-60.8\%\) & \(-26.1\%\) & & & \\ VDP & \(-56.2\%\) & \(-17.3\%\) & +11.9\% & & \\ DCP Static & \(-70.1\%\) & \(-43.7\%\) & \(-23.8\%\) & \(-31.9\%\) & \\ DCP Dynamic & \(-66.7\%\) & \(-37.1\%\) & \(-15.1\%\) & \(-24\%\) & +11.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 15: Comparison in percentage terms of the hourly cost of absorbing electricity from the power supply grid between the different configurations (based on Italian energy market rules). Values in the matrix are the percentage saving/increase of the configuration in the row, with respect to the one in the column.  
Figure 18: Comparison between the different system architectures in terms of the hourly cost of electricity absorbed (active and reactive) during the work cycle. For regenerative configuration, the green parts of the bars represent the hourly energy cost reduction with respect to the standard configuration. For the other configurations, white parts of the bars represent the hourly energy cost reduction with respect to the regenerative configuration.
# Potato Leaf Disease Segmentation Method Based on Improved UNet
## 4 Discussion  
Considering the present repercussions of potato leaf diseases on potato crop yields, precise identification through deep learning assumes considerable significance within the potato industry. It can facilitate farmers in the timely detection and proactive mitigation of potato leaf diseases, providing swifter and more convenient strategies for the preservation of potato crop well-being [35; 36].  
In this study, our focus was on potato leaf disease images, and we successfully trained and validated the improved UNet network model, RS-UNet. This network is designed to handle the task of potato leaf disease image segmentation under a single background and has been compared with various other segmentation network models. Based on these findings, each network had its pros and cons in terms of performance on the potato leaf disease segmentation task. To effectively reduce the loss of small-scale object information and enhance the integration capability of lesion area edge details, innovations were introduced into the UNet architecture. By employing ResNet50 as the encoder, the network model was deepened, and the SE attention mechanism was introduced to improve the extraction of relevant disease spot information while suppressing irrelevant information. Furthermore, the combination of cross-entropy loss and Dice loss improved the accuracy of potato leaf disease spot identification and localization.  
Nonetheless, it is prudent to acknowledge that, as a network model becomes progressively deeper and parameters increase in magnitude, the network's complexity amplifies, thereby introducing greater intricacies in the training process. Interestingly, while our RS-UNet exhibited a shorter training duration in comparison with the basic UNet, it surpassed it in segmentation precision. Moreover, our RS-UNet demonstrated superior performance compared with SegNet, UNet, CENet, and FCN across various metrics, including MIoU, Dice indices, and the quality of the segmented result images.  
Although the current network model exhibited remarkable performance in our present task, we recognize that there exists substantial room for future improvements. Our research  
Figure 11: Comparison of Segmentation Results from Different Network Models.  
was limited to a single background scenario. To this end, we can further optimize the network architecture or explore more refined semantic segmentation models [37; 38; 39], as exemplified by [PERSON] et al. [40], who proposed a segmentation network tailored to the detection of pear leaf diseases in complex natural environments. Simultaneously, the creation of additional potato datasets could be contemplated to augment the model's robustness and precision.
# Deep Residual Network with Sparse Feedback for Image Restoration
## 3 Deep Residual Network with Sparse Feedback Loops
### Network Structure  
Inspired by the residual learning structure, we propose the deep residual network with sparse feedback loops for image restoration, and the structure of FbResNet is shown in Figure 1. It consists of ten layers. \"Convolution\" block is in the first layer. This layer has no \"Batch Normal\" and \"ReLU,\" in other words, the information produced by this layer is the original information after filtering the input image, then it is used to estimate the residual information by feeding back to the middle and the last layer. Eight \"Convolution + Batch Normalization + ReLU\" blocks are in the middle layers. The number behind each middle layer is the dilation factors, which is set to 1, 2, 3, 4, 4, 3, 2 and 1, respectively. By using the increasing dilated factors, the first-half layers can learn the residual information using an enlarged receptive field, and the latter half layers can refine the residual information using the decreasing dilation factors. In order to ensure that the estimated residual information does not deviate greatly, two forward feedbacks from the first layer have been added. The first is connected to the middle of the dilation convolution. The second is connected to the last layer. The main task of FbResNet is to estimate the residual information between the input degraded image and the output clean image.
## 3 Energy-Efficient Optimal Power Allocation
### Energy-Efficient Optimal Power Allocation for Non-Real-Time Applications
#### 3.1.2 Peak Transmit Power Constraint  
When we adopt PTP constraint for the satellite user, the EE maximization problem can be given as  
\[\begin{array}{l}\max_{P_{t}}H\left(P_{t}\right)=\frac{E[\log_{2}(1+\frac{P_ {r}}{N_{\rm s}})]}{E(\xi P_{t}+P_{c})},\\ s.t.\left\{\begin{array}{l}P_{t}\leq P_{m}\quad(a2)\\ \mathrm{E}\left(P_{t}\right)\leq I_{th}\quad(b)\end{array}\right.\end{array} \tag{21}\]  
where \(P_{m}\) is the PTP constraint limit. It can be proved that (21) is also a nonlinear concave fractional programming problem. Therefore, based on the [PERSON]'s method, (21) is equivalent to the following optimization problem  
\[T\left(\eta\right)=\max_{P_{t}\in S_{2}}\mathrm{E}\left[\log_{2}\left(1+\frac{ P_{r}}{N_{\rm s}}\right)\right]-\eta\mathrm{E}\left(\xi P_{t}+P_{c}\right), \tag{22}\]  
where \(\eta\) is a non-negative parameter and \(S_{2}\) denotes the set \(S_{2}=\{P_{t}|P_{t}\in(a2)\cap(b)\}\). Similar to (9), Lagrange duality method can also be employed to solve (22). If the Lagrangian multipliers with respect to \((b)\) is \(\mu\), we can decompose (22) into multiple parallel subproblems with the identical structure for each fading state, which is shown as  
\[\max_{0\leq P_{t}\leq P_{m}}D\left(P_{t}\right)=\log_{2}\left(1+\frac{P_{r}}{ N_{\rm s}}\right)-\eta_{\rm c}^{3}P_{t}-\mu P_{i}. \tag{23}\]  
Then, we can address (22) by iteratively solving (23) for all fading states with a given \(\mu\) and updating \(\mu\) with the subgradient method. Hence, we can finally obtain the optimal allocated power as shown in Theorem 4.  
**Theorem 4**: _The energy-efficient optimal transmit power with PTP constraint for non-real-time applications is given as_  
\[P_{t}^{*}=\min\left(\widehat{P}_{t},P_{m}\right), \tag{24}\]_where \(\widehat{P}_{t}\) can be calculated as_  
\[\widehat{P}_{t}=\bigg{[}\frac{1}{(\eta\xi+\mu G_{l}h_{I})\ln 2}-\frac{N_{s}}{G_{S}h _{S}}\bigg{]}^{+}. \tag{25}\]  
Note that we can efficiently solve (22) via Theorem 4 with a fixed \(\eta\) and obtain the optimal EE by updating \(\eta\) with the [PERSON]'s method. This can be achieved by modifying Algorithm 1, where \(P_{t}^{k}\) is calculated by (24) not (14) in each iteration. Moreover, only one Lagrangian multiplier \(\mu\) need to be updated in the modified algorithm. The details are omitted here for simplicity.