esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Protection Against Vampire Attacks in Routing
### Security Information Distribution  
As previously mentioned, security information, calculated from Section 5.1 (used to determine which nodes are likely to be compromised, together with the probabilities of being compromised) has to be distributed to the nodes in the network for the sake of safer route discovery. For better energy efficiency, security-related information is passed to certain \"cluster heads\" in the first place and then broadcast to surrounding nodes [48], rather than directly flooding them through the entire network. Nodes in the network can then take advantage of this information to select the routes without malicious nodes. In our case, the anchor nodes, which have already been utilized for nodes localization (for more details, see Section 3.2), preferably become \"cluster heads\" that can be employed to distribute security-related information, since they are as follows:  
* Less vulnerable than other normal nodes in the network, since they do not directly participate in data transmissions (in other words, output only);
* More economical (in terms of both energy and cost) since they have already been deployed in the network, and adding some non-heavy duty task to them is preferable to deploy additional nodes for information distribution.
## 5 Simulation Results
### Trajectory Tracking in the Presence of Disturbance  
During the flight, the quadcopter is subjected to external disturbances. The worst type of these are wind disturbances. These kind of disturbances can act on the rotors' propellers affecting the rotors' velocity. In our study, for this section, these kind of disturbances are modeled by a random signal with variable amplitude (Figure 14).  
To analyze the robustness and the trajectory tracking performance of the proposed controllers, the disturbances presented in Figure 14 are considered as random variations in the four rotors speeds. In the simulink diagram representing the nonlinear model of the quadcopter, these disturbances are injected as additive signals for the four inputs of the system (\(U_{1}\), \(U_{2}\), \(U_{3}\), and \(U_{4}\)).
## 1 Introduction  
Carbon capture and storage (CCS) has become well-known as a technology for reducing the emission of greenhouse gases from fossil fuels during power generation and industrial processes [1]. Projects involving the injection of carbon dioxide (CO2) for enhanced oil recovery (EOR) have been operating worldwide since the early 1970s [2] and EOR is expected to be a major driver for CCS by providing an additional revenue stream. Hence, combining the CO2-EOR and CCS into a coupled CCS-EOR approach provides a synergistic effect towards business and environmental protection by offering commercial opportunities to oilfield operators. Consequently, almost 80 million tons (Mt) of CO2 are already being used for CCS-EOR each year [3, 4].  
Methane (CH4) is generated as a by-product of oil extraction during primary and secondary oil production and EOR. As CH4 is a potent greenhouse gas (GHG), the operator often chooses to flare this by-product instead of releasing it directly to the atmosphere if there is little commercial opportunity or sufficient regulatory incentive to bring it to market [5]. Also, CH4 has been used as a re-injection gas for EOR [6; 7]. Previous studies have examined the impacts of impurities in the CO2 stream upon the minimum miscibility pressure (MMP) to find that the presence of CH4 increases the MMP compared to that of CO2, thus negatively impacting upon EOR performance [8; 9; 10; 11; 12; 13; 14]. Moreover, since the global warming potential (GWP) of CH4 over 100 years is 28 times that of CO2 [15], it is worth considering the use of CH4 for CCS projects aimed at mitigating global warming and climate change. Although some studies on the geological storage of CO2 and CH4 in an aquifer have been conducted, there are insufficient studies examining the impact of CH4 on CCS-EOR [16; 17; 18; 19]. Hence, it is necessary to store CH4 in reservoirs in order to mitigate gas flaring and release into the atmosphere by using a CCS-EOR approach that can consider energy security and climate change simultaneously. However, CH4 affects the miscibility as well as the hysteresis and solubility effects for residual and solubility trapping mechanisms and the ultimate EOR performance during CCS-EOR.  
For this reason, compositional simulation models are developed in the present study in order to investigate the effects of CO2-CH4 co-injection upon the CCS-EOR mechanisms and performance. The multiple-mixing-cell method is applied to calculate the MMP of the injected gases (i.e., a mixture of CO2 and CH4) and the reservoir oil. Of the four main CO2 trapping mechanisms (namely: structural, residual, solubility, and mineral trapping), structural and mineral trapping are excluded from the scope of the present study due to their negligible effects upon CCS-EOR in a relatively short period [20]. Here, CO2 can be stored in the reservoir by hysteresis and dissolution in water during the water-alternating-gas (WAG) process, thus indicating residual and solubility trapping mechanisms. A three-phase hysteresis model and Henry's law are applied for the residual trapping and solubility trapping, respectively.
# M-Shape PV Arrangement for Improving Solar Power Generation Efficiency
## 8 Conclusions  
This paper showed a modern design approach for improving PV system power generation efficiency by changing the traditional design of PV arrangement. The proposed scheme was applied to six Asian cities to confirm its robustness and effectiveness. The optimal tilt angles of the S-shape in the six regions are Bangkok (14\({}^{\circ}\)), Hong Kong (14\({}^{\circ}\)), Naha (17\({}^{\circ}\)), Jeju (21\({}^{\circ}\)), Shenyang (23\({}^{\circ}\)), and Darkhan (42\({}^{\circ}\)). The optimal tilt angle of M-shape tends to zero. However, considering the environmental factors,  
Figure 14: Area comparison of Naha and Shenyang.  
Figure 15: Cost comparison for setting the PV panel.  
the angle degree of M-shape was given 15\({}^{\circ}\) to 25\({}^{\circ}\) during the simulation. Moreover, detailed analysis for all case studies was presented with applying different overload rates to ensure the capability of the proposed in different operating conditions even severe ones. This paper's simulation results proved that the east-west direction is the best for M-shape and indicated that the M-shape can save more than half the area of the traditional S-shape. As the latitude increases, the shadow length increases, and the M-shape can make better use of this advantage. When the rated power of the PCS is 10 kW and the overload rate reaches 225%, the power generation efficiency of the M-shape exceeds the conventional S-shape. Analysis of the profit situation over 20 years in Naha shows that the M-shape will be higher than the S-shape. Although this result is small (increase of 0.2 million yen), it is undeniable that the M-shape will improve economic efficiency. Here, that distributed generation accounts for a large proportion, and most of them are based on home roofs and small yards. The area is difficult to change here, so adding solar panels to increase output power is important, and the M-shape is prepared for this. This article analyzed the M-shape regarding its geographical location, floor area, power generation efficiency, and economic benefits. We can confidently point out that the PV array will enter the beginning of the M-shape era, and there will be many problems waiting to be solved.  
Conceptualization, Y.H.; Formal analysis, R.S.; Investigation, R.S. and P.M.; Methodology, Y.H. and R.S.; Software, Y.H.; Supervision, T.S.; Validation, Z.Y.; Visualization, Z.Y.; Writing--original draft, Y.H.; Writing--review & editing, Y.H., A.Y. and P.M. All authors have read and agree to the published version of the manuscript.  
For this paper, the authors acknowledge the University of the Ryukyus. Grateful for advice from [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]. This research received no external funding. The authors declare no conflict of interest.
# UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
## 5 Experiment Evaluation
### UE-eNB Cellular Network Bootstrapping  
We first start our experiment by the bootstrapping procedure of a new UE to be part of the cellular network. The _rrc_procedure_ module, as the name suggests, is responsible for the _rrc_ workflow, which manages cell search and selection, connection establishment, configuration to cell handover, and connection reestablishment. However, all the messages are organized as data structures categorized and saved in one of the following modules _ul_ccch_msg, ul_dcch_msg, dl_ccch_msg, dl_dcch_msg_. These modules stand for uplink  
Figure 4: The experiment implementation setup.  
control messages, uplink data messages, downlink control messages, downlink data messages, respectively.  
A new UE starts operation by scanning for the broadcasted _system info block_ messages published by nearby eNBs. The new UE starts sensing the base station broadcasting within its operating frequency and, as a default, selects the base station with the highest power.  
The next step is initiating the communication toward the eNB via RRC connection. As previously mentioned, the connection workflow is defined in _rrc_procedure_. However, the logic of these functions is described in the _rrc_ module located at the srsUE package. On the eNB side, the _rrc_ module located at the srsENB package is responsible for parsing and defining the workflow for the different _rrc_ messages. The _rrc_ module contains the implementation of the other functions related to UE-eNB connection, such as handling connection messages and sending response messages. Then, the experiment message exchange process is described as follows,  
1. The UE initiates a session to the eNB using a Random Radio Network Temporary Identifier (RA-RNTI).
2. eNB assigns a Cell Radio Network Temporary Identifier (C-RNTI) and sends timing adjustment to the UE.
3. Then, the UE adjusts the timing and sends an RRC Connection Request including UE identity using _send_con_request_ function from _srsue: rrc_ module.
4. Next, the eNB checks on Mobile Management Entity (MME) and Temporary Mobile Subscriber Identity (TMSI) using _handle_rrc_con_req_ function from _srsenb: rrc_ module then sends the RRC Connection Setup to the UE using _send_connection_setup_ function from the same module.
5. The UE acknowledges the message with RRC Connection Complete message using _send_con_setup_complete_ function.
# Rough-Terrain Path Planning Based on Deep Reinforcement Learning
## 5 Conclusions  
In this paper, we have studied the global-path-planning problem for unmanned vehicles operating on rough terrains. We propose the A-DQN algorithm to model unmanned vehicles as agents, constructing a model that includes states, actions, rewards, and strategies, and taking into account constraints on energy consumption and different classified terrains through the reward function. The proposed method effectively solves the problem of the difficult convergence of the ground training due to the large size of the search map through the integration of deep reinforcement learning and heuristic exploration. The experimental results demonstrate rapid convergence, effective terrain recognition, and superior path quality compared to the traditional A*, Dijkstra, and RRT algorithms.  
However, the proposed A-DQN method has certain limitations. Firstly, the extensive training process is computationally demanding, particularly for large-scale rough environments. Secondly, the algorithm's performance is sensitive to the parameters chosen in the reward function, which necessitates manual tuning for an optimal performance across different terrains. Our future work will focus on the development of adaptive reward mechanisms that can automatically adjust parameters according to environmental conditions. Future work will include extensive field tests to validate the algorithm on real-world terrains.  
Methodology, Y.Y. and Z.Z.; software, Z.Z.; validation, Y.Y. and Z.Z.; writing--original draft preparation, Z.Z.; writing--review and editing, Y.Y. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Comparison of paths before and after mud land setting of Map 1: (**a**) three-dimensional view of the muddy area; (**b**) test path map when mud land parameter is 0; (**c**) test path map when mud land parameter is 1.
# High-Precision Combined Tidal Forecasting Model
###### Abstract  
To improve the overall accuracy of tidal forecasting and ameliorate the low accuracy of single harmonic analysis, this paper proposes a combined tidal forecasting model based on harmonic analysis and autoregressive integrated moving average-support vector regression (ARIMA-SVR). In tidal analysis, the resultant tide can be considered as a superposition of the astronomical tide level and the non-astronomical tidal level, which are affected by the tide-generating force and environmental factors, respectively. The tidal data are de-noised via wavelet analysis, and the astronomical tide level is subsequently calculated via harmonic analysis. The residual sequence generated via harmonic analysis is used as the sample dataset of the non-astronomical tidal level, and the tidal height of the system is calculated by the ARIMA-SVR model. Finally, the tidal values are predicted by linearly summing the calculated results of both systems. The simulation results were validated against the measured tidal data at the tidal station of Bay Waveland Yacht Club, USA. By considering the residual non-astronomical tide level effects (which are ignored in traditional harmonic analysis), the combined model improves the accuracy of tidal prediction. Moreover, the combined model is feasible and efficient.  
tidal level prediction; combined model; harmonic analysis method; Support Vector Regression (SVR); Autoregressive Integrated Moving Average Model (ARIMA) +
Footnote †: journal:
## 4 Discussion
### Comparison of Schemes by Metrics  
To demonstrate that the de-noising effect for the proposed scheme is good, four tables were created for clear comparison. Table 1 shows the PSNR and SSIM for a Barbara image using various de-noise schemes. In this image, we padded Gaussian white noise randomly into the image. The proposed scheme shows superiority or is the same as other filters for PSNR or SSIM. This is equal for the Lena image shown in Table 2. Table 3 shows the average PSNR and SSIM for Barbara using various de-noising schemes. There were 20 images in total. The image was randomly padded with various Gaussian white noise, \(\mu=0,\ 0,\ 0,\ 30\), 100; \(\sigma=10,\ 15,\ 20,\ 30,\ 50,\ 60\), on 20%, 50%, 75% of the entire image. The proposed scheme shows superior results to the other filters both for PSNR or SSIM. This is equal for the Lena image shown in Table 4.
## 4 Proposed Collision Warning System and V2V Communications
### Collision Determination  
The fused output of the trajectory information can be applied to estimate the future trajectory of the detected objects. However, in practice, as the measurement has certain level of noise, it is hard to predict trajectories of vehicles correctly. Therefore, we employ a Kalman filter for noise mitigation and precise estimation [70]. We assume that the sensors monitor nearby objects with \(t_{V}\) time interval which is identical to regular video frame. For simplicity, we consider 2-dimensional case mainly in this section, and it can be extended to 3-dimension simply with the same manner. The state and control input of Kalman filter at time \(t\) are defined by \(x[t]\) and \(u[t]\), respectively, which consists of x-y coordinates of positions \((s)\), velocities \((v)\), and accelerations \((a)\) such that  
\[x[t]=(s_{x}[t]\;s_{y}[t]\;v_{x}[t]\;v_{y}[t])^{\mathrm{T}},\;u[t]=(a_{x}[t]\; a_{x}[t])^{\mathrm{T}}. \tag{28}\]  
Figure 7: Example of the considered data communication system and real-time data fusion process. V1 can receive several data within the predetermined cycle (100 ms), and a decision-making algorithm updates the fused output in real-time.  
Let us denote the measurements of position and velocity at time \(t\) as \(y[t]\). Then, the process dynamics can be expressed as  
\[x[t+1]=\begin{bmatrix}1&0&t_{V}&0\\ 0&1&0&t_{V}\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}x[t]+\begin{bmatrix}\frac{(t_{V})^{2}}{2}&0\\ 0&\frac{(t_{V})^{2}}{2}\\ 1&0\\ 0&1\end{bmatrix}u[t]+N_{p}[t],\;y[t]=\begin{bmatrix}1&0&0&0\\ 0&1&0&0\\ 0&0&1\end{bmatrix}x[t]+N_{m}[t], \tag{29}\]  
where \(N_{p}[t]\) and \(N_{m}[t]\) represent process and measurement noises which are white and independent normal distributions with zero mean and noise covariance matrices \(Q\) and \(R\), respectively, i.e., \(N_{p}\sim\mathcal{N}(0,Q)\) and \(N_{m}\sim\mathcal{N}(0,R)\). From (28) and (29), we can estimate trajectory of objects precisely [71].  
Next, in order to determine collision, we define the \"_safety area_\" of the objects [31, 72, 73], which is a circle of radius \(R_{s}\) as depicted in Figure 8. Note that under the proposed system, we can determine collisions precisely by utilizing class information. We decide an accident when safety areas of objects and ego-vehicle are overlapped. In other words, along with the estimated trajectories, vehicles examine whether the safety areas overlap, and the anti-collision system would be triggered if an collision is predicted.
# Millimeter-Wave High-Gain Dual-Polarized Flat Luneburg Lens Antenna with Reflection Cancellation
## 4 Implementation, Measurements and Discussions  
### Implementation of FLLA  
The designed FLLA was fabricated using a standard PCB process and the used substrate was a Rogers RO4003C. A postprocessing was introduced to install both the feeding stacked patch antenna and the FLL using a 3D-printed supporter, as shown in Figure 12. The feeding antenna was assembled with four nylon screws to be mounted with a slidable supporter, allowing it to move in the transverse plane for beam scanning purposes. The fabricated 10-layer PCB boards were assembled using nylon screws as well to compose the FLL, and later mounted onto the same supporter.  
Figure 11: Simulated scanning beams of FLLA: (**a**) E-plane, (**b**) H-plane.  
Figure 10: Simulated radiation patterns of the FLLA: (**a**) E-plane at 27 GHz, (**b**) H-plane at 27 GHz, (**c**) E-plane at 29 GHz, (**d**) H-plane at 29 GHz, (**e**) E-plane at 31 GHz, (**f**) H-plane at 31 GHz.
## 3 Lossless Convex Optimization Algorithm
### Convexification and Discretization
Then, the dynamic equation constraints in discrete form can be written according to (11):  
\[ \begin{array}{l}r[k]-r[k+1]+\frac{\Delta t}{2}v[k]+\frac{\Delta t}{2}v[k+1]=0\\ v[k]-v[k+1]+\frac{\Delta t}{2}u[k]+\frac{\Delta t}{2}u[k+1]=-g\Delta t\\ z[k]-z[k+1]-\frac{\Delta t}{2l_{\text{sp}}g_{0}}\sigma[k]-\frac{\Delta t}{2l_{ \text{sp}}g_{0}}\sigma[k+1]=0\end{array} \quad k=1,\ldots,N-1 \tag{17}\]  
Since the objective function of the discretized problem is a linear function, the equality constraints are affine, and the inequality constraints are a combination of affine constraints and second-order cone constraints. The original continuous optimization problem is completely transformed into a finite-dimensional SOCP. The results can be summarized in the Figure 5.  
The problem can be solved efficiently using a mature SOCP solver. All numerical simulations in the paper are performed on an Intel Core i7-10875H PC with 2.3 GHz and 64 GB memory using the ECOS solver [31]. The algorithm used by the solver is a standard primitive-dual, prediction-correction interior-point algorithm, using Nesterov-Todd (NT) scaling and self-dual embedding technology. ECOS is a small-footprint, high-performance SOCP solver whose accuracy is numerically reliable and beyond that typically required by embedded applications. It is competitive in solving small and medium-sized convex optimization problems.
## 1 Introduction
These databases are composed by complete MR volumes and their corresponding CT volumes acquired for a certain field of view (FOV) of interest. However, finding MR-CT pairs that match those requirements--and including spatial resolution and contrast constrains required to generate those training datasets--in retrospective clinical studies is usually difficult, limiting the flexibility and generalizability of these databases. On the other hand, whole-body acquisition strategies are gaining importance in early diagnosis, staging, and assessment of therapeutic response in oncology [28]. In this scenario, while MR whole-body acquisition has accelerated its deployment and acceptance in clinical practice, in the acquisition of CT images, the FOV is generally reduced in order to lower the radiation dose to the patient. Thus, to create highly flexible and generalizable atlases to train our algorithms in a real clinical setting, we should be able to work with datasets containing lower resolution but bigger FOV MR images alongside low dose and reduced FOV CT images. In this work, we propose a method based on the idea of modality propagation using MR-CT atlases described in preceding developments [11; 29] and a deep learning architecture approach inspired on our previous work [22]. However, our database is composed by head and neck MR images and local portions of CT including the brain, paranasal sinuses, facial orbits, and neck studies. Additionally, the deep learning architecture presented here incorporates novel state of the art techniques. With this work we demonstrate that constructing and using incomplete databases still enables accurate results without major limitations, as Deep Learning methods have the potential to be used with this kind of datasets if certain steps and correction are performed on the pipeline.
## 4 Media Access Control
### Cooperative Sensing
#### 4.2.3 Decision Formulation
Compared with soft fusion, hard combination incurs less transmission and computation overhead, though the lack of detail may result in suboptimal performance [20]. It is argued in [52] that hard fusion is able to perform nearly as well as soft combination since simulations carried out in [90] show that hard combination based on energy detection provides comparable performances when the number of cooperative peers is about 1.6 times that of soft fusion. Some mechanisms incorporate both the soft and hard mechanisms for optimized outcome. The scheme proposed in [91] demonstrates one of the examples which uses dual-mode in order to improve signal detection. Hard combination is adopted by default with predefined upper and lower thresholds which are shared among all users, similar to [92]. The final decision is \(1\) if the result is below the lower threshold and \(0\) if above the higher one. Decisions are then reported to CHs or FC. However, nodes cannot make local decisions if the detected energy level falls between the two thresholds; alternatively, they handle the \"unknown state\" by switching to soft fusion and send local sensing value to cluster-heads or the FC instead. Accordingly, the entity responsible for the fusion process would need to make higher level soft fusion on behalf of those indecisive nodes and then combine all decisions to generate the final judgment [91]. A two-stage hybrid data fusion is proposed in [93]. Nodes firstly make local decisions and report them for fusion. If the result is \(1\), the system perform corresponding adaptations; otherwise, the second stage is invoked where soft data is requested from the adaptive nodes to make more careful decisions [93]. The rationale behind this algorithm is that a double-check mechanism using soft data is launched if no consensus is reached using hard combination.