esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis and Perspectives  
### Analysis of Experiments  
With the development of 5G and 6G communication technologies in recent years, the development of SAGINs has been greatly driven by the development of FANETs as an important relay linking ground-based and space-based networks, and UAV-related research in this field is favored by an increasing number of researchers. Routing algorithms transport packets from source nodes to destination nodes quickly and reliably. The design of routing algorithms for FANETs plays a fundamental role in improving the reliability and network performance of FANETs and SAGINs. We queried the number of UAV routing algorithms researched over 10 years in the Web of Science core database using a side-by-side query with keywords (UAV routing, FANET routing, and drone routing), and the results are shown in Figure 7. The figure clearly shows that research on routing algorithms for UAVs has been increasing year by year in recent years. It also shows that users and operators are demanding more and more operational efficiency, security, and reliability from UAVs, and existing routing techniques cannot meet the increasing number and scale of FANETs. In Table 1, we focus on comparing 48 experimental approaches that have been researched on routing algorithms for UAVs since 2018. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Network Scale (Sorties)** & **UAV Type** & **Method** & **Dataset Type** &
\begin{tabular}{c} **Simulation** \\ **Platform** \\ \end{tabular} \\ \hline
[82] & 76 & Small drones & GA+SA & Real dataset & Win10+Python \\ \hline
[102] & 20–70 & Small drones & MADDGP+LSTM & Self-generated & NS-3 \\ \hline
[103] & 10 & Small drones & MARL & Self-generated & Ubuntu16.04 \\ \hline
[81] & 100 & Small drones & MA+GA & Real dataset & Win10+Python \\ \hline
[111] & 15 & Small drones &
\begin{tabular}{c} Fuzzy \\ Logic+Q-learning \\ \end{tabular} & Self-generated & MATLAB \\ \hline
[112] & 30 & Small drones & FSR+Dijkstra & Self-generated & NS-2 \\ \hline
[88] & 12 & Medium to large drones & GA+BM & Real dataset & MATLAB \\ \hline
[99] & 50 & N/A & Q-learning & Self-generated & NS-3 \\ \hline
[101] & 12 & N/A & MARL+CNN & Self-generated & MATLAB \\ \hline
[77] & 10–30 & Small drones & B\&C & Self-generated & Python+Gurobi \\ \hline
[113] & 5–10 & Medium to large drones & Fuzzy Logic+RL & Self-generated & MATLAB \\ \hline
[100] & 25 & Medium to large drones &
\begin{tabular}{c} Fuzzy Logic+Q- \\ learning+DFS \\ \end{tabular} & Self-generated & NS-3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Analysis of routing algorithms in published FANETs.
# Recent Developments and State of the Art in Flexible and Conformal Reconfigurable Antennas
## 5 Multiple Reconfigurable Features in Antennas
### Frequency and Polarization Reconfigurability  
In Reference [109], an L-shaped stub is introduced in the ground plane of a disc monopole antenna and a parasitic arc is placed around the main radiator. Two-pin diodes are placed between the main radiator and the parasitic arc to switch between two frequencies of 2.45 GHz and 0.92 GHz. The L-shaped stub is also connected to the ground plane with the help of two diodes. Through optimization of the diodes positions on the patch and the connecting and disconnecting the stub to the ground, four states of right-handed circular and left-handed circular polarizations are generated for both frequency bands, separately. The antenna is placed on jeans substrate with \(\varepsilon_{r}=1.7\) and an adhesive copper tape is used as a radiating element and ground plane (Figure 8). In References [1] and [58], the frequency reconfigurability is achieved by manipulating the slot size at the ground plane of the truncated edges of the rectangular patch textile antenna. In Reference [1], three diodes are integrated on a slot at the ground plane of a textile antenna to switch between six frequencies: 1.57 GHz, 1.67 GHz, 1.68 GHz, 2.43 GHz, 2.50 GHz, and 2.55 GHz. ShieldIt Super and felt with \(\varepsilon_{r}=1.22\) at 1.575 GHz is utilized for conductive parts and substrate, respectively. To bias these three diodes, four vertical slots at the edge of the ground plane are added to the structure. Capac  
Polarization reconfigurability between right-handed and left-handed circular polarization at 5 GHz, and linear polarization at 4.7 GHz or 5.2 GHz is presented with a textile antenna based on snap-on buttons in [110]. The polarization is controlled by rotating the module through opening and closing the flap (the slot placed on the circular patch antenna). The metalized nylon Ripstop fabric and Cuming Microwave C-Foam PF-4 foam with \(\varepsilon_{r}=1.06\). are used for conductive parts and substrate, respectively.
# A Positive Flow Control System for Electric Excavators Based on Variable Speed Control
## 4 Simulation Results
### Energy Saving Analysis of Proposed PFS Based on Variable Speed  
The energy consumption of a hydraulic system is mainly reflected in the power consumption, and the hydraulic power is calculated by the flow and pressure. The following is the energy consumption comparison between the PFS based on variable speed and the TSCS based on constant speed.  
Figure 10 shows the pressure and flow rate curves of the main pump outlet of the two systems. As seen from the pressure curves, during the actuator's movement, the changing trend of pump outlet pressure with the load in the two systems is basically the same. According to the flow curves, the pump output flow based on constant speed and constant displacement is almost constant, around 37 L/min. While the output flow of the PFS based on variable speed displacement is controlled by the opening of the electronic control handle, with the increasing of the handle movement amplitude, the output flow increases accordingly. Therefore, it can realize the flow matching between the pump and the load and can reduce the energy loss. This proves that the proposed PFS has good energy-saving ability.  
Figure 11 shows the energy consumption comparison curves of hydraulic pumps of the two systems in one working cycle. The energy consumption of PFS is lower than that of TSCS. In one working cycle, in which the arm cylinder extends and retracts, the energy consumed by TSCS is about 67.5 kJ and that of PFS is about 47.5 kJ. The energy-saving efficiency is up to 29.6%.
# Monitoring Perishable Commodities Using Cellular IoT:
## 2 Materials and Methods
### Software Architecture
#### 2.3.2 Strategies for Communications and Sending Frequency
If a certain threshold \(p_{threshold}^{minimum}\) is not exceeded with respect to the previous measurement for all parameters \(p_{i}\), no data are sent wirelessly, as shown in Equation (1), where \(p_{i}^{current}\) and \(p_{i}^{previous}\) are the current and previous measurements, respectively, and \(i=1,\dots,\ 5\) for luminosity, CO\({}_{2}\) concentration, temperature, humidity and acceleration. If a measurement exceeds the threshold for any \(i\), all data are sent. In any case, the data are always sent after 2 h even if this threshold is not exceeded in order to keep track of the monitoring of the parameters. In the case of humidity, it does not decide on this part (infinity threshold), as there may be occasions in a refrigeration chamber where this magnitude has high oscillations. The predefined thresholds with respect to the previous measurement to be exceeded are shown in Table 3.  
\[\left|p_{i}^{current}-p_{i}^{previous}\right|\leq p_{threshold}^{minimum}\ \forall i \to No\ data\ is\ sent\ wirelessly \tag{1}\]
# On Implementing a Two-Step Interior Point Method for Solving Linear Programs
## 3 Experimental Evaluation
### Experiments on CPU
The average speedup over all 50 instances is between 1.3 and 1.4 for both Algorithms 1 and 2. The average relative reduction is between 1.20 and 1.25 for both Algorithms 1 and 2. If we focus only on 5 of the largest instances then the speedup and relative reduction are comparably higher. This indicates that the method scales well. Algorithm 1 has a greater relative reduction but it does not translate into a greater speedup. Algorithm 2 has a better speedup. The instances indexed 40-45 in the plots show anomalous behaviour, it appears that these are specific types of instances (staircase instances) and the performance of Algorithms 1 and 2 seems to be different (compared to the average) on these instances. One of the prominent features of the new proposed algorithm is its ability to find an appropriate search direction compared to traditional interior-point algorithms. As demonstrated in [20], the new modified Newton method exhibits a faster convergence rate, approximately \(1+\sqrt{2}\). Therefore, the new algorithm leverages this property, allowing it to reach the optimal solution in fewer iterations--a particularly valuable advantage when dealing with high-dimens  
Figure 2: (**Left**) Relative gap, (**Right**) absolute gap vs. the iteration number. scenarios where matrix multiplication and inversion can be computationally intensive, the reduced number of iterations provided by the new algorithm significantly decreases the execution time and enhances overall performance. In Table 1, the average number of iterations and CPU time (measured in seconds) for three different methods--Classical Algorithm, Algorithms 1 and 2--are presented. The Classical Algorithm required an average of 97.82 iterations and 166.71 s of CPU time. In contrast, Algorithm 1 demonstrated improved efficiency with an average of 71.34 iterations and 136.29 s of CPU time. Similarly, Algorithm 2 also exhibited promising results with 72.52 iterations and 135.46 s of CPU time. These findings indicate that both Algorithms 1 and 2 are more effective than the Classical Algorithm in reducing the number of iterations and CPU time. Notably, Algorithm 1 achieved the lowest number of iterations, while Algorithm 2 achieved the lowest CPU time. Moreover, the fourth column presents the standard deviation for the number of iterations for the three algorithms, indicating that Algorithm 1 has the lowest variability, followed by Algorithm 2, with the Classical Algorithm having the highest variability. The details of the problem instances can be found in [29]. The raw data used to build these figures is in the Appendix A and is self-explanatory. The data in Appendix B is divided into blocks of three rows each. The first row in each block is data for the classical algorithm and the other two rows are the data for Algorithms 1 and 2. Table A2 lists the  
Figure 4: Speedup vs. number of instances. Figure 3: Relative reduction. relative gap and the norms and shows the convergence of the three algorithms to an optimal solution on several instances from NETLIB.
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 5 Simulation Results and Performance Evaluation
### Performance Trade Offs  
Table 5 shows the performance trade offs of proposed schemes. In order to avoid repetition, parameters are explained generally because of same behaviour in proposed schemes. Multiple copies of data packets are forwarded in order to achieve reliability. FLMPC-One performs better in terms of active nodes, energy consumption and PRR. However, processing delay at each node in FLMPC-One increases its delay. FLMPC-Two makes smart decisions than FLMPC-One and awakes less number of active nodes which saves energy and increases PRR by reliably transmitting the data packet towards the surface. However, FLMPC-Two pays the cost of delay.
## 3 Network Model
### Quantum Key Distribution Luminescence Network Architecture Based on Hybrid Trusted Relay
It also encompasses the user-network interaction interface, as well as specific business requirements and application scenarios for secure communication. These functionalities are achieved through high-level application interfaces and can be extended with various features based on different application needs.
# Zero-Inflated Patent Data Analysis Using Compound Poisson Models
## 4 Experimental Results  
### Simulation Data Analysis  
First, we analyze a simulation data to illustrate the improved performance of our proposed method. We generated the random numbers from the generalized Poisson distribution with (0.7, 0.3, 0.4) and (0.352, 0.265, 0.342) for rate and dispersion parameters. The number of variables and the sample size are 3 and 10,000, respectively. So, we constructed the intermediate correlation matrix as follows.:  
\[\begin{pmatrix}1&0.4977&0.3671\\ 0.4977&1&0.5049\\ 0.3671&0.5049&1\end{pmatrix}\]  
We used the first variable as response variable y and others as explanatory variables \(x_{1}\) and \(x_{2}\). In this paper, we used the R data language and its packages for the simulation data generation [46; 47]. Table 2 shows summary statistics of all variables.  
From the results in Table 2, we found that the simulation data is zero-inflated, for example, the zero percentage of x\({}_{1}\) is 74.08%. Using the variables, we constructed an analytical model as (13):  
\[\mathrm{y}=\mathrm{f}(x_{1},x_{2})+\varepsilon \tag{13}\]  
In the comparative models, we used \(y\) and \((x_{1},x_{2})\) as response and explanatory variables. Table 3 illustrates the analysis results of comparative models using the simulation data of Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **Min** & **Q1** & **Median** & **Q3** & **Max** & **Mean** & **Percentage of Zero (\%)** \\ \hline Y & 0.0000 & 0.0000 & 1.0000 & 1.0000 & 7 & 0.7365 & 49.44 \\ X\({}_{1}\) & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 4 & 0.3028 & 74.08 \\ X\({}_{2}\) & 0.0000 & 0.0000 & 0.0000 & 1.0000 & 5 & 0.4224 & 66.27 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary statistics of simulation data.  
Figure 2: Procedure of proposed method.  
In Table 3, we compared the compound Poisson model (CP) with comparative models which are linear regression model (LM), generalized linear model with Poisson distribution (GLM) and zero-inflated Poisson model (ZIP). We represented the estimated coefficients and \(p\)-values of \(x_{1}\) and \(x_{2}\) according to the comparative models. Additionally, we showed the AIC value of each model to evaluate model performance between compared models. We found that the AIC value of CP is the smallest among the comparison models. Therefore, we verified the better performance of CP for zero-inflated data analysis. Next, we used patent documents as real data to show the validity of our proposed method.
# Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
## 3 Laboratory Tests and Numerical Approach Method
### Cast Body Flake Method  
Cast body flake analysis is a microscopic analysis of rock flakes with a thickness of around 30 \(\upmu\)m to obtain various petrographic parameters: texture, structure, primary porosity, secondary porosity, fracture type, clay minerals, and cementation type. [PERSON] and [PERSON] used the cast body flake method to analyze the interaction between the filter and the rock  
Figure 1: Arps non-dimensional production integral versus non-dimensional time.  
and indicated that flake analysis is a suitable method to evaluate the formation damage mechanism [35]. Although the cast body flake method can assess the damage due to drilling fluids, especially solid phase deposition, it is difficult to detect foreign solid phase intrusion damage and pore structure alteration in the rock sample [36]. Moreover, the acquired two-dimensional images are only planar views of the pore structure and cannot be used to assess the connectivity and permeability of the samples.
# Joint Energy Supply and Routing Path Selection for
### Contributions  
Based on the above analysis, the major contributions of this paper are summarized as follows:  
* We propose a two-stage energy replenishment strategy with limited energy supply capacity. The strategy uses an initiative power supply method with obvious advantages to charge the sensor nodes. Based on several important parameters such as the residual energy of nodes, the future energy consumption rate of nodes, the charging duration and charging speed of WCD, etc., each node of the charging time can be determined in two stages. Thereinto, we set a charging time update strategy to improve the energy utilization efficiency, which can optimize the energy distribution according to the WCD energy replenishment ability and the nodal energy consumption intensity.
* We propose an algorithm by considering the joint optimization of energy supply and routing. Jointly considering the energy status of nodes, energy consumption and energy supply, we propose a routing selection algorithm. In this algorithm, we consider not only the transmission energy from the current node to the next-hop node, but also the energy consumption of the next-hopnode to the sink node, which impacts the delay and cost of data transmission. In addition, in order to avoid nodal premature death, the estimated residual energy of nodes has been taken into consideration, and the influence of energy supply model on the node transmission capacity has also been joined.
* We analyze the proposed algorithm from the perspectives of parameters and computational complexity. Besides, the algorithm is evaluated from the perspectives of the fusion index, harmonic coefficient, network size, charging duration based on our simulation. We compare the proposed algorithm with other two strategies--the proportional distribution strategy and greedy strategy. The simulation results show that our algorithm can effectively prolong the network lifetime, and it is able to obtain different demands of network delay and energy consumption by dynamically adjusting the relevant parameters.  
The remaining parts of this paper are arranged as follows: Section 2 illustrates the rechargeable wireless sensor network of this article, at the same time, some formal definitions and concepts are presented. In Section 3, there is a detailed description of the proposed algorithm, and we provide the relevant algorithm performance analysis and implementation guidelines. The simulation results and relative discussions are presented in Section 4. Section 5 concludes the paper and briefly suggests some future research directions.
## 4 Simulation Environment  
Network simulator NS2.35 is one of the popular simulation tools which are used to understand and predict the protocols behavior in networks. In order to simulate congestion control protocols, dumbbell topology is a good model to reflect real-world environment. For this, we have chosen dumbbell topology with multiple bottleneck links consisting of ten nodes with different bandwidths (5 Mb, 4 Mb, 10 Mb, 2 Mb) incorporating delay of 10 ms as shown in Figure 2. Packet size of TFRC is 1000 bytes and total simulation time is 175 s.
# Quality of Service Based NOMA Group D2D Communications
## 4 Numerical Results and Discussion
### Comparison between Two Implementations of Q-NOMA Group D2D Communications  
In this section, we compare the performance of two possible implementations of Q-NOMA group D2D communications based on power allocation coefficients Policies I and II. The results for Q-NOMA group D2D communication under Policy I are also obtained by utilising Equation (8), with the exception that \(\{\varphi_{m}^{\max}\}_{m=1}^{M}\) are computed by using fixed power allocation coefficients \(\{\beta_{m}^{\mathrm{F}}\}_{m=1}^{M}\). We consider five different cases for users' targeted rates and compared the outage probabilities achieved by the Q-NOMA group D2D communications under Policies I and II at an SNR of 25 dB. Table 3 summarizes the outage comparison between these two implementations of the group D2D communications. The power allocation coefficients for Policy I are derived as \(\beta_{m}^{\mathrm{F}}=\{0.5,0.33,0.17\}_{m=1}^{M}\), whereas those for Policy II are calculated using Equation (2) based on users' targeted rates and are shown in Table 3.  
It is observed from the results in Table 3 that Q-NOMA under Policy I achieves lower outage probability in cases 1 and 2, whereas it obtains better outage performance in cases 4 and 5 for Policy II. In addition, the results in case 3 indicate the importance of proper power and rate allocation to avoid situations of complete outage. Moreover, it can be observed that the Q-NOMA group D2D communication under Policy I performs better in cases where the users' targeted rates are significantly apart. This is more suitable to D2D communication scenarios where different users may have diverse QoS requirements.  
In order to extend the analysis beyond the case of \(M>3\) users, we further present in Table 4 the average outage comparison between paired D2D and Q-NOMA group D2D communications (based on power allocation coefficients Policies I and II). We consider \(M=5,7,9\) with \(R_{m}\sim\mathcal{U}\left(0.1,2\right),\forall m\in M\) and \(\rho_{t}=25\) dB, where \(\mathcal{U}\left(\cdot,\cdot\right)\) represents the random uniform distribution function. It can be observed that the results in Table 4 are consistent with those presented in Figure 4, for example, Q-NOMA group D2D communications consistently achieve lower outage probability than paired D2D communications.
## 5 Discussion
Only applying the proposed method that generates accurate GCPs from all types of roads seems to be a more straightforward solution that does not substantially affect the total computational cost, especially if enough hardware resources are available. Nevertheless, the division of the image to land cover types would also benefit the proposed road detection method, as different (suitable) parameters could be set for the image segmentation and classification for each land cover. The potential of the proposed method is demonstrated for regions in Slovenia, where accurate reference data is available. However, the method can work also in other parts of the world. Currently, OpenStreetMap road layers are implemented in the processing and they are globally available. Although their accuracy is questionable, they can still be used as a supporting layer or when more accurate data is not available. The images of regions where national reference data is accessible can be easily processed after such data is added in the database. Areas without any roads cannot benefit from the proposed method. In this case, the image of such areas can only be automatically processed without GCPs. The presented results clearly showed the potential of the method for a robust geometric correction of panchromatic images with very-high resolution. The proposed method shows a good performance on images with complex land cover, which is mostly characterized by densely populated urban areas with high buildings. Although a diverse set of images is used, a more extensive VHR images dataset (different sensors and even higher resolutions) would be necessary to additionally test the robustness in regard to different resolutions and sensors, and to improve the processing parameters of the method. It is still mandatory to test the performance of the algorithm where non-ideal atmospheric conditions are present (e.g., clouds or/and haze) or during more extreme landscape conditions (e.g., snow cover).