esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Whilst this is a logical approach for the use of RL, in the work presented here, reinforcement learning is used to explore the full flight envelope based on the use of targeted cost reward structures, building on the manoeuvres currently available from more classical-based control. [PERSON] et al. [2] developed a PPO-based controller for a multirotor. They used the full nine-element rotation matrix, representing the orientation of the vehicle as part of their state input. In this work, a quaternion representation is used, which reduces the overall size of the input vector, while retaining the advantage of eliminating singularities, unlike an Euler angle representation. Similar to other works, they generate a controller that takes an error as part of the input, whereas, here, the error signal is delivered through the reward function. [PERSON] et al. [14] used PPO to develop an attitude control using a small fixed-wing model. The endpoint reward was based on deviation from the control point and reinforcement learning was used to provide attitude control. Demonstrated in simulation, the resultant neural networks were shown to be relatively robust and performed well in comparison with a baseline PID controller. Although it is not clear what the maximum angle of attack the aircraft reached during the tests was, the response was shown to be stable up to plus/minus a twenty degrees pitch angle. The work within this current paper uses a similar framework for the PPO control, but moves away from the steady state and low alpha transitions into the highly nonlinear, high alpha flight regimes to provide agile manoeuvres. Building on this previous work, the key contributions of this current paper are the following:  
1. The introduction of a high fidelity longitudinal aircraft model which allows reinforcement learning to explore the full flight envelope, thereby closing the reality gap to free flight. 2. The generation of aerobatic manoeuvres, exploiting the high-alpha flight regime that classical controllers typically do not enter. 3. The linking together of multiple manoeuvres through the generation of robust neural network-based controllers and the training of a single neural network for more than one manoeuvre. The following section introduces the aircraft model and reinforcement learning environment.
## 4 Experiments and Comparisons
### Experimental Comparison  
We use the remain 100 self-built datasets as experimental data, and we divide them into four groups of test1, test2, test3, and test4, which contain 10, 15, 25, and 50 pairs of pictures respectively. We also compare the subjective vision and objective quality with the existing super-resolution algorithms SRCNN [4], FSRCNN [5], VDSR [6], DRCN [7], LapSRN [8], RCAN [9]. Before making comparison, we analyze the relevant algorithm  
Figure 8: The flow chart of experimental dataset acquisition.  
network structure, as shown in Table 2, which provides a clear list of the input, depth, residual structure and loss function for each of the algorithm network.  
In order to quantitatively analyze the super separation performance of each method, the peak signal to noise ratio (PSNR) [31] and structural similarity (SSIM) [32] were used as evaluation indicators for comparison on the selected experimental data test1, test2, test3 and test4 respectively. The inputs of the model are the test1 experimental data magnified by two, three and four times. The calculated PSNR average results are 37.73 dB, 34.63 dB and 31.15 dB, and the SSIM average results are 0.9349, 0.9075 and 0.8243. Using the same method, experiments are also conducted on experimental data test2, test3, and test4, where the overall end results are then placed together with other results obtained from using advanced super-resolution algorithms such as SRCNN [4], FSRCNN [5], VDSR [6], DRCN [7], LapSRN [8], RCAN [9] for comparison. The results of each method on the image in the figure are shown in the tables listed as below. As shown in Table 3, our algorithm has achieved better performance results compared to the other reconstruction algorithms mentioned above.  
As shown in Table 3, when different magnification and experimental data combinations are used. Our algorithm is superior to six other advanced super-resolution algorithms in which the PSNR value of the proposed algorithm exceeds others. For the 2X test1 experimental data, the PSNR value generated is 0.45 dB higher than that generated by the  
methods are applied under a 2X and 4X of test1 and test4 experimental data, which directly indicates that our algorithm has outperformed others.
## 5 Developments in Improving Biodiesel Production from Animal Fats
The use of a new nano catalyst consisting of CaO/CuFe\({}_{2}\)O\({}_{4}\) during the transesterification process was successfully assayed for biodiesel production from chicken fat [69]. A sodium silicate catalyst that does not saponify with free fatty acids during transesterification was recently assayed to produce biodiesel that could be blended up to 30% with diesel, giving good performance. The brake specific fuel was 26% higher than diesel and the brake thermal efficiency was 4% lower while CO was reduced by 24.4% and hydrocarbons by 22.9%. However, no emission was increased by 11% at full load [25]. Shells of _Mytilus galloprovincialis_--waste from fish industry--containing CaO that can be used as a catalyst, were used for transesterification of jojoba oil. As CaO could be contaminated with CO\({}_{2}\) and H\({}_{2}\)O, it was calcined immediately before use [126]. Calcined scallop shell was also reported as a very active catalyst for transesterification of rapeseed oil [127]. Recently, a cheap and safe catalyst consisting of metal hydrated salts was proposed for the pretreatment of animal fats with a high content of free fatty acids that could be esterified up to 99% with alcohol under mild conditions [128]. The methyl esters remained in the oily phase and could be used for transesterification directly with alkaline catalysts. On the other hand, a bioreining strategy for animal fat waste was proposed for the conversion of free fatty acids into triglycerides that could be blended with fossil diesel and be used in engine combustion systems [129]. Recently, adsorbents like magnesium aluminum hydroxycarbonate and 1,3,5-trimethyl-2,4,6-tris(3,5-ditert-butyl-4-hydroxybenzyl) benzene were proposed to enhance the oxidative stability of biodiesel and its blends and therefore retard their degradation. The acid value could be reduced up to 9%. In this way, the adsorbents can remove the precursors of the aging of biodiesel by stabilizing the generated free radicals and preventing them from starting new oxidation chains [130]. Precipitates of sterol glucosides are found in biodiesel produced from vegetable oils and may produce filter blockage. Their removal is achieved through adsorption with 3% silica at 112 \({}^{\circ}\)C for 72 min [131]. Finally, it is important to mention that Neste renewable diesel is produced through the hydrogen catalyzed conversion of triglycerides into the corresponding alkanes and propane. Nearly 3 million tons are produced in five plants and mixed with fossil diesel for its use in aviation, turbines, generators and ships [132].
## 2 Methodology
### IPSO-LSSVM Model
#### 2.2.2 PSO Algorithm Principle  
Iterative optimization is the basis of the evolutionary computation algorithm known as PSO optimization [28]. The PSO algorithm creates massless particles to represent the birds in the swarm. The particle has two attributes: position and velocity. The position represents the direction of movement, and the velocity represents the speed of movement. To identify the optimal fitness as the present global optimum, each particle independently finds the optimal solution in the search space, records it as the fitness of the current individual, and shares the fitness with other particles in the complete particle swarm. Then, all particles update their velocity and position according to the current individual fitness value and the current global optimum. The following equations are used to update the particle's position and velocity:  
\[V_{id}=\tau V_{id}+C_{1}r_{1}(0,1)(P_{id}-X_{id})+C_{2}r_{2}(P_{gd}-X_{id}) \tag{11}\]  
\[X_{id}=X_{id}+V_{id} \tag{12}\]  
where \(\tau\) is the inertia weight coefficient; \(V_{id}\) and \(X_{id}\) represent the velocity and position of particle \(i\) in \(d\) dimension, respectively; \(r_{1}\) and \(r_{2}\) are a random number between 0 and 1, respectively; \(C_{1}\) and \(C_{2}\) are the learning constants; \(P_{id}\) represents the historical optimal solution of particle \(i\) in \(d\) dimension; and \(P_{gd}\) represents the optimal position of the whole swarm in \(d\) dimension.
# Highway Tunnel Defect Detection Based on Mobile GPR Scanning
## 4 Experiment and Results
### Imaging Result and Analysis  
In order to obtain the detection results such as rebar spacing, second-lining thickness, and disease characteristics, the detection area is imaged by the BBP algorithm. The imaging result is shown in Figure 11.  
Within the range of ZK39 + 753-755, the double-layered dot-like reflective energy concentration is visible, and the depth of these concentrations fluctuates slightly at about 0.25 m. The horizontal direction is evenly distributed in a 4 cm spacing. Consequently, it is presumed to be a double-layered reinforced skeleton inside the structure. After ZK39 + 755, the reflected energy is significantly weakened, and the reflection interface is deformed. Therefore the moisture content at this location is inferred to be relatively high, resulting in rapid attenuation of electromagnetic signals. The upper reflective layer disappears at ZK39 + 757 and 759. Consequently, it is speculated that the seepage channel is developed there. In the range from ZK39 + 758 to 760, the steel layer can be seriously deformed and protrude into the tunnel. It is inferred that there is a defect in the structure. An obvious sub-interface can be seen at a depth of about 0.55 m, which should be the tunnel second lining and primary lining sub-interface.  
Figure 11: With the BBP algorithm, the position of the reinforcement, the thickness of the structural layer, and the deformation of the reinforcement layer are clearer in the image.  
Figure 10: Radar images after preprocess show a distinct layer of rebar reflections, but the reinforcement is not recognizable. Moreover, due to the multiple reflected wave interference caused by the deformed high reinforcement layer, the thickness of the second-lined structure cannot be identified. (**a**) 400 MHz; (**b**) 900 MHz.  
Based on the above analysis, it can be seen that the spacing of the reinforcement in the structure is about 4 cm, and the thickness of the second-lined structure is about 0.55 m, which is consistent with the design. The defects of the tunnel structure are mainly distributed in ZK39 + 755 and ZK39 + 758-760. After the inspection was completed, the corresponding location of the tunnel was inspected for visual quality, and obvious cracks and groundwater seepage (repaired) were visible near ZK39+760, as shown in Figure 12.
# Joint Maintenance Strategy Optimization for Railway
## 3 Optimization Model of the Joint Maintenance Strategy
### Joint Maintenance Probability Model
#### 3.2.1 Maintenance Probability Model
\]  
3 If \(i>\ k+1\), the period of reproriling is not adjacent to the periodic inspection cycle. \[ \begin{array}{ll}L(t^{m-1},i\Delta t)&=\left\{Xw(t^{m-1},i\Delta t)\geq Lsw ^{l},Xw(t^{m-1},k\Delta t)<Lsw^{l},\ldots,Xw(t^{m-1},(i-1)\Delta t)<Lsw^{l},i>k+1 \\ &=\left\{X_{T_{m-1},T_{m-1}+(i\Delta t-i^{m-1})}^{d}\geq Lsw^{l},X_{T_{m-1},T_ {m-1}+(k\Delta t-i^{m-1})}^{d}<Lsw^{l},\ldots,X_{T_{m-1},T_{m-1}+((i-1)\Delta t -i^{m-1})}^{d}<Lsw^{l}\right.\end{array} \right.\end{array}\]  
According to the theory in the last section, the probability \(P(L(t^{m-1},\ i\Delta t))\) is obtained as \[P\big{(}L\big{(}t^{m-1},i\Delta t\big{)}\big{)}=\left\{ \begin{array}{l}1-\Phi \left(\frac{Lss^{\mu}-t_{m-1}^{d}\tau_{m-1}\tau_{m-1}+(\Delta t-\mu-1)}{t_{m-1} ^{d}\tau_{m-1}\tau_{m-1}+(\Delta t-\mu-1)}\right),\ k=i\\ \left[1-\Phi\left(\frac{Lss^{\mu}-t_{m-1}^{d}\tau_{m-1}+(\Delta t-\mu-1)}{t_{m- 1}^{d}\tau_{m-1}\tau_{m-1}+(\Delta t-\mu-1)}\right)\right]\cdot\Phi\left(\frac {Lss^{\mu}-t_{m-1}^{d}\tau_{m-1}\tau_{m-1}+(\Delta t-\mu-1)}{t_{m-1}^{d}\tau_{ m-1}\tau_{m-1}+(\Delta t-\mu-1)}\right),k=i-1\\ \left[1,-,\Phi\left(\frac{Lss^{\mu}-t_{m-1}^{d}\tau_{m-1}+(\Delta t-\mu-1)}{t_ {m-1}^{d}\tau_{m-1}\tau_{m-1}+(\Delta t-\mu-1)}\right)\right]\cdot\prod_{p=1}^ {i-p}\Phi\left(\frac{Lss^{\mu}-t_{m-1}^{d}\tau_{m-1}^{d}\tau_{m-1}+(\Delta t- \mu-1)}{t_{m-1}^{d}\tau_{m-1}\tau_{m-1}+(\Delta t-\mu-1)}\right),t^{m-1}<k \Delta t<(i-1)\Delta t\end{array} \right. \tag{13}\]  
Event \(A(i^{m}\Delta t)\) is denoted as the \(m\)-th preventive maintenance at time \(i^{m}\Delta t\), and event \(B(t^{m})\) is denoted as the \(m\)-th corrective maintenance at time \(t^{m}\). The recursion relations between the \(m\)-th maintenance and the \((m-1)\)-th maintenance is  
\[P(A(i^{m}\Delta t))=\sum_{m^{-1}=m-1}^{i^{m}-1}P(A(i^{m-1}\Delta t))\cdot P(A( i^{m-1}\Delta t,i^{m}\Delta t))+\sum_{k=1}^{i^{m}}\int_{(k-1)\Delta t}^{k \Delta t}dP(B(t^{m-1}))\cdot P(A(t^{m-1},i^{m}\Delta t)) \tag{14}\]  
\[P(B(t^{m}))=\sum_{m^{-1}=m-1}^{i^{m}-1}P(A(i^{m-1}\Delta t))\cdot P(B(i^{m-1} \Delta t,t^{m}))+\sum_{k=1}^{i^{m}}\int_{(k-1)\Delta t}^{k\Delta t}dP(B(t^{m -1}))\cdot P(B(t^{m-1},t^{m})) \tag{15}\]  
In particular, \(P(A(i^{1}\Delta t))=P(A(0,i^{1}\Delta t))\), \(P(B(t^{1}))=P(B(0,t^{1}))\). The \(P(L(i^{m}\Delta t))\) is the probability that the wheel diameter deterioration exceeds the limit in the \(i\)-th cycle after the \(m\)-th reproriling. \[P(L(i^{m}\Delta t))=\sum_{i^{m}=m}^{i-1}P\big{(}A(i^{m}\Delta t)\big{)}\cdot P (L(i^{m}\Delta t,i\Delta t))+\sum_{k=1}^{i}\int_{(k-1)\Delta t}^{k\Delta t}dP( B(t^{m}))\cdot P(L(t^{m},i\Delta t)) \tag{16}\]
# Interactions in Augmented and Mixed Reality: An Overview
## 3 Research Results
### Sensor-Based Modality
#### 3.4.3 Location-Based  
Another context in the sensor-based modality that provides interaction for AR and MR environments is the location-based context. Radio frequency identification (RFID) uses passive, active, or semi-passive electronic tags that store a small amount of data, usually an ID or a link, and need readers to obtain the data from the tags. The communication of readers with the tags is made through RF [169]. Tags are deployed across the environment, and readers are carried on or attached to the positioning subjects. [PERSON] et al. [170], in their work \"Presence and digital tourism\", mention that using GPS/GNSS coordinates and near-field communication (NFC) can create location-based triggered events for user interaction. [PERSON] [171] designed a novel model for evaluating educational AR games. Using the _location-aware triggered events_ method, the participants can interact with virtual historical figures and items, which GPS triggers to appear on their personal digital assistant (PDA). Other ways of acquiring the user's location to achieve interaction regarding navigation or present information include, without being limited, dead reckoning and WiFi [172; 173], visible light communication and cellular networks (GSM, LTE, 5G) [174; 175].
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 2 Related Work
### How Our Work is Different from Baseline Scheme?  
In this paper, similar to LMPC routing protocol, both proposed schemes (FLMPC-One and FLMPC-Two) also direct multiple copies of a data packet. However, they are different from LMPC as follows:  
* LMPC establishes binary tree from the source node. While, FLMPC-One and FLMPC-Two establish binary tree only from the sensor nodes which reside near the layer. It results in energy efficiency.
* LMPC forwards the data packet to the sensor node which lies in the transmission range irrespective of considering void holes. However, both proposed schemes direct data packet on the path which ensures two or three hops neighbor information. It increases PRR of the network.
* In LMPC, data packets not necessarily pass through the sensor nodes residing near the layer. In this way, less copies of data packet are generated from the sparse regions. However, every data packet passes through sensor nodes which lie near the layers to generate multiple copies in both FLMPC-One and FLMPC-Two. This results in reliability and almost elimination of retransmissions.
## 1 Introduction
### Objectives of This Study  
Taking into account the aspects described previously, the main objective of this study involves the development of a new methodology to automatically detect the homologous points from two DEMs (to be assessed and reference) that are suitable to assess the positional accuracy (vertical and horizontal) of the dataset to be analyzed. This implies that points must be well-defined, both on the sources and on the ground (following the ASPRS standard [12]). As a secondary goal, the procedure should allow us to increase the sample size of the points, improving the results given by the manual procedures. Another goal is related to the analysis of those parameters needed to execute the procedure and their relationships with the data characteristics, such as the spatial resolution of both DEMs.  
This paper is structured as follows: First, a description of the proposed methodology is outlined, including the algorithm used to achieve the main objective of this study. After that, a description of the data used in the application of this methodology is given. Subsequently, a summary of the main results obtained after this application is given, including a discussion of the results and their impact on quality assessment, considering those aspects that affect the proposed methodology. Finally, the main conclusions of this study and future works will be presented.
## 5 Experimental Validation
### Emulation Results  
The simulated results were validated using the first test case (the one presenting the highest PV power variability) and the emulation platform under a real time emulation test. The input data were scaled-down according the emulator's power ratings. The outputs of the EMS were sent, periodically (\(T_{EMS}=5\) min), to the devices (emulated). In Figure 10, the experimental results can be observed, showing how the response was very similar to the simulation results. In particular, the same tendency in the diesel units connections and disconnections as well as in the battery utilization can be observed. An important observation is that, generally, the generation was greater than the load because the emulated inverters had power losses.  
Figure 9: Microgrid description.
# Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
## 4 Challenges
H. Evaluation Dilemmas: e-nose technology holds immense promise in detecting volatile compound fingerprints of food samples, enriching organoleptic evaluations encompassing aspects like flavor and aroma [214]. However, the challenge persists in establishing consistent and reliable evaluation methodologies. Traditional sensory evaluations hinge on expert panels, a practice that can be time-intensive, expensive, and susceptible to evaluator-related inconsistencies arising from factors such as fatigue or stress. The fusion of artificial sensing systems into standardizing evaluation approaches represents a leap forward, although this domain's evolution is still in its nascent stages, warranting further exploration [215]. I. Reliability and Prediction: e-nose sensor arrays translate chemical changes into electrical signals, contributing to measurement, control, and prediction systems. Sustaining predictive model performance becomes intricate due to factors such as sensor sensitivity fluctuations, signal source dynamics, and evolving operational conditions. While model training establishes a predictive linkage between input and output, model performance may degrade during testing due to temporal shifts and instrument variability [216]. J. Time Efficiency Pressures: conventional quality evaluations, often involving expert panels, are renowned for their time-intensive nature and resource-heavy demands. These evaluations can be plagued by inconsistencies and subjective biases stemming from evaluator elements like mental and physical states [217]. While e-nose technology opens avenues for streamlining evaluation processes, guaranteeing efficient and objective assessment methodologies across various industries presents an ongoing challenge compared to traditional paradigms [218]. K. Mitigating Over-Fitting: overfitting, a perennial concern in data analysis, is exacerbated by the intricate interplay of numerous variables within e-nose applications. Tackling this challenge mandates the identification of pivotal variables for data analysis and computation. Striking a harmonious balance between transfer sample sizes while evading an undue focus on transfers to avert overfitting stands as a pivotal task. Tailoring models to adequately encompass variations and intricacies in e-nose data and applications is paramount to forestal overfitting and ensure steadfast performance [219]. L. Nuanced Classification: e-nose systems confront challenges stemming from sensor drift, influenced by factors such as sensor longevity and environmental fluctuations. The evolving nature of sensor responses over time can erode the quality of regression and classification models [207]. Crafting classification mechanisms that embody robustness, incorporating stage classifiers and advanced signal processing techniques, emerges as a necessity to precisely capture and classify evolving data patterns. Navigating these challenges assumes pivotal significance in upholding accurate classification within dynamic environments [220].
## 3 Methodology
### Discriminative Attention Multi-Network (DAM-Net)
#### 3.1.2 Specific Part Identification Module (SPIM)  
To overcome the challenge of the OFLM typically detecting incomplete sections of a target, we enhance our model's robustness by introducing a SPIM and a discriminative branch. Analysis of the activation map \(A\) reveals that regions with higher activation values are often associated with crucial parts of the pest, like its head or tail. To capture these informative areas, we employ a sliding window technique that isolates windows containing critical information as discriminative images. This process is further refined by adopting a full CNN, reducing computational effort in a manner similar to how OverFeat [30] processes feature maps. Subsequently, we perform channel-wise aggregation of the activation maps, \(A_{w}\), for each window and determined the mean activation value, following the procedure outlined in Equation (11):  
\[\bar{a}_{w}=\frac{\sum_{x=0}^{W_{w}-1}\sum_{y=0}^{H_{w}-1}A_{w}(x,y)}{H_{w} \times W_{w}} \tag{11}\]  
\(H_{w}\) and \(W_{w}\) represent the height and width of a feature map corresponding to a particular window. We arrange the windows in order based on their \(\bar{a}_{w}\) values, which denote the informativeness of the regions they encompass. A higher \(\bar{a}w\) value signifies greater significance, as shown in Figure 4. To select the most representative windows while avoiding redundancy, we employ the non-maximum suppression (NMS). This technique allows us to choose a diverse set of windows as discriminative images that vary in scale, ensuring more robust and comprehensive localization of target parts.  
Figure 3: The process of the OFLM.
## 3 Design and Implementation of the Multi-Attribute PLA Scheme for LoRaWAN  
This section outlines the design, implementation, and evaluation of a multi-attribute PLA scheme for LoRaWAN-based WSNs. The proposed methodology focuses on securing communication against spoofing attacks, with a forest fire detection scenario as the primary use case.