esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Experiments and Result Analysis
### Analysis of Simulation Experiments
#### 4.2.2 High-Speed Mobile Scenario
In addition, to verify that ICCDRL does not have a large impact on other TCP protocols, the experiments still use the dumbbell topology described above to create four short-life flows, one of which runs the ICCDRL algorithm, while the other four flows run NewReno, Cubic, Westwood and DRL-based DCC, respectively, and count the average throughput of the four flows to obtain the friendliness of the ICCDRL algorithm. shown in Figure 7:1  
It is obvious from Figure 14 that, although ICCDRL occupies a higher share of bandwidth than other algorithms, it does not have a large impact on the throughput of other algorithms. This is due to the fact that ICCDRL uses the PPO2 algorithm for learning and sets congestion sensitivity factors and QoS weights to distinguish the importance of different services, which makes the congestion window change more smoothly and not too aggressively, showing that ICCDRL has good friendliness. (7) Comparison of Convergence Speed  
Figure 7:2 Evaluation of Friendliness. Figure 7:3 Fairness Comparison of ICCDRL at different BER (**a**) BER is 5% and (**b**) BER is 20%. DRL includes many different algorithms such as Deep Q Network (DQN) and Proximal Policy Optimization (PPO2). Among them, DQN uses a neural network to approximate the Q table, and after calculating the value function, the greedy algorithm is used to output Action, which is suitable for a discrete action space, while PPO2 is suitable for a continuous action space. This section compares the convergence speed of the PPO2 algorithm used by ICCDRL with that of DQN, and the experimental results are shown in Figure 7:4  
From the figure, it can be seen that the reward value of PPO2 stabilizes at 80,000 training steps as the number of training steps increases, indicating that the PPO2 algorithm has basically converged at this time. On the contrary, DQN still repeatedly oscillates drastically and fails to converge after training for up to 350,000 steps. It is verified that PPO2 has a fast convergence speed and the ICCDRL algorithm has convergence. In addition, ICCDRL utilizes the PPO2 algorithm for training and designs congestion-sensitive factors to distinguish the QoS weights of different services, resulting in a more fine-grained setting of the threshold for entering slow-start and the congestion window size. ICCDRL has low computational complexity and is more versatile in vehicle communications with low latency.
## 5 Identification Procedures and Validity
### Validity of the Proposed Identification Method  
Numerical experiments are performed to confirm the validity of the formulae for geometric deviations. At first, by using a random number table, the initial values of geometric deviations are selected and substituted into the mathematical model. Six numerical experiments are conducted at \(R_{\text{C}}\) = 50 mm, \(R_{\text{B}}\) = 360 mm, \(R^{\prime\prime}_{\text{B}}\) = 410 mm, \(Z_{\text{C}}\) = 300 mm and \(Z^{\prime}_{\text{C}}\) = 340 mm. Table 6 summarizes the eccentricities obtained at each measurement. Finally, the eccentricities are substituted into the above formulae to calculate the geometric deviations. The differences between the initial values and the identified values are shown in Table 7.  
From the results, it is found that the difference of angular deviations is less than 0.12 arcsecond and the difference of positional deviations is smaller than 0.37 \(\upmu\)m. Therefore, 12 geometric deviations of multi-tasking machine tools, including the squareness deviations, \(\alpha_{\text{YZ}}\) and \(\beta_{\text{YZ}}\), can be identified correctly by using the above formulae.  
Figure 6: Measurement procedures to identify twelve geometric deviations.
## 5 Experiment III: Listening Experience Rating
### Stimuli  
The same audio content as described in Table 2 was used. This was to use different listening cohorts to confirm the validity of the listener-preferred SBRs measured in Expt. II, since the system-suggested SBRs were derived from those values. This also allowed direct comparisons on listening experience provided by the listener-preferred SBRs and the optimal SBRs suggested by the system. For each of the five audio scenes three excerpts were drawn from Expt. II (Section 4.1), leading to 15 excerpts in total. Within each excerpt, the foreground speech and background sound were premixed at three constant SBR levels (\(-9\), \(0\) and \(9\) dB), and three scene-specific levels: \(\text{SBR}_{min}\) predicted by the BiDWGP metric (\(\text{SBR}^{\prime}_{min}\)), the listener-preferred SBR (LP) measured in Section 4.3, and the suggested-optimal SBR (OPT), i.e., \((\text{SBR}^{\prime}_{min}+\Delta)\) dB, where \(\Delta\) was 5.5 dB in all scenes except for 'News broadcasting' where \(\Delta\) was set to 23 dB, in order to match the higher listener-preferred level. In total, six SBR conditions were evaluated. Table 3 further details the SBRs yielded by the three scene-specific systems for each audio scene. The intensity of the foreground speech was equalised over all the audio clips; the background level was adjusted accordingly to meet the overall SBR requirements.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \(\text{SBR}^{\prime}_{min}\) & **LP** & **OPT** \\ \hline Football commentary & \(-3.7\) & \(0.4\) & \(1.8\) \\ News broadcasting & \(-7.5\) & \(15.6\) & \(15.5\) \\ Public speech & \(-2.5\) & \(3.7\) & \(3.0\) \\ Radio drama & \(-12.4\) & \(-6.1\) & \(-6.9\) \\ Radio poetry & \(-4.2\) & \(1.1\) & \(1.3\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: SBR values (dB) of \(\text{SBR}^{\prime}_{min}\), listener-preferred (LP) and optimal (OPT) in the audio scenes.
# Lifting Activity Assessment Using Kinematic Features and Neural Networks
## 4 Discussion
### Limitations and Future Developments  
The results obtained in this study can be applied to the experimental conditions that have been tested but little can be said regarding the applicability of the approach to composite and/or sequential manual lifting jobs characterized by significantly different lifting tasks [8, 39].  
Possible future developments of this study may be: (i) including, in the range of lifting task types those having the same LI but different multiplier values; (ii) testing also lifting conditions with LI values that are outside the quantized set (i.e., lower than 1, between 1 and 2, and between 2 and 3); (iii) analyzing how environment- and subject-related factors (i.e., temperature and humidity, sex,age, work experience, etc.) affect the results; moreover, while the current sample size was chosen to obtain an appropriate statistical power [40] and to adhere to the requirements given by artificial neural network training [41] increase the sample size to include analysis of gender effects. As regards this latter aspect, women and men show different risks of developing these disorders due to their physiological differences in perception of pain [42], fatigability [43], tendon properties [44], hormonal differences [44], anthropometry and muscular entities [45].  
It would be also interesting to test the performance of alternative measuring devices such as wearable inertial sensors and to compare them to the traditional measurements provided by the optoelectronic systems. This evaluation would make it possible to verify the validity of the method, which appears to be much more easily applicable in both indoor and outdoor work environments.
## 4 Discussion
### Feature Engineering  
A series of handcrafted VIs and GLCM textural features were generated, which were examined for their importance with regard to cocoa and non-cocoa classification. The sole use of the four most important VIs and textural features was the weakest input configuration, yielding the lowest accuracy scores (Table 4). When adding the 12 Sentinel-2 bands to these features, both OA and F1 were increased by 4.5% on average using the refined dataset. However, this fusion achieved an average increase of 0.8% for OA and 1% for F1 compared with the sole use of Sentinel-2 bands. Accordingly, the fusion of the Sentinel-2 bands with the 25 initial features (i.e., before selecting the most important ones) improved the mean OA by 0.4% and the F1 by 0.2% compared with the sole use of Sentinel-2 bands. As such, exploring the most important features had an added value in the classification but only when these features were combined with the Sentinel-2 bands. Yet, the accuracy gain from the inclusion of the features to the Sentinel-2 bands relative to the sole use of the Sentinel-2 bands was rather small. This finding is in line with previous classification studies that used a random forest [102; 107].  
Textural features were shown to be particularly beneficial for classification studies in heterogeneous areas [69; 105], and especially in the complex and fragmented landscape of West Africa, where smallholder farms of irregular shapes predominate [48; 108]. However, the window size plays a key role in the effectiveness of texture, and its selection mainly depends on the image resolution and the homogeneity or heterogeneity of the land-cover class [122]. The window size cannot be too small, only partially capturing the land-cover class, or too big, covering the neighbouring class [123]. In our study, we applied a 3 \(\times\) 3-pixel window size following the practice by [PERSON] et al. [45], but this size might have been too small considering the average cocoa plot area of 2.3 hectares and the 10 m spatial resolution of the Sentinel-2 image. Previous studies found that a window size of 5 \(\times\) 5-pixel was more suitable for detecting cocoa agroforestry and coffee using Sentinel-1 images [35; 48]. Therefore, the effectiveness of larger window sizes and/or the combination of various sizes for generating GLCM textural features could be investigated in future cocoa studies using optical data.
# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 2 Materials and Methods  
Figure 1 shows a flow chart summarizing the most important steps of the methodology followed, which include:  
1. Data acquisition and preprocessing.
2. Tree detection.
3. Change detection.
# A Survey of LoRaWAN for IoT: From Technology to Application
## 9 Tackling LoRaWAN Challenges and SWOT Analysis
### Security Challenges  
Several security challenges and vulnerabilities have already been addressed by the new standard [11]. The missing of end-to-end message integrity check leaves application payload data integrity check unprotected. This vulnerability is not addressed with the new standard. The malicious node in-between network server and application server can perform bit flipping attacks and these attacks can go undetected. Even worse, this can end in confidentiality compromise of application data if certain changes in application data provoke observable behavior to the attacker. One solution that can be used to ensure end-to-end MIC is to add another MIC between network server and application server. A single key can be used to generate MIC and check it between network and application server independent of the end node traffic.  
New LoRaWAN standard specifies the possibility for backward compatibility. This means that, when one of the ends, network server or end device, uses old specification standard, then they both fall back to version v1.0. In such a case, all the vulnerabilities of old standard are possible [132].
## 1 Introduction
Their efforts focus on minimizing the use of hazardous solvents during synthesis and purification, avoiding toxic substances, reducing energy consumption, and promoting synthesis under ambient conditions. In this context, deep eutectic solvents (DESs) have gained prominence. DESs are liquid systems formed by the interaction of two or more components, usually a hydrogen bond donor and a hydrogen bond acceptor, in specific molar ratios, resulting in a eutectic mixture with unique solvent properties [23; 24]. DESs represent a new class of low-cost, environmentally friendly solvents with great potential to replace traditional solvents. They emerge as promising substitutes for volatile organic solvents and ionic liquids, mainly due to their simple preparation procedure and the variety of low-cost starting components from natural, biodegradable, and sustainable sources [25]. Due to their high thermal stability, good dispersibility, remarkable ionic conductivity, wide electrochemical window, and self-assembly capability, DESs have recently been selected as suitable media for the chemical synthesis of metal catalysts for electrochemical and photocatalytic use [25; 26]. Moreover, a key advantage of DESs is their compatibility with numerous metal salts, which favors their incorporation into the final product [16; 27; 28; 29]. For these reasons, DESs have recently gained attention for the synthesis of catalytic materials, especially in combination with AOPs for water treatment, offering a sustainable alternative to conventional methods. Therefore, given their non-toxicity, easy accessibility, low price, and the fact that they are considered green solvents, in this article, it was decided to choose a DES as a starting point for the elaboration of a novel catalyst. This research investigates the novel synthesis of a catalyst of a ZnO/g-C\({}_{3}\)N\({}_{4}\) composite using a ternary DES and evaluates its application in different AOPs for the treatment of dye-contaminated water. Through the pyrolysis of the DES, it is intended to obtain composites formed by ZnO and g-C\({}_{3}\)N\({}_{4}\) (ZnO/g-C\({}_{3}\)N\({}_{4}\) composites). The characterization allows us to confirm the correct synthesis of these composites. Photocatalytic, photo-Fenton-like, and electro-Fenton-like tests are performed to evaluate its catalytic capacity and reusability by the degradation of Rhodamine B. This dye is widely used as a model pollutant to evaluate the usefulness of different types of catalysts in various AOPs in recent years [30; 31; 32; 33; 34]. The final objective is to study the feasibility of the synthesized catalyst by evaluating its effectiveness in water treatment applications. Given the limited exploitation of this field, this research aims to provide a more sustainable and cost-effective alternative to existing methods.
# Improving Low Earth Orbit (LEO) Prediction with Accelerometer Data
## 5 Conclusions  
With the development of the LEO related missions and applications, there will be a great demand of real time LEO precise orbit. In this study, three strategies with estimation of different dynamic parameters were conducted and the conclusions are summarized as follows  
1. The precision of LEO satellites orbits could reach the centimeter level with estimation of different kinds of dynamic parameters if precise GNSS products were adopted. However, under the current circumstances, RTS was not available for onboard processing. For the demand of the real time precise LEO orbit, at least a one-hour orbit prediction should be adopted.
2. For the current GNSS technology, LEO orbit prediction by using accelerometer data was feasible. Precision of one hour predicted LEO satellite orbits with initial conditions and dynamic parameters estimated with a 24 h arc could reach the centimeter level by using accelerometer data while that using empirical force models was at the decimeter level.
3. For the LEO orbit prediction over 20 min, the predicted LEO orbit precision with estimation of one set of biases and scales for each direction (strategy 1) was better than that of biases for each direction every 60 min (strategy 2), though the calculated orbit precision with the latter was better than the former method.
4. If there were enough uplink and downlink stations, which mean the interval of uplink and downlink could be shorter than 20 min, all these three strategies could be adopted for the real time applications.  
On all accounts, the accelerometer data had great potential to do the LEO orbit prediction for real time applications in the near future.
# A Reinforcement Learning Model of Multiple UAVs for Transporting Emergency Relief Supplies
## 3 Model Description
### Problem Definition  
In this study, we assumed a scenario in which last-mile distribution to isolated shelters due to tsunami inundation is performed. In this scenario, multi-rotor UAVs are used due to its ability to provide vertical take-off and landing (VTOL). It was assumed that sufficient quantities of relief supplies and batteries for recharging were stored at the nearest depot (distribution center) and that the demands of the isolated shelters were known in advance. To determine the UAV transport routes and the transport supplies strategy, we formulated the UAVRPs as multi-objective, multi-trip, multi-item, and multi-UAVs. Each UAV is subject to battery and payload limitations and cannot meet the demands of all shelters in a single trip; therefore, it makes multiple round trips between shelters and a depot until it completes. The case of road blockage that limits land access and the unavailability of helicopters for air supply transportation is also considered in the presented scenario. Therefore, the exclusive use of multi-UAVs is presented here. We acknowledge that the future incorporation of multi-UAVs in disaster response require specific protocols for the coordination and organization of these units together with other human-operated vehicles and aircrafts. A more detailed explanation is given below.
# Terrain Irregularity Sensing by Evaluating Feet Coordinate Standard Deviation
## 2 \(Sd\) Usage for Terrain Roughness Sensing
Also, the robot's ground clearance highly depends on supporting leg-reachable areas. This paper assumes that robot legs move well within this reachable area. Considering this, we can state that terrain with more than a 160 mm rough height is impassable. Knowing the maximal terrain roughness, the all-terrain roughness is categorized this way:  
* High roughness: 87 mm \(\leq z_{f}(i)<\) 160 mm;
* Average roughness: 43 mm \(\leq z_{f}(i)<\) 87 mm;
* Low roughness: 0 mm \(<z_{f}(i)<\) 43 mm;
* Flat terrain: \(z_{f}(i)=\) 0 mm. So, in our case, we consider terrain to have a high roughness if the robot leg coordinates fall between the maximum possible ground clearance and the ground clearance in a neutral robot stance. The average terrain roughness is when the coordinates fall between the neutral stance ground clearance and half this length (43 mm). Low-roughness terrain is when the coordinates are less than half the neutral stance ground clearance. Even though in this case the flat terrain value is 0 mm, in the case of real flat terrain, some higher value should be selected to avoid noise. \(SD\) values must be calculated for these categories to know what \(SD\) value each terrain roughness can give. \(SD\) value limits will be reached when three robot legs take a favorable extreme position and the other three take a negative position. For example, the \(SD\) limit value for low-roughness terrain will be reached when three legs are at a +43 mm position and the other three at \(-\)43 mm (Figure 3). The terrain roughness \(SD\) values can be categorized this way:  
* High roughness: 94 mm \(\leq SD<\) 175 mm;
* Average roughness: 47 mm \(\leq SD<\) 94 mm;
* Low roughness: 0 mm \(<SD<\) 47 mm;
* Flat terrain: \(SD\) = 0 mm. In most cases, while walking on natural rough terrain, the \(SD\) values (and mean) will be much smaller than the calculated values. So, for example, for the \(SD\) values to always be in high-roughness margins, three feet \(z\) coordinates must be equal to \(+h\), and the other three equal \(-h\), with each step, where h belongs to the high-roughness height range. However, due to mechanical limitations, robot legs cannot be lowered below \(-\)60 mm without changing their body position. However, even if the roughness heights were constant, there was a possibility that the robot's feet would land on the same plane, giving \(SD=0\). Figure 3: Robot feet positions on low-roughness terrain. By dividing the terrain into roughness categories, it becomes easier to select a specific gait for each category. For example, for moving over flat terrain, a faster gait like a tripod could be selected, because it is less important to carefully select foot placement. Gait selection dependence on terrain roughness requires separate research and is not in the scope of this paper.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 4 Challenges That Influence the Design of In-Body Antennas
### Biocompatible Encapsulation  
The process of covering the in-body antenna with biocompatible material is known as encapsulation. In Ref. [78], the effect of encapsulation on radiation efficiency is described through a comparative study between Zirconia and PEEK used as encapsulation shells for in-body antennas. It was observed that Zirconia demonstrates better results than PEEK due to its significantly lower loss and higher dielectric constant, which agrees with a higher concentration of the near field in the low-loss surrounding of the in-body antenna. Furthermore, a thicker encapsulation facilitates overall low losses. However, in the case of PEEK, this effect reaches saturation after a thickness of 2 mm, where the losses are approximately similar for a thickness of 3 mm. It was also noticed that PEEK is the kind of material, which can be handled and manufactured far more easily than Zirconia, thus being more suitable for IBBDs. In summary, low-loss encapsulation helps mitigate the loss by concentrating the near field in a low-loss region.
###### Abstract  
Thermal barrier coatings (TBCs) play a crucial role in safeguarding aero-engine blades from high-temperature environments and enhancing their performance and durability. Accurate evaluation of TBCs' porosity is of paramount importance for aerospace material research. However, existing evaluation methods often involve destructive testing or lack precision. In this study, we proposed a novel nondestructive evaluation method for TBCs' porosity, utilizing terahertz time-domain spectroscopy (THz-TDS) and a machine learning approach. The primary objective was to achieve reliable and precise porosity evaluation without causing damage to the coatings. Multiple feature parameters were extracted from THz-TDS data to characterize porosity variations. Additionally, correlation analysis and \(p\)-value testing were employed to assess the significance and correlations among the feature parameters. Subsequently, the dung-beetle-optimizer-algorithm-optimized random forest (DBO-RF) regression model was applied to accurately predict the porosity. Model performance was evaluated using K-fold cross-validation. Experimental results demonstrated the effectiveness of our proposed method, with the DBO-RF model achieving high precision and robustness in porosity prediction. The model evaluation revealed a root-mean-square error of 1.802, mean absolute error of 1.549, mean absolute percentage error of 8.362, and average regression coefficient of 0.912. This study introduces a novel technique that presents a dependable nondestructive testing solution for the evaluation and prediction of TBCs' porosity, effectively monitoring the service life of TBCs and determining their effectiveness. With its practical applicability in the aerospace industry, this method plays a vital role in the assessment and analysis of TBCs' performance, driving progress in aerospace material research.  
thermal barrier coatings; porosity characterization; terahertz time-domain spectroscopy; nondestructive evaluation; multi-feature fusion; machine-learning-based prediction; aerospace materials 4.0/.