esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography
## 1 Introduction
Despite these findings, there remains a research gap in understanding the precise temporal thermal behavior of different land cover types, particularly under varying atmospheric conditions such as humidity, wind speed, and cloud cover, which influence heat retention and dissipation patterns. Temporal monitoring of LST at different times of the day is essential for capturing diurnal thermal dynamics and understanding how surfaces respond to solar radiation. Studies have shown that asphalt surfaces can experience temperature increases of up to 30 \({}^{\circ}\)C from morning to afternoon, while vegetated areas remain more thermally stable due to their transpiration-driven cooling effects [11]. Water bodies, on the other hand, exhibit minimal temperature fluctuations due to their high thermal inertia, making them effective heat buffers [12]. UAV-based thermal monitoring allows researchers to capture these changes at a high temporal resolution, providing deeper insights into heat accumulation and dissipation patterns. However, challenges remain regarding the accuracy of UAV-derived LST measurements, which can be influenced by factors such as sensor calibration, surface emissivity variations, and atmospheric conditions [13]. Several studies comparing UAV-based thermal imagery with in situ infrared thermometer readings have reported root mean square errors ranging from 1.5 \({}^{\circ}\)C to 5.5 \({}^{\circ}\)C, depending on land cover and meteorological conditions [14]. The current findings demonstrate that sensor calibration and validation are a prerequisite for utilizing UAV-derived thermal data in studies demanding absolute temperature measurement. Despite recent advancements in UAV-based thermal imaging, comprehensive studies integrating these methods with meteorological temperature records to analyze diurnal LST variations across multiple land use/land cover (LULC) types remain scarce. While previous research has largely concentrated on urban heat island assessments or agricultural applications, the high-resolution dynamics of surface temperature over the full diurnal cycle have not been thoroughly investigated--a gap that is increasingly critical given the rising frequency of extreme heat events under climate change. Addressing this need, our study investigates the diurnal thermal behavior of five distinct LULC classes--bare soil, buildings, grassland, paved roads, and water bodies--using UAV-mounted thermal cameras. Data are captured at four key periods--morning, afternoon, evening, and midnight--facilitating a time-specific assessment of LST variations. By integrating high-resolution UAV-based thermal images with robust statistical analyses, this research systematically examines the complete diurnal temperature cycle across diverse surfaces. Such an approach enhances our understanding of surface-atmosphere interactions and localized thermal anomalies while providing critical information for urban microclimate modeling, heat island mitigation strategies, and climate-adaptive urban planning.
## 6 Analysis of Results
### Energy Consumption  
Energy consumption is the total amount of energy consumed in the network throughout the whole simulation. It is known that the energy consumption increases when the number of nodes increases, because more duplicate packets are generated and forwarded. We plot the simulation results of energy consumption by varying the number of nodes and the number of transmission ranges. The energy consumption of EESEVBF is lower than that of ESEVBF, as shown in Figures 13-15. The reason is the instantiation of the duplicate packets. In ESEEVBF, the reproducing nodes generate the duplicate copies and forward them to their PFNs, as discussed in Section 4. The transmission energy on the duplicate packet is consumed from the reproducing node to the sink, which results in increased total energy consumption. In contrast, our proposed protocol not only selects the favorable path with respect to the packet advancement and residual energy at the first and second hop, but also tackles the hidden terminal problem (as discussed in Section 4) resulting in a reasonable reduction in duplicates packets, as shown in Figures 16-18.  
Figure 11: Number of Nodes vs. PDR.  
Figure 12: Number of Nodes vs. PDR.  
Figure 14: Number of Nodes vs. Energy Consumption.  
Figure 13: Number of Nodes vs. Energy Consumption.  
Figure 15: Number of Nodes vs. Energy Consumption.  
The energy consumption increases when the number of nodes increases. This is because some of the packets do not reach their fourth hop in the sparse network, resulting in lower packet delivery ratio and energy consumption. On the contrary, most of the packets are successfully delivered causing higher energy consumption. The difference between EESEVBF and ESEVBF is lessened for the lower number of transmission range and nodes because of the next forwarder selection mechanism. From Equation (1), it is difficult to find the node at the second hop, which competes with the first-hop forwarder nodes in case of a sparse network, and the forwarder is purely selected from the weight of the first-hop PFNs. Therefore, in large transmission ranges and dense networks, the effectiveness of our proposed protocol is clearer. The simulation results show that the proposed protocol reduces the energy consumption by an average of 10.45% in comparison to ESEVBF, as listed in comparison Table 3.  
Figure 16: Number of Nodes vs. Data Copies Forwarded.  
Figure 17: Number of Nodes vs. Data Copy Forwarded.
## 4 Panel Data Analysis-Based Surface Texture Prediction Model
### Prediction Results of Long-Term Surface Texture  
The SMTD development in the long-term (from year 2021 to 2026) is predicted by the model based on the one year (2017), two years (2017 to 2018), three years (2017 to 2019), and four years (2017 to 2020) SMTD panel data. The predicted results are shown in Figure 9. It can be seen that the predicted SMTDs of SRT-14, SRT-7, SRT-9, and SRT-19 display a similar trend in the long-term. However, the prediction model based on only one year SMTD data (year 2017) shows the fastest decrease trend in the long-term, whereas the prediction model based on four years SMTD panel data (from 2017 to 2020) shows the slowest decrease tendency. The longer the scale of input data for the model development, the less the predicted SMTD value decreases.  
Figure 8: Comparison of measured and predicted SMTD based on different time scales of SMTD panel data. (**a**) SRT-14 (Dense-graded asphalt concrete, SAC13-65%). (**b**) SRT-7 (Dense-graded asphalt concrete, SAC13-70%). (**c**) SRT-9 (Porous asphalt concrete, PAC13-80%). (**d**) SRT-19 (Dense-graded asphalt concrete, SMA13-75%).  
Taking SRT-14 as an example, the SMTD prediction models developed based on one year, two years, three years, and four years panel data are used for long-term prediction, and the SMTD value is reduced by 0.14 mm, 0.16 mm, 0.10 mm, and 0.07 mm, respectively, from year 2021 to 2026. Notably, the input SMTD panel data across a short time scale has difficulty in achieving a high-precision long-term prediction. According to the short-term prediction results (Figure 8), the predicted SMTD development in the fourth year (2020) based on the first three years (2017 to 2019) panel data model agrees well with the measured data, while the prediction models based on one year (2017) or two years (2017 to 2018) panel data provide less accurate results. It can be deduced that the predicted SMTD value by the four years panel data-based model is likely closer to the long-term actual SMTD value. The analysis shows that the longer time scale of SMTD panel data for model development can guarantee the robustness of the model's long-term prediction ability.  
Figure 9: Predicted SMTD results for year 2021 to 2026 for different time scales of panel data analysis-based model. (**a**) SRT-14 (Dense-graded asphalt concrete, SAC13-65%). (**b**) SRT-7 (Dense-graded asphalt concrete, SAC13-70%). (**c**) SRT-9 (Porous asphalt concrete, PAC13-80%) (**d**) SRT-19 (Dense-graded asphalt concrete, SMA13-75%).
###### Abstract  
Imbalanced data are a non-trivial problem in deep learning. The high variability in the number of samples composing each category might force learning procedures to become biased towards classes with major cardinality and disregard classes with low instances. To overcome such limitations, common strategies involve data balancing using resampling techniques. The cardinality of overnumbered categories is often lowered by sample deletion, thus reducing the data space where the model can learn from. This paper introduces a new approach based on data balancing without sample deletion, allowing for biasing reduction in instance localization and classification tasks. The method is a multi-stage pipeline starting with data cleaning and data filtering steps and ending with the actual data balancing process, during which overnumbered samples are not deleted but divided into multiple sub-classes. In this way, the model can learn from balanced data distribution in which some classes have a high correlation factor. To evaluate the effectiveness of the method in real-life scenarios, a case study in the field of precision agriculture has been developed, motivated by the fact that the publicly available datasets for pest classification often reflect the real-world imbalanced distribution of pests, making the task challenging. Two models for the localization and recognition of pests belonging to several species are also indicated. The obtained results show the method's validity as the performance both in the detection and classification tasks outperforms the state-of-the-art methods. The general nature of the conceived balancing technique may make the approach useful in other application fields.  
imbalanced data distribution; deep learning; pest detection and classification; precision agriculture; dataset biasing Article
## 2 Materials and Methods
### The State-Dependent Riccati Equation (SDRE) Method  
First, the nonlinear equation is given by  
\[\dot{\mathbf{x}}=\mathbf{f}(\mathbf{x})+\mathbf{g}(\mathbf{x})\mathbf{u},\;\mathbf{x}(0)=\mathbf{x}_{0} \tag{10}\]  
Let us suppose that the origin \(\mathbf{x}(0)=\mathbf{0}\) is an equilibrium point of the system. The control input \(\mathbf{u}\) is such that the following functional \(\mathbf{j}\) is minimized:  
\[J=\frac{1}{2}\int_{0}^{\infty}\Big{(}\mathbf{x}(t)^{T}\mathbf{Q}(\mathbf{x})\mathbf{x}(t)+\mathbf{ u}(t)^{T}\mathbf{R}(\mathbf{x})\mathbf{u}(t)\Big{)}dt \tag{11}\]  
Figure 4: A singular surface (a singularity in the dimension of angular momentum) when \(\beta=30\) degrees (**a**), \(\beta=54.74\) degrees (**b**), and \(\beta=75\) degrees (**c**).  
where \(\mathbf{Q}(\mathbf{x})\geq\mathbf{0}\) is the state-dependent weighting (SDW) matrix of state vector \(\mathbf{x}\), \(\mathbf{R}(\mathbf{x})>\mathbf{0}\) is the SDW matrix of input vector \(\mathbf{u}\), and both of them depend on state \(\mathbf{x}\). To design the controller, if there is an optimal index \(\mathbf{\varphi}(\mathbf{x})\) for the quadratic form evaluation function \(\mathbf{x}^{T}\mathbf{Px}\), its mathematical form can be rewritten in the form of a positive definite symmetric matrix \(\mathbf{P}\) as follows:  
\[\mathbf{\varphi}\rightarrow\mathbf{x}^{T}\mathbf{Px}\text{, }\mathbf{f}(\mathbf{x})\rightarrow\mathbf{ A}_{c}(\mathbf{x})\mathbf{x}\text{, }\mathbf{g}(\mathbf{x})\to\mathbf{B}_{c}(\mathbf{x}) \tag{12}\]  
where a system \(\dot{\mathbf{x}}=\mathbf{A}_{c}(\mathbf{x})\mathbf{x}+\mathbf{B}_{c}(\mathbf{x})\mathbf{u}(\mathbf{x})\) is the SDLR, and the SDLR has a degree of freedom of modeling. Moreover, \(\mathbf{A}_{c}(\mathbf{x})\), \(\mathbf{B}_{c}(\mathbf{x})\) are the state-dependent coefficient (SDC) matrices that depend on state \(\mathbf{x}\).  
This procedure forms the so-called extended linearization control method. We can now define the control input \(\mathbf{u}(\mathbf{x})\) such that  
\[\mathbf{u}(\mathbf{x})=-\mathbf{R}(\mathbf{x})^{-1}\mathbf{B}_{c}(\mathbf{x})^{T}\mathbf{P}(\mathbf{x})\mathbf{x} \tag{13}\]  
where \(\mathbf{P}(\mathbf{x})\) is a solution of the following SDRE:  
\[\mathbf{A}_{c}(\mathbf{x})^{T}\mathbf{P}(\mathbf{x})+\mathbf{P}(\mathbf{x})\mathbf{A}_{c}(\mathbf{x})-\mathbf{P}( \mathbf{x})\mathbf{B}_{c}(\mathbf{x})\mathbf{R}(\mathbf{x})^{-1}\mathbf{B}_{c}(\mathbf{x})^{T}\mathbf{P}(\mathbf{x })+\mathbf{Q}(\mathbf{x})=\mathbf{0} \tag{14}\]  
Figure 5 shows the summary of the above-described flow. In conclusion, the SDRE solution to the nonlinear regulator is a generalization of the time-invariant linear quadratic regulator (LQR) problem, where the matrices are state-dependent [18]. From an operating point of view, at each time step, the method treats SDC matrices as constants because of the \"freezing\" of the state. Therefore, by solving the LQR problem for each control cycle, it is possible to easily obtain the solution.
# Low-Thrust Nonlinear Orbit Control for Very Low Lunar Orbits
## Appendix B Sensitivity Analysis with Respect to the Gravitational Model  
A sensitivity analysis is performed in order to assess the truncation error associated with the use of several harmonics of the selenopotential, as well as third- and fourth-body perturbations. Nominal simulations along the circular target orbit 1 are considered, while retaining an increasing number of harmonics in four different simulation setups: (i) harmonics associated with coefficients \(|J_{lm}|>10^{-5}\), i.e., four zonal harmonics and four sectoral/tesseral harmonics (compare Table 10); (ii) harmonics associated with coefficients \(|J_{lm}|>10^{-6}\), i.e., 34 zonal harmonics and 11 sectoral/tesseral harmonics (compare Table 11); (iii) harmonics up to order 50 (compare Table 12); (iv) harmonics up to order 100 (compare Table 13); (v) harmonics associated with coefficients \(|J_{lm}|>10^{-5}\) without third- and fourth-body effects (compare Table 14). From the inspection of Tables 10 through 14, it is apparent that for very-low-altitude lunar orbits, very accurate results can be obtained by including harmonics associated with coefficients \(|J_{lm}|>10^{-6}\).
## 3 Gray Value Compression
### Huffman Coding  
Huffman encoding is a lossless variable-length prefix encoding. This greedy algorithm considers the occurrence of each symbol in an optimal way, i.e., the characters that occur more frequently receive shorter codewords, while the characters that occur less frequently receive longer codewords [41]. In this way, Huffman coding reduces the number of bits needed to represent a set of symbols.  
In this subsection, EEDC compression without modification of the grey values is compared with JPEG and JPEG 2000 compression methods at five compression ratios (0.8, 0.4, 0.2, 0.1, and 0.05 bpp). The compression analysis is performed for the previously mentioned Figure 1a).  
The smaller the value of the AAE parameter, the better the quality of the reconstructed image. At a compression ratio of 0.8 bpp, as we can see from Table 2, the JPEG and JPEG 2000 compression methods are better than EEDC. At 0.4 bpp, JPEG 2000 achieves the best result, followed by EEDC, while JPEG has the worst result. At 0.2 bpp, JPEG 2000 is still better than EEDC, but, at 0.1 and 0.05 bpp, EEDC achieves the best quality of the reconstructed image. Figure 4 shows a graphical relationship of AAE between the above compression methods at different compression ratios.
## 4 Results
### Global Models' Performance on Validation Data  
Figure 13 displays the validation data evaluation results and highlights the differences in performance across several parameters. The remarkable accuracy rate of 95% of the SVM and KNN models, especially, highlights their ability to categorize traffic accurately. With a precision of 94%, the SVM model shows that it is quite accurate at detecting affirmative cases. It demonstrates that it can capture significant instances of selective forwarding attacks with a 97% recall rate. With 95% F1 scores, precision, and recall, the KNN model performs in a well-balanced manner. This demonstrates its continuing accuracy in data categorization and usefulness in identifying patterns, suggesting focused forwarding attacks. Though it lags in accuracy at 94%, the LR model shows important precision, recall, and F1 score at 92%, 96%, and 94%, in that order. This shows that it can detect positive cases and record pertinent instances of selective forwarding attacks. In contrast, the accuracy of the NB model differs significantly, at 89%. However, its remarkably high recall at 99% raises the possibility that the system can successfully rule out false positives. Even then, the somewhat lower recall of 79% and F1 score of 88% suggest possible difficulties in accurately recognizing all relevant cases of selective forwarding attacks. These data provide a detailed overview of the models' performance environment and perceptive details on their applicability and potential for improvement in detecting selective forwarding attacks in RPL-based IoT networks.  
Figure 12: Global models’ performance on test data.
# A Robust Timing Synchronization Algorithm Based on PSSS for LTE-V2X
## 5 Results and Discussion
### Hardware-in-the-Loop Simulation
In contrast, the SSA achieves a CDR of 0.9 at \(-\)7 dB, which is about 3 dB worse than the improved algorithm. Figure 14 depicts the performance of the improved algorithm and traditional algorithms in the Urban-NLOS scenario. In Figure 14a, the velocity is 0 km/h, and in Figure 14b, the velocity is 50 km/h. From these simulation results, the following observations can be made:  
Figure 13: Comparison of improved and traditional algorithms in Highway-LOS. (**a**) 0 km/h. (**b**) 120 km/h. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **Channel Model** & **Velocity [km/h]** \\ \hline Urban-NLOS & Umi-NLOS micro-cell & 0 \\ Urban-NLOS & Umi-NLOS micro-cell & 50 \\ Highway-LOS & Umi-LOS micro-cell & 0 \\ Highway-LOS & Umi-LOS micro-cell & 120 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Channel models. The improved algorithm appears to have better PSSS detection performance when the velocity is 0 km/h in this scenario. It maintains a CDR close to 0.9 at \(-\)12 dB. In contrast, the SSA achieves a CDR of 0.9 at \(-\)9 dB, indicating a 3 dB performance gap compared to the improved algorithm. The M = 2 BCCA and CCA achieve a CDR close to 0.9 at \(-\)2 dB, highlighting a 10 dB degradation compared to the improved algorithm. 2. The improved algorithm also sustains superior PSSS detection performance when the velocity is 50 km/h in this scenario. At a velocity of 50 km/h, the improved algorithm maintains a CDR close to 0.9 at \(-\)2.5 dB. Conversely, the SSA achieves a CDR close to 0.9 at 2 dB, reflecting a performance gap of approximately 4.5 dB compared to the improved algorithm.
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 3 JRCO Algorithm
### Storage and Retrieving of Neighboring UAV State  
As mentioned earlier, in order to ensure that each UAV could smoothly construct an objective function in a disconnected state, each UAV needed to store and retrieve the state of the neighboring UAVs. The process was as follows: when the \(i\)-th UAV was not in a disconnected state in the \(t\)-th time slot, the \(i\)-th UAV needed to temporarily store \(\bar{\mathbf{r}}_{i}^{t}\), \(\hat{\mathbf{u}}\), and \(\bar{\mathbf{C}}_{\theta,i}^{t}\) at this time. Once the \(i\)-th UAV was in a disconnected state in the \(t\)-th time slot, the \(i\)-th UAV could continue to complete the construction of the objective function by calling the information of \(\bar{\mathbf{r}}_{i}^{t}\), \(\hat{\mathbf{u}}\), and \(\bar{\mathbf{C}}_{\theta,i}^{t}\) in the \(t\)-th time slot. Each UAV stored and retrieved information independently. If the UAV was not in a disconnected state in two consecutive time slots (such as the \(t\)-th and \(t+1\)-th time slots), the stored information in the \(t+1\)-th time slot could delete the information in the \(t\)-th time slot to save memory.  
The vector corresponding to the storage information of the \(i\)-th UAV location in the \(t\)-th time slot could be defined as:  
\[\mathbf{m}_{i,i}^{t}=\left[\mathbf{m}_{i,i,1}^{t},\mathbf{m}_{i,i,2}^{t},\cdots,\mathbf{m}_{i, i,M_{i}}^{t}\right]^{T} \tag{55}\]  
where \(\mathbf{m}_{i,i,k}^{t}=\left[\overline{x}_{i,k}^{t},\overline{y}_{i,k}^{t}, \overline{z}_{i,k}^{t}\right]^{T}\).  
The vector corresponding to the storage information of the composite measurement variance of AOA in the \(t\)-th time slot could be defined as:  
\[\mathbf{m}_{\theta^{\prime},i}^{t}=\left[\left(\sigma_{\theta^{\prime},i,1}^{t} \right)^{2},\left(\sigma_{\theta^{\prime},i,2}^{t}\right)^{2},\cdots,\left( \sigma_{\theta^{\prime},i,M_{i}}^{t}\right)^{2}\right]^{T} \tag{56}\]  
\[\mathbf{m}_{\theta^{\prime},i}^{t}=\left[\left(\sigma_{\theta^{\prime},i,1}^{t} \right)^{2},\left(\sigma_{\theta^{\prime},i,2}^{t}\right)^{2},\cdots,\left( \sigma_{\theta^{\prime},i,M_{i}}^{t}\right)^{2}\right]^{T} \tag{57}\]  
The vector corresponding to the storage information of the target location estimation in the \(t\)-th time slot was defined as:  
\[\mathbf{m}_{u,i}^{t}=[\hat{x}_{u},\hat{y}_{u},\hat{z}_{u}]^{T} \tag{58}\]  
Even if the \(i\)-th UAV could not sense any state information shared by other UAVs within a certain time slot, it could still smoothly generate optimization objectives based on the rules above.
# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 3 Experimental Design
### Materials and Experimental Setup  
While a great deal of open and premium software is available for drive test (DT), Geneva Assistant v16 was used in this study, as it bridges the gap between 3 GPP MBB and the 4G LTE network. Besides being widely available, it also has a friendly interface for interacting with the data monitoring and acquisition process. The features of a DT assistant, such as the GENEX v16, are widely used in the measurement and study of network services and parameters, as they make test procedures practical, accurate, and convenient. Notable features of the DT assistant are:  
* Allows minimal modification of the terminal device and requires less technical knowledge.
* Automatic measurement and logging of DT data at rapid and programmable sessions.
* Mobility and ease of installation across multiple operating systems (for example, Windows, Linux, Mac).
* Universal and applicable across different terminal devices, such as mobile phones, for measuring network parameters without special equipment.
# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 3 Approach  
In this section, we present the main components of our approach. As visualized in Figure 1, the approach is composed of the **Sensor Data Collection** module, which collects the data from smartphone users via an application that has been developed for the purpose of this research and can be found in ref. [19]. After smartphone pressure sensor data are collected, noise is filtered out in the **Signal Filtering** module. The **Reference Pressure Extraction** module has two roles: (1) to filter outdoor data, and (2) to identify locations where pressure readings can be extracted. In the **Stair Removal** module, features that belong to intermediate heights (i.e., stairs or elevators) are rejected. Remaining pressure readings are later used in the barometric formula for **Altitude Estimation**. In the **Data Aggregation** module, we combine data from multiple users, while the **Floor Estimation** module has two roles: (1) to identify the number of floors in a set, and (2) to estimate the altitude of each floor. Finally, in the **CityGML Generator** module, a **CityGML Model** is dynamically generated.