esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
If the training sample is not representative enough, false reliability predictions are possible. Also, due to the increased computational complexity, the method may require a powerful computing environment for operation in real time, which limits its application in embedded systems with limited resources. The disadvantages of the study are the lack of real experiments on radar hardware stands, as simulation data were generated artificially, although using approximations close to the real environment. The issue of interaction with multi-sensor systems or scenarios with a large number of targets was also not considered. Possible directions of development are the expansion of the system to work with multi-position radars and multi-sensor structures. In particular, the integration of additional data channels, the construction of more complex neural network architectures (for example, ConvLSTM for spatio-temporal processing) will increase the adaptability and accuracy of support in complex and dynamic scenarios. Development is also advisable in the direction of including reinforcement learning for automatic learning of the control strategy in online mode. The current version of the LSTM model was trained on synthetic data with Gaussian noise parameters fixed at \(\sigma\) = 0.5 and \(\sigma\) = 2.0 to emulate varying measurement qualities. While this simplifies training and evaluation, we acknowledge that real-world jamming environments often include more complex effects such as pulsed, non-Gaussian, or coherent interference. As a result, future development will focus on augmenting the training dataset with a wider range of interference types and dynamics, including bursty noise, correlated errors, and adversarial patterns, to improve the model's generalization and robustness.
# A Study on the Surrogate-Based Optimization of Flexible Wings Considering a Flutter Constraint +
## 2 Methodology
### Dataset Definition and Generation
#### 2.1.2 Sampling Plan  
For sampling, Latin Hypercube Sampling (LHS) was adopted using the pyDOE2 package [30] due to its uniform coverage across the design space and its efficient sampling scheme. Simulations conducted in SHARPy focused on the wing metrics to be used in the optimization: structural mass, flutter speed, and aerodynamic coefficients (\(C_{L}\) and \(C_{D}\)). For this study, 2000 simulation samples were generated. Despite this number appearing to be modest given the complexity of the problem, the high computational demands of SHARPy made this a significant effort. Each simulation averaged around 93.5 s. The simulations were run on a host computer with Windows 11, an Intel i7-10510U processor, 16 GB DDR4 RAM, and a 477 GB SSD. This machine also used NVIDIA GeForce MX250 graphics. Within this system, a virtual machine operated on Ubuntu 22.10, utilizing 4 cores, 11 GB RAM, and 59.2 GB of storage space. Virtualization was facilitated by Oracle VM VirtualBox 7.0.6.
## 7 Conclusions and Future Scope  
LoRa and Sigfox are perfect candidates for long-range IoT requirements with ultra-low-power operation. It emerges that both LoRa and Sigfox are promising technologies that provide different performances in certain conditions and are highly configurable to adjust to the underlying environment. In the first part of the work, design goals and deep insight into LoRa and Sigfox is given, while in the last part, simulations are shown to understand that as the number of devices increases, the number of collisions also increases, which leads to more re-transmissions that may lead to congestion of the network. Results provided in this paper establish that 200 devices of Sigfox and 100 devices of LoRa can coexist without interference beyond the same collisions occur due to the spatial diversity mechanism and re-transmission procedure used by both technologies. Work also takes PER and delivery ratio of packets as the correct figure for estimation of successful transmissions, which takes into account the collision effects, propagation models. The work carried out in this paper can be substantiated in the future by the deployment of a campus network with hundreds of such devices in close proximity to further understand the actual number of collisions through spectrum sensing. In order to increase scalability, it is proposed that the redundancy mechanism of Sigfox be removed and LoRa devices be encouraged to utilize lower SF while they can reach the gateway, and SF 12 or SF 11 be used only in case a device is not able to reach any gateway.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Work** & **Year** & **Nodes** & **Work Based on** \\ \hline
[17] & 2016 & LoRa; 1600 & DER \\ \hline
[18] & 2017 & LoRa; 10,000 & PDR \\ \hline
[20] & 2020 & Sigfox; 100,000 & BER, SNR \\ \hline
[21] & 2019 & LoRa; 100 & PDR, RSSI \\ \hline
[25] & 2022 & LoRa, Sigfox & PDR, RSSI \\ \hline Proposed & 2024 & LoRa-5000, Sigfox 10,000 & PER \& Delivery Ratio \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative Analysis with Previous Related Work on LoRa and Sigfox.  
Figure 12: LoRa PER and collision while using SF12, worst case (682 ms).  
Author Contributions: Conceptualization of the work has been done by [PERSON] including programming of the traffic generator, supervision has been carried out by [PERSON] and R.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
Author conveys special thanks to Military College of Telecommunication Engineering, Mhow, Indore, India for support in terms of equipment and accessories for practical testing of LoRa and Sigfox devices.  
The authors declare no conflict of interest.
## 4 Materials and Methods
### Analysis of Volatile Organic Compounds (VOCs)  
The composition of volatile organic compounds (VOCs) was investigated using headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME/GC-MS). Honey samples (approximately 2.00 g) were weighed into 10 mL SPME vials, followed by the addition of 2 mL of distilled water and 200 mg of sodium chloride (NaCl) (100 mg/mL \(\sim\) 1.71 mol/L). A pre-extraction incubation was carried out under agitation for 30 min at 40 \({}^{\circ}\)C. VOC extraction was performed using an SPME fiber assembly coated with divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) (Sigma-Aldrich, St. Louis, MO, USA) for 60 min in the headspace. After extraction, the fiber was desorbed in the GC injector for 5 min at 270 \({}^{\circ}\)C.  
Untargeted analyses were performed using a GC-MS system (Shimadzu QP2010 Plus, Kyoto, Japan) consisting of a gas chromatograph coupled to a quadrupole mass spectrometer equipped with an electron ionization (EI) source at 70 eV. Chromatographic separation was achieved using an Rtx-5 MS\({}^{\otimes}\) capillary column (5% phenyl and 95% methylpolysiloxane) (Sulpeco, Bellefonte, PA, USA) (30 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m film thickness) with helium as the carrier gas at a flow rate of 1.0 mL/min. The oven temperature program was as follows: initial temperature of 50 \({}^{\circ}\)C (held for 1 min), increased at 3 \({}^{\circ}\)C/min to 270 \({}^{\circ}\)C (held for 1 min), then increased at 5 \({}^{\circ}\)C/min to 300 \({}^{\circ}\)C (held for 10 min). The injector operated in splitless mode, with helium (99.99% purity) as the carrier gas at a flow rate of 1.2 mL/min.  
The proportion of each compound was estimated by dividing its average peak area by the total average chromatographic area and expressed as relative percentages. Compound identification was based on similarity with entries in the NIST library, elution order, and retention indices (RI). A mixture of linear alkanes (C\({}_{7}\)-C\({}_{30}\)) was analyzed alongside the samples to calculate RI values according to the van den Dool-Kratz equation [80].
## 5 Simulation and Analysis
### Distributed Jammers of Same Topology in Different Scenarios
In the absence of jamming, the signal receiver receives a signal with a BER mean of \(5.259\times 10^{-6}\), a sample standard deviation of \(1.185\times 10^{-6}\), and an overall standard deviation of \(1.182\times 10^{-6}\). This demonstrates that more stable communication and data transmission can be accomplished between the analogue source and the signal receiver of the LEO star when no interference is carried out. When jamming is performed, the signal receiver receives a signal with a BER mean of \(5.331\times 10^{-2}\), a sample standard deviation of \(4.522\times 10^{-3}\) and an overall standard deviation of \(4.417\times 10^{-3}\). This demonstrates the effectiveness and high success rate of jamming while enabling stable jamming.
Although effective in recognition, the SVM model has a weakness that its performance greatly depends on the parameter selection, which is troublesome and time-consuming for manual operation. To improve the performance and work efficiency, various optimizing algorithms have been introduced into the SVM model, such as genetic algorithm (GA) [26; 27] and particle swarm optimization (PSO) [28; 29]. Recently, another intelligent optimization method, fire algorithm (FA) proposed by [PERSON] [30] has become a promise in optimization problems. To our best knowledge, there are few studies applying FA to obtain the optimal parameters of SVM. However, the traditional FA often suffers from being trapped into local optimum, which causes a premature problem. This paper aims to realize the combustion state of flame images coupling radial Chebyshev moment invariants (RCMIs) and improved firefly algorithm-wavelet support vector machine (IFA-WSVM) model. Firstly, the potential flame pixels are segmented from candidate images in the YCbCr color space, and the potential flame contour is extracted by the Canny edge detector. Then, the proposed rotation, translation and scaling (RTS) invariants of radial Chebyshev moments are derived. Next, the RCMIs are calculated to extract the local and global multi-features of candidate images, forming feature vectors. In order to enhance the performance of combustion state recognition, the FA is applied to search the best penalty factor and dilation parameter of SVM model with a Morlet wavelet kernel. Due to the defect of the traditional FA, an improved FA algorithm is proposed to increase the convergence rate and adjust the searching step dynamically. Afterwards, the IFA-WSVM model is utilized to recognize the combustion state of the testing set. To illustrate the performance of proposed method, the comparison with the methods based on HMIs and ZMIs is performed. In addition, a comparison of IFA with traditional FA, GA and PSO is also performed, observing its quality of solution. Finally, the results for case studies demonstrate that the proposed method can increase the recognition rate and achieve the highest rate of 99.07%. The methods combining region moment invariants and contour moment invariants outperform those based on region moment invariants, improving the accuracy of feature extraction. They can also recognize the combustion state in real time, consuming less computational resources. Besides this, the developed IFA can enhance potential solution of optimization, which is superior to other benchmark algorithms. For the images transformed by RTS and small training sets, the proposed method maintains the best recognition performance, proving its validity and stability.
# Computational Approaches for Grocery Home Delivery Services
## 3 Problem Description and Formal Model
### Arrival Times and Feasibility
#### 3.3.1 Earliest and Latest Arrival Times  
We consider a fixed tour \(\mathcal{A}=(0,1,\ldots,n,n+1)\), where 0 and \(n+1\) denote the depot \(d\) and \((1,\ldots,n)\) denote the customers assigned to the tour. We use the concept of earliest and latest arrival time \(e_{i}\) and \(\ell_{i}\), as in reference (2018), which give the earliest (latest) time at which the vehicle may arrive at customer \(i\), while not violating time window and travel time constraints on the remaining tour:  
\[e_{0}:=start_{\mathcal{A}}, e_{j+1}:=\max\Bigl{\{}B_{w(j+1)},\;e_{j}+s(j)+t(j,j+1)\Bigr{\}},j \in[n-1]_{0},\] \[e_{n+1}:=e_{n}+s(n)+t(n,n+1),\] \[\ell_{n+1}:=end_{\mathcal{A}}, \ell_{j-1}:=\min\Bigl{\{}E_{w(j-1)},\;\ell_{j}-t(j-1,j)-s(j-1) \Bigr{\}},j\in[n]\setminus\{1\},\] \[\ell_{0}:=\ell_{1}-t(0,1).\]  
Here, we assume \(s(0)=s(n+1)=0\).  
Following the definitions, vehicles always leave as early as possible from the depot. This generates unnecessary idle time before serving the first customer of a tour. Hence, once the delivery schedule is finalized, we alter the start times of the vehicles accordingly.  
A schedule \(\mathcal{S}\) is _feasible_ if all its tours are feasible. A tour \(\mathcal{A}\) is _feasible_ if it satisfies both of the following conditions:  
\[e_{i}\leq E_{w(i)},\;i\in[n]\quad\land\quad e_{n+1}\leq end_{ \mathcal{A}}, \texttt{TFEAS}(\mathcal{A}),\] \[\sum_{i\in[n]}c(i)\leq C_{\mathcal{A}}, \texttt{CFEAS}(\mathcal{A}).\]  
While \(\texttt{TFEAS}(\mathcal{A})\) ensures that the arrival times at each customer assigned to tour \(\mathcal{A}\) are within their assigned time windows, \(\texttt{CFEAS}(\mathcal{A})\) guarantees that the capacity of \(\mathcal{A}\) is not exceeded. Note that we do not need to check for \(\texttt{TFEAS}(\mathcal{A})\) if \(B_{w(i)}\leq e_{i},\;i\in[n]\), as this is ensured by the definition of \(e_{i}\).
# Enhancing Discoverability: A Metadata Framework for Empirical Research in Theses
## 5 Documentation Standards
### The Data Documentation Initiative (DDI)
#### 5.1.2 Structure  
The DDI relies on XML and organizes metadata using predefined tags. These include information about data collection methods, sampling techniques, population characteristics, spatial and temporal coverage, keywords, access conditions, and more. While Version 2.0 of the DDI focuses on the final documentation stage of a study, Version 3.3 extends coverage to the entire research lifecycle--from design to dissemination--enhancing its utility for secondary research.  
The five main components of a DDI XML document are as follows:  
* **docDscr**: Description of the XML document.
* **stdyDscr**: Study-level metadata.
* **fileDscr**: Information about data files.
* **dataDscr**: Variable-level metadata.
* **othMat**: Supplementary materials.  
While DDI was primarily developed for quantitative empirical research, its open and extensible structure allows partial adaptation for other research types. Its advantages include interoperability, open access, and integration potential with standards like Dublin Core [19].
# Super-Twisting Sliding Mode Control with SVR Disturbance Observer for PMSM Speed Regulation
## 3 STRL Controller Design  
### Super-Twisting Reaching Law  
A sliding mode approach can be divided into two modes. The first mode is the reaching mode. In this phase, the initial state of the system is attracted to a sliding surface \(s=0\). The second mode, called sliding mode, ensures a dynamic performance and limits chattering. The system state slides on the sliding manifold. In this study, STRL is introduced, which is represented as  
\[\dot{s}=-k_{1}|s|^{\frac{1}{2}}\text{sgn}(s)-k_{2}\!\int_{0}^{t}\text{sgn}(s)d\tau, \tag{9}\]  
where \(\text{sgn}(\cdot)\) means a signum function. As STRL contains a continuous function which includes an integration term, the chattering can be diminished [24]. Moreover, the integration term moves the system trajectory around the equilibrium point to guarantee the continuity of the output control signal, which reduces the chattering in the sliding mode control when the moving point reaches the sliding mode surface [25]. The robustness of the reaching law to parametric uncertainties and disturbances is the primary advantage of the proposed control law. In addition, STRL is a higher-order sliding mode which has been widely proposed in traditional sliding mode control. STA has been introduced because finite time convergence can be improved by preserving the role of the first-order sliding mode and reducing chattering [26]. The reaching law of \(\dot{s}\) comprises two components [24]:  
\[\dot{s} =s_{1}(t)+s_{2}(t), \tag{10}\] \[s_{1} =-k_{1}|s|^{\frac{1}{2}}\text{sgn}(s),\] \[s_{2} =-\int_{0}^{t}k_{2}\text{sgn}(s)d\tau,\]  
where \(k_{1}\) and \(k_{2}\) are the positive gain values. The gains \(k_{1}\) and \(k_{2}\) are designed for the controller purpose and anti-disturbance ability. \(k_{2}\) is derived from bound of disturbance to ensure the system stability. The gain \(k_{1}\) is selected to make the system satisfy the performance specification with proper reaching speed [21; 27]. Furthermore, substituting Equation (3) and STRL Equation (9) into Equation (8) yields the control input \(i_{q}\) as follows:  
\[i_{q}=\frac{1}{b}\bigg{(}k_{1}|s|^{\frac{1}{2}}\text{sgn}(s)+k_{2}\int_{0}^{t }\text{sgn}(s)\,d\tau+\frac{B}{J}\omega+\frac{T_{L}}{J}\bigg{)}, \tag{11}\]  
where \(b\) is \(3n_{p}\psi_{f}/2J\). In Equation (11), \(T_{L}/J\) represents lumped disturbances including external load disturbances, and \(B/J\) denotes a coefficient about viscous friction. STRL-SMC makes the state to reach the sliding surface within finite time and slide keeping the error as 0.  
Figure 1: SMC with SVR-DOB of a PMSM.
## 3 Resolutional Analysis Results  
According to the aforementioned data, this section will simulate SAR resolutional analysis, including the system resolution extraction and image resolution on the basis of objective quality metric assessment. The input images are shown in Figures 2a and 3. According to the proposed IPS, the incoherent information of the SAR images is processed to create necessary phase information for the purpose of HMS adverse effects analysis on the system resolution. Firstly, the SAR images will be stretched by the surface reflectivity index on the supposition of a non-backscattering background, which makes the CTT homogenous for system resolution extraction. Next, it is imported to the proposed inversed equalized algorithm routine. The SAR system parameter selection is dependent on the size of input in both fast-time and slow-time.
## 4 Results and Analysis
### Characteristics of Spatiotemporal Variation in NPP
#### 4.3.2 Relative Contributions of Climate and Human Activities to NPP Changes  
The predicted NPP change rate over the past two decades in the study area ranged from \(-\)88.5608 to 127.417, and the anthropogenic NPP slope ranged from \(-\)99.4355 to 92.722 (Figure 18). The combination of the results of climate change and human activity contributions to NPP can be found in Figure 19. The percentage of the area where NPP increased owing to climate change was 61.87%, of which 6.63% was contributed by >80%, primarily in the eastern part of Northeast and North China. The decrease was 38.13%, mainly in the southeastern part of the Qinghai-Tibet Plateau. The region of NPP increased by human activities was 73.92%, of which 38.01% was contributed by >80%, mainly in Central China and South China, central parts of North China, north and south parts of Northeast China, east and south parts of Northwest China, and the Qinghai-Tibet Plateau. The decrease accounted for 26.08%, mainly in the northeastern part of Northwest China and the western part of North China.  
Figure 16: Plot of the multiple correlation coefficient (**a**) and \(F\)-test (**b**).  
Figure 17: Spatial distribution of the compound effect of (**a**) temperature and (**b**) precipitation on NPP.
# Pido: Predictive Delay Optimization for Intertidal Wireless Sensor Networks
###### Abstract  
Intertidal habitats are among the harshest environments on the planet, and have emerged as a model system for exploring the ecological impacts of global climate change. Deploying reliable instrumentation to measure environmental conditions such as temperature is challenging in this environment. The application of wireless sensor networks (WSNs) shows considerable promise as a means of optimizing continuous data collection, but poor link quality and unstable connections between nodes, caused by harsh physical environmental conditions, bring about a delay problem. In this paper, we model and analyze the components of delays in an intertidal wireless sensor network system (IT-WSN). We show that, by properly selecting routing pathways, it is feasible to improve delay. To this end, we propose a Predictive Delay Optimization (Pido) framework, which provides a new metric for routing path selection. Pido incorporates delay introduced by both link quality and node conditions, and designs a classifier to predict future conditions of nodes, i.e., the likely time of aerial exposure at low tide in this case. We evaluate the performance of Pido in both a real IT-WSN system and a large-scale simulation, the result demonstrates that Pido decreases up to 73% of delays on average with limited overhead.  
delay modeling; delay optimization; intertidal WSN; environmental monitoring; ETX 20181464; doi:10.3390/s18051464
## 3 Results
### Frost Heaving-Induced Pressure
#### 3.2.2 Summary of Frost Heaving Pressure Tests  
The ultimate frost heaving pressure (maximum \(p\), defined as \(p_{\text{ult}}\)) increased as \(M\) increased. The \(p_{\text{ult}}\) is located in a \(t\) range of 500-700 min. The \(p_{\text{ult}}\) values are summarized in Figure 8. For \(S=70\%\), \(p_{\text{ult}}\) were 0.1, 3.1, 4.5, and 7.6 kPa for \(M=0\), 5, 15, and 25%, respectively. For \(S=90\%\), a relatively linear increase was observed at \(p_{\text{ult}}=0.3\), 4.5, 9.4, and 16.7 kPa for \(M=0\), 5, 15, and 25%, respectively. The \(p_{\text{ult}}\) for \(S=90\%\) is approximately two times larger than that for \(S=70\%\). Overall, the frost heaving pressure of pure gravel ballast can be neglected under constrained conditions; however, the pressure becomes perceptible as subgrade soil is mixed in the gravel ballast.  
Figure 7: Results of frost heaving pressure tests. (**a**) \(M=0\%\), (**b**) 5%, (**c**) 15%, and (**d**) 25%.