esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Gas Chromatography Coupled to Mass Spectrometry (GC-MS)
Other substances with percentages below 5% identified in _D. punctata_ leaves in all areas were: neophytadiene, stigmasterol and the 4, 4, 6a, 6b, 8a, 11, 11, 14b-octamethyl-1, 4, 4a, 5, 6a, 6b, 7, 8, 8a, 9, 10, 11, 12, 12a, 14, 14A, 14b-octadecahydro-2H-picen-3-one. For the ethanolic extracts of _D. punctata_ branches, the analyses identified 16 compounds, among which hydrocoumarin and lupeol are presented as markers. Lupeol was identified as the major compound present in the plant material collected in areas 1, 4, and 5, with percentages above 5% in the other collection areas (Table 5). Only in the plant material collected in area 1 was (Z)-9-octadecenoic acid (oleic acid) detected (6.8%). Other compounds present in the extract of branches collected in this and other collection areas were (-)-pathulenol, 4-O-methylmannose, n-hexadecanoic acid, bis(2-ethylhexyl)phthalate, stigmasterol, \(\gamma\)-sitosterol, and lup-20(29)-en-3-one (Table 5). Except for 4-O-methylmannose, these compounds were also identified in _D. punctata_ leaves extracts. 4-O-methylmannose was the major constituent in the extract of branches collected in area 2 (85.5%) and in area 3 (25.4%) (Table 5). For area 3, all the constituents of the extract of the branches presented percentages above 5%, with the highest values for this area being for hydrocoumarin (16.8%), (-)-spathulenol (16.9%), and bis(2-ethylhexyl)phthalate (16.9%) (Table 5). In areas 4 and 5, in addition to the common compounds present in the extracts of branches found in all collection areas, the analysis verified the presence of (Z)-9-octadecenal (area 4 with 5.4% and area 5 with 5.1%) and octadecanoic acid (stearic acid) (area 4 with 5.3% and area 5 with 5.2%) (Table 5). The analyses of the extracts of _D. punctata_ residues by GC-MS identified 31 different chemical substances. Among the 14 compounds analyzed in area 1, for example, were n-hexadecanoic acid (5.1%), hexadecanoic acid 2-hydroxy-1-(hydroxymethyl) ethyl ester (5.5%), (S)-isopropyl lactate (6.5%), 2,3-butanediol (7.3%) and, 9-octadecenoic acid (E,E,E)-1,2,3-propanetriyl ester (15.8%), this last being the outstanding compound of this area, and of area 2, with a percentage of 16%, and area 4, with 10.6% (Table 6). In areas 1, 2, and 4, apart from the common major constituents, (-)-spathulenol was also highlighted.
# Security Risk Analysis of LoRaWAN and Future Directions
###### Abstract  
LoRa (along with its upper layers definition--LoRaWAN) is one of the most promising Low Power Wide Area Network (LPWAN) technologies for implementing Internet of Things (IoT)-based applications. Although being a popular technology, several works in the literature have revealed vulnerabilities and risks regarding the security of LoRaWAN v1.0 (the official 1 st specification draft). The LoRa-Alliance has built upon these findings and introduced several improvements in the security and architecture of LoRa. The result of these efforts resulted in LoRaWAN v1.1, released on 11 October 2017. This work aims at reviewing and clarifying the security aspects of LoRaWAN v1.1. By following ETSI guidelines, we provide a comprehensive _Security Risk Analysis_ of the protocol and discuss several remedies to the security risks described. A threat catalog is presented, along with discussions and analysis in view of the scale, impact, and likelihood of each threat. To the best of the authors' knowledge, this work is one of the first of its kind, by providing a detailed security risk analysis related to the latest version of LoRaWAN. Our analysis highlights important practical threats, such as end-device physical capture, rogue gateway and self-replay, which require particular attention by developers and organizations implementing LoRa networks.  
internet of things; sensor node; LPWAN; attacks; threats; vulnerabilities; IoT; analysis; risk; assessment; low power; LoRa; v1.1
# GPS/BDS RTK Positioning Based on Equivalence Principle Using Multiple Reference Stations
## Validation and Analysis
### Static Experiment
#### 3.1.2 Analysis of Positioning Results  
Figure 4 depicts the number of observed GPS satellites and redundancy, as well as the relative dilution of precision (RDOP) value of scheme-three during 1 July 2018. As shown, the average number  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Schemes** & **Reference Stations** & **Rover Station** & **Model** \\ \hline One & CUTA0 & CUT00 & SBRTK \\ Two & CUTA0 + CUTB0 & CUT00 & MBRTK \\ Three & CUTA0 + CUTB0 + CUTC0 & CUT00 & MBRTK \\ \hline \hline \end{tabular}
\end{table}
Table 2: The description of the three schemes for static experiment.  
Figure 3: Distribution of four receivers (**a**) ([[http://saegnss2.curtin.edu.au/ldc/CU-GNSS-receivers-setup](http://saegnss2.curtin.edu.au/ldc/CU-GNSS-receivers-setup)]([http://saegnss2.curtin.edu.au/ldc/CU-GNSS-receivers-setup](http://saegnss2.curtin.edu.au/ldc/CU-GNSS-receivers-setup)). pdf) and the sky plot (**b**) of the visible GPS satellites for station CUT00.  
of GPS observed satellites and redundancy is 7.6 and 16.9, respectively. The average RDOP value is 7.3 for the whole day.  
Figure 5 shows the positioning errors in the North (N), East (E), and Up (U) components for three schemes, and the corresponding statistics are listed in Table 3. The results show that MBRTK has better positioning accuracy than SBRTK. The root mean square (RMS) values of the MBRTK for scheme-two and scheme-three are (2.4, 2.0 and 6.3) mm, and (2.2, 1.9 and 5.9) mm in N, E, and U components, while the corresponding values of the SBRTK are (2.8, 2.3 and 7.3) mm. The ambiguity-fixing rate of the three schemes are all 100%. Compared with the positioning results of the SBRTK, it is apparent that scheme-two and scheme-three have a performance improvement by approximately (14%, 13%, and 14%), and (21%, 17%, and 19%) in N, E, and U components, respectively, validating the feasibility and reliability of the proposed MBRTK.  
Figure 4: Number of observed satellites and redundancy (**left**) and relative dilution of precision (RDOP) value (**right**) for static experiment.  
Figure 5: Errors in the East (E), North (N), and Up (U) of MBRTK for static experiment.
# Releasing Differentially Private Trajectories with Optimized Data Utility
## 5 Experimental Evaluations
### Utility Metrics
#### 5.3.1 Semantic Utility Metrics  
(_i) Periodic pattern Jaccard coefficient_. Periodic patterns are required by numerous semantic mining tasks [9] for providing personalized services, where a periodic pattern is represented by a series of locations that repeatedly occurs in a trajectory dataset, e.g., (school, apartment, school). Given the actual dataset \(D_{A}\) and its obfuscated version \(D_{O}\), we find out top-k periodic patterns \(PP(D_{A})\) and \(PP(D_{O})\) from each dataset, respectively. Then, the semantic utility of the private protection algorithm \(Q(f)\) is evaluated by the Jaccard similarity coefficient over the two sets of periodic patterns, as shown in Equation (12). A greater coefficient indicates a higher utility in terms of semantics. Particularly, for AdaTrace, the geographical space of \(D_{O}\) may be different from that of \(D_{A}\), \(PP(D_{A})\), and \(PP(D_{O})\) may be incomparable. To tackle this issue, trajectories in \(D_{A}\) and \(D_{O}\) are discretized to grid trajectories following [6], where the number of grids is 400.  
\[Q(f)=\frac{|PP(D_{A})\ \bigcap\ PP(D_{O})|}{|PP(D_{A})\ \bigcup\ PP(D_{O})|} \tag{12}\]  
(_ii) Semantic annotation_ is a trajectory data analysis task in many emerging LBS such as location recommendation [10]. It represents the process of attaching category tags such as shopping and nightlife to locations in a trajectory dataset. A common process of semantic annotation is to train a classifier over a dataset where semantic tags are given. We represent the category tags by Foursquare location categories [27]. In our experiments, we acquire category tags for each location in the obfuscated trajectory dataset \(D_{O}\) from Foursquare and then split \(D_{O}\) into training and test subsets with a ratio of 9:1. A widely used semantic annotation approach [8] is exploited to train the classifier over the training subset and predict the semantic tags of the test subset. Specifically, first, it extracts population features and temporal features, e.g., distribution of check-in time, to express the category tag of locations in training data. Second, the relatedness among locations, a.k.a, the regularity of individual check-ins to similar locations, e.g., co-occurrence, is extracted by a related locations network. Finally, the category tags of locations in the test subset can be derived from their related locations in the training subset. Generally, the effectiveness of the semantic annotation task can be measured by _Accuracy_, which is defined as the ratio of the number of correctly annotated locations to the total number of locations. A higher accuracy indicates better performance of the semantic annotation, which further implies higher utility in terms of semantics.
# Characterization of CYGNSS Ocean Surface Wind Speed Products
###### Abstract  
Since its launch in 2016, a number of wind speed retrieval algorithms have been developed for the NASA CYGNSS satellite observations. We assess their accuracy and precision and characterize the dependence of their performance on environmental factors. The dependence of retrieval uncertainty on the wind speed itself is considered. The triple collocation method of validation is used to correct for the quality of the reference wind speed products with which CYGNSS is compared. The dependence of retrieval performance on sea state is also considered, with particular attention being paid to the long wave portion of the surface roughness spectrum that is less closely coupled to the instantaneous local wind speed than the capillary wave portion of the spectrum. The dependence is found to be significant, and the efficacy of the approaches taken to account for it is examined. The dependence of retrieval accuracy on wind speed persistence (the change in wind speed prior to a measurement) is also characterized and is found to be significant when winds have increased markedly in the \(\sim\)2 h preceding an observation.  
ocean surface wind speed; GNSS-R; CYGNSS; bistatic radar 2024 16 2024 1
# NDARTS: A Differentiable Architecture Search Based on the Neumann Series
## 3 Experiment
### Simulation Experiment
#### 3.1.3 Search Space  
1. DARTS search space.  
The DARTS algorithm, serving as a fundamental neural network architecture search approach based on gradient descent, encompasses a substantial quantity of nodes and operation types within the building block (unit). The resulting network structure is achieved by stacking two structural units, thus leading to a high architectural complexity. Consequently, the DARTS search space has the potential to produce network models with superior performance. Currently, the majority of gradient-based methods undergo performance evaluation within the DARTS search space and are subsequently compared with other algorithms.  
The unit structure of the DARTS search space shown in Figure 4, \(c_{k-2},c_{k-1},c_{k}\) represents the outputs of units \(k-2\), \(k-1\), and \(k\), respectively. In the \(k\)-th unit, there are four nodes between the outputs of the first two units and the output of the \(k\)-th unit. Each edge represents a candidate operation, with eight types of operations including extended separable convolutions of \(3\times 3\) and \(5\times 5\), deep separable convolutions of \(3\times 3\) and \(5\times 5\), average pooling of \(3\times 3\), maximum pooling of \(3\times 3\), identity operation, and zero operation. Zero operation indicates that there is no connection between two nodes, while identity  
Figure 3: ImageNet Dataset.  
operation indicates that the data from the previous node is directly transferred to the next node.  
The entire network structure is composed of eight units, which are divided into standard units and down-sampling units. In the down-sampling unit, the first two nodes are connected to other nodes through pooling operations. The network consists of six standard units and two down-sampling units, which are located at one-third and two-thirds of the entire network.  
2. NAS-Bench-201 Search space.  
In current research, a growing number of NAS algorithms have been proposed. Despite their theoretical groundwork, many aspects of these algorithms display significant differences, including distinct search spaces, training strategies for architecture evaluation, and methods used to split validation sets. These differences lead to considerable challenges when comparing the performance of various NAS algorithms. As a result, researchers devote substantial computational resources to traverse and evaluate the performance of different search spaces and neural network structures, as well as their architectures within the designed network structure search space and producing datasets. Subsequent experiments on the NAS-Bench-201 search space [34] can obtain evaluation results through tabular queries without the need for retraining.  
The structural units used in NAS-Bench-201 are shown in Figure 5. Each structural unit contains four nodes and five operation types (\(1\times 1\) convolution, \(3\times 3\) convolution, \(3\times 3\) average pooling, identity operation, zero operation), totaling \(5^{6}\) = 15,625 types of unit structures.
# Energy-Efficient AC Electothermal Microfluidic Pumping via Localized External Heating
###### Abstract  
In this study, we present a comprehensive numerical investigation of alternating-current electrothermal (ACET) pumping strategies tailored for energy-efficient microfluidic applications. Using coupled electrokinetic and thermal multiphysics simulations in narrow microchannels, we systematically explore the effects of channel geometry, electrode asymmetry and external heating on flow performance and thermal management. A rigorous mesh convergence study confirms velocity deviations below \(\pm\)0.006 \(\upmu\)m/s across the entire operating envelope, ensuring reliable prediction of ACET-driven flows. We demonstrate that increasing channel height from 100 \(\upmu\)m to 500 \(\upmu\)m reduces peak temperatures by up to 79 K at a constant 2 W heat input, highlighting the critical role of channel dimensions in convective heat dissipation. Introducing a localized external heat source beneath asymmetric electrode pairs enhances convective circulations, while doubling the fluid's electrical conductivity yields a \(\sim\)29% increase in net flow rate. From these results, we derive practical design guidelines--combining asymmetric electrode layouts, tailored channel heights, and external heat bias--to realize self-regulating, low-power microfluidic pumps. Such devices hold significant promises for on-chip semiconductor cooling, lab-on-a-chip assays and real-time thermal control in high-performance microelectronic and analytical systems.  
microfluidics; alternating-current electrothermal flow (ACET); alternating-current electroosmotic flow (ACEO); heat transfer Article
## 4 Applications of Zeolites in Catalysis, Bioprocesses, and Environmental Remediation
### Zeolites in Environmental Remediation
The advantage offered is the high energy generation by weight in comparison with batteries. Zeolites have been taking part in the development of proton exchange membrane fuel cell, (PEMFC), better in energy efficiency than diesel and gas engines, without generation of pollution or toxic by-products. The application of a composite membrane using sulfonic acid functionalized zeolites plays an essential role stabilizing the dimension and proton conductivity of membranes [353]. In this scenario, the zeolite A-supporting noble metals have helped in the purification of the hydrogen used in this type of cell [354]. Microbial fuel cells have been developed with zeolitic structures associated. These convert organic or inorganic waste in electricity with microorganisms as catalysts decomposing a determined biomass. The eco-friendly aspect involved in this device is the hydrolyzation and oxidation of a liquid waste by bacteria without emission. For this process, the anode material determines the performance of electron transfer and a zeolitic material has been focused to compose it [355]. There are other strategic research areas involving zeolites for processes focusing on environmental protection highlighting energy (thermal energy storage), adsorption of greenhouse gases such as CO\({}_{2}\), and water treatment. Table 1 shows some works on these topics with relevant information to match zeolites with environmental protection. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Zeolite Type** & **Main Considerations** & **Reference** \\ \hline \multirow{8}{*}{**Energy storage**} & \multirow{2}{*}{13X (FAU)} & A mobile thermal energy storage system was developed reducing greenhouse gas emissions. & [356] \\ \cline{2-4}  & & A process of drying was evaluated in relationship with thermal storage energy increasing the performance in 1.6 times. & [357] \\ \cline{1-1} \cline{2-4}  & & The modeling based in TES (thermal energy storage) was created varying zeolite properties with success. & [358] \\ \cline{1-1} \cline{2-4}  & & Generation of a TES that contributes for renewable energy production with thermal and electrical energy driven with reasonable feature. & [359] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Uses of zeolites for environmental protection in different approaches.
# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 2 Materials and Methods
### Methods  
Mountain pine stands have similar morphological and anatomical needle characteristics to other subalpine coniferous trees (e.g., Scots pine (_Pinus sylvestris_) and Swiss pine (_Pinus cembra_)) [3,21] which lead to similar spectral characteristics (Figure 2). As a result, the satellite-based mapping of mountain pines is challenging [6,23,28,29] as they cannot be differentiated from other coniferous tree species based on their spectral properties only [23,28]. Moreover, the spectral confusion between mountain pine areas and alpine grasslands or dwarf shrub species can also reduce the classification accuracy [8,20]. To increase the class separability of mountain pines, we first derived complementary features from satellite and LiDAR data (Section 2.3.1), which we combined in different feature spaces (Section 2.3.2) for the subsequent classification (Section 2.3.3).  
Figure 2: **(a) Study area (in yellow) and settlement areas (in red) in the Sarntal Alps with a black rectangle showing the extent of the example imagery in (b–d). Background: Orthophoto 2020; (b) Orthophoto 2020 (20 cm) (Autonomous Province of Bolzano); (c) PlanetScope (5 July 2020) (3 m); (d) Sentinel-2 (7 July 2020) (10 m) and (e) Spectral signatures of grassland, mountain pines and coniferous forest. Spectral signatures are mean reflectance values from reference polygons within the extent of b–d based on PlanetScope bands (5 July 2020).**
## 3 Optimization Empowerment Combined Model Based on Sliding Window
### Combined Prediction Model under Sliding Window  
This paper assumed a sliding window that continuously updated with a fixed size. When using a sliding window, a crucial step is to determine the window size, denoted as \(V\). If the window is large, it will include more data, but it can sometimes be affected by outliers, making it challenging to accurately represent data fluctuations. On the other hand, if the window is small, it includes fewer data and may have difficulty expressing the overall data fluctuations. Additionally, due to the presence of errors, it inevitably impacts the accuracy. Therefore, determining the sliding window size \(V\) is of significant importance.  
We used the entropy method to determine the window size. In statistics, a larger entropy indicates a more evenly distributed data, while a smaller entropy means a more concentrated data distribution. Therefore, when the entropy is larger, the data errors between different sliding windows are smaller, and the data's fluctuation is reduced. Hence, the choice of window size is more appropriate. To calculate the optimal window size using the entropy method, follow these steps: (1) calculate the absolute prediction error of the \(r\)-th individual prediction method at time \(t_{i}\), along with the average absolute prediction error of \(l\) individual predictions corresponding to time \(t_{i}\). (2) Determine the average absolute prediction error for the \(k\)-th window, where \(k=1,2,\ldots,N-V+1\). (3) Normalize all the calculated average absolute prediction errors. (4) Calculate the entropy using the normalized results. (5) The window size \(V\) that corresponds to the maximum entropy is considered the optimal window size.  
To transform from fixed-weight to variable-weight and determine the weights based on the sliding window size, the sliding window can be integrated into the weight determination process. By using the sliding window to group the observed data, we can obtain the predicted sequence \(\hat{W}_{rt_{i}(k)}=\left(\hat{W}_{rt_{i}(k)},\hat{W}_{rt_{i}(k+1)},\ldots, \hat{W}_{rt_{i}(k+V-1)}\right)\) generated via the \(r\)-th prediction method within the \(k\)-th sub-window of the sliding window. The corresponding measured subsequence is \(W_{t_{i}(k)}=(W_{t_{i}(k)},W_{t_{i}(k+1)},\ldots,W_{t_{i}(k+V-1)})\).  
Because using the sliding window results in the loss of \(V-1\) data points, when applying the three weight calculation methods mentioned above, to reduce the impact of data loss on the accuracy of subsequent data predictions, we can assume that all weights are \(Q_{V}\) in the first sliding window. As we slide to the \(k\)-th window at time \(t_{k+V-1}\), the weightsare updated to \(Q_{k+V-1}=\left(Q^{(1)}{}_{k+V-1},Q^{(2)}{}_{k+V-1},\ldots,Q^{(l)}{}_{k+V-1}\right)\). Then, based on these weights, we can perform a combined prediction, achieving the goal of variable-weight combination prediction.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 5 Future Research Directions  
As indoor localization technologies continue to evolve, several areas demand further research to address existing challenges and enhance system performance across healthcare, industrial, and military domains. The integration of advanced technologies, optimization techniques, and security measures will play a pivotal role in shaping the future of RTLSs.
## 5 DSS Techniques and Generalized Finite State Machine
The job of the spectrum allocation module is to implement different spectrum allocation and access mechanisms for: (1) protecting incumbent terrestrial systems; (2) satisfying the priority of GSO over NGSO systems; and (3) preventing interference among different GSO and NGSO transmissions. Lastly, the most important event from the steady state is interference detection, where interference can be detected in a variety of ways, such as SINR degradation, interference source detection, etc. All active links are monitored when the system is operating in the steady state, and in case of detecting an interference event, the interference management techniques, local DSS enablers, and spectrum decision module will be executed in order to move the system back to the steady state. Two DSS techniques have been designed for managing the interference and support flexible spectrum (re)allocation and release. These two DSS techniques are based on CP (called DSS1) and spectrum sensing (called DSS2).