esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Performing dual-task (DT) activities is essential for independent living among elderly people. No study has investigated motor performance in various cognitive-motor DT activities, utilizing the Timed Up and Go (TUG) test. This study aimed to compare motor performance between four cognitive-motor DT tests in community-dwelling older adults. The sample consisted of 60 older women. The cognitive tasks performed with the TUG test were (a) mental calculation, (b) memory recall, (c) verbal fluency, and (d) reaction to a stimulus. Lower limb muscle strength was assessed with the 30-Second Chair Stand Test, balance with the Four Square Step Test, and balance confidence with the Activities-specific Balance Confidence Scale. Completion times and DT costs were calculated. Mental calculation (r = 0.63, \(p<0.01\)) and verbal fluency (r = 0.65, \(p<0.01\)) tasks were similarly correlated with the TUG test, and significantly impacted motor performance compared to other DT tests. The reaction to a stimulus test showed a high relationship with the TUG test (r = 0.89, \(p<0.01\)) and had the least impact on motor performance. These findings suggest that the cognitive task type can significantly influence motor performance during DT activities. Adding a cognitive load to the TUG test may improve its ability to identify older adults at risk for falls, aiding in the development of targeted interventions. Further research is required to validate these findings.  
Keywords:dual-task; community-dwelling older adults; Timed Up and Go
## 5 Conclusions  
In summary, we report a high-resolution, low-cost, and quickly realized flame temperature detection scheme based on an sCMOS camera. LIBS (Laser-Induced Breakdown Spectroscopy) technology is used to hit the combustion product of nitrocellulose, obtain the characteristic spectral information of nitrocellulose, and select the optimal spectral line based on the principle of two-color temperature measurement to avoid the influence of emissivity on temperature measurement accuracy. The sCMOS camera was combined with 694 nm and 768 nm filters to build a temperature measurement system, and a temperature calibration experiment was conducted to detect and image the flame temperature of the energy-containing material nitrocellulose. The maximum temperature measurement error and temperature measurement uncertainty of this method are 2.10% and 3.43%. Compared to traditional two-color infrared thermometers, it provides a new idea for realizing higher resolution, low cost, and more accurate flame temperature 2D detection technology. In the future, this technology can be applied to the fields of explosion field temperature and industrial boiler temperature.  
Conceptualization, P.P. and X.H.; validation, X.H. and P.P.; formal analysis, S.F.; investigation, T.W., C.X. and P.P.; original draft preparation, P.P. and X.H.; review and editing, X.H. and C.X.; funding acquisition, X.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (52475575); National Natural Science Foundation of China (52075504); State Key Laboratory of Quantum Optics and Quantum Optics Devices (KF202301); Shanxi Provincial Key Research and Development Project (202302150101016); Central Guided Local Science and Technology Development Funding Program (YDZJSX2024D033).  
Not applicable.  
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors thank all the reviewers, editors, and contributors for their contributions and suggestions, as well as all the members of the OSEC Laboratory.  
The authors declare no conflicts of interest.
# A Registration Method for Historical Maps Based on Self-Supervised Feature Matching
## 2 Related Work
### Learning-Based Methods  
Recently, deep learning techniques have emerged as the dominant approach for many traditional computer vision tasks, including semantic segmentation, image registration, classification, and object detection. In the context of image registration, these methods achieve higher accuracy in establishing correspondences, enabling the estimation of geometrically consistent transformation models. Current learning-based registration methods can be categorized into descriptor-based and descriptor-free paradigms.  
Descriptor-based methods learn feature representations end-to-end, replacing hand-crafted descriptors with neural networks. Pioneering works like LIFT and MagicPoint demonstrated the feasibility of learning discriminative descriptors. R2D2 [22] advances this paradigm by jointly predicting reliable keypoints, robust descriptors, and confidence scores to prioritize trustworthy regions. D2 Net [23] unifies keypoint detection and description within a single network, demonstrating robustness to significant intensity variations and viewpoint shifts. Despite their success, feature-based methods often fail to generalize to images with large domain gaps (e.g., stylistic disparities across historical maps) and extreme scale variations.  
Recent detector-free approaches, such as RoMa [11], introduce a coarse-to-fine dense matching framework leveraging hierarchical feature representations. DeDoDe [24] decouples feature detection and description through a modular pipeline, enabling independent optimization of each component. Despite their advancements, state-of-the-art methods like RoMa and DeDoDe require large-scale pretraining on annotated datasets, limiting their applicability to historical maps--scarce, annotation-deficient, and stylistically heterogeneous. To address this, we propose a self-supervised framework specifically designed for historical map registration, eliminating dependency on manual annotations.
## 2 Materials and Methods
### MODIS Data  
The MODIS data used in this study were the MOD09 GA (Terra, collection 6) Terra daily surface reflectance (SREF) L2 Global product for the period from 2014-2017, which came from the Land Processes Distributed Active Archive Center (LP DAAC) with a spatial resolution of 500 m. Reflectances for the red (620-670 nm), near-infrared (841-875 nm), blue (459-479 nm), and shortwave infrared bands (1628-1652 nm) were used to calculate the EVI and the land surface water index (LSWI) for 2014-2017. To match the SIF spatial resolution (0.5\({}^{\circ}\) latitude and longitude resolution), we calculated vegetation indices (EVI and LSWI) for pixels within a square of 0.5\({}^{\circ}\) by 0.5\({}^{\circ}\) around the two flux sites, and good-quality data were selected by using the quality assurance (QA) flags ([[https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod09](https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod09) ga_v006]([https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod09](https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod09) ga_v006)). Then, all pixels were averaged, and the averaged value was later deemed to be the value for each 0.5\({}^{\circ}\) grid cell. To ensure the consistency with the SIF data from GOME-2, the EVI and LSWI values were averaged over a 16-d period to match the 16-d SIF data from GOME-2. The EVI and LSWI values were calculated according to the following expressions:  
\[EVI = \frac{2.5\times(\rho_{nir}-\rho_{red})}{\rho_{nir}+(6\times\rho_{ red}-7.5\times\rho_{blue})+1} \tag{1}\] \[LSWI = \frac{\rho_{nir}-\rho_{swir}}{\rho_{nir}+\rho_{swir}} \tag{2}\]
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 6 Data Pre-Processing and Engineering Module
### Network Feature Extraction  
The Zeek tool is integrated into the system using the docker files available on the Zeek Community website [54]. The tool requires the configuration of the input and output directories. The input directory contains the raw pcap data, which are converted into nine.log files stored in the output directory. Each.log file contains particular traffic flow information. The set of available information and relative descriptions are presented as follows.  
* **conn.log:** Information about each network connection observed by Zeek, including source and destination IP addresses, ports, timestamps, and the connection state;
* **dns.log:** DNS activity observed by Zeek, including DNS queries and responses, along with relevant metadata such as the query type, response code, and DNS transaction IDs;
* **files.log:** Information about files transferred over the network, including file metadata such as file hashes, file sizes, MIME types, and file paths;
* **http.log:** HTTP activity, including HTTP requests and responses, along with details such as user agents, referrers, HTTP methods, status codes, and URIs;
* **ntp.log:** Information related to Network Time Protocol (NTP) activity, including NTP requests and responses, along with timestamps and NTP modes;
* **packet_filter.log:** Logs packet filtering activity, providing details about packets that have been filtered based on defined packet filter rules;
* **ssl.log:** Secure Sockets Layer (SSL) and Transport Layer Security (TLS) activity observed by Zeek, including SSL handshakes, certificates, cipher suites, and SSL/TLS versions;
* **weird.log:** Contains logs of unusual or unexpected network activity that may indicate potential security issues or anomalies detected by Zeek;
* **X509.log:** Logs details about X.509 certificates observed in SSL/TLS handshakes, including certificate subject and issuer information, validity periods, and certificate extensions.  
To extract the information reported in Table 1 from these nine.log files and unify it into a single csv file, a Python script is used. The script accesses the log files, extracts the relevant information for each packet based on the ID, renames it according to Table 1, and saves it in a single csv file. For each packet, all the information from the relevant protocols is reported, if available.
# Characterizing Situations of Dock Overload in Bicycle Sharing Stations
## 3 Methodology
### Mining Occupancy Monitoring Patterns
#### 3.2.2 Proposed Approach for OMP Mining
It follows that the intermittence of \(P\) in period \(TP_{k}\) is equal to the total frequency of all o-itemsets with the same stations as \(P\), such that at least one station (but not all them at the same time) is overloaded in \(TP_{k}\). For the sake of efficiency, our approach avoids generating all these o-itemsets, but instead it proceeds as follows. Let us denote as \(card\_value\) the total number of transactions in period \(TP_{k}\) in the analyzed dataset. It easily follows that \(card\_value\) is equal to the sum of the following three terms: the frequency of the critical o-itemset \(I_{C}\) (\(freq\_value(critical)\)), the frequency of the normal o-itemset \(I_{N}\) (\(freq\_value(normal)\)) and the total frequency of all o-itemsets with the same stations as \(P\), such as at least one station (but not all them at the same time) is overloaded at time \(TP_{k}\). Therefore, we compute the intermittence of \(P\) in period \(TP_{k}\) as \[intermittence=card\_value-(freq\_value(critical)+freq\_value(normal)).\] (2)  
**Example 4**. : \(P\)=\(\{s_{2}\)_, \(s_{3}\}\) is an OMP with criticality equal to 1 and intermittence equal to 2 in time period \(TP_{1}\). These measures are computed based on the frequencies of the critical o-itemset \(\{\langle s_{2},Overloaded,TP_{1}\rangle,\)\(\langle s_{3},Overloaded,TP_{1}\rangle\}\) and of the normal o-itemset \(\{\langle s_{2},Normal,TP_{1}\rangle,\)\(\langle s_{3},Normal,TP_{1}\rangle\}\). The critical o-itemset has frequency equal to 1 being contained in the transaction with TID equal to \(\text{TID}_{1}\). Thus, the criticality of \(P\) is equal to \(freq\_value(critical)=1\). The normal o-itemset has frequency equal to 1 since it is included in the transaction with TID equal to \(\text{TID}_{4}\) (i.e., \(freq\_value(normal)=1\)). \(card\_value\) is equal to 4 because four transactions refer to time period \(TP_{1}\). Based on Equation (2), it follows that the intermittence of \(P\) is computed as intermittence = 4 - (1 + 1) = 2. This intermittence value corresponds to the total frequency of the o-itemsets \(\{\langle s_{2},Normal,TP_{1}\rangle,\)\(\langle s_{3},Overloaded,TP_{1}\rangle\}\) and \(\{\langle s_{2},Overloaded,TP_{1}\rangle,\)\(\langle s_{3},Normal,TP_{1}\rangle\}\), respectively contained in the transactions with TIDs equal to \(\text{TID}_{2}\) and \(\text{TID}_{3}\)._  
In Section 3.2.3, we describe the algorithm used in the BELL framework to mine the OMPs including nearby stations according to the spatial constraint _maxdist_ as well their criticality and intermittence values.
## 4 Results and Discussion
### Practical Applications of NAS in GANs
#### 4.5.3 Anomaly Detection  
Anomaly detection in medical imaging (or industrial settings) requires the precise modeling of a \"normal\" data distribution so that deviations can be identified. NAS-GAN models have been tailored to capture subtle differences between normal and abnormal samples. For example, [PERSON] et al. presented a comprehensive study on GAN-based anomaly detection in medical imaging, demonstrating improved sensitivity and specificity in fracture detection [17]. Additional works have refined these methods by integrating active learning strategies to mitigate overfitting in the discriminator [5].
## 2 Materials and Methods
### Input Variables Selection  
According to previous studies, eight variables, including maximum, minimum and mean air temperature (T\({}_{\text{max}}\), T\({}_{\text{min}}\), T\({}_{\text{mean}}\), \({}^{\circ}\)C), diurnal temperature range (\(\Delta\)T, \({}^{\circ}\)C), total solar radiation (R\({}_{\text{a}}\), MJ m\({}^{-2}\) d\({}^{-1}\)), actual sunshine duration (S, h), relative humidity (RH, %) and wind speed at 2 m height (U\({}_{2}\), m s\({}^{-1}\)), were initially selected [32; 35]. Datasets were chosen from 2018-2020, in which 2018 and 2019 datasets were utilized for training models and datasets fir the year of 2020 were for testing. All the results reported referred to the testing phase. Published literature has shown that the effect of meteorological parameters on the performance of ET\({}_{0}\) estimation models varied markedly across different climatic zones [7]. Therefore, correlation analysis was conducted before the variables were applied to neural network models. With the extent of determination coefficients (r\({}^{2}\)) ranging from 0.10 to 0.93, threshold values were determined using the median (r\({}^{2}\) = 0.50) and third quartile (r\({}^{2}\) = 0.70) values from a box chart after statistical distribution for training and testing models (Table 2).  
Figure 2: Workflow of the proposed genetic algorithm optimized BP neural network models.
## 8 Conclusions and Future Work Directions  
This paper reported the HiL vs. MiL evaluations for two proposed power split strategies intended for a recent topology of passenger vehicles, HEVs equipped with EVT. First, a detailed vehicle model was built at the component level and linked to the provided optimized EMSs in a MATLAB/Simulink(r) environment to obtain the MiL simulation results. Next, a versatile test bench representing the vehicle architecture was prepared in the presence of physical components. Two of the studied control strategies were uploaded into the controller hardware unit and the real-time HiL implementations results for a full driving cycle were achieved. Upon successful HiL and MiL tests over the cycle, the paper performed comparative analyses by applying different statistical agreement and error evaluation indices. The investigation of the results for both studied EMSs indicated that the torque and speed actuation results obtained from the HiL implementations could closely comply with the MiL ones. The evaluations validated the implementation ability of the studied cases with robust accuracy and fast actuation response in such a real-time environment.  
Knowing the driving cycle trends in advance, offline EMSs were tested in this study. Future work directions can include evaluation of online strategies using the provided testbed. Furthermore, investigation of different control strategies linked to the component' optimal sizing concept merits as a future subject matter paving the design way toward vehicle-in-the-loop experiments and validation studies for real-world applications.  
Conceptualization, M.V. and O.H.; Methodology, M.V.; Software, M.V. and F.V.; Validation, M.V.; Formal analysis, M.V.; Investigation, M.V.; Writing--Original draft preparation, M.V.; Writing--Review and editing, M.V., O.H., M.E.B., and F.V.; Visualization, M.V. and F.V.; Supervision, O.H., P.S., and J.V.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors are grateful to VLAIO (ex. IWT) and Flanders Make, national funding schemes in Belgium, for their support to the current work, performed within the EMTechno project (project ID: IWT150513).  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Outer Rotor**} & \multicolumn{2}{c}{**Inner Rotor**} \\ \hline  & Torque & Speed & Torque & Speed \\ R\({}^{2}\) & 0.99 & 0.99 & 0.80 & 0.98 \\ RMSE & 0.12 & 0.03 & 0.91 & 0.18 \\ MSE & 0.01 & 0.001 & 0.84 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical performance analysis results, ECMS-based case study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Outer Rotor**} & \multicolumn{2}{c}{**Inner Rotor**} \\ \hline  & Torque & Speed & Torque & Speed \\ R\({}^{2}\) & 0.98 & 0.99 & 0.98 & 0.99 \\ RMSE & 0.15 & 0.03 & 0.24 & 0.07 \\ MSE & 0.02 & 0.001 & 0.05 & 0.006 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical performance analysis results, LPF-based case study.
# On the Regional Temperature Series Evolution in the South-Eastern Part of Romania
## 4 Conclusions  
The article presents the regional evolution of the annual temperature series (minimum, average, maximum) for 1965-2005 in the Dobrogea region, Romania, using spatially distributed series. The methods were two original algorithms proposed by the author, versions of MPPM. The novelty of this approach is that instead of analyzing only the evolution of the individual series, a unique series for the entire region was built, selecting values from the data. Method II, which employs the clustering algorithm for selecting the series that participate in building the 'Regional series', provided better results than Method I in all situations.  
To validate the results, the best 'Regional series' (in terms of MAE, and MSE) were compared with the series extracted from the ROCADA database (after processing to compute the maximum, minimum, and average series annual series). In all cases, the MAEs and MSEs of the fitted series were smaller than when they were measured with respect to the initial series.  
An increasing trend of the minimum temperatures series was noticed, whereas the maximum (and average, with a smaller amplitude) regional series presented a high variability. More studies will be conducted on more recent data series to confirm these findings.  
An interesting topic is related to the impact of the missing values (when they exist) on fitting the 'Regional series'. Generally, the higher the number of missing values, the higher the error. The missing values may be replaced by values obtained by linear, spline, or wavelet interpolation of the data series recorded at the specified site or by the average of the data series. Another possibility is replacing the missing values with those recorded at neighboring stations. The study of the impact of the missing values will be extensively performed in the future.  
The APC was funded by Transilvania University of Brasov, Romania by the funds for research.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are available on request from the author.  
**Conflicts of Interest:** The author declares no conflict of interest.  
Figure 13: Comparisons between (**a**) MAEs and (**b**) MSEs from the models.
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 4 CR-Based UAVs
### Potential Applications  
In the last few years, emerged a growing trend to rely on UAVs as a support tool on critical missions, such as military surveillance [33], forest fire monitoring [34], crop monitoring [35; 36; 37], traffic surveillance [38], border patrolling [39], natural disasters area surveillance and support [40; 41; 42], and commercial drones [43]. However, these areas usually suffer from infrastructure issues that could possibly lead to spectrum scarcity or anomalous PUs behavior [44; 45]. Thus, an integration between UAVs and CR could be useful for these scenarios due to its spectrum sensing and handover capabilities.  
As well as the spectrum uncertainty scenario set up by emergency conditions, densely populated areas such as big cities can likewise go through overcrowded frequency bands, mainly motivated for the growing volume of wireless network users. Although spectrum scarcity is perceived as a harmless effect, the falling of a UAV in a crowded area due to an unstable communication could inflict much damage depending on the aircraft size and set up [46]. One potential advantage of CR utilization is the potential increase in the Quality of Service (QoS) caused by applying the spectrum handover capabilities to identify and relocate to less occupied frequency bands, thus adding to the stability of the data transmission between UAVs and their base stations.  
In order to avoid the spectrum scarcity caused by densely populated areas, the UAV can previously adjust its flight path considering the spectrum availability, as applied in [47] using cellular data network. To accomplish this application, it can be done a mapping of several frequency bands of the area in which the UAV would be deployed, therefore enabling the occupancy pattern recognition and, subsequently, prediction by machine learning (ML) algorithms [48].
## 1 Introduction
On the one hand, although off-chain storage enhances the scalability of the blockchain, the off-chain storage system is detached from the blockchain, and it can only obtain data from the off-chain storage system without verifying the authenticity of the data [10]. On the other hand, the existing off-chain storage scheme lacks the on-chain and off-chain data consistency and integrity guarantee mechanisms and suffers from the problems of excessively long public parameters, fixed off-chain storage capacity, and difficulty in expansion. The contributions of this paper are as follows: An on-chain and off-chain collaborative storage mechanism based on incremental aggregatable sub-vector commitments is proposed. It improves the on-chain and off-chain collaborative storage scheme built on updatable sub-vector commitments, making it have fixed-length public parameters and improving communication efficiency. It also combines smart contracts to design an aggregation proof protocol to improve query and verification efficiency. The rest of this paper is structured as follows: In Section 2, we delineate the related research and progress lineage of blockchain storage scalability. In Section 3, we describe the security problems of blockchain on-chain and off-chain cooperative storage of big spatio-temporal data and propose a solution for this paper. Section 4 presents a detailed system overview of spatio-temporal big data collaborative storage mechanisms based on incremental aggregation subvector commitment in on-chain and off-chain systems. Section 5 describes the specific scheme design, scheme construction, and aggregation proof protocol, and finally gives the correctness and security analysis. In Section 6, the paper presents an analysis of the performance of the proposed scheme.