esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper proposed a location estimation system using an improved particle filter regarding the improvement of indoor location accuracy by the BLE position fingerprinting method. Improved likelihood functions considering position coordinates in particle filters were designed based on the position fingerprinting method. Especially, by using the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Proposed A** & **Proposed B** & \
\begin{tabular}{c} **k-NN +** \\ **General PF** \\ \end{tabular} &
\begin{tabular}{c} **k-NN** \\ **SVM (RBF)** \\ \end{tabular} \\ \hline Minimum error (m) & 0.30 & 0.18 & 2.83 & 0.00 & 0.03 \\ Maximum error (m) & 18.59 & 24.08 & 18.26 & 25.88 & 19.84 \\ Average error (m) & 5.97 & 5.52 & 6.22 & 6.13 & 6.22 \\ Variance (m\({}^{2}\)) & 3.75 & 3.38 & 3.87 & 5.24 & 4.66 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Estimation errors on dataset.  
Figure 12: Error comparison.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Proposed A** & **Proposed B** & \
\begin{tabular}{c} **k-NN +** \\ **General PF** \\ \end{tabular} &
\begin{tabular}{c} **k-NN** \\ **SVM (RBF)** \\ \end{tabular} \\ \hline Minimum error (m) & 0.38 & 0.32 & 0.41 & 0.76 & 0.77 \\ Maximum error (m) & 3.45 & 3.95 & 3.56 & 3.86 & 3.38 \\ Average error (m) & 1.83 & 1.66 & 1.79 & 2.42 & 2.18 \\ Std Dev (m) & 0.18 & 0.25 & 0.19 & 0.30 & 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Estimation errors by proposed and existing methods.  
likelihood function B, this method can extend position fingerprints with interpolated data and improve estimation accuracy. Meanwhile, the error of location estimation becomes large when the location is close to a wall or obstacle, which is probably because of the extended error of the position fingerprint.  
Anyway, comparison and evaluation between position fingerprint methods based on only k-NN and SVM were conducted by experiments. It was confirmed that the proposed method has improved estimation accuracy and stability.  
In future studies, it is considered possible to demonstrate the accuracy of location estimation drawn by the proposed method in different environments, e.g., location estimation in rooms of different shapes or sizes, in multiple rooms, for multiple people, etc. In addition, to further improve the estimation accuracies, this study will design a method for outliers in the RSSI values of reference fingerprints.  
[PERSON], [PERSON], and [PERSON] suggested the basic concept and the algorithm demonstrated in this study. [PERSON] and [PERSON] designed and performed the experiments. [PERSON], [PERSON], and [PERSON] analyzed the data. W.-S.K. and Y.Y. provided help with algorithms and data analysis. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The RSSI dataset collected for this experiment has been uploaded to Zenodo. [[https://doi.org/10.5281/zenodo.11604290](https://doi.org/10.5281/zenodo.11604290)]([https://doi.org/10.5281/zenodo.11604290](https://doi.org/10.5281/zenodo.11604290)) (accessed on 12 June 2024).  
The authors declare no conflicts of interest.
## 4 Evaluation
### Obfuscation-Resilient Capability  
In this section, we evaluate the obfuscation-resilient capability of TPL detection methods. The obfuscation-resilient capability is an important metric to measure the performance of a TPL detection tool since obfuscation techniques can discount the detection performance. We select the seven ubuntu binary program dataset and use a popular obfuscation tool, Armariris [25], to obfuscate these programs. Armariris is an obfuscator based on the LLVM project for multiple languages and platforms, currently supporting string obfuscation, control flow flatten and instruction substitutions.  
Based on these obfuscated target binary problems, we only compare BBDetector with B2 SFinder, since ModX has no experiment in designing obfuscations and is not open source for the time being, at present. The specific detection results are presented in Table 7. From the table, we can observe that B2 SFinder only achieves an F1-score of 28%, much lower than that of 67% achieved by it with the non-obfuscated binary programs. This is because the obfuscation technique alters the syntactic information of a program, leading to the syntactic features being different from the original TPLs and without a doubt, affecting the detection performance of TPL detection methods based on syntactic features. However, we can see BBDetector achieves an F1-score of 71%, slightly lower than the F1-score of 77% achieved with the non-obfuscated binary programs, which indicates BBDetector is less affected by the code obfuscation technique due to we consider the function semantic features, demonstrating the obfuscation-resilient capability of BBDetector towards code obfuscation technique.
## 2 Sampling, Experimental Work, and Data Collection  
A total of 48 evaporitic rock blocks of various sizes were obtained from the Early Miocene age (23-16 Myr) Lower Fars Formation. This formation comprised gypsiferous layers interbedded with 3-5-m-thick, highly fragile mudstone, and marl. The blocks were collected from a quarry located on the eastern side of Hafit mountain, Al Ain City, UAE (Figure 1) [2; 4]. To avoid possible anisotropy effects of thinly bedded rocks along the gypsiferous layers, rock blocks were carefully selected by visual inspections (macro level-naked eyes) during the field studies. All evaporitic rock blocks were brought to the laboratory for shaping them into NX-sized cylindrical core samples (approximately 54 mm), which were then used to prepare test samples for the BTS tests. In the laboratory, all test specimens for the BTS were carefully inspected with naked eyes for defects of surface failures due to preexisting veins, macro cracks, and fissures, as such defects can cause measurement bias. Then, 124 test samples were prepared and tested according to the ASTM standards [15]. In addition, index tests, namely, the I\({}_{\text{d2}}\) and G\({}_{\text{s}}\) tests [39; 40], as well as the BTS test, were performed. The partial test results of this study are listed in Table 1. I\({}_{\text{d2}}\) (%) values were in the range of 8.42-60.77% with a mean value of 36.42%. BTS was in the range of 1.47-4.39 MPa with a mean value of 2.58 MPa. The G\({}_{\text{s}}\) values were in the range of 2.06-2.36 with a mean value of 2.16. For the analysis, the BTS was defined as the target variable and I\({}_{\text{d2}}\) and G\({}_{\text{s}}\) were the input variables.  
Figure 1: Geological map of the study area and sampling location [4].
# A Framework for the Detection of Search and Rescue Patterns Using Shapelet Classification
## 4 Experimental Evaluation & Results
### Optimization on Synthetic Data  
For evaluating and optimising the parameters of our setup, we used the \"Steps\" vs. \"Random\" pattern case in all the optimisation scenarios. As explained above, there are eight different scenarios, for different numbers of input time series (training samples) and a different number of iterations for the genetic extractor. Every scenario has been repeated 100 times and the 800 accuracy results (using 10-fold cross validation in each experiment) have been recorded. In order to summarize our results, we measured the average accuracy for all the experiments, which was 86.76% and used the following labels to annotate the performance of each experiment:  
* Bad: results with prediction's accuracy score smaller than 80%.
* Acceptable: results with prediction's accuracy score from range 80% to 90%.
* Excellent: results with prediction's accuracy score from range 90% to 99%.
* Perfect: results with perfect prediction's accuracy score with value 100%.  
Table 1 summarizes the results of our optimization experiments. In most of the experiments (83.5%) the accuracy is above the level of 80%.  
Table 2 shows the best performance achieved for each scenario, the times it has been achieved (in the 100 repetitions) and the average execution time. It can be observed the scenarios that have been trained with more time-series (scenarios 5-8) have better results than the others and among them, the scenario that performed the maximum number of iterations in the GENDIS algorithm (20 iterations) is significantly better that all the scenarios 1-4. The execution time, which comprises both the shapelet fit and extraction and the training and testing of the classifier, is less than 5 min per scenario, which is considered acceptable for this task. The parameters training samples and number of GENDIS iterations have been set to 150 and 20 after this experiment.
# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} RAN & Radio access network \\ HetNet & heterogeneous networks \\ NS & Network slicing \\ QoS & Quality of service \\ uRLLC & Ultra-reliability low latency communications \\ eMBB & Enhanced mobile broadband \\ UE & User equipment \\ NOMA & Non-orthogonal multiple access \\ SIC & Successive interference cancellation \\ U-S. A & UE-slice association \\ U-S. P & UE-slice Pairing \\ MBS & Macro base station \\ SBS & Small bas station \\ WBH & Wireless backhaul network \\ H-NOMA & Heterogenous non-orthogonal multiple access \\ ITU & International Telecommunications Union \\ mMTC & massive Machine Type Communication \\ HARQ & Hybrid Automatic Repeat reQuest \\ CN & Core network \\ SDN & Software-Defined Networking \\ NFV & Network Functions Virtualization \\ EDF & Earliest deadline first \\ RSMA & Rate-splitting multiple access \\ CSI & Channel status information \\ MIMO & multiple-input multiple-output \\ RIS & Reconfigurable intelligent surface \\ EPS & Enhanced Pre-emptive Scheduling \\ C-RAN & Cloud radio access network \\ DRL & Deep reinforcement learning \\ MC & Multi-connectivity \\ PrNet & Pointer network \\ RAT & Radio access technology \\ OFDM & Orthogonal frequency-division multiple access \\ TTI & Transmission time interval \\ InP & Infrastructure provider \\ NLOS & Non-line-of-sight \\ PRB & Physical resource block \\ CCI & Co-channel interferences \\ QoE & Quality of experience \\ DAA & Deferred acceptance algorithm \\ EAA & Early acceptance algorithm \\ MAX-SINR & Maximum-signal-to-interference-plus-noise ratio \\ GA & Greedy algorithm \\ OMA & Orthogonal multiple access \\ PUNC & Puncturing \\ C-NOMA & Cooperative non-orthogonal multiple access \\ CR-NOMA & Cognitive radio non-orthogonal multiple access \\ FANET & Flying Ad Hoc Networks \\ \end{tabular}
# A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios
## 5 Conclusions  
For the complex network environments, the IoT is facing serious challenges in terms of security. In this paper, we first discussed the existing wireless routing protocols (i.e., based on geographic location, hierarchy, and data) in detail. To cope with the black hole and Sybil attacks, we proposed a novel GIN protocol, which combined the GEAR protocol and the IN mechanism. Each node in the network was equipped with a reputation function that contained behavioral records of all nodes (i.e., IN, MN and CH nodes). When a node was found to forward packets selectively, the transmission of the information will be terminated. Moreover, the information of energy and location can be exchanged by two neighboring nodes. The GIN protocol utilized the information for selecting the optimal path to the event area. Once the location of a neighboring node was found far beyond the  
Figure 11: The results for GIN in throughput. (**a**) Relationships between the throughput and the number of sensor nodes; (**b**) Relationships between the throughput and the packet size.  
normal signal transmission range, it will be blacklisted and given a bad reputation. The specific model establishment and transmission processes were addressed. Moreover, we utilized the game theory to select INs. To further validate the performances of GIN protocol, we established a network simulation environment by NS3. The results illustrated that our scheme outperformed the other protocols in the network delay, packet loss ratio, and throughput. Comparing with the traditional routing protocols (i.e., flooding, GPSR, and GEAR): the network delay can be further reduced by 67.7%, 43%, and 20%, respectively. The packet loss ratio can be roughly decreased by 68.9%, 56.3%, and 33.3%, respectively. The throughput can be improved by 65.2%, 43.5%, and 20.9% respectively.  
The malicious attack issue had seriously obstructed the development of IoT. A smart, collaborative, and reliable routing protocol could make significant changes for information collection procedures. In the future, we will work on the application of the proposed protocol in multiple scenarios.  
The first two authors contributed equally to this work and share the first authorship. [PERSON] and [PERSON] established the research schedule, performed the validation, and wrote the paper. [PERSON] contributed the main ideas of GIN, suggested the approaches, and provided the improvements.  
This work was supported by the Fundamental Research Funds for the Central Universities under Grant No. 2018 YJS011 and 2017 JBM012.  
We would like to thank all the reviewers and editors for their invaluable comments and efforts on this article.  
The authors declare no conflict of interest.
## 5 Conclusions  
The delocalization of nuclear wavepackets is a natural effect in molecular systems, and constitutes a fundamental property of wavepackets that cannot be easily circumvented. As the signal measured in scattering experiments is the transform of the molecular structure, the natural delocalization of structures during chemical reactions dampens the diffraction signal, particularly at large values of \(q\), and implies a limit to the range of scattering vectors that yield useful information on the molecular dynamics. For the X-ray scattering experiments considered here, our simulations and analytical model suggest that there is little information beyond 10 to 15 A\({}^{-1}\), at least for typical organic molecules. The sensitivity of scattering experiments to the shape of the wavepackets may offer an opportunity to experimentally measure them. Such measurements could be valuable for the development of advanced computational codes.  
Ultrafast X-ray scattering experiments benefit from the exciting development of ultrashort pulsed free electron lasers. A challenge to date has been that the X-ray photon energy is limited, so that only rather small ranges of \(q\) can be probed. However, advances in accelerator technologies, in particular the build-out of LCLS-II at the SLAC National Accelerator Laboratory, promise harder X-rays and a concommittant expanded \(q\) range. With a typical detector geometry covering scattering up to 60 degrees, 30 keV X-ray photons will reach up to \(q=15\) A\({}^{-1}\). It therefore seems likely that the advances offered by LCLS-II will enable experimental determinations of wavepacket motions up to the useful maximum suggested by our simulations.  
[PERSON] acknowledges funding from the Leverhulme Trust (RPG-2013-365), the European Union (FP7-PEOPLE-2013-CIG-NEWLIGHT), and the hospitality of [PERSON] (Uppsala University) including sabbatical support from the Wenner-Gren Foundations. [PERSON] acknowledges support from the National Science Foundation, Grant No. CBET- 1336105, and by Defence Threat Reduction Agency, Grant Number HDTRA1-14-1-0008. This study benefitted from stimulating conversations with [PERSON] and [PERSON] from the SLAC National Accelerator Laboratory.  
[PERSON] and [PERSON] conceived and designed the calculations; [PERSON] performed the calculations; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the article.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
XFEL: X-ray Free-Electron Laser LCLS: Linac Coherent Light Source IAM: Independent Atom Model CHD: 1,3-cyclohexadiene FWHM: Full Width at Half Maximum
## 1 Introduction
### Recommended Values of the \"Inelastic\" Period for the Earthquake Intensity Measure  
The inelastic elongated fundamental period \(T_{in}\) is a key parameter for establishing a rigorous evaluation of structural performance into the non-linear regime; for this reason, \(T_{in}\) can represent the intensity measure required for IDA better than the elastic fundamental period \(T_{el}\). Therefore, \(IM\) could be evaluated by entering the earthquake response spectrum with the inelastic period \(T_{in}\) instead of the elastic period \(T_{el}\).  
Evaluating the lengthening of the period is still an open issue in earthquake engineering. Based on many numerical investigations, some empirical formulas have been proposed in the literature. [PERSON] et al. [24] studied over 100 ground motion records and proposed the following relationship between the elastic initial period \(T_{el}\) and the inelastic final period \(T_{in}\)[24]:  
\[T_{in}=1.07~{}T_{el}\left(\frac{S_{a}(T_{el})}{A_{y}}\right)^{0.45}\leq 2~{}T_{el}, \tag{1}\]  
where \(A_{y}\) is the yield spectral acceleration. Note that Equation (1) limits the value of the elongated period to twice the elastic period.  
To predict the value of the fundamental period shift ratio (\(T_{in}/T_{el}\)) of R/C structures, [PERSON] and [PERSON] [33] provided the following formula:  
\[\frac{T_{in}}{T_{el}}=3.6-2.367\exp\left(-0.006~{}T_{el}^{-2.412}\right) \tag{2}\]  
The above relation produces values of \(T_{in}\) ranging from approximately 3.6 \(T_{el}\) (for very rigid oscillators) to 1.23 \(T_{el}\) (for flexible oscillators). Some corrective parameters are also given in [33] to adjust the \"reference\" values obtained from Equation (2) to account for some deterioration effects.  
By referring to one-degree-of-freedom oscillators equivalent to the considered multi-degree-of-freedom systems, [PERSON] et al. [32] derived the value of the inelastic period at the maximum amplitude of the Fourier spectrum relevant to the structural acceleration under each scaled earthquake. Both regular and irregular multi-story buildings were considered in [32] under hundreds of recorded earthquakes, two different hysteretic rules, and 11 scaling factors (PGA reaching up to 1.7 g). The results presented by [PERSON] et al. [32] showed that the shift ratio \(\frac{T_{in}}{T_{el}}\) is typically lower than 1.7. It should be noted, however, that such results were obtained by carrying out in-plane analyses on the equivalent one-degree-of-freedom oscillators.
## 3 Results and Discussion
### Comparison
In the present case, the vacant sites of SiC NPs are mostly contributed by the external surface, which provides relatively easier access to antibiotic molecules, unlike activated carbon-based adsorbents, where adsorbate molecules have to diffuse through the interstices of the porous structure to find vacant sites for adsorption. As a result, SiC NPs yield a significantly higher rate of antibiotic removal.
# Fatigue Damage Evaluation of Aviation Aluminum Alloy Based on Strain Monitoring
## 4 Conclusions  
This paper investigates the relationship between strain and fatigue damage in aerospace aluminum alloy 7075-T6. The study analyzes the microscopic damage mechanism of low-cycle fatigue using crystal plasticity and fatigue tests. The correlation between strain and fatigue damage prior to the onset of fatigue crack life is explored through multiple sets of unilateral grooved specimens. Fatigue crack initiation life is predicted using two fatigue indicator factors (FIPs) in crystal plasticity simulations, and the accuracy of these FIPs is assessed. The main conclusions of this study are as follows: (1) The fatigue indication of strain energy dissipation in low-cycle fatigue testing of aluminum alloys is more accurate in predicting fatigue crack initiation life, as determined by analyzing the accuracy of the two FIPs. (2) Strain changes were found to be insignificant for monitoring the early stages of work-hardening in fatigue, as evidenced by the comparison of experimental and simulated cycle counts with fatigue damage. Experimentally monitored strains remained essentially constant. (3) After the fatigue cycle enters cyclic saturation, the microstrain at the notch gradually begins to rise, indicating the onset of microcracks. (4) When the strain exceeds 26 \(\mu\epsilon\), microcracks start to appear, and the strain tends to increase as the microcracks expand. Crystal plasticity simulations show a similar pattern in the cumulative plastic slip fatigue indicator factor.  
Figure 11: Simulation results analysis: (**a**) comparison of two FIP predictions and experimental results; (**b**) variation of strain and FIP with fatigue cycles for crystal plasticity model at 0.15% strain amplitude.  
**Author Contributions:** Writing--original draft, Software, Formal analysis and Methodology, J.W.; Conceptualization and Writing--review and editing, W.W.; Writing--review and editing, M.L.; Investigation, Y.H. and J.W. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (No. 12204510) and National Key Research and Development Program (2022 YFB3206802).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 5 Performance Evaluation  
In performance evaluation section, we evaluate the performance of RTC by comparing it with two state-of-the-art schemes: H2-DAB [22] and AUV-PN [24]. The reason to prefer these schemes for the comparison is that these models are similar in functioning with our proposed approach. The simulation and comparison is performed using NS-3, we utilized UAN channel::_Unanchannel_, \(CWMAC802.11 DCF\) MAC layer protocol, NS-3 Packet Error Rate (PER) model::_UnalhyperGendefault_, and to recognize connection breakage underwater traversing is utilized as mentioned in [45]. The parameters selected for the simulation are given in Table 1. For a fair comparison, we take all the parameters same for all methods during the comparison. We kept the same area dimensions, the number of FNs, the network energy, and the transmission radius of nodes for all performed simulations. For the simulations, we consider the horizontal node movements like: 1 m/s to 5 m/s, while the movements in vertical directions are neglected. MS travels on a pre-defined route to collect data from all the RHs. Hello message size is considered fixed and small in accordance to one normal data packet as each Hello message consumes 0.1% of energy resources.