esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of V2X-IoT Standards and Frameworks for ITS Applications
## 3 Commercial V2X Products  
As was already mentioned in the previous chapter, both the automotive industry and the governments, as well as scientific institutions are making significant efforts to develop and promote architectural solutions for hybrid DSRC and C-V2X technologies that would sustain reliable and efficient V2X connections. The design and development of these technologies must not only offer new V2X communication schemes, assess their effectiveness and reliability in various mathematical analyses or computer simulations, but more importantly, demonstrate the real feasibility, i.e., how these theoretical algorithms operate in the real transport environment and whether they can be integrated with the current V2X systems installed by the large vehicle manufacturers. Therefore, this section will review the existing V2X R&D platforms and commercial products that could be used in developing versatile frameworks for ITS applications, research and for equipment deployed on modern vehicles or road infrastructure. In this section, we will also review and analyze on-market radio frequency chips, which could be used to develop new V2X systems that fit the DSRC and C-V2X communication technologies and covering all current standards.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 3 Results
### GWS Trend in Peru (2003-2023)
#### 3.1.2 Results of Validation with In Situ Data  
GWS estimates (mm/year, equivalent water height) were validated using annual groundwater level decline measurements (m/year) from over 1700 wells across six aquifers (Caplina, Tumbes, Chillon-Rimac, Lurin, Chilca, Locumba), obtained from the ANA database.  
In situ data were converted to equivalent water height (mm/year) by multiplying the decline rate (m/year) by the aquifer's specific yield and dividing by the aquifer area (km\({}^{2}\)), ensuring unit consistency with GRACE data.  
The Pearson correlation coefficient (r = 0.95) indicates a strong, statistically significant agreement between GRACE-derived GWS trends and in situ depletion rates, confirming the reliability of satellite gravimetry for regional monitoring, despite its \(\sim\)300 km spatial resolution. In situ data from 1700+ wells were quality-controlled to ensure consistency, though uneven well distribution in remote Andean regions may introduce minor biases.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Zone**} & \multicolumn{1}{c}{**Critical Years**} & \multicolumn{1}{c}{**Most Affected Regions**} & \multicolumn{1}{c}{**GWS Trend**} \\ \hline North & 2007–2010 & Piura, Lambayeque & 20–30\% decline \\ Central & 2007–2010, 2023 & Lima, Ica & 30–40\% decline \\ South & 2007–2010, 2015–2023 & Arequipa, Tacna & 40\% decline \\ \hline \hline \end{tabular}
\end{table}
Table 2: Critical years, most affected regions, and GWS trends by zone in Peru (2003–2023).  
Figure 5: Annual groundwater extraction volume (blue bars) and annual groundwater level decline (red) for six Peruvian aquifers (ANA’s Reports from 2019 to 2022).
## 2 Materials and Methods
### Genome Sequencing and Analysis  
The genomic DNA of strain Q3-6 was extracted by using a genomic DNA extraction kit (Takara Biomedical Technology Co., Ltd., Beijing, China) according to the manufacturer's recommendations. The genomes of strains Q3-6 were sequenced on the Illumina sequence (X PLUS) at Shanghai Majority Bio-pharm Technology Co., Ltd. (Shanghai, China). The raw data were filtered with fastp software v0.23.0 (HaploX Biotechnology Co., Ltd., Shenzhen, China) [31] and assembled by SOAPdenovo v2.04 (BGI Genomics Co., Ltd., Shenzhen, China) [32]. Quality assessment of the genomes was conducted using CheckM v1.1.6 [33]. Related strain genomes were downloaded from the NCBI Prokaryotic reference database, and the draft genome content was annotated via the NCBI Prokaryotic Genome Annotation Pipeline PGAP. Further phylogenetic and taxonomic studies were conducted to further confirm the taxonomic status of strain Q3-6. The average nucleotide identity (ANI) values were calculated by using online tools of the EzBioCloud database ([[https://www.ezbiocloud.net/tools/ani](https://www.ezbiocloud.net/tools/ani)]([https://www.ezbiocloud.net/tools/ani](https://www.ezbiocloud.net/tools/ani)), accessed on 3 June 2024) [34], digital DNA-DNA hybridization (dDDH) values were calculated by using the Genome-to-Genome Distance Calculator v3.0 (ggdc.dsmaz.de/ggdc.php, accessed on 7 August 2024) [35]. The concatenated alignment sequences of 120 ubiquitous single-copy proteins were obtained by GTDB-Tk (v1.3.0) [36], and the phylogenetic tree was reconstructed by FastTree using JTT + CAT parameters and IQ-TREE using the LG + F + I + G4 model with 1000 bootstrap replicates. The genome sequence of strain Q3-6 was deposited in GenBank under accession number PRJNA1093269.  
To investigate the metabolic pathways and potential functions of bacteria, genomes were annotated using the KEGG database (v3.0) [37]. Bacterial resistance genes were annotated through RAST databases (v2.0). Secondary metabolite prediction was performed using the antiSMASH database 6.0 [38].
## 2 Materials and Methods
### Free Amino Acids Analysis  
Free amino acids content in the <3000 Da fractions was determined by fast high-performance liquid chromatography, with the equipment _Acquity Arc_, Waters (Saint-Quentin-en-Yvelines, France) coupled with a fluorescence detector (model FLR 2475: ex/em 266/473, Waters). Samples were derivatized using the Waters AccQ-TagTM ultra reagent kit. For the purpose, 10 uL of each sample were mixed with a borate buffer containing the internal standard (ABBATM). Then, 20 uL of the derivativizing reagent were added to the previous solution and this final mix was heated at 55 \({}^{\circ}\)C for 10 min. For the analysis, 2 uL of sample were injected on the column _Xbridge_ C18 (150 \(\times\) 3.0 mm, 3.5 um) and eluted at the flow rate of 1 mL/min. The separation gradient was generated using four mobile phases: (A) AccQ-Tag eluant A, (B) water/acetonitrile 90/10 + 2% formic acid, (C) water and (D) acetonitrile +2% formic acid. The gradient was defined as follows: from 0 to 12 min A10%/B0%/C90%/D0%; from 12,1 to 15.5 min A10%/B80%/C10%/D0%; from 15.6 to 16.8 min A8%/B16%/C58%/D18%; from 16.9 to 17.5 min A8%/B0%/C71%/D21%; from 17.6 to 19 min A4%/B0%/C36%/D60% and from 19.1 min to the final time of 22.5 min A2%/B0%/C98%/D0%. Data acquisition and chromatograms treatment were performed with the [PERSON] _Empower v.3_. Amino acids concentration was determined based on amino acids calibration standard run at 8 concentrations: 1.25, 2.5, 12.5, 50, 125, 250, 625 and 1250 uM of Ala, Arg, Asn, Asp, Gln, Glu, Gly, His, Hyl, Hyp, Ile, Leu, Lys, Met, Phe, Pro, Ser, Thr, Trp, Tyr, Val. The proportional molar concentration for each amino acid in the peptides solutions was calculated based on the concentration of the standard amino acids.
## 2 Materials and Methods
### Color Measurement  
The color of the oils and mayonnaise samples was evaluated by directly reading the color coordinates of the CIEL \"a \"b \" system (L*--lightness, a*--redness/greenness, and b*--yellowness/blueness) using a Thermo Scientific Evolution 600 UV/VIS spectrophotometer (Thermo Scientific, Waltham, MA, USA) calibrated against a white standard. Readings at five different points were taken for each sample. The total color change (\(\Delta\)E) was calculated for each mayonnaise sample using the equation below:  
\(\Delta\)E = \(\sqrt{\left(\text{L1}-\text{L0}\right)^{2}+\left(\text{a1}-\text{a0}\right)^ {2}+\left(\text{b1}-\text{b0}\right)^{2}}\), where L1, a1, b1 represent color values of mayonnaise samples after 8 weeks of storage, while L0, a0, b0 are color values of fresh mayonnaise samples.
## 2 Methods
### Parametric Simulation
#### 2.4.5 Crash Pulses  
Autonomous vehicle-relevant crash conditions from the literature were used as baseline crash conditions. In one study, 150 head-on accidents collected by a South Korean insurance company were reconstructed using PC-Crash software (DSD, Linz, Austria) [62]. Virtual AEB systems were then activated to observe changes in crash configurations and frequencies [60]. Based on these analyses, the two most common autonomous vehicle-relevant crash scenarios were selected, considering both their frequency and severity (Figure 8a). These configurations represented the most frequent scenarios involving a host vehicle equipped with automatic emergency braking (AEB). Among these, cases with some of the highest crash severities were selected to determine the initial crash conditions. For the 21 KR0040 case, the relative velocity, heading angle, and overlap were 93.8 km/h, 209\({}^{\circ}\), and 43%, respectively. For the 21 KR0090 case, the relative velocity, heading angle, and overlap were 81.2 km/h, 215\({}^{\circ}\), and 90%, respectively. Variations from baseline values were applied to these three parameters to generate crash pulses for parametric simulations (Figure 8b). This approach resulted in 27 variations of crash conditions for each scenario.  
Figure 8: Baseline crash conditions and application of variations. (**a**) Two autonomous vehicle relevant crash scenarios, and (**b**) method for applying variations to crash conditions.  
Figure 7: Types of shoulder belt attachment: (**a**) upright-BPI (B-pillar-integrated), (**b**) upright-BIS (belt-in-seat), (**c**) reclined-BPI, and (**d**) reclined-BIS.  
For these selected scenarios, car-to-car crash simulations were conducted using a 2011 Honda Accord model in LS-Dyna (version R11.1.0, LSTC, Livermore, CA, USA) to generate crash pulses for occupant simulation with the MADYMO software. After crash simulations, crash severity (\(\Delta\)V) and principal direction of forces (PDOF) were calculated (Equations (1) and (2)). The PDOF was computed at every moment during the crash. Its value at the point when \(\Delta\)V reached its maximum was used for further analysis.  
\[\Delta V=max\Big{(}V_{magnitude}\Big{)}-\min\Big{(}V_{magnitude}\Big{)} \tag{1}\]  
\[PDOF=\tan^{-1}\big{(}\Delta V_{x}/\Delta V_{y}\big{)} \tag{2}\]  
To account for occupants seated on both the driver side and the passenger side, 27 mirrored crash pulses reflected along the \(y\)-axis were included while keeping the AHBM in the passenger-side seat to simulate occupant responses in the driver-side seat under identical crash scenarios. It was assumed that the interior environment for the driver side was the same as that for the passenger side. This methodology resulted in a total of 54 crash pulses, with 27 allocated to each of the driver and passenger locations.
## 5 Conclusions  
This study presents a fast satellite selection algorithm, NSWOA, for multi-constellation LEO satellite dynamic opportunistic navigation. Through both theoretical modeling and experiments, the key findings and contributions are summarized as follows:  
The algorithm was evaluated over 2185 dynamic epochs using Doppler signals from multiple LEO constellations, including Starlink, Orbcomm, and Iridium. In terms of computational efficiency, it achieved average runtime reductions of 27.06% compared to GWO, 27.05% compared to PSO, and 68.57% compared to NSGA-II. Moreover, it consistently outperformed these benchmark algorithms in solution quality, demonstrating clear advantages in both speed and optimization performance.  
NSWOA consistently exhibited rapid convergence and reliable performance across varying constraint scenarios. In fixed-number satellite selection, the algorithm required only 11 iterations on average to meet the navigation threshold, confirming its suitability for time-sensitive applications such as UAV-based positioning.  
By introducing Pareto non-dominated sorting into the Whale Optimization Algorithm, the proposed NSWOA simultaneously minimizes both the DGDOP value and the number of selected satellites. It reduces the average number of selected satellites from 22 to fewer than 6 while maintaining a stable preset positioning accuracy of approximately 260 m. This enables a real-time balance between positioning accuracy and computational efficiency, reducing computation time to 54.96% compared to full satellite selection.  
**Author Contributions:** Conceptualization, C.D.; methodology, C.D. and Y.C.; validation, C.D. and Y.C.; formal analysis, C.D. and B.Z.; investigation, Y.C.; resources, C.D. and B.Z.; writing--original draft preparation, C.D. and Y.C.; writing--review and editing, Y.C., C.D. and K.W.; visualization, Y.C.; supervision, C.D. and B.Z.; project administration, C.D., B.Z., L.L., L.Z. and M.W.; funding acquisition, C.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (grant number 61973314) and the National Society Science Foundation of China (grant number 2024 SKJ)B037).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding authors upon reasonable request.  
**Conflicts of Interest:** According to the policy as well as our moral obligation, we declare that there are no relevant conflicts of interest.
# Characterization of the Intelligibility of
## 2 Materials and Methods
### Dataset Creation: VCV Recordings and Noise  
In this study, sets of spoken VSVs in English, French, German, Italian and Portuguese were compared, i.e., five languages among the top 25 languages with 50 or more million first-language speakers each [35]. The list of stimuli included 12 consonants common across the five languages (/b/,/d/, /f/, /g/, /k/, /l/, /m/, /n/, /p/, /r/, /s/, /t/) in the context of the vowel/a/ (e.g., aba, ada). The VSVs were single exemplars spoken in a sound-treated room by a professional language native male speaker who pronounced the VSVs with no prosodic accent and with constant pitch across the list. Stimuli were recorded in a professional recording studio by using a TLM 103 microphone (Neumann,Berlin, Germany), SSL S4000 64 channels mixer, HD192 A/D converters (44.1 kHz, 16 bit) (Motu, Cambridge, MA, USA), and a GENELEC 1025A control room monitor. The level of the recordings was equalized within and across the sets to meet the equal speech level requirement as in the ISO 8253-3:2012 standard [36] and to guarantee equal average levels of the sets of speech materials.  
The VCV recordings were then additively combined with filtered speech-shaped noise. The speech-shaped noise (SSN) was generated by filtering a Gaussian white noise with a filter representing the international long-term average speech spectrum [37] and, subsequently, with a low pass filter (cut-off 1.4 kHz, roll-off slope 100 dB/octave). Adding this kind of noise is a common viable solution used in speech-in-noise tests for screening in multilingual settings, as it allows to differentiate between normal hearing and mild hearing-impaired subjects more accurately than other processed noise (e.g., high pass, modulated, etc.) [19; 20]. To further limit the possible influence of ambient noise levels, a noise floor was added, consisting of the same SSN described above and attenuated by 15 dB [19; 20], thus the final delivered stimulus resulting as the addition of a VCV, the filtered SSN and the filtered SSN attenuated by 15 dB.
## 2 Multi-Body Constellation Design  
The lunar gravitational environment is particularly complex, and simulations are not always straightforward, which is why different types of constellations are analysed in order to choose and develop the one that best suits the coverage requirements. One of the main drivers in the choice of orbits has been safeguarding the space around the lunar surface; developing a constellation system is crucial from the earliest stage of lunar exploration and colonisation, but it is crucial to do so in a responsible way and avoid unnecessarily overcrowding such a delicate near-surface environment, risking compromising the viability of the many future missions planned to our natural satellite.
## 3 Main Technical Research
### Research on Obstacle Risk Detection and Localization Based on Security Boundary Model
#### 3.2.1 Research on Obstacle Detection Based on KD-Tree Nearest Search and Euclidean Clustering  
In order to address the detection of invading foreign object point clouds, this paper employs a method based on KD-tree (K-Dimensional tree) nearest search and Euclidean clustering for accurate positioning and multi-object pose detection and localization. KD-tree, a balanced binary tree structure that divides k-dimensional spatial data points, adopts a divide-and-conquer strategy to partition space into multiple subparts. The core idea of the KD-tree nearest point search algorithm can be divided into two stages: constructing the KD-tree and searching for the nearest point.  
Constructing the KD-tree: As illustrated in Figure 6, for a dataset composed of n-dimensional data, the dataset is divided into two subsets, the left and right subtrees, through a recursive process by selecting the splitting dimension and splitting point. The dimension with the highest data variance is usually chosen as the splitting dimension, and the median of the data in the splitting dimension is selected as the splitting point. The above process is recursively applied to the left and right subtrees until the amount of data in each subtree is smaller than the system-set maximum threshold.  
Searching for the nearest point: Starting from the root node of the KD-tree, the search proceeds downward along the tree structure until the leaf node containing the target point is found. Then, other subtrees are examined during backtracking to find closer points. By comparing the distance between the target point and the current node and the distance between the target point and the current node's splitting dimension, it is determined whether to search the other subtree. If a closer point is found during backtracking, the current nearest point is updated.  
As shown in Figure 7, Euclidean clustering is performed after completing the KD-tree nearest point search. First, a point in space is located, and the nearest n points are found using the KD-tree. The distances are then assessed, and points satisfying the threshold condition are grouped into a cluster. This operation is repeated until no new points are added to the cluster, resulting in a clustering subset.  
Figure 6: KD-tree Partitioning Process (**a**–**d**).
# On the Estimation of the Moving Mass of a TMD Installed on a Lively Structure
## 2 Theoretical Background
Each segment is processed separately by computing the cross power spectral density defined in Equation (5) for two signals \(x(t)\) and \(y(t)\), where \(E\{\cdot\}\) stands for the expected value operator and the star (\(*\)) accounts for the complex conjugate operand. In addition, the auto power spectral densities, \(S_{xx}(\omega)\) and \(S_{yy}(\omega)\), can be computed through the same expression by substituting \(y(t)=x(t)\) in the first case and \(x(t)=y(t)\) in the second. More details about this can be found in [18]. \[ \begin{split} S_{xy}(\omega)&=\int_{-\infty}^{ \infty}R_{xy}(\tau)e^{-j\omega\tau}d\tau\\ R_{xy}(\tau)&=E\{x(t+\tau)y^{*}(t)\}=E\{x(t)y^{*}( t-\tau)\}\end{split}  \tag{5}\]  
The cross and auto power spectral densities associated to every pair of processed segments can be averaged, so a final cross power spectral density, \(S_{xy}(\omega)\), and a couple of auto power spectral densities, \(S_{xx}(\omega)\) and \(S_{yy}(\omega)\), are obtained. To calculate the frequency response function, one of the estimators shown in Equation (6) can be used, where \(S_{yx}(\omega)=S_{xy}^{*}(\omega)\). The main difference between the estimators \(H_{1}(\omega)\) and \(H_{2}(\omega)\) is the assumption of where the noise is mostly concentrated: in the input time series or the output time series. As can be seen, the estimator \(H_{\flat}\) is the geometric mean of \(H_{1}(\omega)\) and \(H_{2}(\omega)\). More details about each approach can be found in [19; 20]. In this work, the \(H_{\flat}(\omega)\) estimator is used, unless otherwise stated, and, as can be seen in the provided code [17], the function tfestimate of MATLAB is used to perform all the aforementioned estimations. \[ \begin{split} H_{1}(\omega)&=\frac{S_{xy}(\omega )}{S_{xx}(\omega)}\\ H_{2}(\omega)&=\frac{S_{yy}(\omega)}{S_{yx}(\omega )}\\ H_{\flat}(\omega)&=\sqrt{H_{1}(\omega)H_{2}( \omega)}=\frac{S_{xy}(\omega)}{|S_{xy}(\omega)|}\sqrt{\frac{S_{yy}(\omega)}{S _{xx}(\omega)}}\end{split}  \tag{6}\]
## 1 Introduction
Nevertheless, it does not take into account no-fly zones, threats, communication topology, and time constraints in addition to the task reassignment in the case of UAV faults. On the basis of this, [27; 28] introduced local communication constraints with communication distance and information hop times to determine whether other UAVs participate in the local task assignment; however, it neglected the communication constraint caused by the swarm specific topology, which is crucial for certain command structures so as to improve operational effectiveness. Compared with [21; 22; 23; 24; 25; 26; 27; 28], the problem investigated in this paper is the dynamic task assignment for the heterogeneous UAV swarm consisting of distributed subsets with specific topology. In this paper, common constraints such as time windows, the UAV capability model, as well as new constraints such as topology constraints are combined to build the complex model of dynamic task assignment. The key contribution of this paper is that it proposes a solution to rectify the problem of heterogeneous UAV swarm cooperative dynamic task assignments with specific hierarchical communication topologies and other multiple constraints. An extended-CNP-based distributed assignment approach is proposed, along with distributed heterogeneous UAV swarm executing reconnaissance and attack tasks as the main scenario. The swarm consists of several subsets. On the swarm discovering new targets, it firstly assigns each target to subsets according to communication topology, and then each subset assigns subtasks to the UAVs within the group. The modified artificial potential field method is adopted to preplan the threat avoidance flight path, and battlefield survivability and fuel consumption are introduced to describe the flight path's impact on the task assignment. Correspondingly, the consumption penalty and threat penalty functions can be designed so as to solve the coupling between task assignment and path planning. Meanwhile, constraints on time and cooperation are introduced to adjust the task executing sequence. The rest of paper is organized as follows. The description of distributed grouping UAV swarm task assignment problem with multiple constraints is presented in Section 2. In Section 3, combined with hierarchical communication topologies of the UAV swarm,the distributed assignment algorithm based on extended contract network protocol is thoroughly addressed. In Section 4, the dynamic cooperative task assignment scheme based on an event-trigger strategy is proposed. Section 5 demonstrates the approach's effectiveness, both in uncertain environments and with UAV failure, by numerical examples, and the whole work is concluded in Section 6.