esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Experiment Setup
#### 3.3.3 Time of the Recordings  
Most of the studies reported that their data collections took place in the months of June [20; 61; 84; 85; 93] and July [61; 84; 85; 88; 93; 65], with a few extending to August [57; 84] and September [99]. Some studies reported that acoustic changes could be seen according to the time of the day [69; 89]. By exploring different acoustic properties during the morning, midday, afternoon, and night, authors in [69] showed that audio frequency changes slowly through the day. Measuring data over a 24 h period was a common setup in many studies [55; 71; 73; 74; 76]. On the other hand, some studies focused on recording audio only when the bees are the most active. For example, 3 p.m.-6 p.m. was used for the purpose of swarming detection [20; 61], while arrival and departure activity was monitored between 7 a.m.-5 p.m. and between 8 a.m.-6 p.m. in [84; 85] and in [86], respectively.
# An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
## 5 Numerical Experiment
### Simulation Scenario  
In order to study the critical problems of mission scheduling, link optimization, collaborative computing, and network topology discovery of spatial information networks, we developed a Chinese version of the Space-based information network satellite ToolKit (CSTK) independently based on the actual satellite and common mission data in spatial information networks. In this system, we simulated satellite data, including satellite load data, satellite orbit data, inter-satellite visible time window and satellite ground visible time window data, satellite resource capacity, and satellite resource quantity. In addition, we simulated data of common Earth observation application scenarios, including Earth observation area, mission execution time demand, mission resource type, mission resource demand, etc. Moreover, we have developed various computational libraries required for the CSTK system. The experiments in this paper were finished based on the CSTK experimental simulation platform and coded in Python. Eventually, the system created a multi-satellite and multi-task earth observation scene. The scene start time is \"13 June 2021 04:00:00.000 UTCG\", and the end time is \"14 June 2021 04:00:00.000 UTCG\". The Earth observation scene contains 10 practical satellites, of which five satellites are used in the Earth observation scene, and the remaining five are communications satellites. The three-dimensional and two-dimensional views of the satellite and the earth in the Earth observation simulation scene are shown in Figures 7 and 8. As shown in Figure 8, we depict the track of the subsatellite point in the two-dimensional plane. CSTK uses the satellite orbit parameter data provided by celestrak ([[https://celestrak.com/](https://celestrak.com/)]([https://celestrak.com/](https://celestrak.com/)), accessed on 27 October 2021) to calculate the track of subsatellite point in the two-dimensional plane. We denote the satellite coordinates in its orbital plane as cr=(\(x_{0}\), \(y_{0}\), \(z_{0}\)). The calculation Formula (5) is as follows,  
\[cr=\begin{cases}x_{0}=-a\big{(}1-e^{2}\big{)}\times\frac{\cos(\theta)}{1+e\cos (\theta)}\\ y_{0}=-a\big{(}1-e^{2}\big{)}\times\frac{\sin(\theta)}{1+e\cos(\theta)}\\ z_{0}=0\end{cases} \tag{5}\]  
where \(a\) denotes semi major axis, \(\theta\) indicates trueanomaly, \(e\) represents eccentricity. We obtain basic satellite information and satellite orbit information from celestrak and third-party satellite simulation calculation packages. The parameters of satellites in the simulation scenario are shown in Table 3.  
To facilitate experimental verification, only the observation and storage resources required by the resource request queue are considered in this experiment.
## 2 Materials and Methods  
### Overview of the Study Area and Data Sources  
#### 2.1.1 Overview of the Study Area  
The Guangxi Zhuang Autonomous Region, situated in southern China, spans a geographical range of \(20^{\circ}54^{\prime}\) to \(26^{\circ}24^{\prime}\) N latitude and \(104^{\circ}27^{\prime}\) to \(112^{\circ}04^{\prime}\) E longitude, representing a typical mountainous zone in the region [10]. The topography is characterized by hills and low mountains with significant elevation variations. As illustrated in Figure 1, elevation distribution maps of China and Guangxi reveal an altitude range from \(-134\) m to 2111 m, highlighting the region's pronounced mountainous features, with roads frequently traversing these natural landscapes [11]. Influenced by a subtropical monsoon climate, Guangxi experiences high and concentrated precipitation, particularly during the rainy season, where intense rainfall often triggers geological hazards such as collapses, posing substantial risks to transportation safety and social development [12]. The interplay of complex geological conditions and diverse geomorphological features further heightens the susceptibility to collapse disasters. This study identified 10 primary collapse-influencing factors in the study area, encompassing critical elements such as topography and geology. By integrating topographic, climatic, and geological data, this study employs a convolutional neural network (CNN) model to synthesize multi-source data from the study area, aiming to enhance the prediction accuracy of collapse disaster risk and provide a scientific foundation for disaster mitigation and road planning [13].
## 2 Materials and Methods
### Methods
#### 2.3.2 STL Decomposition  
The cyclical season pattern caused by the Earth's orbit around the Sun \"carved names\" on many ecological and environmental time series. This annual seasonal oscillation must first be distinguished to comprehend the time series change [21; 46]. The STL decomposition method is a nonparametric statistical method proposed by [19]. It comprises two main components: an inner loop for fitting the trend and period terms and an outer loop for calculating robustness weights to reduce the effects of outliers. The basic formula was shown as follows:  
\[WUE_{t}=T_{t}+S_{t}+R_{t} \tag{2}\]  
where \(WUE_{t}\) is the value observed at time \(t\). \(T_{t}\), \(S_{t}\), and \(R_{t}\) represent the trend term, the seasonal term, and the irregular residual term, respectively. \(T_{t}\) contains long-term and non-periodic trend signals, such as long-term trends caused by climate change. \(S_{t}\) refers to the seasonal pattern caused by the Earth's revolution. \(R_{t}\) involves other residuals that cannot be explained by trends or seasonal effects.  
WUE time series have trend and periodicity characteristics. Decomposing the time series can more clearly reveal the variation patterns of WUE. The STL seasonal decomposition method was used to mine the information inherent in the time series of WUE. We implemented the STL decomposition pixel-by-pixel programmatically using the _terra_ and _forecast_ packages in R [47]. The starting date of the time series was set to 2002-01, and the ending date was set to 2021-46. All parameters in the STL seasonal decomposition process were determined automatically except for the _robust_ parameter, which controls whether a robust fit was used in the locally weighted regression. The robust parameter was set to _FALSE_ because the WUE time series had been smoothed by the S-G filtering beforehand. Figure 4 depicts an example of WUE time series decomposition using the STL method for a grassland pixel from 2002 to 2021.
## 5 Conclusions  
The article has presented a novel approach to the analysis of signals occurring in radars using the forward scattering phenomenon. Based on the concept of the complex STFT phase and the CR estimators known from the literature, the signals from the PFSR radar have been analyzed. In the first part, using a mathematical model, the possibility of applying the proposed method has been tested using simulated signals. A situation has been considered in which one object and two objects with different kinematic parameters intersect the baseline. Simulations have confirmed the applicability of the method, after which the method was verified using real-life data. The role of the illuminator of opportunity has been fulfilled by the GSM transmitter and the transmitter-receiver line was crossed by the car. The analysis allowed the Doppler rate to be determined in the real scenario. In addition, the method gives the Doppler rate estimation, which had not yet been available, not only at the time of the baseline intersection, but also for the entire observed trajectory. A valuable property is the ability to extract temporary changes in velocity, which increases the amount of information describing the observed object.  
The accuracy of the considered tools has been verified by statistical analysis and the comparison of results to the CRLB, which mainly showed the differences between the estimators as well as the expected accuracy. In the future, the authors want to verify the accuracy of the estimators for a different analysis window length. This is an important parameter due to the fact that in the classical STFT the length of the analysis window affects the estimation variance and bias, and because the tools used are based on STFT, the bias and the variance are also related to the analysis window's length.  
A promising point of further work is the extension of the method with the possibility of classifying objects and estimating the targets tracks. Based on the Doppler rate, not only at the intersection of the baseline but also in the wider observation period, it is possible to classify objects. This can be particularly important in security systems, where the detection of fast and maneuvering missiles is difficult for both active and passive radars. The use of the proposed approach for a transmitter located on the ground and a receiver on the ground or in orbit would allow for detection and classification of dangerous rockets and flying objects.  
**Author Contributions:** Project administration, K.A.; formal analysis, K.A. and Z.G.; investigation, K.A.; methodology, K.A. and P.S.; raw radar data collection, P.K.; software, K.A. and P.K.; supervision, P.S.; validation, K.A.; visualization, K.A. and P.K.; Writing--Original Draft, K.A., P.K. and Z.G.; Writing--Review & Editing, PS.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Parametric Cost Estimation Model for Use in the Architectural Planning Stage
### Parameters for Development of the Construction Cost Estimation Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Quantitative Attributes** & **Mean** & **Std** & **Min** & **25\%** & **50\%** & **75\%** & **Max** \\ \hline year & 2017.63 & 1.87 & 2015.00 & 2016.00 & 2018.00 & 2019.00 & 2021.00 \\ type id & 5.09 & 3.19 & 1.00 & 2.00 & 3.00 & 9.00 & 11.00 \\ floor area & 10,651.76 & 48,415.80 & 145.40 & 3466.96 & 7075.51 & 12,114.60 & 1,417,284.00 \\ site area & 51,933.87 & 153,864.43 & 0.00 & 4407.18 & 12,507.05 & 26,216.73 & 1,510,099.00 \\ building area & 3583.01 & 18,647.70 & 0.00 & 1005.64 & 2050.95 & 3672.37 & 535,285.00 \\ landscaping area & 5407.29 & 22,546.71 & 0.00 & 101.51 & 1366.02 & 3115.59 & 381,635.40 \\ base floor height & 8.51 & 151.77 & 0.00 & 3.43 & 3.90 & 4.20 & 4500.00 \\ max height & 18.53 & 13.32 & 0.00 & 13.40 & 18.30 & 22.42 & 232.40 \\ max number of & 0.84 & 0.66 & 0.00 & 0.00 & 1.00 & 1.00 & 5.00 \\ underground floors & 3.90 & 2.32 & 0.00 & 3.00 & 4.00 & 5.00 & 27.00 \\ max number of floors & 484.78 & 308.37 & 0.00 & 360.00 & 450.00 & 600.00 & 6559.00 \\ number of parking & 92.40 & 166.68 & 0.00 & 22.00 & 52.00 & 95.00 & 1830.00 \\ \hline \hline \end{tabular} Note. This table describes the data distribution for the \(12\) quantitative factors. Each statistical value is shown to two decimal places. Std = standard deviation; Min = minimum value; 25% = first quartile (Q1); 50% = second quartile (Q2); 75% = third quartile (Q3); Max = maximum value.  
\end{table} 
Table 3: Statistical description of quantitative attributes. parameter. Considering these constraints and criteria for parameter selection, 7 of the 13 quantitative parameters--including the floor area, building area, site area, landscaping area, maximum number of underground floors, maximum number of floors, and number of parking spaces--were selected as the parameters constituting the construction cost estimation model.
# A Review on 5G Sub-6 GHz Base Station Antenna Design Challenges
## 4 Future Trends in 5G Base Station Antennas
Despite the high throughput advantage of these massive MIMO antennas, the extreme power consumption puts a significant loading on the electrical network. The operators cannot remove the existing 4G BSAs to reduce the power consumption since the current 5G massive MIMO antennas cannot still serve all the frequency bands. Therefore, the industry is leaning towards integrating legacy multiband 4G antennas with 5G massive MIMO antennas. One potential approach is to reduce the number of ports in massive MIMO BSAs from 32 to 16 and provide some space to integrate a legacy antenna. The aim is to strike a balance with operational cost and performance. The combination of legacy 4G and 5G massive MIMO still poses the challenges highlighted in the paper. For example, the low band (under 1 GHz) needs to be decoupled in an even wider band from 1.4 GHzto 4.2 GHz, which is very difficult. Moreover, the antenna profile is required to be slimmer for less wind loading, making the technical design even harder. Possibilities remain open to design novel wideband frequency selective surfaces to overcome some of these challenges to reduce coupling and improve patterns since more bands are now required to be integrated under one radome.
## 3 Results
### Climatic Impacts on Vegetation Asymmetry Indices  
#### 3.3.1 Interannual Variability of Asymmetries in Temperature and Precipitation  
Understanding the climatic constraints that underly seasonal change in plant growth is crucial for explaining climate change effects on global vegetation productivity. Across the entire NEC, results show that AsyTemp and AsyPrcp exhibit remarkable interannual variability (Figure 8). The temporal dynamics of AsyTemp and AsyPrcp do not appear to have a significant cyclicity, but fluctuations in these variables are both positive and negative. These changes in AsyTemp and AsyPrcp imply that seasonal distribution of temperature and precipitation do not consistently play a major role in controlling the interannual variability in vegetation growth. The fluctuating profile of the AsyTemp time series is consistent with that of the AsyPrcp time series, which is also reflected by their high correlation coefficient of 0.8 (Figure 9). More specifically, the obvious peaks around 1992 and 2003 for the two time series generally match each other. The negative amplitude in AsyTemp, on the other hand, is relatively larger than that in AsyPrcp.  
Figure 7: Spatial distribution of correlation coefficient of three asymmetric indices (white cells denote insignificant correlations).  
To compare asymmetries in temperature and precipitation with those in vegetation growth, their relationships were quantified using Spearman correlation analysis, at the scale of the NEC (Figure 9). The results suggest that AsyTemp does not have a significant relationship with AsyV (r = 0.42), but it does have a significant positive correlation with AsyL (-0.81) and a large, significant negative correlation with AsyR (-0.95). AsyPrcp is related to all the vegetation asymmetric indices, with its  
Figure 8: Interannual variability of asymmetries for temperature (**a**) and precipitation (**b**).  
Figure 9: Correlation matrix among the asymmetry time series for temperature, precipitation and vegetation growth asymmetries over the entire Northeast China (NEC) (blank block denotes insignificant correlation).  
most substantial positive relationship being that with AsyL (0.69) and its largest negative relationship (\(-0.83\)) being that with AsyR. AsyPrcp exhibits a weaker correlation with NDVI asymmetry (0.59).  
At the pixel level, averaged asymmetries for temperature and precipitation across the NEC exhibited a slightly similar spatial pattern, with apparent clusters (Figure 10) where positive AsyTemp and AsyPrcp are distributed widely within natural vegetation in the northern and eastern areas. There is a positive AsyTemp for approximately 55% of the vegetated area. Negative AsyTemp accounts for 45% of the area, which is primarily found in the central agricultural lands. Negative values are shown for 19% of AsyPrcp and are mainly clustered in the southwestern NEC. Using the multi-year mean, precipitation has a greater impact later in the season, after the peak rather than before it.
## 2 Results and Discussion
### Interpretation of Product Ion Spectra for Defined Marker Peptides  
The mass spectrum and product ion spectrum for the marker peptides P11, R2, R3, and R4 detected in sika deer gelatin are shown in Figures S3-S6. In electrospray ionization mass spectrometry (ESI-MS), b-ions and y-ions were predominantly generated. In addition to these ions, high-intensity product ions were observed, and their compositions were estimated based on accurate mass data and to examine whether the identified amino acid sequence was reasonable (Figure 2). The precursor ion of P11 was identified as \(m\)/z 780.91 (double-charged). The sequence GETGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGPAGAGPAGPAGPAGPAGPAGPAGPAGPAGPAGAGPAGPAGPAGPAGPAGAGPAGPAGPAGPAGAGPAGPAGPAGPAGAGPAGPAGAGPAGPAGAGPAGPAGAGPAGPAGAGPAGAGPAGPAGPAGAGPAGAGPAGAGPAGAGPAGPAGAGPAGAGPAGAGPAGAGPAGAGPAGAGPAGAGPAGAGPAGAGP  
and 795.37 (b10+). The sequence could be found in COL1A1 from white-tailed deer (UniProt Accession No. A0A6J0Z7P5, aa976-aa993) and bovine (_Bos taurus_) (UniProt Accession No. P02453, aa11066-aa1083). Furthermore, the fragmentation of y15+ or y12+ resulted in generated characteristic product ions at _m/z_ 127.09 [GlyPro \(-\) CO (carbon monoxide) + H]+, 155.08 [GlyPro + H]+, and 226.12 [GlyProAla + H]+, while product ions at _m/z_ 608.84, 637.36, and 687.88 were identified as double-charged ions derived from cleavage at the y14, y15, and y16 positions.  
Figure 2: Product ion spectrum of marker peptides obtained from ESI-MS/MS (P* and Hyp indicate hydroxyproline). (**A**) Peptide P11, (**B**) peptide R2, (**C**) peptide R3, and (**D**) peptide R4.  
The amino acid sequences identified from the analysis of the y-ions and b-ions of the peptides R2, R3, and R4 were consistent with the sequences reported in the literature [26; 32; 37]. These results were further validated through detailed MS/MS fragmentation analysis. Namely, for the peptide R2, in addition to the product ions associated with b-ions and y-ions, specific product ions derived from individual amino acids were observed at _m/z_ 102.05 [Glu \(-\) CO + H]\({}^{+}\), 120.08 [Phe + H]\({}^{+}\), 159.08 [ThGly + H]\({}^{+}\), 171.08 [HypGly + H]\({}^{+}\), 187.07 [GlyGlu + H]\({}^{+}\), and 213.09 [GluThr \(-\) H\({}_{2}\)O + H]\({}^{+}\). Similarly, for the peptide R3, characteristic product ions were observed at _m/z_ 101.07 [Gln \(-\) H\({}_{2}\)O + H]\({}^{+}\), 171.08 [HypGly + H]\({}^{+}\), and 226.12 [GlyProAla + H]\({}^{+}\), while for the peptide R4, distinctive product ions were observed at _m/z_ 127.09 [GlyPro \(-\) CO + H]\({}^{+}\), 143.08 [HypGly \(-\) CO + H]\({}^{+}\), 171.08 [HypGly + H]\({}^{+}\), 226.12 [GlyProAla + H]\({}^{+}\), and 300.12 [HypGlyGlu + H]\({}^{+}\). The analysis of these product ions revealed amino acid combinations consistent with the identified sequences of the peptides R2, R3, and R4, further substantiating their accurate characterization.
# Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads
## 5 DrI-Based Executor
### MDP Formulation  
The scheduling process comprises \(|\mathcal{T}|\) consecutive decision steps. We formalize the MDP defined by a tuple \((\mathcal{S},\mathcal{A},\mathcal{P},\mathcal{R},\mathcal{S}^{\prime})\) as follows:  
**States**\(\mathcal{S}\). The global state \(s^{t}\) at timestep \(t\) consists of a APG state \(s^{t}_{sc}=\langle S(\mathcal{O})^{t},\kappa^{t},\xi^{t}\rangle\) and a thread-worker state \(s^{t}_{c}=(S(C)^{t},\epsilon)\):  
* **APG State**: \(\kappa^{t}\) and \(\xi^{t}\) denote scheduled/unscheduled set of resource edges. For each \(O_{ij}\), \(S(O_{ij})^{t}=\{T(O_{ij})^{t},X(O_{ij})^{t}\}\), representing operation state, where \(X(O_{ij})^{t}\) is a binary indicator (1 for scheduled operation, 0 otherwise), \(T(O_{ij})^{t}\) is scheduled time or the estimated minimum completion time defined as: \[T(O_{ij})^{t}=\begin{cases}\text{Completion time}&\text{if scheduled}\\ T(O_{ij-1})^{t}+\min_{C_{k}\in\mathcal{C}}\rho_{ijk}&\text{otherwise}\end{cases}\] (7)  
**Action**\(\mathcal{A}\). The hierarchical action space contains:  
* **Operation selection**\((a^{t}_{1})\): Choose eligible operation \(O_{ij}\);
* **Thread-worker assignment**\((a^{t}_{2})\): Choose one thread worker for \(O_{ij}\) from candidate set \(\mathcal{M}(O_{ij})\).  
Figure 5: DCoS executor solves the scheduling problem in Example 2.  
**Reward**\(\mathcal{R}\). The incremental reward at step \(t\) is:  
\[r(s^{t},a_{1}^{t},a_{2}^{t})=-\Big{[}M(s^{t+1})-M(s^{t})\Big{]} \tag{8}\]  
The objective is to minimize makespan \(M=\max_{O_{ij}}T(O_{ij})\) by maximizing cumulative rewards \(\sum_{t=1}^{T}r^{t}\).  
**APG Transition**\(\mathcal{P}\). At timestep \(t\), after the agent acts with updating one directed arc, a new APG is generated with a new global state \(\mathcal{S}^{\prime}\) with \(s^{t+1}=(s_{sc}^{t+1},s_{c}^{t+1})\).
# Effect of Chemical Composition of Bio- and Petroleum-Based Modifiers on Asphalt Binder Rheology
###### Abstract  
In recent years, increased use of recycled asphalt materials (RAP) has created a need for softer binders to compensate stiffer binder coming from RAP. Economic alternatives, like recycled oils and proprietary bio-based oils, can be potential modifiers that will reduce the dependence on petroleum-based alternatives. However, there is limited information on the long-term rheological performance of binders modified with proprietary modifiers. These modifiers are chemically complex and their interaction with binders further complicates the binder chemistry. Therefore, the objective of this study was to evaluate the impact of modifier chemistry on modified binders' long-term cracking potential. A base binder of Superpave Performance Grade (PG) 64-22 was used to develop PG 58-28 binder using six different modifiers. An unmodified PG 58-28 was included for a comparative analysis. A few modified binders rheologically outperformed the base binder and others performed similarly. The modifier derived from recycled engine oil showed the worst performance. Chemical analysis indicated that the best performing modified binders had significant amounts of nitrogen in the form of amines. On the other hand, poor performing modified binder had traces of sulfur. Additionally, modifiers with lower average molecular weights appeared to have a positive impact on the performance of aged binders.  
Asphalt modification; modifier chemistry; long-term aging; asphalt rheology; phase angle; delta T\({}_{c}\)