esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
analyzed the relationship between vegetation and precipitation in the Mara River Basin at a seasonal scale and found that the local vegetation lags behind the peak of precipitation by an average of 35.5 days [24]. [PERSON]'s research found that, in Northern China, vegetation coverage exhibits significant temporal cumulative effects in response to meteorological factors, and the primary temporal influence of the climate factors on vegetation coverage is a 0-month lag or a 1-2-month cumulative effect [25]. Furthermore, related studies have shown that ecosystems exhibit significant dependencies in their lag effects on climate [26]. These lag effects mitigate the adverse impacts of the extreme weather and provide sufficient recovery time for vegetation [27; 28], playing a crucial role in the growth and carbon balance of the terrestrial ecosystems. As a significant participant in the field of the global climate change, China, one of the world's most populous countries, is committed to maintaining ecological balance and adapting to future climate changes. In 2021, China announced its dual-carbon goals during the United Nations General Assembly debate. We understand that vegetation plays a vital role in carbon storage and CO\({}_{2}\) absorption. Furthermore, the process of the vegetation absorbing CO\({}_{2}\) from the atmosphere is not instantaneous, as it takes time to grow and absorb, storing carbon within the plant's structure. The Gross Primary Productivity (GPP) refers to the overall rate at which plants in an ecosystem convert solar energy into organic matter, typically in the form of carbon. The GPP reflects the total amount of captured and fixed light energy in the entire plant community [29; 30]. A comprehensive study of the distribution characteristics of GPP in China and its response to climate change is of particular importance. This helps in evaluating China's carbon balance and provides a scientific basis for formulating policies to mitigate climate change. Therefore, the objectives of this study were as follows: (1) to elucidate the spatiotemporal patterns of the GPP and various meteorological factors in different regions of China from 2000 to 2018; (2) to investigate the spatial distribution of lag effects of GPP concerning important meteorological factors; and (3) to quantitatively analyze the response characteristics of the GPP to meteorological elements in different regions and land-use types.
# Coordinated Optimization of Feeder Flex-Route Transit Scheduling for Urban Rail Systems
## 5 Result Analysis  
### Case Description  
This section selects the St Nicholas Av/W 125 St station of the M3 bus line and the 125 St station of the subway A/B line in Manhattan, New York as examples to verify the model and algorithm based on operational data such as traffic flow, lines, and timetables, as shown in Figure 4. In the feeder service area, the M3 route is converted into a flex-route transit system, enabling vehicles to deviate from the baseline to accommodate passengers with flexible travel demands.  
Numerous points of interest (POIs) surround the interchange station, attracting a significant number of passengers traveling between destinations. Passengers are likely to arrive at a specific POI after taking the subway and depart from a POI before boarding the subway. This study primarily addresses the \"last mile\" travel problem for subway passengers, selecting POIs near the transit line and station. A set of travel demands is randomly generated within the selected POIs.  
For simulation analysis, the complexity of the road network is not the focus of this study; therefore, the system is abstracted as an \(L\times W\) service area, where flex-route transit facilitates passenger connections. Given the focus on \"last mile\" short-distance travel, \(L\) and \(W\) are set to 2 miles, indicating that the distance from the central interchange station to the feeder service boundary is 1 mile.  
To ensure efficient transfer service, synchronization between the subway schedule and feeder flex-route transit operations is required. The actual departure interval of the subway line typically ranges from 8 to 15 min. For this study, the subway departure interval is set to 10 min. The flex-route transit system schedules operations based on the temporal and spatial distribution of travel demand, formulating a feasible departure and operation plan.  
Refer to [9; 25; 26; 27; 28; 29; 30] for relevant parameters in the research system. \(\theta_{W}\), \(\theta_{L}\), and \(\theta_{O}\) are 1 USD/min, 1 USD/min, and 1 USD/min, respectively. Considering the public welfare of public transit, as far as possible to meet travel demand, and reduce the rejection rate, and \(\theta_{F}\) is set to 5 USD/passenger. \(H_{\min}\) is 3 min, \(H_{\max}\) is 60 min, \(v\) is 25 miles/h, \(\epsilon\) is 3 min, \(\tau\) is 10 min, and \(t_{m}\) is 0.3 min. Similar to related research [27; 28], linear motion is used to reproduce the real road network.  
This section is based on the Matlab R2021a platform, on the computer of Intel(R) Core(TM) i7-8750H CPU @ 2.20 GHz, 8 GB RAM, windows 11 system, the model is solved by the programming. For each simulation experiment, the parameters of the genetic algorithm are uniformly set as follows: the chromosome population size is 80, the probability of crossover behavior is 0.9, the probability of mutation behavior is 0.1, and the maximum number of iterations is 500.
## 2 Study Area and Data Preparation
### Streamflow Data  
As shown in Figure 1 and Table 1, five streamflow stations are situated in the basin. The daily streamflow data at these five stations from 1995 to 2015 were obtained from the DMH. These stations measure daily streamflow data from 03:00 UTC of the current day to 03:00 UTC of the next day. These historical streamflow data were adopted for the calibration of hydrological models.
# Smart Nanocomposites for Nanosecond Signal Control: The Nano4 waves Approach
## 3 Results and Discussion
### Realization of Efficient Microwaves Absorbers
#### 3.3.1 Multi-Walled Carbon Nanotubes (MWCNTs)-Polymer Absorber  
MWCNTs based nanocomposites were the first studied. Samples ranging from 1% to 5% (%w/w) MWCNTs in polycarbonate (PC) were prepared and characterized. The samples were studied by TEM. MWCNTs were found to disperse very well even at high loading. As an example, Figure 15a shows the TEM micrograph of a 1% PC MWCNT nanocomposite, in this figure a network of individual MWCNTs is clearly observed.  
Figure 14: (**a**) Transmission (S\({}_{12}\)) (Here to help observe the resonance property as the DC magnetic field applied, S\({}_{12}\) is taken as a difference between S\({}_{12}\) at the applied DC magnetic field (1 KOe–9 KOe) and S\({}_{12}\) at 0 Oe) measured for thin film containing GNPs and MNPs, as a function of applied DC magnetic field in range 0–9 kOe; all columns contain the same magnetic weight percentage and all rows contain the same GNPs amount (**b**) TEM images of PC with 2.5% MNPs (left) and 2.5%MNPs+10%GNPs (right) (c) measurements of saturation magnetization obtained with a MPMS-XL5 SQUID magnetometer (Quantum Design, U.S.A.) (**d–f**) size distribution of MNP for 1%, 2.5% and 5% at different concentration of GNPs respectively.  
Figure 15: (**a**) TEM micrograph of PC-CNT nanocomposite (**b**) absorption index from equation (2) for PC-CNT nanocomposite.  
Further, the electromagnetic properties of the samples were characterized (see Figure 15b). All the samples showed broadband absorption in the characterized frequency range. Best absorption, around 50%, was observed at 1 wt% MWCNTs loading. Absorption was found to decrease with increased MWCNTs loading. The reason is linked to higher conductivity of the composite at higher MWCNTs concentration, which increases the reflectivity, and reduces the overall absorption.  
Additionally, nanocomposites with 1% GNPs were mixed with 0.5% MWCNTs to improve the electromagnetic properties. These were indeed found to have better performances compared to the nanocomposite with only 1% GNPs (see Figure 16a). This improvement is postulated to arise from the existence of a network between MWCNTs and GNPs nanoinclusions (Figure 16c). The formation of an interconnected conductive-network, along with micro-and sub-aggregates of nanofillers, induces the AC conductivity in PC-CNT-GNP composites, which may be attributed to the fact that graphene-nanoplatelets serve as a bridge connecting CNTs and vice-versa helping to form a continuous network of fillers in the composites. This explanation is supported by TEM micrographs (see Figure 16b).  
Figure 16: (**a**) Improvement of absorption performance by using a mixture of MWCNTs and GNPs (**b**) TEM micrograph of PC containing MWCNTs-GNPs 0.5: 1% (% w/w) (**c**) Schematic illustration of interconnected conductive nanofillers network formation of CNT and/or GNPs in the polymer nanocomposite (adapted from [18]).
# FF-YOLO: An Improved YOLO11-Based Fatigue Detection Algorithm for Air Traffic Controllers
## 3 Materials and Methods
### FF-YOLO Network  
This study selects YOLO11 as the baseline model for the ATCO fatigue detection task. The YOLO series, as a classic end-to-end detection network [34], demonstrates strong performance in both high precision and efficiency, better satisfying the requirements for real-time fatigue detection. YOLO11, the latest iteration in the series, employs a keypoint prediction mechanism based on an anchor-free detection paradigm. This approach eliminates the constraints of preset anchor boxes, further enhancing the model's precision and detection performance. Consequently, it achieves higher accuracy and faster recognition speed for fatigue identification in complex backgrounds. However, the YOLO11 model still exhibits limitations. For instance, the fine-grained features differentiating fatigued and alert ATCOs--such as darkened eyelid regions and micro-muscular movements of facial expressions--cause its accuracy to degrade under varying indoor and outdoor lighting conditions. Additionally, it may misidentify fatigue when ATCOs wear glasses. To address these issues, this paper proposes the FF-YOLO model. The structure of the FF-YOLO model is shown in Figure 9. FF-YOLO optimizes the backbone, neck, head, and loss function based on YOLO11.
## 3 Statistical Analysis of the Collected Data for Different Seasons
### Pre-Summer and Summer Measurements
However, in Qatar during summer season, as shown  
Figure 7: Comparison of the Refractive index variations for the months of December, January, February and March according to Bendersky, Kopeika, and Blaunstein (BKB) model in Qatar with FSO’s PDR during those months. in the previous section, the weather factors exceed these thresholds. For this reason, the variation of refractive index during summer season is not presented. As discussed earlier, the strength of atmospheric turbulence is highly affected by the increasing temperature. Thus, a notable increase of refractive index during summer season can be seen. The authors have deduced a closed form expression, from the large data collected from the FSO system and the weather station, for the months of June, July and August (summer in Qatar) and it can be used to estimate the FSO performance in terms of PDR using the Temperature, Humidity and Solar Irradiance Environment parameters. The closed form expression along with the statistics are shown below:  
\[PDR(\%)=96.87-0.46\times(T)+0.14\times(H)-0.002\times(I) \tag{6}\]  
for the month of June, July and August. In the above expression \(T\) is Temperature in Celsius, \(H\) is the Relative Humidity in \(\%\) and \(I\) is the Solar Irradiance in W/m\({}^{2}\). The closed form expression can be estimated to have a Maximum Linearity Error of 0.44\(\%\), which can be considered as a considerably good estimation. Similar expressions were not found for the rest of the months as seen in the previous sections that the environment factors were not affecting the PDR during the other months, as they were sufficiently.
# Effect of Radial Height of Helical Static Blade on the Cavitation Performance of Inducer
## 4 Results Analysis
### Analysis of Simulation Results
#### 4.2.1 External Characteristic  
Figure 7a shows the change of the rated head under different radial heights. With the increase in the radial height, the head increases initially and decreases afterwards. The maximum head is 9.97 m at \(k\) = 0.15. When the radial height changes from 0.05 to 0.2, the head increases by 0.9%, 8.4%, 18.8%, 23.3%, 24.3%, 22.4%, and 16.9%, relative to the head of the separate inducer. Figure 7b shows that the rated efficiency gradually decreases with the radial height changing from 0.05 to 0.2; the decreasing amplitude of the efficiency is 6%, 8%, 10%, 13%, 15%, 17%, and 18%, respectively. Therefore, the implement of the helical static blade will improve the head of the inducer but reduce the efficiency at the same time; there is an upper limit for the increase in the head, which is concerned with the radial height of the helical static blade. Because the inducer is not the main pressurizing component in the liquid rocket engine, the loss of efficiency in the inducer will not significantly affect the overall supercharge efficiency of the propulsion system, while the increase in the head will increase the inlet pressure of the centrifugal pump, resulting in the improvement of cavitation performance.  
Figure 6: Cavitation performance curves under different radial height.  
Figure 7: Comparison of external characteristic under different radial height. **(a)** Head, **(b)** efficiency.
# Code Similarity and Location-Awareness Automatic Program Repair
## 3 Our Approach
### Patch Generation
#### 3.2.1 Longest Common Subsequence  
Considering the efficiency of repairing algorithms and the complexity of buggy programs, we chose the longest common subsequence (LCS) algorithm to measure the similarity between the codes. LCS can take into account both the attribute characteristics and structural characteristics of the program in the process of repairing bugs. Program characteristics are represented by identifiers in the program. Program identifiers include common operators, keywords, standard method names, and user-defined identifiers. The attribute characteristics of the program are obtained by classifying and collecting the identifiers in the program. The structural characteristics of a program are captured by the order in which identifiers appear in the program. The LCS algorithm calculates the similarity by comparing the program characteristics. The sequential identifier sets of the two program segments are respectively deleted with zero or more identifiers, but the order of the remaining identifiers is not changed. The resulting longest strictly increasing sequence is called the longest common identifier subsequence.  
Figure 4: Test case prioritization overview.  
Figure 5: An example of patch generation.  
The longest common subsequence (LCS) algorithm is used to measure the similarity between codes at the character level. For two sequences \(X_{i}=(x_{1},x_{2},\ldots,x_{i})\) and \(Y_{j}=\big{(}y_{1},y_{2},\ldots,y_{j}\big{)}\), the following recurrence relation is used to calculate the LCS length:  
(1) when \(i=0\) or \(j=0\), the LCS length of \(X_{i}\) and \(Y_{j}\) is zero.  
(2) when \(x_{i}=y_{j}\), the LCS of \(X_{i}\) and \(Y_{j}\) is the LCS of \(X_{i-1}\) and \(Y_{j-1}\) plus the LCS of \(x_{i}\) (or \(y_{j}\));  
(3) when \(x_{i}\
eq y_{j}\), the LCS of \(X_{i}\) and \(Y_{j}\) is the longer of the LCS of \(X_{i-1}\), \(Y_{j}\) and the LCS of \(X_{i}\), \(Y_{j-1}\).  
Assuming that \(c[i][j]\) represents the length of the LCS of \(X_{i}\) and \(Y_{j}\), the following calculations are performed:  
\[c[i][j]=\left\{\begin{array}{cl}0&\mbox{$i=0$ or $j=0$}\\ c[i-1][j-1]+1&\mbox{$i,j>0$ and $x_{i}=y_{i}$}\\ \max(c[i-1][j],c[i][j-1])&\mbox{$i,j>0$ and $x_{i}\
eq y_{i}$}\end{array}\right. \tag{2}\]
## 3 Packet Reception Rate  
Let us first introduce the necessary mathematical notation, all of which is collectively listed in Table 1. Wireless link quality, e.g., PRR, is a function of Bit Error Rate (BER) and the packet size in digital communications; namely,  
\[PRR=(1-BER)^{f_{data}} \tag{1}\]  
where \(f_{data}\) is the data frame size in bits, and  
\[BER=f\left(\frac{E_{b}}{N_{0}}\right) \tag{2}\]  
where \(E_{b}/N_{0}\) is the ratio of the average energy per bit to the single-sided noise power spectral density.  
Signal to Noise Ratio (SNR) can be calculated as  
\[SNR=\frac{E_{b}}{N_{0}}\frac{R}{B} \tag{3}\]  
where \(R\) is the data bit rate and \(B\) is the channel bandwidth. Note that, by definition, SNR is the ratio of received signal strength to the channel noise power, _i.e._, \(SNR=RSSI/N\). Therefore, Received Signal Strength Indicator (RSSI) is  
\[RSSI=SNR\cdot N=\frac{E_{b}}{N_{0}}\frac{R}{B}N \tag{4}\]  
where \(N\) is channel noise power, which can be calculated as  
\[N=kTB, \tag{5}\]  
where \(k\) is Boltzmann's constant and \(T\) is the effective temperature in degrees Kelvin. For simplicity we ignore receiver system noise generated in the amplification and processing of the signal, which is usually constant.  
By substituting Equation (5) into Equation (4), and re-arranging the terms, we have  
\[\frac{E_{b}}{N_{0}}=\frac{RSSI}{kTR} \tag{6}\]  
Since \(k\), \(T\) are constants, given a specific modulation format (e.g., two-level FSK), \(E_{b}/N_{0}\) only depends upon the RSSI and data rate \(R\). Recall that Bit Error Rate (BER) is a function of \(E_{b}/N_{0}\) (see Equation (2)) and PRR is again a function of BER (see Equation (1)). Consequently, PRR is a function of RSSI and \(R\). We have  
\[PRR=g(\frac{RSSI}{R}) \tag{7}\]
# Ocean Surface Topography Altimetry by Large Baseline Cross-Interferometry from Satellite Formation
## 6 Numerical Results of LB-IRA  
The altimetric accuracy of LB-IRA is evaluated and discussed based on the selected parameters from Table 1. Figure 16a, b shows the absolute altimetric accuracy determined by phase noise alone, under a cross-track baseline length of 629 and 1000 m, respectively. Although the total coherence of LB-IRA is relatively low, as shown in Figure 15, owing to multi-looking, which considerably suppresses the phase noise, the absolute altimetric accuracy still meets the centimetric accuracy requirement at 1 km resolution. Assuming the phase noise of two neighboring grids are totally uncorrelated, then, the relative accuracy will be decreased by a factor of \(\sqrt{2}\), which is still better than 1 cm for most cases.  
Figure 15: Total coherence alongside other major decorrelation factors of LB-IRA. (**a**) Cross-track baseline length of 629 m, and (**b**) cross-track baseline length of 1000 m.  
According to Reference [24], the baseline length error of TSX/TDX is smaller than 1 mm by DGPS evaluation. Figure 17a shows the absolute accuracy determined by a baseline length error of 1 mm, the absolute accuracy is a few centimeters among the variation range of cross-track baseline. The relative accuracy determined by the same baseline length error is shown in Figure 17b. Due to differentiation which cancels out most of the common parts of error, the relative accuracy is far better than the centimetric requirement.  
According to Equation (7), a baseline length error of 1 mm will result in an equivalent inclination error of 0.2\(\sim\)0.3 arcsec, corresponding to the baseline length of 629\(\sim\)1000 m. Figure 18a indicates that the absolute centimetric accuracy cannot be met without further calibration from the ground, which is also the same dilemma faced by SWOT [34]. However, the relative accuracy is as high as 1\(\sim\)2 mm, as shown in Figure 18b, which is quite sufficient for applications such as geostrophic current or marine gravity anomaly inversion.  
Figure 16: The absolute altimetric accuracy determined by phase noise, at 1 km resolution. (a) Cross-track baseline length of 629 m, and (b) cross-track baseline length of 1000 m.  
Figure 17: The altimetric accuracy determined by a baseline length error of 1 mm. (a) The absolute accuracy and (b) the relative accuracy.  
Although the relative altimetric accuracy of LB-IRA can be as high as a few millimeters, the absolute accuracy still cannot fulfill the centimetric accuracy requirement at present. The biggest altimetric error comes from the baseline length error, due to limited ranging accuracy between two satellites. If the ranging accuracy can be improved by an order of magnitude by calibration from the ground, or utilizing other advanced ranging techniques, for example, the K-band ranging system on Gravity Recovery And Climate Experiment (GRACE) satellite whose accuracy is only a couple of microns [35], the altimetric capability of LB-IRA will be improved remarkably.
# A Novel Cross-Layering Power Control Mechanism for AODV +
## 4 DPCM (Density Power Control Mechanism)
### Power Control Mechanism Definition  
To define the power control mechanism is necessary to consider several factors such as the number of neighbors, simulation area, transmission range, etc. The power control mechanism has been designed to be applied in MANETs with AODV routing protocol and networks with symmetrical distribution within a limited area.  
As already indicated in previous sections, the mechanism operates according to the number of neighbors that the AODV routing protocol has detected. The power value that the mechanism should increase or decrease according to the number of neighbors that it detects depends on many parameters such as simulation area, transmission range, minimum power, maximum power, and propagation loss model. The mechanism operates with a conditional logic based on the number of neighbors. Hence, according to this parameter, decisions are made to increase or reduce transmission power. The mechanism employs the following steps: (1) For DPCM to work, it is necessary to have access from the network layer to the physical layer; (2) define variables to store the power values, number of neighbors, and the addition power factor; (3) start the evaluation of the number of neighbors with the four possible cases.  
For this work, it has been considered that the \"ideal\" number of neighbors for a node is four. This is, we set \(lb=4\), one neighbor node at each cardinal point. This number has been chosen due to the uniform distribution of nodes in the simulation scenarios. For the upper bound, we will evaluate the performance of two values in Section 5.  
Algorithm 1 shows our DPCM algorithm. First, in line two, if the number of neighboring nodes is zero, then the new power transmission \(P\) is set to the maximum power \(P_{max}\). Second, in line three if the number of neighboring nodes is less than \(lb\), then the mechanism will increase an \"addition factor\" (discussed later). If the neighbors detected are greater than or equal to \(ub\) (line 9), the mechanism will decrease the current power of the node. This will be done by subtracting the \"addition factor\" from the current power. If it does not meet any condition, then the power value \(P\) will be the current value. Finally, the transmission power is configured.  
```
1:\(P_{current}\):Current power, \(lb\):Lower bound, \(ub\): Upper bound.
2:\(P\) is the new power.
3:if number of neighbors \(=0\)then
4:\(P=P_{max}\)
5:elseif number of neighbors \(<lb\)then
6:if\(P_{current}+\Delta_{power}<P_{max}\)then
7:\(P=P_{current}+\Delta_{power}\)
8:else
9:\(P=P_{max}\)
10:endif
11:elseif number of neighbors \(\geq ub\)then
12:if\(P_{current}-\Delta_{power}>P_{min}\)then
13:\(P=P_{current}-\Delta_{power}\)
14:else
15:\(P=P_{min}\)
16:endif
17:else
18:\(P=P_{current}\)
19:endif
20:return P
```  
**Algorithm 1**\(DPCM(P_{current},lb,ub)\)  
There are some parameters that the power control mechanism considers for its operation. These parameters are detailed now.