esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Notably, the desert steppe's robust adaptability to extreme climate conditions results in a distinct responsepattern compared to other land surfaces, rendering its carbon stocks more sensitive to extreme climate variations than to normal climatic conditions [24]. Consequently, investigating the response mechanisms of terrestrial ecosystems in this region is of paramount importance. The desert steppe at the NFYS, situated between the typical meadow and the desert, is characterized by a singular vegetation type, a fragile ecological environment, and significant soil erosion [25]. This ecosystem exhibits a heightened sensitivity to climate change compared to other terrestrial ecosystems [26]. Among the various influencing factors, precipitation emerged as the predominant driver of vegetation change in this region. Notably, precipitation occurring outside the growing season primarily influenced the underground components of desert steppe vegetation. In contrast, the contributions of temperature, atmospheric circulation factors, solar radiation, and human activities to vegetation change were substantially lower than that of precipitation, with their respective contribution rates being relatively comparable [27; 28]. Previous studies have predominantly focused on investigating the correlations between extreme climatic events and various ecological aspects of desert steppe ecosystems, particularly concerning phenological patterns and productivity dynamics [29; 30]. However, there remains a notable research gap in quantitatively assessing both the direct and indirect relationships between GPP and extreme climate events, as well as normal climatic conditions, while systematically accounting for temporal lag effects in this specific region [31]. This study is conducted in the desert steppe region at the northern foothills of the Yinshan Mountains, with a primary focus on analyzing the spatiotemporal evolution characteristics of long-term GPP and climate change patterns. By employing the random forest method and the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, the research quantitatively elucidates the responses and lag effects of GPP to extreme precipitation events and extremely high temperatures. Additionally, the study further investigates the mutual feedback mechanisms between GPP and extreme climate events under projected future climate change scenarios.
# Partitioning Evapotranspirationiration in a Cotton Field under
## 2 Materials and Methods
### Measurements  
The \(EC\) system was used to monitor the water-carbon fluxes and \(ET\) of cotton fields under multiched drip irrigation. It consisted of a fast-response open-path infrared gas analyzer (LI-7500, LI-COR Inc., Lincoln, NE, USA), a fast-response 3D sonic anemometer (CSAT3, Campbell Inc., Logan, UT, USA), an air temperature and relative humidity sensor (HMP155, Vaisala Inc., Helsinki, Finland), a net radiometer (NR-LITE, Kipp&Zonen, Delft, Netherland), and a datalogger (CR3000, Campbell Inc., Logan, UT, USA). The installation heights of LI-7500 and CSAT3 were both 4 m above the ground. Wind speed, ultrasound virtual temperature, the densities of the atmosphere, water vapor, and CO\({}_{2}\) were measured by them at a frequency of 10 Hz to obtain net ecosystem carbon exchange (\(NE\)) above the canopy, and \(ET\) was determined from the latent heat flux (\(LE\)) identified by the water fluxes. The mean value of the data every 30 min was stored as a group. HMP155 was installed 4 m above the ground to measure air temperature (\(T_{air}\)) and relative humidity (\(RH\)). NR-LITE was installed 3 m above the ground to measure upward and downward longwave and shortwave radiation. All instruments were connected to the CR3000, which recorded and stored data every 30 min. Due to the latitude difference, the apparent solar time in the study area is 2 h later than Chinese standard time, but the crops respond to the apparent solar time. In order to facilitate comparison with other similar studies, Chinese standard time is still used in this paper.
# WiFi Positioning in 3 GPP Indoor Office with Modified Particle Swarm Optimization
## 6 Conclusions  
Generally, in an indoor environment, a user's location is located using mobile communication technologies such as Wi-Fi, Bluetooth, and UWB. However, a positioning error occurs in an indoor environment due to a propagation loss problem because of many walls and obstacles. In this paper, we proposed a positioning method based on the modified PSO to improve the positioning error. The proposed scheme innovatively establishes the initial search region of the traditional PSO. Limiting the initial search region of the PSO helps the intelligent particle converge to the global optimum in the optimization problem. In addition, the time required for convergence to the optimal value can be shortened. Based on the above two advantages, it was confirmed through simulation that the proposed method can provide high positioning accuracy. In the future, we plan to study the positioning performance according to the change of the parameter values of the particles distributed within the limited region. In addition, we plan to verify the performance of the proposed method by building a testbed in a real scenario.  
Conceptualization, S.H.O. and J.G.K.; methodology, S.H.O. and J.G.K.; software, S.H.O.; validation, S.H.O. and J.G.K.; formal analysis, S.H.O. and J.G.K.; investigation, S.H.O.; resources, J.G.K.; data curation, S.H.O.; writing--original draft preparation, S.H.O.; writing--review and editing, J.G.K.; visualization, S.H.O.; supervision, J.G.K.; project administration, J.G.K. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2021R1F1A1063845) and a Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea government (MOTIE) (N0002429, The Competency Development Program for Industry Specialists).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 9: Positioning error CDF according to distance between SPs.
# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
## 3 Intelligent MTD Techniques
### Intelligent MTD Techniques
#### 3.2.1 Towards Affordable
Second, they changed the attack-aware data samples from network data streams to stream table data to reduce the network channel resource occupation. Experiments showed that the hopping frequency of their proposed method was lower than other methods [11; 12; 13; 77], significantly reducing the system overhead. * Methods are designed to minimize the additional overhead when triggering MTD  
[PERSON] et al. [68] proposed an MTD approach for minimizing long-term system costs for DDoS attacks and covert channel attacks. The authors argued that MTD imposes an overhead on currently running applications in the system when dynamically shifting the attack surface; therefore, determining the optimal triggering time for MTD can minimize this additional overhead. The authors modeled this problem as an update reward process in reinforcement learning and proposed an optimal algorithm to determine the correct time to trigger MTD and avoid the additional burden of invalid triggering, thus minimizing long-term cost rates. Designing an inappropriate interval to trigger MTD cannot afford to balance system security and system overhead, so the trend in intelligent MTD development is _towards self-adaptive_. Our investigation shows that most self-adaptive MTD has achieved better results in reducing the additional overhead when triggering MTD. It should be noted that the techniques we listed were optimized regarding system overhead, so we could assume that, in the future, self-adaptive MTD techniques are also likely to address affordability.
###### Abstract  
In this study, we introduce a novel combination of layer-static-weighted attention and ascending feature selection techniques to predict the seriousness level of adverse drug events using the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). We utilized natural language processing (NLP) to analyze the terms in the active substance field, in addition to considering demographic and event information such as patient sex, healthcare provider qualification, and drug characterization. Our ascending feature selection method, which progressively incorporates additional features based on their importance, demonstrated continuous enhancements in prediction performance. Simultaneously, we employed a layer-static-weighted attention technique, which dynamically adjusts the model's focus between natural language processing (NLP) and demographic features. This technique achieved its best performance at a balanced weight of 50%, yielding an average test accuracy of 74.56% and CV ROC score of 0.83 when 4000 features were included, indicating a compelling advantage to include a larger volume of meaningful features. By integrating these methodologies, we constructed a robust model capable of effectively predicting seriousness levels, offering significant potential for improving pharmacovigilance and enhancing drug safety monitoring. The results underscore the value of NLP and demographic data in predicting drug event seriousness and demonstrate the effectiveness of our combined techniques. We encourage further research to refine these methods and evaluate their application to other clinical datasets.  
layer-static-weighted attention; ascending feature selection; natural language processing; drug event seriousness; drug safety monitoring Article
# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 2 Materials and Methods
### Lithology Classification Based on Deep Learning  
It is challenging to classify lithology because the surface has suffered from weathering for years. In this paper, a novel classification method is proposed for lithology classification based on dual-frequency Pol-SAR data and the SSAE classifier. The proposed method has the flow chart as shown in Figure 1, which includes pre-processing of Pol-SAR data, polarimetric feature extraction, and classification with the SSAE.
## 4 Self-Selective Routing (SSR)
### Autonomous Self-Aware and Adaptive Route Repair Technique in ASAART  
Herein, we employ the greedy algorithm [33, 58] to implement the autonomous self-awareness and adaptive route repair technique. By implementing the concept of the self-awareness principle defined in Section 3, the sensor nodes become self-aware and adaptive for both local and global network state information. During route maintenance, the source node initiates a new route repair packet (_RRP_). The route repair packet _RRP_ consists of the source node identification (\(\mathit{Source}_{\mathit{id}}\)) number, packet identification number (\(\mathit{packet}_{\mathit{id}}\)), route metric (\(\mathsf{Route}_{\mathit{Metric}}\)), back-off delay (\(\mathit{AutoSelf-Awareness-Back-offDelay}\)), and route metric sequence number of sources to destination (_RouteMetric\({}_{\text{Seg}}\)_). In this case, when a sensor node \(k\) receives an _RRP_ packet, it processes the packet according to the algorithm given in Figure 2.  
There are two sets of nodes, one that consists of the node's neighbors within its transmission range (_Local-Node-Neighbor\({}_{\text{Info}}\)_), and another that is outside the transmission range that leads to the minimal routing path (_Global-Node-Neighbor\({}_{\text{Info}}\)_). At the initial stage of network creation, sensor node \(k\) updates its global state information _Global-Node-Neighbor\({}_{\text{Info}}\)_ (\(k\)) using the source node's local neighborhood information (_Local-Node-Neighbor\({}_{\text{Info}}\)_) to update its global node information. This approach helps to prevent and avoid both transient and permanent faults and failure in the network. A detail of the route repair technique in ASAART is illustrated in Figure 2.
# 2D-Unitary ESPRIT Based Multi-Target Joint Range and Velocity Estimation Algorithm for FMCW Radar
## 4 Simulations and Analysis
### Range-Velocity Pairing Correctness Comparison in Multi-Target Scenario  
In this simulation, firstly, four targets with different ranges and velocities are set as \(R_{0}=[11,15,5,7.7]\) m and \(v_{0}=[5.8,4.7,8.8,2]\) m/s to verify the feasibility of range-velocity pairing performance of various algorithms. Then, two targets with the same range but different velocities and two targets with different ranges but the same velocity are set as \(R_{0}=[11,11,7.7,15]\) m and \(v_{0}=[5.8,9,3.3,3.3]\) m/s to compare the correctness of the range-velocity pairing performance of various algorithms.  
Figure 8 shows the scatter plot of the range-velocity pairing feasibility of various algorithms when SNR = 10 dB. As can be seen, the correct pairing of range-velocity estimates can be achieved by all algorithms except for the 2D-CZT algorithm. The reason is that the range and velocity spectral functions of the same target have the maximum correlation, which is utilized by the 2D-CZT algorithm for range-velocity pairing, but it has randomness in sawtooth wave frequency modulated signals.  
Figure 9 shows the scatter plot of the range-velocity pairing results of various algorithms when SNR = 10 dB, and Figure 10 shows the correct rate of range-velocity pairing of various algorithms when SNR = 10 dB. As can be seen from Figures 9 and 10, the correct rate of range-velocity pairing can be achieved by the proposed algorithm, the MMEMP algorithm, and the 2D-FFT algorithm without wrong pairing and false detection. An arbitrary false signal is estimated by the 2D-RootMUSIC algorithm when there are targets  
Figure 6: RMSE versus SNR for range estimates of various algorithms in multi-target scenarios. (a) \(R_{0}=11.9\) m. (b) \(R_{0}=12\) m.  
Figure 7: RMSE versus SNR for velocity estimates of various algorithms in multi-target scenarios. (a) \(v_{0}=5.6\) m/s. (b) \(v_{0}=5.8\) m/s.  
with the same range or the same velocity. The 2D-CZT algorithm fails to achieve correct range-velocity pairing due to the same reasons described in the previous simulations.  
Therefore, the proposed algorithm, the MMEMP algorithm, and the 2D-FFT algorithm outperform the other algorithms in terms of range-velocity pairing correctness in multi-target scenarios with the same range or the same velocity, and no false targets or incorrect pairs were detected.  
Based on the results of the above three simulations, the proposed algorithm has the performance of high accuracy of range and velocity estimation, high resolution as well as high accuracy of range-velocity pairing. Therefore, the proposed algorithm has a better robustness than the other algorithms.  
Figure 8: Scatter plot of range-velocity pairing feasibility of various algorithms when SNR = 10 dB.  
Figure 9: Scatter plot of range-velocity pairing results of various algorithms when SNR = 10 dB.
## 3 Materials and Methods
### Traffic Direction  
This section presents a method for extracting ship azimuth data in defined waters and displaying the representative azimuths. Analyzing vessel azimuth data in congested waters is essential for understanding vessel traffic patterns. This study extracts ship azimuth data using AIS data and displays representative azimuths using clustering techniques. [PERSON] et al. [39] applied a data mining approach to categorize ships based on type, size, and destination to identify key waypoints where voyage changes occurred. This methodology helps compress trajectories and cluster ship movements, enabling effective and probabilistic characterization of ship routes and waypoints. [PERSON] et al. [40] used clustering techniques such as principal component analysis (PCA) and k-means to model ship paths, distinguishing between movement patterns at ports and outputs. Bay center averaging was used to further refine the path centerlines, extracting representative path and azimuth vectors. To segment the ship collision azimuth map, we utilized a support vector classification (SVC)-based method. [PERSON] et al. [41] demonstrated the application of the SVC to azimuthally classify ship collisions, enabling accurate collision angles to be mapped across different ocean areas. This classification helps identify high-risk collisions and improves maritime traffic management.
## 4 Results
### Pre-Trained Analysis
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Alg.**} & \multicolumn{4}{c}{**Trained on BL and**} & \multicolumn{4}{c}{**Trained on GB and**} & \multicolumn{4}{c}{**Trained on DL and**} & \multicolumn{4}{c}{**Trained on GB and**} \\  & \multicolumn{4}{c}{**Tested on DL CC**} & \multicolumn{4}{c}{**Tested on DL CC**} & \multicolumn{4}{c}{**Tested on DL SS**} & \multicolumn{4}{c}{**Tested on DL SS**} \\ \cline{2-13}  & **BPCER** & **APCER** & **Liv.** & **BPCER** & **APCER** & **Liv.** & **BPCER** & **APCER** & **Liv.** & **BPCER** & **APCER** & **Liv.** \\  & [**5**] & [**5**] & **Acc.[**5**] & [**5**] & [**5**] & [**4**] & **Acc.[**5**] & [**5**] & [**5**] & **Acc.[**5**] & [**5**] & [**5**] & **Acc.[**5**] & **Acc.[**5**] \\ \hline Col (*) & 1.61 & 26.71 & 99.18 & 1.51 & 0.29 & 99.16 & 1.61 & 58.86 & 67.16 & 1.51 & 61.5 & 65.76 \\ \hline Dob (*) & 1.07 & 0.16 & 99.37 & 1.27 & 0.20 & 99.31 & 1.07 & 31.34 & 82.41 & 1.27 & 26.59 & 84.92 \\ \hline con & 5.27 & 0.28 & 93.46 & 36.44 & 0.16 & 83.35 & 5.27 & 73.94 & 57.27 & 36.44 & 45.50 & 58.07 \\ \hline IJW & 0.68 & 30.65 & 98.16 & 5.51 & 25.08 & 83.81 & 0.68 & 95.12 & 45.41 & 5.51 & 99.92 & 43.00 \\ \hline m\_s(*) & 0.83 & 2.80 & 99.20 & 0.83 & 0.77 & 99.20 & 0.83 & _29.07_ & 83.77 & 0.83 & 29.07 & 83.77 \\ \hline m\_e(*) & 0.24 & 0.77 & 99.87 & 0.24 & 0.04 & 99.87 & 0.24 & 28.66 & 84.26 & 0.24 & 28.66 & 84.26 \\ \hline PAU (*) & 2.68 & 13.13 & 96.16 & 2.68 & 4.80 & 96.16 & 2.68 & 24.72 & 85.30 & 2.68 & 24.72 & 85.30 \\ \hline Bld & 2.59 & 4.80 & 94.28 & 5.85 & 0.37 & 97.14 & 2.59 & 77.97 & 56.30 & 5.85 & 22.03 & 85.32 \\ \hline bb8 & 2.39 & 8.33 & 96.58 & 3.61 & 49.59 & 71.31 & 2.39 & 69.51 & 46.03 & 3.61 & 99.88 & 43.88 \\ \hline r2d2 & 1.27 & 4.27 & 98.03 & 0.73 & 68.29 & 62.42 & 1.27 & 82.11 & 45.85 & 0.73 & 100.00 & 45.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of LivDet 2021 competitors on test sets acquired with the Dermalog sensor. In particular, Derm CC is a test set acquired with the consensual technique, while Derm SS is a test set acquired with the ScreenSpoof technique. (*) indicates training on both GreenBit and Dermalog Livdet training sets. (**) indicates training on additional data with respect to LivDet training sets. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Alg. **} & \multicolumn{4}{c}{**Trained on GB and**} & \multicolumn{4}{c}{**Trained on DL and**} & \multicolumn{4}{c}{**Trained on GB and**} & \multicolumn{4}{c}{**Trained on DL and**} \\  & \multicolumn{4}{c}{**Tested on BK 2013**} & \multicolumn{4}{c}{**Tested on BK 2013**} & \multicolumn{4}{c}{**Tested on BK 2019**} & \multicolumn{4}{c}{**Tested on OR 2019**} \\ \cline{2-13}  & **BPCER** & **APCER** & **Liv. ** & **BPCER** & **APCER** & **Liv. ** & **BPCER** & **APCER** & **Liv. ** & **BPCER** & **APCER** & **Liv. ** \\  & [**5**] & [**5**] & **Acc. [**5**] & [**5**] & [**5**] & **Acc. [**5**] & [**5**] & **Acc. [**5**] & [**5**] & **Acc. [**5**] & [**5**] & **Acc. [**5**]** & **Acc. [**5**]** \\ \hline con & 16.30 & 5.70 & 89.00 & 0.00 & 1
## 3 Results and Discussion
### Effect of Background Chemicals on ILs Adsorption
#### 3.5.2 Effect of Ionic Strength  
In practice, some natural ions are also involved during the washing step after the extraction of oil sands. Figure 8 shows the effects of CaCl\({}_{2}\) and NaCl on [Bmim][BF\({}_{4}\)] adsorption onto the resins. It was observed that the adsorption ratio of [Bmim][BF\({}_{4}\)] declined from 95.32% to 88.67% and 79.13%, respectively, when the concentrations of Ca\({}^{2+}\) and Na\({}^{+}\) increased from 0 to 1000 ppm. The suppressed adsorption would be ascribed to the result of competitive adsorption of coexisting cations with [Bmim] cations. Additionally, the divalent Ca\({}^{2+}\) showed a more noticeable suppression of [Bmim][BF\({}_{4}\)] adsorption than that of monovalent Na\({}^{+}\). This is mostly attributed to the stronger electrostatic attraction to ion-exchange resins. Thus, the Ca\({}^{2+}\) ions would compete against the [Bmim] cations for binding sites more effectively [47; 48].
## 4 Discussion  
The main contributors to the alteration of the surface properties involved on the surface-liquid interaction include adsorption, ion exchange as well as electrostatic forces. This can be seen in the process of transforming a surface from hydrophilic to oleophilic, naturally or artificially induced [13; 53]. Examples of these are the response of the surface when in contact with oil for long enough or when the surface are chemically treated. In both cases an hydrophobic behavior is promoted.  
The use of TD-NMR on two-component systems provides useful information to understand these phenomena. The T\({}_{2}\) distribution, which directly relates with the porous media through the surface relaxivity term in Equation (2), reflects the effect on the pore surfaces. On the other hand, the diffusion coefficient provides complementary information on the effects of the fluid. These two variables can jointly aid to interpret wettability changes. More complex heterogeneity and variability can significantly impact data interpretation. However, similar trends can still be observed in the different data types.  
Wettability effects in complex systems have been widely explored [28; 30; 35; 36]. The use of simpler two-component systems helps to assess wettability changes to a specific factor. Example of this is the wettability alteration induced by low-salinty sulfate brine, analyzed in this study. Although this work does not attempt to resolve the dichotomy related to fluid-fluid and fluid-rock interactions as it pertains to low-salinity waterflooding, namely which one is more dominant, it provides a simpler method to analyze the wettability alteration and the effects that the different fluids can have on it.  
Figure 15: Diffusion coefficient behavior of Berea Sandstone before and after the aging process in 1% Na\({}_{2}\)SO\({}_{4}\) and 100% Na\({}_{2}\)SO\({}_{4}\) at 25 \({}^{\circ}\)C.
# Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites
## 4 Tests and Results
### Packet Loss Test  
This test was performed to check the timeout functionality and to plot the packet success rate graph of the system. Some of the packets were intentionally dropped by a modified code to simulate packet loss. The transmitting and receiving threads ended just after the timeout and successfully returned part of the transmission. Therefore, the first part of the test confirms that the system will not hang out if some or all packets are dropped, and it also confirms that the system correctly saves the received portion of data and generates a report of the operation. In cases where the lost packets are signaling packets, the ground station will not send feedback for the mode of the lost packets, meaning it will not be considered in the next patch. This will not affect the communication session.  
The whole patch of data will only be lost in the case of feedback loss, because of the data rate mismatch between the two ends of the communication terminal.  
To determine the packet success rate, different values of signal-to-noise ratio (SNR) are inputted into the noise source, and for each value (200), packets are transmitted at a fixed data rate. The received packets are monitored. This test was performed for the available modes of transmission, i.e., 9600 bps GMSK, 4800 bps GMSK and 2400 bps GMSK. The results are shown in Figure 16.