esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Benchmark Highway Bridge  
The highway bridge considered in this study is a benchmark bridge. For many years, researchers have suggested control systems and implemented them in different bridges; therefore, it was hard to compare their results. For facilitating comparison among various control devices, the ASCE Committee on structural control in 2003 decided to develop a benchmark structural control model for highway bridges. This model is based on an actual bridge in Southern California.  
The selected bridge is 91/5 freeway overcrossing located in Orange County of Southern California, shown in Figure 1. This is a continuous two-span, cast-in-place prestressed concrete box-girder bridge supported by an outrigger bent at the center. The Whititer-Ellsinore fault is 11.6 km (7.2 miles) to the northeast, and the Newport-Inglewood fault zone is 20 km (12.5 miles) to the southwest of the bridge. The bridge has two spans of 58.5 m (192 ft) long and has two abutments skewed at 33\({}^{\circ}\). The width of the deck along the east span is about 12.95 m (42.5 ft), while along the west span is about 15 m (49.2 ft). The cross-section of the deck consists of three cells. The deck is supported by a 31.4 m (103 ft) long pre-stressed outrigger, which rests on two pile groups, each consisting of 49 driven concrete friction piles. The columns are approximately 6.9 m (22.5 ft) high. Readers are referred to the definition paper for more information about the benchmark bridge [27].  
A three-dimensional finite element model of this bridge was developed using ABAQUS software, in two phases. In phase I, the semi-isolated model, the deck of the bridge is fixed to the center outrigger, and eight seismic isolators are applied to separate the bridge from the end abutments [27]. In phase II, the completely isolated model, two lead rubber bearings are installed between the deck and the center outrigger in addition to the eight aforesaid isolators [28]. Many researchers have implemented their control systems in phases I and II of the benchmark bridge. For example, [PERSON] and [PERSON] [29] presented sample designs for three control systems and applied them in phase I. [PERSON] et al. [30; 31; 32] investigated the performance of different passive and semi-active devices on the phase I bridge. [PERSON] et al. [33; 34; 35] also applied passive control systems on this bridge. Regarding sample controllers in phase II, [PERSON] et al. [36] proposed a damper with SMA for controlling the displacements and studied the impact of temperature changes on the SMA behavior. [PERSON] et al. [37] suggested a new SMA device as a passive damper to reduce the maximum displacement of the base isolators. [PERSON] et al. [38] investigated the performance of a novel negative stiffness device (NSD) and a damper system in seismic control of the benchmark bridge. In this research, the completely isolated benchmark bridge in phase II is applied, which is modelled by [PERSON] et al. [28]. A brief review of the bridge modelling is provided in the following section.
## 3 Steel Truss Experiment
### Sound Signal Feature Extraction  
The collected sound signals from each condition are preprocessed by framing and windowing. The framing parameters are set as follows: frame length of 512 samples, frame shift of 256 samples, and Hanning window function. When the length of the sound signal is set to 1 s, each segment of the signal consists of 77 frames of valid signals. Figures 4-6, respectively, show a typical waveform of loose bolt damage signal, waveforms of two types of strong environmental interference noise (cicada sound and rain sound), short-time energy, and the distribution of MFCC and wavelet packet energy bands of the frame signal with maximum short-  
From Figure 4, it is evident that the short-time energy of the sound signal emitted from the loose bolt is generally higher than that of the exciter signal (in the first 0.3 s segment). Therefore, to exclude the influence of the exciter signal on the recognition results during specific analysis, an initial judgment is made using short-time energy. Frames with short-time energy greater than the warning threshold \(T\) are selected, and the signal analysis is performed sequentially in descending order (in this experiment, \(T\) is set to 0.5). Compared to the loose bolt damage signal, there are significant differences in the distribution patterns of MFCC and wavelet packet energy bands for the two types of typical environmental disturbances.  
Figure 5: Cicada chirping signal map and feature extraction results.  
Figure 6: Rain sound signal map and feature extraction results.  
interference noise. From the distribution of MFCC, it can be observed that the amplitudes of the three types of signals are both positive and negative. When the channel number is relatively small, the amplitude is negative and relatively large. As the channel number increases, the absolute value of the amplitude generally decreases. From the perspective of wavelet packet band energy distribution, the sound of cicadas and the sound of rain have a relatively narrow distribution range. The former is mainly distributed in the fourth frequency band, while the latter is primarily distributed in the second frequency band. In contrast, the signal of bolt loosening has a relatively wide distribution, spreading across the first to fourth and sixth to eighth frequency bands. Overall, the MFCCs of various signals and the energy distribution of wavelet packet frequency bands are not only identical, but it is also difficult to directly discern their patterns through manual judgment, thus distinguishing the sound signals of bolt loosening under different working conditions.
# The Resilience of Vegetation to the 2009/2010 Extreme Drought in Southwest China
## 3 Results
### Vegetation Recovery Time  
The recovery time of vegetation growth and productivity varied with vegetation types (Figure 6). The percentages of the NDVI and LAI recovery times showed that more than 80% of all vegetation recovered from the extreme drought within 5 months, but for GPP, vegetation took more time to recover. Deciduous broadleaf forest recovered faster than the other forest types, with more than 90% of the deciduous broadleaf forest recovering within 5 months for NDVI, LAI, and GPP. For evergreen broadleaf forest, 87.84%, 88.44%, and 62.17% of NDVI, LAI, and GPP recovered to pre-drought levels within 5 months, respectively. The NDVI, LAI, and GPP in deciduous broadleaf shrubland, grassland, and cropland took less time to recover from the extreme drought than that of evergreen broadleaf forest, evergreen needleleaf forest, and broadleaf and needleleaf mixed forest.  
Figure 5: Resistance (**a**), recovery (**b**), and resilience (**c**) of different vegetation types to the extreme drought in the 4 year base period.  
Figure 6: The percentages of recovery time for different vegetation types, characterized by NDVI (**a**), LAI (**b**), and GPP (**c**).
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Baseline** & **12-Month** & \\  & **(Mean \(\pm\) SD)** & **Examination** & **\(p\) Value** \\ \hline Vessel Density (\%) in RA Patients & & & \\ \hline SCP Global Area & 45.3 \(\pm\) 4.6 & 45.7 \(\pm\) 3.8 & 0.78 \\ SCP CSF & 25.2 \(\pm\) 7.5 & 23.7 \(\pm\) 5.1 & 0.46 \\ SCP Inner Ring & 47.8 \(\pm\) 6.3 & 47.4 \(\pm\) 4.9 & 0.85 \\ SCP Temporal & 47.9 \(\pm\) 6.9 & 47.0 \(\pm\) 5.4 & 0.69 \\ SCP Superior & 47.2 \(\pm\) 6.1 & 48.3 \(\pm\) 5.2 & 0.59 \\ SCP Nasal & 46.9 \(\pm\) 6.8 & 46.6 \(\pm\) 4.9 & 0.86 \\ SCP Inferior & 49.1 \(\pm\) 6.7 & 47.8 \(\pm\) 5.2 & 0.50 \\ DCP Global Area & 45.7 \(\pm\) 6.3 & 46.3 \(\pm\) 6.2 & 0.77 \\ DCP CSF & 37.3 \(\pm\) 8.9 & 40.3 \(\pm\) 5.5 & 0.11 \\ DCP Inner Ring & 51.2 \(\pm\) 5.0 & 52.06 \(\pm\) 4.9 & 0.60 \\ DCP Temporal & 52.1 \(\pm\) 5.7 & 53.7 \(\pm\) 5.3 & 0.38 \\ DCP Superior & 50.4 \(\pm\) 5.9 & 52.2 \(\pm\) 5.6 & 0.25 \\ DCP Nasal & 51.7 \(\pm\) 6.0 & 52.3 \(\pm\) 5.2 & 0.68 \\ DCP Inferior & 50.8 \(\pm\) 4.5 & 49.9 \(\pm\) 5.12 & 0.34 \\ FAZ Value mm\({}^{2}\) & 0.22 \(\pm\) 0.07 & 0.22 \(\pm\) 0.06 & 0.95 \\ \hline \hline \end{tabular} Abbreviations: RA—theumatoid arthritis; SCP—superficial capillary plexus; DCP—deep capillary plexus; FAZ—f foveal avascular zone; CSF—central subfield.  
\end{table} 
Table 2: Vessel density analysis of superficial capillary plexus (SCP), deep capillary plexus (DCP) and foveal avascular zone (FAZ) values. A representative case at the baseline and one-year follow-up is illustrated in Figure 1.
## 3 Data and Methodology
### MED16 Ocean Model  
MED16 is a high-resolution implementation of the ocean global circulation model by the Massachusetts Institute of Technology (MITgcm model; [96]), suitable for long-time climatic integrations. The model, recently implemented at ENEA, covers the Mediterranean-Black Sea systemwith a horizontal resolution of \(1/16^{\circ}\) (about 7 km) and 100 vertical levels. The horizontal resolution is further increased at the Gibraltar and Turkish Straits, to adequately resolve the complex local dynamics. Another distinctive feature of the present implementation is the inclusion of the main tidal forcing, both local and propagating from the Atlantic. The model has been validated considering 34 tide gauges. Three simulations of the Mediterranean Sea climate were performed: a hindcast simulation (1980-2010), a historical simulation (1981-2005), and a future climate simulation under the RCP8.5 scenario. The three runs were forced using regional high-resolution (about 12.5 km) downscaling performed with the SMHI-RCA4 atmospheric regional model [97], constrained by the ERA-Interim reanalysis [98] and by present climate and future climate runs of the CMIP5 global model HadGEM2-ES [99], respectively. Further details about the numerical implementation, together with a first analysis of the model results, can be found in [PERSON] et al. (2022) [72]. The purpose of the hindcast simulation was to show that the model can reproduce the climate variability of the past decades. Hindcast fields were found in very good agreement \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station** & **Piombino-Follonica** & **Marina di Campo** & **Alghero-Fertilia** & **Rome** & **Latina Sabaudia** \\ \hline Minimum (m) & \(-0.244\) & \(-0.244\) & \(-0.216\) & \(-0.268\) & \(-0.277\) \\ Maximum (m) & \(0.229\) & \(0.229\) & \(0.193\) & \(0.240\) & \(0.246\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ranges of the astronomical tide in 2023 in four stations close to the study areas (data from ISPRA). Piombino-Follonica and Marina di Campo refer to Livorno (Tuscany), Alghero-Fertilia refers to Porto Torres (western Sardinia), Rome refers to Anzio (central Lazio), and Latina Sabaudia refers to Gaeta (southern Lazio).
## 6 Conclusions  
Traditionally, the concept of GDOP is commonly used to determine the geometric effect of GPS satellite configurations. It is desirable to select a set of satellites with GDOP as small as possible. When the measurements have different error variances or in integrated positioning systems, WGDOP is appropriate to select the measurement units to reduce the positioning error. The matrix inversion method guarantees the optimal subsets but with a significant computational burden. To further reduce the complexity, a novel matrix multiplication scheme is proposed to compute WGDOP. The matrix multiplication scheme not only avoids complex computations, but is also easy to design in hardware architecture. The proposed WGDOP calculation with matrix multiplication does not need to implement  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **Average WGDOP Residual** \\ \hline Proposed formula & \(3.110480\times 10^{-14}\) \\ Previously proposed method [30] & Cannot calculate \\ Previously proposed Rprop-based algorithm for Type 6 [27] & \(0.16803456595604\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of average WGDOP residual for all methods when five measurements are available for location purpose.  
Figure 3: The CDFs curves of WGDOP residual for all methods when five measurements are available for location purpose.  
a training phase and the calculation of matrix inversion. It is designed to only need simple matrix multiplication in which the _a priori_ error information of each measurement is not the same. In addition, we also provide a calculation device applied on the computation of more than four positioning devices. No matter how many measurements are used, the proposed WGDOP scheme has the best computational efficiency. The proposed WGDOP calculation with matrix multiplication provides very precise WGDOP calculation solutions and is very suitable to implement in practical hardware equipment.
## 4 Discussion
### VI Variability  
Various factors including clouds and shadow-polluted pixels, time-based interpolation, smoothing, spatial filtering, cropping assumptions, and mixed pixels can influence the VI values. NDVI and SAVI are more prone to atmospheric effects than EVI, induced by cloud presence. The impurity of the peaks of the remaining clouds makes a big difference in NDVI values. Some limitations in the MODIS cloud mask exist even after using it to decrease cloud properties on per-pixel surface reflectance [54; 55]. Vegetation shifts  
Figure 6: VIs phenological metrics were detected for Punjab Province from 2000 to 2019.  
are misidentified with EVI, degraded by the presence of clouds and aerosols [56]. The reliability of quantified vegetation variations mainly depends on how image classification incorporates atmospheric and cloud transmission information. Affected pixels may be treated by additional masking [56], which targets the resulting inconsistencies through quantifying vegetation variability using the NDVI time series. High VI variation (>40%) can be seen in areas near water bodies, and low vegetation occurs near built-up areas (Figure 4) [54; 55]. VI variation (10-30%) occurs in the vicinity of the river, and the majority of cropland areas occur in the study area. This variation may be due to a combination of minor crop fields. In sparsely vegetated areas, in particular, EVI has more coverage than SAVI and NDVI. The soil composition of these regions largely consists of dry sand. NDVI has been described as vulnerable to the spectral effect of soil moisture and texture in the spaces between desert grassland and shrubland [57; 58]. The great annual diversity observed from EVI may be correlated with a difference in soil history at low levels of VIs.
## 3 Graph Clustering Methods
### I-Louvain  
I-Louvain [43] is a technique for identifying groups within a graph, where each node has numerical attributes. It improves upon the modularity measure [13] and includes an additional measure for further optimization. I-Louvain therefore measures the inertia between the data of two nodes to attempt to group together the most similar elements in the embeddings, which are the numerical vectors of each node. This measure of inertia-based modularity is defined by [PERSON] et al. [43] as follows:  
\[Q_{\text{inertia}}\,=\,\sum_{(v,v^{\prime})\lor V}\biggl{(}\frac{I(V,v)\cdot I( V,v^{\prime})}{(2N.I(V))^{2}}-\frac{\|v-v\prime\|^{2}}{(2N.I(V))}\biggr{)}\delta(C_{v},C_{v \prime}) \tag{2}\]  
Let \(V\) denote a set comprising \(N\) elements, represented within a real vector space, where each element \(v\in V\) is characterized by a vector of attributes \(v=(v_{1},\dots,v_{n})\in\mathbb{R}\). The inertia of \(V\) about its center of gravity, denoted as \(I(V)\), and the inertia of \(V\) about a specific element \(v\), denoted as \(I(V,v)\), are defined as the sum of the squared Euclidean distances between \(v\) and the other elements of \(V\). Similar to the modular quality metric \(Q_{\text{modularity}}\), \(\delta\) represents the Kronecker delta.  
\(Q_{\text{inertia}}\) evaluates the discrepancy between the anticipated and observed distances between pairs of elements \((v,v^{\prime})\) within the same community. If the observed distance is less than the expected distance, it suggests that \(v\) and \(v^{\prime}\) are potential candidates for belonging to the same cluster.  
I-Louvain is a community detection technique tailored for real attributed graphs, leveraging inertia-based modularity \(Q_{\text{inertia}}\) in conjunction with [PERSON]'s \(Q_{\text{inertia}}\). Essentially, this method revolves around optimizing the global criterion \(Q_{I-\text{Louvain}}\), defined as follows:  
\[Q_{I-\text{ Louvain}}\,=\,Q_{\text{modularity}}\,+\,Q_{\text{inertia}} \tag{3}\]  
As with the Louvain method, I-Louvain works by evaluating the gain in \(Q_{I-\text{ Louvain}}\) those results from moving each node \(v\) and its adjacent nodes in the graph to different communities. During each iteration, node \(v\) is assigned to the community that yields the maximum gain in the global criterion \(Q_{I-\text{ Louvain}}\). This process is repeated sequentially for all nodes until no further improvement in \(Q_{I-\text{ Louvain}}\) can be achieved.
## 5 Conclusions  
Aiming at the nonlinearity and time delay of pneumatic constant force actuators, this paper proposes an improved PID control method combining a BPNN and the Smith predictor. An on-line BPNN ANN1 is used for adjusting the PID coefficients intelligently, which can enhance the robustness of a nonlinear system. The Smith predictor, with the aid of off-line neutral network ANN2, compensates the pure time delay, which can improve the adjustment speed of the system. The simulation and experiment results both showed that the BPNN-SP-PID control method was feasible and superior compared with the traditional PID controller.  
The matching error between the theoretical model and the actual model is concerned with the control performance. In a simulation, Smith predictor can compensate almost all of the time delay. In practical applications, the posture of the actuator is time-varying, and the system model will also change accordingly. As the offline ANN2 cannot fully fit the time-varying nonlinear system, the time delay compensation effect is not as good as that found in simulation. Adding a neural network identifier to update ANN2 will be beneficial for identifying the actual model in time so that the adaptability of the controller will be stronger. This will be the focus of future work.  
[PERSON]. ([PERSON]) contributed to the main idea of this paper; [PERSON] performed the simulation and experiments, analyzed the data and wrote the paper; [PERSON] and [PERSON] helped to build the prototype; [PERSON] and [PERSON]. ([PERSON]) helped to design the data acquisition code; [PERSON] and [PERSON]. ([PERSON]) reviewed and edited the paper. All authors have read and agreed to the published version of the manuscript.  
The presented work was supported by the following grants: National Natural Science Foundation of China Grant No. U1713210 and Shenzhen Fundamental Research Program Grant No. JCYJ20170818163928953.  
Data Availability StatementData sharing not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{4}{c}{**Performance Indicators of Three Controllers**} \\ \hline
**Type** & **PID** & **BPNN-PID** & **BPNN-SP-PID** \\ \hline Simulation & & & \\ Step response & & & \\ Steady-state error (N) & \(\sim\)0 & \(\sim\)0 & \(\sim\)0 \\ Settling time1 (s) & 0.76 & 0.76 & 0.59 \\ Maximum overshoot (\%) & 6.10 & 0.45 & 0.15 \\ Sinusoid track & & & \\ Maximum error (N) & 3.22 & 2.65 & 2.63 \\ Average error (N) & 2.09 & 1.68 & 1.59 \\ Experiment & & & \\ Step response & & & \\ Steady-state error (N) & 0.18 & 0.12 & 0.12 \\ Settling time1 (s) & 0.48 & 0.46 & 0.16 \\ Maximum overshoot (\%) & 9.70 & 4.90 & 1.56 \\ Sinusoid track & & & \\ Maximum error (N) & 2.70 & 2.06 & 1.88 \\ Average error (N) & 0.98 & 0.74 & 0.68 \\ \hline \hline \end{tabular}  
* The minimum time required for the system response to reach and maintain a value within 5% of the final value.  
\end{table}
Table 3: Comparison of overall performance indicators of three controllers.
## 3 Experimental Scenarios  
The authors conducted controlled experiments using NN and recurrent NN models to evaluate and compare their proposed framework. Each model was trained separately with adjusted hyperparameters and architectural choices to investigate the performance improvements gained under different data and model configurations. Two distinct deep-learning architectures were implemented and analyzed:  
1. A feedforward neural network, referred to as slideNN.
2. A Gated Recurrent Unit (GRU)-based architecture.  
Both models were evaluated under four input-output configurations using time windows of 64, 128, 256, and 512 h as input, with corresponding prediction horizons of 2, 4, 8, and 16 h of AQI values, respectively. This enabled a consistent comparison of AQI forecasting, revealing how varying the amount of historical input data and forecast length affected model performance. The experiments were performed under identical preprocessing conditions, and both architectures were trained using standardized AQI data to ensure consistency and fairness in comparison.  
The experiments were also differentiated into the two main framework computational deployment cases: Edge computing, and cloud computing. Each scenario was designed to reflect realistic use cases, taking into account constraints on computational resources and inference requirements.  
**Edge Computing case:**: This scenario simulates environments with limited hardware capabilities, such as embedded systems or mobile devices. Both architectures-slideNN (a feedforward neural network) and a GRU-based model architecture were tested under four distinct input-output configurations of \(64\to 2,128\to 4,256\to 8\), and \(512\to 16\) (hours). The variable length GRU model was adjusted using a small number of cells (e.g., 8, 16, 32, and 64) to match the parameter count of the corresponding slideNN sub-model. Enabling a fair and direct comparison of their performance on the same resource-constrained platform.
**Cloud Computing case:**: This scenario represents high-resource environments where model complexity and real-time inferences are not a limiting factor. Only the GRU-based model was tested in this case, using a larger number of cells (specifically 1280) to exploit its full representational capacity. For each of the four input-output configurations mentioned above, the GRU layer was followed by a dense NN sub-network, forming a hybrid architecture that combines deep-temporal recurrent modeling with deep-layered neural network processing.  
The results of the edge GRU models were compared with those of the slideNN models for the same input-output configurations. Performance was measured using RMSE, allowing direct comparison between recurrent and feedforward approaches at matched model capacities. To initiate the evaluation process, we conducted experiments on the feedforward neural network architecture, referred to as slideNN. The model was trained and tested independently for the four input-output configurations, namely 64-2, 128-4, 256-8, and 512-16 (Figure 2), corresponding to the number of hours used for input and prediction, respectively.
# A Novel Relay Selection Scheme Based on Q-Learning in Multi-Hop Wireless Networks
## 2 System Model and Conventional Selection Schemes
### Random Relay Selection Scheme  
The random relay selection scheme selects a relay among several relays randomly. In other words, the random relay selection scheme selects a relay without considering the signal-to-noise ratio (SNR) of the received signal and a channel condition of the source, relay and destination. Therefore, the random relay selection scheme is very simple. However, it may select a relay having a bad channel condition and degrade the performance of the cooperative communication system.
# Fast and Reliable Burst Data Transmission for Backscatter Communications
## 5 Burst Data Transmission Scheme  
In this section, we present the design of our scheme by fragmenting large data packets into blocks. The major goal of our scheme is to achieve fast and reliable burst data transmission and goodput optimization for backscatter communications under dynamic harvesting and channel conditions.