esa-sceva/satcom-chunk-collection huggingface dataset:
# Construction Project Cost Prediction Method Based on Improved BiLSTM
## 6 Experimental Results and Analysis
### Evaluation Indicators  
In this study, we used five evaluation indices to evaluate the model: the mean absolute error (MAE), root-mean-square error (RMSE), mean percentage error (MPE), mean absolute percentage error (MAPE), and coefficient of determination R\({}^{2}\). The smaller the values of MAE, RMSE, and SMAPE, the better the performance of the model, and the larger the value of R\({}^{2}\), the better the model fitting ability. In assessing the feasibility of a project, the consequences of underestimating costs are more serious than those of overestimating them. The MPE was used to measure the directionality of the forecasting index. The specific formulas are as follows:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{26}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & \(N_{batch}\) & \(N_{LSTM}\) & \(N_{Dropout}\) & \(N_{Dense}\) & \(N_{epoch}\) \\ \hline PSO & 5 & 26 & 0.2 & 22 & 200 \\ IPSO & 3 & 32 & 0.1 & 18 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimisation results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Search Scope** \\ \hline \(N_{batch}\) & 1–30 \\ \(N_{LSTM}\) & 1–100 \\ \(N_{Dropout}\) & 0.1–0.5 \\ \(N_{Dense}\) & 1–100 \\ \(N_{epoch}\) & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hyperparameter search ranges.  
Figure 9: Hyperparametric optimisation convergence curves.  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{27}\] \[MPE=\frac{100\%}{n}\sum_{i=1}^{n}\frac{\hat{y}_{i}-y_{i}}{y_{i}}\] (28) \[MAPE=\frac{100\%}{n}\sum_{i=1}^{n}\left|\frac{y_{i}-\hat{y}_{i}}{ y_{i}}\right|\] (29) \[R^{2}=1-\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}/\sum_{i=1}^{n}(y_{ i}-\overline{y})^{2} \tag{30}\]  
where \(y_{i}\) is the true value, \(\hat{y}_{i}\) is the predicted value, \(\overline{y}_{i}\) is the mean of the true values, and \(n\) is the number of data used.
## 3 The Recommendation Pipeline
### Constructing Patient State Vectors
#### 3.3.3 Lab Results and Measurement State  
The dimensions of this state vector consist of the intersection \(\mathbb{M}\) of two components: (i) the space of all measurements and lab results seen across all patients \(\mathbb{P}(\mathcal{S}[\vec{p}])\) that are deemed relevant to the target patient \(\vec{p}\) in state \(\mathcal{S}[\vec{p}]\); (ii) measurements taken of \(\vec{p}\) up to and including the state \(\mathcal{S}[\vec{p}]\). In total, \(\mathbb{M}\) includes 434 distinct lab test results and 763 unique chart event measurements, encompassing a broad range of clinical indicators.  
For example, if \(\mathbb{P}(\mathcal{S}[\vec{p}])\) consists of two patients \(p_{1}\) and \(p_{2}\) with measurements and lab results for \(\{\texttt{bp},\texttt{glucose},\texttt{bodytemp}\}\) and \(\{\texttt{glucose},\texttt{creatinine}\}\), respectively, and the target patient up until \(\mathcal{S}[\vec{p}]\) has measurements \(\{\texttt{bp},\texttt{glucose},\texttt{O2},\texttt{creatinine},\texttt{bodytemp}\}\), then we consider the four measurements \(\{\texttt{bp},\texttt{glucose},\texttt{creatinine},\texttt{bodytemp}\}\) to be the universe of measurements. So \(\mathbb{M}\) depends on the measurements of relevant patients as well as the target patient.  
\[\mathbb{M}:=\]  
Figure 4: The construction of a patient’s _raw_ time-varying state vectors \(\mathcal{S}[p,t]\). The vector encodes information about a patient’s demographics, which do not change with time, as well as time-changing data such as diagnoses, measurements, lab results, and treatments. We highlight in bold updates to the state vector as records from different MIMIC-III tables are processed in time order.  
DataToCare constructs a series of time-varying measurement and lab result vectors for each patient \(p\in\mathbb{P}(\mathcal{S}[\hat{\vec{p}}])\) from the labevents and chartevents tables. Each record in these tables is timestamped and DataToCare processes them sequentially in time order. At time \(t_{0}\), the vector at \(t_{0}\) simply contains the values for any measurements or lab results for all times at \(t_{0}\) or earlier with nulls for all other features. When the \(i\)th lab result or measurement record arrives, a new vector for time \(t_{i}\) is constructed: any new values are inserted, and past values from the previous vector at time \(t_{i-1}\) are retained. Thus, the measurement values associated with time \(t_{i}\) are those that have accumulated up to \(t_{i}\).
## 2 Materials and Methods  
### Study Area, Data and Wind Measuring Stations  
The study domain covers the whole Iranian territory, with an area of approximately 1,648,195 km2, while over the half is mountainous terrain (Figure 1). Iran is known for its rugged topography, featuring high mountains, deep valleys, extensive arid basins, vast desert areas, and topographic low drainage basins [59]. The arid and semi-arid terrain is particularly vulnerable to land degradation and wind erosion, especially during spring and summer, when vegetation coverage is low and the winds are stronger [60; 61; 62; 63]. The topography of the study area can be broadly characterized as a great plateau (Central Iranian Plateau, Kavir and Lut Deserts) surrounded by two high mountainous ranges (see Figure 1). The Alborz Mountains in the north have a long east-west axis with peaks exceeding 3000 m in height, and some reaching over 5000 m. In the west and southwest, the Zagros Mountains extend over a vast distance, with most areas being over 1800 m in elevation, and many summits exceeding 3600 m. The great central plateau, rising to about 1000 m above sea level (asl), dominates most of the country, while some parts, such as the Lut valley and the Sistan Basin, are only around 500 m asl.  
Figure 1 also displays the locations of the stations used in this study, including weather stations, upper air stations, meteorological towers, and operational wind farms. This study utilized four available datasets to verify and compare the model results with observational data. These datasets included:  
1. Ten-meter wind data in meteorological stations: In order to comprehensively assess the model results, we selected 140 weather stations distributed across the country. After conducting quality control on the synoptic data, we narrowed down our selection to 110 stations (as shown in Figure 1) that had complete data available from 2004 to 2020, which were then compared with the wind simulations generated by the model.
2. Data from upper air stations (UAS): The studied area comprises approximately 10 UAS. However, these stations are not operational on a continuous basis and often have gaps in their data records. Most of these stations conduct observations at specific  
Figure 1: The study area and the location of the wind farms, weather stations, meteorological towers (Met. Tower) and upper air stations (UAS) in the Iranian territory.  
times, either at 00 UTC or 1200 UTC. Additionally, the available data from these stations had a time step of 10 s, and the stations did not provide wind data at heights near the ground surface for model verification purposes. Fortunately, data with a time step of 2 s were successfully extracted from four UAS (as shown in Figure 1) for the months of January and July 2013, which were then utilized for verifying the model results.
3. Meteorological mast data: Data from five meteorological masts (Figure 1) were utilized to verify the model results.
4. Available data from the Global Wind Atlas: To compare the simulated wind results with those from The Technical University of Denmark (DTU) Wind Atlas, data from 2008 to 2017 were used.
# A Bird Vocalization Classification Method Based on Bidirectional FBank with Enhanced Robustness
## 2 Related Work  
The sparsity and multi-resolution characteristics of DWT are reflected in its ability to decompose the input signal, yielding a set of approximate coefficients (low-frequency components) and detailed coefficients (high-frequency components) at various decomposition levels [15]. The former is considered a useful signal, while the latter is regarded as noise. This effective denoising approach has been widely applied in birdsong recognition by researchers such as [PERSON] et al. and [PERSON] et al., enhancing the reliability of signal analysis and interpretation [16; 17]. In other domains, [PERSON] et al. integrated the WT, convolutional neural network (CNN), inception module, and long short-term memory (LSTM) network for structural damage diagnosis [18]. Threshold selection and threshold functions are critical factors in denoising performance. VisuShrink [19], RigrSure [20], and MiniMax [21] are classic methods for threshold selection, while the hard threshold proposed by [PERSON] and [PERSON] [19] and the soft threshold introduced by [PERSON] [22] are well-established methods for threshold functions.  
MFCC is the main feature used in bird vocalization classification, but its limitations are frequently addressed by integrating them with other features. For instance, [PERSON] and Alsina-Pages integrated MFCC, gamma-tone cepstral coefficients (GTCCs), and narrow-band (NB) features, employing k-nearest neighbor (kNN), neural network (NN), and Gaussian mixture model (GMM) classifiers [23]. Similarly, [PERSON] et al. utilized MFCC and GTCC for feature extraction, and the birdsong classification accuracy rate was 78.11%, which improved to 78.33% through the use of particle swarm optimization (PSO) for 264 bird vocalizations [24; 25]. In particular, [PERSON] et al. extracted deep features with a CNN to the Hilbert-Huang transform (HHT), short-time Fourier transform (STFT), and wavelet transform (WT), fusing MFCC to achieve excellent results [16].  
In addition to MFCC, FBank features have also widely been used in recognition. [PERSON] et al. identified ten bird vocalizations using FBank with the transformer model, achieving an accuracy of 85.77% [26]. [PERSON] et al. fused FBank with SincNet-based Sinc spectrograms while using 2D Gabor filters for Sinc spectrograms and secondary feature extraction to enhance feature representations [27]. Furthermore, [PERSON] et al. employed a full CNN with FBank, reaching 85.71% accuracy in the classification of 17 birdsongs [28]. [PERSON] et al. adopted a different approach by integrating FBank, delta, and delta-delta FBank (3D-FBank) with a Phylogenetic Perspective Neural Network (PPNN), which led to an accuracy of 89.95% in classifying the songs of 500 bird species [29]. In other fields, [PERSON] et al. utilized FBank in a deep neural network (DNN) to analyze giant panda vocalizations [30], and [PERSON] applied FBank and Linear FBank for human voiceprint recognition [31]. Moreover, [PERSON] et al. employed 3D-FBank with ResNet18 for underwater target identification [32].
# Link Budget Analysis with Laser Energy for Time Transfer Using the Ajisai Satellite
## 2 TLTT Link Budget
### Link Budget Equation for TLTT
#### 2.1.2 Effective Cross Section of Ajisai's Mirror for TLTT  
The optical flux returned to the receiving station by a satellite is a function of satellite optical cross section (\(\sigma\)) and distance between satellite and receiving point. The effective target cross section is defined as:  
\[\sigma=\frac{4\pi}{\Omega}\ \rho_{s}\ dA,\ (5) \tag{5}\]  
where \(\Omega\) is the scattering steradian solid angle of target, \(\rho_{s}\) is the target reflectivity, and \(\ dA\) is the target area.  
The spin axis and spin rate of the Ajisai satellite change slowly due to the Earth's gravitation, magnetic fields and solar irradiation; prediction of the rotational phase of the Ajisai at the sub degree level is nearly impossible, although a highly accurate spin model of Ajisai does exist [8]. This means that the geometrical configuration between the reflecting mirror of the Ajisai and the ground stations is difficult to calculate. To simplify this complicated problem, we take into consideration a simple analytical model in which the normal vector (\(N\)) of the reflective convex mirror of the Ajisai satellite is coincident with the phase vector (\(P\)), which is defined as the product of two vectors pointing to the transmitting (\(R_{t}\)) and receiving (\(R_{r}\)) ground stations with respect to the satellite, as shown in Figure 1.  
The phase angle (\(\theta_{ph}\)) can be calculated using the following position vector equation  
\[\theta_{ph}=\arccos\biggl{(}\frac{R_{t}\cdot R_{r}}{|R_{t}||R_{r}|}\biggr{)} \tag{6}\]  
Figure 1: (**a**) Ajisai satellite (courtesy of JAXA) and (**b**) phase angle model of Ajisai’s convex mirror.  
Then, the effective target cross section in Equation (5) can be modified to account for the specular reflection of the mirror as in:  
\[\sigma=\frac{4\pi}{\Omega}\ \rho_{s}\ dA\cos\left(\frac{\theta_{ph}}{2}\right) \tag{7}\]  
Finally, the link budget equation for TLTT with Ajisai's reflecting convex mirror can be expressed as Equation (8):  
\[n_{p}=\left(E_{t}\frac{\lambda}{hc}\right)\eta_{t}\ \frac{G_{t}}{4\pi R_{t} ^{2}}\ \frac{\rho_{s}\ dA}{\Omega R_{t} ^{2}}\cos\left(\frac{\theta_{ph}}{2}\right)A_{r}\eta_{r}\ \eta_{q}\ T_{at}T_{ct}T_{ar}T_{cr} \tag{8}\]
## 4 Discussion
### Limitations  
This study is not without its limitations. Because the participants were male college basketball players, the results might not be applicable to different sports or female basketball players. Furthermore, vertical, lateral, and horizontal jumps are considered fundamental abilities, while vertical jump shots, lateral jump shots, and step-back jump shots are regarded as sports techniques. When connecting jumping abilities with technical movements, many potential factors remain, such as ball handling and the proficiency of technical movements. Additionally, the distance covered in lateral and horizontal jumps might have been influenced by the athlete's leg length and flexibility, which could have increased the jump distance, but not necessarily result in terms of greater force output. Because this study had a small sample size, the results cannot be broadly generalized. Future research on these three types of jumps, using different intervention measures, is needed.
## 3 Methodology
### RF-CNN Classification  
Relying on only one classifier cannot guarantee the best classification results. Leveraging the corresponding advantages of RF and CNN models, a combination of these two strategies has the potential to improve the final classification performance. Therefore, we build an RF-CNN composite classifier herein.  
Glacier mapping using the RF algorithm based on the CNN model (RF-CNN) to achieve target segmentation of glacier remote sensing images includes the following steps:  
1. Create a training dataset for the study area, and use samples and sample labels as the training data set;
2. First, establish a CNN model; then, use the samples and sample labels in step (1) to train the model, and save the model for later use;
3. Use the CNN model in step (2) to extract the feature layers of the study area--that is, extract the deep features;
4. Extract the shallow features of Landsat 8 images and DEM in the study area, including spectral features, index features, texture features, temperature features, topographic features, and glacier movement speed features;
5. Perform multi-feature combination for the deep features extracted in step (3) and the shallow features extracted in step (4);
6. Use RFs to perform semantic segmentation on the combined features in step (5) to achieve glacier segmentation based on remote sensing images.  
This study uses a two-dimensional convolution (Conv 2D) method to process the data set, and uses a sliding window crop with a repetition rate of 0 to crop the experimental data into data blocks and input them into the network. Subsequently, geometric transformations are performed on the training data (including horizontal, vertical, and diagonal flipping). Data enhancement operations are then used to automatically extract various features of the remote sensing images. Before pooling (MaxPooling 2D), an activation function is needed to increase the nonlinear characteristics of the model. The activation function utilizes the SoftMax function to improve the generalization ability of the network. The technical process of debris-covered glacier classification based on the RF-CNN classifier is shown in Figure 7, and the RF-CNN model parameter settings are given in Table 2.  
Figure 7: Technical process for debris-covered glacier classification based on the RF–CNN classifier.
## 4 Salinity Origin and Its Impacts on Plants  
S Salinity is a cumulative process over time and may have a natural origin (Figure 1) or may be due to anthropogenic practices [39; 40]. Different types of mineral salts (Na+, K+, Cl-, Mg, Ca, etc.) at the origin of salinity in the soil and can prevent impacts from several sources such as climatic factors (erosion and evaporation), and manufacturing. Hemophilic PGP (Plant growth promoting) bacteria present in soil can alleviate salt stress, stimulate vegetal nutrition and growth through enhancement of various vital plant mechanisms.  
S Salinity is a consequence of inorganic salt solubilization, dust deposition, unbalance between precipitation and evapotranspiration rates, or an increased capillary infiltration of saline underground  
Figure 1: Origin of salinity in the soil and the main impacts of salicylic acid produced by PGPB (plant growth promoting bacteria) on plant primary metabolism.  
water [41]. Salinization affects plant development when salt concentrations build up in the root zone [40]. To maintain productivity under saline conditions, crops need higher water and fertilizer inputs, which probably enhances soil salinity in the end [5], extending the zone affected by secondary salinization [42].  
Salinity is a two-phase stress to plants comprising a rapid osmotic phase followed by a slower one related with ion accumulation [43]. During the first phase, salinity elicits disorder of water and ionic homeostasis increasing the energetic costs of cellular metabolism [44; 45] affecting, among others, nutrient uptake [46; 47], photosynthesis and respiration [48], lipid metabolism and protein synthesis [49; 50], phytohormone production, oxidative stress, gene expression, and long distance transport [51], which affects plant seed germination and seedling survival, as well as plant phenotype. The plant osmotic response of salinity stimulates physiological modifications in the plant that are similar to those generated by water stress [52]. Apart from its influence in plant biomass accumulation, salinity may interfere in the process of biomass partitioning between shoots and roots [44].  
Plant resistance or tolerance to salinity involves the root capacity to restrain Cl\({}^{-}\) and Na\({}^{+}\) uptake or to induce their efflux, while maintaining the adequate uptake rates of essential ions such NO\({}_{3}\) and K\({}^{+}\)[51], which may be associated with the accumulation of compatible solutes such as proline and soluble sugar able to participate in the osmotic balance [45; 46; 47; 48; 49; 50; 51; 52; 53]. With varying salt concentrations, transcription of genes responsible for osmoprotectant synthesis may express differentially in some HT-PGPR (Halo-tolerant plant growth promoting rhizobacteria). For example, in _Bacillus. amyloliquefaciens_ FZB42 trehalose and proline synthesis were found to be differentially expressed at 0 and 100 mM NaCl concentrations facilitating stress adaption in _A. thaliana_[54].
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 6 Lessons Learned, Open Issues, and Research Challenges
### Open Issues and Research Challenges
#### 6.2.6 Cross-Layer Design  
In FANETs, the link delay, SINR level, link reliability, UAV residual energy, and relative mobility are the key factors in defining link stability. The optimal transmission power allocation in the physical layer and optimal physical resource allocation, such as the frequency or time slots in the MAC layer, control the SINR and throughput of the links. Joint consideration of these constrained resources can significantly improve the performance of the routing layer (relay selection) because they are highly coupled. Designing a cross-layer routing protocol with optimal resource allocation, such as transmission power according to the changes inter-UAV distances indicated by a topology controller [12], MAC layer frequency, or time slots, can be an interesting research direction.
## 2 Numerical Mean Echo Power Model for SAR Altimetry
### Pulse Compression  
We obtain the range profile of the scatterers via pulse compression. The signal after pulse compression in the \(n\)-th PRI is provided by  
\[\begin{split} C(t_{r},n)&=\int_{-\infty}^{\infty} S_{r}(\tau,n)s_{0}^{*}(\tau-t_{r})\mathrm{d}\tau\\ &\approx A_{0}W_{r}\bigg{(}t_{r}-\frac{2R(t_{r}+nT_{r})}{c} \bigg{)}\exp\bigg{(}-j4\pi f_{0}\frac{R(nT_{r})}{c}\bigg{)},\end{split} \tag{6}\]  
where \(W_{r}(\cdot)\) is the autocorrelation function (ACF) of \(s_{0}(t)\).  
Before proceeding any further, applying approximations to the distance \(R(t)\) can be beneficial for simplifying the expression of \(C(t_{r},n)\). For a small \(N\) and narrow antenna beam, we have  
\[R(t)\approx R_{0}-z-\frac{v(y\cos\mu+h\sin\mu)}{R_{0}}t=R_{0}-z-v_{r}t, \tag{7}\]  
where \(R_{0}=\sqrt{x^{2}+y^{2}+h^{2}}\) and \(v_{r}=v(y\cos\mu+h\sin\mu)/R_{0}\) denotes the relative velocity of the scatterer with respect to the radar. Substituting (7) into the phase history in \(C(t_{r},n)\) and assuming that the variation of \(R(t)\) during a coherent processing interval exceeds the range resolution of radar, we have  
\[C(t_{r},n)\approx A_{1}W_{r}\bigg{(}t_{r}-\frac{2(R_{0}-z)}{c}\bigg{)}\exp(j4 \pi f_{0}(v_{r}/c)nT_{r}). \tag{8}\]  
Here, the phase terms that are independent to \(n\) and \(t_{r}\) are merged into the new factor \(A_{1}=A_{0}\exp(j4\pi f_{0}(-R_{0}+z)/c)\). From (8), the Doppler frequency is provided by  
\[f_{d}=2v_{r}f_{0}/c=\frac{2v_{f0}(y\cos\mu+h\sin\mu)}{R_{0}c}. \tag{9}\]
## 5 Real Experiment  
### Image-Motion Compensation Technology Verification Experiment in Laboratory  
To adequately validate the image-motion compensation capability of the secondary mirror's image-motion compensation mechanism, an experimental platform was constructed, as illustrated in Figure 8. The instruments used for the image-motion compensation experiment mainly included a collimator and a two-axis scanning platform. By adjusting the pitch and roll scanning rate of the two-axis scanning platform, we accurately simulated the attitude rate of the targets (No. 4 resolution target, cross-target) under different speed-to-height ratios and different scanning rates. Simultaneously, the two-axis image-motion compensation mechanism drove the secondary mirror's motion to compensate for the image motion caused by the target movement. In the experiments, the two-axis scanning platform was scanned at a velocity of +60\({}^{\circ}\)/s for the roll and 5\({}^{\circ}\)/s for the pitch.  
The formula for calculating the aerial camera's ability to distinguish the number of line pairs per millimeter \(N\) is as follows:  
\[N=\frac{N_{0}}{f}f_{0}, \tag{16}\]  
where \(N_{0}\) is the number of line pairs per millimeter of the resolution target that the camera can distinguish. The 11 th group of the No. 4 resolution target was selected for this experiment with a value of 11.1 lp/mm. \(f_{0}\) represents the focal length of the collimator, which has a value of 3000 mm, and \(f\) is the focal length of the aerial camera.  
According to the above formula, the number of line pairs per millimeter that the aerial camera could dynamically distinguish was 74 lp/mm, which reached the design value. Furthermore, for cross-target imaging, the image centroid offset of the cross-target before and during the experiment was within 0.25 pixels, indicating that the two-dimensional image-motion compensation accuracy was better than 0.5 pixels.