esa-sceva/satcom-chunk-collection huggingface dataset:
# Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN
## 2 Materials and Methods  
### Study Areas  
The study area (Figure 1) is situated in the Conghua and Zengcheng District of Guangzhou, Guangdong of China (22\({}^{\circ}\)26\({}^{\prime}\)-23\({}^{\circ}\)56\({}^{\prime}\) N, 112\({}^{\circ}\)57\({}^{\prime}\)-114\({}^{\circ}\)03\({}^{\prime}\) E). The annual average temperature is 21 \({}^{\circ}\)C and the annual average precipitation is about 1900 mm, concentrating between April and September [20; 21; 22]. The cultivated land area of Guangzhou in 2015 was 13,485.99 hm\({}^{2}\) with an annual crop yield of 440,900 tons (referencing the Statistical Communique of Guangzhou on the 2015 National Economic and Social Development), of which the paddy field was 11,885.16 hm\({}^{2}\) with the average paddy stand size of about 0.25 hm\({}^{2}\), accounting for 87.91% of the total cultivated land area. The cultivated land is mainly concentrated in Conghua and Zengcheng Districts. Rice is the principal crop in the study area, with an annual double rotation system (early rice: March-June and late rice: August-November).
## 4 Conclusions  
The natural and anthropic radioactivity content of a basalt aggregate for concrete from Sicily, Southern Italy, was analyzed through High-Purity Germanium (HPGe) \(\gamma\)-ray spectrometry. Moreover, calculations of the alpha index (I\({}_{\alpha}\)), the radium equivalent activity (Ra\({}_{\rm eq}\)), the absorbed \(\gamma\)-dose rate (D) and the annual effective dose equivalent outdoor (AEDE\({}_{\rm out}\)) and indoor (AEDE\({}_{\rm in}\)) were performed in order to estimate the radiological hazard for human beings. Of note, the obtained values turned out to be lower than the maximum recommended ones for humans, thereby rationally excluding any significant health impact related to exposure to ionizing radiation. Additionally, the mean specific activity of caesium-137 turned out to be lower than the minimum detectable activity.  
Next, the concentration levels of the heavy metals in the analyzed basalt aggregate for concrete were investigated through Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The resulting values were found to be below the threshold levels established by Italian legislation and thus do not reasonably represent a health risk to humans. In addition, the calculation of various pollution indices was carried out in order to assess the ecological risk from heavy metals imposed on the ecology of the ecosystem. The obtained results show a very minimal enrichment only for copper, probably due to the use of this metal as a soil defense product, as well as the soil texture and its high pH, and in general no pollution by the assessed heavy metals.  
Finally, X-ray diffraction (XRD) was applied to recognize the mineralogical/geochemical composition of the investigated sample and to relate it to the natural radioactivity content. From the results, we can conclude that the analyzed basalt aggregate for concrete was characterized by the presence of Altaite, Anorthite, Augite, Pyrochlore and Hematite, superimposed to a glassy groundmass. Moreover, the natural radionuclides' specific activity reported in the present study underlined a high value of the activity concentration of radium-226, thorium-232 and potassium-40 radionuclides with respect to the average worldwide value. This can be explained by the occurrence of crustal contamination, put in evidence by the presence, in the diffractogram, of pyrochlore, in the composition of which Niobium appears.  
This research received no external funding.  
Not applicable.  
Authors declare no conflict of interest.
## 3 Statistical Analysis  
### Hourly Values  
#### 3.1.1 Solar Radiation Components vs. Solar Zenith Angle and Clearness Index  
The relation of extreme values of global radiation as a function of solar zenith angle and the clearness index is shown in Figure 15. The higher values are recorded at low solar zenith angles (SZA) during clear sky conditions. However, extreme values are recorded when scatter clouds reflect additional solar radiation on the ground surface. Therefore, on such occasions the global irradiance reaches the value of about 1400 W/m\({}^{2}\) (Figure 15). Similar values are reported by [PERSON] and [PERSON] (2014) ([PERSON] and [PERSON], 2014). The lower values of all the radiation components are observed at high SZA (low solar elevation) during cloudy conditions.  
Global irradiance is decreased almost exponentially with increasing SZA for a given \(k_{t}\) interval. This relationship can be expressed as:  
\[G=G_{\rm max}*(\cos\theta_{2})^{b} \tag{27}\]  
where \(G_{\rm max}\) is the maximum global irradiance for each \(k_{t}\) interval and b describes how \(G\) changes with the cosine of SZA (\(\theta_{2}\)). The dependence of \(G_{\rm max}\) on \(k_{t}\) can be described by cubic equation as:  
\[G_{\rm max}=-50.77+2033*k_{t}-4931*k_{t}^{2}+4721*k_{t}^{3} \tag{28}\]  
Almost similar graphs to global irradiances are obtained for direct normal (\(B_{n}\)) and direct horizontal (\(B\)) irradiances. The diffuse irradiance shows high values (about 500 and 600 W/m\({}^{2}\)) at low solar zenith angles but in most cases it remains relatively constant between 50 and 200 W/m\({}^{2}\).  
Figure 14: Time series plots of daily values of all shortwave radiation components including daily sunshine duration.
## 4 Join Berth and Time Coordination Optimization Model
### Input Parameters  
Let \(Q\) denote the set of quays under the control of this optimization model. Let \(C\) denote the set of vessel calls in the port during the time in which we are going to perform the optimization. Each call \(c\in C\) is characterized by the vessel \(v(c)\) making the call and a sequence of so-called stays \(S(c)\). A stay \(s\in S(c)\) represents the anchoring of the vessel \(v(c)\) in a particular quay \(q(s)\) to perform a particular set of operations (e.g., loading/unloading of goods). Stays must be conducted in a particular order; the optimization model is not allowed to interchange them. We denote as \(s_{\textit{first}(c)}\) and \(s_{\textit{last}(c)}\) the first and last starts in the call \(c\), respectively.  
Each stay \(s\) has a set of eligible quays where the involved operations should occur. This set is denoted as \(Q(s)\). Typically, in liquid bulk operations, the \(Q(s)\) sets are composed of a small number of options involving expensive and specific resources that are requested for the loading/unloading operations and are not replicated in multiple quays. The estimated duration of the operations associated with stay \(s\), if performed in quay \(q\), is denoted as \(d\)(s,q). Note that this time may be different among the different eligible quays, e.g., when the loading of liquid goods is made via pipes, and pipes with different throughputs exist in different quays.  
For each call \(c\)\(e\)\(C\), \(t_{min(c)}\) and \(t_{max(c)}\) denote the earliest and latest vessel arrival times to the port in the call. This defines the time flexibility window that the model has to optimize vessel arrival time. Additionally, there are pre-stay time constraints if there are some specific limitations, like a particular operation to be initiated not later than a given time. To accommodate this, \(t_{min(s)}\) and \(t_{max(s)}\) indicate the earliest and latest starting times of stay \(s\). Note that a stay \(s\)\(e\)\(S(c)\) with the earliest starting time \(t_{min(s)}\) is always posterior to the call earliest starting time (\(t_{min(c)}\)) plus the duration of the previous stays in the same call, if any.  
Finally, \(P\) is the set of potential allocation conflicts that can exist between two particular stays of different calls (\(s1\)\(e\)\(S(c1)\), \(s2\)\(e\)\(S(c2)\)) at a particular quay \(q\). Note that for a potential conflict to exist: 1) the two stays must have q among their eligible quays; and 2) the two time intervals [\(t_{min(s1)}\), \(t_{max(s1)}+t(s1,q)\)] and [\(t_{min(s2)}\), \(t_{max(s2)}+t(s2,q)\)] should be overlapping. These intervals are the earliest and latest time in which the quay can be occupied for each stay. If they do not overlap, no time decision can cause these two stays to have a conflict at that quay. Note that the set \(P\), with all of the potential conflicts (\(s1\), \(s2\), \(q\)), can be computed in advance from the input data.
## 2 Literature Review
### Financial Volatility Prediction Using Machine Learning in Finance
#### 2.2.2 Machine Learning Techniques for Cryptocurrency Volatility Forecasting  
Various orders of GARCH hybrid models have been explored for forecasting Bitcoin volatility, leading to the proposal of a new stacking ensemble methodology using Least Absolute Shrinkage and Selection Operator (LASSO) [27]. Higher model orders improve prediction accuracy, with the SVM-based hybrid model performing best. The proposed stacking ensemble, incorporating dimensionality reduction, outperforms general stacking methods. The LASSO-based ensemble surpasses most base models and addresses model selection and order determination issues. Future research should examine the impact of different feature selection and extraction techniques.  
A comparison of 12 statistical and machine learning methods, including Human Activity Recognition (HAR), GARCH, LASSO, and Support Vector Regression (SVR), has been conducted to predict the volatility of Bitcoin, Ethereum, Litecoin, and Monero [28]. No single method dominates, as performance varies by cryptocurrency and forecast period. Notably, Random Forest and LSTM outperform GARCH, with LSTM further improved by genetic algorithms and artificial bee colony optimization. Shapley Additive exPlanations (SHAP) analysis highlights the importance of internal decision factors, and multi-cryptocurrency models surpass single-cryptocurrency ones. This study offers insights for investors and financial institutions on cryptocurrency market dynamics.  
Machine learning has been applied to forecast cryptocurrency volatility, with Random Forest and LSTM significantly outperforming the traditional GARCH model [29]. Optimizing LSTM's hyperparameters using Genetic Algorithm and Artificial Bee Colony substantially improves prediction performance. SHAP analysis highlights the importance of internal determinants in volatility forecasts. Models using data from multiple cryptocurrencies outperform those using data from a single cryptocurrency. This research provides practical insights for investors and financial institutions, aiding in capturing the complex dynamics of the cryptocurrency market.
# Phase Offset Tracking for Free Space Digital Coherent Optical Communication System
###### Abstract  
The coherent receiving method can improve the sensitivity of an optical signal receiver for free space optical communication system effectively. To implement coherent receiving, the phase offset between the local laser in the receiver and the received optical signal through the atmosphere needs to be measured and estimated. The commonly used algorithm is the Viterbi-Viterbi phase offset estimation method (VVPE) and this method always produces great errors especially with low SNR (signal to noise ratio). To improve the estimation performance, we present a new method combing the VVPE with the Kalman filter (VVPE-KF) to estimate the phase offset. This method can lower the estimation error by no less than 60%, when the SNR is low. To verify the performance of this new method, the constant parameter channel and atmosphere turbulence channel are employed to evaluate the algorithm. The impact of the atmosphere turbulence intensity on the tracking error is discussed.  
phase offset; estimation method; Viterbi-Viterbi algorithm; Kalman filter; atmosphere turbulence; estimation error +
Footnote †: journal: Applied Physics Letters
# Symmetric-Key-Based Security for Multicast Communication in Wireless Sensor Networks +
## 4 Security Scheme
### Group Key Construction Phase  
Based on the information received after the registration phase, the multicast groups are created. For each group, a multicast address \(ID_{G}\) is generated. The NMM communicates the address \(ID_{G}\) to each subscriber, denoted by \(S_{s},s=1,\ldots,n\) (with corresponding key material \(A_{s},B_{s}\)). To the publisher of the group, denoted by \(P\) (with corresponding key material \(A_{p},B_{p}\)), key related information of the subscribers in its group is sent using their common shared key \(k_{p}\). This key related information for each subscriber equals to \(k_{ps}=H(ID_{G}\|k_{s})\) and is combined with identity \(B_{s}\), for all \(S_{s}\) with \(s=1,\ldots,n\) in the multicast group. Consequently, the message  
\[E_{k_{p}}(B_{1},k_{p1},\ldots,B_{n},k_{pn})\]  
is sent to the publisher. After decryption of this message, \(P\) possesses a common shared key with each of its subscribers. This information allows \(P\) to share a randomly chosen group key \(k_{g}\) for the multicast communication.  
Here, \(P\) first chooses a random value \(k_{g}\), which will serve as the group key to be shared with the other members in a multicast communication message. It also selects a random value \(h_{0}\) for the construction of a one-way key chain [23] to be used for authentication purposes. Consequently,consecutive hashes are taken, i.e., \(h_{1}=H(h_{0}),h_{2}=H(H(h_{0}))=H^{2}(h_{0}),\ldots,h_{w}=H^{w}(h_{0})\). The value \(h_{w}\) will also be shared with the members.  
Since \(P\) has a common key with each member of the group, two methods can be used to distribute the group key: the multicast and unicast approach. In the multicast approach, a combination of LI, a classical technique from the secret sharing approach, with the communication of an IP multicast packet is applied. In the unicast approach, multiple IP unicast packets are sent to share the encrypted group key to each of the individual nodes. Both approaches are discussed in more detail below.
## 1 Introduction  
Tunnel technology offers numerous benefits, such as breaking firewall restrictions, forcing data to a specified address, hiding private network addresses, providing secure encrypted channels, etc. Tunnel technology is widely used [1], with applications including proxy services, remote access, intrusion control, traffic stealing, etc.  
By installing tunnel clients, users can indirectly forward data packets by bypassing IP blocks by using the proxy service, which mimics socks5. Remote access means building a negotiatory channel between the private network and the company network and then accessing secret data or applications. Intrusion control mainly includes establishing C&C communication [2], stealing private data, realizing long-term control, guarding, etc. Among the specific methods used are malicious Trojan, Bothet, ransomware, and advanced persistent threat (APT) [3] techniques, etc. In traffic stealing, web traffic is encapsulated in free DNS [4], ICMP, and other data packets to deceive the network billing system. Tunnel technology is advancing, its threats are increasing with time, and the identification and classification of tunnel traffic in a network are especially critical.
## 3 DST-LEO Satellite-Traffic Prediction Model
### Model Architecture
#### 3.4.3 Temporal Dependency Extraction Model  
In this paper, the bidirectional long short-term memory (Bi-LSTM) units were selected to capture the temporal dependencies. The number of layers was set to 3, hidden_channels = 64, and the time step num_tags = 24. The traffic volume and link connectivity were processed through the Bi-LSTM layers, respectively. For a unidirectional LSTM unit, the input \(X_{t}\) is the input at time step \(t\), \(h_{t-1}\) is the hidden state at the previous time step, \(C_{t-1}\) is the cell state at the previous time step, and the forward-propagation formula is as follows:  
Input gate: \[i_{t}=\sigma(W_{xi}x_{t}+W_{hi}h_{t-1}+b_{i})\] (3) Forget gate: \[f_{t}=\sigma\Big{(}W_{xf}x_{t}+W_{hf}h_{t-1}+b_{f}\Big{)}\] (4) Candidate cell state: \[\widetilde{C}_{t}=tanh(W_{xc}x_{t}+W_{hc}h_{t-1}+b_{c})\] Cell state update: \[C_{t}=f_{t}\odot C_{t-1}+i_{t}\odot\widetilde{C}_{t}\] (6) Output gate: \[O_{t}=\sigma(W_{wx}x_{t}+W_{ho}h_{t-1}+b_{o})\] Hidden state update: \[h_{t}=o_{t}\odot tanh(c_{t})\] (8) Among them, \(\sigma\) is the Sigmoid activation function, \(\odot\) represents element-wise multiplication,\(W_{xi}\), \(W_{hi}\), \(W_{xf}\), \(W_{hf}\), \(W_{xc}\), \(W_{hc}\), \(W_{xo}\), \(W_{ho}\) are trainable parameter vectors, and \(b_{i}\), \(b_{f}\), \(b_{c}\), \(b_{o}\) are the trainable bias vectors.  
The Bi-LSTM consists of two independent LSTM units: a forward LSTM and a backward LSTM. The forward input sequence is \(x_{1},x_{2},\ldots,x_{t}\), and the backward input sequence is \(x_{t},x_{t-1},\ldots,x_{1}\). The hidden state at time step \(t\) of the backward LSTM is \(\overset{\leftarrow}{h_{t}}\), and the cell state is \(\overset{\leftarrow}{c_{t}}\). The output of the Bi-LSTM is the concatenation of the forward and backward hidden states: \(h_{t}=\left[\overset{\rightarrow}{h_{t}};\overset{\leftarrow}{h_{t}}\right]\), with the shape of (batch_size, 2* hidden_dim).
## 3 Results and Discussion
### Water Absorption and Bulk Density Results  
Both the water absorption results and the bulk density results were affected by the amount of fly ash in the binder without any clear influence of activation itself. Water absorption increased by about 0.5 p.p. when the amount of fly ash in the binder increased from 5% to 25% (Table 8). The bulk density of cement mortar decreased by about 0.1 g/cm\({}^{3}\) on average when the amount of addition changed from 5% to 25% in the mortar mix.  
The slight increase in the water absorption results is in line with the research conducted by [PERSON] et al. [42]. The research team noticed a slight increase in open porosity and capillary absorption of specimens with some tested biomasses of fly ash. They associated the differences in the results with the difference in the particle size distribution of fly ash.
# Status of the SXFEL Facility
## 3 The SXFEL User Facility
### Undulator Lines and the FEL Performance  
There are two undulator lines for the user facility. One is upgraded from the test facility and will be operated with either a cascaded HGHG or EEHG-HGHG mode, as shown in Figure 14a, and hereafter referred to as the seeded line, while the other will be a brand-new line operated in the SASE mode, as shown in Figure 14b.  
With the beam energy boosted to 1.5 GeV and the peak current increased to 700 A, the output wavelength of the SXFEL-UF can cover the water window region. Accordingly, to make the FEL output of the seeded line saturate, the length of the original undulator line should be increased. Here, one radiator is added in the first stage and four planar undulators are added in the second stage. With such a configuration, a fully coherent saturated 3 nm FEL output could be obtained. To fulfill users' demands, two elliptical polarized undulators (EPU) will be added following the radiators of the second stage to form the so-called \"afterburner\" scheme and realize the full control of the soft X-ray FEL's polarization.  
Figure 13: Simulation results of the electron beam distributions: energy distributions and current profiles at (**a**) injector exit; (**b**) BC1 exit; (**c**) BC2 exit and (**d**) L3 exit.  
In the baseline design, the second undulator line is based on the SASE mode, and the undulator is the in-vacuum planar type. The undulator period is 16 mm and the working gap is around 3.7 mm, resulting in a K value of 1.8. For this undulator line, the final FEL output is around 8 nm at a beam energy of 840 MeV, and can be as low as 2 nm at a beam energy of 1.5 GeV, while the output peak power will be greater than 100 MW for both cases.  
With the parameters shown in Table 4, the SXFEL-UF's performance with different undulator lines was simulated with the time-dependent mode of GENESIS. A 265 nm laser pulse with a longitudinal Gaussian profile, 200 MW peak power, and 100 fs (FWHM) pulse length is used as the seed laser for the seeded line. To obtain realistic simulation results, the whole electron beam was tracked through the undulator lines. The simulation results are illustrated in Figure 15 for the seeded line with the cascaded HGHG-HGHG mode, and in Figure 16 for the SASE line.  
Figure 16: Simulated FEL performance of the SASE line at 2 nm output: gain curve and the output pulse in the time domain.  
Figure 14: Schematic layout of the FEL undulator lines for the SXFEL-UF: **(a)** Seeded line of the SXFEL-UF (upgraded from SXFEL-TF); **(b)** new undulator line for the SXFEL-UF.  
Figure 15: Simulated FEL performance of the seeded line at 3 nm output: gain curve and the output spectrum.
# The Bivariate Empirical Mode Decomposition and Its Contribution to Grinding Chatter Detection
## 1 Introduction
### Feature Vectors for Grinding Chatter--Real-Time Variance and Instantaneous Energy  
Another major objective of this paper is to accurately detect and identify the onset of grinding chatter before chatter makes damage the workpiece, so that suitable techniques for chatter suppression can be employed as fast as possible. According to the experimental results, the amplitude of chatter signals exhibits a substantial expansion when a stable grinding state changes into a chatter state. Consequently, early grinding chatter can be detected by comparing changes in the time-domain statistical parameters of the signal mathematically. Therefore, appropriate feature vectors should be defined where real-time variance and instantaneous energy are recognized ideal feature vectors.  
The real-time variance (\(R_{v}\)) indicates the degree of deviation from the mean chatter signal, which in a sense reflects the trend of signal oscillation. The real-time variance can be described as:  
\[R_{v}=\hat{R}-\overline{X}=\frac{1}{N}\sum\limits_{j=1}^{N}X_{j}^{2}-\frac{1} {N}\sum\limits_{j=1}^{N}X_{j}, \tag{15}\]  
where \(X=\{X_{1}\), \(X_{2}\), \(X_{3}\),..., \(X_{N}\}^{T}\) (\(X_{j}\in R\), \(j=1\), 2,..., \(N\), \(T\) is a transpose operator), \(N\) represents the sampling points, and \(\overline{X}\) represents the mean of the signal.  
In practice, any internal fluctuation may result in a vibration of instantaneous energy, which means that the change in instantaneous energy has a direct relation to abnormal system operation. Thus, according to Equations (10) and (11), instantaneous energy can be defined as follows:  
\[E_{k}(t)=\frac{1}{2}\alpha_{k}^{2}(t), \tag{16}\]  
where \(\alpha_{k}(t)\) represents the instantaneous amplitude of the \(k\)th IMF.  
By these definitions, the \(R_{v}\) and \(E\) of each IMF can be calculated out in real time.