esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The identification of small scale faults (SSFs) and fractures provides an improved understanding of geologic structural features and can be exploited for future drilling prospects. Conventional SSF and fracture characterization are challenging and time-consuming. Thus, the current study was conducted with the following aims: (a) to provide an effective way of utilizing the seismic data in the absence of image logs and cores for characterizing SSFs and fractures; (b) to present an unconventional way of data conditioning using geostatistical and structural filtering; (c) to provide an advanced workflow through multi-attributes, neural networks, and ant-colony optimization (ACO) for the recognition of fracture networks; and (d) to identify the fault and fracture orientation parameters within the study area. Initially, a steering cube was generated, and a dip-steered median filter (DSMF), a dip-steered diffusion filter (DSDF), and a fault enhancement filter (FEF) were applied to sharpen the discontinuities. Multiple structural attributes were applied and shortlisted, including dip and curvature attributes, filtered and unfiltered similarity attributes, thinned fault likelihood (TFL), fracture density, and fracture proximity. These shortlisted attributes were computed through unsupervised vector quantization (UVQ) neural networks. The results of the UVQ revealed the orientations, locations, and extensions of fractures in the study area. The ACO proved helpful in identifying the fracture parameters such as fracture length, dip angle, azimuth, and surface area. The adopted workflow also revealed a small scale fault which had an NNW-SSE orientation with minor heave and throw. The implemented workflow of structural interpretation is helpful for the field development of the study area and can be applied worldwide in carbonate, sand, coal, and shale gas fields.  
fracture network; thinned fault likelihood; fracture density; fracture proximity; ant-colony optimization; unsupervised neural network
## 3 Method
### Heterogeneous Enhanced Graph Neural Network Feature Embedding Framework
#### 3.3.4 Feature Fusion  
Through graph convolution operations and attention feature enhancement, the features of operation nodes and machine nodes are updated to extract local features. The graph convolution operation captures local structural information between nodes, while the attention mechanism further enhances the ability of machine nodes to aggregate information from important operation nodes.  
For operation node \(O_{ij}\) and machine node \(M_{k}\), the local feature update formulas are as follows:  
\[\alpha_{ij}^{\prime}=GraphConv\Bigl{(}\alpha_{ij}^{enhanced},\mathcal{N} \bigl{(}O_{ij}\bigr{)}\Bigr{)}a_{ij}^{\text{norm}}=\frac{\alpha_{ij}-min( \alpha)}{max(\alpha)-min(\alpha)} \tag{17}\]  
\[\beta_{k}^{\prime}=Attention\Bigl{(}\beta_{k}^{enhanced},\Bigl{\{}\alpha_{ij}^ {\prime}\Bigr{|}(i,j)\in\mathcal{N}(M_{k})\Bigr{\}}\Bigr{)} \tag{18}\]  
Here, \(\mathcal{N}(O_{ij})\) and \(\mathcal{N}(M_{k})\) denote the sets of neighbor nodes for operation node \(O_{ij}\) and machine node \(M_{k}\), respectively.  
After extracting local features, global features are obtained through mean pooling:  
\[h_{global}=MeanPool\Bigl{(}\Bigl{\{}\alpha_{ij}^{\prime}\mid\forall i,j\Bigr{\}} \cup\{\beta_{k}^{\prime}\mid\forall k\}\Bigr{)} \tag{19}\]  
where \(h_{global}\) is the global state embedding, representing the global features of the current scheduling state and the overall state of the graph.  
Finally, the local features and global features are concatenated to generate the final fused feature representation:  
\[h_{final}=\alpha_{ij}^{\prime}\parallel\beta_{k}^{\prime}\parallel h_{global} \tag{20}\]  
where \(\parallel\) denotes the feature concatenation operation, and \(h_{final}\) is the final fused feature representation.  
Through the above four stages, the proposed HEGNN effectively captures complex relationships between operations and machines, dynamically fuses heterogeneous features, and provides high-dimensional semantic information support for subsequent reinforcement learning strategies.
## 2 Design and Method
### Materials  
We realized our design by using a kind of self-assembled quantum dot material of GaAs QDs embedded in an AlGaAs matrix, which was grown on a GaAs substrate by the molecular beam epitaxy (MBE) method [27]. The QDs were fabricated by etching nano-holes directly in the AlGaAs surface using Al droplets without supplying arsenic. Then, by filling 2 nm GaAs into the holes and capping the filled holes with AlGaAs material, QDs are formed [28]. The top layer of the material containing GaAs QD emitters can be released from the bulk GaAs substrate by etching away a sacrificial layer of AlAs that is grown on the substrate in advance. Then, the QD membrane is transferred to a new substrate. Here, a Si substrate with a 400 nm SiO\({}_{2}\) on top was used in our experiment. It should be noted that the new substrate can be other materials depending on the demand of integration photonic circuits. A waveguide with grating output couplers at both ends was directly fabricated from the quantum dot membrane by the fabrication process of electron beam lithography (EBL) and reactive ion etch (RIE).
## 3 Results  
### Inhibitory Effects of Arachis pintoi against the Growth of Tested Plants  
In laboratory bioassays, _A. pintoi_ demonstrated strong inhibition against the growth of _E. crus-galli_. At doses of 50 g/L and 25 g/L, the length of shoot and root, fresh weight, and dry weight of _E. crus-galli_ were remarkably suppressed, in which the root length was the most inhibited (88.3%), followed by the dry weight (62.3%), fresh weight (58.3%), and shoot length (53.7%), while the germination was less influenced (12.9%). At the lowest dose (6.2 g/L), only the root length of _E. crus-galli_ was inhibited (35.2%), and both shoot length and dry weight were stimulated by 8.8% and 19.1%, respectively (Table 1). However, _A. pintoi_ slightly enhanced the germination rate of _E. crus-galli_ by a range of 3.9% to 7.2%. Similarly, the length and fresh weight of _O. sativa_ were significantly suppressed at doses of 50 g/L by over 89.0%, followed by the dry weight (71.1%) and shoot length (69.7%), and the germination rate was the least affected (66.1%). At the 25 g/L dose, the shoot length, root length, fresh weight, and dry weight of _O. sativa_ were remarkably inhibited by 33.0, 84.2, 44.6, and 29.6%, respectively. The lower doses applied at 12.5 g/L and 6.2 g/L exerted either inhibitory or stimulatory effects. In the _V. radiata_ test shown in Table 1, the growth of shoot length, root length, fresh weight, and dry weight were significantly suppressed at three doses of application (12.5, 25.0, and 50.0 g/L) ranging from 28.5 to 69.7%, 26.4 to 72.9%, and 32.7 to 71.1%, and 31.7 to 71.4%, respectively. At the lowest dose applied (6.2 g/L), only the root length of _V. radiata_ was inhibited by 13.4%, and other growth parameters showed either negligible inhibition or promotion (Table 1).  
Inhibitory Effects of Arachis pintoi against the Growth of Echinola crus-galli under Greenhouse Conditions  
Table 2 shows that _A. pintoi_ powders caused a significant reduction in the growth of _E. crus-galli_ at all applied doses. At the applied doses of 50, 100, and 150 g/m\({}^{2}\), the germination rate was significantly inhibited from 42.6 to 47.7%, respectively. The highest reduction was found in the dose of 200 g/m\({}^{2}\) by over 60%. Similarly, at the applied doses of 100, 150, and 200 g/m\({}^{2}\), the shoot length was significantly inhibited by 21.8, 48.3, and 53.2%, respectively. Both fresh and dry weights were remarkably reduced from 31.7 to 71.8% and 39.8 to 67.4%, respectively. On the average inhibition, it showed that _A. pintoi_ powders reduced _E. crus-galli_ growth by 63.4% at the applied dose of 200 g/m\({}^{2}\), followed by 53.0% (150 g/m\({}^{2}\)), 34.7% (100 g/m\({}^{2}\)) while the lowest was 16.7% (50 g/m\({}^{2}\)) (Table 2).
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 4 Proposed Protocol
### Checkup Phase (CP)  
This phase is performed over an insecure channel and aims at a new mutual authentication between the patient and the cloud server and generation of a new session key for encrypting the doctor's diagnosis before the cloud sends it to the patient. The complete procedure is shown in Figure 6.  
Step 1. The patient generates a new random number \(R_{PCP}\), calculates \(MAC_{PCP}=h_{2}(ID_{P}\parallel R_{PCP})\) and sends _Message 1_ = (\(TID_{P}\), \(R_{PCP}\), \(MAC_{PCP}\), _Request_) with a timestamp \(T_{P}\) to the cloud server. Devices with direct access to the 3 GPP network can send their data directly or use D2D communication to reach the cloud server. Devices without the 3 GPP coverage must send their data through D2D communication.  
Step 2. After receiving _Message 1_ and \(T_{P}\), the cloud server verifies if \(T_{P}\) is valid. If the verification fails, the procedure is terminated. Otherwise, it calculates \(MAC_{PCP^{\prime}}=h_{2}(ID_{P}\parallel R_{PCP})\) and verifies if \(MAC_{PCP^{\prime}}=MAC_{PCP}\). If the verification fails, the procedure ends. Otherwise, it authenticates the patient, selects a random number \(R_{PCP}\), calculates \(MAC_{PCP}=h_{2}(ID_{P}\parallel R_{PCP})\), generates the session key \(K_{PCP}=h_{3}(ID_{P}\parallel R_{PCP})\) and computes \(C_{PCP}=h_{4}(K_{PCP})\). It then uses the session key to encrypt the doctor's diagnosis, \(M_{Diagp}=E_{KPCP}\) (_Doctor's Diagnosis, TID\({}_{P}\), \(C_{PCP}\)_) and sends it to the patient _Message 2_ = (\(MAC_{CCP}\), \(R_{CCP}\), \(M_{Diagp}\)) with a timestamp \(T_{C}\).  
Step 3. The patient receives {_Message 2_, \(T_{C}\)} and checks if \(T_{C}\) is valid. If the validation fails, the procedure is terminated. Otherwise, he/she calculates \(MAC_{CCP}\)' = \(h_{2}(ID_{P}\parallel R_{CCP})\) and verifies if \(MAC_{CCP}\)' = \(MAC_{CCP}\). If the verification fails, the procedure is interrupted. Otherwise, he/she authenticates the cloud server, generates the session key \(K_{PCP}=h_{3}(ID_{P}\parallel R_{PCP}\parallel R_{CCP})\), decrypts the doctor's diagnosis, (_Doctor's Diagnosis, TID\({}_{P}\), \(C_{PCP}\)_) = \(D_{KPCP}(M_{Diagp})\), calculates \(C_{PCP}=h_{4}(K_{PCP})\) and verifies if \(C_{PCP}\)' = \(C_{PCP}\). If the verification fails, it ends the procedure. Otherwise, the patient stores the doctor's diagnosis and looks for a convenient treatment.  
Figure 6: Message exchange in checkup phase (CP).
# Compact Broadband Antenna with Vicsek Fractal Slots for WLAN and WiMAX Applications
## 2 Antenna Geometry Design
### Design Evolution Procedure
The four corner squares and the middle square are kept, while the remaining squares are eliminated, as illustrated in the Vicsek fractal [24] with:  
\[a_{i}=\frac{a_{i-1}}{3} \tag{5}\]  
The Hausdorff dimension of the Vicsek fractal is presented by Equation (5) [24]:  
\[d=\frac{\ln(n)}{\ln(R)}=\frac{\ln(5)}{\ln(3)}=1.46496 \tag{6}\]  
The slotted patch antenna presents a broadband of 4.3 GHz from 2.3 to 6.6 GHz with two resonant frequencies at 3.55 and 5.3 GHz with reflection coefficients \(-42\) dB  
Figure 1:0 Simulated S\({}_{11}\) of the different evolutionary stages of the suggested antenna. Figure 1:1 The iterations of the Vicsek fractal structure. and \(-40\) dB, respectively. Finally, the geometry of the suggested antenna is obtained by introducing the second iteration of Vicsek fractal slots on the octagonal patch antenna, as shown in Figure 2d. The suggested antenna achieves a broadband of 4.35 GHz from 2.3 to 6.65 GHz with good impedance matching at resonant frequencies 3.6 and 5.2 GHz.
## 3 Materials and Methods
### Statistical Evaluation of Model Performance  
We determined the performance of the SCOPE simulations using statistical criteria that evaluated the efficiency with which the SCOPE reproduced the flux tower GPPEC. These criteria provided a measure of the SCOPE model efficiency in simulating daily GPPSIM over the 2016 vegetation growing season. We used the following criteria:  
Root mean square error (RMSE) = \sqrt{\frac{1}{n}\sum_{i=1}^{n}(z_{i}-y_{i})^{2}}\,\] (2) Nash-Sutcliffe efficiency (NSE) = 1 - \[\frac{\sum_{i=1}^{n}(z_{i}-y_{i})^{2}}{\sum_{i=1}^{n}(z_{i}-\overline{z})^{2 }}\,\] (3) Correlation coefficient (COR) = \[\frac{\sum_{i=1}^{n}(z_{i}-\overline{z})(y_{i}-\overline{y})}{\sqrt{\sum_{i=1 }^{n}(z_{i}-\overline{z})^{2}\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}}}\,\] (4)  
where \(n\) is the number of daily simulated GPPSIM (\(y_{i}\)) and daily measured GPPEC (\(z_{i}\)), and \(\overline{z}\) and \(\overline{y}\) represent the mean over the vegetation growing period. The RMSE criterion has the unit of GPP. A low RMSE indicates high accuracy. It is appropriate to compare the simulations under different scenarios (Section 3.3). The NSE [58] can range from \(-\infty\) to 1. An NSE value close to 1 indicates a perfect match of simulations to the observations. Following [PERSON] et al. [59], we assumed that an NSE \(\geq\)0.5 indicates adequate accuracy in GPPSIM. The COR represents the linear relationship betweensimulations and observations, and can vary from -1 to 1. We also used Equations (3) and (4) to evaluate the efficiency with which the RTMo produced TOC reflectance on each day. In this case, \(z_{i}\) and \(y_{i}\) represent the observed and the RTMo-simulated HLS/airborne TOC reflectance at the wavelength \(i\), respectively.  
We compared the vegetation properties retrieved from the HLS data with those retrieved from the airborne data, and the measurements using the percentage difference:  
\[\text{Percentage difference}=\left|\frac{A_{i}-B_{i}}{\frac{A_{i}-B_{i}}{2}} \right|\times 100\;, \tag{5}\]  
where \(A_{i}\) and \(B_{i}\) represent the \(i\)th vegetation property retrieved from HLS data and airborne data, respectively. For comparison with the measurements, \(B_{i}\) represents the \(i\)th measured vegetation property.
## 4 Experiments and Results
### Experiment Setup  
The proposed method was developed using hardware with an Intel Core i5-7300 HQ CPU, 8 GB RAM, and an NVIDIA GeForce GTX 1050 GPU. The software specifications used in this work are Python 3.6 with the machine learning frameworks Keras [49] and TensorFlow, and Windows 10 operating system. The following metrics are used to evaluate the segmentation results: the Dice coefficient, intersection over union (IoU), sensitivity (SENS), and precision (PREC).
## 4 Stator-Side External Retrofit Techniques
### Reactive Power Compensation Device  
The static var compensator (SVC) is a widely used reactive power compensation device with the configuration comprising a thyristor-controlled reactor and thyristor-switched capacitors. By injecting reactive power into the connected bus, the SVC can improve the transient stability of DFIG-WTs and damp the power oscillations in transmission lines.  
The static synchronous compensator (STATCOM) is a power electronic device based on the voltage source converter principle. Generally, the STATCOM comprises a two- or three-level voltage source converter [45], as shown in Figure 14. During steady-state conditions, this device injects reactive power into or absorbs reactive power from the power grid to improve voltage regulation and stability. Thus, the transfer capability can be enhanced. To provide smooth and rapid reactive power compensation for grid voltage recovery, the STATCOM can inject the maximum reactive current into a power grid during fault conditions [1; 45; 46]. Compared with thyristor-controlled static var compensators (SVCs), the STATCOM has a similar capability in stability improvement, but a higher control bandwidth. Furthermore, the STATCOM implemented in DFIG-WTs can compensate current without depending on the voltage level of the connection point, which ensures the effective operation of the STATCOM during serious fault conditions [47; 48; 49]. A GSC controller can successfully return the DC-link voltage to a normal value with the aid of the STATCOM [50], which is a necessary condition for the RSC to restart.  
Figure 14: Static synchronous compensator (STATCOM) protection circuit.  
Figure 13: Series dynamic braking resistor protection circuit.
## 3 Materials and Methods
### ZigBee Communication Layer  
The ZigBee protocol provides an open standard for low-power mesh wireless networking of monitoring and control devices. Working with the IEEE 802.15.4 standard, which focuses on low-rate personal area networking and defines the lower protocol layers (_i.e._, the physical layer, or PHY, and the medium access control layer, or MAC), ZigBee adds network structure, routing, and security (e.g., key management and authentication) to complete the communications suite. On top of this robust wireless engine, ZigBee profiles provide target applications with the interoperability and intercompatibility required to allow similar products from different manufacturers to work seamless defines the upper layers of the protocol stack, from network to application, including application profiles. ZigBee uses the license-free ISM bands, which provide unrestricted geographic use [40; 41; 42; 43; 44].  
In the proposed system the ZigBee layer provides the wireless network with the ability to send the environmental parameters, read by the ARDUINO cards, to the local hub or, _vice versa_, to send commands from the hub to the acquisition cards.  
The communication network is realized using the XBee radio modules by MaxStream already equipped with an on-chip antenna [45; 46]. The characteristics of high radio sensitivity, which reduces the probability to receive corrupted packets (less than 1%), the good operational range (tens of meters indoors) and the low power consumption are particularly suitable for battery-powered applications as ours. In particular, to operate the two-way radio transmission, the necessary current is approximately 50 mA with a 3 V DC source. To insure a further power reduction, the sleep mode ensures a current draw of less than 10 \(\upmu\)A.  
Nevertheless, the choice to use ZigBee technology is due to its performance. Table 2 shows the performance of ZigBee against those of other two famous technologies used for short-range wireless communications, Bluetooth and Wi-Fi. As the table shows, both Wi-Fi and Bluetooth have higher bit rates than ZigBee, but the higher power consumption suggests that the ZigBee technology is the best candidate to create a sensor network for our application. Furthermore, studies have proved that ZigBee is more affordable in terms of power consumption and costs than competitive technologies [3; 29; 30; 31].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Feature** & **ZigBee** & **Wi-Fi** & **Bluetooth** \\ \hline IEEE standard & 802.15.04 & 802.11b/g & 802.15.01 \\ Main application & Control & Broadband & Mobile devices \\ Number of network devices & Up to 65,000 & 32 & 7 \\ Bit rate & 20–250 kb/s & 11/54 Mb/s & 720 kb/s \\ Range & 100 m & 100 m & 10 m \\ Battery life & 100–1000 days & 1–5 days & 1–7 days \\ \hline \end{tabular}
\end{table}
Table 2: ZigBee _vs._ some other wireless network.
## 2 Mathematical Principles
### Ensemble Kalman Filter
Utilizing this multi-iteration EnKF algorithm enables a more accurate approximation of the system's actual state, improves prediction accuracy, and ensures algorithmic convergence.
## 5 The Simulation Results and Discussion
### Discussion
#### 5.4.2 The Robot Tracks the Other Typical Path  
In order to investigate the operating ability of the \"Optimal_MFs_FRs_FLC\", this controller, the \"Expert_FLC\", and the \"Optimal_MFs_FLC\" are used to control the robot to move in several typical different paths including square path, sharp turn path, diamond-shaped path, and straightforward path (see Figures 16-19 and Tables 15-18).  
Figures 16-19 and Tables 15-18 clearly indicate that the performances of the motion control are still very good when the robot is controlled by the \"Optimal_MFs_FRs_FLC\" compared to the \"Expert_FLC\" and the \"Optimal_MFs_FLC\" in all the cases of usingthe given paths. Specifically, a 408.98%, 286.49%, 257.09%, and 178.44% reduction in the kinetic energy loss was achieved using the proposed controller compared to the best of the used controllers when the DDR tracks the square path, the sharp turn path, the diamond-shaped path, and the straightforward path, respectively. Especially, the limitations of the other controllers such as the vibration and the jerk have been well solved by the \"Optimal_MFs_FRs_FLC\". So, it can be also concluded that the \"Optimal_MFs_FRs_FLC\" is the best controller for the DDR on the different given paths.  
As mentioned earlier, the main goal of this study is to design an FLC to drive the DDR from the starting point to the destination through all the given waypoints by the optimization process with the help of the GA based on minimizing the kinetic energy loss criterion. Thus, from all the analyses above, it could be concluded that the \"Optimal_MFs_FRs_FLC\" is the most suitable one in the used FLCs. In other words, the \"Optimal_MFs_FRs_FLC\" is much better than the others in terms of energy-saving while the other performances of motion control are still good.
# HORSIC+: An Efficient Post-Quantum Few-Time Signature Scheme
## 4 Analysis
### Comparison with HORS and Horst
#### 4.3.2 Security for Multiple Messages  
HORSIC+ can be used as a few-time signature scheme in two ways. The first is for the signer and the verifier to maintain their own state information as in [15; 33]. It is a good strategy when HORSIC+ is used in broadcast authentication in wireless sensor networks. However, it is not appropriate when used as a general signature scheme because maintaining the state information means it is stateful. If the state information update fails, then HORSIC+ cannot be used anymore. The second is to use HORSIC+ many times without state information as HORST in [13]. In this case, the security level decreases as the number of signatures using the same key increases.  
To investigate how rapidly the security level decreases as the number of signatures using the same key (\(r\)) increases, we normalize the security level for \(r=1\) to 1 and compare the normalized security level of HORSIC+ and HORS/HORST. For simplicity, we compute the normalized security level of HORSIC+ by solving the subset-resilience problem.  
Figure 3 shows the normalized security level of HORSIC+ and HORS/HORST for multiple messages. The x-axis shows the number of signatures using the same key (\(r\)). We can see that the normalized security level of HORSIC+ decreases more slowly than that of HORS/HORST. It is because HORSIC+ uses smaller \(k\) than HORS/HORST for the same security level.
## 6 Experimental Validation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Antenna**} & \begin{tabular}{c} **No. of** \\ **Bands** \\ \end{tabular} & \begin{tabular}{c} **Frequency** \\ **(GHz)** \\ \end{tabular} & \multirow{2}{*}{**Size (\(\mathbf{\Lambda_{0}}\))**} & \multirow{2}{*}{_ka Value_} & \begin{tabular}{c} **Imp.** \\ **BW(\%)** \\ \end{tabular} & \multirow{2}{*}{\begin{tabular}{c} **Gain** \\ **(dBi)** \\ \end{tabular} } \\ \hline Patch & 1 & 6.2 & 0.417 \(\times\) 0.417 & 1.8 & 3.5\% & 6 \\ \hline Patch & 2 & \begin{tabular}{c} 2.4 \\ 5.2 \\ \end{tabular} & \begin{tabular}{c} 0.164 \(\times\) 0.164 \\ \end{tabular} & \begin{tabular}{c} 0.72 \\ 1.54 \\ \end{tabular} & \
\begin{tabular}{c} 1.63\% \\ 13.15\% \\ \end{tabular} &
\begin{tabular}{c} 3.8 \\ 2.9 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of the proposed antenna with the basic patch antenna. Figure 14: Radiation characteristics of the proposed ESA at (**a**) 2.4 GHz and (**b**) 5.2 GHz. ESA is MTM inspired and it can be observed that it is better than the references in terms of size, percentage bandwidth, and multiband characteristics.