esa-sceva/satcom-chunk-collection huggingface dataset:
# Single-Shot Coherent X-Ray Imaging Instrument at PAL-XFEL
## 1 Introduction  
X-ray free electron lasers (XFELs) and diffraction-limited light sources are regarded as generating high-intensity and nearly coherent X-rays. Such coherent sources do not need a small aperture as a spatial filter to improve the spatial coherence. In contrast, synchrotron X-ray sources are considered as generating relatively low-intensity and partially coherent X-rays. They even lose most of their flux by using this small aperture to enhance the spatial coherence. Coherent diffraction imaging (CDI) of a single virus by synchrotron and XFEL sources is a good way in which to dramatically show the effect of X-ray sources [1, 2]. Although both results revealed 2D structural information of viruses at nanoscale resolution, the data acquisition processes were quite different from each other. XFEL could easily achieve this feat with a single X-ray pulse, while a synchrotron source managed to do it with a large amount of data. Thus, high-intensity coherent XFEL enables us to conveniently perform a single-shot-based experiment. Furthermore, XFEL can be generated only in an ultrashort pulse form, which leads us to study ultrafast phenomena of nanoscience. Taking advantage of these remarkable properties of coherent light sources, CDI, X-ray photon correlation spectroscopy (XPCS), and other applications have been actively applied in various research fields [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13].  
We have developed a single-shot coherent X-ray imaging instrument to fully utilize the features of XFEL beams at the hard X-ray beamline of the Pohang Accelerator LaboratoryX-ray Free Electron Laser (PAL-XFEL). This instrument was established to provide an optimum apparatus for single-shot-based X-ray experiments. In this paper, we present the details of the single-shot coherent X-ray imaging. Additionally, we briefly state the femtosecond laser system for optical pump X-ray probe experiments. Finally, we show the demonstration result of pump-probe coherent diffraction imaging (PPCDI) using this instrument. The melting process of Ag nanospheres is visualized at picosecond temporal and nanometer spatial resolutions.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 4 Cognitive Radio
### Brief Background  
The study of CR is presently a popular and interesting research area because it attempts to address the problem of spectrum underutilization. Over the past 20 years, the wireless communication industry has experienced a surge in the number of new wireless technologies deployed to improve the QoS of end-users [68]. Each newly deployed wireless technology demands new and unique frequencies to be allocated for its operation (i.e., fixed spectrum assignment). However, owing to the dwindling availability of usable RF bands, particularly across the very high frequency/ultra high frequency (VHF/UHF) bands up until the microwave band, it has become obvious to most national frequency managers worldwide that spectrum is becoming scarce (spectrum scarcity). Such a reality of spectrum scarcity poses a significant threat to the growth of the wireless communication industry [69]. However, several research findings have revealed that most allocated frequency bands are often underutilized as opposed to being scarce, with utility levels ranging between 15 and 85% [70]. Thus, the concept of CR was conceived supported by the concurrent advances in the development of SDR technologies [71]. Consequently, the study, development, and deployment of CR technologies has since grown exponentially in the literature following its potential use of free spectra for opportunistic communication.  
Technically, CR is defined by the Federal Communications Commission (FCC) as: \"A radio or system that senses its operational electromagnetic environment and can dynamically and autonomously adjust its radio operating parameters to modify system operation, such as maximize throughput, mitigate interference, facilitate interoperability, access secondary markets\" [68]. This definition highlights some specific CR characteristics as follows: CR technologies must provide adaptive and autonomous spectral awareness (spectrum sensing (SS)), detect available channels (spectrum decision making), dynamically adjust its radio operating parameters (spectrum mobility), and conduct concurrent communication (spectrum sharing). In the next subsection, we present only a brief overview of these characteristics.
## 4 the Proposed Secure Multicast Routing Protocol: DLSMR
### The Proposed Deep Learning-Based Secure Multicast Routing Protocol: DLSMR
Otherwise, CH\({}_{c}\) records the sender's ID, updates the routing table, and forwards to the previous node by unicast transmission; then, go to **step 8**. * **Step 7.2:*
* When the S\({}_{\text{ID}}^{\text{RREP}}\) equals S\({}_{\text{ID}}^{\text{tab}}\), we will predict the best secure next node with input parameter consisting of the node ID, position of the node, destination ID, destination sequence number, and remaining energy. The output parameters consists of the NID\({}_{\text{sec}}^{\text{1}}\), NID\({}_{\text{sec}}^{\text{2}}\), and the status of the node (secure or unsecured) to select the secure next node as the best next node while establishing the route from S to multiple D\({}_{i}\) by using a DNN model; then, go to step **step 7.3**. **Step 7.3:** If the \(\mathsf{Stat}_{\mathsf{sec}}\) equals 1, then \(\mathsf{CH}_{c}\) can determine the secure route to be pursued by the secure next node. Then, \(\mathsf{CH}_{c}\) records the \(\mathsf{ND}_{\mathsf{sec}}\), updates the routing table, and forwards to the previous node. The multicast tree routing table can be summarized in Table 4, where \(\mathsf{S}_{\mathsf{ID}}\) is the source ID, \(\mathsf{MG}_{\mathsf{ID}}\) is the multicast group ID, \(\mathsf{S}_{\mathsf{Seq}}\) is the source sequence number, \(\mathsf{D}_{\mathsf{Seq}}\) is the destination sequence number, \(\mathsf{M}_{\mathsf{ID}}\) is the multicast node ID, \(\mathsf{PN}_{\mathsf{ID}}\) is the previous node ID, \(\mathsf{NN}_{\mathsf{ID}}\) is the next node ID, and \(\mathsf{Hop}\) represents the number of hops to \(\mathsf{D}\). The routing table will be used to determine the next node to the multicast group \(\mathsf{D}\) that data packets will pass through during the data transmission process. Then, go to **step 8**. Otherwise, the packet will be dropped and the process is ended. * **Step 8:** **Source Node Operation for Route Reply** If \(\mathsf{S}\) receives all RREP packets from the multicast group, then go to the data transmission process in **step 9**. Otherwise, go to step **Step 8.1:** **Step 8.1:** **Step 8.1:** **Step 8.2:** **Step 8.3:** **Step 8.4:** **Step 8.5:** **Step 8.6:** **Step 8.7:** **Step 8.8:** **Step  
The list of packets for the DLSMR protocol are summarized in Table 5.  
Figure 5: Flowchart of the proposed deep learning-based secure multicast routing (DLSMR) protocol. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Packet Name** & **Full Name** & **Field Information** \\ \hline JREQ & Join Request & \(\text{Type},\text{S}_{\text{ID}}^{\text{JREQ}},\text{D}_{\text{ID}},\text{Status}\) \\ \hline MRREQ & Multicast Route Request & \(\text{Type},\text{S}_{\text{ID}}^{\text{MRREQ}},\text{M}_{\text{GD}},\text{M}_ {\text{ID}},\text{S}_{\text{Seq}},\text{Hop},\text{TTL}\) \\ \hline RREP & Route Reply & \(\text{Type},\text{S}_{\text{ID}}^{\text{RREP}},\text{D}_{\text{ID}}^{\text{RREP }},\text{M}_{\text{ID}},\text{D}_{\text{Seq}},\text{E},\text{Pos},\text{Hop}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: List of packets for the DLSMR protocol.
# Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap
## 4 Experimental Research
### Data Preprocessing and Determination of the Optimal Time Interval  
The work in [22] showed that the ERS/ERD phenomenon in channels C3 and C4 is the most distinct when imagining the left and right hand movements. Therefore, to maximize the significant  
Figure 3: Timing scheme of the collection experiment.  
difference between ERD and ERS, the signals on channels C3 and C4 were selected in this study; channel \(Cz\) is discarded because of the lack of valid information related to the imaging task [26].  
To determine the optimal time period related to the task and obtain the most distinguishable dataset, the average instantaneous power of MI-EEG over electrodes C3 and C4 under different imaging tasks was calculated, respectively, and this is shown in Figure 4.  
It can be seen from Figure 4 that the average instantaneous power spectra of electrodes C3 and C4 show distinct ERS/ERD phenomena from 3.5 s to 6.5 s under different hand movement imagination tasks. Therefore, the time segment [3.5, 6.5] s is chosen as the optimal time period, on which the data are taken as the experimental sample in the follow-up study.
# State of the Art in LP-WAN Solutions for Industrial IoT Services
## 2 Limitations on Existing IIoT Solutions  
Current enabling technologies for IIoT services can be divided into short-range and long-range approaches. The main impediments found to implement sustainable cost-effective IIoT solutions are related to: (i) network management costs; (ii) scalability and network organization; (iii) edge-nodes' dimensioning and power efficiency; and (iv) coverage. In the following, these points are identified and reviewed for different short-range and long-range technologies that have been employed so far for supporting IIoT applications. Please note that although the list of solutions provided in this section does not intend to be exhaustive, it permits us to identify the principal challenges in deploying these types of M2M networks.
# A Deep Learning Approach to Detect Failures in Bridges Based on the Coherence of Signals
## 2 Experimental Set Up  
The monitoring system installed on this bridge (represented in Figure 1) acquires data continuously. Candia Bridge is a multi-span masonry arch bridge that is monitored mainly by tiltmeters, which are used to assess the static position of the bridge. In particular, MEMS tiltmeters (hereafter denoted by Txx) are placed on the arches skewback and are in charge of recording the transversal rotation (positive if upriver) of the piers. In fact, as an example, a possible flooding scenario can apply a scouring action on the foundation by determining (in the long period) potentially irreversible rotations. Moreover, a weather station is placed in the middle of the bridge to record different environmental factors: the pressure, internal temperature of the station, external air temperature, internal humidity, external humidity, wind speed, speed direction and rain rate. Tilttmeters 2 and 14 are equipped with an internal temperature sensor that is stored during the acquisition.  
To detect extraordinary events, the bridge is equipped with hydraulic and visual sensors too:  
* Hydrometer: measures the river level identifying possible flooding that can be dangerous for the bridge's structure.
* Echo sounder: measures the level of the river bed and can provide information about possible movements of the piers within its proximity.
* Cameras: identify possible detritus stacks at the base of the piers by documenting and characterising the annual process of plant transportation.  
A strengthening intervention was made on the river bed in 2003; it is noticed that the piers located within the working site are more subjected to the scouring action. On these piers, two tiltmeters are installed; to better describe this phenomenon, they are placed on the arch skewback converging on the pier.
# An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition
## 5 Discussion
### Filter Size Setting  
In order to directly show that the strategy of using a decreasing filter size has a better performance than using a constant filter size (5,5), as in previous studies [19], and increasing filter size such as (4,4), (4,4), (5,5), (5,5), and (6,6), we conducted experiments using the ESC-50 dataset. The experimental results in Table 5 show that the best classification performance of 65.79% accuracy in RawNet and 81.09% in MelNet is achieved by using a decreasing filter size. It is indicated that the strategy of using a decreasing filter size outperforms other filter size setting strategies in our model.  
Furthermore, for further evaluation of whether the strategy of using a decreasing filter size outperforms other filter size setting strategies in a statistically significant way, we applied a paired sample \(t\) test. We want to test if: hypothesis (\(H_{0}\)) is true; that is, there is no significant difference between two sets of accuracy values. The results for two paired sample \(t\) tests of accuracy values obtained by three different filter size setting strategies in RawNet and MelNet are summarized in Table 6. On average, the strategy of using a decreasing filter size has improved on the accuracy values of other strategies by 3% and 2.3% in RawNet and 1.7% and 1.8% in MelNet, respectively. The standard deviations of accuracy differences are listed in the fourth column of Table 6. The two-tailed \(p\)-values approach zero for each compared pair of RawNet and approach 0.04 in each compared pair of MelNet, which means that we should reject the \(H_{0}\), since the \(p<0.05\) in each case. It can also be seen from Table 6 that the \(p\) values indicate that the decreasing filter size has a significant effect on the accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Paired Accuracy Statistics**} \\ \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Pair**} & & **Mean** & **N** & **Std. Deviation** & **Std. Error Mean** \\ \hline \multirow{4}{*}{**RawNet**} & \multirow{2}{*}{Pair 1} & Decreasing filter size & 0.6579 & 5 & 0.00640 & 0.00286 \\  & & Constant filter size & 0.6253 & 5 & 0.00844 & 0.00382 \\ \cline{2-6}  & \multirow{2}{*}{Pair 2} & Decreasing filter size & 0.6579 & 5 & 0.00640 & 0.00286 \\  & & Increasing filter size & 0.6344 & 5 & 0.00866 & 0.00387 \\ \hline \multirow{4}{*}{**MelNet**} & \multirow{2}{*}{Pair 1} & Decreasing filter size & 0.8109 & 5 & 0.00577 & 0.00258 \\  & & Constant filter size & 0.7932 & 5 & 0.01547 & 0.00692 \\ \cline{1-1} \cline{2-6}  & \multirow{2}{*}{Pair 2} & Decreasing filter size & 0.8109 & 5 & 0.00577 & 0.00258 \\  & & Increasing filter size & 0.7922 & 5 & 0.01015 & 0.00454 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistics of accuracy achieved by different filter size settings.
## 4 Challenges and Considerations for Farmers
### Soil Analysis to Promote Variety of Crop Growth  
In countries where farming is still done in the conventional way, farmers grow crops by planting without understanding the properties of soil and its quality. As a result of this, in the long run, farmers will not benefit from the amount of money they will earn from their farming. The current soil testing process uses a manual method which first involves the sampling of soil and then the submission of samples to laboratories for analysis. Due to the lengthy and laborious process, this manual process is inefficient. Because of human interference, there is a risk of human error. As a result, farmers could obtain inaccurate information. In that case, there is a need for an automated method for soil testing and crop prediction. Soil testing is crucial because soil testing assists in understanding the fertility of soil and this allows predictions to be made about the potential crop yield. Therefore, a system could be proposed that consists of a handheld device which will provide a pH reading and will help estimate the quantity of nitrogen (N), phosphorus (P), and potassium (K) in the soil. In addition, a classification algorithm can be developed to determine crops suitable for specific applications based on the data obtained from the system, and accordingly the appropriate fertilizers for the field can be provided.
## 5 Comparison and Discussion

Table 10: Statistical indicators sensitivity analysis and comparison of the obtained results for the power transformer and IM. reveal the extent of SC faults, for instance, the used indicators reflected different values for SC at tap 1, lowest SC level and at tap 5, highest SC level. FRA can also be employed to detect SC faults in IMs. The FRA measurements indicate that phase-to-phase fault has the largest variation between the normal and faulty responses over the entire FRA spectrum. Coil-to-coil and coil-to-ground faults results in noticeable variations in the FRA signature within the MF and HF regions. Turn-to-turn and turn-to-ground faults reveal variations in the MF region. The neutral-to-ground in a star-connected IM shows a small variation in the HF region. The OC fault has a different effect on the measured frequency response from star connection to delta connection. For star connection, the measured FRA shows a huge drop in the response at the LF and MF regions with no effect in the HF region. However, a relatively slight variation in the FRA signature can be noticed in the case of OC fault in a delta-connected IM in the LF and MF regions. FRA detects the SC faults in the transformer within the frequency range of 100 Hz to 100 kHz while it detects SC faults in IM within the frequency range of 200 Hz to 500 kHz. FRA also detect the OC turns in the transformer with noticeable FRA variations in the frequency range of 20 Hz to 30 kHz, while this frequency range is 20 Hz to 100 kHz for star-connected IM and 20 Hz to 1 MHz for delta-connected IM. While statistical indicators have been employed for fault identifications in power transformers, no specific limits were identified for most of these indicators to ease the identification and quantification process [37]. Based on the case studies in this paper, and by observing the analysis in Table 10, and the graphical FRA patterns of normal and faulty conditions, threshold values for the employed statistical indicators for power transformers and IMs are proposed, as listed in Table 11. The robustness of the proposed threshold limits can be attested through the for short-circuit and open-circuit case studies for transformer and IM.
## 3 System Architecture: Edge-FLGuard
### Integration with Cybersecurity Workflows  
Edge-FLGuard is designed to integrate with enterprise-grade and open-source security tools, as follows:  
* Alerting and Logging: Supports output to SIEM or SOAR platforms via syslog, REST API, or MQTT topics.
* Policy Enforcement: Integrates with SDN controllers or edge firewalls for automated mitigation (e.g., device quarantine).
* Human-in-the-Loop: Includes dashboard hooks for security analysts to audit decisions or retrain thresholds.  
The system is platform-agnostic, supporting deployment on containerized stacks (Docker 24.0, Kubernetes/K3s 1.29), microcontroller platforms (via TensorFlow Lite 2.14, MicroPython 1.22), or native edge frameworks (e.g., NVIDIA Jetson JetPack 6.0 or Arm Cortex-M55 devices ).
## 3 Mechanical Characterization of Tailing Sand
### Theoretical Basis of Sand Shear Strength  
Shear strength, also known as shear strength, refers to the ultimate strength of materials in the process of shearing. It can reflect the ability of materials to resist shear sliding. The shear strength of sandy soils can be divided into two parts: one is the friction due to the normal stress between particles, and the other is the cohesion, which is independent of the normal stress. When the shear force under the action of external force exceeds the shear  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{C_{u}}\)} & \multirow{2}{*}{\(\mathbf{C_{c}}\)} & **Confining Pressure** & **Dry Density** & \multirow{2}{*}{**Moisture Content (\%)**} \\  & & **(kPa)** & & \\ \hline
**3.394** & **1.042** & & & \\  & **1** & & & \\  & **1.5** & & & \\
**5.5** & **2** & & & \\  & **2.5** & 100 & & \\  & **3** & 200 & 1.68 & 15 \\  & **5** & 300 & & \\
**2** & **6** & & & \\  & **6.5** & & & \\  & **7** & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters before the grading test.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Particle Size (mm)**} & \multirow{2}{*}{**<0.075**} & **0.075–0.1** & **0.1–0.25** & **0.25–0.5** & **0.5–1** \\ \hline
**Confining pressure (kPa)** & & & 50/100/200 & & \\
**Dry density (g/cm\({}^{3}\))** & & & 1.68 & & \\
**Moisture content (\%)** & & & 15 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters before a single particle size saturation test.  
strength of sand, it will cause damage to the structure, and even cause serious collapse. The shear strength can be calculated from the cohesion C and the internal friction angle of the material according to the Mohr-Coulomb theory as follows:  
\[\tau f=c+\sigma\tan\phi \tag{3}\]  
The main factors affecting the shear strength are the mineral composition of sand soil, particle shape and grading, density, moisture content and the stress state, and its damage is summarized as the following five destruction types (shown in Figure 9): strong hardening type and weak hardening type, strong softening type and weak softening type and ideal plasticity model [18; 19]. The abscissa represents strain, and the ordinate indicates stress.
## 3 Discussion
The influence of air- and microwave-drying on the flavonoid content of onion was also revealed from the UPLC/MS derived model (Figure 5C). Notably, freeze dried _A. cepa_ exhibited higher quercetin conjugate content compared to the air and microwave-dried samples (Figure 5D). The observed degradation of flavonoid glycosides upon drying of samples is in agreement with previous reports showing that quercetin glycosides are degraded in oven-dried samples suggestive of plausible degradation of the flavonoid glycosides [27]. Also of note was the change in _A. sativum_ fatty acid content upon sun-drying as evidenced in the detected hydroxylated fatty acid, 9,12,13-trihydroxy octadeca-7-enoic acid, first time reported in garlic (Table 2, Figure 5C). Hydroxy fatty acid derivatives are receiving increasing attention due to their anti-inflammatory, antimicrobial, and cytotoxic activities [28] as well as contributing to _Allium_ effects has yet to be determined. The biological activity of _Allium_ species is also known to vary in response to various processing methods. [PERSON] et al. [29] reported that fresh garlic extract exhibited a stronger anti-candida effect than dried garlic powder extracts. Moreover, other scientific evidence points to the fact that fresh garlic extracts should be preferably used and are widely recommended in cases of microbial infections, as well as those derived from aqueous extracts, since the most prominent effects were achieved by using these forms of garlic preparations [1]. Such findings were also confirmed in the current study, with freeze dried garlic samples which most closely mimic fresh cloves possessing the strongestantibacterial activity against _Bacillus subtilis_, whereas air-dried garlic showed the lowest inhibition and with no effect obtained for the commercial product \"Tomex\". The decrease in _A. sativum_ anti-microbial activity upon drying is attributed to the thermal instability of sulphur compounds mediating its bacteriostatic action. This is supported by the observation that storage at room temperature affects antibacterial effectiveness of garlic extract and is less pronounced if stored at 0-4 \({}^{\circ}\)C [30]. Compared to garlic, freeze-dried onion possessed an attenuated inhibitory response towards _B. subtilis_ likely as the former contains higher levels of sulphur compounds (Figure 1A). Moreover, microwave and air-dried onion extracts failed to exert a bacteriostatic effect against the tested microorganism.