esa-sceva/satcom-chunk-collection huggingface dataset:
# Bot-Based Emergency Software Applications for Natural Disaster Situations
## 3 Bot-Based Applications for Natural Disasters
### Jayma Application  
After a natural disaster strikes, Jayma ([[https://citiaps.usach.cl/portafolio/jayma](https://citiaps.usach.cl/portafolio/jayma)]([https://citiaps.usach.cl/portafolio/jayma](https://citiaps.usach.cl/portafolio/jayma)), accessed on 28 January 2022) gives information about safe places and meeting points, recommends routes to follow, and spreads information through the Messenger bot. For the Messenger bot, the app creates a network of the user's contacts. These contacts are included in the Jayma network after they agree to participate by using the Jayma app.  
The Back-end of the application supports a website for the administrator. The administrator can send disaster alerts to users. When the administrator sends an alert message,  
Figure 3: Virtualization technology used in our proposed platform.  
Figure 2: Example of a distributed platform.  
users receive a notification (Facebook Messenger message) indicating that a natural disaster has occurred. The administrator can also submit relevant information such as safe points. Figure 4 shows some screenshots of the Jayma bot. The application supports the following tasks:  
1. Send alerts: The administrator sends alert messages to the users included in the Jayma network.
2. Access to the main menu: Lists all the application options (Figure 4a).
3. Information: Provides information on meeting points or safe areas. These are safe geographic locations (latitude and longitude) and are activated by the administrator. Upon accessing the link, the bot delivers a Google Maps map with the last known location and the route to the closest meeting point (Figure 4b-d).
4. Report user status: The bot asks the user for the location. Once the bot receives the location, it asks the user if they are well. The bot notifies the user's contacts of their status and location. In case the user is unwell, the bot asks what is the problem (panic/injured/infrastructure problems/needs supplies). See Figure 4e,f.
5. Contact management: Adds new contacts to the Jayma network. It also allows deleting a contact and listing current contacts. To include a person as a new contact, the app sends a message to that person with a link to the bot.  
Figure 4: Screenshots of different tasks provided by the Jayma bot. (**a**) Main menu, (**b**) user status information, (**c**) meeting points, (**d**) evacuation route, (**e**,**f**) reports user problems.
# A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder
###### Abstract  
In modern industrial systems, establishing the optimal maintenance policy for rotating machinery is essential to improve productivity and prevent catastrophic accidents. To this end, many machinery engineers have been interested in condition-based maintenance strategies, which execute the maintenance activity only when the fault symptoms are detected. For more accurate fault detection of rotating machinery, vibration signals have been widely used. However, the vibration signals collected from most real rotating machinery are noisy and nonstationary, and signals from fault states also rarely exist. To address these issues, we newly propose a fault detection framework with a spectrogram and convolutional autoencoder. Firstly, the raw vibration signals are transformed into spectrograms to represent both time- and frequency-related information. Then, a two-dimensional convolutional autoencoder is trained using only normal signals. The encoder part of the convolutional autoencoder is used as a feature extractor of the vibration signals in that it summarizes information on the input spectrogram into the smaller latent feature vector. Finally, we construct the fault detection model by applying the one-class classification algorithm to the latent feature vectors of training signals. We conducted an experimental study using vibration signals collected from a rolling element bearing experimental platform. The results confirm the superiority of the proposed fault detection framework on rotating machinery. In the experimental study, the proposed fault detection framework yielded AUROC values of almost one, and this implies that the proposed framework can be sufficiently applied to real-world fault signal detection problems.  
fault detection; one-class classification; rotating machinery; spectrogram; two-dimensional convolutional autoencoder; vibration signal +
Footnote †: journal: Applied sciences
# A Continuous Monitoring and Reallocation Method for Successful Decisions in Change Management
## 4 Experiments and Results
### First Experiment's Results and Analysis
The CoMoRe method provides the manager with information that can serve as an early warning about the process of change over time, enabling the timely reallocation of human resources to achieve a successful change. Generally, a continuous evaluation of change is achieved using the CoMoRe method. Figure 5 shows the average number of employees that will need to be reallocated during the change process (these results were produced in the experiment with 100.000 repetitions). At the early stages of change, when the RIC is high, the reallocation frequency is higher to avoid a significant drop in performance. Afterward, fewer adjustments have to be made, and the number of reallocations decreases over time. Another interesting result in Figure 5 is that human resources are allocated to their most productive positions at about the halfway point (\(t=8\)) of the total change period, and no further reallocation is required.
# Beyond Prompt Chaining: The TB-CSPN Architecture for
## 5 Implementation Walkthrough
### Core Architectural Principles
#### 5.2.1 Semantic Token Model  
The implementation centers on a unified token abstraction that explicitly encapsulates semantic information with weighted topics. Unlike prompt-based coordination systems, where inter-agent communication relies on unstructured text passing, TB-CSPN tokens carry their semantic context as structured metadata with topic distributions, enabling formal reasoning about relevance and coordination decisions.  
This token model directly implements the Token and Color abstractions defined in the organizational model (Figures 2-5), ensuring consistency between theoretical specification and practical implementation.
# Monte-Carlo-Based Estimation of the X-ray Energy Spectrum for CT Artifact Reduction
## 2 Materials and Methods
### Monte Carlo Simulation  
#### 2.1.1 Calculation of X-ray Spectrum Inside the Flat Panel Detector  
Monte Carlo simulation is a powerful tool for modeling X-ray/gamma radiation based imaging and measuring systems [23; 24; 25; 26; 27; 28; 29; 30]. In this work, the X-ray tube and flat panel detector of the FleXCT system were modeled using Monte Carlo N-Particle (MCNP) code, version X [31]. The FleXCT system covers a wide energy range (20-230 kV), which allows analyzing a wide variety of samples from low absorbing materials (e.g., biological samples, polymers) to dense materials (e.g., stone, rock, metals) [22]. Its X-ray tube (X-ray WorX GmbH, Germany) is a reflection-based type and consists of an electron source (cathode), a tiled Tungsten target (anode) and a 1 mm beryllium window, as depicted in Figure 1. The incident angle between the electron beam and the anode is 43\({}^{\circ}\).  
The flat panel detector was modeled as a 3-layered rectangular uniform plate (see Figure 1). The simulated flat panel detector consists of a carbon layer (750 \(\upmu\)m thickness), a polyethylene layer (250 \(\upmu\)m thickness) and a gadolinium oxysulfide (GadOx, Gd\({}_{2}\)O\({}_{2}\)S:Tb) scintillator layer. The simulated GadOx scintillator layer has a thickness of 250 \(\upmu\)m and a density of 4.8 g/cm\({}^{3}\) (58% GadOx powder with a density of 7.34 g/cm\({}^{3}\) and 42% epoxy glue with a density of 1.23 g/cm\({}^{3}\)). The incident X-ray radiation interacts with the GadOx scintillator and is converted into visible light; the carbon and polyethylene layers are used to protect the scintillator layer of the flat panel detector against visible light and mechanical damage. A silicon layer was included in the model to consider the backscattering photons from the electronics behind the detector. The X-ray energy spectrum was calculated inside the GadOx scintillator layer in an area in the center of the beam using photon energy deposition pulse height tally (*F8:P) and energy bin (E8) cards using the MCNP code. All simulations were performed with low statistical error (less than 0.01) using the STOP card in the input file of MCNP code. The calculated X-ray spectra inside the flat panel detector obtained by simulations in this section were compared with the empirically measured X-ray data using the MLEM method, which is detailed in Section 2.2.
## 1 Introduction
Recently, Ref. [30] studied the atmospheric disturbances after the Beirut explosion using GNSS-derived ionospheric slant total electron content (STEC) and triaxial seismic data. Ref. [31] utilised DMSP ion density data and ionosonde data to confirm the generation of travelling ionospheric disturbances (TIDs) due to the magnetosonic waves generated from this explosion. They also demonstrated that the wave travelled more than 3000 km northwest of the explosion point. The Beirut port explosion has attracted significant interest from several scientists because it occurred during quiet geomagnetic conditions. Therefore, investigating the dynamics of the ionosphere under the influence of this explosion is very important to the space weather community. Until now, all investigations have focused on the region within or around the explosion point; however, as far as we know, nobody has focused on whether the explosion could impact the southern hemisphere. This study seeks to provide observational evidence of conjugate disturbances associated with the Beirut port explosion that occurred on 4 August 2020. Through using multi-Low-Earth-Orbiting (LEO) satellite data during its passage close to the explosion region, such a preliminary study is invaluable for remote sensing and for inferring such distance phenomena using available instruments far from the conjugate regions of such quasi-blast explosions. The remainder of this paper is organised as follows. In Section 2, we explain the datasets and methodology. Section 3 presents the results of the study. In Section 4, we present a discussion. The final section presents the conclusions of this study.
## 2 Materials and Methods  
### Infrared FEL Facility KU-FEL  
As shown in Figure 1, the infrared FEL facility KU-FEL is based on a normal conducting S-band linac with a thermionic radio frequency (RF) gun as the electron source [13]. A multi-bunch electron beam with an energy of 8.4 MeV was generated in the thermionic RF gun driven by a 10 MW lystron. After passing through a dog-leg type energy filter, the electron beam is injected into a 3 m accelerator tube driven by a 20-MW lystron. The electron beam bunches were compressed by passing them through a 180\({}^{\circ}\) arc section and were injected into a 1.8 m hybrid planar undulator with a period of 33 mm for the FEL oscillations. An optical cavity with a length of 5.039 m is installed on the extension of the undulator straight line [14]. The maximum energy of an electron is 40 MeV, and the duration of the electron beam macropulse can be up to 7.8 \(\upmu\)s. Although the charge of the electron beam micropulse increases from 20 to 50 pC owing to the back-bombardment effects on the thermionic RF gun, the electron energy of the micropulse is kept constant during the macropulse duration by applying amplitude-modulated RF pulses to the RF gun and accelerator tube [15]. In this experiment, the electron beam was operated under the electron energy of 40 MeV and macropulse duration of 6.9 \(\upmu\)s. The \(K\) value of the undulator was set to 1.28, and the wavelength of FEL oscillations was 4.9 \(\upmu\)m.  
The FEL power was varied by adjusting the phase of the RF field between the RF gun and the accelerator tube. Figure 2 shows the FEL macropulse for some RF phase differences. Here, the RF phase difference on the crest was defined as zero. As shown in Figure 2, the FEL power was maximized near the crest and the maximum value was approximately 40 mJ. Therefore, the bunch length of the electron beam was expected to be minimal near the crest.
## 4 Parametric Analysis of Side Joints
### Lateral Resistance under Load Case 1
#### 4.1.1 Arrangement Length of URSP Connectors
Moreover, the slip increases and the height of neutral axes in the steel girder decreases while using the URSP connectors. When \(L_{U}=L\), or no interaction between the concrete slab and steel girder exists, the two parts behave independently and the strain profile of the steel girder is similar to that of the bare steel beam. Since the truss action is proportional to the degree of shear connection as is mentioned above, compared with no interaction and without arranging the URSP connectors, the tensile strain at the slab top with studs is obviously larger. Figure 10: Load-strain curves of the bottom flange. Figure 9: Load-displacement results. (**a**) Hogging moment; (**b**) sagging moment. Note: For clarity, some of the data points are not drawn in the figure, which is the same for the following figures. Since the interface slip between the RC slab and steel beam always leads to a reduction of the overall structural stiffness [44; 51; 52], a detailed analysis of the slip effect under different cases is performed to interpret the results presented in Figure 9. The slip distributions along the beams under the hogging moment corresponding to the loading displacement (denoted as \(u\) in the following sections) of 10 mm, 70 mm and 150 mm, which represent the elastic state, ultimate state and maximum displacement state, respectively, are summarized in Figure 12a-c. The origin of the coordinate system is located at the beam-column interface. The values in the right direction are set to be positive. Therefore, the coordinate axis value at the left end of the beam is \"-300\". Compared with models merely connected with studs, the slip increases with the use of URSP connectors. In addition, with the increasing arrangement lengths of URSP connectors, the interface slip increases accordingly. The maximum slip value is still less than 1 mm. Figure 11: Strain distribution along the section height. Figure 12: Interface slip distribution under the hogging moment. (**a**) \(u\) = 10 mm; (**b**) \(u\) = 70 mm; (**c**) \(u\) = 150 mm. For the extreme case where \(L_{U}=L\), the slip increases sharply, especially at the left end of the beam, which explains the decrease of the ultimate loading capacity in Figure 9a. The significant slip differences among beams with a partial (\(L_{U}<L\)) and full (\(L_{U}=L\)) arrangement of URSP connectors demonstrate that the studs can strongly restrain the slip at the left beam end. For \(L_{U}<L\), the slip shows an obvious increase at the transition point where URSP connectors are arranged instead of traditional studs. Based on the simulation results above, it can be concluded that if the URSP connector's length \(L_{U}\) is less than \(L\), small differences are observed among the load-displacement curves with different \(L_{U}\)s. Therefore, only the model with a URSP arrangement length equal to \(0.5L\) (denoted as URSP) is chosen to be compared with the model constructed with all studs (denoted as Stud) in the following parameter analysis for simplicity.
# A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices
## 4 Implementation of DRX Power Savings with Adaptive SPS
### Power Saving through Predictive Allocation  
For the reactive buffer-based allocations, the values of \(0<f_{S}<0.5\) ensures satisfactory delay budget performance but shortens the supported SPS periods which leads to shorter sleep cycles for the M2M devices. The values of \(0.5\leq f_{S}<1\) support longer SPS periods hence longer sleep cycles, but increases the waiting time for the packets in the buffer and the statistical probability of exceeding the delay budget values are much higher if the same buffer-based adaptation functions are used. To leverage the benefit of longer sleep cycles and compensate for the longer SPS periods, the scheduler must use predictive adaptation functions where the traffic arrival rates and BSR data would be utilized to predict the instantaneous amount of data in the device buffer.  
We raise the maximum possible allocated number of PRBs, \(N_{P,max}\) for the traffic classes for low-power devices such that more packets would be served from the device buffers at each uplink transmission opportunity thus decreasing packet delay. The predictive adaptation function is used for the range \(0.5\leq f_{S}<1\) to modify the BSR mapping function \(N_{P,func(B)}\) in Equation (1). The new mapping function is given by Equation (19) as follows:  
\[N_{P,func(B)}=\begin{cases}N_{P,B_{latest}},&for\ \ 0<f_{S}<0.5\\ N_{P,(E(B)+\sigma(B))},&for\ \ 0.5\leq f_{S}<1\end{cases} \tag{19}\]  
where \(N_{P,B_{latest}}\) is the exact number of PRBs required to accommodate the latest reported buffer size, as implemented for \(0<f_{S}<0.5\). But for \(0.5\leq f_{S}<1\), the predictive allocation size is used to accommodate the expected number of packets in the device buffer at each SPS interval i.e., \(E(B)\) plus the standard deviation \(\sigma(B)\) of the historical BSR data over the specified measurement window.  
For any traffic class \(S\) with packet arrival rate of \(\lambda_{S}\), the value of \(E(B)\) is calculated from Equation (20) as follows:  
\[E(B)=\lambda_{S}T_{SPS,S} \tag{20}\]
## 4 Results and Discussion
### Comparison of Natural Gas and Hydrogen
#### 4.2.2 Comparison of the Thermodynamic Properties of Gasses in Wellbores  
Under identical injection and withdrawal parameters, the variations in gas pressure within the wellbore over a three-year injection and withdrawal cycle are illustrated in Figure 6. Similar to the cavity pressure, the pressure changes in the two gasses within the wellbore during a single injection and withdrawal cycle exhibit strikingly similar patterns, though with substantial differences in pressure levels. Under the same injection and withdrawal parameters, the pressure of hydrogen within the wellbore can reach up to 17.1 MPa, whereas the maximum pressure of natural gas is approximately 14.5 MPa. It is worth noting that the pressure within the wellbore is significantly lower than the pressure within the chamber due to the greater potential energy for gas compression within the chamber compared to the wellbore. In addition, the Joule-Thomson effect is also a primary contributor to this phenomenon [32]. The hydrogen takes a divergent path by heating up, which displays a negative Joule-Thomson coefficient. During hydrogen expansion, the weak intermolecular forces cannot effectively counteract the repulsive forces between the molecules. As hydrogen gas expands, the molecules move further apart, and the repulsive forces dominate, leading to an increase in kinetic energy and temperature. Consequently, as hydrogen traverses through the exceptionally narrow borehole into the cavity, there is a sudden increase in temperature.  
The variation in gas temperature within the wellbore over a three-year injection-withdrawal cycle under identical parameters is illustrated in Figure 7. The pattern of gas temperature changes within the wellbore during a single injection-withdrawal cycle closely resembles that within the chamber. Under the same injection-withdrawal parameters, the temperature of hydrogen within the wellbore can reach up to 71 \({}^{\circ}\)C, whereas the maximum temperature of natural gas is approximately 55 \({}^{\circ}\)C. The reason for the aforementioned phenomenon lies in the fact that, during the cyclic injection and extraction process, the variation in gas temperature is influenced not only by compression and expansion but also by thermal exchanges with the surrounding rock.  
Figure 5: Variation in cavity temperature over a three-year injection–withdrawal cycle.
# Infrastructure Elements for Smart Campuses: A Bibliometric Analysis
## 4 Implications of Findings
### Implications of Findings on Procurement of Smart Campus Infrastructure
#### 4.2.2 Implications on Technology and IT Network in a Smart Campus  
As suggested in Section 4.2.1 above, the IoTs forms the core of smart buildings. A smart network grid consisting of a microgrid on campus is an integral part of the smart campus building construction [1; 132]. The existence of a smart campus network grid will be inclusive of cloud computing. Cloud computing infrastructure provides a single-point solution to solving the challenges of smart campus messaging, data processing, business interfaces, human-computer interaction and persuasive computing [132].  
Cloud computing within an IT network must be all-encompassing in supporting the learning and teaching management system whereby students with disabilities are provided with the necessary tools to learn. Likewise, academic staff with disabilities must be included by creating campus-specific applications such as social networks, mobile applications, voice recognition or text-to-speech teaching aids. The construction of new technology on smart campus deal with IT project management. Consequently, there is a need to study the intricacies of contract, construction, and procurement of IT infrastructure on campuses.
## 6 Results and Discussion
### Comparative Analysis with SOTA  
The comparative analysis in terms of the number of trials and models employed and their respective test accuracy are presented in Table 4. The test accuracy of HyperGE across the models is competitive with the SOTA models. _However, HyperGE has learned the hyperparameters using only 80 trials trained over 5% of the dataset over five epochs._ HyperGE achieves near-optimal performance within minimal trials, with less time using a subset of data. Moreover, the flexibility of GE to adapt and scale hyperparameters and their choices  
Figure 12: Statistical analysis in terms of the standard deviation of HyperGE stages across all datasets.  
are added advantages. An example of a BNF grammar with more hyperparameters is presented in Figure 13 which can be a possible future direction for HyperGE.
# A Method for Singular Points Detection Based on Faster-RCNN
## 3 Experimental Results  
In this section, the experimental results are shown to evaluate the proposed method and its comparison with others. In concrete terms, we firstly presented the experimental setups including datasets, and parameter setups. Secondly, we compared the proposed method with other algorithms in terms of singular points detection performance on fingerprint public datasets FVC2002 DB1 and NIST DB4.