esa-sceva/satcom-chunk-collection huggingface dataset:
# Democratizing Deep Learning Applications in Earth and Climate Sciences on the Web: EarthAlHub
## 4 Discussion  
As we briefly discussed in the Introduction section, a common approach to making deep learning accessible is to share trained models through deep learning model repositories, or preparing a typically web-based dedicated platform that allows others to experiment with the model. For the first option, Earth scientists would need technical expertise to train or run specific tensor computing libraries in order to test the shared deep learning model, while the second option brings a burden for deep learning practitioners as they would need to allocate substantially more time and resources to make their research publicly available and accessible to ensure scientific communication. On the contrary, in the previous section, using EarthAIHub, we have shown that with the use of their own data, domain experts from the earth and climate sciences will be able to quickly recreate and test previously trained deep learning models without having to learn how to use a specialized tensor computation library that is utilized in some Earth sciences studies as interface figures suggest that EarthAIHub is a user-friendly web application; thanks to the design choices, it takes advantage of client-side resources to run deep learning models. Furthermore, the configuration middleware between individual models and TensorFlow,js that is included with EarthAIHub makes it hassle-free for deep learning practitioners to utilize the system. The previous section proves that using the configuration for ease of usage in the earth sciences, the platform is made to support a wide range of input/output types that a neural network model may possibly operate over, including comma-separated values (CSV) files, NumPy files (npy), and numerous picture formats.  
As of now, EarthAIHub is designed to run only TensorFlow.js models. It is not always possible to convert a model that has been trained in one programming language and framework pair to TensorFlow and later TensorFlow.js. For instance, in order to convert a PyTorch model to a TensorFlow.js model, one needs to convert that model to ONNX, TensorFlow, and finally TensorFlow.js consecutively. Moreover, some implementations of some architectures are not always available in all of these libraries. For instance, some recurrent neural network structures in PyTorch are implemented differently than they are in TensorFlow. Consequently, one could easily encounter problems when converting that model to TensorFlow.js. The coverage of models and architectures could be extended by incorporating other numeric computing and deep learning libraries in JavaScript, such as ONNX Runtime Web. One downside of not implementing ONNX Runtime Web into EarthAIHub during this study was, as mentioned earlier, that ONNX Runtime Web has still not been fully developed for practical web applications.  
Figure 8: Input/output lines for a streamflow data sequence of 48 h.
# On Smartphone Power Consumption in Acoustic Environment Monitoring Applications
## 3 Results and Discussion  
### Power Consumption in Background Audio Recording with and without Digital Signal Processing  
The second-by-second power consumption readings for all signal processing algorithms were averaged and plotted in Figure 3, along with 99% confidence intervals. As one can see, the difference between the devices with the lowest and highest power consumption may be quite high (up to six times in our experiments). It is also interesting to note that \"simple\" and \"moderately complex\" digital signal processing algorithms do not significantly contribute to overall smartphone power consumption. Only the most complex signal processing algorithm #3 visibly increases smartphone power consumption. Nonetheless, even the extreme optimization of algorithm #3 (for example, down to complexity of algorithm #1) would reduce the power consumption by 28% on average, while in certain smartphones the potential power consumption reduction would be only 7%-15%.  
Figure 4 shows the expected battery life for all 18 tested smartphones in the case of continuous audio recording without any signal processing and in the case of audio recording and simultaneous real-time digital signal processing (algorithm #3). The median expected battery life was approximately 28 h with algorithm #3 and approximately 40 h without any digital signal processing. Nonetheless, 3 out of 18 devices (17%) were not able to operate longer than 22 h in continuous audio recording mode even without any real-time digital signal processing. It is worth mentioning that these results represent a rather idealistic scenario, because we disabled mobile data transfer, Wi-Fi and Bluetooth radios, all third-party background applications, and used the nominal battery capacity to predict battery life. In a realistic device usage scenario, the battery life would be shorter due to the battery degradation effect [26].  
Figure 2: Example of instant power consumption measurements using the developed test application.  
Figure 4: Expected battery life of the fully charged smartphones in audio recording only mode and “Algorithm 3” mode.  
Figure 3: Average power consumption with confidence intervals for the 18 tested Android smartphones in four different modes of operation.
## Materials and Methods
### Axisymmetric Model  
Figure 2a shows a 3D schematic, with a 1/4 cross-sectional cut, of an insulating joint mounted with a UMS. Considering the cylindrical geometry of the UMS, the insulating joint was configured as a 2D axisymmetric model. The center of the UMS was constrained by a rigid insulating spacer, while the outside surface was surrounded by the left and right pipe pups and the retainer. For the models, a second-order Lagrangian element was applied to discretize the geometry.  
Figure 2: Schematic of the insulation joint installed with a U-shaped monolithic seal (UMS). SOP: symmetric open groove; ASOP: asymmetric open groove; SNO: symmetric no groove.  
For the model analyzed in this study, as shown in Figure 2c, three geometric housing restraints in which the UMS could be installed were considered: open grooves on both sides (type 1), an open groove on one side only (type 2), and no groove (type 3). Type 1 represents a symmetric open groove (SOP), type 2 an asymmetric open groove (ASOP), and type 3 a symmetric non-groove (SNO). In addition, values of the UMS thickness ratio (TR) of 0.25, 0.50, 1.00, and 1.50 were considered to examine the behavior of the UMS according to the thickness difference for each type. In this study, the TR is defined as follows:  
\[\text{TR}=\frac{t_{body}}{t_{leg}}, \tag{5}\]  
where \(t_{leg}\) denotes the thickness of the UMS leg, which was fixed to 15 mm. \(t_{body}\) represents the thickness of the UMS body; values of 3.75, 7.50, 15.00, and 22.50 mm were applied to realize TR = 0.25, 0.50, 1.00, and 1.50, respectively. The other geometric dimensions were applied with identical conditions to all 12 cases.  
The axial compression of a UMS during assembly and its subsequent radial compression by internal pressure were implemented numerically in this study. Figure 3a shows an initially inserted UMS in the insulating joints prior to assembly. Figure 3b indicates the axially compressed UMS by axial displacement during assembly. The axial compression of the UMS was conducted until the UMS leg was compressed from 0% to 40% in thickness in intervals of 10%. During assembly, the UMS was compressed symmetrically by the left and right displaced pipe pups while the retainer ring remained stationary. The insulating spacer in the middle of the UMS was allowed with radial deformation. After assembly, the insulating joint was under radial compression by the internal pressure during operation, as shown in Figure 3c. The internal pressure was applied gradually from 0 to 10 MPa in intervals of 2.5 MPa. The results were analyzed in terms of the stress distribution, contact stress, gap distance, and strain energy density after each step.
## 4 Experiments and Measurement Uncertainty Analysis
### Measurement Uncertainty Analysis  
The measurement model for small hole diameters is shown in Equation (1). For simplicity, the measurement model can be expressed as the sum of the macro displacement of the probe \(l\), the probing ball diameter \(d\), and two equal probing gaps \(\delta\) (\(\delta_{1}\) and \(\delta_{2}\) are assumed equal) as shown in Equation (3).  
\[D=l+d+2\delta \tag{3}\]  
The uncertainty model of the diameter measurement is shown in Equation (4):  
\[{u_{D}}^{2}={u_{l}}^{2}+{u_{d}}^{2}+4{u_{\delta}}^{2} \tag{4}\]  
The sources and estimation of the standard uncertainty components are listed in Table 3. The uncertainty is evaluated according to the _Guide to the Estimation of Uncertainty in Calibration of Geometrical Measuring Equipment_ (JJF 1130-2005). The expanded uncertainty of the diameter measurement result of the \(\phi\)20-mm ring gauge standard is \(U=0.2\)\(\upmu\)m (\(k=2\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measurement Number** & **1** & **2** & **3** & **4** & **5** \\ \hline Measurement & 19.99993 & 20.00000 & 20.00001 & 20.00001 & 20.00001 \\ Result/mm & 6 & 7 & 8 & 9 & 10 \\ \hline Measurement & 19.99998 & 19.99993 & 19.99995 & 19.99997 & 20.00001 \\ Result/mm & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Diameter measurement results of \(\phi\)20-mm ring gauge standard.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Lab** & **Diameter Value D (mm)** & **Expand Uncertainty U (\(\upmu\)m, \(k=2\))** \\ \hline PTB & 20.00000 & 0.1 \\ NIM & 20.00030 & 0.5 \\ HIT-UOI & 19.99998 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of diameter calibration results. PTB: Physikalisch Technische Bundesanstalt; NIM: National Institute of Metrology; HIT-UOI: Harbin Institute of Technology, center of Ultra-precision Optoelectronic Instrument.
## 3 Results and Discussion  
### LIBS Spectra  
The mean spectrum of 70 LIBS spectra in the training set sample from each penicillin manufacturer is shown in Figure 2. We analyzed the spectral lines of penicillin with a wavelength of 200-900 nm and intensities greater than 500 counts. The corresponding wavelengths of the elements or molecular bands in the penicillin LIBS spectra are listed in Table 2. We used the National Institute of Standards and Technology (NIST) atomic emission database to identify the elements corresponding to these spectral lines. It can be observed from Figure 2 that the LIBS spectral intensities of AC are lower than those of PPT and ACPT. This might be because the AC samples were prepared through powder compression, and their density and hardness were lower than those of the PPT and ACPT tablet samples.  
Figure 2: _Cont_.  
Figure 2: _Cont._  
It can be observed from Table 1 that the main elements shared by the three types of penicillin are C, H, O, N and S. The element of S was not detected in the LIBS spectra of the three types of penicillin. This is mainly because the content of S is relatively low, and the element of S is difficult to excite. There were two sources of C, H, O and N. On one hand, they came from the penicillin samples themselves, and on the other hand, as the penicillin LIBS experiments were performed in the air, the intensities of the spectral lines of these elements might be partially triggered by the excitation of the air.  
Figure 2: The mean spectrum of 70 LIBS spectra in the training set sample from each type of penicillin manufacturer.  
We can see that the LIBS spectra of PPT and ACPT contain the element of K from Figure 2, while the LIBS spectra of AC do not. K is a metallic element, which is easily excited by lasers. The spectral lines of K in the LIBS spectra of PPT were saturated, and in the subsequent identification of different PPT manufacturers, the spectral lines of K were not used as the input data for the classification algorithms.  
Some trace elements (Mg, Ca, Fe and Na) were also detected in the LIBS spectra of penicillin. The element of Mg was not detected in the samples of PPT or ACPT, but it was detected in the samples of AC. The Ca element was detected in the samples of PPT2, PPT3, PPT4, AC1, ACPT1, ACPT2 and ACPT3, but not in other samples. The element of Fe was detected in the samples of PPT, ACPT1, ACPT2 and ACPT3; however, it was not detected in the samples of AC or ACPT4. The Na element was detected in the samples of PPT, AC1, AC4 and ACPT, but not in other samples. The types and quantities of trace elements in penicillin produced by different manufacturers are different, which is also a standard for identifying different penicillin manufacturers.
# Solar Irradiance Forecasting Using Dynamic Ensemble Selection
###### Abstract  
Solar irradiance forecasting has been an essential topic in renewable energy generation. Forecasting is an important task because it can improve the planning and operation of photovoltaic systems, resulting in economic advantages. Traditionally, single models are employed in this task. However, issues regarding the selection of an inappropriate model, misspecification, or the presence of random fluctuations in the solar irradiance series can result in this approach underperforming. This paper proposes a heterogeneous ensemble dynamic selection model, named HetDS, to forecast solar irradiance. For each unseen test pattern, HetDS chooses the most suitable forecasting model based on a pool of seven well-known literature methods: ARIMA, support vector regression (SVR), multilayer perceptron neural network (MLP), extreme learning machine (ELM), deep belief network (DBN), random forest (RF), and gradient boosting (GB). The experimental evaluation was performed with four data sets of hourly solar irradiance measurements in Brazil. The proposed model attained an overall accuracy that is superior to the single models in terms of five well-known error metrics.  
solar irradiance forecasting; heterogeneous ensemble dynamic selection model; neural networks +
Footnote †: journal: _Sci_  
0
Footnote 0: Copyright** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
###### Abstract  
In order to solve the difficulties that the uncertain characteristics of PV output, such as volatility and intermittency, will bring to the development of microgrid scheduling plans, this paper proposes a Transformer-Bidirectional Long Short-Term Memory (BiLSTM) neural network PV power generation forecasting fusion model based on the Newton-Raphson optimization algorithm (NRBO) and Variational Modal Decomposition (VMD). Firstly, the principle of the VMD technique and the Gray Wolf Optimization (GWO) algorithm's key parameter optimization method for VMD are introduced. Then, the Transformer decoder partially fuses the BiLSTM network and retains the encoder to obtain the body of the prediction model, followed by explaining the principle of the NRBO algorithm. And finally, the VMD-NRBO-Transformer-BiLSTM prediction model and hyperparameter selection are evaluated by the NRBO algorithm. The algorithm sets up a multi-model comparison experiment, and the results show that the prediction model proposed in this paper has the best prediction accuracy and the optimal evaluation index.  
PV; forecasting model; NRBO; VMD; transformer-BiLSTM Article
# Despite for SMEs: A Systematic Review of Socio-Technical Cybersecurity Metrics
## 2 Related Work
### Socio-Technical Cybersecurity  
Humans are often considered the weakest link in cybersecurity [21]. It is vital to recognise the interaction of the social and technical sides of cyber-systems when modelling and measuring cybersecurity, which is why the field of STS has played such an important role in cybersecurity metric research [22]. STS research has uncovered the dangers of considering social and technical elements separately [23] and has offered insight into how to avoid these dangers [7].  
Recognition of the human factor in cybersecurity goes beyond simply including static human actors. This is where behavioural theories such as protection motivation theory (PMT) and self-determination theory (SDT) come in [24; 25]. PMT reserves a prominent role for extrinsic motivators and threat appraisal [26]. SDT includes extrinsic motivation as a central concept but often focuses on moving from extrinsic to increasingly internalised motivation [24]. In the context of SMEs, intrinsic motivation to improve cybersecurity is often hard to find. However, there are solutions to this problem. Committing to improving cybersecurity in an organisation can motivate employees [24]. From the STS perspective, it is common to distinguish between metrics that include the real-life threat environment and those that do not [22]. Threat perception lies at the core of PMT and is important in security applications using SDT [25]. Another solution to promote motivation among SME employees would therefore be to incorporate the real-life threat environment in our cybersecurity metrics. Later in this paper, in Section 4, we describe whether this is indeed something we observe in current research.  
We will address the social dimension using the ADKAR model of [PERSON] [27]. This model, originating from change management, considers five phases in managing the personal side of change: awareness, desire, knowledge, ability, and reinforcement. ADKAR has previously been applied in assessing information security culture within organisations [28]. We apply ADKAR as a means to classify the socio-technical cybersecurity metrics we encounter. We define a socio-technical cybersecurity metric to be _a cybersecurity metric that requires measuring the outcome(s) of the actions of at least one (simulated) human actor_. We do not address the technical dimension explicitly in this definition, as the technical dimension is implicit in the term \"cybersecurity\". We hypothesise that all socio-technical cybersecurity metrics can be linked to one or more of the ADKAR categories.
## 5 Performance Analysis
### Experiment 3  
The following experiment was designed to assess the trade-off between computational complexity and performance in terms of bit error rate (BER) in the proposed system as the sub-block size of OFDM-IM increases. The proposed system is compared against a MIMO-OFDM-IM system with MMSE detection and a conventional MIMO-OFDM system with 4-QAM data modulation. To maintain the same spectral efficiency, the OFDM-IM  
Figure 3: Comparison of the MSEs for the channel estimator in the proposed MIMO-OFDM-IM system versus a conventional MIMO-OFDM.  
modulation was configured with \(N_{C}=4\) and \(M=16\). The simulation parameters are described in Table 3.  
From the results in Figure 5, the following observations can be made: (a) Close performance is maintained at low \(E_{b}/N_{0}\) values between the MIMO-OFDM-IM systems with OSIC and MMSE detection. However, above 20 dB, OSIC detection exhibits a consistently superior trend of approximately 2 dB compared to MMSE detection. (b) Increasing the constellation size degrades the performance of the proposed system, resulting in an approximately 2 dB performance difference compared to the conventional OFDM system above \(E_{b}/N_{0}=15\) dB. However, an increase in the number of subcarriers per sub-block results in a significant reduction in the required computational complexity. (c) The use of IM technique in combination with low-complexity OSIC detection is an attractive solution to address the issues of frequency selectivity and high mobility present in vehicular links.
# A Secure and Efficient Multi-Factor Authentication Algorithm for Mobile Money Applications
## 4 System Implementation  
The requirements for the proposed system are gathered through document reviews [11], surveys [9], and observation. The collected data were analyzed and used as essential inputs to define the necessary features of the proposed system.  
Evolutionary prototyping was employed to develop the G-MoMo applications. Evolutionary prototyping is a software development method where a prototype is developed after receiving initial feedback from the customers [84]. Subsequent prototypes are produced, each with additional functionality until the customer is pleased. The last developed version can be engineered as the final product to be delivered [84]. It is similar to incremental development and has six steps of the prototyping process, namely, (1) requirements gathering and analysis, (2) quick design, (3) build a prototype, (4) initial user evaluation, (5) refine the prototype, and (6) implement the product and maintain [84]. Evolutionary prototyping allows mobile money IT support staff, agents, and users to involve themselves throughout the system development period, leading to user requirement satisfaction. It also allows changes in every phase, thus improving the prototype system; it minimizes severe and critical defects during the system testing and offers a better approach that saves time and effort [84].  
In this research, the system requirements are grouped into functional and non-functional requirements, which provide guidelines for implementing the proposed algorithm. The functional requirements include user registration, user authentication, adding new smartphones for the subscribers, depositing money, withdrawing money, sending money, confirming money withdrawal through QR code scanning, checking balance, bill payments, checking the mini statement, statistics of subscribers, account management, and logout. However, the non-functional requirements are security, privacy, usability, maintainability, performance, reliability, interoperability, flexibility, robustness, availability, responsiveness, scalability, and look and feel.  
The G-MoMo applications were designed using the unified modeling language (UML). UML is a modeling language that provides a standard way to visualize system designs through object-oriented diagramming schemes. The designs give an overview of the system, support the application's construction phase, help clarify the requirements to users, and explain the system to other stakeholders. The UML diagrams, such as use case diagrams, sequence diagrams, and flowcharts, were designed, and each is designed for specific modeling purposes.