esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Stability
### Stability Condition on Metal  
For dielectric-metal interface, we deal with the wave equation \(\mu\frac{\partial^{2}\tilde{D}(\tilde{r}t)}{\partial t^{2}}-\
abla^{2}\vec{E} (\vec{r},t)=\bar{0}\) in dielectric and the differential equation for the metal writes \((b+\frac{\partial}{\partial t})\vec{D}(\vec{r},t)=\epsilon_{0}\left(be_{\infty }+a+e_{\infty}\frac{\partial}{\partial t}\right)\vec{E}(\vec{r},t)\). After discretization, the wave equation is rewritten as [41],  
\[\mu\frac{\partial_{t}}{\Delta_{t}^{2}}\vec{D}^{s}-\left(\frac{\partial_{x}^{2} }{\Delta_{x}^{3}}+\frac{\partial_{y}^{2}}{\Delta_{y}^{3}}+\frac{\partial_{z}^ {2}}{\Delta_{z}^{3}}\right)\bar{E}^{s}=0\]  
and the auxiliary differential equation for the metal,  
\[\left(b\tilde{\Delta}+\tilde{\partial}_{t}\right)\vec{D}^{s}=\epsilon_{0} \big{(}(be_{\infty}+a)\tilde{\Delta}+e_{\infty}\tilde{\partial}_{t}\big{)} \bar{E}^{s}.\]  
Now, we replace the electric field \(\vec{E}^{s}\) and the electric displacement field \(\vec{D}^{s}\) in the last two equations by the Equations (A1) and (A2). Hence, from the wave equation we obtain:  
\[(y-1)^{2}\vec{\tilde{D}}_{0}+4y\epsilon_{\infty}\epsilon_{0}\
u^{2}\vec{\tilde{ E}}_{0}=0\]  
and from the differential equation for the metal:  
\[(\Delta_{t}b(y+1)+2(y-1))\vec{\tilde{D}}_{0}=\Delta_{t}\epsilon_{0}((be_{ \infty}+a)(y+1)+2\epsilon_{0}e_{\infty}(y-1))\vec{\tilde{E}}_{0}\]  
where \(\
u^{2}=\left(\frac{\Delta_{t}}{\sqrt{be_{\infty}e_{0}}}\right)^{2\,x,y,z}\sum \limits_{p}^{2\,x}\frac{\sin^{2}\left(\frac{\tilde{k}_{x}\Delta_{x}^{2}}{2} \right)}{\Delta_{p}^{2}}\). By combining both polynomials we determine the characteristic polynomial \(\Phi(y)=a\Delta_{t}(y+1)(y-1)^{2}+e_{\infty}\big{(}y\big{(}4\
u^{2}+y-2\big{)}+ 1\big{)}\left(b\Delta_{t}(y+1)+2(y-1)\right)\). In order to consider the Routh-Hurwitz criterion we apply the bilinear transformation \(y=\frac{r+1}{r-1}\) and determine the characteristic polynomial \(\Phi(r)=b\Delta_{t}e_{\infty}\
u^{2}r^{3}+2\epsilon_{\infty}\
u^{2}r^{2}+ \Delta_{t}\big{(}a+be_{\infty}\big{(}1-\
u^{2}\big{)}\big{)}r+2\epsilon_{\infty} \big{(}1-\
u^{2}\big{)}\). The first column in the Routh table, which should be positive, are \(c_{1,0}=b\Delta_{t}e_{\infty}\
u^{2}\), \(c_{2,0}=2e_{\infty}\
u^{2}\), \(c_{3,0}=a\Delta_{t}\) and \(c_{4,0}=2e_{\infty}\big{(}1-\
u^{2}\big{)}\). From \(c_{1,0}\) and \(c_{3,0}\) we find out that \(\mathcal{R}[a]\geq 0\) and \(\mathcal{R}[b]\geq 0\). The condition over \(c_{4,0}\geq 0\)means that \(1-v^{2}\geq 0\). This is the classic stability condition based on single-cubic FDTD [42], where \(\Delta_{t}\leq\frac{n_{i}}{c_{0}}\sqrt{\frac{1}{\Delta_{x}^{2}}+\frac{1}{\Delta_{ y}^{2}}+\frac{1}{\Delta_{x}^{2}}}\), where \(c_{0}\) is the speed of light in the vacuum and \(n_{i}\) the refractive index of the media.
## 2 Materials and Methods
### Outcome Measures  
#### 2.5.1 Muscle Strength  
The maximal voluntary eccentric contraction (MVEC) and the maximal voluntary concentric contraction (MVCC) of each dominant lower limb were assessed using the Kineo(r) isokinetic dynamometer (Globus, Codogne, Italy). Each participant sat in an upright position with their hips flexed at 80\({}^{\circ}\), with the position secured by straps around the pelvis. The medial condyle of the femur was aligned with the machine's axis of rotation, and a padded lever arm was placed on the tibia just above the anterior part of the ankle joint. To calculate the MVEC, the test started from a 60\({}^{\circ}\) knee extension angle. The protocol included five maximal eccentric contractions with 60 s of recovery between trials. After a 2 min rest period, the MVCC was calculated. To accomplish this, participants applied maximal concentric force against the leg pad, starting from a 60\({}^{\circ}\) knee flexion position [25]. Participants were verbally instructed to perform the test with maximum force. The recorded data were obtained from the mean value of the five maximal eccentric and concentric contractions.  
Figure 1: Flow-chart diagram of the progress of patients through the study phases.  
Isokinetic dynamometry has reported a high intra-class correlation coefficient (ICC) for maximal concentric (0.95-0.96) and eccentric (0.95-0.97) strength measurement in the quadriceps [26]. The standard error of measurement (SEM) has been established as 4% [27].  
The intervention programs for both groups were then set at 20% of MVCC, according to previous studies [15; 16].
## 2 Literature Review
In contrast to digital twins in manufacturing, where data for modeling are derived from IoT (Internet of Things) sensors, in the case of DTO, data about the progress of organizational business processes are received from information systems. In this case, it is essential to examine such data from various perspectives. From the first perspective, the analysis involves receiving data directly from documents related to the enterprise's transportation activities, which are recorded in the TMS. Such documents include transport orders, waybills, invoices, and other relevant records. However, the formats of these documents are standardized as they are governed by the legislation of the state in which the organization operates. Meanwhile, the TMSs used by specific transportation companies may vary, and the actual business processes of the organization may to a large extent depend on the TMS interface [35]. Consequently, Process Mining methods make it possible to monitor managers' activities within a specific TMS using log files, reconstruct the business processes, and identify user activities that deviate from standard business processes, thus negatively impacting the reliability of transportation. At the same time, the creation of digital twins of organizations remains a complex process involving numerous challenges, as the organizational design of a specific company depends on a multitude of factors [37]. Therefore, in our study, we propose an approach that considers constructing a digital twin of a transportation company whose value proposition is the provision of reliable freight transportation services. We propose, at the initial stage, to develop an ontological model of the enterprise, defining its boundaries as systems and reflecting its connections with the external environment, including those influencing the reliability of transportation. Next, we identify examples of business processes that impact the reliability of transportation. Then, we compare these business processes with the interface of a specific TMS, using the TMS \"Autobase\" as an example. Finally, using Process Mining, we analyze how a manager, interacting with this interface, executes the business processes required for transportation. This stage is the most challenging one because, for constructing a digital twin of the organization, we must ensure that the reconstructed business processes correspond to those currently established as standard within the organization. If they do not correspond, it is necessary to assess whether they indeed lead to failures or not. It is also possible that managers start using the TMS interface to address new emerging tasks. This may lead to the emergence of new standard business processes within the organization, while the previous ones become obsolete.
# IoT IP Overlay Network Security Performance Analysis with Open Source Infrastructure Deployment
## 3 Components of Overlay Architecture
### The Noise Protocol Framework  
The Noise Framework is a versatile and efficient cryptographic library designed to offer various cryptographic tools for secure communication over unreliable networks, such as the Internet. Within the Nebula Overlay, the Noise Framework provides a solid foundation to ensure the security and privacy of communications between network nodes. Here is a technical overview of how the Noise Framework operates within Nebula Overlay: Supported Cryptographic Primitives: The Noise Framework includes a range of cryptographic tools, such as symmetric encryption, Diffie-Hellman key exchange, key authentication, digital signatures, and cryptographic hashes. These tools can be flexibly combined to meet specific security requirements. Noise Protocol Patterns: The Noise Framework employs protocol patterns to outline the phases and operations involved in secure communication. Protocol patterns, like the _Noise IK_ pattern, detail a sequence of cryptographic actions during message exchanges between parties. These patterns provide a standardized method for designing secure and scalable cryptographic protocols. Selection of Protocol Pattern: In Nebula Overlay, the Noise Framework allows for choosing the most suitable protocol pattern based on the application's and network's specific needs. For instance, the _Noise IKpsk2_ pattern might be used for pre-shared key exchange with authentication, while the _Noise NN_ pattern might be suitable for anonymous communication. Configuration of Cryptographic Parameters: The Noise Framework allows the configuration of cryptographic parameters, such as the choice of encryption algorithm, the use of digital signatures, and the setting of Diffie-Hellman parameters. This flexibility enables adjustments to the security level and communication performance based on the application's and network's specific requirements. Integration with Nebula Overlay: In Nebula Overlay, the Noise Framework is used to establish secure and authenticated communication channels between network nodes. Noise-based communication protocols ensure that nodes can exchange data securely, maintaining the confidentiality, integrity, and authenticity of the transmitted information. In summary, the Noise Framework is crucial within Nebula Overlay for ensuring secure communication over unreliable networks. By using flexible protocol patterns and a comprehensive set of cryptographic tools, the Noise Framework provides secure and scalable infrastructure for distributed communication over the Internet.
## 3 Results and Discussion
### Influence of Two-Sided Surcharge/Unloading on the Tunnel Below Using Wet Sand
#### 3.2.3 Effect on Additional Surrounding Pressure  
The additional envelope pressure of the positive central section of the tunnel in Case 4 is shown in Figure 21, where the positive values of the additional envelope pressure represent the pressure and the negative values represent the tension.  
Combining Figures 2 and 21, the following can be seen:  
1. The overall additional envelope pressure in the tunnel in wet sand is significantly larger than that in the tunnel in dry sand under the same conditions when the left surcharge is offset by 0.4 m, which is exactly the opposite of the overall additional envelope pressure in the tunnel in wet sand which, according to the literature, is significantly smaller than that in the tunnel in dry sand under the same conditions when the tunnel is surcharged directly above [8]. This is because the stress \(\sigma\) contours are flatter when the soil is wet sand, and the stress decreases slowly with the increase in the bias distance D. When the soil is dry sand, the stress \(\sigma\) contours are relatively long and thin, and the stress \(\sigma\) decreases rapidly with the increase in the deflection distance D. The stress \(\sigma\) contours for dry and wet sand under two-sided surcharge for Case 2 and Case 4 are shown schematically in Figures 22 and 23, respectively.  
Figure 21: Variation in additional surrounding pressure under different surcharges for Case 4 (unit: kPa).  
In the wet sand, unloading can reduce the additional surrounding pressure of the tunnel to some extent, but the effect is obviously worse than that of dry sand unloading under the same conditions. Taking measurement point 1 as an example, in Case 2, the additional surrounding pressure is 2.3129 kPa when the two-sided surcharge is 172 kg, and the additional surrounding pressure is 1.0271 kPa after the two-sided surcharge is unloaded to 0 kg, with a reduction rate of 55.6%. In Case 4, the additional surrounding pressure is 2.4497 when two-sided surcharge is 172 kg, and the additional surrounding pressure is 1.0271 kPa after the two-sided surcharge is unloaded to 0 kg. kPa, while it is 2.3215 kPa after unloading to 0 kg on both sides, which is only a 5.2% decrease. This is because there is a lag in the reduction in the additional surrounding pressure of the tunnel after unloading in the wet sand condition due to the difference in soil, and a lag in the reduction in the additional surrounding pressure of the tunnel after unloading above the tunnel in the soft clay condition was also found in the literature [6].
## 2 Exergy Analysis of Cascaded Phase Change Heat Storage  
### Analysis of Optimum Phase Change Temperature at all Stages  
The temperature variation of fluid in the heat storage process of cascaded PCMs is shown in Figure 1.  
\(T_{\text{in}}\) and \(T_{\text{out}}\) are the thermal fluid temperature at the inlet and outlet of the cascaded phase change heat storage dehumidifier, K, \(T_{\text{p,}i}\) is the phase change temperature of stage \(i\) of the PCMs, K and \(n\) is the total number of stages.  
For the convenience of theoretical calculation and analysis, the sensible heat and heat loss in the process of heat storage are ignored. The exergy of the thermal fluid in the cascaded phase change heat storage process corresponding to Figure 1 can be expressed as [28]:  
\[Ex=\dot{m}c_{P}\big{(}T_{\text{in}}-T_{\text{p,1}}-\Delta T_{1}\big{)}\left(1 -\frac{T_{0}}{T_{\text{p,1}}}\right)+\sum_{i=2}^{n}\dot{m}c_{P}\big{[}\big{(}T_ {\text{p,}i-1}-\Delta T_{i-1}\big{)}-\big{(}T_{\text{p,}i}+\Delta T_{i}\big{)} \big{]}\left(1-\frac{T_{0}}{T_{\text{p,}i}}\right) \tag{1}\]  
where \(\dot{m}\) is the mass flow of the thermal fluid, kg/s; \(c_{P}\) is the specific heat capacity at constant pressure, kJ/(kg\(\cdot\)K); \(T_{0}\) is the ambient temperature, K and \(\Delta T_{i}\) is the difference between the thermal fluid temperature at the outlet of stage \(i\) and the phase change temperature of PCMs, K.  
Assuming the \(\Delta T_{i}\) of each stage is equal, and \(\Delta T_{i}=\Delta T\), when the number of total PCM stages is \(n\), by deriving the phase change temperatures \(T_{\text{p,}i}\) at all stages of Formula (1), it can be obtained that, when the exergy is the maximum, the expression of the optimal phase change temperature \(T_{\text{opt,}i}\) (K) of the phase change material at stage \(i\) is [28]:  
\[T_{\text{opt,}n,i}=\sqrt[n+1]{\left(T_{\text{in}}-\Delta T\right)^{n-i+1}}T_{0 }^{i}\ (I=1,\ 2,\ 3,\......,\ n) \tag{2}\]  
Table 1 shows that, when the ambient temperature \(T_{0}=298.15\) K, the temperature of the hot and wet fluid entering the cascaded phase change heat storage device \(T_{\text{in}}\) is 328.15 K, taken as \(\Delta T=\) 0, 5 and 10 K, respectively, and the number of phase change material stages \(n\) is 1, 2, 3 and 4, respectively, and the optimal phase change temperatures at all stages of the cascaded phase change heat storage device are \(T_{\text{opt,}u,i}\).  
It can be seen from Table 1 that, when \(n\) and \(\Delta T\) are constant, with the increase of \(i\), \(T_{\text{opt,}n,i}\) decreases; under the same \(n\) and \(i\), the larger \(\Delta T\) is, the smaller \(T_{\text{opt,}n,i}\) is; when \(i\) and \(\Delta T\) are constant, the larger \(n\) is, the larger \(T_{\text{opt,}n,i}\) is.  
Figure 1: Fluid temperature variation in the cascaded phase change heat storage process.
# Effects of the October 2024 Storm over the Global Ionosphere
## 4 Results
### Large-Scale Travelling Ionospheric Disturbances
According to their study, the irregularities initially appeared near 150\({}^{\circ}\)E at around 12:50 UT, and subsequently propagated northwestward and southwestward over the Japanese and Australian sectors, respectively. As the storm evolved, these equatorial irregularities diminished. During the recovery phase, at approximately 01:50 UT, weak ROTI perturbations (\(\sim\)0.4 TECU/min) were observed near the geographic equator, as shown in Figure 18b (highlighted by the red circle), followed by another brief occurrence between 03:05 and 03:25 UT, depicted in Figure 18c. These later disturbances were notably weaker in intensity. Figure 18: (**a–c**) The storm time ROTI–map over the Southeast Asian region (**a**) from 15:15 UT to 17:00 UT of 10 October, (**b**) 01:50 UT to 02:00 UT, and (**c**) 03:05 UT to 03:25 UT of 11 October.
# Two Designs of Automatic Embedded System Energy Consumption Measuring Platforms Using GPIO
## 5 Evaluations and Results
### Energy Consumption of \(\mu\)C/OS System Functions  
\(\mu\)C/OS is a widely used, opensource, real-time operating system that we used in accordance with our design to measure the energy consumption of its system functions. Due to the execution time being so short, it was only measured with the standalone design. In this test, we used an STM32F407 discovery board as the target device, with its processor frequency set to 53.76 MHz. Figure 9 shows the energy consumption of different \(\mu\)C/OS system functions.  
Figure 8: Energy consumption of the STM32F4 processor running at different clock frequencies measured using our designs and the manual measurement.  
Figure 9: Energy consumption of the \(\mu\)C/OS system functions measured with the standalone design.  
For most \(\mu\)C/OS system functions, the execution time is calculated via GPIO level changes generated by calling GPIO functions. For os_init(), the initialization function of \(\mu\)C/OS, the intermediate device changed the level of the RST pin on the target device and marked the start timestamp at the same time; after \(\mu\)C/OS was initialized, a GPIO rising edge was generated, and the intermediate device marked it as the end timestamp. The results showed that our standalone design could be used to measure the energy consumption of small code segments or functions, with even the execution time being quite short. The results also proved that the execution time is an impact factor of energy consumption.
## 2 Material & Methods
### Implant Simulation  
For the simulation of \"CI like\" speech processing, we used a vocoder implemented in Matlab(r) (MathWorks, Natick, MA) to simulate an FC and a CP coding strategy. We did not simulate channel interaction in this study.  
A diagram representing the signal processing performed by the vocoder is shown in Figure 1. The different steps of the signal processing are as follows:  
* The input signal goes through a pre-emphasis filter, which is a high-pass filter (cut-off frequency 1.2 kHz and slope 3 dB/octave).
* The signal is then sampled (16 kHz sampling frequency, 16 bit quantization). A short-term fast Fourier transform (STFFT) is applied to the samples and the frame length is 128 points (8 ms). There is a frame overlap of 6 ms (75% overlap) and a set of pulses is calculated for each frame. Sixty-four spectral bins are extracted in each frame (amplitude and phase). The step between two bins is 125 Hz.
* The spectral bins are then grouped into frequency bands that are logarithmically distributed, according to ear physiology [27]. Considering the usual values taken in CI, we used 20 bands (leading to 20 channels). The corresponding mapping is shown in Table 1.
* In each band, the energy is calculated using the Parseval's formula (the squares of the amplitude of each beam are added). In the FC coding, all the channels were taken. For the CP coding strategy, only the eight most energetic channels were kept. The value n = 8 is a standard in CIs [28].
* Each channel is represented by a narrowband spectrum coming from a white noise spectrum. The amplitude of the narrowband follows the energy detected in the corresponding channel. The synthesis filters covered the corresponding analysis bands but were 70 Hz narrower (35 Hz less on each side). Moreover, filters used here were 20 th order Butterworth bandpass filters to avoid channel interaction. The first two-channels were represented by sine waves.
* The output signal is obtained by summing the selected channels (8 for the CP strategy; 20 for the FC strategy).  
Figure 1: Block diagram representing the signal processing performed by the n-of-m simulator.
# Deep Learning for Short-Term Load Forecasting--Industrial Consumer Case Study
## 1 Introduction  
Types of electric load forecasting techniques fall into three main categories regarding the forecast horizon: STLF (short-term load forecasting), MTLF (medium-term load forecasting), and LTLF (Long term load forecasting). The authors in [1] present a classification based on the data frame used before the forecast, including very short-term load forecasting and a framework based on several points predicted into the future. In our study, we implement short-term forecasting for 24 steps (hours). Load forecasting (LF) can offer great value if the process can be automated and operated without human intervention.  
The approach presented in the article for 24 h ahead forecast facilitates the acces to the DAM (day ahead market) and Intra-day market to minimize the difference between real and forecasted values. This difference is mandatory to be balanced on the balancing market, which represents a financial problem for the supplier. Most large non-residential consumers have a single tariff (price/MWh) and few electricity suppliers offer time differentiated prices and time of use tariffs. This single tariff is calculated to cover the expenses with portfolio balancing. Every supplier needs to balances the portfolio of clients by being a balancing responsible party (BRP) or by submiting these responsibility to other parties. Either way, balancing a portfolio is a chalanging task for every supplier.  
In literature, the classification of forecasting determines two main categories illustrated in Figure 1: qualitative and quantitative. Work developed by [2, 3] is a comprehensive and detailed review on selecting forecasting methods from a business standpoint. Regarding the analogy for electricity load, forecasting has the same foundation principles with few particularities. Qualitative techniques apply empirical knowledge of LF experts to make ajudgment call on the forecast. Qualitative techniques used for medium-long term prediction are primarily expert opinions, such as the Delphi Method, Market Research, or Panel consensus [4].  
Quantitative techniques are better for short-term forecasting and consist of (i) time series forecasting and (ii) causal forecasting. In time series forecasting, the historical data is a set of chronologically ordered observation points where \(X_{t}\) is recorded in time, \((X_{t})_{n=0,\pm 1,\pm 2,...}\), observed at times \(t=1,2,...,n\) (\(X_{1},X_{2},\ldots,X_{n}\)). Contrasting CF, TSF is the natural ordering of the data points. TSF help understand components such as patterns, trends, peak values and any irregularity or variation in the series of data.  
The importance of load forecasting is highlighted by many papers such as [5; 6] which presents a detailed literature review. In Table 1, electricity market participants who can benefit from forecasting are highlighted.