esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control Design with a Symmetric BLF  
### Leader's Velocity Estimation  
Note that the velocity of the leader may be not always accessible in practical applications, in which case a fast convergent observer is designed to estimate the unavailable states. According to Equation (2), let the unknown leader's velocity \(\dot{\eta}_{l}\) pass through a first order filter:  
\[\dot{\phi}=-k\phi+k\dot{\eta}_{l}, \tag{9}\]  
where \(k\in\mathbb{R}\) is a positive scalar. Suppose that \(\dot{\eta}_{l}\) is a differentiable function with time-derivative \(\chi\), the dynamics in Equaiton (9) can be rewritten in the form of a linear system as follows:  
\[\begin{array}{l}\dot{x_{1}}=-kx_{1}+kx_{2},\\ \dot{x}_{2}=\chi,\\ y=x_{1},\end{array} \tag{10}\]  
where \(x_{1}\) and \(x_{2}\) are equal to \(\phi\) and \(\dot{\eta}_{l}\), respectively, \(\chi\) is the unknown input while y is the measurable output of the linear system. The problem of estimating \(\dot{\eta}_{l}\) can be formulated as observing the states of the linear system (10). The following observer can therefore be designed to exactly reconstruct the linear system (10) with finite-time:  
\[\begin{array}{l}\dot{\xi}_{1}=-k\dot{\xi}_{1}+k\dot{\xi}_{2}-k1 sign(\dot{ \pi}_{1})-k2\dot{\pi}_{1},\\ \dot{\xi}_{2}=-k3\dot{\pi}_{1}-k4\lfloor\theta\rfloor^{\frac{p}{q}}-k5 sign( \theta),\end{array} \tag{11}\]  
where \(\dot{\pi}_{1}=\dot{\pi}_{1}-x_{1}\) and \(\dot{\pi}_{2}=\dot{\pi}_{2}-x_{2}\) are the estimation errors and are denoted by \(\dot{\pi}=[\dot{\pi}_{1}^{T},\dot{\pi}_{2}^{T}]^{T}\), \(k_{i}\in\mathbb{R},i=1,\ldots,5\) are positive observer gains to be defined later. The variable \(\theta\) is defined as \(\dot{\phi}=k_{1}sign(\dot{\pi}_{1})\), \(p\) and \(q\) are positive odd integers such that \(p<q\).  
**Theorem 1**.: _The observer designed above ensures that the estimate value can converge to its real value in a finite time, provided that the observer gains are selected as: \(k_{1}>max\{\underline{k}_{1},\overline{k}_{1}\}\) and \(k_{5}k-\gamma>0\), where \(\underline{k}_{1}=(k\frac{k_{4}\sqrt{3}+\sqrt{3}k_{5}+\gamma}{\lambda_{min}(p) }+\delta_{0})^{\frac{q}{q+p}}\) and \(\overline{k}_{1}=(\frac{k(\sqrt{3}k_{5}+\gamma)+\delta_{0}\lambda_{min}(p)}{ \sqrt{3}k_{5}+\gamma+\delta_{0}\lambda_{min}(p)})^{\frac{q}{p}}\) where \(\delta_{0}>0\) is a positive scalar, and \(P\) is a positive definite matrix given as \(P=\left[\begin{array}{cc}(k+k_{2})I_{3}&-k_{3}I_{3}\\ k_{3}I_{3}&0_{3}\end{array}\right]\)._  
Proof.: Detailed proof can be found in [37].
# Toward Long-Term Implantable Glucose Biosensors for Clinical Use
## 2 Materials and Methods  
### Subcutaneous Continuous Glucose Monitoring  
An implantable sensor is a key component of a CGMS. Commercial CGMSs employ subcutaneous sensors that are implanted under the skin and continuously monitor subcutaneous interstitial fluid (ISF) glucose. Despite of the invasive concept of subcutaneous sensors, subcutaneous sensors have gained attention, mainly due to high reliability and easy accessibility. Subcutaneous ISF glucose is a well-known alternative sample to blood glucose [13; 14]. Subcutaneous ISF glucose shows good correlation with time lag of 5-30 min [15]. This indirect sensing inevitably needs in vivo calibration. Most of CGM sensors are calibrated against \"reference blood glucose reading\" using home glucose monitoring devices. Frequent calibration can guarantee higher accuracy, but increase pain and discomfort. Recently, some CGM sensors have reached zero in vivo calibration [16; 17; 18]. Stable and reproducible sensor-production has revolutionize diabetes therapy from in vivo calibration to in vitro factory calibration, \"zero finger picking.\" Details about calibration are described in Section 2.3.
## 5 Research Challenges and Future Directions
### Spectrum Bands  
Due to its unique propagation properties, Terahertz systems will replace current ultrahigh frequency networks in future communications. Massive amounts of spectrum in the mmWave and Terahertz ranges (300 GHz to 3 THz) will be used in 6G networks and beyond to support applications requiring 100 Gbps or more [153]. Nevertheless, paradigm changes in operating these systems are required to make use of these frequency ranges since the properties of these signals are substantially different from standard ultrahigh frequency communications. The attenuation caused by the atmosphere is more severe at higher frequencies (over 800 GHz) due to their physical properties. Nonetheless, narrow beams and directional antennas can compensate. Eavesdropping on communications may be avoided, and secure communication is feasible [154]. However, a greater study of THz signals is required in transceiver designs and the physical layer to provide a viable component. To meet the demanding data rate requirements of THz signals, new modulation techniques are needed that can provide spectral efficiency of at least 14 bits/s/Hz. However, this is not possible with current modulation methods, so additional research into this field is required [155]. To ensure the long-term viability of the spectrum utilization plan, regulatory authorities such as ETSI and ITU are hard at work on the THz spectrum strategy. A spectrum policy that prioritizes their safety and efficiency is needed to keep satellites and other space-based communication systems from being disrupted by new devices operating in the THz spectrum. Lastly, because additional applications employing THz signals may be accessible in the future, it is important to thoroughly investigate the effects of radiation from THz broadcasts on human health.
## 4 Physical Audio Features Extraction Techniques
### Time Domain Physical Features
#### 4.1.1 Zero-Crossing Rate-Based Physical Features  
This kind of physical features are based on the analysis of the sing-rate change of the analyzed audio input, which is a simple yet effective parameterization used in several machine hearing applications.  
* **Zero-crossing rate (ZCR):** it is defined as the number of times the audio signal waveform crosses the zero amplitude level during a one second interval, which provides a rough estimator of the dominant frequency component of the signal (Kedem [39]). Features based on this criterion have been applied to speech/music discrimination, music classification ([PERSON] _et al._[40], [PERSON] _et al._[41], [PERSON] _et al._[42], [PERSON] and [PERSON] [28], [PERSON] _et al._[9])), singing voice detection in music and environmental sound recognition (see the works by [PERSON] _et al._[17] and [PERSON] _et al._[18]), musical instrument classification ([PERSON] _et al._[10]), voice activity detection in noisy conditions ([PERSON] _et al._[43]) or for audio-based surveillance systems (as in [PERSON] _et al._[24]).
* **Linear prediction zero-crossing ratio (LP-ZCR):** this feature is defined as the ratio between the ZCR of the original audio and the ZCR of the prediction error obtained from a linear prediction filter (see [PERSON] _et al._[44]). Its use is intended for discriminating between signals that show different degree of correlation (e.g., between voiced and unvoiced speech).
## 2 Theoretical Background
### Parametric Lifetime Models
#### 2.4.2 Weibull Distribution  
This distribution represents the whole curve in the bathtub curve [21], with shape parameter \(\alpha\) and scale parameter \(\beta\), where \(\alpha>0\) and \(\beta>0\) are defined for the probability-density function \(f(t)\), cumulative-distribution function \(F(t)\), and failure-rate function \(h(t)\), as provided below [17; 19].  
\[f(t;\alpha;\beta)=\begin{cases}0&for\ t<0\\ \frac{\alpha}{\beta}\left(\frac{t}{\beta}\right)^
## 5 Experimental Implementation
### System Design
#### 5.1.4 Local Server  
Although the first three parts of the system would work independently, they would not offer much functionality without a central piece to bring them together. The local server is a TCP server socket and the three previous modules are client sockets. The server is programmed so that, at each reception of data, it sends a response to the client. The response differs according to the sender. This way, every client-server communication is a synchronous exchange of information, but the clients can be asynchronous from one another. So, if one part of the system crashes, it will not stop the others. Only the failing part will need to restart, reconnect to the server, and resume where the other parts are.  
The server also allows the components to be linked easily, even if they are not written in the same language. For example, the API for the sensor and controlled device could be written in C++ and the interface in Python. In a final production environment, the modules can be merged into a single application, but the server's presence for the development enhances the versatility and fluidity of the development.
# Prototyping of Environmental Kit for Georeferenced Transient Outdoor Comfort Assessment
## 4 Data Acquisition and Handling
### Solar Radiation
#### 4.3.1 Illuminance Conversion to Irradiation  
The detection and measurement of electromagnetic radiation are known as radiometry, and the subset of radiometry for a typical human eye response (visible spectrum) is known as photometry. To generalize, irradiance is the radiometric measurements of the solar spectrum that is observed as power per unit area (Watt/meter\({}^{2}\)), while the photometric spectrum is measured in lux (lumen/meter\({}^{2}\)). The amount of radiant flux incident on a known surface area is called irradiance, and the total visible energy emitted by a light source is known as luminous flux.  
The photopic spectral luminous efficiency curve, by the commission of illumination (CIE), gives the spectral response of the human eye to various wavelengths of light and is used to convert between radiometric and photometric units. The wavelength at which the human eye is most sensitive is known as the wavelength of unity (concerning scotopic and photopic curves) with a value of 555 nm. The following reference is used as a scaling factor for the unit conversion:  
1 watt (555 nm) = 683 lumen  
Radiometric power is converted into luminous flux using the following integral equation [27]:  
\[\Phi_{v}=\mathrm{K}\int_{380}^{780}P_{e}(\lambda)\;V(\lambda)\;d\lambda\]  
where \(\Phi_{v}\) = flux (Lumen), Pe = power (Watt), V = photopic response function of the human eye, K = constant (683 Im/W for photopic), and \(\lambda\) = wavelength (380 nm to 780 nm).  
There is a nonstandard estimate value for conversion of illuminance to irradiance in the visible spectrum. The value (0.0079 W/m\({}^{2}\) per lux) can be used to find approximate irradiance using photometric sensors. For example, illuminance of 45,000 lux calculated by an illuminance sensor can be converted into irradiance as follows:  
\[45000\;Lux\times 0.0079\frac{Watts}{meter^{2}}\times\frac{1}{Lux}=355.5\frac{Watts }{meter^{2}}\;\rightarrow\rightarrow\rightarrow\]  
Some studies have compared the illuminance and irradiance graphs of a city's meteorological data. These comparisons show a direct relation between the two scales under different factors for global radiation, direct radiation, and diffused radiation. This information can be used for applications where the efficiency of data is not critical; therefore, by a simple calibration for different sky conditions, one can theoretically estimate the irradiance at a particular location.
# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 5 Pattern Matching-Based Load Anomaly Detector
### Pattern Matching Scheme
#### 5.2.3 Global Re-Evaluation Process  
This step is developed from the perspective of global optimization [5]. The cross-entropy loss function is employed to determine whether the classification result of the real load is acceptable. The cross-entropy loss function is a popular classification/clustering result evaluation method that is simple, accurate, and adaptable for global optimization. The cross-entropy loss function is defined as [25]:  
\[L(y,\hat{y})\ =\ -\sum_{i\ =\ 1}^{L}y_{i}\log(\hat{y}_{i}) \tag{9}\]  
where \(y\) is a real label in the training dataset, \(\hat{y}\) is the classification of the model, and \(L\) is the size of the training dataset. The lower the cross-entropy loss is, the better the classification results. The steps in the function are as follows [5].  
1. Use the cross-entropy loss function to calculate the loss of the benign training data.
2. Input the real load data and the matched pattern obtained from the improved SVM model into the training dataset (to build a new dataset), and use the (new) cross-entropy loss function to recalculate the loss.
3. Calculate the difference in the losses \(\varepsilon\) and compare the difference with the \(threshold_{c}\). If \(\varepsilon\) is smaller than the \(threshold_{c}\), the classification result of the improved SVM is acceptable, and the load is benign. Otherwise, even if the improved SVM indicates that the load is associated with a learned pattern, the algorithm considers the load to be abnormal. Considering the normal power usage of humans over time, according to the results of variation analysis, the _threshold\({}_{c}\)_ in this study is set as a changeable constant that varies with time. The influence of this variable threshold on the detection accuracy will be discussed later. Figure 4 shows a flowchart of the pattern-matching-based anomaly detector.
# Emerging Trends in Hybrid Wireless Communication and Data Management for the Industry 4.0
## 2 Use Cases and Requirements
### Selected Industrial Use Cases
Wireless sensor networks (WSN) facilitate the complex task of monitoring an industrial environment to detect malfunctioning and broken elements in the surrounding environment. Given the simplicity of sensing devices, the data collected is processed by a centralized computing infrastructure such as a mobile data or data center cloud. The computation is referred to as fog computing, multi-access edge computing (MEC), and cloud computing. The traffic patterns generated by the sensor network vary with the type of measurement, and the number of devices in the factory is typically much higher than in the other use cases. Augmented reality. Augmented reality will play an important role to support shop floor workers for process monitoring, receive step-by-step instructions for specific tasks, or remote support, especially given the high flexibility and versatility of the factories of the future. Head-mounted AR devices with see-through display are very attractive but often require offload complex (e.g., video) processing tasks to the network in real time.
## Appendix A Proofs
Which implies that \(I_{0}\) can be expressed as:  
\[I_{0} = \int_{\max\{0,t_{0}\}}^{\max\{0,n-m\alpha\}}dt\left(\frac{t-n\alpha +m}{1-\alpha}-k\right)^{n+m-1}\] (A11)  
Then, Equation (A8) can be computed as follows:  
\[P_{W}=\left\{ \begin{array}{ll}0&,\alpha>\frac{n}{m}\\ \frac{1}{(n+m)!}\sum_{k=0}^{n+m}\left[(-1)^{k}\binom{n+m}{k}\times\\ \times\left((n+m-k)^{n+m}-(n+\frac{m}{1-\alpha})^{n+m}\right)\right]&,\frac{m} {n}<\alpha\leq\frac{n}{m}\\ \frac{1}{(n+m)!}\left(\sum_{k=0}^{n+m}(-1)^{k}\binom{n+m}{k}(n+m-k)^{n+m} \right.\\ -\sum_{k=0}^{k^{*}}(-1)^{k}\binom{n+m}{k}\left(\frac{m-n\alpha}{1-\alpha}-k \right)^{n+m}\end{array} \right.&,\alpha\leq\frac{n}{m},\frac{m}{n}\] (A12)  
\(\square\)  
**Lemma 1**. : In the described scenario (see Figure 7) with \(n\) neighbors (\(n>1\)), there is an auto-collision if the minimum inter-arrival time is smaller than the transmission time for a packet, \(L(p)\). Since inter-arrival times \(\delta_{i}=|T_{j_{i}}-T_{j_{i}+1}|\) are independent, according to [PERSON]'s hypothesis, we have:  
\[Pr(\text{auto-collision}) = P(\min_{0<i\leq n}\{|T_{j_{i}}-T_{j_{i}+1}|\}<L)=\] \[= \prod_{0<i\leq n}Pr(\{|T_{j_{i}}-T_{j_{i}+1}|\}<L)=\] \[= Pr(\{|T_{j_{i}}-T_{j_{i}+1}|\}<L)^{n}\]  
The distribution of \(\delta_{i}\) is exponential with rate \(\lambda\); therefore:  
\[Pr(\text{auto-collision}) = Pr(\{|T_{j_{i}}-T_{j_{i}+1}  
Expression (A13) becomes, for uniform jitter (\(\lambda_{\text{uniform}}=\frac{n}{J_{m}}\)):  
\[Pr(\text{auto-collision})_{\text{uniform}} = \left(e^{-\frac{n}{J_{m}}L}-1\right)^{n}\]  
Additionally, for window jitter, with parameter \(\alpha\) (\(\lambda_{\text{window}}=\frac{n}{(1-\alpha)J_{m}}\)):  
\[Pr(\text{auto-collision})_{\text{uniform}} = \left(e^{-\frac{n}{(1-\alpha)J_{m}}L}-1\right)^{n}\]  
\(\square\)
## 2 Time Series Forecasting for Energy Reduction
### Deep Learning for Time Series  
Deep learning (DL) is a subset of Machine Learning. It refers to learning models based on deep Artificial Neural Network architectures with a large number of layers. One of the advantages of DL over ML algorithms is that, as we input more data, the performance of the network tends to improve, as the network can learn more patterns in contrast to traditional algorithms. In recent years, the use of ANNs and DL algorithms have rocketed due to the rapid evolution in hardware capabilities, shifting the paradigm of avoiding complex algorithms. Complex algorithms can be trained and deployed in a compact form consuming fewer resources, such as in mobile phones and other embedded devices.  
Data from sensors may contain noise, missing values, correlations between different types of magnitudes and/or between data from different sensors, seasonality, and trends. Statistical and classical ML time series forecasting models are limited in their ability to extract information from nonlinear patterns and data in such conditions; therefore, they rely on clean, mostly stationary data and hand-crafted features, which is time-consuming in some cases requiring high processing power and can introduce human biases [16].  
Some architectures are very good at learning from sequential data. One-Dimensional Convolutional Neural Networks (1D-CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Neural Networks (LSTMs), Gated Recurrent Unit (GRU) and combinations of these architectures have been used in many classification and forecasting problems with sequential data [17; 18].
## 5 Data Simulation
### LRI1B Simulation  
The true instantaneous range can be directly computed from the error-free orbital states as  
\[\rho_{\text{inst}}=\sqrt{(\mathbf{r}_{2}-\mathbf{r}_{1})\cdot(\mathbf{r}_{2}-\mathbf{r}_{1})}. \tag{41}\]  
For the simulation of ranging data, Equation (16) can be rewritten as  
\[\rho_{\text{LRI}}(t_{1})\approx\rho_{\text{inst}}+\Delta\rho_{\text{bias}}+ \delta\rho_{\text{TOF}}+\delta\rho_{\text{TTL}}+\delta\rho_{\text{LF}}+ \delta\rho_{\text{timetag}}+\delta\rho_{\text{readout}}, \tag{42}\]  
where \(\Delta\rho_{\text{bias}}\) is a constant denoting the measurement bias; \(\delta\rho_{\text{TOF}}\) denotes the error induced by the time delays due to media and relativistic effects; \(\delta\rho_{\text{TTL}}\) and \(\delta\rho_{\text{LF}}\) denote the error caused by the tilt-to-length coupling and laser frequency instability mentioned in Section 4, respectively; \(\delta\rho_{\text{timetag}}\) denotes the range error due to the time tag error; and \(\delta\rho_{\text{readout}}\) denotes the range error due to the phase readout noise. After adding the range error, the simulated range rate and the range acceleration can be obtained by applying the CRN filter [30]. The ASD of the instantaneous range and the non-constant error terms are shown in Figure 6. Descriptions of the error terms are as provided below.
# Time Domain Analysis of NB-IoT Signals
###### Abstract  
The NB-IoT (NarrowBand-Internet of Things) radio technology is now widely implemented by mobile phone network operators to support the communication of IoT devices such as smart meters, insurance black boxes for cars, network connected waste bins, smart bicycles. In the present work, some LTE800 cells of different mobile phone network operators implementing the NB-IoT technology in the guard band mode have been investigated. The signals, consisting of a PRB (Physical Resource Block) 180 kHz wide, have been analyzed and characterized in the time domain by means of a narrow band instrumental chain equipped with a Rohde & Schwarz FSH8 spectrum analyzer. Time domain analysis allows us to identify, within the transmission frame, the position of the NB-IoT signaling channels such as the Narrowband Reference Signal (NRS), the primary (NPSS) and secondary (NSSS) synchronization signals and the broadcast channel (NPBCH), but, above all, to measure the power received during the transmission of the NBS. This value has been compared with that measured by the NB-IoT decoding module supplied on the same analyzer, in order to verify the equivalence of these measurement methods. This would allow use of a more diffuse and cheaper instrumentation rather than more expensive vector analyzers, currently required to assess electric fields due to the NB-IoT signals through the extrapolation techniques set by Italian CEI 211-7/E technical standard.  
NB-IoT; LTE; EMF exposure; extrapolation techniques; Reference Signals; CEI 211-7/E; spectrum analyzer; time domain analysis +
Footnote †: journal: Applied
# Magneto-Inertial Fusion and Powerful Plasma Installations (A Review)
## 6 Conclusions  
Magneto-Inertial Fusion (MIF) is a concept of magnetically driven inertial fusion that involves magnetization of the fuel, laser pre-heating, and magnetic implosion to create fusion conditions. The ability to create controllable electric fields in plasma opens up wide possibilities for plasma technologies [95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105].  
Magneto-inertial fusion controlled by a plasma jet involves merging plasma jets with high Mach numbers. MIF devices are of great practical interest to experimenters as they allow for a full range of diagnostics, including temperature and density distributions, instability, and microwave diagnostics. Such devices are more convenient for observing the burning process (with ports on the chamber walls and access to the plasma), as this is impossible with metallic liners.  
Further directions of work (mostly for Plasma Jet Driven Magneto-Inertial Fusion):  
* the possibility of adding new factors (in addition to compression forces and magnetic field) to increase the efficiency of uniform jet merging and target compression;
* establishing the dependence of the main parameters of plasma jets (density, plasma temperature, velocity, energy content) on pressure, type of working gas, and discharge energy;
* optimization of the design of plasma guns and the power supply system in order to reduce the mass, dimensions and increase the speed of the jets and efficiency.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest**: The author declares no conflict of interest.