esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Key Findings  
After analyzing the state of art, it is clear that a smart campus faces similar challenges to smart cities and that they share certain use cases and communications technologies. Table 2 summarizes the characteristics of the most relevant smart campuses and related solutions, and compares them with the proposed system for the University of A Coruna. As can be observed, the systems in the table make use of diverse short-range and long-range communications technologies, multiple sensors and actuators, different hardware and software platforms, and provide services for several practical use cases. Although some systems provide a holistic approach to a smart campus, devising potential outdoor use cases and applications, their implementations are mainly focused on environmental aspects, missing other smart fields such as the ones defined in Section 2.1. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Smart Campus** & **Area** & **Access Technology** & **Sensors and Actuators** & **IoT Hardware Platform** & **Software Platform** & **Use Cases** & **Fog Computing Capabilities** & **Network Planning** & **Sustainable Development Goals (SDGs) [107], KPIs or Results \\ \hline \hline School of STEM, University of Washington & - & Zigbbe, BLE, BoeH, PAN & & & & & & & \\ Boethell & & & & & & & & & \\ United States [41] & & & & & & & & & \\ \hline QA & & & & & & & & & \\ Higher Education & & & & & & & & & & \\ (QAHE), University of Business & - & - & NFC and RFID tags, QR codes & & & & & & & \\ Technology, Birmingham & & & & & & & & & \\ United Kingdom & & & & & & & & & \\ Kingdom & & & & & & & & & \\ \([42]\) & & & & & & & & & & \\ \hline \hline Tennessee State & & & & & & & & & & \\ University, & & & & & & & & & \\ Nashville & - & - & - & - & - & - & - & - & - \\ United States [43] & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the main features of the most relevant deployed smart campuses and related solutions.
## 4 System Model
### Communication Model  
Unlike terrestrial communication networks, in underwater communication networks, underwater acoustic signals experience spreading and absorption losses, which are related to the signal frequency \(f\). The total attenuation of the underwater acoustic signal over distance \(l\) can be denoted by [10]  
\[A(l,f)=l^{k}a(f)^{l}, \tag{1}\]  
where \(k\) represents the propagation geometry, and its usual value is 1.5. For \(f\) in kHz, the absorption coefficient \(a(f)\) in dB/km is given by [PERSON]'s formula; we have [26]  
\[10 loga(f)=\frac{0.11f^{2}}{1+f^{2}}+\frac{44f^{2}}{4100+f^{2}}+2.75\times 10^{-4 }f^{2}+0.003. \tag{2}\]  
Based on this, when the signal frequency \(f\), the signal bandwidth \(B\), and the transmission power \(P\) of the underwater acoustic signal are given, the transmission rate \(R\) of data transmitted by the underwater communication device to the device with a distance of \(l\) from it can be expressed by  
\[R(B,P,l,f)=Blog_{2}(1+\frac{P}{A(l,f)N(f)B}), \tag{3}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **Notation** & **Definition** \\ \hline \(X_{i}\) & The required CPU cycles of data \(W_{i}\) \\ \(D_{-i}\) & The input data size of data \(W_{i}\) \\ \(f_{i}^{l}\) & The computation capability of SN i \\ \(a,b,d\) & The transmit decision vectors of SN i \\ \(P_{i,B}\) & The transmit power of SN i to BS \\ \(P_{i,j}\) & The transmit power of SN i to SN j \\ \(B_{i}\) & The available spectrum bandwidth of SN i \\ \(f_{i}\) & The carrier frequency of SN i \\ \(D_{-i}^{l}\) & The data size of data \(W_{i}\) after calculation \\ \(l_{i,B}\) & The distance between the SN i and BS \\ \(l_{i,j}\) & The distance between the SN i and SN j \\ \(Z_{j}\) & The maximum data size of SN j \\ \(R\) & The transmit rate \\ \hline \hline \end{tabular}
\end{table}
Table 1: NOTATION.  
Figure 3: Three-layer transmission model.  
The transmission delay of data with data volume \(D\) can be expressed as  
\[T=\frac{l}{c}+\frac{D}{R(B,P,l,f)}, \tag{4}\]  
where the symbol \(c\) represents the velocity of underwater sound, the power spectral density \(N(f)\) of ambient noise in the ocean has four components; details of \(N(f)\) can be found in [27]. The different types of power spectral density (p.s.d) of noisy sources components in dB are \(\mu\) Pa per Hz and can be obtained as follows:  
\[10\log N_{t}(f)=17-30\log f, \tag{5a}\] \[10\log N_{s}(f)=40+20\bigg{(}s-\frac{1}{2}\bigg{)}+26\log f-60\log(f+0.03),\] (5b) \[10\log N_{w}(f)=50+7.5w^{\frac{1}{2}}+20\log f-40\log(f+0.4),\] (5c) \[10\log N_{th}(f)=-15+20\log f, \tag{5d}\]  
where \(f\) denotes the transmitting frequency. The combined noise \(N(f)\) in the acoustic channel yields  
\[N(f)=N_{t}(f)+N_{s}(f)+N_{w}(f)+N_{th}(f). \tag{6}\]
# Cascaded Nonlinear-Optical Loop Mirror-Based All-Optical PAM Regenerator
## 4 Cascaded NOLM-Based Regeneration Scheme
The amplitude regeneration of PAM-4 or PAM-8 signals has been demonstrated using the cascaded NOLM regenerator. In principle, more NOLMs are necessary for higher-order PAM signals. The common design method is summarized as follows: (1) setting the optimal WPs for all NOLMs in series by optimizing the coupler's splitting ratio from high to low levels; (2) calculating the gains of all inter-stage matching optical amplifiers; (3) optimizing the NPTF and NDG curves for the cascaded NOLM-based regenerator; (4) determining the regenerative WPs and the dynamic ranges for PAM signal; and (5) simulating the regeneration performance for verification.
## 1 Introduction
The arching phenomenon and the load distribution itself is not the main focus of this article; the values of the equivalent load resulting from measurements were adopted for the reinforcement analyses. In the present paper, only the second step of the calculations is analysed (load deflection behaviour of the reinforcement). In this step, the load (B + C) on the reinforcement strip between the columns is the one-dimensional load on the membrane strip, and its distribution is different depending on the model used. Some researchers assume that the vertical stress distribution applied on the geosynthetic is uniform [16; 17; 31; 32; 33], has a triangular shape [19] or an inverse triangular shape [9; 10; 18]. There is no unified approach to both the shape of the equivalent load distribution and the arching model. The three commonly used distributions of equivalent load that model the load of the LTP reinforcement, presented in Figure 1, differ considerably. The behaviour of the membrane varies depending on the adopted distribution. [PERSON] and [PERSON] [34] showed that the distribution of the equivalent load has a significant impact on the behaviour of the membrane modelling the LTP reinforcement. Figure 1: Distributions of the equivalent load modelling the load of the LTP reinforcement. In the present paper, 41 distributions of the equivalent load from inverse triangular to triangular were analysed, and the obtained deflection functions were compared with the results of measurements of reinforcement deflection for 5 cases. Three cases concern a situation where the soil between the columns is not involved in the load transfer (\(k=0\), no soil support). For these cases, 4 deflection curves were analysed. The next two cases were related to situations where the role of soft soil in transferring loads is significant. For these cases, 7 deflection curves were analysed. The aim of the research was to determine which of the considered load distributions best maps the actual behaviour of the geosynthetic reinforcement. The analyses presented so far in the literature (cf. e.g., [9; 10]) concerning the influence of the load distribution on the shape of the deflection curve of the reinforcement consisted in a visual comparison of the shape of the analytical deflection function and the shape of the experimental reinforcement deflection curve, and the assessment of the equivalent load distribution based on the shape of the second derivative plot of the reinforcement deflection curve. The authors proposed the measure of degree of accuracy as a relative error of mapping the deflection curve (see Equation (2)). The smaller the error value, the adopted load distribution allowed to more accurately reproduce the deflection curve resulting from the measurements.
# TDLAS Monitoring of Carbon Dioxide with Temperature Compensation in Power Plant Exhausts
## 2 Absorption Spectroscopy
### Wavelength Modulation Spectroscopy  
Wavelength modulation spectroscopy could be used to improve the sensitivity of measurement. In WMS, the wavelength of the laser used is modulated by a combination of slow triangle wave and a fast-sinusoidal signal while the transmitted light is demodulated at a harmonic of the modulation frequency. For a sufficiently slow ramp, the frequency output of the laser can be expressed as [36]:  
\[\
u(t)=\overline{\
u}+\
u_{0}\cos(2\pi ft) \tag{6}\]  
where \(\overline{\
u}\) is the laser center frequency, \(\
u_{0}\) is the modulation amplitude, and\(f\) is the modulation frequency. The transmitted laser intensity can be expanded in a Fourier cosine series as:  
\[I(t)=I_{0}(t)T[\overline{\
u}+\
u_{0}\cos(2\pi ft)]=I_{0}(t)\sum_{k=0}^{k= \infty}H_{k}(\overline{\
u},\
u_{0})\cos(2\pi kft) \tag{7}\]  
where \(H_{k}(\overline{\
u},\
u_{0})\) is the \(k\)th harmonic Fourier component of the transmission coefficient. The second harmonic Fourier coefficient which is given by:  
\[H_{2}(x,m)=\frac{2 XLPS(T)}{\pi\Delta v_{c}}\bigg{\{}\frac{2}{m^{2}}\left[ \frac{2+m^{2}}{\left(1+m^{2}\right)^{1/2}}-2\right]\bigg{\}} \tag{8}\]Two dimensionless numbers \(x=2(\overline{v}-v_{0})/\Delta v_{c}\), \(m=2a/(\Delta v_{c})\), \(\Delta v_{c}\) (cm\({}^{-1}\)) is the collisional linewidth (half width at half maximum, HWHM) of the probed transition, respectively; \(m\) is the modulation index, a dimensionless parameter defined as the ratio of the wavelength-modulation amplitude to the linewidth \(\Delta v_{c}\). CO\({}_{2}\) concentration \(X\) is inferred explicitly from [37]:  
\[X\propto\frac{I_{0}P_{2f}\pi\Delta v_{c}}{S(T)PL}\left\{\frac{2}{m^{2}}\left[ \frac{2+m^{2}}{\left(1+m^{2}\right)^{1/2}}-2\right]\right\}^{-1} \tag{9}\]  
where \(P_{2f}\) is the measured peak 2f signal at the line center \(v_{0}\); therefore, the measured column density depends on the line intensity and linewidth of the transition, both of which vary with temperature.  
For the optical thin condition due to the weak absorption in the near-infrared and low-gas concentration in industrial emissions, the peak 2f signal is linearly proportional to concentration:  
\[y_{X}=a+bx_{P_{2f}} \tag{10}\]  
The variation of line intensity and collisional linewidth caused by temperature change introduces errors to the above linear fitting. According to Equation (6), parameters like line intensity \(S(T)\) and collisional linewidth \(\Delta v_{c}\) are considered in the correction of temperature influence, which is given by:  
\[y_{X}=a+bx_{\frac{P_{2f}\Delta v_{c}}{S(T)}} \tag{11}\]
# GNSS RTK Positioning Augmented with Large LEO Constellation
## 2 Methods
### Combined GNSS/LEO RTK Observation Model
\begin{array}{l}P_{r_{1}\tau_{2},f,i}^{G_{1}G_{2}}=u_{r_{2},i}^{G_{1} G_{2}}r_{r_{1}\tau_{2},i}+\gamma_{f,G}\widetilde{c}_{r_{1}\tau_{2},i}^{G_{1}G_{2}}+m_{r_{2},i}^{G_{1} G_{2}}\widetilde{c}_{r_{1}\tau_{2},i}^{G_{1}G_{2}}+\widetilde{c}_{r_{1}\tau_{2},f,i}^{G_{1}G_{2}} \\ P_{r_{1}\tau_{2},f,i}^{L_{1}L_{2}}=u_{r_{2},i}^{L_{1}L_{2}}r_{\tau_{1}\tau_{2},i} +\gamma_{f,L}\widetilde{c}_{r_{1}\tau_{2},i}^{L_{1}L_{2}}+m_{r_{2},i}^{L_{1}L_{2} }\widetilde{c}_{r_{1}\tau_{2},i}^{L_{1}L_{2}}+\widetilde{c}_{r_{1}\tau_{2},f,i}^ {L_{1}L_{2}}\end{array} \right. \tag{6}\]  
\[\left\{ \begin{array}{l}\varphi_{r_{1}\tau_{2},f,i}^{G_{1}G_{2}}=u_{r_{2},i}^{G_ {1}G_{2}}r_{\tau_{1}\tau_{2},i}-\gamma_{f,G}\widetilde{c}_{r_{1}\tau_{2},i}^{G_{1} G_{2}}+m_{r_{2},i}^{G_{1}G_{2}}\widetilde{c}_{r_{1}\tau_{2},i}^{G_{1}G_{2}}+ \lambda_{f,G}\widetilde{c}_{r_{1}\tau_{2},f}^{G_{1}G_{2}}+\widetilde{c}_{r_{1} \tau_{2},f,i}^{G_{1}G_{2}}\\ \varphi_{r_{1}\tau_{2},f,i}^{G_{1}G_{2}}=u_{r_{2},i}^{G_{1}G_{2}}r_{\tau_{1}\tau_{2},i}-\gamma_{f,G}\widetilde{c}_{r_{1}\tau_{2},i}^{G_{1}G_{2}}+m_{r_{2},i}^{G_{1} G_{2}}\widetilde{c}_{r_{1}\tau_{2},i}^{G_{1}G_{2}}+\lambda_{f,G}\widetilde{c}_{r_{1} \tau_{2},f}^{G_{1}G_{2}}+\widetilde{c}_{r_{1}\tau_{2},f,i}^{G_{1}G_{2}}\end{array} \right. \tag{7}\]  
where \(p\) and \(\varphi\) are the vectors of the observed-minus-computed code and phase observables, respectively; \(u\) is the corresponding design matrix that captures the relative receiver-satellite geometry; \(r\) is the 3-vector of incremental baseline coordinates; \(\gamma\) denotes the conversion factor of ionosphericdelay from GNSS or LEO first frequency \(f_{1}\) to current frequency \(f_{i}\), \(\gamma=f_{1}^{2}/f_{j}^{2};\widetilde{i}\) is the DD ionospheric slant delay at \(f_{1}\); \(m\) is the mapping function to get wet ZTD \(\tau\); \(n\) is the unknown integer DD ambiguities; \(\widetilde{e}\) and \(\widetilde{e}\) denote the vectors of unmolded effects and measurement noise, respectively. So the unknown parameter vector for medium and long baselines is:  
\[\mathrm{X}=\left[r^{T},\tau,\widetilde{i}^{T},n^{T}\right]^{T} \tag{8}\]  
where \(T\) means transpose of the vector, and we omit subscripts and superscripts in the equation for brevity. In terms of the short baseline, since the relative atmospheric delays can be assumed absent, the unknown parameter vector is:  
\[\mathrm{X}=\left[r^{T},n^{T}\right]^{T} \tag{9}\]  
For the combined GNSS/LEO RTK model, the observations from all satellites are processed simultaneously in a single adjustment. All unknown parameters are obtained by extended Kalman filter (EKF) estimator, then the well-known LAMBDA method is employed to solve the DD ambiguity-fixed problem [29]. After the validation by the simple ratio-test, the fixed solution of the non-ambiguity parameter vectors including the rover positions are obtained based on fixed ambiguity parameters. In addition, the detailed data processing strategy is presented in the subsequent section.
## 1 Introduction
(2019) took the transaction between the microgrid MG and the DN into consideration, and they proposed a new method for energy sharing among MG clusters; a seller level game and a new pricing mechanism were proposed to increase interest. The above literature either improves the reliability of the system operation or improves the economy of the system operation, without considering the economy and reliability of the system at the same time. [PERSON] et al. [PERSON] et al. (2019) proposed a two-layer optimization strategy where distribution network operator (DNO) takes the operational safety constraints of the DN into account to improve system stability while minimizing operating costs. However, the DNO needs to set several different prices for different MGs, which usually increase the complexity of the calculation. Furthermore, every MG needs to develop a buyer or seller role in advance and give the upper and lower limits of the transaction power, which is difficult to handle during actual operation. This paper focuses on energy market management of the DN with MMGs taking the economic benefits of MGs and the reliability and stability of the DN into account. The main contributions of this paper are summarized below:  
1. A novel market energy management framework: A new market management framework for the DN with an MMG is proposed based on a dynamic game. In the game, DNO is the leader and MG operators (MGOs) are the followers; they form a one-leader and many-followers game. They expect to minimize their respective daily operation costs through energy transactions, and they influence each other. 2. Simple pricing mechanism: A direct incentive mechanism for electricity price is proposed. The DNO sets a set of day-ahead selling and buying prices, and each MG responds to the price according to its actual situation without having to decide the trading role and having to set the trading range in advance. The simple price strategy encourages MGs to participate in the energy management of the DN and to reduce the operation costs of the system. 3. Cost analysis and discussion: The cost components of DNO and MGO are analyzed in detail. It is necessary to increase the penalty function to improve the operation reliability of the distribution network. The extensive numerical analysis shows the practicability of the method.
# A Forward\(-\)Backward Splitting Equivalent Source Method Based on S\(-\)Difference
## 3 Numerical Simulations  
In this section, simulation analysis is conducted for two types of sound sources: single source and coherent source. Firstly, the Tikhonov regularization method, WBH, ISTA, and FBS are used to reconstruct the sound field, and reconstruction error analysis is performed. A pulsating sphere source with a radius of 0.01 m is used as the target sound source, with a vibration velocity of 2.5 \(\times\) 10\({}^{-2}\) m/s, a sound speed of 340 m/s, and an air density of 1.29 kg/m\({}^{3}\). For all subsequent simulations, the holographic plane is located 0.1 m away from the sound source plane to meet the near-field measurement requirement. The reconstruction plane is located 0.02 m away from the sound source plane to better restore the radiation sound field of the target sound source. The equivalent source plane is located 0.001 m away from the sound source plane to optimally represent the actual sound source. The four planes mentioned in the simulation are all in the same coordinate system. The equivalent source plane adopts a 21 \(\times\) 21 grid with a grid spacing of 0.05 m. The reconstruction plane adopts a 41 \(\times\) 41 grid with a grid spacing of 0.02 m. A rectangular array of 36 channels is used for near\(-\)field sound pressure measurement, and the arrangement of microphones and sound source positions is shown in Figure 2, which is the most suitable configuration for collecting noise generated by automotive components using a wheel array in practical engineering applications. The reconstruction error is calculated using Equation (15). A 36\(-\)channel rectangular microphone array was arranged on the holographic surface, and the simulations were all performed in matlab2018 on a computer with Windows 10 (AMD Ryzen 5 2600X processor, 3.60 GHz, 16.00 GB RAM).  
In order to quantitatively analyze the reconstruction accuracy of the sound field, the reconstruction error calculation formula is used. Accuracy, the reconstruction error, is calculated as follows:  
\[err=\sqrt{\sum_{i=1}^{N}\left(|p_{i}|-|\bar{p}_{i}|\right)^{2}/}\sqrt{\sum_{i =1}^{N}|\bar{p}_{i}|^{2}}\times 100 \tag{15}\]  
where \(\bar{p}_{i}\) and \(p_{i}\) are the theoretical and reconstructed sound pressure values on the reconstructed surface. The smaller the \(err\) value, the higher the reconstruction accuracy.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 5 Utilization of AI Analysis in the IoMT  
TAI analysis techniques make up an important part of foundation for the IoMT, facilitating systems to become intelligent; AI-powered systems possess the ability to process, interpret, and extrapolate actionable insights from the extensive datasets acquired from both environmental and user interactions. These AI techniques use either ML, DL, or RL algorithms to perform predictive analytics, pattern recognition for normal/anomaly detection, activity recognition, sentiment analysis, and behavior analysis. In turn, these techniques empower IoMT systems to discern user preferences, anticipate their needs, and provide personalized and context-aware services. By integrating the aforementioned AI analysis techniques, IoMT devices can deliver optimal user experiences, making AI a vital component of modern-day smart environments. AI systems typically process data acquired from various sources, such as IoT sensors and devices, which may include images, videos, and text. However, these raw data often contain irrelevant information, noise, or missing values which must be addressed to prevent inaccuracies and misdiagnoses. Consequently, data preprocessing to enhance data quality and reliability is a critical initial step. In supervised learning paradigms, data annotation is a fundamental part of analysis. This process involves delineating the Region of Interest (RoI) to facilitate accurate interpretation of normal and abnormal conditions. Annotation is crucial during the model training phase, as it provides the ground truth necessary for learning. During the testing phase, the model's performance is evaluated on preprocessed data, eliminating the need for further external labeling. The rapid advancement of AI has led to the emergence of a wide array of models, each with distinct capabilities; therefore, selecting and training the most appropriate model is a meticulous process preceding final model derivation. After training, the model undergoes fine-tuning and cross-validation to enhance its accuracy further. Rigorous testing is then performed to ensure the model's robustness and generalization. Upon validation, the final model is deployed in real-world scenarios to predict and detect abnormalities. For example, in neuro-imaging analysis, AI models can directly identify abnormalities and present the results in image format on a monitor screen. In other applications, such as cardio-version devices, outputs are delivered through actuators that restore normal heart rhythm when arrhythmias are detected. This comprehensive AI analysis procedure is illustrated in Figure 4, with the details briefly described in the subsequent sections.
# Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch
## 4 Experimental Evaluation
### Experiment Results
#### 4.2.1 Disaster Recovery
This is because the controller has to install the specific rule in each switch and maintain the rule on a regular basis (see the second rule in Table 2), whose stability depends on quality changes of the wireless link, such as narrow-bandwidth or long latency in bad weather conditions. A more serious problem is the sharp decrease of throughput and relatively long recovery period when the controller is unavailable, which nearly leads to communication loss in the sensor network. On the contrary, the throughput of our proposed methodology stays stable constantly and the recovery time is so short that throughput only has a little fluctuation. When a controller reconnects to the switches, the handoff also has a good performance. These can ensure a stable sensor data transmission between different sensor nodes in agricultural networks. Figure 8: Instant throughput over time. Figure 7: The number of consecutive packet losses during recovery time.