esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Use Case: Smart Buildings  
Smart buildings' concept aims to manage properly building resources and optimizing them by using advanced technology and automation for enhancing the comfort, safety, and productivity of its occupants [32]. To achieve this objective, smart buildings leverage a range of technologies, including sensors, data analytics, machine learning, and Artificial Intelligence (AI), to collect and analyze data on various aspects of building performance, such as energy consumption, indoor air quality, occupancy, and security [16]. A smart building is a complex system that is composed of several subsystems such as lighting, Heating Ventilation Air Condition (HVAC), security, and energy management, among others. Smart buildings' complexity directly affects network performance and management. Consequently, smart buildings are facing network management complexity, scalability, and security issues.  
RINA has the potential to offer several benefits to smart building scenarios such as hierarchical management, scalability, service differentiation, and security, among others. The RINA recursion enables the network designer to re-use a single DIF as many times as it is required. In smart buildings, each DIF can be responsible for a particular function and communicate with adjacent DIFs in a hierarchical manner. Furthermore, as the building's needs change or new devices and sensors are added, additional DIFs can be dynamically created to accommodate the growing network requirements. In fact, each DIF can be designed to allow the prioritization and management of different services with the smart building network. In addition, RINA is secure by design, so a DIF is a secure layer with highly customizable privacy and security levels based on policies.  
This section details the proposed architecture implementation in a smart building scenario to enhance the network scalability and management by using the RINA approach.  
Figure 9: Message exchange between RINA SBI Driver and two MAs for the creation of a network slice.
# Characterization of Narrowband Noise and Channel Capacity for Powerline Communication in France
## 2 Experimental Measurement Setup and Methods
### Experimental Procedure
#### 2.2.1 Measurement of Noise Power Spectrum in the Transformer Substation  
As can be seen in Figure 4, noise power spectrum is captured with a spectrum analyzer (reference: PXA N9030A, Agilent Technologies, Santa Clara, CA, USA) both with linear video signal averaging and RMS detection. The sweep time is set to 412.4 ms, which corresponds to twenty periods of the network. During this sweep time, the frequency is sampled at 801 frequencies in the band from 9 to 500 kHz. The bandwidth resolution (IFBW) has a constant value (i.e., 200 Hz). It is important to note that all measurements are carried out in the same configuration.  
Noise power spectrum is carried out in the transformer substation and at the customer side in parallel to the smart electricity meter with single phase coupling. As can be seen in Figure 5, a capacitive coupler is used. It is equivalent to a bandpass filter from 1 kHz to 1 MHz.
## 4 Application of the Model
### Level 2 Evaluation: Signal Spectrum  
In the second-level evaluation, with the information listed in Tables 5 and 6, the system can directly predict the possible types of spectral interference between the transmitter and the receiver. The radio stations HF1, VHF3 and VHF2, and VHF3 are selected to make up the two sets of devices for evaluating fundamental wave interference based on Equation (9). The radio stations HF1, VHF3 and VHF2, and VHF3 are selected to make up the two sets of devices for evaluating harmonic wave interference based on Equation (10). Based on the frequencies of the transmitter and receiver, we must choose an appropriate value of n to calculate. We then increment the order coefficient n and repeat the calculation until all orders of interest have been analyzed for both sets of devices. The radio stations HF1, VHF2, and VHF3 make up the set of devices for evaluating the intermediation wave interference based on Equation (11). Based on the frequencies of the transmitter and receiver, we chose the appropriate n and m for the calculation. We then incremented the order coefficients m and n and repeated the calculation until all orders of interest had been analyzed.  
From the synthesis of all the calculations in Table 8, we conclude that there is potential fundamental wave interference for VHF2 and VHF3, potential higher-order harmonic interference for HF1 and VHF3, and potential higher-order intermediate interference for HF1, VHF2, and VHF3. Hence, the second-level evaluation result is IM \(>0\), which indicates that some potential EMI is present in the system and that the evaluation should proceed to the next level.
# An Innovative System of Deep In Situ Environment Reconstruction and Core Transfer
## 7 Experiment Results and Discussion
### Experiment of the Fuzzy PID Temperature Control Based on Grey Prediction Model  
Figure 15 shows the temperature curves of the storage chamber using the fuzzy PID control and the grey-prediction-based fuzzy PID control with the target temperature of 85 \({}^{\circ}\)C. te MPa, tempB and tempC are the temperatures of A-segment, B-segment and C-segment of the chamber, respectively. In the experiment the 4-group heating mode was adopted. Table 5 shows the experiment results of 75 \({}^{\circ}\)C, 85 \({}^{\circ}\)C and 95 \({}^{\circ}\)C. It can be seen that compared with the fuzzy PID control, the overshoot and steady-state errors of the grey-prediction-based fuzzy PID control were relatively lower, with the steady-state errors less than \(\pm\)0.13% and the overshoots less than 0.22%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Heating Mode**} & \multicolumn{2}{c}{**UI/\%**} & \multicolumn{2}{c}{**Duration/min**} \\ \cline{2-5}  & **Heating Stage** & **Steady State** & **Heating Stage** & **Cooling Stage** \\ \hline
2-group & 99.20 & 99.12 & 90.23 & 23.54 \\
4-group & 99.07 & 99.23 & 43.50 & 44.52 \\
6-group & 98.14 & 98.96 & 30.24 & 71.15 \\ overall & 91.30 & 98.46 & 32.23 & 79.71 \\ \hline \end{tabular}
\end{table}
Table 4: Experimental results of temperature uniformity and durations of different heating modes.  
Figure 15: Temperature curves of heating experiments of 85 \({}^{\circ}\)C; (a) Fuzzy PID control; (b) Grey-prediction-based Fuzzy PID Control.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Target Temp.**} & \multirow{2}{*}{**Performance**} & \multicolumn{4}{c}{**Fuzzy PID**} & \multicolumn{4}{c}{**Grey Prediction Based Fuzzy PID**} \\ \cline{3-8}  & & **Sect. A** & **Sect. B** & **Sect. C** & **Sect. A** & **Sect. B** & **Sect. C** \\ \hline \multirow{2}{*}{75 \({}^{\circ}\)C} & Overshoot/\% & 0.14 & 0.25 & 0.48 & 0.07 & 0.10 & 0.13 \\  & Steady-state error/\({}^{\circ}\)C & \(-\)0.20 & 0.28 & 0.17 & 0.10 & \(-\)0.11 & 0.12 \\ \hline \multirow{2}{*}{85 \({}^{\circ}\)C} & Overshoot/\% & 0.19 & 0.19 & 0.14 & 0.09 & 0.14 & 0.18 \\  & Steady-state error/\({}^{\circ}\)C & 0.29 & 0.25 & 0.13 & 0.13 & \(-\)0.10 & \(-\)0.10 \\ \hline \multirow{2}{*}{95 \({}^{\circ}\)C} & Overshoot/\% & 0.28 & 0.29 & 0.20 & 0.17 & 0.22 & 0.10 \\  & Steady-state error/\({}^{\circ}\)C & 0.33 & \(-\)0.26 & 0.14 & 0.13 & 0.12 & 0.13 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of experimental results.
## 3 Decentralized Neural Block Control (DNBC-UKF)
### DNBC-UKF Controller Design  
Once the RHONN training has been defined, we design a controller based on the tracking error \(z_{i}\) as follows:  
\[\mathbf{z}^{j}_{i,k}=\mathbf{x}^{j}_{i,k}-\mathbf{x}^{1j}_{\text{id},k} \tag{5}\]  
where \(\mathbf{x}^{j}_{\text{id},k}\) is the desired trajectory signal and \(\mathbf{x}^{j}_{i,k}\) is the NN state [27].  
The new value is obtained as:  
\[\mathbf{z}^{j}_{\text{i,k+1}}=\mathbf{w}^{j}_{\text{i,k}}S(\mathbf{\mathcal{X }}^{1}_{\text{i,k}},\dots,\mathbf{\mathcal{X}}^{j}_{\text{i,k}})+\mathbf{w}^ {{}^{\prime}j}_{\text{i}}\mathbf{u}_{i,k}-\mathbf{x}^{j}_{\text{id},k+1}. \tag{6}\]  
Then, system (2) should be expressed as a function of variables \(\mathbf{z}^{j}_{i,k}\) as:  
\[\mathbf{z}^{j}_{\text{i,k+1}}=k^{j}_{\text{i}}\mathbf{z}^{j}_{\text{i,k}}+ \mathbf{w}^{{}^{\prime}j}_{\text{i}}\mathbf{u}_{\text{i,k}}-\mathbf{x}^{j}_{ \text{id},k+1} \tag{7}\]  
When a sliding mode control strategy is implemented, the control input must be limited by \(u_{\text{i,k}}\) as:  
\[|\mathbf{u}_{\text{i,k}}|\leq\mathbf{u}_{\text{i}}. \tag{8}\]  
The sliding surface is designed as \(S_{D_{1}k}=\mathbf{z}^{r}_{\text{i,k}}=0\); then, system (7) is rewritten as follows:  
\[S_{D_{1}k+1}=\mathbf{w}^{r}_{\text{i,k}}S(\mathbf{\mathcal{X}}^{1}_{\text{i, k}},\dots,\mathbf{\mathcal{X}}^{r}_{\text{i,k}})+\mathbf{w}^{{}^{\prime}r}_{ \text{i}}\mathbf{u}_{\text{i,k}}-\mathbf{x}^{r}_{\text{id},k+1}. \tag{9}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Criteria** & **Formula** \\ \hline Integral Absolute Error (IAE) & \(\sum\limits_{k=0}^{\frac{t}{2}}|\mathbf{e}_{k}|\) \\ Integral Squared Error (ISE) & \(\sum\limits_{k=0}^{\frac{t}{2}}\mathbf{e}_{k}^{2}\) \\ Integral Time-weighted Absolute Error (ITAE) & \(\sum\limits_{k=0}^{\frac{t}{2}}k|\mathbf{e}_{k}|\) \\ Integral Time-weighted Squared Error (ITSE) & \(\sum\limits_{k=0}^{\frac{t}{2}}k\mathbf{e}_{k}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance criteria.  
The proper selection of the sliding manifold [28] presents the possibility of finding a bounded control law by \(u_{0i}\); the control \(u_{i,k}\) is composed as  
\[\mathbf{u}_{i,k}=\begin{cases}\mathbf{u}_{eq_{1},k}&\text{for}\Big{\|}\mathbf{u}_{eq_{1},k} \Big{\|}\leq u_{0i},\\ \mathbf{u}_{0i}\frac{u_{eq_{1},k}}{\|\mathbf{u}_{eq_{1},k}\|}&\text{for}\Big{\|}\mathbf{u}_{ eq_{1},k}\Big{\|}>u_{0i},\end{cases} \tag{10}\]  
where \(\mathbf{u}_{eq_{1},k}\) is calculated from \(S_{D_{1},k+1}=0\) as  
\[\mathbf{u}_{eq_{1},k}=\frac{1}{\mathbf{w}_{1}^{r}}\Big{[}-\mathbf{w}_{i,k}^{r}S(\mathbf{\mathcal{ X}}_{i,k}^{1},\ldots,\mathbf{\mathcal{X}}_{i,k}^{r})+x_{i,d,k+1}^{r}\Big{]}. \tag{11}\]  
Figure 3 illustrates the block diagram of the proposed SI optimization approach.
# Smart Warehouses: Rationale, Challenges and Solution Directions
## 4 Results
### RQ-5: What Are the Current Challenges and Strategies for Transitioning to Smart Warehouses?
#### 4.6.1 Strategies  
The implementation of RFID on systems through integration is an important aspect that needs to be addressed for the warehousing system [25, 26]. Altering the WMS from a materials-tracking to a decision-making system as the former are ineffective in providing accurate and timely order execution information [24], the effective use of AR could present as key decision technology during the transition phase [27]. By expanding the current use of tracking technologies from product to the whole warehouse, so the warehouse becomes a \"smart\" self-regulating and allocating warehouse [25, 30]. To realize this, a storage and output conveyor-based system, where each conveyor is controlled by a programmable logic controller (PLC). The PLC uses a local area network (LAN) to communicate with mounted sensors [13]. The PLC can be substituted or parallel-used with an IMCD [18]. Items are preloaded on pallets/totes (of non-identical size) and are attached with an RFIDtag. Potential RFID-tags could be both active or passive depending on the product criteria and the overall costs [19].  
Another strategy could be to integrate the advanced WMS with the fuzzy clustering technique. This technique suggests the best applicable order-picking method and increases the efficiency of the order-picking process [3]. This advanced WMS can be further developed with the use of CPSs to evolve it into a CPS-WMS, making the system (more) autonomous. According to [PERSON] et al. [23], future WMS might have a collaboration between products, and storage units will determine the storage location. To determine the storage location, the system first applies the product allocation problem (PAP). PAP comes down to distributing and storing products amongst the warehouse while maximizing space and minimizing costs. Second, it checks whether there are compatibility constraints (CC) present. CC are shelves and slots capabilities checks to see if there are any restrictions such as size, capacity, product compatibility, policy, and time. Basically, all factors could limit the stocking of a product. Finally, the \"IoT Infrastructure associates RFID technology with wireless sensors network (WSN), communicating object paradigm and multi-agent systems\" [23]. This all comes together at a multi-agent architecture (MAA) based on IoT for PAP/CC for a collaborative warehouse environment that uses negotiation mechanisms. The negotiation mechanisms check for restraints on products or shelves in relation to other products and shelves and will argue whether the said product can be stored at said shelve. Strategically, this WMS will be supported by developing AGVs into Autonomous Automated Guided Vehicles (AAGV) [15]. Combined with the OPOS or DPQ, it could optimize labor resources, reduce in-warehouse travel distance, and define order-picking priority [19; 20].
# Aircraft Wake Recognition Based on Improved ParNet Convolutional Neural Network
## 4 Aircraft Wake Identification Results and Discussion  
### Evaluation Indicators of Identification Effect  
To evaluate the effectiveness of the improved ParNet proposed herein for wake identification, the accuracy, precision, recall rate, and F1 value were selected as evaluation indicators. The four indicators are popular parameters in the field of pattern recognition. Accuracy is the most important one to evaluate the performance of the model. Precision is closely related to the probability of miss detection. High precision corresponds to a small probability of miss-detection. The recall rate is related to the probability of a false alarm. A large recall rate corresponds to a small probability of a false alarm. \(F1\) is the harmonic average of the accuracy and recall rate. The four indicators are calculated as  
\[P_{\text{Accuracy}}=\frac{TP+TN}{TP+TN+FP+FN} \tag{4}\]  
\[P_{\text{Recall}}=\frac{TP}{TP+FN} \tag{5}\]  
\[P_{\text{Precision}}=\frac{TP}{TP+FP} \tag{6}\]  
\[P_{F1}=\frac{2\times P_{\text{Accuracy}}\times P_{\text{Precision}}}{P_{\text {Accuracy}}+P_{\text{Precision}}} \tag{7}\]  
The wake sample would be positive, and the sample without the wake would be negative. \(TP\) denotes the number of samples identified as positive and with positive truth values. \(FP\) indicates the number of samples identified as positive and with negative truth values. \(FN\) denotes the number of samples identified as negative and with positive truth values. \(TN\) refers to the number of samples identified as negative and with negative truth values.  
Additionally, we used floating point operations (FLOPs) to evaluate the computation complexity of the models in aircraft wake recognition. The million floating point operations (MFLOPs) are normally used.
## 2 Materials and Methods
### Collection of Odor Emission Data  
Data concerning Italian asphalt plants have been collected from studies carried out from all three of the authors' laboratories. In this dataset, some data come from old studies, and information on the plants was not available, as was another dataset of which the samples were collected by the plant directly, and there was no available information regarding the sampling method. The plant dataset considered asphalt concrete (HMA) and bituminous waterproofing membranes as well. Samples were mainly collected from the stack, during normal operation of the facility, at the isokinetic flow sampling point. Fugitive emissions were collected both from the trucks, during loading operations, in the proximity of hot material conveyor belts, and at hot material tank vents. Some facilities had adopted odor control measures, mainly water vapor columns or the use of deodorized liquids around the facilities. A limited number of plants were equipped with abatement systems with air/oil or oil mist separators, mainly for the treatment of diffuse emissions, before they were emitted through the main stack. In the present study, all the odor concentrations data considered for the OEF calculation were used, considering the values before any odor abatement systems present.  
The determination of odor concentration was performed according to EN 13725:2003 [18]. Samples were collected in polyethyleneterephthalate (Naloplan NA(tm)) bags with a depression pump in a vacuum chamber, the \"lung method\", and analyzed within 30 h of collection by dynamic olfactometry. Gases and panel members, data recording, calculation and reporting were conducted according to the European Standard EN 13725. Samples with high moisture concentrations could be pre-diluted with dry odor-free air to prevent condensation on the bag walls. Odor concentrations (\(C_{od}\)) were reported as European olfactory units/m\({}^{3}\) (ou\({}_{E}\)/m\({}^{3}\)).  
To estimate the OEF of bitumen-related production sites, odor concentration data are not enough; to estimate the extensive quantity of odor impact, the \(OER\) is needed. The calculation of OER\({}_{i}\), for each considered plant \(i\), is made with the equation:  
\[OER_{i}=\overline{C_{od,i}}\cdot Q_{air,i} \tag{2}\]  
where \(OER_{i}\) (ou\({}_{E}\)/h) is the odor emission rate representative for each plant \(i\), \(C_{od,i}\) is the geometric mean of the measured odor concentration at the emission of each plant (ou\({}_{E}\)/m\({}^{3}\)), purged by abatement system if present, and \(Q_{air,i}\) is the air flow rate of the emission (m\({}^{3}\)/h), under standard conditions for dynamic olfactometry (293 \({}^{\circ}\)K and 101.3 kPa on a wet basis). Where more than one emission chimney was present in a single production site, the sum of each OER was considered. Due to comparison with annual production data, the days and hours of actual operation are taken into account.  
For each production site \(i\), the total \(OER_{i}\) was divided by the gross production value, obtaining and experimental sample of \(OEF_{i}\). Diffuse emissions were neglected from the present assessments.
# Frequency Domain Full Waveform Inversion Method of Acquiring Rock Wave Velocity in Front of Tunnels
## 2 Method
### Frequency Domain FWI
#### 2.2.2 Gradient Operator and Step Size  
The adjoint state method is the common gradient operator computing method in FWI, which can avoid extra calculation consumption. We apply the second-order Taylor-Lagrange expansion to the objective junction \(E(v)\) at \(v_{0}\). Then, we take the first-order derivative with respect to \(v\) at both sides of the approximation. The adjoint state is defined by \(\frac{\partial E(v)}{\partial v}=0\). [34]:  
\[[\text{Re}(H)+\lambda I]\delta v=-\text{Re}(G^{\intercal}\Delta d^{*}), \tag{8}\]  
where \(H\approx G^{\intercal}G^{*}\) is an approximate Hessian matrix, \(G\) is a Jacobian matrix, \(I\) is a unit matrix, and \(\lambda\) is a regularization parameter. There is one adjoint system per shot and per angular frequency. The matrix A propagates the shot into the earth and \(u_{s}\) is the incident field originating at \(s\). \(\lambda_{s}\) is the backpropagation of the residual field. The gradient operator in the frequency domain is [35]:  
\[\
abla E=-\text{Re}(\sum_{\omega}\sum_{s}\lambda_{s},\frac{\partial A}{ \partial v}u_{sx}). \tag{9}\]  
The iterative step length is obtained using the method of line search. There are two main line search methods: imprecise search method and parabolic search method. This article adopts the two-point parabolic search method, and the process is as follows.  
First, a forward wavefield simulation is required. We give a global trial step size \(\alpha_{0}\) (usually 1% of the background speed). A pseudo-update velocity field \(v_{p}=v_{0}-\partial_{0}\delta\) is obtained at the update rate, and a finite difference forward numerical simulation is performed on the pseudo-update velocity field. Calculate the forward and back propagation wavefield and obtain the pseudo-velocity field objective function \(E(v_{p})\), and do a second-order Taylor expansion around it:  
\[E\big{(}v_{p}\big{)}=E(v)-\alpha_{0}(g(v),g(v))+\frac{\alpha^{2}{}_{0}}{2} \tau=E(v)-\alpha_{0}g(v)^{2}+\frac{\alpha^{2}{}_{0}}{2}\tau, \tag{10}\]where \(\tau\) is a value related to the second derivative of the error norm with respect to velocity. In the case of the current error function \(E(v)\), the error function based on the pseudo-velocity field \(E(v_{p})\), and the test step known, the \(\tau\) can be calculated:  
\[\tau=2\frac{E(v_{p})-E(v)+\alpha_{0}g(v)^{2}}{\alpha_{0}{}^{2}}. \tag{11}\]  
If there is an optimal step size \(\alpha\), it must meet \(\
abla E(\alpha)=0\). The second-order Taylor expansion of the error function of the optimal step is expanded in the same form as speed, at which point the optimal step size is:  
\[\alpha=\frac{g(v)^{2}}{\tau}. \tag{12}\]
## 4 Experimental Test Bed and Measuring Equipment
### Noise Generation Scenarios  
An isolation transformer was used to supply electricity from the mains via the LISN to power and manage the loads (laptop charger, computer monitor, CFL bulb, hairdryer, and so on). The load (or loads) was turned on and off at regular intervals to generate impulsive noise while the data transfer was ongoing. The coupling circuit connected the LISN and the load(s) to the power lines. The coupling circuit connected the USRP (transceiver) to the power line.  
The transmit and receive USRPs were linked to the test bed through a gigabit Ethernet cable connecting the transmitting and receiving host PCs. The transmit and receive host computers were used to record the noisy bit error sequence that happened when the load on the PLC network was turned on and off. The SD-PLC transceiver test bed in the laboratory can send and receive complex digital baseband M-QAM signals by using an appropriate differential coupling circuit explicitly developed for LV-PLC implementations.  
Table 1 presents the fundamental software, hardware, and transmission configuration parameters crucial for this study.  
This study examined the impulse noise error sequence by analyzing two distinct scenarios, namely the \"weakly interrupted\" and \"strongly interrupted\" scenarios. The weak interruption scenario pertains to the connection of a solitary impulse noise generator to the channel, while the strong interruption scenario involves the connection of two indoor electrical devices that serve as noise generators to the channel. In both situations, the channel experiences interruptions of varying magnitude.  
Figure 7: The PLN topology.