esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study Analysis
### Case Study 4: Safety Barrier System
The fourth emerging practice is related to the revised \"maintenance and testing concept\" document, where a new guideline is created to build a model-based maintenance and testing concept. The model-based maintenance and testing concept is a transformation of the document into a parametric model that can be updated over time as it interacts with other documents and models. Therefore, the fifth emerging practice is a new guideline for continuously managing and coordinating interactions between documents and models. In this case, emerging practices have impacted the RBM program's functions and data flow. The valve monitoring system is currently used as automatic reporting for planned functional testing. It also reports the technical condition during opportunistic events (closing or opening valves due to operational needs) and the maintenance program is able to manage data and associated notifications. The execution function was clearly affected when the functional testing became remote and automatic. \"Reporting and Analysis\" functions are affected as a new data source, and the valve monitoring system introduces flow. In this case study, data flow was designed to be dynamic in the future using digital twin and model-based systems engineering technology. The traditional data flow is dominated by documents that make data and information difficult to update, especially when there is a \"mother document\" that feeds many other documents. Therefore, the practice of converting the document into parametric models makes the data flow dynamic in several directions (feed-forwards and feed-backs) rather than the traditional one-way direction. The main benefit of installing the valve monitoring system in this case is to perform functional testing in an automatic and remote manner and to reduce the cost of manual functional testing. Since the valve is normally open or closed on demand, the collected condition data are not yet helpful in detecting failures. There is ongoing development in these types of condition monitoring systems to cover more failure modes, detect and qualify leakage, related failure to root cause, and more direct indicators rather than indirect indicators such as closing or opening times, which are affected by several factors. On the other hand, the model-based maintenance and test concept ensures better communication, has the potential to reduce rework at the handover phase, explores better maintenance concepts at the design phase, and reduces risks due to failures at the operation phase.
## 3 Testing and Results  
Three testing activities were performed in different conditions, with the main purposes being to (i) find out whether the Ublox F9P RTK module is keeping the ambiguity fixing in the proximity of obstacles and occlusions that limit the sky visibility; (ii) assess the time to re-fix the solution after signal interruptions occurred, considering different operational conditions; and (iii) understand in which flying conditions false fixing or undetectable positioning gross errors occur more frequently. All these tests simulate common operational conditions faced by a UAV during any infrastructure inspection: in (i), the platform approaches the infrastructure, progressively reducing the number of visible satellites until the number of satellites is insufficient to deliver an accurate position; in (ii), the UAV platform moves away from the infrastructure (or an indoor environment) until it obtains a sufficient number of satellites to re-fix the ambiguity; and in (iii), the quality of the re-fixed ambiguity is assessed according to the motion of the UAV in front of the infrastructure.  
The testing activities were performed outside the old ITC Faculty building in Enschede, which offered tall walls causing multipath and obstructing the sky visibility (oriented both to North and South), and a short tunnel passing through the southwest facade, simulating an indoor (GNSS-denied) environment. Figure 2 shows details about the location of the three tests. For each of them, the three abovementioned aspects (i.e., keeping the ambiguity fixing, the time to re-fix, and the presence of false fixes) were inspected under different operational conditions.  
In particular, test-1 and test-2 simulated the hovering conditions of a drone moving in and out of a tunnel and operating close to a tall wall of the building. In contrast, test-3 aimed to inspect the positioning performances of the drone flying under the tunnel and moving out of it, passing from partial sky visibility conditions and possible multipath to clear sky conditions.
###### Abstract
In conventional network architecture, the each layer of the protocol stack is independently operated, and data are sent only among the nearby layers [17]. However, this conventional protocol stack is inadequate for the MANET due to its direct dependencies among the physical and the upper layers [18]. However, the data are dynamically swapped to the different protocol layers in the cross-layer design. Hence, the data are directly swapped among any layers of the protocol stack [19]. The transmission medium of wireless networks has many restrictions, such as interference, bandwidth restriction, and dynamic topology of the network [20]. Therefore, the Energy Centric Tunicate Swarm Algorithm (ECTSA) is proposed and simulated. The simulation findings show that the ECTSA solves real-world case studies with complex search spaces and produces better optimized solutions than other competing methods. The major contributions of this research are as follows:  
* The cross-layer design (ECTSA) is utilized in the network to enhance the performance of the high-layer protocols which contain a restricted wired network. * The ECTSA cross layer contains an important merit that is exploited to create interoperability among the layers. * The link stability and energy level of a node's battery are prevented by the data-broadcasting processes of the MANET. The structure of this research is as follows: Section 2 elaborates the literature review of existing work on cross-layer routing; Section 3 describes the process of the ECTSA method and its network model, along with mathematical equations; The performance measures and its comparative analysis are explained in Section 4. Finally, the conclusion is stated in Section 5.
## 3 Evaluation
### Energy Model  
To carry out the experiments to test the proposed method, an energy model is needed to simulate the data communications by the radio devices. Thus, we use the first order radio model, which is widely used in the related literature [5]. The model, depicted in Figure 4, is described in Equations (4)-(6). To check the results of the energy model, we have compared it with the results presented in [32]. The comparison shows that the results are comparable with the measures shown for a IEEE 802.15.4 compliant radio transceiver CC2420 chip at 2.4 GHz for the average distances employed in our experiments.  
\[E_{Tx}(l,d)=f(x)=\begin{cases}l\cdot(E_{elec}+E_{fs}\cdot d^{2}),&d\leq d_{0}\\ l\cdot(E_{elec}+E_{mp}\cdot d^{4}),&d>d_{0}\end{cases} \tag{4}\]  
\[d_{0}=\sqrt{\frac{E_{fs}}{E_{mp}}} \tag{5}\]  
\[E_{Rx}(l)=E_{elec}\cdot l \tag{6}\]  
where:  
* \(l\) is the number of bits of the message.
* \(E_{elec}\) is the energy that consumes the transmitter or the receiver circuitry for each bit.
* \(d\) stands for the distance between the sender and the receiver of the message.
* \(E_{fs}\) is the energy consumed by the amplifier according to the free space model \((d\leq d_{0})\) to get an acceptable bit error rate (in Figure 4, it appears as \(\varepsilon_{amp}\) for both cases).
* \(E_{mp}\) is the energy consumed by the amplifier in the multi-path (mp) model \((d\leq d_{0})\) to obtain an acceptable bit error rate (in Figure 4 appears as \(\varepsilon_{amp}\) for both cases).  
When a CH receives data from a contributing node, in addition to the energy spent in receiving the data, the CHs spend energy in the data aggregation process [33]. In the aggregation process, all the CHs send only a summary of those values, which is usually the result of a statistical operation. The aggregation process reduces the number of bits sent when compared with the transmission of each value independently. Therefore, the amount of energy spent by a CH in the reception and aggregation process of a \(l\)-bit message is defined in Equation (7):  
\[E_{Rx-DA}=(E_{elec}+E_{DA})\cdot l \tag{7}\]  
where \(E_{DA}\) is the energy spent by the processing unit of a CH when it aggregates the data received from a contributing node.  
Figure 4: Illustration of the first order radio model.
# A Clutter Loss Model for Satellite Communication Systems
## 2 Rationale of the Model  
The method presented in this contribution to estimate the Earth-space clutter loss in an urban environment is underpinned by solid physical bases and relies on the approach described in Report ITU-R P.2402 [11]. The idea is to calculate the statistics of the propagation loss (due to the presence of buildings) for several rays of a base station towards the sky, in an urban area; the position and height of the buildings are randomly drawn from the local associated statistics. Compared to the model in [11], the new method features accurate (yet simplified) expressions to calculate the reflection loss, the possibility to consider an unlimited number of reflections (instead of a maximum of two reflections) and diffractions, variable incidence planes (instead of just that orthogonal to the building facade), different wave polarizations and the contribution of ground reflections. Furthermore, as in [11], the diffraction loss is calculated by treating each building like a knife-edge obstacle, but using the real building distance and height, instead of the effective value employed in [11].
## 3 Performance Analysis
### Asymptotic Average BER  
This subsection will derive the asymptotic bound of the ABER when the average transmit optical power \(P_{j}\) is sufficiently large.  
According to the series expression of [PERSON]'s G-function [24], when \(P_{j}\) tends to infinity, we can express (31) as \[P_{\rm e}(j) \approx\frac{1}{2}\exp\left(-\frac{\theta_{\rm FoV,m,j}^{2}}{2\sigma_{ \rm o}^{2}}\right)+\frac{2^{\alpha_{m,j}+\beta_{m,j}-4}\zeta_{m,j}^{2}}{\sqrt{ \pi^{3}}\Gamma(\alpha_{m,j})\Gamma(\beta_{m,j})}\left[1-\exp\left(-\frac{\theta_ {\rm FoV,m,j}^{2}}{2\sigma_{\rm o}^{2}}\right)\right]\] \[\times\frac{2\Gamma\left(\frac{1+\alpha_{m,j}-\psi_{m,j}}{2} \right)\Gamma\left(\frac{\alpha_{m,j}-\psi_{m,j}}{2}\right)\Gamma\left(\frac{1 +\psi_{m,j}}{2}\right)d_{m,j}}{\psi_{m,j}}\] \[\times\left(\frac{\alpha_{m,j}^{2}\beta_{m,j}^{2}\sigma_{j}^{2}}{8 \left(A_{m,j}^{\rm H}\mu_{m,j}P_{\rm T}\right)^{2}}\right)^{\frac{\psi_{m,j}} {2}},\psi_{m,j}=\min\left(\zeta_{m,j}^{2},\beta_{m,j}\right) \tag{35}\]  
where \(d_{m,j}=\Gamma\left(\beta_{m,j}-\zeta_{m,j}^{2}/2\right)\Gamma\left(1+\beta_ {m,j}-\zeta_{m,j}^{2}/2\right)\), if \(\zeta_{m,j}^{2}<\beta_{m,j}\). Moreover, \(d_{m,j}=2\sqrt{\pi}\left(\zeta_{m,j}^{2}-\beta_{m,j}\right)\) if \(\zeta_{m,j}^{2}>\beta_{m,j}\).  
In (35), when the average transmitted optical power \(P_{j}\) tends to infinity, the term containing \(P_{j}\) tends to zero. Thus, the asymptotic expression of \(P_{\rm e}(j)\) can be expressed as  
\[p^{\rm bound}(j)=\frac{1}{2}\exp\left(-\frac{\theta_{\rm FoV,m,j}^{2}}{2\sigma _{\rm o}^{2}}\right) \tag{36}\]  
Substituting (36) into (34), we can obtain the asymptotic expression of the ABER as  
\[P_{\rm e}^{\rm bound}=\frac{1}{2}\sum_{j=1}^{2}\exp\left(-\frac{\theta_{\rm FoV,m,j}^{2}}{2\sigma_{\rm o}^{2}}\right)-\frac{1}{2}\prod_{j=1}^{2}\exp\left(- \frac{\theta_{\rm FoV,m,j}^{2}}{2\sigma_{\rm o}^{2}}\right) \tag{37}\]  
**Remark 2**.: _In (37), when \(P_{j}\rightarrow\infty\), increasing \(P_{j}\) will not further improve the ABER of the system. In this situation, the ABER of the system is only related to the FoV \(\theta_{\rm FoV,m,j}\) and the standard deviation of direction deviation \(\sigma_{\rm o}\) and is independent of the number of relay nodes._
## 4 Results
### Dynamic Simulations
\(\epsilon_{\text{pl,shell,BL}}=1.444\%\), \(\epsilon_{\text{pl,shell,JC}}/\epsilon_{\text{pl,shell,BL}}=0.63\); for further values, see Table 4). The pressure distribution used in Simulations 1.2a and 1.2b, on the other hand, generates von Mises equivalent stresses far below the yield point (Figure 13). The maximum stresses in the roof are 233 MPa, approximately two thirds of the nominal yield point of 345 MPa. The variation in the material formulation therefore shows no effect here; the results are identical. The anchorage remains elastic while a rather low lifting height of 6 mm at most occurs. The approach of shock wave loading using LBE (Simulations 1.3a/-b) generates a stress distribution that reaches the yield limit in the elements of the roof, ring stiffeners and anchorage and causes plastic strains (Figure 13, Table 4). However, these are far below those from Simulations 1.1a/-b (see, e.g., in Table 4, Simulations 1.1a and 1.3a: \(\epsilon_{\text{pl,roof,1.1a}}=0.249\%\gg\epsilon_{\text{pl,roof,1.3a}}=0.067\%\)). At the maximum lifting height of 11 mm, the anchorage undergoes minimal plastic deformation. When applying a suction load to the roof, the stress distributions are essentially qualitatively comparable to the simulations with roof compressive load. Simulations 2.1a and 2.1b show, analogous to Simulations 1.1a and 1.1b, high stresses on tank wall sides in the area and above the yield strength (Figure 13). For both material formulations, slightly lower or equal values result when applying the roof suction load (Table 4) in comparison to compression. Additionally, the stresses of the anchors and the uplifting heights are slightly lower, even though the roof suction load should induce higher uplifting forces. A look at the deformation plots in Figure 14 shows deformations congruent with the stress results. The analyses with the application of the uniform pressure load to half the tank circumference and roof (Simulations 1.1a/-b) show heavily deformed areas on the tank wall sides. The maximum deformations are approximately 1 m. Nearly the same applied to Simulations 2.1a/-b, indicating that the roof load does not influence the tank shell's deformation. Significantly smaller deformations result in Simulations 1.2a/-b; here--with purely elastic material behavior (the minimal plastic strain of the anchorage \(\epsilon_{\text{pl,anchor,2.1a}}=0.005\%\) can be neglected)--maximum deformations of 11 mm result in the tank shell and similarly on the tank wall sides, but with the location of the maximal value being moved a little more to the tank front. The equivalent analyses with roof suction load (Simulations 2.2a/-b) show different behavior here: the maximum deformation of 17 mm is concentrated on the compression ring, which forms the transition to the roof.
## 2 SPME-GC/MS Methods  
### Analysis of PFBOA-Derivatives  
In general, carbonyl compounds contribute to the wine aroma even though they are present in low levels. GC/MS analysis of the \(O\)-pentafluorobenzyl (PFB) derivatives formed by reaction with \(O\)-(2,3,4,5,6-pentafluorobenzyl)-hydroxylamine (PFBOA) by recording in singular-ion-monitoring (SIM) the mass spectrum base peak signal at _m/z_ 181 (characteristic of PFB-oximes) is a selective and sensitive method. On the other hand, derivatization increases the complexity of analyte peaks in the chromatogram due to formation of two oximes for each carbonyl group (isomers \(E\) and \(Z\), except for formaldehyde) [38]. By this method several studies of carbonyl compounds in hydro-alcoholic matrices, such as wine, model wine solutions, and spirits, were performed [30,31,39,40,41,42,43,44,45,46,47]. Malolactic fermentation (MLF) is an important oenological process performed after the alcoholic fermentation for improving the organoleptic characteristics and the microbiological stability of wine [46]. The process is carried out by lactic acid bacteria: it can occur naturally or be induced by the  
Figure 3: Structures of “foxy-smelling” compounds and alkyl methoxypyrazines in grapes. In brackets, the odor descriptor is reported [25]. inoculum of commercial bacteria strains. Conversion of L(-)-malic into L(+)-lactic acid decreases wine acidity [30]; usually, the inoculation of selected bacteria strains enables control over the process [48]. With MLF, together with the other fermentative compounds (esters, sulfur and nitrogen compounds, volatile phenols, and volatile fatty acids), also the carbonyl profile changes, increasing the aromatic complexity of wine [42,49]. HS-SPME and GC/MS analysis of PFBOA derivatives was performed to study the carbonyl compounds in Merlot wines after MLF [46]. In general, PFBOA derivatives are characterized by higher volatility, thermal stability, and affinity for the SPME fiber with respect to the corresponding carbonyl compounds [20]. SPME was performed by using a polydimethylsiloxane/divinylbenzene (PDMS/DVB) fiber; GC/MS analysis was performed by electron impact ionization (EI) and chemical ionization in both positive (PICI) and negative (NCI) mode [50]. NCI coupled to SPME provided lower detection limits for isobutyraldehyde, 2-methylbutanal, isovaleraldehyde, (_E_)-2-hexenal, 1-octen-3-one, (_E_)-2-heptenal, methional, (_E_)-2-octenal, phenylacetaldehyde, and (_E_)-2-nonenal [50]. PICI was used for determination of the principal carbonyl compounds in wine, such as acetaldehyde, diacetyl, and acetoin [46]. By using methane as reagent gas, abundant formation of acetaldehyde derivative [M+H]\({}^{+}\) ion at _m_/_z_ 240, diacetyl mono-derivative [M+H]\({}^{+}\) ion at _m_/_z_ 282, and _o_-chlorobenzaldehyde derivative [M+H]\({}^{+}\) ion at _m_/_z_ 336 (the internal standard), was observed. For acetoin abundant formation of [M+H-18]\({}^{+}\) ion at _m_/_z_ 266 was observed. These signals were used for the quantification of compounds. Experimental PICI conditions used are reported in Table 2[46].
# Reconstruction of Land and Marine Features by Seismic and Surface Geomorphology Techniques
## 3 Seismic Attributes
This attribute provided a dense layer model of the subsurface that contained many structural and stratigraphic details (complement coherence and impedance seismic attributes) where these kinds of geological features were associated with singularities (discontinuities in seismic impedance). In addition, this seismic attribute does not require well controls for enhancing reservoir interpretation of migrated seismic data; nevertheless, well controls give an advance petroleum reservoir characterization. [PERSON] and references therein [99] provided a classification of seismic attributes based on (1) wave kinematics or dynamic categories and (2) geologic reservoir feature categories. These include:  
1. Amplitude (reflection strength, RMS amplitude, etc. ), waveshape (apparent polarity and maximum peak amplitude), frequency (instantaneous frequency and average zero crossing), attenuation (amplitude slope and attenuation of sensitive bandwidth), phase (instantaneous phase and response phase), correlation (length and average), energy (reflection strength and vibration energy), and ratios (ratio of adjacent peak amplitudes). 2. Bright and dim spots (slope of reflection strength), unconformity traps (average correlation), oil and gas bearing anomalies (instantaneous real amplitude), thin layer reservoirs (finite frequency-bandwidth energy), stratigraphic discontinuity (apparent polarity), clastic-carbonate differentiation (ratio of adjacent peak amplitudes), structural discontinuity (maximum-minimum correlation), and lithology pinch-out (cosine of instantaneous phase). Furthermore, seismic attributes are often considered a form of \"inversion\", a process widely used to transform seismic reflection data into valuable and meaningful geomorphological elements and reservoir properties that can later be integrated into detailed geomorphology, reservoir geology, and simulation modeling. This inversion method integrates seismic and wellbore data from which an attribute such as acoustic impedance is derived from sonic and density logs and is subsequently used to populate their properties into the seismic cube [26; 100; 101; 102]. [PERSON], [PERSON], and [PERSON] [89] introduced \"digital image processing\" as a complement for seismic attribute analysis where advances in digital image-processing algorithms and computing technology are able to identify and delineate geological and geomorphological features from seismic reflection data. In addition, the removal of random noises and artifacts from seismic reflection data such as velocity push-down or pull-ups, seabed multiples, acquisition and processing footprints, etc., can be solved utilizing digital image-processing techniques including edge/structure-preserving smoothing algorithms [89]. This kind of technique also provides automatic interpretation based on seismic attribute analysis. In particular, these techniques performed very well on fault and channel detection using several seismic attributes such as edge detection, coherence, dip, curvature, randomness, and spectral decomposition.
## 1 Introduction  
Control algorithms are essential parts of the successful operation of processes. In recent years, data-driven intelligent control algorithms mainly relying on artificial intelligence and soft computing concepts have emerged as a valuable alternative to conventional model-based control methods, especially in cases involving complex systems. A special category of such new algorithms that employ the reinforcement learning principle to tune deep neural networks for optimal control of processes is still in the infancy stage but has shown promising results when applied to the control of highly nonlinear, high-dimensional, time-delayed, time-varying, partially known, or hard-to-mathematically-formalize systems. From this perspective, centrifugal chiller systems (CCSs) from complex Heating Ventilation Air Conditioning (HVAC) are no exception, needing specifically tailored control strategies. In this context, the current paper aims to explore the development and MATLAB/Simulink implementation of two neural reinforcement learning (RL) control algorithms utilizing deep learning neural network (DLNN) frameworks in relation to a specific intricate HVACcentrifugal chiller system. We address the key limitations of existing approaches by making the following contributions:  
* We developed an accurate simplified CCS model in a state-space representation. Based on the input-output measurement dataset of the open loop multi-input multi-output (MIMO) CCS extended nonlinear model, having 39 states, three inputs and two outputs, a MIMO autoregressive moving average with an exogenous input (ARMAX) delayed fourth-order polynomial model of z-representation in the complex domain is obtained. This ARMAX model was converted into a linearized MIMO CCS model with four states, three inputs, and two outputs;
* We designed and tuned a standard PID controller and also a standard model predictive control (MPC) for comparison purposes;
* We built two RL DLNN controllers connected in series in a forward path with the MIMO CCS simplified model represented in state space, for temperature control inside the evaporator subsystem and liquid refrigerant level control within the condenser subsystem;
* We rigorously evaluated the tracking performance for both RL DLNNs and compared it with the results obtained using classic PID or MPC controllers.  
The remainder of this research paper is structured in the following six sections. Section 2 is devoted to a brief literature review and some preliminaries regarding HVAC control systems. Section 3 outlines four conventional closed-loop control methods, designed and implemented in Matlab/Simulink, with three being based on a standard PID controller, and the fourth being an MPC. Section 4 focuses on the design of two RL DLNN controllers. The extensive simulation results are reported in Section 5. Section 6 is dedicated to discussions, and Section 7 concludes the paper, briefly highlighting the main yields and the future work directions.