esa-sceva/satcom-chunk-collection huggingface dataset:
# Compact Quad-Port MIMO Antenna with Ultra-Wideband and High Isolation
## 2 Design of UWB Antenna Unit
### Simulation Results Analysis  
Figure 6 shows the comparison curves between the simulation and test results for the reflection coefficient of the UWB antenna. The green line in Figure 6 represents \(\mathrm{S_{11}}=-10\) dB. The simulated working area of the antenna is the light green area, from \(1.83\,\mathrm{GHz}\) to  
Figure 5: The variation curve of the reflection coefficient of UWB antenna with (**a**) w, (**b**) fw1, (**c**) fw2, (**d**) gw.  
15.69 GHz, which can completely cover the 3.1-10.6 GHz required for UWB communication. The blue dashed line is the S\({}_{11}\) curve obtained by the vector network analyzer. The measured S\({}_{11}\)\(<\)\(-\)10 dB band is 1.8-16.38 GHz, the absolute bandwidth reaches 14.58 GHz and the relative bandwidth is up to 160.4%. At the same time, it can be observed that the test curve of the reflection coefficient basically matches the trend of the simulation curve, and the working band has been expanded to a certain extent, which is related to the loss compensation function of the vector network itself, and the error is mainly due to the manual welding of the antenna sample adapter, the slight movement of the antenna sample during the test, and the wear and tear of the test connection itself.  
The radiation characteristics of the UWB antenna are viewed by the simulation software HFSS, and the radiation patterns of the E-plane (XOZ plane) and H-plane (XOY plane) at four frequency points: 3 GHz, 6.16 GHz, 10.6 GHz and 14.92 GHz are shown in Figure 7. As shown in Figure 7a, the radiation patterns in the E-plane of this UWB antenna are all approximately circular, showing stable omnidirectional radiation characteristics. The radiation pattern in the H-plane shown in Figure 7b approximately shows certain bidirectional radiation characteristics, but with the increase in frequency, there is a certain distortion, especially at the frequency of 14.92 GHz, which is due to the fact that with the increase in frequency, the wavelength gradually becomes smaller and the antenna size can be compared with it, and the radiation pattern is more easily affected.  
Figure 6: Comparison of simulation and test curves for S\({}_{11}\) of the UWB antenna.  
Figure 7: Simulated Radiation pattern of UWB monopole antenna in (**a**) E-plane (\(\varphi=0^{{}^{\circ}}\)) (**b**) H-plane (\(\varphi=90^{{}^{\circ}}\)).
## 3 Results  
### Volatile Compounds of Citrus and Other Nectar Honeys  
Forty-one volatile compounds were identified in citrus and other nectar honey samples. Among these volatiles, thirty-three were identified in citrus honey samples of different geographical origin, and nine additional volatiles were identified in flower and thyme honey samples (grouped as other nectar honey samples). The volatile compounds were grouped into classes: alcohols, aldehydes, esters, ethers, hydrocarbons, ketones, phenolics, and terpenes (Table 1). The most abundant volatile compounds were aldehydes, followed by esters and the other classes of compounds. Total semi-quantitative volatile content (TSQVC) followed the order Morocco = Greece > Egypt > Spain. Figure 1 shows a representative gas chromatogram of citrus honey from Argos (Greece). It should be stressed that of the 41 identified compounds, 30 showed significant differences between the different producing countries, also including the different honey types used for the validation of results (Table 1). This indicates that the volatile fraction of citrus or nectar honey provides potentially useful information enabling its correct geographical and botanical discrimination. In support of this finding, some volatile compounds were characteristic for the other nectar honey samples studied: 1-octanol, 1-nonanol, ethyl hexanoate, ethyl heptanoate, ethyl malonate, undecane, delta-3-carene benzeneethanol, and benzeneacetonitrile. Ethyl malonate (propanediolic acid ethyl ester), however, is for the first time reported in the literature to contribute to the aroma of flower honey from Aitoloakamania. Figure 1: Gas chromatogram of citrus honey from Argos (Greece). Volatile compounds used as potential indicators of citrus honey geographical origin are numbered accordingly. 1: Octane. 2: Benzaldehyde. 3: Herboxide (isomer II). 4: Benzeneacetaldehyde. 5: cis-Linalool oxide. 6: Linalool. 7: Nonanal. 8: Lilac aldehyde C. 9: Lilac aldehyde D. 10: Ethyl octanoate. 11: Decanal. 12: \(\alpha\), 4-Dimethyl-3-cyclohexene-1-acetaldehyde. 13: Ethyl nonanoate. 14: Methyl anthranilate. 15: Ethyl decanoate. 16: Ethyl dodecanoate. 17: Ethyl hexadecanoate. IS: Internal standard.
# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
## 4 Results and Discussion
### Parameter Configuration  
After completing data preprocessing and defining the architecture of the neural network, the model training process was carried out. The loss function used was the categorical cross-entropy, handling non-numeric classes probabilistically. The SGD, RMSprop, and ADAM optimizers were tested, with the latter yielding the best results. Since ADAM adjusts the learning rate automatically, it was not specified manually. In the context of regularization techniques, various dropout values between 0 and 1 were tested, and a value of 0.7 led to the best model performance. Furthermore, weight decay was applied using a penalty coefficient of \(5\cdot 10^{-4}\). The signals were divided into 30 s sequences. For EEG signals sampled at 100 Hz, this is equal to 3000 samples per sequence. During the application of E-LRUS, the training set was balanced to 537 epochs per class, based on the least represented class, N1. Several batch sizes (16, 32, 64, and 128) were tested, with the best results achieved with 64 samples per batch. The learning cycles were set to a total of 100 epochs, with early stopping configured at 30 epochs. These parameters were fine-tuned across training sessions and corresponded to those that led to the best-performing model.
## 5 Conclusions  
This work deals with a scheduling problem for SACTs with WRTCs and chamber cleaning operations which are commonly seen requirements in real semiconductor manufacturing. Different from the studies on scheduling SACTs with purge operations in [22], this work focuses on a more general case in which a chamber at a PM may process more than one wafer before a cleaning operation is required. To do so, this work presents a virtual wafer-based method such that a PM processes either a real wafer or a virtual wafer at a time. Then, based on an optimal one-wafer cyclic schedule that can be obtained by the method in [15], by the proposed virtual wafer-based method, a genetic algorithm is developed to find an optimal or near optimal solution. Extensive experiments are conducted to verify the proposed method. Based on the experimental results, GA can be used to find a high-quality solution within reasonable time. Furthermore, the obtained solution is a periodical wafer loading sequence based on a one-wafer cyclic schedule that can be easily implemented. Therefore, it has a high practical value in semiconductor manufacturing.  
In practice, the PMs in cluster tools are prone to failure [27, 28]. Such a PM failure may result in a deadlock such that the WRTCs in PMs are violated. It is necessary to establish real-time failure response policies to deal with PM failures in different situations. Thus, our future work should deal with PM failures for cluster tools with WRTCs and chamber cleaning operations.  
Conceptualization, J.L. and Y.Q.; methodology, J.L., Y.Q. and S.Z.; validation, J.L. and S.Z.; investigation, T.S.; writing--original draft preparation, J.L.; writing--review and editing, Y.Q., Z.L. and N.W.; supervision, Y.Q., Z.L. and N.W.; funding acquisition, N.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by National Natural Science Foundation of China (NSFC), grant number 61803397 and in part by Science and Technology development fund (FDCT), Macau SAR (File Nos: 0017/2019/A1, 0018/2021/A1, and 0083/2021/A2).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Analytical Quasi-Static Modeling  
The main component parts of the classic monolithic micro-manipulator are eight regular, circular flexure hinges. As shown in Figure 1a, the displacement loads and all of the hinges and levers are symmetric, and a 1/2 model is separated as the analytical objective, as shown in Figure 1b.  
It can be seen in Figure 1b that the normal force, X\({}_{1}\), and bending moment, X\({}_{2}\), will be considered as the redundant forces. The input force, X\({}_{3}\), that is produced by the PZT is not a given force and the vertical displacement of the edge point of the 1/2 model would be considered. Combining above conditions and constrains, the uncanonical equations of the 1/2 model are established as:  
\[\begin{pmatrix}\delta_{11}&\delta_{12}&\delta_{13}\\ \delta_{21}&\delta_{22}&\delta_{23}\\ \delta_{31}&\delta_{32}&\delta_{33}\\ \delta_{11}&\delta_{12}&\delta_{13}\end{pmatrix}\begin{pmatrix}\begin{bmatrix} X_{1}\\ X_{2}\\ X_{3}\end{bmatrix}=\begin{pmatrix}0\\ 0\\ \Delta_{\text{in}}\\ \Delta_{\text{out}}\end{pmatrix} \tag{1}\]  
Figure 1: A displacement mechanical amplifier (**a**) schematic of the amplifier (**b**) schematic of 1/2 model equivalent system.  
where \(\Delta_{m}\) and \(\Delta_{out}\) are the varied input displacement output load and the displacement of the manipulator, respectively. \(\delta_{g}\) is defined as the corresponding displacement and can be obtained as follows:  
\[\delta_{g}=\int\frac{\bar{M}_{s}\bar{M}_{s}}{EI}dx=\int_{s\,d\,\bar{M}_{s}(0) \bar{M}_{s}(0)}d0+\int_{CD}\frac{\bar{M}_{s}(0)\bar{M}_{s}(0)}{EI(0)}d0+\int_{ DO}\frac{\bar{M}_{s}(x)\bar{M}_{s}(x)}{EI}dx \tag{2}\]  
where \(M_{s}\) and \(M_{s}\) can be obtained by bending moment expressions. \(I(0)\) is given by:  
\[I(0)=\frac{Ebw^{2}(0)}{12}=\frac{Eb\left[2r+t-2r\cos(0)\right]^{3}}{12} \tag{3}\]  
According to Equation (1), the displacement ratio \(R\) and the input stiffness \(K_{\mathrm{in}}\) can be expressed as:  
\[R=\frac{\Delta_{out}}{\Delta_{in}}=\frac{\delta_{12}^{2}\delta_{1 3}-\delta_{12}\delta_{13}\delta_{12}-\delta_{11}\delta_{22}\delta_{13}+\delta _{11}\delta_{23}\delta_{12}-\delta_{12}\delta_{23}\delta_{14}+\delta_{13} \delta_{22}\delta_{11}+\delta_{13}\delta_{22}\delta_{11}}{\delta_{33}\delta_{ 12}^{2}-2\delta_{12}\delta_{13}\delta_{22}+\delta_{22}\delta_{13}^{2}+\delta _{11}\delta_{22}^{2}-\delta_{11}\delta_{22}\delta_{33}} \tag{4}\] \[K_{in}=\frac{\chi_{1}}{\Delta_{in}}=\frac{\delta_{12}^{2}-\delta _{11}\delta_{22}}{\delta_{33}\delta_{12}^{2}-2\delta_{12}\delta_{13}\delta_{ 23}+\delta_{22}\delta_{13}^{2}+\delta_{11}\delta_{22}^{2}-\delta_{11}\delta_{ 22}\delta_{33}} \tag{5}\]
## 3 Results and Discussion
### CH\({}_{4}\) and CO\({}_{2}\) Emissions during Storage of Pig Slurry
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline  & \multicolumn{6}{c}{**Methane (CH\({}_{4}\))**} & \multicolumn{6}{c}{**Carbon Dioxide (CO\({}_{3}\))**} \\ \cline{2-13}
**Period** & \multicolumn{3}{c}{**Daily Emissions**} & \multicolumn{3}{c}{**Comulative Emissions**} & \multicolumn{3}{c}{**Daily Emissions**} & \multicolumn{3}{c}{**Cumulative Emissions**} \\ \cline{2-13}  & **FPS** & **CTL** & \(p\)**-Value** & **FPS** & **CTL** & \(p\)**-Value** & **FPS** & **CTL** & \(p\)**-Value** & **FPS** & **CTL** & \(p\)**-Value** \\ \cline{2-13}  & **g kg-1 \({}^{\circ}\)VS-4\({}^{\circ}\)** & & & **g kg-1 \({}^{\circ}\)VS** & & **g kg-1 \({}^{\circ}\)VS-4\({}^{\circ}\)** & & & **g kg-1 \({}^{\circ}\)VS** & \\ \hline
1 st period & 0.14 \(\pm\) 0.05 & 0.16 \(\pm\) 0.06 & 0.829 & 0.97 \(\pm\) 0.32\({}^{\circ}\) & 1.09 \(\pm\) 0.39\({}^{\circ}\) & 0.862 & 0.84 \(\pm\) 0.25 & 0.84 \(\pm\) 0.03 & 0.992 & 5.85 \(\pm\) 1.73\({}^{\circ}\) & 5.58 \(\pm\) 0.18\({}^{\circ}\) & 0.991 \\
2 nd period & 0.15 \(\pm\) 0.04 & 0.54 \(\pm\) 0.21 & 0.203 & 2.05 \(\pm\) 0.06\({}^{\circ}\) & 4.90 \(\pm\) 1.15\({}^{\circ}\) & 0.180 & 0.55 \(\pm\) 0.03 & 0.82 \(\pm\) 0.04 & 0.009 & 9.68 \(\pm\) 1.51\({}^{\circ}\) & 11.64 \(\pm\) 0.26\({}^{\circ}\) & 0.453 \\
3 rd period & 0.07 \(\pm\) 0.01 & 0.06 \(\pm\) 0.01 & 0.629 & 3.00 \(\pm\) 0.11\({}^{\circ}\) & 5.72 \(\pm\) 1.25\({}^{\circ}\) & 0.216 & 0.55 \(\pm\) 0.06 & 1.00 \(\pm\) 0.26 & 0.250 & 17.36 \(\pm\) 2.75\({}^{\circ}\) & 25.63 \(\pm\) 3.97\({}^{\circ}\) & 0.225 \\
4 th period & 0.05 \(\pm\) 0.01 & 0.05 \(\pm\) 0.02 & 1.000 & 3.69 \(\pm\) 0.05\({}^{\circ}\) & 6.40 \(\pm\) 1.17\({}^{\circ}\) & 0.199 & 0.34 \(\pm\) 0.13 & 0.38 \(\pm\) 0.13 & 0.820 & 22.11 \(\pm\) 1.93\({}^{\circ}\) & 31.01 \(\pm\) 5.02\({}^{\circ}\) & 0.248 \\
5 th period & & & & & & & & & & & \\
4(5-56) & 0.08 \(\pm\) 0.02 & 0.10 \(\pm\) 0.03 & 0.601 & 4.75 \(\pm\) 0.32\({}^{\circ}\) & 7.74 \(\pm\) 1.13\({}^{\circ}\) & 0.106 & 0.27 \(\pm\) 0.08 & 0.26 \(\pm\) 0.07 & 0.925 & 25.94 \(\pm\) 2.82\({}^{\circ}\) & 34.61 \(\pm\) 5.35\({}^{\circ}\) & 0.307 \\ \hline \
## 3 Results
### Reproductive Capacity of Control, ROT, and ROT Coexposed to Phytoextracts in Oregon R\({}^{+}\)  
In wild-type _Drosophila melanogaster_, fecundity was decreased in positive control and rotenone-treated flies compared to that in control untreated flies. All the groups having a combination of plant extracts with ROT showed a reduction in total fecundity compared to that of the untreated flies, and more than that of the ROT-fed flies. The mean daily egg laying/female/10 days in all the groups of flies that had been fed the ROT and plant extract combination had statistically significant results concerning both control and ROT-fed flies. The fertility percentage in ROT + PG-p, ROT + CP-P, and ROT + UD showed statistically significant improvement compared to ROT, and was significant with control-fed flies, but did not attain the fertility of control flies. The fertility percentage of ROT + PG-P flies was significant compared to that of the control flies only, and ROT + FV and ROT + TFG were significant compared to ROT-fed flies. Reproductive performance in all ROT + phytoextract groups showed statistically significant values compared to that of the control and ROT-fed flies (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Groups**} & \multirow{2}{*}{**Total Fecundity**} & \multicolumn{2}{c}{**Mean Daily Egg**} & \multirow{2}{*}{**Fertility Percentage**} & \multirow{2}{*}{**Reproductive Performance**} \\  & & **Laying/Female/10 Days** & & & \\ \hline CONTROL & 1186 & 237.2 \(\pm\) 2.87 & 80.99 \(\pm\) 1.15 & 96.1 \(\pm\) 0.44 \\ \hline ROT & 239 & 47.8 \(\pm\) 1.74 * & 35.96 \(\pm\) 3.07 * & 8.5 \(\pm\) 0.08 * \\ \hline ROT + PG-P & 948 & 189.6 \(\pm\) 2.80 * & 40.21 \(\pm\) 0.70 * & 38.1 \(\pm\) 0.10 *.* \\ \hline ROT + PG-P & 709 & 141.8 \(\pm\) 3.63 * & 54.22 \(\pm\) 1.35 * & 38.4 \(\pm\) 0.19 *.* \\ \hline ROT + CP-P & 855 & 171 \(\pm\) 4.90 * & 44.01 \(\pm\) 1.36 * & 37.5 \(\pm\) 0.03 *.* \\ \hline ROT + ND & 733 & 146.6 \(\pm\) 2.73 * & 49.79 \(\pm\) 0.08 * & 36.5 \(\pm\) 0.13 *.* \\ \hline ROT + FV & 751 & 150.2 \(\pm\) 2.28 * & 78.61 \(\pm\) 1.40 * & 59 \(\pm\) 0.17 *.* \\ \hline \multicolumn{4}{l}{**} \\ \hline ROT + TFG & 588 & 117.6 \(\pm\) 1.96 * & 77.85 \(\pm\) 1.43 * & 45.8 \(\pm\) 0.26 *.* \\ \hline \end{tabular}
\end{table}
Table 1: Reproductive capacity of Oregon flies in control, ROT, and combinations of phytoextracts.  
Figure 5: Emergence of flies of _Drosophila melanogaster_ in Oregon R\({}^{+}\). All observations are expressed as the mean of 5 vials per group with 50 larvae per vial. Rotenone, ROT; pomergranate peel, PG-P; pomergranate pulp, PG-P; papaya peel, CP-P; nettle leaves, UD; fennel, FV; and fenugreek, TFG. Each observation was performed three times and is shown as the mean \(\pm\) SEM. Results were significant at \(p\) < 0.05. Significance was * \(p\) < 0.05 compared to control;* \(p\) < 0.05 compared to rotenone.
# Tsunami Modeling and Satellite-Based Emergency Mapping: Workflow Integration Opportunities
## 2 Materials and Methods: Tsunami Modelling and Satellite-Based Emergency Mapping
### CEMS Rapid Mapping Activation Outputs  
Details and products of the activation for this event are available on the public website under activation code [EMSR137]: Earthquake in Chile ([[https://emergency.copernicus.eu/mapping/list-of-components/EMSR137](https://emergency.copernicus.eu/mapping/list-of-components/EMSR137)]([https://emergency.copernicus.eu/mapping/list-of-components/EMSR137](https://emergency.copernicus.eu/mapping/list-of-components/EMSR137))). In their first request the ERCC requested mapping the area of Coquimbo city and surroundings (Detail 01 in Figure 8). The definition of these areas was based on knowledge from the ground and media reports. Soon after, several areas were added over populated places along the  
Figure 7: Adopted fault models. Assumed uniform deformation (**a**) and finite fault model (**b**).  
coast \(\sim\)300 km south of Coquimbo based on an analysis of the tsunami impact estimate released by GDACS (Detail 02 in Figure 8).  
The first results of the satellite-based impact assessment were published 25 h after the earthquake (a series of six maps covering the area of Coquimbo). For the assessment, very high resolution optical imagery (sub-meter resolution), acquired \(\sim\)16 h after the event, was used. Figure 9 shows the main steps from the earthquake to the GDACS alert, the activation of CEMS-RM by the ERCC, the acquisition of the image to the release of the first results of the damage assessment. The timeline for this CEMS-RM activation was extremely good considering that the service provided first information within 16 h after the user request (service target is 24 h). Crucial was the scheduling of the new satellite tasking over the affected areas at such short notice (confirmation of the afternoon tasking during the morning of the same day). As detailed is Section 2.2.1, this generally takes one day, depending on when the request is submitted with respect to the satellite tasking cut-off times. In this specific case by the time the tasking request was submitted, the image provider had already proactively planned a tasking over the area and CEMS-RM could benefit from it.  
Figure 10 below shows an example of the released map products. The result of the assessment highlights affected assets (buildings, facilities, roads) in an area of 100-200 m inland and along the  
Figure 8: Location of areas which were mapped by CEMS-RM (damage assessment).  
Figure 9: Timeline of the CEMS-RM activation. Main time stamps (UTC) of the event and production workflow.  
coast of Coquimbo city (first set of areas, Detail 01 in Figure 8). The assessment of the areas which were defined based on GDACS' impact assessment (Detail 02 in Figure 8) revealed only minor damage.
# Time Domain Spherical Harmonic Processing with Open Spherical Microphones Recording
## 3 Filter Design for Obtaining Time Domain Spherical Coefficients
### Error Analysis  
We define the error \(e_{nm}(v)\) as the difference between the desired time domain spherical harmonic and the coefficients we obtained by the proposed method, which can be decomposed to:  
\[e_{nm}(v)=e_{\text{filter}}(n,m,v)+e_{\text{position}}(n,m)+e_{\text{truncation}}(n), \tag{28}\]  
where \(e_{\text{filter}}(n,m,v)\) is filtering error introduced by \(\rho_{n}(v,R_{Q})\), \(e_{\text{truncation}}(n)\) is the truncation error of order \(N\), and \(e_{\text{position}}(n,m)\) is due to the microphones position error. The qualitative analysis of \(e_{\text{truncation}}(n)\) and \(e_{\text{position}}(n,m)\) based on the frequency domain method are addressed in [3], where we draw a similar conclusion in time domain that with increasing number of microphones and fixed \(N\), \(e_{\text{truncation}}(n)\) decreases. Meanwhile, \(e_{\text{position}}(n,m)\) depends on the nature of inaccurate microphone positioning, referring to the distance between the desired point and microphone location. We mainly focus on \(e_{\text{filter}}(n,m,v)\) here as it is the main error contribution due to the proposed filtering approach.  
According to (27), \(e_{\text{filter}}(n,m,v)\) at a specific order \(n\) and mode \(m\) can be expressed as  
\[e_{\text{filter}}(n,m,v) =|a_{nm}(v)*z_{n}(v)-a_{nm}(v)| \tag{29}\] \[=|a_{nm}(v)*e_{n}(v)|,\]  
where  
\[e_{n}(v)\triangleq\delta(v)-z_{n}(v). \tag{30}\]  
Using (18) and (21), the Fourier transform of \(e_{n}(v)\) is  
\[e_{n}(v)\overset{\mathcal{F}}{\longrightarrow}\ E_{n}(f)=\left\{\begin{array}[ ]{ll}1&|j_{n}(kr)|\leq\epsilon,kr<N\\ 0&\text{otherwise}\end{array}\right., \tag{31}\]  
with the same truncation in frequency as \(\hat{Z}_{n}(f)\). Thus, \(e_{n}(v)\) can be expressed as  
\[e_{n}(v) =\sum_{s=0}^{S-1}\big{(}w_{2s+1}^{(n)}-w_{2s}^{(n)}\big{)}\text{ sinc}\big{(}\frac{w_{2s+1}^{(n)}-w_{2s}^{(n)}}{2}v\big{)} \tag{32}\] \[\quad\times\cos\big{(}\frac{w_{2s+1}^{(n)}+w_{2s}^{(n)}}{2}v\big{)},\]  
where \(S\) and \(w^{(n)}\) have the same definition as in (19) and \(w_{0}^{(n)}=0\).  
With (29) and (32) we can quantitatively calculate the filtering error \(e_{\text{filter}}(n,m,v)\) introduced by \(\rho_{n}(v,R_{Q})\). The total error caused by filtering can be calculated by a summation of \(e_{\text{filter}}(n,m,v)\) over every order of \(n\) and mode of \(m\). As this filtering error is mainly due to Bessel zeros, it can be reduced by limiting the highest order \(N\) of the system, where a smaller \(N\) results in lower Bessel zeros hence a smaller \(e_{\text{filter}}(n,m,v)\). Also, \(N\) depends on the highest wave number \(k\) and the radius of the microphone array \(R_{Q}\). By choosing \(N\) with a pre-knowledge of the frequency limit of the input signals and \(R_{Q}\) also helps to minimize the filtering error \(e_{\text{filter}}(n,m,v)\).
## 3 Related Work
Offloading in hybrid **fog/cloud** systems is discussed in [33; 34; 35]. In [33], the authors present a scheme for the joint optimisation of transmit power control, computation and radio bandwidth allocation when offloading in hybrid fog/cloud systems, while guaranteeing user fairness and maximum tolerable delay. In [34], [PERSON] et al. present a cloud-fog-based architecture in the context of a smart grid. The authors discuss simulation results of using round robin, throttled, and particle swarm optimisation algorithms to schedule requests from devices such as smart meters on the VMs of a fog-cloud architecture. However, it is unclear if there is any collaboration amongst the fog-cloud tiers. [PERSON] and [PERSON] [35] present the EEOA (electric earthworm optimisation algorithm) for efficient resource assignment and work scheduling amongst fog-cloud tiers, considering the makespan, cost, and energy usage. Simulation results show that in many cases, the proposed EEOA performs better than alternative methods. Overall, few papers have considered collaboration amongst all three tiers of edge-fog-cloud [20; 21; 28; 29]. However, amongst all the papers considered in this section, only [20] has used an actual test bed without solely depending on simulations. Although important, simulations lack real-world variability, and do not always capture emergent behaviours arising from the interactions of various components in the edge, fog, and cloud tiers. Only work reported in [19; 20; 22; 29; 30; 31; 32] has provided clear evidence of supporting inter-tier interactions and collaborations, and a majority do not discuss support for dynamic conditions. In this paper, we address the gaps highlighted above.