esa-sceva/satcom-chunk-collection huggingface dataset:
# Computational Modeling of the Thermal Behavior of a Greenhouse
## 2 Materials and Methods  
### Experimental Setup  
The greenhouse under study is located in the city of Covilha at the University of Beira Interior Engineering Faculty campus in the center-east region of Portugal, having the following GPS coordinates: 40\({}^{\circ}\)16\({}^{\prime}\)43\({}^{\prime\prime}\) N 7\({}^{\circ}\)30\({}^{\prime}\)48\({}^{\prime\prime}\) W.  
The greenhouse has a rectangular base with a traditional gable roof, with exterior dimensions of 2.0 m \(\times\) 1.4 m \(\times\) 2.1 m (length \(\times\) width \(\times\) height). Figure 1 shows in more detail the supporting structure of the greenhouse and its dimensions.  
The supporting structure is made from fir wood, with cross-sectional dimensions of 30 mm \(\times\) 30 mm. The cover, which involves all sides of the greenhouse, is made of a commercial polyethylene 215 um in thickness commonly used for this kind of greenhouse. Figure 2 shows the greenhouse with the covering material assembled.  
The greenhouse has two circular openings of 127 mm in diameter located in the bottom-left corner of the south-facing wall and the top-right corner of the north-facing wall  
Figure 1: External dimensions of the greenhouse under study.  
Figure 2: Picture of the greenhouse with the polyethylene cover applied.  
to allow natural or forced ventilation of the interior of the greenhouse. Figure 3 shows one of these openings with a mechanical ventilator duct attached.  
The greenhouse location is surrounded by various buildings and a retaining wall that are tall enough to influence its thermal performance by blocking sunlight during certain periods of the day.  
Temperature and relative humidity data acquisitions were performed by means of two devices, the PCE-T 1200 temperature data logger, using T-type thermocouples (Omega Engineering Inc., Norwalk, CT, USA) and the Lascar Electronics EL-USB-1-LCD portable data logger. The accuracies of the temperature and relative humidity measurements were \(\pm\)0.55 \({}^{\circ}\)C and 2.25%, respectively. In order to ensure stable thermal conditions inside the greenhouse, it was necessary for some tests to use a heating device. The equipment used was a ceramic fan heater with a digital thermostat and maximum power of 1800 W.  
The experimental type A tests included natural and forced ventilation inside the greenhouse. Some tests (type B) included a heating set-point temperature to ensure a minimum value of the internal air temperature by using an auxiliary heater located in the greenhouse. Type C tests included a low airflow rate inside the greenhouse with outside fresh air but keeping the auxiliary air heater turned off. Table 1 lists the parameters of each of the experimental tests. The experimental tests were each conducted over 24 h, and the air temperature and relative humidity were recorded every 10 min. Additional details of the experimental apparatus and experimental tests can be found in [17; 18].
## 3 Network Sliced Distributed Learning
### End-to-End Functional Decomposition of DL
This method can be implemented through a Data Post Processing Function (DPsF) that processes the learned data before its transmission to the outside world. * **Data Collection Function (DCF):** In each DL cycle, parameter servers are required to collect learning updates from the devices and create a global update that can be used for the next round of communication. The data received by the servers may have additional information, encryption, noise, etc., and are required to be processed before taking into account the global model update. The Data Collection Function (DCF) includes the steps to collect data from learning devices and prepare them for the global update to be performed. * **Global Model Update Function (GMUF):** The Global Model Update Function (GMUF) performs the updates of the learning model based on learning data. The DCF function results are further processed with some mechanisms, i.e., the averaging process for creating a global model. These model parameters are sent back to the devices or upper layers for further processing. GMUF function can have methods for generating, pre-processing, and transmitting global model parameters over different distributed nodes. * **Distributed Model Inference Function (DMIF):** Model inference is an important step that must be considered for the successful implementation of AI applications based on DL. End users can adopt various forms of inference mechanism for the successful implementation of DL applications based on resource availability and application requirements. These methods and processes can be included in the Distributed Model Inference Function (DMIF). Based on resource availability and application performance requirements, different model inference strategies can be adopted. If a model in question is simple and requires limited computations, inference can be performed on the device itself, increasing the data security. However, in the case where the model requires a large number of parameters with a large computation cost, inference operations can be performed at edge or cloud layers. In some cases, joint strategies (e.g., device-edge, device-edge-cloud) can also be adopted with model-split operations. This creates different possible deployment options for the DMIF function. This set of functions can be used to implement various DL methods in the IoV scenario using the NS principle, aiming to logically deploy multiple intelligent services at the same time.
## 7 Open Issues and Challenges
### Network Stability  
The network's survival is contingent on the presence of active sensors. Due to the limited processing capabilities of nodes, optimizing transmission costs, data gathering, and load tolerance of nodes to extend their life is a difficult challenge. Clustering optimization, which involves selecting the best energy path for routing, can assist in extending the network's lifespan [67].
## 3 Results and Discussion
### Shifts in Microbial Community Structures with Seasonal Temperature
It was interesting to note that the class _Bacteroidales_ was much more abundant in winter (12.45%) than in summer (6.61%), corresponding to an increase of VFAs in the winter. These results also corresponded with the decrease of COD removal efficiency and biogas production in the winter. Furthermore, the class _Deltaproteobacteria_ was much less abundant in summer (1.94%) than in winter (7.81%). A similar tendency was shown by the class _Alphaproteobacteria_ (4.92% in summer, and 9.57% in winter) and _Betaproteobacteria_ (3.84% in summer, and 7.63% in winter). In order to better evaluate the structures of bacterial communities, the top 10 dominant genera in different periods of the system are presented in Figure 10. Among these genera, although the relative abundance of the bacteria at genus level differed depending on the operation conditions, it appeared that _Bacillus_ was the most abundant genus throughout the whole operation period. _Bacillus_ has been reported to possess the abilities of degrading various organic compounds [50; 51; 52; 53]. The genus _Pseudomonas_, with a chemolithoautotrophic and heterotrophic functionality, has been reported to play an important role in organic compound degradation [54; 55]. The presence of _Bacillus_ and _Pseudomonas_ in summer might be responsible for the degradation of organic matters by the AWFR system. This finding was consistent with the high COD removal efficiency and low effluent concentration of COD in summer. In addition, _VadinBC27_vastewater-sludge_group_ was also observed throughout the operation period. It might be participating in the degradation of amino acids and some refractory organic matters [56; 57; 58]. The genus _Smithella_, which could degrade propionate to acetate [42; 59], was dominant in start-up (3.13%) and winter (3.5%), and this was probably one of the reasons of acetate accumulation. The genus _Acinetobacter_ was observed in autumn and winter, and was probably involved in the oxidation of organic matter or sulfides [13; 60]. Figure 9: Bacterial community shifts at (**a**) phylum and (**b**) class level. Figure 10: The top 10 bacterial genera during (**a**) start-up; (**b**) spring; (**c**) summer; (**d**) autumn; (**e**) winter period.
# Real-Time Stress Field Prediction of Umbilical Based on PyEf-U-Net Convolutional Neural Network
###### Abstract  
Stress field analysis is an essential part of umbilical component layout design. The stress field analysis of an umbilical, via numerical simulation, has commonly been applied in practical engineering. The high economic and time cost associated with numerical simulation and analysis of the stress field in an umbilical has been replaced by data-driven, deep-learning-based, real-time computational methods. In this study, a novel Pyramidal Efficient U-Net (PyEf-U-Net) network is proposed to predict the stress field distribution of the umbilical. The input dataset is obtained via the Differential Evolution-Generalized Lagrange Multiplier (DE-GLM) method, which is entered into the network for training, with a detailed discussion of the effects of hyperparameters such as optimizer, learning rate, and loss function on the performance of the network. The experimental research demonstrates that the proposed PyEf-U-Net can accurately predict the stress field of the umbilical in real time with a prediction accuracy of 94.2%, which is superior to other deep learning networks. The proposed method can provide an effective way for rapid mechanical analysis and design of the umbilical in practical engineering, while the method can be extended to the mechanical analysis and design of other similar marine engineering equipment structures.  
stress field; deep learning; real-time prediction; convolutional neural network; umbilical
# Effect of Bentonite Content and Hydration Time on Mechanical Properties of Sand-Bentonite Mixture
## 3 Results of Calcium Bentonite-Sand Mixture
### Effect of Hydration Time  
Figure 4 shows the effect of bentonite hydration time (24 h, 40 h and 48 h) on the stress-strain behavior. The hydration time of bentonite referred to the time interval from initial saturation to the end of consolidation before the triaxial tests. The test results indicate that the hydration time has less effect on the maximum shear stress and residual stress. However, the hydration time has a significant effect on the stress-strain behavior at low strain levels. As the deviator stress-strain results for mixture specimens with hydration times of 24 h, 40 h and 48 h are compared under confining pressure of 200 kPa, it can be found that the specimen with hydration time of 24 h has the highest shear stress. The peak stress of the specimens with a hydration time of 40 h was close to that of 48 h. In this study, the hydration stage of bentonite was controlled before back pressure saturation. The initial hydration of bentonite prevents the further hydration of bentonite. This is possibly due to the gelation in the soil matrix during this period, which essentially offsets the partial reduction in the intergranular relationship due to the hydration of bentonite.  
Figure 3: Effects of bentonite content on deviator stress–strain relationship for sand–bentonite mixture with hydration time of 48 h under various confining pressures: (**a**) 200 kPa; (**b**) 300 kPa; (**c**) 400 kPa.
## 3 Fluorophore Coupled to a Resonant Cavity in the Strong-Coupling Regime
### Strong-Coupling with a Lossy Cavity  
In order to phenomenologically describe losses [16] let us now consider the pure state (3) dynamics described by a _non-Hermitian_ \"effective\" Hamiltonian \(\hat{H}=\hat{H}_{\text{J-C}}+\hat{H}_{\text{loss}}\) where \(\hat{H}_{\text{loss}}\) is introduced phenomenologically and, in the basis \(\{\left|e,0_{c}\right\rangle,\left|\text{g},1_{c}\right\rangle\}\), reads:  
\[\hat{H}_{\text{loss}}=\hbar\begin{bmatrix}0&0\\ 0&-\text{i}\frac{\gamma_{c}}{2}\end{bmatrix} \tag{8}\]  
where the parameter \(\gamma_{c}\) is called the cavity decay rate.  
The coefficients \(\alpha(t)\) and \(\beta(t)\) are obtained by solving the Schrodinger equation with the non-Hermitian Hamiltonian \(\hat{H}=\hat{H}_{\text{J-C}}+\hat{H}_{\text{loss}}\):  
\[\text{i}\hbar\frac{\partial}{\partial t}\begin{bmatrix}\alpha(t)\\ \beta(t)\end{bmatrix}=\hbar\begin{bmatrix}\omega_{0}&g\\ g&\omega_{c}-\text{i}\frac{\gamma_{c}}{2}\end{bmatrix}\begin{bmatrix}\alpha (t)\\ \beta(t)\end{bmatrix} \tag{9}\]  
In order to solve the dynamics of such a system, let us diagonalize the above matrix. Upon diagonalization, one finds the following eigenvalues:  
\[\omega_{\pm}=\frac{\omega_{0}+\omega_{c}}{2}-\text{i}\,\frac{\gamma_{c}}{4} \pm\frac{\Omega}{2} \tag{10}\]  
where we defined the quantity:  
\[\Omega\equiv\sqrt{4g^{2}+\left(\delta+\text{i}\frac{\gamma_{c}}{2}\right)^{2}} \tag{11}\]  
(where \(\delta\equiv\omega_{0}-\omega_{c}\) represents the detuning). We are first of all interested in the probability of finding the atom in the excited state, the _survival probability_ defined as \(P_{\text{surv}}(t)\equiv\left|\alpha(t)\right|^{2}\), so we want to find the expression of \(\alpha(t)\). Its general expression reads:  
\[\alpha(t)=A_{+}\text{e}^{-\text{i}\omega_{+}t}+A_{-}\text{e}^{-\text{i}\omega _{-}t} \tag{12}\]  
where the constants \(A_{+}\) and \(A_{-}\) are determined by the initial conditions \(\alpha(0)=1\) and \(\dot{\alpha}(0)=-\text{i}\omega_{0}\) (coming from the fact that \(\beta(0)=0\) in Equation (9)):  
\[A_{\pm}=\frac{1}{2}\pm\frac{\delta+\text{i}\gamma_{c}/2}{2\Omega} \tag{13}\]  
It is worth noting that for one excitation (photon), the phenomenological approach is equivalent to a rigorous dissipative approach using a master equation (see, e.g., Ref. [17] Chapter 6, especially Section 6.2, entitled \"Spontaneous emission: From irreversible decay to Rabi oscillations\").
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 4 Algorithm Design
### Link Stability Factor  
In EARR, the link stability factor is used to adjust the learning rate \(\alpha\), which helps obtain more stable routes during route adjustment. The link stability factor can play an important role in the routing decision. A link \(l=(v,w)\), where \(l\in\mathcal{L}\). At time \(t_{i}\), the positions of nodes \(v\) and \(w\) are (\(x_{v}^{t_{i}}\), \(y_{v}^{t_{i}}\)) and (\(x_{w}^{t_{i}}\), \(y_{w}^{t_{i}}\)), respectively. At time \(t_{i+1}=t_{i}+\Delta t\), the positions of nodes \(v\) and \(w\) are (\(x_{v}^{t_{i+1}}\), \(y_{v}^{t_{i+1}}\)) and (\(x_{w}^{t_{i+1}}\), \(y_{w}^{t_{i+1}}\)), respectively, and the location information is collected by the beacon. \(DIST^{t}(v,w)\) and \(DIST^{t_{i+1}}(v,w)\) represent the Euclidean distance between \(v\) and \(w\) at time \(t_{i}\) and \(t_{i+1}\), respectively. The calculation formula is as follows:  
\[DIST^{t_{i}}(v,w)=\sqrt{(x_{v}^{t_{i}}-x_{w}^{t_{i}})^{2}+(y_{v}^{t_{i}}-y_{w}^ {t_{i}})^{2}}. \tag{4}\]  
Figure 5: A 6 \(\times\) 6 grid Q-learning update process.  
Figure 6: Two-lane traffic map.  
For a link \(l=(v,w)\), we define \(|\Delta D(l)|=|DIST^{t_{i+1}}(v,w)-DIST^{t_{i}}(v,w)|\) as the change in distance between node \(v\) and node \(w\) when the time is \(t_{i}\) and \(t_{i+1}\), respectively. The smaller \(|\Delta D(l)|\) is, the more stable the distance between the two nodes \(v\) and \(w\) remains within the time interval \(\Delta t\) (\(\Delta t=t_{i+1}-t_{i}\)). Conversely, if \(|\Delta D(l)|\) is larger, the distance between the two nodes \(v\) and \(w\) in the time interval \(\Delta t\) changes more dynamically. The link stability factor for link \(l=(v,w)\) at interval time is defined as follows:  
\[LSF_{\Delta t}(l)=\frac{|\Delta D(l)|}{\Delta D_{max}}, \tag{5}\]  
where \(\Delta D_{max}=2\cdot sp_{max}\cdot\Delta t\), and \(sp_{max}\) is the maximum node speed. Note that the more stable the distance between two nodes \(v\) and \(w\) is maintained, that is, the smaller \(LSF\) is, the longer the lifetime of the link \((v,w)\) is maintained. Vehicles can stay in relative distance longer. The corresponding Q-learning update process does not need to be adjusted too much.
# Prediction of Delayed Surface Subsidence Based on the Improved Knothe-n Model
## 2 Numerical Simulation Programme
### Numerical Simulation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Types of** & **Density** & **Elastic** & **Poisson’s** & **Bulk** & **Shear** & **Cohesion** & **Friction** & **Tensile** \\
**Rock** & \(\rho\) (g/cm\({}^{3}\)) & **Modulus** & **Ratio**\(v\) & **Radius** & **Modulus** & **G (MPa)** & **c (MPa)** & **Angle \(\varphi\) (\({}^{\circ}\))** & **Strength** \\ \hline Shale & 2.6 & 23,500 & 0.28 & 17,803.00 & 9179.69 & 15.0 & 42 & 5.0 \\ Coal seam & 1.3 & 5000 & 0.32 & 4629.63 & 1893.93 & 1.8 & 30 & 0.1 \\ Mudstone & 2.5 & 14,000 & 0.29 & 11,111.11 & 5426.36 & 3.2 & 37 & 1.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of stratigraphic lithologic parameters. Figure 3: Displacement nephogram of the overall model after coal mining. Figure 2: Three-dimensional model of the study area and layout of the monitoring profile. III-III' monitoring profiles are 4.6 m, 4.5 m, and 4.4 m, respectively. The closer the three monitoring profiles are to the mining area from top to bottom, the greater the deformation, and the vertical displacement decreases in all directions. With distance from the mining area as the center, vertical displacement decreases slowly in all directions from the center of the mining area until it approaches zero.
# On Performance Analysis of Protective Jamming Schemes in Wireless Sensor Networks
###### Abstract  
Wireless sensor networks (WSNs) play an important role in Cyber Physical Social Sensing (CPSS) systems. An eavesdropping attack is one of the most serious threats to WSNs since it is a prerequisite for other malicious attacks. In this paper, we propose a novel anti-eavesdropping mechanism by introducing friendly jammers to wireless sensor networks (WSNs). In particular, we establish a theoretical framework to evaluate the eavesdropping risk of WSNs with friendly jammers and that of WSNs without jammers. Our theoretical model takes into account various channel conditions such as the path loss and Rayleigh fading, the placement schemes of jammers and the power controlling schemes of jammers. Extensive results show that using jammers in WSNs can effectively reduce the eavesdropping risk. Besides, our results also show that the appropriate placement of jammers and the proper assignment of emitting power of jammers can not only mitigate the eavesdropping risk but also may have no significant impairment to the legitimate communications.  
security; wireless sensor networks; friendly jamming; analysis +
Footnote †: journal: Sensors
## 4 Performance Evaluation  
### Performance Evaluation Environment  
A performance evaluation was conducted to demonstrate the effectiveness of the proposed scheme for dynamic graph processing on GPUs. This evaluation compares the proposed scheme with existing approaches to highlight its advantages. Table 2 outlines the performance evaluation environment. For the experiments, an integrated CPU-GPU environment was established. The CPU environment consisted of an AMD Ryzen Threadripper PRO 5955 WX processor with 16 cores running at 2.7 GHz and 32 GB of memory. The GPU environment utilized an NVIDIA GeForce RTX 4090 with 24 GB of memory (Nvidia, Santa Clara, CA, USA). The proposed method was implemented and tested on a Linux (Ubuntu 23.04 LTS) operating system, using GCC 11.4.0 and CUDA 12.2.  
Table 3 presents the datasets used for the evaluation. The following datasets consist of directed graph data provided by the SuiteSparse Matrix Collection [40]. The soc-LiveJournal1 dataset was collected from the LiveJournal online social network. The twitter7 dataset represents the Twitter follower network, while the sk-2005 dataset is based on web crawl data from the.sk domain.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & & AMD Ryzen Threadripper \\  & CPU & PRO 5955 WX 16-Cores @ \\ Hardware Configuration 1 & & 2.7 GHz \\  & Main memory & 64 GB \\  & Secondary storage & 1 TB \\ Hardware Configuration 2 & GPU & NVIDIA GeForce RTX 4090 \\ OS & Memory & 24 GB \\ OS & Linux & Ubuntu 23.04 \\ Software Configuration & GCC & 11.4.0 \\  & CUDA & 12.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance evaluation environment.  
These datasets contain self-loops, meaning that certain vertices have direct edges connecting back to themselves. Additionally, path loops exist, where a vertex can return to itself through a sequence of intermediate vertices. These characteristics are inherent to the datasets and have been explicitly considered in our analysis.
## 4 Results and Discussion
### Hydrogen and Oxygen Isotopes
Waters from the C5 cluster are not considered in the regressions. Figure 9: Relations between \(10^{3}\)\(\delta^{2/1}\)H and \(10^{3}\)\(\delta^{18/16}\)O for the investigated waters (blue dots). Groups A, B, and C are encircled in ochre, purple, and red, respectively. The green dots represent C5a-c waters. The orange dots indicate the rainwater samples analysed during this study. The solid black and the dashed black lines are representative of the meteoric water lines available in Sicily (with emphasis on [62] and [56], respectively). The blue line is the linear interpolation of the groundwater isotopic data. Figure 11 reports the relation between \(10^{3}\)\(\delta^{2/1}\)H and T.U. The evident and strongly significant correlation (R\({}^{2}\) = 0.998, and null hypothesis H\({}_{0(\text{slope}=0)}\) \(<\) 0.001) for the data of the C5 cluster is an intriguing problem. Different explanations for this interesting correlation are proposed below.
## 5 Engineering Validation  
### Experiment Descriptions  
To further verify the proposed TFR-based algorithm, a field experiment was designed and carried out on Sutong Bridge, a long-span cable-stayed bridge with 272 cables. This bridge locates at the entrance of the Yangtze River in China, connecting two cities Nantong and Suzhou. The main girder consists of three spans with lengths of 500 m + 1088 m + 500 m, as shown in Figure 11. The height of the tower is about 300 m.  
A special cable A18 in the south side span (downstream side) was chosen as the experimental object, as re-marked with red color in Figure 12. Specifically, the length and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Frequency Order** & **Error** & **Sensor 1** & **Sensor 2** & **Sensor 3** & **Sensor 4** & **Sensor 5** \\ \hline \multirow{3}{*}{4} & AE (\%) & 0.51 & 0.49 & 0.44 & 0.47 & 0.59 \\  & ME (\%) & 5.70 & 5.16 & 2.49 & 3.10 & 5.06 \\  & AE (\%) & 0.40 & 0.42 & 0.4  
mass per unit length of the cable are 336.811 m and 77.70 kg/m, respectively. These cable parameters were measured in the construction stage [42]. The experimental cable connects the tower and the main girder at the auxiliary pier. A series of strain gauges have also been installed at the bottom of the cable. The measured stress is a reference to validate the proposed method.  
In contrast to the loading experiment in the workshop, the moving loads were adopted in the field experiment instead of the static loads. The photograph of the loading trucks is shown in Figure 13. In particular, four trucks full of sand and stones were used as applied loads in the dynamic test. The trucks started from the North Tower and moved towards the South Tower at a constant designed speed of 30 km/h (Case 1) and 40 km/h (Case 2), respectively.