esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation
The frequency-domain signal and fourth-order modal decomposition at the location of the crack in the outer ring of the FCBB are obtained, as shown in Figures 11 and 12. The contact force between the rolling element and the inner raceway of the outer ring fluctuates due to the existence of mesoscale cracks, resulting in additional displacement, which leads to the existence of small wave peaks in the vibration signal. After FFT, a peak corresponding to the small wave peak appears and \(f_{\text{BPOF}}\) is the fault frequency of the bearing outer ring, as shown in Figure 11. The new peak frequency appears near the rotating frequency \(f_{0}\), and with the increase of crack depth, the amplitude corresponding to the small wave peak in frequency domain increases. As shown in Figure 12, after EMD processing, the intrinsic mode function (IMF) components represent the frequency components of the original signal and are arranged in order from high frequency to low frequency. The first image represents the original signal, followed by the three components obtained from EMD decomposition, called IMF1-IMF3. Each IMF component represents an intrinsic modal component present in the original signal. IMF3 corresponds to the lowest frequency component, and according to the decomposition order of EMD, its amplitude matches the amplitude of the lowest frequency in the frequency domain. Therefore, IMF3 corresponds to the frequency of the small wave peak in the time-domain signal, and its amplitude is an important indicator to determine the evolution of the trend of the mesoscale crack in the outer ring of the FCBB, which provides a reference for the evolution of the subsurface crack in the FCBB. Figure 11: Frequency-domain diagram of simulated fault vibration signal.
# Thermal and Energy-Efficiency Assessment of Hybrid CLT-glass Facade Elements
## 5 Full-Size Numerical Analysis of the Live-Lab Facility
### Thermal Characterization and Performance Assessment
#### 5.3.1 Thermal Characterization of the Building Sections (Prefabricated Panels)  
The heat transfer coefficient was calculated for the prefabricated panels in use to verify that the requirement for dynamic thermal performance could be met with respect to the reference limit conditions (\(U_{\text{max}}=0.30\) W/m\({}^{2}\)K). Following the schematic cross-section in Figure 5, the thermo-physical properties in Table 4 were used for calculations.  
The heat transfer coefficient \(U\) for the building part was obtained based on:  
* The surface resistance, with \(R_{\text{si}}\) = 0.13 m\({}^{2}\)K/W and \(R_{\text{se}}\) = 0.04 m\({}^{2}\)K/W for the internal and external sides, respectively (see also Table 5);
* The thermal resistance of the constituent homogeneous layers, as summarized in Table 4:  
\[R_{T}=R_{si}+\sum\frac{d_{i}}{\lambda_{i}}+R_{se}=5.45\frac{m^{2}K}{W}, \tag{3}\]  
resulting in:  
\[U=\frac{1}{R_{T}+R_{u}}+\Delta U=0.18\frac{\text{W}}{\text{m}^{2}\text{K}}<U_{ max}=0.30\frac{\text{W}}{\text{m}^{2}\text{K}} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Thickness**} & **Specific Heat** & \multirow{2}{*}{**Density**} & **Thermal** & **Differential** \\  & & **Capacity** & & **Conductivity** & **Resistance** \\ \cline{3-6}  & **_d_** & \(\epsilon_{p}\) & \(\rho\) & **\(\lambda\)** & \(S_{d}\)** \\  & **(cm)** & **(J/kgK)** & **(kg/m\({}^{3}\))** & **(W/mK)** & **(m)** \\ \hline
4.09—directional chipboards (OSB) & 1.80 & 1700 & 650 & 0.130 & 0.9 \\ DELTA-DAWI-GP—vapour barrier & 0.02 & 1250 & 180 & 0.190 & 100 \\
7.01—mineral wool (MW) & 20.00 & 1030 & 30 & 0.040 & 0.2 \\
4.09—directional chipboards (OSB) & 1.80 & 1700 & 650 & 0.130 & 0.9 \\ \hline Total & 23.62 & & & & 102 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Nominal layout and thermo–physical properties for the prefabricated panels [51].  
where \(R_{\text{u}}=0\) is the thermal resistance for attic spaces.  
Once the capacity of the mineral wool prefabricated panels was verified (using Equation (4)) to meet the minimum requirements, the design temperature factor was set as [52]  
\[f_{Rsi,design}=\frac{R_{\text{T}}-R_{si}}{R_{\text{T}}}=0.982 \tag{5}\]  
and used to assess potential condensation risk.  
The major results are summarized in Table 5 and Figure 12, where it is possible to see that the expected diffusion flow of water vapour through the walls is rather stationary [53] for the examined climatic conditions. Furthermore, Table 6 shows that the calculated \(f_{Rsi}\) values are in close agreement with the internationally recommended values reported in Section 2. The design temperature factor calculated in Equation (5) is also greater than the maximum required value for \(f_{Rsi}\), further enforcing the good thermal performance estimates.
# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
## 2 Architecture Design
### Perception Layer  
The perception layer is composed of IoT devices, such as various sensors. Sensors are used to collect weather-related data, then a connection with the ground base station through IoT is established, and the data could be uploaded to the cloud for analysis. The data collected through the perception layer constitute the dataset of this experiment.
## 4 Discussion
### Contract Value and Joint Procurement  
Contract value and joint procurement both positively correlate with the likelihood of a contract being green. Joint procurement integrates small demands into one and increases the contract volume, as well as the contract value. As a result, contract value and joint procurement can affect the possibility of a green contract in the same direction and also in similar ways. Larger volume or larger value of a contract signifies more incentives for profit-driven economic operators to win the contract. Compared with a less-volume contract, economic operators are more willing to endeavor their efforts meeting various requirements, including environmental requirements. Some scholars also use term of economics of scale to explain the larger impelling power of a large-volume contract [19].  
A large volume or contract value can also reduce the suppliers' risk to invest in environmental technologies or managements, in the case that they do not fulfill their environmental requirements. [PERSON] et al. [26] states that procurement contracts can reduce market risk because they improve the demand predictability. [PERSON] and [PERSON] [27] explain that the market risk is reduced for innovative firms, since the public purchase is contracted and a certain quantity of sales is guaranteed. Assuming a green bid with a small value is publicized, and only a handful of companies able to fulfill its environmental requirements, then the bid may be cancelled if not enough offers and competition, and the purchaser may change it to a normal bid. However, if the volume or value is attractive enough to companies, they would be willing to invest in the environmental technology or innovations needed to make a green bid. More candidates appear and more green contracts are signed.  
In our interview with [PERSON], civil servant of Rotterdam Municipality, he stated:  
_\"(In the Netherlands) We have the national government, then we have about 12 provinces and about 300 of municipalities where the contracts are relatively small... if you look at mobile phone contracts, we are very insignificant in that market. They sell so much, what we do is a very small percentage of what they sell per year. So your influence is very different. But in the contracts for buildings, roads, we can make a difference. We are significant amount of the annual turnover of companies, so you can be attractive by volume\"._  
He regards the dispersive small demands from different governments as a big problem when implementing and activating the functions of GPP, _\"maybe in Europe but definitely in the Netherlands\"_. In contracts with large values or joint procurement, the contracting authorities possess more power to influence private markets, and they are more willing to apply GPP.
# Traffic-Aware Secured Cooperative Framework for IoT-Based Smart Monitoring in Precision Agriculture
###### Abstract  
In recent decades, networked smart devices and cutting-edge technology have been exploited in many applications for the improvement of agriculture. The deployment of smart sensors and intelligent farming techniques supports real-time information gathering for the agriculture sector and decreases the burden on farmers. Many solutions have been presented to automate the agriculture system using IoT networks; however, the identification of redundant data traffic is one of the most significant research problems. Additionally, farmers do not obtain the information they need in time, such as data on water pressure and soil conditions. Thus, these solutions consequently reduce the production rates and increase costs for farmers. Moreover, controlling all agricultural operations in a controlled manner should also be considered in developing intelligent solutions. Therefore, this study proposes a framework for a system that combines fog computing with smart farming and effectively controls network traffic. Firstly, the proposed framework efficiently monitors redundant information and avoids the inefficient use of communication bandwidth. It also controls the number of re-transmissions in the case of malicious actions and efficiently utilizes the network's resources. Second, a trustworthy chain is built between agricultural sensors by utilizing the fog nodes to address security issues and increase reliability by preventing malicious communication. Through extensive simulation-based experiments, the proposed framework revealed an improved performance for energy efficiency, security, and network connectivity in comparison to other related works.  
agriculture system; Internet of Things; fog system; soil monitoring; green energy +
Footnote †: journal: Sensors
## 5 Results and Discussion
### Discussion on BA Results  
The initialization technique plays a vital role in evolutionary and swarm-based stochastic algorithms. As, the traditional BA is not good in the process of global search. Therefore, the performance of BA can be increased by assigning the robust initial fitness to the particles. This may cause the enhancement in the diversity of swarm. For improving the performance of BA in terms of minimizing the global solution, we proposed three novel techniques of population initialization: the Knuth sequence-based BA (BA-KN), the Torus sequence-based-BA (BA-TO), and the WELL sequence-based BA (BA-WE).  
For evolutionary comparison, the proposed techniques: Knuth sequence-based BA(BA-KN), the Torus sequence-based BA (BA-TO), and the WELL sequence-based BA (BA-WE) are compared with BA with Halton distribution (BA-HA), BA with Sobol distribution (BA-SO), are tested on sixteen standard benchmark functions and compared with the standard BA. The experimental results showed that the proposed techniques, BA based on Halton distribution (BA-HA), BA with Sobol distribution (BA-SO), and BA based on Torus sequence (BA-TO), perform better as compared to the standard BA, where among these three proposed techniques, (BA-TO) outperforms the others such as (BA-HA) and the (BA-SO). The convergence curves for all the techniques are presented in Figures 39-54. The results depict that proposed techniques can help BA to avoid from premature convergence and to find the optimum solution quickly in the search space. The performance of the proposed techniques (BA-KN), (BA-WE), and (BA-TO) are compared with the standard BA and (BA-SO), (BA-HA), and (BA-TO) on standard benchmark functions with different dimensional size. It can be concluded from results that quasi-random sequences are best to create the random number sequences for BA and also for other population-based algorithms.
## 5 Results and Discussion  
### Definition and Designation of Context Parameters  
The bearing capacity results were expressed in graphs, generally comprised of the load-settlement relationships. The parameters used in the following context--q, S, B, SR, and BCR--shall first be defined. The bearing capacity, \"q,\" is expressed in \"kPa,\" which are obtained from the recorded load applied (in \"kgf\") over the surface area of the piston or loading plate. The piston in the mold model test had a diameter of 50 mm (\(\O=B=50\) mm) and a surface area of 1,963.5 mm\({}^{2}\). The diameter of the mold (150 mm) was three times the diameter of the piston (50 mm), and, thus the end effects may have no, or very minimal, significance. Similarly, the square loading plate used in the box model test had \(B\) = 300 mm and a surface area of 90,000 mm\({}^{2}\). The width of the loading plate was five times the length of the specimen box, yet 1.7 times the width of the specimen box. Thus, the end effects may have no or very minimal significance on the longitudinal direction, but may have effects on the traverse direction. The vertical displacement or penetration of the loading plate and piston will be designated, in the following context of this paper, as Settlement, \(S\), and the Settlement Ratio (SR) is computed as \(S\)/\(B\). The Bearing Capacity Ratio (BCR) [10; 11; 15; 21] is computed as  
\[\text{BCR}=\frac{q_{ir}}{q_{i}}, \tag{1}\]  
where \(q_{ir}\) is the bearing capacity of reinforced NMC clay (and sand) at a certain settlement level, and \(q_{i}\) is the bearing capacity of unreinforced NMC clay (Case 1) at a certain settlement level.
# RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR
## 4 Results and Discussion
### Ablation Study  
To verify the contribution of each module in RS-DETR for remote sensing image object detection, we conducted ablation experiments on the TGRS-HRSRD dataset using the same experimental environment and training parameters. The experimental results are shown in Table 5 and Figure 12. The precision, recall, mAP50, and mAP of the baseline model RT-DETR are 89.2%, 85.6%, 90.1%, and 64.6%, respectively. Model 1 integrates CGA into the AIFI module. It achieved a precision of 90.1%, improving by 0.9% over the baseline model, and reached 90.6% on the mAP50 metric, an increase of 0.5% over the baseline. The results indicate that replacing the traditional multi-head self-attention mechanism with CGA enhances intra-scale feature interaction and improves object detection performance. Model 2 incorporates the proposed EBiFPN into the baseline model to improve cross-scale feature fusion. The results show that after adding EBiFPN, the model's recall improved significantly by 1.7%, reducing missed detections in remote sensing image object detection while also showing improvements in precision and mAP. The detection results of Model 3 show that when both CGA and EBiFPN are added to the baseline model, it outperforms RT-DETR in all detection metrics, demonstrating that each module enhances detection performance. Finally, combining the various modules allowed RS-DETR to achieve peak detection performance, with mAP50 reaching 91.6%, an improvement of 1.5% over the baseline model, demonstrating significant enhancement in remote sensing object detection performance.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Backbone** & **FLOPs (G)** & **Params (M)** & **mAP50 (\%)** & **mAP (\%)** \\ \hline Faster R-CNN & ResNet50 & 180 & 42 & 88.4 & 59.1 \\ DETR & ResNet50 & 86 & 41 & 89.5 & 61.2 \\ Deformable DETR & ResNet50 & 173 & 40 & 89.9 & 60.4 \\ Conditional DETR & ResNet50 & 90 & 44 & 90.6 & 64.2 \\ DAB-DETR & ResNet50 & 202 & 44 & 91.5 & 62.5 \\ DN-DETR & ResNet50 & 94 & 44 & 91.1 & 61.3 \\ DINO & ResNet50 & 279 & 47 & **91.8** & 64.2 \\ RT-DETR & ResNet18 & 60 & 20 & 90.1 & 64.6 \\ RS-DETR (ours) & ResNet18 & **53.3** & **17.5** & 91.6 & **65.4** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparative results of different models on the TGRS-HRSRD dataset.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **CGA** & **EBiFPN** & **Focaler-GloU** & **P (\%)** & **R (\%)** & **mAP50 (\%)** & **mAP (\%)** \\ \hline Baseline & & & 89.2 & 85.6 & 90.1 & 64.6 \\ Model 1 & ✓ & & & 90.1 & 85.5 & 90.6 & 64.2 \\ Model 2 & & ✓ & & 90.0 & **87.3** & 90.7 & 64.9 \\ Model 3 & ✓ & ✓ & & 89.5 & 86.1 & 90.9 & 64.5 \\ RS-DETR (ours) & ✓ & ✓ & ✓ & **90.4** & 86.3 & **91.6** & **65.4** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of ablation experiments.  
Figure 11: The visual results of RT-DETR (**left**) and RS-DETR (**right**) on the TGRS-HRRSD dataset.
## 3 Results
### Cell Viability Assay
The effects of the plant extracts were also assessed on a non-tumor BALB/3T3 cell line, a model system commonly used for in vitro cytotoxicity screening of chemical compounds. Cytotoxic effects at the 24 th h were detected only when the cells were treated with the extracts at a concentration of 1000 ug/mL. At the other tested concentrations, significant differences in the cell viability between control and treated cells were not established. After 48 h of treatment, an enhanced effect of the extract from in vitro cultured plants was found. Statistically significant differences between the effects of the extracts obtained from in vitro and wild plants were observed at concentrations of 250 and 500 ug/mL. The half-maximal inhibitory concentrations (IC\({}_{50}\)) of the tested _C. vulgare_ extracts were determined for each cell line using nonlinear regression curve fit analysis and presented in Table 2.  
For each cell line, the wild and in vitro plant extracts showed quite similar IC\({}_{50}\) values at the 24 th h. However, after 48 h of exposure, the IC\({}_{50}\) values of the extracts of in vitro plants were lower than those of the wild plant extracts for all cell lines tested except for the non-tumor cell line HaCaT. From the tested tumor cell lines, the lowest IC\({}_{50}\) values were established for HeLa and HepG-2 cells treated with extracts of in vitro plants for 24 and 48 h, respectively. Figure 2: Effects of _C. vulgare_ extracts on the cell viability of non-tumor cell lines HaCaT and BALB/3T3 assessed by MTT test after 24 and 48 h of exposure. Values are presented as means \(\pm\) SD; * _p_\(<\) 0.05, **_p_\(<\) 0.01, and ***_p_\(<\) 0.001; green and red asterisks indicate significance of the differences between control and extract-treated cells; and black asterisks indicate significance of the differences between cells treated with extracts from wild and in vitro cultured plants.
## 2 Materials and Methods  
### Marine Geophysical Survey  
Bathymetric mapping of the study area was performed with a 180 kHz multibeam echosounder (MBES) (Elac Seabearn 1185). This system operates with 126 beams, and it was motion-compensated by an SMC IMU-108 motion sensor. An RTK GNSS was used to achieve \(<\) 10 cm lateral positioning accuracy. Sound-velocity profiles were acquired using a Valeport Midas CTD, and the sound velocity at the transducers was continuously measured using a Mini-SVS. Data acquisition was made through Hydrostar v.3 software and Hypack 2014 Suite. Processing was managed with Hysweep 2014 software. All grids shown in this paper have a cell size of 1 m, except when specifically mentioned otherwise. In the areas where the breakwater was constructed, data from a very dense trackline network using a single beam echosounder were used (Elac Nautic Hydrostar 4300, Kiel, Germany), which were acquired before the establishment of the harbour breakwater.  
High-resolution sub-bottom profiles were acquired using a Kongsberg Geoacoustics Geopulse Plus sub-bottom profiler system with a Universal transceiver, an acquisition display, and an over-the-side Transducer Mounting (array of four transducers) with a hydrophone. The operating frequencies ranged between 1.5 and 11.5 kHz, which achieved greater penetration with a maximum vertical resolution of 10 cm. Chesapeake Sonarwiz v.6 software was used for processing sub-bottom profiles and extracting acoustic types, reflectors and thicknesses.  
An Edgetech dual Frequency (100 and 400 kHz) digital towfish 4200 SP with an Edgetech 4200-P topside Processor Side-Scan Sonar system was used for emitting and acquiring the two frequencies simultaneously. Moga Software SeaView v.5.1.49 was used for processing and producing side-scan sonar mosaics.  
A dense network of 14 tracklines was acquired, running parallel to the shoreline with a line spacing of 100 m. Furthermore, 46 lines were acquired, running perpendicular to the above (Figure 2). The total length of the grid tracklines was about 90 km.  
A ground-truthing survey was carried out in selected pockmarks using a Subsea Tech Guardian mini-ROV equipped with an underwater position system (USBL, Blueprint Subsea SeaTrac). In total, 3 ROV dives were conducted inside selected pockmarks and on the seafloor outside the pockmarks and photographs and videos were collected.  
The analogue geophysical data (3.5 kHz sub-bottom profiler and Side-Scan Sonar) acquired in the previous surveys [39; 42; 57] was converted to digital and was reprocessed to be comparable with present data. Furthermore, we compared the profiling data with the core sediments retrieved from five drillings from the study area [58].
## 2 Methodology
### Seamless IM-NN Fusion Structure  
The accuracy of the integrated mathematical model of the INS/MNS navigation system is compromised by MNS disruption due to the system's nonlinearity and complexity. The proposed seamless IM-NN fusion structure is precisely based on LSTM to build the \(O_{INS}-Yaw_{Z_{k}}\) relational model. This model captures specific force, angular velocity, and heading angular attitude information.  
Modeling the relationship between the constructed INS output information and the actual measurement \(Z_{k}\), rather than the misaligned mathematical model, provides valuable insights for enhancing the analysis of intricate nonlinear systems. The input and output can be expressed as  
\[\begin{split}\text{Input}:\Big{[}w_{x}^{b},w_{y}^{b},w_{z}^{b},f _{x}^{b},f_{y}^{b},f_{z}^{b}\Big{]}\\ \text{Ouput}:\big{[}Yaw_{Z_{k}}\big{]},\end{split} \tag{20}\]  
where \(w_{x}^{b},w_{y}^{b},\ w_{z}^{b}\) represent the angular velocity of the tri-axial weight measured by the gyroscope; \(f_{x}^{b},f_{y}^{b},f_{z}^{b}\) represent the tri-axial specific force measured by the accelerometer; and \(Yaw_{Z_{k}}\) represents the measured heading angle.  
The proposed seamless IM-NN fusion structure is divided into two mode structures. When the MNS is accessible, the IM-NN structure operates in the training mode structure, as shown in Figure 3a. The heading angle is optimally estimated in adaptive CKF by fusing the heading information provided by the MNS with the attitude information output from the INS solution. Meanwhile, IMU raw data and MNS data are continuously fed into the IM-NN structure based on the LSTM network for prolonged training. This process leads to the development of a dependable input-output relationship model, \(O_{INS}-Yaw_{Z_{k}}\), by the adjustment of weights among the hidden layer neurons. In the training model of IM-NN, inertia and geomagnetism are still fused according to the adaptive CKF algorithm of Equations (4)-(14).  
When MNS interruption is unavailable, the IM-NN structure switches to the prediction mode, as shown in Figure 3b. The trained IM-NN structure utilizes the current IMU data to predict the pseudo-heading attitude angle. This prediction as measured information is then integrated into the adaptive CKF together with the attitude information from the IMU to obtain the optimal estimate of the heading angle. The seamless IM-NN fusion structure is used to maintain navigation accuracy even in the absence of the MNS signal. In the IM-NN prediction mode, \(\textit{Yaw}_{z_{k}}\) output from the trained model is substituted for \(Z_{k}\) in Equation (13) and the seamless fusion process of Equations (4)-(14) is continued.