esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions
The difference between the two case studies is more emphasises when considering only the G values, confirming that the opening surfaces have more influence on MAA indicator in a room with windows with the higher airtightness class;
5. MAA increases when the difference between the indoor and outdoor mean temperature increases, in almost all the studied configurations, with a more emphasized dependence for the configurations with the closed window;
6. An opposite trend for the two case studies was observed when comparing the MAA with the difference of air pressure and air velocity;
7. An influence on MAA indicator specular with respect to the one of the temperature was observed for air relative humidity difference;
8. A more emphasized dependence is found for the configurations with the closed window. Finally, an artificial neural network (ANN) was implemented, able to predict the CO\({}_{2}\) concentration within the room; 12 parameters related to geometric dimension of the room, permeability characteristics of windows, and environmental conditions were provided as input data, while the CO\({}_{2}\) concentration was used as target. A multi-layer perceptron (MLP) neural network was trained by using data of 19 experimental campaigns, while the last 14 ones were used for testing and checking the reliability of generalization of the network. Results show a good agreement between experimental CO\({}_{2}\) concentration and the one predicted by the ANN in all the experimental campaigns; in particular, the starting concentration (at time step 0) simulated by the ANN is always very close to the real one (mean error lower than 1%). A higher mean error was found for the maximum CO\({}_{2}\) concentration reached in about 50% of the surveys. However, it does not involve a wrong evaluation of the MAA indicator; according to the results, the MAA calculated starting from CO\({}_{2}\) predicted by ANN differs from the experimental one of about a few minutes (2-3 min for OD_OW, and maximum 10 min for CD_CW). Therefore, the ability to use ANN for the evaluation of MAA has been demonstrated. It can be also concluded that both window frame airtightness and environmental conditions, especially indoor and outdoor air temperatures, play an important role in air replacement in natural ventilated buildings and in indoor air quality. Therefore, it has to be carefully taken into account when solutions aiming at reducing air infiltration for energy savings are adopted. **Author Contributions:** Conceptualization, C.B. ; methodology, C.B. and D.P. ; investigation, D.P. ; writing--original draft preparation, C.B. and D.P. ; writing--review and editing, C.B. ; supervision, C.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Acknowledgments:** The Authors wish to thank [PERSON] for his precious contribution during the measurement campaigns in Perugia. **Conflicts of Interest:** The authors declare no conflict of interest.
# Experimental and Numerical Evaluation of Calcium-Silicate-Based Mineral Foam for Blast Mitigation
## 4 Results from Simulations
### Parametric Study
#### 4.3.2 Influence of the Deformability of the Front Skin  
Figures 28 and 29 present the maximum deflection at the backplate's center and the absorbed energy by the foam when using a non-deformable front skin. Notably, the results reveal that providing free space for foam expansion outperforms a single foam volume when using a non-deformable front skin. In the previous section, the optimal \(\zeta\) value that minimizes maximum deflection and maximizes foam energy absorption corresponds to \(\zeta\) = 34.3 m\({}^{-1}\) for configuration C. Similar observations are made with a non-deformable front skin. For \(\zeta\) values \(\leq\) 34.3 m\({}^{-1}\), the SC shows comparable effectiveness in terms of reducing maximum deflection regardless of the front skin deformability. However, as \(\zeta\) exceeds 34.3 m\({}^{-1}\), using a non-deformable front skin increases the performance of the SC by 14, 29, and 33% for configurations D, E, and F, respectively. Similarly, the absorbed energy by the foam is increased by less than 10% for \(\zeta\) \(\leq\) 34.3 m\({}^{-1}\) but rises notably by 22, 24, and 34% for configurations D, E,and F, respectively, when using a non-deformable front skin. Furthermore, configuration E demonstrates superior performance over configuration D when using a non-deformable front skin, contrasting with results observed under the same conditions with a deformable front skin. This disparity can be attributed to the enhanced strength of the foam core in configuration E.  
Figure 28: Influence of front skin deformability on the maximum deflection at the backplate’s center across various foam configurations.
# Battery Life Prediction for Ensuring Robust Operation of IoT Devices in Remote Metering
## 3 Remote Metering Life Prediction Method  
### Multi-Stage Discharge Primary Battery  
As previously mentioned in the introduction, we focus on real-time SOC prediction and on triggering a low battery alert before complete discharge. To achieve this, we developed a Multi-Stage Discharge (MSD) primary battery, as illustrated in Figure 2, which exhibits two distinct voltage drop stages. In Figure 2, the point at which the voltage drop initiates is denoted as the primary point. The primary point represents the final moment at which real-time estimation can be performed. For enhanced life prediction reliability and to allow sufficient time for maintenance, we recommend battery replacement when the battery reaches the primary point rather than waiting for the final drop point. This innovative design incorporates two separate voltage drops rather than the single voltage drop characteristic of conventional primary batteries. The MSD primary battery is produced by blending two electrolytes, \(SO_{2}Cl_{2}\) and \(SOCl_{2}\), resulting in two discharge stages governed by the following chemical reactions (see Equations (1) and (2)):  
\[First\:stage: 4 Li+2 SO_{2}Cl_{2}\to 4 LiCl+2 SO_{2}, \tag{1}\] \[Second\:stage: 4 Li+2 SOCl_{2}\to 4 LiCl+SO_{2}+S. \tag{2}\]  
The operation of the MSD primary battery commences with the reaction of \(Li\) with \(SO_{2}Cl_{2}\), which leads to a minor voltage drop to a point termed the primary point. This point can be moved to the 10% and 5% points of \(SOC_{Prim}\), depending on the ratio of the two electrolytes, allowing the MSD primary battery to be used to its full capacity. Subsequently, \(Li\) reacts with \(SOCl_{2}\), inducing a further reduction in voltage to a lower level, and eventually the battery discharges.  
In our implementation, the MSD primary battery demonstrates a total capacity of approximately 17.40 Ah. During the first discharge stage, where the average operating voltage typically stabilizes at around 3.78 V, the battery constitutes about 14.82 Ah of its total capacity. We also observed that this first-stage voltage may vary with temperature: at 60 \({}^{\circ}\)C, the battery maintains around 3.60 V, whereas at \(-20\)\({}^{\circ}\)C, it drops to about 3.30 V, and at ambient temperature (20 \({}^{\circ}\)C), it averages approximately 3.75 V. After passing the primary point, the voltage transitions to the second stage, averaging around 3.47 V. Considering that the remote metering device consumes approximately 11.8 mAh per day, the battery sustains operation for about four years before reaching complete discharge.
# Design and Performance Analysis of an In-Band Full-Duplex MAC Protocol for Ad Hoc Networks
## 5 Result and Performance Analysis
### Throughput Analysis  
The throughput analysis of the proposed AdHoc-FDMAC is performed in a saturation condition where all the nodes have data to transmit. The simulation for the throughput analysis is conducted by MATLAB and the result is shown in Figure 10, where the throughput of the proposed AdHoc-FD MAC is compared with the conventional HD as well as with a recently published MAC protocol named CFFD-MAC [14]. The simulation parameters are shown in Table 2.  
Figure 10: Throughput comparison.  
As shown in the Figure 10, the average throughput for the conventional HD communication is 24.06 Mbps. The average throughput for AdHoc-FDMAC is 40.06 Mbps, which has a 66.50% increase compared with the conventional HD.  
In this simulation, 20% of the nodes are considered as hidden nodes. According to Figure 10, the throughput increases as the number of nodes increases. As the number of nodes increases, the number of hidden nodes also increases and thus the occurrences of having TNFD communications increase. Therefore, the overall throughput increases.  
The throughput of AdHoc-FDMAC and CFFD-MAC are almost same when the number of nodes are 5 to 10 (Figure 10). After that, the throughput for CFFD-MAC decreases gradually compared to the AdHoc-FDMAC, with the increase in the number nodes. This is because, if the number of nodes increases, more TNFD communications take place and the control overhead for TNFD in CFFD-MAC is higher than that for TNFD in AdHoc-FDMAC. Thus, the throughput of CFFD-MAC becomes lower than that of the AdHoc-FDMAC, when the number of nodes increases. The average throughput of AdHoc-FDMAC and CFFD-MAC are 40.06 Mbps and 34.30 Mbps, respectively. Therefore, AdHoc-FDMAC outperforms CFFD-MAC by 16.80% average throughput gain.
## 4 Simulation and Results Discussions  
In this section, we briefly present some metrics to evaluate our proposed architecture implemented in a basic environment and examine the fundamental security considerations underlying our suggested methodology in comparison with related works.  
The outcomes derived for the ANN model, as depicted in Figures 6-8, show a good trend of improvement in the performance metrics with 100 epochs. The accuracy evolution graph shows a continuous convergence between testing and training accuracy to 97.60%, which is a sign of the good generalization of the model. In the learning curve (loss), there is a steady decrease in the loss value, indicating effective learning without apparent signs of over-fitting. Lastly, the precision, recall, and F1-score curves confirm these observations, with consistent improvements and stabilization around high values after approximately 20 epochs, highlighting an excellent balance between precision and recall in malware detection.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Technology** & **Description** \\ \hline ANACONDA & An open-source platform used to develop and manage data science and scientific computing libraries required for deep learning and data processing tasks. \\ \hline Spader & An open-source development environment used for writing, testing, and debugging Python v3.13.2 code, bundled with Anaconda. \\ \hline Python & The main programming language used to implement deep learning models, GAN-based feature selection, and experiment pipelines. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental setup.  
The values obtained for the CNN model, as presented in Figures 9-11, indicate a gradual increase in the performance measures with the progression of the 100 epochs. The plot of accuracy evolution indicates a steady performance in the early epochs and then a drastic improvement in both the training as well as test accuracy after epoch 80 and then going up to 80%. The learning curve (loss) indicates the steady reduction and thus efficient training with no sharp over-fitting issue. Concerning the precision, recall, and F1-score, the values grow in a good direction with precision stabilizing at higher points compared to the  
Figure 8: ANN performance scores.  
Figure 6: ANN accuracy.  
Figure 7: ANN loss.  
levels above 96%. An F1-score of 97.78% is particularly indicative of the good balance the model maintains between precision and recall in favor of its ability to correctly classify malware data with minimal loss.
# Digital Twins, Virtual Devices, and Augmentations for Self-Organising Cyber-Physical Collectives +
## 2 Digital Twins and Virtual Devices for CPS Engineering: A Review and Taxonomy
### The Concepts of Digital Twin and Virtual Node in the Literature  
#### 2.1.1 Digital Twin  
[PERSON] et al. define a _digital twin_ as a connected, virtual model of a physical entity [4]. The connection--which can be uni- or bi-directional--is established through a _digital thread_[5]. A digital thread realises the data flows that are needed to keep the physical and digital counterparts in sync. The concept of a digital twin is related to other engineering concepts such as \"plain old\" models, yet, there are subtle but important differences. For example, [PERSON] et al. make a distinction between a _digital model_ (where a manual flow of data ensures the synchronisation between the physical entity and the digital entity), a _digital shadow_ (where updates on a physical entity are automatically mirrored on the digital entity, however, not in the other way), and a _digital twin_ (where changes are automatically reflected through a bi-directional flow of data between the physical and digital entities) [17]. To make the notion of a digital twin helpful in engineering, it is crucial to clearly demarcate it from related concepts and be precise about its design dimensions and characteristics, enabling to classify its possible realisations. [PERSON] et al. present a taxonomy of digital twins [18]. The authors classify the concept of a digital twin based on the type of data link (uni or bi-directional), the purpose of the twin (repository, transfer, processing), the physical binding (bound or unbound), its accuracy (from identical to partial), its synchronisation (present or not), and the order of creation (first the physical part, first the digital part, both parts created at the same time).
# Applications of Machine Learning in Cyber Security: A Review
## 6 Shortcomings in Existing IDSs
### False Identification
[58] proposed an incrementally convoluted method of Information Gain (IG) and Random Forest (RF) for initial feature selection, followed by Recursive Feature Elimination (RFE) with a Multilayer Perceptron (MLP) network for further refinement, showing a 50% reduction in feature space and leading to a 2% improvement in accuracy. Automated cleaning methods include principal component analysis (PCA), ridge regression, lasso regression, ensemble learning [63], and Recursive Feature Elimination (RFE) [66]. PCA and REF techniques provide higher accuracy and lower false-positive rates in IDS applications [66]. Others are not yet evaluated, while it is unclear if outlier detection [64] is indeed utilized or suitable for CS applications. Furthermore, the Synthetic Minority Over-Sampling Technique (SMOTE) [96] is often used to automatically balance data, while it is unclear if an absolute balance would be truly representative of the problem. Even more advanced approaches such as feature extraction models like VGG-16 and DenseNet are proposed in [68] at a high computation cost. Other studies simply use increasingly complex models (more than 56% of the studies use CNNs, RNNs, DNNs in Table 3) or hybrid approaches to counter the effects of unclean data [61]. It is clear that the research community has not reached a consensus on the most appropriate approach to tackle the issue of false positives or the features needed for IDS models.
## 4 Conclusions  
Briefly, a simple one-pot hydrothermal technique was utilized to prepare cerium-oxide-nanoparticle-decorated zinc oxide for photocatalytic application. It was found that an increase in Ce doping does not affect the morphology of Ce/ZnO, which remains unchanged in comparison with the hexagonal wurtzite structures of pristine ZnO. XRD and XPS analyses revealed the existence of a cubic CeO\({}_{2}\) phase and Ce\({}^{4+}\) ions in Ce-doped ZnO composite samples. Additionally, an increase in Ce doping in ZnO results in narrower optical energy band tunability and reduced charge recombination. When compared with pristine ZnO, the Ce/ZnO catalysts showed superior photocatalytic degradation of methyl orange, which increased from 69.42% (ZnO) to 94.06% (7% Ce/ZnO) under a fluorescent lamp illumination time of 60 min. This may be due to the Ce\({}^{4+}\) ions, which can trap more electrons, resulting in an increase in generated radicals. Moreover, CeO\({}_{2}\)/ZnO heterojunctions were formed, which provide a carrier pathway to obstruct charge recombination and improve the photocatalytic degradation performance. Thus, cerium-oxide-nanoparticle-decorated zinc oxide has great potential as a material for water decontamination applications due to its simple synthesis, low-cost materials, and superior photocatalytic degradation performance.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/5/1697/s1](http://www.mdpi.com/2076-3417/10/5/1697/s1)]([http://www.mdpi.com/2076-3417/10/5/1697/s1](http://www.mdpi.com/2076-3417/10/5/1697/s1)), Figure S1: FE-SEM image and size distribution of pristine ZnO, Figure S2: the emission spectrum of fluorescent lamp (compact fluorescent lamp) and absorption spectrum of MO, Table S1: Calculated crystal size from several reflection planes of pristine ZnO and Ce/ZnO samples, Table S1: XPS peak position and %area of ZnO and 7%Ce/ZnO nanocomposites, Table S3: The comparison of our work and other research in term of % degradation, illumination time, and reaction rate constant.  
This research study was carried out as a collaboration of the aforementioned authors and was supervised by [PERSON]; [PERSON] initiated the idea and planned the experiments; [PERSON] assisted in draft preparation; [PERSON], [PERSON], [PERSON], and [PERSON] helped in data analysis; [PERSON] wrote the manuscript, which was edited by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) by The Ministry of Science, ICT, and Future Planning (2019R1A2B5B02069683).  
The authors declare no conflict of interest.
## Materials and Methods
### Methods Overview  
The monthly surface and subsurface water storage estimation are accomplished using multi-mission EO data and model outputs using the water balance Equation (1). Where TWS represents terrestrial water storage and the components of surface water storage (SWS), soil moisture storage (SMS), and groundwater storage (GWS) are calculated using the following steps (Figure 2): (1) Monthly TWS is derived from five different GRACE products (GFZ, CSR, JPL, CSR mascon, and JPL mascon). (2) SWS is obtained by summing up net surface runoff and reservoirs volume changes according to Equation (2). Inflow and outflow are obtained through in-situ hydrological station measurements. The volume changes of Xiaowan (\(\Delta\)V\({}_{\text{Xiaowan}}\)) and Nuozhadu ( \(\Delta\)V\({}_{\text{Nuozhadu}}\)) reservoirs are estimated from multi-mission-derived WSE and spectral and SAR imagery-derived SWE. (3) The SMS is retrieved from GLDAS and the Modern-Era Retrospective analysis for Research and Applications from NASA Global Modeling and Assimilation Office (MERRA-2). (4) The GWS can be calculated by subtracting SWS and SMS from TWS derived from the above steps according to Equation (3). The units of TWS, SWS, SMS, and GWS are all in centimeters. Both runoff and reservoir volume changes are reported in cubic meters and converted into equivalent water depths (in centimeters) by dividing the area of the study region (S) as shown in Equation (2). The specific information and processing methods of datasets used in this study are described in the following sections. All datasets used in this study are listed in Table 1.  
\[\text{TWS}\ =\ \text{SWS}\ +\text{SMS}\ +\ \text{GWS} \tag{1}\]  
\[\text{SWS}\ =\ (\text{Inflow}\ +\Delta\text{V}_{\text{Xiaowan}}+\Delta\text{V}_{ \text{Nuozhadu}}-\ \text{outflow}) \tag{2}\]  
\[\text{GWS}\ =\ \text{TWS}\ -\text{SWS}\ -\ \text{SMS} \tag{3}\]
# Multi-Technology Multi-Operator Site Sharing: Compliance Distance Analysis for EMF Exposure
###### Abstract  
In recent years, the development of wireless technologies has led to fast growth in mobile networks, especially with the rise of 5G New Radio (5G NR). A huge number of base stations (BSs) are mandatory to serve the growth of mobile services, which has led to concerns about the increase in electromagnetic field (EMF) radiation exposure levels. To control the overall power emitted by EMF transmitters, international bodies have set maximum exposure limits. This paper investigates the compliance distances (CDs) of shared sites by a group of Mobile Network Operators (MNO) as multi-operators operating with multi-technology and sharing the same tower. The study investigated the CDs of the most two commonly used types of sharing sites, macro and indoor-Based solution sites (IBS). In addition, the study analyzed the power densities and total exposure ratios for the general public and occupational workers in each sharing scenario. The results showed that, compared with a single MNO, the CD increased by 41% in the case of two MNOs, 73% for three MNOs, and 100% for four MNOs. The EMF site sharing scale-up formula was used to estimate the increase in CDs for N number of MNOs assuming that all MNOs use the same site configuration. In addition, the results showed that 5G has the highest contribution to the total exposure ratio (TER) at the CD in the main direction of the antennae.  
EMF exposure; power density; total exposure ratio; compliance boundary; 5G; massive MIMO 20232315888  
2023235888
# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
## 3 Results
### The Fluctuation Analysis of GPP, ET, and WUE  
The fluctuations in GPP, ET, and WUE across the Three-North region of China were shown by the VCs. In this study, the VCs were categorized into five grades: high fluctuation, moderate-high fluctuation, moderate fluctuation, moderate-low fluctuation, and low fluctuation, based on the natural breakpoint method, as shown in Figure 6. For GPP, approximately 38.30% of the total area had low fluctuations, while only 1.43% of the total area had high fluctuations. The statistics of WUE were similar to those of GPP. For ET, 28.71% of the total area had low fluctuations, while 5.19% of the total area had high fluctuations. For GPP and WUE, the low fluctuations was mainly distributed in the southern and eastern parts of the research region. The high fluctuations were mainly located at the border areas of Xinjiang Province. For ET, the desert and Gobi regions in the western part of the research area showed large fluctuations, suggesting that the water conditions in the arid areas had large interannual variations. These results suggest that the vegetation growth conditions and WUE in the eastern and southern parts of this region with relatively abundant precipitation conditions are more stable than the regions with limited water supplies.  
Figure 4: Spatial variations in GPP (**a**), ET (**b**), and WUE (**c**) of the Three-North region of China.  
Figure 5: Overall temporal variations in GPP, ET, and WUE of the Three-North region of China.  
Figure 6: Spatial distributions of GPP (**a**), ET (**b**), and WUE (**c**) fluctuations across the Three-North region of China.