esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Security Analysis
### Formal Security Analysis
#### 5.2.1 BAN Logic  
BAN logic is a symbolic logic system that is used for analyzing security protocols. It focuses on ensuring that various security properties are held in a protocol. The detailed BAN logic analysis for the user registration phase of our proposed protocol, including equations and explanations, is given below:  
1. Initial Assumptions; * [PERSON] trusts the server S. * [PERSON] chooses a unique identity \(ID_{D}\) and a strong password \(PSW_{D}\). * The hash function is used for generating \(HID_{D}\) and \(HPSW_{D}\) for secure communication. * The server S securely stores the secret credentials of users, including email addresses and mobile numbers. * The server S verifies the identity of the user based on the stored data in its database. The database is secure and safe.
2. Idealized Protocol Model;
3. Protocol Description;
4. Formal Agreement Analysis.  
**BAN Logic Formal Analysis for User Registration Phase:**  
- Doctor [PERSON] computes hash-based identity \(HID_{D}=\text{hash}(ID_{D})\) and hash-based password \(HPSW_{D}=\text{hash}(ID_{D}\parallel PSW_{D})\).  
**Server S receives \(HID_{D}\) for Verification:**  
- Server S checks the received \(HID_{D}\) against its stored database to verify the identity.
* If the identity is correct, server S proceeds; otherwise, it terminates the connection.  
**Equations and BAN Logic Analysis:**  
1. Initial Assumptions (Idealization); * D believes [S, \(K_{S}\)] is secure: D\(|\)S, \(K_{S}\). * D chooses a unique identity and strong password: D\(|\)\(|\)\(ID_{D}\), \(PSW_{D}\)]. * D believes the hash function is secure: D\(|\)hash\((ID_{D})\). * S securely stores user credentials: S\(|\)[Secrets]. * S verifies identities based on stored data: S\(|\)[Verified].
2. Idealized Protocol Model (Idealization); * Doctor D sends \(HID_{D}\) to server S for verification: D \(\rightarrow\) S: \(\{HID_{D}\}\). * Server S verifies \(HID_{D}\) in its database and verifies the identity: S \(\rightarrow\) D: \(\{\)Verified\(\}\).
3. Protocol Description (Formalization);* Doctor D believes that the server S has received \(HID_{D}\): D\(|\)S: \(\{HID_{D}\}\).
* Doctor D believes that the server S has verified the identity: D\(|\)S: [Verified].
* Formal Agreement Analysis (Inference Rules).
* Doctor D believes that server S has verified the identity based on the received \(HID_{D}\): D \(|\)S: \(\{HID_{D}\), Verified}.
* Server S securely stores user credentials: S\(|\)[Secrets].
* Server S verifies identities based on stored data: S\(|\)[Verified].
* Doctor D has securely registered with server S: D\(|\)S: [Registered].
# Unified Simplified Capacity Model for Beam-Column Joints into RC Moment Resisting Frame
## 4 Conclusions  
The present study proposes a unified analytical model to assess the ultimate capacity of RC beam-column joints. The model was developed according to a generalized approach. It allows the behavior of both T- and X-shaped RC joints to be assessed by means of a single mathematical model.  
The proposed approach allows for the evaluation of several failure modes typically detected in the RC joints. It represents an important improvement, especially in practical applications.  
The present study contributed a theoretical discussion of the failure mechanisms of RC joints. The failure modes can be different, and to have a model able to assess them represents a valid tool, especially for the designers. In particular, predictions can be used to check the most satisfactory mechanism compared to the others. Therefore, the proposed simplified model is useful for this purpose, due to the possibility of checking these differences.  
These predictions represent the basis for the effort to drive the design towards the respect of the strength hierarchy. The advantages of a ductile failure mode in the case of a seismic event are known and largely debated by the scientific community, preserving the joints from premature failures.  
The comparison between the analytical predictions and approximately 500 experimental results confirmed the reliability of the proposed model. The proposed model is able to indicate the failure mode, as well, for each specimen of the large experimental population.  
The impact of the bond condition has been evaluated on several types of RC joints (T- and X-shaped), highlighting the differences between these two shapes. These outcomes provide a useful contribution to understanding which mechanism, as the input parameters change, led to ultimate capacity of the RC joints and their relation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Type**} & \multicolumn{8}{c}{**Prediction Models**} & \multicolumn{2}{c}{**Proposed Model, V\({}_{\text{i}}\) \textgreater{} 0**} \\ \hline \multirow{2}{*}{**T-Shaped**} & **ACI** & **EN1998-** & **NZS** & **CSA** & **AIJ:** & **IS 13920:** & **Proposed Model, V\({}_{\text{i}}\) \textgreater{} 0** & **Proposed Model, V\({}_{\text{i}}\) \textless{} 0** \\  & **318-14** & **1:2004** & **3101:1-2006** & **A23.3-2004** & **2010** & **2016** & \(\tau_{\text{min}}\) & \(\tau_{\text{av}}\) & \(\tau_{\text{max}}\) & \(\tau_{\text{min}}  
**Author Contributions:** Conceptualization, G.P.L.; methodology, G.P.L.; software, G.P.L.; investigation, G.R., G.P.L. and F.F.; data curation, G.R.; writing--original draft preparation, G.R. and G.P.L.; writing--review and editing, F.F. and A.P.; supervision, A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** The data that support the findings of this study are available upon reasonable request.  
The authors have no conflict of interest to declare.
# Preparation of an Asymmetric Membrane from
## 3 Results and Discussion
### Characterization of Cellulose Acetate
#### 3.2.4 XRD of Extracted Cellulose and Cellulose Acetate  
X-ray powder diffraction was used for the determination of cellulose crystallinity. The crystallinity index (CrI) was calculated by the following equation [34]:  
\[CrI=\frac{(I_{200}-I_{am})}{I_{200}}\times 100 \tag{11}\]  
where \(I_{200}\) is the intensity of the peak in the crystalline plan 200 (20 = 22.2\({}^{\circ}\)) and \(I_{am}\) is the intensity of the peak in the amorphous portion (20 = 18.7\({}^{\circ}\)).  
The XRD pattern of extracted cellulose and CA samples is shown in Figure 4. The XRD pattern of CE-1 and CE-2 (Figure 4a,b) show four diffraction peaks at 20 = 14.8\({}^{\circ}\), 16.0\({}^{\circ}\), 22.2\({}^{\circ}\), and 34.0\({}^{\circ}\) typical of cellulose crystal I (natural cellulose) [5; 35]. These signals correspond to diffraction patterns 110, 101, 002 and 040. The shoulder diffraction peak at 16.0\({}^{\circ}\) and a weak peak at 34.0\({}^{\circ}\) indicates the absence of lignin and hemicellulose in the extracted cellulose [36]. CE-2 is purer than CE-1, which is demonstrated by the absence of a peak at 34.0\({}^{\circ}\). This is also consistent with the obtained crystallinity index (CrI) values. CE-1 presents a CrI value of 55.27% and CE-2 presents a CrI value of 58.77%.  
In Figure 4c, for CA-1 a strong peak appears at 8.0\({}^{\circ}\) and weak peaks at 10.3\({}^{\circ}\), 12.4\({}^{\circ}\), 16.6\({}^{\circ}\), 21.5\({}^{\circ}\), 22.2\({}^{\circ}\), assigned to the crystalline peaks of cellulose triacetate II, while CA-2 (in Figure 4d) presents maxima at 7.6\({}^{\circ}\), 17.6\({}^{\circ}\), and 20.9\({}^{\circ}\), corresponding to cellulose triacetate I [37]. In the acetylated process, cellulose acetate I was converted to cellulose acetate II, which may be because of the lignin content in CE-1.  
Figure 4: XRD patterns of extracted cellulose and CA samples synthesized from SB cellulose: (**a**) CE-1, (**b**) CE-2, (**c**) CA-1, (**d**) CA-2.  
Figure 3: \({}^{1}\)H-NMR spectra for (**a**) CA-1 (DS = 2.67) and (**b**) CA-2 (DS = 2.81).
## 2 Materials and Methods
### Derivation of Strain from GNSS Baseline Vectors  
The expansion of dense and continuous GNSS observation networks has facilitated precise monitoring of surface displacements across both tectonically active and stable regions. In geodetic strain analysis, these displacements are represented as baseline vectors between GNSS stations, where temporal variations reflect the accumulation of crustal deformation.  
Strain tensors are derived by converting spatial velocity gradients calculated from GNSS baseline time series, based on the assumptions of linear elasticity. This process enables the construction of regional strain fields and contributes to the understanding of tectonic loading patterns and potential zones of strain concentration.  
Reliable strain field estimation typically requires multiannual velocity data, generally spanning six to seven years, processed using established software packages such as GAMIT/GLOBK or Bernese. These outputs are expressed within globally or regionally consistent reference frames, including ITRF2020 and GSRM v2.1, to ensure compatibility and accuracy [51; 52; 53]. However, methodological differences and reference frame selection may introduce discrepancies, particularly in spatially dense station arrays, thereby necessitating thorough cross-validation and quantitative uncertainty assessment [53; 54; 55; 32].  
Based on this processing framework, the present study computed the satellite-based displacement of five continuously operating reference stations over a ten-year period. As described in Section 3.1, the derived results were compared against the tectonic velocity models of ITRF2020, GSRM v2.1, and APKIM2005-IGN.
## 2 Data
### SEVIRI Cloud Coverage  
The FUB cloud detection scheme provides the probability of cloud coverage (between 0 and 1) based on the analysis of spectral and temporal information from SEVIRI, mounted on the geostationary MSG satellites, using the neural network approach [34]. Several networks, depending on a set of input parameter, e.g., daytime and nighttime schemes, were constructed based on a multi-layer perceptron architecture with one hidden layer of 25 or 20 neurons. A central input parameter is the assumed clear-sky brightness temperature of the 10.8 \(\upmu\)m channel, which is estimated from the analyses of its temporal evolution. The neural network training and test datasets were created by manual classification of cloudy and clear-sky scenes as well as with simulations from a radiative transfer model [35]. The probability of cloud coverage is provided with the temporal resolution of the SEVIRI instrument of 15 min and a spatial resolution of about 4 km \(\times\) 7 km for the German domain.
# Lightweight, Secure, Similar-Document Retrieval over Encrypted Data
## 4 Scheme Overview
### Initialization Stage
#### 5.1.2. Index Construction and Document Encryption  
1. Index construction: _Bob_ uses the secret keys \(K_{1}\) and _kpub_ to build the inverted index from the collection \(D\) as in Algorithm 1.
2. Document encryption: _Bob_ encrypts his collection \(D\) with the secret key _Koll_. He then sends the encrypted collection to the server \(S\). Thus, the privacy of the documents therein is protected. Once the data are outsourced, a data user should be able to match their documents with those held on the remote server. The AES encryption/decryption algorithm is commonly efficiently combined into popular and widespread lightweight devices, such as smart devices and IoTs, by means of hardware [36; 37]. For further security, the counter mode CTR [38] is utilized with AES. For instance, the encryption process is \(Enc_{kcoll}(.)\), where _kcoll_ is the length secret key (128-bit). Note that the key is a smaller size, which significantly speeds up the encryption and decryption process.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline
**Term** & **Vector** & & & & & & & & & & & & & \\ \hline
500 & 1 & 1 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\
520 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 \\
600 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\
680 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\
710 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inverted index example.  
For more protection, the document file is randomly mapped with fake data unrelated to the owner's genuine data to mask its real size. This random mapping is done after the secure searchable inverted index has been built to avoid padding the index with random data.
That is what we have extracted from the PCC-RPL: if we move the security mechanism to another level of topology and specify the controller to make the calculations which were previously performed on the nodes, this would significantly improve the network lifetime, as proven by the derived results. Researchers have proposed trust models to detect and mitigate these attacks, but many of these models are not efficient due to the resource constraints of IoT devices. To address this, a new trust model has been proposed that detects and mitigates wormhole attacks in an efficient manner while minimizing energy consumption and resource utilization. The model has a separate controller layer to calculate trust values for nodes in the network. This study proposes a trust mechanism using a subjective logic framework (SLF) to detect and mitigate wormhole attacks on RPL-based IoT and WSN devices. The proposed model includes following research contributions. * A secure version of RPL-based IoT and WSN devices to detect and mitigate wormhole attacks. * A controller-based hierarchical model to calculate the trust metrics for each node separately utilizing the subjective logic framework. * A system that enhances the QoS-based parameters, keeping in view the resource-constrained nature of the RPL-based IoT and WSN device. This study aims to improve RPL security by detecting and mitigating wormhole attacks on RPL-based IoT and WSN devices while taking into account their constrained nature and resources. The study aims to maximize network life while improving security. The extensive use of IoT and WSN devices in daily life has led to an increase in security vulnerabilities. The limited power source, low memory, high heterogeneity, and limited processing power of these devices have led to security issues in existing routing protocols. Multiple strategies and methodologies for data transmission on the IoT and WSNs have been introduced in various studies. The layered topology of the IoT is described in Figure 1 previously.
## 3 Results
### No-Reference Evaluation Results  
Figure 7 shows the calculated BRISQUE, a blind quality assessment of five reconstruction filters, at a projection angle of 1.2\({}^{\circ}\). At a projection angle of 1.2\({}^{\circ}\), the BRISQUE value was approximately 35.55, 35.03, 42.90, 45.66, and 48.89 for the Ram-Lak, Shepp-Logan, Cosine, Hamming, and Hann filters, respectively. Based on the graph analysis, the Ram-Lak filter, which had the lowest performance in terms of noise and similarity evaluation results, showed the greatest improvement. Conversely, the Hann filter, which exhibited the best performance in the noise and similarity evaluation results, exhibited the lowest performance in the no-reference evaluation results.
# Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks
## 4 Deep CNN-Based Detector
### DCNN Type-II: Training with Original Message  
The training architecture of DCNN type-II is given as in Figure 4, where accurate CSI is not needed. Instead, the output of DCNN \(\widetilde{\mathbf{H}}_{UM}\) is used in MLD and obtain the likelihood of each candidate message as follows:  
\[\hat{q}_{i}=\exp\{-\|y-\widetilde{\mathbf{H}}_{UM}\mathbf{B}\mathbf{x}_{i}\|^ {2}\},x_{i}\in\Omega. \tag{13}\]  
By using of the SoftMax function, the normalized likelihood probability of each candidate message can be given as  
\[q_{i}=\frac{\hat{q}_{i}}{\sum_{i=1}^{|\Omega|}\hat{q}_{i}}. \tag{14}\]  
We use \(p_{i},i\in[1,|\Omega|]\) to denote the correct probability of each candidate message. That is \(p_{i}=1\) if \(i\)-th candidate message is correct, otherwise \(p_{i}=0\). Inspired by the information theory, the cross-entropy can be used to quantify the difference between two probability vectors.  
Figure 3: DCNN type-I: Training model of CNN with accurate CSI.  
Accordingly, for probability distributions of \(q_{i}\)and \(p_{i}\), we can calculate the cross-entropy as follows:  
\[C(p,q)=\sum_{i=1}^{|\Omega|}p_{i}log_{2}\frac{1}{q_{i}}. \tag{15}\]  
Then, the cross-entropy \(C(p,q)\) can be used as the loss function to train the DCNN model.  
Compared with the loss function in (12), only the received signal \(y\), the beamforming matrix \(\mathbf{B}\) and the original message \(x\) are needed, which enlarge the application scenarios of the DCNN type-II. On the other hand, without the help of the accurate CSI, DCNN type-II leads to deteriorated performance compared with type-I, which can be validated in the simulation results.  
Please note that the DCNN model could also output the ground-true symbol \(x\) directly in a supervised manner, and the outdated MIMO channel matrix could be further employed as side information by inputting it to the DCNN. We use DCNN type-III to denote the new DCNN model. Although the detailed architectures of DCNN type-II and the suggested DCNN type-III are different, they are functionally equivalent as a black box with DCNN kernels. In other words, the MLD module in DCNN type-II can be seen as part of the functions of DCNN type-III.
## 4 Numerical Results of the Proposed Reconstruction Method
### Presentation of Reconstruction Results of Velocity Field and Pressure Field
Therefore, the following conclusions can be obtained after simulation: for the velocity field, the proposed method can accurately reconstruct the velocity distribution, whereas for the pressure field, the pressure distribution characteristics can be basically restored by using the proposed method.
## 3 Results and Discussion
### Load Test Database
The aggregate pier lengths, \(L_{p}\), ranged from approximately 2.3 to 14 m with diameters, \(B\), ranging from 0.3 to 1 m. The replacement ratio for the 16 SPs (single isolated pier) ranged from 95% to 122%, whereas 15 footing load tests were performed on ISPs (intermediate single pier), IGPs (intermediate group of piers), and GPs (group of piers) with \(a_{r}\) ranging from 16% to 47%.
## 2 Design Approach
### Design of Porous Microstructures  
Three-dimensional models of square porous microstructures with different porosities were modeled in ABAQUS, as shown in Figure 1. These structures were simulated under a compression test to obtain effective mechanical properties. A 3D cube cellular structure (15 mm \(\times\) 15 mm \(\times\) 15 mm) was used with six different sizes of pores and strut thicknesses, as shown in Figure 1. The minimum selected strut thickness was 0.3 mm due to the resolution of the EOS M 280 printing machine. Multiple unit cells were introduced into cubic samples of 15 mm dimensional length. The proposed designs were considered with 12-23 unit cells enclosed within each dimensional direction of the cubic samples, as shown in Figure 1. Plates were fixed on the top and bottom side of each cube; the top plate was assigned a displacement boundary condition, whereas the bottom plate was tied up with an encastre boundary condition. The bulk Ti-6 Al-4V alloy material properties were assigned to the porous structures. The assembly was meshed using C3D10 quadratic tetrahedral elements with a mesh size of 0.2 mm, which was used based on a sensitivity study driven from stiffness convergence of a solid cube, as shown in Figure 2. Axial displacement on the upper plate was applied to achieve the yield points with increment sizes of 0.05 and with 20 increments to generate a more accurate value of the yield points and modulus of elasticity. The volumetric porosity for the designed porous cubes was calculated based on Equation (1), which was derived from geometric calculations for the designed porous cube. The designed cubes volumetric porosities were compared using the mass properties option in ABAQUS.  
\[Vp=\mathrm{L}_{2}^{2}N^{2}\ (2 Nt+2t) \tag{1}\]  
where \(V_{p}\) = volumetric porosity, \(L_{2}\) = cubic pore inner dimension, \(N\) = number of pores in a single cube face, \(x\) = full cube dimension, and \(t\) = strut thickness.  
Figure 1: Porous structure designs along with finite element model to study the mechanical properties.
###### Abstract
Recently, some reviews have highlighted the role of PGPR as abiotic stress alleviators in soil and the prospects of their application to mitigate soil metal contamination [20; 21; 22]. However, none was directed to describe their impact on soil bioremediation and cereal growth enhancement in arid environments. In this paper, we highlight the importance of using beneficial soil bacteria for both soil quality restoration and plant growth enhancement in arid environments. We also summarize scientific works revealing the place of such soil bacteria in improving wheat and rice production under stress circumstances. It is important to mention that cereals, particularly wheat and rice, are known to be the most important crops in the world. Together with maize, they constitute more than 50% of all the calories consumed by human beings over the world [23]. In addition, human diets, especially in developing countries where aridity is dominant, are essentially based on cereals. For example, global cereal demand is expected to increase from 585 million to 828 million tons by 2025, corresponding to an increase of 42% [2]. Furthermore, developing countries' rice and wheat production is supposed to jump from 4.2 and 3.1 to 4.7 and 3.5 tons/ha, between the year 2015 and 2030, respectively. Such rising cereal production would seem to be unsatisfactory to meet the accelerated growth of the human population that is predicted to reach 8.5 billion by the end of 2030 [1; 24]. Accordingly, this review aims to understand the prospective functions of Plant Growth-Promoting Rhizobacteria (PGPR), which can participate in soil bioremediation, stress alleviation and plant growth restoration. In fact, the positive effects of PGPR could be exploited to promote agricultural practices in stressed environments and help specialists to manage decisions for more ecofriendly practices in agriculture.
# HBIM: Background, Current Trends, and Future Prospects
## 5 Applications and Data Management
### Common Data Requirements
#### 5.1.1 Geometric Data  
The first essential information any BIM model must contain is the geometric data, which will be the basis for defining all other attributes [69]. Having an accurate geometry is particularly important for the preservation of historic buildings [70], especially considering the often complex nature of these structures. Therefore, the key factor to be defined when planning the creation of the model is the level of detail to be achieved. A higher LOD allows for a more precise representation of specific geometric features but comes at the cost of increased model size and a greater workload for its creation. Additionally, it will be limited by the available data collection tools. The state of the art of data collection and modeling tools have been explained in further detail in Section 4.