esa-sceva/satcom-chunk-collection huggingface dataset:
# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 3 Security Challenges and Mechanisms
### IoT in the Sea
#### Internet of Underwater Things  
The Internet of Underwater Things (IoUT) has been used in data aquisition [192], smart oceans [193] and similar areas.  
Researchers have considered the following security challenges and mechanisms regarding the IoUT:  
* Security Challenges
* Privacy The IoUT is the network of devices used in sensing, communication, and controlling the environment. Recently, proposed schemes focus on information collection and position estimation in the IoUT. The localization systems proposed in these are based around an anchor node. In these schemes, location privacy is not correctly protected, which makes the anchor node vulnerable to attack. The authors of [194] propose a privacy-preserving localization system. The scheme uses a privacy-preserving asynchronous transmission protocol to detect malicious anchor nodes and hide the location information of anchor nodes while not being synchronized to a clock. To localize the target location, a privacy-preserving estimator with ray compensation is used to avoid localization bias. Through testing simulations and experiments, the authors prove the scheme to be an effective system of localization.  
In addition to security, energy efficiency [195] has been considered as a design objective in recent research on the Internet of Underwater Things.
## 3 Security Implications and Fundamental Approaches of Blockchain-Based Data Storage Methods
### Blockchain-Based Data Integrity and Traceability Solutions
#### 3.5.1 Ensuring High Data Integrity with On-Chain Methods  
On-chain data storage methods utilize hash-based verification mechanisms to provide high reliability and full traceability. These methods are particularly suitable for applications in healthcare and finance, where data integrity is critical [31]. However, high transaction costs and scalability limitations can restrict their usability in large datasets or high-volume processing scenarios [29].  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Storage Method** & **Data Integrity Mechanism** & **Traceability** & **Sectoral Applications** & **Advantages** & **Disadvantages** & **Reference Number** \\ \hline On-chain & Hash-based verification & High & Healthcare, finance & Reliable verification, full traceability & High transaction cost & [31] \\ \hline Off-chain & Only hash data on blockchain & Medium & Education, Healthcare, logistic & Low cost, efficiency & Inadequate in areas requiring data security & [36] \\ \hline Hybrid & On-chain and off-chain integration & High & Healthcare, IoT & Provides balanced cost and safety & Requires complex configuration & [6; 28; 32; 33; 37; 47; 165] \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparative overview of data integrity and traceability solutions in blockchain-based storage methods.
# Optimization of PV and Battery Energy Storage Size in Grid-Connected Microgrid
## 5 Conclusions  
This study presents a PSO-based algorithm with a new energy management strategy to find the optimum PV and BESS size for a grid-connected MG. The MG can operate in island mode and, if necessary, in grid-connected mode with some limitations. The MG structure is designed in such a way that it can demand energy from the grid when there is not enough energy in the PV system and BESS. However, the amount of demanded energy is limited by the system authorities. The aim is to find an optimum PV and BESS size by considering the defined energy cost. This allows the microgrid to be supported from the grid in critical situations, although supplying loads from the RES has priority, regardless of whether the system will demand energy from the grid and/or the amount of energy to be demanded from the grid can be configured with the proposed energy management method. Therefore, the energy management algorithm can be reconfigured and used for various systems and different constraints. To validate the proposed approach, various calculations are carried out for different PV and BESS sizes. Furthermore, to prove the effectiveness of the new energy management method with PSO, it has been compared with GA. Results show that the PSO-based algorithm with the energy management strategy can determine the optimum PV and BESS size, with the minimum cost defining the system constraints. Consequently, PV and battery sizes have been optimized together with the proposed PSO algorithm and novel energy management system. The effectiveness of the system is also explained by comparing the results with different algorithms.  
Conceptualization, S.G. and S.O.; methodology, S.G. and S.O.; software, S.G. and S.O.; validation, S.G. and S.O.; formal analysis, S.G. and S.O.; investigation, S.G. and S.O.; resources, S.G. and S.O.; data curation, S.G. and S.O.; writing--original draft preparation, S.G. and S.O.; writing--review and editing, S.G. and S.O.; visualization, S.G. and S.O.; supervision, S.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The dataset associated with this paper can be found in the online version, at [[https://www.elia.be/en/grid-data?cstr=17010711133344377898](https://www.elia.be/en/grid-data?cstr=17010711133344377898)]([https://www.elia.be/en/grid-data?cstr=17010711133344377898](https://www.elia.be/en/grid-data?cstr=17010711133344377898)) (accessed on 30 July 2022).  
The authors
## 3 Results  
### Pull-Off Phase  
#### 3.1.1 Kinematics  
The kinematic analysis revealed that the knee joint was in a flexed position at the initial contact (IC) moment of the jump shot under all knee brace stiffness conditions. Significant differences in knee flexion angle were observed among the brace conditions (\(p=0.001\)). Additionally, the knee adduction angle exhibited significant differences (\(p=0.001\)), while no significant differences were found in the knee rotation angle (Table 2). When landing with no-stiffness knee braces, the knee joint experienced the maximum internal rotation angle, while wearing low-stiffness and high-stiffness knee braces results in smaller knee joint internal rotation angles compared to the no-stiffness condition. During the whole push-off phase, no significant differences were observed in the maximum knee flexion angle among knee braces with different stiffness levels. Similarly, there were no significant differences between low-stiffness and high-stiffness knee braces in terms of the maximum knee flexion angle or the maximum internal and external rotation angles. However, significant differences were found in the maximum knee flexion angles (\(p<0.001\)), and the ROM of the knee in the sagittal, frontal, and horizontal planes was significantly different (\(p=0.007\), \(p=0.001\), \(p=0.003\)) (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & & **No Stiffness** & **Low Stiffness** & **High Stiffness** & \(p\)** & **F** \\ \hline Pop & & & & & & \\ IC Angle (\({}^{\circ}\)) & flexion & \(-27.98\) (\(7.11\)) & \(-26.65\) (\(3.3\)) \# & \(-25.58\) (\(4.03\)) \^{} & **0.001** & 5.452 \\  & adduction & \(-0.58\) (\(1.31\)) & \(0.46\) (\(1.11\)) \# & \(0.45\) (\(1.67\)) \^{} & **0.001** & 5.452 \\  & external rotation & \(-7.14\) (\(0.91\)) & \(-5.18\) (\(2.04\)) & \(-2.11\) (\(2.20\)) & \(0.325\) & 1.165 \\ Max Angle (\({}^{\circ}\)) & flexion & \(-100.17\) (\(12.39\)) & \(-98.54\) (\(12.81\)) \# & \(-98.83\) (\(9.14\)) \^{} & \(0.062\) & 2.499 \\  & adduction & \(-0.60\) (\(3.95\)) & \(6.76\) (\(0.81\)) \# \^{} & \(4.39\) (\(1.65\)) \^{} & **0.001** & 4.767 \\  & external rotation & \(-16.92\) (\(5.68\)) & \(-12.73\) (\(6.09\)) & \(-9.78\) (\(7.52\)) & 0.955 & 0.108 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The knee joint’s IC moment angle, maximum angle, peak moment in three planes, and sagittal plane.
# Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes
## 4 Security Analysis
### Formal Security Analysis  
In this section, the widely deployed Burrows-Abadi-Needham (BAN) logic is utilized to demonstrate that the beacons exchanged are protected against any form of eavesdropping. Therefore, the communicating entities can trust each other as they communicate over insecure channels. Table 3 presents the BAN logic notations (BLNs) deployed in this formal verification process. For easy of analysis, all the messages exchanged during the AKA procedures are transformed into some idealized format (IME) as shown in Table 4. \begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**SNo.**} & \multicolumn{1}{c}{**Symbol**} & \multicolumn{1}{c}{**Description**} \\ \hline BLN\({}_{1}\) & SK & One-time session key \\ \hline BLN\({}_{2}\) & D/=E & D believes statement E \\ \hline BLN\({}_{3}\) & D/ -E & D once said statement E \\ \hline BLN\({}_{4}\) & D-dE & D sees statement E \\ \hline BLN\({}_{5}\) & \#E & Statement E is fresh \\ \hline BLN\({}_{6}\) & \textless{E}\textgreater{}F & Statement E is combined with secret statement F \\ \hline BLN\({}_{7}\) & \(\{\text{E}\}_{\text{G}}\) & Statement E is protected by secret key G \\ \hline BLN\({}_{8}\) & D \(\textless{A}\) H & D and H share secret key G \\ \hline BLN\({}_{9}\) & D \(\Rightarrow\) E & D has jurisdiction over E \\ \hline \hline \end{tabular}
\end{table} 
Table 3: BAN logic notations.
# Feature-Oriented CBCT Self-Calibration Parameter Estimator for Arbitrary Trajectories: FORCAST-EST
## 1 Introduction  
To reconstruct a Cone-Beam Computed Tomography image, several X-ray images are required and must be input into a reconstruction algorithm. This algorithm must know at which position and orientation each X-ray image was made. Several methods have been developed to solve this problem, and they come in two broad categories, offline and online calibration.  
Offline calibration uses phantoms with markers, which are often small metal beads that are imaged for each projection where the position and orientation can be calculated [1; 2; 3; 4; 5; 6; 7]. The phantom is imaged with a trajectory, and the correction factors are calculated. Then, these correction factors are used when reconstructing the images from this trajectory. This requires a dedicated run of the trajectory with the phantom.  
Online calibration uses the acquired projections and then uses prior information about the imaged object [8; 9; 10; 11], or it minimizes a cost function that is defined on the reconstructed image [12; 13; 14; 15]. A review of current approaches was published by [PERSON] et al. [16]. This approach does not need a dedicated phantom nor an extra run of the trajectory for the calibration, but it does have other constraints. Some use a prior image on which theprojections are registered when using 2D-3D registration algorithms, others require the trajectory to have a specific, often circular, shape.  
Using a 2D-3D registration of the acquired projections on a prior image allows for the calibration of fully arbitrary trajectories [17], and the registration is faster if the initial parameters are close to the actual parameters. The position reported by the CBCT system is usually close enough for a quick and good calibration with state-of-the-art algorithms. But, if this information is not available (e.g., portable C-Arms, continuous acquisition/fluoroscopy mode of the Artis Zeego), other means of obtaining these initial parameters are required.  
One option is to track the C-Arm externally with inertia sensors [18; 19]. These sensors can be attached to the C-Arm and, after calibrating the sensors, they track the inertia in all three dimensions. With this inertia data and a known starting position, the movement of the C-Arm can be calculated.  
Also possible is the use of 3D cameras [20; 21]. Here, the position of the C-Arm is observed by tracking optical markers through using either a camera attached to the X-ray source or two external cameras.  
This paper presents another option that uses a prior image to simulate forward projections from different angles, and then uses feature matching to find the one that fits bests for each acquired projection. In this way, the parameters for each projection can be estimated.
## 5 Threat Analysis and Risk Assessment (TARA)
### Risk Value Determination  
The analysis of risk values was achieved by applying a sequence of risk formulas that incorporate impact ratings and attack feasibility ratings before being determined. Initially, a translation of potential impact and attack feasibility ratings into corresponding scores was performed. Subsequently, for each distinct threat scenario, the average of all impact scores was computed and then multiplied by the feasibility score. The resultant risk scores were eventually subject to a transformation process, wherein they were calibrated into risk values based on a predefined mapping. The scale for risk values included the following values, ranked in descending order of danger: high, medium, low, very low. The impact factor was computed for each feature on the basis of four aspects: safety (\(S\)), financial (\(F\)), operational (\(O\)), and privacy (\(P\)). It is possible now to define an equation for computing the risk for each threat:  
\[Risk=Avg(S+F+O+P)\times Feasibility \tag{1}\]  
The value obtained from the equation can be converted to a risk level following Table 3.  
Considering the context of our system, which is strictly related to human life, operational and safety impacts are the most important impact metrics. Anyway, considering the economic impact of the components involved and the data exchanged when these components are used, financial and privacy impacts are also relevant. For this reason, we decided to consider all the metrics with an equal impact on the risk.  
Nevertheless, we choose to alter the natural distribution of risk level classes in line with Table 8 defined in ISO/SAE 21434. We started by splitting half the maximum value (\(40\times 40=1600/2=800\)) and assigning the highest part of this to high-level threats. For the remaining half, we performed another splitting (\(800/2=400\)), assigning the highest part to the medium-level threats and for the remaining part half and half to low- and very low level threats.  
These operations are needed to have a good threat model, able to consider as high risk all the threats that can lead to possible harm to the driver.
# A 100 Gbps OFDM-Based 28 GHz Millimeter-Wave Radio over Fiber Fronthaul System for 5G
## 2 Baseband Unit (BBU) System
The behavior of the MZM modulator is expressed as:  
\[\text{E}_{\text{out}}(\text{t})=\frac{\text{E}_{\text{in}}(\text{t})}{10^{( \text{IL}/20)}}\cdot\Big{[}\text{b}\cdot\text{e}^{(|\pi\text{v}_{2}(\text{t})/ V_{\text{nRF}}+|\pi\text{v}_{\text{bias}2}/V_{\text{nDC}})}\Big{]}+\Big{[}(1- \text{b})\cdot\text{e}^{(|\pi\text{v}_{1}(\text{t})/V_{\text{nRF}}+|\pi\text{v }_{\text{bias}1}/V_{\text{nDC}})}\Big{]} \tag{8}\]  
where \(\text{E}_{\text{in}}\) is the input optical signal, IL is the insertion loss, \(\text{v}_{1}(\text{t})\) and \(\text{v}_{2}(\text{t})\) are the input electrical voltages for the upper and lower modulator arms, \(\text{V}_{\text{bias}1}\) and \(\text{V}_{\text{bias}2}\) are the settings of bias voltage 1 and 2 of the modulator, \(\text{V}_{\text{nRF}}\) and \(\text{V}_{\text{nDC}}\) are switching modulation voltage and switching bias voltage respectively. \(\text{$\psi$}\) is the power splitting ratio of the arm branch written as;  
\[\text{$\psi$}=\Big{(}1-\frac{1}{\text{$\varepsilon$}_{\text{r}}}\Big{)},\text { where $\varepsilon$}_{\text{r}}=1=10^{\frac{\text{ExtRatio}}{10}} \tag{9}\]  
Extinction ratio (ExtRatio) is a parameter linked to the optical power of the external modulator. In this work, an ExtRatio of 60 dB is specified for the LiNbO\({}_{3}\) MZM. Also, the CW laser which is used to generate the optical signal at a center frequency of 193.1 THz has a phase noise modeled using the Probability Density Function (PDF) and expressed as;\[\mathrm{f}(\Delta\varphi)=\frac{1}{2\pi\sqrt{\Delta\mathrm{fdt}}}\,\mathrm{e}^{- \frac{\Delta\varphi^{2}}{4\pi\Delta\mathrm{fdt}}} \tag{10}\]  
where \(\Delta\varphi\) is the phase difference between two successive time instants and dt is the time discretization. However, \(2\pi\sqrt{\Delta\mathrm{f}}\) has been assumed as a Gaussian random variable with zero mean and variance with \(\Delta\)f the laser line-width.
# A Novel Slicing Strategy to Print Overhangs without Support Material
## 5 Conclusions  
New 3D printer kinematics require adapted slicing strategies and algorithms for adequate path planning. An algorithm with a geometrical transformation of the generated STL file, followed by a conventional slicing process and a back-transformation of the G-code is presented in this paper. Analysis of two printed reference parts led to the following observations:  
* The study demonstrates that the novel slicing strategy with cone shaped layers works very well on the 4-axis printer.
* It is possible to print parts with 90\({}^{\circ}\) overhangs without the use of support material. The downward oriented surfaces have the same quality as the upward oriented surfaces.
* The accuracy of the printed part geometry and the surface roughness as well are not yet as good compared to a conventional 3D printer with orthogonal kinematics.  
The motivation for this work was the development of a system capable of printing parts with large overhangs without support material. The outcomes of this R&D prove the feasibility of the chosen approach.  
Conceptualization, M.W., M.G. and W.J.E.; methodology, M.W. and M.G.; software, M.G.; validation, W.J.E. and M.W.; investigation, M.W.; resources, M.W.; writing--original draft preparation, M.W.; writing--review and editing, W.J.E., M.G. and C.J.; visualization, M.W., W.J.E. and M.G.; supervision, W.J.E. and M.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
3 Dp 3D printing AM Additive manufacturing FDM Fused Deposition Modeling CNC Computer Numeric Control DED Direct Energy Deposition (aka DMD) ZHAW Zurich University of Applied Sciences STL Standard Tessellation Language
## 3 Materials and Methods
### Hand-Crafted Feature Extraction
#### 3.6.3 Hue-Saturation-Value (HSV)  
Color-based analysis is essential for detecting discoloration and chlorophyll depletion, which are key symptoms of pest damage. The Hue-Saturation-Value (HSV) color space was chosen instead of the RGB model due to its perceptual relevance and robustness to lighting variations [28]. The transformation from RGB to HSV is defined as:\[H=\begin{cases}0^{\circ},&\text{if }\Delta=0\\ 60^{\circ}\times\left(\frac{G-B}{\Delta}\mod 6\right),&\text{if }V=R\\ 60^{\circ}\times\left(\frac{B-R}{\Delta}+2\right),&\text{if }V=G\\ 60^{\circ}\times\left(\frac{R-G}{\Delta}+4\right),&\text{if }V=B\end{cases} \tag{2}\]  
where \(\Delta=\max(R,G,B)-\min(R,G,B)\), and \(S=\hat{\psi}\) represents saturation, while \(V=\max(R,G,B)\) denotes brightness.  
By converting maize plant images to HSV, the hue component was utilized to detect discoloration patterns, saturation was analyzed to assess chlorophyll depletion, and brightness variations were studied to highlight infection areas. These color-based features complement both Gabor and GLCM-based texture features, ensuring a comprehensive feature representation.
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 6 Conclusions  
Self-organizing network (SON) technologies constitute one of the key enablers for B5G network deployment and operation since network management, optimization, and maintenance processes are expected to become even more complicated because of the diversity of services and equipment and the escalation of the number of devices in the network. Without SON, these tasks will become unfeasible. Academia and industry managed to make network automation a reality through SON and embedded machine learning (ML) and artificial intelligence (AI) technologies in mobile networks, while the adoption of SON platforms by many operators worldwide over the last few years has yielded impressive results in terms of KPI advancement and tremendous changes in terms of network management processes.  
In this paper, we presented and analyzed in detail the rationale of operation of SON systems and their evolution across generations, and proposed design methodologies for SON applications in B5G environments. We described in detail the design and the components of a C-SON system able to handle 4G and 5G networks and the dimensioning principles that must be considered prior to a SON system deployment in a RAN, including 2G/3G/4G/5G technologies. As far as the applications are concerned, we provided a deep insight into ANR, MLB, MRO, and CCO and explained their operation rationale.  
Moreover, we described the key AI and ML algorithms needed for successful SON deployment and described the most recent related research directions such as RL and DRL. These specific algorithms are ideal for B5G SON systems due to their ability to interact with the environment and collect information about the next steps without the need for previous knowledge or training information. Finally, we highlighted the most important points that SON research should focus on in order to guarantee the successful application of SON solutions in future networks such as B5G or 6G. Key points regard the interaction of SON functions with NFV, Big Data, backhaul management, IoT infrastructures, important B5G technology enablers such as mmWave and massive MIMO, and finally the interaction with basic 5G use cases such as URLLC.  
Conceptualization, A.G.P. and G.C.P.; Methodology, A.G.P. and G.C.P.; Investigation and writing, A.G.P. and G.C.P.; Review and editing: A.G.P. and G.C.P.; Supervision, G.C.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Not applicable; the study does not report any specific data.  
The authors declare no conflict of interest.