esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Blockchain Technology for Enhancing Digital Twins' Security and Transparency
## 1 Introduction
These solutions primarily focus on the integrity and confidentiality of data but often neglect the underlying security system's vulnerability to a direct attack. This research proposes an innovative approach that leverages the adaptability potential of blockchain technology, a decentralized system that has experienced rapid adoption in recent years. Refs. [10; 11] discuss the widespread adoption of blockchain across industries, highlighting its versatility in securing transactional and operational data. Beyond its initial application in cryptocurrencies, blockchain has found widespread use in various domains. Ref. [12] examines its integration into supply chains, showing how blockchain improves traceability and accountability. The decentralized nature of blockchain enables the secure and immutable storage of diverse data types, including documents, facts, packets, and transactions, as [13; 14] demonstrate in their respective studies. However, despite these advances, a significant gap remains in ensuring the transparent and secure integration of digital twin management systems with robust security frameworks. This research aims to address this challenge by pioneering an innovative approach that bridges digital twin ecosystems with blockchain technology. The intent is to create a seamless integration that not only enhances data security and traceability but also ensures transparency in operations without compromising system performance. By enabling real-time, secure interactions between digital twins and blockchain systems in a symbiotic relationship, this approach advances the state of the art, transforming how industrial ecosystems manage and secure their data. The following paragraphs explore a potential application of this technology within the context of digital twin security, aiming to minimize communication between the physical system and its blockchain-based counterpart. In Section 4, we evaluate the proposed system's performance, focusing on scalability and its contribution to enhanced security while ensuring the correct functioning of the solution proposed through multiple tests. Section 5 demonstrates its real-world application using a real system and the newly integrated security system.
## 4 Discussion and Conclusions
### Influence of the Geomechanical Parameters of the Rock Mass  
The influence of the rock type, characterised by Hoek's parameter m\({}_{\text{i}}\), is, for practical purposes, negligible in the cases with small load eccentricity with respect to the cavern axis (E < 3a). For eccentricity values (E/a) from 3 to 8, this influence is lower than 10% (Figure 10).  
The strength and conditions of the rock mass (parameters UCS and GSI-RMR\({}_{\text{b}}\)) have relatively little influence on the results obtained (Figure 11). By analysing them with greater accuracy, it can bestated that there is a threshold for a change in behaviour for eccentricity E = 2a. For higher values, the relative ultimate bearing capacity is again higher than 70%; therefore, the reduction is, for practical purposes, of minor importance. For lower eccentricity values (E < 2a), the relative ultimate bearing capacity is considerably reduced, and as expected, the minimum value is obtained with the null eccentricity. A significant influence can be observed, with differences in the relative ultimate bearing capacity between 10% and 30%, according to the values of the parameters UCS and GSI, when that eccentricity is lower, and, therefore, the existence of the cavern is determined (Figure 12).  
This conclusion is of great practical importance given that, if an accuracy level on the order of +/-10% is assumed in the calculation of the ultimate bearing capacity, the curves in Figures 11 and 12 can be used for all types of rock masses.
## 4 Comparison
### Comparison of Security Algorithms in Drone Routing  
Based on the discussion in Section 3.3, in this subsection, different security strategies in drone-based delivery systems are compared with each other in terms of their main idea, advantages, limitations, evaluated metrics, performance objectives, and innovative ideas. In Table 6, drone-based delivery techniques that emphasize security issues for drone-based delivery are compared in terms of their main goal, advantages, and limitations. It can be observed that blockchain technology is the most widely used approach to guarantee security and privacy in drone-based delivery systems. The drones being used for delivering packages are generally considered as the nodes in the blockchain. The two major advantages of blockchain technology are immutability and decentralized architecture, ensuring the overall framework is secure. Different approaches have considered a variety of security issues such as intrusion detection, trajectory planning for avoiding restricted areas, and security of user and product data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Main Idea** & **Advantages** & **Limitations** \\ \hline \multirow{3}{*}{Covadel [56]} & The scheme considers queuing & Effect of weather on drone & A secure collision avoidance \\  & schedule for the delivery of the & energy consumption and & mechanism for drone-based \\  & goods with minimal & charging mechanism & delivery is designed based on \\  & communication overhead. & were ignored. & light intensity. \\ \hline \multirow{3}{*}{PA-NOP [57]} & Study of collision avoidance & The proposed scheme provides & \\  & among multiple drones from & a secure delivery flight & Scalability of the proposed \\  & different organizations to provide & operation by introducing & mechanism is not guaranteed. \\  & secure delivery service. & blockchain mechanism. & \\ \hline \multirow{3}{*}{GaRuDa [58]} & Considered the security aspects & The system can offer better & \multirow{3}{*}{The co-channel and \multirow{3}{*}{cross-channel interference} issues are overlooked.} \\  & for healthcare-based product & & \\  & delivery using Internet of drones. & data storage. & \\ \hline \multirow{3}{*}{Delivery-Coin [60]} & Proposed an intrusion detection & Proposed framework has the & \multirow{3}{*}{Security issues of edge} \\  & technique to provide privacy & lower consensus latency with & computing in the proposed \\ \cline{1-1}  & preserving drone-based & respect to other & \multirow{3}{*}{framework were ignored.} \\ \cline{1-1}  & delivery systems. & conventional schemes. & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of security algorithms in drone routing in terms of main idea, advantages, and limitations.
## 3 Results
### Guidelines concerning the Optimal Therapeutic Approach for Patients with COVID-19 Infection  
Various therapies have been developed to target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Some of these therapies are effective for patients with mild lung involvement, while others are more suited for those with severe lung damage. CT scans are only performed on COVID-19 positive patients when they are hospitalized. Hence, a severity score is needed to determine the optimal therapeutic approach and patient management framework for these patients.  
The COVID-19 lung involvement score, which can be objectively determined using the masks yielded by the U-Net-based model, plays a crucial role in establishing patient treatment plans.  
Here are the updated guidelines for the optimal therapeutic approach for COVID-19 patients with different severity levels [28]:  
1. Severity scores of 1 and 2--patients in stable condition who do not require oxygen supplementation: * Do not use dexamethasone or other corticosteroids; * Use remdesivir for patients who are at high risk of progressing to severe COVID-19; * Administer a prophylactic dose of heparin;
2. Severity score of 3--patients who require conventional oxygen supplementation: * Use remdesivir for patients who require minimal oxygen supplementation; * For most patients, administer dexamethasone and remdesivir or just dexamethasone- some (if remdesivir is unavailable); * Add either baricitinib or tocilizumab if the oxygen is needed and inflammation markers are rapidly increasing; * Administer a therapeutic dose of heparin for nonpregnant patients with D-dimer levels above the upper limit of normal without increased bleeding risk;
3. Severity score of 4--patients who require high-flow nasal cannula oxygen supplementation or noninvasive ventilation: * Use just dexamethasone, dexamethasone with baricitinib, or dexamethasone with tocilizumab; * Administer a prophylactic dose of heparin;
4. Severity score of 5--patients who require mechanical ventilation or extracorporeal membrane oxygenation: * Use just dexamethasone, dexamethasone with baricitinib, or dexamethasone with tocilizumab; * Administer a prophylactic dose of heparin.  
Figure 11 summarizes the guidelines and highlights the most important information based on lung damage severity [28].
## 6 The Proposed RAWC Scheme
### RAWC Algorithm
It is divided into two cases: 1 Only one QKD service request arrives at a certain time, in which case there is no wavelength conflict, as shown in Algorithm 1; 2 Multiple QKD service requests arrive simultaneously at a certain time and there are wavelength conflicts, as shown in Algorithm 2. The schemes for both cases are divided into three steps, i.e., routing calculation, wavelength assignment, and quantum key assignment. In the routing computation, case 1 needs to adaptively calculate the communication path with the Dijkstra algorithm for the QKD service request arriving at each moment. Case 2 first uses the KSP algorithm to calculate K alternative paths for each QKD service request request request, and then calculates the wavelength conflict degree of each alternative path. **Algorithm 3** The scheme without wavelength conflicts  
The schemes for both cases are divided into three steps, i.e., routing calculation,  
```
0:\(G(V,E)\), \(R_{i}(s_{R_{i}},\,d_{R_{i}},\,K_{R_{i}})\), \(\lambda_{ini}\), \(K_{ini}\), \(\alpha\), \(\beta\)
0: SR, WRU, QKU of QKD service requests
1:for simultaneous \(m\) (\(m\geq 2\)) service requests \(R_{1}\), \(R_{2}\),..., \(R_{m}\)do
2: path selection for each service request based on the wavelength conflict degree;
3:for each service request \(R_{i}(1\leq i\leq m)\)do
4: routing computation with the KSP algorithm according to Equation (1);
5:for each computed alternative path \(p_{j}^{i}(1\leq j\leq K)\)do
6: wavelength conflict computation referring to Section 6.2.2;
7:endfor
8: select the path with the least wavelength conflict as the communication path \(p^{i}\);
9: search available wavelength resources \(W(p^{i})\) through the path \(p^{i}\);
10:if\(W(p^{i})\
eq\varnothing\)then
11: select an available wavelength with the FF algorithm;
12: update each link wavelength resource status of path \(p^{i}\);
13:else
14: the service request is blocked;
15:continue;
16:endif
17:for each link\((u,v)\) of path \(p^{i}\)do
18: quantum key assignment;
19:if\(K_{R_{i}}\leq K_{aaa}(u,v)\)then
20: select \(K_{R_{i}}\) quantum keys from QKP for \(R_{i}\) with the FF algorithm;
21: update the remaining quantum key status of each node of path \(p^{i}\);
22:else
23: the service is insecure and blocked;
24:endif
25:endfor
26:endfor
27:endfor
```  
**Algorithm 2** The case with wavelength conflicts
## 2 Materials and Methods
### Kinematic Observation Equation  
The dual-frequency code and carrier phase observations between the LEO satellite receiver (subscript \(L\)) and the GPS satellite PRN \(k\) (superscript \(G_{k}\)) at time epoch \(t\) can be described as follows [33]:  
\[\begin{array}{rcl}P_{i,L}^{G_{k}}&=&\rho_{L,0}^{G_{k}}+\overset{\to G_{L}}{ \leftarrow}\overset{\to G_{k}}{\leftarrow}\overset{\to G_{k}}{\leftarrow} \overset{\to G_{k}}{\leftarrow}+c(dt^{G_{k}}-dt_{L})+\alpha_{i}I_{L}^{G_{k}}+ \varepsilon_{P,L}^{G_{k}}\\ L_{i,L}^{G_{k}}&=&\rho_{L,0}^{G_{k}}+\overset{\to G_{k}}{\leftarrow} \overset{\to G_{k}}{\leftarrow}\overset{\to G_{k}}{\leftarrow}+c(dt^{G_{k}} -dt_{L})+\lambda_{i}N_{i,L}^{G_{k}}-\alpha_{i}I_{L}^{G_{k}}+\varepsilon_{i,L}^ {G_{k}}\end{array} \tag{1}\]  
where \(\rho_{L,0}^{G_{k}}\) is the geometric distance between the GPS satellite's true position \(\overset{\to}{\overset{\to}{r}}^{G_{k}}\) and the LEO satellite's approximate position \(\overset{\to}{r}\overset{\to}{L,0}\); \(\overset{\to G_{k}}{\leftarrow}\overset{\to G_{k}}{\leftarrow}\overset{ \to G_{k}}{\leftarrow}\overset{\to G_{k}}{\leftarrow}\overset{\to G_{k}}{ \leftarrow}\overset{\to G_{k}}{\leftarrow}\) is the unit direction vector between the GPS and LEO satellites; \(d\overset{\to}{r}\overset{\to}{L}\) is the position correction vector \(d\overset{\to}{r}\overset{\to}{L}\) = \(\overset{\to}{r}\overset{\to}{L}-\overset{\to}{r}\overset{\to}{L,0}\overset{ \to}{r}\overset{\to}{L}\) is the true position of the LEO satellite; \(c\) is the velocity of light in a vacuum; \(P_{i,L}^{G_{k}}\) and \(L_{i,L}^{G_{k}}\) are the dual-frequency code and carrier-phase observations, respectively; \(\lambda_{i}\) and \(N_{i,L}^{G_{k}}\) are the carrier-phase wavelength (in a vacuum) and ambiguity at frequency \(f_{i}(i~{}=~{}1,2)\), respectively; \(dt^{G_{k}}\) and \(dt_{L}\) are the clock errors of the GPS and LEO satellite receivers, respectively; \(\alpha_{1}~{}=~{}1,\alpha_{2}~{}=~{}f_{1}^{2}/f_{2}^{2}\), and \(I_{L}^{G_{k}}\) is the ionospheric delay on \(L_{l}\); and \(\varepsilon_{P,L}^{G_{k}}\) and \(\varepsilon_{i,L}^{G_{k}}\) are the code and carrier phase observation errors (e.g., multipath), respectively. Generally, \(\overset{\to}{r}^{G_{k}}\) and \(dt^{G_{k}}\) can be obtained from the precise ephemeris and clock products released by CODE or the International GNSS Service (IGS) [34]. The relativistic effect and phase wind-up effect (due to the relative rotation between a transmitting and receiving antennas), which can be calculated using known correction models, are not shown in Equation (1). Moreover, for simplicity, subscript \(L\), superscript \(G_{k}\), \(\rho_{L,0}^{G_{k}}\), and \(dt^{G_{k}}\) are omitted in some of the following equations, without causing confusion.
# Visualising the Relevance of Climate Change for Spatial Planning by the Example of Serbia
## 2 Data and Methods
### Data Collection Methods  
The Law on Planning and Construction (2023) [32] defines four types of spatial plans in Serbia: (1) spatial plans for the Republic of Serbia, (2) regional spatial plans, (3) spatial plans for special-purpose areas, and (4) spatial plans for local self-governing units. The spatial plan for the Republic of Serbia, as an overarching strategic document, providesstrategic guidelines for regional and other spatial plans at lower planning levels. The whole territory of Serbia is encompassed by regional plans as well as local plans. The relationship between climate change and spatial planning is analysed here through the current set of national and regional documents, i.e., the national spatial plan, all nine regional plans, and some additional planning documents that accompany them such as strategic environmental assessment (SEA) and implementation plans: the Spatial Plan of the Republic of Serbia (2010) [33] and its SEA (2010) [34], and Implementation Program (2011) [35]; the Regional Spatial Plan for Juzno Pomoravlje (2010) [36]; the Regional Spatial Plan for the Administrative Area of the City of Belgrade (2011) [37]; the Regional Spatial Plan for the Autonomous Region of Vojvodina (2011) [38], with its Implementation Program (2013) [39]; the Regional Spatial Plan for Timocka Krajina (2011) [40] and its Implementation Program (2015) [41]; the Regional Spatial Plan for Nisavski, Toplicki, and Pirotski Districts (2013) [42]; the Regional Spatial Plan for Zlatiborski and Moravicki Districts (2013) [43]; the Regional Spatial Plan for Sumadiiski, Pomoravski, Raski, and Rasinski Districts (2014) [44]; the Regional Spatial Plan for Podunavski and Branicevski Districts (2015) [45]; and the Regional Spatial Plan for Kolubarski and Macvanski Districts (2015) [46].
## 3 5G Technology Enablers for V2X Use Cases
### The Evolution of V2X Communications
#### 3.1.2 3 GPP Release 14  
In 2017, 3 GPP Release 14 was introduced. This set of standards includes V2X communication, which is basically an extension of the Release 12 LTE Proximity Services used for ad hoc direct communication services. The communication methodology is similar to the LTE uplink communication of the Release 14 standard. This implies that the same subchanneling mechanisms are applied, which requires tight time synchronization, SC-FDM modulation, and turbo coding. The Release 14 communication defines Mode 3 and Mode 4 transmission scheduling modes. In Mode 3, the cellular network is responsible for resource management (subchannel assignment). Mode 4 defines a distributed operation, which is essential in the V2X world due to safety reasons, as depicted in [59]. In Mode 4, the tight time synchronization requirement was solved via precise GNSS signals. This also means that the Release 14 distributed communication is not operable without a GNSS signal [8; 30; 58].
###### Abstract  
In this study, the termination criteria at each drought response stage were shown to increase storage volume while maintaining the stability of the water supply service during drought in multi-purpose dams. For 52 of the Representative Concentration Pathway (RCP) 4.5/8.5 scenarios of the IPCC (Intergovernmental Panel on Climate Change) fifth assessment report (AR5), dam inflows were calculated through the precipitation-runoff modeling system (PRMS), and time reliability and supply reliability were estimated. Moreover, given the application of termination criteria at each drought response stage, the volume of additional water supply was calculated, while the number of days for additional supply availability of water for residential and industrial use and water for residential, industrial, and agricultural use and river maintenance was quantified as well. The CMCC-CMS(RCP4.5) GCM exhibited the largest volume of additional water supply at 74.15 million m\({}^{3}\), which corresponds to 52.0 days of water for residential and industrial use and to 47.7 days of water for residential, industrial, and agricultural use and river maintenance. The analysis revealed that the volume and the number of days of additional water supply also increased for other GCMs due to the application of termination criteria at each drought response stage.  
Keywords:multi-purpose dam; standard flow index (SFI); storage capacity; maintenance days; termination criteria at each drought response stage +
Footnote †: journal: Applied sciences
## 3 Proposed Model
### Hub Object Detection
We define \(D\) as a set denoting all information including scores \((S_{i})\), positions\((x_{i},y_{i})\) and bounding box dimensions \((h_{i},w_{i})\) for all detected candidates. Therefore, we can write \(D\) as below:  
\[D=\{D_{i}|i=1,2,..,N;D_{i}=(S_{i},x_{i},y_{i},h_{i},w_{i})\} \tag{1}\]  
where \(N\) is the total number of objects detected for the current image. In order to suppress weak objects, first we should sort D regarding to object scores to get \(D_{s}\):  
\[D_{s}=sort(D|S_{i},i=1,2,..N)=\{D_{j}|j=j_{1}^{*},j_{2}^{*},...,j_{N}^{*}\} \tag{2}\]  
To obtain it, we define Equation (3):  
\[I_{k}=j_{k}^{*}=Argmax(S_{i}|i=1,2,..,N,i\
otin J_{k-1}^{*}) \tag{3}\]  
where \(j_{k}^{*}\) is the argmax for \(k^{\prime}\)th iteration, and we can write Equation (4):  
\[J_{0}^{*}=\phi,J_{1}^{*}=\{J_{0}^{*},j_{1}^{*}\},J_{2}^{*}=\{J_{1}^{*},j_{2}^{* }\},...,J_{N}^{*}=\{1,2,..,N\} \tag{4}\]  
Sometimes there are some detections even with high scores while they are false positives (FPs). In order to reject these types, we apply dimensional filtering for objects in sorted detection set \(D_{s}\). According to Equation (5), the _best object_ **(Bo)** can be found by selecting the lowest amount of \(k\), which satisfies both conditions \(h_{j_{k}^{*}}>k_{h}\times\bar{H}\) and \(w_{j_{k}^{*}}>k_{w}\times\bar{W}\) :  
\[ \begin{split} Bo=j_{k}^{*}|h_{j_{k}^{*}}>k_{h}\times\bar{H}& \&\&\qquad w_{j_{k}^{*}}>k_{w}\times\bar{w}\\ for&\quad min(k),k\in\{1,2,..,N\}\\ \bar{H}=1/N\times\sum_{i=1}^{N}h_{i}\\ \bar{W}=1/N\times\sum_{i=1}^{N}w_{i}\end{split}  \tag{5}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Values** & **Description** \\ \hline H & 300 & Height of the input images \\ \hline W & 480 & Width of the input images \\ \hline iC & 3 & Number of color channels of the input images \\ \hline n\_classes & 1 & Number of positive classes \\ \hline Scales & [0.08, 0.16, 0.32, 0.64, 0.96] & An explicit list of anchor box scaling factors. \\ \hline AR(aspect ratios) & [0.5, 1.0, 2.0] & The list of aspect ratios for the anchor boxes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Primary configurations for SSD7.
## 4 Discussion
### Research Implications and Limitations  
Calibrating Sentinel-2 data using fine-scale UAV estimates can produce LAI datasets that are less biased and better capture the within-field variability when compared to ground measurements. In an operational context, our two-stage UAV calibration approach could be applied, for instance, at some sample within-field areas to capture the variability in crop growth. This calibration could then be applied to larger extents of the Sentinel-2 dataset in order to generate improved estimates of wheat LAI at the farm and landscape scales.  
In our study, for the first time, we have deployed a UAV sensor with Sentinel-2 matching band-pass filters. Past studies comparing UAV to satellite-scale observations (e.g., [38; 56]) have used UAV instruments with different spectral bands to those of the satellite sensor. For instance, research in [PERSON] et al. [61], which involved comparing NDVI derived from a UAV to that from the RipidEye satellite sensor, attributed the disparities in NDVI to differences in sensor spectral properties. In our research, by matching spectral observations, we reduce errors due to band-positioning off-sets. This UAV instrument, linked to the GPR modelling, provided a unique opportunity to scale up the ground measurements, whilst tracking the uncertainty of hand-held instruments, to the spatial extents of UAV flights that could then be compared to the Sentinel-2 data. We recommend future studies take advantage of such band-matching approaches.  
Our study is limited to the use of spatially and temporally concurrent data (i.e., ground, UAV and Sentinel-2 acquisitions) for only one wheat growing season across two farm sites. Calibration and validation across additional growing seasons and sites would be of value to further test the performance of our two-stage calibration approach. While the calibration is specific, we would not expect our approach to be limited to wheat. We anticipate a comparably strong performance of the two-stage calibration when applied to other arable crops, and even to other vegetation types. Research in [PERSON] et al. [38], for instance, demonstrated the calibration of Sentinel-2 NDVI using UAV data for quantifying damages to rice crops across large spatial extents. Given the typical scale mismatches between ground measurements and Sentinel-2 data, we would also expect that the two-stage calibration would be equally as valid for improving the retrieval of additional crop variables that covary with LAI,including fAPAR and canopy chlorophyll content [7; 62; 63]. In particular, our approach overcomes widespread calibration challenges related to resolving the spatial heterogeneity of vegetation, which are even more pressing in unmanaged/natural ecosystems. We would, therefore, recommend that the two-stage calibration, involving the use of fine-scale UAV observations, should be adopted as a standard when using Earth observations for vegetation upscaling activities.