esa-sceva/satcom-chunk-collection huggingface dataset:
# SNR Analysis for Quantitative Comparison of Line Detection Methods
## 4 Method--SNR of SGAD for Line Detection
### Dispersion of Gradient Angle Differences
, \tag{54}\]  
where  
\[P_{1} =12+16\rho_{1}-8\rho_{2}-16\rho_{\sqrt{5}}-4\rho_{2\sqrt{2}},\] \[P_{2} = -6-8\rho_{1}+10\rho_{2}+16\rho_{\sqrt{5}}+4\rho_{2\sqrt{2}}-6\rho _{4}-8\rho_{\sqrt{17}}-2\rho_{2\sqrt{5}},\] \[P_{3} =2+8\rho_{1}+10\rho_{2}-8\rho_{\sqrt{5}}-12\rho_{2\sqrt{2}}+8 \rho_{3}-8\rho_{\sqrt{13}}+2\rho_{4}-2\rho_{2\sqrt{5}}.\]  
Furthermore, the dispersion of the vector of the gradients in Equation (50) when \(i=1\) and \(j=8\) is derived as follows:  
\[D\left\{ \begin{bmatrix}g_{\xi_{1}}\\ g_{\xi_{1}}\\ g_{\xi_{5}}\\ g_{\xi_{8}}\\ \end{bmatrix} \right\}=\frac{\sigma_{\mathbf{n_{s}}\mathbf{s}}^{2}}{64} \begin{bmatrix} Q_{1}&0&Q_{2}&Q_{3}\\ 0&Q_{1}&Q_{3}&Q_{2}\\ Q_{2}&Q_{3}&Q_{1}&0\\ Q_{3}&Q_{2}&0&Q_{1}\\ \end{bmatrix} , \tag{55}\]  
where  
\[Q_{1} =12+16\rho_{1}-8\rho_{2}-16\rho_{\sqrt{5}}-4\rho_{2\sqrt{2}},\] \[Q_{2} = -1-4\rho_{1}-4\rho_{2}+8\rho_{\sqrt{5}}+12\rho_{2\sqrt{2}}-4\rho _{3}+8\rho_{\sqrt{13}}-2\rho_{4}-4\rho_{\sqrt{13}}\] \[\quad-4\rho_{2\sqrt{5}}-4\rho_{5}-\rho_{4\sqrt{2}},\] \[Q_{3} = -1-4\rho_{1}-4\rho_{\sqrt{2}}+4\rho_{3}+8\rho_{\sqrt{10}}+2\rho_ {4}+4\rho_{\sqrt{17}}-4\rho_{3\sqrt{2}}-4\rho_{5}-\rho_{4\sqrt{2}}.\]  
Thus, the dispersion of the angle difference in Equation (48), \(D\{\theta_{i}\text{-}\theta_{j}\}\), when \(i=4\) and \(j=5\) is derived as follows:  
\[\sigma_{\theta_{4}\text{-}\theta_{5}}^{2}=D\{\theta_{4}\text{-}\theta_{5}\}= \frac{\sigma_{n}^{2}}{256\pi\sigma_{5}^{2}}\big{[}\frac{P_{1}}{g_{\xi_{4}}^{2} +g_{\xi_{4}}^{2}}+\frac{P_{1}}{g_{\xi_{5}}^{2}+g_{\xi_{5}}^{2}}-2\cdot\frac{g _{\xi_{4}}g_{\tau_{5}}p_{2}+g_{\xi_{4}}g_{\varsigma_{5}}p_{3}}{\big{(}g_{\xi_{4}} ^{2}+g_{\xi_{4}}^{2}\big{)}\big{(}g_{\xi_{5}}^{2}+g_{\tau_{5}}^{2}\big{)}}\big{]}. \tag{56}\]  
Figure 7: Neighborhood pixels for calculating gradient angle difference. (**a**) Column direction; (**b**) Diagonal direction. Moreover, the dispersion of the angle difference in Equation (48), \(D\{\theta_{i}\text{-}\theta_{j}\}\), when \(i=1\) and \(j=8\) is derived as follows:  
\[ \begin{split}\sigma_{\theta_{1}\text{-}\theta_{8}}^{2}& =D\{\theta_{1}\text{-}\theta_{8}\}\\ &=\frac{\sigma_{n}^{2}}{256\pi\sigma_{s}^{2}}\big{[}\frac{Q_{1}}{ g_{c_{1}}^{2}+g_{r_{1}}^{2}}+\frac{Q_{1}}{g_{c_{8}}^{2}+g_{r_{8}}^{2}}-2Q_{2} \cdot\frac{g_{r_{1}}g_{r_{8}}+g_{c_{4}}g_{c_{8}}}{\big{(}g_{c_{1}}^{2}+g_{r_{1} }^{2}\big{)}\big{(}g_{c_{8}}^{2}+g_{r_{8}}^{2}\big{)}}\\ &\quad+2Q_{3}\cdot\frac{g_{r_{1}}g_{c_{8}}+g_{c_{1}}g_{r_{8}}}{ \big{(}g_{c_{1}}^{2}+g_{r_{1}}^{2}\big{)}\big{(}g_{c_{8}}^{2}+g_{r_{8}}^{2} \big{)}}\big{]}.\end{split}  \tag{57}\]
# Efficient Route Management Method for Mobile Nodes in 6 TiSCH Network
## 5 Conclusions  
In this paper, we proposed a scheduling technique in which a link assignment with a preferred parent node is released owing to the movement of a moving node in a 6 TiSCH network. Cell allocation, based on link information from moving and neighboring nodes, quickly resets the path with the preferred parent node and reduces latency.  
We compared the scheduling performance of our proposed algorithm with that of Orchestra with EMA-RPL, which supports distributed scheduling and existing moving nodes. Experiments show that when the proposed algorithm is released from link assignment with the preferred parent node because of the movement of the mobile node, it quickly allocates cells through link information from the mobile and neighboring nodes, complementing the path simultaneously, minimizing latency by up to 7\(\times\) and reducing collision of approximately 2.5\(\times\) control packets in the shared cell. It was also demonstrated that the proposed algorithm can support mobility better than conventional algorithms by the approximately 33% decrease in network engagement time for mobile nodes.  
We focused on algorithms that moved and quickly recovered after the mobile node deviated from the topology. For future research, we plan to extend the study to algorithms with better network quality by registering RSSI values or directional information of mobile nodes as parameters to predict the moving position of mobile nodes in advance.  
Conceptualization, M.-J.K.; methodology, M.-J.K.; software, M.-J.K.; validation, M.-J.K. and S.-H.C.; formal analysis, M.-J.K. and S.-H.C.; investigation, M.-J.K. and S.-H.C.; resources, M.-J.K. and S.-H.C.; data curation, M.-J.K.; writing--original draft preparation, M.-J.K.; writing--review and editing, M.-J.K. and S.-H.C.; visualization, M.-J.K.; supervision, S.-H.C.; project administration, S.-H.C.; funding acquisition, S.-H.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program(IITP-2021-2016-0-00318) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation).  
All the data generated or analyzed during the current study are included in the published article.  
The authors declare no conflict of interest.
## 5 Fraud Risk Management in the Honey Sector--The VACCP Approach and Innovative Technologies
In Poland, despite growing awareness among food business operators, the implementation of the VACCP system continues to face organizational, competency-based, and technological barriers. Studies indicate that vulnerability assessments are often conducted in a formalistic manner, lacking meaningful integration with broader quality management systems [79,80]. The need for sector-specific guidance and the improved operationalization of fraud prevention measures remains particularly urgent in high-risk supply chains such as those involving honey [81,82]. For the honey sector, it is recommended to update existing checklists by including specific indicators, such as the presence of atypical pollen, inconsistencies between declared and actual botanical composition, supplier trade history, and the use of AI-supported analytics. These measures align with international recommendations on supply chain integrity and traceability, particularly those outlined by FAO/WHO and EFSA [83,84,85]. In practical applications, predictive tools and digital risk assessment systems are becoming increasingly important. The integration of data from multiple sources, such as chemical analyses, AI results, traceability documentation, and commercial data, enables the creation of dynamic decision support models. This approach supports both internal audits and regulatory oversight, increasing operational resilience and transparency in the supply chain. VACCP typically considers four key categories of vulnerability: (1) economic attractiveness--the potential economic benefit of fraud; (2) technical feasibility--the ability to carry out and conceal fraudulent activities; (3) effectiveness of oversight systems--thestrength of existing control measures; and (4) supply chain transparency--the degree of visibility and traceability within the supply chain. These categories are widely recognized in the literature as fundamental components of food fraud vulnerability assessments [3,86]. A detailed overview of these vulnerability areas, along with proposed preventive measures within the VACCP system, is presented in Table 3.  
Recent studies suggest that the implementation of advanced digital technologies, particularly artificial intelligence (AI), can significantly improve the effectiveness of VACCP systems in the honey sector. AI-based predictive models enable not only the verification of
# Internal Force Analysis of Buried-boring Piles in the Yuanzishan Landslide
## 2 Methodology and Validation  
### Simplified Calculations of Buried-Boring Piles during a Landslide  
#### 2.1.1 Basic Assumptions  
Three assumptions were made in order to simplify the buried-boring pile calculations:  
1. it was assumed that the driving force of the landslide was transmitted to the foundation of the bored pile, with the friction force at the top of the pile and the reaction force at the pile bottom assumed to be negligible;
2. the bored pile was assumed to be in a fragile state, such that the displacement of the soil in front of the pile was equal to the pile displacement above the slip surface. The pile was simplified as a cantilever beam; thus, the resistance force of the soil in front of the pile is negligible;the potential fracture surface in the slope body was assumed to be the Coulomb fracture surface [25], such that the shear strength of the soil above the slip surface was constant at \(\varphi\) and the slope, \(a\), of the slip surface at the pile location was parallel to the ground slope. By making these assumptions, the thrust distribution was simplified to a trapezoidal load.  
According to the above assumptions, the stress model of a buried-boring pile is shown in Figure 1. The stress calculations involved three steps: (1) determination of the pile length above the slip surface; (2) calculation of the internal force of the pile above the slip surface; and (3) calculation of the internal force of the pile below the slip surface.
## 4 Discussion
### Effects of Heating Power  
Figure 10 depicts the effects of heating power on the \(V_{g}\) and \(R_{gw}\) within 1080 days with WWH&CH. When the heating power is increased from 100 to 200, 300, 400, and 500 W/m, the \(V_{g}\) increases from 4.055 \(\times\) 10\({}^{6}\) to 4.120 \(\times\) 10\({}^{6}\), 4.161 \(\times\) 10\({}^{6}\), 4.197 \(\times\) 10\({}^{6}\), and 4.227 \(\times\) 10\({}^{6}\) ST m\({}^{3}\), increasing by 101.60%, 102.61%, 103.05%, and 104.24%, respectively. The results show that a heating power of 100 W/m is sufficient to prevent the formation of secondary hydrates in the TPL when the production pressure difference is set to 4 MPa. Increasing heating power further does not significantly increase gas output. A heating power of 100 W/m offers an almost identical heating effect as a 500 W/m heating power but with less heating energy consumption. The \(R_{gw}\) in the aforementioned cases stays around 200 after 1080 days of production, showing no discernible change.  
Figure 9: Evolution diagram of gas saturation without and with heating within 1080 days.  
To further investigate the relationship between the calorific value of increased production and the additional heat to drive production, we introduced a custom production efficiency index \(EROL_{custom}\) based on previous work [48; 49; 50; 51]. The specific definition is as follows:  
\[EROL_{custom}=E_{O}/E_{I} \tag{3}\]  
where \(E_{O}\) is the energy output, which is the increased gas production times the heating value of natural gas (approximately 35.8 MJ/m\({}^{3}\)), and \(E_{I}\) is the energy input, which is the energy consumed by in situ electric heating [48; 49; 50; 51]. Figure 11 shows the \(EROL_{custom}\) curves with different heating power. All curves show a trend of first increasing and then decreasing and tend to remain constant in the later stage. The peak value of the curve indicates that heating has the most significant effect on increasing production in the initial stages of production. Among them, a heating power of 100 W/m has the best energy output-to-input ratio.
## 2 Materials and Methods
### Experimental Design  
The treatments were applied as two crop sizes in combination with two different canopy heights. The two crop sizes were regulated via cluster thinning, which was manually performed at the beginning of the veraison (on 28 July 2017 and 23 July 2018) at grapevine growth stage 35 according to the modified E-L system [42], when approximately 3-5% of the berries started to change color and the concentration of soluble solids in the grape juice was approximately 9 Brix. Cluster thinning treatment (CT) was obtained via thinning 35% of the clusters, whereas in full crop treatment (FC) the clusters were not removed. Two canopy heights were obtained via trimming the vines to 130 cm of total canopy height (high canopy; HC) or removing the apical part of the shoots via severe shoot trimming (SST) to 70 cm of the total canopy height at late veraison (on 14 August 2017 and 10 August 2018) and grapevine growth stage 36, when approximately 80-90% of berries changed color and the concentration of soluble solids in grape juice was approximately 14-15 Brix. Treatments were set up as a 2 \(\times\) 2 factorial design, leading to four combinations: FC-HC (full crop, high canopy), FC-SST (full crop, severe shoot trimming), CT-HC (cluster thinning, high canopy), and CT-SST (cluster thinning, severe shoot trimming). The experiment was set as a randomized complete block design in three adjacent rows, with each row as a block. In each row, four sections of two post spaces (containing 12 vines per plot) were randomly assigned to the four combinations of treatments. Two post spaces at the beginning and end of each row were not included in the experiment. In both seasons, treatments were applied on the same vines. The grapes were harvested manually on 20 September 2017 and 24 September 2018.
# SMS: A Secure Healthcare Model for Smart Cities
## 1 Introduction  
The amalgamation of computer science, electronics, and other related technologies has created a synergistic relationship leading to the birth of one of the most prominent technologies branded as the \"Internet of Things (IoT)\" [1]. The advances in ICT and the emergence of IoT have attracted interests of researchers across the globe and helped in the realization of smart city ecosystems. Since the time IoT was first coined, there have been numerous researches in this field ranging from its application areas to implementation challenges and issues. Today, IoT has been imprinted in almost every area of computing, leading to the concept of \"Smart-Environment\", where all the participating objects are interconnected and are capable of sensing, storing, communicating, and sharing information [1]. The advancement in ICT and networked technologies like Bluetooth, Wi-Fi, 3G/4G/5G, NFC/RFID, LiFi, and Smart dust has further expanded this concept [2]. It has also enabled humans to be an active part of the network as one of the primary entities.  
These modern-day technologies have their impact on all aspects of human lives including the way they live and their surrounding environment. The emerging concept of smart cities is an example of how modern-day technologies can revolutionize the traditional processes.  
A typical smart city is primarily dependent on IoT and other related network technologies for performing its functionalities. The ability to automate the events on the basis of preconditions and dynamically improvising with respect to the changing environment constitutes the major advantages of smart cities. A city is called \"smart\" if all or most of its services (education, healthcare, transportation, agriculture, businesses, etc.), infrastructure (building, homes, warehouses, etc.), and processing are smart and automated, and can be managed with optimum human interventions [2; 3; 4].  
As IoT-based environments allow human interaction with the physical world objects and entities, it has found its applications in almost all spheres of a smart city ecosystem. One such application area that has grown in recent years is healthcare. The growth of ICT and related technologies has led to the emergence of a smart healthcare ecosystem where patients, doctors, medical equipment, and hospitals are interconnected to create a smart medical information system. However, to utilize the true capabilities of IoT technology in the healthcare domain, there is a need to develop a secure and privacy-preserved framework that patients and medical fraternity could trust in terms of security, privacy, reliability, and performance [5; 6; 7]. This paper proposes a conceptual framework for an \"IoT-based healthcare system\" for a smart city ecosystem to provide privacy-preserved data capturing, storing, and processing. The proposal also contemplates the use of the Mobile Edge Computing (MEC) paradigm [8; 9] for processing the data on the edges of the network in close vicinity to the origin of the data and therefore increase the performance and scalability of the entire system.
# A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks
## 3 Approach
### Threat Model  
In this paper, we assume that the blockchain network is trusted, that is, no attacker can control the blockchain network by controlling more than half of the server nodes. We further assume that the routing nodes are untrusted and the vulnerable routing nodes may be controlled by malicious attackers. In a routing scheduling process, a malicious routing node can falsely claim to have sent a  
Figure 2: Loop routing problem in routing algorithms.  
certain number of packets to a routing node or deny receiving packets sent by other routing nodes. The malicious routing nodes can release false routing information on the routing network, such as queue length information, thus affecting the routing scheduling process. They can also act as black hole attack nodes and refuse to forward packets. However, we do not consider the collusion attack by two routing nodes to complete invalid blockchain transactions. We further assume that a routing node can only act as a normal or malicious node, which means attacks are by no means intermittent. Meanwhile, we do not consider the occasional abnormal behavior caused by the performance of the node (e.g., a node does not send a message in time or loses the wireless spectrum).
# Realizing Efficient Security and Privacy in IoT Networks
## 5 Numerical Analysis and Results
### Realized Privacy Capacity  
We performed a transmission performance evaluation for a three-node transmission under power and rate constraints as shown in Figures 3 and 4. A comparison of three separate transmission scenarios with respect to the privacy capacity of their transmissions is shown in the experiment.  
Figure 3 indicates privacy capacity against [PERSON], whereas Figure 4 represents the data rate of the transmission between the source and destination _(Source-to-Destination)_ specifically realizing for the entire \(A_{m}<C_{m},\forall m\in\mathbb{N}\). This clearly implies that, for the entire set of subcarriers, the Eve's channel is stronger from the Source \((T_{x})\) than the Destination \((R_{x})\) has from the Source.  
For Transmission 1 (the first transmission scenario), at the Source and Destination terminals, data rate is optimized subject to power constraints. However, this occurs without the constraint for privacy  
Figure 4: Realized privacy capacity for Source-to-Destination \((C_{SOD}^{*}=0.8C_{SOD,1})\) transmission under power and rate constraints.  
Figure 3: Realized privacy capacity against eavesdroppers under power and rate constraints.  
capacity. The subsequent obtainable data rate is signified by \(C_{SOD,1}\). On the other hand, as expected for this channel, the subsequent privacy capacity \(S_{c,1}\) is realized as zero.  
For Transmission 2, the transmissions privacy capacity is optimized subject to power constraints at _Source_ and _Destination_ likewise a rate constraint of _Source-to-Destination_. Setting the lesser bound on the rate (i.e., the constrained rate) at \(C_{SOD}^{*}=0.8C_{SOD,1}\), the equivalent rate realized at the channel transmission is signified by \(C_{SOD,2}\), as anticipated, the curve is vague from that of \(C_{SOD}^{*}\). \(S_{c,2}\) represents the subsequent attained transmission privacy capacity and it is huge and quite close to Transmission 3.  
In Transmission 3 (the third transmission scenario), the privacy capacity is optimized with only power constraint at _Source_ and _Destination_ without considering any rate constraint. Subsequently, we represented the privacy capacity as \(S_{c,3}\) while the data transmission rate is signified as \(C_{SOD,3}\).  
Considering the three transmission scenarios, it is observed that the result obtained at the second scenario _(Transmission 2)_ outperform the other two transmission scenarios in terms of _source-to-destination_ data rate trade-off and the transmission's privacy capacity.
## 4 Results and Discussion
### Harmonic Feature Test  
This work tested various combinations of 12 chroma, 12 Chroma CENS, 40 MFCC, and 18 statistical harmonic features. The model performances are listed in Table 7, along with the confusion matrix in Figure 13 and the respective ROC in Figure 14.  
For harmonic features, the following combinations were tested: chroma only, Chroma CENS only, MFCC only, statistical only, chroma + statistical, Chroma CENS + statistical, MFCC + statistical, and all feature combinations. While the combinations of all the harmonic features produced a validation accuracy of 93.80%, the chroma harmonic features produced a validation accuracy of 93.40%. Compared to all feature combinations, the chroma-only experiment observed that the recall for normal label falls by 0.01 and for abnormal label precision, the F1-score falls by 0.01. This resulted in a fall in accuracy of 1.00%. This is a significant change, yet the chroma features performed well, with only 12 features compared to 82 in the previous test. Additionally, the 18 statistical features performed well in the validation accuracy with 92.70% but showed a slight drop in performance for the abnormal label.  
The Chroma CENS harmonic features produced a validation accuracy of 80.30%, which is 13.10% less than the chroma-only features. Additionally, for the abnormal label, it did not perform well. On the other hand, the MFCC harmonic features produced a validation accuracy of 92.60% and performed fairly well on normal labels. However, abnormal labels, showed significantly lower recall metrics.  
The combination of chroma and statistical harmonic features produced a validation accuracy of 92.90%. It was observed that the recall and F1-score for the abnormal class dropped by 0.04 and 0.02 points. Additionally, for the normal class, the precision fell 0.01 points. Meanwhile, a combination of the Chroma CENS and statistical harmonic features produced a validation accuracy of 92.40%, which is a significant improvement in comparison to Chroma-CENS-only features. In comparison to all the features, it was observed that the recall and F1-score for an abnormal class dropped by 0.04 and 0.02 points. Also, for the normal class, the precision fell by 0.01 points.  
Finally, the combination of MFCC and statistical harmonic features produced a validation accuracy of 93.50%. It was observed that the abnormal class precision, recall, and F1-score improved and were close to the full harmonic feature set, but compared to the chroma-only feature test, it required more computations.
## 2 Calculation Methodology of the PVCS Global Cost
### Investment Cost  
The total investment cost \(C_{i}\) for a PVCS is calculated as follows:  
\[C_{i}=C_{PV}+C_{bat}+C_{ter}+C_{infra} \tag{2}\]  
where \(C_{PV}\), \(C_{bat}\), \(C_{ter}\), and \(C_{infra}\) are the investment costs (\(\mathbf{\xi}\)) of PV system, stationary battery, terminals, and infrastructure, respectively.  
Thus, the total investment cost of the PVCS is obtained by adding the investment costs related to the entire energy chain and those of the infrastructure.