esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Challenges and Limitations
#### 4.4.3 Interoperability and Standards  
Enterprise system integration presents perhaps the most formidable practical challenge for blockchain traceability deployment, as most supply chain participants operate legacy Enterprise Resource Planning (ERP) systems, Warehouse Management Systems (WMS), and Manufacturing Execution Systems (MES) that were not designed for blockchain interoperability. These established platforms, often representing expensive multi-million-dollar investments with decades of customization, cannot be readily replaced to accommodate new blockchain architectures. Integration typically requires developing sophisticated middleware layers that can translate between blockchain transaction formats and legacy system data structures while maintaining real-time synchronization across heterogeneous platforms. For instance, the incumbent SAP or Oracle-based ERP systems must interface with blockchain smart contracts through Application Programming Interfaces (APIs) that can handle the asynchronous nature of blockchain consensus mechanisms while preserving the synchronous transaction expectations of traditional enterprise workflows.  
The complexity multiplies when considering the diverse technological landscapes across global supply chains, where upstream suppliers in developing economies may operate on entirely different platforms or even manual systems, while downstream retailers employ cutting-edge digital infrastructure. This technological stratification necessitates tiered integration approaches that can accommodate participants with varying levels of system sophistication. Cloud-based integration platforms and standardized data exchange protocols (like EPCIS [46], GS1's flagship standard) become essential for bridging these gaps, but they also introduce additional points of failure and potential security vulnerabilities. Furthermore, legacy systems often contain proprietary data formats and business logic that must be carefully mapped to blockchain asset models without compromising existing operational workflows or regulatory compliance requirements, requiring extensive system analysis and custom development work that can significantly extend implementation timelines and costs. Our framework was somewhat tailored (e.g., specific asset names); we anchored some design on ISO 9001 [47] and general traceability principles, but future work could map our data model to standard ontologies so that data from WEave could feed into global traceability networks or reporting platforms.
## 1 Introduction
The use of remote sensor networks for the collection and processing of structured data aimed at improving the transportation energy efficiency and safety under varying weather conditions is also discussed in Section 3, along with some practical examples of remote sensor use for improving railway transport safety and energy efficiency. Section 4 discusses the recent advances in creating a new IoT-based paradigm within the Industry 4.0 concept for railway transport, with applications such as predictive maintenance, multimodal transport, and railway system resilience improvement measures. The roles and benefits of smart grids in future railway transport are also assessed. For each of these categories, practical examples are used to illustrate the benefits of emerging technologies. The concluding remarks are summarised in Section 5, which also gives guidelines for future research directions.
## 2 Materials and Methods
### Robust Satellite Techniques (RST)  
The RST technique is an advanced change detection scheme (whose detailed description can be found in [24]), which considers each anomaly in space-time domain as a deviation from a \"normal\" state, which may be determined by analyzing multi-annual time series of homogeneous (e.g., same calendar month and overpass time) cloud-free satellite records. A statistically based index, named ALICE (Absolutely Local--double \"l\" is used to strengthen the concept that the index is local in space as well as in time [25]--Index of Change of the Environment [25]), taking into account the signal variability due to natural (e.g., different land covers, solar exposition) and observational (e.g., sun zenith angle, satellite view angles) conditions, enables the identification of anomalous events in the space-time domain, guaranteeing a high trade-off between reliability and sensitivity. The ALICE index, in its general formulation, is computed as follows:  
\[\otimes_{V}(x,y,t)=\frac{V(x,y,t)-\mu_{V}(x,y)}{\sigma_{V}(x,y)} \tag{1}\]  
where \(V(x,y,t)\) is the signal measured at the time \(t\) and location \((x,y)\), \(\mu_{V}(x,y)\), and \(\sigma_{V}(x,y)\) are corresponding expected value (generally the temporal mean) and standard deviation computed using a homogeneous dataset (see above).  
The RST detection scheme was used in several previous studies to identify hotspots associated with volcanic activity (e.g., [2,4,12]), forest fires (e.g., [26]), gas flaring (e.g., [27]) and other events (e.g., [28]) analyzing MIR and/or TIR signal. Here, it is implemented, for the first time, on ASTER TIR data by assessing its performance in mapping thermal anomalies associated with volcanic activity.
# Mediterranean Aquaponics: Fasting and Refeeding in a Polyculture Aquaponic System
## 3 Results
### Fish, Prawn, and Plant Growth Performance Indices
\begin{table}
\begin{tabular}{l c c c} \hline  & **A** & **B** & **C** \\ \hline \multicolumn{4}{c}{European sea bass} \\ \hline Initial body weight (IBW, g) & 3.387 \(\pm\) 0.031  & 3.385 \(\pm\) 0.040  & 3.401 \(\pm\) 0.041  \\ Initial body length (IBL, cm) & 7.03 \(\pm\) 0.05  & 7.02 \(\pm\) 0.06  \\ Final body weight (FBW, g) & 15.61 \(\pm\) 0.12  & 16.57 \(\pm\) 0.21  \\ Final body length (FBL, cm) & 15.39 \(\pm\) 0.09  & 18.32 \(\pm\) 0.28  & 21.70 \(\pm\) 0.13  \\ Fulton Factor (K, g/cm\({}^{3}\)) & 0.991 \(\pm\) 0.022  & 0.990 \(\pm\) 0.021  & 0.982 \(\pm\) 0.079  \\ Weight gain (WG, g) & 12.23 \(\pm\) 0.12  & 14.44 \(\pm\) 0.08  & 13.18 \(\pm\) 0.21  \\ SGR (\%/days) & 2.040 \(\pm\) 0.018  & 2.216 \(\pm\) 0.020  & 2.112 \(\pm\) 0.029  \\ Feed conversion ratio (FCR) & 0.916 \(\pm\) 0.010  & 0.773 \(\pm\) 0.009  & 0.853 \(\pm\) 0.013  \\ Protein efficiency ratio (PER) & 2.029 \(\pm\) 0.022  & 2.396 \(\pm\) 0.014  & 2.188 \(\pm\) 0.032  \\ Nitrogen retention (NR, \%) & 37.94 \(\pm\) 0.38  & 42.91 \(\pm\) 0.25  & 43.94 \(\pm\) 0.71  \\ Lipid retention (LR, \%) & 9.213 \(\pm\) 0.68  & 2.173 \(\pm\) 1.253  & 5.995 \(\pm\) 0.261  \\ Survival (S, \%) & 86.67 \(\pm\) 0.00  & 93.3 \(\pm\) 0.00  & 84.45 \(\pm\) 1.11  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline \multicolumn{4}{c}{Prawns} \\ \hline Initial prawn weight (IBWp, g) & 1.140 \(\pm\) 0.051 [FOOTNOTE:2]Footnote 2: Data are expressed as meandifferences between the experimental treatments, with the highest concentration observed in treatment B in comparison to the other two treatments. The ash content of the glasswort branches exhibited a statistically significant difference, with the highest value observed in treatment C. The moisture content of the plant demonstrated statistically significant variations, with the lowest value noted in treatment A.
# Experimental Investigations on the Effects of Fatigue Crack in Urban Metro Welded Bogie Frame
## 4 Conclusions  
An extensive investigation of the fatigue crack development of positioning block welded to the spring sleeve in metro bogie frame was conducted. Systematic studies of fracture and metallographic analysis were employed to obtain failure modes and fracture characteristics of the weld toe of positioning block. On-track testing was carried out to obtain acceleration and stress response information of the bogie. After that, the test data analysis in the time domain and the frequency domain was conducted. The results are as follows:  
(1) The weld toe of positioning block was the cracking initiation site, in which micro cracks were observed.  
(2) The vibration frequency generated by the wheel polygon and the rail corrugation is highly coherent with the dominant frequency of dynamic stress at the cracked area, indicating that the wheel polygon and the rail corrugation have a significant effect on the dynamic stress amplitude. Further, the response of the amplitude and frequency of dynamic stress is higher with running speed increasing.  
(3) The fractography and the on-track tests show that optimizing the weld technology, improving the weld quality, profiling the wheels, grinding the rails and decreasing the train running speed are all effective to reduce the failure probability and elongate the bogie frame life.  
Conceptualization, W.J.W.; formal analysis, W.J.W.; methodology, W.J.W.; project administration, P.Y.Z.; resources, W.J.W. and J.Z.; writing original draft, J.Y.B.; writing--review and editing, W.J.W. and S.C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Beijing Nova Program Interdisciplinary Cooperation Project, grant number Z191100001119011, National Natural Science Foundation of China, grant number 11572267 and Major Systematic Project of CHINA RAILWAY, grant number P2018J003.  
The authors declare no conflict of interest.
# Analysis of Dynamic Behavior of Spray Boom under Step Excitation
## 3 Results and Discussion
### Analysis of Influence of Spray Boom Length on Spray Boom Vibration  
The previous section verifies the accuracy of the geometric continuous model of the spray boom based on multi-body vibration. This section discusses the influence of lengths of the unilateral spray boom on the vertical vibration of the spray boom. Figure 13 shows the vertical vibration at the extremity of the unilateral spray boom with seven lengths of type A cross-sectional structure when the boom sprayer passes through 0.1 \(\times\) 0.1 m square obstacles at a speed of 6 km\(\cdot\)h\({}^{-1}\). With an increment in the length of the unilateral spray boom, the amplitudes of the first and second peaks of vertical vibration increase slightly and greatly respectively, indicating that when the cross-section is the same, the elastic deformation of the spray boom is intensified with the increase in its length resulting in increased amplitude at the extremity of the spray boom [28; 29; 30; 31]. When the length of the unilateral spray boom reaches 5 m, the first peak amplitude maximizes and then decreases. When the length of the unilateral spray boom reaches 6 m, the amplitude of the second peak maximizes and then declines. Hence, when the unilateral spray boom is longer than 6 m, with the increase in the length of the spray boom, the spray boom is more prone to elastic deformation under excitation. The influence of excitation on the extremity of the spray boom is gradually reduced when the length of the spray boom exceeds the strength support range of the spray boom structure, and the elastic deformation of the spray boom will inhibit the vibration transmission to the extremity of the spray boom [30; 31]. At present, the length of unilateral spray booms manufactured by various manufacturers is less than 6 m.  
Figure 12: Model predicted values of the vibration of the extremity of the spray boom when the boom sprayer passes through obstacles at different speeds.
## 8 Security Analysis
### Security Assumptions  
Our security analysis relies on the following assumptions:  
* **Account Ownership:** The ownership of Ethereum accounts used by publishers, subscribers, and the broker is guaranteed. We assume there are no impersonation activities, i.e., an attacker cannot take over a participant's account.
* **Hash Function Properties:** The hash functions used for the construction of hashchains and Merkle hash trees (MHT) are collision and pre-image resistant. This means that it is computationally unlikely to find two different inputs that produce the same hash (collision resistance) or to find an input whose hash matches a given output (pre-image resistance).
# Controlling Industrial Robotic Arms Using Gyroscopic and Gesture Inputs from a Smartwatch
## 2 The State of the Art
### Contribution to Future Work Directions  
The core innovation introduced in this paper was fully conceptualized and implemented by the authors. This includes the design of the smartwatch-based gesture recognition interface for robot control, the development of a custom signal mapping layer linking IMU features to robot actions, and the integration with a real-time cloud monitoring platform (ThingWorx 9.3.16 is a IIoT Platform). Although existing APIs such as TouchSDK 0.7.0 and WowMouse were used for gesture sensing, the implementation of the multi-layer control logic, gesture classification thresholds, experimental setup, and evaluation protocol are entirely the result of the authors' work. The architecture was designed to enable modularity, scalability, and low-latency interaction, prioritizing usability in collaborative industrial environments.  
The suggested approach directly adds to various future research paths identified in the literature, especially concerning intuitive, wearable interfaces for collaborative robots. Our smartwatch-based control technology illustrates the efficacy of consumer-grade wearables as ergonomic and mobile human-machine interfaces (HMIs) for industrial robotic arms. We specifically focus on the necessity for low-latency, real-time control through inertial sensor fusion, resilient gesture detection, and wireless data transmission. Our approach demonstrates scalable, user-friendly robotic interaction with little calibration by mapping gyroscopic input to joint velocity instructions and connecting with platforms such as ThingWorx for monitoring. This clearly endorses research trends advocating for context-aware, multimodal control systems, real-time responsiveness, and diminished training requirements for operators. Furthermore, our research aligns with the goal of integrating wearable HMIs into Industry 5.0 paradigms, where human-centric automation and intuitive robotic support are essential.
# A Security Framework for the Internet of Things in the Future Internet Architecture
## 6 Delegation-Based Key Provisioning Protocol
### Protocol Design Choices and Considerations
#### SIGMA Protocol  
The Diffie-Hellman (DH) key exchange protocol [48; 49] is one of the most fundamental two-party key exchange protocols. Though itself is not an authenticated key agreement protocol, it is the basis for many popular authenticated protocols. One serious vulnerability of the Diffie-Hellman key exchange is the man-in-the-middle-attack [50]. To address the weaknesses of the Diffie-Hellman protocol, a variety of variant two-party key exchange protocols have been proposed, including the STS protocol [51], Photuris protocol [52] and SIGMA [44] protocol.  
Unfortunately, many of those variants have flaws that were identified later on. To the best of our knowledge, SIGMA is still a secure two-party authenticated key exchange protocol that has no effective attack against it. SIGMA takes advantage of the public key infrastructure and adopts _\"SIGnt-and-MAc\"_ approach to authenticated Diffie-Hellman with digital signatures. It serves as the cryptographic basis for the Internet Key Exchange (IKE) standard (version 1 and version 2) [53; 54; 44]. In our key provisioning protocol, SIGMA is chosen as a building block and is overlaid with the 3 PKD protocol to enable the Guardian and the Parent to authenticate each other on demand. This choice also brings other benefits, such as forward secrecy.
## 2 Theory Based on Resonance Method
### Thickness Extension (TE)  
Table 1 presents the geometric shape and material constants of a thin square plate in which the directions of polarization and vibration are oriented along the depth (thickness). When the aspect ratios of the component satisfy \(l>10t\) and \(w>10t\), we use five specimens with dimensions \(l=46\pm 0.1\) mm, \(w=46\pm 0.1\) mm, and \(t=1.2\) mm, and the following equations can be used to calculate their material constants:  
\[k_{t}^{2}=\frac{\pi}{2}\frac{f_{r}}{f_{a}}\cot(\frac{\pi}{2}\frac{f_{r}}{f_{a}}) \tag{7}\]  
\[c_{33}^{D}=4\rho(f_{a})^{2} \tag{8}\]  
\[c_{33}^{E}=c_{33}^{D}\big{(}1-k_{t}^{2}\big{)} \tag{9}\]  
where \(k_{t}\) is the electromechanical coupling coefficient by thickness extension mode, \(f_{r}\)is resonant frequency, \(f_{a}\) is anti-resonant frequency, \(t\) is the thickness of specimen, and \(\rho\) is the density of specimen.  
For the dielectric constant, the capacitance measured using an impedance analyzer at a specific frequency (1 kHz) can be substituted into the following equations, which will produce \(\varepsilon_{33}^{T}\), the dielectric constant in constant stress, and \(\varepsilon_{33}^{S}\), the dielectric constant in constant strain:  
\[\varepsilon_{33}^{T}=\frac{C_{l}\cdot t}{A} \tag{10}\]  
\[\varepsilon_{33}^{S}=\frac{C_{h}\cdot t}{A} \tag{11}\]  
Figure 1: Schematics in resonant and anti-resonant frequencies obtained by impedance analyzer.  
where \(A\) is the area of the electrode, \(C_{l}\) is the capacitance under 1 kHz, and \(C_{h}\) is the capacitance at twice the anti-resonant frequency (\(2f_{a}\)).
## 3 Control of Thermal Deformation Optimization Strategy
### Thermal Deformation Analysis after Optimization
#### 3.4.2 Thermal Deformation Analysis Results  
According to the temperature of every single machine and plate as input, the angle variation of the laser communication load caused by thermal deformation and the star sensor in the full satellite simulation model is calculated. The full satellite simulation model is shown in Figure 21. The cloud map of thermal deformation is shown in Figure 22.  
The coordinate values after thermal deformation of the installation points of the star sensor and laser communication loads extracted after calculation are shown in Table 9. ID indicates the number of the installation plane point.  
Figure 21: Full satellite simulation model.  
Figure 20: Temperature cloud map of deformation control support.  
The least square method was used to fit the expressions of two device installation planes, as shown in (5).  
\[z=b_{1}+b_{2}\cdot x+b_{3}\cdot y \tag{5}\]  
The \(b_{1}\), \(b_{2}\), and \(b_{3}\) parameters of the two planes are shown in Table 10.  
The included angle variation between the two plane normal vectors fitted after thermal deformation can be calculated as 14.25\". The calculation results show that the optimization scheme has an obvious effect.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & \(\mathbf{b_{1}}\) & \(\mathbf{b_{2}}\) & \(\mathbf{b_{3}}\) \\ \hline Star sensor & \(-3988.777117\) & \(-0.000264139\) & \(-0.000186277\) \\ Laser communication load & \(-3988.750815\) & \(-0.000330447\) & \(-0.000205741\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Fitting parameters of the installation plane.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**Equipment**} & \multirow{3}{*}{**ID**} & \multicolumn{3}{c}{**Coordinates after Thermal Deformation of the**} \\  & & \multicolumn{3}{c}{**Installation Points of Star Sensor and Laser**} \\  & & \multicolumn{3}{c}{**Communication Loads/mm**} \\ \cline{3-5}  & & X & Y & Z \\ \hline \multirow{4}{*}{Star sensor} & 1 & \(498.022332\) & \(590.592472\) & \(616.777184\) \\  & 2 & \(-195.021749\) & \(-310.980372\) & \(-237.124945\) \\  & 3 & \(-3988.873092\) & \(-3988.875943\) & \(-3988.895106\) \\  & 4 & \(498.022332\) & \(590.592472\) & \(616.777184\) \\ \hline \multirow{4}{*}{Laser communication load} & 5 & \(555.885593\) & \(299.889329\) & \(578.953251\) \\  & 6 & \(-654.633767\) & \(-654.700147\) & \(-466.758511\) \\ \cline{1-1}  & 7 & \(-3988.799787\) & \(-3988.715247\) & \(-3988.846126\) \\ \cline{1-1}  & 8 & \(555.885593\) & \(299.889329\) & \(578.953251\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Coordinates after thermal deformation of the installation points of the star sensor and laser communication load.  
Figure 22: Simulation results of thermal deformation.
# Diagnosis and Treatment of MODY: An Updated Mini Review
## 4 MODY Subtypes and Their Treatments
### Gck-Mody (mody 2)  
Glucokinase (GCK), also known as Hexokinase IV or D, belongs to the hexokinase family, and is one of the most common forms of MODY. The _GCK_ gene plays a significant role in glucose-stimulated insulin secretion in the pancreas, while it contributes to glucose uptake and glycogen conversion in the liver [40; 41]. _GCK_ gene mutations result in MODY 2 [41; 42] and have been shown to cause abnormal glucose sensing, resulting in a higher threshold for glucose-stimulated insulin secretion initiation. HbA1c levels are typically less than 7.3-7.5% [22; 23]. The vast majority of MODY 2 patients have slightly elevated fasting plasma glucose levels but no postprandial hyperglycemia, meaning they produce enough insulin in response to a rise in blood glucose levels after eating [27]. _GCK_-MODY treatment is usually unnecessary; patients with confirmed _GCK_-MODY do not need treatment other than dietary alterations, because their long-term outcomes are comparable to those of healthy individuals [40; 43; 44]. However, insulin should be administered during pregnancy to reduce the risk of fetal macrosonia [45]. Although the fetal genotype is not always known, serial ultrasound measurements may be used to determine growth. If no evidence of accelerated growth is found, the fetus is assumed to have inherited the _GCK_ mutation, and maternal hyperglycemia is not treated [46].
## 3 Results and Discussion
### GC-MS Analysis of Volatile Profile of Dried Citrus Peel as Effected by Drying Methods
#### 3.1.7 Sesquiterpenes  
For sesquiterpenes (C\({}_{15}\)H\({}_{24}\): MW 204), they were also the other important part in volatiles of dried citrus peel, and some sesquiterpenes, such as copaene, \(\beta\)-elemene, caryophyllene and farnesene were common in different citrus germplasms [17; 37]. The sesquiterpenes were reported to have efficacy in defending against insects and fungus [38; 39]. As shown in Figure 1a and Table 1, FD was also unfavorable to preserve sesquiterpenes; however, AD was the best way to preserve sesquiterpenes, which was unlike monoterpenes. The EIC (\(m\)/\(\underline{z}\) 204) of all dried peel are also shown in Figure 2d to clearly demonstrate that the sesquiterpene composition is influenced by drying methods in the two cultivars. It can be seen from Figure 2d that the content and number of sesquiterpene isomers was highest in AD peel for both two cultivars. Moreover, each cultivar peel had its characteristic sesquiterpenes, such as \(\gamma\)-elemene, trans-\(\beta\)-farnesene and germacrene B only in dried Ponkan peel while \(\alpha\)-humulene and (Z, E)-\(\alpha\)- farnesene only in dried Chachi peel; and dried Chachi peel contains much higher content of \(\alpha\)-farnesene (the most sesquiterpene detected in dried Chachi peel) than dried Ponkan peel, resulting in a higher level of total sesquiterpenes in dried Chachi peel.