esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating the IoT and Blockchain Technology for the Next Generation of Mining Inspection Systems
## 2 Related Works
Furthermore, the use of blockchain-based smart contracts makes asset monitoring and validation less human-dependent and prone to errors,while the inherent properties of blockchain increase the security and transparency of the transacted data. Despite the growing interest in technology, integrating IoT and blockchain still faces several open challenges. As thoroughly described in [18], these challenges can be grouped into three major areas: privacy preservation, scalability, and utilizing blockchains in scenarios involving devices with constrained capabilities. For privacy preservation and scalability, the current research focuses on the trade-off between public and private blockchain networks or architectures using a combination of both [6]. In these scenarios, a certain level of trust among the system users exists, reducing some security concerns while increasing the overall performance. Nonetheless, there has been little discussion about using blockchains with devices with constrained capabilities such as IoT sensors, which typically have stringent computational and networking limitations and a very restricted energy budget. On the one hand, IoT devices can be users of the blockchain, where blockchain provides support to IoT devices for access control, firmware updates, and other services in a decentralized way. In this case, the IoT does not impose demanding requirements, and the challenges are related to interfaces and implementation [18]. On the other hand, IoT devices can be a part of the blockchain data on-chaining system, as the reliable provisioning of blockchain-external data to smart contracts [25]. In this case, IoT devices are considered \"hardware oracles\" [26], meaning that they are the direct data sources of the physical phenomena they are sensing. Oracles have a tremendous responsibility to the blockchain-based systems as the insertion of incorrect information creates an immutable record that could trigger an irreversible action [20]. Therefore, these IoT devices face several challenges to become trustworthy oracles [25], such as reporting readings without sacrificing data security [19] while maintaining low-cost constrained computing capabilities [27]. In summary, these studies outline a clear need to understand the inherent challenges, issues, and limitations of integrating new technologies in an end-to-end mining inspection system. On the one hand, blockchain can provide a transparent and auditable repository of information, enabling trust between the unknown stakeholders involved in the inspection process. Nonetheless, current literature lacks a description of architectures to achieve this goal. On the other hand, IoT-based measuring instruments could work as trusted oracles that vouch for the truthfulness of the collected data during the inspection. However, there has been little quantitative analysis using low-cost IoT devices with blockchain-based systems, particularly for mining inspections.
# Real-Time Location-Positioning Technologies for Managing Cart Operations at a Distribution Facility
## 1 Introduction
The scientific objective of our research is to demonstrate the effectiveness of RFID technologies with the use of triangulation of signals in identifying tagged objects in a crowded fast-moving environment. We also believe that this application has a high practical impact on supply chain management due to the prevalence of electronic retailing in recent years. The remainder of our paper is organized as follows. In Section 2, we review the existing work on RFID and their use in supply chain management. In Section 3, we present the architecture of our proposed platform and discuss its potential benefits to distribution centers. Section 4 provides the case study of our actual implementation at the AMC and presents the analytics powered by the RFID tracking data collected. Section 5 concludes our work.
# Gait Biomechanics for Fall Prevention among Older Adults
## 4 Biomechanics of Dynamic Balance
### Margin of Stability (MOS)  
A spatial description of dynamic balance can be provided by the margin of stability (MOS), as illustrated in Figure 5. The fundamental concept of MOS is the distance between the extrapolated centre of mass (XCOM) and the BOS boundary relevant to the particular balance loss type.  
\[\text{MOS}=\text{BOS}-\text{XCOM}\]  
In the calculation of dynamic balance, XCOM is the projected COM position based on current velocity factors, therefore, differentiating'moving' COM from the static condition. Falls direction can be determined by which BOS boundary COM is likely to cross. Tripping and slipping are the primary causes of forward and backward balance loss, respectively [40]. Although the MOS concept may appear to ignore vertical COM information, the calculation of XCOM requires the limb length, accounting for the vertical kinematic factor for dynamic balance. As in Figure 5 (left bottom), inverted pendulum gait mechanics [55] account for the arch-like COM motion path.  
\[\text{XCOM}=\text{COM}+\frac{\text{COMvel}}{\sqrt{\frac{\text{g}}{\text{I}}}}\]  
Figure 5: (**Top left**) Dynamic balance characteristics at MFC—tripping-related COM indicated by a circle, XCOM indicated by X, (**bottom left**) inverted pendulum model during single support; (**right**) dynamic balance characteristics at heel contact—slipping related; MOS = margin of stability COM indicated by a circle, XCOM indicated by X. ART = available response time, COM = centre of mass. XCOM = COM + COMvelocity + \(\surd\)g/I, where g = gravitational acceleration and 1 = limb length; MOS = BOS—XCOM as illustrated above.  
Originally, the most traditional concept for dynamic balance was viewed in the transverse plane (Figure 5) [55] with resultant anterior-posterior and medio-lateral components together. However, MOS studies do not focus on actual (resultant) COM movement in the transverse plane due to attempts to conduct sagittal and/or frontal plane analyses [56; 57; 58; 59; 60]. Viewing MOS in the sagittal or frontal plane, however, masks the actual COM movement direction and has absolutely no advantages over a transverse plane analysis. Even when there is sufficient anterior MOS, for example, balance can still be destabilised in the medio-lateral directions and vice versa. MOS should thus be considered in the transverse plane (Figure 5) and utilising the actual COM direction but not in the sagittal or frontal plane.
# Green Approach in Water-Based Drilling Mud Design to Increase Wellbore Stability
###### Abstract  
Wellbore instability is one of the most serious problems that can occur during drilling, mainly during drilling oil or gas wells through rocks that contain a higher proportion of clay, such as shales. To prevent wellbore instability, oil companies apply different approaches to strengthen wellbore walls, and use different shale swelling inhibitors. The aim of this research was to apply a green approach and the concept of the circular economy in mud design, and to determine whether mandarin peel powder, which is a waste material, can be used as an inhibitor of shale swelling. For that purpose, pellets consisting of bentonite and quartz in a 50:50 ratio were prepared using a compactor, and bentonite-based drilling mud (BM) with and without mandarin peel powder in concentrations of 0.5, 1, 1.5 and 2% by volume of water. The swelling of quartz-bentonite pellets after 2 and 24 h in each drilling-mud sample was determined at room temperature and 90 \({}^{\circ}\)C using a linear swell meter. On the basis of laboratory research, we concluded that increasing the concentration of mandarin peel powder reduces pellet swelling. By adding mandarin peel powder particles between 0.1 and 0.16 mm to the base mud at a concentration of 2% by volume of water, the following was achieved: 44% reduction in API filtration, 61.54% reduction in PPT filtration, 45% reduction in pellet swelling after 24 h at room temperature, and 48.6% reduction of pellet swelling after 24 h at 90 \({}^{\circ}\)C.  
waste mandarin peel; wellbore instability; drilling mud; quartz-bentonite pellet; swelling; mud filtration
# Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
## 2 Methods
### Data Collection and Extraction  
The data collection and extraction procedures were conducted by a single reviewer, following a standardized protocol to ensure methodological consistency and minimize potential bias. After completing the database searches, all retrieved records were imported into the Rayyan platform for systematic reviews (Rayyan Systems Inc., 2025), which enabledthe automatic identification and removal of duplicate entries. Title and abstract screening was also performed in Rayyan, using predefined inclusion and exclusion criteria to assess the relevance of each record. Studies meeting the initial eligibility criteria were then subjected to full-text review to determine final inclusion. This systematic and transparent process ensured that only studies directly addressing the review's research questions were retained for synthesis.  
For data extraction, a structured spreadsheet was developed in Microsoft Excel (Microsoft Corporation, Version 16.77.1, 2025) to systematically collect key information from each included study. The extraction process was supported by ChatGPT (OpenAI, GPT-4, 2025), which assisted in identifying and organizing study details such as objectives, data collection methods, analytical techniques, principal findings, types of monitoring technologies, monitored variables, monitoring frequency, and temporal coverage. All extracted information was manually reviewed and cross-verified against the original study reports by the reviewer to ensure accuracy and consistency.
## 3 Problem Statement and Input Data Representation Methods
### Traffic Data and Their Unique Characteristics
#### 3.1.1 Spatial Dependencies  
To describe the spatial dependencies within traffic data, the representation can be primarily categorized into the following three ways.  
* Stacked VectorWe can use domain knowledge to stack data from multiple related spatial units according to predefined rules to capture spatial dependency, forming a multi-dimensional vector. The data for each unit can include traffic flow, speed, density, etc. Through stacking, these vectors can simultaneously represent the traffic conditions of multiple geographic locations.  
* Matrix/Grid Representation  
To capture spatial dependencies, spatial data, such as points or areas within a traffic network (a traffic matrix is a two-dimensional matrix with its \(ij\)-th element \(t_{ij}\) denoting the amount of traffic sourcing from node \(i\) and exiting at node \(j\)), are mapped onto a two-dimensional matrix or grid. In this matrix or grid, each row and column represents a specific geographic location, and each cell in the matrix contains the traffic data for that location. This treats spatial information as two-dimensional Euclidean data composed of geographic information, representing it like an image. This method can be called matrix-based representation or grid-based map segmentation. As shown in Figure 3, the city map is divided into a grid-based map format.  
* Graph Representation  
Compared to image data, traffic network data exhibit more complex spatial dependencies, primarily because these dependencies cannot be solely explained by Euclidean geometry. For example, traffic networks are inherently graph like rather than grid like. While image data naturally align into a regular grid where each pixel relates primarily to its immediate neighbors, traffic nodes (such as intersections and bus stops) connect in more complex patterns that often do not correspond to physical proximity.  
A graph consists of multiple nodes and edges that connect these nodes. Each node represents a specific spatial unit. In the representation of a graph, a matrix of size of \(N\times N\) is commonly used to describe the complex relationships between nodes, where \(N\) is the total number of nodes. This matrix is called an adjacency matrix \(A\), and its element \((i,j)\) represents the connectivity status between the \(i\)th and \(j\)-th spatial units. These connections may involve distance, connectivity properties, and other more complex spatial relationships, often beyond simple Euclidean distance definitions.  
Figure 3: Grid-based map segmentation in Shenzhen.
## 4 Simulation Modeling
### Externally Modulated Laser (EML) Chirp Simulation Model
This performance deterioration at the higher IF frequencies is attributed to the combination of two effects: (a) the chromatic dispersion of the fiber and (b) the chirp effect of the EML. All the above simulated results which were directly compared with the experimental results reported in [41], confirming the successful modeling of the fiber transmission and the EML for various channel setting (modulation order, number of IF sub-bands, fiber length).
## 3 Retrieval
### Setup
#### 3.1.6 Jacobian matrix \(\mathbf{K}\)  
The Jacobian matrix includes the first order derivatives of the forward model with respect to all state vector elements and has a dimensionality of \(m\times n\). A measurement can only include information on those state vector elements which have sufficiently linearly independent derivatives. Figure 6 illustrates the content of a typical example of a Jacobian matrix. Note that the sensitivity to SIF has artificially been set to zero in the \(O_{2}\) fit window in order to ensure, that the SIF information solely comes from the SIF fit window and misinterpretations with scattering parameters are avoided [29].  
Figure 6: Jacobian matrix computed with FOCAL for the geophysical _Rayleigh_ scenario and the _3-Scat_ retrieval setup. Within the CO2 fit windows, an additional line in light colors shows the partial derivatives according to \(\tau_{s}\) and \(p_{s}\) scaled by a factor of 10 and 20, respectively.
# Thermal Energy Diffusion Incorporating Generalized Einstein Relation for Degenerate Semiconductors
## Appendix A  
The kinetic energy density tensor \(\mathbf{Q}\) included in Equation (22) can be decomposed into physically meaningful parts by the following successive vector deduction and addition process:  
\[\begin{array}{l}\frac{1}{n}\mathbf{Q}=\langle\frac{\hbar^{2}}{m_{k}}\mathbf{k} \mathbf{k}\rangle=\langle\frac{\hbar^{2}}{m_{k}}(\mathbf{k}-\mathbf{k}_{o}+ \mathbf{k}_{o})\mathbf{k}\rangle=\langle\frac{\hbar^{2}}{m_{k}}(\mathbf{k}- \mathbf{k}_{o})\mathbf{k}\rangle+\langle\frac{\hbar^{2}}{m_{k}}\mathbf{k}_{o} \mathbf{k}\rangle\\ =\langle\frac{\hbar^{2}}{m_{k}}(\mathbf{k}-\mathbf{k}_{o})(\mathbf{k}-\mathbf{ k}_{o}+\mathbf{k}_{o})\rangle+\langle\frac{\hbar^{2}}{m_{k}}\mathbf{k}_{o}( \mathbf{k}-\mathbf{k}_{o}+\mathbf{k}_{o})\rangle\\ =\langle\frac{\hbar^{2}}{m_{k}}(\mathbf{k}-\mathbf{k}_{o})(\mathbf{k}-\mathbf{ k}_{o})\rangle+\langle\frac{\hbar^{2}}{m_{k}}(\mathbf{k}-\mathbf{k}_{o}) \mathbf{k}_{o}\rangle+\langle\frac{\hbar^{2}}{m_{k}}\mathbf{k}_{o}(\mathbf{k} -\mathbf{k}_{o})\rangle+\langle\frac{\hbar^{2}}{m_{k}}\mathbf{k}_{o}\mathbf{ k}_{o}\rangle\\ \equiv A+B+C+D,\end{array}\]  
where \(\mathbf{k}_{o}\) is such that \(\mathbf{P}=\ n\langle\hbar\mathbf{k}\rangle=n\hbar\mathbf{k}_{o}\).  
Since \(\mathbf{v}=\langle\hbar\mathbf{k}/m_{k}\rangle\),  
\[\begin{array}{l}A=\langle\frac{\hbar^{2}}{m_{k}}(\mathbf{k}-\mathbf{k}_{o})( \mathbf{k}-\mathbf{k}_{o})\rangle=k_{B}\mathbf{T},\\ B=\langle\frac{\hbar^{2}}{m_{k}}(\mathbf{k}-\mathbf{k}_{o})\mathbf{k}_{o} \rangle=\frac{1}{n}\langle\frac{\hbar}{m_{k}}(\mathbf{k}-\mathbf{k}_{o}) \rangle\mathbf{P}=\frac{1}{n}(\mathbf{v}-\mathbf{v}_{o})\mathbf{P},\\ C=\langle\frac{\hbar^{2}}{m_{k}}\mathbf{k}_{o}(\mathbf{k}-\mathbf{k}_{o}) \rangle=\frac{1}{n}\mathbf{P}(\mathbf{v}-\mathbf{v}_{o}),\end{array}\]  
and  
\[D=\langle\frac{\hbar^{2}}{m_{k}}\mathbf{k}_{o}\mathbf{k}_{o}\rangle=\frac{1}{ n}\mathbf{v}_{o}\mathbf{P},\]  
where  
\[\mathbf{v}_{o}=\frac{1}{n}\langle\frac{1}{m_{k}}\rangle\mathbf{P}=\frac{1}{m_{ o}^{*}(w)n}\mathbf{P}\ (\text{i.e., }m_{o}^{*}(w)\equiv\langle m_{k}\rangle=m_{c}(1+2\alpha w))\]  
The result is:  
\[\mathbf{Q}=nk_{B}\mathbf{T}+(\mathbf{v}-\mathbf{v}_{o})\mathbf{P}+\mathbf{P}( \mathbf{v}-\mathbf{v}_{o})+\mathbf{v}_{o}\mathbf{P}=nk_{B}\mathbf{T}+\mathbf{ v}\mathbf{P}+\mathbf{P}(\mathbf{v}-\mathbf{v}_{o}).\]  
In a special case where \(m_{o}^{*}(w)=m^{*}(w)=\) a constant, \(\mathbf{v}\) is equal to \(\mathbf{v}_{o}\) by the relations \(\mathbf{P}=nm_{o}^{*}(w)\mathbf{v}_{o}\) and \(\mathbf{P}=nm^{*}(w)\mathbf{v}\), and \(\mathbf{Q}\) is simplified to:  
\[\mathbf{Q}=nk_{B}\mathbf{T}+\mathbf{v}\mathbf{P},\]  
which is the expression obtained for parabolic energy bands (\(\alpha=0\)).
# A Comprehensive Review of Acoustic Methods for Locating Underground Pipelines
## 7 Conclusions and Future Research
\begin{table}
\begin{tabular}{l l} \hline \hline
**Methods** & **Applications and Limitations** \\ \hline Trace wires (TW) [150] & \begin{tabular}{l} A reliable method when present \\ • Multiple trace wires in the same area can affect the locating accuracy \\ • Trace wire damage or absent frequently happens \\ • \\ \end{tabular} \\ \hline \multirow{3}{*}{Groun penetrating radar (GFR) [5]} & \begin{tabular}{l} • Applicable for both metallic and non-metallic objects \\ • Greater signal attenuation with conductive soil such as clay and saturated soil \\ • Detection results require professional operators to interpret \\ \end{tabular} \\ \hline \multirow{3}{*}{Electromagnetic methods (EM) [9]} & \begin{tabular}{l} • Can only detect metallic objects \\ • Sufffer from attenuation in the presence of conductive soil \\ • Possible interference by other near-by metallic pipes \\ \end{tabular} \\ \hline \multirow{2}{*}{Magnetic methods (MM) [151]} & \
\begin{tabular}{l} • Only applicable for detecting and tracing ferrous targets \\ • The accuracy can be affected by nearby objects \\ \end{tabular} \\ \hline Infrared thermography (IP) [18] &
\begin{tabular}{l} • Based on variations in thermal property \\ • Can be affected by ground cover, wind speed and moisture content of surrounding soil \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Applications and limitations of different non-acoustic localization technologies.
## 5 Conclusions  
To deliver the full range of potential capabilities promised by 5G networks, efficient dynamic spectrum allocation schemes are required. Furthermore, heterogeneous deployment, i.e., small cells, is considered a major network topology in 5G networks. Such multi-tier network layouts will enhance the coverage and capacity of the network 1000-fold. However, such architecture introduces severe challenges for the 5G-based CRNs. The reason is that the PU will be required to be more active because of the small coverage area, with more active users per unit area, more base stations, random user arrivals, and spectrum movement. Besides, handover operations must be executed in multi-tier networks. Therefore, efficient handover management is needed for such networks. CRNs have great potential to enhance the spectrum utilization. In this regard, we propose the analytical formulation and evaluation of spectrum utilization for the PU and SU, considering different sets of accessible channels. We consider an interweave-based CRN under different physical topologies based on the spatial and temporal variations of the PU activities. We present a realistic scenario of multiple narrowband channels for the SU to utilize the licensed band more efficiently. The SU can switch to the next channel in the case of PU interruptions and ensures the reliability of the transmitted packet, hence increasing spectrum utilization. The sum utilization of spectrum is analyzed through closed-form expressions. In addition, we evaluate the impact of different network and sensing parameters over the sum utilization gain, achieved with the multi-channel scenario. Specifically, numerical results indicate that a different number of channels, PU activities, sensing outcomes, and network topologies have significant impact on the sum utilization of spectrum. In the future, we would like to extend our approach of investigating the utilization of spectrum for more complicated scenarios such as cooperative, unlicensed (femtocells), and HARQ-enabled SUs in dense CRNs.  
[PERSON] and [PERSON] designed and developed the proposed idea, and also formulated the mathematical model. [PERSON] wrote the paper. [PERSON] supervised and finalized the manuscript for submission.  
**Acknowledgments: This research was supported by a 2018 Yeungnam University Research Grant.**  
**Conflicts of Interest: The authors declare no conflict of interest.**  
Figure 12: Probability of sum utilization of spectrum with different PU active state probabilities for single- and multiple-channel scenarios under topology-2.
## 2 Background on Physical-Layer Security in LoRaWAN and WSNs
### Evolution of Multi-Attribute PLA Schemes  
The evolution of PLA schemes reflects a significant shift from single-attribute methods to multi-attribute approaches, driven by the increasing complexity of physical-layer threats. Early PLA schemes relied on single attributes, such as RSSI, which, while lightweight, were vulnerable to environmental interference and dynamic conditions [9]. Recognizing these limitations, researchers began integrating additional attributes to enhance detection accuracy and robustness. Ref. [14] introduced a pioneering approach by combining RSSI with Radio Frequency Fingerprints (RFF) using hardware-specific imperfections for improved spoofing detection. Ref. [16] extended this concept by incorporating timing-based features, such as signal phase and delay, to address adaptability in highly dynamic environments. These studies demonstrated that multi-attribute PLA schemes could outperform single-attribute methods in both static and dynamic scenarios.  
The application of machine learning models further advanced PLA schemes, enabling real-time detection of physical-layer threats. Ref. [10] proposed a supervised learning model for jamming detection in multi-hop IoT networks, combining RSSI and Signal-to-Noise Ratio (SNR). This approach achieved high detection accuracy while maintaining computational efficiency, underscoring the scalability of multi-attribute PLA for IoT systems.  
Machine-learning techniques, such as LR and RF, are particularly well suited for resource-constrained sensors due to their low computational overhead compared to more complex deep-learning models. These models require minimal processing power while achieving high detection accuracy, making them effective for real-time spoofing and jamming detection in IoT environments [17]. Recent studies, such as Ref. [18], have exploredlightweight ML techniques, highlighting their computational efficiency and suitability for resource-constrained environments. Their work demonstrates the potential of models like Naive Bayes and SVMs for achieving high accuracy in detecting physical-layer threats with minimal resource usage. Similarly, Ref. [19] presented an effective combination of CNN and LSTM models for the real-time detection of physical-layer attacks in IoT. Their approach adapts to dynamic network environments. Further, Ref. [20] compared traditional ML methods, such as decision trees and RF, with deep-learning models like CNN for RSSI-based jamming detection. Their findings highlighted trade-offs between accuracy and computational requirements, providing insights into optimizing PLA schemes for diverse IoT scenarios.  
While this progress represents significant progress, they also highlight the need for novel features and enhanced adaptability, particularly in resource-constrained environments. This need motivates our work, which integrates additional dynamic and spatial attributes to extend the capabilities of existing PLA methods.