esa-sceva/satcom-chunk-collection huggingface dataset:
# Sigma Routing Metric for RPL Protocol
## 5 Design and Simulation of the Network  
The simulation of the network wasdone with a single sink node, using a topology in order to distribute the nodes in a square area with a side L = 500 m. Also, the RPL network was designed using OF0, MRHOF, PH-ETX, and the SIGMA-ETX metric, proposed for its comparison with simulations under different numbers of nodes, with a receiver node or additional sink. Following this, an RX value of 100% was used and the behavior of RPL was investigated with regard to packet delivery ratio and power consumption. The main RPL default parameters used in the simulations are shown in Table 2.  
Figure 5: DAO format.  
Figure 6: Pseudocode scheme.  
The simulation is based on the IPv6 architecture and uses IEEE 802.15.4 at the PHY and MAC layer to form LLNs. With the IEEE 802.15.4 standard, a device can be set to operate in the beacon-enabled mode or the non-beacon enabled mode. This work of this paper is based on the un-slotted non-beacon-enabled mode with CSMA/CA, in the 2.4 GHz ISM band, which is the default configuration of ContikiOS. On top of that, it uses 6 LoWPAN, RPL, and IPv6 to provide end-to-end two-way communication to each node.
# Reduced Overhead Routing in Short-Range Low-Power and Lossy Wireless Networks +
## 3 Related Work
This work demonstrates that TinyRPL and ContikiRPL implementation do not function as expected under network partitioning. Based on RPL's standard specification, the authors model protocol's dynamic behavior, and it has been demonstrated that a standard compliant implementation of RPL functions as expected under network partitioning. In [16], an end-to-end route registration for P2 MP communication and a novel policy to manage neighbor tables in RPL are presented with an aim to enhance RPL's scalability in dense and large networks. It is shown through experiments that the new features improve RPL's packet reception rate in large and dense networks. The lack of mechanisms towards emerging security threads in LLNs pose a significant challenge in the continued adoption of RPL. To mitigate security challenges, a number of enhancements to RPL are proposed. In [17], an algorithm is presented to identify misbehaving nodes in a RPL network. Each node in a network monitors its preferred parent forwarding behavior to observe the packet loss rate. Each node compares the observed packet loss rate with the packet loss rate obtained from one-hop neighbors. Using this process, a node is bale to identify a misbehaving preferred parent node. In SPLIT [18] a secure extension to RPL is proposed. SPLIT uses a lightweight remote attestation technique to ensure software integrity of network nodes. SPLIT reduces the control overhead of remote attestation by piggybacking the attestation process onto RPL control messages. In [2], RPL is used to enable multi-hop communications in Long Range (LoRa); a wide-area low-power wireless networking technology. A new RPL OF is presented that aims to discover forwarding paths with minimum air time. Minimizing air time can result in lower energy consumption, a key feature in LLNs. It has been shown that enabling multi-hop communication in LoRa using RPL can support more IoT use cases compared to what can be supported with LoRaWAN's traditional star topology. The existing work on RPL largely focuses on protocol analysis for different scenarios, interoperability between storing and non-storing MoP, reducing memory requirement for the storing MoP, security enhancements, and reactive discovery of P2P data forwarding paths. Existing work on reactive discovery of P2P paths requires additional control overhead, which requires changes to the standard protocol and typically requires more memory, which can be problematic for resource-constrained nodes. Moreover, an approach that reduces the source routing overhead in RPL's non-storing MoP does not exist. Therefore, this paper focuses on the following: \((i)\) improved P2P routing and forwarding in RPL without using additional control messages and with minimal change to the protocol so as to maintain interoperability with standard RPL implementations, and \((ii)\) reduced source routing overhead for P2 MP and P2P communications in RPL's non-sorting MoP. These aspects are largely missing in the existing literature. Furthermore, this paper extends the work presented in [19].
# Rough-Terrain Path Planning Based on Deep Reinforcement Learning
###### Abstract  
Road undulations have a significant impact on path lengths and energy consumption, so rough-terrain path planning for unmanned vehicles is of great research importance for performing more tasks with limited energy. This paper proposes a Deep Q-Network (DQN)-based path-planning method, which shapes the reward by introducing a slope penalty function and a terrain penalty function. For the problem of the low exploration efficiency of the \(\epsilon\)-greedy strategy, a hybrid exploration strategy combining stochastic exploration and the A* algorithm is proposed, after which the agent is trained on rough terrain. The results show that the algorithm can efficiently plan energy-saving paths, converge quickly, and compared with the traditional A* algorithm and RRT algorithm, performs better under three-dimensional terrain and can choose paths more rationally.  
Keywords:reinforcement learning; A* algorithm; path planning; DQN +
Footnote †: journal: Applied Sciences
# Spectrum Demand Forecasting for IoT Services
## 5 Study Case Implementation: Colombia
### Market Study: Number of Devices  
According to all statistics and time-series predictions, we constructed a peak case for each service. These are summarized in Table 2. The attentive reader should consider that Colombian cities are densely populated. For all peak cases, we have considered the capital Bogota (one of the most densely populated cities in the world), where the density averages 250 inhabitants per hectare.  
Figure 2: **Left:** Ideal spectral efficiency increase in a 10-year period. **Right**: Optimistic and pessimistic 10-year forecast for IoT penetration [41; 42].
## 8 Results
### Four-Aircraft-Formation Scenario  
There are four UAVs in a certain airspace, which are divided into two flying formations. The first formation includes aircraft A and B, which fly from south to north. The second includes aircraft C and D, which fly from north to south. The trajectories of the two formations intersect in a narrow space,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **A** & **B** \\ \hline Initial recording position & & \\ (m, m, m) & (1200,0,3248) & (0,0,3280) \\ Initial recording speed & & \\ (m/s, m/s, m/s) & (80,0,0) & (80,0,0) \\ Initial attitude angle & & \\ (rad, rad, rad) & (5,180,0) & (5,0,0) \\ Initial angular velocity & & \\ (rad/s, rad/s, rad/s) & (0,0,0) & (0,0,0) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Initial state.  
Figure 16: 2D flight path.  
Figure 17: Relative distance.  
Figure 15: 3D flight path.  
and there will be chain collisions between the aircraft if they fly along the original route without taking maneuvers. Table 7 records the initial state of the four aircraft.  
As is illustrated in Figures 18 and 19, the trajectories of the aircraft in the whole process are demonstrated in three dimensions and on the horizontal plane, respectively. In the whole process, each aircraft generates and evaluates maneuvers according to the current situation and agree on combinations of maneuvers through a data link. At 3 s, the collision avoidance maneuvers are activated at the same time among participants. The first formation chooses to climb to avoid collision, while the second formation chooses to roll and climb to avoid collision. The maneuver commanded is shown in Table 8. When only maneuvering once, each aircraft achieves collision avoidance. The minimum distance between pairs is shown in Table 9, and the relative distance is shown in Figure 20. The minimum distance between pairs is larger than a safe distance.
## 3 Information-Theoretic Large-Scale Semi-Supervised Metric Learning via Proxies
### Entropy Regularization  
The above Equation (8) only considers the labeled data information. In semi-supervised settings, the amount of labeled data is usually limited, applying Equation (8) to these conditions may easily come across the over-fitting problem. To solve this issue, researchers propose to simultaneously mine the discriminant information of unlabeled data. Multiple tricks have been taken in the literature, such as manifold-based regularization [20; 21; 33] and entropy regularization [28]. However, these regularizations are usually of high time or space complexity. By using the proxy vector, we can show that the time complexity of the entropy regularization can be significantly reduced.  
In the field of information theory, the information entropy measures the degree of \"uncertainty\" of the given random variable. For a given random variable \(\mathbf{\bar{p}}=(p_{1},\cdots,p_{m})\), its definition of the information entropy can be expressed as \(\mathrm{H}_{m}(\mathbf{\bar{p}})=-\sum_{i=1}^{m}p_{m}\text{log}p_{m}\). When \(p_{1}=\cdots=p_{i-1}=p_{i+1}=\cdots=p_{m}=0,p_{i}=1,\forall i=1,\cdots,m\), \(\mathrm{H}_{m}(\mathbf{\bar{p}})\) reaches its minimal value. In this case, the system has minimal \"uncertainty\" [34].  
The intuition of entropy regularization used in semi-supervised learning follows the low-density separation assumption [35; 36], which encourages the unlabeled data to be predicted with high probability. In ISMLP, it is desirable for unlabeled data to locate around a certain proxy vector. In other words, the distribution in Equation (7) should be a perky one. To achieve this goal, according to the above analysis of the minimization of information entropy, the following entropy regularization can be constructed:  
\[\min_{\mathbf{M}\in\mathcal{S}_{+}^{d}}-\frac{1}{n_{u}}\sum_{i=n_{i}+1}^{n}\sum _{k=1}^{m}q_{ik}\text{log}q_{ik}, \tag{9}\]  
where \(\mathbf{q}_{ij}\) is the probability of \(i\)-th instance choosing the \(j\)-th proxy-vector as a neighborhood, and it is defined by Equation (6). Minimizing Equation (9) will shrink the distances between the unlabeled instances and their corresponding proxy vectors while keeping a large distance to the other proxy vectors.
## 6 Discussion  
### The Deformation Mechanism  
In existing research, scholars often use displacement sensors such as wire displacement meters and laser displacement meters to measure the displacement of the subgrade. The cracks on the subgrade surface are also analyzed by the naked eye and rulers. These experimental methods can only monitor the displacement at one or a few points and cannot accurately analyze the displacement and crack development on the subgrade and foundation surface during different stages of fault dislocation. This paper employs the PIV monitoring method, commonly used in physical model tests of fault dislocation in sandboxes, to achieve a refined analysis of the subgrade and the foundation deformation under different amounts of fault dislocation.  
While common discrete element numerical simulation methods can accurately depict the impact of fault dislocation on subgrades, they have a slow computation speed and are generally used to study small-sized two-dimensional models. Therefore, they cannot conduct multi-condition three-dimensional numerical simulations efficiently. Additionally, previous finite element analyses have not detailed the development patterns of the subgrade and the foundation cracks under strike-slip fault dislocation through equivalent plastic strain analysis. Hence, this study utilizes the GDEM numerical simulation software, independently developed by the Institute of Mechanics, Chinese Academy of Sciences. By monitoring the change in the combined damage factor, the subgrade and its lower foundation fracture induced by a fault dislocation are analyzed. Compared to same-sized discrete element simulation models, this method shortens the computation time.  
In summary, by improving model testing and numerical simulation methods, this paper reveals the development patterns of S-shaped displacement, tension-shear cracks, and compressive crushed zones in railway subgrades under strike-slip fault dislocation.  
Figure 24: The deformation curve perpendicular to the subgrade strike of the centerline of the subgrade top surface. (**a**) \(\alpha=22.5^{\circ}\), \(45^{\circ}\), \(67.5^{\circ}\), and \(90^{\circ}\); (**b**) \(\alpha=90^{\circ}\), \(112.5^{\circ}\), \(135^{\circ}\), and \(157.5^{\circ}\).  
The impact of different angles between the fault strike and the subgrade strike on subgrade deformation is also analyzed. The deformation mechanism of the railway subgrade under strike-slip fault dislocation is revealed more comprehensively.
# NFT-Based Framework for Digital Twin Management in Aviation Component Lifecycle Tracking
## 1 Introduction
### Background and Motivation
Third, NFTs integrate effectively with digital twins, thereby providing enhanced monitoring capabilities. Each NFT can be linked to a component's digital twin, ensuring that the digital representation is associated with a verified physical asset. This linkage guarantees that all real-time operational data captured in the digital twin corresponds to a validated component, increasing both accuracy and trust. As a result, the combination of NFTs and digital twins enables stakeholders to monitor the state of each component accurately, using data that are verified and reflective of its real-world counterpart. NFTs streamline compliance and regulatory auditing by securely recording every transaction, repair, or inspection associated with each component [17]. Regulatory authorities can access this blockchain record directly, simplifying the audit process and reducing administrative burdens on CAMOs and MROs. By replacing paper-based records with secure digital logs, NFTs support more efficient compliance management, as the verifiable record simplifies auditing and facilitates more streamlined compliance monitoring. NFTs enable interoperability across diverse stakeholders and systems by creating a universal, blockchain-based ledger. Each stakeholder, irrespective of their internal system, can access the NFT as a consistent source of truth for component data, reducing delays due to data discrepancies. This shared access model promotes cross-stakeholder collaboration, which is often hampered by fragmented data practices, thus enabling better-informed decision-making and smoother operational processes. The goal of this paper is to present a comprehensive framework that combines the capabilities of NFTs and digital twins to create a secure, traceable, and interoperable system for managing aviation component lifecycles.
# Modulation Characteristics of High-Speed Transistor Lasers
## 3 Modulation Characteristics of TL  
In current research on TL modulation and data transmission characteristics, approaches such as optimization of QW, optimization of the base region, introduction of intra-cavity photon-assisted tunneling, and introduction of external circuit assistance have been proposed. These approaches reduce the carrier recombination lifetime or photon lifetime; further, they can increase the photon density or increase the rate of change of the output optical power with bias in different ways.
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 5 Discussion and Conclusions  
Based on the above descriptions, we can find that early fault diagnosis (EFD) of rotating machinery has achieved a large number of successful applications. Since the literature on this subject is huge and diverse, a review of all of the literature is impossible, and omission of some papers would be inevitable. From the previous section, some issues that should be studied in-depth for the real EFD applications are pointed out as follows:  
1. Research on EFD based on multi-information fusion should be developed. In real applications, usually, multiple channel signals are measured simultaneously, such as vibration signals, current signals, torque signals, and rotating encoder signals. The extension of EFD techniques to multivariate versions can extract more characteristic fault information, which is vital for detection of weak fault symptoms at an early fault stage.
2. The calculation efficiency of EFD techniques deserves further research. Many EFD methods are proposed to improve the early fault detection ability at the cost of time consumption, which cannot meet the requirements of online condition monitoring. Therefore, how to improve the calculation efficiency of EFD is another research topic for early fault detection.
3. Most EFD methods are tested to be powerful on one test rig and the reliability test results on other machines are unknown. In real applications, the robustness of EFD methods should be studied, aiming to be effective for multiple machines.
# Analyzing Data Reduction Techniques: An Experimental Perspective
## 9 Discussion
### Lossy Data Reduction Algorithms  
When examining the results of the lossy data reduction algorithms, a tangible trend appeared in the \"Percentage Variation\" column. Linear Regression was the algorithm that had the best results in percentage variation across all the data types, with the percentage variation ranging from \(-75.36\%\) to \(-80.63\%\).  
Regarding the processing time, the algorithm efficiency varied across the datasets. For Daily Minimum Temperatures in Melbourne (temporal and numeric), Linear Regression and Simple Random Sampling were the algorithms that took less time to process data, each taking \(0.0538\) to process \(1\) MB. As for the Electric Production dataset (temporal and numeric), the algorithm that could process data in less time was Quantization, taking \(0.0082\) ms to process \(1\) MB. For the Monthly Beer Production in Austria dataset (temporal and numeric), the algorithm with the best processing time per MB was Simple Random Sampling, taking \(0.0290\) ms to process \(1\) MB. As for the Accelerometer and the Smoke Detection datasets, Random Projection was the algorithm that took less time to process \(1\) MB, taking \(0.0019\) ms and \(0.0012\) ms, respectively. The faster algorithm for the IoT Temperatures dataset was Linear Regression, taking \(0.0012\) ms to process \(1\) MB.  
When we look at the data type results in our study (see Table 14), the algorithm that reduced more data across the different data types was Linear Regression. But when looking at the best processing time per MB column, we can see that, for the temporal and numeric data types, the algorithm that appears the most is Simple Random Sampling. Random Projection was the algorithm that performed faster in numeric data type datasets, and Linear Regression performed faster in the temporal- and text-type datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Data Type**} & \multirow{2}{*}{**Dataset Name**} & **Algorithm with the Best** & **Algorithm with the Best** \\  & & **Percentage** & **Variance** & **Processing Time per MB** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Temporal, \\ Numeric \\ \end{tabular} } & Daily Minimum Temperatures in Melbourne & Linear Regression & Linear \\  & Temperature & Linear Regression & Regression/Simple \\  & Melbourne & & Random Sampling \\  & Electric Production & Linear Regression & Quantization \\  & Monthly Beer & & Simple Random \\  & Production in Austria & & Sampling \\ \hline \hline \end{tabular}
\end{table}
Table 14: Best lossy data reduction algorithms.  
This distinctive research highlights the algorithms' effectiveness and dataset-dependent nuances in achieving optimal tradeoffs between data reduction and processing times.
# Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning
## 4 Performance Evaluation
### Model Performance  
The performance of the predictive models for hazard-type and damage-level classification is evaluated separately in this section. In each case, besides the straightforward confusion matrix, the scalar accuracy measures, including the \(F_{1}\) score, the overall accuracy (\(OA\)), the average accuracy (\(AA\)), and the area under the ROC curve (\(AUC\)) are jointly considered. The graphical ROC and PRC of the best models selected in this paper are further used to examine the model capacity and robustness.
# Transforming Customer Digital Footprints into Decision Enablers in Hospitality
## 3 Materials and Methods
### Emotion Modelling  
The emotions articulated by customers enable a comprehensive understanding of their overall experience; however, limited attention has been given to the extraction of emotions at a granular level related to different individual hotel aspects in previous studies. A combination of NLP and machine learning techniques were applied to extract different emotional expressions from online reviews for this research.  
The emotion modelling was based on the highly cited and endorsed psychological emotion model proposed by [PERSON] [33] which demonstrates eight basic emotion states, \"Joy\", \"Anger\", \"Sad\", \"Surprise\", \"Fear\", \"Anticipation\", \"Trust\" and \"Disgust\". Initially, a generic list of emotion words was used to build the dictionary with the use of a published emotion-lexicon dictionary [34]. A standard series of text pre-processing steps of stemming, lemmatization and stop word removal was carried out to clean the textual review. Next, a keyword extraction method was developed to identify emotional expressions presented in social media content [35]. For this purpose, regular expression-based text mining was conducted to handle the negations of emotional expressions.  
To further enrich the initial emotion terms and to contextually align the emotion expressions with the hospitality industry, we used a customisation process for the emotion lexicon as noisy or incomplete vocabularies could lead to errors in the emotion extraction. For this purpose, a word embedding model was trained using the Word2 Vec approach [36], which can capture the context of a word in a document and identify the semantic and syntactic similarity with other words. The Word2 Vec model was trained based on 250,000 online reviews related to tourism [37] and was then used to generate similar words for each emotion based on hotel review data. The similar words were generated based on the neighbourhood of the seed word (emotion). The generated terms were reviewed by the authors to determine the applicability. Using this expanded vocabulary, it was possible to capture indirect, contextual emotional expressions which are otherwise challenging to capture from a dictionary-based approach. A few examples of contextual emotion expressions are noted below:  
* \"Joy\" \(\rightarrow\) {'tasty, 'fragrant','serenity'};
* \"Anger\" \(\rightarrow\) {'dirty', 'rude', 'noisy'};
* \"Disgust\" \(\rightarrow\) {'smelly', 'rotten', 'filthy'}.  
Figure 1: The architecture of the proposed methodology.  
The following Table 1 represents an excerpt from the tourism-related emotion dictionary which was created to conduct the emotion analysis.  
The following Figure 2 illustrates the high-level process of the emotion extraction engine.  
The extracted emotions were then aligned with hotel aspects to create informed insights related to customer experience.