esa-sceva/satcom-chunk-collection huggingface dataset:
### Web 3.0 Applications  
#### 3.3.1 DeFi: Decentralized Finance  
Decentralized finance, in short DeFi, aims to revolutionize the way financial systems work, removing intermediaries and empowering individuals and peer-to-peer transactions at reduced costs in a matter of seconds.  
DeFi heavily relies on tokens [4] and smart contracts empowered by blockchain as opposed to traditional finance systems. Access to finances in a DeFi environment is primarily anonymous and can be done in a matter of seconds without a central authority to validate users' identities.  
Some centralized finance concepts have also been implemented in the DeFi environment. However, since the users' identity is unknown, concepts such as collateral and interest have been adjusted to ensure that the systems work with zero knowledge of user-related information.  
Lending and borrowing tokens require token liquidity [5], whereas, in real life, a physical asset would be required.  
Decentralized exchanges (DeX) have paved the way for an innovative way of trading tokens. In centralized exchanges, authorities would facilitate trading between peers, and market makers would stimulate trading whenever a buy order was not present to fulfill the order [6]. On the other hand, matching buy-sell orders in a decentralized environment is relatively tricky, so the entire system relies on liquidity pools. If trading X token for Y token, there should be enough Y tokens available in the liquidity pool to fulfill the order. The balance between the two pools would eventually determine the buying price.
## 4 AI Agents: A Primer for Data Annotation
### Classification of AI Agents  
We categorize AI agents based on their architectural design and functional capabilities, where each type is aligned to specific data annotation tasks:  
Figure 2: Schematic representation of an agent-driven interaction loop for data annotation. The central agent integrates multiple LLM-based modules: the reflector module evaluates past actions and outcomes; the assessment module analyzes annotation quality; and the policy module determines subsequent strategies. A critique agent provides targeted feedback to enhance decision-making within the interaction environment. The iterative loop culminates in the production of a refined labeled dataset, promoting scalability, consistency, and efficiency in AI-assisted annotation workflows.  
* Rule-Based Agents: Operate on predefined conditional logic and excel at structured annotation tasks with clear decision boundaries. These agents are commonly used for format validation, basic text classification, and quality control checks in annotation workflows. For example, rule-based agents automatically classify customer support tickets based on keyword patterns or validate the completeness of bounding box annotations [57; 58].
* Model-Based Reflex Agents: Maintain internal representations of annotation environments to handle contextually dependent labeling tasks. These agents excel at sequential annotation tasks such as named entity recognition in text or object tracking across video frames, where current decisions depend on understanding previous annotation states [59; 60].
* Goal-Based Agents: Plan and execute multi-step annotation strategies to achieve specific data quality and coverage objectives. These agents decompose complex annotation projects into manageable workflows, prioritize annotation tasks based on model training needs, and adapt strategies to meet quality targets. Examples include agents that orchestrate active learning pipelines to maximize model improvement per annotation effort [61].
* Utility-Based Agents: Evaluate annotation decisions by optimizing multiple factors including confidence scores, cost effectiveness, and expected model performance gains. These agents are particularly valuable in active learning scenarios, where they select the most informative samples for annotation while balancing annotation costs against anticipated improvements in model accuracy [61; 62].
* Learning Agents: Continuously adapt annotation strategies based on feedback from human annotators, annotation quality metrics, and model performance indicators. These agents improve over time by refining their understanding of annotation guidelines, reducing error rates, and becoming more efficient at identifying cases requiring human intervention [46; 47].
## 4 Results  
### Selection of Conditioning Factors  
The IV evaluation method was carried out to analyze the importance of landslide conditioning factors, and the results are presented in Table 2. It can be observed that the distance to roads gained a highest IV value of 3.329 among all factors, followed by elevation (2.694), land use (1.136), NDVI (1.0894), distance to faults (0.733), ERG (0.396), slope angle (0.176), profile curvature (0.105), slope aspect (0.065), annual rainfall (0.052), distance to rivers (0.050), and plan curvature (0), respectively. This result indicates that distance to roads was the most important factor responsible for landslide susceptibility assessment in the study area. Previous studies emphasized the role of roads, as excavation activities and removal of vegetation during road constructions usually disturb natural terrain and interfere with slope stability [32]. In addition, the effect of faults can never be neglected in landslide analysis. The integrity of rock in the neighboring area of faults is disturbed by fault activities, which makes it prone to weathering and benefits to landslide [77]. Cracks and joints induced by faults also contribute to the enrichment and infiltration process of rainwater and groundwater. It should be noted that the spatial variation of plan curvature fails to distinguish the landslide-prone areas effectively. Therefore, plan curvature was eliminated from landslide modeling owing to its incompetence.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Conditioning** \\ **Factors** \\ \end{tabular} } & \multirow{2}{*}{**Classes**} & \begin{tabular}{c} **Numbers of** \\ **Landside** \\ **Pixel** \\ \end{tabular} & \begin{tabular}{c} **Numbers of** \\ **Non- Landside** \\ **Pixel** \\ \end{tabular} & \begin{tabular}{c} **IV of** \\ **Classes** \\ \end{tabular} & \
\begin{tabular}{c} **IV of** \\ **Factors** \\ \end{tabular} \\ \hline \multirow{5}{*}{
\begin{tabular}{c} Distance to roads \\ (m) \\ \end{tabular} } & 0–67.26 & 144 & 27,281 & 0.408 & \multirow{5}{*}{3.329} \\  & 67.26–179.97 & 115 & 35,161 & 0.180 & \\  & 179.97–430.47 & 75 & 51,765 & 0.003 & \\  & 430.47–803.09 & 32 & 45,260 & 0.036 & \\  & 803.09–2048.00 & 19 & 70,570 & 0.261 & \\  & 2048.00–2556.69 & 3 & 16,078 & 0.108 & \\  & \(>\)2556.69 & 0 & 64,101 & 2.333 & \\  & 740.00–1043.67 & 88 & 12,262 & 0.334 & \\  & 1043.67–2138.90 & 271 & 105,623 & 0.254 & \\  & 2138.90–2389.03 & 25 & 35,515 & 0.025 & \\  & 2389.03–4160.00 & 4 & 156,816 & 2.081 & \\  & Residential area & 36 & 1951 & 0.233 & \\  & Forest & 146 & 247,017 & 0.315 & \\  & Grassland & 20 & 26,894 & 0.018 & \\ Land use & Farmland & 33 & 3918 & 0.138 & 1.136 \\  & Bare land & 131 & 28,544 & 0.319 & \\  & Engineering land & 2 & 169 & 0.010 & \\  & Other & 20 & 1723 & 0.103 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information value (IV) analysis of landslide conditioning factors.
# An Application of a LPWAN for Upgrading Proximal Soil Sensing Systems
## 1 Introduction
To meet the needs of long-term field work, and considering the limitations of sensor battery power supply, LPWAN technology needs to be integrated and applied in proximal soil sensor devices. However, such devices generally need to be redesigned at a high cost, and this leads to the elimination of the previous generation of inventoried devices, due to their outdated technology. At the same time, the redesigned sensor not only needs to have its communication function tested, but also requires more practice to verify its sensing technology [13]. Therefore, this paper selected an inventoried soil moisture sensor based on an RS-485 interface as the research object; designed and adapted the attachment hardware system (AHS), according to its electrical specifications and communication protocol, integrated LPWAN technology; and realized the technological upgrade of the sensor, so that it not only retained the function and performance of the original sensor, but also had the attributes of ultralow power consumption and long-distance transmission, while supporting long-term battery power supply, easy deployment, and simple management. At the same time, the elimination of an inventoried device due to the application of new technology was avoided, and resources and costs were saved, because the design was based on the inventoried device. The main contributions of this paper are as follows:  
1. This paper put forward a new idea for applying the emerging LPWAN technology in proximal soil sensing systems and carried out engineering practice. 2. Instead of directly eliminating the inventoried proximal soil sensor device with outdated technology, this paper upgraded it by designing an AHS; the new device not only retained the original mature sensing technology of the sensor device, but also exhibited ultralow power consumption and long-distance transmission. In addition, this paper gave full play to the application value and economic value of the devices stored in the inventory. 3. The proposed approach also provides a reference for applying LPWAN technology to a wider range of inventoried sensor devices for technical upgrading. The rest of this paper is organized as follows: Section 2 presents the overall architecture design of the system. Section 3 describes the hardware design of the AHS. Section 4 introduces the software design of the system. Section 5 tests and analyzes the sensor device, after loading the AHS, and also discusses the relevant factors affecting the communication quality. The last section summarizes this paper and discusses its significance.
# CommC: A Multi-Purpose COMModity Hardware Cluster
## 3 System Architecture  
The high-level architecture of our proposed system is illustrated in Figure 1. Our cluster is composed of multiple physical nodes that are equipped with a similar set of components, and they collectively form a cohesive virtualized environment managed through a centralized cluster management layer.  
The _Virtualization Cluster_ is represented at the top, symbolizing the unified virtualized environment that spans across all physical nodes. This centralized layer ensures that all nodes work in harmony, managing resources and Virtual Machines (VMs) across the entire cluster. By linking multiple nodes together, the cluster provides redundancy, load-balancing, and scalability, allowing the virtualized environment to handle varying workloads and ensure high availability. In its current state, CommC operates under a hierarchical resource management structure. At a higher level, an administrator is responsible for allocating the necessary resources for each virtual machine, similar to how resource management is handled in cloud-based environments. The actual distribution of tasks and resource assignment within the system is managed by the cluster orchestrator, which, in this case, is Kubernetes.  
Each physical node contains three main components:  
1. _GUI_ (in yellow): This provides a graphical user interface for interacting with the node locally. The GUI offers a user-friendly way to interact with the virtual machines running on the cluster.
2. _Cluster Manager_ (in blue): This component handles the orchestration of resources within the node and coordinates with the other nodes in the cluster. The Cluster Manager communicates with the centralized Virtualization Cluster layer, ensuring that resources are allocated effectively and that VMs can be managed across nodes.
3. _Virtual Machines_ (in green): These are the actual virtualized instances running on each physical node. VMs are isolated environments that simulate separate operating systems, allowing multiple applications to run concurrently on a single physical server. The hypervisor on each node allocates resources to these VMs and ensures their isolation from each other.  
At the bottom of the diagram, the _Kubernetes_ icon indicates that some of the VMs within the cluster are running a separate Kubernetes cluster. This setup enables Kubernetes to orchestrate containerized applications on top of virtualized infrastructure, providing an additional layer of flexibility. By running Kubernetes within VMs, the architecture can support containerized workloads alongside traditional virtualized applications, enabling a hybrid environment within each physical node.  
In summary, each node is equipped with local management and monitoring capabilities, and virtual machines provide isolated environments for running both standard applications and a Kubernetes cluster.
# Voice Transformation Using Two-Level Dynamic Warping and Neural Networks
## 8 Summary and Conclusions  
VT has been achieved by employing two-level DW in combination with NN structures. The DW does both temporal and spectral alignment. The path warping functions from DFW are used to train a NN to produce the warping functions. These warping functions were interpolated to provide vectors of constant length. The warped amplitude spectra are shaped by phase reconstruction, which was shown to dramatically improve the speech quality.  
To provide sufficient representation capability on larger training data sets, we found it necessary to cluster the training data, training each segment separately then combining the results. This produced low error in the representation of the warping functions.  
Based on comparisons with mel-cepstral scores, the VT approach provides performance competitive with previous works.  
The primary focus of this work has been on the development of the two-level DW and design of a NN architecture to do VT. Future work will consider transformation among different classes of speakers, including dealing with speakers with different accents.  
Conceptualization, A.-W.A.-D., T.K.M., J.H.G.; methodology, A.-W.A.-D., T.K.M.; software, A.-W.A.-D.; validation, A.-W.A.-D.; formal analysis, A.-W.A.-D.; investigation, A.-W.A.-D.; resources, T.K.M., J.H.G.; writing--original draft preparation, A.-W.A.-D.; writing--review and editing, T.K.M., J.H.G.; supervision, T.K.M.; project administration, T.K.M. All authors have read and agreed to the published version of the manuscript.  
This research received no funding.  
Not applicable.  
The code, data, and transformed sounds can be found here: [[https://digitalcommons.usu.edu/etd/8030](https://digitalcommons.usu.edu/etd/8030)]([https://digitalcommons.usu.edu/etd/8030](https://digitalcommons.usu.edu/etd/8030)), [[https://github.com/[PERSON]/Voice-Transformation-.git](https://github.com/[PERSON]/Voice-Transformation-.git)]([https://github.com/[PERSON]/Voice-Transformation-.git](https://github.com/[PERSON]/Voice-Transformation-.git)) (accessed on 23 May 2021).  
The authors declare no conflict of interest.
# Integrated Earthquake Catalog of the Ossetian Sector of the Greater Caucasus
## 3 Results
### Statistics of the Integrated Catalog of the Ossetian Sector of the Greater Caucasus  
Figure 13 shows the distribution of earthquake epicenters from the integrated catalog of the Ossetian sector of the Greater Caucasus. The catalog contains 16,285 events from 1962-2022. It has to be noted that the integrated catalog contains 837 events from the ISC and 15,717 earthquakes from the national Soviet, Russian, and Georgian catalogs.  
The distribution of magnitudes in time and differential magnitude-frequency graphs in different time periods are presented in Figure 14. Before 1982, most events had integer or half-integer classes, which caused magnitude sampling in 0.5 increments. During the same period, earthquake epicenters in the original catalogs are given with an accuracy of 0.1\({}^{\circ}\) (see Supplementary Figure S3). The representativeness of the catalog varies significantly over time, with significant dips in registration levels from 1967 to 1970, as well as from 1988 to 1991. Registration levels improved significantly in 1995 and then in 2005.  
Figure 13: Map of earthquake epicenters \(M\geq\) 2.0 of the integrated catalog of the Ossetian sector of the Greater Caucasus, 1962–2022. GSR is the integration of Soviet, Russian, and Georgian catalogs (Figure 2). The solid black line is the state border of the Russian Federation; the thin line is the border of the Republic North Ossetia–Alania.  
Figure 15a presents spatial-temporal variations in the magnitude of complete registration since 1982. To determine spatial-temporal variations of the magnitude of complete registration, the multi-scale method was used [31]. It was designed by authors to analyze heterogeneous catalogs with significant variations in the registration level. Maps of spatial variations of \(Mc\) were also constructed for the periods 1995-2004 (Figure 15b) and 2005-2022 (Figure 15c) when \(Mc<3.2\) and \(Mc<2.2\), respectively. In the western part of the considered region, the level of registration is better than in the east and \(Mc=3.0\) in 1995-2004, and \(Mc=2.0\) in 2005-2022, respectively.  
Figure 14: Distribution of magnitudes in the integrated catalog of the Ossetian sector of the Greater Caucasus 1962–2022 in time and differential magnitude–frequency graphs in different time periods.
# Development of a Submillimetric GNSS-Based Distance Meter for Length Metrology +
## 2 GNSS Data Processing Approach
### Functional Model for the Initial Coordinates  
The GBDM technique starts with the determination of the absolute coordinates of the ends of the measured distance. This initial solution has to provide three-dimensional coordinates with an accuracy of a few centimetres in the same coordinate reference frame as the precise ephemerides used in the second step. This requirement is not very difficult to fulfil, and can be achieved by using different strategies, but we recommend the use of a common Precise Point Position (PPP) solution. This approach, which is based on an epoch-wise processing strategy that uses an ionosphere-free combination with floating ambiguities, is relatively easy to implement or can be alternatively obtained from online services like CSRS-PPP [30].  
Some advantages of this straightforward approach are that the bulk of the ionosphere error is removed, slow changes of the tropospheric delays can be easily absorbed by parametrization, the receiver clock offsets are well determined and the obtained ionosphere-free residuals are expected to be mostly multipath error and possible antenna calibration mismodelling.  
Concerning the functional model, a widely used equation for receiver \(i\) and satellite \(k\) is (see, e.g., [31])  
\[h_{\Phi_{IF}}=\begin{array}{c}\rho_{i}^{k}+c\delta_{i}+A_{i}^{k}+m_{G}(\theta _{i}^{k})T_{zwd}\\ +m_{G}(\theta_{i}^{k})\cot(\theta_{i}^{k})[G_{N}\cos\psi_{i}^{k}+G_{E}\sin\psi _{i}^{k}]\end{array} \tag{4}\]  
where:  
\begin{tabular}{l l} \(h_{\Phi_{IF}}\) & ionosphere-free combination (m) \\ \(\rho_{i}^{k}\) & modelled geometric range (m) \\ \(c\) & speed of light in vacuum (m/s) \\ \(\delta_{i}\) & receiver clock offset (s) \\ \(A_{i}^{k}\) & non-integer ambiguity term (m) \\ \(\theta_{i}^{k},\psi_{i}^{k}\) & elevation and azimuth angles (rad) \\ \(T_{zwd}\) & zenith wet troposphere delay (m) \\ \(m_{G}()\) & Global Mapping Function (GMF) \\ \(G_{N},G_{E}\) & north and east troposphere gradients \\ \end{tabular}  
The non-integer ambiguity term \(A_{i}^{k}\) comprises the integer ambiguity, the initial phase offsets for both frequencies, and the hardware delays. The vector of Kalman filter states that are estimated is  
\[\vec{x}=(X_{i}\ Y_{i}\ Z_{i}\ c\delta_{i}\ T_{zwd}\ G_{N}\ G_{E}\ A_{i}^{1} \cdots A_{i}^{m})^{T} \tag{5}\]  
Taking into account the implemented PPP approach, the obtained ionosphere-free residuals are expected to be mostly multipath error and possible antenna calibration mismodelling [32].
# A Protocol Layer Trust-Based Intrusion Detection Scheme for Wireless Sensor Networks
## 4 Performance Analysis  
The purpose of the analysis is to derive mathematical results of the false positive and false negative probabilities. The false positive probability is the probability that a node is evaluated as compromised whereas it is not. On the other hand, false negative probability is the probability that a node is evaluated as not compromised whereas it is. The expressions for the false positive and false negative probabilities are derived using a statistical approach. We also calculate the communication overhead of our scheme.
## 3 Data Description and Data Preprocessing
### Data Preprocessing
#### 3.2.1 Data Normalization  
The dimensions and value ranges of the different parameters vary greatly, and so they cannot be compared horizontally, nor can they be directly input into the model.  
In order to ensure that the dimensions and value ranges between different features will not have an adverse effect on the model training, the input data need to be normalized before they are input into the model for training. This paper chooses the min-max normalization method to process the data, thus mapping the data to the range of [0, 1].  
\[x^{*}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{10}\]  
where \(x\) is the original data, \(x^{*}\) is the data after normalization, and \(x_{\text{min}}\) and \(x_{\text{max}}\) are the minimum and maximum values of the data, respectively.  
Processing data with minimum-maximum standardization solves the problem of large differences in dimensions and value ranges between different features, making different features comparable and helping to improve the training speed and model performance.
# Mobility-Aware Hybrid Flow Rule Cache Scheme in Software-Defined Access Networks
## 4 Proposed Hybrid Flow Rule Cache Scheme  
In this section, the proposed hybrid flow rule cache scheme is described. Let us consider that there are \(N\) number of ADs and \(M_{i}\) number of mobile flows into the \(i\)th AD. This paper assumes that the predicted target ADs are determined for the mobile flows. To find the optimal number of proactive flow rules, the proposed scheme can be formulated as an integer linear programming (ILP) problem, and the problem can be solved using the heuristic method.