esa-sceva/satcom-chunk-collection huggingface dataset:
# MDFULog: Multi-Feature Deep Fusion of Unstable Log Anomaly Detection Model
###### Abstract  
Effective log anomaly detection can help operators locate and solve problems quickly, ensure the rapid recovery of the system, and reduce economic losses. However, recent log anomaly detection studies have shown some drawbacks, such as concept drift, noise problems, and fuzzy feature relation extraction, which cause data instability and abnormal misjudgment, leading to significant performance degradation. This paper proposes a multi-feature deep fusion of an unstable log anomaly detection model (MDFULog) for the above problems. The MDFULog model uses a novel log resolution method to eliminate the dynamic interference caused by noise. This paper proposes a feature enhancement mechanism that fully uses the correlation between semantic information, time information, and sequence features to detect various types of log exceptions. The introduced semantic feature extraction model based on Bert preserves the semantics of log messages and maps them to log vectors, effectively eliminating worker randomness and noise injection caused by log template updates. An Informer anomaly detection classification model is proposed to extract practical information from a global perspective and predict outliers quickly and accurately. Experiments were conducted on HDFS, OpenStack, and unstable datasets, showing that the anomaly detection method in this paper performs significantly better than available algorithms.  
Bert; Informer; anomaly detection; cybersecurity; log
## 3 Results
### Test Setup
#### 3.1.2 PERFORM Case Study II: Multiple Obstacles  
Similar to Case Study I, the second case study observes a waypoint-to-waypoint task (as shown in Figure 9). In this scenario, two obstacles are present and positioned to analyze the decision-making autonomy of the vehicle with regards to weighting the total distance traveled against vehicle safety (as determined by CPA). To reach the goal, the autonomy engine must decide between maneuvering between the two obstacles and increasing its safety risk or increasing the total distance traveled by taking a more conservative path to avoid the obstacles. Dependent on the intended use of the vehicle, the scoring in the IT2-FL techniques may be modeled to reflect these test goals with appropriate modifications to the rule base. CPA is chosen to be the minimum of the shortest distance between the vehicle and each obstacle. The reader should note that this approach is flexible in its application to an environment with varying numbers of obstacles and types of configurations. For example, an average value calculated from the CPA to each hazard, for instance, may be used instead.  
For Case Study II, the rule base (as shown in Table 3) is adjusted to incentivize a heavier weighting of the safety of the vehicle. Here, a higher CPA score corresponds to the linguistic term \"far\".  
Figure 9: Testbed for Case Study II (obstacles represented by black boxes).
# EconLedger: A Proof-of-ENF Consensus Based Lightweight Distributed Ledger for IoVT Networks
## 2 Background and Related Work
### State of the Art on IoT-Blockchain  
To support security and lightweight features required in IoT systems, the IoTChain [25] proposes a three-tier blockchain-based IoT architecture, which allows regional nodes to perform any lightweight consensus, such as PoS and PBFT. IoTChain only provides simulation results on communication cost of transactions; however, key metrics in the consensus layer, such as computation, storage, and throughput, are not considered. FogBus [26] proposes a lightweight framework for integrating blockchain into fog-cloud infrastructure, which aims to ensure data integrity as transferring confidential data over IoT-based systems. In FogBus, master nodes deployed at the fog layer are allowed to perform PoW mining, while IoT devices send transactions to master nodes as trust intermediates to interact with blockchain. However, using PoW as the backbone consensus protocol still results in high energy consumption and low throughout.  
HybridIoT [27] proposes hybrid blockchain-IoT architecture in order to improve scalability and interoperability among sub-blockchains. In HybridIoT, a BFT inter-connector framework functions as a global consortium blockchain to link multiple PoW sub-blockchains. However, using PoW consensus in sub-blockchain networks still imports computation and storage overhead on IoT devices if they are deployed as full nodes. IoTA [28] aims to enable cryptocurrency designed for the IoT industry, and it leverages a directed acyclic graph (DAG), called tangle [29], to record transactions rather than chained structure of the ledger. IoTA provides a secure data communication protocol and zero fee micro-transaction for IoT/machine-to-machine (M2M), and it demonstrates high throughput and good scalability. However, existing IoTA networks still rely on hard-coded coordinators, which employ PoW to finalize the path of recorded transactions in DAG.  
Unlike the above mentioned IoT-Blockchain solutions, which either adopt computation intensive PoW as their backbone consensus mechanism or rely on an intermediate fog layer to execute consensus protocol, EconLedger aims to provide a partially decentralized and lightweight blockchain for resource constrained IoVT devices at the edge without relying on any intermediate consensus layer deployed at fog level. Moreover, EconLedger leverages DDB technology to enable trusted off-chain storage, which reduces storage overhead caused by directly storing raw data on the public distributed ledger.
## 2 Materials and Methods
### Data Analyses
#### 2.4.4 Social Network Comparison between Recorded Video and GNSS Data  
To determine the accuracy of the RTK rovers for studying social networks in sheep, the position of individuals relative to each other were examined based on (1) recorded videos by the installed camera at the north corner of the paddock (Figure 6), (2) social network analyses of GNSS locational data, and (3) plotted GNSS location of animals. For this purpose, four 30 s time periods during the third day of the study were selected based on the criteria of good performance of all sensors, videos of high enough resolution to distinguish individual animal's positions relative to each other, and minimal movement by the animals. Static images at the beginning of the 30 s periods were extracted from the video recordings and were compared with the social network graph and GNSS positional plot. The distance between individuals was calculated using the spatsoc package [29]. The social network graph was then drawn using the package of ggplot2 [41] within the 30 s time periods with a threshold distance of 30 cm. The width of the edges in the graph was set based on the distance between individuals so that the width increased as the distance between dyads increased. The location of individuals was also plotted based on GNSS positional data using the package of ggplot2 [41].  
Figure 5: Some examples of individual animal movement behaviour at 10 min intervals on the second day of the study to rank the animals based on their movement trajectories and select the animal (s) who moved first. Plot (**a**) shows where all individuals were assigned a different rank, (**b**) shows where all individuals received the same rank, (**c**) shows where one animal (B) did not move. Individual animals are represented by separate colours and letters (A to G), and the direction of the arrow indicates the direction of travel at the conclusion of the 10 min period. The length of the lines indicates the distance travelled. For clarity, the specific paddock area the animals were within is displayed, hence the different axis values for each plot.
# An FPGA-Oriented Baseband Modulator Architecture for 4G/5G Communication Scenarios
###### Abstract  
The next evolution in cellular communications will not only improve upon the performance of previous generations, but also represent an unparalleled expansion in the number of services and use cases. One of the foundations for this evolution is the design of highly flexible, versatile, and resource-/power-efficient hardware components. This paper proposes and evaluates an FPGA-oriented baseband processing architecture suitable for communication scenarios such as non-contiguous carrier aggregation, centralized Cloud Radio Access Network (C-RAN) processing, and 4G/5G waveform coexistence. Our system is upgradeable, resource-efficient, cost-effective, and provides support for three 5G waveform candidates. Exploring Dynamic Partial Reconfiguration (DPR), the proposed architecture expands the design space exploration beyond the available hardware resources on the Zynq xc7z020 through hardware virtualization. Additionally, Dynamic Frequency Scaling (DFS) allows for run-time adjustment of processing throughput and reduces power consumption up to 88%. The resource overhead for DPR and DFS is residual, and the reconfiguration latency is two orders of magnitude below the control plane latency requirements proposed for 5G communications.  
reconfigurable hardware; FPGA; dynamic partial reconfiguration; baseband processing; carrier aggregation; 4G/5G coexistence; cloud-RAN
## 3 Results  
Analysis of the measurement repeatability and reproducibility demonstrated that the error associated with the method did not affect the results. The repeatability and reproducibility of the measurements taken for \(\alpha 3\) and \(\alpha 5\) reached 1.3% (Table 1) and 0.7%, respectively (Table 2). This showed that the variability in the measurements strongly depended on the tooth shape in 98.7% in relation to \(\alpha 3\) and in 99.3% in relation to \(\alpha 5\).  
In our findings, the mean values of the \(\alpha 3\) and \(\alpha 5\) angles in lateral incisors demonstrated statistically significant variation. Contrarily, the \(\alpha\) angle measurements of the central incisors did not reveal any statistically significant differences (Table 3). Notably, the \(p\)-value for the left central incisor was marginally close to the conventional significance threshold, recorded at just above 0.05. In general, the lateral incisors demonstrated greater variability than the central incisors. In particular, the right lateral incisor was the most diverse, presenting mean values of \(\alpha 3\) and \(\alpha 5\) angles equal to \(-1.4^{\circ}\) and \(+3.5^{\circ}\), respectively. The corresponding values of the \(\alpha 3\) and \(\alpha 5\) angles measured on the left lateral incisor reached \(-0.7^{\circ}\) and \(+2.7^{\circ}\), respectively. The \(\alpha 3\) and \(\alpha 5\) values of the maxillary central incisors showed additional palatal root torque from \(+0.6^{\circ}\) to \(+1.6^{\circ}\) for the right central incisor and from \(+1.3^{\circ}\) to \(+1.4^{\circ}\) for the left central incisor.  
Figure 5: Schematic of the bracket positioned at 4 mm with the base adjusted to Section B of the CrVS.
# An Analysis of the Short Utterance Problem for Speaker Characterization +
###### Abstract  
Speaker characterization has always been conditioned by the length of the evaluated utterances. Despite performing well with large amounts of audio, significant degradations in performance are obtained when short utterances are considered. In this work we present an analysis of the short utterance problem providing an alternative point of view. From our perspective the performance in the evaluation of short utterances is highly influenced by the phonetic similarity between enrollment and test utterances. Both enrollment and test should contain similar phonemes to properly discriminate, being degraded otherwise. In this study we also interpret short utterances as incomplete long utterances where some acoustic units are either unbalanced or just missing. These missing units are responsible for the speaker representations to be unreliable. These unreliable representations are biased with respect to the reference counterparts, obtained from long utterances. These undesired shifts increase the intra-speaker variability, causing a significant loss of performance. According to our experiments, short utterances (3-60 s) can perform as accurate as if long utterances were involved by just reassuring the phonetic distributions. This analysis is determined by the current embedding extraction approach, based on the accumulation of local short-time information. Thus it is applicable to most of the state-of-the-art embeddings, including traditional i-vectors and Deep Neural Network (DNN) xvectors.  
speaker recognition; short utterances; phonetic content +
Footnote †: This paper is an extended version of our paper published in IberSPEECH2018.
## 2 Preliminaries and Basic Idea
### Proposed Scheme  
The main idea of this research is to follow the motor's status through its operation and try to keep it working unless there is a real threat to the motor. Figure 3 shows the basic idea of this study for monitoring the three-phase induction motor by measuring its currents, stator winding temperature, vibration, and speed. Based on these measurements, a single-phasing fault can be detected as a faulty operation. In parallel with detecting single phasing, the proposed monitoring scheme can also detect any abnormal increase in winding temperature. The previous measurements are considered vital signs of the motor, and based on them, a decision unit or drive circuit decides the best action for the motor without harming it, as well as its continuity of operation, such as reducing its speed or reducing the load applied to the motor (if it is possible), or shutting down the motor if its continuity of operation will affect its lifetime or if it will probably damage it. The proposed monitoring scheme provides a reliable and efficient method for maintaining the optimal operation of motors in various industrial applications.
# Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors
## 3 Main Reflectance Indices  
### Vegetation Indices  
Vegetation indices (VIs) are widely used for the estimation of the biomass production in plants and the analysis of their health. VIs are measured on the basis of the reflectance in the red (600-700 nm) and part of the near-infrared (NIR) (700-900 nm) spectral regions [10]. The red region of this reflectance is related to light absorption by photosynthetic pigments; the reflectance in the NIR spectral region is dependent on the structure of the leaf and canopy [58; 59; 60]. Using combinations of reflectance in these regions of wavelengths provides the long-term estimation of the photosynthetic capacity, which is related to the plant productivity. The main VIs are summarized in Part 1 of Table 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reflectance Index** & **Formula** & **References** \\ \hline \multicolumn{3}{c}{Vegetation indices} \\ \hline Difference Vegetation Index (DVI) & \(DVI=R_{NIR}-R_{Rad}\) & [61] \\ \hline Simple Ratio Index (SR) & \(SR=\frac{R_{NIR}}{K_{Rad}}\) & [58] \\ \hline Normalized Difference Vegetation Index (NDVI) & \(NDVI=\frac{R_{NIR}-R_{Rad}}{R_{NIR}+R_{Rad}}\) & [58] \\ \hline Green Normalized Difference Vegetation Index (SNDVI) & \(GNDVI=\frac{NIR-G_{Gross}}{NIR+Gross}\) & [62] \\ \hline Soil-adjusted Vegetation Index (SAVI) & \(SAVI=\frac{(R_{NIR}-R_{Rad})(1+L)}{R_{NIR}+R_{Rad}+L}\) & [63] \\ \hline Optimized Soil-Adjusted Vegetation Index (OSAVI) & \(OSAVI=1.16\frac{R_{NIR}-R_{Sun}}{R_{Sun}-R_{Sun}+0.16}\) & [64] \\ \hline Modified Soil-Adjusted Vegetation Index (MSAVI) & \(MSAVI=0.5\bigg{[}2R_{800}+1-\sqrt{(2R_{800}+1)^{2}-8(R_{800}-R_{970})}\bigg{]}\) & [65] \\ \hline Transformed Soil-Adjusted Vegetation Index (TSAVI) & \(TSAVI=\frac{a(R_{NIR}-aR_{Rad}-b)}{R_{Rad}+aR_{NIR}-ab}\) & [66] \\ \hline Global Environment Monitoring Index (GEMI) & \(GEMI=n(1-0.25n)-\frac{R_{dd}-0.125}{1-R_{ad}}\) & [67] \\ \hline Enhanced Vegetation Index (EVI) & \(EVI=2.5\frac{R_{NIR}-R_{c}}{R_{NIR}+C_{1}R_{c}-C_{2}R_{c}+L}\) & [68] \\ \hline Wide Dynamic Range Vegetation Index (WDRVI) & \(WDRVI=\frac{aR_{NIR}-R_{Rad}}{aR_{NIR}+R_{Rad}}\) & [69] \\ \hline Atmospherically effect resistant vegetation index (IAVI) & \(IAVI=\frac{R_{NIR}-(R_{Rad}-\gamma(R_{R_{R_{R}}}-R_{Rad}))}{R_{NIR}+(R_{Rad}- \gamma(R_{R_{R_{R}}}-R_{Rad}))}\) & [70] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main reflectance indices used for the plant remote sensing.
## 2 VLC System Model
### Color Shift Keying Modulation  
As we mentioned above, it is necessary to assign a transmission wavelength to each of the three transmitters. This assignment must meet the following two criteria:  
Figure 1: General outline of the proposed localization system.  
* Optical transmitters must emit light at wavelengths suitable for the hospital environment.
* Optical transmitters must emit light at wavelengths sufficiently separated from each other.  
Although no data are transmitted through the optical medium, according to the aforementioned premises, the modulation scheme based on CSK specified in PHY III of the IEEE 802.15.7 standard [34] is adopted and modified. This mechanism is designed to provide high data rates in indoor environments and uses the Commission Internationale de l'Eclairage (CIE) 1931 \(xy\) color diagram. This standard is exemplified by three RGB LEDs. However, given the hospital environment, it is not possible to use RGB LEDs. While the CIE color diagram is primarily focused on specifying colors, the concept of illuminants can be considered in the context of white light. To achieve the desired goal, represented by the coordinate \((x_{c},y_{c})\) within the CIE 1931 system, a careful selection of three LEDs is made. These LEDs are chosen to emit light with coordinates that closely align with \((x_{c},y_{c})\), forming a triangle within the diagram. When controlling the intensity of these light sources, different variations of white light can be achieved within the geometric shape defined by the coordinates of the three LEDs. This approach ensures that significant color differences are minimized, allowing the system to maintain a consistent and harmonious lighting output suitable for the hospital environment.  
To set the position of the LEDs, we consider \((x_{i},y_{i})\) as the coordinates of each of the three LEDs within the CIE 1931 \(xy\) system, where \(i\in\{1,2,3\}\). These coordinates form a triangle within the diagram. Let us assume that \((x_{c},y_{c})\) lies within this triangle. Based on the intensity \(I_{i}\) of each LED, it is possible to generate the color described at this point. In other words, the intensity \(I_{i}\) is associated with the transformation of the coordinate \((x_{i},y_{i})\). The relationship between these coordinates and the intensities is specified in the mentioned standard as [34]  
\[x_{c}=\sum_{i=1}^{3}x_{i}*I_{i}, \tag{1}\]  
\[y_{c}=\sum_{i=1}^{3}y_{i}*I_{i}, \tag{2}\]  
\[\sum_{i=1}^{3}I_{i}=1. \tag{3}\]  
In this context, the optical receiver must be able to differentiate between the three transmitters on the basis of the wavelength emitted by each of them.
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 6 Data Pre-Processing and Engineering Module
### Image Conversion  
Following the data sampling process, each feature within the time window is characterized by three metrics: the minimum, maximum, and mean values for numerical features and the most frequent, second most frequent, and least frequent values for categorical features. This representation offers a comprehensive summary of the value distribution within the time window and is adaptable to new network and sensor data. To reduce data dimensionality, a 3:1 representation ratio is employed, where each feature value is mapped to a specific pixel intensity, as represented by an RGB (Red-Green-Blue) pixel in an image. This approach is motivated by two key factors:  
* Representing each of the three features as an RGB pixel reduces the amount of data by one-third, which is crucial for achieving the sustainability goal of the data engineering system.
* Representing data as images enables the use of consolidated DNN technologies such as convolutional neural networks. By converting data into image format, we can harness the power of CNNs, which have been widely adopted and proven successful in various image-related tasks, such as object detection, image classification, and segmentation [61].  
Furthermore, this approach is supported by the increasingly adopted techniques of Gramian Angular Fields (GAF) [62] and Markov Transition Fields (MTF) [63], which have  
Figure 3: Data pre-processing and engineering system architecture.  
been successfully used to convert time series into image representations while preserving temporal relationships. The developed custom image conversion technique follows the same principles as these approaches; however, we acknowledge that the level of abstraction introduced could be validated in future experiments using statistical analysis of features (e.g., Pearson coefficient) between the original and converted data.  
To perform this task, data are re-shaped into three-dimensional matrices, as shown in Figure 4, to be later converted to images:  
* The sensor data are transformed into a \(6\times 3\times 3\) matrix. Each row of the matrix represents a sensor, and each cell contains three timed metrics for a feature. If a sensor only has two measures, NaN values are used to fill the last cell of the row.
* The network data are transformed into a \(6\times 7\times 3\) matrix, with each cell containing three timed metrics for a specific feature. NaN values are used to fill the remaining positions in the matrix.  
In both matrices, values are normalized to the range of [0:255] to represent pixel color intensities, with NaN values converted to zeros (black pixels). The matrices are then transformed into images using the Python Imaging Library (PIL) [64], where each matrix sub-value is mapped to the intensity of the red, green, or blue channel of a pixel. Figure 5 shows the network matrix transformed into a 2D matrix with separated and unified color channels, while Figure 6 depicts the corresponding transformation for the sensor data.