esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Term Forecasting Potential of Photo-Voltaic Electricity Generation and Demand Using R
## 2 Materials and Methods  
### Data Description  
An overview of the parameter names and units is given in Table 1.  
* Micro-grid data, two parameters. An initial cost analysis was performed on the hourly data from the micro-grid by [8] from April 2013 to March 2018. These data were recorded and obtained from a different data storage point in the Energy Management System and not yet cleansed. The data used in this study concerns per second data, from January 2015 to December 2017, which was obtained from the micro-grid data storage point and was cleansed as described in [9]. It was converted into hourly data to enable analysis with hourly weather data by means of averaging. Parameters of interest are purchased electricity and total solar power generation.  
For the purpose of this study, battery charging and discharging is ignored as it is of negligible proportions. The electricity amount and cost reduction function of the battery is active only during July, August, and September. Previous studies showed this amount is maximally 4.86% of the total building energy demand [10].
* Japan Meteorology Agency (JMA) data, six parameters. The hourly weather parameters for the corresponding time period of January 2015 to December 2017 were downloaded in csv files from the JMA website (freely accessible) [11] in batches of one month due to size restrictions. Parameters of interest are temperature, precipitation, sunlight, wind speed, solar irradiation, and humidity.
* Time parameters (6): year (note that due to the total data volume each year is calculated separately), month, day of month, day of week, hour, holiday.
* Electricity cost data, one parameter (see Figure 1). These data were obtained from the Facility Management Office of NIMS, based on the yearly renewed contract with the Tokyo Electric Power Company. The Feed in Tariff surcharge for renewable energy is included. Other factors such as fuel regulatory costs, basic connection to the electricity grid and spare line fees are not included since these are shared by the entire site and independent of building electricity use.
## 7 Security
### Countermeasures
Also, the interference is quite limited, and nearby frequencies are interfered with in addition to the target frequency [143]. * Replay: This attack consists of repeated data transmission between two devices without the data being modified in any way. The attacker intercepts the original communication and later retransmits it to gain unauthorized access [144]. For connected vehicles, it can also be used to break the encryption key while replaying the ARP protocol. It is mainly used when the connection between the vehicle and other devices is secure [145]. * Buffer overflow: This is an attack intended to fill the vehicle's memory buffer with more data than it can process. Such data overflow can cause the vehicle to malfunction and execute arbitrary code, which is exploited to gain control of the device [146]. * DoS: This is performed either by deauthentication or Wi-Fi jamming, which causes devices in the vehicular networks to crash [147]. * ARP cache poisoning (ARP spoofing): An attacker sends spoofed ARP messages into the network in order to deceive other devices trying to communicate with the device. These spoofed ARP messages contain a physical MAC address to which other devices will send their messages. However, this MAC address is spoofed and it is the attacker's device, which gives him access to the data being sent and allows him to modify it and send it on. In vehicular networks, it is used to send malicious scripts repeatedly [148].
## 5 Methodology: AGO Construction
### First Phase: Definition of the Scope and Knowledge Acquisition  
The first stage is an analysis phase and aims to establish the concrete scope of the use case. This determines which objects are included in the taxonomy of classes. This step requires having a deep knowledge of the domain of interest and can be the most expensive phase in terms of person-hours. In this case, several large-scale open datasets for the development of AD technologies and the Safety Pool taxonomy were analyzed for acquiring knowledge and defining the elements of the ontology. The number of elements is summarized in Table 1. The goal was to define generic classes that cover as many elements of the analyzed resources as possible to enhance interoperability, while maintaining a sufficient level of detail for the applications. Thus, the desired output was a domain-specific hierarchically structured knowledge graph.  
Figure 3: Pipeline of the methodology for ontology building.  
Figure 2: Example of an AGO Class definition in OWL.  
In general, these datasets do not define classes in a hierarchy but often constitute a one-level list. In addition, only two of the listed datasets cover a few classes related to maneuvers (Action classes in the AGO domain). However, actions are presented as special attributes of the corresponding objects. Due to the manner in which they are defined, these datasets do not consider the need of making a distinction between Action and Events. Moreover, they do not present semantically relevant relations among these elements and the objects. Taking into account that most of these datasets do not consider the traffic maneuvers, the \"_ALKS Regulation UN R157_\" ([[https://undocs.org/ECE/TRANS/WP.29/020/81](https://undocs.org/ECE/TRANS/WP.29/020/81)]([https://undocs.org/ECE/TRANS/WP.29/020/81](https://undocs.org/ECE/TRANS/WP.29/020/81)) accessed on 20 July 2021) was included in AGO so that the ontology gathers the critical scenarios listed in the regulation as Actions, along with some additional Object classes and properties. As a result, AGO is an ontology that offers flexibility for present and future labeling needs, supported by the established mechanism to connect Objects, Actions, and Events through Relations. AGO also includes, in its current form, a rich set of object classes, which are easily extensible, and an equivalently wide number of actions and maneuvers that can serve as a basis for multiple applications in the domain (e.g., test coverage analysis, online data recording, and digital twins).
## 7 Discussion  
There are many trade-offs in the design of the communication aspects of a VLP system using a QADA receiver and most of these are shared by VLC systems in general. However, there are also trade-offs between the communications performance and the positioning performance.  
Increasing the area of the PD increases both the received signal power and the shot noise power, and results in an overall improvement of SNR [34]. This increase in SNR is at the cost of a decrease in receiver bandwidth caused by the increased stray capacitance associated with the larger PD. Increasing the area of the PD will also increase the positioning accuracy [16]; however, a bulky receiver is generally not desirable in many applications. Our previous work in [24] provides further detail on other trade-offs related to positioning performance, as well as data on the positioning accuracy of the QADA receiver.  
Most modulation schemes that can be used in VLC can also be used in QADA-based VLP. There has recently been substantial interest in different forms of optical OFDM [41]. In the simulations we use 4-QAM ACO-OFDM which is a very power-efficient form of OFDM to show the robustness of the system. The data rate can be increased at the cost of increased BER simply by increasing the ACO-OFDM constellation size and in the experiments 16-QAM ACO-OFDM rather than 4-QAM had to be used before any bit errors were detected. Recently several new modulation schemes based on ACO-OFDM have been developed. These increase the bandwidth efficiency at the cost of power efficiency [42] and all of these could be used in a VLP system but could increase the signal processing complexity of both the transmitter and the receiver.  
The data rate required for indoor positioning is, in general, quite low. It depends on precisely what position information is transmitted by the luminaires. For example, whether the global coordinates of the luminaire are transmitted or simply the LED-ID. Another consideration is how often position updates are required: slow moving users require less frequent updates than faster moving users. In scenarios with multiple transmitting luminaires, the available bandwidth must be shared and the data rate available for each luminaire depends on the detailed design of the multiplexing system. In location-based services, where information is broadcast in addition to the positioning information, or where the VLP is combined with VLC, higher data rates may be required.  
There are many avenues for future research that can be investigated, including exploring methods capable of achieving higher data rates, evaluating the effect of movement on communications and positioning accuracy and testing the combination of a QADA with other technologies for improved positioning.
# A Simplistic Downlink Channel Estimation Method for NB-IoT
## 1 Introduction
### Contributions  
In this paper, a channel estimation method is proposed for NB-IoT downlink. The method features a simple approach for populating channel coefficients based on pilot estimates. Sparse NRS pilots are estimated using the conventional LS algorithm. Next, the channel coefficients for all resource elements in a transmission frame are calculated through interpolation in the time (symbol) domain, followed by either interpolation or averaging in the frequency (subcarrier) domain. The two approaches in the frequency domain are compared to evaluate their performance in fading channel conditions. This paper evaluates the SNR performance of the proposed scheme to verify that it does not have a higher level of channel estimation error compared to the method that uses a more complex interpolation scheme. Moreover, the computational complexity was investigated by comparing the processing delays among methods. The main objective of this paper is to determine whether a simplified channel coefficient population scheme has a negative impact on the quality of channel estimation in a narrowband frequency channel designated for NB-IoT downlink.  
It is worth noting that this paper presents the results obtained by recording actual NB-IoT signals generated by a professional radio communication tester. This is in contrast to other papers on this topic, which often focus solely on theoretical analysis and software simulation results. Using a hardware testbed requires a modified channel estimation evaluation methodology because the values of the original channel coefficients are not accessible. Two performance indicators are introduced in this paper to evaluate channel estimation. These indicators rely solely on information recovered from received signals.
## 2 Materials and Methods
### Random Forest Algorithm  
This study employed the Random Forest (RF) classifier, a robust and widely used machine learning algorithm, for the classification of segmented image objects. The Random Forest algorithm has been proven to be a reliable and accurate classification technique in numerous remote sensing applications [36; 37; 38; 39; 40]. It was selected for this study due to its ability to handle high-dimensional data effectively and its capacity to assess feature importance.  
Random Forest is an ensemble machine learning method designed to enhance the performance of Classification and Regression Trees (CARTs). It operates by constructing multiple decision trees and aggregating their outputs. In a classification analysis, each tree casts a vote for the most probable class of the input data, with the final classification determined by a majority vote. The RF algorithm employs two key techniques: bagging and random subspace selection. The process begins with the creation of multiple binary classification trees (_ntrees_) using bootstrap samples drawn with replacements from the original dataset [41]. For this study, the _ntrees_ parameter was set to 100 trees in classification mode, balancing computational efficiency with classification accuracy.  
An important feature of the Random Forest algorithm is its use of out-of-bag (OOB) samples. These are observations not included in a particular bootstrap sample during the tree-building process. OOB samples serve a dual purpose in the RF model: they help estimate the misclassification error, provide an internal validation mechanism, and assist in assessing the importance of different variables in the model.
# End-to-End Multi-Modal Speaker Change Detection with Pre-Trained Models
## 3 Approach
### Multi-Modal SCD Model
#### 3.2.1 Audio Feature Extraction  
In the model, audio features are extracted using the pre-trained Wav2 Vec2 encoder, which transforms raw audio signals into contextualized hidden representations. Given a 1 s raw audio waveform segment represented as a vector \(\mathbf{X}\in\mathbb{R}^{L}\), where \(L\) is the number of audio samples (e.g., \(L=16{,}000\) for 16 kHz sampling), we pass this input through the Wav2 Vec2 encoder to obtain a sequence of feature vectors:  
\[\mathbf{z}^{\text{audio}}=\text{Wav2 Vec2}(\mathbf{X})\in\mathbb{R}^{T\times d} \tag{1}\]  
where \(T\) is the number of time frames after subsampling by the encoder, and \(d=768\) is the dimensionality of each feature vector.  
To enhance this representation, we further apply a self-attention mechanism that enables the model to re-weight the importance of different time steps based on their relevance to speaker change detection. The attention-enhanced audio representation is computed using the output of Equation (1):  
\[\mathbf{h}^{\text{audio}}=\text{SelfAttention}(\mathbf{z}^{\text{audio}}) \in\mathbb{R}^{T\times d}, \tag{2}\]  
To convert the temporal sequence \(\mathbf{h}^{\text{audio}}\) into a fixed-size vector suitable for classification, we apply mean pooling across the time dimension to the output of the attention layer:  
\[\mathbf{\bar{h}}^{\text{audio}}=\frac{1}{T}\sum_{t=1}^{T}\mathbf{h}^{\text{ audio}}_{t}\in\mathbb{R}^{d}. \tag{3}\]  
This final vector \(\mathbf{\bar{h}}^{\text{audio}}\) serves as the aggregated audio embedding, capturing both global and locally attended information from the original speech segment. It is used either directly (in audio-only mode) or concatenated with text embeddings (in multi-modal mode) for downstream classification.
## 5 Conclusions  
The fast and simple way to sense the incoming pulses by using a LA-based protocol was introduced in this work. To this end, different scenarios have been devised in order to test the functional characteristics of the proposed solution. The algorithm supervises the concentration of the received pulse, and it determines, based on a set of parameters, if it is an incoming message transmission. Injected with the capability of a learning automaton to adapt to the network changes, the proposed algorithm outperforms in bit error rate solutions with both fixed and adaptive threshold detection. The algorithm seems to have a consistent performance for various network configurations. More specifically, lower concentration pulses seem to fit better to its operation principles, as the time-on-air for these messages is lower that for messages with higher values of concentration. On the other hand, for high concentration pulses, the algorithm may require additional time for the reception process, yet it seems to have a more consistent outcome.  
In comparison with existing similar solutions, the LA has comparable performance to the established works related to high transfer data rates; however, as the time between transmissions increases, the algorithm is capable of eliminating any reception errors, as long as the channel characteristics remain the same. Then again, the algorithm can adapt to the channel changes due to the LA operation and can provide increased reliability and a higher transfer data rate.  
We should take into consideration a detail that was mentioned in the previous section about the ligand-receptor binding model. The transmitted message reaching the vicinity of the node should find a free receptor protein in order to bind and transfer its DNA to the receiver. If at the time of arrival at the node's membrane, there are no free receptors, then the signaling molecule will move away. As a result, the receiver node will not sense the increased density. As this communication model is based on the presence of free receptors in the cell's periphery, it is obvious that there are additional parameters that should be taken into consideration when designing communication protocols for biological nanonetworks.  
Take together, a multifaceted approach and a careful design is required for the development of communication protocols in biological nanonetwork, as there are different parameters that take part in the communication process. The algorithm in the present work helps to mitigate the effects of the volatile network environment of the biological nanonetworks.  
This research received no external funding.  
Not applicable.  
The author declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BNs & Biological Nanonetworks \\ LA & Learning Automaton \\ MC & Molecular Communication \\ BS & Base Station \\ \end{tabular}
# Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning
## 2 Prognostics Method Based on Manifold Learning and a Wavelet Neural Network
### High-Dimensional Feature Signal Extraction  
The fault prognostics of bearing faults can be considered as a map between two spaces where one is the fault feature set and the other is the state of faults. It is crucial to accurately find the corresponding fault features, i.e., feature extraction. Too many or too few features will produce difficulties in prognostics. In order to obtain fault features from nonlinear and unstable roller bearing vibration signals, traditional methods such as the Hilbert Huang transform, Fast Fourier Transformation (FFT), signal envelope, Empirical Mode Decomposition (EMD) and wavelet transform were taken to extract features from the monitoring time domain vibration signals. It is obvious that the feature signals extracted by these methods are redundant, nonlinear, and uncertain. Therefore, manifold learning is utilized to reduce dimensionality. Improved Laplacian Eigenmaps (ILE) are proposed when considering the local field selection of manifold learning.
# A Review of Physical Layer Security Techniques for Internet of Things: Challenges and Solutions
## 2 Literature Review of PLS Techniques for IoT
### PLS Schemes for IoT: State of the Art
#### 2.2.3 Bit Flipping  
The bit flipping technique is mainly applicable to securing the communications between the massive sensor nodes and the legitimate fusion center (LFC). In this approach, sensor nodes are divided into two groups (a strong group and a weak group) based on the strength of their channel gains to the LFC. The sensors with worse channel qualities, which are categorized into the weak group, are required to send the bit-flipped data, i.e., false data, to interfere with the eavesdropping fusion center (EFC), while the sensors with better channel qualities (i.e., those belonging to the strong group) are utilized to send the information-bearing data. Due to the statistical independence between the legitimate channel and the wiretap channel, with high probability, the received SNR at the EFC is much lower than that at the LFC, thus yielding a significant performance degradation at the EFC. Ref. [42] proposed a thresholding based bit flipping scheme. In this scheme, LFC first broadcasts two thresholds \(\tau_{\mathrm{s}}\) and \(\tau_{w}\) to the sensors, which then compare their channel gains with the thresholds \(\tau_{\mathrm{s}}\) and \(\tau_{w}\) to autonomously classify themselves into strong or weak groups. During actual data transmissions, the sensors that belong to the strong group (i.e., those satisfying \(|h_{i}|^{2}>\tau_{\mathrm{s}}\)) transmit the real data, while the sensors that belong to the weak group (i.e., those satisfying \(|h_{i}|^{2}<\tau_{w}\)) send the bit-flipped false data to confuse the EFC. Following the work in [42; 43], a three-threshold transmission scheme was further proposed where all the sensors are divided into three groups. Besides the sensors transmitting the real data and the false data, there are also some other sensors that remain silent during the transmission procedure. Compared with the scheme in [42], the three-threshold method in [43] further improves the energy utilization efficiency under the total energy constraint.
## 3 Results and Discussion
### Accuracy of the Numerical Solution
We demonstrate more than three times improvements of computational efficiency compared to the standard approach.
# Social Milieu Oriented Routing: A New Dimension to Enhance Network Security in WSNs
## 3 Algorithm Description
### Quantum Annealing (QA)  
In statistical mechanics, a physical process called annealing is often performed in order to relax the system to the state with the minimum energy. In the basic form of simulated annealing (SA), it first generates an initial solution as the current feasible solution using the Metropolis algorithm [20]. Then, another solution is selected in the neighborhood of the current solution and replaces the current solution with the new one with the following transition probability given by the Metropolis criterion. With the temperature decreasing, only the better deterioration configuration can be accepted. Simulated annealing is a particularly promising minimization technique. It has, for example, proved effective in finding the global minimum of multidimensional functions having large numbers of local minima [21]. However, it is worth mentioning that introducing thermal fluctuation is not the only way to perform annealing. QA depends on quantum fluctuation instead [22]. A prominent advantage of quantum fluctuation over thermal fluctuation originates from the fundamental property of the quantum theory, namely, the possibility of tunneling through classically impenetrable potential barriers between energy valleys. Consequently, methods of quantum search, in principle, could be more efficient than the classical search methods [23]. Furthermore, the effect of quantum tunneling is shown to be crucial for solving many computationally difficult problems, including the class of non-deterministic polynomial time problems. A practical implementation of QA will need to solve the time-dependent Schrodinger equation in a very large and exponentially growing Hilbert space, which can only rely on a robust quantum computer. Recent research in this area has been carried out by the configuration path-integral Monte Carlo (CPIMC) approach [13] using quantum-classical mapping with the aid of a Suzuki-Trotter transformation [24; 25; 26], inspired from which we apply QA to the OSTP selection in WSNs. To the best of our knowledge, this is the first application of QA to this problem and experiment results show QA based searching algorithm has better performance than its heuristic opponent does.