esa-sceva/satcom-chunk-collection huggingface dataset:
# Intrinsic Interference Use for FBMC-IOTA Systems
## 2 MAP Detection on Weight Matrix
According to [PERSON] rule, we have  
\[p(\mathbf{a}|\mathbf{p})\propto p(\mathbf{p}|\mathbf{a})Pr(\mathbf{a}) \tag{8}\]  
where \(Pr(\mathbf{a})\) is the joint a priori PMF of all symbols assuming that they are independent to each other. Therefore, the estimation function can be modified to  
\[\hat{a}_{k,l}=\arg\max_{a_{m,n}\in\mathbb{X}}\sum_{\mathbf{a}\in\mathbb{X}^{N,L}}p (\mathbf{p}|\mathbf{a})\prod_{\mathbf{a}\in\mathbf{a}}Pr(a) \tag{9}\]  
As can be seen from (9) this computation includes a marginalization process which is NP-hard [20]. Furthermore, with the assumption that the noise vector is identically and independently distributed (i.i.d.) and is uncorrelated with the transmitted symbols, we can factorize  
\[p(\mathbf{p}|\mathbf{a})=\prod_{k^{\prime},l^{\prime}}\ p(P_{k^{\prime},l^{\prime}}| \mathbf{a}) \tag{10}\]  
Please note that \(P_{k,l}\) is the parity symbol related to the symbol to be detected. Therefore, in this equation a general form of \(P_{k^{\prime},l^{\prime}}\) is applied. Since a limited number of symbols interfere on each parity symbol, the calculation of this function can be simplified as  
\[p(\mathbf{p}|\mathbf{a})=\prod_{k^{\prime},l^{\prime}}\ p(P_{k^{\prime},l^{\prime}}| \mathbf{a}^{[k^{\prime},l^{\prime}]}) \tag{11}\]  
where \(\mathbf{a}^{[k^{\prime},l^{\prime}]}\) is the vector of symbols that interfere on the parity symbol with indices \(k^{\prime},l^{\prime}\). Substituting (11) into (9) yields  
\[\hat{a}_{k,l}=\arg\max_{a_{m,n}\in\mathbb{X}}\sum_{\mathbf{a}\in\mathbb{X}^{N,L}}Pr (\mathbf{a})\prod_{k^{\prime},l^{\prime}}\ p(P_{k^{\prime},l^{\prime}}|\mathbf{a}^{[ k^{\prime},l^{\prime}]}) \tag{12}\]From (12), we can see that the FBMC-IOTA structure allows us to translate the MAP detection problem into the marginalize product of functions (MPF) problem. The local observation at parity symbol \(P_{k^{\prime},l^{\prime}}\) is given by  
\[p(P_{k^{\prime},l^{\prime}}|\mathbf{a}^{[k^{\prime},l^{\prime}]})=\frac{1}{\sqrt{2 \pi}\sigma}\exp\left(\frac{1}{2\pi\sigma^{2}}\left\|P_{k^{\prime},l^{\prime}}- \mathbf{a}^{[k^{\prime},l^{\prime}]}\mathbf{s}^{T}\right\|^{2}\right) \tag{13}\]  
where \(\mathbf{s}\) is the vector of calculated weights according to (3) and (4).
## 2 Background
### Energy Consumption Forecasting
They are also capable of handling non-linear relationships between input and output variables, which is important in energy forecasting, where the relationships may be complex. Finally, LSTMs could process sequential data of varying lengths, which is useful for handling variable-length TS data in energy forecasting.
# A Design of a Time Synchronization Protocol Based on Dynamic Route and Forwarding Certification
## 5 Analysis of the Simulation and Test
### Field Test on an Embedded Linux Platform  
The field test used nine embedded Linux platforms as nodes, and carried out the porting of DRFC-TSP on the platform. The field tests on the multi-hop error accumulation, synchronization accuracy, and adaptability of the protocol to different topologies were evaluated. The frequency offset of the nine nodes relative to node 0 is shown in Table 2.
## 2 Discussion
### Neurobiological, Psychosocial, and Behavioral Components of Clinical Practice
#### 2.4.2 Specific Examples of Neuroscientific Findings  
Although a \"fledgling\" scientific field, specific findings will be enumerated, which serve as springboards for ongoing research:  
* The thickness of the left caudal anterior cingulate cortex was found to be inversely correlated with patients' trust in physicians using structural magnetic resonance imaging (MRI) [38]. Furthermore, using interactive functional near-infrared spectroscopy of both physicians and patients exhibiting a high level of trust in each other revealed increased inter-brain synchronization in the bilateral tempo-parietal junction and right inferior frontal gyrus [39].
* Functional MRI (fMRI) neuroimaging studies of physicians administering anesthesia to patients undergoing experimentally induced pain revealed the activation of the medial frontal brain regions [40]. More recent similar studies by the same group demonstrated the activation of additional brain regions of the physicians, including the right ventrolateral and dorsolateral prefrontal cortices [41].
* More intricate recent studies were conducted, consisting of interacting whole-brain mapping and fMRI of physicians administering anesthesia to patients during experimentally induced acute and repetitive (chronic) pain stimuli. Significant brain-to-brain concordance with the dynamic coupling of brain nodes was demonstrated among physicians and patients (both of which with previously established rapport) [42].
* As per a systematic review of fMRI studies, the recently discovered mirror neuron system of specific areas of the brain appears to be important in establishing the basis for empathy: ventral premotor cortex, parietal and somatosensory areas, and limbic and paralimbic structures [43]. The mirror neuron system is a group of specialized neurons that fire when an individual is performing an activity, and it also fires in the same pattern when the same individual observes another person performing the same previous activity (as if the observer was performing the activity again). This neuronal system is also implicated in neurocognitive functions and neuropsychiatric disorders [44].
* The neuroendocrine system has also been implicated in correlates in the physician-patient relationship as manifested by fluctuations in the secretion of stress hormones (cortisol and epinephrine) as opposed to those secreted during \"peaceful\" activities (oxytocin) [45]. In fact, field-labeled socio-physiology is developing, which involves the associations between social behavior and physiology in multiple areas of medicine.
## 5 Simulation  
### Localized Algorithm Simulation  
A flow diagram of the sensor node localization is illustrated in Figure 7. Initially, the type of node distribution is chosen as per the application, which may be uniform or random. Furthermore, node density is determined to check the number of nodes present in the area (measured in square meters). The algorithm computes these input data and results in sensor node distribution patterns. After confirming the communication link among the sensor nodes, a hybrid range-based localization technique was chosen because it is more effective. Localization is crucial to determine the sensor node target tracking location. Here, we first grouped the algorithms into free range-based and hybrid range-based. Furthermore, we analyzed the suitable localization techniques. Range free-based localization is preferred because of its low power consumption, whereas hybrid range-based localization is preferred depending on the applications. Because the land is not uniform in nature, hybrid range-based localization is widely preferred in agriculture. Most IoT-based applications require sensor node localization because these applications are easy and convenient to monitor. Simulation studies were carried out to compute the localization of algorithms. The effect of the node density, data rate, and signal strength was analyzed to develop the optimal algorithm for our application. Furthermore, we undertook a comparative analysis of the range-based agriculture applications, and concluded that range free-based localization was more suitable.  
Figure 6: Block diagram of IoT-based farm automation using a solar panel.
## 5 Numerical Results
### Performance Evaluation in a Multiple-EHD Scenario
#### 5.2.2 Performance Evaluation in a Dense Urban Environment  
Next, we evaluate our proposed methods in a Dense Urban environment with \(r_{\max}=100\) m and \(r_{\max}=150\) m, respectively.  
Figure 7(a),b exhibit a comparison of the average overall charging time and the average execution time of various methods when \(r_{\max}=100\) m. It is shown that proposed method #1 achieves a near-optimal charging time over the entire range, while significantly reducing the execution time compared to the optimal design. Moreover, proposed method #2 achieves the shortest execution time among all methods, while outperforming the conventional methods in terms of overall charging time. However, it results in a longer charging time than proposed method #1. In comparison to the charging time performance when \(r_{\max}=100\) in the Urban environment, as shown in Figure 4(a), the overall charging time increases in the Dense Urban environment. The reason is that the LoS probability decreases in the Dense Urban environment compared to the Urban environment. On the other hand, Figure 7(b) shows that the average execution time in the Dense Urban environment demonstrates similar performance to that in the Urban environment, as depicted in Figure 4(b). This result confirms that the proposed methods reduce computational complexity compared to other methods, even in the Dense Urban environment.  
We also evaluate the various methods when \(r_{\max}=150\) m. Proposed method #1 achieves a significantly reduced charging time and yields a much shorter execution time compared to the optimal design. Also, the shortest execution time for all methods is achieved using proposed method #2. However, proposed method #2 is outperformed by the optimal EB with \(H_{\max}\) in terms of overall charging time when the number of EHDs exceeds 12. Figure 8(a) also shows that the overall charging time increases in the Dense Urban environment compared to the charging time performance when \(r_{\max}=150\) in the Urban environment, as depicted in Figure 6(a). Notably, Figure 8(b) shows that the average execution time in the Dense Urban environment demonstrates similar performance to that in the Urban environment, which verifies the robustness of the proposed methods in terms of complexity reduction.  
Figure 7: Performance comparison of various methods when \(r_{\max}=150\) m in an Urban environment.  
These results verify that our proposed methods significantly outperforms conventional methods in terms of overall charging time, while achieving near-optimal performance and substantially reducing execution time across various practical scenarios.
## 4 A Distributed Mapping Service
### An Algorithm for Distributed Map Construction
#### 4.1.4. Dynamically Update the Partial and Complete Maps  
In many real-world applications of WSNs, the sensed modalities are mostly unchanged over time [5]. One method for updating maps in the presence of such changes is for each node to use the Map Generation service to build a local accuracy model, which can be used by the node to base its decisions of reporting a change in its local map to its cluster head. The local accuracy model is defined such that a node is able to estimate its own reading using other observation points within some level of tolerance. This level of tolerance can be derived from the global cluster map accuracy. When the accuracy level changes sufficiently, the sensor node saves the new calculated map using the most recent readings and it reports these changes to its corresponding cluster head. If several updates arrive at the cluster head about the same time, they are grouped into one update transaction before being sent to the sink. Algorithm 1 outlines this approach.  
```
1. IF node is a sensing node THEN
2. create a local accuracy model
3. classify each new reading according to the local accuracy model
4. IF local accuracy deviates THEN
5. update the local map
6. forward the new reading to its cluster head
7. END IF
8. END IF
9. IF node is a cluster head THEN
10. WHEN receive update message
11. update local map
12. send the update message to the sink
13. END IF
14. sink combines up-to-date local maps
```  
**Algorithm 1** Incremental update of local and global maps.  
Figure 2 shows how the proposed approach operates on a generic WSN. Sensor nodes at the bottom layer of the network collect data from their neighbours to build their support set, which is fed into the mapping services to generate the nodes local view. Then, sensor nodes forward their mapping data to their cluster head, where it is merged to generate the cluster view. Maps from all clusters are merged at the sink to generate the global map. Maps at different levels are updated progressively from bottom all the way up to the sink.
# Assessment of Interventions in Fuel Management Zones Using Remote Sensing
## 3 Results
### Estimation of FMZs Intervention Date  
This work aims to classify if FMZs are treated and maintained but it is also interesting to establish when those same FMZs are treated. For this purpose, an extra experiment was carried out to try to estimate the dates on which interventions in FMZs occurred. To achieve this, some clusters sections were selected for further analysis. These sections were treated at the same time of the year, but are part of two different locations: Site A (Figure 13(a)) and Site B (Figure 13(b)).  
These FMZs in particular were selected due to their official data not being yet available by the Macao City Hall, and because the intervention of FMZs around roads is more easily noticeable in satellite images. As it can be seen in Figure 15, for Site A, the NDVI values before and after intervention have drastic changes. The same can be observed for Site B in Figure 16. A simple threshold value was used as the classifier using the temporal pattern \(\Delta dif_{ NDVI}\) (Equation (4)) which calculates the  
Figure 14: Sections used to estimate the intervention date.  
Figure 13: F1-score values for all algorithms and data sets using Sentinel-1 and Sentinel-2 data.  
difference of the \(diff_{NDVI}\) (Equation (2)) in two consecutive dates. By establishing a threshold of \(l=0.10\), an FMZ is assumed to be treated when \(\Delta{\it{if}}_{NDVI}>l\). This index represents a sudden drop of the FMZ NDVI values in consecutive months, while the values on the outer buffer have little variation.  
The results for both sites are presented in Figure 17. In both cases, most of the estimated intervention dates correspond to the manual observed date, only failing in 3 cluster sections out of 28. However, for several sections, there were no results. In sum, 16 sections were estimated correctly, 3 incorrectly and 9 had no attribution of an estimated intervention date (i.e., \(diff_{NDVI}=<0.10\)).  
Figure 16: NDVI before and after the intervention for Site B. (a) NDVI on 15 May 2018. (b) NDVI on 19 June 2018.  
Figure 17: Intervention date forecast results for Site A (a) and Site B (b). Presented in red where the estimate was wrong, in green where it was correct, and in black which did not obtain results.  
Figure 15: NDVI before and after the intervention for Site A. (a) NDVI on 15 May 2018. (b) NDVI on 19 June 2018.
## 3 Results and Discussion
### Comparison with the Oil Boiler System
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Athens**} & \multicolumn{2}{c}{**Thessaloniki**} \\ \cline{2-5}
**Parameter** & **Heat Pump** & **Oil Boiler** & **Heat Pump** & **Oil Boiler** \\ \hline Heating load (kWh/year) & 6785 & 6785 & 12,591 & 12,591 \\ Electricity demand (kWh/year) & 3189 & - & 6299 & - \\ Oil demand (kWh/year) & - & 9046 & - & 16,965 \\ Primary energy demand (kWh/year) & 9247 & 9951 & 18,267 & 18,661 \\ Mean yearly COP & 2.128 & - & 2.013 & - \\ LCC (EUR) & 18,196 & 24,644 & 33,055 & 46,216 \\ Lifetime CO\({}_{2}\) emissions (kgCO\({}_{2}\)) & 38,823 & 59,706 & 76,690 & 111,968 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the performance of the two heating systems for both locations. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Athens** & **Thessaloniki** \\ \hline Lifetime primary energy reduction (kWh) & 17,588 & 9857 \\ \hline Lifetime net savings (EUR) & 6448 & 13,161 \\ \hline Simple payback period of the investment (years) & 8.03 & 6.11 \\ \hline Lifetime CO\({}_{2}\) emissions reduction (kgCO\({}_{2}\)) & 20,882 & 35,278 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Final assessment of both technologies. Thus, it is strongly recommended to install high-temperature heat pumps for heating purposes due to the multiple advantages compared to conventional systems. Moreover, the use of advanced heat pump cycles with internal heat exchangers, ejectors, etc. can improve the COP and lead to greater benefits. Also, the incorporation of solar thermal systems and/or geothermal energy into the system can make it more efficient, and this fact has to be examined with the aim to achieve higher performance.
## 2 Material and Methods
### Feature Extraction and Selection  
Recently, efforts have been made to automate time-series feature extraction methods and calculate hundreds of different features [17; 36]. However, these high dimensional features lead to challenges when calculating, predicting, storing, and even understanding the correlation of data with the target/outcome [37]. A common technique used for time-series feature extraction is windowing (data are divided into smaller windows and features are extracted for each window) [38]. Features such as mean can reduce signal noise, but averages the overall signal. On the contrary, some features such as maximum can be unduly affected by noise [36]. As automated feature extraction has limitations and motivates the need for a systematic process of feature selection along with feature extraction [12], this study implements a three-stage feature extraction and selection algorithm. Each stage is explained in the following subsection, illustrated in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Time Series Signals** & **Data Points** & **Recording Time** \\ \hline Blood Volume Pulse (BVP) & 212,234 & \\ \hline Heart Rate (beats per min) & 3308 & \(\sim\)50 min \\ \hline Respiratory Rate (breaths per minute) & 3308 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The average number of entries (per participant).  
Figure 2: Participants with increased stress levels during each task.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 6 Evaluation
### Limitations and Future Directions  
In the current implementation of our proposed protocols, use cases in the area of distributed systems and network scenarios with congestion or high mobility are not currently considered. While the proposed protocols are designed for adoption in various use  
Figure 14: Live streaming frame retransmission rates: (**a**) PQC–AES-256 (with F512). (**b**) PQC–AES-256 (with F1024).  
Figure 13: Live streaming total throughputs: (**a**) PQC–AES-256 (with F512). (**b**) PQC–AES-256 (with F1024).  
cases, extensive adaptation to mitigate challenges common in these areas requires further study to ensure correct implementation and migration of applications associated with such scenarios to the quantum-secure protocol. To further improve our proposed protocols, as part of our future efforts, we will consider expansion to various systems such as distributed architectures, network scenarios with high overhead impacts, and mobility.  
Furthermore, while our current implementation ensures MAC synchronization and consistency between the client and the server, formal modeling remains an open issue. In future work, we will consider the use of formal verification tools to analyze the robustness of MAC synchronization in scenarios where delays, packet loss, or concurrent events may occur. Additionally, we will expand the proposed protocols to include additional network environments and platforms, including energy consumption analysis on ARM and other IoT-specific hardware. Moreover, the current implementation of the proposed protocols focuses on the application layer and does not directly address side-channel attacks at the lower levels. A detailed side-channel analysis using tools such as ChipWhisper and constant-time execution testing in embedded scenarios is necessary and will be considered as part of the expansion of the proposed protocols' implementation.
# An Adversarial DBN-LSTM Method for Detecting and Defending against DDoS Attacks in SDN Environments
## 4 Experimental Results and Discussion
### Experimental Setup  
To improve the performance of the adversarial DBN-LSTM anomaly detection module, we evaluate the impact of the minibatch size, the learning rate on the DBN algorithm, and the number of layers of LSTM in the Classifier model. As for the GAN model, part parameters were set as dropout with 0.1, the learning rate with 0.001, and the optimizer with Adam. These parameters can achieve quite good performance.  
Firstly, we set six different minibatch sizes to evaluate the length of epoch required for the convergence of different sizes. As shown in Figure 4, when we set the minibatch size to 32, we can obtain convergence with 86 batches. The convergence speed is superior to other minibatch sizes.  
Then, we compare the effect of the learning rate on the convergence training time. As shown in Figure 5, we can obtain the best result when we set the learning rate as 1.00. Therefore, the learning rate should be set as 1.00.  
Finally, we need to determine the effect of an LSTM structure with different layers on the results. We selected LSTM structures with two, three, and four layers for a performance test in the rate of decline of training loss and the training accuracy of discrimination. As shown in Figures 6 and 7, four layers of LSTM obtain the best performance.  
To avoid the difference between the training and test datasets, we compare the accuracy of LSTM structures with different layers in the test dataset. As shown in Figure 8, four-layer LSTM obtained higher accuracy than other LSTM structures. Therefore, we use a four-layer LSTM to construct the Classifier model with DBN.