esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things for Modern Energy Systems: State-of-the-Art, Challenges, and Open Issues
## 3 IoT Challenges in Energy Systems
### Big Data (BD) Processing  
As the amount of information becomes large in an IoT-based system, data handling with traditional methods becomes impractical. BD analysis is one of the main challenges in IoT systems, as systems should be able to store data, analyze it, and plan for future, based on the present and the past data. In designing algorithms to deal with this condition, uncertainty and lack of data should also be considered. On the other hand, data is collected from different sources, and BD analysis in real time is a key to apply IoT systems in real world situations [94]. Therefore, modern methods with specific architecture are needed for BD processing. In this regard, some toolboxes such as toolboxes in Matlab are developed to deal with BD [95; 96]. One solution to deal with BD is using localize processing of data. In this regard, devices are aware of the state of the main server and their neighbors, which makes it possible to save more network bandwidth. In addition, by using localized algorithm, it is possible to deal with BD, as data mostly process locally [94].  
In respect of BD analysis, as the system enjoys more detailed information, data processor systems are able to analyze information in more detail. On the other hand, system security will be affected by the large amount of information [97]. Despite challenges introduced by BD analysis, advanced machine learning algorithms can enjoy large scale information for better training in their process [95; 96; 98].  
Assume that there is an Advanced Metering Infrastructure (AMI) to collect data of the system. The very first step after gathering the data is to use a method to extract information. This can be done through cluster-based computing systems. For example, in 2004, Google introduced a solution based on the Google File System, which used different methods to process data. In 2012, Hadoop with an Apache license introduced another method to deal with BD, and used Spark SQL as a database system. Later, this BD processing framework became the most dominant one [99].  
After useful knowledge extraction from BD, the system should be able to further classify the information and provide certain services (such as sending signals to the actuators or alerting systems). As an example, in energy systems, data collected from electricity and heat consumption can be subjected to consumers' type like industrial and residential, and each of the consumption level can be further classified into off, standby, and active operating mode. Based on this argumentation, a BD architecture is introduced in [100].
## 4 Implementation
### BER Estimation for Optical OOK Method  
In this communication modulation process, the received electrical signal can be calculated as [26]  
\[r(t)=I(t)+\sum_{i=-\infty}^{+\infty}I(t)a_{i}g(t-iT_{s})+n(t), \tag{4}\]  
where \(a_{i}\) is the level of the \(i\)-th symbol and \(a_{i}\)\(\in\) {\(-\)1,1}; the transmission probabilities of bit \(-\)1 and 1 are \(P_{0}\) and \(P_{1}\), respectively, \(g(t)\) is the rectangular pulse shape function and \(T_{s}\) is the symbol time. When there is no turbulence, and only Additive white Gaussian noise (AWGN) is present, the BER can be written as: [27]  
\[P_{e}=\frac{1}{2}erfc(\sqrt{\frac{E_{b}}{2\sigma_{n}^{2}}}). \tag{5}\]  
As the transmitted signal corresponding to bit \"0\" is zero, the received signal for this bit will only have AWGN. However, both turbulence noise and AWGN are present for a bit \"1.\" Therefore, the signal currents for bits \"0\" and \"1\" can be written as:  
\[r(t)=\left\{\begin{array}{l}n(t),a_{i}=-1\\ 2I(t)+n(t),a_{i}=1\end{array}\right.. \tag{6}\]  
Figure 6 shows the bit error probability of Optical-On Off Keying(O-OOK) modulation versus pixel E\({}_{\text{b}}\)/N\({}_{0}\) based on the AWGN channel. The paragraph illustrates that O-OOK schemes require a pixel E\({}_{\text{b}}\)/N\({}_{0}\) of at least 11 dB to achieve BER of \(10^{-4}\). Comparing with the theoretical pixel E\({}_{\text{b}}\)/N\({}_{0}\) calculation in Figure 2 and the result of experimental SNR as shown in Figure 6, the proposed scheme can be gained BER of \(10^{-4}\) at distance of 10 m or more by controlling suitably the exposure time of the camera. However, as mentioned below, the SNR can be improved by changing a longer exposure time, however, it causes the reducing of the communication bandwidth. From that, the setting of exposure time needs to be considered carefully to achieve better performance.  
Figure 6: Bit error probability curve for on-off keying (OOK) modulation.
# Analytical Model and Feedback Predictor Optimization for Combined Early-HARQ and HARQ
## 1 Introduction
In [14; 15], prediction schemes in appliance with turbo decoder are presented and investigated from the point of false prediction rates. In [16], the authors introduce the prediction algorithm, which exploits the substructures of the Low-Density Parity-Check (LDPC) codes to start feedback calculation already on partially received codewords, and therefore, provides an excellent opportunity to send ACK/NACK report before the full reception of the codeword. The BER estimations are corrected by including the results of a few LDPC-based decoding operations performed on the partial codewords. In our previous work in [17], we address this problem by proposing an analytical model that studies e-HARQ prediction computed on half of the actual codeword and present comparative analysis of various performance measures. However, we did not investigate the role of a threshold parameter, which is known to be of critical importance for the given e-HARQ schemes. Moreover, by analyzing only the case in which the prediction is performed on half of the codeword, we could not get full picture of the given approach. Other research papers, such as [18; 19], investigate ACK/NACK prediction schemes based on channel state estimation. The contribution of this paper is twofold. First, we propose a new two-phase analytical model in discrete time, which evaluates the behavior of a e-HARQ prediction performed for any partially received codeword in a 5G transmission. The model allows obtaining the main performance measures, including the misprediction errors. Second, we formalize the optimization problem to find the optimal values used in a predictor's classification, including a threshold value, a packet transmission length, and a length of the codeword used for prediction. By performing the substantial comparative analysis for various transmission and prediction lengths, we show the maximum gain that can be achieved in terms of performance measures by using e-HARQ prediction in comparison with the traditional HARQ approach. To the best of our knowledge, this is the first time an analytical model that covers parallel processing of e-HARQ and HARQ mechanisms hasbeen proposed. Note that the given model can be applied also to other e-HARQ prediction schemes besides the LDPC-based one. The paper is organized as follows: in Section 2 we firstly detail e-HARQ and HARQ mechanisms, introduce the basic model assumptions, and then present a system model with an incorporated e-HARQ and HARQ schemes, derive its balance equations and performance measures. Section 3 presents numerical results, including validation of the model and comparative analysis of schemes for various simulation assumptions. Moreover, we provide results of the optimization analysis, and the best achievable gains that can be found in terms of main performance measures in comparison with traditional HARQ approach without prediction. Finally, Section 4 concludes the paper.
# A Survey of Ledger Technology-Based Databases
## 4 Permissioned Blockchain Based Databases
### FalconDB
#### 4.3.2 Transaction Processing  
FalconDB is incentive model-based and creates the opportunity for low hardware requirement clients to use an honest service in which servers are incentivised to provide services and penalised for dishonest behaviour. At the core of this model are smart contracts (e.g., on Ethereum [44]). Clients and servers can communicate with one another and make deposits through these contracts. As a result of the incentivised model, either the client or the server must pay a fee, or if a server is malicious, all funds in the smart contract account will be lost. There are two key aspects to the smart contacts:  
* Service fee contract: a server receives a fee from the client to process queries and requests.
* Authentication contract: for the duration of this contract the server's account is temporarily frozen until it shares proof regarding the requested smart contract.  
The architecture of FalconDB can be split into two layers. These are the authentication layer and the consensus layer. To better understand the basics of FalconDB, let us take a look at the details of a sent query through the layers.  
**Consensus layer:** Queries are sent from a client to the server nodes via the service fee smart contract. The server account receives a fee for the processing of the queries. Immediately upon executing the query, the result is returned to the client. After the agreement through the BFT consensus protocol, the new block will be committed in the blockchain, and the new generated digest is available for all clients to update their own digest.  
**Authentication layer:** As has been mentioned, clients have the opportunity to use the authentication contract to be reassured of the success of the request. However, in this case, an extra fee must be paid to the server from the client. Thus, ADS proof is generated by the server to validate the digest by the client.
# Multidisciplinary Approach to Characterizing the Fingerprint of Italian EVOO
## 2 Results and Discussion
### Oil Chromatograms
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Rt** & **Name** & \begin{tabular}{c} **Western Garda** \\ **EVOO 1** \\ \end{tabular} & \begin{tabular}{c} **Western Garda** \\ **EVOO 2** \\ \end{tabular} & \
\begin{tabular}{c} **Esatern Garda** \\ **EVOO 1** \\ \end{tabular} &
\begin{tabular}{c} **Esatern Garda** \\ **EVOO 2** \\ \end{tabular} \\ \hline
2.40 & 4,4-Dimethylboxazolidine & 0 & 0 & 0 & \(2.12\times 10^{4}\) \\
2.85 & 1-Fluorooctane & 0 & 0 & 0 & \(5.53\times 10^{5}\) \\
4.17 & 4-Allyl-5-furan-2-y1-2-4-dihydro-11,2,4|triazole-3-thione & 0 & 0 & \(0\) & \(1.43\times 10^{4}\) \\
4.37 & 3-S-Dimethyloctane & 0 & 0 & 0 & \(2.56\times 10^{5}\) \\
5.33 &
\begin{tabular}{c} Hepptlybenzene \\ \end{tabular} & 0 & 0 & 0 & \(3.39\times 10^{4}\) \\
7.89 & Cyclobut-1-enylmethanol & 0 & \(8.99\times 10^{4}\) & 0 & 0 \\
10.52 & trans-1,2-bis-(1-methylmethyl)cyclobutane & \(2.84\times 10^{5}\) & 0 & 0 & 0 \\
14.08 & 1-3 Cyclohex-1-y1-ethanone & \(1.21\times 10^{5}\) & 0 & 0 & 0 \\
18.12 & Methylhepthene & \(1.83\times 10^{5}\) & 0 & 0 & 0 \\
20.04 & 4-Butoxy-1-butene & \(1.47\times 10^{5}\) & 0 & 0 & 0 \\
24.52 & 2,6-Dimethyl-1,3,5,5-catetraene, \(E\),\(E\)- & \(1.45\times 10^{5}\) & 0 & 0 & 0 \\
25.15 & 1-Octa-3-olol & 0 & \(5.46\times 10^{4}\) & 0 & 0 \\
28.77 & 3,5-Octaiden-2-one, (\(E\),\(E\))- & \(1.95\times 10^{5}\) & 0 & 0 & 0 \\
30.80 & 1-Decay-d-araphiol & 0 & \(1.64\times 10^{5}\) & 0 & 0 \\
64.37 & \(n\)-Hexadecanoic acid & 0 & 0 & \(1.36\times 10^{5}\) & 0 \\
69.21 & Silver decanoate & 0 & 0 & \(1.03\times 10^{5}\) & 0 \\
76.49 & Cholester-7-en-39,5a-diol-6a-benzoate & 0 & 0 & \(2.60\times 10^{5}\) & 0 \\  & tert-butyldimethylsilyl & \(1.20\times 10^{5}\) & 0 & 0 & 0 \\
81.33 & 2-(2-(2-Butoxyoxy)ethoxy)acetate & \(1.20\times 10^{5}\) & 0 & 0 & 0 \\
84.29 & 1-(+)-Asorbic acid 2,6-dihexadecanoate & 0 & 0 & \(5.54\times 10^{6}\) & 0 \\
86.46 & Bis(1-chloro-2-propyl) (3-chloro-1-propyl)phosphate & \(1.84\times 10^{5}\) & 0 & 0 & 0 \\
90.55 & Octadecanoic acid & 0 & 0 & \(1.17\times 10^{7}\) & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of unique compounds for Garda PDO EVOOs, with their retention time (min) and areas of the peaks (arbitrary unit). list of compounds found exclusively in one of the analyzed samples since they are an index of the treatment of plants and the production process.
# CCAIB: Congestion Control Based on Adaptive Integral Backstepping for Wireless Multi-Router Network
## 4 Performance Evaluation  
To show the effectiveness and superiority of CCAIB, simulations are conducted. First of all, comparison between CCAIB and Hoo approaches proposed in [53] is conducted. Hoo is an effective tool to deal with external disturbance and uncertainty in time delay. When adopting Hoo algorithm in congestion control problems, the queue length of the router will be maintained at targeted level, which is implied by the asymptotical stability of the system, with minimum sensitivity to the fluctuation of the available link bandwidth [53]. A detailed description of this algorithm can be found in reference [53] and the references therein.  
Algorithm proposed in [53] is chosen as the comparison algorithm because it is also designed for wireless networks. Many of the classic congestion control algorithms have not been used for comparison because it makes little sense to compare algorithms designed for wired networks with algorithms designed for wireless networks. Besides, although there have been some literatures dealing with congestion control in wireless networks scenarios, in almost these studies, except [53] to the best of our knowledge, the wireless networks are modeled using linear model, which cannot reflect actual behavior and dynamics of realistic wireless networks with high fidelity. Besides, ref. [53] considers the wireless access networks consisting of only one bottleneck router and the model is constructed accordingly. In this work, the wireless network model is expanded to multi-router networks which consider the inter-relationships between connecting routers. Thus, the comparison is implemented only on a single router. Afterwards, CCAIB is simulated with multiple bottleneck routers and impact of network parameters on CCAIB is evaluated.
# A Feature-Oriented Reconstruction Method for Surface-Defect Detection on Aluminum Profiles
## 3 Proposed Methods  
The framework is divided into three parts, which are aluminum profile image adaptive preprocessing, essential feature learning, and surface-defect detection, as shown in Figure 1.  
1. Adaptive preprocessing of aluminum profile images: Images of aluminum profiles with different background colors, lighting, and placement angles are extracted by adaptive boundary extraction, removal of background colors and data normalization to obtain images with no background, only the main part of aluminum profiles, and uniform specifications.
2. Essential Feature Learning: Firstly, the boundary extraction, background removal, and data normalization operations are performed on the non-defective images aluminum profile dataset. Then these are cropped to 224 \(\times\) 224 specifications and input into the model one by one. The model performs feature extraction on them, removes the masked image blocks based on the randomly generated mask image (mask rate of 75%), and inputs 25% of the image blocks that are not removed into the encoder and decoder for prediction of the removed image blocks. Finally, the loss constraint is utilized to make the reconstructed image as consistent as possible with the input image. (c) Surface-defect detection: Firstly, the image to be detected is adaptively preprocessed to get the aluminum profile image with uniform specifications. Then it is cropped to obtain all of the area images, and the area images are input into the model one by one. The feature-reconstructed image is then compared with the area-feature image by mean structural similarity index measure (MSSIM) comparison to determine whether the input image is a defective image. Finally, the detection results of all area images of the image to be detected are used to determine the final detection results.
# Coherent Chaotic Communication Using Generalized Runge-Kutta Method
## 3 Investigation of Chaotic Communication System with \(\alpha\)-Based Signal Modulation
### Quantified Return Map Analysis (QRMA)  
This subsection describes the evaluation of the secrecy of the designed chaotic communication system using specialized analysis methods and software. The underlying algorithm is a further development of the well-known return map analysis method. Let us apply the QRMA method that was described in [43]. To evaluate the secrecy of transmission using this method, one should find the values of the local maxima of the signal amplitude and determine the intervals between them. One point on the desired return map can be obtained by using a pair of values: the value of the maxima and the interval between the two peaks. The first maximum is omitted. Figure 13 displays the signal with the marked maxima.  
The return maps of the chaotic signal for PM for the third experimental case without noise in the communication channel are in Figure 14. The return maps of the parameters differ from each other, indicating that this set of parameters lacks confidentiality.  
Figure 12: BER vs. SNR estimation.  
Figure 13: Signal with mapped maximums and inter-peak intervals.  
The following formulae are used to calculate the difference between two return maps:  
\[\theta(x)=\begin{cases}x&x\geq\epsilon;\\ 0&x<\epsilon.\end{cases}\epsilon\in\mathbb{N}_{1}; \tag{13}\]  
\[\Delta_{i,j}=|X_{i,j}-Y_{i,j}|\cdot|\Theta(X_{i,j})-\Theta(Y_{i,j})|,\ i,j\in[1,N]; \tag{14}\]  
\[D=\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}\frac{\theta(\Delta_{i,j})}{\theta (X_{i,j})+\theta(Y_{i,j})}, \tag{15}\]  
where \(\Theta\) is the Heaviside step function, \(N\) (\(N\times N\)) is the 2D histogram resolution, and \(\epsilon\) is a threshold for cutting off divergent points that affect the calculation of the difference between two return maps [43]. In all our calculations, we define the resolution of the 2D histogram as \(N=80\) and \(\epsilon=3\).  
Quantified return map analysis was applied to transmissions conducted with different signal-to-noise ratios and various ranges of \(\alpha\) and parameter value variations. We investigate the secrecy of chaotic communications implemented using \(\alpha\)M and PM modulation techniques. Figure 15 displays the obtained results.  
The experimental results for security evaluation show that the proposed \(\alpha\)M technique outperforms the broadly used PM modulation in all considered scenarios.  
Figure 14: Return map of the chaotic signal for PM.
## 3 Proposed Approach
### NSP and Its Limitations  
The NSP task in the original BERT model was designed to predict whether two sentences are sequentially related [21; 22]. In this process, the model feeds two input sentences through the BERT encoder, leveraging the CLS token to classify their relationship. As illustrated in Figure 3, only the representation of the CLS token from the encoder's final hidden layer is used for the sentence prediction task. The label that indicates sentence continuity is set to 1 for a randomly selected following sentence (random next) and 0 for a consecutive sentence from the same document (actual subsequent).  
Figure 3: Comparison of NSP and CRL Procedures in BERT. NSP relies on a softmax classifier to determine sentence continuity, while CRL applies a distance-based objective to better capture underlying semantic relationships between sentences.  
A random next is taken from a different document, whereas an actual next is the immediately following sentence in the same document. The model increases the probability of predicting the correct sentence label via cross-entropy loss. While this approach succeeds in identifying sequence continuity, it lacks the capacity to capture more intricate relational dynamics between sentences. Since the cross-entropy objective merely focuses on whether two sentences follow in order, it undervalues deeper semantic similarities. Consequently, the model often struggles with understanding complex context shifts, cause-effect relationships, and other nuanced clinical inferences--critical for tasks such as patient condition analysis and discharge summary comprehension. The proposed CRPT method directly addresses these shortcomings by leveraging contrastive learning to align semantically similar sentences and separate those that are semantically divergent.
# Day-Ahead PV Generation Scheduling in Incentive Program for Accurate Renewable Forecasting
## 4 Numerical Results
### Result of the Revenue of the PV Site  
The daily revenue of the PV site was analyzed for one year in all cases. Tables 2-4 present the expected daily revenue of the PV site, including selected dates of high, medium, and low levels of PV generation, respectively. On days with high PV generation levels, the expected revenue from incentives for case 5 increased by approximately 10% compared to case 1, resulting in a daily revenue increase of 0.76%. Similarly, on days with low PV generation levels, compared to case 1, the expected revenue from incentives for case 5 increased by 1.14%, resulting in a daily revenue increase of 1.15%. These results indicate that the proposed generation scheduling method can improve the daily income of PV sites, regardless of PV generation levels. The results of case 1 and case 2 demonstrate that adjusting the schedule by considering the characteristics of PV energy deviation can increase incentives compared to submitting the predicted power generation without changes. Generally, PV curtailment can reduce power generation revenues. However, the results of case 3 demonstrate that incentives can be increased, despite PV curtailing, by adjusting the schedule simultaneously. Furthermore, in case 5, consideration of PV curtailment and ESS operation can increase the daily total revenue.
# CI-UNet: Application of Segmentation of Medical Images of the Human Torso
## 4 Discussion
### Model Performance  
Comparing different backbone networks, we found that the mPA metrics were better when using the ResNet50 backbone network for the multi-organ segmentation task; however, in the spine segmentation experiments, we found that the VGG16 network had even better mIoU metrics. Since the network designed in this paper is mainly used for the application of spine segmentation, the overlap between the predicted image and Ground truth is more critical for spine segmentation, so we choose the VGG network as the backbone feature extraction network of CI-UNet in this paper. Comparing the three different attention mechanisms, we find that the Polarized Self-Attention mechanism fits the network model more closely and improves accuracy. Compared with PSPNet, DeepLabv3+, and other MRI-specific networks, our model is more advantageous. Comparing the three different attention mechanisms, we find that the Polarized Self-Attention mechanism fits the network model more closely and improves accuracy. When comparing with PSPNet, DeepLabv3+ networks, and MRI-specific networks, we found that our model is superior.