esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study: Purse Seiner in the Adriatic Sea
### LCA Models for Different Power Systems
#### 3.2.2 The LCA of an LNG-Powered Ship  
The uptake of LNG as an alternative fuel onboard fishing vessels is increasing [37]. LNG-powered ships are powered by dual-fuel engines in which LNG and diesel are used, e.g., modern dual-fuel by Wartsila [16]. It is assumed that the purse seiner considered operates only in a dual-fuel mode with 95% LNG (\(x_{LNG}\)) and 5% diesel (\(x_{P\text{-}LNG}\)), used as a pilot fuel to initiate combustion. According to [PERSON] et al. [62], general equations for the calculation of fuel consumption for a dual-fuel engine are:  
\[FC_{f}=x_{f}\cdot EC\cdot SFC_{f}, \tag{11}\]  
\[FC_{P-f}=x_{p-f}\cdot EC\cdot SFC_{p-f}, \tag{12}\]  
where \(x\) represents the share of fuel in a dual-fuel engine, while the subscripts \(f\) and \(p\)-\(f\) refer to the second and pilot fuels, respectively. Specific fuel consumptions are obtained from [62]. The processes included in the LCA of an LNG-powered ship are presented in Figure 7.  
The environmental impact of the engine is the same as that of the diesel engine in Section 3.2.1. The WTT phase includes natural gas extraction in Qatar, where, before transportation to Croatia (for 7000 km), the natural gas is liquefied on site. According to [PERSON] et al. [62], the general equation for the calculation of TTW emissions released from dual-fuel engines is:  
\[E_{TTW,\;i}=EF_{f,i}\cdot FC_{f}+EF_{D,i}\cdot FC_{P-f}\,, \tag{13}\]  
where \(EF_{f}\) and \(EF_{D}\) refer to the emission factors of the second fuel and pilot fuel (diesel). The emission factors are presented in Table 3. The methane slip in the TTW phase is calculated by multiplying 5.5 g CH\({}_{4}\)/kWh [95] by energy consumption and added to CH\({}_{4}\) emissions calculated with Equation (13).
## 5 Proposed Hardware Architecture
### Arithmetic Logic Unit  
The proposed two-stage pipelined architecture includes adder, multiplier, and square units, as shown in Figure 1. To implement the adder unit, m bitwise exclusive OR gates are used, where m represents the key length. Multiplication is a crucial operation in cryptographic applications, and there are several techniques available in the literature. In this work, we utilize the Montgomery radix-2 multiplier, which is discussed in detail in Section 5.5.1. The square unit is placed after the multiplier unit, as seen in Figure 1. By adding a \"0\" after each input data value, the square unit is implemented, as described in [31]. The square unit's purpose is to minimize the total number of clock cycles (CCs) required for PM calculation, as instructions such as \((A\times B)^{2}\) can be computed using the square unit. After each polynomial multiplication and squaring unit, an inverse operation is necessary. The quad block Itoh-Tsuiji method [15] is employed using the multiplier and squarer units to execute the inversion operation.
## 3 Attacks in Blockchain
### Detecting and Mitigating Malicious Blockchain Attacks
#### 3.3.5 Race Attack  
Race attacks have been extensively studied, with several detection techniques proposed. A key detection method is the Listening Period, during which vendors associate each transaction with a listening time frame. During this period, transactional records are scrutinized for any signs of a race attack. If no malicious activity is detected within this time frame, the vendor proceeds with the transaction [97]. Another effective technique is the Insertion of Observers, where vendors deploy monitoring nodes within the network. These observers transmit all transactions to the vendor, enabling swift detection of race or double-spending attacks [97].  
To prevent race attacks, several mitigation strategies can be employed. Timestamping all transactions ensures that the processing time is recorded, allowing the first recorded transaction to be considered valid. Cryptographic signatures authenticate transactions, verifying that they were submitted by the intended user. Multi-party signatures enhance security by requiring multiple parties to approve a transaction, reducing the risk of unauthorized activities. Increasing the block processing time also mitigates race attacks, as attackers face greater difficulty in executing multiple fraudulent transactions within a restricted timeframe [64].
# DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
## 5 Experiments  
### Dataset  
We adopted two commonly-used benchmark datasets to evaluate the performance of our model. The first one is _Movelens-1m_ ([[https://grouplens.org/datasets/movielens/1m](https://grouplens.org/datasets/movielens/1m)]([https://grouplens.org/datasets/movielens/1m](https://grouplens.org/datasets/movielens/1m)), accessed at 14 September 2021), whichcontains interaction logs of more than 6000 users and 3000 movies. The other is _Tmall_ ([[https://tianchi.aliyun.com/dataset/dataDetail?dataId=53](https://tianchi.aliyun.com/dataset/dataDetail?dataId=53)]([https://tianchi.aliyun.com/dataset/dataDetail?dataId=53](https://tianchi.aliyun.com/dataset/dataDetail?dataId=53)), accessed at 3 January 2022), which includes user behavior logs on an e-commerce platform. We retained the \"buy\" behaviors for the Tmall dataset. For both datasets, we sorted each user's behaviors according to the timestamp. We followed a 5-core and 20-core strategy for the MI-1m and Tmall dataset that removes the users and items whose number of occurrences is less than 5 or 20. We also applied the leave-one-out evaluation protocol [24]. The latest clicked item of a user belongs to the testing set, and the previous one of this item belongs to the validation set. The remaining sequences construct the training set. To augment the training data, we extended the user's sequence following [3,22]. We set the maximum sequence length equal to 200 and 100 for the MI-1m and Tmall dataset, respectively. The statistics are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Statistics** & **MI-1m** & **Tmall** \\ \hline Number of users & 6040 & 31,854 \\ Number of Items & 3883 & 58,343 \\ Number of Training Sequences & 981,504 & 832,603 \\ Number of Testing Sequences & 6040 & 31,854 \\ Number of Attributes & 18 & 70 \\ Average Length of Sequence & 164.50 & 28.13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of Datasets.
## 2 Materials and Methods  
### Research Methodology  
The study's focal point is the detection of abnormal PGs using the MIBI-early, MIBI-late, and TeO4 images (thyroid scan) accompanying the scintigraphic outcome. Medical experts perform localisation in the everyday routine, compare the three images, and determine positive findings identified on the MIBI-late image. The study involves seven methodological steps, as presented in Table 1 and Figure 1.  
The first stage involves the necessary data pre-processing to create the study's dataset. This stage includes region of interest (ROI) reduction and image normalisation. In the second stage, ParaNet+ is deployed to identify normal and abnormal scintigraphic images. Per-patient agreement with the experts is recorded and discussed. The Grad-CAM++ algorithm visualises the essential CNN-suggested features on the MIBI-late image. Medical experts perform a visual assessment during the fourth stage.  
The fifth stage involves external testing using patient data from the year 2022. Patient data included 100 surgically verified cases. The assessment is on a patient-level and PG-level basis. The sixth and seventh stages include stability tests and comparisons with state-of-the-art approaches and previous works by the author group.  
\begin{table}
\begin{tabular}{r c} \hline \hline
**Stage** & **Information** \\ \hline
1 & Data pre-processing—the creation of the dataset (years 2010–2020). \\  & Develop ParaNet+. Train and validate the model using the human experts’ \\
2 & labels as the reference. Inspect the agreement with the experts on a patient-level basis. \\
3 & Employ Grad-CAM++ to localise abnormal PGs. \\
4 & Experts perform the visual assessment and compute the agreement on a PG-level basis. \\
5 & Evaluate the model on the external thyroidectomy dataset. \\
6 & Conduct statistical significance tests and inspect the reproducibility of the experiments by performing multiple repetitions. \\
7 & Compare ParaNet+ with state-of-the-art approaches. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Research methodology.  
Figure 1: Research methodology. MIBI stands for the radioactive tracer \({}^{99m}\)Tc-Sestamibi and TeO4 stands for the \({}^{99m}\)Tc-petrechnetate for thyroid delineation.
## 2 Method
### Model Development and Validation
#### 2.2.3 Transient Model Validation  
The experimental data were also used in transient engine running conditions to validate the developed 1D tool. Figure 9 gives information on the transient profile comparison between the experimental and the simulation results. As mentioned earlier, the tip-in simulates an abrupt load demand, from 2 bar BMEP to full load, until an almost steady-state operation is achieved.  
The strategy consisted of imposing a constant engine speed operation (as for the experiments). The experimental boost was the simulation target through the calculation managing the VGT position, imposed according to the procedure followed in [25]. The simulation in Figure 9 shows a 1500 RPM engine speed in open VGO mode. During the initial low load (corresponding to approximately 30 Nm), the desired boost pressure attained in the experiment and simulation is about 1.18 bar. Due to achieving similar boost pressure in both experimental results and simulation prediction, the relative error in mass flow rate at low load was almost negligible. The model p3 prediction was highly accurate. No errors were identified during the low steady state before the full-load demand.  
Figure 9: Simulation data compared to testing during sudden transient demand at 1500 RPM: Boost pressure (**A**), Air mass flow rate (**B**), Turbine inlet pressure (**C**), and BMEP (**D**).  
Once the load demand started (approximately at 1.1 s), until second 2, the model perfectly targeted the desired boost, air mass flow, and BMEP. After 2.1 s, there was some mismatch regarding boost pressure. The model evolution was not as fast as the experiments, which is attributed to a possible mismatch in VGT management, which is also reflected in p3 growth. In summary, the relative error in the exhaust manifold pressure was lower than 5%.  
Finally, as the engine reaches stability at full-load conditions, model prediction provided accurate results regarding boost (which converges with the experimental value), with the subsequent proper air mass flow and BMEP prediction.
# Improving Automated Machine-Learning Systems through Green AI
## 3 Bayesian Optimization for Hyperparameter Optimization
### Bayesian Optimization  
In recent years, there has been a growing interest in using Bayesian optimization for hyperparameter optimization. Bayesian optimization is a sequential model-based optimization algorithm that learns a probabilistic model of the objective function \(f(\Theta)\) and uses this model to guide its search for the optimal hyperparameter configuration \(\Theta^{*}\). The Bayesian optimization algorithm can be formalized as follows:  
1. Initialize: Start with a prior distribution \(p(\Theta)\) over the hyperparameter space \(\Theta\).
2. Evaluate: Evaluate the objective function \(f(\Theta)\) at a new hyperparameter configuration \(\Theta_{t}\).
3. Update: Update the prior distribution \(p(\Theta)\) using the information from the objective function evaluation.
4. Select: Select the next hyperparameter configuration \(\Theta_{t+1}\) using an acquisition function.
5. Repeat: Repeat steps 2-4 until a stopping criterion is met.  
The acquisition function is a function that measures the expected improvement in the objective function \(f(\Theta)\) from evaluating the objective function at a new hyperparameter configuration \(\Theta\).  
A frequently used acquisition function is the expected improvement (EI) function, which is defined as follows:  
\[EI(\Theta)=\mathbb{E}_{\theta\sim p(\Theta)}[\max(f(\Theta_{t})-f(\theta),0)]\]where \(f(\Theta)\) is the objective function evaluated at \(\Theta\).  
The Bayesian optimization algorithm can be used to find the optimal hyperparameter configuration for a wide range of machine-learning models (see Algorithm 1). The algorithm is particularly effective for problems with many hyperparameters, as it can efficiently explore the hyperparameter space.  
```
1:Initialize: \(p(\Theta)\sim\mathcal{N}(\mu_{0},\Sigma_{0})\)
2:for\(t=1,2,\ldots\)do
3: Evaluate: \(f(\Theta_{t})\)
4: Update: \(p(\Theta)\propto p(\Theta_{t}|f(\Theta_{t}))\)
5: Select: \(\Theta_{t+1}=\underset{\Theta}{\operatorname{argmax}}EI(\Theta)\)
6:endfor
```  
**Algorithm 1** Bayesian optimization.  
In this algorithm, the following variables are utilized:  
* \(p(\Theta)\) is the prior distribution over the hyperparameter space \(\Theta\).
* \(f(\Theta_{t})\) is the objective function.
* \(EI(\Theta)\) is the expected improvement acquisition function.
* \(\mu_{0}\) and \(\Sigma_{0}\) are the mean and covariance of the prior distribution \(p(\Theta)\).
* \(p(\Theta_{t})\mid f(\Theta_{t})\) is the posterior distribution over the hyperparameter space \(\Theta\)given the objective function evaluation \(f(\Theta_{t})\).
## 4 Discussion
### Applications  
* Research advancement:  
A key application stemming from this research is the integration of advanced climate modeling techniques. By utilizing climate models to predict future weather patterns, WWTPs can proactively adapt to seasonal fluctuations, improving their operational strategies and treatment efficiency in varying weather conditions.  
* Consolidation of sensor technologies:  
Incorporating real-time sensor technologies into WWTPs is another potential application of this research. The continuous monitoring of parameters impacted by the temperature and rainfall--such as the COD, BOD, TSS, and nitrogen levels--can provide more dynamic control over treatment processes, enabling operators to adjust processes in real time to maintain optimal performance.  
* Climate-receptive treatment strategies:Based on the seasonal fluctuations observed in our study, we recommend the development of climate-responsive treatment strategies. These could include adaptive chemical dosages or changes in treatment processes depending on anticipated weather patterns. Such strategies would allow WWTPs to better handle variations in influent quality and flow during different seasons.  
* Green infrastructure solutions: Our findings also suggest that green infrastructure solutions, such as engineered wetlands, rain gardens, and permeable surfaces, could mitigate the impact of variations in rainfall on WWTPs. These solutions would help manage stormwater runoff, reduce pollutant loads entering the plant, and improve the overall treatment efficiency during periods of intense rainfall.
* Artificial intelligence: The application of machine learning and AI algorithms in WWTPs offers significant potential for optimizing plant operations. By processing real-time environmental data, these technologies can enhance resource efficiency, reduce energy consumption, and improve treatment outcomes in dynamic weather conditions.
* Longitudinal monitoring: To build on our findings, longitudinal monitoring is crucial for understanding long-term trends and improving forecasting systems. By continuously monitoring WWTPs' performance over extended periods, operators can better predict the impact of future climatic conditions and adjust treatment processes accordingly.
* Synergetic research opportunities: Lastly, this study advocates for synergistic research initiatives that bring together academic, industry, and government bodies to address climate variability in wastewater treatment. Collaboration across disciplines can accelerate the development of innovative solutions that improve the resilience and efficiency of WWTPs under changing environmental conditions.
## 1 Introduction
To summarize, the analytical methods avoid iterative solutions with lower accuracy predictions under high values of parasitic elements of the amplifier components because the maximum amplifier efficiency of a real Class-E could be outside of the ZVS condition [15]. However, when the design specifications are not achieved following these methods, the designer could be made a tuning process under hard switching operation. This paper proposes a set of analytical expressions to determine the maximum values of the currents and voltages present in the components of the ideal Class-E PA with FDI. These mathematical expressions are developed based on the model proposed in [6] and extended in [7], and they could be integrated easily into the design methodology proposed in [8]. To the best of the authors' knowledge, these expressions have not been reported in the literature before. Some of these mathematical expressions are simplified using curve fitting tools. The expressions accuracy were analyzed by the simulation (carry out on the Orcad Pspice Designer(r) software) of twelve ideal amplifiers, which were designed to meet a wide spectrum of application scenarios. The Mean Absolute Percentage Error (MAPE) of the estimation was 2.64%, with a standard deviation of 2.58%. Additionally, the maximum estimation error was 14.89%. The rest of the article is divided as follows. Section 2 presents the ideal Class-E PA with FDI model. Section 3 presents the basic design set of the ideal Class-E PA with FDI. Section 4 develops the maximum current and voltage expressions for each component. Section 5 reports the Class-E PA validation by simulation of the expressions found in Section 4. Finally, Sections 6 and 7 present the conclusions and the future work, respectively.
## Appendix A Noise in HF Band: Gulf of Guinea  
The first document that any HF system designer should consider is the ITU-R Recommendation P.372-11, issued September 2013. Here, charts and graphs describing HF noise levels all over the world can be found. (Alternatively, the very same can be achieved with software package GHNoise ([[http://www.greg-hand.com/noise/](http://www.greg-hand.com/noise/)]([http://www.greg-hand.com/noise/](http://www.greg-hand.com/noise/)))). It is important to note that noise in the HF band depends on the season and time of a day.  
Based on this data, noise levels on a given location can be estimated (see Figure 11).  
Figure 12: Yearly statistics.  
Figure 11 Clarification:  
* Wi--Winter, Sp--Spring, Su--Summer and Au--Autumn
* 08 h, 16 h and 24 h represent time of a day,
* Y axis values represent noise power above noise floor (\(-174\) dBm//Hz)
* Values are plotted for 6.8 MHz frequency  
According to Figure 11 average noise level varies between \(-125\) and \(-112\) dBm/Hz. Measurements from the field (Figure 11) confirm the validity of the data presented in Figure 11. These measurements are taken using a monopole antenna centered at 6.8 MHz (VSWR less than 1.1) and a low loss cables.  
Taking into account that the HFSWR receiver bandwidth is 1 MHz, the average noise level the radar needs to handle varies between \(-65\) and \(-52\) dBm/MHz.  
In order to demonstrate higher noise level effects on the radar we need to compare SNR levels after signal processing in the laboratory and in the field.  
* Setup for the laboratory measurements consists of a radar transmitter with attenuators (needed to simulate signal path) and a radar receiver with signal processing.
* Setup for the field test consists of an operating radar and the surrounding environment.  
The test signal was run through the both setups and obtained results are shown Figure 14.  
From Figure 14 it can be seen that the SNR level measured in the laboratory is 120 dB, while the SNR level in the field is barley 80 dB. It can be seen that the additional noise reduces the system's dynamics by nearly 40 dB, which directly reduces the radar range.
# LogEDL: Log Anomaly Detection via Evidential Deep Learning
## 5 Experiment and Analysis
### Experiment Setting
#### 5.1.3 Evaluation Metrics  
In this study, similar to previous research, we use the \(F1\)_score_, _precision_, and _recall_ as evaluation metrics to validate the effectiveness of the LogEDL model for anomaly detection. _precision_ represents the percentage of true anomalies among all detected anomalies, while _recall_ represents the percentage of detected anomalies in the anomalous dataset. The \(F1\)_score_ is calculated as the harmonic mean of these two metrics, as shown below:  
\[precision=\frac{TP}{TP+FP} \tag{10}\]  
\[recall=\frac{TP}{TP+FN} \tag{11}\]  
\[F1\;score=2\cdot\frac{precision\cdot recall}{precision+recall} \tag{12}\]  
where \(TP\) is true positive, \(FP\) is false positive, and \(FN\) is false negative. The \(F1\)_score_ ranges from 0 to 1, with higher values indicating better performance of the classifier. A higher \(F1\)_score_ signifies that the model has a good balance between _precision_ and _recall_, effectively capturing the overall performance in anomaly detection. This makes the \(F1\)_score_ a comprehensive metric for evaluating the model's effectiveness in identifying anomalies.
# Privacy-Preserving Solution for European Union Digital Vaccine Certificates
## 4 Cryptographic Preliminaries  
In this section, at first, we outline the used notation and the security assumptions needed to understand our scheme and our security proofs. Second, we briefly introduce the attribute-based credential scheme [21] and the elliptic curve integrated encryption scheme [22] as the main building blocks of the cryptographic core of our privacy-preserving solution for EU digital vaccine certificates.
## 2 Applications and Background of Uwsn
### Applications of Uwsn
#### 2.1.1 Natural Hazard Detection  
[PERSON] et al. work on the topic of early warning of different natural events including hazards [8, 9]. Authors proposed a low power underwater sensor network enabling system for early warning generation of natural hazards like earthquakes, tsunamis, hurricanes, floods. Data is collected by the deployment of Uwsn at the ocean sea bed. In the front end of the system, Uwsn collects and transfers the necessary information from the sea surface to the floating surface station. This surface station relays the data to the satellite which eventually down-link the data to the data collection center. At this center, the collected data is analyzed and a warning is generated if necessary. [PERSON] et al. propose a Uwsn architecture for tsunami detection [10]. The system is based on sense and detects mechanism. It collects the seismic information from the sea bed and relay that data with directed diffusion routing protocol [11].