esa-sceva/satcom-chunk-collection huggingface dataset:
# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
## 4 Adaptive Channel Estimation Scheme Based on Preamble
### Calculation of Adaptation Algorithm  
To select the best channel estimation technique, the adaptation algorithm is calculated for each channel estimation technique using the channel estimates and average SNR obtained from the prior steps. The adaptation algorithm derived above according to each channel estimation is expressed as follows.  
\[\zeta_{STA}=\left|\frac{1}{\gamma}-\tilde{\delta}_{STA}\right| \tag{22}\]  
\[\zeta_{MMSE}=\left|\frac{1}{\gamma}-\tilde{\delta}_{MMSE}\right| \tag{23}\]  
\[\zeta_{TRFI}=\left|\frac{1}{\gamma}-\tilde{\delta}_{TRFI}\right|. \tag{24}\]  
When switching between STA and MMSE, (21) and (22) are compared, and when switching between STA and TRFI, (21) and (23) are compared.
## 4 Discussion
### Importance and Clinical Applicability of the Study  
The computational method described in this paper addresses many of the current barriers in clinical radiology. This method offers a standardized approach to resolve intra- and inter-observer variability amongst radiologists in the assessment and quantification of WMLs and is easily implemented even in low resource settings. It is likely that, in the near future, reporting WML load in clinical neuroradiology reports will become standard, at least in certain pathologies such as multiple sclerosis and dementia, as this information has diagnostic and prognostic value [34]. For example, in multiple sclerosis, one could track the load of WML over time to get a temporal assessment of drug efficacy. One could also measure  
Figure 6: Radiological measures of the lesions of different brain regions. (**a**) Average strength of lesion (FLAIR signal), (**b**) heterogeneity of the lesion (FLAIR), (**c**) entropy/randomness in FLAIR signal, (**d**) shape/appearance of the signal (sphericity), (**e**) size of the lesion, and (**f**) homogeneity in FLAIR signal.  
lesion load in specific white matter structures, e.g., the corpus callosum. The spatial distribution of white matter lesions could have both diagnostic and prognostic value in mild cognitive impairment (MCI) and dementia, for example, WML load in the temporal regions may predict progression to MCI or dementia in asymptomatic patients. Moreover, the amount and size of WML in the periventricular and subcortical regions may predict severity of dementia [35]. As these techniques become more robust, the referring physicians will probably demand this kind of quantification from neuro-radiologists. Importantly, although this study is focused on WML, the same approach could also be used for similar lesion types.
# A Blockchain-Based Protocol for Fair Delivery for Receips
## 9 Cost Assessment
### Trade-Off Between Cost and Delay  
The economic viability of the proposed solution was assessed on the basis of the average cost of executing each function on the blockchain. However, the latency introduced by the use of the blockchain is another key factor that can affect the overall cost.  
When a transaction is published to the blockchain, there is both a cost and a delay before it is confirmed and added to the chain. This delay is influenced by numerous factors. Network congestion is a primary factor; a high volume of pending transactions increases competition for inclusion into the next block. The fee offered by users is a critical determinant because miners prioritize transactions with higher fees, as these provide better financial incentives. Therefore, to minimize delays, users can offer higher fees to encourage miners to include their transactions earlier.  
We used the data provided in [31] to obtain the required gas price for the same period as in Section 9.2. Figure 10 shows the daily costs of executing the _accept_ and _publish_ functions of our proposed solution. The maximum price to be paid by the recipient is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Polygon**} & \multicolumn{2}{c}{**Ethereum**} \\ \cline{2-5}  & **First** & **Next** & **First** & **Next** \\  & **Delivery** & **Delivery** & **Delivery** & **Delivery** \\ \hline
**Avg.** & 0.053 & 0.011 & 41.21 & 10.37 \\
**Max.** & 0.226 & 0.045 & 183.51 & 46.17 \\
**Min.** & 0.013 & 0.002 & 5.875 & 1.478 \\
**Std.** & 0.037 & 0.007 & 34.08 & 8.577 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparing the costs (in USD) of our solution using both Polygon and Ethereum: Maximum, minimum, and average (with the standard deviation) costs (in USD) required for each blockchain network.  
Figure 9: Estimated average cost of our solution (measured in USD) in 2024 (from January 2024 to July 2024).  
approximately USD 0.034, while the cost for the sender is approximately USD 0.013. In this case, the transaction publication time for both the sender and the recipient is between 5 and 10 s, which can be considered both fast and cost-effective. Furthermore, the deadline can be adjusted depending on the scenario in which the solution is used and the estimated publication cost at any given time.  
Understanding the delay in publishing transactions to the blockchain is critical for applications with strict time-sensitive requirements. While in some scenarios, the delay may not significantly impact functionality, in other use cases, such as real-time financial trading, supply chain tracking, and automated smart contract execution, timeliness can be critical. In these situations, it is essential to analyze the expected delay to ensure that the blockchain meets the requirements of the application.
# AI-Driven Phishing Detection: Enhancing Cybersecurity with Reinforcement Learning
## 3 Classification of Phishing Detection Strategies
### Mathematical Advantages of RL over Traditional ML
#### 3.5.3 Reduced False Positive Rate Through Reward-Penalty Mechanism  
False positives in phishing detection are costly, leading to user frustration and loss of trust. In RL, we can explicitly define penalties for false positives, forcing the agent to improve its decision-making process. A reward function example is as follows:  
\[R(s,a)=\left\{\begin{array}{ll}+1&\text{if correct classification }(\text{TP or TN})\\ -1&\text{if false positive }(\text{FP})\\ -x&\text{if false negative }(\text{FN}),\ x\geq 0\end{array}\right.\]  
where the following variables are used:  
\(R(s,a)\) is the reward function based on state s and action \(a\).  
TP is true positive, correctly identifying a phishing attempt.  
TN is true negative, correctly identifying benign content.  
FP is false positive, incorrectly classifying benign content as phishing.  
FN is false negative, missing an actual phishing email.  
\(x\) is a penalty value, with \(x\geq 0\) applied for false negatives, ensuring heavier penalties for missing actual threats.  
This differential penalty system ensures that false negatives (missing an actual phishing email) are penalized more heavily than false positives, aligning with real-world cyber-security priorities.
## 3 Experimental Methods
### Configuring the Mobile Robotic Platform for the Experiments
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Model** & **Vendor Details** \\ \hline
**Common to all experiments** & & KUKA Deutschland GmbH, \\ Mobile robotic platform & KMR 200 & Augsburg, Germany \\ Robotic arm & LBR iwa 14 R820 & KUKA Roboter GmbH, Augsburg, \\ Robotic gripper & Robotiq 3-Finger Adaptive Robot & Rebotiq inc., St-Nicolas, Canada \\ Vision system & Visor V20C-RO-P3-W-W-M2-L & SensoPart Industriesensorik \\ Programming software & Sunrise.Workbench—1.16.19 & GmbH, Gottenheim, Germany \\ Gripper configuration & KUKA.WorkVisual & KUKA Deitschland GmbH, \\ V5.0.5\_Build0600 & Germany & Germany \\ Designing software & Autodesk Inventor 2021 & States \\
3D printer & Prusa i3 MK3S & Republic \\ PLA filament & RS PRO 1.75 mm Black PLA 3D & Radionics Ltd., Dublin, Ireland \\
**Experiment 1 specific equipment** & Corning 50 mL clear & \\ Centrifugal tube & polypropylene (PP) self-standing centrifuge tubes- 430921 & Corning, New York, United States \\
**Experiment 2 specific equipment** & & \\ Bioprocessing container bag & 500 mL Labtainer BPC, 2D system & Thermo Scientific, Massachusetts, United States \\
**Experiment 3 specific equipment** & Irradiated TSA media 3P & bioRxiv Inc., Durham, North \\ Petri dish & environmental monitoring media & Carolina, United States \\ \hline \hline \end{tabular}
\end{table} 
Table 5: List of equipment used in the experiments. communication. The robot, the arm and the vision system are able to communicate with a remote computer (Operator's desktop computer) through a wireless router integrated with the mobile robotic base. This allows the operator to send any control commands or process parameters. The robot uses this communication channel to transfer information about task progress to the operator. The KUKA Sunrise.Workbench suite is used as a user interface for this. The communication channel also allows other external software to schedule and initialise any given task by communicating the task parameters to the robot. Hence, instead of KUKA Sunrise.Workbench suite, a LIMS or a cloud-based scheduling system can be used to schedule robotic tasks. During the experiments described in the below sections, the mobile robotic platform, the robotic arm, the robotic gripper, and the vision system all remain the same. A different control program is used for each experiment, and specific additional equipment to be manipulated by the robot is used in each experiment, as detailed in Table 5.
###### Abstract  
Microwave Breast Imaging (MBI) is an emerging non-ionizing imaging modality, with the potential to support breast diagnosis and management. Wavelia is an MBI system prototype, of 1 st generation, which has recently completed a First-In-Human (FiH) clinical investigation on a 25-symptomatic patient cohort, to explore the capacity of the technology to detect and characterize malignant (invasive carcinoma) and benign (fibroadenoma, cyst) breast disease. Two recent publications presented promising results demonstrated by the device in this FiH study in detecting and localizing, as well as delineating size and malignancy risk, of malignant and benign palpable breast lesions. In this paper, the methodology that has been employed in the Wavelia semi-automated Quantitative Imaging Function (QIF), to support breast lesion detection and characterization in the FiH clinical investigation of the device, is presented and the critical design parameters are highlighted.  
breast cancer detection; microwave breast imaging; computer-aided diagnosis (CAD); first-in-human (FiH) study +
Footnote †: journal: Medical Imaging Department, MVG Industries, 91140 Villejust, France; [EMAIL_ADDRESS] (J.D.G.C.)  
Footnote †: journal: Medical Imaging Department, 91140 Villejust, France; luc.duchesne@mvg-world.  
WaveliaTM is an MBI system prototype, of 1 st generation, which demonstrated the ability to detect dielectric contrast between tumor phantoms and synthetic fibroglandular tissue in preclinical studies [13] and has recently completed a First-In-Human (FiH) clinical investigation on a 25-symptomatic patient cohort, hosted in NUIG Clinical Research Facility Galway, Ireland. In this study (ClinicalTrials.gov NCT03475992), the Wavelia MBI system was evaluated in the clinical setting for the first time, using mammography as the reference conventional imaging modality and post-surgery histology data to assess the size of the cancers. Ultrasound, MRI and core biopsy data were also collected as a reference and were available as part of the patient's standard of care. In this FiH study, Wavelia demonstrated the capacity to detect and approximate underlying breast abnormalities to the appropriate location, in patients with palpable biopsy-confirmed invasive carcinomas and benign breast lesions, such as cysts and fibroadenomas [12]. The device also demonstrated promising results in delineating the size and malignancy risk of the detected breast lesions [14].  
The methodology that was employed in the Wavelia semi-automated Quantitative Imaging Function (QIF) during this FiH study, to support morphological breast lesion detection based on persistence, lesion sizing and lesion characterization in a low-dimensional feature space, spanning shape and texture-based features, is presented in this article.
## 3 Control Strategies
### Learning-Based Incremental Conductance (LINC) Technique
\right]=\frac{2}{3}\left[ \begin{array}{cc}\cos\theta&\cos \left(\theta-\frac{2\pi}{3}\right)&\cos\left(\theta+\frac{2\pi}{3}\right)\\ -\sin\theta&-\sin\left(\theta-\frac{2\pi}{3}\right)&-\sin\left(\theta+\frac{2 \pi}{3}\right)\\ \frac{1}{2}&\frac{1}{2}&\frac{1}{2}\end{array} \right]\left[ \begin{array}{c}i _{La}\\ i_{Lb}\\ i_{Lc}\end{array} \right] \tag{12}\]  
where, \(\theta\) denotes the angular position of the rotating frame. The rotating frame revolves synchronously with three-phase AC voltages using a three-phase PLL. The currents in \(dq\) frame are found using [PERSON]'s transformation, presented in Equation (13), and are processed through AMDWPT to obtain the DC parts of \(i_{d}\) and \(i_{q}\). The fundamental and harmonic components of \(d\) and \(q\) frame currents are given as:  
\[i_{d}=i_{d-dc}+i_{d-ac} \tag{13}\]  
\[i_{d}=\frac{\sqrt{2}}{3}\left[i_{La}\cos(\omega t)+i_{Lb}\cos\left(\omega t- \frac{2\pi}{3}\right)+i_{Lb}\cos\left(\omega t+\frac{2\pi}{3}\right)\right] \tag{14}\]  
where, \(i_{d}\) and \(i_{q}\) are the direct and quadrature axis current. Again,  
\[i_{q}=i_{q-dc}+i_{q-ac} \tag{15}\]  
\[i_{q}=\frac{\sqrt{2}}{3}\left[-i_{La}\sin(\omega t)-i_{Lb}\sin\left(\omega t- \frac{2\pi}{3}\right)-i_{Lc}\sin\left(\omega t+\frac{2\pi}{3}\right)\right] \tag{16}\]  
Finally, the transformation from \(dq\) to \(abc\) is done for generating three-phase reference currents (\(i_{sa}^{*}\), \(i_{sb}^{*}\) and \(i_{sc}^{*}\)). \[ \begin{array}{c}i_{sa}^{*}=\frac{\sqrt{2}}{3}\left[i_{d,ac}\cos(\omega t)-i_ {q}\sin(\omega t)\right]\\ i_{sb}^{*}=\frac{\sqrt{2}}{3}\left[i_{d,ac}\cos\left(\omega t-\frac{2\pi}{3} \right)-i_{q}\sin\left(\omega t-\frac{2\pi}{3}\right)\right]\\ i_{sc}^{*}=\frac{\sqrt{2}}{3}\left[i_{d,ac}\cos\left(\omega t+\frac{2\pi}{3} \right)-i_{q}\sin\left(\omega t+\frac{2\pi}{3}\right)\right]\end{array}  \tag{17}\]  
The reference currents obtained in Equation (17) are then processed further and are fed to the hysteresis controller for switching signal generation.
# Paper-Mill Wastes for Bioethanol Production in Relation to Circular Economy Concepts: A Review
## 5 RPS for Energy Applications
### Bioethanol Production from Lignocellulosic Biomass
In this method, soluble sugars are immediately fermented into ethanol, improving both enzymatic hydrolysis efficiency and ethanol yield. In CBP, enzymes are produced alongside hydrolysis and fermentation in a single step [22]. Generally, at the end of the fermentation step, the bioethanol value stands at 5% wt. This is a lower value compared to first-generation ethanol, which stands at 12% wt. The ethanol broth is distilled in a stripper column to reach a 20% wt. concentration and then further concentrated in a rectifier column to no higher than 95.6% wt. ethanol in water. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pre-Treatment Method** & **Advantages** & & **Disadvantages** \\ \hline \multirow{4}{*}{Steam explosion} & \(\bullet\) & Allows lignin & \(\bullet\) & Partial hemicellulose \\  & & transformation and & Degradation. \\  & & hemicellulose & \(\bullet\) & Formation of toxic \\  & & solubilization. & & Compounds. \\  & \(\bullet\) & Cost-effective. & \(\bullet\) & Requires high pressure \\  & & & & and temperature. \\ \hline \multirow{4}{*}{Ozonolysis} & \(\bullet\) & Reduces lignin content. & \(\bullet\) & High costs and energy \\  & \(\bullet\) & No generation of toxic & \(\bullet\) & demand. \\  & & compounds. & \(\bullet\) & Requires high amount of \\  & & & & ozone. \\ \hline \multirow{4}{*}{Ammonia fiber Explosion} & \(\bullet\) & Minimal formation of inhibitors. & \(\bullet\) & Not suitable for \\  & & Requires low & & softwood. \\  & & temperature. & \(\bullet\) & Not efficient for raw \\  & & & & materials with high \\  & & & temperature. & lignin content. \\  & & \(\bullet\) & Increases accessible & \(\bullet\) \\  & & surface area. & \(\bullet\) & Requires large amount of \\  & & & \(\bullet\) & ammonia. \\  & & & & High costs. \\ \hline \multirow{4}{*}{Organosolv} & \(\bullet\) & Promotes lignin and & \(\bullet\) & Solvents need to be \\  & & hemicellulose & & recycled. \\  & & hydrolysis. & \(\bullet\) & High costs. \\  & & & & \(\bullet\) \\ \hline \multirow{4}{*}{Concentrated acid} & \(\bullet\) & Requires ambient/low temperatures. & \(\bullet\) & High cost of acid. \\  & & High glucose yield. & \(\bullet\) & Requires recovery of \\  & & & & acids. \\  & & & & \(\bullet\)  Reactor corrosion. \\  & & & & Formation of inhibitors. \\ \hline \multirow{4}{*}{Diluted acid} & \(\bullet\) & Removal of & \multirow{2}{*}{\(\bullet\)} & Generation of \\  & & hemicellulose and part & & degradation products. \\ \cline{1-1}  & & Less formation of & \(\bullet\) & Production of low sugar \\ \cline{1-1}  & & inhibitors. & & concentration. \\ \cline{1-1}  & & \(\bullet\) & Less corrosion problems. & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Current industrial pre-treatment methods, adapted from [11; 132; 133]. Distillation is an energy-demanding step, accounting for 60-80% of the total separation cost of bioethanol from water [22].
# Measurement and Analysis of Downhole Drill String Vibration Signal
## 4 Conclusions  
(1) The article analysis results show that the stick-slip vibration can be qualitatively judged by the wave packets appearing regularly in the measured three-axis acceleration signal. And the period of the stick-slip vibration can be determined by the time length of the wave packet 'appearing-disappearing' process. Radial acceleration is more sensitive to the change of speed in stick-slip vibration, while tangential acceleration can more intuitively indicate lateral vibration.  
(2) Time-frequency analysis of the stick-slip vibration acceleration signal shows that 0.1221 Hz corresponds to the period of stick-slip vibration. Additionally, from the amplitude corresponding to 0.1221 Hz, it can be seen that radial acceleration can more intuitively represent stick-slip vibration than tangential acceleration.  
(3) We analyzed the vortex acceleration signal. When whirl occurred, the lateral vibration and torsional vibration of the drill string were of equal severity. The mean value of radial acceleration and tangential acceleration were quite different: 0.05341 Hz is the frequency of whirl, which approximately matches the period of whirl 19 s.  
(4) Through time-frequency analysis of the three-axis acceleration signal of drill string vibration, it was found that the occurrence of torsional vibration and whirl in igneous rock drilling is very likely to be related to the use of torsional vibration impactor and jar in the drill tool assembly. It is recommended not to use torsional vibration impactor and jar at the same time in actual drilling.  
Writing--original draft preparation, Y.L.; investigation, J.W.; visualization, Y.S. and C.W.; supervision, Y.H. All authors have read and agreed to the published version of the manuscript.  
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Natural Science Foundation of China (42072341), National Key R&D Program of China (2016 YFE0202200), Basic research fund of Chinese Academy of Geological Sciences (JY\(\gamma\)WF20180501), Fundamental Research Funds for the Central Universities (2-9-2018-096).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Vibration Pattern**} & \multicolumn{3}{c}{**Main Frequency (Hz)**} \\  & **Radial** & **Tangential** & **Axial** \\ \hline \multirow{8}{*}{Stick slip} & 0.1221 & 0.1221 & 0.1221 \\  & 0.3967 & & \\ \cline{1-1}  & 145.9 & 145.9 & 145.9 \\ \cline{1-1}  & 155.5 & 155.5 & 155.5 \\ \cline{1-1}  & 216.5 & 216.5 & \\ \cline{1-1}  & 361.7 & 361.7 & \\ \cline{1-1}  & 391.8 & & \\ \cline{1-1}  & 0.05341 & 0.05341 & 0.05341 \\ \cline{1-1}  & & 145.9 & 145.9 \\ \cline{1-1}  & & 155.5 & 155.5 \\ \cline{1-1}  & & 216.5 & \\ \cline{1-1}  & & 361.7 & \\ \cline{1-1}  & & 391.8 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Feature frequency statistics when different vibration patterns occur.
## 4 Evaluation of the Proposed Algorithm
### The Energy Consumption  
In the first scenario, the base station location is at (0,0); Figure 6 shows that the genetic and LEACH (traditional) algorithms lose 80% of the energy after approximately 350 rounds and 370 rounds, respectively, whereas the COY algorithm at 400 rounds and (AO and HHO) algorithms lose the same amount at 450 rounds. The presented AO algorithm in this case has the best loss energy consumption at 450 rounds.  
In the second scenario, the BS location is at (50,50); Figure 7 shows that the genetic and LEACH (traditional) algorithms lose 80% of the energy after 350 rounds and 400 rounds respectively, whereas the COY algorithm at 700 rounds and (AO and HHO) algorithms lose the same amount at 800 rounds. In this case, the presented AO algorithm has the best loss energy consumption at 800 rounds.  
In the third scenario, the BS location is at (100,100); Figure 8 shows that the genetic and LEACH (traditional) algorithms lose 70% of the energy after approximately 350 rounds, whereas COY, and (HHO and AO) algorithms lose the same amount at approximately 400 rounds. In this case, the presented AO algorithm has a normal loss rate at 400 rounds.  
Thus, in terms of the network lifetime and the network performance, the proposed AO algorithm has the best value of energy consumption compared with the existing algorithms, whereas LEACH and genetic algorithms have the least network performance. This is an important observation of the proposed algorithm over the traditional and metaheuristic  
Figure 4: Number of alive nodes in case of scenario 2: BS at (50,50).  
Figure 5: Number of alive nodes in case of Scenario 3: BS at (100,100).  
COY, HHO, and GA algorithms. In this case, the presented AO algorithm has the best loss rate at 1800 rounds with BS location (50,50). Additionally, the means of the normalized energy of the proposed AO algorithm in three scenarios are 450, 800, and 400 rounds, respectively, outperforming the LEACH, COY, HHO, and GA algorithms. In this case, the presented AO algorithm has the best energy loss rate at 800 rounds with a BS location (50,50), allowing us to conclude that AO has a positive effect on system stability.