esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Advanced Oxidation Processes
### Persulfate, Peroxymonosulfate, and Hydrogen Peroxide
The optimum ratio of \(\mathrm{H_{2}O_{2}}\) needs to be determined experimentally, and it is dependent on the experimental conditions [42; 45; 62] and effluent characteristics. This reagent can only be used as an AOP technology when it is in the presence of radiation (UV, solar, etc.) or catalyst [42], and its good characteristics include good market availability, good thermal stability, infinite solubility in water, its ability to be stored in the operational site, and lack of mass transfer problems associated with gases [42; 45]. The use of UV with \(\mathrm{H_{2}O_{2}}\) reagent leads to the generation of two hydroxyl radicals (Equation (31)) which should enhance the removal performance [35; 75; 76] compared to a single \(\mathrm{H_{2}O_{2}}\) reagent that cannot produce these types of radicals. Furthermore, organic and inorganic peroxyl radicals can be generated using \(\mathrm{H_{2}O_{2}}\)/UV in the presence of oxygen and other organic compounds [45]. However, this technology also shows disadvantages, since low-pressure UV lamps require high concentrations of \(\mathrm{H_{2}O_{2}}\) for good OH generation, but the existence of \(\mathrm{H_{2}O_{2}}\) at high concentrations can lead to a scavenging effect of hydroxyl radicals [49]. Therefore, the use of \(\mathrm{H_{2}O_{2}}\) with UV radiation may not be recommended since it can have a high scavenging effect in secondary and tertiary wastewater treatments [49]. \[\text{S}_{2}\text{O}_{8}\)\({}^{2-}+\text{H}^{+}->\text{H}^{+}+\text{SO}_{4}\)\({}^{2-}+\cdot\text{SO}_{4}\)\({}^{-} \tag{22}\]0  
At alkaline pH, several studies report an interesting behavior of \(\mathrm{H_{2}O_{2}}\) reagent, even when it is used alone. In fact, it has already obtained degradation efficiencies higher than 90% in 60 min regarding dibutylsulfide (DBS) degradation [77], higher DBS disappearance rate for single \(\mathrm{H_{2}O_{2}}\) when compared to single ozone or \(\mathrm{O_{3}}\)/\(\mathrm{H_{2}O_{2}}\) system for pH \(<\) 12 [77], and about 52% of 4-chloro-2 nitrogenhol degradation at pH = 10 [78], which showed better results than at pH \(<\) 10, with UV use, or in Fenton's (from pH = 6 until pH = 10) [78]. Regarding its use in real wastewater treatments, \(\mathrm{H_{2}O_{2}}\) achieved COD reduction of about 35%, 29%, and 27% in 180 min for pH = 5, 7, and 9, respectively, regarding the treatment of linear alkyl benzene industrial wastewater [79]. Furthermore, olive wastewater treatment allowed a maximum phenol degradation around 65%, 78%, and 81% and COD maximum abatement of about 20%, 25%, and 23% for pH = 9, 10, and 11, respectively [80]. Other work for olive oil wastewater treatment led to a phenol degradation result of 65%, 74.4%, and 64.4% using single \(\mathrm{H_{2}O_{2}}\), while the UV/\(\mathrm{H_{2}O_{2}}\) system achieved 66%, 59.2%, and 72.3% for \(\mathrm{H_{2}O_{2}}\) concentrations of 0.11, 0.55, and 2 M in 150 min at pH = 9, which can show a bit better behavior in the single hydrogen peroxide system when compared to UV/\(\mathrm{H_{2}O_{2}}\)[81].
## 8 Performance Analysis
### Computation Overhead  
We compared the computation costs of the proposed scheme with existing schemes [6; 20; 21; 28]. We define the parameters based on the work of [PERSON] et al.'s scheme [6]. \(T_{cert\_ver}\), \(T_{cert}\), \(T_{h}\)\(T_{s}\), \(T_{e}\), \(T_{m}\), \(T_{eca}\), \(T_{ecm}\), and \(T_{b}\) denote public key certificate verification, public key certificate generation, one-way hash function, symmetric encryption/decryption, modular exponentiation, multiplication, ECC point addition, ECC point multiplication, and bilinear pairing, respectively. Based on the works in [21; 34], we present the execution time for various cryptographic operations in Table 5 and assume \(\{T_{s}\approx T_{h}\), \(T_{m}\approx T_{e}\}\) is negligible because it requires very low execution time. We also assume \(T_{eca}\ll T_{e}\) and \(T_{eca}\approx T_{h}\).  
In authentication process, total computation overheads of proposed scheme and [PERSON] et al.'s scheme are \(16T_{h}\) and \(12T_{h}+4T_{ecm}\), respectively. Based on the works in [21; 34], the total computational overheads of our scheme is 0.011 s and 0.05 ms, which is implemented on HiPerSmart card and Pentium IV platform, respectively. Therefore, we provide better efficiency than existing schemes because our protocol utilizes only hash function and XOR operation. Table 6 shows the analysis result of computation overhead compared to existing schemes.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Subsample**} & \multicolumn{2}{c}{**Q1**} & \multicolumn{2}{c}{**Q2**} & \multicolumn{2}{c}{**Q3**} & \multicolumn{2}{c}{**Q4**} & & \\ \cline{2-13}  & \multicolumn{2}{c}{**Category**} & & & & & & & & & & & & \\ \hline \multirow{5}{*}{Entire Sample} & \multicolumn{2}{c}{Total} & \(n\) & \(n\) & \% & \(n\) & \% & \(n\) & \% & \(n\) & \% & \(\chi^{2}\) & \(p\) & \(w\) \\ \cline{2-13}  & Total & 578 & 141 & 24.4 & 153 & 26.5 & 164 & 28.4 & 120 & 20.7 & 7.370 & 0.061 & 0.11 \\ \cline{2-13}  & Individual & 229 & 62 & 27.0 & 53 & 23.1 & 61 & 26.6 & 53 & 23.1 & 1.270 & 0.736 & 0.07 \\  & Group & 349 & 79 & 22.6 & 100 & 28.7 & 103 & 29.5 & 67 & 19.2 \(\downarrow\) & 10.186 & 0.017 & 0.17 \\ \hline \multirow{5}{*}{Junior} & \multicolumn{2}{c}{Total} & 356 & 89 & 24.8 & 99 & 27.9 & 103 & 29.0 & 65 & 18.3 \(\downarrow\) & 9.833 & 0.020 & 0.17 \\ \cline{2-13}  & \multicolumn{2}{c}{13 y} & 43 & 13 & 30.2 & 15 & 34.9 & 8 & 18.6 & 7 & 16.3 & 4.162 & 0.244 & 0.31 \\ \cline{1-1}  & \multicolumn{2}{c}{14 y} & 130 & 37 & 28.5 & 38 & 29.2 & 32 & 24.6 & 23 & 17.7 & 4.338 & 0.227 & 0.18 \\ \cline{1-1}  & \multicolumn{2}{c}{15 y} & 183 & 39 & 21.3 & 46 & 25.2 & 63 & 34.4 \(\uparrow\) & 35 & 19.1 & 10.027 & 0.001 & 0.23 \\ \cline{1-1} \cline{2-13}  & Individual & 141 & 38 & 26.9 & 40 & 28.4 & 39 & 27.7 & 24 & 17.0 & 4.843 & 0.183 & 0.19 \\ \cline{1-1}  & Group & 215 & 51 & 23.7 & 59 & 27.4 & 64 & 29.8 & 41 & 19.1 & 5.633 & 0.131 & 0.16 \\ \hline \multirow{2}{*}{Senior} & Total & 222 & 52 & 23.4 & 54 & 24.3 & 61 & 27.5 & 55 & 24.8 & 0.810 & 0.846 & 0.06 \\ \cline{1-1} \cline{2-13}  & Individual & 88 & 24 & 27.3 & 13 & 14.8 & 22 & 25.0 & 29 & 32.9 & 6.091 & 0.107 & 0.26 \\ \cline{1-1}  & Group & 134 & 28 & 20.9 & 41 & 30.6 & 39 & 29.1 & 26 & 19.4 & 5.160 & 0.160 & 0.20 \\ \hline \hline \end{tabular} Note: \(\uparrow\) = significant (\(p<0.05\)) over-representation of participants compared to the expected value in the quartile; \(\downarrow\) = significant (\(p<0.05\)) under-representation of participants compared to the expected value in the quartile. Expected quartile distributions were equal (i.e., \(0.25\%\)).  
\end{table} 
Table 1: Percentage distribution of RGs birthdates and \(\chi^{2}\) test results by age category and competition format (individual/group) in the Entire Sample.
## 1 Introduction  
Prostate cancer (PCa) is the most prevalent oncological disease in males in Western Europe, including Lithuania [1]. Before the multiparametric magnetic resonance imaging (mpMRI) era, the principal method to diagnose PCa and determine the risk was a systematic 12-core transrectal ultrasound (TRUS)-guided biopsy [2]. Unfortunately, TRUS-guided biopsy is performed randomly, without targeting the tumor, and leads to over-diagnosing of clinically insignificant cancer in up to 50% of cases and not diagnosing clinically significant cancer in up to 18% of men, particularly if the tumor is in the apical or anterior regions of the prostate [3,4]. In addition, TRUS-guided biopsy causes side effects including bleeding, pain, and severe systematic infection [5,6]. To tackle this problem, new methods are being established which lead to increased biopsy accuracy based on the prostate mpMRI. One of the most promising and easily accessible prostate biopsy techniques is the mpMRI and ultrasound fusion (mpMRI/US)-guided prostate biopsy, which uses a specially designed system and software to fuse mpMRI images with real-time TRUS [7]. This technique allows us to target suspicious lesions much more accurately than systemic TRUS-guided prostate biopsy, a little bit more accurately than transrectal cognitive guidance prostate biopsy, and gives advantages of this tactic mainly due to fewer biopsy cores taken that give lower rates of complications and better tolerance for the patient [8, 9, 10]. Based on the Cochrane meta-analysis, MRT-FIRST and 4M clinical trials, the absolute added value of systematic biopsy for the detection of clinically significant PCa is known to be lower than that of targeted biopsy [11]. Nowadays, it is important to get an answer whether targeted prostate biopsy enhances the detection of clinically significant PCa and safely obviates the need for systemic biopsy in primary patients, as some controversies remain [12, 13]. This study aimed to prove the high accuracy of mpMRI/US-guided targeted prostate biopsy for the detection of clinically significant PCa and demonstrate that adapted systematic biopsy (AdSB) does not provide additional benefit for the detection of clinically significant PCa.
# Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications
## 3 Network Energy Models (Methods)
### Energy Consumption Models  
We used a first order radio model as the energy consumption model for the purpose of the transmission/reception of messages of the same length, \(n\)-bits. This model computes energy consumption \(E_{i}\) for the transmission of IoT nodes during one round. The energy consumed to transmit \(n\)-bits of data to a node located at a distance \(d\) (using Euclidean distance metric) was estimated as follows [9]:  
\[E_{tx}(d)=\left(\frac{n}{r}\right)\left(\Phi_{amp}d^{n}+\Phi_{cit}\right)\mid \alpha=\left\{\begin{array}{ll}2,\ d<d_{cr}\\ 4,\ d>d_{cr}\end{array}\right. \tag{1}\]  
where \(\Phi_{cit}\) represents the power consumption during the operation of the transmitter circuit, and \(\Phi_{amp}\) is the power consumed by amplifier. \(\alpha\) is the exponent indicating the path loss component with the range [2; 4], and \(d_{cr}\) is the crossover distance based on free path loss and multipath loss. Here, \(\alpha\) is 4 and 2 for multi-path loss and free path loss, respectively. The consumption of energy at the receiving node at rate \(r\) depends entirely on the operation of the circuit, which is represented as follows:  
\[E_{rx}(d)=(n\ /r\ )\Phi_{cir} \tag{2}\]  
Thus, for an intermediate sensor node \(i\) at a single-hop distance, the energy consumption \(E_{i}\) for transmission and reception for relaying over the distance \(d\) is given as follows:  
\[E_{i}(d)=E_{tx}(d)+E_{rx}(d)=\Big{(}\ \frac{n}{r}\ \Big{)}(\Phi_{amp}d^{ \alpha}+2\Phi_{cir}\Big{)} \tag{3}\]
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 2 Metal Sulfide Thin Films
### Indium Sulfate Films  
The n-In2S3 films were produced by using different precursors, namely indium (III) chloride (InCl3) and indium (III) nitrate (In(NO3)3). Generally, these films were orange and turned red during the annealing process. The films prepared by using In(NO3)3 showed uniform morphology and strong diffraction peaks along the (440) plane. The XRD data supported the films produced by using InCl3, were crystalline (as heated at 400 \({}^{\circ}\)C), and exhibited excellent photo responses [60]. The depositions of films took place on amorphous glass substrates, by using indium (III) sulfate (In2(SO4)3), sodium sulfide, and complexing agents (TEA and hydrazine hydrate). The obtained films were very smooth and the band gap value was 2.7 eV [61]. [PERSON] and co-workers [62] proposed that thin films were synthesized under different rinsing and dipping times. Bigger grain sizes could be detected in longer dipping times. [PERSON] and co-workers [63] reported that these films could be employed for solar energy conversion for several reasons, e.g., low toxicity with good stability, excellent carrier mobility, and excellent photo-absorption coefficients.  
Figure 3: FTIR spectrum of the SILAR-deposited CdS reproduced with permission from [57], [PERSON], 2014.
## 4 GNC Subsystem Architecture
### Guidance and Control  
#### 4.3.1 Propulsion System  
The propulsion system used for the CANYVAL-C mission and equipped in the 2U was the Nanoprop of GomSpace. It has four nozzles at the center of each \(xz\) and \(yz\) plane and can fire in \(\hat{b}_{x}\) and \(\hat{b}_{y}\) directions of the body frame. The maximum magnitude of the thrust was 1.0 mN for each nozzle, and one or two nozzles were simultaneously fired towards the desired direction. Because the nozzles lie on the \(xy\) plane of the body frame, the z-component of the control error is uncontrollable. Hence, for an orbit maneuver with three degrees of freedom, the attitude of the spacecraft must be adjusted simultaneously. The target thrust vector is projected onto the thrust plane, and the error angle between the target vector and the projected vector is calculated and controlled by the reaction wheel torque. According to the specifications of the reaction wheels, the maximum torque is 1.414 Nm and the spacecraft is controlled within 1.0\({}^{\circ}\)/s (Table 6).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Maximum Thrust** & **Thrust Resolution** & **Nozelle Configuration** & **Firing Accuracy (3\(\sigma\))** \\ \hline
1.0 mN & 0.01 mN & \(\pm\hat{b}_{x}\)\(\pm\hat{b}_{y}\) & \textless{3.0 deg, 15\%} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Specification and configuration of the propulsion system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Absolute Orbit Determination** & \multicolumn{2}{c}{**Relative Orbit Determination**} \\  & **Coarse Solution** & **Fine Solution** \\ \hline \(<\)10.0 m, 0.1 m/s & \(<\)20.0 m, 0.2 m/s & \(<\)1.0 m, 0.01 m/s \\ (3\(\sigma\), for each axis) & \(<\)(3\(\sigma\), for each axis) & (3\(\sigma\), for each axis) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Requirement of navigation subsystem [37].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attitude Determination** & **Attitude Control** \\ \hline \(<\)1.5 deg, 0.15 deg/s & \(<\)3.0 deg, 0.5 deg/s \\ (3\(\sigma\), for each axis) & (3\(\sigma\), for each axis) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Requirements of attitude determination and control subsystem [38].
## 4 Discussion
### Spectral Peak Shift  
One straightforward idea is that droplet/mist ejection in the detection path for the THz wave emission in the transmission and the reflection directions may cause additional absorption for THz wave emission as shown in Figure 4b. The water absorption spectrum shows its higher intensity at the higher frequency as reported previously [56], which will cause an apparent peak shift toward the lower frequency as the droplet/mist ejection takes place.  
Second, interferometric effects for THz wave emission due to the transient surface roughness/ripple formation on the flow surface are considered to be shown in Figure 4b. It was reported that laser excitation of LiNbO\({}_{3}\) by transient grating controls THz emission spectra within the range of 0.5-3 THz [57]. Similar studies with InAs proved that periodic structures of THz wave emission source show the peak shift toward the lower frequency as the period increases from 190 \(\upmu\)m to 360 \(\upmu\)m [58]. Under the current experimental condition for the pre-pulse irradiation to induce laser ablation, transient surface roughness is considered to be composed of different orders of spatial frequencies from a few tens of nano-meters to micrometers [52]. Such surface structures can cause the dynamic spectral shift under the delayed main-pulse irradiation.
# AI-Driven Phishing Detection: Enhancing Cybersecurity with Reinforcement Learning
## 1 Introduction
This is particularly important given the growing sophistication of phishing techniques, including the use of social engineering and advanced obfuscation tactics [15; 16]. To validate the effectiveness of the proposed framework, we compare its performance against state-of-the-art phishing detection models, including machine learning-based approaches. The methodology uses an RL-agent that is trained to identify phishing emails and websites by interacting with a simulated environment. Through iterative learning, the agent refines its detection strategies based on feedback, continuously improving its ability to distinguish between legitimate and phishing threats. The following list outlines our key contributions:  
* Introduction of a RL-based phishing detection framework: The paper proposes a new phishing detection framework leveraging RL, specifically using DQN architecture. This approach enables dynamic learning from real-time interactions, which contrasts with static, traditional ML models. * Enhanced adaptability to emerging phishing threats: Unlike traditional ML models that require frequent retraining with new data, the proposed RL-based model adapts continuously through trial-and-error learning. This makes it highly effective against evolving phishing techniques, including spear-phishing and PaaS attacks. * Reduction of false positives in phishing detection: One of the key challenges in phishing detection is the high false positive rate. The paper introduces a reward-penalty mechanism within the RL model to penalize false positives, thereby improving decision-making accuracy and reducing false alarms to just 4%, compared to 10-12% in traditional models. * Benchmarking against developed models: The proposed framework is benchmarked against state-of-the-art models, such as SVMs and Random Forests. The RL-based model achieved a 95% accuracy, outperforming traditional ML models (which achieved an 85-87% accuracy). * Comprehensive experimental validation: The model was trained on a real-world dataset comprising 5000 emails (2500 phishing and 2500 benign), with 4000 emails used for training and 1000 emails for testing. It achieved a 95% accuracy, 96% precision, and 94% recall on the real-world dataset and maintained a 93% accuracy and 94% precision and a 4% false positive rate during external validation using a synthetic phishing dataset comprising 1000 generated samples simulating unseen attacks.
## 4 Proposed Path Planning Techniques  
Path planning is defined as a process of determining a path in configuration space between the initial and target position of the robot [30]. The generated path should maintain the kinematic constraints of the mobile robot.  
Indeed, the robot should not collide with obstacles (static or dynamic). Path planning can be classified into two types: Global path planning and local navigation path planning. In global path planning, prior knowledge of the environment (updated map and static obstacles) should be available. Many methods have been developed for global path planning, such as the A* algorithm [31], Cell decomposition method [32], RRT (Rapidly exploring Random Tree) and RRT* [33], and PRM (Probabilistic Roadmaps) [34]. In local navigation, the motion and direction of the robot are controlled autonomously by using onboard sensors, like ultrasonic range finder sensors, LIDAR sensors, IMU sensors, infrared range sensors, and vision (camera) sensors, etc.  
A flowchart is shown in Figure 3, in which the implementation of the path planning code is shown. The code is initialized by recording the start and end positions, and it scans for the offline map. After that, the intended algorithm is calculated, and the desired path is generated and sent to the 4 OWMR. If the robot faces an obstacle, it sends the data to the master controller, which stops the robot immediately while updating the map to generate a new path. If there is no new obstacle, the robot goes to a second check that checks if the robot has reached the target or not. If no obstacle is found, the robot continuously moves according to generated path till it reaches the final destination.  
Several algorithms are chosen for implementation on the 4 OWMR. The PRM, RRT, RRT\({}^{\star}\), and A* algorithms are described theoretically, applied, and compared experimentally, and their results are enhanced by using the genetic algorithm optimization technique (GA), as can be seen in the later sections. The theoretical explanation for the path planning algorithms is explained in the following subsections.
# Partial Discharge Wideband Full-Band High-Gain Resonant Cavity UHF Sensor Research
## 6 Conclusions  
For the current demand for UHF antenna sensors for PD detection of power equipment with broadband full-band high-gain antenna sensors, this paper carries out the research of broadband high-gain resonant cavity monopole UHF antenna for PD detection of power equipment, using curved-flow technology and slot double resonant structure to optimize the monopole antenna. Based on the idea of Fabry-Perot resonant cavity antenna for high gain antenna, the designed antenna was analyzed and verified by experiments:  
* Optimization of monopole patch antenna by using the idea of curved-flow technology and slot dual resonance structure, which greatly broadens the operating band of the antenna sensor and improves the antenna gain directionality. Based on the idea of Fabry-Perot resonant cavity antenna, partial reflection cladding was set up, so that the antenna sensor can obtain full-band high gain performance. The simulated and measured results show that the designed resonant cavity monopole antenna has VSWR \(\leq\) 5 in the frequency band of 0.37 GHz-3 GHz, excellent VSWR performance, average gain of 4.92 dBi, peak gain of 7.16 dBi at the main resonant frequency, simple fabrication process, and meets the design requirements of PD UHF antenna sensors.  
Figure 19: Detection of PD signal spectrum and noise spectrum by different sensor antennas: (**a**) high-performance flexible monopole antenna; (**b**) resonant cavity monopole antenna.  
Figure 18: Waveforms of PD signals collected by different sensor antennas.  
* Through the constructed partial discharge test platform, the designed resonant cavity monopole antenna is used to detect the typical air gap discharge defects, and compared with the mature flexible monopole UHF antenna, the results show that the resonant cavity monopole antenna can detect the PD signal more obviously, which proves the feasibility of resonant cavity monopole antenna for PD detection and reflecting the PD radiation electromagnetic wave energy spectrum characteristics.  
[PERSON] and [PERSON] proposed the research direction and supervised the writing of the paper. [PERSON]. ([PERSON]), [PERSON] and [PERSON]. ([PERSON]) designed the antenna model, improved the antenna model, completed the simulation and experiment, and wrote the manuscript. [PERSON] and [PERSON] revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Natural Science Foundation of China (52107144); This research was funded by Science and Technology Project of Hubei Education Department of China (Q20211401).  
Not applicable.  
The data presented in the article is original and has not been inappropriately selected, manipulated, enhanced, or fabricated by us.  
The authors declare no conflict of interest.