esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation of Numerical Methods  
### Validation of the Simulation of the Ice Constitutive Model  
The selected ice constitutive model is the 13 th material model \"MAT_ISTROPIC_ELASTIC_FAILURE [29] from the LS-DYNA material library. When the effective plastic strain reaches the failure strain or the pressure reaches the failure criterion, the unit loses the ability to carry the stress, and the bias stress is set to zero. This means the material is realized as a fluid state.  
To verify the accuracy of the ice principal model, this study numerically simulates the rigid plate impacting spherical head ice and compares it with the P-A theory curve proposed by [PERSON] in 2009 [30]. The model is shown in Figure 4.  
\[P=7.4A^{(-0.7)} \tag{10}\]  
where \(P\) is the pressure (MPa) and \(A\) is the contact area between the rigid plate and the ice (m\({}^{2}\)).  
Spherical ice is modeled using solid units. The model consists of a solid hemisphere with a radius of 1 m and a solid cylinder; the height of the solid cylinder is 1 m. The material parameters of the sea ice are shown in Table 2. The rigid plate is modeled as a solid with a side length of 2.5 m; the material parameters are shown in Table 3. The rigid plate is given a constant downward initial velocity v = 2 m/s to hit the spherical head ice.  
Contact is made between the spherical head ice and the rigid plate using the erosion contact keyword \"CONTACT_ERODING_SURFACE_TO_SURFACE in the LS-DYNA soft  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Density**} & **Shear** & **Yield** & **Harden-** & **Bulk** & **Plastic** & **Cut Off** \\  & **Modulus** & **Stress** & **Modulus** & **Modulus** & **Failure** & **Pressure** \\ \hline
**(kg/m\({}^{3}\))** & **(GPa)** & **(MPa)** & **(GPa)** & **(GPa)** & & **(MPa)** \\ \hline
910 & 2.2 & 2.12 & 4.26 & 5.26 & 0.35 & \(-4\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material parameters of ice.  
Figure 4: The rigid plate hits the ball head ice.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Density**} & **Density** & **Elasticity Modulus** & **Poisson’s Ratio** \\  & **(kg/m\({}^{3}\))** & **(GPa)** & **(GPa)** & & **(MPa)** \\ \hline
7850 & 200 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material parameters of rigid plate.  
ware. This software can be used to automatically delete the failed unit and automatically search for the effective unit to establish contact when the plastic strain of the ice unit reaches the failure strain or the pressure reaches the failure truncation pressure.  
Figure 5 shows a comparison of the P-A curves obtained from the rigid plate-bulb ice in this study with the ISO standard curves. As seen, the results fit well, thus verifying the reliability of the principal ice model material used in the collision simulation.  
It should be noted that, considering the changes in material parameters caused by the size of the ice, these parameters can be modified in the software when conducting different simulations.
## 3 Simulation Results
Furthermore, the simulation results confirmed that the proposed hybrid converter can simultaneously support two separate loads with opposing energy flows, showcasing its adaptability and utility in complex power distribution scenarios. In the context of the second application, the paper provided an analysis of the total harmonic distortion (THD) for the grid current. The results demonstrated that the new hybrid converter topology achieved a THD of 21.11% for the grid current. This indicates its effectiveness in mitigating harmonics and improving power quality in smart grid environments. Overall, the simulation and experimental study validated the advantages and performance enhancements offered by the proposed hybrid DC-DC converter. Its ability to achieve higher voltage gain, reduce voltage stress, support multiple loads, and maintain synchronization between output voltages makes it a promising solution for smart grid applications, including renewable energy systems and electric vehicles. The outcomes of this study underscore the potential of the proposed hybrid converter to contribute to the advancement of power electronics and enhance the efficiency and reliability of modern power conversion technologies.
# Clustering Analysis of Wind Turbine Alarm Sequences Based on Domain Knowledge-Fused Word2 vec
## 5 Results and Discussion
### Results
#### 5.3.2 The Clustering Results of All Alarm Sequences  
Based on the clustering results using labeled alarm sequences, it is determined that the optimal clustering performance for K-means clustering, using the distance metric WRD, is achieved when DK-Word2 vec has a weight of 0.4 and a semantic window length of 4. Consequently, these parameters were employed to cluster all the alarm sequences, including the labeled and unlabeled data. The average SC was computed for different numbers of clusters, as illustrated in Figure 10. The highest average SC is observed when there are 18 clusters, indicating that the clustering performance is optimal.  
Figure 10: Average silhouette coefficient scores obtained for different numbers of clusters. The highest average SC is observed when there are 18 clusters.  
Figure 9: The clustering results based on AVD: (**a**) The results of Purity; (**b**) the result of ARI.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**The Numerical Representation Methods of Alarms** & **Purity** & **ARI** \\ \hline Binary vector & 0.629 & 0.394 \\ Binary vector and feature Extraction & 0.662 & 0.467 \\ Skip-gram model & 0.766 & 0.624 \\ DK-Word2 vec & 0.831 & 0.729 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The comparison results of alarm numerical representation methods.  
Based on the number of labeled alarm sequences in each cluster and existing domain knowledge, we analyzed the alarm sequences within each cluster and identified the corresponding fault events. Furthermore, we conducted a statistical analysis of the number of alarm sequences within each cluster. The results are presented in Figure 11. Compared to the fault events corresponding to labeled alarm sequences, the wind turbine unwinding control, the restart of the wind turbine, tower faults, and low grease tank liquid levels are newly identified fault types. Therefore, these faults are primarily associated with unlabeled alarm sequences. Consultation with wind turbine maintenance experts revealed that the wind turbine unwinding control is a routine operation during wind turbine operation and does not require maintenance. Restart of the wind turbine is a predefined self-check and protection action in the wind turbine's main control system. Tower faults and Low grease tank liquid levels are faults that field operators tend to overlook.
## 3 Efficiency Analysis of Herringbone Planetary Gear
### Bearing Contact Power Loss  
Under heavy load and high-speed conditions, the herringbone planetary drive relies on the line contact between the rollers of the cylindrical roller bearing and raceway to withstand large radial loads. However, sliding and rolling contact between the cylindrical roller and inner and outer raceway surfaces, as well as viscosity of the lubricant and contact between the cylindrical roller and bearing cage, generate friction torque. Because the bearings were under high-speed and heavy-load conditions, the calculation model of friction torque introduced by SKF is more accurate. The amount of friction torque will depend on the type of bearing and its geometry. The parameters of the bearing are listed in Table 1.  
In Table 1, \(R_{1}\) is a geometric constant based on bearing type; \(S_{1}\) is a constant based on bearing type; \(S_{2}\) is a constant based on bearing type and seal; \(K_{r0}\), \(K_{L0}\), and \(K_{Z0}\) are geometric constants based on bearing type; and \(V_{M0}\) is the drag loss variable.  
The frictional moment of the cylindrical roller bearing is [26; 27; 28]  
\[M_{0}=\phi_{rl}\phi_{rs}M_{r0}+M_{s0}+M_{s0}+M_{d0} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & \(R_{1}\) & \(S_{1}\) & \(S_{2}\) & \(K_{L0}\) & \(K_{Z0}\) & \(K_{rs}\) & \(V_{M0}\) \\ \hline Value & \(1.48\times 10^{-6}\) & 0.16 & \(1.5\times 10^{-3}\) & 0.65 & 5.1 & \(6\times 10^{-8}\) & \(1.2\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of cylindrical roller bearing.  
Figure 4: Main factors affecting gear transmission power loss.  
where \(M_{0}\) is total friction torque of the bearing, \(M_{r0}\) is rolling friction torque of the bearing, \(M_{s0}\) is sliding friction torque of the bearing, \(M_{s0}\) is friction torque due to eddy current drag and splash, \(\phi_{ri}\) is the heat reduction factor, and \(\phi_{rs}\) is the lubricating oil backfill reduction factor.  
A single row of cylindrical roller bearings (SKF bearing series) that are not fully loaded is adopted in the study with no seal ring structure; therefore, the effect of the frictional moment of the seal ring is not considered, \(M_{s0}=0\).
## 2 Materials and Methods  
### Study Population  
A retrospective study, approved by the Institutional Ethics Committee of Arad County Emergency Clinical Hospital (approval number 92/20.01.20252), was conducted to determine age- and sex-related patterns in meniscal tears in patients undergoing first-time meniscal surgery. This investigation was performed in compliance with the principles outlined in the Declaration of Helsinki. Medical records of patients who visited the hospital between August 2015 and January 2025 for knee discomfort were reviewed to identify those who underwent their first surgical procedure for a meniscal injury. The study population included patients across the entire age continuum, irrespective of sex. The primary inclusion criteria were as follows: (_i_) first-time arthroscopic intervention for symptomatic meniscal tears confirmed via clinical evaluation and magnetic resonance imaging (MRI); (_ii_) intraoperative arthroscopic confirmation of meniscus tears (lateral, medial, or both, with or without chondropathy); (_iii_) availability of complete clinical and demographic data for all variables of interest--specifically, age, sex, diagnosis, and meniscal tear type; and (_iv_) documented failure of conservative treatment, primarily > 4-6 weeks of physical therapy and/or non-steroidal anti-inflammatory drugs.  
Exclusion criteria included (_i_) previous surgery on the affected knee, including arthroscopy, ligament reconstruction, or cartilage repair; (_ii_) concomitant ligament injuries such as anterior cruciate ligament, posterior cruciate ligament, or collateral ligament tears, confirmed via MRI or intraoperative findings; (_iii_) presence of systemic inflammatory conditions affecting joint health (e.g., rheumatoid arthritis, systemic lupus erythematosus, psoriatic arthritis); (_iv_) neurological or musculoskeletal conditions affecting gait, joint stability, or loading (e.g., multiple sclerosis, Parkinson's disease, cerebral palsy); (_v_) history of significant acute trauma to the affected knee (e.g., high-energy fractures, dislocations, or blunt force trauma) unrelated to isolated meniscal tears; (_vi_) cases with uncontrolled chronic diseases (e.g., diabetes, cardiovascular diseases); and (_vii_) incomplete or inconsistent clinical documentation, including missing or unclear preoperative imaging, surgical reports, or demographic information essential for eligibility verification and statistical modeling. All patients provided informed consent in accordance with ethical guidelines.
# Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG Data
## 2 Methods  
### Algorithm  
#### 2.1.1 Overview  
First, our algorithm (Figure 1) preprocesses the EEG potential signal to prepare for later analyses [1, 3]. Next, it applies multitaper analysis to the signal from each channel (4 s intervals with 3 s overlap) to create a spectrogram that provides, for each 1 s segment, a vector of power values, with one value corresponding to each 0.25 Hz frequency band between 0.5 and 32.5 Hz. It computes the Spearman correlation coefficient between the power vectors for each 1 s segment for every pair of channels, yielding 15 time series of correlations in power--one time series for each pair of channels. From these 15 pair-specific time series, the algorithm creates six channel-specific time series of average correlations by averaging the five pairwise correlations that share a common channel segment by segment. In each channel-specific average time series, the algorithm identifies \"bad\" segments using a moving window-based local threshold approach [9] and considers all remaining segments \"good\" segments. A channel-specific global threshold for outlier detection is derived using the pool of \"good\" segments for that channel.  
In a second pass, a 1 s segment within a channel is declared an outlier using a global threshold based on the pool of all \"good\" segments for the channel. Any segments falling between two outliers that are within 5 min (10 epochs) of each other are also declared outliers. Because the average correlation between any given channel and each of the other five channels can be affected by outliers in any one of the five, the algorithm proceeds iteratively. A channel with more than 5% of its segments declared as outliers is excluded from subsequent channel-specific averages of pairwise correlations but is not declared an outlier channel yet. The process is repeated until no additional channels can be excluded (Figure 1).
## 4 Comparative Discussion
### Performance  
The performance of the above mentioned protocols were evaluated and compared in terms of maximum temperature rise, average packet delivery delay and packet drop rate in Figures 1-3 of the work [25], respectively. The shortest hop routing (SHR) protocol was evaluated as the representative of non-temperature-aware routing protocols, and the four temperature-aware protocols of TARA, LTR, HPR and TSHR were evaluated quantitatively.  
In terms of maximum temperature rise, the TSHR protocol outperforms all other protocols, while the SHR protocol is the worst. As for the packet delivery delay, SHR is the best and HPR is the second best. The TARA protocol performs the worst, because, in TARA, every time a node encounters a hotspot, it will send the packet back to its previous sender and try to find another path. This mechanism increases the packet delivery delay. The TARA protocol, again, performs the worst with regards to the packet drop rate, as opposed to SHR and TSHR, in the first and second place. A longer path in TARA causes the higher packet drop rate, whereas, in SHR and TSHR, the shorter path tends to reduce the packet drop rate. For more information, interested readers may refer to the performance evaluation results in [25].  
The performance of the RAIN protocol is compared with that of a flooding algorithm, called C-FLOOD in [26]. It is reported that RAIN outperforms C-FLOOD in terms of maximum temperature rise, energy consumption, packet delivery ratio and average packet delivery delay.
## 5 Results and Discussion
### Throughput  
The throughput performance of the MRLAM, MP-OLSR, and MP-OLSRv2 routing schemes with increasing node speed shown in Figure 4 indicates that the MRLAM scheme constantly provides acceptable and better throughput performance than the MP-OLSR and MP-OLSRv2 routing schemes in all scenarios of node speed. Higher throughput achieved through the MRLAM scheme is indicative of the mobility awareness consideration factored in during the optimal route selection process, whereas the other two routing schemes do not consider mobility awareness, especially when a link failure occurs as a result of high node's mobility. The MP-OLSR routing employs route recovery and loop detection, while the MP-OLSRv2 employs the topology sensing and route computation mechanism for the selection of an intermediate node in the route. These mechanisms need to transmit extra packets in the network when the route is established, which lead to reduce number of packets received at the destination at the particular time period. The route selected by the MRLAM scheme usually has lower mobility or more energy level than other routes; therefore, the link is more stable and ultimately experiences very few packet drops, which in turn maximizes the throughput. The link failure of nodesin MRLAM routing is estimated by using ETX and EAX parameters. Based on the values of these parameters, the proposed routing scheme diverts the packet flow toward the intermediate nodes which have better link quality. As the node speed increases, the number of link breaks with MRLAM remains lower than MP-OLSR and MP-OLSRv2 routing schemes and accordingly, the throughput decreases constantly. Throughput decreases from 47.39 kbps to 39.87 kbps for MP-OLSR, from 49.17 kbps to 40.87 kbps for MP-OLSRv2, and from 50.86 kbps to 42.63 kbps for MRLAM when the node speed varies from 10 m/s to 60 m/s respectively. The proposed routing scheme selects the route based on the status of link quality metric, which reduces the number of transmissions and increases channel utilization efficiency upon which reliable link is selected. This, in turn, increases the throughput of the whole network. Compared with the other schemes, critical analysis of the simulation results demonstrates that MRLAM scheme clearly performs better when it comes to selecting intermediate nodes that are more qualified in terms of low mobility, energy level, and link quality. As a result, the route recovery process invokes less frequent link failure; therefore, improved overall network throughput is achieved.
## 2 Background and Related Work
### Background
#### 2.1.2 Attack Tree  
An attack tree [9] is a well-known threat modeling technique. The attack tree expresses possible security threats to the system through a tree and sequentially connects a series of actions to achieve the goal. The attack tree generally consists of root, leaf, and intermediate nodes [10]. The root node is at the top of the tree, meaning the attack's ultimate goal. The leaf node (or the terminal node) is located at the bottom of the attack tree, consisting of initial attacks to reach the upper node. The intermediate node represents the condition to direct the upper node to achieve the goal. These nodes are generally connected to upper nodes by gates, such as AND and OR gates. The AND gate indicates that all lower nodes must be successfully executed to reach the upper node (subgoal or goal). On the other hand, the OR gate indicates that the upper node can be reached even if only one of the lower nodes is successfully executed. The attack tree is used in various threat modeling studies [18; 19]. Recently, an augmented attack tree with notions, such as sequential AND (SAND) gate and priority, has been employed by extending the conventional attack tree [20; 21; 22; 23].
## 3 Data and Methods
### Cool Roofs  
Cool roofs are painted with bright colors using thermal barrier paint that reflects and radiates both sunlight and solar heat; these effects result in less heat accumulation by the roof and less heat transfer into the building. Cool roofs were installed at 134 locations (23,264 m\({}^{2}\)) within the study area, excluding roof materials such as slate and asphalt shingles, to which it is physically impossible to apply thermal barrier paint. Our experiment involved four households that were able to cooperate for the duration of the study period. Experimental sites were matched with control sites that had the same roofing material (slab or panel; see Table 1); matched sites were selected within nearby locations. Surface  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Cool Roof** & **Cool pavement** \\ \hline Experimental date & 26 August & 19 September & 6 October & 26 October \\ Measurement times & 1 h intervals (9:00–15:00) & 2 h intervals (07:00–21:00) \\ Location & \multicolumn{2}{c}{Residential area *} & Traditional market & Residential area \\ Type of material & Slab & Panel & Parking lot & Alley \\ Control group & R-1 & R-2 & P-1 & A-1, 2 \\ Experimental group & R’-1 & R’-2 & P’-1\(\sim\)6 & A’-1\(\sim\)4 \\ Monitoring & Thermal imaging camera, & & Thermal imaging camera \\ equipment & \multicolumn{2}{c}{temperature data logger} & \\ Material & \multicolumn{2}{c}{Heat-reflective paint Agdereoncat (NCK Global Paint, Singapore)} & \\ Solar reflectance & \multicolumn{2}{c}{RGB 96\%, Infrared 89.4\%} \\ \hline \hline \end{tabular} \({}^{\star}\)Indoor and outdoor.  
\end{table}
Table 1: Description of experimental setup for surface temperature measurements at heat mitigation facilities in 2019.  
Figure 2: Study area in Jangyumugye district. (**a**) Residential area with cool roof and cool pavement systems. (**b**) Traditional market with cool pavement installed.  
The monitoring of slab and panel roofs was conducted on 26 August and 19 September 2019, respectively. A thermal imaging camera was used to measure roof surface temperature; a wireless temperature data logger was used to measure indoor air temperature (Table 3). From 9:00 to 15:00, the roof surface temperature and indoor air temperature were measured at 1 h intervals.
# Synthesis and Biological Activity of 1,3,4-Oxadiazoles Used in Medicine and Agriculture
## 4 1,3,4-Oxadiazoles Used Commercially in Agriculture and Medicine
An interesting compound with anticancer activity is _Zibotentan_ **(12)**. It is a specific ETA receptor antagonist used in the treatment of severe prostate tumors. The mechanism of action of _Zibotentan_ is based on the inhibition of apoptosis and cell proliferation. At higher concentrations, it is also able to stop the growth of blood vessels within neoplastic tissue [93]. It has been proven in preclinical studies that the combination of _Zibotentan_ and _Paclitaxel_ exhibits a synergistic effect; in particular, it increases apoptosis [94]. Research is ongoing on the effectiveness of this chemotherapeutic in terms of its use against ovarian and breast cancer [95]. An example of a medicinal preparation used in cases of cardiovascular diseases is _Thiodiazosin_ **(13)**. This compound includes a quinazoline structure and a 1,3,4-oxadiazole  
Figure 5: Compounds with herbicidal, insecticidal, and antibacterial activity: 3,5-dihalophenyloxypyridines [78], oxadiazole [86], 2,5-bis(2,4-dichlorophenyl)-1,3,4-oxadiazole (DCPO) [87], and indole-1,3,4-oxadiazole hybrid [85]. core. It has antihypertensive activity. The mechanism of action is based on blocking adrenergic receptors, leading to the relaxation of vascular smooth muscles and the inhibition of the secretion of norepinephrine secreted from the adrenal glands. _Thiodazosin_ is used as a first line treatment when there is a need to treat cardiovascular disease related to hypertension. An additional benefit of _Thiodazosin_ is its prolonged half-life in blood plasma compared to another drug with a similar effect--_Prazosin_. As a result, the therapeutic concentration of the drug in the blood is prolonged, extending its action in vivo [96]. Another compound used in medicine is 2-amino-5-[2-(5-nitro-2-furyl)-1-(2-furyl)-vinyl]-1,3,4-oxadiazol, commonly known as _Furamizole_ (**9**). This molecule is a derivative of nitrofuran, which has strong antibacterial activity [20].