esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Physicochemical Properties of EHN/SVO Double Blends and D/EHN/SVO Triple Blends
Performance of EHN/SVO Double Blends and D/EHN/SVO Triple Blends in a Diesel Engine Operating as an Electric Generator  
The diesel engine performance using the triple blends was evaluated experimentally to determine the optimal ratios of EHN and straight vegetable oils (SVO) in these formulations, and it is shown in Tables 3 and 4. Figure 1 provides a comparative analysis of the power output under varying engine loads for triple blends containing sunflower oil (Figure 1a) and castor oil (Figure 1b). Additionally, the performance of conventional fossil diesel and pure biofuels (Bio100 SO and Bio100 CO) is included for reference, offering a comprehensive overview of the blends' effectiveness. The results indicate that the type of SVO used in the triple blends has no significant effect on performance, as the power output increases nearly linearly with demand, from 1 kW to 5 kW. Furthermore, all the blends demonstrated higher power output compared to pure fossil diesel, suggesting that the addition of EHN and vegetable oils enhances the engine power generation capacity across various load levels. It is noteworthy that the pure double blend Bio100 CO outperforms all other blends, showing a 12.6% improvement in power output over fossil diesel. Similarly, the Bio100 SO blend performs comparably to fossil diesel, achieving an 8% increase in power output  
Figure 1: Power generated (in watts) based on the power demanded (in kW) when operating with different triple blends: diesel/EHN/sunflower oil (**a**) and diesel/EHN/castor oil (**b**). These results highlight the potential of both pure biofuels, Bio100 SO and Bio100 CO, as viable alternatives to fossil diesel, offering full replacement capabilities without sacrificing power generation. Additionally, all the triple blends also generate higher power output while enabling a significant reduction in fossil diesel utilization. Among these blends, Bio60 SO and Bio60 CO are the most efficient, delivering the highest power output across all the blends. This suggests that triple blends can offer an optimal balance between renewable content and engine performance. Despite the excellent results of the triple blends, both pure biofuels, Bio100 SO and Bio100 CO, remain viable for fully replacing fossil diesel without any reduction in power generation. The remarkable performance of Bio100 CO also points to its potential for reduced fuel consumption, making it a particularly efficient and sustainable option.
## 3 Simulation Analysis of PD Signal Denoising  
### Simulation Signal Generation  
According to research, the original noise-free PD signal can be simulated by four distinct models with parameters in Tables 1 and 2 [29]:  
The most prevalent types of noise encountered in on-site PD signals are narrowband noise and white noise [30]. This simulation includes the \(-20\) dB Gaussian white noise and narrowband noise to bring the PD simulation signal closer to the actual signal. Narrowband noise in the engineering environment consists primarily of carrier communication interference and broadcast communication (medium band 0.5-1.6 MHz, short band 2.3-25 MHz, FM band 88-108 MHz). Combining sinusoidal signals with varying amplitudes allows us to simulate narrowband noise. In this paper, the frequency of simulating narrowband interference is set to 9 MHz, 14 MHz, and 96 MHz. Consequently, the original noiseless and polluted PD signal can be successfully simulated in Figure 3:  
Apparently, the original PD signal has been totally submerged in noise.  
\begin{table}
\begin{tabular}{l l} \hline
**Model** & **Equation** \\ \hline Single exponential decay model: & \(f(t)=Ae^{-\frac{t}{t}}\) \\ Double exponential decay model: & \(f(t)=A\Big{(}e^{-13\frac{t}{t}}-e^{-2.2\frac{t}{t}}\Big{)}\) \\ Single exponential oscillation decay model: & \(f(t)=Ae^{-\frac{t}{t}}\sin(2\pi f_{c}t)\) \\ Double exponential oscillation decay model: & \(f(t)=A\Big{(}e^{-13\frac{t}{t}}-e^{-2.2\frac{t}{t}}\Big{)}\sin(2\pi f_{c}t)\) \\ \hline \end{tabular}
\end{table}
Table 1: Decay models and their equations.  
Figure 3: Simulated noiseless and polluted PD signal.
## 4 Strengths and Limitations  
To the best of our knowledge, this is the first time the DISCERN instrument has been used for evaluating the content of the World Wide Web. From the study, we learned that those websites that appeared on 3 rd and 4 th click of the search engine are not necessarily of good quality.  
On the other hand, there are several constraints that must be kept in mind when applying the findings of this work.  
The search engines are continuously updating and improving their search strategies. The same query for a search may produce different search items in the future which may lead to different results. Also, because we chose the first 20 hits (first 2 pages) from each search engine, we may have missed high quality websites just because they were not amongst the first 20 search results. Although DISCERN is a reliable tool, it does have its own limitations. For example, it does not tell anything about display/presentation of information or browsing and locating information on a website. We also excluded scientific websites from the list of candidate websites due to the limitations of DISCERN.  
Similarly, there are certain limitations of the Flesch-Kincaid tool. For example, most of the medical terminologies are long in length, due to which the number of characters in a sentence increases. Thus, the complexity of an article increases according to the Flesch-Kincaid tool which is not a fair reflection of the actual result. Furthermore, a website may have a greater level of complexity if it has large information content compared to a smaller one. Similarly, complexity and readability of a document does not depend only on the length of words and sentences but also on other factors [17]. Previous studies show that readability formulas do not take graphic or pictorial material into account [40], so it may also affect the overall readability of a website. Furthermore, this study is restricted to websites written in the English language only; quality information may exist on non-English language websites too. There may also be a difference in search results depending on region. Our findings are based on search queries conducted in Malakand, in the Khyber Pakhtunkhwa region of Pakistan. The mentioned reasons may produce variation in results.
# Dbrutinib Could Suppress CA-125 in Ovarian Cancer: A Hypothesis
## 3 Hypothesis  
In our case report, we describe a patient with low-grade ovarian cancer experiencing a prolonged normalization in CA-125 under concurrent brtutinib treatment. Although not necessarily causal, the timely fashion of the protein decline and subsequently incline after termination of treatment is staggering.  
From these observations, the next step was to formulate a bio-molecular hypothesis that could mechanistically explain a decrease in CA-125 after birutinib intake. We proposed the following model, as depicted in Figure 2. Ibrutinib inhibits the phosphorylation of EGFR (1-4) as well as Src [9] and the FGFR in mesenchymal cells [10]. This blocks downstream effectors and stops EGFR/Src from activating RAS [11]. Subsequently, the oncogenic RAS pathway, which has been shown to encourage MUC16 upregulation via RAF, MEK, and ERK [8], is not activated. Finally, MUC16 production and CA-125 shedding diminish. MEK inhibitors affect proteins further downstream the pathway and have been described to normalize CA-125 levels [12].  
Figure 2: Proposed mechanism of ibrutinib-mediated suppression of CA-125 production along the RAS–RAF–MEK–ERK pathway including additional inhibitory sites (created with Biorender.com).
## 4 Results and Discussion
### Element Associations
#### 4.3.1 Fluvial Channel and Mid-Channel Downstream Accreting Bar  
At site III, the combination of the fluvial channel and mid-channel downstream accreting bar with a genetic relationship is identified in a 40 m long longitudinal profile with an east-west orientation (Figure 6). The bottom erosion interface of the combination unit deposited 20-30 cm thick sparse gravel lag flooring, which is also a fifth-level boundary surface for the channel belt [54], but due to the exposure problem, the corresponding fifth-level boundary surface is not recognized on the upper boundary. The head of the downstream accreting bar has been eroded by the upstream channel to varying degrees. When viewed in the direction of the source, the shape of the channel is an elongated wedge that erodes into the head of the bar for approximately 15 m. When observed in the direction perpendicular to the source, the downstream accreting bar has a lenticular shape with a flat bottom and a convex top. The axial part is the thickest, and it gradually becomes thinner toward the side. The directions of inclination are opposite, with dip angles of 6\({}^{\circ}\) to the northwest side and 8\({}^{\circ}\) to the southeast side, which is consistent with the internal bedding, but the top of the bar is eroded in most cases (Figure 5). The channel on either side of the bar is usually relatively flat and has an inconspicuous asymmetrical lenticular (flat top, convex bottom) shape. Thin channel deposits overlying the bar are sometimes possible to see. However, the channel deposits at the side edge of the bar usually maintain a similar sedimentary stratification to the side edge of the bar, but the grain size is significantly fine-silt and silty mudstone. In this study, six combination units of fluvial channel and mid-channel downstream accreting bar with a genetic relationship are identified, among which the thickness range of the single-channel thread is 1.4- 6.5 m and the width range is 30-120 m. The thickness of the downstream accreting bar in each period is between 1.5 and 6 m, with an average thickness of approximately 2.4 m. The thickness of multiple periods can reach 10 m, with a horizontal extent ranging from 40 to 160 m.
# DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios
## 1 Introduction
**Dimension-Augmented TimesFM:** A pretrained time series model enhanced with cross-pool data augmentation, leveraging DTW-based similarity matching to align and integrate related time series while mitigating distribution shifts;
3. **Comprehensive Validation:** Extensive experiments on real-world cloud resource data from a Chinese telecom operator demonstrate that our framework reduces prediction errors by 73.7-82.1% (MAE), 73.8-79.1% (MAPE), and 72.9-81.7% (RMSE) compared to state-of-the-art baselines, while exhibiting superior robustness in challenging scenarios. This work provides enterprises with a practical solution to align resource allocation with dynamic demand patterns, ultimately reducing operational costs and improving sustainability in cloud ecosystems.
## 3 Results and Discussion
### Relating Samples to VOCs by Univariate Analysis of Variance (ANOVA)  
ANOVA is a first, straightforward approach to relate spoiled or normal samples with VOCs. Because of the data structure, the analysis was performed after their _clr_ transformation and constraining the sum of model coefficients to 0 (null). The significant positive standardised coefficient for a sample means a relevant contribution to the model (high concentration of the compound in the sample). In contrast, low negative contributions correspond to low presence/absence [6], and their interpretations (out of the scope of the work) will not be intended. In this line, the contribution of F508, affected by sulfidic spoilage, to the response (level of dimethyl sulfide (V2)) is outstanding (Figure 2A) as well as that of F282 and F283 to methyl butanoate (V13) (Figure 2B). Therefore, dimethyl sulfide and methyl butanoate should be considered as, at least, one of the components that characterise sulfidic (F508) and butyric (F282 and F283) spoilages, respectively. The low (but significant) contribution of F562 to the dimethyl sulfide (V2) and F502 to methyl butanoate (V13) models could be interpreted as the result of an overcome initial spoilage.  
Figure 1: pH (**A**) and salt (**B**) values obtained for the different fermentation vessels at 1 month of fermentation. The horizontal line stands for habitual values under normal conditions for lye treated olive post-fermentation storage [1]. * Spoiled samples.  
Including all the ANOVA graphs in the text is not feasible. Instead, the coefficients corresponding to the samples with significant contributions to the different VOCs' models are summarised in Supplementary Table S2. Therein, the most relevant values have been highlighted in bold. The following comments will be focused on them.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### Location-Based Routing Schemes
#### EMGGR
Moreover high transmission power also increases interference and contention. QERP  
QERP [30] is a cluster-based quality of service aware routing protocol. It aims to mitigate the effects of underwater channel impairments to provide QoS guarantees, such as high packet delivery ratio, low delays, etc. To achieve this end, QERP uses a small clustering technique, in which sensor nodes are connected into a hierarchy, which helps in even distribution of energy and traffic load. Routing path selection and forwarding decisions are made in a greedy manner, keeping in view the link quality between clusterheads. Moreover, the use of quality of service aware shortest path selection algorithm enables reduction in route loops, thereby reducing network delays, and achieving better energy efficiency. Furthermore, failure of a forwarding node is dealt with by adjusting transmission power based on information from routing tables, thus avoiding connectivity voids. QERP improves  
Figure 6: Network model. packet delivery ratio and reliability by selecting relays based on link condition and the shortest distance to sink node. However, due to the relatively higher load, clusterheads may consume their energy quickly, thus creating energy holes. EULC [31] is a cluster-based routing scheme. In EULC, the network is divided into layers of unequal width, with the shallower layers having less width compared to deeper layers. This layering strategy mitigates the \"hot spot\" problem by regulating transmission power levels based on a node's distance from the sink node. Clusters are formed within each layer. Clusterheads are selected depending upon the remaining energy of the candidate node, degree of the candidate node (which is a function of the neighboring nodes of the candidate node), and its distance from sink. Data transmission starts after setting up clusters and clusterheads. Routing is done taking into account the distance between the sender and a candidate forwarder, and the residual energy level of the candidate forwarder. EULC increases network life by setting contending radii of CHs based on its distance from sink, thus reducing cluster scale near the sink. Moreover it balances energy dissipation by selecting CHs based on residual energy level.
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 6 Tsunamis
### Reviewed Use Cases for Tsunami EW
When the distance between the sensor and the water raises above a certain threshold, the GSM module mounted on the IoT devices sends an SMS with a warning message before the water level becomes dangerously high. A sound indicator is also installed on the device to alert nearby people. The prototype was successfully tested but the ultrasonic sensor cannot read any water level data if put at a distance higher than 3.19 m from seawater, and as such specific data about seawater conditions are needed before installation. [PERSON] et al. [85] designed a buoys satellite system for ocean monitoring and tsunami early warning. The first deployed system consisted of a buoy equipped with a Global Navigation Satellite System (GNSS) antenna able to transmit data to the coast before the wave reaches it. The received data (sea level, buoys position) allows users to visualize the motion of the sea surface at the buoys in real time. The development of this solution began in 1997, with subsequent deployments in 2001-2003, 2004-2006, and 2011. These previous solutions showed that buoys placed 20 km from the coast did not provide enough time to warn the coast and evacuate it. They also provided data to address some critical issues about buoys design, with the goal of each buoy surviving for at least 10 years. Since the required buoy distances could be of above 100 km, the Precise Point Positioning algorithm is proposed to know the position of the buoys alongside satellite communications. Field experiments showed accuracy of a few centimeters in buoys positioning. One early experiment showed data gaps, possibly due to the tilting of the buoy degrading the communication, so a mechanism to stabilize the antenna was used in subsequent ones. The paper also proposes the use of buoys to make ocean bottom motion measurements, which provides less accuracy than solutions such as S-Net but better resolution. The experiment used three transponders placed on the bottom of the ocean and communicating with the buoys with an acoustic link. Ocean crust motion measurement can be obtained by estimating the transponders position. [PERSON] et al. [86] designed an IoT device to generate early warnings when anomalies in wave behavior are detected, using Fuzzy Logic for accurate predictions. The sensor node consists of a gyroscope, an accelerometer, and a LoRa module for data transmission. The sensor floats in the sea and senses and processes data to obtain information on wave height and wave speed. These two parameters are then used to classify the wave using the Fuzzy Logic algorithm embedded in the sensor unit. Sensed data is then sent to a server using the LoRa module. The classification algorithm provides both a danger label (safe, standby, dangerous) and a danger level (a value from 0 to 100) by computing height and speed of the waves. The Fuzzy Logic algorithm bears a 98% to 100% accuracy in testing phase. The LoRa communication was also tested, with 4.6279 s of delay and minimal error rate.
# Numerical Analysis of the Settlement Behavior of Soft Soil Improved with Stone Columns
## 5 Conclusions  
The present parametric numerical modeling study was carried out to investigate the influence of key design parameters on the settlement performance of narrow footings supported by small groups of stone columns at the Bothkennar site. Key parameters that were studied are column spacing, column length, presence of a crust layer, footing shape, column diameter, and column strength. The following conclusions are drawn based on this work:  
* A working applied pressure of 50 kPa is deemed acceptable to avoid excessive settlement and can be used as a guidance when designing pad footings on top of stone columns in soft soils.
* The ground surface settlement profile diminishes with distance from the centerline of the pad footing, reaching zero settlement at a distance of two to three times the footing length.
* Increasing the column length beyond the critical length does not enhance the bearing capacity performance but will contribute to improving the settlement performance.
* The stiff crust layer at a shallow depth has a minor influence upon the settlement performance for low-load increments. However, the rate of settlement increases at higher applied pressures (bearing capacity performance) for a footing resting on top of the soft clay (no crust layer).
* The predicted settlement of a square pad footing is comparable with the settlement of a narrow strip footing for the same area replacement ratio. The stress depth influence is deeper for the square pad footing; hence, the crust layer may be playing a significant role in masking the beneficial contribution of the strip footing.
* A decrease in settlement is observed with increasing stone column diameter and strength.
* Drained and undrained analyses are found to agree very well in predicting the long-term settlement.
* The Priebe design method [37] significantly underestimates the settlement performance of the trial footings at the Bothkennar site. It is recommended to use the Priebe method with caution and verify the outcomes with field trials or numerical analysis when stone columns are installed in sensitive clays and when a crust layer is present.  
Conceptualization, W.A.-A.; methodology, W.A.-A. and D.W.; software, W.A.-A.; validation, W.A.-A.; formal analysis, W.A.-A.; investigation, W.A.-A.; writing--original draft preparation, A.G.; writing--review and editing, D.W. and W.A.-A.; supervision, D.W. and A.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 4 Experimental Results  
In the experiments, we first describe the characteristics of the eight selected classification problems; secondly, we explain the assessment measures and the values of the learning parameters, and then we show the results obtained through cross-validation. Finally, we illustrate an example of a generated ruleset. In this part, a key research question is whether the proposed rule extraction technique provides good results with respect to other known algorithms.
## 3 Results and Discussion
### Economic and Resilience Analysis of Microgrid Systems  
Economic viability and resilience are essential considerations for the successful deployment and long-term operation of microgrid systems. While microgrids improve energy reliability and support the integration of RES, their development depends on achieving financial sustainability and design strategies capable of managing operational risks.
# Depth Evaluation of Tiny Defects on or near Surface Based on Convolutional Neural Network
## 2 Materials and Methods
### Experimental System and Methods  
The non-contact laser ultrasonic data acquisition system is shown in Figure 2. The ultrasound is excited by a 1064 nm pulsed laser (WEDGE HB 1064 from Bright Solutions, Pavia, Italy)) with a pulse duration of 1.5 ns and an operating frequency of 2 kHz. The max pulse energy is 2.12 mJ. A galvanometer (JD1105 from Sino-Galvo, Beijing, China) with a dynamic focal lens is used for pulsed laser beam scanning with a maximum angle of \(\pm\)15\({}^{\circ}\) and scanning speed of 7 m/s. The laser beam is focused to a spot beam, and the spot size is 100 \(\upmu\)m. A two-wave mixing interferometer (QUARTET--1500 from Bossa Nova Technologies, Culver City, CA, USA) with bandwidth of 100 MHz is used for the reception of the ultrasound on the sample surface. The laser used for the interferometer is a 532 nm continuous-wave laser. The power of the laser is 1 W, and the spot size is 50 \(\upmu\)m. When a trigger signal is transmitted from the galvanometer, the pulse laser emits, and the acquisition card (NI PXIe--5260 from National Instruments, State of Texas, USA)) receives an ultrasonic A--scan signal with a sampling frequency of 1.25 GHz and a 16-bit resolution.  
Figure 1: Schematic diagram for the sample.  
Figure 2: The testing system: (**a**) schematic diagram; (**b**) photograph of the real product.  
The different functional modules of the testing system are shown in Figure 3a, and the C-scan mode is used for data acquisition (Figure 3b). The excitation laser and receiver laser scan simultaneously with a fixed distance of 2 mm during scanning. The scanning range should cover the target area. The scanning step is set to be comparable to the size of target defect. The raw data are stored in a three-dimensional matrix, which provides abundant ultrasonic data for further signal processing (Figure 3c). The C-scan image could be plotted if the two-dimensional amplitude matrix at a specific time is extracted from the raw data matrix, and the A--scan can be extracted from each collection point of the data matrix.