esa-sceva/satcom-chunk-collection huggingface dataset:
# Fire-Induced Changes in Soil and Implications on Soil Sorption Capacity and Remediation Methods
## 2 Factors Which Determine the Effect of Fire Events on Soil Properties
### Type of Vegetation  
Vegetation is the main fuel responsible for wildfire events [21; 22]. Fire intensity generally increases with increase in vegetation cover because of availability of fuel. It is not only the quantity, but the type of vegetation that dictates fire intensity and, consequently, the changes in soil properties. Vegetation flammability varies throughout the year due to changes in its chemical composition, moisture content, leaf biomass and the proportion of dead to live plants [27]. The intensity and duration of fire could therefore also vary throughout the year according to vegetation characteristics. [PERSON] et al. [28] showed that higher soil temperatures are encountered during fire events fueled by woody species compared to those fueled by grasses and shrubs. Furthermore, fires on soils with woody species are hotter and also burn for longer periods, a characteristic which has been attributed to the high content of lignin in woody species. Lignin is a high-molecular-weight insoluble polymer composed of methoxylated derivatives of benzene and are thus aromatic in nature. This complex aromatic structure locks up a higher amount of energy in its carbon chain compared to aliphatic structures. Lignin therefore provides fuel over a longer period of time, hence the longer duration of fire on soils covered with woody species. On croplands with plant residue that comprise of cellulose- and glucose-rich polysaccharides (aliphatic compounds), wildfire events would cause soils to be heated for shorter periods because these compounds have a less complex structure with lower amounts of energy when compared with lignin. Soils under woody vegetation are therefore most likely heated to higher temperatures and for longer periods during fire events compared to soils under non-woody vegetation. [PERSON] et al. [29] have reported that soils in areas that are sparsely vegetated experience higher temperatures during fire events than more densely vegetated areas. This, however, contradicts the findings of [PERSON] et al. [28], who established a positive correlation between high vegetation density and high soil temperatures during fire events. Other factors besides fuel content could therefore be responsible for soil temperatures attained during fire events. The presence of stumps, logs, roots and peat in soils also provide fuel for combustion and could result in soil temperatures of up to 1500 \({}^{\circ}\)C during fire events [25]. Vegetation not only influences the temperature of surface soils, but through the network of roots below the soil surface, it also affects the depth to which subsurface soils are heated.
## 3 Results and Discussion  
### n-Hexadecane Assimilation  
Previous studies showed that the strain _Y. lipolytica_ CMGB32 has high ability to assimilate \(n\)-hexadecane when grown on a minimal BS medium, with a plateau between the 6 th and the 10 th day of growth [39]. This period corresponds to the time required for the DCPIP color change observed on the 6 th and 9 th days of the experiment using the YPG- and YP-grown inoculum, respectively. The longer period needed for DCPIP decolorization by the YP-hexadecane-grown inoculum can be related to a slower cell metabolism caused by the changes induced in the expression of genes associated with the assimilation of hydrophobic substrates, compared to the cells growing on glucose, in the case of YPG medium inoculum. Although the expression of genes associated with non-conventional carbon sources (such as \(n\)-alkanes) is essential for the \(n\)-hexadecaneassimilation of the metabolic shift, in the case of yeasts, it is time dependent and requires prolonged exposure to support an exponential growth in the first hours of cultivation. Meanwhile, no decolorization was observed for the inoculum and substrate control tubes.  
The information on the DCPIP method used for biodegradability tests on yeasts is rather poor. However, ref. [40] described a DCPIP decolorization time ranging from 8 to 29 h for a _Candida vishvanathi_ strain able to degrade various biodiesel blends. Also, ref. [41] reported two strains--_Rlodotuta aurantiaca_ UFPEDA 845 and _Candida enrobii_ UFPEDA 862--as inducing a positive response in the presence of DCPIP and diesel oil within 16 and 24 h, respectively. The shorted DCPIP decolorization periods mentioned in the cited literature can be most probably related to the composition of the carbon substrate, which is more accessible for the yeast cells compared to our experiment, where the sole carbon source is represented by the \(n\)-hexadecane.  
The MATH value (82.61%) determined after three days in the presence of \(n\)-hexadecane revealed the high hydrophobicity of the _Y. lipolytica_ CMGB32 cells. The result corresponds to other studies [29] in which _Y. lipolytica_ cells showed 76.5-80.2% hydrophobicity when grown on a mixture of C\({}_{12}\)-C\({}_{16}\) alkanes, but after seven days of incubation. Our results suggest a higher ability of _Y. lipolytica_ CMGB32 cells to adapt to hydrocarbons substrates, such as \(n\)-hexadecane. The ability of yeast cells to attach to the alkane droplets was also evaluated using an optic microscope. Yeast cells and large pseudohyphae corresponding to morphological modifications related to the adaptation to hydrocarbons substrate adhere to \(n\)-hexadecane droplets (Figure 1).
## 2 Materials and Methods
### Cloning of shRNAs and gRNAs  
The gRNA for correction of the _DNMT3A_ R882H mutation in SET-2 cells using the adenine base editor ABESe was designed manually. For validation of the screen results with RNAi, the two top scoring shRNAs per candidate gene, according to the obtained screen data, were selected (Table S2). For validation of the screen results with the CRISPR system, GPP sgRNA Designer [24], the CHOPCHOP V3 [25] and InDelphi [26] tools were used. All oligos were synthesized by metabon international AG (Planegg, Germany). The oligos were processed and cloned into pLenti-U6-tdTomato-P2A-BlasR (LRT2B) (a gift from [PERSON], Addgene #110854), pRS12.U6-shRNA.UbiC-GFP-T2A-Puromycin (Cellecta) or pL.CRISPR.EFS.GFP (a gift from [PERSON], Addgene plasmid # 57818), respectively, following the protocol described in [27]. In brief, the oligos were phosphorylated and annealed with T4 PNK in T4 ligation buffer, digested with BbsI and ligated with T4 ligase in Tango buffer (all from New England Biolabs, Ipswich, MA, USA). The ligate was transformed into home-made electrocompetent XL1 blue cells at 1700 V. Home-made SOC medium was added, and the bacteria were plated on Ampicillin agar plates. The colonies were picked after overnight incubation at 37 \({}^{\circ}\)C and a colony PCR was performed with MyTaq Red Polymerase (Bioline Meridian Bioscience Inc., Cincinnati, OH, USA) following the manufacturer's instructions using the primers U6-seq-F and shRNA-seq-R (for shRNA inserts) or U6-seq-F and gRNA-seq-R (for gRNA inserts) (Table S1) with the indicated annealing temperatures, an extension time of 20 s and 30 cycles. The PCR products were sent to the sequencing service provider Microsynth Seqlab GmbH (Gottingen, Germany). The positive clones were grown overnight in 230 mL LB under Ampicillin selection (50 \(\upmu\)g/mL) and the cultures were processed with Qiagen Plasmid Maxi Kit (Qiagen, Hilden, Germany) according to manufacturer's instructions. The obtained transfer plasmids were used for virus production (see Section 2.9 Virus production).
## 4 Multi-Objective Optimization Framework
### Chaotic Mapping-Driven Population Initialization and Updating Mechanism  
This optimization algorithm employs an enhanced logistics circle composite chaotic mapping for population initialization to improve solution diversity and exploration capability. Chaotic mapping generates heterogeneous initial solution sets due to its intrinsic unpredictability and complex dynamical behavior, which facilitates escaping local optima and enhances global search performance. By increasing population diversity, the algorithm effectively explores the search space and mitigates premature convergence. The initial solutions are generated with high ergodicity, where \(r\) (control parameter of the chaotic mapping) is initialized at 3.99, and \(\mu\), \(k\) (parameters of the circle mapping) are set to 0.5 and 1.0, respectively, to modulate the mapping behavior. Compared to the random initialization in the original DMOA [45], this method prevents population clustering in specific regions and significantly improves initial solution diversity. A chaotic update operation is introduced every 100 generations to reorganize the worst-performing 10% of individuals. Nonlinear perturbation is achieved through dynamic adjustment of the chaotic parameter \(r\), continuously injecting fresh solutions to avoid local optima. Here, \(X_{k}\) denotes the current iterative value, \(\theta_{k}\) represents another chaotic variable, and \(t\) indicates the current iteration count. The governing Equations (33) and (34) are formulated as follows.  
\[r=3.99+0.1\times\sin\biggl{(}\frac{t}{100}\biggr{)} \tag{33}\]  
\[\begin{split} X_{k+1}=\biggl{(}& r\times X_{k}(1-X_{k })+\mu\\ &-\frac{k}{2\pi}\sin(2\pi\theta_{k})\biggr{)}\mod 1\end{split} \tag{34}\]
# Object/Scene Recognition Based on a Directional Pixel Voting Descriptor
## 1 Introduction  
Detecting objects in images is crucial in multiple applications, including surveillance, autonomous navigation, augmented reality, and so on [1, 2, 3, 4]. While AI-based approaches such as Convolutional Neural Networks (CNNs) have demonstrated high performance in object detection [5, 6], in scenarios where object/scene being recognized is unknown it is difficult to achieve accurate recognition. i.e., without prior training data on that specific object, traditional AI methods reach poor performance.  
On the other hand, feature-based approaches like SIFT [7], SURF [8], and ORB [9] offer capability to search any template but have limitations under complex visual variations. These methods extract distinctive features from an image and use them to detect objects [10, 11]. By carefully selecting and combining these feature-based techniques, developers can achieve efficient object detection solutions without compromising performance.  
Another approach to object detection involves high-level abstraction descriptors such as those based on edges and shapes. These methods combine the advantages of both CNNs and feature-based techniques [1, 12]. They aim to be more robust than feature-based approaches, akin to CNNs, and at the same time they can maintain relatively simple complexity allowing to search any template, akin to feature-based methods.  
In this work, we present an edge-based object/scene recognition method. We propose that the utilization of shapes obtained from edge points should achieve high performance given any type of template. Our contribution lies in the introduction of a novel directional pixel voting descriptor using image segments. The experimental results demonstrated high performance under complex visual variations. Furthermore, our methodology enables both recognition of both objects and scenes. This dual capability opens up promising avenues for future exploration and practical real-world applications.
# Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review
## 5 Conclusions  
This review thoroughly examined the progress in ultrasound image quality enhancement using deep learning, with a focus on applications suitable for POCUS. Ultrasound image enhancement through deep learning is a vibrant research field. However, the majority of performed studies focuses on single aspects of quality enhancement, which are less effective for POCUS devices that suffer from multiple distortion types. Studies addressing multiple quality aspects demonstrate the potential for substantial image quality improvements across various ultrasound devices. The PSNR values for low-quality input images ranged from 8.65 to 29.24, improving to 13.99 to 36.59 for the enhanced images. Similarly, the SSIM values ranged from 0.03 to 0.71 and 0.30 to 0.93 for the low-quality input images and the enhanced images, respectively. However, quantifying the expected performance gain precisely remains challenging due to the heterogeneous nature of the studies. It is important to note that studies often neglect to report computation times, which is a crucial factor for enabling real-time applications. Future research should prioritize the development of standardized evaluation metrics, report computational efficiency, and ensure reproducibility by sharing source code. Additionally, creating accurate paired datasets with POCUS devices and high-end US images is essential for advancing this field and achieving reliable real-time image enhancement.  
Conceptualization, F.G. and B.D.; methodology, H.G.A.v.d.P., LM.v.K., M.W., F.G. and B.D.; software, H.G.A.v.d.P.; validation, H.G.A.v.d.P., LM.v.K., M.W., F.G. and B.D.; formal analysis, H.G.A.v.d.P., F.G. and B.D.; investigation, H.G.A.v.d.P. and F.G.; resources, B.D.; data curation, H.G.A.v.d.P. and F.G.; writing--original draft preparation, H.G.A.v.d.P. and F.G.; writing--review and editing, H.G.A.v.d.P., L.M.v.K., M.W., F.G. and B.D.; visualization, H.G.A.v.d.P. and F.G.; supervision, F.G. and B.D.; project administration, B.D. All authors have read and agreed to the published version of the manuscript.  
Research at The Netherlands Cancer Institute has been supported by institutional grants of the Dutch Cancer Society and of the Dutch Ministry of Health, Welfare and Sport.  
Data Availability Statement: Data sharing is not applicable.  
The authors declare no conflicts of interest.
# Collision Avoidance Adaptive Data Rate Algorithm for LoRaWAN
## 6 Results and Discussion
### Total Energy Consumption  
The proposed SSFIR-ADR model exhibits a notable decrease in the global energy utilisation of the network, as illustrated in Figure 4 when considering different application data intervals, and Figure 5 when examining various node densities. When comparing SSFIR-ADR1 and SSFIR-ADR2 against the standard ADR algorithm, both variants demonstrate average energy savings of 27% and 24%, respectively. It is vital to take note that the energy consumption of the ADR algorithm directly correlates with the network size, as depicted in Figure 5. Consequently, as the application data interval increases, the energy consumption decreases for a fixed number of end devices within the network. This improvement in energy efficiency can be attributed to the reduction in interference, leading to fewer collisions and subsequently fewer retransmissions. By minimising these disruptions, the proposed algorithm effectively conserves energy resources. As a result, the proposed SSFIR-ADR algorithm presents superior performance in comparison with the ns3-ADR protocol, although it is outperformed by the FL-ADR scheme in terms of saving energy.  
Figure 4: Data interval vs. Total consumed energy.
# Fatigue Life Analysis of Cylindrical Roller Bearings Considering Elastohydrodynamic Lubrications
###### Abstract  
Cylindrical roller bearings are widely used in industrial machinery, automotive systems, and aerospace applications, where their reliability directly affects the performance and safety of mechanical systems. The fatigue life of cylindrical roller bearings is significantly affected by their elastohydrodynamic lubrication condition, with variations potentially reaching multiple times. However, conventional quasi-static models often neglect lubrication effects. This study establishes a quasi-static analysis model for cylindrical roller bearings that incorporates the effects of elastohydrodynamic lubrication by integrating elastohydrodynamic lubrication theory with the Lundberg-Palmgren life model. The isothermal line contact elastohydrodynamic lubrication equations are solved using the multigrid method, and the contact load distribution is determined through nonlinear iterative techniques to calculate bearing fatigue life. Taking the N324 support bearing on the main shaft of an SFW250-8/850 horizontal hydro-generator as an example, the influences of radial load, inner race speed, and lubricant viscosity on fatigue life are comparatively analyzed. Experimental validation is conducted under both light-load and heavy-load operating conditions. The results demonstrate that elastohydrodynamic lubrication markedly increases contact loads, leading to a reduced predicted fatigue life compared with that of the De Mul model (which ignores lubrication). The proposed lubrication-integrated model achieves an average deviation of 5.3% from the experimental data, representing a 16.1% improvement in prediction accuracy over the De Mul model. Additionally, increased rotational speed and lubricant viscosity accelerate fatigue life degradation.  
rolling bearing; fatigue life; elastohydrodynamic lubrication; quasi-static model; multigrid method 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (ICRS), Vol. 2, No. 1, pp. 1-10, 2008. IEEE.
# Impact of Exhaust Gas Recirculation on Gaseous Emissions of Turbocharged Spark-Ignition Engines
###### Abstract  
Exhaust gas recirculation is one of the technologies that can be used to improve the efficiency of spark-ignition engines. However, apart from fuel consumption reduction, this technology has a significant impact on exhaust gaseous emissions, inducing a significant reduction in nitrogen oxides and an increase in unburned hydrocarbons and carbon monoxide, which can affect operation of the aftertreatment system. In order to evaluate these effects, data extracted from design of experiments done on a multi-cylinder 1.3 L turbocharged spark-ignition engine with variable valve timing and low-pressure exhaust gas recirculation (EGR) are used. The test campaign covers the area of interest for the engine to be used in new-generation hybrid electric platforms. In general, external EGR provides an approximately linear decrease of nitrogen oxides and deterioration of unburned hydrocarbon emissions due to thermal and flame quenching effects. At low load, the impact on emissions is directly linked to actuation of the variable valve timing system due to the interaction of EGR with internal residuals. For the same external EGR rate, running with high valve overlap increases the amount of internal residuals trapped inside the cylinder, slowing down combustion and increasing Unburnt hydrocarbon (HC) emissions. However, low valve overlap (i.e., low internal residuals) operation implies a decrease in oxygen concentration in the exhaust line for the same air-fuel ratio inside the cylinders. At high load, interaction with the variable valve timing system is reduced, and general trends of HC increase and of oxygen and carbon monoxide decrease appear as EGR is introduced. Finally, a simple stoichiometric model evaluates the potential performance of a catalyst targeted for EGR operation. The results highlight that the decrease of nitrogen oxides and oxygen availability together with the increase of unburned hydrocarbons results in a huge reduction of the margin in oxygen availability to achieve a complete oxidation from a theoretical perspective. This implies the need to rely on the oxygen storage capability of the catalyst or the possibility to control at slightly lean conditions, taking advantage of the nitrogen oxide reduction at engine-out with EGR.  
spark-ignition engines; emissions; fuel consumption; exhaust gas recirculation Article
# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 4 Navigation Integration Enhanced by the ANN
### Hybrid Methods
#### 4.2.2 NN as the INS Errors Predictor for Error Compensation
On the model predictive filter neural network (MPFNN) for INS error compensation, an NN learning technique is suggested to increase the positional precision of the GPS/INS combined navigation system in the absence of GPS. During GPS outages, MPFNN is used to anticipate INS inaccuracies. The MPF technique is used to correct the model error of neural networks during training to account for variations between the actual and desired output. A land vehicle navigation test was used to empirically confirm the performance of the suggested technique. According to comparison data, the suggested approach can successfully deliver extremely precise adjustments to the solo INS during GPS outages.
## 2 Literature Review
### Direct Measurement Techniques  
Among direct seawater salinity measurement techniques, chemo-resistive sensors are widely used [10; 11; 12]. A recent study in [13] investigated the sensitivity and selectivity of an ITO (Indium-Tin-Oxide) nanoparticle-based sensor for detecting liquid chemicals diluted in brine. This eco-friendly sensor performance is investigated using representative analytes such as methanol (MeOH), ethanol (EtOH), and isopropyl alcohol (IPA) diluted in brine (NaCl 3.5 wt.%). Direct measurement techniques are usually based on different chemical reactions, which could result in changing the chemical properties of the solution to be tested. Although direct measurement techniques could be very simple to implement, they would not be suitable to many applications where the chemical properties of the solution to be tested should stay unchanged.
# Physical Side-Channel Attacks on Embedded Neural Networks: A Survey
## 4 Physical SCA Attacks on DNNs
### Recovering Network Architecture and Parameters
#### 4.2.2 FPGA Targets
The authors propose a model based on the HD of previous and current cycles summations in each register of the second stage of the adder. They developed a cycle-accurate simulator of the adder pipeline in order to simplify their attack, which functions as follows: (i) computes per cycle the value in each register for all possible couples of weight and a certain known input; (ii) computes the HD of each register for each cycle; and (iii) computes the total power dissipation for each cycle by adding the HDs of all the registers. Finally, by using [PERSON]'s correlation between the weight guess and power measurements, the authors show that the correct weight value can be deduced. The authors note that this method can also be applied in order to deduce the bias as well by attacking the 10 th stage of the adder tree since it is added to the final summation or by attacking the activation output. This attack methodology is evaluated through the implementation of a BNN with three fully connected layers of 1024 neurons each used for MNIST digit recognition (28-by-28 pixel images) on a SAKURA-X board with a Xilinx Kintex-7 FPGA. The results show that 45K measurements were necessary to obtain the correlation between the correct weight value guess and power measurements with 99.99% confidence. **Discussion and limitations:** In addition to the attack strategy, the main contribution of this paper is also the proposal of a masking-based mitigation against power-based SCA (discussed in Section 5). The implementation of this attack is also used for evaluation purposes. This attack requires the attacker to use a publicly available network architecture and to know the hardware implementation details, which are not often public information.
## 4 Experimental Results
### Attention Analysis
Our current strategy uses random rotation and flipping; however, super-resolution as a pre-processing step could improve model robustness and classification accuracy. Future research may use GAN-based super-resolution models to enhance MRI images before feeding them into the GATransformer architecture to reduce misclassification due to low picture quality. Super-resolution and deep learning can bridge the gap between AI-based tumor classification and clinically relevant decision support systems, improving diagnostic reliability and model generalization. Figure 14: Analysis of the proposed model—training dataset: FigShare; testing dataset: Kaggle.