esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Acquisition  
In this study, the PP insulation underwent accelerated thermal aging tests in accordance with the IEC standard [14] to ensure long-term reliability. Aging temperatures of\(110\)\({}^{\circ}\)C and \(130\)\({}^{\circ}\)C were selected based on the maximum allowable temperature for PP. The aging duration was 960 h.  
Figure 1 shows the structure of a McKeown electrode comprising spherical electrodes. The test specimen underwent thermal degradation after the cable was processed into a ribbon type. Post-degradation, it took the form of a circular specimen and was positioned at the center of the electrode. Subsequently, the electrode was encapsulated with epoxy to prevent surface discharge during the dielectric BD test.  
Figure 2 illustrates the specimen processing and the configuration of the McKeown electrodes [15; 16]. Following thermal aging, the ribbon-type specimens were further processed into circular specimens with a diameter of 8 \(\Phi\). The AC BD strength tests were conducted using McKeown electrodes with a diameter of 10 \(\Phi\). Moldings were applied around the electrodes to prevent surface discharge, and tests were conducted in insulating oil. The right side of the figure displays views from the side, top, and bottom of the McKeown electrode. The side view allows for the verification of the specimen's centering within the electrode. The top figure presents the state before molding the spherical electrode, emphasizing the need to confirm appropriate specimen positioning before molding the electrode.  
The tests entailed the application of an AC voltage at a rate of 1 kV/s until breakdown, and the breakdown voltage (BDV) was duly recorded. To convert the obtained voltage into an electric field, the thickness of the position where the breakdown occurred in the specimen was measured. Roughly 20 samples were acquired for each degradation condition. If the breakdown did not occur at the center of the electrode during processing, the specimen was deemed problematic and subsequently excluded from the data analysis. Table 2 provides a detailed overview of the BD strength test conditions.  
Figure 1: Structure of McKeown electrode and specimen.  
Figure 2: Configuration of AC BD test with electrodes.
# DDR4 BER Degradation Due to Crack in FBGA Package Solder Ball
## 5 Measurement Results  
Figure 11(a) shows DQ3 channel data eye. The width of the data eye is measured at either Vih or Vil. The channel loss gives losses in data channel due to transmission line and discontinuities in the channel. The channel loss is measured by subtracting eye opening from unit bit interval [19]. For DDR4 operating at 2.133 Gbps the unit interval is 468.8 ps. The data input valid window (V\({}_{\text{difvw}}\)) for micron DDR4 is 136 mV [20]. V\({}_{\text{difvw}}\) is the vertical opening of the DDR4 receiver RX Mask. DQ3 data channel has no defect in solder ball. DQ3 data eye width and channel loss measurements are given as following.  
* DQ3 data eye width = 303.8 ps
* DQ3 channel loss = 165 ps  
Figure 11(b) shows DQ2 channel data eye. DQ2 channel has capacitive loading to mimic the crack in solder ball. DQ2 data channel data eye width and data channel loss measurements are given as following.  
* DQ2 data eye width = 267.8 ps
* DQ2 channel loss = 201 ps  
The comparison of DQ2 and DQ3 shows that DQ2 channel has 36 ps more channel loss due to the eye width decrease introduced by the data eye degradation.  
Figure 11: Measurement system showing memory FPGA based memory controller and DDR4 test fixture.  
Figure 10: DDR4 RDIMM test fixture with a capacitor loaded data line.  
DQ2 and DQ3 data eyes are used to create bathtub plots, as shown in Figure 13. DQ3 channel bathtub plot shows that it has more eye opening for same bit error rate as DQ2. DQ2 channel with the solder ball defect has smaller eye opening for same bit error rate as DQ3. Figure 13 also shows that if both DQ2 and DQ3 have eye opening of 0.46 UI, DQ2 has bit error rate of 10e-11 whereas DQ3 has bit error rate of 10e-20. This shows how the crack degrades the bit error rate of DDR4 data channel.
# Effect of Washing Temperature on Adsorption of Cationic Dyes by Raw Lignocellulosic Biomass
## 2 Materials and Methods  
### Preparation of the Biosorbents  
To conduct various characterization and adsorption experiments, substantial quantities of wild carob (_Ceratonia siliqua_) and _Maclura pomifera_ (Osage orange) were collected in Setif. The raw materials were washed with tap water to remove impurities, particularly soil sludge. They were then dried using solar energy in an oven at temperatures between 70 and 80 \({}^{\circ}\)C to prevent any alteration in the physico-chemical properties of the materials. After cooling, the materials were ground.  
To demonstrate the effect of wash water temperature on the adsorption capacity of raw organic materials, wild carob and _Maclura pomifera_ were washed under agitation with either cold distilled water at a room temperature of 60 \({}^{\circ}\)C or brought to boiling (100 \({}^{\circ}\)C). This washing process was repeated several times until the residual water was as clear as possible. The biomasses were then dried in an oven at 60 \({}^{\circ}\)C, ground, and sieved to retain the fraction between 250 and 500 \(\mu\)m. The powdered biomaterials were stored in hermetically sealed plastic containers and were used for analysis as well as for adsorption experiments.  
Names of Materials Prepared  
The materials prepared were as follows:  
* CC: Carob washed in cold water;
* CW: Carob washed with warm water (60 \({}^{\circ}\)C);
* CH: Carob washed in hot water (100 \({}^{\circ}\)C);
* MPC: _Maclura pomifera_ washed in cold water;
* MPW: _Maclura pomifera_ washed in warm water;
* MPH: _Maclura pomifera_ washed in hot water.
# Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review
## 4 Results
### Pricing Strategies
#### 4.2.3 Pollution and Congestion in Urban Areas: The Effects of Low-Emission Zones [20]  
This study examines the effectiveness of Low-Emission Zones (LEZs) in mitigating pollution and compares their impact with urban tolls, focusing on large European urban areas from 2008 to 2016. It utilizes a unique panel dataset to evaluate congestion using data from TomTom and pollution (PM2.5) from environmental sciences. The key contributions are as follows:  
* Impact on Pollution: LEZs are particularly effective in reducing PM2.5 emissions, especially in cities with high initial pollution levels, broad coverage areas, and stringent restrictions on vehicle types. For instance, previous studies indicate average reductions in PM10 emissions of up to 13% in German cities and 33% for PM10 in Rome's restricted areas.
* Ineffectiveness on Congestion: LEZs have a minimal impact on congestion mitigation. The renewal of car fleets with cleaner vehicles compliant with LEZ regulations may initially reduce congestion but is counterbalanced by latent demand and car substitution effects in multi-car households.
* Trade-offs in Policy Goals: The study highlights the differing goals of LEZs (curbing pollution) and urban tolls (reducing congestion), showing that while both can address externalities simultaneously, their primary objectives result in varying effectiveness.
* Support for LEZs: LEZs are generally more socially acceptable than tolls, primarily affecting older, more polluting vehicles while leaving compliant drivers unaffected. This acceptability contributes to their widespread adoption across European cities.  
The importance of tailoring LEZ policies based on local pollution severity is described, together with their coverage areas and regulation stringency. It suggests that dynamic LEZs could optimize their impact by integrating real-time data to address congestion spillovers and latent demand effects while maintaining public acceptance.
## 2 Materials and Methods
### Input Data for Simulations
#### 2.3.2 Solar Radiation Measurements and Uncertainty
A cloudless day is shown in (**a**) and (**c**) and a partially cloudy day is shown in (**b**) and (**d**). The top row shows the absolute uncertainty (in W/m\({}^{2}\)) as shaded bands around the measured value. The bottom row shows the uncertainty in percentage. While it is well-established that the uncertainty of pyrheliometer (DNI) measurements is lower than global pyranometer (GHI) measurements due to the non-ideal cosine response of pyranometers [50], this is not always the case in the present work because hourly averages are used for the simulations. During periods of high cloud variability, the variance of hourly DNI is significantly higher than the variance of hourly GHI. The uncertainty due to this variation is included in the uncertainty model using the standard error of the hourly averaged value. Figure 3 shows that the uncertainty of hourly averaged DNI is as low as \(\pm\)2.2% during hours with little to no cloud variability, but under high solar variability, the uncertainty can exceed \(\pm\)15%. Please note that the hourly DNI tends to be low in absolute (W-m\({}^{-2}\)) terms during such times of passing clouds. As illustrated in Figure 3, the uncertainty of a PV performance simulation is dependent on which combination of GHI, DHI, and/or DNI is used as input. Table 1 shows which two components each software uses to calculate \(G_{POA,Front}\) and \(G_{POA,Rear}\)--some use GHI and DHI, while others use DHI and DNI. When simulations use hourly GHI and DHI, only under clear sky conditions will this simulation be associated with higher uncertainty than simulations that use hourly DNI and DHI. Under variably cloudy sky conditions, simulations that use hourly DNI and DHI are likely to be associated with higher uncertainty.
# Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation
###### Abstract
Inspired by cross-domain sentiment classification, to alleviate the problem of inconsistency in the domain feature space of the source domain dataset and target domain dataset, we propose a Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation (DAN-CDERC) model, which transfers the knowledge from the domain adversarial model to the emotion recognition model, instead of directly modeling historical information and speaker information. The domain adversarial model consists of the encoders, a generator and a domain discriminator. The encoders are used to learn sequence knowledge from the massive amounts of the generative dataset. The generator generates an utterance in the source domain. The discriminator performs domain adaptation by discriminating the domain, which plays an essential role in reducing the domain inconsistency for domain adaptation. Since both the generative conversational task and the  
Figure 1: A conversation example with emotion labels from the MELD dataset. ERC task also need to model the dialogue context, DAN-CDERC can use the dialogue sequence knowledge from the large-scale dialogue generation dataset to assist the ERC in modeling the dialogue context. Due to the use of the domain adversarial network, our DAN-CDERC avoids the dissimilarity of the domain and vector space during the transferring process. In this paper, we try to achieve the same effect of these models without explicitly modeling speakers. For sentence-level classification, the domain discriminator is used to discriminate sentences. Our discriminator can discriminate each utterance in the conversation that belongs to the source domain or the target domain, rather than the whole conversation. We believe this is important. On the one hand, our model is relatively simple, with only two encoder layers. It is challenging to represent the whole conversation with a vector effectively. On the other hand, using sequential utterances is more conducive to learning and transferring sequence knowledge. In summary, our contributions are as follows:  
* To alleviate the problem of the small scale of the ERC task dataset, we propose Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation, which not only learns knowledge from large-scale generative conversational datasets, but also utilizes adversarial networks to reduce the difference between source and target domains;
* We use two large-scale generative conversational datasets and three emotion recognition datasets to verify model performance. The empirical studies illustrate the effectiveness of the proposed model, even without modeling information dependencies such as speakers. The rest of the paper is organized as follows: Section 2 discusses related work; Section 3 provides details of our model; Section 4 shows and interprets the experimental results; Section 5 analyses and discusses the experimental results; and finally, Section 6 concludes the paper.
## 3 Results and Discussion
### Removal of Cd from Contaminated Soil  
Unlike the water phase, the condition in the soil was much more complicated. As shown in Figure 7, the leachable Cd concentration increased with increasing amino acid concentration from 0 to 1000 \(\upmu\)mol/L. In the distilled water (control treatment), the leachable Cd was only 231.97 \(\upmu\)g/L. The maximum extraction of Cd by the studied amino acids was 793.51 \(\upmu\)g/L, 764.75 \(\upmu\)g/L, 724.62 \(\upmu\)g/L, 708.72 \(\upmu\)g/L, 694.66 \(\upmu\)g/L, and 652.94 \(\upmu\)g/L for 1000 \(\upmu\)mol/L Thr, Glu, Gly, Ala, Leu, and Phe, respectively. The Cd removal efficiencies by Thr, Glu, Gly, Ala, Leu, and Phe were 38.88%, 37.47%, 35.5%, 34.72%, 34.04%, and 31.99%, respectively. Thus, it could be concluded that Cd was complexed with the functional groups of amino acids to form soluble complexes, inhibiting the adsorption of Cd on the surface of the soil. Among the six studied amino acids, importantly, Thr and Glu exhibited the highest extraction and removal ability toward Cd in soil, which is consistent with a previous study [7].
## 5 Conclusions  
We investigated the patterns of blowdowns in tropical forests, including their potential mapping using Landsat-8 and PlanetScope satellite optical sensors, estimating their damage and tracking post-disturbance forest regeneration and dynamics. We concluded that the higher temporal and spatial resolution of PlanetScope NICFI imagery, compared to Landsat-8 OLI, allowed for monitoring the effects of blowdown disturbances and vegetation recovery much more consistently. We highlighted the importance of detecting the precise date of disturbance in order to consistently estimate tree mortality associated with blowdowns. The regular monthly time scale of the NICFI product used here can be considered as a minimum requisite for estimating the date of occurrence of the blowdown events. This information can be further refined with daily observations of other products.  
The consistent estimation of tree mortality associated with blowdown occurrence in future studies should require the detection of blowdown occurrence. Otherwise, damage may be significantly underestimated, for instance, in 52.93% in only four months of delay from disturbance to the first acquired image. Moreover, our study revealed that the NPV fraction obtained from the PlanetScope NICFI monthly mosaics recovered to pre-disturbance levels (95% confidence interval) after an average of 12.2 months since disturbance. We note the recovery may vary with differences in gap size. Thus, the stratification of blowdowns by gap size is a future research direction that deserves further investigation.  
Conceptualization, D.P., R.D. and P.d.C.B.; methodology, D.P., R.D. and P.d.C.B.; software, D.P.; validation, D.P.; formal analysis, D.P. and R.D.; investigation, D.P. and R.D.; resources, D.P., R.D. and P.d.C.B.; data curation, D.P.; writing--original draft preparation, D.P.; writing--review and editing, R.D., L.S.G., B.N., F.W., D.M.S. and P.d.C.B.; visualization, D.P.; supervision, R.D. and P.d.C.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The shapefile of polygons of blowdown events in this study are available at: [[https://zenodo.org/record/7036241](https://zenodo.org/record/7036241)]([https://zenodo.org/record/7036241](https://zenodo.org/record/7036241)) (accessed on 20 April 2023).  
The authors thank the Department of Geography, School of Environment Education and Development (SEED) at the University of Manchester (UK) for the support on this research.  
The authors declare no conflict of interest.
# Quantification of Optical Chirality in Cellulose Nanocrystal Films Prepared by Shear-Coating
## 2 Materials and Methods
### Characterization Techniques  
The films were imaged using polarized optical microscopy (POM) with an Olympus BX60 optical microscope equipped with a Hitachi KP-D50 color digital camera. Left- and right-handed sheet polarizers (Edmund Optics, Barrington, NJ, USA) were used to evidence optical chirality. Mueller matrix measurements in transmission and reflection modes were performed with a dual rotating compensator spectroscopic ellipsometer (RC2, J. A. Woollam Co., Inc., Lincoln, NE, USA). A collimated beam (4 mm in diameter) was used for measurements in transmission mode at normal incidence in the wavelength (\(\lambda\)) range of 210-1690 nm. For these measurements, the shear direction was placed vertically  
Figure 1: Schematics of the preparation method: (**a**) a drop of cellulose nanocrystal (CNC) suspension is deposited onto a glass substrate; (**b**) another glass slide is placed so that the suspension adheres to it; (**c**) the coater plate is moved to distribute the suspension; (**d**) the sample is placed inside a petri dish for the slow evaporation of water in a humid environment.  
(along the \(y\)-axis) and the beam path was assumed along the z-axis. In the reflection mode the measurements were taken at angles of incidence (\(\theta\)) from 20\({}^{\circ}\) to 70\({}^{\circ}\) in steps of 5\({}^{\circ}\) using focusing probes, whereby areas with a diameter below 100 \(\upmu\)m in were sampled [19; 21]. The use of focusing probes limited the wavelength range to 210-1000 nm. Regression analysis was performed with the WVASE software (RC2, J. A. Woollam Co., Inc., Lincoln, NE, USA). The reflectance spectra of unpolarized light were measured in the wavelength range of 350-1000 nm with a CHEM4 system (Ocean Optics, Inc., Dunedin, FL, USA) using an optical fibre probe. The latter had six illumination fibres around one read fibre. The effective angle of incidence was 25\({}^{\circ}\) with a spot size of about 0.4 mm in diameter. A silicon wafer was used to calibrate the measurements. The thicknesses of the films were determined from scanning electron microscopy images using Phillips XL30 ESEM equipment. To avoid overcharging, a thin layer of conductive graphite was deposited using a Denton Vacuum Desk V using argon as the carrier gas.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
###### Abstract  
Accurate estimation of forest carbon stock is critical for understanding ecosystem carbon dynamics and informing climate mitigation strategies. This study presents a deep learning framework that integrates Sentinel-2 multispectral imagery with an enhanced residual neural network for estimating aboveground forest carbon stock in the Liuchong River Basin, Bijie City, Guizhou Province, China. The proposed model incorporates multiscale residual blocks and channel attention mechanisms to improve spatial feature extraction and spectral dependency modeling. A dataset of 150 ground inventory plots was employed for supervised training and validation. Comparative experiments with Random Forest, Gradient Boosting Decision Trees (GBDT), and Vision Transformer (ViT) demonstrate that the enhanced ResNet achieves the best performance, with a root mean square error (RMSE) of 23.02 Mg/ha and a coefficient of determination (\(R^{2}\)) of 0.773 on the test set. Spatial mapping results further reveal that the model effectively captures fine-scale carbon stock variations across mountainous forested landscapes. These findings underscore the potential of combining multispectral remote sensing and advanced neural architectures for scalable, high-resolution forest carbon estimation in complex terrain.  
carbon sink; forest; estimation; remote sensing; multi-spectral image Article
## 2 Materials and Methods
### Cell cytotoxicity
#### Cytotoxicity assay  
All experiments were carried out in triplicate using CellTiter 96 Aqueous One Solution Cell Proliferation Assay (Promega, Madison, WI, USA). The reagent mixture used contained 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxy methoxy-phenyl)-2-(4-sulfophenyl)-2-H-tetrazolium (MTS), inner salt, and phenazine ethosulfate (PES), which has high chemical stability and can be combined with MTS to form a stable solution. An MTS-based colorimetric assay was performed in 96-well microtiter plates with a flat bottom (Falcon). The assay was based on the ability of metabolically active cells to reduce MTS, a yellow tetrazolium salt, to the colored formazan that is soluble in the culture medium. Each well contained 1 \(\times\) 10\({}^{4}\) cells in 100 uL of culture medium cultured for 24 h. After discarding the culture supernatants, normal and cancer cells were treated with increasing concentrations of compounds or oncolytic drugs for 24 and 48 h. After incubation, 20 uL reagents containing MTS and PES were added to each well. The plates were then incubated for 4 h at 37 \({}^{\circ}\)C with mild agitation every 15 min. The reduction in the tetrazolium compound to formazan was measured spectrophotometrically at \(\lambda\) = 492 nm using a Dynex plate reader (DYNEX Technologies MRS, Chantilly, VA, USA). The percentage of viability compared to untreated cells (considered 100% viable) was calculated using Equation (3).  
Cell viability (%) = (absorbance of treated cells \(-\) absorbance of culture medium)/(absorbance of untreated cells \(-\) absorbance of culture medium) \(\times\) 100  
The mean \(\pm\) standard deviation (SD) of the experiments obtained in triplicate was used to calculate the percentage of viability compared to untreated cells [45; 46].