esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Scanning Electron Microscope and Elementary Analysis  
Figure 7a-d shows the SEM images of the untreated biofilm and of the biofilm formed after NAC addition at the same magnification. On the right, an SE detector was used, while on the left, a BSE detector was used.  
Figure 7: Scanning electron microscopy (SEM) image, using a back-scattered electrons (BSE) detector (left) or a Scattered Electrons (SE) detector (right), of: (**a**) untreated biofilm; (**b**) biofilm with 0.5 mg\(\cdot\)mL\({}^{-1}\) of NAC; (**c**) biofilm with 2 mg\(\cdot\)mL\({}^{-1}\) of NAC; (**d**) biofilm with 4 mg\(\cdot\)mL\({}^{-1}\) of NAC; (**e**) clean steel-surface.  
The distinction between the biofilm presence and the clean steel can be observed as the analyses were performed using BSE detector. Figure 7b-d shows the biofilm with increasing NAC concentration (0.5-4 mg\(\cdot\)mL\({}^{-1}\)), together with biofilm untreated as a control (Figure 7a).  
As the concentration of the active increases, the biofilm decreases in thickness, until it is possible to see only the clean steel layer as in Figure 7d, where there is only a small organic residue.  
Figure 8A-D show a higher magnification of biofilm images at different NAC concentrations.  
Thanks to the analysis with the SEM it was possible to verify even visually the presence of the biofilm, as clearly shown in Figure 8A-C, in which _Pseudomonas aeruginosa_ appeared immersed in the organic matrix.  
Energy Dispersive X-Ray Spectroscopy (EDS) analysis was performed to quantify the biofilm on each individual steel disk. Figure 9 and Table 1 show the plotted spectra of elements and the Sulfur atom %, of the control disk (CTR) and of the disk with the active (ACTIVE), respectively.  
This semiquantitative analysis allowed us to confirm that the active leads to a thinning of the biofilm.  
What can be seen is that as the quantity of biofilms decreases sulfur decreases, while iron increases, demonstrating that the thickness of the biofilm decreases, while the elements of steel emerge. On the contrary, when the biofilm is thick enough, such as in the control, the elements of the steel decrease because they are hidden by the thick organic film. However, the iron value has a limited meaning in this instance, as a small amount of iron is released when cells die.  
The high standard deviation (Table 1) is due to the inhomogeneity of the samples treated with the active, as the surface does not have a homogeneous layer of biofilm, as shown in Figure 7b-d. While the standard deviation in the control appears smaller because in this case a thick and equal film has been obtained across the full surface.
# Deep Learning for Sarcasm Identification in News Headlines
## 1 Introduction  
The Oxford Language Dictionary defines sarcasm as the use of irony to mock or express content. It is a commonly used form of expression that presents a challenge in identifying sarcasm in the written text [1; 2]. The challenge in detecting sarcasm arises from the ambiguity of the language employed and the intention of the speaker or writer, which can be in opposition to the literal meaning. Sarcasm is often used as a tool to attract attention and to express negative sentiments more unequivocally. With the widespread use of online platforms such as Facebook, Instagram, and Twitter, people can express their opinions on various topics such as politics, entertainment, and products. The complex structure of sentences in these online messages makes it difficult for both machines and humans to understand their meaning, making sentiment analysis and sarcasm detection a rapidly growing area of interest in machine learning [3].  
Previous research on sarcasm detection has primarily focused on using Twitter data, such as tweets. The methods used in these studies have varied from rule-based to moreadvanced approaches, and a rule-based method was employed to categorize text incongruuity. However, previous studies were constrained due to limitations arising from unclear labelling, and the data were not explicitly collected to detect sarcasm.  
Recently, researchers have tried to accurately identify sarcasm by exploring the characteristics of sarcastic phrases compared to non-sarcastic ones. Despite these efforts, the field of sarcasm detection remains under investigation. Neural Networks have been used to develop sarcasm detectors for different types of content, including tweets, Flickr photos, etc.  
This study proposes an improved version of the Long Short-Term Memory (LSTM) model for sarcasm detection, called the GMP-LSTM. The GMP-LSTM incorporates a GlobalMaxPooling layer, which allows the model to capture essential features from temporal sequences. The GMP-LSTM is designed to capture higher-level patterns in the data compared to the original LSTM. The proposed model was evaluated on two sarcasm datasets and achieved a higher accuracy than the original LSTM. These results suggest that the GMP-LSTM can be a useful tool for detecting sarcasm in textual data. The paper is structured as follows: In Section 2, we review previous works on sarcasm detection using machine learning. In Section 3, we outline the approach used in this study, where we propose a deep learning architecture-based model to detect sarcasm in news headlines. In Section 4, the implementation of the classifier and the results of the implemented classifies are presented. Finally, in Section 5, we summarize the findings of this study and discuss its implications.
# Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy
## 2 Materials and Methods
### NIR Instrument and Spectra Acquisition  
The spectral information of rice grain samples was collected by a NIR optical spectrum instrument (NIRez, Isuzu Optics Corp, Shanghai, China). This instrument is reflective, with two integrated tungsten halogen lamps, whose collection range is 950-1650 nm; optical resolution is 10 nm and the signal-noise ratio is 5000:1 in a one-second scan. The spectrometer can measure the penetration and reflectivity of the measured object. The spectrometer itself carries a light source that can be placed in the photosensitive window to directly obtain the spectrum. The reflectance spectrum is directed through the collimator to the sensor. There is only one sensor pixel and when the scanning test works, it can acquire the spectrum at different positions and at different times. When the spectrometer works in reflective mode, the light is acquired through a lens coupled to the spectrometer. Prior to spectral determination, the instrument was turned on and preheated for 15 min to its operating conditions. Then the blackboard and whiteboard correction was conducted. During the spectral acquisition process, the 90 rice grain samples prepared as described in Section 2.1 were loaded into a hollow cylinder with a diameter of 30 mm in sequence, and the corresponding full NIR spectral information was collected using this NIR optical spectrum instrument. The scan range was 950-1650 nm and the obtained spectrum for each sample was the average of three scans.
# Pentane Depletion by a Surface DBD and Catalysis Processing
## 4 Discussion
As an example, a recent study investigated the abatement of another alkane (n-undecane) and the related CO\({}_{x}\) selectivity, achievable through the application of a packed-bed DBD reactor and using \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) spheres as packing material [34]. The removal efficiency of n-undecane and the CO\({}_{x}\) selectivity increased from 40% to 80-92% and from 37% to 40-80%, respectively, when loading \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) with CeO\({}_{2}\) at different weight fractions. In light of the numerous studies on cold atmospheric plasmas and their potentials for VOC abatement/conversion, such devices could be successfully employed in a broad spectrum of applications concerning VOC removal. The applications might be differentdepending on the mode NTP devices used. Under static conditions, as in the present study, NTP devices show great potential in the decontamination of polluted environmental matrices such as contaminated air in confined volumes or contaminated soil [35]. NTP devices could also be applied to the treatment of contaminated air streams, thus under dynamic conditions. In this case, optimizing energy efficiency is a challenge that plasma technologies should face in the future, especially in applications regarding the treatment of high airflow rates. The use of catalysts, the combination with other removal technologies, or the study of peculiar configurations [36] may help to reduce the energy consumption in order to achieve the same targets. The use of SDBD instead of volume DBDs also seems to be promising for energy optimization purposes. In a review paper, Vandenbroucke et al. [2] compared the results obtained by Oda et al. [37; 38], who measured the abatement performance of trichloroethylene (TCE) by: (1) an SDBD reactor with a V\({}_{2}\)O\({}_{5}\)/TiO\({}_{2}\) catalyst, and (2) a volume DBD with a TiO\({}_{2}\) catalyst. At the same airflow rate (400 mL/min) and TCE concentration (1000 ppm), the first configuration allowed for the acquisition of the same removal efficiency for the second configuration (\(>\)95%) by using only 25-48% of the energy consumed by the latter. Other authors [39] carried out a comparison between an SDBD and a volume DBD reactor in terms of ozone generation. The authors found out that under the same operating conditions and specific energy density, the ozone generation obtained with the SDBD exceeds the amount of ozone generated by the volume DBD by a factor of 2.5-3.5. Similar results were obtained in a more recent study comparing the application of an SDBD and a volume DBD reactor to ethanol in air [40]. Under the same operating conditions and specific energy density, the authors obtained ethanol removal efficiencies of 80% and 60% when using the SDBD and the volume DBD reactor, respectively.
## 3 Asymptotic Confidence Intervals  
According to [19], let Q represent the complete data and U represent the observed data. We denote \(I_{U}(\theta)\) as the observed information and \(I_{Q}(\theta)\) as the complete information, and \(\theta=(\alpha,\lambda)\). Subsequently, we introduce a new function, \(I_{Q|U}(\theta)\), to represent the missing information.  
\[I_{Q|U}(\theta)+I_{U}(\theta)=I_{Q}(\theta), \tag{16}\]  
where  
\[I_{Q}(\theta)=-E\bigg{[}\frac{\partial^{2}\ln L_{c}(Q;\theta)}{\partial \theta^{2}}\bigg{]}. \tag{17}\]  
With the \(i\)-th observed data \(x_{i}\), we derive the \(I_{Q|U}^{(i)}(\theta)\), which represents the Fisher information for censored data.  
\[I_{Q|U}^{(i)}(\theta)=-E_{Z_{i}|x_{(i)}}\bigg{[}\frac{\partial^{2}\ln f_{Z_{i }}(z_{i}|x_{(i)},\theta)}{\partial\theta^{2}}\bigg{]}. \tag{18}\]  
As a result, we can obtain the complete missing information as follows:  
\[I_{Q|U}(\theta)=\sum_{i=1}^{m}R_{i}I_{Q|U}^{(i)}(\theta). \tag{19}\]  
Additionally, we employ a numerical technique to obtain \(I_{Q}(\theta)\):  
\[I_{Q}(\theta)=\bigg{[}\begin{array}{cc}C_{11}&C_{12}\\ C_{21}&C_{22}\end{array}\bigg{]}. \tag{20}\]  
The expressions for \(C_{11}\), \(C_{12}\), \(C_{21}\), and \(C_{22}\) are provided in the Appendix A.  
Next, we derive the \(I_{Q|U}^{(i)}(\theta)\) and consider its observed information, which encompasses the expectations outlined in (18):  
\[I_{Q|U}^{(i)}(\theta)=\bigg{[}\begin{array}{cc}D_{11}(x_{(i)},\alpha,\lambda )&D_{12}(x_{(i)},\alpha,\lambda)\\ D_{21}(x_{(i)},\alpha,\lambda)&D_{22}(x_{(i)},\alpha,\lambda)\end{array} \bigg{]} \tag{21}\]  
The representations of \(D_{11}\), \(D_{12}\), \(D_{21}\), and \(D_{22}\) are provided in the Appendix A.  
By employing the two matrices referenced above, we can derive the information matrix \(I_{U}(\theta)\). Subsequently, we can calculate the variances of the maximum likelihood estimates for the parameters individually. The asymptotic variance-covariance matrix of the MLEs of the parameters is given by \(I_{U}^{-1}(\hat{\theta})\).  
This allows us to establish \(100(1-\kappa)\%\) asymptotic confidence intervals (ACIs) for the estimates, where \(0<\kappa<1\). By applying the principle of asymptotic normality to the MLE of \(\hat{\theta}\), defined as \(\hat{\theta}=\big{(}\hat{\alpha},\hat{\lambda}\big{)}\), we recognize that \(\hat{\theta}\) follows a normal distribution, specifically \(N(\theta,I_{U}^{-1}(\hat{\theta}))\). Therefore, the ACIs for \(\lambda\) can be defined as follows:  
\[\bigg{(}\hat{\lambda}-u_{\frac{\kappa}{2}}\sqrt{Var(\hat{\lambda})},\hat{ \lambda}+u_{\frac{\kappa}{2}}\sqrt{Var(\hat{\lambda})}\bigg{)}, \tag{22}\]  
where \(u_{\frac{\kappa}{2}}\) denotes the upper \(\frac{\kappa}{2}\)-th quantile of the standard normal distribution. The term \(Var(\hat{\lambda})\) refers to the principal diagonal element of \(I^{-1}(\hat{\theta})\). We can similarly derive the ACIs for \(\alpha\).
## 4 Results and Discussion
### Comparison with Previous Studies  
We compared the obtained results with other related models implemented based on accuracy, model parameters, training epochs, inference time, and time complexity. Table 3 shows that our proposed model has 2.3 million parameters, less than the existing networks. Significantly, the acquired recognition rate of the Massey ASL dataset is 99.22%, and the accuracy of the ISL dataset is 99.79%, which is higher than the previous studies with fewer training epochs and an excellent average inference time on testing images.
## Appendix B Simulation Setup for LTE
### LTE Channel Conditions  
In LTE, RB is assigned to user equipment (UE) from eNB in the downlink channel. The LTE signal suffers from signal degradation due to a dynamic change of the distance between eNB and UE, radio power level, noise level, and multipath fading effects.  
The downlink unlicensed-LTE signal is affected by the following: (1) LTE moving channel of propagation conditions which implements _scenario 1_ for an extended typical urban environment using an (ETU200) Rayleigh fading model with 200 Hz Doppler shift and changing delays, (2) LTE moving channel with _scenario 2_ which represents a single non-fading model as specified in [85; 86], (3) LTE fading channel for a multipath fading MIMO channel propagation conditions using a Generalized Method of Exact Doppler Spread (GMEDS) for a Rayleigh fading model type [87], and (4) AWGN.  
In our simulation, the LTE radio signals are exposed across a range of light to serve noise conditions where the SNR ranges from 0 dB to 20 dB. Besides, the LTE downlink signal is affected by three main propagation channel models and the AWGN. The downlink LTE signal is affected by (1) LTE moving channel of propagation conditions which implements _scenario 1_, (2) LTE moving channel with _scenario 2_, (3) LTE fading channel, and (4) AWGN. The LTE moving propagation scenarios are implemented as specified in TS 36.104, Annex B.4 [85]. The used parameters of the LTE fading channel model are specified in [87]. A summary of the implemented channel conditions for unlicensed-LTE is presented in Table 10.
## 4 Conclusions  
The nonlinear regression analysis showed that the Peleg model was adequate to describe the water sorption isotherms of all dried apple purece products as well as freeze-dried agar gel. The water adsorption isotherms of most dried gels represented type III sorption behaviour, which is common for a high sugar food. It can be linked with an increase in sugar concentration in gels due to the addition of apple purece. The different shapes of the sorption isotherm (represented type II) of the freeze-dried agar gel (without apple purece) was a result of the low concentration of solids in the material. The higher amount of adsorbed water at lower water activity was observed for the vacuum-dried apple purece gel, which can be related to expanded structure with large pores. However, the thick walls of pores created during the vacuum-drying impeded the hygroscopicity of the material. Low porosity and thick walls of pores in air-dried gels affected the lower moisture sorption and the increase in glass transition temperature. The open-pore structure and the presence of many pores with thin walls in freeze-dried apple gels led to the higher hygroscopicity of samples. The application of this drying method also caused a decrease in glass transition temperature and critical water activity of studied materials. Additionally, freeze-dried gel without maltodextrin was characterised by the low value of glass transitions, which may indicate the presence of the amorphous state. It may lead to the undesirable changes of texture (collapse of structure). The increase in apple purece addition gels from 25 to 40% caused the increase in hygroscopicity during the storage, which can be linked with the higher concentration of low molecular substances. This study shows that the composition of gel and the application of certain drying methods enabled obtaining products with tailored structure and sorption properties. The air-dried apple purece gels can be recommended as the snack's products with the lowest hygroscopicity and the highest stability at room conditions (the highest glass transition temperature and the critical water activity). In addition, a decrease in the amount of apple purece from 40 to 25% in dried gels improved their sorption properties.  
Conceptualisation, E.J.; methodology, E.J.; investigation, E.J., A.K.-D., E.O.-L., A.G. ([PERSON]), M.W.-W., D.M.-J., A.G. ([PERSON]); formal analysis, E.J.; writing--original draft preparation, E.J.; writing--review and editing, E.J., A.G. ([PERSON]) and J.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Data Availability StatementThe data generated or analysed during this study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
# Towards Feasible Capsule Network for Vision Tasks
## 4 Experiments
### Results
#### 4.3.3 Pretrained and Fine-Tuned Evaluation  
In this study, our main objective is to investigate whether CapsNet can enhance the fine-tuning model in a downstream task. We aim to assess the potential benefits of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Backbone** & **Head** & **Dicescore** \\ \hline \multirow{3}{*}{CT-Lung-Scan} & FCN & \multirow{3}{*}{CNN} & 96.06 \\  & Deeplab & & 97.69 \\ \cline{2-3}  & FCN & \multirow{3}{*}{CapsHead} & **97.59** \\  & Deeplab & & **97.70** \\ \hline \multirow{3}{*}{VOC 2012} & FCN & \multirow{3}{*}{CNN} & 80.24 \\  & Deeplab & & 80.27 \\ \cline{2-3}  & FCN & \multirow{3}{*}{CapsHead} & **86.58** \\  & Deeplab & & **86.11** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of CapsHead compared to the CNN baselines on segmentation tasks.  
Figure 2: Segmentation masks of Lung-CT-Scan (**left**) and VOC2012 (**right**) datasets from: (**first row**) FCN-CapsHead, (**second row**) FCN. Comparing segmentation result in read circles we clearly see that CapsHead produces better overlapping between a mask and ground truth.  
incorporating capsule structures in mainstream CNNs. We conduct experiments involving two variations of CapsNet configurations:  
* CapsNet with a pre-trained backbone: The pre-trained model serves as the starting point, and we subsequently fine-tune the entire network, including the CapsHead.
* CapsNet without a pre-trained backbone: Here, the entire network, including the capsule structures, is trained from scratch on the target downstream task.  
For CIFAR10, when CapsHead is trained from scratch, it achieves an accuracy of 83.7% with ResNet18 and 86.03% with DenseNet. However, with a pre-trained backbone, the performance significantly improves to 94.08% with ResNet18 and 94.97% with DenseNet. Similarly, for CIFAR100, CapsHead with a pre-trained ResNet18 backbone achieves an accuracy of 73.03%, which is notably higher than the 54.29% achieved when trained from scratch. Additionally, CapsHead with a pre-trained DenseNet backbone achieves a significant accuracy of 79.91%. The results in Table 3 indicates that leveraging a pre-trained backbone significantly boosts the performance of CapsHead in both CIFAR10 and CIFAR100 datasets, indicating the importance of transfer learning in enhancing the capabilities of CapsNets for vision tasks. Figure 3 also clearly shows that Pretrained backbones outperform scratch training and DenseNet backbones are better than ResNet backbones. By utilizing the knowledge learned from a large-scale dataset, CapsHead can effectively capture intricate spatial relationships and achieve impressive accuracy for image classification tasks.
## 3 Network Models and Related Definitions
### Perceptual Model  
Considering practical application scenarios, the coverage probability of a spherical probabilistic perception model varies with the distance of the nodes. Traditional spherical sensing usually uses a Boolean model, which can lead to errors between the actual coverage and the calculated results. The effect of signal attenuation on the coverage model is considered to avoid the cliff-like perceptual boundaries of traditional Boolean perception models and to match the actual propagation of the node-sensing signal more closely. In this paper, we use a probabilistic perception model that can reflect the properties of the nodes' perceived distance [38]:  
\[P\big{(}s_{i},e_{j}\big{)}=\begin{cases}1&d\big{(}s_{i},e_{j}\big{)}\leq R_{s}- R_{a}\\ e^{-\alpha\lambda^{\beta}}R_{s}-R_{a}<d\big{(}s_{i},e_{j}\big{)}<R_{s}+R_{a},\\ 0&d\big{(}s_{i},e_{j}\big{)}\geq R_{s}+R_{a}\end{cases} \tag{1}\]  
Figure 1: Distribution of hybrid node target region.  
where \(d\big{(}s_{i},e_{j}\big{)}\) is the distance between node \(s_{i}\) and target point \(e_{j}\), \(d\big{(}s_{i},e_{j}\big{)}=\sqrt{\big{(}x_{i}-x_{j}\big{)}^{2}+\big{(}y_{i}-y_{j }\big{)}^{2}+\big{(}z_{i}-z_{j}\big{)}^{2}}\). \(R_{a}\) describes the uncertainty perception capability of the node. \(\lambda=d\big{(}s_{i},e_{j}\big{)}-R_{s}-R_{a}\), \(\alpha\) and \(\beta\) are the attenuation factors that determine the degree of attenuation. Define \(\alpha=\beta=0.5\).