esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials, Datasets and Methods  
ERA5 global reanalysis is a highly visible datasets within the Copernicus Climate Change Service (C3S) [20], providing an improved and consistent record of a large number of ECVs for the C3S Climate Data Store (CDS) [21]. Additionally, there is a considerable increase in resolution (both horizontal and vertical) and the benefit of 10 years of model and data assimilation (DA) developments [22]. The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as Outgoing Longwave Radiation (OLR). The top net thermal radiation equals the negative of OLR. This parameter represents a mean over a particular time period (the processing period), which depends on the extracted data. [PERSON] et al. [23] proposed a genesis potential parameter (GPP), which includes both dynamic and thermodynamic factors over the northern Indian Ocean.  
\[\text{GPP}=\text{RV}\times\text{M}\times\text{I}\times\text{VWS} \tag{1}\]  
RV = low-level relative vorticity (at 850 hPa).  
M = Middle troposphere relative humidity (M= (RH - 40)/30).  
I = (T850 - T500) Middle-tropospheric instability (K).  
VWS = Vertical wind shear (wind speed and direction) difference in between 200 and 850 hPa levels (m/s).  
ERA5 Reanalysis Datasets are used to analyze the TC Environmental Conditions over the Bay of Bengal ([[https://www.ecmwf.int](https://www.ecmwf.int)]([https://www.ecmwf.int](https://www.ecmwf.int)), accessed on 26 February 2025).  
NOAA SST v2, a high-resolution blended analysis of daily SST and ice datasets, incorporates observations from various platforms (satellites, ships, buoys, and Argo floats) into a regular global grid [24]. These datasets are used, and details are discussed in ([[https://pslnoaa.gov/data/gridded/data.noaa.oisstv2.highres.html](https://pslnoaa.gov/data/gridded/data.noaa.oisstv2.highres.html)]([https://pslnoaa.gov/data/gridded/data.noaa.oisstv2.highres.html](https://pslnoaa.gov/data/gridded/data.noaa.oisstv2.highres.html)), accessed on 26 February 2025).
## 1 Introduction
These architectures help extract spatial and temporal dependencies, enhancing model accuracy and adaptability under variable loads at treatment facilities [34; 35]. However, working with time series data requires high-quality preprocessing and the regular measurement of parameters, which in many cases is challenging to achieve. This study addresses the issue of accumulating training datasets in modeling not based on the principle of time series. This is particularly relevant in cases where the technical infrastructure of wastewater treatment plants does not allow for the regular measurements of the characteristics necessary for constructing a time series. One of the primary objectives of this work is to develop principles for obtaining training datasets for model training, which can subsequently be applied to operational treatment facilities. The feasibility of using laboratory sample data as a training set, followed by validation against data from real treatment plants, is examined. An essential stage of the research involved is analyzing the significance of predictors for establishing guidelines regarding the placement of analytical equipment at wastewater treatment stations [36]. In this work, models compatible with [PERSON]'s value-based analysis tools (using the SHAP library) are employed, which enabled the compilation of a list of predictors for each specific task. Thus, the following research objectives have been formulated:  
* To establish a system that facilitates the collection of the necessary amount of data for training supervised models based on a laboratory setup. * To test the hypothesis regarding the equality of the ratios of input and output parameters of wastewater in the populations of the laboratory setup and the operational treatment plants under investigation. * To develop a strategy for data preprocessing. * To implement the addition and selection of predictors for model training. * To identify the most effective models with optimal hyperparameters for classification and regression tasks using cross-validation tools. * To evaluate the models on a test dataset using selected quality metrics. To achieve these objectives, an experiment was conducted using a laboratory-scale bioreactor simulating the wastewater treatment process. Statistical tests were carried out to confirm the applicability of the data collected in laboratory conditions as a training dataset. The test dataset comprised data from full-scale wastewater treatment plants (WWTPs). This approach assesses the feasibility of training a machine learning model under conditions of limited historical data from real wastewater treatment plants. Well-established regression and classification machine learning models were employed for training, with performance evaluated across multiple metrics, significant parameters identified through SHAP analysis, and rigorous quality assessment of the model predictions.
## 3 Related Technologies
### Node Remote Reprogramming Mechanisms  
In the general case, wireless node reprogramming, also named over-the-air reprogramming, is a useful tool for remote uploading new codes or for changing the functionalities of existing codes [42]. Remote reprogramming is especially useful on large networks installed on harsh or difficult access environments. In our case, the proposed network access control framework relies on remote reprogramming as a means to enforce node security, _i.e._, to guarantee that legitimate nodes are using security free codes before being connected to the network.  
Remote software installation is more challenging on LoWPAN networks mainly because of the resource constraints of the nodes, the fact that a single network might have thousands of nodes and the fact of communications being over multihop links. Moreover, a program image is relatively large to be transmitted over low-power wireless links where link losses and collisions often occur. Consequently, efficient mechanisms must be used to upload the software, minimizing the nodes energy consumption. Significant research has been done in order to address the resource constraints nature of LoWPAN networks. The first proposed reprogramming mechanisms have only assumed single-hop networks. In this case, only nodes with direct links with the border router (usually selected to store the new software because it has more resources than the other nodes) can be reprogrammed. Recently, more mechanisms were proposed to address multi-hop reprogramming [43]. In fact, multi-hop reprogramming can be more efficient, both in terms of time and energy, when compared with single-hop reprogramming. First, shorter links can be used, which reduces the retransmissions due to collisions. Second, the multi-hop protocols divide the entire code image into pages and when a node completes downloading a single page, it can send it to other nodes in the network while downloading the next page (this process is referred to as spatial multiplexing).  
Remote reprogramming research is organized under the following three categories [44] (Figure 3): the sensor node execution environment, the protocols for update dissemination, and the image size reduction. Power efficiency, performance, security and reliability are common issues addressed by the solutions proposed for each category.
# Neural Machine Reading Comprehension: Methods and Trends
## 3 Deep-Learning-Based Methods
### General Architecture  
As presented in Figure 5, a typical neural machine reading comprehension system, which takes the context and question as inputs and the answer as output, contains four key modules: embeddings, feature extraction, context-question interaction and answer prediction. The function of each module can be interpreted as follows.  
Embeddings: As the machine is unable to understand natural language directly, the embedding module is indispensable to change input words into fixed-length vectors at the beginning of the MRC systems. Taking the context and question as inputs, this module outputs context and question embeddings by various approaches. Classical word representation methods such as one-hot and Word2 Vec, sometimes combined with other linguistic features, i.e., part-of-speech, name entity, and question category, are usually used to represent semantic and syntactic information in the words. Moreover, contextualized word representations pre-trained by a large corpus also show promising performance in encoding contextual information.  
Feature Extraction: After the embedding module, context and question embeddings are fed to the feature extraction module. To better understand the context and question, this module is aimed at extracting more contextual information. Some typical deep neural networks, such as recurrent neural networks (RNNs) and convolution neural networks (CNNs) are applied to further mine contextual features from context and question embeddings.  
Context-Question Interaction: The correlation between the context and the question plays a significant role in predicting the answer. With such information, the machine can find out which  
Figure 4: Comparison of different MRC tasks.  
Figure 5: The general architecture of machine reading comprehension system.  
parts in the context are more important to answering the question. To achieve that goal, the attention mechanism, unidirectional or bidirectional, is widely used in this module to emphasize parts of the context relevant to the query. To sufficiently extract their correlation, the interaction between the context and the question sometimes involves multiple hops, which simulates the rereading process of human comprehension.
* Answer Prediction: Answer prediction module, the last component of an MRC system, outputs the final answer based on all the information accumulated from previous modules. As MRC tasks can be categorized according to answer forms, this module is highly related to different tasks. For cloze tests, the output of this module is a word or entity in the original context, while the multiple-choice task requires selecting the correct answer from candidate answers. In terms of span extraction, this module extracts a subsequence of the given context as the answer. Some generation techniques are used in this module for the free answering task, as it has nearly no constraint on answer forms.
# Dynamic Characterization of Cohesive Material Based on Wave Velocity Measurements
## 3 Results and Discussion
### Frequency Dependency
In addition to this, a high frequency wave attenuates faster than the low frequency one, provided the medium is the same. The fact that the use of a high frequency gives the clearest output signals remains undisputed. In the case of compression waves (Figure 10), the authors carried out research in the frequency scope of up to 10 kHz. A very large dispersion of the results and a lack of dependence of \(\upsilon_{\mathrm{P}}\) on the frequency ratio, as well as on mean effective stress can be clearly noticed. The authors could not find a proper relationship of \(\upsilon_{\mathrm{P}}\)_vs._ frequency and mean effective stress. Empirical equations expressing the variation of P-wave velocity with frequency are characterized, however, by the low coefficients of determination (\(R^{2}\)), listed in Figure 10. In general, the differences in compression wave velocities with the different frequencies reach high values, from 54 to 110 m\(\cdot\) s\({}^{-1}\) and from 19 to 57 m\(\cdot\) s\({}^{-1}\), depending on the technique used to measure the travel time. In Figures 11 and 12 the effect of excitation frequency on shear modulus (Figure 11) and Young's modulus (Figure 12) is presented. The excitation frequencies varied from 1 to 100 kHz. The specimens were subjected to various isotropic effective confining pressures. It was observed (black frame in the figures) that the best signal, and consequently the greatest values of shear modulus (\(G\)), were received at the excitation frequency, whose values were equal 20 kHz for the peak-to-peak method (Figure 11a) and 50 kHz for the start-to-start method (Figure 11b). The excitation frequency being equal to (Figure 12b) or smaller than 50 kHz (Figure 12a) gave the greatest values of Young's modulus (\(E\)), as well. It is worth paying attention to a decrease in the modulus values along with the decreasing frequency, although \(G_{\mathrm{max}}\) and \(E_{\mathrm{max}}\) values were not obtained for the highest frequency, as already mentioned above. The average \(G_{\mathrm{max}}\) value obtained from all applied values of pressure ranged from 139 MPa to 194 MPa, depending on the measurement of the travel time, while average \(E_{\mathrm{max}}\) was around 405 MPa and 557 MPa. In the case of the minimal dynamic properties of the studied soils, using the lowest frequencies resulted in the smallest values of \(G\) and \(E\), with the average minimal \(G\) ranging from 116 MPa to 152 MPa and the minimal \(E\) ranging from 379 MPa to 509 MPa. It should be also pointed out that the values of \(E\) are arranged in some trend with increasing frequency, whereas the values of \(\upsilon_{\text{P}}\) do not show any dependence on frequency. This may seem incorrect, because Young's modulus is relevant to the compression wave velocity, due to the motion of the particles. However, it must be remembered that the authors used the results of \(\upsilon_{\text{P}}\) only indirectly, in order to determine the proper value of \(\
u\).
## 4 Modeling Process and Results  
The modeling process (see Figure 4) can be divided into the following six stages: (1) According to historical landslides, a landslide inventory map was created and subsequently divided into a training dataset (70%) and a test dataset (30%). (2) Twenty initial landslide influencing factors were selected to construct a spatial database. These factors were then overlaid with the landslide inventory map and reclassified. (3) The SI method was used to assign weights to each class of factors to obtain the SI model. (4) The factors were screened using GeoDetector combined with recursive feature elimination, and the GD-SI model was obtained. (5) The weights of continuous factors were optimized using GPR, and the final hybrid model GD-GPR-SI was obtained. (6) The performances of SI, GD-SI, and GD-GPR-SI were compared and evaluated, and landslide susceptibility maps were finally drawn.
# 47.5 GHz Membrane-III-V-on-Si Directly Modulated Laser for Sub-pJ/bit 100-Gbps Transmission
Moreover, these results pose a \(\sim\) 3.9x times improvement in the power consumption compared to similar PPR-based InP DMLs [11] and a significant improvement in terms of both power consumption and 3-dB bandwidth compared to previously demonstrated PPR-based III-V/Si DMLs [15]. In addition, we used this DML to demonstrate 60 GBaud 4-level pulse-amplitude modulated (PAM-4) signals below the Ethernet's KP4 forward-error correction (KP4-FEC) threshold [19] (i.e., a net rate of 113 Gbps) after 2-km of standard single-mode fiber (SSMF) transmission, with a DC energy-efficiency of 0.17 pJ/bit at a room temperature (RT). Equally important, we further operated the laser at 50 \({}^{\circ}\)C to study its eligibility for semi-cool operation in intra-DCNs environments. At this temperature, we were able to demonstrate a similar 60 GBaud PAM-4 (net 113 Gbps) transmission over 2-km of SSMF, with a DC energy-efficiency of 0.34 pJ/bit and a 3-dB bandwidth of 42.5 GHz. Deploying such DMLs in future integrated multichannel transceivers should ensure their suitability for a sustainable evolution towards TbE links and OBO sub-systems.
# Physics of Within-Tissue Wave Propagation Generated by Pulse Propagation in the Carotid Artery
## 3 Materials and Methods
### Parametric Study
#### 3.2.3 Patient-Specific Carotid Artery  
To study the geometrical effect of a realistic artery on the wave propagation analysis, a patient-specific carotid artery (CA) model was investigated (see Figure 1b). The geometry consisted of the CCA (length \(\sim\)34 mm) and the bifurcation region (BIF; length \(\sim\)16 mm), where the CCA splits into the internal (ICA) and external (ECA) carotid artery. The patient-specific CA was embedded in a cuboidal domain (width 18 mm, length 50 mm, height 26 mm and averaged CCA depth d \(\sim\)15 mm). Due to the complexity of the geometry, we used software developed in-house Pyformex (Pyformex 1.0, Ghent University, Belgium; [[http://pyformex.org](http://pyformex.org)]([http://pyformex.org](http://pyformex.org))) to model and mesh the geometry using both hexahedral and tetrahedral elements. The boundary conditions were the same as for the CCA model: infinite elements were placed on the external boundaries of the domain to avoid wave reflections except for the free top surface, as shown in  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Material Characteristics**} \\ \hline \(\rho\) [kg/m\({}^{3}\)] & 1045.5 \\ E [kPa] & 73.0 \\ \(v\) [-] & 0.49999 \\ g\({}_{1}\) [-] & \(4.04\times 10^{-3}\) \\ \(\tau_{1}\) [s] & \(99.8\times 10^{-6}\) \\ g\({}_{2}\) [-] & \(7.04\times 10^{-2}\) \\ \(\tau_{2}\) [s] & 77.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the viscoelastic material characteristics of polyvinyl alcohol (PVA) [23].  
Figure 1b. The element size definition of the mesh varied depending on the vicinity of the BIF, but the largest overall element size was chosen based on the outcome of our mesh sensitivity analysis of the CCA model. The accuracy of the selected mesh was verified by modeling shear wave propagation in a similar manner to that described for the CCA model in Section 3.1.2. After this verification step, a pressure impulse traveling along the centerline of the artery with a velocity of 4, 7 and 10 m/s (magnitude and spatial profile similar to that defined in Section 3.2.1) was imposed on the model for a time of 13 ms.
# A Low-Complexity Pilot-Based Frequency-Domain Channel Estimation for ICI Mitigation in OFDM Systems
## 5 Numerical Results
This is because our proposed pilot pattern and low complexity channel estimation allow obtaining the coupling matrix without ICI. In addition, the spline interpolation allows to obtain accurately the coupling matrix for the OFDM data symbols. The achieved BER in the high SNR regime is even smaller than with ideal channel estimation (ideal) of the main diagonal, since ICI mitigation is not performed in this case. The second best results (in terms of BER) are achieved by the comb type pattern (comb). This is because such a pattern offers pilots at every OFDM symbol, and hence, the channel information is not outdated for equalization purposes. However, the performance at the high SNR regime is limited by ICI in channel estimation and after equalization. The LTE pilot pattern provides a higher BER than previous pilot patters. On the other hand, ZOH interpolation provides the worst BER results whereas spline method achieves the lowest BER. This is because there is ICI in the channel estimation, the channel is outdated between OFDM symbols without pilots, and additionally, ICI is not mitigated with the equalization. Lastly, results related to frequency-flat channel are illustrated on Figure 8 to asses the performance of the proposed technique. To illustrate the bound on achievable performance, results of the perfect (ideal) estimation of the full coupling matrix (i.e., not banded assumption), with full MMSE matrix inversion is shown with a purple dash line. As can be observed in the noise limited region (i.e., in the low SNR regime), the ideal channel estimate with 1-tap (blue dashed line) and the ideal coupling matrix estimate with MMSE equalization achieves the same performance. Nevertheless, as the SNR increases the knowledge of the coupling matrix allows perfect ICI mitigation. Besides, in this figure it is shown the proposed technique for different values of \(Q\). It is shown that increasing \(Q\) does not improve the performance. Nevertheless, with all these values of \(Q\) BER is smaller that with the LTE pattern in the high SNR regime, which was also observed with Figure 7. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Legend** & **Pilot Pattern** & **Time Interpolation** & **Equalization** \\ \hline \(H_{n^{\prime}k}\) ideal 1-tap ZF & none & none & ZF \\ \hline \(\underline{\mathbf{H}}_{df}(d,f)\) ideal MMSE & none & none & MMSE \\ \hline comb & comb-type & none & ZF \\ \hline LTE-ZOH & LTE & ZOH & ZF \\ \hline LTE-LIN & LTE & linear & ZF \\ \hline LTE-SPL & LTE & spline & ZF \\ \hline prop-SLP & prop. & spline & \(LDL^{H}\)-MMSE \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters.
## 3 Results and Discussion
### Required Sampling Volumes  
For each sorbent, there is an optimal range of flow rates but a typical range is between 10 and 250 mL/min. If the flow rate is too high, the analytes will not have enough time to interact with the sorbent material and if the flow rate is too slow, there is a risk of back-diffusion.  
Several factors affect the detection limit of a TC-GC-MS method; among others some of the parameters of the thermal desorption such as the inlet split ratio during the primary (tube) desorption and the outlet split ratio during the secondary (trap) desorption. Moreover, the total detection limit is also affected by the sensitivity of the detector. Another important factor affecting the limit of detection is the volume sampled; the larger the volume sampled the lower is the detection limit. However, for a given analyte, there is a maximum volume that can be sampled before the analyte passes out the back end of the sorbent bed (breakthrough).  
The sampling volumes required to ensure that the analytical method will meet the thresholds set in ISO 14687-2 [2] for hydrocarbons, sulphur compounds and halogenated compounds have been estimated here based on several assumptions. (1) The quantification limit (LOQ) for each family was calculated using the criterion stated in ISO/CD 21087 [10]; LOQ + uLOQ \(<\) thresholds set in ISO 14687-2; for the calculation, uLOQ was estimated to be 50%. Based on these assumptions, the method's LOQs are 1 \(\upmu\)mol/mol for total hydrocarbons, 0.002 \(\upmu\)mol/mol for total sulphur compounds and 0.025 \(\upmu\)mol/mol for total halogenated compounds; (2) In order to estimate what would then be the required LOQ for each individual compound, we used the method for missing data where it is common practice to replace every value below LOQ by a constant such as LOQ/2 or LOQ/\(\upmu\)2. Here we chose LOQ/2. Based on this assumption, the method's LOQs are 0.5 \(\upmu\)mol/mol for each hydrocarbon, 0.001 \(\upmu\)mol/mol for each sulphur compound and 0.0125 \(\upmu\)mol/mol for each halogenated compound. (3) Finally, it was assumed that the LOQ for any compound with the analytical methods used in this study is 2 ng. This limit is fairly easy to obtain with most systems based on TD-GC-MS.  
Using the three assumptions, the minimum volume of hydrogen that should be sampled on sorbent tubes to reach the thresholds set in ISO 14687-2 is presented in Table 5. The minimum volume of hydrogen sampled also depends on the amount of the sorbent in sorption tubes; in this case, 200 mg in each tube.  
The minimum sampling volume required for hydrocarbons and halogenated hydrocarbons with the method used in this study is very low for sorbent tubes (typical sampling volumes range from 0.5 L up to at least 10 L). However, due to the stringent threshold set for sulphur compounds, this minimum volume is higher for this family.