esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimization of HS-SPME
#### 3.2.1 The effect of Extraction Temperature  
In the extraction process, VOCs need to be evaporated from the sample solution to the gas phase by heating the sample, but with the increase of temperature, the SPME coating/headspace partition coefficient decreases. Suitable extraction temperature enables solid phase microextraction to stably adsorb more VOCs. The effect of temperature on extraction efficiency was investigated under the temperature of 25, 35, 45 and 55 \({}^{\circ}\)C and with an extraction time of 30 min. As shown in Figure 4a, with the increase of temperature, more volatile components were detected, and the extraction efficiency was generally enhanced. When the temperature exceeded 35 \({}^{\circ}\)C, however, the TN remained basically unchanged, TA increased slightly and SA decreased gradually. It is known that the adsorption on fibers is an exothermic process [26]. When temperature exceeded 35 \({}^{\circ}\)C, although the peak area of VOCs were increased to some extent, the total number of volatile compounds remained unchanged, and the sulfur adsorption capacity of the fiber decreased sharply, indicating that the sulfur adsorption was inhibited at an elevated temperature. Therefore, 35 \({}^{\circ}\)C was selected as the optimal heating temperature for extracting volatile compounds from landfill leachate, and the balance between volatility and absorption was achieved.  
Figure 4: Effect of (**a**) extraction temperature and (**b**) extraction time on the extraction efficiency of the VOCs.
# Biosimilar Medicines: From Development Process to Marketing Authorization by the EMA and the FDA
## 4 Development of Biosimilar Medicines
### Selection and Characterization of the Reference Product  
The first step in biosimilar development is the selection of an appropriate reference product. This should be a biological medicine with a well-known safety and efficacy profile that has already been approved by the regulatory authorities [38]. An in-depth characterization of the chosen reference medicine is performed through extensive analytical studies to identify the products' key structural and functional characteristics using sophisticated laboratory techniques. This step is crucial because it provides valuable data for setting up acceptance criteria through the definition of ranges for crucial parameters such as potency, purity, impurities and structural characteristics [37; 38; 39; 40; 41]. Table 4 presents some examples of the relevant attributes of the reference product and the techniques used to evaluate each one.  
After characterizing the product that will serve as a reference, a manufacturing process that allows for the final biosimilar to have the same CQAs as the reference product must be developed in order to achieve the established QTPP. This process is divided into four stages, as shown in Figure 4, namely, cell line creation, cultivation and production; isolation and purification; and finally formulation, filling and finishing, and requires the use of very complex and advanced techniques [42; 43].  
\begin{table}
\begin{tabular}{p{85.4 pt} p{284.5 pt}} \hline \hline
**Attribute** & **Methods** \\ \hline Primary structure & Peptide mapping: liquid chromatography (LC) and mass spectrometry (MS). Peptide mass fingerprint: matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). \\ \hline Higher order structure & Nuclear magnetic resonance (NMR). Surface plasmon resonance (SPR). \\ \hline Post-translational modifications & Normal-phase high-performance liquid chromatography coupled with mass spectrometry (NP-HPLC-MS). Gas chromatography–mass spectrometry (GC-MS). \\ \hline Size, detection of aggregates & Sodium dodecyl sulfate-polyacrylamideamide gel electrophoresis (SDS-PAGE). High-performance size exclusion chromatography (HP-SEC). \\ \hline Binding & Surface plasmon resonance (SPR). Enzyme-linked immunosorbent assay (ELISA). \\ \hline Biological activity & Cell assays. In vivo assays. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Examples of analytical methods for characterizing structural and functional attributes of a reference biological medicine [7].  
Figure 4: Biosimilar medicines manufacturing process [38].
# Dynamic Behavior Analysis of I-Shaped RC Beams under Combined Blast and Impact Loads
## 4 Numerical Results of I-Shaped RC Beams under Combined Loads
### Influences of Different Load Application Sequences  
By varying the start and end times of the blast and impact loads, the effects of different load sequences on the dynamic response of the structure were studied. When impact loads are applied to the structure prior to blast loads, they are called \"impact-blast\" loads. When blast loads are applied to the structure prior to impact loads, they are called \"blast-impact\" loads.  
Figure 6 shows the effects of different explosion proportional distances (Z), impact velocities (V), and load loading sequences on the failure behavior of the structure. The results show that under the impact-blast load, the concrete was more severely damaged in the depth direction, and the damage location was mainly concentrated in the mid-span. The damage to the concrete in the depth direction increased with the decrease in the blast proportion and the increase in the impact velocity. However, under the blast-impact load, the concrete of the top plate was partially damaged by compression, and the concrete of the bottom plate was damaged by tension along the span direction of the beam. With the decrease in the blast proportion and the increase in the impact velocity, the overall failure mode of the beam changes from local damage to flexural-shear failure.  
Figure 7 shows the structural damage changes during the load application. The main reason for the different structural dynamic responses is the different mechanism of the load. The impact load is applied to the structure in the form of concentrated force, which tends to cause obvious local flexural-shear stress in the impact zone. When the subsequent blast load is applied to the structure, it causes the spread of combined stresses. However, when the blast load is first applied to the structure, the uniform load will not cause local stress concentration, and the beam will undergo overall flexural and shear failure. The structure still has a strong bearing capacity when subjected to the subsequent impact loads. Damage along the span direction occurs in the tensile zone of the concrete. Figure 8 shows the time-history curve of the mid-span displacement of the beam under the explosion proportional distance of 0.32 m/kg\({}^{1/3}\) and the impact velocity of 6 m/s. It can be seen that the maximum mid-span displacement of the beam under the impact-explosion load is greater than the blast-impact load, only blast, and only impact.  
Figure 5: Numerical analysis model of I-shaped RC beam under combined load.  
Figure 8: Time-history curves of mid-span displacement of beams under different loading sequences.  
Figure 6: Numerical analysis results of damage behavior of I-shaped RC beams under different impact rates and proportional distances.  
Figure 7: Damage states of the beam progressing under different combined loadings with \(V\) = 6.86 m/s and \(Z\) = 0.32 m/kg\({}^{1/3}\).
## 2 Related Works  
Even though that routing requirements in Smart Farming were not explicitly considered by RPL, recent studies show the important and promising adoption efforts of the IoT in agriculture [2; 15; 16; 17]. In fact, the first advantage of IoT is to enable Machine-to-Machine (M2M) communications, between all farming infrastructure components. This provides farmers an advanced automation level, enabling the supervision and control of farm sectors with minimum farm-worker intervention. Indeed, in this extremely heterogeneous context, RPL enables interoperability among network components [18]. This interoperability, between densely deployed smart farm IP-enabled sensor/actuator nodes, generates substantial data exchanges that should be routed by RPL. Recall that RPL was mainly conceived for low data rate network. As a result of this, it is reasonable to state that one way to enable RPL [5] to be efficiently scalable in smart farms, is to find a way to reduce the routed data streams.  
In the literature, recent research contributions considered applying the RPL protocol in the context of SF. In [19], the authors defined a scalable context-aware objective function (SCAOF) that can adapt RPL to the monitoring of a smart farm. This combined the awareness of energy, reliability, and robustness according to a composite routing metric approach. In [20] authors proposedagriculture data aggregation scheme for low-power and lossy SF networks. They first use the RPL routing protocol in agricultural system, especially in greenhouses. Then, they collect and analyze the sensed data before transmission up to the sink. In [21], the authors predicted the optimum values of the DODAG Information Object (DIO) interval rate and redundancy frequency that minimizes the battery consumption. In [22], the authors explored how to apply RPL for smart irrigation system. They identified the scalability as a main concern of RPL in smart farming context because of the high number and density of nodes, through the farm.  
Most of the aforementioned works have focused on various aspects such as reducing the power consumption (using a related metric in the OF), bounding the delay, fostering the use of efficient links, etc. However, only few of them have paid enough attention to the optimization of the built routing structure, i.e., the DODAG, serving as the backbone infrastructure for any in-network aggregation we can imagine. In this paper, we focus on enhancing RPL in the SF context, by proposing a new objective function that optimizes the generated routing DODAG, so that it reflects the spatial partitioning of the monitored farm. Our proposal will be referred to as the Partition Aware-RPL (PA-RPL).
## 4 Proposed Approach
### Cluster Head Election Phase
When the neighbors of these nodes receive the \(CH\_Announcement\) message, they update their \(CRL\) lists and join the CH with the highest weight using rule \(R1\), then execute \(R3\) to select the shortest path toward their CHs using neighbors information. At this stage, two clusters are formed in the network: \(C1\)\(\{CH:1;\ CMs:2,3,4,9\}\), \(C2\)\(\{CH:11;\ CMs:12,13,14\}\). Next (Figure 6e), as new members joined the cluster, the members send \(LSV\) message that contains the updated cluster information to their surrounding neighbors. Therefore, in the last state (Figure 6g) the same situation can be repeated for the rest of the nodes. The remaining unallocated nodes have received the clustering information and updated their \(CRL\) lists. These nodes join the cluster with the weightiest CH, then update their distance using \(R3\) (\(Dist=2\)) to form the final two hops clusters. Figure 5: The cluster head election phase. Figure 6: Execution scenario of Distributed Clustering based 2-Hop Connectivity (DC2 HC) clustering process.
# Analysis of a Landslide on a Railway Track Using Laser Scanning and FEM Numerical Modelling
## 2 Landslide Area and Methods
The SRM is based on the assumption of an iterative reduction in the subsequent calculation steps of the strength parameters of the soil: friction angle and cohesion until the numerical stability of the model is achieved. The value of the FoS was calculated for the natural (dry model) and fully saturated (wet model) numerical models. The boundary conditions were introduced in such a way as to block the possibility of horizontal displacements on both side frames and vertical displacements on the bottom frame. The terrain surface in the numerical model was developed based on LIDAR data. The geometry of the geological structure was developed based on the extrapolation of data from the four geological profiles. Analysis of the stability of the railway embankment slope was undertaken on the basis of geological-engineering documentation [27]. Results of Remote Sensing Test to Model the Morphology of the Area before and after the Landslide Occurred  
To model the morphology before the landslide occurred, we used data available at www.geoportal.gov.pl (Figure 5). In this way, the morphology of the area before the landslide was reconstructed. As can be seen, the area is suitable for the accumulation of rainwater from trench No. 1. In location No. 2, the accumulation of the flowing water from trench No. 1 occurred, and a water-flow channel was created (Figure 6). The digital terrain model after the landslide was developed using a terrestrial laser scanner is shown in Figure 6. Using the Riskan Pro Cloud Punk, the point clouds were combined in one area into a cloud of 3D points. Even at this analysis stage, a surface trough (No. 1) can be seen at the location of the landslide (Figure 7). Figure 5: Digital model of the area obtained from LIDAR air photos (based on www.geoportal.gov.pl). Description: 1–trench used to collect rainwater; 2–the pass where rainwater flows out from the trench 1; 3–position of the railway track. Based on the terrestrial measurements, a digital model of the ground surface was created (Figure 8). The obtained model allows the analysis of morphology changes in the context of the possible activation of the landslide. As a result of the water, the landslide main scarp formed, and a deeply grooved trough was created. Figure 9 shows a model of the area after the landslide; the model was constructed using the data obtained from terrestrial laser scanning, which was superposed by the digital model obtained from the LIDAR data. The location of the object No. 2 from Figure 5 is probably where the landslide process was activated. Figure 6: Narrow pass No. 2 resulted from the loss of the stability of the railway embankment (phot. [PERSON], [PERSON]). Figure 7: The digital model of the landslide consisting of cloud points obtained from terrestrial laser scanning. Figure 8: A digital model of the area based on terrestrial laser scanning. Figure 3:0 The area of trough No. 2 obtained by terrestrial laser scanning and superposed by LIDAR data.
# Fault-Free Protection Level Equation for CLAS PPP-RTK and Experimental Evaluations
## 5 Discussion  
The broadcast \(\sigma_{i}\) of the correction messages in SBAS or CLAS is typically much larger than the actual correction error distribution because it is intentionally inflated to overbound the residual errors in range measurements such that the protection levels also overbound the position errors after applying the correction messages. In VRS-RTK, the uncertainty of the double-difference code and carrier phase measurement, \(\mathbf{Q}_{\rho}\) and \(\mathbf{Q}_{\Phi}\), respectively, is used to solve the float baseline and integer ambiguities. Then, the float integer ambiguities and their associated covariance matrices are used in the re-parameterization and search procedure for integer ambiguities. If \(\mathbf{Q}_{\rho}\) and \(\mathbf{Q}_{\Phi}\) are constructed using the broadcasted \(\sigma_{i}\) from CLAS, the LAMBDA process has a very large search space and a high chance of finding incorrect integer ambiguities. Therefore, the realistic values of \(\mathbf{Q}_{\rho}\) and \(\mathbf{Q}_{\Phi}\) should be used to successfully resolve the integer ambiguities in LAMBDA.  
In the test results, the RMS errors in the up direction are relatively larger than in the East and North directions. The mean value of the vertical position errors had a bias of approximately 12 cm. We also observed a similar bias from zero-baseline VRS-RTK position errors with the same dataset using an open-source software tool [28]. Therefore,  
Figure 8: Comparison of the horizontal protection levels and horizontal position errors from the test dataset.  
Figure 9: Comparison of the vertical protection levels and vertical position errors from the test dataset.  
it is presumed that there was a bias in the reported true antenna positions of the GNSS dataset used in the test. This issue will be investigated further.  
In fact, whether the receiver moves or not, protection levels can be computed in the same way as long the PPP-RTK process is adequately implemented. However, a dynamic receiver may suffer from a frequent loss and inclusion of satellites, which may overall lower the _PCF_ of integer ambiguities and result in incorrect integer ambiguities. The research on this issue will also be carried out as future work.
# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
## 2 Methodology
### Segmentation Evaluation Metrics  
The accuracy of the trained models was assessed against the 20% test labeled data as explained in Section 2.1. The results of each algorithm on the test were evaluated using several evaluation metrics including overall accuracy, intersection over union (_IoU_), and boundary F1 score (_BF score_). The metrics were assessed over the entire dataset and per-class version. They were computed using four measures: true positives (_TP_), false positives (_FP_), true negatives (_TN_), and false negatives (_FN_). _TP_ was image pixels that were correctly classified as belonging to the water region, while _TN_ was the number of pixels that were correctly classified to the non-water (background) class. _FP_ was the number of pixels that did not belong to the water region but was wrongly classified as water and _FN_ was the pixels that were supposed to be in the water class but were incorrectly associated with the background region.  
Overall accuracy is the ratio between the number of pixels that were correctly identified to the total number of pixels without concerning which class the pixels belonged to. It was computed using Equation (2).  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{2}\]  
Intersection over union (_IoU_) or also known as the Jaccard coefficient was the standard measure in evaluating the segmentation results [66]. _IoU_ was computed by measuring the overlapping area between the predicted segmentation and the ground truth region. _IoU_ is defined by Equation (3).  
\[IoU=\frac{TP}{(TP+FP+FN)} \tag{3}\]  
Boundary F1 score (_BF score_) was also used to get detailed information on the accuracy of the segmented boundaries as the two above-mentioned metrics provided more region-based accuracies [67]. _BF score_ measured how close the boundary of the predicted segmentation and the ground truth segmentation was. It was computed from the precision and recall values considering a distance error tolerance over the boundary pixels [68].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **DeepLabv3+** & **Value** \\ \hline Momentum & 0.9 & \\ Epochs & 30 & \\ L2 regularization & 0.005 & 0.0005 \\ Learning rate & 0.003 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameters set for this work.  
Precision is the number of correctly classified positive results divided by all positive results and recall is the number correctly classified positive results divided by the number of samples that should have been classified as positive. The mathematical representation of the _BF score_ is defined as in Equation (4).  
\[\textit{BF score}=2\times\frac{\textit{Precision}\times\textit{Recall}}{\textit{ Precision}+\textit{Recall}} \tag{4}\]
## 7 Overview of Algorithms for Energy Optimization UAV-Based Cellular Networks
### Machine Learning Algorithms
#### 7.2.4 Deep Learning (DL)  
DL is a branch of ML that mimics the functioning of the human brain in the data processing. It allows machines to learn without human supervision. It gives the ability to perceive spoken, translate, identify objects, and make decisions. Despite being a branch of ML, DL systems do not have limited learning capabilities like traditional ML algorithms. Instead, DL systems can continually improve their performance as they are fed larger and more consistent data [165].  
DL was realized with the help of artificial neural networks. Numerous sensors, the artificial counterparts of neurons, have come together to form artificial neural networks. The term \"deep\" is used to denote the number of hidden layers that neural networks have. Each layer of the neural network processes the input data and transmits them to the next layer until the data reach the output layer, where the desired output is obtained. DL algorithms have a sophisticated architecture comprising in-built feature extraction and representation capabilities absent in traditional ML algorithms. This enables them to process very complex data such as images, videos, natural language, play games, etc. [166].  
Another way to understand how DL works is to look at convolutional neural networks (CNNs). Convolutional neural networks detect features directly, eliminating the need for manual labeling of data. None of the features are previously taught to the machine; instead, it trains itself on the given set of images. This automatic feature detection capability makes DL models useful for object classification and other computer vision applications. The reason deep neural networks are so sensitive in identifying features and classifying images is because of the hundreds of layers they contain. Each layer learns to describe certain features, and as the number of layers increases, the complexity of the learned image features changes in direct proportion [167; 168; 166].
# Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service
## 4 Results  
In this section, we initially evaluate the results obtained from the prediction of the QoE and QoS directly based on the gathered EEG data. We subsequently shift the evaluation to additionally consider the availability of user-centric profiles, expressed in terms of the z-score of the EEG measurements, EEGz. Throughout this section, we employ the second degree polynomial extensions \(d=2\) in our evaluations, to enable direct comparison with other results obtained for binocular augmented vision scenarios in [2]. We additionally note that the overall trends observed in prior works [29] apply here, as well, i.e., the second degree is a sensible trade-off between computational complexity and prediction performance.
## 3 Applications
### Sensing  
Nanostructured titania has been investigated to develop a variety of electrochemical sensors, especially as applied to biomarkers for early disease diagnosis. Volatile organic compounds (VOCs) can be useful biomarkers for the early detection of a variety of pathological states, and therefore chemoresistive breath sensors for their detection are investigated, often using semiconducting metal oxides. To this end, various forms of nanostructured titania can be used, such as nanoparticles, nanotube arrays, and nanosheets, as recently reviewed [224]. Zinc-doped titania nanoparticles have been also proposed to develop glutamate sensors, as glutamate is a common food additive but also a useful biomarker, since abnormal levels were linked to pathologies such as epilepsy, Alzheimer's and Parkinson's diseases, ischemia, and amyotrophic lateral sclerosis [225].  
Figure 9: Scheme of the fundamental reaction pathways of ammonia degradation photocatalyzed by titania. Reprinted with permission from [214], Copyright © 2020, American Chemical Society.  
For all these sensing applications, the inclusion of carbon nanostructures can be beneficial to enhance sensitivity and the performance of the devices generally, as recently reviewed [10]. This is true for a variety of carbon nanomorphologies. For instance, the inclusion of CNTs allowed a lower detection limit and an increased linear range of operativity for an epinephrine sensor [226]. In another recent example, an rGO/titania nanohybrid deposited on a glassy carbon electrode displayed a better performance for the electrochemical detection of a red dye relative to the sensor made with either component, thanks to a larger electroactive surface area and lower charge-transferred resistance [227]. Finally, graphene quantum dots also allowed for an enhanced photoresponse, with higher photocurrents and improved charge carrier separation efficiency, for an NO gas sensor based on titania that operated under UV-light irradiation [228].