esa-sceva/satcom-chunk-collection huggingface dataset:
# A Simulation Study on Pressure Control in Oil Well Drilling Using Gain-Scheduled PID Controllers
## 4 Results
### GS Controller and MRAC: Simulation
#### 4.3.3. Kick  
The problem of influx (kick) depends on the magnitude of the pressure increase rate. Figure 8 shows that the model was simulated related to this disturbance through the increase in the flow rate from the reservoir to the well. A positive ramp function was used in the reservoir flow rate variable (\(q_{r}\)), for a range of 40 to 80 s, starting at 0.001 to 0.02 m\({}^{3}\)/s [39]. During operation, influx is a phenomenon that can occur in even shorter moments of time, but for the purpose of better illustrating the system response when the two controllers act, a time of 40 s was adopted. It could be noted in these simulations that the system pressure was already at the desired pressure (190 bar). The system was simulated and the responses for the GS and MRAC controllers, as already specified, were  
Figure 10: Loss of circulation simulation for different depths. (**a**) 500 m, (**b**) 1000 m, (**c**) 2000 m, (**d**) 3000 m, (**e**) 4000 m, and (**f**) 5000 m.  
obtained. The response behavior related to the controller performances in the presence of inflows was evaluated for different drilling depths (500, 1000, 2000, 3000, 4000, and 5000 m). The system responses related to inflow problems are illustrated, as shown in Figure 11.
# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
## 2 Datasets and Observations  
### The Swarm Satellites  
The Swarm constellation is the first ESA constellation program for geomagnetic observation, whose main scientific objective is to study the delicate structure of the Earth's magnetic field, its dynamics, and its interaction with Earth systems [24; 25]. The constellation consists of three identically equipped satellites, Swarm A (Alpha), B (Bravo), and C (Charlie), which were launched together into a near-polar orbit on 22 November 2013, and finalized their commissioning on 17 April 2014. For the main purpose of the Swarm constellation, each satellite is equipped with a high-precision magnetometer in addition to several other sensors to increment the scientific satellites' capabilities. Among them, the charged particle sensor (Langmuir detector) provides a new avenue for the study of the seismic ionosphere phenomena.
# Convolutional Neural Network Architecture for Recovering Watermark Synchronization
## 4 Proposed Method
### Application Method for Images of Various Sizes  
Because the input size is fixed in CNN, all descriptions are based on 512 \(\times\) 512 images according to the network input size. In the real world, however, there are images of various sizes, so all images must be resized to 512 \(\times\) 512 to insert and extract the template and watermark. However, if the image is resized for watermark and template insertion, image quality degradation caused by the resizing cannot be avoided.  
To avoid image degradation due to resizing, the embedding process is performed as shown in Figure 7. First, the image is resized to 512 \(\times\) 512 and a template and watermark are inserted into the resized image to create a stego image. Subtracting the 512 \(\times\) 512 image before the embedding step will leave only the stego signal, which contains the template and watermark signals. By resizing the stego signal into the original image size and adding it to the original image, the template and watermark can be inserted into the image without image quality degradation.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 9 Performance Analysis
### On-Board Active System: Performance Analysis  
Some representative DI traces acquired along a test track and at various testing speeds by means of the prototype of on-board active defect detection system described in Section 5.1 are illustrated in Figure 22; note that the SNR of the DI traces degrades as the train speed increases. The plots of Figure 22 are generated on the basis of the data available in Ref. [47].  
Figure 22: Example of damage index traces collected during tests (dashed lines = locations of defects; dashed-dotted lines = locations of welds; dotted lines = locations of joints). Testing speeds: (**a**) 1.6 km/h; (**b**) 8 km/h; (**c**) 16 km/h; (**d**) 24 km/h.  
The performance of the above-mentioned prototype, expressed in terms of ROC, are illustrated in Figure 23 (its plots have been generated on the basis of the data available in Ref. [47]).  
The probabilities of detection computed for four different PFAs on the basis of the cumulative curves shown in Figure 23 are listed in Table 2. From Figure 23 and Table 2, it is easily inferred that the performance of the considered defect detection system gets worse as the speed at which the test is accomplished increases [47]. Moreover, the experimental campaigns accomplished for this system have proven that it is sensitive to both transverse-type defects and mixed-mode cracks (vertical split heads or compound fractures). However, its performance is limited by the fact that air-coupled ultrasound transduction in steel suffers from a loss of energy due to the large impedance mismatch between air and steel, both in transmission and in reception. This explains the low SNR characterizing received signals [40] and limits the test speed [47].
## 4 Discussion
Therefore, a combination of 5-FU with MEK/ERK inhibition may be a prospective approach in order to enhance the effectiveness of cancer pharmacologic therapies for OSCC treatment. The transmembrane protein PD-L1, a co-inhibitory factor of the immune response, is considered pro-tumorigenic because its expression in cancer cells can render these cells to escape the antitumor immunity [19]. There has been increasing evidence to indicate PD-L1 expression in tumors as a poor prognostic marker for OSCC patients [21; 22]. It was reported that various cytotoxic drugs, including paclitaxel, etoposide, and 5-FU, induced PD-L1 surface expression in human breast cancer cells to promote PD-L1-mediated T-cell apoptosis, suggesting a relationship between chemotherapy and cancer immunoresistance [27]. Another study concluded that 5-FU enhanced PD-L1 expression in head and neck squamous cell carcinoma cells [25]. Similarly, our research also provided the evidence linking PD-L1 upregulation, both the whole-cell expression and surface expression, with 5-FU treatment in SCC4 cell line, indicative of a positive association. The literature has indicated the MAPK/ERK signaling pathway as a crucial mechanism that regulates the expression of PD-L1 in cancer cells [19; 20], contributing to the chemoresistance [38]. Therefore, the inhibition of the MAPK/ERK signaling pathway may have a double effect on cancer cells, reducing PD-L1 expression and suppressing cancer cell survival. A previous study demonstrated the increases in ERK phosphorylation and PD-L1 expression in esophageal squamous cell carcinoma cells following standard chemotherapy treatments, which were attenuated by the inhibition of MEK [39]. Similarly, our research reported that treatment with 5-FU combined with the blockage of MEK/ERK signaling pathway reduced PD-L1 expression in the SCC4 cell line because the inhibition of MEK1/2 using either Refametinib or U0126 reduced the high levels of PD-L1 expression in the 5-FU-treated SCC4 cells. In contrast with the substantial reduction in the total levels of PD-L1 protein, the significant expression of PD-L1 on the surface of the 5-FU-treated cells remained nearly unchanged following the inhibition of MEK1/2, as determined with flow cytometry. In fact,the literature has reported that the profile expression of PD-L1 includes membrane-bound, extracellular soluble, and exosomal forms [40]. Thus, the differences between two analyses herein may reflect the presence of non-surface forms of PD-L1 and the influences of dead or dying cells upon treatment which could be excluded from the flow analysis. Moreover, PD-L1 expression is regulated by multiple factors at different levels of transcription, post-transcription, and post-translation [20]. Therefore, our findings still support a rationale for the combination of 5-FU with MEK/ERK-targeted therapy to boost the anticancer effects in OSCC by mitigating PD-L1 upregulation, which represents a key mechanism for the immune evasion and chemoresistance.
## 6 Results and Discussion
### Optimal Number of Selected Features for the Proposed Classification System  
Since the number of selected features obtained from the feature selection process typically has an impact on the classification accuracy, we firstly investigated the optimal number of selected features for our PQD classification. Table 4 shows the classification accuracy of various numbers of selected features, with the results based on the adaptive SSA. This table indicated that with over 5 selected features, higher precision than 1 to 4 selected features is obtained. In particular, the highest classification accuracy of 98.77% can be achieved with 12 optimally selected features. In addition, Table 5 shows further detail of classification accuracy at each iteration having different selected features by the adaptive SSA. It shows that the highest accuracy with the optimally selected features indicated is achieved at iteration 79. The results displayed in the next sections are based on these 12 optimal features.
## 4 Clutter Suppression Method
### Clutter Removal  
For the correlation coefficient matrix, the Ostu algorithm [23] is used to extract the location of the clutter. First, initial threshold \(T_{0}\) is set, dividing the correlation coefficient matrix into two parts, \(C_{0}\) and \(C_{1}\), where the value of pixels in \(C_{0}\) is smaller than that of \(T_{0}\), and pixels in \(C_{1}\) are opposite. The between-cluster variance is defined by  
\[\delta_{B}^{2}=\omega_{0}\omega_{1}(\mu_{0}-\mu_{1})^{2}, \tag{18}\]  
where \(\omega_{0}\) and \(\omega_{1}\) are the ratios of the pixels of \(C_{0}\) and \(C_{1}\) to the total pixels of the matrix, \(\mu_{0}\) and \(\mu_{1}\) are separately the weighted averages of \(C_{0}\) and \(C_{1}\). We adjust the value of \(T_{0}\) to maximize \(\delta_{B}^{2}\). When between-cluster variance \(\delta_{B}^{2}\) is maximized, threshold \(T_{0}\) is adjusted to optimal value \(T_{a}\).  
The positions of elements with values greater than \(T_{a}\) are extracted and divided into \(D\) parts through the K-means clustering algorithm.  
They are denoted by \(C_{d}=[x_{d},y_{d}]^{T},d=1,2,\ldots,D\), i.e.,  
\[\left\{\begin{array}{l}x_{d}=\frac{1}{N_{d}}\sum\limits_{n=1}^{N_{d}}x_{nd} \\ y_{d}=\frac{1}{N_{d}}\sum\limits_{n=1}^{N_{d}}y_{nd}\end{array}\right., \tag{19}\]  
where \([x_{d},y_{d}]^{T},d=1,2,\ldots,D\) is the centroid of the \(d\)-\(th\) cluster, \(N_{d}\) denotes the number of pixels of the \(d\)-\(th\) cluster, and \([x_{nd},y_{nd}]^{T}\) is the n-\(th\) point of the \(d\)-\(th\) cluster.  
Figure 9: The specific operation of cross-correlation.  
We correspond positions \(x_{d}\) and \(y_{d}\) back to the original range-slow time matrix, which correlate, respectively, with row \(d\) in \(\mathbf{S}_{1}\) and row \(d^{\prime}\) in \(\mathbf{S}_{2}\). This is where a pair of symmetric clutter appears. Their pairwise elimination can be expressed as  
\[\widetilde{s}_{2d^{\prime}}=s_{2d^{\prime}}-s_{1d}, \tag{20}\]  
where \(s_{2d^{\prime}}\) denotes row \(d^{\prime}\) in \(\mathbf{S}_{2}\) and \(s_{1d}\) denotes row \(d\) in \(\mathbf{S}_{1}\).
# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 3 Materials and Methods
### Enhanced Pose Estimation
#### 3.5.1 ICP Pose Initialization  
To obtain a relatively good initialization, we utilized the 3D ICP technique on the 3D reconstruction of image lane markings and HD map lane markings. A simple 3D reconstruction calculation is executed on image lane markings based on the camera model, as shown in Figure 7a. We manually choose two points, p1 \((u_{1},v_{1})\) and p2 \((u_{2},v_{2})\) on parallel lane markings from the image, measure physical lane width \(w\) (\(w=x_{c1}-x_{c2}\)) of the corresponding point pair p1 \((x_{c1},y_{c1},z_{c1})\) and p2 \((x_{c2},y_{c2},z_{c2})\) from the HD map. We use vector \(A_{3\times 1}\) to represent the transformation from point \((u,v)\) to \((x_{c},y_{c},z_{c})\), as described by (10):  
\[\begin{pmatrix}\frac{x_{c}}{z_{c}}\\ \frac{y_{c}}{z_{c}}\\ 1\end{pmatrix}=K^{-1}\begin{pmatrix}u\\ v\\ 1\end{pmatrix}=A_{3\times 1} \tag{10}\]  
Use \(\alpha\) to record \(x_{c}/y_{c}\) with (11):  
\[\alpha=\frac{x_{c}}{y_{c}}=\frac{A_{0}}{A_{1}} \tag{11}\]  
Figure 7: 3D ICP Process on Lane Marking Points. (**a**) Calculation of Camera Height; (**b**) 3D ICP.  
Then the camera equipped height \(h\) can be obtained according to (12):  
\[h=y_{c}=\frac{w}{\alpha_{1}-\alpha_{2}} \tag{12}\]  
The image lane markings can be reconstructed to the 3D camera frame using the obtained \((x_{c},y_{c},z_{c})\) coordinates. A general ICP procedure shown in Figure 7b is executed between the reconstructed lane markings and the local HD map in the 3D ego frame to estimate the pose between the camera frame and the ego frame. The resulting camera pose is then used as an initialization value of EKF for the subsequent precise alignment.
# Review of Renewable Energy-Based Charging Infrastructure for Electric Vehicles
## 2 Technological Infrastructure
### Storage Battery and Controller  
Solar-powered batteries can fulfill unreliable grid electricity demands, which are strong charge, discharge, and intermittent full-charging periods. A range of battery types fulfills these specific criteria. The major battery storage subgroups reviewed for solar energy include a lead-acid battery, lithium-ion battery, and flow battery [38; 39].  
To save the additional energy produced by photovoltaics, a central controller is required to redirect the generated power to the battery, as illustrated in Figure 1. Many scholars have investigated the sequence of controllers that are used in photovoltaics. They highlighted that it is essential to improve the productivity of solar energy generation through a maximum power point tracker (MPPT) and pulse width modulated (PWM) technologies [40].
## 1 Introduction
kluyveri_ and _M. pulcherrina_[10] decreased, while _T. delbrueckii_[11; 12; 13] and _L. thermotolerans_[13] increased the concentration of 2-phenylethanol in sequential or co-inoculation compared to pure _S. cerevisiae_ fermentation. Although non-_Saccharomyces_ species may have a positive impact on wine profile, there are also studies reporting the opposite, so the results were in many cases contrasting, even for the same yeast species. _Pichia kluyveri_ was shown to be able to both reduce [10; 14] and increase [15] the concentration of hexanol in co-fermutation with _S. cerevisiae_. Similar was observed for _T. delbrueckii_, causing either a decrease [11; 12] or an increase [13; 16] in isoamyl acetate concentration. In mixed cultures, non-_Saccharomyces_ yeasts can modulate wine aroma by their own activity but also by changing the genomic expression of _S. cerevisiae_ while coexisting during wine fermentation [17]. Multiple factors, such as the genetic predispositions of a particular non-_Saccharomyces_ yeast, the availability of yeast nutrients, and the general composition of grape must certainly play a significant role in determining the outcomes of such fermentations. Considering such interactions and the fact that most previous studies investigated the influence of co-fermutation of non-_Saccharomyces_ and _S. cerevisiae_ yeasts based on the aroma of the final wine, the actual aromatic potential of non-_Saccharomyces_ yeasts in co-fermentations was often not distinguishable. On the other hand, studies that investigated volatile profiles of pure culture fermentations of various non-_Saccharomyces_ yeasts were mainly conducted at the end of monoculture fermentations [4; 18; 19; 20]. However, this is not representative of their enological application, as they are exclusively used in mixed cultures with _S. cerevisiae_. Indeed, the production of volatile compounds during fermentation fluctuates [21], and the final volatile profiles of non-_Saccharomyces_ monocultures do not necessarily reflect their status at earlier fermentatation stages (e.g., when _S. cerevisiae_ is added). To our knowledge, only one study compared early fermentation volatile profiles of inoculated non-_Saccharomyces_ yeasts, highlighting the unique behaviour of each yeast [22], which warrants further research. The main premise of this study was that in the initial phase of alcoholic fermentation, the species-specific effects of non-_Saccharomyces_ yeasts on the grape must's volatile profile would be more distinguishable than in the finished wines produced by mixed fermentation with _S. cerevisiae_. Therefore, the aim was to investigate the production of the volatile aroma compounds by five commercially available non-_Saccharomyces_ yeasts in the early stage of fermentation, before the inoculation of _S. cerevisiae_. In this manner, their effect would be more distinguishable compared to finished wines produced by mixed fermentations with _S. cerevisiae_ and their monoculture fermentations.
# From Cyber-Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs
## 6 The Digital Twin Technology
### The Digital Transformation
#### 6.2.1 The Network Digital Twin  
The functional decomposition of a DT can either be (i) horizontal, i.e., life of a packet from the edge to the cloud, or (ii) vertical, which distributes decisions to improve overall efficiency, i.e., DT and multi-scale AI. In addition, the architecture of the network DT (NDT), similar to the traditional DT, essentially consists of both the physical and the digital and includes three layers [101]:  
* hardware layer, which comprises the physical components of the DT, such as routers, actuator IoT sensors, as well as edge servers;
* middleware layer, which is all about data governance, processing, integration, visualization, modeling, connectivity, and control; and
* software layer, which consists of analytics engines, ML models, data dashboards, as well as modeling and simulation software.  
The explosive growth of new use cases in (i) time-sensitive, (ii) safety critical scenarios, and (iii) stringent requirements have created a huge demand for cost-effective verification methods [102]. The NDT, as the DT technologies continues its rapid diffusion into numerous industries through interdisciplinary advances in the industrial IoT, cloud and edge computing, ML, AI, as well as through advanced analytics, is also permeating into wireless communication space. In wireless networking, DTs are utilized to drive the current network testing environments towards being sufficiently ready for the demands of the future. In the NDT is where the agents perform the configuration and function changes [33]. For instance, when new configurations are applied on a new AI model or applied on a new network software, it is of paramount importance to know its performance before it is commissioned to the whole network. The components of a DT in wireless networks can be defined in terms of two platforms, which are described as follows:  
* Data Platform: A data platform is one of the main DT components, which ensures secure data ingestion and processing, as well as steady performance, normalization, management, ML, AI analytics, micro-services, and integration.
* Autonomous Network Platform: This is the digital domain of the DT framework on wireless communication consisting of four main components, i.e., network state prediction, expert knowledge, AI algorithm, and DT network. Therefore, in a DT exists an autonomous platform that enables a transition to a world in which computers use digital maps as a life-like representation of the physical network. The autonomous platform forms a foundation for simulations and virtual reality environments that are meant to fool the human mind and make it to believe that it is actually located where it is not. This module is between the physical network and the DT and enables the DT to analyze the outcome of a set of inputs and predict the outcome without affecting the physical network. If the outcomes are what is intended, then the new configurations can be transferred to the physical network as updates [103].
## 3 Results and Discussion
### Physical Properties of Freeze-Dried Edible Insects Larvae
#### 3.2.2 Rehydration Rate and Hygroscopic Properties of Freeze-Dried Edible Insects Larvae  
The rehydration attributes of freeze-dried insects are summarized in Table 2. Rehydration is a process opposite to the drying process, and it is an important indicator of the quality of the dried products. The primary goal of the rehydration is to obtain a product similar to those before the drying process. During rehydration, water permeates the dried tissue, while soluble solid compounds migrate into the water, leading to an expansion in the weight and volume of the rehydrated tissue. The structural transformations that occur in tissue during both drying and pretreatment exert an influence on its capacity to bind water, consequently affecting the restoration of the original material volume [42; 52]. Despite the lack of significant differences, the use of PEF pretreatment slightly increased the rehydration rate of the dried product samples (Table 2), with the exception of _T. molitor_ larvae which were treated at an energy intake of 5 kJ/kg. PEF treatment can cause various tissue changes, e.g., an increase in the number of pores, and thus allow better solvent penetration or a modified chitin fraction and protein structure forming the exoskeleton of insects [53]. As [PERSON] et al. [54] explains, at very low water activity, the lipid oxidation process can increase, which primarily affects protein oxidation and thus decrease its solubility, which in turn enhances its ability to bind water [9; 55]. Connected with rehydration is also the loss of the water-soluble (SSL) compounds of the dry matter. The results for SSL loss after 3 h provided that the influence of PEF pretreatment, both for _H. illucens_ and _T. molitor_ larvae, was not significant (Table 2). The hygroscopic properties of freeze-dried _H. illucens_ and _T. molitor_ larvae are shown in Figure 2 and in Table 2 (which presents the results of hygroscopicity after 72 h). The PEF-treated insects more intensively adsorbed water vapor before freeze drying (Figure 2a). The pretreated samples presented similar or higher hygroscopicity than the untreated ones. In the case of _H. illucens_ larvae, it was stated that during the adsorption time the pattern of changes for PEF-treated samples was similar, i.e., samples treated by PEF showed a statistically higher hygroscopicity than untreated samples. Meanwhile, for _T. molitor_ larvae, the effect of pre-treatment on hygroscopicity was found only for the sample subjected to 20 kJ/kg, which showed the highest hygroscopic properties throughout the measurement (Figure 2b). Even though after 72 h the hygroscopicity for this sample was higher by 8.4% in comparison to untreated sample, the changes were still not statistically significant (Table 2). Changes in the hygroscopicity may be related to the electroporation phenomenon, which may lead to cellular structural disruption and the release of certain polar compounds into