esa-sceva/satcom-chunk-collection huggingface dataset:
# Value Creation with Digital Twins: Application-Oriented Conceptual Framework and Case Study
## 6 Discussion  
This chapter first summarizes value creation with DTs before outlining further developments of the presented use case in a second part. This is followed by a discussion of the contribution to practice by indicating why the application-oriented DTF is useful for practitioners. Finally, we discuss to what extent the calls for a conceptual reference framework for value creation with DTs presented in Section 1.3.2 could be answered.  
**Value Creation with Digital Twins--**DTs are considered integrators of the physical and digital worlds and internal and external value creation. Further, creating DTs requires, by definition, the use of digital data. Hence, the proposed application-oriented DTF includes the following dimensions for every DT: data resources, external value creation, and internal value creation.  
**Internal value creation--**As DTs enable the connection and interaction of internal and external processes and stakeholders, they become the hub of data-based value creation in digitized product service systems. In the dimension of internal value creation, DTs connect the product management hierarchy with the processes that manage their life cycles. In this way, they contribute to reducing and mastering the complexity and variety of variants in the internal value-creation processes. They achieve this by linking the necessary data from isolated internal systems, external systems, and the IoT ecosystem and making it usable through data processing. DTs also enable a generational view of the relevant elements for value creation by storing historical data and making it available for optimizing the present and anticipating the future.  
**External value creation--**The resulting data-based services form the core of the value proposition, which can be communicated to internal and external users. In the dimension of external value creation, DTs connect the SCP, systems, and systems of systems in their use phase with the MoL processes of the providers. They make it possible for these to have \"control\", \"optimization\", and even \"autonomy\" capabilities and thus qualify to be described as \"smart\". Only through these capabilities do DTs enable providers to ensure that the services they offer create the value promised and agreed upon in the value proposition regarding availability, performance, and quality in real-world use.  
**Data resources--**DTs also enable users to be involved in data structuring and interpretation, for example, making it easier for humans to interpret data by providing optimally prepared decision-making bases. DTs can learn from the interpretation processes and decisions of users and, over time, will be able to interpret an increasing proportion of the data themselves. This ability forms the basis for achieving the \"autonomy\" level regarding smartness maturity. Overall, DTs have been shown to offer an integrative approach to value creation that includes internal services, market-side services, and comprehensive data provision.
## 3 Results and Discussion
### Effect of 30 Kc19\(\alpha\) on NADPH/NADP\({}^{+}\) Ratio of EPO-CHO Cells  
Nicotinamide adenine dinucleotide phosphate (NADPH), as a coenzyme, is a substrate for several metabolic redox reactions. It is known that NADPH plays a central role in a variety of biosynthetic pathways such as nucleic acid metabolism and protein synthesis [32; 33; 34]. In addition, the intracellular NADPH/NADP\({}^{+}\) (oxidized form of NADPH) ratio is respon  
Figure 5: Comparison of effects of 30 Kc19\(\alpha\) and full-length 30 Kc19 on nHEPO production in CHO cells. Cells were dually treated with the indicated concentration of 30 Kc19\(\alpha\) or 30 Kc19, respectively. Subsequently, the conditioned media were collected on day 2 of culture in serum-free medium. Titers of nHEPO were determined by ELISA. Error bars indicate standard deviations (\(n=3\) per group). Statistical significance was determined using Student’s \(t\)-test. \({}^{*}\), \(p<0.05\); \({}^{***}\), \(p<0.005\) compared to the nontreated control; n.s., not significant in comparison between 30 Kc19\(\alpha\) and 30 Kc19-treated group.  
sible for the detoxification of reactive oxygen species (ROS) by maintaining appropriate reduced/oxidized glutathione (GSH/GSSG) [35; 36]. As a result of comparing EPO-CHO cells treated dually with 24 \(\upmu\)M of 30 Kc19\(\alpha\) to those in the nontreated control group, the intracellular NADPH/NADP\({}^{+}\) ratio was significantly increased by the 30 Kc19\(\alpha\) treatment (Figure 6). Our previous studies have demonstrated that mitochondrial membrane potential and adenosine triphosphate (ATP) generation can be enhanced by the addition of the recombinant 30 Kc19 protein as well as by the overexpression of the 30_Kc19_ gene [24]. Such enhancements are due to the stabilizing effect of 30 Kc19 on intracellular enzymes involved in the mitochondrial electron transport system [27]. Likewise, intracellularly delivered 30 Kc19\(\alpha\) might have affected the activities (or stabilities) of several enzymes related to the flux of reducing power from cytosol to mitochondria, leading to the enhancement of the NADPH/NADP\({}^{+}\) ratio and the subsequent recombinant EPO biosynthesis.
## 2 Materials and Methods
### Machine Learning Methods
#### 2.1.4 Regression Trees and Tree-Based Ensemble Methods
Unlike RF, in which all trees are constructed independently, each using a subset of the training dataset, GBR uses gradient boosting as an ensemble technique [61]. ```
Let \(M\) be the number of boosting iterations. Given a learning rate \(\
u,\ 0<v\leq 1\). Initialise \(f_{0}(x)=\arg\min_{\gamma}\sum_{i=1}^{m}L(y_{i},\gamma)\). for\(k=1\to M\)do for\(i=1\to m\)do  Compute \(r_{ik}=-\left[\frac{\partial L(y_{i}f(x_{i}))}{\partial f(x_{i})}\right]_{f=f _{k-1}}\). endfor  Fit a regression tree to the targets \(r_{ik}\) giving terminal regions \(R_{jk}\), \(j=1,2,\ldots,J_{k}\). for\(j=1\to J_{k}\)do  Compute \(\gamma_{jk}=\arg\min_{\gamma}\sum_{x_{i}\in R_{jk}}L(y_{i},f_{k-1}(x_{i})+ \gamma)\). endfor  Update \(f_{k}(x)=f_{k-1}(x)+\
u\sum_{j=1}^{J_{k}}\gamma_{jk}I(x\in R_{jk})\). endfor return\(f(x)=f_{M}(x)\). ```  
**Algorithm 1** GBR algorithm  
In this approach, an additive model is built such that at each stage, an RT is fitted to the negative gradient of the given loss function. Note that at each boosting iteration \(k\), a tree is added that modifies the overall model. The added trees are weak learners in the sense that their performance is slightly better than random chance. An important parameter associated with gradient-based optimisers is the learning rate, which controls the contribution of each tree to the final outcome. If the learning rate is low, more boosting stages (i.e., more trees) are needed in the training process. In general, models that learn slowly perform better. However, the number of boosting iterations must be carefully selected due to the high risk of overfitting [59]. In addition, the extreme GBR (or XGBoost) method may be used to reduce overfitting. This is an extension of the gradient boosting method that employs advanced regularisation techniques such as Lasso regularisation (based on the L1 norm), Ridge regularisation (based on the L2 norm), and dropout techniques. The Lasso and Ridge regularisation methods introduce an additional term into the loss function to penalise large weights [62]. The dropout technique [63] is an adaptive regularisation method in which a random percentage of trees is ignored in the training phase so that no tree manages to memorise part of the inputs [62].
## 4 Implementation
### Proposed Scheme  
#### 4.4.1 Data Frame Structure  
Figure 7 illustrates the proposed data frame structure for the MIMO-OOK scheme. To access many users, we added the node ID part to each frame. Each user will be defined by a unique ID so that the receiver can categorize the signal from different users. Defining users by ID nodes helps the system accept dozens of users, up to hundreds of users. Furthermore, when module devices have many sensors, the amount of data is large. As such, we propose the use of multiple LEDs to increase the data rate. Additionally, we propose the addition of LED-ID to the data frame structure to support the receiver's detection and collection of data, as shown in Figure 7.
## 2 Standard Nondestructive Testing and Evaluation Techniques
### Eddy Current Testing  
Eddy current testing (ECT) is an NDT technique that uses the principle of electromagnetic induction to inspect and characterize materials [29]. Figure 3 shows a demonstration of ECT. A primary coil generates a magnetic field transmitted to the inspected specimen, which should be conductive. This primary magnetic field induces a current on the specimen, generating a secondary magnetic field. Another coil measures this secondary magnetic field. Furthermore, the presence of a defect deforms the induced current on the specimen surface, which in turn changes the secondary magnetic field. This secondary field is measured using the receiver coil, which enables the detection of the defect.  
ECT has been researched thoroughly in the literature. For example, the authors of [6] investigated a pipe crack detection system using an ECT platform. This paper presented a novel design for a probe incorporated into a distributed ECT system. The system is distributed because the probe consists of a rotating excitation primary coil and an array of receiver coils; this enables the probe to rotate inside a pipe while conducting measurements. Moreover, the system was utilized to inspect cracks within the interior of a carbon steel pipe sample in colorredreal time. Their methodology demonstrated that their system was highly efficient in detecting defects, with a maximum percentage error of \(-2.11\%\) for a circumferential defect within the pipe [6], thus advocating for ECT's usability in accurate defect detection.  
Another example is in the paper by [PERSON] et al. [30], where the system utilized a phased array ultrasound to detect volumetric defects within a weld bead volume, with ECT being used to identify surface and subsurface cracks [30]. The phased array ultrasound results showcased that temperature affects sound attenuation, thus the detection ability; this can be compensated for up to 200 \({}^{\circ}\)C. In contrast, the eddy current results showed no notable influence for temperatures up to 300 \({}^{\circ}\)C [30].  
A third example is the system in [PERSON] et al. [31], which utilized pulsed eddy current (PEC) sensing to inspect defects or cracks in the critical water pipe sectors. PEC signals have a broad frequency spectrum; thus, they can penetrate into different depths within the specimen. This enables the extraction of more information about the specimen under test. The authors designed and implemented an exciter-detector coil sensor and presented a calibration strategy. Furthermore, they investigated the effect of various parameters like lift-off on the accuracy of estimating thickness.  
Despite the successes with ECT, a major concern is its poor effectiveness when used on materials or pipes with low electrical conductivity, which might be the case in some industries and applications.
## 3 Results of Numerical Simulations and Case Studies
### Mercury-Based Scenario  
This specific mission scenario, thanks to the non-negligible value of the eccentricity \(e_{0}\), allows the reader to appreciate the impact of the initial true anomaly \(v_{0}\), i.e., the angular position of the Sun-facing diffractive sailcraft along the reference elliptical orbit at the beginning of the phasing maneuver, on the minimum value of the flight time \(\Delta t\).  
In this regard, Figure 3 shows the value of \(\Delta t\) as a function of \(v_{0}\), when \(\Delta\phi\in[-8,\,8]\,\text{deg}\), and the value of the characteristic acceleration is \(0.1\,\text{mm}/\text{s}^{2}\), i.e., a value in the range of \(a_{c}\) considered in this work; see Section 2.1. The figure clearly shows how, for a fixed value of \(\Delta\phi\), there is a notable dependence of the minimum flight time on the initial true anomaly \(v_{0}\). This characteristic is evident even for small values of the phasing angle since, for example, there are differences in flight time of about \(20\,\text{days}\) when \(\Delta\phi=5\,\text{deg}\) starting from different points of the reference elliptical orbit; see Figure 2(a). On the other hand, as expected, for a given value of \(|\Delta\phi|\), a phasing ahead requires a longer flight time than a phasing behind, since, in the second case, the displacement of the virtual point on its reference (elliptical) Keplerian orbit is exploited.  
Figure 3: _Cont._  
Finally, Figure 6 shows the minimum flight time for a phasing ahead in which \(\Delta\phi\in(0,\,60]\) deg and \(a_{c}=0.1\,\mathrm{mm}/\mathrm{s}^{2}\), as a function of the initial angular position. In particular, this figure highlights how the value of the pair \(\{v_{0},\Delta\phi\}\) influences the performance when the value of the eccentricity \(e_{0}\) is non-negligible.
# ExtractEO, a Pipeline for Disaster Extent Mapping in the Context of Emergency Management
## 2 Materials and Methods
### Fully Automated End-to-End Chains
#### 2.4.2 Burnt Area Mapping Pipeline  
The approach is different from the water chain. This pipeline (Figure 6) is dedicated solely to optical images with SWIR bands, and has been initially developed through the ASAPTERRA project [19]. The fire mapping guidelines from the International Working Group on Satellite-Based Emergency Mapping (IWG-SEM), which are available online [20], are followed.  
The fire extraction workflow illustrated by Figure 6 presents the different steps:  
1. Data Acquisition--the burnt areas are derived from a change detection method by processing both post- and pre-event images;
2. Processing: 1. Two models based on the pre- and post-NBR (Normalized Burn Ratio) and BAI (Burned Area Index) indices are computed to derive a potentially burnt area mask. 2. Then, zonal statistic operations are applied and features are filtered to reduce false positives using NIR and SWIR post-event bands, and the NBR and BAI for Sentinel-2 [21] indices;
3. Post-processing 1. Mosaicking of the results coming from tiles with the same date and projection; 2. Minimum mapping unit (MMU) sieving to remove small features and fill holes; 3. Vectorization step in all classic formats, including GeoJSON, ESRI Shapefile, or Keyhole Markup Language (KML).  
The operator only has to choose the pre-event data (and the processing resolution). The reference image selection is a crucial step in this methodology. The results are enhanced when images are fairly close chronologically, or have vegetation in a similar state (the same season).
## 3 Methodology
### Model Structure
#### 3.2.1 Unimodal Encoders  
Similar to EMT [33], we employ the pretrained BERT model to encode textual tokens into context-aware word embeddings. Specifically, we notice that the [CLS] token of the BERT model contains a sequential representation of the text modality. Therefore, we use this token as the utterance representation for the text sequence, denoted as \(u_{t}\in\mathbb{R}^{d_{t}}\). For the audio and visual modalities, we use LSTM recurrent neural networks to extract temporal information from the feature sequences. Ultimately, we select the hidden state of the last time step of the LSTM network for both the audio and visual modalities as their respective utterance representations: \(u_{a}\in\mathbb{R}^{d_{a}}\) and \(u_{v}\in\mathbb{R}^{d_{v}}\). Simultaneously, we need to process other tokens output by the BERT model and hidden states from LSTMs at different time steps for later use in self-attention and cross-attention mechanisms. These representations are denoted as \(F_{m}\in\mathbb{R}^{T_{m}\times d_{m}},m\in\{t,a,v\}\), representing the text, audio, and visual modalities, respectively.  
\[\begin{split} F_{t}&=BERT(X_{t})\\ F_{a}&=LSTM(X_{a})\\ F_{v}&=LSTM(X_{v})\\ \end{split} \tag{1}\]
## 4 Simulation Results and Discussion
### Load Transient  
In this case, the simulation time is reduced from 140 s to 20 s. To observe the performance of VSG during the load transition, a group of load with the power consumption, 1.5 kW (25%), is connected into the system at the time \(t=3\) s with the initial consumption 4.2 kW, and disconnected around 45% at the time \(t=10\) s as shown in Figure 16. The VSG here will be compared with the modified droop control [27]. In Figure 17, \(blue-graph\) is the frequency response of the VSG, and \(red-graph\) is the frequency response of the modified droop control. It is clearly seen that the VSG which emulates the synchronous generator dynamic characteristic could provide the inertia and damping support to the system, as a result, it has smaller frequency overshoot/undershoot compare to the modified droop control when there is a sudden increase in power demand.  
Figure 15: Power & SoC of hybrid energy storage system.
## 3 Simulation Setup and Validation
### Numerical Engine Model  
#### 3.2.1 Model Summary and Simulation Procedure  
The ECFM-3Z model is used in this paper, which is a very robust combustion model, especially for diesel engines. The models related to the calculation of the combustion process are summarized in Table 4. In order to fully verify the reliability and accuracy of the model, the in-cylinder combustion and emission data are used for model validation under the BMEP of 1.1 MPa and 1400 rpm. The specific parameters of this operating point are listed in Table 5.  
The operating process from the moment of intake valve closure (IVC) to the moment of exhaust valve opening (EVO) is chosen as the model computation interval.  
Figure 5 shows the mesh model of the combustion chamber. By dividing the entire combustion chamber evenly by the number of injector nozzles, the calculation grid uses the partial combustion chamber grid model created by AVL Fire's ESE Diesel module. The combustion chamber is divided into a 1/8 combustion chamber grid model because the injector nozzles have 8 nozzles, as shown in Figure 5. The grid is constructed with a compensation volume to guarantee the compression ratio, and the compensation component of the radial direction should contain at least three layers of the grid to guarantee the calculation's accuracy. The ESE dynamic grid is used to generate the grid corresponding to the crank angle between 0 and 360 \({}^{\circ}\)CA, which contains a complete piston reciprocation process. The grid corresponding to the crank angle between 30 \({}^{\circ}\)CA before and after the upper stop is encrypted, and the important spray area at each moment is also encrypted. To validate the model, the model's calculation results are compared to the outcomes of the tests. Then, utilizing the validated numerical model, a thorough investigation of the impact of injection timing and EGR on in-cylinder combustion and NOx and soot emissions is conducted.
## 3 Raw Network Traffic Analysis
### Early Flow Classification  
One advantage of using only the first packet of a flow is simplicity. If no packets ever need to be correlated and analyzed together, the complexity of the required framework is greatly reduced. For example, for TCP flows, with one-packet images, only SYN packets need to be analyzed, which can be done without processing additional packets in that flow. Another advantage is that the time to detect a malicious flow is decreased. By beginning analysis upon receipt of the very first connection request, there is as little delay as possible from connection to identification. Waiting for additional packets also introduces additional delays, which may be crucial in cases of attacks where the damage done quickly, such as DDoS attacks, where the very act of connecting is the core of the attack.  
One advantage of two-packet analysis is that malicious flow detection accuracy based on the first two packets can be significantly higher in some cases than that based on the first packet [3; 5]. Our results also indicate that certain types of attacks are detected with a higher degree of accuracy when the second packet is included in analysis. For flows initiated by the external clients, the second packet of a flow is sent by an internal server quickly. Therefore, the additional wait time and the extra preprocessing overheads are not too high.
# Digital twins: The New Frontier for Personalized Medicine?
###### Abstract  
Digital twins are virtual replicas of physical objects or systems. This new technology is increasingly being adopted in industry to improve the monitoring and efficiency of products and organizations. In healthcare, digital human twins (DHTs) represent virtual copies of patients, including tissues, organs, and physiological processes. Their application has the potential to transform patient care in the direction of increasingly personalized data-driven medicine. The use of DHTs can be integrated with digital twins of healthcare institutions to improve organizational management processes and resource allocation. By modeling the complex multi-omics interactions between genetic and environmental factors, DHTs help monitor disease progression and optimize treatment plans. Through digital simulation, DHT models enable the selection of the most appropriate molecular therapy and accurate 3D representation for precision surgical planning, together with augmented reality tools. Furthermore, they allow for the development of tailored early diagnosis protocols and new targeted drugs. Furthermore, digital twins can facilitate medical training and education. By creating virtual anatomy and physiology models, medical students can practice procedures, enhance their skills, and improve their understanding of the human body. Overall, digital twins have immense potential to revolutionize healthcare, improving patient care and outcomes, reducing costs, and enhancing medical research and education. However, challenges such as data security, data quality, and data interoperability must be addressed before the widespread adoption of digital twins in healthcare. We aim to propose a narrative review on this hot topic to provide an overview of the potential applications of digital twins to improve treatment and diagnostics, but also of the challenges related to their development and widespread diffusion.  
digital twin; artificial intelligence; surgical planning; drug development; transplant; human digital twin; DHT
## 4 Proposed Epileptic Seizures Detection Model
### Feature Selection Techniques
#### 4.3.2 Discrete Wavelet Transform (DWT)  
DWT is a powerful signal processing technique that decomposes EEG signals into different frequency components, allowing for the analysis of both time and frequency characteristics. This method is particularly effective in capturing the transient features of EEG signals, which are essential for detecting epileptic seizures. DWT generates coefficients that serve as significant features, capturing the nuances of EEG signal variations. The choice of DWT is motivated by its proven efficacy in retaining the most relevant features of non-stationary signals like EEG, which exhibit complex patterns and require sophisticated techniques to extract meaningful information.