esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Acute myeloid leukemia (AML) is a malignant cancer of the hematopoietic system. It is characterized by uncontrolled proliferation of immature blood cells, called leukemic blasts, caused by the impairment of differentiation programs in hematopoietic stem and progenitor cells (HSPCs). Conventional chemotherapy treatment can efficiently eliminate the leukemic blasts; however, it typically fails to eradicate the immature, quiescent populations of leukemic progenitors [1; 2] These can drive leukemogenesis anew, creating a risk of disease recurrence (relapse), often with a more aggressive phenotype and a worse prognosis [3]. Thus, scientific efforts have been directed towards the development of targeted therapies that would eliminate these immature cell populations and therefore prevent recurrence of the disease in patients in remission.  
Unlike leukemic blasts that harbor several driver mutations, which can potentially be exploited as therapeutic targets (e.g., FLT3-ITD [4]), leukemic progenitors often lack therapeutically actionable mutated targets. A prominent example of this is normal karyotype AML (NK-AML) with mutations in _DNMT3A_. Recent studies in primary cells from AML patients have shown that the _DNMT3A_ R882 mutation marks immature cells that are resistant to chemotherapy and represent a reservoir for AML relapse [5]. Remarkably, AML patients with _DNMT3A_ mutations have shorter survival than _DNMT3 AWT_ patients, and this effect seems to be independent of the characteristic late co-mutations in _FLT3_ or _NPM1 [6]_. The mutant DNMT3A R882X protein induces epigenetic changes in HSCs that distort the balance between self-renewal and differentiation [7; 8], however detailed knowledge about the downstream targets of these changes remains scarce [9]. Thus, there is a clinical need for discovering vulnerabilities of this cell population that can be exploited as a therapeutic approach.  
High-throughput genetic screens are a well-established technique for revealing genotype-phenotype connections. Genetic screens in cancer cell lines have been used to identify cancer vulnerabilities and in recent years very comprehensive databases of such vulnerabilities have been generated [10; 11; 12; 13]. Nevertheless, confirming these dependencies in patient-derived material remains a challenging task, exposing the drawbacks of cell lines as AML models. While AML cell lines largely reflect the cytogenetics of the original malignancy [14], the transcriptional and epigenetic profiles of cancer cell lines may differ dramatically from those of primary cancer cells [15; 16; 17; 18]. Previous work from our lab has shown that high-throughput genetic screens in primary AML cells are feasible and a powerful tool to nominate patient-specific targets [19]. Here, we describe a high-throughput RNAi-based screening approach to functionally profile primary _DNMT3 AWT/R882X_ cells from AML patients.
# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 3 Algorithm
### Detection of Type 2 Vibrations
#### 3.5.3 Refinement of Type 2 Vibrations  
While detected peaks \(\hat{p}_{i,n}\) and samples \([x,y]\) were considered during vibration scanning, Type 2 vibrations were then refined by considering linear-scale spectrograms. For this, normalised samples \([x,y]\) were converted back to peak locations \(l_{i,n}\). In addition, boundaries were adapted from the normalised range to \(\epsilon=\epsilon\cdot\hat{N}\) and models \(y_{2}(x)\) were adapted to \(y_{2}(i)\). Then, refinement was conducted for each Type 2 vibration \(j=1,...,J_{2}\):  
* A grid with elements \(d_{i,k}\) was created with \(i=1,...,L_{G}\) and \(k=1,...,\frac{\hat{N}}{2}\).
* Each model \(y_{2,j}\) was refined by using spectra information as fitting weights for data points \(d_{i,k}\), \[w_{i,k}=\begin{cases}\hat{S}_{\hat{r}_{i}}(k)\ \ \text{if}\ \ |d_{i,k}-y_{2}(i)|\leq \epsilon_{2,j}\\ 0\ \ \text{else}.\end{cases}\] (9)
* Robust fitting was used as \(f_{v2,j_{2}}(\hat{r})=p_{2}\hat{r}^{2}+p_{1}\hat{r}+p_{0}\) with parameters \(p_{0}\), \(p_{1}\) and \(p_{2}\).  
An example of two Type 2 vibrations for the exemplary sensor is displayed in Figure 10.  
Figure 9: Vibration scanning for detecting Type 2 vibrations. Peaks related to Type 2 vibrations \(P_{v2}\) displayed as samples with x and y axes normalised to [0,1]. (**Left**) Exemplary fitting for first-degree polynomial \(y_{1}(x)\) and second-degree polynomial \(y_{2}(x)\) and corresponding ranges \(\epsilon_{1}\) and \(\epsilon_{2}\). (**Right**) Models \(y_{2,j}(x)\) corresponding to Type 2 vibrations \(V_{2,j_{2}}\) for vibrations \(j_{2}=1,...,7\).
## 5 Performance Evaluation  
In this section, we first describe a set of experiments that were carried out to characterize a real propagation environment in order to obtain realistic models to simulate the performance of the proposed strategy. Next, we include a numerical evaluation of the proposed transmission strategy that compares its performance with the performance of other transmission strategies in several scenarios.
# Valorization Potential of Oilseed Cakes by Subcritical Water Extraction
## Materials and Methods
### Determination of Ca, K, Na and P  
Calcium (Ca), sodium (Na) and potassium (K) were determined by flame photometry (Evans Electroselenium LTD, Halstead, Essex, UK). Calibration curves were made using standard solutions of sodium, potassium and calcium. The standard solutions were prepared by pipetting 0.5-20 mL aliquots of the standard stock solutions into a 25-mL volumetric flask. Responses obtained on the galvanometer for all standard concentrations and extracts were read and the calculation was made according to the equation C(\(\mu\)g/mL) = V \(\times\) T \(\times\) 10\({}^{6}\)\(\times\) R/25 (V, volume of standard solution/extract (ml); T, titer of standard stock solution (g/mL); R, dilution factor). The results of Ca, K and Na content were expressed as mg/100 g of dry extract \(\pm\) 2 SD.  
Phosphorus (P) was determined by photoelectric photometer (Iskra MA 9506, Kranj, Slovenia). The extract (0.1-0.3 g) was placed in the 25-mL quartz Kjeldahl flask. Hydrochloric acid (3 mL) and 10 drops of concentrated HNO\({}_{3}\) were added into the flask. The solution was heated until becoming colourless. The resulting solution was cooled at room temperature, transferred into volumetric flask and mixed with 2 drops of 1% phenolphthalein, 30% NaOH until becoming red and 10% H\({}_{2}\)SO\({}_{4}\) until becoming colourless. Enough distilled water was added to make the solution up to 100 mL. The extract (10-20 mL) solution was mixed with 5 mL of 5% ammonium heptamolybdate, 1 mL of 11% sodium sulphite and 1 mL of 0.5% hydroquinone. The volume of the mix solution was made up to 100 mL by adding distilled water. Blank and standard solutions were also run through the steps as above using 0-4 mL of monopotassium phosphate instead of the extract as calibration standard. After incubation at room temperature for 35 min in a dark place, the colour of the final solution turned blue. The solutions were taken in the cuvettes and introduced to a photoelectric photometer. The absorbances were read for all extract solutions and the calculation was made according to the equation P = X \(\times\) 10\({}^{-6}\)\(\times\) 10,000/V \(\times\) m (X, phosphorus content in extract solution (\(\mu\)g); V, volume of the extract (mL); m, mass of the extract (g)). The result of the P content was expressed as mg/100 g of dry extract \(\pm\) 2 SD.
## 3 Results and Discussion
### Impact of HAuCl4 Concentrations on the Morphology of PDA-Au  
Previous studies on the plasma-assisted synthesis of AuNPs emphasized that this method is affected by various factors such as the type of plasma reactors, operating parameters, working gas, and the concentration of precursors. Among these, the concentration of HAuCl4 is considered the most crucial factor influencing the formation and morphology of AuNPs. Thus, we further investigate the impact of the Au ion precursor on the morphology of PDA-Au by altering the HAuCl4 concentration (0.2, 0.5, and 1 mM).  
Figure 4a-f present the SEM images of PDA-Au synthesized at different HAuCl4 concentrations (0.2 mM, 0.5 mM, and 1 mM). The average particles size of the decorated AuNPs for HAuCl4 0.2 mM, 0.5 mM and 1 mM are 10.3 \(\pm\) 2.0 nm, 11.2 \(\pm\) 2.6 nm, 21.4 \(\pm\) 5.7 nm, respectively (Figure 4g-i). As the precursor concentration increases, significant changes in both the morphology and distribution of AuNPs on the PDA surface are observed. At the lowest HAuCl4 concentration of 0.2 mM (Figure 4a,b), a sparse and relatively homogeneous distribution of small AuNPs can be observed on the PDA surface. The nanoparticles are well dispersed with low aggregation. When the concentration increases to 0.5 mM, the density of AuNPs on PDA NSs increases significantly (Figure 4c,d). In this condition, the AuNPs appear more closely packed with a larger number of particles, thus covering a larger portion of the PDA surface compared to 0.2 mM. At the highest HAuCl4 concentration of 1 mM, a different morphology is observed as larger particles are formed on the PDA surface (Figure 4e,f). However, in this case, the number of AuNPs on the PDA NS surface is much lower compared to the two lower concentrations. These findings suggest that the HAuCl4 concentration significantly influences the size, distribution, and uniformity of AuNPs formed on PDA. This can be attributed to the fact that the reduction of HAuCl4 during plasma treatment is highly dependent on the concentration of Au\({}^{3+}\) ions. An excessive concentration of HAuCl4 generally leads to the formation of a fewer number of nanoparticles with larger particle sizes [40; 41]. We evidenced that the HAuCl4 concentration of 0.5 mM yielded the most homogeneous decoration of AuNPs, whereas an excessive concentration of 1 mM resulted in the formation of larger particles. These larger particles may affect the performance of the final product in applications such as catalysis or sensing.  
Figure 3: Characterizations of plasma synthesized PDA-Au and PDA NSs: (**a**) X-ray diffraction patterns and (**b**) Fourier-transform infrared spectra.
# Conductive Textiles for Signal Sensing and Technical Applications
## 2 Materials Used to Impart Conductivity on Textiles
### Conductive Metals
#### 2.2.4 Metal Oxide Nanoparticles (MONPs)  
MONPs are renowned for their environmental remediation and electronic applications. The particles can generate charge carriers through energy absorption. TiO\({}_{2}\) and ZnO are widely used in textile finishing [51]. Nano-iron oxide, nano-copper oxide, nano-cobaltoxide, and nano-manganese oxide in cotton fabrics can be created for high antibacterial efficiency. In addition, UV protection and flame retardation can be developed by in situ preparation of nanometal oxides [58].
# Forecasting Multivariate Time Series
### Data Profiling
#### 2.2.3 Identifying the Type of Missing Data  
In handling missing data, three categories are identified: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). In univariate analysis, treating time as a variable adds complexity, requiring consideration of time series traits and covariance for imputation. For MCAR data, univariate imputation is relatively straightforward. However, in multivariate analysis, the association between columns is taken into account. MCAR data are randomly missing and can often be replaced with mean or median values, as the missing data are considered recoverable. MCAR is given by (1), where \(R\) is missing data and \(Y\) is the complete data.  
\[P(Ri|Yi)=P(Ri) \tag{1}\]  
MCAR data lack significant association according to measures like Chi-Square, Cramer's rule, or the correlation coefficient, and typically contain fewer null values. For large datasets, instances with missing data may be omitted or imputed. Deletion methods for MCAR include list-wise deletion, removing records with any null value, suitable when missing data are minimal, and pairwise deletion, retaining data for pairs of quantities with available data points, preserving more data. Imputation becomes necessary when deletion methods are unsuitable. Imputation involves substituting missing data with expected values, with various techniques applied depending on the nature of the data. In the case of MAR, missing data may follow a pattern or sequence, occasionally appearing outside the sequence. When a relationship between missing and existing data is identified, it is considered MAR. Imputation techniques such as mean, median, mode, or interpolation can be employed to handle MAR, with multiple imputation techniques available for non-time series data and interpolation for time series data. MAR is given by (2)  
\[P(R_{i}\mid Y_{i})= P(R_{i}\mid Y_{i,\rho}) \tag{2}\]  
In MNAR, specific instances share missing data, making prediction challenging as it relies on unobserved information. If missingness in one column shows little or no correlation with other columns and no strong associations are found upon investigation, and the frequency of missing values is high, this is considered MNAR. MNAR is non-ignorable, with no straightforward method for handling it other than obtaining the missing data. MNAR typically has more null values compared to MCAR and can only be identified through domain knowledge. MNAR is given by (3):  
\[P(R_{i}\mid Y_{i})\
eq P(R_{i}\mid Y_{i},\rho) \tag{3}\]
# Biosimilar Medicines: From Development Process to Marketing Authorization by the EMA and the FDA
## 1 Introduction  
In recent years, biological medicines have significantly impacted the care of numerous patients with diverse medical conditions, including diabetes, autoimmune diseases and various types of cancer [1, 2]. In contrast to chemically synthesized drugs, which have a well-known structure, biological medicines are complex products generated using biotechnological processes. These processes involve the identification, sequencing and manipulation of DNA, with the aim of producing therapeutic or medical diagnostic products [3, 4, 5].  
When a biological medicine is placed on the market, it is under patent protection during a specified period of time. Once this period expires, the biological medicine is used as a \"reference medicine\" for the creation of a very similar product, designated as biosimilar medicine [1]. As the name suggests, these products are highly similar, but not identical, to its reference medicine and are subjected to fewer studies, reaching the market faster and at a lower price [1, 2, 6]. However, due to their nature and unique manufacturing process, they can contain some variations and therefore, to be placed on the market, they must demonstrate similar safety, efficacy and quality compared to their reference biological products [7, 8].  
This review article aims to clarify the concept of biosimilar medicines, comparing them with innovative biological medicines, intended copies, biobetters, stand-alone biologics and generic medicines. The regulatory framework for these products are presented, along with the European Medicines Agency (EMA) and Food and Drug Administration (FDA) guidelines specifically developed for these products. A detailed explanation about the manufacturing process of these products is provided, as well as key concepts related to the establishment of similarity between the biosimilar and its reference medicine. The data requirements set by the regulatory authorities for the approval of biosimilar medicines are presented, together with the different pathways that may be pursued to obtain such approval. Lastly, an evaluation of the current market for biosimilar medicines is performed, providing a comprehensive and up-to-date overview of this topic.
# Enhancing Robotic-Assisted Lower Limb Rehabilitation Using Augmented Reality and Serious Gaming
## 4 Proposed Methodology for the Lower Limb Rehabilitation Treatment
### Augmented Reality Training for Robot-HoloLens Interaction  
The goal of the training session is to familiarize both patients and therapists with the AR interface, controls, and functionalities, ensuring that the system is easy to use, intuitive, and does not add cognitive or physical strain during rehabilitation exercises. Based on the work of [PERSON] et al., presented in [38], the following training strategy has been established to address aspects such as the following:  
Making users comfortable with the HoloLens 2 device and its basic operation.  
Ensuring patients understand how their real-time movements are replicated in the AR environment.  
Teaching users how to interact with the game modes and provide feedback to the system.  
The training session is planned to be delivered in the following stages:  
**A. Introduction to AR Technology**  
**Therapist Orientation**: Before involving the patients, therapists will undergo a brief orientation session that includes the following:  
Understanding the hardware (HoloLens 2) and how it integrates with the LegUp parallel robot and electro-goniometers.  
Learning how to monitor and control the system, including starting/stopping sessions, adjusting difficulty settings, and accessing patient performance metrics.  
Patient Orientation**: Patients are introduced to AR in a simple, user-friendly way. The initial phase involves the following:  
Explaining how their physical movements interact with virtual objects in the AR environment.  
Demonstrating how AR helps visualize joint positions and game targets.  
**B. Hands-On Practice with HoloLens 2**  
**Patient Familiarization**: Patients practice wearing HoloLens 2 to get used to the feel and field of view. They learn basic interactions (looking at and interacting with virtual targets) and hand-eye coordination within the AR environment.  
**C. Game Mode Demonstration**  
**Game Explanation**: The two game modes are explained in terms of how they assist with rehabilitation. Each game mode is demonstrated with the patient in control, so they experience the visual and auditory feedback.  
**Guided Participation**: The patient's participation in the game is made under a therapist's supervision to help the patient understand the AR interface. The patient receives instructions on how to reach the game targets and what kind of movements are expected for success.  
**D. Therapist Supervision and Feedback**  
**Therapist-Controlled Adjustments**: Therapists are trained on how to adjust the settings to personalize the session (e.g., game difficulty, range of motion, speed of targets). They also learn how to provide patients with additional feedback based on their performance data, helping to reinforce correct movements.  
**Ongoing Monitoring**: Therapists can monitor patient progress in real time through streaming from Hololens in the user interface of the robot and adjusting therapy sessions based on the patient's development.
## 2 Materials and Methods
### Poisson Mixture Distributions  
The Poisson mixture distribution comprised of \(k\) components is represented similarly in Equation (14) where \(\Phi=\begin{bmatrix}k\\ \theta\\ \pi\end{bmatrix}\) is the parameter vector [24]. The probability function of Poisson mixture distribution \(f(x;\Phi)\) is given in Equation (16) and the sum of the proportions is equal to 1.  
\[f(x;\Phi)=\sum_{i=1}^{k}\pi_{i}\frac{\alpha_{i}^{x}e^{\alpha_{i}}}{x!} \tag{16}\]  
\(\pi_{i}\): proportion of the \(i\)th Poisson distribution (\(i=1,\dots,k\))  
\(\alpha_{i}\): mean number of the \(i\)th Poisson distribution (\(i=1,\dots,k\))  
The mean of the Poisson mixture distribution (\(\alpha_{m}\)) comprised of \(k\) components can be written as in Equation (17).  
\[\alpha_{m}=\sum_{i=1}^{k}\pi_{i}\alpha_{i},\ \ 0<\pi_{i}<1 \tag{17}\]  
The variance of the Poisson mixture distribution (\(\sigma_{m}^{2}\)) can be calculated by using Equations (18)-(20).  
\[var(X)=E[X^{2}]-(E[X])^{2} \tag{18}\]  
\[var(X)=E[X^{2}|\pi_{i},\alpha_{i}]-(E[X|\pi_{i},\alpha_{i}])^{2} \tag{19}\]  
\[\sigma_{m}^{2}=\sum_{i=1}^{k}\pi_{i}(\sigma_{i}^{2}+\alpha_{i}^{2})-\alpha_{m} ^{2}. \tag{20}\]  
By capitalizing on the equality between the variance and mean of a Poisson distribution, we make a substitution of \(\alpha_{i}\) instead of \(\sigma_{i}^{2}\), leading to the derivation of the formula presented in Equation (21).  
\[\sigma_{m}^{2}=\sum_{i=1}^{k}\pi_{i}(\alpha_{i}+\alpha_{i}^{2})-\alpha_{m}^{2}. \tag{21}\]  
It is necessary to estimate the number of components \(k\), and the parameters of the distribution, \(\pi_{1},\pi_{2},\dots\pi_{k}\) and \(\alpha_{1},\alpha_{2},\dots,\alpha_{k}\) to fit with a mixture distribution. \(k\) is determined using model selection criteria that are Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and log-likelihood (LL). The likelihood function and log-likelihood function are given in Equation (22) and in Equation (23), respectively.  
\[L(\Psi)=\prod_{i=1}^{n}f(x_{i},\Psi)=\prod_{i=1}^{n}[\sum_{j=1}^{k}\pi_{j}f(x _{i},\theta_{j})] \tag{22}\]  
\[lnL(\Psi)=\sum_{i=1}^{n}lnf(x_{i},\Psi)=\sum_{i=1}^{n}ln[\sum_{j=1}^{k}\pi_{j }f(x_{i},\theta_{j})] \tag{23}\]  
Consider \(L(\Psi)\) as the maximum value of the likelihood function for a mixed distribution model with the estimated number of components \(k\). Subsequently, the AIC and BIC are computed using the formulas \(2k-2 lnL(\Psi)\) and \(-ln(n)k-2 lnL(\Psi)\), respectively. When deciding the number of components using model selection criteria, the value of \(k\) is selected based on the criterion of having the lowest AIC and BIC values, while simultaneously taking into account the highest LL value.
## 1 Introduction
### Related Work
Fortunately, by harvesting the energy from the surrounding environments, wireless power can be obtained to address this problem. Radio frequency (RF) energy harvesting is applied in cognitive radio networks [24; 25; 26], which indicated that the energy signal can be harvested from the radio-frequency (RF) signals. Therefore, RF-based energy harvesting technique can provide flexible, sustainable and stable energy supply for wireless applications [27; 28; 29; 30; 31]. To provide battery charging, the primary network allows the secondary users dynamically decided to sense the spectrum or harvest the primary RF energy as in [28; 29]. In other trends, throughput improvement in the secondary network can be achieved by utilizing both the optimal channel selection method and the harvested RF energy as in [30]. The energy consumption in cognitive radio sensor networks benefits from RF energy harvesting in [31]. The authors in [32] considered Simultaneous Wireless Information and Power Transfer (SWIPT) to implement cognitive radio with NOMA. In vehicular networks, the base station (BS) and roadside unit (RSU) in 5G-enabled networks are widely used facilities, especially in V2I communications. In particular, RSU presents a new way to provide high data-rate services and reliable connections to vehicles. An energy-efficient relay assisted transmission scheme is studied in the uplink cellular networks for V2X communications to provide delay insensitive applications [33]. The authors in [34] proposed a new approach for power allocation with energy efficiency optimization to employ in cellular device-to-device-assisted V2X communication network.