esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Scaling Gene and Cell Therapy ATMPs from Research to the Clinic with AI  
Gene and cell therapies show immense potential as advanced medicinal therapies, but scaling initial proof-of-concepts into robust clinical processes poses difficulties and limitations. The sensitivity of living systems means research protocols often fail when directly applied at the larger volumes required for patient treatments. Healthcare centers developing gene therapy ATMPs must optimize this scale-up, to progress from early studies to clinical manufacturing. AI and real-time metabolomics analysis can allow enhanced process understanding, to bridge the research-to-clinic scale-up gap.  
During initial studies, gene delivery mechanisms and editing efficiency are evaluated at laboratory scale. However, small-scale conditions rarely translate directly to the larger volumes required for patient doses. Cell growth kinetics, vector productivity, and other critical quality attributes commonly diverge during scale-up. Extensive empirical optimization trials have traditionally been needed to adapt processes to clinical scale. AI technologies offer tools to accelerate scale-up by revealing key biological factors affected by production volume. ML algorithms integrating multivariate data can identify patterns linking process parameters to cell health across scales [35]. This model-driven approach rapidly highlights stress factors impairing growth or productivity when scaling up batches. AI control systems can then automatically optimize dynamic feeding, pH, dissolved oxygen, and other bioreactor parameters based on continuous metabolite monitoring.  
Cellular metabolism provides integral indicators of the biological effects of gene engineering and culture environment. High-throughput metabolomics profiling coupled with AI analysis elucidates metabolic phenotypes and clarifies discrepancies between research and clinical protocols. Tracking key metabolites and pathways disrupted during scale-up highlights target areas for process improvements. For instance, glutamine limitation or build-up of lactate may only occur above certain volumes. AI modelling can integrate multi-omics datasets with process data to reveal the metabolic shifts responsible for reduced clinical-scale productivity. This understanding enables tailored nutrient supplementation or gene modifications, to restore a favorable metabolism. Additionally, real-time biosensors monitoring metabolites allow adaptive AI control systems to dynamically optimize nutrients and parameters throughout production. Maintaining cultures in ideal metabolic regimes enhances process robustness during scale-up.  
Analysis of the Role and Potential Impact of AI in Academic ATMP Manufacturing, with Additional Details on the Key Advantages AI Can Provide
# Development of a Pre-Evaluation and Health Monitoring System for FAST Cable-Net Structure
## 4 Pre-Evaluation and Health Monitoring System of FAST  
In order to take advantage of the theoretical results, a PHM system for FAST was developed and derived from the updated MST model. The system consists of a pre-evaluation system and a health monitoring system. The framework diagram is shown in Figure 7.  
Figure 6: Errors between the measurement and analysis of cable force.
# Bayesian Modeling of Traffic Accident Rates in Poland Based on Weather Conditions
## 2 Materials and Methods
### Data
#### 2.1.2 Nationwide Modeling Data  
For the nationwide weekly analysis, a different aggregation approach was employed. Temperature was calculated as the average of all mean daily temperatures across counties within each week. Humidity was processed using the same methodology. Precipitation was represented as the total sum of daily precipitation measurements across all counties for each week. Socioeconomic indicators were incorporated as the national aggregates for the corresponding year. The variables used in these models and their descriptions are provided in Table 2.  
\begin{table}
\begin{tabular}{l l} \hline
**Variable** & **Description** \\ \hline _county\_mean\_temperature_ & Annual average of daily mean temperatures per county (\({}^{\circ}\)C) \\ _county\_mean\_humidity_ & Annual average of daily mean relative humidity per county (\%) \\ _county\_total\_precipitation_ & Annual accumulated precipitation per county (mm) \\ _county\_population\_density_ & Population per square kilometer \\ _county\_passenger\_cars_ & Number of registered passenger vehicles per county \\ _county\_road\_density_ & Length of paved municipal and county roads per 100 km\({}^{2}\) \\ \hline \end{tabular}
\end{table}
Table 1: Variables used in the yearly county-level accident count modeling.  
Figure 1: Normalized distributions of predictors used in county-level yearly accident modeling. Top row: Weather factors including (**a**) mean temperature (z-score normalized), (**b**) mean humidity (z-score normalized), and (**c**) total precipitation (log1p normalized). Bottom row: Socioeconomic factors including (**d**) population density, (**e**) passenger car count, and (**f**) road density, all log1p normalized. The distributions illustrate the variability of these factors across Polish counties during the 2020–2023 study period.  
The normalization approach for weekly data followed a similar pattern to that used for county-level data, with certain adaptations to account for the different temporal scales. Temperature and humidity underwent z-score normalization to standardize seasonal variations. Precipitation was subjected to log1p normalization to address its right-skewed distribution. The yearly national socioeconomic data were min-max normalized to facilitate meaningful year-to-year comparisons while maintaining the relative scale of annual changes. The resulting distributions of the predictors are presented in Figure 2.
# Update: Heart Failure in Systemic Lupus Erythermatosus
## 3 Pathophysiology and Risk Factors of Heart Failure in Systemic Lupus Erythermatosus
On the other hand, activated components of the complement system cause cardiomyocyte damage. In Libman-Sacks inflammation, fibrin and thrombocytes are deposited on the heart valves, causing their destruction [45; 48; 49; 50]. The predominance of the HFpEF type in SLE patients may be related to the pathophysiology of the disease and closely related to the effect of endothelin-1 (ET-1). ET-1 secretion increases in response to elevated blood pressure, leading to disturbances specific to HFpEF: endothelial dysfunction, myocardial fibrosis, increased vascular stiffness, and diastolic dysfunction. On the other hand, in HFrEF, cardiomyocyte damage leads to eccentric left ventricular remodeling and systolic dysfunction, which is an infrequent process in SLE [51; 52; 53]. Pathophysiological research is supported by the results of interventional studies since it has been shown that appropriate anti-inflammatory treatment significantly improves physical fitness. These observations were confirmed by the improvement of echocardiographic results and cardiopulmonary exercise results, as well as by the betterment of serological parameters and heart failure biomarkers during the observation period [12]. In conclusion, HF in SLE patients may be caused by multiple mechanisms, including myocarditis, cardiomyopathy, valvular heart disease, as well as genetic factors. Crucially, SLE itself may be responsible for 50% of HF cases in this group of patients. The prevalence of subclinical heart failure may be underestimated, which indicates a strong need for early diagnosis and implementation of cardiac function monitoring in these patients. Chronic inflammation plays a key role in the pathogenesis of HF development in SLE patients. Available data suggest anti-inflammatory therapy as a method to improve cardiac function and physical fitness of patients, as indicated by echocardiographic and cardiac marker results. This fact may be of practical importance in future attempts to develop standards for monitoring and treatment of patients with SLE. However, further studies are needed to better understand the mechanisms leading to HF in SLE and to construct more effective therapeutic strategies. A summary of risk factors responsible for the development of heart failure in SLE is presented in Figure 1.  
Figure 1: Summary of risk factors responsible for the development of heart failure in SLE. Source: own study.
# A Novel Cell-Based Adaptive Cartesian Grid Approach for Complex Flow Simulations
## 4 Results and Discussion
### A Mach 3 Wind Tunnel with a Forward Facing Step
In this test case, the comparison of the dimensionless computational expense between cell-based AMR grids and uniform grids is shown in Table 1. Under the uniform grid, the compute time was approximately proportional to the total number of cells (a quarter-scale reduction results in a sixteen-fold increase in the overall number of cells). Regarding the computational time for the AMR setup, considering the evolving and unsteady nature of the flow within the computational domain, the AMR strategy dynamically refined the mesh in regions with steep flow gradients and coarsened it in areas with smoother flow. The table serves as a point of reference, listing the number of grids at the moments previously discussed. Overall, AMR efficiently captured flow details and achieved a significant reduction in computational costs. As indicated by the results in the table, with three layers of solution adaptivity, the number of cells at the final time step and the overall computational expense were reduced to 48.9% and 47.6% of those for the uniform grid; when the level of adaptivity was elevated to five layers, these ratios further decreased to 15.8% and 19.5%, respectively. This demonstrates that increasing the number of solution adaptation layers significantly enhanced the efficiency of the simulation.
## 1 Introduction
### Synchronization in OFDM  
The synchronization procedure in the OFDM system generally involves three steps. During the first step of the procedure, the receiver exploits the redundant information to find the FFO and initial STO [9; 10; 11], which is performed in the time domain. In continuous transmission mode, such as terrestrial broadcasting systems, the cyclic prefix can be used for these synchronization tasks. On the other hand, dedicated training symbols are inserted at the beginning of the OFDM frame in the case of burst-mode OFDM systems, which are used for time and frequency synchronization. After FFO compensation and cyclic prefix removal, the frequency domain samples are obtained via fast Fourier transform (FFT) processing. Once the first step is completed, the IFO is estimated together with residual STO, which can usually be done in the frequency domain [12; 13; 14; 15; 16; 17; 18]. After frequency synchronization, there are residual CFO and SFO errors that continuously rotate the phase of the OFDM symbol. During the tracking phase, the residual CFO and SFO are typically estimated based on a set of dedicated pilots, which are estimated and compensated periodically because of its time-accumulative nature [19; 20; 21]. Since data demodulation can be correctly performed only after successful compensation of the IFO, the IFO represents an important task in the overall synchronization procedure. Hence, we focus on the estimation of the IFO in this paper.  
The IFO estimation can be classified into two main categories--namely, blind estimation and pilot-aided estimation. The blind estimation method relies on the utilization of any inherent structural information in the OFDM symbol, which is preferred due to its bandwidth efficiency. In [12; 13; 14; 15], the blind approaches were proposed utilizing the combination of data subcarriers and null subcarriers (NSCs) present at the edges of the OFDM spectrum. The main disadvantage of such a blind estimation strategy is that it requires an exhaustive search for IFO estimates. Unlike the blind approach, a pilot-aided IFO estimation can be accomplished using periodically transmitted pilot information [14; 15; 16; 17]. Maximum likelihood (ML) estimation has been successfully used to estimate the IFO [15], based on the observation of pilot information. The ML estimator achieves accurate CFO estimation at the sacrifice of spectral efficiency and computational load. The work in [17] proposes a new pilot structure together with its pilot-specific estimation algorithm. Another strategy for data-aided IFO detection has been recently presented, exploiting the spatial redundancy introduced by the use of space-frequency block codes [18]. Despite showing good detection capability, these estimation schemes developed in [17; 18] are incompatible with legacy ISDB-T devices.
# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
## 5 Performance Analysis
### Security Analysis
#### 5.1.1 Collusion Attack  
Passive attackers may collude with some users to get additional information about other users or collude with the LBS server to predict sensitive information about legitimate users. If the probability of successfully guessing the real location of a vehicle user among \(k\) locations in the service query does not increase with the number of collusion users, the proposed method can resist collusion attack.  
We consider a situation that collusion occurs between a group of users aiming to acquire the user's real location from \(k\) locations. In RR-DLS method, each user can only know the service request probability and road condition collected by itself. When eavesdroppingthe service query sent to the LBS server, the attacker cannot filter out some invalid locations through additional information since \(k\) locations in the service query have the same or similar service request probability and are on the road.  
One extreme case for the passive adversary is that it can acquire the global information by compromising the LBS server as well as RSUs. In this case, it becomes an active adversary and can perform inference attack as discussed in the following.
## 4 Results
### Deep-Learning Methods
The final decisions of the five classification networks are finally merged to improve the overall performance. The authors of [125] propose a deep convolutional neural network, named Classification of Skin Lesion Network (CSLNet), to perform multi-class classification of skin lesions. The network consists of concatenated basic blocks with a total of 68 convolutional layers, each preceded by a batch normalization layer and a LeakyRelu layer. Finally, a Global Average Pooling layer precedes the last FC layer before the output layer. In the paper [126] a deep convolutional ensemble neural network is created to perform classification of dermoscopic images into three classes: melanoma, nevus, and seborrheic keratosis. The classification layers of four different deep neural networks are fused, two pre-trained (ResNet and GoogLeNet), and two with weights initialized to random values (VGGNet and AlexNet). The final classification is obtained by performing the weighted sum of the maximal probabilities (SMP) of each network. To classify melanoma images into malignant and benign, in [127] a pretrained MobileNetV2 network is used as the basis of the model and adds a global average pooling followed by two fully connected final layers. Evaluation of the model on four different datasets shows poor accuracy in classifying malignant lesions, a result likely related to the imbalance between classes. As it is designed, the proposed model can also be implemented on mobile devices. In [128], the addition of features in the layers of a CNN is proposed. Specifically, features are extracted from segmented dermoscopic images and used as additional input to the CNN network layer. The handcrafted features, which include shape, color, and texture features (extracted by GLCM and scatter wavelet transform), and the features extracted by CNN are concatenated at the fully connected layer leading to high performance in classifying various skin lesions. In [129], a deep convolutional neural network framework for multiclass classification of skin lesions is proposed, including the outcome of binary classification (healthy/diseased)in the final probabilities. To accomplish this, the pretrained GoogLeNet-InceptionV3 network is used to perform multiclass and binary classification simultaneously, and the respective softmax outputs are merged on a support training layer. This layer multiplies the confidence of multiclass classification with the corresponding confidence of binary classification. Table 5 summarizes the deep-learning methods previously described.
# Durability of Mortars with Partial Cement Replacement by Recycled Brick Powder
###### Abstract  
Over the past decade, global cement production has exceeded 4 billion tonnes annually. This process has a considerable environmental impact and is estimated to account for approximately 5% to 7% of the total annual CO\({}_{2}\) emissions released into the atmosphere. To mitigate the environmental consequences of cement production, extensive research has been conducted on alternative materials that can partially replace cement in concrete manufacturing, thereby reducing its carbon footprint. This study explores the utilisation of recycled brick powder as a supplementary cementitious material in mortar production, with replacement levels ranging from 5% to 45%. In addition to evaluating their mechanical properties, such as flexural and compressive strength, this research investigates the durability of the newly developed mortars. The results indicate a reduction in porosity of up to 50% and an enhancement in chloride penetration resistance by up to fourfold when the substitution rate is between 35% and 40% compared with mortar without replacement. Although no improvements were observed in carbonation resistance, the new mixtures exhibited an increase in resistivity of up to ninefold.  
recycled brick powder; mortar; durability; sustainability Article
at basic pH and are transformed into catechol quinones, which can react with various functional groups, including thiols, amines, and other catechols [23; 24; 25]. In addition to forming covalent bonds, the catechol groups interact with various substrates via electrostatic interactions, hydrogen bonds, \(\pi\)-\(\pi\) stacking, and \(\pi\)-cation interactions [25; 26]. Inspired by the mussel adhesion behavior, catechol-conjugated polymers have been synthesized to improve adhesion of fluids [27; 28; 29; 30; 31]. For instance, catechol-conjugated chitosan has excellent solubility at neutral pH, excellent adhesive properties in tissues, and superior hemostatic capability compared to unmodified chitosan [32; 33; 34; 35; 36]. Therefore, the introduction of catechol groups into the HA backbone can increase HA mechanical properties and adhesiveness in wet environments.  
In this review, we briefly describe the methodologies used for the preparation and characterization of HA-catechol. In addition, we focus on the biomedical uses of HA-catechol, which are categorized into the following four subsections: antifouling, wound healing, drug/gene delivery carriers, and tissue engineering scaffolds. Furthermore, we discuss the current challenges and opportunities for technologies using HA-catechol, which require further fundamental research, and its use in translational medicine.
# Trajectory-Tracking Control of Unmanned Vehicles Based on Adaptive Variable Parameter MPC
## 6 Conclusions  
In order to improve the adaptive ability of trajectory-tracking control of unmanned vehicles under complex working conditions, we estimated the tire lateral force online based on the SRCFK algorithm to correct the tire cornering stiffness and realize the real-time correction of the MPC state matrix and the control matrix. The accuracy of the vehicle model in the control algorithm was improved by transforming the complex and unpredictable empirical formula fitting problem into an estimation problem based on the energetic state quantities. The proposed Gaussian-based adaptive time-domain strategy was able to adjust the time-domain size according to the changes in road conditions, which can effectively coordinate the trajectory-tracking accuracy and lateral stability, smoothing the control output and further satisfying the stability requirements of the vehicle.  
The simulation experiments of the proposed method were carried out by building a joint simulation model of CarSim and Simulink, and the final experimental results show that, compared with the traditional MPC, the proposed adaptiveness variable-parameter MPC strategy has good adaptive capability and robustness under complex working conditions, and it can greatly improve the tracking accuracy and lateral stability under the double-shifted line condition, which is of great significance for improving the adaptive and robustness of the control system of intelligent automobiles. The proposed adaptive variable-parameter MPC strategy has good adaptive capability and robustness under complex conditions.  
[PERSON] wrote the paper and conducted the final editing; [PERSON] and [PERSON] developed the methodology, analyzed the data, and structured the discussion; [PERSON] reviewed the results and supervised the conclusions. All authors have read and agreed to the published version of the manuscript.  
Beijing Municipal Education Commission Research Project Funding (No. KM202311232015, KM202311232016) and the Beijing Natural Science Foundation Project (No. 4244091). Supported by the Open Project of Beijing Key Laboratory of High Dynamic Navigation Technology.  
Not applicable.  
The original contributions presented in the study are included in the article.  
The authors declare no conflicts of interest.  
Figure 14: Prediction time domain and control time domain variation curve.