esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Post-Treatment of the Active Layer
The increase in domain size presumably promotes the geminate  
Figure 3: AFM and phase images of the photoactive layers on PEDOT:PSS fabricated by using (**a–c**) ITO coated glass and (**d–f**) ITO coated PET substrates and treated by (**a–d**) TA, (**b–e**) SVA-DCM, and (**c–f**) SVA-THF. recombination of charge carriers with a subsequent decrease of photogenerated current, V\({}_{\rm oc}\) and FF. Figure 4 and Figure S5a shows the results of the JHA treatment. The cell performance improves exponentially during the first 20 min of treatment, where the PCE is seen to increase six-fold. After 20 min, the V\({}_{\rm oc}\) and J\({}_{\rm sc}\) remain stable, but the FF decreases, probably due to resistive losses. This behavior is assigned to the increase of the nanodomains of the active layer that result in the initial enhancement of the J\({}_{\rm sc}\) and V\({}_{\rm oc}\). However, the extent to which the JHA can be used to increase the size of the domains is limited in contrast to the other post-deposition techniques. SVA and the thermal treatment can induce a continuous growth of the crystal domains, which, at the beginning, has a faster effect on the performance, mainly due to an increase in the film crystallinity [19], but a faster detrimental effect as well because of the increase in the charge recombination. However, JHA has a smoother effect, which is also limited by the value of the applied current, and thus, the growth of the domains can be controlled. The post-deposition treatment was studied on flexible devices due to their potential and ease of application on roll-to-roll coating systems. The average temperature was measured by using a thermal camera under operating conditions (see Figure S5b). The thermographic image indicates that the cell reached temperatures between 65 and 80 \({}^{\circ}\)C, which is compatible with flexible substrates. With this data, we adapted the thermal annealing modified from 110 \({}^{\circ}\)C to 65 \({}^{\circ}\)C, keep it for 5 min at 65 \({}^{\circ}\)C and then up to 80 \({}^{\circ}\)C annealing until the color changes from blue to purple, obtaining better results (see Table 4). Therefore, the JHA might be used to find the optimal annealing temperature since this is a post-treatment that can be applied 'in operando' conditions. We could also confirm that the changes induced by JHA are not reversible and that they are not related to any excess or saturation of charges, as can be observed in other kinds of solar cells as a 'light soaking' effect [28; 29].
# A Guideline for Implementing a Robust Optimization of a Complex Multi-Stage Manufacturing Process
## 2 Materials  
Ultrasound diagnostic technology is generally related to imaging of biological tissue by using a probe. The latter is typically placed on the body surface or internal to a body lumen of a patient in a selected imaging region. Today, the ultrasonography is becoming more and more useful for the management of COVID-19 (Corona virus desease-19) with respiratory involvement and for the rapid assessment of the severity of SARS-CoV-2 (Severe acute respiratory syndrome coronavirus (2) pneumonia [30, 31]. The safety, repeatability, absence of radiation, portability, and low cost are the main characteristics for lungs inspection. The tracking of the evolution of disease, the monitoring of lung recruitment maneuvers, and the fast response for making decisions related to weaning the patient from ventilatory support are some of the benefits of ultrasound probes for medical imaging.  
The US probe geometrical precision and the guarantee of high process capability by limiting the materials damage are continuous tasks that the engineers must chase. In fact, the multi-stage manufacturing process of ultrasound probes for medical imaging is composed by the assembly of layers with different physical and chemical properties and by mechanical dicing of the multi-layers structure with the thickness around some micrometers. The choice of materials involved in the US probe is very important for assuring at the same time the best image quality during the medical diagnosis, the manufacturability and stability of the process, and the cost reduction for manufacturing.
###### Abstract  
A joint compensation scheme based on pilot symbols aiding adaptive Kalman filter (AKF) for phase noise and polarization cross-talk is proposed and investigated via numerical simulation and experimental demonstration. In the proposed scheme, the optimizing parameter Q is adaptively adjusted according to signal parameters or channel conditions. This improvement avoids the drawback of conventional extended Kalman filter (EKF), and its performance is strongly dependent on Q. Another improvement is that the convergence speed of AKF is improved. Pilot quadrature phase shift keyin (QPSK) symbols are inserted into 16 quadrature amplitude modulation (QAM) signals periodically. Besides accelerating convergence speed, the employment of pilot symbols also improves the tracking capability of AKF. The format ratio between pilot symbols and payload symbols is suggested under different system environments, for instance, optical signal-to-noise rate, polarization rotation frequency drift rate, and laser linewidth. With the proposed scheme, it has excellent tolerance to initial parameter Q and dramatically improves the performances in convergence speed, polarization rotation frequency drift rate tracking, and carrier phase recovery. Both the numerical simulation and experimental demonstration achieve convergence improvement for around 40 times than the original AKF. Additionally, the improvements in tracking ability are also demonstrated.  
coherent optical communication; pilot aided adaptive Kalman filter; phase noise; dynamic polarization rotation +
Footnote †: journal: Applied Sciences
# Molecular Imaging with \({}^{68}\)Ga Radio-Nanomaterials: Shedding Light on Nanoparticles
## 4 Biomedical Applications of \({}^{68}\)Ga-Labeled Nanoparticles
### Iron Oxide Nanoparticles (IONPs)
In a posterior study performed by [PERSON] et al. (2016), an analogous iron oxide-based tracer was synthesized following the same procedure previously described, using citrate as a surfactant instead of dextran. Radiolabeling yield obtained was 92%. Nanoradiotracer was functionalized with a highly hydrophobic neutrophil-specific peptide, N-cinnamoyl-F-(D)L-F-(D)L-F (c-FLFLF), to visualize inflammation processes in vivo. Functionalized nanoradiotracer was injected in vivo in acute lung inflammation and chronic inflammation murine models. PET imaging revealed high in vivo labeling efficiency of the nanoradiotracer and yielded high quality images of neutrophil recruitment. Probe specificity for neutrophils, was checked using a neutrophil-depleted model (Figure 6). Figure 5: (**a**) PET/CT imaging 3 h post subcutaneous injection of \({}^{68}\)Ga-SPION. Arrow shows nanoparticle accumulation in SLN (popliteal) and iliac nodes; and (**b**) axial PET/CT slice 3 h post subcutaneous injection of \({}^{68}\)Ga-SPION. Arrows point SLN and reference nodes. Adapted from [PERSON] et al. (2016).
# View-Aware Contrastive Learning for Incomplete Tabular Data with Low-Label Regimes
## 4 Results and Discussion
### Impact of View Quantity on Model Performance Under Incomplete Supervision  
This experiment aimed to investigate the influence of the number of views \(k\) on model performance. By adjusting \(k\) from 2 to 8, the effects on classification accuracy, F1-score, AUC, and normalized MSE were analyzed. All other aspects of the model architecture and training configuration were held constant to ensure the comparability of results under different \(k\) values.  
As shown in Table 9 and Figure 7, the model achieved optimal performance when the number of views was set to \(k=4\), with an accuracy of 0.87, F1-score of 0.83, AUC of 0.90, and normalized MSE reduced to 0.072. In contrast, performance declined when \(k\) was set to 2 or 6, particularly for \(k=2\), where accuracy dropped to 0.70, suggesting that insufficient view diversity may result in inadequate feature coverage. Increasing the number of views to \(k=8\) led to further degradation, yielding the lowest performance across all metrics. This indicates that excessive partitioning introduces subspace noise and disrupts semantic alignment and integration. From a theoretical perspective, the number of views determines the granularity of feature subspace decomposition, which directly influences the amount of structural information captured by each encoder. With too few views, the model fails to encompass diverse feature combinations, resulting in under-represented latent patterns. Conversely, an overly large number of views reduces the information density within each subspace, diluting signal strength and complicating contrastive pair construction due to sparse associations, which may cause instability during training and degrade generalization. Setting \(k=4\) achieves a desirable tradeoff between diversity and contextual integrity. Each view retains sufficient structural context, while the consistency objectives across views effectively align latent representations from different subspaces, facilitating the construction of a coherent semantic space. These findings highlight the importance of selecting an appropriate number of views in multi-view self-supervised frameworks to maintain the quality and stability of the learned representations.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Contrastive Setting** & **Accuracy** & **F1-Score** & **AUC** & **Normalized MSE** \\ \hline Intra-View Only & 0.73 & 0.70 & 0.76 & 0.157 \\ Cross-View Only & 0.79 & 0.75 & 0.82 & 0.113 \\ Intra + Cross View & 0.87 & 0.83 & 0.90 & 0.072 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of different contrastive loss settings under missing labels and incomplete features.
# Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources
## 3 System Model: Instantiating the vBBU within the Linux Kernel
### Instantiating the vBBU within the Linux RT-Kernel  
When instantiated in the Linux RT-Kernel, a vBBU operates as a multi-threaded user-space process, incorporating a mix of both RT and non-RT threads. The method of instantiation for these threads can vary subject to the specific implementation, such as OpenAirInterface (OAI) [51] and SrsLTE [52]. This paper centers around the LTE-eNB implementation by OAI, utilized as the vBBU. This LTE-eNB implementation, when operating under the Linux RT-Kernel, initiates a selection of RT threads and non-RT threads as depicted in Table 1.  
The subset of RT threads is responsible for embedding and processing the L1 and L2 functions of the vBBU within the Linux RT-Kernel. These threads are designated as RT user-space threads due to the strict timing requirements associated with their operations. Such strict timing constraints are critical, for instance, in adhering to the Hybrid Automatic Repeat reQuest deadline [32]. In particular, the ru-thread (\(\tau_{1}\)) handles low-level L1 functions, primarily dealing with tasks such as reading from and processing signals for the RRU receiver and processing and writing signals for the transmitter. Likewise, the fep_processing thread (\(\tau_{3}\)) handles L1 functions through the Front End Process. This involves defining procedures for the Uplink (UL) and processing precoding and Single Carrier Frequency Division Multiple Access signals. For the Downlink (DL), the feptx_thread (\(\tau_{4}\)) serves as the FEP for TX and manages DL procedures, pre-coding, and Orthogonal Frequency Division Multiplexing signal processing [40]. Meanwhile, the lte-softmodem thread (\(\tau_{2}\)) takes on the processing of the remaining L1 functions along with L2 functions.  
Figure 3: Mobile network scenario: monolithic vBBU running on a bare-metal GPP managed by Linux RT-Kernel. The vEPC is deployed on bare metal GPP managed by Linux generic Kernel.  
Conversely, the non-RT thread subset includes L3 processing functions and control procedures such as RRC, STCP, and S1 AP. Also categorized as non-RT threads are functions tasked with processing user data plane traffic through the GTP tunnel, an example of which is TASK_GTPV1_U (\(\tau_{5}\)). This uses the transport protocol UDP, as seen in tasks like TASK_UDP (\(\tau_{6}\)).
## 1 Introduction
[31] reported a reduction of 4.0-5.0 Log\({}_{10}\) CFU/mL of _E. coli_, _S. typhimurium_, _S. aureus_, _L. monocytogenes_, and _E. faecalis_ inoculated on spinach leaves, working with 100 mg/L and 200 mg/L of FAC. The sanitizers used in washing RTE vegetables have a different influence on bacterial microbiota. Some sections of the bacteria composition of plants affect the survival of pathogens through competition for limited nutrients or production of growth inhibitors [16; 19; 33], and others facilitate the growth of pathogens through the metabolism of different carbon sources [24]. Chlorine-based washing has previously been reported to reduce the number of microbes that inhibit the growth of pathogens in lettuce and spinach [18]. [PERSON] et al. [25] observed changes in the bacteria community in spinach leaves washed with chlorine. [PERSON] et al. [34] reported a reduction in the richness of the bacterial community of RTE spinach without affecting bacterial diversity after washing the spinach leaves with vinegar. However, the effect of washing with ECAS on the composition of the microbiome of RTE spinach leaves compared to that of PAA sanitizer has not previously been assessed. This study evaluated the effect of an ECAS at neutral pH with proven efficacy against foodborne pathogens and in reducing the overall bacterial load in RTE spinach [28; 35; 36; 37] focusing on the structure of the bacterial community present on RTE spinach leaves. We compared the changes in the profile of the bacterial microbiome in minimally processed fresh spinach leaves washed with tap water, PAA (50 mg/L), and ECAS (50 mg/L and 85 mg/L of FAC) on days 0, 5, and 10 after the sanitizing wash and storage at \(4\pm 1\)\({}^{\circ}\)C. Furthermore, a comparative analysis of the bacterial composition was performed through an analysis of the composition of microbiomes among all the treatment types and sampling days.
# Development of a Path Tracker Based on a 4 WS Vehicle for Low-Speed Automated Driving Systems
###### Abstract  
With the increasing demand for various autonomous driving services in urban environments, low-speed autonomous vehicles, such as autonomous shuttles and purpose-built vehicles, equipped with enhanced driving characteristics suitable for urban roads featuring narrow streets, intersections, congested traffic, and small radii, are emerging. In particular, the 4 WS (four-wheel steering) system, which is being integrated into these vehicles, is designed to steer both the front and rear wheels. This system improves steering responsiveness and stability, providing maneuverability under various driving conditions and making it highly suitable for urban environments. However, the 4 WS system involves complex dynamic modeling and poses challenges in designing a path tracker, especially if factors such as the vehicle's turning radius and road curvature are not properly considered. To address these challenges, this paper proposes a path tracker for a low-speed autonomous driving system based on a 4 WS system, optimized for the characteristics of urban roads to minimize the vehicle's turning radius and enhance driving performance. The proposed path tracker independently controls the front and rear wheels and incorporates road curvature and vehicle turning radius as feedforward terms to improve the response performance of the path tracker. The performance of the proposed path tracker was evaluated through simulations and real-car experiments.  
automated driving systems; path tracker; lateral controller; curvature; feedforward; four-wheel steering Article
## 3 Convergence Study and Calculation Validation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Case** & **Cell Count (million)** & \(\overline{C}_{d}\) & \(C_{l}^{\prime}\) & \(S_{t}\) & \(-\overline{C}_{pm}\) & \(-\overline{C}_{pb}\) \\ \hline Coarse Mesh & 1.9 & 0.367 & 0.032 & 0.383 & 2.313 & 0.424 \\ Medium Mesh & 3.8 & 0.377 & 0.038 & 0.383 & 2.318 & 0.419 \\ Fine Mesh & 7.4 & 0.378 & 0.046 & 0.383 & 2.326 & 0.437 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Grid density sensitivity study results of a single cylinder at \(Re=3\times 10^{6}\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Researchers** & \(Re\) & **Description** & \(\overline{C}_{d}\) & \(C_{l}^{\prime}\) & \(S_{t}\) & \(-\overline{C}_{pb}\) \\ \hline Present study & \(3\times 10^{6}\) & \(k\)–\(\omega\) IDDES & 0.377 & 0.033 & 0.383 & 0.419 \\ [PERSON] [40] & \(1\times 10^{6}\) & Experiments & 0.30 & – & – & 0.37 \\  & \(1.8\times 10^{6}\) & Experiments & 0.42 & – & – & 0.62 \\  & \(3.5\times 10^{6}\) & Experiments & 0.69 & – & – & 0.85 \\ [PERSON] [41] & (2.6–3.3) \(\times\) \(10^{6}\) & Experiments & 0.25–0.44 & – & – & 0.51–0.61 \\ [PERSON] et al. [42] & \(1\times 10^{6}\) & Experiments & 0.21 & – & – & 0.53 \\  & \(2.9\times 10^{6}\) & Experiments & 0.53 & – & – & 0.59 \\  & \(3.7\times 10^{6}\) & Experiments & 0.56 & – & – & 0.61 \\ [PERSON] [24] & \(3\times 10^{6}\) & Experiments & 0.45 & – & – & – \\ [PERSON] et al. [43] & (3–3.1) \(\times\) \(10^{6}\) & Experiments & 0.35–0.38 & – & – & 0.45–0.46 \\ [PERSON] et al. [44] & \(3\times 10^{6}\) & DES & 0.41 & 0.06 & 0.35 & 0.53 \\ [PERSON] al. [45] & \(1\times 10^{6}\) & LES & 0.31 & – & 0.35 & 0.32 \\ [PERSON] et al. [46] & \(3.6\times 10^{6}\) & \(k\)–\(\epsilon\) URANS & 0.457 & 0.077 & 0.305 & – \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of present and previous studies for flow past a single cylinder. predicted in the present study agrees well with the previous experimental results [49] and other numerical results [44; 46]. The overprediction of \(C_{f}\) for \(\theta<90^{\circ}\) in the present study compared with the experimental values is also observed from other numerical studies which used wall functions [44; 45; 46]. This results from the wall function method's assumption of a completely turbulent boundary layer [50]. In general, the present calculation of flow past a single cylinder is in accordance with the outcomes of other published papers. Thus, it is reasonable to assume that the calculation method is also valid for calculating the triangular arrangement of the three cylinders.
# Parallel Mechanism of Spectral Feature-Enhanced Maps in EEG-Based Cognitive Workload Classification
## 5 Results and Discussions
### Generalization  
In actual usage of cognitive workload classification, the final accuracy of new subjects is not necessarily so high. Although the training and test set don't overlap, they are all from the same 13 subjects, which results in features of specific subjects being already trained by deep neural networks and the accuracy would be higher. In the testing of generalization after using PMSFE maps, trails of specific subjects are identified as the test set, and these trails from the remaining 12 subjects is used as training set. For example, in Table 3 the EEG data from subject No.1 is not included when training the network and is only used as the test set. This method simulates the actual application and guarantees the validity generalization.  
ResNet-152 which performs best in final accuracy is used to test the generalization. From Table 3, the effect of a parallel mechanism can be identified clearly. Most subjects have an increase in the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subject** & **Spectral Maps (\%)** & **PMSFE Maps (\%)** & **Increased Rate (\%)** \\ \hline
1 & 55.68 & 58.38 & 2.70 \\
2 & 70.28 & 72.17 & 1.89 \\
3 & 88.95 & 91.46 & 2.51 \\
4 & 99.50 & 99.50 & 0 \\
5 & 99.49 & 100 & 0.51 \\
6 & 99.00 & 99.5 & 0.50 \\
7 & 99.00 & 100 & 1.00 \\
8 & 100 & 100 & - \\
9 & 100 & 100 & - \\
10 & 100 & 100 & - \\
11 & 98.62 & 99.08 & 0.46 \\
12 & 72.25 & 72.25 & 0 \\
13 & 46.36 & 47.73 & 1.37 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The accuracy of spectral maps and PMSFE maps tested on 13 subjects and the rate of increase after using PMSFE maps.  
Figure 10: The confusion matrix of spectral maps and PMSFE maps. (**a**) The confusion matrix of spectral maps in AlexNet. (**b**) The confusion matrix of spectral maps in VGGNet-19. (**c**) The confusion matrix of spectral maps in ResNet-152. (**d**) The confusion matrix of spectral maps in DenseNet-200. (**e**) The confusion matrix of PMSFE maps in AlexNet. (**f**) The confusion matrix of PMSFE maps in VGGNet-19. (**g**) The confusion matrix of PMSFE maps in ResNet-152. (**h**) The confusion matrix of PMSFE maps in DenseNet-200.  
range from 0.46% to 2.70%, due to the spectral value of different trails from different subjects having different ranges.
## 7 Discussion
### Metrics Used to Evaluate Segmentation and Quantification
DSC is the reported Dice coefficient, \(r\) is the Pearson correlation coefficient, AUC is the area under curve measurement and \(\rho\) is the Spearman correlation coefficient. Segmentation time is the reported time to segment a single CT scan. Alongside segmentation accuracy and efficiency, in medical image segmentation, an important metric is segmentation precision or reliability [57]. Even if a segmentation algorithm is highly accurate for most examples, if it fails dramatically on other examples it will be hard to use the method in clinical practice. The simplest metric to evaluate reliability is by calculating and reporting the standard deviation of the segmentation metrics over the evaluation dataset. Other ways used to show reliability by the papers mentioned in this review include the inter-quartile range [7], or the minimum and maximum value of the metric [24; 42]. Where reported by the authors, we have included these metrics in the Result columns in Tables 1 and 2. Despite the importance of segmentation reliability, 10 papers mentioned in this review do not report any reliability metrics for segmentation performance. Future authors in this field should present the standard deviation of their metrics at a minimum, but also present more detailed reliability metrics or plots such as box plots across their evaluation dataset. This would add a dimension along which to more objectively compare methods. For evaluating quantification performance, papers mentioned in this review mostly use correlation measures such as the Pearson correlation coefficient (\(r\)) or the Spearman correlation coefficient (\(\rho\)). While these metrics have some descriptive power of the quantification performance, they can potentially mislead if the sample size is large enough or if the measurements themselves have a low variability [58]. For evaluating quantification in medical imaging, a better evaluation tool is the Bland-Altman plot, which shows agreement and not correlation. An example of a Bland-Altman plot is shown in Figure 13. Measuring the agreement of EAT or PAT volume between expert measurements and a computerized segmentation method or between two segmentation methods is a more objective indicator of quantification performance than a single correlation measure. Several papers mentioned in this review do not perform a Bland-Altman analysis of their results. We recommend that future authors use Bland-Altman analysis and other measurement agreement metrics when evaluating quantification performance.