esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The recently developed high-throughput system for cell spheroid generation (SpheroWell) is a promising technology for cost- and time-efficient in vitro analysis of, for example, chondrogenic differentiation. It is a compartmental growth surface where spheroids develop from a cell monolayer by self-assembling and aggregation. In order to automatize the analysis of spheroids, we aimed to develop imaging software and improve the localization of cell compartments and fully formed spheroids. Our workflow provides automated detection and localization of spheroids in different formation stages within Petri dishes based on images created with a low-budget camera imaging setup. This automated detection enables a fast and inexpensive analysis workflow by processing a stack of images within a short period of time, which is essential for the extraction of early readout parameters. Our workflow combines image processing algorithms and deep learning-based image localization/segmentation methods like Mask R-CNN and Unet++. These methods are refined by an evolution strategy for automated grid detection, which is able to improve the overall segmentation and classification quality. Besides the already pre-trained neural networks and predefined image processing parameters, our evolution-based post-processing provides the required adaptability for our workflow to deliver a consistent and reproducible quality. This is especially important due to the use of a low-budget imaging setup with various light conditions. The to-be-detected objects of the three different stages show improved results using our evolutionary post-processing for monolayer and starting aggregation with Dice coefficients of 0.7301 and 0.8562, respectively, compared with the raw scores of 0.2879 and 0.8187. The Dice coefficient of the fully formed spheroids in both cases is 0.8829. With our algorithm, we provide automated analyses of cell spheroid by self-assembling in SpheroWell dishes, even if the images are created using a low-budget camera setup.  
heuristic optimization; evolution strategy; computer vision; deep learning; machine learning; image processing; spheroids; quantification; high-throughput screening +
Footnote †: journal: Applied Sciences  
_Applied_  
_Acticle_  
Evolutionary Grid Optimization and Deep Learning for Improved In Vitro Cellular Spheroid Localization
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline
**Phase** & **Interval** & **Duration (s)** & **CV (\%)** & **Duration (\%)** \\ \hline \multirow{4}{*}{Phase 1} & Int 1 & 0.561 \(\pm\) 0.232 & 41 & 32.02 \(\pm\) 13.24 \\  & Int 2 & 0.713 \(\pm\) 0.241 & 34 & 32.12 \(\pm\) 10.86 \\  & Int 3 & 0.985 \(\pm\) 0.321 & 33 & 31.60 \(\pm\) 10.30 \\ \hline \multirow{4}{*}{Phase 2} & Int 1 & 0.364 \(\pm\) 0.066 & 18 & 20.78 \(\pm\) 3.77 \\  & Int 2 & 0.423 \(\pm\) 0.093 & 22 & 19.05 \(\pm\) 4.19 \\  & Int 3 & 0.481 \(\pm\) 0.165 & 34 & 15.43 \(\pm\) 5.29 \\ \hline \multirow{4}{*}{Phase 3} & Int 1 & 0.377 \(\pm\) 0.067 & 18 & 21.52 \(\pm\) 3.82 \\  & Int 2 & 0.444 \(\pm\) 0.106 & 24 & 20.00 \(\pm\) 4.77 \\  & Int 3 & 0.611 \(\pm\) 0.335 & 55 & 19.60 \(\pm\) 10.75 \\ \hline \multirow{4}{*}{Phase 4} & Int 1 & 0.450 \(\pm\) 0.102 & 23 & 25.68 \(\pm\) 5.82 \\  & Int 2 & 0.640 \(\pm\) 0.287 & 45 & 28.83 \(\pm\) 12.93 \\ \cline{1-1}  & Int 3 & 1.040 \(\pm\) 0.537 & 52 & 33.37 \(\pm\) 17.23 \\ \hline \end{tabular} Duration (s): mean \(\pm\) standard deviation (SD) of phase duration; CV: coefficient of variation (SD/Mean) as percentage; Duration (%): mean \(\pm\) standard deviation (SD) of phase duration as percentage; Interval: intensity (load) interval.  
\end{table} 
Table 1: Duration of the different phases of the bench press exercise and their coefficient of variation for each load condition. Figure 2: Barbell movement description: position (_m_), velocity (_m\(\cdot\)s\({}^{-1}\)_), acceleration (_m\(\cdot\)s\({}^{-2}\)_) and mechanical power (_W_) and phases (separated by dotted lines) of one interval 1 subject sequence (5 repetitions). Lines represent the mean and the shaded areas represent standard deviation. The comparison between the different intensity intervals for P\({}_{\text{MAX}}\), P\({}_{\text{MEAN}}\) and W during the eccentric phase (P2) of the BP is displayed in Table 3. The results of repeated measures ANOVA revealed significant differences among intensity intervals in P\({}_{\text{MAX}}\) (\(p<0.001\), \(\eta\)p2 = 0.341), P\({}_{\text{MEAN}}\) (\(p<0.005\), \(\eta\)p2 = 0.259) and W (\(p=0.002\), \(\eta\)p2 = 0.307). Specifically, pairwise comparisons showed that P\({}_{\text{MAX}}\) and P\({}_{\text{MEAN}}\) were significantly greater during interval 1 compared to interval 2 (P\({}_{\text{MAX}}\): \(p=0.015\), \(g=-0.51\); P\({}_{\text{MEAN}}\): \(p=0.025\), \(g=-0.45\)) and interval 3 (P\({}_{\text{MEAN}}\): \(p=0.003\), \(g=-0.86\); MP: \(p=0.015\), \(g=0.7\)). In contrast, there were no differences in P\({}_{\text{MAX}}\), P\({}_{\text{MEAN}}\) and W when intervals 2 and 3 were compared (P\({}_{\text{MAX}}\): \(p=0.67\), \(g=-0.5\); P\({}_{\text{MEAN}}\): \(p=0.322\), \(g=-0.34\); W: \(p=0.051\), \(g=-0.46\)).
## 2 Literature Review
### Origin-Destination Matrix Estimation and Prediction  
Predicting the passenger OD matrices within urban rail transit systems remains challenging primarily due to the spatiotemporally high dimensionality of the problem [12; 13]. Existing research approaches can be broadly categorized into three distinct groups: classical models, deep learning (DL) models, and causal inference-based methods.  
Classical models primarily rely on dimensionality reduction and assumptions of structural regularity. [PERSON], et al. [7] and [PERSON], et al. [5] have proposed that the OD ma trices exhibit low-rank properties, thus reducing the matrices to smaller manageable sizes amenable to traditional autoregressive modeling techniques. Similarly, [PERSON], et al. [13] and [PERSON], et al. [12] introduced a simplifying assumption that the ratio between station-to-station passenger flows and the total inflow at stations remains relatively constant, thereby significantly simplifying OD estimation once inflow data are known. Additionally, station-level embedding methods have also been applied to overcome dimensionality constraints [14], enhancing prediction effectiveness.  
In contrast, deep learning (DL) models have recently garnered substantial attention due to their capability to model high-dimensional and nonlinear spatiotemporal interactions. Temporal modeling typically leverages recurrent architectures, such as Long Short-Term Memory (LSTM) networks, to capture inherent temporal dependencies [15; 16]. Other innovative approaches have involved transforms and incorporating periodic characteristics of passenger flows across daily and weekly as different views [17]. Moreover, attention mechanisms have been increasingly integrated into these frameworks to dynamically emphasize significant temporal features [18]. In terms of spatial modeling, convolutional neural networks (CNNs) have been applied to uncover intricate spatial correlations embedded within historical OD matrices [19]. Graph neural networks (GNNs), exemplified by studies [20; 21], represent another prevalent approach, effectively modeling relationships among urban rail transit stations. Other research has implemented multi-resolution analysis through wavelet transforms to derive spatial features at varying granularities [22].  
Lastly, causal inference-based methods, although less common, offer interpretability through explicit modeling of causal relationships in passenger flows. Such approaches quantify how specific disruptions or external factors influence transit dynamics, enhancing the understanding of system resilience under disruptions. Dual-transformer architectures have also emerged, simultaneously capturing spatial-temporal relationships to improve OD flow predictions [23]. Nevertheless, despite numerous advancements, existing research predominantly emphasizes general operational scenarios, with insufficient exploration of incident and emergency conditions.
## 3 Plastics Used for Food Packaging
### Classification of Plastics  
Currently, the food packaging industry depends on fossil-based plastics which in turn originate from a finite raw material feedstock [8]. The finite resources issue induces a movement towards reducing the usage of virgin plastics towards a plastic production based on alternative raw materials such as renewable resources and biomass waste that have the potential to become plastic alternatives [44; 94].  
Next to conventional plastics that are fossil-based and non-biodegradable (e.g., PE, PP, poly(ethylene terephthalate) (PET), \"bioplastics\" were developed. According to the European Bioplastics association, \"a plastic material is defined as a bioplastic if it is either bio-based, biodegradable, or features both properties\" [95]. Thus, bioplastics involve a range of materials that show different properties and applications [1]. Figure 1 illustrates the categories of the plastics used for food packaging applications.  
Thus, biodegradable (and even more compostability) is considered as a useful characteristic providing one option to reduce plastic waste. Biodegradation occurs when a product undergoes a significant change in chemical structure under specific environmental conditions. Biodegradable polymers can, for example, be decomposed to natural substances (CO\({}_{2}\) or methane (CH\({}_{4}\)) and water (H\({}_{2}\)O)) by microorganisms that are found in the environment like algae, fungi, and bacteria [1; 89; 96; 8]. As biodegradation depends on the chemical structure of the material compound rather than on its origin, the basis of biodegradable plastics are not necessarily renewable resources [1; 97; 98].  
Biopolymers such as proteins, polysaccharides, and lignin are natural polymers produced by the cells of living organisms (e.g., forestry and agricultural crops, terrestrial and marine animals) [29; 30]. These biopolymers can be used for the manufacturing of packaging materials and therefore have a high potential to replace synthetic plastics [99; 100]. Most common biopolymers currently used for food packaging applications are synthesized [34; 83; 101; 102].  
In the following subchapters, the review focuses on different groups of bio-based polymers and their characteristics regarding food packaging applications starting with synthetically manufactured polymers using natural monomers (Section 3.2.) followed by polymers isolated from renewable resources (Section 3.3).
# Improving the Solving of Optimization Problems:
## 4 Quantum Annealer  
Quantum annealers [10, 11] are special-purpose quantum computers designed to solve QUBO problems. They are defined as analog quantum computers because they exploit quantum systems' continuous, analog properties to explore the problem solution space. Unlike digital quantum computers, which rely on discrete operations and gates, quantum annealers use smooth, gradual changes in physical parameters (such as magnetic fields) to steer the system toward a low-energy solution. They are the physical implementation of the quantum annealing optimization process (Section 4.2), which is based on Adiabatic Quantum Computing (Section 4.1).
## 3 Results
### ASE Triggers Different Cell Death Features  
Active ingredients of _Artemisia_ species were shown to induce various cell death modes (apoptosis, autophagy, ferptptosis, necrosis, necroptosis, and oncosis) through downregulating specific signal transducers [28]. Thus, ASE's cytotoxicity toward NSCLC cells was evaluated by a flow cytometry analysis. The results of Annexin/7-AAD staining revealed that the ASE induced a dose-dependent increase in cell death but displayed different patterns in the two cell lines (Figure 2A). ASE treatment at the higher concentration caused A549 cells to cluster in the upper left quadrant (7-AAD+, Annexin V\(-\)) from 1.9% \(\pm\) 0.7% to 34.8% \(\pm\) 4.8%, whereas the apoptotic rate was significantly higher in the positive control group (Docetaxel (DTX)). In contrast, in H23 cells, ASE induced early and late apoptosis (in the right quadrant; Annexin V+) from 11.1% \(\pm\) 3.3% to 28.9% \(\pm\) 3.5% at the higher concentration of ASE. These results indicate that ASE triggered more apoptotic death in H23 cells, whereas A549 cells displayed a more necrotic form of cell death. This was consistent with the data analyzed in Figure 2B. This conclusion was proven by a Western blot analysis with the potential implication of apoptosis-related proteins, where the ASE (100 and 200 ug/mL) dose-dependently reduced proaspase-3 expression after 48 h, while concurrently increasing cleavage of caspase-3 in H23 cells (Figure 2C). In A549 cells, however, no significant change in proaspase-3 or cleaved caspase-3 levels was detected, even at the higher ASE dose (Figure 2D). These data suggest that the ASE may predominantly trigger caspase-dependent apoptosis in H23 cells as well as a caspase-independent non-apoptotic pathway in A549 cells. Consequently, both cell lines ultimately underwent cell destruction while concurrently showing distinct cell death features.  
Figure 1: Effect of _Artemisia sutolini_ethanol extract (ASE) on cell growth of different non-small cell lung cancer (NSCLC) cell lines. Lung cancer cells were treated with different concentrations of the ASE for 24, 48, and 72 h. Cell viability of A549 (**A**) and H23 cells (**B**) was assessed by SRB assay. (**C**) The 50% inhibitory concentration (IC50) values were calculated at different time points. All data are the mean \(\pm\) SE of three replicate experiments. (**D**) Light microscopic changes of cellular shape during ferptptosis. A549 and H23 cells were exposed for indicated durations to 200 ug/mL. ASE and observed by phase-contrast microscopy. Ferroptotic cells, presenting a typical rounded “ballooning” phenotype morphology, are indicated by red arrowheads. (**E**) Morphological changes after ASE treatment evaluated with Liu’s stain. Black arrows indicate a few examples of rounded, dying cells. Representative fields are shown. Scale bar = 50 μm.  
Figure 2: _Cont._
# Robust Speed Tracking of Induction Motors: An Arduino-Implemented Intelligent Control Approach +
## 2 System Description  
For the velocity control of the V/f controlled IM, the system mainly consists of three parts: IM, V/f controller and speed controller. The block diagram of the closed-loop system structure is shown in Figure 1 (cf. Figure 7.18 in [2]).  
For the IM, its input phase voltages are transformed from three-phase stator rms voltage inputs \(v_{as}(t)\), \(v_{bs}(t)\) and \(v_{cs}(t)\), which are generated by the outputs of the V/f controller via a three-phase sine wave generator as follows:  
\[v_{as}(t)=V_{m}(t)\cdot\sin(\omega_{s}(t)\cdot t) \tag{1}\]  
\[v_{bs}(t)=V_{m}(t)\cdot\sin(\omega_{s}(t)\cdot t-\frac{2\pi}{3}) \tag{2}\]  
\[v_{cs}(t)=V_{m}(t)\cdot\sin(\omega_{s}(t)\cdot t+\frac{2\pi}{3}) \tag{3}\]  
where \(V_{m}(t)\) and \(\omega_{s}(t)\) denote the supply voltage and the electrical angular velocity, respectively. A simplified IM model represented by the input-output relation between the electromagnetic torque \(T_{m}(t)\) and the mechanical angular velocity \(\omega_{rm}(t)\) (rad/s) can be written as  
\[T_{m}(t)=J_{m}\frac{d}{dt}\omega_{rm}(t)+B_{m}\omega_{rm}(t)+T_{L}(t) \tag{4}\]  
where \(T_{L}(t)\) represents an external load torque; \(J_{m}\) and \(B_{m}\) stand for the rotor inertia and the coefficient of viscous damping, respectively.  
For the V/f controller, its inputs are \(f_{s}(t)\) and \(V_{0}\), which stand for a stator frequency command (Hz) and an offset voltage to counter the stator resistive voltage drop, respectively. The stator frequency command is set as  
\[f_{s}(t)=\frac{1}{2\pi}\cdot(\omega_{sl}(t)+\omega_{rm}(t)) \tag{5}\]  
where \(\omega_{sl}(t)\) is the slip speed. The outputs of the V/f controller are \(\omega_{s}(t)\cdot t\) and \(V_{m}(t)\). The input and output relations are given by  
\[\omega_{s}(t)\cdot t=2\pi\cdot f_{s}(t)\cdot\int dt \tag{6}\]  
and  
\[V_{m}(t)=K_{m}\cdot(K_{pf}\cdot f_{s}(t)+V_{o}) \tag{7}\]  
where \(K_{pf}\) is a constant ratio between the stator-phase voltage and the stator frequency, and \(K_{m}\) is a constant gain.  
Figure 1: Functional structure of speed control of a V/f controlled induction machine (IM).  
For the speed controller, its input is the difference error  
\[e(t)=\omega_{rm}^{ref}(t)-\omega_{rm}(t) \tag{8}\]  
and its output is the control command \(T_{e}(t)\) where \(\omega_{rm}^{ref}(t)\) denotes the reference speed (rad/s). The input of the limiter is \(T_{e}(t)\) and its output is given by  
\[\omega_{sl}(t)=\left\{\begin{array}{ll}\frac{R_{r}}{L_{r}+L_{s}}&\text{if }T_{e}(t)\geq\frac{R_{r}}{L_{r}+L_{s}}\\ -\frac{R_{r}}{L_{r}+L_{s}}&\text{if }T_{e}(t)\leq-\frac{R_{r}}{L_{r}+L_{s}}\\ T_{e}(t)&\text{otherwise}\end{array}\right. \tag{9}\]  
where \(R_{r}\) denotes the rotor resistance; \(L_{s}\) and \(L_{r}\) stand for the stator and rotor inductances, respectively.
# Unveiling Accurate Seismic Imaging through the Advanced Target-Oriented Kirchhoff Migration Method
## 3 Results and Discussions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Full-Scale** & **Target-Oriented** \\ \hline Grid parameters & 1200 \(\times\) 648 & 700 \(\times\) 401 \\ Computational time, min & 4545 & 2535 \\ Normalized time & 1.0 & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The comparison of computational costs between full-scale Kirchhoff migration and target-oriented Kirchhoff migration based on field dataset. The computational time is proven reduced significantly. Figure 12: A comparison of frequency band between full-scale surface image and target-oriented redatumed image. By performing seismic redatuming prior to seismic migration, the frequency band of redatumed image is enhanced. Consequently, this enhancement supports the generation of higher-quality images, as illustrated in Figure 11c compared to Figure 11b. It has been proven that the proposed workflow Indeed enhanced the quality of the migrated image within the reservoir level while also leading to a substantial reduction in migration costs. However, it is important to keep in mind that the quality of the proposed method relies on the quality of the seismic redatuming method. The quality of the redatumed data can be influenced by two factors: (1) well-sampled sources and receivers and (2) the accuracy of wavelet prediction. These input factors are what affect the quality of the redatumed data. Additionally, it is important to note that the proposed workflow has solely been tested with a streamer dataset thus far. To establish its robustness and applicability, it should be subjected to testing with various acquisition datasets, including but not limited to ocean bottom cable (OBC) datasets, which encompass both PP and PS data types. We believe further research is required to come out with a detailed workflow for target-oriented migration based on seismic redatuming especially for field dataset.
## 3 Advancements in Imaging and Augmented Reality (AR)
### Immersive Surgical Planning in the Metaverse  
Beyond intraoperative overlays, a new horizon is emerging with immersive virtual planning environments, often referred to as the surgical \"metaverse\". Using VR headsets and high-speed connectivity, multiple surgical stakeholders can now enter a shared virtual space to review and manipulate patient-specific 3 DVMs. [PERSON] et al. (2024) reported a novel application where avatars of surgeons and moderators discussed partial nephrectomy cases in a fully virtual meeting room, interacting with hyperaccurate kidney models and simulating resection and clamping strategies prior to the actual surgery [20]. This technology not only overcomes geographic barriers but enhances spatial orientation and team collaboration. Preliminary findings suggest that such preoperative discussions can enrich planning quality and contribute to more precise surgical execution.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 6 Discussion
### Stability Versus Plasticity in Federated Learning  
The two benchmark tasks provide a clear illustration of the stability-plasticity trade-off in FL. On the highly skewed UNSW-NB15 intrusion task, FedAvgSurfiers from large oscillations in accuracy due to disjoint class distributions. Introducing entropy regularization via ERFO dramatically smooths these oscillations, yielding both greater stability and improved minority-class recall.  
By contrast, on PneumoniaMNIST each client already possesses both classes, and the task is simpler. Here, FedAvg is inherently stable (Section 5.2, Figure 2), and ERFO's impact is essentially neutral: it preserves the model's _plasticity_ to learn local patterns while maintaining stability. Crucially, ERFO does not over-regularize in this scenario. Despite the entropy term, clients still fully fit their local data (which aligns with the global objective) and achieve high final accuracy.  
Compared with FedProx--which enforces stability by uniformly constraining the norm of every update--ERFO's advantage is its _adaptive_ regularization. In UNSW-NB15, FedProx attains strong stabilization and the highest overall accuracy, but ERFO attains a better balance of per-class performance (Table 2). In PneumoniaMNIST, FedProx converges more slowly than both FedAvg and ERFO (Table 3), since its rigid norm penalty unnecessarily limits beneficial client updates. ERFO, in contrast, allows large updates, provided they remain _information-rich_ (high-entropy), thus avoiding undue interference with local learning.  
In summary, ERFO achieves stability when it is needed--by damping excessively concentrated updates--while retaining plasticity when data are naturally well-aligned. This makes ERFO a more flexible form of regularization: it does not uniformly shrink all updates, but rather shapes their distribution to strike an effective stability-plasticity balance.
# Optimal Base Station Density of Dense Network:
## 3 The Expected Link Rate of the SmBS Tier
### Distribution of the Distance from a User to Its Serving BS in the SmBS Tier  
Without loss of generality, we assume a user is at the origin. In the SmBS tier, since the transmit power in the same tier is identical, the SmBS closest to the user offers the strongest received power so the user will associate with it. \(R_{\rm m}\) denotes the distance between them. Its distribution can be obtained from the following lemma.  
**Lemma 1**.: _The probability distribution function (PDF) of the distance from the user to its serving SmBS is_  
\[f_{R_{\rm m}}\left(r\right)=2\pi\lambda_{\rm m}r\exp(-\pi\lambda_{\rm m}r^{2}). \tag{4}\]  
Proof of Lemma 1.: The result can be proofed by modifying the proof of Lemma 3 of [24].  
Equation (4) indicates that \(R_{\rm m}\) is more likely to be small as the density \(\lambda_{\rm m}\) increases. This is because users can find and connect to an SmBS more easily when the SmBS network becomes denser.
# Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA
## 5 Proposed Algorithm
### System Model  
We consider one BS and \(K\) cellular users as \(UE_{1}\), \(UE_{2}\),..., \(UE_{K}\). Among them, \(UE_{1}\) is the closest to the base station with the best channel conditions, and \(UE_{K}\) is at the edge of the cell with the worst channel conditions. In the two-stage NOMA-D2D model, \(UE_{2}\) can act as a relay for \(UE_{1}\), while \(UE_{2}\) can directly communicate with \(UE_{3}\) as D2D transmitter. In the first stage, the BS sends signals to \(UE_{1}\) and \(UE_{2}\) in NOMA mode; in the second stage, \(UE_{2}\) acts as a relay and D2D transmitter and also sends signals to other users in GADIA mode.