esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### NIPP affects the Cytoplasmic Membrane of Cancer Cells and Causes the Influx of FITC-Labeled Dextran  
To evaluate if NIPP exposure increased the cytoplasmic membrane permeability to low molecular weight compounds, breast cancer cells were incubated with FITC-labeled  
Figure 3: Basic NIPP characterization. (**a**) Superficial (FLIR One infrared camera, Teledyne Flir) and (**b**) submerged (Lab Thermometer IP65 with an LT-101 temperature sensor, TFA Dostmann) temperature variations of cell culture medium at room temperature during 120 s of NIPP treatment. (**c**) Flame pattern after ignition of the NIPP flame at carrier gas flow rates from 1.0 to 6.5 L/ min. The flow rates are shown in the device’s own display. (**d**) H\({}_{2}\)O\({}_{2}\) formation after NIPP treatment. Ultrapure water was treated with NIPP at flow rates of 4.0, 4.5, and 5.0 L/ min and treatment durations of 5 to 80 s. The relative H\({}_{2}\)O\({}_{2}\) concentrations were determined by a Molecular Probes Amplex Red Hydrogen Peroxide/Peroxidase Assay Kit (Thermo Fisher Scientific).  
10 kDa dextran after 20 and 60 s of NIPP treatment. Microscopic analysis of the intracellular FITC signal revealed that significantly higher amounts of dextran-FITC penetrated the cells after both NIPP treatment durations (Figure 4a,c). This effect was observed for both cancer cell lines, MCF-7 and MDA-MB-231, although no dependence on NIPP exposure time was observed (Figure 4b,d).  
NIPP-Induced Alteration of the Cytoplasmic Membrane Enables the Efflux of Non-Membrane-Permeable Fluorescein  
In the following, data were confirmed using an alternative technique. NIPP-treated cells were loaded with fluorescein diacetate (FDA), washed, and subsequently analyzed by flow cytometry. FDA is particularly suitable for such studies because the lipophilic FDA is converted intracellularly by cellular esterases to the hydrophilic, membrane-impermeable dye fluorescein, which accumulates in the cell [19].  
Flow cytometric evaluation of the FDA-loaded cells showed that they released the fluorescent dye significantly after NIPP treatment in comparison to argon-treated control cells (Figure 5). Although the enzymatic cleavage of FDA to fluorescein should have prevented the dye from bleeding out of the cells, the dye accumulated significantly less in the breast cancer cells after 20 and 60 s of NIPP treatment. Again, the NIPP exposure time did not seem to affect the strength of the effect.  
Figure 4: Microscopic dextran-fluorescein isothicyanate (FITC) uptake assay. Breast cancer cells MCF-7 (**a,b**) and MDA-MB-231 (**c,d**) were seeded on coverslips, treated with NIPP for 20 and 60 s, and stained with 10 \(\upmu\)L of dextran-FITC and 100 \(\upmu\)L DAPI for 2 min. Cells were analyzed by fluorescence microscopy (BZ-9000 microscope with BZ-II analyzer software, scale bars represent 50 \(\upmu\)m). For analysis, the FITC-positive area was determined and correlated to the number of DAPI-positive nuclei (FITC signal/cell). The results are displayed as mean \(\pm\) standard deviation (\(p\leq 0.05\)*; \(p\leq 0.01\)**; \(p\leq 0.001\)**).
# Experimental Detection of Nonlinear Dynamics Using a Laser Profilometer
## 1 Introduction
In this work, the experimental time domain responses obtained from non-zero initial conditions are measured using a Keyence Laser profilometer, conventionally adopted for product shape detections in online industrial applications [38; 39; 40]. Experimental tests are carried out and the frequency contents are examined to spot the nonlinear characteristics with respect to the linearised configurations of the structure. Moreover, numerical simulations with LUPOS [41; 42] are performed to fully interpret the obtained results. Finally, experimental test frequency contents are post-processed with the use of different time-frequency analysis approaches: the Fast Fourier Transform (FFT) converting signals to frequency domains as the summation of harmonics; the Continuous Wavelet Transform (CWT) [43; 44; 45; 46], now widespread in Structural Health Monitoring [47; 48], which employs fundamental waves called \"wavelets\" of various shapes, allowing the description of different time scale phenomena occurring; and the Hilbert transform [49; 50] allowing the monitoring of instantaneous frequency of nonlinear single DoF systems. The paper is organised in sections. In Section 2, the experimental test-rig is shown with attention to the system equipment. In Section 3, the experimental results are depicted and discussed; the FFT and the CWT time-frequency analyses are adopted and compared in results; also, Hilbert transform and Finite Element models of linearised configurations of the system are adopted in the analysis. Finally, some comments on this work are discussed in the conclusions in Section 4.
## 3 Long Short-Term Memory Network  
An LSTM network is a type of RNN. The LSTM network has been effectively used in the field of deep learning. The main advantage of an LSTM network is ease of training because it does not face problems such as gradient vanishing or exploding. LSTM can process entire input (source code, video,speech, image) sequences. An LSTM network memory unit consists of four attributes, namely a forget gate, a cell state, an input gate, and an output gate. The cell state remembers the information of the entire sequence and the three gates control the input and output of the cell, as shown in Figure 1.  
At the start of the process, the forget gate checks which information to throw away and which information to keep in the cell state. Equation (1) is used for the forget gate. It is calculated at cell state \(c_{t\text{-}1}\) using hidden state \(h_{t\text{-}1}\) and input \(x_{t}\). The output of the forget gate, between 0 and 1, is produced by the sigmoid function. An output value of 1 (0) means keep (remove) all information in (from) the cell state.  
\[f_{t}=\sigma\big{(}W_{f}[h_{t-1},x_{t}]+b_{f}\big{)} \tag{1}\]  
To store a new piece of information in the cell state, the input gate decides which value will be updated using the sigmoid function. The tanh function creates a new candidate value \(\overline{c_{t}}\) for the cell state.  
\[i_{t}=\sigma(w_{i}\,.\,[h_{t-1},x_{t}]+b_{i}) \tag{2}\]  
\[\overline{c_{t}}=\tanh(w_{c}\,.\,[h_{t-1},x_{t}]+b_{c}) \tag{3}\]  
Then, the old cell state \(c_{t\text{-}1}\) is used to update \(c_{t}\).  
\[c_{t}=f_{t}\,*\,c_{t-1}+i_{t}\,*\,\overline{c_{t}} \tag{4}\]  
We can now calculate the output of LSTM, which is based on a filtered version of the cell state. The sigmoid function decides which part of the cell state is going to the output and then updates the weight accordingly.  
\[o_{t}=\sigma(w_{o}\,.\,[h_{t-1},x_{t}]+b_{o}) \tag{5}\]  
\[h_{t}=o_{t}\,*\tan\h(c_{t}) \tag{6}\]  
The combination of the attention mechanism with LSTM improves model performance for fault assessment and detection and the classification of source code.
## 2 Materials and Methods
### Binary Semantic Segmentation
#### 2.1.4 Metrics  
To quantitatively assess the performance of our training strategies and facilitate comparison, the predictions of our networks were compared to the ground truth by calculating the following well-established segmentation metrics: the Jaccard index (JI) and the Dice coefficient (DC). The Jaccard index evaluates the similarity between two sets by evaluating the ratio of the size of their intersection to the size of their union.  
\[JI(A,B)=\frac{\left|A\cap B\right|}{\left|A\cup B\right|} \tag{1}\]  
The Dice coefficient serves as a metric for quantifying the similarity between two sets. Specifically, it is calculated by dividing the size of the intersection of the sets by the average size of the sets.  
\[DC(A,B)=\frac{2\cdot\left|A\cap B\right|}{\left|A\right|+\left|B\right|} \tag{2}\]  
In the given context, set A comprises pixels labeled as positive (e.g., bone pixels) in the ground truth, while set B encompasses pixels predicted as bone by the CNN being evaluated. Both coefficients have a range from 0 to 1, with 1 being the optimal value.
## 4 Qualitative Findings and Discussion
### Key Design Challenges in Improving LSAD
#### 4.1.3 Comfort  
The shared perspective among participants was that the majority of LSAD currently available are excessively heavy, contributing to issues such as poor breathability. This issue primarily arises during the summer, with some participants experiencing varying degrees of skin problems due to moisture and heat accumulation around the waist: \"I don't think I can use it at all in the summer because its heat dissipation is so poor, [...] it's very thick, like wrapping a wet towel around, I feel like my waist is about to rot\" (Participant 7). In addition, female participants highlighted the significance of the edge material and craftsmanship of LSAD, particularly during the menstrual period: \"You may not know, but some LSAD have either rough or hard edges, and that spot under the breasts is uncomfortable to be pushed up against, especially during a girl's menstruation [...] I hope that the design will be more considerate and not bring extra discomfort to women\" (Participant 14).  
The in-depth investigation into LSAD user experience has afforded us an opportunity to delve into the personal perspectives of individuals suffering from lower-back pain, enabling us to explore novel insights beyond anticipated or previously known factors. This overarching theme reinforces participants' desire to achieve favorable rehabilitation outcomes through the improvement of LSAD design. However, negative experiences during the wearing process have, to some extent, impacted the perceived efficacy of LSAD, thereby diminishing participants' confidence in sustained usage. Participants typically associate stability and fit with the effectiveness of LSAD. Therefore, enhancing stability and fit may potentially increase compliance in patients with lower-back pain during usage. This can be achieved through design measures. For stability, we recommend a particular focus on the relationship between LSAD and the lower body/pants. Regarding fit, patients should be allowed to make minor adjustments to the hard plastic for a better fit.  
Additionally, according to the results of QUEST 2.0, comfort stands out as the most prominent issue. Therefore, in the design process, attention should be paid to the selection of materials. This may involve the use of soft and breathable materials, as well as materials with anti-microbial and odor-resistant properties, to ensure that patients with lower-back pain feel comfortable while wearing LSAD [38], thereby increasing both their willingness to use it and its effectiveness. In the design of LSAD edges, gender differences should be taken into full consideration to minimize inconvenience for female patients.  
However, unexpectedly, almost all participants reported the presence of the three key experiential issues with their LSAD. Nevertheless, a majority of participants expressed their intention to continue using it, and we attribute this phenomenon to users deriving optimal benefits from it [31].
## 4 MTAPSP Model
### Multilayer LSTM Based on a Multi-Head Self-Attention Mechanism
#### 4.2.1 Multilayer LSTM  
Traditional recurrent neural networks (RNN) suffer from the problem of gradient vanishing for long sequence data like OULAD, in contrast, LSTM can efficiently convey and express the information of long-time sequences and solve the problem of long-term dependency [4]. As shown in Figure 2, compared to single-layer LSTM, a three-layer LSTM can enhance the extraction of long-distance memory and contextual information, better capture the long-distance dependencies in the sequence of students' learning time behaviors, and improve the model's generalization ability and robustness.  
The input data \(X_{t}\in R^{t\times m}\) is a two-dimensional tensor, where \(t\) represents the number of course days for a student, and \(m\) denotes the number of features in the input data. LSTM uses a gating mechanism to control the flow of information by feeding data \(X_{t}\) into the LSTM network, which sequentially goes through the computational process of forgetting gates, input gates, and output gates in LSTM. In the forget gate, LSTM integrates new input data and the output from the previous time step to determine the omission of irrelevant information. The computational formula is expressed as Equation (1).  
Figure 1: MTAPSP model structure.  
Figure 2: Three-layer LSTM network structure.  
\[f_{t}=\text{{Sigmoid}}(W_{f}[h_{t-1},x_{t}]+b_{f}) \tag{1}\]  
In the input gate, LSTM filters and updates the information by multiplying the current input data with the hidden state of the previous moment, which is calculated as shown in Equations (2)-(4).  
\[i_{t}=\text{{Sigmoid}}(W_{i}[h_{t-1},x_{t}]+b_{i}) \tag{2}\]  
\[\widetilde{C}_{t}=\text{tanh}(W_{c}[h_{t-1},x_{t}]+b_{c}) \tag{3}\]  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\widetilde{C}_{t} \tag{4}\]  
In the output gate, LSTM extracts relevant information from the vector obtained by integrating the current input data with the previous time step's output value, determining the information to be outputted. The computational formulas as shown in Equations (5) and (6).  
\[o_{t}=\text{{Sigmoid}}(W_{o}[h_{t-1},x_{t}]+b_{o}) \tag{5}\]  
\[h_{t}=o_{t}*\text{tanh}(C_{t}) \tag{6}\]  
The above equations, where \(b_{f}\), \(b_{i}\), \(b_{c}\), \(b_{o}\), \(W_{f}\), \(W_{i}\), \(W_{c}\), \(W_{o}\) are the biases and weights that correspond to the three gates, and denote the activation function.
# A Framework for Enhancing Big Data Integration in Biological Domain Using Distributed Processing
## 3 Literature Review
In addition, Wu-Palmer similarity measure is designed for simple concepts, but it does not consider how far the concepts are semantical. Moreover, semantic proximity is context-dependent, leading to uncertainty in cases where objects can be similar in one context and dissimilar in another. Therefore, these similarity measures are not suitable in some fields, such as biomedicine, where similarity measuring is not a simple task; it is achieved by comparing features that describe the objects in addition to the hierarchal relationships between these features. For instance, measuring the similarity between genes or gene products by comparing the gene ontology annotation terms is not enough since there is a relationship between the gene expression's and gene ontology's semantic similarity [53]. In addition, gene ontology annotations are not consistent where edges at one level may have various semantic measures; terms at the same level may have a different level of details, and nodes may have a variable density of terms [18]. Therefore, some semantic similarity measures are defined specifically for the biology field to measure the similarity between genes and gene's products. Moreover, the best SSMs illustrated in the background chapter work in a single ontology, which means that they cannot be used to calculate the semantic similarity of two genes located on two different ontologies. Therefore, we need to integrate these genes into a single ontology to be able to calculate their semantic similarity. According to the problems we mentioned previously, which are related to big data characteristics, the way in which previous work introduced upper-layer ontology or domain ontology and other issues was related to semantic similarity measures. This is a great opportunity to enhance the semantic big data integration process with a new big data integration framework that integrates big data in the biology domain using distributed processing. In addition, we can advance the biological domain with a new big biological ontology that can be used for further research and for calculating the semantic similarity between genes and gene products.
## 4 Results and Discussion
### Spatial Variation of Precipitation, Maximum Temperature, and Minimum Temperature  
4.3.1. Precipitation  
Precipitation shows considerable variation across the Upper Bhima sub-basin. The spatial distribution of average annual precipitation over the study area, for uncorrected output from the ACCESS1-3 CMIP5 GCM model and after applying various bias correction methods, is compared against the observed precipitation data, as shown in Figure 9. Figure 9a-f shows the spatial variation of the model-simulated raw precipitation data, observed precipitation data, LS-corrected, LOCI-corrected, PT-corrected, and DM-corrected data, respectively.  
There is a discrepancy in the pattern, as shown in Figure 9a, as there exists biases in the model-simulated raw precipitation data. It shows a clear underestimation of precipitation over the whole Upper Bhima sub-basin. The spatial variation of the model-simulated raw data shows only two classes over the watershed, ranging from 200 to 500 mm per year in the Western Ghat region and from 0 to 200 mm per year in the remaining area, which is significantly less compared to the observed data. On the other hand, after bias correction methods are applied, there is a remarkable enhancement in the model output as its magnitude approaches the observed values. Figure 9b shows the spatial variation of the observed precipitation data for various classes over the Upper Bhima sub-basin, ranging from 200 to 500 mm per year in the central region, which is the lowest rainfall, and increasing to 2500 to 3000 mm per year in the Western Ghat region.  
All the bias correction methods show a significant and similar level of ability in bringing the model output results closer when comparing the climatological distribution of annual average precipitation over the Upper Bhima sub-basin against the observed data for the uncorrected and corrected model data. Figure 9c-e are the spatial variations of the LS, LOCI, and PT correction methods and they show a similar spatial pattern of precipitation over the Upper Bhima sub-basin. But, Figure 9c-e do not show the class of lowest rainfall in the central part of the study area, which is 200 to 500 mm per year, as seen in the observed data (Figure 9b). The best improvement is seen in Figure 9f, showing the spatial variation of the DM-corrected precipitation data and relatively matching the pattern of spatial variation of the observed data, including all classes throughout the study area. Also, some central  
Figure 9: Spatial variation of annual average precipitation (mm/year) for the period from 1979 to 2005: (**a**) model-simulated raw data, (**b**) observed data, (**c**) LS-corrected, (**d**) LOCI-corrected, (**e**) PT method, and (**f**) DM method.  
and eastern parts of the study area show the lowest rainfall, which can also be seen in the spatial variation of the observed data (Figure 9b).
# Evaluation of Deviations Produced by Soft Tissue Fitting in Virtually Planned Orthopathic Surgery
###### Abstract  
Orthopathic surgery (OS) is a complex procedure commonly used to treat dentofacial deformities (DFDs). These conditions, related to jaw position or size and often involving malocclusion, affect approximately 15% of the population. Due to the complexity of OS, accurate planning is essential. Digital assessment using computer-aided design (CAD) and computer-aided manufacturing (CAM) tools enhances surgical predictability. However, limitations in soft tissue simulation often require surgeon input to optimize aesthetic results and minimize surgical impact. This study aimed to evaluate the accuracy of virtual surgery planning (VSP) by analyzing the relationship between planning deviations and surgical satisfaction. A single-center, retrospective study was conducted on 16 patients who underwent OS at San Pedro University Hospital of La Rioja. VSP was based on CT scans using Dolphin Imaging software (v12.0, Patterson Dental, St. Paul, MN, USA) and surgeries were guided by VSP-designed occlusal splints. Outcomes were assessed using the Orthopathic Quality of Life (OQOL) questionnaire and deviations were measured through pre- and postoperative imaging. The results showed high satisfaction scores and good overall outcomes, despite moderate deviations from the virtual plan in many cases, particularly among Class II patients. A total of 63% of patients required VSP modifications due to poor soft tissue fitting, with 72% of these being Class II DFDs. Most deviations involved less maxillary advancement than planned, while maintaining optimal occlusion. This suggests that VSP may overestimate advancement needs, especially in Class II cases. No significant differences in satisfaction were observed between patients with low (<2 mm) and high (>2 mm) deviations. These findings support the use of VSP as a valuable planningtool for OS. However, surgeon experience remains essential, especially in managing soft tissue behavior. Improvements in soft tissue prediction are needed to enhance accuracy, particularly for Class II DFDs.  
computer-aided design; computer-assisted manufacturing; computer-aided surgery; orthognathic surgery; dentofacial deformities; maxillofacial surgery; bioengineering; 3D printing
## 3 Empirical Data and Technical Parameters  
### Cost of Pollution Control and Effectiveness of Pollution Reduction  
The TAPCAP being implemented by the Taiwan EPA incorporates 14 pollution control measures; these include power sector regulations, installation of pollution control devices in factories, and replacement of heavy-oil boilers with gas boilers. Old diesel vehicles will also be retired, while newer vehicles will be equipped with smoke filters. As the implementation of the TAPCAP began only in 2017, its pollution control measures were only expected to be fully implemented in 2019. Therefore, it was expected that the pollution-reducing effects of the TAPCAP would start being realized in 2019. The technical costs and expected pollution reductions of each of the pollution control measures of the TAPCAP are listed in Table 1.  
The 14 measures included in the TAPCAP can be categorized as administrative or technical measures. Administrative measures pertain to the use of legal measures or administrative methods to reduce pollution. In this case, pollution reductions are derived from increments in technical efficiency, rather than the incorporation of pollution control technologies. For this reason, administrative measures do not require expenditure on pollution control devices. The measures that fall under this category include (1) regulation of fugitive dust from construction sites and stockpiles of dust-generating materials, (2) control of fugitive dust from riverbeds, (3) elimination of 1 million two-stroke motorcycles, and (4) introduction of new vehicle regulations. Technical measures require the use of various pollution control techniques, which need additional investments on equipment. These investment costs are defined as the technology cost of pollution control measures.
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
## 1 Introduction
The plausibility and suitability of the simulations are validated against field measurements. * To generate realistic synthetic waveform datasets for voltage notch studies, a Monte Carlo simulation approach is adopted. This method considers the dependence of key parameters and typical conditions of distribution networks, such as common short-circuit power levels, enhancing the realism and applicability of the generated data. This paper is organized as follows. Section 2 introduces the fundamentals of voltage notches. Section 3 presents the design of the simulation platform in Simulink. Section 4 analyses the interrelations, limitations, and impact of key parameters on voltage notch waveforms. Section 5 validates the plausibility and suitability of simulations against field measurements from German industrial LV networks. Section 6 proposes a Monte Carlo simulation approach to generate realistic synthetic waveform datasets, and a case study is developed considering the conditions of German LV networks. Section 7 provides a discussion of the key findings, limitations, and implications of this study. Finally, Section 8 provides the closing remarks and offers potential further research.