esa-sceva/satcom-chunk-collection huggingface dataset:
# Vertical Seismic-Profile Data Local Full-Waveform Inversion Based on Marchenko Redatuming
## 2 Principles and Methods
### Marchenko Source-Receiver Redatuming with VSP Data
Then, we also iteratively compute the focus function \(f^{\pm}(\mathrm{x}_{i},\mathrm{x}_{s},\omega)\) by the standard Marchenko method, where \(\mathrm{x}_{i}\) (X: 3500 m; Z: 4170-5020 m) is an array of virtual receivers along the left vertical white line in Figure 1. We substitute \(G(\mathrm{x}_{j},\mathrm{x}_{s})\) and \(R_{VSP}(\mathrm{x}_{j},\mathrm{x}_{s})\), respectively, into Equation (16) to estimate the source-receiver redatumed Green's functions \(G(\mathrm{x}_{i},\mathrm{x}_{j})\) between the virtual source \(\mathrm{x}_{j}\) and virtual receivers \(\mathrm{x}_{i}\). The results of the standard [PERSON] source-receiver redatuming method and the Marchenko source-receiver redatuming with VSP data are shown in Figure 5. As a benchmark, Figure 5c shows the actual signal received by the \(\mathrm{x}_{i}\) with the source at point \(\mathrm{x}_{j}\). It can be clearly noticed in Figure 5 that the standard Marchenko method cannot retrieve the laterally propagating wave components at the locations indicated by the red arrows. However, the VSP Marchenko method is consistent with the recorded signals. This proves that the results of the Marchenko method with VSP data can accurately reconstruct the lateral propagation waves. In the next section, we will describe how to incorporate the VSP Marchenko redatum data into the local FWI process to improve the accuracy of the inversion results.
## 4 Experimental Results
Figure 2: The impulse response used in simulation below. increment of \(\sigma\). The PAPMCC algorithm had high estimation error when \(\sigma\) took a larger value since MCC behaved similar LMS when the value of \(\sigma\) was very large. Thirdly, the effects of \(\mu\) on the convergence for the PAPMCC algorithm was investigated using colored noise as the input signal. From the above simulation results, \(\sigma=1.0\) was selected, and other parameters were the same as the first experiments, and the results are presented in Figure 5. Parameter \(\mu\) controls the convergence speed of the PAPMCC. With the increment of \(\mu\), the normalized misalignment was decreased, while the convergence rate became fast. Consequently, the parameters \(\mu\) and \(\sigma\) are supposed to be reasonably selected in practical application. Figure 4: The effects of the kernel width \(\sigma\) on PAPMCC algorithm. Figure 5: The effects of the step size \(\mu\) on PAPMCC algorithm.
# In Vitro Effect of Putty Calcium Silicate Materials on Human Periodantal Ligament Stem Cells
## 1 Introduction  
In our daily practice, it is common to find in some problematic cases with lesions such as external or internal resorption, iatrogenic perforations, or periapical lesions that have a doubtful prognosis especially when there is a radiolucent area adjacent to the perforation site, and therefore it is necessary to perform apical surgery with a retrograde root filling [1; 2].  
Periapical surgery is the last option to save a tooth before performing the extraction when a periapiapical lesion cannot be managed with conventional endodontic retreatment. To achieve the treatment, the material is placed in the root-end cavity sealing the entire area [3; 4; 5]. This area is where the stem cells from the periodontal ligament (PDLSCs) are located and are in direct contact with the retrograde root filling material [6; 7]. The interaction between the PDLSCs and the material should improve the regeneration of the area; several studies have proven that it favors the healing of the cement, alveolar bone, gingiva, and periodontal ligament [8; 9]. Therefore, it is crucial to conduct cytocompatibility studies of these calcium silicate-based materials and because it is a complicated and challenging treatment, we need the properties of the materials to be optimal [10].  
Over the years, the most commonly used materials to perform a retrograde root filling have been mineral trioxide aggregate (MTA), Biodentine, amalgam, super ethoxy benzoic acid (super EBA). However, new materials are appearing on the market, such as calcium silicate materials [2].  
Calcium silicate-based materials with numerous properties are appearing on the market that makes them ideal for these cases [11]. Calcium silicate materials are cements with a formulation based on MTA-like material or with minor modifications. Different cements have been studied in the last years and have demonstrated their numerous advantages [12; 13]. So far, there is no specific material that meets all the necessary properties (antimicrobial, physical, chemical, and ease of handling) for these situations [14; 15; 16]. It is important to note that one material with all these requirements would cause an improvement in the prognosis of these treatments.  
Bio-C Repair (Angelus, Londrina, Pr, Brazil) (BCR) is available in an individual syringe and TotalFill BC RRM putty (FGK, Dentaire SA, La-Chaux-de-fonds, Switzerland) (TFB) is available in a jar; both have an excellent consistency to facilitate handling unlike MTA or amalgam, and according to the manufacturer, are bioactive, antibacterial, nonstaining, and promote healing. Some tests were used to check whether these substances are cytotoxic [17].  
This study aims to compare the cytotoxic effects and the bioactivity potential of two premixed materials used in retrograde root filling surgery such as BCR and TFB with PDLSCs.
# Influence of Fatigue and Defensive Pressure on Three-Point Jump-Shot Kinematics in Basketball
## 4 Discussion  
The aim of this study was to investigate the influence of fatigue and defensive pressure on the kinematic parameters of the three-point jump shot in basketball. The results of this study provide valuable insights into the effects of fatigue and defensive pressure on the kinematic parameters of the three-point jump shot in basketball. Both factors significantly influenced various aspects of shooting mechanics, reinforcing the importance of considering these conditions in training and game preparation.  
The fatigue protocol required maximal effort from the players, resulting in an average heart rate (HR) of 187.07 beats per minute (BPM) at the end of the test. The highest HR during shooting was observed in condition 4 (CON4), where players shot after reaching fatigue and faced a defensive player, with an average HR of 186.66 BPM. Previous studies reported that peak HR during live game situations can reach approximately 180 BPM [20; 31], confirming that the fatigue levels induced in this test are comparable to those experienced in real-game scenarios.
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 5 Experiments with Real Data
### Data Processing  
#### 5.1.1 Camera Calibration  
In this study, the Camera Calibrator toolbox [23] in MATLAB was used by [PERSON] for the checkerboard grid calibration, and the internal reference matrix was obtained as follows:  
\[\begin{bmatrix}6377.341875&0&2994.3\\ 0&4251.56125&1978.42\\ 0&0&0\end{bmatrix}\]  
The distortion parameter was as follows:  
\[\begin{bmatrix}-0.03484541&0.06374797&-0.01231441&-0.00002336&0.00036276\end{bmatrix}\]  
The overall error in calibration did not exceed 0.7 pixels, and the average error did not exceed 0.4 pixels.
In [19], the authors presented a silicon-based chip, creating a \"lab-on-a-skin\" platform that enables the measurement of the K\({}^{+}\) ion concentration in the sweat in real time (interval of 20 min demonstrated in the article). An elastomeric styrene block copolymer was used in the paper [20] to create a stretchable microfluidic and electronic system which can be applied on the skin and can collect and measure sweat chloride levels in humans in an aquatic medium. A patch-like sensor was proposed in [21] for measuring the sweat secretion rate and total ionic charge concentration using an interdigitated electrode structure embedded in a microfluidic channel. In reference [22], the authors presented platforms composed of a triple-electrode array patterned on a PET substrate and connected with an electronic circuit on the printed circuit board for sensing caffeine from sweat. In paper [23], a CO thread was used to guide the sweat sample to the paper-based sensor, and with the assistance of a smartphone, the glucose concentration was monitored by observation of colorimetric changes. Nowadays, many people worldwide suffer from some type of cancer. One of the symptoms is night sweats [24]. Treatment with cytostatic drugs is still a part of standard protocol in the vast majority of hospitals. Keeping this in mind, it is important to investigate the concentration of cytostatics in sweat as a noninvasive procedure for subjects. This is significant not only for the estimation of drug treatment efficacy, but also for the explanation of some skin-related side effects, such as [PERSON]'s disease, which appears as a consequence of the concentration of chemotherapy drug metabolites in sweat [25; 26]. However, the above-mentioned solutions demand complex and costly technological processes for manufacturing of the whole device, and the systems are not based on textile materials. In this study, a textile-based microfluidic platform fabricated using a xerographic technique for fast determination of the cytostatic drug concentration in artificial sweat is discussed. The core of the multilayered microfluidic structure is CO or PET fabric coated with PANI/TiO\({}_{2}\) nanocomposites. In situ polymerization of PANI/TiO\({}_{2}\) nanocomposites imparted different conductivity/resistivity values to the textile substrates. The microfluidic platform provides a fast tool for sweat manipulation, and the conductivity of the textile composite is changed with various sweat samples soaking the textile material. The performance of the platform was tested through monitoring different concentrations of the cytostatic drug cyclophosphamide in artificial sweat. The examination was realized by measuring the variation of electrical resistance at two terminals of the created microfluidic device in order to provide a proof of concept.
## 8 Experiments
### Results  
#### 8.3.1 Statistical Models  
Character-based statistical models were taken as the baselines. The evaluation of different datasets is presented in Table 7. The best WER was obtained on the Artur_J dataset, and the worst was on Gos. The same conclusions were obtained with BLEU and chrF metrics. CER shows a different perspective of the obtained results. The best CER was obtained on the GosVL dataset and the worst on the Artur_N dataset. CER takes into account that even if normalization at the word level is incorrect, only certain letters might be wrong. This information is particularly important if automatic normalizations are manually corrected afterward. CER provides an estimate of the required effort for manual corrections.  
Table 8 reports the results of statistical models when using words as basic units. Using words instead of characters changed the context, which was considered in the next token prediction. All metrics indicated improved results for the datasets Gos, Artur_N, and All. All metrics except for CER indicated improved results for the dataset GosVL as well. Word-based models are a better choice than character-based models if we have enough training data and if the training data contain all (or almost all) instances of spoken forms that differ from the standard forms. Word forms that are the same in spoken language as in the standardized language do not pose a problem, as the translation model copies them from spoken to normalized language unchanged.  
Using subword models, we aimed to reduce the problem of unknown word forms in spoken language. The results are collected in Table 9. According to WER, all subword models improve the baseline results of the Artur_N dataset. Subword-based models with vocabulary sizes of 5000 and 1000 also improve the baseline for the Gos dataset. According to CER, all subword-based models improve the baselines for all datasets, except GosVL.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Dataset** & **WER [\%]** & **CER [\%]** & **BLEU** & **chrF** \\ \hline GosVL & 3.42 & 0.82 & 91.86 & 97.49 \\ [PERSON] & 5.21 & 3.70 & 88.36 & 95.61 \\ [PERSON]\_J & 2.34 & 2.79 & 94.34 & 98.29 \\ [PERSON]\_N & 4.29 & 4.01 & 89.82 & 96.39 \\ [PERSON]\_P & 2.76 & 3.35 & 93.47 & 98.20 \\ All & 3.87 & 3.34 & 90.97 & 97.00 \\ \hline \end{tabular}
\end{table}
Table 7: Evaluation of character-based statistical models.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Dataset** & **WER [\%]** & **CER [\%]** & **BLEU** & **chrF** \\ \hline [PERSON] & **2.63** & 2.86 & **93.67** & **97.96** \\ [PERSON] & **3.75** & **3.32** & **91.48** & **96.55** \\ [PERSON]\_J & 2.62 & 2.88 & 93.72 & 97.85 \\ [PERSON]\_N & **3.83** & **3.88** & **91.16** & **96.51** \\ [PERSON]\_P & 3.70 & 3.80 & 90.28 & 97.06 \\ [PERSON] & **3.24** & **3.25** & **92.33** & **97.19** \\ \hline \end{tabular}
\end{table}
Table 8: Evaluation of word-based statistical models. Results that surpass the baseline are highlighted in bold.
## 4 Experimental Results
### Real-World Experimentation and Validation  
To corroborate the simulation findings and validate the practical applicability of the proposed scheduling approach, experiments were conducted on the real-world TSN testbed described in Section 4.1.2, utilizing the ZIGGO platform.  
In a representative experiment, the Gate Control List (GCL) generated by our algorithm was deployed to the FPGA-based TSN switch. The test scenario involved a high-rate Time-Triggered (TT) stream, configured to transmit 1500-byte packets, and lower-priority background traffic, all scheduled within a 33 ms hyperperiod. The GCL allocated specific, protected time slots for the TT stream to ensure its deterministic delivery.  
The key performance metrics from this real-world experiment are summarized in Figure 8. Subplot (a) confirms that the prerequisite of precise time synchronization was met, while subplot (b) presents the end-to-end latency performance of the TT stream.  
The analysis of the real-world results provides strong validation for our proposed scheduling algorithm:  
1. Time Synchronization Efficacy: As shown in Figure 8a, the gPTP time synchronization mechanism functioned effectively on the hardware testbed. The plot illustrates that the clock offset between nodes rapidly converged from an initial larger value and remained stable within a tight bound of approximately \(\pm 50\) ns, establishing the high-precision, shared sense of time required for TAS execution.
2. Deterministic Latency and Low Jitter: Figure 8b presents the latency distribution for thousands of received TT packets. The histogram reveals an extremely narrow and sharp peak, indicating highly consistent end-to-end latency. The data, extracted and analyzed from the testbed logs, shows a mean latency of approximately 26.24 us. Critically, the variation around this mean (the jitter) was exceptionally low, with the vast majority of packets arriving within a 100 ns window.  
The results from the physical testbed align with the simulation findings. They confirm that the GCLs generated by our algorithm, when deployed on real TSN hardware, successfully enable the deterministic transmission of time-critical data streams with low, predictable latency and extremely low jitter, validating the practical effectiveness of our approach. This successful validation on physical hardware provides strong confidence in the applicability of our scheduling framework for real-world deployments of future internet technologies.  
Figure 8: Performance validation on the real-world TSN testbed. (**a**) Time synchronization performance, showing the clock offset between a slave node and the gPTP master converging to and remaining within a tight nanosecond-level bound. (**b**) End-to-end latency distribution for the received Time-Triggered (TT) stream, illustrating extremely low jitter.
# Soy Isoflavones and PCOS: Role in Hormonal and Metabolic Mechanisms
## 4 Polycystic Ovary Syndrome and Soy Isoflavones
### Endocrine Mechanisms  
#### 4.1.1 Hyperandrogenism  
The main feature of the three PCOS phenotypes (A-C) is an above-normal level of androgens, which triggers other characteristic symptoms such as menstrual disorders, infertility, excessive hair growth, acne, and many others [94; 101; 102]. In women of reproductive age without hormonal disorders, androgen production occurs in similar amounts in the ovaries and adrenal glands, and the main androgens are androstenedione and testosterone [101; 102]. When steroidogenesis is disrupted, as in PCOS, the ovaries and, less commonly, the adrenal glands produce excessive amounts of androgens [101]. The causes of ovarian dysfunction in this regard remain unclear [95; 101; 102; 103; 104]. Some studies indicate that the cause of functional ovarian hyperandrogenism (FOH) is the overexpression of mRNA of steroidogenic enzymes in isolated theca cells, especially cytochrome P450c17 and luteinizing hormone (LH) receptors [105; 106]. This defect in theca cells leads to excessive androgen production due to higher activity of steroidogenic enzymes [105; 106]. Increased androgen secretion through various mechanisms is also observed in response to IR occurring in PCOS [95]. This condition affects both slim women with PCOS and those who are overweight or obese [107]. Excess insulin increases LH production, which is often elevated in women with PCOS. High LH levels stimulate excessive androgen production mainly in the ovaries (to a lesser extent in the adrenal glands), activating steroidogenic enzymes, including 17\(\alpha\)-hydroxylase, 17,20c-lyase, and cytochrome P450c17 [95; 108]. Due to the increase in LH levels, relatively low FSH levels lead to an increase in the imbalance between LH-dependent androgen synthesis and follicle-stimulating hormone (FSH)-stimulated oestrogen synthesis [108; 109]. Hyperinsulinemia also affects the decrease in the production of sex hormone-binding globulin (SHBG) in the liver, which in turn increases the level of free androgens [96; 110; 111]. Selected pathways leading to hyperandrogenism in PCOS are shown in the figure below (see Figure 5).  
Ovarian hyperandrogenism is considered to be the main mechanism leading to excess androgens in PCOS. However, functional adrenal hyperandrogenism (FAH) may also play a role, although it is more common in lean women with PCOS [101; 104; 112]. In FAH, serum levels of dihydroepiandrosterone sulphate (DHEAS), secreted by the adrenal cortex, are elevated. The causes, similar to FOH, are believed to be related to disturbances in steroidogenesis pathways through hyperinsulinemia, but also increased adrenal sensitivity to ACTH and, consequently, increased activity of the enzymes 17a-hydroxylase and 17,20
## 3 Congestion-Aware EFL-Based Model Aggregation Policy in Edge Computing
### System Architecture
#### 3.1.3 Controller Tier  
In this sub-section, we delve into the controller and virtualization layers, addressing their roles in adapting virtual edge model storage, network functions, and computing capacities in accordance with the proposed policy. The controller functions as an integrated virtualized infrastructure manager (VIM), overseeing the orchestration between pooled virtualization and physical resources. To efficiently apply policies to the primary controller, the incoming local model \(M_{n}\) with defined \(Y_{n}^{m}\) is channeled into a subordinate processing module. This module pre-calculates metrics such as size-based processing time (\(pt\Big{[}M_{n(t)}\Big{]}\)), tolerable latency (\(tl\Big{[}M_{n(t)}\Big{]}\)), and service criticality (\(sc\Big{[}M_{n(t)}\Big{]}\)) for the given model. These metrics enhance the hidden states of processing speed, latency tolerance, and service criticality. The resulting time series of model loading durations are stored for the purposes of congestion control and adaptive configuration adjustments. Within the subordinate module of each virtual EFL server, specific parameters are retrieved for the selected participant's models \(M_{n(t)}\), including the time slot (\(ts\Big{[}M_{n(t)}\Big{]}\)), buffer size (\(bs\Big{[}M_{n(t)}\Big{]}\)), and queuing index (\(qi\Big{[}M_{n(t)}\Big{]}\)). The observation of these parameters is enabled through SDN interfaces integrated into the system architecture. The gathered network status conditions at the current \(t\)-index are appended to the policy orchestration. Through the formulation of predictive models, the congestion levels are translated into three subsequent threads of action as follows:  
1. During non-congestion states, the offloading decision-maker modules adhere to the current policy, retaining incoming local models.
2. In cases of congestion, the scheduling and aggregation destinations for that specific time slot are altered by modifying forwarding rules within the primary controller.
3. For heavy congestion, adjustments are made to the virtual resource placement properties within each functional instance's descriptor. This adaptation is guided by the proposed policy and prioritized service types at that time slot.
# AgriMFLN: Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews
## 3 Materials and Methods
### Experimental Datasets
#### 3.2.2 The Private Dataset  
Considering the limited data volume of the public dataset, we utilized web crawling techniques to automatically obtain review data from e-commerce platforms named 'Jing-Dong' and constructed our own private dataset. We first specified search keywords and filtering conditions to ensure that the obtained review data were relevant to the agricultural products, and then removed the reviews which were too short or invalid. Finally, we annotated the sentiment polarity of the product reviews. The product reviews obtained through web crawling have the characteristics of large data volume, high authenticity, and high timeliness, laying the foundation for the subsequent experiments. We obtained 14,222 product reviews, including 10,013 positive reviews and 4209 negative reviews.  
Two datasets were used, respectively, for training and conducting testing. These comments were randomly divided into a training set and a test set, respectively, which are independent of each other, following a ratio of 8.2. The training sets were used to build the classification model, and then the model was used to test the data of the test set to evaluate the quality of the model. The dataset sample is shown in Table 2.  
Observing two datasets, we noticed that the length of agricultural product context text varies greatly. Some product reviews may simply provide a brief evaluation of the product, while others may have longer comments where consumers describe their purchasing experience with the agricultural product from multiple perspectives. These may include the fruit quality, the speed of delivery, the quality of service provided by the merchant, and the price of the agricultural product. These dimensions may all affect the emotional tendencies expressed by consumers in their comments.  
Additionally, we found that complex language is frequently used in agricultural product reviews to express emotions. The context is strongly related to the product, and consumers may not directly say that the product is poor, but rather use modifiers and metaphors to describe its quality. For example, the sentence \"I don't know if what I bought is juibbe or pear. Can this kind of business last? Don't tell me that the Xinjiang pears are supposed to be this big. Luckily, I'm not a first-time buyer or eater. This is just outrageous\" is a description of poor fruit taste and quality.