esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Uniform Design of Experiment  
Design of experiment (DOE) is the sampling plan within the design variable domain whereby experimental design levels are set for each of the contributing factors, and the objective is to obtain a limited number of simulations (due to computational expense) covering the entire design region.  
Figure 2: Decision variables and objective functions defining the optimization process of the integrated biobutanol production process.  
Factorial experimental design is commonly used to select experimental points within the design variables domain, thereby allowing for the determination of the relationship between the inputs of a process and its responses, which is encapsulated in regression models. When the relationship between the inputs and the responses of the process is nonlinear or the experimental domain is large, a two-level fractional design does not provide sufficient information to develop a representative model, because most of the information is obtained at the periphery of the experimental domain. To circumvent this limitation, uniform design (UD) can be used, where the design runs are uniformly and optimally scattered within the experimental domain using a small set of design points [19]. UD attempts to choose design points that cover the design region uniformly with limited experimental runs. A website published by Hong Kong Baptist University tabulates the design points for a uniform design, U\({}_{\text{n}}\)(q\({}^{\text{s}}\)), given the number of runs (n), factors (s), and levels (q) [20]. Each table provides the desired number of design points; each design point contains the number of factors (or decision variables) and their respective level, which can be used to determine the actual values of the decision variables using their allowable ranges. These design points (decision variables) are then used to generate the respective objective functions. In this investigation, uniform design U\({}_{30}\)(5\({}^{\text{s}}\)) and U\({}_{10}\)(5\({}^{\text{s}}\)) were used to generate the learning and validation datasets for the artificial neural network (ANN), respectively.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 9 Conclusions  
In this review article we have discussed the development of telemedicine systems, with a particular focus on the various communication technologies employed. Communication technologies such as BAN, remote communication with telemedical servers, and communication based on telemedical applications have been classified and reviewed. The use of internet, satellite, WAN and GSM communications, as mentioned and used by different researchers for the transmission of different types of data from a local point (patient) to a remote server (remote physician location) have been discussed in detail. The choice and use of any type depends on the availability of the infrastructure, type of application, and cost. The different connectivity protocols/standards used for communication were examined, with certain protocols noted to be better adapted to certain specific communication contexts based on the data type to be transmitted. The use of ML algorithms for telemedicine was considered. The overall purpose of this review is to provide a broad overview of the recent wireless communication technologies deployed in telemedicine systems, with a particular emphasis on implementation issues, challenges, and research gaps. Findings from the various articles reviewed here suggest the need to implement systems that combine multiple wireless networks in order to guarantee QoS and allow mobility for both clients/patients and remote care-givers. Despite the numerous studies conducted in this field, medical data transmission using a standard format that is universally compatible with other wireless devices remains lacking. Future telemedicine designs need to address issues such as scheduling, data security and privacy, and medical video streaming employing modern compression techniques. Another issue is that the design of wearable medical devices needs to take cognizance of energy efficiency, the absorption rate of radiation from wireless sensors, and the need for skin protection. Finally, the quality and integrity of data used for learning by ML algorithms is a crucial requirement for building reliable models to be used in telemedicine research and development.  
Authors C.O.A., H.O.O., A.O.A., A.J.O., E.E.O., A.I.B., S.A.O., E.K. and B.A. contributed equally to this work. Conceptualization, C.O.A., H.O.O., A.O.A., A.J.O.; methodology, H.O, A.J.O., A.O.A.; writing--original draft preparation, A.O.A., E.E.O., A.I.B., S.A.O.; writing--reviewand editing, C.O.A., A.J.O., H.O.O., A.O.A., E.K., A.I.B.; supervision, C.O.A., A.J.O., H.O.O., A.O.A.; funding acquisition, C.O.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the TETFund, Nigeria, with project number TETF/ES/DR& D-1674 CE/NRF2020/SET1/67/VOL.1.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Thermomechanically Induced Precipitation in High-Performance Ferritic (HiperFer) Stainless Steels
## 3 Results and Discussion
### Impact of Deformation Parameter Variation
#### 3.2.2 Deformation  
When using thermal annealing only (cf. micrograph \"\(\varphi\) = 0\" in Figure 8a; image analysis results, Figure 8b), precipitation of fine particles was not achievable at 800 \({}^{\circ}\)C. Even at short holding times (60 s), all the particles in the \"solely annealed\" alloy (\(\varphi\) = 0, in Figure 8) were larger than 800 nm, and most were larger than 1000 nm. The ambient-temperature ultimate tensile strength after \"solely annealing\" was less than 400 MPa (Figure 8c), which was below even the level of recrystallized material. The coarsened particles thus obtained obviously did not benefit material strength. In fact, the consumption of W and Nb from the matrix additionally decreased solution strengthening and led to the observed drop below the strength level of solution-treated material.  
Figure 7: Impact of TMP temperature on (**a**) particle density and area fraction, (**b**) particle size distribution, and (**c**) ambient-temperature tensile properties (\(\varphi\) = 0.5, \(t_{\text{H}}\) = 60 s, T = 800/950 \({}^{\circ}\)C).  
At a processing temperature of 800 \({}^{\circ}\)C, a clear trend towards higher tensile strength (Figure 8c) resulted from the introduction of dislocations by TMP (deformation degrees 0.2 and 0.5). Furthermore, it led to increased density and area fraction of Laves-phase precipitates via introduction of additional nucleation sites. The particle size distribution diagrams (Figure 8b) reveal the formation of a multitude of finer precipitates in a size range of generally less than 200 nm, with a dual-peak distribution (around 20 nm and 80 nm), in the TMP material. At higher deformation degrees (\(\varphi\) = 0.5), the particle microstructure appeared to be more uniform, with a more pronounced linear arrangement of precipitates than at lower deformation degrees (\(\varphi\) = 0.2; cf. Figure 8a). Quantitative analysis did not reflect significant differences concerning particle density and area fraction, which were almost constant within the range of scatter. Consequently, few differences in tensile properties were encountered (Figure 8c). The higher density of dislocations induced by increased deformation might be considered responsible for the more pronounced linear arrangement of precipitates. Nevertheless, the material deformed by \(\varphi\) = 0.5 presented slightly higher values for offset yield and ultimate tensile strength (approx. 40 MPa higher than \(\varphi\) = 0.2).  
Figure 8: Influence of TMP deformation grade on (**a**) particle density and area fraction, (**b**) particle size distribution, and (**c**) ambient-temperature tensile properties (\(t_{\text{H}}\) = 60 s, T = 800 \({}^{\circ}\)C, \(\varphi\) = 0, 0.2, 0.5).
## 5 Experiments  
### Benchmarks and Evaluation Metrics  
**ACE2005-Chinese**, which is a widely used benchmark for event-detection tasks, encompassing a collection of documents from various domains, such as news texts, broadcast dialogues, and blogs. We specifically selected the Chinese portion of this dataset. It defines 9 major event categories and 33 subcategories.  
**DuEE**[36], which is currently the largest Chinese event-detection dataset, was developed jointly by Baidu, the Chinese Computer Federation, the Chinese Information Processing Society, and data resource developers from universities and enterprises. The dataset includes a substantial amount of text from Baidu News, with a relatively consistent annotation style. It defines 9 major event categories and 65 subcategories.  
The distribution of event quantities in these two datasets is illustrated in Figure 3.  
We selected the top-10 event types with the highest frequency from both the ACE and DuEE datasets to construct a class-incremental learning setup with 5 sub-tasks. Each sub-task includes 2 event types, and experiments were conducted with 2-way 5-shot and 2-way 10-shot settings. The data partitioning for these two datasets is detailed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Task** & **Training Set** & **Validation Set** & **Test Set** \\ \hline \multirow{2}{*}{ACE2005} & \(T_{base}\) & 100 & 30 & 30 \\  & \(T_{incre}\) & 5/10 & 30 & 30 \\ \hline \multirow{2}{*}{DuEE2005} & \(T_{base}\) & 70 & 20 & 20 \\  & \(T_{incre}\) & 5/10 & 20 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Class-incremental learning-task data partitioning.  
Figure 3: The distribution of event counts in the ACE2005-Chinese and DuEE datasets.  
The strict matching standard was adopted; that is, a result is considered correct only if both the boundary of the trigger and the event type are all correct. \(Recall@K\) on the test set was used to evaluate the model, where \(K\) denotes the number of candidates.  
\[Recall@K=\frac{N_{recall@K}}{N_{gold}}, \tag{15}\]  
where \(N_{gold}\) denotes the total number of gold triggers and \(N_{recall@K}\) denotes the number of recalled gold triggers when \(K\) candidates are recommended.  
It is important to note that our previous research [1] indicated that the overall efficiency of the recommendation model is highest when recommending three candidate items. Therefore, the subsequent experiments will use \(Recall@3\) as the evaluation metric. To ensure that the overall efficiency is not adversely affected by excessively low precision, we require that the model's precision for potential event detection must exceed 33% at the time of model saving.
## 3 Results and Discussion
Figure 7: Averaged over 10\({}^{4}\) realizations waveforms of the time-varying component of the probe radiation power U(t) in experiments with synthetic crystalline quartz (different color curves correspond to different positions of the crystal under test and, accordingly, to its different spatial regions): (**a**) Waveforms obtained when the cells were filled with ambient air (black negative) and argon (black positive) and the crystal was in the same position, and waveforms obtained when the cells were filled with argon and the crystal position was varied perpendicular to the beam axis (color); rectangular function reflects the influence of electronic nonlinearity of the refractive index (black rectangular); (**b**) Waveforms shown in (**a**), obtained after subtracting the rectangular function from them; (**c**) Waveforms shown in (**b**), normalized to their maximum amplitude. To evaluate the sensitivity of measurements in quartz glass, the following factors should be taken into consideration. First, it has approximately four times the value of the thermo-optic parameter [13,40]. Secondly, the effect of heat diffusion, which reduces the signal value in the crystal by 1.7 times at a heating pulse duration of 1 ms, has little effect in quartz glass. In this case, the sensitivity of absorption coefficient measurements will be \(\approx\)\(1.5\times 10^{-9}\) cm\({}^{-1}\).
# Decellularized Skeletal Muscles Support the Generation of In Vitro Neuromuscular Tissue Models
## 3 Results  
### Decellular Muscles Allow 3D Co-Culture of Human Myogenic Cells and oSpC Sections  
With the aim to repopulate dSkM with human myogenic cells, we first proceeded with their characterization (Figure 1). Cell myogenic identity was confirmed in standard culture conditions by the presence of MyoD-positive cells and by the presence of multinucleated myotubes in both proliferating and differentiating culture conditions (Figure 1A). According to the evidence that fibroblasts are necessary to drive dSkM repopulation by myogenic cell in vitro [16], human myogenic cells were combined with murine fibroblasts in a defined ratio [16], seeded within the dSkM, and co-cultured for 21 days (Figure 1B). Fluorescence stereomicroscope imaging of calcein incorporation demonstrated the viability of the cells (Figure 1B, left panels). Further investigation performed by 2-photon calcein live imaging analysis confirmed the presence of living cells within the dSkM with elongated morphology (Figure 1B, right panel).  
Based on our previous results and on the neurotrophic properties of dSkM [16; 17], we generated the neuromuscular construct by co-seeding human myogenic cells and murine fibroblasts into the dSkM together with oSpC onto the scaffold (Figure 2A). Calcein live-imaging performed at the fluorescence stereomicroscope, confirmed the presence of viable cells after 21 days of co-culture (Figure 2B). We did not observe significant differences in biological and technical replicates of the developed co-cultured models in terms of cell viability, morphology, and scaffold repopulation at the end of the experiments. Interestingly,2-photon imaging analysis revealed the presence of elongated cells resembling myotubes in strict proximity to cellular projections similar to neural axons (Figure 2C). Confirmation of the preservation of both neural and myogenic cells was provided by immunofluorescence analysis for the marker class III-beta tubulin (Tuj1) and desmin, respectively (Figure 2D). Moreover, these data also confirmed that neural extensions and myogenic cells were in close proximity to each other within the dSKM (Figure 2D). Based on these findings, we wanted to investigate whether such co-culture could have a basal physiological activity. To do so, 3D neuromuscular constructs were cultured for 21 days, treated with the Fluo-4 calcium dye, and subjected to live imaging analysis to eventually reveal calcium fluxes into the myogenic compartment. Despite the fact that monitoring Fluo-4 within the 3D neuromuscular construct was not possible due to thickness and autofluorescence of the construct, myotubes located to the margins between the spinal cord and the dSKM clearly showed physiological activity (Figure 2E and Supplementary Video S1) with quantifiable calcium spikes (Figure 2E, left panel).
## 4 Propagation Characteristics and Phenomena of LMS Communication
### Additional Path Loss  
The additional path loss, as can be seen in [21], decreases when increasing the elevation angle, and it is zero when the angle is equal to 90 degrees. It is very complex to take into account the different losses due to the different layers of the atmosphere, and a simple corrective factor is to be considered both for the uplink (\(-(A_{p})_{u}\)) and the downlink (\(-(A_{p})_{d}\)) channels. A further loss is considered for the climatic conditions (\(A_{r}\)). Thus, the following equation is considered for the received power \(p_{r}\):  
\[\begin{array}{c}P_{r}=\\ P_{t}+G_{t}+G_{r}+147.6-20 log(f\cdot d)-A_{p}-A_{r}=\\ EIRP+G_{r}+147.6-20 log(f\cdot d)-A_{p}-A_{r},\end{array} \tag{7}\]  
where \(EIRP\) is the equivalent isotropic radiated power, \(147.6-20 log(f\cdot d)\) represents the attenuation of FSL, and \(A_{p}\) represents the loss due to atmosphere on the generic link (uplink or downlink). The term \(d\) is a length in km: \(d=42,643.7\cdot\sqrt{1-0.295577\cdot(cos\phi\cdot cos\delta)}\)where \(\phi\) is the latitude and \(\delta\) is the longitude of the ground station. Through these angles, it is possible to determine the elevation angle (\(\theta\)) from the horizon.
# Experimental Investigation on Effective Distances of Acoustic Emission in Concrete Structures
## 2 Experimental Program  
### Test Setup  
A drop experiment was conducted to investigate the propagation characteristics of AE according to the impact energy. In the experiment, reinforced concrete structures 20 m \(\times\) 0.2 m \(\times\) 1.2 m in length, width, and height, respectively, and AE sensors with data acquisition equipment were prepared. In each specimen, steel reinforcements 10 mm in diameter were placed with the direction shown in Figure 3. The spacing of the steel reinforcement was 200 mm in the transverse direction and 250 mm in the vertical direction. The AE sensor used in the experiment is R.45I (R.45I sensor, MISTRAS Group Inc, Benicia, CA USA), which includes 40 dB preamp, and is internally amplified with a resonance frequency of 20 kHz and an operating frequency of 1-30 kHz with very high sensitivity [26]. The sensor is equipped with a data transmission line and a standard Bayonet Neill-Conelman (BNC) connector on the side and is connected to the AE system (AEwin Software for Express-8 E5.91, MISTRAS Group Inc, Benicia, CA, USA, 2014.), which contains real-time data acquisition and replay software developed by MISTRAS [27]. The sampling rate was set as 1 MHz, and the threshold for filtering noise was set to 40 dB in the data acquisition. In addition, even though the AE signal is amplified by the internal amplifier in the sensor, the AE parameters were calculated with the original signal magnitude in the data acquisition. It should be noted that because no filtering process was used (except for the threshold setting), the measured AE signals contained noise that might have occurred during the experiment. Thus, an analog filter was used in the system with settings of 1 kHz and 400 kHz for low- and high-pass filters, respectively. Furthermore, according to the recommendation of AEwin for Express-8, the pre-trigger was 256 us, the peak definition time (PDT) was 200 us, the hit definition time (HDT) was 800 us, and hit lockout time (HLT) was 1000 us.  
In the experiment, elastic waves were generated by dropping a steel ball vertically from a specific height. The generated waves were recorded by AE sensors installed on the surface of the structure and were transformed into electric waveforms [28; 29]. Figure 3 shows a schematic diagram of the experiment parameters. The measurement points were 0.1 m, 0.3 m, 1.0 m, 4.0 m, 8.0 m, 11.0 m, 14.0 m, 16.0 m, and 17.0 m from the impact point.  
Figure 3: Schematic diagram of experiments.
## 5 Industrial Applications of CFD Simulations in Food Technology
### Quality Control and Monitoring in Thermal Processes Through CFD Simulations  
CFD simulations offer a comprehensive approach for real-time quality control and the monitoring of thermal processes in food production. By simulating the entire thermal cycle--from preheating to cooling--it is possible to not only predict but also actively monitor the temperature distribution throughout the product. This is crucial for ensuring consistent product quality, particularly in processes where precise temperature control is essential, such as pasteurization or sterilization. CFD provides insights into the temperature fluctuations within the food product and its environment, allowing for a deeper understanding of how thermal profiles affect microbiological safety and product quality [117]. To enhance the monitoring and control of thermal processes, CFD simulations can be integrated with sensors, real-time feedback systems, and control algorithms. This integration enables dynamic adjustments to the thermal process based on live data, ensuring that any deviation from the desired conditions can be promptly corrected. For example, by comparing real-time temperature measurements with the CFD predictions, operators can identify discrepancies in heat transfer or flow patterns and adjust parameters, such as heating times, temperatures, or fluid flow rates, in real-time. This adaptive approach is essential for maintaining product consistency, optimizing energy usage, and reducing waste during production. Moreover, CFD simulations allow for the early detection of potential deviations from optimal thermal conditions, facilitating preventive actions before the product reaches the consumer. By continuously monitoring and adjusting the process in response to simulation outcomes, manufacturers can ensure that the thermal process remains within the optimal range, reducing the risk of quality issues, such as underpasteurization or overcooking [14; 148].
## 4 Analysis and Discussion
### Analysis of Cavern Group Excavation Scheme
#### 4.1.3 Displacement Analysis of SR  
After the cavern group excavation was completed, the optimized DDARF program automatically measured the displacement contours of the SR of the cavern in three schemes, as shown in Figure 12a-c.  
In order to obtain more accurate displacement data, at the arch, the left and right side walls and the bottom floor of each cavern, there are four key points set to measure the change in displacement in the process of excavation. The setting of measuring points is shown in Figure 13.  
Figure 12: Displacement contours of three schemes after excavation completion: (**a**) Scheme 1; (**b**) Scheme 2; (**c**) Scheme 3.  
Figure 13: Setting of key points of cavern group. (Note: The numbers in the picture represent the key point number).  
The displacement of the key points of the three schemes after the completion of the excavation process can be obtained through the data of the measuring points. The specific statistics are shown in Figures 14 and 15.  
Figure 14: Vertical displacements (mm) of the arch of cavern group in three schemes after excavation completion.  
Figure 15: Vertical displacement of bottom floor of cavern group in three schemes after excavation (mm).  
By comparing the data in Figures 12, 14 and 15, it can be summarized as follows:  
* Among the vertical displacements of the arch, Scheme 2 has the smallest vertical displacement of the MP cavern (493.762 mm), MTC cavern (489.204 mm) and TST cavern (517.279 mm), respectively. It can be concluded that from the vertical displacement of the arch that Scheme 2 is the optimal scheme.
* In the vertical displacement of the bottom floor, Scheme 2 has the smallest vertical displacement of the MP cavern (308.156 mm), MTC cavern (365.59 mm) and TST cavern (320.562 mm), respectively. It can be concluded that from the vertical displacement of the bottom floor that Scheme 2 is the optimal scheme.  
The vertical displacements of the arch and bottom floor of the cavern group are the smallest in Scheme 2. Therefore, from the displacement analysis of the SR of the cavern, Scheme 2 is also the optimal scheme.