esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Observation System for CER Spectrum  
The outline of the observation system for the CER is shown in Figure 1[12]. The CER beam is extracted from the optical cavity by inserting a flat mirror tilted at an angle of 45\({}^{\circ}\) to the optical axis in the horizontal plane. The distance from the light source of the downstream bending magnet to the flat mirror was 0.56 m. The effective area of the deflection mirror projected onto the plane perpendicular to the optical axis was 20 mm\({}^{2}\). The electron beam generated CER at both the ends of the undulator, the exit of the upstream  
Figure 1: Schematic outline of KU-FEL and observation system for the CER beam.  
bending magnet, and the entrance of the downstream bending magnet in the undulator straight section. However, the magnetic field at the end of the undulator was significantly lower than that at the bending magnet, and the CER generated at the undulator was negligible compared to that generated at the bending magnet [16]. The CER generated at the upstream bending magnet was reflected to the inner surface of the vacuum chamber of the undulator multiple times; therefore, it did not reach the flat mirror while maintaining coherence. Therefore, only the CER beam generated at the downstream bending magnet was extracted to the atmosphere through a vacuum window made of z-cut crystal quartz with a thickness of 4 mm. The extracted CER beam was deflected by a concave mirror with a diameter of 76 mm and radius of curvature of 1 m positioned at 0.8 m from the radiation point and was transported to a Michelson interferometer. A nitrocellulose pellicle with a thickness and diameter of 2 \(\upmu\)m and 76 mm, respectively, identical to that of the beam splitter used for the Michelson interferometer, was inserted between the concave mirror and Michelson interferometer. The pellicle was coated by a nickel-based alloy, and the transmittance and reflectance in the THz region were approximately 60% and 10%, respectively [12]. The component reflected by the pellicle is used as reference light. A movable mirror with a diameter of 50 mm was employed to manipulate the optical path difference of the interferometer by up to 100 mm. The autocorrelation signal was concentrated by a parabolic mirror with a focal length of 76 mm and diameter of 51 mm and measured using a pyroelectric energy meter (Sensor-und Lasertechnik Inc. (Wildau, Germany), THz10) with a sensor diameter of 10 mm. A black polyethylene film with a thickness of 60 \(\upmu\)m was installed in front of the energy meter to suppress the unwanted infrared radiation. The reference light was also measured by separate pyroelectric energy meter with a different black polyethylene film.
## 3 Results  
### Change in pH and DIC Concentration with CO\({}_{2}\) Absorption  
The pH in both absorption columns and PBRs repeated reduction and elevation every 24 hours upon the medium circulation and CO\({}_{2}\) gas replenishment (Figure 2), but the range was smaller in PBRs than in absorption columns. Most of the supplied CO\({}_{2}\) was absorbed in Runs 1 and 2, while the removal efficiency in Run 3 was 63% (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Run**} & \multirow{2}{*}{**CO\({}_{2}\) Supply Rate**} & \multicolumn{2}{c}{**CO\({}_{2}\) Recovery**} & \multicolumn{2}{c}{**CO\({}_{2}\) Removal**} & \multirow{2}{*}{**pH before CO\({}_{2}\)**} & \multirow{2}{*}{**pH after CO\({}_{2}\)**} \\  & & **Rate, \(R_{CO2}\)** & **Efficiency** & & **Supply** & **Supply** \\ \cline{2-5}  & **(gC L-PBR\({}^{-1}\) d\({}^{-1}\))** & **(gC L-PBR\({}^{-1}\) d\({}^{-1}\))** & **(\%)** & & \\ \hline
1 & 0.20 & 0.227 \(\pm\) 0.052 & 115 b  & 9.85 \(\pm\) 0.08 & 9.43 \(\pm\) 0.09 \\
2 & 0.39 & 0.369 \(\pm\) 0.058 & 94 & 9.35 \(\pm\) 0.11 & 8.33 \(\pm\) 0.17 \\
3 & 0.59 & 0.369 \(\pm\) 0.055 & 63 & 9.34 \(\pm\) 0.05 & 8.13 \(\pm\) 0.16 \\ \hline \hline \end{tabular}  
* Values from steady-state (Day 10–18) were averaged. b The value includes CO\({}_{2}\) from headspace replacement.  
\end{table}
Table 2: Average CO\({}_{2}\) fixing rate and pH before and after CO\({}_{2}\) supply in absorption columns a.  
Figure 2: Time course of pH of each reactor (photobionectors: PBR; and CO\({}_{2}\) absorption columns). There are two points in a day, representing pH change due to the circulation of medium between PBR and absorption column.  
To elucidate the pH buffer characteristics of the modified SOT medium, the relationship between CO2 absorption and medium pH was modeled using Equation (4). The modeled curve showed a close similarity to the experimental data (Figure 3a). The accuracy was confirmed with regression, in which R2 was 0.8593 and residual mean square error (RSME) was 0.307 with an average pH 9.29, indicating nearly 97% accuracy.  
Increase in \(\Delta\)_DIC_ (CO2 absorption) lowered the pH of the solution, but a buffer region existed where \(\Delta\)_DIC_ was 0-0.17 mol L-1 (pH range: 8.9-10.4; Figure 3a), at which buffer intensity \(\beta\) was over half the maximum (>0.10 mol L-1; Figure 3b). In Runs 2 and 3 after Day 3, the CO2 absorbent received so large amount of CO2 gas that \(\Delta\)_DIC_ exceeded the buffer region, causing drastic drops of pH down to approximately 8.5 (Figure 2). Although the DIC in the PBR increased with a longer experimental duration in all conditions, the maximum value was 0.43 mol L-1 (\(\Delta\)_DIC_ = 0.16 mol L-1) in Run 3, which was within the buffer region (0-0.17 mol L-1). As a result, the pH in PBR remained over 9 even in Runs 2 and 3 (Figure 2). Equilibrated CO2 gas concentration was also drawn in Figure 3a based on Equation (6). With a slight increase in \(\Delta\)_DIC_ less than 0.02 mol L-1, equilibrated CO2 gas exceeded atmospheric concentration (c.a. 0.04%).
###### Abstract  
As far as taking-away of the symmetry constraints is concerned, as a scientifically symmetry problem, the global synthesis for antenna arrays that produce the desired radiation pattern is also a highly nonlinear optimization issue in fact. Besides this, the built criteria offer the reasonable power patterns. The consequent synthesis could be implemented by looking for a nominal pattern. When the criteria are already sufficient, it can simply do the whole synthesis process. To utilize multiple antennae, a method to choose a transmit antenna for the linear dispersion codes (LDC-TAS) is implemented in this paper. The authors used the max-min-post-signal to noise ratio (SNR) criteria to select these optimal transmitting antennae while this dependent, linear receiver is applied to the varying and slow channel. The simulated results illustrate that this max-min-post-SNR criterion outperforms the Bell Labs layered space time transmitting antenna selection (BLAST-TAS) applying the same spectral efficiency than space-time block codes (STBC)-TAS in the environment with low SNR. Furthermore, once the M antennae are selected under the selection criteria, a max-min-post-SNR rule, a novel linear antenna synthesis to linear dispersion codes on the basis of an innovative HYBRID (of mixed characters or solutions) genetic algorithm has been presented and evaluated to formulate and address the optimal problem to non-uniformly spaced and linear arrays. The restricted side-lobes level, the main-lobe width, and the shaped beam pattern are contemporarily concerned via maximizing a pretty suitable cost function through the innovational advanced genetic-algorithm-based algorithm. The method proposed in this paper can provide flexibility and a simple insertion of the a priori knowledge under a small computing pressure. At last, a computing simulation is completed well and the results are shown. It should be noticed that some extensions of the presented method could also be easily utilized without an obvious increase in the algorithm complexity.  
multiple antennae; linear codes; antenna synthesis; beam pattern; complexity +
Footnote †: journal:  
_Article_  
A Novel Linear Antenna Synthesis for Linear Dispersion Codes Based on an Innovative HYBRID Genetic Algorithm
## 4 The Concept of GORBA for Fast Rescheduling  
GORBA executes a two-stage planning process. In the first stage, the actual scheduling task is checked for plausibility firstly. The critical path of each new application job is calculated and it is checked whether there are resources available to process it at all and within the given time and at the given costs. In this way, unrealistic tasks can be identified before the scheduling process itself starts by applying two sets of heuristics. The first set originates from early experiments aimed at the evaluation of the scheduling procedures. The grid is empty and the scheduling task reflects some sort of initial planning situation. The second set utilizes the interrupted plan, also called the _old plan_. If no allocation conflicts and no violations of restrictions occur, the best heuristic result is regarded as the final outcome and the second stage is dropped.  
In the second phase, the Evolutionary Algorithm (EA) described later on is applied using all results from the first stage to seed its initial population. Since we use an elitist EA, the final outcome has the quality of the best heuristic at the minimum. Furthermore, seeding the start population improves the quality of the evolutionary search [20]. The main motivation for the usage of a meta-heuristic like an Evolutionary or Memetic Algorithm is the ability of this algorithm class to come up quickly with a \"good\" or at least feasible solution. For this area of application, optimality is not a necessary requirement, as schedules are usually processed to a small extent only. A thorough discussion of further reasons can be found in [28].
## 3 Results
### Body Composition and Recovery  
When comparing the body composition BIA parameters at baseline in patients with a worse recovery (\(n\) = 34) or with a better recovery (\(n\) = 32) (Figure 1), we detected a significant difference in the lean mass parameters. Patients with a better recovery showed higher values of FFM (54.1 \(\pm\) 9.8 vs. 47.5 \(\pm\) 9.5, \(p\) = 0.011), FMI (19.0 \(\pm\) 2.5 vs. 17.5 \(\pm\) 2.8, \(p\) = 0.031), ASMM (20.5 \(\pm\) 4.8 vs. 17.3 \(\pm\) 5.1, \(p\) = 0.010), SMI (7.2 \(\pm\) 1.2 vs. 6.3 \(\pm\) 1.5, \(p\) = 0.007), BCM (25.6 \(\pm\) 6.7 vs. 21.1 \(\pm\) 6.9, \(p\) = 0.013) and BCMI (9.0 \(\pm\) 2.2 vs. 7.8 \(\pm\) 2.3, \(p\) = 0.022) with respect to the patients with worse recovery. On the contrary, no significant  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Baseline Characteristics** & \multicolumn{2}{c}{**Sample Group**} \\  & **(\(n\) = 66)** \\ \hline _Cumulative Illness Rating Scale_ (CIRS) & & \\ CIRS severity & 1.9 \(\pm\) 0.8 \\ CIRS comorbidity & 5.6 \(\pm\) 1.7 \\ \hline Time from stroke onset (days) & 103 \(\pm\) 51 \\ _Activity of daily living (ADL) Assessment_ & & \\ Modified Barthel Index T0 (0–100) & 45 \(\pm\) 19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Body Composition of the Sample (\(n\) = 66)** & & \\ \hline  & **T0** & **T1** & \(p\) \\ \hline _Body Fat composition_ & & & \\ FM (kg) & 18.6 \(\pm\) 9.2 & 17.9 \(\pm\) 8.3 & 0.220 \\ FMI (kg/m\({}^{2}\)) & 6.8 \(\pm\) 3.4 & 6.5 \(\pm\) 3.1 & 0.155 \\ \hline _Body lean mass composition_ & & & \\ FFM (kg) & 50.7 \(\pm\) 10.1 & 51.2 \(\pm\) 10.6 & 0.221 \\ FFMI (kg/m\({}^{2}\)) & 18.3 \(\pm\) 2.7 & 18.4 \(\pm\) 2.8 & 0.279 \\ ASMM (Kg) & 18.9 \(\pm\) 5.2 & 19.0 \(\pm\) 5.2 & 0.289 \\ SMI (kg/m\({}^{2}\)) & 6.8 \(\pm\) 1.4 & 6.8 \(\pm\) 1.4 & 0.253 \\ BCM (kg) & 23.3 \(\pm\) 7.1 & 23.8 \(\pm\) 7.6 & 0.257 \\ BCMI (kg/m\({}^{2}\)) & 8.4 \(\pm\) 2.3 & 8.6 \(\pm\) 2.5 & 0.217 \\ \hline _Muscle quality_ & & & \\ PhA (degree) & 4.6 \(\pm\) 1.1 & 4.7 \(\pm\) 1.2 & 0.097 \\ \hline \hline \end{tabular} Data are reported in mean \(\pm\) standard deviation. \(p\)-values refer to the [PERSON] signed rank test. FM, fat mass; FMI, fat mass index; FFM, fat-free mass; FFMI, fat-free mass index; ASMM, appendicular skeletal muscle mass; SMI, skeletal muscle index; BCM, body cell mass; BCMI, body cell mass index; PhA, phase angle.  
\end{table}
Table 2: Bioelectrical impedance analysis parameters at baseline (T0) and at discharge (T1) of patients enrolled (\(n\) = 66).  
differences were found in the body fat mass parameters. Finally, even if not significantly, PhA values appeared higher in patients with better recovery, with respect to those with worse recovery (4.9 \(\pm\) 1.1 vs. 4.4 \(\pm\) 1.0, \(p\) = 0.078).
## 2 System Overview and Design
### On-Chip Identification  
By on-chip identification, we mean an addressing protocol by which chips are able to positively conclude they are being addressed. This can be accomplished with knowledge of the reliability of individual ID generator bits. Each bit of the ID generator can be classified as reliable or unreliable,based on each bit's value of \(p\). Although \(p\) is a continuous random variable, we can approximate it as a discrete random variable \(p^{\prime}\), where  
\[p^{\prime}=\begin{cases}1-\epsilon&1-\epsilon\leq p\leq 1\\ 1/2&\epsilon<p<1-\epsilon\\ \epsilon&0\leq p\leq\epsilon\end{cases} \tag{2}\]  
The variable \(\epsilon\) is the threshold of reliability: reliable bits have a probability \(\epsilon\) of flipping during read, and unreliable bits are treated as evaluating to either \"0\" or \"1\" with equal probability.
## 3 Results and Discussion
### Variation of the Cross-Linker Concentration On Particle Formulations
SEM micrographs of PMMA-_co_-EGDMA-_co_-AUPDS and of PS-_co_-DVB-_co_-AUPDS using 10 mol% of cross-linker are displayed in Figure 3. It can be observed that the particles are on the same size range of non-cross-linked particles, but are slightly more polydisperse. Reproducibility DLS data on the formulations using 10 mol% cross-linker are displayed in Table S2. On average, PMMA-_co_-EGDMA-_co_-AUPDS had a Z-average size of 166.4 \(\pm\) 3.1 nm, PI of 0.09 \(\pm\) 0.01 and ZP of 41.1 \(\pm\) 8.1 mV, whereas PS-co-DVB-co-AUPDS presented a Z-average size of 147.9 \(\pm\) 9.7 nm, PI of 0.04 \(\pm\) 0.01, and ZP of 29.2 \(\pm\) 3.9 mV. All optimized formulations presented a satisfactory reproducibility with a low standard deviation.
# A Review on Computer Vision Technology for Physical Exercise Monitoring
## 3 Discussion about Articles
### Other Approaches
#### 3.8.2 Facial Expression  
The human face is a door for expressing feelings yielded either by physical or mental condition. Pain, tiredness, and illness due to exertion is reflected in facial expressions; therefore, monitoring exercise intensity level by analyzing facial expression might be an interesting idea.  
[PERSON] [44; 89] explored various methods of automatic classification of exercise intensities using computer vision techniques of a subject performing sub-maximal incremental exercise on a cycle ergometer. The facial expression was analyzed by extracting 70 facial feature points. The exercise intensity was classified according to the distance between points and stage of the incremental exercise. The intensity was classified into two, three, and four classes using kNN, Support Vector Machine (SVM), and discriminant analyses. The results showed that facial expression is a good method to identify exercise intensity levels. A regression based facial color analysis to estimate the heart rate at particular instances of time was presented by [PERSON] et al. [89] where the autoregression model is proposed to predict the heart rate from the facial color changes.  
A different way of using facial expression to analyze physical effort was presented by [PERSON] [85]. The facial images were analyzed at different levels of resistance training. The authors evaluated the changes in facial expression using Facial Action Coding System (FACS) and the facial muscle activity using surface electromyography. The association of these parameters was mild, however, statistically significant.  
[PERSON] [103] also presented an analysis of the reliability in tracking data from facial features across incremental exercise on a cycle ergometer. The results differed according to the face parts analyzed, but higher reliability was found for the lower face. A non-linear relationship between facial movement and power output were also determined. The power output, heart rate, RPE, blood lactate, positive and negative effects in corresponding exercise intensity were satisfied in the two blood lactate thresholds and maximum a posterioriprobability MAP. These results show the potential in using the tracking of facial features as a non-invasive way of obtaining psychophysiological measures to access exercise intensity.  
Still regarding the use of facial features to evaluate levels of exertion, the mouth and eyes are particularly interesting parts that express information by muscle actions. Thus, there is a variety of facial expressions and emotions heavily oriented by the eyes and mouth. Therefore, tracking the movement of these parts could be a key idea to analyze exercise intensity [45]. Recently, the eye-blink rate and open-close rate of the mouth were tracked using [PERSON] and [PERSON] algorithm for image processing [104] during sub-maximal exercise on a cycle ergometer. The eye-blinking rate was correctly identified with 96% accuracy. Additionally, the higher the exercise intensity, the higher the eye and mouth movement.
## 4 Target Model and Frequency Offset Extraction Method
### Model Establishment
#### 4.1.3 Rotating Target Measurement Scene  
The rotating target measurement scene established based on the bistatic radar model is shown in Figure 5a. TX is the 5G signal-transmitting antenna, and RX is the signal-receiving antenna. \(L_{T0}\) is the projected distance from the target rotation center to the transmitting antenna on the antenna connection line, and \(L_{R0}\) is the projected distance from the target rotation center to the receiving antenna on the antenna connection line. \(L_{r}\) is the distance from the rotation center to the antenna connection, and \(r\) is the target rotation radius. In the simulation, the carrier frequency is 3.45 GHz, \(L_{T0}=3.5\) m, \(L_{R0}=1\) m, \(L=L_{T0}+L_{R0}=4.5\) m, \(L_{r}=1\) m, and \(r=0.3\) m. The frequency offset curves of different types of rotating targets are discussed below.
## 4 Performance Optimization and Testing  
### Computation and Communication Overlap  
Computation and Communication Overlap is an optimization strategy used to improve the performance of computer programs in parallel computing environments. Its goal is to perform data communication concurrently with computational tasks, effectively hiding communication within the computation. By ensuring that the computation time slightly exceeds the communication time, computation and communication overlap can be achieved.  
Before implementing computation and communication overlap, the algorithm structure needs to be adjusted. Originally, the grid data were computed first, followed by the communication of boundary data, so that the communicated data could be used in the next iteration. However, this approach would cause communication to be blocked. In this study, the communication operations were placed before the computation. As long as communication starts with the second iteration step, it can satisfy the requirement of communication before computation. Then, MPI blocking communication functions, MPI_Send and MPI_Recv, were replaced with non-blocking functions, MPI_Isend and MPI_Irecv. The calls to these interfaces mean that communication starts without hindering the execution of the main function. The MPI_Waitall interface was used to check if the non-blocking communication had been completed. Before initiating the communication operations, the data to be sent or received were preloaded into the cache, allowing the data in the cache to be used during the communication operations, thereby achieving computation and communication overlap.  
In this experiment, strong scalability testing was performed on the heterogeneous parallel algorithm before and after overlap. The number of nodes was gradually increased to verify if the program's parallel computing capability could improve linearly or nearly linearly with the increase in computational resources. The computation time before and after overlap was compared, as shown in Figure 10. Since a one-dimensional partition along the x-axis was used in this study, and the computational grid for both DCUs and CPUs was also divided along the x-axis, a grid size of 10,240 \(\times\) 1024 was tested, where each node computed a grid size of (10,240/node) \(\times\) 1024, and each DCU and CPU computed a grid size of (10,240/(node \(\times\) 5)) \(\times\) 1024. Each test involved 1000 iterations of computation time, and three tests were conducted to obtain the average value.  
Figure 10: Strong scalability test chart before and after overlapping.
## 6 Results
### Chloride Penetration Test  
Chloride penetration can cause steel bars corrosion in RCC buildings; therefore, studying concrete susceptibility against chloride penetration is very necessary from the life span and serviceability of concrete buildings in mediums bearing chloride such as soils, seawater, etc. Values of the chloride penetration of concrete samples are presented in Figure 9. An  
Figure 8: Water Absorption of Concrete.  
increasing dose of recycled concrete aggregate also increases chloride penetration. Recycled concrete aggregate higher capacity for water absorption than natural concrete aggregate made it easy for the solution to penetrate having (CI'). Moreover, utilization of silica fume is helping to improve durability against chloride penetration, RCA0-SF20 sample showed 18% and 29% less chloride penetration than the reference sample at 28 and 90 days of curing. Authors in the study [57] defined that silica fume decreased the size of the pore in concrete enhances the resistance to chloride penetration, it is because adding silica fume slows the movement of chloride ions. Resistance to chloride penetration is enhanced because of an increase in the quantity of alumina in cement [58]. This causes the formation of a dense C-S-H structure and C\({}_{3}\)A which could bind the chloride ions [57]. Silica fume also enhances chloride penetration of concrete samples having recycled concrete aggregate. Concrete samples with 20% silica fume and 50% recycled concrete aggregate showed similar chloride penetration as reference samples.  
Past researches have similarly revealed the detrimental effect of fibers such as (polypropylene, steel) [59] on concrete chloride permeability. The outcomes of chloride penetration are similar to water absorption results. Since durability performance relies upon concrete accessible porosity. Adding silica fume is useful to nylon fiber reinforced concrete resistance against chloride penetration, NFRC samples which had 50% recycled concrete aggregate and 20% silica fume showed chloride penetration as similar to the reference sample because of silica fume. It can be concluded, to sustain concrete durability similar to plain concrete samples, it is necessary to use nylon fibers with SCMs [60].