esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup  
The proposed common-path interferometer for optical angle measurement is shown in Figure 3. A He-Ne laser with a 632.8 nm wavelength is launched through a polarizer (PL) at 45\({}^{\circ}\). The input light is coupled into a lithium niobate Zn-Indiffused electro-optic modulator (ZIEOM) [18] through an objective lens (L1). The homemade ZIEOM is a waveguide-type electro-optic device that was fabricated in an \(x\)-cut/\(z\)-propagation lithium niobate substrate. The detailed fabrication process and the applications of heterodyne and homodyne interferometry have been reported in our previous work [18,19]. In the heterodyne interferometry, the frequency shifted between two orthogonal lights in the ZIEOM can be generated by applying a prompt sawtooth voltage driven from a function generator (FG). The driving voltage and frequency are 7 V and 100 kHz, respectively. The light is then focused by another lens (L2). A pinhole (PH) is used to block the scattering light. The incident light is divided into two different paths after passing through a beam splitter (BS). The transmitted and reflected paths are used as sensing (Is) and reference (Ir) lights, respectively. The OAS was made using a 1 mm thick BP placed in a cylindrical glass cell filled with different liquids, as shown schematically in the inset of Figure 3.  
Figure 3: Experimental setup for optical angle measurement and the structure of optical angle sensor.  
Figure 4 illustrates the fabricated OASs with the MgO:LN and KTP plates immersed in different liquids. In the initial development stage, a glass plate is placed directly on the top of the cylindrical glass cell. The covered plate was attached to the glass cell by using a UV curing step. Therefore, the high refractive index liquid can be sealed easily in the cell. The OAS is put on a rotation stage to allow changes in angle. The beam shape after AL2 is focused again, using a cylindrical lens (CL). After passing through two analyzers AL1 and AL2 oriented at 45\({}^{\circ}\), both signals detected by PD1 and PD2 are processed by the lock-in-amplifier (LIA). The phase difference between them can be further extracted for the measurements of angle variations.
## 4 Discussion
### Computation Load  
In this study, FLOPs (Floating-Point Operations per Second) and the number of parameters are used to measure the computational cost of the models. FLOPs represent the total number of floating-point operations required during the inference process and serve as a key metric for evaluating computational complexity. The input data size is set to (1, 6, 256, 256), where 1 denotes the batch size, 6 represents the number of input channels,  
Figure 12: Quantitative evaluation results of BCSR-STF, BCSR-STF-NT, and BCSR-STF-NS.  
Figure 13: Quantitative evaluation results of BCSR-STF, BCSR-STF-NA, and BCSR-STF-OA.  
and 256 \(\times\) 256 corresponds to the resolution of the input image. By calculating FLOPs and the number of parameters, the computational efficiency and resource requirements of the models are comprehensively assessed. The results are presented in Table 4.  
As shown in Table 4, although the parameter count of BCSR-STF reaches 34.80 M, which is higher than that of other lightweight models, its FLOPs are only 2.71 \(\times\) 10\({}^{10}\), significantly lower than the self-attention-based models STF-Trans and CTSTFM. This advantage is primarily attributed to its pyramid structure and windowed attention mechanism, which strike a well-balanced trade-off between computational efficiency and model performance. Furthermore, the results from three experimental datasets demonstrate that BCSR-STF exhibits the best robustness, effectively adapting to diverse scenarios and varying data distributions. For dense prediction tasks such as image fusion, a moderate increase in parameter count is generally accepted in exchange for improved model expressiveness and performance.
## 1 Introduction  
Intensive care units (ICU) enhance the survival of critically ill patients through constant active monitoring of their vital signs. Heartbeat, blood pressure, respiratory rate, and oxygen saturation are recorded continuously by default [1]. Setting upper and lower limits per vital parameter can then trigger off alarms with different levels of urgency. If the system registers pathological values, the medical staff is alerted by different alarm severity levels. A red alarm signifies a potentially life-threatening situation, prompting the team to respond to the alarm immediately [2]. Monitoring agitated or delirious patients at the ICU is particularly challenging since the attached wires are often not tolerated and may be (un-)intentionally removed by patients. Monitoring is even more difficult in acutely suicidal patients, who deliberately lure for situations with reduced care attention to remove sensors and hurt themselves, e.g., work breaks or shift changes of the responsible nurses [3, 4].  
If the standard monitoring system is no longer attached, \"alarm notifications\" are triggered to remind the intensive care staff to check the sensor and reattach the cabling.  
Such alarms can reiterate multiple times per shift and lead to a phenomenon known as \"alarm fatigue\", i.e., the reaction time of the responsible nurse decreases [5; 6]. During this unobserved period, no adequate alarm can be triggered, even if medical emergencies occur, such as life-threatening arrhythmias or cardiopulmonary arrest. Although relatively rare, the consequences of these scenarios may be fatal for the patient and legal for the medical staff. More often, these patients lie in their beds, palpate their surroundings with their hands, or prepare to stand up--in other words, they move [7].  
Accordingly, the simultaneous detection of patients' motions could provide information on the ability to move, thus filling an essential gap in the monitoring continuum until the traditional monitoring system is reconnected.  
Motion detectors have been repeatedly tested at the ICU for various indications as measuring patients' activity or mobility levels [8; 9; 10], assessing patients' sleep quality [11], or reducing the number of artefact alarms in vital sign monitoring [12]. To date, no study has examined whether motion detectors can be used to identify emergencies. However, detecting immobility following an agitation episode may be highly suspicious for an urgent, vital threat requiring immediate alarm response.  
In a proof-of-concept study, we aimed to evaluate whether ICU staff may reliably detect the simulated emergency situation of \"lying dead-still\" with the help of a noncontact motion detector. We hypothesized that \"motionlessness\" can be accurately identified, thus enabling correct alerts of medical staff to alarm situations without the traditional monitoring system.
# Influence of Oak Chips and Oak Barrel Ageing on Volatile Profile in Chardonnay Wine of Romania
## 3 Results
### Volatile Profile of White Wines Aged with Non-Toasted and Light-Toasted Oak Chips
#### 3.2.4 Lactones  
Among lactones, butylolactone showed the highest concentration and depending significantly on the studied factors, as in the case of pantolactone and 3,4-dimethyl-2(5)-furanone. Thus, for control sample, an amount of 478 ug/L of total lactones was quantified. Regardless of the ageing method, wines in which non-toasted oak chips were added (N1M-N3M) and in non-toasted oak barrel (BAR1M-BAR3M) presented greater butylolactone concentrations than the corresponding light-toasted chips (L1M-L3M) wines.
# Optoelectronic Instrumentation and Measurement Strategies for Optical Chemical (Bio)Sensing
## 4 Design of Applications
### Fiber Optic pH Sensor Based on Fluorescent Radiometric Intensity Measurement
#### 4.1.1 Optical Sensor Characterization  
We prepared the active pH phase by entrapping the pH indicator in a sol-gel silica matrix during the polymerization process. Once the optical sensor was prepared, we obtained its excitation, emission, and reflection spectra by means of a fluorescent spectrometer for different pH values, as shown in Figure 14. From this figure, three important consequences can be observed that will affect the design of the system:  
(1) The pH value increases the emission of the fluorescent light in a quasilinear manner;  
Figure 13: Design flow for an optoelectronic instrumentation system involving optical sensors.  
2. The excitation and emission peaks of the sensor are close to each other (\(\lambda_{\text{exc}}\) = 528 nm, \(\lambda_{\text{emi}}\) = 549 nm). To avoid overlapping between both spectra, a light source with an emission peak below the sensing phase excitation maximum may be adequate;
3. The excitation spectra depend on the pH and their intensity change in the same way as the emission spectra.
# Low-Latency QC-LDPC Encoder Design for 5G NR
## 4 Encoder Design Details  
According to the encoding algorithm and the structure characteristics of the 5G QC-LDPC code, the cyclic shift of encoder and XOR operation in GF(2) is purely logic operations. The hardware complexity of the encoder will increase with the increase of the code length. Especially for CRC calculation, the complexity of encoding hardware will increase linearly with code length. The design of this paper is based on the requirements of 5G NR. In the eMBB scenario, NR requires large-scale data transmission. In the URLLC scenario, NR requires ultra-low latency and high reliability. Hence we need to design an encoder whose performance is close to the latency limit. The solution is to maximize the parallelism of encoding calculations; at the same time, the hardware must be configurable and have reasonable resource utilization. Therefore, this paper optimizes the parallelism of CRC and QC-LDPC encoding and selects a suitable architecture for the flexible configuration.
## 3 Case Study
### Collapse Risk Assessment Based on Instrument Monitoring Data
When C is 0.05, 0.1, 0.15 and 0.2, the model classification accuracies are 63.6%, 68.3%, 68.3% and 68.3%, respectively. This is due to the fact that the larger the parameter C is, the easier the model is to be overfitted. From the above analysis, the optimal value of C is 0.1. Figure 5b shows the classification probability distribution chart when C is 0.1, and gamma are 0.001, 0.002, 0.004 and 0.006, respectively. It can be seen that there are two points where it has clearly deviated from the other points when gamma is 0.001, and the level is II. There is one point where it has clearly deviated from the other points when gamma is 0.002, and the level is II. However, all points are gathered together when gamma is 0.004, 0.006, and the level is II. When gamma is 0.001, 0.002, 0.004 and 0.006, the model classification accuracies are 63.6%, 68.3%, 68.3% and 68.3%, respectively. This is due to the fact that the larger the parameter gamma is, the easier the model is to be overfitted. From the above analysis, the optimal value of gamma is 0.004. In summary, the optimal parameter C is 0.1, and the optimal parameter gamma is 0.004. The collapse risk assessment of the test samples based on instrument monitoring data is predicted, and the results are shown in Table 8, which can obtain risk assessment accuracy. Obviously, a large part of the prediction results deviates from the true value. This is due to the error of monitoring data caused by the operation error of construction personnel, or the judgment error caused by the fact that the vault settlement displacement and horizontal convergence displacement cannot fully reflect the surrounding rock support state. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Tunnel Collapse Level** & **Daily Deformation (mm/d)** & **Cumulative Deformation (mm)** \\ \hline I (Safe) & \(0\leq\text{x}<2\) & \(0\leq\text{y}<50\) \\ II (Deformation is formed) & \(2\leq\text{x}<5\) & \(50\leq\text{y}<100\) \\ III (Small-scale collapse) & \(5\leq\text{x}<10\) & \(100\leq\text{y}<200\) \\ IV (Large-scale collapse) & \(10\leq\text{x}<20\) & \(200\leq\text{y}<300\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification of monitoring measurement data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Distance from Monitoring** & **1B** & **2B** & **3B** & **4–6B** \\
**Point to Palm Face (D)** & **1B** & **2B** & **3B** & **4–6B** \\ \hline \(\xi\) & 0.5 & 0.75 & 0.85 & \(1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Coefficient (\(\xi\)) of cumulative deformation (y).
# Radiometric Calibration of the Near-Infrared Bands of GF-5-02/DPC for Water Vapor Retrieval
## 4 Results
### Radiometric Calibration Results of the DPC at the NIR09 Band  
The matching results between the actual observations of the DPC at the NIR09 band and the simulated observations of the DPC at the NIR09 band based on the observations of the recalibrated DPC at the NIR08 band and AERONET PWV data are illustrated in Figure 6. The actual and simulated TOA reflectance of the DPC at the NIR09 band exhibit a high degree of linear correlation, and the radiometric calibration coefficient for the DPC at the NIR09 band (i.e., \(k2\)) is 1.03.  
Figure 5: The distribution of the values of \(R^{\prime}_{865 nm}/R_{865 nm}\). The black line represents the normal distribution function corresponding to \(R^{\prime}_{865 nm}/R_{865 nm}\).  
Figure 6: Matching results between the simulated and actual TOA reflectance data of the 910 nm band of the DPC.  
In this study, \(k2\) is used to characterize the systematic bias in the observations of the 910 nm band of the DPC, and is equal to the mean of the ratio between the actual observation of the DPC at the NIR09 band (i.e., \(R^{\prime}_{910 nm}\)) and the simulated observation of the NIR09 band (i.e., \(R_{910 nm}\)) calculated based on AERONET PWV data and the observations of the 865 nm band of the DPC after radiometric calibration. The method used to calculate the uncertainty of \(k2\) is as follows. The distribution of the values of \(R^{\prime}_{910 nm}/R_{910 nm}\) is shown in Figure 7. The mean (i.e., expected value) and standard deviation of the normal distribution corresponding to \(R^{\prime}_{910 nm}/R_{910 nm}\) are 1.03 and 0.03, respectively. This indicates that the uncertainty of \(k2\) is 0.03. In addition, the expected value of \(k2\) is 1.03, which is consistent with the above calibration result.
# Effects of Co-Solvents on the Performance of PEDOT:PSS Films and Hybrid Photovoltaic Devices
## 3 Results
### Effect of Co-Solvents on the Anti-Reflective Properties of Devices  
Although quantitatively characterizing the thickness and irregularity of the PEDOT:PSS films on textured substrates is challenging, measuring the anti-reflective properties of the devices allows us to directly assess the impact of the spin conditions on the photo collection efficiency of the device and empirically deduce the resulting irregularity of the films. Figure 4 shows the UV-VIS reflection spectrum collected for the PEDOT:PSS films spin-coated on textured silicon wafers with different co-solvent compositions. It can be readily seen that the PEDOT:PSS films significantly reduce the reflectivity of the devices, confirming its anti-reflective properties. We can also observe that the addition of co-solvents does not affect the anti-reflective properties of the resulting films significantly. PEDOT:PSS has a transmission of more than 90% throughout the solar spectrum.  
Nevertheless, there are some slight differences between spin recipes that are important to point out. Particularly, PEDOT:PSS with 7 wt% EG has a better anti-reflective performance in the visible spectrum when spin-coated at 4000 rpm than 2250 rpm. DMSO, on the other hand, seems to perform just as well at either spin speed. The difference in anti-reflective performance seen for EG may be due to the rheological properties of the dispersion and their implications in spin-coating on a textured substrate. Although it is difficult to confirm the specific impacts of the rheological properties on the thickness distribution of the films obtained in these cases, the anti-reflective performance suggests that, at least for EG, 4000 rpm produces films with a thickness closer to 50 nm throughout the silicon substrate [12]. In other words, dispersions with EG are more sensitive to pooling which can be overcome by spinning at higher speeds. DMSO, on the other hand, produces films which are more resistant to pooling and thus are more homogeneous. The implications of the difference in homogeneity  
Figure 4: The reflectivity of PEDOT:PSS films on textured Si substrates.  
of the films coated on textured substrates are important to consider in order to optimize their electrical performance. The more inhomogeneous films produced with EG at 2250 rpm will not only have a lower anti-reflective performance but may also have higher sheet resistance, as this property is limited by the thinnest regions of the film.
# Factors Affecting Cybersecurity Awareness among University Students
## 4 Results  
### Demographic Data  
Demographic data in this research is in the form of respondent data: gender, age, education level, computer skills, and how often respondents make online purchases. The distribution of demographic data can be seen in Table 1.  
In Table 1, it can be seen that the number of female respondents (52.8%) is greater than that of male respondents. Most of the respondents were aged 20-35 years (53.3%), followed by respondents aged less than 20 years (39.5%). A large number of respondents were in this age group because the main target of this research is students, the level of education  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Category** & **Number** & **Percentage (\%)** \\ \hline \multirow{2}{*}{Gender} & Male & 238 & 52.8\% \\  & Female & 212 & 47.2\% \\ \hline \multirow{4}{*}{Age} & \textless{}20 & 178 & 39.5\% \\  & 20–35 & 240 & 53.3\% \\  & 36–49 & 28 & 6.2\% \\  & 50–65 & 4 & 1\% \\ \hline \multirow{4}{*}{Education} & Diploma & 16 & 3.6\% \\  & Bachelor’s Degree & 417 & 92.6\% \\  & Master’s Degree & 10 & 2.2\% \\  & PhD & 7 & 1.6\% \\ \hline \multirow{4}{*}{Computer Skill} & Beginner & 67 & 14.9\% \\  & Intermediate & 237 & 52.7\% \\  & Advance & 146 & 32.4\% \\ \hline \multirow{2}{*}{Purchase Online} & Rarely & 132 & 30\% \\  & Frequently & 318 & 70\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of research respondent demographic data (n = 450).  
attained by most of the students in this study being a bachelor's degree (92.6%). Based on computer skills, most respondents have skills in using computers at an intermediate level (52.7%), followed by respondents who are proficient in using computers (32.4%). In the field of online purchasing, it can be seen that most of the respondents often purchase online (70%).
## 2 Equipment and Methods of Capturing and Preparing Data
### Data Preprocessing  
The original data were preprocessed before being passed to the model. First, it was necessary to ensure that each participant had four files with measurements of various types of breathing (standard, thoracic, abdominal, and mixed). For six participants, we found that the same type of breathing was recorded more than once. Duplicate files were deleted. After that, it was essential to confirm that the measurement lasted no less than a minute in each file. Eight measurements lasting significantly less than a minute were found; they were removed as statistical outliers. It was also noticed that in some files, the fractional part of the numbers was separated by a comma instead of a dot. Such data were converted to a single format: the separation of the integer and fractional parts of the number by the dot symbol.  
The remaining statistical outliers were searched using the interquartile range method. The interquartile range is the difference between the third (\(Q3\)) and the first (\(Q1\)) quartiles of the sample. This value helps estimate the spread of 50% of the sample elements without taking into account outliers [25; 26; 27]. Statistical outliers were records for which the frequency or amplitude values were 1.5 times greater or 1.5 times lower than the interquartile range (\(IQR\)), i.e., all the records that had a frequency or amplitude value less than the corresponding lower limit or upper limit according to (1).  
Figure 3: During data collection.  
\[IQR=Q3-Q1\] \[Lowerlimit=Q1-1.5\cdot IQR \tag{1}\] \[Upperlimit=Q3+1.5\cdot IQR\]  
In addition, all numerical characteristics were scaled using the StandardScaler from the scikit learn (sklearn) Python library before being passed to the model for training; all categorical characteristics were represented appropriately using the get_dummies method from the Python Pandas library. The dataset was divided into training (60% of records) and test (40% of records) samples, using the train_test_split method from the Python scikit learn (sklearn) library.  
We chose the ratio of training and test samples based on [28], which supposed using 60% of the original data for the training sample, 20% for the validation sample, and 20% for the test sample; those recommendations are suitable for the data volume we had. The validation sample is used when the model independently selects optimal hyperparameters, which was not done in our study. Therefore, 60% of the data was allocated for the training sample. The training and test samples data do not intersect: the separation was performed before the training. The data were balanced in the test and training samples because the \"stratify\" parameter was used in the train_test_split method. That method performs a random split: it shuffles the sequence of all the data to eliminate the factor of models' selection of false (\"biased\") features associated with the training data sequence.