esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligibility and Listening Effort of Spanish Oesophageal Speech +
## 4 Experiment 2: Listening Effort for High-Intelligibility Oesophageal Speech
### Materials and Methods
#### 4.1.3 Listening Test  
Sixteen native Spanish speakers (7 female, 9 male; age range: 19-35, mean = 26.56, SD = 4.50) participated in the study. All participants were native Spanish speakers from South America, except one who was from Spain. They were given monetary compensation for participating in the test. Ethics for conducting the experiment was approved by the local ethics committee of the University of Oldenburg. All participants had normal hearing except one participant with a 55 dB hearing loss in the left ear. The inclusion of this participant did not alter the observations of the study and hence, we chose to keep this participant.  
The test began with the LE task first, where 60 (30 OS and 30 HS) out of the 70 available LE sentences were played in 3 blocks of 20 sentences (randomised) each. For 15 sentences of each block, participants were prompted to provide LE ratings. In the other 5 sentences (presented at random intervals), they were asked to repeat the last word of the sentence. This was to ensure that they were attentive and actively listening to the stimuli. A 13-point scale from 'Ningun esfuerzo' (No effort) to 'Muchisimo esfuerzo' (Extreme effort) was used to rate LE. This scale was based on the one used in [23]. There were 7 labels in all, but also intermediate labels (\"-\") that allowed participants to choose in-between options. The LE rating labels, their English translations, and their values are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**LE Rating Labels** & **English Translations** & **Values Assigned** \\ \hline Muchisimo esfuerzo & Extreme effort & 13 \\ – & – & 12 \\ Muchos estuerozo & A lot of effort & 11 \\ – & – & 10 \\ Esfuerzo considerable & Considerable effort & 9 \\ – & – & 8 \\ Esfuerzo moderado & Moderate effort & 7 \\ – & – & 6 \\ Poco esfuerzo & Little effort & 5 \\ – & – & 4 \\ Muy poco esfuerzo & Very little effort & 3 \\ – & – & 2 \\ Ningún esfuerzo & No effort & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: LE rating labels, their English translations, and the values assigned.  
After the LE task, the participants got a break and then they proceeded to the intelligibility task. In this task, they listened to a sentence and received a prompt on the screen to repeat the sentence that they heard. They provided oral responses for the 30 sentences. The whole session of the intelligibility test was recorded with a microphone so that it could be transcribed later.  
Stimuli were played at a comfortable listening level of 60 dB SPL.
## 6 Carboxymethyl Cellulose (CMC)
### CMC-Based Nanofibers
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Type of Structure** & **Chemical Modification** & **Fabrication Method** & **Bioactive Substance** & **The Test Model** & **Significant Findings** & **Ref.** \\ \hline CMC & - & \begin{tabular}{c} Hydrogel- \\ solvent casting \\ \end{tabular} & Ibuprofen & \begin{tabular}{c} Compartmentalized \\ in vitro drug release \\ system \\ \end{tabular} & \begin{tabular}{c} Adequate mechanical and \\ swelling properties, \\ proper pH, and \\ controllable drug release \\ \end{tabular} & [101] \\ \hline CMC/mesoporous silica & - & \begin{tabular}{c} Hydrogel- \\ sonication \\ \end{tabular} & Tetracycline Methylene blue & _S. aureus_ & \begin{tabular}{c} Improved swelling, water \\ vapor, and oxygen \\ permeability, and a \\ significant antibacterial activity \\ \end{tabular} & [79] \\ \hline DCMC/Gel/PEG & - & \begin{tabular}{c} Hydrogel-gel \\ spinning and \\ crosslinking \\ \end{tabular} & NA & L929 fibroblasts & \begin{tabular}{c} High mechanical property \\ and low toxicity \\ \end{tabular} & [102] \\ \hline CMC/Ag NPs & - & \begin{tabular}{c} Hydrogel in \\ situ reduction \\ of silver nitrate \\ \end{tabular} & Ag NPs & Primary fibroblasts and \begin{tabular}{c} \\ keratinocytes \\ \end{tabular} & \begin{tabular}{c} High saline absorbance, \\ moisture donation, proper \\ cytocompatibility, and \\ anti-microbial activity \\ \end{tabular} & [103] \\ \hline CMC & \begin{tabular}{c} Hydrogel- \\ freeze-drying \\ \end{tabular} & Sericin & Human keratinocytes & \begin{tabular}{c} Adequate release of \\ growth factor, no \\ cytotoxicity, and low \\ inflammatory response \\ \end{tabular} & [103] \\ \hline NaCMC/rGO & - & \
\begin{tabular}{c} Hydrogel- \\ interactions \\ \end{tabular} & NA &
\begin{tabular}{c} _C. elegans_ \\ _S. aureus_ \\ _P. aeruginosa_ \\ Primary human dermal \\ fibroblast cells (HDF) \\ \end{tabular} & [74] \\ \hline \hline \end{tabular}  
* **Abbreviations:** Cu-HAP@CA: Cu-modified hydroxyapatite encapsulated in cellulose acetate, STZ: Streptozoxin, PAC: Pure asiatis-coside, CACE: Asiaticoside crude extract, PVP: Poly(vinylpyrrolidone), MH: Manuka honey, BZC: Benzocaine, BCG: Bromoresol green, PCL: poly(\(\varepsilon\)-polycaprolactone), NPs: nanoparticles, Gel: Gelatin, ZM: Zataria multiflora, PEU: Polyester urethane, PHMB: Poly(hexamethylene biguanide), PLA: poly(lactic acid), HBP: N-(3,4-diamino-7-(benzo[d]thiazol-2-yl)-6-oxo-1H-pyrazol[4,3-clpyridin-5(6H)-yl]benzenesulffoanmide, PEO: poly(ethylene oxide), GA: Gallic acid, SSD: Silver sulfadiazine, AAC: Acrylic acid, IPN: Interpenetrating polymer network, BP: Benzophenone.  
\end{table} 
Table 2: Cont. (_w/v_)/ PEO 1% (_w/v_), and it showed excellent bactericidal activity against various bacteria, indicating a highly promising material for applications such as wound-dressing.
# Service Life Prediction and Life Cycle Costs of Light Weight Partitions
## 4 Field Survey Results and Discussion  
The study's sample encompassed 133 residential interior partitions, with ages ranging from 1 to 26 years, that were categorized into three types: gypsum board, aerated concrete block, and hollow concrete block partitions. The site selection criteria aimed to guarantee a representative sample of residential buildings by considering geographic location, building type (low-rise up to four floors and high-rise above four floors), construction method (concrete frame buildings with conventional or industrialized ceilings and cladding components), and occupancy date. Including a site in the sample depended on the availability of detailed prior information about the implementation of partitions. The list of sites and the distribution of the number of apartments, made in accordance with the sample classification are provided in Table 2, which offers a detailed overview of the survey's scope, presenting a comprehensive breakdown of the apartments surveyed in each sample category.  
The apartment selection within each building was random, not pre-determined, and apartments at various levels, including on top, middle, and bottom floors, were surveyed. The investigation process for each apartment included interviewing residents with a questionnaire and visual inspection of the partitions that rated them on the basis of their physical and visual condition through marking, on a scale from 0 to 100%, that reflected the partition's performance in various aspects.  
The age range of most observations in the gypsum block partition sample was 1-4 years, and an additional three observations were eight years old. This age distribution did not allow for reliable conclusions to be drawn from statistical analysis, and service life expectancy prediction or life cycle cost calculation were therefore not performed for this partition type, and it was not included in the research conclusions. The main findings for the remaining three partition types are detailed later in the study.
## 4 Experiments and Results
The Bland-Altman analysis demonstrated a mean difference of 0.6 bpm between the six-axis sensor and the reference PPG, indicating a negligible positive bias  
Figure 8: MAE of HR estimation at different time windows using [PERSON]’s method: (**a**) Mean absolute error (MAE) of heart rate (HR) estimation using the Welch method across different analysis window lengths (30 s, 20 s, 8 s, and 4 s). (**b**) Comparison of MAE before and after applying Welch-based power spectral density (PSD) analysis. with high agreement. This suggests strong consistency and practical interchangeability of the proposed method with conventional PPG-based HR monitoring. Furthermore, although the signal processing pipeline was modified to include adjustments to filter parameters and Welch-based PSD analysis, the mean absolute error (MAE) remained at a comparable level of 4.75 bpm. This consistency is likely due to both pipelines effectively preserving the dominant heartbeat components within the 0.8-2.0 Hz range, as also confirmed by the Bland-Altman analysis.
## 3 Experiments and Discussions
### Preparations
#### 3.1.1 Datasets  
Several real-world image datasets are selected to evaluate the effectiveness of our methods. The public facial datasets are widely adopted for machine learning recognition tasks, including AT&T, ORL, AR, Yale and YaleB. AT&T [30], which is a commonly used dataset for face recognition, consisting of 400 grayscale images of 40 individuals. Each person has 10 images captured under different conditions (varying angles, lighting, facial expressions, etc.). ORL [31] is another widely used dataset for face recognition, comprising 400 grayscale images of 40 individuals. Each person has 10 images taken at different times and under different states (with glasses, without glasses, etc.). The AR (Augmented Reality) Face Database [32] contains 2600 images of 126 individuals. Each person has 20 images, with 10 unoccluded and 10 occluded (with obstructions such as glasses, scarfs, etc.). Here, we conduct a mini-batch of AR with 260 samples of 10 individuals. Yale [33] consists of 165 grayscale images of 15 individuals. Each person has 11 images captured under varying lighting conditions. YaleB [34] is an extension of the Yale Face Database, containing 2540 grayscale images of 38 individuals. Each person has 64 images covering different lighting conditions and facial expressions. The partial samples are presented in Figure 3.  
Figure 3: The partial samples of the five real-world facial datasets.
## 2 Materials and Methods
### Methods
#### 2.2.5 Performance Evaluation  
Choosing appropriate performance evaluation metrics is essential for model development. True positives (TP), false negatives (FN), true negatives (TN) and false positives (FP) are the most basic model evaluation standards. Among them, TP and TN represent samples that are true positives or negatives and are correctly classified by the model, while FN and FP represent positive or negative samples that are incorrectly classified by the model. Based on these four metrics, we derived the following six commonly used metrics to evaluate the performance of the model in this paper:  
Accuracy (Acc): this metric measures the proportion of correctly classified instances out of all the instances in the dataset.  
\[\mathrm{Acc}=\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{FP}+\mathrm{ TN}+\mathrm{FN}} \tag{7}\]  
Precision (Pre): this metric measures the proportion of true positives out of all the instances that were predicted as positive.  
\[\mathrm{Pre}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}} \tag{8}\]Sensitivity (Sen): this metric, also known as recall or true positive rate (TPR), measures the proportion of true positives out of all the instances that are actually positive.  
\[\text{Sen}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{9}\]  
Specificity (Spe): this metric measures the proportion of true negatives out of all the instances that are actually negative.  
\[\text{Spe}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{10}\]  
F-score: this metric is a harmonic mean of precision and recall and is useful when both high precision and high recall are important.  
\[\text{F}-\text{score}=\frac{2\times\text{TP}}{2\text{TP}+\text{FP}+\text{FN}} \tag{11}\]  
Matthews Correlation Coefficient (MCC): This metric is a correlation coefficient between the observed and predicted binary classifications. It ranges from \(-1\) to \(1\), where \(1\) represents a perfect prediction, \(0\) represents a random prediction, and \(-1\) represents a completely wrong prediction. It takes into account true positives, true negatives, false positives and false negatives.  
\[\text{MCC}=\frac{(\text{TP}\times\text{TN})-(\text{FN}\times\text{FP})}{\sqrt {(\text{TP}+\text{FN})\times(\text{TN}+\text{FP})\times(\text{TP}+\text{FP} )\times(\text{TN}+\text{FN})}} \tag{12}\]  
In addition, we use receiver operating characteristic (ROC) and area under curve (AUC) to more intuitively demonstrate the model's ability to identify druggable proteins.
## 2 Methods
### System Parameter Analysis  
After adding a valve control to a VSPC system, the parameters of LVPC systems will have the following characteristics:  
(1) Identical hydraulic natural frequency. Equations (9) and (10) indicate that the introduction of valve control does not change the system's hydraulic natural frequency and the LVPC system has the same hydraulic natural frequency as the VSPC system.  
(2) Larger and variable total leakage coefficients. In general, total leakage coefficients of pump control systems \(C_{l}\) is small and stable. However, after introducing valve control, the total leakage coefficient of LVPC systems becomes larger and varies widely with system pressure \(P_{h}\) and control inputs \(u_{v}\). This is due to the pressure-flow gain \(K_{c}\) being much greater compared to \(C_{t}\) and changing with \(P_{h}\) and \(u_{v}\), according to \(K_{c}=C_{sv}u_{v}/\left(2\sqrt{P_{h}}\right)\).  
(3) Greater and variable damping ratios. Because the damping ratio is proportional to the total leakage coefficient, the damping ratio of LVPC systems is much greater than that of VSPC systems and varies widely with \(P_{h}\) and \(u_{v}\). Greater damping ratios will benefit system stability but values will cause difficulty with parameter prediction and system control.  
(4) Lower velocity stiffness. Equation (15) indicates that velocity stiffness decreases with the increase of total leakage coefficients and \(C_{t}\ll C_{l}\). Thus, compared with VSPC systems, LVPC systems have lower velocity stiffness and will be more susceptible to load disturbance.  
(5) Faster dynamic response. It is well known that increasing open loop gain will speed system response. As long as the weight factors \(K_{v}\)_and_\(K_{p}\) are appropriate, the open loop gain of LVPC systems will always be greater than that of VSPC systems (i.e. \(K_{sl}>K_{sp}\)) and therefore the LVPC system will respond faster than the VSPC system.
## 2 Introduction of Fast Delay Estimation Based on BCC  
### Signal Model  
Assume that the two broadband signals obtained by the acquisition system are expressed as follows:  
\[x_{1}(kT) =s(kT)+n_{1}(kT)\] \[x_{2}(kT) =\mathbf{A}s(kT-I_{0}T-\tau_{0}T)+n_{2}(kT) \tag{1}\]  
where \(T\) denotes the sampling period; \(s(kT)\) represents the complex signal with a frequency bandwidth of \([-B/2,B/2]\) Hz and power \(\sigma_{s}^{2}\); and \(n_{1}(kT),n_{2}(kT)\) represent two independent complex Gaussian noises with zero mean and a variance of \(\sigma_{n}^{2}\) that are independent of \(s(kT)\). Considering \(x_{1}(kT)\) as the reference channel signal, \(I_{0}\) and \(\tau_{0}\in[-0.5,+0.5]\) denote the integral period and fractional delay relative to the reference signal. \(\mathbf{A}\) indicates the channel gain. Suppose that \(s(kT)\) is a zero-mean stationary stochastic process consisting of a real part \(s_{r}(kT)\) and an imaginary part \(s_{i}(kT)\). Its autocorrelation functions (ACFs) can be expressed as \(R_{s}(\tau)=E\{\Re(s^{*}(kT)s(kT+\tau T))\}\). Let \(\{n_{r1},n_{r2}\}\) and \(\{n_{i1},n_{i2}\}\) denote the real parts and imaginary parts of \(\{n_{1},n_{2}\}\), respectively. According to the properties of Gaussian noise, we can obtain  
\[\begin{cases}R_{n_{1}}(\tau)=R_{n_{2}}(\tau)=0&\text{ if }\tau\
eq 0\\ R_{n_{1}}(\tau)=R_{n_{2}}(\tau)=\sigma_{n}^{2}&\text{ if }\tau=0\end{cases} \tag{2}\]  
The time delay \(IT+\tau T\) is estimated according to the correlation function algorithm as follows:  
\[\hat{I}+\tau=\arg\max_{I+\tau}\hat{R}_{x_{1}x_{2}}(I+\tau)\] \[\hat{R}_{x_{1}x_{2}}(I+\tau)=\frac{1}{N}\sum_{k=0}^{N-1}\Re(x_{1} ^{*}(kT)x_{2}(kT+IT+\tau T)) \tag{3}\]  
where \(E(\hat{R}_{x_{1}x_{2}}(I+\tau))=\mathbf{A}R_{s}(I+\tau-I_{0}-\tau_{0})\) and \(N\cdot T\) indicates the observation time. The estimates are obtained by solving Equation (3) for the ideal case \(\hat{I}=I_{0}\), \(\hat{\tau}=\tau_{0}\).
# An FPGA-Based Four-Channel 128k-Point FFT Processor Suitable for Spaceborne SAR
## 4 Experimental Evaluation and Results
### Experimental Settings  
#### 4.1.1 Experimental Data Settings  
The proposed processor was used to process 32-bit signed fixed-point numbers without setting integer bits, that is, the range of input data was \(-1\) to 0.999999995, with the data width of 64 bit, since the input data were complex numbers, including real and imaginary parts. The data length was 128 \(\times\) 1024 points. Therefore, the overall size of the input data was 64 \(\times\) 124 \(\times\) 1024 bit = 8 Mbit. Four sets of test signals were selected in the experiment as experimental data, including real sine signal, complex sine signal, periodic square wave signal and periodic sawtooth signal. The function expressions of the four test signals are shown in Table 6; the range of variable t is from 0 to 128 \(\times\) 1024 \(-\) 1. The square function generates a square wave with period 2\(\pi\) for the elements of the time array \(t\). The default amplitude is 1, and the duty cycle is 50%. The sawtooth function generates a sawtooth wave with period 2\(\pi\) for the elements of the time array t. The default amplitude is 1, which linearly changes from \(-1\) to 1 in one cycle.  
Figure 13: Serial to parallel module decomposition rules.
# Research on Non-Contact and Non-Fixed Cable Force Measurement Based on Smartphone
## 2 The Proposed Method for Cable Force Determination
### Cable Force Determination  
#### 2.3.1 Determination of Fundamental Frequency  
According to string theory, the natural frequency of stay cable is an integer multiple of the fundamental frequency [2]. The fundamental frequency is generally determined as the (average) difference between two adjacent peak frequencies [14], as indicated in Equations (4) and (5).  
\[f_{1}=\Delta f=f_{n}-f_{n-1},\ n=2,3,4\ldots \tag{4}\]  
\[f_{1}=f_{n}/n \tag{5}\]  
Based on the corrected target displacements, a fast Fourier transform (FFT) is performed in order to obtain the frequency spectrum. Take the displacement-time history in Figure 7 as an example, the corresponding spectrum analysis results are shown in Figure 8 where the first peak represents the direct current component and the second peak occurs at the frequency of 2.609 Hz. The exemplified spectrum does not present a pattern of multiple peaks with constant frequency difference.  
Figure 7: Displacement-time history.  
In the case where there is only one peak frequency (excluding the direct current component), its corresponding order can be determined using either of the following two methods:  
1. Manual inference: Since the variations of both fundamental frequency and cable force are relatively small during the construction phase, an initial measurement of cable forces for the entire bridge can be carried out using a dynamic tester. On top of the direct force measurements, the range of natural frequencies can be identified and used to infer the order of corresponding peak frequency.
2. Automatic recommendations from software: It is first assumed that the order of peak frequency is \(n\), where \(2\leq n\leq 5\). The range of [2; 5] is empirical and the observable order of natural frequencies for stay cables near the bridge end normally falls in it. The possibility that a peak represents the \(n\)-th order of natural frequencies is denoted by \(P_{n}\). The peak frequency is denoted by \(F_{h}\), where \(h\) is the sequence position of the peak in a series of frequency data. The possibility \(P_{n}\). can be calculated using Equation (6), where \(m\) is the range for data comparison. \[P_{n}=\left(\left(\sum_{j=2}^{5}\sum_{k=-m}^{m}signum\Big{(}F_{int(\frac{h}{2} \times j)}-F_{int(\frac{h}{2}\times j+k)}\Big{)}\right)/(2\times 4\times m)+0.5 \right)\times 100\%\] (6)  
Again, take the displacement-time history data in Figure 7 as an example for illustration. When the variable \(m\) is 3, the possibilities of the peak representing the \(n\)-th order of natural frequencies can be calculated, as shown in Table 3.
# On Performance Analysis of Protective Jamming Schemes in Wireless Sensor Networks
## 3 System Models
### Fri-Jam Schemes  
In this paper, we assume that the network is placed in a torus [30]. In this manner, the border effect can be ignored. We consider three types of users in our network: legitimate users, eavesdroppers and friendly jammers. The legitimate users are distributed according to homogeneous Poisson point process (PPP). Legitimate users transmit data packets, which might be passively snooped by eavesdroppers while legitimate users are unaware of the reconnaissance. Similar to [28], we assume that the eavesdropper is located at the center of the network without loss of generality since the network is placed in a symmetric torus.  
The interference caused by friendly jammers heavily depends on the location of jammers and the emitting power of each jammer. In this paper, we consider two placement strategies of friendly jammers in WSNs: (i) FJ-Reg scheme, in which jammers are regularly placed at deterministic locations and (ii) FJ-Ran scheme, in which jammers are regularly placed at random locations. Specifically, in the FJ-Reg scheme, friendly jammers are regularly placed in a grid manner, as shown in Figure 1. In the FJ-Ran scheme, friendly jammers are randomly distributed according to according to PPP, as shown in Figure 2. In addition to FJ-Reg and FJ-Ran schemes, we also consider adjusting the emitting power of jammers. In particular, we consider a modified FJ-Reg scheme with power control (named FJ-PC scheme) in this paper.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Encryption** & **Artificial Noise** & **Power Control** \\ \hline References & [8; 9; 10; 11; 18; 21; 22; 23; 24] & [25; 26; 27] & [28] \\ \hline Limitations & computational intensive & too specific (only apply for & deteriorate legitimate \\  & and power consuming & some specific scenarios) & communications \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of related anti-eavesdropping schemes in WSNs.