esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Design
### Ranging Measurement
The related equation will be presented later in this section. The third term can be calibrated precisely before the electronic apparatus employed in the synchronization loop are installed into the payload, but is expected to be within 0.1 nanosecond. The fourth term can be effectively compensated through the use of the dual frequency ionospheric correction algorithm, which is described in [24] as:  
\[R=\frac{f_{1}^{2}R_{1}-f_{2}^{2}R_{2}}{f_{1}^{2}-f_{2}^{2}} \tag{13}\]  
where \(f_{i}\) is the radio frequency, and \(R_{i}\) is the corresponding pseudo-range measurement. Certain frequencies can be adopted for different scenarios and goals of synchronization precision. The fifth term can be obtained by means of Equation (10), although the on-board clocks normally have good stabilities and as such, this term only has a small effect during a short time interval. The sixth term, which denotes the other delays, include the tropospheric delay and Sagnac delay in the GSM. The European Geostationary Navigation Overlay Service (EGNOS) tropospheric correction model can be employed for the GSM to eliminate most of the tropospheric delay [25]. Meanwhile, a Sagnac-effect correction method can be applied to the propagation time of the signal in such a case [26]. An applicable solution for this specific requirement is proposed in this paper. The relative motion error consists of two parts, namely the ephemeris error and the Doppler shift. In the proposed system, we assume that the slave satellites only have the pseudo-range observation and the ephemeris of the entire system members. Figure 5 shows how the positions of the master satellite and the slave satellite change. We define \(\mathbf{r}(t_{i})\) as the displacement from the satellite \(i\) to the other satellite at \(t_{i}\), while \(\mathbf{r_{SM}}(t_{i})\) and \(\mathbf{r_{MS}}(t_{i})\) are the displacements from satellite M/S to satellite S/M during the period of \(\tau_{SM}(t_{i})\) and \(\tau_{MS}(t_{i})\), such as \(\mathbf{r_{SM}}(t_{i})=\tau_{SM}(t_{i})\cdot c\), \(\mathbf{r_{MS}}(t_{i})=\tau_{MS}(t_{i})\cdot c\). They are modeled by:\[\left|\mathbf{r}\left(t_{S}\right)\right|=f\left(\mathbf{r}_{\mathbf{ SM}},\mathbf{\Delta}_{\mathbf{M}}\right)=\sqrt{\left(\mathbf{r}_{\mathbf{ SM}}-\mathbf{\Delta}_{\mathbf{M}}\right)^{T}\left(\mathbf{r}_{\mathbf{ SM}}-\mathbf{\Delta}_{\mathbf{M}}\right)} \tag{14}\]  
where \(\mathbf{\Delta}_{\mathbf{M}}\) denotes the change of position from the instance that the slave satellite transmits the ranging signal to the instance that the master satellite receives the signal. It can be expressed as \(\mathbf{\Delta}_{\mathbf{M}}=\mathbf{v}_{\mathbf{M}}\cdot\mathbf{\tau}_{SM}(t _{S})\), where \(\mathbf{v}_{\mathbf{M}}\) is the velocity of the master satellite, which varies over time.
## 2 Materials and Methods  
### Design and Fabrication of Microtool  
#### 2.1.1 Microtool Concept  
Figure 1 shows a microtool that is designed to push cells. The microtool consists of two parts, namely, a V-shaped head and handle for manipulation of cells and for interaction with optical tweezers, respectively (Figure 1a). The V-shaped head has a 2 \(\upmu\)m-wide flattened bottom and two 6 \(\upmu\)m-length sides at 135\({}^{\circ}\). The V-shaped head has a height of 6 \(\upmu\)m. Further, the handle part is an equilateral triangle with a 4 \(\upmu\)m microsphere at each vertex and 17 \(\upmu\)m-length square bar at each side. The square bar has a thickness of 1 \(\upmu\)m. Figure 1c shows detailed dimensions of the microtool. In addition, the microtool size and shape can be readily adapted to be larger or smaller depending on the target cells due to the simple fabrication process.  
The design has the following advantages. First, the V-shaped head is applicable to the manipulation of different sizes of cells. As the microtool moves forward, the V-shaped head will generate forces (see \(F_{a}\) and \(F_{b}\) in Figure 1b) to trap the cell at the center bottom of the V-shaped head. Therefore, the microtool allows stable manipulation without cells adhering to the microtool. The cells, regardless of their sizes, will be minimally exposed to the laser beams. Second, the microtool is optimized for optical manipulation. The pose of the microtool can be manipulated by controlling the positions of the three handling microspheres with three optical traps. The formation of three microspheres in an equilateral triangle will be of benefit to the visual servo control method due to the simplified image processing process. The diameter of each optical handling microsphere is set to 4 \(\upmu\)m by the experiment. The diameter is much larger than the 1064 nm wavelength of the optical tweezer, and thus conventional ray-optics theory can be used to analyze the trapping actions of the optical traps on the microspheres. Moreover, to prevent the optical handling spheres from escaping the traps and achieve high-precision manipulation, a control constraint [22] could be introduced in the control strategy. During optical manipulation, the deviations of the trap center from the center of the microsphere will be smaller than 1.4 \(\upmu\)m, which is seven-tenths of the microsphere radius. Therefore, utilizing optical traps that have the same parameters as those in our previous work [22] and considering cells with radii ranging from 2 \(\upmu\)m to 10 \(\upmu\)m, the minimum distance between the trap center and cell edge is twice that of the sphere radius (i.e., 4 \(\upmu\)m, as shown in Figure 1c). The change of the laser beam as it overlaps with cells is further decreased. Hence, cell exposure to the laser beam can be effectively reduced.  
Figure 1: Designed microtool. (**a**) Schematics of the proposed microtool. (**b**) Two push force, namely, Fa and Fb, are generated by the baffles on the left and right sides of the V-shaped head of the microtool. (**c**) Dimensions of the microtool.
## 2 Materials and Methods
### Estimation of Model Parameters
#### 2.3.2 Estimation of Transmissibility  
The estimation of the transmissibility of the \(j\)-th day, \(\beta_{j}\) at \(t_{j}=t_{0}+j\), from the initial day \(t_{0}\) is described (Algorithm 1, line 3). The estimated transmissibility of a day before, \(\beta_{j-1}^{\text{opt}}\), is used in order to obtain the state of the variables at time \(t_{j}\): \((S(t_{j}),E(t_{j}),I(t_{j}),R(t_{j}))\). This state is adopted as the initial condition for estimating the transmissibility \(\beta_{j}\) by performing the Bayesian method as follows:  
\[P(\beta_{j},\sigma_{j}^{\text{R}}|D_{j}^{\text{R}})~{}\propto~{}P(D_{j}^{ \text{R}}|\beta_{j},\sigma_{j}^{\text{R}})\,P(\beta_{j},\sigma_{j}^{\text{R}}). \tag{16}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Search Range** & **Prior Distribution** \\ \hline \(E(t_{0})\) & \([0,100]\) & Uniform \\ \(I(t_{0})\) & \([0,100]\) & Uniform \\ \(\beta\) & \([0,0.8]\) & Uniform \\ \(\lambda_{I\to H}\) & \([0,0.15]\) & Uniform \\ \(\lambda_{I\to F}\) & \([0,0.004]\) & Uniform \\ \(\lambda_{H\to U}\) & \([0,0.3]\) & Uniform \\ \(\lambda_{H\to F}\) & \([0,0.25]\) & Uniform \\ \(\lambda_{U\to F}\) & \([0.2,1]\) & Normal: \(\mu=0.4\), \(\sigma=0.1\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Priors for SEIR-H parameters.  
The interpretation of the different terms of this equation is similar to the Equation (10), but in a time-window of \(W\) starting on \(t_{j}\): The observed data is \(D_{j}^{R}=\{D^{R}(t_{j}+k)|k=1,\dots,W\}\), and these data are assumed to be normal-distributed around the daily reported cases \(dR^{\beta_{j}}\) from the simulation results with standard deviation \(\sigma_{j}^{R}\) (restricted to the interval [0, 10,000]). The likelihood function can be expressed as:  
\[P(D_{j}^{R}|\beta_{j},\sigma_{j}^{R})=\prod_{k=1}^{W}\frac{1}{\sqrt{2\pi} \sigma_{j}^{R}}\text{exp}\left[\frac{\left(D^{R}(t_{j}+k)-dR^{\beta_{j}}(t_{k} )\right)^{2}}{2(\sigma_{j}^{R})^{2}}\right], \tag{17}\]  
where, the daily reported cases \(dR^{\beta_{j}}\) are obtained from:  
\[dR^{\beta_{j}}(t_{k})=R^{\beta_{j}}(t_{k})-R^{\beta_{j}}(t_{k}-1),\qquad\text{ for }j<k\leq j+W, \tag{18}\]  
where \(R^{\beta_{j}}\) is the accumulated count of reported cases from the simulation using the parameter \(\beta_{j}\). The priors for \(\beta_{j}\) are also restricted to uniform distributions with a search range of \([0,0.8]\) (see Table 3). The value for \(\beta_{j}\) is estimated as the one that gives the maximum log probability (the logarithm of the right-hand side of the Equation (16)) performed using MCMC: \(\beta_{j}^{\text{opt}}=\{\beta_{j_{m}}|\text{max}_{m\in[1,N_{\text{iter}}]}\;l _{p}\}\). 30 Markov-chains with 3000 iterations for each chain, including 1000 warm-up iterations are used, and the average proposal acceptance probability adapt_delta[31] is set initially to 0.94. This average proposal acceptance probability is dynamically augmented by 0.01 when divergent transitions after warm-up are observed. In most cases, this value is remained in 0.94, and there are a few cases when it is augmented up to 0.97.
## 4 Simulation and Analysis  
From the analysis in Section 2, it can be seen that the multi-spot position detection accuracy of the QD based on OFDM is related to four key parameters: \(\omega\), \(\left(x_{j},y_{j}\right)\), \(R_{SN}\), and \(N\). Section 4 analyzes the impact of the four key parameters on the accuracy of multi-spot position detection based on the Monte Carlo simulation system. It is assumed that the radius of the QD is 4 mm, and the light beams were excited by 1 MHz, 2 MHz, and 3 MHz Cosine signals, which are sampled at a frequency of 32 MHz. This paper will discuss conditions when the three spots are located at the center of the QD and at the (0.1, 0), (0, 0), and (\(-\)0.05, 0).
## 3 Results
### Model 1: Order of 24-h Recalls and Nutrient Intakes
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Nutrients** & \begin{tabular}{c} **Mean of First 24-h** \\ **Recall and 95\% CI of the** \\ **Mean** \\ \end{tabular} & \begin{tabular}{c} **Mean of Second 24-h** \\ **Recall and 95\% CI of the** \\ **Mean** \\ \end{tabular} & \
\begin{tabular}{c} **Mean of Third 24-h Recall** \\ **and 95\% CI of the Mean** \\ \end{tabular} &
\begin{tabular}{c} **Mean of Fourth 24-h** \\ **Recall and 95\% CI of the Mean** \\ **Mean** \\ \end{tabular} \\ \hline Energy (kcal) & **1641.7** & **1557.7–1725.7** & **1480.4** & **1401.2–1559.5** & 1578.3 & 1498.5–1658.1 & 1561.6 & 1475.7–1647.6 \\ \hline Protein (g) & 78.1 & 74.3–82.0 & 74.4 & 70.2–78.6 & 76.3 & 72.3–80.3 & 77.4 & 73.3–81.5 \\ \hline Fat (g) & 68.0 & 63.4–72.6 & 62.2 & 57.8–66.6 & 64.7 & 60.2–69.2 & 65.7 & 61.0–70.4 \\ \hline Carbohydrates (g) & 182.1 & 172.0–192.2 & **161.5** & **152.4–170.6** & 174.5 & 165.2–183.8 & 166.9 & 157.0–176.8 \\ \hline Vitamin C (mg) & 74.0 & 65.0–83.0 & 57.6 & 50.6–64.6 & 58.2 & 50.5–65.9 & **55.1** & **48.8–61.4** \\ \hline Vitamin D (IU) & 67.6 & 58.6–76.6 & 65.6 & 52.8–78.5 & 82.3 & 65.7–99.0 & 70.2 & 58.8–81.6 \\ \hline Vitamin A (\(\mu\)g) & 602.4 & 480.1–724.7 & 599.0 & 490.1–708.0 & 594.0 & 480.2–707.8 & 499.6 & 412.8–586.3 \\ \hline Iron (mg) & 12.3 & 11.5–13.0 & 11.3 & 10.5–12.0 & 11.8 & 11.0–12.5 & 11.4 & 10.6–12.2 \\ \hline Calcium (mg) & 1078.2 & 1002.4–1153.9 & **931.2** & **864.5–998.0** & 1013.9 & 945.4–1082.3 & 955.4 & 889.6–1021.1 \\ \hline Magnesium (mg) & 241.8 & 225.5–258.2 & 223.2 & 208.3–238.0 & 224.7 & 211.3–238.0 & 216.6 & 202.1–231.0 \\ \hline Total water (g) & 2704.5 & 2590.5–2818.5 & 2528.8 & 2426.1–2631.5 & 2664.3 & 2567.6–2760.9 & 2615.8 & 2510.2–2721.4 \\ \hline Fiber (g) & 17.3 & 16.3–18.4 & 15.4 & 14.4–16.4 & 16.2 & 15.0–17.3 & **15.8** & **14.6–16.9** \\ \hline Vitamin K (ug) & 96.7 & 78.4–115.0 & 78.0 & 64.5–91.4 & 76.6 & 63.1–90.0 & 70.5 & 60.2–80.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean intakes of energy and of 36 macro- and micronutrients from 24-h dietary recalls (n = 388 individuals with obesity).
###### Abstract  
An improved neural network-based equalization method is proposed and experimentally demonstrated. The up-to-137 Gb/s transmission of four level pulse amplitude modulation (PAM-4) signals with 25 G class 850 nm optical devices is achieved over an in-house fabricated 40 cm optical backplane. An in-depth investigation is conducted regarding the impact of delayed taps and spans on equalization performance. A performance comparison of the proposed method with the traditional maximum likelihood sequence estimation (MLSE) and decision feedback equalization (DFE) is also undertaken. For the bit rate from 80 to 100 Gb/s, the proposed method achieves an adopted hard-decision forward error correction (HD-FEC) requirement at a received optical power (RoP) of \(-9\) and \(-8\) dBm, while DFE and MLSE cannot meet the HD-FEC requirement. When the bit rate increases from 120 to 137 Gb/s, the proposed equalization method still successfully maintains the acceptable system performance at an RoP of \(-4\) and \(-2.5\) dBm. Furthermore, the specific bit error rate (BER) performances for varied maximum achievable bit rate under different RoPs by applying MLSE and the proposed method are also analyzed. This provides an important potential solution to realize the future data centers.  
neural network; optical backplane; equalization +
Footnote †: journal: Applied Physics Letters  
137 Gb/s PAM-4 Transmissions at 850 nm over 40 cm Optical Backplane with 25 G Devices with Improved Neural Network-Based Equalization [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]  
Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200072, China; [EMAIL_ADDRESS] (Q.Z.); [EMAIL_ADDRESS] (Y.J.); [EMAIL_ADDRESS] (H.Z.); [EMAIL_ADDRESS] (C.D.); [EMAIL_ADDRESS] (S.D.); [EMAIL_ADDRESS] (Z.W.); [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (Y.S.); [EMAIL_ADDRESS] (J.Z.); [EMAIL_ADDRESS] (Y.W.); [EMAIL_ADDRESS] (T.W.); [EMAIL_ADDRESS] (M.W.)  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]
## 2 Automatic Geometric Corrections
### Geometric Modelling
#### 2.2.3 Model Refinement  
The remaining GCPs are used to correct the biases and random errors that are present in the supplied RPC coefficients. To remove the errors, the GCPs are introduced in refinement functions that are provided by the RPC adjustment equations [34]:  
\[Line_{i}=\Delta p+p(\phi_{i},\lambda_{i},h_{i})+\varepsilon_{L_{i}}, \tag{1}\]  
\[Sample_{i}=\Delta r+r(\phi_{i},\lambda_{i},h_{i})+\varepsilon_{S_{i}}. \tag{2}\]  
Equations (1) and (2) are formed for each GCP, where \(Line\) and \(Sample\) are the measured image coordinates, \((\phi_{i},\ \lambda_{i},\ h_{i})\) are object coordinates, \(\Delta p\) and \(\Delta r\) are refinement functions expressing the differences between the measured and the image coordinates calculated using RPCs, \(\varepsilon_{L_{i}}\) and \(\varepsilon_{S_{i}}\) are random errors, and \(p\) and \(r\) are the image coordinates obtained with RPCs.  
\(\Delta p\) and \(\Delta r\) are the refinement functions expressed as first order polynomials to calculate six unknown adjustment parameters (\(a_{0},\ a_{S},\ a_{L},\ b_{0},b_{S},b_{L}\)):  
\[\Delta p=a_{0}+a_{S}\cdot Sample+a_{L}\cdot Line, \tag{3}\]  
\[\Delta r=b_{0}+b_{S}\cdot Sample+b_{L}\cdot Line. \tag{4}\]  
Each non-eliminated GCP is used to compute the six parameters with least square adjustment and that are later used to correct the pixel positions during orthorectification. The adjustment can work with a single image or a block of images. Currently, the supplied RPCs, the adjustment parameters and a suitable DEM can be used to orthorectify VHR images in the reference coordinate system by either anindirect or a direct method. The indirect method projects the pixels from the object space to the raw image, while the direct method works in the opposite direction [26].
## 3 Recent Developments and Progress
### GNSS Ionospheric Sounding
[PERSON] and [PERSON] [47] conducted a detailed study and revealed the varying characteristics of ionospheric disturbances during Typhoon Chandu, utilizing data from GPS and GLONASS, including the amplitude of the disturbances and the speeds (Figure 9). Figure 8: Daily average TEC from GIM, GEDM, IRI-2020 in 2010 (**left**), and 2014 (**right**) (Reprinted with permission from Ref. [45]. 2024 [PERSON]). Figure 7: The spatial distribution of TEC at 13:00 UT during DOY 80, 170, 270, and 360 in 2023 for the double-layer SH model (**left**) and GIM-IGS (**right**) (Reprinted with permission from Ref. [44]. 2024 [PERSON]).
# A Case Study on the Design and Implementation of a Platform for Hand Rehabilitation
## 5 Results
### RehabHand Engineer Viewpoint
#### 5.2.2 Code Generation  
The semantics of the RehabHand is described using the language Xtend [49]. Xtend is a general-purpose programming language for the Java Virtual Machine. More specifically, Xtend is a dialect of Java programming language but has a more concise syntax. Any Java library can be used with Xtend. Usually, Xtend is used for code generation of a DSL developed with Xtext.  
Translators from RehabHand notation to three different devices were defined from Xtend specifications in Figure 6 inside _if-else_ statements (method doGenerate) which create variable hand with help of three different classes: BionicRobotHand, ArduinoRobotHand, and CytonHand.  
In the semantics of the doGenerate method, an attribute result carries the translated program through this method and finally outputs it in the file (see the last statement of the DoGenerate() method). Translated code in the attribute result is constructed during processing the DSL program. We read any library includes, calibration, and evaluate the repetition of actions with hand/fingers from the DSL program (see _for_ loop inside the doGenerate() method).  
Figure 7 presents a high-level part of the Xtend code to a support specific rehabilitation hand. The object model of any code that controls the rehabilitation device needs to correspond to the abstract class IRobotArm, where basic functions are defined. Variable hand corresponds to this abstract class (see Figure 6, again).  
Implementation of a bionic hand with the concrete class BionicRobotHand (Figure 7) corresponds to this abstract class. This class implements all abstract methods (calibration(), libraries(), repeat(), and fileName()) of its super abstract class IRobotArm. Very gen  
Figure 7: Top level implementation of Bionic rehabilitation hand code generation.  
erally speaking, the idea behind these methods is to provide part of the generated code returned for a specific rehabilitation hand.
# Assessing Gender Bias in Auditory-Perceptual Ratings of Tracheoesophageal Speakers
###### Abstract  
**Objective:** This study examined the relationship between gender and auditory-perceptual evaluation of tracheoesophageal (TE) speech. **Method:** We collected auditory-perceptual judgments of two features, _speech acceptability_ and _listener comfort_, from normal-hearing young adult listeners (\(n=16\)) who were naive to TE speech. Auditory-perceptual judgments were made for 12 TE speakers (6 men and 6 women) on two occasions separated by between 7 and 14 days. During the first session, listeners were deceived about the gender of the voice samples presented, and in the second session, listeners were informed of the true gender of the voice samples. **Results:** The findings suggest that a gender bias exists in perceptions of TE speech, and that female TE speakers tend to be disproportionately penalized when compared to their male counterparts when gender is known. **Conclusions:** These data provide insights into the potential influence of speaker gender on listener judgments of TE speech and the impact that such factors may have on communication. Our data indicate that listeners rate female TE speaker samples as less acceptable and less comfortable to listen to when the samples are known to be female speakers.  
**Keywords:** tracheoesophageal speech; auditory-perceptual evaluation; gender; laryngectomy; perceptual psychophysics +
Footnote †: journal: Medical  
into the vocal tract (i.e., the pharynx, oral, and nasal cavities), where it is modulated by the articulators to produce speech.  
Despite the success of restoring voice and speech to those who have undergone total laryngectomy, TE speech is nevertheless distinctly different from the normal laryngeal voice; these changes have been documented in several acoustic studies [2; 3; 4]. Despite the restoration of voice, TE speech remains altered in its overall perceptual voice quality to the listener. TE speech is characterized by variable levels of substantial aperiodicity (noise) secondary to the aerodynamic influence on the new voice source that is comprised of muscular tissues of the pharynx and esophagus [5]. While much individual variation in the auditory-perceptual quality of TE voices exists, it is clear when listening to TE speakers that one's voice is quite unlike that of a normal laryngeal speaker [6]. Because judgments of a speaker's voice quality are based on a construct that fundamentally evolves from the listener's perception, auditory-perceptual ratings are considered the gold standard for voice description in the areas of both clinical and experimental research [7; 8].
# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 3 Algorithm to Detect Vacant Parking Spaces
### Parking Space Detection Stage
#### 3.2.2 Feature Classification  
Because the features we intend to extract are corners at junctions between parking space lines, we defined two separate criteria to eliminate corners that are not part of the parking space lines: circular intensity proportion and maximum vertical intensity profile in the \(x\)-axis.  
1. Circular Intensity Proportion Figure 6a illustrates the circular intensity proportion of a given corner. The green dots indicate the corners, and the red circle indicates the area of intensity proportion. Firstly, to clearly distinguish parking space lines from the road, binary processing of the image is performed. Next, using the corner as the center of a circle, we use the radius r to calculate its circular intensity proportion. From this, it can be observed that 75% of the circular area is high intensity (i.e., parking space markings) and 25% of the circular area is low intensity (i.e., road). Therefore, we used the criteria to eliminate corners that are not part of the parking space lines. The process for this elimination is shown in Figure 6b.
2. Maximum Vertical Intensity Profile Lane boundary lines are represented as long, straight lines of high brightness in AVM images. We used this characteristic to project intensity values onto the \(x\)-axis, as shown in Figure 7, in which  
Figure 5: Feature detection results.  
Figure 6: Circular intensity proportion. (**a**) Diagram; (**b**) Flowchart.  
red dotted lines mark the region of interest. The areas of maximum vertical intensity profile fall approximately near the lane boundary line; therefore, we can eliminate corners that are not near the lane boundary line as they are not part of the parking space. Figure 8 shows the results of feature classification.