esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low BER DB-PAM4 Adaptive Equalizer for Large Channel Loss in Wireline Receivers
## 3 Circuit Design  
### Tunable CTLE Equalizer with Complementary Symmetric Structure  
Due to the low-pass characteristics of the channel itself, the analog equalization module at the front end of the receiver plays a very important role, and its equalization directly determines the effect of DB processing. CTLE is essentially a high-pass filter whose function is to compensate for the channel bandwidth while generating a roughly Doubinary channel through adaptive adjustment. In a high-loss channel of \(-35\) dB, CTLE should provide about 16 dB of equalization strength. The traditional CTLE with a source-level degenerate CML structure composed of only NMOS cannot provide a large gm, which often leads to insufficient equalization strength. In this paper, CTLE with a complementary symmetrical structure is used to improve the gain and linearity of the equalizer. The complementary gm circuit shown in Figure 5a consists of PMOS and NMOS differential transistor pairs to increase the total gm. \(g_{mm1}\) and \(g_{mp1}\) are the transconductance of the input NMOS transistor and PMOS transistor, respectively. When \(g_{mm1}=g_{mp1}=g_{m}\), the CTLE output function of the complementary symmetric structure is expressed as [9]:  
\[H(s)=2\times\left(\frac{g_{m}}{1+\frac{g_{m}R_{s}}{2}}\times\frac{1+sR_{s}C_{ s}}{1+\frac{sR_{s}C_{s}}{1+\frac{sR_{s}C_{s}}{2}}}\right)\times\frac{R_{L}+sL_{s}}{1+s^{2} C_{L}L_{s}+sR_{L}C_{L}} \tag{3}\]Compared with the CTLE of the conventional structure, the high-frequency gain is improved by about two times. Although this complementary topology requires a higher supply voltage, the power dissipation can be reduced by \(1/2\) because the same current flows through the NMOS and PMOS transistors. This scheme requires a 20% higher supply voltage than the NMOS-only scheme to keep both devices in saturation. Combining lower currents with higher supply voltages results in power savings of about 40%. Since the DB-PAM4 signal can compress the signal energy to low frequencies, CTLE can provide equalization slightly below the Nyquist frequency. Through Matlab simulation analysis, the equalization frequency of CTLE is at [0.4-0.5] \(\times\) fband, which reduces the design difficulty of analog equalization. As shown in Figure 5b, the output of the resistor array is controlled by an adaptive algorithm to achieve adjustable equalization strength. The capacitor array can be manually controlled. However, under the high-speed data of 112 Gb/s, when the data frequency at the receiving end does not change, the degradation capacitance value Cs does not change.
# Early Predictability of Grasping Movements by Neurofunctional Representations: A Feasibility Study
## 4 Discussion  
The present study investigated whether EEG data can be used to discriminate between different grasping movements for a prosthetic application, i.e., within a sufficient time window. To this aim, different combinations of classifiers, feature extraction methods, time windows, EEG frequency bands, and developed learning scenarios were analyzed. We also examined whether pre-training with data from different subjects would enable the subsequent fine-tuning of classifiers for a target subject, which could simplify the efforts of amputees when training their individual prosthetic control.  
To our knowledge, the present results are the first to have used neurofunctional representations for a real multiclass prediction within the required time window for upper limb prosthetics, i.e., before preparation of the hand movement sequence for the respective grasping task. In contrast to other EEG studies [24; 25; 26; 27], our results are based on real, physically executed grasping kinematics and not on action recognition. Previous studies with similar aims but lower accuracies used only a two-class approach to discriminate between lateral and palmar grasps [46; 47]. Additionally, when discrimination was investigated for more than two different grasping types, either only a single object for the respective grasping pattern was investigated [48], standardized grasping taxonomies were not considered [27], or signal intervals unsuitable for prosthetic tasks were used [28; 29]. However, the discrimination accuracy of ADLs found in the present study seems to depend on several factors, which will now be discussed.  
Figure 7: Balanced discrimination accuracies (mean \(\pm\) sd over all grasps) of the classifiers LR in tangent space (TS_LR) and SVM in tangent space (TS_SVM), and scenarios A–D from Figure 4 trained with the grasping posture categorization C5 as a function of EEG signal frequency bands and epochs \(E_{L}\) (\(-1\) s–\(0.5\) s) and \(E_{S}\) (\(-1\) s–\(0\) s).
# Machine Learning Methods with Decision Forests for Parkinson's Detection
## 2 Literature Review
ERT has been proposed to build on the training samples rather than bootstrap samples. However, another decision forest called Forest by Penalizing Attributes (ForestPA) Decision Forest uses bootstrap samples to build the forest. After generating samples, ForestPA selects the splitting attribute for a node using a merit value of the attribute. The merit value of attributes is dynamic and is computed by multiplying the classification capacity with the current weight of the attribute. The default weight for each attribute is initialized to 1.0 while building the first decision tree in the forest. The weights are kept on changing during the tree building process. Machine Learning (ML) proved to be a great tool in many fields of medical science [41; 42; 43; 44; 45; 46; 47; 48; 49] including but not limited to Parkinson's Detection [50; 51; 52; 53; 54; 55; 56; 57]. For Parkinson's Disease Detection (PDD) Random Forest as a decision forest algorithm is used in combination with other promising preprocessing algorithms. For instance, [PERSON] et al. [54] proposed a PD detection technique using a combination of Random Forest and Mann-Whitney U test algorithm to predict Parkinson. Their approach reveals an impressive accuracy of 84.96%, sensitivity of 84.52% and specificity of 85.71%. Random Forest is also used along with Principal Component Analysis (PCA) for segregation of PD from control subjects [24]. The features generated by PCA boost the detection accuracy by up to 96.83%. This is due to the little number of features results in the formation of adequate trees for prediction. [PERSON] et al. [9] compared many supervised classification techniques along with Random Forest on acoustic signals to predict Parkinson. The research reveals that the Random Forest impressively detects the presence of [PERSON]'s disease from the bandwidth, formant frequencies, intensity, and vocal-based features. [PERSON] et al. [23] proposed a PD detection model using Random Forest and Synthetic Minority Over-sampling Technique (SMOTE). The tree formation realizes a detection accuracy of 94.89%. Similarly, an automated detection of [PERSON]'s disease based on Minimum Average Maximum Tree (MAMT) been proposed for [PERSON]'s detection [55]. The MAMT approach took the help of singular value decomposition method to achieve a superb detection accuracy of 92.46%. Further, boosting approaches such as XGBoost [56] and Gaussian Mixture Model (GMM) [57] proved to be a reliable [PERSON]'s detection approach. These boosting models not only efficiently detect the presence of [PERSON] but also found to be precise in the detection process.
## 2 Design of APK with VSA  
Compared with rigid actuators, researchers have validated several attractive performances of SEAs, including low mechanical output impedance, impact absorption, and passive mechanical energy storage [34]. The internal elasticity in SEAs makes it possible to convert the force control of the joint into position control [35]. SEAs have been widely utilized in cooperative robots, rehabilitation, and assistive robotics [36; 37]. However, a lighter and more flexible bionic knee prosthesis remains a challenge.  
Based on the above mentioned, a compact variable stiffness actuator (VSA) is developed for the active prosthetic knee in this paper. Figure 1 illustrates the composition of our VSA active knee joint, and the main parameters of the APK are shown in Table 1. This structure is improved compared to the previous work [38]. The VSA-Joint utilizes two different size motors to regulate the knee angle and the stiffness of joints, respectively. That is, there are two types of motion in the VSA-Joint: (1) joint rotation provided by the main motor; and (2) stiffness regulating motion provided by stiffness tuning the motor.  
For the joint rotation motion, the main motor provides the power for knee joint motion, which firstly is passed on the damping mechanism through a spur gear. The damping mechanism mainly consists of four parts: a cam disk, cam rollers, a roller cage, and a rectangular spring. The cam disk is combined to the joint base and rotates under the drive of the spur gear. Three cam rollers are fixed to the roller cage, and the roller cage is connected to the guiding shaft. There are three curve-surface slots evenly machined on the cam disk along the axial direction. Then, the cam rollers can roll on the slots, and the torque from the cam disk is indirectly transmitted to the guiding shaft; at the same time, the rectangular spring is compressed by the upward motion of the rollers. This mechanism supplies cushion corresponding to the deflection of the flexible joint. The main motion generated by the main motor is implemented to a level gear whose driving gear is fixed to the guiding shaft, and the follower gear is connected with the joint shaft. The thigh connection terminal is fixed to the joint shaft. Finally, the main motor drives the knee joint to rotate.  
For the stiffness motion, the joint stiffness can be controlled by the stiffness regulator, which involves a stiffness tuning motor, a spring support plate, a ball screw, and a small spur gear. The stiffness tuning motor is utilized to adjust the rectangular spring precompression, which can be decreased or increased by actuating the spring support plate up and down through the ball screw.
# Validation of Copernicus Sea Level Altimetry Products in the Baltic Sea and Estonian Lakes
## 2 Material and Methods
### In Situ Data
When the difference between the \(SSH_{GNSS}\) from two receivers on the same time tag was larger than \(\pm 0.10\) m, the measurement was excluded from further processing. Cleaned heights from two receivers on the same time tag were averaged. Next, the temporally moving-averaged \(SSH_{GNSS}\) was calculated in order to smooth out the fluctuations of the vessels. The window size for the moving average filter was selected based on the velocity of the vessel and the GNSS data rate (2 s) in order to obtain window size, which is close to the altimeter along-track resolution (300 m). For example, if the vessel's velocity was 10 m/s and the GNSS sample rate was 2 s, the moving average window 15 was chosen, since \(15\times 2\times 10=300\) m. Spatially moving-averaged \(SSH_{GNSS}\) above WGS84 ellipsoid were compared with \(SSH_{SA}\) for altimetry validation:  
\[\Delta SSH_{GNSS}=SSH_{SA}-SSH_{GNSS}. \tag{6}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Waterbody** & **Gulf of Finland** & **Gulf of Riga** & **Lake Peipus** & **Lake V\%tsjärv** \\ \hline Date & 25 April 2019 & 20 June 2019 & 19 June 2019 & 13 July 2019 \\ Weather conditions & Light wind, waves & Calm, & Calm, & Calm, \\  & up to 1 m & no waves & no waves & no waves \\ Profile length & 25 & 54 & 33 & 10 \\ (km) & & & & \\  & & & & \\  & & & & \\ Vessel & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: General parameters of the GNSS kinematic measurements. Figure 5: Research vessel SALME (**a**) and the GNSS receivers on top of the wheelhouse (**b**). The total station (**a**) was used to connect the GNSS antenna’s reference point with sea level. Note that the error sources for the determination of \(\Delta SSH_{\text{GNSS}}\) are: (i) errors in the determination of the GNSS antenna height from the water level; the uncertainty of the sea surface height from the GNSS antenna is at least \(\pm\)0.020 m and it is possible that the GNSS antenna connection to the water level could also cause a systematic shift; and (ii) the accuracy of the GNSS solution itself (Table 1). Multiple-frequency Trimble R8s and Trimble R4-3 receivers were used for the GNSS kinematic measurements on the boat, but the vertical uncertainty could be \(\pm\)0.015 m + 1 ppm RMS according to the manufacturer's specification. In addition, a pressure sensor-based buoy (Figure 6) was deployed on the seafloor of the Gulf of Riga (Figure 2) for five months (from June to November 2019). The crossing point of the S3A pass 186 and 397 ground tracks (58\({}^{\circ}\)07\({}^{\prime}\)12\({}^{\prime}\) N and 23\({}^{\circ}\)32\({}^{\prime}\)06\({}^{\prime}\) E) was chosen for the buoy location. Sea-level data were recorded by buoy at 5-min intervals. The buoy's records referred to ellipsoid WGS84. For this, the water column height above the sensor was measured by tape and sea surface height above the ellipsoid WGS84 was measured with GNSS during the buoy mounting.
## 4 Data Obtained
### Calculating Confidence Intervals  
Confidence intervals [13] represent an estimate of a range of values pertaining to a population of values. This parameter is found through a sample model calculated from the collected data.  
The confidence interval is expressed in percentages or confidence levels, with 90%, 95%, and 99% being the most commonly used [14]. In order to calculate the lower and upper limits of the confidence intervals, the following formulas were used:  
\[Inferior\ Limit=\bar{x}-z.\frac{\sigma}{\sqrt{n}} \tag{2}\]  
\[Superior\ Limit=\bar{x}+z.\frac{\sigma}{\sqrt{n}} \tag{3}\]  
Following the formula, \(\bar{x}\) is the sample mean value; \(\sigma\) is the population's standard deviation; \(n\) represents the sample size; and finally, Z* represents the appropriate value of the normal distribution for the desired confidence level. For the cases illustrated, we had:  
Z=1.645, for 90% confidence.  
Z=1.96, for 95% confidence.  
Z=2.58, for 99% confidence.  
The 90%, 95%, and 99% confidence intervals were then calculated, resulting in a total of 3 distinct ranges of CI (confidence intervals), corresponding to degrees of confidence, for each threshold.
###### Abstract
Figure 1 shows a block diagram of the color-independent visual-MIMO method based on the color space [10]. At the transmitter, it performs the color-space-based modulation on the encoded data. Each constellation point in the light color space represents a corresponding color and a target color is the average of all appropriate constellation points. Here, target color indicates the wanted color of the LEA lighting. The target color corresponding to any VLC signal can be chosen from the gamut area and an information data stream can then be sent by choosing the appropriate constellation diagram  
Figure 1: Color-space-based color-independent visual-MIMO transceiving procedure using image processing. corresponding to this target color. The proposed visual-MIMO system thus enables color-independent communication. Then, the serial to parallel conversion is performed on the modulated symbols by the LEA size. These symbols are then mapped onto LEA as prescribed order. At the receiver, a set of symbols (colors) are detected by the image sensor and the symbol (color) decision corresponding to each LED is performed by using image processing. Finally, the output of demapping in the color space is converted into a serial data stream that is sent to an information sink. Because the camera is used as a receiver, a variety of distortions might occur when projected onto the image sensor, thereby adversely affecting the performance of color-independent visual-MIMO. In this paper, we propose an LEA detection and tracking method, shown in the image processing step in Figure 1, so as to improve SER performance. The remainder of this paper is organized as follows: Section 2 provides a detailed explanation of the proposed LEA detection and tracking techniques; Section 3 represents the results and discussion; finally, Section 4 concludes the paper.
## 3 Experimental and Results
### Mineral Composition  
X-ray diffraction, XRD, was used to determine the majority and minority (with content up to 1-2% (weight percent).) minerals in the kurkar stone, see Figure 11.  
The main minerals determined were calcite, CaCO\({}_{3}\), and quartz, SiO\({}_{2}\). The secondary minerals found in the kurkar sandstone were albite, NaAlSi\({}_{3}\)O\({}_{8}\), and biotite K(Fe, Mg)\({}_{3}\)AlSi\({}_{3}\)O\({}_{10}\).  
Petrographic analysis of the kurkar stone was performed in a thin section by polarized light microscope Olympus-2. The petrographic thin sections were prepared after impregnation with polystyrene resin under vacuum, then slicing and polishing a slide to 0.03 mm thickness. The film sections were also stained with Alizarin Red to differentiate calcite from dolomite. The petrographic descriptions were done according to [17; 18].  
The investigated kurkar stone consists of well-sorted subangular to subrounded quartz, minor amounts of feldspar, heavy minerals, and allocations, all ca. 0.2 mm in size and constituting no less than 50% of the texture, Figure 12a. Sand grains are firmly cemented by micro-sparitic to dominantly micritic calcite of the different grain-size with minor porosity. Figure 12 shows the contact between the micritic cement (upper part of the picture) and the microsparitic, coarser calcite cement (lower part of the photo). Calcitic cement of kurkar is very heterogeneous, Figure 12a. Most pores are in micritic cement, Figure 12b. There were observed signs of recrystallization of calcite in the pores, Figure 12b.  
Figure 11: XRD diffraction of kurkar sandstone.
## 3 The Study
### Simulation Setup  
The parameters for the networks A and B mainly consist of the following:  
* Network A: \(E=2\) eNBs and \(S=6\) sensors. The sensors are equally distributed to the eNBs and directly transmit packets to the server every \(t_{per}^{A}=1\) s in the periodic and \(t_{ala}^{A}=0.5\) s in the alarm conditions.
* Network B: \(E=2\) eNBs, \(G=2\) aggregators and \(S=6\) sensors (equally distributed between the aggregators). The sensors always transmit data to the aggregator every \(1\) s.  
In the periodic condition, the aggregators communicate to the server every \(t_{per}^{B}=2\) s, while in the alarm condition, they transmit to the server every \(t_{ala}^{B}=1\) s.  
Then, we simulated two scenarios for each network and conditions: (i) varying the number of sensors, i.e., \(S=\{3;\,6;\,10;\,40;\,50\}\), each generating packets at the application layer with a fixed size of \(P=40\) B, and the number of aggregators \(G=\{1;2;\,2;\,4;\,5\}\) for the network B, respectively, and (ii) varying the packet's size \(P\), i.e., \(P=\{40;\,80;\,120;\,400;\,800;\,1000;\,2000\}\) bytes, with the number of sensors referred in the two networks above.  
The performance indices considered in these simulations are the End-to-End (E2E) delay (from the sensor to the server) and the goodput, i.e., the valuable sensor's measurement data that reach the collector application on the server in the unit of time. For those two indices, we computed their average over all the packets received by the server.  
Finally, the default simulation run lasts five minutes.
## 2 Mathematical Model
### Model Assumptions and Construction  
The following simplified assumptions were made about the model:  
1. Assuming that there is equal heat exchange between the working medium side and the water side, the heat loss is not taken into account.
2. The flow pressure drop in the condenser is ignored, that is, the momentum equation is not considered.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Name** & **M** & \(\mathbf{T_{crit}}\)/\({}^{o}\)C** & \(\mathbf{P_{crit}}\)/MPa & **ODP-GWP** & \(\mathbf{T_{bp}}\)/\({}^{o}\)C** & \(\mathbf{T_{tp}}\)/\({}^{o}\)C & **Safety a** \\ \hline R245 fa & 134 & 154.01 & 3.651 & 0-790 & 15.14 & \(-\)102.1 & B1 \\ R114 & 171 & 145.68 & 3.257 & 0.85-4 & 3.59 & \(-\)92.5 & A1 \\ R123 & 153 & 183.68 & 3.662 & 0.02-77 & 27.82 & \(-\)107.15 & B1 \\ R601a & 72 & 187.20 & 3.378 & 0-20 & 27.83 & \(-\)160.5 & A3 \\ R601 & 72 & 196.55 & 3.370 & 0-20 & 36.06 & \(-\)129.68 & A3 \\ R141b & 117 & 204.35 & 4.212 & 0.086-0.15 & 32.05 & \(-\)103.47 & A1 \\ R113 & 187 & 214.06 & 3.392 & 0.9-1.55 & 47.59 & \(-\)36.22 & A1 \\ \hline \hline \end{tabular}  
* Refrigerant safety group classification. 1: No flammability; 2: Lower flammability; 3. Higher flammability; A: Lower toxicity; B: Higher toxicity.  
\end{table}
Table 1: Properties of 7 kinds of working fluids.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline \(v_{cw}\) & 0.2 & m/s \\ \(T_{cw}\) & 20 & \({}^{\circ}\)C \\ \(P_{cw}\) & 1000 & kPa \\ \(P_{hw}\) & 1000 & kPa \\ \(n_{hw}\) & 10 & kg/s \\ \(T_{air}\) & 25 & \({}^{\circ}\)C \\ \(T_{sh}\) & 5 & K \\ \(T_{sc}\) & 0 & K \\ \(\Delta T_{p}\) & 10 & K \\ \(n_{exp}\) & 1500 & r/min \\ \(D_{exp}\) & 0.3556 & m \\ \(\eta_{pump}\) & 0.7 & – \\ \(T_{thr,initial}\) & 125 & \({}^{\circ}\)C \\ \(T_{c,initial}\) & 45 & \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 2: The basic input operating parameters.  
Figure 2: Working fluid calculation comparison.  
3. The steady flow satisfies the mass equation (continuity equation), only considering the energy equation. 4. The mass flow rate at each position in the heat exchanger remains unchanged. The following steps were included in the model's construction process: 1. Under the initial design values in Table 2, calculate and obtain the initial value of the evaporation pressure when the expander output power is maximum, which was 827.46 kPa. 2. Calculate the pressure drop and actual heat exchange area of the heat exchanger, then select the appropriate type from the 12 brazed plate heat exchangers provided by the manufacturer [48]. 3. Establish the range of changing working conditions through the self-regulation of the thermal parameters like the heat transfer coefficient and mass flow rate, etc., based on the structural and thermal parameters of the chosen heat exchanger type. The altering rules of the parameters under various working conditions were therefore obtained. The structural parameters of the selected heat exchanger are shown in Table 3.