esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Research Opportunities for Medical XAI
### Tailoring of Explanations to Diverse User Needs  
Choosing the right XAI approach for each case is not a straightforward task, as the decision depends on many factors, such as characteristics of the concrete problem, of the application environment or of the available data, as different applications have different interpretability needs [112]. However, the most important aspect involves the requirement of the actual end-users of a system: clinicians and, in some cases, patients.  
The questions from the users will vary depending on their expertise and knowledge of the domain, and their views on an application problem. A clinician using an AI system to confirm a diagnosis who wants to know if the model is working properly but has no technical knowledge will need different explanations than the system builder who wants to check the models' performance, or a patient who is using an AI system by themselves. For these reasons, the kind and extent of explanations need to be adjusted to the specific type of user needs in order to build trust [141], without falling into over-trust [1].  
Depending on the prospective end users of the explanations, whether patients, clinicians or technical designers, it might be preferable to design different types of explainability, about the exhaustive workings of the model or about the relevant features being used, for example [16]. It should be identified in each case why users want explanations from the model, what information they need that said explanations contain, and how they prefer them to be presented [142; 143] (RQ3).  
If the factors above are not considered, accurate explanations will not match the needs of the users and be informative and understandable to them [144]. Achieving this user understanding might also need interaction between the system and its human user, in order to obtain further answers to different questions [11].  
To summarise, in order to enable collaboration between humans and AI, XAI outcomes have to be appropriately tailored to different end users, so more attention has to focus on these aspects of research: human-machine interaction and users' mental models. It is most likely that general solutions will not be feasible, so the context of the problem has to be taken into account, preferably with interdisciplinary collaboration, and combining different types of explainability to fulfill users' needs [145].
## 2 Methodology
### Power Matching  
The target engine is comprised of a single shaft, and the compressor and turbine operate at the same rotational speed. When the micro gas turbine engine is operating at a constant rotational speed, the torque produced by the turbine and the torque required by the compressor are identical, referred to as power matching. However, CFX does not provide a distinct model or option to reflect power matching. So, an additional subroutine was applied to adjust the fuel flow rate based on the torque of the compressor and turbine, as determined by the torque ratio of the compressor and turbine. This subroutine was incorporated into the analysis model to control the fuel flow rate during computation. The subroutine is based on the previous research [21], and the equation is as follows:  
\[\dot{m}_{i+1}=\dot{m}_{i}\times\left(\tau_{comp}/\tau_{turb}\right) \tag{1}\]  
In Equation (1), \(\tau\) represents the torque of the compressor or turbine, and \(\dot{m}_{i}\) represents the fuel mass flow rate.  
However, when Equation (1) is applied, the amount of change in the fuel flow rate becomes significant when the torque ratio changes rapidly. In such cases, convergence may take a considerable amount of time, and in severe instances, the analysis may diverge. To enhance the convergence of the analysis, a relaxation factor (RF) is introduced. As shown in Equation (2), the RF value of 0.005 is selected in this study.  
\[\dot{m}_{i+1}=\dot{m}_{i}\times(1-RF)+\dot{m}_{i}\times RF\times\left(\tau_{ comp}/\tau_{turb}\right) \tag{2}\]
# Analysis of the Propulsion Performance and Internal Flow Field of an Underwater Launcher
## 2 Experimental Research  
### Experimental Device  
The experimental system is depicted schematically in Figure 1, which comprises primarily a launching platform, a water tank, a high-speed video recorder, a pressure tracking system, and an ignition source.  
The structural diagram and dimensions of three gas-curtain projectiles with varying jet structures are illustrated in Figure 2 and Table 1, respectively.  
It can be found that the jet structure and size of projectile A is a central nozzle with a diameter of 3 mm, whereas the jet structure of projectile B is a 4 mm diameter central nozzle. The jet structure of projectile C is relatively complex, consisting of a central nozzle, four lateral nozzles, and four sidewall grooves. The head construction of the three projectiles is consistent, and their mass is equalized by slightly extending the projectile's length. All three projectiles use the same ammunition belt.
## 3 Fundamental Experiments
### Experiment 1: Different Torques  
Within the measuring range, the torque applied by the weights must be accurately measured in sine wave form. For this experiment, various torques were applied by varying the weight and the weight arm length with the fixed moment arm length of the load cell (\(d_{LC}=100\) mm); the maximum applied torques were \(\tau_{app}=0.0117\), \(0.1177\), and \(1.1772\) Nm (\(1.2\), \(12\) and \(120\) g\(\cdot\)m), respectively.  
In order to measure the applied torque accurately, calibration must be performed prior to the measurement. Generally, the sensor measures the output voltage without any applied torque, and then the output voltage is measured again by applying a known torque to determine the voltage offset and the gain value \(K\): _expected force or torque \(=K\times\)_(_measured voltage\(-\)offset_). However, in this experiment, we detect the two values by fitting the measured data to the sine curve by rotating a known weight. With this method, the two parameters can be determined with a single measurement, without the need for the formal two-step measurement for calibration. For the curve fitting, we can utilize MATLAB _lsqcurvefit_ function, which solves nonlinear curve-fitting problems in least-squares sense [30]. The curve fitting is performed after two revolutions of the weight, then standard deviations of the measured torques are computed based on the fitted curves.  
Figure 9 shows the measured torque after calibration with the fitted curves for the three cases. The computed RMSE for the three cases were \(0.1902\), \(1.2463\) and \(18.9636\) mNm, respectively. Therefore, the ratio of the RMSE to the maximum applied torques correspond to \(1.62\), \(1.06\) and \(1.54\%\), respectively, indicating fairly high accuracy. It was difficult to find particular correlation between the maximum applied torque and the measured RMSE ratio. One of the important reasons for these non-negligible measurement errors must be an immature control strategy for the actuating motor. Actually, a chattering phenomenon was clearly observed at high rotation speed. Improving the control method is expected to equalize this measurement accuracy. Additionally, in the data curves, minor fluctuations or shifts are repeatedly observed when the measured torque reaches \(0\) on the \(x\)-axis. In other words, as shown on Figure 9b, the ascent line and the descent line intersect near the origin, and the resultant graphs appear as elongated eight-shaped loops. The main cause of this hysteresis must be the backlash inside the motor; the average backlash of the gearhead combined with the motor in the catalog provided by the manufacturer is \(1.9^{\circ}\). It is not a small enough value to be ignored, and therefore the effect of the actuator backlash on this hysteresis phenomenon will be discussed in Section 5. Other causes of this hysteresis are assumed to be the clearance in the rod ends and the friction between components. However, they are minor factors and may not have a significant impact on the measurement results.
## 3 Results and Discussion  
### GCMS Measurements  
The analysis of the commercial essential oils used in this study was performed using gas chromatography (Agilent 7890B, Santa Clara, CA, USA) with an electron-ionization mass-selective detector (Agilent Technologies 5977A, Santa Clara, CA, USA). A sample was dissolved 10,000 times in n-hexane and injected (2 \(\upmu\)L) into a split/splitless inlet at 220 \({}^{\circ}\)C. The carrier gas, helium (purity 99.999%), was used with a constant flow of 1.0 mL/min. The compounds were separated on an Agilent Technologies HP-5 MS column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) using the following temperature program: start at 60 \({}^{\circ}\)C, 3 \({}^{\circ}\)C/min to 246 \({}^{\circ}\)C. The total run time was 62 min. Eluate was delivered to the mass spectrometer via a transfer line held at 240 \({}^{\circ}\)C. Ion source temperature was maintained at 230 \({}^{\circ}\)C, electron energy at 70 eV, and quadrupole temperature at 150 \({}^{\circ}\)C. Data were acquired in scan mode (\(m/z\) range 40-400). The compounds were identified by mass spectra comparison with libraries (NIST/EPA/NIH Mass Spectral Library 05 and Mass Hunter workstation Software, Unknown Analysis). The n-alkane mixture, consisting of a mixture of C\({}_{9}\)-C\({}_{28}\) n-alkanes corresponding to 820-2800 KI, was used as a standard for the determination of retention indices. Relative amounts of components, expressed in percentages (%), were calculated by normalization measurement according to the peak area in the total ion chromatogram. The results of the GCMS measurements can be found in the Supplementary Materials (Chromatograms of cinnamon bark, clary sage and tea tree, table with list of compounds).
# Super-Twisting Sliding Mode Control with SVR Disturbance Observer for PMSM Speed Regulation
## 3 STRL Controller Design
### Stability Analysis  
This section describes the stability of the proposed STRL-SMC method. We chose the Lyapunov function, as shown in Equation (18):  
\[V=\frac{1}{2}s^{T}s+\frac{1}{2\gamma}\hat{W}^{T}\hat{W}. \tag{18}\]  
where \(\tilde{W}\) represents the weight vector of SVR-DOB as \([\tilde{w}_{1},\tilde{w}_{2},\cdots\ \tilde{w}_{M}]^{T}\). The time derivative of Equation (18) can be obtained as follows:  
\[\dot{V}=s^{T}\Big{(}-bi_{q}^{*}-d(t)\Big{)}+\frac{1}{\gamma}\hat{W}^{T}\dot{ \tilde{W}}. \tag{19}\]  
Substituting Equation (17) into Equation (19), Equation (20) can be written as follows:  
\[\dot{V}=s^{T}\bigg{(}-k_{1}|s|^{\frac{1}{2}}\text{sgn}(s)-k_{2}\int_{0}^{t} \text{sgn}(s)d\tau+\hat{r}\bigg{)}+\tilde{W}^{T}\bigg{(}s^{T}\mathbf{x}-\frac {1}{\gamma}\dot{\tilde{W}}\bigg{)}. \tag{20}\]  
In addition, the adaptive control law can be designed as Equation (20).  
\[\begin{array}{l}\dot{\tilde{W}}=-\dot{\tilde{W}},\\ \dot{\tilde{W}}=\gamma s^{T}\mathbf{x}_{i},\end{array} \tag{21}\]  
where \(\gamma\) is a positive constant. Using Equation (21), the Equation (22) can be derived as follows:  
\[\dot{V}=s^{T}\bigg{(}-k_{1}|s|^{\frac{1}{2}}\text{sgn}(s)-k_{2}\int_{0}^{t} \text{sgn}(s)d\tau+\int_{0}^{t}|\dot{r}|d\tau\bigg{)}, \tag{22}\]  
where \(k_{2}\geq|\dot{r}|\). Therefore, it is proved through Equation (23) that the time derivative of Equation (18) is less than 0.  
\[\begin{array}{l}\dot{V}=s^{T}\Big{(}-k_{1}|s|^{\frac{1}{2}}\text{sgn}(s)-k_ {2}\int_{0}^{t}\text{sgn}(s)d\tau+\hat{r}\Big{)}\\ \leq s^{T}\Big{(}-k_{1}|s|^{\frac{1}{2}}\text{sgn}(s)\Big{)}\leq 0\end{array} \tag{23}\]  
Thus, the stability of speed loop with disturbance can be guaranteed via Equation (18) to Equation (23).
## Multipath Routing in Wireless Sensor Networks: Survey and Research Challenges  
**[PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 2,* * 1**Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia (UTM), Johor 81310, Malaysia; E-Mails: [EMAIL_ADDRESS] (M.R.); [EMAIL_ADDRESS] (B.D.); [EMAIL_ADDRESS] (K.A.B.)
2 Center for Advanced Image and Information Technology, School of Electronics & Information Engineering, ChonBuk National University, 664-14, 1 Ga, DeokJin-Dong, Jeonju, ChonBuk 561-756, Korea  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-63-270-3993; Fax: +82-63-270-2394.  
_Received: 8 November 2011; in revised form: 3 January 2012 / Accepted: 4 January 2012 / Published: 9 January 2012_  
**Abstract:** A wireless sensor network is a large collection of sensor nodes with limited power supply and constrained computational capability. Due to the restricted communication range and high density of sensor nodes, packet forwarding in sensor networks is usually performed through multi-hop data transmission. Therefore, routing in wireless sensor networks has been considered an important field of research over the past decade. Nowadays, multipath routing approach is widely used in wireless sensor networks to improve network performance through efficient utilization of available network resources. Accordingly, the main aim of this survey is to present the concept of the multipath routing approach and its fundamental challenges, as well as the basic motivations for utilizing this technique in wireless sensor networks. In addition, we present a comprehensive taxonomy on the existing multipath routing protocols, which are especially designed for wireless sensor networks. We highlight the primary motivation behind the development of each protocol category and explain the operation of different protocols in detail, with emphasis on their advantages and disadvantages. Furthermore, this paper compares and summarizes the state-of-the-art multipath routing techniques from the network application point of view. Finally, we identify open issues for further research in the development of multipath routing protocols for wireless sensor networks.  
keywords: wireless sensor networks; multipath routing; concurrent multipath routing; alternative path routing; load distribution; energy efficiency; reliability; QoS
## 2 Materials and Methods  
### Cell Cultures  
Human bone marrow-derived MSCs were purchased from LONZA (LONZA, Basel, Switzerland). The cells were cultured in the growth medium composed of DMEM (Sigma-Aldrich, Osaka, Japan), 10% fetal bovine serum (FBS; Hyclone, Logan, UT, USA), 100 ug/mL streptomycin (Sigma-Aldrich), and 100 U/mL penicillin (Sigma-Aldrich).  
To generate MSC spheroids, the hanging drop technique with minor modifications was employed [23]. Briefly, cells were suspended at concentrations of 2.5 \(\times\) 10\({}^{4}\), 5.0 \(\times\) 10\({}^{4}\), 10.0 \(\times\) 10\({}^{4}\), and 20.0 \(\times\) 10\({}^{4}\) cells in 20 \(\mu\)L of growth medium supplemented with 50 ug/mL L-ascorbic acid. These cell suspensions were then plated as hanging drops on the inner lid of a petri dish and incubated for 48 h. Once the cells aggregated, they were transferred to a 96-well ultra-low-binding plate (Corning Inc., Corning, NY, USA) and cultured for an additional 48 h. Finally, the cell aggregates were maintained in growth medium for 5 days to form MSC spheroids (Figure 1A; upper schematic image).  
C-MSCs were developed as previously reported [24]. Specifically, to produce C-MSCs of varying sizes, trypsinized MSCs were seeded onto 96-well, 48-well, and 24-well plates (Corning) at cell densities of 5.0 \(\times\) 10\({}^{4}\), 10.0 \(\times\) 10\({}^{4}\), and 20.0 \(\times\) 10\({}^{4}\) cells/well, respectively. The cells were cultured in a growth medium supplemented with 50 ug/mL L-ascorbic acid for 4 days. The confluent cells, which formed a cellular sheet consisting of ECM proteins, were detached from the culture plate by scratching the edge with a micropipette tip. The detached cellular sheets were then transferred to a 48-well ultra-low-binding plate (Corning) and maintained in a growth medium. To evaluate the efficiency of this transfer process from a 2D plastic plate to a 3D floating culture condition, the cell number before and after the transfer was assessed by trypsin dispersion. It was confirmed that 96.29 \(\pm\) 4.79% of cells successfully transitioned to the floating condition, indicating a highly efficient transfer process. During the culture period, the floating cellular sheet contracted to form a round clump of cells. After 5 days of incubation, mature round cell aggregates, known as C-MSCs, were obtained (Figure 1A; lower schematic image).  
Figure 1: (**A**) Schematic image of MSC spheroids and C-MSC culture schedule. (**B,C**) MSC spheroids and C-MSCs were generated from the indicated number of cells (within a range from 25,000 (25 K) to 200,000 (200 K) cells) as described in the Section 2. (**B**) Macroscopic images of each cell aggregate at day 5 are shown. (**C**) Graph indicates the average diameter of each aggregate calculated as (major diameter + minor diameter)/2. Values represent mean \(\pm\) S.D. of four samples per group. * \(p\) < 0.05, **_p_ < 0.01 (ANOVA). All images and graphs are representative of three independent experiments.
## 3 Optimal Design of the FCPM Model  
Figure 4 shows the optimization process. For optimization, first, the design of the experiment (DOE) should be considered. The DOE is essentially conducted using the Latin hypercube sampling (LHS) technique that has no overlap between the design variables and is easy to implement. There is a disadvantage that it takes a long time to conduct experiments or simulations, but the LHS technique can efficiently analyze the variable space in minimal time [24].  
A meta-modeling technique is used to obtain an approximate model that can replace the actual model. It also has the advantage of being easy to approximate and implement a complex design space using average values and deviations [25]. Among various meta-modeling techniques, this study used the kriging modeling technique. A kriging modeling technique with multiple design variables and a strong nonlinearity is used to set the objective function. Also, the kriging modeling technique can accurately interpolate sample data and it is also possible to model multiple local poles [26; 27].  
Also, a genetic algorithm (GA) was used as the optimization algorithm. The GA efficiently converges to the optimal design value while searching the entire range of the optimization design variables and has a low error rate and high reliability [28]. GAs first distribute a plurality of design points in the initial design phase and thereafter consider the degree of violation of the objective function value and constraints. This makes it suitable for each design point. GA can search for a better design point because the higher the suitability of the individual point, the higher the probability of participating in the mating and variant processes. Through this repetitive process, the GA converges to a better design point [29].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Unit** & \begin{tabular}{c} **Typical** \\ **Flared IPM Model** \\ \end{tabular} & \
\begin{tabular}{c} **Proposed** \\ **FCPM Model** \\ \end{tabular} &
\begin{tabular}{c} **Relative** \\ **Difference** \\ \end{tabular} \\ \hline Back-EMF & Vrms & 45.46 & 28.32 & \(\blacktriangledown\)37.7\% \\ (Phase) & \% & 16.3 & 45.8 & \(\blacktriangle\)29.5\% \\ THD & \% & 0.210 & 0.134 & \(\blacktriangledown\)36.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis results at no-load.  
The characteristic analysis for each sample case is performed through 2D FEM, and the electromagnetic field analysis was performed using JMAG Designer v17.1.
## 3 Results and Discussion
### Bias Stress and Recovery  
The conductivities of the polymers decreased slightly after depositing the SE, which was indicative of partial polymer dedoping [24]. Therefore, we included a negative V\({}_{\text{GS}}\) voltage during the transfer curve measurements. The transfer curve sweeping was typically carried out from \(-1.0\) V to 1.25 V, then back to \(-1.0\) V to scan the full switching potential of the device (i.e., including complete dedoping and redoping of the polymer; see Appendix A.3 Figure A3). A previous study showed that bias stress arose from the incomplete movement of ions between the SE and polymer [20]. Therefore, if the V\({}_{\text{GS}}\) scan rate (sweeping speed) is too slow or the V\({}_{\text{GS}}\) scan window is too large, then the bias stress will not be visible. Moreover, the drain-source voltage (V\({}_{\text{DS}}\)) should also be small enough to avoid current stress (see Appendix A.4 Figure A4). We performed five-hundred successive transfer curve measurements (from purple to red gradient in Figure 2a). This measurement, lasting for about 6 h, showed some bias stress effect in our device (i.e., the shift of the transfer curves). Nevertheless, a recovery measurement after the bias stressing indicated that the device completely recovered even after prolonged operation. The latter reported measurements were taken after giving sufficient pauses to avoid bias stress.  
Figure 2: **(a)** Bias stress stability measurement in an OECT where the PEDOT:Tos film is annealed for 1 h at 70 \({}^{\circ}\)C. The measurement spans approximately six hours, with a recovery measurement showing no evidence of device degradation. **(b)** Demonstration of a scan-rate-dependent transfer curve measurement where the inlet shows the hysteresis strength \(\Downarrow\)[19; 25]. Such a measurement reveals two distinct regimes of hysteresis, which we name kinetic hysteresis (blue gradients) or non-kinetic hysteresis (orange gradients), depending on whether they change for a further reduction of the scan rates. Kinetic hysteresis at higher scan rates is related to the incomplete movement of ions, whereas non-kinetic hysteresis observed at lower scan rates results presumably from a combination of multiple factors. The channel width W, length L, and gate distance g are 150 μm, 150 μm, and 50 μm, respectively. The film thickness d for the bias stress measurement is 80 nm and for the scan-rate-dependent measurement is 119 nm. The drain–source voltage (V\({}_{\text{DS}}\)) is kept constant at \(-0.2\) V. The scan speed for bias stress measurement is \(\approx\)180 mV s\({}^{-1}\).
# A Practical Positioning Method in End-Plate Surface Distance Measurement with Nano-Meter Precision
## 4 Accuracy Enhancing Methods
### Signal Processing System
#### 4.2.2 Measurements  
Besides the signal processing methods, the bidirectional measurement and averaging multiple measurement were adopted. The measurement experiment for the same step in two measurement directions was done in two days. In Figure 17, it is obvious that when the measurement direction  
Figure 16: Positioning signal of second surface.  
Figure 15: Positioning signal of first surface.  
was different, the result was relatively changed. On the first day, the measurement results were unsymmetrical to the direction in an approximate stair shape. On the second day, the measurement result was symmetrical to the direction in an approximate triangle shape. A system error was coupled into the measurement result, and it was changed with the time and the measurement direction. If there was no averaging, the measurement result difference was divergent and the maximum different was more than 0.7 \(\upmu\)m. If averaging, the final result was convergent, and the difference was less than 0.03 \(\upmu\)m.