esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Slope Movement Velocity Analysis
(6) The working face in panel A did not pass through M07 and M08 and the displacement velocity at these monitoring points was less than 20 mm/d, and remained in a stable state. (7) Monitoring point M05 was located on the ground in a position corresponding to the center of panel B. As the working face in panel B moved gradually closer to and away from M05, the displacement velocity first increased and then decreased, and finally remained around 20 mm/d. From the velocity curve, Vx \(<\) Vy \(<\) Vz, and the maximum moving velocity in the plane occurred earlier in the direction in which the land was sinking. The results could be summarized as follows. (1) The change in the speed of ground movement at the monitoring points was mainly affected by factors such as mining, topography (elevation, slope, and slope direction), and rainfall. When the speed of ground movement at the monitoring point was only slightly affected by mining, the velocity was within 20 mm/d. At this time, although the overlying rock formation was active, the ground stability was acceptable. (2) Because the position of the monitoring points in the two panels and the corresponding terrain were different, the magnitude of the speed of ground movement in the same direction and the time required to achieve stability in the two panels  
Figure 5: Rainfall statistics in the slope area. were different. (3) Under the influence of rainfall, the moving velocity suddenly increased. Although the duration of the increase was short, continuous rainfall over a short period of time resulted in a large disturbance to the stability of the slope body.
# Improvement of Power Converters Performance by an Efficient Use of Dead Time Compensation Technique
## 3 Proposed Dead Time Compensation Technique
### Improvement in Power Quality Parameters of Induction Motor  
The induction motors are major energy-using equipment, any issues with their smooth operation are extremely significant. The unavoidable delay in signal switching can cause sub-harmonics leading to waveform distortion in the current signal. Additionally, it can also lead to pulsation in torque, and reduction in the rotational speed of the motor, manifesting in heat dissipation from the motors. These distortions and the concomitant damages can be significantly reduced by employing an efficient DTC technique. The DTC technique can restore the current to one looking more like the sinusoidal curve which entails the remedy of the aforementioned drawbacks in motor performance. DTC insertion in the inverter circuitry has been shown to mitigate the harmonic distortion and load torque pulsation. Furthermore, DTC can lead to practical improvements in motor performance such as smooth operation, limited torque ripples, low noise, and enhance efficiency in operation due to lower harmonic losses.  
The proposed DTC technique has been applied to the inverter circuit with the harmonic distortion mentioned above. Figure 12a-c show the improvement in the current waveform distortion, motor speed, and torque pulsation respectively. In Figure 12a-c, using DTC application shows better performance in comparison with using DTC application for the motor speed and the torque pulsation, respectively. The left side of these figures represents these parameters before the application of DTC, while the right sides represent the post DTC parameters' behavior.  
Figure 11: Without/with 10 \(\upmu\)s DTC (**a**) fundamental voltage (\(V_{a}\)) magnitude (**b**) fundamental voltage (\(V_{a}\)) phase angle.
## 2 Basic Theory
### Basic Concepts of Neighborhood Rough Sets
The classification ability of a decision information system can be measured by approximate classification quality (also called approximate dependence). In the neighborhood approximation space \((U,N)\), the approximate classification qualities of \(d\) with respect to \(B\) and \(\delta\) are:  
\[\gamma_{B}^{\delta}(d)=\text{Card}(POS_{B}^{\delta}(d))/\text{Card}(U) \tag{7}\]  
where \(\text{Card}(\cdot)\) is the cardinality of the set. Let \(B\subset C\), from Formula (3), for any \(a\in C-B\), The following equation is obtained  
\[\delta_{B\cup\{a\}}(x)=\delta_{B}(x)\cap\delta_{a}(x)\subseteq\delta_{B}(x) \tag{8}\]  
Based on Equations (5)-(7), it follows that  
\[POS_{B\cup\{a\}}^{\delta}(d)=\underset{Y\in U/\{d\}}{\cup}\underline{N}_{B \cup\{a\}}^{\delta}(Y)\supseteq\underset{Y\in U/\{d\}}{\cup}\underline{N}_{B}^ {\delta}(Y)=POS_{B}^{\delta}(d) \tag{9}\]  
\[\gamma_{B\cup\{a\}}^{\delta}(d)=\text{Card}(POS_{B\cup\{a\}}^{\delta}(d))/ \text{Card}(U)\geq\text{Card}(POS_{B}^{\delta}(d))/\text{Card}(U)=\gamma_{B}^{ \delta}(d) \tag{10}\]  
Equations (8)-(10) indicate that the neighborhood decreases as the number of features increases, while the relative positive region and approximate classification quality increase with an increasing number of features. By utilizing the monotonicity property of approximate classification quality, a forward-type feature selection algorithm can be constructed, also known as the one-by-one addition method. This method involves selecting one feature at a time from the candidate subset and adding it to the minimal feature set until there is no further change in its approximate classification quality.
# Quasi-Isotropic Hybrid Dielectric Resonator Antenna--Bow-Tie Patch with Harmonic Suppression
## 1 Introduction
### Design of Proposed Antenna Having a Quasi-Isotropic Pattern  
Figure 1 shows the configuration of the proposed antenna. The bow-tie patch is loaded into the DRA to implement the quasi-isotropic pattern. The microstrip seventh-order Chebyshev low-pass filter is used in the feed line to suppress harmonics. Here, the DRA and bow-tie patch antenna is simultaneously fed by the metal post located at pole_d from the feeding direction and has a length of pole_h and is electrically connected to the microstrip filter located in the feeding line. Operating in the TM01 mode, the DRA is equivalent to a vertical dipole and the bow-tie patch is equivalent to a horizontal dipole to realize a quasi-isotropic pattern. The DRA and the ground size were \(39\times 40.18\times 16.76\) mm\({}^{3}\) and \(40.6\times 49.4\) mm\({}^{2}\), respectively. The substrate used was Taconic RF-35 (\(\varepsilon=3.5\), \(tan\delta=0.0025\)),  
and the thickness was 1.52 mm. The alumina (\(\epsilon=9.6\), \(tan\delta=0.0002\)) was used to design DRA. The design parameters are presented in Table 1.  
Figure 2 shows the principle of implementing the quasi-isotropic pattern of the proposed antenna. As mentioned above, to implement a quasi-isotropic pattern, two omnidirectional patterns that have an orthogonal relationship are required. Figure 2a shows the DRA and bow-tie patch antenna used to implement the quasi-isotropic pattern in this paper. The metal post inside the DRA feeds the DRA and the bow-tie patch antenna, creating \(E_{1}\) and \(E_{2}\) fields, respectively. The \(E_{1}\) and \(E_{2}\) fields are orthogonal to each other, and as shown in Figure 2b, an omnidirectional pattern that has an orthogonal relationship is implemented. Therefore, two omnidirectional patterns that have an orthogonal relationship are synthesized to realize a quasi-isotropic pattern.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline gnd\_x & 40.6 & tri\_d & 22.29 & l\({}_{1}\)1 & 1.5 \\ gnd\_y & 49.4 & radii & 2.5 & w\({}_{1}\)2 & 11.77 \\ dr\_x & 39 & outer\_radi & 8.28 & l\({}_{2}\) & 3.30 \\ dr\_y & 40.18 & pole\_d & 28.19 & w\({}_{1}\)3 & 0.425 \\ dr\_z & 16.76 & pole\_h & 18.28 & l\({}_{1}\)3 & 5.40 \\ bow\_x & 25 & w\({}_{1}\) & 3.344 & w\({}_{1}\)4 & 11.77 \\ bow\_y & 14 & l\({}_{1}\) & 3.29 & l\({}_{1}\)4 & 4.5 \\ tri\_y & 3.28 & w\({}_{1}\)1 & 0.425 & l\({}_{1}\)8 & 4.33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters of the proposed antenna (unit: mm).  
Figure 1: Configuration of the proposed antenna: (**a**) overall view; (**b**) side-cut view (yz plane); (**c**) back side-cut view of ground (xy plane); and (**d**) front side-cut view of ground.
# Review of Advanced Medical Telerobots
## 3 Autonomy Levels  
This section aims at describing different levels of autonomy and embedded intelligence in various telerobotic solutions in the context of teletherapy. We consider a gradual increase from direct telerobots with no intelligence to fully-autonomous telerobots.  
The gradual increase in autonomy was first defined by [PERSON] et al. in 1978 [311] with ten levels of autonomy (including none and full autonomy). [PERSON] et al. reduced this scale in 1990 [312] to (1) direct continuous teleoperator control, (2) shared continuous teleoperator control, (3) discrete command control by the human operator, and (4) supervisory control. In 2016, [PERSON] et al. [313] modernized the terms into bilateral teleoperation, shared control, supervised control and supervisory control, modified the order of shared and supervised control, and introduced a new level, i.e., traded control, which corresponds to a mix in time between teleoperation and shared control, and formalized a generic software architecture related to that. [PERSON] et al. [314] and [PERSON] et al. [304] also revised these terms (with six levels). In this paper, we propose to bring some refinements to the scale proposed in [313], taking into account [304; 314], and proposing a rising score of autonomy order for tele robotic systems (details can be found below and in Figure 7):  
1. Bilateral teleoperation: features any exchange of position (and force) between the leader and the follower robots (only position exchanges were envisaged in [313]) and uses a SL/SF topology defined in Section 2.1;
2. Shared control which can be split in two subcategories: * Assisted Shared Control: the operator is assisted with an auxiliary feedback added to the teleoperation such as continuous vibratory feedback, or virtual fixture. This is in agreement with the definition of \"shared control\" in [312; 313];
* Multi-user Shared Control: corresponds to architectures which include several operators sharing the control of the same telerobot;
3. Traded Control where the users alternate between bilateral teleoperation and shared control [313] or supervisory control [314]. They may switch assisted/automated control off when it does not comply with the task requirements.
4. Supervisory Control, where some high-level information (parameters and/or offline programming) is sent to the follower robot to be reproduced with some degree of controlled autonomy, knowing that in main medical applications, there are no very long-distance constraints.
## 3 LDAF Background and Overview  
The Low-bandwidth Distributed Application Framework (LDAF) [40] is comprised of a LDAF server and corresponding client entities--Figure 1. A LDAF client runs on an end device, e.g., an (constrained) IoT device, and connects to the LDAF server via one of the available connection mechanisms. At the moment WebSockets, HTTP, and The Things Network (TTN) [41] are implemented. The LDAF server connects to arbitrary cloud APIs, e.g., cloud platforms for IoT, Google services, Ethereum client API, etc., to enable the following.  
* API aggregation: the LDAF server adapts to specific external API requirements (communication protocols, message formats, and authentication) and adapts it to a common message exchange between itself and the client.
* Efficient binary message serialization: the content of the messages between the client and the server is binary encoded using Protocol buffers (ProtoBuf, PB) [42] or similar binary encoding mechanism to reduce the message size. This is especially efficient compared to exchanging structured text--based messages (e.g., JSON and XML).
* Application adaptation: server can adapt the application logic, e.g., to reduce the number of messages passed to the client to further reduce the communication requirements. It can also take over a part of a device's application execution (heavy computations, hash calculations, caching, etc.), and thus not only reduces the communication bitrate, but the processing and storage requirements at the end device too. This allows for applications beyond simple aggregation of APIs.  
The LDAF framework is characterized by modular architecture, as depicted in Figure 1. It enables the flexible addition of new connection mechanisms and new services. A service is a particular mapping between an API, or APIs, and the clients that are communicating with these APIs through the LDAF server. There can be an arbitrary number of services implemented in LDAF server.  
Without LDAF the device would connect directly to the external API, and would therefore have to comply with the protocols, syntax, semantics, and application logic imposed by the API. These might not be well adapted to the device constraints.
## 3 Physical Layer  
The physical layer (PHY) represents the boundary between the computational domain and the physical medium. Its role in adaptive communications is twofold. First, it is able to capture medium information necessary for adaptation processes. In the specific case of addressing spectrum scarcity in _IoT_, the technique to probe the spectrum utilization is termed _spectrum sensing_, which is one of the foundations of adaptive communication strategies. Second, it plays an important part in putting adaptation decisions into practice, since a major aspect of adaptations involve modifying the PHY layer parameters like operating frequency and transmission power.
## 4 Neuoprotective Peptides and Their Therapeutic Application in HIE
### Studies Using CARPs in Animal Models of HIE
#### 4.4.8 Tat-NR2B9c/na-1  
The development of NR2B9c as a neuroprotective agent has recently been discussed in detail [146]. Briefly, NR2B9c is a 9 amino acid peptide (KLSSIESDV\({}_{1479-1487}\)) derived from the intracellular terminal carboxyl region of the NMDA receptor NR2B subunit protein, a site that binds the adaptor protein postsynaptic density-95 (PSD-95). PSD-95 couples nNOS to the NR2B subunit and following NMDA receptor activation stimulates NO generation. The NR2B9c peptide was developed on the basis that it blocks NR2B subunit and PSD-95 binding and thereby inhibiting NO production following NMDA receptor stimulation.  
TAT-NR2B9c (YGRKKRRQRRR-KLSSIESDV; net charge +7) administered (19,850 nmol/kg; IP) either 110 min or 20 min before hypoxia in mice reduced brain injury at 24 h post-HI [147]. In addition, TAT-NR2B9c treatment significantly improved behavioral outcomes up to 7 days after HI. Interestingly, a 3000 nmol/kg dose (IP), administered 20 min before hypoxia was ineffective.  
term fetal sheep assessed Peptide 5 and hypothermia (32 \(\pm\) 1 \({}^{\circ}\)C; 72 h), as individual treatments and when combined. Peptide 5 administration 3 h after hypoxia (50,000 nmol/kg for 1 h and then for 24 h; ICV) and hypothermia treatments alone, improved EEG power, neuronal and oligodendrocyte cell survival at 7 days post-HI. However, combined Peptide 5 and hypothermia treatment did not result in any additional improvement in EEG power or histological outcomes [165]. This suggests that neuroprotective targets, other than hemichannels, may be more appropriate to improve the therapeutic effects of hypothermia for HIE.  
Other Cx43 mimetic peptides (Gap 26: VCYDKSFPISHVR; net charge +1 and Gap 27: SRPTEKITIFI; net charge +1) have demonstrated neuroprotective efficacy in a P7 rat model of HIE [166]. Gap 26 (0.64, 3.22, 6.44, 16.1 and 32.2 nmol/kg; IP) and Gap 27 (16.1 nmol/kg; IP) administered 1 h before hypoxia, reduced infarct volume at 48 h post-HI; however, the two lower doses of Gap 26 were ineffective. In addition, multiple administrations of Gap 26 (32.2 nmol/kg: IP) every day for 7 days, beginning 24 h after hypoxia reduced brain injury and improved neurological function, up to 3 weeks post-HI.  
Since Cx43 mimetic peptides Peptide 5, Gap26 and Gap 27 have only one arginine residue and low cationic charge, it would seem unlikely the peptides are operating via a CARP mechanism of action.
## 3 Discretization
\[V_{i,m}^{Ei}=\int\limits_{T_{i,m}}f_{i,m}(\mathbf{r})\cdot\mathbf{E}_{i}^{\text{ source}}ds,\ \ V_{i+1,m}^{Ei}=\int\limits_{T_{i,m}}f_{i,m}(\mathbf{r})\cdot\mathbf{E}_{i+1}^{\text{ source}}ds, \tag{37}\]  
\[V_{i,m}^{Hi}=\int\limits_{T_{i,m}}f_{i,m}(\mathbf{r})\cdot\mathbf{H}_{i}^{\text{ source}}ds,\ \ V_{i+1,m}^{Hi}=\int\limits_{T_{i,m}}f_{i,m}(\mathbf{r})\cdot\mathbf{H}_{i+1}^{\text{ source}}ds. \tag{38}\]  
According to Equation (22), the matrix equation for all surfaces with block tridiagonal matrix equation is derived as follows:  
\[ \begin{pmatrix}\mathbf{Z}_{0}^{0}&\mathbf{Z}_{1}^{0}&\mathbf{0}\\ \mathbf{Z}_{0}^{1}&\mathbf{Z}_{1}^{1}&\mathbf{Z}_{2}^{1}&\mathbf{0}\\ \mathbf{0}&\mathbf{Z}_{1}^{2}&\mathbf{Z}_{2}^{2}&\mathbf{Z}_{3}^{2}&\mathbf{0 }\\ &\ddots&\\ &\mathbf{0}&\mathbf{Z}_{K-4}^{K-3}&\mathbf{Z}_{K-3}^{K-3}&\mathbf{Z}_{K-2}^{K -3}&\mathbf{0}\\ &&\mathbf{0}&\mathbf{Z}_{K-3}^{K-2}&\mathbf{Z}_{K-2}^{K-2}&\mathbf{Z}_{K-1}^{ K-2}\\ &&&\mathbf{0}&\mathbf{Z}_{K-2}^{K-1}&\mathbf{Z}_{K-1}^{K-1}\end{pmatrix} \left( \begin{array}{c}\mathbf{I}_{0}\\ \mathbf{I}_{1}\\ \mathbf{I}_{2}\\ \mathbf{I}_{3}\\ \vdots\\ \mathbf{V}_{K-3}^{\text{EH}}\\ \mathbf{V}_{K-1}^{\text{EH}}\end{array} \right). \tag{39}\]  
Equation (39) is the relation between the surface current, impedance matrix and incidence fields in \(K\)-layered dissipative medium structures. The impedance matrix is a square matrix with the number of elements equal to \(\sum\limits_{i=0}^{K-1}2N_{i}\times\sum\limits_{i=0}^{K-1}2N_{i}\), with \(\sum\limits_{i=0}^{K-1}(2N_{i})(2N_{i-1}+2N_{i}+2N_{i+1})\) non-zero data in the impedance matrix. Both the number of unknown surface current coefficients and excitation source matrix are \(\sum\limits_{i=0}^{K-1}2N_{i}\times 1\). Therefore, the surface current coefficients can be obtained by solving the matrix equation.
## 2 Hyperspectral Imaging Configurations
### Instrumentation
For hyperspectral imaging applications, high performance CCD cameras, such as electron-multiplying, intensified and back thinned (or back illuminated), are often necessary, because they have better QE responses and higher signal-to-noise ratios. In recent years, complementary metal-oxide-semiconductor (CMOS) has also been increasingly used for hyperspectral imaging, due to its advantages of low cost, low power consumption, small size, and fast signal transfer. CMOS, however, has some drawbacks including high noise and dark current and low dynamic range and sensitivity. For applications over the NIR region, indium gallium arsenide (InGaAs) detectors are often used, which typically cover the wavelength range of about 900-1700 nm, and can be extended to 2.6 \(\upmu\)m.
## 5 Performance Evaluation of the TPCT
### Laboratory Tests  
The laboratory test serves to examine the stability of the roadbed and the related performance of the TPCT by reducing the real embankment structure by a similar ratio, so that the real environmental conditions can be simulated in a shorter time inside the experimental model box. The common experimental box is shown below in Figure 9 For evaluating the working performance of the TPCTs, numerous tests have been performed.  
Figure 8: The cross-sections of the two embankments [71]. (**a**) Composite embankment (AA\({}^{\prime}\) cross section in Figure 7); (**b**) Contrast embankment (BB\({}^{\prime}\) cross section in Figure 7).  
[PERSON] et al. [38] presented an L-shaped thermosyphon with crushed-rock revetment and insulation. The results indicate that the combined measures can keep the thermal stability of large-width embankment in cold regions. [PERSON] et al. [40] conducted a series of experiments to explore the geo-temperature control process. The optimal inclined angle with efficient control ability is 70 \({}^{\circ}\). [PERSON] et al. [15] found that the TPCT has a significant thermal semi-conduction effect and it can work efficiently with the startup temperature difference of about \(-\)0.2 \({}^{\circ}\)C. [PERSON] et al. [16] indicated that the TPCTs can effectively cool down the embankment center and prevent it from degrading. [PERSON] et al. [14] also designed a self-adaption TPCT which has a better ability to solve the side-slope faces and severe environment.  
Generally, the experiments test can enable rapid test results to be obtained than the observation. But its results may not be reliable. Therefore, we need the numerical simulations to check it in a few years' time.
## 4 Conclusions  
An interference power control model for V2X based on a deep learning algorithm was proposed to enhance the connectivity between V and X, thus providing efficient road traffic communications. The proposed model is expressed using the Lagrange optimization technique to determine the optimum required interference power, thus satisfying the system requirements. Based on this optimization model, the system QoS was evaluated in terms of reliability and effectiveness for V2X communications. Furthermore, a 1D-CNN deep learning model was proposed to calculate the optimum interference transmission power (\(P_{t}\)) in real-life applications. The proposed deep learning model was compared to standard benchmarks in a 10-fold cross-validation split and provided an improvement in terms of mean absolute error. Moreover, further assessment of the model was performed to compare it to the analytical model. It has been shown that the optimum required interference power can vary under certain environmental conditions such as the outage probability, vehicle transmission power, path loss, and \(SINR_{th}\). The proposed model can be identified given the optimum required interference power in a vehicular network. The findings of this work can be used to enhance the efficiency and reliability of the vehicular network for improving road traffic information and increasing road safety under different environmental conditions. The results showed that the proposed control interference power can dynamically achieve the preferable QoS, in terms of achieving the highest achievable data rate, packet delivery ratio with the lower possible packet loss rate, and average end-to-end delay.  
Conceptualization, R.A.O.; data curation, R.A.O.; formal analysis, R.A.O., S.N.S., and Y.N.M.S.; investigation, R.A.O. and S.N.S.; methodology, R.A.O. and S.N.S.; resources, R.A.O., S.N.S., Y.N.M.S., and M.N.E.; software, R.A.O. and S.N.S.; validation, R.A.O. and S.N.S.; visualization, R.A.O., S.N.S., and Y.N.M.S.; writing--original draft, R.A.O., S.N.S., Y.N.M.S., and M.N.E.; writing--review and editing, R.A.O., S.N.S., Y.N.M.S., and M.N.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.