esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
All of these systems were concentrated on reducing the bit error rate, instead of increasing the data rate, reducing energy consumption and providing accurate posterior probability estimates that can be exploited by a soft-input channel decoder to achieve capacity. The contribution of this paper is to cover high bit rate, low bit error rate (BER) and low energy consumption by applying the SVM technique. A numerical example is used in giving a brief demonstration of the SVM detector and the design parameters have been considered and investigated for the purpose of optimization and simplification. Other related issues such as the kernel selection, features extraction and reducing complexity of the detector have also been analyzed. In addition, we give the analysis of state-of-the-art nonlinear detector together with the channel decoder. The remainder of this paper is organized as follows: Section 2 is devoted to introducing the efficient modulation. We present the receiver scheme in Section 3 and briefly describe the SVM classification and PPEs for LDPC decoding. In Section 4, we include illustrative experiments to compare the performance of the SVM detector. We conclude in Section 5 with some final comments.
## 4 Field Experiments
### Testing of Modulation Circuitry and PMT Detection Output Waveforms  
In the laboratory, the optoelectronic signals of the transmitter and receiver were tested using an oscilloscope. The communication rate in the test environment was 150 kbps, as depicted in Figure 12.  
Figure 11: (**a**) Distance display on map. (**b**) Physical setup diagram for field experiment.  
Figure 12: (**a**) Testing of modulation circuitry output waveforms. (**b**) Testing of PMT detection output waveforms.  
Figure 12a illustrates the waveform signal output from the transmission modulation circuit, while Figure 12b displays the waveform signal of photoelectrons detected by the PMT. Each photoelectron pulse corresponds to one or multiple photoelectrons detected by the PMT. Due to the limited laboratory space, the path loss caused by channel attenuation is relatively low, resulting in a denser detection of photoelectrons by the PMT at this time.
# A Lunar-Orbiting Satellite Constellation for Wireless Energy Supply
## 3 System Architecture and Key Technologies
### On-Board Storage System  
Concerning the storage system, lithium-ion batteries are preferred because they offer high energy density, high efficiency, long service life, low self-discharge, low self-heating rate, and are used in many space applications including interplanetary missions and space stations. In [55], a simulation of battery charge/discharge of a small satellite can be found. This simulation was taken as a starting point for the Simulink model in this paper.The study presented below thus combines an optimization between the number of satellites and the choice of orbit. The main driver in this optimization is to transfer as much energy as possible for the future lunar base from orbit to ground through a high-power laser payload.
###### Abstract  
Background: The Midfacial Skeletal Expander (MSE) produces a greater skeletal effect than its dentoalveolar side effects. The aim of this study was to quantify the stability of the different components of MSE expansion post-orthodontic treatment. Methods: Fourteen subjects (mean age of 20.4 \(\pm\) 3.5 years) were treated with the MSE. The pre-expansion (T0), post-expansion (T1), and post-treatment (T2) CBCT records were superimposed and compared. The rotational fulcrum of the zygomatomaxillary complex was identified, and angular measurements were generated to assess changes in the zygomatomaxillary complex (skeletal expansion), dentoalveolar bone (alveolar bone bending), and maxillary first molars (dental tipping). The stability of all three components after orthodontic treatment was also assessed by comparing changes between T0-T1 and T0-T2. Results: Post-expansion, angular measurements showed that skeletal expansion accounted for 87.50% and 88.56% of total expansion, alveolar bone bending for 7.09% and 5.23%, and dental tipping for 5.41% and 6.21% on the right and left sides, respectively. Post-treatment skeletal expansion relapsed by 11.20% and 13.28% on the right and left sides, respectively. Conclusions: The MSE mainly produces skeletal changes with insignificant and negligible dentoalveolar changes immediately after expansion. In the long term, the majority of skeletal expansion was maintained.  
expansion; alveolar bone bending; dental tipping; cone-beam computed tomography (CBCT); micro-implant-assisted rapid palatal expansion (MARPE)
## Data and Methods
### Data Preprocessing  
#### 2.2.1 PALSAR Dataset and Preprocessing  
The 25 m ALOS PALSAR Fine Beam Dual polarization (FBD) products from 2007 to 2017 generated with the selected image acquired between the months of June and October [22] can be downloaded from the Earth Observation Research Center, Japan Aerospace Exploration (JAXA) ([[http://www.eorc.jaxa.jp/ALOS/en/palsarfnf/data/](http://www.eorc.jaxa.jp/ALOS/en/palsarfnf/data/)]([http://www.eorc.jaxa.jp/ALOS/en/palsarfnf/data/](http://www.eorc.jaxa.jp/ALOS/en/palsarfnf/data/))). The PALSAR (Table 1) 25 m orthorectified mosaic data, provided by JAXA, is created globally using images in the ascending path. The original PALSAR images have an off-nadir angle of 34.3 deg and cover wet and dry seasons, or winter and summer seasons, and were resampled into the 25 x 25 m mosaic to create one composite per year. Polarized signals can be transmitted and received in the horizontal or vertical dimensions by the L-band PALSAR, allowing for horizontal transmission and reception (HH) and horizontal transmission and vertical reception (HV). While the ALOS has launched, the mosaic of HH and HV polarization data, local incidence, and the mask information (effective area, layover, ocean flag, shadowing, and void area) were included in the datasets. PALSAR HH and HV backscatter data were slope corrected and ortho-rectified with a geometric accuracy of about 12 m to guarantee the geometric accuracy, using the 90 m Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) [34], before being radiometrically calibrated. The Digital Number (DN) values (amplitude values) were converted into gamma-naught backscattering coefficients in decibels (\(\gamma^{\circ}\)) using a calibration coefficient from JAXA [35; 36]  
\[\gamma^{\circ}=10\,\times\,\log_{10}\textless\text{DN}^{2}\textgreater\,+\, \text{CF}, \tag{1}\]  
where \(\gamma^{\circ}\) is the backscattering coefficient, DN is the digital number value in HH or HV, and CF is the absolute calibration factor of \(-\)83 [36]. Further, the Ratio and Difference values were calculated using the resultant HH and HV backscattering coefficient in decibels. In which, HH and HV are raster data with 25 m resolution because of their effective classification of the land cover [25; 37; 38]. The calculation of backscatter coefficients and generation of ratio and difference bands was conducted following the below equations.  
\[\text{Difference}\,=\,\text{HH}-\text{HV}, \tag{2}\]  
\[\text{Ratio}\,=\,\text{HH}/\text{HV}. \tag{3}\]  
We also resampled the 25 m PALSAR images (HH, HV, Difference and Ration layers) to 30 m PALSAR images using the neighbor sampling method to match Landsat images at 30 m spatial resolution using the nearest neighbor method. The 30 m PALSAR resultant images were then combined with Landsat images to generate a PALSAR/Landsat forest map.
Future Internet_**2015**, \(7\), 342-362; doi:10.3390/fi7030342  
_future internet_  
ISSN **1999-5903**  
www.mdpi.com/journal/futureinternet  
Enhanced Secure Trusted AODV (ESTA) Protocol to Mitigate Blackhole Attack in Mobile _Ad Hoc_ Networks  
**[PERSON] * and [PERSON]**  
Department of Computer Engineering, Punjabi University, Patiala 147002, Punjab, India;  
E-Mail: [PERSON]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +91-781-456-6122.  
Academic Editor: [PERSON]  
_Received: 15 July 2015 / Accepted: 14 September 2015 / Published: 23 September 2015_  
**Abstract:** The self-organizing nature of the Mobile _Ad hoc_ Networks (MANETs) provide a communication channel anywhere, anytime without any pre-existing network infrastructure. However, it is exposed to various vulnerabilities that may be exploited by the malicious nodes. One such malicious behavior is introduced by blackhole nodes, which can be easily introduced in the network and, in turn, such nodes try to crumble the working of the network by dropping the maximum data under transmission. In this paper, a new protocol is proposed which is based on the widely used _Ad hoc_ On-Demand Distance Vector (AODV) protocol, Enhanced Secure Trusted AODV (ESTA), which makes use of multiple paths along with use of trust and asymmetric cryptography to ensure data security. The results, based on NS-3 simulation, reveal that the proposed protocol is effectively able to counter the blackhole nodes in three different scenarios.  
**Keywords:** AODV; Multipath ROUTING; encryption; blackhole attack; trust
## 3 Materials and Methods
### Testing Procedures
#### 3.5.5 Environmental Impact and Reliability  
Environmental impact was assessed by weighing support structures and failed prints, then classifying waste streams as biodegradable, recyclable, or hazardous. Print reliability was quantified as the number of successful prints per total attempts, with dominant failure modes (e.g., warping for FDM, incomplete curing for SLA) documented.
# Linear Method for Diagnosis of Inter-Turn Short Circuits in 3-Phase Induction Motors
## 3 D-Q Transformation
### D-Q Stationary Reference Frame  
In the stationary reference frame, the 3-phase variables \(I_{a}\), \(I_{b}\), and \(I_{c}\) can be projected to the \(d^{s}\), \(q^{s}\) axis. The projected values of the variables to the \(d^{s}\), \(q^{s}\) axis can be obtained by Equations (1) and (2):  
\[I_{d}^{s}=I_{a}\cos(0)+I_{b}\cos(-\frac{2}{3}\pi)+I_{c}\cos(-\frac{4}{3}\pi) \tag{1}\]  
\[I_{q}^{s}=I_{a}\sin(0)+I_{b}\sin(-\frac{2}{3}\pi)+I_{c}\sin(-\frac{4}{3}\pi) \tag{2}\]  
Figure 2: D-Q transformation process: (**a**) D-Q stationary reference frame (**b**) D-Q synchronous reference frame.  
In Equation (1), \(I_{d}^{s}\) is the current of the stationary reference frame projected to the \(d^{s}\) axis and \(I_{q}^{s}\) is the current of the stationary reference frame projected to the \(q^{s}\) axis.  
Because the \(d^{s}-q^{s}\) axis is rotating, the 3-phase variable needs to be changed to a rotating \(d^{\omega}-q^{\omega}\) axis variable. The general equation for transforming 3-phase data to orthogonal axes where the 3-phase variable is converted to a random angular velocity \(\omega\) is as follows:  
\[I_{dq}=T(\theta)I_{abc} \tag{3}\]  
where \(I_{dq}=\left[I_{d^{s}}^{s},I_{q}^{s}\right]^{T}\), and \(I_{abc}=\left[I_{a},I_{b},I_{c}\right]^{T}\). This is the result of generating the transposed matrix of angular velocity. The transformed T(\(\theta\)) can be expressed as follows:  
\[\text{T}(\theta)=\frac{2}{3}\left[\begin{array}{cc}cos\theta&\cos\left( \theta-\frac{2}{3}\pi\right)&\cos\left(\theta-\frac{4}{3}\pi\right)\\ -sin\theta&-sin\left(\theta-\frac{2}{3}\pi\right)&-\sin\left(\theta-\frac{4}{3 }\pi\right)\end{array}\right] \tag{4}\]  
In Equation (4), \(\theta\) denotes the rotation angle of the reference frame, and 2/3 is the coefficient of the transformation matrix. The effective values of voltage and current are the same, but the power and torque are reduced by 2/3. In this study, the coefficient of 2/3 was applied because the current data was used.  
When Equation (4) is transformed into a matrix, we get the following equation:  
\[\left[\begin{array}{c}I_{d}^{s}\\ I_{q}^{s}\end{array}\right]=\frac{2}{3}\left[\begin{array}{cc}1&-\frac{1}{2}&- \frac{1}{2}\\ 0&\frac{\sqrt{3}}{2}&-\frac{\sqrt{3}}{2}\end{array}\right]\left[\begin{array}[] {c}I_{a}\\ I_{b}\\ I_{c}\end{array}\right] \tag{5}\]  
The method of obtaining the stationary reference frame as in Equation (5) is also called Clark's Transformation.  
This equation can be simplified as follows:  
\[I_{d}^{s}=\frac{2I_{a}-I_{b}-I_{c}}{3} \tag{6}\]  
\[I_{q}^{s}=\frac{1}{\sqrt{3}}(I_{b}-I_{c}) \tag{7}\]  
Here, if the induction motor is ideal, the 3-phase winding is symmetrical. If the neutral point is not connected to another circuit, the sum of the physical quantities, such as the current of the 3-phase winding, is \(I_{a}+I_{b}+I_{c}=0\). Thus, we get \(I_{d}^{s}=I_{a}\) from Equation (6). Therefore, the \(d\)-axis variable of the stationary reference frame is always the same as the phase A variable.
# Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili
## 2 Swahili Language Structure  
Swahili is one of the Bantu languages widely spoken in East and Central Africa, with two main dialects being Unguja (which is spoken in Zanzibar) and Mvita (which spoken in Mombasa) [35]. It is influenced by languages such as Arabic, Persian, German, Portuguese, English and French [35]. This explains the presence of a couple of loan words such as _slukrani_ (thanks) and _polisi_ (police). Swahili is also very contextual with high agglutinative and polysemous features [19; 36]. The following two sentences demonstrate the polysemy feature of the word _panda_:  
1. Walimuua ndege kwa panda (they killed a bird using a catapult); and
2. Walipanda mti (they climbed a tree or they planted a tree).  
The Swahili morphology depends on prefixes and suffixes, which are syllables. It has a large noun class system with distinctive singular and plural forms [37], which is achieved by using syllable pairs, for example, _mtu_ (person) and _watu_ (people), or _kitabu_ (book) and _vitabu_ (books). In fact, [PERSON] [38] observed that synthetic and functional information can be derived from these connotation bearing affixes attached to nouns. However, verbs, pronouns, adjectives and demonstrations must match with the noun class to guarantee effective Swahili communication. In addition, Swahili verbs are very agglutinative, and may consist of subject, tense, relative and object prefixes in addition to roots and extensions. The following list provides a few examples of the verbal agglutinative components:  
* subject prefix: a, m, ni, wa, tu, si, hatu;
* tense prefix: na, ta, li, me, ngeli, ja, singali;
* relative: po, ko, o, cho, mo, yo, vyo, lo;
* object prefix: wa, ni, ji, ku, i, ya, vi, zi;
* root: sema, kuja, ahidi, acha, aga, ajiri; and
* extension: ia, iwa, lia, shwa, ana, isha, kia.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 2 Background and Preliminaries
### Smart-Grid-Based Cybersecurity Standards
#### 2.5.5 IEC 62351  
The IEC 62351 series addresses security measures for power system communication protocols, specifically those that surround the standards of IEC 60870-5 [54], IEC 61850, and DNP3. This offers confidentiality, integrity, and authentication across SCADA communications yet lacks support for modern security protocols such as TLS or IPsec. Its specifications cover message authentication and encryption, secure key management, Role-Based Access Control (RBAC), and secure logging and auditing [55].
# A Multibody Model Calibration Method for Vehicle Misuse Testing
## 2 The Overall Calibration Workflow with Commercial Software
### The Overall Calibration Workflow  
The overall calibration workflow for vehicle misuse tests is illustrated in Figure 2. Starting from the designed vehicle parameters, the full processes are mainly divided into three phases: (a) road tests of the real vehicle, (b) calibration loops using virtual simulation in Adams/Car, and (c) a theoretical auxiliary analysis using the modified quarter-vehicle model.  
During the road test phase, the vehicle is tuned, loaded, tested, and recorded. Chassis tuning should be conducted before testing to ensure the chassis is in the optimal condition for daily use. In the Class II tests, the chassis is raised by an additional 30 mm to facilitate passage over deep potholes. This tuning results in variations in the air spring stiffness, shock absorber damping, and the stopper gap. The loads also need to be adjusted from a half-load to a full-load counterweight for the misuse tests, as the initial vehicle model is typically designed for durability analyses under the former conditions. Next, the altered vehicle is tested by passing over various obstacles at a given speed, and the responses are recorded using sensors and outputted in *.s3t format, which can be processed using experimental data software, such as nCode(11.1).  
During the Adams/Car calibration phase, the detailed multibody model is built, simulated, compared, and adjusted. As described above, the Adams/Car model was initially built for durability analyses. Simulating on virtual ground with the given obstacles,as depicted in Figure 3, the numerical results are output in *.rsp format to be read in nCode as well. The simulation results are compared with the test results, and the errors in both the amplitudes and the periods are assessed. If the errors are small, the parameter sensitivity is calculated using the finite difference method, with additional simulations for parameter perturbation. Subsequently, assuming a linear input-output relationship, the vehicle parameters are adjusted using the least squares optimization method. The simulation loop continues until the errors are found to have converged, at which point the calibration process is concluded.  
If the errors are large or medium, the input-output relation may become highly nonlinear. Improper setting of the vehicle parameters may result in simulation failure or divergences in Adams/Car, interrupting the parameter iteration loop. In this case, the theoretical analysis is necessary to help calibrate a set of parameters that are sufficiently close to initiate the iteration. As depicted in the workflow in Figure 2, the theoretical analysis method proposes a modified quarter-vehicle model and divides the misuse process into three stages: obstacle contact, free detaching, and ground recontact. Contact and modal analyses are conducted theoretically to calibrate the vehicle parameters by comparing the response amplitudes and periods, a process that will be detailed in subsequent subsections.
# A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation
## 3 Experimental Results
### Datasets
#### 3.1.1 OASIS  
The Open Access Series of Imaging Studies (OASIS), is a publicly available standard MRI dataset (See the \"Open Access Series of Imaging Studies\" (OASIS) project's web site at [[https://www.oasis-brains.org/](https://www.oasis-brains.org/)]([https://www.oasis-brains.org/](https://www.oasis-brains.org/)) (accessed on 19 April 2022)). This dataset consists of 416 cross-sectional data from subjects aged between 18 and 96. The images in the dataset are of 1.25 mm thickness and of \(256\times 256\times 128\) resolution.
# Surface Subsidence Monitoring in Kunming City with Time-Series InSAR and GNSS
## 4 Results and Discussion
### The Eastern Area of Dianchi Lake  
Figure 8 shows the deformation results in eastern of Dianchi Lake. As can be seen from the figure that a relatively obvious settlement occurred. Xinjie town is taken as the settlement center and moved eastward to Pingzheng village-Sanjieqiao area, with the average annual subsidence rate is \(-33.39\) mm/year-- \(9.57\) mm/year. The area with the largest deformation magnitude is located near the central hospital of Xinjie town, with the  
Figure 6: Velocity maps in line of sight (LOS) of the study area.  
maximum settlement rate exceeding \(-33\) mm/year. The time series cumulative settlement diagram of a typical deformation P4 is selected, and it can be seen that with the passage of time, there is a continuous downward trend, and the subsidence rate is fast. By the end of 2020, the cumulative settlement has exceeded 75 mm.
## 2 Results and Discussion  
### Comparison of Headspace Extraction Methods  
The GC profiles of extracts obtained in static or dynamic headspace using conventional HS-SPME-Car/PDMS (static), D-HS-Tenax (dynamic), and D-HS-trilayer (dynamic) were first analyzed and compared. Then, three variations of the latter methods were applied, including (1) a static headspace extraction with ten Car/PDMS SPME fibers placed simultaneously into the headspace of the milk sample (S-HS-MultiSPME); (2) a dynamic headspace extraction using the same number and type of SPME fibers (D-HS-MultiSPME); and (3) a dynamic extraction procedure using a tube containing Tenax sorbent enhanced with these 10 SPME fibers (D-HS-MultiSPME/Tenax). The VOCs profiles of extracts obtained by these three innovative headspace extraction methods were also analyzed and compared from a qualitative and semi-quantitative point of view. Following a non-targeted approach aiming to detect a maximum of compounds in breast milk, the mass spectrometer was used in TIC (total ionic current) mode to obtain qualitative information on the identity of the detected compounds. However, data from the flame ionization detector (FID) were preferred for a semi-quantitative approach, in accordance with good practices [45; 46] and standard recommendations [47]. Indeed, although the response factors are different from one molecule to another for FID, the disparities observed in the response factors for different compounds are smaller than for the mass spectrometer and generate less distortion of the chromatographic profiles with respect to the real compositional profile of the extract or product analyzed.  
The resulting profiles of the six methods reveal that the recovery of volatile compounds and their chemical classes differ significantly according to the extraction method, as shown by the FID area and mass spectrometry responses (Table 1). Summarizing these results, Figure 1 represents the profiles of the volatile compounds (total peak area per chemical class) for each extraction method (except for the D-HS-trilayer method that leads to unexplotable results; see explanation hereafter). In total, 54 compounds were identified from at least eight chemical classes.  
Figure 1: Total FID peak areas, number of peaks corresponding to compounds, and their belonging to different chemical classes obtained from human milk using the different headspace extraction methods.  
+++ ==ERROR: No output for this page==
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**LRL\({}_{\text{spient}}\)**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**CAS**} & \multicolumn{2}{c}{**Major**} & \multirow{2}{*}{**LRL**} & \multicolumn{2}{c}{**RS-SPR-Car/PDMS**} & \multicolumn{2}{c}{**D-RS-Trans**} & \multicolumn{2}{c}{**S-SMS-MidS/STHE**} & \multicolumn{2}{c}{**D-RS-MultiS/STHE**} & \multicolumn{2}{c}{**AnOVA**} & \multicolumn{2}{c}{**Lissurance**} \\  & & & **Name**