esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Technologies Used in the Proposed Solution  
The optical communication technique called optical camera communications (OCC) is described in [5,6]. OCC allows the use of huge unregulated bandwidths in the optical domain spectrally located between microwave and X-ray wavelengths, as shown in Figure 1. In such systems, an image sensor and a camera are used to demodulate the transmitted signal, which has been modulated according to on-off keying (OOK). Current available devices are smart devices equipped with LED flash and cameras. These provide a pragmatic form of an Optical Wireless Communication (OWC) where LED projectors deliver the Visible Light spectrum (VLC) component and a camera as the receiving module, creating a transceiver pair.  
The OCC system uses commercial LED lighting sources that include LED-based infrastructure lighting, LED flashes, LED tags, displays, laser diodes, image patterns, and some current generation projectors. The major driving forces of OCC deployment are the widespread availability of visible light (VL) LEDs and the possibility of utilizing the camera in the smart devices to decode LED modulated data. Therefore, these LED infrastructures can be used for data transmissions using on-off keying (OOK).  
A typical OCC system is shown in Figure 2, where a camera is used as a receiver that consists of an imaging lens, an image sensor, and a readout circuit.  
Optical communications comprise an LED, Infra-Red or Lazier projector, and a high-speed camera embedded in a mobile phone. The projector projects a beam of light toward the direction of the camera. The camera has an embedded CMOS image sensor that captures the projected beam. The beam on/off projection duration and frequency is according to an encoded pattern coordinated with the receiving camera. The receiving camera records a video of the projection session and saves it in its internal  
Figure 1: Electromagnetic spectrum range.  
Figure 2: A schematic view of the optical camera communication (OCC) system.  
storage. The recorded projection can be decoded into bits, where for an \"on\" beam, the corresponding decoding bit is set to \"1\", and otherwise, it is decoded as \"0\". The video in the camera can be further transmitted to the target receiver through a public network connection. The beam may be visible (normal lighting) or invisible (Infra-Red and Lazier). When the CMOS image sensor is operated, images are captured. These images are the source for extracting data by decoding it. Figure 3 depicts the three phases of the received signal processing. The left image is the original recorded beam impact, the one to the right is the original image after it was crystalized, and the third image to the right depicts the final stage of the process. The third image is the input for decoding the beam stream into a bit string.  
Figure 4 depicts the encoding process, starting with processing the image and translating it into a sequence of a signal chart (the top chart). The bottom chart depicts the final bit sequence.  
Figure 4: Optical camera communication architecture.  
Figure 3: Three phases of fringe signal processing ([PERSON] and [PERSON], 2017).
# A Channel Allocation Mechanism for Cellular Networks
## 2 Related Work
### Channel Allocation Mechanisms  
In cellular networks, when one MS wants to communicate with another MS, it must get a free channel provided from BS to serve it. Thus, if the BS has a free channel, the connection between two  
Figure 1: The GSM network architecture [18].  
Figure 2: The UMTS network architecture [18].  
MSs will be built successfully; otherwise, the connection of communication fails. Therefore, to allocate the adaptable number of channels for communication is a critical issue. In this subsection, we will introduce two types of channel allocation mechanisms, which are the traditional SCA mechanism and the DCA mechanism, respectively.
# A Review of Deep Learning Based Speech Synthesis
## 2 An Overview of Speech Synthesis  
### Basic Concept of Speech Synthesis  
Speech synthesis or TTS is to convert any text information into standard and smooth speech in real time. It involves many disciplines such as acoustics, linguistics, digital signal processing, computer science, etc. It is a cutting-edge technology in the field of information processing [15], especially for the current intelligent speech interaction systems.
## 2 State of the Art
### Four-Dimensional Modelling
#### 2.2.4 Structure Recognition from Plan Data  
Although building footprints are still mainly manually extracted from historical maps, various AI-based approaches support this task. CNNs and--more recently--transformer approaches are used for the segmentation of historical maps [145; 146; 147; 148; 149]. Another approach to automatically generating 3D/4D models comprises building footprint recognition and parametric modelling. Footprint recognition via semantic segmentation for aerial/satelliteimagery [150; 151; 152; 153] or from current cadastral data [154] and for contemporary photography [155] has been frequently researched. One issue in boundary detection workflows is overlapping building boundaries and texts. Consequently, many approaches combine text recognition and boundary delineation [156; 157; 158; 159] to trace building footprints.  
Although current classifiers achieve good results for specific map styles and ages, they are not very transferable, so structure recognition has not reached a sufficient quality yet to be transferable without prior manual retraining of the detection system.
## 5 Simulations and Comparison
### Vehicle Models and Test Conditions
#### 5.1.2. Kambara Test Using Proposed Algorithm
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & **Linear** & **Trajectory** & **Cycloid** & **Trajectory** \\ \hline
**Index** & & **C. Dist.** & **V. Dist.** & **C. Dist.** & **V. Dist.** \\ \hline MIA & \(x_{e}^{E}\) & 0.2573 & 0.2583 & 0.0530 & 0.0700 \\  & \(y_{e}^{E}\) & 0.1371 & 0.1425 & 0.0855 & 0.0793 \\  & \(\psi_{e}\) & 0.0518 & 0.0516 & 0.0302 & 0.0223 \\  & \(x_{e}\) & 0.2566 & 0.2582 & 0.0531 & 0.0702 \\  & \(y_{e}\) & 0.1380 & 0.1432 & 0.0853 & 0.0791 \\  & \(u_{e}\) & 6.2570 & 6.2901 & 1.1692 & 1.6046 \\  & \(r_{e}\) & 0.0505 & 0.0544 & 0.0321 & 0.0371 \\ MIAC & \(\tau_{u}\) & 64.652 & 74.632 & 96.729 & 106.75 \\  & \(\tau_{e}\) & 1.8703 & 1.1113 & 1.8144 & 1.1432 \\ RMS & \(||e||\) & 0.9303 & 0.9671 & 0.3643 & 0.3950 \\ KE & \(K_{m}\) & 19.679 & 19.662 & 33.637 & 33.635 \\ \hline \end{tabular}
\end{table} 
Table 3: Performance for Kambara. Figure 9: Simulation results for Kambara—IQV controller and cycloid trajectory (variable disturbances): (**a**) desired and realized trajectory; (**b**) position errors (E-frame); (**c**) position errors (B-frame) and angular error; (**d**) desired and realized velocity (\(u_{d},u\)); (**e**) desired and realized velocity (\(r_{d},r\)); (**f**) realized velocities; (**g**) applied force and torque.
## 3 Objective and Scope  
Even though typical maps provided to robots describe structural layouts of buildings, the accuracy of localization is significantly impacted by non-structural building elements and common items, such as doors, appliances, and furniture, which a robot can recognize using its sensors. However, none of the mentioned approaches have enhanced robot localization by dynamically updating the semantic building representation with non-structural elements. It is clear from related works on semantic maps and object recognition that there have been interesting solutions and approaches proposed to improve robot localization. State-of-the-art studies have not proposed an approach that considers altering the navigation map dynamically. This study presents a new method to enhance the accuracy of indoor robot localization by automatically updating the environment state in a semantic 3D building map. This study also presents a modified AMCL that integrates object recognition and map updating based on traditional probabilistic localization. For that, we developed a robot localization system that analyzes real-time data from sensors, detects and classifies objects using object-recognition techniques, acquires geometric information about detected objects using the depth point cloud, and creates a semantic building map reflecting the current environment state.
## 3 Numerical Simulation of Direct Shear Test of Bonded and Naturally Rough Concrete-Rock Interfaces
### Preparation of the Numerical Simulation
#### 3.3.2 Generation of the Mesh  
Before defining the mesh for the parts, some considerations need to be taken into account. First, meshing 3D geometrical parts in contact, with the contact interface being rough or not perfectly flat, usually results in the phenomenon of overclosure [53], meaning that while fitting the elements to the interface, some nodes of elements of one part end up intruding the other parts. This makes it necessary to use a node adjustment routine [53]. Second, the 3D morphology of the interface in this simulation is used to add the influence of macro-roughness (millimetric scale) in the simulation, and consequently, the elements generated by the meshing must have a millimetric size. Last, the idealization made in Section 3.1 and the presentation of the model in Section 3.2 support that the simulation depends on the constraints at the interface and the explicit geometrical representation of the interface, therefore the meshing must be such that the elements at the interfaces lead to an appropriate representation of macro-roughness and sufficient proportion of constraints to correctly represent the micro-roughness.  
With these considerations and after verifying that reducing the mesh size further does not significantly change the result of the simulations but increases the size of the problem and the influence of micro-roughness, it was adopted an average element size of 4 mm. (Figure 17).  
The element sizes selected are in the same range as the acceptable element size of 2 mm reported by [PERSON] et al. [44] for the numerical simulation of the direct shear test of unbonded rough concrete-rock interface with a cylindrical sample having a diameter between 83 mm and 145 mm.  
The size of the sample used in the numerical simulation is similar to the one used experimentally. The type of element used is the fully integrated linear brick element called C3D8. This type of element is appropriate for this specific type of 3D contact problem.
# Fault Location in Double Circuit Medium Power Distribution Networks Using an Impedance-Based Method
## 4 Simulation Results
### Numerical Results
#### 4.2.1 The Effect of the Fault's Resistance  
The fault's resistance is one of the factors affecting the fault location's accuracy. To evaluate the effect of the fault's resistance, a simulation with single-phase faults to the ground with a range of resistances between 0 to 100 \(\Omega\) was done. The results are shown in Figure 4. According to the results presented in Table 3, we can conclude that the fault's resistance had little effect on the proposed method (even for \(R_{F}\) larger than \
## 6 Evaluation
### Pruning with Same Pruning Ratio per Layer  
Tables 2-4 present the application of pruning followed by a postquantization on the plant seedlings dataset for models VGGNet16, AlexNet, LeNet5, respectively. The comparison was made in terms of size and accuracy at different pruning levels. Results show that by using pruning techniques during compression and postquantization we can reduce the size of the models by an average factor of 38. In other words, we can achieve a reduction of factor 3.5, 4.2, 5.3, and 7.5 by removing, respectively, 80%, 85%, 90%, and 95% of trainable weights without considerable variation. We can then apply the postquantization and obtain a reduction of factor 12, factor 16, factor 22, and factor 38 with the same precision. In all the following tables, bold numbers indicate the best accuracy.  
We obtained similar results by applying the same process on the Flavia database. Since only the last layer is changed in the architecture of the models applied to these two datasets, the variation in terms of size is exceedingly small. Table 5 presents the different accuracy values obtained by applying the pruning on LeNet5, AlexNet, and VGG16 with a sparsity level of 80%, 85%, 90%, and 95% for all the architectures.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**VGGNet16** & **Original** & **80\% Pruning** & **85\% Pruning** & **90\% Pruning** & **95\% Pruning** \\ \hline \multicolumn{6}{c}{**Pruning**} \\ \hline Size & 152.61 Mb & 43.47 Mb & 36.12 Mb & 28.45 Mb & 20.17 Mb \\ \hline Accuracy & 88.67 & 88.40 & **89.48** & 86.88 & 88.85 \\ \hline \multicolumn{6}{c}{**Pruning + postquantization**} \\ \hline Size & 36.76 Mb & 11.84 Mb & 9.41 Mb & 6.87 Mb & 3.98 Mb \\ \hline Accuracy & 88.67 & 88.23 & 89.03 & **89.84** & 89.03 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison in terms of size before and after compression on VGGNet16 and the plant seedlings dataset.
## 11 The Concept of Temperature Accumulation  
According to Figure 7, under the same external conditions, the temperature change curves of various asphalt mixtures are very similar; the temperature change of four asphalt mixtures represents a uniform law, with only some differences in aspect of temperature change rate. The difference in temperature change rate reflects the difference in the thermal stability of asphalt mixtures, and it can be reflected by the material thermal storage coefficient in traditional research.  
While a single material layer with enough thickness is affected by harmonic thermal action on the surface, the surface wave amplitude ratio of heat flux and temperature is defined as a thermal storage coefficient. It can be seen from the definition that it is very difficult to obtain the thermal storage coefficient of asphalt mixture accurately, whether during laboratory tests or numerical simulations. Therefore, the concept of temperature accumulation (TA) was introduced to describe the thermal stability differences of various materials under the same environmental conditions.  
Temperature accumulation is widely used in biology. It usually refers to the cumulative value of the effective temperature required by plants and oviparous animals to complete a certain development stage, such as the egg hatching process of poultry animals. Similar phenomena also exist in the pavement, such as the obvious acceleration of rutting development when the pavement temperature is higher than a certain limit value. In this paper, TA is introduced mainly to describe the thermal stability of the material, so TA is expressed as the product value of temperature absolute value and time.  
As Figure 10 shows, temperature accumulation is actually the area between the temperature-time curve and the coordinate axes. For two materials with the same initial temperature and environmental temperature, faster cooling means a smaller TA. As shown in the figure, _TA2_ is significantly smaller than _TA1_. In contrast, during the heating process, the larger TA means temperature increasing more rapidly. Therefore, with the help of the concept of TA, when describing the changing law of the temperature field of different materials under the same temperature boundary condition, many similar curves can be omitted, and the thermal stabilities of materials can be directly reflected by temperature accumulation.
## 3 Data Modeling Methodology
### Differential Evolution (DE)
#### 3.2.1 Population Initialization and Variation  
After the DE algorithm performs a random initialization of the population \(X_{i}(0)=\{x_{i,1}(0),x_{i,2}(0),x_{i,3}(0)\ldots,x_{i,n}(0)\)\(i=1,2,\ldots,n\}\), the DE algorithm will get the individuals of dimension \(n\), where \(x_{i,j}(0)\) is the value on dimension \(j\) of the \(i\)-individual in the population in generation \(0\), and then continue with the mutation operation. The DE algorithm will first select three individuals \(X_{p1}(g)\), \(X_{p2}(g)\), \(X_{p3}(g)\) in the variation operation. After \(X_{p2}(g)\) and \(X_{p3}(g)\) get the differential value by scaling factor, it will add linearly with \(X_{p1}(g)\), and the formula is as follows:  
\[X_{i}(g)=X_{p1}(g)+F\big{[}X_{p2}(g)+X_{p3}(g)\big{]} \tag{8}\]  
where \(x_{p2}(g)+x_{p3}(g)\) is the differential vector; \(F\) is the scaling factor, which determines the step size of the individual differences of the population. The larger the \(F\)-value is, the global optimization ability of the algorithm will be enhanced, but the overall convergence speed of the algorithm will be affected.
## 2 Materials and Methods  
The system is composed of several key components, each contributing to the overall functionality of measuring soil dielectric permittivity:  
* Voltage-Controlled Oscillator (VCO) : This device generates a Continuous Wave (CW) signal, which is essential for the measurement process.
* Measurement Module : This module includes the following components:
* Power Detector : Measures the power levels of the signals.
* Reflectometer : Performs reflectometry measurements in the frequency domain, enabling precise determination of reflection coefficients.
* Processing Module : Driven by an Analog-to-Digital Converter (ADC), this module orchestrates the calibration process and displays the data collected from the measurement module. It ensures accurate interpretation and visualization of the data.
* Probe : The probe is employed to measure the dielectric properties of the soil. It integrates various components to harmonize with the soil environment, ensuring accurate and reliable measurements.  
This paper focused on the design of the probe and addressed the various challenges associated with its development, as shown in Figure 1[14]:  
Figure 1: Vegetal soil characterization system.
# Assessment of Healing after Diode Laser Gingivectomy Prior to Prosthetic Procedures
###### Abstract  
The current pilot study investigates the effects following removal of excessive gingival tissue, which is often necessary in fixed prosthodonic cases. The aim of the study is to assess gingival healing after diode laser gingivectomy prior to prosthetic procedures. Materials and methods: The healing process of the gingiva after the diode laser gingivectomy of 41 teeth was assessed. The following parameters were examined: recovery time of the gingiva, possibility to take the impression at the same visit, duration of the manipulation, bleeding during the procedure, tissue adherence to the instrument, postoperative hemorrhage on probing, postoperative pain, and wound healing in regard to tissue color, tissue contour, and appearance of the wound. Results: At the 24 th hour, all gingival wounds were covered with fibrinous plaque and an erythemata halo. At the 72 nd hour, in two cases (4.9%), this wound's characteristic had already passed through to the next healing stage--granulated surface and normal pale pink color. A granulated surface in the 1 st week was reported in 10 (24.4%) cases, and in all of the other 31 (75.6%) cases, complete healing was reported. In the second week, all wound surfaces had a normal pale pink color. Bleeding was self-limiting in most of the cases and negligible from a clinical point of view. The postoperative pain levels were low or nonexistent. There was lack of postoperative hemorrhage on probing after the first week in all examined cases. Tissue recovery in terms of tissue contour, color, and appearance of the wound was asymptomatic. Conclusion: within the limitations of this pilot study, the outcomes of the wound healing after laser gingivectomy aided the clinicians to perform the prosthetic procedures in the same visit.  
laser gingivectomy; laser crown lengthening; laser surgery; prosthetics
## 3 Experiment Results and Analysis
### Time Complexity Analysis  
To assess the time complexity of our RDHEI method, we conducted a detailed analysis of its key operations, which are presented in Algorithms 3 and 4. We considered factors such as the size of the input image \(n\times m\) and the number of bit planes processed. Algorithm 3 involves initiating the \(mappedPredictionError()\) function, followed by sequential calls to \(quadtreeDecomposition()\), \(calculateAuxInfo()\), and \(bitPlaneReordering()\) for the embedded bit planes (up to 8 in the worst case), and finally calling the \(encrypt()\) function. The complexity for most functions is \(\mathcal{O}(nm)\), except for \(quadtreeDecomposition()\), which has a time complexity of \(\mathcal{O}(nm\log(nm))\). Consequently, the overall time complexity of the \(processImage\) algorithm is deduced to be \(\mathcal{O}(nm\log(nm))\) as the quadtree decomposition process imposes the most significant computational load compared to the other processes. In the data embedding Algorithm 4, three functions--encrypt(), readPartialInf(), and embedBits()--are employed, with each having a time complexity upper-bounded by \(\mathcal{O}(nm)\). Considering both algorithms, it can be inferred that the overall time complexity of our RDHEI method is \(\mathcal{O}(nm\log(nm))\).  
Upon comparing the complexity analysis of the various studied algorithms, it is noteworthy that a significant portion of these algorithms exhibit a time complexity that approximates \(\mathcal{O}(nm)\). However, our proposed approach, with a time complexity of \(\mathcal{O}(nm\log(nm))\), while slightly higher, remains well within the bounds of efficiency. It is important to emphasize that this \(\mathcal{O}(nm\log(nm))\) complexity offers a favorable trade-off between computational cost and performance gains when compared to the \(\mathcal{O}(nm)\) alternatives. The computational efficiency is more than acceptable, making our approach a compelling choice.
# Engineering Biomimetic Gelatin Based Nanostructures as Synthetic Substrates for Cell Culture
###### Abstract  
There is a need for synthetic substrates that replicate the natural environment for in vitro intestinal models. Electrospinning is one of the most versatile and cost-effective techniques to produce nanofibrous scaffolds mimicking the basement membrane topography. In this study, three different novel electrospun nanofibrous scaffolds made of a polycaprolactone (PCL), gelatin, and poloxamer 188 (P188) blend were produced and compared with PCL and PCL/gelatin fibers produced using the same solvent system and electrospinning parameters. Each polymer solution used in this experiment was electrospun at four different voltages to study its influence on fiber diameter. The morphology and physical characteristics of the fibers were studied using scanning electron microscopy and atomic force microscopy. The average fiber diameter of all scaffolds was within 200-600 nm and no significant decrease in diameter with an increase in voltage was observed. Attenuated total reflection Fourier transform infrared spectroscopy was used to determine the chemical characteristics of the nanofibrous scaffold. The conductivity of the polymer solutions was also analyzed. Biocompatibility of the scaffolds was determined by a cell proliferation study performed using colorectal carcinoma (Caco-2) cells. PCL/gelatin/P188 scaffolds exhibited higher cell proliferation compared to PCL, PCL/gelatin scaffolds, and the control (tissue culture multi-well plate) with PCL/gelatin/P188 80:10:10 sample showing the highest cell proliferation.  
electrospinning; polycaprolactone (PCL); gelatin; nanofibers; P188 +
Footnote †: journal: Applied Physics Letters
## 4 Overall Design and System Analysis
### Theoretical Analysis
#### 4.2.2 Theoretical Basis Derivation
We can see that along with an increase in communication rate, reliability, and timing accuracy, measured noise can be reduced. That means that combining additional information from other services will improve the performance of each service. In other words, by integrating CPNT with more information, positioning error can be reduced, and positioning and navigation performance will be further improved.