esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 General Encoding and Decoding Algorithm
Figure 5: Convolutional decoding operation through Viterbi algorithm. 3. **There are separated errors in the received codeword. ** Figure 7 shows that the receiver received a codeword with a separated two-bit error. The receiver, depending on Viterbi algorithm, found the error bit and corrected the code depending on the survival path (the path with the minimum hamming distance value; here, it is **2**). 4. **There are continuous (contiguous) errors in the received codeword. ** Figure 8 show that the receiver received a codeword with continuous errors. The receiver in this situation could not detect the errors and the receiver could correct only 64.29% of the received codeword, and 35.71% of the received codeword could not be corrected, and this shows the disadvantages of this type of coding and decoding method. Figure 6: Received a codeword with one-bit error. Figure 7: Received a codeword with two separated-bit error. The provided examples merely serve an illustrative purpose of explaining the operational mechanism of these codes and do not accurately depict the characteristics of the noise encountered by GPS signal systems. The primary concern for GPS systems is multi-path fading, which induces bit flipping due to the occurrence of Reflection Errors and Attenuation or Signal Loss. Convolutional codes are used in GPS systems to mitigate errors caused by multipath fading, including reflection errors and attenuation or signal loss. Here is how convolutional codes contribute to the robustness, accuracy, and stability of GPS signals:  
1. Reflection Errors: Convolutional codes work by introducing redundancy into the transmitted signal. By adding extra bits to the original data stream, the receiver can detect and correct errors caused by reflection. When the reflected signal arrives at the receiver, the redundancy introduced by convolutional coding enables the receiver to differentiate between the original and reflected signals and correct any bit errors that may have occurred due to reflections. 2. Attenuation or Signal Loss: When signals are weakened or lost due to attenuation or signal loss, convolutional codes help in recovering the original data. By adding redundancy in the form of additional bits, convolutional codes allow the receiver to reconstruct the original signal, even if some bits are missing or corrupted. These codes use mathematical techniques to reconstruct the most likely sequence of bits, ensuring accurate and reliable data reception despite signal attenuation or loss. Convolutional codes provide robustness, accuracy, and stability to GPS signals by offering error detection and correction capabilities. By detecting and correcting bit errors caused by reflection and compensating for attenuated or lost signals, convolutional codes ensure that the received data is as close to the original transmission as possible. This improves the reliability and accuracy of GPS positioning, allowing for more precise and consistent location measurements.
# An Overview of Shade Selection in Clinical Dentistry
## 2 Literature Review
### Factors Affecting Shade Selection
#### 2.5.1 Age  
Shade-matching skills become unfavorable due to aging because the cornea and lens of the eye yellow with age, conveying a yellow-brown bias. This change starts at the age of 30; it becomes more obvious at 50 years of age and has medical importance after the age of 60. After the age of 60, many people face problems in distinguishing blue and purple colors. Images are observed as more yellowish and brownish with an increase in eye age [53].
# Popularity-Aware Closeness Based Caching in NDN Edge Networks
## 2 Background and Motivations
### Caching Strategies in the Literature
Given a content \(x\), the closest node to the requesters of \(x\) would be able to deliver the content with the lowest delay. Therefore, to take advantage of the limited cache capacity at the edge, it is necessary to select the most popular contents and cache them in the closest nodes along the delivery paths. In addition to reducing the content retrieval latency, caching contents close to the consumers allows for the reduction of intra-domain traffic. Indeed, contents may traverse a lower number of hops and free bandwidth resources over edge links. Table 1 compares the main features of the related caching strategies available in the literature and our proposal.
# Effective Management System for Solar PV Using Real-Time Data with Hybrid Energy Storage System
## 5 Results and Discussion
### Simulation Results  
The simulation results of the proposed are presented in Figure 11. The analysis between the boost converter and IBC system results are shown in Figure 12. In the boost converter, the high ripple voltage and currents are presented in Figure 12a. The voltage drop at the DC link is more at the instant t5 in Figure 12a by 8V/s during sudden variations in the load at time t5 by 4A/s in Figure 11c; thus it affects the fast-acting DC link voltage regulation, and the high width of the current ripples and conduction losses in SC are presented in Figure 12a. It decreases the efficacy of the system. The IBC efficiently  
\begin{table}
\begin{tabular}{c c} \hline \hline
**PV Pack Specifications at STC** & **Values** \\ \hline Open circuit voltage (V\({}_{\text{pv}}\)) & 40 V \\ Short circuit current (I\({}_{\text{SC}}\)) & 25 A \\ \hline
**Battery Pack Specifications** & **Values** \\ \hline Type & Li-ion \\ Rated capacity & 12 Ah \\ Terminal voltage & 12 V \\ No. of batteries in series & 4 \\ \hline
**SC pack parameters** & **Values** \\ \hline Terminal voltage (V\({}_{\text{sc}}\)) & 12 V \\ No. of SCs in series & 4 \\ Max. peak current (I\({}_{\text{p}}\)) & 200 A \\ Capacitance (C\({}_{\text{sc}}\)) & 58 F \\ Max. continuous current (I\({}_{\text{mc}}\)) & 19 A \\ \hline
**Converter Parameters** & **Values** \\ \hline PV parameters & \begin{tabular}{c} L\({}_{1}\) = L\({}_{2}\) = 10 mH \\ C\({}_{\text{PV}}\) = 500 \(\mu\)F \\ \end{tabular} \\ \hline Battery parameters & \begin{tabular}{c} L\({}_{\text{b1}}\) = L\({}_{\text{b2}}\) = 10 mH \\ C\({}_{\text{b1}}\) = C\({}_{\text{b2}}\) = 500 \(\mu\)F \\ \end{tabular} \\ \hline SC parameters & \
\begin{tabular}{c} L\({}_{\text{sc}}\) = 10 mH \\ C\({}_{\text{sc}}\) = 500 \(\mu\)F \\ \end{tabular} \\ \hline DC load parameter &
\begin{tabular}{c} R\({}_{\text{dd}}\) = 100 \(\Omega\) \\ \end{tabular} \\ \hline
**DC link parameter** &
\begin{tabular}{c} **V\({}_{\text{dc}}\) = 80 V** \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: System parameters.  
Figure 15: (a) Battery-1 SOC, (b) battery-2 SOC.  
reduces the current and voltage ripples as shown in Figure 12b, and increases the performance and reliability of the system.
# Electromagnetic Switching Multiple Grasping Modes Robot Hand
## 4 Mechanical Analysis of ESMM Hand
### Kinematic Analysis of the ESMGM Hand
#### 4.1.2 Coupled and Self-Adaptive Grasping Mode (COSA Mode)  
The mechanical analysis of ESMGM hand under COSA mode is shown in Figure 10. When the ESMGM hand is in coupled grasping mode, the rotational angles of both joints are equal.  
Figure 9: Kinematic analysis of the ESMGM hand (PASA mode): (**a**) Trajectory along the \(x\)-axis; (**b**) trajectory along the \(y\)-axis.  
Similarly, the coordinate of the end of the distal phalange of the ESMGM hand can be represented as:  
\[x_{d}=l_{1}\sin\alpha+l_{2}\sin 2\alpha=l_{1}\sin\frac{6n_{0}t}{i}+l_{2}\sin\frac{12n _{0}t}{i}, \tag{13}\]  
\[y_{d}=l_{1}\cos\alpha+l_{2}\cos 2\alpha=l_{1}\cos\frac{6n_{0}t}{i}+l_{2}\cos \frac{12n_{0}t}{i}, \tag{14}\]  
Under the coupled grasping mode, when the distal phalange rotates coupled with the proximal phalange, the partial effective driven disc rotates reversely with the same angular speed. Thus, when assembling the device, a return stroke of the partial effective mechanism should be reserved to guarantee enough range for the coupled grasping mode motion.  
Due to the limitation of the stroke angle of the partial effective transmission mechanism, the joint angle of proximal phalange \(\alpha\) and operation time under the coupled grasping period \(t_{\alpha}\) have the following ranges:  
\[0\leq\alpha\leq\psi_{0}, \tag{15}\]  
\[0\leq t_{\alpha}\leq\frac{i\psi_{0}}{6n_{0}}, \tag{16}\]  
After the coupled grasping mode, the ESMGM hand will execute a period of the idle-stroke phase, and the idle-stroke phase time \(t_{\varphi}\) is relative to the state of the partial effective transmission mechanism and can be calculated as follows:  
\[t_{\varphi}=\frac{i\varphi_{0}}{6n_{0}}+t_{\alpha}, \tag{17}\]  
Then, the ESMGM hand will enter self-adaptive grasping mode, the proximal phalange keeps still, and the distal phalange is turned, so the coordinate of the end of the distal phalange can be represented as:  
\[x_{d}=l_{1}\sin\alpha+l_{2}\sin(\alpha+\beta)=l_{1}\sin\frac{6n_{0}t_{\alpha} }{i}+l_{2}\sin(\frac{6n_{0}t}{i}-\varphi_{0}), \tag{18}\]  
\[y_{d}=l_{1}\cos\alpha+l_{2}\cos(\alpha+\beta)=l_{1}\cos\frac{6n_{0}t_{\alpha} }{i}+l_{2}\cos(\frac{6n_{0}t}{i}-\varphi_{0}), \tag{19}\]  
Figure 10: Mechanical analysis of the ESMGM hand (COSA mode): (**a**) Coupled grasping; (**b**) self-adaptive grasping.  
\[t_{\beta}\geq\frac{i\varphi_{0}}{6n_{0}}+2t_{\alpha}, \tag{20}\]  
Thus, one can draw the trajectory of the distal phalange end during the COSA mode, as shown in Figure 11. From Figure 11, it can be observed that the grasping action under the coupled grasping mode is more rapid, but the period of the idle-stroke phase is much longer compared with that in the PASA mode.
## 3 Methodology
### Flow-Focused Global and Local Moran Indice  
Global and local Moran Indices have become popular methods for measuring spatial autocorrelation, which is a form of spatial dependence. In this case study, both indices were employed for two purposes: detecting clustering patterns and defining clusters of flow, and examining the reduction of spatial auto-correlation in the residuals of global and local models of transport flows. The calculation of spatial autocorrelation depends on spatial weights. The distance threshold method is commonly used in the calculation of spatial weights.  
Considering the unique spatial dependence of flows, spatial weight is defined by the spatial distance between OD flows [31]. Equation (3) is used to calculate the spatial distance between flows. Adjacent OD flows within a specified critical distance will be given a weight and have a significant impact on the calculation of the target OD flow. Adjacent OD flows that exceed a specified critical distance will be assigned a weight of zero and will not have any effect on the calculation of the target OD flow. The calculation method of the space weight is called the fixed distance band, and its equation is as follows:  
\[\omega_{\mathrm{ij}}=\left\{\begin{array}{ll}\frac{1}{\mathrm{d}_{\mathrm{ ij}}^{2}},&\mathrm{if}\;\mathrm{d}_{\mathrm{ij}}\leq b\\ 0,&\mathrm{otherwise}\end{array}\right. \tag{3}\]  
where b is the fixed distance band, which should satisfy the requirement of ensuring at least one adjacent flow for each flow, and \(\mathrm{d}_{\mathrm{ij}}\) is the spatial distance between flow i and flow j. The Global Moran Indice is used to detect the pattern of transport flows: random, dispersed or clustered. The Local Moran Indice enables flows to be classified into four categories: HH (high-high), LL (low-low) and LH (low-high) and HL (high-low). HH means high volumes of flows are surrounded by a high volume of flows, and are therefore recognized as a hot cluster. Conversely, LL defines a cold cluster. LH and HL are defined as spatial outliers.
# Blockchain Technology for Intelligent Environments
## 3 Noteworthy Blockchain Implementations
### Private/Permissioned Blockchains
#### 3.2.4 Corda  
Corda [52] is a blockchain-inspired distributed ledger by R3, a financial technological company based in New York. Corda is meant to serve the purpose of exchanging information between nodes belonging to different companies, to record, manage, and synchronize agreements and transfer value. It is designed to be fast and easy to adapt for building customer specific solutions.  
Corda relies on various existing technologies including Java and enterprise technologies, relational databases and the Advanced Message Queuing Protocol (AMQP). It does not have any internal Corda coin. Corda focuses on the critical issues of privacy, transaction finality, legally identifiable participants, scaling, developer productivity, and easy enterprise integration.  
Corda goals include: assurance that the parties involved in the same transaction agree on the facts, a mechanism for reaching consensus on the state of the ledger, and immutability of the ledger and transactions. Corda is based on: a point-to-point architecture, a pluggable consensus, and a multilateral ledger.  
Corda transactions are similar to those of Bitcoin: they have inputs, outputs and signatures, but, unlike Bitcoin, they can contain arbitrary data [53]. Corda does not order transactions using a blockchain and does not use miners or PoW. Instead, each state points to a notary, which is a service that guarantees it will sign a transaction only if all the input states are un-consumed. It is a permissioned DLT and identities are used throughout and at all levels. No broadcast is used. This identity does not have to be a legal or true identity.  
A Corda network has one or more notary services which provide transaction ordering and timestamping. Notaries are typically composed of multiple mutually distrusting parties who use a standard consensus algorithm. Notaries consider transactions submitted to them and either sign or reject them. The presence of an appropriate notary signature indicates transaction finality.
## 3 Theory
### Estimation of the Inherent Parameters \(T_{ng}\), \(T_{g}\) and \(C\)
#### 3.2.2 Estimation of \(\alpha_{g}\) and \(\Delta N_{g}\)  
To calculate the total energy decay, Equation (8) in Section 3.1, the number of modes in each sector and the corresponding reverberation times must be known. In this paragraph and the following paragraph, we will firstly estimate the representative absorption coefficients \(\alpha_{g}\) and \(\alpha_{ng}\) for the grazing and non-grazing sectors in Figure 2, as well as the number of modes \(\Delta N_{g}\) and \(\Delta N_{ng}\) in each sector.  
Figure 3: Local vs. extended reaction. Calculations according to the SEA model in a classroom with a 15 mm thick porous ceiling absorber with a mounting height of 200 mm (case 4 in the Section 5). Calculated local reaction (red), measured (blue), calculated and extended reaction (green).  
The grazing sector is defined by an angle \(\theta_{g}\) given by  
\[\theta_{g}=\arccos\left(\frac{c}{4 fL_{x}}\right) \tag{26}\]  
The derivation of \(\theta_{g}\) is given in Appendix A. The grazing sector in the wavenumber space is illustrated in Figure 4.  
By knowing the surface impedance of the ceiling, the angle-dependent absorption coefficient can be calculated. The grazing absorption coefficient \(\alpha_{g}\) is then calculated as the average absorption coefficient in the grazing region, i.e., between \(\pi/2-\theta_{g}\) and \(\pi/2\). This absorption is often quite small but not negligible when compared to the total absorption for the grazing field. Equation (26) is a high-frequency estimation. At low frequencies, i.e., at 125 Hz and 250 Hz, the grazing absorption is estimated by  
\[\alpha_{g,ceiling}=\pi\rho cA^{\prime}_{xl} \tag{27}\]  
where \(A^{\prime}_{xl}\) is the real part of the admittance for the ceiling absorber. \(A^{\prime}_{xl}\) is given by the real part of \(1/Z\), where \(Z\) is given by Equation (22), assuming an extended reaction. The derivation of Equation (27) is given in Appendix B.  
The number of grazing modes in the frequency band \(\Delta f\) is given by [55]  
\[\Delta N_{g}\big{(}\theta_{g}\big{)}=\bigg{[}\bigg{(}\frac{4\pi f^{2}V}{c^{3} }\bigg{)}\cos\left(\frac{\pi}{2}-\theta_{g}\right)+\bigg{(}\frac{2f}{c^{2}} \bigg{)}\big{(}\pi L_{y}L_{z}+\theta_{g}\big{(}L_{x}L_{z}+L_{x}L_{y}\big{)} \big{)}+\bigg{(}\frac{1}{c}\big{)}\big{(}L_{y}+L_{z}\big{)}\bigg{]}\Delta f \tag{28}\]  
where \(V\) is the volume and \(L_{x}\), \(L_{y}\), and \(L_{z}\) are height, length, and width of the room, respectively. As \(\theta_{g}\to 0\), the number of grazing modes corresponds to the tangential and axial modes in the \(yz\) plan.
# A Comprehensive Review of Micro UAV Charging Techniques
## 8 Potential Challenges in UAV Charging Techniques
### Optical Communication Problem  
In future, LPT systems might be able to combine efficient optical communication. By using a PV cell as a receiver for communication, control information and forward command might be delivered, along with power transmission, which could result in a dual-use system. The technique to modulate communication links onto transmitted photon energy will revolutionize robust free space optical communication. However, it is still an open challenge to incorporate communication signals into the optical energy path.
# Survey and Perspectives of Vehicular Wi-Fi versus Sidelink Cellular-V2X in the 5G Era
## 2 Overview of the Technologies and Comparative Discussion  
After the studies on short-range communications between vehicles carried out in the eighties and nineties, probably the first milestone was the allocation of dedicated bandwidth in the US in 1999. In particular, seven channels of 10 MHz each were reserved around 5.9 GHz for what was called dedicated short range communication (DSRC) (for this reason, DSRC is often used to denote IEEE 802.11p and the related American standards). A similar allocation was issued in Europe in 2008 (similar frequencies, with only five channels [16]). In both cases, the allocations were implicitly addressing IEEE 802.11p.  
Since the first allocations in the US, for more than one decade all remained confined to research efforts and there was no clear program for a real deployment. Everything has instead accelerated in the latest years, after large scale tests in the US and the so-called European Telecommunications Standards Institute (ETSI) Plugtests in Europe have proved the feasibility of massive use of short-range V2X. As a consequence, various governments are working at the opportunity to mandate or at least encourage the installation on new vehicles.  
In the meantime, 3 GPP have recognized the relevance of this vertical and the urgency of specific effort inside the cellular ecosystem. For this reason, in 2016, it created the so-called C-V2X within LTE Release 14, frozen in 2017, and included a short-range interface that can be used also outside the cellular coverage. The new short-range LTE-V2X interface clearly appears as an alternative to IEEE 802.11p. The main events are also summarized in Table 1, where it is possible to observe at a glance that, while the IEEE 802.11 working group spent about 15 years to make commercial products available on the market, 3 GPP has taken a leap forward and promises to have LTE-V2X devices soon available (despite the decidedly lower series of experimentations).  
In this section, the main characteristics of the two technologies, the planned evolutions, and a comparative discussion based on related work are reported.
# Adding a Tail in Classes of Perfect Graphs
## 5 \(P_{4}\)-Sparse Graphs
### The Algorithm
\((V_{j}\cap N_{G}(a))\setminus N_{G}(u)=\emptyset\) which implies that \(t_{j}\) is a 1-node. Requirement 4 implies that \(V_{j}\cap N_{G}(u)=V_{j}\) which together with Requirement 2 imply that \(N_{G}[a]=V_{j}\); that is, \(a\) is universal in \(G[V_{j}]\) and we have the second case in the statement of the lemma. Now we are ready to describe our algorithm for counting the number of fill edges in a minimum \(P_{4}\)-sparse completion of the graph \(G+uw\). Note that every graph on 4 vertices is \(P_{4}\)-sparse since every forbidden graph for the class of \(P_{4}\)-sparse graphs has 5 vertices (Figure 3). Algorithm 1 can be easily augmented to return a minimum cardinality set of fill edges. The correctness of the algorithm follows from Lemmas 5, 6, 7 and 8 and Corollary 1. Let \(G\) be the given graph and let \(n\) be the number of its vertices. If the \(P_{4}\)-sparse tree \(T_{G}\) of \(G\) is given, an \(O(n)\)-time traversal of the tree enables us to compute the path \(t_{0}t_{1}\cdots t_{h}u\), the sets \(V_{0},\ldots,V_{h}\) and the number of neighbors and non-neighbors of \(u\) in each of these sets; additionally, the height of \(T_{G}\) is \(O(n)\) and thus \(h=O(n)\). To avoid duplicate work in the recursive calls, we store the numbers of neighbors and non-neighbors of \(u\) in each of the sets \(V_{0},\ldots,V_{h}\) for easy access and work from the highest 2-node and up, leaving the rest for the recursive call at that node. Since all conditions can be checked in \(O(1)\)-time, the entire algorithm runs in \(O(n)\) time. Theorem 1: _Let \(G\) be a \(P_{4}\)-sparse graph on \(n\) vertices and let \(uw\) be a tail attached at node \(u\) of \(G\). If the \(P_{4}\)-sparse tree of \(G\) is given, Algorithm \(P_{4}\)-sparse-Tail-Addition computes the minimum number of fill edges to be added to \(G+uw\) so that the resulting graph is \(P_{4}\)-sparse in \(O(|V(G)|)\) time._  
If the \(P_{4}\)-sparse tree \(T_{G}\) of \(G\) is not given, then it can be computed in \(O(n+m)\) time where \(m\) is the number of edges of \(G\)[37] and the entire algorithm takes \(O(n+m)\) time. ```
Input: a \(P_{4}\)-sparse graph \(G\), a vertex \(u\in V(G)\) and a tail \(uw\) to be added to \(G\). Output: the number of fill edges (excluding the tail \(uw\)) needed in a minimum \(P_{4}\)-sparse completion of the graph \(G+uw\).