esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Test Scenarios
### Case 2: Database Connection  
In this scenario, an example of connection to a database by means of naturalistic queries is presented. SQL is a query language with a high level of expressiveness, a feature which facilitates implementation in the SN language without major problems. Two very simple abstractions are presented below, _Car_ and _Person_:  
noun Car:  attribute model as a String.  attribute maxSpeed as a Integer Number.  
noun Person:  attribute name as a String.  attribute age as a Integer Number.  
To enable a separation between the database connection and the nouns, the adjective _Storable_ is presented, thus refining the adjective _DBEntity_.  
1 adjective Storable is DBEntity:
2 circumstance:executeassignthiswhenthisiscreated.
3 verbassignitself:
4 the driveris''org.postgresql.Driver''.
5 thejaris''C:\pgsql.jar''.
6 the useris''admin''.
7 the passwordis''admin1''.
8 the URLis''jdbc:postgresql: //localhost:5432/agenda''.  
Based on these abstractions, the following example presents a mechanism that enables three to be converted into database entities with the ability to query, insert, delete, and modify information:  
1 main Select:
2 a DBPersistent String with''Person1''asvalue.
3 an Storable Person with the String as name.  
some Strings.
5 add ''name'' to these.
6 add ''age'' to the Strings.
7 select the Strings from the Person.
8 System prints these and newline.  
Line 1 describes an abstraction _main_, which is the starting point of any program in SN. Line 2 describes the instance of a noun _String_ that is complemented with an adjective _DBPersistent_, which turns the string into an abstraction storable in an attribute of a database tuple. Line 3 indicates the creation of an instance of the noun _Person_ that is complemented with _Storable_, in order that this instance functions as an element equivalent to a tuple of a database. Line 4 indicates the creation of a plural type _Strings_, which will store the attributes of the table to be accessed. Lines 5 and 6 add the strings _name_ and _age_, which are the attributes of the table _Person_ that you want to extract from the database. On Line 7, the database query is made by means of the verb _select_, which returns a plural of the type Person, if a plural is not defined, and the type _Things_, the root of plurals, is then returned. Finally, Line 8 prints the values. Other database-related operations are shown in Appendix C.
# Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud
## 4 Experimental Results Analysis
### Time Cost  
In order to test the time cost of the system, we took 50 test cases for each action in the filtered and clustered segmentation processed data for a total of 250 test cases to conduct experiments on the time cost of individual action recognition. The average test time for each action is shown in Table 2. The average recognition time for the action with the longest recognition time is 75 ms, and the average recognition time for the action with the shortest recognition time is 59 ms. The average recognition time for all the actions is 66.4 ms. Subsequently, we processed our captured data using RadHAR's processing flow, and similarly, we sampled 50 test cases for each action to conduct experiments on the time cost of individual action recognition. The results are shown in Table 3. The time cost of the proposed system in this paper is much lower than the average 785.8 ms elapsed time of RadHAR. Most of the previous studies used voxelization for point cloud data processing. Voxelization not only loses part of the information in the data, but also produces many empty voxels, resulting in the rise in the recognition time cost. In this paper, by overlaying multiple frames and discarding the \(y\)-axis data with relatively less information, we significantly reduce the data dimension, data volume, and the time cost required for recognition.
## 3 Proposed Techniques  
This paper introduces two techniques for transmitting the sensor data to the BS: a static and a dynamic technique. Thus, we suppose that the lifetime of the network is split into rounds. Every round comprises of four phases: the initial phase, the announcement phase, the tables preparation phase, and the routing phase. It is worth mentioning that the basic difference between the two techniques appears in the routing phase. This study assumes that the nodes of the network are all stationary and scattered randomly along the environment. Additionally, they will be initiated by a limited quantity of energy. To compute the energy receding in the network nodes, we use the energy dissipation model, which was proposed in [14]. By doing so, the amount of energy which is required to transmit and receive \(k\)-bit packets can be calculated using the following equations, respectively [14]:  
\[E_{Tx}(k,\,d)=\left\{\begin{array}{ll}E_{elec}\times k+\varepsilon_{fs} \times k\times d^{2}\,,&d<d_{o}\\ E_{elec}\times k+\varepsilon_{mp}\times k\times d^{4},&d\geq d_{o}\end{array}\right. \tag{2}\]  
\[E_{Rx}(k)=E_{elec}\times k \tag{3}\]  
Here, the \(E_{elec}\) is the energy that is consumed per bit in order to process the data that will be transmitted and received along the network. The \(\varepsilon_{fs}\) indicates the free space propagation model, which is utilized in evaluating the energy needed if the transmission distance is less than a threshold distance (\(d_{o}\)). The \(\varepsilon_{mp}\) refers to the two-ray propagation model and is used when the transmission distance is equal to or longer than \(d_{o}\). The value of the \(d_{o}\) can be found as follows [14]:  
\[d_{o}=\sqrt{\varepsilon_{fs}/\varepsilon_{mp}} \tag{4}\]
# A Coordination Technique for Improving Scalability of Byzantine Fault-Tolerant Consensus
## 3 A Coordination Technique for Scalable BFT Consensus  
This section presents a coordination technique for achieving the scalability of BFT-based consensus algorithms. Our approach is composed of two parts: _Our Coordination Technique_ and _BFT-based Consensus Algorithm_ (Figure 2). The BFT-based Consensus Algorithm part located at the bottom of the figure indicates traditional BFT-based algorithms such as PBFT and IBFT. It is composed of one _prime node_ that controls consensus process and other _general nodes_ similar to general BFT-based blockchain platforms. Each node has _BFT-Module_ controlling the consensus process and generating new blocks and _Transaction Pool_ maintaining unconfirmed transactions. Thus, the BFT-Module accesses transactions of the transaction pool regularly and executes a BFT-based consensus algorithm. Once all nodes have been achieved, the consensus of transactions, the BFT-Module produces a new block after accumulating agreed transactions.  
Our Coordination Technique part corresponds to the top of the figure, and it is also located to the part above the BFT-based Consensus Algorithm part. In the technique, we newly introduce the _Consensus Coordinator_ that controls each node's BFT-based consensus algorithms' conditional execution depending on the equality of transactions. Our consensus coordination technique consists of four steps. (1) The prime node is elected among all participating nodes. (2) The coordinator collects all transactions that existed in the transaction pool of each node. (3) The coordinator checks the equality of transactions and classifies transactions into _common_ and _trouble_ transactions. For trouble transactions, the coordinator requests a prime node to execute a consensus algorithm and obtains _agreed_ transactions. (4) The coordinator merges common and agreed transactions and requests the controller of all nodes to execute block generation with merged transactions. In the following subsections, the steps are described in more detail.
# A Fuzzy Method for Joint Resource Allocation and Stable Pairing in D2D Communications
## 2 System Model
### Construing the Fuzzy Membership Function  
In the mentioned network, a stable connection is a connection in which the data rate is acceptable while the link does not disconnect due to a low battery condition. The set of all nodes suitable for pairing has a fuzzy interpretation. Let the set \(T=\{t_{ki}\,|\,t_{ki}\) is a stable transmitter\(\}\) be the fuzzy set, where \(k\in\{1,\ldots,K\}\) and \(i\in\{1,\ldots,N\}\). We tend to construct a fuzzy membership function for each node in \(T\). Two network parameters, the normalized data rate (\(R\)) and battery level (\(E\)) of each node, are considered. To measure the stability, two thresholds \(R_{0}\) and \(E_{0}\) are set. Considering the limitations of the network protocols, these thresholds guarantee the stability of the D2D link.  
Concerning the mentioned explanations, the proposed membership function \(\mu:T\rightarrow[0,1]\) is defined as follows.  
\[\mu(t_{ki})=\lambda(R_{ki},E_{ki}), \tag{6}\]where \(E_{kl}\) is the battery level of transmitter \(t_{ki}\), and  
\[\lambda(R_{ki},E_{ki})=\begin{cases}R_{ki};&(E_{0}\leq E_{ki}\leq 1,\\ &0\leq R_{ki}<R_{0})\\ E_{ki};&(0\leq E_{ki}<E_{0},\\ &R_{0}\leq R_{ki}\leq 1)\\ \tau(R_{ki},E_{ki})(R_{ki}^{2}+E_{ki}^{2});&(0<E_{ki}<E_{0},\\ &0<R_{ki}<R_{0})\\ \tau(R_{ki},E_{ki});&(E_{0}\leq E_{ki}\leq 1,\\ &R_{0}\leq R_{ki}\leq 1).\end{cases} \tag{7}\]  
Note that the Einstein sum is applied as  
\[\tau(R_{ki},E_{ki})=\frac{R_{ki}+E_{ki}}{1+R_{ki}E_{ki}}.\]  
Since those fuzzy nodes with a high membership degree have both a higher battery level and a higher data rate among their neighbors, it can be said that the proposed membership function is an appropriate metric for measuring stability. For this reason, when transferring high volume contents, the connection is less likely to fail.
# Research on Waveform Adaptability Based on Lunar Channels
###### Abstract  
In recent years, the focus of space research and exploration by various countries and international space agencies has been on the return of humans to the moon. Astronauts on lunar missions need to utilize network communication and exchange data. Against this backdrop, it is necessary to consider the performance of communication systems and the extreme conditions of the lunar environment, such as signal attenuation and frequency selection, to ensure the reliability and stability of communication systems. Therefore, providing technical performance adapted to the lunar environment is crucial. In this article, we investigated the applicability of Orthogonal Frequency Division Multiple Access (OFDMA) and Single-Carrier Frequency Division Multiple Access (SC-FDMA) waveforms in the lunar communication environment. Specifically, we used Peak-to-Average Power Ratio (PAPR) and Bit Error Rate (BER) as performance indicators. By studying the impact of different modulation schemes and cyclic prefix lengths on communication performance, we completed the research on waveform adaptability based on lunar channels. Simulation results indicate that the transmission structure we designed can meet the system-level performance requirements of lunar communications. This research provides valuable insights for the design and optimization of communication systems for future lunar missions, paving the way for the seamless integration of advanced ground technologies in extraterrestrial environments.  
lunar communication system; orthogonal frequency division multiple access; single-carrier frequency division multiple access; peak-to-average power ratio; bit error rate +
Footnote †: journal: Electronics Electronics
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 3 Hybrid RF-FSO Systems Techniques and Their Advanced Achievements
### Space Optical Satellite Communications
Regarding free space being a non-homogenous medium, and non-absorbing as well, a soliton pulse also has an opportunity to be implemented as the signal modulation in DSORN. A soliton pulse has some benefits for high-speed signal transmission into space to support a DSORN as the major communication platform carrying high-data-rate transmission.
# An NFC on Two-Coil WPT Link for Implantable Biomedical Sensors under Ultra-Weak Coupling
## 4 Minimizing Interactions between NFC and WPT
### Minimizing the Impact of the NFC Downlink on the Power Link  
When the downlink data are modulated on the power carrier, the modulation could induce serious amplitude variation on the power carrier. We use BPSK for the downlink, because it theoretically has a constant carrier amplitude and a constant carrier frequency, thus bringing less amplitude variation on the power carrier compared to ASK and less link gain variation on the resonated inductive link compared to FSK.  
However, BPSK modulation could generate a phase jump on the power carrier. The phase jump will seriously reduce the transmitting power of the Class E PA, because the Class E PA is very sensitive to its optimal condition (narrow bandwidth). In order to reduce this impact, we introduce a shaping filter (raise-cosine finite-impulse-response digital filter) in the out-body downlink modulator, as seen in Figure 8. The shaping filter smooths the downlink baseband signal. Then, the smooth baseband signal controls the phase of the PWM generator to generate a PWM with smooth BPSK modulation. By feeding the Class E PA, the PWM with smooth BPSK modulation, the transmitting power of the Class E PA will not decrease seriously. Figure 9a shows the impact of phase jump without shaping filter. By using the shaping filter, the phase shift is accomplished slowly in multiple power carrier periods. The phase jump thus is avoided, and then, the serious variations of the Class E transmitted power and \(V_{\rm load}\) (the voltage on in-body coil) are eliminated, as seen in the waves in Figure 9b.  
Figure 8: The block diagram of the out-body downlink modulator and a shaping filter is introduced to reduce the phase jump impact of BPSK on Class E PA.  
Figure 7: Calculated PTE and received uplink signal power versus \(R_{\rm pa}\), when \(k=0.01\).
## 3 Materials and Methods
### \(\mu\)-Raman Measurements  
An inVia confocal Renishaw Raman microscope was used to perform the molecular characterization. The spectrometer was equipped with a Peltier-cooled CCD detector coupled with a DMLM Leica microscope, featuring a motorized XYZ positioning stage with integrated position sensors on the \(X\) and \(Y\) axes. The measurements were carried out using the 20\(\times\) and 50\(\times\) objectives, together with a 785 nm excitation laser. The measurement conditions were adjusted as needed for each sample to minimize fluorescence interference and prevent photothermal degradation. Instrumental calibration was performed daily using the characteristic silicon band at 520.5 cm\({}^{-1}\). Raman spectra were acquired and processed using the Wire 3.0 Renishaw software. For spectral interpretation and phase identification, reference data from the RUFF spectral database were employed [42].
## 2 Study Area and Data
### Traffic Accident Data  
We used accident records collected by the Traffic Accident Analysis System (TASS) operated by the South Korean Road Traffic Authority [12], focusing on accidents in Seoul (the most urbanized city in South Korea) during the rainy season (April to October) from 2007 to 2017. A total of 259,088 traffic accident cases were collected for this period. Among the collected 259,088 cases, 24,797 accidents occurred in rain conditions, and the remained occurred in absence of rainfall. Each collected accident record included the date and time of the accident combined with details such as the number of fatalities, number and degree of injuries, gender and age of those involved, and accident summaries. Among the 24,797 accidents that occurred in rain conditions, 9039 accidents involved fatalities or severe injury. The accident reports also included the records of weather conditions (rainfall, fog, etc.) by the police handling the accident.  
From the 24,797 accidents that occurred in rain conditions, we extracted the data on weather conditions occurring one week prior and one week after the accidents. The extraction of this data allowed consideration of data from a similar period during which no rain was falling. Classification of rainfall/no-rain conditions in the record was based on weather conditions directly recognized by the police officer handling the accident, without using weather information observed at the time. Therefore, rainfall conditions based on accident reports were defined as actual weather conditions at the time in that location. The geographic locations of all selected accidents were used when collecting relevant gauge- and radar-derived rainfall data.
# Integrity Monitoring of GNSS Constellations with Only LEO-PNT Satellites +
## 3 User-Based Integrity with LEO-PNT Formulation (Option 2)
### Performance Assessment
#### 3.5.3 Simulation Results  
The following results are from the 1-day SVS simulation run for a 20 \(\times\) 20 user grid with a time step of 1 h.  
Basic LEO Barrier Results  
Table 6 shows the comparison of the basic sigma monitor, as a function of the LEOs in view, and the phase LERE budget for both the full constellation and the IoD constellation. The 19 cm target of the sigma monitor is achieved 95% of the time (2-sigma) when there are  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Constellation ID**} & **Number of** & **Number of** & **Altitude** & **Inclination** \\  & **Satellites** & **Planes** & **[km]** & **[deg]** \\ \hline \multirow{3}{*}{
\begin{tabular}{c} HIGH\_LEO \\ (220 satellites) \\ \end{tabular} } & 42 & 6 & 1200 & 80 \\  & 81 & 9 & 1200 & 65 \\  & 49 & 7 & 1200 & 50 \\  & 48 & 8 & 1200 & 10 \\ \hline IoD (4 satellites) & 4 & 2 & 561 & 97.6354 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Constellation definition.  
Figure 2: SVS processing diagram.
# Flow Sensing-Based Congestion Detection for D2D Streaming on a 5G gNB
## 4 Simulation Results
We will consider this parameter in the future. Figure 12 shows the average throughput performance with flow block sizes ranging from 200 MB to 1.5 GB. We simulated the performance as FV increased and had a better result than others when FV was 0.8 and 0.9. Figure 10: Average congestion control ratio with FV. The proposed algorithm exhibited better results compared with the link scheduling and clustering algorithms, achieving excellent performance. The proposed algorithm is not affected by 5G gNB link constraints, flow characteristics, CC, and other overhead constraints. Hence, the proposed algorithm operates an efficient flow-congestion-control mechanism, and D2D streaming services are maintained in a stable state. As a result, this paper showed that the proposed FS-CDA mechanism has an effect on the performance metrics of the system.
## 4 Proposed Protocol
### CH Selection
#### 4.1.1 Step 1: Selection of Candidate CHs  
Because nodes cannot be charged and their energy is limited, nodes with sufficient energy should be selected as CHs to improve network efficiency. The nodes with energy levels higher than the energy threshold are considered candidate CHs. The set of candidate CHs selected can be mathematically expressed by Equation (4):  
\[S_{CH}=\{N_{i}|E(N_{i})\geq E_{threshold}\} \tag{4}\]  
where \(E(N_{i})\) is the residual energy of the node \(i\) and \(E_{threshold}\) represents the average energy of the existing alive nodes:  
\[E_{threshold}=\frac{\sum_{i=1}^{N_{size}}E(N_{i})}{N_{alice}} \tag{5}\]  
Equation (5) is used to determine the nodes with enough energy to be selected for CHs.
## 2 Related Works
### Hamming Code  
Hamming codes [30; 31] have been widely used for error control in digital communications and data storage. It has interesting properties that make encoding and decoding tasks easier. When \(r\) is a non-negative integer that is a parity space dimension, the length of the codeword becomes \(\mathcal{N}=2^{r}-1\) and the data bits become \(\mathcal{K}=\mathcal{N}-r\). A single error-correcting linear block code with a minimum distance of 3 for all codewords was selected. Let \(x\) be a \(\mathcal{K}\)-bit information word. An \(\mathcal{N}\)-bit codeword \(y\) is generated using \(y=xG\), where \(G\) is a \(\mathcal{K}\)-by-\(\mathcal{N}\) generator matrix.  
It is in fact the space of vectors \(y\in\mathbb{F}_{2}^{n}\) such that \(Hy=0\) for the parity check matrix \(H\). The parity check matrix for the HC(7,4) Hamming code is as follows  
\[H=\begin{bmatrix}0&0&0&1&1&1&1\\ 0&1&1&0&0&1&1\\ 1&0&1&0&1&0&1\end{bmatrix} \tag{6}\]  
Let us assume that the codeword \(y^{\prime}\) has an error such as \(e=(y\oplus y^{\prime})\). In this case, we could correct one error \((e=y-y^{\prime})\) from the codeword \(y^{\prime}\) by using the syndrome \(\eta=y^{\prime}\cdot H^{T}\), where the syndrome denotes the position of the error in the codeword. As show in Equation (7), the error \(e\) can be obtained.  
\[\left\{\begin{aligned} \hat{y}\cdot H^{T}&=(e\oplus y) \cdot H^{T}&=e\cdot H^{T}+y\cdot H^{T}\\ (y\cdot H^{T})&=(x\cdot G)\cdot H^{T}&=x\cdot(G\cdot H^{T})=0 \end{aligned}\right. \tag{7}\] \[\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad =e\cdot H^{T}+0=e\cdot H^{T}\]  
Figure 1: An example of AMBTC: (a) an original block; (b) a bitmap block; (c) a reconstruction block.  
The following are check matrices for HC(15, 11) binary Hamming codes (Equation (8)).  
\[H=\left[\begin{array}{cccccccccccccccccccc}0&0&0&0&0&0&0&1&1&1&1&1&1&1&1\\ 0&0&0&1&1&1&1&0&0&0&0&1&1&1&1\\ 0&1&1&0&0&1&1&0&0&1&1&0&0&1&1\\ 1&0&1&0&1&0&1&0&1&0&1&0&1&0&1\end{array}\right] \tag{8}\]  
**Example 2**.: _Suppose [PERSON] wants to send codeword \(r=[1\ 0\ 1\ 0\ 1\ 0\ 1\ 0\ 1\ 0\ 1\ 0\ 1\ 0\ 1\ 0]\) to [PERSON]. Here, the codeword \(r\) contains an error in the second bit. That is, [PERSON] receives codeword \(\bar{r}=[1\ 1\ 1\ 0\ 1\ 0\ 1\ 0\ 1\ 0\ 1\ 0\ 1\ 0]\) with an error. [PERSON] computes the syndrome \(\eta\) and finds the error e. As a result, he obtains the codeword \(r=[1\ 0\ 1\ 0\ 1\ 0\ 1\ 0\ 1\ 0\ 1\ 0\ 1\ 0\ 1\ 0]\) which has repaired the error._
# Channel and Bit Adaptive Power Control Strategy for Uplink NOMA VLC Systems
## 3 Adaptive Power Control Strategy for Uplink NOMA VLC
### Low-Complexity Receiver Design  
Intended for our proposed efficient adaptive power control strategy in the last subsection, in this subsection, our task is to develop an efficient detection algorithm for the receiver.  
Let \(\tilde{D}=h_{1}\check{\gamma}_{1}\). Then, according to Property 1, the received signal \(y=\sum_{i=1}^{N}h_{i}\check{\gamma}_{i}s_{i}+\zeta\) can be rewritten by \(y=\tilde{D}(s_{1}+2^{K_{1}}s_{2}+\cdots+2^{\sum_{i=1}^{N-1}}s_{N})+\zeta\). Thus, we have the following low-complexity demodulation and decoding algorithm for signal detection.  
**Algorithm 2**.: _(Fast ML Demodulation and Decoding) Let \(g\in\{\sum_{i=1}^{N}h_{i}\check{\gamma}_{i}s_{i}:s_{i}\in\mathcal{S}_{i}\}\) denote the sum signal. Then, the optimal ML estimate of \(g\) can be attained by_  
\[\hat{g}=\tilde{D}\times\left\{\begin{array}{ll}0,&\frac{y}{\tilde{D}}\leq 0 \\ \lfloor\frac{y}{\tilde{D}}+\frac{1}{2}\rfloor,&0<\frac{y}{\tilde{D}}\leq 2^{ \sum_{i=1}^{N}K_{i}}-1\\ 2^{\sum_{i=1}^{N}K_{i}}-1,&\frac{y}{\tilde{D}}>2^{\sum_{i=1}^{N}K_{i}}-1.\end{array}\right. \tag{6}\]  
_where \(y\) is the received signal and it is denoted by (1). Then, the estimate signal \(\hat{s}_{i}\) of \(s_{i}\) at the PD receiver is given as follows._  
\[\left\{\begin{array}{l}\hat{s}_{1}=\frac{\hat{g}}{\tilde{D}} mod2^{K_{1}},\\ \hat{s}_{i}=\frac{\hat{g}-\frac{\hat{g}}{\tilde{D}}mod2^{\sum_{i=1}^{i-1}K_{j}}} {2^{\sum_{i=1}^{i-1}K_{j}}}mod2^{K_{i}},i\geq 2.\end{array}\right. \tag{7}\]  
From Algorithm 2, we can find that our developed efficient detection algorithm is essentially a joint ML detection and thus, it is optimal for the sum signal detection. In addition, its computational complexity is much lower than conventional ML detection.