esa-sceva/satcom-chunk-collection huggingface dataset:
# Single-Carrier Rotation-Interleaved Space-Time Code for Frequency-Selective Fading Channels +
\begin{table}
\begin{tabular}{l l l l l l l} \hline Schemes & [17] & [19] & [20] & [21] & [22] & [23] \\ \hline Modulation & Any & Any & Any & Specific 1 & Any & Specific \\ \hline No. of Tx Antennas & Any & Any & Any & Specific & Fixed & Specific \\ \hline Full-Rate & \(\times\) & \(\times\) & \(\times\) & ✓ & ✓ & ✓ \\ \hline Full space-time-multipath diversity & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline Numerical Search/Verification & \(\times\) & \(\times\) & \(\times\) & ✓ & \(\times\) & ✓ \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of Single-carrier Space-time Codes for Frequency-Selective Fading Channel (1 Specific means that the scheme may require further numerical searches/verification to find the codewords for other types of modulations (or different number of Tx antennas) not shown in their present work). in this paper by including Propositions 2 and 3 on deterministic algebraic rotation and their proofs as well as the proposed frequency-domain iterative receiver to lower the receiver complexity. The remainder of the paper is as follows. In Section 2, the system model and the performance criteria of space-time code are presented. In Section 3, the design detail and the performance analysis of the proposed scheme are presented. In Section 4, we present some simulation results for the proposed design, and finally, Section 5 concludes the paper.
# Estimation of the Measurement Accuracy of Wireless Passive Resonance Sensors
## 3 Cramer-Rao Lower Bounds for Exponentially Decaying Phasors
### Cramer-Rao Lower Bounds  
Due to the finite sample with N noisy values, the parameters in \(\chi\) can only be determined with a finite precision, the so-called asymptotic Cramer-Rao lower bounds. This case is analogous to the uncertainty in a mean value, which is calculated from a finite number of noisy constant measurement values.  
The Cramer-Rao lower bounds are calculated using the Fisher information matrix \(J\). In the case where we have additive white Gaussian noise, unbiased complex data and unknown real parameters in \(\chi\), the Fisher information matrix can be calculated using [50] (p. 49).  
\[J_{ij}(x)=\frac{2}{N_{p}}\Re\left(\sum_{n=0}^{N-1}\left[\frac{\partial\mu_{n} ^{*}}{\partial\chi_{i}}\frac{\partial\mu_{n}}{\partial\chi_{j}}\right]\right), \tag{35}\]  
with \(\Re\{x\}\) being the real part of \(x\). Appendix A shows the calculation of the Fisher information matrix for the parameters in \(\chi\) as well as its inverse matrix. The asymptotic Cramer-Rao lower bounds for the unknown parameters in \(\chi\) are obtained from the main diagonal of \(J^{-1}(\chi)\). We obtain (see Equations (A37) and (A38)).  
\[\sigma_{\dot{\omega}}{}^{2} = \sigma_{\alpha}{}^{2} \tag{36}\] \[\geq \frac{\left(1-e^{-2\alpha NT}\right)\left(1-e^{-2\alpha T}\right) ^{3}}{2T^{2}\eta\left[-N^{2}e^{-2\alpha NT}\left(1-e^{-2\alpha T}\right)^{2}+ e^{-2\alpha T}\left(1-e^{-2\alpha NT}\right)^{2}\right]}\] \[\frac{\sigma_{\dot{b}}{}^{2}}{b^{2}} = \sigma_{\dot{\varphi}}{}^{2}\] (37) \[\geq \frac{1-e^{-2\alpha T}}{2\eta}\left[1+\frac{-e^{-\alpha(4N+2)T}+ (2N-1)e^{-\alpha(2N+4)T}+\ e^{-\alpha(2N+2)T}+e^{-4\alpha T}}{-N^{2}e^{-2\alpha NT }\left(1-e^{-2\alpha T}\right)^{2}+e^{-2\alpha T}\left(1-e^{-2\alpha NT} \right)^{2}}\right].\]  
Since \(\omega\) is known exactly, the variance \(\sigma_{\dot{\omega}}{}^{2}\) is identical to the variance \(\sigma_{\omega_{d}}{}^{2}\). The variances \(\sigma_{\omega_{d}}{}^{2}\) and \(\sigma_{\alpha}{}^{2}\) can be rewritten as  
\[\sigma_{\omega_{d}}{}^{2}=\sigma_{\alpha}{}^{2}\geq\frac{\left(1-e^{-2\alpha T }\right)^{3}}{2T^{2}e^{-2\alpha T}\eta}\cdot r(\alpha NT), \tag{38}\]  
where \(r(\alpha NT)\) is the residual of the above approximation with  
\[r(\alpha NT)=\frac{e^{-2\alpha T}\cdot\left(1-e^{-2\alpha NT}\right)}{\left[- N^{2}e^{-2\alpha NT}\left(1-e^{-2\alpha T}\right)^{2}+e^{-2\alpha T}\left(1-e^{-2 \alpha NT}\right)^{2}\right]}. \tag{39}\]
# Applications of Nanomaterials in RFID Wireless Sensor Components
## 1 Introduction  
Wireless communication technology facilitates the exchange of information between distributed points without the need for physical lines or direct plug-ins. With the rapid expansion of the Internet of Things (IoT), the integration of sensors and communication functions into various objects for internet connectivity has gained increased significance [1, 2, 3, 4, 5]. Wireless Sensor Network (WSN) is a specific type of wireless communication technology that collaboratively exchanges data by centrally collecting spatially distributed monitoring records [6, 7, 8]. WSN establishes a network structure by gathering data from sensor nodes and transmitting them externally to sink nodes [9, 10].  
Radio Frequency Identification (RFID), a subset of WSN technologies, enables the real-time collection and processing of information using radio frequency waves as a communication medium [11, 12, 13, 14, 15]. RFID is extensively employed in everyday life and industry due to its notable attributes such as high data throughput, contactless recognition, and data readability from multiple directions [16, 17, 18, 19, 20, 21, 22, 23]. Notably, ongoing research is exploring the establishment of a wireless sensor network based on RFID communication methods capable of Wireless Information and Power Transfer (WIPT), with applications to various sensor devices [24, 25, 26].  
Nanomaterials, characterized by a high surface area for chemical reactions and a unique surface structure that facilitates the implementation of flexible elements, enhance the superior performance and efficiency of RFID sensors [27; 28; 29; 30]. Thoughtful material selection and the exploration of diverse structures contribute to the sensor's heightened reliability, selectivity, and rapid response and recovery performance to external stimuli [31; 32; 33; 34; 35]. Utilizing various materials in the fabrication of RFID sensor components allows for the effective integration of RFID wireless communication technology and sensing capabilities [36; 37; 38; 39; 40].  
This review assesses the impact of applying various nanomaterials to RFID systems on wireless sensor performance, summarizing their contributions. Additionally, it introduces the fundamental mechanisms based on the configuration of RFID sensor systems and changes in electrical resistance. The review explores research trends related to various materials such as polymers, metal nanoparticles, and carbon-based materials applied to each component. Finally, an analysis of RFID sensor systems applied in diverse fields aims to predict the applicability and prospects of next-generation wireless sensors.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 3 Proposed Methods
### Region Filling
#### 3.4.6 The Region-Filling Algorithm  
The algorithm fixes coverage circles for the base stations at \((0,0)\) and \((m,n)\), as shown in Figure 13, in order to utilize the maximum possible heights for the drones near the base stations. We proceed by filling the circles along the \(x\) and \(y\) axes. We then move to the inner layer filling to cover the entire region. The detailed procedure is provided in Algorithm 5. The above method uses the minimum number of drones possible. ```
Input:\(m,n,H,SH,\alpha,B,W,C,H_{max},H_{min},h_{td}\) Output:Ordered collection of drone objects \(D\)
1:\(D\leftarrow\{\}\), \(h\leftarrow\frac{W+H_{max}-d}{W+\tan(\alpha)}\), \(r\gets h\times\tan(\alpha)\)
2:\(x\leftarrow\frac{r}{\sqrt{2}},y\leftarrow\frac{r}{\sqrt{2}}\)
3:\(h_{\mathrm{b}_{1}}\gets H_{0,0},h_{\mathrm{b}_{2}}\gets H_{m,n}\)
4:\((b,h_{1},r,e)\leftarrow FindDroneParameters(x,y,h_{\mathrm{b}_{1}},h)\)
5:\(x\leftarrow\frac{r}{\sqrt{2}},y\leftarrow\frac{r}{\sqrt{2}}\)
6:\(d\gets Drone(x,y,r,h_{1},b,e,h_{\mathrm{b}_{1}})\)
7:coordinates \(\leftarrow\{\}\)
8:while\((d.x,d.y)\
otin\) coordinatesdo
9:coordinates \(\leftarrow\) coordinates \(\cup\)\((d.x,d.y)\)
10:\((d.b,d.h,d.r,d.e)\leftarrow FindDroneParameters(d.x,d.y,d.h,d.h_{ref})\)
11:\(d.x\leftarrow\frac{r}{\sqrt{2}},d.y\leftarrow\frac{r}{\sqrt{2}}\)
12:endwhile
13:\(D\gets D\cup\{d\}\)
14:if\((d.x-m)^{2}+(d.y-n)^{2}>d.r^{2}\)then
15:\((b,h_{1},r,e)\leftarrow FindDroneParameters(m-d.x,n-d.y,h_{\mathrm{b}_{2}},h)\)
16:\(x\gets m-\frac{r}{\sqrt{2}},y\gets n-\frac{r}{\sqrt{2}}\)
17:\(d_{1}\leftarrow Drone(x,y,r,h_{1},b,e,h_{\mathrm{b}_{2}})\)
18:coordinates \(\leftarrow\{\}\)
19:while\((d_{1}.x,d_{1}.y)\
otin\) coordinatesdo
20:coordinates \(\leftarrow\) coordinates \(\cup\{(d_{1}.x,d_{1}.y)\}\)
21:\((d_{1}.b,d_{1}.h,d_{1}.r,d_{1}.e)\leftarrow FindDroneParameters(d_{1}.x,d_{1}.y, d_{1}.h,d_{1}.h_{ref})\)
22:\(d_{1}.x\gets m-\frac{d_{1}.y}{\sqrt{2}},d_{1}.y\gets n-\frac{d_{1}.y}{ \sqrt{2}}\)
23:endwhile
24:\(D\gets D\cup\{d_{1}\}\)
25:\((x_{1},y_{1})\leftarrow ArbitraryDistant(d.x,d.y,d_{1}.x,d_{1}.y,d.r,d_{1}.r,0)\)
26:\((x_{2},y_{2})\leftarrow ArbitraryDistant(d.x,d.y,d_{1}.x,d_{1}.y,d.r,d_{1}.r,0,1)\)
27:if\((x_{1},y_{1})\) are not defined or \(,(x_{1},y_{1})\) are defined and \(Maximum(y_{1},y_{2})<n\) or \(Minimum(y_{1},y_{2})>0\)then
```  
**Algorithm 5** Region Filling  
Figure 13: The region-filling algorithm initially places the circles at the two base stations.
# Towards a Counting Point Detector for Nanosecond Coherent X-ray Science
## 5 Conclusions  
We have demonstrated the capability of utilizing photon counting of X-ray pulses at the full repetition rate of the PLS-II storage ring source. The high X-ray flux available at synchrotrons, especially operating in dense fill mode, can cause saturation of photon-counting, which results in distortions in the measured signals. We use combinations of high-speed APD and the analysis of dead-time effects to make full use of the dynamic range of the detector scheme. Our approach is applicable to any single-event counting measurements with electronics and detector dead times. Thus, the correction scheme will find wide application in all time-resolved photon counting experiments at upcoming light sources where high transverse coherence and a very dense electron fill pattern are expected, such as 4 GSR.  
Finally, we note that the optimal count rate is approximately \(T/2\) for a detector working in the isolated dead-time regime ([34]); above this counting rate (250 MHz in this case), the dominating source of count rate uncertainty is no longer counting statistics but  
Figure 5: The observed APD count rate saturation that occurs as aluminum X-ray transmission filters are removed from in front of the detector is fitted to both an isolated dead-time model (Equation (1)) and a non-extended dead-time model (Equation (2)). Both models indicate the detection system dead-time is comparable to the minimum X-ray pulses separation of 2.00 ns. The error bars from counting statistics in the observed count rate are less than 1% for observed count rates above 100 MHz and too small to be seen on this plot.  
instead becomes the under-counting of X-rays when multiple photons arrive in a single pulse. For application to XPCS, achieving isolated dead-time is extremely important since single-pulse dead-time correction only results in a correction to the true count rate and not a correction to temporal correlations, unlike detectors that experience a recovery time longer than \(T\). The ability of 4 GSR sources to provide these high count rates in real samples of interest at relevant scattering angles is yet to be determined; however, we anticipate that single-point counting detectors such as the one demonstrated here will be able to handle this task and potentially permit the measurement of atomic length-scale fluctuations at atomic time-scales.  
Conceptualization, S.L. and E.C.L.; methodology, all authors; analysis, S.L. and E.C.L.; writing-original draft preparation, S.L. and E.C.L.; and writing-review and editing, all authors. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The data presented in the study are available on request from the corresponding author.  
We acknowledge useful discussions regarding APD development with [PERSON] of Enertrex Corporation and on the topic of detector response time correction with [PERSON] of DePaul University. Experiments at 1C PAL-KRISS beamline at PLS-II were supported in part by MSIT and POSTECH.  
The authors declare no conflict of interest.
## 5 Results and Discussions
### RMS Delay Spread  
The RMS delay spread is a parameter that allows us to quantify the temporal dispersion of a signal, in this case the optical signal, in indoor environments. For our study, this is specifically underground mining environments. Despite not being the focus of our work, it is important to make a brief analysis of the multi-path dispersion that exists in the scenarios presented due to the NLoS components. Therefore, the signals received in \(R_{x}\), which are copies of the transmitted signals, they arrive at different times at \(R_{x}\), due to the different lengths of the NLoS paths, which can induce ISI, significantly affecting the channel capacity and bandwidth. This is reflected in the maximum data rate (\(R_{b}\)) of the VLC channel, which will be limited by \(R_{b}\leq 1/(10\ D_{RMS})\)[11, 49], where \(D_{RMS}\) is the RMS delay spread. Therefore, time dispersion parameters are calculated with a truncation time (\(T_{TR}\)). The mean excess delay (\(\mu_{RMS}\)) and the \(D_{RMS}\) can be represented in terms of the channel impulse response, and are given by the following expressions [11, 49]  
\[\int_{0}^{T_{TR}}h^{2}(t)dt=0.97\int_{0}^{\infty}h^{2}(t)dt, \tag{42}\]  
\[\mu_{RMS}=\frac{\int_{0}^{\infty}t\ h^{2}(t)dt}{\int_{0}^{\infty}h^{2}(t)dt}, \tag{43}\]  
\[D_{RMS}=\sqrt{\frac{\int_{0}^{\infty}(t-\mu_{RMS})^{2}\ h^{2}(t)dt}{\int_{0}^ {\infty}h^{2}(t)dt}}. \tag{44}\]  
RMS delay spread for the mining roadway and mine working face scenarios are illustrated in Figure 8a,b, respectively. It can be noted that the maximum values are 2.208 \(\times\) 10\({}^{-4}\) s and 5.043 \(\times\) 10\({}^{-6}\) s for the first and second scenarios, respectively. Likewise, the minimum values are 0.213 \(\times\) 10\({}^{-4}\) s and 0.512 \(\times\) 10\({}^{-6}\) s, respectively. In addition, we observe in both scenarios that the RMS delay spread distribution is not uniform because the physical environments presented are not closed cuboids. Consequently, the LoS and NLoS components arrive at each scenario points at different times. However, it can be seen that the RMS delay spread decreases when the \(R_{x}\) moves away from the \(T_{x}\)s.
## 3 DC-SoC Consortium Chain Network Model
### Data Dissemination
#### 3.3.1 Push Mechanism  
Once clustering and topology are established, all nodes within each cluster are organized in an adjacency list, where the core object serves as the header, collectively forming a directed acyclic graph. In the block propagation phase, the network can be conceptualized as a broadcast multi-way tree. Newly generated blocks are initiated at the master node, and then propagated layer by layer until all leaf nodes receive them. Leaf nodes refer to the tail-end nodes in the network structure. The following operational steps outline the block-push mechanism within this propagation structure:  
* When the master node receives a new block from the ordering service, it records the block in its ledger and stores it in a buffer.
* Once the buffer is full or the push interval expires, the block is propagated to nodes in the current neighborhood and routing nodes in other clusters.
* Upon receipt, the nodes compute the sender's social credibility via a social mechanism, deciding whether to accept the block based on the sender's reputation level.
* The nodes then decrypt the hash value stored in the block header, using it to check their ledger for any prior receipt of the same block. If no corresponding record is found, the block content is recorded, effectively appending the block to the chain.
* If the current node is a core object, it continues to propagate the block to other nodes in the neighborhood until all core objects have completed their propagation tasks. Non-core nodes are only required to perform the block-chaining operation.
* Once all core nodes have completed propagation, they must send a confirmation message to their superior core object. If the master node confirms that it has received a sufficient number of confirmation messages, the push process is considered complete, and the block content is fully synchronized. Figure 3 presents a graphical illustration of the aforementioned operational example.  
Figure 3: The process of pushing block in density structure.
## 2 Optimal Control Model for PMCU Path Planning  
In order to solve the PMCU pushing path planning problem using optimal control techniques, the path planning problem of PMCU pushing must be modeled. This section first introduces the basis of optimal control and then gives a specific description of the PMCU path planning optimal control model.
## 5 Results and Discussion
### Performance Units
#### 5.2.3 Packet Loss Ratio (\(P_{LR}\))  
It is determined as the amount of data packets that fail to reach their target (i.e., amount of packet loss--APL) to the total number of packets transmitted (i.e., overall amount of packets disseminated--APD), as defined in Equation (5). The main reason for packet loss is due to network congestion.  
\[P_{LR}=\frac{APL}{APD} \tag{5}\]  
Figure 8 shows that MAMC-MAC performs well with a high possibility of message delivery.
# Pressure Fluctuation and Flow-Induced Noise of the Fin and Rudder in a Water Tunnel
## 1 Introduction
[16] established a method for predicting the flow-induced noise of a wall-mounted airfoil. The prediction results show that the intensity of pressure fluctuations on the airfoil surface determines the magnitude of low-frequency radiated noise. [PERSON] et al. [17] and [PERSON] et al. [18] simulated the flow-induced noise of an airfoil and a hydrofoil, respectively. Based on the LES model and acoustic analogy theory, they found that the directivity of radiated noise on both sides of the airfoil/hydrofoil were greater than that in the direction of inlet flow and wake flow. With the increasing demand for stable and maneuverable underwater vehicles, various types of rudders have been designed, but the flow-induced noise characteristics of these rudders have not been discussed in detail. Therefore, the purpose of the present paper is to analyze the flow field and radiated noise resulting from water flowing through a fin and rudder. Pressure fluctuations and noise generation are analyzed via experimental measurements and LES simulations. Section 2 provides a detailed description of the geometry of the rudder and fin, the experimental platform, and the numerical model. Section 3 introduces the LES and Lighthill acoustic analogy theories employed in this study and verifies the mesh independence. In Section 4, the pressure fluctuations tested in the experiment and predicted by the numerical model are compared and analyzed. Section 5 introduces the flow-induced noise results derived at different flow velocities. Finally, Section 6 summarizes the conclusions of the present work.
# UVCS: Unit Virtual Coordinate System for UAV Intra-Swarm Routing in GPS-Denied Environment
## 7 Conclusions  
This paper proposes an approach to organize and maintain the topology of a distributed network. The proposed UVCS algorithm is designed to find the coordinates of a network node, operating only with local information available at the node itself. One of the advantages of the UVCS is its minimal input information requirements. Further, the virtual coordinates can be used to implement geographic routing in the network. In the performance evaluation, we assessed the UVCS by topological and routing metrics. Numerical analysis showed that the virtual coordinates found with the UVCS algorithm are close to the physical coordinates. Secondly, the UVCS algorithm outperforms all known algorithms in terms of the total number of discovered paths, which can be critical to maintaining the connectivity of a UAV swarm. However, the main advantage of the UVCS is its fast convergence. In future, the problem can be developed in the direction of building virtual coordinate systems with anchor nodes, while the number and rule for assigning nodes as an anchor can become the subject of research to maintain a balance is between positioning accuracy and overheads due to the amount of service traffic when implementing the algorithm. Analysis of data transmission delay depending on the route scheme is also a part of this research.  
Conceptualization, K.S.; methodology, K.S.; software, K.S. and Y.G.; validation, K.S. and Y.G.; formal analysis, Y.G.; investigation, K.S. and Y.G.; resources, K.S.; data curation, K.S.; writing--original draft preparation, Y.G.; writing--review and editing, K.S.; visualization, Y.G.; supervision, K.S.; project administration, K.S.; funding acquisition, Y.G. All authors have read and agreed to the published version of the manuscript.  
The reported study was funded by RSF, project number 22-29-00694, [[https://rscf.ru/en/project/22-29-00694/](https://rscf.ru/en/project/22-29-00694/)]([https://rscf.ru/en/project/22-29-00694/](https://rscf.ru/en/project/22-29-00694/)) (accessed on 21 December 2022).  
Not applicable.  
The authors thank [PERSON] for the ideas that led to the development of the iterative algorithm.  
The authors declare no conflicts of interest.
## 3 Results
### Prediction of Mutagenicity  
The second example aimed to predict the mutagenicity and involved the dataset collected by [PERSON] and co-workers including 4337 molecular structures subdivided into 2401 mutagens and 1936 non-mutagens based on the corresponding Ames test data [12]. Here, the variable selection kept 24 attributes which include (1) stereo-electronic parameters (such as LUMO Energy and Parr and Pople absolute hardness [31]) which encode for the reactivity/electrophilicity of the molecule; (2) constitutive descriptors (such as number of unsaturated systems, number of H-bonding groups) which may indirectly encode for the presence of reactive moieties; (3) 13 Kier-Hall E-state descriptors which reasonably play the same role as described for constitutive attributes [28].  
The final model obtained by the random forest algorithm reaches satisfactory results, as confirmed by an MCC equal to 0.644 and an AUC equal to 0.896. Table 2 summarizes the performances for each class, where 0 and 1 indicate non-permeant and permeant molecules, respectively, and shows very similar performances for both classes. In detail, the model correctly predicted 3575 molecules out of 4337, with an overall accuracy equal to 82.43%, truly superimposable to that obtained by the original study (82.5%). For this second example, Tree2C was utilized to generate the corresponding C-script embedded within the VEGA environment by which new predictions can be easily performed. Notice that since the predictive model comprises stereo-electronic descriptors, the corresponding script also includes the source code required to calculate them by linking the script with the MOPAC2016 program. The interested reader can find the corresponding source code in the Supporting Material (File S1).  
Figure 2: The graphic interface of the web service based on the code generated by Tree2C to predict the BBB permeation.