esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Sub-Time Slot Analysis  
In this paper, we assume all channels follow quasi-static fading, i.e., the channel gains remain to be constant within each transmission block. The auction process is applied in every block interval. The secondary relays can get instantaneous CSI between two receivers by the pilot, and the relays don't share the CSI among them. Therefore, potential relays are with incomplete information. One of the main advantages is the reduction of the overhead of CSI interaction. We assume the primary network selects the \(R_{k}\) as the cooperative relay, where \(R_{k}\in\{R_{1},R_{2},\cdots,R_{N}\}\). \(H_{XY}\) denotes the channel between X and Y, where X, Y could be \(S\), \(R_{k}\), \(D\), and \(C_{k}\), respectively. The channel power gain is written as \(G_{XY}=|H_{XY}|^{2}\). Furthermore, if the satellite \(S\) chooses \(R_{k}\) as a relay, it indicates that \(H_{SR_{k}}\) is always better than \(H_{SD}\). For the assistance of the primary satellite communication, the secondary relay chooses DF or AF relay protocols for the transmission. In the considered system, each node has a single antenna, working in a half-duplex mode (which means that the node cannot transmit and receive simultaneously).  
In this paper, \(x_{p}\) and \(x_{k}\) denotes the signal of satellite transmitter and the \(k\)th relay respectively, where \(\mathrm{E}\left[\left|x_{p}\right|^{2}\right]=\mathrm{E}\left[\left|x_{k} \right|^{2}\right]=1\), and \(\mathrm{E}\left[\cdot\right]\) means the expectation operation. The satellite sends the signal to the cognitive relay in the first sub-time slot, which can be expressed as  
\[y_{SR_{k}}=\sqrt{P_{S}}H_{SR_{k}}x_{p}+n_{0}, \tag{1}\]  
where \(P_{S}\) is the satellite transmitter power, and \(n_{0}\) is the additive white Gaussian noise (AWGN) with mean zero and variance \(\sigma_{0}^{2}\). For the second sub-time slot, the chosen relay could choose the DF or AF relay protocols. For DF relay protocols, the received signal of PU is given by  
\[y_{R_{k}D}=\sqrt{P_{k}}H_{R_{k}D}x_{p}+n_{1}, \tag{2}\]  
Figure 1: System model.  
where \(P_{k}\) is the transmitter power of \(R_{k}\), we assume the same AWGN noise power is \(\sigma_{1}^{2}\). For AF relay protocols, the second sub-time slot received signal of PU is  
\[y_{R_{k}D}=\sqrt{P_{k}}\frac{H_{R_{k}D}y_{SR_{k}}}{\sqrt{\left|y_{SR_{k}}\right|^ {2}}}+n_{1}. \tag{3}\]  
In the third sub-time slot, the chosen relay would transmit own signal to the SU, the received signal is given by  
\[y_{R_{k}C_{k}}=\sqrt{P_{k}}H_{R_{k}n}x_{k}+n_{2}, \tag{4}\]  
where the AWGN noise power is \(\sigma_{2}^{2}\).
# A Reconfigurable Mesh-Ring Topology for Bluetooth Sensor Networks
## 3 Configurable Algorithm
### Problem Statement  
During the topology maintenance phase, each master locally in the ring subnet can be connected with up to five more mesh links due to node leaves. As shown in Figure 1, the master initially connects with the other master that has the maximum hop length, such as M1 to M5. If a slave node leaves the M1 piconet, then the master is able to connect with one additional master with a mesh link via the maximum path criterion. The second connected master is M6, the third one is M4, the fourth one is M7,and the final one is M3. On the other hand, if a slave node joins the M1 piconet, then the M3 link is first removed, then the M7 link, and so forth in reverse, until arriving at the second mesh link. At least one mesh link in a piconet is required to maintain the Mesh-Ring topology. As a result, mesh connectivity in the ring subnet can be classified into three possible cases, including the piconet case, where the mesh links are located within a piconet as shown in Figure 3. The other one is the scatternet case, where the mesh links are distributed among several piconets, as shown in Figure 4. A hybrid case combines the mesh connectivity of Figures 3 and 4. In addition, the execution round for the first mesh link is \(p\) and the last mesh link is 0, thus, the average execution round is \(p/2\) to determine the optimal connectivity of mesh links.  
Figure 4: The possible mesh link connection example for each master in a scatternet: (**a**) The first mesh link connection; (**b**) The second mesh link connection; (**c**) The third mesh link connection; (**d**) The fourth mesh link connection.  
Figure 3: The possible mesh link connection examples for each master in a piconet: (**a**) The first mesh link connection; (**b**) The second mesh link connection; (**c**) The third mesh link connection; (**d**) The fourth mesh link connection; (**e**) The fifth mesh link connection.  
In each piconet, there exist up to 5 links that can be connected as mesh links. In the piconet case, the 5 links are concentrated within a piconet, and the mesh links can also potentially be distributed among various piconets for the other scatternet cases. In general, the mesh links can be distributed among different piconets, and a combined configuration of piconet and scatternet cases is possible. Among the configurations above, the main research challenge issue in this study is how to determine the best throughput performance for the Mesh-Ring topology.
# Communication Architecture for Grid Integration of Cyber Physical Wind Energy Systems
## 3 CPWES Modeling and Assumption
### Wind Farm Timing Requirements  
In a power system, it is important to meet the latency requirement for different real-time monitoring and protection applications. Therefore, the WF communication network should ensure  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Type** & **Level** & **Sampling Rate** & **Data Rate** & **Type** & **Level** & **Sampling Rate** & **Data Rate** \\ \hline Anemometer & 97 m & 3 Hz & 6 bytes/s & Anemometer & 56 m & 3 Hz & 6 bytes/s \\ Anemometer & 96 m & 3 Hz & 6 bytes/s & Wind Vane & 56 m & 3 Hz & 6 bytes/s \\ Wind Vane & 96 m & 3 Hz & 6 bytes/s & Anemometer & 46 m & 3 Hz & 6 bytes/s \\ Temperature & 93 m & 1 Hz & 2 bytes/s & Wind Vane & 46 m & 3 Hz & 6 bytes/s \\ Humidity & 93 m & 1 Hz & 2 bytes/s & Anemometer & 26 m & 3 Hz & 6 bytes/s \\ Pressure Sensor & 93 m & 100 Hz & 200 bytes/s & Humidity & 14 m & 1 Hz & 2 bytes/s \\ Anemometer & 86 m & 3 Hz & 6 bytes/s & Temperature & 14 m & 1 Hz & 2 bytes/s \\ Anemometer & 76 m & 3 Hz & 6 bytes/s & Pressure Sensor & 14 m & 100 Hz & 200 bytes/s \\ Wind Vane & 76 m & 3 Hz & 6 bytes/s & Rain Sensor & 10 m & 4 Hz & 8 bytes/s \\ Anemometer & 66 m & 3 Hz & 6 bytes/s & ADCP & Foundation & - & 150 bytes/s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Locations, types of sensors, and measurement devices installed at the met-mast.  
Figure 6: Schematic diagram of the cyber physical model for met-mast.  
the end-to-end delay for data transmission between wind turbines, the met-mast, the substation, and the control center. The IEC 61400-25 standard does not provide any specific timing requirements for the WF. In this study, we considered the latency requirement based on the electric power system. Table 7 shows the communication timing requirements for electric substation automation based on IEEE 1646 [29]. We mapped WF monitoring and control information as follows: protection information from IEDs were mapped for protection information, analogue measurement and status information were mapped for monitoring and control information, and meteorological data from the met-mast were mapped for operation and maintenance.
# SCMs: Systematic Conglomerated Models for Audio Cough Signal Classification
## 4 Proposed Technique 3: Efficient Feature Selection and Machine Learning Classification  
### Analysis of TQWT  
Depending on the oscillation characteristics of the audio cough signal, an improvised version of the Wavelet Transform is proposed, known as TQWT [41]. With the aid of real-valued sampling factors, reversible oversampling filters are used by TQWT. Through the low-pass and high-pass filters, the passing of the input is performed in every decomposition stage. For the low-pass and high-pass filter in TQWT, the frequency responses are indicated as \(G_{0}(w)\) and \(G_{1}(w)\) and expressed as follows:  
\[G_{0}(w)=\begin{cases}1&|w|\leq(1-\sigma)\pi\\ \theta\Big{[}\frac{w+(\sigma-1)\pi}{\mu+\sigma-1}\Big{]}&(1-\sigma)\pi<|w|<\mu \pi\\ 0&\mu\pi\leq|w|\leq\pi\end{cases} \tag{44}\]  
\[G_{1}(w)=\begin{cases}0&|w|\leq(1-\sigma)\pi\\ \theta\Big{[}\frac{\mu\pi-w}{\mu+\sigma-1}\Big{]}&(1-\sigma)\pi<|w|<\mu\pi\\ 1&\mu\pi\leq|w|\leq\pi\end{cases} \tag{45}\]  
Figure 2: Proposed stacked CAE for audio cough classification.  
where the low-pass scaling factor is denoted by \(\mu\) and the high-pass scaling factor is denoted by \(\sigma\), and it is assumed that \(0<\mu<1\), \(0<\sigma\leq 1\) and \(\mu+\sigma>1\). The Daubechies filter is considered as \(\theta(w)\), which helps to construct the transition bands \(G_{0}(w)\) and \(G_{1}(w)\), giving rise to the following equation:  
\[\theta(w)=\frac{1}{2}(1+\cos w)\sqrt{2-\cos w},|w|\leq\pi \tag{46}\]  
The low-pass filter output \(w_{L}\) of every stage is the preceding stage input in this multi-layer decomposition technique. For each layer, the wavelet coefficient is \(w_{H}\). To obtain a novel set, the processing of every coefficient is conducted after the decomposition is performed. With the help of TQWT inverse transformation, the reconstruction of the signal can be performed. The oversampling rate \(r\), quality factor \(Q\) and the decomposition level \(D\) are the main parameters here. \(Q=\frac{f_{0}}{B_{w}}\) is the ration between the center frequency \(f_{0}\) and the bandwidth of the signal \(B_{w}\). The number of wavelet coefficients can be expressed by \(r\), and it assesses the spectral overlap occurring between the corresponding bandpass filters. In our work, \(r=5\) is considered. \(D+1\) sub-bands are produced by \(D\)-level decomposition. The filter's bandwidth limitation is assessed, and the following is obtained:  
\[D_{\text{max}}=\left[\frac{\lg(\sigma N_{s}/8)}{\lg(1/\mu)}\right] \tag{47}\]  
Once the parameters are determined, \(\sigma=\frac{2}{Q+1}\) and \(\mu=1-\frac{2}{(Q+1)r}\) can be computed.
# Refining Zero-Shot Text-to-SQL Benchmarks via Prompt Strategies with Large Language Models
## 5 Results
### Cross-Round Accuracy  
Cross-round accuracy retested persistent errors, showing improved performance on our dataset versus BIRD. O1-Preview's high accuracy and error drop reflect robust reasoning, bolstered by logical alignment. Figures 12-14 provide further granularity on model performance across different domains, difficulty levels, and error types, respectively.  
Figure 12: Error comparison across domains for six models.  
1. Domain Analysis: Figure 12 shows O1-Preview's edge, particularly in complex domains.
2. Difficulty Analysis: Figure 13 highlights O1-Preview's robustness on challenging tasks compared to others.
3. Error Analysis: Figure 14 details seven error types (T1-T7). On BIRD, errors rose significantly. Type 5 (query logic) dominated but was cut by 33% with logical alignment. O1-Preview's performance confirms refinement enhances reasoning. While this error typing provides more insight than a simple pass/fail metric, future work could explore even more granular partial correctness evaluations.
# Performance of Dense Wireless Networks in 5G and beyond Using Stochastic Geometry
## 5 Outage Analysis in the Presence of NLOS, Noise and Blockage
### Blockage
Recall that the number of UEs served in Figure 18 refers to the UEs allocated in the same spectrum, and thus by knowing the average number of UEs assignable on the same spectrum region, one can pre-design the largest number of users that a resource scheduler can expect to assign (not covered in this paper). Figure 17: Average service probability vs. the probability or blockage of all the links, for \(\lambda=\{5\times 10^{-3},5\times 10^{-2}\}\,\mathrm{m}^{-2}\), and number of paths \(L=\{1,4\}\), where \(L=1\) means that only LOS link exists. Parameters: \([P_{rx}]_{dB}=20\) dB, \(NF=7\) dB, \(BW=400\) MHz, \(F_{c}=28\) GHz, \(|\beta_{\ell}|=\sqrt{P_{tx}}\,4\pi F_{c}/c\) for every path \(\ell\), service area = \(100\,\mathrm{m}\times 100\) m square, AP height \(h=10\) m, signal-to-noise-and-interference (SINR) threshold \([T]_{dB}=0\), \(R_{max}=400\) m.
# Automated Data Acquisition in Construction with Remote Sensing Technologies
###### Abstract  
Near real-time tracking of construction operations and timely progress reporting are essential for effective management of construction projects. This does not only mitigate potential negative impact of schedule delays and cost overruns but also helps to improve safety on site. Such timely tracking circumvents the drawbacks of conventional methods for data acquisition, which are manual, labor-intensive, and not reliable enough for various construction purposes. To address these issues, a wide range of automated site data acquisition, including remote sensing (RS) technologies, has been introduced. This review article describes the capabilities and limitations of various scenarios employing RS enabling technologies for localization, with a focus on multi-sensor data fusion models. In particular, we have considered integration of real-time location systems (RTLSs) including GPS and UWB with other sensing technologies such as RFID, WSN, and digital imaging for their use in construction. This integrated use of technologies, along with information models (e.g., BIM models) is expected to enhance the efficiency of automated site data acquisition. It is also hoped that this review will prompt researchers to investigate fusion-based data capturing and processing.  
automated data acquisition; remote sensing technologies; automated progress reporting; data fusion; tracking resources 2020 rights rights rights
## 3 Results and Discussion  
### MicrodaughLab Mixing Curves  
The impact of Cc additions on dough mixing properties was evaluated and the mixing parameters, summarized in Table 2, were used to characterize the effect of Cc additions on mixing behavior.  
From Table 2, it can be observed that up to 10% of incorporation, no significant (\(p>0.05\)) impact on water absorption (WA), dough development time (DDT), dough stability (DST), and dough softening (DSF), was registered. However, for higher levels of Cc tested, 15% and 20%, the WA increased, probably due to the increased amounts of proteins in dough matrix, requiring more water to improve the connectivity of the network developed by starch-protein-xanthan gum, reinforced by hydrogen bonds [19]. The DDT also increased with 15% of Cc addition, resulting from the increase in solids content associated with difficulties in dough homogenization. In terms of dough stability, no significant differences were observed. However, Cc additions led to a significant (\(p<0.05\)) increase of dough softening after 15% of Cc addition, reflecting the weakening of dough structure. Although the Cc addition promoted a slight impact on dough mixing properties, in terms of DDT and DSF, these effects can be considered as an additional support of denatured WP (coming from Cc) to dough plasticity or extensibility, which can be crucial during the fermentation process to dough expansion and gas retention.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & **WA (\%)** & **DDT (min)** & **DST (min)** & **DSF (mNm)** \\ \hline CD & 66.30 \(\pm\) 0.60 \({}^{\text{a}}\) & 0.80 \(\pm\) 0.10 \({}^{\text{a}}\) & 0.80 \(\pm\) 0.10 \({}^{\text{a}}\) & 3.30 \(\pm\) 0.61 \({}^{\text{a}}\) \\ CcD5\% & 66.70 \(\pm\) 0.60 \({}^{\text{a}}\) & 0.70 \(\pm\) 0.10 \({}^{\text{a}}\) & 0.60 \(\pm\) 0.20 \({}^{\text{a}}\) & 3.00 \(\pm\) 0.12 \({}^{\text{a}}\) \\ CcD10\% & 67.70 \(\pm\) 0.50 \({}^{\text{a}}\) & 0.70 \(\pm\) 0.20 \({}^{\text{a}}\) & 0.60 \(\pm\) 0.10 \({}^{\text{a}}\) & 3.00 \(\pm\) 0.63 \({}^{\text{a}}\) \\ CcD15\% & 69.30 \(\pm\) 0.50 \({}^{\text{ab}}\) & 0.90 \(\pm\) 0.50 \({}^{\text{ab}}\) & 0.60 \(\pm\) 0.15 \({}^{\text{a}}\) & 5.30 \(\pm\) 0.60 \({}^{\text{b}}\) \\ CcD20\% & 70.30 \(\pm\) 0.60 \({}^{\text{b}}\) & 1.00 \(\pm\) 0.10 \({}^{\text{b}}\) & 0.60 \(\pm\) 0.10 \({}^{\text{a}}\) & 6.00 \(\pm\) 0.30 \({}^{\text{b}}\) \\ \hline \hline \end{tabular} Different superscripts (\({}^{\text{a}}\), \({}^{\text{b}}\)) indicate significantly statistical differences between CB and CcB. (at \(p\leq 0.05\), Tukey test).  
\end{table}
Table 2: Comparison of Microdaughlab mixing parameters: WA—water absorption (%), DDT—dough development time (min), DST—dough stability (min), DSF—dough softening degree (mNm), for control dough (CD) and curd cheese doughs (CcD5% up to CcD20%).
## 3 Simulation Results
### BLER in Different Subcarrier Spacing  
When different subcarrier spacings exist within the channel bandwidth, a coexistence case of the subbands with different subcarrier spacings is shown in Figure 7a, where Subband 3 has a subcarrier spacing of 960 kHz while the rest have a spacing of 480 kHz. The traditional CP-OFDM cannot process the data from different subbands together during the IFFT. However, the proposed GFB-OFDM can handle all subbands together, demonstrating flexibility for various requirements. The specific procedure is illustrated in Figure 8. Specifically, after performing the first-level IFFT over the adjacent symbols of Subband 3 separately and then adding CP, they are concatenated and then serve as the input of the second-level IFFT along with the other subbands. In this simulation, the CP-OFDM waveform is obtained by the oversampled IFFT operations on the data with a subcarrier spacing of 480 kHz and 960 kHz, respectively, using 16,384 points. Subsequently, the two sets of the time-domain data are combined into one set.  
Figure 8: Subband data-processing procedure for different subcarrier spacing with the number of modulated symbols \(K=8192\), the number of subbands \(N=8\), and the number of subcarriers in each subband \(M=1024\) or \(M=512\).  
Figure 7: The locations of the eight subbands in the frequency domain under two cases. (**a**) represents the mixture scenarios of different subcarrier spacings, which were 480 kHz and 960 kHz, respectively. (**b**) represents single-carrier and multi-carrier mixture scenarios.  
Figure 9 presents a comparison of the BLER performance in demodulating data transmitted on subband 3. Due to the interference from the other subbands with different subcarrier spacings, the BLER performance of subband 3 degrades. It can be also observed that GRB-OFDM outperforms CP-OFDM since GRB-OFDM employs the polyphase filtering, resulting in low out-of-band leakage for each subband and thus smaller inter-subband interference compared to the CP-OFDM. Therefore, GRB-OFDM outperforms CP-OFDM in BLER in the different subcarrier spacing scenario, which facilitates a dynamic and flexible adjustment scheme for the subcarrier spacing between the subbands in regard to 6G application.
# A Survey on Penetration Path Planning in Automated Penetration Testing
## 4 Planning Models in Penetration Path Planning  
### Graphical Security Model  
The graphical security model [29; 30] is utilized for the visual construction of network attack paths. It depicts various network elements, such as hosts, services, and vulnerabilities, as vertices within a graph. The interactions among these elements are denoted by edges. Attack graphs and attack trees are the classical models within the graphical security model.
## 1 Introduction
Moreover, the dynamic analysis model of the rotation transformation matrix is established to solve the dynamic rotation transformation matrix of each node. The dynamic rotation transformation matrix and distributed data fusion algorithm are used to fuse the data of all nodes to obtain the navigation state, and then the flight parameters of INS are calculated by the inertial velocity. In addition, polynomial fitting is performed on the pressure values measured by FADS to obtain the flight parameters solved by FADS. Finally, the variable gain complementary filtering algorithm is used to fuse the flight parameters of inertial navigation and FADS to obtain high-precision flight parameters.
# Dissolved Oxygen Modeling by a Bayesian-Optimized Explainable Artificial Intelligence Approach
## 2 Methodology
### Bayesian Optimization  
Hyperparameter tuning is a critical step in developing accurate ML models. This study combines BO with the ML algorithms to achieve optimal model performance. The choice is based on its optimization effectiveness and computational efficiency [35]. This method employs probabilistic modeling and decision theory to explore the hyperparameter space and find the optimal values. The model first evaluates the objective function at a few initial points and then fits a surrogate model to the observed data. The objective function \(f(x)\) representing the performance metric of a model is often noisy and expensive to evaluate, rendering a direct optimization approach (e.g., grid search) impractical. As a result, a surrogate model (Gaussian process, GP) is used to approximate the objective function. The GP is defined by a mean function \(\mu_{0}(x)\) and a covariance function \(k_{0}(x,x^{\prime})\). Given observed data points \(\left\{(x_{i},y_{i})\right\}_{i=1}^{n}\), the GP estimates the objective function by using the following expression:  
\[f(x)\sim\text{GP}\big{(}\mu_{0}(x),k_{0}\big{(}x,x^{\prime}\big{)}\big{)} \tag{9}\]  
Figure 2: Illustration of a boosting ensemble (adapted from [PERSON] and [PERSON] [32]).  
The mean and covariance functions are updated based on the observed data. The mean function represents the expected value of the objective function at a given point. The covariance function quantifies the uncertainty in the prediction. After evaluating a new point, the GP updates its posterior distribution by combining prior beliefs with the likelihood of the observed data. This is achieved using [PERSON] theorem, which incorporates the new data into the model, adjusting the mean and covariance to reflect the observed values better.  
BO employs an acquisition function to determine the next point for evaluation. This function quantifies the expected improvement (EI) over the current best observation. It balances exploration and exploitation by accounting for both the predicted mean and the associated uncertainty. Mathematically, if \(y^{+}\) is the current best value:  
\[\text{EI}(x)=\text{E}\big{[}\text{max}\big{(}f(x)-y^{+},0\big{)}\big{]} \tag{10}\]  
where E = expectation operator. Using the GP's predictive mean \(\mu_{0}(x)\) and variance \(\sigma_{0}(x)\), EI is computed as follows:  
\[\text{EI}(x)=\big{(}\mu_{0}(x)-y^{+}\big{)}\Phi\bigg{(}\frac{\mu_{0}(x)-y^{+} }{\sigma_{0}(x)}\bigg{)}+\sigma_{0}(x)\phi\bigg{(}\frac{\mu_{0}(x)-y^{+}}{ \sigma_{0}(x)}\bigg{)} \tag{11}\]  
where \(\Phi\) = cumulative distribution function and \(\phi\) = probability density function of the standard normal distribution. Subsequently, the new hyperparameters are evaluated, and the surrogate model is updated by adding the new data point and refitting the GP model. The iteration involves optimizing the acquisition function, assessing the objective function, and updating the surrogate model. This process continues until a stopping criterion is met.