esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Impact of Channel Numbers on Performance Indicators  
During the experiment to test the influence of input length on the accuracy of the model, this paper found that when the number of model channels was set to five, i.e., when the data of five adjacent heartbeats (3 s) were selected as the input, the accuracy was the highest. Table 2 shows the experimental results of different heartbeats. Compared with the input length of 10 heartbeats (6 s), the accuracy, sensitivity, specificity, and F1 increased by 1.1%, 1.7%, 4.6%, and 1.4%, respectively; and compared with the input length of 15 heartbeats (9 s), the accuracy, sensitivity, specificity, and F1 indicators increased by 2.7%, 4.6%, 6.0%, and 3.4%, respectively.  
Thus, the model channel was set to five, and we used five heartbeats as the input to obtain the classification effect confusion matrix as shown in Figure 6, which indicates that the model could identify 98% of myocardial infarction ECG. The ROC curve is shown in Figure 7. The AUC value of 0.9868 indicates that the model had excellent classification performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Input Length** & _Acc_ & _Sen_ & _Spec_ & **F1** \\ \hline
3 s & 95.4\% & 98.2\% & 86.5\% & 96.8\% \\
6 s & 94.3\% & 96.5\% & 81.9\% & 95.4\% \\
9 s & 92.7\% & 93.6\% & 80.5\% & 93.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of different heartbeats.
## 3 Proposed Method
### Hybrid Fragment Horde Bland Lobo Optimization  
#### 3.3.1 Traditional PSO Algorithm  
Three vectors are used in the computations of the PSO algorithm. They are x-vectors, p-vectors, and v-vectors, accordingly [35]. The p-vector (pbest) designates the place where the particle has found the object, and the x-vector tracks the particle's current location inside the search zone (best response yet). Particle velocity, which forecasts where each succeeding particle will move throughout the course of that iteration, is likewise included in the v-vector. Initial random displacement of the particles occurs in preset directions. Gradually altering the particle's orientation allowed it to start moving on its own in the direction of the prior best position. Then it searches the area for the optimal spots to perform some fitness-related tasks, using the formula \(fit=S^{m}-S\). Here, the particle's position is specified as \(\overrightarrow{M}\in s^{m}\), although its speed is given as \(\overrightarrow{w}\). These two variables are initially chosen at random, and they are subsequently updated repeatedly using the two formulas in Equation (14):  
\[\overrightarrow{w}=\omega\overrightarrow{w}+c_{1}r_{1}(\overrightarrow{q}- \overrightarrow{M})+c_{2}r_{2}(\overrightarrow{f}-\overrightarrow{M}) \tag{14}\]  
The inertia weight, or \(w\) in this instance, is a user-defined behavioural parameter that controls the degree of particle velocity recurrence. The particles implicitly interact with one another since the even-before position of the particle (pbest position) is q, and the prior-best location of the particle inside the swarm (gbest position) is \(\overrightarrow{f}\). The acceleration constants are \(c_{1},c_{2}\). Utilizing the stochastic variables, this is graded, \(r_{1},r_{2}\sim U(0,1)\). The equation shows that the particle is moved to the next site in the search zone by adding the velocity to its present position (15), as shown in Figure 6, regardless of fitness gains [36].  
\[\overrightarrow{M}\leftarrow\overrightarrow{M}+\overrightarrow{w} \tag{15}\]
# Research on Wellbore Trajectory Prediction Based on a Pi-GRU Model
## 2 Materials and Methods
### Feature Encoding for Nontime Series Data  
In non-time series data feature modeling tasks, the lithology types of strata and the types of drill bits are typical nonnumerical discrete variables, and they usually need to be transformed into digital data through one-hot encoding to satisfy the input requirements of machine learning models. Taking lithology classification as an example, suppose that there are different lithology categories, such as sandstone, shale, and limestone. Through one-hot encoding, each category can be converted into a unique binary vector. For example, if three lithology types are present, sandstone, shale, and limestone can be represented as [1, 0, 0], [0, 1, 0], and [0, 0, 1], respectively. This encoding method can clearly present the differences between different categories and is easy for computers to understand and process. The encoding process is shown in Figure 4.  
However, when the number of layer types is large, direct one-hot encoding results in a high-dimensional sparse matrix, which may lead to poor generalization performance and difficulty in terms of effectively learning high-dimensional sparse features. Therefore, the word embedding algorithm is used to reduce the dimensionality of the high-dimensional sparse data obtained after performing one-hot encoding. Word embedding technology has been widely applied in the field of natural language processing. It can map high-dimensional sparse word vectors to a low-dimensional continuous vector space while  
Figure 4: One-hot encoding process.  
preserving the semantic relationships between words. In the context of stratum information processing, through word embedding technology, high-dimensional stratum information vectors can be transformed into low-dimensional dense vectors. These low-dimensional vectors can significantly reduce the data dimensionality while retaining the key stratum information features, thereby reducing the imposed computational burden and improving the training efficiency of the model.
## 3 Results and Discussion
### ZnO Nanoparticle Characterization Analysis
#### 3.3.2 Minimum Bactericidal/Fungicide Concentration (MBC/MFC) and Inhibitory Concentration (MIC)  
Tables 3 and 4 and Figures 2 and 3 show the MICs and MBCs/MFCs of the ZnO nanoparticles loaded with the _L. pubescens_ shoot methanol extract. As can be seen, the MICs and MBCs/MFCs of the 50 mg and 100 mg LP-ZnO NPs were significantly higher (7.22 and 4.88 and 10.59 and 10.81 mg/mL, respectively) compared with those of the LPME and ZnO. Irrespective of the antimicrobial substance used, all the substances showed significantly lower MICs and MBCs/MFCs in water compared with DMSO (Figures 2 and 3). Overall, the MICs of the tested substances against microbes were significantly better in DMSO than in water (estimated marginal means of 4.40 vs. 8.54 mg/mL, respectively) (Figure 2b), while the opposite was true in the case of the MBCs/MFCs, with estimated marginal means of 9.76 and 10.25 mg/mL in water and DMSO, respectively (Figure 2c). In more detail, all the substances had lower MICs and MBCs/MFCs against almost all the tested bacteria and fungi in both water and DMSO, except for _P. aeruginosa_ in water, which suggests the higher antimicrobial potential of nanoparticles. It is worth noting that the substances were significantly more active against _S. aureus_, _A. terrus_, and _A. niger_ in water than DMSO (Figure 2b,c). The results of the interaction effects of the microorganisms, solvents, and treatments are displayed in Figure 3a-c, which provides a clearer picture of how the tested microbes interacted with the ZnO nanoparticles in water and DMSO. The functionalization of the ZnO by the LPME improved the antibacterial/antifungal activity of the ZnO alone in water and DMSO, which confirmed the inhibition-zone findings. However, the 100 mg LP-ZnO NPs exhibited the highest activity against both bacteria and fungi in DMSO, as seen in the MIC and MBC results (Figure 3b,c). Both the 50 mg and 100 mg LP-ZnO NPs in water had similar antimicrobial effects (Figure 3b,c).
## 6 Summary of Experimental Results
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Quadrant (s)** & **SNC-4 (s)** \\ \hline Broadcast & 0.525 & 0.553 \\ Gather & 0.301 & 0.297 \\ AllReduce & 2.168 & 2.421 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Communication time between Quadrant mode and SNC-4 mode in Host environment. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **DDR4/MCDRAM (\%)** & **Number of Processes** \\ \hline Broadcast & 105 & 68 \\ Gather & 108 & 64 \\ AllReduce & 102 & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of C communication time between DDR4 and MCDRAM in four Docker Containers environment (Flat mode). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Quadrant (s)** & **SNC-4 (s)** \\ \hline Broadcast & 0.525 & 0.553 \\ Gather & 0.301 & 0.297 \\ AllReduce & 2.168 & 2.421 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Communication time of Quadrant mode and SNC-4 mode in four Docker Containers environment. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Broadcast (s)** & **Gather (s)** & **AllReduce (s)** \\ \hline Host & 0.285 & 0.194 & 1.261 \\ One Container & 0.288 & 0.199 & 1.271 \\ Two Containers & 1.991 & 0.508 & 4.447 \\ Four Containers & 2.231 & 0.628 & 4.677 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Communication time according to the increase in the number of Docker Containers. Our performance evaluation can be summarized by the following three points:  
* The combination of MCDRAM usage and Python buffer communication has greatly improved the performance of Python's MPI collective communication. This result shows that MCDRAM was efficiently used in the Python environment by wrapping Memkind library. And it was confirmed that Python's buffer communication is more efficient than Python's object communication. In addition, in Memory mode of Knights Landing Process, the use of MCDRAM in Flat mode showed better performance than Cache mode using MCDRAM as a cache (Tables 1-4). * C communication is still fast in a distributed Docker environment. All experiments showed similar communication performance on the Host and one Docker Container environments. In this result, we confirmed Docker's claim that the performance difference between the main Host and Docker's Container is not significant because Docker's Container directly uses the Host's resources. However, multiple Containers show relatively less efficient communication than communication in the Host environment (Table 5). * The Quadrant mode showed more efficient experimental results than the SNC-4 mode. SNC-4 mode has a low latency when communication is maintained within the NUMA node. Therefore, it is difficult to efficiently utilize the SNC-4 mode when its process of communication is not in the same domain. Due to the characteristic of MPI communication, the advantages of SNC-4 mode cannot be utilized. In Cluster mode, Quadrant mode, which ensures transparency, is an efficient method for MPI communication (Tables 6 and 7).
# Q-LBR: Q-Learning Based Load Balancing Routing for UAV-Assisted VANET
## 4 Proposed Q-LBR Design
### Network Load Estimation
#### 4.2.2 UAV Relay Congestion Identification  
The URCI, given by Equation (7), is calculated through the URN's own queue load from the UAV relay node \(u\).  
\[URCI_{u}(t)=\frac{AQL_{u}(t)}{MQL_{u}} \tag{7}\]  
With \(URCI_{u}\), however, it can be recognized that the closer \(AQL_{u}(t)\) is to \(MQL_{u}\), and when considering the load balancing aspect, the greater the throughput within the maximum range the UAV can accommodate.
# Dispersive FSO Performance Estimation with Gaussian Pulses and Laplace Modeled Time Jitter
## 2 System Model  
Here, the link was implemented via optical, longitudinal, Gaussian modelled pulses, which were supposed to act as information carriers and mimic real FSO pulses, propagating and fading in either link direction. Furthermore, provided these pulses are transmitted in a memoryless, stationary, ergodic, dispersive, lossy, and noisy channel with independent and identically distributed (i.i.d.) intensity fading statistics suffered from additive white Gaussian noise (AWGN), \(n\), characterized by zero mean value and variance equal to \(N_{0}/2\), with \(N_{0}\) being the noise power density, [28; 29], then the electrical signal, \(r\), at the receiver's output is given by the following equation [30; 31; 32]:  
\[r=xs+n=\pi\eta I+n, \tag{1}\]  
where \(x\) modulates the \"0\" or \"1\" bit signal, \(s=\eta I\) denotes the instantaneous beam intensity, \(\eta\) represents the receiver's effective photo-current conversion ratio, and \(I=I_{r}/I_{0}=LBI_{T}\) is the instantaneous normalized irradiance if \(I_{0}\) denotes the initially transmitted irradiance and \(I_{r}\) denotes the finally received irradiance. The normalized irradiance can be assumed as a multifactorial product of the total power losses, \(L\); the chromatic dispersion influence, \(B\); and the TJ effect, \(I_{T}\).
## 3 Related Works  
### Early Prediction and DSS  
ML models' usage to assist in decision making has received the attention of researchers in the last years. For instance, [PERSON] [28] describes a framework based on a ranking and feature selection algorithm to assist physicians' decision-making on cardiovascular diseases' most relevant risk factors. The author also applies machine learning techniques to enable identifying the risk factors.  
[PERSON] and [PERSON] [29] developed an artificial neural network (ANN) system to assist physicians and patients in selecting pancreatic cancer treatment. The system determines the 7-month survival or mortality of patients based on a specific treatment decision. [PERSON] et al. [31] propose a decision support methodology guided by a Bayesian belief network algorithm to predict kidney transplantation's graft survival. The authors use a database with more than 31,000 U.S. patients and argue that the methodology can be reused in other datasets.  
[PERSON] et al. [30] evaluate a murine model, induced by intravenous Adriamycin injection, using optical coherence tomography (OCT) to assess the CKD progression by images of rat kidneys. The authors highlight that OCT images contain relevant data about kidney histopathology. [PERSON], [PERSON], and [PERSON] [32] propose a fuzzy expert system to assist the medical diagnosis, focusing initially on kidney diseases. The system is guided by the experience of physicians to indicate disease profiles. [PERSON] et al. [34] present a DSS to identify acute kidney injury and CKD using knowledge representation and reasoning procedures based on logic programming and ANN. [PERSON] et al. [33] used the support vector machine technique and the two-feature selection methods wrapper and filter to conduct the CKD identification early. The authors justify the computer-aided diagnosis based on high mortality rates of CKD. Finally, [PERSON] and [PERSON] [35] presented a DSS for CKD prediction (CKD or non-CKD) using a logistic regression model.  
However, these CKD studies have some limitations. For example, relevant topics are the ML technique used to identify the disease and the costs of required examinations (predictors). Most of the studies use many predictors and apply complex analysis, increasing costs and making physician double-checking results problematic. Indeed, this type of functionality is relevant because other clinical conditions influence CKD, and the diagnosis is usually improved when physicians collaborate to conclude.
## 4 Experiments
### Ablation Experiments
While the LB model uses prior knowledge to set fixed convolution kernel weights to enhance the details of the network with the least parameters in the shallow network, details can be lost as the network becomes deeper. The CLAU module is applied in the deep network to supplement detailed information and guide the network to independently integrate semantic and detailed features. Therefore, a single LB module alone cannot solve the issue of detail loss in the deep network, and a single CLAU module alone can only supplement the details of the deep network. Consequently, combining the LB module and CLAU module allows the LC-YOLO and Tiny LC-YOLO models to use the enhanced detailed features in the shallow network to supplement the deep network at the cost of adding only a few parameters, thereby significantly improving detection performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Parameters** & **Inference** & **Calculation** \\  & **(M)** & **(ms)** & **Quantity** \\  & & & **(GFLOPs)** \\ \hline YOLOv5s & 7.06 & 10.8 & 16.3 \\ YOLOv5s+LB & 7.06 & 13.8 & 16.3 \\ YOLOv5s+CLAU & 7.30 & 12.1 & 17.6 \\ LC-YOLO & 7.30 & 15.6 & 17.6 \\ \hline YOLOv5n & 1.77 & 10.2 & 4.3 \\ YOLOv5n+LB & 1.77 & 13.1 & 4.3 \\ YOLOv5n+CLAU & 1.83 & 11.6 & 4.6 \\ Tiny LC-YOLO & 1.83 & 14.4 & 4.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of parameters, inference time, and computation in ablation experiments on the UCAS-AOD dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**AP@.5 (\%)**} & \\ \cline{2-4}  & **Car** & **Airplane** & **mAP@.5 (\%)** \\ \hline YOLOv5s & 87.16 & 96.15 & 91.66 \\ YOLOv5s+LB & **90.32 (+3.07)** & 93.55 & **91.94 (+0.28)** \\ YOLOv5s+CLAU & **90.64 (+3.48)** & **97.58 (+1.43)** & **94.11 (+2.45)** \\ LC-YOLO & **91.82 (+4.66)** & **98.10 (+1.95)** & **94.96 (+3.30)** \\ \hline YOLOv5n & 86.64 & 96.09 & 91.36 \\ YOLOv5n+LB & **89.88 (+3.24)** & 95.23 & **92.10 (+0.74)** \\ YOLOv5n+CLAU & **90.86 (+4.22)** & 92.51 & **91.69 (+0.33)** \\ Tiny LC-YOLO & **90.84 (+4.20)** & **97.51 (+1.45)** & **94.17 (+2.81)** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The AP@.5 and mAP@.5 results for ablation experiments on the UCAS-AOD dataset.
# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
## 5 LoRa under Noisy Channel  
In this section, we will evaluate the performance of CSS modulation adopted by LoRa technology in an industrial environment focusing on the PER. We will essentially consider the \"one-to-many\" and \"many-to-one\" architectures specific to the LoRaWAN network, which is based on a star topology [44], as shown in Figure 7. Typically, in the LoRa context, this star configuration allows end devices to exchange with one or more gateways via the LoRa physical layer. These gateways are then connected to a central server using the standard IP protocol. This server is linked to an application server and can also interact downlink with the end devices via the gateway. Figure 8 illustrates the evaluated architecture, primarily consisting of the modulator and demodulator, in addition to an industrial channel.  
Figure 8: Basic LoRa architecture.  
Figure 6: FIR filter structure.  
Figure 7: LoRa Network topology.  
The various parameters required to define the context of the simulations are presented in Table 2.  
Two configurations, LOS and NLOS, were considered. In this work, we have considered that the industrial and AWGN channels are propagation channels with Rician fading in the LOS configuration and Rayleigh fading in the NLOS configuration at a frequency of 868 MHz [22; 39].  
In all the communication modes considered, the data frames for each transmitter were binary, with different lengths of 32 bits randomly generated. This approach stems from the fact that industrial sensors transmit short packets of data [22].
## 4 Performance Analysis
### Symbol Error Rate  
The confidential messages can be recovered only when estimates of MP-WFRFT parameters, the number information of LUs, and the corresponding modulation symbols are all correct. Here, we consider that the MP-WFRFT parameters are securely shared between transmitter station and LUs. Therefore, in order to calculate the overall SER \(P_{s}\), we should consider the probability of error \(P_{a}\) for the estimates of number information and the SER \(P_{m}\) of corresponding modulation symbols. The overall SER \(P_{s}\) can be calculated as  
\[P_{s}=1-(1-P_{a})(1-P_{m}). \tag{19}\]First, the \(P_{m}\) of corresponding modulation symbols is calculated. Only BPSK modulation, i.e., \(M=2\), is considered throughout this paper. Moreover, the number of active LU is \(Q=2\). The theoretical SER \(P_{m}\) over AWGN channel can be obtained by  
\[P_{m}=1-(1-Q(\sqrt{r}_{k}))^{2}=2Q(\sqrt{r}_{k})-Q^{2}(\sqrt{r}_{k}), \tag{20}\]  
where \(Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}\exp(-\frac{x^{2}}{2})dx\) is the complementary error function. \(r_{k}\) is the signal to interference-plus-noise ratio (SINR). According to Equation (16), we can calculate the \(r_{k}\) of LU \(k\) as  
\[r_{k}=\frac{\zeta_{k}}{\sigma_{L}^{2}}. \tag{21}\]  
According to the Equations (10), (11), and (12), the total transmit power \(P_{s}\) can be calculated as  
\[P_{s}=\sum\limits_{i=1}^{K}\|\mathbf{d}_{k}(t,\mathbf{f})\|_{2}^{2}. \tag{22}\]  
By contrast, with the conventional AN-DM shceme, we need allocate power to AN to suppress the signal received by [PERSON]. Therefore, the transmit power of confidential message can be expressed as \(P_{m}=\beta^{2}P_{s}\), and the SINR \(r_{k}\) of LU \(k\) can be obtained by  
\[r_{k}^{AN}=\frac{\beta^{2}\zeta_{k}}{\sigma_{L}^{2}}=\beta^{2}r_{k}. \tag{23}\]  
where \(\beta\) is the power splitting coefficient for confidential messages.  
According to the Equation (19), we can calculate the \(r_{k}\) of \(j\)-th [PERSON] intercepting \(k\)-th LU's information as  
\[r_{k}=\frac{\left|\mathbf{h}_{E_{j}}^{H}(t)\mathbf{d}_{k}(t)\right|^{2}}{ \sum\limits_{i=1,i\
eq k}^{K}\left|\mathbf{h}_{E_{j}}^{H}(t)\mathbf{d}_{i}(t) w_{0}\right|^{2}+\sum\limits_{i=1}^{K}\left|\mathbf{h}_{E_{j}}^{H}(t)\mathbf{d}_{i}(t) b_{i}\right|^{2}+\sigma_{E}^{2}}.\]  
Next, the probability error \(P_{a}\) at LUs and Eves is calculated, respectively. Here, we consider the probability error \(P_{a}\) for the LUs. In this paper, we assume that the LUs know the SM mapping in advance and that Eves cannot wiretap any SM information. Meanwhile, each independent LU effectively receives the confidential signal, which is verified in the next subsection. Therefore, we consider a reasonable approximation \(P_{a}\approx 0\) at LUs. Then, we calculate \(P_{a}\) at Eves. Due to the arbitrary locations, Eves cannot wiretap any number information. Therefore, \(P_{a}\) at Eves can be obtained by  
\[P_{a}=1-\frac{1}{2^{K}-1}. \tag{24}\]
# An On-Demand Scheduling-Based MAC Protocol for UW-WiFi Networks
## 5 Experiments and Results  
In this section, we first perform throughput simulations on the SDDA MAC protocol. Then, we also use simulations to compare SDDA with another two MAC protocols, S-FAMA [6] and TDMA, in aspects of channel utilization, end-to-end delay, and packet delivery ratio. The time slot length of S-FAMA is equal to the maximum propagation delay plus the CTS's transmission delay. In TDMA, the number of slots in a periodic time frame is the number of nodes in the UW-WiFi network, and the slot length is set to the sum of the maximum propagation delay and a data packet's transmission delay. In addition, we implement the SDDA protocol and build a WiFi network system in a real marine environment. In the real network system, we carry out two types of trials to demonstrate the performance of the proposed MAC protocol.