esa-sceva/satcom-chunk-collection huggingface dataset:
# Fine-Tuning a Personalized OpenBioLLM Using Offline Reinforcement Learning
## 4 Experiment
### Ablation Experiment  
This experiment evaluates the role of the DTE module and the InfCTI module in the treatment model through ablation experiments as shown in Table 1.  
The experimental results show that under the experimental condition of removing the DTE module, the InfCTI module shows significant effectiveness in inferring treatment intentions. The model can formulate a treatment decision that is highly consistent with the clinician's decision based on the patient's current health status. In the surviving patient set, the action similarity between the treatment model and the clinician reached 89.6%, and in the dead patient set, the similarity reached 88.5%. Therefore, it can be considered that the model can accurately simulate the clinician's treatment intention and show high consistency in two different patient groups.  
After removing the InfCTI module in the experiment, the similarity between the treatment decision and the clinician's decision in the treatment model on both the surviving patient set and the dead patient set decreased. In the surviving patient set, the action similarity between the treatment model and the clinician's decision reached 77.2%. Especially in the dead patient set, the similarity decreased more significantly, reaching 59.6%. This phenomenon may be due to the fact that clinicians tend to take more radical treatment measures when facing patients with a higher risk of death, and this strategy may cause harm to patients. Relatively speaking, the treatment model that lacks the InfCTI module but retains the DTE module is more inclined to choose a lower-risk and more conservative treatment strategy to avoid potential damage due to its comprehensive state representation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Dataset** & **Survival Dataset** & **Death Dataset** \\ \hline All & 90.4\% & 72.3\% \\ Without DTE & 89.6\% & 88.5\% \\ Without InfCTI & 77.2\% & 59.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Similarity of treatment models to clinical actions: survival and death datasets.  
Through this ablation experiment, it is shown that the InfCTI module can provide the full treatment model with information about the clinician's treatment intention, and the DTE module can provide the full treatment model with sufficient patient status information. After the integration of these two modules, the full treatment model produces a safe and reliable personalized treatment strategy that contains the clinician's treatment intention.
## 2 Spectrum Sensing for Enhanced Communications in Multi-Agent Systems  
A second group of papers focuses on spectrum-sensing techniques for enhanced communications in scenarios with multiple users or agents.  
In the paper, \"Dynamic Carrier-Sense Threshold Selection for Improving Spatial Reuse in Dense Wireless LANs\", by [PERSON] and [PERSON] [9], the problem of increasing the system spectral efficiency in wireless LANs is addressed by acting on carrier-sense threshold, since, frequently, transmissions are unnecessarily blocked by carrier sensing. Using high carrier-sense threshold and allowing nodes to transmit aggressively may increase the system throughput, but this approach can lead to unfair channel sharing and cause starvation for the edge nodes. The authors propose a new medium access control (MAC) protocol based on a dual-threshold, which can achieve higher system throughput compared to using a single carrier-sense threshold, without penalizing edge nodes.  
Spectrum sensing to find Television White Spaces (TVWS) to be exploited by cognitive radio systems is investigated in the paper, \"An Accurate Probabilistic Model for TVWS Identification\", by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [10], as a means to improve spectrum efficiency. Considering false alarm and detection probabilities jointly, a probabilistic model is proposed which can identify TVWS with improved accuracy. For demonstration purposes, a low-cost mobile prototype was designed and used for experiments in the UHF-TV spectrum. Analysis of the collected data showed that the proposed model improves the TVWS detection compared to other models, reducing the complexity while maintaining the accuracy.
## 3 Experiments and Discussions  
### Computer Simulation  
To validate the proposed method, we use simulated second heart sound signals with well-defined split. According to the model proposed by [PERSON] et al. [3], the aortic component and pulmonary component are modeled as narrow-band chirp signals, i.e.,  
\[A_{2}(t)=A_{m_{A}}(t)\sin(\varphi_{A}(t)) \tag{15}\]  
\[P_{2}(t)=P_{m_{P}}(t)\sin(\varphi_{P}(t)) \tag{16}\]  
where \(A_{m_{A}}(t)\) and \(P_{m_{P}}(t)\) are the amplitudes of \(A_{2}\) and \(P_{2}\), \(\varphi_{A}(t)\) and \(\varphi_{P}(t)\) are the phases. An S2 signal is simulated as  
\[S_{2}(t)=A_{2}(t)+P_{2}(t-t_{s}) \tag{17}\]  
where \(t_{s}\) is the split. To simulate varying split, it assumes that there are six hundred S2s and the splits of the S2s change in adjacent cardiac cycles from 25 ms to 80 ms like a sine wave. Then, the proposed method, the HVD method [7] and the STFT method [4] are used to detect the split variations. The simulated S2s are shown in Figure 10a. The extracted aortic and pulmonary components are shown in Figure 10b,c. The measured varying splits are illustrated in Figure 10d. A visual check shows that all the measured splits by the three methods are close to the true splits. However, in quantity, the root mean square errors of the proposed, HVD, STFT are 0.98 ms, 0.84 ms, and 1.1 ms, respectively. This computer simulation proves that the proposed method works as the authors expected.
## 4 Experiment and Results  
### Datasets and Criteria  
Because there are no existing definitive Chinese datasets that are randomly selected and annotated, we compiled a crawler tool based on the scrapy framework [32]. This tool can collect data from Sina Weibo. To ensure topic-independence of the dataset, we randomly selected target users and retrieved the first five pages of their microblogs as experimental data. Ten volunteers were employed to manually annotate the polarity of each microblog. In order to eliminate arbitrary results, the ten volunteers had different ages, genders, and professional background, and the final results were averaged over the annotation of the ten volunteers. To keep balance of the corpus, the numbers of positive and negative training samples were both 5000, and the number of test data was 1000.  
We take the precision (denoted by \(\text{P}_{pos}\) and \(\text{P}_{neg}\)), recall (denoted by \(\text{R}_{pos}\) and \(\text{R}_{neg}\)), and F1 score (denoted by \(\text{F1}_{pos}\) and \(\text{F1}_{neg}\)) as the evaluation criteria. We describe TP as the number of correctly classified positive samples, TN as the number of correctly classified negative samples, FP as the number of falsely classified positive samples, and FN as the number of falsely classified negative samples. The criteria are expressed as follows:  
\[\text{P}_{pos}=\frac{TP}{TP+FP} \tag{19}\]  
\[\text{P}_{neg}=\frac{TN}{TN+FN} \tag{20}\]  
\[\text{R}_{pos}=\frac{TP}{TP+FN} \tag{21}\]  
\[\text{R}_{neg}=\frac{TN}{TN+FP} \tag{22}\]\[\mathrm{F1}_{pos} =\frac{2\mathrm{P}_{pos}\times\mathrm{R}_{pos}}{\mathrm{P}_{pos}+ \mathrm{R}_{pos}} \tag{23}\] \[\mathrm{F1}_{neg} =\frac{2\mathrm{P}_{neg}\times\mathrm{R}_{neg}}{\mathrm{P}_{neg}+ \mathrm{R}_{neg}} \tag{24}\]
# Flexible, Brain-Inspired Communication in Massive Wireless Networks
## 1 Introduction
It was already shown in [14] that the digital system with coding can be more energy efficient for longer links while the uncoded digital system is more energy efficient for shorter links. We wanted to extend this research to an analog system that requires higher transmission power (to provide higher SNR at the receiver as confirmed by Figure 6), but lower power is consumed by signal processing. Finding the balance between these two factors and its relation to coded and uncoded digital systems is a novel research topic up to our knowledge. In this paper, we consider brain-inspired analog and digital transmission in the context of energy-efficient ultra-dense networks, i.e., consisting of short links with moderate attenuation and distortions. The energy-efficiency metric has been compared between these two types of communication taking the circuit power consumption model into account. Moreover, we assumed that the transmitted data came from analog sensors in the considered systems. Although currently many sensors are available as digital output only, a number of physical phenomenons are measured initially as analog signals, e.g., voltage or resistance. Therefore, in the case of the digital transmission, the input data are fed first to the analog-to-digital converter. At the receiving side, the decoded data are converted back to analog form. The rest of the paper is organized as follows. In Section 2, the system model for analog and digital transmission is described. The numerical results with their detailed analysis are presented in Section 3, while the conclusions are provided in Section 5.
# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 5 Tool Wear Monitoring Method Based on Deep Convolutional Neural Network
### Network Model Structure for Tool Wear Monitoring
[125] used the images of the spindle current clutter to identify the wear condition of the milling cutter and formulated a deep CNN model, as shown in Figure 11. The network model consists of seven layers: an input layer, two convolutional layers, two pooling layers, a fully connected layer, and an output layer. The spindle current clutter feature is normalized and binarized into an image with a size of \(140\times 80\), used as an input. The convolution kernel sizes of convolution layer I and II are \(13\times 15\) and \(9\times 9\), respectively. The convolution step size is \(1\times 1\). The ReLU function is selected as the activation function. The core size of pooling layer I is \(4\times 4\), and the step size is \(4\times 2\). The core size of pooling layer II is \(8\times 8\), and the step size is \(8\times 8\). Random pooling is selected for the pooling layers. Table 2 lists the main characteristics, experimental conditions and experimental results of the above network structure.
# Non-Probabilistic Reliability Analysis of Slopes Based on a Multidimensional Parallelepiped Model
## 2 Non-Probabilistic Reliability Analysis Method Based on a Multidimensional Parallelepiped Model  
A non-probabilistic reliability analysis method is a structural reliability assessment method that uses a non-probabilistic convex model theory to describe parameter uncertainty [19]. The convex set models used in this method mainly include interval models and ellipsoid models; an interval model can only deal with independent interval variables, while an ellipsoid model can only deal with correlated interval variables. Multidimensional parallelepiped models are a new class of convex set models that can consider both independent interval variables and correlated interval variables [15]. According to the way of measuring the correlation of interval variables and the way of constructing the shape kernel matrix, multidimensional parallelepiped models can be subdivided into the following types: the MP-1 model, the MP-2 model, the rectangular model, the lower triangular model, and the upper triangular model. Among these models, the MP-1 model and the MP-2 model are the most representative. In this paper, the MP-1 model and the MP-2 model are used for non-probabilistic reliability analysis.
## 2 Study Area and Data
### Data
#### 2.2.2 Vegetation Index Data  
The normalized difference vegetation index (NDVI) is one of the most widely used vegetation indices, which can be calculated from near-infrared and red light bands. Moderate Resolution Imaging Spectroradiometer (MODIS) provides monthly NDVI products with a spatial resolution of 1 km. The monthly NDVI (MOD13A3) data spanning from 2001 to 2020 were downloaded from Google Earth Engine (GEE).
# Dermoscopy Images Enhancement via Multi-Scale Morphological Operations
## 4 Results and Discussions
### Comparison of the Proposed Algorithm with State-of-the-Art Algorithms
#### 4.2.2 Visual Assessment by the Dermatologist  
In Figures 8 and 9, the images enhanced with different state-of-the-art algorithms can be visualized. The images obtained by the multiscale MM based algorithms use an iteration number \(n=4\).  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline  & \multicolumn{2}{c}{**Algorithms**} & \multicolumn{4}{c}{**Metrics**} \\ \cline{3-5}  & & E & REC & PSNR \\ \hline \multirow{4}{*}{MGRTH-I} & Negative ranks & 33 & - & - \\  & Positive ranks & 203 & - & - \\  & Z & \(-11.55\) & - & - \\  & Sig. asymptotic (bilateral) & \(\approx\)0 & - & - \\ \hline \multirow{4}{*}{MGRTH-GRMMCE} & Negative ranks & 40 & 160 & 133 \\  & Positive ranks & 196 & 76 & 103 \\  & Z & \(-10.871\) & \(-6.987\) & \(-1.63\) \\  & Sig. asymptotic (bilateral) & \(\approx\)0 & \(\approx\)0 & \(0.103\) \\ \hline \multirow{4}{*}{MGRTH-MMALCER} & Negative ranks & 32 & 29 & 232 \\  & Positive ranks & 204 & 207 & 4 \\  & Z & \(-10.188\) & \(-11.524\) & \(-13.267\) \\  & Sig. asymptotic (bilateral) & \(\approx\)0 & \(\approx\)0 & \(\approx\)0 \\ \hline \multirow{4}{*}{MGRTH-HE} & Negative ranks & 6 & 218 & 4 \\  & Positive ranks & 230 & 18 & 232 \\  & Z & \(-13.228\) & \(-12.981\) & \(-13.287\) \\  & Sig. asymptotic (bilateral) & \(\approx\)0 & \(\approx\)0 & \(\approx\)0 \\ \hline \multirow{4}{*}{MGRTH-BBHE} & Negative ranks & 8 & 214 & 15 \\  & Positive ranks & 228 & 22 & 221 \\  & Z & \(-13.156\) & \(-12.789\) & \(-12.698\) \\  & Sig. asymptotic (bilateral) & \(\approx\)0 & \(\approx\)0 & \(\approx\)0 \\ \hline \multirow{4}{*}{MGRTH-MMBEBHE} & Negative ranks & 9 & 212 & 30 \\  & Positive ranks & 227 & 24 & 206 \\  & Z & \(-13.167\) & \(-12.547\) & \(-11.935\) \\  & Sig. asymptotic (bilateral) & \(\approx\)0 & \(\approx\)0 & \(\approx\)0 \\ \hline \multirow{4}{*}{MGRTH-QHELC} & Negative ranks & 24 & 48 & 232 \\  & Positive ranks & 212 & 188 & 4 \\  & Z & \(-12.182\) & \(-10.467\) & \(-13.303\) \\ \cline{1-1}  & Sig. asymptotic (bilateral) & \(\approx\)0 & \(\approx\)0 & \(\approx\)0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The Wilcoxon signed rank test for paired observations.  
It can be seen that MGRTH and MMALCER are the algorithms that preserve the most features and provide the best sharpness. They also avoid adding unnecessary brightness and improve the visualization of circumscribed skin. These algorithms are possibly the most applicable for dermoscopic image assessment and classification.
## 6 Conclusions  
A cost-effective solution for SAC-OCDMA technology that provides a solid platform for multiple access networks named CCIS is described. CCIS design combines a CW laser (coherent source) with an LED (incoherent source) and improves system performance considerably. CCIS design trades off low-cost LED sources with a low output power and expensive CW laser sources with a high output power. Furthermore, the study conducted and validated performance analysis on the CW laser, CCIS, and LED optical sources in both FSO and AAPS systems. Further investigation is made in the sky wave communication, which showed the possibility of using the FSO system in the sky mesh network based on the  
Figure 15: Eye diagrams at (a) 60 km, (b) 38 km, and (c) 6 km.  
CCIS-OCDMA system. As means of comparison at an FSO transmission distance of 9 km, the values of BER are 1.86 \(\times\) 10\({}^{-7}\), 1.96 \(\times\) 10\({}^{-6}\), and 1.83 \(\times\) 10\({}^{-5}\) for the laser, CCIS, and LED, respectively. This is because low haze weather causes a lower attenuation coefficient (1.537 dB/km) compared to that of medium (4.285 dB/km) and heavy (10.115 dB/km) hazes weather conditions, so the information signal can propagate a longer range under it. The FSO ranges under low haze, moderate haze, and heavy haze are 3.7 km, 2.5 km, and 1.5 km, respectively. At 1.25 Gbps data rate with a total of 16 subscribers at 0 dBm received power, the AAPS-OCDMA system for the CW laser, LED optical sources, and CCIS showed an acceptable BER of 3.3 \(\times\) 10\({}^{-14}\), 4.4 \(\times\) 10\({}^{-13}\), and 1.5 \(\times\) 10\({}^{-12}\), respectively.  
It is concluded that, as the FSO range and the haze level increase, the BER also increases. Additionally, distance is the main factor that limits performance, regardless of the type of optical source being used. In conclusion, a reasonable and dependable solution for future optical communication systems might be created by combining technologies effectively.  
[PERSON] designed the research and undertook analytical derivation; [PERSON] performed the simulations and obtained the results; [PERSON] and [PERSON] analyzed the obtained results; [PERSON], [PERSON] and [PERSON] wrote the paper, which was further reviewed by [PERSON] and [PERSON]-M. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research through project number **IF2/PSAU/2022/01/2079**.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to their large size.  
The authors declare no conflict of interest.
## 2 Construction and Characteristics of Digital Track Map
### Definition and Accuracy of Required Digital Track Map
For example, when the radius of curvature is 1000 m, the distance \(D\) of the discrete points on the map is taken to be 80 m, and the maximum lateral error will still be only 0.08 m.  
Figure 2: Cross-track error due to interpolation and curvature.
## 3 Results and Discussion
### Fatigue Lives Predictions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Series-Crack Type**} & \multicolumn{2}{c}{**Crack Sizes**} & \multicolumn{2}{c}{**FCGR**} & \multicolumn{2}{c}{**Fatigue Life**} \\ \cline{2-9}  & **a\({}_{\text{i}}\) c\({}_{\text{i}}\)** (mm)** & **a\({}_{\text{f}}\) c\({}_{\text{f}}\)** (mm)** & **A\({}_{\text{f}}\)** (mm)** & **A\({}^{\star}\), n\({}^{\star}\)** & **Y Value** & **Experimental** & **Estimated** & **Ratio** \\ \hline S2-extended & & & & & & & (Cycles) & **(Cycles)** & **(Cycles)** & **Ratio** \\ \hline S2-extended & & & & & & & & & \\ (Figure 10a) & 0.02, 12.5 & 0.96, 12.5 & 2 \(\times\) 10\({}^{-9}\), 3.2017 & 1.08 & 1.13 & 22,922 & 20,792 & 1.10 \\ S2-extended & & & & & & & & \\ (Figure 10a) & 0.02, 12.5 & 1.22, 12.5 & 2 \(\times\) 10\({}^{-9}\), 3.2017 & 1.08 & 1.13 & 26,225 & 21,043 & 1.25 \\ S1-semi-elliptical & & & & & & & & \\ (Figure 10b) & 0.15, 0.46 & 1.62, 2.61 & 7 \(\times\) 10\({}^{-10}\), 3.5965 & 1.05 & 1.10 & 29,264 & 25,485 & 1.15 \\ S1-semi-elliptical & & & & & & & & \\ (Figure 10c) & 0.10, 0.69 & 2.01, 4.83 & 7 \(\times\) 10\({}^{-10}\), 3.5965 & 1.01 & 1.01 & 34,127 & 38,642 & 0.88 \\ S3-corner crack & & & & & & & & \\ (Figure 10d) & 0.21, 1.15 & 3.00, 4.81 & 6 \(\times\) 10\({}^{-11}\), 4.4175 & 1.00 & 1.15 & 33,762 & 35,065 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Data and parameters for the estimation of the fatigue life. expressions from the literature, [8; 25] was consulted for the Y value and [42] for the M\({}_{\rm k}\) factor. As can be seen in Tables 5 and 6, the M\({}_{\rm k}\) factor that considers the effect of local stress concentration, due to the excess weld on cracks starting from the undercuts, had a small effect, mainly due to the small width of the weld beads. For the case of cracks starting from the underfill, the Mk factor were assumed equal to one. The M\({}_{\rm k}\) factor according to an expression in [42], considers the weld bead features: flank angle, attachment, excess-weld high and joint thickness. As observed in Table 5, fatigue lives were generally underestimated, being less than 25%, although the relation between the experimental results and the predictions of the fatigue lives achieved in Table 6 can be considered acceptable for the data, parameters and assumptions used; the analysis showed that at high stress levels the appropriate values of parameters A, n and initial crack depth, a\({}_{\rm i}\), must be used with a correct assignment of the type of crack to a given failure mode, as long as the initiation and coalescence stages can really be neglected. For specimens of the S5 series there were no predictions of the fatigue life due to the FCGR not being appropriate, as was described in Sections 3.3 and 3.4.
# Impact of Patient Size and Positioning on Radiation Dose in Multiphase Liver CT Examinations
## 3 Results
### Does the Isocenter Have a Statistically Significant Impact on X-Ray Tube Output Parameters?
#### 3.6.1 Group with Abdomen and Pelvis Imaging  
The analysis of the 'Abdomen' group encompassed 80 patient observations. In this group, the presence of an isocenter did not demonstrate a statistically significant impact on the measured parameters. The \(p\)-values for E (mSv), kV, mA I phase, mA II phase, and mA III phase were 0.3225, N/ A (due to uniformity across the sample), 0.5942, 0.5940, and 0.6262, respectively (Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Mean with** & **Mean Without** \\  & **Isocenter** & **Isocenter** & **\(p\)-Value (\(t\)-test)** \\ \hline kV & 120 & 120 & [Not applicable] \\ mA I Phase & 800 & 794.87 & 0.5942 \\ mA II Phase & 800 & 794.83 & 0.5940 \\ mA II Phase & 765.90 & 744.37 & 0.6262 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of X-ray tube output parameters based on the presence or absence of an isocenter in a group limited to the abdomen.  
Figure 16: Correlation coefficients between patients’ weight, height, and BMI and the various X-ray tube output parameters for cases involving the abdomen and pelvis region.
## 6 Simulation Results and Discussion
### Comparative Analysis  
The effectiveness of the proposed approach for the optimal sizing and allocation of DSTATCOMs in RDS is demonstrated through a comparison with other algorithms such as LSA [7], BFOA [9], CSA [10], MOSCA, and MOPSO, both from Reference [11]. All methodologies were assessed using the IEEE 33-bus and IEEE 69-bus systems based on installing three DSTATCOMs for a fair and consistent comparison. Notably, no method was identi ed in the literature reviewed that used the IEEE 85-bus test system. Each technique from the literature was aimed at the common objective of minimizing active power losses. It is important to mention that this comparison with these literature methods does not consider the presence of renewable generation sources and assumes a xed load demand.  
Tables 7 and 8 indicate that the proposed method outperforms the other techniques in terms of active power loss minimization, enhancement of voltage pro les, and voltage stability.  
Figure 16: (a) Voltage pro le for the IEEE 85-bus network without DSTATCOMs; ( b) voltage pro le for the IEEE 85-bus network with DSTATCOMs.  
Figure 15: (a) Voltage pro le for the IEEE 69-bus network without DSTATCOMs; ( b) voltage pro le for the IEEE 69-bus network with DSTATCOMs.