esa-sceva/satcom-chunk-collection huggingface dataset:
# Surgical Complications of Orthognathic Surgery
## 3 Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Skeletal Jaw Relations** & **Number (\%) of Patients** & **Secondary Diagnosis** & **Number (\% within Group) of Patients \({}^{*}\)** \\ \hline \multirow{4}{*}{Class III} & \multirow{4}{*}{70 (67.9\%)} & None & 25 (36\%) \\ \cline{3-3}  & & Open Bite & 38 (54\%) \\ \cline{3-3}  & & Asymmetry & 14 (20\%) \\ \cline{3-3}  & & None & 13 (62\%) \\ \cline{3-3}  & & None & 8 (38\%) \\ \cline{3-3}  & & Asymmetry & 0 (0\%) \\ \hline \multirow{4}{*}{Class I} & \multirow{4}{*}{12 (11.6\%)} & None & 9 (75\%) \\ \cline{3-3}  & & Open Bite & 2 (17\%) \\ \cline{3-3}  & & Asymmetry & 1 (8\%) \\ \hline Total & 103 (100\%) & & \\ \hline \hline \end{tabular} \({}^{*}\)Possibility of results exceeding 100%, as there could be more than one secondary diagnosis.  
\end{table} 
Table 1: Diagnoses Distribution. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Surgical Procedure** & **Number** & **\%** \\ \hline BSSO & 4 & 3.8\% \\ \hline BSSO + GP & 9 & 8.7\% \\ \hline LF1 & 14 & 13.6\% \\ \hline LF1 + GP & 8 & 7.70\% \\ \hline LF1 + BSSO & 55 & 53.4\% \\ \hline LF1 + BSSO + GP & 5 & 4.8\% \\ \hline LF1 2p & 4 & 3.8\% \\ \hline LF1 2p + Genioplasty & 1 & 0.97\% \\ \hline LF1 2p + BSSO & 1 & 0.97\% \\ \hline LF1 + HVRO & 2 & 1.94\% \\ \hline \hline \end{tabular} Legend: BSSO—Bilateral Sagittal Split Osteotomy, LF1—Le-Fort 1 Osteotomy, LF1 2p—Two Piece Maxilla Le-Fort 1 Osteotomy, HVRO—High Vertical Ramus Osteotomy, GP—Genioplasty.  
\end{table} 
Table 2: Surgeries Distribution. Risk) for complications in two-jaw surgery was 2.67, mainly due to higher complication rates related to hardware issues. Analyzing for relationships between complications and initial diagnosis revealed that patients with CL III have a significantly lower (\(p\) = 0.022, Pearson Chi-Square Test) chance of post-operative complications (OR 1.69 for CL I and 2.75 for CL II) as shown in Table 5. As the surgical team involved both faculty and trainees, no difference among trainees or faculty could be distinguished.
## 4 Role of Ap4A: Functions in Normal and Cancer Cells
### Stress Response Modulation  
Cancer cells frequently encounter diverse forms of stress, including oxidative stress and changes in the extracellular environment. Their ability to effectively respond to these stressors is crucial for their survival, proliferation, and resistance to therapy.  
In response to stress, Ap4A levels rise rapidly, triggering defensive mechanisms that enhance cell survival. Elevated Ap4A levels are believed to mitigate oxidative damage caused by reactive oxygen species (ROS) from UV light or toxins [2]. Additionally, Ap4A levels increase during heat shock, aiding cells in managing thermal stress by facilitating protein repair and stabilization. Therefore, Ap4A induces the expression of Heat Shock Proteins (HSPs), which assist in refolding denatured proteins and degrading irreversibly damaged ones [10; 19]. In addition, stress response involves the upregulation of antioxidant defences and repair mechanisms, including the activation of nuclear factor erythroid 2-related factor 2 (Nrf2), which orchestrates the expression of genes involved in detoxifying ROS and repairing oxidative damage [30].  
Furthermore, Ap4A's role in stress response extends to its interaction with purinergic receptors, such as P2X and P2Y receptors. These receptors are involved in various intracellular signaling pathways that regulate cellular stress responses. For example, Ap4A, by binding to P2X receptors, can activate the ATP-sensitive K+ channels, influencing cellular ion homeostasis and helping cells manage stress-induced changes in membrane potential and intracellular calcium levels. Similarly, Ap4A's interaction with P2Y receptors can modulate intracellular signaling cascades, such as the MAPK/ERK and PI3K/AKT pathways, which are involved in stress response and survival signaling [31].  
Cancer cells' ability to withstand and adapt to stress is a hallmark of their malignancy and contributes significantly to their resistance to conventional therapies. Elevated levels of Ap4A in cancer cells often correlate with increased resistance to chemotherapy and radiation therapy, as the enhanced stress response mechanisms facilitate cancer cell survival and repair in the face of therapeutic results. For instance, Ap4A can promote DNA repair pathways that counteract the damage induced by chemotherapeutic agents, thereby reducing their efficacy and contributing to the development of therapy-resistant tumors [2].  
In summary, Ap4A plays a crucial role in stress response modulation in cancer cells by enhancing their ability to survive and adapt to various forms of stress. Understanding Ap4A's role in these processes may provide new insights into developing strategies to overcome cancer cell resistance and improve treatment outcomes (Figure 2).
## 2 Materials and Methods
### Toxicity Prediction  
The best-predicted toxic effect of the screened mushroom-derived database of bioactive compounds was analyzed using the ProTox 3.0 software [35]. This software evaluates the compounds' toxicity by calculating several parameters, including organ toxicity, such as hepatotoxicityoxicity, neurotoxicity, nephrotoxicity, respiratory toxicity, and cardiotoxicity. In addition, ProTox 3.0 calculates toxicity endpoints such as carcinogenicity, immunotoxicity, mutagenicity, cytotoxicity, blood-brain barrier (BBB) toxicity, ecotoxicity, clinical toxicity, and nutritional toxicity. Regarding nuclear receptor signaling pathways, the effects of each compound in different relevant proteins were also calculated, including the aryl hydrocarbon receptor (AhR), androgen receptor (AR), ligand-binding domain (AR-LBD), aromatase estrogen receptor alpha (ER), estrogen receptor ligand-binding domain (ER-LBD), and peroxisome proliferator-activated receptor gamma (PPAR-Gamma). Cellular stress response pathways were also investigated, including nuclear factor (erythroid-derived 2)-like 2/antioxidant responsive element (nrf2/ARE), heat shock factor response element (HSE), mitochondrial membrane potential (MMP), phosphoprotein (tumor suppressor) p53, and ATPase family AAA domain-containing protein 5 (ATAD5). The analysis of molecular initiating events included thyroid hormone receptor alpha (THRa), thyroid hormone receptor beta (THR\(\beta\)), transtyretrin (TTR), ryanodine receptor (RYR), GABA receptor (GABAR), glutamate N-methyl-D-aspartate receptor (NMDAR), alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionate receptor (AMPAR), kainate receptor (KAR), acetylcholinesterase-increase (AChE), constitutive androstane receptor (CAR), pregnane X receptor (PXR), NADH-quinone oxidoreductase (NADHOX), the voltage-gated sodium channel (VGSC), and Na+/1- symporter (NIS). Finally, the effect on the metabolism of the compounds was analyzed, considering the cytochromes CYP1A2, CYP2C19, CYP2C9, CYP2D6, CYP3A4, and CYP2E1. A qualitative score was created to classify the compounds, in which the 'inactive' state received 1 point, indicating that the compound did not present toxicity in the evaluated parameter, while the 'active' state received 0 points, indicating toxicity. Thus, the higher the score, the less toxic the compound was, considering a qualitative evaluation of the analyzed parameters. In this way, at the end of the evaluation, the compounds with the highest scores exhibited less toxicity, helping to select those with the most significant potential.
## 5 Results  
In order to conduct a comprehensive analysis of DDSMC, the 116 selected articles in the systematic literature review (Section 4) were categorized according to the SMC schemes, data-driven techniques, intelligent algorithms and applications. Figure 10 shows how each category is classified, and all articles were analyzed this way. Initially, the analysis focused on determining the applications and identifying their relationships with SMC schemes and data-driven techniques. Both relationships are presented in Tables 1 and 2. Classic SMC includes the sliding mode methodology presented by [PERSON] [6] or [PERSON] et al. [77]. According to the information in Table 1, the most popular application is robotics. This category included air, ground, underwater and surface vehicles; manipulators; and various types of robots. The second category covers applications for electromechanical systems such as engines and machines. Third, the generic systems category appears, including applications that do not fit into the main categories or those that do not describe a specific application, such as MIMO (multi-input-multi-output) systems, high-order systems, and multi-agent systems, among others. The fourth place is in energy conversion systems applications. Fifth, chemical processes are presented, including tank systems, heat exchanges, and reactors. Finally, medical applications appear last, including glycemia regulation systems, neuronal disorders treatment, etc. Figure 11 presents the percentage of each application according to Table 1.  
Figure 10: Classification of techniques and applications DDSMC. Continuing with the analysis of Table 1, the most widely used control scheme is the classical approach, followed by terminal, fractional-order SMC, and high-order SMC. It should be noted that the TSMC, NTSMC, FTSMC, ITSMC, and HOTSMC schemes were grouped into the terminal category. TSMC has gained interest, especially the most frequently reported combination of nonsingular and fast terminal approaches, followed by integral TSMC. Some proposals combine TSMC with fractional order for applications such as robotics, electromechanical, and generic systems. The data-driven techniques are presented in Table 2. This study considered four main techniques. The first is adaptive control, which offers significant advantages for enhancing the performance of industrial processes. It allows systems to respond agilely to dynamic environments by automatically adjusting parameters based on real-time data
## 1 Introduction
It optimized the parameters of the SVM and established an IGWO-SVM cutting pattern recognition model. Finally, the effectiveness of the proposed method was verified by establishing a shearer simulation cutting experimental system. The experimental results show that the method proposed in this paper can accurately identify the cutting mode of the shearer, which provides a new research idea for the recognition of the shearer's cutting pattern based on the cutting sound signal. The novelty of this paper lies in the following four aspects. (1) It first established a combined model based on ICEMDAN-IGWO-SVM for shearer cutting pattern recognition. (2) Based on permutation entropy and multi-scale entropy, it proposed a composite multi-scale permutation entropy as the feature of the cutting mode. (3) The paper built a cutting pattern recognition model based on the SVM combined with the cutting sound signal data. It introduced the DE algorithm and nonlinear convergence factor to improve the GWO algorithm and optimized the identification model parameters. (4) Meanwhile, it established several different cutting recognition models and analyzed the experimental results to reflect the performance of the proposed method. The rest of the paper is arranged as follows. Section 2 introduces the theoretical basis for feature extraction of the cutting sound signals. Furthermore, Section 3 proposes the IGWO algorithm and the method of optimizing SVM parameters. Section 4 establishes the cutting pattern recognition model of the shearer. Moreover, Section 5 describes the design and analysis of the shearer simulation cutting experiment. Finally, Section 6 contains some conclusions.
## 3 Methods
### Datasets  
To assess SAM 2's performance in segmenting medical images, compare it with the original SAM, and test our proposed approach to medical image segmentation, we selected two widely recognized public lung datasets, one comprising axial lung scans and the other containing frontal chest X-rays, ensuring representation of both image modalities in our experiments. These datasets were chosen due to their accessibility, publication status, and frequent use in previous scientific studies, underscoring their significance in comparative research. Furthermore, they are the same datasets used in our previous work [9], allowing for a direct comparison between the models. Image dimensions are described in width \(\times\) height.  
Both datasets provide lung masks verified by radiology specialists, ensuring the availability of well-labeled data for research. The first dataset (COVID-19) includes 20 axial lung scans [18], divided into two subsets: Coronacases (see Figure 1a) and Radiopaedia (see Figure 1b). The quality of the CT slices in the Coronacases subset is superior to that of the Radiopaedia subset, the latter being composed of cone beam CTs with lower resolution, hence the disparity in quality. Moreover, while the Coronacases subset consists exclusively of 512 px \(\times\) 512 px slices, the Radiopaedia subset is more varied, including 829,630 px \(\times\) 630 px slices and 110,630 px \(\times\) 401 px slices. Coronacase CT scans in this dataset average 258.1 slices, while Radiopaedia averages 93.9, providing 3520 slices for evaluating SAM 2's performance. This dataset is also utilized to evaluate the performance of DMDF-Net, as reported by [PERSON] et al. [19]. The differences between these two kinds of CT make them ideal for a separate study so that they will be treated as different datasets during this work.  
\begin{table}
\begin{tabular}{c c|c c} \hline \hline  & **SAM** & \multicolumn{2}{c}{**SAM 2**} \\ \hline
**Name** & **Size** & **Name** & **Size** \\ \hline ViT-H & 2.4 GB & **Large** & **856.4 MB** \\
**ViT-L** & **1.2 GB** & Base+ & 308.5 MB \\ ViT-B & 357.7 MB & Small & 175.8 MB \\  & & Tiny & 148.7 MB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model checkpoint sizes (chosen checkpoints in bold).  
Figure 1: Prediction examples (yellow box: target location; green circles: lung centers; red circle: background).  
The second dataset (Montgomery) comprises 138 frontal chest X-rays [20], provided in Portable Network Graphics (PNG) format, along with corresponding left and right lung masks (see Figure 1c). [PERSON] et al. [21] used this dataset, referred to as Montgomery after the collection site in Montgomery County, MD, USA, to evaluate the performance of TransAttUnet. This dataset contains 414,892 px \(\times\) 4020 px slices and 4020 px \(\times\) 4892 px slices.
# Reduced Overhead Routing in Short-Range Low-Power and Lossy Wireless Networks +
## 2 RPL: Routing Protocol for Low-Power and Lossy Networks  
RPL [1] is an IETF standardised routing framework for LLNs. Generally, in a RPL network there is a border router also known as a gateway/root that connects different nodes in the network to an external network, such as the Internet. Within a network, RPL supports MP2P, P2 MP, and P2P communications. RPL constructs a routing topology called destination-oriented directed a-cyclic graph (DODAG) rooted at the gateway. To discover data forwarding paths, RPL uses the following control messages:  
1. [label=(0)]
2. DIO: DODAG Information Object (used to discover forwarding paths for MP2P communication)2. DIS: Destination Information Solicitation (a node multicasts this message to discover the route to the gateway)
3. DAO: Destination Advertisement Object (used to discover forwarding paths for M2 MP and P2P communications)
## 3 Congestion Recognition Based on the ACA  
### Ant Colony Algorithm (ACA)  
The ACA (ant colony algorithm) is a self-organizing and swarm intelligence-based algorithm that mimics the food search behaviour of ants to find optimal paths in a network [16; 17]. It effectively addresses path-planning problems in both static and dynamic environments. In dynamic environments, traditional path-planning algorithms struggle to handle the constant changes in obstacles. However, the ACA leverages the concept of pheromones to guide ants in finding new shortest paths, thereby enhancing the efficiency and accuracy of path planning. ACA's adaptability to dynamic environments makes it a powerful tool in path-planning applications [18; 19].  
The ACA finds application in various domains of intelligent transportation systems. It enables path planning for intelligent vehicles, facilitates the identification and resolution of traffic congestion, and facilitates traffic light control. In the aerospace sector, the ACA proves valuable for UAV path planning and flight control [20]. Additionally, the ACA finds utility in optimizing routing protocols and enhancing transmission quality in the field of data transmission [21]. Its versatility and effectiveness make the ACA a versatile tool across multiple domains [22].
# Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems
## 1 Introduction
As the main results of this analysis, we derive an analytical expression for the error probability of the AQC system when using the quasi-Bell state. Then, by investigating the numerical characteristics of the system using this analytical expression with various reflectivities and the average number of photons, we demonstrate that the AQC system using the quasi-Bell state is not only always superior to the ACC system, but also is asymptotically superior to the same AQC system using the TSVS. Additionally, in contrast to reference [9], which shows that using the EPR state asymptotically outperforms using the coherent state, this paper shows that the ACC system asymptotically outperforms the AQC system when using the TSVS. Finally, we show that the AQC system using the quasi-Bell state may almost reach the universal lower bound on the error probability, unlike the AQC system using the TSVS. The rest of this paper is organized as follows. Section 2 describes the TSVS, the quasi-Bell state, and the ACC system. Section 3 describes the model of the AQC system when using the entangled states, and also provides a description of Bob's received quantum states in the AQC system. Section 4 presents an analytical expression for the error probability of the AQC system when using the quasi-Bell state. In Section 5, by using the analytical expression obtained in Section 4, the system error performance is given and is compared numerically with the error probabilities of both the ACC system and the AQC system when using the TSVS and the universal lower bound on the error probability.
# Scenario-Based Configuration Refinement for High-Load Cellular Networks: An Operator View
## 6 Conclusions  
In this paper, taking the perspective of telecommunication operators, we refine the cellular network configuration to improve QoS and user experience for different scenarios. We analyze the traffic features of high traffic load cells based on the field data and propose basic principles of network configuration refinement. Moreover, we conduct case studies for different typical scenarios of high traffic loads, including high population density, emergencies, and high-speed mobility. In general, after following the proposed configuration refinement, the access success rate is up to 99.6% in Jiangsu province, with a low call dropping ratio around 0.18%. The switching success rate is up to 98.87%. The access success rate of CSFB in high-speed railways is up to 90% with 25 Mbps as a stable download data rate. Therefore, the proposed analysis flow and the refinement policy can significantly increase the quality of users' experience and the service quality of cellular networks.  
Conceptualization, F.D., R.S., G.H. and Q.Q.; methodology, R.S., F.D., G.H. and Q.Q.; writing, R.S. and M.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Jiangsu Province, Grant No. BK20190733, the NUPTSF, Grant No. NY219166., the Ministry of Education-China Mobile Research Foundation, Grant No. MCM20170205, the Six Talent Peaks Project of Jiangsu Province, Grant No. DZXX-008, the China Postdoctoral Science Foundation, Grant No. 2019M661900, the Postdoctoral Research Funding Program of Jiangsu Province, Grant No. 2019K026, the NUPTSF Grant No. NY220028, and the project of \"Localization and tracking system of IoUT\", Grant No. 2021W159.  
Not applicable.  
The authors declare no conflict of interest.
# Kalman-Filter-Based Tension Control Design for Industrial Roll-to-Roll System
## 3 Design of a Signal Filter
### Linear State Space Model  
To design a KF, the linear state space model must be first obtained by modeling the target system. The performance of the KF depends largely on how similar the system model is to the actual system. Equation (8) can be described by the following differential equation:  
\[\tau_{n}\dot{F_{T}}+F_{T}-2 cos\theta{R_{n}}^{-1}\tau_{\varepsilon}-F_{ \varepsilon}=0 \tag{9}\]  
where \(R_{n}\) is the nominal radius of the web and \(F_{\varepsilon}\) is the sensor noise. Defining the state variables \(x(t)=F_{T}(t)\), \(w(t)=F_{\varepsilon}(t)\), the state differential equation is  
\[x(t)=-\frac{1}{\tau_{n}}x(t)+\frac{2 cos\theta{R_{n}(t)}^{-1}}{\tau_{n}}u(t)+w(t) \tag{10}\]  
\[z(t)=x(t)+v(t) \tag{11}\]  
where \(x\) is the state of the system, \(z\) is the output of the system, \(u\) is the system input, \(w\) is the system noise, and \(v\) is the measurement noise.
# A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks
## 5 Simulation Results and Findings
### V2V Communication Reliability  
Figure 6 paints a compelling picture of how data size impacts V2V transmission probability under various schemes. While benchmark schemes exhibited a predictable decline in transmission success as data size increased, the proposed QMIX scheme stood out. At small data sizes, QMIX outperformed with a remarkably higher transmission probability, thereby highlighting its effectiveness in handling light communication loads. This consistent success makes it an ideal choice for scenarios where reliable transmission of smaller packets is crucial.  
From the Figure 6, it is evident that as the data payload increased, the proposed QMIX technique continued to outperform by a significant margin. For example, we have improvements of 3.09%, 8.89%, 19.5%, and 26.67% for data sizes of \(3\times 1060\), \(4\times 1060\), \(5\times 1060\), and \(6\times 1060\) bytes, respectively. Figure 6 shows that the average improvement of the proposed technique over MARL was 14% in the given scenario.  
This consistent performance across varying data sizes signifies the strength and potential of QMIX. It underscores its ability to deliver enhanced transmission performance, even in the dynamic and challenging environment of V2V communication. The QMIX adaptation in V2X networks can significantly improve data communication performance.  
In addition, it is important to note that the number of training episodes greatly influences the effectiveness of the QMIX method. When the training duration is very short, agents cannot adequately explore their environment, and reinforcement learning-based strategies may not perform better than random baseline approaches. This behavior is demonstrated in Figure 7, where the performance of the V2I (Figure 7a) and V2V (Figure 7b) links is shown for a fixed payload size. We can observe that, particularly in scenarios with limited training time--around 2000 training episodes--the QMIX approach offered significant benefits.