esa-sceva/satcom-chunk-collection huggingface dataset:
# The Emerging Roles of RNA Modifications in Glioblastoma
## 4 Potential Clinical Implications of RNA Modifications in GBM
### Potential Therapeutical Implications of RNA Modifications in GBM  
Covalent modifications in both DNA, histones and other proteins have shown promising probability for cancer treatment. Actually, some epigenetic drugs such as azacitiidine, 5-aza-2\({}^{\prime}\)-deoxycytidine, suberoylanilide hydroxamic acid (SAHA), romideppsin, belinostat, panobinostat, has been approved by the United States Food and Drug Administration (FDA) and childamide has been approved by China FDA to treat some T-cell lymphoma or multiple myeloma, and many other epigenetic drugs on cancers are ongoing clinical trials [10]. Besides, drugs that target modulators of protein post-translational modifications, such as E3 ubiquitin ligases including mouse double minute 2 homolog (MDM2), phase kinaseassociated protein 2 (SKP2), speckle type BTB/POZ protein (SPOP), cellular inhibitor of apoptosis 2 (clAP) and anaphase-promoting complex/cyclosome (APC/C), are also been evaluated in clinical and preclinical settings [168]. Importantly, some epigenetic drugs such as vorinostat, panobinostat, romideppsin and valpro acid alone or in combination with other chemotherapeutic methods such as TMZ treatment or radiotherapy are studying in phase I/II clinical trials [169].  
As kinds of covalent modifications in RNA, their potentials in cancers like malignant GBM are being explored. Since more than 170 kinds of modifications, it is a wider world for us to explore. Specific antagonist against writers, erasers and readers of RNA modifications needs to be screened, designed and identified, as well as their biological functions and clinical applications in cancer treatment also need to be extensively studied. In fact, several pharmaceutical companies are planning to develop small-molecule inhibitors of the METTL3-METTL14 complex and ADAR [170]. Several compounds with piperidine and piperazine rings have exceptionally high docking efficiencies to METTL3-METTL14-WTAP complex, thereby activating RNA methylation [171]. Recently, a small molecule inhibitor of METTL3 is also shown to curb the development of acute myeloid leukemia (AML) in vivo [172; 173]. We believe that effective and efficient therapies regarding RNA modifications will soon be developed to treat malignant neoplasms such as GBM.
###### Abstract  
Accurately measuring the lower extremities and L5/S1 moments is important since L5/S1 moments are the principal parameters that measure the risk of musculoskeletal diseases during lifting. In this study, protocol that predicts lower extremities and L5/S1 moments with an inside sensor was proposed to replace the prior methods that have spatial constraints. The protocol is hierarchically composed of a classification model and a regression model to predict joint moments. Additionally, a single LSTM model was developed to compare with proposed protocol. To optimize hyperparameters of the machine learning model and input feature, Bayesian optimization method was adopted. As a result, the proposed protocol showed a relative root mean square error (rRMSE) of 8.06-13.88% while the single LSTM showed 9.30-18.66% rRMSE. This protocol in this research is expected to be a starting point for developing a system for estimating the lower extremity and L5/S1 moment during lifting that can replace the complex prior method and adopted to workplace environments. This novel study has the potential to precisely design a feedback iterative control system of an exoskeleton for the appropriate generation of an actuator torque.  
Keywords:human motion analysis; lifting task; machine learning; lower body joint moment; work-related musculoskeletal disorders; insole system +
Footnote †: journal: Applied Sciences
## 6 Discussion
### Sources of Uncertainties  
In this paper, the point-to-pixel comparison method was implemented to assess the performance of the GPPs throughout Iran [83; 84; 85]. Accordingly, the results may undervalue the exact accuracies of the GPPs due to mismatches between point measurements (i.e., synoptic stations) and pixel measurements (i.e., GPPs), which is acceptable for any in situ based validation studies [35]. This issue may also be a source of uncertainty as the spatial resolution of GPPs differ, and the limited representativeness of point observations could affect the results. For instance, the temporal inconsistency between the Coordinated Universal Time (UTC) and local time of recording in situ could probably introduce some uncertainties into the daily validations [109]. Furthermore, the inherent spatial mismatch between in situ data and GPPs, as well as the precipitation under-catch of in situ instruments due to the wind effect, are other sources of uncertainties [110; 111]. However, it is worth noting that the point-to-pixel and pixel-to-pixel comparisons would provide similar statistical results and may be negligible for the inter-comparison and ranking of GPPs [112]. Therefore, it is expected that the actual accuracy of GPPs would be higher than the ones reported in this study and also other validation studies. Although, the current state of GPPs required further modifications and enhancement to provide more accurate data.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 4 Mathematical Justification  
This chapter provides a rigorous mathematical foundation to demonstrate that the proposed M2D2 framework can achieve the following objectives in multi-modal and multi-machine industrial settings:  
1. Short-Term Drift Detection Consistency: When \"short-term drift\" (acute anomaly) occurs, the framework can detect it with high probability given a finite number of observations (corresponding to the Bayesian threshold in Phase 2).
2. Long-Term Drift Detection Consistency: When \"long-term drift\" (gradual anomaly) develops over time, the framework uses distributional metrics such as \(D_{KL}\), Wasserstein distance, or other measures to detect the deviation in extended time windows (corresponding to Phase 3).
3. Federated Aggregation Robustness: In \"drift-aware federated learning\" (Phase 6), the system introduces a drift score \(\mathcal{E}_{k}^{\prime}\) for each machine \(k\) to dynamically adjust aggregation weights or trigger micro-rounds/drop strategies. We show that this mechanism for ensuring the risk minimization of the global model is not jeopardized by severe local drifts, converging under reasonable assumptions at (or near) the global optimum.  
In the subsections that follow, we present the core theorems and proofs addressing these three objectives.
## 3 Dependence of Geometric Placement of Antenna Array Elements in Regards to Radio Link Improvements  
In this section, we analyze the geometric placement of the antenna array elements. In particular, we identify the beamwidth and steerable of the main beam capacities, which are fundamental antenna features to improve the DPS and CT metrics; the planar and circular geometries are analyzed, as well as the special case of concentric rings.
# A Formula-Structure-Embedding-Based GNN for Formula Equivalence Determination
## 1 Introduction  
Mathematical formulas, as structured information in scientific literature, constitute a fundamental part of scientific literature and serve as abstract representations of domain-specific knowledge. Different from texts, mathematical formulas have the advantage of accurately conveying complex mathematical meaning and logical relationships. Correct understanding of mathematical formulas not only helps to grasp the core content of scientific literature, but also is significant for knowledge extraction, automatic reasoning, and literature retrieval. Formula equivalence determination refers to the process of determining whether two or more formulas are equivalent under specific conditions, which facilitates researchers in overcoming superficial symbolic differences to semantically grasp the underlying meaning of formulas. It not only can help to improve the accuracy of mathematical information retrieval, but also can provide a solid foundation for intelligent processing tasks, e.g., mathematical text analysis and automatic reasoning, which has significant research value and potential for broad applications.  
The methods for math formula equivalence determination can be divided into three categories: one is a category of methods based on symbolic reasoning [1, 2], where a logic system or a computer algebra system is used to perform precise mathematical reasoning to determine formula equivalence. The methods of this category show a high degree of accuracy and interpretability, but have high requirements for professional mathematicalknowledge and a low degree of automation. This makes it difficult to adapt to the needs of large-scale data processing. The second category of methods is mainly based on formula structure comparison [3,4]. These methods reduce manual intervention by defining structural similarities. However, explicitly representing and comparing all substructures demands significant computational resources, which limits their scalability and efficiency in practical applications. The third category of methods is based on machine learning [5]. These methods encode formulas into low-dimensional vectors via word embeddings or by pre-trained language models. Compared with the methods of two previous categories, methods of this category have the advantages of high computational efficiency and low computing resource requirements. However, their structure information is often lost during formula embedding.  
In order to eliminate the effect of formula structure information losing in machine-learning-based methods, this paper proposes a multi-level node feature embedding based GNN (FSCMGNN) for formula equivalence determination. It integrates with formula standardization, formula node structure embedding and attention mechanisms to extract structure information effectively. Experiments show that this method achieves a significant performance improvement in the task of formula equivalence determination in calculus.
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Biochemical Approaches to Identify mTOR Signaling Pathway Components
#### 4.1.2 G\(\beta\)L (G Protein \(\beta\)-Subunit-Like Protein) Was Identified by TOR-IPs  
G\(\beta\)L was originally identified with Raptor in mTOR immunoprecipitations from HEK293T cells [30] and further confirmed by mTOR immunoprecipitations from \(2\times 10^{8}\) HEK293T cells lysed with CHAPS buffer, using blocking peptides towards the mTOR antibody as a negative control [31]. In yeast, LST8 (G\(\beta\)L) was biochemically purified from yeast as described above [29].  
1.3 Sin1 (Stress Activated MAP Kinase-Interacting Protein, or MAPKAP1) Was Identified by Differential Raptor and Rictor-IPs  
Given it has been determined that Raptor is unique to mTOR1 while Rictor depicts mTOR2, [PERSON] et al. immunoprecipitated either Rictor or Raptor using a 0.3% CHAPS buffer, and coimmuno precipitated proteins were analyzed by mass spectrometry. Sin1 was identified to be specifically associated with Rictor but not Raptor, which was further confirmed by proteomic analysis of the Sin1 immunoprecipitated complexes [32]. In a separate study, TAP-tagged G\(\beta\)L and TAP-tagged Raptor stably expressed in HEK293T cells were used as baits to pulldown 150-200 mg HEK293T whole cell extracts, and Sin1 was identified by mass spectrometry analysis as a protein binding to G\(\beta\)L but not to Raptor [33]. Further Sin1 depletion and deletion studies revealed that Sin1 maintains mTOR2 complex integrity and kinase activity [32; 33]. Thus, Sin1 was identified as an essential component of mTOR2, but not of mTORC1.
# A Key Node Mining Method Based on K-Shell and Neighborhood Information
## 4 Experiments and Results
### Metrics  
#### 4.2.1 Robustness Metrics  
Connectivity tests serve as a reliable method for assessing the significance of nodes and are a classic approach for gauging their importance. During experiments, we commonly employ important node-mining algorithms to rank the importance of nodes in the network. Subsequently, nodes are systematically removed, commencing with the most crucial ones. If the network undergoes substantial deterioration following the removal of a node, that node is deemed important. The robustness indicator \(R\), often used to assess the degree of network degradation [49], is defined as follows:  
\[R=\frac{1}{N}\sum_{i=1}^{N}r(i) \tag{17}\]  
In this formula, \(1/\mathrm{N}\) represents the normalization factor, while r(i) indicates the change in the network after the removal of node \(i\). Specifically, it reflects the ratio of the size of the largest connected component post-removal of node \(i\) to the total number of nodes in the network after the removal. \(R\) represents the area under the curve formed by the r(i) values plotted against Cartesian coordinates. A rapid decline in r(i) upon the removal of crucial nodes signifies severe network impairment. Consequently, higher accuracy in node importance mining corresponds to a smaller \(R\)-value.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Networks** & **N** & **E** & **\textless{K}\textgreater{}** & **Kmax** & **C** & **r** \\ \hline Windsurfer & 43 & 336 & 15.63 & 31 & 0.6534 & \(-\)0.147 \\ WikiVote & 889 & 2914 & 6.56 & 102 & 0.1528 & \(-\)0.0288 \\ USpowerGrid & 4941 & 6594 & 2.67 & 19 & 0.0801 & 0.0035 \\ Tribes & 16 & 58 & 7.25 & 10 & 0.5392 & 0.0499 \\ Seventh & 29 & 250 & 17.24 & 28 & 0.7767 & \(-\)0.1575 \\ Rhesus & 16 & 69 & 8.63 & 12 & 0.7085 & \(-\)0.1091 \\ HepPh & 11,204 & 117,619 & 19.00 & 491 & 0.6115 & 0.6323 \\ Health & 2539 & 10,455 & 8.24 & 27 & 0.1467 & 0.2513 \\ Dolphins & 62 & 159 & 5.13 & 12 & 0.259 & \(-\)0.0436 \\ C\_ElegansNeural & 297 & 2148 & 14.46 & 134 & 0.2924 & \(-\)0.1632 \\ \hline \end{tabular}
\end{table}
Table 2: Basic topological features of network data. The leftmost column presents the names of the networks, \(N\) and \(E\) represent the number of nodes and edges in the network; \(<\)K\(>\) and \(K_{max}\) represent the average degree and maximum degree of the network; C and \(r\) indicate the global clustering coefficient and assortativity coefficient of the network.
## 2 Related Works  
### Research Status of WMRAs  
Currently, there is a growing body of research on WMRAs that can perform simple planar grasping tasks and navigate through home environments. [PERSON] [1] combined voice interaction with arm and head sensors to increase interaction options and improve task efficiency. [PERSON] et al. [2] segmented the door opening task into multiple stages and used visual information alone, without an environmental map, to complete the task by narrowing down the error through multiple positionings. [PERSON] [3] proposed a visual navigation framework that guided the wheelchair through a corridor using visual servoing methods.  
All of the above WMRAs perform navigation and grasping tasks separately. However, they do not consider the effect of the base placement on arm operations, leading to the fixed selection of the base placement, which cannot adapt to various grasping tasks, resulting in a low overall task completion efficiency and success rate.  
To improve the navigation efficiency and obstacle avoidance ability of the WMRA, obtaining information about the surrounding environment is crucial. The most common method for collecting environmental information is using the SLAM algorithm to construct occupied grid maps. By matching sensor information and map information, self-positioning can be achieved, thus avoiding obstacles during navigation.
# Multi-Strategy Hybrid Whale Optimization Algorithm Improvement
## 5 Practical Application  
### Pressure Vessel Design  
Pressure vessel design is a classic engineering problem where the pressure vessel consists of several components, including the connector, manhole, head, cylinder, bearing, and level gauge, as shown in Figure 10. The primary objective of the pressure vessel design is to optimize the welding cost, material usage, and the shape of the vessel. This problem involves satisfying four constraint conditions, and four variables are utilized in calculating the objective function: shell thickness (\(z_{1}\)), head thickness (\(z_{2}\)), internal radius (\(x_{3}\)), and vessel length without the head (\(x_{4}\)).  
The problem can be formulated as follows:  
Minimize:  
\[f(\overline{x})=1.7781z_{2}x_{3}^{2}+0.6224z_{1}x_{3}x_{4}+3.1661z_{1}^{2}x_{4 }+19.84z_{1}^{2}x_{3} \tag{26}\]  
Subject to:  
\[\left\{\begin{aligned} g_{1}(\overline{x})=0.00954x_{3}\leq z_{2} \\ g_{2}(\overline{x})=0.0193x_{3}\leq z_{1}\\ g_{3}(\overline{x})=x_{4}\leq 240\\ g_{4}(\overline{x})=-\pi x_{3}^{2}x_{4}-\frac{4}{3}\pi x_{3}^{3} \leq-1,296,000\end{aligned}\right. \tag{27}\]  
Figure 9: Convergence comparison for CEC2022 with dimension 20.  
where  
\[\begin{cases}z_{1}=0.0625x_{1}\\ z_{2}=0.0625x_{2}\end{cases} \tag{28}\]  
With bounds:  
\[\left\{\begin{aligned} x_{1}\leq 99\\ x_{2}\geq 1\\ x_{3}\leq 200\\ x_{3}\geq 10\end{aligned}\right. \tag{29}\]  
By incorporating MHWOA into the pressure vessel design problem and comparing it with standard swarm intelligence algorithms, Figure 11 shows that MHWOA exhibits the fastest convergence speed and highest computational accuracy when dealing with the problem compared with all the compared objects.  
Figure 11: Convergence visibility of the pressure vessel design.  
Figure 10: An illustration of the pressure vessel design.  
Based on the comparison of the best cost, mean cost, and worst cost obtained from 30 runs, the cost savings rate of each algorithm can be statistically determined. Taking MHWOA as the benchmark, the cost savings rates compared with other algorithms are shown in Table 10:  
MHWOA has a cost savings rate of 10.96% over GWO; 11.87% over AVOA; 13.65% over AVO; 24.09% over AO; 27.08% over GTO; 26.93% over SO; 78.38% over BWO; 90.55% over WOA.
# Multi-Pedestrian Tracking Based on KC-YOLO Detection and Identity Validity Discrimination Module
## 4 Experiments and Analyses
### Analysis of Experimental Results
#### 4.3.3 Pedestrian Tracking Algorithm Ablation Experiments  
To check whether the IVDM in our proposed multi-pedestrian tracking method is a positive improvement, we conducted ablation experiments on the IVDM.  
The results of ablation experiments with the IVDM are shown in Table 5. They clearly show that the addition of the IVDM improves the _MOTA_ of the multi-pedestrian tracking method by 3.0% and the _IDF1_ by 2.8%, and the _IDs_ are also significantly reduced. The role of the IVDM is mainly to eliminate false and erroneous identity data due to occlusion and to maintain the purity of the original tracking target while reducing the generation of redundant data. After the introduction of the IVDM, the overall performance of the task is improved.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**k-Means++** & **CBAM** & **Precision** & **Recall** & **AP** \\ \hline \(\times\) & \(\times\) & 0.85 & 0.78 & 0.85 \\ \(\times\) & \(\surd\) & 0.89 & 0.83 & 0.88 \\ \(\surd\) & \(\times\) & 0.88 & 0.84 & 0.85 \\ \(\surd\) & \(\surd\) & 0.91 & 0.85 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 4: YOLOv5 ablation experiment.  
Figure 9: Pedestrian ablation test results: (**a**) AP change curve; (**b**) precision change curve; (**c**) loss change curve.  
To illustrate more intuitively the advantages of the improvements made to the tracking method in this study, we present visual comparisons of the tracking experiments conducted on the MOT16 training dataset. Figure 10 compares the demonstrations of the multi-pedestrian tracking method with and without the IVDM.  
As shown in Figure 10, when using the multi-pedestrian tracking without adding the IVDM, the target pedestrians with ID number 154 and ID number 183 changed their ID numbers due to brief occlusion, which also means that the tracking failed and generated redundant and incorrect ID numbers at the same time. However, the problem of tracking failures due to transient occlusion is well solved after adding the IVDM.
## 1 Introduction
On the contrary, the semi-empirical model is presented by a closed-form formula, and thus, the required computation time is much reduced as compared to the ECBE model. Another advantage of the semi-empirical method is the ease of obtaining parameters, such as k1, k2, and k3, which describes the internal state of the battery by implementing a single formula. The parameters (k1, k2, and k3) calculated in the work can also be used to study the changes in battery internal structure, such as capacity losses at normal conditions, as well as cycling at high temperatures. In this work, we use the ECBE model as a reference. Several empirical prediction models have been proposed in recent years. [PERSON]. [PERSON] et al. developed a calendar and cycle life model, which account for the time, temperature, and SoC of batteries [17]. [PERSON] established the cycle-life models that followed capacity loss related to time or charge to estimate the capacity fade of LiFePO4 [18]. The mechanical-fatigue method adopts damage-accumulation relationships to evaluate the capacity fade [19]. And some studies use a coupled model to achieve life-prediction [20; 21]. However, these prediction models consider several factors, such as temperature, depth of discharge (DOD), current rate, etc., rendering their applications to describe capacity fade complicated as temperature, DOD, and current are independent variables. In contrast, the semi-empirical model chosen in this work eliminates the dependency of these factors, making it more practical to the estimation of SoH. In this work, we simplify this semi-empirical model so that it can be presented by a closed-form equation, reducing the required computation time significantly as compared to ECBE and other models. The parameters of this equation can also be obtained easily, and these parameters describe the internal states of the battery. The present paper is organized as follows: The experimental settings and approach are given in Section 2, and an introduction of the principle and architecture of the semi-empirical capacity fading model is discussed in the subsequent Section. The differences in the estimation results with ECBE are presented from our experimental results. Conclusions and future works are given in the last Section.