esa-sceva/satcom-chunk-collection huggingface dataset:
# Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining
## 5 Discussion
### The ER Perspective  
The use of process mining and data mining techniques allowed answering a question frequently posed by ER experts. The most significant contributions of this research relate to the data model for linking data from ER and the methodology that demonstrates the necessary steps for obtaining data and process models in order to answer the frequently-posed questions.  
The data extraction process is the most critical stage of the entire methodology, since in the absence of complete and accurate data in the correct format, the construction of the event logs and the subsequent stages will not produce the desired results. An analysis undertaken by a process mining expert is needed with regard to the minimum data required (timestamp and activity names), in addition to one by an ER expert in order to identify the clinical information relevant to the process analysis (critical activities, such as inter-consultations). The provision of a data model establishes the bases for obtaining the minimum data required to construct an event log for analysis purposes in ER, eliminating the dependency on the experts. It also provides a data structure for storing information stemming from the HIS or other existing data sources in the medical facility.  
Furthermore, the methodology acts as a tool to guide an ER expert to use data and process mining analysis techniques in the absence of an expert in those particular fields, after the minimum required data are identified and stored in the data model.  
The ER expert fulfills a crucial role in the data extraction process because he/she is able to identify the most important data within the process that, in turn, will help to guide him/her to answer the posed question. For example, if the question relates to a specific neurological diagnosis, the expert can emphasize the importance of the inter-consultation to neurology and the magnetic resonance imaging tests; knowledge that, in most cases, process mining experts lack.  
In the stage of verification by the experts, the method includes certain tools for analyzing whether the answer obtained is correct. In case the ER expert is the one leading the analysis, some examples are provided in order to be able to verify the answers with other ER experts.  
Regarding the results obtained through the process models and data, it is important to note that such findings do not only provide answers to the frequently posed questions, but they also help to acquire additional knowledge about the process. For example, they can help to do the following: identify the stages undertaken in response to different cases in ER; identify activities undertaken in a sequential or parallel manner or those performed specifically for certain patients; verify whether medical regulations and protocols are being adhered to; identify organizational aspects, such as the work of the team as a whole and individuals' roles therein; and provide performance information relating to each case.
## 2 Review of Existing Studies
### Global Studies
They used multiple satellite sensor and ancillary data including AVHRR 3 band and NDVI; SPOT VGT NDVI; monthly rainfall and temperature; elevation; Japanese Earth Resources Satellite-1 Synthetic Aperture Radar (JERS-1 SAR) data for the rain forests; and a map of global tree cover. In the first phase, the global land surfaces were segmented based on elevation, temperature and precipitation zones. Next, classification was performed within these segments using class spectra derived from unsupervised classification; these were matched with ideal or target spectra. Finally, the mixed classes were resolved based on decision tree algorithms and spatial modeling. Whenever that did not work, the problem class was used to mask and re-classify the original input data and the class identification and labeling protocol was repeated. The sub-pixel area calculations were performed by multiplying full-pixel areas with irrigated area fractions for every class. The most important property of this methodology is that it delineates areas most likely to be rainfed. Such mapping is essential in the entire classification process as it can be overlaid with a map of green areas and only the non-rainfed green areas can then be considered to be irrigated. The methodology described here to create a map of Potentially Irrigated Areas (PIA) is based on Droogers and Aerts [10].
## 3 Results and Discussion
### Packet Delivery Ratio Comparison
Although DEM improves upon the unoptimized protocols, its performance is more vulnerable to increased traffic, particularly in the case of GPSR [19]. These observations reinforce the importance of aligning protocol optimization strategies with the specific performance priorities of the network environment, whether the goal is maximizing relia  
Figure 6: PDR comparison between (a) QOS-AODV, QOS-AODV-TOM, and QOS-AODV-DEN and (b) GPSR, GPSR-TOM, and GPSR-DEN for different traffic generations. bility (PDR), minimizing latency, improving throughput, or achieving a balanced trade-off. Moreover, the insights gained from this study contribute to the broader understanding of control parameter tuning in wireless networks, with promising implications for enhancing the reliability and efficiency of urban vehicular communication systems [20].
## 1 Introduction
In recent years, with the increasing application of energy-absorbing components, many scholars have proposed new structures based on this energy-absorbing principle, the influence of relevant parameters on the energy absorption capacity of the structure has been analysed in detail, and the relevant design and optimization methods have been proposed [25; 26]. From the paper on the existing metal-cutting energy-absorbing structures, it is found that the researchers are most concerned about the parameters affecting the change of cutting force, because these are closely related to the energy-absorbing capacity of the structure. For the influencing factors of cutting force, in addition to the experimental test, simulation and theoretical derivation were used. [PERSON] obtained the cutting force calculation formulae for turning and groove cutting through mathematical derivation [27]. The general simulation analysis method for analysing different machining methods can better predict the cutting force in machining. To sum up, many people have carried out research on the energy absorption characteristics of metal plastic deformation-type energy-absorbing elements [28; 29; 30], and there are an increasing amount of metal-cutting energy-absorbing elements to be researched. In order to improve the crashworthiness of the structure, the buffering structure needs to increase the energy-absorbing stroke [31]. [PERSON], [PERSON], [PERSON], et al., applied the variable-buffer force structure to a rail vehicle, which greatly improved the energy absorption capacity of the vehicle, and effectively improved its crashworthiness [32; 33; 34]. [PERSON] et al. [35] introduced a novel method to enhance absorbed energy by square sections. Specific absorbed energy by open and closed sections is calculated. [PERSON] et al. [36] investigated the cutting strain of the combined honeycomb core considering the effects of core diameter, explosive charge mass and scaled distance, with their results demonstrating that either a decreasing scaled distance or an increasing charge mass results in a larger cutting strain. [PERSON] et al. [37] conducted a quasi-static cutting experiment on an aluminium tube, and it was shown that the cutting aluminium tube absorber presents a stable deformation mode. The purpose of this paper is to propose a cutting energy-absorbing structure that can control the buffer force according to the actual situation, and to study the influencing factors of the buffer force of the metal cutting energy-absorbing structure under high speed and large cutting depth. A calculation formula for cutting buffer force considering parameters was obtained, and provides a design basis for variable cutting force energy-absorbing structures.
## 2 Problems Statement and Preliminaries
### Initial Knowledge Preparation  
It is not difficult to prove that the system has a unique equilibrium point \((M_{e},P_{e})\). The equilibrium point is found by making both equations of the system equal to zero. The calculation process uses the zero point theorem, derivatives, etc. The specific procedure is not described in this section. Introducing two new variables \(m(t)=M(t)-M_{e}\) and \(p(t)=P(t)-P_{e}\), system (1) becomes \[\begin{array}{l}\dot{m}(t)=\frac{k}{1+((p(t-\tau)+P_{e})/p_{0})^{n}}-\mu(m(t)+M_{e} )\\ \dot{p}(t)=m(t)+M_{e}-d(p(t)+P_{e})\end{array} \tag{2}\]  
It can be seen that Equation (2) is modeled based on Equation (1) by moving the equilibrium point of the system to the origin.  
In general, it is very important to consider the linear part of the system, and we study the local stability of the system by the linear equation of the system and consider the Hopf bifurcation on this basis. Therefore, for the study of the linear part of the system, it is necessary to turn system (2) into system (3), and we have the following matrix to represent the linear part of the system:  
\[\begin{array}{l}\dot{m}(t)=-\mu m(t)-Kp(t-\tau)\\ \dot{p}(t)=m(t)-dp(t)\end{array} \tag{3}\]  
within the static delayed state feedback control gain \(K=\frac{k\eta\beta}{P_{e}(1+\beta)^{2}}\), and \(\beta=(P_{e}/p_{0})^{n}\).  
The system (3) can be written as the following matrix equation:  
\[\dot{u}(t)=Au(t)+Bu(t-\tau) \tag{4}\]  
within  
\[A=\left[\begin{array}{cc}-\mu&0\\ 1&-d\end{array}\right],\ B=\left[\begin{array}{cc}0&-K\\ 0&0\end{array}\right]\ \text{and}\ u(t)=\left[\begin{array}{c}m(t)\\ p(t)\end{array}\right]\]  
and the corresponding characteristic polynomial of Equation (4) is  
\[P(\lambda)=det(\lambda I-A-Be^{-\tau\lambda}) \tag{5}\]  
Substituting the values of the matrix into Equation (5), we can obtain the characteristic polynomial of the system, which is  
\[P(\lambda)=\lambda^{2}+(\mu+d)\lambda+\mu d+Ke^{-\tau\lambda} \tag{6}\]  
A sufficient and necessary condition for the stability of the above linear system is that all eigenvalues lie on the left half-plane of the complex plane. In terms of the calculation of the stability interval of the system with delay, we consider the \(\tau\)-decomposition strategy to be an efficient computational method.
## 3 Results and Discussion
### Isotherms  
Several non-linear isotherm models (Langmuir, Freundlich, Sips, Bi-Langmuir, Toth, Temkin, and Dubinin-Radushkevich) were applied to the experimental data. However, according to the R\({}^{2}\) values and RMSEs, the Sips model was clearly the best fit (R\({}^{2}\) = 0.95, RMSE = 1.81). For that reason, only the parameters of the most traditional Langmuir and Freundlich isotherms, in addition to the Sips isotherm, are presented in Table 3. Corresponding fits are illustrated in Figure 5. The applicability of the Sips model is a very reasonable result, as it is generally known to represent adsorption well on heterogenous surfaces, which is the case with biomass-based activated carbon materials.  
Figure 4: Zinc removal [%] and adsorption capacity [mg/g] as a function of the mass of the adsorbent. Experiments were done duplicated and the range of results were at maximum 3 percentage points in values of removal % and at maximum 0.5 mg/g in adsorption capacity determinations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Parameters and Errors**} & **Value** \\ \hline Experiment & Max. \(q_{e}\) [mg/g] & 21.44 \\ \hline \multirow{4}{*}{**Langmuir**} & \(q_{m}\) [mg/g] & 20.78 \\  & \(b_{L}\) [L/mg] & 0.29 \\  & R\({}^{2}\) & 0.87 \\  & RMSE & 2.68 \\ \hline \multirow{4}{*}{**Freundlich**} & \(n_{F}\) & 5.86 \\  & \(K_{F}\) [(mg/g)/(mg/L)\({}^{n}\)] & 8.50 \\ \cline{1-1}  & R\({}^{2}\) & 0.71 \\ \cline{1-1}  & RMSE & 4.06 \\ \hline \multirow{4}{*}{**Sips**} & \(q_{m}\) [mg/g] & 19.10 \\  & b\({}_{S}\) [L/mg] & 0.41 \\ \cline{1-1}  & \(n_{S}\) & 4.02 \\ \cline{1-1}  & R\({}^{2}\) & 0.95 \\ \cline{1-1}  & RMSE & 1.81 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Isotherm parameters of Langmuir, Freundlich, and Sips models.
## 1 Introduction
Therefore, by coupling tumor specific molecular recognition units to magnetite nanoparticles, the newly created nanocariers can be directed to attach specifically to tumor sites, where they can enhance MRI contrast, and potentially enable the early detection of cancer [17; 19; 20]. The receptor for luteinizing hormone-releasing hormone (LHRH) has been shown to be expressed in over 50% of TNBC, and can be used for the specific targeting of receptors on the surfaces of TNBC [21; 22; 23]. On the design of LHRH-specific targets for TNBC, efforts have been made to engineer LHRH-conjugated magnetite nanoparticles as MRI contrast agents, aiming for specific signal enhancement of the lesion for the early detection of TNBC [24; 25; 26; 27]. Prior work by [PERSON] et al. [24] demonstrated that LHRH-conjugated bare superparamagnetic iron oxide nanoparticles brighten excised breast tumors in vitro in T2-weighted MR imaging. However, bare magnetite nanoparticles without protective surface coatings tend to aggregate and are subjected to rapid clearing from the body by the reticuloendothelial system (RES) [17]. Hence, in an effort to achieve nanoparticle monodispersity, and to extend the circulation time of nanoparticles in vivo, polyethylene glycol (PEG) coated magnetite nanoparticles (MNPs) and LHRH conjugated MNPs (LHRH-MNPs) were formulated [25; 26]. The specific adhesion and enhanced cellular uptake between LHRH-MNPs and TNBC cells have also been demonstrated under in vitro conditions. However, in vivo applications of coated LHRH-MNPs have not been demonstrated in the enhancement of MRI contrast usingsubcutaneous xenograft tumor models. Such demonstration is important, since it is known that the use of inert coatings can significantly enhance or reduce the nanoparticle magnetization [28]. Hence, in this study, we explore the effectiveness of MNPs and LHRH-MNPs as MRI contrast agents for the specific imaging of TNBC through in vitro and in vivo experiments. To investigate the specificity of LHRH targeting in vivo, MRI images of mice bearing MDA-MB-231 xenograft tumors were obtained before and after intratumoral and intravenous injections of MNPs or LHRH-MNPs. The implications of the results are discussed for the use of LHRH-MNPs in the detection and treatment of TNBC.
# A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks
###### Abstract
Our emphasis lies in the 3 GPP cellular V2X technology, where a reservoir of radio resources is at the disposal of vehicles, thus enabling them to judiciously select optimal resources for their communication needs [9]. The spectrum is currently allocated for V2I (Vehicle-to-Infrastructure) links utilizing OFDM, thus incorporating essential interference management and power control mechanisms. For V2V (Vehicle-to-Vehicle) links, effective strategies are required to efficiently share the preoccupied spectrum with V2I. This involves the selection of optimal sub-bands and power levels. Similarly, in [10], a task offloading strategy has been developed to maximize computation efficiency between vehicles. This work introduces a novel Multi-Agent Reinforcement Learning (MARL) algorithm with QMIX cooperation for dynamic resource allocation in heterogeneous V2X networks [11]. It tackles the challenging scenario of multiple V2V links having a pool of radio resources that users can autonomously select for V2V communication. By selecting the optimal power level and spectrum sub-band, the algorithm dynamically adapts to the changing vehicular environment, thereby aiming to achieve increased network efficiency, an enhanced data rate, and improved reliability. Compared to existing methods, our QMIX-based MARL excels in promoting V2V cooperation, real-time adaptation through an online MARL approach, and joint network optimization for superior efficiency and fairness. This innovative solution presents significant contributions to dynamic resource allocation in V2X networks. The article layout is organized as follows: Section 2 discusses the related studies in detail, and Section 3 describes the system model with various subsections to explicitly talk about the necessary techniques. Section 4 explains the construction of the hybrid model and respective algorithms, and Section 5 presents the simulation results of the proposed framework. Section 6 concludes the article.
# Finding the Least Motion-Blurred Image by Reusing Early Features of Object Detection Network
## 2 Materials and Methods
### Motivation and Method for Finding the Least Motion-Blurred Image
#### 2.1.1 Proposed Method
During the construction of the gradient boosting model, the maximum tree leaves for the base learners parameter (num_leaves) was set to 41, boosting learning rate (learning_rate) was set to 0.02, and the number of boosted trees to fit (n_estimators) was set to 10,000. The collection and construction of the training set are described in Section 2.3. After the ranker model is trained, the key steps of the image quality evaluation using the proposed method are the following:  
1. Detection of the head region (BBox) (the BBox found in the head's background removal step is reused). 2. BBox is transferred from the image coordinates to the selected feature map (refer to Figure 2) coordinates, i.e., BBox coordinates are scaled using the factor how the spatial size of the feature map is smaller than the spatial size of the image (refer to Figure 3). 3. Limiting the feature map of the L-th convolutional layer to the region corresponding to the detected head BBox, we get a subarray of K-dimensional feature vectors. Figure 3: A diagram explaining the sampling process of features for image quality (motion blur level) evaluation that is based on the LightGBM ranker model. At first, a subarray of \(K\)-dimensional feature vectors (part of the feature map) (**b**) corresponding to the detected head BBox in the input image (**a**) is determined. The said region of the feature map describes the image patch containing the face. The spatial position of the region in the feature map (**b**) is relatively the same (proportional) as the position of the head’s bounding box (BBox) in the image (**a**). Later, only part of the feature vectors from the region are selected (**c**): feature vectors randomly sampled for the training dataset and uniformly sampled for testing/evaluation. A subset of 100 feature vectors is uniformly sampled from the subarray of K-dimensional feature vectors for testing/evaluation. For comparison, a subset of 50 feature vectors is randomly sampled for training data. * Sampled features are provided as inputs for the LightGBM ranker model during the inference (image quality assessment). For comparison, sampled features together with the outputs (ranking information) are given to the LightGBM ranker model during training. * Making a decision: a higher score indicates less motion blur (better quality).
## 2 Methods
### Datasets  
Input for the Terrestrial Ecosystem Model (TEM)  
The input climate variables for TEM are temperature, diurnal temperature range, vapor pressure, incoming shortwave radiation, precipitation, and surface wind speed. MACA-downscaled and bias-corrected data were used to provide future climate data. The area sizes in the LULC dataset provided from the imagery classification were used to run the model based on several different landscape regimes. CO\({}_{2}\) levels were taken from historical data (1979-2019) [21] and future data from the RCP8.5 (2020-2099) scenario [22]. TEM also requires soil texture and elevation, ozone, and nitrogen deposition, which were taken from standard datasets [10], with ozone and nitrogen deposition levels held constant for the future. The products of TEM include carbon, nitrogen, and water fluxes.  
Crops were fertilized but not irrigated, because Lehigh Valley is in a mesic climate. The lawn cohort was fertilized and irrigated. Pasture was neither fertilized nor irrigated but received natural fertilization from the livestock, as explained in Felzer [4]. The pasture, lawn, and crop cohorts were irrigated. For irrigated cohorts, if grid precipitation in a month during the growing season is less than 200 mm, the cohort was irrigated to the equivalence of 200 mm of precipitation. Cropland and lawn were fertilized starting in 1979, at around 15 gN/m\({}^{2}\)yr\({}^{-1}\), while the pasture cohort was fertilized with 5 gNm\({}^{-2}\)yr\({}^{-1}\). Management processes are further described in [PERSON] [4]. Impervious surfaces were treated as 100% clay, as water cannot penetrate as easily through clay; additionally, the size of the bucket was set to zero to prevent any water from penetrating.
# Improving Adhesive Bonding of Al Alloy by Laser-Induced Micro-Nano Structures
## 1 Introduction  
Aluminum (Al) alloy is popularly applied in the fields of aviation, aerospace and vehicles, owing to its high strength, light weight and good corrosion resistance [1]. Since Al alloy is used as the material for adhesively bonded joints, its surface pretreatment is one of the most important processes before adhesive bonding [2]. Traditionally, the surface of Al alloy is treated by sandblasting to increasing the shear strength. However, dust contaminants may adhere to the surface of the substrate after sandblasting, which affects the shear strength and would lead to hazards of the products.  
To enhance the adhesion on Al alloy, the surface pretreatment methods including chemical etching, electroplating and sandblasting have been developed in recent years [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. As demonstrated by [PERSON] et al., the shear strength of Al alloy after chemical etching was as high as 20.4 MPa [13]. [PERSON]'s group showed the shear strength pretreated with different chemical reagents was 20% higher [14]. [PERSON] used the anodizing method to treat Al alloy pieces and enhanced the bonding behaviors [15]. [PERSON] combined chemical modification and post-sandblasting, which resulted in better shear strength. However, the above methods still faced serious challenges, namely, high labor cost, complex processes and chemical pollution [16].  
Laser surface treatment, a non-contact and environment-friendly technique, is promising for use in industrial products over conventional processing methods [17; 18; 19]. [PERSON] showed that the shear strength of laser-treated Al alloy surface was similar to that by chemical-etching and electroplating [20]. [PERSON] used excimer lasers to perform the surface treatments on alloy and the shear strength was controlled by laser fluence [21]. [PERSON] found that microstructures produced in the laser irradiation could result in an increase of surface area and therefore in better bonding performance [22]. Recently, it was found thatthe type of laser-produced microtexture on the surface also had a significant impact on bonding performance [23; 24; 25].  
It has been proven by previous investigation [26] that nano-structured surface could enhance the bonding behavior of metals. Our current work was focused on the influence of micro/nano structures on the adhesive bonding strength of Al alloy. The surface roughness, chemical composition, contact angle and shear strength were analyzed before and after surface treatment. By controlling the laser spot spacing, the laser-induced micro-nano composite structures were formed on the surface of Al alloy and its shear strength became larger than the sandblasting method. These results offer an alternative approach in the field of surface pre-treatment of aluminum and its alloys.