esa-sceva/satcom-chunk-collection huggingface dataset:
# Targeting KRAS in PDAC: A New Way to Cure It?
## 3 KRAS Mutations in PDAC
### Progress of PDAC with KRAS Mutations  
The link between KRAS mutations and PDAC prognosis has been the focus of research, and several recent studies have further illustrated their relationship. [PERSON] and colleagues analyzed the personal information of 110 PDAC patients who underwent histological diagnosis from 2017 to 2019. All of these patients underwent first-line therapy with gemcitabine and nab-paclitaxel. Patients were analyzed for the presence of KRAS mutations and grouped through the quenching probe method. Then, progression-free survival (PPS) and overall survival (OS) were compared between the two groups. The study showed that patients with wild-type KRAS genes had much longer PFS and OS than patients with KRAS mutations (6.9/5.3 months (\(p\) = 0.044) vs. 19.9/11.8 months (\(p\) = 0.037), respectively) [64]. In patients with surgically resectable tumors, KRAS gene mutations can also affect their prognosis after undergoing surgery. The analysis of patient data collected from Memorial Sloan Kettering (MSK) showed that patients with KRAS mutations had a worse prognosis after the surgical removal of the tumor [65].  
With the development of next-generation sequencing (NGS), it has become possible to measure the mutation frequency of the alleles in tumor samples [66; 67]. As PDAC tumors are highly heterogeneous [68], the proportion of malignant cells in tumors may vary greatly from patient to patient. [PERSON] and colleagues studied microdissection samples from 144 PDAC patients who had undergone classic pancreaticoduodenectomy (PD) (classic Whipple) or pylorus-preserving PD (PPPD). KRAS mutations were present in 121 patients (84%). Studies show that patients with a high frequency of KRAS mutations (more than or equal to 20%, \(n\) = 29) have larger tumors, higher postoperative distal recurrence rates, and shorter disease-free survival after surgery than those with a low frequency of KRAS mutations (less than 20%, \(n\) = 29) [69]. Another study found that PDAC patients who received FOLFIRINOX chemotherapy followed by the surgical resection of tumors had new KRAS mutations in their cell-free DNA compared to those before treatment [70]. The relationship between increased KRAS mutations and chemotherapy, as well as the surgical resection of tumors, still warrants further exploration.  
Research has progressed on the specific molecular mechanisms by which KRAS gene mutations worsen the prognosis of PDAC patients. It has been shown that KRAS\({}^{\text{GI2D}}\)the most predominant KRAS mutant phenotype in PDAC, induces the overexpression of SUMO-activating enzyme subunit 1 (SAE1), which can lead to heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1) being SUMOylated. SUMOylated hnRNPA1 is packaged by extracellular vesicles (EVs) and transported to human lymphatic endothelial cells (HLECs), ultimately promoting lymphatic vessel proliferation and lymph node metastasis [11; 61].
## 1 Introduction  
Stilbene compounds consisting of heterocycles such as benzoxazole, oxadiazole andtriazole are used commercially as fluorescent materials [1; 2; 3]. One of these dyes, the 4,4\({}^{\prime}\)-bis(2-benzoxazoleyl)[tstilbene (BBS), is utilized as a fluorescent brightener in textiles, sensors [4], and thermochromic materials [5], which can be attributed to its high fluorescence quantum yield and a mono-exponential decay time [6]. There is a sensitive response that results in the formation of a thermally excimer in BBS dye, making it useful as a sensor or probe. Despite these optical advantages, a major shortcoming of BBS is its limited solubility in organic solvents, except for chlorinated solvents, such as tetrachloroethane (C\({}_{2}\)H\({}_{2}\)Cl\({}_{4}\)) [6]. Moreover, similar to other fluorescent dyes, its planar structure acts as the main drawback because it causes the self-quenching and red shift in an aggregated state, such as a solid or concentrated solution state [7; 8].  
In this work, it was attempted to synthesize some dyes by introducing additional substituents symmetrically at the 3,3\({}^{\prime}\)-position of stilbene rings starting from the 4,4\({}^{\prime}\)-bis(2-benzoxazolyl)[tstilbene (BBS), which can be expected to improve solubility and luminescent properties in the aggregated state, compared to the BBS moiety. Dyes containing nitro or amino groups were prepared as precursors of the final fluorescent dyes, then \(n\)-butyl, 2-butyl, \(n\)-hexyl, anilino or pyrroliaryl-substituted dyes were subsequently synthesized from the precursor dyes. The absorption and emission maxima of the five blue fluorescence dyes were investigated, and the solubility of each dye in the dichloromethane was also examined.
# Grid-Connected PV Systems Controlled by Sliding via Wireless Communication
## 4 Experimental Results  
### Experimental Platform  
A commercial solar module has been used to generate DC power in the experimental platform. The DC/DC and DC/AC converters has been designed and implemented by the authors. The output of these converter stages has been connected to the laboratory AC electrical system. Figure 7 shows a global vision of the experimental system including some details of the converters boxes. Part number 1 is the PC where all the data are visualized and the control is implemented. Part number 2 is the router responsible for the Wi-Fi communication between the PC and the grid-connected PV system whereas part number 3 and 4 are the DC/DC converter and the DC/AC converter respectively. Finally, part number 5 is the grid connection. The experimental system has been supervised and controlled via wireless.  
The DC/DC converter input voltages ranges from 10 V to 70 V whereas 100 V is its maximum output voltage. The power that can be transferred by the buck-boost converter is 70 W. The DC/AC converter has a maximum input voltage and current of 120 V and 4.5 A, respectively. The inverter maximum output voltage is 85 V and it can transfer until 400 W.  
The sensors used to measure the currents and voltages are LEM sensors, LA25-NP and LV25-P respectively. The controller is implemented in a dsPIC30F microcontroller. The temperature is measured with a LM35 sensor and a commercial meter is used for the irradiance. Figure 8 presents a developed virtual instrument, using Visual Basic programming, to supervise the tests.  
Figure 6: PI current control scheme.  
Figure 8: Screen of the designed virtual instrument.  
Figure 7: Experimental platform.
# Student Dropout Prediction for University with High Precision and Recall
###### Abstract  
Since a high dropout rate for university students is a significant risk to local communities and countries, a dropout prediction model using machine learning is an active research domain to prevent students from dropping out. However, it is challenging to fulfill the needs of consulting institutes and the office of academic affairs. To the consulting institute, the accuracy in the prediction is of the utmost importance; to the offices of academic affairs and other offices, the reason for dropping out is essential. This paper proposes a Student Dropout Prediction (SDP) system, a hybrid model to predict the students who are about to drop out of the university. The model tries to increase the dropout precision and the dropout recall rate in predicting the dropouts. We then analyzed the reason for dropping out by compressing the feature set with PCA and applying K-means clustering to the compressed feature set. The SDP system showed a precision value of 0.963, which is 0.093 higher than the highest-precision model of the existing works. The dropout recall and F1 scores, 0.766 and 0.808, respectively, were also better than those of gradient boosting by 0.117 and 0.011, making them the highest among the existing works; Then, we classified the reasons for dropping out into four categories: \"Employed\", \"Did Not Register\", \"Personal Issue\", and \"Admitted to Other University.\" The dropout precision of \"Admitted to Other University\" was the highest, at 0.672. In post-verification, the SDP system increased counseling efficiency by accurately predicting dropouts with high dropout precision in the \"High-Risk\" group while including more dropouts in total dropouts. In addition, by predicting the reasons for dropouts and presenting guidelines to each department, the students could receive personalized counseling.  
dropout precision; dropout recall; machine learning; imbalanced data processing; hybrid method; big data; academic data; principle component analysis; K-means clustering +
Footnote †: journal: Applied  
1
# A Sensitivity Study of POD Using Dual-Frequency GPS for CubeSats Data Limitation and Resources
###### Abstract  
Making use of dual-frequency (DF) global navigation satellite system (GNSS) observations and good dynamic models, the precise orbit determination (POD) for the satellites on low earth orbits has been intensively investigated in the last decades and has achieved an accuracy of centimeters. With the rapidly increasing number of the CubeSat missions in recent years, the POD of CubeSats were also attempted with combined dynamic models and GNSS DF observations. While comprehensive dynamic models are allowed to be used in the postprocessing mode, strong constraints on the data completeness, continuity, and restricted resources due to the power and size limits of CubeSats still hamper the high-accuracy POD. An analysis of these constraints and their impact on the achievable orbital accuracy thus needs to be considered in the planning phase. In this study, with the focus put on the use of DF GNSS data in postprocessing CubeSat POD, a detailed sensitivity analysis of the orbital accuracy was performed w.r.t. the data continuity, completeness, observation sampling interval, latency requirements, availability of the attitude information, and arc length. It is found that the overlapping of several constraints often causes a relatively large degradation in the orbital accuracy, especially when one of the constraints is related to a low duty-cycle of, e.g., below 40% of time. Assuming that the GNSS data is properly tracked except for the assumed constraints, and using the International GNSS Service (IGS) final products or products from the IGS real-time service, the 3D orbital accuracy for arcs of 6 h to 24 h should generally be within or around 1 dm, provided that the limitation on data is not too severe, i.e., with a duty-cycle not lower than 40% and an observation sampling interval not larger than 60 s.  
CubeSat; precise orbit determination; GNSS; low earth orbit +
Footnote †: journal: remote sensing
## 10 Effective Medium Ratio and Comparison with Recent Study  
The effective medium ratio (EMR) is the fundamental aspect of metamaterial related applications. EMR stands for the smallness and usefulness of metamaterials. Many devices operate at low resonant frequencies, but the small size of the metamaterial makes it difficult to achieve low resonant frequencies. A high EMR represents the integrity and achievement of metamaterial design standards. If the EMR is less than 4, the subwavelength criteria of the metamaterial are not met. The operating frequency and size of metamaterials are inversely related to each other. Therefore, the EMR should be considered to design the metamaterial. EMR is the ratio of wavelength to unit cell length and is expressed by Equation (13).  
\[\text{EMR}=\frac{\text{Wavelength, }\lambda}{\text{Unit cell length, }\text{L}} \tag{13}\]  
The modified Coptic cross shape-based metamaterial unit cell exhibits an EMR of 13.50, 3.90, 2.73, and 1.89 at the frequency of 2.02, 6.985, 9.985, and 14.425 GHz. An EMR of 13.50 at 2.02 GHz shows that the proposed unit cell is compact and effective. The main advantages of high EMR are: (1) improved characteristic uniformity, (2) shorter electrical dimension without fabrication limitations, and (3) reduced transmission coupling effects. The unit cell reveals an electrical dimension of 0.074\(\lambda\)\(\times\) 0.074\(\lambda\); \(\lambda\) is calculated at the frequency of 2.02 GHz which fulfills the criteria of high EMR condition.  
The performance of the designed metamaterial is compared with recently published work considering unit cell size (physical and electrical), a range of resonance frequencies  
Figure 12: Scattering parameters plot using different types of array (**a**) \(S_{11}\) (**b**) \(S_{21}\).  
with covered bands, and the effective medium ratio of the metamaterial. The comparison is shown in Table 2. The EMR is an important aspect for metamaterial. The proposed metamaterial exhibits an excellent EMR of 13.50, compared to other works listed in Table 2. The table shows that Refs. [24; 30; 31; 38; 39] have the highest electrical dimensions and lower EMRs compared to the proposed work; the proposed work is, therefore, the most effective. The proposed metamaterial contains moderate size and quadruple resonance with higher EMR, which makes this design more fulfilling than others shown in the comparison Table 2.
# A Topological Sensitive Node Importance Evaluation Method in Aerospace Information Networks
## 3 Node Importance Evaluation Model  
### Node Initial Importance  
For a connected graph, each node in the graph has a different influence. For example, the root node is often of greater importance than the leaf nodes. Once an important node fails, it impacts the connectivity and robustness of the network. Therefore, we draw the idea of a node deletion method that removes individual nodes in the network in turn and evaluates the changes in network structure and efficiency before and after deletion. Once a node in the network fails, its adjacent edges will also break at the same time and information transmission cannot be carried out at this time. The effect of deleting a node may be very different. If a node is very important, removing it will lead to changes in the network topology and a sharp decline in network efficiency. If it is a terminal node, its removal has almost no effect. Finally, this paper proposes that the initial value \(C_{i}\) of the node importance in the network is defined by the following equation:  
\[C_{i}=\frac{E(G)-E(G^{\prime})}{E(G)} \tag{3}\]  
Among them, \(E(G)\) is the network efficiency and \(E(G^{\prime})\) the network efficiency after deleting the node. This metric reflects the change in network efficiency after the node is deleted. The larger the change the more important the node is and the importance is quantified from a global perspective.
## 3 The Closest Resemblance Algorithm
### Phase 1: Learning Phase  
Suppose that a set of \(L\) labeled examples \(X=\{x_{1},x_{2},\ldots,x_{L}\}\) with a set of \(n\) features \(F=\{f_{1},f_{2},\ldots,f_{n}\}\) are given. This example set is known as the training set. Each example \(x_{i}\) is represented by a vector \(f(x_{i})=<f_{1}(x_{i}),f_{2}(x_{i}),\ldots,f_{n}(x_{i})>,i=1,\ldots,L\), where \(f(x_{i})\) is an \(n\)-dimensional vector, where each component \(f_{j}(x_{i})\) represents the value of the feature \(f_{j}\) for the training example \(x_{i}\). The training examples have exactly one of \(K\) distinct class labels. In the learning phase, a prototype of each class is determined using the training set \(X\). For each class \(C^{h},h\in\{1,\ldots,K\}\), \(CR\) determines a prototype \(P(C^{h})\) from training set \(X\). The prototypes are considered as good representatives of their class and are described by the score of each of the \(n\) features. More precisely, for each feature \(f_{j}\) and for each class \(C^{h}\), an interval \(I^{h}_{j}\) is determined. The interval \(I^{h}_{j}\) is the value range of feature \(f_{j}\) with respect to class \(C^{h}\). An \(n\)-dimensional prototype of each class \(C^{h},h=1,\ldots,k\) is built as presented in Equation (1):  
\[p(C^{h})=\left\{I(f_{1},C^{h}),I(f_{2},C^{h}),\ldots,I(f_{j},C^{h}),\ldots,I(f _{n},C^{h})\right\} \tag{1}\]  
The value domain of each dimension \(f_{j}\) is the value range or interval \(I^{h}_{j}=I(f_{j},C^{h})=\left[I^{1}(f_{j},C^{h}),I^{2}(f_{j},C^{h})\right]\), where \(I^{1}(f_{j},C^{h})\) is the lower bound and \(I^{2}(f_{j},C^{h})\) is the upper bound interval of feature \(f_{j}\) for the class \(C^{h}\).  
In the learning phase, we have used two approaches to build the prototype of classes. Both approaches utilize prototypes defined by reference intervals for each feature in a class. In this work, we have chosen simple approaches to determine the interval of each feature for each class (see Figure 2). The first one is based on the mean \(\mu\) and the standard deviation \(\sigma\) and the second one is based on the percentiles. The reference intervals based on percentiles and the mean and standard deviation are non-parametric methods as they do not require any assumptions about the underlying distribution of the data.
## 2 Optimal Scheduling Models for AC/DC Distribution Systems
### DC Ring Network Constraints  
The DC ring network operation is subject to various constraints to ensure feasibility and operational integrity.  
The DC power flow constraints are modeled using Equations (25)-(27) from [6]:  
\[\widetilde{U}_{j,t}^{\text{DC}}-\widetilde{U}_{i,t}^{\text{DC}}=2\tau_{ij}^{ \text{DC}}P_{ij,t}^{\text{DC}}-(r_{ij}^{\text{DC}})^{2}\widetilde{I}_{ij,t}^{ \text{DC}}\forall ij\in L^{\text{DC}},\forall t\in T \tag{25}\]  
\[P_{j,t}^{inj}-\sum_{k\geq i\to k}P_{j,t}^{\text{DC}}+\sum_{i\geq i \to j}(P_{ij,t}^{\text{DC}}-r_{ij}^{\text{DC}}\widetilde{I}_{ij,t}^{ \text{DC}})=0,\forall j\in B^{\text{DC}},\forall t\in T \tag{26}\]  
\[\left\|\widetilde{I}_{ij,t}^{\text{DC}}-\widetilde{U}_{j,t}^{\text{DC}} \right\|_{2}\leq\widetilde{I}_{ij,t}^{\text{DC}}+\widetilde{U}_{j,t}^{\text{ DC}},\forall ij\in L^{\text{DC}},\forall t\in T \tag{27}\]  
where \(\widetilde{U}_{i,t}^{\text{DC}}\) and \(\widetilde{U}_{j,t}^{\text{DC}}\) are the squared DC voltage magnitudes at nodes \(i\) and \(j\) at time \(t\); \(P_{ij,t}^{\text{DC}}\) is the DC power flow on branch \(ij\) at time \(t\); \(L^{\text{DC}}\) is the set of branches in the DC ring network; and \(I_{ij,t}^{\text{DC}}\) is the squared DC current magnitude on branch \(ij\) at time \(t\).  
The power balance at each DC node must be maintained according to Equation (28):  
\[P_{j,t}^{inj}=P_{j,t}^{\text{PV}}+P_{j,t}^{\text{VSC,DC}}-P_{j,t}^{load}, \forall j\in B^{\text{DC}},\forall t\in T \tag{28}\]  
DC operational safety constraints and RES output constraints are similar to those of the AC subnetwork, as shown in Equations (19)-(24), but do not contain reactive power constraints, which will not be repeated here.
# Investigation of the Influence of Sublayer Thickness on Pairing of Metallic MEMS Shutter Blades
## 2 3D MEMS Shutter Blades: Paired and Unpaired Geometries  
The fabricated and studied shutter blades are rectangular in shape, with dimensions of 150 \(\times\) 400 \(\upmu\)m\({}^{2}\), and they are fixed along one edge via a hinge structure to an anchor and arranged in 2D arrays. The shutter blades represent a multilayer hybrid metal stack consisting of aluminium (Al1), chromium (Cr), and aluminium (Al2). The individual thicknesses of the layer system are between 15 and 99 nm. Such a sandwich system possesses tailored intrinsic stress due to the involved Al and Cr thin film deposition characteristics. The tailored intrinsic stress produces the curling and freestanding features of the shutter blades. By finetuning the deposition parameters (i.e., the layer thicknesses), different curling geometries can be achieved (see Figure 3, left side (unpaired shutters)) and adapted to fit the targeted investigation goal. It has been noticed [16,29] that the fabricated curled shutter blades, which are initially in an open/curled state that is called 'unpaired', can self-assemble into paired shutters. The pairing occurs between two neighbouring shutters from two consecutive rows. Once the blades of two shutters are in contact, they are nearly inseparable. Figure 3 shows the different curling states of the unpaired shutters on the left hand side and the paired ones on the right hand side involving shutters A and B. The finally paired situation closely resembles a Casimir system of two uncharged metal plates that are brought together at a very close distance and experience strong attraction forces. Using theoretical model calculations [16], we could demonstrate that a separation of less than 21 nm is already enough to keep the two blades tightly attached only via Casimir forces. SEM micrographs of the trenches made by a focused Ga ion beam within the overlapping area of the paired shutter blades revealed an even smaller distance. This led to our strong belief in the key role of Casimir forces between the paired micro-shutter blades.
## 3 Results  
The mean SBS values of the lithium disilicate discs on dentin surfaces are presented in Table 1. Statistically significant differences were observed among the groups (df = 2; F = 7.79; \(p\) = 0.004). Group B had the highest SBS, while Group C had the lowest. Group C (5.84 \(\pm\) 1.4 MPa) had a significant difference in SBS values compared to group B (11.45 \(\pm\) 2.4 MPa) (\(p\) = 0.008) and group A (10.29 \(\pm\) 2.5 MPa) (\(p\) = 0.012). There was no significant difference between groups A and B (\(p\) = 0.823). Groups A and C mainly showed an adhesive type of failure, while Group B mainly showed a mixed type of failure (Table 2). The representative SEM micrographs of the specimen surfaces after SBS tests are presented in Figure 3a-c. The pull-out of the dentin cuffs was observed for Group A (Figure 3a) and Group B (Figure 3c) specimens around the dentin tubules. The shear bond strength values of the study were evaluated with Weibull analysis (Figure 4). Group A had the highest Weibull modulus (m1 = 4.53), followed by Group B (m2 = 2.82) and Group C (m3 = 1.65). Unlike the Weibull modulus, the R\({}^{2}\) value for Group C (0.96) was higher than that of Group A (0.91) and Group B (0.86).  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Mean \(\pm\) SD** & **Weibull Modulus** & **R\({}^{2}\) for Weibull** \\  & **(MPa)** & **Modulus** & **Modulus** \\ \hline Group A & 10.29 \(\pm\) 2.5 \({}^{\text{A}}\) & 4.53 & 0.913 \\ \hline Group B & 11.45 \(\pm\) 2.4 \({}^{\text{A}}\) & 2.82 & 0.862 \\ \hline Group C & 5.84 \(\pm\) 1.4 \({}^{\text{B}}\) & 1.65 & 0.955 \\ \hline \(p\) & 0.004 & - & - \\ \hline \end{tabular} Different superscripted uppercase letters indicate significant different means within each column (\(p<0.05\)).  
\end{table}
Table 1: Mean shear bond strength (MPa) and Weibull parameters (n = 10).  
Figure 2: Schematic view of shear bond strength test.