esa-sceva/satcom-chunk-collection huggingface dataset:
# Targeting KRAS in PDAC: A New Way to Cure It?
## 6 Strategies to Circumvent Drug Resistance  
Combination therapy is currently an effective way to overcome drug resistance, and the exploration of combination therapy with KRAS inhibitors is underway. Several clinical trials of combination therapy for KRAS\({}^{\text{G12C}}\) inhibitors have been conducted (Table 1). The combination of KRAS\({}^{\text{G12C}}\) inhibitors with RAS upstream pathway inhibitors is a hot research topic nowadays, among which the combination with SHP2 inhibitors is the most popular. SHP2 is a non-receptor protein tyrosine phosphatase encoded by the PTPN11 gene, which can be activated by dephosphorylate KRAS proteins through binding to them. KRAS is phosphorylated via Src kinase on a conserved tyrosine at position 32 of the switch I region. This phosphorylation inhibits the binding of the effector Raf while promoting the involvement of GAPs and the hydrolysis of GTP [107]. SHP2 inhibition not only rescued RTK-driven acquired resistance to MEK inhibition but also had inhibitory effects on preclinical tumor models containing mutations in RAS-related pathways [108; 109; 110]. Moreover, the combination of SHP2 inhibitors with KRAS\({}^{\text{G12C}}\) inhibitors has been shown to overcome KRAS\({}^{\text{G12C}}\) inhibitor resistance and direct a better tumor microenvironment in PDAC models [111]. Related drug combination clinical trials are also in progress [112].  
The combination of KRAS inhibitors with downstream pathway inhibitors is less commonly reported, and the combined targeting of the MAPK-ERK and PI3K-AKT-mTOR pathways was ineffective in pancreatic cancer models. However, this has been changed with the addition of histone deacetylase (HDAC, a kind of epigenetic modifier) to the above combination targeting regimen [113]. In fact, the inhibitory effect of HDAC was dominant, and the combined targeting of the MAPK-ERK and PI3K-AKT-mTOR pathways enhanced this inhibitory effect [114; 115; 116].  
Additionally, the combined targeting of KRAS\({}^{\text{G12C}}\) with cell cycle checkpoints or immune checkpoints is promising [50; 72; 117]; the triple combined inhibition of KRAS\({}^{\text{G12C}}\)/SHP2/PD-L1 leads to severe tumor regression in PDAC mouse models [108]. Recent studies have shown that the nuclear export protein exportin 1 (XPO1) relieves tumor cells from resistance to KRAS\({}^{\text{G12C}}\) inhibitors [118]. This effect has been demonstrated in a mouse xenograft model. XPO1 has the function of transporting protein cargo from the nucleus to the cell, thus maintaining cellular homeostasis [119]. Additionally, KRAS mutant NSCLC cells are dependent on this mode of transport [120]. This combination therapy may also be applicable to the treatment of patients with PDAC containing the KRAS\({}^{\text{G12C}}\) mutation.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 2 Synthesis Methods
### Chemical Methods
#### 2.2.7 Pyrolysis Methods  
**Spray pyrolysis** is a technique commonly used for the synthesis of various materials, including MNPs. This method involves the formation of fine droplets of precursor solutions that are sprayed into a high-temperature environment. The solvent evaporates, and the remaining solute undergoes chemical reactions to form the desired material, collected on a substrate [58].  
In precise context of MNP synthesis, spray pyrolysis offers several advantages: controlled particle size, homogeneous coating, scalability, versatility, and tunable magnetic properties [59].  
**Laser pyrolysis** is a method which involves the use of CO\({}_{2}\) laser irradiation to induce chemical reactions in a precursor material, leading to the formation of NPs. The principle consists of focusing a high-intensity laser beam on a precursor material in the gas or aerosol phase. The laser energy is absorbed by the precursor, leading to its rapid heating and decomposition. The resulting chemical reactions lead to the formation of NPs, which are then collected on a substrate [3]. This technique offers both advantages (rapid heating, high purity, fine tuning) and characteristics that need to be improved (scalability and cost) [60].
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 1 Introduction
The experimental results show that the efficient storage and exchange engine for SAR imaging data designed in this paper has a storage access bandwidth of up to 16.6 GB/s in the range direction, which is the read bandwidth in the range direction. In addition, the write access bandwidth in the range direction of DDR3 is 16.0 GB/s. In the azimuth direction, the storage access bandwidth can reach up to 20.0 GB/s, which is read bandwidth in the azimuth, and the write bandwidth in the azimuth is 18.3 GB/s. The method proposed in this paper is better than the existing implementations. The rest of this paper is organized as follows. Section 2 introduces the SAR imaging algorithm and analyzes the DDR3 data access characteristics, and provides an overview of the currently available data exchange methods. Section 3 provides a detailed introduction of the data storage and exchange method based on superscalar pipeline. The proposed hardware architecture and register configuration will be described in Section 4 of this paper. Section 5 shows the experiments and results. Finally, in Section 6, the conclusions are presented.
## 4 Mechanical Energy Transport Model of the Towed Cable
### Dynamic Response between Wake and Cable under Rotating Motion  
To accurately capture the impact of the towed cable's attitude and position on the complex flow of lateral separation in rotational motion, it is assumed that the vehicle and the fluid are rotating at the same angular velocity, and there is no relative motion between the fluid and the wall, only subject to the effect of the static pressure field generated by the fluid due to rotational motion. The specific parameters of the cable, the time step used in the calculation, and the basic assumptions are the same as those for straight navigation. By operating the rudder at angles of \(2^{\circ}\), \(5^{\circ}\), \(8^{\circ}\), and \(12^{\circ}\), respectively, we observed the relationship between the attitude and position of the cable and the wake.  
As shown in Figure 12b, as the angle of drift increases, the lateral separation flow around the vehicle body gradually becomes stronger, and the wake motion generated by separation becomes more intense. Unlike the straight state, the spatial motion curvature of the cable under rotation motion is large and affected by the angle of drift, the wake shifts towards the y-axis, and the cable immersed in the wake also shifts in the y-direction. However, under different angles of drift, the wake changes slightly in the longitudinal direction, so the wake has a small difference in the longitudinal displacement of the cable under different angles of drift. However, it can be clearly observed that the larger the angle of drift, the greater the adsorption effect of the wake on the cable. As shown in Figure 12a, the larger the angle of drift, the closer the cable is to the wake.  
Figure 11: Vorticity dissipation curve.  
Figure 12: \(Cont\).  
Furthermore, under an 8\({}^{\circ}\) drift angle, multiple positions were selected for cable placement, as shown in Figure 12(a),b. It was found that the attitude and position of the cable at each position was basically consistent with the trend of the cable under direct navigation, but the attitude and position of the cable under the effect of the drift angle had significant differences in both the longitudinal and transverse directions from direct navigation. In the longitudinal direction, it was closer to the vehicle body than direct navigation, and the ideal position of the cable angle with the vehicle body was 10\({}^{\circ}\), which was approximately 75% less than the direct navigation case.  
Figure 12: Wake trajectory and cable position of towed system at different drift angles.  
The lateral deviation angle of the tail end of the cable in the y-direction is greater than 0.4\({}^{\circ}\) but less than 0.9\({}^{\circ}\), while this lateral deviation can be ignored in the case of direct navigation and angle of attack. As shown in Figure 13c, under the influence of an 8\({}^{\circ}\) drift angle, the vorticity at the trailing edge of the enclosure differs by up to 18 s\({}^{-1}\) from the corresponding position vorticity during direct navigation, with a relatively large difference.
_Toxins_**2010**, \(2\), 229-261; doi:10.3390/toxins2020229  
_toxins_  
**ISSN 2072-6651**  
www.mdpi.com/journal/toxins  
_Review_  
**Mycotoxin Contamination of Beverages: Occurrence of Patulin in Apple Juice and Ochratoxin A in Coffee, Beer and Wue and Their Control Methods**  
**[PERSON] 1,*, [PERSON] 2,*, [PERSON] 3, [PERSON] and [PERSON] 1**  
1 School of Biological Sciences, Universiti Sains Malaysia, 17180 USM, Penang Malaysia; E-Mail: [EMAIL_ADDRESS] (B.S.)  
2 National Biological Control Laboratory, Crop Genetics Research Unit Agricultural Research Service, US Department of Agriculture, 59 Lee Rd, PO Box 60, Stoneville, MS 38776, USA  
3 Corn Insects and Crop Genetics Research Unit, Agricultural Research Service, US Department of Agriculture, Room 401, Genetics Building, Nova State University, Ames, IA 50011, USA; E-Mail: [EMAIL_ADDRESS] (C.A.A.)  
4 Department of Medicinal Chemistry, College of Pharmacy, University of Minnesota, 308 Harvard St., SE, Minneapolis, MN 5855, USA; E-Mail: [EMAIL_ADDRESS] (W.-T.S.)  
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (K.R.N.R.); [PERSON]L_ADDRESS] (H.-K.A.); Tel.: +604-6534001 (K.R.N.R.); 1-662-686-5313 (H.-K.A.) Mobile: +604-4341405 (K.R.N.R.); Fax: 1-662-686-5281 (H.-K.A.).  
_Received: 10 December 2009; in revised form: 18 January 2010 / Accepted: 21 January 2010 / Published February 2010_  
**Abstract:** Mycotoxins are toxic secondary metabolites produced under special conditions of moisture and temperature by aerobic, microscopic fungi that can colonize a variety of foods from before harvest up to consumer use. Contamination of beverages, including that of apple juice by patulin and coffee, beer and wine by ochratoxin A has gained worldwide interest in recent years with the revelation of the effect of these toxins on human health. Patulin and ochratoxin A, which are produced by species of _Penicillium, Byssochlamys_ and _Aspergillus_, pose the greatest threat to human health and are subject to government regulation in juices and beverages worldwide. This review provides an overview of the prevalence of these mycotoxins in beverages and control methods which help in establishing and carrying out proper management strategies. A detailed investigation on contamination of juices and beverages by toxic compounds helps to provide safe products for consumption and export, and they allow us to prioritize future research efforts.  
**Keywords:** patulin; ochratoxin A; beverages
# CycleGAN-Driven MR-Based Pseudo-CT Synthesis for Knee Imaging Studies
## 4 Discussion
Further improvement of the current model could be achieved by increasing the number of patients included in the dataset and/or training the CycleGAN with better, higher-resolution MR images. This is supported by the fact that, although after epoch 50, the noticeable improvement in image differences reduced, the loss function graph (MAE) continued to converge gradually. We believe that this convergence and, thus, the pseudo-CT results, might be enhanced with a larger patient dataset. Collaborating with several medical institutions to create a multi-center database would be a promising strategy to increase the diversity and number of patients and therefore enhance the robustness and reliance of our DL model. However, despite the small number of patients in the dataset, the comprehensive evaluation presented here encompasses a variety of cases, reflecting diverse patient anatomies and knee conditions. This diversity is essential to ensure that our knee pseudo-CT synthesis approach is consistent and reliable across a spectrum of clinical scenarios. Second, even if the age of patients was in the range of [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70], their mean age was 45.9 \(\pm\) 13.2, which does not ensure an even distribution across age groups. Moreover, since all patients are aged over 20 years, applying the proposed method directly to pediatric patients is challenging due to the intrinsic variations in anatomy and bone densities. Third, we have assessed the impact of our method on the synthesis of only a limited number of patients, assessing the main anatomical structures and small implants (screws). However, a further assessment including patients with different pathologies would be desirable. Finally, although we obtained encouraging results, the reproducibility of our method needs to be properly assessed in subjects who underwent repeated examinations.
## 4 Conclusions  
Biobased, cationic WPUUs were successfully constructed by varying the CO content, where stable aqueous dispersion was obtained even when N-MDEA loading was as low as 3.16 wt%. A bonus of this low hydrophilic moiety was the excellent adhesive strength, whose T-peel strength could reach 36.8 N/25 mm, about 114% higher than that of WPU (17.2 N/25 mm) without any CO content. The elongation at break of CO\({}_{7.40\%}\)-WPU was 391%. The mechanical performance, film-forming property, flexibility, and adhesion of the cationic WPU films could be tuned by adjusting the CO loading. WPUUs containing higher content of CO demonstrated a remarkable enhancement in homogeneity among particles and CO\({}_{7.40\%}\)-WPU had the highest adhesion strength. The combination of CO has a positive effect on the dispersion and stability properties of WPUUs. On increasing the CO content, the tensile strength of WPU films was observed to decrease, and the elongation at break increased owing to the average hydroxyl functionality of 2.7 chains introduced in the PU backbone chain, along with a reduction in the content of the hard segments. In addition, the incorporation of CO hindered the PCL chain packing, leading to a reduced crystallinity in the WPU films. Overall, using CO as an addition of second material improved the mechanical properties, wettability, water resistance and adhesive strength of PCL-WPUs. The experimental findings indicated that the CO-modified PCL-WPUs displayed excellent film-forming property, flexibility, and adhesion. Collectively, this study provides a novel strategy to synthesize eco-friendly, high performance, biodegradable polymers for diverse applications. Further detailed investigations on the synthesis and properties of the CO-modified PCL-WPUs are underway.  
Conceptualization, Y.L.; methodology, S.C. and S.Z.; software, M.L.; R.L. and W.X.; validation, J.S.; formal analysis, J.S.; investigation, J.S.; resources, J.S.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing. Y.L.; supervision, J.S.; project administration, J.S. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (31971582).  
Not applicable.  
The authors declare no conflict of interest.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 4 Mathematical Justification
### Short-Term Drift Detection Consistency  
In Phase 2, for each new data point \(z^{(m)}(t)\), we compute the reconstruction error,  
\[E_{recon}^{(m)}(t)=\left\|z^{(m)}(t)-z^{(m)}(t)\right\|^{2}, \tag{19}\]  
and employ a Bayesian threshold mechanism (with prior \(p(\tau)\)) to decide if a sudden drift has occurred [25; 26]. Formally, we define  
\[\tau_{t}^{\star}\sim p\Big{(}\tau\big{|}E_{recon}^{(m)}(t)\Big{)}. \tag{20}\]  
If \(E_{recon}^{(m)}(t)>\tau_{t}^{\star}\), the system flags an acute anomaly, setting \(y^{(m)}(t)=1\). The following theorem states that if the underlying data distribution truly shifts abruptly, the detection probability quickly approaches 1, even with a limited number of samples.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Time** & **Machine ID** & **Wafer ID** & \begin{tabular}{c} **Drift** \\ **Score** \\ \end{tabular} & \begin{tabular}{c} **Short-Term** \\ **Label** \\ \end{tabular} & \
\begin{tabular}{c} **Long-Term** \\ **Label** \\ \end{tabular} &
\begin{tabular}{c} **Root Cause** \\ \end{tabular} \\ \hline
1 January 2025 & Machine 1 & 001 & 0.15 & 0 & 0 \\ \hline
1 January 2025 & Machine 2 & 002 & 0.65 & 1 & 0 & “Pressure anomaly” \\ \hline
1 January 2025 & Machine 1 & 003 & 0.85 & 1 & 1 & “Current fluctuations” \\ \hline
1 January 2025 & Machine 2 & 003 & 0.30 & 0 & 0 & “Maintenance” \\ \hline \hline \end{tabular}
\end{table}
Table 3: Example of drift detection results.  
**Theorem 1**: (Short-Term Detection with High Confidence). _Let \(\mathbf{\mathcal{H}}_{0}\) be the distribution of the reconstruction error under the normal condition, and \(\mathbf{\mathcal{H}}_{1}\) be the distribution under a short-term drift event. If_  
\[\mathbf{\mathcal{H}}_{0}\cap\mathbf{\mathcal{H}}_{1}=\varnothing\text{ or }D_{KL}(\,\mathbf{ \mathcal{H}}_{1}\|\mathbf{\mathcal{H}}_{0})>0, \tag{21}\]  
_then there exists a threshold \(\tau^{\dagger}\) such that, as \(n\to\infty\), the probability that \(\{E_{\text{recon}}^{(m)}(t)>\tau^{\dagger}\}\) occurs under \(\mathbf{\mathcal{H}}_{1}\) converges to 1. In other words, the system can detect acute drift with high confidence in finite time._  
**Proof.**  
1. If \(\mathbf{\mathcal{H}}_{0}\) and \(\mathbf{\mathcal{H}}_{1}\) are disjointed, then there is a critical point \(\tau^{\dagger}\) such that the probability of exceeding that threshold is 0 under \(\mathbf{\mathcal{H}}_{0}\) and 1 under \(\mathbf{\mathcal{H}}_{1}\).
2. If \(D_{KL}(\,\mathbf{\mathcal{H}}_{1}\|\mathbf{\mathcal{H}}_{0})>0\), Chernoff bounds or [PERSON]'s theorem imply that, as the sample size \(n\) grows, the probability of exceeding a certain threshold \(\tau^{\dagger}\) rises exponentially under \(\mathbf{\mathcal{H}}_{1}\)[27].
3. Incorporating the Bayesian threshold mechanism, \(\tau^{*}_{t}\) adjusts posteriorly to favor higher drift likelihood as reconstruction errors increase, and so the detection probability approaches 1 over time.
4. Consequently, under a genuine acute drift event, detection's success probability converges to 1 as \(n\to\infty\).
## 2 Sensor Platform LSI Network System
### Sensor Platform LSI
#### 2.2.3 Event-Driven Function  
For further relieving data congestion, the sensor platform LSI has the event-driven function to reduce the number of sensor platform LSIs that attempt to send data packets on the bus line. According to our design, we can configure the threshold value for each capacitance or resistance channel of the sensor platform LSI. When a sensor connected to a channel of a sensor platform LSI is stimulated, the converted digital value from the channel changes. If the digital value exceeds the configured threshold value of the channel, the sensor platform LSI generates a data packet including sensing data  
Figure 5: Adaptation function of sensor platform LSI.  
from all the eight capacitance or resistance channels, and tries to send the data packet on the bus. We adopted this strategy because our MEMS tactile sensor uses six or eight channels individually. Smarter event-driven functions such as adaptive threshold [21] and send-on-delta system [22] were reported, but the simple one used in this study is still useful to reduce the number of packets on the serial bus.  
Figure 6 shows the signals of the bus on which one sensor platform LSI is operating with different configurations. When neither adaptation nor event-driven function is turned on (Figure 6a), the bus is busy with a small idle time ratio. By enabling event-driven (Figure 6b) and adaptation (Figure 6c) functions in order, the bus idle time ratio becomes larger. For the adaptation function configuration in Figure 6c, \"Data Wait Increment\", \"Data Wait Times\", \"Bus Wait Increment\" and \"Bus Wait Times\" were configured to 80, 64, 1 and 16, respectively. Note that Figure 6 shows the effects of the adaptation and event-driven functions when only one LSI operates to help illustrate these two functions. In the experiment, only the adaptation function was used.
## 4 DHD-MEPO Algorithm
### Calculation of cell Coverage  
Based on the divided cell side length \(c\), it is determined that there are \(c^{3}\) target points within each cell, and the coverage ratio of each cell is calculated to determine whether there are coverage holes within each cell, and thus the position of the coverage blind zones. A sensor node can cover multiple target points, while the same target point is also covered by multiple sensor nodes, as illustrated in Figure 5. Target point \(e_{2}\) is covered by sensor nodes \(s_{1},s_{2}\), and \(s_{6}\) at the same time, indicating that there are three sensor nodes covering target point \(e_{2}\) at the same time, then the coverage of sensor node \(s_{1}\) on target point \(e_{2}\) at this time is \(1/3\), and so on. Then, any one sensor node \(s_{i}\) within the cell has a coverage of all target points \(e_{j},j=1,2,\ldots,c^{3}\) within the cell, the sum of coverage can be calculated as:  
\[P\left(s_{i},c^{3}\right)=\sum_{j=1}^{c^{3}}P\left(s_{i},e_{j}\right), \tag{15}\]  
The coverage of all nodes within the cell for all target points within the cell in aggregate, i.e., cell coverage, is:  
\[P_{D_{(x,y,z)}}=\frac{\sum_{i=1}^{t}P\left(s_{i},c^{3}\right)}{c^{3}}, \tag{16}\]  
where \(t\) is the quantity of sensor nodes distributed within the cell \(D_{(x,y,z)}\).  
If the coverage of a cell is greater than 90%, it means that there is no coverage hole in the cell; otherwise, there is a coverage hole in the cell. As shown in Figure 6, the cell marked in red indicates that there is a hole in the cell and the redundant nodes in the target region need to be moved to cover the hole.  
Figure 5: Cell \(D_{(x,y,z)}\) node monitoring graph.  
Figure 6: Target region coverage hole map.  
```
1:Initialization: edge length \(k\) of the network, number of network cells \(M\), cell ID number \(D_{(x,y,z)}\), cell edge length \(c\), number of nodes \(N\), node perception range \(V\)
2:\(\left[N_{ideal}\right]=\frac{k^{3}}{V}\), \(D_{ratio}=\frac{\left[N_{ideal}\right]}{N}\)//Calculation of the node ratio based on the quantity of nodes and the node sensing range
3: The quantity of cells is divided according to \(D_{ratio}\), with each cell ID being \(D_{(x,y,z)}\)
4:\(CV_{s_{i}}=Aggregate\left(E_{res_{i}}+\frac{1}{Ad\cup_{s_{i}}}\right)//\)Cumulative values according to static node remaining energy and average distance values
5: Select the group nodes for each cell \(D_{(x,y,z)}\)
6: for (\(i=1\); \(i\leq N\); \(i\)++) do
7:\(P\left(s_{i},c^{3}\right)=\sum_{j=1}^{c^{3}}P\left(s_{i},e_{j}\right)//\)Calculate the sum of each node's coverage of all target points within the cell
8:\(P_{D_{(x,y,z)}}=\frac{\sum_{i=1}^{c^{3}}P\left(s_{i},c^{3}\right)}{c^{3}}//\)Calculate the coverage of each cell
9: if Cell coverage >90% then
10: No holes within the unit
11: else
12: The unit has a hole in it
13: end if
14: end for
```  
**Algorithm 1**Detection phase algorithm.