esa-sceva/satcom-chunk-collection huggingface dataset:
# Peptides That Block RAS-p21 Protein-Induced Cell Transformation
## 3 Anti-RAS-p21 Peptides from Expression Libraries
### Peptides and Small Molecules from Libraries That Bind to Multiple Forms of RAS-p21-Gpp-NHp
Using the plasmid expression of BR2 linked to the single chain variable fragment or scFv of the anti-RAS-p21 Y13-259 antibody, a fusion polypeptide or BR2-scFv Y13 259 was synthesized as BR2-V\({}_{\text{H}}\)-(GGGGGS)\({}_{3}\)-V\({}_{\text{L}}\)-H\({}_{6}\) and tested for its ability to kill RAS-p21-transformed tumor cells [38]. The specificity of this fusion polypeptide for cancer cells is provided by the BR2 sequence. Once the polypeptide enters cancer cells, the anti-RAS-p21 mini antibody blocks ras activation. This construct was tested on HCT 116 (human colon cancer) cells which contain oncogenic substitutions for Gly 13 [38]. The results showed that 67 percent of the cells treated with BR2-scFv underwent apoptosis while about 48 percent of the cells underwent apoptosis when treated with Tat-scFv, and 68 percent of the cells underwent apoptosis when treated with the apoptosis-inducing agent, staurosporine. On the other hand, only 25 percent of the cells treated with scFv alone with no CPP were found to undergo apoptosis [38]. In concurrent experiments, RAS-p21 activity was assayed by measuring its binding to the RBD of RAF. Over a two hour period, 2 uM tat-scFv and BR2-scFv caused a 50 and 60 percent drop in RAS-p21 activity. BR2-scFv induced about a 60 percent reduction in cell viability using the MTT assay at 2 uM concentration while scFv itself was found to have only a minimal effect [38]. Further evaluation awaits testing of other cancer cells and normal cells since BR2 may enter into normal cells despite this being significantly less than into cancer cells.
## 3 Results and Discussion
### Antioxidant Activity  
Antioxidants are very important for human health, as they scavenge free radicals that can cause various diseases [25]. In this study, the antioxidant activities of UL, GTL, and BTL were determined by DDPH', ABTS\({}^{+}\), and FRAP assays, and the IC\({}_{50}\) values of DPPH' and ABTS\({}^{+}\) were calculated using probit regression analysis. The inhibition rate of DPPH' and ABTS\({}^{+}\) radicals were similar, with the scavenging activity of both decreasing in the following order: VE > GTL > BTL > UL (Figure 3). The maximum DPPH' radical inhibition activity reached 87.73% (201 \(\mathrm{\SIUnitSymbolMicro g}\)/mL), 87.30% (201 \(\mathrm{\SIUnitSymbolMicro g}\)/mL), and 87.64% (201 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) for the extracts of GTL, BTL, and UL, respectively; while the maximum ABTS\({}^{+}\) radical inhibition activity reached 86.06% (67.12 \(\mathrm{\SIUnitSymbolMicro g}\)/mL), 85.45% (100.46 \(\mathrm{\SIUnitSymbolMicro g}\)/mL), and 87.64% (90.52 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) for the extracts of GTL, BTL, and UL, respectively. The extracts of GTL and BTL efficiently inhibited DPPH' and ABTS\({}^{+}\) radicals at lower concentrations than those of UL. FRAP assays were performed to determine the transfer electron ability for the reduction of Fe\({}^{3+}\) to Fe\({}^{2+}\) in the presence of TPTZ [26]. Due to differences in reaction mechanisms, the absorbance values measured by FRAP assay exhibited dose-dependent growth trending in the order of VE > GTL > UL > BTL, indicating the reliability of the reducing power assay for different sample concentrations. The IC\({}_{50}\) values of the DPPH' and ABTS\({}^{+}\) radicals are shown in Figure 3. The differences in IC\({}_{50}\) values among UL, BTL, and GTL reached significant levels (\(p\) < 0.05) in the following order: VE (17.41 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) < GTL (36.10 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) < BTL (39.99 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) < UL (45.46 \(\mathrm{\SIUnitSymbolMicro g}\)/mL), demonstrating the potent in vitro antioxidant activity of _P. chinense_ leaves after processing into green and black tea. For the IC\({}_{50}\) values of ABTS\({}^{+}\), the order was similar to that of DPPH', but the IC\({}_{50}\) value of GTL (28.43 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) was slightly higher than that of VE (26.39 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, \(p\) > 0.05) and significantly lower than that of UL (37.99 \(\mathrm{\SIUnitSymbolMicro g}\)/mL). In general, the antioxidant activity was directly related to the phenolic content [12]. Besides, the tea making involving enzymatic and non-enzymatic processes results in producing various active metabolites [27]. Therefore, the results of the DPPH' and ABTS\({}^{+}\) indicated that the antioxidant activity of GTL and BTL were similar and higher than UL, and thus, GTL and BTL showed more promise as functional foods after processing.
###### Abstract
In [37], dual watermarks were formed by combining a copyright watermark, which utilized copyright information, with another watermark composed of features selected via fuzzy c-means clustering from the vector geographic data. Subsequently, these newly composed watermarks were embedded into the cover data using a single watermarking algorithm. The watermarks, designed based on QR codes (Quick Response Codes), were embedded into the polar coordinates of the vector geographic vertices in [38]. Prior to embedding all of the watermarks, the locations for embedding should be reserved according to the number of watermarks intended for insertion. In order to ensure the preservation of all watermarks in the watermarked data, even after common adding and deleting attacks, and when embedding sufficient watermarks in vector geographic data containing fewer coordinate vertices, we proposed a novel multiple watermarking algorithm based on multiple QIM for vector geographic data. The subsequent sections of this paper are structured as follows: Section 2 presents the proposed method, multiple quantization index modulation. Subsequently, Section 3 provides a detailed introduction to the multiple watermarking algorithm for vector geographic data. The experimental results and performance evaluations are presented in Section 4. Finally, concluding remarks and the study's identified limitations are discussed in Section 5.
# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 3 Materials and Methods
### Mapping Regions of Interest  
A fuzzification (by fuzzy overlay) was computed to identify possible regions of interest (ROIs) for iron oxides in Freixeda. The fuzzy overlay combines raster data, based on the selected overlay type [39]. By applying fuzzification, the integration of data from multiple sensors enables a more robust and reliable delineation of target zones [40; 41; 42].  
Identifying ROIs is crucial to compare the results of the satellites and to understand the capability of the PS image to identify the same ROIs.  
Two tests were conducted using the GAMMA function of fuzzy overlay. Test 1--Using all the BR from each satellite as input (\(\gamma\) = 0.9); test 2--A fuzzification (Sentinel-2, ASTER., Landsat 9) without PS (\(\gamma\) = 0); this will allow us to understand the impact of PS in the final result. The final result was analyzed with the help of high-resolution true color composites and geological maps to discard false positives and choose the ROIs.
## 4 Investigation of Influence of Model Size and Joint Dip Angle on Rock Brittleness
### Experimental Results  
A representative relationship between stress and strain is illustrated in Figure 6. The deformation process of the jointed specimens under all conditions is similar to intact rock specimens and it includes the crack closure phase, elastic deformation phase, crack growth phase and post-peak phase. Unlike intact rock, the jointed specimens may exhibit \"multi-peak\" characteristics in their deformation curves due to the presence of joints.  
The uniaxial compression strength (UCS) is an indispensable strength indicator of the rock mass. The UCS values of jointed specimens present an overall V-shaped distribution with the increasing joint dip angle (Figure 7). Specifically, the UCS value originally declines and then grows as the dip angle of joints increases. This conclusion agrees with the law obtained from the existing compression experiments of joints [34; 35]. Generally, the UCS values of the rock mass decline with the growth in the model size, which indicates an obvious size effect.  
Figure 5: Experimental equipment and loading conditions.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Specimen No.** & \(L\) **(mm)** & \(\alpha\) **(\(\circ\))** & **Specimen No.** & \(L\) **(mm  
Elastic modulus is an important deformation index of the rock mass. Similarly to the uniaxial compression strength, the elastic modulus generally falls down and then grows  
Figure 6: Stress–strain curves of jointed specimens.  
Figure 7: Variation in UCS of jointed specimens with joint dip angle.  
with the increasing joint dip angle. Elastic modulus presents an overall downward trend as the model size grows. It is worth emphasizing that the above experimental results reveal the model size effect of rock mass, which indicates it is necessary to obtain the equivalent mechanical parameters of rock mass through a representative element volume (REV) [36] in practical engineering design.
# Innovation in Company Labor Productivity Management: Data Science Methods Application
## 2 Methodology of Research
### Materials and Methods
#### 2.2.4 Self-Organizing Maps  
Self-organizing maps (SOM) are one of the varieties of neural network algorithms [54]. The main difference between this technology and neural networks trained by the backpropagation algorithm is that the teaching method is unsupervised, that is, the training result depends only on the structure of the input data. The algorithm for the functioning of SOM is one of the options for clustering multidimensional vectors. An example of such algorithms is the \(k\)-means algorithm. An important difference of the SOM algorithm is that in it all neurons (nodes, centers of classes) are ordered into some structure (usually a two-dimensional grid). During training, not only the winning neuron is modified, but also its neighbors, but to a lesser extent. SOM can be considered as one of the methods for projecting multidimensional space into space with a lower dimension. When using this algorithm, vectors that are similar in the original space turn out to be nearby on the resulting map.
## 2 Related Works  
Anomaly detection in industrial control devices involves monitoring their operational states and behaviors to promptly identify irregularities or unauthorized access, ensuring safety and normal functioning. As depicted in Figure 1, the process typically includes data collection, fingerprint extraction, model training, and anomaly detection. The quality of ICS device fingerprints and the design of the anomaly detection model significantly impact detection performance.  
Figure 1: Industrial control equipment anomaly detection process.  
Effective fingerprint extraction is crucial for accurately distinguishing between normal and abnormal states. ICS device fingerprints uniquely represent operational characteristics--such as parameters, performance data, and response times--precisely describing normal behavioral patterns. Incomplete or inaccurate fingerprints can lead to misjudgments, resulting in false positives or negatives. Moreover, ICSs operate in dynamic environments, requiring fingerprints to reflect device behaviors under varying conditions. Failure to capture these variations can reduce detection accuracy and increase false alarm rates.  
The design of anomaly detection models directly affects their ability to differentiate between normal and abnormal states. An effective model must extract key features from ICS device fingerprints to identify abnormal behaviors. Poorly designed models with inappropriate feature selection or oversimplification may yield suboptimal performance. Models need good generalization capabilities to maintain high accuracy on unseen data and adapt to changes like equipment aging or new devices. Balancing the detection of true anomalies and reducing false positives is essential to maintain reliability without disrupting normal operations.  
ICS device fingerprinting is a technology used to identify and track industrial control devices, encompassing hardware, software, and behavioral information to determine device model, version, configuration, and operational state. Fingerprint extraction methods are mainly categorized into passive and active detection.
## 4 Field Monitoring and Stress Analysis
### Monitoring Results  
The wave velocity test results of the surrounding rock prior to the first blasting cycle are shown in Figure 22. The extracted results of measuring points 1-5 are shown in Table 5. Substituting the data into Equation (7), the calculated damage factors for monitoring Points 1 to 5 are 0.20, 0.18, 0.14, 0.10, and 0.09, respectively. Taking the radial strain test at monitoring Point 1 during the first blasting cycle as an example, the measured voltage signal is shown in Figure 23. The stress peak occurs at approximately 55 ms, closely aligning with the 50 ms delay associated with the highest charge, a single time-delay interval of the cut holes, confirming the reliability of the dynamic strain measurements. The wave velocities and dynamic strain data from the remaining four points were substituted into Equations (7), (26) and (27) to calculate the corresponding damage factors and dynamic stresses. The results are presented in Table 6, where \"--\" indicates that dynamic strain data were not obtained at certain points due field complexity and signal interference. The uncertainty in the peak stress and peak voltage is \(\pm\)0.02.  
Figure 21: On-site Monitoring Layout.  
Figure 23: Measured voltage signal (radial direction at monitoring point 1 during the first blasting).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Point 1** & **Point 2** & **Point 3** & **Point 4** & **Point 5** \\ \hline hole depth (m) & 1.0 & 2.0 & 3.0 & 4.0 & 5.0 \\ original rock wave velocity (km/s) & 3.036 & 3.036 & 3.036 & 3.036 & 3.036 \\ wave velocity before the first blasting (km/s) & 2.716 & 2.750 & 2.816 & 2.881 & 2.897 \\ D & 0.20 & 0.18 & 0.14 & 0.10 & 0.09 \\ \hline \end{tabular}
\end{table}
Table 5: Sonic wave test results for measuring points 1–5.  
Figure 22: Rock wave velocity test results (first blasting).  
As shown in Table 6, at each monitoring point, the stresses hierarchy is as follows: radial stress > axial stress > hoop stress. Additionally, all three stresses decrease with the components of increasing rock mass damage and with greater distance from the blast source.
## 4 Circuit Description  
In this section, we present a printed circuit board (PCB) which is designed to demonstrate the ability of the SCMWC for spectrum sensing. The board consists of four parts: RF front-end, periodic sequence generator, mixer and low pass filter. The input signal is firstly fed to the RF front-end so as to catch the interested signal. Then the output signal is mixed with a periodic waveform through a wideband active mixer. Finally the output of the mixer is low-pass filtered before being sampled. In our lab experiments, we use a oscilloscope to sample and store the waveform for simplification. However, in the future, we will use commercial ADC devices for that task. Figure 4 presents a block diagram of the board. We then describe each part in detail.
## 3 Methodology
### Passenger Flow Prediction Model  
We propose the PFP-XPOI model for passenger flow prediction. The features selected for this model comprise the following three dimensions. One dimension is the information related to the line and the station, such as the line code of the station and the latitude and longitude of the station. Another dimension is the time period and the date when the IC card data are generated. The third dimension is the number of different types of POI around the station, including the number of companies and research institutions, etc.  
This model consists of two parts. One part is the calibration of the service radius between the bus station and POI data, and the other part is the training of the passenger flow prediction model for each line. We built the PFP-XPOI model based on the following steps. The dataset \(D_{S}\) is a sample space, and we can represent the machine learning model as  
\[M(x\in D_{S})\to y. \tag{1}\]  
where \(M\) means a mapping from data point \(x\) to its true value \(y\). After taking POI into account, we can add a new dataset to the original one. Namely  
\[D_{n}=f_{dis}(D_{S},D_{P}). \tag{2}\]  
where \(D_{n}\) is the updated sample space. \(D_{P}\) means the POI data set. \(f_{dis}\) refers to a distance-based function between bus stations and POI. In this model, distances were set to 100, 200, 300, 400 and 500 m, forming 5 datasets. After that, to obtain the optimal service radius \(d^{*}\), the machine learning model for different datasets was trained. Finally, \(d^{*}\) can help us find the best dataset. We trained a passenger flow prediction model for each line based on this dataset using XGBoost. The detail of the PFP-XPOI model is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Meaning** \\ \hline key & Users apply for API types on the official website of AMap. \\ \hline location & Longitude and latitude are divided by ”,”. Longitude is the former and latitude is the latter. The decimal point of latitude and longitude should not exceed 6 digits. \\ \hline coordsys & The original coordinate system. \\ \hline types & POI types. The classification code consists of six digits. The first two numbers represent large categories, the middle two represent medium categories and the last two represent small categories. \\ \hline city & City of inquiry. \\ \hline radius & Radius of the inquiry. The value range is from 0 to 50,000. \\ \hline Output & Return to data format type. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coordinate conversion and peripheral searching parameters using Amap API.
## 2 Background Information
### TX Plant Simulation  
PLM (Product Lifecycle Management) Siemens is a software platform developed by Siemens Digital Industries Software that helps organizations manage the entire lifecycle of a product, from ideation and design through to manufacturing, maintenance, and eventual retirement. PLM Siemens provides a comprehensive set of tools and capabilities for managing product data, processes, and collaboration across different teams and stakeholders. Some of the key features of PLM Siemens include:  
* Design and Engineering: The software provides tools for creating and managing product designs, including 3D modeling, simulation, and analysis capabilities.
* Manufacturing: PLM Siemens offers tools for creating and managing manufacturing processes, including process planning, production scheduling, and quality management.
* Maintenance and Service: The software helps organizations manage product maintenance and service activities, including maintenance planning, repair management, and service documentation [28].  
PLM Siemens is used by a wide range of organizations across various industries, including those of automotive, aerospace, defense, and consumer goods. It can help organizations improve efficiency, reduce costs, and accelerate the time-to-market for new products.  
Tecnomatix Plant Simulation is a software tool used for modeling, simulating, and analyzing manufacturing processes. Developed by Siemens Digital Industries Software, Tecnomatix Plant Simulation allows users to create digital models of manufacturing systems and simulate various scenarios to optimize production efficiency and reduce costs. With Tecnomatix Plant Simulation, users can create 2D and 3D models of manufacturing processes, including production lines, supply chain networks, and logistics systems. The software features a comprehensive library of predefined objects and components, making it easy to model complex manufacturing processes quickly.  
Tecnomatix Plant Simulation enables users to simulate various scenarios and analyze the performance of their manufacturing processes. With the software's powerful analysis tools, users can identify bottlenecks and inefficiencies in their production systems, optimize production schedules, and reduce lead times. One of the key features of Tecnomatix Plant Simulation is its ability to integrate with other software tools and systems. The software can interface with enterprise resource planning (ERP) systems, manufacturing execution systems (MES), and other production planning software tools, allowing users to optimize their manufacturing processes end-to-end. Tecnomatix Plant Simulation is a powerful software tool for manufacturers looking to improve efficiency, reduce costs, and optimize their production processes.
## 2 CHS T-joints  
A schematic representation of a non-retrofitted CHS T-joint including a chord member, a brace member, and their geometric features are shown in Figure 1. Based on the literature, the most important normalized geometric properties of non-retrofitted joints, which are studied in this research, are the ratio of the brace radius to its thickness (\(r/t\)), the ratio of the chord radius to its thickness (\(R/T\)), the ratio of the brace to the chord radius (\(r/R\)), and the ratio of the brace length to its radius (\(l/r\)). Independent varying of the geometric parameters will lead to a huge number of models; hence, in this study the radii of the members are considered as constant: \(r=70\) mm and \(R=80\) mm. Therefore, the \(r/R\) ratio is constant but the remaining normalized parameters are independently varied over reasonable ranges in order to investigate their effects on the buckling response of the generated CHS T-joints. The selected parameters and ranges are tabulated in Table 1. Accordingly, more than 180 non-retrofitted CHS T-joints are modeled to find out the effects of the geometric parameters on the governing buckling modes and their critical loads.  
After investigation of the non-retrofitted joints, three joints with different buckling behavior (i.e., the local and overall buckling of the brace and the chord ovalization) were chosen to be retrofitted. When the joints were retrofitted with FRP wrapping, additional parameters were added to the problem. These were the number of plies in the composite patches (2-12 plies in this study), the ply orientation, and the length of the composite patch, which was changed from half to the total brace length. In practice, the CFRP wrapping method is directly disarrayed because of the T-shape of the joint; hence, in this numerical study, just practical wrappings are considered. The buckling state of the retrofitted joints and their post-buckling behavior were assessed and then compared with the corresponding non-retrofitted joints. In this way, the effects of the FRP wrapping on the load-bearing capacity and the deformed shape of the joints were investigated. Composite failures in the retrofitted joints were also evaluated.