esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
As one of the most important transcription factors in cells, c-MYC (hereafter MYC) is involved in cell development and regulates cellular functions, including cell cycle control, cell metabolism, proliferation, differentiation and apoptosis [1, 2]. Once deregulated, MYC acts as a proto-oncogene contributing to tumorigenesis by increasing cell proliferation and inhibiting cell differentiation [3, 4]. _MYC_, located on chromosome 8q24.21, is overexpressed in up to 45% of solid tumors [5] and translocated in up to 15% of diffuse large B-cell lymphomas (DLBCL) [6] and multiple myeloma (MM) [7] patients, and in 95-100% of all Burkitt lymphoma (BL) patients [8, 9]. Tumors overexpressing MYC often display resistance to chemotherapy, leading to higher incidences of relapse and elevated mortality rates [10, 11, 12, 13, 14]. Successful immunotherapeutic approaches for lymphoma, such as the anti-CD20 antibody rituximab in combination with chemotherapy, are also less effective in high-grade B-celllymphomas displaying _MYC_ rearrangements [14; 15; 16], suggesting the involvement of MYC in tumor escape from novel immunotherapeutic approaches. Supporting this idea, combining MYC inhibitors with anti-PD1 therapy effectively reduced tumor volumes in recent murine studies of prostate and pancreatic cancer [17; 18]. While such evidence points to a significant contribution of MYC to immunotherapy resistance [19], the exact impact of MYC on the efficacy of immunotherapeutic approaches and possible mechanisms thereof remains largely unexplored.  
To gain more insight into this, we used synthetic MYC inhibitor 10058-F4 in a relatively broad panel of MYC overexpressing cell lines and primary tumor cells from B-cell malignancies, including DLBCL, BL and MM. We tested the susceptibility of all MYC downregulated cells against effective and clinically approved conventional antibodies targeting CD20 (rituximab) and CD38 (daratumumab) and a bispecific T-cell engager targeting CD19 (blinatumombab). We generated mutated MYC alleles in OCI-LY18 cells using CRISPR-Cas9-gene-editing technology and subsequently repaired mutated _MYC_ coding sequences by CRISPR-Cas9-mediated homologous recombination to confirm the direct involvement of MYC in escape from conventional and T cell-redirecting antibodies. Our results reveal the involvement of MYC in tumor escape from NK cell- and T cell-mediated cytotoxicity through diverse, non-overlapping mechanisms. The results provide novel insights to improve the efficacy of immunotherapeutic approaches for MYC-driven malignancies.
## 2 Methodology  
We selected 120 global evenly distributed MGEX stations that support the B1I/B3I and B1C/B2a observations of BDS-3, during the period from 1 October to 31 October 2023. The distribution of the chosen BDS-3 stations is illustrated in Figure 1. Our ERP data processing schemes are outlined in Table 1. Our data processing was performed on BSW54 [25]. The dataset spans one month, covering days of the year (DOY) 274 to 304 in 2023, and we utilized double-difference observations with ionosphere-free (IF) combinations. To achieve high-precision orbit and ERP results, we implemented a two-step process: initially generating 1-day arc solutions, followed by stacking consecutive normal equations of 3 days to obtain the final 3-day arc solutions [26]. ERP estimation was performed with a 24 h resolution, represented by epochs at noon and midnight, utilizing piece-wise linear functions for parameterization. For reference frame selection, we opted for IGS20, which is a realization of ITRF2020 for GNSS [2]. The datum definition was achieved by using IGS20 core stations with minimal constraints including NNR and NNT to align with IGS20. To maintain consistency, we employed antenna models from IGS20.txt for both receivers and satellites, and the phase center variations (PCV) of BDS satellites were not taken into consideration. In this study, the SRP model employed ECOM2 with seven parameters [27]. Sub-daily variations were corrected using the Desai-Sibois model, which is recommended by the 3 rd IGS reprocessing campaign [28]. Because of the correlation between orbital elements and UT1-UTC, the GNSS technology could not directly estimate the UT1-UTC parameter [29]. Consequently, we tightly constrained the UT1-UTC of the first epoch to the a priori value from IERS EOP 20C04, which originates from VLBI technology. For the troposphere estimation, we use the Vienna Mapping Function (VMF3) grid product as the a priori model and zenith wet delay was parametrized as piecewise constants with a temporal resolution of 2 h. We use Chen-Herring model as an a priori model and 24 h resolution for troposphere gradients estimates.  
Figure 1: Distribution of 120 selected BDS-3 stations support both B1I/B3I observation and B1C/B2a observation.
# High-Efficiency Navigation of Nonholonomic Mobile Robots Based on Improved Hybrid A* Algorithm
## 2 Path Planning for Nonholonomic Robots  
### Map Construction and Robot Positioning  
Simultaneous localization and mapping (SLAM) [28], which includes laser SLAM, visual SLAM, and multi-sensor fusion SLAM according to the type of sensors employed, was the commonly used method in the construction of maps for different working environments [29]. In this study, laser SLAM was chosen as the localization and mapping method due to its higher accuracy, faster speed, and wide-ranging capabilities with the using of LiDAR. The sensor emits laser signals to perceive the surrounding environment, and the obstacles reflected back to the receiver by the laser are used to construct the map based on the sizes, shapes, and positions of the obstacles. In plotting the map, the geometric mapping approach, the topological mapping approach, and the occupancy grid mapping approach are the commonly used methods. In this study, the occupancy grid map approach, which plots the map by small squares, was employed. The number of squares represents the size of the map, and the probability of the obstacle in a square is higher when the number is larger. Then, the plotted map was meshed, and the values in the range of 0-255 were defined to each cell presenting different occupied characters (vacancy, obstacles, etc.). The computer uses this feature to discriminate obstacles. A two-dimensional map was constructed digitally via the G-mapping, which is a widely used SLAM algorithm in ROS. This algorithm is an improvement upon the Rao-Blackwellized particle filter (RBPF) approach, which prioritizes positioning before map construction.
## 2 Materials and Methods
### Evaluation Metrics
#### 2.3.10. Curve \(F_{1}\)-Score-Threshold  
This curve represents the \(F_{1}\)-score metric for different threshold values. \(F_{1}\)-score-Threshold complements the information provided by the PR curve considering jointly the values of the precision and recall measures, compacted in their harmonic mean. It allows the evaluation of the stability of the system performance for different threshold values. In a high-performance, stable system, the curve is nearly a straight line; the value of \(F_{1}\)-score remains approximately constant and close to 1 for the full range of possible threshold values.
## 3 Results and Discussion
### Effects of Thermal and pH Treatments on the Stability of P2, P4 and P6  
Figure 9A indicated that the scavenging activity of P2, P4 and P6 on hydroxyl radicals (presented as EC\({}_{50}\) value) has the same varying tendency. Heat treatments can greatly affect their activity and the EC\({}_{50}\) values of P2, P4, or P6 treated at 20 and 40 \({}^{\circ}\)C were significantly (\(p>0.05\)) different from those treated at 60, 80, and 100 \({}^{\circ}\)C, respectively (\(p<0.05\)). On the other hand, there was no significant difference in their EC\({}_{50}\) values on hydroxyl radicals when P2, P4, or P6 were disposed at 20 and 40 \({}^{\circ}\)C for 0.5 h, respectively (\(p>0.05\)).  
Figure 9B shows the influences of acid and alkali treatments on P2, P4 and P6. The results indicated that the EC\({}_{50}\) values of P2, P4, or P6 showed no significant difference when the pH value ranged from 5 to 9, but they were significantly different from the EC\({}_{50}\) values of P2, P4, or P6 treated at pH 3 or pH 11. In addition, the alkaline treatment had a stronger effect on P2 and P6 compared with P4.
# The Spotting Distribution of Wildfires
## 1 Introduction
### Prior and Concurrent Models Coupling Spotting with Local Spread  
A recent cellular automata model [33] was the first of its kind to incorporate spotting. The authors assumed a simple two-dimensional normal distribution for the spotting distribution, where the spotting distribution represents the probability of ignition occurring in a non-burning cell in a given time step. This model was considered first separate from local spread, in which non-burning cells can be ignited by their burning neighbours, with some probability related to the rate of spread indicated by the empirically-based Fire Behaviour Prediction system [34]. In addition, burning cells become burnt-out in each time step with some probability. The authors of the paper then used distributions to describe the spotfire distribution in the plane, neglecting topographical variation. The room for improvement of the normal distributions from [33] provided some of the initial motivation for examining more realistic spotting kernels, as in this paper.  
The cellular automata model was unique in that it provided a coherent mathematical model in which spotting and local spread were simultaneously present. Since then other stochastic models have been developed, such as the augmentation of Discrete Event System Specification models (which employ novel Lagrangian point-advancement techniques), to include spotting [35]. This particular reference improved on the spotting distribution of [33], employing the more realistic log-normal distribution.  
It must be mentioned that computer implementations of spotting have been employed in computer-based local spread simulators (like PROMETHEUS[20], or FARSITE [19]). Spotting models, such as those developed by [PERSON] [1], have been incorporated in an ad-hoc way [36], focussing on the maximum spotting distance, rather than the downwind spotting _distribution_. Most of the [PERSON] models deal with _tording_, where ladder fuels allow at most a few isolated trees to crown and spot at the same time, and with _line thermals_[13], which are well-mixed rising columns of air which lift firebrands--this framework is more appropriate for a large, very intense forest fire, as we consider here.  
The above computational models provide fire front prediction through explicit curve tracking. The level-set method is an alternative and very powerful method for front tracking. Consequently, several authors use level set methods for the propagation of wild fires [37; 38; 39]. Our approach for the spotting distribution studied here differs from the level-set approach, as we are not only interested in the location of the fire front, but we also like to understand the distribution of spot fires in a continuous region ahead of the fire. While here we focus on the spotting process, a detailed probabilistic model for wildfire spread including spotting and local effects has been developed in the thesis of [PERSON] [29]. We briefly discuss these models in the Discussion Section 4.1, though we emphasize that the present work is devoted to the spotting model.
# Feasibility Study for Using Piezoelectric-Based Weigh-In-Motion (WIM) System on Public Roadway
## 4 Discussion  
### Force Transmission  
While transmitting the force from vehicles' wheels to the piezoelectric materials, some force is absorbed and wasted by the protective package and the surrounding pavement. Therefore the actual loading applied to the piezoelectric materials is less than the loading applied on the surface of the P-WIMs. Since the voltage generated by each disk is the same and all the piezoceramic disks are connected in parallel, the stress applied on each disk by the loading axle can be calculated as:  
\[\sigma=V/(g_{33}\times t) \tag{1}\]  
Figure 5: A sample data and instruction of finding the reference peak voltage value.  
where g\({}_{33}\) is the voltage constant, and t is the thickness of the disks. The g\({}_{33}\) of the 850 series PZT material is 0.024 Vm/N and the thickness of the disks is 0.02m. The total loading actually taken by all 9 disks in the P-WIM can be calculated as:  
\[F_{i}=\sigma\times\pi r^{2}\times 9 \tag{2}\]  
where r is the radius of the disks which is 0.02m, and i is the ID of the P-WIMs. The force transmission percentage, P\({}_{T}\), of the force applied from vehicles' axles can be calculated as:  
\[P_{T}=\frac{\sum_{i=1}^{n=4}F_{i}}{F_{0}}=[\sum_{i=1}^{n=4}\frac{9V_{i}\pi r^{ 2}}{(g_{33}\times t)}]/F_{0} \tag{3}\]  
where V\({}_{i}\) is the voltage measured from each of the installed P-WIM and F\({}_{0}\) is the vehicle axle loading provided by VDMV. All the data from VDMV was recorded with a time log. The time setting on the testing computer was reset to match the time setting used in VDMV (weigh station) computer. As an example, the axle loading of one trailer truck passed the weigh station is listed in Table 1. The peak voltage outputs from the P-WIMs corresponding to this trailer truck are listed in Table 2. Table 3 listed the calculated actual loading applied to the 9 disks in each P-WIM and the force transmission percentage. The average force transmission percentage for this measurement is about 15.16%. Thirty-three sets of data measured on 25 October 2012 are generated from full contact and valid to perform the analysis. The average force transmission percentage of them is about 14.84% with 0.63% of the variance and 0.79% of standard deviation.  
engineering plastic and copper alloy electrodes. Additional testing tools are also required for axle loading measurements, such as a National Instrument digital multimeter and a personal computer. According to the research report of MnDOT published in 2016, the cost of conventional four-lane Lineas sensors and a commercial controller used by MnDOT is estimated at $133,072 [31]. The detailed comparison between the cost of P-WIM and a permanent WIM for four lanes are listed in Table 4. The total cost, including the system and installation of P-WIM, is 80% less than the Permanent WIM system used by MnDOT.
## 2 Overview
### IoT  
#### 2.1.1 Architecture  
IoT is a combination of interconnected embedded sensors and heterogeneous devices, where they share common features such as limited processing capabilities, small memory, low power, and unique identifiers. IoT users can remotely provision data and access services through deployed gateways that connect the IoT network with the outside world [2].  
As stated by [27], \"The Internet of Things allows people and things to be connected anytime, anyplace, with anything and anyone, ideally using any path/network and any service\". Various IoT architectures are proposed, each representing distinct perspectives and functions. From a deep technical perspective in wireless networks, ref. [28] described IoT architecture in a three-layer/tier manner, such as interfaces/services, network/ communication, and perception/hardware [29]. However, some other applied researchers and industrial experts consider a fourth layer. In [30], this is called the support layer, which participates in fog computing, smart computing, cloud computing, etc. Another interpretation of the fourth layer approach is depicted by Figure 1, where each layer represents different technology approaches and the scale of architectural elements [31].  
From a top-down perspective, the four distinguishable key parts of generic IoT architectures are application, data processing, network, as well as sensors and actuators layers. To accomplish various applications (for example, healthcare, smart home, and smart transportation) of IoT devices, the application layer implements and delivers the results of the data processing (i.e., transport) layer [32]. The application layer is a user-centric layer that performs different functions on behalf of the user. The data processing layer analyzes the data acquired in the sensing layer and determines based on the findings. The data processing layer in various IoT devices (e.g., smartwatches, smart home hubs, etc.) also stores the results of earlier analyses to offer a better user experience. The network layer exchanges the results of data processing with other linked devices. The network layer facilitates sending data from the sensing and actuators layer to other connected devices as it serves as a communication channel. Data can be transferred across connected IoT devices using various communication technologies, such as Z-Wave, cellular network, Bluetooth, Wi-Fi, and Zigbee [33]. The primary function of the sensors and actuators layer is to identify any events occurring in the device's periphery and to collect real-time data [32].  
Figure 1: The generic architecture of IoT systems in a four-layered approach [31].
## 3 Results  
### Effect of W/C Ratios on the Setting Time of SLMKPC Slurry  
Figure 3 presents the relationship between the setting time and W/C ratios of SLMKPC. It is noticeable that W/C ratios had a great influence on setting time. With the increase of W/C, the setting time of SLMKPC was prolonged. When W/C was less than 0.3, the setting time increased greatly. When W/C = 0.25, the setting time increased by 31% as compared to that with W/C = 0.2. When W/C = 0.3, the setting time was 24% higher than that with W/C = 0.25. However, the increment of setting time became slow after W/C = 0.3, and the setting time increased by 15% and 8%, respectively, as compared to those with W/C between 0.35 and 0.3, 0.4, and 0.35.  
The SLMKPC slurry r  
Figure 4 displays the variation trend of the hydration exothermic curve of SLMKPC with W/C in 400 min. The hydration exothermal curve of the SLMKPC slurry had an acceleration period in the beginning, and the hydration heat reached the peak and decreased slowly with the prolonged time. The exothermic peak of hydration manifested different characteristics with the changing W/C ratio.  
When W/C < 0.3, only one exothermic peak was detected and the maximum exothermic peak temperatures corresponding to W/C = 0.2, 0.25, and 0.3 were 63.2 \({}^{\circ}\)C, 68.5 \({}^{\circ}\)C, and 69.7 \({}^{\circ}\)C, respectively. It implies that the hydration reaction of SLMKPC became more violent in the W/C range of 0.2-0.3. When W/C = 0.2, the moisture content in the SLMKPC slurry was insufficient to allow all CMR and KH2 PO4 to react. When the W/C ratio increased from 0.2 to 0.3, CMR and KH2 PO4 completely reacted with water, and subsequently, the highest exothermic temperature of hydration was obtained at W/C = 0.3.  
Figure 4: Influence of W/C ratios on the hydration heat release characteristics of SLMKPC slurry.  
Figure 3: Influence of W/C ratios on the setting time of SLMKPC slurry.  
When the W/C ratio increased from 0.35 to 0.4, the number of hydration exothermic peaks changed from one to two, and the maximum temperature of the first exothermic peak was lower than that of the second one. When W/C = 0.4, the exothermic peak temperature of hydration was less than that with W/C = 0.35, indicating that the excessive liquid played a certain blocking effect on the reaction of the slurry and reduced the exothermic reaction temperature.
## 3 Drone System  
Conventional drones primarily rely on manual control or GPS-assisted navigation, which limits their effectiveness in indoor environments where GPS signals are unavailable, and space is often constrained. In such scenarios, human operators, especially inexperienced ones, face challenges in maintaining stable and collision-free flight due to limited spatial awareness and reaction time. This becomes particularly critical during indoor inspection tasks, where obstacles such as walls, equipment, and structural elements are densely distributed.  
To address these challenges, the custom-designed 360-degree sensor module proposed in this research was integrated into a PX4 Development Kit-X500 v2 drone platform [30; 31], shown in Figure 9. The sensor module enhances the drone's perception capabilities by providing real-time distance measurements using eight Time-of-Flight (ToF) distance sensors arranged around the drone. This configuration enables autonomous collision avoidance by detecting nearby obstacles and dynamically modifying the drone's motion commands. The integration preserves manual flight capability while adding a layer of autonomous safety, ensuring efficient and secure operations in complex, GPS-denied environments. The drone used for experimentation and testing is a custom quadcopter equipped with standard commercial-grade components. Table 2 summarizes the drone's main specifications. The total additional weight introduced by the sensor module and microcontroller was less than 100 g, which is well within the payload capability of the drone. During test flights, the drone demonstrated reliable performance with minimal impact on flight stability or power efficiency.
# Investigation of Biochars in Terms of Vitamin E Adsorption Capacity
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were conducted using SigmaPlot 15.0. (Systat Software Inc., San Jose, CA, USA). Data for each mesopore class, as well as the theoretical and experimental adsorption capacity of \(\alpha\)-tocopherol acetate, were tested for normal distribution using the Shapiro-Wilk test and the Brown-Forsythe test for equal variance, with \(\alpha=0.05\). For the experimental adsorption of \(\alpha\)-tocopherol acetate, the tests performed were passed, and the data were consequently analysed for significant differences using One-Way Analysis of Variance (ANOVA) and [PERSON]'s multiple comparisons test, with \(\alpha=0.05\). For comparisons of mesopore class and theoretical adsorption capacity between biochars, neither normality testing nor equal variance testing was passed, which is why the data were transformed using logarithms, roots, reciprocals, or squaring. As the data were still not normally distributed (\(p\geq 0.05\)), a Kruskal-Wallis One-Way ANOVA on ranks, with [PERSON]'s multiple comparison test, was conducted, with \(\alpha=0.05\). However, the ANOVA on ranks showed no significant differences (\(p\geq 0.05\)), even if large differences existed between the respective results. This might be attributed to \(n=2\), as the porosity analysis was only conducted in duplicate. Thus, despite the failed normality (\(p\geq 0.05\)) and/or the failed equal variance tests (\(p\geq 0.05\)), a One-Way ANOVA was conducted, showing significant differences between the biochars (\(p<0.05\)). The same procedure was conducted for the comparison of the mesopore classes for each biochar. Significant differences (\(p<0.05\)) between biochars are indicated with small letters (abcdef), whereas significant differences (\(p<0.05\)) between mesopore classes for each biochar are indicated with capital letters (ABCDEF).  
Regression was analysed between the BJH surface area and the relative adsorption capacity of \(\alpha\)-tocopherol acetate using the polynomial linear equation\(f(x)=y_{0}+ax\). The decision for this regression analysis was based on the assumption of monomolecular linear adsorption. As results, the strength and direction of the linear relationship, the correlation coefficient \(r\), and the coefficient of determination \(r^{2}\), as well as the adjusted coefficient of determination \(r^{2}\)_{adjusted}_, are shown.