esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Outer Membrane Vesicles as Cancer Vaccines: A New Toolkit for Cancer Therapy
## 2 Vaccines in Cancer Therapy
### Status of Clinical Trials  
A large number of preclinical and clinical studies involving cancer vaccines with varying levels of success have been described over the last two decades. However, with the exception of Sipleuucl T, an ex vivo DC vaccine for prostate cancer, and T-VEC, an injectable modified herpes virus for advanced melanoma, no therapeutic cancer vaccine has yet shown clinical efficacy in phase III randomized trials, mainly because of the type of patients recruited in the various clinical studies and reasons including the trial design, the specific vaccination approach, and host-related factors [30; 31; 32]. Rindopepimut (CDX-110) is a peptide vaccine that targets epidermal growth factor receptor variant III (EGFRVII) to cure glioblastoma. It showed clinical benefit and significant efficacy in phase II clinical trials; however, phase III trials were terminated as it was deemed likely the study would fail to meet its primary end point [33; 34]. Additionally, this antigen was mixed with granulocyte-macrophage colony-stimulating factor (GM-CSF) and used to vaccinate patients with breast cancer in the exploratory phase I-II trials [35].  
Unfortunately, the results from clinical settings so far have been disappointing. In a cumulative analysis of several vaccine trials including 936 patients with different types of solid tumors, [PERSON] et al. used response rate as a measure of positive outcome and concluded that only 3.6% of patients had an objective benefit from vaccination [36]. However, disappointing, lessons learned from recent studies associated with the immune-suppressive microenvironment of tumor cells have suggested further strategies for improvement [37; 38]. Indeed, the clinical success of checkpoint blockades has underscored the role of peripheral tolerance mechanisms in limiting vaccine responses and highlighted the potential for applying combination therapies [39]. In conclusion, the future of cancer vaccines looks bright; however, there remain important problems to be addressed as follows: (1) there is still a need for sufficient quantity of tumor cell antigens; and (2) lasting and effective immune responses are not induced. Furthermore, the knowledge of immune-editing and immune-suppressive mechanisms that operate in each individual patient with cancer is gaining attention in the efforts to design new cancer vaccines.
# Energy Efficient Resource Allocation for M2M Devices in LTE/LTE-A
## 5 Conclusions  
In this paper, we introduced two uplink scheduling algorithms for M2M devices over LTE. Both of them take into consideration the channel quality when allocating resource blocks to devices, which has the advantage of reducing the number of RBs necessary to send data, and consequently, reducing the energy consumed by devices. CBC-M can achieve the desired objective, which is reducing energy consumption comparing to RR scheduler; however, it cannot guarantee meeting the delay requirements of M2M devices. It was also shown that RME-M, which considers delay constraint, allows one to satisfy the QoS requirements of a large number of MTC devices in terms of delay and reducing power consumption. In this paper, we have also demonstrated the superiority of our approaches, in terms of spectral efficiency.  
**Author Contributions:** Supervision, C.B. and P.B.; Writing--original draft, H.B.R.  
**Funding:** This research was funded by iQsim company and Association Nationale Recherche technologie (ANRT).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Research on UAV Path-Planning Problem
### Environmental Modeling
#### 2.3.3 Exclusion Zone Model  
In actual flights, drones are often subject to restrictions from special areas such as the military and government. Here, these restrictions are set as spherical no-fly zones. The sphere can be expressed by the following equation:  
\[s_{i}=\left\{(x,y,z)|\left(x-c_{x(i)}\right)^{2}+\left(y-c_{y(i)}\right)^{2}+ \left(z-c_{z(i)}\right)^{2}\leq\left(r_{(i)}\right)^{2}\right\} \tag{12}\]  
The set of spheres is:  
\[s=\bigcup_{i=1}^{n_{s}}s_{i} \tag{13}\]  
In the above two formulas,\(s_{i}\) is the range of the region of the \(i\) sphere, \(\left(c_{x(i)},c_{y(i)},c_{z(i)}\right)\) are the center coordinates (sphere centers) of the \(i\) sphere, and \(r_{i}\) represents the radius of  
Figure 1: Polynomial interpolation method for curve fitting.  
the \(i\) sphere; \(n_{s}\) denotes the total number of spheres, and \(s\) denotes the set formed by all the spheres.
## 4 Discussion
### Deficiency and Implication  
There are still some problems in the application of deep-learning methods for LULC classification and LUCC detection based on time-series NDVI, which leads to the deviation of classification results. First, the annotation of deep learning samples consumes a lot of time and energy and is prone to errors, resulting in model deviation; second, there are geometric distortions in remote-sensing images of different periods, such as road deformation and house distortion. These geometric changes will be incorrectly classified as change areas; third, unstable objects can interfere with change detection, such as (1) moving aircraft and factory chimney emissions of gas, (2) agricultural greenhouses that vary significantly with the seasons, and (3) the influence of a partial sun angle and cloud shadows. Fourth, the complexity of ground feature types in urban areas can reduce the accuracy of classification. The characteristics of land cover, such as the unbalanced proportion of each category and the fragmentation of land-cover patches, make it difficult to identify and extract features. Three kinds of problems were found in the classification results: (1) the extraction effect of tidal flats with a small and variable area ratio was not ideal; (2) in the north, agricultural land and towns are intersected and scattered, while in the south, woodlands are interspersed with some small areas of agricultural land. The boundary contour of different categories is not clear enough, leading to classification errors; (3) a large area of northern water was misclassified as agricultural land, and some water was also found in the agricultural land classification. This is because there is a large area of fish ponds and paddy fields in this area. Although the semantics are different, the spectral characteristics are similar, which affects the classification of water and agricultural land.  
In view of the problems in this experiment, the LULC classification method used in this article still has a lot of room for progress. First, it is necessary to optimize the model algorithm to reduce the workload of manual annotation as much as possible while ensuring classification accuracy. Second, making full use of the contextual information in the image to improve the classification accuracy is the direction of efforts; third, the data sources used in this study are relatively simple. A good research direction might involve fully integrating the advantages of different data sources (e.g., DEM, radar images, and spatial-temporal big data) to improve classification accuracy.
# Improving VulRepair's Perfect Prediction by Leveraging the LION Optimizer
## 6 Limitations  
We acknowledge several constraints of our study. The T5 model was neither developed nor trained by us. Adjustments to the deep learning layers, whether horizontally or vertically, were not feasible as we were limited to using only the binary file. We depended on the models provided by [PERSON] et al. Additionally, Colab was chosen as it offers a conducive environment for enhancing VulRepair, with its ease of setup and pre-installed libraries. Our experiments required an Internet connection to Colab. Although an updated NVIDIA 4090 GPU is available, we did not access one on a local machine for testing. Despite achieving stable results, local testing could potentially eliminate Internet connectivity issues and enhance stability. Moreover, while some studies have shown improvements in VulRepair using AdamW, we did not explore the potential enhancements from the LION optimizer due to time and cost constraints. Such investigations may be more appropriate for researchers who manage their models.
## 6 A Method to Calculate \(\boldsymbol{k_{b/m}}\)
### Condition 1: \(p_{i}\geq 0\)  
Combining Equation (22) with \(p_{i}\geq 0\), we get that:  
\[\frac{\hat{\phi}_{b}\cdot\hat{n}_{b}+\hat{\phi}_{m}\cdot(2-\hat{n}_{m})}{2\left( \hat{\phi}_{b}+\hat{\phi}_{m}\right)}\geq 0 \tag{29}\]  
Remember that Equation (28), which is the denominator of Equation (29) must also be fulfilled.  
Thus, we need:  
\[\hat{\phi}_{b}\cdot\hat{n}_{b}+\hat{\phi}_{m}\cdot(2-\hat{n}_{m})\geq 0 \tag{30}\]  
After that, we substitute Equation (24) in Equation (30) to get the following first condition to be fulfilled by \(k_{b/m}\):  
\[\frac{k_{b}\cdot MOS_{b}\cdot\hat{n}_{b}}{J_{b}}+\frac{k_{m}\cdot MOS_{m}\cdot( 2-\hat{n}_{m})}{J_{m}}\geq 0 \tag{31}\]  
Now, if we multiply the whole inequation Equation (31) by \(\frac{1}{k_{m}}\)\((\forall\;\;k_{m}\;\in\;\mathbb{R}^{+})\) and rename \(\frac{k_{b}}{k_{m}}\) by \(k_{b/m}\):  
\[\frac{k_{b/m}\cdot MOS_{b}\cdot\hat{n}_{b}}{J_{b}}+\frac{MOS_{m}\cdot(2-\hat{ n}_{m})}{J_{m}}\geq 0 \tag{32}\]  
\[\frac{k_{b/m}\cdot MOS_{b}\cdot\hat{n}_{b}}{J_{b}}\geq\frac{MOS_{m}\cdot(\hat {n}_{m}-2)}{J_{m}} \tag{33}\]  
\[k_{b/m}\geq\frac{MOS_{m}\cdot J_{b}\cdot(\hat{n}_{m}-2)}{MOS_{b}\cdot J_{m} \cdot\hat{n}_{b}} \tag{34}\]  
Figure 6: Subjective video quality measured by means of the mean opinion score (MOS) as a function of the fraction of packet losses (FPL).
## 2 Materials and Methods
### Design and Procedure  
The probe commenced at 7:00 a.m. and the participating athlete had their last food intake before 9:00 p.m. the previous day. The hour before starting and immediately after finishing the ultraendurance event, we analyzed the following parameters based on previous studies conducted on ultraendurance probes [12; 13; 14].  
Body mass was measured using a SECA scale model 714 with a precision of 100 g (range 0.1-130 kg). The scale was placed on a flat and smooth surface and calibrated at zero. Participants were barefoot and minimally clothed. They stood in the center of the platform without their bodies in contact with surrounding objects, evenly distributing their weight on both feet while facing forward.  
Cortical arousal was analyzed using the Critical Flicker Fusion Threshold (CEFT) in a viewing chamber (Lafayette Instrument Flicker Fusion Control Unit Model 12021) following established procedures from previous studies. An increase in CEFT suggests heightened cortical arousal and information processing, while values falling below baseline indicate reduced efficiency in information processing and central nervous system fatigue.  
Isometric hand-grip strength was assessed using a TKK 5402 dynamometer (Takei Scientific Instruments Co., Ltd., Niigata, Japan). The athlete's hand grip strength was measured in the dominant hand. The athlete sat with 0 degrees of shoulder flexion, 90 degrees of elbow flexion, and the forearm in a neutral position. The highest result from two trials was recorded.  
Lower limb strength was evaluated through a horizontal jump test. The athlete stood behind a line marked on the ground with their feet at shoulder width. The athlete performed three attempts, and the highest result was analyzed.  
Heart Rate Variability (HRV) was recorded using a Polar V800 HRV monitor (Kempele, Finland). The measurement began minutes before the start of the event and concluded at the end of the event.  
Reaction Time was measured using a mobile app. The screen of the phone would turn entirely white and randomly change to a different color, requiring the subject to react immediately by tapping the screen. Participants were familiarized with the app beforehand, and three measurements were taken before and after the probe. The mean of these three moments represented the final value.  
The following parameters were analyzed immediately after the event following previous protocols [12; 13; 14]:  
Hydration status was assessed using a colorimetry procedure with a urine color chart, which helped identify the pH status and the presence of glucose, nitrites, protein, and glucose in the urine strip.  
Blood glucose concentration was determined by analyzing 5 uL of capillary blood from the finger using a portable analyzer (One Touch Basic, LifeScan Inc., Madrid, Spain).  
Blood lactate concentration was measured by collecting a 5 uL sample of capillary blood from the subject's finger and analyzing it with the Lactate Pro II system (Arkay, Inc., Kyoto, Japan).
# Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection
## 3 Problem Formulation  
In this section, the main problem of inlier detection is mathematically formulated. First, the fundamental theories of two-view epipolar geometry are explained, and our fast solution to this problem is presented. Then, a threshold-free objective function for robust estimation is formulated.  
Notation: Column vectors are represented by italic, bold lower case letters, such as \(\mathbf{x}\). Therefore, \(\mathbf{x}^{\intercal}\) (the transpose of \(x\)) is a row vector. Matrices are denoted by italic uppercase letters, such as \(F\). The elements of the matrix are denoted as \(F_{ij}\), where \(i\) represents the row index and \(j\) represents the column index. Sets are denoted by italic, bold uppercase letters, such as \(\mathbf{U}\).
# Collaborative Mobile Robotics for Semantic Mapping: A Survey
## 3 Collaborative Semantic Mapping
### Multi-Robot Data Association and Semantic Maps Fusion  
The data association and fusion process can be performed in two modes, either a \"_real-time incremental mode_\" or a \"_one-shot mode_\". In the first, the individual robots incrementally create their partial semantic maps and communicate them in real time to a fusion module to progressively build the global semantic map. In the second mode, the semantic maps of individual robots are created in advance and then fed to the fusion module to create the global semantic map in one step. Table 2 specifies the fusion mode for the reviewed works and provides an overview of the input data type of the fusion module, the methods used to solve this task, and the resulting map representation.  
This process can be viewed as two main sub-problems: the data-association problem and the fusion and optimization problem. Thus, the data-association problem consists of establishing correspondences between local semantic maps using geometric and semantic data. In addition, the fusion and optimization problem consists in integrating multi-robot data into a global representation by using data associations and dealing with data dissimilarities. In what follows, we review the techniques proposed to address these two problems.
# A Review of Recent Advancements in Knock Detection in Spark Ignition Engines
## 3 Advancements in Knock Detection
### Other Signals
#### 3.4.4 Ion Radiation across Spark Plug Gap  
One area that has gained significant traction over the years is related to measuring the ion current across the spark plug leads, with a number of studies exploring this possibility. In 2007, [PERSON] et al. [45] performed a feasibility analysis and found that knock could be predicted by using this approach. Combustion events generate ionized particles in the air-fuel mixture, forming a conductive path between the spark plug electrodes. The autoignition event that results in knock causes changes in the composition of the ionized particles. As such, analyzing changes in the ion current signal in real time provides insight into the occurrence of knock.  
[PERSON] et al. [46] further analyzed this method, correlating knocking cycles to changes in the ion current signal. This approach was able to accurately classify 95.7 percent of knocking cycles. In a separate study, [PERSON] et al. [47] improved the accuracy to 98.4 percent through the use of artificial neural networks. Similar analyses were used by [PERSON] et al. [48] and [PERSON] et al. [49]. Another study by [PERSON] et al. [50] analyzed the results from a number of development efforts related to using ion detection to assess engine knock, then successfully implemented this technique in a Formula SAE racing car for Chongqing University in 2020. Regardless of this success, it is likely that significant development is required for the integration of this technique into a commercial vehicle.
# STPA-RL: Integrating Reinforcement Learning into STPA for Loss Scenario Exploration
## 2 Background  
### System-Theoretic Process Analysis  
STPA is a systems-thinking-based hazard analysis and risk assessment method that can be used to proactively identify control and feedback failures within safety-critical systems [13]. It aims to identify unsafe controls that may pose risks in control relationships by considering interactions rather than individual components of the system. Therefore, in STPA, the system is considered a collection of interacting control loops [5].  
STPA is composed of four stages, as shown in Figure 1: First, we define the accidents and hazards to determine the scope of the system that is controllable. A hazard is a system state or condition that may cause accidents. System-level safety constraints refer to the state or behavior of the system to prevent accidents or losses, corresponding to the previously defined hazards. Second, the subject and object of the control relationship of the system is configured, along with the control and response, using the schematic of the control structure. A control algorithm and Process Model are defined for the controller to control the controlled process. The Process Model contains information that the controller uses to command control actions, e.g., the state of the controlled process, the state of the environment, and the state of other system components [14]. Each control action is assessed for its potential contribution to the hazard. Third, Unsafe Control Actions (UCA), an unsafe form of control commands that can cause system risks, are identified. STPA views the hazard state as a result of UCAs. These UCAs may or may not exist in the actual system. This is a hypothesis that should be validated or rejected based on an investigation of actions when the system is designed and constructed. Therefore, to ensure a comprehensive assessment, each control action must be examined in turn [15]. Fourth, a loss scenario is derived to track the causal factors of the UCA performed, creating a scenario that caused the loss based on the factors causing improper performance or failure of the control command provided [16]. To identify loss scenarios in STPA, analysts have generally used context tables [1], which offer a tabular framework for organizing system states, control actions, controlled variables, and hazardous states. Each row of the context table corresponds to a specific scenario, describing potential sequences of events or conditions leading to hazardous situations [17]. By populating the table with relevant information about the system's behavior, the analysis identifies causal factors and their combinations contributing to hazardous states.  
In this paper, we focus on the fourth stage of STPA and utilize reinforcement learning as a distinct approach to trace and derive loss scenarios by identifying causes from state transitions, rather than relying on the analyst's manual suggestions.
# Dynamic Single-Electron Transistor Modeling for High-Frequency Capacitance Characterization
## 3 Simulation Results
### Temperature Dependence of Capacitances  
Figure 6 shows the gate input capacitance \(C_{\rm gg}\) as a function of temperature at low and high drain voltages of \(e/C_{\rm\Sigma}/100\) and \(e/C_{\rm\Sigma}/2\), respectively. The simulation is performed up to the temperature of 15.5 K [0.5] because the proposed model is accurate up to this temperature, as was previously discussed.  
For a small \(V_{\rm ds}\), \(C_{\rm gg}\) increases as the temperature decreases almost inversely proportional to the temperature as predicted by the quantum capacitance \(C_{\rm q}\) (dashed line) [7]. This is due to the steeper change of the island charge with respect to the \(V_{\rm gs}\) at lower temperatures. For a large \(V_{\rm ds}\), the change of the island charge is gentle due to the wider transition region between \(n=0\) and 1, and thus the \(C_{\rm gg}\) is small and insensitive to the temperature.  
At higher temperatures, the \(C_{\rm gg}\) assumes the common and nearly constant value as the Coulomb blockade is lifted, and the proposed model can describe the behavior up to the normalized temperature of 0.5 in contrast to the simplified equation of \(C_{\rm q}\).
# An Evaluation of Image Slicing and YOLO Architectures for Object Detection in UAV Images
## 6 Conclusions  
Object detection in aerial images is an important research area with many potential applications. The results of the data we collected to support the bottom view of flying cars, which is a future technology, were examined with different slicing sizes in the ISA preprocessing structure. ISA is one of the effective methods for object detection in aerial images that can increase detection accuracy by eliminating pixel loss in very high-resolution image data. However, determining the slice sizes to be applied is very important for the  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline  & \multicolumn{3}{c}{**YOLOv7**} & \multicolumn{3}{c}{**YOLOv8**} & \multicolumn{3}{c}{**YOLOv9**} \\ \hline
**Slicing Size** & **512** & **640** & **1280** & **512** & **640** & **1280** & **512** & **640** & **1280** \\ Precision & **90.2** & 74.6 & 75.1 & 55.2 & 67.3 & 74.1 & 67.0 & 78.9 & 78.4 \\ Recall & 67.0 & 73.1 & **79.8** & 34.2 & 59.1 & 58.2 & 67.1 & 43.0 & 73.0 \\ mAP\_0.5 & 74.7 & 77.6 & **88.2** & 42.5 & 56.3 & 62.8 & 60.1 & 51.9 & 87.7 \\ mAP\_0.5:0.95 & 61.9 & 62.9 & **73.2** & 34.0 & 43.2 & 50.2 & 48.2 & 38.7 & 71.6 \\ \hline \end{tabular}
\end{table}
Table 6: Test results of three different architectures of the YOLO algorithm for different slice sizes.  
Figure 9: Precision-Recall graph for YOLOv7x.  
integrity of the objects and their detection by the model. The approach is relatively simple but significantly affects the overall sensitivity of multi-class object detection studies. In experimental studies conducted with three different versions of the YOLO algorithm, the effect of the slice size on object detection accuracy and FPS was examined. When the experimental results were examined, the highest object detection accuracy was obtained at 88.2 in the structure, where the slicing size was determined to be 1280. In comparison to the slicing sizes of 640 and 512, a lower FPS value of 28.70 was obtained. The approach can be further evaluated in different domains and used in industrial AI object detection challenges [28]. The presented method can simply be integrated with existing object recognition models for future studies and can be further evaluated with other datasets and environments.  
Methodology, M.T., D.A. and S.K.; Software, M.T. and D.A.; Resources, S.K.; Writing--original draft, M.T.; Writing--review & editing, D.A. and S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to \"[EMAIL_ADDRESS]\".  
Author [PERSON] was employed by the company Sakarya University Technology Development Zones Management CO. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.