esa-sceva/satcom-chunk-collection huggingface dataset:
# Check for special characters and punctuation
### System Deployment
#### 3.4.1 Deployment Architecture  
The system employs a practical architecture:  
1. Frontend:  
* Streamlit-based web interface for encoding/decoding operations
* Secure authentication via SHA-512 password hashingBackend:  
* Containerized Python (version 3.7) services using Docker
* MongoDB document storage for operation logs and reports
* OpenAI API integration for GPT-4-omni access  
The deployment architecture is defined in standard docker compose format:  
version: '3.7' services: synsteg: build:. ports: - \"80:8501\" volumes: -.:/synsteg networks: - net
## 3 Results and Discussion
### Bioactive Compound
#### 3.2.3 Antioxidant Capacity  
The antioxidant capacity based on FRAP for treated and un-treated fresh-cut Granny Smith apples were greatly impacted by the combination of storage time and treatment methods (\(p\leq 0.05\)). For both AEW and AIEW treatments, a continuous increase was observed in FRAP, while this was in contrast to the control samples which did not change significantly during storage. The lowest FRAP was recorded for the control, in comparison to treated fresh-cut apples (Figure 4). AEW-treated fruit maintained the highest antioxidant capacity in fresh-cut 'Granny Smith' apples followed by AIEW-treated samples at the end of storage day 9. As the polyphenol content in apples increases, the overall antioxidant capacity of the fruit also tends to increase (Supplementary Figures S1 and S2). This is because polyphenols are effective at donating electrons to free radicals, thereby neutralizing them and reducing oxidative stress [13]. Furthermore, by reducing oxidative stress, antioxidants help protect cells from damage, which can contribute to potentially lowering the risk of chronic diseases and improving overall health [4].  
The findings from this work agree with the observation from [PERSON] et al. [21]. The authors reported that antioxidant activity for fresh-cut 'Florina' and 'Lonathan' apples treated with AEW was higher than the non-treated samples. Furthermore, [PERSON] et al. [26] evaluated the influence of SAEW treatments on fresh-cut caulifi  
Figure 4: Effects of AEW and AIEW on fresh-cut ‘Granny Smith’ apples. Ferric reducing antioxidant power (FRAP) during storage at 2 °C and 95% RH for 9 days. Error bars represent standard deviation (SD) of mean values of treatments (n = 3) tested using the Duncan multi-range test at 95% confident interval (\(p\leq 0.05\)) and different lower-case letters indicate significant difference in means (\(p<0.05\)). Similar lower-case letters are not significantly different. Continuous dashed line indicates baseline measurement. The descriptions of treatments: AEW = acidic electrolyzed water (200 mg L\({}^{-1}\)); AIEW = alkaline electrolyzed water (200 mg L\({}^{-1}\)).  
that the SAEW effectively preserved fresh-cut cauliflower antioxidant activity. Other studies focused on EW treatments on fresh fruit agree with the findings from this study. They demonstrated that various EW treatments better maintained or elevated levels of antioxidant capacity compared to control samples [27]. [PERSON] et al. [27] investigated minimally processed apples treated with AEW. The authors showed that AEW-treated apple slices had higher antioxidant activity than the control and organic acid treated samples. The authors attributed this measured response to a lower pH which slows down the loss of nutrients and inhibits enzyme activities. Additionally, it has been reported that blueberries treated with AEW had higher antioxidant enzyme activities than samples treated with distilled water [28], which may explain some of the observed variations in antioxidant activity observed in this study.
## 4 Experiments and Discussion  
### Data Set  
The raw data was provided by Jinan Public Transportation Corporation, Jinan 250100, China. It includes line number, bus identification, station number, arrival time, departure time, length of the road segment, number of intersections, number of lanes, whether the bus lane exists, and the number of passengers. In order to establish the data set, we first cleaned the raw data. Then the arrival time and departure time of each station were converted to the travel time and dwell time.  
The data set then included the line number, station number, travel time, dwell time, length of the road segment, number of intersections, number of lanes, whether the bus lane exists, and the number of passengers. As shown in Figure 3, there are 50 stations in 1 route, and the route is divided into 49 road segments. We marked the departure station and the terminal in the map because this bus route has not been included in English maps such as Google Maps. The data set contains a total of 2064 run cycles of the bus. We selected the first 1600 cycles as the training set, the middle 200 cycles as the validation set, and the last 264 cycles as the test set.
# The Perpetual Vector Mosquito Threat and Its Eco-Friendly Nemeses
## 4 Effective \"Green\" Technologies
### CRISPR-Based Engineering of Mosquitoes Refractive to Pathogens  
4.6.1. CRISPR/Cas9  
Engineering mosquitoes to be refractive to pathogens by disrupting both their life cycles is also promising. [PERSON] et al. [209] showed that disruption of the fibronectin-related protein 1 (FREP1) gene in _Anopheles gambiae_ conferred a profound effect in suppressing infection by _Plasmodium falciparum_ and _Plasmodium berghei_, which are malarial parasites of human and rodents, respectively. The engineered mosquitoes were less robust in blood-feeding, fecundity, and egg-hatching. Moreover, the fitness cost extended to poor larval and pupal development and a shorter life span after a blood meal.  
More recent studies show that disruption of the _Anopheles gambiae_\(\gamma\)-interferon-inducible thiol reductase (mosGILT) gene impaired the ovarian development and the production and accumulation of yolk in the developing oocytest (vitellogenesis), and was less permissive for the human and rodent _Plasmodium_ parasites [210]. Although these mutant mosquitoes are partially refractive to malarial parasites, the imposed fitness cost likely excludes further development for their use in malaria control. These mutants will not be able to compete successfully in the wild. In any case, at present, such strategies may not be a practical priority, given the recent successes of SIT, IIT, and RIDL.
# Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA
## 2 Model and Data
### LAI Data  
The GEOV1 LAI product is distributed by the Copernicus Global Land Service (CGLS). It is a satellite-based estimate of LAI that is derived from observations from the PROBA-V satellite. The product is generated using a statistical algorithm [50], specifically, a neural network trained on two other pre-existing products, and is provided globally with a spatial resolution of 1 km \(\times\) 1 km and a sampling time of 10 days.
## 4 Evolutionary Algorithms in VANETs
### Self-Organization and Adjustability  
Even though VANET is a dynamic system, a self-organizing mechanism is required each time an automobile joins or exits the network. The routes must be adjusted to the node's entry/exit and the information updated for all nodes. The AntHocNet [31] algorithm is focused on ants' self-organization conduct to find food particles. The AntHocNet algorithms follow two methodologies for gathering path data that can be broadcast to other network nodes. An ant follows the most frequently used route from origin to target in the first method. A second method, referred to as pheromone propagation, is used by ants to accumulate routing information regarding frequently visited nodes. The information collected can then be used by other nodes to determine the natural path to take. The technique exhibited significant improvements in packet delivery ratio (PDR), overhead routing, and end-to-end latency when compared to well-known algorithms such as Ad Hoc On-demand Distance Vector (AODV) and Optimized Link State Routing Protocol (OLSR). AODV is a routing protocol for ad hoc networks that is loop-free. It is intended to be self-starting in a mobile node environment, withstanding a wide range of network characteristics such as node mobility, connection failures, and packet losses. OLSR, on the other hand, acts as a proactive, table-driven protocol, exchanging topological information with other network nodes on a frequent basis [29].
## 2 Deep Learning Algorithms
### Fundamentals of CNNs
* Stride [35]: Stride defines the step size at which the filter moves across the input image. Common values are 1 and 2, with larger strides reducing the size of output feature maps. * Activation Function [36]: Common activation functions include ReLU (Rectified Linear Unit), Leaky ReLU, and Sigmoid. ReLU is widely favored for its simplicity and effectiveness. * Padding [35]:Padding can be applied to maintain the spatial dimensions of the input when convolving with filters. Common padding options are'same' and 'valid'. * Pooling [37]: Pooling layers downsample feature maps to reduce spatial dimensions. Max-pooling and average pooling are prevalent types, often with a pool size of \(2\times 2\). * Fully Connected Layers [38]: The number of neurons in fully connected layers can vary based on task complexity. The output layer size depends on the number of classes in the classification task. Figure 1: Representation of the convolutional and activation layers, as well as the pooling operation process. * Dropout [39]: Dropout, a regularization technique, randomly sets a fraction of neurons to zero during training to prevent overfitting. Common dropout rates range from 0.2 to 0.5. * Batch Size [40]: This parameter signifies the number of samples used in each iteration during training. Smaller batch sizes are more computationally intensive but can lead to better convergence. * Number of Epochs [41]: This refers to the number of times the entire training dataset is fed through the network during training. * Learning Rate [42]: The learning rate governs the step size during optimization. A smaller rate leads to slower convergence, while a larger rate can lead to instability. * Optimizer: Common optimizers in CNNs include Stochastic Gradient Descent (SGD) [43], Adam, and RMSprop. Selecting the appropriate values for these parameters depends on the specific problem, dataset, and available computing resources. Hyperparameter tuning and experimentation are often necessary to determine the optimal settings for a given CNN architecture and task.
# Design of a Novel Linear Shaped Charge and Factors Influencing its Penetration Performance
## 2 Experiment
### Experimental Bi-Apex-Angle Liner Shaped Charge  
The BLSC used in the experiment consisted of the main charge, a bi-apex-angle liner, and a shell, as shown in Figure 4. The main charge was an 8701 explosive, which was placed in a press mold and subjected to a pressure load at room temperature. The height of the main charge (H) was 17 mm. Because of the design of bi-apex-angle liner, the mass of the BLSC main charge decreased by 1.1% compared to an LSC charge under the same density and the height (H). The bi-apex-angle liner was made from a 1.0 mm T2 copper plank. The shell was made from polymeric methyl methacrylate (PMMA). It provided a 10 mm standoff distance (the same as the width), and the influences of the shell were neglected.
## 3 Experiment and Result  
### Experimental Facilities  
Our experimental facilities are shown in Figure 9, including a constant voltage source, turtlebot3 robot, an industry camera, 4 LEDs, a personal computer (ThinkPad E475, Windows 10, 4G RAM A10-9600P [EMAIL_ADDRESS] GHz, Lenovo, Beijing, China.), and a high-speed video transmission line. The LEDs were light signal transmitters with unique IDs, supplied with a constant voltage source. The turtlebot3 robot served as the carrier for the industry camera, which can move along a fixed route if a script is written beforehand. The industry camera was connected to the PC by a long and high-speed video transmission line. The time of transmission was included in the processing time of our VLP system. The combination of turtlebot3 and industry camera stimulated the moving object requiring indoor navigation. The personal computer received the image captured by the industry camera and processed it with our proposed algorithm in real time.  
The size of our platform was 190 cm \(\times\) 100 \(\times\) 190 cm. Four of the LEDs were used to realize the VLP system, whose world coordinates were (100,145,190), (0,145,190), (0,45,190) and (100,45,190), respectively. The specific parameters of the industry camera, turtlebot3, constant voltage source, and experiment platform are shown in Table 1.  
The open source computer vision library (Opencv2.4.9) was used to process the received images in our experiment, and C++ was used as the programming language. Also, we recorded several image sequences in different situations with the same route of turtlebot3 to better analyze the performance of our proposed algorithm, which were all captured by the industry camera mentioned above. The route of turtlebot3 was constant, realized by a script written beforehand. Moreover, the single-lamp positioning technology was introduced into our VLP systems, meaning that we did not need to acquire all the LEDs' coordinates but just one to locate the target, which reduced the computational cost and enhanced the real-time ability of our VLP algorithm.  
Furthermore, to learn the ground truth reference of the robot's position, another camera was set directly above the experimental platform. Grids and coordinates were drawn on the ground before doing the experiment. When the industry camera on the turtlebot3 started working, the camera above the experimental platform started recording. The videos captured by the two cameras were one-to-one in the number of frames. The frames with the same number were taken at the same time. When we wanted to confirm the ground truth position of the turtlebot3 at a certain frame, we only needed to record the number of this frame and then find the frame with the same number in the videos recorded  
Figure 9: The platform and hardware devices for experimental setup.  
by the camera above the platform. Finally, we read the coordinates from the grids on the ground where the turtlebot3 stood from the image of this frame. Through this method, we learned the ground truth reference of the turtlebot3's position, which can be utilized to measure the positioning accuracy of our VLP system.
# Effect Mechanism of Connection Joints in Fabricated Station Structures
## 2 Three-Dimensional Viscoelastic Boundary and Verification
### Verification of the Artificial Viscoelastic Boundary  
In the validation example of the artificial viscoelastic boundary, a 3D uniform semi-infinite elastic FE model is illustrated in Figure 1. Point A is the midpoint of the top boundary and point B is the midpoint of the bottom boundary.  
A finite area of 400 m in length and width and 600 m in height was cut from a uniform three-dimensional elastic half-space. The elastic modulus of the model was 4.88 GPa, the density was 2000 kg/m\({}^{3}\), and Poisson's ratio was 0.22. The incident \(p\)-wave velocity was  
Figure 1: FE model for the validation example.  
1669.05 m/s and the S-wave velocity was 1000 m/s. The time step was taken as 0.01 s. The grid size was 20 m in three directions, with 13,671 nodes and 12,000 hexahedral cells. In the numerical analysis, the input of seismic waves was implemented into ABAQUS [38] using a displacement time history. The displacement time history of the Dirac impulse function was imposed on the bottom boundary of the model. The displacement time history of the Dirac impulse function can be expressed as:  
\[P(\tau)=16P_{0}\bigg{[}G(\tau)-4G(\tau-\frac{1}{4})+6G(\tau-\frac{1}{2})-4G( \tau-\frac{3}{4})+G(\tau-1)\bigg{]} \tag{4}\]  
where \(G(\tau)=\tau^{3}H(\tau)\), \(\tau=t/T_{0}\left(T_{0}=1\,\mathrm{s}\right)\), \(P_{0}=0.5\) m, and \(H(\tau)\) is the Heaviside function.  
The calculated and theoretical values for points A and B are given in Figure 2. The numerical and theoretical solutions were compared for each condition, and the results showed that they were very consistent. The time history and amplitude of the S and P waves in the numerical verification were pretty accurate, which showed the validity of the wave input method used in this study.
# Back to the Metrics: Exploration of Distance Metrics in Anomaly Detection
## 3 Experiments
### Performance Comparison  
#### 3.4.1 Anomaly Detection on MVTec-3D AD  
To compare the anomaly detection (AD) performance, we evaluated BTM against several 3D, RGB, and RGB + 3D multimodal methods on the MVTec-3D dataset. As shown in Table 1, under the RGB + 3D multimodal setting, BTM exhibited an average AD performance improvement of 5.7% compared to the previous BTF method; this advantage was also observed in each individual category. In the RGB and 3D modalities, BTM achieved average performance improvements of 3.1% and 3.9%, respectively, over BTF; again, this improvement was consistent across each category.  
We also replicated M3 DM using the code and corresponding model parameters from the official GitHub repository. In comparing our results with the replication, our method demonstrated strong competitiveness overall. Specifically, in the RGB + 3D multimodal setting, BTM outperformed M3 DM by 0.4%, achieving a performance of 93%. It is worth noting that unlike M3 DM, we did not use manually designed foreground templates, leaving significant room for further AD performance enhancement. We prefer to validate the model by preserving the foreground and background to ensure its robustness.
## 2 Proposed Resistive-Bridge Readout Integrated Circuit
### AOCL, LPF, ADC Driving Buffer, and 12-Bit SAR ADC  
The proposed CFIA circuit used chopper stabilization techniques for low-noise performance, but the undesired offset of the resistive-bridge sensor also be amplified at the output stage of the CFIA. To solve this problem, we added an automatic offset calibration loop (AOCL) for offset cancellation. The AOCL consists of a comparator, a 12-bit SAR logic block, and a 12-bit R-2R digital-to-analog converter (DAC). When offset is amplified through the CFIA, the output of the CFIA is compared through the comparator input connected to the CFIA output. The output of the comparator passes through the 12-bit SAR logic to generate the corresponding 12-bit digital code and is applied as an input to the 12-bit R-2R DAC, which generates the corresponding compensation voltage to compensate the offset. The low-frequency noise, modulated at the chopper frequency, is shown at the output as a ripple; the 2 nd-order 500 Hz LPF was used to remove the chopper ripple. The signal amplified through the IA is converted to the digital signal through the 12-bit SAR ADC, after ADC buffer. The detailed circuit implementations of the 500 Hz 2 nd-order LPF, buffers for driving ADC, 12-bit SAR ADC, and AOCL are described in [14; 15; 16; 17].
## 3 Computer-Aided Analytical Modeling of Class-E Series-Tuned RFPAs  
### Key Design Parameter: Quality Factor and Its Trade-Offs  
The resonant quality \(Q\) factor is a dimensionless parameter that describes the performance of a resonant circuit, such as an \(L\)-\(C\) tank circuit. It quantifies the sharpness of the resonance peak and the efficiency of the circuit. A higher \(Q\) value indicates a narrower bandwidth and greater selectivity, suggesting that the circuit has less energy loss and superior performance at its resonant frequency. The quality factor (\(Q_{s}\)) of the series-tuned tank circuit, which consists of an inductor \(L_{s}\) and a capacitor \(C_{s}\), is defined by Equations (28) and (29), regardless of the specific Class-E topology used.  
\[C_{s}=\frac{1}{\omega_{0}Q_{s}} \tag{28}\] \[L_{s}=\frac{Q_{s}}{\omega_{o}} \tag{29}\]  
HB simulations are performed using the commercially available Keysight Advanced Design System (ADS) tool [25]. The ADS simulation setup of Topology 2.2 is depicted in Figure 3. For a comparative analysis, the input parameters are normalized to \(f=1\), \(V_{\text{cc}}=1\), and \(P_{\text{out}}=1\). This setup utilizes an input source, \(V_{f\_pulse}=1\), representing a voltage source with a Fourier series expansion of a periodic rectangular wave, characterized by various pulse widths and duty ratios, within an HB simulator setup. All other important parameters required to run the simulation setup are shown in Figure 3.  
Figure 3: ADS simulation setup for idealized topology 2.2, Class-E.
# Open Collaborative Platform for Multi-Drones to Support Search and Rescue Operations
## 4 Performance Evaluation
### Simulation Study
#### 4.2.2 Implemented Schemes  
To verify different searching path designs of the proposed platform, four schemes were implemented and compared--_Random_ approach, _Centralized_ approach, _Distributed_ approach, and _Hybrid_ approach.  
1. In the _Random_ approach, all drones moved in a random waypoint mobility model in which a random destination was selected. The drone moved to the selected destination before another random destination was selected.
2. In the _Centralized_ approach, all drones followed an assigned searching path (i.e., a sweeping curve that staggered with each other) from the Open Collaborative Platform.
3. In the _Distributed_ approach, each drone randomly selected a sub-region and searched the selected sub-region using a sweeping curve. Each sub-region was 200 m by 200 m. When a drone finished searching the selected sub-region, it randomly selected another sub-region and searched the subsequently selected sub-region.  
Figure 18: Number of planes with dynamic searching path.  
In the _Hybrid_ approach, all drones can exchange information about searched sub-regions by themselves or with other drones. Two drones can exchange information when they were within 50 m of each other. With the exchanged information, drones avoided selecting those already searched sub-regions.
## 2 Methods
### Monitoring
#### 2.12.2. Ray Performance Dashboard  
For deployments utilizing the **scaling mode**, JAVIS integrates the **Ray performance dashboard**, which offers real-time monitoring of system performance and resource utilization. Key functionalities include the following:  
* **Replica status monitoring**, which displays the status of all active replicas, ensuring that each instance of LLMs is functioning correctly.
* **Resource utilization metrics**, which provide real-time data on CPU and GPU usage, as well as memory consumption, allowing for immediate identification of potential bottlenecks.
* **A cluster health overview**, which offers a comprehensive view of the overall health of the computing cluster, including network performance and node status.
* **Model serving loads**, which track the load on each model serving instance, facilitating the balanced distribution of requests and preventing overloading of individual nodes.  
The Ray performance dashboard enables administrators to perform prompt interventions, optimize resource allocation, and make informed scaling decisions to maintain optimal system performance under varying demand conditions.