esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Perspectives
### Limitations and Challenges of Implementing the System in Real-World Scenarios  
Although the simulations conducted in this study demonstrate the effectiveness of the cooperative SCARA system in virtual environments, it is necessary to consider several limitations and potential challenges for its implementation in real-world scenarios. The most important of these include the following:  
Variation in environmental parameters: Real-world environments are often less predictable and more variable than controlled virtual environments, so the system may need to handle unexpected obstacles, changes in lighting, temperature fluctuations, and other environmental factors that were not considered in the simulations.  
Use of adequate sensors: The accuracy and reliability of sensors used for navigation and object detection in real-world scenarios can vary, as inaccurate sensor data can lead to errors in trajectory planning and collision avoidance, requiring robust sensor fusion algorithms and error correction.  
Integration with existing systems: Integrating the cooperative SCARA system with existing manufacturing systems and workflows can be challenging, as compatibility issues may arise, requiring customized interfaces and protocols to ensure proper communication and system integration.  
Real-time processing and latency: Real-time processing requirements are critical for effective coordination and collision avoidance in dynamic environments. This implies a high computational demand and may require the use of advanced hardware and optimized algorithms to minimize latency and ensure timely responses.  
Safety: Implementing the system in environments with human presence requires adherence to strict safety standards and regulations, such as signaling, emergency stop mechanisms, compatible control strategies, among others.  
Costs: The cost of implementing and maintaining a robotic system can be significant, so a thorough study of the economic resources that can be generated by implementing a robotic system is necessary.  
Conceptualization, C.U., P.S., J.K. and H.T.; methodology, C.U., P.S., J.K. and H.T.; software, C.U., P.S., J.K. and H.T.; validation, C.U., P.S., J.K. and H.T.; formal analysis, C.U., P.S., J.K. and H.T.; investigation, C.U., P.S., J.K. and H.T.; resources, C.U., P.S., J.K. and H.T.; data curation, C.U., P.S., J.K. and H.T.; writing--original draft preparation, C.U. and P.S.; writing--review and editing, C.U., P.S., J.K. and H.T.; visualization, C.U., P.S., J.K. and H.T.; supervision, C.U. and J.K.; project administration, C.U., J.K. and H.T.; funding acquisition, C.U. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
This work has been supported by the Faculty of Engineering of the Universidad de Santiago de Chile, Chile.  
The authors declare no conflicts of interest.
## 5 System Outline
### Transmit Hardware Description  
The transmit hardware is an M8190A arbitrary waveform generator (AWG) (Keysight Technologies, Santa Rosa, CA, USA),which supports up to two channels per device and allows users to import custom waveforms created in MATLAB. The M8190A AWG has an 8 Gs/s sampling rate, and can achieve skew times of 6 ps between the two channels by adjusting the fine and coarse delays [48].
## 1 Introduction
### Background and Related Work
#### 1.1.3 Spine Segmentation  
Segmentation and volumetric 3D modelling of individual vertebrae or the complete human spine is an important task for surgeon pre-operation preparation. Combined with 3D printing or virtual reality systems for spine model examination, the surgeon gets much deeper understanding of the patient's problem. As the input images are usually thoracic scans, simple methods like thresholding are unable to distinguish between all bones in the thoracic region and spine segmentation is a difficult task. Spine segmentation is a challenging problem also because the input images contain less distinctive features compared to brain segmentation. Also when scanning younger children with not fully ossified bones, the contrast is very low and the spine tissue is not easily distinguishable from surrounding tissues.  
Recent work using deep learning U-Net architecture [16] achieved highly precise results and by combining more neural network architectures in a chain even a higher precision can be achieved [17].
# Smart Low-Cost Control System for Fish Farm Facilities
## 5 Smart Control Algorithm for Aquaculture
### Included Algorithms in the Complete System
#### 5.1.2 Energy Efficiency Algorithms  
The energy might be a limiting factor, mainly in buoy sensor nodes, buoy sensor actuators, and the buoy gateway. Thus, the efficient management of energy in those elements of the system is crucial.  
Some of the energy efficiency algorithms are based on event-triggered algorithms that define the existence or absence of the need to send data to DB. The first example is when sensed data are within the expected values, no data indicate an abnormal situation, and no alarm is generated; data can be stored and sent later. Generally, when a packet is sent, the useful payload, the portion of the packet that belongs to the sensed data, is infinite. Thus, it is better to store the data and send them when the stored data size is similar to the payload size. Nonetheless, if newly gathered data represent a variation in the previous patterns, or can trigger a predefined control algorithm, data must be sent immediately to the DB to have feedback.  
Other examples of energy efficiency are related to wireless communication. The first example is for multi-hop networks, the selection of the most suitable node for the next hop basin on the remaining energy in the available neighbors. It is preferred to send the information to a node with a high percentage of energy since the node that received the packet will need to forward it, which supposes energy consumption. Thus, the node can balance the data among its neighbors according to the remaining energy to avoid depleting the energy of one node.
## 3 Results
### Task Performance Measures at Different Automation Levels and Statuses  
Figure 2a describes the ST ratios at different automation levels and statuses. We observed that the ST ratios are significantly increased by the status (\(F\) (1,82) = 4.012, \(p\) < 0.05) and level \(\times\) status (\(F\) (3,163) = 3.101, \(p\) < 0.05), but not by level (\(F\) (1,82) = 3.687, \(p\) = 0.0583). As expected, High and Routine automation showed significantly lower ST ratios than Low and Routine automation, whereas pairwise comparison of both Failed automations did not show a statistically significant difference (\(F\) (3,163) = 2.472, \(p\) = 0.0636).  
Figure 2b describes the SA ratios at different automation levels and statuses. The SA ratios were significantly affected by level (\(F\) (1, 84) = 4.733, \(p\) < 0.05), status (\(F\) (1, 84) = 5.092, \(p\) < 0.05), and level*status (\(F\) (3, 167) = 2.756, \(p\) < 0.05). Similar to the ST ratio patterns, High and Routine automation shows significantly high SA ratios on automation statuses, and Low and Routine automation shows the second-highest accuracy, whereas pairwise comparison of Failed automation did not show the significant difference (\(F\) (3,167) = 3.121, \(p\) = 0.717). The differences in SA ratios were not significant in the Failed automation (\(F\) (1, 84) = 3.372, \(p\) = 0.0699).  
Longitudinal performance changes were examined for ST and SA ratios, as shown in Figures 3 and 4, respectively. These figures demonstrate how the users adapted to Routine and Failed automations over a prolonged period. As a result, except for SA ratios in High and Routine automation, no significant performance changes were observed. An ANOVA on SA ratios in High and Routine automation yielded a significant difference among sessions, \(F\) (3, 42) = 4.29, \(p<0.05\). A post-hoc test showed that SA ratios in the session 1 and in the session 4 differed significantly at \(p<0.05\). In addition, we could confirm that there was no longitudinal difference in statistically similar performances in Failed automation.  
Figure 3: Variations in longitudinal SA ratios in the four sessions.  
Figure 2: (**a**) Standardized task completion time (ST) ratios of four degrees of automation. (**b**) Standardized task completion accuracy (SA) ratios of four degrees of automation.
## 2 Related Work
### Ship Trajectory Prediction
By processing AIS data and optimizing SVR model parameters, the model effectively enhanced prediction accuracy and real-time performance. [PERSON] [30] proposed a new method for inland ship trajectory prediction based on a Genetic Algorithm-optimized LSTM (GA-LSTM), significantly improving prediction accuracy and computational efficiency. [PERSON] [31] presented a ship trajectory prediction model based on an Attention-enhanced Bi-directional LSTM combined with a Whale Optimization Algorithm (WOA) for hyperparameter optimization. This model comprehensively considered the context information of time series data, improving prediction accuracy and adaptability.
Remote Sens._**2015**, \(7\), 15295-15317; doi:10.3390/rs71115295  
_remote sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
**Mapping Cropland in Smallholder-Dominated Savannas: Integrating Remote Sensing Techniques and Probabilistic Modeling**  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 3,4 and [PERSON] 1,5**  
1 Center for the study of Institutions, Populations, and Environmental Change (CIPEC), Indiana University, Bloomington, IN 47408, USA  
2 Department of Government and Justice Studies, Appalachian State University, Boone, NC 28607, USA; E-Mail: [EMAIL_ADDRESS]  
3 Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA; E-Mail: [EMAIL_ADDRESS]  
4 Woodrow Wilson School, Princeton University, Princeton, NJ 08544, USA  
5 Department of Geography, Indiana University, Bloomington, IN 47408, USA; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-812-320-8268.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 23 September 2015 / Accepted: 10 November 2015 / Published: 13 November 2015_  
**Abstract**: Traditional smallholder farming systems dominate the savanna range countries of sub-Saharan Africa and provide the foundation for the region's food security. Despite continued expansion of smallholder farming into the surrounding savanna landscapes, food insecurity in the region persists. Central to the monitoring of food security in these countries, and to understanding the processes behind it, are reliable, high-quality datasets of cultivated land. Remote sensing has been frequently used for this purpose but distinguishing crops under certain stages of growth from savanna woodlands has remained a major challenge. Yet, crop production in dryland ecosystems is most vulnerable to seasonal climate variability, amplifying the need for high quality products showing the distribution and extent of cropland. The key objective in this analysis is the development of a classification protocol for African savanna landscapes, emphasizing the delineation of cropland. We integrate remote sensing techniques with probabilistic modeling into an innovative workflow. Wepresent summary results for this methodology applied to a land cover classification of Zambia's Southern Province. Five primary land cover categories are classified for the study area, producing an overall map accuracy of 88.18%. Omission error within the cropland class is 12.11% and commission error 9.76%.  
Keywords:cropland; agriculture; savanna; food security; spectral mixture analysis; multi-temporal; logistic regression; land cover; classification; Landsat +
Footnote †: journal: Journal of the American Statistical Association
## 4 Experimental Verification
### Comparison Experiments
#### 4.2.2 Comparison of Compensation Effects  
The optimal solutions obtained by the four algorithms were used as the initial weights and biases for training the BP neural network. To ensure the fairness of the comparison, each model was trained with the number of samples required for its maximum prediction deviation to begin converging. After training, the test dataset was fed into the four models as well as the pure BP model to obtain the total error range, average absolute error, and standard deviation, as shown in Table 2.  
As shown in Table 2, after error compensation, the total error for each method is significantly lower than before compensation, demonstrating the effectiveness and feasibility of these methods in improving the positioning accuracy of the rock-drilling robot arm. After compensation using the BP, GA-BP, PSO-BP, SBOA-BP, and ISBOA-BP algorithms, the maximum positioning errors at the end of the drill arm are reduced to 85.965 mm, 54.572 mm, 48.331 mm, 37.742 mm, and 36.427 mm, respectively. In addition, the standard deviations of the positioning errors at the end of the drill arm are reduced to 13.608 mm, 10.724 mm, 10.471 mm, 9.864 mm, and 9.423 mm. These results indicate that the method proposed in this paper is more effective in compensating for errors in the rock-drilling robot arm.
## 2 Materials and Methods
### Total RNA Extraction and cDNA Synthesis  
Total RNA was extracted from the gill and hepatopancreas tissues in each experimental and control group using TRIzol reagent (Life Technologies, Carlsbad, CA, USA), per the manufacturer's guidelines. DNase I treatment (Takara, Japan) was performed on the total RNA samples to remove any genomic DNA contaminants. The purity and quality of the treated RNA were validated using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and 1% agarose gel electrophoresis. The samples were then aliquoted and stored at \(-\)80 \({}^{\circ}\)C. Subsequently, the total RNA was utilized as a template to synthesize complementary DNA (cDNA) employing a PrimeScript(tm) 1 st Strand cDNA Synthesis kit (Takara, Japan), as described in our previous research [26; 27].
## 4 Materials and Methods
### Immunological Quantitation of c-di-AMP  
To quantify the concentration of c-di-AMP in the parental strain YB955 and mutants lacking _cdaA_ and/or _disA_, a competitive ELISA kit (Cayman Chemical Company; Ann Arbor, MI, USA) was used. For preparation of cell lysates, the parental and mutant strains were independently grown to an OD\({}_{600\mathrm{nm}}\) of 1.0; at this point, samples (2 mL) of each culture were collected and the cells were pelleted by centrifugation (4800\(\times\)\(g\)/3 min/4 \({}^{\circ}\)C). The pellets were washed twice with 20 mM Tris/HCl buffer pH = 7.5 and stored at \(-\)20 \({}^{\circ}\)C until used. Next, cells resuspended in 0.3 mL of 20 mM Tris/HCl buffer pH = 7.5 were incubated with 0.1 mL of lysozyme (10 mg/mL) and 0.002 mL of DNAse I (1 U/mL) for 15 min at 37 \({}^{\circ}\)C. After disruption, cell lysates were centrifuged for 10 min at 4800\(\times\)\(g\)/4 \({}^{\circ}\)C and the supernatants were employed to prepare half serial dilutions, samples (0.05 mL) of these dilutions were transferred in triplicate to the wells of an enzyme-linked immunosorbent assay (ELISA) plate pre-coated with a goat anti-mouse IgG along with different amounts of a standard c-di-AMP solution to obtain a standard curve. 0.05 mL of a c-di-AMP tracer linked to horseradish peroxidase (HRP) and 0.05 mL of a mouse anti-c-di-AMP monoclonal antibody were added to each well. Plates were incubated for 2 h, washed and then supplemented with 0.0175 mL of the colorimetric substrate 3,3',5.5'-tetramethylbenzidiene (TMB) to determine the concentrations of the bound c-di-AMP-HRP tracer. Plates were incubated with TMB for 30 min, then the reaction was stopped with 0.075 mL of HRP-stop solution and plates were read in a microplate reader (Varioskan Flash Multimode Reader; Thermo Scientific, Pittsburgh, PA, USA) set at 450 nm and quantitation of c-di-AMP was calculated from the standard curve. Results were collected from at least two different dilutions for each sample, per triplicate and adjusted to a specific number of cells determined by viable counts from the cultures of each strain.
## 5 Limitations  
This study aims at conducting an SLR of DL-based web attacks detection research works. The lack of comparison between selected studies is the primary limitation of this study. Although we reported the results of selected studies, we could not compare their performance because they used different types of datasets and performance measures. In the searching phase, we collected studies from the most extensively utilized six digital libraries (Scopus, Web Of Science, ScienceDirect, IEEE, ACM, and Springer). Still, some resources might be left out. Thus, it is hard to declare that our search strategy covered all relevant studies. Moreover, our selection was conducted in two phases: firstly, we selected title and abstract-based studies, and secondly, the selection was based on the full text of each study. However, there is still a possibility that a suitable study might be excluded mistakenly. We would also like to mention that we genuinely reported the experiment results of the reviewed studies, but we did not perform their models and we did not reproduce their experimentation.
## 2 Electro-Thermal Modeling and Analysis of Composite Materials Under Lightning Strike
### Finite Element Model  
In the electro-thermal coupling model, interactions between different layers in the composite laminate are comprehensively considered. Each layer's response to the electric field depends not only on its own conductivity properties (both longitudinal and transverse) but is also influenced by adjacent layers. At interfaces, electrical potential and current density are assumed to be continuous, implying no additional contact resistance. The model also accounts for temperature effects on material properties, where conductivity changes with local temperature, further affecting the electric field distribution and heat generation.
# Studying the Impact of a UDP DoS Attack on the Parameters of VoIP Voice and Video Streams
## 6 Discussion  
### Analysis of Results When Only Voice Traffic Is Exchanged  
From the carried-out study and the obtained results, it was found that the UDP DoS attack does not affect the voice flow parameters. The two main parameters that are monitored, mean jitter and packet loss, are always within the norm--during the attack, packet loss increases to 0.02%, with an allowable 1% packet loss. The average jitter also increases to 2.86 ms, which is very far from the allowable 30 ms limit. These increases are in the background of flooding the server with UDP packets, of size 950 bytes each.  
Furthermore, this attack does not lead to Asterisk being blocked either. Even though the server is flooded with UDP packets, the system functions normally. This was confirmed by the obtained and presented results. Figure 38 presents what the CPU load of the Asterisk is for the day when only voice streams were exchanged--for normal mode and during the UDP DoS attack. As can be seen from the graph, the average CPU utilization for the whole day varies around 20%.  
Figure 36: Total number of UDP packets sent during the attack—video calls only.  
Figure 37: Ping test result during the attack for the video calls only.  
Figure 39 shows what the memory load was for the day. As can be seen from the graph, only 15% of the memory is used, and the rest of the memory is free. As can be seen from the graph, there is no significant difference in the memory load when Asterisk is attacked and when it is not attacked--always only 15% of the memory is in use.  
These graphs explain the success rate of the ping command during the UDP DoS attack--the server's hardware resources are not 100% occupied, which is the goal of a UDP DoS attack--to completely block the system.
## 2 Background Overview
### Software Defined Networking  
SDN is recently adopted in VANETs to deal with network management issues because it is an approach that has a better view of the network, and therefore offers higher flexibility, more scalability, and better programmability. SDN is a mechanism that takes advantage of the decoupling between the control plane and data plane, allowing for complex network management. For such dynamic networks like VANETs, with an abundance of vehicles that cause frequent topology changes and the presence of heterogeneous nodes, SDN can make better decisions for coordination and management of resources, since it considers the requirements of the entire network and avoids interference with other networks. In addition, greater control is achieved by comprehensively knowing the requirements of the VANET applications and by exploiting the available resources of the network [32]. The network adapts to the dynamic changes of the network and also supports the emergency situations by prioritizing the application requirements in terms of bandwidth, propagation, delay, and processing resources.
## 1 Introduction
### Channel Assignment Framework  
In [8], [PERSON] formalized this optimization problem using graph theory, establishing its equivalence to generalized graph coloring problems. Within this context, the resultant optimization problem is fundamentally combinatorial and falls into the category of NP-hardness problems [8]. The CAP problem in wireless networks is commonly addressed through four standard formulations, as outlined in [2]:  
1. The Maximum Service and Minimum Blocking Frequency Assignment Problem (referred to as Max-FAP) aims to assign the largest number of channels required by a cell in a wireless network while ensuring interference restrictions are met. This approach is essential for determining the maximum capacity of the network under quality of service conditions.
2. The Minimum Order Frequency Assignment Problem (MO-FAP) seeks to reduce the number of different channels used in the wireless network while adhering to interference restrictions. This formulation is crucial for achieving a cost-effective network for the utilization of wireless frequencies.
3. The Minimum Span Frequency Assignment Problem (MS-FAP) focuses on minimizing the bandwidth of the networks, ensuring that channel demand and interference restrictions are met. This approach plays a critical role in determining the channels required for the network's service grade.
4. The Minimum Interference Frequency Assignment Problem (MI-FAP) aims to decrease the overall interference created by the wireless network to fulfill channel demand. This approach is relevant in scenarios where the network is extensive, and a solution without interference is not feasible.