esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
coli_ isolates from cows with mastitis in Switzerland were fully susceptible to all antimicrobials tested. The authors observed low resistance rates for gentamicin (3.7%), amoxicillin/clavulanic acid (2.4%), and ceftiofur (1.2%) [34]. The low resistance rates were also reported among mastitis _E. coli_ isolates in Denmark with no resistance to gentamicin and ceftiofur [35]. On the other hand, [PERSON] et al. [18] observed resistance to ceftaxome, ceftaxome, gentamicin, and the ESBL phenotype in 23% of isolates derived from the milk of cows with subclinical mastitis. One of the most efficient ways of mastitis case reduction in dairy farms is pre-milking and post-milking feat disinfection. In the present study, antibacterial activity of three dipping agents, i.e., stabilized iodine, povidone iodine, and chlorhexidine was evaluated. All three agents significantly reduced the bacteria number (from 96.16 to 97.92%). The strain susceptibility was not correlated with its origin. There were no significant differences in the bactericidal efficacy between the dipping agents. This supports our previous study showing the high effectiveness of all three chemicals against _E. coli_, _S. aureus_, _L. monocytogenes_, and _S. marcescens_[17]. Additionally, [PERSON] et al. [36] showed high effectiveness of preparations based on iodine at various concentrations in teat dip. One property helping the bacteria to survive in the mammary gland and persist in dairy herds is a biofilm formation ability [11]. Biofilm production might hinder the treatment of recurrent infection. In this study, we compared this ability between a group of strains isolated from cows with subclinical and clinical mastitisti. To date, no significant link between biofilm formation capability and bovine mastitis was demonstrated [37]. In our study, we did not observe a significant difference in the biofilm intensity between both groups of isolates. On the contrary, [PERSON] et al. [38] reported that all 27 _E. coli_ strains isolated from cows with mastitis possessed the capability to form biofilm, although with different strength. [PERSON] et al. [39], however, demonstrated that biofilm production is strictly associated with the medium and growth conditions applied. They revealed that lower temperature (20 \({}^{\circ}\)C) and low nutrient media promoted the biofilm formation ability of _E. coli_ mastitisti' strains. In turn, cultivation in TSB at 37 \({}^{\circ}\)C inhibited this property in almost 80% of the tested strains [39].
## 1 Introduction
### Tsp  
The TSP is a combinatorial optimization problem [29] that is simple to explain but difficult to solve, and it is categorized as one of the problems that cannot be solved in polynomial time; in other words, it is an NP-hard issue [30].  
The TSP's purpose is to determine a complete, cheap tour in which a salesman must visit each of the n provided cities only once. The TSP's search space consists of permutations of n cities. Each combination of n cities generates a solution, and given n cities, the size of the overall TSP search space is \(s((n-1)!/2)\)[8; 31].  
Due to its importance and application in multiple areas of daily life, the common NP-hard problem known as the TSP has been comprehensively and widely explored in many fields, especially across the domains of computer science and operations studies; however, it remains an open challenge, and the TSP is widely regarded as a standard testbed of multiple combinatorial optimization techniques [27; 32].  
However, one can question whether the TSP issue is significant enough to warrant all of the attention it has received. Much attention has been paid to the problem because it is a reasonably straightforward problem, and it is so easy to describe but so hard to solve. The TSP is important because it is a representation of a wider category of problems defined as combinatorial optimization problems. The TSP belongs to the group of problems identified as NP-complete problems. In addition, if a polynomial-time-efficient method can be developed for the TSP, then efficient algorithms might be developed for every other NP-complete problem. There are, however, significant examples of actual difficulties that may be expressed as TSPs, and many additional problems are extensions of this problem [33], such as the drilling of printed circuit boards, the overhaul of gas turbine engines, the design of global navigation satellite system surveying networks, wallpaper cutting, computer wiring, X-ray crystallography, vehicle routing, etc. [29; 34].  
Many techniques for solving the TSP have been proposed, including exact and approximation algorithms [35]. Exact approaches are not generally appropriate for large-scale TSPs. On the contrary, approximation algorithms, particularly many bioinspired algorithms, may produce acceptable solutions to many NP-hard problems in a (reasonably) short running time [10; 29; 36], such as genetic algorithms, ant colony optimization (ACO), particle swarm optimization (PSO), and artificial neural networks (ANNs) [37; 38].  
[PERSON] [39] presented the first GA for the TSP in 1985. Over time, the GA implementations evolved from being general to being more specialized to the TSP. Many population seeding, encoding, mutation, and crossover methods were developed throughout the years, further specializing GAs for the TSP [40].
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
## 5 Performance Analysis
### Security Analysis
PKAEF provides a verification mechanism to detect the invalid partial signatures and a substitution scheme to maintain enough participating nodes for report generation. When a compromised non-CH node participating in report generation refuses to provide \(k_{j}P\) or a partial signature, or provides an incorrect partial signature, then a non-participating cluster node will substitute it to participate in report generation. Therefore, PKAEF is more resilient to report disruption attacks launched by a non-CH node than DSEDA. PDF does not provide any measure to combat report disruption attacks launched by a non-CH node, so it cannot resist such attacks. \(\square\)  
**Theorem 4**: _PKAEF can resist report disruption attacks launched by a CH node, while DSEDA and PDF cannot resist such attacks._  
Looking at PKAEF, if a CH node refuses to send out the signed event report or sends out an event report with an illegitimate signature, then a new CH will substitute it, and a new signed event report will be generated. Therefore, PKAEF can resist report disruption attacks launched by a CH node. DSEDA and PDF, however, do not provide any measure to combat such attacks, so they cannot resist such attacks. **Theorem 5**: _PKAEF can resist selective forwarding attacks, while DSEDA and PDF cannot resist such attacks._  
PKAEF provides an efficient report-forwarding protocol. When the selected forwarding node does not forward the report, the sender will try other trusted upstream nodes until the report is forwarded; therefore, PKAEF can resist selective forwarding attacks. DSEDA and PDF, however, do not provide any mechanism to combat selective forwarding attacks, so they cannot resist such attacks. **Theorem 6**: _PKAEF can detect and isolate the malicious nodes while DSEDA and PDF cannot._  
PKAEF provides a trust value-based approach to detect and isolate the malicious nodes. Looking at PKAEF, each node establishes a trusted neighbor list to store the trust values of its neighbors. When a node has any malicious behavior, its trust value will be reduced, and it will be isolated if its trust value falls below the predefined threshold. DSEDA and PDF, however, do not provide any mechanism to detect and isolate the malicious nodes. Finally, the security performance of DSEDA, PDF, and PKAEF is summarized in Table 1, in which \"Poor\" means the scheme cannot resist such attacks, while \"High\", \"Moderate\" and \"Low\" indicate the relative ability of these schemes to resist such attacks.
# Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks
## 2 Related Works  
### NOMA-Enabled Task Offloading in MEC Scenarios  
Video surveillance systems have been used extensively in various industries, and are gradually becoming intelligent, for example, face recognition and object detection [13; 14], etc. Along with the rapidly growing number of video monitoring applications, it is necessary to transfer and analyze an increasing amount of video data. MEC has great potential in terms of reducing delays [15; 16; 17; 18], and energy consumption [19; 20; 21] due to its intelligence in computing and caching. It is possible to reduce the transmission burden and latency by offloading the computation-intensive and latency-sensitive tasks to the MEC server. In order to further optimize the spectrum resource allocation and achieve high speed transmission and wide coverage, most studies combined NOMA and MEC. The authors in [22] focused on the partial offloading and binary offloading problems under time division multiple access and NOMA and tried to maximize the computing efficiency of the system. The authors in [23] proposed the ultra-dense heterogeneous network (UDHN) based NOMA-MEC system and studied the resource allocation problem of multi-SBS and multi-users to minimize user energy consumption and task delay. The authors in [24] focused on reducing the transmission delay and optimizing workload offloading allocation in a downlink NOMA-based MEC system. To solve this problem, they designed a channel quality ranking algorithm to obtain the optimal offloading decision. The authors in [25] considered the random task arrival and the uncertainty of channel conditions, and they proposed a decentralized DRL framework to solve the problem of power allocation, where the state was based on local observations. In addition, NOMA technology is also applied in many practical situations to improve spectrum utilization, such as robotics, unmanned aerial vehicle (UAV), and smart healthcare scenarios, e.g., where multiple users offload tasks to the MEC simultaneously. The authors in [26] proposed a communication enabled indoor intelligent robots (IRs) service framework, which adopted the NOMA to support the highly reliable communications. The efficiency and communication reliability of the IRs was maximized using a DRL-based algorithm. The authors in [27] considered a framework for computation offloading in which UAVs used NOMA and MEC techniques to serve mobile users. They introduced federated learning and reinforcement learning to solve the problem of privacy restriction between the UAVs. In order to satisfy the ultra-reliable low-latency connectivity requirements of the remote-e-Health systems, the authors in [28] considered applying a NOMA to the e-Health systems and proved that the NOMA exhibits an excellent performance in the scenarios of fifth generation and beyond. These solutions provide certain insights in to applying a NOMA to enable efficient task offloading in MEC scenarios, while also providing a feasible scheme through which to solve the efficient transmission of video data.
### Related Work
Similarly, the methods mostly analyze spatiotemporal data from a single dimension, either time or space, without considering spatial-temporal dependencies. So, the traditional matrix decomposition method cannot adapt to current huge data volume, and the previously used clustering algorithms cannot capture well the spatial-temporal dependency in spatiotemporal data. Meanwhile, co-clustering algorithms are currently used in other fields, such as gene analysis. This paper aims to introduce a novel spatial-temporal co-clustering algorithm based on information divergence into the marine field to explore the spatial-temporal patterns of SST.
## 4 Results and Discussion
### Results Analysis
#### 4.2.2 Dataset 1  
The first dataset analyzed showed that the stalling events were detected as they are forming. At around 03:01 (Figure 10), there is an event that was not detected by the system as a stall. This is due to the _WOB_ not being high enough, and the PDMSstatus output did not reach a value higher than 0.5. This can be considered normal as maybe the motor started to reduce its RPM; however, the restriction was removed by the mill before the _WOB_ or CP could build up further.  
Figure 9: Plot for Pre-Test Case datapoints and FIS outputs.  
At around 06:00, a FlowRestriction event is detected (denoted by the red vertical line) and this could be due to the fact that the motor was stalled and, while still having trapped pressure inside the housing, the operator started to remove the _WOB_ by pulling up the CT string, which decreased the CTW_deviation back to N (Normal), and the FlowRestriction output was activated. This action of not waiting on the pressure to bleed off before moving up the tool string is an operational error by the operator side and could result in over-spinning of the PDM and, in the worst case, could lead to the rotor unscrewing from the PDM body and immediate failure of the tool. In a real case scenario, the PDMSatus output could be connected to an actuator on the CT unit that would not allow the CT pipe to move up even if the operator moves the lever to do so.  
The FIS detected 7 out of 8 stalls present in this data.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 4 Dimensioning Tools: Interference and Fading Characterization
### Single-User Case with Multiantenna Receiver  
We assume a SIMO configuration depicted in Figure 4 which \(n_{r}\) antennas are used at the receiver and only a single antenna at each sensor node. Let \(F_{1},\ldots,F_{n_{r}}\) denote the fading coefficients between the sensor device and the \(n_{r}\) receive antennas. Assume that the receiver antennas are sufficiently separated to assume that the random exponential fading coefficients are independent.  
Figure 4: Receiver diversity with SIMO configuration.  
Two transmission modes are considered: the first transmission is the antenna selection in which the transmission is performed on the path with highest fading coefficient. The second transmission is the Maximum Ratio Combiner (MRC), in which the received signals at the different antenna are combined.
## 5 Proposed Models
### Transformer-Enhanced CNN-RNN (TE-CNN-RNN)
#### 5.1.1 Model Architecture Overview  
The TE-CNN-RNN architecture begins with a 3D convolutional layer (Conv3D) that acts as the encoder. Its purpose is to extract spatial features from the input data and encapsulate the dimensional intricacies of network traffic. After this spatial feature extraction, the Multi Transformer Blocks process these features, considering their sequential relationships and enriching the model's understanding of temporal patterns. The GRU layer sequentially explores the dynamics over time, providing a profound temporal analysis. This processing sequence concludes with a fusion mechanism that integrates the derived spatial and temporal features to output a refined prediction ready for effective network management.
# Enhanced Trajectory Tracking via Disturbance-Observer-Based Modified Sliding Mode Control
## 2 Control Design
### ESO-Based PIDSMC
#### 2.2.2 Perturbation Effects on Sliding Variable  
The actual sliding-variable dynamics (\(\hat{s}\)) are affected by the estimation error of perturbation (\(\widetilde{x}_{3}\)) (Appendix C):  
\[\dot{s}=-K^{\prime}\text{sat}(s)+\widetilde{x}_{3}, \tag{45}\]  
**Assumption 4**.: _It is assumed that sliding variable is within boundary layer (\(s<|\epsilon_{c}|\)); therefore, saturation function in Equation (45) can be defined as:_  
\[\text{sat}(s)=\frac{s}{\epsilon_{c}}, \tag{46}\]  
_based on Assumption 4, Equation (45) can be written as:_  
\[\dot{s}=-K^{\prime}\bigg{(}\frac{s}{\epsilon_{c}}\bigg{)}+\widetilde{x}_{3}, \tag{47}\]  
Apply Laplace transformation to Equation (47):  
\[sp=-K^{\prime}\bigg{(}\frac{s}{\epsilon_{c}}\bigg{)}+\widetilde{x}_{3}, \tag{48}\]  
\[s\bigg{(}p+\frac{K^{\prime}}{\epsilon_{c}}\bigg{)}=\widetilde{x}_{3}, \tag{49}\]  
the relation between the estimated error of perturbation (\(\widetilde{x}_{3}\)) and the actual perturbation (\(x_{3}\)) is defined as (Appendix B):  
\[\widetilde{x}_{3}=  
\[\frac{s}{x_{3}}=\frac{\epsilon_{0}p^{3}+l_{1}p^{2}+l_{2}p}{\epsilon_{0}p^{4}+A_{1} p^{3}+A_{2}p^{2}+A_{3}p+A_{4}}, \tag{53}\]  
where \(A_{1}=l_{1}+\frac{K^{\prime}\cdot\epsilon_{0}}{\epsilon_{c}}\), \(A_{2}=l_{2}+\frac{K^{\prime}\cdot l_{1}}{\epsilon_{c}}\), \(A_{3}=l_{3}+\frac{K^{\prime}\cdot l_{3}}{\epsilon_{c}}\), and \(A_{4}=\frac{K^{\prime}\cdot l_{3}}{\epsilon_{c}}\).  
It can be understood from the Bode plot of the relation (53) that the designed algorithm effectively attenuates the low- and high-frequency elements of the perturbation (Figure 3) (\(a=30\), \(K=20\), \(c_{1}=5\), \(c_{2}=2.5\), \(\epsilon_{c}=1\), \(\epsilon_{o}=1\)).
# Fast Path Planning for Autonomous Ships in Restricted Waters
## 2 Materials and Methods
### Path Planning Methods
#### 2.3.2 Online Path Planning in Dynamic Environment
If the potential at the position of the own ship is less than \(\lambda\), it can be ensured that the own ship does not cross the static obstacle area or keeps a safe distance from it. The current position of the own ship may be A, B or C, and the subsequent position after time \(t_{c}\) (e.g., 4 min) is p\({}_{2}\) when keeping the current heading \(\psi_{c}\). The algorithm continuously calculates the potential value of position p\({}_{2}\), i.e., \(f(p_{2})\geq\lambda\), there is a collision risk for the own ship taking the current course, so the necessary course alteration should be taken. The course alteration amplitude at one time step is designed as \(\pm\)_maximum_ (\"\(-\)\" means left turn and \"\(+\)\" means right turn) according to the own ship's maneuverability. It is supposed that the own ship's positions after time \(t_{c}\) in the two directions '\(\psi_{c}+\)_maximum_' and '\(\psi_{c}-\)_maximum_' are p\({}_{1}\) and p\({}_{3}\), respectively, and their potential values are written as \(f(p_{1})\) and \(f(p_{3})\). As far as the solution of (9) is obtained, the optimal direction to avoid static obstacles can be determined by the direction of \(\rm{w^{*}}\) corresponding to positions \(p_{1}\) and \(p_{3}\). For example, in Figure 9, at the next time step, the own ship should take a left turn at position A and a right turn at positions B and C. Then, the same method is used to acquire the optimal direction to avoid static obstacles at subsequent time steps until all static obstacles are avoided. \[\rm{w^{*}}=\arg_{\rm{w}\in\{1,3\}}\min\{f(p_{w})\}, \tag{9}\]  
Figure 8: The vectors and CA strategy for emergency ship-to-ship encounters; distance to the closest point of approach (DCPA).
# Calibration of Finn Model and UBSCAN Model for Simplified Liquefaction Analysis Procedures
## 4 Results and Discussion
### Comparisons of Undrained Cyclic DSS Responses
The relationship between the CRR and \(N\) can be approximated with a power function:  
\[\text{CRR}=a\times N^{-b}, \tag{14}\]  
where \(a\) and \(b\) are determined by regression against the experimental data. The normalized CRR (CRR/CRR\({}_{N=15}\)) versus \(N_{liq}\) curves (the weighting curves) are plotted in Figure 6 against the \(b=0.34\) (typical value for clean sand) curve [30]. The weighting curves of both models follow the trend of the typical clean sand curve, indicating that the effects of earthquake magnitude on the CRR could be adequately modeled. Figure 5: Accumulation of excess pore water pressure ratio and associated cycle ratio of the UBC-SAND model. (**a**) HBF; (**b**) JRA96; (**c**) NCEER; (**d**) T-Y. Figure 6: Weighting curves of four simplified liquefaction analysis procedures. (**a**) Finn model; (**b**) UBCSAND model. The effects of the overburden stress and the static shear stress on the CRR were compared with published relationships for cases of the NCEER procedure (Figures 7 and 8) because only the NCEER procedure includes these effects in the CRR calculation. The overburden stress effect is represented by \(K_{\sigma}\)[30] and compared with the proposed relationships [10]:  
\[K_{\sigma}=(\sigma_{\text{rc}}\,^{\prime}/P_{a})^{(f-1)}, \tag{15}\]  
where \(\sigma_{\text{rc}}\,^{\prime}\) is the vertical effective consolidation stress, \(P_{a}\) is the atmospheric pressure, and \(f\) is the model constant (\(D_{R}\) = 40\(\sim\)60% and \(f\) = 0.8\(\sim\)0.7, \(D_{R}\) = 60\(\sim\)80% and \(f\) = 0.7\(\sim\)0.6). The parameter \(m\_{\textit{flfac1}}\) (refer to Tables 2 and 3) of the UBCSAND model is used to include the overburden stress effect. Using the calibrated relationships of \(m\_{\textit{flfac1}}\)[19], Figure 7 shows that the \(K_{\sigma}\) of the UBCSAND model was in good agreement with the proposed curves [10]. The Finn model could capture the decrease in \(K_{\sigma}\) with increasing \(\sigma_{\text{rc}}\,^{\prime}\), but the effect of \(D_{R}\) on \(K_{\sigma}\) did not coincide well with those of the curves from the NCEER procedure. Figure 8: Static shear stress effects on the CRR and comparison with curves in the NCEER procedure. (**a**) Finn model; (**b**) model (modified from [PERSON] and [PERSON], 2011) [19]. Figure 7: Overburden stress effects on the CRR and comparison with curves in the NCEER procedure. (**a**) Finn model; (**b**) UBCSAND model (modified from [PERSON] and [PERSON], 2011) [19]. The effect of static shear stress on the CRR is represented by \(K_{\alpha}\)[31] and compared with experimental data [32]. Figure 8 shows that the \(K_{\alpha}\) of the UBCSAND model was in good agreement with experimental data. The Finn model captured the trend of \(K_{\alpha}\) at the case of (N1)\({}_{60}\) = 5 only. In summary, \(K_{\sigma}\) and \(K_{\alpha}\) were poorly modeled by the Finn model because the dilatant behavior of sand cannot be modeled appropriately by the simple formulation of the Finn model.
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 6 Existing Methods and Proposed Solution  
### Fixed Power Control (FPA)  
Fixed power allocation (FPA) is a standard industry technique for interference mitigation and is, therefore, used as a baseline algorithm. It assigns a specific value for the transmit signal power. FPA does not use interference coordination. The total transmission power is uniformly allocated across all physical resource blocks (PRBs), resulting in a constant power level. The gNB retains its power level and adjusts the code schemes and modulation techniques. This is termed link adaptation. The link is adapted based on the measurements reported by the UAV back to gNB. The link is adapted either based on periodic or aperiodic measurements because the transmit power is fixed, resulting in a higher effective SINR. There are no measurement reports sent to the interfering gNB [44]. The total power is simply divided among the PRBs:  
\[P_{\text{TX},b}[t]:=P_{\text{gNB}}^{\text{max}}-10\log N_{\text{PRB}}+10\log N _{\text{PRB},b}[t]\quad\text{(dBm)} \tag{6}\]