esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Source and Methods
Seafloor temperature is a composite dataset developed by the Geological Survey of Spain (IGME) using CTD data downloaded from the World Ocean Database ([[https://www.ncei.noaa.gov/products/world-ocean-database](https://www.ncei.noaa.gov/products/world-ocean-database)]([https://www.ncei.noaa.gov/products/world-ocean-database](https://www.ncei.noaa.gov/products/world-ocean-database)); accessed on 1 February 2019) and the British Oceanographic Data Centre ([[https://www.bodc.ac.uk/](https://www.bodc.ac.uk/)]([https://www.bodc.ac.uk/](https://www.bodc.ac.uk/)); data given by British Geological Survey to GARAH project on 30 November 2018). Finally, bathymetry was obtained from three sources: the EMODnet Bathymetry portal ([[https://www.emodnet-bathymetry.eu/](https://www.emodnet-bathymetry.eu/)]([https://www.emodnet-bathymetry.eu/](https://www.emodnet-bathymetry.eu/)); accessed on 31 March 2020), IBCAO ([[https://www.gebco.net/data_and_products/gridded_bathymetry_data/arctic_ocean/](https://www.gebco.net/data_and_products/gridded_bathymetry_data/arctic_ocean/)]([https://www.gebco.net/data_and_products/gridded_bathymetry_data/arctic_ocean/](https://www.gebco.net/data_and_products/gridded_bathymetry_data/arctic_ocean/)); accessed on 1 February 2019) and GEBCO ([[https://www.gebco.net/](https://www.gebco.net/)]([https://www.gebco.net/](https://www.gebco.net/)); accessed on 31 March 2020). Several records were added or updated using regional data from scientific organizations: (i) British Geological Survey (BGS) technical reports for geophysical indicators in the north of the British Islands ([PERSON] and others, 2018; [PERSON] and others, 2019; [PERSON] and others, 2019); (ii) the BGS 250k map series/MCA Civil Hydrography Prog data for pockmark mapping; (iii) marine gas hydrate evidence in the Black Sea from SRDE-Geonform of Ukraine; and (iv) regional models of the base of the GHSZ for CH\({}_{4}\) and/or CO\({}_{2}\) in the Biscay Bay from the Bureau de Recherches Geologiques et Minieres (BRGM). The thickness of the GHSZ was taken from Nunez-Varela (Nunez-Varela, 2019). The hydrate stability field has been calculated with the CSMHYD program (Nunez-Varela, 2019) for a standard composition of biogenic gas (Nunez-Varela, 2019) and a salinity assumption of 36 psi for all the study area.
## 1 Introduction
This paper integrates DInSAR technology, visual interpretation methods, and a combined subjective-objective weighting model to construct an ecological and geological environmental risk assessment model for the Wangwa mining area. The research objectives are as follows: (1) Quantify the impact of coal mining activities on the surface of the mining area using DInSAR technology and visual interpretation methods. (2) Couple AHP and the entropy weight method to construct an ecological and geological risk assessment model for the Wangwa mining area. (3) Analyze the spatiotemporal distribution characteristics of EGER to provide a basis for the formulation of effective prevention and control measures in the mining area.
# The Role of Fucoidan in Controlling _Listeria monocytogenes_ Biofilms on Seafood-Contact Surfaces
The increasing resistance of microorganisms to common antibiotics has intensified the search for alternative antib biofilm agent [19; 20]. Fucoidan (natural compound) has gained attention due to their reported minimal adverse effects and cost-effectiveness, positioning them as promising candidates for antimicrobial drug development [21; 22; 23]. Food additive plant-derived polyphenols, such as quercetin, have shown promising antimicrobial and antibiofilm properties. Quercetin, a flavonoid with known antioxidant activity, has been demonstrated to significantly decrease _Salmonella_ biofilms on food and abiotic (food-contact) surfaces at sub-inhibitory concentrations (31.25-125 \(\mu\)g/mL), with log CFU/cm\({}^{2}\) reductions ranging from 1.50 to 2.61 [8]. In addition to reducing microbial counts, quercetin interferes with key bacterial functions by downregulating genes accompanyingwith _avrA_, _hilA_ (virulence), _rpos_ (stress response), and _luxS_ (QS), thereby disrupting coordinated biofilm development. Specifically, increasing concentrations of quercetin markedly suppressed swarming and swimming motility and the expression of flagellar function (_flaA_, _flgL_), biofilm-related (_vp0952_, _vp0962_), virulence (_VopQ_, _vp0450_), and QS (_aphA_, _luxS_) (_p \(<\) 0.05). The reduction in viable cell counts ranged from 0.10 to 2.17 and 0.26 to 2.31 log CFU/cm\({}^{2}\), respectively, on stainless steel (SS) and hand gloves (HG), depending on the quercetin concentration [24]. Recent studies suggest that fucoidan not only exerts antibacterial effects but also interferes with biofilm formation and destabilizes preformed in several bacterial species biofilms, highlighting its prospective role in preventing biofilm-associated contaminations [22]. Additionally, effectiveness in reducing _L. monocytogenes_ biofilms on seafood surfaces, particularly HG, silicon rubber (SR), and SS, persists mostly unknown. Aims to assess the effects of fucoidan at sub-minimum inhibitory concentrations (subMIC) on the reduction in biofilm of _L. monocytogenes_ on commonly used seafood contact surfaces, including HG, SR, and SS. Furthermore, it examines how fucoidan influences the relative genes associated with biofilm development and pathogenicity at the molecular level. The outcomes of this research will offer important insights into the potential of fucoidan as a natural antibacterial solution in the seafood processing surroundings, supporting enhanced food safety.
## 2 Synchronous ZETA Converter  
### Mathematical Model  
The sZETA converter is schematized in Figure 1. In its simplest form, it is composed by an input voltage source \(V_{in}\) and five linear elements, namely two capacitors with capacitance \(C_{1}\) and \(C_{2}\), two coils characterized by the inductance \(L_{1}\) and \(L_{2}\), and the load resistance \(R\). Additionally, two synchronous switches complete the design: whenever the control signal \(u=1\), the main switch is set to the ON position and the complementary switch is automatically set to the OFF position (\(\bar{u}=0\)), and vice versa. The state of the sZETA converter is completely described by the currents flowing through the coils (\(i_{1}\) and \(i_{2}\)) and the voltages across the capacitors \(v_{1}\) and \(v_{2}\). Notice that, since we are interested in a theoretical approach to the stability of the converter, all elements are assumed to be ideal. The output voltage of the converter is given by \(v_{2}\). Furthermore, with the aim of defining a PI control in Section 2.2, we extend the state vector to include the integral of the error between a desired output \(V_{ref}\) and the output voltage, namely \(e=V_{ref}-v_{2}\). Altogether, we can fully describe the sZETA converter via the state vector:  
\[\mathbf{x}=\begin{bmatrix}x_{1}\\ x_{2}\\ x_{3}\\ x_{4}\\ x_{5}\end{bmatrix}:=\begin{bmatrix}i_{1}\\ i_{2}\\ v_{1}\\ v_{2}\\ \int\limits\limits\limits\limits\limits\limits\limits\limits\limits\limits \limits\limits\limits\limits\limits\limits\limits \tag{1}\]  
which evolves according to the following equations:  
\[\dot{\mathbf{x}}=\left\{\begin{array}{ccc}\mathbf{f}_{\text{ON}}(\mathbf{x})& \text{if}&u=1\\ \mathbf{f}_{\text{OFF}}(\mathbf{x})&\text{if}&u=0\end{array}\right. \tag{2}\]  
where  
\[\mathbf{f}_{\text{ON}}=\begin{bmatrix}V_{in}/L_{1}\\ v_{1}/L_{2}-v_{2}/L_{2}+V_{in}/L_{2}\\ -i_{2}/C_{1}\\ i_{2}/C_{2}-v_{2}/(RC_{2})\\ V_{ref}-v_{2}\end{bmatrix}\quad\mathbf{f}_{\text{OFF}}=\begin{bmatrix}-v_{1}/L_ {1}\\ -v_{2}/L_{2}\\ i_{1}/C_{1}\\ i_{2}/C_{2}-v_{2}/(RC_{2})\\ V_{ref}-v_{2}\end{bmatrix} \tag{3}\]  
are the vector fields that govern the dynamics in each one of the topologies defined by the position of the main switch. The gain of the sZETA converter depends on the value of the duty cycle \(d\), i.e., the fraction of time during a PWM cycle that the switch remains in the ON position. It can be shown that the gain of the controller is given by [18]  
\[\mathcal{G}=\frac{d}{1-d} \tag{4}\]  
which means that the sZETA can work as a step-down (\(d<0.5\)) or step-up converter (\(d>0.5\)).
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 6 Discussion  
### Limitation of the Proposed Flow Table Saturation Attacks  
One limitation is that the attack effect is impacted by the probing accuracy. In the maintaining phase, the attacker needs to measure the RTT of testing packets to check whether the corresponding flow rules are still stored in the flow table. If the accuracy of RTT measurements in a realistic network scenario is not good enough, the attacker can mistakenly infer the corresponding flow rule is deleted. To cope with this, we can use the \(t\)-test method [38] to improve its accuracy. However, the \(t\)-test method requires more testing packets, thus incurring higher traffic rates and attack costs.  
Another limitation is that we only consider the general timeout assignments. We assume that the flow rule state transitions are impacted by the packet inter-arrival times, which is the most common feature of existing adaptive timeout mechanisms. Since the main purpose of flow table management is to reduce the packet-in rates and improve the flow table utilization, assigning suitable timeouts covering the potential packet intervals is a practical design. However, if the timeout transition is impacted by other factors, such as the matched number of packets and bytes in each flow, the assumed timeout state transition in the inferring phase can fail. To overcome this, we can generate attack flows with more probing packets and mimic the behavior of benign flows to perform the state transition. In each state, we send multiple packets to hit the flow rule before intentionally triggering a table-miss. After that, we send packets with a larger packet-interval time to trigger the new flow rule and probe if the timeout is larger.
## 6 Performance Evaluation and Discussion
### Adhesive Spraying Results  
Due to the transparency of the water-based adhesive, a UV fluorescent dye was added for post-application inspection. Under UV light, the adhesive-covered regions fluoresse, enabling a direct comparison with a manually drawn reference bonding line on the lasted upper. As shown in Figure 28, the applied adhesive closely matches the reference lines along the toe, heel, and lateral areas, confirming the success of customized path planning and accurate execution by the robotic arm. Most areas fall within the industry-accepted 1 mm tolerance, with only slight deviation observed at the toe cap--an issue further discussed in the next section.
## Abbreviations  
**Abbreviation**  
**Full Form**  
AI Artificial Intelligence  
ANN Artificial Neural Network  
ASPP Atrous Spatial Pyramid Pooling  
AWCM Auto-adaptive Window Cropping Method  
BIM Building information modelling  
BN Batch Normalisation  
CBD Concrete Bridge Defect  
CNN Convolutional neural network  
COCO Common Objects in COntext  
DBRNet Dense boundary refinement network  
DL Deep Learning  
ELMs Extreme learning machines  
FCNs Fully Convolutional Networks  
FRCNN Fast Region-based CNN  
FPS Frames Per Second  
GA Genetic Algorithm  
GIS Geographic information system  
GLCM Grey-Level Co-occurrence Matrix  
GPR Ground penetrating radar  
ICA Imperial Competitive Algorithm  
IoU Intersection over Union  
InSAR Interferometric Synthetic Aperture Radar  
LiDAR Light detection and ranging  
MAE Mean Absolute Error  
mAP Mean average precision  
ML Machine Learning  
NDT Non-Destructive Testing
## 2 Selected Wireless Sensor Networks Standards: State-of-the-art  
Wireless communication in industrial automation is mostly based on standardized technologies, such as the IEEE 802.11 [5] and IEEE 802.15 standard families [6], also designated as _Wireless Local Area Networks_ (WLAN) and _Wireless Personal Area Networks_ (WPAN). Both of these standard families were conceived for application purposes different than industrial automation. In fact, the IEEE 802.11-based standards offer high data rates in the order of tens of Mbit/s and ranges up to tens/hundreds of meters, while the IEEE 802.15-based standards only supports data rates of hundreds of kbit/s to several Mbits/s with ranges from a few meters up to hundreds of meters. However, to provide greater data rate and range, IEEE 802.11 technology consumes a greater energy budget that can limit the benefits obtained by wireless communications. Indeed, the sensor nodes are either powered by cables or batteries. In the former case, the advantages provided by wireless communication are partially negated, whereas in the latter case, the scarce energy resource has to be parsimoniously consumed in order to avoid frequent human interventions to recharge the batteries. Energy is thus a major concern in both previous cases and we therefore focus on the IEEE 802.15-based standards, and particularly on the IEEE 802.15.1 [7] and IEEE 802.15.4 [8] standard, within the scope of this work.  
Figure 1: From the sensors to the customers.
# Unit Commitment with Ancillary Services in a Day-Ahead Power Market
###### Abstract
In terms of safety and unit commitment emergency analysis, [15] used N-k fault analysis to seek the work of the demand response in ancillary services, in order to address disasters caused by emergencies. Since microgrids have more flexibility in coordinating power sources and loads, they are usually [16] considered as a candidate for the provision of ancillary services. Ref. [17] used a renewable ancillary service to enhance power system operations. Regarding the ancillary services of frequency control, voltage regulation, and active power management, the allocation and management of costs are the most important issues of system operation [18; 19; 20; 21]. Ref. [22] used pumped storage hydropower plants to provide the ancillary services for balancing supply and demand. The AGC unit's quantitative evaluation, unit selection [23], and power transmission line in ancillary service cost allocation [24] are all discussed. Real-time ancillary service is now a non-negligible function in an electrical deregulated environment. This paper proposes a hybrid DPSO-SQP by combining Discrete Particle Swarm Optimization (DPSO) [25] with Sequential Quadratic Programming (SQP) [26] to compute the power scheduling of various units and ancillary services in the power market. PSO is introduced with simplicity, easy implementation, and mutual independence in simulation, whereby the particles only need to exchange information once with the group optimum. However, the performance of the PSO is related to the initial distribution of the swarm, meaning that if the initial states of the swarm are near the local optimum region, the swarm may become trapped, which is the disadvantage of PSO. Unit commitment in ancillary services can be considered as two sub problems: \"determining the on/off state of units in various time intervals\" and \"electricity generation assignment\", which has many constraints. Generally speaking, while appropriate PSO parameter settings can accelerate the convergence, a good set of parameters may not be easy to come by. It is generally obtained from many experiences or from other settings with sophisticated enumeration process, thus, the problem is further complicated. This paper uses DPSO to compute the discrete mode of the \"on/off state of units\" for various time intervals, where parameter settings can be effectively reduced. SQP will then calculate power generation and ancillary services. The proposed DPSO-SQP used the thermal units of TPC and Independent Power Producers (IPPs) as the test sample, and the simulation results are compared with other algorithms, including the Genetic Algorithm (GA), Evolutionary Programming (EP), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) [27; 28; 29; 30]. The accuracy and robustness of the algorithm also show a higher probability of reaching a global optimum.
## 3 The key Technology of the Multi-DOF Automatic Image Acquisition System  
The key metrics of this multi-DOF automatic image acquisition system are the high quality of dataset (rich and diverse data) and short acquisition time. Therefore, the system mainly involves two key technologies: the coordination of the camera angle and the moving position of the object as well as the optimization of the random motion path.
## 3 Results and Discussion
### Molecular Weight of PWPs  
The molecular weight distribution was evaluated using SEC-MALLS analysis. As shown in Table 2, PWPs was characterized by a weight-average molecular mass (Mw) of 66 kDa and number-average molecular mass (Mn) of 38.3 kDa. The value of polydispersity index (Mw/Mn = 1.72) assumed a slight heterogeneous nature for PWPs. According to literature, the heterogeneity of PWPs could confirm the presence of a pectin type polysaccharide as it was largely described in literature about pectins [45]. In their study on _Plantago major_ leaves, [PERSON] et al. [24; 26] defined a pectic fraction from water-extractable polysaccharide with Mw of 46-48 kDa and Mw/Mn = 1.4-1.9, and also an arabinocalatan II fraction with Mw of 77-80 kDa and Mw/Mn = 1.1-1.2. [PERSON] et al. [46] reported a pectic fraction from _P. palmata_ leaves with Mw of about 1200 kDa, Mw/Mn = 1.05, and similar monosaccharide composition to those extracted from _P. major_ leaves. Finally, a lot of gel chromatography studies on _P. media_ and _P. depressa_ leaves established that all polysaccharide fractions were heterogeneous and did not have a common characteristic Mw distribution. They contained three groups of polymers, respectively with Mw > 150 kDa, Mw = 50-110 kDa, and Mw = 8-20 kDa [47].
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 3 Background
### System Model
#### 3.1.1 Energy Consumption Model  
Underwater channel attenuation over a distance \(l\) can be demonstrated as [29]:  
\[10 logA(l,f)=c.10 logl+l.10 loga(f). \tag{1}\]  
The first term of this equation represents the spreading loss, and the second term shows the absorption loss, where \(c\) is the spreading coefficient, which states the geometry of propagation, i.e., \(c=1\) is cylindrical spreading in shallow water, two is for spherical spreading in deep water and 1.5 depicts the practical spreading, where, \(\alpha(f)\) is the absorption coefficient.  
Figure 2: Proposed system model illustrating Adaptive transmission range in WDFAD-Depth-Based Routing (DBR) and Cluster-based WDFAD-DBR (C-DBR).  
In UWSNs, the acoustic signal is affected by different noises, such as turbulence \(N_{t}(f)\), shipping \(N_{s}(f)\), waves \(N_{w}(f)\) and thermal noise \(N_{th}(f)\)[17; 29; 30; 31]. These noises can be expressed as,  
\[N(f)=N_{t}(f)+N_{s}(f)+N_{w}(f)+N_{th}(f). \tag{2}\]  
For the acoustic signal, the Signal to Noise Ratio (SNR) with frequency \(f\) and distance \(l\) can be expressed as:  
\[SNR(f,l)=T_{p}(f)-A(l,f)-N(f)+D_{i}, \tag{3}\]  
where \(T_{p}(f)\) represents the transmission power with frequency \(f\). \(D_{i}\) denotes the directivity index to evade unnecessary noise. During the reception of an acoustic signal, if \(SNR(f,l)\) becomes greater or equal to detection threshold \(D_{t}\), then the received signal is decoded correctly.
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 2 Latency Definition and the Ideal Case Study
### Physical Layer Latency  
The present work defines PPDU payload latency as the target performance metric, focusing solely on latency incurred in the PHY. This encompasses the processing time at the TX and RX digital basebands (DBBs) and includes data propagation through the channel. It is analogous to tracking the E2E delay of individual MAC layer protocol data units (MPDUs) (the terms PPDU payload and MPDU are used interchangeably in the manuscript). from entering the TX PHY, to exiting the RX PHY and heading upwards through the network stack. Marking the timing start and stop points, Figure 1 depicts MPDU propagation and the latency it encounters through the PHY.
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 3 Improved Security AODV Routing Protocol
### Secure Routing Scheme Based on ECC
#### 3.2.2 The Process of Routing Discovery
Finally, the source node \(S\) receives the RREP message, and after all verifications are passed, it also decrypts \(T_{d}\) with its own private key, and the process of route discovery ends. After the source node and the destination node receive \(T_{d}\) and \(T_{s}\), respectively, their shared session key can be calculated. The calculation process of source node \(S\) is as follows:  
\[V_{d}=P_{d}+h(ID_{d})\times G+[(X(P_{d})+h(ID_{d}))\text{mod}n]\times P_{SA} \tag{7}\]  
\[SK=X_{s}\times V_{d}+S_{s}\times T_{d}=(X_{s}S_{d}\text{mod}n)\times G+(S_{s} X_{d}\text{mod}n)\times G \tag{8}\]  
The calculation process of destination node \(D\) is as follows:  
\[V_{s}=P_{s}+h(ID_{s})\times G+[(X(P_{s})+h(ID_{s}))\text{mod}n]\times P_{SA} \tag{9}\]  
\[SK=X_{d}\times V_{s}+S_{d}\times T_{s}=(X_{d}S_{s}\text{mod}n)\times G+(S_{d} X_{s}\text{mod}n)\times G \tag{10}\]  
In this way, the source node \(S\) and the destination node \(D\) have the shared session key \(SK\). In the next stage of data transmission, the efficient symmetric cryptosystem can be used to complete the secure transmission of a large number of real-time data.
# Hybrid Multi-Strategy Improved Butterfly Optimization Algorithm
## 3 Improvement of the BOA
### Time Complexity  
In the Improved Butterfly Optimization Algorithm (IBOA) proposed in this paper, with a population size of N, dimension of D, and a maximum number of iterations of T_MAX, the time complexity for generating the population through the Sine-Plane Map (SPM) and reverse learning is O(2N). The time complexity for updating the position of each butterfly individual is O(N \(\times\) D), and the time complexity for the simulated annealing algorithm to determine whether to accept a worse solution is O(N). Therefore, the total time complexity is O((2N + N \(\times\) D + N) \(\times\) T_MAX) = O (N \(\times\) D \(\times\) T_MAX), which is comparable to the time complexity of the original BOA.