esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Conclusions  
With the increasing adoption of IoT devices in our daily activities and in widespread services, it becomes increasingly critical to secure and manage the vast amounts of data they generate. SDN has emerged as a promising solution to manage the network traffic generated by IoT devices, while blockchain technology offers a secure and decentralized platform for data sharing and trust management. Consequently, security is crucial for ensuring the integrity of data generated by IoT devices, while DDoS attacks pose a significant threat to the availability of IoT services. In this context, this paper studied DDoS threat in IoT and SDN, as well as the vulnerability of these environments against DDoS attack. Furthermore, different DDoS attack mitigations techniques based on cutting-edge technologies such as, blockchain, and machine learning were reviewed. In fact, this paper aims to investigate innovative techniques and methods that could help IoT services and applications to be delivered in a secure way. For this purpose, the benefit of the integration of SDN paradigms as well as blockchain technology and machine learning models in such environments were deeply analyzed. A holistic secure IoT system that considers multicontroller SDNs with augmented security by blockchain and ANN models was introduced to contribute in detecting and mitigating DDoS attacks. The data plane also could be protected via firewalls and encryption systems. Afterward, the implementation of the ANN-based detection system was detailed, and it proved its high performance. In the future, we plan to develop the implemented solution to deploy the whole system in mini-net emulators with multiple ONOS controllers that perform east-west communications using hyperledger fabric.  
Conceptualization, R.J. and W.G.; methodology, R.J., W.G. and R.G.; software, R.J. and W.G.; validation, A.S.A., B.M.A. and H.A.; formal analysis, R.J. and R.G.; investigation, H.A.; resources, R.J. and R.G; data curation, R.J. and W.G.; writing--original draft preparation, R.J., W.G. and R.G.; visualization, B.M.A. and A.S.A.; supervision, R.J.; project administration, R.G.; funding acquisition, A.S.A., B.M.A. and H.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Scientific Research Deanship at University of Ha'il--Saudi Arabia through project number RG-21 137.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussions
### Interactivity Analysis of RSM Model Process Parameters  
Upon comparison of the data in Tables 4 and 5 (corresponding to depth) and Tables 6 and 7 (corresponding to hardness), it is evident that the significance of certain parameters varies between the two frequencies. This discrepancy may be attributed to the differing mechanisms by which the process parameters influence the depth and hardness of the hardened layer at different induction frequencies, thus necessitating further analysis.  
Based on the multiple regression equations and analysis of variance, three-dimensional surface plots can be generated to illustrate the interaction effects of the experimental factors. Figure 2 provides an intuitive representation of the effects of the laser power, the scanning speed, and the induced power, along with their interactions, on the depth of the hardened layer when the induction frequency is 10 and 15 kHz, respectively.  
Among the three process parameters, only scanning speed (B) exhibits a negative correlation with the depth of the hardened layer. As scanning speed increases, the total heat input per unit time and area decreases, leading to a lower average surface temperature and a shallower region reaching the austenitizing temperature. In contrast, laser power (A) and induction power (C) show a positive correlation with the depth of the hardened layer. Increased heat input raises the temperature, allowing a deeper region of the 42 CrMo steel to reach the austenitizing temperature. Furthermore, analysis of the interaction effect plots reveals that lower induction frequencies favor deeper hardened layers, with maximum depth increasing from 7.022 mm at 15 kHz to 7.817 mm at 10 kHz, indicating that induction heating penetrates deeper in this range.  
Figure 3 shows the effects of laser power, scanning speed, and induction power on the hardened layer at induction frequencies of 10 kHz and 15 kHz. The key to surface  
Figure 2: Interaction effect of process parameters on the depth of the hardened layer. (**a**) Laser power and scanning speed (10 kHz); (**b**) laser power and induced power (10 kHz); (**c**) scanning speed and induced power (10 kHz); (**d**) laser power and scanning speed (15 kHz); (**e**) laser power and induced power (15 kHz); (**f**) scanning speed and induced power (15 kHz).  
hardening of 42 CrMo steel is the martensitic structure from phase transformation. At a constant scanning speed, increasing laser power boosts energy input, which raises the material's temperature and extends the region reaching the austenitizing temperature. This leads to faster cooling and more martensite, increasing hardness. However, too much laser power can overload the heat input, insulating the specimen and slowing cooling, which cuts martensite formation and caps hardness gains. Consequently, simply increasing laser power, as shown in Figure 3a,d, will not enhance hardness. The complexity of multiple factors makes hardness analysis tricky, rendering single-factor evaluations less reliable.
# Designing a Cleavable Cell Surface Protein for Cytotherapy and Drug Delivery Applications
## 2 Materials and Methods
### Nanoparticle Characterization  
The hydrodynamic size and zeta potential measurements of the prepared nanoparticles were analyzed by dynamic light scattering (DLS) using a Zeta sizer Nano ZSP (Malvern, Worcestershire, UK) (Figure 3A). The Smoluchowski model was used to calculate the zeta potential value (Figure 3B). All data represent the average OF triplicate measurements of samples prepared in different preparations. The morphology of the prepared BNP was further analyzed using transmission electron microscope (TEM, Tecnia G2, Spirit Bio TWIN, Los Angeles, CA, USA). TEM sample was prepared by drop casting and evaporation technique using a fomvar-coated copper grid (400 mesh). TEM images were analyzed by GATAN digital imaging system (GATAN, Inc., Pleasanton, CA, USA) (Figure 3D).  
Figure 3: Physicochemical characterization of biotin functionalized polymeric nanoparticles. (**A**) Hydrodynamic size of PLGA nanoparticles showing Z-average of 95 \(\pm\) 5 nm. (**B**) Zeta potential of PLGA nanoparticles demonstrates the negative charge property of PLGA nanoparticles. (**C**) A stability of PLGA nanoparticles in ionic condition (pH7.4). (**D**) Representative TEM image of PLGA nanoparticles with core-shell structure.  
The stability of the nanoparticles in physiological ionic condition was investigated at pH 7.4 using PBS. In brief, 500 \(\upmu\)L of 1 mg/mL nanoparticles in PBS and incubated at 37 \({}^{\circ}\)C with rotating motion for 7 days. The stability of nanoparticles was determined by measuring the particle size and PDI every 24 h for 7 days using DLS (Figure 3C).
# Wavelet-Based Optimum Identification of Vehicle Axles Using Bridge Measurements
###### Abstract  
Accurate vehicle configurations (vehicle speed, number of axles, and axle spacing) are commonly required in bridge health monitoring systems and are prerequisites in bridge weigh-in-motion (BWIM) systems. Using the 'nothing on the road' principle, this data is found using axle detecting sensors, usually strain gauges, placed at particular locations on the underside of the bridge. To improve axle detection in the measured signals, this paper proposes a wavelet transform and Shannon entropy with a correlation factor. The proposed approach is first verified by numerical simulation and is then tested in two field trials. The fidelity of the proposed approach is investigated including noise in the measurement, multiple presence, different vehicle velocities, different types of vehicle and in real traffic flow.  
axle detection; wavelet analysis; bridge weigh-in-motion; scale; field experiment +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Molecular Analysis  
The bioinformatic analysis evidenced high uniformity, as all genes associated with biofilm were found in all the sequenced strains. Out of the analyzed genes, some play a more important role in the process of biofilm formation, and _luxR_ genes can be regarded as such because it was proven that they are crucial for the quorum sensing which plays a vital role in the mechanisms of biofilm creation [49; 50]. However, _luxR_ is not only typical for _Aeromonas_ but also for other genera of bacteria [49; 50]. The finding of a group of genes, including _filM_, which are responsible for encoding the polar flagella, which is again considered crucial for the initial stages of biofilm formation, is certainly highlighted in our study. The polar flagella is connected with a mechanosensation phenomenon (surface sensing), which is the bacteria's ability to determine exactly where (how far) the surface is, and how it can approach it and attach itself to it [51]. Again, this factor is not only crucial for biofilms in _Aeromonas salmonicida_ subsp. _salmonicida_ but also in most other motile species, including _Pseudomonas aeruginosa_, _Bacillus subtilis_, _Vibrio cholerae_, _V. parahaemolyticus_, and _P. mirabilis_[52]. Although [PERSON] [25] emphasized in a doctoral dissertation that the _vapA_ gene, which encodes the production of the A-monolayer, is extremely important for the molecular differentiation between different isolates of _A. salmonicida_ subsp. _salmonicida_, in our research, it was shown that all strains are identical based on the genetic sequences of _vapA_. The sequences (in the translation test) differed in 1 amino acid, which is considered an insignificant finding, on the basis of which it cannot be concluded that the strains had a different origin.  
Genetical homogeneity between different strains of _Aeromonas salmonicida_ subsp. _salmonicida_ isolated across Bavaria and their similarity to the reference genome cannot explain the strong differences in the ability to produce biofilms between the tested strains, and this strongly emphasizes the important role of epigenetics, and thus gene expression, in the adaptation and biofilm creation processes of this pathogen.
# Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach
## 3 System Model  
### Overview  
In the proposed work, an IoT enabled IEEE 802.15.4 network for small-to-medium scale buildings is considered (Figure 1). Each building has smart facilities (e.g., energy management, door locks, heat ventilation air-conditioning, security and surveillance, lights, entertainment, and water management) with a central control within the premises which communicates with all smart installations for management operations and control. In addition to regular communication, the performance data consisting of parameters and metrics of interest is periodically transmitted from the site of deployment to the service provider through this central control. A server collects this data and runs deep learning algorithms to identify the relationships between performance metrics of interest (\(PDR\) and \(EC\) in this case) and communication parameters (\(TP,DT,PS\), etc.), as highlighted in Figure 1. This learning process keeps adapting as different aspects of the network evolve (e.g, change in; QoS requirements, communication parameters including interference, size, and dimensions of the network, and channel quality, etc.), and new data becomes available. Against each performance metric constraint, a set of recommended values for the communication parameters that meet (maximize/minimize) the required goals for the metric are sent to the central controller within the consumer premises. This information is sent in the form of a table as shown in Figure 1. This table contains a set of values for each metric and recommended values for a list of relevant parameters that may help meet the constraint for the metric under consideration. Thus, for the end-device, it is a simple table lookup operation. The controller at the user site uses this information to select the suitable values for the parameters considering performance constraints.
# Risk-Quantification Method for Car-Following Behavior Considering Driving-Style Propensity
## 5 Experimental Analysis
### LightGBM-Based Risk Prediction for Car Following  
5.3.1. Selection of Characteristic Indicators  
LightGBM was chosen to predict the risk level of car following. As a gradient enhancement algorithm, the LightGBM algorithm adopts a leaf growth strategy with depth limitation to improve the accuracy of the algorithm while avoiding the problem of overfitting.  
Car-following involves real-time interactions with adjacent vehicles; hence, the motion characteristics (E) extracted for risk assessment encompass not only the dynamics of the following vehicle but also those of the leading and following vehicles (the interaction traits of nearby vehicles). The predictive outcome (F) spans four categories: safe, low risk, medium risk, and high risk, with the Collision Frequency Rate (CFR) being computed and sorted based on predefined thresholds to serve as the classification labels. In contrast to the feature selection in the behavioral recognition module, risk prediction attributes account for the target vehicle's relative lateral distance and velocity in relation to nearby vehicles, as well as the driving style tendencies of those vehicles concerning the assessed risk level. A set of eight attributes outlined in Table 5 were identified for the purpose of car-following risk prediction.
## 2 The Standard PTP
### PTP Operation
#### 2.1.1 Establishing the Master-Slave Synchronization Hierarchy  
The first step for clock synchronization in the PTP system is establishing the master-slave synchronization hierarchy, which defines the clock synchronization flow from the highest accurate clock in the network (GMC) toward the end devices. The synchronization hierarchy is represented as a synchronization tree structure that only includes the ports of the OCs and BCs, as shown in Figure 1.  
Specifically, GMC represents the root of the synchronization tree. In fact, the intermediate nodes of the tree are composed of BCs; thus, each port of these BCs is considered to be an independent clock (MC or SC) in the synchronization hierarchy. More importantly, the leaves of each level of the synchronization tree are represented as OCs, and they are considered to be SCs (Figure 1). However, TCs and non-PTP devices are not considered in the master-slave synchronization hierarchy, and they are represented as a connection point in the synchronization tree.  
The establishment of the master-slave synchronization hierarchy can be accomplished by using the BMC algorithm; this algorithm determines the BMC in the network segment and elects the GMC depending on the clock accuracy of the nodes to represent the root of the synchronization  
Figure 1: Example of the PTP master–slave synchronization hierarchy. PTP: Precision Time Protocol; GMC: grandmaster clock.  
hierarchy. Subsequently, the algorithm determines the MCs for the remaining levels of the hierarchy, which is considered to be the source of the clock synchronization at each level. Consequently, the BMC algorithm determines each port state for all nodes in the PTP system as MCs and SCs, as shown in Figure 2. The structure of the master-slave synchronization hierarchy differs from the physical topology structure, which is converted by the BCM algorithm. Because the remaining ports of the intermediate nodes (BCs) are considered to be loop-free ports (passive-state ports) that will be determined by BMC algorithm, these ports prevent the loop inside the network.  
Afterward, the timing flow is forwarded from the GMC toward the slave ports in the first level of the synchronization hierarchy. Subsequently, the MC ports in each level of the hierarchy distribute the time toward the slave ports until it reaches the leaf ports in the synchronization tree.
## 3 2-D Frequency-Domain Focusing Based on BiCoT-ESRM  
Based on the proposed BiCoT-ESRM, a 2-D frequency-domain imaging method is derived for GEO-LEO BiSAR in sliding-spotlight mode. Three problems need to be addressed for the accurate focusing of echo data. First, both the azimuth variance of Doppler centroid in GEO-LEO BiSAR and the steering of antenna beam in sliding-spotlight mode will result in the increase of the azimuth bandwidth, which causes azimuth aliasing. The two-step preprocessing scheme [56] is modified to remove the Doppler aliasing in azimuth frequency domain. Second, the equivalent circular orbit trajectory in (11) introduces azimuth variance of the motion parameters. In this paper, an azimuth trajectory scaling is proposed and integrated in the preprocessing scheme to resample the azimuth trajectory to a uniform linear motion. The third problem is the 2-D spatial variance of echo data brought by GEO-LEO BiSAR imaging configuration. This problem is solved by a modified 2-D \(\omega-k\) method in Section 3.2. Next, we will analyze the modified azimuth preprocessing and 2-D \(\omega-k\) method in detail.
## 4 Results
### Static Results
#### 4.1.3 Dilution of Precision  
The dilution of precision is a term used in geomatics engineering and satellite navigation to describe the mathematical impact of the geometry of navigation satellites on the accuracy of positional measurements. Figure 7 displays this situation and demonstrates how the precision of the linearly polarized devices performed better in all scenarios. DOP is an approximation of the ratio between the accuracy of measurements and positioning.  
Figure 6: Average of the satellites used by each device in a static position inside a building with large windows. The position of this device was moved toward the interior of the building.  
Figure 7 displays the position DOP (PDOP), which depicts the influence of satellite geometry on position estimates while accounting for the precision attained both horizontally and vertically (in height). A smaller number implies a more exact accuracy value, hence the lower the DOP (in this instance PDOP), the better, as it is a variable that determines the accuracy of a device in obtaining geolocation.  
To quantify the quality of the results, it can be observed that results between 1 and 2 were deemed excellent, those between 2 and 5 were considered good, those between 5 and 10 were deemed moderate, and outcomes over 10 were deemed fair or poor [22]. It was also possible to note a correlation between geolocation accuracy and window distance with the previous results; the accuracy increased with increasing distance from the window.
# Video over DSL with LDGM Codes for Interactive Applications
## 3 Methodology
### Evaluating the Video Response  
To provide a realistic evaluation of video distortion, tests used the reference video sequence _Football_, with plenty of motion activity, which increases the temporal compression coding dependencies. In order to judge the video distortion, a video trace was fed into a numerical simulator (refer to Figure 4) where ADSL packetization took place. After numerical simulation, data from the ADSL packets judged lost were removed from the compressed video bitstream, prior to passing through the H.264/Advanced Video Coding (AVC) [29] decoder. The resulting bitstreams (before and after LDGM repair) were compared to the YUV video input to determine the Peak Signal-to-Noise Ratio (PSNR).  
To allow the gain from combining FEC with built-in error resiliency, HEVC was not used as it has limited support for error resiliency. Instead the video sequence was encoded with the H.264/AVC JM 14.2 codec in Common Intermediate Format (352 \(\times\) 288 pixels/frame) at 30 frames/s with a Constant BitRate (CBR) of 1 Mbps. The frame structure was an initial intra-coded frame followed by all predictively-coded P-frames. Two percent intra-coded macroblocks (MBs) were included in the P-frames to guard against temporal error propagation. The IPPP...frame structure with intra-coded MBs included, is also suitable for streaming to mobile devices, as there is reduced computation because bi-predictive B-frames are no longer employed. Channel switching, for which periodic I-frames are useful, is not expected in a telemedicine or video-conferencing application. Data partitioning was also turned on at the codec as an additional form of error resilience, with constrained intra prediction also configured. These video settings conform to the recommendations of [30].
# OPMSS: Optimal Producer Mobility Support Solution for Named Data Networking
## 4 Formulation Analysis of OPMSS  
The formulations of handoff performance are derived from the network setup in Figure 5 for both the proposed OPMSS and the benchmarked Kite solution. The network analysis model shown in Figure 5 contains links between the intermediate routers, the consumer, and the producer. The wireless link or hop between the consumer or producer  
Figure 4: Interest packet transmission in OPMSS.  
and the intermediate router is labeled with a, and the wired links or hops between routers are labeled with \(b\). For every link between two nodes, there is a compounded link delay \(Ldw\) and \(Ldwl\) associated with wired or wireless, a queuing delay \(Qd\), and required bandwidths \(Bw\) and \(Bwl\) for certain transmissions of data \(S_{int/TL\_TD}\), where \(S\) represents a packet for data, interest, update, \(MI\), \(TI\) or \(TD\), etc. The total delay for wired or wireless links between two nodes can be represented in Equations (1) and (2) with respect to the probability of link failure \(q\).  
\[Lwl_{int/TI\_TD}\ =\ \left(\frac{S_{int/TI\_TD}}{(Bw)}+(Ldw+Qd)\right) \tag{1}\]  
\[Lwl_{int/TI\_TD}=\left(\frac{1+q}{1-q}\right)\times\left(\frac{S_{int/TI\_TD} }{Bwl}+Ldwl\right) \tag{2}\]  
Therefore, the transmission latency between the consumer, the producer, and the CRs can be expressed as:  
\[TLat_{wl}=a\times Lwl_{int/TI\_TD}\]  
\[TLat_{w}=b\times Lw_{int\_MI/TI\_TD}.\]  
Looking at Figure 5, three scenarios for the analysis of numerical handoff latency, handoff signaling cost, and data packet delivery cost can be formulated. The formulation for the three scenarios is covered in Equations (3)-(14) for both the Kite solution and our proposed solution. From Figure 5, the label \(a\) represents a wireless link between the producer or consumer and the router, while the \(b\) represents the link between routers. The D1 and D2 represent the two different domains, and CR is a content router and RDR is the restricted domain router, which can serve as a content router at the same time.