esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Communication and Channel Modeling
### Channel Modeling  
The received signal at the UE is affected by the channel gain between the transmitter and that point as the following:  
\[\psi=\sqrt{P_{T}}~{}\mathcal{g}_{LoS}^{TR}+\omega, \tag{1}\]  
where \(\psi\) denotes the received signal, \(P_{T}\) denotes the transmitted power, \(\mathcal{g}_{LoS}^{TR}\) denotes the LoS channel gain, \(\beta\) denotes the coded signal, and \(\omega\approx\mathcal{CN}(0,~{}\sigma^{2})\) is a complex Gaussian additive noise with zero mean and covariance \(\sigma^{2}\) for modeling scattering components. The channel gain may be adversely affected by the environment, resulting in NLoS multipath signals due to scattering and consequence random reflections, diffractions, and refractions, see Figure 2.  
In future networks, the LoS singles are assumed to be dominant compared to NLoS multipath signals that are negligible based on real-world experimental [38]. The channel  
Figure 2: Illustration of direct LoS path and multi-path.
# An AUV-Aided Cross-Layer Mobile Data Gathering Protocol for Underwater Sensor Networks +
## 2 Related Work  
The underwater environment has its own physical constraints and unique characteristics which should be taken into account during the design of the underwater routing and Medium Access Control (MAC) protocols over an aquatic network. These include slow propagation speed, limited bandwidth, energy constraints, node mobility with water current, and high deployment costs. UWSNs are significantly different from terrestrial (radio) sensor networks as they utilize acoustic waves with comparatively lower loss and longer range in the underwater environment than electromagnetic and optical waves [13; 14; 15; 16; 17].  
Recently, a cross-layer protocol has increasingly been growing in network design due to its better performance [18; 19]. Some variances based on the cross-layer group have been proposed.  
An AUV in an AUV-aided Energy-Efficient Routing Protocol (AEERP) [20] travels in a planned elliptical trajectory in each data gathering process. Data packets are received and delivered to an AUV by selected sensors as the gateway. The gateway sensors selection is based on the residual energy and the distance to the AUV trajectory. Using a Shortest Path Tree (SPT) every member is assigned to a gateway. However, no bound on the hop distance from members to a gateway causes higher energy consumption. Moreover, a group of nodes may be isolated and unable to reach any gateway near the AUV trajectory that constitutes the void problem. Furthermore, there is no recovery mechanism for the dropped packets that are generated by these nodes.  
In another mobile data gathering scheme AUV_PN [21], the network is divided into a number cluster, which is subdivided into several subcluster. To gather information from members, subclusters have a Path Node (PN). In every data-gathering operation, the AUV visits the nearest CH to regain the list of PNs and afterwards travels to every PN to obtain the data. This procedure repeat for next nearest CH after all PNs are visited in a cluster until all clusters are visited. The AUV returns to the base stations to pass all the aggregated data. Although the base station has received all the information, the constructed tour by AUV overpasses over itself and is not optimal. Moreover, in the larger network, the higher distance between PNs and members requires a higher power for transmission. Besides, network partitioning is also a complicated and energy-consuming process.  
In a Cluster-based Mobile Data Gathering scheme (CMDG) [22], a group of sensors is selected as cluster heads in a distributed manner to collect data from other sensors. Then, an AUV tour is planned to collect these data from cluster heads with minimum latency. Although CMDG is effective in terms of the network layer, it still suffers from a lack of capability in terms of the MAC layer. The problem is that the network and MAC layer are not coordinated to increase the performance.
## 5 Analysis of Factors Affecting Bearing Capacity of Inclined Pillar
### In-Situ Stress Ratio Effect on the Bearing Capacity of Pillar  
When the inclination of the ore-body is constant, the variation law of the contribution factor of shear load (_CFSL_) is obtained when the \(k\) is from 0.3 to 3.5, in which the dip angle \(\theta\) increases in increments of 10\({}^{\circ}\) from 0\({}^{\circ}\), as shown in Figure 12. When the in-situ stress ratio \(k\)\(\in\)[0.3, 3.5], the _CFSL_ decreases gradually with the increase of the in-situ stress ratio. When \(k\)\(\in\)[1, 3.5], the _CFSL_ is negative, which shows that the direction of the shear load has changed. When \(\theta\) = 0\({}^{\circ}\) or 90\({}^{\circ}\), the _CFSL_ is equal to 0; the pillar is loaded under pure pressure and is not affected by the external shear load.  
Figure 12 demonstrates that, except for 0\({}^{\circ}\) and 90\({}^{\circ}\), at any inclination angle, the _CFSL_ decreases with the increase of the in-situ stress ratio. To explore the root cause, the partial derivative of both sides of Equation (16) to the in-situ stress ratio is obtained.  
\[\frac{\partial(\tan\beta)}{\partial k}=-\frac{2\sin 2\theta}{\left[(1+k)+(1-k) \cos 2\theta\right]^{2}} \tag{26}\]  
From Equation (20), the partial derivative of \(\tan\beta\) to \(k\) is always less than zero, indicating that \(\tan\beta\) is a monotone decreasing function. Additionally, the monotonicity of _CFSL_ is affected by the monotonicity of \(\tan\beta\). When \(\theta\)\(\in\)(0\({}^{\circ}\), 90\({}^{\circ}\)), with the increase in the in-situ stress ratio, the _CFSL_ decreases gradually and changes from positive to negative. It shows that the bearing capacity of the pillar increases firstly and then decreases. In addition, the pillar with a small dip angle and a large in-situ stress ratio has a high shear stress concentration, which leads to low bearing capacity.
# What Advanced Treatments Can Be Used to Minimize the Production of Sewage Sludge in WWTPs?
## 3 Advanced Treatments and Technologies
### Chemical Treatments
#### 3.1.3 Fenton and Photo-Fenton  
The Fenton process is an advanced oxidation process (AOP) useful for minimizing the SS production and increase sludge dewaterability [33; 34]. This process is composed of four subsequent stages, which are: (i) pH adjustment at low acidic values, (ii) oxidation, (iii) neutralization and (iv) coagulation [35]. The Fenton reaction produces OH\({}^{\bullet}\) in acidic solutions by iron catalysed decomposition of hydrogen peroxide [36]. During the last decade, the possibility of sludge dewatering by Fenton oxidation has been studied and the experimental results indicated that Fenton oxidation had a positive effect [33; 37]. Moreover, the Fenton process allows us to minimize the SS production. In fact, used as pre-treatment, the Fenton process allows us to increase sludge solubilization and biodegradability, degrading extracellular polymeric substances, and breaking the microbial cell walls [34; 38]; therefore anaerobic digestion is improved significantly [33; 34; 39]. As a result of this application, the volatile solids (VSs) are reduced by 27%, and consequently higher biogas production is achieved [34]. In the literature, the combined application of the Fenton process with biological treatments, such as a membrane biological reactor (MBR), are studied. For instance, [PERSON] and [PERSON] [40] found that the average sludge yield decreased from 0.15 g\({}_{\text{MLSS}}\) g\({}_{\text{COD}}\)\({}^{-1}\) to 0.006 g\({}_{\text{MLSS}}\) gCOD\({}^{-1}\) during 60 days of operation when an MBR system was combine with the Fenton process.  
During recent years, the integration of the Fenton process with ultraviolet or visible light has been investigated in order to increase the stabilization of organic substances and SS dewatering [36; 41]. The use of UV rays stimulates the production of OH\({}^{\bullet}\) radicals and allows us to re-convert Fe\({}^{3+}\) into Fe\({}^{2+}\). In this way, the concentration of ferrous ions is increased, and this allows us to further speed up the reaction [42; 43]. For instance, [PERSON] et al. [44] tested photo-Fenton pre-treatment followed by anaerobic digestion of secondary sludge and they found 75.7% total VS reduction, 81.5% COD removal, and 0.29-0.31 m\({}^{3}\) kgys\({}^{-1}\) d\({}^{-1}\) biogas production rate, compared to 40.7% total VS reduction, 54.7% COD removal, and 0.12-0.17 m\({}^{3}\) kgys\({}^{-1}\) d\({}^{-1}\) biogas production rate without the photo-Fenton pre-treatment. Other applications of photo-Fenton processes for SS are reported in Table 1.  
One of the main disadvantages of the Fenton and Photo-Fenton processes is the production of chemical sludge that must be managed and disposed of. In order to minimize this production, zero valent iron in the form of powder (Fe0, ZVI) can also be used as a catalyst in the Fenton process. The most important advantage of this modified Fenton process is the lower chemical sludge production compared to conventional ones [35].
## 4 Simulation and Analysis
### Simulation on Main Lobe Rendezvous Scheme Based on Location Prediction  
Each UAV in a FANET can discover all neighbors according to the BR-DA-FANET protocol. As a UAV prepares to communicate with any neighbor, it needs to discover the neighbor quickly according to the ND-LP protocol. The random waypoint mobility model is adopted as the mobility model to mimic the movement of a flying UAV [36; 37].  
Once the initial access for the FANET is completed, each UAV discovers one of its neighbors 99 times. After neighbor discovery, each UAV moves in a random direction at a certain random speed for a period of time. The neighbor discovery delay is calculated as the average of 10,000 runs in our simulation.  
The simulation result is shown in Figure 29. As shown, the delay for the first neighbor discovery of the ND-LP protocol is as large as that of the BR-DA protocol. However, from the second neighbor discovery onwards, the delay decreases quickly due to the location prediction based on the information obtained from the previous neighbor discovery.  
The communication range of the switched beam antenna model overtakes the omnidirectional antenna because of high gain. Therefore, the design of double-directional antennas can reduce the number of hops required in comparison to using the omnidirectional antenna. To validate this performance improvement, we conducted simulations. In our simulations, the gains of the switched beam antenna and phased array antenna are 10 dB and 20 dB, respectively. The gain of the omnidirectional antenna is 0 dB. According to the Friis transmission equation [31], the minimum number of hops required for the omnidirectional antenna to reach the maximum communication range of the phased array antenna is 10, while for the switched beam antenna, it is only 4 hops.  
The communication range of the omnidirectional antenna is 10 m. Hence, the communication ranges of the switched beam antenna and phased array antenna are \(10*10^{0.5}\) m and \(10^{2}\) m, respectively. In our simulation scenario, there is a pair of randomly positioned UAVs with a distance ranging from 0 to 100 m between them. Several relay UAVs are placed between this pair to facilitate multihop communication. Both the omnidirectional antenna and switched beam antenna extend the communication range to outnumber the  
Figure 29: Discovery delay of continuous 100 times neighbor discovery under different numbers of sectors.  
distance between this pair of UAVs by multihops. To compare the performance of the omnidirectional antenna and the switched beam antenna, we measure the number of hops required to reach the distance between this pair of UAVs, as presented in Figure 30. Our simulation demonstrates that the switched beam antenna significantly reduces the number of hops compared to the omnidirectional antenna.
## 3 RAR: Reliability-Aware Routing
### Calculation of Physical Distance  
After the sensor nodes exchange the vital information within a predefined interval of time, a control packet is sent by sink comprising of its own ID and the time of transmission. A node that receives this packet computes the distance by which it is apart from the sink using the time of reception of the packet [18]. The computed distance is then incorporated within the packet along with the ID and time of transmission by the broadcaster node. This process is repeated in the same manner as in Figure 2. However, this process is different from Figure 2 in that the packet is generated by the sink node at the top of the water surface. In this way, all nodes come to know physical distance of one another from the sink node as well as their mutual physical distance from one another. Such information later helps in building the reliable paths during data routing. The suppression of redundant packets in this case follows the same strategy as in Figure 2. The hello packet sent by the sink node is expired after the preset interval of time. This interval of time is set in such a way that even the farthest node in the network receives the hello packet.
## 6 Simulation Results and Discussion
### Throughput Comparisons  
To compare the throughputs achieved by the three strategies, we ran four simulation sets in total: for the first two, we used a random topology, and for the other two, we used a linear topology. For both topologies, we applied the two scenarios regarding the timing relationship of the transferring and processing stages.
## 2 Results  
### Identification and Sequence Analysis  
A total of 26 CCO family members were identified in the _S. miltiorrhiza_ genome database, with 1500 bp upstream of the start codon as the promoter sequence. The length of the gene, the coding sequence and protein, the isoelectric point (PI), and the molecular weight of the SmCCO gene family members are shown in Table S1. The length of the SmCCO gene ranges from 252 bp to 6508 bp. The length of the SmCCO CDS and protein ranges from 252 bp and 83 aa to 2409 bp and 82 aa. Among them, seven genes have the same length as the CDS, indicating that these genes only have exons and no introns. The predicted MW of the proteins ranges from 9.61 kDa to 88.49 kDa, and the PI ranges from 4.55 to 9.45. As shown in Figure 1A, a neighbor-joining phylogenetic tree of 26 SmCCO protein sequences using default parameters was constructed. According to the phylogenetic analysis, 26 SmCCO proteins could be classified into six groups. The gene structure analysis of SmCCO is shown in Figure 1B. There are a total of seven genes with only one exon and no introns, while the number of introns in other genes ranges from one to three. The result of the motif analysis is shown in Figure 1C. SmCCO members in the same group have similar motif composition.
# Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead
## 5 Evaluation
### Impact of Contact Information Size on Message Forwarding  
Section 3.2 explained the impact of contact information size on message forwarding in terms of data exchange success and energy consumption on nodes. In order to verify these claims, we allow the simulation to run without generating data messages, so that only contact information is exchanged when nodes encounter each other. Hence, in the simulation, nodes consume energy due to exchanging contact information alone. In order to investigate the impact of contact information size on data exchange success, we record the percent of contact information transfer attempts that were successful when nodes run under PRoPHET and PoiFord in Figure 9. Since nodes can forward messages only after a successful transfer of contact information, successfully transferring more number of contact information means more chances of exchanging messages. In order words, nodes running on PoiFord are able to forward more messages during encounters due to less contact information size.  
Figure 8: Theoretical and simulated average contact information size on nodes running on PRoPHET and PoiFord over the small, medium and large scenario.  
Figure 9 also shows that this allows PoiFord to consume less energy per contact information transfer, as it reduces the average and maximum energy consumed with PRoPHET in a day by 17.7% and 21.2%, respectively.
# Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches
## 2 Study Area and Data  
### Study Area  
A part of East Asia was selected as the study area based on the spatial extent of the obtained reference data (i.e., PIREPs). The area (i.e., 94-141\({}^{\circ}\) E and 5-47\({}^{\circ}\) N) covers the major countries of East Asia (i.e., the Korean Peninsula, Japan, China, Hong Kong, and Taiwan). Based on the total air passenger traffic in 2016, one quarter of the world's top 20 airports are located in this region [32].
# Path Tracking Control Strategy Based on Adaptive MPC for Intelligent Vehicles
## 4 Condition-Adaptive MPC Controller
### Division of Steady-State Steering Conditions  
In order to enhance the tracking accuracy of the MPC algorithm across a range of vehicle speeds and varying curvature conditions, it is essential to incorporate feedback mechanisms involving longitudinal vehicle speed and road curvature radius into the MPC controller. This integration enables the analysis and matching of optimal control parameters, facilitating the achievement of adaptive control parameters that better align with real-time driving conditions. Consequently, the dynamic characteristics of intelligent vehicles are more accurately captured, thereby enabling precise path tracking even under complex driving scenarios. As a reference, Table 3 presents the minimum road curvature radius corresponding to different design speeds, as outlined in the China National Highway Route Design Specification JTG D20-2017.  
When a vehicle is in motion, its turning radius must not fall below the threshold value defined by the minimum road curvature radius corresponding to the design speed of the current road section. In accordance with the uniform design principle and the minimum road curvature radius stipulated by the design speed, 100 distinct groups of steady-state steering conditions were categorized, as illustrated in Table 4.  
Figure 4: Influence of speed and road curvature on path tracking.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline Speed/km/h & 20 & 30 & 40 & 60 & 80 & 100 & 120 \\ \hline Minimum radius of curvature (general value)/m & 30 & 65 & 100 & 200 & 400 & 700 & 1000 \\ Minimum radius of curvature (limit value)/m & 15 & 30 & 60 & 125 & 250 & 400 & 650 \\ \hline \end{tabular}
\end{table}
Table 3: Minimum road curvature radius corresponding to different design speeds.
## 4 Experiments and Results
### Results and Analysis
#### 4.2.3 Proactive Defense Method  
Retraining the model is a common training practice used to reduce losses and increase its accuracy. This is one of the most effective proactive strategies for defense against adversarial attacks on deep learning models. The resultant retrained network should be able to withstand the adversarial attacks used to generate adversarial samples during the training phase. These adversarial attacks target neural networks to cause misclassification. After the attacks, these assaults and the correct labels are used to teach the neural network. Such proactive strategies cause a minor decline in the predictability of a deep learning model. However, the resilience that they provide against adversarial attacks is thought to make up for this decline. A model of this type of operation is illustrated in Figure 6, below.  
Figure 5: The variations in adversarial FGSM attack content (epsilon): (**a**) subject 1, (**b**) subject 2.  
Figure 6: The proactive defense method.  
Figure 7 and Table 8 show an increase in accuracy, as well as in other performance metrics, after retraining the model for both subjects.  
Analyzing the scenarios where both attacks succeeded in fooling the DL model shows that deep learning models have weaknesses that may lead to misclassifying normal behavior as abnormal behavior. In the healthcare domain, our investigations revealed that adversarial attacks can reduce accuracy when utilizing the most recent deep learning model and, therefore, lead to severe and dangerous consequences. Thus, retraining the model is a proactive defensive method that helps to make sure that such attacks fail to fool the deep learning model and makes the model more robust.
# An Architecture for Reliable Transportation of Delicate Goods
## 1 Introduction
In short, it is fundamental to provide transport services of better quality and with more guarantees than simply meeting the delivery deadline. The solution naturally depends on the conditions created by the transporters and remainder logistics operators, but it is also up to the market to demand more effective guarantees that the service is provided under the agreed conditions. Producers, carriers (including sub-contractors) and end costumers would all benefit from it. The authors present in this article an open and generic architecture and protocols, designed to guarantee the quality of the transportation service for all the involved, including the customer. The architecture was designed to be adopted by any carrier that wants to provide guarantee of the conditions agreed for the transportation of the merchandise, without requiring significant changes to the companies' operational model, with a reduced investment and a low operational cost. The remainder of this article is organized as follows: the most novel solutions available on the market that can be compared with this work are presented in Section 2; the technical options that are available (with a focus on their advantages and limitations) are discussed in Section 3; the high level architecture, the IoT devices that support it and the topologies idealized to enable a widespread use of the solution are presented in Section 4; the technical details of the protocols, including the procedures for the aggregation and disaggregation of merchandise, are described in Section 5; an overall discussion of this proposal, together with the conclusions and directions for future work, are presented in the final section.
###### Abstract  
This paper presents an experimental evaluation and validation of a standalone photovoltaic (PV) renewable energy system using a perturb and observe MPPT-based voltage controller (POVC) for application on the reduced component multilevel inverter (MLI). The objective is to verify the effectiveness of POVC and asymmetrical MLI for real PV interconnection applications, which has never been reported before. An asymmetrical 17-level MLI topology is selected in this research. It requires four isolated DC sources to operate. A boost converter is deployed as one of the inputs while isolated DC power supplies power to the others. POVC is implemented at the boost converter to control the DC voltage from the PV panel. From the experimental results, the capability of the POVC to produce constant voltage levels under varying conditions is proven. It is also capable of delivering maximum power under maximum load. The full system is tested using different load types, including nonlinear loads. The MLI produces expected outputs under all operating conditions with efficiencies above 97%. The viability of the proposed PV system is firstly analysed through MATLAB/Simulink simulations. The results are then evaluated experimentally.  
multilevel inverter; asymmetric; PV system; voltage feedback control Article