esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Damage Characterization, Objective Functions, and Optimization Algorithm
### Optimization Algorithms
#### 5.4.3 Multi-Objective Optimization Algorithms  
All the above optimization algorithms are referred to the single-objective problem. Sometimes, the damage detection problems are lack of a clear objective function and need simultaneous optimizations of several objectives, which is referred to the multi-objective optimization problem. These problems do not have a unique optimal solution. Instead, they have a set of alternative solutions named the Pareto Frontier or Functional Efficient Boundary. Therefore, special optimization techniques are needed to solve this type of problems. In general, the two main optimization strategies (i.e., gradient-based and evolutionary algorithms) can be used for solving the multi-objective problems. [PERSON] et al. [191] and [PERSON] et al. [192] presented a detailed introduction to the multi-objective optimization techniques. For the application in structure damage detection, [PERSON] and [PERSON] [147] used a multi-objective optimization technique to optimize two objective functions simultaneously, which overcomes the difficulty of weighing the individual objective function in conventional FE model updating procedure. [PERSON] et al. [143; 193; 194] developed an evolutionary (GA and PSO) multi-objective framework for structural damage localization and quantification with objective functions based on the modal flexibilities, frequencies, and modal shapes. [PERSON] and [PERSON] [195] proposed hybrid multi-objective optimization algorithms based on modal strain energy to detect damages in various three-dimensional steel structures. In general, the gradient-based multi-objective algorithms are recommended for smooth (differentiable) problems but not suitable for explicit time integration, while the evolutionary-based algorithms are recommended for all other cases [174]. The advantages of using evolutionary algorithms to solve multi-objective problems are their ability of dealing simultaneously with a set of possible solutions in a single run, instead of having to perform a series of separate runs as in the case of the gradient-based techniques, and the feature of less susceptible to the shape or continuity of the Pareto front [191].
## 4 Results and Discussion
### Multi-Step Ahead PV Power Forecasts
For Data Set 1, the five-step-ahead forecasting R2's for all models except ConvLSTM is around 0.90 (Table 5). Results in Table 5 show that for five-steps ahead forecasting based Data Set 2 almost all models provide relatively good forecasting accuracy in terms of \(R2\) which is around 0.94. It is worthwhile noticing that for a ten-step -ahead forecast, the accuracy of all models starts to decrease and achieve \(R_{2}\) values around 0.86. In the fifteen-step -ahead forecasting, we observed that LSTM, BiLSTM, and ConvLSTM achieved poor forecasting performance. The other models are still providing acceptable forecasting accuracy. We notice that the SAE model outperforms slightly the other models with higher \(R_{2}\) and lowest forecasting errors. The overall forecasting performance of the RNN, GRU, RBM, SAE, and VAE model was satisfying, and they can maintain a reasonable forecasting performance to forecast solar PV power output as the number of steps increases. The error for the second dataset is large compared to the first one. The first dataset is 15 min time resolution recorded for one year, while the second data is of one-minute time resolution recorded for three years. Moreover, we used 90% of data for both datasets for training and 10% for testing. The one-minute data is very dynamic, which explains the large error compared to the first dataset. It is challenging to tell which models were absolutely superior on the basis of the R2, MAPE, and RMSE values. The results of this study show that RNN, GRU, and VAE performs slightly better on average than the other models in most cases for one- and multi-step-ahead forecasting. The obtained results demonstrate that both RNNs with supervised learning and VAE with unsupervised learning can perform a one-step and multi-step forecasting accurately. Overall, the VAE deep learning model gives an effective way to model and forecast PV power output, and it has emerged as a serious competitor to the RNN-driven models (i.e., RNN, GRU, and LSTM).
# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 5 Discussion
### Strengths, Limitations and Future Directions
Unlike other approaches that may require the complete temporal series to perform irrigation mapping or detection, the proposed method could be a near real time application for irrigation detection. The classification results obtained in Section 4.4 show that the proposed method could be a competing method for classifying irrigated plots versus machine-learning approaches that require an extensive training database in order to obtain good classification results. For example, [PERSON] et al. [11] and [PERSON] et al. [12] used machine learning models such as the random forest and the convolutional neural network with S1 temporal series to classify irrigated/non-irrigated plots in Catalonia, Spain. Both studies achieved an overall accuracy of 82% and 90%, respectively. Using our approach, we were able to separate irrigated and non-irrigated plots with an overall accuracy of 85.9% over the same study site (4.4). Generally, machine-learning models used for classification tasks usually depend on the studied sites and the studied period of the year. In our case, the proposed method was tested over three different study sites with different geographical and climatic properties. This indicates that the proposed method is not site-dependent. Since no specific site calibration is required in the proposed approach, the proposed method is thus capable of providing a non-supervised tool for monitoring irrigation activities at plot scale. This reflects the strength of the proposed method against the transfer of the machine-learning models from one site to the other. However, the threshold values used could still be modified to obtain optimum results over another study sites. The change of the thresholds, if needed, does not require extensive dataset from in situ observations for irrigated plots. By analyzing the number of obtained irrigation points on each field (as for Section 4.3), our proposed method could be used to generate labelled samples of irrigated/non-irrigated plots. This means that the proposed method could be used to create a training/validation dataset, over any study site, which could be then used in machine-learning models such as random forest or deep-learning approaches. Our future work will concentrate on merging our proposed method with machine learning methods to obtain a semi-supervised classification model in which the training/validation data will first be selected using the proposed approach in this study, and then a machine-learning model will be built to perform further irrigation classification. In this case, the proposed method could replace the terrain campaigns usually done for collecting rich set of labelled samples in order to perform machine-learning classification approaches
# A Co-Design-Based Reliable Low-Latency and Energy-Efficient Transmission Protocol for UWSNs
## 4 Problem Statement  
### Network Model  
We consider a 3D UWSN architecture consisting of a number of sensor nodes that are randomly and densely deployed in an underwater region, as illustrated in Figure 1. A single sink is located at the center of the network on the water surface. A remote base station is deployed on the ground. Sensor nodes sense and record data, and after that the data are delivered to the sink hop-by-hop. The sink then wirelessly (RF) transmits data to a remote base station. When deploying underwater sensor nodes, a unique ID is given to each node \(n\in N\), where \(N\) is the set of deployed sensors. During the initialization of the underwater network, sensor nodes are layered as shown in Figure 1. Let \(l_{n}\) express the layer number of node \(n\) (e.g., the layer number of node i in Figure 1 is 3). Each node in the network is assigned to a specific layer number according to its distance relative to the sink. For convenience, the layer number of the sink is assigned to 0.  
After network initialization, each node maintains a neighbor table \(T\) that contains the information of its neighbors. The neighbor table includes neighbor ID, neighbor layer number and two-hop neighbor information. Each node classifies its first-hop neighbors to lower-layer neighbors (LN) and higher-layer neighbors (HN) according to the layer number. The LN of \(n\) expresses the neighbor whose layer number is lower than node \(n\), and HN expresses the opposite.  
The following assumptions are made in the design of the scheme to facilitate our research:  
Figure 1: The Network Instruction.  
All sensor nodes are randomly deployed in the network with a random walk mobility model with average speed 1.5 m/s [29].
2. The transmission power and receiver power of the node are fixed. Therefore, the transmission range of each node is also fixed.
3. All sensor nodes have identical initial energy and fixed idling power. When no data packet is sent or received, energy consumption of each node is fixed.
4. The network does not vary at a rate faster than a round trip time and adjacent layers perform data forwarding at different time slices to reduce collision.
5. The network is time-synchronized.  
Unlike most depth-based routing scenarios, the sink node can deploy underwater or on the sea surface in CDTP. Sensor nodes can deployed randomly around the sink node to forward the data to the sink node through multi-hop forwarding. CDTP is also suitable for the multi-sink scenario. CDTP uses a one-sink model for convenience of description and is suitable for different scenarios.
## 6 Results and Discussion
### Case 3  
In this case, the moving load is applied with transverse eccentricity equal to 1.0 m to the right from the center of the ferry (+X direction). Figure 27 illustrates that the deformations are unsymmetrical about the center of the ferry. The ferry is tilted towards the same load position (about Z direction) as the previous cases but with higher values as the rotational angle at the hinged point at the beginning of the ferry is increased. Additionally, the ferry is warped more than the previous case.  
Figure 26: Stress change with time for case 2.  
Figure 25: Stress distribution over ferries for case 2.  
Figure 28 shows the maximum deformation values with time for the ferry for both materials. The figure illustrates that the deformation in this case has the same manner as in case 2 but with higher values due to the increase in the transverse eccentricity. The deformations are increased by about 12% for the steel ferry and by about 19.5% for the composite ferry from case 1.  
The maximum deformation is increased to 1.262 m for the steel ferry and 0.279 m for the composite ferry. The above results illustrate that the increasing in the vertical displacement is caused by the eccentricity effect. The stress distribution is illustrated in Figure 29.  
Figure 27: The total deformation in case 3.  
Figure 28: The total deformations with time for case 3.  
Figure 30 shows the stresses with time in case 3. The stresses are increased with a very high percentage from case 1. The increase is about 10 times for the steel ferry and about 4.65 times for the composite ferry.  
In this case of loading, the von-Mises stresses are higher than the stresses in the previous cases. The maximum stress for this case of loading equals \(1.58\times 10^{9}\) Pa for the steel ferry at t = 1.8 s, which exceeds the ultimate strength of the used steel; additionally, the results illustrates its insufficient torsional stiffness to resist the twisting moment from the eccentric loading. Therefore, the steel ferry is not safe in this case of loading. For the composite ferry, the stress equals \(1.62\times 10^{8}\) Pa at t = 2 s, which is higher than the values in the previous cases of loading. The stresses curve is changed as the tank load is applied at the connections between two pontoons.
# Quarantining Malicious IoT Devices in Intelligent Sliced Mobile Networks
## 3 Proposed Solution
### Detection of Potential Malicious Devices  
An SDN application running on top of the SDN controller periodically samples the flow tables of the SDN switches, obtaining the numbers of packets and bytes transmitted in the sampling period for each flow rule in each SDN switch. Since the number of users connected to each base station can be up to 300,000 [28], installing a flow rule per each UE in each SDN switch is not feasible, because the tables  
Figure 1: Proposed quarantine architecture.  
of an SDN switch can efficiently support up to 4000 flow rules [29]. Therefore, in our solution each flow rule in the SDN switch corresponds to an aggregation of UEs. Then, the flows are the elements that the SDN application monitors and assigns to the slices. Consequently, the SDN application will detect suspicious flows, rather than malicious individual devices (note that we follow SDN terminology, in which the term flow denotes a sequence of packets that share the same set of packet header values).  
A flow will be considered suspicious when its throughput exceeds a certain threshold. Thus, this approach requires knowing the typical traffic in a flow without malicious devices. Fortunately, there are several models for the behavior of IoT devices, and in particular their traffic patterns. IoT sensors are often sending data in a deterministic periodic manner [30; 31; 32], and other authors have also proposed deep learning methods for IoT traffic load prediction [33] for IoT devices with more complex communication patterns. Malicious devices usually perform scanning operations, attempts to infect other devices, and DDoS attacks (currently most IoT botnets are used for DDoS attacks) [34; 35], thus increasing the regular traffic load.  
An undesired side-effect of this aggregation is that legitimate devices may be temporarily assigned to the Quarantine NS due to the aggregation of nodes inside a flow. Nevertheless, those devices will not lose connectivity nor session continuity while they are diverted to the Quarantine NS, and they will be returned back to the IoT NS after actual malicious nodes are identified.  
Another side-effect of the aggregation is that some flows containing malicious devices may not be properly detected if their activity does not exceed the threshold, so that they will never be sent to the Quarantine NS. However, although it is desirable to detect all suspicious devices, they will cause no harm if they are few. Furthermore, this problem can be alleviated by simply setting lower thresholds for tagging a flow as suspicious. These threshold values will determine the number of flows that are sent to the Quarantine NS.
# User-Information-Aware D2D Multicast File Distribution Mechanism
## 7 Simulation Analysis
### Effects of Various Parameters on Performance of All Four Schemes
#### 7.3.2 Effects of Various Parameters on Transmission Rate  
By predicting the potential requester's willingness, the proposed user-information-aware D2D multicast file distribution mechanism pushes the video to potential requesters whose willingness greater than willingness threshold. The throughput and resource utilization efficiency are improved greatly by increasing the number of D2D multicast receivers. The utility values of all four schemes have studied in the above subsection, and the D2D transmission rate of all four schemes are studied in this subsection.  
The relationships between the D2D transmission rate and the number of service requesters \(|U_{R}|\) are shown in Figure 7, and we can easily find that the transmission rate of the proposed scheme is  
Figure 6: The relationship between utility and \(|F|\): **(a)**\(P_{th}^{accept}=0.5\); **(b)**\(\lambda=0.6\).  
greater than the other three schemes. With the number of service requesters increases, the performance of the user-information-aware scheme is still better than other three schemes, although the transmission rate of all four schemes has declined to varying degrees. The interference-aware scheme is better than social-aware scheme and the Random cluster-head selection scheme. What's more, the decline of user-information-aware scheme slower than other three schemes, followed by the interference-aware scheme. Compared with the social-aware scheme and the random cluster-head selection scheme, the channel quality information between users is considered in the interference-aware scheme, which makes the transmission rate of the interference-aware scheme is better than the above two schemes. Although the purpose of the proposed scheme is to maximize the utility value, the transmission rate is also better than the other schemes. It can be seen from Equation (22) that this scheme considers the channel quality information and social relationship information at the same time, and owns more members in the D2D multicast cluster to facilitate the selection of suitable cluster head.  
Since the values of \(\lambda\) and \(P_{th}^{accept}\) affect the number of potential service requesters satisfying the condition \(p_{pi,l}^{accept}>P_{th}^{accept}\), the selection of cluster head is affected. Therefore, the performance of the proposed scheme differs under different parameter settings, but it is still superior to the comparison schemes.
# Encapsulated Probiotics: Potential Techniques and Coating Materials for Non-Dairy Food Applications
## 4 Biomaterials Utilized for Probiotics Encapsulation
### Why Proteins  
Why proteins are a complex mixture of globular proteins isolated from whey, which refers to the liquid part of milk (by-product) that separates during the cheese-making process. Whey is mainly constituted of \(\beta\)-lactoglobulin (85%), \(\alpha\)-lactalbumin (10%), and bovine serum albumin (5%). \(\beta\)-lactoglobulin, the major protein in whey, is rich in rigid \(\beta\)-sheet structure and two disulfide bonds. These two unique features of \(\beta\)-lactoglobulin provide whey a high resistance and stability against pepsin digestion, making whey protein a suitable encapsulation material for the controlled release of probiotics [86]. Previously, whey proteins have been found to increase the resistance of probiotics against gastrointestinal conditions for up to 3 h [45, 88]. Whey proteins, including whey protein concentrates (35\(-\)85% protein) and whey protein isolates (\(>\)95% protein), have been used in probiotic food products as encapsulating materials [41]. Whey proteins are a suitable medium to preserve and deliver probiotics owing to their high nutritional composition (containing soluble milk proteins and lactose). Whey proteins also possess amphoteric character, good gelation properties, thermal stability, hydration, and emulsification properties (pre-treated by heat-induced denaturation). Hence, they can interact, entrap, and protect probiotics components [87]. In probiotic encapsulation, whey proteins have been used as wall materials together with gum Arabic, maltodextrin, and pectin. The synergistic effects between whey proteins and polysaccharides have been reported to enhance the encapsulation efficiency of whey proteins [86].
# FBG-Based Monitoring of Geohazards: Current Status and Trends
## 3 Development of FBG-Based Geohazard Monitoring Systems
### Installation and Protection Techniques
conducted real-time monitoring of the Ripley Landslide using FBG and BOTDR sensors [41; 42]. They stated that, after three months' monitoring, their field instrumentation works stopped as the optical fiber cables were damaged by black bear activity. Epoxy resin and caulking products also failed during prolonged intervals of sub-zero temperatures, and the cables could not be firmly attached to the retaining wall. Their experiences indicate that periodic on-site visits are recommended to trouble-shoot hardware, software and telemetry issues.
# Performance Analysis of Ground Target Detection Utilizing Beidou Satellite Reflected Signals
## 3 Experimental Campaign
### Description of the Second Experiment  
In the first experiment, the fact that the target was put in and out by a human would have impacted the detection results. At the same time, the target position was fixed and static, so it was not easy to observe the influence of other satellite reflection signals. In order to solve this problem, a second experiment was carried out in this paper. In this experiment, the signal acquisition equipment, antenna erection height, and the experimental site were all kept unchanged, and the experimental time was 2019_04_04_15_52_13. The experimental setup is shown in Figure 7.  
It should be pointed out that the LHCP antenna was oriented to the south, and the tripod was placed in the middle of the road surface. The target moved between AB, with a width of 6 m. Point C indicates that the target moved directly below the antenna. The target was towed by two ropes, moving uniformly in the east and west directions, as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Length** & **Width** & **Height** \\ \hline
40 cm & 33 cm & 20 cm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions of the metal box, which was regarded as the target in the experiment.  
Figure 7: Measurement setup for ground target detection using BeiDou reflected signals in the second experiment.  
In Figure 8, the \(x\)-axis was west-orientated, and the \(y\)-axis was south-oriented. EFG is the position of the tripod, and the blue quadrilateral area ABCD is the moving range of the target. The vertical distance between CD and EF was 0.5 m, and the distance of AB was 6 m.  
There were some buffer data when the dual-channel collector collected BeiDou signals. Therefore, in the experiment, the target moved from position A in about 10 s after data acquisition, and the data acquisition time was about 3 min. At the same time, the stopwatch was used to record the specific time when the target passed directly below the antenna. The main information is shown in Table 2. Based on this, the moving speed of the target was calculated to be about 0.5 m/s.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of the Target Passing through the Antenna** & **Time (s)** \\ \hline
1 & 15.05 \\
2 & 28.08 \\
3 & 40.09 \\
4 & 53.05 \\
5 & 64.05 \\
6 & 77.04 \\
7 & 89.02 \\
8 & 107.07 \\
9 & 113.04 \\
10 & 125.09 \\
11 & 138.02 \\
12 & 150.00 \\
13 & 162.00 \\
14 & 174.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time information for the target passing through the antenna.  
Figure 8: Relationship between the target and the LHCP antenna in the second experiment.