esa-sceva/satcom-chunk-collection huggingface dataset:
# A Small-Sample Scenario Optimization Scheduling Method Based on Multidimensional Data Expansion
## 3 XGBoost PV Power Prediction Method Considering Data Imbalance in Small-Sample Scenarios
### Algorithm Flow  
The framework of the XGBoost-DDPG optimal scheduling method for small-sample scenarios based on multidimensional data extension proposed in this paper is shown in Figure 6. The specific processes are as follows:  
Figure 5: Flowchart of the DDPG algorithms.  
(1) First, to address the issue of scarce historical data for the new integrated energy system, the geographic distances between neighboring PV power plants are accurately calculated using the Haversine formula, and candidate power plants with strong spatial correlation are identified and screened out. On this basis, the multicore maximum mean difference (MK-MMD) algorithm is used to quantitatively match the distribution of meteorological features, ensuring that the selected source domain power stations have highly similar meteorological conditions to those of the target power stations. The screened high-quality source domain data are transformed into the output data applicable to the target power station through the capacity conversion formula. Meanwhile, for the power load data, based on the dynamic enhancement strategy of day type division, the difference in load patterns between weekdays and weekends is fully considered. A uniform random sampling method is used to generate synthetic data that conform to the characteristics of the real distribution.  
(2) Secondly, the expanded PV output data are processed with sample imbalance, and the real data in the dataset are oversampled to increase the proportion of real data. A PV output prediction model is established using XGBoost, and the expanded dataset is utilized to train the model. The integrated learning framework combines meteorological features, historical load, and time cycle characteristics, utilizing a regularized objective function and second-order gradient optimization to accurately predict ultra-short-term PV power. The prediction result is then used as a key input parameter for optimal scheduling.  
(3) Finally, the integrated energy system optimal scheduling problem is transformed into a Markov decision-making process, which is solved using the Deep Deterministic Policy Gradient (DDPG) algorithm. The Actor-Critical framework of the DDPG algorithm can effectively deal with the optimization problem in the continuous action space and approximate the optimal policy function and action value function by a deep neural network. During the training process, the goal network and soft update mechanism are used to ensure the algorithm's stability. At the same time, a reasonable reward function  
Figure 6: The framework diagram of the XGBoost-DDPG optimal scheduling method for IES small-sample scenarios based on multidimensional data extension.  
is designed to guide the intelligence in minimizing operational costs while satisfying system constraints.
# Evaluating Slope Deformation of Earth Dams Due to Earthquake Shaking Using MARS and GMDH Techniques
## 2 Case Study of Earth Dam  
In this study, a dataset of the slope changes of earth dams and embankments that are created during earthquakes was gathered from around the world. This dataset includes a various range of dams, including homogeneous and non-homogeneous earth dams, embankment dams with concrete facing, rockfill dams, and a few natural slopes. Various recorded data of these samples have been presented in studies and reports [68, 69, 70, 71, 72, 73, 74, 75]. The gathered parameters include earthquake magnitude (Mw), yield acceleration ratio (ay/amax), fundamental period ratio (Td/Tp), and value of slope deformation (Dave) for the collected results that have been obtained from 103 real cases. Figure 1 shows the different distributions of these data.  
The Dave parameter indicates the movement toward the lower section of the earth along the sliding surface that is created by an earthquake. In fact, this value is a point of the vertical and horizontal components of deformations and is calculated in the form of a single vector in the direction of the base of the sliding surface. Using the critical failure arising from the quasi-static analyses, the base inclination angle is derived. Other parameters, such as Mw, amax, and Tp, refer to the earthquake magnitude, the horizontal component of the maximum acceleration of earthquake and predominant earthquake period, respectively, which are introduced as seismic loading features. The two parameters of ay and Td are also called geotechnical features of earth dams and imply the yield acceleration and fundamental period of the earth dam, respectively. The threshold acceleration occurs when a mass is sliding downward, and using the quasi-static analyses, this value is obtained as yield acceleration as in [76]. The ay parameter, in fact, is the same acceleration value that is obtained equal to 1 in the safety coefficient of static analyses. To obtain the parameter of the fundamental period of the earth dam, generally, the reported previous resources are used. Otherwise, it is computed using the relation of Td = 4H/Vs, which has been suggested by several scholars [4]. In this regard, the parameters of H and Vs show the height of the dam and the shear wave velocity in the body of the dam, respectively. In this study, five parameters of Mw, ay/amax, and Td/Tp, which were the key parameters obtained by the review of the previous studies, are used as the input of the intelligent models [3, 77].  
In the modeling stage, according to the previous studies, 80% of data are used for training systems and 20% for the test stage [78, 79, 80, 81]. The data are selected by random and this process is repeated several times until the final outputs are obtained, ideally containing less error. The parameters are selected the same in both sections. Finally, the models are obtained for the next analyses and compared with the existing models. The statistical information of the used data (5 parameters as input) are presented in Table 1.
## 2 Materials and Methods
### Evaluation Parameters
#### 2.3.4 User Experience  
User experience was measured using the User Experience Questionnaire (UEQ) [66]. The UEQ consists of 26 dual-pole items divided into the following six dimensions:  
* Attractiveness: describes the general impression of the product.
* Perspicuity: describes the user's feeling that interaction with a product is easy, predictable, and controllable.
* Efficiency: describes how quickly and efficiently the user can use the product.
* Dependability: describes the feeling of being in control of the system.
* Stimulation: describes the user's interest and enthusiasm for the product.
* Novelty: describes whether product design is perceived as innovative or creative.  
Participants rated the items using a seven-point Likert scale. Each box on the Likert scale was assigned a score between \(-3\) and \(+3\), where \(+3\) corresponded to an adjective with positive connotation. The UEQ score of a dimension is the mean of its respective scores [67].
# Enhancing Data Security Through VLSM Subnetting and TCP/IP Model in an ENT
## 2 Literature Review
### Variable Length Subnet Mask  
A suitable subnetting approach with the introduction of Classless Inter-Domain Routing (CIDR). While CIDR offers granularity compared to classful addressing with masks relating to \"/AA\" representation--where \"AA\" is the number after the IP address with a preceding slash--variable length subnet masks relate to a varied length between 0 and 32 bits. In Figure 2, the 24 takes the place of \"AA\". The numbers grouped as static represent network identifiers. They are fixed and do no change. The number indicating dynamic is a host identifier, which changes proportionally to the static bits--CIDR of 24. The higher the subnet and subnet mask, the lower the number of hosts the network can accommodate and CIDR used. The values highlighted indicate the network is expected to use a CIDR of /27 with 8 subnets and can accept 32 hosts (2 of the 32 hosts are reserved and the remaining 30 hosts are useable). With a single given IP address of 192.168.5.0/24, the fourth octet becomes 0, 32, 64, 96, 128, 160, 192, 224 based on a subnet mask of /27 with a total host of 32. The useable IP address will be 192.168.5.1-192.168.4.32.  
VLSM is an IP allocation technique commonly used in hierarchical network designs and a fundamental concept in IP address planning [17; 18]. In a traditional setting, a subnet mask will be allocated to an entire network resulting in subnets of fixed sizes. However, CIDR depends on VLSMs to determine the routes. Recent research focused on optimisation algorithms and operational aspects of implementing VLSM. Correcting inadequacies of a Fixed Length Subnet Mask in a network where subnets must be a fixed sized leads to many unusable IP addresses. Ref. [18] demonstrated DHCP and VLSM optimal IP address allocation in a network. VLSM allows for the division of a large network into as many smaller subnets of different sizes as a function of the number of locations and sizes of the locations to be catered for. In essence, a long mask is used on subnets with few hosts, and a short mask on subnets with many hosts resulting to the effective use of IP addresses. DHCP uses a client-server architecture to allocate logical addresses in such a manner that a joining host (DHCP client) broadcasts a logical address request. The DHCP server responds to this request with an IP address, subnet mask, and a rent time during which the IP address is valid. Ref. [18] further argue that since IP addresses are allocated on the fly,  
Figure 2: CIDR with VLSM subnetting chart. The two downward arrows indicate increases in the number of Subnets and number of bits allocated to the Network portion of the IP address respectively. The upward arrow depicts an increase in the number of hosts per subnet. The highlighted number means for a network with 8 subnets, there are 32 hosts in each subnet and network boundaries is aligned to the CIDR of 27.  
such a network can grow provided the number of hosts does not outnumber the number of useable IP addresses.
# Testing a Vision-Based Autonomous Drone Navigation Model in a Forest Environment
## 3 The Proposed Method
### Object Detection
By unifying three independent models, the network is able to replace the selective search method entirely. Faster R-CNN computes all the feature maps by using a single forward-pass approach over the entire image. Then, the region proposals share the feature matrices that are later used for the bounding box regressors and learning the object classifier. The workflow of Faster R-CNN for tree object detection is shown in Figure 3.  
As shown in Figure 4, the Faster RCNN detection architecture uses ResNet-101 as the ConvNet backbone to extract features. The Resnet101 Conv block consists of five fully convolution layers (_conv1_x, conv2_x, conv3_x, conv 4_x, conv5_x_) [35]. There are denoted as _C1, C2, C3, C4, and C5_ for _conv1, conv2, conv3, conv4, and conv5_. Each conv layer consists of a 3 \(\times\) 3 convolution size with a fixed feature map dimension. The _conv1_ layer has a size of 3 \(\times\) 3 filters. The _conv2, conv3, and conv4_ layers have 3 \(\times\) 3 filters with 128, 256, and 512 feature map dimensions, respectively. The final layer _conv5_ is connected to the RPN to generate the regions proposals, and then, undergoes ROI pooling. First, the input image is fed into the ConvNe backbone of the RPN network. In the process, the ResNet-101 CNN extracts each feature vector with a corresponding fixed feature map dimension. The network has to learn at every point of the output feature map to estimate its size at the corresponding location and also to decide whether an object is located in the image. Then, a sliding window runs spatially on the convolutional feature maps of the image at the spatial pooling stage. For each sliding window, we need to predict multiple regions of various scales and ratios simultaneously. Hence, anchor boxes are  
Figure 3: Overview of the Faster R-CNN pipeline. introduced in Faster R-CNN to resolve the variations in the ratios and scales of the objects. In total, a set of nine anchors are generated that all have the same center (_cx,cy_), as shown in Figure 5.  
All these anchors have different aspect ratios and different scales. At each location, these anchor boxes predict the probability of the corresponding object in the image. Each of the anchors calculates how much it overlaps with the ground-truth bounding boxes. In the final step, all the bounding boxes are processed by applying spatial pooling. Then, the feature maps are flattened and fed into two fully connected neural networks with 2048 dimensions. The spatial features extracted from the convolutional feature maps are computed in a smaller network for classification and regression. For this reason, bounding box regression is applied to improve the anchor boxes at each location. The output regressors determine a final predicted bounding box with \((x,y,w,h)\) coordinates. The output of the classification determines the probability of whether the predicted box contains an object or not, as shown in Figure 6.  
Figure 4: The Faster R-CNN ReseNet-101 architecture for object detection. Figure 5: Generate anchor boxes.
# Dynamic Similitude Design Method of the Distorted Model on Variable Thickness Cantilever Plates
## 5 Conclusions  
Much research has been done on the similitude design of thin walled structures, such as plates and cylindrical shells. However, in engineering practice, the real structures do not always have the same thickness characteristic. The variable thickness can significantly affect the dynamic properties of the structure such that the prediction of the simple scaled down model may fail in its accuracy.  
In order to address this issue, a new technique for the similitude design of a cantilever VT plate is proposed in the present study based on the transfer matrix method. In this approach, a simplified ST plate is introduced as a connection between the VT plate and the equivalent thin plate, such that the distorted similitude design method of the thin walled structure can be directly applied in designing the scaled model. The transfer matrices of the ST plate and the thin plate have been derived in this study, and the equivalent thickness \(h_{e,n}\) of the thin plate corresponding to the specific vibration modal of the VT plate are calculated according to these transfer matrices. Moreover, a unified model plate is defined as the Model Thin (MT) plate such that different orders' dynamic properties of the prototype VT plate can be predicted by using only one scaled model with the thickness \(h_{Uini}\), and the scaling law between the scaled model and the prototype are also derived as (35). Finally, a case study is employed to validate the proposed approach in the present study, and a general process of determining the scaled model and its scaling laws are summarized to emphasis its significance in engineering practice.  
This work was supported by the National Science Foundation of China (grant numbers 11572082); the Fundamental Research Funds for the Central Universities of China (grant numbers N150304004, N140301001); the Key Laboratory for Precision & Non-Traditional Machining of Ministry of Education, Dalian University of Technology (grant numbers JMT220160); and the Excellent Talents Support Program in Institutions of Higher Learning in Liaoning Province of China (grant numbers LJQ2015038).  
[PERSON] conceived and designed the study. [PERSON] wrote the manuscript. [PERSON] and [PERSON] reviewed and edited the manuscript. All authors read and approved the manuscript.  
The authors declare no conflict of interest.
## 5 Cased Study  
### Monitoring System of Foundation Displacement of Long-Span Bridge  
In order to verify the accuracy of the proposed method in this paper, GNSS observation stations are established in a highway-railway cable-stayed bridge in the construction stage with a main span of 1092 m, as shown in Figure 7. HWA-BM-300 dual-frequency receiver is arranged at each corner of the cushion cap to receive signals of GPS and BDS satellites at the same time. Whereas, the data serving analysis is mainly from GPS + BDS signals, which can monitor the deformation of the cushion cap. The reference station is set up in the south bank (within 2 km), in which the GNSS base station used for measuring and lofting has been built. The reference station antenna is set up side by side with the original antenna on the top of the steel pipe pile of the base station (without mutual interference), and the wireless transmission and solar power supply are adopted, as shown in Figure 8. In addition, a base station for monitoring the deformation of bearing platform is set up independently.  
Figure 5: Correction and prediction of GNSS data.  
Figure 6: The flow chart of the proposed method.  
Figures 9 and 10 show the satellite visibility of the GNNS at the observation station and reference station on 14 March 2018. As shown, GNSS receivers can obtain signal from more than four satellites, meeting the needs of three-dimensional positioning. Since all the monitoring stations are not far from this site, they can have a similar observation condition.  
Figure 8: The observation and reference station: (**a**) observation station, (**b**) reference station.  
Figure 7: The general bridge and monitoring system.  
Figure 9: The number of available satellites.
## 3 Traffic Data Sensing and Gathering  
As discussed in the previous sections, data sensing and gathering focuses on scalable collection of road information from a large number of heterogeneous sources. The presently deployed systems in use by the traffic management companies collect data across a variety of formats, time scales, and degrees of granularity. This is because of the fact that said systems have been deployed at different time periods, all having limited functionality for the purposes of the data exchange between them. In contrast, the emerging TMSs are expected to employ a number of traffic related information collection principles as demanded by the city authorities in order to identify gaps where new communications technologies and systems could be deployed to improve the (i) accuracy; (ii) timeliness; and (iii) cost efficiency of data gathering.  
Following the aforementioned requirements, the current trends in development of TMSs comprise the use of advanced communications and sensing technologies (e.g., wireless sensor networks (WSNs), cellular networks, mobile sensing, and social media feeds) as potential solutions to extend the limitations of the existing systems in the upcoming era of Smart Cities. As the main wireless technology utilized for data sensing and gathering on the road networks, embedded devices in the role of small sensors are being deployed ubiquitously--they can be mounted on the vehicles, at the roadside units (RSUs), or even under the pavement to sense and report the unexpected events.  
In case of embedded in-vehicle sensors, the parameters related to the car operations are monitored and measured. Further, the data is disseminated to the nearby vehicles and/or the RSUs. In case of embedded devices, sensors are primarily used for measuring the speed of passing vehicles, the traffic  
Figure 2: Overall architecture of a modern traffic management system.  
volumes, or other parameters of the environment. A modern TMS needs to therefore concentrate on leveraging the innovative solutions that enable collection of data from a specific region of interest under particular time constraints while minimizing the cost and spectrum usage as well as maximizing the system utilization.
## Materials and Methods
### Adsorption Experiments AB113 in Solution Data Analysis  
Batch adsorption experiments were performed at room temperature adding 50 mg of \(\Phi_{x}\)-PPH at different volumes (a range of volumes was used) of 10\({}^{-5}\) M of AB113. After one day, the solid was removed by centrifugation and the solution was analysed by UV-Vis spectroscopy. The Langmuir model assumes that the sorption sites are identical and energetically equivalent due to its homogeneous structure [41; 42]. The equilibrium is obtained when the monolayer formation on the sorbent occurs. The Langmuir isotherm is described according to the following equation (Equation (1)):  
\[q_{e}=\ \frac{q_{m}K_{L}C_{e}}{1+\ K_{L}C_{e}} \tag{1}\]  
where, \(q_{e}\) (mol/kg) and \(C_{e}\) (mol/L) are the equilibrium concentrations of AB113 in the solid and the liquid phase, respectively, \(q_{m}\) (mmol/g) is the maximum sorption capacity and \(K_{L}\) (L/kg) is the Langmuir constant related to the energy of adsorption. \(q_{e}\) is obtained according to Equation (2):  
\[q_{e}=\Big{(}C_{i}\ -\ C_{f}\Big{)}\frac{V}{m} \tag{2}\]where, \(C_{i}\) and \(C_{f}\) are the concentrations of AB113 at the beginning and the end of the adsorption process, \(V\) is the solution volume used during batch experiments (a range of volumes was used) and m is the mass of \(\Phi_{5}\)-PPH used.  
The Freundlich model was used to describe the adsorption of contaminants on heterogeneous surfaces consisting of sites with different exponential distribution and energies [41; 42]. The equation of the Freundlich sorption isotherm is expressed by Equation (3):  
\[q_{e}=\ K_{F}C_{e}^{n} \tag{3}\]  
where, \(K_{F}\) and \(n\) are the Freundlich adsorption isotherm constants, being indicative of the adsorption extension and the degree of the surface heterogeneity. The SIPS isotherm combines both Freundlich and Langmuir isotherms, which at low adsorbate concentration behaves as the Freundlich isotherm [41] and at high concentration it predicts a monolayer adsorption capacity characteristic to the Langmuir model [41]. The mathematical representation of this model is given by Equation (4):  
\[q_{e}=\ \frac{q_{m}(K_{g}C_{e})^{n}}{1+\left(K_{s}C_{e}\right)^{n}} \tag{4}\]  
where, \(q_{m}\) (mol/kg) is the maximum adsorption capacity, which can also be expressed as Nt, a measure of the total number of binding sites available per g of sorbent, \(K_{S}\) is the affinity constant for adsorption (L/kg) and \(n\) is the Freundlich parameter that takes into account the system heterogeneity. The SIPS isotherm is reduced to the Langmuir form for \(n=1\) and a homogeneous surface is considered. The greater the difference from this value, the greater the solid surface heterogeneity.