esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mapping Model of Propeller RANS and LES Flow Fields Based on Deep Learning Methods
## 4 Experiments
### Experimental Results of Feature Extraction Model for Flow Fields
#### 4.2.2 Experiments of Input Flow Field Sizes  
We initially conducted experiments to investigate the impact of different input flow field sizes on the experimental outcomes, as detailed in Table 1. We began by comparing tests performed on the entire flow field and a central region around the propeller in the flow field. For the central region prediction, only the velocity information within that specific area was forecasted, while the remaining portions were directly compared using original RANS flow field data. The ultimate findings indicated significantly improved performance when focusing on the central region, outperforming direct predictions of the entire flow field. Subsequently, further experiments were conducted with varying flow field sizes. Initially, the image dimensions were set based on the propeller diameter, and experiments were conducted within a specific range around this size. Ultimately, a size of 0.26 \(\times\) 0.26 m\({}^{2}\) was identified as the optimal result. All subsequent experiments employed this size as the input.
# A Novel Method of Local Anode Effect Prediction for Large Aluminum Reduction Cell
## 3 Anode Rod Sequence Fluctuation Detection Algorithm Based on Sliding Window
### Sliding Window Current Series Segmentation  
First, current sequence data was segmented by sliding window, and set the current sequence as:  
\[I_{anode}=(i(t_{1}),i(t_{2}),i(t_{3}),i(t_{4}),\cdots i(t_{n})) \tag{1}\]  
where, \(I_{anode}\) is current data of the acquisition point at each time, and \(t_{1}-t_{n}\) represents the time. In the current sequence, a fixed length sliding window \(m(m\ll n)\) is used for sliding segmentation of the current sequence. The principle is shown in Figure 3. The window  
Figure 2: Partition diagram of anode rod of 400 kA aluminum reduction cell.  
moves backward one step length \(l\) for each sliding, and slides \((n-m)/l\) times to form \((n-m)/l+1\) subsequences.  
Assuming one of the subsequences is \(Y_{j}(1\leq j\leq(n-m)/l+1)\), the slope between any two adjacent data points in the subsequences can be calculated according to Formula (2):  
\[k_{i}=\frac{Y_{j}(i)-Y_{j}(i-1)}{t_{i}-t_{i-1}},(2\leq i\leq m) \tag{2}\]  
In this time window \(Y_{j}\), there are \(m-1\) slopes of adjacent current data. \(K_{mean}\) is the average of the current slope of the time window, while \(c\) is the average of the current data within the time window. The window length \(m\), the average slope value \(K_{mean}\), and the average value \(c\) form an array \(X_{j}(m,K_{mean},c)\). Then, the current sequence sliding window data is analyzed, and its fluctuation is determined by the standard deviation and the change in the slope of the window data.
## 4 Conclusions  
In this paper, aiming at the thrust performance test requirements during the development of ILET, a micro-newton torsional thrust balance was developed, and detailed experimental calibrations were carried out to examine its characteristics. The results showed that the torsional thrust balance exhibits good linearity in the range of 2 \(\upmu\)N-30 \(\upmu\)N, the settle time response to a step force is less than 7 s, and the thrust resolution is 0.09 \(\upmu\)N at \(\sim\)2.5 \(\upmu\)N and 0.21 \(\upmu\)N at \(\sim\)27.8 \(\upmu\)N. This validated performance shows that the balance can fulfill the thrust measurement requirements of the ILET operating in the mode of alternating work voltage polarity. However, the thrust balance has intrinsic drift, and the average deviation rate within 1000 s is about 0.0023 \(\upmu\)m/s. For long duration thrust measurement, the drift must be taken into account. In addition, we found that the electrostatic force produced by the thruster power supply wire has a non-negligible impact on the thrust measurement results in this design. The influence of the wire voltage was quantitatively analyzed, and it was found that a wire with 2 kV can cause 1 \(\upmu\)N thrust measurement deviation.  
Conceptualization, Y.Z. and D.G.; methodology, Y.Z. and D.G.; validation, D.G. and Y.Z.; Software, D.G. and Y.Y.; writing--original draft preparation, Y.Z.; writing--review and editing, D.G. and Y.Y.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the relevant regulations of the author's institution.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
Figure 21: The arm displacement response to different thrust with 2 kV wire voltage. (**a**) 3 \(\upmu\)N, (**b**) 30 \(\upmu\)N.
# Nonlinear Optical Microscopy of Interface Layers of Epitaxial Gamet Films
## 2 Experimental Procedure and Samples under Study
This allows the study of the nonlinear and magnetic properties of the interfaces of garnet films with the surrounding media (air and GGG) using the SHG or THG microscopy technique. Besides the evidently high in-plane spatial resolution of the nonlinear optical probe provided by strong focusing of the pump radiation, the in-depth resolution is mostly determined by small escape lengths at harmonics' frequences at the wavelengths shorter than 600 nm. Importantly, the in-depth resolution of the nonlinear microscopy techniques exceeds that of the linear optical one due to the nonlinear dependence of the response on the pump intensity; the higher the order of the nonlinearity, the stronger the spatial localization of the nonlinear optical response at the second or third harmonics' wavelengths.
# Experimental Evaluation of Unicast and Multicast CoAP Group Communication
## 3 Related Work
Next to these, there exist other solutions to realize group-like communication in constrained environments without using CoAP. For example, the Message Queue Telemetry Transport (MQTT) protocol is another application layer protocol designed for constrained devices [20]. MQTT uses a topic-based publish-subscribe architecture, i.e., clientsutilize the services of a broker to subscribe to topics and get all of the messages that are published to that topic. Unlike CoAP, MQTT relies on TCP as the underlying transport protocol and, thus, inherits its reliability. While the base CoAP does not provide any Quality of Service (QoS), MQTT provides its own QoS mechanism. MQTT provides its own way of group communication, by allowing multiple publishers to publish to the same topic and by allowing multiple clients to subscribe to it. This can be seen as a form of group communication, which exhibits some similarities with our proposed approach. However, it does not adhere to the REST principles that are commonly used for the Internet, and additionally, it does not provide the possibility to aggregate and manipulate notifications that are sent to the clients. MQTT for Sensor Networks (MQTT-SN) is an adapted version of MQTT that is aimed at embedded devices and Wireless Sensor Networks (WSNs) [21]. MQTT-SN extends MQTT beyond the reach of TCP/IP networks by taking into consideration the peculiarities of wireless communication environments. Similar to our solution, MQTT-SN relies on the presence of a gateway to act as a proxy between the clients and the servers (brokers in the MQTT case). However, like MQTT, MQTT-SN does not adhere to the REST principles.
# An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series
## 1 Introduction
Given that there is a low-vegetation-cover period triggered by site preparations (including soil clearing and amelioration) and planted forest seedlings with unenclosed canopies (intact natural forests maintain a relatively high vegetation cover), planted forests can be detected from natural forests [18]. Owing to the advantages of the longest historical archives (nearly 50 years) and the most comprehensive geographical record of Earth observations, Landsat data are appropriate for describing the inter-annual characteristics during the growth of forests. The normalized difference vegetation index (NDVI), which represents greenness, has been widely employed in forest cover monitoring [19]. [PERSON] et al. [20] proposed a shapelet-based algorithm to discern rubber plantations from natural forests by detecting low-value segments (i.e., shapelets) of the Landsat-NDVI time series of forest pixels caused by planting activities. A shapelet is a sub-sequence that captures discriminative features while eliminating redundancy or even noise in a time series, providing deeper insights into the data [21]. Thus, the shapelet-based algorithm has been widely applied in image-outline classification, motion capture, and spectrographs [22]. Furthermore, planted forests are shaped relatively regular due to human intervention, and the forest structure is relatively simple [23]. Efforts to establish mixed species in recent decades are conducive to optimizing the forest structure and improving the stability and biodiversity of forestry ecosystems [24; 25; 26]. For medium- or high-resolution images (e.g., Landsat and Sentinel-2), the pixels capture only partial stand information, so they are not the optimal spatial units for describing the planted forests. More recently, object-based image analysis (OBIA) has made comprehensive use of contextual information and has mitigated the \"salt-and-pepper effect,\" thereby focusing attention on the object-based classification of either individual trees or forest stands [27]. [PERSON] et al. [13] and [PERSON] et al. [28] clustered pixels with the same features as homogenous plantation objects using image segmentation to reduce the effects of fragmentation and noise. The superiority of OBIA in image classification offers opportunities for the planted forest mapping and time-series monitoring of stand information over large areas. In this study, according to the vegetation cover changes caused by the planting activities, we developed an object- and shapelet-based (OASB) method using the annual Landsat-NDVI time series and taking an example of the Ningxia Hui Autonomous Region (NHAR), to achieve two objectives: (1) develop and test the OASB method for distinguishing planted forests from natural forests in 2020 and (2) estimating the planting year from 1991 to 2020 to monitor the expansion of planted forests.
## 5 Application to Face Recognition  
For face recognition in short video, where video \(\mathbb{F}=\left\{F_{1},\cdots,F_{k}\right\}\) can be considered as a sequence of face images, we take the first frame with clear facial features as the key frame and define four square ROIs \(\left\{ROI_{i}\right\}_{i=1}^{4}\) located in the four regions around the two eyes, nose, and mouth as a pattern (see Figure 1a). Using the continuous frame extraction strategy to trace ROI backward, we can string four time series of ROIs \(\left\{\mathbb{ROI}_{i}\right\}_{i=1}^{4}\), where \(\mathbb{ROI}_{i}=\left\{ROI_{i,1},\cdots,ROI_{i,r_{i}}\right\}(1\leq i\leq 4)\), with embedding SPD matrices \(\left\{\mathbb{X}_{R_{i}}\right\}_{i=1}^{4}\) employing Equations (23) and (25). At the same time, in order to combine the global spatial features, we also take the global face \(G_{1}\) in the key frame as the starting point, and link the next two nearest face images in time. For the time series of global spatial features \(\left\{G_{1},G_{2},G_{3}\right\}\), we have time series of their embedding SPD matrices \(\mathbb{X}_{G}=\left\{X_{G_{1}},X_{G_{2}},X_{G_{3}}\right\}\). Then, we extend time series of ROIs and global faces extracted from one short video to a family of feature curves \(\left\{I_{G},\left\{I_{R_{i}}\right\}_{i=1}^{4}\right\}\) on the SPD manifold.  
We basically set the weight of the four ROIs and the whole face as equal and we adopted a pre-classification strategy with KNN-DTW to classify feature curves in the SPD manifold. Given that a query video consists of a family of five feature curves \(\left\{I_{G}^{query},\left\{I_{R_{i}}^{query}\right\}_{i=1}^{4}\right\}\) and training curves \(\left\{\left\{\left\{I_{G}^{j},\left\{I_{R_{i}}^{j}\right\}_{i=1}^{4}\right\},I_{j}^{V}\right\}_{j=1}^{n}\) from sample videos \(\left\{V_{1},\cdots,V_{n}\right\}\) with their associated labels, taking \(\delta_{DTW}\) as a similarity measure in KNN classifier, each feature curves in \(\left\{I_{G}^{query},\left\{I_{R_{i}}^{query}\right\}_{i=1}^{4}\right\}\) is pre-classified independently. For one query curve \(\Gamma^{query}\), we found out the \(K\) training curves closest to the query curve and defined the set of \(K\) training curves as \(N_{K(\Gamma)}\). Solving the label of query curve \(\Gamma^{query}\),  
\[l^{query}=\underset{l}{max}\sum_{\Gamma\in N_{K(\Gamma)}}I\left(l,l^{v}_{i} \right)i=1,\cdots,K, \tag{31}\]where \(I(l,l_{i})=\left\{\begin{array}{ll}1,&\text{if }l=l_{i}^{V}\\ 0,&\text{if }l\
eq l_{i}^{V}\end{array}\right.,l_{i}^{V}\) is the label of \(K\) closest curves \(\Gamma\in N_{K(\Gamma)}\). Then, the final decision is based on \(\left\{l_{G}^{query},\left\{l_{R_{i}}^{query}\right\}_{i=1}^{4}\right\}\) with the law of large number.
## 3 MG-Based BTS Operation
### Processing and Energy Management Systems
#### 3.4.2 Energy Management System
The EMS operates in the normal priority mode most of the time, as the very high priority mode only takes place when a grid outage occurs and the high priority mode two moments a day. During the OT period, the BESS is charged to the desired SoC level, which is set by the designer according to the BESS characteristics. Once the BESS is charged, it goes into a standby mode. Conversely, during the PT period, the BESS is discharged in an appropriate manner, considering their characteristics, to guarantee suitable discharge currents and levels to prolong its lifespan and optimize its total capacity [55]. At the same time, PV production is considered in order to choose the moments when it is appropriate to use the island mode of operation in which PV and BESS are responsible for powering the entire BTS by disconnecting it from the grid. It is important to note that all state transitions in this mode of operation are made by applying hysteresis to the PV thresholds of state switching to ensure as few transitions as possible, thus avoiding high-frequency state changes. Following this approach, the Cloud State is implemented to prevent that if a drop in PV production is produced by an occasional cloud, no reconnection to the grid takes place. This strategy is aimed at optimizing the management of the BESS by ensuring that the discharge currents of the BESS are within the parameters set by the manufacturer and that the depth of discharge chosen maximizes the relationship between the number of BESS cycles and the capacity of the BESS [55]. As an extension of this EMS, in installations where more PV power is installed than the amount of load demanded by the BTS, a new battery state could be considered where the BESS is also charged during the PT period with the surplus of PV. To this end, the BTS PV production forecasts [56] could be used to calculate the periods during the day when this surplus could be produced and, in this way, support the OT charging strategy. To realize the economic study of the savings obtained with the implemented EMS, it is essential to know the price differential between the PT and OT, as well as the BESS characteristics: battery efficiency, optimal depth of discharge and number of life cycles. For a PV system, it is fundamental to determine the amount of power generated according to the location of the installation as well as the prices in the production hours. In Section 4.4, a study is carried out for the specific case study, also obtaining general conclusions for any market and location.
# Analysis of Machine Learning Techniques for Information Classification in Mobile Applications
## 4 Image and Video Classification Algorithms on Mobile Devices
### EfficientNet
#### 4.2.2 EfficientNet Variants  
The EfficientNet model is one of the most relevant among the latest models. This model achieves an assumption of 84.4%. As discussed, EfficientNets models rely on AutoML and composite scaling to increase resource efficiency and achieve superior performance. Following this method, different versions of EfficientNet have been developed; starting from B0, improving the composite scaling method, the EfficientNet B1 to B7 versions were obtained [48]. Although the number of parameters increases, the increase is not significant, while the accuracy increases significantly in contrast to other CNN models [49].  
For the generation of new versions of EfficientNet, it is only necessary to scale the network when a set of heuristic scaling characteristics of the base network (B0) is present, which enables the production of increasingly larger networks. In short, each step of a larger network requires a square amount of computation. Therefore, a large amount of training time is necessary to deploy a network that has good accuracy-related results [32]. Table 3 shows the characteristics of each of the EfficientNet-derived models from B0 to B7, as well as the input size in pixels, the number of parameters and the accuracy [22; 50].
# Overview of FEM-Based Resistance Models for Local Buckling of Welded Steel Box Section Columns
## 4 Verification and Validation of the Numerical Model
### Model Validation and Evaluation of Imperfection and RS Models  
#### 4.2.1 Evaluation Strategy  
Factors directly related to the decrease in load-carrying capacity of thin-walled welded elements include the shape and magnitude of the residual stress pattern and a realistic geometric imperfection. To test all the models found in academic research and conduct subsequent evaluations, a pre-validation process was undertaken based on specimens that provided measurements of actual residual stresses. From the collected research, 22 test specimens were utilized, which gave the validation domain within the current research program. For these specimens not only the ultimate load and material properties, but also the residual stresses and geometric imperfections were also known from measurement results. Throughout the validation process, the ratio of ultimate experimental resistance to numerical analysis outcomes is assessed. Models are considered within the limitations of their respective research. Subsequent adjustments were made using the academic literature and building codes to develop an adequate and general expression for the local buckling phenomenon. Various scenarios were tested to achieve the best combination of residual stress distribution pattern and realistic geometrical imperfections. The strategy of the validation process can be summarized as follows:  
1. Testing different geometrical imperfection models (using the same residual stress model for all cases, called ECCS_Eq model--see details later)--Section 4.2.2;  
Figure 8: Convergence analysis for 200 \(\times\) 5 mm square section (red dots indicates the individual iteration steps).  
2. Modifying the already existing geometrical imperfection models to develop more general models that can more accurately reproduce experimental resistances--Section 4.2.2;
3. Testing the different residual stress models (using the best two geometrical models based on previous step)--Section 4.2.3;
4. Modifying the already existing residual stress models to develop general model that can more accurately reproduce experimental resistances--Section 4.2.4;
5. Execute the final validation step using the best geometrical imperfection and residual stress models but using the actual measured residual stress values published in the corresponding papers (so at this step only the shape of the residual stress pattern according to the model is used, but the actual compressive and tensile stresses are taken from the measurements)--Section 4.2.5.
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
## 4 Results
### Experiment Three--MAUI ROS Live
#### 4.3.3 Findings  
Our first two experiments provide an understanding of how the MLS protocol (sans Delivery Service) performs with respect to network capacity and the common UxS operating system (shown in Figure 11). With these additional data, our final experiment is the real-world application of the MLS protocol that demonstrates the viability of obtaining the security gains of the protocol for UxS use cases. The final test environment also shows the viability of MLS as a software-based module, that can be implemented for achieving decentralized device-agnostic encrypted communication. Additionally, our experiments show the protocol capability of MLS to provide real-time on-the-fly flexible encryption key updates as needed translates into the UxS environment.
## 1 Introduction
### Previous Implementations of ARIA Block Cipher  
There are many ARIA implementations on various environments. We firstly explore previous implementations, especially on other embedded processors.  
[PERSON] et al. [10] presented hardware architecture of ARIA block cipher. It divided plaintext into eight16-bit blocks to make smaller hardware size. They proposed a new design for the substitution and the memory block. The presented implementation used Verilog-HDL, and the proposed ARIA-128 implementation took 400 cycles at encryption step.  
[PERSON] et al. [11] showed a 32-bit structure small hardware implementation of ARIA block cipher. Since it uses a 32-bit input value unit, they redesigned four kinds of Sboxes. The proposed 32-bit ARIA operator is on 0.25 \(\upmu\)m standard CMOS cell process. The result takes 278 clock cycles for the ARIA-128 operation.  
[PERSON] and [PERSON] [12] proposed a 16-bit optimization design for ARIA block cipher. They proposed a 16-bit computation for ARIA diffusion layer from 32-bit optimized technique [13]. It mainly used matrix multiplication of matrix form 16 \(\times\) 16 involutional block diagonal matrix and 16 \(\times\) 16 involutional matrix. Additionally, it has its own Sbox with \(8\times 32\) lookup table. The proposed implementation takes about 600 microseconds for ARIA-128 encryption on the target platform, Atmel ATmega2560 microcontrollers.  
[PERSON] et al. [14] targeted two processors, 16-bit MSP430 and 32-bit ARM Cortex-M3. It provided two optimized implementations. First, on the MSP430 processor, they mainly used a 16-bit word-wise operator to implement ARIA block cipher. Second, on the ARM Cortex-M3, implementation used an \(8\times 32\) lookup-table-based implementation, but was further optimized by effective memory access. The proposed method rescheduled memory access to fully utilize three-stage pipelining. In addition, they proposed optimized implementation with counter mode of operation that applied precomputation techniques. These proposed ARIA-128 implementations took 209 and 96 cycles per byte on MSP430 and ARM Cortex-M3 processors, respectively.  
[PERSON] et al. [15] shows several kinds of block cipher implementations, but on the same target platform as the 32-bit RISC-V processor. The RISC-V processor has limited registers, so they proposed efficient registers scheduling. To implement optimized ARIA block cipher, it also used a lookup table at the substitution layer. The result of optimized ARIA-128 implementation on RISC-V took 295 cycles per byte for encryption.  
[PERSON] et al. [16] also targeted the 32-bit RISC-V processor, but its approach is different. The proposed method used only 10 kinds of RISC-V instructions. It did not use a lookup table for the substitution step. However, it made new architecture of ARIA substitution. For this implementation, most of the operations used composite fields. The operation was performed on SPIKE simulator, and its ARIA-128 implementation took 319 clock cycles.