esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Current Ripple Parameter-Free MPCC of Grid-Connected Inverters for PV Systems
## 3 Control Algorithm
### Delay Compensation  
In the digital control system, a delay is hard to avoid due to the different moments between the current sampling and the new switching state application. As observed in Figure 7a, the former switching state is still applied during this delay interval, and thus the VV selected at \((k-1)\)th continues to be used at \(k\)th. In this case, the current deviates from the reference value. By analogy, the switching state chosen by sampling value at \(k\)th will be employed around \((k+1)\)th. No doubt the intrinsic delay makes the current oscillate around its reference, yielding the worsening current ripple.  
To compensate for the delay caused by the sampling and calculation process, one-step delay compensation is conducted by building the prediction model. As seen in Figure 7b, at \(k\)th, the applied VV is the state chosen at \((k-1)\)th, and the future current can be predicted  
Figure 6: Schematic of selecting candidate triangle: (**a**) selecting right-angle side; (**b**) selecting candidate triangle.  
Figure 7: Compared operation results of the predictive current control: (**a**) without one-step compensation; (**b**) with one-step compensation.  
by (18). Furthermore, the current of \((k+2)\)th can be evaluated by all possible candidate VVs, as  
\[\begin{bmatrix}i_{d}(k+2)\\ i_{q}(k+2)\end{bmatrix}=T_{s}\begin{bmatrix}\overset{\wedge}{F_{d}}(k+1)\\ \overset{\wedge}{F_{q}}(k+1)\end{bmatrix}+T_{s}\begin{bmatrix}\overset{\wedge}{a _{d}}(k+1)&0\\ 0&\overset{\wedge}{a_{q}}(k+1)\end{bmatrix}\begin{bmatrix}u_{d}(k+1)\\ u_{q}(k+1)\end{bmatrix}+\begin{bmatrix}i_{d}(k+1)\\ i_{q}(k+1)\end{bmatrix} \tag{19}\]  
Thereafter, the predictive currents from (19) are employed to (20) to evaluate the cost function of the candidate VVs in turn. The VV that minimizes cost function can be selected to drive the GCI for the following control period.  
\[g=\left|i_{d}^{ref}(k+2)-i_{d}(k+2)\right|+\left|i_{q}^{ref}(k+2)-i_{q}(k+2)\right| \tag{20}\]  
The overall diagram of the proposed MPCC control for grid-connected inverters is depicted in Figure 8. The reference voltage is obtained by maximum power point tracking. Only the sampling information is utilized in the ultralocal model to predict future current, which performs high robustness. Furthermore, VVVs have been reduced to seven via the triangle candidate strategy by which the calculation burden is successfully relieved. Finally, the applied switching states are selected after the delay compensation control.
## 5 Evaluation of the Use of Assessment Templates
### Test Scenario Execution for 5G Communication
A significant relationship exists between traffic load and latency, as an increased load results in network queues. The concept of real-time bandwidth aims to maximise the bandwidth while preserving low latency. The measurement tool employs Ericsson's SCReAM algorithm [44], a mobile-optimised congestion-control algorithm. SCReAM dynamically adjusts the bandwidth based on various metrics, including the round trip time (RTT). As depicted in Figure 11, SCReAM responds by reducing the bandwidth when the RTT increases, effectively minimising the latency. Therefore, the RT BW refers to the data delivered within a reasonably bounded RTT delay. Both the bandwidth and throughput serve as indicators of the network performance. While the bandwidth indicates the available or predicted network capacity, the throughput represents the transmitted data. Given the susceptibility of the intended networks to congestion, mainly as they are not private, the throughput is a more pertinent measurement in this context. Accurately predicting handover issues between cellular network cells is vital for coverage testing. Assessment criteria such as the signal strength, quality, and latency are used to identify potential challenges during the handover procedure, as shown between cell one and cell two in Figure 12. Through scenario simulations and a network-performance analysis, operators can improve handover algorithms and configurations to maintain connectivity. Conducting a specialised ODD assessment is necessary to validate and assess the results. Figure 11: Stream round trip time (RTT) and stream bandwidth (BW) over time. Points of interest are circled in red. Using an assessment template, as shown in Table 4, improves the efficiency of the site assessments for ADSs. This template, which links predefined performance indicators with test scenarios, provides a structured framework that reduces the effort required. It establishes a starting point for developing a more-detailed and customised assessment strategy, as Table 5 exemplifies. The advantage of this approach lies in its focus on test scenarios closely connected to KET and ODD dependencies, especially concerning connectivity and positioning. These scenarios form a pertinent baseline suite to test whether the conditions for activating and maintaining the ADS features throughout the ODD are met. Compared to approaches without such templates, this method offers a more organised and comprehensive way to conduct tests. It ensures that pertinent scenarios and performance indicators are considered, which is crucial to accurately assess an ADS's capabilities and limitations. Using KET-assessment templates makes the assessment process a systematic exercise in evaluating the ADS's functionality and performance within the defined ODD. The perceived efficiency gain can be attributed to:  
1. Structured testing: ensures the comprehensive coverage of essential test scenarios and indicators, reducing the risk of missing critical evaluation aspects. Figure 12: Three cells are involved in the coverage.
# Techniques and Paradigms in Modern Game AI Systems
## 5 Paradigms and Trends
### Techniques for Game Features
#### 5.2.3 Heterogeneous Agents  
MOBA games are usually designed to have heterogeneous agents with quite different mechanics and strategies. Such games provide another challenge for AI research since an AI system has to train different models for each kind of agent or a unified model which can generalize to distinct policies under different settings. There are three races of agents in StarCraft with different mechanics, so AlphaStar trains separate models for them by creating distinct populations of main agents, main exploiters, and league exploiters for each race. However, in games like Dota 2 and HoK, the number of heterogeneous agents called heroes is large, and there is an exponential number of hero combinations in the 5-versus-5 setting. OpenAI Five trains a unified model to control different heroes by limiting its application to 17 heroes and randomly sampling hero combinations for each game during the training. It suffers from a slow convergence speed and is hard to extend to more heroes, as shown in their further experiments with 25 heroes.  
JueWu proposes a new training paradigm under the setting of MOBA games by noticing that many heroes can be classified into several positions with similar strategies for each position, and most of the hero combinations are improper and not preferred by human players. JueWu chooses some typical hero combinations based on human data and trains a separate teacher model for each combination. Knowledge distillation is then used to train a student model to learn the general strategy under different hero combinations by imitating the behavior of teacher models. Such a student model is further trained under random hero combinations to generalize to arbitrary settings. This idea of curriculum learning that learns general behavior from specific settings is the key to the scalability of JueWu, which achieves superhuman performance with a hero pool of 40 heroes.
# Transmission Performance Analysis of RV Reducers Influenced by Profile Modification and Load
## 3 Effects of Modification Coefficients and Load
### Influence of Load for Case 1 Modification Method  
Changes in the transmission performance metrics of an RV reducer were obtained using the case 1 modification method, as shown in Figure 12. As can be seen from Figure 12a, an increase in the TPM significantly reduces the contact ratio. However, as the torque load increases, this effect is effectively reduced. Contrary to the changing trend of the contact area shown in Figure 12a, a TPM will increase the transmission efficiency of the RV reducer. The smaller the load torque, the more obvious the increase in transmission efficiency, as shown in Figure 12b. As can be seen from Figure 12c,d, the TPM increases the maximum contact force and friction. Under different torque load conditions, the change trend is basically the same. At the same time, the TPM will increase the mean value of the TE, as shown in Figure 12e. However, when the amount of modification is less than 0.04 mm, the TPM reduces the amplitude of the TE. When the amount of modification is greater than 0.04 mm, the TPM will increase the amplitude of the TE, as shown in Figure 12f.
## 2 Related Work
### Chinese NER Based on External Resource Enhancement  
In order to further improve the recognition performance of NER models, researchers try to integrate external knowledge such as a lexicon or syntactic characteristics into the model. [PERSON] et al. [21] proposed SDI-NER, which uses a graph neural network to learn syntactic dependency knowledge and integrates it into the BiLSTM-CRF model, so as to enhance the understanding of sentence semantics by the NER model. [PERSON] et al. [22] proposes FLAT, which causes potential matching words to interact with characters directly by improving the position encoding and attention mechanism, and improves the parallel computing efficiency of the NER model. [PERSON] et al. [23] proposed AESINER, which integrates part-of-speech labels, syntactic constituents and dependency relations in sentences into an NER model through a key-value memory network. [PERSON] et al. [24] proposed PLTE, which models all characters and matched words in parallel with batch processing by extending the Transformer encoder. [PERSON] et al. [25] proposed KGNER, a model that incorporates a knowledge graph into the Chinese NER task, and uses a new position encoding method and masking matrix to limit the impact of knowledge graph information on the original semantic meaning of sentences. [PERSON] et al. [26] proposed LEBERT, a model that integrates the matched lexicon knowledge into the underlying layer of BERT through a lexicon adapter, achieving the deep fusion of character and vocabulary knowledge. [PERSON] et al. [27] proposed MW-NER, which integrates word embeddings and n-gram embeddings through two independent attention networks to obtain multi-granular word information. It also attempts different fusion strategies to integrate character information and multi-granular word information, reducing the interference of word segmentation errors and noise in model performance. However, the performance of such methods is easily affected by the quality of external resources, such as the accuracy of parsing tools. At the same time, for entity recognition tasks in some special fields, it is necessary to build the corresponding lexicon or knowledge graph first, and then provide these methods for entity recognition.
## 5 DASH7 Communication System Implementation
### The Receiving Process  
The receiving process is, in essence, the opposite operation of the transmission process. It comprises four steps: reception, demodulation, time synchronisation, and decoding.
# A BIM Based Framework for Damage Segmentation, Modeling, and Visualization Using IFC
## 3 Methodology
### Damage Segmentation  
Convolution Neural Networks (CNN's) have been proven to provide superior performance in object recognition, that has been repeatedly demonstrated at the Kaggle challenges. Several works have proved these advantages in the case of spalling detection and segmentation as well [41; 45; 46]. As no point cloud dataset of damages has been compiled so far, the time consuming, and loaborious manual annotation process of damage classes they require for processing such dataset to allow retraining a CNN model for point cloud segmentation, and the impracticability of the reliance on unsupervised learning models for point cloud segmentation to produce unlabelled point clouds that have to be later identified  
Figure 3: Overview of the framework with the damage input data, damage data processing, model states, and possible use cases. Pictograms have been taken from the Noun Project [8; 11; 12].  
manually, this study has focused on damage recognition from images. Hence, this study relies also on a CNN for semantic segmentation of spalling defects from images. Out of numerous CNN models available, the TeranusNet16 has been chosen for this study. From the acquired inspection images, the spalling could be identified at pixel level via semantic segmentation inferred from the TeranusNet16 retrained on a dataset containing images of spalls via transfer learning. The original inspection image is then provided as an input to the retrained model, which in turn outputs a probability map representing the probability of each pixel in the image as a decimal value in between 0 and 1. This probability map could be then visualized as a grey-scale image and thresholded at a specific probability value above which the pixel is to be classified as a spalling to produce a binary map of values containing either 0 or 1 only [47]. Figure 4 shows some exemplary photos for the steps taken during segmentation. Figure 4a shows the photo with the damage, Figure 4 shows the damage probability map, and Figure 4 shows the overlay of both after thresholding.
# Investigation of Ti65 Powder Spreading Behavior in Multi-Layer Laser Powder Bed Fusion
## 4 Results and Discussion  
### Variation in Powder Bed Quality over Successive Layers  
The morphologies of the powder bed from layer 1 to layer 6 in the multi-layer PBF-LB process are shown in Figure 6. Both top and side views are provided to enable a qualitative understanding of how powder spreads and accumulates over different layers. As shown, the presence of previously printed/solidified regions significantly influences the spreading behavior of the subsequent powder layers. These printed regions introduce local topographical variations that can alter the powder flow, ultimately affecting the quality and dimensional accuracy of the printed part. Therefore, both macroscopic properties (packing density, surface roughness, and uniformity) and microscopic properties (particle rearrangement, coordination number, and void distribution) of the powder beds are analyzed across each layer to assess the evolution of spreading quality.  
Figure 6: Morphologies of powder beds over successive printed layers (\(V=0.100\) m/s).
# Design of a Filtering Power Divider with Simple Symmetric Structure Using Stubs
## 3 Structure of the Proposed WPD
### Output Short-Ended Stub  
At this step, a short-ended stub is incorporated between the output ports of the divider. As mentioned before, isolation resistance is needed between the divider's output ports, so this resistance is divided into two parts and the short-ended stub is added between them. The frequency response and layout of the initial design power divider are depicted in Figure 3.  
Figure 1: Schematic of the typical WPD.  
Figure 2: Simulated structure and frequency response of a typical WPD operating at 2 GHz: (**a**) Layout; (**b**) \(\mathrm{S_{21}}\) and \(\mathrm{|S_{11}}\) ; (**c**) \(\mathrm{|S_{22}}\) | and \(\mathrm{|S_{32}}\) |.  
Comparing the initial WPD and typical WPD results, it can be shown that adding the short-ended stub between the divider output ports resulted in a frequency response improvement, such that the isolation and return loss decreased below \(-30\) dB attenuation. Furthermore, the \(-3\) dB fractional bandwidth (FBW) increased by \(80\%\).
# Analysis of Cultivated Land Productivity in Southern China: Stability and Drivers
## 4 Discussion
### Cultivated Land NPP Stability: A Core Indicator of System Resilience
Improvements in infrastructure and farmland regulation have enhanced the resilience of the cultivated land system and further promoted the increase in NPP [51]. The size, regularity, and continuity of cultivated land are important factors affecting agricultural efficiency and the stability of cultivated land productivity [52]. The size, shape regularity, and contiguity of cultivated land are affected by many factors, including topography, geomorphology, regional settlement morphology, and traffic-water system distribution [53]. The rationality of cultivated land spatial distribution is another key factor in enhancing the resilience of cultivated land systems. Generally, highly concentrated plots facilitate large-scale and mechanized planting, improve production efficiency and incomes, and reduce productivity variations [54]. Trends in scale and mechanization have enhanced the ability of cultivated land to resist external interference. Conversely, fragmentation affects the efficiency of mechanized operations and cultivated land management and increases the risk of abandoned land [55]. Therefore, by optimizing the layout of cultivated land and reducing fragmentation, the resilience of cultivated land systems can be increased, and the stability of productivity can be maintained.
## 1 Introduction
There are some existing beam theories exploring a general procedure to define deformation modes. The generalized beam theory (GBT) [24] considers the deformed configuration through a linear combination of deformation modes. These modes are derived with the cross-section analysis [25], which provides deformation modes being hierarchically organized. At present, GBT has been well-established as an efficient and versatile tool to perform first-order [26], buckling [27], vibration [28], post buckling [29] and dynamic analyses [30] of thin-walled structures in elasticity. Another procedure to define deformation modes was proposed by [PERSON] et al. [31]. It considers an enrichment of the displacement field on the cross-section through a set of interpolation functions defined over a mesh of the cross-section, and a set of uncoupled deformation modes representing higher order effects are derived based on the solution of a polynomial eigenvalue problem [32; 33]. Thus, the resulting higher order model is capable to cope with the loss of accuracy inherent from the reduction of a 3D elasticity formulation to a 1D model [34]. The present paper focused on a systemic but simple procedure to identify deformation modes considering dynamic behaviours of thin-walled structures. The procedure was supposed to obtain an exhaustive set of deformation modes for a thin-walled cross-section and open to determine the hierarchy with the structural geometry and material parameters, as well as boundary conditions taken into account. To this end, three steps should be taken. First, preliminary deformation modes (PDM) were defined through kinematic assumptions and integrated in governing equations. They were uncoupled by solving the generalized eigenvalue problem associated with homogeneous governing equations. Second, PDM were assembled to generate final deformation modes. Within each eigenvector, the collinear amplitude functions can be viewed as a set of quantities describing the same structural behaviour. In this sense, the set of PDM can be assembled together to generate a final deformation mode. The key was to address the issues including the determination of PDM with collinear amplitude functions, the assignation of the weights indicating the participation and the decomposition of an amplitude function when related to several structural behaviours. Finally, the assembled deformation modes were hierarchically organized, and further employed to derive a reduced higher order beam model. In order to illustrate the application of the proposed approach, numerical examples were presented concerning representative cross-sections.
## 5 Results and Discussion
### Long-Term Projections  
For the long-term projections, the ten years of calendric life are considered. This is done because, as seen in the previous subsection, the maximum 6000 cycles will not be reached before the ten years considered the storage device's maximum calendric life.  
For these projections, it is assumed that the cost of electricity remains the same (single-rate: 0.1629 \(\epsilon\); 2-TOU: 0.0982 \(\epsilon\)_off-peak_ and 0.1894 \(\epsilon\)_peak_). The bill reduction after ten years is calculated using Equation (5), where \(Y_{1}BR\) is the bill reduction after one year (presented and discussed in the previous sub-section) and the \(SOH_{t}\) is the battery SOH at year \(t\).  
\[10 YBR=Y_{1}BR+\sum_{t=2}^{10}Y_{1}BR\times SOH_{t} \tag{5}\]  
For comparison purposes, this work also considers two discounted prices for the BESS, namely 50% and 75%. Finally, the results are evaluated based on two performance indicators: (1) payback time in years (PBT); and (2) balance per Euro invested after ten years (10Y%PEI). The former is given by Equation (6) and the latter by Equation (7). \(Y_{0}I\) is the initial investment, i.e., acquisition of inverter and battery.  
\[PBT=\frac{Y_{0}I}{10 YBR\times 0.1} \tag{6}\]  
\[10Y\%PEI=\frac{10 YBR}{Y_{0}I} \tag{7}\]  
The obtained results are presented in Table 12. The heatmap from blue to red indicates the best options for each UPAC (dark blue, best; dark red, worst).  
As can be observed from the %PEI and Payback Time columns, it is impossible to recover the investment before ten years with the current storage prices. Even with a price discount of 50% (average rate of 256 \(\upxi\)/kWh, including the inverter), only the larger UPACs can recover the initial investment and make a small profit. Interestingly, due to its very high net demand, UPAC C already makes some profit in the pre-charge control strategy with a 50% discount rate.  
Nevertheless, it is only at a 75% discount price (average price of 128 \(\upxi\)/kWh, including the inverter) that storage becomes profitable in all but one case (UPAC A). Naturally, profits are higher for larger UPACs, which also make the initial investments more substantial. For example, in UPAC F, the smallest BESS combination will yield a profit of 239% of the initial investment. That is, for an investment of 374 Euros in Year 1, there would be a profit of 897 euros after ten years.
## 4 Results and Discussion
### Energy Consumption per Sensor Node  
Figure 9 shows the average energy consumption. The trend is similar to the previous figure, and it can be noted that EQPD-MAC outperforms MPQ-MAC, RMP-MAC, and PMME-MAC by up to 7.5%, 7% and 30.3%, respectively. However, it may seem that percentage gains are not as high especially against MPQ-MAC and RMP-MAC. This is because EQPD-MAC transmits more packets than these protocols, which consumes more energy. Nevertheless, EQPD-MAC still provides the best performance, and it is able to provide this while delivering the highest number of packets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Packet Rate & 1 packet/s \\ \hline SIFS & 0.192 ms \\ \hline Size of Tx-beacon & 14 bytes \\ \hline Size of Rx-beacon & 13 bytes \\ \hline Size of SYNC Packet & 5 bytes \\ \hline Size of Data Packet & 28 bytes \\ \hline Size of ACK Packet & 11 bytes \\ \hline Initial Energy & 18738 J \\ \hline Buffer Size & 100 packets \\ \hline Retransmission limit & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._