esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Distribution Grid Consumption Strategies Containing Distributed Photovoltaics
## 1 Introduction  
With the proposal of a \"double carbon\" target [1], new energy generation technology has set off a new wave of development. The use of renewable energy to replace fossil energy with large carbon emissions is one of the important ways to realize sustainable development. In this context, photovoltaic (PV) power generation technology has been developed in an unprecedentedly superior way. Solar energy is a clean energy source that can be utilized in a sustainable manner, and although the non-recyclability of PV panels is a potential pollution, the overall damage to the ecological environment is weak. In addition, solar energy is characterized by abundant reserves, a good economy, and a long supply time, so it has been highly valued in the development of clean energy. China is a vast country with abundant solar energy resources [2], and according to the data shown in Figure 1, it can be seen that the installed capacity of photovoltaics in China has achieved significant growth.  
Not only that but in addition to China, other countries are also vigorously developing solar energy resources; according to the data released by the European Photovoltaic Association, in 2022, the EU 27 countries' new PV installed 41.4 GW, an increase of nearly 50%,optimistic that the annual installed capacity of PV is expected to be close to 120 GW in 2026. The European countries' PV installed situation is shown in Figure 2, 2022e; the figure represents the additional capacity in 2022. According to Fitch and Brazil's Ministry of Mines and Energy statistics, Brazil's total installed capacity of PV in 2022 reached 2 GW, with a new capacity of 9.0 GW and a new installed capacity year-on-year growth of 73.3%. According to research data from JMK, an Indian PV consultancy, 13.96 GW of solar PV systems were installed in India in 2022, a year-on-year increase of nearly 40%. This includes 11.3 GW of utility-scale PV, a year-on-year increase of about 47%, and some rooftop distributed PV and off-grid/distributed capacity. According to Fitch and the U.S. Energy Information Administration (EIA), in 2022, Japan's installed PV capacity reached 77.6 GW, up 4.4% year-on-year, with 3.1 GW of new PV capacity. As major economies strive to develop PV, it is worth noting that the U.S. PV market is one of the few to see a recession.  
With the continuous development of photovoltaic (PV) power generation, solving the problem of distribution grid consumption [3] containing distributed PV has become a key link. In this paper, we will discuss the main technologies and strategies for PV consumption. This includes distributed PV power generation, energy storage technology, microgrids, load-side management, and many other methods. These technologies and strategies have their own advantages and limitations in different application scenarios and need to be selected and combined according to the actual situation. This paper will provide useful insights and references for researchers and practitioners in related fields by reviewing and analyzing PV consumption.
# An Interactive Transient Model Correction Active Sonar Target Tracking Method
## 2 Description of the TMC Tracking Method
### System Model
#### 2.1.2 Measurement Equation  
In the rectangular coordinate system, the measurement equation is:  
\[\mathbf{Z}(k)=\mathbf{H}\mathbf{X}(k)+\mathbf{V}(k) \tag{12}\]  
In the formula, \(\mathbf{Z}(k)\) represents the measurement vector, where \(\mathbf{Z}(k)=\big{[}r_{x}\ r_{y}\big{]}^{\mathrm{T}}\). \(\mathbf{H}\) is the measurement matrix:  
\[\mathbf{H}=\left[\begin{array}{ccc}1&0&0&0\\ 0&0&1&0\end{array}\right] \tag{13}\]  
\(\mathbf{V}(k)\) is the measurement noise matrix, and the measurement noise is Gaussian white noise with a mean of zero, variance of \(r\), and a normal distribution. Its covariance matrix \(\mathbf{R}(k)\) is defined as:  
\[\mathbf{R}(k)=\mathbf{E}\Big{[}\mathbf{V}(k)\mathbf{V}^{T}(k)\Big{]}=\int_{0 }^{\mathrm{T}}\begin{bmatrix}1\\ 1\end{bmatrix}[1\ 1]rd\tau=\begin{bmatrix}\mathrm{T}&\mathrm{T}\\ \mathrm{T}&\mathrm{T}\end{bmatrix}r \tag{14}\]
# Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles
## 6 Experimental Results and Discussion
Camera sensor failure: In this case, the pipelines free navigation space segmentation is affected, but it is still able to properly segment using LiDAR depth map alone. Detecting and tracking objects is unaffected and done via the late fused (object level) LiDAR and radar data with the EKF algorithm. 2. Radar sensor failure: In this case, LiDAR can measure the speed of the objects via the range and ToF measurements. However this is slightly less accurate, but still usable for EKF to track the state of objects detected. The road segmentation and object classification remain unaffected. 3. LiDAR sensor failure will not fail the framework. Since the camera and radar essentially are doing the segmentation and tracking tasks on their own, not having LiDAR data will not completely derail or disturb the fusion flow. However, not having LiDAR depth map or point cloud position coordinates information can reduce the segmentation quality as well as tracking accuracy. In all three scenarios, corrupted inputs can potentially influence the quality of both segmentation, detection and tracking. A concrete analysis of robustness is left for future work.
## 5 Threat Analysis and Risk Assessment (TARA)
### Threat Scenario Identification  
Threat scenarios were identified using the Medini Analyze 2023 R1 software, employing the spoofing; tampering; repudiation; information disclosure; denial of service; elevation of privilege (STRIDE) model as the foundational framework. This model correlates each cybersecurity property--authenticity, integrity, non-repudiability, confidentiality, availability, and authorization--with potential threats: spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege, respectively. Each identified threat scenario is characterized by a set of three elements: the specific asset under threat, the compromised cybersecurity property affecting the asset, and the underlying cause behind the compromise of said cybersecurity property. To define the cybersecurity properties pertinent to each asset, a manual process was followed. Subsequently, the software facilitated the extraction of the corresponding threat scenarios associated with these attributes.
# Mapping Higher-Order Network Flows in Memory and Multilayer Networks with Infomap
## 2 Modeling Network Flows
### First-Order Network Flows  
In a first-order network representation, the flow direction only depends on the previously visited physical node. That is, for a random walker that moves between physical nodes \(i\in\{1,2,\ldots,N_{P}\}\), which represent objects in a complex system, and in \(t\) steps generates a sequence of random variables \(X_{1},X_{2},\ldots,X_{t}\), the transition probabilities  
\[P(X_{t}|X_{t-1},X_{t-2},\ldots)=P(X_{t}|X_{t-1}) \tag{1}\]  
only depend on the previously visited node's outlinks. With link weights \(w_{ij}\) between physical nodes \(i\) and \(j\), and total outlink weights \(w_{i}=\sum_{j}w_{ij}\) of node \(i\), the first-order transition probabilities are  
\[P(i\to j)=P_{ij}=\frac{w_{ij}}{w_{i}}, \tag{2}\]  
which give the stationary visit rates  
\[\pi_{i}=\sum_{j}\pi_{j}P_{ji}. \tag{3}\]  
To ensure ergodic stationary visit rates, from each node we can let the random walker teleport with probability \(\tau\), or with probability 1 if the node has no outlinks, to a random target node proportional to the target node's inlink weight [22].  
While a first-order model is sufficient to capture flow dynamics in some systems, recent studies have shown that higher-order flows are required to capture important dynamics in many complex systems [5; 6; 23]. The standard approach of modeling dynamical processes on networks with first-order flows oversimplifies the real dynamics and sets a limit of what can actually be detected in the system (Figure 1). Capturing critical phenomena in the dynamics and function of complex systems therefore often requires models of higher-order network flows [5; 6; 7; 23; 24; 25; 26; 27; 28].
# An Efficient and Reliable Algorithm for Wireless Sensor Network
## 6 Data Forwarding in Extended ERASCA Protocol (EERASCA)
### Algorithm of EERASCA  
Depending on the two situations, a mechanism of unicasting and multicasting is used in this paper. The Algorithm 1 (Stage 8) contains four steps which are as follows:  
```
/*****the initialization phase*****/ 1 Broadcast \"SD message\" about connected neighbors from core node 2 Receiving of \"SD message\" about the neighbors /*****The running phase*****/ 3 while (receive a data packet D by Mesh member M)
4 Calculate the connected neighbors of M
5 Update the routing table of M and assume that neighbors are represented by G
6 if (G \(\leq\) 3) then
7 Cancel the multicasting of M;
8 else
9 update the routing table of M;
10 if (G \(\geq\) 4) then
11 multicast M;
12 Break from while loop;
```  
**Algorithm 1**The Proposed Algorithm of EERASCA.
# Multi-Temporal InSAR Structural Damage Assessment: The London Crossrail Case Study
## 5 Conclusions  
In this paper we applied two techniques in the field of remote sensing and structural engineering to assess building damage due to tunneling. Several procedures have been proposed in literature for predicting structural damage induced by underground excavations. The quantification of the soil-structure interaction effect is one of the key factors for an accurate damage assessment. Attempts to include this effect in large scale urban damage assessment procedures have been performed connecting the building-induced variation of the settlement profile with the relative stiffness between the structure and the soil [29; 30; 31; 32]. However, the lack of assessment and validation against large datasets of field  
Figure 6: Greenfield-based vs. InSAR-based building strains.  
Figure 7: Schematic representation of the proposed method of the T-InSAR/relative stiffness method.  
measurements has highlighted the difficulty of assessing their potential and consequently their practical application [34].  
The proposed approach demonstrates how the use of MT-InSAR time-series analysis can be coupled to the relative stiffness method in order to calculate building modification factors. These modification factors can then be used to assess building damage caused by tunneling-induced subsidence. We have demonstrated the feasibility of this approach jointly exploiting two differential techniques, bridging the gaps between remote-sensing space-borne observations and structural engineering analysis. We validated our PS cumulative displacement fields using ground based leveling measurements and found a standard deviation of 2.5 mm for PS with temporal coherence higher than 0.9. The results show the difference between building and greenfield movements (Figure 6), allowing quantification of the effect of the building on the settlement profile. As expected the greenfield deformation assessment tends to provide a conservative estimate in the majority of cases (10 of the 14 buildings), overestimating tensile strains up to 50%. Future research will point toward the creation of an automatic system capable of generating tunneling induced damage maps including PS time-series analysis, building and soil characteristics.  
Original COSMO-SkyMed product ASI Agenzia Spaziale Italiana (2011-2016). We wish to thank [PERSON] and [PERSON] for providing the leveling data included in Figure 3. We wish to thank [PERSON] for hosting [PERSON] at NASA-JPL while writing this paper. This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.  
[PERSON] and [PERSON] Processed the SAR data. [PERSON] Supervised the SAR data processing. [PERSON] and [PERSON] worked on the structural engineering calculations. [PERSON] wrote the paper and interpreted the results supported by [PERSON], [PERSON], [PERSON] and [PERSON].  
The authors declare no conflict of interest.
# TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4
## 5 Performance Evaluation
However, this has a tradeoff of consuming more bandwidth [35]. Since TARS does not rely on retransmission, TARS can also be theoretically evaluated to understand its  
Figure 14: PDR with no retransmission versus density. Figure 13: Resource collision probability; (**a**) with respect to density and, (**b**) a confidence interval of 95%. Figure 15: PDR with retransmission versus density. bandwidth savings. By way of an example, the periodic message size is between 50 B and 300 B if no cryptography codes are used. Vehicle safety applications have a typical message frequency of 5 Hz to 50 Hz. If we assume a 10 ms scheduling period and one RB for every transmission, then in a 10 MHz bandwidth, 2 dB of noise, and 28 dBm of signal power, we are left with approximately 19 Mbps of savings in bandwidth.
# Spatial Small Target Detection Method Based on Multi-Scale Feature Fusion Pyramid
## 2 Related Work
### Vision Transformers  
Currently, the Transformer model is heavily used in natural language processing with success; researchers link CV (Computer Vision) and NLP (Natural Language Processing) domain knowledge to apply the Transformer model to computer vision tasks. CNNs lead to loss of spatial information in convolution and pooling; in contrast, Transformer introduces location information into the model through positional encoding, which introduces location information into the model. ViT [23] divides the image into 16\(\times\)16 patches and projects each patch as a fixed-length vector to use a Transformer for type prediction, achieving excellent results and providing a new paradigm for visual feature learning. Subsequently, DeiT [24] and PVT [25] improve on ViT to reduce the computational complexity and memory occupation of the algorithm and improve its performance in various downstream tasks. In addition, Swin Transformer is able to capture long-range dependencies between different regions in an image through multi-level windowed self-attention operations, which effectively enhances the modeling capability of the model. Meanwhile, the introduction of windowed self-attention operation can effectively model the spatial features in the image and improve the accuracy of image classification.
## 1 Introduction
### Structure of the Paper  
The organization of this paper is designed to provide a coherent exploration of the theoretical and practical elements involved in using big data to personalize foreign language learning. The structure is crafted to guide the reader through a logical progression from the conceptual foundations to practical applications and critical evaluations.  
The paper begins with the current section, introducing the research background and objectives, followed by a comprehensive review of the literature in Section 2. This section delves into the evolution of language learning theories, the role of big data in educational research, and the development of personalized learning systems. It sets the stage for understanding the gap this research aims to fill.  
Section 3 presents the theoretical foundations that underpin the study, detailing key concepts in language acquisition and the impact of technological advancements on learning processes. It also discusses how big data can be integrated effectively into language learning strategies.  
In Section 4, the paper describes the conceptual framework of the Data-Driven Personalized Learning Model (DDPLM), outlining its components and demonstrating how it can be applied to create tailored learning experiences. This section is central to the paper, as it introduces the model developed through this research.  
Section 5 applies the DDPLM in two distinct case studies--Edmodo and Duolingo--to empirically test the framework's effectiveness and adaptability. This section provides a detailed analysis of each case study, including background, implementation processes, and key findings.  
Section 6 discusses the implications of the findings, comparing them with existing models and discussing their significance for the broader field of language learning and educational technology.  
Section 7 concludes the paper with a summary of the key findings, recommendations for future research, and a discussion of this study's limitations. This final section reflects on the broader impact of the research and its contributions to the field of education.
## 2 Materials and Methods
### Formulation of the Reduced-Order Model
#### 2.1.3 Calculation of the Flow Rate  
In order to calculate the pressure distribution in the glottis from Equation (3), besides the prediction of the flow resistance coefficient \(f_{r}\), the value of the flow rate \(Q\) is also required. In previous reduced-order modeling of the vocal folds [20; 21; 22], the flow rate was obtained by assuming that the pressure at the minimum sectional area equals to zero and not including the viscous effects. In this subsection, we propose a more accurate way of predicting the flow rate based on the modified Bernoulli model.  
Let the index \(i=n\) in Equation (3); we have:  
\[P_{n}=P_{s}+\frac{\rho}{2}(\frac{Q}{A_{s}})^{2}-\frac{\rho}{2}(\frac{Q}{A_{n}} )^{2}-\frac{\rho Q^{2}}{2}\sum_{j=1}^{n}\frac{f_{r_{j}}}{A_{j}^{2}}. \tag{4}\]  
By substituting Equation (4) into Equation (2), we can easily derive the expression of the flow rate \(Q\) as follows:  
\[Q=\sqrt{\frac{2(P_{s}-P_{c})}{\rho\Big{[}-\frac{1}{A_{s}^{2}}+\frac{1-2\frac{ A_{n}}{A_{c}}(1-\frac{A_{n}}{A_{c}})}{A_{n}^{2}}+\sum_{j=1}^{n}\frac{f_{r_{j}}}{A_{ j}^{2}}\Big{]}}}. \tag{5}\]  
Once the value of \(f_{r}\) on each section of the glottis has been predicted by the DNN model, the flow rate \(Q\) can be directly obtained from Equation (5).
## 2 Proposed Approach
### Proposed Workflow of the Proposed Method for Strip Nonuniformity Correction  
The overall flow chart of the proposed algorithm in this paper is shown in Figure 3. There are specific steps in the algorithm.  
1. A portion of the raw infrared scan image is intercepted and used to calculate the linear correction coefficients. After the experiments (see Section 3.2), the linear correction coefficients are calculated using the 1500 image columns chosen for this work.  
2. The selected image is used to eliminate the horizontal strip caused by nonuniformity with the guided filtering model proposed in Section 2.1.
3. We use the linear fitting model based on guided filtering proposed in Section 2.2 to calculate the linear correction coefficients \(a(i),b(i)\).
4. The whole frame of the infrared image is corrected using linear coefficients as shown in the Formula (12).
## 5 Discussion
### Land Subsidence Caused by Land Reclamation
The average subsidence rates at p1 and p3 were approximately 27.6 and 11.4 mm/a, respectively, and then gradually slowed down in January 2019. Different from the deformation evolution in Region J, the time series deformation in Region K presented an approximate linear trend, where the deformation rates in Figure 13d,e were 31.0 mm/a and 22.3 mm/y, respectively. Land subsidence associated with land reclamation mainly experiences two consolidation processes: primary consolidation and long-term secondary compression, as recorded in [1; 2; 16; 25; 47]. The primary stage is governed by the dissipation of pore pressure and the secondary stage is creep under constant effective stress. Coastal soft soils are characterized by clear secondary consolidation. After the completion of primary consolidation, in turn, the pore pressure dissipates and a long-term compression process occurs [25]. The subsidence in the primary stage is faster than that in the secondary stage. The compression magnitude and velocity of land subsidence after land reclamation activities largely depend on the type and thickness of geotechnical parameters. Such a subsidence process can be  
Figure 13: Deformation analysis at Region K. (**a**) the deformation rate map; (**b,c**) the Google Earth images acquired in January 2015 and September 2019, respectively; (**d,e**) the deformation time series at feature points p1 and p2, marked in (**a**). explained by the well-known Terzaghi theory of consolidation [47], a typical time-subsidence prediction curve for both primary consolidation and secondary compaction. We explain the temporal evolution of surface deformation in Regions G, J and K using the Terzaghi theory of consolidation. Therefore, we can infer from the graph of the deformation time series that, before the SAR observed period, the reclamation project in Region J was basically completed; therefore, it suffered primary consolidation and then entered the secondary stage, which evidently presents a typical time-subsidence prediction curve under the reclamation, especially for Figure 12c. However, Regions G and K are the new reclamation areas and are currently still in the primary consolidation stage with an approximate linear deformation and larger deformation rate. Accordingly, significant land subsidence will continue for some time and finally enter a long-term slow compression stage. InSAR-derived results account for only a small part compared to the entire compaction process due to the short-term observation period. By integrating the geotechnical parameters of reclamation materials, the whole subsidence will be forecasted.
## 3 Measurement Setup
### Data Analysis Method
Equations (1), (2) and (5), and the 'pwelch' function are used to calculate each propagation channel characteristic from the PDP. The procedure of the post-processing script is shown in Figure 2.  
Figure 2: The procedure of post-processing Matlab script. In order to further analysis each communication crossing environment, we compared the path loss exponents that we obtained to those suggested by the Third Generation Partnership Project (3 GPP) models [27]. Specifically, we considered the following 3 GPP models based on our measurement locations: Rural Macro Line-of-Sight (RMa LoS), Rural Macro Non-LoS (RMa NLoS), Urban Macro LoS (UMa NLoS), Urban Macro NLoS (UMa NLoS), Suburban Macro LoS (SMa LoS), and Suburban Macro Non-LoS (SMa NLoS), [25]. Each 3 GPP path loss equation for a specific scenario can be described as follow:  
Figure 1:0  
Figure 1:1  
Figure 1:2  
Figure 1:3  
Figure 1:4  
Figure 1:5  
The averaged propagation channel characteristics over the tested track were evaluated. For each crossing, the data were collected by three runs of the train through the tested regions. Therefore, the propagation channel characteristics averaged over the test track and three measured data sets.