esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Co-Registration and Individual Tree Matching
#### 1.2.4 Research Gap  
Existing methods for co-registration are challenged by complex and variable stands (unevenly or evenly aged, mixed stands, or purely coniferous/deciduous)--especially if the number of survey trees is low. The limited number of survey plots particularly impedes the parametrization of the algorithms. To overcome these limitations, a procedure is needed which uses synthetic (modeled) training data to achieve optimal parameters and to reduce the need for additional validation data.  
Since mismatched surveyed and detected tree pairs reduce the performance of empirical models (e.g., individual tree-based), a higher-order valuation of reliability (e.g., an posteriori matching probability) is needed. This information could be suitable to enhance the performance these models by increasing the amount of usable reference data. This could be achieved by weighting the importance of a training dataset in relation to its reliability.
# Comprehensive Review of Storage Optimization Techniques in Blockchain Systems
## 3 Background
### Blockchain Storage  
Blockchain storage is a distributed and decentralized data storage method based on blockchain technology [25]. It is maintained collaboratively by multiple nodes, with each node holding a complete copy of the blockchain data. During blockchain transactions, nodes participate in verification and consensus processes to ensure data integrity and consistency. Compared to traditional data storage methods, blockchain storage has the following key features:  
1. Distributed Storage: Blockchain technology disperses data across multiple nodes, significantly reducing the risk of system failure caused by a single point of failure. Traditional centralized storage typically relies on one or a few central servers, and any failure in these servers could lead to data loss and severely impact system operation. In contrast, blockchain storage distributes data across many nodes, so even if one node fails, others can continue operating, ensuring high reliability and stability.  
2. Decentralization: Blockchain storage makes the storage system fairer and more transparent by removing control from a centralized institution. Traditional centralized storage methods often involve data control and management by a central institution, which can lead to unfair handling and tampering of data. Blockchain storage, on the other hand, ensures the fairness and transparency of data through consensus algorithms and collaboration among distributed nodes, thus enhancing the credibility of the system.
3. Immutability: Blockchain storage leverages the inherent immutability of blockchain technology, ensuring that once data are written, they cannot be deleted or altered, thereby safeguarding data integrity, authenticity, and trustworthiness. Each data block contains the hash of the previous block, and any modification to the data will change the hash, which can be quickly detected by other nodes. Even if an attacker attempts to alter the data, other nodes can promptly identify and correct it, ensuring data security and consistency.  
In summary, the characteristics of blockchain storage--distribution [42; 43; 44], decentralization [45; 46; 47], and immutability [48; 49; 50]--not only enhance data storage security and reliability but also bring transparency and fairness to data management. These features give blockchain storage significant application value and broad potential in modern data storage systems.
## 7 Simulations and Results
### Simulation Model  
The aim of the simulation is to evaluate whether the EPF based on the SDN-NDN-IoT system can provide us with better results in terms of network efficiency, efficient forwarding, and energy consumption of sensor nodes. To that end, the implementation of our code is in ndnSim and evaluation of EPF is with the GIF and native-flooding mechanism of NDN. In our simulation environment, IEEE 802.15.4 MAC protocol used. We deployed sensor nodes in a lattice and grid topology. The grid topology is defined as the intersection of the nodes of the network. The network topology is defined as the intersection of the nodes of the network topology.  
size is 10 \(\times\) 10 nodes. The transmission range of each node was 100 m. The transmission network range was 1000 m \(\times\) 1000 m. We evaluated our simulation results over an average of 20 independent runs and computed the results after that. For energy related computations, energy model of NDN is used in the simulation scenario. The initial energy of the nodes was 50 joules. Implemented simulation parameters are shown in Table 2.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 12 Applications of HCFs in Networks and Systems
### Stable Latency Applications
#### 12.2.4 Time Distribution  
The conclusion is identical to the one in Section 12.2.3, especially considering that all facilities in need of precise distribution/comparison of time and frequency from atomic clocks already have access to dark fibers of SMF type and Gigabit Ethernet (GbE) data links. HCFs may, however, be used in short intra-facility time distribution links.
# Progressive Poling of Large Area, High r\({}_{33}\) Electro-Optic Polymer SEO100c
## 1 Introduction
EO-Modulator designs may require a larger electrode for their end application, such as in the case of an EO antenna [11]. Additionally large area poling can be used in poling full wafers before individual devices are diced. The use of a large poling electrode will result in the poling of a larger section of the core material but increases the risk of shorting through the device and rendering it unusable. Because a larger area is being poled there is higher probability of the poling E-field finding a material imperfection. Any imperfection such as a dust particle or an air bubble in the poled area can result in dielectric breakdown. Inherent to the polling process of EO-polymers is the risk of dielectric breakdown. This risk is introduced by the combination of heating the polymer to the glass transition temperature which lowers the breakdown voltage while applying a high strength field to the device. For these reasons the effects of poling SEO100c at lower voltages and lower temperatures have been examined, minimizing of the risk of dielectric breakdown at the expense of a lower r\({}_{33}\). A large r\({}_{33}\) of 226 pm/V has been reported in a TiO\({}_{2}\)/SEO100c/TiO\({}_{2}\) multilayer structure measured using the teng-man method [8]. An r\({}_{33}\) of 166 pm/V has been reported for a directional coupler using SEO100c with a proprietary cladding material [4]. This paper reports and quantifies the research of r\({}_{33}\) measurements poled under increasingly stronger electric fields of the EO polymer SEO100c for application designers. Of particular interest is the achievable r\({}_{33}\) in a practical waveguide EO modulator, constructed using commercial polymers under various poling conditions. A polymer modulator made from commercially available products demonstrates the progress that has been made in EO polymer design.
## 4 Variational Inference Model for Non-Linear Total Variability Embedding
### Non-Linear Feature Extraction and Speaker Verification  
The encoder network of the proposed VAE generates the latent variable mean \(\mu(\mathbf{X})\) and the log-variance \(\log\sigma^{2}(\mathbf{X})\). Once the VAE has been trained, the encoder network is used as a feature extraction model, as shown in Figure 2. Similar to the conventional i-vector extractor, the encoder network takes the Baum-Welch statistics of the input speech utterance and generates a random variable with a Gaussian distribution, which contains essential information for modeling an utterance-dependent GMM. The mean of the latent variable \(\mu(\mathbf{X})\) is exploited as a compact representation of the variability within the GMM distribution dependent on \(\mathbf{X}\). Moreover, since the variance of the latent variable \(\sigma^{2}(\mathbf{X})\) represents the variability of the distribution, it is used as a proxy for the uncertainty caused by the short duration of the given speech samples. The features extracted by the proposed VAE can be transformed via feature compensation techniques (e.g., linear discriminant analysis (LDA) [1]) in order to improve the discriminability of the features.  
Given a set of \(N(p)\) enrollment speech samples spoken by an arbitrary speaker \(p\)  
\[\mathbf{X}(p)=\{\mathbf{X}_{1}(p),\mathbf{X}_{2}(p),\cdots,\mathbf{X}_{N(p)}(p )\}, \tag{18}\]  
the speaker model for \(p\) is obtained by averaging the features extracted from each speech sample. To determine whether a test utterance \(\mathbf{X}_{test}\) is spoken by the speaker \(p\), analogous to the i-vector  
Figure 2: Flow chart of the speaker verification process using the proposed feature extraction scheme.  
framework, probabilistic linear discriminant analysis (PLDA) is used to compute the similarity between the feature extracted from \(\mathbf{X}_{test}\) and the speaker model of \(p\).  
Unlike the conventional i-vector framework, which only uses the mean of the latent variable as feature, the proposed scheme utilizes both the mean and variance of the latent variable to take the uncertainty into account. Providing the speaker decision model (e.g., PLDA) with information about the uncertainty within the input speech, which is represented by the variance of the latent variable, may improve the speaker recognition performance. This is verified in the experiments shown in Section 5.
## 4 Multicast Routing Approaches in DTN
### Buffer-Efficient Multicast Routing in DTN
#### 4.2.2 Reduce Replication  
Non-Replication Multicasting Scheme: [PERSON] and [PERSON] [52] proposed a compare-split scheme to construct a dynamic multicast tree for single-copy message forwarding. The goal of the scheme is to minimize the number of forwarders to reach multicast destinations. The compare-split is performed  
Figure 9: The buffer management strategy in RMDA [48].  
during contact with potential relays. The scheme makes a comparison during the encounter of a node without any destination subset and decides whether the split will occur based on the contact rate level. Ratio-based-Split (RS) is used to divide the destination subset according to the activity levels of two encountered nodes until only one destination remains in the destination set. The scheme will either wait until it meets the destination or forward the message to a node with a higher encounter rate level with the destination. This scheme exploits the contact state information to construct a dynamic non-replication tree for multicast with a small number of relay nodes, thus reducing buffer resource consumption.  
Space Minimizing Tree-Based Routing: [PERSON] [53] proposed a generic tree computation algorithm to compute multicast paths with a lower number of relay nodes. The algorithm leads to the reduction of the number of message copies spawned in the network. Space Minimizing Tree-Based Routing (SMTBR) selects relays based on the importance metric. The importance of a node refers to the likelihood that the node will be selected in a multicast path. As shown in Figure 10, a thin multicast tree structure that has fewer participating nodes can be obtained from SMTBR using a generic tree computation algorithm. The solid circles represent the relays that utilize the discovered path to forward a single-copy of the message until they reach the branch points where the message is replicated as needed for delivery to the destinations (represented by hollow circles in Figure 10). Two specifications of the algorithm have been discussed. The first updates the importance of a node for every iteration. The node with higher importance is most likely to be selected as a relay. In the second specification, the algorithm updates only once to distinguish important nodes from unimportant nodes. There is a trade-off between message copy reduction and message delivery latency. The delay in message delivery increases for both specifications.
## 4 Experiment and Analysis
### Build Experimental Datasets  
Continuous wavelet transform (CWT) is used to convert one-dimensional vibration signals into time-frequency graphs. Time-frequency graphs contain the time-domain and frequency-domain features of faults, which can avoid the influence of single feature of network learning on diagnostic accuracy. Therefore, in this study, the continuous wavelet transform was used to convert the vibration signal into a two-dimensional time-frequency image as the input of the network [27].  
Firstly, in order to expand the number of datasets, an overlapping sampling technique was employed in each health condition dataset [28]. As shown in the Figure 6, the original vibration signal is sliced with a window of 1024 points, and each data sample contains 1024 points. Then, CWT was used to convert the selected 1024 points into a time-frequency image with a size of 256 \(\times\) 256, the wavelet base was selected as cmor3-3, and the size sequence length was 64. In addition, another 1024 continuous sampling points were selected in the way of overlapping sampling to generate another time-frequency image; each sample overlaps 500 points.  
The number of obtained health status samples is shown in Table 2. The 10 health states under four different loads were alternately used as \(D_{s}\) and \(D_{t}\) for learning and transfer. Samples in the \(D_{t}\) were not labeled during the network learning process to represent different fault samples under unknown working conditions.  
For example, in the task \"Working condition 0-1\", 10 types of health status data under 0 HP load were used as source domain samples, and another 10 under 1 HP load were used as target domain samples for the transfer learning task.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial Number** & **Healthy State** & **Fault Size/mm** \\ \hline NA & Normal & 0 \\ IF-0.1 & Inner ring fault & 0.1778 \\ OF-0.1 & Outer ring fault & 0.1778 \\ BF-0.1 & Ball bearing fault & 0.1778 \\ IF-0.3 & Inner ring fault & 0.3556 \\ OF-0.3 & Outer ring fault & 0.3556 \\ BF-0.3 & Ball bearing fault & 0.3556 \\ IF-0.5 & Inner ring fault & 0.5334 \\ OF-0.5 & Outer ring fault & 0.5334 \\ BF-0.5 & Ball bearing fault & 0.5334 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the CWRU dataset.  
Figure 6: Data argument with overlap.
## 2 Theoretical Analysis
### Diffractive Analysis of the SMB  
To comprehensively analyze the diffraction effects of a finite-size baffle, two different cases should be considered. When a microspeaker driver is measured on a baffle, the sound radiated on the front side and rear side of the microspeaker can be regarded as two separate sources vibrating simultaneously with the same amplitude, but opposite phases. Under these circumstances, diffractions caused by sources on both sides should be considered. When a closed-box microspeaker module is measured on the baffle, only the diffractions caused by the source on the front side should be considered.  
Figure 2a shows the SMB dimensions specified in IEC 63034 [1]. During the measurement process, the microspeaker is specified to be installed at the position (35.6 mm, 36.5 mm), and the microphone shall be placed directly in front of the microspeaker with a measuring distance of 0.1 m. Notably, diffractions up to the third order were considered in this study which can provide sufficient information about the SMB diffraction effects on the microspeaker FRs. Typical diffracted propagation paths describing the first-, second-, and third-order SMB diffractions are shown in Figure 2b. Considering the diffractions caused by the front-side source S, SAR is the first-order diffraction path, and SBCR and SBDER are the second- and third-order diffraction paths, respectively. Similarly, S'A'B'R and S'A'B'C'R are the second- and third-order diffractions caused by the rear-side source S'.  
Based on the extended BTM technique, the impulse response of the diffracted sound can be calculated using Equations (6)-(8) in the time domain. The diffractions in the frequency domain can be obtained by applying fast Fourier transform (FFT) to transform the impulse response to the frequency domain. The total response received at the measuring point is obtained as the superposition of the direct and diffracted sounds. Therefore,  
\[p_{total}(\omega)=p_{direct}(\omega)\,+p_{diff\_f}(\omega)\,-p_{diff\_r}( \omega), \tag{9}\]  
where \(p_{direct}(\omega)\) is the direct sound, and \(p_{diff\_f}(\omega)\) and \(p_{diff\_r}(\omega)\) are the diffracted sounds from the front-side and rear-side sources, respectively. The minus sign in Equation (9) denotes the opposite phase of the source in the rear side. \(p_{diff\_r}(\omega)\) is set to zero when a closed-box microspeaker module is considered. To describe the SMB diffraction effects more explicitly, a normalized FR can be built as [15]:  
\[H(\omega)=p_{total}(\omega)\ /\ p_{direct}(\omega) \tag{10}\]  
which is the transfer function between the total sound and the direct sound, characterizing the degree of influence on the FR of a microspeaker by the SMB diffraction effects.
## 4 Flexible Route Planning Method Combining Q-learning and Graph Search  
### Outline of the Proposed Method  
A combined method of a graph search algorithm (A* algorithm), and Q-learning, a reinforcement learning method, is applied to solve the route planning problem of multiple AGVs. The framework of the proposed method for the route planning problem of multiple AGVs is shown in Figure 4. After the initial and destination nodes are given to each AGV as a task, each AGV independently generates the shortest path from \(S_{\tau}\) to \(D_{\tau}\) by using the A* algorithm. If the a possible collision between AGVs is detected, Q-table created by the Q-learning is used to update the actions to avoid the collision. Q-learning is prepared in advance such that AGVs avoid collisions and interference with each other.  
Figure 4: Framework for the proposed method. Each AGV performs A* and Q–learning independently. Collision determination is performed by a central computing system that integrates the positions of all AGVs.  
Figure 3: A model of warehousing system with obstacles. Each AGV is initially located at its standby node. Tasks are then assigned to each AGV, which moves to the indicated loading and unloading nodes in that order.
# Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions
## 4 Summary of Selected Research
### Fog Computing
The approach is evaluated using two scenarios in iFogSim, a static and a mobile application, and compared against Modified Best-Fit-Decreasing (MBFD) and Maximum Density Consolidation (MDC) algorithms. Results suggest that the average energy consumption of the proposed heuristic is 20.36% and 33.84% less than benchmark algorithms. [PERSON] et al. (2019) [70] use iFogSim to explore the latency of user nodes offloading tasks in a hyper-dense fog-to-cloud computing network. [PERSON] et al. introduce adaptive low latency networks, irrespective of the number of user nodes, based on game theory. Game theory was applied to reduce energy consumption on the fog nodes as well as maintaining an acceptable latency. They also develop automated distributed fog computing for computational offloading using the theory of minority games. Resource allocation was performed in a distributed manner for better network management. Simulation results suggest that the proposed algorithm achieves the user satisfaction latency deadline as well as the required QoE. Moreover, it guarantees an adaptive equilibrium level of the Fog-to-Cloud computing system, which is suitable for heterogeneous wireless networks. [PERSON] et al. (2019) [67] propose an Effective Load Balancing Strategy (ELBS) for fog computing environments using data from a medical sensor application use case. Their solution includes a Priority Assigning Strategy (PAS), a Data Scheduling Algorithm (DSA), an External Data Requesting Algorithm (EDRA), a Server Requesting Algorithm (SRA), and a Probabilistic Neural Network based Matching Algorithm (PMA). PAS is used to classify incoming tasks based on their priority. DSA is used to locate the data on a local file system for task execution, and EDRA used to fetch data from remote file system if it is not present at the task processing location. The SRA algorithm allocates resources to process tasks by liaising between the Fog Master Server and Master Server Manager. Finally, PMA is used to match tasks with the server in a master priority queue. The proposed approach efficiency is demonstrated by modelling the system and medical sensor data use-case in iFogSim. Simulation results suggest that ELBS outperforms benchmarks achieving the lowest average turnaround time and failure rate. Accordingly, ELBS may be a suitable strategy for load balancing in the fog as it supports reliable execution for real-time applications. [PERSON] et al. (2019) [69] focus on resource provisioning in fog environments. They propose a lightweight decentralised service placement policy for performance optimisation in fog computing in order to optimise service placement and so reduce latency and network usage. iFogSim is used for evaluation and results compared to the existing (Edgewards) placement policy, which support the efficiency claims of the proposed approach.
## 5 Mathematical Formulations for Vehicle Travel and Charging
### Charge Consumption  
As soon as the AEV moves, it gradually discharges the battery. Therefore, the consumption of charge [43] of AEV \(k\) on road segment \((i,j)\) can be expressed as \[E_{ij}^{k}(t)=\int_{0}^{\tau_{ij}^{k,r}}\Bigg{(}\bigg{(}\frac{\rho}{2}\varphi A^{k} \Big{(}\frac{V_{ij}}{1+\alpha\big{(}\eta_{ij}(t)\big{)}^{\beta}}+v_{f}\Big{)}^{2 }+M^{k}g\Big{(}f_{r}\cos\theta_{ij}+\sin\theta_{ij}\Big{)}\bigg{)}\frac{V_{ij}}{ 1+\alpha\big{(}\eta_{ij}(t)\big{)}^{\beta}}\Bigg{)}dt, \tag{16}\]  
where \(\tau_{ij}^{k,r}\) represents the estimated travel time of AEV \(k\) on segment \((i,j)\). \(f_{r}^{k}\) is the rolling friction coefficient of AEV \(k\), \(M^{k}\) is the total mass and load of AEV \(k\), \(g\) is the gravitational acceleration, \(\rho\) is the air density, \(\varphi\) is the air resistance coefficient, \(A^{k}\) is the frontal area of AEV \(k\), and \(v_{f}\) is the wind speed. \(\theta_{ij}\) is the slope of segment \((i,j)\).  
The SoC of AEV traveling from node \(i\) to node \(j\) can be expressed as  
\[C_{j}^{k}=C_{i}^{k}(t_{i}^{k})-E_{ij}^{k},\quad j\in O(i), \tag{17}\]  
where \(C_{j}^{k}\) represents the SoC when the AEV \(k\) reaches road node \(j\).