esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
### Overview of JPEG Systems Part 4: Privacy and Security  
JPEG Systems is a multi-part specification. Among them, the JPEG Privacy and Security standards in Part 4 is a framework that defines standards and guidelines for protecting the privacy and security of images in any JPEG format. The goal of JPEG Privacy and Security is to provide a comprehensive set of tools for preventing unauthorized access to JPEG images, detecting and recovering from tampering and other forms of image manipulation, and ensuring the authenticity and integrity of JPEG images. The JPEG Privacy and Security framework defines standards for secure image transfer and storage, secure image compression, and secure image authentication and verification. These standards define several mechanisms for protecting images from various types of attacks, such as eavesdropping, modification, and unauthorized access. The JPEG Privacy and Security framework also includes guidelines for implementing security measures in JPEG applications, such as image editors and viewers. These guidelines cover topics such as access control, user authentication, and secure communication protocols. Overall, JPEG Privacy and Security is an important framework for ensuring the privacy and security of JPEG images, particularly in situations where sensitive or confidential information is involved.
## 4 Results
### The Direction and Magnitude of LULC Change
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & & & & & & & & & & \\ \hline
**LULC Types** & **High Coverage** & **Medium** & **Low Coverage** & **Low Coverage** & **Parameter loci** & **Shady Land and** & **Sand, Gobi and** & **Base Land** & **Desert** & **2004** \\  & **Grasband** & **Crossing** & **Crossing** & **Crossing** & **Crossing** & **and Snow** & **Susamp** & **Base Land** & **Desert** & **2004** \\ \hline high-coverage & & & & & & & & & & \\ grassland & 139457 & 2.86 & 0.19 & 0.17 & 0.00 & 0.00 & 0.00 & 0.00 & 13982.0 \\ medium-coverage & & & & & & & & & & \\ grassland & 0.18 & 2441.39 & 0.78 & 0.06 & 0.00 & 0.00 & 0.00 & 0.00 & 2442.42 \\ \hline low-coverage & & & & & & & & & \\ grassland & 0.84 & 5.32 & 8138.59 & 8.64 & 0.00 & 0.27 & 7.70 & 0.00 & 8163.36 \\ \hline water bodies & & & & & & & & & \\ permanent ice and & 0.00 & 0.00 & 0.00 & 994.19 & 0.00 & 0.03 & 0.00 & 0.00 & 994.30 \\ snow & & & & & & & & & \\ shobly land and & 7.26 & 3.93 & 1.79 & 3.90 & 0.00 & 462.19 & 0.00 & 0.00 & 479.10 \\ snow & & & & & & & & & \\ sand, gobi and & 0.02 & 0.95 & 43.02 & 5.70 & 0.00 & 0.00 & 2202.96 & 0.00 & 22032.67 \\ land & & & & & & & & & \\ desert & 0.03 & 1.19 & 1.64 & 5.55 & 0.00 & 0.00 & 0.00 & 3205.43 & 3244.21 \\ \hline
2012 & 1403,09 & 2655,65 & 8186,09 & **(0)** The headwaters of the Yellow River & & & & & \\ \hline
**LULC Types** & **Forest** & **Shady** & **High Coverage** & **Medium** & **Low Coverage** & **Susamp** & **Small and** & **Sand, Gobi and** & **Base Land** & **2004** \\  & **Crossing** & **Crossing** & **Crossing** & **Crossing** & **and** & **Susamp** & **Base Land** & **2004** \\  & **Forest** & **30.20** & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 30.29 \\  & shrub & 0.00 & 665.58 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 665.58 \\ high-coverage & & & & & & & & & & \\ grassland & 0.00 & 0.00 & 11,957.12 & 21.44 & 1.56 & 0.39 & 1.65 & 0.00 & 0.00 & 11942.56 \\ archcom-coverage & & & & & & & & & & \\ grassland & 0.00 & 0.00 & 190.10 & 2721.07 & 1.67 & 7.21 & 1.50 & 0.00 & 0.00 & 7471.55 \\ live-coverage & & & & & & & & & & \\ grassland & 0.00 & 0.00 & 18.74 & 627.85 & 14,329.64 & 43.27 & 46.07 & 0.00 & 0.00 & 15045.96 \\ water & & & & & & & & & & \\ bodies & 0.00 & 0.00 & 0.00 & 0.27 & 6.23 & 1972.29 & 0.06 & 0.00 & 0.00 & 1978.85 \\ hably & & & & & & & & & & \\ land and & 0.00 & 0.00 & 3.31 & 0.00 & 8.39 & 112.78 & 1899.16 & 0.00 & 0.00 & 2023.64 \\ warning & & & & & & & & & & \\ build-up & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 10.38 & 0.00 & 10.38 \\ snow & & & & & & & & & & \\ sand, & & & & & & & & & & \\ gall and & 0.00 & 0.00 & 0.00 & 0.63 & 209.17 & 5.98 & 2.99 & 0.00 & 469.47 & 4688.24 \\ snow land & & & & & & & & & & \\ \hline
2012 & 30.29 & 665.58 & 12,169.27 & 7921.25 & 14,507.06 & 241.92 & 1951.83 & 10.38 & 469.47 & 43,938.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Direction and magnitude of LULC changes from 2004 to 2012 in the headwaters of the **(A)** Yangtze River and **(B)** Yellow River (unit: km\({}^{2}\)).
## 6 Test Results
### Professional Grade Receiver
A summary of the results of the tests with default C/N\({}_{0}\) settings is given in Table 6. The maximum horizontal and vertical errors are computed for the interval in which the interference is present and the receiver also offers a position fix. As already discussed, the position fix availability during the interference interval for the mass-market receiver is high (97.91%) at the expense of position accuracy. On the other hand, the professional grade RUT preserves the position accuracy at the expense of solution availability (58.58%). The maximum horizontal and vertical errors in the test case are only slightly larger than in the baseline case. It is important to recall here that the availability is computed only for the duration when interference is active. It can be observed from Table 6 that, when manufacturer's default receiver settings are used, the mass market RUT has much higher sensitivity as compared to the professional grade RUT. The former is able to withstand the interference event through the entire rising ramp of the interference power profile and for most of the falling ramp duration. The position fix is lost after the interference power peak has been reached (precisely, at J/S = 30 dB) and it is regained after a few seconds. The behavior is different for the PRO RUT which instead stops offering a position solution as soon as the interference reaches a power level such that J/S is 45 dB. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test Case**} & **Maximum** & **Maximum Vertical** & **Position Fix** & **J/S\({}_{\text{PVT, lost}}\)** & **J/S\({}_{\text{PVT,reobulined}}\)** & **TTRP * (s)** \\  & **Proilation Error (m)** & **Position Error (m)** & **Availability** & **(dB)** & **(dB)** & **(dB)** \\ \hline Baseline & 0.28 & 0.41 & 100\% & - & - & - \\ \hline PRO01 & 3.76 & 1.89 & 66.36\% & 55 & 35 & 9 \\ \hline PRO02 & 0.66 & 1.88 & 66.09\% & 55 & 35 & 10 \\ \hline PRO03 & 0.97 & 2.19 & 66.04\% & 45 & 35 & 10 \\ \hline PRO04 & 0.78 & 0.97 & 63.16\% & 40 & 45 & 6 \\ \hline PRO05 & 2.63 & 1.55 & 61.29\% & 40 & 40 & 6 \\ \hline \hline \end{tabular}  
* * TTRP values are otained with TTRP test cases and the other matrices (e.g., maximum horizontal position error, maximum vertical position error, etc.) are obtained either in baseline test case or in different sensitivity test cases, as represented by the test id.  
\end{table} 
Table 5: Test results for PRO RUT. Figure 8: East-North-Up deviations for (**a**) MM RUT and (**b**) PRO RUT in the presence of threat signature 03 when C/N\({}_{0}\) default settings are used in both the receivers. Figure 9: Drop in average C/N\({}_{0}\) for (**a**) MM RUT and (**b**) PRO RUT in the presence of threat signature 03 when C/N\({}_{0}\) default settings are used in the receivers.
## 3 Results  
### Details of the Experimental Areas  
We tested and verified the performance of the multi-hot LM in four evaluation areas. These were KorQuAD v1.0 ([[https://korquad.github.io/KorQuad%201.0](https://korquad.github.io/KorQuad%201.0)]([https://korquad.github.io/KorQuad%201.0](https://korquad.github.io/KorQuad%201.0)), (accessed on 28 January 2019)), Named Entity Recognition (NER), Semantic Role Labeling (SRL), and Naver Sentiment Movie Corpus (NSMC) ([[https://github.com/e9t/nsmc](https://github.com/e9t/nsmc)]([https://github.com/e9t/nsmc](https://github.com/e9t/nsmc)), (accessed on 28 June 2016)). All the data used a morpheme-analyzed corpus using UTagger, and answers in raw corpus format were reprocessed according to morpheme units. The following is a detailed description of the four areas.  
* KorQuAD v1.0--A Korean QA corpus constructed by LG CNS. It consists of 60,407 training data and 5774 validation data. This corpus was used to compare machine reading comprehension performance with that of existing language models. An exact match for the answers and the F1-score were used for the evaluation.
* NER and SRL (NIKL MODU Corpus)--The 'NIKL MODU corpus', constructed by the National Institute of Korean Language, was used for NER and SRL. The two domains  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **BPE Base** & **BPE + Syllabe** & **Dictionary + Syllabe** \\ \hline All Tokens & 37,027 & 20,892 & 37,822 \\ Syllabe Tokens &. & 1994 & 1994 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of tokens by generation method.  
were used to test the model's semantic analysis of natural languages. NER consists of 15 tags, while SRL consists of 18 tags. A BIO-based method was used for NER, and for SRL, the word segments of the target predicates were attached after the natural language sentence to be analyzed. The NER corpus contains 120,066 sentences of training data and 30,018 sentences of test data. The SRL corpus contains 108,509 sentences of training data and 27,501 sentences of test data. The F1-score was used for the evaluation.
* NSMC (Naver Sentiment Movie Corpus)--The NSMC analyzes the positive and negative emotions in review comments. Unlike the above domains, NSMC was used to determine the suitability of the language model for a spoken corpus in which everyday terms were used, rather than a written corpus like the newspaper corpus. The corpus contains 150,000 training data and 50,000 test data. It was evaluated based on the accuracy.  
Figure 2 represents the model used in each experimental area. In order to compare the performance of the language models, the experimental model was simply constructed. This was to reduce the influence on the performance of the experimental area by other factors.
## 3 Simulation Results and Discussion
### Single Element Transducer  
Figure 7 shows the unfolded schematic of the longitudinal wave radiating from a single element transducer in the pulse-echo mode.  
Figure 8 shows the received displacement amplitude of the fundamental and second harmonics for the single-element transducer. Note that the received displacement amplitude of the fundamental wave gradually decreases in Figure 8a. This behavior compares well with the behavior of the plane wave, where the received amplitude is constant at all distances (see Figure 5a). This is due to the diffraction effects of the finite size transducer in radiation and reception.  
Figure 8b shows the received displacement amplitude of the second harmonic for rigid (\(R=1\)) and stress-free (\(R=-1\)) boundaries. The overall behavior of \(R=-1\) is almost identical to that of the plane wave, except that the total second harmonic amplitude at the initial position does not completely disappear. This is due to the diffraction effect of the finite size transducer. It is noted that the results of \(R=1\) continue to increase linearly. This is known as the cumulative effect of the second harmonic, which provides a major advantage in experimental measurement of the nonlinear effect [21]. The signal-to-noise ratio improves simply by letting the wave propagate for a longer distance.  
The phase analysis results for the received displacement of Figure 8 are shown in Figure 9. Comparing Figure 9 to Figure 6, the results of the two cases are almost the same, so all discussions of Figure 6 can be applied to Figure 9. One thing to note is that the phase difference of the two second harmonic components for the stress-free boundary (\(R=-1\)) is not exactly \(\pi\) at the source position because of the diffraction effect.  
Figure 8: Changes in the amplitude of displacement received from the single-element transducer: (a) fundamental wave and (b) second harmonic wave.  
Figure 7: Longitudinal wave propagation from a single\(-\)element transducer and reception in the pulse-echo mode.
## 1 Introduction
The stereo imagery acquired by optical sensors onboard an Unmanned Aerial Vehicle (UAV) could be one choice for the collection of reference data of forest AGB. Studies on the measurements of forest spatial structures using stereo imagery have been booming in recent years due to the rapid development of UAV platforms and the automatic processing algorithms of stereo imagery [22; 23; 24; 25; 26; 27]. The stereo images acquired by consumer-grade cameras are good enough for stereoscopic processing using algorithms from computer vision [26]. This facilitated the application of low-cost UAV with low payload capability in the acquisition of stereo imagery. The application of UAV stereo imagery as a practical sampling tool of forest AGB is hindered by the unavailability of ground surface elevation under forest canopies. The results derived from stereo imagery are the digital surface model (DSM) of the forest canopy top. A digital terrain model (DTM) of the ground surface under forest from other data sources is needed to extract the forest's vertical structures. Many studies on the extraction of forest spatial structures using airborne stereo imagery used the DTM from lidar data [28; 29; 30]. In this way, the application of UAV stereo imagery is limited within the area where the lidar DTM is available. Some studies reported that point cloud of stereo imagery acquired in leaf-off conditions could see the ground surface through the deciduous forest canopy [26; 31]. Our previous studies evaluated the impact of forward overlaps and image resolutions for the mapping of forest three-dimensional structures using UAV stereo imagery [32], and also reported the estimation of the forest leaf area index using height and canopy cover extracted from UAV stereo imagery [33]. This study reported our new results on the inventory of forest aboveground biomass over deciduous forest through the synthesis of leaf-on and leaf-off UAV stereo imagery by taking lidar data as references.
## 2 Materials and Methods  
### Approach to Analysis  
Validation is essential for numerical calculations. A model is called valid when there is an agreement between the measured response and the prediction of the numerical model [26]. The Finite Element Method (FEM) is used to perform a full validation of constants of some selected constitutive relations, which are available in the literature and were experimentally determined. The sets of constants under consideration are available in [14; 19; 22; 23]. Computational 3D models of mouse carotid arteries are formulated in the Abaqus 2019 FEM environment to recreate the classical inflation test, and the numerical results are verified against the experimental data. The aim is to verify whether the laws with the already existing constant sets are able to recreate all the phenomena that are observed during such inflation and also the loss of stability if reported. Only then can the constitutive relation and the proposed model be called a valid one. The necessary details of the FEM method can be found, for example, in [27; 28].  
There are advantages of such a computational approach. Finite element analysis (FEA) is not restricted by the geometry of the domain nor its complexity. Therefore, FEM models are versatile, whereas analytical solutions of partial differential equations, still popular in the field of bioengineering analyses, can be obtained only for a limited number of cases with simple geometries and loading and boundary conditions. In consequence, many complex bioengineering behaviors may be studied in detail only with the aid of FEM, which enables improvement in the quality of data to be used for medical purposes. The application of FEM models can also facilitate the process of problem identification. Typically, experiments and medical examinations are conducted to address this issue. These can be effectively supplemented by calculations (see [29; 30]). That is the reason why FEM analyses are preferred and promoted in this article. Nevertheless, it must be kept in mind that only validated models are reliable. Hence, the validation problems are strongly underlined here.  
Moreover, it is emphasized here that although the issues related to mice arteries' material laws are studied, a general problem is discussed. Hence, this is also a starting point for the reliable analysis of the human body's response.
## 3 Results
### Qualitative Analysis of Grain Size Parameters  
To further understand the similarities and discrepancies between the two methods, we analyzed the consistency of the results in terms of the qualitative descriptions of the sorting coefficient, skewness, and kurtosis (Figure 3). Among the 143 samples analyzed  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Grain Size Parameters** & **Calculation Method** & **Min.** & **Max.** & **Average** & **Standard Deviation** \\ \hline \multirow{2}{*}{Mean grain size} & GM & 3.74 & 6.25 & 5.03 & 0.45 \\  & MM & 3.52 & 6.26 & 4.95 & 0.46 \\ \hline \multirow{2}{*}{Sorting coefficient} & GM & 1.22 & 1.81 & 1.34 & 0.10 \\  & MM & 0.92 & 1.75 & 1.28 & 0.15 \\ \hline \multirow{2}{*}{Skewness} & GM & 0 & 0.61 & 0.30 & 0.08 \\  & MM & 0.91 & 2.07 & 1.39 & 0.18 \\ \hline \multirow{2}{*}{Kurtosis} & GM & 0.84 & 2.06 & 1.33 & 0.19 \\  & MM & 1.58 & 2.61 & 1.95 & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic grain size parameter values obtained via the graphic method (GM) and moment method (MM).  
for the sorting coefficient, 141 and 2 exhibited consistent (shown in the yellow area) and inconsistent results, respectively, yielding a consistency rate of 98.61% (Figure 3a). Similarly, among the 143 samples analyzed for skewness, 114 exhibited consistent outcomes and 29 were inconsistent, yielding a consistency rate of 79.17% (Figure 3b). In contrast, only one of the 143 samples analyzed for kurtosis produced consistent results and 142 out of 143 samples presented as inconsistent, yielding a consistency rate of 0.70% (Figure 3c).  
Figure 3: Comparison of the physically descriptive terms obtained using the graphic method (GM) and moment method (MM). (**a–c**) Relationships of the sorting coefficient, skewness, and kurtosis between the GM and MM (The yellow rectangles are result agreement area, and others are inconsistent).  
Figure 2: Numerical values and regression equations for the grain size parameters calculated using the graphic method (GM) and moment method (MM).  
Therefore, among the qualitative descriptive term results determined according to the two methods, the sorting coefficient was consistent, whereas the skewness and kurtosis demonstrated evident differences.
# A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks
## 4 Matrix-Based Data Relay Algorithm  
In large-scale distributed sensor networks, because of the huge system size and energy constraints of sensors, sensors are unlikely to have a global observation to support optimal communication decisions. Sensors only make rational communication decisions solely based on their local dataset from their previously transmitted messages around the network. To make rational communication decisions, sensors need local topology of the network to indicate who are potential receivers, the local data distribution to indicate what data neighbors have, and the utility between data pieces to figure out the benefit of broadcasting one piece of data.  
In this section, a Proactive Energy-Saving Data Relay algorithm \(CDU\) will be proposed to help sensors compute the benefit and cost for all the data in \(pendingQue\) by neat matrix computations. The framework is shown in Figure 2. First, sensors need local states to support their decisions. In our model, three parts are necessary to a sensor: connection matrix \(\mathbf{C}\) denotes its local network topology, data distribution matrix \(\mathbf{D}\) about the local data distribution of the sensor and its neighbors and the data Utility matrix \(\mathbf{U}\). For each time step \(t\), each sensor \(i\) is required to update CDU matrixes by the model maintenance function introduced in the next section. Next, a neat computation with the CDU matrixes can produce the expected benefit of transmitting pending data \(B_{i}^{t}\). In addition, connection matrix \(C_{i}^{t}\) also be used to predict the cost of transmitting data \(E_{i}^{t}\). By balancing between \(B_{i}^{t}\) and \(E_{i}^{t}\), if the sensor find that the expected utility \(F_{i}^{t}\) is positive, it will broadcast the data as the network will be benefit of more likely fusing valuable information.
# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 4 Methods  
This section introduces the proposed voice conversion algorithm, Multi-Dimensional Perception Flow Matching. The overall architecture of the MPFM-VC model is illustrated in Figure 3. In addition to the content features, \(I_{content}\), extracted from the input speech, the model also incorporates auxiliary features, such as pitch (\(I_{pitch}\)), energy (\(I_{energy}\)), and prosody (\(I_{prosody}\)), to generate the target mel spectrogram, \(O_{mel}\), that corresponds to the speaker's voiceprint, \(I_{spk}\). We provide a detailed explanation of the core components of the proposed model in the following, including flow matching for multi-dimensional perception, the multi-dimensional perception network, the content perturbation-based training enhancement method, and the adversarial training mechanism based on the voiceprint.
## 4 Methodology
### NIST Test Suite  
The NIST Test Suite is made up of 17 different empirical tests [32] that were developed to analyze binary sequence bitstreams. The tests investigate the unpredictability of data through the use of a variety of bit statistics as well as bit block statistics. The NIST STS tests all investigate the degree to which entire bitstreams are governed by randomness. Multiple tests that can detect local nonrandomness break the bitstreams down into several generally large pieces and calculate a bit characteristic for each segment of the data. After that, the test statistic is computed by using each of these partial attributes in turn. Checking the randomness of the sequence is a part of each of the NIST STS tests, and each test is specified by one of the three types of test statistic. For example:  
1. Bits: These tests examine a variety of aspects of bits, such as the proportion of bits, the frequency of bit change for example, runs, and cumulative sums;
2. M-bit blocks: These tests examine the allocation of m-bit blocks (where m is typically less than 30 bits) inside the sequence or its portions;
3. M-bit parts: These tests examine the complex properties of M-bit parts (where M is typically greater than 1000 bits) of the sequence, such as the rank of the sequence as seen in a matrix, the sequence's spectrum, or the linear complexity of the bitstreams.  
Some tests that are part of the NIST STS are carried out in a greater number of variations. For instance, they may carry out a number of sub-tests and investigate additional qualities of a sequence of the same type. For example, the cumulative sum test and the serial test look at a sequence forward and backward orders.