esa-sceva/satcom-chunk-collection huggingface dataset:
# Research and Applied Perspective to Blockchain Technology: A Comprehensive Survey
## 5 Consensus Algorithms
### Consensus Algorithms Used by Various Cryptocurrencies
#### 5.1.8. Directed Acyclic Graph (DAG)  
In directed acyclic graph (DAG) the data is stored topologically in graph manner. DAG can overcome the problems of data processing, compression and routing. One of the disadvantages of PoW is the creation time of the block which is 10 min. DAG instead of implementing a single chain, works on \"side chains\" [73]. Therefore, to reduce the time of block creation and validation different transactions are performed on multiple chains. Mining also is a waste of time and energy so in DAG all the transactions are maintained and directed in a certain sequence. DAG is acyclic so there is no chance of finding the parent node as it's a tree of nodes and not the loop of nodes. Some of the basic concepts of DAG are:  
* No More Double Spending: In traditional blockchain, a scenario of more than one miner tries to validate the same blockchain leads to double spending. This also leads to hard or soft forks. The DAG is safer and robust as it validates a particular transaction based on the previous number of transactions.
* Less Width: The transaction in another consensus algorithm gets added to the whole network but in DAG the transaction is added to the transaction graph. This makes the whole network less bulky and easy to validate a particular transaction.
* Smarter and Faster: As the blockless nature of DAG, it is much faster than PoW and PoS.
* Favorable to the Smaller Transactions: Bitcoin and Ethereum are not much friendly for the smaller amounts rather DAG seems perfect as transaction fee is neglected.
# Surround by Sound: A Review of Spatial Audio Recording and Reproduction
## 2 Binaural Recording and Rendering
### Binaural Recording and HRTF Measurement  
In both binaural recording and the HRTF measurement, a typical setup is to record acoustic properties of the dummy head or head and torso simulator (HATS), which are designed based on the average dimension of a human head/torso and have two high fidelity microphones inserted within each ear to record the two-ear signals. Some widely used binaural recording packages include (a) Dummy head: Neumann KU-100 (Berlin, Germany) [19], (b) head-and-torso simulator (HATS): Bruel & Kjaer 4128D/C (Narum, Denmark) [20], and (c) 3 Dio Free Space Binaural Microphone (Vancouver, WA, USA) [21], as shown in Figure 1.  
The dummy head recordings are supposed to achieve a good trade-off between individual variability and good spatial perception. However, as each human body has a unique size and shape features, it could cause confusion, especially in terms of elevation localization using the dummy head recordings directly for everyone's ears [22]. This problem is also known as one of the HRTF characteristics--that HRTFs differ greatly from person to person.  
To understand the perceptual cues to spatial hearing, HRTFs are measured on both the HATS and listening subjects, and further analyzed for its characteristic variation as a function of source positions. The source positions are typically sampled at a predefined set of azimuths and elevations on a spherical surface with a radius of 1-2 m, which is when the HRTFs are assumed to be distance independent [23; 24]. The whole measurement process, which involves the measurement system design/setup, is logistically complicated and takes at least 15-20 min to complete [25].
## 3 Experimental Results and Analysis  
### Mechanical Performance Results  
Performance tests on the slump and expansion of steel slag micro powder UHPC and recording the data in units of mm with an accuracy of 1 mm were simultaneously conducted. The statistical results are accurate to 5 mm. Cube specimens measuring 100 mm \(\times\) 100 mm \(\times\) 100 mm were prepared for compressive and splitting strength tests, while prism specimens measuring 100 mm \(\times\) 100 mm \(\times\) 300 mm were used for axial compression and elastic modulus measurements. The specific results are shown in Table 3.  
The analysis of Table 3 reveals that the average compressive strength of the cube specimens for different mix proportions of steel slag micropowder UHPC reaches 105.7 MPa at 3 days, 117.4 MPa at 7 days, and 128.4 MPa at 28 days. It can be observed that the compressive strength of the steel slag micropowder UHPC cubes develops at an early stage, with the average compressive strength at 3 days reaching 82% of the 28-day value. The average compressive strength at 7 days reaches 91% of the 28-day value. The average splitting tensile strength of the steel slag micropowder UHPC is 10.3 MPa, and the average elastic modulus is 46.6 GPa. The average axial compression strength of the prism specimens is 103.3 MPa. Among the experimental groups, Group SSP2 exhibits the highest elastic modulus and the highest compressive strength for both cube and prism specimens. The axial compression strength of the steel slag micropowder UHPC prism specimens is consistently above 95.1 MPa, with an average of 105.1 MPa. The ratio of prism axial compression strength to cube compressive strength ranges from 0.80 to 0.85, which is higher compared to that of ordinary high-strength concrete (with a ratio of 0.77 to 0.82) [23]. Under vertical loading, the increased elastic modulus of the steel slag microppowder UHPC prism specimens reduces lateral deformation, narrows the range of constraint effects from the loading plate, and leads to a compressive failure mode similar to that of the cube specimens. Therefore, the ratio is higher, which is consistent with the findings [24].
## 4 Results and Discussion
### Coverage Prediction via Ray-Tracing
#### 4.3.1 Line-of-Sight  
For a simulation with the same TX and RX location as in the LOS measurement, the minimum PL of 82 dB corresponds to the direct LOS path and is equal to the measured PL. Two paths exist with a total PL of 97 dB, i.e., the path via a first-order ceiling reflection, as well as the path with reflections on windows 3 and 4. This last path corresponds to the measured PL of 99 dB using the channel sounder, with AoA 132\({}^{\circ}\). With the TG platform, we have not measured this path, as it is outside the 90\({}^{\circ}\) azimuth scanning range. Ceiling reflections were not measured with the channel sounder or TG platform, due to the limited HPBW in the elevation plane. A path with reflection on furniture, followed by a reflection on window 2 and a ground reflection, has a PL of 99 dB. Two paths have a PL of 111 dB. We conclude that simulated PL corresponds well to measured PL, even though we do not have a detailed representation of the environment. The throughput estimation of 4.62 Gbps, when using a TX power of 13 dBm and antenna gain of 20 dBi, corresponds to the throughput estimation using the TG platform, and is in line with the PER measurement results.
# Automatic Selective Encryption of DICOM Images
## 4 Performance Analysis and Discussion
### Approach I
#### 4.1.2 Security Evaluation  
PSNR, entropy [38], correlation coefficients (CORR) [39], NPCR [39; 40] and UACI [39], and Chi-square [41] metrics are used to evaluate the robustness of the proposed encryption approach. Table 1 indicates the performance overview of the proposed approach, which shows a good level of robustness. The low correlation value indicates robustness against linear attacks as it shows that the proposed approach has a low correlation between the original and the cipher images. The low PSNR and high entropy values show that the encrypted images have a good randomness level. The MAE value shows that the encrypted images are significantly different from the original images. The high NPCR/UACI scores show robustness against differential attacks, which depicts a large difference between the plain and cipher images. On the other hand, the Chi-squared metric value indicates that the encrypted images' distribution is greatly different from the original images.
## 2 Materials and Methods
### Research Methods
#### 2.3.3 Persistence Analysis  
Hurst Index (H) [32] is one of the main methods for quantitative description of long-range dependence, which is widely used in hydrology, climate, earthquakes, and geology. Later, it was gradually applied to remote sensing and vegetation [33; 34; 35; 36]. In this study, R/S analysis method was adopted to calculate the change trend of vegetation phenology in Northeastern China by pixel to reflect the persistence of the change trend. The value range of H is between [0 and 1], and the strength of vegetation phenology persistence can be judged by the size of the H value. The value of H is generally divided into three forms:  
Figure 2: Double Logistic (D-L) filtering of SIF time series in Northeastern China during 2001–2020 (**a**); D-L filtering of SIF time series in 2005 (**b**); D-L filtering of SIF time series in 2010 (**c**); D-L filtering of SIF time series in 2015 (**d**); D-L filtering of SIF time series in 2020 (**e**).  
1. When 0.5 < H < 1, which indicates that the time series is sustainable or persistent with the past long-term change; the greater the H value, the stronger the sustainability or persistence;
2. When H = 0.5, which indicates that the time series is random and long-term correlation does not exist;
3. When 0 < H < 0.5, which indicates that time series has anti-sustainability or opposite direction with the past long-term change; the smaller the H value, the stronger the anti-sustainability.  
On this basis, the H value of vegetation phenological time series in Northeastern China is divided into four types: strong anti-sustainability (0.15 \(\leq\) H < 0.35), abbreviated as SAS; weak anti-sustainability (0.35 \(\leq\) H < 0.5), abbreviated as WAS; weak sustainability (0.5 < H < 0.75), abbreviated WS; strong sustainability (H \(\geq\) 0.75), abbreviated SS.  
The change trend of vegetation phenological parameters and sustainability results were superimposed to obtain the change trend-sustainability results shown in Table 1.
## 3 Demonstration Experiment of MDPO  
In order to evaluate the performance of the MDPO algorithm with real pseudorange observations, we applied it to GNSS pseudorange measurements obtained from a pair of GNSS ground stations emulating a lander and a rover on the Moon. In this experiment, two neighbor International GNSS Services (IGS) stations, KGNI and MTKA, were selected. Table 1 describes their locations. The KGNI and MTKA stations were considered as the rover and the lander vehicles, respectively, and the baseline length between them was approximately 5 km. Both the observation and navigation data were obtained from the Crustal Dynamics Data Information System (CDDIS) archive in the form of RINEX format.
## 4 New Tools for Digital Pathology in the Era of Artificial Intelligence
### HistoQC  
HistoQC is a Python-based algorithm developed for the quality control of whole slide images that also enables the detection of artifacts and the identification of batch effects (such as slides with darker staining compared with the other slides), making use of the inspection of color distribution, edge, and smoothness detectors. Moreover, HistoQC provides an interactive and user-friendly interface, which allows for the exploration of data [45]. HistoQC was used to assess the role of tissue quality in the application of deep learning approaches in computational pathology [46]. HistoQC was also used to automatically detect image artifacts in comparison with a manual review of WSI images in the context of renal histomorphometry [47]. Another study [48], aiming to quantify peritubular capillary attributes using conventional and whole slide images, used HistoQC to identify WSI images that represented outliers within a cohort and to identify artifact-free regions (the normalized peritubular capillary aspect ratio was below 0.6; a 0.1 increase in the normalized aspect ratio was significantly associated with disease progression, with a hazard ratio of 1.28).
# A Vision Servo System for Automated Harvest of Sweet Pepper in Korean Greenhouse Environment
###### Abstract  
Recently, farmers of sweet pepper suffer from the increase of its unit production costs due to the rise of labor costs. The rise of unit production costs of sweet pepper, on the other hand, decreases its productivity and causes the lack of its farming expertise, thus resulting in the quality degradation of products. In this regard, it is necessary to introduce an automated robot harvest system into the farming of sweet pepper. In this study, the authors developed an image-based closed-loop control system (a vision servo system) and an automated sweet pepper harvesting robot system and then carried out experiments to verify its efficiency. The working area of the manipulator that detects products through an imaging sensor in the farming environment of sweet pepper, decides whether to harvest it or not, and then informs the location of the product to the control center, which is set up at the distance scope of 350-600 mm from the center of the system and 1000 mm vertically. In order to confirm the performance of the sweet pepper recognition in this study, 269 sweet pepper images were used to extract fruits. Of 269 sweet pepper images, 82.16% were recognized successfully. The harvesting experiment of the system developed in this study was carried out with 100 sweet peppers. The result of experiment with 100 sweet peppers presents the fact that its approach rate to peduncle is about 86.7%, and via four sessions of repetitive harvest experiment it achieves a maximal 70% harvest rate, and its average time of harvest is 51.1 s.  
sweet pepper harvesting automation; cylindrical robot; vision servo system; image processing-based; momentum backpropagation +
Footnote †: journal: Applied Sciences
# Resource Allocation of Multiple Base Stations for Throughput Enhancement in UAV Relay Networks
## 4 Comparison: Single Transmission from the Single Base Station in the URN
### mBS-Based URN (mBS-URN)  
The UAV receives a signal via a wireless backhaul link in the first time slot only from mBS, even when the UAV is within an sBS service range. This is an equivalent model considered in [30]; however, with interference from the sBS. Because it does not receive a signal from sBS, \(SINR_{S,UAV}^{1 st}=0\) in (2).  
With the condition that \(SINR_{S,UAV}^{1^{st}}=0\),  
\[prob\Big{(}R_{U_{a}}^{(1)}\geq\eta\Big{)} \tag{12a}\] \[=prob(\frac{P_{mBS,UAV}^{1^{st}}SmBS,UAV}{P_{sBS}S_{sBS,UAV}+P_{mBS, UAV}^{1^{st}}S_{mBS,UAV}+\sigma_{UAV}^{2}}\geq 2^{\omega^{+}}-1)\] (12b) \[\overset{(a)}{=}E_{h_{mBS,UAV}}\Bigg{[}\frac{\gamma\big{(}k_{sBS, UAV},k_{sBS,UAV}\psi^{mBS-URN}(h_{mBS,UAV})\big{)}}{\Gamma(k_{sBS,UAV})} \Bigg{]}\] (12c) \[\overset{(b)}{=}\int\limits_{x_{min}}^{\infty}\frac{\gamma\big{(} k_{sBS,UAV},k_{sBS,UAV}\psi^{mBS-URN}(x)\big{)}}{\Gamma(k_{sBS,UAV})}f_{h_{mBS, UAV}}(x)dx, \tag{12d}\]  
where  
\[x_{min}=(\psi^{mBS-URN})^{-1}(0),\] \[\psi^{mBS-URN}(h_{mBS,UAV})=\frac{(P_{mBS,UAV}^{1^{st}}-(2^{ \omega^{+}}-1)P_{mBS,UAV}^{1^{st}})}{(2^{\omega^{+}}-1)P_{sBS}}\frac{l_{mBS, UAV}}{l_{sBS,UAV}}h_{mBS,UAV}-\frac{\sigma_{UAV}^{2}}{l_{sBS,UAV}P_{sBS}},\] \[f_{h_{mBS,UAV}}(x)=\frac{k_{mBS,UAV}k_{mBS,UAV}}{\Gamma(k_{mBS, UAV})}x^{k_{mBS,UAV}-1}e^{-k_{mBS,UAV}x},\,x\geq 0,\]  
and \((\psi^{mBS-URN})^{-1}\) is an inverse function of \(\psi^{mBS-URN}\). \((a)\) in (12c) takes the expectation with respect to \(h_{mBS,UAV}\); \((b)\) in (12d) follows that \(h_{mBS,UAV}\) has a \(Gamma(k_{mBS,UAV},\)\(1/k_{mBS,UAV})\) with a gamma function \(\Gamma(t)=\int\limits_{0}^{\infty}z^{t-1}e^{-z}dz\) and a lower incomplete gamma function \(\gamma(s,x)=\int\limits_{0}^{x}z^{s-1}e^{-z}dz\).
# Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration
## 3 Proposed Artificial Neural Network (ANN) Model
### Network Training
#### 3.1.1 Bayesian Neural Network (BNNs)  
A Bayesian neural network is a form of artificial neural network (ANN) that combines the flexibility and versatility of ANNs, with the ability to handle the uncertainty of the model's parameters. It establishes the probability distribution of the model's parameters using Bayesian inference, as opposed to point estimations, unlike conventional ANNs. This enables BNNs to incorporate uncertainty into their predictions and provide more accurate results. BNNs have been implemented in a variety of applications, including image classification, natural language processing, and reinforcement learning [36].  
[PERSON] [37] explains the Bayesian framework for neural networks; his Bayesian research approach focuses on the probabilistic interpretations of network architectures. In contrast to conventional network training, in which the optimum weights are obtained by minimizing the error function, the Bayesian approach employs a probability distribution of the network weights. Therefore, the general network's prediction is based on a probability distribution. In the Bayesian concept, the network weights are random variables, and their posterior distribution may be modified according to [PERSON] rule [38].  
Therefore, the equation is as follows.  
\[\mathrm{k}\;(\omega\;|C,\alpha,\beta,L)=\frac{K(C\;|\omega,\beta,L)K(\omega\;| \alpha,L)}{K(C\;|\alpha,\beta,L)} \tag{6}\]  
where the main neural network model is represented as \(K\), the training sample is represented as \(C\), the distribution of the weights is described as \(k\left(\omega\;|\alpha L\right)=\left(\frac{\alpha}{2\pi}\right)^{m/2}\exp\; \left\{-\frac{\alpha}{2}\omega^{1}\omega\right\}\), \(L\) is one of the particular ANNs, and \(\omega\) represents the vector of the network with their weights. \(K\left(\omega\;|\alpha,L\right)\) describes the main state of knowledge before the target data are collected, and \(K(C\;|\omega,\beta,L)\) is a similar function, which is the probability of the data occurring given the weights. The posterior equation for the BNN network is stated in Equation (7).  
\[Posterior=\frac{Likelihood\times prior}{Evidence} \tag{7}\]  
A BNN can create probabilistic commitments for its predictions, and the distribution of the parameters it has learned from the observations. Hence, one may derive the type and form of the neural network's learned parameters from the parameter space. These two aspects make them BNNs attractive to both theorists and practitioners. Hence, due to its performance and accuracy of the as a predictive model, we have proposed a BNN in this study, to predict the blast induced ground vibration of Mikurahana quarry site, based on the eight input parameters, one output parameters and one hundred blasting datasets. The overall flow of a Bayesian neural network (BNN) is summarized in Figure 7.
# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 3 IberSpeech-RTVE 2022 Evaluation Tasks
### Evaluation Metric  
In search on speech systems (both for STD and QbE STD tasks), a hypothesized occurrence is called a detection; if the detection corresponds to an actual occurrence, it is called a hit; otherwise, it is called a false alarm. If an actual occurrence is not detected, this is called a miss. The main metric for the evaluation is the actual term weighted value (ATWV) metric proposed by NIST [17]. This metric combines the hit rate and false alarm rate of each query and averages over all the queries, as shown in Equation (13):  
\[ATWV=\frac{1}{|\Delta|}\sum_{Q\in\Delta}(\frac{N_{hit}^{Q}}{N_{true}^{Q}}- \beta\frac{N_{FA}^{Q}}{T-N_{true}^{Q}}), \tag{13}\]  
where \(\Delta\) denotes the set of queries and \(|\Delta|\) is the number of queries in this set. \(N_{hit}^{Q}\) and \(N_{FA}^{Q}\) represent the numbers of hits and false alarms of query \(Q\), respectively and \(N_{true}^{Q}\) is the number of actual occurrences of query \(Q\) in the audio. \(T\) denotes the audio length in seconds and \(\beta\) is a weight factor set to 999.9, as in the ATWV proposed by NIST [17]. This weight factor causes an emphasis placed on recall compared to precision with a 10:1 ratio.  
ATWV represents the term weighted value (TWV) for an optimal threshold given by the system (usually tuned on the development data). An additional metric, called maximum term weighted value (MTWV) [17] is also used to evaluate the upper-bound system performance regardless of the decision threshold.  
Additionally, p(Miss) and p(FA), which represent the probability of miss and false alarm of the system as defined in Equations (14) and (15), respectively, are also reported:  
\[p(Miss)=1-\frac{N_{hit}}{N_{true}} \tag{14}\]  
\[p(FA)=\frac{N_{FA}}{T-N_{true}}, \tag{15}\]  
where \(N_{hit}\) is the number of hits obtained by the system, \(N_{true}\) is the actual number of occurrences of the queries in the audio, \(N_{FA}\) is the number of false alarms produced by the system and \(T\) denotes the audio length (in seconds). These values, therefore, provide a quantitative way to measure system performance in terms of misses (or equivalently, hits) and false alarms.  
In addition to ATWV, MTWV, p(Miss) and p(FA) figures, NIST also proposed a detection error tradeoff (DET) curve [19] that evaluates the performance of a system at various miss/FA ratios. Although DET curves were not used for the evaluation itself, they are also presented in this paper for system comparison.  
The NIST STD evaluation tool [20] was employed to compute the MTWV, ATWV, p(Miss) and p(FA) figures, along with the DET curves.
# A Public Infrastructure for a Trusted Wireless World
## 6 Security within the System  
Java card is an industry standard technology platform developed by Sun Microsystems (now Oracle) to enable Java-based applications that run on smart cards. These Java-based applications are called applets. Java card helps developers build, test, and deploy smart-card-based applications quickly and efficiently with an object-oriented programming model and off-the-shelf development tools. Since Java Card 3.0, the card has been extended to support a Web application model with servlets running on the card and TCP/IP as basic protocol. The virtual machine and runtime environment have been upgraded as well to support multithreading and hierarchical class loaders. The Java Card platform can run on contact and contactless devices since it runs on secure elements that power the card emulation mode in NFC. NXP Semiconductors makes a smart card using the Java Card Open Platform (JCOP) operating system. The JCOP operating system has a Java Card Virtual Machine (JCVM). NXP's smart cards are more popular in Europe than in the United States. The most popular smart card using NXP's JCOP is MiFare [33].  
Applying digital certificates to online voting systems following the ICAM framework ensures the integrity and availability of each system. The built-in security of modernizing PKI with blockchains ensures that every system can be checked for fraud and the tampering of a citizen's vote. In the traditional method of voting, citizens must show identification to vote. If traditional methods of identification such as driver's license and passport were updated using a Java Card that has a digital certificate, then citizens could utilize the ICAM Framework to cast their votes online with confidentiality and security. Only the system that verifies the identity, the verification authority, would need to confirm the identity of the citizen to ensure they may vote and vote only once; then, the voting system would ensure the confidentiality of the vote by only recording the votes with no identities attached. Again, the votes would be secured and stored within the system using blockchain-based PKI so that the integrity and security of the registered votes are ensured. Any attempts to alter a blockchain would be easily detected and the blockchain technology would revert to the untampered, originally cast vote.