esa-sceva/satcom-chunk-collection huggingface dataset:
# Post-Processing for Shadow Detection in Drone-Acquired Images Using U-NET
## 3 Materials and Methods
### Post-Processing
#### 3.5.3 Fully Connected Conditional Random Field  
In this work, the implementation of dense CRF post-processing is similar to binary segmentation implementation in [38; 39]. CRF is pixel-wise labeling, forming a graph called Markov Random Field based on pixel labels as random variables, modeled as nodes, and global observation from image for the edges. The pixel-wise labeling can be modeled as CRF characterized by Gibbs distribution [27]. Gibbs' energy function in dense CRF is defined as:  
\[E(x)=\sum_{i}\psi_{u}(x_{i})+\sum_{i}\sum_{i<j}\psi_{p}\big{(}x_{i},x_{j}\big{)} \tag{4}\]  
where \(x\) denotes the assignment of labels for pixels. \(u\) represents unary potential, where \(\psi_{u}(x_{i})\) is taken from outputs from U-Net prediction. \(\psi_{p}\) represents pairwise potential, which \(\psi_{p}(x_{i})\) is described as:  
\[\psi_{p}\big{(}x_{i},x_{j}\big{)}=\mu\big{(}x_{i},x_{j}\big{)}\underbrace{\sum _{m=1}^{K}w^{(m)}k^{(m)}(f_{i}f_{j})}_{k(f_{i}f_{j})} \tag{5}\]  
where \(\mu\big{(}x_{i},x_{j}\big{)}\) represents the compatibility of label and feature vectors \(f_{i}\) and \(f_{j}\). Each \(k^{(m)}\) for \(m=1,\,\ldots,\,K\) is a Gaussian Kernel that is applied on feature vectors, and \(w^{(m)}\) are the linear combination weights.  
Figure 5: Visualization of 3 \(\times\) 3 rectangle shape structuring element.  
Figure 6: Visualization of 3 \(\times\) 3 cross shape structuring element.
# Engineering Resource-Efficient Data Management for Smart Cities with Apache Kafka +
## 5 Evaluation of the Setup
### Hdd  
For the purposes of evaluating the efficiency of HDD, we took into account the industrial best practices in the related application sectors. We identified Kafka setup guidelines used by credible industrial service providers. For example, Microsoft recommends that it would be better to constrain the existing partitions per broker (including replicas) to a number not more than 1000. In another example, Confluent recommends setting the number of partitions per broker to at least \(100\cdot B\). Consequently, combining the essence of these configuration recommendations, we arrive at the following benchmark method, called MS-CNFL: \(P=\min\left(P\in_{R}[1\ldots\frac{1000\cdot B}{r}],P\in_{R}[1\ldots 100\cdot B]\right)\) and \(b\in_{R}[1\ldots B]\), where \(\in_{R}\) denotes uniformly random selection. We measure the system throughput, captured by the ultimate number of partitions selected by each algorithm (our algorithms being BroMin and BroMax of [26]); the replication latency, captured by the amount of time that is needed to process each message, in the sense of time required for data to be stored or retrieved; the numbers or costs of the application's infrastructure, captured by the number of brokers used in the Apache Kafka cluster; and the OS load metric via the open file handles and the unavailability metric via the unavailability time. We perform the measurements for variable numbers of consumers. Indicatively, we display the performance in terms of throughput (number of partitions) and replication latency, in Figure 9. We can see that the HDD maintains equivalent numbers of partitions (and therefore throughput), but, at the same time, does not violate the latency constraint (as the benchmark is doing). The complexity analysis details can be found in [26].
###### Abstract  
A challenging problem in the aquaculture industry is bacterial disease outbreaks, which result in the global reduction in fish supply and foodborne outbreaks. Biofilms in marine pathogens protect against antimicrobial treatment and host immune defense. Zeolites are minerals of volcanic origin made from crystalline aluminosilicates, which are useful in agriculture and in environmental management. In this study, silver-ion-exchanged zeolite A of four concentrations; 0.25 M (AgZ1), 0.50 M (AgZ2), 1.00 M (AgZ3) and 1.50 M (AgZ4) were investigated for biofilm inhibition and antimicrobial properties against two predominant marine pathogens, _V. campbelli_ and _V. parahemolyticus_, by employing the minimum inhibitory concentration (MIC) and crystal violet biofilm quantification assays as well as scanning electron microscopy. In the first instance, all zeolite samples AgZ1-AgZ4 showed antimicrobial activity for both pathogens. For _V. campbellii_, AgZ4 exhibited the highest MIC at 125.00 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, while for _V. parahemolyticus_, the highest MIC was observed for AgZ3 at 62.50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. At sublethal concentration, biofilm inhibition of _V. campbelli_ and _V. parahemolyticus_ by AgZ4 was observed at 60.2 and 77.3% inhibition, respectively. Scanning electron microscopy exhibited profound structural alteration of the biofilm matrix by AgZ4. This is the first known study that highlights the potential application of ion-exchanged zeolite A against marine pathogens and their biofilms.  
biofilms; zeolite A; _Vibrio_ spp.; antimicrobials +
Footnote †: journal: Applied Sci.  
**2021**
## 6 Experimental Results
### Experimental Equipment  
The transmitting and receiving devices were a laptop computer with an Intel 5300 wireless network card and a commercial Wi-Fi device. The commercial Wi-Fi device was used as the transmitter, and the laptop computer functioned as the receiver.  
MATLAB was used to pre-process the dataset, feature extraction, and classification recognition of actions.  
To demonstrate that the method proposed in this study can effectively achieve gesture recognition, the obtained feature sets were randomly arranged. Next, a 10-fold crossover was taken for validation; i.e., a subset was selected as the test set each time, and the average crossover validation recognition correct rate of 10 times was taken as the result. In this method, all samples were used as training and test sets. Each sample was verified once. The experimental results are presented in Figure 11 (below).  
The average recognition rate for push, swipe, draw triangle, draw N, draw O, and clap was 93.1% (each gesture scored 94.7%, 95.5%, 93.1%, 94.1%, 88.4%, and 92.3%, respectively).  
Figure 10: Types of gestures used for experiments.  
Figure 9: The picture above shows the experimental environment and Gesture drawing direction: (**a**) Hall; (**b**) Office; (**c**) Classroom; (**d**) Gesture drawing direction.
## 4. SoSCO-Based Architectural Steps and Application Example  
This study describes the proposed architectural process using a search and rescue (SAR) case study used widely in SoS-related research [51; 52]. The civilian maritime SAR scenario involving a distressed yacht is modeled using UAF. Initially, a monitoring device receives the distress signal from the ship and transmits it to a command and control (C2) center. Subsequently, the C2 center coordinates the search and rescue operation  
Figure 6. Core terminology of SoSOO.  
involving helicopters, naval ships, and civilian sea rescue organizations. A mainstream SoS architecture modeling platform, Cameo Enterprise Architecture, models the SAR case.  
The strategic architecture is primarily anchored on the capability defined by the factors and relationships specified in the SoSCO. This section outlines ontology-based modeling steps for strategic architecture, which leverage the SoSCO to identify capabilities that match desired attributes and relationships. Figure 7 illustrates four architectural steps in the strategic domain, where the parallelograms denote elements such as mission and _CapabilityFactor_, and white rectangles represent engineering activities or methods. The green boxes denote the architecture views, while the orange boxes indicate the utilization of _CapabilityRelationship_.
# Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili
## 6 Results and Discussion
### Our Model Results (WEFSE)
#### 6.2.2 Word Analogy Task  
We further evaluated the WEFSE using the word analogy test. This test uses a triplet of words with the goal being to guess the fourth word such that given A is to B (A:B) and C is to D (C:D). In this case, the vectors of A, B and C are used to evaluate the vector of D. The vector of D would be computed by \(X_{B}-X_{A}+X_{C}\) where \(X_{B}\), \(X_{A}\) and \(X_{C}\) are the vectors of words B, A and C, respectively. A typical example of a word analogy question is \"Boy:Girl:Man:?\". The average accuracy over the entire corpus provides the performance measure for the analogy.  
The non-existence of a Swahili word analogy dataset forced us to develop a dataset based on the English dataset introduced by [PERSON] et al. [24]. Therefore, because there is no direct translation for some categories such as comparatives, superlatives, city-in-state and antonyms, we removed or replaced them accordingly. For example, we added a new category for sounds (_tanakali_) and replaced the cities with constituencies and their corresponding counties in Kenya. The resultant analogy dataset has **12,864** questions on which we experimented using word2 vec and our WEFSE. The results in Table 8 show that our model outperforms the word2 vec model, which is based on words. This demonstrates that word embeddings generated from syllables embeddings are better than word-based embeddings.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Wanakutana** & **Mchezo** & **Mulluga** & **[PERSON]** \\ \hline word2 vec & italeta & mtoto & - & - \\  & amri & aliwataka & - & - \\  & frank & upande & - & - \\  & kujhusisha & mawaziri & - & - \\  & waveze & kiongozi & - & - \\ \hline WFSE & wanasubiri & mchele & mulinda & kusota \\  & wanaamini & mchepuo & mulanda & kusoma \\  & wanatakiwa & mchakato & muhimu & kusomea \\  & wanachama & mchango & mulla & kusomewa \\  & wanachi & mchaka & musa & kusonga \\ \hline \hline \end{tabular}
\end{table}
Table 7: The syntactically and semantically similar words using nearest neighbors.
## 1 Introduction
This is what happens when: (1) the Galerkin scheme is adopted, and (2) the selected expansion functions are orthonormal eigenfunctions of a suitable operator containing the most singular part of the integral operator at hand. Such an approach, appropriately called method of analytical preconditioning, is very effective, as clearly shown in the literature devoted to the study of the scattering, propagation, and radiation problems [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. Another way to obtain a guaranteed-convergence consists in solving numerically the singular integral equation by means of a Nystrom-type discretization scheme taking into account the singularity of the integral equation and the behavior of the unknowns at the edges [34; 35; 36; 37; 38]. When dealing with thin disks, the complexity of the problem can be significantly reduced [39; 40; 41; 42; 43]. Indeed, the Fourier series expansion of the unknowns and the orthogonality property of the cylindrical harmonics allow recasting the surface integral equations as infinite sets of independent 1-D integral equations for the harmonics of the effective surface current densities. The strategy of combining the vector Hankel transform (VHT) [44] and the Helmholtz decomposition [45] is particularly suitable because the spectral domain counterpart of the surface curl-free and divergence-free contributions of the currents are scalar functions. After assuming such functions as new unknowns in the spectral domain, the method of analytical preconditioning can be readily applied. The selected expansion functions in the spectral domain, diagonalizing the most singular part of the integral operator, have a closed-form spatial domain counterpart reconstructing the behavior of the unknowns at the disk rim and around the centre of the disk. As a result, the projection integrals reduce to algebraic products and the convergence is very fast. Moreover, the 1-D improper integrals of the coefficient matrix can be quickly and accurately evaluated by adopting the analytical technique developed in [46] and generalized in [40]. In this paper, the method described above is successfully generalized to the analysis of a lossy Fabry-Perot resonator realized with two equiaxial thin resistive disks with the same radius. As will be clearly shown: (1) The proposed method is fast convergent and allowsone to accurately and efficiently reconstruct both near-field and far-field parameters; (2) The resonance frequencies of the structure are simply individuated by the peaks of the total scattering cross-section (TSCS) and the absorption cross-section (ACS), which are expressed in closed form; (3) The proposed method drastically outperforms the general-purpose commercial software CST Microwave Studio (CST-MWS) in terms of both CPU time and memory occupation. This paper is organized as follows. Section 2 is devoted to the description of the proposed method. In Section 3, the obtained numerical results are presented, and the conclusions are summarized in Section 4.
## 1 Introduction
In [26], the researchers used a new offset-free input to design a fuzzy controller. A predictive control method based on the fuzzy Hammerstein model was shown in [27]. However, MPC has some typical drawbacks in actual industrial processes. Firstly, it is difficult to obtain a reliable first-principles model for an object with highly nonlinear characteristics. Secondly, the stability analysis of MPC is often hard to obtain and yet can be easily achieved via other control methods like model-free adaptive control [22; 23]. Finally, the cost of calculation for MPC is quite large [26; 28; 29]. To avoid the disadvantages of MPC, an adaptive tracking constrained control method is proposed in this paper. The proposed control method depends on the modified PID control algorithm, whose exponential parameters can be estimated by an adaptive law based on a back-propagation training algorithm. Moreover, the complex dynamics of SOFC can be approximated via the Wiener-type neural network, and stability analysis of the proposed control algorithm can be easily completed by the Lyapunov function. Furthermore, its calculation is reduced in contrast with MPC. In this paper, we first present an innovative, novel adaptive constrained control design based on the Wiener-type neural network identification for SOFCs. There are various amplitudes and rate constraints of the control inputs in the SOFC control system; based on the online Wiener-type neural network model identification and exponential PID control, an innovative dynamic anti-windup compensator is added to eliminate the negative effects of control input constraints. Furthermore, a stability analysis of the SOFC is given for the presented adaptive constrained approach via the Lyapunov function. Simulations of the SOFC prove that our adaptive tracking constrained control strategy based on Wiener model identification is highly effective. The main innovation points in this paper are as follows:  
1. An innovative method based on the Wiener-type neural network model can be used to identify the system dynamics of an SOFC. A new dynamic anti-windup compensator combined with an exponential PID control method is proposed to eliminate the influence of amplitude and rate constraints for control input in the SOFC system. 3. The Lyapunov function is used to verify that the SOFC closed-loop control system is stable. The rest of this paper is as follows. The problem formulation of the SOFC voltage control system is illustrated in Section 2. In Section 3, the description of the constrained adaptive tracking control based on the Wiener model is presented. Simulation results for the SOFC voltage control system are given in Section 4. Some conclusions for the whole paper are drawn in Section 5.
# An Audio Distortion Dynamic Range Index for Evaluating the Performance of Audio Systems
## 3 Critical Influence Factors
### Span of FFT Bins of the Specified Signal  
Under the ideal measurement conditions, the fundamental frequency and harmonics only have one point peak in the FFT spectrum. But in fact, because the sampling frequency and the test signal frequency cannot be absolutely synchronized, the fundamental frequency and harmonics will have a few point-wide peaks. In order to improve the calculation accuracy, it is necessary to add more points during calculating the fundamental energy and harmonic energy.  
The FFT bin span of the signal directly determines the power of the fundamental signal, which is of great significance to the accuracy of the ADDR index. IEC standard specifies the span of the bins in the frequency domain to distinguish signal from noises by a boundary at 1.5 times above and below the fundamental frequency [15], but a narrower window size was chosen in [7]. Generally, the span should be adjusted according to the signal amplitude and leakage. Assume \(X_{k},(k=0,1,2..N)\) is the averaged spectrum of the DUT output, and \(k\) is the index of each FFT bin. Referring to the method used in signal analyzer, an efficient and effective roll-off method determining the span of FFT bins of the signal is as follows:  
Step 1: Find the FFT bin closest to the input frequency: \(X_{in}\), and \(in\) is the corresponding index of this FFT bin;  
Step 2: Find the largest local peak around the input frequency: \(X_{peak}\) = max \(\{X_{in-1},X_{in},X_{in+1}\}\), and _peak_ is the corresponding index of the FFT bin;  
Step 3: Initialize the left and right boundary indexes respectively: \(idxL=peak-1\), \(idxR=peak+1\);  
Step 4: Role down slope to left by repeating the process \(idxL=idxL+1\), until the condition \(idxL>0\) and \(X_{idxL}\leq X_{idxL+1}\) do not satisfy at the same time;  
Step 5: Role down slope to right by repeating the process \(idxR=idxR-1\), until the condition \(idxR\leq N\) and \(X_{idxR}\leq X_{idxR-1}\) do not satisfy at the same time;  
Step 6: Obtain the left and right boundary index (inclusive) of the FFT bins of signal respectively: \(idxL=idxL+1\), \(idxR=idxR-1\).  
Figure 4 shows the effectiveness of the roll-off method by weakly using the averaging operation and setting a larger \(\beta\). It is obvious that different categories of spectral components are extracted completely.  
Above all, when measuring DUTs for comparison, the parameter conditions in the critical influencing factors mentioned above must be individually adjusted to fit each DUT before being used, which will help to obtain measurement results closer to the true value and achieve a fair comparison. This means that the parameter conditions may be different for DUTs. More details of the parameter setting of the digital signal processing and its influence please refer to [30].  
Figure 4: Roll-off method is used to determine the boundaries of FFT bins of the fundamental, harmonic, noise and DC spectral components, respectively. Spectrum averaged times: 10; Kaiser window (\(\beta=38\)).
# Reverse Coarsening and the Control of Particle Size Distribution through Surfactant
## 2 Results and Discussion
### Simulation of Reverse Coarsening in the Particle Ensemble
From our simulations, we conclude that the main signature of reverse coarsening is the dissolution of the largest particles in the ensemble. While both the mean and the maximum particle radii increase in ordinary coarsening, the maximum radius in reverse coarsening will, at some point in time, decrease, indicating partial dissolution of the biggest particles. This feature is crucial for experimental identification of the reverse coarsening process.
# A Combinational Buffer Management Scheme in Mobile Opportunistic Network
## 5 Conclusions  
In MONs, transfer information between nodes adopts a storing-carrying-forwarding paradigm due to the node mobility and intermittent connectivity of the network. Node buffer management will directly affect the message delivery ratio due to limited storage space, and rational management strategies will effectively avoid excessive copies of messages and the waste of network resources. The existing buffer management research for opportunistic network is fragmented. In order to solve the present issue, the proposed CBM strategy designs the utility value from the view of messages and nodes. It mainly includes a reasonable queuing strategy and a novel message migration method. The novelty of the strategy is that we proactively forward messages to the neighbor node in order to avoid the reduced delivery ratio of the message caused by deleting messages when there is node buffer congestion. Compared with other classical buffer management strategies, CBM aims to maximize delivery ratio and minimize the number of forwarding hops, overhead ratio and average delays.  
In the future, we will further study the popularity of the message when calculating the utility value of the message, and use the node position and the node centrality as the criterion of the node utility value. More refined schedule methods will be designed and evaluated. At the same time, we can further verify the correctness and advantages of the CBM strategy by collecting and using more real data sets. At the same time, we should address buffer management in terms of average delay time of messages in future work.  
This work was supported by the National Natural Science Foundation of China under Grant No. U1404602, the Young Scholar Program of Henan Province under Grant No.2015 GGJS-086, the Science and Technology Foundation of Henan Province under Grant No.172102210341, the Dr. Startup Project of Henan Normal University under Grant No.qd14136, and the Young Scholar Program of Henan Normal University with No. 15018.  
[PERSON] conceived the idea for the study; [PERSON] and [PERSON] did the analyses; [PERSON] performed the experiments and wrote the paper; [PERSON] revised the manuscript.  
The authors declare no conflict of interest.  
Figure 6: Impact of buffer size of the delivery ratio.
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 6 Implementation and Evaluation
### Evaluation
#### 6.3.5 Energy Consumption Issue  
Based on the previous experiment, we compare the energy consumption for each approach, as shown in Figure 10. This energy measurement is based on the time on-air (ToA) of the forwarded packet. Hence, the distance and the packet size will affect energy consumption. For calculating energy, we use transmission power 90 mA and voltage 3 V.  
\[E=\sum_{n=1}^{k}ToA*mA*V \tag{7}\]  
As shown in the figure, our OOD and OODC consume less energy for transmission. Compared to FP, both OOD and OODC forward packet efficiently so that it achieves good reliability. The OODC energy consumption is slightly higher than the OODC because of the header overhead and more transmissions needed. For CPR, it incurs high energy consumption as it combines several packets, makes the relay packet get bigger, and, consequently, the packet needs to be transmitted longer.  
Figure 9: Performance evaluations on network topology 2. (**a**) PDR results. (**b**) The number of packet collision and unnecessary forwarding.