esa-sceva/satcom-chunk-collection huggingface dataset:
# A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics +
## 3 Proposed Methodology
### Ranking Geo-Cluster Tags  
Since among the purposes of this work is one to attach a semantically meaningful label to each detected AOI, we opt for a well-known approach commonly encountered in relevant research works (e.g., [33; 39; 40; 41; 42]). A typical first step is to extract the most representative tags of an AOI. This is not a trivial task and is commonly tackled using several heuristic-based measures, such as the tag frequency, popularity within the users, or even visual features of their corresponding photos [43]. In this work, given the top-\(L\) tags of an AOI, that are extracted as mentioned in Section 3.1.2, in order to acquire the most representative one we choose to adopt a variation of the TF-IDF model as a tag-ranking metric. This particular weighting scheme acts as a statistical measure so as to ascertain the uniqueness of a word found in a document, by taking into consideration how often it appears both in the document itself and across the examined corpus. The TF-IDF metric is given by  
\[\text{tf-idf}_{t,d}=\text{tf}_{t,d}\times\text{idf}_{t}\, \tag{2}\]  
where \(\text{tf}_{t,d}\) measures the frequency of a specific word within a document and \(\text{idf}_{t}\) denotes its importance (i.e., the overall number of documents in the collection that contain it). The inverse document frequency acts essentially as a mechanism that limits the weight of non-informative words that occur frequently across the corpus and increases that of more unique and meaningful ones. In our case, the term frequency of a user-generated tag is determined by the number of photos associated with it inside a region.  
Considering both the limited spatial boundaries of the examined area in the context of our experiments, along with the fact that it is rather usual for a city to contain a relative small number of AOIs, we calculate the inverse document frequency for a given tag \(x\) by dividing the total number of photos contained in the dataset by the number of those that include \(x\), instead of relying solely on the fraction of regions where it appears. As a result, it is possible to limit the negative effect that the inverse document frequency has on a tag that can be spotted on multiple AOIs (e.g., _Acropolis_ and _Parthenon_), yet still retain its usefulness for more generic and widespread tags. For the sake of clarity, we present pseudocode of the TF-IDF process in Algorithm 4, which works on a clustering \(C_{p}\) and produces a set of ranked tags \(\widehat{T}_{i,p}\) for each geo-cluster within \(C_{p}\).
# Aerial Grasping of an Object in the Strong Wind: Robust Control of an Aerial Manipulator
## 5 Conclusions  
In this paper, in order to give an aerial manipulator the ability to work in the wind, we present an acceleration feedback enhanced \(H\infty\) controller. The proposed controller is based on the hierarchical inner-outer loop structure, and composed of a robust \(H\infty\) controller and the acceleration feedback enhanced term, which is used to reject the wind disturbance. This controller can improve the position control accuracy of an aerial manipulator in the wind, so that it can complete grasping tasks with wind disturbance. In future work, we will pay more attention to impedance control of the aerial manipulator to complete more complicated tasks.  
Conceptualization, G.Z. and Y.H.; methodology, G.Z.; software, G.Z. and B.D.; validation, G.Z. and B.D.; formal analysis, F.G. and L.Y.; investigation, G.Z.; resources, Y.H.; data curation, G.Z. and B.D.; writing--original draft preparation, G.Z., J.H. and G.L.; writing--review and editing, G.Z., J.H. and G.L.; visualization, G.Z.; supervision, Y.H., J.H. and G.L.; project administration, F.G. and L.Y.; funding acquisition, Y.H. and F.G.  
This research was funded by National Key Research and Development Program of China grant number 2017 YFC1405401, National Nature Sciences Foundation of China, grant number 61433016 and U1608253, and Science and Technology Planning Project of Guangdong Province, grant number 2017B010116002.  
The authors declare no competing financial interests.  
Figure 11: Aerial grasping of target object in the wind and the hex-rotor controlled without (up) and with (down) the acceleration feedback enhanced term.  
Figure 10: Target position in the operational space when hex-rotor is controlled with and without the acceleration feedback enhanced term, respectively.
## 5 Discussion
### Data Visibility Analysis  
According to the geometric relationships and categories mentioned in Section 3.3, we calculated the visual area of the image and combined it with radar incident angle and DEM data. The incident angles of the ascending Sentinel\(-1\)A and the ALOS/PALSAR\(-2\) ascending satellite used in this paper are 37.76\({}^{\circ}\) and 36.18\({}^{\circ}\), respectively, so the difference is small. The proportion of visible area of Sentinel\(-1\)A is 88.50%, and that of ALOS/PALSAR\(-2\) is 87.55% (Figure 13). The visibility of Sentinel\(-1\)A is slightly higher than that of ALOS/PALSAR\(-2\), but the proportions of visible and geometrically distorted areas are almost the same. Therefore, the difference in the detection results is not likely to have been caused by geometric distortion.  
Figure 12: The number of disaster points detected by two data-collection systems.
## 3 Supply Chain Mathematical Model
### Symbols and Formulation  
* _I_: Number of centers. * _J_: Number of products. * \(\xi_{ij}\): Uncertain supply availability of product \(j\) from supplier \(i\), where \(\xi_{ij}\) are independent variables with probability distributions \(\phi_{ij}\), and \(\theta_{ij}\) are independent uncertain variables with uncertainty distributions \(T_{ij}\), respectively. * \(\xi=(\xi_{11},\xi_{12},\ldots,\xi_{IJ})\): Uncertain vector. Variables and Indices
* \(s\): Index for suppliers \(s=1,\ldots,S\). * \(r\): Index for raw materials \(r=1,\ldots,R\). \(p\): Index for plants \(p=1,\ldots,P\). * \(k\): Index for finished products \(k=1,\ldots,K\). * \(d\): Index for distribution centers \(d=1,\ldots,D\). * \(c\): Index for customers \(c=1,\ldots,C\). * \(t\): Index for planning periods \(t=1,\ldots,T\). _Decision Variables_  
* \(X_{k,p,t}\): Binary variable, 1 if product \(k\) is produced by plant \(p\) in period \(t\). * \(IRP_{r,p,t}\): Inventory level of raw material \(r\) in plant \(p\) at the end of period \(t\). * \(IKP_{k,p,t}\): Inventory level of product \(k\) in plant \(p\) at the end of period \(t\). * \(IKD_{k,d,t}\): Inventory level of product \(k\) in distribution center \(d\) at the end of period \(t\). * \(QRSP_{r,s,p,t}\): Quantity of raw material \(r\) dispatched from supplier \(s\) to plant \(p\) in period \(t\). * \(QKP_{k,p,t}\): Quantity of finished product \(k\) produced in plant \(p\) in period \(t\). * \(QKPD_{k,p,d,t}\): Quantity of finished product \(k\) dispatched from plant \(p\) to distribution center \(d\) in period \(t\). * \(QKDC_{k,d,c,t}\): Quantity of finished product \(k\) dispatched from distribution center \(d\) to customer \(c\) in period \(t\). * \(BQK_{k,c,t}\): Quantity of backorder for product \(k\) incurred by customer \(c\) in period \(t\). _3.3. Mathematical Model Formulation_  
Objective Functions  
* **Minimize total costs (MinOF1)** : \[ \begin{split}\text{MinOF}_{1}=&\sum_{r\in R}\sum_{s \in S}\sum_{p\in P}\sum_{t\in T}TCSP_{r,s,p,t}\cdot QRSP_{r,s,p,t}\\ &+\sum_{r\in R}\sum_{p\in P}\sum_{t\in T}CIRP_{r,p,t}\cdot IRP_{r,p,t}\\ &+\sum_{k\in K}\sum_{p\in P}\sum_{t\in T}CIRP_{k,p,t}\cdot IKP_{k,p,t}\\ &+\sum_{k\in K}\sum_{d\in D}\sum_{t\in T}CKP_{k,p,t}\cdot QKP_{k,p,t}\\ &+\sum_{k\in K}\sum_{d\in D}\sum_{t\in T}CKS_{k,p,t}\cdot X_{k,d, t}\\ &+\sum_{k\in P}\sum_{p\in P}\sum_{d\in D}\sum_{t\in T}TCPD_{k,p, d,t}\cdot QKPD_{k,p,d,t}\\ &+\sum_{k\in K}\sum_{d\in D}\sum_{c\in C}\sum_{t\in T}TCDC_{k,d, c,t}\cdot QKDC_{k,d,c,t}\\ &+\sum_{k\in K}\sum_{c\in C}\sum_{t\in T}BOC_{k,c,t}\cdot BQKC_{k, c,t}\end{split} \]
* **Maximize on-time deliveries (MaxOF2):** \[ \begin{split}\text{MaxOF}_{2}=&\sum_{r\in R}\sum_{s \in S}\sum_{p\in P}\sum_{t\in T}\eta_{r,s,p,t}\cdot QRSP_{r,s,p,t}\end{split}
_Appl. Sci._**2014**, \(4\), 128-147; doi:10.3390/app4020128  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Compressed Sensing-Based Distributed Image Compression**  
**[PERSON] 1, [PERSON] 2,* and [PERSON] 3**  
\({}^{1}\) School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) School of Engineering and Advanced Technology, Massey University, Albany, Auckland, New Zealand  
\({}^{3}\) Asia-Pacific Broadcasting Union, Angkasapuri, Kuala Lumpur 50614, Malaysia;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +64-9-4140800  
_Received: 7 October 2013; in revised form: 21 January 2014 / Accepted: 28 February 2014 / Published: 31 March 2014_  
**Abstract:** In this paper, a new distributed block-based image compression method based on the principles of compressed sensing (CS) is introduced. The coding and decoding processes are performed entirely in the CS measurement domain. Image blocks are classified into key and non-key blocks and encoded at different rates. The encoder makes use of a new adaptive block classification scheme that is based on the mean square error of the CS measurements between blocks. At the decoder, a simple, but effective, side information generation method is used for the decoding of the non-key blocks. Experimental results show that our coding scheme achieves better results than existing CS-based image coding methods.  
**Keywords:** distributed image coding; compressed sensing
## 3 Analysis of Energy Efficiency and Network Connectivity  
In this section, we first describe the physical-layer underwater energy consumption model, which tells how large the energy consumption is in one transmission given a transmission range. Then, we analyze the average energy consumption w.r.t. an end-to-end packet transmission, _i.e._, from a source node to the sink. After that, we provide the analysis on the connectivity for such a random network.
## 3 Estimation of Stray Capacitance of Single-Phase CMIs
### Total CM Capacitance  
As shown in Figure 6, the parallel stray capacitance \(C_{\rm p}\) is obtained as the ladder circuit of the turn-to-turn capacitance and turn-to-core capacitance. Based on the delta-wye transformation, Equation (18) is derived [20].  
\[C_{\rm p}(N)=\left\{\begin{array}{cc}C_{\rm t-t}+\frac{C_{\rm t-c}}{2}&(N=2) \\ \frac{C_{\rm t-t}}{2}+\frac{C_{\rm t-c}}{2}&(N=3)\\ \frac{C_{\rm p}(N-2)\cdot\frac{C_{\rm t-c}}{2}}{C_{\rm p}(N-2)+\frac{C_{\rm t- c}}{2}}+\frac{C_{\rm t-c}}{2}&(N\geq 4)\end{array}\right. \tag{18}\]  
Note that the total CM capacitance \(C_{\rm CM}\) of single-phase CMIs is the parallel connected \(C_{\rm p}\) and is obtained as \(C_{\rm CM}=2C_{\rm p}\).  
One possible error factor in the stray capacitance estimation method described above is that straight lines approximate the electric force lines between conductors of adjacent turns. In toroidal core-based CMIs, the distance between conductors of adjacent turns becomes large when the turn number is small or when the winding covers the core at a wide angle. As is well-known, the electric force lines between conductors are represented by arcs. On the other hand, this article approximates the electric force lines between conductors by straight lines. This approximation may lead to the overestimated turn-to-turn capacitance because the length of the electric force line is estimated to be shorter than its actual length when the winding is applied sparsely to the toroidal core. The possible error of the turn-to-turn capacitance due to the above approximation of the electric force lines increases as the distance between conductors of adjacent turns increases.  
On the other hand, in this article, the parallel stray capacitance of the winding is calculated as the ladder circuit of the turn-to-turn capacitance and the turn-to-core capacitance. The turn-to-core capacitance does not depend on the distance between conductors of adjacent turns, and windings are usually applied closely to the core. Therefore, the turn-to-core capacitance is dominant in the parallel stray capacitance of the winding when the winding is sparsely applied to the core. In other words, even if the turn-to-turn capacitance is overestimated, its impact on the parallel stray capacitance may be ignored.  
The turn-to-core capacitance can be reduced when the distance between the winding and the core increases. In such a case, the turn-to-turn capacitance may dominate the parallel stray capacitance even if the winding is sparsely applied to the toroidal core. However, windings are usually applied tightly to the core. Thus, the distance between the winding and the core is not considered in Equations (16) and (17) for calculating the turn-to-core capacitance.
## 5 Application to GCM Mode's GHASH Function Implementation
used the C version of the 128-bit reduction method in their \(BF\) multiplication method [10], we have implemented a fast reduction method similar to the fast reduction algorithm in [14] in assembly language, and the running time of the reduction is 350 \(cc\). Note that including the final one additional \(BF\) multiplication for computing \(V_{m+n+1}\) in Figure 5, 2, 5, and 17 128-bit \(BF\) multiplications are required for 16 byte, 64 byte, and 256 byte messages, respectively. Table 3 compares the performance of our GHASH implementation and the previous work. Actually, since [PERSON] et al. did not provide timing results for 64 byte and 256 byte messages in their paper [10], and we have implemented their method and measured the timings for 64 byte and 256 byte messages. Our proposed implementation provides improved performance by over 42% compared to the previous best result from [9]. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **16 Bytes** & **64 Bytes** & **256 Bytes** \\ \hline [PERSON] et al.’s implementation [10] & 13,816 & 34,156 & 115,516 \\ This work & 7834 & 19,594 & 66,634 \\ Improvement ratio & 43.30\% & 42.6\% & 42.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Timing costs for GHASH function and comparison to previous work (timings are measured by clock cycles (\(cc\))). Figure 5: GHASH function process \((A_{1},\ldots,A_{m}\) are additional authenticated data (AAD) and \(C_{1},\ldots,C_{n}\) are blocks encrypted by CTR mode).
## 4 Experimental Analysis
### Datasets
#### 4.1.1 BONN EEG Dataset  
One of the datasets used in this research is the BONN dataset, which contains single-channel intracranial EEG data from epilepsy patients. The dataset is curated at the University of Bonn by the Department of Epileptology in Germany [48,49]. There are five subsets in the data, with each subset consisting of 100 segments of a single-channel EEG signal. Each data segment consists of 23.6 s of EEG signals collected at a sampling rate of 173.61 Hz. The total number of sample points in each segment is 173.61 \(\times\) 23.6 = \(\sim\)4097. The subsets are labeled as Z, O, N, F, and S. The data are collected from five individuals in good health with their eyes in the open and closed states using the international 10/20 electrode system. An intracranial electrode placement system is used to collect subsets N, F, and S from five patients diagnosed with epilepsy. Subset S is recorded during seizures, while subsets labeled N and F are collected during periods without any seizure events.
# An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System
## 2 Related Work
### Deep Neural Network  
Deep learning approaches stimulate the researchers over the last decade, this method, initially presented by [PERSON] [35; 36], has exposed a robust and efficient machine-learning (ML) approach in multiple domains, including facial recognition [37; 38], speech recognition [39], and NLP, i.e., natural language processing [40]. Deep learning networks are also effective for forecasting-related issues for sequential data [41; 42]. In the current research, these deep learning networks have been utilized for financial prediction [43; 44; 45] and attained 63%-73% accuracy in forecasting the direction of alteration in trade. For forecasting-related issues, restricted Boltzmann machines and different autoencoders have been effectively utilized for the unsupervised removal of abstract input features [46; 47]. In financial forecasting, the method was also shown to be very effective [43]. The techniques for pretraining deep neural networks with the SAE and stacked Boltzmann are briefly described in [43; 48; 49; 50; 51]. Current improvements in CNN i.e., convolutional neural network, make it more appealing for classification and forecasting-related issues [52] centered on time series data. CNNs are great for financial forecasting because of two main causes: Firstly, noise filters and dimensionality reduction approaches help to select crafted input features. Secondly, the local association amongst successive observations can be suppressed to lessen the number of parameters to be considered in the network by linking just a small number of end-to-end inputs to each hidden layer's unit. The units in the convolutional neural network get input from the small adjoining subsections of space in the input, known as the respective field, that covers the whole input feature's set. This lets units act as local kernels and exploit correlation amongst adjoining inputs.
## 3 4-SSB System Design  
### Introduction to Single-Sideband Modulation  
In general, the single-sideband (SSB) [18] is extracted from the double sideband (DSB) which contains two sidebands: the upper sideband (USB) and lower sideband (LSB). Both contain the complete information of the original baseband signal. The SSB modulation only requires half of the signal bandwidth. In Figure 1, without loss of generality, let \(x(t)\) be the baseband signal, then the mathematical model of two sidebands USB and LSB in the time domain can be expressed as:  
\[x_{\text{USB}}(t)=\frac{1}{2}[x(t)+j\hat{x}(t)], \tag{1}\]  
\[x_{\text{LSB}}(t)=\frac{1}{2}[x(t)-j\hat{x}(t)], \tag{2}\]  
where \(\hat{x}(t)\) is unknown and \(j\) is a complex value equal to \(\sqrt{-1}\).  
Then, to identify \(\hat{x}(t)\), we apply the Fourier transform in one sideband of SSB with the opposite sign as follows:  
\[X_{\text{USB}}(f)=X(f)U(f)\ =\frac{1}{2}X(f)[1+\text{sgn}(f)]\ =\frac{1}{2}X(f)+\frac{1}{2}X(f) \text{sgn}(f), \tag{3}\]  
where \(U(f)\) is a step function in the frequency domain.  
We observe the Fourier transform: \(j\hat{x}(t)\leftrightarrow X(f)\text{sgn}(f)\). Therefore:  
\[\hat{X}(f)=-jX(f)\text{sgn}(f), \tag{4}\]  
where \(\text{sgn}(f)\) denotes a signum function in the frequency domain.  
As the inverse Fourier transform for \(-j\text{sgn}(f)\) in time domain is \(\frac{1}{n!}\), (4) in time domain is equivalent to:  
\[\hat{x}(t)=x(t)\ \ast\frac{1}{n!}\, \tag{5}\]  
where \(\ast\) denotes the convolution operation, i.e.:  
\[\hat{x}(t)=\frac{1}{\pi}\int_{-\infty}^{\infty}\frac{x(\tau)}{t-\tau}d\tau. \tag{6}\]  
Specifically, we apply the unit step function to define Hilbert impulse response in the time domain by using a Fourier transform. Then, we finalize the Hilbert transform model as shown in Equations (5) and (6).  
The SSB signal generated by the Hilbert transform then can be expressed as:  
\[x(t)=\ \frac{1}{2}\ x(t)cos(2\pi f_{c}t)\pm\ \frac{1}{2}\hat{x}(t)sin(2\pi f_{c} t), \tag{7}\]  
Figure 1: Single-sideband (SSB) generated by the Hilbert transform.  
where \(f_{c}\) is the carrier frequency and \(\pm\) denotes the sign of two sidebands: the minus sign is for USB and the positive sign is for LSB. Also, \(\hat{x}(t)\) is a Hilbert transform of signal \(x(t)\) and the second term of the right side shows the output generated by Hilbert transform (Figure 1) where \(x(t)\) can be represented in the complex form as: \(x(t)=x(t)+j\hat{x}(t)\).
## 2 Results and Discussion
### Fluorescent Quenching of Screened Compounds on XO  
Since protein exhibits fluorescence spectrum light, fluorescent quenching could be used to investigate the binding between active compounds and protein. The fluorescence emission spectrum of XO and the spectra with the addition of gardenin B and eupatorin, respectively, are shown in Figure 8. XO showed a fluorescence emission peak at 337 nm with the excitation wavelength at 280 nm. Different levels of fluorescence quenchings could be observed when increasing concentrations of gardenin B and eupatorin were added. Therefore, the Stern-Volmer plots for XO fluorescence quenching by gardenin B and eupatorin were further investigated.  
The plots are shown in Figure 9 and the binding parameters are summarized in Table 2. The Stern-Volmer plots of gardenin B and eupatorin were linear and the fitting degrees were 0.9988 and 0.9957, respectively. The satisfactory results reflected that the calculations of \(K_{\rm q}\) and \(K_{\rm sv}\) were  
Figure 8: The quenching effects of (**a**) gardenin B and (**b**) eupatorin on xanthine oxidase (XO) fluorescence spectra at 300.15 K. \(\upmu\)ex = 280 nm; XO, 1.0 \(\upmu\)mol/L; the addition of gardenin B was from 0.5 to 4.0 \(\upmu\)mol; the addition of eupatorin was from 5 to 40 \(\upmu\)mol.  
Figure 7: The chromatograms of (a) extract of Clerodendanthus spicatus, (b) eupatorin and (c) gardenin B.  
suitable. The linear Stern-Volmer plots indicated a single way of quenching, which meant that either dynamic quenching or static quenching played a key role. The value of \(K_{\mathrm{q}}\) reflected the efficiency of quenching and the accessibility of fluorophores. Based on previous research, if \(K_{\mathrm{q}}\) is near 1.0 \(\times\) 10\({}^{10}\) L/mol/s, the quenching can be considered as dynamic quenching. Meanwhile, if \(K_{\mathrm{q}}\) is greater than 2.0 \(\times\) 10\({}^{10}\) L/mol/s, the quenching can be considered as static quenching [29]. In this study, the \(K_{\mathrm{q}}\) values of gardenin B and eupatorin were 600-fold (1.2194 \(\times\) 10\({}^{13}\) L/mol/s) and 500-fold (1.0097 \(\times\) 10\({}^{13}\) L/mol/s) greater than 2.0 \(\times\) 10\({}^{10}\) L/mol/s, respectively. This suggested that the quenching effects of gardenin B and eupatorin belong to static quenching mechanisms.