esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Flexible Method for Solving Multi-Expert Multi-Criterion Decision-Making Problems
### Hesitant Fuzzy Linguistic Term Set  
Extending the concept of fuzzy sets, [PERSON] [18] proposed the hesitant fuzzy set (HFT) in order to manage situations containing hesitation in determining the membership degree of the elements in human decision-making processes. HFT can be explained as follows.  
**Definition 5**[18].: _If \(X\) is a reference set, regarding the HFT \(E\) of \(X\), this function returns a subset of [0, 1] when applied to \(X\), can be defined as follows._  
\[E=\{\langle x,\ h_{E}(x)\rangle|x\in X\}\]  
_where \(h_{E}(x)\) represents the degree of membership of elements \(x\in X\) to set \(E\), and its values are in the interval [0, 1]._  
[PERSON] et al. [17] first introduced HFLTS to optimize the computational capabilities of conventional linguistic methods, in situations wherein experts hesitate between some values for evaluating data [45,46]. The basic concept of the HFLTS is based on the HFT and fuzzy linguistic approaches, and can be explained as follows.  
**Definition 6**[21].: _An HFLTS \((H_{s})\) is an ordered finite subset of continuous semantic terms of \(S\), \(S=\left\{s_{0},s_{1},\ldots,s_{S}\right\}\)._  
**Definition 7**[21].: _The full HFLTS and empty HFLTS for a semantic variable \(\alpha\) are explained as follows._  
\[\mathrm{Full\;HFLTS:}H_{s}(\alpha)=S;\]  
\[\mathrm{Empty\;HFLTS:}H_{s}(\alpha)=\varnothing.\]  
**Definition 8**[47].: _Let \(S=\left\{s_{0},s_{1},\ldots,s_{S}\right\}\) be a linguistic term set. \(H_{s}\), \(H_{S}^{1}\), and \(H_{S}^{2}\) are the arbitrary HFLTS on \(S\), and then their intersection, union, and complement operations are explained as follows._  
\[H_{S}^{1}\cap H_{S}^{2}=\left\{s_{i}|s_{i}\in H_{S}^{1}\text{ and }s_{i}\in H_{S}^{2}\right\};\]  
\[H_{S}^{1}\cup H_{S}^{2}=\left\{s_{i}|s_{i}\in H_{S}^{1}\text{ or }s_{i}\in H_{S}^{2}\right\};\]  
\[H_{S}^{C}=S-H_{s}=\left\{s_{i}|s_{i}\in S\text{ and }s_{i}\
otin H_{s}\right\}.\]  
**Definition 9**[17].: _The upper bound \(H_{s^{+}}\) and lower bound \(H_{s^{-}}\) of HFLTS are explained as follows._  
\[H_{s^{+}}=max(s_{i})=s_{j},\ s_{i}\in H_{s},\ \mathrm{and}\ s_{i}\leq s _{j}\forall i;\]  
\[H_{s^{-}}=min(s_{i})=s_{j},\ s_{i}\in H_{s},\ \mathrm{and}\ s_{i}\geq s_{j} \forall i.\]  
**Definition 10**[47].: _The envelope of an HFLTS, \(env(H_{s})\), is a linguistic interval, which is restricted by its lower bound \(H_{s^{-}}\) and upper bound \(H_{s^{+}}\)._  
\[env(H_{s})=[H_{s^{-}},H_{s^{+}}]\]
# A Novel Framework for Identifying Customers' Unmet Needs on Online Social Media Using Context Tree
## 6 Discussion  
### Which Is Right for Identifying Customer Needs: Tweets, Blog Posts, and Other Alternatives  
The data used in this study are tweets and blog posts. As comparing the two, it is easier to identify the keyword-based user contexts about home appliances using tweets than blog posts. A single tweet is a short sentence within 140 characters (not strict criteria), while a blog post is long and contains a lot of nested topics compared to tweets. For example, when the word 'oven' appears on a tweet, the subject of the tweet is often highly related to the 'oven'. On the other hand, even though the word 'oven' is included in a blog post, the main topic of that blog post may be the daily record or house interior, which are not significantly related to the oven.  
Of course, the above example is not always true. For some subjects, there may be many posts that deal with them in detail, and even the average length of those posts is long. It is a good idea to check whether our target social media data is proper to extract users' contexts through preliminary analysis. However, if you do not have much time to collect and analyze all types of data, it is strongly recommended to use short texts, such as tweets, product reviews, etc.  
Except for tweets and blog posts, some websites and applications that deal with specific topics in-depth are good for extracting users' contexts and identifying customers' needs. These types of websites and applications include:  
* Yelp: reviews on restaurants and dishes
* Amazon: reviews on many products (in specific categories related to target subjects)
* IMDB and Rotten Tomatoes: reviews on movies and TV shows
* Yahoo Answer: questions and corresponding answers about target subjects
* Reddit: Opinions on target subjects
## 2 Syntchitic Aperture Radar
### SAR Raw Data  
SAR raw data are an array of chirp signals (pulses of electromagnetic waves) backscattered from the surfaces of interest. Figure 7 shows the general structure of SAR raw data for pulsed and continuous SAR radars, and SAR raw data have no direct application and cannot distinguish targets or surfaces of interest; therefore, SAR images are generated by focusing algorithms.  
The SAR raw data in Figure 7 are sampled in the azimuth direction using the slow time sampling frequency or pulse repetition frequency \((PRF=1/PRI)\), the inverse of the pulse repetition frequency \((PRF)\) is called the pulse repetition interval \((PRI)\); the data are also sampled in the range direction using the fast time sampling frequency or sampling frequency \((f_{s})\).  
Figure 5: Transmitter \((Tx)\) and receiver \((Rx)\) of the LFM-CW SAR system.  
Figure 6: Backscattered signals at the receiving antenna Rx in the LFM-CW SAR system [19].
## 2 Related Work
### MR-Based Solutions  
Most publications on prostate segmentation focus on MR data. The U-Net model and its variations remain among the most widely used architectures for medical image segmentation. In the study by [10], a benchmark of U-Net variants for prostate segmentation across various datasets is presented, with the conclusion that nnU-Net and Attention U-Net performed best in most tests. In [11], a Dice coefficient of over 99% was achieved for the prostate segmentation task; however, the input data were multi-modal, consisting of a fusion of T2-weighted and diffusion-weighted MR imaging. In [12], another U-Net-inspired 2D model, called Dense-2U-Net, was introduced, achieving a Dice coefficient of 92.1 \(\pm\) 0.8%.  
EfficientDet [13] is another model that has achieved promising results. The authors of [13] compared this architecture with other models, including V-Net, 2D U-Net, and GAN, obtaining a 91.4% Dice coefficient for EfficientDet. A promising research direction is described in [14], where the authors achieved a 95% Dice coefficient using a self-constructed transformer-based model. However, it is important to note that transformer-based models typically require more training data than CNN-based solutions.
# IRS-Enabled Ultra-Low-Power Wireless Sensor Networks: Scheduling and Transmission Schemes
## 3 Transmission Scheme
### ISN Transmission and Rate Analysis  
When ISN \(l\) is activated by the beacon beam, it starts modulating the phase shift matrix \(\mathbf{\Theta}_{l}(t)\) as follows:  
\[\mathbf{\Theta}_{l}(t)=\mathbf{\Phi}_{l}X_{l}(t), \tag{16}\]  
where \(X_{l}(t)\in\{-1,1\}\) according to a BPSK signal and \(\mathbf{\Phi}_{l}=\mathrm{diag}(e^{i\theta_{l,1}},\dots,e^{i\theta_{l,N}})\). To highlight the information modulation, (12) becomes  
\[Y=\sqrt{P_{l}}(\mathbf{f}_{l}^{H}\mathbf{H}_{1,l}\mathbf{\Phi}_{l}\mathbf{h}_ {2,l})X_{l}(t)+Z, \tag{17}\]  
Note that a phase shift of \(\pi\) does not deter the effective channel magnitude, as \(\|\mathbf{G}_{l}\mathbf{v}_{l}\|^{2}=\|\mathbf{G}_{l}\mathbf{v}_{l}e^{j\pi}\|^ {2}\). Thus, this modulation scheme does not change the signal-to-noise ratio; moreover, \(\mathbf{\Phi}_{l}\forall l\) is constant during the frame, which in turn means that the scheduling decision remains valid.  
For a BPSK input for (17), we know that \(X_{l}(t)\in\{-1,1\}\) with equal probability. The theoretical achievable rate formulated by [PERSON] (in bits per transmission) is then [17]  
schedule. However, this scheduling scheme is suboptimal, as it does not take advantage of all the degrees of freedom in the system.  
Furthermore, we define a variation of this scheme called RR with optimal phases, which we use as a baseline for simulations; in this scheme, we assume that backhaul links exist and that IRSs choose optimal phases while scheduled in an RR fashion. This scheme is helpful to examine the advantages of scheduling vs. passive beamforming at the IRSs.  
The next scheduling scheme lies between RR and OS, and can be tweaked in order to achieve appropriate fairness/performance criteria.
## 3 Proposed Protocol: ELEACH-DFL
### Cluster Configuration  
After deciding on CHs, clusters are formed with several non-CH nodes. The second FIS considers the energies of the CHs, distance from BS, and distances between nodes and CHs among on-CH nodes, and obtains the chance values from these three inputs. Figure 8 is the second FIS block diagram for the cluster configuration process. The inputs and output of the second FIS are described in regard to variable names and meanings in Table 3. Membership functions of the FIS are set like Figure 9 to get optimal cluster formations to get optimal performances in terms of energy.  
Figure 8: Second FIS for cluster configuration.  
Figure 9: Membership functions of inputs and output in second FIS.  
Once clusters are formed, each node transmits the measured data to its CH, and then, CHs collect the data and aggregate the collected data if needed or necessary, and send the fuzzed data to the BS. The clustering formation process is described in Figure 10 in detail.  
The following Figure 11 shows the proposed algorithm's pseudocode. Lines 13-49 of the code are the CH selection and cluster configuration section. Line 15 is the part that determines whether the node's random number is less than the \(T(n)\) of LEACH if the node is alive. If true, the CH selection section, lines 17-31 will be executed. Line 18 is a FIS operation function, and lines 3-12 are its executing code. If the conditions of line 15 are false, the node becomes a member node and progresses to lines 35-48, which are part of the cluster configuration.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{Input variable} & Energy & Residual energy of the node \\ \cline{2-3}  & dtoCH & Distance from the node to the CH \\ \cline{2-3}  & dCHtoBS & Distance from CH to the BS \\ \hline Output Variable & Chance(output1) & The threshold value (fuzzy crisp output) to be selected as the CH \\ \hline \hline \end{tabular}
\end{table}
Table 3: FIS input and output variables during cluster formation.  
Figure 10: Flowchart of cluster configuration process.
# A Method for Modeling and Testing Near-Real-Time System Scenarios
## 4 Modeling the System Scenarios with Finite-State Machines  
### Validation and Verification of Requirements  
Quality control in requirements engineering consists of two basic activities: requirements validation and requirements verification. Validation is usually understood as the activity of checking that requirements are correct, acceptable, and prioritized. The purpose of requirements verification is to familiarize the user with the requirements and to confirm their completeness, consistency, and unambiguity. Quality control of requirements is difficult when end users lack technical knowledge or expertise in a particular field. Moreover, requirements are documented in natural language, which may lead to their ambiguous interpretation.  
The presented certification method consists of mapping requirements in the form of system use cases to certification requirements and system safety functions, which allows protecting designers from preparing a solution for which essential safety requirements have been omitted. Each requirement specification can be checked to be complete, correct, unambiguous, necessary, prioritized, verifiable, and concise.  
A requirement is considered complete when it is sufficient to carry out further design work at an appropriate level of detail. The required level of completeness varies depending on the methodology and the stage in the life cycle at which the requirement is studied or represented. Using the DO-178 design methodology when building an unmanned system, the designer can be sure that none of the essential requirements will be omitted. A requirement is considered consistent with the identified stakeholder's needs when it is not inconsistent with other requirements. This is ensured by the FHA functional decomposition verified by the certifying authority. A requirement is considered to be stated concisely when it does not contain superfluous and redundant content. A requirement is feasible when it is feasible within the agreed risk. Moreover, it should be confirmed by experiments or prototypes. Building unmanned systems based on prototyping models is a commonly used approach. A requirement is considered unambiguous when it is defined in such a way that it can be verified whether the solution meets the user's needs or not. In the case of a set of requirements mapped to FHA, the designer is able to quickly verify their unambiguity. A requirement is considered testable when the designer can verify, using simulator tests or tests performed during UAV flights, whether the requirements have been met. Acceptable levels of compliance verification depend on the system function to which the requirement applies. Each requirement must be prioritized, which allows requirements to be grouped by severity and value over all other requirements. This provides a demonstrated approach to merging requirements with the FHA.  
Before designers of unmanned systems begin testing the system on simulators and then in flight in real weather conditions, the formal system model must be theoretically checked. The method presented in this work is used for this purpose.
## 3 Example Test and Result Analysis
### Test Results and Analysis  
The method described in this paper was used to obtain the three-dimensional coordinate values of the public marker points from four perspectives, as shown in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Angle of View**} & \multirow{2}{*}{
\begin{tabular}{c} **Flag Point** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{**X Coordinate/m**} & \multirow{2}{*}{**Y Coordinate/m**} & \multirow{2}{*}{**Z Coordinate/m**} \\ \hline  & A1 & 0.59 & \(-\)3.69 & 4.56 \\  & A2 & 1.74 & \(-\)4.46 & 4.05 \\  & A3 & 2.62 & \(-\)5.95 & 3.16 \\ Upper & A4 & 4.37 & \(-\)6.79 & 2.18 \\ perspective & A5 & 6.99 & \(-\)8.12 & 4.77 \\  & A6 & 9.93 & \(-\)9.22 & 3.98 \\  & A7 & 10.58 & \(-\)9.65 & 3.57 \\  & A8 & 12.99 & \(-\)15.55 & 4.18 \\ \hline  & B1 & \(-\)4.59 & \(-\)5.72 & 2.34 \\  & B2 & \(-\)1.23 & \(-\)6.99 & 2.89 \\  & B3 & \(-\)5.99 & \(-\)5.11 & 1.29 \\ Left perspective & B4 & \(-\)3.59 & \(-\)1.28 & 0.89 \\  & B5 & \(-\)3.87 & \(-\)2.38 & 1.49 \\  & B6 & \(-\)2.94 & \(-\)1.49 & 3.36 \\  & B7 & \(-\)6.05 & \(-\)4.38 & 4.09 \\  & B8 & \(-\)4.95 & \(-\)3.54 & 3.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Three-dimensional coordinate values of public sign points. Figure 2: Data collection results of inverted arch point cloud in the soft rock tunnel. (**a**) Upper view point cloud data. (**b**) Left view point cloud data. (**c**) Right view point cloud data. (**d**) Lower view point cloud data. According to Table 1, the method used in this paper can effectively obtain the three-dimensional coordinate values of each public landmark point from four perspectives. According to the three-dimensional coordinate values of each public landmark point, the public landmark points are automatically matched to obtain public landmark point pairs, and the point cloud is spliced according to the public landmark point pairs to obtain the point cloud data of the soft rock tunnel's inverted arch after splicing. The point cloud splicing results are shown in Figure 3.  
According to Figure 3, the method used in this paper can effectively splice point clouds based on public marker points to obtain complete soft rock tunnel inverted arch point cloud data. However, the articulated soft rock tunnel inverted arch point cloud data have poor clarity and contain noise. Therefore, we continued to use the method in this paper to reconstruct the splice point cloud data. The result of splice point cloud reconstruction is shown in Figure 4.  
According to Figure 4, the method used in this paper can effectively remove noise and better retain terrain feature information, so that the filtered point cloud data can more accurately express terrain information. This experiment proves that the method in this paper is able to reconstruct point cloud data and effectively obtain the inverted arch point cloud data of soft rock tunnels with better clarity, which is conducive to improving the monitoring accuracy of inverted arch uplift deformations of soft rock tunnels in the future.
# A Frequency-Domain Estimation Scheme for Frequency Offset with Large Range in OFDM Systems
## 2 System Description
### The OFDM System Model
In this work, we mainly focus on the frequency offset estimation module in Figure 1 and consider the Doppler with additive white Gaussian noise (AWGN) channel. The received time-domain OFDM signal \(\mathbf{y}\) is given as [6; 10]  
\[\mathbf{y}(n)=\mathbf{x}(n)e^{\frac{2\pi n/d}{N-3}}+\mathbf{w}(n),n=0,1,2,\ldots,N-1. \tag{1}\]  
where \(\mathbf{x}(n)\) and \(\mathbf{y}(n)\) are the \(n\)-th element of \(\mathbf{x}\) and \(\mathbf{y}\), respectively, \(f_{d}\) is the Doppler shift, \(\Delta f\) is the subcarrier spacing, and \(\mathbf{w}(n)\) is the noise corresponding to the \(n\)-th received symbol \(\mathbf{y}(n)\). Similarly to previous works [3; 5; 6; 10], we define the normalized frequency offset as \(\epsilon=f_{d}/\Delta f\) in this paper. The received frequency-domain OFDM vector \(\mathbf{Y}\) is calculated by FFT module, and the \(k\)-th element of \(\mathbf{Y}\) is given by  
\[\mathbf{Y}(k)=\sum_{n=0}^{N-1}\mathbf{y}(n)e^{-\frac{2\pi n+k}{N}}=\sum_{n=0}^{N-1}\bm {x}(n)e^{\frac{-2\pi n(k-\epsilon)}{N}}+\mathbf{W}(k),k=0,1,2,\ldots,N-1. \tag{2}\]  
where \(\mathbf{W}(k)\) is the corresponding frequency-domain noise. According to (2), if \(\epsilon\) is an integer, the relationship between the received vector \(\mathbf{Y}\) and the transmitted vector \(\mathbf{X}\) is  
\[\mathbf{Y}[\text{mod}(k+\epsilon,N)]=\mathbf{X}(k)+\mathbf{W}(k),k=0,1,2,\ldots,N-1. \tag{3}\]  
where the function \(\text{mod}(x,N)\) denotes \(x\) modulo \(N\). Equation (3) indicates that the received vector \(\mathbf{Y}\) could be directly obtained by cyclically right-shifting \(\epsilon\) elements in \(\mathbf{X}\), if regardless of the noise.
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 4 Acoustic Emission Source Localization Techniques for Three-Dimensional (3D) Structures
### Point Location Based on Arrival Time Approach  
Using this method requires many single component sensors. These sensors are installed in appropriate locations that can effectively detect acoustic emission/microseismic (AE/MS) activity. Then, based on the observed arrival time, velocity model and sensor coordinates, an arrival time function is established for each sensor. The following is the simplest form of this function:  
\[\sqrt{\left(x_{i}-x\right)^{2}+\left(y_{i}-y\right)^{2}+\left(z_{i}-z\right)^{2} }=v(t_{i}-t) \tag{1}\]  
where the unknowns, \(x\), \(y\) and \(z\) are the coordinates of the source, t is the origin time of the event, \(x_{i}\), \(y_{i}\) and z\({}_{i}\) are the coordinates of the \(i\)th transducer, \(t_{i}\) is the arrival time at the ith transducer, \(v\) is stress wave propagation velocity and \(i=1\), \(2\), \(\ldots\), N, \(\ldots\), M.  
When M > N, the problem becomes overdetermined and a regression method has to be used for the optimum solution.  
When solving this nonlinear equation, it can be solved iteratively or non-iteratively. So, based on the type of equation being solved, point location can be divided into non-iterative methods and iterative methods.  
Non iterative methods include USBM [97; 98] and Inglada [99], while non-iterative methods include Geiger [100; 101], Thurber [102] and Simplex [103]. [PERSON] [67; 77] provided a systematic review of iterative and non-iterative methods.
## 4 Results and Discussion
### Rice Map Accuracy Assessment  
The accuracy assessment is a comparison of the classified rice map against ground-truth data. The spatial resolution of the rice maps ranges from 3 m to 15 m. However, the ANLD filtering processes (see Section 3.1) reduce the effective resolution by performing locally adaptive smoothing and edge detection. To account for this lower resolution and the horizontal accuracy of the handheld GPS units relative to the pixel size, the validation data were collected in areas that had homogeneous land cover in a 15-m radius around each GPS point for sites using 3-m resolution imagery and a 50-m radius for sites using 10-m or 15-m resolution imagery (see Section 2.2). The observed land cover at the GPS validation points was compared to the mode value of the rice map pixels within a window that matched the radius used in the validation land cover assessment. The accuracy assessment for the rice maps was conductedon a rice/non-rice basis, where all other land cover types were grouped into a single non-rice class. Across the 1,334 points, 696 (52.2%) were rice, 233 (17.5%) were other annual, perennial or plantation crops (such as banana, coconut, cotton, fruit orchards, cassava, corn, sugarcane, groundnut and black gram), 85 (6.4%) were built-up areas, 75 (5.6%) were forest, 72 (5.4%) were grassland, 35 (2.6%) were water bodies, with barren, mixed, shrublands, and others made up the remaining 10.3%. This rich non-rice dataset (638 points) could be further exploited in the future to assess the SAR signatures of other land cover types commonly found in rice-growing areas. The same signatures could also be used to generate new bounding limits (based on the temporal signatures for other crops and urban and water surfaces, for example) to further guide parameter selection in the rule-based classifier. Given the large amount of _in situ_ data collection across 13 sites and six countries, we adopted a rapid and practical approach to the sampling method for the map validation (see Section 2.2) as opposed to a regular sampling grid, for example. There is substantial variability in the spatial distribution of the validation points across the sites (Figure 5a-m), which reflects a combination of the following constraints: (i) variability in the level of road access; (ii) the human resources available; (iii) the local teams' familiarity with the landscape; and (iv) availability of ancillary spatial data to guide the team. In many cases (Site 1, Takeo, Site 6, Nam Dinh, Site 8, Cuddalore, Site 9, Thanjavur, Site 10, Sivaganga, Site 11, Muang Yang, Site 12, Suphan Buri, and Site 13, Nueva Ecija), additional validation dates were added to increase the spatial coverage of the points. In total, 37 days of travel and field data collection were required to collect 1334 validation points across the 13 footprints, an average of 36 points per day and 103 points per site (Table 5). It is clear that even this rapid appraisal approach was resource intensive, especially in areas where accessibility was limited.