esa-sceva/satcom-chunk-collection huggingface dataset:
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Document Pre-Processing in Short Text Clustering
#### 3.1.2 Stop-Word Removal  
Stop words are utilized as a grammatical function of the language when a document lacks context instead of specifying a semantic function or meaning. Stop words are considered less useful in text than other terms. Generally, they have a direct effect on the meaning of the text. In most cases, documents include many unnecessary words in English. Stop words are typically utilized by writers to improve the structure of their writing linguistically. Examples of stop words include demonstratives such as 'this', 'that' and 'those' and articles such as 'the', 'a' and 'an.' Removing stop words from a document is typical and positively affects document clustering. This is because the capacity of the terms' space is significantly reduced upon completion of word removal. Every language has its unique collection of stop words [56]. By removing these frequently used stop words from the text documents, the number of words each search term has to be matched against is reduced, significantly increasing the time it takes for queries to receive a result without affecting accuracy.  
These words often communicate more grammatical functions than semantic functions, which may increase the conversational or informative aspects of the document's content. Considering this, removing unnecessary words results in an improved ability to transmit the meaning of the text or document content and leads to an easier time understanding it using machine learning approaches. Many search engines have implemented stop word removal to help users or writers with queries obtain improved results by searching for information or meaning instead of searching for functional words [55]. Figure 5 displays the word document sample after removing stop words.  
Figure 4: Tokenization and normalization steps.  
Figure 5: Sample text after stop word removal.
# Random Noise Suppression Method of Micro-Seismic Data Based on CEEMDAN-FE-TFPF
## 2 Materials and Methods
### TFPF Method Based on CEEMDAN-FE  
In order to solve the influence of fixed window length for TFPF, [PERSON] et al. gave the selection rules of window length for TFPF [2; 5] as follows:  
\[1\leq W_{L}\leq\frac{0.384f_{s}}{f_{d}} \tag{21}\]  
where \(f_{s}\) is the sampling frequency and \(f_{d}\) is the main frequency.  
It can be seen from the above equations that the window length used by TFPF is directly proportional to the sampling frequency, and inversely proportional to the main frequency. If \(f_{s}\) increases or \(f_{d}\) decreases, the window length used should increase, and the value of window length is usually odd. Therefore, when TFPF is used to attenuate random noise in high frequency signals, a short window length should be used. In contrast, a long window length should be used for low-frequency signals. The flow of this method is as follows:  
1. The noisy data is decomposed by CEEMDAN into several IMFs, which are ordered from high frequency to low frequency.
2. The noisy critical point of the IMFs is judged by the discriminant factor. If it is higher than the critical point, it is considered that the IMFs needs to be denoised. In this way, the IMFs which need to be denoised are screened out. Therefore, the IMFs are divided into two groups, including the processing group for denoising and the residual group.
3. According to the principle of window length selection, the high-frequency IMFs of the processing group are denoised by TFPF with a short window length, and the low-frequency IMFs of the processing group are denoised by TFPF with a long window length.
4. The IMF processing group after denoising and the IMF residual group are used to reconstruct the denoised data. In this way, the purpose of random noise suppressing is achieved.
## 1 Introduction
Especially under long-term exposure to ultraviolet radiation and salt mist conditions in the marine environment, degradation may occur, weakening the protective and self-repairing functions of the coating [25]. In this study, a novel self-healing microcapsule was prepared using PMMA-DVB polymer, known for its excellent mechanical properties as the shell material, and EPR resin, which is non-toxic, exhibits good film-forming ability and weather resistance and has excellent compatibility with epoxy resin, as the core material. This microcapsule can effectively enable self-healing functionality in coatings. Through examining the influence patterns of process parameters on microcapsule properties, the optimal parameters for microcapsule preparation were identified. The particle size distribution, surface morphology, chemical structure, thermal stability, and self-healing performance of the microcapsules prepared under these optimized parameters were then tested and characterized. The successful preparation of this microcapsule enriches the types of microcapsules for self-healing coatings, provides a basis for the preparation of microcapsules for self-healing coatings, and is of great significance for enhancing the service life of surface coatings on steel structures of offshore drilling platforms.
# Megalos: A Scalable Architecture for the Virtualization of Large Network Scenarios +
## 7 Evaluating the Scalability
### Virtual LAN Scalability  
To demonstrate that Megalos can achieve scalability in terms of number of virtual LANs, we measured the amount of virtual LANs that can be deployed in a cluster. The results of the test were very positive. Indeed, we were able to start on a single worker more than 500K different virtual LANs distributed among 1000 VDs. However this does not come for free: starting a high number of virtual LANs on the same worker implies spending a considerable startup time. Indeed, our experiment took about 90 min on a worker equipped with dual Intel Xeon Gold 6126 CPU, 768 GB of RAM, 9\(\times\) SAS SSDs Raid 5, and Debian 9 as OS.
# Data-Decoupled Scattering Imaging Method Based on Autocorrelation Enhancement
## 4 Discussion  
The following conclusions can be drawn based on the reconstruction results presented in Section 3. The prior physical knowledge of autocorrelation is used as a constraint due to its distinct structural characteristics of scattered autocorrelation. However, for unconstrained models, the recovery results are indeed noise resistant, but they also increase the training time and the risk of overfitting. It also needs to overcome the limitations of the training data set. For this reason, the robustness to noise can be improved in speckle imaging, and the limitation of the data domain can be solved by constructing basic-element datasets to recover complex targets. For cases involving low signal-to-noise ratios, autocorrelation-enhanced recovery is necessary, first by employing a deep-learning model. The generator was built using U-Net and ResNet and included five jump layers to fuse high-level information with low-level features. The discriminator was a five-layer convolutional neural network. To further enhance the robustness of the model, basic elements were used in the training set because a complex target is geometrically derived from the simple target, which in turn achieves data decoupling and completes the autocorrelation output of  
\begin{table}
\begin{tabular}{c c c c} \hline  & \(PSNR\)/\(\mathbf{dB}\) & \(SSIM\) & \(MI\) \\ \hline U-Net & 23.62 & 0.73 & 0.76 \\ HNN & 25.48 & 0.81 & 0.91 \\ Ours & **35.22** & **0.95** & **1.21** \\ \hline \end{tabular}
\end{table}
Table 1: Test results for complex object datasets and comparison of related methods (_PSNR_, _SSIM_, and _MI_).  
complex target speckle images with a high signal-to-noise ratio. At the same time, target reconstruction is achieved in conjunction with traditional phase recovery.
# Upgrade of the Universal Testing Machine for the Possibilities of Fatigue Tests in a Limited Mode
## 8 TD1 and TD2 Transducer Calibration Processes
### TD1 Transducer Calibration Process  
For the calibration of the TD1 transducer, a specially developed single-purpose test jig according to Figure 7a with contact surfaces for clamping the TD1 transducer arms was clamped in the clamping jaws of the FPZ 100/1 machine instead of the test specimen. A total of two LARM MSL30.152 PA measurement probes [20] were integrated into this jig. The probes are used for precise measurement of length coordinates in the measurement range of 30 mm. The LARM measuring probe is equipped with a return spring with a defined maximum pressure of 1 N. The supply voltage of the probe is +5 V and its measuring step is 5 \(\upmu\)m. The optical information about the position of the measuring rod is transformed by electronic circuits into electrical pulses of incremental type. The obtained signal was processed by Matlab software. The test fixture together with the LARM probes and the TD1 transducer is shown in Figure 7b. The electrical diagram of the interconnection of the FPZ 100/1 machine, the MF624 I/O card, the PC, the Signal Conditioning Module SCM and the pair of LARM MSL30.152 PA measurement probes for the purpose of calibration of the TD1  
Figure 6: Transducers TD1 and TD2 for measuring absolute elongations \(\Delta l\) based on inductive transducers, used in calibration and low-cycle fatigue analysis processes: (**a**) TD1 in the free unapplied position; (**b**) TD2 in the calibration mode.  
transducer is shown in Figure 8. The output of the LARM probes was fed to the MF624 I/O card via its incremental sensor inputs during the measurement.  
The correct function of the extensometer was verified by several measurements on two measured lengths of the fictitious measurement base \(l_{0}\) 20 mm and 100 mm and at all available Scale Factors SF of 10:1, 20:1 and 40:1. During the calibration, the elongation values \(\Delta l\) [mm] from the FPZ 100/1 machine indicator and the related length change values in the electrical voltage variable \(U_{\Delta l}\) [V] simultaneously recorded by the I/O card in the PC and the elongation or length change values \(\Delta l\) [mm] obtained from the LARM MSL30 sensors were recorded. To create the calibration dependencies and curves, the \(\Delta l\) values obtained from the FPZ 100/1 machine indicator had to be converted to units of [mm] mm using the appropriate Scale Factors SF. The values so adjusted were plotted on a graph versus the values of the change in length \(\Delta l\) [mm] obtained from the LARM MSL30 sensors. Detailed calibration results are given in a separate chapter.  
Figure 8: Electrical diagram of the interconnection of the FPZ 100/1 machine, I/O card MF624, PC, Signal Conditioning Module SCM and a pair of LARM MSL30.152 PA measurement probes for the calibration of the TD1 transducer.  
Figure 7: (**a**) Test jig with integrated LARM MSL30 measurement probes for the TD1 transducer calibration process; (**b**) test fixture clamped in the jaws of the FPZ 100/1 in the TD1 transducer calibration process.
## 2 Literature Review
### Cost Estimation Techniques  
Empirical studies indicate that the size of a project team and the project budget are key structural variables affecting the quality of the NPD process and success of the project [7]. An investigation of new product forecasting practices in industrial companies shows that the most popular techniques include customer/market research, looks-like analysis, trend line analysis, moving average, scenario analysis, and multi-attribute models [8; 9]. To a lesser extent, companies use forecasting techniques related to nonlinear regression, expert systems, neural, and neuro-fuzzy networks [8; 10].  
Cost estimation techniques can be divided into the following groups [11; 12]: intuitive, analogical, parametric, and analytical. Intuitive methods use past experience of an estimator. Analogical methods  
Figure 1: Product development cash flow.  
estimate the cost of new products using similarity to previous products. Parametric methods estimate the cost of a new product from parameters that significantly influence the cost. In turn, analytical methods estimate the cost of a product by decomposing product development into elementary tasks with the known cost.  
Parametric estimation techniques may be based on regression analysis [13,14], artificial neural networks [15,16], fuzzy logic systems [17,18], or hybrid systems such as neuro-fuzzy systems [16,19] and genetic fuzzy systems [20,21]. Engineering approaches are predicated on a detailed analysis of product features and the manufacturing process. For example, the cost of a new product is calculated in this approach as the sum of the resources used to design and produce each component of the product (e.g., raw materials, labor, equipment). As a result, the engineering approach is best used in the final phases of product development, in which the product and the manufacturing process are well defined.
# Coded-GFDM for Reliable Communication in Underwater Acoustic Channels
## 3 System Architecture
### Shallow Underwater Acoustic Channel  
The underwater acoustic channel has both time selective as well as frequency selective characteristics making it doubly selective [31]. Due to the low speed of sound, multipath and ambient noise, the bandwidth is severely limited [32]. The delay spread of the underwater channel is usually between 10 ms to 50 ms and sometimes as large as 100 ms [33]. A classic underwater channel with \(L\) time shifted paths [31] is given as:  
\[H(t,\tau)=\sum_{x=1}^{L}A_{x}(t)\delta(\tau-\tau_{x}(t)) \tag{10}\]  
where \(A_{x}(t)\) represents the amplitude, whereas \(\tau_{x}(t)\) is \(x\)th multipath and \(\delta(t)\) is the Dirac delta function. The channel envelop response can be divided into two components, the deterministic component and a random fading component [8]. This channel model is close to a real underwater channel as it contains absorption and angular pathloss, multipath fading in addition to frequency dependent noise of a realistic underwater channel. The mathematical model utilizes Rician distribution for multipath fading. For the deterministic component, the absorption is modeled as a wave propagation equation and the transfer function now becomes \(H_{a}(f,d)\)[34; 35] which is written as:  
\[H_{a}(f,d)=A_{d}e^{-\gamma(f)d} \tag{11}\]  
where \(\gamma\) is the sum of absorption coefficient and phase constant while \(A_{d}\) represent the scaling constant. It was observed in several studies that the multipath fading in a shallow underwater channel is better represented and modeled using Rician distribution [36; 37]. We use \(k=2.0;\ m=0.4\)[36] as Rician fading model parameters. The noise observed in shallow underwater acoustic channels is impulsive in nature [38] and a number of techniques have been proposed for modeling it including colored and white noise models. Comparing traditional models against the experimentally observed noise sampling reveals that a Gaussian distribution model cannot statistically represent data affected by impulsive noise whereas, stable distributions are more appropriate [39] for noise estimation purposes. Underwater ambient noise is mainly a combination of four sources including shipping activity, thermal, waves, and noise due to turbulence. The frequency dependent noise is added to the faded signal, whose power spectral density [40] is given by:  
\[N(f)=10\log\biggl{(}10^{\frac{N_{b}(f)}{10}}+10^{\frac{N_{m}(f)}{10}}+10^{ \frac{N_{m}(f)}{10}}+10^{\frac{N_{m}(f)}{10}}\biggr{)} \tag{12}\]  
where \(N_{s},\ N_{tu},\ N_{w}\), and \(N_{th}\) are the values associated with shipping, turbulence, wave, and thermal noise, respectively.
## 2 Materials and Methods
### PSSM Feature Extraction
#### 2.3.1 AADP-PSSM  
The AADP-PSSM [20] method combines AAC and dipeptide composition (DPC) features derived from the normalized PSSMs.  
AAC-PSSM: For each protein sequence, AAC-PSSM is calculated by averaging the normalized PSSM values across all positions for each of the 20 standard amino acids, resulting in a 20-dimensional feature vector.  
\[\text{AAC-PSSM}_{i}=\frac{1}{L}\sum_{j=1}^{L}P_{ji}^{\text{norm}}\quad\text{ for }1\leq i\leq 20\]  
where \(L\) is the length of the protein sequence, and \(P_{ji}^{\text{norm}}\) is the normalized PSSM value at position \(j\) for amino acid type \(i\).  
DPC-PSSM: DPC-PSSM captures the frequency of dipeptide combinations within the evolutionary context. It is calculated by averaging the product of normalized PSSM values for adjacent amino acid pairs across all positions, resulting in a 400-dimensional feature vector.  
\[\text{DPC-PSSM}_{i,j}=\frac{1}{L-1}\sum_{k=1}^{L-1}P_{k,i}^{\text{norm}}\cdot P _{k+1,j}^{\text{norm}}\quad\text{for }1\leq i,j\leq 20\]  
where \(L\) is the length of the protein sequence and \(P_{k,i}^{\text{norm}}\) and \(P_{k+1,j}^{\text{norm}}\) are the normalized PSSM values at positions \(k\) and \(k+1\) for amino acid types \(i\) and \(j\), respectively.  
The final AADP-PSSM feature vector is a concatenation of the AAC-PSSM and DPC-PSSM features, resulting in a 420-dimensional feature vector.
# Characterization of the Key Aroma Compounds in Different Aroma Types of Chinese Yellow Tea
## 2 Materials and Methods
### Qualitative and Quantitative Analysis  
The volatile compounds were analyzed by the HS-SPME/GC-MS method according to our previous study [12]. Briefly, each sample was initially ground and homogenized. The tea powder (1 g), NaCl (0.5 g), and ethyl decanoate (10 \(\mu\)L, 8.64 mg/L) were introduced into a 20 mL headspace bottle and infused with 5 mL of boiling water. The vial was immediately sealed and kept at 80 \({}^{\circ}\)C for 10 min. A 65-\(\mu\)m polydimethylsiloxane/divinylbenzene (PDMS/DVB) fiber (Supelco, Bellefonte, PA, USA) was exposed to the sample for 40 min. Next, the SPME fiber was inserted into GC injection port (230 \({}^{\circ}\)C for 5.0 min) and subsequently analyzed. Each sample was analyzed three times.  
GC conditions: An Agilent 7890B GC system coupled with an Agilent 5977A MSD mass spectrometer (Agilent, Santa Clara, CA, USA) was used. An Agilent HP-5 MS capillary column (30 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\mu\)m film thickness) was used for separation; the carrier gas was helium (purity \(>\) 99.999%) with a constant flow rate of 1 mL/min. The injection mode was set to splitless. The oven temperature was initially held at 50 \({}^{\circ}\)C for 3 min, raised to 170 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min, raised to 190 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min, then raised to 250 \({}^{\circ}\)C at a rate of 15 \({}^{\circ}\)C/min, and finally held at temperature for 2 min. MS conditions: EI ionization energy, ion source temperature, quadrupole temperature, and mass scanning range were set to 70 eV, 230 \({}^{\circ}\)C, 150 \({}^{\circ}\)C, and 35-400 atomic mass units (amu), respectively. Retention indices (RIs), authentic standards, and mass spectra matching in the standard NIST17 library were used for identification. RIs were calculated after analyzing C7-C40 n-alkane series under the same chromatographic conditions. Moreover, some commercially available standards were applied to verify the analysis results under the same conditions. The volatile compounds with available standards were quantitated according to our previous study. For the volatile compounds without the available standards, the quantitation was carried out using the standard that had the same carbon atom or a similar functional structure [12].
# Event-Related Coherence in Visual Cortex and Brain Noise: An MEG Study
## 2 Materials and Methods
### MEG Acquisition  
MEG recordings were performed with an Elekta-Neuromag system with 306 channels that was housed in a magnetically shielded room at the Centro de Tecnologia Biomedica, Universidad Politecnica de Madrid. The head position was continuously tracked with head position indicator (HPI) coils and co-registered in the device and head coordinate system with three fiducial points (nasion, left, and right preauricular points) and around 300 scalp surface points digitized by a Polhemus Fastrak system. A vertical electrooculogram (EOG) and electrocardiogram (ECG) were placed to capture eye blinks and cardiac activity, respectively. The data were sampled at 1000 Hz.  
The experiments for all 17 subjects lasted 4 days. Along with MEG recordings of the subjects, the MEG data were also collected daily in an empty room. All data were passed through an online anti-alias bandpass (0.1-330)-Hz filter. MaxFilter software was used for the temporal signal-space separation (tSSS) to reduce magnetic interference and perform head movement compensation. A 56-ms delay between event triggers and the actual stimulus was measured separately using a photodiode.
# Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees
## 4 MADM Handover Algorithm  
Having described the operation of the network architecture model as well as the WBAN traffic categories, we now present the proposed MADM approach, including the following steps. In the first step, the algorithm will monitor and collect the information that is related to the QoS of the running application requirements, and the QoS attributes are presented in detail. Then, we discuss the handover decision making that accounts for the QoS application requirements to choose the most efficient network connection. The algorithm also considers the weight vectors for the classified WBAN applications that are computed by using a pairwise comparison of the attributes.
## 4 Experiments
### Datasets
#### 4.1.2 SWaT  
The Secure Water Treatment (SWaT) dataset is a water treatment testbed, which contains data from the water treatment plant and water treatment process generated. It includes 7 days of normal data with 496,800 samples and 4 days of abnormal data with 449,919 samples. The abnormal data contain 36 attacks in total. The number of sensors and actuators in the testbed for data collection is 51.