esa-sceva/satcom-chunk-collection huggingface dataset:
# Near Infrared Illumination Optimization for Vein Detection: Hardware and Software Approaches
## 1 Introduction
There has also been substantial work in designing mobile devices with biomedical and biometric capabilities. Although various vein viewer devices are commercially available, the high cost has become the bottleneck for making the assisted devices used extensively by medical practitioners in hospitals and clinics. Most commercial vein viewers were developed by using complex systems with the integration of laser scanning mechanisms making the cost for fabrication of such devices higher. Hence, the alternative solution is to utilize low-cost near-infrared vein medical imaging. To date, various approaches in hardware and software by the utilization of low-cost commercial off-shelf electronic sensors are still progressing [9,10].
# Topic Word Embedding-Based Methods for Automatically Extracting Main Aspects from Product Reviews
## 1 Introduction
Empirically, the number of topics that yields the highest topic coherence value is chosen while the topic model with different numbers of topics is executing iteratively. * Secondly, we propose a novel Topic Cleaning (TC) method that correct raw topics output by the existing topic model through topic splitting and merging processes. To split and merge incorrect topics, we propose a new post-processing method based on the word embedding and unsupervised clustering techniques in which we consider word2 vec [3], GloVe [4], fastText [5], ELMo [6], and BERT [7] as the word embedding model and Density-based Spatial Clustering of Applications with Noise (DBSCAN) [8] as the unsupervised clustering method. Figure 1: The overall system of the proposed approach. Next, we propose three automatic methods of automatically labelling clean topics after topic splitting and merging. Each topic is given a label (title) of the topic that represents the overall meaning of the topic as a single keyword. It is also assigned sub-labels that assist the semantics of the topic. Our proposed methods are based on (1) topic words, (2) head words obtained by dependency parsing of the sentences related to a given topic, or (3) a hybrid of topic and head words. To the best of our knowledge, the proposed approach is the first study to (1) develop word embedding-based topic coherence measure, (2) to correct dirty topics generated by an existing topic model using the word embedding and unsupervised clustering techniques, and (3) to generate main and sub aspects based on clean topics and dependency parsing of topic sentences. In addition, to find top-\(k\) sentences relevant with a given topic, we propose a new probabilistic model based on Maximum Likelihood Estimation (MLE). Our experimental results show that the proposed approach outperforms LDA as the baseline method. The remainder of this article is organized as follows--in Section 2, we introduce both background knowledge and existing methods related to this work. In particular, we discuss the novelty of our method, in addition to main differences between previous studies and our work. In Section 3, we describe the details of the proposed method and explain/analyze experimental set-up and results in Section 4. We also discuss how the results can be interpreted in perspective of previous studies and limitations of the work in Section 5. Finally, we summarize our work and future direction in Section 6.
# Ground-Based Cloud Image Segmentation Method Based on Improved U-Net
## 2 Materials and Methods
### Decoder  
The goal of the decoder network is to obtain cloud mask output images of the same size as the input images to the encoder module. The decoding module in this paper consists of four up-blocks and a 1 \(\times\) 1 convolution. Among them, each up-block module includes two convolutional layers with dimensions of 3 \(\times\) 3 and adds batch normalization processing and activation layer functions. Advanced cloud feature alternates through up-sampling layers and up-block convolution blocks, finally obtaining a cloud mask of size 128 \(\times\) 128, with the same resolution as the input image.  
Compared with non-pooling, up-sampling not only preserves the position information but also fills reasonable values during the interpolation process. Compared with deconvolution, up-sampling has better computational efficiency [22]. To improve the resolution of features, this paper uses bicubic interpolation to replace the traditional transposed convolution up-sampling method.  
Bicubic interpolation is an interpolation method based on cubic polynomials. It considers the pixel values of a 4 \(\times\) 4 grid surrounding the target pixel and calculates the interpolation result based on the grayscale values and positional relationship of the 16 points. Assume that \(f(x,y)\) is the pixel value function of the input image. For the target point \(P\), it is obtained by weighting and averaging the 4 \(\times\) 4 sampling points in the rectangular grid, so the interpolation function is  
\[P(x,y)=\sum_{i=0}^{3}\sum_{j=0}^{3}a_{ij}x^{i}y^{j} \tag{1}\]  
where \(i\) and \(j\) represent the horizontal and vertical positions of the image respectively, and \(a_{ij}\) represent the weighting coefficient. The solution equation is as follows:  
\[W(x)=\begin{cases}(a+2)|x|_{3}-(a+3)|x|_{2}+1&\text{for }|x|\leq 1\\ a|x|_{3}-5a|x|_{2}+8a|x|-4a&\text{for }1<|x|<2\\ 0&\text{otherwise}\end{cases} \tag{2}\]  
where \(a=-0.5\).  
After the above calculation, \(P(x,y)\) is the desired result of bicubic interpolation.  
Therefore, the bicubic interpolation method considers the 4 \(\times\) 4 sampling points around each target pixel in the cloud image. First, it calculates the weighted interpolation in the horizontal direction using cubic polynomials based on distance, and then, in the vertical direction, it calculates the final interpolation result using the weights based on the intermediate results obtained from the horizontal interpolation. The method is used to increase the image scale by increasing its resolution while minimizing the loss of image quality that occurs due to the enlargement of the image.
# Preamble-Based Noncoherent Synchronization in Molecular Communication: A Machine Learning Approach
###### Abstract
Additionally, we investigate method to enhance synchronization performance by correcting errors in the preamble signal using machine learning (ML). As mentioned earlier, methods for mitigating channel delay and ISI are crucial for MC channels. In an MC channel, molecules travel through diffusion-a process characterized by random migration without a specific direction or velocity. During this process, molecules may naturally degrade, and the chemical reactions between molecules and ligand receptors at the receiving end are not strictly proportional to the concentration of molecules. These factors introduce nonlinearities in the MC channel. Due to this nonlinear nature, traditional linear mathematical modeling approaches, such as linear approximation [20] and parameter optimization through mathematical modeling [21; 22], face limitations in improving communication performance. To address these challenges, ML has been applied to model and enhance MC [23; 24]. ML methods can capture both linear and nonlinear channel characteristics, quantify the effects of the channel, and estimate parameters such as channel delay and ISI impact. Based on these learned patterns, errors occurring during data reception can be corrected, resulting in improved synchronization performance. Simulation results show that as the transmission distance increases, the influence of noise and channel effects reduces accuracy. However, ML compensates for these issues by learning nonlinear properties, ultimately improving synchronization performance. The structure of this paper is as follows: Section 2 discusses the characteristics of diffusion-based channel and concentration-encoded MC. Section 3 explains the preamble signal structure and the proposed synchronization method in MC system. Section 4 presents the ML structure and methodology. Section 5 evaluates the experimental performance of the proposed method. Finally, Section 6 concludes the paper.
## 5 Conclusions  
Artificial intelligence, especially neural networks, has been increasingly utilized in ultrasonic signal classification. Conventional approaches necessarily require us to select and extract either time- or frequency-domain features from original ultrasonic signals manually for training the neural network model. However, this procedure is very subjective, empirical and time-consuming, and in most cases, it is very difficult to extract the appropriate features that are most relevant to the defects to be detected. In this paper, an automatic classification method using a convolutional neural network is proposed. The novelty of this work is to accomplish the automatic extraction of features from the original ultrasonic signals with raw time-domain data as input, avoiding the necessity and inaccuracy caused by manual feature selection.  
An ultrasonic detection experimental setup was first used for data collection from the specimen with internal slots. Through the optimization of network structures, a network structure equipping the configuration of 500 neurons in fully connected network hidden layers was utilized for signal classification. To further validate the effectiveness of the proposed method, the handcrafted features either in the time- or frequency-domain approach were employed for comparison. The results demonstrate that the recognition rates in each individual domain are lower than the proposed method but the fusion performance can slightly outperform the accuracy of the proposed method. The proposed method shows good robustness and avoids the massive workload of manually extracting features. Finally, we visualized classification results to present and characterize detailed information about defects. Compared with the traditional C-scan based on amplitude of collected signals, our method is able to provide a higher credibility of C-scan visualization for defect determination.  
The main objective of this work is to prove the feasibility and effectiveness of the proposed method, and therefore, only the specimen with internal slots is investigated. In real industrial applications, as long as sufficient data with regard to the various defect types (dimensions, orientations, locations and shapes) can be acquired, the developed method can achieve the accurate classification of corresponding defects.  
Conceptualization, Y.S.; Data curation, W.X.; Methodology, Y.S.; Validation, W.X.; Writing--original draft, Y.S.; Writing--review & editing, W.X., J.Z. and X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (Grant No. 2018 YFB1106100).  
The data presented in this article are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Tripartite Quantum Key Distribution Implemented with Imperfect Sources
## 5 Estimation of Key Rate  
Note that in the previous section, for simplicity of this analysis, it is assumed that [PERSON] has a single-photon source. However, it must be emphasized that this analysis can be applied to the case where [PERSON] uses an SPDC source that occasionally emits more than one photon in each mode. The signals produced by this source contain photons with a probability distribution provided by [49; 52]  
\[p(k|\gamma)=\frac{\gamma^{k}}{(1+\gamma)^{k+1}}, \tag{28}\]  
where \(k\) is the number of photons and \(\gamma\) is the average intensity of the laser source. In this case, the decoy-state method is employed to estimate all of the quantities corresponding to the single-photon pulses needed to apply the loss tolerant method. In the decoy-state method, in order to mitigate against a possible photon-number splitting attack [PERSON] prepares photons using the intensities \((\mu,\
u,\omega)\), where \(\mu\) denotes the intensity of the signal states, \(\
u\) represents decoy states, and \(\omega\) denotes vacuum states [47]. These intensities are typically chosen according to \(P_{\mu}>P_{\
u}>P_{\omega}\), whereby \(P_{\mu},P_{\
u}\) and \(P_{\omega}\) correspond to the probabilities for the signal, decoy and vacuum states, respectively. Therefore, the corresponding key generation rate for the proposed RFI QKD protocol is provided by [11; 14; 39]\[r=Q_{Z_{A}Z_{B}Z_{C}}^{\mu,1}(1-I_{E}^{U})-f_{EC}Q_{Z_{A}Z_{B}Z_{C}}^{\mu}\max\{h(E _{Z_{A}Z_{B}}^{\mu}),h(E_{Z_{A}Z_{C}}^{\mu})\}, \tag{29}\]  
where \(Q_{Z_{A}Z_{B}Z_{C}}^{\mu,1}\) is the gain of signals prepared using the \(Z\) basis and intensity \(\mu\), and measured by [PERSON] and [PERSON] in the \(Z\) basis. The term \(Q_{Z_{A}Z_{B}Z_{C}}^{\mu,1}\) denotes the gain of single-photon components. The parameters \(E_{Z_{A}Z_{B}}^{\mu}(E_{Z_{A}Z_{C}}^{\mu})\) correspond to the QBER between [PERSON] and [PERSON] ([PERSON]) for signals transmitted using intensity \(\mu\) and measured in the \(Z\) basis. Here \(h(x)=-x\text{log}_{2}(x)-(1-x)\text{log}_{2}(1-x)\) represents the Shannon binary entropy. Moreover, the term \(f_{EC}\geq 1\) denotes the error correction efficiency and \(I_{E}^{U}\) denotes the upper bound on the knowledge gained by [PERSON] about the key. The relevant parameters in Equation (29) are derived in Appendix A. The key rate formula in Equation (29) is a direct generalization of the bipartite scenario in Ref. [39] to the tripartite case. In addition, note that the key difference between the key rate formula for the three-party RFI QKD and the one in Refs. [11, 14] lies in the method used to estimate [PERSON]'s knowledge about the key.
## Nomenclature  
\begin{tabular}{l l l l} Symbols & Description & Symbols & Description \\ \(E\) & Electric field [V/m] & \(u(z)\) & Function number of different inputs \\ \(I\) & Electric current [A] & \(V\) & Voltage [V] \\ \(I\) & Current density [A m\({}^{-2}\)] & Voc & Open circuit voltage [V] \\ \(k^{\prime}\) & Thermal conductivity [W/m K] & \(Z_{p,n}\) & Figure of merit of p-type and n-type junctions [1/K] \\ \(K_{n}\) & n-type Thermal conductivity [W m\({}^{-1}\)K\({}^{-1}\)] & \(\varmathbb{Z}T\) & Figure of merit, Dimensionless [unit Less] \\ \(K_{p}\) & p-type Thermal conductivity [W m\({}^{-1}\)K\({}^{-1}\)] & \(\alpha\) & Seebeck coefficient [V K\({}^{-1}\)] \\ \(Q_{C}\) & Dissipated heat of TEG [W] & \(\alpha_{n}\) & Seebeck coefficient of n-type thermoelements [V K\({}^{-1}\)] \\ \(Q_{L}\) & Absorbed heat of TEG [W] & \(\alpha_{p}\) & Seebeck coefficient of p-type thermoelements [V K\({}^{-1}\)] \\ \(R\) & Electrical resistance [\(\Omega\)] & \(\varmathbb{\Delta}T\) & Temperature difference [K] \\ \(\overline{T}\) & Mean temperature & \(\varmathbb{\Delta}V\) & voltage difference [V] \\ \(T_{c}\) & Cold side temperature [K] & \(\lambda\) & Thermal conductivity [W/m K] \\ \(TEG\) & Thermoelectric generator & \(\sigma\) & Electrical conductivity [\(\Omega^{-1}\) m\({}^{-1}\)] \\ \(T_{h}\) & Hot side temperature [K] & \(\eta_{max}\) & Maximum efficiency [\%] \\ \(u_{n}\) & Standard uncertainty & \(y_{1}\), \(\Delta y_{2}\) & Potential errors \\ \end{tabular}
## 2 Research Methods  
Figure 1 shows the specific appearance of roof damage after a typhoon. In Figure 1a, the roof is clearly divided into three parts. The left part with pink background represents the roof without damage, the middle part with purple background is the roof covered with blue tarps (coverage area of blue tarps increases from left to right), and the right part with green background is the roof completely destroyed (function of the roof was completely lost, thus, was not necessary to cover using blue tarps). Thus, With the direction of the red arrow in the Figure 1, the degree of roof damage increases. In Figure 1f, according to \"the guideline for disaster prevention\" [3] by the Japan cabinet office, the level of roof damage after a typhoon is divided into five levels: levels 0-4. The roof without damage is level 0. The roof covered with blue tarps is divided into levels 1-3 according to the proportion of blue tarps in the roof. Finally, the roof completely destroyed is classified as level 4. The specific distinction is shown in Figure 1.  
In this study, it was found that dividing roofs (for instance, the roof which is neither covered with blue tarps nor completely destroyed, but with some holes,) into two types can greatly improve the accuracy of classification. One type is a roof with a small hole (less than approximately 5% of the roof area). The hole will not affect the normal life of residents and thus, it can be marked as \"roof without damage\". The other roof type can be marked as \"roof completely destroyed\" in the case of a roof with huge holes (more than 80% of the roof area). This is because such holes are not covered with blue tarps. Such houses are completely unfit for living, or the residents have moved to another place during  
Figure 1: Appearance and level of roof damage in Typhoon Faxai in 2019. The left part with pink background represents the roof without damage, the middle part with purple background is the roof covered with blue tarps, and the right part with green background is the roof completely destroyed. Thus, With the direction of the red arrow in the Figure 1, the degree of roof damage increases.  
the field investigation [15]. Such a classification method may lead to detection omission, such as, classifying an actual \"roof with damage\" as \"roof without damage;\" however, it is beneficial to the overall accuracy of \"roof completely destroyed\".  
This study was mainly divided into three parts. First, based on the aerial photos taken after the typhoon Faxai, the images of roofs were obtained. Thereafter, the obtained images were used to render samples for training a deep learning model. Finally, the model was used to automatically detect three types of objects: blue tarps, roof outline, and roofs completely destroyed, and the level of roof damage was classified by combining three types of objects (Figure 2).
## 2 Electronic Area Protection  
The main goal of electronic area protection is to catch the trespasser before or directly intruding into the protected area. Secondly to locate and monitor its movement with technical tools to eliminate it or prevent its illegal activity. If we detected the intruder before breaking, or just after breaking into the territory of the interest, the security service would have enough time to react and eliminate the given threat.  
Such devices and systems are required problem-free functionality and reliability. The reliability of the system is affected, not just by the quality of components, but by their resistance to the wind and other environmental effects, especially if these devices are located within the reach of people or animals. These conditions could be the source of unforeseen complications. It is important to prevent these types of malfunctions by placing detectors outside the visible perimeter or using the appropriate masking to blend to the surrounding environment. It is important to use high-quality sensors and software, which ensure the correct detection of space violations without false alarms and malfunctions. The external electronic area protection has to be designed and constructed correctly to fulfill its purpose. Technological progress makes it possible to enter protected areas in new ways.  
New systems and equipment could easily eliminate security. Staying up-to-date is necessary when researching and developing new systems and sensors for external use. Various intrusion detection systems are used to secure a defined guarded area of interest. According to the principle used to detect intrusion into the protected area, we can operate sensors with infrared detectors, microwave detectors, accelerometers, magnetometers, sound detectors, various hygrometers and thermometers detectors, microwave barriers, ultrasonic sensors and optical laser sensors, FMCW (Frequency Modulated Continuous Wave Radar), radar systems that measure the distance and speed of moving objects and Fence intrusion detection systems which use a various combination of detection sensors.
## 4 Case Study
### Solution of Non-Splittable Model Under Adequate and Insufficient Capacity Scenarios
#### 4.2.1 Train Transportation Mode Selection Results  
The results of the mode selection for 12 trains and the number of special trains were obtained using the non-splittable model (M3), as shown in Table 12. Under the adequate capacity condition, 11 passenger trains are used with the \"passenger carry\" mode, no confirmation trains are employed, and no special freight trains are set. This choice is due to the high management costs associated with confirmation, reservation, and special train modes. Additionally, using reservation or special train modes could potentially result in passenger capacity loss. To minimize operating costs and impacts on passenger capacity, the passenger carry mode was chosen when its capacity allows.  
In the insufficient capacity scenario, to meet transportation demand, one additional express freight train was added. The confirmation train mode was used for express delivery, with nine passenger trains operating in the passenger carry mode and two trains in the reservation mode.
## 4 Discussion
### Effectiveness of the Optimal PLSR Model Constructed Based on FOD Spectra
Additionally, spectral indices based on low-order FOD spectra have been shown to effectively capture variations in leaf dry matter content [30]. These findings suggest that low-order derivatives enhance spectral features related to leaf biochemical composition, including chlorophyll content and structural features, which are critical for ChlF estimation. Therefore, low-order FOD spectra serve as an effective tool for capturing ChlF parameters. In our previous study, we demonstrated that novel spectral indices derived from low-order FOD spectra enhanced the estimation of ChlF parameters, surpassing the performance of empirical spectral indices [32]. However, the study also highlighted the need to consider species specificity and leaf type (sunlit and shaded). Consequently, the generalizability of retrieving ChlF parameters based on spectral indices remains constrained. The present study builds upon our previous research by demonstrating the potential of low-order FOD spectra in capturing ChlF parameters and by showing that the optimal PLSR model mitigates the effects of species specificity and spatiotemporal heterogeneity, achieving satisfactory prediction results in the test dataset. Table 1 presents the predictive performance of the optimal PLSR model across different species. The results indicate that, despite species-specific variations, the model maintained reasonable predictive accuracy for the majority of ChlF parameters, demonstrating its robustness in handling species heterogeneity. However, differences were observed, particularly for _P. japonica_, where the model performed poorly. This may be due to its unique biophysical and biochemical traits as a low evergreen shrub, which is likely to exhibit different response mechanisms compared to other species, thereby affecting the ability of reflectance spectra to accurately capture ChlF parameters [20]. While the findings demonstrate the robustness of the method in a temperate deciduous forest, further validation in different ecosystems, such as evergreen forests, grasslands, and agricultural crops, would be beneficial.
## 3 Principals of Routing Protocols and Their Breakdowns
### Energy Aware Multi-Path Routing Protocols
#### 3.1.18. Rer [41]  
The reliable and energy-efficient underwater routing protocol (RER) [41] establishes a path from the source to the destination to reduce transmission latency. The source node initially sends an RTS packet to the neighboring nodes, and the adjacent nodes return the packet transmission delay to the source node. The source node evaluates packet transmission delays and chooses the hop with the shortest transmission delay as the next hop. Every adjacent node receives a broadcast of the newly formed route.