esa-sceva/satcom-chunk-collection huggingface dataset:
# Generating Activity-Based Mobility Plans from Trip-Based Models and Mobility Surveys
## 2 The Related Literature and Problem Statement
### The Related Literature
#### 2.1.1 Trip-Based Mobility Models  
The traditional trip-based model predicts aggregated traffic flows. It consists of four steps, namely trip generation, trip distribution, mode choice, and assignment. The modeled area is discretized into traffic analysis zones (TAZs), and the time of day is discretized into time bins. The trip generation step determines the number of trips originating from or terminating in each TAZ based on its sociodemographic and spatial attributes. During trip distribution, origins and destinations are linked to create trips. This step results in an origin-destination matrix, quantifying the traffic flow between any two TAZs. During mode choice, the trips are distributed onto the available modes of transport based on distance or travel time. The assignment step assigns the modal traffic flow between the TAZs to the transport network, resulting in a prediction of the aggregated traffic flow on each network link. Any interested readers can refer to [PERSON] and [PERSON] [41], who provide further information on the four-step model. Disaggregated versions of the traditional model differentiate between different groups of persons or even individuals to allow for a more realistic assignment of activities and modes, rather than assuming everyone in a TAZ behaves the same way. These modifications are made to overcome some of the traditional model's disadvantages, such as the aggregation bias, yet do not provide spatially or temporally consistent mobility plans on a microscopic, individual level [42]. Tour-based models additionally prevent the traditional model's knowledge loss and reduce inconsistencies. Rather than considering trips independently, they are combined into tours starting and ending at home. Thus, all trips can be traced back to households with their associated sociodemographic properties. Constraints ensuring the consistency of the sequence, location, time, and mode of trips may be introduced [7; 43].
## 5 Conclusions  
In this study, a detailed review of the image processing techniques for satellite image analysis is presented. This work attempts to provide an insight into the importance of image processing techniques in remote sensing applications. A roadmap of the research activities in various image processing stages have been explained with relevance to remote sensing applications. With the emergence of technology and frequent availability of satellite imagery, image processing in remote sensing is a topic of growing interest. The survey explores various image processing techniques and points out the advantages and limitations associated with these methods. Further the techniques specific to various applications are discussed and the performance measures are explained. Low contrast of satellite imagery, improper threshold selection for image segmentation, misinterpretation of image pixels as part of change detection are some of the challenges in image processing. The complexity of satellite image further poses a difficulty in real world classification applications and can be solved by involving computationally intelligent paradigms like machine learning. In current scenario, machine learning is an evolving research area with its ability to make data driven decisions effectively and learn and perform intelligently. The quality of the input image and the complexity of image features are some factors responsible for deciding the image processing technique to be applied. Researches are currently being extended into hybridized image processing techniques to improve the robustness of the existing techniques. The future work can be extended to apply the explained techniques in various practical areas in remote sensing and extending the implementation of quantum algorithms for remote sensing applications.  
[PERSON] performed the formal analysis; [PERSON] supervised and designed the methodology; [PERSON] performed the validation; [PERSON] and A.N. analyzed the results; [PERSON] performed the investigation and data curation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Colorectal Cancer Detection Tool Developed with Neural Networks
## 2 Materials and Methods
### CNN-Based Tool  
The training of the CNN utilized the following architecture. Initially, three convolutional layers and three pooling layers are employed, with the incorporation of normalization and ReLU (rectified linear unit) layers. The first convolutional layer is characterized by a 3-by-3 filter size and eight filters, with the padding option set to'same.' The second convolutional layer maintains the same filter size, but the number of filters is augmented to 16. The final convolutional layer contains 16 filters, each set to the same dimensions of 3 by 3. Maximum pooling is employed in all pooling layers, with dimensions set at 2 by 2.  
After this segment, a fully connected layer is implemented, accompanied by the integration of classification and softmax layers. The configuration of these layers is determined by the number of outputs utilized, with 2 or 3, respectively. The dimensions of the images are required as inputs for the input layer. The number of labels (outputs) is required for the fully connected layer.  
ReLU layer: The role of the ReLU layers is to linearize the computation increase, which without these layers would grow exponentially.  
Normalization layer: The normalization layer is used to normalize the output of the previous layers; it also allows the network to learn more independently from layer to layer.  
Convolutional layer: The convolutional layer is the main building block of any CNN, and it is usually a set of filters that are determined while the network is trained. Due to the convolutional layers creating feature maps, with each convolutional layer the dimensions of the maps increase.  
Pooling layer: To reduce the dimensions of the feature maps, pooling layers are used.  
A fully connected layer is a feed-forward neural network that has as its input the flattened output of the final convolutional and pooling layer.  
Classification layer: usually used to compute the cross-entropy loss for classification tasks.  
Softmax layer: used to predict a multinomial probability distribution for multiclass classification problems where two or more class labels exist.  
After the architecture is set, some parameters need to be given, such as the initial learning rate, set at 0.01, and the number of epochs, set at 100. The data are then shuffled around after every epoch, mainly to improve accuracy. Then, the validation data are given; for every type of NN, there is a requirement of having training data and validation data. Lastly, validation is performed every 30 iterations.
# Multiuser Equalizer for Hybrid Massive MIMO mmWave CE-OFDM Systems
## 3 Precoder and Equalizer Design
### Analog Precoder  
The aim of the analog precoder is to maximize the beamforming gain between each UT and the BS. To compute the analog precoder, let us consider:  
\[\mathbf{A}_{tx,u}=[\mathbf{a}_{tx,u}([\boldsymbol{\phi}]_{u,1}^{tx}),\ldots, \mathbf{a}_{tx,u}([\boldsymbol{\phi}]_{u,N_{d}}^{tx})]\in\mathbb{C}^{N_{tx} \times N_{d}}, \tag{8}\]  
where \(\mathbf{a}_{tx,u},u=1,\ldots,U\) is the normalized array response vector, which for ULA case is given by (5), and \([\boldsymbol{\phi}]_{u,q}^{tx},u=1,\ldots,U_{d}=1,\ldots,N_{cl}\), is the average AoD for \(q\)th cluster of \(u\)th user channel. Notice that by assuming only the knowledge of the average AoD we reduce the feedback information from the receiver to the transmitter, making our design more useful for practical applications. The analog  
Figure 2: Proposed receiver structure. FFT: fast Fourier transform; IFFT: inverse fast Fourier transform; CP: cyclic prefix; RF: radio frequency.  
precoder is obtained from the eigenvalue decomposition of correlation matrix \(\mathbf{A}_{tx,\mu}\mathbf{A}_{tx,\mu}^{H}\), i.e., we compute \(\mathbf{A}_{tx,\mu}\mathbf{A}_{tx,\mu}^{H}=\mathbf{\Lambda}_{tx,\mu}\mathbf{ \Sigma}_{tx,\mu}\mathbf{A}_{tx,\mu}^{H}\). Therefore, the analog precoder vector of the \(u\)th user is given by:  
\[\mathbf{f}_{a,\mu}(n)=\frac{1}{\sqrt{N_{tx}}}\exp\{\mathrm{jarg}(\mathbf{ \Lambda}_{tx,\mu}(n,1))\},\quad n=1,\ldots,N_{tx}. \tag{9}\]  
From Equation (9), we can see that the analog precoder selects the vector of \(\mathbf{\Lambda}_{tx,\mu}\) that corresponds to the largest eigenvalue of \(\mathbf{A}_{tx,\mu}\mathbf{A}_{tx,\mu}^{H}\) and then projects its elements along the unit magnitude set; therefore, it points the beam along the dominant channel direction. The discrete transmitted baseband signal of the \(u\)th user, at \(k\)th subcarrier, \(\mathbf{x}_{u,k}\in\mathbb{C}^{N_{tx}}\), is given by:  
\[\mathbf{x}_{u,k}=\mathbf{f}_{a,u}c_{u,k}. \tag{10}\]
# LEO-Based Satellite Constellation for Moving Target Detection
## 3 Results
### Multi-Source Fusion CFAR Detection Results
The comparison results with different methods are given in Figure 12d-f, and one can see that the two target regions can be extracted more precisely by means of LRWB method, which further verify the effectiveness of our theoretical analysis. Figure 11: Detection performance analysis. (**a**) The detection probability with different methods; (**b**) The detection result using LRB detection method with a single platform; (**c**) The detection result using LRB detection method with multiple platforms; (**d**) The detection result using LRWB detection method with multiple platforms. In conclusion, the LRWB detection method based on the exponential weighted likelihood ratio could effectively improve the target detection performance with multi-source platforms. Figure 12: Multi-source fusion results with regard to multiple targets scenario. (**a**) Multi-source fusion result; (**b**) The weight distribution of the contrast ratio based on the sliding window; (**c**) The exponential weighted likelihood ratio distribution corresponding to the fusion result; (**d**) The detection result using LRB detection method with a single platform; (**e**) The detection result using LRB detection method with multiple platforms; (**f**) The detection result using LRWB detection method with multiple platforms.
## 5 Experiments  
In this section, we evaluate the effectiveness of the proposed sandstorm image restoration algorithm by conducting comparative experiments with several state-of-the-art methods, including Gray World [42], Shades of Gray [43], the fusion-based enhancing approach (FBE) [3], the halo-reduced dark channel prior (HRDCP) [11], normalized gamma transformation (NGT) [4], ROP [13], successive color balance (SCB) [5], ROP+ [14], chromatic variance consistency (CVC) [12], and DedustGAN [10]. The experiments are conducted on the following platform: Intel i5-12500H CPU @ 3.10 GHz, 16.00 GB RAM Windows 11 using MATLAB R2020b and Python 3.8. Due to the lack of publicly available datasets, we collect approximately 1500 real-world sandstorm images from the internet and research papers, selecting 500 high-quality images as our test dataset. For fairness, the codes for the comparative algorithms are provided by the original authors.
## 5 Discussion  
The reason for a color shift in the numerical simulation is discussed. In comparison with the experimental results, the results of the numerical simulation show that the color of the reconstructed images shifts remarkably when bit depth of wavelength-multiplexed holograms is low. Here, the value of the wavelength difference is focused on, and \(\lambda_{2}\) is set to 561 nm instead of 532 nm to adjust the wavelength difference for three wavelengths in the numerical simulation described in Section 3.2. The numerical results when setting the wavelengths to 488, 561, and 640 nm are shown in Figure 8. The images indicate that at least 2-bit resolution per hologram at a wavelength is required, and a color 3D image with a small color shift by using an image sensor that has more than 2-bit resolution per wavelength. However, color shift was obviously decreased by increasing the difference of neighboring wavelengths \(\lambda_{2}\) and \(\lambda_{3}\). This trend can be explained by the fact that, as described in Section 2, WSPS-DH is enabled by the wavelength dependency of the intensity change induced by wavelength-dependent phase shifts of interference light. When the difference between \(\lambda_{2}\) and \(\lambda_{3}\) was small, the effect for wavelength-dependent intensity change also became small. Selective extraction of object waves at \(\lambda_{2}\) and \(\lambda_{3}\) were difficult as the bit resolution was decreased because the wavelength-dependent intensity change was small and an image sensor with low bit resolution was not able to detect the change. As a result, the CC of \(\lambda_{1}\) was relatively high and the RMSE was relatively low, so the object-intensity image at \(\lambda_{1}\) was clearly reconstructed in comparison to those at \(\lambda_{2}\) and \(\lambda_{3}\). Quantitative evaluations shown in Figure 5 supported this finding because the CC was higher and the RMSE was lower at \(\lambda_{1}\). In contrast, in the simulation shown in Figure 8, the wavelength-dependent intensity change became large by increasing wavelength-dependent phase shifts, and therefore each of three object waves was reconstructed from holograms with 4-bit resolution. As a result, the color was improved. From the experimental results presented in Section 3.1 and the numerical results presented in this section, it is clear that the color 3D-image sensing can be achieved when using an image sensor with more than 2-bit resolution per wavelength.  
Figure 7: Quantitative evaluations of reconstructed images at \(\lambda_{2}\): CC of reconstructed (**a**) amplitude and (**b**) phase images, and RMSE of reconstructed (**c**) amplitude and (**d**) phase images.
# MinHash-Based Fuzzy Keyword Search of Encrypted Data across Multiple Cloud Servers
## The Proposed Fuzzy Keyword Search Scheme Based on MinHash
### Implementation of the Algorithm  
#### 3.4.1 Keyword Fingerprint Generation Based on MinHash  
The traditional MinHash algorithm can be used to generate a fingerprint for each document, making it suitable only for applicable document query scenarios. For information retrieval, the use of keywords to query documents has become more common. Therefore, the MinHash algorithm needs to be improved so that it can be used to generate more than one fingerprints for keywords. To extract more than one feature for each keyword, the following scheme makes use of the n-gram method to process keywords. In addition, since the traditional MinHash algorithm is mainly used in the plaintext data, only ordinary hash functions are used. Since our proposed scheme is to be applied to encrypted data, to improve the security of the scheme, this paper uses a keyed one-way hash function h (hk, -) instead. The specific steps of generating a keyword fingerprint based on MinHash are as follows:  
1. Input the keyword w that needs to be processed and initialize both \(\tau\)-dimensional vectors V and S to 0 in which the value of the \(\delta_{\rm i}\) is the same as the number of finger of hash value generated by hash function h.
2. Process the keyword w by n-gram to obtain multiple features of the keyword. In this scenario, to get more accurate search results, keywords need to be divided as much as possible, i.e., n = 2. For example, when w is encrypted, after 2-gram processing, gramset = {en, nc, ry, yp, pt} is obtained, where each element in gramset is a feature of the keyword w.
3. Use keyed one-way hash function h to calculate a hash value for each element in the gramset to protect the privacy of keywords and indexes. Hash function h can be Hmac-SHA1 or Hmac-MD5. Different hash functions will generate hash values of different sizes, which will affect the accuracy of the final search and the large the size, the more accurate the search.
4. Map the hash values of these elements into vector V one by one in which if the i-th bit of the hash value is 1, the i-th bit of the vector V gets incremented by 1 and if the i-th bit of the hash value is 0, the i-th bit of the vector V gets decremented by 1.
5. Finally, map vector V into vector S in which if the i-th bit of vector V is no less than 0, the value of the i-th bit of vector S is set to 1 and if the i-th bit of vector V is less than 0, the value of i-th bit of vector S is set to 0.
6. Output S as the fingerprint of the keyword. An example of using the above algorithm is illustrated in Figure 2:
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 3 Air Pollution Monitoring Equipment
### Gas Sensor  
Nowadays, many different technologies for gas detection are available, each with certain advantages and disadvantages. To date, there are five types most suitable and widely used low-cost portable gas sensors, namely electrochemical sensors, catalytic sensors, solid-state (semiconductor) sensors, non-dispersive infrared radiation absorption (NDIR) and photo-ionization detector (PID) sensors (see Table 7). All of these sensors are low cost, light weight (less than one hundred grams) and with fast response time (in tenths seconds or few minutes). However, no single type of sensors is able to measure all the hazard gases (hundreds of hazard gases have been identified). Each type of sensors is sensitive to specific kinds of hazard gases.  
Although, till now there is no low-cost portable gas sensor can achieve the same data accuracy and quality as conventional monitoring instruments. The low-cost portable gas sensors provide a fair enough accuracy and detection range [46]. What is more, all sensors need to be calibrated (When calibrating a sensor, the sensor is exposed to a specific kind of pollution gas with predefined concentration, the parameters of the sensor are adjusted such that the difference between the predefined gas concentration and the sensor output is minimized.) before operation and after a specific operational time. The necessity of calibration and the calibration procedures can be found in [83].  
As described in Section 2, there are four types of hazard gases that we want to monitor most. They are carbon monoxide (CO), nitrogen dioxide (NO\({}_{2}\)), ground level ozone (O\({}_{3}\)) and sulfur dioxide (SO\({}_{2}\)). Combining the descriptions in [46,84] and the comparisons (with respect to sensor detectable gases, linearity, cross sensitivity, power consumption, maintenance, response time and life expectancy) in Table 7, two best types of sensors for these four types of hazard gases are determined.  
* CO: Can be well detected by solid-state and electrochemical sensors.
* NO\({}_{2}\): Can be well detected by solid-state and electrochemical sensors. Need to consider the interference gas O\({}_{3}\). Proper methods can be applied to reduce the interference.
* O\({}_{3}\): Can be well detected by solid-state and electrochemical sensors. Need to consider the interference gas NO\({}_{2}\). Proper methods can be applied to reduce the interference.
* SO\({}_{2}\): Can only be well detected by solid-state and electrochemical sensors. It poisons the catalytic sensors. The sensitivity of NDIR sensors is not high enough.  
In a word, the solid-state and electrochemical sensors are the most suitable types of sensors to monitor these four types of hazard gases in building the TNGAPMS scenario. In fact, these two types of sensors are the basic elements in most of the existing works presented in Section 4. The operational principles of these two types of sensors are introduced as follows.
# An Image-Processing Method for Extracting Kinematic Characteristics of Droplets during Pulsed GMAW
## 3 Extraction of Droplet Region by Image Processing
### The Period with an Undetached Droplet  
The droplet and wire are still connected in the period with an undetached droplet, as seen in Figure 3. As observed in Figure 2b, the droplet is disconnected from the wire at the neck. That is to say, the detached droplet comes from the part below the minimum neck of the droplet in the period with an undetached droplet. Thus, in this study, the droplet was divided at the minimum diameter of the neck. The residual droplet part of the solid wire was above the dividing line, while the droplet below the dividing line was taken as the research object for the subsequent extraction of kinematic characteristics. For the convenience of study, the droplet below the dividing line is called the target droplet in this paper.  
Based on the above ideas, the following image-processing scheme is proposed for the recognition of the target droplet in the period with an undetached droplet: (1) image enhancement; (2) position identification of the minimum diameter of the neck; (3) droplet cutting and recognition.  
Figure 2: Typical pulse current waveform and contrast images under different \(\theta\) of the neutral density (ND) filter: (**a**) pulse current waveform; (**b**) translucent arc; (**c**) transparent arc.