esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Signal Evaluation Approach under the Local Despreading Waveform Mismatch
### Multipath Effects under Unmatched Receiving  
In this subsection, the effect of multipath under unmatched receiving is discussed. Consider a basic and representative model with a one-path specular reflection [18]. To evaluate the average influence of multipath, the average range error envelope [18] is utilized, defined as:  
\[\Gamma(\delta_{m})=\frac{1}{\delta_{m}}\int_{0}^{\delta_{m}}\left(\max_{\phi}( \alpha,u,\phi)-\min_{\phi}(\alpha,u,\phi)\right)du \tag{22}\]  
where \(\alpha\) is the multipath-to-direct ratio (MDR), \(\delta_{m}\) is the relative delay of multipath signal compared with the direct signal, \(\phi\) is the relative carrier phase of multipath signal compared with the direct signal. In Equation (22), \(\varepsilon(\alpha,u,\phi)\) is the error caused by zero crossing bias of discriminator curve. The discriminator curve in the present of multipath is given by [19]:  
\[\begin{array}{ll}D(\tau)=&R_{\overrightarrow{g\overrightarrow{g}}}^{2} \left(\tau+\frac{\Delta}{2}\right)-R_{\overrightarrow{g\overrightarrow{g}}}^{2} \left(\tau-\frac{\Delta}{2}\right)+\alpha^{2}\left[R_{\overrightarrow{g \overrightarrow{g}}}^{2}\left(\tau+\frac{\Delta}{2}-\delta_{m}\right)-R_{ \overrightarrow{g\overrightarrow{g}}}^{2}\left(\tau-\frac{\Delta}{2}- \delta_{m}\right)\right]\\ &+2\alpha\text{cos}\phi\cdot\left[R_{\overrightarrow{g\overrightarrow{g}}}^{2 }\left(\tau+\frac{\Delta}{2}\right)\cdot R_{\overrightarrow{g\overrightarrow {g}}}^{2}\left(\tau+\frac{\Delta}{2}-\delta_{m}\right)-R_{\overrightarrow{g \overrightarrow{g}}}^{2}\left(\tau-\frac{\Delta}{2}-\delta_{m}\right)\right] \end{array} \tag{23}\]  
where \(R_{\overrightarrow{g\overrightarrow{g}}}\) is the cross-correlation function between received signal and local despreading signal.  
As can be seen, both the multipath component and the mismatch of local signal and received signal together influence the zero-crossing point of DLL discriminator curve, thus resulting in different characteristics in the ranging error than the multipath effect under matched receiving.  
To give an overall evaluation of the multipath effect in unmatched receiving, simulation of the multipath resistance performance is provided for ACE-BOC and MBOC signals in Sections 6.2.4 and 7.2.4.
## 4 Implementation and Performance Evaluation
### Implementation  
Experiments regarding a deep-belief-network-based \(\alpha\)-\(\beta\) filter, a deep-extreme-learning-machine-based \(\alpha\)-\(\beta\) filter, and a support-vector-machine-based alpha-beta filter were carried out on MSI DESKTOP-SC4U005. The computer has an \"11 th Gen Intel(R) Core (TM) i7-11700 KF CPU @ 3.60 GHz, 32 GB ram, Nvidia Quadro M1200 4 GB graphics card, and MATLAB R2022a\". The implementation and simulation configuration are shown in Table 2. For the analysis of our models and the performance assessment of the multi-learning-algorithm-based \(\alpha\)-\(\beta\) filter, we used the real weather dataset [2] of Korea and the data gathered by temperature and humidity sensors over three years, comprising hourly data with simulated noisy sensor readings, and some errors were added to the data. The total number of days for three years was 365 \(\times\) 3 = 1095, and there were 26,280 total data instances. Initially, when the filter-readings sensor obtained values via the typical method, the root mean square error, a value of 5.21, was obtained, which is very high in terms of the RMSE. By using these three algorithms, we attempted to reduce the error. The data representation is shown in Figure 6.  
Figure 6: Representation of humidity, sensor readings, temperature, and error.
## 2 Data Acquisition and Processing
### Navigational Environment Data Acquisition and Processing  
Navigational environment data (included meteorological and sea condition data) are sourced from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Copernicus Marine Service. These data are collected hourly and downloaded in grid format (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)).  
Meteorological and sea condition data are matched with the longitude, latitude, and time in AIS. Wind speed and current rate components are combined to determine the actual wind speed, wind direction, current speed, and current direction. The relative direction of the case ship is calculated by combing course and direction data. Three parts of the data are fused based on time in Figure 1.
# Multi-Satellite Observation-Relay Transmission-Downloading Coupling Scheduling Method
## 2 Problem Description
### Improvement of Observation Solution Local Search Based on Satellite Cluster
#### 3.3.1 Observation Solution Search Neighborhood  
The observation solution local search process is constructing the next set of solutions based on the current observation solution and the set of unallocated missions, following certain local search rules. Relevant state variables of the current solution are denoted by the subscript \"Cur\", and the state variables of the next set of solutions are denoted by \"Next\".  
In this article, referring to [45], the insertion neighborhood, deletion neighborhood, scheduled mission exchange neighborhood, unscheduled mission exchange neighborhood, and rearrangement neighborhood have been designed. The observation search neighborhoods are described as follows:  
1. Insertion neighborhood. The process entails selecting a mission \(i\) from the unselected mission set and choosing a satellite \(k\) and a position in the current observation result sequence of satellite \(k\) to insert mission \(i\) into the sequence;
2. Deletion neighborhood. The process involves selecting a satellite \(k\) and removing an observation result \(i\) from the observation result sequence of satellite \(k\);
3. Scheduled mission exchange neighborhood. The process entails selecting two missions from the observation result sequences of satellites \(k\) and \(l\), then swapping the positions of the two missions. If satellite \(k\) and satellite \(l\) are the same, the selected missions must be different;
4. Unscheduled mission exchange neighborhood. The process involves selecting a mission \(i\) from the unselected mission set and choosing a satellite \(k\) with a mission \(j\) in its observation result sequence. Mission \(i\) is then swapped with mission \(j\), which means mission \(i\) takes the position of mission \(j\) and is removed from the unselected mission set, while mission \(j\) is removed from the observation result sequence and added to the unselected mission set;
5. Rearrangement neighborhood. The process includes taking out mission \(i\) from the observation result sequence of satellite \(k\), then selecting another satellite \(l\) and an insertion position in the observation result sequence, and inserting mission \(i\) into satellite \(l\)'s mission observation result sequence.
# A Review of Artificial Intelligence Research in Peer-Reviewed Communication Journals
###### Abstract  
This study analyzes artificial intelligence (AI) research in communication scholarship through a content analysis of published articles between 2006 and 2022. It aims to understand the status of AI research between 2006 and 2022 and identify directions for future inquiry. Findings indicate that the number of articles about AI has increased over the years and scholars should continue applying existing theoretical frameworks or proposing new ones to investigate diverse topics across cultural and sociopolitical contexts.  
review studies; AI research; peer-reviewed articles; communication scholarship 2020 IEEE/RSJ Web Conference 2020  
While the discussion surrounding AI within the communication field has seen a surge in articles, there remains a notable absence of a systematic review of the literature. Such an analysis is crucial because it allows for scholars to discern the prevalent research topics, theoretical frameworks, and methodological approaches employed within this domain, thereby guiding future research endeavors [11]. Consequently, this study endeavors to bridge this gap by conducting a comprehensive examination of published peer-reviewed articles focusing on AI within the communication field. Thus, this article serves several critical purposes within the academic landscape. First, it addresses the pressing need for a systematic review of published peer-reviewed articles on AI within the communication field, a topic experiencing burgeoning interest but lacking comprehensive analysis. By undertaking this review, this article aims to provide scholars with a comprehensive understanding of the trends, topical themes, theoretical frameworks, and methodological approaches shaping AI research in communication. Second, it seeks to offer insights into the diverse applications of AI in communication, ranging from chatbots and deepfakes to social bots and algorithms, thereby highlighting the multifaceted nature of AI's impact. Additionally, this article endeavors to establish a robust and nuanced definition of AI within the context of communication studies, fostering clarity and coherence in future research endeavors. Finally, by identifying gaps and areas for further exploration, this study aims to guide and inspire future research efforts in this dynamic and rapidly evolving field. Overall, this article's significance lies in its potential to inform and shape the trajectory of AI research in communication, facilitating advancements in theory, practice, and policy.
# Assessing the Functional Response to Streamside Fencing of Pastoral Waikato Streams, New Zealand
## 2 Materials and Methods
### Functional Indicators
#### 2.3.2 Ecosystem Metabolism  
We estimated the combination of gross primary production (GPP) and ecosystem respiration (ER) for each paired site using Odum's open-system two-station analysis of diel oxygen curves [57], as modified by [PERSON] et al. [58]. This required measurements of the diel changes in dissolved oxygen (DO) saturation at the upstream and downstream ends of each reach [59].  
At each paired site, DO saturation and water temperature were continuously recorded with optical fluorescence probes (D-Opto, Zebra-Tech Ltd, Nelson, New Zealand) during stable flows at 15-min intervals for at least a 48-h period during March and April 2012. Paired sites were recorded simultaneously. Prior to data recording, the oxygen probes were calibrated in air-saturated water at sea level. To determine the differences in the calibration resulting from instrument drift and between sondes, all loggers were deployed together for one hour at the beginning of each recording periodso that any differences could be corrected before data analysis. To measure hydraulic travel times between upstream and downstream DO loggers (aimed at a minimum of 1 hour), we released 50 mL of Rhodamine(r) WT liquid dye across the stream width upstream of the upper DO sonde and tracked water travel time by monitoring Rhodamine(r) concentrations at the downstream DO sonde with a C3 TM Submersible Field Fluorometer (Turner Designs, San Jose, CA, USA).  
The rate of ER and the re-aeration coefficient (_k_) were determined following the methods from [PERSON] [59], except \(k\) was estimated using the night-time regression method [60] instead of using tracer gases. This method assumes low surface turbulence, as was the case in this study. The ratio of GPP:ER (P/R) was also determined. P/R relates to the balance between primary production and ecosystem respiration and determines whether the reach is autotrophic (P/R > 1) or heterotrophic (P/R < 1) [57].
## 2 Methodology for the 3D Quality Assessment of UUSR
### AHP-EWM Weight Method  
The weighting of indicators plays a crucial role in the UUSR assessment, and it is generally divided into subjective and objective weights. AHP is one of the most representative subjective weight methods and is widely used in the UUSR assessment. It is a multicriteriadecision-making method introduced by [PERSON] [45]. The index weights are determined by the judgment matrix, which can be determined using the expert scoring method. EWM is more and more widely used due to its objectivity based on data, especially for sensitive data [46]. It is a commonly used weighting method that measures value dispersion in the decision making proposed by [PERSON] [47]. In each dataset associated with different indicators, a higher degree of dispersion signifies a greater degree of differentiation and yields more information. Consequently, the weight assigned to this particular dataset increases within the entire dataset.  
In this study, the AHP for calculating subjective weights and the EWM for calculating objective weights are jointly employed, as shown in Figure 2. Firstly, the assessment indexes are divided into two levels. The AHP method is utilized to calculate the weight of the qualitive first-level indexes (\(w_{G}\), \(w_{H}\), \(w_{S}\)). Then, the EWM method is utilized to calculate the weight of quantitative geotechnical parameters \(G\) (\(w_{c1}\), \(w_{\phi 1}\), \(w_{k1}\)) due to its sensitivity to spatial variation. Meanwhile, the AHP method is utilized to determine the weight of qualitive spatial variables \(S\) (\(w_{h1}\), \(w_{dg1}\), \(w_{ds1}\)). Finally, the weight of each indicator is calculated by multiplying the weight of first-level and secondary indexes, i.e., \(w\) = (\(w_{G}\)\(\times\)\(w_{\mathrm{c1}}\), \(w_{G}\)\(\times\)\(w_{\mathrm{\phi 1}}\), \(w_{G}\)\(\times\)\(w_{k1}\), \(w_{h}\), \(w_{S}\)\(\times\)\(w_{h1}\), \(w_{S}\)\(\times\)\(w_{ds1}\)).
## 3 Results and Discussion
Therefore, the value of the absorption capacity \(\mathbf{Q_{1}}\) was lower than that of CCBs. However, the actual adsorption amount of CZIF-8/CNTs/CCBs was much higher, due to the porous structure with large volume and the loading of CZIF-8 and CNTs, resulting in a higher absorption capacity \(\mathbf{Q_{2}}\) value. Therefore, the loading of ZIF-8 and CNTs plays a critical role in improving the adsorption performance of CZIF-8/CNTs/CCBs. As shown in Table 1, the absorption capacity of CZIF-8/CNTs/CCBs is higher than that of various previously reported sorbent materials [31; 39; 40; 41; 42; 43; 44; 45]. Although the sample shows lower absorption capacity than the reported materials [46; 47; 48], the precursor material is cheaper, and the preparation method is simpler. To investigate the recyclability of the sample, the absorption-distillation test was repeated up to nine times. As displayed in  
Figure 6: (**a**) FTIR spectra of CBs, ZIF-8/CNTs/CBs, and CZIF-8/CNTs/CCBs. (**b**) XRD patterns for CBs, ZIF-8/CNTs/CBs, and CZIF-8/CNTs/CCBs. Figure 9c, CZIF-8/CNTs/CCBs retained its high absorption capacity for n-heptane after nine cycles, indicating good recyclability. Based on these results, CZIF-8/CNTs/CCBs could be used as an ideal material for efficient separation of oil-water mixtures, due to its superhydrophobic properties. Figure 8: (**a**) Sorption process of dichloromethane (dyed with Oil Red O) by C ZIF-8/CNTs/CCBs. (**b**) Sorption process of n-hexane (dyed with Oil Red O) by C ZIF-8/CNTs/CCBs. Figure 7: (**a**–**c**) The water static contact angle measurement for CBs, ZIF-8/CNTs/CBs, and CZIF-8/CNTs/CCBs. (**d**) Adhesion images of the water droplet on C ZIF-8/CNTs/CCBs.
## 2 Material and Methods
### Datasets  
The shortage of datasets is the main challenge in pattern recognition, and it is one of the limiting factors in developing reliable systems for Amharic OCR. There are few databases used in the various works on Amharic OCR reported in literature. As reported in [16], the authors considered the most frequently used Amharic characters. A later work by [PERSON] et al. [1] uses 76,800 character images with different font types and sizes which belong to 231 classes.  
Other researchers' work on Amharic OCR [15; 20; 21] reported that they used their own private databases, but none of them were made publicly available for research purposes. Promising work on Amharic OCR is reported in [9], where the authors employed an LSTM-based neural network for Amharic OCR text-line image recognition; they also introduce a dataset called ADOCR, which is made public and freely accessible at [[http://www.dfki.uni-kl.de/~belay/](http://www.dfki.uni-kl.de/~belay/)]([http://www.dfki.uni-kl.de/~belay/](http://www.dfki.uni-kl.de/~belay/)).  
In this paper, we use the ADOCR database introduced by [9]. This dataset contains 337,337 Amharic text-line images which are written with the Visual Geez and Power Geez fonts using 280 unique Amharic characters and punctuation marks.All images are greyscale and normalized to 48 by 128 pixels, while the maximum string length of the ground-truth text is 32 characters including true blank spaces.  
From the total text-line images in the dataset, 40,929 are printed text-line images written with the Power Geez font; 197,484 and 98,924 images are synthetic text-line images generated with different levels of degradation using the Power Geez and Visual Geez fonts, respectively. All characters that exist in the test dataset also exist in the training set, but some words in the test dataset do not exist in training dataset. Sample printed and synthetically generated text-line images taken from the database are shown in Figure 2.  
The details of the dataset (text-line images, unique Amharic characters, and punctuation marks) used in this experiment are summarized in Table 1 and Figure 3.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Printed** & \multicolumn{2}{c}{**Synthetic**} \\ \hline
**Font Type** & **Power Geez** & **Power Geez** & **Visual Geez** \\ \hline Number of samples & 40,929 & 197,484 & 98,924 \\ No. of test samples & 2907 & 9245 & 6479 \\ No. of training samples & 38,022 & 188,239 & 92,445 \\ No. of unique chars. & 280 & 261 & 210 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The details of the text-line images in the dataset [9].  
Figure 2: Sample Amharic text-line images that are normalized to a size of 32 by 128 pixels: (**a**) Printed text-line images written with the Power Geez font type. (**b**) Synthetically generated text-line images with the Visual Geez font type. (**c**) Synthetically generated text-line images with the Power Geez font type.
# Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models
## 2 Current-Voltage Curves
### Hot Spot Faults  
Hot spot faults in solar modules primarily arise from mismatched power output among some solar cells within the module. Cells that are mismatched can become reverse-biased, transforming from power-generating elements to load-bearing ones, consuming energy and generating significant heat. When a solar module exhibits a hot spot fault [19], its current-voltage (I-V) curve will display characteristics that vary depending on the distribution of the hot spots. Cells affected by hot spot faults exhibit lower short-circuit currents than normal cells, leading to power mismatches among them.  
As shown in Figure 14, among all fault types, only this fault employs module parallel connection. To simulate the distribution of hot spots in half-cut cells, the solar cell string is divided into three modules. Two of them are connected in series, with one being a normal module and the other experiencing hot spot faults. This string of solar cell modules is then paralleled with another set of normal solar cell modules; a resistor is paralleled on a solar module experiencing a hot spot fault. By changing its internal parameters \(I_{sc}\) and the value of the parallel resistor, a distorted curve hot spot is simulated as shown in Figure 15. As shown in Figure 16, three solar cell modules are connected in series; the top module is normal, while the two below exhibit varying degrees of hot spot faults with different resistor values paralleled, simulating a double-step curve hot spot as shown in Figure 17.  
Figure 14: Distorted curve hot spot fault current–voltage curve simulation circuit diagram.  
Figure 13: Short-circuit fault current–voltage curve.
## 2 Materials and Methods Background
### Distributed Systems and Cloud Computing
Messages are exchanged between UDP endpoints in CoAP. CoAP was created by the IETF (Internet Engineering Task Force) CoRE (Constrained RESTful Environments) and became an Internet standard in 2014 [35]. 5. 6 LoWPAN  
6 LoWPAN (Low-power Wireless Personal Area Networks) is a protocol that falls into the datalink layer of IoT. Within this network, communication takes place via the IPv6 network protocol. The protocol has a lower baud rate as well as lower operation cost [35]. The fourth version of the Internet Protocol, IPv4, will not cover the range of public addresses assigned to objects in the future. Therefore, IPv6 has been created to provide more address space. Compared to IPv4, the address is 128-bits long and, thus, approximately 3.4 \(\times\) 1038 unique IP addresses can be assigned. 6. Transmission Systems  
All data measured, read, or entered by a user must be transmitted to a central collection system for further processing. A suitable communication system is required for this transmission, most often wireless technology. There are many communication technologiessuitable for IoT. These are mainly wireless technologies that allow connections from a few centimeters (RFID) to hundreds of kilometers (Sigfox, LoRa). Important parameters for choosing the right technology include range, data transmission rate, energy consumption, and security. Within Smart City, LPWANs (Low-PowerWide-Area Networks) are predominantly used, including Sigfox, LoRa, and NB-It. These networks are energy-efficient and have a range of tens of kilometers. Short-range networks include RFID, Bluetooth, ZigBee, and Z-Wave. Medium-range networks include IQRF and WiFi. Long-range networks include Sigfox, LoRa, NarrowBandlo T, GPRS, HSDPA (3G), LTE (4G), and 5G.
## 2 Model Problem of the Allen-Cahn Equation and Finite Difference Approximation
### Model Problem  
Consider the Allen-Cahn equation of the form  
\[\frac{\partial u}{\partial t}=\epsilon\frac{\partial^{2}u}{\partial x^{2}}+F(u ),\quad x\in\Omega,\quad t\geq 0 \tag{1}\]  
with \(F(u)=u-u^{3}\), and \(\Omega=\left[a,b\right],\) for some real number \(a,b\) with \(a<b\) where the initial condition : \(u(x,0)=f(x)\), and the boundary conditions : \(u(a,t)=g_{1}(t),\quad u(b,t)=g_{2}(t),\quad t>0\). Here, we have variables \(t\) for time and \(x\) for position, and \(F(u)\) is the nonlinear reaction term. The boundary conditions may depend on the problem for a given domain \(\Omega\). In this context, \(u=u(x,t)\) denotes the phase function of two kinds of liquid at each position \(x\) in space, and time \(t\). The parameter \(\epsilon\) is a small positive number, which is related to the width of the interface of two kinds of liquid or the interaction length and can be used to describe the thickness of phase boundary in the laboratory scale [1]. The term \(\epsilon\frac{\partial^{2}u}{\partial x^{2}}\) represents the diffusion of the liquid, and the term \(F(u)\) is the kinetic potential of the liquid. For \(u(x_{1},t_{1})=1\), it means that at the time \(t_{1}\), the position \(x_{1}\) is filled only one kind of liquid; for \(u(x_{2},t_{2})=-1\), it means that at the time \(t_{2}\), the position \(x_{2}\) is filled only the other kind of liquid. For \(t=0\), we will provide the initial data of \(u(x,0)\). This equation has three constant steady states, \(u=-1\), \(u=0\), and \(u=1\). The middle state is unstable, but the states \(u=1\), and \(u=-1\) are attracting, and solutions tend to exhibit at areas close to these values separated by interfaces that may coalesce or vanish on a long time scale, a phenomenon known as metastability. Thus it is called a bistable reaction-diffusion equation or a reaction-diffusion equation with bistable nonlinearity.