esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Overview of Traditional A* and DWA Algorithms
#### 2.1.3 Traditional A\({}^{\star}\) and DWA Fusion Algorithm  
The traditional A\({}^{\star}\) and DWA fusion algorithm can simultaneously consider the global static environment as well as the layout dynamic environment. The USV relies on the map resources pre-loaded into the database and its own sensors to develop situational awareness of the global and local environments. The global optimal path is first obtained according to the A\({}^{\star}\) algorithm, whereby the optimal path is discretized into a number of point traces. Subsequently, the locally optimal path is generated according to the target point of the DWA algorithm, as determined by the point traces, and combined with the location of surrounding obstacles to generate navigational information to guide the USV. The fusion algorithm can continuously update the heading state of the USV, considering the  
Figure 1: Flowchart of algorithm A\({}^{\star}\).  
navigational capability constraints and environmental information, thus achieving more efficient and accurate path planning. The algorithmic principle is illustrated in Figure 2.  
In Figure 2, the global path is first generated using the traditional A* algorithm based on the position, size, and other information on the USV target and obstacles in the environment. Then, the local guide target is set according to the distance from the USV and used as the target point for the DWA algorithm. Finally, the traditional DWA algorithm is used to generate the dynamic window and guide the navigation.
## 2 Results and Discussion
### Sequence Features of the Pc-fps Promoter Region  
Bioinformatics analysis revealed that the _Pc-fps_ promoter carried typical eukaryotic promoter elements (Figure 2). Three predicted TATA boxes were found at position \(-\)567, \(-\)410 and \(-\)367 with respect to the ATG codon, as well as 10 putative CAAT boxes. Three possible core promoter regions were found at \(-\)607 to \(-\)558, \(-\)450 to \(-\)401 and \(-\)373 to \(-\)324 and scores from 0.98 to 1.0 were predicted by using the NNPP v.2.2 for promoter prediction. Potential regulatory elements associated with sterol biosynthesis, hormone, and stress-related responses were also found in the _Pc-fps_ promoter region; there was a Sp1 binding site, an NF-Y binding site, and an E-box motif. Some regulatory elements were homologous to the consensus elements (Inv-SRE-3, SRE-1) of the human squalene synthase promoter [17]. Two methyl jasmonate (MeJA) responsive elements (TGACG) were found at position \(-\)235 to \(-\)230 and \(-\)152 to \(-\)147. Analysis of these specific motifs of the _Pc-fps_ promoter region might identify useful cis-acting elements and thus provide some clues to the mechanism by which this gene responds to stress signals.  
Nucleotide sequence of the promoter region for the _Pc-fps_ promoter. The translational start site ATG was defined as position \(+\)1. The transcriptional regulatory elements of TATA-box, Sp1 binding site, NF-Y binding site, E-box motif, YY1 binding site and MeJA responsive elements are labeled and underlined.  
\(-\)738 TTCATGATGCAGGCGACCACACAGCGTCTAGAGACAATCCACAGCGAAAGTTTATG  
\(-\)680 AGGAGCAGATGGTGCTACACGGATTTTGGAAACACACATCACATCCTTCCACCCCC  
\(-\)624 AGCATGATGGCCTTCCAATATATATAAAGGACTGAATTCTGGTCCGACT  
\(\mathbf{NF-Y binding site}\)\(\mathbf{TATA-Box}\)  
\(-\)564 TTGCTCTTTCCCCATTGCACCTTCAATAATCCGAATTCTCCAT
## 4 Case Study of Data Collaboration
### Space-Time Travel Data Mining
#### 4.2.5 Spatial Travel Hotspots Mining Based on Gaussian Kernel Density Estimation  
In order to capture the spatial distribution characteristics of map-matching data more intuitively, we used the kernel density estimation(KDE) method to mine the travel hotspots, and the Gaussian function was selected as the kernel function. Generally, this paper selects two working days and weekends a week for mining the hotspots. As shown in Figure 7, the data presents obvious spatial distribution characteristics, and the weekday and weekend have their own characteristics. By extracting high-density hotspots, it can be seen that Sanyang Square (red area) in the Liangxi District of Wuxi City is the central area of the city, which will be further excavated in the next chapter.
## 2 System Model
When destination \(D\) receives the RREQ packet from CH\({}_{4}\), it generates a RREP packet and switches to channel 3 in order to send the RREP packet to CH\({}_{4}\). CH\({}_{4}\) continues to forward the RREP packet to GW\({}_{4}\) without switching channel. GW\({}_{4}\) switches to channel 2 and forwards the RREP packet to CH\({}_{2}\). CH\({}_{2}\) continues to forward the RREP packet to GW\({}_{3}\) without switching channel. GW\({}_{3}\) continues to forward the RREP packet to GW\({}_{3}\) without switching channel. GW\({}_{3}\) continues to forward the RREP packet to GW\({}_{1}\) without switching channel. GW\({}_{1}\) switches to channel 1 and forwards the RREP packet to CH\({}_{1}\). CH\({}_{1}\) continues to forward the RREP packet to source \(S\) without switching channel. When source \(S\) receives an RREP packet, it sends data to the destination. We note that each node in routing process records the next hop and a sending channel into its routing table, which is used to send data from the source to destination \(D\). In this paper, we mainly focus on the network layer to establish a route in MANET, where mobile nodes have to exchange their information together by using control packets. The energy consumption is primarily affected by transmitting and receiving control packets. Moreover, the energy consumption of data transmission from a source to a destination mainly depends on transmission energy of sender end reception energy of receiver. Thus, we only consider the transmission energy and reception energy issues that affect clearly on the network layer and considering other energy issues in physical and data link layer is indeed out of the scope of the paper. We apply the energy model in physical layer taken from References [38; 39] to evaluate the energy efficiency of the proposed routing protocol. The energy consumption varies from 230 mA in receiving mode to 330 mA in transmitting mode, using a 3.3 V or 5.0 V energy supply. These values correspond to a 2400 MHz WaveLAN implementation of IEEE 802.11. The transmission energy and reception energy (in Joules), denoted by \(E_{TX}\) and \(E_{RX}\), respectively, depend on packet size (in bits) and they can be expressed as  
\[E_{TX}=\left(330\times 5\times PacketSize\right)/(2\times 10^{6}). \tag{1}\]  
\[E_{RX}=\left(230\times 5\times PacketSize\right)/(2\times 10^{6}). \tag{2}\]  
In MANETs, multiple channels are usually supported by the modern wireless MAC protocols [40]. For example, it is known that the IEEE 802.11 b/g standard provides 14 channels and each node can use multiple channels to communicate with other nodes. Each node has two transceivers, one is always on common channel 0 for controlling packets while the other one can change its channel number from 1 to \(N_{c}-1\) for transmitting data packets, where \(N_{c}\) denotes the total number of channels.
## 4 Simulation Experiment
### Compare the Conventional Carrier Reuse System with Our Scheme  
When the optical switch in Figure 1 is on and the electrical switch is off, the system is the conventional carrier reuse system. In this case, the central frequency of the FBG in the BS is shifted to 193.02 THz from 193.1 THz, as shown in Figure 1.  
Figure 15 shows the relationship between BER and the received power of the PDs in the conventional carrier reuse system and our scheme.  
As the BER is less than \(10^{-9}\), the improvements in the sensitivity of the receivers are listed in Table 2 for the transmission distances of 0 km,10 km, and 20 km, respectively.  
We can see that improvement in the sensitivity of receivers is increased with the increase in the fiber transmission distance.
## 6 Profound Impact Potential on Decision-Making Processes
### Modernizing Military Weather Monitoring  
The U.S. military depends heavily on space-based meteorological systems to support Joint All-Domain Command and Control (JADC2) operations, which require near-real-time, high-resolution meteorological data to enable agile, informed responses. Existing limitations in terrestrial sensors, combined with the vast and complex Indo-Pacific theater, underscore the need for advanced space-based weather solutions [35].  
The Weather System Follow-on Microwave (WSF-M) program directly addresses capability gaps left by the aging DMSP constellation. The first WSF-M satellite was launched aboard a SpaceX Falcon 9 rocket in April 2024. This satellite enhances the military's ability to measure ocean surface winds, monitor tropical cyclone development, and detect charged atmospheric particles. Beyond military applications, WSF-M provides valuable environmental data, including sea ice mapping, soil moisture content, and snow depth measurements. Future plans include launching a second WSF-M satellite in 2028, complemented by additional platforms developed under the Electro-Optical/Infrared Weather System (EWS) program, scheduled for deployment in 2025 and 2027 [36]. These programs ensure redundancy, resilience, and continuity in space-based weather monitoring [37].
# Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai
## 2 Related Work  
### Analysis of Network Traffic  
Network traffic analysis is based on different network performance indicators, such as traffic volume, latency, and packet loss rate [18]. [PERSON] et al. [19] found that the UK government's lockdown policy amid the COVID-19 pandemic had dramatically changed people's mobility patterns and Internet usage. Based on the data of UK mobile operators, the changes in user mobility and their impact on the mobile network were analyzed in detail. The overall user mobility declined by 50%, with deviations varying by region. While traffic characteristics change, operators maintained stable service as radio load decreased and per-user throughput was probably application-limited. [PERSON] et al. [20] examinedthe WAN traffic characteristics in Baidu's data centers network (DCN). They found that a significant percentage of traffic left the cluster flowing from the DC to the WAN. They further observed that traffic communications among DCs were imbalanced, i.e., a large amount of traffic was generated by a small number of DC pairs. Meanwhile, the traffic among DC services had stability over time and the stability made it possible to predict overall traffic demand.  
Many prior works have analyzed the characteristics of IPv6 network traffic. [PERSON] et al. [9] studied the traffic characteristics and user behavior of the IPv4 and IPv6 networks of Xi'an Jiaotong University. They analyzed the average packet size, flow size, flow duration, and self-similarity of IPv4 and IPv6 traffic and found a significant difference between IPv4 and IPv6 traffic. [PERSON] et al. [21] compared the changes in IPv6 network activities before, during, and after World IPv6 Day (8 June 2011). They found that native IPv6 traffic increased significantly while tunneled traffic did not change much during that time. [PERSON] et al. [22] analyzed IPv6 traffic based on a large-scale IPv6-based campus network with one-week traffic data. They investigated the development of IPv6 networks and studied the composition of aggregate traffic. [PERSON] et al. [10] utilized the traffic data collected on the backbone network of the China Science and Technology Network (CSTNET) and found that IPv6 traffic had been increasing rapidly from 2011 to 2013. [PERSON] et al. [11] conducted a statistical analysis of the IPv6 data in Yahoo and obtained the daily and weekly recurring patterns.  
However, IPv6 networks are developing rapidly, and past analysis findings might not be suitable for today's IPv6 networks. Our research analyzes the latest IPv6 traffic over campus networks to reveal the current IPv6 traffic development and leverages various analytical approaches from past work to provide a more comprehensive analysis of IPv6 traffic. In addition, we use two campus networks' data over two months for analysis, which makes our analysis results more conclusive than many previous works.
# Research Progress of Visual Inspection Technology of Steel Products--A Review
## 6 Analysis
### Market Size Analysis of Visual Inspection  
In 2017, the size of the global machine vision market was about USD $7.2 billion, growing 6.8% year-on-year. The market size is expected to be USD $7.7 billion in 2018, and could break through USD $9 billion in 2021, with an expected average annual compound growth rate of around 7.5% for 2018-2021. Germany and the United States are the world's two largest national machine vision markets, accounting for more than 30% of the worldwide market in 2017. China's machine vision industry has emerged since 2010, and is now in a period of rapid development. China's market size in 2017 was CNY $2.9 billion (about USD $42.64 million), accounting for 6.41% of the global market, and up 18.3% year-on-year. With the deepening of automation and the intellectualization of various industries, it is estimated that the average annual growth rate of China's machine vision market will be around 20% in 2018-2021, which was higher than the global average growth rate, as shown in Figure 12[90].  
The world's major machine vision manufacturers include Keenshi, Konrad, Darsa, Panasonic, and Omron. In 2016, their combined market share was about 38.0%. Typical Chinese enterprises are Daheng, New Epoch Technology, and Shenzhen JT Automation Equipment, which are less competitive compared with international well-known players, and each made up less than 1.5% of the global market in 2016.  
At present, Chinese machine vision products are mainly used in semiconductor, electronic manufacturing, automobile, and other fields. The demand for machine vision in these fields accounted for nearly 60% of total demand in 2017.  
Figure 11: Distribution of classification methods.
## 5 Conclusions  
In this paper, we propose a method for text-line image recognition of Amharic, an old Semitic language. Amharic has it own indigenous script and is rich in a bulk of historically printed and handwritten documents. However, it is an underprivileged group of scripts in Natural Language Processing (NLP) due to the lack of extensive research in the area and the lack of annotated datasets. Therefore, in this paper, we present an end-to-end trainable neural network architecture which consists of CNN (the feature extractor), LSTM (the predictor), and CTC (the transcriber) in a unified framework. The proposed model is evaluated using a publicly available Amharic database called ADOCR, and it outperforms the state-of-art methods employed on this benchmark dataset [9] by a large margin.  
As part of future work, the proposed method will extended for handwritten Amharic document image recognition. Similarly, we have planned to develop an OCR system that should recognize some complex Amharic documents, such as historical and scene Amharic text images.  
Figure 7: Learning loss comparison: (**a**) The training and validation losses of CTC in the original paper [9] recorded for 50 epochs. (**b**) The training and validation CTC-loss of the proposed model recorded for 10 epochs.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **No. of Text-Lines** & **Image Type** & **Font Type** & **CER (\%)** \\ \hline Addis [24]\({}^{*}\) & 12 pages & printed & - & 2.12\% \\ Belay [9] & 2,907 & Printed & Power Geez & 8.54\% \\ Belay [9] & 9,245 & Synthetic & Power Geez & 4.24\% \\ Belay [9] & 6,479 & Synthetic & Visual Geez & 2.28\% \\ Ours & 2,907 & Printed & Power Geez & 1.56\% \\ Ours & 9,245 & Synthetic & Power Geez & 3.73\% \\ Ours & 6,479 & Synthetic & Visual Geez & 1.05\% \\ \hline \hline \end{tabular} \({}^{*}\)Denotes methods tested on different datasets.  
\end{table}
Table 5: Comparison of test results (CER).  
**Author Contributions:** Conceptualization, B.B.; Methodology, B.B. and T.H.; Validation, B.B.; Writing--original draft preparation, B.B.; Writing--review and editing, T.H., M.M., and G.B. Supervision, M.L. and D.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The first author was supported by a DAAD scholarship (Funding program No. 57375975). This research was carried out at the Augmented Vision lab at DFKI, Kaiserslautern, Germany, and it was partially funded by the BMBF project VIDETE (01 IW1800).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Green's Function for Acoustic Problems in Pekeris Waveguide Using a Rigorous Image Source Method
## 1 Introduction
To meet the requirements of studying the structural acoustic radiations in shallow water, in which the near- and far-field problem will be involved, a common strategy is to apply a combination of the near- and far-field GFs to ensure high computational efficiency and accuracy (e.g., the image source method (ISM) and the NM for the far- and near-field sound fields, respectively) [15; 16]. However, there are some limitations to such a strategy. First, it will involve a distance connection problem of the near and far fields, which is inconvenient to execute in the calculation program. Additionally, the near-field ISM involves the problem of the difference between the symmetry of the incident spherical wave and the form of the fluid-fluid interface. The classical ISM, in which a process that uses the incident spherical wave multiplied directly by the constant reflection coefficient of the plane wave to represent the reflected sound field is feasible for an ideal seabed since the reflection coefficient is always constant (i.e., 1) at any angle, where each order of plane wave in a spherical wave multiplied by a constant is equal to a spherical wave multiplied directly by a constant. However, for the liquid seabed, for example, the Pekeris waveguide, the reflection coefficient of a plane wave at the liquid-liquid interface is not always constant, and when there is sound absorption in the seabed medium, there can be no total reflection. It is inaccurate to represent the reflected sound field by the spherical wave directly multiplied by a certain reflection coefficient. Therefore, the strategy of using a combination of the ISM and NM cannot accurately solve the far- and near-field acoustic problem, and a more adaptable GF needs to be explored. In this paper, a rigorous ISM model of the interaction between the spherical wave and the plane interface on the liquid-liquid boundary is established by combining classical ISM with the wave acoustics, an accurate GF is derived for the near and far fields in the Pekeris waveguide, and the sound attenuation in a homogeneous liquid seabed is introduced. This theoretical model mainly consists of three parts. First, the spherical wave of a point source is decomposed into plane waves in different directions based on the Fourier transform. Then, we need to make the symmetry of the sound field waves unified with the form of the plane interface, as we are more familiar with the theory of reflection and transmission of plane waves. Thus, it is necessary to convert the spherical coordinates to Cartesian coordinates, then each plane wave is multiplied by the corresponding reflection coefficient to obtain the reflected sound field. Finally, all of the plane wave components of the reflected sound field are superimposed to obtain the spherical wave reflected by the liquid-liquid interface. The results calculated by this rigorous ISM are compared with the standard WI to verify the accuracy for the near- and far-field acoustic problem in the Pekeris waveguide.
## 4 Baseband Modulation Instability
### Analytical Approach  
To study the correlation between rogue waves and modulation instability, plane waves with identical amplitude are considered,  
\[\begin{array}{l}u_{0}=\rho\exp\left[i(-3\sigma\rho^{2}z)\right],\\ v_{0}=\rho\exp\left[i(-3\sigma\rho^{2}z)\right],\\ w_{0}=\rho\exp\left[i(-3\sigma\rho^{2}z)\right].\end{array} \tag{4}\]  
Small perturbations of the form \(\exp[i(Kt-Wz)]\) would be governed by  
\[\begin{array}{l}W^{6}-\left[3K^{4}+2\left(\delta^{2}+3\sigma\rho^{2}\right)K ^{2}\right]W^{4}+\left(3K^{8}+12\sigma\rho^{2}K^{6}+\delta^{4}K^{4}\right)W^{2 }\\ -\left[K^{12}+2\left(-\delta^{2}+3\sigma\rho^{2}\right)K^{10}+\delta^{2}\left( \delta^{2}-8\sigma\rho^{2}\right)K^{8}+2\sigma\rho^{2}\delta^{4}K^{6}\right]=0.\end{array} \tag{5}\]  
Focusing on the instability of low frequency disturbances (\(K\to 0\)), \(c=W/K=O(1)\) is determined from \(c^{6}-2\left(\delta^{2}+3\sigma\rho^{2}\right)c^{4}+\delta^{4}c^{2}-2\sigma \rho^{2}\delta^{4}=0\).  
This is identical to the dispersion relation Equation (3) with a slight change in variable: \(\left(i\Omega_{0}\right)^{6}-2\left(\delta^{2}+3\sigma\rho^{2}\right)\left(i \Omega_{0}\right)^{4}+\delta^{4}\left(i\Omega_{0}\right)^{2}-2\delta^{4}\sigma \rho^{2}=0\), confirming again the relation between baseband modulation instability and rogue waves.  
Figure 1: The rogue wave solution given by Equations (2) and (3) with \(\sigma=1\), \(\sigma=1\), \(\delta=0.1\) and \(\Omega_{0}=0.0537+0.0537i\): (**a**) \(u\)-component (bright FPRW); (**b**) \(v\)-component (EDRW); (**c**) \(w\)-component (FPRW); (**d**) the top view of (**a**); (**e**) the top view of (**b**); (**f**) the top view of (**c**).