esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Analysis  
Each wireless back-haul link--either the one with the long path of 1.8 km or the short path of 300 m--can support up to 4.5 Gbps and 2.8 Gbps using 256-QAM and 32-QAM modulations under clear sky and light rain conditions. To maintain the link availability during heavy rain, the total throughput was sacrificed by reducing the number of transmission bits by applying the adaptive modulation technique. Detailed analysis for utilization of adaptive modulation modes along 1.8-km and 300-m links are shown in Figures 5 and 6. Figure 5 shows that at 98% of time, the 1.8-km link is available using high-order modulation of 256 QAM to support the maximum throughput of 4.5 Gbps for the link. For short link of 300 m, Figure 6 shows that the link is available at 98.83% of time with 32 QAM (representing the highest order modulation for short link based on the link setup) to support the maximum throughput of 2.8 Gbps for the link. As both links of 73.5 and 83.5 GHz have the same performance of rain attenuation either in long distance (1.8 km) or short distance (300 m), the measured throughput represents the full-duplex wireless back-haul link.  
Figure 4: Cumulative distribution for maximum rain attenuation along 300-m LOS links.  
Figure 3: Cumulative distribution for maximum rain attenuation along 1.8-km LOS links.  
Figures 7 and 8 present the measured throughput in the full-duplex E-bands links with different rain rate for long (1.8 km) and short (300 m) links, respectively. From visual inspection of the figures, it can be noted that the throughput degrades with increasing rain rate. It can be shown that the maximum throughput of 4.5 Gbps and 2.8 Gbps can be achieved at any rain rate below 5 mm/h and 10 mm/h along 1.8 km and 300 m, respectively. For the long link of 1.8-km LOS path, Figure 7 shows that the rain rates above 5 mm/h and below 32 mm/h degrade the throughput by 1 Gbps. For heavy rain rates above 80 mm/h, the throughput is around 283.2 Mbps, which is dramatically degradation compared to low rain rates below 20 mm/h. For short link of 300 m LOS path, Figure 8 shows that the rain rates above 10 mm/h and below 50 mm/h degrade the throughput by 0.6 Gbps. For heavy rain rates above 110 mm/h, the throughput is around 283.2 Mbps, which is dramatically degraded compared to low rain rates below 65 mm/h.  
If we define the rain rate as variable \(r\) and maximum throughput as constant \(c\), the measured throughput degradation with rain rate can be modeled as  
\[\text{Throughput} =-44.97\times r+c, \text{where }\ d =1.8\ \text{km}\] \[\text{Throughput} =-18.72\times r+c, \text{where }\ d =0.3\ \text{km}. \tag{3}\]  
The sum square errors of (3) \(R^{2}\) are found to be 0.9753 and 0.8993 for 1.8 km and 300 m, respectively.  
Figure 5: Adaptive modulation modes for full-duplex wireless back-haul E-band link along 1.8 km.  
Figure 6: Adaptive modulation modes for full-duplex wireless back-haul E-band link along 300 m.
# Next Generation Wireless Technologies for Internet of Things
## 2 A Review of the Contributions in This Special Issue
Moreover, a decision mechanism of the broadcasting data is employed to enhance the security and reliability of the positioning information. The obtained results reveal that the proposed algorithm efficiently improves the speed and precision of the positioning, also reducing the possibility of the anchor nodes suffering abnormal interference, and meeting the needs of energy efficiency. Finally, a monitoring system, based on a line information reader and line information generator for optical fiber communication networks, is proposed in [13]. The proposed setup applies to control and manage defaulted optical lines. Simulation results demonstrate that the smaller the radius of curvature and the longer the wavelength of the transmission optical signal, the more the optical radiation field leakage of the fiber bending.
## 2 Problem Context  
Given the existence and use of UAVs for assessing damage and identifying areas to deploy SAR teams to in the aftermath of a tornado, multiple methods can be used for routing deployed UAVs to identify damaged areas as quickly as possible. For example, the UAVs may be routed through the entire identified search area [11]. However, the actual damaged area is significantly smaller than a proposed search area.  
An example of such an area can be seen in Figure 1. Here, we have a red polygon, which represents an area that may have tornado damage, with the cyan polygon representing the actual damage caused by the tornado. The objective of this paper is to identify the area denoted in cyan, in relation to the initial search area outlined in red. Note that the tornado damage area may not be totally encompassed by the SBW.  
To effectively search the area, previous works assume that people in need of aid would be within some radius of a road, which has been denoted \(r_{scan}\). The performance metrics outlined in [1, 6, 11, 12] yield an \(r_{scan}\) of 300 m. Using the road network as a proxy for the location of people (as in [11]), and heuristically finding the minimum number of waypoints with a radius of \(r_{scan}\) that can cover the road network in a given area. An example of these waypoints can be seen in Figure 2. Given the context of Figure 2, a UAV that is scanning the area is attempting to identify the waypoints in green by searching through the collection of blue waypoints. The waypoints in green will not be known when the UAV is launched. Previously, the UAV only considers waypoints inside the SBW and will never inspect the damaged waypoints outside the SBW [11, 12]. That is, \"damaged waypoints\" outside the SBW (for example, the waypoints west of the red box outlined in Figures 1 and 2) would never be inspected in the previous literature. This paper attempts to allow the UAV to scan the entire area as well as identify damage that may occur outside of a search area.
# Integrated Hydrological Analysis of Little Akaki Watershed Using SWAT-MODFLOW, Ethiopia
## 2 Materials and Methods  
### Study Area Description  
Little Akaki catchment is one of the suppliers to the flow of the Akaki river. It is located in the west of Addis Ababa and within Oromia Region. It is located between UTM grids 453,334.5 m to 469,476 m East and 991,929 m to 1009,619 m north. The elevation of the catchment area varies from 2370 to 3368 m above sea level (Figure 1). The physiographic components in this area are rolling plains, valleys, steep river banks, hills, and mountains.  
The daily average temperatures in the area range from around 10 to 25 \({}^{\circ}\)C and the aerial average annual catchment rainfall is 1204 mm, as estimated at Addis Ababa Observatory. Like other parts of Ethiopian highlands, the climate of the Little Akaki catchment is described by two separate seasonal weather patterns. The main rainy season extends from June to September and contributing around 70% of the yearly total rainfall (Figure 2). A small rainy season from mid-February to mid-April contributes additional moisture to the region.  
The geology of the Little Akaki watershed is a basic part of the growth and advancement of the Ethiopian Plateau and the rift system. The catchment system is protected by volcanic rocks covered by fluvial and residual soils, in which black cotton soils are the predominant, varying in thickness from a few centimeters to about 20 m. The leading lithologies include basalts, rhyolites, trachytes, scoria, trachy-basalts, ignimbrites, and tuff. The geological structures, fractures, and faults are mainly related to the rift tectonic extensions. These greatly weathered, broken lithologies favor the movement and storage of groundwater [11; 15]. The aquifer properties and the hydraulic complexity of these volcanic rocks in the area are controlled by the litho-stratigraphy of the volcanic rocks and their complex spatial distribution, their different joint stratigraphic relationships, their substantial compositional, structural, and textural variability, and their diverse stages of tectonization and weathering [9; 16].
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Optimal Coding
#### 3.5.2 Fiber/Wireless-Based Autoencoder  
The parameters of the fiber channel or wireless channel are very important for end-to-end learning, so that the back-propagation algorithm of the neural network can be effectively calculated. In an optical fiber communication system, the channel is governed by the nonlinear Schrodinger equation (NLSE). Therefore, in order to apply autoencoder in optical fiber communication, an appropriate equivalent deep neural network of NLSE has to be established [87; 88; 89; 90]. The experimental results demonstrated that the input information was mapped to a set of robust transmitted waveforms via autoencoder and detected with a measured BER under FEC threshold in intensity-modulation direct-detection (IM/DD) optical-fiber systems [87; 88]. The successful demonstrations in both wireless communication and optical fiber systems clearly validate that end-to-end learning can be a promising technology to fundamentally reconsider communication-system design [77; 87].
# On Transform Domain Communication Systems under Spectrum Sensing Mismatch: A Deterministic Analysis
## 2 System Models and Modulation Schemes
### Two Waveform Modulation Schemes  
In a typical CCSK, the cyclic-shifted sequence (namely the FMW \(\mathbf{b}\) in the TDCS) is used to compose a data symbol. For \(M\)-ary CCSK signaling, \(\log_{2}(M)\) bits of data are gathered to map a complete data  
Figure 1: An example of the spectrum sensing result for vector A.  
Figure 2: Block diagram of the TDCS transmitter (two modulation methods in one general diagram).  
symbol, i.e., \(S_{C}\in\{0,1,...,M\}\) (data1 in Figure 2), referring to any given alphabet (we assume \(M=N\) in this work for convenience). When an \(M\)-ary order CCSK is used, the signal to be transmitted is generated by cyclically shifting by \(\tau\in\{0,1,...,M\}\):  
\[(\mathbf{x})^{(CCSK)}=\{x_{0},x_{1},...,x_{N-1}\}=\{b_{\tau},b_{\tau+1},...,b_{N },b_{0},...,b_{\tau-1}\} \tag{3}\]  
According to [PERSON]'s OFDM-based implementation [25], the CCSK operation can be replaced by using FFT to reduce computational complexity:  
\[(x_{n})^{(CCSK)}=\lambda\frac{1}{\sqrt{N}}\sum_{k\in\Omega^{C}}\left(e^{jm_{k} }e^{-j2\pi kS_{C}/N}\right)e^{j2\pi kn/N} \tag{4}\]  
Compared the CCSK-based TDCS, the QPSK-based TDCS removes the cyclic-shift operation and acts more like a multi-carrier CDMA (MC-CDMA). Before IFFT operation, a given QPSK symbol, \(S_{Q}=e^{j\varphi}\) (data2 in Figure 2), is spread directly over each subcarrier of the spectral waveform \(\mathbf{B}\), as depicted in Figure 2. The corresponding transmitted signal is given as:  
\[(x_{n})^{(QPSK)}=\lambda\frac{1}{\sqrt{N}}\sum_{k\in\Omega^{C}}\left(e^{jm_{k }}e^{j\varphi}\right)e^{j2\pi kn/N} \tag{5}\]  
Comparing [26] with [25], different modulations result in different transmitting waveforms. Despite different modulation methods are employed, the above-mentioned two TDCS models are fully compatible with OFDM transmission due to its multicarrier structure, as mentioned in [27].
# Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks
## 3 Game Theoretic Framework and Utility Function Design
Let Equation (1) be the utility function to be maximized by link \(i\in\mathcal{M}\) at time slot \(t\), such that_  
\[u_{i}(s_{i}(t)=k)=\gamma_{i,k} \tag{5}\]  
_The utility function in Equation (5) corresponds to utility obtained by link \(i\) on subchannel \(k\) assuming \(\boldsymbol{s}_{-i}(t)=\boldsymbol{s}_{-i}(t-1)\), i.e., all other links continue to use the subchannel selected in a previous time slot. This subchannel allocation game has eight possible subchannel allocation profiles, \((s_{1},s_{2},s_{3})\); two same-subchannel profiles are \((1,1,1)\) and \((2,2,2)\), and six mixed-subchannel profiles are \((1,1,2),(1,2,1),(1,2,2),(2,1,1),(2,1,2)\) and \((2,2,1)\)._  
_Case I: If initially at time slot \(t=0\), a same-subchannel profile is considered, say \((s_{1},s_{2},s_{3})=(1,1,1)\), then all links have an incentive to change subchannel at next time slot \(t=1\) since \(\text{BR}_{i}(s_{-i})=\{2\}\), \(\forall i\in\mathcal{M}\). This will change the strategy profile \((1,1,1)\) to any one of the mixed-subchannel profiles: \((1,1,2)\), \((1,2,1)\) or \((2,1,1)\), depending on which link is chosen to update their strategy at the next time slot \(t=1\)._  
_Case II: If initially at time slot \(t=0\) a mixed-subchannel profile is considered, say \((s_{1},s_{2},s_{3})=(1,1,2)\), then at next time slot \(t=1\), link \(1\) has an incentive to change to subchannel \(2\) since \(\text{BR}_{1}(s_{-i})=\{2\}\). The sequenceof subchannel switching is \((1,1,2)\rightarrow(2,1,2)\rightarrow(2,1,1)\rightarrow(2,2,1)\rightarrow(1,2,1) \rightarrow(1,2,2)\rightarrow(1,1,2)\rightarrow\ldots\); i.e., it does not converge to a pure strategy NE. In fact, none of the six mixed-subchannel profiles can satisfy Equation (4). This subchannel allocation game does not converge to a pure strategy NE._
# Railway Bridge Condition Monitoring Using Numerically Calculated Responses from Batches of Trains
###### Abstract  
This study introduces a novel method to determine apparent profile of the track and detect railway bridge condition using sensors on in-service trains. The concept uses a type of Inverse Newmark-\(\beta\) integration scheme on data from a batch of trains. In a self-calibration process, an optimization algorithm is used to find vehicle dynamic properties and speed. For bridge health monitoring, the apparent profile of the bridge is first determined, i.e., the true profile plus components of ballast and bridge deflection under the moving train. The apparent profile is used, in turn, to calculate the moving reference deflection influence line, i.e., the deflection due to a moving (static) unit load. The moving reference influence line is shown to be a good indicator of bridge stiffness. This numerical approach is assessed using an elaborate finite element model operated by an independent research group. The results show that the moving reference influence line can be found accurately and that it constitutes an effective indicator of the condition of a bridge.  
drive-by; optimisation; track profile; bridge damage; apparent profile; moving reference; influence line +
Footnote †: journal: Applied sciences
# Analyzing Zone-Based Registration Using a Three Zone System: A Semi-Markov Process Approach
## 4 Numerical Results
### Total Cost for Various CMRs  
Figure 9 shows the total costs of 1Z, 2Z and 3Z systems for various call-to-mobility (CMR = \(\lambda_{i}\)/\(\mu\)) ratios. CMR = 1/2 indicates that the UE entered two zones between incoming calls. Note that a smaller CMR means that the UE entered more zones between incoming calls. The CMR value is considered from 0.125 to 1.00 to consider real situations. From the figure, we can see that 3 ZR outperforms not only 1Z but also 2Z. We can also see that, as CMR decreases, the relative gap between 1Z/2Z and 3Z increases. Even if 3Z is expected not to outperform 1Z/2Z especially when CMR is very high, such a high CMR seems to be unrealistic. In summary, 3 ZR generally outperforms 1Z and 2Z in real situations.
## 3 Materials and Methods
### Dataset
#### 3.1.1 Description  
The original Corsican dataset [10] comprises almost 2000 wildfires captured with different camera configurations. The pictures were shot in the visible (Please refer to Figure 2b) and near-infrared (Please refer to Figure 2c) spectral ranges at a resolution of 1024 \(\times\) 768 pixels and stored in portable graphics format (png). The present study selected 1175 pictures from the available image collections, spotting heterogenous color flames with proper background conditions (light and textures). The dataset comprises a collection of multimodal images (see Figure 2a) captured with a \"JAI AD-080 GE\" camera [10]. This sort of camera can simultaneously capture images in the visible and near-infrared spectra using the same optics that are aligned. The Corsican dataset is built adopting a homography matrix transform-based picture registration technique, and every image is annotated with its corresponding segmentation mask.  
Figure 1: The proposed framework for fire detection. First, the dataset is labeled and correctly formatted; later, the model is trained to locate the flames. Then the deeplabv3+ model is used to generate the segmentation mask.  
The FLAME [11] dataset is another public aerial fire dataset adopted in the present study. The FLAME dataset pictures were captured using drones and are primarily composed of videos; some were transformed into frames for segmentation purposes. The designated videos were shot using a Zenmuse X4S and a phantom 3 camera with a resolution of 640 \(\times\) 512 and a frame rate of 30 frames per second. The dataset consists of 2003 pictures already annotated with corresponding ground truths for every picture.  
Firefront Gestosa is an unlabeled aerial private dataset (which will be made public in the future) obtained during the Firefront project team's Gestosa mission [37]. This dataset primarily consists of five main recorded videos that last between two and three minutes. Please refer to Table 1 for further information about each video. The last video is not an actual recording of the mission; it is a compilation of multiple perspectives from the first five videos. Indeed, this dataset poses a significant challenge as it comprises images completely obscured by smoke, making it quite hard to distinguish the flame location from the rest of the image.  
Two hundred thirty-eight fire frames were selected for the present study experimentation through the already existing videos. The fire images were manually labeled. More details on the data annotation procedure are explained briefly in the subsequent section.  
Figure 2: Examples of fire pictures are included in our simulation dataset: (**a**) Multimodal, (**b**) RGB spectra, (**c**) IR spectra pictures taken from the Corsican dataset. (**d**) RGB pictures from FLAME dataset, (**e**) pictures from Firefront_Gestosa.
## 6 Conclusions  
In this study, we propose 6-DoF pose estimation and CAD model retrieval methods to align the projection of the CAD model with an object in an RGB image. The pose estimation is performed by using a deep learning network which is based on the PoseContrast network. To enhance the performance of the class-agnostic PoseContrast network, we add two sub-networks to emphasize the feature weight from the original ResNet50. Variance uncertainty weight and attention sub-network modules enhance the performance of pose estimation. After the pose estimation of the RGB object, we retrieve the matching CAD model from a CAD dataset. The quantitative analysis of the correlation between the 6-DoF pose estimation and the CAD model retrieval has been conducted. We evaluate the performance of pose estimation on Pascal3D+, Pix3D, and ObjectNet3D datasets. Through the experiments, we show that the proposed network reduces median errors in calculating the rotation angle of a 3D object in the RGB image. Furthermore, it is shown that the matching CAD model can be effectively retrieved using two similarity measurements (SM-AT and SM-NI). In addition, we find that there is a positive correlation between the pose estimation and CAD model retrieval performances.  
In our experiments, there is a limitation in the training stage of the proposed network. The Pascal3D+ dataset, used for training, exhibits a bias in the distribution of Euler angles of the RGB objects. This bias may affect the network's ability to estimate the pose of the objects. In future studies, we will focus on developing a new training dataset with evenly distributed training angles.  
Conceptualization, S.-Y.P.; methodology, software, validation, formal analysis, S.P., M.M. and W.-J.J.; investigation, S.P., W.-J.J., M.M. and S.-Y.P.; writing--original draft preparation, S.P. and W.-J.J.; writing--review and editing, S.-Y.P.; supervision, S.-Y.P.; project administration, S.-Y.P.; funding acquisition, S.-Y.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported partly by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2021-0-00320, Manipulation and Augmentation for XR in the Real-World Environment) and partly by Culture, Sports and Tourism R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture, Sports and Tourism in 2024 (No. RS-2024-00341886).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 1 Introduction
The framework now incorporates two low-resource languages, namely Urdu and Arabic. However, there are several challenges associated with including these languages in the Digital News Stories Archive (DNSA), mainly due to their low-resource nature. This study identifies various challenges related to different aspects of low-resource languages, making it difficult to incorporate them into the digital news stories archive. These challenges encompass issues related to volume, variety, and velocity during archival information packaging; technical difficulties encountered during the creation of the archive; and challenges with the dissemination of archived content. One of the major obstacles is the scarcity of resources available for low-resource languages. For example, the tokenization of Urdu scripts is less efficient and inconsistent in terms of space than that of high-resource languages, and comprehensive dictionaries, which are fundamental linguistic resources, are not available for either Arabic or Urdu. As a result, extensive preprocessing becomes necessary during the preservation process to compensate for the lack of these resources. Establishing linking mechanisms and metadata is crucial during the preservation process to ensure the efficient dissemination of archived news articles sourced from multiple languages and diverse sources [7]. The second part of this paper introduces bilingual news linking mechanisms, specifically the \"Common Ratio Measure for Dual Language (CRMDL)\", which is based on the ratio of common terms, and the \"Similarity Measure based on Transliteration Words (SMTW)\", which relies on words translated from the English language in Urdu news articles. These mechanisms are implemented to facilitate the accessibility of news articles that were extracted and archived from various sources during the preservation phase. This paper compares these linking algorithms and discusses the effectiveness of the results. By incorporating different linking mechanisms, the Digital news stories preservation (DNSP) framework is enriched and enhanced to ensure future accessibility of the archived content. Section 2 and subsections differentiate low-resource languages from high-resource languages and introduce challenges in IRLs, with a brief overview of the Urdu and Arabic languages. Section 3 presents details about the digital news story preservation framework initiative, the importance of preservation, research challenges, DNSP framework enhancement, the multilingual archive and its structure, and major issues encountered in enhancing the extraction tool. In Section 4, extraction quantification is comprehensively discussed. Section 6.3.2 compares the results of bilingual linking mechanisms, and in the last section, the findings are summarized.