esa-sceva/satcom-chunk-collection huggingface dataset:
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 4 Results and Discussion
### Computational Efficiency and Memory Usage of the Model  
This section presents an analysis of the technical performance of the polynomial model. Two key metrics are considered: computational efficiency, which is measured by the execution time of the noise figure evaluation functions, and memory usage, which includes both the storage size of the model files and Python scripts in local memory, as well as the amount of RAM required for the library to perform computations. These metrics were assessed for the Python implementation of the model evaluation class, though they may vary depending on the specific solution used.
## 5 Discussion  
### Influence of the Use of a Cosine-Corrected Foreoptic  
Hemispherical measurements enable the sampling of a wider area [23], and so a cosine-corrected foreoptic can also be used to collect the up-welling radiance reflected by the vegetation. The resulting spectral measurement footprint matches better with the eddy flux observations. [PERSON] et al. [42] have stated that for hemispherical up-welling observations using a cosine-corrected foreoptic, 90% of the total radiation comes from a field of view (FOV) of 144\({}^{\circ}\), which corresponds to 75.68% of the area of the flux measurements. Therefore, for a heterogeneous forest ecosystem, a cosine-corrected foreoptic should be used to collect the up-welling radiance. According to the statistical results [57] from 2011, about one-third of the observation systems at flux towers use hemispherical observations of the upward vegetation irradiance. The up-welling radiance is collected using a bare fiber for crop and grassland sites, whereas a cosine-corrected foreoptic is used for forest sites.  
However, there are also some disadvantages of using a cosine-corrected foreoptic to collect the up-welling radiance. Firstly, the large light-loss fraction (about 70% at the far red band) increases the risk of a mismatch between the two channels because the detector integrating time is longer under weak illumination. Secondly, the SNR will slightly decrease thanks to the low transmittance of the cosine corrector, which will influence the quality of the SIF retrieval results [42]. Thirdly, the shadow of the tower will be included in the hemispherical field of view. The size of this effect will vary with the solar zenith angle and influence the potential application of the observations [23]. Finally, the calculation of the radiation transfer path is more complex for hemispherical observations, which is an important consideration when conducting atmospheric corrections. [PERSON] et al. [42] found the equivalent radiance transfer path length to be twice the height of the sensor for tower-based hemispherical observations. However, this was based on assumptions that ignored the heterogeneity of the surface and the directional characteristics of the SIF and reflectance [42].
# A Data-Intelligence-Driven Digital Twin Framework for Improving Sustainability in Logistics
## 1 Introduction
### Case Study  
The case study is simply that of the pick-up and drop-off segment of the transport company in which assets are transported based on customer orders across the UK. A fleet consists of a truck and trailer. Figure 1 provides an overview of a sample daily collection of jobs at a given time and the real-time location of vehicles/drivers, depicting a non-optimized plan with no routes included.  
The business requirements for this case study are to develop a solution that will track the fleet in real time and apply a model that can recommend optimized job allocations and routes to minimize the occurrence of empty runs and cut down emissions. Therefore, the objective of the DT framework is to achieve the following.  
* Track emissions and empty runs in real time from journeys related to jobs.
* Apply an intelligent model that recommends optimized job plans and routes.
* Facilitate feedback mechanisms from human agents to inform and improve the model's accuracy.
## 2 Related Work
### Description of Data Clustering Problem  
In data clustering problems, data objects sharing similar characteristics are grouped together into a cluster, such that data objects in one cluster are different from data objects in other clusters. In most cases, the number of clusters is specified, while in some cases,especially in high dimensional datasets, predetermining the number of clusters is not feasible. In optimization terms, data objects are clustered, based on the similarity or dissimilarity between them, such that the inter-cluster similarity is minimized (maximizing inter-cluster dissimilarity), while maximizing the intra-cluster similarity (or minimizing intra-cluster dissimilarity). The description of a clustering problem as an optimization problem is given below.  
Given a dataset \(X=(x_{1},\ x_{2}\ldots,x_{n})\) of dimension \(d\) which represents the number of attributes or features of the data objects in the datasets such that \(x_{i}\ (i=1,2,\ldots,n)\) where \(n\) is the number of data objects in the dataset. Each data object \(x_{i}=(x_{i1},\ x_{i2},\ \ldots,x_{id})\), where \(x_{i1},\ x_{i2},\ \ldots,x_{id}\) represents all the features for data object \(x_{i}\). The dataset \(X\) can be represented in a matrix form as shown in Equation (15):  
\[X=\begin{bmatrix}x_{1}\\ x_{2}\\ \vdots\\ x_{i}\\ \vdots\\ x_{n}\end{bmatrix}=\begin{bmatrix}x_{1,1}&x_{1,2}&\ldots&x_{1,d}\\ x_{2,1}&x_{2,2}&\ldots&x_{2,d}\\ \vdots&\vdots&\vdots&\vdots\\ x_{i,1}&x_{i,2}&x_{i,j}&x_{i,d}\\ \vdots&\vdots&\ddots&\vdots\\ x_{n,1}&x_{n,2}&\ldots&x_{n,d}\end{bmatrix} \tag{15}\]  
\(X\) is required to be grouped into \(k\) number of clusters to satisfy Equations (1)-(3), using Equation (4) as the objective function, with each cluster having a cluster center defined in Equations (5) and (6). For an automatic clustering, the number of clusters is not defined. Therefore, finding the optimum number of clusters represented by equation (6) becomes the optimization problem that seeks to optimize the function \(f(CC,\ D)\) over all possible clustering of \(X\) where function \(f\) represents the global validity index for obtaining the best quality clustering solution and \(D\) represents the distance metric measure, stated in Equation (4).
# Minimum Distance Optimization with Chord Edge Growth for High Girth Non-Binary LDPC Codes
## 2 Non-Binary LDPC Codes Based on High Girth Hamiltonian Graphs  
In this section, the Hamiltonian representation of NB-LDPC codes, the FRC of cycles, and a conventional optimization method of NB-LDPC codes are reviewed. First, a class of NB-LDPC codeswith variable nodes of the degree of two and the Hamiltonian graph representation of these codes are described. Subsequently, the cycles and the FRC of the cycles are introduced, which are the foundations of the proposed design method. Finally, the method proposed by [PERSON] et al. [15] that improves the error floor while using the algebraic properties of cycles and stopping sets is briefly presented.
# Study on Components Determination and Performance Evaluation of LS Pre-Maintenance Agent
## 5 Road Performance Test Results Analysis
### Restore Performance  
The results of the matrix asphalt with adding 25, 35, and 45% asphalt mass of LS pre-maintenance agent and Rhinophilat are shown in Table 7.  
From Table 7, the softening point declined with the increase of the LS pre-maintenance agent. 25 \({}^{\circ}\)C penetration increased significantly compared to 3 h aging. The softening point in adding 45% LS and Rhinophilat agent decreased by 63% and 57%. 25 \({}^{\circ}\)C penetration increased by 261% and 247% and it can be seen that the LS pre-maintenance agent was similar to Rhinophilat on restorative performance. The softening point and penetration of the aged asphalt varied greatly with the increase of the agent. In general, when 25 \({}^{\circ}\)C penetration drops below 2 mm, severe cracking of the road surface occurs.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Indexes** & **Original** & **Aging 3 hrs** & **25\% LS** & **35\% LS** & **45\% LS** & **25\%** & **35\%** & **45\%** \\  & & & & & & & Rhinophilat & Rhinophilat \\ \hline Softening point/\({}^{\circ}\)C & 45.9 & 53.3 & 51.5 & 36.6 & 20.3 & 43.3 & 39.8 & 22.1 \\
25 \({}^{\circ}\)C Penetration/0.1 mm & 100.2 & 62.3 & 75.7 & 197 & 223 & 95.8 & 207 & 218 \\
15 \({}^{\circ}\)C Ductility/cm & 158.4 & 67.1 & 33 & 29.6 & 20.9 & 31.5 & 27.9 & 20.3 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The asphalt indexes comparison results.  
Figure 8: The comparison of average penetration depth. (**a**) 0.5 kg/m\({}^{2}\); (**b**) 1 kg/m\({}^{2}\).  
Above 3 mm acquires good crack resistance ability, which means the LS pre-maintenance agent can significantly improve the anti-cracking ability of asphalt at low temperature. For the softening point, it is an important index to show the temperature sensitivity of the asphalt. Reducing the softening point after adding the LS pre-maintenance agent is very beneficial and improves the low temperature crack resistance of the asphalt. As for ductility, it is generally accepted that the road surface is in a crackable state if 15 \({}^{\circ}\)C ductility is less than 5 cm. 15 \({}^{\circ}\)C ductility in adding two agents were both more than 20 cm, which can meet the requirements of anti-cracking and water sealing. The effect of the two agents on the asphalt indexes were slightly different, because of the solvent oil in the LS pre-maintenance agent, which was more volatile than the naphtha in Rhinophilt, this produced the difference during the process of heating. In summary, the LS pre-maintenance agent had good restorative ability to the aged asphalt, which can effectively improve asphalt low temperature performance.
## 5 Performance Evaluation
### Experiment Setup
#### 5.1.3 Metric  
Based on the above data sets and methods to carry out the experiment, the evaluation criteria used in the experiment are considered from the following perspectives:  
1. Single-step training time  
The single-step training time is used to evaluate the acceleration effect of pipeline parallel training. The single-step training time is the time that it takes to complete a batch of forward pass, backward pass, and parameter update. In order to avoid the error of the actual environment operation, the experiment selects 100 training batch running time results. After that, the average value is taken as the single-step running time of the current allocation plan. The shorter the training time used, the better the acceleration effect of the task distribution plan.
2. Proportion of communication time  
The ratio of communication time to total training time is used to evaluate the optimization effect of pipeline parallel training communication. The smaller the proportion of communication time used, the better the effect the pipeline parallel training overlapped computing and communication tasks achieved--this will speed up the. Suppose that the DNN model layer is divided into s groups, corresponding to s stages in the pipeline. The optimization objective of task allocation method can be expressed as follows:  
\[T=\text{min}\sum_{s=1}^{S}\left(A_{s}+C_{s}\right) \tag{5}\]  
where \(A_{s}\) is the total calculation time in stage s, and \(C_{s}\) is the communication time between stage s and stage s + 1. The goal of task allocation method is to find the optimal pipelined parallel task allocation to minimize \(T\).
3. Training accuracy  
The model training accuracy is used to evaluate the effectiveness of the pipeline parallel training method. The experiment performed 100 epoch training for the model and recorded model training accuracy and time changes. The higher the accuracy, the better the effect achieved by the pipeline parallel training.
## 4 Proposed Model
### Delay and Latency Model
Algorithm 1 presents the request transmission method. Here, node \(N\) is the node that has been selected for the first time; we set the capacity of a node as 5 high priority requests at a time, more than that will lead it to be considered an overloaded node, (1) = high priority, and (0) = low priority. Figure 4 presents the workflow of our algorithm. Figure 3: Graphical representation of proposed model.
## 2 Methodology  
### Analysis of Smart Grid Network Attack Path  
The network attack path of the power system is shown in Figure 1; a remote terminal unit (RTU) was widely used to collect measured data. The measured data were transmitted to the Power Control Center (PCC) through the communication network system. The PCC estimated the state of the power system based on the measured data. The abnormal estimation results were filtered out by bad data detection (BDD), and then the accurate state estimation was carried out again [25; 26]. Based on the final estimation results, the energy management system (EMS) made control decisions on the power system such as capacity control, emergency time analysis, and electric power flow optimization. It then sent the final control signals to the physical grid through the communication system to maintain the power system's normal operation [27]. In recent years, with the deepening of academic research on power system network attacks, a variety of potential network attack modes have been proven to possibly threaten the normal operation of power systems, such as false data injection attacks, control signal tampering attacks, and fragile device attack. This article mainly analyzes these three types of network attacks. The network attack path is shown in Figure 1.  
Among them, when a false data injection attack occurred, the underlying measurement data were uploaded to the power control center through the communication system. In addition, the data had been injected and bypassed the bad data detection mechanism, resulting in erroneous state estimation. The malicious control signal tampering attack modified the directives sent by the control center to the physical device, causing the device to lose control. The fragile device attack occurred on specific power equipment, and the equipment information was maliciously tampered with, which was not controlled by the power control center. The network attack on the CPPS seriously endangered the stable operation of the power system. To maintain the normal operation of the power system, a network attack detection method was urgently needed to predict the occurrence of network attacks or faults in the power system.
## 2 Related Works
### CNN-Based Remote Sensing Image Segmentation  
CNNs have achieved significant success in the field of remote sensing semantic segmentation due to their powerful capabilities in local feature extraction and spatial relationship modeling. Early CNN architectures like AlexNet [18] and VGGNet [23] achieved hierarchical abstraction of image features through stacked convolutional layers, laying the groundwork for subsequent remote sensing image analysis. The fully convolutional network (FCN) [24] replaced fully connected layers with convolutional layers to enable end-to-end pixel-level predictions, marking a milestone in remote sensing segmentation.  
Building on the FCN framework, U-Net [25] introduced an encoder-decoder architecture with skip connections, effectively addressing the challenge of detail recovery in remote sensing images. This design enables the network to fuse low-level edge information with high-level semantic features during decoding, making it especially suited for accurately delineating complex object boundaries in remote sensing scenarios. The DeepLab series [26] further expanded the receptive field by introducing atrous convolution while preserving spatial resolution--crucial for capturing multi-scale features in remote sensing images. Notably, the atrous spatial pyramid pooling (ASPP) module in DeepLabV3+ effectively captures multi-scale contextual information, enhancing segmentation accuracy for objects of varying sizes. To address the unique characteristics of remote sensing imagery, [PERSON] et al. [27] proposed a CNN model based on attention mechanisms that learns to weigh important features, thereby improving recognition of complex land covers. [PERSON] et al. [28] introduced a multi-scale feature fusion strategy to effectively handle large variations in object scale within remote sensing data. However, traditional CNNs have limitations in modeling long-range dependencies, making it challenging to capture broad contextual and non-local features in remote sensing images. This has prompted researchers to explore more advanced model architectures.
## 4 Classification of Energy Efficient MAC Protocols for UWSNs
### Slotted-ALOHA (S-ALOHA)
#### (xi) Slotted-Buffering-ALOHA  
The Slotted-Buffering-ALOHA protocol had been used to save energy and increase the lifetime of networks by researcher in [123]. They mentioned that when it comes to energy efficiency, there is a trade-off with delay in time. This protocol is designed to fix issues that have been identified in previous sections. More conditions are employed in this protocol to ensure that a collision does not occur. The first condition of which is to prevent the node from sending any data packets before the time window begins. The alternate option is to employ carrier sensing (CS) to send abbreviated messages over the control channel to characterize the state of the connection channel. Ensuring that no data is present in the communication channel and thus no collision or loss of data packet happens. Another important feature of this protocol is the usage of buffers to aid in the creation of numerous data packets.
# Blockchain Interoperability for Future Telecoms
## 2 Study Background
### Blockchain Interoperability Use Cases in Telecommunications
#### 2.2.2 Sixth-Generation Mobile Networks  
With the evolution of 6G, many research papers and surveys, such as [24; 25], have aimed to address the new opportunities that blockchain technologies provide. A comprehensive survey by [PERSON] et al. [23] demonstrated the essential use cases, technical aspects, and challenges of blockchain technologies for 6G technology. [PERSON] et al. discussed the challenges of deploying blockchain technologies, including their interoperability. According to [23], interoperability is frequently mentioned as a challenge and a topic of open discussion regarding telecom use cases. According to [24], using blockchain for 3D networking in 6G can provide decentralized and secure data access, offloading, and interoperability solutions. Global coverage is a significant feature of cellular network connectivity in 6G. Based on the initial requirements [26] of 6G mobile networks, three-dimensional (3D) global coverage has been envisioned. The international communication coverage in 6G encompasses the air, space, underground space, and sea. Such coverage requires continuous interactions and strictly controlled access requirements between the layers and other networks. In this sense, smart contracts, distributed ledger technologies, secure identity management, and interoperability introduced by blockchain are strong candidates. These technical requirements are significant features that allow seamless 6G access and coverage [24]. They also facilitate further use cases, such as Industry 5.0, Smart Healthcare, unmanned aerial vehicle (UAV) applications, connected autonomous vehicles (CAVs), Energy Internet, Extended Reality (XR), Holographic Telepresence, and Smart Cities.  
Figure 2 depicts the 6G SAGIN [27] architecture, which is expected to be deployed in 6G mobile networks by 2030. Future directions, opportunities, and challenges for deploying seamless, integrated, and converged architectures have been discussed in [28]. The following sections discuss how blockchain interoperability can be deployed through the use of SAGIN [27] architectures.
## 3 Methods
### Model Implementation and Comparison  
Regional-scale studies were performed at a spatial resolution of 1000 m for the time period spanning from 2001 to 2012. The TL-LUE, VPM, and GPD models were implemented using the MODIS data and the Daymet meteorological data. The modeled daily GPP at 1000 m resolution was then summed for the calendar year to obtain the corresponding annual GPP. Vegetation GPP from the preprocessed 8-day 1000 m MOD17A2H data and from the 8-day 1000 m BESS product were multiplied by 8 for the first 45 values/year and by 5 (or 6 in a leap year) for the final period and then summed to obtain annual values. The yearly 1000 m GPP data from the EC-MOD product were extracted directly for use. All model results are resampled using the nearest neighborhood approach to the MODIS Sinusoid projection to allow for direct comparisons. To quantitatively compare the spatiotemporal patterns of the modeling results, annual GPP was first temporally averaged for the years from 2001 to 2012 and was then zonally and meridionally averaged for every 1 degree along latitude and longitude, respectively [55].  
Site-scale studies were further conducted to understand how the models perform as evaluated against flux tower measurement data on both the 8-day and the yearly bases. For studies on the 8-day basis, daily GPP derived from the TL-LUE, VPM, and GPD models and those obtained from flux tower measurements are temporally averaged for each 8-day time period in a calendar year. All modeled results on both the 8-day and the yearly bases were extracted for the pixel that contains the corresponding flux tower site from the 1000 m datasets. To understand how different climate input data influence the modeling results, the TL-LUE, VPM, and GPD models were also driven using the meteorological data measured at flux tower sites. The commonly used metrics, including the coefficient of determination (R\({}^{2}\)), the root-mean-squared error (RMSE), and the mean bias error (bias), were used for model assessment and comparisons.
## 5 Results and Discussion
### PCA Model  
For PCA modeling, the number of components (\(\vartheta\)) was used as the significant parameter for turning the performance of the PCA model. A trial and error procedure with three PCA models (\(\vartheta\) = 1, 2, respectively) was conducted. As a result, the best PCA model reached \(\vartheta\) = 2 as computed in Table 4. Note that, the k-fold cross-validation method was also used for the development of the PCA model.
###### Abstract  
Incorporating MIMO technology with 3D geometry-based stochastic models (GBSM) is a promising channel modeling technique for 5G and beyond. These models could be extended to high-speed train (HST) environments at mmWave bands. In this paper, the proposed 3D MIMO model is composed of the line of sight component (LOS), the non-line of sight component (NLOS) from one sphere, and multiple stochastic confocal elliptic cylinders. The model is applied in the viaduct and cutting environments with a time-varying Rician K-factor. The local channel statistical properties such as the auto correlation function, stationarity distance, and the level crossing rate (LCR) are derived and thoroughly investigated at the 41 GHz frequency. These properties are compared with the corresponding measured results at the same wave frequency for an HST wireless channel. There is a strong correlation between the results from the derived model and the measured results. Therefore, this model can be extended to be used for viaduct and cutting channel modeling at the mmWave band.  
mm waveband; geometry-based stochastic model; viaduct; cutting; high-speed train; channel statistics; MIMO +
Footnote †: journal: 2022  
Article A 3D MIMO Channel Model for a High-Speed Train Millimeter Wave Communication System under Cutting and Viaduct Environments 202211020222  
20221102022
## 5 Applications
### Space Missions  
The compactness and robustness of DFB laser based sensing systems enables their uses even in harsh environments such as outer space. The capacity of TDLS to provide unambiguous information about occurrence and amount of target species and even selected isotope ratios places them in an ideal position to supplement more conventional measurement techniques such as gas chromatography or mass spectrometry [65, 66].