esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Architectural Framework of IBSN and Its Components
### Communication Strategies
#### 2.5.2 Medium Access Mechanisms  
As mentioned earlier, the wireless radio is the most power consuming block of the sensor node. Ideally, turning on the radio should result in a successful data transfer in order to achieve 100% energy-efficiency and reliability. However, IBSN faces interference in the radio channel, collision, over-hearing of data, and packet-errors. Access mechanisms enable the nodes to efficiently use the medium by eliminating all these problems. The communication strategies of the IBSN should handle all aspects of wireless communication in compliance to the international rules of the wireless spectrum.
## 4 Two-Dimensional Benchmark Cases
### Solid Circular Conductor  
The solid conductor has an identical section area to the 3D models which is 2711 mm\({}^{2}\). When an AC current at 50 Hz is applied, the skin effect causes concentration of the current near the outer periphery, resulting in a non-uniform distribution. Figure 7a reveals the current density colour map, showing higher current density near the surface of the cable.  
Figure 6: \(BN_{\text{ERR}}\) error across different models at 0 Hz (DC) and 50 Hz.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**2D Model** & \begin{tabular}{c} **R\({}_{\text{DC-2D}}\)** \\ **(\(\mu\Omega\)/m)** \\ \end{tabular} & \
\begin{tabular}{c} **R\({}_{\text{AC-2D}}\)** \\ **(\(\mu\Omega\)/m)** \\ \end{tabular} &
\begin{tabular}{c} **R\({}_{\text{AC-2D}}\)** \\ **R\({}_{\text{DC-2D}}\)** \\ \end{tabular} \\ \hline Solid Round Conductor & 6.36 & 11.72 & 1.843 \\ Five-Segment & 6.36 & 11.07 & 1.741 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Per-unit-length resistances for the two 2D benchmark cases at 50 Hz.  
Figure 7: Two-dimensional results for the solid-round-cable current density \(J\) (A/m\({}^{2}\)) at 50 Hz, presented as a colour map (**a**) and plotted along the path shown in the colour map (**b**).  
In Figure 7b, the light blue curve shows the current density in the solid round conductor under AC excitation at 50 Hz, plotted against the radial distance from the center. This profile highlights the skin effect, which forces the current toward the conductor's outer periphery. By contrast, the purple horizontal line corresponds to the uniform current density that would be observed under DC conditions, where neither skin nor proximity effects distort the current distribution.
## 2 Background
### Access Control Limitations
Finally, task-based access control (TBAC) augments the association between both the user and the object. This process is conceived through the fusion of both task-based and contextual data. However, TBAC does have limitations. For example, its primary focus is on contexts that are connected to specific duties within the infrastructure. However, EPRs cannot always be identified as duties/tasks. Team-based access control (TeBAC) groups users within an infrastructure network. It then associates what is referred to as a collaboration context with actions [16]. However, both TBAC and TeBAC have not been established to a level where they can be deployed. As a result, there remains some uncertainty on how the implementation can be achieved within stochastic and ever-changing environments.
# Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling
## 3 Problem Description and Model Design
### Improved Informer Modeling  
The above study provides a detailed analysis of the basic principles of the Informer model and its adaptability in long-sequence time series prediction. The Informer model exhibits significant advantages in long-sequence time series prediction. However, there is a limitation in that it does not consider sequence spatial features. In response to the characteristics of logistics transportation supply data, this paper primarily focuses on improving the Informer model in the following two aspects: (1) establishing multidimensional feature engineering for input data to increase data complexity and enhance model representational capacity and (2) designing spatiotemporal convolutional networks to extract spatiotemporal features of supply quantity, incorporating LSTM modules to further extract both short-term and long-term temporal dependencies.
## 3 Results
### Frequency Analysis: Complex Mode Indicator Functions  
To visualize and compare all the data of the entire surface of the TM across different manipulations, the complex mode indicator function (CMIF) was used as a generalized presentation of the membrane's frequency response. Figure 11 shows CMIFs computed from all three temporal bones with different middle-ear manipulations. The horizontal axis is the frequency in kHz (kilohertz), and the vertical axis is the singular value in decibels, which are the square root of the eigenvalues of the frequency response function (FRF) matrix. We observed that CMIF changes with different middle-ear conditions. We defined the closed cavity condition, as shown in the second row of Figure 11, as the baseline and subtracted other obtained CMIF data from this baseline for further comparison in Figure 12. The open cavity condition generally decreases CMIF amplitude at low frequencies around 1 kHz and induces a major peak at 3 to 5 kHz in all three TBs. A half fluid condition in TB3 and TB4 shows lower CMIF amplitude across the entire frequency spectrum compared to the baseline closed cavity case, although the decrease of CMIF in TB5 is relatively small. Also, for the full fluid case, all three samples have the lowest CMIF amplitudes, consistent with TM motions that are significantly reduced by fluid inside the middle-ear cavity. The CMIFs under the stapes immobilization condition at low-frequency ranges (0.5-2.5 kHz) for TB3 and TB4 are significantly reduced. However, this reduction is not obvious in TB5. Finally, the IS joint interruption condition introduces several additional peaks for CMIFs, particularly at mid- and high-frequency ranges.  
Figure 10: Waveforms of six points of the surface of the TM of TB3 when the middle-ear cavity is open. (**a**) Wrapped optical phase 0.56 ms after excitation; (**b**) TM surface normal displacement 0.56 ms after excitation; and (**c**) time waveforms of six points marked in part (**c**).
## 4 Discussion
For this reason, this study installed an underwater camera to supplement the moored echosounder data by distinguishing and identifying fish species accurately and comparing and analyzing the results. However, it was challenging to identify the different species accurately due to issues in the internal battery of the camera in the underwater mount. Thus, follow-up research is required to repeat the same survey and analysis of the collected data using a camera, in order to identify the fish species accurately and analyze the acoustic data.
## 3 Application Cases
### Calculation of Feature Category Weight and Feature Factor Score  
Based on Table 1, which outlines the indicators for morphological feature categories and cognitive intent discernment factors, a 9-point Likert scale was employed. Smaller numerical values indicate weaker cognitive intent towards earthquake science experience facilities, while larger values indicate stronger cognitive intent. It is important to note that the fourth discernment factor, cognitive load, is a negative indicator. Therefore, during data processing, it should be standardized using Formula (4) for negative indicators. A total of 56 survey forms were distributed to users for the experiment, with 53 valid questionnaires returned. The average values for feature categories and feature factors were calculated separately, as presented in Table 8.  
Utilizing the entropy weight method to determine the weights of feature categories, variables were subjected to both positive and negative standardization according to Formulas (3) and (4). Formulas (5)-(8) were then employed to calculate the probability, entropy value, coefficient of variation, and weights of variables, with the results displayed in Table 9.
# Low-Complexity Chromatic Dispersion Equalization FIR Digital Filter for Coherent Receiver
## 5 Conclusions  
This paper uses a Nuttall second-order self-convolution window with the smallest side-lobe peak value and the fastest side-lobe attenuation rate. The uniform quantization of tap coefficients results in a high diversity of real and imaginary parts; a low complexity TD-CDE for DSP is proposed. Using Optisystem 15 to build a coherent optical communication system for simulation verification, the following three points have been obtained. (I) Compared with rectangular windows, a Nuttall second-order self-convolution window is used to optimize the weight of the TD-CDE filter, which can effectively reduce the effect energy leakage caused by the truncation effect. (II) Compared with the standard TD-CDE, by using a Nuttall second-order self-convolution window function to optimize the weight of the filter,  
Figure 8: The influence of fiber transmission distance on the optimization performance of real number multiplication under different quantization stages M.  
and then uniformly quantifying the filter coefficient, rationally using the multiplication distribution law, addition combination law, and the symmetry of the filter coefficients, while maintaining equalization performance, the demand for multipliers and adders in the convolutional operation process can be greatly reduced, and it is more suitable for commercial coherent optical communication systems. The real number multiplication operation reduces by 99%, the real number addition operation reduces by 43%, and the performance only reduces by 5% when the quantization coefficient M = 16. (III) From the analysis of the quality factor Q in Section 3 and the constant-coefficient \(\eta_{\text{mul}}\) in Section 4, it can be seen that in the long-distance optical fiber link, considering the certain equilibrium performance loss, the proposed CQWO-TD-CDE method can be reduced by more than 60% compared with the commonly used FD-CDE method, and the optimization effect is highlighted with the increase in the transmission distance, effectively reducing the complexity of the hardware implementation of commercial coherent receivers.  
In general, it has been proven that the proposed equalizer can achieve the double guarantee of performance and computational complexity. It can become an effective alternative to FD-CDE in long-distance fiber communication.  
Conceptualization, Z.W. and Z.H.; methodology, Z.W.; software, Z.W.; supervision, Z.H.; valida-tion, S.L.; writing (original draft), Z.W.; data curation, F.S.; writing, review and editing, Y.Z.; All authors have read and agreed to the published version of the manuscript.  
This research was supported by Nation Natural Science Foundation of China, grant number 51575517 and Natural Science Foundation of Hunan Province, grant number 2019J50121.  
Not applicable.  
The authors would like to thank the editors and anonymous reviewers for giving valuable suggestions that have helped to improve the quality of the manuscript.  
The authors declare no conflicts of interest.
# Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection
## 4 Results and Discussion
### Performance Evaluation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Damage Class** & **Models** & **Precision** & **Recall** & **F1 Score** & **IoU** \\ \hline \multirow{8}{*}{Cracks} & ENet & 0.58 & 0.71 & 0.64 (91.31) * & 0.45 (84.00) ** \\  & CGNet & 0.50 & 0.71 & 0.59 (84.09) * & 0.42 (77.74) ** \\  & ESNet & 0.56 & 0.74 & 0.64 (91.61) * & 0.46 (86.77) ** \\  & DDRNet-23-Slim & 0.48 & 0.66 & 0.56 (80.24) * & 0.38 (70.75) ** \\  & DeepLabV3+ & 0.62 & 0.79 & 0.70 (100.00) * & 0.54 (100.00) ** \\ \hline \multirow{8}{*}{Efflorescence} & ENet & 0.76 & 0.67 & 0.71 (91.41) * & 0.55 (88.15) ** \\  & CGNet & 0.71 & 0.71 & 0.71 (91.69) * & 0.59 (94.54) ** \\ \cline{1-1}  & ESNet & 0.81 & 0.61 & 0.69 (89.25) * & 0.52 (82.25) ** \\ \cline{1-1}  & DDRNet-23-Slim & 0.74 & 0.68 & 0.71 (90.96) * & 0.56 (89.63) ** \\ \cline{1-1}  & DeepLabV3+ & 0.81 & 0.75 & 0.78 (100.00) * & 0.63 (100.00) ** \\ \hline \multirow{8}{*}{Rebar Exposure} & ENet & 0.72 & 0.56 & 0.63 (85.26) * & 0.45 (77.01) ** \\  & CGNet & 0.68 & 0.62 & 0.65 (87.70) * & 0.47 (79.91) ** \\ \cline{1-1}  & ESNet & 0.66 & 0.57 & 0.61 (82.86) * & 0.43 (74.32) ** \\ \cline{1-1}  & DDRNet-23-Slim & 0.63 & 0.45 & 0.53 (07.98) * & 0.33 (57.01) ** \\ \cline{1-1}  & DeepLabV3+ & 0.77 & 0.72 & 0.74 (100.00) * & 0.58 (100.00) ** \\ \hline \multirow{8}{*}{Spalling} & ENet & 0.72 & 0.71 & 0.71 (91.55) * & 0.54 (83.90) ** \\  & CGNet & 0.77 & 0.72 & 0.75 (95.83) * & 0.61 (94.36) ** \\ \cline{1-1}  & ESNet & 0.72 & 0.65 & 0.68(87.63) * & 0.51 (79.68) ** \\ \cline{1-1}  & DDRNet-23-Slim & 0.75 & 0.65 & 0.70 (89.75) * & 0.52 (81.44) ** \\ \cline{1-1}  & DeepLabV3+ & 0.80 & 0.75 & 0.78 (100.00) * & 0.64 (100.00) ** \\ \hline \hline \end{tabular}  
* Ratio of F1 score of all models to DeepLabV3+. ** Ratio of IoU to DeepLabV3+.  
\end{table} 
Table 1: Evaluation results of all trained models corresponding to each damage class.
# Improved Bilateral Filtering for a Gaussian Pyramid Structure-Based Image Enhancement Algorithm
## 1 Introduction
A new technique, similar to conventional enhancement methods in which the illumination component of the processed image is manipulated, was introduced in [21]. The technique makes use of an augmented Lagrange multiplier based algorithm for optimization of simultaneous estimation of a smoothed illumination component and the reflectance component of the image. While Retinex-based algorithms are able to enhance most images adequately, the output image can sometimes be optimized through visual analysis, depending on the user's experience. As previously stated, with traditional Retinex methods, the image enhancement effect depends on the scale factor of the Gaussian function, leading to some limitations. Hence, we propose an improved Retinex image enhancement algorithm based on Gaussian pyramid transformation with bilateral filtering to eliminate the impact of outliers in the histogram of the image and further improve its contrast, thus improving the overall visual performance of the Retinex algorithm. The rest of the paper is structured as follows. The improved Retinex model is briefly described in Section 2. Details of the improved bilateral filtering function are discussed in Section 3. An overview of the Gaussian-Laplacian multi-scale pyramid algorithm is given in Section 4. Analysis of the results of experiments is given in Section 5, and conclusions are presented in Section 6.
## 2 Materials and Methods: Description of the Measurement Setup
### Description of the Measurement Scenario  
The three areas selected for the measurement campaign are located at altitudes comparable to the position where the C-band receiver of the SRT is installed. This approach ensures that each antenna pointing direction at low elevation angles (i.e., down to 5 degrees) is covered, and the results presented in this paper correspond to all signals detectable during operational sessions. The three selected locations are shown in the map in Figure 1 and have the following geographic characteristics:  
* Area number 1: a point on Monte lxi mountain (lat. 39.50075958202559\({}^{\circ}\) N, long. 9.26616912945711\({}^{\circ}\) E), located approximately 2 km from the SRT as the crow flies;
* Area number 2: a point in the Union of Municipalities of Gerrei, called Colonia Montana (lat. 39.4900647290649\({}^{\circ}\) N, long. 9.23939203600374\({}^{\circ}\) E), located at about 600 m from the SRT as the crow flies;
* Area number 3: a point on the pasture land in front of the telescope (lat. 39.50381409440-4156\({}^{\circ}\) N, long. 9.245117265226954\({}^{\circ}\) E), placed approximately 1 km from the SRT as the crow flies.  
The measurements were conducted in stable weather conditions, with clear skies and variable winds, where average gusts did not exceed 15 km/h.
## 3 TR-AC: Token-Revocation Access Control
### Decryption  
The details of the decryption stage are as follows:  
* **Authentic token generation.*
* The decryption token is computed at the server side, by executing the Token-Generation algorithm. In TR-AC, if the \(UID\) weighted authentic level \(WAL_{UID}=[wb_{UID},wd_{UID},wu_{UID}]_{t}\) overcome \(\tilde{N}_{t}\), based on the comparison between two authentic levels defined in [13], the authentic decryption token generated by \(UID\). The detailed authentic token generation process is shown in Algorithm 1. The \(UID\) has a secret key \(SK_{c_{j}}=z_{j}\) and the managing \(AID\) will provide the public key \(PK_{c_{j}}=g^{c_{j}}\). Then, \(c_{j},z_{j}\in\mathbb{Z}_{p}^{*}\) is randomly chosen. In addition, \(s_{c_{j}}\in\mathbb{Z}_{p}^{*}\), and \(\{\delta_{i}\}_{\forall i\in I}\) are randomly chosen. The exponent \(f=\sum_{i\in I}\delta_{i}\lambda_{i}\mid f\in\mathbb{Z}_{p}^{*}\) is reconstructed. Where \(\{\lambda_{i}\}_{\forall i\in I}\) are valid, \(f\) shares: \[DT =\prod_{k=1}^{N_{A}}\frac{e(C^{\prime},K_{c_{j},k})\cdot e(R_{c_{ j},k},C^{\prime\prime})^{-1}}{\prod\limits_{i\in I}[e(C_{i},GPK_{c_{j}})\cdot e(D_{1,i},K_{c_{j},\rho(i)})\cdot e(D_{2,i},L_{c_{j},k})]^{\delta_{i}}}\] \[=\frac{e(g,g)^{f\cdot a\cdot c_{j}\cdot N_{A}}\prod\limits_{k=1}^ {N_{A}}e(g,g)^{\frac{f\cdot a_{k}}{z_{j}}}}{e(g,g)^{a\cdot c_{j}\cdot N_{A} \cdot f}}\] (1) \[=\prod_{k=1}^{N_{A}}e(g,g)^{\frac{a_{k}}{z_{j}}f}\] If the user was unable to successfully generate the authentic decryption token, he will not be able to generate the original data \(\mathcal{D}\).
* After generating the \((DT=\prod_{k=1}^{N_{A}}e(g,g)^{\frac{a_{k}}{z_{j}}f})\) using the \(UID\) with \(c_{j}\) using Algorithm 1. The user can obtain the original data by decrypting the ciphertext using a decryption token and his secret key \(SK_{c_{j}}=z_{j}\). The content key will be generated as follows: \[\{ck_{k}=\frac{C}{DT^{z_{j}}}\}_{\forall k=1:N_{A}}\] where \(C=ck_{k}\cdot(\prod_{k=1}^{N_{A}}e(g,g)^{a_{k}})^{f}\). The generated \(ck_{k}\) is used to decrypt the ciphertext. Algorithm 2 is the detailed decryption algorithm. It takes as input the ciphertext to be decrypted, \((CT=\{CT_{k}\}_{\forall k\in K})\). It has multiple components The first component \(\{C_{k}=ck_{k}\cdot(\prod_{k\in K}e(g,g)^{a_{k}})^{f}\}_{\forall k\in K}\) generates the content key for that authority by the user. \((DT=\prod_{k=1}^{N_{A}}e(g,g)^{\frac{a_{k}}{z_{j}}f})\), and \((GSK_{c_{j}}=z_{j})\). It outputs the decrypted data \((\mathcal{D})\) by recovering it from \(\{CT_{k}\}_{\forall k\in K}\) using \(ck_{k}\) for each \(AID\).
# LM-DeeplabV3+: A Lightweight Image Segmentation Algorithm Based on Multi-Scale Feature Interaction
## 1 Introduction
Under limited computing resources, semantic segmentation models cannot achieve good segmentation results. However, many existing lightweight segmentation models (such as BiseNetv2 [19]) use detail and semantic branches to balance low-level and high-level semantic information. Although this model effectively reduces the number of network parameters, its segmentation accuracy is not optimistic. In 2017, Google proposed the MobileNet network, which is a representative of lightweight networks aimed at significantly reducing model size and accelerating model computation speed while sacrificing model performance. There are currently three versions of MobileNet, namely MobileNetV1 [20], MobileNetV2 [21], and MobileNetV3 [22]. MobileNetV2 performs well in semantic segmentation tasks. As a lightweight convolutional neural network, it maintains high accuracy while having fewer parameters and lower computational complexity. Previous studies have shown that using MobileNetV2 for the lightweight improvement of DeeplabV3+ achieves good effects [23; 24]. In this regard, this paper proposes an improved algorithm based on the mainstream semantic segmentation model DeeplabV3+ to solve the problem of high model resource consumption, while taking into account segmentation accuracy and obtaining key category information. The main contributions of this paper are summarized as follows:  
(1) On the basis of using lightweight network MobileNetV2 as the backbone network to alleviate network architecture, an ECA attention mechanism is introduced after extracting shallow features in MobileNetV2, improving feature representation ability without damaging the network's structure. (2) Improvements are made to the ASPP module: replacing ASPP pooling with strip pooling effectively expands the receptive field range of the network, making it suitable for more complex scenarios. Furthermore, the EPSA attention module is introduced to effectively establish long-term dependencies between multi-scale channel attention modules and achieve cross-dimensional channel attention interaction between important features. (3) CE loss may have the problem of training results shifting towards a larger number of categories when faced with class imbalance, and the measurement of CE loss usually cannot indicate segmentation quality well on the validation set. Therefore, we have designed a loss that combines CE loss and Lovasz loss and named it CL loss. This paper validates the effectiveness of the method on the Cityspaces dataset and trains new pre-training weights that are more suitable for the semantic segmentation of city street-view images.
## 4 Identification of Surface Crack Location
### Location of Surface Crack on Cylinders with Different Diameters
The variation of the PSR with diameter is shown in Figure 13. The fit curve of the PSR with different diameters is PSR = 22.98 exp(\(-\)0.05R). As the diameter of the cylinder is decreased, the PSR is increased and the phase shift changes faster. When the diameter of the cylinder is infinite, the specimen is a plate. At this time, the phase shift rate is 0, which also indicates that there is no phase shift for the surface waves on a plate. Therefore, it can be deduced that the SLLS peaks of the surface waves in the plate will remain constant when the attenuation of the surface wave propagation is neglected. Figure 11: **(a–e)** The peak-to-peak amplitudes of surfaces waves on the cracked cylinder with a diameter of 10 mm, when \(\theta\) is 30\({}^{\circ}\), 60\({}^{\circ}\), 90\({}^{\circ}\), 120\({}^{\circ}\), and 150\({}^{\circ}\). **(f)** The surface waves on the intact cylinder when \(\theta\) is 30\({}^{\circ}\), 60\({}^{\circ}\), 90\({}^{\circ}\), 120\({}^{\circ}\), and 150\({}^{\circ}\). Figure 12: Phase shifts of the surface waves on the intact cylinder and the RWs on the cracked cylinder, both with a 10 mm diameter. In light of the above discussion, for cylinders with different diameters, the steps of locating the surface crack can be described as follows: First, the PSR corresponding to the respective diameter is obtained from the phase rate curve. Second, the phase shift curve of the intact cylinder is constructed by the phase shift rate and the initial phase, which is usually ignored. Third, the unipolar position of the surface waves is obtained from the phase shift curve. Finally, the SLLS method is implemented for the location of a surface crack on the cylinder, while the unipolar position is used as the optimal detection position.