esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Implication  
In the process of practical field application, special attention must be paid to the oxidation of toluene-like compounds and the reduction of iron. When considering the co-existence of toluene-like compounds and iron pollution in a site, it is not sufficient to rely solely on the iron content to assess the original quantity of contaminants. Evaluating the age of contaminants requires a comprehensive consideration of the iron content and cannot simply rely on specific coupling ratios. Additionally, factors such as the type of iron oxides, specific coupling conditions, and the delay in electron transfer must be taken into account. Therefore, in practical applications, it is essential to consider multiple factors comprehensively for a more accurate assessment and treatment of organic and iron pollution on the site.  
The integration of our proposed model applies not only to organic contaminants, but also extends to biomass and organic acids. The coupling interaction with iron minerals, leading to the formation of complexes with iron, significantly influences the solubility and mobility of iron. We can further explore the significance of the interaction between carbon and iron in soil ecosystems. Microbes, through metabolic activities such as the decomposition of organic matter and redox reactions, play a regulatory role in the cycling of carbon and iron in the soil. The specific organic substances released by microbes, such as biochar and lignin, may mediate the reduction process of iron, thereby affecting the form and bioavailability of iron in the soil [49]. Our model provides a mechanism to elucidate this influence, particularly in the context of microbial involvement in oxidation-reduction processes.  
Based on a comprehensive understanding of toluene degradation and iron reduction processes, the model can reasonably interpret experimental results and possesses a certain degree of universality. However, the model is only in its preliminary qualitative stage and is also subject to limitations imposed by experimental conditions and environmental factors, necessitating further investigation. The model provides important insights into the mechanisms and kinetics of organic degradation and iron reduction processes. Further research could reveal the time lag between toluene degradation and iron generation, providing deeper insights into the mechanism of toluene degradation.  
The effect of temperature on the reaction rate is a classic phenomenon in chemical kinetics. Changes in temperature usually do not alter the fundamental mechanistic processes of a reaction [50]. Temperatures may vary the rate constant (k) and may affect the final results of the conceptual model curves, but they do not alter the reaction equations.  
Therefore, the presentation may vary at different temperatures, but the established model remains applicable because individual reaction mechanisms remain unchanged.
## 3 Analysis of Experimental Results
### Analysis of Information Recognition Effectiveness for Secondary Cabinet Terminal Blocks
#### 3.5.1 Analysis of Diagram Recognition Effectiveness for Secondary Cabinet Terminal Strips  
The model proposed in Section 2.2 is employed for diagram recognition. The fully trained model is applied to new schematic recognition tasks, and its performance is evaluated based on the recognition of graphical elements within the diagrams. Figure 9 illustrates the model's recognition results for graphical elements such as grounding symbols, terminal connectors, and cells.  
The results of the ablation experiments are shown in Table 3. The YOLOv7+Mash+SPD used in the algorithm of the article improves the mAP value by 25.7%, 10.6%, and 8.9%, respectively, compared with the initial YOLOv7, YOLOv7+Mash, and YOLOv7+SPD, and, comprehensively, the algorithm of this article adopts the combination of the integration of [PERSON]'s self-attention mechanism and the SPD convolutional building block. The improvement effect is the best, and its detection effect is better than other combinations in the complex scenario of end-row drawing microelement targets, especially compared with the baseline YOLOv7, to detect the drawings, as the mAP value is significantly improved by 25.7% points, and the leakage rate is reduced by 61.5% points.  
As can be seen from Figure 9, the model proposed in this study is capable of accurately recognizing the graphical elements in the diagrams. To validate the effectiveness of the algorithm further, metrics such as mean average precision (mAP), recall, and false negative rate are employed as evaluation criteria for the model's performance. The model's performance is compared with traditional object detection algorithms such as SSD [29], R-CNN, YOLOv5 [10], and conventional YOLOv7 to demonstrate its effectiveness. The specific results are presented in Table 4.  
As can be seen from Table 4, when compared with traditional algorithms like SSD, R-CNN, YOLOv5, and conventional YOLOv7, the proposed algorithm in this study shows a significant improvement in mean average precision (mAP) of 70.3%, 48.2%, 30.5%, and 25.7%, respectively, and a reduction in the false negative rate of 79%, 75.4%, 64.8%, and 61.5%. This indicates that the improved algorithm effectively enhances the detection capability for small entities.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Precision** & **mAP** & **Recall** & **False Negative Rate** \\ \hline YOLOv7 & 0.795 & 0.653 & 0.712 & 0.288 \\ YOLOv7+Mash & 0.852 & 0.742 & 0.875 & 0.125 \\ YOLOv7+SPD & 0.861 & 0.754 & 0.878 & 0.122 \\ YOLOv7+Mash+SPD & **0.887** & **0.821** & **0.889** & **0.111** \\ \hline \end{tabular}
\end{table}
Table 3: Ablation experiment with different improved modules.  
Figure 9: Performance of the proposed model on different entities in schematic diagrams. (**a**) Grounding Symbols. (**b**) Grounding Symbols. (**c**) Cells.
# Reliability of Supply and the Impact of Weather Exposure and Protection System Failures +
## 4 Case Study
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Set Information**} & \multicolumn{4}{c}{**Method**} \\ \hline  & & \multicolumn{2}{c}{**Approximate**} & \multicolumn{2}{c}{**Time Series**} \\ \cline{3-6}
**Connection** & **Components** & **No PS** & **PS** & **No PS** & **PS** \\ \hline - & Cable & 15.390 & 15.641 & 15.372 & 15.624 \\ - & Line & 130.780 & 134.747 & 129.571 & 133.921 \\ - & Transformer & 76.800 & 77.462 & 76.570 & 77.230 \\ \hline None & Cable-Cable & 0.005 & 0.005 & 0.005 & 0.005 \\ None & Cable-Line & 0.206 & 0.215 & 0.203 & 0.212 \\ None & Cable-Trans & 0.094 & 0.097 & 0.094 & 0.096 \\ None & Line-Line & 0.826 & 0.876 & 3.415 & 3.575 \\ None & Line-Trans & 0.988 & 1.027 & 0.976 & 1.009 \\ None & Trans-Trans & 0.162 & 0.164 & 0.161 & 0.163 \\ \hline
1 bus & Cable-Line & 0.024 & 0.109 & 0.024 & 0.109 \\
1 bus & Cable-Trans & 0.041 & 0.053 & 0.040 & 0.053 \\
1 bus & Line-Line & 0.111 & 0.842 & 0.630 & 1.395 \\
1 bus & Line-Trans & 0.158 & 0.319 & 0.156 & 0.318 \\
1 bus & Trans-Trans & 0.108 & 0.112 & 0.107 & 0.111 \\ \hline
2 buses & Line-Line & 0.008 & 0.109 & 0.057 & 0.161 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Annual unavailability for outages within different set categories \([h/y]\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Cut-Set Information**} & \multicolumn{2}{c}{**Method**} \\ \hline  & \multicolumn{2}{c}{**Approximate**} & \multicolumn{2}{c}{**Time Series**} \\ \cline{3-5}
**Connection** & **No PS** & **PS** & **No PS** & **PS** \\ \hline Unconnected & 373 & 390 & 999 & 1032 \\ of which is Line-Line & 211 & 222 & 839 & 867 \\ Connected at 1 bus & 22 & 125 & 107 & 214 \\ of which is Line-Line & 9 & 71 & 94 & 160 \\ \hline Total & 395 & 515 & 1107 & 1246 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Energy Not Supplied (ENS) due to second order cut-sets, calculated using different methods; in MWh/year. The approach can support decision makers in taking appropriate actions based on risk and socioeconomic considerations. The revelation of cut-sets with a high ENS may guide prioritization in grid hardening efforts, such as enhancing robustness of lines, undergrounding of overhead transmission lines, or investment in new transmission routes. Identification of adjacent cut-sets with high failure rates due to propagating failures may similarly support prioritized inspection and maintenance efforts of the associated protection systems.
# Blockchain-Based Multiple Authorities Attribute-Based Encryption for EHR Access Control Scheme
## 5 Scheme Implementation
### Encryption Phase  
As shown in Figure 5, the DO is responsible for encrypting the medical data.  
\(\textbf{{Encryption}}(\textbf{{KEY}},\textbf{{M}}_{\textit{data}},\textbf{{AP}}, \textbf{{GP}},\textbf{{PAK}})\to\textbf{{CT}}\)The data user first encrypts plaintext with a symmetric key and then encrypts the symmetric key with _the linear secret sharing scheme_ (\(\textit{{LSSS}}\)). First, DO generates a symmetric key \(KEY\in G_{2}\) to encrypt the data \(AESEncrypt(KEY,\textbf{{M}}_{\textit{data}})=CT_{AES}\). Then DO defines an access policy \(AP\) and converts \(AP\) into an \(\textit{{LSSS}}\) shared generation matrix over the associated attributes and a mapping function: \((M_{l\times m},\rho)\), where \(M\) is an \(l\times m\) matrix. The function \(\rho\) maps the \(x\)th row of \(M\) to the attributes \(\rho(x)\in\{attr_{1},\ldots,attr_{j}\}\), where \(j\in[J]\), \(J\) is an attribute in the access policy \(AP\), and \(attr_{j}\) is an attribute in the access policy. DO generates the ciphertext according to the following two steps.  
1. To hide the attributes in the matrix and protect the privacy of the data sharing parties, DO needs to confuse the attribute mapping function: DO randomly chooses \(r\in Z_{p}^{*}\), computes \(\sigma_{j}=e((g^{\delta})^{r},H(attr_{j}))\forall j\in[J]\). DO substitutes \(\sigma_{j}\) for the attributes mapped by the rows of the shared generation matrix: \(\rho^{\prime}(x)=e((g^{\delta})^{r},H(attr_{x}))\), where \(\in l\). The access policy \(AP\) is then transformed into an LSSS scheme \(\Pi:(M_{l\to m},\rho^{\prime})\) over the attributes of interest.
2. For each row of the matrix \(M\), DO randomly selects a set of vectors \(v=(s,r_{1},r_{2},\ldots,r_{m})\), where the secret values \(s\in Z_{p}^{*},\textit{and}r_{1},r_{2},\ldots,r_{m}\in Zp*\). Let \(\lambda_{x}=M_{x}v^{T}\) where \(M_{x}\) is the \(x\)th row of the matrix M. DO randomly select a set of vectors \(h=(0,h_{1},h_{2},\ldots,h_{lm})\in Z_{p}^{*}\), compute \(c_{x}=M_{x}h^{T}\). And randomly choosing \(u_{1},u_{2},\ldots,u_{l}\in Z_{p}^{*}\). Then the ciphertext is calculated as \(\text{CT}_{KEY}=KEY\cdot e(g,g)^{y*}\) \(C_{1,x}=e(g,g)^{a_{\rho(x)}\cdot u_{x}}\) \(C_{2,x}=g^{u_{x}}\) \(C_{3,x}=g^{c_{x}}\cdot g^{\delta_{\rho(x)}\cdot u_{x}}\) \(C_{4,x}=g^{c_{x}}\cdot g^{y*}\lambda_{x}\) \(C_{v}=H(e(g,g)^{y*})\) where \(C_{v}\) is used to verify the correctness of the outsourced decryption result. DO uploads the verification hash \(C_{v}\) to the blockchain, and then the blockchain returns the transaction identifier TxID. Finally, DO uploads the ciphertext \(CT=((M,\rho^{\prime}),g^{r},CT_{KEY},C_{1,x},C_{2,x},C_{3,r},\)\(C_{4,x},C_{5})\) and TxID to the cloud CSP.  
Figure 5: Encryption phase.
# Automatic Creation of Heuristic-Based Truck Movement Paths for Construction Equipment Control
## 2 Earthwork Background  
### Earthwork in General  
Earthwork is a basic process in most construction works, and it has a great influence on productivity and construction cost. To date, various forms of research and development have been conducted to increase the efficiency of earthwork. Earthwork can be divided into 2D-based earthwork (such as land development) and linear earthwork (such as road work and railroad work). In the 2D-based earthwork, a two-dimensional configured target area is divided into cells of a certain size, divided into cut cells and fill cells, and various optimization techniques are applied to minimize the distance for soil distribution. In the case of linear earthwork, the mass-haul diagram method and the traditional linear model are mainly used to establish an optimized distribution plan for cut cells and fill cells. There are several methodologies applied in the establishment of the soil distribution plans [23; 24; 25; 26; 27; 28]. Past earthwork-related studies established a plan for the entire earthwork site, and later studies suggested a methodology for establishing a plan that excluded the deterrent and various obstacle areas. However, the difficulty of applying the proposed methodologies to the sites in Korea is that there is a lot of variability in the process of defining soil allocation. For example, an exclusion zone could randomly occur due to unaccepted land expropriation, which could affect the movement path of a truck within the site. Previously, the soil distribution plan was used only for the purpose of establishing the process plan and calculating construction costs. However, from the perspective of applying the system to smart construction, the volume distribution plan should be linked as an input for construction equipment, and it should be used as essential information to maximize efficiency of the work site. In this respect, the soil distribution plan should reflect the actual site situation while guaranteeing a minimum travel distance for the vehicle.  
Examining the representative earthwork planning and earthwork-related commercial systems, Sokkia proposes a system that enables high-precision work by combining 3D information, GPS, various sensors, and communication technologies [29]. Trimble provides the function of accurately performing earthmoving work without installing survey piles by applying machine guidance technology based on GPS, graphic technology, communication technology, and MMI (man-machine interface). It also provides a function for managing a fleet [30]. Fujita and Maeda Construction in Japan developed a system for remote earthmoving work while ensuring the safety of workers. In Korea, Doosan Infracore and several universities collaborated to develop an intelligent excavator [31] and conducted field experiments to demonstrate the potential of unmanned earthmoving. Additionally, a fleet management system [32; 33] that can establish an optimal earthwork plan and support the operation of construction equipment, while sharing necessary information in real time, has been developed.
## 3 CFD Simulation Results
### Engine Performance and Specific Fuel Oil Consumption  
Figure 10 presents a comparison of cylinder power and ISFOC when using the three types of piston bowls. It is clear that the cylinder power increased when using the \(\omega\)-type and re-entrant piston bowls instead of the U-type bowl. Because the injected fuel in the three cases was kept unchanged, an increase in the cylinder power resulted in a decrease in ISFOC. Among these types of piston bowls, the re-entrant type produced the highest cylinder power and lowest ISFOC. As mentioned in Section 3.1, the \(\omega\)-type and re-entrant piston bowls increased the TKE of the in-cylinder flow, leading to an increase in the fuel/air mixing quality between the injected fuel and in-cylinder air. The higher fuel/air mixing quality resulted in better combustion quality and then consequently enhanced the cylinder power.  
In addition, as can be observed in Figure 9, even though the peak pressures in the U-type and \(\omega\)-type piston bowl cases were higher than in the re-entrant piston bowl case, the in-cylinder pressures at TDC of the engine in these cases were also higher than in the re-entrant piston bowl case. This resulted in a reduction in the cycle work of the cylinder in the U-type and \(\omega\)-type piston bowl cases in comparison with the re-entrant piston bowl case, as calculated by the software based on Formula (2).  
The \(\omega\)-type and re-entrant piston bowls increased the cylinder power by 1.26% and 2.67%, respectively, compared to the U-type bowl. Meanwhile, the ISFOC decreased by 1.06% and 1.60%, respectively, when using the \(\omega\)-type and re-entrant piston bowls instead of the U-type bowl. [PERSON] and [PERSON] also reported in [19] that the re-entrant bowl improved the fuel/air mixing quality, resulting in a performance improvement.
In this study, we will compare STAR-processed Spire products with those from UCAR to evaluate the uncertainty due to the differences in the implementations of processing algorithms. We will examine the Spire data quality during the CWDP Delivery-Order 3 (DO3, from 8 September 2021 to 15 March 2022) and Delivery-Order 4 (DO4, from 16 March 2022 to 16 January 2023) periods. The NOAA purchased about 3000 Spire RO profiles daily in DO3 and about \(\sim\)5000-6000 in DO4 (access on 1 June 2023). We will conduct a quality assessment of the UCAR Spire neutral atmospheric profiles regarding their stability, precision, and accuracy. To better compare the RO retrieval profiles from two missions, we develop an efficient simultaneous limb overpass RO (SRO) method for predicting and collecting RO limb-sounding events between GNSS emitters and LEO RO receivers for inter-satellite comparison purposes [32]. The procedures for obtaining coplanar SRO pairs were introduced by [32; 50]. Because the Spire satellites are in the Sun-synchronized orbits that cover the globe, we can collect many coplanar Spire-Spire pairs and Spire-COSMIC-2 pairs at all latitudes during the performance period, which is critical for examining the climate quality for RO data. We first describe the Spire data spatial and temporal distribution in Section 2. We also detail the data used to validate the Spire retrievals and present the SRO method for our studies to collect the Spire-Spire pairs, Spire-other-RO-mission pairs, and COSMIC-2-COSMIC-2 pairs in Section 2. We quantify the Spire penetration, precision, and stability in Section 3. Section 4 quantifies the UCAR Spire retrieval accuracy and uncertainties using STAR Spire retrievals, the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5), and RS41 radiosondes. We further estimate the Spire error covariance matrix for the NWP data assimilation system in Section 5. We discuss the results in Section 6 and conclude this paper in Section 7.
# Uncertainty-Aware Adaptive Multiscale U-Net for Low-Contrast Cardiac Image Segmentation
## 3 Experimental Results Analysis
### Ablation Study  
Table 1 presents an ablation study of AMU-Net to evaluate the contributions of its submodules: MPCM, DMSA, AFAB, and SFFH. The baseline model achieves a Dice score of 87.29, Precision of 88.74, and Recall of 86.92. When individual submodules are added, a slight improvement in performance is observed, with MPCM achieving a Dice of 87.96, followed by DMSA (87.58), AFAB (87.33), and SFFH (87.38). Combining multiple submodules demonstrates a cumulative improvement, indicating their complementary contributions. For example, integrating MPCM and DMSA achieves a Dice score of 89.59, while MPCM with AFAB yields 88.98. The best performance is achieved when all submodules are combined, resulting in a Dice score of 93.35, Precision of 94.12, and Recall of 92.78, highlighting their collective effectiveness. The results clearly show that each  
module individually improves the baseline, but their combined use significantly enhances segmentation accuracy, precision, and boundary refinement. This analysis demonstrates the importance of integrating multi-scale, attention-based, and fusion mechanisms in achieving state-of-the-art performance.  
Table 2 presents the ablation study of the loss functions used in the AMU-Net model. The principal loss \(L_{PL}\) is the core loss function required to initiate training and was used in all configurations. The inclusion of the auxiliary agreement loss, \(L_{aux}\) alongside \(L_{PL}\) shows an improvement in performance metrics, including Dice, Precision, and Recall. This highlights the complementary role of \(L_{aux}\) in enhancing segmentation performance when combined with the principal loss.
###### Abstract  
We proposed the statistical misalignment model and the power-efficient configuration of transceivers for bi-directional multi-input and multi-output (MIMO) based vertical free space optical (FSO) links. Spatial diversity based MIMO FSO systems could be used to mitigate atmospheric fading issues. However, the increased number of channels can cause additional pointing error in pointing, acquisition and tracking (PAT) systems. The statistical misalignment model for detecting misalignment error is derived from the multiple transceivers. For the bi-directional characteristics of non-terrestrial back-haul networks, transmission performance is down-leveled to the worse in the asymmetric MIMO configuration of transceivers. The symmetric structure can mitigate the effect of increased pointing error to improve transmission performance. The proposed technique can be applied to the design of power-efficient FSO systems for non-terrestrial wireless back-haul networks.  
atmospheric turbulence; diversity gain; free space optical communications; multi-input multi-output MIMO; pointing error; spatial diversity technique +
Footnote †: journal: Sensors
# Recent Advances in Tunable External Cavity Diode Lasers
## 3 Research Progress of ECDL
### Dual-Wavelength External-Cavity Diode Laser
The tunable external cavity was formed between the diffraction grating and the front facet of the superluminescent diode. Finally, a tunable spectral separation of the dual-wavelength ranging from +6.42 nm (2.63 THz) to \(-\)16.94 nm (7.1 THz) was observed. [PERSON] et al. demonstrated high-power tunable dual-wavelength composite external cavity configuration, utilizing a holographic grating and a volume Bragg grating [67]. The frequency difference between the two wavelengths can be tuned from 0.41 THz to 3.89 THz. Additionally, the intensities of the two corresponding wavelengths remain nearly equal, with a minimal power difference of just 0.10%. Quantum dot lasers can also be served as gain chips in dual-wavelength external cavity lasers. [PERSON] et al. demonstrated a tunable double-mode external cavity InAs/InP quantum dot laser employing two diffraction gratings [75]. Dual-mode operation was accomplished by adjusting just one grating while keeping the other fixed. In the external cavity structure, an attenuation slice was placed in the external optical feedback paths to equalize the intensity of the dual modes to decrease the mode competition. Furthermore, the tuning range spanned both the ground and excited states of the quantum dot assembly, demonstrating that both states can be effectively utilized for dual-mode operation. This approach highlights the versatility of quantum dot lasers in enabling wide tuning range and stable dual-wavelength performance in external cavity systems. Dual-wavelength output can also be achieved through a distributed feedback laser diode with a single external-cavity, as shown in Figure 6c [76]. A volume Bragg grating was served as the output coupler for the external-cavity DFB laser, enabling the emission of an additional stable wavelength beam. The wavelength tuning of the DFB laser was achieved through temperature modulation. Meanwhile, the linewidth of the laser could be further narrowed by feeding the laser output back into the active region of DFB using the volume grating. As the operating temperature of the DFB laser increased from 16.1 \({}^{\circ}\)C to 29.8 \({}^{\circ}\)C, the tunable spectral separation between the two wavelengths varied from 1.08 nm (0.5 THz) to 1.92 nm (0.88 THz). This demonstrates the capability of the system for precise dual-wavelength tuning, enhanced stability, and improved spectral performance. Figure 6: \(Cont.\)