esa-sceva/satcom-chunk-collection huggingface dataset:
# A Bacterial Form I' Rubisco Has a Smaller Carbon Isotope Fractionation than Its Form I Counterpart
###### Abstract  
Form I rubiscos evolved in Cyanobacteria \(\geq\) 2.5 billion years ago and are enzymatically unique due to the presence of small subunits (RbcS) capping both ends of an octameric large subunit (RbcL) rubisco assembly to form a hexadecameric (I\({}_{6}\)S) holoenzyme. Although RbcS was previously thought to be integral to Form I rubisco stability, the recent discovery of a closely related sister clade of octameric rubiscos (Form I'; I\({}_{8}\)) demonstrates that the I\({}_{8}\) complex can assemble without small subunits ([PERSON] et al. 2020). Rubisco also displays a kinetic isotope effect (KIE) where the 3 PG product is depleted in \({}^{13}\)C relative to \({}^{12}\)C. In Cyanobacteria, only two Form I KIE measurements exist, making interpretation of bacterial carbon isotope data difficult. To aid comparison, we measured in vitro the KIEs of Form I' (_Candidatus_ Promineofilum breve) and Form I (_Synchococcus elongatus_ PCC 6301) rubiscos and found the KIE to be smaller in the L\({}_{8}\) rubisco (16.25 \(\pm\) 1.36% vs. 22.42 \(\pm\) 2.37%, respectively). Therefore, while small subunits may not be necessary for protein stability, they may affect the KIE. Our findings may provide insight into the function of RbcS and allow more refined interpretation of environmental carbon isotope data.  
carbon isotope fractionation; cyanobacteria; rubisco; evolution Article
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 4 Implementation and Results
### Step 5 Reporting and Remediation  
Finally, we leveraged the capabilities of LLMs to generate a report on the penetration-testing process. The report not only detailed the identified vulnerabilities but also provided specific and actionable suggestions for repairing the vulnerabilities, thus facilitating more effective security-enhancement measures.
## 2 Overview of the Study Area  
Qiemo and Ruoqiang are located in the southeastern fringe of the Taklimakan Desert. An area covering both counties (37.77\({}^{\circ}\)-39.38\({}^{\circ}\) N, 84.99\({}^{\circ}\)-88.74\({}^{\circ}\) E) was selected as the study area (Figure 1). The study area has a typical continental temperate arid climate, with annual average precipitation of less than 20 mm and annual average evaporation of over 2500 mm. The evaporation is intense, and the diurnal temperature range is high. The annual average temperature, highest temperature, and lowest temperature of the study area are 11.7, 41.3, and \(-\)26.4 \({}^{\circ}\)C [4], respectively. The northeesterly wind dominates the study area and has many windy days, especially blowing sand and floating dust days. Fortunately, the Qarqan River and several other water systems in the southeastern fringe of the Taklimakan Desert have nourished the Qiemo Oasis, made outstanding contributions to ecological protection projects in the county, and created favorable conditions for biological sand control along railways. The Hotan-Ruoqiang Railway connecting the two counties passes through the piedmont alluvial-proluvial inclined plain (Ruoqiang DK0+000-Washixia DK80+500) and the alluvial-proluvial inclined plain in the southern margin of the Tarim Basin (Washixia DK80+500-Qiemo DK261+000). The railway includes both passenger and freight traffic, with trains traveling through the desert daily. The aeolian sand environment along the railway is harsh, and the surface is dominated by extremely fine sand, fine sand, and medium sand. The complex topography and characteristics of the sandy environment affect the formation and development of dunes [25]. Due to abundant sand sources, wind-sand disasters are very serious here, posing grave threats to traffic safety. The angle between the railway trend and sand-moving wind direction is under constant change and first increases and then decreases from Ruoqiang to Qiemo. Due to the variation in the angle, the railway is exposed to sand hazards to varying extents. For sand control, mechanical and biological measures have been combined along the railway in the study area.
# Research on Evaluation Methods of Black Soil Farmland Productivity Based on Field Block Scale
## 2 Materials and Methods
### Machine Learning Model Selection and Its Optimization
#### 2.3.2 Support Vector Machine (SVM) Model and Its Optimization  
SVM is a powerful machine learning model for regression and classification tasks. In this study, we propose a regression prediction model based on a simulated annealing algorithm to optimize the hyperparameters of the support vector machine, as well as the type of kernel function: the SA-SVM (Figure 2).  
The traditional SVM model constructs several different types of kernel functions based on non-linear sample datasets. It involves training samples with input features and iteratively adjusting parameters until optimal performance is achieved. The selection of an appropriate kernel function is a crucial step in SVM model development. In our approach, we use the Radial Basis Function (RBF) kernel. To prevent overfitting and balance the model's fit and complexity, we introduce a regularization parameter C. However, the traditional SVM model is time-consuming during parameter tuning, and the obtained result may be a local optimum. Additionally, different kernel function types can impact parameter settings, influencing the performance of the kernel function. After exploring various optimization algorithms, we opted for the Simulated Annealing (SA) algorithm to optimize SVM model parameters. SA is a global optimization algorithm that searches for the optimal combination of hyperparameters, kernel function parameters, and regularization parameter C in the parameter space to enhance the model's performance. The optimization objective of the SA algorithm is to minimize the loss function, representing the error between the model-predicted yield and the actual yield.
## 3 Aircraft Observation
### Second Encounter--Radar Observations and Windshear Alerts  
After conducting a missed approach, the aircraft turned north and then to the east. Near the location of the TDWR, it was moving to the southeast. At around that time, the intense radar echoes originally affecting HKIA had been moving further east, and the aircraft just flew into them. The observational data mainly come from the weather radars at that time (around 21:42 Hong Kong time of 30 April 2024).  
From the TDWR, significant divergence was observed to be associated with the intense radar echoes with a difference in radial winds of about 15 m/s over a distance of around 6 km as shown in Figure 9a. EDR map had been provided by the Tate's Cairn weather radar at that time. The maps at heights of 1 km and 2 km above the sea level can be found in Figure 10. The EDR was in the order of 0.35 m\({}^{2/3}\)s\({}^{-1}\) or more, and MOG turbulence could be expected. Because of the significant divergence, based on TDWR data, the WTWS also provided a microburst alert of \(-30\) knots at that region as shown in Figure 11. After encountering that microburst, the aircraft continued to move south and reached the waters to the south of Hong Kong.  
Figure 8: The various kinds of alerts including windshear alerts from the ground-based anemometers (empty red square) as well as windshear (empty red ellipse) and microburst alerts (filled red ellipse) from the BP TDWR shown by the HKO’s Windshear and Turbulence Warning System at 13:35 UTC on 30 April 2024. The white arrow shows the intended landing path of the aircraft along runway 25 right approach (R25 RA). The area enclosed by the white dash rectangle indicates the eastern corridor of the north runway which comprises the R25 RA and R07 LD.  
Figure 9: The (**a**) radial velocity and (**b**) reflectivity plots over HKIA by the BP TDWR at 13:42 UTC on 30 April 2024.  
Figure 10: EDR (in units of m\({}^{2/3}\)s\({}^{-1}\)) map at height of **(a)** 1 km and **(b)** 2 km based on the spectral width data of the weather radar at TC at 13:42 UTC on 30 April 2024. The blue star shows the location of HKIA.
# An Empirical Study of a Trustworthy Cloud Common Data Model Using Decentralized Identifiers
## 3 The Extended to Identify Management Scheme for Cloud CDM  
### The Cloud CDM Model  
In order to collect and integrate clinical data of multiple hospitals, it is required to solve the heterogeneity of data structure and format, differences in quality and quantity of data, technical limitations of interoperability, and security issues. CDM should support the linking of common analysis codes for electronic medical record (EMR) resource linkage to support integrated data analysis of research institutions, without leaking sensitive personal information.  
Data extracted from EMRs tend to be stored in different relational database schemas. Figure 2 illustrates the conventional concept of CDM and its operation scheme derived from several sources of EMR in hospitals.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Traditional Identity Management**} & \\ \cline{2-5}  & **Isolated** & **Centralized** & **Federated** & **DID** \\ \hline Privacy and protection & Low & Low & Low & High \\ User control and consent & Low & Low & Moderate & High \\ Dependency & Moderate & High & High & Low \\ Fault tolerance & High & High & Moderate & Low \\ Usability & Low & Low & Moderate & High \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of traditional identity managements and DID.  
The cloud CDM reference model shown in Figure 3 is a partial result from the previous works and consists of several CDM providers and CDM consumers participating [10]. Using this presented reference model, clinical researchers can isolate and securely distribute CDM data.  
* Cryptography can be used for protecting information, using a hash value to maintain management of large-capacity CDMs. Encryption can be used to protect information using symmetric and asymmetric keys to maintain the management of large-capacity CDMs.
* A distributed ledger is used to provide data integrity and share information through a CDM signature.  
Figure 3: Concept of the Secure-Cloud Common Data Model (SC-CDM).  
Figure 2: Conventional concept of the Common Data Model (CDM) and operation scheme.  
In the process of data creation and use, the distributed ledger guarantees data integrity, and transparently signed CDM can be accessed.
# A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET
## 2 Related Work
### Discussion  
The complexity of congestion control in VANET, due to its dynamic nature, diversity, and mobility, has created significant challenges. As previously mentioned, adjusting transmission rate or power can have a negative impact on awareness, while a combination of both is an ideal solution but with an optimization problem that is not convex [11]. To tackle this challenge, researchers have proposed machine learning-based algorithms for congestion control that are based on real-time states instead of predetermined rules, allowing for better adaptability to the dynamic network environment. Supervised and unsupervised learning techniques have been used in the past, but these methods are limited in their ability to handle real-world network conditions as they are trained offline and cannot classify wireless and congestion loss accurately [35]. In contrast, RL techniques offer advantages such as online training capabilities.  
This paper proposes a Q-learning-based congestion control algorithm for the field of VANET congestion and awareness control, demonstrating the application of RL-based methodology in this area. The proposed algorithm builds upon a previous publication [36] by providing a more comprehensive description of the RL framework and updating the Q-learning-based congestion control algorithm. A new estimation function has been introduced to accurately predict the CBR based on vehicle density and transmission rate, which was not present in the original publication. The paper also includes an expanded literature review and new simulation results for various performance metrics, including Packet Delivery Ratio (PDR) and BER.
## 3 Materials and Methods
### Chemical Syntheses
#### 3.2.5 Preparation of Phenyl(4-benzyloxy)-7-morpholinobenzo[_d_]thiazol-2-yl)carbamate (6)  
Pyridine (243 \(\mu\)L, 3 mmol) was added to a well-stirred and pre-cooled (0-5 \({}^{\circ}\)C, ice bath) suspension of **5** (341 mg, 1 mmol) in anhydrous THF (7.5 mL). After stirring for 5 min, phenyl chloroform (144 \(\mu\)L, 1.15 mmol) was slowly added and the mixture was stirred for another 5 min. The ice bath was removed, the reaction mixture was stirred at ambient temperature for 0.5 h (TLC: sample in acetone **not** MeOH, ethyl acetate/hexane, 80/20, R\({}_{\rm fsm}\). 0.70, R\({}_{\rm fp}\) 0.90) and then diluted with dichloromethane (50 mL) and water (15 mL). The organic layer was separated, washed with aqueous HCl (0.5 N, 50 mL) and brine (50 mL), dried over anhydrous Na\({}_{2}\)SO\({}_{4}\) and concentrated under reduced pressure. Extraction of the residue with hot ethanol followed by cooling to ambient temperature, filtration and drying at 60 \({}^{\circ}\)C afforded the title compound (433 mg, 0.94 mmol, 94%) as porcelain-colored crystals, mp 229 \({}^{\circ}\)C (dec). \({}^{1}\)H-NMR (400 MHz, DMSO-\(d_{6}\)) \(\delta\) 3.00 (t, 4H, \(J\) = 4.5 Hz, CH\({}_{2}\)NCH\({}_{2}\)), 3.75 (t, 4H, \(J\) = 4.5 Hz, CH\({}_{2}\)OCH\({}_{2}\)), 5.24 (s, 2H, Bn-CH\({}_{2}\)), 6.89 (d, 1H, \(J\) = 8.5 Hz, \(H^{6}\)), 7.05 (d, 1H, \(J\) = 8.5 Hz, \(H^{5}\)), 7.26-7.54 (m, 10H, \(2\)\(\times\) C\({}_{6}\)H\({}_{5}\)), 12.70 (s\({}_{\rm br}\), 1H, NH). \({}^{13}\)C-NMR (100 MHz, DMSO-\(d_{6}\)) \(\delta\) 158.3 (C\({}^{2}\)), 51.9 (CNC), 67.0 (COC), 70.7 (Bn-CH\({}_{2}\)), 110.3 (C\({}^{5}\)), 113.4 (C\({}^{6}\)), 127.4 (C\({}^{7a}\)), 126.6 (Bn-C\({}^{2,3,4}\) + Ph-C\({}^{2,3,4}\)), 136.8 (Bn-C\({}^{1}\)), 137.6 (Bn-C\({}^{1}\)), 140.6 (C\({}^{3a}\)), 145.7 (C\({}^{7}\)), 147.6 (C\({}^{4}\)), 153.1 (C=O). Anal. calcd for C\({}_{25}\)H\({}_{23}\)N\({}_{3}\)O\({}_{4}\)S: C, 65.06; H, 5.02; N, 9.10; found: C, 65.08; H, 5.00; N, 9.08. HRMS \(m/z\): [M + H]\({}^{*}\) calcd: 462.1482; found: 462.1480. 3.2.6. Preparation of \(N\)-(4-(Benzyloxy)-7-morpholinobenzo[_d_]thiazol-2-yl)-4-hydroxy-4-methylpiperidine-1-carboxamide (7) [25]  
4-Hydroxy-4-methylpiperidine (126 mg, 1.08 mmol) was added to a stirred solution (prepared under argon) of 6 (461 mg, 1 mmol) in anhydrous DMSO (10 mL) and the clear brown mixture was stirred at 50 \({}^{\circ}\)C for 1 h (TLC: sample in acetone **not** MeOH, do **not** dry with a heat gun, ethyl acetate/hexane, 80/20, R\({}_{\text{Carbamate}}\) 0.90, R\({}_{\text{R/Urea}}\) 0.35, R\({}_{\text{Pfhenol}}\) 0.98). After cooling to ambient temperature, 50% brine (40 mL) was added and the resulting precipitate was extracted into ethyl acetate (2 \(\times\) 50 mL). The organic phase was washed with 10% citric acid (50 mL), saturated aqueous Na\({}_{2}\)CO\({}_{3}\) (50 mL), water and brine and dried over anhydrous Na\({}_{2}\)SO\({}_{4}\). Concentration under reduced pressure produced a solid that was treated with boiling ethanol (ca. 7 mL) and cooled overnight.
# Hybrid Precoding Design for Subarray-Structure-Enabled mmWave URLLC System
## 3 Problem Solution  
In Section 3.1, we calculate the optimal all-digital precoding matrix, and the optimal solution will be substituted into the next subsection as the reference value. In Section 3.2, the Euclidean distance between the hybrid and all-digital precoding matrix is expected to be minimal to obtain a hybrid precoding matrix.
## 2 Materials and Methods
### Determination of Plasma Temperatures  
Nitrogen and nitrogen ion bands are widely used to determine rotational temperature. Most often, the method is a comparison of the N\({}_{2}\) (C\({}^{3}\Pi_{u}^{+}\to B^{3}\Pi_{g}^{+}\)) transition at 337.1 nm with the simulated emission spectra. In our case, the band showed spectral overlap with NO \((B-X)\), so it was excluded. The first positive band of N\({}_{2}\) (\(B^{3}\Pi_{g}^{+}\to A^{3}\Sigma_{u}^{+}\)) can also be used for determination, but studies indicate low accuracy for high temperatures [17].  
Therefore, the 0-0 band of the N\({}_{2}^{+}\) (\(B^{2}\Sigma_{u}^{+},v=0\to X^{2}\Sigma_{g}^{+},v^{\prime}=0\)) transition at 391.1 nm was chosen to determine rotational temperature as depicted in Figure 2a. Rotational temperature \(T_{\text{rot}}\) was determined from the slope of the Boltzmann plot, depicted in Figure 2b and given by  
\[\ln\left[\frac{I_{nm}\lambda_{nm}^{4}}{(N^{\prime}+N^{\prime\prime}+1)}\right] \propto-\frac{hcB_{v}}{k_{\text{B}}T_{\text{rot}}}\ N^{\prime}(N^{\prime}+1) \tag{1}\]  
with the intensity of the transition \(I_{nm}\) at the specific wavelength \(\lambda_{nm}\), quantum numbers of the upper state \(N^{\prime}\) and \(N^{\prime\prime}\) and \(h\), \(c\), \(k_{\text{B}}\) being the [PERSON] constant, speed of light and [PERSON]'s constant, respectively. The rotational constant \(B_{v}\) was calculated according to Equation (2) with \(B_{e}=2.075\,\text{cm}^{-1}\) and \(\alpha_{e}=0.024\,\text{cm}^{-1}\) taken from [22]:  
\[B_{v}=B_{\text{e}}-\alpha_{e}(v+0.5). \tag{2}\]  
For the analysis, spectral lines of the P-branch were chosen, and these showed no spectral overlap with lines from the R-branch [17, 23]. Rotational lines were identified using values from the literature and are given in Table 1[24].  
Figure 2: Determination of the rotational temperature of the N\({}_{2}^{+}\) (\(B^{2}\Sigma_{u}^{+},v=0\to X^{2}\Sigma_{g}^{+},v^{\prime}=0\)) transition at a flow rate of 45 L min\({}^{-1}\) and 2 mm distance from the nozzle. (**a**) Measured spectra with selected lines. (**b**) Boltzmann plot.  
A set of atomic nitrogen lines was selected to determine the excitation temperature by the Boltzmann plot method and is given by  
\[\ln\left[\frac{I_{\text{ki}}\lambda_{\text{ki}}}{g_{\text{k}}A_{\text{ki}}}\right] \propto-\frac{-E_{\text{k}}}{k_{\text{B}}T_{\text{exc}}}, \tag{3}\]  
where \(I_{\text{ki}}\), \(\lambda_{\text{ki}}\) and \(A_{\text{ki}}\) are the spectral line intensity, wavelength and transition probability of the selected transition, respectively, and \(g_{\text{k}}\) and \(E_{\text{k}}\) are the statistical weight and the energy of the upper level. The selected spectral lines and necessary parameters taken from [25], are listed in Table 2. Uncertainty of \(A_{\text{ki}}\) is less than \(\pm\)10% for all transitions.
## 4 Discussion
### Comparison with Other Studies
The image classification model, classifying images into 13 classes of 5\({}^{\circ}\) each from 0\({}^{\circ}\) to 60\({}^{\circ}\), showed high accuracy with an average AUC of 0.953. To clarify the analysis of our results, we examined why the classification accuracy varied significantly across different angles. Specifically, we discussed the mechanisms that may contribute to higher AUC scores at certain angles such as 0\({}^{\circ}\) and 60\({}^{\circ}\), considering factors like the unique characteristics of these images and their representation in the training dataset. Despite the confusion matrix appearing inaccurate, the high AUC was due to the difference between the target and output angles being within \(\pm\) 5\({}^{\circ}\) for most test data. For instance, for the 30\({}^{\circ}\) target, 67 out of 68 images were classified as 25\({}^{\circ}\) to 35\({}^{\circ}\). However, for the 0\({}^{\circ}\) target, more images were classified as 5\({}^{\circ}\) than 0\({}^{\circ}\), resulting in lower accuracy. Further, we considered the reasons behind the notably lower classification accuracy for most angles except the 60\({}^{\circ}\) classes. This was likely because the number of learned images for 0\({}^{\circ}\) was half that of other angles, as +x\({}^{\circ}\) and \(-\)x\({}^{\circ}\) were used as x\({}^{\circ}\), resulting in twice the number of images for angles other than 0\({}^{\circ}\). To address the concerns regarding the data volume of the 0\({}^{\circ}\) category, we should analyze in a future study whether the reduced number of images for this angle was solely responsible for its decreased accuracy or if it also contributed to lower accuracy across other categories. Consequently, it can be speculated that the model did not learn enough 0\({}^{\circ}\) lumbar spine X-ray images, which may have led to the observed decrease in accuracy.
## 3 Efficiency and Secure Key Rates
### Finite Key Analysis for LM05  
In this work, we will consider the LM05 protocol with [PERSON]'s choice for qubit preparation and measurement in the \(Z\) bases is preferred (\(p_{Z}>p_{X}\)) and [PERSON] either encodes with anyone of her transformations with probability \(c/4\) or make measurements in the \(X\) bases only with probability \(1-c\). Furthermore, the deliberations ensue would be focused on the RR scenario. [PERSON]'s and [PERSON]'s choice of measuring \(2M(n,k)\) qubit pairs in the basis \(X\) with probability \(1-c\) results in \(n_{e}=n+\sqrt{nk}\) number of bits derived from EM and \(k\) for CM. Hence \(\sqrt{nk}\) pairs would be wasted due to bases mismatch when [PERSON] measures in \(X\) while [PERSON] chooses \(Z\) (notice that \(2\sqrt{nk}\) qubits are wasted in [9] for similar reasons).  
Let us write the string [PERSON] has in the EM and CM as \(s_{B}^{\text{EM}}\) and \(s_{B}^{\text{CM}}\) respectively as well as [PERSON]'s as \(s_{A}^{\text{EM}}\) and \(s_{A}^{\text{CM}}\) respectively. As described above, let us consider a _gedankenexperiment_ where all qubits are measured using \(X\) basis (for CM), we can therefore write an uncertainty relation from Equation (11) to bound [PERSON]'s information as  
\[H_{min}^{\epsilon}(s_{B}^{\text{EM}}|E)+H_{max}^{\epsilon}(s_{B}^{\text{CM}}| s_{A}^{\text{CM}})\geq n_{e}. \tag{20}\]  
Similar to earlier discussions, the first term in the left hand side of the above equation is the smooth min-entropy reflecting the number of bits contained in [PERSON]'s string, \(s_{B}^{\text{EM}}\) that are \(\epsilon\)-close to a uniform distribution and independent of [PERSON]. The second term \(H_{max}^{\epsilon}(s_{B}^{\text{CM}}|s_{A}^{\text{CM}})\) reflects the correlation between [PERSON]'s and [PERSON]'s strings and gives the number of bits needed to reconstruct \(s_{B}^{\text{CM}}\) from \(s_{A}^{\text{CM}}\) up to a probability of failure \(\epsilon\),  
\[H_{max}^{\epsilon}(s_{B}^{\text{CM}}|s_{A}^{\text{CM}})\leq n_{e}h_{2}\left( \frac{q}{2}+\mu(n_{e},k)\right). \tag{21}\]  
With \(Q_{f}\) as the error rate in EM, noting \(H_{max}^{\epsilon}(s_{B}^{\text{EM}}|s_{A}^{\text{EM}})\leq n_{e}h_{2}(Q_{f})\) and following Equations (14), (20) and (21), the key length after error correction and privacy amplification is given by  
\[L_{LM05}\leq n_{e}-n_{e}h_{2}\left(\frac{q}{2}+\mu(n_{e},k)\right)-n_{e}h_{2}( Q_{f})-\log\frac{2}{\epsilon_{S}^{2}}. \tag{22}\]
## 2 Methods and Materials
### Materials  
Five corrosion-resistant nickel-based alloys were selected as the objects of investigation in this study: Hastelloy\({}^{\tiny\textregistered