esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The meta-heuristic is generally known as an approach that cansolve network reconfiguration problems. Several meta-heuristic algorithms have been proposed in the literature for solving these problems [17,18]. Comparative studies on various optimization methods show that thegenetic algorithm (GA)and particle swarm optimization (PSO)approaches are the most widely used methods for solving combinatorial problems [19, 20, 21]. One of the advantages of these algorithms is that the solution of a problem can still be obtained even there is no analytical solution for the problem [22]. Therefore, the emphasis will be placed on the use of GA and PSO algorithms to solve the problem of finding an optimal configuration of a distribution network. In a distribution network reconfiguration problem, aload flow algorithm is a fundamental tool for analysis, optimization, and planning. It is an important step for each generation of optimization algorithms. In this paper, the new development introduced in the GA and PSO application to the distribution network reconfiguration problem is the proposed load flow algorithm. Several load flow algorithms specificallydesigned for distribution systems have been proposed in the literature [23]. From those methods, the Gauss implicit matrix method is one of the most commonly used methods; however, this method does not explicitly exploit the radial and weakly meshed network structure of distribution systems. Recent research proposed some new ideas on how to deal with the special topological characteristics of distribution systems [24], but these ideas require new data formats or some data manipulations. To cope with this problem, in this study, a new load-flow method was proposed and introduced in the GA and PSO algorithms for solving the distribution network reconfiguration problem in order to enhance reliability and reduce power losses. The only input data of this algorithm is the conventional bus-branch oriented data used by most utilities. The novelty in this study is to develop a formulation, which takes advantage of the topological characteristics of distribution systems and solves the distribution load flow directly. It means that the time-consuming forward/backward substitution of the Jacobian matrix or the admittance matrix, required in the traditional Newton Raphson and Gauss implicit matrix algorithms, are not necessary in the new development. Two developed matrices, the bus-injection to branch-current matrix and the branch-current to bus-voltage matrix, and a simple matrix multiplication are utilized to obtain load flow solutions. This paper is organized as follows: Section 2 presents a mathematical simulation of the distribution network reconfiguration problem, as well as all the constraints that must be met and the methods for solving the optimization problem. Section 3 discusses the application of these optimization tools ina distribution network. Section 4 finally draws conclusions on the results of the study.
## 9 A Complete Pattern: WS-Trust
### Consequences  
The WS-Trust pattern presents the following _advantages_:  
* _Security._ By extending the WS-Security mechanisms, we can handle security issues such as security tokens (the possibility of a token substitution attack), and signing (where all private elements should be included in the scope of the signature and where this signature must include a timestamp).
* _Trust._ With this solution, we have the choice of implementing the WS-Policy framework to support trust partners by expressing and exchanging their statements of trust. The description of this expected behavior within the security space can also be expressed as a trust policy.
* _Confidentiality._ We can achieve confidentiality of users' information. Since Policy providers now can use mechanisms provided by other web services specifications such as WS-Security [ibm09b] to secure access to the policy, XML Digital Signature [24] to authenticate sensitive information, and WS-Metadata Exchange [37].
* All the security tokens exchanged between the involved parties are signed and stamped with unique keys that are known only to the recipients.  
* _Time validity_. We can specify time constraints in the parameters of a security token issued by STS. This constraint will specify for how long that security token is valid. Upon expiring, the security token's holder may renew or cancel it.  
The WS-Trust pattern presents the following _liabilities:_  
* The efficiency of WS-Trust may suffer from the repeated round-trips for multiple token requests. We need to make an effort to reduce the number of messages exchanged.
* The WS-Trust standard is a lengthy document and several details were left to avoid making the pattern too complex. The interested reader can find more details in the WS-Trust Standard web page [17].
### Contributions  
To design our network structure, the concept of SDN routing architecture is added. We hope to consider the historical density and directionality of vehicles between grids, and modify the way that Q-learning generates the Q-table to determine the macrotransfer direction of the V2X packet. Then, according to the historical trajectories of the real vehicles, we can select the most appropriate packet delivery path, that is, the microscopic delivery path. This paper makes the following contributions:  
1. We design the Software-Defined Directional QGrid (SD-QGrid) network architecture, which combines the centralized control concept of the SDN Control Node (CN), and proposes the offline Q-learning training process and the online routing decision process in a V2X network architecture with reference to vehicle trajectories and three types of directionalities.
2. Using the number of vehicles and the trajectory and directionality of vehicles between grids as parameters, the offline Q-learning training process of this paper modifies the Q-learning of reinforcement learning by increasing the moving directions between neighboring grids from the original four to eight and using the corresponding Q-tables for different time periods [22] as the macroreference for intergrid routing packet delivery.
3. In the online routing decision process, a two-hop trajectory-based routing (THTR) algorithm is proposed, referring to the historical trajectory of the vehicle and the two-hop transmission path for calculating the future trajectory. This algorithm selects the neighbor node which is most suitable for the direction along the best next-hop grid and the best two-hop next grid, and then forwards packets to it, increasing the probability to meet the destination node and resulting in a shorter packet transmission path and lower packet transmission delay.
4. We conduct simulation experiments to analyze and find the routing paths in V2X, and then obtain real and meaningful performance data of the routing algorithm using the real vehicle movement trajectories in Taipei City.
## 2 System Modeling
### Inter-Plane and Inter-Shell Configurations  
This research evaluates two distinct configurations of ISLs, inter-plane and inter-shell links, to identify effective strategies for mitigating solar irradiance interference. The inter-plane configuration refers to constellations where all the orbital planes share uniform characteristics, such as the number of satellites per plane, the orbital inclination, and the altitude. On the other hand, the inter-shell configuration is defined as a collection of inter-plane systems, especially for those with different altitudes. Figure 7 provides a cross-sectional view of an inter-shell configuration, enabling a detailed examination of altitude variations across different orbital planes.  
Figure 6: Multi-directional transceiver model based on satellite-centric coordinates, including the solar irradiance as (**a**) non-blinded and within FoV, (**b**) non-blinded but outside FoV, and (**c**) within FoV but blinded.  
Figure 7: The cross-sectional view of the inter-shell configuration: orange (540 km, the lowest layer), magenta (550 km, second layer), cyan and blue (560 km, third layer highlighted to represent the same altitude), and green (570 km, the highest layer).  
To ensure practical relevance, this study adopts the real-world constellation design of Starlink [17] for its inter-shell model and simulation environment, as introduced in Table 2. The specific parameters and components of the Starlink inter-shell configuration are summarized in Table 3, which serves as the basis for further analysis and simulation in this research.
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 4 Security Hardening of Kubernetes Components
### Image and Workload Hardening
#### Disabling Containers to Run as Root  
To enhance the overall security of the container environment, it is essential to investigate not running containers as a root user. The principal advantage of executing containers with non-root privileges is to protect the application environment. Kubernetes enables the specification of a security context for pods and containers, delineating privilege and access control configurations. The objective is to guarantee their operation adheres to the principle of least privilege. Running containers without root privileges inhibits malicious code from acquiring elevated permissions on the container host.  
This limitation guarantees that unauthorized individuals who obtain the container from a public registry cannot access essential server resources. When containers provide elevated privileges, unauthorized users can execute undesirable processes or access confidential information. That is an elucidation of the crucial fields we may utilize in Kubernetes YAML files to achieve this objective:  
* runAsNonRoot: guarantees that the container operates without root user privileges; Kubernetes will inhibit the container's initiation if the UID is configured to 0 (root).
* runAsUser and runAsGroup: designate the user and group ID under which the container operates, facilitating the enforcement of a specific non-root user.
* allowPrivilegeEscalation: inhibits a process from acquiring elevated privileges.
* privileged: When enabled, the container gains access to host devices and can alter the kernel.  
Non-root containers are crucial in multi-tenant Kubernetes environments, where workload isolation and the enforcement of stringent security policies are imperative.  
This configuration inhibits attackers from exploiting vulnerable applications to obtain root access and control the host system, as the security context delineates privilege and access control settings at the pod and container levels. The suggested pod definition can be implemented in the cluster using the command kubectl apply -f file_name.yaml, where file_name.yaml is the configuration file name.  
Turning off privileged mode prevents the container from accessing host resources such as device files, kernel modules, or root processes, thereby protecting the host operating system from direct interference. Eliminating all capabilities guarantees that the container process receives minimal permissions, thereby obstructing attackers from executing privileged operations such as altering the system time, binding to privileged ports, or adjusting network configurations.
## 3 Results
### Comparison with Citrus Agricultural Areas  
Once the areas most affected by frosts were identified and their trends over recent years were analyzed, we aimed to determine whether these frosts indeed impact the regions where citrus fruits are cultivated and thus pose a risk to growers. To this end, we compared the frost damage intensity map with the citrus cultivation area map provided by the Spanish National Geographical Information Centre. In Figure 17, the citrus map of the Valencian Community is shown, rescaled to the same spatial resolution as the MSG images. Analyzing the frost trends over the study period in these citrus areas, we can see that the citrus areas correspond to regions in the community (central and coastal areas) where no trend is detected.  
Figure 16: Result map of the seasonal Kendall trend test conducted on the overall frost damage intensity data for the study period (2004–2023) in the Valencian Community.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Intensity (I) Levels** & **Pixels** & \
\begin{tabular}{c} **Pixels with** \\ **Decreasing Trend** \\ \end{tabular} &
\begin{tabular}{c} **Percentage of Pixels with** \\ **Decreasing Trend** \\ \end{tabular} \\ \hline I \(>\) 0 & 856 & 310 & 36\% \\ \hline I \(>\) 50 & 317 & 161 & 51\% \\ \hline I \(>\) 100 & 194 & 125 & 64\% \\ \hline I \(>\) 150 & 129 & 97 & 75\% \\ \hline I \(>\) 200 & 76 & 64 & 84\% \\ \hline I \(>\) 250 & 50 & 43 & 86\% \\ \hline I \(>\) 300 & 30 & 28 & 93\% \\ \hline I \(>\) 350 & 10 & 10 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Areas with the presence of damage intensity and the percentages with a decreasing trend based on the intensity levels throughout the study period (2004–2023) in the Valencian Community.  
However, when we select only the points where frost intensity is above the average for the study period, we find that these areas do not overlap with those where citrus fruits are cultivated (Figure 18).  
Figure 17: Citrus map of the Valencian Community provided by the Spanish National Geographical Information Centre, rescaled to the same spatial resolution as the MSG images.  
Figure 18: Overlay map of areas where above average intensity frosts occurs, and areas cultivated with citrus in the Valencian Community.
# Research and Application of Slag-Nanosilica Stabilizer for Silt Subgrade
###### Abstract  
With the rapid development of construction and road engineering, the accumulation of silting waste soil is becoming more and more serious. In order to recycle the silt, a new type of stabilizer was developed in this study to improve its mechanical properties and applicability on roads. The optimal ratio of stabilizer components was determined by orthogonal test and grey correlation analysis. The effects of stabilizer on the macroscopic mechanical properties of silt were investigated by unconfined compressive strength (UCS) test and split test. The water stability test and freeze-thaw cycle test were carried out to study the durability and road performance of stabilized soil. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) methods were used to study the effect of the stabilizer on the microstructure and mechanism of silt. The results showed that the optimal mixture ratio of the new type of stabilizer was quicklime: nanosiliac: slag = 32:3:65. Adding 10% stabilizer is a reasonable and effective method to strengthen silt, which has the characteristics of high strength and strong durability in the early period. The addition of stabilizer will result in hydration reaction, pozzolanic reaction, and cation exchange on the surface of soil particles with silt, which will enhance the intermolecular force of soil particles, reduce the porosity of soil, and strengthen the connection between soil particles.  
highway; stabilization; orthogonal mix design; grey correlation analysis; silt; subgrade +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Pca  
To further investigate the key odorants to distinguish the spirits produced by the distillation process of pervaporation process, PCA analysis was performed using the concentrations of all of the key aroma compounds in the peach spirits (Figure 4). The first principal component (PC1) and the second principal component (PC2) explained 93.4% and 5.1%, respectively, indicating these two principal components could represent the original data. Figure 4a showed that PC1 could separate the distilled peach spirit and pervaporation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**NO.**} & \multirow{2}{*}{**Absence of Compounds**} & \multicolumn{2}{c}{**DS**} & \multicolumn{2}{c}{**PS**} \\  & & **N \({}^{\text{a}}\)** & **Significance \({}^{\text{b}}\)** & \(n\) & **Significance** \\ \hline
1 & All esters & 9 & * & 14 & *** \\
1-1 & Ethyl acetate & 7 & - & 8 & - \\
1-2 & Ethyl isobutyrate & - & - & 13 & *** \\
1-3 & Isobutyl acetate & - & - & 10 & * \\
1-4 & Ethyl isovalerate & - & - & 12 & *** \\
1-5 & Ethyl butyrate & 6 & - & 8 & - \\
1-6 & Isoamyl acetate & 11 & ** & 12 & *** \\
1-7 & Ethyl hexanoate & 15 & *** & 13 & *** \\
1-8 & Ethyl octanoate & 9 & * & 14 & *** \\
1-9 & Ethyl decanoate & - & - & 9 & * \\
2 & 1,1-Diethoxyethane & 13 & *** & 15 & *** \\
3 & All alcohols & 14 & *** & 13 & *** \\
3-1 & Isobutanol & - & - & 10 & * \\
3-2 & 3-Methyl-1-butanol & 10 & * & 10 & * \\
3-3 & 1-Hexanol & 11 & ** & 12 & *** \\
4 & All terpen & 8 & - & 12 & *** \\
4-1 & Linalool & 8 & - & - & - \\
4-2 & Citronellol & 9 & * & - & - \\
4-3 & (E)-\(\beta\)-damascenone & 9 & * & 15 & *** \\
4-4 & Eugenol & - & - & 13 & *** \\
5 & All acids & - & - & 14 & *** \\
5-1 & 2-Methylpropanoic acid & - & - & 13 & *** \\
5-2 & 3-Methylbutanoic acid & - & - & 14 & *** \\
6 & Furfural & 6 & - & 14 & ** \\
7 & \(\gamma\)-Butyrolactone & 13 & *** & 13 & *** \\
8 & \(\gamma\)-Decalactone & - & - & 12 & *** \\
9 & Phenethyl acetate & 12 & *** & - & - \\
10 & Phenethyl alcohol & - & - & 7 & - \\ \hline \hline \end{tabular}  
* Number of panelists who identified the aroma difference in the triangle tests correctly. \({}^{\text{b}}\) Significance: \({}^{\text{*}}\), 0.05 significant level; **, 0.01 significant level; \({}^{\text{***}}\), 0.001 significant level.  
\end{table}
Table 3: Results of omission experiments from reconstitutions.  
produced peach spirit well, distributed by the negative and positive half-axes, respectively. Ethyl hexanoate, ethyl isobutyrate, isobutyl acetate, 3-methylbutyl acetate, ethyl isovalerate, \(\gamma\)-decalactone, ethyl decanoate, isobutanol, 1-hexanol, \(\gamma\)-butylrolactone, 2-methylpropanoic acid, 3-methylbutanoic acid, eugenol, and furfural were positively corresponding to the aroma characterization of pervaporation produced peach spirit, while 1,1-diethoxyethane, phenethyl acetate, citronellal, (E)-\(\beta\)-damascenone, and ethyl octanoate were highly related to the overall aroma of distilled peach spirit. The distinctive aroma compounds and aroma profiles of peach spirits were formed by different separation methods.
## 3 Results and Discussion
### Microbiogical Results  
The counts of microbiological groups in salami made with different starter cultures are shown in Table 2. The use of starter cultures with different acidification rates does not seem to have affected the microbiological profile of the products (SM and SR), en  
Figure 1: The pH monitoring during the ripening process of two types of Salame Napoli made with Euroferment Medio (SM) and Euroferment Rapido (SR) as starter cultures.  
Figure 2: Visual checking of two types of Salame Napoli: on the left, **(a)** salami with Euroferment Rapid, SR, and on the right, **(b)** salami with Euroferment Medium (M) at the end of the drying phase (Drying 5).  
suring the safety of the final products regardless of the starter culture used. The total mesophilic aerobic bacterial counts (TAB 30 \({}^{\circ}\)C) were within the expected range (around 8 Log CFU g\({}^{-1}\)) for fermented salami [28], and no significant differences were found between SM and SR. SM salami showed a higher count in terms of TAB 30 \({}^{\circ}\)C and mesophilic lactic acid bacteria (LAB) than SR, attributed to the slower and more progressive pH decrease. On the other hand, SR salami revealed a higher count in yeasts and molds. This difference could probably be explained by the faster exhaustion of LAB activity, non-homogeneous water loss, and uneven mold distribution on the external surface of the SR salami, which could be influenced by the expected metabolic activities [37]. In fact, to produce traditional Italian Salame Napoli, it is ideal that the pH does not fall below the value of 5.2-5.3 even at the end of drying. The gradual and slower pH drop allows Staphylococcus and molds better opportunities for development and activity during the slow maturation of the sausage, resulting in products of good sensory quality, appreciated by the consumer [38]. On the contrary, the excessive acidity could make the finished product less pleasant in his organoleptic qualities and could be responsible for unwanted microbial flora [39]. The LAB, as a main factor behind the pH decrease, could be playing a crucial role in preserving meat products from spoilage microorganism and pathogenic bacteria [10]. Regarding _Pseudomonas_ spp., these bacteria exhibited pH-dependent variations, showing a lower value in SR than SM at the end of the ripening process [26]. The results of _E. coli_, _Bacillus cereus_, and _Clostridium perfringens_ detections showed counts within the limits imposed by current regulations on hygienic criteria. It could be possible that the acidification of the meat due to the presence of starter cultures stopped the growth of undesirable microorganisms through different mechanisms, such as the pH and \(\mathrm{a_{w}}\) decrease [2]. _Listeria monocytogenes_ and _Salmonella_ spp. were not detected in SM and SR salami (absence in 25 g).
# ODEM-GAN: An Object Deformation Enhancement Model Based on Generative Adversarial Networks
## 3 Approach
### Network Architecture of the ODEM-GAN Module
#### 3.2.2 Loss of the GAN Branch  
Since \(L2\) loss will lead to blurred sample generation, \(L1\) loss is used for the autoencoder, as shown in Equation (2):  
\[L(x)=\left\|x-D(x)\right\|_{1}, \tag{2}\]  
where \(L(x)\) is the loss of the autoencoder, \(D(x)\) is the output of the autoencoder, and \(x\) is the input of the autoencoder. The loss of generator \(G\) is shown in Equation (3):  
\[L_{G}=L(x^{s}), \tag{3}\]  
where \(x^{s}\) is the sample generated by generator \(G\). The loss of the discriminator is shown in Equation (4):  
\[L_{D}=L(x^{r})-L(x^{s}), \tag{4}\]  
where \(x^{r}\) are samples created by the online deformable sample creation module. To enhance the diversity of the samples generated, a coefficient of balance \(k_{t}\) balances the generator and discriminator in each alternate training. The update of \(k_{t}\) is shown in Equation (5):  
\[k_{t+1}=k_{t}+\zeta(\eta L(x^{r})-L(x^{s})), \tag{5}\]  
where \(\zeta\) is the learning rate of \(k_{t}\), and the parameter \(\eta\) can control the proportion of sample diversity. In this article, we set \(\zeta=0.01\) and \(\eta=0.5\). After adding \(k_{t}\), the loss function of the discriminator \(L_{D}\) becomes the following Equation (6):  
\[L_{D}=L(x^{r})-k_{t}L(x^{s}). \tag{6}\]  
Although generator \(G\) can generate sufficiently realistic hard samples by minimizing Equations (2) and (5), to make the generated hard samples help improve the regression accuracy of the detector, we define that positioning accuracy loss is introduced here. The positioning accuracy loss \(L_{lp}\) is as shown in (7):  
\[L_{lp}=\begin{cases}|t_{i,j}^{s}-t_{i,j}^{g}|^{2}*0.5*0.9,|t_{i,j}^{s}-t_{i,j} ^{g}|<1/9\\ |t_{i,j}^{s}-t_{i,j}^{g}|-\frac{0.5}{0.9},else.\end{cases} \tag{7}\]  
The hard sample generated by generator \(G\) is taken as the input of the detector regression module, and after the forward propagation of the regression module, the 4-dimensional offset \(t_{i,j}^{g}=[t_{i,j_{t}^{g}}^{g}t_{i,j}^{g},t_{i,j}^{g}]\) of the object bounding box will be output. Then, the smooth \(L1\) loss of \(t_{i,j}^{g}=[t_{i,j_{t}^{g}}^{g}t_{i,j_{t}^{g}}^{g}t_{i,j}^{g}]\) and the 4-dimensional offset \(t_{i,j}^{s}\) of the ground-truth box are calculated.  
The purpose of the positioning accuracy loss is to make the hard samples generated by the generator improve the regression accuracy of the object detector. Therefore, \(L_{lp}\) is added to the loss of generator \(G\). The loss function of generator \(L_{G}\) becomes the following Equation (8):  
\[L_{G}=L(x^{s})+\lambda_{lp}L_{lp}, \tag{8}\]  
where \(\lambda_{lp}\) can control the weight of \(L(x^{s})\) and \(L_{lp}\). In this article, we set \(\lambda_{lp}=0.1\). By minimizing Equations (6) and (8) and alternately training the discriminator \(D\) and generator \(G\), the generator can generate sufficiently realistic hard samples.