esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data
### Iasi  
The Infrared Atmospheric Sounding Interferometer (IASI) IWV product is provided by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The product is based on the all-sky Piece-Wise Linear Regression (PWLR) algorithm (version 6.2 of EUMETSAT's operational IASI L2 processor) and does not only use IASI measurements, but also radiances from collocated Advanced Microwave Sounding Unit (AMSU) and the Microwave Humidity Sounding (MHS) [22]. IASI is operational on the polar-orbiting satellites Metop-A and Metop-B and provides a near-global coverage twice a day. The spatial resolution of IASI is 12 km at nadir, and it has a swath width of about 2200 km. In this study, the IASI IWV data is used only if the atmospheric humidity quality indicator (average uncertainties along the profile in dew point temperature) is below 2 (personal communication [PERSON], EUMETSAT).
# Humans and Automation: Augmenting Security Operation Centers
## 3 Results and Findings
### SOC Automation Application Areas
#### 3.2.2 Automated Incident Detection  
Where applicable, we attempted to code SOC automation application areas by the incident response lifecycle (IR) phases. We focused on opposite spectrums of the IR lifecycle, namely detection and analysis at the one end and recovery and response at the other. This was performed because traditional cybersecurity automation is typically employed in the earlier phases. However, as technology progresses, particularly with the rapid evolution of AI and ML tools, new recovery and response solutions are beginning to emerge. For example, Microsoft's Security Co-Pilot and Crowdstrike's Charlotte AI (neither mentioned in any reviewed articles) represent first-to-market large-language models for use in SOCs.  
One of the essential components of harnessing automation within SOCs is to support analysts in identifying threats by providing them with the highest priority security alerts to act upon [53, 54]. Automation that specifically flags the most severe threats (those demanding immediate action) leads to beneficial efficiency gains. For this reason, we coded the sub-theme of automated \"alert analysis.\" Here, our results yielded two categories of solutions: the automation of the alert correlation process [49, 55, 56] and the automation of the triage process [43, 47, 48]. One study developed a semi-autonomous tool interacting with security analysts to identify advanced persistent threats [57]. Another study aimed to detect malware attacks by monitoring host machines and their potential communication connections with malicious domains hosted at command-and-control centers [54]. Through 24 interviews conducted with SOC analysts, the effectiveness of automated tools identifying suspicious activity primarily depends on their configuration [12]. Based on a mixed-methods approach with 20 SOC analysts, ref. [44] designed a framework for configuring tools to generate reliable, explainable, analytical, contextual, and transferrable alerts. Various authors suggested corroborating alerts in conjunction with the preceding and proceeding alerts (i.e., the alerts around the flagged alert) [41, 51, 56, 57].  
**Findings:** Overall, in many of the examined articles, automation was commonly used for three primary purposes in the detection phase:  
1. Connecting and correlating alerts from different security tools integrated into the SOC.  
Gathering suspicious alerts in context with their surrounding (and related) alerts to provide richer context.
* Grouping alerts based on similar characteristics and IOCs (often called clustering). For instance, ref. [55] advised that automation should analyze flagged alerts in context with similar previous incidents to allow analysts to determine how to remediate them.  
These three purposes illustrate that the automation of incident detection can be implemented to varying degrees. It is recommended that all three aspects be adhered to.
# A Hierarchical Resource Allocation Scheme Based on Nash Bargaining Game in VANET
## 2 Hierarchical Resource Allocation Scheme Based on Nash Bargaining Game
### The Formulation of Hierarchical Resource Allocation Scheme in VANET  
In this section, a hierarchical resource allocation scheme based on Nash bargaining game is proposed. In the first layer, we analyze the resource allocation between central cloud and RSU cloud.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Variables** & **Definition** \\ \hline \(K\) & The number of RSUs \\ \(b_{k}\) & The resources allocated to the \(k\)th RSU \\ \(B^{TOTAL}\) & The total spectrum resources in the central cloud \\ \(R_{k}\) & Transmission rate of the \(k\)th RSU \\ \(R_{k}^{min}\) & The lowest transmission rate expected by the \(k\)th RSU \\ \(\overline{\eta}_{k}\) & The modulation factor for the \(k\)th RSU \\ \(C^{k}\) & The number of subcarriers of the \(k\)th RSU \\ \(N_{k}\) & The number of vehicles requested services from the \(k\)th RSU \\ \(\omega_{k}^{0}\) & Subcarrier bandwidth of the \(k\)th RSU \\ \(h_{ij}\) & Channel gain of the \(j\)th subcarrier for the \(i\)th VUE \\ \(P_{ij}\) & Transmission power of the \(j\)th subcarrier for the \(i\)th VUE \\ \(pMAX\) & The sum of the transmitted power for all subcarriers \\ \(a_{i}^{k}\) & The matching degree between the \(k\)th RSU and the \(i\)th VUE \\ \(\tau_{i}\) & The size of time slot assigned to the \(i\)th VUE for requesting services \\ \(r_{i}^{min}\) & The minimum transmission rate expected for the \(i\)th VUE \\ \hline \hline \end{tabular}
\end{table}
Table 1: The description of variables in hierarchical resource allocation scheme.  
Thereafter, based on the matching degree between VUEs and RSUs, We study the resources allocations problem between them in the second layer.
## 3 Results and Discussion
### Optimizing Synthesis Conditions
#### 3.1.5 Effect of Reaction Time  
Figure 7 shows the effect of the polymerization reaction time on the intrinsic viscosity of the hybrid flocculant. According to Figure 7, as the reaction time was prolonged, the intrinsic viscosity of the hybrid flocculant initially increased and then decreased. The maximum intrinsic viscosity was achieved at 3 h. This behavior can be attributed to an increase in free radicals within the system during a prolonged reaction time, facilitating polymerization reactions. However, after a certain point, the monomer concentration gradually decreased, leading to a slower reaction rate and a reduced degree of polymerization, resulting in a decrease in the product's intrinsic viscosity.  
Figure 5: The influence of initiator on intrinsic viscosity.  
Figure 6: The influence of reaction temperature on intrinsic viscosity.  
According to the test results, the optimum conditions for preparation of the PAC-PDMDAAC hybrid flocculant were as follows: DMDAAC mass fraction of 30%; initiator mass fraction of 0.7%; reaction temperature of 60 \({}^{\circ}\)C; reaction time of 3 h. Three sets of parallel verification experiments were conducted under these experimental conditions, and the measured epeiviscosity values of the products were 912.85 mL/g, 937.35 mL/g, and 928.88 mL/g, respectively, with an average value of 926.36 mL/g.
## 3 System Model
### Problem Formulation  
To reduce the number of users whose with low throughput, we formulate the dynamic antenna control problem into maximizing the throughput of users. The throughput \(\mathcal{T}\) of a user can be obtained by the following equation:  
\[\mathcal{T}=b\times\log_{2}(1+\gamma), \tag{4}\]  
where \(\gamma\) denotes the SINR at the user, \(b=B/K\) denotes the bandwidth assigned to the user, \(B\) denotes the bandwidth of each antenna array and \(K\) denotes the number of users in an antenna array coverage.  
Figure 4: Example of an antenna pattern for vertical or horizontal polarization.  
Figure 3: Antenna parameters.  
As obvious from Equation (4) and the x-z plane in Figure 5, we can know that the rising of the throughput with the increase in the SINR is very slow when the bandwidth is very small. Thus, considering making users' bandwidth as large as possible is necessary, particularly in the non-uniform user distribution scenario. Compared with the conventional methods, which reduce the SLL [24] or improve the SINR at users [35], this formulation not only improves the SINR at the user's end, but also improves the bandwidth at the user.
# A Case Study on Distresses of Concrete Pavements Supported on a Retaining Wall
## 3 Evaluation of the Performance of the Subject Section
### Evaluation of Soil Conditions in MSE Wall
#### 3.3.2 Soil Properties from Laboratory Tests  
A total of four soil borings were performed on 30 and 31 March 2023 to take soil samples in the MSE wall and beneath it, and a series of laboratory tests were conducted on the soil samples. Soils in the MSE wall were high-plastic clays, with average values of LL of 70 and PI of 37, and classified as CH per USCS and A-7-6 per AASHTO. Soils beneath the MSE wall were also high-plastic clays, with average values of LL of 70 and PI of 40, and classified as CH per USCS and A-7-6 per AASHTO. The water contents of the soils inside the MSE wall ranged between 30% and 39.4%, with an average value of 34.4%. Table 2 summarizes the results of the laboratory tests conducted on the soil samples.  
The existence of high-plastic clays (CH) with high water contents in the MSE wall suggests that the high shrink-swell potential of CH soils, combined with repeated traffic loading, might have contributed to the differential movement among lanes and facilitated the water infiltration. Furthermore, the large amount of water infiltrated into the MSE would have applied large lateral pressure onto the MSE wall due to the low hydraulic conductivity of CH soils, explaining the tensile failure of tie-bars presented in Figure 7a. More details of potential distress mechanisms are presented in the following section.
## 4 Methodology
### Statistical Analysis  
The sampling with replacement bootstrap method was applied to the data collected from the tests. The bootstrap method was chosen because of its simplicity, the possibility to use the method for complex distribution with many strata (visual variables, different visual encoding values, and different partial overlap levels), and provision of accurate confidence intervals [38].  
From the proposed test scenarios, two hypotheses were considered:  
* Alternative hypothesis (\(H_{a}\))--the performance analyzed from the overlap level (highest accuracy) and the resolution time (quickest resolution time) for the control group (0% occlusion) must return the best results;
* Null hypothesis (\(H_{0}\))--that the application of occlusion would not affect the accuracy and resolution time.  
The procedures followed by the sampling with replacement bootstrap algorithm, developed in the R programming language, are shown in Figure 6:  
* The control group (0% occlusion) with 216 elements per visual variable was separated from the other groups (50%, 60%, and 70% occlusion) with 648 elements in total;  
Figure 5: Organization of tasks by group of participants, considering visual variables, the number of distinct visual encodes, and the partial overlap level.  
* The algorithm then drew 216 elements at random from each of the groups, always replacing the previously drawn element;
* After the simulation's final drawing round of 216 elements (for each group), the algorithm calculated the average of each group;
* If the control group value obtained was greater than that of the other occlusion levels group, then the alternative hypothesis would receive one point;
* According to [25], this procedure was repeated 20,000 times for each analyzed variable;
* _number of points_)/20,000;
* \(p\)-Value classifying: lack of significance (\(p\geq\) 0.05 ), moderate significance (0.01 \(\leq p<\) 0.05), strong significance (0.001 \(\leq p<\) 0.01), and extreme significance (\(p<\) 0.001).
## Materials and Methods
### Caesar-Lisflood LEM: Model Description and Calibration
The map has been converted in a raster grid and was introduced in the model in order to set the depth of bedrock and grain-size distribution within the active layer where erosion and deposition processes are estimated by LEM. The layer of the bedrock has been derived in a GIS environment using a map algebra tool based on a subtraction between the soil thickness map and the DEM. Hourly rainfall dataset was derived from a weather station located near the catchment outlet. Rainfall record covers a time interval of about 22 years, and it was used also for the dam-removal scenario. Input data and model parameters are summarized in Table 1 (see also [34]). It is worthy of note that the estimation of the sedimentary budget base on the LEM simulation was already tested through a comparison between the total amount of eroded sediment volumes coming from the model results and direct measurement of the short-term (i.e., about 20 years) sediment storage within the artificial reservoir. The good accordance between the model results and direct measurements [34] suggests that the model can be used to simulate the dam removal scenario in a consistent way. Manning coefficient values were assigned (Table 2) according to a revision of the values proposed in previous works (see for example [46; 47]) whereas the spatial distribution of the Manning coefficient follows the boundaries of the land use map of the study area (Figure 2b). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Caesar-Lisflood Parameter** & **Value** \\ \hline
1 & Grain-size features (m) & 0.0005, 0.001, 0.002, 0.004, 0.008, 0.016, 0.032, 0.064, 0.128 \\
2 & Grain-size distribution (total 1) & 0.20, 0.18, 0.12, 0.06, 0.03, 0.03, 0.1, 0.25 \\
3 & Type of rainfall record & Hourly \\
4 & Sediment transport equation & Einstein \\
5 & Max erode limit (m) & 0.01 \\
6 & Active layer thickness (m) & 0.1 \\
7 & Lateral edge smoothing passes & 40 \\
8 & Manning coefficient & 0.015–0.1 (derived by land-use map) \\
9 & Soil creep/diffusion value & 0.0025 \\
10 & Slope failure threshold & 40 \\
11 & Vegetation critical stress & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model parameters.
## 2 Related Works
In [21], a low Earth orbit-medium Earth orbit-geostationary equatorial orbit (LEO-MEO-GEO), layer structure was produced to reduce the communication costs from LEO routing by dividing into MEO and GEO layers. In [22], the MEO layer collects the link state and calculates the routing table for the LEO layer. This reduces the burden on LEO satellite computing resources for maintaining routing tables. In [23], to increase network reliability, a failure recovery mechanism was applied to the failure cases of the LEO and MEO layers. In the hierarchical architecture, LEO/MEO layers cooperate with each other. Even a single link change causes all routing tables in the entire network to be updated, wasting the limited power of the satellite. Therefore, studies are conducted to deliver link failure information only to restricted areas. In [24], the authors proposed a fault-tolerant adaptive routing based on a minimal-connected-component model. A faulty and useless node is pre-detoured by fault region boundary diffusion. In [9], the author proposed a survivable routing algorithm. In this algorithm, a vector-based next-hop selection mechanism is used for the routing path calculation. It assigns scores based on whether each path is the shortest or a detour, prioritizing the path with the highest score for selection. When a link failure is detected on a high-priority path, the algorithm switches to the path with the next-highest priority. In [11], the authors proposed a disruption-tolerant routing algorithm. In contrast to traditional flooding methods, this algorithm employs a restricted flooding area with a predefined hop. Nodes that receive flooding are sent in a direction different from the shortest path to detour link failure. The previous routing algorithms cannot be directly implemented in LEO satellite networks. Distributed routing can reduce communication overhead by maintaining the routing table independently; however, it is vulnerable to link failures caused by topological differences. With the massive number of satellites in LEO constellations, clustering and topology pruning may incur substantial communication overhead from frequent information exchange in the presence of topological differences and link failures. Hierarchical structures can share the burden of LEO satellites on other layers but cannot address communication delays between layers. Fault link information shared only to restricted areas is using the flooding method. Flooding can be used to generate an optimal detour path instead of generating massive communication overhead throughout the network. Distributed routing using the \(n\)-hop flooding method incurs a relatively small communication overhead but cannot create the shortest detour path. Considering the onboard power limitation of LEO satellites and detour path delay, we propose a distributed detour routing scheme to minimize the communication overhead and delay of the detour path.
## 6 Sixth-Generation (6G) Testbeds and Platforms
### Testbeds for 6G Technologies
#### 6.2.5 Cell-Free Systems Testbeds  
Cell-free systems, a strong candidate for 6G networking, are being rigorously tested. Ericsson introduced radio stripes, a distributed MIMO deployment, in 2019. Samsung presented a Distributed-Full Duplex-MIMO (D-FD-MIMO) system [251], and HUAWEI tested a user-centric 5G indoor distributed massive MIMO solution in 2021, enhancing user experience rates. The REINDEER project, launched in 2021, aims to design cell-free protocols and real-time real-space interactive application processing algorithms. As part of this project, an open 6G modular testbed called Techtile was proposed in 2022 [252]. Southeast University built a cloud-based cell-free distributed massive MIMO system, supporting scenarios up to 128 \(\times\) 128 antenna scale [253].
## 2 Materials and Methods
### Experimental Trials
#### 2.2.1 Effect of Basal Culture Medium and Trace Metals Solution  
A standard heterotrophic medium, MB10, was used as a starting point for the optimization of the culture medium. In the first trial, this basal medium was compared to MT1, a culture medium that was designed in accordance with the information available in the literature [2, 6, 10, 11, 14, 17, 20, 26, 27, 28, 29, 30, 31, 32, 33, 34]. The MT1 medium was tested using two trace metals solutions: (i) TM1, the trace metals solution of MB10; and (ii) TM2, formulated based on previous results obtained in the literature [6, 8, 10, 14, 16, 17, 20, 24, 26, 27, 23, 32, 33, 34].  
MB10 was composed of 9.0 g/L K\({}_{2}\)HPO\({}_{4}\), 8.7 g/L NaH\({}_{2}\)PO\({}_{4}\).H\({}_{2}\)O, 5.0 g/L MgSO\({}_{4}\).7H\({}_{2}\)O, 1.2 g/L citric acid, 6.3 g/L ammonium sulfate, 0.3 g/L CaCl\({}_{2}\), 2.0 g/L (monosodium) glutamate and 15 g/L NaCl, supplemented with 20 mL/L of TM1 and 1.5 mL of vitamins solution (VIT).  
MT1 was formulated to contain 4 g/L K\({}_{2}\)HPO\({}_{4}\), 2 g/L MgSO\({}_{4}\).7H\({}_{2}\)O, 0.8 g/L of ammonium sulfate, 0.1 g/L CaCl\({}_{2}\), 20 g/L (monosodium) glutamate, 0.5 g/L of KCl, 10 g/L NaSO\({}_{4}\) and 4 g/L of yeast extract and was further supplemented with 20 mL/L of TM1 or TM2 and 1.5 mL of VIT1.  
The composition of TM1 was as follows: 0.1 g/L CuSO\({}_{4}\).5H\({}_{2}\)O, 3.3 g/L H\({}_{3}\)BO\({}_{3}\), 14 g/L ZnSO\({}_{4}\).7H\({}_{2}\)O, 8.1 g/L MnSO\({}_{4}\).H\({}_{2}\)O, 0.39 g/L Na\({}_{2}\)MoO\({}_{4}\).2H\({}_{2}\)O, 0.13 g/L NiCl\({}_{2}\).6H\({}_{2}\)O, 33 g/L monohydrated citric acid and 1.1 g/L FeSO\({}_{4}\).7H\({}_{2}\)O. TM2 was formulated to contain 0.6 g/L CuSO\({}_{4}\).5H\({}_{2}\)O, 0.8 g/L ZnSO\({}_{4}\).7H\({}_{2}\)O, 0.86 g/L MnCl\({}_{2}\).4H\({}_{2}\)O, 0.01 g/L Na\({}_{2}\)MoO\({}_{4}\).2H\({}_{2}\)O, 0.06 g/L NiSO\({}_{4}\).6H\({}_{2}\)O, 0.29 g/L FeSO\({}_{4}\).7H\({}_{2}\)O, 0.01 g/L CoCl\({}_{2}\).6H\({}_{2}\)O and 6 g/L of disodium EDTA.  
VIT1 was composed of 1 g/L thiamine-HCl (vitamin B1), 0.015 g/L d-biotin (vitamin B7), 0.012 g/L cyanocobalamin (vitamin B12), 0.030 g/L pantotheanate (vitamin B5) and 0.060 g/L p-aminobenzoic acid (PABA).  
The carbon source was a glucose stock solution at 50% w/v, which was given in order to obtain a final concentration of 50 g/L of glucose in the culture's medium in all treatments.  
Henceforth, MT1 supplemented with TM1 and VIT1 and glucose at 50 g/L was the medium used in the following assays unless stated otherwise.
# A Cross-Chain Solution to Connect Multiple DNS Blockchains in Consensus Roots System
## 3 System Model  
This study aims to find a solution for the cross-chain interaction between heterogeneous DNS blockchain systems. Suppose Region A and Region B have different DNS blockchain systems. This paper proposes a scheme to build a relay platform between them, as well as a consensus roots system for Region A and Region B to support cross-chain interaction. This way, different regional DNS blockchain systems can communicate and coordinate, achieving more efficient and secure domain name resolution.  
Our proposed scheme uses a master-subchain 1 + N structure design. As shown in Figure 2, it consists of a master chain, subchains, and an atomic swap transaction. The master chain is a decentralized consortium blockchain based on relay chain technology, which acts as a relay platform, supports homogeneous and heterogeneous chain access, and verifies cross-chain transaction data. The subchains are DNS blockchains that need cross-chain interaction, and they form an efficient and secure cross-chain structure with the master chain. The atomic swap transaction ensures the atomicity of cross-chain data transmission. The master chain design can improve the system efficiency and security. The scheme can also provide the technical support for building a consensus roots system for Region A and Region B. The specific parts are as follows.