esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Importance of Thermal Condition Monitoring
### Storage Batteries  
The need for continuous monitoring of batteries temperature and thermal control becomes paramount as operating temperatures affect many energy storage devices when they operate out of their thermal threshold [41]. Many applications require the battery cells or super-chargers to be stacked to meet the required voltage or power levels to perform efficiently. Uneven temperatures continue to impact the life of the battery operations, hence the need for the temperature monitoring system for rapid measurements. The thermal condition monitoring system should promptly indicate the measurements beyond the operational setup margins. For this purpose, real-time online measurements and performance of state estimation are essential. The temperature sensors are supposed to be in place for readily monitoring and transmitting thermal behaviors [42; 43]. Cell performance may change abruptly with temperature fluctuations due to batteries exposure to sudden temperature changes.  
The situation can result in limited performance, which is attributed to the change in cell chemistry, especially at low temperatures [42]. Such a scenario ascribes to the electrochemical characteristics of the active chemicals used in the cell and for which the nominal voltage of a galvanic cell sets. The terminal voltage always associates with the load currents and internal resistance of the cell's properties. These are influenced by the temperature of the cell charge state and the age of the cell [43]. Repetitive charging at low temperatures can cause the accumulation of internal pressure of cells, causing electrolyte leakage by high-temperature conditions. As a result, it is ideal for critical monitoring of the batteries for temperature variations. The DERs system assigned for the grid voltage coordination and power level regulation should be monitored against unfavorable situations. Batteries dedicatedfor such applications need to be in order to keep them at the specified temperatures, above the freezing point, and at the predetermined thermal threshold through real-time online monitoring system [43].
## 3 Results and Discussions  
The summary of the test results with the standard deviations is tabulated in Tables 5 and 6.  
Figure 4: Freeze–thaw cycle and compressive strength test after a freeze–thaw cycle.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Mix ID**} & \multicolumn{2}{c}{**Permeability**} & \multicolumn{2}{c}{**Compressive**} & \multicolumn{2}{c}{**Flexural**} & \multicolumn{2}{c}{**Flexural**} \\  & **Coefficient (mm/s)** & **Strength (MPa)** & **Strength (MPa)** & **Strength (\(\times 10^{-6}\))** \\ \cline{2-7}  & **Mean** & **St. Dev.** & **Mean** & **St. Dev.** & **Mean** & **St. Dev.** & **Mean** & **St. Dev.** \\ \hline Control & 3.91 & 0.12 & 22.2 & 0.50 & 4.84 & 0.09 & 1354 & 21 \\ CCR-2 & 3.89 & 0.14 & 20.4 & 0.43 & 4.48 & 0.10 & 1372 & 25 \\ CCR-4 & 3.64 & 0.15 & 18.6 & 0.24 & 3.96 & 0.07 & 1444 & 37 \\ CCR-6 & 3.54 & 0.19 & 16.9 & 0.21 & 3.31 & 0.06 & 1492 & 25 \\ CCR-8 & 3.23 & 0.18 & 14.6 & 0.45 & 2.87 & 0.11 & 1654 & 30 \\ CCR-2 & 3.92 & 0.09 & 21.9 & 0.37 & 4.80 & 0.05 & 1611 & 29 \\ CCR-4 & 3.75 & 0.11 & 21.2 & 0.41 & 4.47 & 0.10 & 1811 & 25 \\ CCR-6 & 3.50 & 0.15 & 20.4 & 0.33 & 4.31 & 0.08 & 2104 & 32 \\ CCR-8 & 3.36 & 0.12 & 19.7 & 0.33 & 4.01 & 0.06 & 2482 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mechanical properties and permeability results for rubber-modified pervious concrete (RMPC).
## 1 Introduction
The bioaugmentation-assisted phytoremediation method exploits the potential of micro-organisms present in the inoculant to biodegrade petroleum pollutants and their influence on the phytoremediation process. Increasing the concentration of bacteria, fungi and yeast in the root zone has been proven to promote root growth, allowing it to extract more nutrients from the soil, resulting in better growth. Moreover, the symbiotic interaction of micro-organisms with the root system allows for a better degradation of pollutants, transformation of complex organic molecules into simpler and less toxic compounds and their transfer to the plant [18; 19; 20; 21]. It should be remembered that the inoculant used in bioaugmentation treatments should contain micro-organisms capable of degrading a broad spectrum of contaminants. The bacteria most commonly described in the literature for use in remediating land contaminated with petroleum hydrocarbons include _Achromobacter_ sp. [22; 23], _Bacillus_ sp. [24; 25; 26; 27; 28; 29], _Dietzia_ sp. [30; 31], _Gordonia_ sp. [32; 33], _Mycolicibacterium_ sp. [34; 35; 36; 37; 38; 39], _Pseudomonas_ sp. [40; 41; 42; 43; 44; 45], _Rhodococcus_ sp. [46; 47; 48; 49; 50; 51]. Among the fungi capable of metabolizing oil-derived substances are _Aspergillus_ sp. [52; 53], _Candidia_ sp. [54; 55], _Cladosporium_ sp. [56; 57], _Fusarium_ sp. [58; 59] and _Penicillium_ sp. [60; 61]. It should also be remembered that in order to ensure the best possible efficiency of the bioaugmentation process, it is advisable to use inoculants developed on the basis of selected micro-organisms isolated from the natural environment. This avoids antagonistic interactions with naturally occurring micro-organisms in the tested soil [17]. In addition, substances can be added to the inoculant to ensure better microbial growth by providing an easily assimilable carbon source or using a natural biosurfactant, such as \(\gamma\)-PGA [62; 3; 17; 63]. Bioaugmentation and phytoremediation are technologies, which have been studied individually many times, but there are few publications describing their interaction in the bioremediation of soils polluted with TPHs and PAHs. It was assumed that the correct choice of inoculant in the bioaugmentation-assisted phytoremediation method would help ensure high efficiency of the treatment process of contaminated land. In the present study, it was decided to experimentally test the effectiveness of the combination of phytoremediation and bioaugmentation in several variants of conducting the process on real samples of land contaminated with TPHs and PAHs, and to evaluate the change in toxicity of soils after treatment.
## 4 Hyperconverged TPM  
In the solutions found in the literature to provide security for vTPMs, the anchoring of the vTPM to the TPM is managed and performed individually and locally on the node hosting the VM, which brings two main limitations. First, there is the prerequisite that the node hosting the VMs must have a physical TPM. Although the adoption of TPM chips is growing, establishing such a requirement limits the use of this approach to contexts where all nodes in the system have a TPM. In different data centers, hybrid scenarios are common, where some hosts have the chip and others do not.  
Secondly, even in environments where all the nodes have physical TPMs, it may be appropriate to create replicas of the anchoring. Replicating an anchoring refers to anchoring the vTPM in more than one TPM chip. This is especially important for critical systems that require guarantees of availability, integrity, and security. In this way, if one of the anchorings becomes inaccessible, the replicas ensure that the vTPM remains attested and verified. Furthermore, in cases of VM migration to another host, the replica of the anchoring allows for easier migration of the vTPM.  
Both situations presented can be solved by removing the requirement for the vTPM to be anchored only to the TPM chip of the host where it is physically allocated. Therefore, in this work, we propose an approach for hyperconverged TPMs. In the proposed solution, the resources of all the physical TPMs are combined and managed jointly by a single software layer. In this way, all the services in the data center can make use of the hardware TPM resource, even if the chip is not physically available in the host where the service is allocated. In other words, although the machine hosting the vTPM does not have a physical TPM, its anchoring can be performed on another machine with a TPM in a way that is transparent to the user. In this approach for a hyperconverged TPM, each node provides its individual TPM to create a distributed TPM across the nodes of the data center.
## 2 Background and Related Work
### Background
#### 2.1.3 Vehicle Safety System (In-Vehicle Network)  
Autonomous and connected vehicles are transforming transportation through the integration of three key systems: safety, connectivity, and diagnostics. These systems, along with their supporting sensor technologies, enable advanced functionalities and enhance the driving experience. However, this increased complexity also introduces significant security vulnerabilities. This section examines the core systems and sensor technologies of autonomous and connected vehicles, outlines fundamental security requirements, and analyzes common attack vectors that threaten their operation.  
The evolution of autonomous and connected vehicles hinges on the integration of three foundational systems--safety, connectivity, and diagnostics--each serving distinct yet synergistic roles in redefining modern transportation. As illustrated in Figure 2, these systems collectively enhance vehicular functionality, prioritize occupant safety, and elevate user experience through advanced technological frameworks.  
The Safety system comprises active and passive safety mechanisms. Active safety systems assist drivers in accident prevention by utilizing sensing, processing, and actuation technologies. In contrast, passive safety systems include components such as airbags that mitigate risks and protect occupants in the event of an accident [26]. The Connectivity system encompasses all connected vehicle features and is divided into three subcategories: Vehicle-to-Everything (V2X) communication, in-vehicle connections, and smart connected features that enhance user comfort [26].  
V2X communication, illustrated in Figure 3, consists of four key types of connectivity: Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Network (V2N), and Vehicle-to-Pedestrian (V2P). V2P facilitates the direct communication between vehicles and vulnerable road users. V2I enables vehicles to share information with infrastructure-related devices such as cameras and traffic lights. V2V allows vehicles to exchange data regarding location, speed, and other parameters to improve traffic flow and prevent collisions. V2N leverages cloud services to enhance communication between vehicles, traffic management systems, and road users [26].  
The Diagnostics system, depicted in Figure 4, describes the methods through which vehicles can be accessed for diagnostic and software updates. This can be achieved either remotely over-the-air (OTA) or through a physical connection. OTA updates enable the Original Equipment Manufacturer (OEM) server to connect to the vehicle's main electronic  
Figure 2: In-vehicle network.  
control unit (ECU). Alternatively, physical diagnostics can be performed using On-Board Diagnostics (OBD) ports [26].
# A Ku-Band Fully Differential Low-Power High-Input TdB Low-Noise Amplifier
## 3 Measurement  
Figure 4 shows a microphotograph of a chip fabricated with 65 nm bulk CMOS technology. The size of the chip was 0.75 mm \(\times\) 0.35 mm (0.26 mm\({}^{2}\)). An on-chip GSG probe was used with a network analyzer for S-parameter measurements. The operating voltage of the chip was 1 V, and it consumed 11 mW of static power. Additionally, the power consumption measured at the OP1 dB point at 14 GHz was 30 mW. Figure 5 presents the S-parameter, noise figure, input P1 dB, and K-factor (stability) measurement results. The maximum gain was 19.92 dB at 12.2 GHz with a \(-\)3 dB bandwidth of 4.65 GHz (11.24-15.89 GHz). The noise figure reached its minimum value of 2.7 dB at 13.6 GHz. Additionally, at a frequency of 15.6 GHz, the input P1 dB was \(-\)7.45 dBm and the output P1 dB was 10.09 dBm. Figure 6 shows the third-order intermodulation distortion (IMD3) measured using tones with an interval of 100 MHz at 14 GHz. It shows a low IMD3 value of less than \(-\)30 dBc, even at output powers above 0 dBm or higher. Table 1 compares the measurement results of the proposed LNA with those of the latest Ku-band LNA, which utilizes a process comparable to the 65 nm CMOS process used in the proposed LNA. For a fair comparison, two figure of merits were considered [15]. Utilizing comparison tables and figure of merit (FoM) enable a fair assessment of the performance of the LNA. From the comparison table, it can be confirmed that the proposed LNA exhibits the best input P1 dB performance. Regarding figure of merit I, it demonstrates adequate performance. However,  
Figure 3: Simulation results of the proposed low-noise amplifier according to the second-stage bias (VG2) condition at 14 GHz. (**a**) AM-AM, (**b**) input power versus output power.
# Intelligent Network Service Optimization in the Context of 5G/NFV
## 5 Experimental Results  
By exploiting the features of the DLS library, various types of NNs can be quickly trained and deployed. For the purposes of this study, a simple FFNN was utilized, structured as a three-layered NN. A combination of infrastructure-oriented, application-level, and QoE-based metrics were employed as inputs to the FFNN. A summary and a more detailed description of the incorporated metrics are given in Table 2 and Section 4.4, respectively. The outputs of our FFNN are the optimization actions of horizontal scaling of services, as summarized in Table 3 and detailed in Section 4.6.
## 2 Stiffness Reduction Method
### Stiffness Reduction Model
Figure 3a shows the bending moment diagram of a PUTCGE under the action of the loadings when the beam-spring model was adopted, where the magnitudes of the bending moments at the upper and lower ends of the sidewalls are denoted by \(M_{t}\) and \(M_{b}\), respectively, and the magnitude of the bending moment at the middle-height of the sidewalls is denoted by \(M_{m}\). Figure 3b shows the initial bending moment diagram of a PUTCGE under the action of the loadings when the stiffness reduction model was adopted, where the magnitudes of the initial bending moments at the upper and lower ends of the sidewalls are denoted by \(M_{t1}\) and \(M_{b1}\), respectively, and the magnitude of the initial bending moment at the middle-height of the sidewalls is denoted by \(M_{m1}\). Then, the bending moment adjustment coefficients at the upper and lower ends of the sidewalls are defined as \(\xi_{t}\) and \(\xi_{b}\), respectively, as follows:  
\[\xi_{t}=\frac{M_{t}}{M_{t1}}-1 \tag{2}\]  
Figure 3: Bending moment diagram: (**a**) beam-spring model and (**b**) stiffness reduction model. \[\xi_{b}=\frac{M_{b}}{M_{b1}}-1 \tag{3}\]  
Then, the magnitudes of the initial bending moments, shown in Figure 3b, were fine-tuned (adjusted) as follows:  
\[M_{12}=M_{t1}(1+\xi_{t}) \tag{4}\]  
\[M_{b2}=M_{b1}(1+\xi_{b}) \tag{5}\]  
\[M_{m2}=M_{m1}-\frac{M_{t1}\xi_{t}+M_{b1}\xi_{b}}{2} \tag{6}\]  
where \(M_{12}\) and \(M_{b2}\) are the final adjusted magnitudes of the bending moments at the upper and lower ends of the sidewalls, and \(M_{m2}\) is the final adjusted magnitude of the bending moment at the middle-height of the sidewalls. It was noted that these final adjusted magnitudes of bending moments \(M_{12}\), \(M_{b2}\), and \(M_{m2}\) were correspondingly equal to those of bending moments \(M_{t}\), \(M_{b}\), and \(M_{m}\) shown in Figure 3a. The bending moments at the ends of the top and bottom slabs could be obtained accordingly based on the equilibrium of moments at the ends. It should be mentioned that both the uniform stiffness model and the beam-spring model were used here to mainly illustrate the concepts of the stiffness reduction coefficient and the bending moment coefficient, but these two models were not really needed, as the stiffness reduction model could supersede these two models in the analysis of PUTCGEs with the help of an equation shown in Section 3.5. Details of this can also be found in the calculation steps in Section 4, Model Validation.
## 4 Chitosan as Catalytic Support
### Chitosan-Containing Composite Biocatalysts
The enhancement in catalytic activity of the enzyme in the presence of silver by a factor of 1.23 was found for the reaction of lactose hydrolysis. Both immobilized enzyme preparations exhibited remarkably high stability against pH (2-9) and temperature (20-80 \({}^{\circ}\)C) compared to the free form. PANI-CS-Ag-bound \(\beta\)-galactosidase was able to retain 94% of its initial activity after ten repeated uses. It was underlined that both PANI-CS and PANI-CS-Ag-bound \(\beta\)-galactosidase can serve as powerful recognition probes in biosensor applications for lactose detection. LIP from _Mucor miehei_ was immobilized on CS hydrogels by [PERSON] et al. [218] without using any crosslinking or activating agents. CS was dissolved in acidic solution (pH = 3), and then LIP solution was added. The obtained biocatalytic material was tested in a model esterification reaction of propylaurate synthesis. The immobilized LIP showed an optimum operation temperature of 30 \({}^{\circ}\)C and maintained high catalytic activity in various nonpolar organic solvents. The biocatalyst presented excellent reusability, with only 10%  
Figure 32: Scheme of SiO\({}_{2}\)-Ca–CS–LAC [216]. loss of activity after 12 uses, which was higher than that obtained fromLIP-containing CS-beads prepared with a crosslinker. The residual activity decreased to 62% of the initial one after one day of storage, and to 42% after 4 days, remaining at the same level for about month under freeze (\(-20\)\({}^{\circ}\)C). Some comparative characteristics of biocatalysts are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Enzyme** & **Support** & **Shape** & \begin{tabular}{c} **Cosslinking** \\ **Agent** \\ \end{tabular}  & \begin{tabular}{c} **Additional Activating** \\ **Agent** \\ \end{tabular}  & \ \begin{tabular}{c} **Recycling** \\ **Stability** \\ \end{tabular}  & \begin{tabular}{c} **Storage** \\ **Stability** \\ \end{tabular}  & **Ref.
# Effects of Grazer Exclusion on Carbon Cycling in Created Freshwater Wetlands
## 3 Results
### Decomposition Rates  
Decomposition rates varied significantly across species; _N. odorata_ decomposed faster than _T. latifolia_, _P. cordata_ and _S. latifolia_ (F\({}_{3,35}\) = 255, \(p\) < 0.001, Figure 3). Across all species measured, _N. odorata_ was also the species with the lowest litter C:N (Table 2). Differences in these ratios were largely driven by species-specific differences in %N, with values ranging from a low of 1.3 \(\pm\) 0.02% in _P. cordata_ to 2.5 \(\pm\) 0.01% in _N. odorata_ (Table 2). Decomposition was greater in caged plots overall (F\({}_{1,35}\) = 7.3, \(p\) = 0.01), but the significant interaction between species and treatment showed that the increase in decomposition when grazers were excluded only occurred for _N. odorata_ (F\({}_{3,35}\) = 6.5, \(p\) = 0.001, Figure 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **\%C** & **\%N** & **C:N** \\ \hline
**Vegetation** & & & \\ _S. latifolia_ & 42.0 \(\pm\) 0.01 & 2.0 \(\pm\) 0.05 & 24.9 \(\pm\) 0.6 \\ _N. odorata_ & 41.9 \(\pm\) 0.23 & 2.5 \(\pm\) 0.01 & 19.8 \(\pm\) 0.2 \\ _T. latifolia_ & 44.4 \(\pm\) 0.14 & 2.1 \(\pm\) 0.08 & 24.6 \(\pm\) 0.8 \\ _P. cordata_ & 41.2 \(\pm\)0.07 & 1.3 \(\pm\) 0.02 & 37.3 \(\pm\) 0.6 \\ _L. oryzaeides_ & 43.1 \(\pm\) 0.03 & 1.4 \(\pm\) 0.13 & 35.9 \(\pm\) 3.3 \\ \hline
**Belowground biomass** & & & \\ Caged & 35.9 \(\pm\) 1.66 & 1.6 \(\pm\) 0.07 & 26.0 \(\pm\) 1.3 \\ Uncaged & 35.5 \(\pm\) 2.00 & 1.6 \(\pm\) 0.14 & 25.7 \(\pm\) 1.7 \\ \hline
**Soil** & & & \\ Caged & **6.5 \(\pm\) 0.33** & 0.5 \(\pm\) 0.13 & 19.7 \(\pm\) 0.7 \\ Uncaged & **5.1 \(\pm\) 0.36** & 0.3 \(\pm\) 0.02 & 20.8 \(\pm\) 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Elemental composition of dominant plant species at the study site and belowground biomass and soil from caged and uncaged plots, mean \(\pm\) SE. Bold values indicate significant differences between caged an uncaged plots.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 4 4G and NSA-Based 5G V2X Services
### RAN Aspects Support for the 4G & 5G V2X  
4G access network is supported by the new radio (5G-NR) features for enhancing the ecosystem of V2X services. The 3 GPP technical document (_TR 37.985 Rel-16_) reports overall RAN aspects: specifications, features design and operations, and interfaces (Uu and PC5 to cover communication types) for 4G & 5G V2X services and applications. To expand the support, the report also elaborates on resource allocation, congestion control, physical layer, higher-layer protocols, network aspects, and V2X via the Uu interface for 4G V2X and 5G V2X services implying that, each technology is considered for the given operations. Many use case scenarios are now considering both 4G and 5G radio-based communication networks working together and forming a multi-RAT (radio access technology) V2X. Hence, the existing scenarios consider both 4G & 5G V2X sidelink communications. This dictates the support for aV2X scenarios grouped into platooning, extended sensors, advanced driving, and remote driving as documented in the technical specification (_TS 22.186 Rel-16_). We would like to draw the attention of the reader from the automotive industry to these developments.
## 4 Results
### Experiment in Corridor  
#### 4.1.1 Long Distance Tracking  
We designed this experiment to verify if the proposed method can eliminate the cumulative error of RGB-D SLAM in long distance tracking in corridor scene with poor texture information. The experiments were performed in a travel distance of 100 m using a laptop (Dell precision7530 GPU work station)--12 Intel Core i7-8750H CPU, 2.20 GHz frequency, 16 GB RAM memory, 6G NVIDIA corporation GP104 GLM. Figure 12a,b shows the sparse maps constructed by the two methods.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Camera** & \(f_{x}\) & \(f_{y}\) & \(c_{x}\) & \(c_{y}\) & \(k_{1}\) & \(k_{2}\) & \(k_{3}\) & \(p_{1}\) & \(p_{2}\) \\ \hline Kinect v1 & 514.994 & 513.758 & 321.045 & 244.587 & 0.0110 & \(-\)0.0521 & 0021 & 0028 & \(-\)0.0064 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calibration results of RGB-D camera.  
To quantitatively assess the accuracy of the model, Table 3 has been provided, which shows the matching error between the ground truth LiDAR data and the sparse map generated by the two methods. As seen in Table 3, the mean error of our method is 0.08 m, whereas that of ORB-SLAM2 is 1.18 m.