esa-sceva/satcom-chunk-collection huggingface dataset:
# How to Isolate Non-Public Networks in B5G: A Review
## 6 Open Research Challenges  
### Wireless Throughput Capacity with Spectrum Isolation  
Spectrum isolation should be achieved in the B5G system with NPNs. At present, there is no theory to reveal how spectrum isolation for NPNs affects the wireless throughput capacity in the B5G system. Since the capacity boundary is unknown, the B5G system has to reserve enough spectrum resources to guarantee that the isolation performance can meet the requirements, which leads to wasting valuable spectrum [19; 31]. Therefore, it is important to study the wireless capacity of the B5G system with spectrum isolation for NPNs. To achieve this, it may be necessary to define the key parameters determining the performance of the wireless capacity and spectrum isolation [23; 32]. Then, the relationships between/among the parameters should be derived, as these can reveal the quantitative decreases in wireless capacity with the corresponding spectrum isolation requirements. Finally, spectrum assignment among NPNs should be undertaken based on the theory of wireless throughput capacity with spectrum isolation [17; 33]. The former potential research line would bring great benefits by maximizing the capacity of the B5G system and sharing spectrum among NPNs efficiently.
## 2 Experiments
### Experimental Apparatus and Main Procedures
#### 2.2.2 Core Displacement  
A core displacement experiment is a laboratory method that we used to model and examine fluid flow and displacement processes in a porous medium. It is sometimes referred to as a displacement test or core flood test. To investigate the displacement efficiency, fluid flow behavior, and several factors influencing fluid-fluid and fluid-rock interactions, one fluid (LSW, NF, and a mixed hybrid solution) must be injected into a prepared core sample saturated with another fluid (light crude oil). The detailed core flooding experimental steps are as follows and shown in Figures 2 and 3:  
1. Core Preparation: Cylindrical rock samples, known as core plugs, are extracted from reservoir rocks and prepared for experimentation. The cores are cleaned, dried, and  
Figure 1: Wettability measurement experiment flow chart.  
saturated with light crude oil. Saturation is carried out by using kerosene. First, we vacuum-cleaned rock cores to be sure that any other foreign matter was not left in the rock pores. After that, we saturated them with kerosene at 5 MPa for 1 day.
7. Saturation and Injection: Core plugs were placed in a core holder, and the desired fluid (low-salinity water, nanofluid, and mixed hybrid solution) was injected into the core with a certain pressure and flow rate. The injection was performed using a displacement pump, also known as an ISCO pump.
8. Fluid Flow Monitoring: To evaluate fluid flow behavior, saturation profiles, and recovery efficiency during the injection process, injected fluid volumes and the pressure drop of the core were tracked. These data aid in assessing how well flooding with nanofluid and low-salinity water works to remove oil from the core.
9. Recovery and Analysis: After the displacement process, the core was extracted, and the residual oil saturation was measured. Additional analysis, such as fluid composition analysis or imaging techniques, can be performed to further evaluate the fluid-rock interactions and recovery mechanisms.
## Appendix B Simulation Results of the Limit Capacity of Transmission Towers under Winds  
Considering the ranges of the wind angle of attack (0\({}^{\circ}\)-90\({}^{\circ}\)) and horizontal span (100-400 m), we conducted a series of simulations on the finite element model of tower _ZY_ and the capacity curves of tower _ZY_ under different wind loading cases generated by the explicit algorithm-based non-linear static analysis (Figure A1). The capacity curve was drawn by relating the tower top displacement to the wind speed and to the bottom shear force, respectively (see Figure A1a,b). From Figure A1, the mechanical behavior of the tower structure can be divided into two stages: one is the linear/elastic stage and the other is the nonlinear/plastic stage. The transition of the two stages is defined as the damage threshold of the structure. The wind speed magnitude that corresponds to the transition point was taken as the limit wind speed that the tower can sustain. Figure A1 further notes the proportions of three wind load components (i.e., \(H_{t}\), \(H_{l}\), and \(H_{w}\)) at the limit/transition point of the capacity curves. From Figure A1, we have the plastic stage gets shorter with the proportion of the sum of \(H_{t}\) and \(H_{l}\) increasing. Figure A2 further shows the failure modes of tower _ZY_ under different combinations/ratios of three wind load components.
## 2 Materials and Methods
### Instrument Conditions  
Plasma concentrations of enavogliflozin and IS were analyzed using an Agilent Infinity 1260 HPLC system coupled with an Agilent 6430 triple quadrupole mass spectrometer (Agilent Technologies, Santa Clara, CA, USA) equipped with an electrospray ionization source. The analytes were separated on a Synergi Polar RP column (4 \(\mu\)m particle size, 2.0 mm \(\times\) 150 mm; Phenomenex, Torrance, CA, USA) with isocratic elution. The mobile phase consisted of water containing 0.1% formic acid and methanol containing 0.1% formic acid (15:85, \(v/v\)) at a flow rate of 0.25 mL/min. The column oven temperature was maintained at 30 \({}^{\circ}\)C. The gas flow rate was 10 L/min with a gas temperature of 310 \({}^{\circ}\)C. Nebulizer and capillary voltages were set at 35 psi and 4000 V, respectively. Multiple reaction monitoring (MRM) transitions were set to \(m/z\) 464.2 \(\rightarrow\) 131.0 for enavogliflozin and \(m/z\) 468.1 \(\rightarrow\) 135.2 for d4-enavogliflozin (IS) in a positive ion mode. The collision energy for both analytes was set to 25 eV.
## 6 Security Analysis
### Authentication Proof Using BAN Logic
* \(S_{14}\): Using similar arguments of \(S_{8}\) and \(S_{13}\) for Message 3 and Message 4, we can obtain \(S_{n}\mid\equiv\left(S_{n}\overset{R_{i,n}}{\leftrightarrow}P_{j}\right)\) (**G3** achieved) and \(P_{j}\mid\equiv\left(P_{j}\overset{R_{i,m}}{\leftrightarrow}S_{n}\right)\) (**G4** achieved), respectively. * \(S_{15}\): Based on A4, A5, A6, \(S_{11}\) and \(K_{i,j}=a_{i,m}.b_{m,i}.Y_{n,j}\), we obtain \(P_{i}\mid\equiv\left(P_{i}\overset{K_{i,j}}{\leftrightarrow}P_{j}\right)\) (**G5** achieved). * \(S_{16}\): Using similar arguments of \(S_{15}\), we can obtain \(P_{j}\mid\equiv\left(P_{j}\overset{K_{i,j}}{\leftrightarrow}P_{j}\right)\) (**G6** achieved). Therefore, the proposed protocol achieves **G1**, **G2**, **G3**, **G4**, **G5** and **G6**. Hence, it can assure that both \(P_{i}\) and \(P_{j}\) mutually authenticate each other.
## 2 Problem Formulation
### MIMO Radar System Model with Mutual Coupling
Mutual coupling coefficients are factors related to the distance between the two antennas [23], and for the \(k+1\) non-zero coefficients, they satisfy \(0<|c_{ik}|<...<|c_{i1}|<|c_{i0}|=1\). Besides, for the ULA coupling model, just a few mutual coupling coefficients are non-zero ones [22; 23; 24], and without loss of generality, \(2k<\min\{M,N\}\) is assumed. Then, by collecting \(J\) snapshots, the received data in MIMO radar with mutual coupling is represented as follows:  
\[\mathbf{X}=\mathbf{CAS}+\mathbf{N} \tag{4}\]  
where \(\mathbf{X}\in\mathbb{C}^{MN\times J}\) is the data matrix, \(\mathbf{C}=(\mathbf{C}_{t}\otimes\mathbf{C}_{r})\in\mathbb{C}^{MN\times MN}\) is the transmit-receive mutual coupling matrix. \(\mathbf{A}=[\mathbf{a}(\theta_{1}),\ldots,\mathbf{a}(\theta_{P})]\in\mathbb{ C}^{MN\times P}\) is the steering matrix, with the steering vector \(\mathbf{a}(\theta_{p})=\mathbf{a}_{t}(\theta_{p})\otimes\mathbf{a}_{r}( \theta_{p}),p=1,2,\ldots P\). \(\mathbf{S}=[\mathbf{s}(t_{1}),\ldots,\mathbf{s}(t_{I})]\in\mathbb{C}^{P \times J}\) and \(\mathbf{N}=[\mathbf{n}(t_{1}),\ldots,\mathbf{n}(t_{I})]\in\mathbb{C}^{MN \times J}\) are the noncircular signal matrix and the complex Gaussian white noise matrix, respectively. Figure 1: Configuration of MIMO radar in the presence of mutual coupling.
# Educational 5G Edge Computing: Framework and Experimental Study
## 4 Measurements and Experiments
### Evaluation of our Proposal
#### 4.2.5 The Virtual Simulation Test on Different Systems Performance Testing  
By comparing the virtual simulation experiment's effect on colleges under different architectures, we can test the differences in network, computing, and other capabilities. To test the system performance under different concurrent numbers, we conducted virtual simulation under different terminals (PC, VR glasses, 5G CPE(5G SA sim card)) at three places. The time was 10:00 a.m. on a working day, and the statistical result is the average value of 20 independent runs. The test simulated different numbers (5 users to 100 users, concurrently) of students carrying out virtual teaching in various scenarios under the normal teaching mode. The end-to-end delay of the virtual simulation application is within 3 s, and the application runs smoothly. See Figure 11 for test results.  
Under our proposal architecture, the access delay is about 10 ms, the application runs smoothly, and the user does not feel any delay. As the number of concurrent users increased from 5 to 100, the application open time did not change obviously, eventually rising from 2 s to 3 s. The network latency increased slightly, from 10 ms to 15 ms, and the computing power consumption increased from 10 percent to 95 percent. Application opening, delay test, computing power consumption, etc. are shown on the left side of Figure 11.  
Under the traditional architecture, the access delay is about 40 ms, and the application runs smoothly without affecting the users at all. As the number of concurrent users increased from 5 to 100, the application open time did not change obviously, eventually rising from 2 s to 3 s. The network latency does not change obviously, fluctuating around 40 ms, and the computing power consumption increases from 10 percent to 98 percent. Application opening, delay test, and computing power consumption are shown on the right side of Figure 11.  
Based on the above analysis, we can conclude that the computing and storage capacity of different architectures mainly comes from the gap in hardware resources. Under similar configurations, this part has the same ability to provide applications. The end-to-end network delay in the proposed system is much better than the traditional systems. The gap mainly comes from the impact of data offloading on the network delay brought by the design.  
In the current situation that colleges do not comprehensively upgrade the existing system and hardware, the compromise business optimizations include:  
1. Viewing the control delay through the end-to-end delay of the network;
2. Modifying the network bandwidth/video frame rate of different clients to improve the fluency of course control;
3. Optimizing the configuration encoding process and reducing the amount of transmitted data.  
These operations improve the processing capacity of the server (that is, the concurrent performance of the system) and reduce the delay at the same time.
# Calibration and Impact of BeiDou Satellite-Dependent Timing Group Delay Bias
###### Abstract  
The accuracy of the timing group delay (TGD) transmitted in the broadcast ephemeris is an important factor that affects the service performance of a GNSS system. In this contribution, an apparent bias is found by comparing the orbit and clock difference using half-year data of the BeiDou navigation satellite system (BDS) broadcast ephemeris and precise post-processed products. The bias differs at each satellite on each frequency and shows a general systematic difference between BDS-2 and BDS-3. We attribute this to the satellite-dependent TGD bias of the BDS broadcast ephemeris, which is subsequently calibrated. Moreover, to calibrate the bias independently, a network solution strategy is proposed based on 87 globally distributed multi-GNSS experiment (MGEX) stations spanning 25 weeks. The estimated bias shows good agreement with the values observed from the orbit and clock comparison. For the validation of the bias, we compared the signal-in-space range error (SISRE) performance with and without the TGD bias correction. The results show that the SISRE of the BDS improved from 0.71, 0.81, and 1.40 m to 0.64, 0.66, and 0.64 m in the B1I, B3I, and B1I/B3I frequencies. For BDS-3, the SISRE is well within 0.50 m after the bias correction. To further validate the bias, a week's data were collected at 97 globally distributed MGEX stations. When the TGD bias is corrected, the root mean square (RMS) of single point positioning (SPP) can be improved by 5.6, 8.4, and 21.6% in the B1I, B3I, and B1I/B3I frequencies. Meanwhile, the SISRE and SPP assessment results also indicate that the TGD bias should be corrected by each satellite rather than only corrected between BDS-2 and BDS-3.  
BeiDou navigation satellite system; broadcast ephemeris; timing group delay; differential code bias; signal-in-space range error; single point positioning +
Footnote †: journal: _Journal of the Royal Society B: Series B_, 12, 192; doi:10.3390/rs12010192
# The Perspective of Large-Scale Production of Algae Biodiesel
## 3 Production of Algal Biodiesel
### The Environmental Impact of Algal Biodesel Production
#### Land Use and Location  
The significant advantage of algae farming is the ability to exploit less valuable land, thus minimizing competition for food production. Due to the high production yields of microalgae, a smaller land area is required compared to conventional energy crops.  
Topographic constraints reduce the availability of land for outdoor pond systems because the relatively flat terrain is required. The porosity/permeability of the soil also has an impact on the construction of the pond. Solar irradiation is one of the critical factors that influence algae growth and  
Figure 5: GHG emissions and emissions reduction comparison of advanced biofuel pathways [14].  
yields, and there should be small seasonal variations. Therefore, for practical purposes, warm places near the equator are most appropriate.
# Forest Volatile Organic Compounds and Their Effects on Human Health: A State-of-the-Art Review
## 3 Results
### Limonene
#### 3.5.1 Anti-Inflammatory Activity  
In a systematic review investigating the anti-inflammatory potential of forest BVOCs, D-limonene was shown to be able to modulate many pro-inflammatory mediators such as TNF-\(\alpha\), NO, IL-1\(\beta\), IL-6, enzymes involved in the inflammatory response (5-LOX, COX-2, iNOS), transcription factors (NF-\(\kappa\)B), and signal transduction kinases (p38, JNK, ERK) [4]. In particular, the modulation of inflammatory substances and transcription factors was demonstrated in various animal models (Wistar rats, BALB/c mice, fruit flies) and in some cell cultures (raw 264.7 cell lines, human chondrocytes) [4]. These activities seemed responsible both for preventative action and for a direct effect on inflammation [24]. It was also noted that the (\(l\))-(+)-limonene enantiomer was approximately three-fold less active than the (\(S\))-(-)-limonene one in 5-lipoxygenase inhibitory activity [107].  
On the specific role of limonene in respiratory tract inflammation, the review by [PERSON] and colleagues concluded that the monoterpene has effective anti-inflammatory activity in both preventing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Plant Source** & **Botanical Family** &
\begin{tabular}{c} **Part of the Plant** \\ **Used** \\ \end{tabular} & **Content (\%) \(\ast\)** \\ \hline _Boswellia rivac_ Engl. & Burseraceae & Oleoresin & 28.0–45.0 \\ \hline _Boswellia sacra_ [PERSON] & Burseraceae & Oleoresin & 6.0–21.9 \\ \hline _Bursera gravelens_ & Burseraceae & Oleoresin & 58.6–63.3 \\ ([PERSON]) Triana et Planch & Burseraceae & Oleoresin & 26.9–65.0 \\ \hline _Canarium Inzonicum_ & & & \\ ([PERSON]) A. Gray, C. _vulgare_ [PERSON]. & Burseraceae & Oleoresin & 26.9–65.0 \\ \hline _Ravensara aromatica_ & & & \\ Sonnerat & & & \\ \hline _Abies allu_ Mill. & Pinaceae & Leaves and branches & 28.5–54.7 \\ \hline _Abies specatabilis_ ([PERSON]) & Pinaceae & Leaves and branches & 29.6 \\ Spach & & & \\ \hline _Pinus mugo_ Turra & Pinaceae & Leaves and branches & 6.1–37.1 \\ \hline _Citrus_ spp. & Rutaceae & Fruit peel & 27.0–95.0 \\ \hline \end{tabular} \(\ast\) Content (%) = the relative quantity, expressed as a range of percentages, of D-limonene included in tree-derived essential oils.  
\end{table}
Table 3: Average content of D-limonene in some tree-derived essential oils [106].  
and controlling respiratory system injuries [108], and this points towards a possible role for limonene in the management of allergic airway diseases and asthma [24].  
In a clinical trial on \"inflamm-ageing\" and oxidative stress in healthy elderly subjects, the intake of 10 mg/kg of limonene as a food supplement in the context of a specific diet resulted in a decrease in fibrinogen, glucose, and insulin levels and in an amelioration of the HOMA-IR index, a homeostatic model assessment used to quantify insulin resistance and beta-cell function [109].
# Dissemination of Enterococcuscal Genetic Lineages: A One Health Perspective
## 4 Enterococcal Genetic Traits: Dissemination in the One Health Continuum
### Human Healthcare-Associated Genetic Traits
Recent studies [83; 84] have demonstrated significant genome flexibility and specific clonal outbreaks in certain hospitals, resulting in new clonal lineages with novel adaptative genetic traits, belonging to CC17. In Germany hospitals, _E. ficeum_ ST117 significantly increased from 2010 to 2019 and became a highly widespread lineage of CC17 [85]. This sequence type is frequently linked to _vanB_-type resistance, as seen by the formerly dominant ST192 [85]. In contrast, one of the most prevalent CC17 clones in hospitals in China and Japan has been ST78 VRE, which carries a linear plasmid with a distinctive structural _Tn1546_ including _vanA_[86; 87; 88]. As in other countries, the emergence of _E. ficeum_ clones (belonging to CC17) has also occurred in Australia, namely ST203, ST796, ST1421, and ST17 [67; 70; 71; 89]. Initially, ST203 isolates were susceptible to vancomycin, but the acquisition of _vanB_, encoded by _Tn1549_, was responsible for the emergence of VRE ST203 strains [90]. In 2011, ST203 became the most prevalent cause of _E. ficeum_ bloodstream infections in Australian hospitals [70]. Three years later, in 2014, ST203 was completely replaced by _vanB_ ST796 _E. ficeum_[70]. This clone caused several outbreaks in Australia and New Zealand, before quickly spreading to other countries in Europe, such as Switzerland in 2017 [91; 92]. The presence of _vanA_ in some ST796 clones from Switzerland has also been reported [92]. However, a change in the molecular epidemiology of VREfm occurred between 2013 and 2020 in Australia, leading to a large rise in the incidence of _vanA_ VREfm in 2016, due to the emergence of _vanA_ ST1421 [71; 89]. More recently, a decrease in _vanA_ VREfm was observed, mainly due to a decrease in ST1421 and ST1424 clones and an increase in _vanB_ ST17, which became the most predominant clone in 2020 [89]. Therefore, some clones are endemic to specific geographic regions. For example, _E. ficealis_ CC87 is still present in hospitals in Poland [78]; _E. ficeum_ ST796 was present in Australia and New Zealand in 2014, being later detected in Europe (Switzerland) [70; 93]; the first _E. ficeum_ ST192 and more recently ST117 in Germany [85]; _E. ficealis_ ST525 in Brazil [94]; _E. ficeum_ ST80 associated with the spread of a _vanA_-containing plasmid and acquisition of a heterogeneous accessory genome in Denmark [95]; and _E. ficeum_ ST1421, which became the predominant _vanA_-type clone from 2015 to 2019 in some Denmark regions [96], among others. Overall, the prevalence and spread of enterococcal genetic traits are not just a concern in clinical practice, but also a threat to public health, concerning animals, the environment, and the community, due to enterococcal ubiquity across all sectors of the One Health continuum.
## 2 Related Works
### Loss Functions for Bounding Box Regression  
\(l_{n}-norm\) losses, such as L1 Loss and L2 Loss, primarily optimize the target by minimizing the positional difference between the predicted box and the target box. Consideringtheir sensitivity to outliers and anomalies, [PERSON] et al. [29] introduced Smooth L1 Loss, which alleviates gradient explosion while retaining sensitivity to large errors. Nevertheless, none of these losses explicitly consider geometric relationships, making them less suitable for bounding-box regression in complex object detection scenarios. Consequently, Intersection over Union (IoU)-based loss functions, which directly consider the overlap between two bounding boxes, have become more widely adopted in object detection. The IoU loss [30], introduced as the earliest IoU-based loss function, enhances the detection accuracy by minimizing the difference between the intersection and union ratio of the ground truth and predicted boxes. Although IoU loss effectively measures the overlap between two bounding boxes, it becomes ineffective when there is no overlap, degenerating to a constant value of 1. This limitation hampers the ability to update the network parameters efficiently, thus impacting training efficiency. To overcome this challenge, enhanced IoU losses such as DIoU [31], GIoU [32], and CIoU [33] have been developed by introducing additional penalty terms that address cases with no overlap.  
Among these, CIoU loss extends DIoU loss by integrating factors such as IoU, centroid distance, and aspect ratio, thereby improving bounding box regression accuracy. It has been proven to improve the accuracy of bounding-box regression by taking into account multiple geometric factors, such as location, shape, and size, which is particularly advantageous for complex object detection tasks.
## 4 MIP Problem Formulation
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{l}{**Sets and Parameters**} \\ \hline \(\mathcal{V}\) & set of SDM optical nodes \\ \(\mathcal{E}\) & set of MCF links \\ \(\mathcal{C}\) & set of cores available at each MCF link \\ \(\mathcal{C}(c)\) & set of cores adjacent to core \(c\) \\ \(\mathcal{S}\) & set of frequency slices of a fixed width; \(\mathcal{S}=\{s_{1},s_{2},\ldots,s_{|\mathcal{S}|}\}\) \\ \(\mathcal{M}\) & set of available modulation formats \\ \(\mathcal{D}\) & set of traffic demands \\ \(\mathcal{L}(d)\) & set of allowable lightpaths for demand \(d\) \\ \(\mathcal{L}\) & set of all allowable lightpaths; \(\mathcal{L}=\cup_{\mathcal{L}\in\mathcal{D}}\mathcal{L}(d)\) \\ \(\mathcal{L}(e,c,s)\) & set of lightpaths routed through link \(e\), core \(c\), and slice \(s\) \\ \(\mathcal{E}(l)\) & set of links belonging to the routing path of lightpath \(l\); \(\mathcal{E}(l)\subseteq\mathcal{E}\) \\ \(\mathcal{S}(l)\) & set of frequency slices assigned to lightpath \(l\); \(\mathcal{S}(l)\subseteq\mathcal{S}\) \\ \(c(l)\) & core assigned to lightpath \(l\); \(c(l)\in\mathcal{C}\) \\ \(m(l)\) & modulation format used by lightpath \(l\); \(m(l)\in\mathcal{M}\) \\ \(d(l)\) & demand realized by lightpath \(l\) \\ \(Q(m)\) & the inverse of the required SNR value for modulation format \(m\) \\ \hline \multicolumn{2}{l}{**Variables**} \\ \hline \(x_{dl}\) & binary, equal to 1 if lightpath \(l\in\mathcal{L}(d)\) is selected for demand \(d\in\mathcal{D}\) \\ \(y_{cs}\) & binary, equal to 1 if slice \(s\in\mathcal{S}\) of core \(c\in\mathcal{C}\) of link \(e\in\mathcal{E}\) is used be a lightpath \\ \(y_{s}\) & binary, equal to 1 if slice \(s\in\mathcal{S}\) is used in the network \\ \(w_{cs}\) & continuous, represents the inverse of the SNR value in slice \(s\in\mathcal{S}\) of core \(c\in\mathcal{C}\) of link \(e\in\mathcal{E}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notation.