esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Biomass, Lipid, and Pigment Analysis  
Microalgal biomass was determined using a microplate reader synergy H1 (BioTek, Winooski, VT, USA) spectra-fluorometer every alternate day. About 200 \(\mathrm{\SIUnitSymbolMicro mL}\) of culture was loaded onto 96-well plates, and the absorbance was recorded at 680 nm to calculate the biomass concentration using Equations (8)-(11) [25].  
\[\mathrm{Biomass\ concentration\left(g\cdot L^{-1}\right)}=0.675\times A_{680}-0.0841 \tag{8}\]  
where \(\mathrm{A_{680}}\) depicts the density of algae at 680 nm wavelength.  
\[\mathrm{Biomass\ productivity\left(g\cdot L^{-1}d^{-1}\right)}=\frac{\mathrm{Biomass\ concentration\left(g\cdot L^{-1}\right)}}{\mathrm{Cultivation\ duration\ (d)}} \tag{9}\]  
\[\mathrm{Specific\ growth\ rate\ (u)(d^{-1})}=\frac{\mathrm{ln\ (X_{2}/X_{1})}}{\mathrm{T_{2}-T_{1}}} \tag{10}\]  
\[\mathrm{Doubing\ time\ (d)}=\frac{\mathrm{ln2}}{\mathrm{\SIUnitSymbolMicro\mu}} \tag{11}\]  
where T represents time in days; \(X_{2}\) and \(X_{1}\) showed final and initial biomass concentration; and \(\mathrm{T_{2}}\) and \(\mathrm{T_{1}}\) represent the final and initial time, respectively.  
The lipid-sensitive fluorescent dye Nile Red was used to visualize and estimate triacylglycerlevel (TAG) accumulation in microalgae at different stages [26]. The quantification of neutral lipid was performed in duplicates on a black 96-well plate (Nunc maxisorp, Waltham, MA, USA) at each time interval (on the 1 st, 5 th, 9 th, 13 th, 18 th, 23 rd, 25 th, and 30 th day). About 500 \(\mathrm{\SIUnitSymbolMicro mL}\) of the culture was mixed with 1 \(\mathrm{\SIUnitSymbolMicro mL}\) of stock Nile Red (stock concentration) containing 499 \(\mathrm{\SIUnitSymbolMicro mL}\) of dimethyl sulfoxide (DMSO) solution. The sample was loaded onto the 96-well plate, and the fluorescence was recorded using a Synergi H1 spectra-fluorometer at an excitation wavelength of 530 nm and emission wavelength of 575 nm.  
The microalgae pigment was determined by loading 200 \(\mathrm{\SIUnitSymbolMicro mL}\) of the culture onto a 96-well plate using a microplate reader synergy H1 spectra-fluorometer on alternate days. Pigment content was determined as chlorophyll a, chlorophyll b, and total carotenoid by Equations (12), (13), and (14), respectively [27].  
\[\mathrm{Chl\ }a\ \left(\mathrm{\SIUnitSymbolMicro\mu g\cdot mL^{-1}}\right)=13. 36\ A_{664}-5.19A_{648} \tag{12}\]  
\[\mathrm{Chl\ }b\ \left(\mathrm{\SIUnitSymbolMicro\mu g\cdot mL^{-1}}\right)=27. 43\ A_{648}-8.12A_{664} \tag{13}\]  
\[\mathrm{Car\ }(\mathrm{\SIUnitSymbolMicro\mu g\cdot mL^{-1}})=\frac{\left(1000\ A _{470}-2.13 Chl\mathrm{\SIUnitSymbolMicro\mu g\cdot 64\ }\mathrm{\SIUnitSymbolMicro\mathrm{Chl }b}\right)}{209} \tag{14}\]  
where \(\mathrm{Chl\ }a\), \(\mathrm{Chl\ }b\), and \(\mathrm{Car}\) are chlorophyll \(a\), chlorophyll \(b\), and total carotenoids, respectively. \(A_{664}\), \(A_{648}\), and \(A_{470}\) are the absorbances recorded at wavelength 664, 648, and 470 nm, respectively.
###### Abstract  
In this paper, a reliability assessment and prediction method based on bearing vibration signals is proposed, which combines Adaptive Cyclostationary Blind Deconvolution (ACYCBD) and AdaBoost-Mixed Kernel Relevance Vector Machine. Firstly, CYCBD parameters were optimized by the Ivy optimization algorithm to enhance the noise reduction effect, and then multidimensional features were extracted and dimensionalization was reduced by PaCMAP. Based on dimensionality reduction features, logistic regression was used to evaluate reliability, and AdaBoost-MKRVM was combined to predict reliability. The experimental results show that the mean absolute error (MAE) of the proposed method on the bearing life dataset of Xi 'an Jiaotong University is 0.052, which is better than the traditional method, and provides a new idea for the performance prediction of rolling bearings.  
AdaBoost; maximum cyclostationary blind deconvolution (CYCBD); mixed kernel relevance vector machine (MKRVM); pairwise controlled manifold approximation (PaCMAP); reliability evaluation and prediction; rolling bearing _algorithms_
# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 4 Evaluation
### Testing Scenarios
#### 4.1.1 Scenario S1  
In this scenario, we examine localization accuracy in a non-self-similar environment. The second room to be visited is distinct in its dimensions from other rooms. Thus, the first correct localization can be observed in frame 8, when the first room has been completely observed and the second room has been observed halfway. Figure 6 shows that the accuracy of localization is below 30 cm and 1\({}^{\circ}\). The average translational error for correctly localized poses is 16.3 cm, and the average rotational error is 0.402\({}^{\circ}\). The ground estimation has an average error of 4 cm, the highest of the three scenarios, which correlates with the area covered. The best accuracy in this scenario can be found when the device first observes the second room. After this, the localization accuracy decreases again, which points to inaccuracies of the device accumulating over time. Older point-cloud data may become shifted due to accumulative errors in the device's internal tracking systems.
# A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications
## 2 Network Based Systems
### Range Free
#### 2.2.2 Fingerprinting
The creation of the database samples requires an intensive campaign of measurements in order to collect the fingerprints of the radio signals and create a radio map of the indoor environment. This process is time consuming and vulnerable to environmental changes. Furthermore, the accuracy of the system depends on the assumption of similar wireless conditions between the collection of the fingerprints and the current signals [8]. The movement of humans or objects inside the building will produce differences between the database and the online collected measurements that will cause an increase in the positioning error. The accuracy of a fingerprinting method is related to the number of points of the calibrated radio map [80]. However, the dimension of the radio map is determined by the application and type of fingerprinting method employed. For example, deterministic methods generally imply less calibration effort than probabilistic methods because the later imply the computation of the statistics of the signals at each calibration point [81]. Similarly, the localization of a person moving around an indoor environment will require a larger calibration campaign than the localization of a static object. Recently researchers have shown an increasing interest in reducing the effort of the calibration process [8]. Besides the typical point to point calibration process that involves a larger calibration effort, the authors proposed different calibration procedures as for example the collection of measurements through a walking path or crowdsourcing between the measurements collected by different users. In [82] authors evaluate the performance of different radio map construction methods. More information about calibration methods for fingerprinting systems can be found in [8]. Independently of the source of fingerprints the main disadvantage of fingerprinting methods is the effort needed for the collection of the database samples, which increases the cost and reduces the scalability.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### AI-Enabled Interference Mitigation
#### 4.4.6 Standardization Mapping and Industry Maturity  
Many of the IM techniques discussed in this review are addressed across 3 GPP Releases 15 through 18. For instance, elCIC and CoMP, first introduced in earlier releases, have seen ongoing enhancements in Release 15 and beyond, including integration with dynamic scheduling and beam coordination. Dynamic TDD coordination has evolved in Release 16 with guard period adaptation and slot alignment for UL/DOL balancing [205].  
The integration of AI and ML into RAN architectures is a significant aspect of Release 17 and 18. These releases introduce the concept of near-RT and non-RT RICs, aligning with the open RAN Alliance architecture, to enable AI-driven interference detection and mitigation [206]. FL for traffic steering and IPR is actively studied in Release 18 [207], although normative specifications are still under development.  
AI-native IM techniques such as RL for BF or scheduling are being trialed in early deployments and open testbeds, but full industry adoption requires overcoming challenges related to model interpretability, cross-vendor interoperability, and trust management. These issues are reflected in joint efforts by 3 GPP and open RAN to define key performance indicators, lifecycle hooks, and interfaces for AI explainability and orchestration [208].  
A summary of key IM techniques mapped to their respective 3 GPP releases and maturity levels is shown in Table 9.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**IM Technique** & **3 GPP Release** & **Standardized Under** & **Maturity Level** \\ \hline elCIC & Rel-10/Rel-15+ & TS 36.300/38.300 series & Widely deployed \\ \hline CoMP (JP, DPS) & Rel-11/Rel-15+ & TS 36.819/TS 38.214 & Deployed with limitations \\ \hline Dynamic TDD coordination & Rel-16 & TS 38.331 & Field-tested in UDN trials \\ \hline RIC-based AI (non-RT/near-RT) & Rel-17/Rel-18 & TS 38.533/open RAN WG2/WG3 & Pre-commercial/maturing \\ \hline FL-based IM & Rel-18 (study) & TR 38.838/TS 28.104, 28.105 & Research phase \\ \hline \hline \end{tabular}
\end{table}
Table 9: Mapping of IM techniques to 3 GPP releases and maturity levels.  
Although 3 GPP-standardized techniques such as CoMP and eICIC offer substantial theoretical gains, their effectiveness depends heavily on deployment conditions. For instance, CoMP requires robust and low latency backhaul connectivity and precise synchronization among BSs conditions not always available in rural or legacy systems. AI-driven RICs further depend on high computational capacity and continuous data collection, which may be difficult to sustain at the network edge.
# Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency
## 4 Results  
### Results of Multicollinearity Analysis  
The results of the collinearity assessment for landslide assessment factors across four time ranges in this study are presented in Table 1. As introduced in the Section 3, when the VIF result of an assessment factor approaches 10, it indicates a certain degree of collinearity with other factors. In this study, the VIF values of the landslide assessment factors are all less than 5, indicating that there are no significant issues of multicollinearity among the assessment factors. This also suggests that the selection of landslide assessmentfactors in this study is relatively robust, which is conducive to improving the performance of the model.
## 4 Mathematical Model
### Decision Variables  
* \(X_{iljk}\): binary variable that equal to 1 if operation \(j\in J_{i}\) of sub-lot \(l\in L_{i}\) of job \(i\in N\) is assigned to the material resource \(k\in M_{ij}\), and 0 otherwise.
* \(F_{iljk}l^{\prime}gk\): binary variable that is equal to 1 if operation \(j\in J_{i}\) of sub-lot \(l\in L_{i}\) of job \(i\in N\) directly precedes operation \(g\in J_{h}\) of sub-lot \(l^{\prime}\in L_{h}\) of job \(h\in N\) on the material resource \(k\in M_{ij}\cap M_{hg}\), and 0 otherwise.
* \(Z_{ilil^{\prime}k}\): binary variable that is equal to 1 if operation \(j\in J_{i}\) of sub-lot \(l\in L_{i}\) of job \(i\in N\) starts and finishes at the same time as the operation \(j\in J_{i}\) of sub-lot \(l^{\prime}\in L_{i}\) of job \(i\in N\) on the material resource \(k\in M_{ij}\), and 0 otherwise.
* \(S_{iljk}\): starting time of operation \(j\in J_{i}\) of sub-lot \(l\in L_{i}\) of job \(i\in N\) on the material resource \(k\in M_{ij}\).
* \(C_{iljk}\): completion time of operation \(j\in J_{i}\) of sub-lot \(l\in L_{i}\) of job \(i\in N\) on the material resource \(k\in M_{ij}\).
* \(C_{i}\): completion time of job \(i\in N\).
## 4 Data and Methods
### Design of the Performance Evaluation
#### 4.6.2 Experimental Setup of the Multi-Frequency SAR Analysis  
As second major part, the capability of SAR data to remove cloud cover from multi-spectral images by means of the given algorithm had to be tested using a huge variety of constellations in order to find the best setup for cloud removal. The tested data constellations are shown in Figure 6. Therefore, the backscatter intensities of the SAR data were included as reference data in the image reconstruction as single frequency information as well as a multi-frequency dataset of X-, C- and L-Band data. To enhance the dimensionality of the SAR information, texture measures were included since texture is supposed to increase the separability of structural features within the images [43]. Hence, a set of co-occurrence texture measures was estimated from the SAR intensity images with a 3 \(\times\) 3 moving window and added to the stack of SAR data. The texture measures Contrast, Dissimilarity, Homogeneity, Mean and Variance were derived [43,44].  
Figure 6: Overview of the data constellations tested within this study. Cloud cover was varied in 10% steps from 10%-30%. All SAR frequencies are included as single frequencies (X, C and L are indicating the respective frequency band) as well as a multi-frequency combination (XL, XCL). The label “I” indicates the reconstruction solely based on backscatter intensities. The label “I_T” indicates the reconstruction based on a combination of backscatter intensities and texture measures.
## 3 Methodology
### Implementation of Mathematical Model of CR-NBEER
#### 3.1.6 Node Path Loss Phase (NPLP)  
The nodes are embedded with functionalities to calculate the path loss in dB and transfer the data to the nearest possible node.
# Suitability Evaluation of the Lining Form Based on Combination Weighting-Set Pair Analysis
## 2 The Suitability Level of lining Form
\[C\geq\frac{\sigma_{min}}{\gamma_{w}h_{\mathrm{s}}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation** & **Extremely** & **More** & **Basically** & **Relatively** & **Extremely** \\
**Level** & **Suitable (I)** & **Suitable (II)** & **Suitable (III)** & **Unsuitable (IV)** & **Unsuitable (V)** \\ \hline \(A\) & \textgreater{}1.5 & 1.3–1.5 & 1.2–1.3 & 1.0–1.2 & \textless{}1.0 \\ \(B\) & \textgreater{}1.3 & 1.2–1.3 & 1.1–1.2 & 1.0–1.1 & \textless{}1.0 \\ \(C\) & \textgreater{}1.3 & 1.2–1.3 & 1.1–1.2 & 1.0–1.1 & \textless{}1.0 \\ \(D\) & \textgreater{}3 & 3–6 & 6–10 & 10–15 & \textgreater{}15 \\ \(d) Evaluation index \(D\) is based on the hydraulic gradient of high-pressure pipelines to evaluate the suitability of reinforced concrete lining. The hydraulic gradient is closely related to the hydraulic fracturing criterion and is an important parameter for determining the permeability stability of the surrounding rock. According to the material and fracture development in the borehole fault fracture zone of high-pressure pipelines, there is a time-varying phenomenon along the fault fracture zone, and it is relatively strong. Combined with similar engineering experience, the high-pressure hydraulic gradient is generally not greater than 10-15 under I-III class hard surrounding rock conditions, and the hydraulic gradient in fault fracture zone and influence zone is recommended to be 3.0-6.0. Therefore, the hydraulic gradient value range is 6\(\sim\)10, which is basically suitable. (e) Evaluation index \(E\) evaluates the suitability of reinforced concrete linings according to rock mass classification of high-pressure pipelines. This index is also an important impact parameter for the above criterions. In drilling exploration, the rock quality designation (RQD) is usually selected to determine the rock mass classification. However, many researchers pointed out that the RQD cannot directly reflect the rock structure type and rock block composition [16; 17; 18]. As shown in Figure 1, when the distance (intercept) between joints is 10 cm or less, the RQD is 0%, whereas when the distance is 11 cm or more, the RQD is 100%. Compared with RQD, the rock mass block index (RBI) is able to reflect the size of the rock mass [19]. For a rock mass with RQD equal to 90%, although its block composition varies among 10-30, \(>\)50, \(>\)100 cm, etc., the RBI values corresponding to each case are different. RBI is more accurate than RQD in reflecting the structural characteristics of the rock mass [20]. Therefore, RBI is an improvement and supplement to RQD, and the formula is shown in Equation (4). \[\text{RBI}=3C_{r3}+10C_{r10}+30C_{r30}+50C_{r50}+100C_{r100} \tag{4}\]  
where, \(C_{r3}\), \(C_{r10}\), \(C_{r30}\), \(C_{r50}\), and \(C_{r100}\) are the core acquisition rates with lengths of 3\(\sim\)10, 10-30, 30-50, 50-100, and \(>\)100 cm, respectively. Furthermore, 3, 10, 30, 50, and 100 are all coefficients.
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 5 The Taxonomy of Middleware Architectures for WSNs  
The middleware architectures for WSNs have been used widely to reduce the complexity of WSN applications. The classification of middleware architectures approaches are proposed in the literature [22; 31]. The middleware architectures based on SOA for WSNs can be classified based on the applications targeted as shown in Figure 4. Additionally, Table 1 presents the comparison between different middleware architectures designed for WSNs.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Middleware** & **Scalability** & **Heterogeneity** & **Ese to Used** & \begin{tabular}{c} **Power** \\ **Awareness** \\ \end{tabular} & \begin{tabular}{c} **Application** \\ **Type** \\ \end{tabular} & **Security** & **QoS** \\ \hline Database & Not & none & Yes & None & \begin{tabular}{c} Event \\ driven \\ applications \\ \end{tabular} & None & None \\ \hline Virtual Machine & Supported & Not fully & Little & Supported & \begin{tabular}{c} Dynamic \\ Applications \\ \end{tabular} & Yes & None \\ \hline Message & Supported & Not fully & Yes & Supported & \begin{tabular}{c} Event \\ driven \\ applications \\ \end{tabular} & Little & None \\ \hline Modular & Supported & None & Yes & Supported & \
\begin{tabular}{c} Dynamic \\ Applications \\ \end{tabular} & Yes & None \\ \hline Application & & & & & & \\ Driven & Supported & None & Yes & None\(/\)Partial &
\begin{tabular}{c} Real-time \\ applications \\ \end{tabular} & None & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: A Comparison of Different Middleware Architectures Approaches.
## 5 Performance Evaluation
### Handover Authentication
#### 5.3.2 Signaling Cost  
In the context of a communication protocol involving an edge device, a 5G service provider, and a fog node, this section compares different signaling messages exchanged among these entities. The focus is on evaluating the efficiency and effectiveness of various protocols in terms of the number of signaling messages and overall performance. The findings are presented in Table 17 and Figure 19, which outline the characteristics of different protocols. The proposed protocol stands out for its efficiency. It involves only two signaling messages that are exchanged between the edge device, 5G service provider, and a fog node. These two messages serve the purposes of handover and key agreement. In contrast, another protocol proposed by [PERSON] et al. [55] is highlighted as having the highest signaling cost, involving six signaling messages. This comparison suggests that [PERSON] et al.'s scheme might result in higher communication overhead and potentially increased latency. Comparing the proposed protocol with two other schemes, namely, FogHA and quantum-resistant handover authentication protocols, the designed protocol has lower signaling costs. Specifically, FogHA [44] involves five signaling messages, and the quantum-resistant protocol [62] involves four. In this regard, the proposed protocol stands out as having the smallest signaling costs among the three. Consequently, the proposed 3-Tier AKA protocol is the optimal choice among the evaluated options. Its key advantage lies in its minimal signaling costs compared to other protocols.
## 4 Principle and Operation of the Metamaterials
### Future towards Software-Defined Metamaterials/Metasurfaces  
Tunability in the alteration of unit cell structural configurations is more challenging for a tunable metasurface. This method involves connecting sections of the unit cell to achieve various structural configurations. A hardware system that applies the software primitives and effectively reconfigures the metasurface is needed to realize a software-defined metasurface. Some researchers propose that a network of microscopic controllers be integrated into the metasurface structure and wirelessly interfaced with an external entity [85]. Each controller can interpret global commands and act locally, for example, by tuning its associated varactors to produce the required impedance configuration. The key problems are constructing an ultra-low-cost network of controllers and connecting it with the metasurface within a single structure [86].  
The metasurface profile refers to the systematic registration of functionalities offered by a certain metasurface. This profile is the linchpin between academic knowledge and real-world applications in the software-defined platform. The profile's information enables software developers and engineers to create systems that incorporate object electromagnetic behavior into their control loops without understanding the underlying physics. Software-defined metasurfaces may give the IoT a new application field in the electromagnetic behavior of things in the future. IoT can be extended to the Internet-of-Materials (IoM), providing unparalleled capabilities combined with attempts to enable control over mechanical qualities [87].