esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Microalgae, Soil and Liquid Digestate Origin  
Microscopically identified _Monoraphidium_ sp. was isolated from a lake in Lescar (France) and cultivated in BG-11 medium (Sigma Aldrich) ([PERSON] et al., 2018). _Monoraphidium_ sp. is a unicellular microalgae sizing 2-182 um in length and 1-8 um in width; the cells are straight to lunate to sigmoid or helically twisted, often with elongate ends ([[https://www.algaebase.org](https://www.algaebase.org)]([https://www.algaebase.org](https://www.algaebase.org))). ALD was obtained from an agricultural biogas plant located in Prechacq-Navarrenx (France), which is operated in mesophilic conditions using as feeding substrates mainly crop residues and animal manure. In this case, the biogas plant separates the digestate into a liquid and solid fraction by a screw press separator. Once collected, the ALD was centrifuged and filtered with a 25 um filter in the laboratory in order to prepare it for further use as a growth medium supplement for microalgae cultivation and was conserved at 3 \({}^{\circ}\)C until its use. The chemical characterization of ALD has been reported in Table 1. The pH (25 \({}^{\circ}\)C) was of 8.6 and the conductivity of 3640 \(\mu\)s cm\({}^{-1}\). [PERSON] et al. (2016) reported value of pH varying from 7.6 to 8.2 for digestate of various origin (i.e., municipal wastewater, sewage sludge or agro-waste treatment plants) [13]. For the other parameters, results are in adequation with previous studies that investigated the chemical composition of liquid digestate [6; 9; 13]. The TKN was of 455 g N/kg TS and the ammonium content of 255 g N/kg representing 56% of the TKN. [PERSON] et al. (2017) reported on several agro-industrial digestates proportion of ammonium ranging from 16% to 72% of the TKN [6]. As concerns the heavy metals, higher concentrations were found for Cu and Zn with 79.5 and 207 mg/kg TS respectively and these are generally due to the use of animal slurries in the feeding of the AD plant [9]. Indeed, these two elements are used as additives to stimulate livestock growth and prevent pig and cattle diseases [9].  
The soil employed for the plant growth tests was obtained from a 10 cm to 30 cm layer from a farm located in Montpellier region in France (Inrae from Mauguio). The soil was composed of 20% of clay, 30% of sand and 50% of silt. Air-dried soil was sieved (<4 mm) and stored (15 \({}^{\circ}\)C) until the beginning of the experiments. The soil was composed of organic matter (1.9%), C/N ratio (10.1), pH (7.4), total nitrogen (0.11%), P\({}_{2}\)O\({}_{5}\) (0.096 g/kg), K\({}_{2}\)O (0.386 g/kg) and MgO (0.195 g/kg).
## 2 Problem Formulation
### Discussion on Assumptions  
The requirement that \(H(z,\zeta)\) remains stable for all \(\zeta\) is very reasonable. In practical engineering, most systems are designed to operate stably across a range of parameter values. Many physical systems--such as electrical circuits, mechanical structures, and control systems--can be modeled this way, and stability under parametric variation is often a core design goal.  
The assumption that \(H(z,\zeta)\) has no repeated poles is moderately restrictive but still often realistic. In practice, repeated poles are uncommon in physical systems due to inherent damping and parameter variability. While idealized or overly simplified models may include repeated poles, most real-world systems do not. If this assumption is not met, the system may exhibit more complex transient behavior and be more sensitive to small changes in parameters, which complicates both analysis and implementation.  
The condition \(m<n\), ensuring a proper transfer function, is very standard and reflects the fact that real systems are causal and physically realizable. Nearly all real-world dynamic systems--across domains such as electronics, mechanics, and thermal processes--satisfy this assumption.  
Lastly, the assumption that each element of \(H(z,\zeta)\) admits a partial fraction expansion is common and reasonable. Most linear time-invariant systems with rational transfer functions can be expressed this way. If this assumption does not hold, it may indicate that the system is not described by a rational function, which limits the use of standard analytical and numerical tools in engineering practice.  
Overall, these assumptions are not overly restrictive and align well with the characteristics of many real-world systems. They ensure that the model remains both mathematically tractable and physically meaningful.
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 5 Discussion
### Performance of Kriging  
All reconstruction methods take assumptions about the input signal, mostly that the signal is bandlimited and time-infinite or periodic. If the assumptions are fully met, perfect reconstruction is feasible. Kriging assumes that the signal originates from a statistical process, and therefore does not provide perfect reconstruction, but the best possible estimate, the so-called best linear unbiased estimator (BLUE) [23]. This BLUE property is only achieved if the variance of the process over time or space depends only on the variogram.  
In practise, these assumptions cannot be fully met. Either the signal is time-limited, or, in the case of Kriging, there is only a raw estimation for the variogram available from limited experimental data. Variogram parameters are normally estimated in the time domain, however, the settings of the variogram to achieve a good frequency response, as described in this paper (Sections 3.5 and 4.4), led to highly accurate reconstructions in the group-delay scenario.  
In the single-delay scenario, the weighting of the input samples can no longer be interpreted as symmetric filter. Kriging was developed as a space- or time-domain interpolator. As such, Kriging provides good interpolation, even if design in the frequency domain is no longer feasible after filter symmetry is lost due to truncation of the input window.  
As a statistical method, Kriging is suitable for handling noise in both scenarios (Figure 8). Distortions are mainly caused by high-frequency components of the noise. The contribution of the spectral components of the noise can be estimated based on the RMSE caused by different sine frequencies (Figure 11b). The amplification of noise distortions by Kriging with \(f_{L}>0.3\) Hz is limited to 4 dB or a factor of 1.6 in the single-delay scenario (Figure 11b), whereas high frequency noise distortions are amplified by up to 21 dB for Margolis and 28 dB for Lagrange (beyond plotting scale in Figure 11b).  
Kriging is the only method that can make use of a higher number of input samples by increasing HW, without increasing its noise sensitive at the same time. This noise stability requires to set the nugget value in the single-delay scenario (Section 4.4) with the disadvantage that the corner frequency of the equivalent filter is reduced (Figure 4).
# Dynamic Cooperative MAC Protocol for Navigation Carrier Ad Hoc Networks: A DiffServ-Based Approach
## 5 Performance Analysis  
This section analyzes the searching probability and throughput performances of DDC-MAC. Note that PC mode can proceed without collision, and thus can achieve an approximately 100% packet delivery ratio (PDR), and relatively fixed throughput. Thus, the analysis is mainly for OC mode, with the assumptions that: (i) the nodes are distributed uniformly in a 3D cluster network, and (ii) all senders are fully backlogged, and the size of the data payload is fixed.
## 2 Experimental Section
### Description of the \"COMMODORE\" Solution
#### 2.1.4 Cloud Solution  
As mentioned in the previous section the captured data are transmitted to Unitron cloud (uCloud). Data being sent via either NB-IoT or LTE-M are transferred to the uCloud via the MQTT-SN (Message Queuing Telemetry Transport for Sensor Networks) messaging protocol. The selection of MQTT-SN stems from its adeptness in catering to the intricacies of IoT communications, manifesting in robustness, reliability, data integrity, and the perpetuation of sensor longevity via enhanced battery utilization.  
Within the framework of MQTT-SN, the devices, referred to as clients, dispatch messages to a central broker. This broker, in turn, receives these messages and disseminates them to designated subscribers. The classification of each message is contingent on its associated topic--an addressing mechanism assigned to each published message. By subscribing to one or more specific topics, the recipients gain access to messages allocated to those topics.  
Upon arrival at uCloud, the transmitted measurements are meticulously stored and systematically organized within a dedicated database. To interact with this database, a web application serves as the interface. Furthermore, accessibility to the database extends to authorized personnel, who can engage in the processing and analysis of the archived measurements. This analytical exploration lends itself to the refinement and visualization of the stored data, enabling a comprehensive understanding of the information at hand.
# Rapid Estimation of TVWS: A Probabilistic Approach Based on Sensed Signal Parameters
## 4 Proposed Rapid Estimation Method
### REM Block
* Parameter \(\tau_{I}\) refers to the distance measured from the \(I\) intersection line and \(Z_{1}X\). It can be interpreted as the separation of the noise and the PU components in the composite signal \(X(p)\). * Parameter \(\tau_{T}\) refers to the distance measured from the defined value of \(T\) and \(Z_{1}X\). It provides an idea of how spread out the \(P_{d}\) AUC is. * Parameter \(\tau_{\overline{X}}\) refers to the distance measured from the \(\overline{X}\) and \(Z_{1}X\), and it provides an idea of how spread out the composite signal \(X(p)\) is. By finding \(\tau_{\overline{W}}\tau_{\overline{X}}\), we can identify how close the \(W(p)\) and \(X(p)\) signals are. The closer they are, the less _PU_s are found. * Parameter \(\Phi\) refers to the numerical ratio obtained when dividing the standard deviation for the composite signal to the standard deviation of the sensed noise, as expressed by \[\Phi=\frac{\sigma_{X}}{\sigma_{W}}\] (12) This parameter allows us to find the relation between the \(\sigma_{X}\) and \(\sigma_{W}\) with the purpose to know how spread out \(X(p)\) is to estimate the presence of _PU_. Based on this rate, we can affirm that, when the value of \(\Phi\) is lower, it means the less PUs in \(X(p)\). On the other hand, when the value of \(\Phi\) is higher, it means there is less TVWS in the composite signal. The value for \(\Phi\simeq 1\) represents that the only signal present in the TV-UHF band is the noise \(W(p)\). Here, we can use the Rose criterion explained in [22] and mentioning that a \(\Phi\simeq 4\) would be an adequate reference value to estimate the presence of \(W(p)\) and _PU_ signals. The auxiliary parameters explained are presented in Figure 14, where coloured arrows indicate the measured distance for each \(\tau_{i}\). Numerical values for the auxiliary parameters are summarized in Table 4, where parameters of the ten sensed points have been obtained by following the previously explained procedures. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ord** & **From** & **To** & **Name** \\ \hline
1 & \(\overline{W}\) & \(Z_{1}X\) & \(\tau_{\overline{W}}\) \\
2 & \(I\) & \(Z_{1}X\) & \(\tau_{I}\) \\
3 & \(\overline{X}\) & \(Z_{1}X\) & \(\tau_{\overline{X}}\) \\
4 & \(T\) & \(Z_{1}X\) & \(\tau_{T}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Distance \(\tau\) measured from different statistical parameters.
## 3 WBAN Sensor Techniques
In addition, sensors generate various types of data that require different processing to ensure the specific requirements are met. Types include general data, delay sensitive data, emergency data and reliability data. Due to the various data rates of sensors of human physiological parameters, how to merge multi-sensor data to describe one's health condition in a uniform standard is difficult. To address these problems, several researchers [55; 56; 57] have designed novel flexible sensors to provide comfortable, soft and stretchable wearable systems for human health monitoring, enabling effective monitoring of physiological signs without affecting patients' daily activities. Some studies have designed signal extraction schemes for each different body parameter which adapt to body status and environmental movement [58; 59; 60], and explored high-speed preprocessing and transmission algorithms for the weak physiological parameter signals of the human body [61; 62]. There are also some studies [63; 64; 65] that proposed data fusion strategies for multiple sensor nodes based on different sensing mechanisms and physiological information relationships, forming a series of body parameter detection methods required for sensor node microarchitecture optimization [66; 67; 68]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Working Mechanism** & **Power Consumption** & **Data Rate** \\ \hline \multirow{3}{*}{Blood sugar} & Uses non-invasive methods & \multirow{3}{*}{Very low} & \multirow{3}{*}{Low} \\  & such as optical measurement at & & \\  & the eye and breath analysis & & \\ \hline \multirow{2}{*}{Blood pressure} & Measures systolic and & \multirow{2}{*}{High} & \multirow{2}{*}{Low} \\  & diastolic pressure & & \\ \hline \multirow{2}{*}{ECG/EEG/EMG} & Differential measurement via & \multirow{2}{*}{Low} & \multirow{2}{*}{High} \\  & electrodes placed on the body & & \\ \hline \multirow{2}{*}{Temperature} & Uses an integrated circuit to & \multirow{2}{*}{Low} & \multirow{2}{*}{Very low} \\  & detect the temperature changes & & \\  & by measuring resistance & & \\ \hline \multirow{4}{*}{Respiration} & Measures the dissolved oxygen & \multirow{4}{*}{Low} & \multirow{2}{*}{Low} \\  & in a liquid with two electrodes, & & \\ \cline{1-1}  & a cathode and an anode & & \\ \cline{1-1}  & covered by a thin membrane & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Working mechanism of biosensors and their data rates in WBANs. \begin{table}
\begin{tabular}{c c} \hline \hline
**Non-Invasive Sensors** & **Invasive/Implantable Sensors** \\ \hline EEG/ECG/EMG & Pacemaker \\ Position/Motion sensor & Deep brain stimulator \\ BP/SpO\({}_{2}\) & Implantable defibrillators \\ Glucose sensor & Cochlear implants \\ Temperature/Pressure sensor & Electronic pill for drug delivery \\ Pulse oximeter & Wireless capsule endoscope (electronic pill) \\ Oxygen, pH value & Retina implants \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Non-invasive and invasive sensors.
## 2 Literature Review
### Clustering Techniques  
Similar to the requirements of the PHM model to have the Weibull and covariate parameters known, it is also necessary to define the operational state bands of an asset. Typically, when the model involves one or two covariates, expert judgment can readily establish these ranges. However, when dealing with multiple covariates, interpreting these variables becomes more complex, and expert judgment may struggle to define the ranges accurately. Therefore, machine learning tools such as clustering algorithms are valuable for understanding the data behavior and automatically generating these bands. Clustering algorithms are unsupervised techniques that facilitate the detection of standard features in data, enabling their grouping into subgroups, which aids in categorizing new data [4].  
One well-regarded clustering technique is K-means, which creates \"K\" clusters. In K-means, data points in each group are closest to the center of that cluster [24; 25; 26]. However, there can be challenges when applying K-means to small datasets, especially when determining the optimal number of clusters [27].  
Gaussian Mixture Modeling (GMM), instead of assuming data points have a single center, assumes they follow a mix of normal distributions. This approach lets us describe each group by its average and standard deviation. Consequently, GMM offers insights into the likelihood of data points belonging to specific classes or clusters by utilizing the probability distribution [26; 28; 29].  
GMM has various uses in research. For example, in Ref. [30], GMM was used with a weighted Principal Component Analysis (PCA) to diagnose bearing faults more accurately. The authors also used an algorithm called expectation selection maximization to improve feature selection and diagnosis. In another study [31], GMM helped classify corrosion severity in building materials, guiding maintenance planning. Additionally, in Ref. [32], GMM and PCA are combined to diagnose faults in air conditioning systems more precisely and efficiently.  
In Refs. [5; 27], GMM was compared to K-means for estimating state ranges using PHM. This facilitated calculating transition probabilities for reliability and remaining useful life estimation where the main finding was that GMM enhanced the model's robustness. While the latter study utilized an industrial database and effectively established decision rules, the covariates used were solely determined by expert judgment, without any exploration of the changes in optimization parameters or covariate selection. Hence, this current research endeavored to address these aspects and integrate them for the development of a predictive modeling framework.
# Study on the Ground Pressure Manifestation Patterns of Roof Cutting and Pressure Relief
## 4 Ground Pressure Monitoring Data Analysis  
### Analysis of Surrounding Rock Displacement Evolution  
The ground pressure monitoring stations in the headgate of Panel 81308 were categorized into three groups (advance, central, and rear monitoring sections). Average values from each station group were calculated to plot the characteristic surrounding rock deformation patterns versus distance from the working face, as shown in Figure 8. Analysis of Figure 7 and Table 3 reveals the following:  
1. The average total roof-to-floor deformation measures 607 mm, including 443 mm (73%) of floor heave. Rib deformation averages 170 mm, predominantly occurring at the pillar side. Both roof-floor and rib deformations exhibit similar evolutionary patterns: rapid initial growth, followed by gradual stabilization. The roof-floor deformation rate significantly exceeds the rib deformation rate, with floor heave dominating roof subsidence, and pillar-side deformation being primary in rib convergence.
2. Maximum roof-floor convergence occurs within 0-88 m behind the face (primary deformation zone), decreasing through 88-142 m, and stabilizing beyond 178 m. The initial deformation mechanism is governed by composite roof structure movement, while later-stage deformation manifests as progressive settlement from goaf fracture zone compaction. Pillarless roof-cut roadways demonstrate \"wide disturbance range and prolonged residual deformation\" characteristics under mining-induced stresses.  
Figure 8: Cont.
# A Restoring Force Model for Prefabricated Concrete Shear Walls with Built-In Steel Sections
###### Abstract
[13] developed a restoring force model for a steel high-performance concrete composite structural wall and demonstrated the application of nonlinear dynamics analysis as well as static structural calculations. [PERSON] [14] established a restoring force model of a fabricated double-slab concrete shear wall. Through dimensionless processing of skeleton curves, the key points of skeleton curves and the calculation methods are determined. [PERSON] [15] developed a modified method for strength and stiffness calculation of fabricated concrete shear wall models based on the bending calculation theory and established its restoring force mathematical model. [PERSON] et al. [16] developed a simplified method to assess the seismic behavior of reinforced concrete columns that failed in different modes. [PERSON] et el. [17] developed a cyclic restoring force model for high-strength foamed concrete (HFC)-filled cold-formed thin-walled steel (CFS) shear walls based on experimental studies, which investigated the characteristics of hysteretic and skeleton curves. [PERSON] et el. [18] analyzed the restoring force characteristics of shear walls subjected to horizontal two-directional loading. Based on the results of static loading tests of the box type and cylindrical type shear walls, it was verified that the resultant shear force-total deformation angle relationship under two-directional loading is analogous to that under one-directional loading. It is observed that although stiffness calculation methods of different types of shear walls have been developed, including research on the resilience models of prefabricated concrete shear walls, no research has been conducted on restoring force models of prefabricated concrete shear walls with built-in steel sections. Furthermore, even general research on the behavior of steel-section-reinforced shear walls is very limited. Steel sections can provide temporal support to the wall components before completion of the connections and make the assembling process smooth during the construction. The steel sections also contribute significantly to the load resistance of the connections. Therefore, it is worth further investigation on the performance of prefabricated shear walls strengthened with steel sections. This paper proposes a new shear wall structure that is prefabricated and field-connected with steel sections and reinforcement bars. This paper presents an experimental and numerical study on the proposed wall structure and develops a restoring force model based on the test results. The performance of the connections at different heights of the walls are compared. This study provides basic information on the seismic performance of steel-section-reinforced shear walls based on their restoring force models that can serve as experimental and theoretical support for further formulation of the relevant standards and specifications for prefabricated composite shear walls.
# Blasting Vibration Control and Signal Analysis of Adjacent Existing Deterioration Tunnels
## 4 Field Experiment of Blasting Vibration Control
### Comparison of Vibration Velocity  
Three experimental schemes were carried out for full-section detonator blasting and mechanical cutting blasting. The values of fractional velocity and resultant velocity at the measuring points of each experiment were recorded. The vibration velocity is shown in Figure 13. In the figure, Vx1 represents the vibration velocity in the X direction in the first experimental scheme, and Vr represents the resultant velocity.  
The peak vibration velocity of each measuring point and the reduction rate of vibration velocity of each measuring point under the two schemes are shown in Figure 14.  
As can be seen from Figure 14, mechanical cutting combined with an ordinary detonator blasting scheme can significantly reduce blasting vibration velocity in field experiments. The vibration reduction rate of measuring point 1 is the largest, reaching 38.01%, followed by 26.23% at measuring point 2, 19.54% at measuring point 4, and 18.44% at measuring  
Figure 12: Mechanical cut hole layout. (The numbers represent the segment).  
Figure 13: Measuring point vibration velocity. (**a**) Normal detonator blasting; (**b**) mechanical cutting blasting.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Hole Class**} & \multirow{2}{*}{**Number**} & **Hole Diameter** & \multirow{2}{*}{**Segment**} & **Hole Depth** & \multirow{2}{*}{**Charge in a**} & \multirow{2}{*}{**Cumulative Charge**} \\  & & **(mm)** & & **(m)** & **Single Hole (kg)** & **(kg)** \\ \hline \multirow{2}{*}{Auxiliary hole} & 10 & 42 & 3 & 1.3 & 0.3 & 3.0 \\  & 10 & 42 & 5 & 1.3 & 0.3 & 3.0 \\ \hline \multirow{2}{*}{Peripheral hole} & 15 & 42 & 7 & 1.3 & 0.3 & 4.5 \\  & 20 & 42 & 9 & 1.3 & 0.3 & 6.0 \\ \hline \multirow{2}{*}{Bottom hole Total} & 11 & 42 & 11 & 1.3 & 0.3 & 3.3 \\  & 66 & & & & & 19.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mechanical cut blasting scheme.  
point 3. Therefore, mechanical cutting can be used in engineering and has a better vibration reduction effect, which ensures the safety of existing deterioration tunnels.