esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Geospatial Data Preparation
#### 2.2.1 Fire Density over the Past 10 Years  
The historical fire data used in this study span from 2012 to 2021 and are sourced from the Keelung City Fire Bureau. Over the past 10 years, there have been approximately 1095 fire incidents recorded by the Fire Bureau [16]. In this study, the focus is on analyzing indoor fire disasters, excluding non-building-related fires. Within the 10-year period, there were 811 incidents of indoor fire disasters. Detailed information on the frequency and spatial distribution of fire incidents in Keelung City can be found in Figure 2a where the fire density in Keelung City over the past 10 years is depicted. The definition of the fire incident density over the past 10 years is as in Equation (1).  
\[F_{1}=\frac{N_{f}}{A}, \tag{1}\]  
where \(F_{1}\) denotes the fire density over the past 10 years, \(N_{f}\) denotes the number of fire incidents in each village, and \(A\) denotes the area of each village (measured in square meters).
## 2 Direct Under-Sampling Compressive Sensing Method
### Direct Under-Sampling Compressive Sensing Method (DUS-CS)  
#### 2.2.1 Direct Under-Sampling (DUS) Implementation  
The direct under-sampling method [27] is modified based on the traditional sampling circuit. The sampling trigger time is controlled by pseudo-random timing control, which samples along the rising edge (or falling edge) of each trigger signal, and the corresponding signal is collected in the data buffer. Compared to traditional sampling, only the design of the sampling circuit is changed. Applying DUS to the reception of echo signals can reduce both the complexity of the receiving system and its cost. Before the A/D conversion, the control of the signal sampling time has low speed and is non-uniform. At each instance, the sampling trigger is initiated at a different location and at different times, which can ensure the resulting signal is a discrete sample.
## 3 Applications of the CDS for the Fiber Optic Link
### Perceptor
#### 3.1.3 Bayesian Equation  
The Bayesian equation is another subsystem that is shown in Figure 7. If the BER is under the FEC threshold, \(P(X_{n}^{k}\mid\hat{Y}_{n}^{k})\) model extracted in Layer III will be used and the CDS operates in the BER improvement mode. In this mode, the estimation is as follows,  
\[P(X_{n}^{k}\mid Y_{n}^{k})\cong\frac{P(\hat{Y}_{n}^{k}\mid\hat{X}_{n}^{k})P(X_ {n}^{k})}{P(\hat{Y}_{n}^{k})} \tag{1}\]  
Here, the index \(k\) is PAC number in CDS. If the BER exceeds the FEC threshold, the CDS will use the model \(P(\hat{Y}_{n}^{k-1}\mid\hat{X}_{n}^{k-1})\) stored in the model library to approximate \(P^{k}(\hat{Y}_{n}^{k}\mid\hat{X}_{n}^{k})\) as predictive Bayesian model:  
\[P(X_{n}^{k}\mid Y_{n}^{k})\cong\frac{P(\hat{Y}_{n}^{k-1}\mid\hat{X}_{n}^{k-1} )P(X_{n}^{k})}{P(\hat{Y}_{n}^{k})} \tag{2}\]  
The model \(P(\hat{Y}_{n}^{k-1}\mid\hat{X}_{n}^{k-1})\) is the closest model to the current system state. Hence, the CDS can guarantee maximum possible data rates using the prediction based on previous models and improve the BER. In statistical processing using the Bayesian approach, it is well known that the evidence \(P(\hat{Y}_{n}^{k})\) can be considered as the scaling factor. Therefore, the evidence can be neglected [7; 8; 9; 10].  
We accomplish the task of model extraction by cycling through the prediction mode and BER improvement mode, as will be explained in Section 5.
## 3 Results Analysis
### Wave Profiles  
Wave profiles recorded in Test 3 are shown in Figure 5. As can be seen in Figure 5, the first wave reaches W1 at about 7 s and reaches W8 at 15 s. The waveform is relatively regular at W1-W6, which are far away from the revetment breakwater, while the waveform is irregular due to the effect of topography and the wave reflection at W7 and W8 near the revetment breakwater.
# Carbon Footprint Assessment on the Viability of Utilizing Brewer's Spent Grain to Produce Biochar
## 3 Results
### Landfilling BSG Comparison  
A study conducted a cost-benefit analysis of greenhouse gas emissions associated with diverting food waste from landfills [30]. The food waste in question was generated by a restaurant during a 6-day work week, stored in bins without refrigeration, and picked up on a weekly basis. The study used a functional unit of 1 kg unprocessed food waste. It accounted for CO\({}_{2}\) emissions resulting from the usage of grid electricity at the waste producer site; CO\({}_{2}\) emissions from fossil fuel consumption during transportation over a distance of 15 km; and CH\({}_{4}\) emissions originating from the food waste at the landfill [30].  
In the modeled scenario, the gases were released into the environment rather than being collected for use.  
The study revealed that the primary contributor to CO2e emissions from landfilling food waste is the release of fugitive methane (3 kg of CO2e per kg of food waste) [30]. Comparatively, transportation of the food waste to the landfill was a minor contributor, at 0.006 kg of CO2e per kg of food waste [30]. The net emissions associated with landfilling increased proportionally with the volume of uncaptured methane released. Furthermore, the study demonstrated that capturing methane onsite at a landfill can reduce the environmental burden of landfilling BSG. Adapting the functional unit of the present study, the total emissions from landfilling 1 kg of unprocessed BSG would be 3.01 kg CO2e.  
The [PERSON] et al. study [30] also examined various other scenarios, including: centralized composing; centralized computing of dehydrated food waste; food waste or diversion to wastewater treatment plants as a result of using aerobic digesters with liquid outputs; or in-sink disposal. In all these scenarios, the CO2e values were significantly lower than landfilling; they ranged from 0.04 to 0.7 kg CO2e per kg of food waste [30]. More specifically, composing and aerobic digestion both proved to be more favorable compared to landfilling, with CO2e values of 0.3 and 0.11 kg CO2e per kg of food waste, respectively [30].  
However, the [PERSON] et al. study [30] did not provide data on the composition of the food waste utilized. Methane is primarily produced in a landfill setting when an oxygen-limited environment is formed, leading to anaerobic bacteria fermenting carbohydrates and amino acids. Since the carbohydrate and amino acid concentration of BSG is considerably smaller than that of the generic food waste evaluated in the [PERSON] et al. study, it can be presumed that the actual CO2e generation is slightly less than 3 kg per kg of unprocessed BSG (See Chapter 1, Table 2 for BSG composition data), but still greater than 1 kg CO2e per kg of unprocessed BSG.  
Figure 3: Mass-flow diagram of BSG-to-biochar production process.
## 4 Conclusions  
### Key Findings  
The planning of rehabilitation measures and the rational management of in-service concrete pavements is a difficult task, mainly due to the special properties of concrete and constructional aspects such as curing time, climatic conditions during the cutting process, etc. In the present study, an in-service JPCP structure was investigated without records of the initial condition as well as its condition over the lifespan. Three types of data were collected and analyzed based on FWD tests: deflection indicators, durability data, and load transfer data. The authors treated the collected data with descriptive statistics and distribution fitting analysis. The main findings of the study are as follows:  
* In situ deflectometric testing with the FWD detected changes in pavement slab deflections along the length of the lanes, with ranges of 50-1450 \(\upmu\)m and 6-630 \(\upmu\)m for the maximum deflection and the deflection of the outer geophone of the FWD, respectively.
* The age of the pavements and the observed deterioration in the serviceability of the pavements were related to these deflection changes observed for both indicators.
* Since field data reflect the \"as is\" condition of the pavement structures, decision-makers should rely on the produced results and their technoeconomic models for pavement management. Herein, the observed deflection ranges necessitated a probability analysis to define characteristic values that could reflect the condition of the majority of the pavement slabs investigated. These values were then increased to establish threshold values (or control values) that could classify the condition of the slabs and assist in the strategic management of pavements. In the present study, threshold values of D\({}_{0}\) = 220 \(\upmu\)m and D\({}_{1800}\) = 20 \(\upmu\)m were proposed.
* The assessment of durability proved to be less critical, as individual cases with durability problems at the slab corners were already accounted for by the high values  
Figure 13: Example of preservation strategies according to individual slab condition ranking.  
of the deflectometric indicators. In the present study, threshold values found in the literature [28] were proposed.
* The LTE assessment showed relatively good conditions at the joints, while at the cracks, lower LTE values were consistent with the deflectometric indicators, indicating that lower LTE corresponded to higher deflectometric records. In the present study, a threshold value of 60% was proposed.  
Based on the methodology followed with the FWD inspection and the above findings, an attempt was made to develop a framework to classify the condition of each slab based on the values of all deflection indicators analyzed. It was emphasized that, in parallel with the classification of slab condition, the technoeconomic feasibility of alternative maintenance strategies must be investigated to achieve optimal pavement management.
# Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review
## 2 Overview of Concept Drift  
In this section, we introduce the definition and causes of concept drift, different types of concept drift, and the process of concept drift adaptation methods. Concept drift was first proposed by [PERSON] et al. [2] in 1986 and mainly refers to the fact that the underlying data stream distribution changes over time [18; 19].
# Application of Acoustic Emission Technique in Landslide Monitoring and Early Warning: A Review
## 4 Discussion
### Monitoring of Rock Slopes  
Rock slopes experience failures differently from soil slopes due to anisotropic lithological variations determining strength and disturbance zones depending on discontinuity orientations. Brittle cracks nucleate under stress perturbations, permitting sudden collapses once thresholds are surpassed. Successful AE applications provide insights into failure precursors, potentially forecasting impending breakdowns.  
Investigations were performed to examine cliff instabilities [123]. Five seismic stations monitored chalk cliffs in Normandy, France, observing rising activity hours preceding collapse interpreted as progressive damaging. Three months of monitoring of unstable columns in Vercors massif limestone cliffs distinguished fracture process regimes from microseismicity [124].  
Large-scale mining-induced slope failures motivated the monitoring of collapsed opencast coal mines in Indonesia using seismic sensors [125]. Back-analyses established empirical relationships between precursor bursts and unstable mass dimensions, permitting predictive modeling simulating failure propagation sequences and outcomes.  
Laboratory studies established correlations between AE parameters emitted during true-triaxial tests and failure processes within Aztec sandstone [126]. Results suggest the feasibility of employing AE techniques for seismic hazard evaluation from earthquakes, mining-induced seismicity, dynamite explosions, etc.  
Waveguides offered the means of embedding multiple sensors inside boreholes for enhanced detection. Wireless earthquake detection devices incorporated fiber-optic waveguides transmitting Raman backscatter-modulated strain signals from fractured rock masses [127]. Simulations showed the potential for structural health monitoring as damage signals were resolved.  
Recent studies have utilized a convolutional neural network (CNN) to automatically classify seismic signals collected over a 15-year period on the Aknes rock slope in Western Norway [128]. The CNN effectively distinguished and categorized eight types of events, achieving an accuracy rate of approximately 80%. This analysis yielded important findings regarding the microseismic activity at Aknes, which exhibited pronounced seasonality correlated with annual temperature fluctuations. By automating the classification process, this research makes a notable advancement in landslide monitoring, enabling the real-time analysis of seismic events associated with slope movements.  
Overall, rock slope monitoring through borehole-embedded passive/active AE waveguides furnished direct access into internal damage evolution when sub-surface interrogation was not possible through surface techniques. Emerging machine learning capabilities promise augmented quantitative understandings linking the onset and growth of precursor emissions to slope kinematics, improving forecast lead-times in urgent demand for advanced warning applications.
## 4 Discussion
### The FOM Advantages over the S/N Ratio  
The quality characteristic as defined in this study is FOM (cf. Equation (3)), which integrates not only the difference between the practical survey and theoretical preset but also the average, stdev, \(\beta\), and correlation coefficient altogether. The FOM shows its excellent capability in compiling the individual imaging-quality characteristics and reveals a good suggestion with many quantified values, although the [PERSON] suggested signal-to-noise ratio in dynamic analysis still has its advantage in practical consideration. Define the signal-to-noise as Equation (4) to revise the derived data in this study;  
\[\frac{S}{N}(\eta_{i})=-10\log\Bigl{[}(stdev/avg)^{2}\Bigr{]} \tag{4}\]  
where stdev is the standard deviation as defined in Equation (2), and avg is the average of individual differences between practical measurements and theoretical preset in this study. As clearly depicted in Figure 4, the dominant factors become factor B (signal gain), C (resolution vs. speed), D (dynamic range), E (XRES), and G (time gain compensation). The trend of each factor has significantly different behaviors, as implied in Figure 3. Factor A (angle of probe) becomes minor, whereas D (dynamic range) becomes dominant. Note that S/N focuses only on the stdev and average of the difference and ignores other contributions from \(\beta\) (sensitivity) or \(r^{2}\) (correlation coefficient) (cf. Equation (3)) in reality. Accordingly, the newly proposed FOM might be an alternative option in correlate research since it compiles more imaging quality characteristics in reality.
## 2 Materials and Methods
### Model Evaluation  
In a machine learning study, the model's performance is enhanced through optimization of the parameters according to the dataset. Four commonly used statistical methods were used to evaluate the prediction accuracy of the models. These methods are the coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), defined as Equations (2)-(5) [43]. R\({}^{2}\) indicates how well the independent variable x explains the dependent variable y in a regression model. The fact that the R\({}^{2}\) value tends toward 1 when the variables are correlated linearly shows that the independent factors account for the majority of the variation in the dependent variable. RMSE is a quadratic metric used to quantify the error magnitude of a machine learning model. It is usually employed to evaluate the discrepancy between the predicted and actual values, i.e., the distance between them. RMSE is calculated as the square root of the average of squared prediction errors, representing the standard deviation of the prediction errors. MAE measures the differences in errors between matched observations that express the same phenomenon. Examples of Y against X include contrasts between predicted and observed data, subsequent time and starting time, and one measuring technique and another measurement technique. MAPE is often used as a loss function in regression problems and model evaluation because of its very intuitive interpretation of relative error.  
\[R^{2}=\frac{\sum_{i=1}^{n}\left(Y_{i}-\dot{Y}_{i}\right)^{2}}{\sum_{i=1}^{n} \left(Y_{i}-\dot{Y}_{i}\right)^{2}} \tag{2}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(Y_{i}-\dot{Y}_{i}\right)^{2}} \tag{3}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|Y_{i}-\dot{Y}_{i}\right| \tag{4}\]  
\[MAPE=\left(\frac{1}{n}\sum_{i=1}^{n}\frac{\left|Y_{i}-\dot{Y}_{i}\right|}{ \left|Y_{i}\right|}\right)*100 \tag{5}\]  
where \(Y\) is the actual value, Y is the predicted value, Y is the mean of the actual value, and n is the number of samples.
# An Overview on Atomization and Its Drug Delivery and Biomedical Applications
## 7 Electrospray Atomization and Its Characteristics
### Working Principle and Components of Electrospray  
Electrospray constitutes a syringe setup with a metallic needle to which an electric current is connected from a high voltage generator. The needle of the syringe acts as an orifice through which the fluid comes out. The syringe is connected to a syringe pump with which we can apply the required pressure to push the working fluid in the syringe. There is a collector which is grounded so that the atomised drops/particles can be collected on its surface [78]. Figure 6 shows the basic components in an electrospray and also the forces acting on the Taylor cone to develop droplets.  
Sometimes the whole setup explained above will be enclosed in a chamber through which air or nitrogen will be allowed to flow continuously so that the atomized particles will  
Figure 5: Schematic representation of electrospinning and electrospray; reproduced with permission from [82].  
Figure 6: Components in an electrospray setup and the forces acting on Taylor cone; reproduced with permission from [86].  
be transported to the collector region. This also enhances the production of small, smooth and uniform droplets [87; 88]. The forces acting on the Taylor cone are the gravitational pull which will be negligible if the viscosity of the working fluid is high, viscosity and surface tension of the fluid and the working parameter that is the electric field.  
Table 4 compares electrospray to other common techniques in generating micro/nano polymeric particles applied for drug delivery applications.
## 3 System Design
### Telemetry Decoding
#### 3.8.2 Sync Frame Confirmation  
The telemetry decoder checks that at least a certain percentage of preambles is accurately detected at the expected timings. If the detection of the telemetry sync patterns is confirmed, then the receiver assumes correct sync frame detection and proceeds to the third step, which involves TOW estimation. Otherwise, the receiver assumes a synchronization issue and goes back to the sync frame detection.
# Shear Mechanical Properties of Bolt-Grout Interface under Different Bolt Surface Profiles
## 5 Conclusions  
This study examines the impact of rib spacing, rib angle, and normal stress on the shear behavior and failure mode of the Bolt-Grout interface. The following conclusions can be drawn:  
1. The shear strength of the Bolt-Grout interface demonstrated a pattern of decreasing and then increasing with the rise of rib spacing. With smaller rib spacings, the effect of rib spacing on peak shear strength was more apparent. Furthermore, the changes in shear strength with varying rib angles followed a similar pattern, displaying an initial increase, followed by a decrease, and then another increase. It is possible to speculate that, at a 45\({}^{\circ}\) rib angle, the peak and residual shear strength reached their maximum values.
2. The shear process with different rib spacing and angles was accompanied by a normal deformation phenomenon. When the rib spacing was small, the normal displacement decreased in the test's early phases due to the compression of the internal gap of the specimen. Thereafter, the shear-slip phenomenon occurred, and the normal displacement gradually increased. Finally, due to the existence of the \"climbing effect\", the normal displacement produced a cyclic change of decreasing and then increasing. When the rib spacing was large, the normal displacement changes were roughly the same and showed an initial decrease, followed by an increase, and then tended to flatten. The shear dilation curves under various rib angle conditions shared a similar morphology, with normal displacements exhibiting a pattern of decrease, increase, and then stabilization.
3. The failure modes of the interface can be categorized as shear-slip failure, shear-fracture failure, and composite failure. Although the interface failure modes universally manifest as combined failures, the proportion of shear-slip failure and shear-break failure undergoes variations due to the influences of rib spacing, rib angle, and normal stress.  
Conceptualization, Z.Z. and J.L.; methodology, Y.J. and H.L.; investigation, Z.Z. and Y.W.; resources, H.L. and Y.J.; data curation, S.Z. and Q.S.; writing--original draft preparation, Z.Z., J.L. and Y.W.; writing--review and editing, H.L. and C.W.; visualization, S.Z. and Q.S.; funding acquisition, H.L. and Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 52104093) and the Shandong Provincial Natural Science Foundation (No. YDZX2022141).  
Not applicable.  
Data Availability Statement: Data associated with this research are available and can be obtained by contacting the corresponding author upon reasonable request.  
Author [PERSON] was employed by the company Dongtan Coal Mine, Yankuang Energy Group Company Limited. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Popularity-Aware Service Provisioning Framework in Cloud Environment
## 5 Evaluation Results
### Effect of \(\theta_{R}\)  
Figure 4a,b show the effect of the response time requirement of the service, \(\theta_{R}\), on the average OPEX of the service provider and the average response time of the service, \(\zeta_{O}\) and \(\psi_{R}\), respectively. From Figure 4a, it can be observed that the average OPEX, \(\zeta_{O}\), of PASPF decreased as \(\theta_{R}\) increased. This can be explained in the following way. A large \(\theta_{R}\) implies that the service does not require a tight response time. ThePASPF recognizes this fact and then requests less resources for the service. Thus, as shown in Figure 4b, a longer average response time of the service is expected. Note that the average response time of the service \(\psi_{R}\) of the PASPF did not exceed the response time requirement of the service \(\theta_{R}\). In contrast, the other comparison schemes maintained their policy even when the service required a more relaxed response time. Thus, their \(\zeta_{O}\) and \(\psi_{R}\) were constant regardless of \(\theta_{R}\) (see Figure 4a,b).