esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The renewal of underground infrastructure is an emerging challenge for most municipalities in the United States. As compared to trenchless cured-in-place pipes (CIPPs), excavation technologies (ETs) have adverse impacts on the environment. Due to its lower ecological impact, trenchless technology is preferred in comparison to conventional pipe replacement. The selection of the most appropriate method depends on factors such as the existing sewer network, traffic disruption, soil conditions, and environmental safety. Recent concerns pertaining to environmental impact have increased the demand for reduced carbon footprints. The objectives of this paper are the following: (1) to present a comprehensive review on the achievements achieved over the years in understanding the factors influencing environmental emissions from the use of CIPP and ETs and (2) to analyze and compare the environmental emissions produced from CIPPs and ETs for 8-inch-, 10-inch-, and 12-inch-diameter pipes. Published papers from 1990 through 2024 have been included, which reported emissions from both alternatives. A comparison of total environmental emissions produced from both the processes is presented. The literature review and analysis suggest that higher emissions are a result of higher fuel consumption, material use, and input allocation. The emissions of pipeline renewal methods were evaluated using USEPA's TRACI 2.1 methodology within SimaPro software. The analysis showed that CIPP renewal greatly reduced carbon emissions when compared with ET. CIPPs exhibited approximately 70% less ecological impact, 75% less impact on human health, and 60% less depletion of resources. CIPPs reduced carbon emissions by 78-100% in comparison to ETs. The recycling materials used in CIPPs potentially reduce the environmental impact by 10%, making them highly sustainable. The installation phase should therefore be carefully analyzed for factors like the pipe material and the pipes' external diameter in view of achieving the greatest sustainability of these methods, as these characteristics affect emissions. It can be inferred that the comparison of the emissions of both alternatives is extremely vital for sustainable underground infrastructure development.  
open-cut; CIPP; environmental emissions; sanitary sewers; carbon footprint 1
## 2 Context of the Experiments
### Control Algorithm
#### 2.7.5 SOC Estimation  
The value of the SOC estimated at a given time t\({}_{0}\) is further noted SOC estimate(t\({}_{0}\)). This estimation is computed using the initial value of the state of charge SOCinit and using the values of the energy meter, read every dt equal to 2 min. For many charging stations, it is the minimum time to retrieve data without any problem of synchronisation or timestamp  
Figure 2: Illustration of the computation of the available power.  
issue. The power withdrawn by the car \(\mathrm{P_{w}(t_{0})}\) is first estimated from the values measured by the energy meters at the considered time \(\mathrm{t_{0}}\), noted \(\mathrm{E_{w}(t_{0})}\), and Q dt minutes before \(\mathrm{t_{0}}\), noted \(\mathrm{E_{w}(t_{-Q})}\). The power \(\mathrm{P_{w}(t_{0})}\) is computed according to the following formula:  
\[\mathrm{P_{w}(t_{0})}=\frac{\mathrm{E_{w}(t_{0})}-\mathrm{E_{w}(t_{-Q})}}{ \mathrm{Q}\times\mathrm{dt}} \tag{2}\]  
Q is a parameter chosen equal to 3 to obtain an accurate value of the power as quickly as possible (i.e., every 6 min). We consider that if the car does not withdraw energy during the six minutes before \(\mathrm{t_{0}}\), the measured withdrawn power \(\mathrm{P_{w}(t_{0})}\) is equal to zero. This phenomenon occurs because the car battery is fully charged, and then the \(\mathrm{SOC_{estimate}}\) is equal to 100%.  
If \(\mathrm{P_{w}(t_{0})}\) is not equal to zero, we compute \(\mathrm{SOC_{temporary}(t_{0})}\) according to Equation (3). In this equation, we consider \(\mathrm{t_{init}}\) being the time at the start of the charging process, and \(\mathrm{Y}\) being the yield of the charging process (supposed to be equal to 95%).  
\[\mathrm{SOC_{temporary}(t_{0})}=\mathrm{SOC_{init}}+\mathrm{Y}\,\times 100\times \frac{\mathrm{E_{w}(t_{0})}-\,\mathrm{E_{w}(t_{init})}}{\mathrm{E_{Max}}} \tag{3}\]  
If the car keeps charging (i.e., \(\mathrm{P_{w}(t_{0})}\) > 0) while \(\mathrm{SOC_{temporary}(t_{0})}\) equals a threshold value (typically 99%), we consider that the value of \(\mathrm{SOC_{temporary}}\) is overestimated compared to the real SOC. In that case, without any other information about the real SOC, we consider that \(\mathrm{SOC_{estimate}}\) stays equal to the threshold value. In other cases, we consider that \(\mathrm{SOC_{estimate}}\) is equal to \(\mathrm{SOC_{temporary}}\).
# Leveraging Large Language Models for Sensor Data Retrieval
## 3 Architecture of the System
### Data Retrieval  
Once sensor data have been collected and properly formatted, the second component of the system manages user requests for that data. This component consists of a table-based search system that accepts user requests in the form of an initial dataset in tabular format. It then retrieves the most relevant indexed sensor datasets related to the user-provided dataset. This fulfils the requirement to retrieve sensor information that can complement the user's initial data.  
At the core of this component lies the utilisation of word embeddings to conduct semantic comparisons between different sensor datasets. This component involves a three-step process: data preprocessing, word embeddings indexing, and the final stage of search and retrieval. The following lines describe each of them in more detail.
## 4 Discussion
### Fluorescence and the Canopy--Air Temperature Difference  
Our main findings were that forest stand age classes and diurnal processes significantly affected the emitted fluorescence within a LP forest. As reported previously by many researchers, the far-red fluorescence radiance (F740) was generally higher than red fluorescence (F685) across all age groups and throughout the observed diurnal cycle. However, F685 radiances (and/or F685 yields) provided a more reliable indicator of stand age, canopy chemistry, and physiological status than F740 radiances (or F740 yields), and was stable over older aged, closed canopy stands. This conclusion was also reached in a sister study for this campaign [34]. However, F685 radiances were non-linearly affected by the forest temperature difference (canopy-air, \({}^{\circ}\)C) throughout the diurnal cycle. All LP age group stands showed this hysteresis response--an increase in red fluorescence from mid-morning to midday, with a return to original values in mid-afternoon. However, the amplitudes of the fluorescence and  
Figure 14: Hysteresis at four times over the diurnal cycle is expressed in the combined responses of F685 and the PRI: (**A**) the mature LP stand at the NC2 tower site; and (**B**) all ROI stands, including the NC2 tower stand.  
temperature changes, driven by the daily irradiance regime, were especially pronounced in young, open stands (Figure 12B). The young stands, as compared to older stands, exhibited relatively low TChl and PRI values, coupled with higher F685 yields and Fratios. We note that thermal effects on the PRI and chlorophyll fluorescence have been studied in olive groves [45; 46] and other field studies [47].
## 1 Introduction  
For more than two decades, gallium nitride (GaN) has attracted interest with regard to applications in a wide range of optical devices, such as light-emitting diodes (LEDs) and laser diodes (LDs) [1; 2]. Recently, GaN-based vertical-cavity surface-emitting lasers (VCSELs) have received considerable attention as a result of their superior characteristics, including low threshold currents, array formation capability, applicability to high-frequency operation, low manufacturing costs and high efficiencies [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. Due to these advantages, GaN-based VCSELs have the potential to replace conventional LEDs and edge emitting LDs as light sources for optical storage, laser printers, projectors, displays, solid state lighting, optical communications, biosensors and many other applications.  
This review summarizes recent progress in the development of GaN-based VCSELs. In the former sections, an overview of GaAs-based VCSELs is initially provided, followed by a discussion of hybrid DBR GaN-based VCSELs, in which the DBRs are made of semiconductors and dielectric materials. In the latter sections, all-dielectric-DBR VCSELs reported in the early stage of research into such devices are examined, after which the latest progress in other types of all-dielectric-DBR VCSELs based on epitaxial lateral overgrowth [14; 15] and curved mirrors [16; 17] is summarized.
## 3 Experimental Results
### Hysteresis Curve  
The hysteresis curve represents the relationship of the restoring force and the lateral displacement of a shear wall under horizontal earthquake action, which is of great importance to the evaluation of the seismic performance of the shear wall. Figure 7 presents the obtained hysteresis curves of the test specimens.  
In summary, each specimen, except PMW-3B, exhibited a reasonably symmetric load-displacement relationship in the push and pull directions. The asymmetric hysteretic response of PMW-3B was due to the sudden failure of a displacement sensor. In spite of this, the results of PMW-3B, recorded before the failure, were considered to be reliable for the subsequent analysis. At the beginning of the test, the restoring force of each specimen varied linearly with the imposed displacement, and the loading curve nearly overlapped the unloading curve, proving that the test walls were in the elastic stage.  
As the loading displacement increased, the specimens turned to the elastic-plastic stage. In this study, PMW-1 was taken as an example to display the development of the hysteresis curves at this stage. Figure 8 presents the hysteresis loops of PMW-1 at the first yielding of vertical reinforcement, at maximum load, at about 1% drift, and at 15% strength degradation. As observed, the hysteresis loop of the specimen at yield was pretty thin, indicating poor energy dissipation. The figure shows that the area of the hysteresis loop increased obviously with the increasing displacement, which indicates a higher level of dissipated energy, especially at the post-peak stage. Meanwhile, stiffness degradation gradually occurred with the increasing lateral displacement, as observed from the flatter loading curve of each loading cycle [24]. It should be noted that obvious pinching behavior was observed during the cyclic loading, which was due to the slide occurring between the surfaces of the rebar and the cracking concrete [25].  
Figure 7: Force–displacement hysteresis curves of the tested walls: (**a**) PMW-1; (**b**) PMW-2; (**c**) PMW-3A; (**d**) PMW-3B; and (**e**) PMW-4.
# Object Tracking Based on (_m,k_)-firm Model in Multimedia Wireless Sensor Networks +
## 3 Performance Evaluation  
Performance of the proposed scheme is proved by simulations. We use ns-2 as the simulator. The simulation terrain is set as a 500 m \(\times\) 500 m field with 50 nodes. A sink is located at the lower right corner of the field so that the end-to-end hop-count ranges from 4 to 9 hops with an average of 6 hops. Each node has a radio range of 40m. Propagation model is set to be Two-Ray Ground where protocols for physical is set to be WirelessPHY. We assume that the location of object is known to all nodes so there is no impact of detection and update algorithm.  
Deadline for real-time packet on each node is set to average-link-delay x number of shortest hops, respectively without special mentioning. For the (_m,k_)-firm stream, we introduce three different requirements such as (1,4), (2,4) and (3,4) where they are represented as 14, 24 and 34 in the Figure 3. Evaluation result is presented as stream dynamic failure ratio (SDFR) where the 95% confidence intervals on the mean are utilized. We vary the update interval for object tracking to observe the impact of traffic for the SDFR.  
By referring to Figure 3, we can find out that SDFD is affected by the update interval. More accurately, lower SDFD is observed when the update interval become short. However, it maintains the acceptable SDFD for (_m,k_)-firm stream by two level adjustment. Among the three traffic model, (3,4) suffers from the lowest SDFD due to strict requirement for real-time. But, due to this requirements, performance is limited to some amount even though adaptive schemes are applied. Also, severe degradation of (_m,k_)-firm stream in previous session can affect the current session  
Figure 2: QoS routing algorithm.  
significantly, performance enhancement is not great in some cases. However, in the most cases, adaptive routing and DBP control for sessions are feasible to meet (_m,k_)-firm requirements.
## 3 Application
### Analysis of Test Results
The propagation velocity of elastic waves in the layer within the depth range of 14-43 m was always stable at about 1650 m/s and did not vary significantly with the distance from the pile. Above 43 m depth, with the distance from the pile decreasing, the propagation velocity of the elastic wave in the layer increased from 1650 m/s away from the pile to about 2250 m/s, and the velocity change rule was opposite to that of before grouting. The closer to the pile, the faster the propagation velocity of elastic wave increased. From Figure 9, it can be seen that the velocity field of the C3-C5 profile also changed significantly after grouting (right panel). Above the depth of 14 m at the partition interface, from far to near to the pile, the change trend of the propagation velocity of elastic wave in the layer still decreased, from about 1000 m/s to 900 m/s; at the depth of 14-33 m, from far to near to the pile, the soil wave velocity decreased from about 1700 m/s to 1550 m/s; in the range of depth 33-43 m, the change of the propagation velocity of elastic wave in the layer was not obvious and was basically stable at 2800 m/s; while below the 43 m depth from far to near to the pile, the propagation velocity of elastic wave in the layer showed an increasing trend from 2400 m/s to 2700 m/s. Figure 9: C3-C5 profile before and after grouting. The variation of velocity before and after grouting across the pile profile is shown in Table 4. The above test results show that the derived law was basically the same. This indicates that the base grouting, the cement paste under pressure, has a reinforcing effect on the soil around the pile, which makes the layer disturbed during the borehole be reinforced. Moreover, the closer to the pile the more obvious the reinforcing effect is. According to the velocity variation range in the cloud, the maximum reinforcement radius of this post-base grouting can reach 2 m, and the height of cement paste return along the pile was about 12 m.
# A Test Method for Acoustic Emission Properties of Natural Cellulose Fiber-Reinforced Composites
## 2 Experiments
### Acoustic Vibration Test Device
#### 2.2.1 Specimen Fixing  
Two typical methods for fixing the specimen are one with free boundary conditions and the other with fixed boundary conditions. If the test specimen is large, fixed boundary conditions are preferred, while for a small specimen, free boundary conditions are preferable due to potential external factors that can influence testing results. In the current study, small specimens were used for development of materials and thus, free boundary conditions were adopted.  
For the case of free boundary conditions, the specimen should be completely isolated from the environment by hanging it with a string such that the specimen can vibrate in all modes. Two factors are considered for the system, namely, the positions of the hanging strings on the specimen and the natural frequency of the system determined by the length and the stiffness of the string. The position where the string was tied to the suspended specimen should not interfere with any form of vibration. The distance between the hanging points and the near end of the specimen must be 0.224 L, in which \(L\) is the length of the specimen (Figure 2) according to the literature [20]. If the whole system is considered as a spring with the mass of the specimen, the spring constant, \(K\), is inversely proportional to the length and positively proportional to the stiffness of the string as follows [21]:  
\[K=\frac{F}{\Delta x}=\frac{2\pi R^{2}\sigma}{\frac{\sigma}{L}L_{0}}=\frac{2 \pi R^{2}E}{L_{0}} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Fabrics**} & **Fabric Count** & **Weight** & **Thickness** \\  & **(end \(\times\) pick/10 cm)** & **(g/m\({}^{2}\))** & **(mm)** \\ \hline Hemp & \(108\times 88\) & 538 & 0.96 \\ Ramie & \(100\times 126\) & 619 & 1.136 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the fabrics.  
Figure 2: Schematic diagram and real image of lateral free vibration testing system. The dimensions in the figure are all in mm.  
where \(F\) is the force applied, \(\Delta\)x is the extension of the string, \(\sigma\) is the stress, \(R\) is the radius of the string, \(E\) is the Young's modulus of the string and \(L_{0}\) is the length of the string.  
The natural frequency, \(f_{0}\), of the system is as follows:  
\[f_{0}=\frac{1}{2\pi}\sqrt{\frac{K}{m}}=\sqrt{\frac{ER^{2}}{\pi L_{0}m}} \tag{2}\]  
where \(m\) is the mass of the specimen. The criteria for selection of the string length and stiffness are in place to ensure that the natural frequency of the system is far away from the vibrational frequency of the specimen being tested. In the current study, a polylactic acid string (19.5 tex) with a Young's modulus of about 3 GPa and a length of 150 mm was used to hang the specimen. The calculated system natural frequency was in the range of 30-50 Hz, which is indeed far away from the frequency of the acoustic waves (200-5000 Hz) emitted from the composite samples.
# Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals
## 2 Experimental Method  
### Subjects  
In our study, 10 male subjects (age range: 24-28 years old, height range: 173.0-176.0 cm, weight range: 62.1-71 kg) without any illness in their musculoskeletal and nervous systems, were selected from graduate students who were skilled in using a treadmill (Brother WL-332). Before the experiment, they were not allowed to take part in physical exercise to avoid complications arising from fatigue. The experiment was approved by the local human ethics committee of Zhejiang University, and all the volunteers provided us with informed consents in advance.
# Numerical Simulation-Based Investigation of the Limits of Different Quasistatic Models
## 4 Numerical Validation
### Industrial Application
#### 4.1.4 Measurement and Simulation Comparison  
In Figure 8, the evolution of the impedance as a function of the frequency obtained from Darwin model, as well as the phase, are presented.  
First, the impedance Z corresponds to the DC resistance of the winding when the frequency tends towards zero. Indeed, we have Z = R\({}_{\text{DC}}\) = 6.8 m\(\Omega\).  
Second, for \(f<500\) kHz, the results obtained with the Darwin are in good agreement with the measurement results, which present the resistive-inductive phenomena, such as the MQS regime.  
Third, the resonant frequency is around 1 MHz; the simulation result of the Darwin model shows a close result at the chosen point of the frequency (7 th point), just before the resonant frequency. It can be found that this point is no longer in the MQS regime, since the impedance at this point is not linear to the previous points. A similar behavior can be observed for the phase as a function of the frequency.
## 5 Process Parameter Optimization of Laser Control System for MCR Frequency Modulation
### Laser Characteristics Analysis of MCR Frequency Modulation
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Essential Factor**} & **A (Laser Power)** & **B (Laser Frequency)** & **C (Processing Speed)** \\ \cline{2-4}  & **Over-Frequency Rate/\%** & **Over-Frequency Rate/\%** & **Over-Frequency Rate/\%** \\ \hline Ki1 & 4.74 & 7.33 & 9.01 \\ Ki2 & 6.68 & 7.41 & 8.21 \\ Ki3 & 8.82 & 7.56 & 6.74 \\ Ki4 & 9.3 & 7.6 & 5.94 \\ Ki1 & 1.185 & 1.8325 & 2.2525 \\ Ki2 & 1.67 & 1.8525 & 2.0525 \\ Ki3 & 2.08 & 1.89 & 1.685 \\ Ki4 & 2.325 & 1.9 & 1.485 \\ Ri & 1.14 & 0.0675 & 0.54 \\ Optimal level & A1 & B1 & C4 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Range analysis table of over-frequency rate of MCR frequency modulation. processing speed, and the influence of laser frequency is the smallest. In the variance results of the processing time of MCR frequency modulation, the order of F ratios is FC > FA > FB, that is, the influence of laser processing speed on processing time is the most obvious, followed by that of laser power, and the influence of laser frequency is the least. When \(\alpha=0.05\), according to the F distribution table, Since F\({}_{\alpha}(3,6)=4.76\). Since F\({}_{\beta}<\) F\({}_{0.05}(3,6)\), it can be determined that laser frequency has no significant effect on the MCR frequency modulation. The results of variance analysis and range analysis show that laser power and processing speed have significant influence on the effect of MCR frequency modulation, while the laser frequency has no significant influence. Combined with the requirements of over-frequency rate, processing time and appearance in the actual processing process, the optimal combination of laser control parameters for MCR frequency modulation are determined as A1, B4 and C1, as listed in Table 13.