esa-sceva/satcom-chunk-collection huggingface dataset:
# On Smartphone Power Consumption in Acoustic Environment Monitoring Applications
## 6 Conclusions  
Our analysis provides ample evidence that the major impact on smartphone power consumption of background audio monitoring applications is caused by the core audio recording functionality, whereas moderately complex audio processing algorithms (such as sound pressure level estimation, acoustic feature extraction, audio fingerprinting, etc.) that run simultaneously with background audio recording do not significantly impact overall smartphone power consumption and battery life. Therefore, in the majority of cases, the optimization of real-time digital audio signal processing algorithms may not provide any noticeable battery life savings if the mobile application runs in the background and performs continuous microphone sensing.  
The power consumption caused by background audio recording varies significantly from model to model, and the difference between the best and worst smartphone may be significant. In most cases, the power consumption with audio recording enabled is much higher than the power consumption in wakelock mode.  
Power consumption is likely influenced by multiple factors (both hardware-related and software-related), and we found no obvious correlation between power consumption in background audio recording mode and factors such as year of release, the OS version, and the CPU, processor, or system-on-chip model. However, batteries in newer smartphone models tend to last longer than those in older models, mostly because newer smartphones use larger-capacity batteries.  
We believe that our results may be useful to developers of mobile applications that rely on background audio recording and real-time digital audio signal processing. The results may also be interesting to mobile device manufacturers.  
**Acknowledgments:** This work was funded by Cifrasoft Ltd.  
**Author Contributions:** [PERSON] designed the research, analyzed the data, and wrote the paper; [PERSON] co-designed the experiment, developed the test application, and performed the experiments; [PERSON] co-designed the experiment, developed the signal processing code, and prepared the test equipment; and [PERSON] performed the algorithm complexity measurements.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Structure and Fabrication  
Figure 1a shows the schematic structure of the present SRN ridge waveguide with the total height, rib height and width denoted as \(H\), \(h\), and \(w\), respectively. A thick SRN layer was needed for tightly confining light in the SRN core and obtaining a proper dispersion profile. Here, we used a low-pressure chemical vapor deposition (LPCVD) machine to deposit an 810 nm SRN film on 2.5 \(\upmu\)m thermal silicon dioxide (SiO\({}_{2}\)). The SRN film was first deposited by a reaction between dichlorsilane (SiH\({}_{2}\)Cl\({}_{2}\) at 80 sccm) and ammonia (NH\({}_{3}\) at 20 sccm) at 830 \({}^{\circ}\)C and a pressure of 120 mTorr, and then annealed for three hours at 1150 \({}^{\circ}\)C. After annealing, the Si excess was measured to be \(\sim\)18.7% and the SRN film was condensed to 800 nm. A thick resist (CSAR, \(\sim\)1.1 \(\upmu\)m) was then spun and patterned by the electron beam lithography. The patterns were then transferred to the SRN film by using an inductive coupled plasma machine at -10 \({}^{\circ}\)C with flows of the etching gases SF\({}_{6}\)/Ar/CF\({}_{4}\)/CH\({}_{4}\) being 4/10/4/2 sccm. SRN ridge waveguides with various widths and rib heights were fabricated with a fixed \(H\) of 800 nm. After residual resist stripping, the ridge waveguides were finalized with air as the upper cladding and the dimensions were measured, as shown by an exemplified scanning electron microscopy (SEM) image in Figure 1b. RIs at near infrared wavelengths of the deposited and annealed SRN film were measured by an ellipsometry method and fitted with the Sellmeier formula to extract the RIs around 2 \(\upmu\)m wavelengths. Here, the RI was calculated to be 2.123 at 1950 nm. Figure 1c shows the simulated  
mode profile of the fundamental transverse-electric (TE\({}_{0}\)) mode for a SRN ridge waveguide with \(w\) = 1.32 \(\upmu\)m and \(h\) = 640 nm at the wavelength 1950 nm. The power confinement ratio in the SRN core was calculated to be 89.2%, indicating strong confinement of the SRN ridge waveguide.
## 1 Introduction
In this respect, several researchers [28; 29; 30; 31; 32; 33; 34; 35; 36] have attempted to identify the relationship between various material properties and P-wave velocity. These studies indicated that sound velocity is closely related to physico-mechanical properties such as uniaxial compressive strength, modulus of elasticity, hardness, effective porosity, water absorption and both saturated and dry density. P-wave velocity test is non-destructive, offers several advantages to researchers and can be easily applied both in laboratory and in situ. It is mentioned that P-wave velocity is related with the characteristics of the whole body of the material tested [29]. The Schmidt hammer test is equally acceptable and used for estimating indirectly material hardness and strength, through the determined \(R_{L}\) value, which depends on the properties of the specimen. The operation of the device is based on the principle that the rebound of an elastic mass impacting on a surface depends on its hardness and thus the harder the surface the higher is the rebound distance [37]. Several societies, including the International Society for Rock Mechanics [38; 39] and the American Society for Testing and Materials [40], have proposed test methods and standards, respectively, for the proper operation of the device [41]. Other options for determining such material properties with the use of the Schmidt hammer have also been proposed [42; 43; 44]. Breakage rate parameters depend on material properties, so it is very useful and practical from a laboratory and industrial point of view if correlations between them are established. In this regard, [PERSON] [45] used limestone as test material and identified the relationship between Bond's grindability and breakage rate parameters, while [PERSON] [46] established important correlations between the point load index and Bond's grindability as well as breakage rate parameters. However, so far no correlations between other material properties such as uniaxial compressive strength or modulus of elasticity and breakage rate parameters have been established. This study has a certain degree of novelty since it attempts to establish potential correlations between material properties derived from the application of destructive and non-destructive tests, and breakage rate parameters.
## 4 Results and Analysis
### Comparative Testing of Transmission Mechanisms and Results Analysis
#### 4.2.4 Throughput Test  
For different packet sizes, the time taken to 1000 consecutive transmissions was measured for both RDMA transmission mechanisms. This measurement was repeated 10 times to calculate the average throughput. Figure 13 presents a comparison of throughput between the different RDMA transmission mechanisms. As illustrated in Figure 13, the throughput of the status-byte-assisted RDMA transmission mechanism is significantly higher than that of the sliding window RDMA transmission mechanism, especially for smaller packet sizes.  
To illustrate throughput for different data packet sizes, Figure 14 compares the throughput of different RDMA transmission mechanisms: (a) for small data packet sizes (64 B to 4 KB) and (b) for large data packet sizes (8 KB to 22 MB). As shown in Figure 14, the throughput of the proposed status-byte-assisted RDMA transmission mechanism is notably higher than that of the sliding window RDMA transmission mechanism, especially for smaller packet sizes. For packets smaller than 1 KB, the sliding window RDMA transmission mechanism experiences high control latency that often exceeds the actual data transmission time, severely limiting its throughput. In contrast, the status-byte-assisted RDMA transmission mechanism shifts synchronization control to the sender and employs low-latency unilateral operations to query control information in real time, significantly reducing the control latency. This results in higher throughput for small packet sizes. As the packet size increases, the transmission time lengthens, which reduces the number of packets transmitted per unit of time. Consequently, the effect of communication control latency on throughput diminishes and the throughput of both transmission mechanisms converges. The throughput peaks when transmitting packets of 1 MB. However, for packets larger than 1 MB, the throughput tends to stabilize at approximately 7000 MB/s due to the increased overhead of retransmissions for excessively large packets.  
Figure 13: Comparison of the throughput of various RDMA transmission mechanisms for different packet sizes.  
In summary, the status-byte-assisted RDMA transmission mechanism significantly outperforms the sliding window RDMA transmission mechanism for packet sizes smaller than 512 KB, and the throughput of both transmission mechanisms becomes relatively similar for packet sizes larger than 512 KB.
# Segmentation of Rice Seedings Using the YCrCb Color Space and an Improved Otsu Method
## 2 Materials and Methods
### Improved Otsu Method for Rice Seeding Segmentation
#### 2.5.1 The Otsu Method  
The Otsu technique chooses the threshold by minimizing the within-class variance or maximizing the between-class variance. Suppose that the pixels in a given image be represented in \(L\) gray levels ranging from 0 to \(L\)-1. Let \(n_{i}\) denote the number of pixels with the level \(i\) and \(N\) denote the total number of pixels. The  
variances are in contrast to each other. However, the threshold biases toward the class with the larger variance (as shown in Figure 7B) when the histogram with a single peak is unimodal or near-unimodal. Hence, it is necessary to improve the Otsu method for selecting the desired threshold in unimodal or near-unimodal histograms of images.
## 2 Related Work and Problem Description
### Parallel Subnetwork Routing Architecture  
The introduction of the optical interconnection parallel subnetwork algorithm aims to address the issue of excessive hops in large Low Earth Orbit (LEO) constellations. The core idea of this algorithm is to partition these satellites into multiple secondary subnetworks based on the positional information and connectivity status of the LEO satellites. Subsequently, the algorithm establishes optical links within each subnetwork and between different subnetworks. The design of the secondary subnetworks and the scheduling of optical connections are based on snapshots of the satellite topology at specific time points. In theory, the partitioning of secondary subnetworks and the selection of connected satellite pairs will evolve with changes in the current link conditions. Due to the periodic nature of satellite movement, these changes exhibit periodic characteristics globally. Additionally, the fundamental requirement for establishing long-distance links is the presence of satellite pairs with line-of-sight visibility and sufficient available power in both subnetworks [4].  
When applied to routing and transmission in Low Earth Orbit constellations, this algorithm only considers single services and single paths. However, in practical scenarios, especially in time-sensitive service transmission scenarios, multiple services and paths may exist simultaneously within the same period. Therefore, this paper has made improvements to the algorithm to account for these multi-service and multi-path transmission scenarios.
# Joint Resource Allocation and Learning Optimization for UAV-Assisted Federated Learning
## 3 Joint Optimization Algorithm  
Because the above problem is not convex, it is difficult to solve it directly. Moreover, the UAV's altitude affects the users' access, but the number of users depends on the distribution of the users, so we first fixed the altitude of the UAV, then, the remaining variables \(\mathbf{q}_{m}\), \(\eta\), \(\mathbf{f}\), \(\mathbf{t}\), \(\mathbf{p}\), \(\mathbf{b}\) are assigned a value satisfying the feasible domain. Then, we transformed it into three subproblems: UAV horizontal placement, local accuracy and computation and communication resources, and used the corresponding algorithm to solve them. For the sake of understanding, the dependency relationships among algorithms are shown in Figure 2.
## 2 Mechanical Behavior of Metal Anchors
### Characteristic Resistance of Anchors under Tensile Loading
#### 2.1.2 Mechanical Anchors  
The main types of failure expected for mechanical anchors are analogous to the failure modes described for adhesive anchors (see Figure 1): (a) steel failure; (b) pull-out or interface failure; (c) substrate cone; (d) combined pull-out and cone; (e) pull-out of one brick; and (f) blow-out failure.  
Considering the steel failure of the anchor, Equations (1) and (2) can be applied to mechanical fasteners as well.  
The pull-out or interface failure of mechanical anchors, herein called T6, may occur at moderate embedment lengths in low-strength substrate material or in cases when the drill hole has a greater diameter than the fastener [9]. The following expression can be used to calculate the characteristic resistance associated with this failure mode [17]:  
\[N_{Rk}=k_{2}\cdot A_{h}\cdot f_{ck}, \tag{8}\]  
where \(k_{2}\) (-) is a semi-empirical parameter, \(f_{ck}\) (MPa) is the characteristic compressive strength of concrete, and \(A_{h}\) (mm\({}^{2}\)) is the load-bearing area of the head of the fastener, which, in turn, is defined as:  
\[A_{h}=\frac{\pi}{4}\cdot\Big{(}d_{h}^{2}-d_{a}^{2}\Big{)}, \tag{9}\]  
where \(d_{h}\) (mm) is the diameter of the anchor head, and \(d_{a}\) (mm) is the diameter of the anchor.  
The pull-out or interface failure in mechanical anchors can be associated with moderate embedment lengths in substrates with low strength or in cases where the drill hole is greater than the diameter of the anchor [9]. The standard EN 1992-4 [17] establishes \(k_{2}\) values of 7.5 for cracked concrete and 10.5 for non-cracked concrete.  
Regarding the substrate cone failure in mechanical anchors, herein referred to as T7, the characteristic resistance can be estimated through [22]:  
\[N_{Rk}=k_{1}\cdot(f_{ck})^{0.5}\cdot h_{ef}^{1.5}, \tag{10}\]  
where \(k_{1}\) (N\({}^{0.5}\)/mm\({}^{0.5}\)) is the same semi-empirical parameter presented previously for the substrate cone failure in adhesive anchors (T3).
## 3 Receiver Test Methodology
### Sensitivity Test Method  
This test is conducted by varying the power of the interfering signal. The interference is turned on 10 min after the simulation starts and it follows a two-peak ramp power profile. The initial interference power is such that J/S is 5 dB and then the interference power is increased by 5 dB every 45 s until reaching the first power peak, which corresponds to a J/S of 65 dB. After it reaches the first peak, the interference power is decreased. The power step and the dwell duration per power step are again, respectively, 5 dB and 45 s. The entire process is repeated a second time. The profile of this test methodology, whose total duration is 60 min, is illustrated in Figure 3.  
Figure 1: Receiver test platform.  
Figure 2: TTRP test profile.  
In order to assess the performance of the RUT in the presence of interference, different metrics are observed. The following outputs from the GNSS receiver are recorded and analyzed for all the test methodologies:  
1. Number of tracked satellites;
2. Position fix indicator;
3. Number of satellites used in fix;
4. Carrier-to-Noise density (C/N\({}_{0}\)) ratio;
5. East-North-Up deviations of the receiver's solution from the true position.  
Moreover, depending on the test methodology, additional parameters are evaluated. In the case of TTRP test method, the time taken for the RUT to re-obtain a position fix after a strong interference event is measured. Other metrics are of interest in the case of the sensitivity test method. In particular, the Jamming-to-Signal ratio at which position solution is no longer available and the availability of the position solution during the interference event are computed in the sensitivity tests. Furthermore, the maximum horizontal and vertical position errors are computed for the interval in which the interference is present when the receiver offers a valid position fix.
# Multiellipsoidal Mapping Algorithm
## 4 Experiments
### Comparison to Spherical Mapping  
Multiplanar mapping algorithms have been used for many applications such as urban mapping and office-like environments representation; however, these algorithms are not suitable to mapping free form objects. To solve this problem, dense multiplanar representations have been developed [5]; however, another problem then arises because, in such cases, the planes boundaries must be defined.  
Spherical mapping [8; 9] solves both problems by approximating the cloud point with spheres. Because the proposed approach is an extension of the spherical mapping, both approaches are empirically compared to show that ellipsoids can more accurately represent a point cloud.  
Let us consider the model error for the point cloud of experiment 1. Let \(x\in S_{i}\) represent the points in the cluster \(S_{i}\) and \(d\) be the closest to \(x\) three-dimensional points on the spherical or ellipsoidal surface. Then, the RMSE defined by (13) is shown, where \(i\) is the number of the cluster. Figure 12 shows the ellipsoidal and spherical approximations with \(k=100\). The spherical mapping is created in the exactly same way as ellipsoidal, except for adapting only one radius.  
\[e_{i}=\sqrt{\frac{\sum_{x\in S_{i}}(d(x)-x)^{2}}{S_{i}}} \tag{13}\]  
In Figure 13, we present the histograms of error of the ellipsoidal and the spherical approaches. In the left, the error \(e_{i}\) calculated using Equation (13) of each ellipsoid is shown. In the same way, in the right we have the calculated error for each sphere. In Table 4, their statistical measures are provided.  
Figure 11: Experiment 9.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multicolumn{2}{c}{**Point Cloud**} & \multicolumn{2}{c}{**Multiellipsoidal Map**} & \multirow{2}{*}{**Percentage Cost**} \\ \cline{2-2} \cline{4-6}  & **Points** & **Bytes** & **Ellipsoids** & **Bytes** & \\ \hline Experiment 8 & 714,446 & 8,573,352 & 8880 & 213,120 & 2.4858\% \\ Experiment 9 & 70,447 & 845,364 & 5920 & 142,080 & 16.8069\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Memory cost of the representation—part 2.  
Figure 12: Maps of experiment 10.  
Figure 13: Errors of experiment 10.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approximation** & **Mean** & **STD** & **Maximum** & **Minimum** & **Total** \\ \hline Ellipsoidal & 0.2079 & 0.0451 & 0.3604 & 0.1351 & 20.7936 \\ Spherical & 0.2258 & 0.0540 & 0.4647 & 0.1475 & 22.5769 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Errors of ellipsoidal and spherical approximations.  
Note that the multiellipsoidal mapping has a smaller error than the spherical representation due to the degrees of freedom of the ellipsoid. Hence, in this experiment, it has been empirically shown that the ellipsoidal mapping represents an improvement in the spherical mapping algorithm.
## 6 Discussion and Conclusions  
### Discussion and Future Directions  
Existing studies primarily explore the impact of individual factors on household energy consumption (HErc), often analyzing them in isolation. However, these factors are interconnected, and single-factor analysis tends to overlook interactive effects. For example, household size influences energy consumption but also intertwines with education level, income, and assets, forming a multi-layered impact mechanism. Households with higher education levels may exhibit stronger environmental awareness, making them more inclined to adopt low-carbon energy sources. However, higher income and consumption capacity can simultaneously drive demand for high-carbon products, creating a dual effect that adds complexity and uncertainty to energy consumption behavior.  
Additionally, income growth not only increases energy consumption directly but also indirectly by altering household consumption habits and lifestyles. These effects are further shaped by household size, cultural background, and policy environments. The lack of consideration for cross-factor interactions in existing studies introduces significant uncertainties in predicting household energy consumption. Future research should adopt a more integrated and dynamic analytical framework to capture these interdependencies, providing policymakers with scientifically grounded insights for addressing complex energy transitions.  
Despite progress in household carbon emission forecasting, existing models still face critical limitations. First, due to data constraints, certain dynamic factors that are difficult to quantify, such as individual lifestyle choices and cultural influences on energy consumption, have not been fully explored. Future research should incorporate more detailed micro-level or high-frequency data to improve model accuracy. Second, while existing predictive models capture economic and technological trends, they still face challenges in addressing uncertainties related to policy interventions and behavioral changes. Lastly, the impact of cultural, behavioral, and psychological factors on household energy consumption warrants further investigation. Integrating perspectives from psychology, sociology, and behavioral economics will facilitate a more comprehensive understanding of household energy consumption dynamics and provide more precise scientific evidence for policy formulation.
# Algorithm Development for the Non-Destructive Testing of Structural Damage
## 4 Experimental Setup  
### ML-Based Damage Detection for Concrete and RC Beams  
Six concrete beams and four reinforced concrete (RC) beams are fabricated and tested. The 3-point and 4-point bending tests are carried out on beams until failure occurs on the specimens. Table 1 depicts the specifications of our experimental setup.  
The size of the concrete beams is 400 mm \(\times\) 100 mm \(\times\) 100 mm, with a distance of 200 mm between the sensor and actuator, and the size of the RC beams is 1700 mm \(\times\) 150 mm \(\times\) 250 mm, with a distance of 400 mm between the sensor and actuator [39]. An N16 steel bar was used in this experiment as the longitudinal and transverse reinforcement size. The Australian Portland cement type GB, Natural river sand (Fine aggregate), Natural river gravel (Coarse aggregate), and Tap water are used as materials of concrete mixture [39]. The experimental tests are carried out through the Instron high bandwidth system commonly known as the \"Universal Test Machine (UTM)\", consisting of a loading cell, for monitoring in real time. Figure 2a,b shows our bending test setup on the beams.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Concrete Beam** & **Size (mm)** & **Loading Capacity (kN)** & \begin{tabular}{c} **Displacement** \\ **Movement (mm/min)** \\ \end{tabular} & \begin{tabular}{c} **Loading Cell** \\ **Starting Point** \\ \end{tabular} \\ \hline \
\begin{tabular}{c} Simple \\ Reinforced \\ \end{tabular} & \(400\times 100\times 100\) & 200 & 0.01 & 0 \\
\begin{tabular}{c} Reinforced \\ \end{tabular} & \(1700\times 150\times 250\) & 1000 & 0.009 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications for experimental setup.  
Figure 1: Flowchart of the developed Fuzzy c-mean thresholding algorithm.