esa-sceva/satcom-chunk-collection huggingface dataset:
# No Free Lunch--Characterizing the Performance of 6 TiSCH When Using Different Physical Layers
## 6 Experimental Results
### Battery Lifetime  
We have the motes report what portion of the time their radio is active (\"radio duty cycle\"), and what portion of the time the radio is transmitting (\"transmit duty cycle\"). We then combine that with the current draw of the radio in transmit and receive states, the supply voltage and the energy contained in a battery to compute a battery lifetime. This computation does not take into account the possible current draw of other electronics, and assumes the battery is a perfect bucket of charge (i.e., ideal battery). Even though this is not an accurate prediction of the lifetime of the mote on a real battery, it is good enough to compare the effect on lifetime of the different PHYs (We are aware that current peaks shorten the effective mote lifetime compared to the \"ideal battery\" lifetime. It turns out that the PHYs leading to the shorter \"ideal battery\" lifetime in our survey also have the higher peak current. This reinforces our conclusions.).  
We compute a mote's radio duty cycle as \(T_{on}/T_{total}\) (see Section 5.2). Figure 9 shows the evolution of the duty cycle over time. We expect the duty cycle to decrease with data rate. This does _not_ hold for OFDM 868 MHz because of the time to issue SPI commands to the AT86 RF215, which can take up to 1 ms as explained in Section 4 and shown in Figure 1.  
Given \(T_{TX}\) and \(T_{on}\), we use Equation (2) to compute reception time \(T_{RX}\), the transmit duty cycle \(DC_{TX}\) and the receive duty cycle \(DC_{RX}\).  
\[\left\{\begin{array}{lcl}T_{RX}&=&T_{on}-T_{TX}\,,\\ DC_{TX}&=&\frac{T_{TX}}{T_{total}}\,,\\ DC_{RX}&=&\frac{T_{RX}}{T_{total}}.\end{array}\right. \tag{2}\]  
Table 4 details the calculation of battery lifetime, assuming a mote is powered by a pair of AA batteries holding 8.2 Wh of energy. O-QPSK 2.4 GHz exhibits a battery lifetime 5 times larger than OFDM 868 MHz and 10 times larger than FSK 868 MHz. Despite the advantages of FSK 868 MHz and OFDM 868 MHz in range, reliability and latency, their utilization leads to more frequent battery replacement.  
Figure 8: End-to-end latency in the network recorded over the entire 90 min experiments. The longer the PHY range, the lower the latency. (**a**) Time serie; (**b**) Cumulative Density Function (CDF); (**c**) Probability Density Function (PDF).
## 3 Results and Discussion
### Limitations on the Use of the SE Margin of the Ria de Arosa
#### 3.2.5 Comprehensive Risk Mapping of the SE Margin of the Ria de Arosa  
This section integrates the various risks identified in Section 3.2 into a single, comprehensive mapping (Figure 8). The areas around the coastline are those that pose the greatest challenges, as it is here that a higher number of natural risks converge. Therefore, the limitations on land use must place special emphasis on these areas. The development of anthropogenic infrastructure for residential or industrial purposes would not be advisable without a thorough prior analysis.  
Cliff sectors, particularly well represented along the western margin of the Castrove Peninsula, are among the most critical constraints. These areas feature steep slopes, low vegetation density, and are generally unsuitable for construction due to their instability and high susceptibility to erosion and landslides.  
Coastal areas with gentler slopes, while less prone to landslides or surface erosion (depending on the consolidation of the substrate), present problems related to coastal flooding, especially in low-lying areas.  
Furthermore, the southern margin of Umia-Grove Bay, characterized by active fan and alluvial cone systems, presents constraints due to the mobile nature of the substrate, which increases the risk from a geomorphological and geotechnical perspective. Finally, areas with residual relief, such as Monte Sirdella or the northwest part of Arosa Island, are not recommended for construction. These areas have poor slope stability and can be affected by block falls and landslides, making them particularly susceptible to ground movement. The comprehensive risk mapping provides a unified view of the potential natural threats described in the study area [52; 53; 54; 55], which is highly useful for identifying the most problematic zones where multiple hazards converge. This contributes positively to risk prevention strategies that should be considered during the pre-project planning phase. The qualitative overlay analysis of multiple layers (QOA) proves to be an appropriate and effective method for generating a comprehensive multi-hazard map [55]. It enables a holistic visualization (i.e., integrated and combined) of areas where several hazards coexist. This cartographic product translates technical hazard information into a planning tool that is both easily interpretable and aimed at minimizing potential impacts in the context of coastal development [7; 8].
## 1 Introduction  
Spinal implants--pedicle screws, spinal cages, and spinal rods--are medical devices used in the surgical treatment of patients with spinal diseases. They play a crucial role in supporting the spine through effective selection and combination. Currently, titanium alloys, such as Ti-6 Al-4V, are used for spinal implants because of their lower range of motion (ROM) and higher modulus of elasticity compared to living tissue. However, the spinal implants curb the movement of the spine and damage adjacent living tissues, such as adjacent bones and intervertebral discs (IVDs), leading to problems such as increased pain, loss of function, and risk of revision surgery. Recently, a flexible material-based spinal device was proposed to achieve a more uniform load distribution and to reduce side effects. Materials such as polyetheretherketone (PEEK), which is a relatively more flexible material that can reduce stress shielding and perform uniform load distribution, have been proposed as alternatives to metals. Recent studies have shown that PEEK spinal implants are similar to titanium alloy spinal implants in terms of biocompatibility, but they have a lighter weight and lower elastic modulus compared to the metal implants, which can reduce heterogeneity and minimize damage to adjacent living tissues. However, there are problems in the fusion of the spinal cage and difficulty in further deformation into a shape suitable for the patient during surgery [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11].  
Carbon fiber-reinforced PEEK (CFR-PEEK) has been used in several studies to reduce the elastic modulus of spinal implants, and the stress on adjacent bone, and thus solve the problem of metal implants affecting the medical imaging of tumor patients [12; 13; 14; 15; 16; 17]. [PERSON] et al., [PERSON] et al., and others have implemented a porous structural implant using 3D printing technology to improve spinal motion, minimize damage to adjacent vertebral tissues, and improve bone fusion [18; 19; 20; 21; 22; 23; 24; 25]. Spinal implants were applied to the vertebral body, and their behavior under spinal motion was investigated using the finite element method (FEM) [26; 27; 28; 29; 30; 31]. However, these studies were limited to retrospective clinical studies, or new materials, such as porous metal structure implants and CFR-PEEK, were applied only to a portion of the spinal implant. Therefore, investigation of the application of porous structure to metal spinal implants, such as pedicle screws, spinal cages, and spinal rods, and study of the behavior of the vertebral body using FEM has been insufficient in conventional studies.  
In this study, the safety and applicability of porous spinal implants of various porosities, developed using 3D printing, were evaluated using FEM to resolve the problems of metal spinal implants. Nine values of porosity, ranging from 0% to 81%, were applied to a porous pedicle screw, spinal cage, and spinal rod to evaluate the ROM of each spinal motion, the equivalent stress on the adjacent bone and IVD, and the safety factor of the implant.
## 5 Simulation Results
Secondly, with a high hard delay constraint, more data packets can be accumulated for transmission, resulting in a longer coding blocklength, further contributing to the reduction in power consumption. Figure 7 depicts the change in average power consumption as a function of the iteration number of the gradient descent policy. As the iteration number increases, a deceleration in descent speed is observed, pointing to an eventual convergence. Notably, the rate of convergence of the gradient descent policy slows down with increases in the hard delay constraint. This slowdown can be attributed to the scale of the Markov chain states and the scale of the channel truncation threshold parameters. The increased number of parameters  
Figure 4: The average power of the hard-delay-constrained policy, which changes with the hard delay constraint. leads to a slower convergence rate. Consistent with the observations in Figure 4, Figure 7 also illustrates a gradual decrease in average power consumption with an increase in the hard delay constraint. Figure 8 illustrates the comparison of our presented scheme with the CSIT and the CSIR scheme in [18]. To clearly demonstrate the comparison between the two policies, we modified some simulation parameters. Here, we set the packet size to \(A=128\) bits, the channel bandwidth to \(B=2.5\times 10^{3}\) Hz, and the coherence time to \(T=3\times 10^{-3}\) s. Additionally, we showcase the performance of the CSIT scheme and the CSIR scheme under different block error probabilities. It is worth noting that in the CSIR scheme, channel inversion truncation is not required. Therefore, in this simulation, we set the maximum  
Figure 5: The transmission power changes with the threshold of truncated inversion. Figure 6: A comparison between the first heuristic algorithm and the second heuristic algorithm. queue length equal to the maximum coding blocklength. In Figure 8, we observe that our presented CSIT scheme performs better when the hard delay constraint is relatively small. For instance, when the block error probability \(\epsilon=10^{-3}\) and the hard delay constraint \(D\leq 14\), our presented CSIT scheme outperforms the CSIR scheme. Similarly, when the block error probability \(\epsilon=10^{-7}\) and the hard delay constraint \(D\leq 38\), our presented CSIT scheme outperforms the CSIR scheme. Therefore, we find that as the block error probability decreases, the advantage interval of our presented CSIT scheme increases. Figure 8: A comparison of the CSIT and CSIR schemes. Figure 7: The numerical result of the gradient descent policy, which changes with the iteration number.
## 3 Results and Discussion
### Optical Absorbance and Photocurrent Measurement  
The light absorbance for the prepared Zn-Fe LDH is shown by the UV-visible DRS spectrum presented in Figure 5a. It is obvious form the absorption that Zn-Fe LDH absorbs a good portion of visible light, up to the wavelength of 550 nm, with the absorption peak appearing at the wavelength of 490 nm. As established for photocatalytic materials, the photogeneration of electrons and holes in Zn-Fe LDH is dependent on light photons, which are very important to initiate the photocatalytic reaction and provide an active surface for the reaction [26]. The DRS spectrum thus obtained clearly demonstrates that the surface of Zn-Fe LDH presented greater electrons and holes produced by the absorption of visible light due to a narrow band gap [30]. The band gap estimated from the absorption data was found to be 2.53 eV for Zn-Fe. The band gap was estimated by the band edge equation which represents the relationship between band gap energy and wavelength maxima [31; 32] of an absorbing material, \(\mathrm{E_{bg}}\) (eV) = 1240/\(\lambda_{\mathrm{m}}\).  
Figure 5b shows the photocurrent density of Zn-Fe LDH with respect to the illumination time (20 s illumination interval). It is obvious that the photocurrent density of the  
Figure 4: FT-IR spectrum of the Zn–Fe LDH synthesized by a co-precipitation method.  
Figure 5: (a) UV–Visible Diffuse Reflectance and (b) photocurrent density of Zn–Fe LDH.  
Zn-Fe LDH was approximately around 0.10 mA cm\({}^{-2}\) compared to a sample in the dark, thus supporting the role of Zn-Fe LDH in the photocatalytic conversion of biomass to valuable products.
###### Abstract  
Fractal geometry has developed rapidly, and is widely used in various disciplines. However, only a few fractal dimension methods and techniques have been applied to the processing of gravity data, especially in the detection of geological edges and interfaces. In this paper, the definition, properties and characteristics of fractal dimensions are used to improve the edge detection of gravity anomalies, and a theoretical gravity model is established. At the same time, a new method of fractal surface variance statistics is applied and compared with traditional methods. The fractal gravity anomaly processing methods in different directions are analyzed, and the results show that the maximum value of the fractal surface variance statistical method on a fixed window can be used to delineate the geological edge of the ore body. When the method in this paper is applied to the Luobusha chromite deposit in Tibet, China, the fractal dimension corresponds well with the structural development zones of various faults, and it is also helpful to delineate the boundary of the chromite deposit and identify the interface with an obvious difference in gravity anomaly density.  
Keywords:edge detection; gravity anomaly; surface variance statistics; fractal dimension +
Footnote †: journal: Applied Sciences
## 4 Experiment
### Comparision Experiment  
We compared the proposed detection algorithm with other mainstream few-shot object detection algorithms with a self-built few-shot defect dataset on a power transmission line; the results are presented in Table 1 for comparison. The training parameters of each model refer to the optimized experimental parameters in the mmfewshot algorithm framework, ensuring that the models achieve optimal performance. The detection results are shown in Figure 6.  
Table 3 presents and compares the detection performance of the proposed detection algorithm with other mainstream few-shot object detection algorithms based on the fully trained models. The following conclusions can be drawn: (1) Our proposed method achieved the highest overall mAP among mainstream methods, with significant performance improvements, indicating that Meta PowerNet is more suitable for defect detection tasks of transmission lines under small sample conditions. Specifically, the detection accuracy of normal components such as insulators and vibration dampers is slightly improved compared to other methods, but the detection performance of defect components such as insulator self-explosions and missing vibration dampers is significantly better than other methods, indicating that the main advantage of our method compared to other methods is the high detection accuracy of defect parts. (2) Meta PowerNet also performed well in detecting pins and pins missing, indicating that the model has better detection performance for small target objects and can be used for the defect detection of components with small sizes on transmission lines. (3) When detecting different types of defects of the same component, such as insulator self-explosion and insulator damage, our proposed method also demonstrated good discrimination.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**MA-RPN**} & \multirow{2}{*}{**DFRM**} & \multicolumn{6}{c}{**Novel Classes**} \\ \cline{3-6}  & & **Insulator Self-Explosion** & **Insulator Damage** & **Damper Missing** & **Pin Missing** & **Mean** \\ \hline \multirow{3}{*}{\(\surd\)} & & 78.5 & 72.6 & 80.6 & 46.2 & 69.4 \\  & & 79.3 & 73.1 & 82.2 & 47.7 & 70.5 \\ \cline{1-1}  & \(\surd\) & 80.9 & 75.0 & 82.2 & 47.5 & 71.4 \\ \cline{1-1}  & \(\surd\) & 81.9 & 76.2 & 82.4 & 49.8 & 72.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of ablation experimental.  
Figure 6: Detection result.
# Instantaneous Networking Service Availability for Disaster Recovery
## 4 Networking Service Availability: An Integrated Measure of User Service Restoration  
### Networking Service Availability for Users  
Let X and Y denote communication source and destination, and Ax and Ay denote the local areas where X and Y reside. For example, in Figure 1 the set of areas where [PERSON] may reside is expressed as \(AxSet=\{A,B,C\}\). The set of areas in which Y possibly resides is denoted by \(AySet=\{A,B,C,G\}\), where G refers to globally connected areas. Hence, the combinational cases of X and Y location are represented by the Cartesian product set of \(AxSet\) and \(AySet\), being denoted by \(AxySet=AxSet\times AxySet=\{AA,AB,AC,AG,BA,BB,BC,BG,CA,CB,CC,CG\}\). Due to the same communication requirement, we combine the cases of AB and BA, BC and CB, and AC and CA to simplify the number of cases to analyze. Hence, we simply express the cases of X and Y location combination, by \(Axy=\{AA,AB,AC,AG,BB,BC,BG,CC,CG\}\). In this expression and what follows, AB refers to the fact that either X is in A and Y in B, or X is in B and Y in A. The same expression rule is applied to cases of AC and BC.  
Figure 2: LACS structure overview.  
To explicitly measure the speed of network restoration procedure, we adopt the metric of the instant networking service availability (I-NSA) between X and Y at a time \(t\) as the probability that [PERSON] is able to communicate with Y at the time \(t\). We note that the I-NSA of a user X in a disaster region correlates with the integrated availability of local servers and global connectivity of the local areas that [PERSON] and [PERSON] reside.  
In a conventional way, the conditions that [PERSON] can communicate with [PERSON] are: (1) the area where [PERSON] resides is locally recovered with both local and the Internet connections, and (2) the area where [PERSON] exists is provided with both local and global network connections.  
We can generally write the I-NSA of X at time \(t\), for x in Ax, and y in Ay, as  
\[\begin{split} P_{Convention}(X\sim Y)(t)&=P(\text{ X in Ax, Y in Ay, t})\cdot P_{local}(Ax,t)\\ &\cdot P_{global}(Ax,t)\cdot P_{local}(Ay,t)\cdot P_{global}( Ay,t),\end{split} \tag{1}\]  
where \(P_{local}(Ax,t)\) refers to the probability that the local server at Ax is launched, \(P_{local}(Ay,t)\) refers to the probability that the local server at Ax is launched, \(P_{global}(Ax,t)\) refers to the probability that the global connection at Ax is launched, and \(P_{global}(Ay,t)\) refers to the probability that the global connection at Ay is launched.
# Hardware-Based Architecture for DNN Wireless Communication Models
## 5 Hardware Architectures
### The Quantized Neural Network Hardware Architecture  
#### 5.2.1 Quantization Technique  
Quantization is a technique to help map numbers from the domain of real numbers to the domain of integer numbers (quantization numbers). This technique helps reduce the cost of storing memory. Instead of storing the floating-point numbers, it only stores the \(n\) bits of integer numbers. Besides, this technique also reduces computation time compared to floating-point numbers and number sharing.  
Although quantization has many advantages, its accuracy is affected. The accuracy depends on what the quantization number is applied to and the bit-width of the quantization number. The entire formula of the quantization number \(x\in[\alpha,\beta]\to x_{q}\in[\alpha_{q},\beta_{q}]\) is represented in the following equations  
\[Scale =\frac{\beta-\alpha}{\beta_{q}-\alpha_{q}} \tag{19}\] \[Offset =round(\frac{\beta\alpha_{q}-\alpha\beta_{q}}{\beta-\alpha})\] \[x_{q} =\frac{x}{Scale}+Offset\]  
where, \(\alpha\) and \(\beta\) are the smallest and largest values in the form of real numbers, respectively. \(\alpha_{q}\) and \(\beta_{q}\) are the smallest (\(-2^{(n-1)}\)), largest (\(2^{(n-1)}-1\)) values in the form of integer numbers, and \(n\) is the number of bits representing the quantized real number as an integer. \(x\) and \(x_{q}\) are the values in the float domain and the values in the integer domain, respectively.
## 3 Results
### Effects of NDVI, LST, and Precipitation Changes on SWUE Variability  
To reveal the response of SWUE changes to vegetation dynamics in northeast China, we calculated the correlation coefficients between SWUE and NDVI in the period of 2007-2015 (Figure 6a). We found that 98.7% of the study area showed strong correlation (\(0.6\leq r<1\), \(p<0.05\)), while weak to moderate correlation was observed in 1.3% of the landscape. SWUE showed a strong and positive correlation with NDVI. In the region with greater NDVI, which indicated enhanced vegetation activity, the SWUE of vegetation might also have improved.  
In Figure 6b, the correlation between SWUE and LST in northeast China from 2007 to 2015 is presented. SWUE was highly and moderately correlated with LST, accounting for 19.0% and 63.1% of the land that passed the significance test (taking up 96.8% of the study area, \(p<0.05\)), respectively. The area showing weak correlation occupied only 17.9% of the study area. Overall, LST in the growing season had positive effects on SWUE variations in the study area. As LST increased, SWUE might have improved.  
As a supply of soil moisture, precipitation affects vegetation growth and the maintenance of ecosystem services, especially in arid and semi-arid areas. We resampled the time-series SWUE data to a \(0.5^{\circ}\) spatial resolution to match the precipitation data, using the bilinear interpolation method. The correlation coefficient between eight-day SWUE and precipitation in the growing season was then calculated (Figure 6c). It was found that SWUE was significantly positive correlated with precipitation in 84% of the study area (\(r>0\), \(p<0.05\)), with 14.2% and 85.8% of the area presenting moderate and weak correlation, respectively. In the last nine years, the vegetation SWUE was positively correlated with the precipitation in northeast China, although the correlation was not strong. With the increase of precipitation, the value of SWUE increased.