esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### The Inferior Alveolar Canal: Clinical Insights  
In the field of mandibular surgery (trauma, dental, or reconstructive), the inferior alveolar bundle must be carefully identified and preserved as a noble structure [13]. Consisting of the inferior alveolar nerve and the homonymous vessels (artery and vein), it runs in a bony canal inside the mandible known as the \"inferior alveolar canal\" (IAC), or improperly called mandibular canal [14] (Figure 1). This canal begins at the level of the lingual aspect of the mandibular ramus (just above the Spix spine) and runs through the ramus, the angle and the mandibular body up to the mental foramen (usually located between the first and second premolar) [15; 16]. Its identification during radiological examinations is a key moment in surgical planning [13].  
Accurate preoperative identification of the inferior alveolar nerve (IAN) course is crucial in various maxillofacial surgical procedures, such as dental anesthesia, extraction of impacted third molars, placement of endosseous implants, mandibular osteotomies for orthognathic purposes, and fracture plating of the mandibular ramus or angle [17; 18; 19]. Even though permanent damage to the inferior alveolar bundle is rare, these surgeries can lead to temporary damage to the nerve function. Extraction of impacted teeth, in particular, is the most frequent cause of temporary damage (40.8%), which occurs in up to 7% of cases [20; 21; 22]. This is the relevant data, considering that dental extraction is one of the most common surgical interventions.  
Endosseous dental implant is one of the most frequent dental procedures. However, the risk of neurosensory disorders following implant positioning in the mandible is quite high, and has been estimated at around 13.50/100 person-years (confidence interval (CI): 10.98-16.03) [23]. The persistence of the neurosensory deficit can occur as a result of incorrect positioning of the implant, and can be disabling for the patient [24]. An accurate pre-operative assessment of the three-dimensional position of the IAC within the mandible is recommended, thus allowing for proper planning of implant procedures.  
Generally speaking, an injury of the IAN would result in a partial or total loss of lower lip sensitivity and ipsilateral lower dental arch, or painful dysesthesia, together with a proprioceptive alteration of the lower lip. Although not disabling in terms of mobility, neurological damage would certainly cause discomfort to the patient and should, therefore, be avoided if possible [25; 26]. In addition, many patients who require elective surgery near the IAN are young and healthy [20]. Hence, it is essential to set up a pre-operative plan as accurately as possible to reduce post-operative complications.
# ForecastNet Wind Power Prediction Based on Spatio-Temporal Distribution
## 4 Conclusions  
This paper focuses on predicting the spatio-temporal distribution of wind power from different turbines in wind farms and proposes a ForecastNet prediction model based on the spatio-temporal distribution. In order to simplify the prediction, the data of N wind turbines with the highest output power in the wind farm are selected to train the model, where the determination of the N value is based on the deterministic prediction results. Then, the uncertainty prediction is performed on the wind power data. The prediction results of ForecastNet variant models based on different hidden layers are compared with those of common prediction models. The results show that the ADFN model has better prediction results when considering the reliability index and acuity index. In addition, the prediction model in this paper has the following features:  
* The model uses Euclidean distance and difference distance combined with a graph attention network to aggregate spatio-temporal information on input features.
* In order to avoid the gradient problem when the neural network model predicts wind power in the short term, the time-varying characteristics and interleaved output characteristics of ForecastNet are used to improve the prediction effect.
* The model can predict the probability density curve of wind power at future moments, which can provide more effective information for grid decision makers.  
Figure 16: Probability density function of wind power at different moments.  
**Author Contributions:** Conceptualization, S.P., X.L. and J.P.; methodology, S.P. and J.P.; software, L.G. and J.P.; validation, L.G., H.H. and J.P.; writing--original draft preparation, L.G. and J.P.; writing--review and editing, L.G., H.H. and J.P.; supervision, S.P. and X.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported in part by National Natural Science Foundation of China (grant number: No. 62003218) and in part by the Stable Support Projects for Shenzhen Higher Education Institutions (grant number: No. 20220717223051001).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All the data used in this paper can be obtained by contacting the authors of this study.  
**Conflicts of Interest:** Author [PERSON] was employed by the company State Grid Zhuzhou Power Supply Company. 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.
## 2 Modeling and Parking Path Planning
### Analysis of Parking Space Constraints  
The path curve should not only satisfy the requirements of the geometric characteristics of the movement of the vehicle but also ensure that the process does not result in an accident. Therefore, it is necessary to establish the corresponding constraints, and plan the appropriate parking path curve so that the parking process is safe and accurate. This section analyzes the possible collision points in the parking process.  
According to the planned parking path, namely, the rear parking trajectory function, the theoretical curvature \(\rho\) of the vehicle at an arbitrary point in the parking process can be given as follows:  
\[\rho=\frac{y^{\prime\prime}}{\left[1+\left(y^{\prime}\right)^{2}\right]^{3/2}}\]  
According to the relation of the Ackerman angle:  
\[\tan\varphi=\frac{L}{R}\]  
where, \(L\) refers to the wheel base, \(R\) represents the radius of the turning circle, and \(R=1/\rho\).  
According to the above equations, the Ackermann angle of the vehicle at arbitrary point is as follows:  
\[\varphi=\arctan(\frac{L\cdot y^{\prime\prime}}{\left[1+\left(y^{\prime}\right) ^{2}\right]^{3/2}})\]  
Based on the coordinates of the midpoints of the rear axle and their mutual relationship, the trajectories of A, B, C and D can be obtained as follows:  
\[\left\{\begin{array}{l}x_{A}=x+\left(L_{f}+L\right)\cdot\cos\theta+\frac{W}{ 2}\cdot\sin\theta\\ y_{A}=y-\left(L_{f}+L\right)\cdot\sin\theta+\frac{W}{2}\cdot\cos\theta\\ \end{array}\right.\left\{\begin{array}{l}x_{B}=x+\left(L_{f}+L\right)\cdot \cos\theta-\frac{W}{2}\cdot\sin\theta\\ y_{B}=y-\left(L_{f}+L\right)\cdot\sin\theta-\frac{W}{2}\cdot\cos\theta\\ \end{array}\right.\]  
\[\left\{\begin{array}{l}x_{C}=x-L_{r}\cdot\cos\theta-\frac{W}{2}\cdot\sin \theta\\ y_{C}=y+L_{r}\cdot\sin\theta-\frac{W}{2}\cdot\cos\theta\\ \end{array}\right.\left\{\begin{array}{l}x_{D}=x-L_{r}\cdot\cos\theta+ \frac{W}{2}\cdot\sin\theta\\ y_{D}=y+L_{r}\cdot\sin\theta+\frac{W}{2}\cdot\cos\theta\\ \end{array}\right.\]  
where, \(L_{f}\) refers to the length of the front overhang, and \(L_{r}\) represents the length of the rear overhang.  
According to the established parking kinematics model, there are four positions at which danger exists in the process of parallel parking, as shown in Figure 3.  
To ensure that no collision occurs during the parking process, the trajectory function for vertical parking has to satisfy the following conditions:  
When \(x_{A}\in[0,S-L_{2}]\), \(y_{A}<h\);  
When \(x_{A}\in[0,S-L_{r}]\),\(y_{A}<h+L_{1}\);  
When \(x_{B}\in[0,S-L_{2}]\),\(  
The analysis of the kinematics constraints in the process of parking ensures the safety of the running vehicle during the parking operation, and lays a foundation for the vehicle trajectory planning and path tracking in the parking process.
## 1 Introduction
The conditional eddy covariance method considered both stomatal and nonstomatal processes in the effective jet information and extended it to half-hourly flux results for evapotranspiration components [17]. The flux variance similarity approach is based on the processes of stomatal and nonstomatal components at the same time and applies the flux variance similarity principle to each process, resolving the correlation coefficients of the respective components between water and carbon fluctuations with the estimation of leaf-scale water use efficiency to achieve direct partitioning for ecosystem transpiration and soil evaporation [18; 19]. The double-layer eddy covariance method simultaneously observes the turbulent exchange within and above the canopy; it is used to explain or complement the contribution of understory vegetation and soil in energy fluxes or carbon fluxes to the fluxes above the canopy. In the study of the carbon dioxide balance of forest ecosystems, this method is used to investigate the effect of subcanopy decoupling or advection on the above-canopy carbon dioxide exchange and to further correct the carbon balance of forest ecosystems under decoupling conditions [20; 21]. However, relatively few studies have focused on how to effectively separate ecosystem evapotranspiration components by using the double-layer eddy covariance method [9; 12; 22]. Recently, [PERSON] et al. improved on the known limitations of this method and developed systematic treatments to achieve the goal of partitioning evapotranspiration in forest ecosystems by simultaneously using EC measurements below and above the canopy [9]. The DLEC method has encountered more limitations in its subsequent use, including the test and delineation of coupling between layers [23] and inverse gradient transport of eddies within the canopy [24]. The most important is the coupling state of airflows crossing the canopy vertical layer. The effect of different coupling states on the application of the DLEC method to separate evapotranspiration is unknown for specific sites, so it is necessary to delineate and interpret the coupling states of air masses below and above the canopy for specific flux sites, especially in the study of carbon balance and evapotranspiration separation in forest ecosystems [20; 21; 23; 25]. The diagnosis of the coupling state ensures an effective understanding of the mixing of the vertical-layer gas masses [26], further helping to determine the best decoupling index for comparison with the performance of the CEC, FVS, and MREA methods for separating evapotranspiration when full coupling conditions are met. In this study, our objectives are (1) to investigate state of turbulence coupling using EC measurements within and above the poplar plantation canopy; (2) to apply different approaches to determine coupling and decoupling in the _Poplar_ plantation and further partition E and T from ET accordingly; and (3) to access the performance and accuracy of evapotranspiration partitioning based on the CEC method, FVS method and MREA method consistently under turbulence coupling conditions.
## 3 Results
### Biologicals in Asthma and Allergic Rhinitis
#### 3.3.2 Mepolizumab and Reslizumab (Anti-IL-5)  
In 2015 the FDA authorized mepolizumab [210] for patients aged 6 years and older with severe eosinophil asthma. The recommended dose by subcutaneous injection every 4 weeks depends on the patient's age [15].  
Mepolizumab effectiveness was evaluated for the treatment of severe eosinophil asthma in the MENSA study (NCT01691521) [211]. Mepolizumab reduced asthma exacerbations by 47% in patients who received intravenous doses and by 53% in those receiving subcutaneous doses as compared with the group receiving placebo. Recently, the COSMEX study (NCT02135692) showed the long-term efficacy of mepolizumab [212]. This Phase IIIb clinical trial involved 340 patients with eosinophil asthma who had previously participated in the SIRIUS (NCT01691508), MENSA (NCT01691521), and COSMOS (NCT01842607) [211, 213, 214]. In the COSMEX study, patients received mepolizumab subcutaneously every 4 weeks for at least 188 weeks, showing a fivefold reduction in the rate of exacerbations per year [212].  
On the other hand, reslizumab was approved by FDA one year after mepolizumab [215]. Its mechanism of action is the same as mepolizumab, but reslizumab has weight-based dosing, so it is useful for patients with high body mass index [216]. It is authorized for patients aged 18 years and older with severe eosinophil asthma, recommended to administer 3 mg/kg by intravenous infusion every 4 weeks [15]. In 2016, [PERSON] et al. showed the efficacy of reslizumab in a randomized, double-blind Phase III trial (NCT01508936) [217]. In this study, the administration of reslizumab once a month for 16 weeks in 492 patients with asthma exacerbations demonstrated an improvement in exacerbations, especially in those patients with baseline eosinophils \(\geq 400\) cells/\(\mu\)L.
# A Voxel-Based Watermarking Scheme for Additive Manufacturing
## 2 Materials and Methods
### ROI Segmentation  
After converting the input geometric model into a distance field, the proposed watermarking procedure extracts some adjacent levels from the distance field. These distance levels are confined by the following constraint:  
\[t_{1}\leq D(x,y,z)\leq t_{2}. \tag{2}\]  
When \(t_{1}\) and \(t_{2}\) are the lower and upper bounds of the adjacent levels. Then, an oriented bounding box (OBB) is manually constructed by the users via a graphical user interface. Those voxels, belonging to these distance levels and the OBB, are automatically collected by the encoder to form a region of interest (ROI). They will be utilized to carry the watermark in a latter computation.  
An example is shown in part (a) of Figure 3 to demonstrate the ROI construction process. The input model is a bowl, rendered in white color. The green edges reveal the OBB of the ROI; the light blue region is the resultant ROI.  
Figure 2: (a) a polygonal model, (b) voxelization results, (c) levels of the distance field shaded in colors.
## 3 Results  
Nineteen subjects took part in the research. For each of them, 5 SBP values were measured using the oscillometric technique, and 5 SBP values were estimated using the proposed method. Therefore, the measured values vector \(\mathbf{x}\) and estimated values vector \(\mathbf{y}\) contains N = 95 values.  
The difference between measured and estimated SBP was calculated for each examination, including the mean difference (MD), the standard deviation (SD), and the relative error (\(e_{r}\)), in order to evaluate the performance of the proposed method (Table 1):  
A Pearson's correlation value shows that the estimated and measured SBP values correlated well (r = 0.86, \(p<0.001\)). This statistically significant tight correlation shows that the proposed method is suitable for continuous systolic blood pressure estimation using PPG recording devices with a low sampling rate (up to 60 Hz) and low resolution (up to 8 bits).  
The measured MD and SD values are within the requirements of the AAMI standard (MD = \(\pm\)5 mmHg and SD = \(\pm\)8 mmHg) [49], since the mean difference MD = \(-0.043\) and standard deviation SD = 6.79, though the study does not comply with the AAMI protocol, which requires more subjects with different blood pressures and ausculatory technique as a reference method to evaluate the accuracy of the proposed method.  
The Bland-Altman plot, as discussed in Reference [50], is commonly used for the comparison of clinical results obtained by using different methods. In Figure 15, we present the agreements of the measured and estimated blood pressure values. The horizontal part of the plot corresponds to the mean values of the measured and estimated SBP, while the vertical axis shows the difference between these values. The mean difference of all the subjects is calculated and represented as a horizontal line, and this horizontal line means better results if it is closer to zero.  
The comparison of estimated and measured values in Figure 15 shows that the error bias, \(-0.043\) mmHg between the gold standard and measured SBP values, is close to zero, while the values of the SD of bias 6.79 mmHg for SBP are very similar to the results of other authors. The systematic trends in Figure 15 show, that there are some errors caused by the method itself, most likely due to rounding procedures.  
Table 2 shows the comparison of the proposed SBP estimation method with other authors' results:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**r (p < 0.001)** & **MD** & **SD** & **e\({}_{r}\) \%** \\ \hline
0.86 & \(-0.043\) & 6.79 & \(0.025\pm 5.44\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of the proposed method.  
Figure 15: Bland–Altman plot presenting the values of the difference (\(SBP_{e}\)–\(SBP_{m}\)) as a function of the mean of \(SBP_{m}\) and \(SBP_{e}\).  
As can be seen in Table 2, the proposed SBP estimation method gives similar or better results even when the sampling rate is almost two times lower.
## 3 Three-Dimensional FDTD Parallel Algorithm on the CUDA Platform
### Data Transmission Scheme Based on NCCL  
#### 3.2.1 GPUs Communicate Indirectly Through the CPU  
As shown in Figure 4, the existing way to realize communication between multiple GPUs is to transmit data through the CPU, which is slow and has a low computational efficiency.  
The implementation steps of this data exchange mode are as follows:  
1. GPU copies data to CPU: Firstly, the GPU copies the data from its computation results that need to be shared with other GPUs into the CPU memory. This is because, in the traditional multi-GPU communication model, the CPU is responsible for coordinating and forwarding the data, so the data must first be transferred to the CPU's memory.
2. The CPU exchanges the data between adjacent processes through MPI (Message Passing Interface) technology: After the data are copied to the CPU memory, the CPU uses MPI technology to exchange the data between adjacent processes. An MPI is a commonly used communication protocol in parallel computing, which can transmit information between multiple processors and ensure that the necessary electromagnetic field data can be shared between different GPUs.
3. The CPU copies the exchanged data to the GPU memory: After the data exchange is completed, the CPU copies the updated data back to the memory of each GPU so that the GPUs can continue to perform calculations. This step ensures that each GPU has the latest electromagnetic field data so that they can be processed correctly in the following calculations.  
From the above steps, it can be seen that the traditional multi-GPU communication mode requires data forwarding through the CPU each time, which significantly increases the delay of data exchange and thus reduces the overall computational efficiency. Due to the dependence on the CPU for data transmission and processing, the CPU has becomes a communication bottleneck in large-scale computing tasks, affecting the parallel computing performance of the GPUs. Therefore, developing a GPU direct communication technology is the key to improving computing efficiency.
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
Herein, this work presents the development of a non-intermittent self-powered UHF passive tag prototype for biomedical temperature monitoring using a 0.18 \(\upmu\)m CMOS technology. The proposed device shows a reading range of 9.5 m using an RF link budget of 1 W EIRP. This device can measure temperature alterations from 0 \({}^{\circ}\)C to 60 \({}^{\circ}\)C with a sensitivity of 823.29 Hz/\({}^{\circ}\)C and a standard error of 13.67 Hz/\({}^{\circ}\)C over linear regression. The overall size of the self-powered passive tag is 8 cm \(\times\) 2 cm, regarding both chip and antenna. The proposed sensor could have potential application for human body temperature monitoring because it is a portable system which does not require conventional batteries. This work is organized as follows. Section 2 presents the architecture of the proposed self-powered sensor and discusses the required circuits. Section 3 introduces a bottom-up design methodology for the proposed sensor prototype and shows post-layout simulations. Section 4 presents and discusses the implemented test-benches used to measure the fabricated prototype. Finally, Section 5 draws the conclusions.
## 2 Materials and Methods
### Chemicals  
Ethyl acetate (99.5%), 3-methylbutanal (99%), 2,3-butanedione (98%), 2,3-pentanedione (97%), ethyl 2-methylbutanoate (99%), 3-methylbutanol (99%), 1-octen-3-one (97%), 2,5-dimethylpyrazine (99%), 2,6-dimethylpyrazine (98%), 2-ethylpyrazine (98%), dimethyl trisulfide (98%), 2-ethyl-5-methylpyrazine (98%), nonanal (95%), 2,3,5-trimethylpyrazine (99%), propionic acid (99%), linaloal (98%), ethyl 3-acetylpropionate (98%), 2-methylpropionic acid (99%), butanoic acid (99%), 3-methylbutanoic acid (99%), 2-furanmethanol (98%), me thionol (98%), pentanoic acid (99%), ethyl phenylacetate (99%), 4-methylpentanoic acid (99%), methylcyclopentone (99%), guaiaicol (99%), maltol (99%), 4-ethylguaiaicol (98%) and 2-octanol (99 %, internal standard) were purchased from J&K Chemical Ltd. (Beijing, China). 2-methylbutanal (98%), ethyl propanoate (99.5%), ethyl 3-methylbutanoate (99%), hexanal (97%), octanal (99%), methional (98%), 2-ethyl-3,5-dimethylpyrazine (99%), 3-methyl-2-isobutyl pyrazine (>98%), benzeneacetaldehyde (95%), phenethyl alcohol (99%) and vanillin (>98%) were bought from Macklin Biochemical Co., Ltd. (Shanghai, China). Ethyl butanoate (>98%), ethyl 2-hydroxy-4-methylpentanoate (98%), (E,Z)-2,6-nonadienal (>95%), 4-ethylphenol (>97%) and 2,6-dimethoxyphenol (99%) were obtained from TCI (Shanghai, China). Ethyl 2-methylpropanoate (>98%), 2,3-diethyl-5-methylpyrazine (>98%), 2-acetylpyrazine (>98%), 3-methylpentanoic acid (>98%) and \(\gamma\)-dodecalactone (>98%) were supplied by Adamas reagent Co., Ltd. (Shanghai, China). Ethanol (>99%), acetic acid (>99%), anhydrous sodium sulfate and dichloromethane were purchased from Sinopharm Chemical Reagent Co., Ltd. (Beijing, China). Methanethiol (2000 \(\mu\)g/mL in toluene), 4-hydroxy-2,5-dimethyl-3(2H)-furanone (98%), 5-ethyl-4-hydroxy-2-methyl-3(2H)-furanone (97%) and phenylacetic acid (95%) were supplied by AccuStandard (New Haven, CT, USA), Aladdin Reagents (Shanghai, China) Co., Ltd., Ark Pharm Inc. (Chicago, IL, USA), Key Organics (Cornwall, England), respectively. C6-C28 normal alkanes were bought from Aldrich Chemical Co., Ltd. (Shanghai, China). Dichloromethane was freshly distilled prior to experiments.