esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions and Future Prospects  
MIR spectral trace gas sensing is particularly attractive for its unique and strong fingerprint absorption. Higher sensitivity has been achieved by solving all the challenges of the spectral feature, i.e., broad, serried, crowding, and even overlapping in the MIR region. Benefiting from the methods mentioned above, multicomponent simultaneous detection is an expected achievement. The invention and commercialization of high-performance MIR lasers, i.e., DFB-QCL and DFB-ICL, promoted the development and application of MIR tunable laser-based trace gas sensors, especially in miniaturized and portable applications. Though more than 100 types of gas have been detected by tunable laser-based sensors, there are huge demands for higher detection sensitivity, or, in extreme conditions or scientific exploration, more other types of gas need to be detected. Thus, we believe greater progress will be achieved in the next decade, which may include:  
**(1) Compact integrated gas sensors**. Compact sensors with lower power consumption or battery power could benefit from VCSEL, DFB-ICL, and iHWG. Even a sensor system on a chip will become possible using integrated optics with the fabrication of miniaturized devices integrating the electronics and optics. These sensors could play a vital role in portable and wearable applications that could be applied for breath analysis, diagnostics, metabolomics, environmental safety detection, and related applications. **(2) Multicomponent gas sensors**. Multicomponent sensors will achieve more progress, benefiting from the wider wavelength coverage by integrated laser arrays, OFC, or EC-QCL. More species could be detected simultaneously by a particularly devised broadband laser, which could expand their applications in scientific research, including combustion diagnosis, chemical reaction process dynamics, exhaled breath analysis, and metabolomics. **(3) Standoff remote sensing**. The techniques of open-path standoff detection by backscattered MIR light provide a promising method of prompt and flexible assessment of atmospheric environmental, leaks, explosive, and security in handheld devices or UAV. The detection sensitivity could be substantially improved by newly developed high-performance MIR detectors and progress in high-power DFB-QCL. **(4) Ultra-sensitive sensing**. With the development of a mid-infrared laser source and high-performance detector, combined with cavity enhancement technology and noise immunity technology, ultra-high detection sensitivity becomes possible.
# Evaluation of IEEE802.15.4g for Environmental Observations
## 2 Materials and Methods
### Regional Regulations  
Duty cycle, maximum bandwidth per communication channel, maximum dwell time and maximum transmission power are some of the parameters each region/country regulates. This section summarizes the regulations in Europe, the United States and Japan.
## 2 Background: Partial Discharge Inception Field Modeling and \"Three-Leg\" Approach  
Recently, a new procedure, named the \"three-leg\" approach, has been proposed to achieve PD-free design of insulation systems. It holds for both AC and DC, and it is based on the following three steps (legs):  
1. electric field (and thermal stress) simulation,
2. extrinsic aging threshold modeling (PD inception field and voltage in combination with leg 1),
3. PD measurements for validation of the first two legs.  
When the maximum field calculated under leg 1 matches the inception PD field estimated by the model from leg 2, then PD will be triggered in the insulation system under consideration. The voltage at which this occurs is the partial discharge inception voltage (PDIV), which can be validated by proper PD detection. An experimental validation is performed (leg 3) to support the PDIV estimation thus obtained. However, since PD is not just magnitude and repetition rate, PDIV measurements must be carried out with the support of proper hardware/software tools, allowing separation, recognition, and, eventually, identification of the different typologies of source-generating PD, i.e., internal, surface, and corona discharge (according to the descending order of harmfulness).  
The most recent innovative approach (described, e.g., in [17]) possesses software for PD acquisition and analysis that is fully automatic and outputs the identification of the type of source-generating PD [18].
# Vitamin K Status of Patients Undergoing Hemodialysis: Insights from a Cross-Sectional Study
###### Abstract
A warfarin prescription for ESRD patients is implied in order to reduce the stroke risk in patients with atrial fibrillation or prosthetic valves, but sometimes also in order to preserve vascular access patency. Warfarin acts through the inhibition of a vitamin K epoxide reductase enzyme and contributes to ectopic calcification processes by reducing the activity level of endogenous mineralization inhibitors [8]. On the other hand, there are several therapeutic interventions available in order to control CKD-MBD, and thus, reduce vascular calcifications. However, the interaction between vitamin K and phosphate binders could potentially limit vitamin K bioavailability because the formed calcium phosphate salt can bind vitamin K as well [9]. Moreover, current vitamin K statuses are not routinely assessed in ESRD patients; therefore, reports regarding the possible interactions between concurrent medications and vitamin K deficiency are scarce. A method used to assess vitamin K status is to measure the fragments of VKDPs; increased levels reflect a vitamin K deficiency. MGP is transformed in an active form through phosphorylation and vitamin K-dependent carboxylations. The MGP inactive form (dephosphorylated-uncarboxylated MGP- dp-ucMGP) reflects the subject's vitamin K status, and higher levels have been associated with more ectopic calcifications [10]. The completely unfunctional form of MGP (dp-ucMGP) has a low affinity for calcium and matrix vesicles, and only after being carboxylated, it forms a mass with calcium phosphate, thus preventing vascular calcifications. This is the explanation for why high levels of dp-ucMGP reflect an impaired vitamin K status [11]. Osteocalcin (OC) is produced by osteoblasts and has a role in increasing mineral bone density. OC transformation from its uncarboxylated form to its fully functional carboxylated form is vitamin K-mediated. Increased uncarboxylated OC is a marker of vitamin K deficiency [12]. However, the immunoassays commonly used in the literature to measure the different fragments of OC do not provide complete information on how many of the fragments are actually carboxylated, which leads to the idea of using the total OC concentration instead of truncated fragments [13]. The total osteoclastvine (that recognizes both intact and N-MID fragments of osteoclastcin), which we determined in our study, is even more stable compared to the use of assays measuring intact osteoclastcin only. The present study aims to assess the total OC and dp-ucMGP levels in a cohort of hemodialysis patients and their relationship with CKD-MBD markers, nutritional status as well as concomitant medications.
# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 4 Neutralization Systems
### Kinetic-Mechanical Neutralizers
#### 4.2.1 Neutralizers Based on Projectiles  
These neutralizers are real weapons using projectiles capable of destroying m-drones. They include machine guns, munitions, guided missiles, artillery, mortars and rockets. Some of them (guided missiles) may require a guidance and tracking system in order to track and hit the drone target, while others can be equipped with an optical sensor for object detection and tracking. They are an expensive solution (the cost per shot is high) and typically used in military contexts. Finally, they are also capable of causing collateral effects, as the hit drone can fall to the ground causing damage to people and/or infrastructures.
## 6 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} \\ \cline{2-5}  & **RMSE** & **R\({}^{2}\)** & **RMSE** & **R\({}^{2}\)** \\ \hline RF & 0.057 & 0.963 & 0.060 & 0.894 \\ CART & 0.052 & 0.945 & 0.052 & 0.924 \\ KNN & 0.074 & 0.904 & 0.067 & 0.867 \\
**PSO-SVR-L** & **0.036** & **0.963** & **0.040** & **0.954** \\ PSO-SVR-P & 0.040 & 0.962 & 0.042 & 0.948 \\ PSO-SVR-RBF & 0.041 & 0.962 & 0.043 & 0.946 \\ \hline \hline \end{tabular} Note: The bold type represents the best model. RMSE denotes root-mean-square error; R\({}^{2}\) denotes determination coefficient.  
\end{table} 
Table 5: Performance indices of the PM\({}_{10}\) concentration predictive models.
## 5 Results and Discussion
### Summary of Results  
On the basis of the dynamic behavior of a dam under near-fault ground motions, it can be seen that the near-fault pulse ground motions are more destructive than the non-pulse ground motions. When the incidence angle is 75\({}^{\circ}\), the average maximum relative displacement of dam crest point with respect to bottom left (bottom right) corner under pulse records is 1.62 (1.71) times higher than that under non-pulse records. The response of the dam increase with the ratio of PGV/PGA.  
With an increase of the P wave incident angle, the displacement of the dam crest and the damage of the dam first increase, and then decrease; when the incident angle of P wave is 60\({}^{\circ}\) or 75\({}^{\circ}\), the dynamic response of the dam reaches the maximum. The maximum relative displacement of dam crest point with respect to bottom left (right) corner under the three near-fault non-pulse ground motion records reaches the maximum value at 75\({}^{\circ}\) (60\({}^{\circ}\)), and they are 4.31, 4.14, 3.02 (2.24, 2.02, 3.82) times higher than that with the vertical incidence of seismic waves, respectively.  
In addition, the damage of the gravity dam under non-pulse records with a small incident angle of the P wave is more severe than that under pulse records with a large incident angle of the P wave. For example, in three earthquakes, the damage of the dam under non-pulse ground motions with the incident angle of 75\({}^{\circ}\) is more serious than that under pulse ground motions with the incident angle of 0\({}^{\circ}\).  
Moreover, the worst damage of the combined dam structure occurs at the interface structures, and the spatial variation of the damage on the interface of different materials varies with different types of near-fault earthquakes and seismic incident angles. The maximum damage occurs at different part of the interface under different earthquakes.
# A Two-Stage Optimization Model for Airport Stand Allocation and Ground Support Vehicle Scheduling
## 4 Modeling
### Multi-Type Ground Support Vehicle Scheduling Model
#### 4.3.1 Vehicle Service Process and Synergy Analysis
Baggage and cargo handling staff, along with the equipment, should be in position 5 min before the scheduled/estimated arrival time, and the loading of cargo and baggage must be completed 5 min before the scheduled/target departure time. Based on the task associations of vehicles, ground support vehicles can be divided into three types of collaborative relationships:  
1. Sequential Task Relationships Sequential task relationships are characterized by a clear time order between tasks. For example, ferry vehicles and fuel trucks, as fueling is generally not permitted while passengers are on board. Therefore, after the ferry vehicle completes its task, the fuel truck can service the aircraft. The fueling task must be completed before the ferry vehicle for departing passengers arrives. 2. Parallel Task Relationships Parallel task relationships mean there are no constraints on the order of task execution. For example, water trucks, garbage trucks, and catering trucks operate independently and do not need to follow one another in a specific order. 3. Joint Task Relationships Joint task relationships refer to situations where multiple vehicles must work together to complete a single task, with each being indispensable. For example, baggage tractors and baggage belt loaders work in tandem to handle baggage loading and unloading. Since the baggage tractor needs to shuttle between the baggage handling center and the apron, which takes some time, the baggage belt loader should arrive at the designated task point before the baggage tractor to avoid delays. Once the baggage handling is completed, the tasks for both types of vehicles are finished.
## 5 Conclusions  
This study involved the development of two convolutional neural network models utilizing SST, longitude, latitude, and temporal data. A comparative analysis was undertaken to assess the performance and stability of these models. Following this, an experiment was conducted to evaluate the lightweight model. The outcomes of this study yielded the subsequent results and conclusions:  
1. The AlexNetMini model, which is a lightweight version, was developed and is one-third the size of the original AlexNet model while maintaining comparable predictive performance.
2. The optimal dropout rates identified for the AlexNetMini model were 0 and 0.2, with an optimal training set proportion (TSP) of 0.8.
3. The average F1-score for the AlexNetMini model was recorded at 0.7486 when utilizing DPM-S3 and 0.7495 with DPM-S2. Ultimately, Scenario 3, as determined through a cluster analysis of fishing ground centers, was selected as the most effective division of the training dataset.  
In conclusion, the AlexNetMini model demonstrated an F1-score comparable to that of the AlexNet model while exhibiting a reduced size in this study. Consequently, it was deemed appropriate for the practical prediction of fishing grounds for the purpleback flying squid in the high sea of the Northwest Indian Ocean. Furthermore, there is potential for the application of deep learning models across various fish species to establish a more precise fishery ground prediction system. Looking ahead, we intend to optimize the utilizationof big data in fisheries and contribute to the advancement of a high-precision, intelligent fishing ground prediction system.  
Conceptualization, S.Z., J.C. and F.T.; methodology, J.C., S.Z. and H.H.; data curation, J.C., H.H., F.T. and X.C.; writing--review and editing, S.Z., J.C., H.H. and X.C.; visualization, J.C. and Y.S.; validation, Y.S.; funding acquisition, S.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Laoshan Laboratory, grant number LSKJ202201804.  
Not applicable.  
The data supporting the results of this research are not publicly available.  
The authors declare no conflicts of interest.
## 3 Yaw Angle Control Design
### Controller Design  
In Formula (23), the desired yaw rate for manipulating the yaw angle is designed as follows:  
\[r_{d}(k)=\omega_{1}[\psi_{d}(k)-\psi(k)] \tag{27}\]  
According to the predictive functional control theory [50; 51], the system input can be formulated using  
\[\left\{\begin{array}{l}\delta_{r}(k+i)=\delta_{1}+i\cdot\delta_{2}\\ \Delta\delta_{r}(k+i)=\delta_{2}\end{array}\right. \tag{28}\]  
Then, in Formula (23), in the absence of \(f_{r}\), a predictive model for the yaw rate model can be given using  
\[\left\{\begin{array}{l}r(k+1)=r(k)+Tb_{r}\delta_{1}\\ r(k+2)=r(k)+Tb_{r}(\delta_{1}+\delta_{2})\\ r(k+3)=r(k)+Tb_{r}(\delta_{1}+2\delta_{2})\\ \vdots\\ r(k+n)=r(k)+Tb_{r}[\delta_{1}+(n-1)\delta_{2}]\end{array}\right. \tag{29}\]  
The following receding horizon performance index function is selected:  
\[J(k)=\frac{1}{2}\sum_{j=1}^{2}\left[r\big{(}k+n_{j}\big{)}-r_{d}\big{(}k+n_{j} \big{)}\right] \tag{30}\]  
where \(n_{1}\) and \(n_{2}\), which are two positive integers, are the lengths of the receding horizon.  
Denote \(u(k)=[\delta_{1},\delta_{2}]^{T}\). Then, by letting \(\frac{\partial J(k)}{\partial u(k)}=0\), the optimal rudder deflection angle can be derived as follows:  
\[\delta_{r}(k)=[1,0]\begin{bmatrix}Tb_{r}&Tb_{r}(n_{1}-1)\\ Tb_{r}&Tb_{r}(n_{2}-1)\end{bmatrix}^{-1}\begin{bmatrix}r_{d}(k+n_{1})-r(k)\\ r_{d}(k+n_{1})-r(k)\end{bmatrix} \tag{31}\]  
Through combing Formulas (26) and (31), the final control law is summarized as  
\[\delta_{r}(k)=[1,0]\begin{bmatrix}Tb_{r}&Tb_{r}(n_{1}-1)\\ Tb_{r}&Tb_{r}(n_{2}-1)\end{bmatrix}^{-1}\begin{bmatrix}r_{d}(k+n_{1})-r(k)\\ r_{d}(k+n_{1})-r(k)\end{bmatrix}-\frac{1}{b_{r}}\hat{f}_{r}(k) \tag{32}\]
# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 4 Simulation Model
### Channel Scheduling for WirelessHART
For this reason, we investigated the behavior of alternative coloring policies such as those proposed in [35] trying to build superframes optimized for both path traversal directions. In this case, we obtained far larger superframe sizes (as shown in Figure 2, _Up-down_ data series), which might negatively affect latencies. Since we consider the requirements of an industrial environment, we finally resorted to use the _Broadcast_ policy in our simulations. We modeled queues in the MAC layer as multiple FIFO queues, one for each destination, so that a node may send a frame to a neighbor as soon as a slot for that neighbor occurs, also if older frames to other neighbors exist (but in the respective, different, queue). In the next section, the simulation conditions are detailed, including the number of consumers and proxies configured for each simulation.
That simplifies the communication network and is definitely appropriate for coordinating distributed resources with participation in the frequency regulation [23]. However, this control strategy requires the power tracking and energy balancing for fair utilization of available resources. The first application of the control algorithm is distribution networks voltage control incorporated into large-scale energy storage systems and photovoltaic systems [24; 25]. [PERSON] et al., developed the distributed pinning control framework embedded with a leader-following communication protocol [26]. A compound distributed control strategy by a combination of model predictive control and consensus control strategy is designed [27]. In order to improve the convergence of the frequency control method, the finite-time consensus protocol has been coupled with the distributed control strategy [28]. Due to a lack of dynamic frequency behaviors in inverter ACs, the finite-time consensus approach has rarely been utilized to control both the power outputs and energy states for aggregated ACs in current studies. Compared with previous research, the main contributions of this paper are as follows:  
1. A comprehensive derivation of the virtual battery model of inverter ACs is developed based on the ETP model, which contributes to the practical application of inverter ACs for ancillary services. To the authors' knowledge, this is the first published model mentioning the dynamic characteristic of aggregated inverter ACs for frequency regulation. 2. A distributed control framework is established to integrate the aggregated ACs into the conventional control. A well-designed control strategy is proposed on the basis of an adjustable control error based on unit capacity (UPCE), which significantly improves the control performance with the participation of heterogeneous ACs. 3. A distributed pinning control algorithm is developed to coordinate the numerous ACs. The desired dynamic performance, which refers to the convergence of power tracking and state-of-charge (SOC) balancing of inverter ACs, is ensured by the finite-time consensus protocol. The structure of this paper is presented as follows: The virtual battery model of an individual inverter AC is derived in Section 2. Section 3 proposes the hierarchical frequency control scheme. Simulation tests are performed in Section 4. The contributions of this paper are concluded in Section 5.
## 4 Results and Analysis
### MODIS NPP  
versus _Terrestrial NPP_  
Since three different daily climate data sets are available for deriving MODIS NPP across Austria, we explore the impact of different daily climate data on the resulting MODIS NPP computations. We compute the MOD17 NPP estimates for the years 2000-2011 using three different daily climate data sets: (i) GMAO; (ii) NCEP2 and (iii) ZAMG. For all three settings, the improved method for estimating NPP according to [PERSON] _et al._[44] is used.  
The mean values for the NPP results using the local climate data set (ZAMG) 568.0 gC/m\({}^{2}\)/year (median 579.8 gC/m\({}^{2}\)/year, standard deviation 113.6 gC/m\({}^{2}\)/year), using the climate data of NCEP2 728.6 gC/m\({}^{2}\)/year (median 738.9 gC/m\({}^{2}\)/year, standard deviation 59.6 gC/m\({}^{2}\)/year) and of GMAO 731.5 gC/m\({}^{2}\)/year (median 742.3 gC/m\({}^{2}\)/year, standard deviation 79.2 gC/m\({}^{2}\)/year) (Figure 2). The NPP estimates result from the computations according to Equations (1) and (2) and the cited references. The varying NPP computation results are due to the differences in the daily climate data sets [44], as all other input parameters were kept constant.  
For the same forest inventory plots we calculate the terrestrial NPP according to Equations (3)-(10) and the information given in the Appendix. Note that due to the inventory design the results cover all trees with a \(dbh\geq\)5 cm. The terrestrial NPP results (NFI) are also presented in Figure 2 and show a mean of 525.2 gC/m\({}^{2}\)/year (median 486.3 gC/m\({}^{2}\)/year, standard deviation 251.4 gC/m\({}^{2}\)/year). The numbers above the boxplots give the sample size. The difference between NFI and MODIS of 222 are the inventory plots, where MODIS classify a land cover other than forest. The root mean square error (RMSE) between MODIS and terrestrial NPP is for ZAMG 276.1 gC/m\({}^{2}\)/year, for NCEP2 335.5 gC/m\({}^{2}\)/year and for GMAO 340.3 gC/m\({}^{2}\)/year.
## 3 Numerical Simulation and Verification  
### Simulation Setup  
For simplification, only Steps 2 and 3 in Section 2.3 are carried out in the simulation. As in Figure 7, the side length of the iron plate is 1.5 m; the thickness and material properties are the same as in Section 2.1. The original point \(O_{2}\) is set in the vertex of the lower right corner; the defect is 0.825 m from \(O_{2}\) with a size of \(1~{}\times 10\) mm, and the angular position \(\alpha_{1}\) is \(10^{\circ}\). The defect is motivated with a tone-burst signal, which is acquired by filtering a sinusoidal signal of multiple cycles with a Hanning window, as in Figure 8. The main frequency is 128 kHz. With the angular step \(\theta_{\text{s}}\) of 0.5\({}^{\circ}\), 91 receiving points are set at the radius 1.3 m, which are marked from RP1 to RP91. The MST is always towards the original point during the signal being received from RP1 to RP91.The sound field of the MST for receiving is the same as that for generation, and so the signals collected at these receiving points are modulated by the sound field of the MST to construct the B-scan image.  
Figure 7: The diagram of the numerical model.  
Figure 8: The excitation signals with different cycles and their amplitude spectrums: (**a**) three cycles; (**b**) five cycles; (**c**) seven cycles; (**d**) nine cycles. The half-decline frequency bandwidth of the signals is marked.