esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Flexural Behaviors with RC  
The tensile capacity of the carbon grids in the carbon grid-reinforced specimens was 0.6 to 5.0 times higher than that of the steel in specimen RC (Table 1); however, the maximum load of the carbon grid-reinforced specimens ranged from 60 to 110% of the maximum load of the steel specimen RC (Figure 18), because specimen RC reached its maximum load owing to the yielding of steel, whereas the carbon grid-reinforced specimens reached their maximum load because of mortar crushing. In this case, only tensile stress corresponding to 38-82% of the tensile strength was observed on the longitudinal strands, as indicated in Table 7. In the case of specimens J4 and J5 reinforced with cross-laminate structured carbon grids FTG-CR13 and FTG-CR16, which were calculated to have a flexural capacity similar to that of specimen RC, the maximum load ranged from 60 to 79% of the maximum load of specimen RC.  
The stiffness of specimens J4 and J5 was significantly lower than that of specimen RC after cracking (Figure 19) because the elastic modulus of the longitudinal strands was 0.5 times lower than that of steel, whereas the total cross-sectional areas of the strands in the carbon grids arranged in specimens J4 and J5 were similar to or larger than that of steel by 0.91 and 1.40 times, respectively. Consequently, the mid-span deflections of specimens J4 and J5 under the maximum load were 2.00 and 1.35 times higher than that of specimen  
Figure 17: Stiffness ratios for experimental and calculation results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{\(I_{cr}\) (mm\({}^{4}\))} & \multirow{2}{*}{\(I_{e}\) (mm\({}^{4}\))} & \multicolumn{2}{c}{**Stiffness of Cracked Section (N/mm)**} \\ \cline{3-5}  & & & **Based on \(I_{cr}\)** & **Based on \(I_{e}\)** \\ \hline G1 & 287,945 & 295,566 & 538 & 524 \\ G2 & 278,931 & 286,721 & 522 & 508 \\ G3 & 151,084 & 158,556 & 289 & 275 \\ J1 & 217,652 & 225,827 & 411 & 396 \\ J2 & 437,175 & 447,188 & 814 & 796 \\ J3 & 296,093 & 304,562 & 554 & 539 \\ J4 & 600,818 & 609,404 & 1109 & 1094 \\ J5 & 484,276 & 495,148 & 901 & 881 \\ J6 & 676,201 & 687,438 & 1251 & 1231 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Stiffness of cracked sections by effective and cracked transformed moment of inertia.  
RC, respectively (Table 5). Furthermore, in the case of specimen RC, the load gradually decreased even after reaching the maximum load owing to the ductile behavior after the yielding of steel; approximately 81% of the maximum load was maintained at the end of the experiment. However, in the case of specimens J4 and J5, the load sharply decreased to approximately 67 and 61% of the maximum load, respectively, as the load resisted by the mortar was transferred to the carbon grids after reaching the maximum load due to the heterogeneous bond between the mortar and carbon grids [27]. The load finally decreased to approximately 53 and 36% of the maximum load for specimens J4 and J5, respectively, without carbon grid fracture at the end of the experiment.
## 3 System Model
### Eavesdropping and Jamming Model
As shown in Figure 2b, \(UAV_{L}\) chooses \(UAV_{ST}\) for eavesdropping and \(UAV_{SR}\) for jamming. In this case, the channel gain in the eavesdropping link is the same as in Equation (6), and because of the jamming object selection of \(UAV_{SR}\), the channel gain in the jamming link is changed as:  
\[H_{j}(x)=\frac{\lambda H_{j}(x-1)+n\sqrt{1-\lambda^{2}}}{d_{2}^{\alpha_{1}}(x)} \tag{9}\]  
where \(d_{2}(x)=\sqrt{D{(x)}^{2}-d_{1}^{2}(x)}\). Accordingly, the Signal to Noise Ratio (SNR) in the jamming link denotes as:  
\[\gamma_{j}(x)=\sqrt{\frac{H_{j}(x)\cdot K_{2}^{-1}\ln\frac{K_{1}}{\epsilon} \cdot\left(2^{\rho(x)}-1\right)}{N_{0}}} \tag{10}\]  
**Case 3:**_UAV\({}_{L}\)_ _eavesdropps and jams_ UAV\({}_{SR}\)_._  
_As shown in Figure 2c,_ UAV\({}_{L}\) _only chooses_ UAV\({}_{ST}\) _for eavesdropping and jamming. The channel gains for eavesdropping and jamming links are denoted as:_  
\[H_{e}(x)=H_{j}(x)=\frac{\lambda H_{e}(x-1)+n\sqrt{1-\lambda^{2}}}{d_{2}^{a_{1}} (x)} \tag{11}\]  
_where_ \(d_{2}(x)=\sqrt{D(x)^{2}-d_{1}^{2}(x)}\)_. Accordingly, the Signal to Noise Ratio (SNR) in the jamming link is the same as in Equation (7)._  
**Case 4:**_UAV\({}_{L}\)_ _eavesdropps_ UAV\({}_{SR}\) _by jamming_ UAV\({}_{ST}\)_._  
_As shown in Figure 2d,_ UAV\({}_{L}\) _chooses_ UAV\({}_{ST}\) _for jamming and_ UAV\({}_{SR}\) _for eavesdropping. In this case, the channel gain in the eavesdropping link is the same as in Equation (11), and the channel gain in the jamming link is the same as in Equation (6)._
###### Abstract  
Symmetry-based sub-connected hybrid precoding is an energy-friendly structure in wireless communications. Most of the prior work set a diagonal constraint on the analog precoder and used a randomly set matrix as the initial analog precoder, which did not match the optimal channel conditions, leading to a decrease in spectral efficiency, and some had huge complexity when calculating the digital precoder. Aiming to solve these problems, this paper proposed a low-complexity hybrid precoding algorithm based on Initial value Acceleration-based Alternating Minimization (IAAM). Leveraging the special structure of analog precoder in sub-connected scheme, we design the analog precoder through low-complexity quadratic programming and use the least square method to obtain the digital precoder. Moreover, we design a heuristic algorithm with the objective function of maximizing the effective channel gain to calculate the initial analog precoder as the starting point for alternating minimization. The simulation results show that the spectral efficiency of this algorithm is at least 17.5% higher than the existing two traditional sub-connected algorithms. Additionally, it increases energy efficiency by at least 12.8% compa with the Orthogonal Matching Pursuit (OMP) algorithm. Its algorithm convergence speed is fast, which increases with the number of RF chains.  
massive MIMO; millimeter wave; dynamic sub-connected; hybrid precoding +
Footnote †: journal:  
_Article_
## 4 Simulations
### Comparison of Mal-Function Probabilities in Normal and Abnormal Communication Conditions  
(1) False trip probability  
In normal communication conditions, the expected inconsistent delay is relatively small and acceptable. The current differential protection operates correctly. In other words, the false trip probability is very low under normal communication conditions. However, when a disturbance occurs in the communication channel, the inconsistent delay may increase and fluctuate. The change of inconsistent delay will affect protection's false trip probability.  
The inconsistent delay during a period of time is shown in Figure 16. At \(t=1\) s, a fault occurs to the communication channel, which results in an inconsistent delay increase. Accordingly, during the period of time, the real time false trip probability of the protection was obtained as shown in Figure 17.  
Figure 16: Inconsistent delay sequence.  
Figure 15: The relationship between the threshold and refuse-operation probability.  
It can be seen from Figure 17 that the false trip probability is low before the time instant \(t=1\) s, while the false trip probability becomes large after the time instant \(t=1\) s. The probability of stepping from a low level to a high level at the time instant \(t=1\) s is because of the communication condition deterioration. The simulation results suggest that worse communication conditions increase the false trip probability of differential protection.  
(2) Refuse-operation probability  
In China, under normal conditions, the consistent delay of communication is less than 8 ms, and the communication bit error rate is lower than \(10^{-7}\). When a fault occurs in the communication channel, the consistent delay and bit error rate may become larger and more volatile.  
The consistent delay and communication bit error rate sequence during a period of time are shown in Figures 18 and 19, respectively. At \(t=1\) s, a fault happens in the communication channel, which causes an increase  
In Figure 20, before the time instant \(t=1\) s, the refuse-operation probability is relatively low, while the refuse-operation probability becomes larger after the time instant \(t=1\) s. The refuse-operation probability experiences a step change at the time instant \(t=1\) s because a fault happens at that time instant. The simulation results indicate that worse communication conditions increase the refuse-operation probability of differential protection.
# Coordination of Macro Base Stations for 5G Network with User Clustering
###### Abstract
The cooperation among all BSs in the network is achieved through BS sleeping, user allocation, and power transmission methods. (2) A user clustering method based on users' geographic distribution is proposed to reduce the computational burden of the first-step problem. Benders decomposition is applied to accelerate the solution of the optimal scheduling of standard equipment. The remainder of this paper is organized as follows. The two-step energy management model for both communication equipment and standard equipment is proposed in Section 2. Section 3 introduces the problem reformulation using the user clustering method and Benders decomposition algorithm. Section 4 presents and analyzes the simulation results, and the optimality and efficiency of the proposed model are verified, followed by the conclusions.
## 2 Data and Methodology  
### Study Area  
The Yellow River Basin (32-42\({}^{\circ}\)N, 96-119\({}^{\circ}\)E) spans the Tibetan Plateau to the North China Plain, with a total area of 79.5 \(\times\) 104 km\({}^{2}\) (Figure 1). The topography descends stepwise across four principal geomorphological units--the Tibetan Plateau, Inner Mongolian Plateau, Loess Plateau, and North China Plain--along a west-east axis, governing the transition of hydrothermal gradients from northwestern arid zones to southeastern semi-humid regions [15]. Seventy-five percent of the basin's vegetation consists of cropland (southeastern plains), grasslands (arid and northeastern semi-arid zones), and meadows (Qinghai-Tibetan Plateau). The Loess Plateau, a critical ecologically fragile region in the Yellow River Basin, has been the primary implementation zone for ecological restoration projects since the 1980s [32]. Large-scale initiatives, including the Three-North Shelterbelt Forest Program (TNSP), Taiwaning Mountain Afforestation Project (TMAP), and Middle Yellow River Shelterbelt Project (MYRSP), have cumulatively achieved 1.7 million hectares of afforestation and 3.47 million hectares of grassland rehabilitation. Numerous studies have confirmed its lasting and far-reaching influence on the vegetation cover of the land surface, which has become one of the anthropogenic factors that cannot be ignored in the study of vegetation dynamics of land surface ecosystems in the Yellow River Basin [33; 34].  
Figure 1: Study Area.
# Deriving a Dilution of Precision Indicator for GNSS Factor Graph Optimization Solutions +
## 3 Results  
### Validation Method of the Proposed Batch DOP Formula  
The formula, Equation (11), to obtain uncertainty for a batch solution from the snapshot scalar uncertainty has been validated for a single scalar state.  
However, when combining the uncertainty of several scalar states to compute a DOP indicator, the correlated impact of an observation error on the different states should be accounted. This is not the case for the batch DOP computed using Equation (16), since the diagonal elements of the covariance matrix of the batch solution are approximated by considering each scalar state uncertainty independently from the other states.  
The following subsections aim at quantifying the impact of this approximation. To do so, the batch DOP obtained from Equation (16) and the exact batch DOP, obtained by computing the covariance of the full state vectors with Equation (10), are compared based on the difference of HDOP and the ratio of HDOP. The results are summarized in Table 1.
## 4 Discussion
### Strengths and Limitations  
The primary strength of PRATER study is the capture of RW data from leading hospital institutions that treat NSCLC in Austria using robust datasets. The sample of 319 patients of the primary study part met the originally planned size. A large population was analyzed for the exploratory part: 7846 patients representing \(\sim\)46% of all NSCLC diagnoses during 2018-2021 in Austria (estimating \(\sim\)17,000 cases based on an average \(\sim\)5000 newly diagnosed lung cancer cases annually and \(\sim\)85% being NSCLC). Furthermore, a close alignment of treatment patterns was observed between the two study parts, thereby lending credibility to the study findings.  
As with any retrospective study, PRATER bears several limitations, including patient selection, confounding and information bias. Efforts were made to minimize such bias by applying consecutive sampling and enrolling patients with sufficient medical records (applicable for the primary part only). Furthermore, stage distribution could be biased due to non-probability sampling for study site selection (e.g., three thoracic surgery departments were included). Additionally, although disease staging in the primary part was mostly based on AJCC/UICC TNM 8 th Edition criteria, this information is lacking for the exploratory part due to the data being collected in a cumulative high-level aggregate form, as per study design. Therefore, interpretations of the exploratory part results should take into consideration that differences in staging system may exist across hospital sites.  
Regarding biomarker testing, no specific central laboratory testing was enforced, thus the possibility of inherent inter- and intra-assay/laboratory/observer bias exists. Another source of information bias arises from the fact that, for commercially available panels, the specific biomarkers that tested negative were not collected/recorded, thus relevant data were excluded from the analysis.  
Lastly, the study was not designed to statistically analyze differences between subpopulations, thus any observations are presented in a purely descriptive approach. Any interpretation of treatment patterns should also take into account that differences may exist between substages which were not captured herein. Future substage analyses are warranted in light of the new ICI/TT indications in the non-metastatic setting.
## 3 Results
### UV Sensor  
This experiment was particularly designed to test the functionality of UV sensor. The general regulation of experiments, set up sampling rate, and the configuration were the same as mentioned earlier. We did not impose any additional conditions but strictly have asked the subject to maintain the following: 1- do not put the left-hand in pockets, 2- do not cover the surface of the prototype with any types of external objects, and 3- the test must be performed only outdoor. As clouds filter UV light, we selected a windy and cloudy day to test the UV sensor. Our prototype was worn by a user who logged the sun occlusions over a period of 20 min (Figure 9--right). The opening window of the UV sensor covers at most up to angle of 130\({}^{\circ}\). With partly occlusions, the measured UV index is not exceeding 2.0. After about 7 min, the thick clouds covered the sun completely and did not release it anymore. Therefore, the measured UV index is zero after about 400 samples (Figure 9--right).  
In particular, the photo diode detects the 240-370 nm range of light (which covers UVB and most of UVA spectrum). It delivers a signal of nano amperes. We tossed on an operational amplifier to yield an output voltage level of UV detection in the range of 0.1 to 1 V. Hence, for calibration and conversion from voltage to UV index, the output needs to be divided by 10 (Table 5).  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline Voltage (mV) & 97 & 196 & 302 & 405 & 498 & 601 & 708 & 801 & 891 \\ \hline UV index & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\ \hline \end{tabular}
\end{table}
Table 5: Voltage to UV index conversion.  
Figure 10: NO\({}_{2}\) test in a chemical laboratory. Vessels 1 to 7 indicate the output of microwave digestion; the yellow and red horizontals mark the danger level of 2 ppm and 5 ppm, respectively.
## 2 Description of Dynamic Modelling of AUV
However, the viscous damping term \(X_{u}\), \(Y_{v}\), \(N_{r}\), \(X_{u|u|}\), \(Y_{v|v|}\), \(N_{r|r}\) is difficult to obtain precisely, and may not even be available at all. Since the second-order viscous damping coefficient has little effect on the control system, this paper ignores \(X_{u|u|}\), \(Y_{v|v|}\), \(N_{r|r|}\) in modeling. The actual operation of an AUV needs its attitude and speed information when calculating the parameters required by the controller and the control force and torque. This information is generally obtained through the speed and position sensors carried by the AUV itself. Nevertheless, these data are based on the ground coordinate system, and the controller is designed according to the body coordinate system, so coordinate transformation is required. When calculating the pose and other parameters of the AUV based on the control force and moment, it is necessary to convert the control force and moment in the body coordinate system to the ground coordinate system. At this stage, Euler angles are used to describe the attitude of the underwater vehicle in the ground coordinate system. The following gives the relationship between the object frame and the fixed frame at the linear velocity according to the literature:  
\[\dot{\eta}=R(\psi)v \tag{4}\]  
where \(R(\eta)\) is the kinematic transformation matrix, and it is in the following form:  
\[R(\psi)= \begin{bmatrix}\cos\psi&-\sin\psi&0\\ \sin\psi&\cos\psi&0\\ 0&0&1\end{bmatrix}  \tag{5}\]  
According to the characteristics of the underwater vehicle system, the dynamic model is generalized, and a generalized mathematical model is obtained:  
\[\left\{ \begin{array}{l}m_{1}\dot{u}=-X_{u}u+m_{2}vr+f_{1}(u,t)+F_{u}\\ m_{2}\dot{v}=-Y_{v}v-m_{1}ur\\ m_{3}\dot{r}=-N_{r}r+(m_{1}-m_{2})uv+f_{2}(r,t)+F\end{array} \right. \tag{6}\]  
where  
\[m_{1}=m-X_{ii},\ m_{2}=m-Y_{\dot{v}},\ m_{3}=I_{z}-N_{r}\]  
\(f_{1}(u,t)\) and \(f_{2}(r,t)\) represent the nonlinear perturbation of the marine environment to the underwater vehicle. The above equation shows that the underwater target vehicle has only two control inputs, which is a standard underactuated system. The mass \(m\), and the viscous damping coefficient \([X_{u},Y_{v},N_{r}]\) are all positive numbers, and \(\left[F_{u},F_{r},u,r,\dot{u},\dot{v}\right]\) are bounded. Since the AUV is not driven in the sway direction, it is necessary to demonstrate the boundedness of the sway velocity \(v\). Let us define a Lyapunov function as:  
\[V=\frac{1}{2}m_{2}v^{2} \tag{7}\]  
Its first derivative is:  
\[ \begin{array}{ll}\dot{V}&=m_{2}v\dot{v}\\ &=v(-Y_{v}v-m_{1}ur+f_{2}(v,t))\\ &=-Y_{v}v^{2}-vm_{1}ur+vf_{2}(v,t)\\ &<-Y_{v}v^{2}+Q|v|\end{array}  \tag{8}\]  
where  
\[Q=\max(|m_{1}ur|+|f_{2}(v,t)|)\]  
The following can be obtained: \(\dot{V}\leq-\frac{1}{2}Y_{v}v^{2},\sqrt{|v|}\geq\frac{2Q}{Y_{v}}\). Therefore, we can prove that the vehicle lateral motion speed \(v\) is stable and bounded.
## 3 Results and Discussion
### Heat Sensitivity of Bacteriophage K5
In contrast, heat treatment at 80 \({}^{\circ}\)C showed particles with higher stability, which was explained by partial protein denaturation. Although the bacteriophages were not tested for plaque-forming ability in the study, it can be assumed that the bacteriophages were no longer capable of attaching to their host cells, injecting their DNA, and lysing the host cells [60]. [PERSON] and [PERSON] treated the _Escherichia coli_ bacteriophages T4 and \(\lambda\) in nutrient broth at temperatures between 50 and 75 \({}^{\circ}\)C. They observed linear reductions of approximately 1.5 log units at 65 \({}^{\circ}\)C after 70 min, whereas the same reduction was reached at 70 \({}^{\circ}\)C after 15 min. Below or at 60 \({}^{\circ}\)C, the bacteriophages appeared to be almost stable [61]. Compared with bacteriophage K5 in the present study, these two other bacteriophages seem more heat resistant. On the other hand, similar behavior to bacteriophage K5 was observed with the _Bacillus amyloiquefaciens_ bacteriophage BA01. It was inactivated by 5 log units within 30 min at 65 \({}^{\circ}\)C and by 7-8 log units (estimated, since bacteriophage titers were below the limit of quantification) within 5 min at 70 \({}^{\circ}\)C [62]. A large amount of research has focused on the heat inactivation of bacteriophages against lactic acid bacteria, especially _Lactococcus lactis_, _Streptococcus thermophilus_, and _Leuconstoc_ spp., as their inactivation is of great importance for the processing of fermented milk products [63]. Among them, there are heat sensitive, as well as heat resistant, bacteriophages. For instance, _Lactococcus lactis_ bacteriophage P001 was inactivated in nutrient broth by 7 log units within 10 min at 70 \({}^{\circ}\)C, whereas bacteriophage P008 was inactivated by only 2.5 log units at the same temperature and time [46]. The particularly heat resistant bacteriophage P680 was inactivated at 70 \({}^{\circ}\)C, only by 1 log unit at 75 \({}^{\circ}\)C within 10 min, and still detectable after 1 min at 95 \({}^{\circ}\)C [45; 44]. The comparison to other bacteriophages leads to the statement that bacteriophage K5 has to be considered as heat sensitive, therefore further processes, i.e., encapsulation and incorporation into food, must be designed as mild as possible. If such processes are aligned to the weakest bacteriophage, they will work with many other bacteriophages as well.
## 4 Method for UAV Electromagnetic Interference Suppression
### Anti-Electromagnetic Interference Method of UAV Data Link
#### 4.1.4 UAV Electromagnetic Interference Adaptive Scheme  
There are many studies that comprehensively adopt a variety of anti-interference technologies to propose an overall solution for UAV data link anti-electromagnetic interference, which has a high reference value for UAV electromagnetic compatibility issues in the converter station scenario.  
Traditional anti-jamming methods of UAV data link are mainly implemented from the perspective of spread spectrum and coding, but the room for improvement of anti jamming performance is limited and the technical bottleneck is high. Take frequency hopping communication as an example: Although the frequency hopping map has flexible and changeable random characteristics, the lack of autonomous perception will cause the operating frequency to fall into the broadband interference interval, affecting the normal operation of the data link. In addition, traditional power control methods mainly reduce the influence of electromagnetic interference on the crosstalk between data link codes by selecting power gears. This method relies too much on experience accumulation and has poor adjustability [66].  
The literature [67] proposed a new method for UAV data link electromagnetic interference adaptation based on electromagnetic environment perception, which uses airborne antennas and UAV telemetry links to achieve key frequency band monitoring and measurement information return. Correlation vector machine regression dynamic data link electromagnetic interference effect threshold prediction model and UAV data electromagnetic interference level judgment method, and finally a set of relatively complete and enlightening adaptive implementation method of UAV data link electromagnetic interference is proposed (Figure 5).