esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
As may be observed, the addition of recycled carbon fibers in quantities of 3 kg/m\({}^{3}\) and 6 kg/m\({}^{3}\) improved the behavior of concrete subjected to flexural or impact compared to conventional concrete and, more importantly, when compared to concretes containing the same amounts of polypropylene fibers. This represents an improvement from an environmental point of view both due to the possibility of recycling carbon fiber fabrics and due to the reduction in energy as well as in raw materials required to manufacture the polypropylene fibers. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Compression Test**} & \multicolumn{4}{c}{**Compression Test**} \\  & \multicolumn{4}{c}{**(Cylindrical Specimens)**} & \multicolumn{4}{c}{**(Cubic Specimens)**} \\ \cline{2-13}  & **C** & **C-PP3** & **C-PP6** & **C-FC3** & **C-FC6** & **CU** & **CP3** & **CP-PP6** & **CU-FC3** & **CU-FC6** & **CU-FC6** & \\ \hline C & — & \(-4.60\) & \(-2.25\) & \(-8.62\) & \(-10.46\) & — & \(-3.04\) & \(-3.84\) & \(-5.99\) & \(-7.55\) & CU \\ \hline P & — & \(-1.81\) & \(+3.99\) & \(+22.76\) & \(+6.10\) & — & \(+15.82\) & \(+32.01\) & \(+27.93\) & \(+45.68\) & L \\ \hline  & P & P-PP3 & P-PP6 & P-FC3 & P-FC6 & L & L-PP3 & L-PP6 & L-FC3 & L-FC6 & \\ \cline{2-13}  & \multicolumn{4}{c}{Flexural test} & \multicolumn{4}{c}{Impact test} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Relationship between strengths as a percentage depending on the addition type and amount. Figure 7: Mean values for the fracture energy in the impact testing for each concrete type studied.
## 4 Main Result and Stability Analysis
\[\dot{v}\leq-av \tag{44}\]  
By solving (44) inequality, we get the following answer:  
\[v\leq e^{-at}v(0) \tag{45}\]  
Based on Lemma 1 of [41], for an arbitrary \(\epsilon>0\), we according to different definitions can define the ellipsoid. \[\left\{ \begin{array}{l}R_{1}:=\{\mathbf{z}:\lambda(\eta)|\mathbf{z}|^{2}\leq \lambda_{min}(\eta)k^{2}M^{2}\}\\ R_{2}:=\{\mathbf{z}:\lambda(\eta)|\mathbf{z}|^{2}\leq\lambda_{max}(\eta)((1+\epsilon) \phi_{max})^{2}\}\end{array} \right. \tag{46}\]  
According to (46), \(R_{1}\) and \(R_{2}\) are invariant regions for the multi-agent system (28). With this setting, the trajectories of (28) staring in \(R_{1}\) will approach \(R_{2}\) in finite time. We can provide the estimates (44) of the convergence rate as in [35]. With the estimation of the upper bound of the convergence time that staring in \(R_{1}\) enter \(R_{2}\) as  
\[\psi=\frac{1}{a}ln\frac{\lambda_{min}(\eta)(M)^{2}}{\lambda_{max}(\eta)(1+ \epsilon)^{2}\phi_{max}^{2}} \tag{47}\]  
To guarantee the asymptotic stability of the whole system, the Liberzon's design strategy [41] is employed. The control scheme contains two steps: \"zooming-out\" to detect measurement of states by increasing \(k\); \"zooming-in\" to achieve more accurate quantization by decreasing \(k\). When the initial state is in ellipsoid \(R_{1}\) or \(R_{2}\) with the initial zooming variable \(k(t_{0})\), the zooming-in phase starts with the update interval \(\psi\). Let \(k(t)=k(t_{0})\) for \(t\in[t_{0},t_{0}+\psi]\), where \(\psi\) is given by (47). Then \(\bar{z}(t_{0}+\psi)\) belongs to the ellipsoid \(R_{1}\). The equation about \(k\) is as follows:  
\[k=\pi^{6}k_{0} \tag{48}\]  
\[\pi= \frac{\sqrt{\lambda_{max}(\eta)}(1+\epsilon)\phi_{max}}{\sqrt{\lambda_{ min}(\eta)}M} \tag{49}\]  
where \(k=k(t)\), \(k_{0}=k_{0}(t)\), \(\psi\) is defined as (47) and \(\beta\) is the number of update times. It is easy to check \(\pi<1\) and \(k<k_{0}\) according to (48) and (49). To decrease \(k\) by means of multiplying it by the scaling factor \(\pi\), we have \(\lim_{t\rightarrow\infty}k=0\), (44) implies \(\lim_{t\rightarrow\infty}\bar{z}=0\). As the initial states of multi-agent systems generally known for quantizers, we can select \(k_{0}<1\) in advance to keep the systems starts in the ellipsoids \(R_{1}\) (46). The steps for proof \(\lim_{t\rightarrow\infty}\bar{z}(t)=0\) under novel dynamic quantizer, are summarized as follows:  
(1) Design the novel dynamic quantizer (6) as shown in Section 2. _(2) The design transforms the multi-agent consensus problem into the origin stabilization problem in Section 3._  
_(3) Lyapunov function are defined in (36) and prove that in Section 4._
# Integration and Field Evaluation of an IoV System for Enhancing Road Safety
## 3 Human-Machine Interface Architecture
### Warnings and Message Flow Architecture  
The HMI is designed to function as part of a C-ITS which targets both equipped and non-equipped and must have seamless communication with different discrete devices at the same time. To achieve this, it aims at taking advantage of the vehicle-infrastructure sensing and communication components and thus optimizing the security and effectiveness of the provided information through a decentralized decision-making architecture. Consequently, the dynamics of the user interface rely more on fast connectivity and less on the computing power of the devices themselves.  
Figure 3 illustrates the flow of warnings/messages for the scenario of road works taking place in the lane of a passing vehicle.
# Coverage Path Planning with Semantic Segmentation for UAV in PV Plants
## 3 Results and Discussion
### Results of the CPP Method
#### 3.2.1 The Three First Experiments with a Typhoon UAV
For the experiment conducted at the PV plant Unioeste 1, the graph in Figure 12a shows that the BECD method had the lowest energy consumption when the CPP width was in the range of 5 to 16 m, while GBSTC and GBWC had the lower energy consumption when the CPP width was in the range of 16 to 20 m. For the experiment tested at the Arak PV plant, the graph in Figure 12b shows that the BECD method had the lowest energy consumption when the CPP width was in the range of 5 to 10 m, while the GBSTC and GBWC had similar energy consumption when the CPP width was in the range of 10 to 15 m, and GBWC had the lowest energy consumption when the CPP width was in the range of 15 and 20 m. For tests conducted at the CSUSL PV plant, the graph in Figure 12c shows that the BECD method had the lowest energy consumption when the CPP width was in the range of 1 to 5.5 m, while the method GBSTC had the lowest energy consumption when the CPP width was in the range of 5.5 to 8 m. All plants were recreated in a simulation environment with their real dimensions. Figure 11: Performance metrics of Typhoon UAV on CSUSL PV plant (Experiment 3).
# 5G Physical Layer-Based Procedure to Support Time-Sensitive Networking
## 3 Time-Sensitive-Enabled Small Cell
### Virtual Clock Domain Accuracy and TSN Slice  
A virtual clock domain enables the coordination of independent clocks across the network nodes to provide a distributed common timescale with defined accuracy. The accuracy of a virtual clock domain is based on the uncertainty of each clock relative to the reference time. Figure 3 illustrates a virtual clock domain including controllers, SCs, and Robots.  
The controller clock is synchronized with the reference clock and provides a controller with a local time, which may be used in scheduling events or interpreting incoming signals. SC clocks are synchronized with the reference clock. Robot clocks are synchronized with SC clocks and, by extension, the reference clock. In this way, each clock in the virtual clock domain has a common reference time.  
In connection with providing TSN functionality, the network should provide message delivery with guaranteed delivery of the time-constrained messages or signals. Quality of Service (QoS) policies may be implemented to prioritize the performance of different types of traffic from different UEs to guarantee message delivery. In our proposal, the QoS may be implemented by deploying a dedicated Network Slice. Network Slices are isolated, virtual end-to-end networks configured to fulfill specific parameters or requirements. For example, a network slice may be configured to provide specific QoS requirements regarding the latency and reliability of the TSN application.  
Figure 4 illustrates a network including a dedicated virtual TSN slice. For this, resources from the AMF and the UPF may be dedicated to the virtual TSN slice. Moreover, network resources that provide access to the AMF and the UPF may allocate dedicated resources to the separate network slices providing different services. Virtual network slices may be implemented by dedicating a portion of computing capacity to provide a dedicated function or service to devices communicating with the network slice. Here, the TSN slice may be deployed together with a standard network slice to support the standard 5G smartphones and UEs. For example, the TSN slice may include dedicated resources from the AMF and the UPF to provide functionality to the robots. Additional AMF and UPF resources may be allocated to a virtual eMBB slice. The eMBB slice provides 5G network communication to smartphones.  
Figure 3: Virtual Clock Domain.
# Constant-Beamwidth LCMV Beamformer with Rectangular Arrays
## 4 Constant-Beamwidth LCMV Beamformer with Rectangular Arrays
### Rectangular Convolutional Kronecker Product Beamforming  
Let \(S_{1}\) be a subarray of \(S\), including the \(M_{1,\times}\times M_{1,\mathrm{y}}\) first sensors of \(S\), with steering vector \(\mathbf{d}_{1}\), and similarly for \(S_{2}\). These arrays sizes are such that \(M_{1,\times}+M_{2,\times}-1=M_{\times}\), and \(M_{1,\mathrm{y}}+M_{2,\mathrm{y}}-1=M_{\mathrm{y}}\). By designing beamformers \(\mathbf{h}_{1}\) and \(\mathbf{h}_{2}\) for \(S_{1}\) and \(S_{2}\), respectively, we show in Appendix A that it is possible to synthesize a beamformer for full array \(S\) through  
\[\mathbf{h}=\mathcal{V}\Big{(}\mathcal{V}^{-1}\big{(}\mathbf{h}_{1};\,M_{1, \mathrm{y}}\big{)}\,\,\,\,\,\,\,\,\,with representing the 2-D convolution operation. We call this the rectangular convolutional Kronecker product (RCKP) method. A simple implementation of the proposed RCKP method is presented in Algorithm A1 in Appendix B, which is written in a Python-like pseudolanguage.  
In the same way as with the KP and LCKP methods, with the RCKP, one can design beamformers for subarrays \(S_{1}\) and \(S_{2}\), and one can synthesize the URA's beamformer \(\mathbf{h}\) through the 2-D convolution (and vectorization processes). Accordingly, \(\mathbf{h}\) is an \(M\times 1\) vector; however, in this scenario we have a total of \(M^{\prime}=M_{1,\times}M_{1,\times}M_{2,\times}M_{2,\times}\) virtual sensors. Assuming that all \(M\)s are \(\geq\)1, it is trivial to see that \(M^{\prime}\geq M\) (equality happens if \(S_{1}\) and \(S_{2}\) are perpendicular ULAs, or if one of them has only one sensor). Therefore, we have more sensors virtually than if \(S\) was split into two VAs through the KP method.  
It is easy to verify that the properties in (16) are also valid for the proposed RCKP. With this, like for the KP and LCKP, the full-array beamformer obtained through the RCKP inherits the beampattern and distortionless features from the subarray beamformers. Also, since convolution is commutative and associative, one can split the \(S\) array into more than two virtual arrays, design a beamformer for each subarray, and combine all the beamformers through the RCKP without the loss of generalization. In this case, assuming that we are using \(K\) beamformers, each with size \(M_{k,\times}\times M_{k,y}\), then their dimensions must be such that  
\[\sum_{k=1}^{K}M_{k,\times}-(K-1) =M_{\times}, \tag{18a}\] \[\sum_{k=1}^{K}M_{k,y}-(K-1) =M_{y}. \tag{18b}\]  
This is easily verifiable by repeating the synthesis operation in Equation (17) \(K-1\) times.
## 10 Experimental Implementations  
There are a considerable number of groups that have implemented SV-DD- and KK-based schemes using different models. In this part, we consider some key experimental implementations. Based on this, we give a fair comparison between both schemes regarding the required CSPR value and the system complexity.  
In general, regarding an optical amplifier noise-limited system, an SV-DD can effectively operate at 0 dB CSPR while other direct-detection-based schemes normally require a relatively higher CSPR value. For instance, Table 11 illustrates various direct-detection-based experimental implementations that are grouped into SV-DD- and KK-based schemes. It can be inferred that while most of the SV-DD-based demonstrations demand 0 dB CSPR for signal transmission over more than 100 km SSMF, the KK-based demonstrations require \(\sim\) 8 to 12 dB. This outstanding advantage can be attributed to the fact that the SSBN can be entirely separated from the desired signal by the SV-DD scheme. In this context, the SV-DD-based scheme offers the most efficient approach for the SSBN cancellation with the lowest required OSNR. This can be achieved without sacrificing the electrical bandwidth. Nevertheless, a conventional SV-DD-based scheme implementation demands a higher number of components, resulting in a more complex receiver structure compared with other single-PD-based schemes. Consequently, the SV-DD-based scheme receiver complexity can present significant challenges regarding cost and power consumption. Based on this, modified SV-DD-based schemes with a fewer components are highly imperative [19; 151]. In this regard, a self-heterodyne SV-DD (SH-SVD) scheme that is based on an SSB signal and requires fewer components was presented in [151]. In spite of the fact that the scheme can considerably reduce the associated receiver cost and power consumption compared with a three- or four-PD-based SV-DD scheme, the related increase in the required sampling rate and bandwidth is another concern.
## 2 System Model  
### Coexistence Model  
Consider a spectrum-sharing scenario between the UD-LEO satellite communication (UD-LSC) system and a GEO beam-hopping satellite communication system, as shown in Figure 1. There are \(M\) LEO beam cells (LBCs) on the ground, which are served by the UD-LSC system. As a result of the dense topology of LEO satellites of the UD-LSC system, more than one LEO satellite is available for each ground LBC at every time snapshot. We assume that there are \(K\) LEO satellites at time snapshot \(t\) that can totally provide \(K\cdot L\) beams to serve the ground \(M\) LBCs, where \(L\) is the number of provided beams of each LEO satellite. Simultaneously, a GEO satellite forms \(G\) GEO beam cells (GBCs) that overlap the same coverage area with the UD-LSC system, as shown in Figure 1 (beams with green lines and black lines). The GEO satellite system employs the beam-hopping technique that lets partial beams work to realize flexible resource management. With the improvement of bandwidth requirements for multimedia services and considering the shortage of frequency resources of both GEO and LEO satellite communication systems, we assume that multiple beams of each satellite communication system provide services in a full-frequency reuse (FFR) manner. The inter-system interference (IGI) between the UD-LSC and GEO systems should be seriously considered.
## 3 Experiments and Discussion
### Measurement Experiment Using a Multi-Sensor CMM
#### 3.4.2 Measurement of an f-Theta Lens Surface
Future work will be focused on the quantitative analysis of the influence of the systematic error on the fusion result. Practically, more attention should be paid to correcting the systematic error, especially that which is caused by the optical sensors such as the laser sensor. Another approach to this issue when considering the systematic error is generating adaptive weighting using a Bayesian framework [28]. In this situation, the fault-inducing systematic error may be detected and the weighting for those points can be adjusted accordingly.
# The Emerging Role of Long Noncoding RNAs in Sorafenib Resistance Within Hepatocellular Carcinoma
### Secondary Resistance
#### 1.3.3 Ferroptosis
In HCC cells, the high level of lncRNA GABPB1 antisense RNA 1 enhances erastin-induced ferroptosis by blocking the translation of GA-binding protein subunit beta-1 (GABPB1) and suppressing peroxidiredoxin-5 peroxidase, leading to reduced cellular antioxidant capacity and cell viability [88]. LncRNA PVT1 has been reported to regulate the expression of ferroptosis-related genes and is associated with sorafenib resistance in HCC. By targeting lncRNA PVT1, studies have shown an increase in ferroptosis sensitivity, suggesting that PVT1 silencing could help overcome resistance [87; 89]. The combination of ferroptosis and lncRNA has shown promise in prognostic prediction for HCC and RCC. A prognostic model for HCC has been developed based on ferroptosis-associated differentially expressed lncRNAs, which could be used for prognosis prediction and the selection of cancer groups for therapies [88]. Therefore, ferroptosis-related lncRNAs are potential new therapeutic targets to overcome sorafenib resistance.
## 4 5G and V2X Deployment Projects
### 5g-Carmen  
The 5G-CARMEN project was launched on the first of November 2018 for deploying 5G solutions on the critical corridor along 600 km of roads, crossing Bavaria in Germany, Tirol in Austria, and Trentino/South Tyrol in Italy, connecting three European regions, which are also known by Munich-Bologna as a 5G corridor for connected vehicles. The 5G-CARMEN project aimed to provide a multi-tenant platform for the automotive sector to make mobility smarter, greener, and more intelligent, focusing on the ultimate goal of enabling self-driving vehicles.  
The 5G-CARMEN project consists of V2V, V2I, and V2N communications and investigates four 5G V2X use cases and application scenarios. The first use case is cooperative maneuvering by providing a platform enabling vehicles to exchange trajectories/maneuvers and determine optimal behavior to avoid dangerous situations and positions, using speeds and other helpful data. The second use case in 5G-CARMEN is extended situation awareness using V2I communication to obtain information about the surrounding environment and detect potentially abnormal situations. Video streaming is the third use case in 5G-CARMEN, which is exploring different network architectures and configurations to achieve permanently available high-quality video streaming, including in cross-border scenarios. The fourth use case is green driving by promoting greener driving styles due to providing intelligent solutions, such as supporting road operators to determine decisions to help limit the negative impact of transportation on the environment and promote the approach of hybrid vehicles [89].
## 2 Materials and Methods
### QA/QC Analysis  
QA/QC samples were dictated by the sampling criteria outlined in Section 2.1. Samples with an XRF in situ reading above 20 mg/kg and a AAS result above 20 mg/kg were considered as an acceptable prediction by XRF. False negatives (Type II error) were samples with a mean XRF in situ reading below 20 mg/kg and a mean AAS result above 20 mg/kg. False-negative percentages were found using only QA/QC samples at 0-2 cm, 2-10 cm, and a combination of all depths (excluding litter).  
To determine the percentage of false positives (Type I error), samples were identified with a mean XRF in situ reading (regardless of which XRF) above 20 mg/kg and mean AAS result: below 20 mg/kg, below the United States Regional Screening Level (RSL) (23 mg/kg), or below the Kyrgyz standard (2.1 mg/kg). For this analysis, all samples were used, divided by depths 0-2 cm, 2-10 cm, and a combination of all depths (excluding litter).
# An ANN Model for Predicting the Compressive Strength of Concrete
## 5 Conclusions  
In this study, the database of real concrete mix proportioning listed in [12] is used to establish an ANN prediction model for the concrete compressive strength. The ANN structure chosen in this study is the Back Propagation network with 1 hidden layer. The same database was also used in [16], and the number of hidden layers in the ANN is also the same. The database is divided into two sets, the training set and the testing set, respectively. In comparison with [16], we use fewer data for testing and more data for testing. The testing set comprises 15% of the data while in [16] it was just 4%. Considering the possible effect of over-fitting, the number of neurons in the hidden layer is finally chosen as 7, which is half the number used in [16]. The present ANN model outperforms  
Figure 5: Comparison of predicted compressive strength with the actual strength of the 76 experimental concrete mixtures in [15] (Unit: MPa).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Ingredients (\(\text{gs/m}\))**} & \multicolumn{3}{c}{**Compressive Strength**} & \multicolumn{3}{c}{**Ingredients (\(\text{gs/m}\))**} & \multicolumn{3}{c}{**Compressive Strength**} \\  & & & & **(MPa)** & & & & & & & & & & \\ \hline
**Water** & **Cement** & **Fine** & **Conse** & **Fip** & **Actual** & **Predicted** & **Water** & **Cement** & **Fine** & **Conse** & **Fip** & **Actual** & **Predicted** \\  & & **Assigne** & **Assigne** & **Assigne** & **Assigne** & **Assigne** & **Assigne** & **Assigne** & **Assigne** & **Assigne** & **Assigne** & **Assigne** \\ \hline
198  
the model in [16] in the verification in which we compare the predicted results with the experimental data. For corroborating that our model performs well in further practical use, we use experimental data from [15] for validation. A good agreement has also been found.  
The synaptic weights and the thresholds are all listed in this article. With these numbers and using Equations (1)-(6), anyone can predict the compressive strength of concrete according to the concrete mix proportioning on his/her own.  
Conceptualization, N.-J.W.; methodology, N.-J.W.; coding, N.-J.W.; investigation, C.-J.L.; data curation, C.-J.L.; formal analysis, C.-J.L. and N.-J.W.; writing--original draft preparation, C.-J.L.; writing--review and editing, N.-J.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study  
All the data are available in the tables and can be traced back to references cited in this paper.  
The authors declare no conflict of interest.