esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
On the example of the analysis of the stiffness and load-carrying capacity of beams overloaded before strengthening, it can be concluded that strengthening with composite strips can be an effective treatment for increasing the load class of bridges related to road repairs and modernization, as well as it can be a solution to the problem of the safety of operation of structures at risk of unforeseen, oversized loads. * The results of the analysis of deformation of composite strips during laboratory tests showed that, in the case of passive strengthening of RC beams with carbon strips, it is not possible to fully use their potential before the beam failure. Increasing the level of strip deformation may occur only when using an active way of strengthening, i.e., with initial pre-tension of the CFRP strip before sticking it to the concrete surface [14]. * The results of the analysis of preloaded beams before strengthening indicate that totally relieving them prior to strip application turned out to be the most beneficial. A more favorable strengthening effect is obtained if, in the analysis, we ignore the initial deflections and deformations occurring in the steps preceding the strengthening of beams. This approach best corresponds to the cases of actual strengthening of reinforced concrete structures (usually, the previously created permanent deflection of the beam is not taken into account); therefore, it is the most reliable in determining the effectiveness of strengthening the beams with a composite strip. * The finite-element method (FEM) proved to be useful in the analysis of RC beams strengthened with CFRP elements. Validation of the numerical model and the whole procedure of numerical modeling based on the results of experiments positively ended. This proves that numerical analyses carried out on positively verified numerical models can, in many cases, replace usually costly and long-term studies on samples on a real scale. * Summarizing the above given conclusions, it can be firmly stated that the effect of strengthening reinforced concrete beams with carbon strips not only is determined by the amount of added strengthening material and its location but also largely depends on the deformation state and crack layout of the RC element being strengthened. This applies both to the analysis of the load-carrying capacity of the RC beam strengthened using CFRP strips (in the ultimate limit states) and to the analysis of the deformation level and stiffness of such beam (in the serviceability limit states). This research received no external funding. Not applicable. The prepared samples were examined at Poznan University of Technology located in Poznan 60-965, ul. Piotrowo 5, Poland. The author declares no conflict of interest.
# Numerical Simulation of Non-Isothermal Mixing Flow Characteristics with ELES Method
## 2 Turbulence Model and CFD Methodology
### Governing Equations of SST k-\(\omega\) Model  
The k-omega SST model was used in the RANS portion to obtain the inlet velocity and turbulence quantities. The equations of the turbulence kinetic energy (\(k\)) and the specific dissipation rate (\(\omega\)) for the SST \(k\)-\(\omega\) model are as follows [28; 29]:  
\[\frac{\partial}{\partial x_{i}}(\rho ku_{i})=\frac{\partial}{\partial x_{j}} \big{[}(\mu+\mu_{t}/\sigma_{k})\partial k/\partial x_{j}\big{]}+G_{k}-Y_{k} \tag{11}\]  
\[\frac{\partial}{\partial x_{i}}(\rho\omega u_{i})=\frac{\partial}{\partial x_ {j}}\big{[}(\mu+\mu_{t}/\sigma_{\omega})\partial\omega/\partial x_{j}\big{]}+ G_{\omega}-Y_{\omega}+D_{\omega} \tag{12}\]  
The turbulent viscosity \(\mu_{t}\) and terms such as \(G_{k}\), \(Y_{k}\), \(G_{\omega}\), \(Y_{\omega}\), \(D_{\omega}\) are defined as:  
\[\mu_{t}=\frac{\rho k}{\omega}\bigg{[}\text{max}\bigg{(}\frac{1}{\alpha^{*}}, \frac{SF_{2}}{4\alpha_{1}\omega}\bigg{)}\bigg{]}^{-1} \tag{13}\]  
\[G_{k}=\text{min}\big{(}\mu_{t}S^{2},10\rho\beta^{*}k\omega\big{)}\quad Y_{k}= \rho\beta^{*}k\omega \tag{14}\]  
\[G_{\omega}=\frac{\alpha_{w}}{\alpha^{*}}\Big{(}\frac{\alpha_{0}+Re_{t}/2.95}{ 1+Re_{t}/2.95}\Big{)}\rho S^{2}\quad Y_{\omega}=\rho\beta^{*}\omega^{2} \tag{15}\]  
\[D_{\omega}=2(1-F_{1})\rho\sigma_{\omega,2}\frac{1}{\omega}\frac{\partial k}{ \partial x_{j}}\frac{\partial\omega}{\partial x_{j}} \tag{16}\]
# A New Design of Codebook for Hybrid Precoding in Millimeter-Wave Massive MIMO Systems
## 4 Simulation Results
### Comparison and Analysis of Spectral Efficiency Performance
Through the data analysis of Table 3, we can see that when the number of RF chains equals to 10, the spectral efficiency of the algorithm proposed in this paper is 5% higher than that of the OMP-based joint \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Spectral Efficiency (bps/Hz)**} \\ \cline{2-4}  & **SNR = 0 dB** & **SNR = 5 dB** & **SNR = 10 dB** \\ \hline Hybrid Precoding with quantization [1] & 14.4082 & 17.5505 & 20.8505 \\ \hline OMP-based Codebook [4] & 15.3462 & 18.3034 & 21.5461 \\ \hline Proposed Codebook & 15.8109 & 18.7715 & 22.0345 \\ \hline Optimal unconstrained precoding & 16.5443 & 20.0332 & 24.0119 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Spectral efficiency of different algorithms with SNR. Figure 3: Spectral efficiency varying with SNR in different precoding schemes. codebook algorithm [4] and 10% higher than that of the hybrid precoding algorithm with quantization [1]. When the number of RF chains equals 12, the spectral efficiency of the algorithm proposed is, at most, 6.3% higher than that of the OMP-based joint codebook algorithm [4] and 16.7% higher than that of the hybrid precoding algorithm with quantization [1]. As the number of RF chains increases, the spectral efficiency of the proposed codebook design algorithm gradually decreases, but it can still reach more than 83.9% of the optimal unconstrained precoder. All in all, the proposed codebook design algorithm generally performs better than the OMP-based joint codebook [4] algorithm and the hybrid precoding algorithm with quantization [1].
# UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios
## 5 Performance Evaluation and Analysis
### Result Analysis
It can be seen that the complexity of the deep reinforcement learning algorithm is higher than that of the traditional reinforcement learning algorithm, because deep reinforcement learning uses deep neural networks instead of Q-tables in Q-learning. It overcomes the difficulty for traditional reinforcement learning to adapt to high-dimensional state spaces, but it also leads to an increase in the complexity of the algorithm. Deep reinforcement learning avoids the problem of dimensional explosion and has stable performance over a large state space. Therefore, the average energy consumption of the DRL-UCTO and DQN algorithms is lower than that of the Q-learning algorithm in scenarios with a higher number of users. This paper next explores the impact of deploying different numbers of UAVs in the same environment on the energy consumption of the system, as shown in Figure 7. It can be simply seen that as the number of UAVs deployed in the system increases, the energy consumption generated by the system also grows, because deploying more UAVs in the system requires correspondingly more energy to be consumed for the propulsion energy overhead generated by maintaining the UAVs in flight. The energy consumption of DQN and Greedy algorithms grows faster as the number of UAVs increases, especially the DQN algorithm, because it is prone to overestimation problems that lead to excessive UAV exploration in areas without ground users, thus causing the UAV to spend more energy on flight overheads. On the contrary, the DRL-UCTO algorithm can quickly move the UAV to the area of the ground user to complete the task-offloading requirements of the user due to its stable action selection policy. As a result, the DRL-UCTO algorithm generates relatively low energy consumption and grows more slowly, which means that the DRL-UCTO algorithm is more adaptable and stable than the other two baseline algorithms. Deploying more UAVs in the system will lead to an increase in energy consumption, but it  
Figure 6: Comparison of system average throughput and average energy consumption under a different number of user scenarios. (**a**) System average throughput versus number of users. (**b**) System average energy consumption versus number of users. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Complexity** &
\begin{tabular}{c} **Average Energy** \\ **Consumption (KJ)** \\ \end{tabular} \\ \hline DRL-UCTO & \(O(SAT)\) & 76.68 \\ DQN & \(O(SAT)\) & 85.51 \\ Q-learning & \(O(SA)\) & 89.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of DRL-UCTO with other reinforcement learning algorithms. will likewise result in an effective increase in the computing power of the system, which can provide stronger task-offloading services to ground users.
## 2 Literature Review
### Related Work
#### 2.3.5 Hybrid Models
A hybrid anomaly detection approach based on Naive Bayes, Random Forest, and J48 (C4.5) using Deep Neural Networks and Classical Autoencoders is presented in the study by [PERSON] et al. [39]. The model has an accuracy rate of 91.29%, significantly higher than previous methods. However, the study is based on a single dataset, which should be more recent. The rapid growth of IoT environments and increasing threats necessitate the assistance of existing ML algorithms to identify new attacks, as existing algorithms need to adapt to the growing threat landscape. In [PERSON] et al. [40], a hybrid anomaly detection system incorporating Deep Neural Networks and Classical Autoencoders was proposed. Based on the UNSW-NB15 benchmark dataset, the model was found to be significantly more accurate than previous models, with an accuracy rate of 91.29%. A single dataset was used in the study, which should have been of a more recent date. Since IoT environments are rapidly growing, existing machine learning algorithms are insufficient for detecting new threats, making the proposed model more appropriate for research in the future. [PERSON] and [PERSON] [41] suggested using hybrid IDS techniques in a cloud environment along with GAs and SVM. The hybrid IDS method categorizes network data into normal and abnormal behavior by using a GA. Based on the KDD CUP 99 dataset, the proposed model achieved a 99.3% accuracy level. However, other datasets or recent attacks may affect the accuracy of the proposed model.
## 3 System Model
### Sum Rate
#### 3.2.2 Data Rate in the Licensed Spectrum  
In the licensed spectrum, each CU can one of the orthogonal subchannels to communicate. The D2D users will share the subchannel of the CU, which results in co-subchannel interference. As we mentioned above, all the licensed subchannels in the licensed spectrum of the system are used, and only two kinds of rates are required to calculate, i.e., the rate of the CUs on subchannel \(l\) and the rate of the D2D users reusing subchannel \(l\).  
Figure 2: Duty-cycle Mechanism Schematic Diagram.  
The signal-to-interference-plus-noise-ratio (SINR) of CU \(C_{m}\) over licensed spectrum \(l\) can be expressed as:  
\[\gamma_{m,b}^{l}=\frac{x_{m}^{l}p_{m}h_{m,b}^{l}}{\sum_{n=1}^{N}y_{n}^{l}p_{n}h_{ n,b}^{l}+B_{l}\sigma_{N}^{2}}, \tag{4}\]  
where \(h_{m,b}^{l}=d_{m,b}^{-a}h_{0,mb}^{2}\) is the channel gain between CU \(C_{m}\) and the BS on subchannel \(l\), \(d_{m,b}\) is the distance between the BS and CU \(C_{m}\), \(\alpha\) is the path-loss exponent corresponding to the large-scale fading of the transmission channel [31], and \(h_{0,mb}\) is the Rayleigh fading channel coefficient, which obeys the standard normal distribution. \(p_{c}\) is the transmit power of CU \(C_{m}\). \(h_{n,b}^{l}\) represent the channel gain between D2D transmitter \(D_{n}^{l}\) and the BS on subchannel \(l\). \(p_{n}\) is the transmit power of D2D transmitter \(n\). \(\sigma_{N}^{2}\) represents the noise power density. The data rate of CU \(C_{m}\) over licensed subchannel \(l\) is given by  
\[R_{m,b}^{l}=B_{l}\log_{2}(1+\gamma_{m,b}^{l}). \tag{5}\]  
Correspondingly, the SINR of D2D receiver \(D_{n}^{r}\) over licensed subchannel \(l\) can be expressed as  
\[\gamma_{n}^{l}=\frac{y_{n}^{l}p_{n}h_{n}^{l}}{\sum_{m=1}^{M}x_{m}^{l}p_{m}h_{ m,n}^{l}+\sum\limits_{n^{\prime}=1,n^{\prime}\
eq n}^{N}y_{n^{\prime}}^{l}p_{n ^{\prime}}h_{n^{\prime}}^{l^{\prime}}+B_{l}\sigma_{N}^{2}}, \tag{6}\]  
where \(h_{n}^{l}\) is the channel gains between D2D transmitter \(D_{n}^{t}\) and receiver \(D_{n}^{r}\) on licensed subchannel \(l\), \(h_{m,n}^{l}\) represents the channel gains from CU \(C_{m}\) to D2D receiver \(D_{n}^{r}\) on licensed subchannel \(l\), and \(h_{n^{\prime}}^{l}\) represents the channel gains between D2D transmitter \(D_{n^{\prime}}^{t}\), and receiver \(D_{n}^{r}\) on licensed subchannel \(l\). \(p_{n}\) is the transmit power of D2D transmitter \(D_{n}^{t}\). Thus, the rate of D2D user \(D_{n}\) is given by:  
\[R_{n}^{l}=B_{l}\log_{2}(1+\gamma_{n}^{l}). \tag{7}\]
## 4 Experimental Results
### Experimental Settings  
In this experiment, the FeatureNet dataset (see Figure 8), which contains 144,000 (24 classes \(\times\) 1000 models \(\times\) 6 orientations) different CAD models stored in stereolithography (STL) files, is used as a benchmark. Because the approaches involved in the comparisons only accept 3D voxels as input, the STL files must be converted to binvox data format [14]. In addition, the MSV representation is captured from the voxel grid and also requires voxel as input [20]. In this study, 3D CAD models are voxelized in a \(64\times 64\times 64\) grid resolution. For fair comparison, the entire dataset is divided into a training set (80% of the total dataset), a validation set (10%), and a test set (10%) in accordance with [20]. All the training tasks were conducted on a cloud server with Intel Xeon Gold 6230R CPU, 32 GB memory, and NVIDIA Tesla V100S (32 GB) GPU. For reasonable comparison on personal computer, all the latency testing tasks were carried out on a consumer laptop with Intel Core i7-7700 CPU, 16 GB memory, and NVIDIA GeForce GTX 1070 (8 GB) GPU. All experiments were implemented on PyTorch 1.9.0 [57]. The inference latency values are the average over 1000 runs.  
Accuracy, which is a widely used metric in machine learning, is used to evaluate the performance of the approaches. It measures the proportion of correctly classified instances among all instances in the dataset. Its simplicity and ease of interpretation make it a popular choice, particularly for balanced classification problems. Accuracy is defined as follows:  
\[\text{Accuracy}=\frac{\text{Number of correctly classified instances}}{\text{Total number of instances}} \tag{10}\]
## 3 Methodology and Data
### Study Area and Data Sources
Considering the information flow between cities is asymmetrical, the resulting information network is a directed weighted network, where the direction of edge \(e_{ij}\) indicates information flow from city \(i\) to city \(j\). (2) Investment network, \(G_{C}\): \(r_{xy}\) represents the invested capital where company \(x\) registered in city \(i\) has shareholder \(y\) registered in city \(j\). The investment relationship weight \(w_{ji}\) from city \(j\) to city \(i\) is calculated using Equation (5):  
\[\mathbf{w}_{ji}=\sum_{x}^{x\leq i}\sum_{y}^{y\leq j}\mathbf{r}_{xy} \tag{5}\]  
Similar to the information network, the investment network is also a directed weighted network, where edge direction indicates investment from city \(i\) to city \(j\). (3) High-speed rail network, \(G_{R}\): This study uses a P-space network construction method. According to the P-space network definition, if a high-speed rail route \(L\) from city \(i\) to city \(j\) stops at cities \(m\), \(n\), and \(l\), edges are established between all pairs of nodes \(i\), \(m\), \(n\), \(l\), and \(j\). The edge weight is calculated based on the number of high-speed rail services \(n_{ij}^{L}\) between cities, where \(w_{ii}=n_{ii}^{L}\), \(w_{ii}\in W_{R}\). (4) Highway network, \(G_{H}\): Similar to the high-speed rail network, this study uses the P-space network construction method for the highway network, establishing edges between sample cities along national highway routes. However, unlike the high-speed rail network, edge weights are determined using geographical distance \(d_{ii}\) between cities to evaluate relationship intensity. Closer cities have more frequent relationships. Additionally, the number of highways m between cities increases relationship frequency. Therefore, the edge weight \(w_{ii}\) in the highway network is calculated using Equation (6):  
\[w_{ij}=(\frac{1}{d_{ij}})^{m},w_{ij}\in W_{H} \tag{6}\]It should be noted that since most high-speed rail services in China operate bidirectionally and highway connectivity is symmetric, both the high-speed rail network and highway network are undirected weighted networks. Based on the construction of the four types of urban networks described above, this paper extracts the seven urban agglomerations as subnetworks from the overall network, forming the urban agglomeration networks analyzed in this study. To focus on the internal structure of the urban agglomerations, inter-cluster links and their weights are naturally discarded during the subnetwork analysis. At the same time, considering the impact of network weights on the analysis of urban agglomerations, a backbone network extraction method was employed to remove low-weight links [54] and highlight the core structural features of the urban network. This method allows us to construct a concise network model that emphasizes core interactions. The topology of the extracted network is shown in Figure 4.  
Figure 4: Topology of the four types of urban networks: (**a**) information network; (**b**) investment network; (**c**) high-speed rail network; (**d**) highway network.
## 2 Materials and Methods
### Applications of the Automated SHS Quantification Algorithm
#### 2.4.2 Pre-Ablation BE SHS and RFA Treatment Response  
The objective of this study was to investigate whether SHS in the BE region was associated with RFA treatment response. Data from a subset of 21 patients imaged by VLE, treated by RFA on the same visit, and with EGD at follow-up within 1-4 months was analyzed to investigate the association between the pre-ablation BE SHS characteristics and whether the BE region achieved neosquamous epithelium. The mean age of the patients was 71.1 \(\pm\) 9.7 and all were male. The mean BE length was 2.1 \(\pm\) 2.3 cm.  
We defined the 1 cm segment region distal to M as the BE region of interest (ROI\({}_{\text{BE}}\)) to perform SHS quantification (Figure 3B). Note that the ROI\({}_{\text{BE}}\) may not be entirely BE if M was not coincident with the most proximal circumferential extent of BE. The SHS characteristics quantified over this region were compared between patients whose ROI\({}_{\text{BE}}\) had neosquamous epithelium (neo-SE) versus remaining BE at follow-up EGD. Here, the ROI\({}_{\text{BE}}\) was required to be completely squamous epithelium to be counted as neo-SE on follow-up (Figure 3B). Therefore, the RFA treatment response here was specifically related to the post-ablation response of the 1 cm segment ROI\({}_{\text{BE}}\), rather than the standard definition of CRIM.
# Congestion Control and Traffic Differentiation for Heterogeneous 6 TiSCH Networks in IIoT
###### Abstract  
The Routing Protocol for Low power and lossy networks (RPL) has been introduced as the de-facto routing protocol for the Industrial Internet of Things (IIoT). In heavy load scenarios, particular parent nodes are likely prone to congestion, which in turn degrades the network performance, in terms of packet delivery and delay. Moreover, there is no explicit strategy in RPL to prioritize the transmission of different traffic types in heterogeneous 6 TiSCH networks, each according to its criticality. In this paper, we address the aforementioned issues by introducing a congestion control and service differentiation strategies to support heterogeneous 6 TiSCH networks in IIoT applications. First, we introduce a congestion control mechanism to achieve load balancing under heavy traffic scenarios. The congestion is detected through monitoring and sharing the status of the queue backlog among neighbor nodes. We define a new routing metric that considers the queue occupancy when selecting the new parent node in congestion situations. In addition, we design a multi-queue model to provide prioritized data transmission for critical data over the non-critical ones. Each traffic type is placed in a separate queue and scheduled for transmission based on the assigned queue priority, where critical data are always transmitted first. The performance of the proposed work is evaluated through extensive simulations and compared with existing work to demonstrate its effectiveness. The results show that our proposal achieves improved packet delivery and low queue losses under heavy load scenarios, as well as improved delay performance of critical traffic.  
Industrial IoT; 6 TiSCH; RPL; trickle timer; priority; congestion; traffic differentiation +
Footnote †: journal: Sensors
# Profiling Taste and Aroma Compound Metabolism during Apricot Fruit Development and Ripening
## 2 Results
### Changes in Sugars during Fruit Development and Ripening  
As shown in Figure 3a, all sugars including fructose, glucose, sucrose and total sugars kept increasing during the whole development period. From the turning stage (S3) to the full-ripe stage (S5), the sugar contents increased especially steeply. Trace sorbitol was detected in fruit tested at all developmental stages. Even though no significant differences of sugar content during the early development (from S1 to S2) were found in all tested fruits, significantly higher fructose was observed in Danxing (DX), Luntaixiaobaxixing (LT) and Baixing than those in Hongyu (HY) and Yechengheiyexing (YC) during ripening (from S3 to S5). The content of all sugars identified and total sugars in Luntaixiaobaxixing (LT) were found significantly higher than those in other cultivars during ripening. Though higher contents of fructose, glucose, sucrose and total sugars were found in Danxing (DX), Yechengheiyexing (YC) and Luntaixiaobaxixing (LT) peels than their pulps during the whole development period, significantly lower level of sugars were found in Hongyvuxing (HY) pulps than those in its peels. At the same time, no marked difference of sugar content was found between Bainxin peels and pulps during the whole development period. Therefore, the difference of sugar contents between peels and pulps may dependent on the cultivar.  
Figure 3: _Cont._  
As for the ratio of sugars (Figure 3b), on the whole, ratio of glucose in fruit decreased significantly during the whole development period, it decreased from 70.17% to 19.43% in peels and 70.17% to 19.17% in pulps of tested cultivars, respectively. However, glucose was the predominant sugar during the early development (from S1 to S3) and still accounted for 38.6%-71.42% of total sugar in peels and 45.08%-70.68% of total sugar in pulps. Similar to glucose, the ratio of fructose also decreased from 21.46% to 4.56% in peels and from 21.46% to 4.82% in pulps during the whole development period. As the second major sugar at the early stage, fructose accounted for 11.41%-21.46% of total sugar in peels and 13.1%-21.46% of total sugar in pulps. However, the ratio of sucrose increased from 10.98% to 69.92% in peels of tested cultivars and 10.98% to 72.22% in pulps of tested cultivars during the whole development period, and become the major sugar in all cultivars tested during ripening (from S3 to S5), accounting for 35.94%-69.92% of total sugars in peels and 27.55%-71.62% of total sugars. Glucose become the second major sugar during ripening, it represented 19.43%-55.64% of total sugar in peels and 19.17%-55.88% in pulps of cultivars tested. During the ripening period, fructose was found at the lowest level of all tested sugars, only accounting for 4.56%-12.14% in peels and 4.82%-16.57% in pulps of cultivars tested.