esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
(1) Both C40 and S1 PP0.2 HyFRC have temperature enhancement effects at temperatures ranging from 25 \({}^{\circ}\)C to 200 \({}^{\circ}\)C. The peak strength of the sample is the highest at 200 \({}^{\circ}\)C, and the peak strength of the sample also increases significantly with the increase in impact pressure. When the temperature gradient exceeds 200 \({}^{\circ}\)C, both types of concrete experience temperature damage effects. In addition, both C40 plain concrete and S1 PP0.2 HyFRC exhibit enhanced dynamic peak strain effects with increasing temperature and impact air pressure.  
(2) With the increase in temperature, the damage forms of C40 plain concrete and S1 PP0.2 HyFRC are different in the uniaxial dynamic impact test, and the damage degree becomes more and more prominent. At 25 \({}^{\circ}\)C and 100 \({}^{\circ}\)C, the failure mode of C40 plain concrete is surface spalling; at 200 \({}^{\circ}\)C and 400 \({}^{\circ}\)C, the failure mode is mainly core failure; and at 600 \({}^{\circ}\)C and 800 \({}^{\circ}\)C, the failure mode develops into mass fragmentation and crushing. In contrast, S1 PP0.2 HyFRC shows no obvious damage at 25 \({}^{\circ}\)C and 100 \({}^{\circ}\)C; at 200 \({}^{\circ}\)C and 400 \({}^{\circ}\)C, its failure mode is surface spalling; and at 600 \({}^{\circ}\)C and 800 \({}^{\circ}\)C, its failure mode is dominated by core failure. Hybrid fiber concrete has an obvious reinforcement effect.  
(3) Uniaxial dynamic impact compression tests are simulated using modified constitutive model parameters for C40 concrete and HyFRC. The obtained impact waveforms and stress-strain curves are consistent with the test results, with the comparison error between peak stress and peak strain effectively controlled within 6%. The HJC constitutive model is suitable for studying the dynamic mechanical properties of concrete, allowing for accurate characterization of changes in the mechanical properties of concrete under the dynamic impact.  
Figure 24: Impact compression damage process of S1 PP0.2 HyFRC at different impact velocities (impact loading from the right side).  
**Author Contributions:** Conceptualization, J.D. and X.S.; methodology, X.L., X.S. and L.L.; validation, X.L., L.L. and X.C.; resources, J.D.; data curation, L.L.; writing--original draft preparation, X.S., X.L. and L.L.; writing--review and editing, X.C., X.L. and L.L.; supervision, J.D.; project administration, J.D.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the Special Basic Cooperative Research Programs of the Yunnan Provincial Undergraduate University's Association (202301 BA070001-012, 202101 BA070001-137).  
**Data Availability Statement:** The original contributions presented in this study are included in this article, and further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare that this research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflicts of interest.
# Feature Interaction in Terms of Prediction Performance
## 4 Discussion
In the classification task, an evaluation value of 0.01 indicates that the interaction increases the prediction accuracy by 0.01. In the regression task, an evaluation value of 0.01 indicates that the interaction decreases the predicted RSME by 0.01. The evaluation value proposed in this paper was also compared across prediction models, which helps users clearly understand the feature interaction effects either in a specific model or across various models. Table 3 summarizes the above discussion. Feature interaction can be defined variously and has differing characteristics according to the definition.
# A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs +
## 2 Overview of blackhole Attack  
One kind of network security risks that affects wireless networks, particularly VANETs, is the blackhole attack. In the blackhole attack, a malicious node can attract all packets by falsely claiming a fresh route to the destination and then absorbing them (dropping all packets) without forwarding them to the destination [2; 29]. This causes other nodes in the network to route their packets through the malicious node, which then drops or consumes the packets instead of forwarding them as expected. It is also known as a sequence number attack because it uses sequence numbers for dropping the packet [30]. The sequence number is a numbering scheme kept by the source node of the route request (RREQ) and route reply (RREP) to help maintain the freshness of the routing information.  
In the routing process, the source node starts a route discovery if the route toward the destination is not found or if the route is no longer active. In this case, the source node broadcasts RREQ to the neighbor nodes (NN). If the destination node receives this RREQ, it responds to the source node with RREP containing updated information such as source identifier, destination identifier, sequence number, broadcast ID, and hop count. A blackhole node can exploit this process to present itself as having the freshest route to the destination by quickly responding with a fake RREP with a significantly higher sequence number than the normal nodes in the network without checking its routing table. A higher value of destination sequence number means a fresher route [31]. In this way, a blackhole node becomes part of the route. Then, the source selects this route, and the blackhole node starts to drop the packets, degrading the overall performance of the VANETs. Dropping these packets in highly dynamic VANETs could result in road fatalities, accidents, traffic jams, and congestion. Hence, our research focuses on addressing the blackhole attack in VANETs and proposes an efficient solution to avoid it.  
A blackhole attack can be detected by studying the behavior of blackhole nodes in a network. Therefore, by using the behavioral characteristics of a blackhole attack, we can make a detection system to classify the type of node between a blackhole node and a normal node [11; 32; 33; 34], which can be summarized as follows:  
* The blackhole node has a higher destination sequence number.
* The blackhole node has a lower number of hop counts toward the destination node.
* The blackhole node has higher remaining energy.
* The blackhole node responds to all RREQ packets by sending an RREP packet.
* The blackhole node never broadcasts any RREQ packet received.
* The blackhole node drops the received data packet in the network.
## 5 Conclusions  
In this paper, a functionally graded strategy is proposed to investigate the effect of filling forms and direction gradient on the energy absorption of TPMS structures. First, the selective laser melting technique was utilized to fabricate four cellular structures based on the TPMS, in which the volume fraction of AlSi10 Mg was 35%. The stress-strain curves, deformation behavior, and energy absorption of four uniform TPMS structures and three functionally graded structures under compression conditions were experimentally investigated. The following conclusions were drawn.  
1. For the same type of TPMS-based cellular structure at the same volume fraction, the sheet structures exhibited more stable deformation patterns and stress plateaus than the skeletal structures during compression, which improved the energy absorption capacity of the structures. In addition, the sheet TPMS cellular structure improved the mechanical properties in terms of compressive strength, yield strength, and plateau stress.
2. A variable functionally graded TPMS-based cellular structure design methodology was proposed to design a transverse gradient sheet TPMS structure, and the results show that the transverse gradient transformation can change the deformation pattern and cannot improve the load-bearing and energy-absorbing capacity compared with the uniform sheet TPMS structure.
3. For the degraded TPMS cellular structure, which varied the same as the compression direction, the structure was able to guide a stable deformation pattern during  
Figure 18: Comparison of the characteristics of TPMS structures: (**a**) SEA and EA, (**b**) MCF and PCF, (**c**) CFE.  
compression, which reduced the initial peak force and improved the crushing force efficiency. In addition, the structure exhibited a trapezoidal shape during compression, which increased the contact area and improved the compression distance. The degraded TPMS structure helps delay densification, which in turn enhances the energy absorption performance.  
Conceptualization, J.Z. and J.S.; methodology, J.S.; software, M.W.; validation, J.S. and D.L.; investigation, J.Z.; data curation, M.W.; writing--original draft preparation, J.S. and M.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Research Project of Liaoning Provincial Education Department (No. JYTQN2023007 and No. IJKMZ20220847), Dalian Science and Technology Talent Innovation Support Policy Plan (No. 2023 RQ089).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 6 Therapeutic Implications: Targeted Strategies, Metabolic Targets, and Combined Approaches
### Metabolite Adjuvants: Synergistic Modulation of Response  
The targeted use of metabolites or cofactors represents a promising strategy to enhance antibiotic efficacy by shifting bacterial metabolism toward more vulnerable states. Notable examples include:  
* Administration of \(\alpha\)-ketoglutarate: enhances aminoglycoside efficacy by increasing ROS production, thereby contributing to bacterial lethality [142].
* Use of fructose and mannose: improves aminoglycoside uptake by boosting glycolysis and the proton motive force, demonstrating efficacy against persister cells [143].
* Application of nitric oxide (NO) donors: induces oxidative and nitrosative stress, increasing the lethality of ciprofloxacin in _E. coli_[144].  
These metabolic strategies have shown efficacy in both in vitro and in vivo models, including against persister cells, underscoring the therapeutic potential of metabolism-based approaches to enhance antibiotic activity and counteract tolerance.  
As detailed in Table 10, metabolite adjuvants can modulate bacterial metabolism to enhance the efficacy of conventional antibiotics through diverse mechanisms, including redox modulation and uptake facilitation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Adjuvant Agent** & **Mechanism of Action** & **Potentiated Antibiotic(s)** & **References** \\ \hline \(\alpha\)-Ketoglutarate & Promotes ROS accumulation & Aminoglycosides & [142] \\ \hline \multirow{2}{*}{Fructose, Mannose} & Boost glycolysis and proton & Aminoglycosides (e.g., & \\  & motive force & gentamicin) & [143] \\ \hline Nitric Oxide (NO) Donors & Induce oxidative/nitrosative stress & Ciprofloxacinacin & [144] \\ \hline \multirow{2}{*}{Sulfonamides + Trimethoprim} & Sequential folate & Broad-spectrum & [144] \\  & pathway inhibition & bactericidal synergy & [144] \\ \hline \multirow{2}{*}{Isoniazid} & Disrupts NAD\({}^{+}\)-dependent mycolic & \multirow{2}{*}{Antimycobacterial regimens} & \multirow{2}{*}{[143]} \\  & acid synthesis in mycobacteria & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Metabolic adjuvants enhancing antibiotic efficacy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Enzyme Target** & **Function in Bacteria** & **Therapeutic Rationale** & **References** \\ \hline \multirow{2}{*}{Isocitrate Lyase (ICL)} & Glyoxylate cycle enzyme & Target in _M. tuberculosis_ for latent & [83] \\  & critical in latency & phase survival & [83] \\ \hline \multirow{2}{*}{Glutamate Dehydrogenase} & Redox balance and & \multirow{2}{*}{Target to disrupt redox homeostasis} & \multirow{2}{*}{[141]} \\  & nitrogen metabolism & & \\ \hline \multirow{2}{*}{Glutathionee Biosynthetic Enzymes} & Maintain intracellular & Inhibition sensitiizes bacteria to & \\  & ROS detoxification & oxidative antibiotics & [141] \\ \hline \multicolumn{4}{c}{ROS: Reactive Oxygen Species.} \\ \end{tabular}
\end{table}
Table 9: Enzymatic targets for metabolism-based antibacterial therapy.
## 3 System Development for Verification  
The modules and the development procedure for the tool condition monitoring system implemented in this study are shown in Figure 4. The modules of this system include signal transformation, feature selection, and classifier design. The fast Fourier transformation (FFT), combined with the class-mean scatter criterion, was adopted in this study for signal transformation and correlation analysis of the features and tool wear. The Fisher linear classifier [43] was chosen in this study as the model for the classifier.  
The time domain signals usually exhibit lower sensitivity to the change in micro tool wear and are transformed into the feature space for the analysis in this study. After transforming the time domain signals into the frequency domain, some frequency signal features can be more sensitive to the change in tool wear than others and may therefore be considered valuable features as the input elements to the classifier for identifying the tool condition.  
To evaluate the sensitivity of each feature in the frequency domain regarding the change in micro tool wear, the class-mean scatter criterion was adopted, and the classifier index \(J=\frac{R_{c}(k,k)}{R(k,k)}\) which estimates the between-scatter to within-class scatter ratio is used for analysis [44]. The between-class scatter \(R_{c}\) and within-class scatter \(R\) are defined as follows.  
The mean of the features for each class \(\widetilde{Y}_{i}(k)\) was obtained using individual features \(Y_{ij}(k)\) in Equation (1) [44]:  
\[\widetilde{Y}_{i}(k)=\frac{1}{M_{i}}\sum_{j=1}^{M_{i}}Y_{ij}(k) \tag{1}\]  
Figure 4: Schematic of tool wear monitoring system for tool wear classification.  
Figure 3: Implementation of the cutting test for the micro milling process.  
where \(i=i^{th}\) class \(j=j^{th}\) pattern in a class \(k=k^{th}\) feature \(M_{i}=\text{ the number of patterns in class }C_{i}\).  
The overall system mean \(\overset{-}{Y}\) is determined as follows:  
\[\overset{-}{Y}(k)=\sum_{i=1}^{C}p_{i}\overset{-}{Y}_{i}(k) \tag{2}\]  
where \(p_{i}\) a priori probability of class \(C_{i}\)\(C=\text{ number of classes.}\)  
The within-class scatter and individual-class scatter are defined as in Equations (3) and (4), respectively:  
\[R_{i}(k)=\frac{1}{M_{i}}\sum_{j=1}^{M_{i}}\left(Y_{ij}(k)-\overset{-}{Y}_{ij}( k)\right)\left(Y_{ij}(k)-\overset{-}{Y}_{ij}(k)\right)^{T} \tag{3}\]  
\[R_{c}(k)=\sum_{i=1}^{C}p_{i}\left(\overset{-}{Y}_{i}(k)-\overset{-}{Y}(k) \right)\left(\overset{-}{Y}_{i}(k)-\overset{-}{Y}(k)\right)^{T} \tag{4}\]  
The \(R(k)\) is defined as in Equation (5) as follows:  
\[R(k)=\sum_{i=1}^{C}R_{i}(k) \tag{5}\]
# Load Balancing of Two-Sided Assembly Line Based on Deep Reinforcement Learning
## 4 Deep Reinforcement Learning Algorithm Based on DPPO and CNN (DPPO-CNN)
### Overall Flow of DPPO-CNN  
The overall flow of the proposed DPPO-CNN for TALBP-BL (Algorithm 1) is as follows.  
```
1: Initializes the Actor-Critic network parameters \(\theta\), \(\varphi\) for the main process, maximum iteration number, experience pool capacity buffer size, maximum experience pool capacity max buffer size, batch data size, number of network updates of each round epoch; Subprocess Actor-Critic network parameters \(\theta\), \(\varphi\).
2:for each episode do:
3:\(t=1\).
4: The main process empties the experience pool, sends its Actor-Critic network parameters \(\theta\), \(\varphi\) to each child process; each sub-process bilateral assembly line environment 1, 2, 3,..., \(m\) is initialized, generates states \(S^{1}_{1}\), \(S^{2}_{t}\), \(S^{1}_{t}\),..., \(S^{m}_{t}\); each agent 1, 2, 3,..., \(m\) loads the network parameters of the main process.
5:while buffer size < max buffer size do:
6:while all tasks of \(m\)'s respective bilateral assembly lines have not been allocated do:
7: Agent 1, 2, 3,..., \(m\) observes the environment status \(S^{1}_{t}\), \(S^{2}_{t}\), \(S^{1}_{t}\),..., \(S^{m}_{t}\), respectively, and takes the tasks \(a^{1}_{t}\), \(a^{2}_{t}\), \(a^{1}_{t}\),..., \(a^{m}_{t}\) to be assigned according to strategy \(p_{\theta}(a_{t}|s_{1})\).
8: Environment 1, 2, 3,..., \(m\) allocates tasks \(a^{1}_{t}\), \(a^{2}_{t}\), \(a^{1}_{t}\),..., \(a^{m}_{t}\) separately, and feedback reward \(r^{1}_{t}\), \(r^{2}_{t}\), \(r^{1}_{t}\),..., \(r^{m}_{t}\).
9:\(t=t+1\).
10: Environment 1, 2, 3,..., \(m\) update states \(S^{1}_{t}\), \(S^{2}_{t}\), \(S^{1}_{t}\),..., \(S^{m}_{t}\).
11:endwhile
12: Agent 1, 2, 3,..., \(m\) stores the interactive trajectory (solving experience) \(\tau_{1}\), \(\tau_{2}\), \(\tau_{3}\),..., \(\tau_{m}\), respectively, in the experience pool and packages it as training data.
13:endwhile
14:for epoch in [1, 2,..., _epochs_] do:
15: Randomly extract the training data with the size of batch size from the experience pool.
16: Calculate the network loss function of the actor strategy of the master process; evaluate the critic loss function of the master process.
17: Update the main process Actor's policy network \(p_{\theta}(a_{t}|s_{t})\).
18: Update the main process Critic's evaluation network \(p_{\varphi}(s_{t},a_{t})\).
19:endfor
20:\(\theta_{old}\), \(\varphi_{old}\leftarrow\theta\), \(\varphi\).
21:endfor
```  
**Algorithm 1** Load balancing-oriented TALBP based on DPPO-CNN
## 2 Materials and Methods
### Metal Interaction Experiments
#### 2.2.2 Ferrozine Method  
Principle: Ferrozine is a specific indicator forming a magenta-colored complex with ferrous ions.  
To evaluate iron chelation, solutions of the tested compounds (50 uL) dissolved in DMSO at various concentrations up to 10 mM were mixed in buffers with ferrous or ferric ions for 2 min (50 uL, 250 uM). Ferrozine (50 uL, 5 mM) was then added in the case of ferrous ions. Hydroxylamine (50 uL, 10 mM) was added prior to ferrozine at pH 7.5 to inhibit ferrous oxidation at this pH. This hydroxylamine solution was also used in the case of total iron chelation at pH 4.5 to reduce the remaining ferric to ferrous ions, which then later formed the previously mentioned complex with ferrozine. Absorbance was measured immediately after the addition of ferrozine and 5 min later at 562 nm.  
To determine the degree of ferric ion reduction, various concentrations of the tested compounds were mixed for 2 min with ferric ions in buffers. Afterwards, ferrozine was added and absorbance was measured both immediately and 5 min later. Hydroxylamine was used as the positive control (100% reduction).
## 4 Discussion
Despite extensive cadaveric dissection studies or morphometric studies in the dry skull, accurate data on the base of the skull is uncertain. Some interventions, such as ultrasound procedures and computerized tomography scans, can contribute to more definitive data. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Parameters** & **Sex** & \multicolumn{2}{c}{**Present Study (2022)**} & **[PERSON] et al., 2017 [7]** & **[PERSON] et al., 2013 [17]** & **[PERSON] et al., 2005 [18]** \\ \cline{3-8}  & & & **Left** & **Right** & **Left** & **Right** & **Left** & **Right** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Foramen \\ Magnum \\ \end{tabular} } & Anteroposterior diameter & Male & 34.80 \(\pm\) 2.31 & 36.16 \(\pm\) 2.29 & 36.20 \(\pm\) 3.39 & 33.3 \\  & & Female & 32.84 \(\pm\) 2.31 & 33.28 \(\pm\) 2.51 & 30.92 \(\pm\) 3.15 & \\  & & Male & 30.04 \(\pm\) 2.68 & 31.32 \(\pm\) 2.51 & 30.92 \(\pm\) 3.15 & \\  & & Female & 28.57 \(\pm\) 2.19 & 28.97 \(\pm\) 2.32 & 29.61 \(\pm\) 2.08 & 27.9 \\  & & & Male & 21.38 \(\pm\) 2.83 & 21.17 \(\pm\) 2.21 & 19.82 \(\pm\) 3.19 & \\  & & Female & 20.39 \(\pm\) 3.15 & 20.05 \(\pm\) 2.45 & 18.77 \(\pm\) 3.26 & \\  & & & Male & 43.88 \(\pm\) 2.85 & 43.36 \(\pm\) 3.35 & 52.80 \(\pm\) 4.93 & \\ \multirow{3}{*}{\
\begin{tabular}{c} Occipital \\ Condyle \\ \end{tabular} } & Between posterior tips & Female & 41.48 \(\pm\) 2.74 & 41.23 \(\pm\) 3.30 & 50.13 \(\pm\) 4.71 & \\  & & Male & 25.24 \(\pm\) 2.88 & 24.08 \(\pm\) 2.12 & 24.07 \(\pm\) 2.59 & 24.33 \(\pm\) 2.57 & 26.48 \(\pm\) 2.80 & 26.30 \(\pm\) 2.92 \\  & & Female & 23.21 \(\pm\) 2.53 & 23.23 \(\pm\) 2.73 & 22.32 \(\pm\) 2.71 & 29.25 \(\pm\) 2.96 & 24.57 \(\pm\) 2.13 & 24.70 \(\pm\) 2.66 \\  & & & Male & 13.82 \(\pm\) 1.63 & 13.11 \(\pm\) 1.62 & 12.12 \(\pm\) 1.66 & 12.10 \(\pm\) 1.50 & 13.24 \(\pm\) 2.20 & 13.13 \(\pm\) 2.01 & 14.7 \\  & & Female & 12.92 \(\pm\) 1.63 & 12.16 \(\pm\) 1.50 & 11.46 \(\pm\) 1.51 & 11.43 \(\pm\) 1.47 & 122.74 \(\pm\) 1.63 & 130.4 \(\pm\) 1.99 & \\  & & Male & 13.61 \(\pm\) 1.23 & 12.60 \(\pm\) 1.28 & 8.38 \(\pm\) 1.12 & 8.30 \(\pm\) 1.30 & No data & No data & \\ \multirow{3}{*}{
\begin{tabular}{c} Hypoglossal \\ Canal \\ \end{tabular} } & Western occipital condyle to & Male & 17.17 \(\pm\) 1.73 & 16.97 \(\pm\) 1.25 & No data & No data & No data & No data & No data \\  & & Female & 16.47 \(\pm\) 1.68 & 16.55 \(\pm\) 1.53 & No data & No data & No data & No data \\  & & Female & 16.47 \(\pm\) 1.68 & 15.75 \(\pm\) 1.53 & No data & No data & No data & No data \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of foramen magnum, occipital condyle and hypoglossal cans among several studies (diameters are expressed in mm).
## 2 Materials and Methods
### Using Chamber Experiments  
For each pig, \(n=6\) jejun samples starting from the third meter distal to the pylorus were stripped of outer muscle layers and immediately mounted between the two halves of an Nessing chamber (Physiologic Instruments, San Diego, CA, USA). Each segment was bathed on its mucosal and serosal surfaces (exposed area 1.0 cm\({}^{2}\)) with the same corresponding KRB buffer. Specifically, each chamber was filled with 4 mL KRB buffer (115 mM NaCl, 2.4 mM K\({}_{2}\)HPO\({}_{4}\), 0.4 mM KH\({}_{2}\)PO\({}_{4}\), 1.2 mM CaCl\({}_{2}\), 1.2 mM MgCl\({}_{2}\), 25 mM NaHCO\({}^{3-}\)). The serosal buffer (pH 7.4) also contained 10 mM glucose as an energy source that was osmotically balanced by 10 mM mannitol in the mucosal buffer (pH 7.4). Indomethacin was added in both mucosal and serosal buffers (0.01 mM). Buffers were continuously perfused with a 95% O\({}_{2}\) and 5% CO\({}_{2}\) gas mixture. The temperature was maintained at 37 \({}^{\circ}\)C by a circulating water bath.  
After a stabilization period of 30 min, three different treatments were randomly assigned to each chamber in duplicate: KRB (as control), 2.5 mM pure Met and 2.5 mM of Met-ADS after release tests protocol at pH 3 was applied. The effect of the KRB Buffer pH 3 used for the Met release from the carrier was tested in a different experiment by adding the equivalent volume of the KRB Buffer (pH3) without Met-ADS as a control. Each addition was kept in equilibrated osmotic condition by the addition of equimolar mannitol on the serosal side. Forskolin (10 \(\upmu\)M) was added to the serosal compartment at the end of the experiment to test tissue viability. During the experiments, aliquots (500 \(\upmu\)L) of both apical and basolateral sides were collected immediately before (T0) and after 3 h (T1) of adding Met. Aliquots were immediately frozen (\(-20\)\({}^{\circ}\)C) until further analysis. Trans-epithelial potential difference (TEER) and the short-circuit current (Isc) were continuously monitored using a computer-controlled device,. Tissues were voltage-clamped at 0 mV by an external current after correction for solution resistance.