esa-sceva/satcom-chunk-collection huggingface dataset:
# Calculation Method of the Blasting Throwing Energy and Its Variation Affected by the Burden
## 5 Conclusions  
The casting velocity and throwing energy of rock fragments were calculated through blasting basic theory and dimensional analysis theory. In addition, combined with a large number of model tests, the variation law of the throwing energy of the broken rock mass with the burden under the explosive load of the explosive in the blast hole was obtained:  
1. Based on dimensional theory, the calculation equations for the casting velocity and throwing energy of the rock fragments were constructed during spherical cartridge and step columnar cartridge blasting. Model tests verified the feasibility of the calculated equation;
2. Through high-speed photography technology, it was found that the casting velocity of rock in the broken zone of step blasting has different burdens. This shows a normal distribution law along the vertical direction of the steps, and the fit correlation is high;
3. The model experiment successfully obtained the energy consumption of throwing broken rocks under explosive load. This shows a decreasing trend with an exponential relationship with the increasing burden. The trend of the energy proportion is similar.  
It is generally considered that the casting velocity and energy consumption of broken rock belong to the useless work of explosives to break the rock. It wastes energy and produces harmful conditions such as individual blasting fly-rock. However, cast blasting uses part of the energy to throw the broken rock mass to a fixed point, which saves a significant mechanical transportation procedures and costs. It can provide a reference for predicting throwing distance and energy consumption. The manuscript uses only the minimum burden as a single influencing factor. Many influencing factors such as lithology, charge structure, time difference of initiation, and so on are not involved. The law of multiple factors affecting the minimum burden, lithology, blasting parameters, and blasting technology needs further study.  
[PERSON] led the experiment and wrote the first draft of the paper, [PERSON] ([PERSON]) assisted the experiment and participated in the revision of the paper, [PERSON] ([PERSON]) gave valuable opinions on the work, [PERSON] processed the experimental data, [PERSON] is responsible for coordinating and communicating with team members, [PERSON] used data to draw charts. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the National Natural Science Foundation of China (52064025, 52164009, 52164010). Its support is gratefully appreciated.  
Not applicable.  
We declare that we do not have any commercial or associative interest that presents a conflict of interest in connection with the work.
## 5 Discussion
proposed a dynamic state-space model based on deep learning to analyze accident risk, which can better solve the heterogeneity problem in relevant collision data and was found to be a superior alternative to traffic collision estimation and prediction [39]. [PERSON] et al. proposed a traffic conflict identification method using vehicle trajectory data in a work zone, analyzed the spatial distribution characteristics of traffic conflicts in the transition area, and found that the probability of conflicts in the front and rear of the transition area was greater than in the middle; the faster the vehicle speed, the greater the probability of serious conflicts. The spacing between the transition areas should be appropriately increased to reduce the probability of conflicts [40]. [PERSON] et al. proposed a conflict prediction method according to artificial neural networks, which can only use work zone configurations and design parameters for conflict prediction [41]. [PERSON] et al. used deep learning models to classify and predict conflicts in work zones based on natural driving datasets, and found that driving behavior, driving tasks, and traffic volume have a significant impact on traffic safety risks in work zones [42]. It is difficult to obtain accident information and matching historical traffic flow data for work zones. Existing research has gradually shifted to analyzing traffic safety levels based on traffic conflict data and dangerous driving behavior data, but the research is mostly based on static traffic flow states. Different driving behaviors can lead to changes in traffic status and driving workload within work zones. In the future, the configuration, driving behavior, environmental factors (lighting and weather, etc. ), driver factors (gender, age, driving experience, etc. ), and driving workload of work zones can be integrated with deep learning methods to construct an integrated learning model for predicting traffic conflicts. Furthermore, a fusion model for safety assessment in work zones can be constructed to achieve traffic safety analysis.
# Recent Advances in Nanoparticle-Mediated Treatment of Inflammatory Bowel Diseases
## 9 Lyophilized Probiotic Extract Encapsulated in Plga-Nanoparticles  
Since probiotics are mainly considered adjunctive therapies in the treatment of IBD, lyophilized probiotic cell free extract (LPE) was encapsulated in nanoparticles and delivered to inflamed tissues [27]. LPE was encapsulated in PLGA-NPs and demonstrated its efficiency in an immune-based rat model of colitis. The authors used the easily induced and highly reproducible experimental colitis model based on TNBS. Following induction of colitis with TNBS, macroscopic and histological damage was improved by LPE, showing decreased neutrophil infiltration and reducing inflammation. Results showed that LPE alone and LPE-NPs significantly reduced TNF-alpha and IL-1b levels in the colon tissue. They both had positive effects in reducing MPO, the major protein in neutrophil granules, in a dose-dependent manner. Moreover, they determined a lower expression of LPE in colon tissue. The findings demonstrated that the formulated compound containing LPE was as efficient as, or even superior to oral treatment with dexamethasone. The study outcome paved the way for a new form of probiotic-based treatment that is safe and efficient in experimental colitis.
# Real-Time Performance Measurement Application via Bluetooth Signals for Signalized Intersections
## 3 Results
In Equation (1), an example calculation is given for the Q5 traffic flow fixed-time signaling system for the 13:00-14:00 interval. \[\text{Hourly delay per vehicle}=(71665+6500)/(914+127)=75.09\ \text{s/veh} \tag{1}\]  
Hourly delay graphs of fixed-time and fully adaptive signaling systems on 15 May 2024 and 16 May 2024, detected by the Bluetooth traffic sensors in the study, are given in Figures 8-10. In these figures, it is observed how the delays in traffic flows change during the day. It is observed that the delays in traffic flows increase during the peak traffic hours in both management methods. However, as can be seen from the figures, traffic flows with high vehicle numbers show a better performance in fully adaptive signaling management. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Traffic Flow**} & **Bluetooth Configuration** & \multirow{2}{*}{**Road Distance between Sensors (m)**} & \multirow{2}{*}{**Travel Time under Normal Conditions (s)**} \\  & **(From -> To)** & & \\ \hline Q5 & Bluetooth 4 -> Bluetooth 2 & 536 & 39 \\ Q5 & Bluetooth 4 -> Bluetooth 1 & 553 & 40 \\ Q6 & Bluetooth 2 -> Bluetooth 4 & 536 & 39 \\ Q6 & Bluetooth 2 -> Bluetooth 3 & 536 & 39 \\ Q7 & Bluetooth 4 -> Bluetooth 3 & 515 & 37 \\ Q8 & Bluetooth 2 -> Bluetooth 1 & 584 & 42 \\ Q9 & Bluetooth 1 -> Bluetooth 2 & 584 & 42 \\ Q9 & Bluetooth 1 -> Bluetooth 3 & 418 & 30 \\ Q9 & Bluetooth 1 -> Bluetooth 4 & 553 & 40 \\ Q10 & Bluetooth 3 -> Bluetooth 1 & 418 & 30 \\ Q10 & Bluetooth 3 -> Bluetooth 2 & 536 & 39 \\ Q10 & Bluetooth 3 -> Bluetooth 4 & 515 & 37 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Distance between Bluetooth sensors and travel time under normal conditions. The traffic-flow-based daily average delay per vehicle is calculated based on the total delay and total number of real vehicles in Table 8. The traffic-flow-based daily average delay per vehicle is found by dividing the total delay by the total number of real vehicles. The daily average delay per vehicle for the intersection is calculated by adding up the total delays of all traffic flows and dividing by the total daily real vehicle number of the intersection. The delay performances daily per vehicle at the intersection are shown in Table 9.  
The performance measurement of fixed-time and fully adaptive signaling systems is carried out with the real-time performance measurement system designed in the study. According to the measurement results, the daily average delay per vehicle in the fixed-time signaling system is determined as 58.1 seconds/vehicle, and the daily average delay per vehicle in the fully adaptive signaling system is determined as 45.3 seconds/vehicle. According to the Bluetooth-based performance measurement results, an approximately
## 4 Experimental Results
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref.** & \begin{tabular}{c} **Manufacturing/** \\ **Material + Plating** \\ \end{tabular} & \begin{tabular}{c} **Order \(n\)**/** \\ **Cells** \\ \end{tabular} & \begin{tabular}{c} \(f_{o}\) \\ **(GHz)** \\ \end{tabular} & \begin{tabular}{c} \(FBW\) \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} \(RL\) \\ **(dB)** \\ \end{tabular} & \begin{tabular}{c} \(IL\) \\ **(dB)** \\ \end{tabular} & \
\begin{tabular}{c} \(RLSB\) \\ **(dB)** \\ \end{tabular} &
\begin{tabular}{c} \(Q_{u}\) \\ **(\(\lambda_{0}\times\lambda_{0}\))** \\ \end{tabular} \\ \hline
[30] Figure 11b &
\begin{tabular}{c} CNC machining/ \\ aluminum \\ \end{tabular} & 5 & 14 & 1 & 13 & 1 & 80 & 3480 & \(5.1\times 1.13\) \\ \hline
[31] Figure 9 &
\begin{tabular}{c} CNC machining/ \\ aluminum \\ \end{tabular} & 4 & 40 & 2.5 & 19 & 1.1 & 32 & 1005 & \(4.1\times 1\) \\ \hline
[32] Figure 5 &
\begin{tabular}{c} CNC machining/ \\ brass + silver \\ \end{tabular} & 5 & 59.7 & 1.34 & 9 & 1.7 & 55 & 1046 & \(6.92\times 2.43\) \\ \hline
[33] Figure 9 &
\begin{tabular}{c} CNC machining/ \\ aluminum \\ \end{tabular} & 5 & 11.58 & 0.62 & 10 & 0.9 & 60 & - & \(6\times 4.4\) \\ \hline
[34] Figure 11 &
\begin{tabular}{c} CNC machining/ \\ aluminum \\ \end{tabular} & 5 & 11.59 & 0.86 & 19 & 0.7 & 55 & 5402 & \(8\times 1.8\) \\ \hline
[35] Figure 5 &
\begin{tabular}{c} CNC machining/ \\ aluminum \\ \end{tabular} & 3 & 16 & 2 & 21.2 & 0.53 & 40 & 1560 & \(6.2\times 1.7\) \\ \hline
[36] Figure 7 &
\begin{tabular}{c} 3D printing/ \\ resin + silver \\ \end{tabular} & 5 & 94 & 3.2 & 15 & 0.46 & 50 & 2210 & \(10.2\times 1.8\) \\ \hline
[37] Figure 15 &
\begin{tabular}{c} CNC machining/ \\ aluminum \\ \end{tabular} & 7 & 37.3 & 1.8 & 17 & 1.5 & 70 & 1691 & \(9.62\times 1.48\) \\ \hline
[38] Figure 7 &
\begin{tabular}{c} CNC machining/ \\ brass + silver \\ \end{tabular} & 5 & 61 & 2.5 & 13 & 1.5 & 50 & 928 & \(4.54\times 1.16\) \\ \hline
[39] Figure 18 &
\begin{tabular}{c} CNC machining/ \\ brass + silver \\ \end{tabular} & 3 & 35 & 1 & 9 & 1 & 35 & 1091 & \(1.48\times 2.08\) \\ \hline
[40] Figure 11 &
\begin{tabular}{c} CNC machining/ \\ unknown \\ \end{tabular} & 5 & 12.08 & 0.5 & 15 & 2 & 70 & 44 & \(5.12\times 1.37\) \\ \hline
[41] Figure 7 &
\begin{tabular}{c} 3D printing/ \\ polymer + copper \\ \end{tabular} & 4 & 35.65 & 1.4 & 11.7 & 0.5 & 48 & 1275 & \(2.5\times 2.5\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between the manufactured fifth-order wideband GGW Chebyshev filters and other GGW bandpass filters reported in the literature.
# Dental Hydrogels with Essential Oils with Potential Activity in Periodontitis
## 3 UV-Vis Absorption Spectroscopy of the Hydrogels
### Antimicrobial Activity of the Experimental Gels Containing Natural Essential Oils
In particular, carvacrol has extensively been tested as an antimicrobial agent in food to control gram-positive and gram-negative pathogens, including _Bacillus cereus_, _Enterococcus faecalis_, _Listeria monocytogenes_, _Staphylococcus aureus_, _Escherichia coli_, _Pseudomonas fluorescens_, _Salmonella typhimurium_, _Vibrio cholerae_, and _V. vulnificus_[35; 36]. The most striking fact to emerge from these data is that all of the pathogens are the main etiologies for oral pathologies concerning the mouth cavity and also gastrointestinal diseases. These results further widen our knowledge about the importance of phytotherapy and specific natural mini constituents with potent biological activity. Our formula captures the results of the chemical analysis of Oregon(r) essential oil, which reveals the presence of several ingredients, most of which possess important antioxidant and antimicrobial properties [37]. Carvacrol and thymol, two phenols with antibacterial properties, make up the majority of oregano essential oil (78-85%) [38]. Additionally, small components, including the monoterpene hydrocarbons p-cymene and -terpinene, contributed to the oil's antimicrobial action [39]. There are several references on the chemical makeup, antibacterial properties, and use as antimicrobials and antioxidants of some oregano species' essential oils [40]. With the completion of these two test procedures, further data were produced on the effectiveness of the gels, demonstrating that the Thieves(r) hydrogel had the least effective effect and oregano-based gels had the most optimal effect. However, a PubMed search revealed only three other study teams utilizing Oregon(r) essential oil and none employing Frankincense(r) or Thieves(r) for the treatment of periodontitis or other oral disorders, emphasizing the uniqueness of the current work. Our work has some limitations due to the lack of antibacterial testing on all of the specific bacteria which are considered the etiological causes of periodontitis. After thoroughly testing all three essential oils on the above-mentioned spectrum of bacteria, the best should be chosen, and further specific tests should be carried out to demonstrate the biological effect of the new hydrogels prior to in vivo testing. Other specific tests could include immunofluorescence assays and spectrophotometry analyses allowing us to determine the best essential oil that could be used in the synthesis of the hydrogel, being able to stand out as a singular therapy in periodontitis. Despite all of this, we believe our work could be a starting point in establishing new treatments for oral pathologies.
# Electric Load Forecasting Based on Deep Ensemble Learning
## 2 Materials and Methods  
This paper proposes an anomaly detection method based on the CNN-BiLSTM-RF model for electric load data. This method employs CNN to extract temporal and spatial information from the load data. The extracted features are then processed through a BiLSTM to capture the temporal dependencies in the data more effectively. An improved random forest is utilized as a classifier for anomaly detection, and a panel logistic regression model is employed to analyze the causes of sudden changes in the electric load across various industries ([PERSON] et al., 2018). After ranking the features based on significance, features with higher importance are assigned greater weights, while those with lower priority receive reduced weights.  
Subsequently, the paper presents a deep ensemble learning model named DCC-EL, which integrates dilated causal convolutional neural networks with ensemble learning and mitigates model overfitting through cross-validation ([PERSON] et al., 2018). Initially, XGBoost, CATBoost, and LightGBM models are the base learners in ensemble learning to understand deep cross features. The prediction outcomes from ensemble learning are adopted as a new feature set for a secondary ensemble. Simultaneously, the dilated convolutional neural networks extend the receptive field of the convolutional kernel. Features and their assigned weights are then introduced into the DCC-EL model to predict future power load values. The DCC-EL model incorporates feature weights in its predictions.
# Total Internal Reflection Lens for Optical Wireless Receivers
## 3 Measurements
### Data Transmission Results  
Figure 7a-c show the measured eye diagrams for the channel without the lens and Figure 7d-f for the channel with the lens. Table 2 lists the BERT data. The BER measurement is clipped at \(10^{-12}\), when no bit errors occurred after 1000 s. For all measurements, the eye opening increases with a TIR lens. The improvement varies with the received signal power and thus the APD bias that is adjusted to avoid saturation. The improvement is clearly visible for rather high distances with a weak input signal. For instance, at \(Z_{\text{o}}\) = 150 cm, there is practically an error-free transmission with the TIR lens, but no transmission at all without it. The avalanche effect leads to stronger noise at the high level compared to the low level [33] (pp. 58-60). This can be seen in particular for the weak signals in Figure 7b,c, where the APD gain is high.
## Abbreviations  
\begin{tabular}{l l} \((i:j)\) & The binary value of the integrated shadow map \\ \(B_{c}(i,j)\) & The binary value of the point \((i,j)\) in the horizontal shadow map \\ \(\overline{B_{c}}(i,j)\) & The binary value of the point \((i,j)\) in the vertical shadow map \\ \(l_{c}(i,j)\) & The integrated shadow map \\ \(K_{i}\) & The aspect ratio of the shadow group \\ \(\underline{L_{i}}\) & The total length of the shadow group \\ \(\overline{W_{i}}\) & The average width of the shadow group \\ \(R_{k}\) & The aspect ratio threshold, \(R_{k}=10\) \\ \(P_{i}\) & The average profile width difference rate of shadow group \(i\) \\ \(N_{i}\) & The total number of profiles of shadow group \(i\) \\ \(W_{ij}\) & The width of the \(j\)th section of shadow group \(i\) \\ \(R_{P}\) & The threshold value of the average profile width difference rate, \(R_{P}=1.0\) \\ \(V_{i,j}\) & The current direction of the third section of shadow group \(i\) \\ \(\widetilde{V}_{i,j}\) & The next direction of the \(j\)th section of shadow group \(i\) \\ \(P_{i,j}\) & The center point of the section of shadow group \(i\) \\ \(T_{i}\) & The center point of shadow group \(i\) \\ \(R_{t}\) & The average direction difference threshold, \(R_{t}=\pi/12\) \\ \(M_{j}\) & A set of input feature maps. \\ \(b\) & The bias for each output feature map. \\ \(n\) & The subsampling layer number. \\ \(up(\cdot)\) & An operation of up-sampling which tiles each pixel in the input horizontally and vertically \(n\) times in the output if the subsampling layer subsamples by a factor of \(n\). \\ \(b_{j}\) & The bias. \\ \(\textbf{k}_{ij}^{l}\) & The kernel weight. \\ \(\left(\textbf{p}_{i}^{l-1}\right)_{up}\) & The patch in \(\textbf{x}_{i}^{l-1}\) multiplied by \(k_{ij}^{l}\) via the element-wise procedure to calculate the element at \((u,\,v)\) in the output map \(\textbf{x}_{j}^{l}\) in the convolutional layer. \\ \(\textbf{x}_{j}^{l}\) & The input maps in the convolutional layer. \\ \(down(\cdot)\) & A sub-sampling procedure. \\ \(\beta\) and \(b\) & The bias parameters. \\ \(P\) & The accuracy rate \\ \(R\) & The regression rate \\ \(F\) & F- score \\ \(3\)D-SM & Three-dimensional Shadow Modeling \\ CNN & Convolutional Neural Network \\ SHM & Structural Health Monitoring \\ I/P & Input \\ O/P & Output \\ \(N_{TP}\) & The number of true-positive elements \\ \(N_{FP}\) & The number of false-positive elements \\ \(N_{FN}\) & The number of false-negative elements \\ \end{tabular}
## 5 Production of Carotenoids by Haloarchae
### Bacterioruberin Biological Roles  
_Bacterioruberin as antioxidant compound:_ The scavenging capacity of the oxygen reactive species (ROS) is dependent on the carotenoid concentration as it has been described so far. On the other hand, the antioxidant capacity of carotenoids in general is related to the length of the carbon chain, the number of pairs of conjugated double bonds and the carotenoids concentration [118; 119; 120]. As mentioned before, bacterioruberin contains 13 pairs of conjugated double bonds _versus_ the nine pairs of conjugated double bonds of the \(\beta\)-carotene. Therefore, bacterioruberin is much better than \(\beta\)-carotene as radical scavenger [5; 121]. It has been demonstrated that it protects the cells against oxidative damage. As a consequence of this important biological role, haloarchaea escape from fatal injury under strong light, and resist oxidative DNA damage resulting from radiography, UV irradiation, high doses (5 kGy) of gamma irradiation and H\({}_{2}\)O\({}_{2}\) exposure [107; 122]. What is clearly stated up to now is that the carotenoids of halophilic microorganisms present higher antioxidant capacity than the carotenoids produced by the other microorganism (extremophilic or not extremophilic).  
_Bacterioruberin controls membrane rigidity:_ With 4 hydroxyl substitutes in this dipolar C\({}_{50}\) carotenoid, bacterioruberin was suggested to act as a \"rivet\" in the membrane cells. This carotenoid has some effect on fluidity of the membrane, acts as a barrier to water and allows permeability to oxygen and other molecules, so strains can survive in hypersaline or low-temperature conditions [105; 123].  
_Bacterioruberin as part of the rhodopsin complexes:_ Archaehrodopsin-2 (aR2) is a retinal protein-carotenoid complex found in the claret membrane of _Halorubrum_ sp. as well as in other species [124; 125; 126]. It functions as a light-driven proton pump highly important for haloarchaea cells to obtain energy. Using crystallographic studies it has been demonstrated that bacterioruberin binds to crevices between the subunits of the archaehrodopsin structure, which is a trimer. So, bacterioruberin sustains structural support related to the archaehrodopsin structure [127]. Bacterioruberin is also part of a complex constituted by this carotenoid and halorhodopsin in haloarchae membranes such as those from _Natronomonas pharaonis_. Halorhodopsin is a retinal protein with a seven-transmembrane helix and acts as an inward light-driven Cl(\(-\)) pump [128].
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 2 Energy-Efficient IoT
### Existing Energy-Saving Techniques
#### 2.3.4 Data-Management Technology  
Data aggregation refers to the process of combining multiple data sources or sensors into a single unified data set. Data aggregation lessens the power consumption required for communication by reducing the amount of data transmitted. In-network processing, on the other hand, strives to reduce the energy consumption by processing data at the edge of a network, with only the necessary information or processing results being sent to the cloud.