esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Aiming to address the actual problem of the rear row original rock strain, this paper proposed a three-hole simultaneous blasting technology. The feasibility of the technology was verified through onsite blasting experiments and numerical simulations. Based on the obtained results, the following conclusions are drawn:  
1. Through onsite blasting tests, it was found that the three-hole simultaneous blasting technology yielded a good blasting effect, with fewer flying stones and more concentrated blasting piles. Additionally, the original rock in the rear row had no obvious tensile damage, and the block size was relatively uniform, with no large blocks produced.
2. Numerical simulations concluded that compared to the hole-by-hole blasting method, the three-hole simultaneous blasting method provided more advantages. A transverse crack formed in the direction of the connection line at the center of the blast hole. This was consistent with the findings of the onsite blasting experiments.
3. The three-hole simultaneous blasting technology proposed in this paper combined the stepwise blasting technology and the smooth blasting theory. It could effectively avoid the problem of rear row strain in the blasting area. Based on the preliminary results reported in this work, the proposed technology could be promoted and applied to similar mining bench blasting.
4. In the future, it is necessary to further strengthen the on-site testing and numerical simulation research of the three-hole step-by-step detonation technology in different mines.  
Conceptualization, X.Z. and Z.L.; Methodology, X.Z. and Z.L.; Validation, W.G.; Formal analysis, X.Z. and Z.W.; Investigation, X.Z., Z.W. and W.G.; Resources, Z.L.; Data curation, Z.L.; Writing--original draft, X.Z., Z.W. and W.G.; Writing--review & editing, X.Z. and Z.L.; Visualization, Z.W. and W.G.; Supervision, X.Z. and Z.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Beijing Postdoctoral Research Foundation (2022-ZZ-100), the Chaoyang District Postdoctoral Work Foundation, and the Foundation of Hubei Key Laboratory of Blasting Engineering.  
Not applicable.  
Data availability Statement: Data are contained within the article.  
Author [PERSON] was employed by the company Inner Mongolia Kinergy Blasting Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Acoustic Models
### Energy Consumption Model  
To analyze the energy consumption model [9] for acoustic communication, we first use the passive sonar equation to calculate Signal-to-Noise Ratio (SNR) in an acoustic channel.  
\[SNR=SL-TL-NL+DI\geq DT \tag{1}\]  
In the above equation, _SL_ and _TL_ denote Source Level and Transmission Loss, respectively. Moreover, _NL_ is Noise Loss, _DI_ is Directive Index and _DT_ is the Detection Threshold of the sonar. Units of all the quantities are in \(dBre\mu Pa\).  
Transmission loss may be computed by using Thorp model [26] as follows:  
\[TL=10 log(d)+\alpha d\times 10^{-3} \tag{2}\]  
where, \(\alpha\) is the absorption coefficient and \(d\) is the distance between sender and receiver nodes.  
Another important factor, NL [27], consists of four noise components, which are calculated by using the equations given below. _NL_ depends upon frequency of the signal:  
\[10 log(N_{t}(f))=17-30 log(f) \tag{3}\]\[10 log(N_{s}(f))=40+20(s-0.5)+26 log(f)-60 log(f+0.03) \tag{4}\]  
where \(s\) is a shipping constant and \(w\) is a wind constant.  
\[10 log(N_{w}(f))=50+7.5w^{1/2}+20 log(f)-40 log(f+0.4) \tag{5}\]  
\[10 log(N_{th}(f))=-15+20 log(f) \tag{6}\]  
where, \(N_{w}(f)\), \(N_{s}(f)\), \(N_{t}(f)\) and \(N_{th}(f)\) show the noise produced due to wind, shipping, turbulence and thermal activities, respectively. All these factors largely depend on frequency (_f_) as noise increases with the increase in frequency of signal. _NL_ is calculated as:  
\[NL=N_{t}(f)+N_{s}(f)+N_{w}(f)+N_{th}(f) \tag{7}\]  
_SL_ can also be calculated by using passive sonar equation.  
\[SL=SNR+TL+NL-DI \tag{8}\]  
Transmitted signal Intensity, \(I_{T}\), may also be calculated by using SL.  
\[I_{T}=10^{SL/10}\times 0.67\times 10^{-18} \tag{9}\]  
Source transmitted power, \(P_{T}(d)\), can be calculated by using the following equation:  
\[P_{T}(d)=2\pi\times 1m\times H\times I_{T} \tag{10}\]  
In the above equation, \(H\) shows the depth of the network. \(P_{T}(d)\) can also be written as:  
\[P_{T}(d)=2\pi H\times 1m\times H\times 10^{SL/10}\times 0.67\times 10^{-18} \tag{11}\]  
Energy dissipation, \(E_{TX}(k,d)\), in sending \(k\) bits over a distance \(d\) is given as follows:  
\[E_{TX}(k,d)=P_{T}(d)\times T_{TX} \tag{12}\]  
where \(T_{TX}\) is the transmission time in seconds.
## 2 Integration of Artificial Intelligence (AI) in Robotic Surgery  
The integration of artificial intelligence (AI) into robotic surgery represents a transformative leap in surgical precision and efficiency. AI, especially through machine learning (ML) and deep learning (DL), plays an instrumental role in advancing surgical capabilities by enabling real-time data analysis and providing decision support tools to surgeons. While early applications are promising, most AI tools in surgery remain in early validation phases and lack prospective multicenter trials. Their clinical deployment is still largely confined to high-volume tertiary centers with robust informatics infrastructure. Critically, these applications--ranging from intraoperative decision support to postoperative prediction--are not yet uniformly validated. Clinical integration depends on factors such as data quality, surgeon compliance, and regulatory approval. Future efforts should prioritize prospective validation and cost-effectiveness analysis to facilitate broader adoption.
# Treatment of Biofilm Communities: An Update on New Tools from the Nanosized World
## 6 Antibiofilm Application of Nanotechnology
### Physical Factors  
Combination of physical removal of biofilm with other treatments is regarded with increasing attention since the sole mechanic disruption may lead to dispersal of small residues, therefore increasing the risk of infection [7; 73]. Photothermal activity has been studied in conjunction with gold nanoparticles, owing to their intrinsic physico-chemical properties, resulting in significant biofilm reduction of both Gram-negative and Gram-positive bacteria [128; 129]. Photodynamic treatment of biofilm in combination with organic compounds is another strategy that has been tested and yielded good results on staphylococcal biofilms [39; 130; 131; 132].  
Therefore, a wide range of nanotechnological applications are now available to tackle biofilm infection. Many of them showed promising features at laboratory level. Research effort should befocused on translating these principles into clinical application to reduce the well-known medical burden of biofilm communities.
## 2 Derivation of Functionality Loss Evaluation Model for Railway Network Components
### 2.2. Establishment of Functionality Loss Evaluation Models for Railway Network Components  
2.2.1. Seismic Fragility Curves for Railway Network Components  
We applied previously researched fragility curves for railway network components--bridges, tunnels, and embankments. For Korean railway bridges, PSC box girder bridges are predominant, and [PERSON] et al. [13] proposed fragility curves for railway PSC box girder bridges by linearly combining three prior models. In this study, the proposed seismic fragility curves were applied to evaluate the seismic resilience of bridge components in the  
Figure 1: Procedure for development functionality loss curve evaluation.  
railway network. The fragility curves are shown in Table 1 and Figure 2. The formula for deriving the seismic fragility function is presented in Equation (1).  
\[\mathrm{P[ds|IM]}=\Phi\bigg{(}\frac{\mathrm{ln}X-\mu}{\beta}\bigg{)} \tag{1}\]  
where X represents the Peak Ground Acceleration, \(\mu\) denotes the median (g), and \(\beta\) represents the standard deviation (g).  
For embankment structures, the seismic fragility curves proposed by [PERSON] and [PERSON] [16] are the most widely used. In this study, the seismic fragility functions for embankment structures were applied based on subsoil stiffness and embankment height as proposed by [PERSON] and [PERSON] [16]. The applied seismic fragility curves vary with the softness of the subsoil. These fragility curves are shown in Table 2 and Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**(a) Soft ground (H = 6 m)** & \\ \hline
**Damage State** & **Median (g)** & **Standard Deviation (\(\beta\))** \\ \hline Slight & 0.12 & \\ \hline Moderate & 0.2 & 0.8 \\ \hline Extensive & 0.34 & \\ \hline
**(b) Soft ground (H = 4 m)** & \\ \hline
**Damage State** & **Median (g)** & **Standard Deviation (\(\beta\))** \\ \hline Slight & 0.25 & \\ \hline Moderate & 0.37 & 0.8 \\ \hline Extensive & 0.54 & \\ \hline \end{tabular}
\end{table}
Table 2: Fragility curve for embankment [16].  
\begin{table}
\begin{tabular}{c c c} \hline
**Damage State** & **Median (g)** & **Standard Deviation (\(\beta\))** \\ \hline Slight & 0.293 & 0.632 \\ Moderate & 0.464 & 0.677 \\ Extensive & 0.596 & 0.699 \\ Complete & 0.853 & 0.691 \\ \hline \end{tabular}
\end{table}
Table 1: Fragility curve for railway bridge [13].  
Figure 2: Fragility curve for a railway bridge.  
Figure 4: Fragility curve for railway tunnel. (**a**) Shallow tunnel; (**b**) deep tunnel.  
Figure 3: Fragility curve for railway embankment. (**a**) Soft ground, H = 6 m; (**b**) soft ground, H = 4 m; (**c**) soft ground, H = 2 m; (**d**) stiff ground, H = 6 m; (**e**) stiff ground, H = 4 m; (**f**) stiff ground, H = 2 m.
## 3 Results  
### Trueness  
The trueness values measured for the crowns fabricated with the three ceramic materials are shown in Table 2. The first null hypothesis was rejected for trueness (\(p<0.001\)) because the trueness values varied according to the ceramic material. Crowns fabricated with HASS Rosetta had the lowest trueness value (\(26.5\pm 2.5\) um), which was significantly different from the crowns fabricated with IPS e.max (\(33\pm 8.8\) um) or VITA Suprinity (\(38.4\pm 5.4\) um) (\(p<0.05\)); however, trueness values were not significantly different between crowns fabricated with IPS e.max CAD and VITA Suprinity (\(p\geq 0.05\)).  
Figure 3: Procedure for measuring marginal and internal fit. (**A**) A fabricated model without silicone material. (**B**) A fabricated model with silicone material. (**C**) A scanned model without silicone material. (**D**) A scanned model with silicone material. (**E**) Superimposition of (**B**,**D**). (**F**) Marginal and internal fit measurements on virtual planes by measuring the thickness of the replicated silicone material.  
Comparisons of trueness using color difference maps (Figure 4) showed the same trend as the data in Table 2. Crowns fabricated with HASS Rosetta had the smallest area of blue and red, while the extent of blue and red in the color difference maps from crowns fabricated with IPS e.max CAD and VITA Suprinity were similar to each other, but greater than that of HASS Rosetta (Figure 4).
## 4 Proposed Scheme
### Step 1: Forming the Inner Circle  
Note that nodes located in proximity to the base station must forward packets from other nodes; therefore, the traffic of the whole network is converged to a specific area near base station, resulting in energy hole. This extra task puts a burden on these nodes that can be alleviated through the charging of nearby nodes by drones starting from the base station in the proposed scheme.  
To prevent the energy hole phenomenon caused by traffic converging near the base station, the scheme constructs an inner circle with a radius \(D_{dmax}/2\), centered on the base station. Here, \(D_{dmax}\) is defined as:  
\[D_{dmax}=\frac{e_{\max,d}-(e_{\max,sn}-e_{\min,sn})}{r_{d}} \tag{10}\]  
In the proposed scheme, nodes located within the inner circle are placed in to set \(S_{in\_d}\), and charged by drones waiting at the base station. Based on Equation (10), the drones always have sufficient energy to fly from the base station, charge nodes in \(S_{in\_d}\) and then return to base station.  
Algorithm 1 outlines the procedure for forming the inner circle.  
Input: A requesting node set \(S\), base station \(s_{0}\), the maximum flight distance of drone \(D_{dmax}\).  
Output: \(S_{in\_d}\)  
Step 1: Compute the Euclidean distance from every node to \(s_{0}\) in \(S\).  
Step 2: for each \(s\) in \(S\)  
if \(d(s,s_{0})\leq D_{dmax}/2\),  
\(s\to S_{in\_d}\)  
Step 3: Output \(S_{in\_d}\)  
**Algorithm 1. Forming the inner circle**
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 6 Promising Bio-Energy Solutions, Lessons Learned
### Enzymatic Bio Fuel Cells  
Since biofuel cells with abiotic electrocatalysts like platinum [427] or microbial [428] catalysts should not be implanted in living organisms for high inflammation risks, enzyme-immobilized EBFCs remained as the only viable option and many research groups have focused on this specific field. However, host immune response is still an important bottleneck [262] because the collagen capsule, generated as a natural response to an external implant, can limit the access of analytes [268] and initiate degradation of immobilized electrons [269]. We have summarized recent EBFC works based on their concentrations in Table 2.  
At this point the major challenges for implementing EBFCs in the real world are:  
1. Insufficient output voltage level [429].
2. Limited performance due to incomplete oxidation by the dedicated enzymes [259].
3. Demand for an operating range of pH and temperature [259].
4. Selection (unavailability) of optimal substrates inside a living body which will provide fuel for the cell, without causing immune response in the host and can accommodate with the bio-fouling conditions caused by the host body [262, 387].
5. Durability is the other bottleneck factor since lithium batteries for implanted devices can operate on average for five years [430], while the longest in vivo operating time for EBFCs is about two months [35].
6. In terms of EBFC fabrication, the major challenge lies in effective enzyme wiring for efficient direct electron transfer mechanism [267].
7. An additional concern is low oxygen concentration at cathodes which limits the performance of such biofuel cells.  
Micro-fluid structures for EBFCs have also been widely studied since they offer advantages in terms of easier fluid manipulation, faster chemical response and higher surface to volume ratio [401]. From Table 2, it can also been seen that interest has shifted towards partial [280] or completely external [283] EBFC configurations in order to overcome the major bottleneck of internal host body reaction. Studies on the use of an enzymatic biofuel cell on a contact lens [280, 281, 282] and skin patches [283, 284] support this hypothesis. For such externally wearable configurations, more concentration will be made towards developing base component for EBFCs which would be compact, flexible, biocompatible as well as mechanically and chemically stable [262]. Another alternative approach towards the solution of reducing host body immune system could be merging biodegradable and edible electronics [431] and targeted drug-delivery [432] technology with EBFCs which would stay in the host body system only for a short while and could be taken as regular medication. Application of EBFC fueled bio-energy harvesters in cyborg animals [34, 258, 433] are also found which show the promise of a new generation of convenient and energy autonomous environmental and agricultural monitors.
## 3 Proposed Model
### Question Constraint Extraction
#### 3.3.2 Constraint Detection  
When the model receives the question \(S_{S}\), it is preprocessed and delivered to the semantic encoding layer, which uses Bert as the pretrained model to generate the matrixed representation \(\mathbf{E}_{CD}\) of the question. Following that, \(\mathbf{E}_{CD}\) is input into the semantic reasoning layer, which employs a bidirectional recurrent neural network BiGRU to capture the contextual information of the question's words and improve the model's reasoning abilities. The question's forward encoding \(\overrightarrow{\mathbf{E}}_{CD}=\big{[}\overrightarrow{\mathbf{e}_{1}}, \overrightarrow{\mathbf{e}_{2}},\ldots,\overrightarrow{\mathbf{e}_{n}}\big{]}\) and backward encoding \(\overleftarrow{\mathbf{E}}_{CD}=\big{[}\overleftarrow{\mathbf{e}_{1}}, \overleftarrow{\mathbf{e}_{2}},\ldots,\overleftarrow{\mathbf{e}_{n}}\big{]}\) are then obtained, and the two are concatenated to generate \(\overleftarrow{\mathbf{E}}_{CD}\), which is added to the original input \(\mathbf{E}_{CD}\) to fuse the information contexts. Finally, this layer produces the encoded information for the question \(\mathbf{H}_{W}\in\mathbb{R}^{n^{*}q}\), where \(q\) is the word vector dimension. Equations (10) and (11) are used in the process:  
\[\overleftarrow{\mathbf{E}}_{CD}=concat(\overrightarrow{\mathbf{E}}_{CD}, \overleftarrow{\mathbf{E}}_{CD}) \tag{10}\]  
\[\mathbf{H}_{W}=\overleftarrow{\mathbf{E}}_{CD}+\mathbf{E}_{CD} \tag{11}\]  
\(\mathbf{H}_{W}\) is then sent into the constraint detection layer, which we model as a sequence tagging task, tagging each token with entity tags using **TLNO** tag (**T** is a temporal constraint, \(\mathbf{L}\) is a spatial constraint, \(\mathbf{N}\) is a numerical constraint, and \(\mathbf{O}\) is other). The constraint detection layer first employs a trainable matrix \(\mathbf{W}_{D}\in\mathbb{R}^{q^{*}m_{t}}\) to reduce the dimensionality of \(\mathbf{H}_{W}\), where \(m_{t}\) is the number of tag types, here \(m_{t}=4\), and then employs softmax as a classifier to predict the corresponding tag \(\mathbf{P}_{D}\in\mathbb{R}^{n^{*}m_{t}}\) for each token, implying the probability of the label corresponding to each token predicted by the classifier, for which the loss function is calculated using cross entropy, as shown in Equation (12):  
\[Loss_{3}=-\frac{1}{n}\sum_{i=1}^{n}\sum_{j=1}^{m_{t}}y_{i,j}\log p_{i,j} \tag{12}\]  
where \(y_{i,j}\in\{0,1\}\), if the \(i\)th token's real tag is \(j\) then \(y_{i,j}=1\), otherwise \(y_{i,j}=0\). \(p_{i,j}\) is the probability of the \(i\)th token belongs to the \(j\)th tag.
## 3 Experiments and Datasets  
### Case 1: Validation with Generated Interferences and Four-Channel Simulations  
The signals used in the algorithm validations include the following two types: Case 1, separation of rail cracks under simulated noise interference, and Case 2, Separation of rail cracks under real additive railway noise. The simulation validation (Case 1) was used to make sure the feasibility of the proposed method in a standard separation task with a specific known source number. The experimental validation (Case 2) was used to verify the applicability of the proposed method in an actual railway environment. The proposed method is first compared with different BSS methods in a three-source and four-channel simulation. In the simulated experiment, the actual burst-type rail crack signal is adopted as the investigated signal. These actual crack signals were previously acquired in tensile  
Figure 3: Flow chart of the proposed TFSNN employed in the AE signal separation.  
tests of a rail specimen [29]. A detailed description of the tensile test system has been given previously and will not be repeated here. To match the actual crack signal, the sampling rates of the source signals are all 5 MHz with a length of 0.4 ms. The ramp waves and square pulses represent the periodic and impulse interference signals, which normally exist in a typical railway analysis. The frequency of the ramp wave is 10 kHz, while the frequency of square pulse is 100 kHz with a 20 percent duty cycle. The original sources are shown in Figure 4.  
To simulate the signals received by a sensor array and compare the methods under the same conditions, for the simulation the four source signals are mixed using an arbitrarily chosen mixing matrix in Equation (10).  
\[\mathbf{A}=\begin{bmatrix}0.545&0.588&0.993\\ 0.172&0.121&0.253\\ 0.853&0.554&0.533\\ 0.180&0.082&0.892\end{bmatrix} \tag{10}\]  
Furthermore, a uniformly-distributed random noise, which has a \(-20\) dB amplitude level against the noise-free observation, is added to all channels of mixtures to testify to the robustness against noise interference, and the final observed signals of this simulation are shown in Figure 5.  
Figure 4: Original sources in the simulation: (**a**) Crack source; (**b**) Square pulse source and (**c**) Ramp wave source.  
Figure 5: Observation mixture signals in the simulation: (**a**) Channel 1; (**b**) Channel 2; (**c**) Channel 3 and (**d**) Channel 4.
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 4 Material and Methods
### Change Detection Algorithms
#### 4.2.2 Eddm  
The early drift detection method (EDDM) [13] is a modified technique that enhances the DDM method. It is designed to detect drifts as early as possible, even before significant changes in the data distribution occur. EDDM has been shown to be effective in detecting both abrupt and gradual drifts.  
The algorithm computes the minimum distance between the reference distribution and the observed distribution in each window and updates it as new data points arrive. When the minimum distance exceeds a certain threshold, the algorithm signals a drift. The threshold is calculated using statistical tests, such as the Student's \(t\)-test and the Chi-squared test, which are sensitive to small changes in data distribution. EDDM uses different statistical tests than DDM and has been shown to be effective in detecting both abrupt and gradual drifts.