esa-sceva/satcom-chunk-collection huggingface dataset:
# Delayed Combination of Feature Embedding in Bidirectional LSTM CRF for NER
## 2 Related Work
### Model Architecture for NER Task  
The most common model architecture used for NER task in previous works is the bidirectional LSTM CRF [2; 3; 4; 6; 23; 30; 31; 32; 33; 34; 35]. Except for the works of [PERSON] et al., 2015 and [PERSON] and [PERSON], 2019, all these previous works combined various feature encodings like pre-trained word embedding, contextual word embedding, word or character-level representations and dictionary-based representation at the embedding layer to feed them into the bidirectional LSTM CRF network. These early combination models combined many feature encodings by just concatenating into one long embedding vector except [PERSON] et al., 2016 suggested a weighted sum based on attention mechanism instead of concatenation.  
In contrast, [PERSON] et al., 2015, and [PERSON] and [PERSON], 2019, bypassed some feature encodings and combined them with the output of the bidirectional LSTM blocks. We call these models delayed combination in contrast with the early combination models. [PERSON] et al., 2015, passed the pre-trained word embedding through the bidirectional LSTM blocks and then concatenated the output with additional word encodings based on word spelling, n-gram context of word, POS and chunk, and gazetteer. [PERSON] and [PERSON], 2019 first combined pre-trained word embedding with dependency encoding at the embedding layer and then passed it through dependency-guided bidirectional LSTM blocks. They secondly combined the output of the blocks with ELMO embedding just before the CRF layer.  
When the features are combined, the feature collision may occur ([PERSON] et al. [36]) and this could cause that some important information may disappear after combination. This may limit the performance improvement to be obtained from various feature encodings. [PERSON] et al., 2015 also suggested that this delayed combination could accelerate the training process with similar performance.  
However, [PERSON] et al., 2015 and [PERSON] and [PERSON], 2019 did not provide and analyze any comparison between their delayed combination approach and the common early combination approach to show how effective the delayed combination is.  
In this paper, we compare the delayed combination model with the early combination model by evaluating our own implementation of the two approaches and also compare our result to the previous works having similar model architecture and features to show the effectiveness of the delayed model.
## 4 Case Study--Numerical Experiments
### Out-Bound Logistics Process
#### 4.2.1 DES Model for SM's Outbound Logistics Operation  
A DES model, as illustrated in Figure 9, has been constructed to emulate the outbound logistics process of SM. This simulation model incorporates a finite OQN framework within the Anylogic software platform. Subsequently, optimisation configurations are inputted into the model to ascertain the optimal buffer sizes for each service station.  
The simulation-optimisation experiments conducted in this study involve using various customer arrival rates to represent distinct scenarios. The identified customer arrival rates and the ratio between standard and spot customers are presented in Table 8. The objective of the optimisation study is to maximise the throughput, which corresponds to the number of successfully fulfilled customer orders. A constraint is imposed with the budget limit for buffer spaces, with each space costing EUR 100. The total budget allocated for buffer spaces is EUR 8000. The optimisation experiment is executed over 12 h, with a maximum of 1000 iteration runs. These experimental settings align with the methodology employed in the previous section.
# Fast Sound Source Localization Based on SRP-PHAT Using Density Peaks Clustering
## 7 Conclusions  
Among the existing sound source localization algorithms, SRP-PHAT has the advantages of anti-noise and robustness against reverberation. However, the heavy computational load prevents it from applying in real-time situations. To solve the problem, an improved procedure called ODB-SRP-PHAT was proposed by the authors. The basic idea of ODB-SRP-PHAT is constructing an ODB prior to real-time sound source localization.  
Sound signals were emitted from all the possible sound source positions in turn artificially, and were collected by a microphone array. These signals were then evaluated with SRP-PHAT to localize each sound source position. To improve the localization accuracy, DPC was used. The center of each cluster, i.e., the sound source locations, was stored in an ODB. In real-time localization, the SRP values of only the points in the ODB would be computed and the maximum value indicated the sound source. Hence the heavy computational load of searching the whole space was considerably reduced.  
Simulations and tests in a real environment verified the localization accuracy, the anti-noise ability, and the robustness against reverberation of the proposed method. Compared with SRP-PHAT, ODB-SRP-PHAT exhibited better performance and higher efficiency and, therefore, is applicable for real-time localization. However, ODB-SRP-PHAT is designed to be suitable for the situation of fixed sound sources. In future work, a study using a microphone array with better elevation resolution in more complicated circumstances will be performed to further validate the performance of ODB-SRP-PHAT.  
[PERSON]-B.Z. and H.C conceived and designed the theoretical verifications; [PERSON]-B.Z. performed the simulations and experiments; [PERSON]-B.Z. and H.C. analyzed the results and wrote the paper; [PERSON] revised the manuscript to improve the quality of English. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Scientific research project of the Hunan Education Department, grant number 20C1512.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Photodegradation Experiments  
Methylene blue dye (MB) and Rhodamin B dye (RhB) purchased from Sigma-Aldrich (St. Louis. MO, USA) have been used for monitoring the photoactivity of the synthesized TiO2@rGO nanocomposites. Each pollutant was dissolved in MilliQ water in order to reach a concentration of 10 mg/L. The photocatalysts (4.5 mg) were dispersed in 9 mL of the contaminant solution within a modified beaker reactor with quartz cover. As-prepared samples were left for 60 min in a chamber without irradiation. After the irradiation was turned on, the photocatalytic efficiency was monitored over 120 min. The magnetic stirrer was continuously turned on. Concentration change was measured by the adsorption decrease using a UV/VIS spectrophotometer (PerkinElmer Lambda 950) in set time intervals while irradiated by light in a climatic chamber equipped with the cooling system, using Osram's Ultra Vitalux lamp. The lamp produces a mix of radiation intervals that can be found in the natural solar radiation spectrum. Therefore, the term solar-like radiation for this radiation mix was used. The lamp and the solution in the reactor were set up at a distance of 20 cm.  
From the slope of the straight line, the first-order rate constant was calculated, represented with the following equation [31]:  
\[A_{t} =A_{0}\cdot e^{-k\cdot t} \tag{1}\]  
where \(k\) (min\({}^{-1}\)) is the rate constant of photodecomposition of dye, \(A\) is the absorption of dye at the time of the photocatalytic process, and \(A_{0}\) is the absorption at the beginning of the experiment [32; 33].  
The calculation of half-life (\(t_{1/2}\)) was done using the following equation [31]:  
\[t_{1/2}=\frac{ln2}{k} \tag{2}\]  
The percentage of photocatalytic degradation efficiency of the prepared photocatalysts were calculated using the following equation [31]:  
\[\eta=\frac{A_{0}-A_{t}}{A_{0}}\cdot 100\% \tag{3}\]where \(\eta\) is the percentage efficiency of the photodecomposition of dye, \(A_{0}\) is the absorbance of the starting content of dye, and \(A_{t}\) is the content of dye after irradiation at the time, \(t\) (min) during the photocatalytic experiment.
## 4 Experimental Results and Analysis  
We designed two different experiments for the proposed scheme to demonstrate the construction process and effectiveness of our approach. The overall performance is compared with the state-of-the-art schemes shown in Section 4.2.
# A Heuristics-Guided Simplified Discrete Harmony Search Algorithm for Solving 0-1 Knapsack Problem
## 3 Simplified Discrete Harmony Search Algorithm for the 0-1 KP
### Memory Consideration Operator  
Algorithm 3 is the memory consideration operator which is used to construct a feasible solution from scratch. In Algorithm 3, items are processed in non-ascending order of the profit-weight ratio under the constriction of the capacity constraint. This heuristic-guided strategy has been widely used by other metaheuristics, such as [13; 21; 23; 24; 44; 45], to design the greedy repair operator to repair an infeasible solution.  
```
1:\(x_{j}^{new}=0\), for \(j=1,2,...,n\)
2:\(v=0\)
3:\(c=0\)
4:for\(k=1\) to \(n\)do
5:\(j=lk_{k}\)
6:if\(c+w_{j}\leq W\)then
7: Randomly select a harmony \(x^{i}\) from the harmony memory
8:\(x_{j}^{new}=x_{j}^{i}\)
9:if\(x_{j}^{new}==1\)then
10:\(v=v+p_{j}\)
11:\(c=c+w_{j}\)
12:endif
13:endif
14:endfor
```  
**Algorithm 3** The memory consideration operator.  
In Algorithm 3, variable \(x^{new}\) is a vector which represents the new constructed solution. Each element \(x_{j}^{new}\) of \(x^{new}\) indicates whether item \(j\) is in the knapsack. In the first line of Algorithm 3, each element of \(x^{new}\) is set to 0, which means no item is selected in the beginning. Variable \(v\) and variable \(c\) represent the total profit and the total weight of the items in the knapsack, respectively. Variable \(h\) is a vector which stores the items in non-ascending order of the profit-weight ratio. From line 4 to line 14 of Algorithm 3, items are processed in non-ascending order of the profit-weight ratio. If the selection of an item \(j\) does not violate the capacity constraint, then a harmony \(x^{i}\) is randomly selected from the harmony memory and the value of \(x_{j}^{i}\) is assigned to \(x_{j}^{new}\). If the value of \(x_{j}^{new}\) is equal to 1, which means item \(j\) is selected, then the profit and weight of item \(j\) is added to \(v\) and  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Operator** & **Algorithm** & **Time Complexity** & **Parameters** & **Num Params** \\ \hline Initialization & HS & O(\(hms\times n\)) & Size of the harmony memory (\(hms\)) & 1 \\  & SDHS & O(\(hms\times n\)) & Size of the harmony memory (\(hms\)) & 1 \\ \hline Memory Consideration & HS & O(\(n\)) & Harmony Memory Consideration Rate (\(hms\)) & 1 \\  & SDHS & O(\(n\)) & None & 0 \\ \hline Pitch Adjustment & HS & O(\(n\)) & Pitch Adjustment Rate (\(par\)), Bandwidth (\(bw\)) & 2 \\  & SDHS & O(\(n\)) & None & 0 \\ \hline Random Selection & HS & O(\(n\)) & None & 0 \\  & SDHS & None & None & 0 \\ \hline Update Harmony Memory & HS & O(\(hms\)) & None & 0 \\  & SDHS & O(\(\log_{2}hms\)) & None & 0 \\ \hline Overall per iteration & HS & O(\(n+hms\)) & & \\  & SDHS & O(\(n+\log_{2}hms\)) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time complexity and parameter comparison between HS and SDHS.  
\(c\), respectively. Apparently, the constructed solution is a feasible solution, and the time complexity of Algorithm 3 is \(O(n)\). Figure 2 is the corresponding flowchart.
## 3 Results and Discussion
### Temperature Sensors and TEC Performance  
An automated TEC test procedure implemented as part of the firmware was executed. This test procedure first heats the system with maximum power for a duration of 1 h and then reduces the power \(u_{T}\) in steps of 5% after fixed intervals. This test covers the range of operation of the TEC module in both heating and cooling modes (100% to \(-\)100%). Temperature changes inside and outside the chamber and the corresponding power applied were recorded, as shown in Figure 11a.  
In the first hour, the temperature inside chamber rises to about 44 \({}^{\circ}\)C and slowly saturates. When the power is reduced from 100% to 85%, the temperature raises by about 0.7 \({}^{\circ}\)C. This can be explained by the additional heating in the TEC due to the ripple voltage generated from the digital to analog conversion. At 100% power, there are no voltage ripples and therefore the thermal output is reduced. The range from 20% to \(-\)20% power produces no change in temperature. This behavior is again the result of digital to analog conversion where the PWM corresponding to this range does not produce voltage high enough to conduct current across the TEC. In the heating mode, the temperature inside the chamber could be raised to 20 \({}^{\circ}\)C above the ambient temperature. However, in cooling mode, the temperature could only be lowered by 10 \({}^{\circ}\)C. This phenomenon could be explained by Equation (3), representing a TEC model [33; 34]. The heat transferred (W) by the TEC is given by  
\[Q_{\mathrm{TEC}}=\alpha_{\mathrm{q}}I_{\mathrm{q}}\delta T+(I_{\mathrm{q}}{}^{2 }R_{\mathrm{q}})/2, \tag{3}\]  
Figure 11: Evaluation of sensor and actuator performance: (**a**) TEC actuator and temperature sensor test; (**b**) humidifier and humidity sensor test; (**c**) gas exchange test; and (**d**) light spectrum and circadian pattern generation. E1 and R1 correspond to measurements made outside and inside the chamber, respectively, using a reference sensor. Data represented by \(u\) correspond to actuator state.  
where \(\alpha_{\rm q}\) is the Seebeck coefficient (V K\({}^{-1}\)), \(I_{\rm q}\) is the current (A) flowing through, \(\delta T\) is the temperature difference (K) between the hot and cold sides, and \(R_{\rm q}\) is the series resistance (\(\Omega\)) of the TEC module. It can be seen that the joule heating term, \(I_{\rm q}^{2}R_{\rm q}\), contributes positively to the heating mode while affecting the performance negatively in the cooling mode. It can also be noticed that the outside temperature also increases during the initial heating phase. This results from the heat transferring from the chamber to the outside. It can be concluded from this observation that the thermal conductivity of the chamber walls needs to be reduced to increase the thermal insulation of the chamber.
## 3 Results
### Microstructure of Mortar/SAPs Specimens
#### 3.2.2 Image Segmentation and Phase Identification  
There are a number of different approaches on the matter of image segmentation and phase identification via microCT Analysis. One that stands out as it is widely used is the global thresholding method in GSH. The GSH with the Gaussian deconvolution procedure performed for the mortars examined in this work, are given in Figure 8.  
Figure 8: Grey Scale Histogram with the Gaussian deconvolution fit for mortar-reference (**a**), mortar-SAPs-1 (**b**), mortar-SAPs-2 (**c**).  
The deconvolution of the initial GSH as described earlier, produced four distinct curves in each case, curves 1 to 4 and the corresponding peaks (Figure 8). Curve 1--Peak 1 were attributed to pores, curve 2 and curve 3 were assigned to hydrated cement phases and curve 4 was ascribed to unhydrated cement phases and SAPs. We observed that for all specimens the main peak in the GSH was deconvoluted in two separate peaks which were attributed to different hydrated products probably owed to the main hydrated cement products which are Calcium Silicate Hydrates, C-S-H and Calcium Hydroxide, Ca(OH)\({}_{2}\).  
Then, using the GSH data, the quantitative determination of the different phases identified in the mortar specimens was performed and the corresponding results were tabulated in Table 4. Taking into consideration the data in Table 4, it is observed that in the case of mortar-SAPs-1 the unhydrated cement products together with the incorporated SAPs (1% bwoc) represented only the 5% of the total material when the corresponding values for both mortar-reference and mortar-SAPs-2 were 36%. This indicates that the progress of the hydration reactions for this material was remarkably enhanced compared to mortar-reference and mortar-SAPs-2, as a result of SAPs incorporation in the cementitious matrix. This is attributed to more effective SAPs dispersion in the cementitious matrix and therefore enhancement of their functionality to extend hydration reactions during cement curing, thus promoting this as the optimal SAPs concentration in mortars in respect to microstructure evaluation.
# Improved U2 Net-Based Surface Defect Detection Method for Blister Tablets
## 5 Experiments and Analysis of Results
### Ablation Experiment  
In order to determine the impact of each part of the model studied in this paper on the performance of the segmentation detection effect, ablation experiments were carried out using the same training and validation sets, with parameters set to a batch_size of 4, an initial learning rate of 0.001, and the choice of the Adam optimizer. In the first step, the original U2 net was used to train, test, and derive metrics. In the second step, the RSLU module is used instead of the original RSU module to derive the metrics, and then the loss function of Gloss is used instead of the original loss function to derive the metrics as well. Finally, the local means and thresholds of the OSTU method are combined with the previous RSLU and Gloss to derive the metrics. In Table 2, \(\times\) indicates that this module is not available, while \(\surd\) indicates that it is available.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Acc** & **P** & **R** & **F\({}_{\mathbf{1}}\)-Score** & **IoU** & **Testing Time(s)** \\ \hline Improved U2 Net & 0.998 & 0.963 & 0.903 & 0.932 & 0.845 & 0.05 \\ U2 Net & 0.986 & 0.944 & 0.834 & 0.885 & 0.832 & 0.047 \\ Pspnet & 0.942 & 0.578 & 0.67 & 0.621 & 0.581 & 0.07 \\ Deeplabv3 & 0.981 & 0.934 & 0.851 & 0.891 & 0.792 & 0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between the method proposed in this article and other models in blister tablet data.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**RSLU** & **Gloss** & **Local Mean and OTSU** & **Acc** & **P** & **R** & **F\({}_{\mathbf{1}}\)-Score** & **IoU** \\ \hline \(\times\) & \(\times\) & \(\times\) & 0.986 & 0.944 & 0.834 & 0.885 & 0.832 \\ \(\surd\) & \(\times\) & \(\times\) & 0.994 & 0.953 & 0.899 & 0.925 & 0.843 \\ \(\surd\) & \(\surd\) & \(\times\) & 0.994  
As can be seen from the results in Table 2, each of the modules studied in this paper has significantly improved the final results by comparison. Among them, by using the RSLU module consisting of LKA in the model with a dual-dimensional focus on channel and space, which increases the model's attention to the defects in obvious places, the model is able to capture the details of the defects at a higher resolution, and this strengthens the accuracy of the model in dealing with the defect boundaries of the image; the introduction of a new loss function strengthens the model's sensitivity to the defective region, whose boundaries make the model more sensitive to the defect boundary detection; and, finally, the use of local mean and OTSU in the inference stage of the model again strengthens the accuracy of the final results of the model.
## 6 Findings and Discussion
Another finding of this research is the high efficiency of blockchain usage in attack detection systems. While some believe that blockchain can just be used as a secure data storage and sharing medium for this type of application, blockchain offers many other services and benefits. The immutability of stored data is one of the key features of blockchain that improve the robustness and accuracy of the detection techniques and thus the detection system's overall reliability. In our proposal, the dependency and trustworthiness of the immutably stored behaviours are very high. In addition, the transparency and non-repudiation of the stored data is another feature of utilising blockchain in detection systems; this feature comes from the fact that any behaviours will be recorded, along with its owner, whether they are trusted or real-time events, and will be accessible by all participants in the network. This feature is utilised implicitly in our proposal via the detected attack analyser technique, where it will be better exploited in the coming enhanced proposals. Traceability is another feature gained from blockchain, which allows for the tracing of a set of related events and then memorisation of the whole behaviour structure to be utilised as a trusted input of a detection mechanism designed for this level of detection. Overall, it could be argued that our proposal has achieved its objectives and overcome the limitations mentioned in previous proposals. Figure 10: Target system performance speed vs. detection system response speed among detection modes. Based on our experiment, we can generally indicate that adopting blockchain in any detection system will increase the above-mentioned beneficial features in comparison to the detection systems that apply any other distributed method of data storage and exchange. However, it could be argued that as a consequence of the costly mining for each behaviour's event, the overall performance may be affected, especially when data is written to the blockchain, where the effect is based on the applied consensus protocol such as PoW or PoA and network throughput. In fact, it should be said that these effects should be tolerated in order to obtain the required advantages of the blockchain. Therefore, it is observed that there is another trade-off between immutability and performance by adopting blockchain or not, respectively.
# The Vibration Response Characteristics of Neighboring Tunnels Induced by Shield Construction
## 4 Numerical Model Validation
### Calibration of Model Parameters  
The numerical model parameters were determined based on the engineering geological report and triaxial tests of the soil, as shown in Table 1. In the DEM-FDM coupled model, the selection of damping parameters was optimized through continuous adjustment to ensure that the model's output aligns with the monitored data response range. Specifically, the damping coefficients were set based on the field survey data and experimental results, referencing values from similar projects. These parameters were fine-tuned in the model to match the actual measurement data. Data from 300 s of monitoring the shield machine cutterhead were selected as the vibration source input to verify the feasibility of the model. The response displacement data on the adjacent tunnel side obtained through monitoring are shown in Figure 14, with a vibration displacement range of 0-0.03 mm, which matches the displacement range at the parallel section of the cutterhead shown in Figure 8. This indicates that the numerical model is feasible for dynamic analysis, and the damping parameters within the model accurately reflect the actual vibration response. Subsequently, for the simplification of calculations, a theoretical sine wave with an amplitude of 1 mm was applied. Monitoring observations were made by inputting a sinusoidal theoretical wave of 1 mm. The wave was applied to the vibration excavation face on the construction-side tunnel of the model and was loaded in the axial direction for 10 s.  
Figure 13: PFC3D numerical model diagram.