esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Attention Mechanism  
After designing different network structures and developing training strategies for the model, the performance of ship detection in SAR images was significantly improved. However, there are still some difficulties in the detection of multiscale ships. Therefore, global features and suppressing unnecessary and confusing information are the keys to improving the performance of ship detection in SAR images.  
When processing information, the attention mechanism only focuses on the part of the regional information that is beneficial to task realization, which not only describes the focus of the model, but also improves the representation of features. The importance of attention mechanisms has been extensively studied in the literature [33].  
[PERSON] et al. [10] designed a lightweight nonlocal attention module embedded side-by-side into the stem network to suppress background disturbances. [PERSON] et al. [34] designed a new strip pool-based attention mechanism and attached it to the extracted features to enhance the representation.  
The above SAR ship target attention mechanism was intended to imitate human vision, focus attention on the sea area where ship targets are concentrated, eliminate land scattering and other scattering interference, and reduce the scale sensitivity of the network.  
In view of the characteristics of the ship target in the SAR image, the characteristics of the data image were analyzed. A common scenario in the SSDD dataset is shown in Figure 1. In Figure 1a, the ship target is near the shore, with high target density and strong land scattering around it. In Figure 1b, the ship target appears in the ocean far from the land, and the target scattering is less. As shown in Figure 1, different data sets show different characteristics. This part will be introduced in the following content. Ship targets appeared in the water, and after land interference was ruled out, there were still large areas. If all the waters in the picture were traversed and detected, the detection efficiency would be greatly reduced. We proposed an improved spatial attention mechanism, which is improved according to the characteristics of SAR images and improves the detection efficiency while ensuring the detection effect.
###### Abstract  
Background: This pilot prospective study analysed the clinical use of a new bioceramic premixed CaSi-containing sealer in association with a warm carrier-based technique. Methodology: Healthy patients (\(n=38\)) requiring 40 root canal treatments were enrolled. Peripheral X-rays were taken preoperatively, after root canal filling and after 1, 6, and 12 months. Two evaluators assessed the Periapical Index (PAI) and the sealer extrusion. The healing rate and survival rate were also evaluated. Barnard test was used to assess the relationship of each potential prognostic factor with periapical index (PAI) at 12-month follow-up. The significance level was set at 0.05. Results: Root canal treatments (\(n=38\)) were analysed at the end-line (12 months). The total drop-out was 5% (two patients; two teeth). A total of 31 teeth (82%) (PAI 1-2) showed complete healing, while 7 (18%) are still healing. Cumulative survival was 100%. Apical extrusion of the sealers was observed in 18 cases (47%). Of these extrusions, nine (50%) resulted radiographically undetectable after 12 months. Conclusions: The study supports the use of premixed CaSi-based bioceramic sealers in association with carrier-based techniques. Periapical extrusion of the sealer and its radiographic modification or disappearance are possible events reported in the first 12 months.  
carrier-based techniques; premixed bioceramic sealer; periapical healing; apical extrusion; post-operative pain Article  
Clinical Evaluation of a Novel Premixed Tricalcium Silicate Containing Bioceramic Sealer Used with Warm Carrier-Based Technique: A 12-Month Prospective Pilot Study
# Constitutive Expression of a Cytotoxic Anticancer Protein in Tumor-Colonizing Bacteria
## 3 Results
### Fate of ppGpp-Defective S. Gallinarum Injected into Tumor-Bearing Mice
At the indicated time points, samples were collected to measure cell mass (A\({}_{600}\)) and red fluorescent signals using a fluorometer at an excitation wavelength of 550 nm and an emission wavelength of 610 nm. A.U. = red fluorescent signal/A\({}_{600}\). **(C)**\(\Delta\)ppGpp \(S\). Gallinarum was intravenously injected into CT26 tumor-bearing mice (\(n\) = 4/group, 1 \(\times\) 10\({}^{8}\) CFU/mouse) when tumor size reached 130-150 nm\({}^{3}\). Bacterial loads from isolated tumors, livers, and spleens were determined at the indicated times after the injection (related to data shown in Figure S3). **(D)** The fluorescent bacteria were intravenously injected into mice grafted with CT26 cancer cells (\(n\) = 5, 1 \(\times\) 10\({}^{8}\) CFU/mouse). The Salmonella in tumors, spleens, and livers at the indicated time points were observed under a confocal microscope. Bacteria cross-linked with Flamma® Fluors 552 NHS ester are shown in red, bacteria stained with a fluorescent antibody are shown in green, and DAPI-stained nuclei are shown in blue. Yellow arrows indicate merged red and green bacterial signals. Scale bar = 10 \(\mu\)m for 800\(\times\) magnification. **(E)** The fractions of red fluorescent bacteria (indicated by arrows) among green bacteria (total number) in each organ and tumor tissue at 12 h postinjection are shown in a graph (fraction of red fluorescent bacteria = red/green \(\times\) 100, %). Results are expressed as the mean \(\pm\) SD from five sections, unpaired Student’s _t_-tests, _p_\(<\) 0.0001. **(F)**\(\Delta\)ppGpp \(S\). Gallinarum carrying prrmBp1-gfpOVA (SMP4001) was administered to CT26-grafted mice (\(n\) = 5/group) via the tail vein, and gfpOVA expression from the rrmB P1 promoter in the bacteria in tumors, livers, and spleens at the indicated time points was determined by quantitative real-time PCR. The gfpOVA Ct values were calculated relative to \(S\). Gallinarum rpoB Ct values in triplicate. All data are expressed as the mean \(\pm\) SD. Significant differences are shown as _p_\(<\) 0.05 (unpaired Student’s _t_-tests; ns: not significant).
# An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction
## 2 Basic Theory and Algorithm
### Lstm  
Recurrent neural network (RNN) is a type of neural network dedicated to processing time-series data samples. Each layer of its output is output to the next layer and to a hidden state, which is used by the current layer when processing the next sample, as shown in Figure 1. Module M of the RNN reads the input \(x^{(t)}\) and obtains the output \(h^{(t)}\). Circulation is used to complete the transfer to the next step of information from the current step.  
The above chain network structure reveals that RNN is essentially sequence-dependent. However, in practical applications, problems of gradient disappearance and gradient explosion occur. To solve these problems faced by RNN, [PERSON] et al. [30] constructed a LSTM architecture that involves a memory cell. This model resembles a standard RNN with a hidden layer. Each repeating module has a simple tanh layer. The LSTM has the same structure, but the only difference is that the structure inside each module is different, each node in the ordinary hidden layer is replaced by a storage units. The specific structure [34] of the model is shown in Figure 2. This structure ensures the RNN model has the long short-term memory in the form of weights and ephemeral activations.  
\(x^{(t)}\) is the input vector at the current time, \(h^{(t-1)}\) is the hidden layer state value of the previous time\((t-1)\), and the memory unit is the memory of the neuron state, which is used to record the current time state. The forget gate in the LSTM decides what information is retained or forgotten. The forgetting gate is calculated by the sigmoid function. The input gate decides whether to update  
Figure 1: Recurrent neural network (RNN) expansion structure.  
Figure 2: The architecture of a long short-term memory (LSTM) memory cell.  
the state of the LSTM using the current input; the output gate decides whether to pass on the hidden state to the next iteration.  
\[g^{(t)} = \tanh\left(W^{gX}x^{(t)}+W^{gh}h^{(t-1)}+b_{g}\right),\] \[i^{(t)} = \sigma\left(W^{iX}x^{(t)}+W^{ih}h^{(t-1)}+b_{i}\right),\] \[f^{(t)} = \sigma\left(W^{f^{X}}x^{(t)}+W^{fh}h^{(t-1)}+b_{f}\right),\] \[o^{(t)} = \sigma\left(W^{oX}x^{(t)}+W^{oh}h^{(t-1)}+b_{o}\right),\] \[s^{(t)} = g^{(t)}\times i^{(t)}+s^{(t-1)}\times f^{(t)},\] \[h^{(t)} = \tanh\left(s^{(t)}\right)\times o^{(t)}.\]  
where \(W\) and \(b\) values are the layer weights and biases, respectively; \(\sigma\) and \(tanh\) represent the sigmoid activation function and hyperbolic tangent activation function, respectively; \(x^{(t)}\) and \(h^{(t-1)}\) are the input layer and hidden layer at time \(t\), respectively; \(g^{(t)}\), \(i^{(t)}\), \(f^{(t)}\), and \(o^{(t)}\) are the output values of the input node, the input gate, the forget gate, and the output gate, respectively; and \(s^{(t)}\) is an internal state at the current time.
## 2 Distributed Fair Rate Allocation Algorithm
### Proposed Algorithm
#### 2.4.1 Dual Subproblems  
From \(\mathbf{\mu}^{k}\), each sensor computes \(\lambda_{j}^{k}=[\mathbf{A}^{T}]_{j}\mathbf{\mu}^{k}\) as in Equation (10). Thereafter, the sensor solves the following optimization problem,  
\[d_{j}(\lambda_{j}^{k})=\left\{\begin{array}{ll}\max_{r_{j},y_{j}^{k}}&U_{j} (r_{j})-\lambda_{j}^{k}\,y_{j}^{k}\\ s.t.&m_{j}\leq r_{j}\leq M_{j}\\ &r_{j}\leq y_{j}^{k}\end{array}\right. \tag{13}\]  
Note that Problem (13) coincides with Problem (8) if we take Equation (10) into account and thus we keep the nomenclature, that is, we refer to Problem (13) as the dual subproblems of the CDM. As a result of computing the dual subproblems, the optimal values of the inner optimization variables \(y_{j}^{k}\) given \(\lambda_{j}^{k}\), _i.e._, \(y_{j}^{*,k}(\lambda_{j}^{k})\) are found as well. However, these values are not necessarily primal feasible, _i.e._, they may not satisfy \(\mathbf{A}\mathbf{y}\preceq\mathbf{c}\), and there is no guarantee that they accomplish the necessary slackness conditions in Equation (11). In the second step of the method, called the primal projection, we modify the resulting allocation \(\mathbf{y}^{k}\) in order to satisfy both conditions.
## 4 Experimental Results
### Training Performance Evaluation  
We conducted training using TD3 both with and without CER on driving tasks to evaluate whether CER enhances training performance. Figure 5(a) illustrates the average episode length curve per 1000 episodes over the complete training steps, with a maximum episode length of 1000 steps. Additionally, Figure 5(b) depicts the reward curves for the baseline method and the proposed method. As shown in Figure 5(b), the use of CER contributes to enhanced training speed and performance.
## 2 Materials and Methods  
The framework of this study is shown in Figure 1. Firstly, we processed the OCO-2 SIF and MODIS EVI data to generate county-level means of SIF and EVI for corn and soybean, respectively (Sections 2.2.3 and 2.3.1). Secondly, climate data from the Daily Surface Weather and Climatological Summaries (DAYMET) databases [68], including temperature, precipitation, and vapor pressure (VP), were processed for each county using the Google Earth Engine (GEE) platform. Lastly, DNN models were trained with census yields and above processed data (Section 2.3.2).
## 1 Introduction
A recent comparative study showcased the efficacy in dentin mineralization and tubule occlusion of an alternative technology based on stabilized microparticles of amorphous calcium magnesium phosphate (ACMP, marketed as CAPOSAL(r)) [16]. In the study, CAPOSAL(r) was the only technology resulting in complete occlusion and deep mineralization of the dentin tubules. The mineralization layer was also able to withstand an acid challenge, increasing the chances for long-term sensitivity relief. The mode of action of these particles, as well as the characteristics of intra-tubular mineralization layers, have been demonstrated in separate studies [17; 18]. Combined, these studies demonstrate that the application of ACMP particles is one of the most effective approaches for promoting dentin remineralization and tubule occlusion, caused by the high aqueous solubility that supplies a high local concentration of calcium and phosphate ions. The metastable nature of ACMP particles also means that they are easily transformed into more stable calcium phosphate phases in situ, such as hydroxyapatite. In fact, amorphous calcium phosphate has long been considered a precursor to natural apatite in teeth and bone and therefore plays an important role in endogenous mineralization [19; 20], and its unique properties have rendered it a highly interesting material for dental and other biomedical applications [21; 22]. With the innovative CAPOSAL(r) technology, the previously elusive nature of amorphous calcium phosphate particles as a functional ingredient in oral care products has been made commercially available. Until now, however, the ACMP particles have relied on separate application and external supply of fluoride ions to form the more acid- and caries-resistant fluorapatite mineralization layer [18]. In the current study, we present a development of the ACMP particles to also contain a fluoride inclusion, making the particles essentially self-sustaining in the process of remineralizing dentin for the proposed reduction of dentin hypersensitivity and the prevention of caries.
## 3 Results and Discussion
### Stability of Gold Nanoparticles
#### 3.2.3 pH Stability of GNPs  
pH plays an important role in controlling nanoparticle size by affecting the surface charge stability and particle interaction. A digital pH meter (model 510, Oakton, Eutech, Vernon Hills, IL, USA) was used for the pH measurements of gold nanoparticles. To check the pH effect, acidic (HCl) and alkaline (NaOH) solutions were prepared and added to nanoparticles solution. This solution was kept for 2 days, and their stability was checked by observing absorption on a UV-visible spectrophotometer. The cleared solution and UV results supported the stability of nanoparticles at all pH values (Figure 4).  
Figure 3: Effect of heat on GNPs.  
Figure 2: Change in absorption with the passage of time.
## 4 Experiment
### YOLOv8 for Non-PPE Detection  
In many fields, including construction, YOLO-based methods rank among the most widely adopted for object detection [11; 31; 32]. Over successive releases, YOLO has under  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Purpose**} & \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Images** \\ \end{tabular} } & \multicolumn{6}{c}{**Class**} \\ \cline{3-8}  & & **Non-Helmet** & **Non-Mask** & **Non-Glove** & **Non-Vest** & **Non-Shoes** & **Total** \\ \hline Training & 1382 & 126 & 1082 & 741 & 424 & 547 & 2920 \\ Augmentation & 8292 & 756 & 6492 & 4446 & 2544 & 3282 & 17,520 \\ Validation & 460 & 48 & 352 & 238 & 137 & 182 & 957 \\ Test & 460 & 51 & 337 & 211 & 158 & 177 & 934 \\ Total & 10,594 & 981 & 8263 & 5636 & 3263 & 4188 & 22,331 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comprehensive dataset distribution.  
Figure 10: Examples of annotation process for detecting non-PPE.  
gone significant enhancements, culminating in YOLOv8, which was chosen here for its high accuracy and flexibility. Compared to earlier versions, YOLOv8 offers substantial improvements in feature extraction and resilience to varied object sizes and occlusions, with its modular design allowing extensive customization--particularly in selecting a backbone architecture for balancing detection accuracy and computational efficiency [6].  
Building on this foundation, three prominent transformer-based backbones--Vision Transformer (ViT), Swin Transformer, and Pyramid Vision Transformer (PVT)--are integrated, each recognized for robust performance across diverse object detection tasks [33; 34; 35]. Although YOLOv8 has thus far seen less application in construction environments than other YOLO variants, these backbones offer complementary approaches to feature extraction: ViT employs a patch-based global attention mechanism but can be computationally demanding at high resolutions, Swin Transformer adopts a hierarchical, windowed attention structure for improved scalability, and PVT utilizes a progressive shrinking pyramid to balance accuracy and efficiency.  
Finally, to detect the presence of non-PPE, the final classification layer in each backbone was adapted to distinguish six classes--\"Non-helmet\", \"Non-mask\", \"Non-glove\", \"Non-vest\", \"Non-shoes\", or \"background\"--reflecting the range of non-PPE categories relevant to construction-site safety.
## 3 DL-Based Ontology
### Top-Level Entities
To describe indicator values, fourteen top-level data relations named \(\mathit{hasSupportVolume}\)-\(\mathit{num3}\), \(\mathit{hasBuildTime}\)-\(\mathit{h}\), \(\mathit{hasBuildCost}\)-\(\mathit{GBP}\), \(\mathit{hasDimensionalError}\), \(\mathit{hasGeometricError}\)-\(\mathit{num}\), \(\mathit{hasVolumetricError}\)-\(\mathit{num3}\), \(\mathit{hasSurfaceRoughness}\)-\(\mathit{un}\), \(\mathit{hasDensity}\)-\(\mathit{g}\)/\(\mathit{cm3}\), \(\mathit{hasHardness}\)-\(\mathit{HV}\), \(\mathit{hasFieldStrength}\)-\(\mathit{MPa}\), \(\mathit{hasTensileStrength}\)-\(\mathit{MPa}\), \(\mathit{hasElongation}\)-\(\mathit{Pct}\), \(\mathit{hasResidualStress}\)-\(\mathit{MPa}\), and \(\mathit{hasFatigueStrength}\)-\(\mathit{MPa}\) were created. The domain and range of each data relation are respectively _LbpfPart_ and xsd:double. This means that an LPBF part has predicted indicator value of xsd:double. The widely known LPBF materials include Ti6 Al4V, AlSi10 Mg, stainless steel, and tool steel. Recently, several other types of LPBF materials, such as tungsten, zinc alloy, magnesium alloy, and metal matrix composite, have been developed [56]. Therefore, eight assertional axioms were created as follows: _LbpfMaterial_(_ti6 Al4V_); _LbpfMaterial_(_alSi10 Mg_); _LbpfMaterial_(_stainlessSteel_); _LbpfMaterial_(_toolSteel_); _LbpfMaterial_(_tungsten_); _LbpfMaterial_(_zincAlloy_); _LbpfMaterial_(_magnesiumAlloy_); _LbpfMaterial_(_metalMatrixComposite_). According to the Senvol Database [54], there have been two hundred and thirty-six LPBF machines available in the market to date. The names of these LPBF machines were used to instantiate the concept _LbpfMachine_ to create two hundred and thirty-six assertional axioms. For example, in the database, EOSINT M270 is an LPBF machine. An assertional axiom _LbpfMachine_(_cosintM270_) was created to make this assertion.
# Incorporating Epistemic Uncertainties in Ship Operability Study
## 2 Methods
### Seakeeping Analysis
#### 2.1.4 Model Uncertainty of Transfer Functions  
The Frequency Independent Model Error (FIME) is developed and utilized to formulate the model uncertainty of transfer functions [31]. The fundamental concept of FIME involves multiplying transfer functions by a constant fit factor \(\varphi\), which is expressed as follows:  
\[\varphi=\frac{\sum_{i}\hat{H}_{i}H_{i}}{\sum_{i}H_{i}^{2}}, \tag{13}\]  
where \(\hat{H}_{i}\) and \(H_{i}\) are measured and calculated transfer functions at the frequency \(\omega_{i}\), respectively. This allows the transfer function to be adjusted to match, on average, with the experimental results at all frequencies.  
According to its geometrical meaning, \(\varphi\) is the slope of the regression line that must pass through the origin when comparing experimental and calculated transfer functions. The theory overestimates the experimental results when \(\varphi\) is less than 1, whereas it underestimates readings when \(\varphi\) is more than 1 [17].  
By comparing transfer functions computed by CFEs with model-scale experiments for 13 ships of various types, speeds, and relative heading angles, the parameter \(\varphi\) was found for heave and pitch in [23]. Using linear multivariate regression analysis, the dependence of \(\varphi\) on the main parameters was expressed as:  
\[\varphi=\mathrm{A}+\mathrm{B}\beta+\mathrm{CC}_{b}+\mathrm{DF}_{n}, \tag{14}\]  
where \(F_{n}\), \(C_{b}\), and \(\beta\) are Froude number, block coefficient, and heading angle, respectively. Table 1 provides the regression coefficients and intercept [23].  
Values in Table 1 represent a mean value for each speed and heading angle and a standard deviation of 0.19 and 0.27 is determined for pitch and heave, respectively, in [23]. Due to the lack of relevant comparisons with experiments, mean value and standard deviation for the model uncertainty of the roll angle are assumed in the present study as 2.5 and 0.2, respectively. The normal distribution is used to represent the model uncertainty of the seakeeping calculations.
# Real-Time Path Planning for Strategic Missions
## 3 The Path Planning Strategy  
Figure 1 illustrates the flowchart of the path planning strategy for a mobile robot, considering the possibility of environment changes, such as the appearance of unknown or unpredicted obstacles. Algorithm 1 assists the flowchart interpretation.  
Figure 1: The route planning strategy flowchart for a mobile robot.  
Initially, the presence of obstacles in the environment is verified. After checking, the LPA* algorithm [9] provides a feasible route. If there are any changes in the environment that make it impossible to follow the route previously found, the Algorithm 1 recomputes and redesigns the route. If a feasible solution cannot be found to reach the goal, the robot stops (or returns to the starting position) and sets an \"Impossible Route\" warning. If, after replanning, the robot is not on the calculated new route, a collision-free \"shortcut\" is established, using [PERSON]'s algorithm. Finally, the mission is accomplished once the robot reaches the goal.  
From our point of view, the main contribution of this paper is the way the LPA* and Dijkstra algorithms collaborate to find a feasible and optimal path linking the robot to its goal, whenever an unpredicted or unforeseen event occurs.  
The following functions were used to implement the LPA* algorithm:  
* \(T(n)\) is the transition function that takes a \(n\) state and returns all successor states of \(n\).
* \(g(n)\) returns the cost between the initial \(S_{init}\) state and the \(n\) state.
* \(C(n,c)\) returns the cost of the transition from state \(n\) to state \(c\).
* \(h(n)\) is a heuristic function that returns an estimation of the cost between state \(n\) and goal \(S_{g}\).
* \(f(n)\) returns the result of \(g(n)+h(n)\).  
```
Input:\(S_{init}\), \(S_{g}\), \(CLOSED\) Output: Route from \(S_{init}\) to \(S_{g}\) ifCLOSED is emptythen  Insert \(S_{init}\) in OPEN and CLOSED else  OPEN gets CLOSED and it is ordered based on \(f\) whileOPEN isn't emptydo \(n\) receives and takes first element of OPEN if\(n=S_{g}\)then  Returns the route between \(S_{init}\) and \(n\) break  end if \(i\) receives all successor states from state \(n\) by applying the transition function \(T(n)\) for\(c\) ranging in each state of \(i\)do if\(c\) not in CLOSEDthen  Insert \(c\) in OPEN and CLOSED  end if if\(c\) in CLOSED and \(f(c)>g(n)+C(n,c)+h(c)\)then  Upgrade \(g(c)\) gets \(g(n)+C(n,c)\)  Upgrade \(f(c)\) using the new value of \(g(c)\)  end if  end if  Order based on \(f\) states in OPEN in ascending order  end if  end if
```  
**Algorithm 1**LPA*