esa-sceva/satcom-chunk-collection huggingface dataset:
# Uncertainty in Automated Ontology Matching: Lessons from an Empirical Evaluation
## 5 Case Study: Spatio-Temporal Alignment of Indicators
### Experimental Results
#### 5.3.1 Disambiguation
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline Exp. & **Tool** & **Threshold** & \(\mathcal{R}\)\({}^{\dagger}\) & \(\mathcal{A}\)\({}^{\ddagger}\) & _Amb\({}^{\lx@sectionsign}\)_ & _Precision_ & _Recall_ & _F-Measure_ & _Overall_ & _Ambiguity_ \\ \hline \multirow{2}{*}{**Exp. 1**} & LogMap & – & 267 & 212 & 2 & 0.995 & 0.79 & 0.881 & 0.786 & 0.94\% \\  & AML & 0.77 & & 209 & 138 & 0.32 & 0.25 & 0.281 & \(-\)0.28 & 66\% \\ \hline \multirow{2}{*}{**Exp. 2**} & LogMap & – & 446 & 200 & 0 & 1.0 & 0.448 & 0.619 & 0.448 & 0\% \\  & AML & – & 446 & 200 & 0 & 1.0 & 0.448 & 0.619 & 0.448 & 0\% \\  & AML & – & 446 & 227 & 15 & 0.929 & 0.473 & 0.627 & 0.437 & 6.6\% \\ \hline \multirow{2}{*}{**Exp. 3**} & LogMap & – & 434 & 194 & 0 & 0.984 & 0.44 & 0.6 & 0.433 & 0\% \\  & AML & – & 227 & 25 & 0.903 & 0.472 & 0.62 & 0.421 & 11\% \\ \hline \multirow{2}{*}{**Exp. 4**} & LogMap & – & 555 & 61 & 0 & 0.426 & 0.472 & 0.448 & \(-\)0.163 & 0\% \\  & AML & 0.49 & & 36 & 23 & 0.3 & 0.2 & 0.241 & \(-\)0.254 & 63.88\% \\ \hline \multirow{2}{*}{**Exp. 5**} & LogMap & – & 137 & 3 & 0 & 1.0 & 0.021 & 0.042 & 0.021 & 0\% \\  & AML & – & & 3 & 0 & 1.0 & 0.021 & 0.042 & 0.021 & 0\% \\ \hline \multirow{2}{*}{**Exp. 6**} & LogMap & – & 102 & 31 & 0 & 1.0 & 0.3 & 0.466 & 0.3 & 0\% \\  & AML & 0.46 & & 36 & 8 & 0.777 & 0.274 & 0.4 & 0.196 & 22.22\% \\ \hline \multirow{2}{*}{**Exp. 7**} & LogMap & – & 521 & 290 & 0 & 0.996 & 0.554 & 0.712 & 0.552 & 0\% \\  & AML & 0.51 & & 50 & 0 & 0.989 & 0.094 & 0.171 & 0.092 & 0\% \\ \hline \multirow{2}{*}{**Exp. 8**} & LogMap & – & 195 & 190 & 0 & 1.0 & 0.974 & 0.987 & 0.974 & 0\% \\  & AML & – & 190 & 2 & 0.989 & 0.964 & 0.976 & 0.953 & 1\% \\ \hline \multirow{2}{*}{**Exp. 9**} & LogMap & – & 177 & 0 & 1.0 & 0.9 & 0.949 & 0.9 & 0\% \\  & AML & – & 192 & 2 & 0.973 & 0.954 & 0.963 & 0.928 & 1\% \\ \hline \multirow{2}{*}{**Exp. 10**} & LogMap & – & 197 & 179 & 0 & 1.0 & 0.91 & 0.952 & 0.91 & 0\% \\  & AML & – & 187 & 0 & 0.994 & 0.944 & 0.968 & 0.939 & 0\% \\ \hline \hline \end{tabular} \({}^{\dagger}\)\(\mathcal{R}\): expected correspondences. \({}^{\ddagger}\)\(\mathcal{A}\): detected correspondences. \({}^{\lx@sectionsign}\)_Amb_: ambiguous correspondences in \(\mathcal{A}\).  
\end{table} 
Table 4: Results after alignment _trimming_.
# MIU-Net: MIX-Attention and Inception U-Net for Histopathology Image Nuclei Segmentation
## 4 Experimental Setup
### Evaluation Metrics  
All the pixel points are divided into two channels after onehot, which are the foreground channel and the background channel. We conducted a series of experiments on public datasets to evaluate the proposed network. Evaluation indicators are visualization effects and specific numerical indicators. We chose Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD) to measure the segmentation performance.  
\(DSC\) is usually used to calculate the similarity of two samples in the range of 0 to 1. The closer value is to 1, the more similar the automatic segmentation is to the manual annotation segmentation. \(DSC\) implies precision and recall, which are equal to \(F1\) in value.  
\[DSC=\frac{2 TP}{2 TP+FP+FN} \tag{2}\]  
where \(TP\) represents the accurately segmented region, \(FP\) represents error nuclei region, \(FN\) represents error background region.  
Hausdorff Distance (HD) is also a commonly segmentation metric in medical image. \(HD\) is mainly applied to measure the segmentation accuracy of nuclei boundary. We use tools of surface-distance to calculate \(HD\). \(A\) and \(B\) stands for two sets of points on the edge of the nucleus. Hausdorff distance measures the contour similarity. Let \(A=\{a_{1},\cdots,a_{p}\}\), \(B=\{b_{1},\cdots,b_{p}\}\), the \(HD\) between these two point sets is defined as:  
\[h(A,B)=\underset{a\in A}{max}\{\underset{b\in B}{min}\|A-B\|\} \tag{3}\]  
\[h(B,A)=\underset{b\in B}{max}\{\underset{a\in A}{min}\|B-A\|\} \tag{4}\]
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
## 4 Results
### Experiment Two--MAUI ROS
#### 4.2.3 Findings  
This second experiment primarily considers UxS performance due to varying form factors associated with UxS types ranging from handheld devices to full-scale unmanned aircraft and ships. From this performance experiment, ciphersuites X448_AES256 GGCM_SHA512_Ed448, X448_CHACHA20 POLY1305_SHA512_Ed448, and X25519_CHACHA20 POLY1305_SHA256_Ed25519 appear to provide a good performance balance between MLS setup time and message handling. However, if the UxS chosen has significant processing power limitations or if concerns with network capacity exist, as explained in our first experiment, we recommend X25519_CHACHA20 POLY1305_SHA256_Ed25519. This ciphersuite provides a slightly lower overhead per message by reducing the cryptographic hash functions from SHA512 to SHA256.
## 5 Experiment  
The experimental setup, shown in Figure 12, consists of a robot controller, a welding robotic arm, a laser vision sensor, a simulated welding torch, a welding workpiece, and an industrial control computer. The laser vision sensor uses an MV-CB016-10 GM-C board-level CMOS camera from Hikvision, with a resolution of 1440 \(\times\) 1080 and a pixel size of 3.45 \(\upmu\)m \(\times\) 3.45 \(\upmu\)m. The lens features a fixed focal length of 12 mm. An inclined mount  
Figure 11: Posture adjustment flowchart.  
with a tilt angle of 6\({}^{\circ}\) was fabricated based on the geometric constraints of the constant-focus optical path. To filter out stray light, the line laser emitter wavelength was chosen to be 658 \(\pm\) 5 nm, and a narrowband filter corresponding to this wavelength range was installed in front of the lens. The robotic arm used is a KUKA KR5 R1400 six-axis industrial robot. A small local area network (LAN) was established, with the host computer serving as the server, and the robot and sensor as clients. Data transmission is facilitated through the TCP/IP protocol.  
The experiment proceeds with hardware setup, sensor calibration, and hand-eye calibration, followed by the weld seam tracking experiment. The detailed workflow is as follows. The laser vision sensor captures images of the welding workpiece's surface profile and transmits them to the industrial computer via Gigabit Ethernet for processing. Using the sensor's measurement model, the industrial computer calculates the precise location of the welding workpiece's surface profile based on the images and the parameters obtained from sensor calibration. It then employs a trajectory planning algorithm to compute the weld seam trajectory in real-time and sends the control data to the robotic arm control cabinet via Gigabit Ethernet to execute the corresponding motion commands. Upon receiving the motion commands, the control cabinet directs the robotic arm equipped with the simulated welding torch to follow the specified motion trajectory, thereby achieving weld seam tracking.
## 3 Model Description and Formulation  
### Definition of a New Truck Appointment System(TAS)  
In this paper, we consider a new TAS between the transport companies (forwarders) and the terminal operator at the gate and the yard of the container terminal. There is variability in truck arrivals due to the operational complexity of the container terminal and the uncertainty of inland flows [12]. The terminal operating system assigns tasks continuously, but to reduce the complexity of the continuity problem, the TAS is expressed in a discrete form (see Figure 3).  
The TAS consists of yard blocks (space concept, see Figure 4) and time windows (time concept) in a two-dimensional matrix, and each area is called a slot, and the slot capacity refers to the workload of the yard crane. The terminal operator determines the number of tasks a truck can reserve per slot, and the transport companies (forwarders) assign trucks per slot. Then, the yard crane assigned to each block performs the reserved tasks.  
From the perspective of the transport company (forwarder), the mathematical model assigns a truck schedule to the slots that can be reserved and set by the terminal operator for each yard block and time window. To do this, we minimize the waiting time for the truck and the number of rehandling for carry-out containers. From the perspective of the terminal operator, the mathematical model optimizes the number of tasks a truck can reserve according to the truck allocations set by the transport companies (forwarders) for each yard block and time window. To do this, we consider whether there is unassigned slot capacity other than the number of tasks a truck can reserve and whether there is unreserved slot capacity. From the perspective of cooperation between the transport companies (forwarders) and the terminal operator, the mathematical model simultaneously determines the number of tasks a truck can reserve and the truck allocations.  
Figure 4: The configuration of the yard block.  
Figure 3: The Truck Appointment System (TAS) expressed in a discrete form.  
We consider the following assumptions to formulate a mathematical model:  
1. The container handling process only includes loading/discharging, carry-in/out, and rehandling, not re-marshaling.
2. The priority of loading/discharging is highest and carry-in/out proceeds when there is no loading/discharging. (The truck arrives at the yard block according to the carry-in/out schedule).
3. All export containers arrive at the yard before loading, all import containers leave the container terminal after discharging, and we do not consider other exceptions.
4. We consider rehandling for carry-out, but not rehandling for loading.
5. We apply different stacking rules according to the task type of the container. (In the case of import containers, the carry-out times are different, so they are spread low to reduce rehandling. In the case of export containers, the loading times are similar, so they are stacked high with a small number of rows to facilitate shipment).
6. Carry-in/out or rehandling operations belonging to the same yard block and time window start with the truck that arrives first.
## 3 Results and Discussions
### Incident P Wave  
Figure 2 gives the theoretical amplitude spectrum of gauge combination \(FV_{2}\) for borehole tensor strainmeters. The amplitude of \(FV_{2}\) increases from zero-frequency gain (the values of gauge combinations at \(f\) = 0) with the increase of incident wave frequency \(f\), and reaches a peak value at a certain \(f\), then roll-offs (an indicator of decay rate) at a certain approximate dB/decade of \(f\), which is similar to the frequency responses of second-order low pass filters of a certain quality factor. Because the peak value is about than 10% larger compared with zero-frequency gain, it will not satisfy the 1% specification of 3 dB bandwidth waviness. The 1% bandwidth definition is adopted to determine the bandwidth of gauge combinations, where the cut-off frequency is taken when the amplitude spectrum is 101% compared with zero-frequency gain. The 1% bandwidth for \(FV_{2}\) is 1214 Hz. We also compared the theoretical amplitude spectrum of gauge combination \(FV_{2}\) for the borehole tensor strainmeter with those for the empty borehole without grout and instrument casing, shown in Figure 2. The existence of grout and stainless steel casing will decrease the zero-frequency and peak value of \(FV_{2}\), and shift the corresponding \(f\) for peak value right relative to the empty borehole, which means the increase of resonance frequency of the empty borehole in infinite space. The response spectrums of \(FV_{4}\), \(FS\), \(TV\) and \(TS\) are similar to that of \(FV_{2}\), but have different zero-frequency gains, peak values,\(f\) for peak values, roll-offs and bandwidth. Table 1 shows characteristic values for the incident P wave amplitude spectrum of \(FV_{2}\), \(FV_{4}\), \(FS\), \(TV\) and \(TS\) at different \(\theta_{1}\). The gauge combinations, which reflect the far-field  
areal strain that represents a strain invariant, have the same change characteristics, while \(TS\) also has the same change characteristics as \(FS\). Because the grout condensation in the borehole is difficult to control after installation, the mechanical properties of the grout have evident uncertainty. Figure 3 shows the influence of the Young's modulus of grout on the theoretical amplitude spectrum of gauge combinations \(FV_{2}\) for borehole tensor strainmeters. The Young's modulus of grout has a large effect on zero-frequency gain and the peak value of \(FV_{2}\). The effect of the Poisson's ratio of the grout and the density of the grout on the theoretical amplitude spectrum of gauge combinations \(FV_{2}\) for borehole tensor strainmeters are shown in Figures 4 and 5, respectively. The density has no influence on zero-frequency gain but has a large influence on the peak value of \(FV_{2}\), and the Poisson's ratio has large influence on both zero-frequency gain and peak value.
# A Lightweight Uav Swarm Detection Method Integrated Attention Mechanism
## 3 Materials and Methods
### Model Training
#### 3.6.2 Loss Function  
The goal of network training is to reduce the loss function and make the prediction box close to the ground truth box to obtain a more robust model. The loss function of object detection needs to indicate the proximity between the prediction box and the ground truth, whether the prediction box contains the target to be detected, and whether the object category in the prediction box is true. As predicted by the head network, the loss function consists of three parts, which are given by Formula (4):  
\[Loss=Loss_{Reg}+Loss_{Obj}+Loss_{Cls}\, \tag{4}\]  
(1) Regression loss (\(Loss_{Reg}\)) is the loss of position error between the prediction box and the ground truth. The x, y, w, and h parameters predicted by the model can locate the position of the prediction box, and the loss is calculated based on the DIoU of the ground truth and the prediction box. Figure 10 shows the principle for DIoU to calculate regression loss, and the corresponding calculation Formula is (5):  
\[DiU=IoU-\frac{\rho^{2}(b,b^{gt})}{c^{2}}=IoU-\frac{d^{2}}{c^{2}}\, \tag{5}\]  
where \(b\) represents the parameter of the center coordinate of the prediction box and \(b^{gt}\) represents the parameter of the center coordinate of the ground truth; \(d\) is the distance between the center point of the prediction box and the ground truth; and \(c\) represents the diagonal length of the maximum bounding rectangle of the union of the prediction box and ground truth. IoU measures the intersection ratio between the prediction box and the ground truth. However, if there is no intersection between them, the result of IoU will always be 0. When one of the two boxes is inside the other, if the size of the box remains unchanged, the calculated IoU value will not change, which will make the model difficult  
Figure 9: The enhancement effect of Mixup.  
to optimize. If DIoU is used to calculate regression loss, this problem can be effectively solved and a good measurement effect can be obtained.  
(2) Object loss (\(Loss_{Obj}\)) is to determine whether there is an object in the predicted box, which is a binary classification problem. According to the result predicted by the head network, whether the target is included can be known, while the feature points corresponding to all ground truths are positive samples, and the remaining feature points are negative samples. The Binary Cross-Entropy loss is calculated according to the prediction results of whether the positive and negative samples include the target.  
(3) Classification loss (\(Loss_{Cls}\)) is applied to reflect the error in object classification. According to the feature points predicted by the model, the predicted category results of the feature points are extracted, and then the Binary Cross-Entropy loss is calculated according to the category of the ground truth and prediction results.
# Dispatching Strategy for Low-Carbon Flexible Operation of Park-Level Integrated Energy System
###### Abstract
Many existing studies focus on the operation flexibility of energy systems. In [17], an integrated energy system optimal allocation method that considers the flexibility requirements was designed to strike a balance between operation economy and safety in system planning. In [18], a mathematical expression of the flexibility demand was derived, and a mathematical model of the operation flexibility of gas and heat supply networks was established. This analysis showed that the system operation cost can be effectively reduced by improving the operation flexibility. To mitigate the uncertainty in renewable energy and load, [19] proposed an integrated energy system optimization strategy considering multi-energy flexibility. This analysis validated that the proposed model can enhance the system's operation flexibility and mitigate system power fluctuations by coordinating the unit output. A joint operation market mechanism with flexible regulation of resources was designed in [20], which showed that this method was highly effective in improving the level and flexibility of new energy consumption. The majority of existing studies have employed controllable devices to regulate system flexibility, with very few considering the introduction of load-side dispatchable resources for this task. Furthermore, the existing literature has seldom considered the synergistic optimization of low-carbon levels, economy, and flexibility in the dispatching objectives of PIES. To address the above problems, this paper introduces CCE based on RPLTCM to explore the synergistic operation mechanism of both, with the aim of further improving the system's carbon control capability. In terms of flexibility, PFI is adopted to quantify the system flexibility, and the load-side dispatchable resources are introduced to enhance system operation flexibility based on the load demand response characteristics. In addition, an optimal dispatching model that takes into account the carbon emission cost, operation cost, and flexibility supply power is constructed to derive a rational day-ahead dispatching plan to accomplish the synergistic optimization of these three aspects.
# Modal Properties of Photonic Crystal Cavities and Applications to Lasers
## 4 Line-Defect Cavities
### Slow-Light Photonic Crystal Lasers
The output power is expressed as \(P_{\text{out}}=\left(\hbar\omega_{s}/q\right)\text{SE}\times(I-I_{\text{th}})\), where \(\text{SE}=\eta_{i}\frac{Q_{i}}{Q_{i}+Q_{c}}\) is the slope efficiency. To single out the impact of the slow-down on output power and slope efficiency, we select a given cavity length. By tuning the mirror phases, one may vary the oscillation frequency and subsequently the slow-down factor, with the cavity length being fixed. As an example, Figure 12d displays the output power (left) and slope efficiency (right) for \(L=5a\) versus the slow-down factor. Here, we have assumed the active region to consist of 3 active layers, each being 8 nm thick, leading to \(\Gamma_{y}=12\%\). Furthermore, we have neglected the variation of \(\Gamma_{\text{FF}}\) with the oscillation frequency and assumed \(\Gamma_{\text{FF}}\approx 63\%\). The active region width is \(w=500\,\text{nm}\). Other parameters are \(g_{N}=5\times 10^{24}\,\text{cm}^{2}\), \(N_{\text{tr}}=10^{18}\,\text{cm}^{-3}\), \(\tau=0.5\,\text{ns}\) and \(\eta_{i}=0.4\), and the bias current is \(I=50\,\text{\mu A}\). The output power initially builds up as the slow-down factor grows, owing to the increasingly smaller threshold current. However, as the slow-down factor is further increased, the output power saturates and subsequently diminishes, because of the degradation of the slope efficiency. Consequently, while a larger slow-down factor always improves the threshold current, an optimum value exists which maximizes the output power. We notice that experimental results on PhC lasers have shown an optimum cavity length, where the threshold density is minimum [75]. This is interpreted as being due to enhanced backscattering loss as the slow-down factor increases. A discussion of this effect is outside the scope of this paper.
# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 2 Materials and Methods
### Stacked Sparse Autoencoder  
The stacked sparse autoencoder is a deep learning architecture that can extract the deep features of the input data and achieve land classification by the softmax classifier. It has been used widely in image processing and pattern recognition due to its simple process and high classification accuracy.
# Dynamic Graph Attention Network for Skeleton-Based Action Recognition
## 3 Method
### Partition Strategy  
In skeleton-based human action recognition, human actions typically involve coordinated movements of multiple joints [15; 21; 33]. To effectively capture interactions among critical joints and incorporate spatial priors into the attention mechanism, we employ a spatial encoding strategy based on node partitioning. Specifically, we define a binary partition matrix \(P\in\mathbb{R}^{|V|\times R_{\text{num}}}\), where \(P_{\text{num}}\) represents the number of partitions. Each row of \(P\) contains exactly one element equal to 1, with all others set to zero, indicating the spatial partition assignment for each node. Based on this matrix, we construct the corresponding degree matrix \(D\in\mathbb{R}^{P_{\text{num}}\times P_{\text{num}}}\) to derive the partition bias \(K_{\text{p}}\):  
\[K_{P}=PD^{-1}P^{\top}XW_{p}, \tag{5}\]  
where \(W_{p}\in\mathbb{R}^{d\times d}\) is a projection matrix for node features \(X\in\mathbb{R}^{V\times d}\) from the previous layer, and \(d\) denotes feature dimensionality. \(K_{P}\) encodes spatial biases for each node within its corresponding partition, highlighting the most collaborative joints or body parts relevant to the action context.  
Figure 3a illustrates the node partition strategy employed in our method. To incorporate this partition bias into the masked attention mechanism, we extend Equations (3) and (4) by introducing the partition bias \(K_{P}\) into the original key vectors. This extension explicitly differentiates between partitions during attention computation.  
The final spatial encoding strategy and the corresponding spatiotemporal encoding strategy are formulated as follows:  
\[X_{l}=\text{softmax}\Bigg{(}M_{l}+\frac{Q_{l}(K_{l}^{\top}+K_{P}^{\top})}{ \sqrt{d_{k}}}\Bigg{)}V_{l}+X_{l-1}, \tag{6}\]  
\[X_{l}=\text{ReLU}\Bigg{(}\text{MSTC}\Bigg{(}\text{softmax}\Bigg{(}M_{l}+\frac{Q _{l}(K_{l}^{\top}+K_{Pl}^{\top})}{\sqrt{d_{k}}}\Bigg{)}V_{l}+X_{l-1}\Bigg{)}+X_{ l-1}\Bigg{)}, \tag{7}\]  
Figure 3: Comparison of different partition strategies: (**a**) proposed partition, (**b**) learned partition, (**c**) empirical partition based on symmetry.  
where \(Q_{l}\), \(K_{l}\), and \(V_{l}\) denote query, key, and value matrices at layer \(l\), \(d_{k}\) represents the feature dimension, and \(M_{l}\) denotes the attention mask matrix. \(X_{l}\) represents the node features after attention encoding at layer \(l\).