esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality-Aware Resource Model Discovery
## 3 Quality-Aware Resource Model Discovery
### Resource Model Generation
#### 3.2.2 Case-Annotated Transition Systems  
Case-annotated transition systems are generated by annotating the cases recorded in event logs into transition systems. Definition 3 gives the formal explanation for annotating the cases into transition systems and deriving the case-annotated transition systems \(CTS=(S,\,E,\,T,\,A^{s},\,A^{e})\). The state and event annotation functions \(A^{s}\in S\to C\) and \(A^{e}\in E\to C\) adds the cases into states and events, respectively. Figure 3 gives an example of transition systems that annotate cases based on the event log in Table 1 and the process model in Figure 2b. Figure 3 provides a case-annotated transition system \((S,\,E,\,T,\,A^{s},\,A^{e})\) with \(rep^{s}(\sigma)=\{\beta_{u}(\sigma(|\sigma|))\}\) and \(rep^{e}(e)=\beta_{u}(e)\). For example, the annotated cases on the state M1, i.e., \(A^{s}(M1)\), are [C1, C2, C3], while the cases of the arc between the state M1 and M2, i.e., \(A^{e}(M2)\), are [C1, C3].  
**Definition 3**.: ( _Case-annotated transition system)Let \(L\) be an event log, and \(TS=(S,\,E,\,T)\) be a transition system based on a state representation function \(rep^{s}\) and an event representation function \(rep^{e}\). The annotation functions \(A^{s}\in S\to L\) and \(A^{e}\in E\to L\) where for any state \(s\in S\) and any event \(e\in E\) :_  
\[A^{s}(s)=\Big{\{}c\in L\Big{|}\sigma=\beta_{t}(c)\wedge 0\leq k\leq|\sigma| \wedge s=rep^{s}\Big{(}hd^{k}(\sigma)\Big{)}\Big{\}}\]  
\[A^{e}(e)=\{c\in L|\sigma=\beta_{t}(c)\wedge 0\leq k\leq|\sigma|,\,e=rep^{e}( \sigma(k))\}\]  
_A case-annotated transition system \(CTS\) is defined such that \((S,E,T,A^{s},A^{e})\)._
# Determination of Cyanotoxins and Prymnesins in Water, Fish Tissue, and Other Matrices: A Review
## 3 Current Detection Methods for Cyanobacterial Toxins  
A wide range of detection methods is available for the analysis of cyanotoxins. The most commonly used methods for different toxins are listed in Table 2. These methods are primarily targeted for water matrices; however, some can potentially be adapted for use with other matrices, such as fish tissue, shellfish, or organs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Common Name** & **Commonly Used Analytical Detection Techniques** \\ \hline Microcystins & Immunoassay, LC–PDA, LC–MS *, GC–MS, PPIA \\ \hline Nodularins & Immunoassay, LC–PDA, LC–MS *, PPIA \\ \hline Anatoxin-a & Immunoassay, LC–UV, LC–MS *, IMS \\ \hline Savitoxins & Immunoassay, LC–FLD, LC–MS * \\ \hline BMAA & Immunoassay, LC–FLD, LC–MS * \\ \hline Cylindrospermopsins & Immunoassay, LC–UV, LC–MS * \\ \hline Prymnesins & MS–MS, HRMS, qTOF, NMR \\ \hline \hline \end{tabular}  
* Includes LC–MS, LC–MS/MS, and high-resolution mass spectrometric technologies.  
\end{table}
Table 2: Analytical methods available for the detection of cyanotoxins and pyrmnesins (primarily used for water but adapted for use with fish tissue and other matrices).  
The quantification of cyanotoxins in water matrices is at this point a well-studied problem, with multiple EPA standard methods developed for drinking water and an extensive literature presence. However, studies on the extraction and quantification of cyanotoxins in more complex matrices, such as shellfish, fish tissue, and organs, are more limited in quantity and scope. This review will present a summary of studies evaluating cyanotoxins in various matrices, and their extraction, preconcentration, and clean-up stages will be discussed, along with the specific quantitation techniques employed.
## 4 Discussion and Conclusions  
A high priority in prosthetic hand design is the number of DOFs, which depends on the usage scenario, actuation method, control algorithm and so on [12,40]. Ab/d DOFs are frequently neglected in robotic hand design, especially for commercial prosthetic hands. In this paper, the effect of ab/d DOFs on grasping is studied. Experiments based on grasping a set of disks with various diameters by human hand, simulator, and robotic hand were conducted, which indicated that ab/d DOFs could distinctly promote the grasp size, grasp force, grasp quality, and grasp success rate. Furthermore, experimental results under four various conditions show that possessing at least one ab/d DOF can significantly improve  
Figure 10: Comparison of grasping success rate with and without abduction of a soft robotic hand.  
Figure 9: Average grasp quality with the simulator under 4 conditions.  
the performance of the prosthetic hand, which is consistent with the conclusion mentioned in [12; 13] and can be applied to robotic hand design.  
One of the crucial reasons that ab/d DOF promotes grasp size, force, quality and success rate is probably the more extensive active range of motion (ROM) of the fingers [41], enabling a larger grasp space and more dexterous contact points between fingers and objects. To further verify this, we studied the contact point under four conditions when grasping a disk with a diameter of 120 mm, whose size is close to a CD. A glove with markers attached to the fingertips was used (Figure 11a). The contact points during a tenfold grasp process were recorded, as shown in Figure 11b. We can see that the points under the free condition were most dispersive, possibly implying more stability during grasping, and the points under the IMRL-L condition were most centralized.  
In addition, the size of objects may also influence the results of grasping. Intuitively, a larger object requires a larger grasp space so that having ab/d DOF is an advantage. In [42; 43], the mean optimal handle diameter was calculated to be 38-40 mm. When the size of the grasped object exceeds this value, an increase in the object size leads to a reduction in the grasp force [43]. Six subjects carried out a Box and Block test [34] under four conditions to evaluate it. The results are shown in Figure 12. We can see that the numbers of blocks grasped per minute under the four conditions are basically equal because the blocks in this test are small (wood cubes with sides of approximately 24 mm). In this case, the grasp space brought by ab/d DOFs is redundant. However, [PERSON] et al. showed that the complete free state of the MCP joint significantly improved the grasping operation with tasks involving a tripod grasp [12]. This may be caused by the fact that the operation after grasping is the key factor in some specific operations. Therefore, the flexibility of the MCP joint has advantages, even in grasping small objects.  
Figure 11: (**a**) A glove with markers can be captured by a motion tracking system; (**b**) contact point distribution of grasping a 120 mm diameter disk under four conditions. The unit of coordinate system is millimeters.
## 3 Experimental Results and Analysis
### Grasping Algorithm Experiments
#### 3.6.1 Registration Algorithm Experiment  
To verify the superiority of the proposed algorithm and evaluate the acceleration effect of integrating KD-Tree search on the overall process of point cloud processing, this paper presents a comparative experiment. The experiment takes the entire point cloud processing module as the evaluation object, with the version excluding KD-Tree-based segmentation used as the control group. To comprehensively evaluate the performance of the algorithms, three metrics are used for comparison:  
1. Program running time--to measure computational efficiency;  
2. Root mean square error (_RMSE_)--to reflect the overall level of point cloud registration error (see Equation (10));  
3. Mean absolute error (_MAE_)--to quantify the absolute deviation of registration errors (see Equation (11)).  
\[RMSE=\sqrt{\frac{1}{n}\frac{n}{i=1}\|T(p_{i})-q_{i}\|^{2}} \tag{12}\]  
\[MAE=\frac{1}{n}\underset{i=1}{\overset{N}{\sum}}\|T(p_{i})-q_{i}\| \tag{13}\]  
In the equation, \(n\) denotes the number of point pairs involved in the registration from the template point cloud, \(p_{i}\) represents the \(i\)-th point in the template point cloud, \(T(p_{i})\) is the transformed position of \(p_{i}\) after registration, and \(q_{i}\) is the corresponding point in the target point cloud matched to \(p_{i}\).  
As shown in Table 9, the proposed algorithm rapidly locates the target point cloud within a limited search space through KD-Tree search. This significantly improves the processing speed and real-time performance, while also reducing _RMSE_ and _MAE_ to a certain extent, thereby satisfying the requirements for point cloud registration.  
Figure 16: Grasping experimental environment: 1. STEP-700E; 2. pneumatic gripper; 3. Tuyang depth camera module; 4. workpiece to be grasped.
# Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks
## 4 Performance Evaluation
### Evaluation against SkySensor
#### 4.4.2 Data Loss  
In this experiment, dimension of attribute was fixed at 2 and data generation rate was varied from 0.02 packets/node/second to 0.1 packets/node/second. Data loss metric is defined as the ratio of the number of data packets actually stored to the total number of packets generated. From Figure 16b, it can be seen that the percentage of data loss in SkySensor increases with an increasing data rate. This happens because there are only two clusters in SkySensor, which is clearly insufficient if the application or simulation runs for a long time. In addition, with a higher data generation rate, a hot spot or bottleneck would be created surrounding the cluster or edge nodes. On the other hand, regardless of the number of dimensions, the number of sectors or clusters is fixed ensuring an increased participation of nodes in data storage with uniform distribution.
## 2 Methods
### Graph-Based Wall Topology Analysis
#### 2.2.3 Graph-Based Closed-Loop Analysis  
Just like the minimum closed-loop in the RTG that often indicates the roof corners, the closed-loops in the WLTG will represent closed building boundary courtyard walls. As shown in Figure 3c, the closed loop \(\{1,2,3,4\}\) formed by the vertices represents the boundary of the compound, and the closed loop formed by the vertices \(\{1,5,6,7,8\}\) represents the boundary of the house. The calculation of the minimum closed loop is rather simple: repeatedly delete a graph edge and then use the Dijkstra algorithm to find the shortest path between the two endpoint points of the edge. The closed loops are then extracted by connecting the path with edge endpoints.  
For the task of the cadastral survey, further distinguishing of the courtyard walls from the buildings is required. Additionally, according to the latest regulation of building estate measurement and register, unenclosed balconies and concave doorways are calculated by half-areas, thus needing specially extracted. We distinguish them based on the point clouds of the building roof. Rough segmentation results for the building roof can be generated by the height above DEM (set as 2 m) and the point cloud roughness estimated by the tensor voting algorithm [35], since building roofs are considered to be much higher than the ground and have a significant plate tensor. Since the building polygon will be filled up by the roof points, the percentage of grids having roof points within the boundary are taken as the standard to distinguish courtyard walls from the buildings, and a simple 80% threshold is enough for most cases. And, those areas having roof points while existing outside the boundary will be further checked according to regulation standards. The areas and types of boundaries can then be calculated and marked for further usage in the cadastral survey.
# Modeling and Performance Analysis of mmWave and WiFi-Based Vehicle Communications
## 3 Performance Analysis
### Average Throughput
#### 3.2.1 WiFi Communications Scenario  
(a) LoS Scenario:  
The expression of the average throughput in an LoS WiFi communication scenario can be written as follows:  
\[AT_{{}_{WiFi}}^{{}^{LoS}}=B_{{}_{WiFi}}\,\mathbb{E}\Big{[}\text{log}_{2}(1+SNR _{{}_{WiFi}})\Big{]}=\frac{B_{{}_{WiFi}}}{\ln(2)}\mathbb{E}[\text{ln}(1+SNR_{ {}_{WiFi}})], \tag{30}\]  
where \(B_{{}_{WiFi}}\) is the WiFi bandwidth and \(A{}E_{{}_{WiFi}}\) is the average ergodic capacity, which can be derived by using the following theorem [36]:  
**Theorem 1**. : _Let \(R\) be an arbitrary positive random variable, then_  
\[\mathbb{E}\left[\text{ln}\bigg{(}1+\frac{S}{P}\bigg{)}\right]=\int_{0}^{\infty }\!\!\left[\frac{1-L_{R}\big{(}\frac{x}{P}\big{)}}{x}\right]\exp(-x)\;dx, \tag{31}\]  
_where \(L_{R}\) is Laplace transform, which is expressed by_  
\[L_{R}\Big{(}\frac{x}{P}\Big{)}=\mathbb{E}\bigg{[}\exp\left(-\frac{R\;x}{P} \right)\bigg{]}, \tag{32}\]  
_Based on that, and by using Laguerre theorem, the expression of \(AT_{{}_{WiFi}}^{{}^{LoS}}\) is given by_  
\[AT_{{}_{WiFi}}^{{}^{LoS}}=\frac{B_{{}_{WiFi}}}{\ln(2)}\sum_{n=1}^{N}\bigg{\{} \frac{w_{n}}{x_{n}}\bigg{[}1-L_{R_{1}}\bigg{(}\frac{x_{n}}{\mathcal{P}_{N}} \bigg{)}\bigg{]}\bigg{\}}, \tag{33}\]  
_where \(R_{1}=\mathcal{P}_{{}_{T}}H_{1}A_{{}_{W}}(\hat{d}^{2}+[\chi-\eta]^{2})^{-\frac {\delta_{1}}{2}}\).
# Influence of Quality of Mounting Process of RF Transistors on Their Thermal Parameters and Lifetime
## 4 Measurement Method and Set-Up
This allowed to minimize the measurement error and stabilize the ambient temperature during the measurements. In order to measure thermal resistance Rthc-a, these transistors were controlled by the voltage source E1 and voltage V\({}_{\text{CS}}\) was measured with the voltmeter V\({}_{1}\). The drain current I\({}_{\text{D}}\) was controlled by the current source G\({}_{2}\). The transistor output voltage V\({}_{\text{DS}}\) was measured with the voltmeter V\({}_{2}\). It should be noted that the BLC10G22 LS-240 PVT transistor includes two LDMOS dies [36]. In the measurement system, one of the gates was disconnected and the flow of the drain current I\({}_{\text{D}}\) was possible due to the diode present in the die and marked in Figure 7b. During the measurements, the tested transistor should be properly controlled in order to dissipate power P\({}_{\text{D}}\) in it. The value of this power should be sufficient to obtain an increase in the transistor case temperature above the ambient temperature exceeding 40 \({}^{\circ}\)C. As a result, the measurement error of thermal resistance Rth did not exceed a few percent [44]. Figure 8: View of the measuring stand (**a**) and the inside of the measuring chamber (**b**). Figure 7: Set-ups for measuring thermal parameters of the BLC9G22 LS-160 VT transistor (**a**) and the BLC10G22 LS-240 PVT transistor (**b**). The visible difference between the ambient and case temperatures of the transistor allowed determining R\({}_{\text{thc-a}}\) from the Formula (2). For each of the transistors, two measurements of R\({}_{\text{thc-a}}\) were made at two different power values. It should be added that setting the transistor's operating point at the given electrical parameters is a time-consuming process, because the thermally steady state is obtained after the time often exceeding 30 min, and sometimes even up to 1 h. From the thermographs obtained using the IR camera, apart from the temperature in the hottest point on the transistor case, the information about the temperature distribution along the cross-section of the transistor case was also obtained. This was to check the effect of selecting a point on the case where the T\({}_{\text{C}}\) value was measured on the obtained R\({}_{\text{thc-a}}\) values. It was also checked whether the changes in the temperature T\({}_{\text{C}}\) distribution depend on the void level, which was previously measured for the tested transistors.
###### Abstract  
Liver cancer is a major global health challenge, significantly contributing to mortality rates. The accurate segmentation of liver and tumors from clinical CT images plays a crucial role in selecting therapeutic strategies for liver disease and treatment monitoring but remains challenging due to liver shape variability, proximity to other organs, low contrast between tumors and healthy tissues, and unclear lesion boundaries. To address these challenges, we propose the Deep Residual Dual-Attention Network (DRDA-Net), a novel model for end-to-end liver and tumor segmentation. DRDA-Net integrates a Residual UNet architecture with dual-attention mechanisms, multi-scale tile and patch extraction, and an Ensemble method. The dual-attention mechanisms enhance focus on key regions, addressing variations in size, shape, and intensity, while the multi-scale approach captures fine details and broader contexts. Additionally, we introduce a unique pre-processing pipeline employing a two-channel denoising technique using convolutional neural networks (CNNs) and stationary wavelet transforms (SWTs) to reduce noise while preserving structural details. Evaluated on the 3 DIRCADb dataset, DRDA-Net achieved Dice scores of 97.03% and 75.4% for liver and tumor segmentation, respectively, outperforming state-of-the-art methods. These results demonstrate the model's effectiveness in overcoming segmentation challenges and highlight its potential to improve liver cancer diagnostics and treatment planning.  
liver and tumor segmentation; deep learning; residual UNet; dual-attention mechanisms; CT Images +
Footnote †: journal: Applied sciences  
Article
## 2 System Model
### Optimal Noncoherent PPM Receiver  
The optimal PPM receiver also recovers the pulse signal through envelope detection and then determines the received information by judging which of the \(2^{M}\) time slots the pulse signal is in. The decision process is shown in Figure 3.  
1. Start symbol decision;
2. Sample the envelope values of time slots \(1,2,\ldots 2^{M}\);
3. Select the maximum time slot \(U_{k}\) in the sampling values \(U_{1}\), \(U_{2},\cdots\), \(U_{2^{M}}\); determine the time slot where the signal is located; and output data \(k\);
4. End decision.  
Figure 2: Symbol decision flow of a suboptimal receiver.
# Cascade or Direct Speech Translation? A Case Study +
## 4 Baseline Models
### Cascade Models  
The speech-to-text cascade models are based on separate components for speech recognition and machine translation, each trained on their own datasets, either on the in-domain mintzai-ST corpus only or on a combination of the corpus with additional data. We describe each component in turn below.  
For the additional dataset, we selected publicly available corpora, close to the mintzai-ST domain, to support a straightforward reproduction of our results. For this language pair, only text-based datasets were available with these characteristics, and we selected the OpenDataEuskadi corpus [16], prepared from public translation memories by the translation services of the Basque public administration (the corpus is available at the following address: [[http://hltshare.fbk.eu/IWSLT2018/OpendataBasqueSpanish.tgz](http://hltshare.fbk.eu/IWSLT2018/OpendataBasqueSpanish.tgz)]([http://hltshare.fbk.eu/IWSLT2018/OpendataBasqueSpanish.tgz](http://hltshare.fbk.eu/IWSLT2018/OpendataBasqueSpanish.tgz)), accessed on 30 October 2018). This corpus is close enough to the mintzai-ST domain to be meaningfully combined and large enough to contribute significantly to different components of the cascade models. The corpus amounts to 963,391 parallel sentences, with 23,413,116 words in Spanish and 17,802,212 in Basque.  
To connect the components, the best hypothesis of the ASR model was fed to the MT model, after generating punctuation as described in Section 4.1.1. Although considering alternative hypotheses in the n-best ASR output might provide additional robustness and accuracy to the overall system, we left an evaluation along these lines for future research.
# Prediction of Shallow Failure on a Slope Using Volumetric Water Content Gradient Characteristics
## 2 Materials and Methods  
### VWC Gradient  
[PERSON] and [PERSON] [21] proposed a failure prediction method according to the VWC characteristics. According to this method, the VWC responds directly to water that penetrates into the soil slope, and the maximum value is achieved for a long time before reaching failure. The VWC gradient was calculated using the time of the increase in the VWC and the increment during the failure process through an indoor experiment, and it was compared with the VWC gradient calculated using the VWC values measured in testbeds in which failure did not occur.  
To express the amount of water required for the VWC to reach the maximum value, the concept of effective cumulative rainfall (ECR) was used. ECR can be calculated according to Equation (1).  
\[\text{ECR}=\text{intensity rainfall}\left[\text{mm}/\text{min}\right]\times\text {t}\left[\text{min}\right] \tag{1}\]  
where t is the time from the moment when the VWC begins to increase to the moment when the maximum VWC is reached.  
To calculate the VWC gradient, the VWC change was normalized using the ratio (t/tmax) of the VWC increase start time (t) to the maximum VWC arrival time (tmax) as shown in Figure 1, and the slope by linear regression analysis was used as the VWC gradient.  
This method can predict failure by monitoring the VWC increase patterns in real time (Figure 2).  
Figure 1: Example showing the calculation of the volumetric water content (VWC) gradient.  
Figure 2: Relationship between the VWC gradient and rainfall (Reprinted with permission from ref. [21]).
###### Abstract  
Agile Earth observation satellite scheduling is crucial for space-based remote-sensing services. The sharply rising demands and explosion of the solution space pose significant challenges to the optimization of observation task scheduling. To address this issue, we propose a deep reinforcement learning-based attention decision network (ADN) to determine the task scheduling sequence. We also construct a Markov decision process model in which the original and direct attributes are defined to describe the environment and used as the input of the ADN. Moreover, a start-time-shift-based local search is proposed to improve the observation plan generated by the ADN model. A comprehensive experiment was conducted, and the results proved that the attention mechanism in our ADN was beneficial for the training process to converge to better strategies. Compared with other advanced algorithms, the proposed method obtained a better total profit in the test sets. Furthermore, our methods exhibit considerable time efficiency, even for large-scale problems.  
earth observation; scheduling; attention network; deep reinforcement learning; local search 202416436  
1
## 4 Results and Discussion
### Binder Testing Results and Discussion
#### 4.1.3. Ductility Healing Rate  
Table 6 shows the results of ductility test for the different binders. As can be seen in the first row, the average ductility (without 24 h time interval) of virgin binder is 15.2 cm, while the average ductility 4% EMAA-modified binder decreases by 15.8%. This indicates that EMAA has a negative impact on ductility. In contrast, SBR or EMAA/SBR binders show a positive effect on ductility with 6.6% and 33.6% improvement, respectively. After a 24 h time interval, the ductility of all fractured samples increased, indicating a positive effect on self-healing. Another important parameter can be compared between varied binders is healing rate (\(D\)). It can see in the last row of Table 6 that the binders with EMAA modification show higher healing rate than virgin binder and SBR-modified binder. An interesting observation is that adding the combination of EMAA and SBR improves the self-healing behavior of binder significantly. The healing rate of EMAA/SBR compound-modified binder is 2.04 and 1.53 times than that of virgin binder and SBR-modified binder, respectively. The reason is not only the EMAAs own self-healing capacity, but also is probably because of the complex chemical and physical reactions that occurred among the binder and two kinds of additives.