esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Contour Error Determination  
The contour error determination plays a crucial role in achieving a high-precision multiple LMs control system. As shown in Figure 2, \(P_{i}(x,y_{i})\) and \(D_{i}(x_{d},y_{id})\) denote the actual and desired positions; \(x_{d}\) and \(y_{id}\) are the desired displacements of the actual displacements \(x\) and \(y_{i}\). \(D_{i}^{\prime}(x_{d}^{\prime},y_{id}^{\prime})\) denotes previously desired positions. Further, the linear contour degree \(\theta_{i}\) is an angle between the line vector \(D_{i}D^{\prime}\) and \(x\)-axis which can be determined as  
\[\theta_{i}=\left\{\begin{array}{c}\tan^{-1}(\frac{y_{id}-y_{id}^{\prime}}{x_ {d}-x_{d}^{\prime}})\ \ if\ (\frac{y_{id}-y_{id}^{\prime}}{x_{d}-x_{d}^{\prime}})\geq 0 \\ \pi-\tan^{-1}(\frac{y_{id}-y_{id}^{\prime}}{x_{d}-x_{d}^{\prime}})\ \ if\ (\frac{y_{id}-y_{id}^{\prime}}{x_{d}-x_{d}^{\prime}})<0 \end{array}\right.. \tag{15}\]  
Figure 1(a),b indicate the value of linear contour degree \(\theta_{i}\) is within the ranges of 0 to \(\pi\)/2 and \(\pi\)/2 to \(\pi\) degrees, respectively. \(e_{x}\) and \(e_{yi}\) are the axial errors between the desired and the actual positions defined as  
\[e_{x}=x_{d}-x, \tag{16}\]  
\[e_{yi}=y_{id}-y_{i}. \tag{17}\]  
Then, the contour error in the first situation is [16; 17]  
\[\varepsilon_{i}=e_{x}\sin\theta_{i}-e_{yi}\cos\theta_{i}. \tag{18}\]  
Figure 2: Contour error determination in a biaxial motion system: (**a**) The first situation; (**b**) the second situation.  
The contour error in the second situation is  
\[\varepsilon_{i}=e_{x}\sin\theta_{ci}+e_{yi}\cos\theta_{ci}. \tag{19}\]  
According to the geometry shown in Figure 2, a supplementary angle \(\theta_{ci}\) equals to \((\pi-\theta_{i})\) that indicates \(\cos\theta_{ci}\) equals to \(-\cos\theta_{i}\), and, so, both situations can apply the calculation method as shown in (18). Hence, the contour errors in \(x\)-direction and \(y_{i}\)-direction are as follows:  
\[\varepsilon_{ix}=\varepsilon_{i}\sin\theta_{i}, \tag{20}\]  
\[\varepsilon_{iy}=-\varepsilon_{i}\cos\theta_{i}. \tag{21}\]  
From (20) and (21), \(\varepsilon_{ix}\) and \(\varepsilon_{iy}\) are negative and positive, respectively, if \(\varepsilon_{i}\) is negative as shown in Figure 2a. However, \(\varepsilon_{ix}\) and \(\varepsilon_{iy}\) are both positive if \(\varepsilon_{i}\) is positive as shown in Figure 2b. In addition, it should be noted that decreasing the tracking error of each LM does not imply decreasing the contour error.
# Research on Chinese Nested Entity Recognition Based on IDCNNLR and GlobalPointer
To validate the nested entity recognition capability of our model on medical information, the paper selected the CMeEE dataset as the first experimental dataset. The CMeEE dataset is a nested entity recognition dataset for Chinese medical texts, categorizing medical named entities into nine classes: diseases (dis), clinical symptoms (sym), medications (dru), medical equipment (equ), medical procedures (pro), body parts (bod), medical examination items (ite), microorganisms (mic), and departments (dep). By performing nested entity recognition on medical information, medical institutions can efficiently extract useful entities from medical data, thereby accelerating the operational efficiency of medical institutions. Currently, the content of real-life texts covers a wide range, and a paragraph of text may contain various types of data. To validate the entity recognition capability of our model on everyday life texts, the paper selected the CLUENER2020 dataset as the second dataset. The Chinese fine-grained named entity recognition dataset CLUENER2020 is based on the THUCTC text classification dataset released by Tsinghua University, with a portion of the data selected for fine-grained named entity annotation. CLUENER2020 includes a total of 10 label types: organization, name, address, company, government, book title, game, movie, organization institution, and scenic spot. The \"company\" and \"government\" categories are quite similar and both belong to the \"institution\" category. These two categories are not as easily distinguishable as the \"address\" and \"institution\" categories. Therefore, this dataset can verify the model's ability to differentiate similar fine-grained categories. In entity recognition, the number of entity categories is about ten, and this entity recognition task is called fine-grained entity recognition. This paper is divided into five chapters: introduction, related work, model, experiments, conclusion, and future work. In terms of structure, the introduction and related work sections first present the current status and significance of nested entity recognition research. Then, the model section describes the specific details of the model. The effectiveness of the model is analyzed through experiments. Finally, the conclusion and future work sections summarize the main contributions of this paper and provide prospects for future research in nested entity recognition.
# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 3 Problem Definition
tures, uncovering the distinguishability between spectrum envelopes (discussed in the next section), thereby determining the presence and number of signals and subsequently identifying the class of each TT&C signal \(x_{i}(t)\).
## 2 Preliminaries
### Broadcast Algorithms
Power grid management relies on broadcast to distribute essential information about electricity generation, consumption, and distribution, enabling effective coordination of energy resources [55]. Similarly, in supply chain management, broadcast plays a key role in disseminating information about inventory levels, production schedules, and shipping updates, promoting seamless logistics operations [56]. In healthcare systems, broadcast is used to distribute vital information about patient conditions, treatments, and outcomes, supporting comprehensive and accurate healthcare delivery [57].
## 5 Simulation Results and Analysis
### Pressure Field
The maximum mass flow rate at the outlet of the combined profile is 4.4% higher than that of the uniform wall thickness profile. During the operation of the vortex compressor, the processes of suction, compression, and exhaust occur simultaneously. Under the condition of equal suction chamber volume in both the combined profile and uniform wall thickness vortex compressors, the length of the leakage line in the uniform wall thickness involute profile is 25% longer than that in the optimized new variable diameter dual circular arc combined profile, intensifying tangential leakage during operation. Therefore, the average mass flow rate at the outlet of the combined profile is 0.74% higher than that of the uniform wall thickness profile. Additionally, since the working fluid in the vortex compressor is transient compressible flow, the mass flow rates at the inlet and outlet are not equal. After the vortex compressor stabilizes in operation, the mass flow rates at the inlet and outlet per unit rotation angle are not equal. This is mainly due to tangential leakage caused by the radial gap and the compression of the working fluid in the working chamber by the vortex teeth, which causes abrupt changes in speed and direction, resulting in vortices of different sizes. The combined profile vortex compressor has fewer radial engagement points at any given moment compared to the uniform wall thickness vortex compressor, hence reducing the gas leakage per unit rotation angle by 3.2%. Specific comparative parameters of mass flow rates for both the combined profile and uniform wall thickness vortex compressors are shown in Table 5. The greater the leakage, the less the amount of gas involved in the actual work of the scroll compressor, and the lower the volumetric efficiency of the compressor. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Numerical Value (B)** & **Numerical Value (D)** \\ \hline Maximum total outlet pressure/pa & 857,706 & 822,064 \\ Average total outlet pressure/pa & 663,460 & 648,044 \\ Average total inlet pressure/pa & 107,405 & 107,362 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pressure comparison value of different profile. Figure 8: Comparison of inlet and outlet pressure.
## 3 Trajectory Optimization Design
### Objective Function and Motion Constraints
#### 3.2.2 Motion Constraints  
The constraint of angular velocity  
The angular velocity of the joint \(i\) should meet the conditions:  
\[\max\left\{\left|\dot{\theta}_{i}(t)\right|\right\}\leq\dot{\theta}_{i\max} \left(i=1,2,3,\cdots,5\right)\text{,} \tag{14}\]  
In the formula, \(\dot{\theta}_{i}(t)\) is the angular velocity of the joint \(i\) at time \(t\), and \(\dot{\theta}_{i\max}\) is the maximum angular velocity of the joint \(i\).  
According to the conditions and work requirements of the pickup manipulator, the settings of joint constraints are shown in Table 1.  
Constraint of angular acceleration  
The angular acceleration of the joint \(i\) should meet the conditions:  
\[\max\{\left|\ddot{\theta}_{i}(t)\right|\}\leq\ddot{\theta}_{\max}\left(i=1,2,3, \cdots,5\right), \tag{15}\]  
In the formula, \(\ddot{\theta}_{i}(t)\) is the angular acceleration of the joint \(i\) at time \(t\), and \(\ddot{\theta}_{\max}\) is the maximum angular acceleration of the joint \(i\).
# Fiber Bragg Grating-Based Optical Signal Processing: Review and Survey
## 3 Survey of Signal Processing Units based on FBGs  
The development of the design tools described above, and especially the synthesis tools, triggered a vast development of linear optical signal processors based on FBGs. Since the FBG implements an LTI filter, the design of the targeted optical signal processor requires the knowledge of the spectral responses of both the input signal \(X(f)\) and the target output \(Y(f)\)[43]. Hence, the transfer function of the processor is determined by \(H(f)=Y(f)/X(f)\), being the impulse response \(h(t)=\Im^{-1}[H(f)]\), its inverse Fourier transform, as depicted in Figure 5.  
The performance of the FBG-based optical signal processor has been typically evaluated by three parameters [17]: the energy efficiency, the cross-correlation coefficient, \(C_{C}\); and the Time-Bandwidth Product (TBP). The energy efficiency is obtained as the output-to-input waveform energy ratio. The \(C_{C}\) coefficient measures the processing error and is given by  
\[C_{C}[\%]=\frac{\int_{-\infty}^{+\infty}y(t)\cdot y_{ideal}(t)\cdot dt}{\sqrt {\int_{-\infty}^{+\infty}y^{2}(t)dt}\cdot\sqrt{\int_{-\infty}^{+\infty}y_{ideal }^{2}(t)dt}}\times 100 \tag{40}\]  
where \(y(t)\) and \(y_{ideal}(t)\) are the actual and ideal output waveforms, respectively. This \(C_{C}\) coefficient provides a precise estimation of the similarity between the obtained waveform and the ideal waveform, thus allowing calculating the TBP of the proposed design. Finally, the TBP is calculated as the ratio between the maximum and the minimum input pulse 3 dB-bandwidth with \(C_{C}\) higher than a fixed value, e.g., 95%.  
In this section, we describe different signal processing units that have been presented in the literature along the past two decades, as well as the method employed for their design.
## 4 Biocontrol Methods
### Lactic Acid Bacteria
The study carried out highlighted the importance of selecting and specifically testing LAB strains in food matrices since their in vitro efficiency may differ from that in real products. [PERSON] et al. (2022) [92] reported that native strains of LAB from Calabria effectively inhibited _L. monocytogenes_ in cheeses, and strains such as _Lactobacillus sakei_ and _Lactobacillus plantarum_ reduced the pathogenic load by 0.5-1.0 log cfu/g. The importance of these studies lies in the fact that the selected strains do not affect the fermentation capacity of the initial cultures, improving food safety without compromising their quality. In another study, _Lactococcus lactis_, combined with lactic acid or sodium lactate, was found to be effective against _L. monocytogenes_ on ggorgonzola cheese at 4 \({}^{\circ}\)C. The combination reduced the pathogen load below the detection limit after 60 days of ripening. _Carnobacterium divergens_ was also tested, and greater antimicrobial activity was observed as the pH of the cheese increased during ripening, indicating the potential for the sequential use of LAB strains at different stages of production [81]. The use of _Lactococcus_ spp. in dairy products for biopreservation has increased significantly. For example, in Moroccan fermented milk, the addition of _L. lactis_ completely inhibited _L. monocytogenes_ within 24 h of storage at 7 \({}^{\circ}\)C, even in the case of high initial contamination. _L. plantarum_, a plantaricin producer, has also been shown to be effective in inhibiting _L. monocytogenes_ in cheeses, especially in combination with nisin producers, reducing the pathogen load below detectable levels within 4 weeks [93]. Other studies have investigated the use of LAB in raw milk cheeses, which are appreciated worldwide. Selected strains, such as _Lactobacillus brevis_ and _Enterococcus faecalis_, reduced _L. monocytogenes_ by 4 log cfu within a few weeks and demonstrated efficacy during cold storage of soft cheeses. Pathogen reduction is also influenced by the genus of LAB and the type of food, which highlights the need to test strains in each specific food category [94]. Studies have shown that _L. brevis_, _L. plantarum_, and _E. faecalis_ have bacteriostatic effects on _L. monocytogenes_ in soft cheese and bactericidal effects in semi-hard cheese, with promising results for periods of up to 20 days. Also, the addition of LAB and lactic acid (a safe food additive) to ripened cheeses can inhibit the growth of _L. monocytogenes_ for long periods [95]. Another mechanism for controlling _L. monocytogenes_ in biofilms is the use of competitive exclusion bacteria. [PERSON] et al. (2004) [96] demonstrated that a combination of _L. lactis_ and _Enterococcus mutans_ significantly reduced _L. monocytogenes_ in biofilms over a wide range of temperatures. These bacteria are able to survive and compete with _L. monocytogenes_ in food environments, offering a practical and economical solution for preventing contamination with this pathogen [97].
# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 2 Basics
### Graph Neural Network Basics
These topologies can vary widely in structure, including dynamic environments where nodes frequently change their positions (such as UAV networks) or those characterized by highly interconnected nodes (like mesh networks) [90]. GNNs excel in these scenarios because they can naturally model the interdependencies between nodes and adapt their learning based on the unique relationships defined by the graph structure. This adaptability is particularly important for routing optimization in complex networks, where traditional algorithms may struggle to provide efficient solutions due to their inherent rigidity and reliance on predefined heuristics [91].
# A New Dynamic Game-Based Pricing Model for Cloud Environment
## 5 Experimental Results: Performance Evaluation of the Proposed Method
### Comparison under Different System Loads  
A good pricing algorithm should perform well in different system loads. The algorithm must respond appropriately to the decreases and increases in demand. During the simulation period, the consideration of different system loads leads to a variation in the number of requests that should be submitted in each simulation run. Figures 8 and 9 depict the mean profit of users as well as the overall profit of the cloud provider when faced with varying system loads, respectively.  
Figure 8: Comparison of the proposed method with [16; 39] under different system loads from the viewpoint of average user’s profit.  
Figure 7: Comparison of the proposed method with [16; 39] from the viewpoint of the number of users’ requests that can be met.  
As can be seen, the performance of the proposed method is better than the two other methods in terms of both average user profit and total provider profit for different loads. The superiority of the proposed method in lighter loads compared to other competitors is also more evident. The reason for this behavior is that in a light workload, the options ahead to allocate the appropriate resource to a request are more, and the maneuverability of the proposed method is higher. Since the proposed method has a more comprehensive view of the factors influencing the finding of a good resource and its appropriate price, compared to the other two methods, increasing the number of options ahead will naturally lead to better performance as expected. The method presented in [16] increases the profit of users due to offering a lower price so that at the highest system load, it has the best performance in this regard among different methods; still, on the other hand, it results in the lowest profit for the cloud provider. On the other hand, the proposed method, considering more parameters that are effective on cloud operating costs and resource efficiency, while providing a fairer price, has also followed a more appropriate mapping of tasks. Therefore, in all cases, both the profit of the user and the profit of the cloud provider have increased.  
The experiments that were conducted demonstrate that taking into account a wide range of factors in the pricing model for cloud services can contribute to creating more equitable and lucrative prices for both users and cloud providers. Acceptance of a task with a high chance of failure due to the penalties it can bring to the provider should be considered in the pricing model. It should be acknowledged that the factors taken into account in this study (specifically, the likelihood of achievement, the level of energy utilized, and the potential disruption among tasks) might not be readily and promptly accessible. As mentioned, many researchers have investigated and predicted these factors, but usually, determining the amount of their effects is associated with computational overhead and inaccuracy.
## 3 Results
### Conventional Gray-Scale US  
In order to define the BIRADS-US score for each nodule, the ultrasound malignancy markers according to the ACR [27] were analyzed. The results can be seen in the following table (Table 2), where for each malignancy marker, the total number of occurrences is stated. Next, the absolute and relative values for malignancy, as checked against later investigations by histopathological results or a combination of mammography and MRI, were obtained.  
The diagnostic power was computed for each conventional US characteristic for identifying breast cancer. The analysis results are embodied by the following measures of diagnostic accuracy--Sensitivity (Se), Specificity (Sp), Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Accuracy  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Absolute Value** & **Relative Value [\%]** \\ \hline
**Benign** & 1241 & 88.33 \\
**Cancer** & 90 & 6.4 \\
**CIS** & 3 & 0.21 \\
**Fibrocystic Mastosis** & 4 & 0.28 \\
**Adenosis** & 67 & 4.77 \\
**Total** & 1405 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Malignancy results for the considered nodules.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Total** & \multicolumn{2}{c}{**Benign**} & \multicolumn{2}{c}{**Malignant**} \\ \hline  & & **Absolute** & **Relative [\%]** & **Absolute** & **Relative [\%]** \\
**Intense hypecheogenic** & 163 & 140 & 85.88 & 23 & 14.12 \\
**Inhomogeneous** & 257 & 213 & 82.88 & 44 & 17.12 \\
**Ill-defined borders** & 94 & 68 & 72.34 & 26 & 27.66 \\
**“Taller than wide”** & 39 & 28 & 71.79 & 11 & 28.21 \\
**Spiculate and anfracuous margins** & 70 & 49 & 70.00 & 21 & 30.00 \\
**Posterior acoustic shadowing** & 13 & 9 & 69.23 & 4 & 30.77 \\
**Intralesional microcalcifications** & 39 & 20 & 51.28 & 19 & 48.72 \\
**Tortuous internal vascularity** & 295 & 245 & 83.05 & 50 & 16.95 \\
**Altered lymph nodes with no evidence of hilium,** & 25 & 14 & 56.00 & 11 & 44.00 \\
**eccentric hilium or cortical edema** & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Conventional breast ultrasound malignancy markers and malignancy (1405 nodules).  
(ACC)--all expressed as percentages (Table 3). Although not explicitly used later in our study, the diagnostic power of the considered malignancy markers is relevant, as the BIRADS-US score assessment is based on their occurrence as described by the WFUMB guidelines [24].  
Based on the malignancy marker occurrence shown in Table 2 and the WFUMB guidelines [24], for each nodule, a BIRADS score was attributed. In the following table (Table 4), the total number of occurrences for each of the four considered BIRADS possible scores is stated. Of the possible BIRADS scores of one through five, BIRADS scores of one and two were attributed to normal breast and cystic lesions and were excluded from the study. After the assessment, the results were checked against later investigations by histopathological results or a combination of mammography and MRI. The numbers of malignant and benign confirmed nodules are shown for each BIRADS score.
## 4 Attack via Demand Response
### Area-Wise Attacks  
We examine six variations of the manipulation of load attacks, which are based on the following factors: contingency, response, and attack (CRA). These factors state in which area contingency occurs, in which area power utility responds to that contingency, and, finally, in which area an adversary performs an attack. Suppose X and Y represent the two areas shown in Figure 1. Table 2 shows six different attack scenarios, which follow the threat model described in Section 4.1.  
**Attack 1:** Suppose A% generation loss occurs in area X. Since a response is taken in the same area, it is taken by DRAS X. DRAS X asks customers to curtail B% of the load to maintain area frequency within secure limits. An adversary leverages the information about the contingency and issues extra C% load curtailment commands from compromised DRAS X.  
**Attack 2:** Similar to Attack 1, but here, an adversary issues C% load curtailment commands from compromised DRAS Y in area Y, which is different from X.  
**Attack 3:** This is a combination of Attack 1 and 2, where an adversary issues load curtailment command in both areas X (C%) and Y (D%).  
**Attack 4:** Suppose A% generation loss occurs in area X. Since a response is taken in area Y, it is taken by PU which issues commands to DRAS Y. Then, DRAS Y asks customers to curtail B% of the load to maintain area frequency within secure limits. An adversary issues C% load curtailment commands from compromised DRAS X in area X.  
**Attack 5:** Similar to Attack 4. An adversary issues extra C% load curtailment commands from compromised DRAS Y in area Y.  
**Attack 6:** This is a combination of Attack 1 and 5, where the system responds in both areas X and Y, and an adversary issues malicious load curtailment command in both area X (C%) and Y (D%).  
The primary idea behind different attack scenarios is that DRAS provides local safety, checking whether there is any contingency in an area it is responsible for. If yes, it automatically sends load curtailment or shedding commands. It is unaware of the scenario in another area and, thus, it can be leveraged by a malicious entity to perform complex attacks that are hard to detect and stop.