esa-sceva/satcom-chunk-collection huggingface dataset:
# Bio-Inspired Algorithms and Its Applications for Optimization in Fuzzy Clustering
## 2 Fuzzy Clustering  
The objective function for fuzzy clustering denoted by \(J_{m}\) is given by the equation:  
\[J_{m}(P,V)=\sum_{i=1}^{k}\sum_{j=1}^{n}\mu_{ij}{}^{m}\parallel x_{j}-v_{i} \parallel^{2},\quad m>1 \tag{1}\]  
where \(P\) denotes a fuzzy partition of a data set \(X\) formed by clusters \(C_{1}\), \(C_{2}\),..., \(C_{k}\). The \(m\) parameter is a weight that determines the degree with which each datum belongs to a cluster in a partial way, and of course this parameter affects the clustering result. The fuzzy c-means (FCM) algorithm aims at finding a good partition by finding the centers of clusters \(v_{i}\) that minimize the objective function \(J_{m}\). The FCM algorithm also finds the membership values \(\mu_{ij}\) that minimize \(J_{m}\). To achieve both goals of finding a good partition and membership values, a condition is needed to guarantee a local minima of \(J_{m}\), which can be mathematically derived from Equation (1) by calculating the partial derivatives and finding where they have a zero value. This mathematical derivation will produce the following two expressions to find the memberships and centers of clusters, respectively [21].  
\[\mu_{ij}=\frac{1}{\sum_{i=1}^{k}\left(\frac{\|x_{j}-v_{i}\|^{2}}{\|x_{j}-v_{k} \|^{2}}\right)^{\frac{2}{m-1}}},\quad 1\leq i\leq k,\;1\leq j\leq n \tag{2}\]  
Based on Equations (2) and (3), the fuzzy c-means algorithm can be used for iteratively updating the centers of clusters and the membership degrees until a convergence criteria is satisfied. As a consequence, the FCM algorithm can be outlined with the following steps:  
1. Given a preselected number of clusters \(k\) and a selected value of \(m\), an initialization of memberships \(\mu_{ij}\) of \(x_{j}\) from cluster \(i\), with values from 0 to 1 is given.  
Calculate the centroids \(v_{i}\) of the fuzzy cluster for \(i=1,2,\ldots,c\) with Equation (3).
* Update the memberships \(\mu_{ij}\) using Equation (2).
* If \(\mid\!\!J_{m}(t)-J_{m}(t-1)\!\mid\) is lower than a threshold \(\varepsilon\), then the process is stopped, if not then go to Step 2.  
This is the original proposal of the fuzzy clustering that was put forward by [PERSON], but now there exist many improved versions of the original FCM algorithm that can be found in the literature. Improved versions have been aimed at improving convergence or the quality of the clusters, but the original idea is the same [21].
# An Improved Ciphertext-Policy Attribute-Based Encryption Scheme in Power Cloud Access Control
## 5 Security Analysis and Performance Analysis
### Computation Overhead  
In this section, our PCAC scheme is compared with the Waters' scheme [12] and the RAAC scheme [5]. The computation overhead of them is analyzed. The result is shown in Table 2. In the table, \(N_{U}\) is the number of users, \(N_{S}\) is the number of attributes in the system, and \(N_{as}\) is the average number of attributes in the user's attribute set (these attributes are also contained in the access structure). O(1)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Agencies** & **PC** & **CKA** & **KS** & **U** & **DS/Owner** \\ \hline Waters [12] & \(N/A\) & \(2L_{p}\) & \(N/A\) & \((1S_{U}\mid+3)L_{p}+L_{Zp}\) & \((1S\mid+3)L_{p}+L_{Zp}\) \\ RAAC [5] & \((6+2\mid S\mid)L_{p}+4L_{Zp}\) & \(N/A\) & \(N/A\) & \((1S_{U}\mid+5)L_{p}+L_{Zp}\) & \((1S\mid+5)L_{p}+L_{Zp}\) \\ PCAC & \((3+1N_{KS}\mid)L_{p}+2L_{Zp}\) & \(3L_{p}+1S\mid L_{p}\) & \(L_{p}\) & \(4L_{p}+L_{Zp}\) & \(L_{Zp}+L_{p}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Storage Overhead of Our PCAC, the Waters Scheme, and the RAAC model.  
means that the computation overhead is the same irrespective of the number of times the input data is increased.  
As can be seen in the table, the computation overhead of the encryption and decryption phases is the same as that in the Waters scheme and the RAAC scheme. The Gen_Stru phase consumes low computational complexity. The computation overhead of Act_Audit phase in the PCAC is the same as that in the RAAC. However, in RAAC it needs to be executed after the key-generation phase, so that if the audit fails, the computation overhead is wasted. Our audit phase is executed before the key-generation phase, so the PCAC can reduce some unnecessary computation overhead.
# The Perpetual Vector Mosquito Threat and Its Eco-Friendly Nemeses
## 1 Introduction
### A Climpse of the Toll of Mosquito-Borne Diseases in Animals
#### 1.2.2 Japanese Encephalitis Virus (JEV)  
Another informative example is the introduction of the Japanese encephalitis virus (JEV) on the Australian mainland. JEV is transmitted principally by _Culex_ species, and the disease is endemic in Asia-Pacific tropical and temperate regions, which are home to \(\sim\)3 billion people. In humans, the annual morbidity is \(\sim\)100,000 and the mortality is \(\sim\)25,000, and the disease remains a leading cause of lethal encephalitis in Asia [24]. Pigs are also susceptible to JEV, and as severe infections can result in encephalopathies and reproductive failures, the spread of the virus in piggeries poses a severe economic threat to local farming and commercial industries [25; 26]. Moreover, transmission from swine reservoirs facilitated by mosquitoes to dead-end human and horse hosts is of concern.  
Before 2021, JEV was largely confined to islands of the Torres Straits and the peak of the Cape York Peninsula. A human case was detected in the Tiwi Islands, and a year later, outbreaks occurred in pig farms in southern Queensland and other regions on the continent, followed by 42 human cases with 7 deaths (16.7%). As a result, a program was initiated to control mosquitoes around piggeries and to vaccinate at-risk human populations, estimated to be \(\sim\)850,000 based on modeling studies [27]. Due to these events in Australia, the Swine Health Information Center of the US Department of Agriculture Animal and Plant Health Inspection Service (USDA APHIS) initiated steps to explore and mitigate the potential JEV threat in the United States [28].  
In summary, these events and scenarios are indicative of challenges that arise with the inadvertent introduction, transmission, and rapid dissemination of potentially lethal pathogens in animals. Perhaps it is likely these events occur at unnoticeable levels in less populated areas or in smaller farms and isolated villages where a limited spread to the local animal and human population is inconspicuous.
# Improving the Sensory Quality of Black Tea by Blending Varieties During Processing
###### Abstract  
Tea blending technology is based on finished tea. Blending fresh leaves during processing has not been proposed and investigated anywhere. This study investigates the impact of blending fresh leaves from different varieties on the flavor quality of black tea. The main taste components, including catechins, theaflavins, and free amino acids, were analyzed using HPLC, while the volatile components were analyzed using GC-MS. The results show that adding fresh _Jinguanyin_ or _Jinxuan_ leaves to _Fudingdabai_ can regulate the ratio of esterified to non-esterified catechins, increase the content of theaflavins and amino acids, and positively impact the strength and freshness of the black tea. The sensory evaluation results show that the taste scores of FJG (black tea made from the blend of fresh _Fudingdabai_ and _Jinguanyin_ tea leaves) (92.14 \(\pm\) 0.41 b) and FJX (black tea made from the blend of fresh _Fudingdabai_ and _Jinxuan_ tea leaves) (93.80 \(\pm\) 0.19 a) are significantly higher than those of _Fudingdabai_ (90.05 \(\pm\) 0.31 d), _Jinguanyin_ (86.10 \(\pm\) 0.45 e), and _Jinxuan_ (91.03 \(\pm\) 0.26 c). Furthermore, adding fresh _Jinguanyin_ or _Jinxuan_ leaves to _Fudingdabai_ can also enhance the floral compounds in the black tea, specifically phenylacetaldehyde, linalool, benzyl alcohol, and oxidized linalool (linalool-type pyran), which make important contributions to the floral aroma of the black tea. Conclusions: Blending fresh leaves for processing can enhance the sensory quality of black tea. This work proposes new insights and methods to enhance black tea sensory quality via the blending of fresh tea leaves with different varieties during processing.  
f _ foods_ **2025**, _14_, 941
# Long-Term Structural State Trend Forecasting Based on an FFT-Informer Model
## 3 Experiments and Discussion
### Datasets and Evaluating Measurements  
The SMC data set [38] is a real data set. It is provided by SMC of Harbin Institute of Technology. More than 150 sensors were installed onto the Yonghe bridge, including 14 uniaxial accelerometers on the deck and one biaxial accelerometer at the top of the south tower. From 2005 to 2007, a large amount of data was collected, as shown in Figure 6a.  
The ASCE benchmark data set [39] is an official data set. The data set was provided by the International Society for Structural Control and the American Society of Civil Engineering. The ASCE benchmark structural model was built in the Earthquake Engineering Research Laboratory at the University of British Columbia, Canada, as shown in Figure 6b. Three types of excitation were used to test it, including an electrodynamic shaker, an impact hammer, and ambient vibration. In our experiments, the data under ambient vibration was used for evaluation, which is the natural structural state without external force.  
Figure 6: (**a**) Arrangement of accelerometers on Yonghe Bridge; (**b**) diagram of ASCE benchmark structural model.  
In order to keep the structure in a progressive state, segment data were randomly selected from a sensor as the experimental data. Multiple segment data from multiple sensors were extracted to train and test the prediction performance, respectively. There are 150,000 data points in each segment of the SMC data set and 60,000 points in each segment of the ASCE data set. A total of 70% of each segment data set was used as the training set and the remaining 30% was used as the testing set.  
In order to evaluate the forecasting accuracy and facilitate comparison, two classical evaluation metrics were applied, mean absolute error (MAE) and mean square error (MSE), as defined in (17) and (18), respectively.  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-g_{i}| \tag{17}\]  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}{(y_{i}-g_{i})^{2}} \tag{18}\]  
where \(y_{i}\) is the actual value, and \(y\) is the estimated forecasting value of the \(i\)-th test value.
## 5 A Framework for SDN-SDR-Based Maritime Communications
### The SDR Approach  
The International Convention for the Safety of Life at Sea (SOLAS) requires maritime operators to comply with a host of requirements that specify certain constraints on ship-board radio equipment [43]. Likewise, the International Telecommunication Union (ITU) has specified equipment to comply with the SOLAS Convention requirements [44]. Among these requirements are two-way VHF voice communication and, depending on the vessel, could also include AIS, satellite equipment, and emergency-position-indicating radio beacons. The ITU also identified trends that include E-navigation, VHF data exchange systems, and VHF voice digitalization.
# Generation and Inhibition of SO\({}_{3}\) in Lead Smelting Flue Gas
## 3 Results and Discussion
### Generation of SO\({}_{3}\) in Simulated Lead Smelting Flue Gas
#### 3.1.1 The Effect of Equilibrium Temperature on SO\({}_{3}\) Generation  
As illustrated in Figure 1, temperature exerts a significant thermodynamic influence on both the equilibrium SO\({}_{3}\) concentration and its conversion rate. A strong inverse correlation was observed: lower temperatures correspond to higher equilibrium SO\({}_{3}\) concentrations. At 400 \({}^{\circ}\)C, the equilibrium SO\({}_{3}\) concentration reached 15.83%, whereas it plummeted to 0.14% at 1300 \({}^{\circ}\)C. Notably, an inflection point in SO\({}_{3}\) concentration occurs at 400 \({}^{\circ}\)C, attributed to partial SO\({}_{3}\) consumption through sulfuric acid (H\({}_{2}\)SO\({}_{4}\)) formation within the 300-400 \({}^{\circ}\)C range.  
The SO\({}_{3}\) conversion rate followed a trend similar to that of its concentration profile, with the maximum conversion (99.70%) achieved at 300 \({}^{\circ}\)C, indicating the near-complete oxidation of sulfur species to SO\({}_{3}\). Conversely, the conversion rate declined sharply to 0.74% at 1300 \({}^{\circ}\)C, reflecting the thermodynamic instability of SO\({}_{3}\) at elevated temperatures. These results demonstrate that low-temperature regimes (<500 \({}^{\circ}\)C) strongly favor SO\({}_{3}\) formation and retention in the gas phase, while high-temperature conditions promote SO\({}_{3}\) decomposition.  
This analysis highlights the critical role of thermal management in controlling SO\({}_{3}\) levels during pyrometallurgical processes, with implications for corrosion mitigation and emission control strategies.  
An increase in the SO\({}_{3}\) concentration in a flue gas system proportionally increases the ADP, thereby exacerbating the corrosion risk of downstream equipment. SO\({}_{3}\) levels should be kept below 1 vol% through engineered suppression strategies to mitigate the degradation of waste heat boilers and particulate control systems. The complexity of SO\({}_{3}\) formation in lead smelting flue gases stems from its diffuse generation pathways, heterogeneous catalytic oxidation, homogeneous gas-phase reactions, and sulfate decomposition mechanisms. The dominant formation route is as follows:  
\[\text{SO}_{3}\left(\text{g}\right)+\text{H}_{2}\text{O}\left(\text{g}\right) =\text{H}_{2}\text{SO}_{4}\left(\text{g}\right)\]  
\[\text{SO}_{2}\left(\text{g}\right)+1/2\text{O}_{2}\left(\text{g}\right)+\text {H}_{2}\text{O}\left(\text{g}\right)=\text{H}_{2}\text{SO}_{4}\left(\text{g}\right)\]  
Figure 1: Effect of equilibrium temperature on final-gas-phase sulfur trioxide.
## 1 Introduction
In [1,2], it is proven that a job-shop problem with equal (unit) durations of operations with the objective of minimising a schedule length (i.e., makespan) reduces to a problem of finding the optimal colouring of the mixed graph \(G=(V,A,E)\). In this paper, we show that finding the optimal colouring \(c(G)\) reduces to the problem of constructing a schedule that minimizes a maximal lateness for processing multi-processor tasks with integer durations, integer due dates and allowed operation interruptions. The converse is also proven. Processing a multi-processor task requires several dedicated processors; see Chapter 10 of the monograph [5]. Moreover, two types of precedence constraints can be specified for the multi-processor tasks, and the unit-time operations of the indicated subset must be processed simultaneously. The remaining part of this paper is organised as follows. The settings of considered scheduling problems, preliminary results and notation are given in Section 2. Related papers are cited, where it has been shown that the problem of finding an optimal colouring of the mixed graph reduces to finding a makespan-optimal schedule for partially ordered operations with unit processing times. Scheduling problems are discussed in increasing order of their complexity. Section 3 demonstrates that the problem of optimal colouring of the mixed graph reduces to finding a makespan-optimal schedule of the multi-processor tasks with equal durations. In Section 4, we discuss scheduling problems of minimising maximal lateness of completing jobs with respect to integer due dates. New results are proven, demonstrated, and discussed in Sections 4-8. The possibility of interruptions processing integer-time tasks is studied in Section 5. A theorem is proven on the reduction of the optimal colouring \(c(G)\) to the scheduling problems with a minimisation of the makespan for the interrupted integer-time tasks with integer release dates of the jobs. An illustrative example of the studied scheduling problem is solved in Section 6. Section 7 includes a theorem on the equivalence of scheduling problems with a maximal lateness objective and the problem of finding optimal colourings of the mixed graphs. Due to the proven equivalence of the problems of constructing optimal schedules and mixed graph colouring problems, many claims that have been proven for scheduling problems can be applied to the problems of optimal mixed graph colourings, and vice versa. In Section 8, we discuss new findings and their potential implications. Section 9 contains concluding remarks and promising future research directions. The new theorems and most of the theorems presented in the survey section are demonstrated in the numerical examples in the main text and in Appendices A and B.
## 2 International Typical Transmitting Antenna Reference
### Four-Tower Antenna Form  
The antenna is supported by four towers of equal height between 200 and 280 m. The radiating body consists of a square top load, main and lower leads and auxiliary lower leads. A small tower is built at the center of the lower leads to provide fixed closure. A schematic diagram of the four-tower inverted-cone antenna is shown in Figure 6. Owing to the low effective height and radiation resistance of the four-tower inverted-cone antenna, a large-scale ground network needs to be designed to achieve better radiation efficiency. The ground network is radial with the feeding point as its center, which is also buried underground. Table 2 shows the performance indexes of the four-tower transmitting antennas designed with reference to Figure 6 at different heights [10,11].  
Figure 5: Schematic diagram of a single-tower umbrella antenna model.  
Figure 6: Schematic diagram of the four-tower inverted-cone antenna model.  
The main advantage of the four-tower transmitting antenna is that the tower height is relatively low and the bandwidth is wide. All iron towers are directly grounded to avoid the use of an insulator at the base of the iron tower. In areas with severe environments such as multiple snow disasters and earthquakes, the system has high safety, reliability and good seismic performance. Its main shortcomings are relatively low radiation efficiency, a large number of towers, large quantities and high cost.
# A Corrected Adaptive Balancing Approach of Motorized Spindle Considering Air Gap Unbalance
## 2 Unbalance and Force of Motorized Spindle  
### Mechanical Unbalance and Force  
When mechanical unbalance is discussed, the stator of motor for a motorized spindle is not considered, and the combined rotor composed of a shaft, holder, and tool is the main research object. As shown in Figure 1, for the combined rotor of a shaft, holder, and tool, the mass unbalance in cutting process mainly appears in this tool due to the inevitable tool wear, breakage, and change, and the cutting tool can be reduced to a circular plane because of its small axial size.  
In Figure 1, assuming that the tool in the A-A plane generates a mass eccentric vector \(m\mathbf{e}\), the mechanical unbalance vector \(\mathbf{U}\) can be shown as  
\[\mathbf{U}=m\mathbf{e}=M\mathbf{e}^{\prime}, \tag{1}\]  
where \(m\) is the eccentric mass, and \(\mathbf{e}\) is the position vector of \(m\) in polar coordinates, \(M\) is the total mass of a combined rotor, \(\mathbf{e}^{\prime}\) is the equivalent eccentricity vector of the combined rotor.  
When the motorized spindle rotor is operating at the angular speed of \(\omega\), \(\mathbf{U}\) causes an undesired inertia force \(\mathbf{F}\), which can be expressed as  
\[\mathbf{F}=\omega^{2}\mathbf{U}. \tag{2}\]  
The force vector \(\mathbf{F}\) can be decomposed and converted into two components of \(F_{x}\) and \(F_{y}\) along the horizontal and vertical directions in the Cartesian coordinate system.  
\[\left\{\begin{array}{l}F_{x}=me\omega^{2}\cos\omega t\\ F_{y}=me\omega^{2}\sin\omega t\end{array}\right.. \tag{3}\]  
According to Equation (3), the unbalance-induced force components are characterized by a frequency equal to the rotating angular frequency \(\omega\), which causes the mechanical vibration of the spindle system.  
Figure 1: Schematic diagram of a combined rotor and its mass unbalance for a motorized spindle: (**a**) combined rotor of shaft, holder, and tool; (**b**) mass unbalance of tool.
## 2 Background  
### Autonomous Driving Technology  
Automobiles are designed with the aims of increasing their efficiency of mobility and ensuring the safety of the driver. Research is being actively conducted into the development of intelligent vehicles that are equipped with various driving assistance functions that can reduce the user's driving burden [8]. The study of advanced driver assistance systems is based on the premise that the primary cause of traffic accidents is driver carelessness; moreover, inefficiency in driving is also attributed to drivers. \"Autonomous driving\" means that a car drives to a destination by recognizing the surrounding environment, assessing the driving situation, and controlling the vehicle itself, without driver intervention [17]. Autonomous vehicles are emerging as a future means of personal transportation that can reduce traffic accidents, increase traffic efficiency, save fuel, and increase driver convenience [18].  
Autonomous driving technology can be classified into stages, according to the level and role of a function. The Society of Automotive Engineers classifies autonomous driving technologies into six levels (0-5), depending on the steering control, speed adjustment, driving controller, driving situation, and environmental monitoring agents [19]. Level 0 (no automation) refers to complete manual driving, without any driving assistance functions. Level 1 (driver assistance) automation is equipped with certain auxiliary functions that help the driver; this is the most commonly used level of driving assistance technology. Level 2 (partial automation) requires users to look ahead, though the system can perform the main control functions, such as steering and speed adjustment. From Level 3 (conditional automation) upwards, the system monitors the driving environment; although the system performs the main control functions, an emergency response must be inputted by the driver, and the responsibility lies with them. From Level 4 (high automation), no user intervention is required, and the system performs control functions, monitors the driving environment, and handles emergency situations at a high level of automation; however, such autonomous driving is possible only within a specific area, where it is fully learned. In Level 5 (full automation), the autonomous driving system can control all the autonomous driving systems without a driver, and it can autonomously drive to a location without restrictions. Therefore, it is important to identify the technical issues related to the user acceptance in the development of autonomous driving technology to ensure that they evolve simultaneously.