esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 UAV Path Planning Method Based Modified Fruit Fly Optimization Algorithm
### Msfoa  
#### 3.1.1 Motivation  
The FOA converges quickly when applied to a single UAV path planning. Yet when it is applied to planning the path of multi-UAVs, it is easy to get lost in the local optimal solution. The original FOA is random when searching the solution, which means the convergence speed is fast in the early stage, but it is difficult to converge towards the optimal solution in the later stage. When the fruit fly's path is near the optimal solution, the algorithm may ignore the path, because the path length and the threading level and other constraints are considered. The optimal path is often between the edge of the threading cylinder or the valley. When the fly finds a path near the optimal solution, the path usually falls into the threading cylinder or through the interior of the mountain. This path with a high smell concentration is easy to eliminate but near the optimal solution. When the algorithm is used to plan the path for a simple scenario, it can be repeated many times to get the optimal solution, but when the algorithm is applied to some complex scenarios, such as multi-UAVs path planning or path planning in complex scenarios, the convergence ability of FOA is obviously insufficient.  
In FOA, there is no communication between fruit flies and the direction of the path depends only on the given random direction and the random distance, so the ability to converge in solving complex problems is insufficient. In order to overcome the shortfalls of the FOA, increasing the searching ability and communication between fruit flies can improve the convergence ability of the algorithm. At thesame time, in order to avoid the problem of possible collision when planning multiple UAVs paths, this algorithm also adds the method of checking for collisions.
## 6 Discussion and Open Issues
* Cross-layer paradigms could be employed to conserve more energy, hence prolonging network lifetime. To this end, network layers can use power control mechanisms at the physical layer to restrict transmission range at the time of packet forwarding. This can reduce the energy consumed by a transmitter dramatically [16]. * One of the drawbacks of mobile sink methods is that in practice, the mobile sink may not be allowed to move along straight lines; for instance, boundaries or obstacles may block the moving paths of the sink. The solution of this problem is known as _obstacle avoidance methods_[57]. More research should be done to overcome this problem. * Imaging and video sensors in real-time applications pose issues like Quality of Service (QoS). In QoS algorithms some metrics such as delay and bandwidth should be guaranteed during the network functionalities. Satisfying these metrics, especially in mobile sink scenarios, may be in conflict with achieving more energy-efficiency [58]. Improving sink mobility methods or using multi-path routing approaches [14] in QoS algorithms is needed to conserve much more energy. * Most of the multi-sink approaches [5,6] suffer from control overhead caused by sinks' advertisement flooding in the gradient field construction phase. Mitigating the negative effect of such overhead on energy-efficiency is an open research issue. * Mobile sink mechanisms are mostly used for time-driven applications in which the sink moves across the network and collects the data from CHs [11] or source nodes themselves [8, 9, 10]. However, there are a lot of applications that employ event-driven paradigms [56] for data forwarding. Therefore, mobile sink strategy should be improved for data gathering in these kinds of networks. * The main drawback of power control schemes is the execution cost due to calculating the RSSI/LQI parameters [17, 18, 19]. Therefore, mitigating the overhead caused by such calculation could be an open issue for future trend. * Current energy-efficient mechanisms that are proposed to prolong network lifetime do not consider security since their final goal is not satisfying this matter. For example, in multi-sink strategy [5, 6, 7], the probability that a malicious node plays the role of sink node and collects the data is very high. In this way, it can negatively manipulate the identity of data. Thus, the security issues in all mentioned mechanisms should be more taken in to consideration. * Finally, a strategy based on joint routing protocols and energy-efficient schemes like sink mobility could be very efficient, since routing and these techniques have a close interaction on each other [9].
## 2 Materials and Methods
### Data Preprocessing
#### 2.2.2 sEMG Data  
To obtain sEMG waveform parameters, the sEMG signals were processed using a fourth-order Butterworth band-pass filter (25-500 Hz) and a fourth-order Butterworth band-stop filter (49.5-50.5 Hz), using zero-phase filtering. To obtain muscle activation, the signals were also rectified and smoothed using Gaussian smoothing [22], and then normalised to the maximum recorded value for each participant across all movement cycles (five RBBT and five VBBT).
# Prediction of Tooth Profile Deviation for WEDM Rigid Gears Based on ISSA-LSSVM
###### Abstract  
This study aimed to develop and validate an improved sparrow search algorithm (ISSA)-optimized Least Squares Support Vector Machine (LSSVM) model for accurately predicting the tooth profile deviation of rigid gears produced by wire electrical discharge machining (WEDM). The ISSA was obtained by optimizing the sparrow search algorithm (SSA) using Tent chaotic mapping, adaptive adjustment strategy, dynamic inertia weights, and grey wolf hierarchy strategy. The effectiveness of the ISSA was verified using four different classes of benchmark test functions. Four main process parameters (peak current, pulse width, pulse interval, and tracking) were taken as inputs and the tooth profile deviations of rigid gears were considered as outputs to develop an ISSA-LSSVM-based profile deviation prediction model. The prediction performance of the ISSA-LSSVM model was evaluated by comparing it with the LSSVM model optimized by three standard algorithms. The prediction results of the ISSA-LSSVM model were \(R^{2}\) = 0.9828, \(RMSE\) = 0.0029, and \(MAPE\) = 0.0156. The results showed that the established model exhibits high prediction accuracy and can provide reliable theoretical guidance for predicting the tooth profile deviation of rigid gears.  
rigid gears; wire electrical discharge machining; tooth profile deviation; least squares support vector machine; improved sparrow search algorithm +
Footnote †: journal: Applied  
0000-0001-999-7882]Z. [PERSON]
# How to Use the Osteoclast Identifier Software
## 1 Introduction
### Alternative Software for Cell Sorting and Identification
#### 1.2.2 ImageJ: A Traditional Software for Cellular Analysis  
Imagej is a very powerful software for analyzing microscopic images. However, when applied to the classification and counting of osteoclasts at different maturation stages in optical microscopy images, its efficiency is even lower than manual counting.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Software**} & \multicolumn{2}{c}{**Automated**} & \multirow{2}{*}{\begin{tabular}{c} **Automatic Cell** \\ **Osteoclast** \\ **Identification** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Automatic Cell** \\ **Classification** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Automatic Cell** \\ **Counting** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Automatic Cell** \\ **Proportion** \\ **Calculation** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **User-Friendly** \\ **Graphical** \\ **Score** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Application** \\ **Scope** \\ \end{tabular} } \\ \hline Manual & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & All information \\  & & & & & & \\ OC\_Identifier & \(\surd\) & 4 categories & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\  & & & & & & \\ OC\_Finder (V 1.0.0) & & & & & & \\
[2,3] & \(\surd\) & 2 categories & \(\surd\) & \(\times\) & \(\times\) & \(\times\) \\ Image \(\{V\) (1.54p) & \(\times\) & Plugin + manual & Plugin + preprocessing & Plugin or & \(\surd\) & Microscopic \\ CellPose (V3.0) & Requires & Requires & Requires & Requires & Requires & \(\surd\) \\
[6,7] & self-training & self-training & self-written code & self-written code & & \(\surd\) \\ DeepCell (V 0.12.10) & Requires & Requires & Requires & Requires & & \\
[8,9] & self-training & self-training & self-written code & self-written code & & \(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of various software alternatives for the analysis of microscopic cell photos.  
This limitation stems from the intricate morphological characteristics of osteoclasts. It is difficult to classify their maturation stage using conventional size and circularity filters (e.g., osteoclasts with 1-2 nuclei and those with \(\geq\)3 nuclei may exhibit similar sizes). Additionally, sustained living osteoclasts have low contrast against the background due to minimal differences in grayscale values, making it challenging to separate cells from the background using threshold adjustment (Image > Adjust > Threshold). Furthermore, ImageJ requires the use of the Watershed plugin (Process > Binary > Watershed) to manually split overlapping cells for accurate counting. Without this step, ImageJ may mistakenly merge multiple adjacent cells into a single cell, resulting in counting errors.
# BIM-Based Management System for Off-Site Construction Projects
## 5 System Application and Effects
### Installation Management and Progress Monitoring  
Using the proposed system, the administrators could more rapidly, accurately, and conveniently manage the installation status of the components onsite. Moreover, the users could effectively monitor the project progress by leveraging the BIM-based visualization  
Figure 8: BIM- and RTLS-based logistics management.  
data. In the existing project management environment that did not support BIM, the participants printed out 2D drawings for each floor to visualize the installation status, pasted them on the wall, and then colored them manually after checking whether each component was installed (Figure 9A).  
This tedious task was highly time-intensive and had to be repeated periodically. Consequently, installation management was error-prone. In several cases, drawings or installation records (logs) were inaccurate, or the staff colored the wrong component. When the progress/cost execution rate or the numerical data were derived based on these inaccurate data, a notable ripple effect was observed on the project. Moreover, when fundamental aspects such as the design were changed, a complete rework had to be performed, resulting in a considerable waste of time and money.  
The proposed system could overcome the abovementioned limitations. The users could effectively manage the project status. The site managers could easily examine the installation status of the components through a 3D view of the system implemented based on BIM (Figure 9B). In addition, project performance indicators such as the progress rate could be derived using data linked by the BIM without additional calculations. Therefore, the users could check the installation status of the project at a specific point in time through color-based 3D views and numerical information. Because the GUIDs recognized each component as a unique object, the corresponding information could be managed through the CDM in the system DB. Furthermore, the information to be stored in the sub-properties of the component model was matched only when it matched the model GUID, to ensure the connection accuracy. In this manner, the testers could streamline the cumbersome and repetitive tasks required to be performed in the non-BIM-based environment. Therefore, the time required to determine the progress status was shortened, and more accurate data-based monitoring could be accomplished.
## 2 Research Status of Design Pattern Detection  
### Non-Machine Learning Design Pattern Detection Methods  
As mentioned above, at present, scholars have proposed many design pattern detection methods based on non-machine learning technologies. These non-machine learning technologies include logical reasoning [5, 6], graph theory [7, 8, 9], extensible markup language (XML) [10, 11, 12], abstract syntax tree (AST) [13, 14], ontology technology [12, 15], abstract semantic graph (ASG) [16], formal technologies [17], rules [18, 19, 20], etc.  
Non-machine learning design pattern detection methods are generally divided into two stages: detection rule acquisition stage and pattern detection stage. The detection rule acquisition stage acquires detection rules from the theoretical description of design patterns and stores them in advance. The general process of the pattern detection stage is first extract the relevant information of the system to be recognized from the source code or system design, such as classes, attributes, operations, and different relationships between classes; then, according to the extracted system information, convert the system into a certain form with strict semantics; after the system is converted into a form with strict semantics, the system is divided into smaller units to be recognized, and the units to be recognized can be matched with the template design patterns according to the detection rules. If a to-be-identified unit matches a template design pattern successfully, the unit is considered to be a (candidate) instance of this pattern, otherwise, it is excluded (as shown in Figure 1).  
These methods have had some success. However, the identification rules for these methods are all derived from theoretical descriptions of design patterns. The use of design patterns is very flexible, and there are often various pattern variants in practical engineering projects. It is difficult for the rules derived from the theoretical description of design patterns to take into account all situations. In addition, there are also cases where multiple classes in a pattern instance are associated with the same pattern role or a subsystem contains multiple instances of the same pattern, while these rules are often only for cases where one class is associated with the same pattern role and a subsystem contains only one instance of the same pattern. Therefore, using rules derived from theoretical descriptions of design patterns for design pattern detection will introduce a large number of false-negative or false-positive instances, limiting the precision and recall of these methods.
# Surface Plasmon Resonance-Based Fiber Optic Sensors Utilizing Molecular Imprinting
###### Abstract  
Molecular imprinting is earning worldwide attention from researchers in the field of sensing and diagnostic applications, due to its properties of inevitable specific affinity for the template molecule. The fabrication of complementary template imprints allows this technique to achieve high selectivity for the analyte to be sensed. Sensors incorporating this technique along with surface plasmon or localized surface plasmon resonance (SPR/LSPR) provide highly sensitive real time detection with quick response times. Unfolding these techniques with optical fiber provide the additional advantages of miniaturized probes with ease of handling, online monitoring and remote sensing. In this review a summary of optical fiber sensors using the combined approaches of molecularly imprinted polymer (MIP) and the SPR/LSPR technique is discussed. An overview of the fundamentals of SPR/LSPR implementation on optical fiber is provided. The review also covers the molecular imprinting technology (MIT) with its elementary study, synthesis procedures and its applications for chemical and biological anlayte detection with different sensing methods. In conclusion, we explore the advantages, challenges and the future perspectives of developing highly sensitive and selective methods for the detection of analytes utilizing MIT with the SPR/LSPR phenomenon on optical fiber platforms.  
optical fiber; sensor; surface plasmon resonance; molecular imprinting 1  
[PERSON], [PERSON] and [PERSON]
# Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation
## 3 Domain Adversarial Network for Emotion Recognition in Conversation  
In this paper, there are two domains: a source domain \(D_{s}\) and a target domain \(D_{t}\). Because the source domain dataset is used to train the generative task, it has no emotional label. For the source domain, given a dialogue containing \(m\) utterances \(d_{s}\) = {\(u_{1}\), \(u_{2}\),..., \(u_{m}\)}, \(m\) is the length of dialogue, we can leverage {\(u_{1}\), \(u_{2}\),..., \(u_{m-1}\)} and {\(\{a_{2}\), \(\hat{u}_{3}\),..., \(\hat{u}_{m}\}\)} to train a generative conversational task. For the target domain, given a dialogue containing \(n\) utterances \(d_{t}\) = {\(u_{1}\), \(u_{2}\),..., \(u_{n}\)} and \(n\) labels \(Y_{d}\) = {\(\hat{y}_{1}\), \(\hat{y}_{2}\),..., \(\hat{y}_{n}\)}, \(n\) is the length of dialogue. Our goal is to predict the emotion labels of the \(d_{t}\).  
This study proposes a _Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation_ (DAN-CDERC) model to address emotion recognition with generative conversation. DAN-CDERC contains two key components: the _Domain Adversarial model_ and the _Emotion Recognition model_. Figure 2 shows the architecture of the _Domain Adversarial model_, where the input is {\(u_{1}\), \(u_{2}\),..., \(u_{m}\)} for the source domain and {\(u_{1}\), \(u_{2}\),..., \(u_{n}\)} for the source target domain. The output of the generator is the generated response sequence {\(\hat{u}_{2}\), \(\hat{u}_{3}\),..., \(\hat{u}_{m}\)}, and the output of the discriminator is the domain labels. Figure 3 shows the architecture of the _Emotion Recognition model_, where the input is {\(u_{1}\), \(u_{2}\),..., \(u_{n}\)} and the output is emotion labels \(Y_{d}\) = {\(\hat{y}_{1}\), \(\hat{y}_{2}\),..., \(\hat{y}_{n}\)}.  
Figure 2: Overall architecture of the Domain Adversarial Network model.  
The _Domain Adversarial model_ contains the _Encoder Layers_, the _Generator model_ and the _Discriminator_. The _Encoder Layers_ and the _Generator_, from Hierarchical Recurrent encoder-decoder (HRED) [16], are used to learn sequence knowledge from the massive amounts of generative dataset. The _Discriminator_ performs domain adaptation by discriminating the domain, which plays an essential role in reducing the domain inconsistency for domain adaptation. For the emotion recognition model, we leverage BERT [45] to encode utterances, and LSTM (context encoder) to encode context, which learns context weights from the generative conversational model. First, we leverage \(d_{s}=\{u_{1},\,u_{2},\,\ldots,\,u_{m-1}\}\) and \(\{\hat{u}_{2},\,\hat{u}_{3},\,\ldots,\,\hat{u}_{m}\}\) to train a generative conversational model, and leverage \(d_{s}\) and \(d_{t}\) to train the domain-distinguish task. Then a part of the parameters of the generative model is transferred to the emotion recognition model (target task). We leverage \(d_{t}=\{u_{1},\,u_{2},\,\ldots,\,u_{m}\}\) and \(Y_{d}=\{\hat{y}_{1},\,\hat{y}_{2},\,\ldots,\,\hat{y}_{n}\}\) to train the emotion recognition model.
## 2 Materials and Methods
### Statistical Methods
#### 2.8.1 Multivariate Logistic Regression  
To identify predictors of marginal bone loss, peri-implant parameters, implant survival rate, and surgical and prosthetic complications, a multivariate logistic regression was performed. The independent variables included in the model were implant site, type of prosthetic rehabilitation, presence of systemic disease, and smoking. The results were expressed as odds ratios (ORs) with 95% confidence intervals. To assess the influence of systemic and clinical variables on the main implant outcomes, a series of null hypotheses were formulated predicting the absence of statistically significant associations between the covariables analyzed and the clinical outcomes considered. Specifically, it was hypothesized that the presence of systemic pathology, smoking habit, implant site (anterior or posterior maxilla and anterior or posterior mandible), and type of prosthetic rehabilitation (single crown or bridge) did not significantly influence any of the following dependent variables: implant survival, marginal bone loss, peri-implant clinical parameters (such as probing depth and bleeding on probing), and incidence of surgical or prosthetic complications. More specifically, a specific null hypothesis was tested for each combination of independent variable and clinical outcome, assuming that any differences observed in the data were attributable to chance. The level of statistical significance (\(\alpha\)) was set at 0.05; therefore, a \(p\)-value below this threshold was considered indicative of the rejection of the null hypothesis and the existence of a statistically significant association between the independent variable and the outcome under investigation.
## 4 Establishment of Simulated HSI Dataset Based on Experimental Measurement
### Mixed Pixel Simulation  
Section 4.2 carried out independent measurements of reflectivity data for six types of ground objects. However, in practice, due to the low spatial resolution of HSIs, each pixel may contain spectral information of multiple basic objects at the same time. This type of pixel is called \"mixed pixel\". The proportion of each type of feature in a mixed pixel is called the \"abundance\" of a ground object [38]. The existence of mixed pixels makes the feature space develop in the opposite direction; that is, the intra-class difference becomes larger, and the inter-class coupling becomes stronger, which has a great negative impact on the classification [39]. In order to verify the influence of mixed pixels on the results obtained by the wavelength selection algorithm, mixed pixel data with an abundance range of 0.75-0.25 and a step size of 0.05 were added to the dataset. Hyperspectral unmixing, that is, extracting basic ground objects from mixed pixels and calculating the abundance of each basic ground object in mixed pixels has become a key preprocessing technique for HSI analysis [40]. The mixed pixels simulation based on the measured object points hyperspectral data is equivalent to the inverse process of hyperspectral pixel unmixing. The samples reflectance data measured in Section 4.2 are used as the endmembers' spectral information to participate in the calculation of the mixed pixels. The existing mixed models mainly include linear models and nonlinear models. For the following two reasons: (a) the linear spectral mixture model has the advantages of simplicity, high efficiency, and well-defined physical meaning; (b) for hyperspectral images with spatial resolution below the meter level, the linear spectral mixing model can better describe the actual spectral mixing phenomenon. In this paper, the Linear Mixing Model (LMM) is selected for the calculation of mixed pixels. It can be described by Equation (12):  
\[L_{mixed}(x,y,\lambda)=A_{i}(x,y)\times L_{i}(\lambda)+[1-A_{i}(x,y)]\times L_ {j}(x,y,\lambda)+e \tag{12}\]  
where \(L_{mixed}(x,y,\lambda)\) is the pixel value of the mixed position on the image, \(A_{i}(x,y)\) is the abundance of the object sample \(i\) in the pixel \((x,y)\),\(L_{i}(\lambda)\) is the spectrum of the object sample \(i\), \(L_{j}(\lambda)\) is the spectrum of the ground object sample \(j\), and \(e\) is the Gaussian noise. In this paper, \(L(\lambda)\) is the hyperspectral reflectance of the sample measured by ASD. Figure 6 is a schematic diagram of the mixed reflectance of pixel \(i\) and pixel \(j\). The pixel abundance is 0.75-0.25, and abundance changes in steps of 0.05.  
In order to test the classification performance of the wavelength selected by the DR method under different noises, this paper sets a total of eight non-uniformly spaced signal to noise ratio (SNR) conditions from SNR = 1280 to SNR = 10. The SNRs are (1280, 640, 320, 160, 80, 40, 20, 10).