esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### LEACH (Low-Energy Adaptive Clustering Hierarchy)  
The structure of a network can be classified according to node uniformity, in Flat Networks Routing Protocols (FNRP) and Hierarchical Networks Routing Protocol (HNRP) [17].  
LEACH is one of HNRP in sensor networks proposed by [PERSON] [18]. This is a typical routing protocol that effectively reduces transmission energy consumption by forming a hierarchical structure between nodes. As shown in Figure 3, LEACH is a method in which the cluster head collects and processes data from the member nodes of the cluster and delivers it directly to the BS. If this is used, the cluster head is selected by the ratio of all sensor nodes, and the cluster head is determined by calculation inside the sensor node, so it is a distributed system.  
The operation of LEACH is divided into rounds, and each round begins with a setup phase when the cluster is formed, and consists of a steady state phase when data is transmitted to the base station. In order to minimize overhead, the steady state phase is longer than the set phase, as shown in Equation (1) [19]. In Equation (1), the setup is: each node decides to depend on CH of the current round. This decision is based on choosing a random number between 0 and 1 if the number is less than the threshold T(\(n\)), and the node becomes the cluster head of the current round. The threshold is set as follows. If the cluster head is selected, the state is reported using the Carrier Sense Multiple Access (CSMA) Medium Access Control (MAC) protocol. The remained nodes make decisions about the current round's cluster head according to the received signal strength of the advertisement message. The Time Division Multiple Access (TDMA) scheduling is applied to all members of the cluster group to send a message to the CH and then from the cluster head to the base station. As soon as a cluster head is selected for a region, the steady state phase begins. Steady: the cluster is created when the TDMA scheduling is fixed and data transfer can begin. If the node always has the data to send, it sends  
Figure 3: Diagram of WSN. (**a**) Flat networks routing protocols. (**b**) Hierarchical routing protocols (Low-Energy Adaptive Clustering Hierarchy (LEACH)).  
Figure 2: Image of disposable sensor.  
it to the cluster head for the allotted transmission time. Each head node can be turned off until the node allocates the transmission time, thus minimizing energy loss in these nodes [20].  
\[T(n)=\left\{\begin{array}{c}\frac{\rho}{1-\rho(r\bmod(\frac{1}{\rho}))},\ \ \text{if}\ n\in G\\ 0,\ \text{otherwise}\end{array}\right. \tag{1}\]  
\[T(n)=\text{Threshold};\]  
\[r=\text{current round};\]  
\[G=\text{The set of nodes that have not been CH;}\]  
\[\rho=\text{the percentage of cluster - heads.}\]
# Quorum Sensing and Quorum Quenching in _Agrobacteriumum_: A \"Go/No Go System\"?
## 3 TraM Acts as a Quorum Quenching Regulator of an Unusual Quorum Sensing System  
In the archetypical view of QS regulation, the above described system should respond to the cell concentration of agrobacteria. Thus, at low cell concentrations, low amounts of OOHL are produced, and the _tra_ and _trb_ operons, hence the _tral_ genes, should not be or be only poorly expressed, even in the presence of agrocinopines. At high cell density, and in the presence of agrocinopines, the transcription of the _arc_ operon is induced. TraR is therefore produced, and upon binding of OOHL, becomes activated; the _tra_ and _trb_ operons are expressed, thus permitting the conjugative transfer of the Ti plasmid.  
The TraR/TraI system and the encoded proteins are, however, peculiar. First, the affinity of TraR for OOHL is extremely high, in the range of 10 pM to 1 nM [30]. Second, in _Agrobacterium_, the activity of TraR is modulated by a small protein, TraM, which is also encoded by a Ti plasmid gene [31; 32] (Figure 1). Indeed, TraM can bind TraR and prevent its association with OOHL [33]. Quorum quenching (QQ) refers to all processes involved in the disturbance of QS, and therefore, encompasses both the degradation of QS signals and disruption of signal sensing devices (for a review on QQ, see [34]). In this scheme, TraM may therefore be regarded as an embedded QQ regulator targeting the AHL sensor/receptor protein TraR. In support, strains defective for TraM do transfer their Ti plasmid constitutively [30; 31; 32]. It therefore appears that even in the absence of agrocinopines, _traR_ is expressed at a level that is sufficient to bind low amounts of OOHL present in the cell and its environment [30] to activate all _tra_ and _trb_ operons at subquorate bacterial concentrations. Under those conditions, can the TraR/TraI system borne on the Ti plasmid be regarded as an ordinary QS system?
## 2 Facility Layout Problem with Relative Positioning Constraints
### The FLPRC Formulation in a Matrix Form
#### 2.3.9 Overall Problem Formulation in Matrix Form  
We define the overall _constraint matrix_\(A\in\mathbb{R}^{(5/2N^{2}+(11/2+M)N)\times(4N+N(N-1))}\) and _constraint vector_\(\mathbf{b}\in\mathbb{R}^{(5/2N^{2}+(11/2+M)N)}\) as  
\[A=\begin{bmatrix}A_{d}&-I\\ -A_{d}&-I\\ A_{r}&O\end{bmatrix},\quad\mathbf{b}=\begin{bmatrix}\mathbf{0}\\ \mathbf{0}\\ \mathbf{b}_{r}\end{bmatrix}\]  
and \(A_{r}\in\mathbb{R}^{(1/2N^{2}+(15/2+M)N)\times(4N+N(N-1))}\) and \(\mathbf{b}_{r}\in\mathbb{R}^{(1/2N^{2}+(15/2+M)N)}\) are defined as follows:  
\[A_{r}=\begin{bmatrix}A_{wb}\\ A_{ar}\\ A_{s}\\ A_{rp}\end{bmatrix},\quad\mathbf{b}_{r}=\begin{bmatrix}\mathbf{b}_{\mathbf{wb}} \\ \mathbf{b}_{\mathbf{z}}\\ \mathbf{b}_{\mathbf{y}}\\ \mathbf{b}_{\mathbf{rp}}\end{bmatrix}\]  
The first equation (26a) that follows from the gradient of Lagrangian must be zero, since the optimal point \(\mathbf{v}^{*}\) minimizes the \(L(\mathbf{v},\lambda)\) over \(\mathbf{v}\). The second equation (26b) is the _complementary slackness_. The third and the last inequality (26c) and (26d) are the constraints with respect to the _primal_ and _dual feasibility_, respectively.  
If (and only if) there are \((\mathbf{v}^{*},\lambda^{*})\) that satisfies the KKT condition (26), then the \(\mathbf{v}^{*}\) is the optimal point. In other words, solving LP (23) is equivalent to the problem of finding the point \((\mathbf{v}^{*},\lambda^{*})\) that satisfies the KKT conditions. As is the case in most other convex problems, however, it is difficult to directly find such points due to a large number of linear inequalities to maintain the primal and dual feasibility in the KKT conditions (26).  
The interior-point method is a technique used to solve those inequality-constrained problems by reducing it to a sequence of problems without inequalities. The details are described in the next section.
## 5 Case Simulation and Verification
### Results and Analysis  
Table 4 compared the stress and deformation characteristics of the glass pane under different supporting pillar radii and spacing distances. In general, under permanent stress from atmospheric pressure and the supporting pillars, the deformation concentration point of the glass pane was located at the center of the square region, generated by the adjacent supporting pillars. Specifically, the largest deformation value occurred between the outermost row and second row of the supporting pillars. Simultaneously, the maximum stress point on the inner surface of the glass pane appeared at the position of the supporting pillar. These trends were consistent with previous research [26].  
According to the National Standard of China \"Safety glazing materials in building--Part 2: Tempered glass\" (GB15763.2-2005), the concentrated load formed on the glass surface was suggested to be below 90 MPa. The ANSYS simulation showed that the stress peaks under the three treatments had a trend of 90 MPa > R2a63 > R3a63 > R3a50, which indicated that the VIG structures under R2a63, R3a63 and R3a50 treatments met the design requirements.  
In order to ensure the mechanical stability of the VIG products, larger supporting pillar radii and smaller spacing distances could obtain better mechanical properties. In this study, a multi-objective solution was conducted based on the published literature [26], and a critical spacing distance for the supporting pillars was solved, i.e., R2a63 treatment. The stress peak of the glass pane under the R2a63 treatment, under a 0.2 mm supporting pillar radius and a 63 mm spacing distance was close to the recommended value (90 MPa). This demonstrated that the radius and spacing distance should be 0.2 mm or more and 63 mm or less, respectively, under this case, which verified the feasibility of the multi-objective solution with the critical load value as the constraint condition.  
[PERSON] et al. [32] found that the critical supporting spacing for VIG was 70 mm when the glass substrate thickness was 5 mm and the supporting radius of the supporting pillar was 0.15 mm (guaranteeing the mechanical stability with the minimum supporting pillar number). However, the simulated results of ANSYS, using the above parameters, showed that the maximum stress on the inner surface was 109.05 MPa, which had exceeded the recommended value of national standard. The reason may be that the tensile stress inside the glass was not comprehensively considered a constraint condition. Meanwhile, supporting form such as spherical supporting, as well as the placement way also impacted stress concentration.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Treatments** & **Supporting Pillar Radius (mm)** & **Supporting Pillar Spacing (mm)** \\ \hline R3a63 & 0.3 & 63 \\ R3a50 \({}^{\star 1\sigma}\) & 0.3 & 50 \\ R2a63 & 0.2 & 63 \\ \hline \hline \end{tabular} \({}^{\dagger}\) Supporting pillar radius and spacing were consistent with reference [30].  
\end{table}
Table 3: Experimental design for ANSYS simulation.
# Fault Diagnosis System of Power Grid Based on Multi-Data Sources
## 4 Fault Tracking Quasi-Real-Time Processing System and Fault Diagnosis Batch Processing System  
### Implementation of Fault Tracking Quasi-Real-Time Processing System  
When a real-time processing system transmits the sets (\(CB\_1\), \(CB\_2\)) of maloperation and refuse-operation circuit breakers and the sets (\(P\_1\), \(P\_2\)) of maloperation and refuse-operation protection to substations, the substations can call the warning information of corresponding relay protection devices and the online monitoring information of corresponding circuit breakers to screen fault features of these devices. According to the fault features of these devices and the relation table between fault reasons and features, fault reasons can be tracked immediately through the corresponding algorithm.
# Vision-Based System for Black Rubber Roller Surface Inspection
## 4 Discussion  
### Comparing the Two Methods  
Edge detection, segmentation, and line detection, which are common manufacturing techniques, are unable to accurately extract the internal structure of any defect. Normally, the damage is treated as a random target, and the difference in reflectivity between the target and the background is used for detection. The rubber roller's qualification is then evaluated based on the position and area of the target. However, this approach often leads to misidentification of textures, stains, oil stains, etc., as defects. This results in low accuracy and a reduced recall rate during the detection process. In some cases, we need to know how many different kinds of defects there are and how often they happen, so we can adjust the production process accordingly. Conventional manufacturing surface inspection techniques cannot accomplish this. Deep learning has emerged to make up for the drawbacks of traditional algorithms. Since deep learning algorithms have shown better performance in object recognition and classification tasks, deep neural networks can be used to detect different types of defects and identify similarities between them. Accurate fault classification can be obtained from large amounts of data.  
After applying two methods, we find that each method has its own advantages and disadvantages, and both methods give quite good defect identification results and can be applied in industry.  
* Traditional method  
This method does not require multiple input images to train the model and can be widely applied in manufacturing industries to detect defects, with low cost and good defect detection ability. However, its accuracy is highly dependent on the lighting system. Therefore, if the lighting system changes, the accuracy of the detection may also change. This makes it difficult to identify specific errors using this method.  
* YOLOv7  
The deep learning method is able to classify specific types of defects on the rubber surface such as damage, erosion, etc.; multiple input images can be used to increase accuracy, and it can be applied in rubber product visual inspection systems to classify defects and improve reliability during inspection.  
However, it is necessary to use many input images to train the model, and a certain level of deep learning knowledge is required.
# The Quest for Low Loss High Refractive Index Dielectric Materials for UV Photonic Applications
## 2 Methods
### Temperature Calculations  
The general heat generation process involves optical absorption, heat generation by itself and heat transfer to the surrounding medium. For the mechanism of heat release, the electric field strongly drives mobile carriers inside the NPs, and the energy gained turns into heat. Then, the heat diffuses away from the NP and leads to an increase of the temperature of the surrounding medium [40]. In metallic nanoparticles, the heat generation rate is especially strong under plasmon resonance. However, in semiconductors, this is much weaker since heat dissipation occurs through an interband absorption process with the creation of a single mobile electron-hole pair (exciton). In absence of phase transformations, the temperature distribution around an optically excited NP is described by the heat transfer equation [40; 41]  
\[\rho(\mathbf{r})c(\mathbf{r})\frac{\partial T(\mathbf{r},\mathbf{t})}{\partial t}=\
abla k(\bm {r})\
abla T(\mathbf{r})+Q_{e}(\mathbf{r},t), \tag{3}\]  
where \(\mathbf{r}\) and \(t\) are the spatial coordinate and time, respectively. \(T(\mathbf{r}\),\(t)\) is the local temperature, and the material parameters \(\rho(\mathbf{r})\), \(c(\mathbf{r})\) and \(\kappa(\mathbf{r})\) are the density, specific heat and thermal conductivity, respectively. The magnitude \(Q_{e}(\mathbf{r}\),\(t)\) represents a heat source produced by light dissipation in NPs. This term is composed by both resistive and magnetic losses. \(Q_{e}(\mathbf{r}\),\(t)\) has been obtained by solving a system of Maxwell's equations including appropriate boundary conditions [5]. In our case, the whole process of light absorption and subsequent heat transfer between the nanostructure and the surrounding medium has been modeled by means of finite element simulations implemented in COMSOL Multiphysics 5.2 [39] (following the same method as in [2; 3]).
# Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet
## 3 Cross-Guide-UNet (CG-UNet)
### Cross-Guide Module
#### 3.1.3 Cgm  
In previous edge detection algorithms, the fusion between the encoder and decoder was limited to simple splicing, failing to extract the relation between cascade features effectively. Hence, we designed a CGM. By computing the attention weights of the encoder and decoder and employing a cross-conferring mechanism, we enhanced the connection of cascade features, thereby effectively guiding the generation of edge images. The CGM not only achieves crosslayer interaction and fusion of encoder and decoder features but also weights and filters features at different levels through channel attention and spatial attention mechanisms, enabling the model to focus more on key features related to ice edges and enhancing feature representation capabilities.
# Physicochemical and Sensory Evaluation of Yanggaeng Treated with Roasted Guateman Coffee Extracts
## 3 Results and Discussion
### Consumer Preferences of Yanggaeng
These sensory differences can be attributed to chemical transformations that occur during roasting. The Maillard reaction and caramelization processes in coffee beans lead to the formation of various volatile compounds such as pyrazines, furans, and phenolic compounds, which contribute to the characteristic roasted aroma and deeper color [50; 51]. Darker roasts, which produce higher concentrations of these compounds, likely enhance the richness and complexity of coffee-infused Yanggaeng, aligning with the observed consumer preferences. The increase in sweetness preference in the M6 sample and the consistently highest overall acceptance in the dark-roasted group indicate a favorable impact of darker roasting on the flavor profile, likely due to the development of richer and more complex flavors during roasting. These results have practical implications for product development. Manufacturers aiming to create coffee-infused Yanggaeng should consider using dark-roasted coffee extract at concentrations between 4% and 6% to optimize sensory attributes such as color, aroma, and overall taste. Additionally, these findings highlight the importance of balancing bitterness and sweetness because excessively high concentrations or roasting levels may lead to undesirable flavor outcomes. Future research could explore the underlying chemical and physical interactions between coffee compounds and Yanggaeng ingredients, as well as consumer perception changes over time and across different demographic groups. Further understanding could guide the more precise tailoring of product formulations to enhance market acceptance and satisfaction.
## 1 Introduction
Finally, the contact or team fixes the problem and tries to identify the main reason for the problem. The dynamic and complex nature of cyberattacks on computer networks causes existing intrusion detection systems to be insufficient in this regard [9]. This is because new and obfuscated malicious attacks make the detection process challenging and can escape from the network security systems. These reasons raise the need in order to develop new methods in the IDS world. This paper aims to contribute to the solution of wireless local area network security problems by using the latest technological improvements. The paper has three main contributions:  
* In order to detect attacks efficiently, the first contribution was made with the feature selection approach (FSAP). * Afterwards, a hybrid classification technique (SABADT) was presented to detect attacks with high accuracy. * Finally, an application was made on the KDD '99 datasets in the literature for the performance evaluation of the suggested approaches and techniques. The KDD '99 as well as UNSW-NB15 training datasets were analyzed while extracting the signatures and rules to train the model. The created model was tested by applying to the KDD '99 and UNSW-NB15 testing datasets. According to the test results, the attacks as well as the attacks' categories were obtained with 99.89% and 98.84% accuracy rates, respectively. Compared to other state-of-the-art studies, there are not many studies proposing new methods for both feature selection as well as classification stages at the same time. By integrating signature-based and anomaly-based methods, the detection time was reduced while the accuracy rate was increased. In addition, the proposed model was tested on the updated CIC-IDS2017 dataset. Attacks were detected with a 99.51-99.91% accuracy rate. More detailed analysis will be conducted on this data set in future research. In summary, within the scope of this study, intrusion attacks were recognized by examining the traffic behaviors in wireless local area networks, and the attacks' types were determined. The remaining sections of the paper are structured as follows: Section 2 provides an overview of the rationale for feature selection and explores various approaches that have been proposed in the literature for selecting relevant features in intrusion detection systems. Section 3 details the proposed feature selection and classification methods. Section 4 describes the implementation of the suggested approach. Section 5 presents the results and discussion of the findings. Section 6 discusses the limitations of the study and outlines directions for future research. Finally, Section 7 provides the conclusion of the paper.
# DanceCaps: Pseudo-Captioning for Dance Videos Using Large Language Models
## 3 Pseudo-Captioning Based Dance Caption Generation
### Tags
#### 3.1.3 Music Tags  
We extracted musical information by focusing on tempo, beat positions, pitch levels, mood, and instruments. Initially, we utilized librosa [42] to determine the tempo, beat positions, and average pitch level of the music. Tempo was categorized into low, medium, and high based on BPM standards like adagio, andante, and allegro in Western music. The average pitch level was calculated from the extracted pitches, classified into high (above 800 Hz), low (below 500 Hz), and mid-range. Beat positions were identified using onset strength from librosa and were aligned with the video's frame rate to correlate beats with specific frames. Music genres, such as hip-hop, jazz, and electronic, were classified using a genre classification model [43]. Results were compared with those from auto-tagging [44], and where applicable, overlapping and differing outcomes were both included. Mood and instrument data were also extracted using auto-tagging techniques. To enhance accuracy, we imposed limits, selecting only 2-3 instruments and moods for representation, while genre tags were further refined using metadata. Other music captioning methods [23; 29] were excluded due to the tendency to generate excessive music-related details that overshadowed movement descriptions, or because they produced numerous inaccurate tags.
# Magnitude Modeling of Personalized HRTF Based on Ear Images and Anthropometric Measurements
## 4 Performance and Evaluation
### SHT Reconstruction Results  
To demonstrate the validity of SHT to represent full-space HRTF, this paper plots the result of SHT performed at approximately 5 kHz for subject003 HRTF magnitude pattern. At the same time, we compare the original HRTF, the reconstructed HRTF (obtained through SHT), and another subject (subject010) HRTF, and calculate the root mean square error (RMSE).  
Figure 9a shows the magnitude comparison of the original HRTF and the reconstructed HRTF of subject003 in 1250 directions, with an RMSE value of 0.19868 dB. It can be seen that due to the truncation of SHT, the reconstructed HRTF is smoother than the original value. According to previous studies, these minor spectral distortions are perceptually indistinguishable. Figure 9b shows the magnitude comparison of subject003 and subject010, which has an RMSE value of 0.63139 dB. This means that the reconstructed HRTF still has personalized features, which can be used to study personalized HRTF in this paper. Figure 9c shows the RMSE values of the original HRTF and the reconstructed HRTF at different frequencies. It can be seen that the reconstruction error varies with frequency, but still preserves individualized information. Figure 9d shows the magnitude comparison of the original HRTF and the reconstructed HRTF of subject003 at order \(L=7\), with an RMSE value of 0.14383 dB. Compared with Figure 9a, the higher the truncation order, the closer the reconstructed HRTF is to the original HRTF.  
Figure 8: CMC curve.