esa-sceva/satcom-chunk-collection huggingface dataset:
# Region-Based Approach for Machining Time Improvement in Robot Surface Finishing
## 3 Materials and Methods
### Surface Subdivision and Machining Time Estimation
#### 3.3.2 Surface Regionalization and Boundary Definition  
In the previous chapter, the K-means algorithm was employed to partition the dataset into clusters, as seen in Figure 2a. These clusters are essentially groups of data points that share similar characteristics, but do not necessarily form coherent spatial boundaries. To interpret the results in a spatial context better, these clusters are processed further to create what will be referred to as regions. A critical step in this process involves defining clear boundaries for these regions to facilitate efficient region-based analysis or machining operations. To achieve this, the alpha shape algorithm [43] was applied, which refines the clusters by generating well-defined boundaries for each region. The algorithm works by constructing a shape, enveloping the set of points with the smallest possible predefined radius \(\alpha\), effectively capturing the concavities of the region, and ignoring outliers or noise. Figure 2b showcases the refined regions after applying the alpha shape algorithm, with each distinct region outlined clearly and separated from others, thus creating separation machining regions, each representing a single continuous space with VVF.
# Deep Reinforcement Learning for UAV-Based SDWSN Data Collection
## 2 Related Work
In [30], the authors aim to design an energy-efficient route for UAVs undertaking long-distance sensing tasks. They propose a DRL-based framework that employs convolutional neural networks (CNNs) for feature extraction and a deep Q-network (DQN) for decision-making. Research on the cooperation of computation tasks between UAV and WSN [31] suggests that tasks can be processed by nodes, UAV, and sink nodes. To manage randomly generated tasks, the UAV needs to dynamically adjust its trajectory to provide computing services for the nodes. Herein, DRL is used for UAV path optimization to enhance energy efficiency during the computation process. However, this research does not address the potential for node failure during this process or the need to restructure the network to re-establish the computation process. Other researchers [32; 33] have also highlighted the minimized energy usage through an optimized path for UAV-assisted WSNs. Nonetheless, an approach that offers flexible restructuring of WSNs when needed has not been considered. In summary, the existing literature on integrating UAV with WSN has not focused on creating a flexible network that can efficiently communicate with UAV, especially when node reconfigurability for various processing and computation tasks is required. Additionally, current algorithms for UAV flight path design have not adequately considered the adaptability of the path to meet the sensor networks' needs. While significant efforts have been made to develop energy-efficient UAV paths, there has been little attention given to optimizing paths that not only save UAV energy but also enable data collection from WSN with minimal downtime, even when structural changes necessitate reconfiguration. Therefore, we propose SDWSN where nodes can be configured with multiple functions to handle various tasks and facilitate efficient data collection by UAV. Furthermore, we propose an optimized path using DRL to ensure energy-efficient UAV flight rounds during data collection from flexible WSN.
## 3 Modeling Approach
### Track Irregularity and Train Grouping  
#### 3.2.1 Track Irregularity  
This study focuses on the dynamic behavior of simply supported beam bridges for medium- and low-speed maglev (MLSM) transportation systems. The bridge is designed to accommodate MLSM trains with a maximum operating speed of 100 km/h. To account for track irregularities, the German low-interference spectrum is applied based on the vehicle-bridge interaction parameters. The corresponding power spectral density (PSD) function is calculated as follows:  
Longitudinal Irregularity:  
\[S_{a}(\Omega)=\frac{A_{a}\Omega_{c}^{2}}{(\Omega^{2}+\Omega_{r}^{2})(\Omega^{2 }+\Omega_{c}^{2})} \tag{1}\]  
Vertical Irregularity:  
\[S_{v}(\Omega)=\frac{A_{v}\Omega_{c}^{2}}{(\Omega^{2}+\Omega_{r}^{2})(\Omega^{2 }+\Omega_{c}^{2})} \tag{2}\]  
Lateral Irregularity:  
\[S_{c}(\Omega)=\frac{A_{v}b^{-2}\Omega_{c}^{2}\Omega^{2}}{(\Omega^{2}+\Omega_{ r}^{2})(\Omega^{2}+\Omega_{c}^{2})(\Omega^{2}+\Omega_{s}^{2})} \tag{3}\]  
In the above formula, the units of \(S_{v}(\Omega)\) and \(S_{a}(\Omega)\) are m\({}^{2}\)/(rad/m); the unit of \(S_{c}(\Omega)\) is 1/(rad/m); \(\Omega\) is the spatial angular frequency; the unit of \(\Omega\) is rad/m; \(\Omega_{c}\), \(\Omega_{r}\), and \(\Omega_{s}\) are the truncated frequency and values of these variables are \(\Omega_{c}\) = 0.8246 rad/m, \(\Omega_{r}\) = 0.0206 rad/m, and \(\Omega_{s}\) = 0.438 rad/m; \(A_{a}\) and \(A_{v}\) are roughness constants, where \(A_{a}\) = 2.119 \(\times\) 10\({}^{-7}\) cm\({}^{2}\)-rad/m and \(A_{v}\) = 4.031 \(\times\) 10\({}^{-7}\) cm\({}^{2}\)-rad/m; and b is half of the distance of the left and right rolling circles, taking 0.75 m.  
Figure 8: Longitudinal floating mode of the first order of 10 \(\times\) 25 m simply supported beam bridge.
###### Abstract  
Automated medical diagnosis is one of the important machine learning applications in the domain of healthcare. In this regard, most of the approaches primarily focus on optimizing the accuracy of classification models. In this research, we argue that, unlike general-purpose classification problems, medical applications, such as chronic kidney disease (CKD) diagnosis, require special treatment. In the case of CKD, apart from model performance, other factors such as the cost of data acquisition may also be taken into account to enhance the applicability of the automated diagnosis system. In this research, we proposed two techniques for cost-sensitive feature ranking. An ensemble of decision tree models is employed in both the techniques for computing the worth of a feature in the CKD dataset. An automatic threshold selection heuristic is also introduced which is based on the intersection of features' worth and their accumulated cost. A set of experiments are conducted to evaluate the efficacy of the proposed techniques on both tree-based and non tree-based classification models. The proposed approaches were also evaluated against several comparative techniques. Furthermore, it is demonstrated that the proposed techniques select around 1/4 th of the original CKD features while reducing the cost by a factor of 7.42 of the original feature set. Based on the extensive experimentation, it is concluded that the proposed techniques employing feature-cost interaction heuristic tend to select feature subsets that are both useful and cost-effective.  
Keywords:cost-sensitive feature selection; ensemble models; decision tree classifiers; chronic kidney disease; random forest; gradient boosted trees +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion  
### P-Wave Velocity of Materials  
The P-wave velocity for each material is shown in Figure 6 in Box and Whisker plots which were designed with the use of Statistica v.10 software (StatSoft Inc., Tulsa, OK, USA). In these plots the central rectangle spans the first to the third quartile, while the segment inside the rectangle represents the median (second quartile). Whiskers above and below the rectangle show the maximum and minimum values and identify the non-outlier range. Any value outside that range is plotted as independent point and is called outlier.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mill** & \multicolumn{2}{c}{**Balls**} & \multicolumn{2}{c}{**Material**} \\ \hline diameter, \(D\) (cm) & 20.4 & diameter, \(d\) (mm) & 25.4 & quartz (2.65) \\ \hline length, \(L\) (cm) & 16.6 & number & 77 & marble (2.72) \\ \hline volume, \(V\) (cm\({}^{3}\)) & 5423 & weight (g) & 5149 & density (g/cm\({}^{3}\)) & quartzite (2.59) \\ \hline operational speed, \(N\) (rpm) & 66 & density (g/cm\({}^{3}\)) & 7.85 & metasandstone (2.11) \\ \hline critical speed, \(N_{c}\) (rpm) & 93.7 & porosity (\%) & 40 & material filling & \\  & & & volume, \(f_{c}\) (\%) & 4 \\ \hline  & & ball filling volume, \(J\) (\%) & 20 & interstitial filling, \(U\) (\%) & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grinding conditions.  
Figure 6 shows that outliers are present, just below the minimum value, only for quartz and quartzite. It is also observed that the descending order of materials with respect to the median \(V_{p}\) value is quartz, marble, quartzite and metasandstone. The statistical results of P-wave velocity also show that the mean \(V_{p}\) values of the materials follow the same order as the median values. Furthermore, low standard deviation is observed for \(V_{p}\) for all the materials tested and thus the data are clustered around the mean \(V_{p}\) values. It is also mentioned that similar values were obtained in other studies for marble and quartzite [51], sandstone [52] and quartz [53].
# A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms
## 4 Simulation Result
### Simulation for Relative Navigation  
In the simulation, the swarm is composed of a leader and nine followers, the formation structure is as shown in Figure 11 and the distance between drones is set to 100 m. Taking the fourth drone as an example, the front camera can observe 0, 1 and 2, the left camera can observe 1, 3, 6 and 7 and the right camera can observe 2, 5, 8 and 9. The relative navigation based on the vision and UWB results is shown in Figure 12. We detect the IDs of adjacent drones through a camera and measure the relative line of sight and azimuth angle, obtaining the relative distance between UAVs through airborne UWB sensors. Based on the measurement results of the camera and UWB, we calculate the relative positions between UAVs using the theoretical tools discussed in Section 2.2. Using the truth values provided by the simulation system as a reference, according to Figure 12, the maximum relative position error is 0.75 m (<0.75% of the distance between adjacent drones). According to (19), the accuracy of the relative position mainly depends on the line of sight and azimuth angle measured by the camera. The error from UWB is only 10 cm, which is a small amount compared to the distance between machines. On the other hand, under the condition of a fixed angle error, the farther the distance between drones, the greater the relative navigation error.  
Figure 10: The absolute error of ISMN simulation.  
Figure 9: The trajectory of ISMN simulation.
## 4 Methodology  
### The Objective Function  
In the proposed method, the objective function is based on identifying the most suitable hospital regarding the patient's status and the admission time. The admission time of a hospital \(H\) is computed as the maximum time \(Max_{t}\) of reach time \(H1_{t}\), and admission time for the patient to get medical care \(H1_{td}\). The patient status is represented by \(P_{com}\), while the preparation of hospital \(H1\) is denoted by \(H1_{md}\). A matching criterion has been developed to identify each symptom with patient status. The match function generates a number that shows the degree of readiness that the hospital must receive the patient. For each hospital, a solution is constructed of hospital number, match value, and admission time. Based on the three criteria, the method varies among different hospitals.  
The method considers patients with severe conditions as it directs those patients to hospitals with the least admission time regardless of the readiness of the hospital to the patient status. This can save the patient's life because he/she needs immediate medical attention. For patients with non-severe cases, they are directed to hospitals with maximum match value and smallest admission time. The method considers the appropriateness of the hospital as the first objective to be optimized, then the admission time. The way to  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline \(h_{t_{ad}}\) & the time needed for a patient to be admitted in a hospital \\ \(h_{t_{t}}\) & the time needed for a patient to reach a hospital location \\ \(h_{md}\) & the medical devices possessed by a hospital \\ \(P_{com}\) & the comorbidities of a patient \\ \(t\) & the time at which the event happens \\ \(d_{pt}\) & the demand of patient \(p\) in time \(t\) \\ \(h_{mb}\) & the maximum number of beds in a hospital \(h\) \\ \(MHB\) & maximum number of beds in hospitals \\ \(R_{hpt}\) & the response time of the system to identify a hospital \(h\) for patient \(p\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: A description of the main parameters of the problem definition.  
compute objective function for two hospitals \(H1,H2\), and the identification of the best hospital is given as:  
\[H1_{Max_{t}}=Max(H1_{t_{r}},H1_{t_{ad}})\] \[H1_{m}=Match(P_{com},H1_{md})\] \[H1=(H1_{m},H1_{Max_{t}})\] \[H2=(H2_{m},H2_{Max_{t}})\]  
The \(H1\) hospital can be identified to dominate \(H2\) if the readiness value of \(H1\) is bigger than or equal to the value of \(H2\) and the admission time of \(H1\) is less than or equal to the admission time of \(H2\). This can be represented mathematically as:  
\[H1_{m}\quad\geq\quad H2_{m}\quad\quad\text{and}\quad\quad H1_{Max_{t}}\quad \leq\quad H2_{Max_{t}} \tag{9}\]
# YOLO-SE: Improved YOLOv8 for Remote Sensing Object Detection and Recognition
## 2 Related Work  
### Object-Detection Networks for Remote Sensing Images  
While deep learning has demonstrated remarkable success in object detection for remote sensing images, effectively detecting multi-scale and small objects continues to pose a substantial challenge. Researchers have made notable contributions to address these challenges. [PERSON] et al. (2016) improved upon Faster R-CNN by proposing a method for identifying medium- and small-sized animals in large-scale images. They utilized the HRNet feature extraction network to enhance small-object detection. [PERSON] et al. (2017) presented the partial-based convolutional neural network (PBNet) to address compound object detection in high-resolution optical remote sensing images. [PERSON] et al. (2018) devised a feature extraction module that integrates convolutional neural networks (CNNs) and multi-head attention, leading to an expanded receptive field and the development of the STC-YOLO algorithm. Additionally, they introduced the Normalized Gaussian Wasserstein Distance (NWD) metric to enhance sensitivity to small-object position deviations. [PERSON] et al. (2019) proposed the Ghost module for building efficient neural network architectures. GhostNet, constructed using this new module, achieved a balance between efficiency and accuracy. [PERSON] et al. (2020) introduced a Scale-Aware Aggregation Module (SMT) that effectively simulates the transition from local to global dependencies with network depth, offering better performance with fewer parameters. [PERSON] et al. (2021) presented the YOLO-HR algorithm for high-resolution optical remote sensing object recognition. This algorithm employs multiple detection heads for object detection and reuses output features from the feature pyramid, further enhancing detection performance. [PERSON] et al. (2022) proposed a multi-scale remote sensing object detection model based on YOLOv3. They improved the existing feature extraction network by introducing DenseNet. This method exhibited good performance in multi-scale remote sensing object detection. [PERSON] et al. (2020) proposed a GhostConv-based backbone lightweight YOLO network (GCL_YOLO). The network first establishes a lightweight backbone network based on ghost convolutions with a minimal number of parameters. Subsequently, a novel small-object prediction head is designed to replace the existing large-object prediction head used for natural scene objects. Finally, the network utilizes the focus-effective intersection over union (Focus-EIOU) loss as the localization loss.  
The above-mentioned research endeavors have led to various improvements in object-detection algorithms for remote sensing images, contributing to the advancement of this field. In light of the challenges posed by multi-scale and small-object detection, this paper aims to further enhance the state-of-the-art YOLOv8 algorithm.
# Markov-Modulated Poisson Process Modeling for Machine-to-Machine Heterogeneous Traffic
## 6 Simulations and Result Discussions  
### Simulator  
In this section, we present our created simulation model. It has the ability to generate both M2M and H2H traffic, as well as add priority or queuing for any kind of traffic.  
Let us assume that our LTE-A network has c = 100 resources in total. Six resources are used by the LTE-M network to process M2M requests (rb(m) = 6). Therefore, the remaining 94 resources (rb(h) = 94) will handle H2H traffic.  
Based on the architecture suggested in [18], we run numerous simulations to examine the interactions between H2H and M2M traffic in either regular or emergency situations. It is simpler to compare empirical results with equivalent theoretical conclusions when the architecture uses queuing theory to provide accurate theoretical results for particular performance measurements. The model goes through multiple improvements to be consistent with our CHANAL architecture, as described in Section 4.  
These improvements include the following:  
1. The design has two servers and two traffic sources (H2H and M2M). Poisson processes determine arrivals using two parameters, \(\lambda_{\text{(h)}}\) and \(\lambda_{\text{(m)}}\), respectively.
2. The mean service time is represented by \(1/\mu\), and the service times follow an exponential distribution with a rate parameter of \(\mu\).
3. We assume a fixed arrival rate \(\lambda_{\text{(h)}}\) for H2H traffic and a service rate \(\mu_{\text{(h)}}=1\).
4. In addition, we assume that M2M heterogeneous traffic has a service rate of \(\mu_{\text{(m)}}=1\) and five distinct variable average arrival rates, denoted as \(\lambda_{\text{(m)}}=\{5;10;15;20;25\}\).
5. M2M and H2H traffic are prioritized equally.
6. For H2H and M2M traffic, respectively, queue sizes of n = o = 0 are taken into account when using a FIFO queue type.
7. The simulation lasts for 1000 s.
# Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation
## 2 Methods
### SwinUnet  
SwinUnet is a transformer-based network proposed by [PERSON] et al., in 2021 [37]. SwinUnet consists of an encoder, a channel, a decoder, and a jump connection, as shown in Figure 5.  
Inspired by the Unet network, SwinUnet is designed with a symmetric decoder with a patch extension layer based on the Swin Transformer. Unlike the traditional multihead self-attention (_MSA_), the Swin Transformer module is built based on a shifted window. Figure 6 shows the structure of the Swin Transformer module, which consists of LayerNorm (_LN_) layers, a multihead self-attention module, a residual connection, and a two-layer MLP with Gaussian error linear units (GELUs) nonlinearity. The window-based multihead self-attention module (_W-MSA_) and the translational window-based multihead self-attention (_SW-MSA_) module are used in two consecutive Swin Transformer modules, respectively. For such a modular composition, the continuous Swin Transformer can be expressed as follows:  
\[z^{l}=W-MSA\left(LN\left(z^{l-1}\right)\right)+z^{l-1} \tag{7}\]  
Figure 5: SwinUnet structure.  
\[z^{l}=MLP\Big{(}LN\Big{(}z^{l}\Big{)}\Big{)}+z^{l} \tag{8}\]  
\[z^{l+1}=SW-MSA\Big{(}LN\Big{(}z^{l}\Big{)}\Big{)}+z^{l} \tag{9}\]  
\[z^{l+1}=MLP\Big{(}LN\Big{(}z^{l+1}\Big{)}\Big{)}+z^{l+1} \tag{10}\]  
\(\hat{z}^{l}\) and \(z^{l}\) denote the output of the (S)W-MSA module and the MLP module of the \(l\)th block, respectively. Then the attention function can be expressed as follows:  
\[Attention(Q,K,V)=softmax\Big{(}\frac{QK^{T}}{\sqrt{d}}+B\Big{)}V \tag{11}\]  
where \(Q\), \(K\), and \(V\in\mathbb{R}^{M^{2}\times d}\) denote the matrix of queries, keys, and values; \(M^{2}\) and \(d\) denote the number of patches in the window and the dimension of the query or key, respectively; and \(B\) comes from the bias matrix \(\hat{B}\in\mathbb{R}^{(2M-1)\times(2M+1)}\).  
SwinUnet successfully puts the transformer module into the encoder and decoder, and together with the jump connection of the Unet network, the SwinUnet network can more quickly and comprehensively acquire the global and local feature information of images, avoiding the limitation of the CNN model, which cannot acquire global and long-range feature information.
###### Abstract  
Deep learning models that require vast amounts of training data struggle to achieve good animal sound classification (ASC) performance. Among recent few-shot ASC methods to address the data shortage problem regarding animals that are difficult to observe, model-agnostic meta-learning (MAML) has shown new possibilities by encoding common prior knowledge derived from different tasks into the model parameter initialization of target tasks. However, when the knowledge on animal sounds is difficult to generalize due to its diversity, MAML exhibits poor ASC performance due to the static initialization setting. In this paper, we propose a novel task-adaptive parameter transformation scheme called few-shot ASC. TAPT generates transformation variables while learning common knowledge and uses the variables to make parameters specific to the target task. Owing to this transformation, TAPT can reduce overfitting and enhance adaptability, training speed, and performance in heterogeneous tasks compared to MAML. In experiments on two public datasets on the same backbone network, we show that TAPT outperforms the existing few-shot ASC schemes in terms of classification accuracy, and in particular a performance improvement of 20.32% compared to the state-of-the-art scheme. In addition, we show that TAPT is robust to hyperparameters and efficient for training.  
animal sound classification; bioacoustics classification; deep learning; few-shot learning; meta-learning; MAML +
Footnote †: journal: Applied sciences
# On-Line Tendency Control of Dissolved Oxygen Concentration during Aerobic Fed-Batch Fermentations
## 3 Results and Discussion
### Performance during C. glutamicum Fermentation
#### 3.1.2 DO Tendency Control in Production Phase  
The tendency control method was applied to maintenance of DO levels in production phase during _C. glutamicum_ fed-batch fermentation, the results are shown in Figure 6 and Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Different Methods** & **From 1565 to 1609 min** \\ \hline Manual control & 27 times \\ Tendency control & 2 times \\ \hline \end{tabular}
\end{table}
Table 2: Action times of actuator for different methods.  
Figure 5: Performance in cell growth phase during _C. glutamicum_ fermentation. (**A**) DO control using the tendency control strategy and (**B**) comparison of the adjustment of stirrer speed.  
As shown in Figure 6B, the stirrer speed was gradually decreased from 646 rpm at 1565 min to 625 rpm at 1609 min. As shown in Figure 4E, after crossing the maximum, the stirrer speed gradually decreased in the latter part of the fermentation process. This case may be due to decrease of oxygen requirements in the production phase. As seen in Figure 6, the range of DO concentrations was fairly large. The actuator was frequently under action during manual control of DO; in contrast, the actuator was much less active when the tendency control method was used. From 1565 to 1609 min, the actuator acted only 2 times. Furthermore, b \(>\) 0.8 and all DO \(>\) low limit were preconditions for the initialization of tendency control. Although the tendency variation values for DO were b4 = 0.98, b5 = 0.71, b6 = 0.39, b7 = 1.25, and b8 = 3.04 respectively, all of which were bigger than 0, the actuator acted only at 1570 and 1603 min. The tendency control algorithm also works well in the production phase of glutamate fermentation.