esa-sceva/satcom-chunk-collection huggingface dataset:
# Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
## 3 Proposed Method  
This paper proposes a method for precise detection of both small and large objects simultaneously through a YOLO-based ML-MHA, with the goal of achieving high real-time performance and accuracy. The focus is on improving object detection performance under challenging conditions, such as with IR camera footage, which has limited resolution information. The core idea of the proposed method is to effectively detect objects of various sizes by combining SSCA and SA, utilizing the efficient features extracted from the backbone and neck of YOLOv8.  
The strength of this approach lies in its ability to balance detection performance and computational efficiency. As a CNN-based model, YOLO excels in real-time object detection tasks, and the proposed method retains this advantage. The backbone and neck of YOLOv8 are responsible for extracting multi-resolution features from input images, leveraging the powerful local pattern extraction capabilities of CNN. This is especially useful for IR camera footage, which typically has low resolution and lacks detailed information. By first extracting key patterns through CNN, the SSCA module can then more effectively classify small and difficult-to-detect objects. Unlike pure attention-based models like ViT, which require large amounts of data and high-resolution images, CNNs perform exceptionally well even with smaller datasets and lower-resolution images, setting this method apart.  
Additionally, while ViT requires position embedding to compensate for the lack of spatial information during the self-attention process after dividing the input into \(n\) patches, SSCA naturally learns the position information of each pixel within a CNN-based structure. As a result, SSCA can effectively handle spatial information without the need for position embedding, reducing computational overhead and making the structure more streamlined. This approach maintains efficiency and delivers robust performance even when detecting objects at varying scales, without sacrificing accuracy.  
Additionally, the ML-MHA structure is designed to efficiently detect both small and large objects. SSCA is applied for small object detection, while SA is used for large object detection, ensuring optimal performance for objects of varying sizes. To further enhance performance on low-resolution IR camera footage, color map preprocessing is applied, adding color information to the single-channel images to improve the contrast and clarity of the IR footage. This contributes to improving object detection performance, particularly in challenging environments such as nighttime or adverse weather conditions.
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Congestion Control Mechanisms in CoAP
#### 4.1.16 CoAP-R  
Another rate-based congestion control mechanism is proposed by [PERSON] et al. in [32] to tackle the performance issues of CoCoA in case of light and bursty traffic conditions and the problem of unfair bandwidth allocation in different traffic scenarios.  
The proposed method is named as CoAP-R which uses the tree-based routing structure of IoT deployments to help in the discovery of bottleneck links. Using the information of bottleneck links, the proposed method employs a max-min fair allocation of available bandwidth in the network in a distributed manner. This process then regulates the message transmission rates of CoAP devices accordingly. However, since the proposed scheme is for tree-based routing structure, if a node is inactive in tree, it would miss the time when bandwidth allocation is performed. Also, with inactive node, link capacity estimation would not be possible and this would lead to wrong allocation of bandwidth among other nodes.
# A Novel Hybrid Ant Colony Optimization for a Multicast Routing Problem
## 4 ACO&CM Algorithm
### Tree Construction  
Though the paths discovered cover a source node and destination nodes, these paths can not directly form a multicast tree because they include some repeated edges. Therefore, to obtain a real multicast tree, we should prune the paths to remove these repeated edges. The completed routing paths \(PL\) is defined as the input parameter of tree construction. Let \(V_{T}\) be a node set, and \(T\) be an edge set. Randomly choose a route path from the path set \(PL\) and add the nodes and edges of this path to \(V_{T}\) and \(T\), respectively. Repeat this procedure until the terminating criterion is met. The output parameter would be a multicast tree spanning a source nodes \(s\) and destinations in \(M\). The steps of tree construction procedure should be described as the following:  
**Step 1**: Initialize \(T=\emptyset\), \(V_{T}=\emptyset\);  
**Step 2**: Choose a route path \(p\) randomly from the set \(PL\) and suppose \(p\) is defined as \(v_{0}\),\(v_{1}\),... \(v_{k}\) node sequence;  
**Step 3**: \(i=0\);  
**Step 4**: Check whether the node \(v_{i}\) is in \(V_{T}\). If the node \(v_{i}\) is in \(V_{T}\), then go to Step 5; otherwise, put \(v_{i}\) into \(V_{T}\), \(V_{T}=V_{T}\cup\{v_{i}\}\), and add the edge (\(v_{i}\), \(v_{i-1}\)) to \(T\), \(T=T\cup\{(v_{i},v_{i-1})\}\);  
**Step 5**: \(i++\);  
**Step 6**: Check whether the condition \(i<k\) is met, if \(i<k\), then go to step 4;  
**Step 7**: Delete \(p\) from \(PL\), \(PL=PL/\{p\}\);  
**Step 8**: If the termination condition \(PL=\emptyset\) is met, and then return to \(T\); if not go to Step 2.
## 2 Materials and Methods
### Fatigue Tests  
Figure 1b shows the geometry of the fatigue specimen [53]. The fatigue tests were conducted on a UBM rotary bending testing machine (Walter + Bai AG, Switzerland) with a loading frequency of 50 Hz in air. [PERSON]'s accelerated method was used to determine the fatigue limits. This method is based on the Palmgren-Miner linear damage hypothesis, which is a specific case of a general cumulative damage theory [54]. To determine the fatigue limit of a material, this method requires only one specimen, which is then subjected to a stepwise increasing load (Figure 3a). The difference \(\Delta\sigma\) of the stress amplitudes between any two adjacent levels and the number of cycles n for each step are constant quantities. The load is increased until the test specimen fails. To calculate the fatigue limit, a minimum of three assumed S-N curves must be selected for the given material. The difference \(\Delta\sigma_{-1}\) between the fatigue limits of two adjacent S-N curves is a constant value, and the curves are equidistant. According to the linear hypothesis, failure occurs when the sum of accumulated damages D is equal to unity:  
\[\text{D}=\sum_{\text{i}=1}^{\text{k}}\left(\text{n}_{\text{i}}/\text{N}_{ \text{i}}\right)=1\]  
where k is the number of steps, and N\({}_{\text{i}}\) is the number of cycles required for failure to occur during the i - th load step for the corresponding S-N curve.  
For each of the selected fatigue curves, the sum of the accumulated damage D is calculated. The obtained values are plotted in the \"fatigue limit-sum of accumulated damage D\" coordinate system. The stress obtained by interpolating for D = 1 in the \"fatigue limit-damage\" curve is the target fatigue limit for the studied material (Figure 3b).  
In this study, three specimens were used for each material condition (i.e., three for each finishing technique). The fatigue limit was calculated as the arithmetic mean of the three specimens for each material condition.  
Five groups of specimens (each containing three samples) were used. The first group was treated by turning and polishing and was used as a reference condition (RC). The purpose of polishing after turning is to satisfy the requirements of the standard [53] in terms of the surface roughness parameter Ra (see Figure 1b). The remaining four groups were treated by DB under flood lubrication (F), air cooling (A), nitrogen cooling (N), and dry without cooling (D) conditions. The DB processes were performed after turning without polishing.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Measuring Device** & **Bruker D8 Advance Diffractometer** \\ \hline Voltage & 30 kV \\ Current & 40 mA \\ Step size & 0.5\({}^{\circ}\) \\ Time for step & 1 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 3: Graphical interpretation of [PERSON]’s method: (**a**) step load; (**b**) damage–stress curve.
# Predicting Fruit's Sweetness Using Artificial Intelligence--Case Study: Orange
## Materials and Methods
### Measuring the Percentage of Sugar and the Amount of Water  
Sugars are the main component of the soluble solids that are found in fruit juice, so the percentage of sugars may be used to estimate the degree of the fruit's sweetness. A pocket molecular scanner SCo (Consumer Physics Ltd., Illinois, USA) was used to measure the percentage of sugar (Brix) in oranges with an accuracy of 78%. The SCo was considered to be the first accurate pocket-size spectrophotometer that is near-infrared (NIR) and smartphone operated and also connected to the cloud. Using the abovementioned device, the amount of water (moisture content) in the orange fruits was measured. The results found that there is an inverse relationship between the amount of water and the sugar content. This has also been previously suggested by other researchers [46].
## 2 Materials and Methods
### Empirical Models (HLD and HLD\({}_{\text{N}}\))  
The HLD of SDHS was determined with Equation (1) using constants, including K\({}_{\text{i}}\) = 0.13 and \(\sigma\) = \(-\)1.13, that were supported by our previous work [51]. The f(A) and the \(\sigma_{\text{T}}(\text{T}-\text{T}_{\text{ref}})\) values were zero because this study did not add a cosurfactant, and the temperature was controlled at 25 \(\pm\) 2 \({}^{\circ}\)C. The constants for the HLD\({}_{\text{nonionic}}\) of AAE(6 EO4 PO) were investigated by the optimal salt concentration and Excel Solver as mentioned in the study by [PERSON] et al. (2016) [56]. At the optimal condition with no cosurfactant addition, the values of b, K\({}_{\text{n}}\), c\({}_{\text{T}}\) and \(\beta\) were the remaining constants. The assumption for Excel Solver to solve these constants was the minimum of the squared difference between the predicted optimal salt concentration and the optimal salt concentration obtained from the experiment. The values of b, K\({}_{\text{n}}\), c\({}_{\text{T}}\) and \(\beta\) were proposed as the average of all the model oils and all the temperatures. For the mixed system, \(\frac{\text{G}_{\text{C}}}{\text{H}}\) was examined by Equations (3) and (4). The constants (A\({}_{1}\) and A\({}_{2}\)) in the Margules expression were also specified as the average of all the mixing ratios.  
The HLD\({}_{\text{N}}\) of SDHS used the same constants as mentioned previously, while the HLD\({}_{\text{N}}\) of AAE(6 EO4 PO) employed the results of HLD\({}_{\text{AAE(6 EO4 PO)}}\). The HLD equation is divided by the K coefficient to determine the HLD\({}_{\text{N}}\) equations (Equation (5)). Regarding the mixed systems, the interaction terms were examined for each anionic-nonionic ratio and each model oil. The HLD\({}_{\text{N}}\) is zero at the optimal condition, and hence the interaction terms in Equation (6) were determined by:  
\[\text{Interaction term }=\text{ X}_{\text{i}}\text{ HLD}_{\text{N},\text{i}}+\text{ X}_{\text{n}}\text{ HLD}_{\text{N},\text{n}} \tag{7}\]  
To formulate these empirical equations, the model oils included n-pentane, n-heptane, n-octane, and n-decane. The commercial diesel oil was applied to predict the optimal salt concentrations compared to the results from HLD and HLD\({}_{\text{N}}\).
# A New Hybrid Prime Code for OCDMA Network Multimedia Applications
## 2 Literature Review
Furthermore, the system BER performance based on this code was improved compared with the other existing codes.
# Experimental and Numerical Dynamic Identification of an Aerostatic Electro-Spindle
## 2 Experimental Setup
### Experimental Characterization at Null Rotational Speed  
The damped natural frequencies, \(\omega_{d}\), and the damping factors, \(\zeta\), were experimentally investigated by means of impulse response tests. A vertical impulse was applied to the nose in correspondence of the tool artifact, and the spindle trajectory was detected by the capacitive sensors (capaNCDT CS05 by Microepsilion) (see Figure 1a). The tests were performed at null rotational speed and absolute supply pressures \(p_{s}\) = 0.3, 0.5, 0.7 and 0.9 MPa. Each test was repeated four times, and signals were sampled with sampling frequency \(f_{s}\) = 50 kHz.  
The spectra of signals \(y_{1}\) and \(y_{2}\), measured by sensors on the front and rear measuring planes, were evaluated through fast Fourier transform (FFT) in order to visualize the number of modes captured and their frequencies. Figure 3 shows one of the computed spectra (at 0.5 MPa supply pressure), while the related time signals are depicted in Figure 4. In all the investigated cases, only one rigid mode shape is visible. Meanwhile, the higher frequency can be attributed to the flexural mode of the spindle since it does not depend on the supply pressure of the air bearings. As can be seen from Figure 4, the first mode shape is conical since \(y_{1}\) and \(y_{2}\) present a 180\({}^{\circ}\) phase shift.  
Figure 4: Example time signals at 0.5 MPa supply pressure.  
Figure 3: Example of spectra measured at 0.5 MPa supply pressure.
## 2 Material and Methods  
The solution developed for this work (named FarmDay) addresses the need to integrate VR exercises with a functional component inside an existing rehabilitation programme, currently performed in a clinic by therapists. It is hoped that these VR exercises will enable a telerehabilitation setup that the patients can use to increase their volumes of work without needing to leave their houses. This rehabilitation programme focuses on patients that have suffered a stroke or other neurological damage.  
Various research studies have confirmed that exercises aimed at improving ADL have greater acceptance among adults, increase motivation, and improve adherence to the treatment [38; 39; 40]. Therefore, the exercises developed must have an intentional goal that makes the user perform one or more of the functional movements present in ADL.
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 5 Implementation Setup and Results Discussion
### Scenario 2: One-Way Delay Calculated at the Sender  
One-way delay is calculated by the sender when it receives the feedback packets from receivers. One-way delay gives sender, the idea of network congestion level and helps in adjusting the playout time. This scenario actually demonstrates the one-way delay value calculated at the sender. Figure 12, shows that the one-way delay remains very consistent (i.e., OWD is mostly 109 ms and 110 ms). However, at two points, we can see large values of up to 125 ms. these two packets were delayed may be due to the functionality of nano sleep [18] that we used to impose network delay. The variation in network delay will result in variation in one way delay values calculated at the sender. One-way delay plays important role in the calculation of playout time. For instance, if some particular receiver is facing network congestion, it will consistently result in the very large value of one-way delay. This will intimate sender to take some action either to adjust the playout time or to slow down the packet sending rate.
# AoI Analysis of Satellite-UAV Synergy Real-Time Remote Sensing System
## 2 System Model
### Communication Model  
This subsection presents the communication model under the FFR strategy for the scenario, which mainly includes the channel gain from SAT to GU \(k\) in \(A_{SAT}\), the channel gain from UAV to GU \(k\) in \(A_{UAV}\), and the signal to interference plus noise ratio (SINR) at GU \(k\).
## 1 Introduction
In addition to regression methods, artificial intelligence is also employed to establish the relationship between process parameters and machine tool power consumption [14]. However, these models lack physical interpretability, and the energy consumption model based on artificial intelligence algorithms relies on extensive testing data [15; 16]. Due to factors such as tool wear, tool deflection, and workpiece material properties, accurately modeling cutting forces presents challenges [17]. Consequently, establishing a cutting power model based solely on cutting forces may deviate from actual results [18; 19]. Micro-milling is a key technology for manufacturing precision components and finds widespread applications in various fields. Due to the influence of size effects, micro-milling has a higher specific energy consumption, which is particularly pronounced when processing difficult-to-machine materials such as Ti-6 Al-4V [20]. Although many researchers have analyzed the power consumption characteristics of cutting processes and established corresponding power consumption models using various methods, most of these models are focused on general-machining machine tools and still have certain limitations, particularly a lack of in-depth explanations of the power consumption mechanisms and unclear physical meanings [21; 22; 23; 24]. Therefore, it is crucial to determine a more generalized process-parameter-based power consumption modeling approach for micro-milling processes. To reduce the energy consumption in micro-milling processes, this paper carried out power consumption modeling using power flow analysis. Based on the established model, the optimization of process parameters was undertaken with the objective of reducing the specific energy consumption, considering constraints on machining quality and efficiency, thereby achieving comprehensive improvements in machinability and sustainability.
## 3 Applications to Kinetic Models
### Simulation Using Synthetic Data  
We tested the KF performance using synthetic data generated using model (24) and the measurement model (25). Now let:  
\[\Delta t=1\,,\sigma_{w}^{2}=0.02^{2}\ \text{rad}^{2}/\text{s}^{2},\sigma_{v}^{2}=0.5 ^{2}\ \text{m}^{2}/\text{s}^{2},\sigma_{\gamma}^{2}=diag\Bigl{(}10^{2},10^{2}\Bigr{)} \ \text{m}^{2}.\]Now, we set the ground truth:  
\[p_{0}=0\,0\ \text{m}_{\text{\text{\textminus}}}-\frac{\pi}{2}\text{rad},\ w_{t}=0.04\ \text{rad}\cdot\text{s},v_{t}=10\ \text{m}/\text{s}.\]  
The yaw rate measurements and the speed measurements were synthesized according to \(\hat{w}_{t}\sim N\big{(}w_{t},\sigma_{w}^{2}\big{)}\) and \(\hat{v}_{t}\sim N\big{(}v_{t},\sigma_{v}^{2}\big{)}\).  
The GPS measurements were synthesized according to \(\hat{z}_{t}\sim N\big{(}p_{t},\sigma_{\gamma}^{2}\big{)}\).  
Then, the KF was applied to the synthetic data and estimates of the state of the vehicle were obtained. The results can be seen in Figure 1, in which:  
* The red line represents the estimated vehicle trajectory.
* The black line stands for the ground truth of the vehicle trajectory.
* The blue crosses are the GPS measurements of the position of the vehicle.  
As we can see from the above figure, the estimated trajectory of the vehicle was smoother than the jumping measurements. Figure 2 shows the results of 100 Monte Carlo trials. As we can observe, the measurement errors were larger than the estimated errors, just like in the previous example.  
We also calculated the orientation estimate errors and the results of the same 100 Monte Carlo simulations are shown in Figure 3. As we can observe, these errors varied around 0.05 radians.  
Hence, we concluded that, without using estimations in this application, we could not have any estimate of the vehicle's orientation except a random guess, which could be as large as \(\pi/2\) radians.  
This last example shows another important utility of the estimation, i.e., a proper estimation of the Gaussian errors could reveal the state part, which is not directly measurable.  
Figure 1: Estimates and position measurements for one trial.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 6 Approaches and Algorithms
### Global Spectrum Planning
#### 6.1.3 Data Mining  
This module employs various machine learning algorithms, including LSTM networks, correlation calculation methods, causal regression models, and anomaly detection techniques. These algorithms analyze historical data to uncover the temporal and spatial patterns or 'laws' governing spectrum usage.
## 2 Facility Layout Problem with Relative Positioning Constraints
### Reducing Redundant Inequalities by Exploiting Relative-Positioning Constraints Structure
#### 2.2.3 Redundancy in Relative-Positioning Constraints  
Finally, we mention the redundant inequalities in relative-positioning constraints. It is obvious that, if the conditions \(\mathcal{H}_{ij}=1\) and \(\mathcal{H}_{jk}=1\) hold, then the condition \(\mathcal{H}_{ik}=1\) must hold. Using these relations, we can reduce the redundant inequalities.  
A minimal set of relative-positioning constraints can easily be obtained through eliminating the indirect path in \(\mathcal{H}_{ij}\) or \(\mathcal{V}_{ij}\) by the _Warshall-Floyd_ algorithm.
## Proposed Approach
### Research Problem  
Studies have been focused on determining the optimum number of lanes in a roundabout in order to greatest capacity. The authors of the current article approach the issue in a different way, by formulating the following thesis: traffic rule modification may lead to the increase of roundabout capacity without changing the roundabout structure. To prove the thesis, the author has analysed the roundabout traffic, developed a model and run experimental studies.  
The roundabout traffic analysis was performed based on several month long observations of traffic at various roundabouts, including large and small, single-lane as well as 2- or 3-lane ones. The observations revealed that excessive congestion was found in the outer lane of a roundabout, while the occupancy of the inner lane (in the case of 2-lane roundabouts) or of the inner and middle lanes (in the case of 3-lane roundabouts) was moderate. Figure 1 presents averaged results of the observations, providing numeric values regarding the mean quantity of vehicles that entered the roundabout directly into the outer lane and those which did not take the first possible exit even though  
\begin{table}
\begin{tabular}{c c c} \hline
**Vehicle Type** & **Vehicle Length [m]** & **Vehicle Length [cells]** \\ \hline Motorcycle & 2.5 & 1 \\ Car & 5 & 2 \\ Van & 7.5 & 3 \\ Minibus & 10 & 4 \\ Bus, commercial van & 12.5 & 5 \\ Truck & 15 & 6 \\ \hline \end{tabular}
\end{table}
Table 1: Vehicle classification (by length).  
\begin{table}
\begin{tabular}{c c c} \hline
**Velocity [cells/s]** & **Velocity in N–Sch Model [km/h]** & **Velocity in the Presented Approach [km/h]** \\ \hline
0 & 0 & 0 \\
1 & 27 & 9 \\
2 & 54 & 18 \\
3 & 81 & 27 \\
4 & 108 & 36 \\
5 & 135 & 45 \\
6 & 162 & 54 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of velocities in the Nagel–Schreckenberg (N–Sch) model and the presented approach.  
they were moving along the outer lane. The analysis shows that in the case of 2-lane roundabouts as many as 69.3% of the drivers entering the roundabout get into the outer lane and then only 19.2% of them take the first possible exit. In the case of 3-lane roundabouts, the values were 57.8% and 21.9%, respectively. The other drivers (80.8% and 78.1%) continue driving in the outer lane up to the subsequent exit or further.  
Pursuant to regulations, such behaviour is legal in most countries. However, the roundabout traffic observations have revealed that such behaviour causes an unnecessary restriction of vehicles queuing in feeder roads to enter the intersection (especially when the distance between the adjacent entrances is small). The above defined thesis may be further specified by stating that a reduction in the occupancy of the outer lane of a roundabout may increase the chances for the queuing vehicles to enter the roundabout, and increase its capacity. Shorter queues of waiting vehicles also provide the benefits of lower emissions and reduced local pollution.