esa-sceva/satcom-chunk-collection huggingface dataset:
# A MSA-YOLO Obstacle Detection Algorithm for Rail Transit in Foggy Weather
## 3 Methodology
### A Multi-Scale Adaptive Module  
The multi-scale adaptive network module demonstrates exceptional performance in the field of image processing, particularly in tasks such as defogging and various filtering operations. By employing convolution kernels of different scales, this module effectively processes multi-level information in images, thereby enhancing image quality and detail representation. Specifically, the module includes three different scales of convolution kernels: \(7\times 7\), \(5\times 5\), and \(3\times 3\).  
The input to the network is a foggy image, and the architecture consists of three parallel branches for multi-scale feature extraction and fusion. The first branch employs five layers of \(7\times 7\) convolutions, designed to cover a larger receptive field and capture global features and long-range dependencies of the image. This branch effectively handles images with complex backgrounds and extracts large-scale global information through multiple layers of \(7\times 7\) convolutions. The second branch uses five layers of \(5\times 5\) convolutions, balancing receptive field size and computational complexity. Compared to the \(7\times 7\) convolutions, the \(5\times 5\) convolutions better capture medium-scale features while being more computationally efficient. This branch enriches the overall feature representation by extracting medium-scale features through multiple layers of \(5\times 5\) convolutions. The third branch directly inputs  
Figure 1: The MSA-YOLO framework.  
the original foggy image, preserving the initial information and details. This ensures that the original image information is retained during feature extraction, providing a reliable foundation for subsequent processing.  
At the output stage, the outputs of the three branches are summed to achieve multi-scale feature fusion. By combining features from different scales, the network can comprehensively represent the image information, enhancing defogging effectiveness and feature representation capability. The fused features are then passed through five layers of 3 \(\times\) 3 convolutions for further detail extraction. The 3 \(\times\) 3 convolutions, with their smaller receptive field, capture fine structures and textures in the image. This multi-layered approach allows for fine-grained processing of the fused features, enhancing the detailed representation of the image.  
Finally, the output is processed through two dense layers for higher-level feature extraction and classification. These dense layers linearly combine the features extracted by the convolutional layers and enhance the model's expressive power through nonlinear activation functions. The processing through the two dense layers results in a high-quality, defogged image.
# Multiplexing Multi-Scale Features Network for Salient Target Detection
## 3 Interpretation
### Experimental Evaluation Index  
In our salient object detection experiment, in order to distinguish the accuracy of the experimental results of different networks and comprehensively evaluate the performance of our salient object detection model, our proposed network was evaluated by both subjective perception and objective evaluation. From the objective point of view, three evaluation methods were used as the objective evaluation indicators of this network: the average absolute error (MAE), \(F\) value (F-Measure), and structural similarity (SSIM). These three measures are introduced as follows:  
(1) Mean Absolute Error  
The mean absolute error (MAE) reflects the error of the predicted value and is calculated using Equation (4).  
\[\text{MAE}=\frac{1}{W\times H}\sum_{x=1}^{W}\sum_{y=1}^{H}|\bar{S}(x,y)-\bar{G }(x,y)| \tag{4}\]  
where \(W\) and \(H\) are long and high, respectively, represent the significance detection map, represents the true value, and \((x,y)\) represents the pixel location; its value ranges from 0 to 1, and its data size is inversely proportional to the network's performance.  
(2) \(F\) value  
The \(F\) value is a comprehensive evaluation index which requires the joint calculation of the accuracy rate (precision) and the recall rate (recall) of the significance detection map. The precision value and the recall value of the significance detection map are as follows:  
\[\text{Precision}=\frac{|S\cap G|}{S}\text{ Recall}=\frac{|S\cap G|}{G} \tag{5}\]  
Here, the significance detection map is binarized to obtain \(S\), while the true value is binarized to obtain \(G\).  
In this evaluation index, \(F\)-Measure is the weighted and harmonic average of precision and recall, and a parameter \(\beta^{2}\) is set to control the importance of the accuracy and recall rate to meet different needs. The larger the value is, the better the network's performance is. To ensure the fairness of the experiment, as with the contrast algorithm for salient target detection, the value of \(\beta^{2}\) was taken to be 0.3.  
\[F_{\beta}=\frac{(1+\beta^{2})\text{Precision}\times\text{Recall}}{\beta^{2} \text{Precision}+\text{Recall}} \tag{6}\]  
(3) Structural Similarity  
Structural similarity (SSIM) is an indicator of the structural similarity between the original image and the test image. The values of this indicator range from 0 to 1. The closer the predicted map and the true value, the closer the value of the SSIM is to 1. The value of the SSIM is calculated using Equation (7):  
\[\text{SSIM}=\frac{2\pi y}{(x)^{2}+(y)^{2}}\times\frac{2\sigma_{x}\sigma_{y}}{ \sigma_{x}^{2}+\sigma_{y}^{2}}\times\frac{\sigma_{xy}}{\sigma_{x}\sigma_{y}} \tag{7}\]where \(x\) and \(y\) represent the significance detection graph and the true value, respectively; \(\bar{x}\), \(\bar{y}\), \(\sigma_{x}\), and \(\sigma_{y}\) are the mean and standard deviation of \(x\) and \(y\), respectively; and \(\sigma_{xy}\) is the covariance.
# Bindings and RESTlets: A Novel Set of CoAP-Based Application Enablers to Build IoT Applications
## 6 Implementation and Evaluation
### Impact of Difference of Data Arrival Time  
All the above tests showed that, the higher the number of packets that are being generated and transmitted inside the constrained network, the performance of both solutions, especially the NO-RESTlet solution, suffers. We run additional tests to observe the impact of the data arrival time difference on the latency by inserting an artificial gap in the data generation at the sensor nodes. All data generation nodes are made to generate data randomly between 0 ms and a maximum interval (this represent real world cases where multiple sensors observe the same physical phenomenon almost simultaneously). We used 500, 1000, 1500 and 2000 ms as maximum interval. The topology used is the same as the first test (Figure 18). We also run the experiment to observe the impact for number of data generation nodes.  
As can be seen in Figure 23, when there is no data generation gap (0 ms gap), the NO-RESTlet solution has much higher latency in most cases. The frequent arrival of packets at the border router creates congestion at the node. Due to the limited queue size of the constrained nodes, some packets  
Figure 21: Network Topology including a Node Generating Side Traffic.  
Figure 22: Impact of Side Traffic on Latency. (**a**) CONfirmable; (**b**) NON-Confirmable transaction.  
will be dropped requiring retransmissions. This is the reason for the significantly high latency at 0 ms gap. When we look at the general trend in all graphs, the latency for both cases reduces until 1000 ms data generation gap and starts rising slightly after that. The reason is simple. The introduction of artificial delays at the sensor node results in an additional delay in the end-to-end transmission. This means, the performance gain obtained by separating the arrival times will be countered by the artificial delay and as a result the overall latency starts to rise.
## 1 Introduction
The 10 wt% addition of Al\({}_{2}\)O\({}_{3}\) nanoparticles enhanced heat conductivity in PDMS composite up to 0.364 Wm\({}^{-1}\) K\({}^{-1}\)[19]. Boron nitride (BN) has been widely used as a ceramic filler for thermal management. Hexagonal-BN nanoparticles showed unique thermal and electrical properties, getting interesting for TIM applications [20; 21; 22]. Metal-based fillers can also be used to increase thermal conductivity of a polymer matrix. Epoxy resin and PVC matrix were filled with metal powders and the variation of k has been studied. Copper and nickel powders with different particle shapes, irregular and spheroidal, respectively, were used as fillers showing the effect of shape and particle distribution [3]. [PERSON] et al. [23] used copper (Cu) powder, aluminum (Al), and silver (Ag) in an epoxy resin (commercially available as E-51) reaching a maximum value of 1.1 Wm\({}^{-1}\) K\({}^{-1}\) for Al powders. Both thermoset polymers and thermoplastic material, as polyamide [24] or polypropylene [25] has been used for composites filled with Cu powders or fibers. Recent studies showed great potential application in commercial electronics based on the result achieved by metal nanowires and hybrid structures [2; 26]. In this paper, the effect on thermal conductivity of three different types of fillers (metals, ceramic, and carbon-based) dispersed into a silicon matrix (Sylgard 184) thermally cured, was investigated. The novelty of the study was the comprehensive investigation and evaluation of how the thermal-conductive filler parameters such as nature, shape, dimension, and surface/volume ratio will influence the enhancement of k value. ATR-FTIR analysis was employed to follow thermal curing. A complete study on kinetics was performed by DSC. Thermo-mechanical properties were tested by DMTA and tensile tests. SEM analyses were performed to elucidate distribution and polymer-filler interaction for the different composites. Thermal conductivity was measured as well as thermal diffusivity.
## 3 Results and Discussions
### Data Collection  
The CDT swipes the tag when she starts and ends working in the station (Figure 9). The data is transmitted to ThingPeak via Wi-Fi. Only when the transmission is successfully done does the buzzer sounds. After ThingPeak successfully receives the data, the data is presented as graphs shown in Figure 10. The upper left graph shows the serial number, which represents the number of swipes at the station and is recalculated if there is a break. The upper right graph shows the serial number of the product, representing the number of prostheses made. The lower left graph shows the standard working hours, recording the reasonable working hours of CDTs. The bottom right graph shows the CDT identification code, which records the information of CDT in the station.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**CDT ID**} & \multirow{2}{*}{**Seniority**} & \multirow{2}{*}{**Technical Level**} & \multirow{2}{*}{**Responsibility**} & \multirow{2}{*}{**Working Hours**} & \multicolumn{2}{c}{**Reasonable or not in**} \\  & & & & & & **Working Hours** \\ \hline CD-001 & Low (1) & Low (1) & High(3) & Morning(1) & NO \\ CD-002 & Low (1) & Medium (2) & Low(1) & Morning (1) & YES \\ CD-003 & High (3) & High (3) & High (3) & Morning (1) & YES \\ CD-004 & Medium (2) & High (3) & Medium(2) & Morning (1) & YES \\ CD-001 & Low (1) & Low (1) & High (3) & Afternoon(2) & NO \\ CD-002 & Low (1) & Medium (2) & Low (1) & Afternoon (2) & NO \\ CD-003 & High (3) & High (3) & High (3) & Afternoon (2) & YES \\ CD-004 & Medium (2) & High (3) & Medium (2) & Afternoon (2) & YES \\ CD-001 & Low (1) & Low (1) & High (3) & Evening(3) & NO \\ CD-002 & Low (1) & Medium (2) & Low (1) & Evening (3) & NO \\ CD-003 & High (3) & High (3) & High (3) & Evening (3) & YES \\ CD-004 & Medium (2) & High (3) & Medium (2) & Evening (3) & NO \\ \hline \hline \end{tabular}
\end{table}
Table 1: Decision table of denture process hours.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Procedure A** & **Procedure B** & **Procedure C** & **Procedure D** \\ \hline Time (Minute) & 40 & 30 & 20 & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reasonable working time of each process.
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**2010 LC Maps**} & \multicolumn{4}{c}{**2020 LC Maps**} \\ \cline{2-9}  & \multicolumn{2}{c}{**RF Classifier**} & \multicolumn{2}{c}{**XGBoost Classifier**} & \multicolumn{2}{c}{**RF Classifier**} & \multicolumn{2}{c}{**XGBoost Classifier**} \\ \hline
**Accuracy** & **F1 Score** & **Producer** & **F1 Score** & **Producer** & **F1 Score** & **Producer** & **F1 Score** & **Producer** \\
**Matrices** & **F1 Score** & **Accuracy** & **F1 Score** & **Accuracy** & **F1 Score** & **Accuracy** & **F1 Score** & **Accuracy** \\ \hline Built-up & NaN & 0 & NaN & 0.00 & 0.47 & 0.44 & 0.53 & 0.56 \\ Water & 0.67 & 0.70 & 0.84 & 0.80 & 0.73 & 1.00 & 0.80 & 1.00 \\ Forest & 0.97 & 0.93 & 0.97 & 0.98 & 0.97 & 0.98 & 0.96 & 0.96 \\ Rocky/barren & 0.88 & 0.88 & 0.84 & 0.80 & 0.88 & 0.82 & 0.84 & 0.78 \\ Cultivation & 0.88 & 0.92 & 0.90 & 0.96 & 0.88 & 0.88 & 0.84 & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: F1 score and producer accuracy for Landsat imagery used in the study. From the LC change map analysis, we can observe a major change in the main commercial area of the Kaski district, i.e., Pokhara. Pokhara's LC has had tremendous change due to the development of recreational activities and improvements in the sightseeing area. The new international airport construction has converted about 1% of the total agricultural area to barren land. The built-up area has even shown an increasing trend. From the change map, we can also notice that the forest area has increased in the past decade. This may be due to the result of the increasing community forest plantation program. One other major change is from rocky to snow and vice versa. A major cause for this transition is the weather and climatic factors in this area. Rocky, barren land and cultivation areas have decreased from the previous year. The water area has not shown much change in terms of area, but the course of the river has changed in the past decade. The changes in area are shown in Figure 9.  
The Sankey diagram from Figure 9 is the best graphical tool to represent the LC class change [68]. We can visualize how much area has changed from one category to every other category. We can see most of the land classes have not changed from 2010 to 2020. There was a huge change in rocky/ barren to snow in interclass change, i.e., approximately 104 sq km area was converted to snow. Another considerable transformation was the cultivation area. About 67.13 sq km of cultivation land was converted to forest in 2020. This implies a positive sign due to the increment of the community forest plantation program in the district in the last decade.
## 3 System Model  
This section elaborates on the system model. First, the structure of the transmitter is shown. Then, the algorithm of the PPI interleaver mechanism is described. Afterwards, the simulations on the channel considerations are shown, where delay profiles for multipath channel are presented. Finally, the messages of the structure depicted in Figure 2 are presented, and the communication process is discussed.
## 2 Experimental Design
### Laboratory Experiments
#### 2.3.4 Test Protocol  
_RGT Elicitation test:_ The RGT procedure used for the elicitation test was identical to the one used in the in-situ test, apart from the fact that the subjects could instantly switch between the stimuli for each triad and provided their responses in a text box on the test GUI (Figure 3a). Before starting the test, each subject completed a familiarisation stage where they were presented with the GUI and listened to all stimuli. As in the in-situ test, the listening test consisted of two separate stages: elicitation and construct rating for each stimulus. Subjects were required to take a short break after approximately 20 min in order to avoid listening fatigue.  
Figure 3: Graphical user interfaces used for the lab experiments. (**a**) Elicitation test (**b**) Audibility test.  
_Audibility test:_ An additional audibility test was conducted for the reason described in Section 2.1.2. In each trial of the test, each subject was asked to listen to and compare all of the stimuli provided on the GUI (Figure 3b) and rate the perceived magnitude of the difference among all of the conditions for each of their own bipolar construct of descriptive terms resulted from the RGT elicitation. A continuous scale with the internal scale from 0 to 100 and semantic anchors dividing the scale into four sections were used: just audible (0-25), slightly audible (25-50), audible (50-75) and very audible (75-100). The order of bipolar constructs to rate in each trial was randomised. After completing the audibility test, subjects were asked to verbally group some of their elicited attributes in terms of similarity. This additional step was used during the analysis along with attribute clustering for obtaining a better understanding of different verbalisations used for the same perceptual effect.
# A Practical Positioning Method in End-Plate Surface Distance Measurement with Nano-Meter Precision
## 4 Accuracy Enhancing Methods
### Optical and Mechanical Aspects
#### 4.1.2 Laser Wavelength  
The laser wavelength is an important parameter for length measurement with a high precision. In order to ensure the length measurement accuracy, the length measurement system should use the frequency stabilized laser. Furthermore, the interference system should be placed in vacuum environment for length measurement with nanometer precision [8; 9; 10; 11]. The experiment measurement repeatability was affected by laser wavelength stability. Both an ordinary semiconductor laser and HP5517 frequency stabilized laser as input light sources were experienced, respectively. The results show that the measurement repeatability was better than 5 nm when using the HP5517 frequency stabilized laser. The wavelength of the semiconductor laser was monitored by the laser wavelength meter with a precision of \(2\times 10^{-8}\). The result is shown in Figure 11 that there was about a maximum of 0.06 nm variation for about 40 min of continuously monitoring. It was indicated that for the average wavelength of 634.286 nm, the correspondent relative variation was \(9\times 10^{-5}\), and for about 15 min of repeatability measurement, the relative variation of the wavelength was \(4\times 10^{-5}\). For the length of 0.1 mm, the correspondent variation was about 4 nm. Analytical results are consistent with the experiment results.
## 2 Rule-Based Strategies
### Fuzzy Logic-Based Strategy
As for the prediction of vehicle operation in the future, the trip information can be considered as one of the determining factors, and can be obtained by intelligent transportation systems (ITSs), geographical information systems (GISs), global positioning systems (GPSs), or advanced traffic flow modeling techniques [47; 48]. When trip information is predictable to a large extent, EMS becomes a global optimization problem, which can utilize DP to improve the optimality of RB strategy in the process of offline optimization. As described in [49], a rule-based EMS was proposed for PHEVs. Based on historical traffic information, the driving cycle is modeled, and the DP algorithm is applied to reinforce the charge-depletion control to ensure that SOC drops to a predefined value at the end of the driving cycle. In summary, rule-based control strategies have been widely used in EMS for HEVs due to their convenience in adjusting parameters and practical applications. Computational time and memory storage of rule-based strategies are acceptable for online control. However, the drawback of such strategies is that they tend to fall into local optimal solutions rather than global optimal solutions, that is, they cannot fully optimize fuel consumption over the whole trip.
# A Motion Control of Soft Gait Assistive Suit by Gait Phase Detection Using Pressure Information
## 3 Gait Phase Detection Method  
### Forward Walking Sequence and Target Assist Timing  
The leg motion is classified into several phases by using the method developed by the Rancho Los Amigos national rehabilitation center [37] as shown in Figure 5.  
In Figure 5, a horizontal axis represents a gait percentage in the one cycle motion. The gait phases of a white color leg are classified in a stance phase, a pre-swing phase, and a swing phase, and an opposite side leg is also classified in the same manner.  
In this paper, the pre-swing phase is set as a target detection phase. The pre-swing phase starts from the heel contact of the opposite side foot and ends at lifting off of the toe. By detecting the pre-swing phase of both legs, a walking cycle time can be obtained, and it is used to calculate proper assist timings and durations in each PARM from the gait analysis data [37]. The suit assists the trainee's leg motion as raising up the toe in the swing phase and landing the foot in the stance phase. The assist force will decrease the walking load of the leg muscles and also helps to avoid stumble and fall of the trainee. When the pre-swing phase, the posterior surface of the calf is stretched and contracted along with the heel lifting as shown in Figure 5. The CB PARMs attached to the trainee will be deformed  
Figure 4: Block diagram of pneumatic pressure control. \(P_{ref}\) and \(P\) (kPa) are reference pressure and current pressure respectively. \(K_{p}\) (V/kPa) and \(K_{i}\) (V/kPa s) are a proportional and an integral gains. \(u\) (V) is a control voltage, and \(s\) is a Laplace operator.  
Figure 3: Schematic of the pneumatic control system. The control unit is connected to the attachment unit by the air tube. The pressure in the PARMs is measured by the pressure sensors, and the analog-to-digital (A/D) board sends the pressure information to the computer. Command voltages for the servo valves are calculated by the computer, and are send by the digital-to-analog (D/A) board. The pressure information of the calf back (CB) PARMs is used for an assist timing detection. Air flows into or out from the CB PARMs are closed by the 2 port valves, and these PARMs are used as deformable closed chambers.  
along with the leg motion. We focus to detect the pre-swing phase from the pressure information in the CB PARMs.  
The pressure of the CB PARMs will be affected by the impact force of foot landing, and it will make worse the signal to noise ratio of the pressure information. Therefore, a low-pass filter is used for smoothing the pressure information.