esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential of Sub-GHz Wireless for Future IoT Wearables and Design of Compact 915 MHz Antenna
## 1 Introduction
From this point of view, Sub-GHz frequencies have a strong potential for wearable medical applications, as using these frequencies, the interaction between human body and antenna decreases with less deterioration in antenna and wireless communications performance [23]. Power consumption represents another critical factor for the design of battery powered medical wearable devices. In particular, the availability of ultra-low power radio transceivers allows the extension of the battery life-time and hence the reduction of battery recharging or replacement operations. In this work, the aim of the authors is to investigate the use of a Sub-GHz wireless frequencies as an alternative to 2.45 GHz. In Section 2, the potential of the 915 MHz and 2.45 GHz ISM bands are compared focusing on wireless link characteristics, device form factor and power consumption. This analysis shows the potential benefits of using the 915 MHz ISM band but also outlines a number of antenna design challenges that need to be addressed and overcome. In Section 3, the antenna design process is discussed in detail, with a particular focus on the 3D electromagnetic modeling of the structure and on the antenna topology. In Section 4, the results of full-wave electromagnetic simulations are presented in detail. In Section 5, a prototype antenna is demonstrated. The measured results show that the antenna exhibits desirable bandwidth characteristics as well as a robustness to detuning. Finally, in Section 6, the potential benefits of 915 MHz operation for future IoT devices and in particular for wrist-worn devices is highlighted and is followed by a summary of the main conclusions of this work in Section 7.
# Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter
## 4 Experimental Results
### Tracking Performance of Deep Feature Based Tracker and the Proposed Method Based Tracker  
[PERSON] et al. evaluated handcrafted feature-based trackers and deep feature-based trackers using their own datasets [6]. In that evaluation, MDNet-based tracker provided the highest accuracy at the cost of a slow processing speed, which is a common problem of various deep feature-based trackers. Under the streaming condition, the processing speed of a tracker is directly related to the tracking accuracy. In other word, a slow tracker loses a number of frames for each tracking period, which results in accuracy degradation.  
Figure 8: Time based ground truth.  
Figure 7: Dataset conversion into movie clip of mp4 format.  
In evaluating the MDNet-based tracker, tracking accuracy was measured under three conditions including (i) local dataset using a GPU, (ii) streaming sequences using a GPU, and (iii) streaming sequences using only CPU. Intersection over Unions (IoUs) are collected from each frame and average of them was used as the final accuracy for each dataset. As shown in Figure 9, the accuracy of tracking was significantly decreased using only CPU under the streaming condition, which demonstrated that the speed of tracker is an important factor to determine the accuracy in the streaming condition.  
We compared the accuracy of the proposed method, MDnet-based tracker and GOTURN-based tracker under a streaming condition as shown in Figure 10. More specifically, Figure 10a shows that the proposed method yielded an acceptable accuracy, where the position of targets drastically change and the reduced frame rate degrades the tracking accuracy. Some sequences include challenging scenes such as occlusions or blurring, which requires tracking failure detection and restarting the tracking process after detecting the failure. In CAU_Ped1, CAU_Ped2, CAU_Ped3 and CAU_Ped4, videos recorded by CCTV watching pedestrians on the street, occlusions frequently occur. For that case, the proposed tracker has been proved suitable for those applications.  
Figure 10: Accuracy evaluation of the proposed method, MDNet-based tracker and GOTURN-based tracker under a streaming condition. (**a**) Datasets in which significant target movements or challenging scenes exist. (**b**) Datasets in which no significant target movements and challenging scenes exist.  
Figure 9: Accuracy evaluation of MDNet based tracker under 3 conditions: local dataset with GPU processing, streaming sequences with GPU processing, and streaming sequences with CPU processing.  
On the other hand, deep feature-based trackers can successfully track a continuously blurred and slightly moving target which is still close to the disappearing location as shown in Figure 10b. In that case, the proposed method keeps spending time for the re-tracking process because tracking failures are detected with a degraded accuracy.  
In real applications, a low processing speed would directly degrade tracking accuracy because a camera cannot follow the target by itself.
## 2 Study Area  
The Baekkwang Mineral Products Yeongcheon underground mine (37\({}^{\circ}\)4'14\", 128\({}^{\circ}\)18'48\"), located in Danyang-gun, Chungcheongbuk-do, South Korea, was selected as the study area. This mine produces 1.2 million tons of limestone annually. The produced limestone is sorted according to its quality and shipped for use in steel, chemicals, and construction materials. The mine interior consists of drifts and three levels, while the exterior includes offices, a crusher, and two storage yards (see Figure 1a).  
The study area is equipped with an ICT-based mine safety management system. In such a system, a wireless network environment is created within the underground mine to track the movement of the equipment and workers in real time and to monitor the work environment to prevent casualties. In the study area, 37 wireless access points (APs) were installed in the pit and 3 were installed in the exterior crushing area to create a wireless communication network (see Figure 1b). Wireless APs recognize the tags attached to the equipment and workers, and monitor the work environment in the pit, transmitting the collected information to a server in real time. The web server accumulates real-time data, including whether the equipment and workers have entered the pit, the work times, and data regarding the pit environment (for example, the temperature, humidity, and hazardous gas concentrations). Therefore, the mine safety management system creates big data, which records the mining work on a web server. Further details on the study area mine safety management system and big data can be found in Baek and Choi [23].  
In the mine, 25-ton dump trucks travel back and forth between the interior and exterior, hauling ore and waste (see Figure 1c). The production manager considers the average quality of the ore that will be delivered that day, determines the positions and number of production work areas, and notifies the truck drivers of their destinations. The trucks that are provided with their destinations enter  
Figure 1: Schematics of study area: (**a**) mine map indicating underground drifts and outside crushing area; (**b**) location map of wireless APs along haulage road in underground mine; (**c**) process of haulage operation system.  
through the underground mine entrance and travel to the loading areas via Wireless APs 01, 02, and 03. After a loader has loaded a truck with ore, the truck exits the mine again, travels to the crushing area, and determines whether or not it can unload the ore in a hopper. If there are no trucks waiting to unload, the ore is immediately unloaded in the hopper. If there are trucks waiting, each truck waits for a fixed time. Trucks that have completed unloading enter the mine again to perform further haulage work.
## 3 Results and Discussions
### Analysis of the Optimal Combination of Room Parameters
It is worth noting that the room energy consumption per square meter in January of the optimal indoor air temperature combination S26 is 5.2 kW-h/m\({}^{2}\), which is only 0.4 kW-h/m\({}^{2}\) higher than S27, and 2.8 kW-h/m\({}^{2}\) lower than the highest energy consumption combination S2 which is 8.0 kW-h/m\({}^{2}\). However, the maximum indoor air temperature of S27 is much lower than that of S26, with a difference of 3.2 \({}^{\circ}\)C. Based on the above analysis, this study determines that the scheme S26 is the optimal combination. That is, the length of the room, the width of the room, the width of the opening on the north wall of the room, and the distance from the lower edge of the opening to the floor are, respectively, 12,300 mm, 5100 mm, 1500 mm and 0 mm (Figure 10). Under the corresponding room and opening parameters of this scheme, the room with a T-wall installed can have the highest indoor air temperature, while the room's energy consumption per square meter is at a low level. Figure 8: The indoor air temperature in January of 27 schemes.
## 5 Experimental Results
### Comparative Experiment
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Method** & **Model** & **DSC\(\uparrow\)** & **HD95\(\downarrow\)** & **Aorta** & **Gaillbladder** & **Kidney (J)** & **Kidney (B)** & **Liver** & **Pancreas** & **Spleen** & **Stomach** \\ \hline \multirow{8}{*}{Transformer} & R50 ViT [15] & 71.29 & 32.87 & 73.73 & 55.13 & 75.80 & 72.20 & 91.51 & 45.99 & 81.99 & 73.95 \\  & ViT [15] & 61.50 & 39.61 & 44.38 & 39.59 & 67.46 & 62.94 & 89.21 & 43.14 & 75.45 & 69.78 \\  & TransUNet [15] & 77.48 & 31.69 & 87.23 & 63.13 & 81.87 & 77.02 & 94.08 & 55.86 & 85.08 & 75.62 \\  & Swin-UNet [17] & 79.13 & 21.55 & 85.47 & 66.53 & 83.28 & 79.61 & 94.29 & 56.58 & 90.66 & 76.60 \\  & MT-UNet [41] & 78.59 & 26.59 & 87.92 & 64.99 & 81.47 & 77.29 & 93.06 & 59.46 & 87.75 & 76.81 \\  & HiFormer [42] & 80.39 & 14.70 & 86.21 & 65.69 & 85.23 & 79.77 & 94.61 & 59.52 & 90.99 & 81.08 \\  & TransASCADE [43] & 82.68 & 17.34 & 86.63 & 68.48 & 87.66 & 84.56 & 94.43 & 65.33 & 90.79 & 83.52\({}^{1}\) \\  & MISFormer [44] & 81.96 & 18.20 & 86.99 & 68.65 & 85.21 & 82.00 & 94.41 & 65.67 & 91.92 & 80.81 \\  & DAE-Former [27] & 82.43 & 17.46 & 88.96 & 72.30\({}^{1}\) & 86.08 & 80.88 & 94.98 & 65.12 & 91.94\({}^{1}\) & 79.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont\). Our model improves the segmentation results for almost all organs, and this all-around improvement confirms the effectiveness of our method. Our DAT module, which combines an effective attention mechanism and external attention mechanism, focuses on both intra-sample correlation and inter-sample correlation, and this multifaceted information effectively improves the overall segmentation performance of the model. Meanwhile, the CTrans module fully combines multi-scale information by cross-fusing the channel information from feature maps of different scales to further enhance the performance of the model. Our model is highly competitive, against both CNN-based models and Transformer-based models. On the Synapse dataset, our model attains a mean DSC (%) sum of 83.6%, while the HD95 (mm) is reduced to 13.99, achieving the best result. Similarly, on the ACDC dataset, our model's DSC (%) reached 90.35, which is also the best result.
## 2 State of the Art
### Model-Based System Engineering and Safety Assessment
#### 2.1.1 Model-Based System Engineering (MBSE)  
The usual approaches for system engineering have been using documents to describe the requirements, environment, architecture, and other views of the system. Althoughthose documents are sometimes created in tools such as Excel or Doors, they are textual and rely on natural language.  
The model-based approach used in system engineering finds its origin in model-based software development [6]. Software developers use such approaches to represent software architecture, with tools such as the UML notation [7]. This language was derived into the SysML notation [8], which system engineers use to describe complex systems.  
The model-based system engineering tools such as SysML or Capella use diagrams to represent different system views. Structural and behavioral aspects are represented, with diagrams such as requirement diagrams allowing for representation of the requirements, activities diagrams allowing for representation of the functions, and block definition and internal block diagrams allowing for representation of the architecture. The MBSE languages present a lack of semantics [9], which causes ambiguities without the use of a third-party methodology. Therefore architecture grids--such as CESAM [10] or the methodology from [11] for SysML or Arcadia for the Capella tool--provide guidelines to create MBSE models.  
Although we often use the name MBSE for such diagrammatic models, system engineering encompasses various disciplines. Therefore, some domain-specific languages (DSL), such as languages for multi-physics or safety assessment, can be interpreted as part of MBSE.
## 3 Methodology
### Calculation of Structural Similarity Index/Peak Signal-to-Noise Ratio
#### 3.5.1 Structural Similarity Index  
The SSIM is responsible for comparing each frame of the original video and degraded video sequences to quantify the video quality. The SSIM is based on the idea that natural images are highly structured; their pixels have a strong dependency, particularly when they are spatially close. Thus, a strong dependency returns an index close to 1 (higher quality), whereas a weak dependency returns an index close to 0 (lower quality) [39]. The SSIM is given by Equation (2).  
\[SSIM_{(x,y)}=\frac{(2\mu_{x}\mu_{y}+c_{1})(2\sigma_{xy}+c_{2})}{(\mu_{x}^{2}+ \mu_{y}^{2}+c_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2})} \tag{2}\]  
Figure 5: BERxEbN0/RS-CONV over AWGN channel.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tap** & **Delay (s)** & **Gain (dB)** \\ \hline
1 & 0 & 0 \\ \hline
2 & 0.15 \(\times 10^{-6}\) & \(-13.8\) \\ \hline
3 & 2.22 \(\times 10^{-6}\) & \(-16.2\) \\ \hline
4 & 3.05 \(\times 10^{-6}\) & \(-14.9\) \\ \hline
5 & 5.86 \(\times 10^{-6}\) & \(-13.6\) \\ \hline
6 & 5.93 \(\times 10^{-6}\) & \(-16.4\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Rayleigh channel parameters.  
where the following abbreviations apply:  
* \(x\) and \(y\) are the dimensions of the frame;
* \(\mu_{x}\) and \(\mu_{x}\) are the means of \(x\) and \(y\), respectively;
* \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) are the variances of \(x\) and \(y\), respectively;
* \(\sigma_{xy}\) is the variance of \(x\) and \(y\);
* \(c_{1}\) and \(c_{2}\) are variables for stabilizing the division by a minimum.
## 1 Introduction
Some authors have considered alternative process selection (i.e., every combination of the process has a feasible tolerance range, and for a given process combination, the cost of machining is the function of the tolerance value) for optimum tolerance allocation of both simple and complex assemblies [4; 10; 13; 15]. [PERSON] et al. (2009) [52] dealt with the distribution of tolerance on the component dimension of a complex assembly with alternative process selection. Authors have attempted to reduce the manufacturing cost of a product using the Lagrange multiplier method for complex assemblies with the bottom curve follower method. Alternative Machine Selection (AMS): It is possible to reduce the manufacturing cost of a product by choosing the suitable machine for the correct process. Several researchers have considered machine selection as one of the criteria for minimizing manufacturing costs in recent years. However, very few authors [9; 17; 34] have discussed alternative machine selection for optimum tolerance allocation, and even fewer studies [34] on minimizing both cost and machining time with process and machine selection for optimum allocation of tolerance have been reported in the literature. From this literature review, it appears that no significant effort has been made to consider machine time as an objective in optimum tolerance allocation, even though machine time is a crucial manufacturing parameter. Therefore, in the present study, both tolerance cost and machining time are optimized. Realizing the complexity of the problem, a combined heuristic and univariate search method is introduced to select the best machine for each process and the optimum tolerance for each component using alternative process and machine selection, with available machine time as a constraint.
## 5 Analysis of Long-Term Prestressing Force (PF)  
### Importance of Temperature Correction  
The PFs of the full-scale specimen and the PSC girder bridge were measured using Smart Strands for as long as possible, but the data measured for 318 days and 476 days after tensioning for the full-scale specimen and for the PSC girder bridge, respectively, were analyzed in this study. During the long-term measurement, the strains measured by sensors are affected by seasonal and daily temperature variations, and such temperature-dependent variation must be corrected or compensated for to derive the purely mechanical strain of interest that is directly related to stress. The correction can be accomplished by applying Equation (2) instead of Equation (1). Figure 6 compares the temporal PF variation before and after the temperature correction, which was measured in an FBG of a Smart Strand in the full-scale specimen. The temperatures were measured simultaneously when the wavelengths at FBGs were measured. The PF fluctuated in line with the trend of temperature variation without the correction, whereas consistent long-term prestress losses were obtained by applying the correction. Consequently, the following analyses are based on the temperature-corrected data. [PERSON] and [PERSON] [9] also performed temperature compensation for the long-term strains obtained using FBGs. However, they did not rely on Equation (2), which was derived by taking the principles of FBGs into account, but simply applied a general temperature compensation procedure for concrete strain. This could be a potential problem due to the decreased accuracy of mechanical strain.  
Figure 5: Prestressed concrete (PSC) girder bridge: (**a**) Tensioning of strands, (**b**) view after completion, (**c**) arrangement of tendons, and (**d**) Smart Strand with fiber Bragg gratings (FBGs).  
Figure 6: Effect of temperature correction on the prestressing force (PF).
## 3 Microalgal/Cyanobacterial Metabolites and Phytohormones with Potential Interest for Agriculture
### Polysaccharides  
Polysaccharides are polymers of carbohydrate molecules, which correspond to monosaccharide chains bonded by glycosidic interactions. A great diversity of polysaccharides can be obtained, being their characteristics determined by the building blocks, that is, the monosaccharides (e.g., hexose, pentose, uronic acid and methyl pentose) [2; 7]. These polysaccharides are used by other organisms as carbon and energy sources, being excreted by algae under both normal and stress conditions. Production of a great diversity of extracellular polysaccharides has been reported for microalgae of different genera, such as _Cylindrotheca_, _Navicula_, _Phaeodactylum_, _Chlamydomonas_, _Chorella_, _Dunaliella_, _Aphanothece_, _Arthospira_, _Nostoc_, _Scytonema_, _Porphyridium_ and _Rhodella_, being the production yields comprised between 0.01 and 2.9 g L\({}^{-1}\)[37]. Other polysaccharides have structural functions in the cell walls of different organisms. These include arabinoxylans, cellulose, chitin and pectins, and their production has been associated with marine and freshwater algae, including brown, red, green and blue-green algae [7]. In addition, some polysaccharides (e.g., starch and glycogen) act as energy storage molecules. Starch is typically produced by red and green algae, whereas glycogen is mainly accumulated by species of _Porphyridium_ and blue-green algae [7]. Polysaccharides' synthesis by microalgae and cyanobacteria accounts for 0.5-20 g L\({}^{-1}\)[37]. These polymers have been widely applied as thickening, gelling, stabiliser and emulsifier agents [45]. Moreover, antibacterial, anticoagulant, antioxidant, anticancer and anti-inflammatory properties have been described for these compounds [38; 43; 44]. In agriculture, polysaccharides have a preponderant role in the improvement of soil quality, with major activities as soil conditioners, nutrient carriers and as safe release systems for agrochemicals [35; 36; 41; 42]. Despite their impact on soils, some studies have also demonstrated that polysaccharides can act on plant growth stimulation and on plant defence against biotic and abiotic factors [34; 36; 39; 40; 46].
# Time-Dependence of the Mechanical Behavior of Loess after Dry-Wet Cycles
## 2 Triaxial Creep Tests  
### Sampling Site and Dry-Wet Cycle Process  
The loess samples used in test were collected from the back scalp of the Dabuzi landslide in Jingyang County, Shaanxi Province (Figure 1). The sampling site was about 20 m from the edge of the platform, and the collected samples were identified as Lishi loess (Q2) according to previous studies [31]. The loess in this area have suffered from different degrees of dry-wet cycles as a result of the long-term repeat rise and fall of the groundwater level due to the farmland irrigation (Figure 2a) [32]. Therefore, the mechanical properties of loess deteriorates, resulting in the occurrence of the Dabuzi landslide (Figure 2b). All loess samples were firstly sealed and kept in iron buckets. Then, samples were brought back to the laboratory and cut immediately to a cylindrical specimen with a diameter of 61.8 mm and height of 125 mm. The procedures based on the ASTM standards were conducted to determine the index properties, namely Afterberg limits [33], specific gravity and density [34], and moisture content [35]. The basic physical index properties of loess are listed in Table 1.  
Figure 2: The sampling site. (**a**) Irrigation canal, (**b**) landslide boundary.  
A two-step process for preparation of the sample was used with each dry-wet cycle. The first step is sample saturation, which is briefly described as follows. The specimens were saturated by the vacuum saturation method. Filter papers and porous stones were firstly placed on the top and bottom of the soil sample. Then, the soil sample was fixed in a saturator chamber, as shown in Figure 3a, and then the saturator chamber with the soil sample was tightened with screw nuts and placed in a vacuum container (Figure 3b). After that, the soil specimen was submerged in de-aired water inside a vacuum container for at least 24 h to achieve a degree of saturation greater than 97% [36; 37]. The drying phase was followed by wetting. After saturation, the soil sample was taken out by loosening the screw nuts and removing the filter papers and porous stones. Then, the soil sample sealed by the saturator chamber was placed in a drying oven at about 105 \({}^{\circ}\)C for 48 h to fully remove its water content [38; 39]. Next, the soil sample was taken out and cooled down. This process, consisting of saturation and drying phases, is called one dry-wet cycle in this study. After the last dry-wet cycle, the corresponding water required to obtain the target water content was dripped on the top surface of the soil sample, and then the soil sample was wrapped with a plastic wrap and placed in a humidifier for 48 h to obtain a homogeneous sample in room temperature [40; 41]. In this study, samples with different dry-wet cycles (0, 5, 10, 15, and 20) were prepared and the sample after 10 dry-wet cycles was shown in Figure 3c.