esa-sceva/satcom-chunk-collection huggingface dataset:
# Management Platforms and Protocols for Internet of Things: A Survey
## 8 IoT Device Management Platforms
### Onem2m  
ONEM2M was first released in 2015 and is a partnership project created to establish access-independent M2M service layers specifications. For the management protocol, it has its own technology called Device Management (DM) and it is also evaluating the possibility to implement OMA-DM, OMA-LWM2M or even CWMP [98; 99]. This platform has its own system and protocols, as described in [100].  
Two basic types of entities make up the functional architecture of ONEM2M: AE (Application Entity) and CSE (Common Services Entity), as shown in Figure 25. Northbound and southbound connected devices are considered an AE. The AE needs to be aware of management data protocols or models. Device Management (DMG) enables Device Management capabilities in MNs (for example, M2M Gateways), ASNs, and ADNs (for example, M2M devices). Connected devices residing in an M2M network are managed by services provided by DMG. The information obtained from the AE is used for network administrative actions (e.g., diagnostics, troubleshooting) [101].  
The Management Server/Client interface is the Mcc, which uses a Device Management technology (e.g., CWMP, OMA-DM, and LWM2M). Device management technology is used to manage the entities (MN, ASN or ADN, and DMG) and translates requests from other CSEs or from AEs to the Device Management technology. The Mcc interface is technology dependent, as above-described.  
_Pros: Interoperability and compliance of services. Cons: Maturity and artificial intelligence._  
The home lighting use case [102] performs remote control of the lights in a home through a user's smartphone in the following manner: (i) the lights are deployed and communicate with home gateway; (ii) the home gateway communicates with the cloud platform, making it possible to control the lights remotely with the smartphone; (iii) the cloud platform supports services to enable the smartphone to control the lights, e.g., discovery, data management, group management, publisher/subscriber, and others; (iv) the user's smartphone hosts an application used to remotely control the lights, i.e., change light state (ON/OFF), discover available lights in the house, among other functions.
## 3 Numerical Model  
### Geometry Model  
Vibratory open-ended pile driving can be treated as an axisymmetric problem. Figure 3 presents an axisymmetric model to reproduce the experiment mentioned above. The radial and vertical sizes of the soil model are \(10D\) and \(24D\), respectively.  
A zipper-type technique, which has been exploited to simulate vibratory open-ended pile driving [6; 7; 11], is adopted in this paper to incorporate the formation of pile-soil contact. According to the zipper-type technique, a fictitious and frictionless rigid tube is embedded in the soil beforehand, as depicted in Figure 3. The tube thickness is 0.03 cm, one-tenth of the pile wall thickness. When the pile penetrates the soil, the pile wall slides along the fixed tube, and the soil is separated from the tube, establishing a pile-soil contact.  
Figure 2: Overview of the experiment of [PERSON] et al. [8]: (**a**) photo of the test system; (**b**) schematic sketch of the test setup [unit: cm].  
In numerical analyses, finer mesh resolution often results in accuracy improvement of the numerical solution, but always accompanies with a higher computation burden. It is therefore important to choose a suitable grid density, especially for dynamic numerical analyses. In order to better model the pile-soil interaction, the mesh of soil elements was refined near the pile shaft. The smallest size of soil elements is around 0.25 cm, about one-tenth of the pile diameter. The whole model has 9420 elements and 20,406 grid points, whose mesh diagram is shown in Figure 3.  
As for boundary conditions, the right and bottom boundaries were all fixed, as they were in the experiment. The pore pressure was set to zero at the top surface, and other boundaries were defined as impermeable. Particularly, the large-strain mode in FLAC3D was used to mitigate the probable mesh distortion. In the large-strain mode, node coordinates were updated, and stress rotation was corrected [21].
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 2 Candidate Wireless Technologies
### Alternative Low-Power Wireless Technologies
#### 2.2.6. Z-Wave  
Z-Wave [72] is a proprietary wireless communication protocol mainly designed for automation in home and light commercial environments. Z-Wave was initially developed by ZenSys (now a division of Sigma Designs) [73] and is currently managed by the Z-Wave Alliance [74]. One of the main advantages of Z-Wave compared to some other technologies is that it operates in the sub-1 GHz band (around 900 MHz) which avoids interference with other wireless technologies operating in the crowded 2.4 GHz band such as WiFi, Bluetooth and ZigBee. Z-Wave technology utilizes a number of low-cost low-power RF transceiver chips which are embedded into home electronic devices such as lighting, intercom and entertainment systems. Z-Wave uses a low-power wireless technology to communicate with Z-Wave-based devices. This technology is optimized to provide reliable transmission of small data packets from a control unit to Z-Wave devices in a network. Z-Wave protocol utilizes frame check sequence, frame acknowledgement, retransmission, CSMA/CA and complex routing algorithms to ensure reliable communication in multipath environment of a residential house. Z-Wave supports mesh networking, provides 9.6 kb/s and 40 kb/s data rates, and uses Gaussian Frequency-Shift Keying (GFSK) modulation scheme. Z-Wave recently introduced the Z-Wave 500 series, a next generation upgrade to the Z-Wave chip and module which supports a higher data rate of up to 100 kb/s. Z-Wave is able to include up to 232 nodes in a Z-Wave network. This technology defines two different types of devices: controllers and slaves. Controllers have unidirectional control over slave devices. They are responsible for sending commands to slave devices, which receive the task and send back the corresponding answer [75].
## 3 Results and Discussion
### The Results of Analysis of Inhibitor Adsorption on Metal Surface
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\Delta G_{ads}\) & \(K_{ads}\) (M\({}^{-1}\)) & \(K_{ads}\) (ppm\({}^{-1}\)) & \(K_{ads}\)\({}^{-1}\) (ppm) & Time (h) & Inhibitor Type \\ \hline \(-38.7156\) & \(4.89298\times 10^{-9}\) & \(0.001497\) & \(667.89\) & \(6\) & \\ \(-37.9684\) & \(6.55088\times 10^{-9}\) & \(0.002005\) & \(498.86\) & \(24\) & banana peel \\ \(-37.6778\) & \(7.33799\times 10^{-9}\) & \(0.002245\) & \(445.35\) & \(48\) & \\ \(-38.3650\) & \(5.61107\times 10^{-9}\) & \(0.003277\) & \(305.17\) & \(6\) & \\ \(-37.6571\) & \(7.39763\times 10^{-9}\) & \(0.00432\) & \(231.47\) & \(24\) & rice straw \\ \(-37.5484\) & \(7.71841\times 10^{-9}\) & \(0.004508\) & \(221.85\) & \(48\) & \\ \(-37.1160\) & \(9.1383\times 10^{-9}\) & \(0.004321\) & \(231.45\) & \(6\) & banana peel \\ \(-36.6501\) & \(1.09617\times 10^{-8}\) & \(0.005183\) & \(192.95\) & \(24\) & (\(40\%\)): rice straw \\ \(-36.6689\) & \(1.08816\times 10^{-8}\) & \(0.005145\) & \(194.37\) & \(48\) & (\(60\%\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Adsorption constant and Gibbs free energy in the presence of banana peel extract, rice straw extract, and a mixture of banana peel (40%) and rice straw (60%) obtained using the Langmuir adsorption isotherm at different test durations (from Figure 11). Figure 11: Adsorption analysis of banana peel (**a**), rice straw (**b**), and a mixture of banana peel (40%) and rice straw (60%) (**c**) extracts using the Langmuir adsorption isotherm at 35 \({}^{\circ}\)C and test durations of 6, 24, and 48 h.
# DALLoc: Deep-Learning Accelerated Visual Localization Based on Mesh Representation
## 5 Experimental Evaluation  
### Experiment Prerequisites and Dataset  
In this section, we validate both the accuracy and efficiency of our DLALoc localization algorithm on two large scene datasets namely Aachen Day-night v1.1 and RobotCar Seasons. We also examine how the number of anchors and different RANSAC sampling times of our additional RANSAC implementation affect the algorithm's performance. The Aachen Day-Night v1.1 dataset contains 6697 database images captured in an old European town, Aachen, Germany. All database images were taken under daytime conditions over different seasons. This dataset also contains 824 daytime and 191 nighttime query images captured with different mobile devices. We use both daytime and more challenging nighttime images for evaluation. The RobotCar Season datasets is a benchmark dataset for long-term autonomous driving localization. The images were taken on an urban road that spans multiple city blocks with the RobotCar platform. This dataset contains 20,862 overcast database images, and a total of 11,934 images for query taken under different conditions like sun, dusk, and rain. To generate the mesh model, we first use colmap [13] to generate sfm point cloud from stereo images; then, we use this multiview stereo point cloud to generate mesh by using screened Poisson surface Reconstruction [41]. All the testing experiments are conducted in Ubuntu version 18.04 operating system equipped with Intel(r) Core(tm) i7-7700 CPU @ 3.60 GHz\(\times\)8 CPU, NVIDIA GeForce RTX 3070 8 GB/PCIe/SSE4 Graphics, and 32 GB Memory.
## 5 Conclusions  
This article considered an integrated problem of one batching and two scheduling decisions between a manufacturing plant and multi-delivery sites. Many jobs ordered by multiple customers are firstly manufactured by one of machines in the plant. In this problem, two scheduling (machine and delivery truck scheduling) problems and one batching problem must be simultaneously determined to minimize the total tardiness. To find the optimal solution, a MILP model was developed. We mainly found an optimal solution using CPLEX for small-sized groups, but it was inefficient and impractical to find the optimal solution for the problems of large-sized groups. Thus, two VNS algorithms, which were applied with different local search schemes, were applied to improve the performance of the algorithm. We conclude that the VNS algorithm with dynamic case selection probability finds better solutions in reasonable CPU times, compared with the VNS algorithm with static case selection probability and the GA based on the test results.  
Conceptualization, B.S.K.; methodology, C.M.J. and B.S.K.; software, C.M.J. and B.S.K.; validation, C.M.J.; formal analysis, C.M.J. and B.S.K.; investigation, B.S.K.; data curation, B.S.K.; writing--original draft preparation, C.M.J. and B.S.K.; writing--review and editing, C.M.J. and B.S.K.; visualization, C.M.J.; funding acquisition, B.S.K.  
This work was supported by the Incheon National University Research Grant in 2017.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Test Problems** & \multicolumn{3}{c}{**VNS\_S**} & \multicolumn{3}{c}{**VNS\_D**} & \multicolumn{3}{c}{**GA**} \\ \hline \(\delta\) & \(J\) & **RDI** & **MAD** & **CPU** & **RDI** & **MAD** & **CPU** & **RDI** & **MAD** & **CPU** \\ \hline
0.1 & 20 & 0.14 & 0.76 & 1.09 & 0.08 & 0.64 & 2.50 & 0.60 & 2.25 & 1.51 \\  & 40 & 0.18 & 1.18 & 9.51 & 0.09 & 0.91 & 15.07 & 0.70 & 2.68 & 8.63 \\  & 60 & 0.25 & 1.88 & 37.45 & 0.12 & 1.42 & 55.08 & 0.79 & 2.48 & 24.16 \\  & 80 & 0.15 & 11.44 & 78.75 & 0.05 & 8.79 & 102.96 & 0.85 & 5.94 & 37.43 \\  & 100 & 0.08 & 19.77 & 200.82 & 0.05 & 27.14 & 195.62 & 0.86 & 7.27 & 67.41 \\
0.3 & 20 & 0.14 & 2.51 & 6.92 & 0.08 & 1.84 & 11.34 & 0.59 & 6.18 & 1.43 \\  & 40 & 0.24 & 5.14 & 6.67 & 0.14 & 3.76 & 9.90 & 0.77 & 5.64 & 7.20 \\  & 60 & 0.22 & 8.25 & 22.32 & 0.12 & 9.92 & 27.40 & 0.73 & 9.93 & 20.31 \\  & 80 & 0.07 & 41.01 & 74.15 & 0.02 & 57.11 & 83.61 & 0.84 & 9.51 & 36.95 \\  & 100 & 0.05 & 38.68 & 166.17 & 0.03 & 32.83 & 101.86 & 0.83 & 10.20 & 67.53 \\
0.5 & 20 & 0.04 & 38.78 & 3.06 & 0.05 & 23.04 & 3.18 & 0.31 & 38.74 & 1.36 \\  & 40 & 0.01 & 4.76 & 1.17 & 0.01 & 8.78 & 1.37 & 0.00 & 0.00 & 7.21 \\  & 60 & 0.03 & 1.88 & 4.00 & 0.02 & 2.29 & 4.08 & 0.12 & 10.75 & 18.70 \\  & 80 & 0.04 & 27.80 & 50.01 & 0.03 & 33.98 & 40.95 & 0.76 & 16.56 & 36.80 \\  & 100 & 0.03 & 55.51 & 111.13 & 0.02 & 21.86 & 69.68 & 0.72 & 18.65 & 66.95 \\ \hline Average & 0.11 & 17.29 & 51.55 & 0.06 & 15.62 & 48.31 & 0.64 & 9.83 & 26.92 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test results of large-sized group.
## 1 Introduction
Fatty acids and sterols are not naturally volatile and require derivatization to increase their volatility, stability at high temperatures, and ionization yield for MS detection. These compounds can be derivatized using silylation and alkylation [35]. The silylation of fatty acids or sterols occurs at high temperatures (around 100 \({}^{\circ}\)C) via the use N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) in pyridine or N.O-bis(trimethylsilyl) trifluoroacetamide (BSTFA) with trimethyl chlorosilane yielding TMS derivatives [29; 36; 37; 38]. Fatty acid methyl esters (FAMEs) resulting from alkylation using acids or bases and acetyl chloride sodium methoxide (CH\({}_{3}\)NaO) can also be analyzed via MS [35; 39; 40; 41; 42]. The available literature reports on specific lipid classes present in yeasts, grape juice and wine extracted and analyzed using different methods. For example, fatty acids were analyzed using gas chromatography-mass spectrometry (GC-MS) [26; 43] and gas chromatography-flame ionization detection (GC-FID) [44]. In another study, fatty acids extracted from Koshu and Pinot Noir berries were detected using gas chromatography coupled with tandem mass spectrometry (GC-MS/MS) whereas glycerolipids were analyzed using liquid chromatography-mass spectrometry (LC-MS) [28]. Recently, liquid chromatography tandem-mass spectrometry (LC-MS/MS) was used to analyze fatty acids in grape skin and seeds [45]. The fatty acids' profile was determined quantitatively by GC-MS and qualitatively by Fourier transform infrared-attenuated total reflection (FTIR-ATR) in a study focused on the recovery of bioactive compounds in grape waste (in this case, seeds) [46]. Sterols in the grape matrix can also be analyzed using GC-MS [47] and GC-FID [29; 39; 48]. Even when both sterols and fatty acid were extracted from yeast using the same protocol and instrumental method (GC-FID), the proposed protocol was long and required large volumes of sample and of solvent [29]. In this context, the aim of the current study was to optimize a fatty acid and sterol extraction protocol in a synthetic medium mimicking grape juice (synthetic grape juice medium or SGM) and a synthetic medium used to cultivate yeasts prior fermentation (yeast nutrient base or YNB), as well as yeast using minimal sample and solvent volume. In addition, the optimization of a GC-MS method that allows the analysis of more than one lipid class in one single run was performed.
## 1 Introduction  
On construction sites, Personal Protective Equipment (PPE)--including safety helmets, vests, and other protective gear--is crucial for preventing accidents. Nevertheless, many workers choose not to wear PPE due to discomfort or limited awareness of safety measures [1]. Ensuring that all workers follow PPE guidelines is, therefore, paramount to reducing risks. Traditionally, site inspections rely on surveillance footage reviews and physical patrols--methods that are both time-intensive and labor-intensive. Moreover, inspectors who monitor multiple screens for extended periods may miss violations due to fatigue [2].  
A promising approach to identify non-PPE use involves deep-learning-based object detection applied to images captured by surveillance or body-worn cameras. By employing bounding boxes, this technique localizes and classifies pertinent objects within each image [3]. Training these models on extensive, labeled datasets encompassing diverse conditions--such as varying lighting or object sizes--automates most of the inspection process and delivers robust generalization [4]. However, acquiring a broad selection of non-PPE images from real construction sites can be difficult since regulations typically enforce adherence, making actual non-PPE instances relatively scarce.  
To overcome this data limitation, image augmentation serves as an effective method to expand the training set without collecting additional images. Through transformations like rotations, flips, scaling, and color adjustments, it is possible to simulate real-world variations and create a wider range of training examples. This strategy addresses the insufficient diversity in the original dataset and significantly boosts model generalization.  
Nonetheless, if augmented data are too detached from realistic conditions, they can increase computational costs during training and may degrade performance [5]. Inappropriate features within the dataset can distract the model from focusing on essential features, thereby reducing accuracy [6]. Consequently, it is vital to select augmentation techniques that accurately represent real-world situations.  
This paper introduces image augmentation strategies, grounded in deep learning, that aim to enhance the accuracy of non-PPE detection. Although previous research has not thoroughly investigated the influence of augmentation for detecting non-PPE, multiple datasets--both original and augmented--were developed to assess the efficacy of these techniques. Model performance was then compared with and without the proposed augmentations, demonstrating how well-chosen augmentation strategies can improve non-PPE detection.
## 2 Background
### Selecting a MOEA  
MOEAs do not generate a single final solution but a set of non-dominated solutions which are considered to be of the same quality. The performance comparison and selection of the best one among alternative algorithms can just be done using _quality indicators_. We can classify these indicators according to what they focus: convergence or diversity.  
Convergence focuses on measuring the closeness of a given non-dominated solution set to the Pareto optimal front. Diversity focus on measuring how diverse the obtained solution set is along the Pareto optimal front [63].  
Some quality indicators focus both on convergence and diversity: Hypervolume [64], Hyper-area Ratio (HR) [65], and Pareto Dominance Indicator (ER) [66]. Others focus on diversity: Uniform distribution (UD) [67] and a Ratio of Non-dominated Solutions (RNI) [67]. There are also other quality indicators, for example, Algorithm Effort (AE) [67], which focus on diversity but at the same time considers the computational time.  
We can use quality indicators to determine which MOEA is the best for solving a given problem. However, due to the stochastic nature of these algorithms, we can just say an algorithm \(A\) is better than \(B\) according to a given quality indicator after running multiple trials of both, taking indicator averages and performing a statistical comparison. One way to reduce the overall computational effort is assigning an algorithm to do this task, in this case, a hyper-heuristic.
# Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems
## 3 Results
Figure 6: Energy efficiency versus spectral efficiency of the proposed UAV-based olive irrigation system for different numbers of IoT devices \(K_{I}\) with the number of GBS antennas as \(M=20\). the throughput, the higher the circuit power (CP), due to the larger number of antennas deployed in massive MIMO communication. In Figures 8 and 9, we illustrate the energy efficiency of the proposed UAV-based olive irrigation system as a function of \(K_{I}\) and \(M\). We consider the MR combiner (respectively, the M-MMSE combiner) in Figure 8 (respectively, in Figure 9). We aim to determine the optimal number \(M\) of GBS antennas as well as the optimal number \(K_{I}\) of IoT devices (UAVs and actuators) that can be served. We note that the energy efficiency surfaces are concave and a global optimum exists for each combiner. Indeed, with the M-MMSE combiner, a maximal energy efficiency of 19.57 Mbit/Joule is achieved with \((K_{I},M)=(20,50)\). Moreover, with the MR combiner, a maximal energy efficiency of 9.68 Mbit/Joule is achieved with \((K_{I},M)=(10,40)\). As we can see, the M-MMSE combiner outperforms the MR combiner.
# Integrated Communication and Measurement System with BOC-Assisted OFDM
## 4 Simulation Results and Performance Analysis  
In this section, we study the performance in terms of composite signal acquisition, tracking, and the BER.
# Retrieval of Nearshore Bathymetry around Ganquan Island from LiDAR Waveform and QuickBird Image
## 3 Materials and Methods  
### Research Area and Data  
#### 3.1.1 Research Area  
Ganquan Island (111\({}^{\circ}\)35\({}^{\prime}\)10\({}^{\prime}\) east longitude and 16\({}^{\circ}\)30\({}^{\prime}\)28\({}^{\prime}\) north latitude) belongs to the Xisha Islands in China. The island has a tropical monsoon climate, and it is approximately 500 m from east to west (width) and 700 m from north to south (length), with an area of approximately 0.3 km\({}^{2}\). The waters around Ganquan Island are clear and limpid and largely unaffected by human activities. The study area and multispectral image are shown in the Figure 1a,b respectively.  
Figure 1: (**a**) The geographic location of Ganquan Island is marked with red five-star; (**b**) multispectral image of Ganquan Island.
# Applying Deep Learning to Medical Imaging: A Review
## 4 Deep Learning for Specific Medical Imaging Modalities
### Positron Emission Tomography (PET)
#### 4.3.2 Challenges and Future Directions  
One of the challenges in applying DL techniques to PET is the limited availability of annotated datasets, particularly for rare diseases. Moreover, PET images suffer from low spatial resolution and high noise levels, which can affect the performance of DL models. Therefore, developing robust DL models that can handle these challenges is an important future direction. Additionally, developing transferable models that can handle variations in imaging protocols and patient populations is also an important future direction.