esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Wireless Communication for Maritime Applications  
Due to a lack of infrastructures such as optic fibers and base stations, maritime communications face a set of complex challenges that their terrestrial counterparts do not. Additionally, the marine environment is volatile, uncontrollable, and prone to sudden weather conditions such as rain or storms [8]. These factors, as are explained below, heavily influence the quality and reliability of wireless communication in the ocean.  
Wireless propagation over the ocean can be analyzed in two distinct regions: near-shore and open sea. In the vicinity of the shore, ports, buildings, vessels, and the water's surface are the main obstacles to wireless signal propagation. In the open sea, where communication distances are generally much higher, the curvature of the earth, the water's surface, and passing vessels are the biggest issues [7].  
The roughness of the sea can cause radiated wireless signals to reflect and scatter power in other directions, leading to a lower achievable range. Additionally, by tilting the antennas with the undulation, the range of the link is also affected. The earth can be considered a flat surface when wireless links are established over short distances. However, for ranges beyond several kilometers, the earth's curvature causes the reception and transmission antennas to lose lines of sight, potentially disrupting the communication link. Buildings beside the coast can reflect the RF signals, leading to a multipath propagation of the signal into the reception antenna. Similarly, ports produce the same effect, as well as shadowing, especially considering they can house large metal vessels [7].  
When it comes to establishing wireless communication networks in the ocean, several technical issues arise. Datasheets of communication modules report an estimated range where the device's functionality is guaranteed; however, this range is much lower in the ocean due to conditions previously described and the fact that air humidity is also higher, which attenuates the RF signal. Furthermore, due to physical constraints and logistics, it is not possible to place antennas at high altitudes in the ocean, and the transmission power cannot be liberally increased due to energy scarcity in remote areas.
## 1 Introduction
### ERP Systems and Carbon Footprint Management  
Enterprise Resource Planning (ERP) systems serve as comprehensive management tools that enable businesses to integrate various operational functions into a unified platform. These systems facilitate seamless data flow across departments, optimizing critical processes such as resource allocation, production planning, and inventory management, thereby enhancing operational efficiency [17; 18]. By centralizing data management, ERP systems eliminate redundancies and support decision-making processes through real-time analytical capabilities. This enables organizations to monitor key performance indicators and adapt swiftly to changing market dynamics [18]ERP systems play a pivotal role in digital transformation by integrating business processes into a single information hub. This capability empowers organizations to automate production processes, monitor operations in real time, and respond promptly to market shifts, thus strengthening competitiveness while supporting sustainability goals [19]. Additionally, ERP systems contribute significantly to effective waste management, resource consumption reduction, and the management of complex sustainable supply chains. These advancements combine environmental sustainability with economic performance, thereby enhancing strategic decision-making capabilities for organizations [20; 21].  
Moreover, ERP systems are critical for monitoring energy consumption and emissions, making significant contributions to sustainability efforts. By integrating organizational resources and enabling efficient management, ERP systems facilitate the adoption of Sustainable ERP (S-ERP) practices within the manufacturing sector. These practices enhance economic, environmental, and social sustainability performance, providing benefits such as waste reduction and sustainable supply chain management [4; 20; 22].  
In addition, ERP systems leverage technologies like cloud computing, artificial intelligence (AI), and the Internet of Things (IoT) to streamline the collection and analysis of carbon footprint data. This integration aligns with Industry 4.0 and 5.0 advancements, enabling enterprises to adapt to evolving technological landscapes while maintaining a focus on sustainability [23]. By supporting business analytics and decision-making processes, ERP systems provide organizations with a competitive advantage and enhance strategic decision-making frameworks [19].  
This comprehensive approach underscores the importance of ERP systems in achieving sustainability goals and addressing the challenges of carbon footprint management in modern manufacturing environments.
# Simulation of Track-Soft Soil Interactions Using a Discrete Element Method
## 3 Results and Discussion  
### Mechanical Property Verification of Scaled-Up Particles  
The accumulated shape, obtained by pixel superposition, is shown in Figure 15. With the increase in sand particle content, the accumulation height of soil decreases gradually, and the flow state becomes more obvious. When the content of sand particles is low, the interaction between soil particles is mainly a cohesive force. However, with the increase in the content of sand particles, pore water film between the clay particles is broken. This leads to the molecular force between clay particles becoming smaller, and soil cohesion also decreases. Moreover, when the soil is mainly composed of sand particles, the pores between sand particles are filled with clay particles. These high-moisture-content clay particles affect lubrication, which causes the soil to become a flow state.  
Figure 15: Accumulations of tests and simulations with five types of soil. The edge curves of soil accumulation and DEM particle accumulation shapes were extracted. Two edge curves are compared by pixel superposition. (**a**) Pixel superposition of sand. (**b**) Pixel superposition of clay. (**c**) Pixel superposition of mixed soil 1#. (**d**) Pixel superposition of mixed soil 2#. (**e**) Pixel superposition of mixed soil 3#.  
As analyzed above, the AOR of clay particles and sand particles is different. The simulation of soil accumulation can reproduce the accumulation test results with the properly adjusted friction and cohesion parameters of soil. Friction and cohesion characteristics of different soils are shown in Figure 16.  
The above results indicate that both cohesive and frictional properties exist in clay particles. However, friction is the main characteristic of sand. Meanwhile, the friction characteristic curves of soils with different compositions are nonlinear. Clay and sand are modeled as spherical particles, which means that the friction parameters between particles are set as larger than that of the actual situation. The significance of accumulation is to explore appropriate contact parameters according to different soil compositions.  
It should be noted that the friction and cohesion characteristics in this study were based on a soil particle size of 1 mm. Additionally, the calibration of friction and cohesion are greatly affected by particle size. The coordinate ordinate axes represent the friction coefficient and surface energy of soil particles based on spherical scaled-up particles.
# Practical Inner Product Encryption with Constant Private Key +
## 1 Introduction
### Related Works
#### Lattice-Based IPE Schemes  
To fend off attack from quantum computers in the future, [PERSON] et al. [8] proposed the first IPE scheme based on the lattice hard assumption (i.e., the learning with error assumption, which is believed to be able to withstand quantum attacks); to do so, they modified an identity-based encryption approach proposed by [PERSON] et al. [31]. [PERSON] [9], inspired by the work of [PERSON] et al., proposed an improved lattice-based IPE scheme that reduced the size of public parameters and ciphertext. [PERSON] et al. [10] proposed a lattice-based IPE scheme that further reduced the size of public parameters and ciphertext. In contrast to [9], their work reduced the size by a factor of \(\log n\), where \(n\) is the security parameter. [PERSON] et al. [11] recently proposed the first compact IPE scheme that employs an IPE scheme [9], fully homomorphic encryption [32] and vector-encoding schemes [33]. Although these constructions are thought to be able to withstand quantum computer attacks, they are based on the learning with errors assumption, resulting in key lengths that are still too large to be practical.
## 3 Hybrid Visual SLAM with Deep Keypoints, Depths, Poses, and Line Features  
### Framework  
Figure 1 illustrates our proposed D3L-SLAM, a novel hybrid SLAM framework that integrates learning-based keypoints, poses, depths, and line detectors. In the front end, we utilize a learning-based local feature extractor to identify keypoints from images. To enhance feature detection in low-texture regions, we incorporate a handcrafted line extractor. Additionally, to ensure that the detected features are scaled, we introduce depth estimates from a self-supervised depth estimation network. Although these depth estimates lack absolute scale, they maintain global consistency. We further transform monocular SLAM into virtual stereo SLAM by designing a virtual baseline. Leveraging these learned depths, D3L-SLAM can recognize relative object distances in the scene, mitigating scale drift caused by insufficient tracking features. In traditional model-based SLAM, the initial selection range for adjacent frame feature matching is determined by a uniform motion model. However, this model is often too idealized and does not reflect real-world scenarios, thereby limiting feature matching accuracy. To address this limitation, we introduce relative pose estimates derived from a self-supervised pose estimation network as a prior. These  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Deep**} & \multicolumn{2}{c}{**Line**} & \multirow{2}{*}{**Deep Depth**} & \multirow{2}{*}{**Deep Pose**} & \multirow{2}{*}{**Loopclosing**} \\  & **Keypoint** & & & & & \\ \hline GCN-SLAM & ✓ & & & & & ✓ \\ DX-SLAM & ✓ & & & & & ✓ \\ LIFT-SLAM & ✓ & ✓ & & & ✓ \\ SC-Depth & & & ✓ & ✓ & ✓ \\ Pseudo & & & & ✓ & & ✓ \\ RGBD- & & & ✓ & & ✓ \\ SLAM & & & & & & ✓ \\ AirVO & ✓ & ✓ & & & & \\ SL-SLAM & ✓ & ✓ & & & ✓ \\ D3 VO & & & ✓ & & ✓ & \\ PLDS-SLAM & & ✓ & & & ✓ \\ RGBL-SLAM & & & ✓ & & ✓ \\ D3L-SLAM & ✓ & ✓ & ✓ & ✓ & ✓ \\ (Ours) & & & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison of key visual SLAM modules of our proposed D3L-SLAM with other representative works.  
estimates, obtained through extensive data training with self-supervised methods based on photometric loss, are more accurate and robust compared to uniform motion models. Finally, in the back end, we design an online learning bag-of-words (BoW) module that uses binary descriptors for online training. This module converts float descriptors from selected keyframes into binary descriptors. The online learning-based BoW addresses the issue of low generalization in learning-based descriptors. The detected loop closures are then utilized to correct accumulated errors in pose estimation.
## 2 Materials and Methods
### PCR Amplification  
Polymerase chain reaction (PCR) amplification of the ITS1 region was performed with primer pair ITS1F (5\({}^{\prime}\)-ACTTGCTATTTAGAGGAAGTAA-3\({}^{\prime}\)) and ITS2 (5\({}^{\prime}\)-GCTGCGTTCT
# Unobtrusive Vital Sign Monitoring in Automotive Environments--A Review
## 5 Optical Methods
### Other Camera-Based Automotive Monitoring  
#### 5.4.1 Automotive Monitoring Using Visible Light (VIS)  
With respect to early camera applications in the automotive arena, driver drowsiness detection as well as driver attention monitoring were among the first topics of interest.  
As early as 2003, [PERSON] et al. introduced an algorithm to monitor driver face orientation based on a single camera [128]. Shortly after, in 2004, [PERSON] and colleagues [129] reported on mouth tracking and shape analysis using CCD cameras to quantify drowsiness employing neural network (Perceptron-type) classifiers.  
Figure 11: Temperature variation around the nostrils during inspiration and expiration. Thermograms of the nose during (**a**) inhalation and (**b**) exhalation from [127] (Figure 1).  
In 2007, [PERSON] and coworkers investigated camera-based systems aiming at correlating the visual contextual information of vehicle interior and vehicle exterior [130]. Topics like occupant position and posture, driver distraction and estimation of face orientation were examined using a multi-camera system during daylight to look in and look out of the vehicle (LiLo concept). Shortly after, in 2008, [PERSON] et al. reported on driver inattention analysis using a dual-camera system in the visible light range [131]. Their face detector was based on the known [PERSON] face detection algorithm [132].  
Capturing and quantifying emotions from facial images is a task that has applications far beyond the automotive scenario, but is of course quite important to access driver state. In fact, stress and emotions might be quantifiable based on vital signs (like heart and breathing rate and derivatives), but also by image processing. Correspondingly, in 2016, [PERSON] and colleagues published an article on detecting stress and pain of drivers by analyzing facial expressions based on tracking dedicated fiducial points [133]. For classification of emotions, the authors employed support vector machines.
# Low-Resolution Target Detection with High-Frequency Information Preservation
## 4 Experiments
### Evaluation on Object Detection  
We aim to evaluate the effectiveness and universality of our method by evaluating its performance on object detection models that differ in terms of paradigms, architectures, and scales. For this purpose, our lineup includes variously scaled versions of the YOLOv5 series, the YOLOF, with its single-scale feature structure, and the well-established two-stage detection model Faster-RCNN. As shown in Table 1, when evaluating the YOLOv5 models at different scales, the models with HFIP achieve better performance compared to YOLOv5 and YOLOv5 with SPD-Conv. Specifically, the YOLOv5m, YOLOv5s, and YOLOv5m models achieve 3.2%, 4.6%, and 1.9% improvements in mAP and 5.3%, 4%, and 2.4% improvements in mAP (small), respectively, when using HFIP compared to SPD-Conv. The HFIP-based models achieved significant improvements in mAP and mAP (small), ranging from 8.8% to 16.2%, compared to the original YOLOv5 models. The conclusions for the YOLOF model are similar to those obtained for YOLOv5 (n, s, m). Notably, SPD relies more heavily on multiscale features. On the single-scale feature architecture, SPD exhibits suboptimal performance, yielding only marginal gains in efficiency. In contrast, HFIP achieves a more significant performance enhancement by strengthening the retention of high-frequency information. Compared to the original Faster R-CNN models, the Faster R-CNN using HFIP still achieves the best mAP and mAP (small). Compared to the corresponding model using SPD-Conv, the mAP and mAP (small) improved by 2.8% and 2.5%, respectively. Compared to the original model, the mAP improved by 7.8% and the mAP (small) improved by 16%.
# Computer Vision in Autonomous Unmanned Aerial Vehicles--A Systematic Mapping Study
## 3 Results
### RQ3: What Types of UAVs Have Been Used in the Proposed Solutions?
#### 3.3.1 Distribution and Annual Trend per UAV Class  
The UAV class distribution over the total of 144 papers is shown in Figure 11. This graph shows that, without any doubt, the most widely used UAV platform corresponds to the rotatory-wing class (102 papers, 71%), followed by 25% that represents the fixed-wing class (37 papers). Only one paper uses an airship UAV. Finally, three papers do not specify the type of UAV. The only class of UAV not present in the systematic mapping study is, therefore, flapping-wing aircraft. Consequently, there is an interesting gap on which future research should be focused. Moreover, considering the characteristics of flapping-wing UAVs, they could be very useful for indoor applications and/or approaches requiring a small aircraft.  
The above data are also endorsed when observing the annual trend considering the UAV class (see Figure 12). This graph presents the annual evolution of the number of works. The data again provide evidence of a greater use of rotatory-wing UAVs. Another noteworthy fact is that the use of rotary-wing aircraft has continued since 1999, also showing a clear trend of growth, which can be considered almost exponential in recent years. However, the other UAV classes have a discontinuous evolution over time. With the exception of fixed-wing UAVs, for which some evolution can be appreciated, no conclusions can be drawn for the remaining cases, given the small number of works.
## 1 Introduction
Recently, a hybrid optical CDMA/DWDM system considering the effects of inter- and intra-channel FWM and inter-symbol interference (ISI) was proposed [19]. The analysis revealed that the generated inter-channel FWM effect contributes not only as an additional crosstalk component, but also produces other noise terms, namely, signal-FWM, Multiple Access Interference (MAI-FWM), and FWM-FWM noise. It has been shown that the CDMA technology, where the bit's energy is spread over the identification code sequence, could minimize the effect of inter-channel FWM. In addition, the electro-optic phase modulator (EOPM) is used to suppress the intra-channel FWM effect in the hybrid optical CDMA/DWDM system [19]. To implement systems that can support a high data rate, the focus should be more on the modulation formats and line coding, which are used to mitigate the linear and nonlinear impairments of fiber optic transmission [20]. DWDM has been analyzed under the influence of the FWM effect using different modulation formats such as carrier suppressed return to zero (CSRZ) [21], modified duobinary return to zero (MDRZ) [22], differential phase shift keying (DPSK) [23; 24], and differential quadrature phase shift keying (DQPSK) [25]. All the modulation formats were proposed as alternative formats to return to zero (RZ) and non-return to zero (NRZ) [26; 27; 28]. By extending the work in [18], the target of this paper is to examine the impact of different types of modulation formats (namely return to zero (RZ), carrier suppressed RZ (CSRZ) and the modified duobinary RZ (MD-RZ) formats-based OOK signaling) on the bit error rate (BER) as a key figure of merit performance of the hybrid optical CDMA/DWDM system. The influence of inter- and intra-channel FWM, ISI, and MAI have been considered.
## 3 Regulation of DAPG Biosynthesis
### Co-regulation of DAPG and Pyoluteorin Production  
Several strains belonging to the _P. protegens_ subgroup have been reported to produce the phenolic antibiotic pyoluteorin [67; 68] in addition to DAPG and MAPG. The amounts of DAPG and pyoluteorin being produced have been shown to be inversely correlated [15; 67]. This has been especially demonstrated in a CHA0 mutant impaired in pyoluteorin production, which overproduced DAPG and MAPG [69], suggesting a co-regulation between both biosynthetic pathway. In addition, some medium were shown to favour either DAPG or pyoluteorin production [67]. DAPG and pyoluteorin both act as autoinducers of their own biosynthesis [15; 70] and the addition of pyoluteorin has been shown to repress _phLA_ expression and DAPG production [15; 70]. The effect of phloroglucinol derivatives on pyoluteorin production is, however, more complex. The addition of high concentrations of phloroglucinol derivatives (DAPG or phloroglucinol) was shown to repress the expression of genes involved in pyoluteorin production [43; 70]. Phloroglucinol production by PhlD was shown, however, to be essential for pyoluteorin production, as a _phlD_ mutant was unable to produce pyoluteorin [43]. The addition of low concentrations of exogenous phloroglucinol restored pyoluteorin production in the _phlD_ mutant [43], suggesting that small quantities of phloroglucinol are required for pyoluteorin biosynthesis. Interestingly,poluteorin-producing _P. aeruginosa_ strains do not produce DAPG, but they harbour a _phlD_ gene adjacent to the pyoluteorin gene biosynthetic cluster, which allows them to produce phloroglucinol [43]. More recently, [PERSON] and colleagues [71] found that phloroglucinol is the substrate of the FADH\({}_{2}\)-dependent halogenase PltM, which converts phloroglucinol into chlorinated phloroglucinol. Chlorinated phloroglucinol activates, probably via the pathway-specific regulator PltR, the expression of pyoluteorin biosynthetic genes [71]. This co-regulation mechanism likely attenuates the metabolic cost of producing several antibiotics, while still providing the option for the bacteria to produce one or the other depending on the situation.
# Hardware Development and Safety Control Strategy Design for a Mobile Rehabilitation Robot
## 5 Experimental Validation
### Experiment Setup  
The MRR experimental system consists of an AMBSS, an LES, a control box, and a panel PC shown in Figure 4. A real-time embedded system (myRIO-1900 (National Instruments, Austin, TX, America)), an industrial touchscreen computer (Intel(r) Core TM i Celeron(r), 3.1 GHz), direct-current servo motor modules, LiDAR, and a power supply module are installed inside the control box. The power supply can output 5 V, 10 V, 12 V, and 24 V voltage. Actuator control commands and sensor measurement signals can be transmitted through the D/A port and I/O ports on the real-time embedded system myRIO-1900. The LES is attached to the lower limb of the subject. The sampling interval of the controllers is 0.005 s.