esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Combined Orchard Harvesting Robot Navigation System
## 5 Conclusions  
Based on the process of commercial apple harvesting, our research developed an autonomous navigation system for a combined orchard harvesting robot using two master-slave navigation methods, Wi-Fi point-to-point communication technology, target feature extraction, and pure tracking control technology, in which the transport robot is the master and the picking robot is the slave. The performance of this combined harvesting robot navigation system was verified through orchard trials.  
The experimental results show that the packet loss rate of the communication system was less than 1.2% when the interval distance of the joint harvesting robots was within 50 m, which meets the communication requirements of the master and slave robots in the orchard. The combined harvesting robot achieved the stop-drive function according to the apple tree planting situation when driving in the row at a speed of 0.5 m/s, and the system ran smoothly. However, the impact of the change in trailer heading when the master vehicle was towing the trailer around a turn resulted in a significant error when the slave vehicle entered the next row and came to a stop. The error value gradually decreased after changing the master-slave navigation method and can be used for in-row cooperative operations. The maximum value of the position error of the master robot was 0.053 m, the minimum value was 0.008 m, the mean value of the error was 0.024 m, and the standard deviation of the error was 0.009 m. The maximum value of the position error of the slave robot was 0.397 m, the minimum value was 0.011 m, the mean value of the error was 0.041 m, and the standard deviation of the error was 0.056 m. The safety distance between master and slave robots was within the set threshold (0.35 m), where the maximum value of the following error was 0.044 m, the mean value of error was 0.013 m, and the standard deviation of error was 0.01 m. Therefore, the system can meet the demands of cooperative operation without collision. The combined harvesting robots could achieve smooth turns according to commanded navigation during the turns, but the course deviation (greater than 10\({}^{\circ}\) less than 90\({}^{\circ}\)) and lateral deviation (greater than 0.3 m less than 1 m) between robots during the turns were largely due to the trailer motion model.
# Monocular Real Time Full Resolution Depth Estimation Arrangement with a Tunable Lens
## 2 Materials and Methods
### Setup
#### 2.1.2 Camera  
The camera requirements need to be chosen according to the selected optics and to the desired resolution of the depth information. In the setup used for this paper, the variable focus lens is C-mount. The lens acquires six different focus planes by sweeping from the nearest focus to the farthest focus (infinity) without an overlapping depth of field. The camera must be C-mount compatible and have at least a frame rate of 150 frames per second (fps) to simulate a real time 25 fps camera. Using a global shutter is mandatory to avoid artifacts while capturing at high frame rate speed. Additionally, an Opto-isolated trigger input/output is needed to perform the correct synchronization with the lens and a high-speed interface for data transfer. To send this amount of data, at least a 3 Gb/s speed bus is needed using Bayer pattern following Equation (1):  
\[AD=\frac{F\times W\times H\times C\times 8}{1024^{3}} \tag{1}\]  
where \(AD\) is the amount of data in Gb/s, \(F\) the number of frames per second and \(W\), \(H\), \(C\) are width, height, and channels, respectively. Usable interfaces that can transfer such high amounts of data are, but not limited to, the Camera Link (CL), CoaXPress (CYP) and MIPI, depending on the version and lanes. For the setup used in the camera presented here, a frame grabber was used to read the output data. The camera and frame grabber used in this setup are the Flare2 MP [23] and Matrox Radient eV-CL [24]. The capture is done at 156 fps with an exposure time of 6 milliseconds.  
Figure 1: Setup diagram. Presents the different visible part of the prototype.
# Context-Aware System for Information Flow Management in Factories of the Future
## 2 Theoretical Framework
### Microservices  
Microservices are a growing trend in software architecture. They were inspired by Service Oriented Computing (SOC) and are small applications that can be implemented, scaled and tested independently [28]. They deal with complexity by breaking down large systems into independent services [29].  
The microservices approach advocates the decentralization of processes, where each one is designed to develop a specific part of the system [30]. Each microservice is designed and operated as an independent, small-scale system, providing access to its internal logic and data via a well-defined network interface [31]. In a microservice architecture, microservices interconnect to perform a specific function [30]. Each microservice exhibits a unique behavior, and the system is formed by combining and coordinating these microservices through messages [29].  
The main characteristics of a microservice system are [29]:  
* Flexibility: The system has the ability to easily adapt to changes in the environment and support all necessary modifications;
* Modularity: The system is made up of isolated components, each of which is responsible for a specific task, rather than relying on a single component that offers all the functionality;
* Evolution: It is possible to maintain the system while it evolves, adding new functionalities independently.  
The use of microservices has several advantages, such as technological diversity (the possibility of implementing different technologies in the same system), independent implementation (the ability to develop and implement a service in isolation, without depending directly on other services in the system), scalability (the ability to expand services as necessary, without affecting the system as a whole) and ease of maintenance (the ability to modify or replace services without affecting the entire application) [32].
# Efficient Small-Object Detection in Underwater Images Using the Enhanced YOLOv8 Network
## 5 Discussion
### The Impact of Low-Resolution Feature Maps on Large Object Detection  
Similarly, we also analyzed the advantage of low-resolution feature maps for detecting large objects. Figure 8 shows the large object sample image from UTDAC2020 dataset with the detection results using our method (1280). We analyzed the three different scale feature maps (320 \(\times\) 184, 160 \(\times\) 92, 80 \(\times\) 46) with our method (1280), using Grad-CAM [62] attention maps. As shown in Figure 9, there is a more concentrated attention on the large object in the 80 \(\times\) 46 feature map, while it appears more dispersed in the 320 \(\times\) 184 and 160 \(\times\) 92 feature maps. This suggests that a low-resolution feature map is more favorable for detecting large objects.  
Figure 8: The large-object sample image from UTDAC2020 dataset with the detection results using our method (1280).  
Figure 7: The feature maps of corresponding detection heads using different methods (YOLOv8s 640, ours 640 and ours 1280) for the underwater image of Figure 6.
# Modulation Characteristics of High-Speed Transistor Lasers
## 2 Inherent Advantages of TL
### Drastically Reduced Effective Carrier Recombination Lifetime
The eye diagram of TL is wider because the rise and fall times of the digital signal transmission of the TL are shorter than that of the DML, which means that the signal transmitted by the DML is more prone to distortion [28]. A low H(\(\omega_{R}\)) of TL means that the area available for signal transmission is widened, and the actual signal transmission rate is increased. Although the bandwidth of the GaAs -based TL reported by [PERSON] et al. was approximately 17 GHz, the data transfer rate reached 40 Gb/s. Therefore, the ratio of transmission rate to bandwidth is as high as 2.35, which is significantly higher than the 1.25 ratio of DML [13].
## 1 Introduction
### Directions  
Based on the works in recent years, the conceptual development of AI Fairness has focused on the following directions:  
* Fairness and bias [15; 16; 17]: Introduction of widely used fairness metrics, such as disparate impact, equal opportunity, and statistical parity. Evaluation of their applicability and limitations in different contexts, contributing to a nuanced understanding of group fairness assessment.
* Algorithmic bias mitigation [18; 19; 20]: Exploration of techniques, like pre-processing, in-processing, and post-processing, to mitigate algorithmic biases. Critical analysis of their effectiveness in different scenarios, offering insights into the trade-offs between bias reduction and model performance.
* Fair representation learning [21; 22; 23]: Introduction of techniques for learning fair representations, including adversarial debiasing and adversarial learning frameworks. Investigation into their potential for producing fair and informative representations, fostering a deeper comprehension of their role in mitigating biases, understanding the true sources of disparities, aiding in the design of more targeted interventions.  
Based on the above conceptual directions, we condense and analyze the methodology and technical analysis involved, and focus on the major key elements in this paper, including definition and problem formulation, bias analysis, fair training, and corresponding applications and practices.  
This article undertakes a comprehensive exploration of the critical subject of fairness issues within artificial intelligence (AI) systems. The overarching scope of this survey is to provide an in-depth analysis of the multifaceted landscape of AI fairness, covering its foundational aspects, developmental trajectory, practical applications, and emerging research directions. By delving into these dimensions, the survey aims to shed light on the complex challenges linked to fairness in AI, while offering insights into potential remedies and avenues for future exploration.
## 4 Conclusions
Combining the rigid button PCB that included the wireless sensor module with the textile layers in the proposed design provides several benefits, including high stability in operation resonance frequency, impedance matching level, radiation patterns, less path loss, less attenuation through materials, improved wireless communication range, and reduced SAR values. The simulation and measurement results validate the concept of making a button antenna that provides wireless connectivity in a relatively small package. The unique aspect of this work is that it provides a systematic approach to the design of a complete transceiver in a wearable package with the option of wearing it as a button or a wristband, and the small package includes everything needed for communication with WiFi routers. While the wearable antenna is the heart of this design, the transmit/receive module with the matching elements completes it as a standalone unit. The unit is compatible with IEEE 802.11 b/g/n standards. The sensor is characterized for various conditions and is shown to be an effective and good candidate for WBAN health monitoring systems. For the on-body (chest and arm) positions, we measured the reflection coefficient (S\({}_{11}\)) of \(-\)23.07 (\(-\)27.07) dB and \(-\)30.76 (\(-\)31.12) dB, and gain of 2.09 (6.7) dBi, and 2.16 (5.67) dBi, and radiation efficiency of 65.12 (81.63)% and 75.0 (85.0)%, for the chest and arm locations, in lower (upper) bands, respectively. In addition, the performance of the button sensor antenna was tested under extreme bending and wet conditions, and the design was found to work within the specified operating band. The 10 g and 1 g average tissue SAR tests were performed, and the results were within the standard limits. The measured results showed that the 2.45 GHz device platform has a communication range of 40 m, which is better than many other wearable devices used in healthcare reported in the literature. Through simulations and measurements, we have shown that the proposed design platform has the potential to be used in future wearable health monitoring applications. An example of these applications is a novel body-worn hearing aid for the elderly that is based on WBAN systems and utilizes beamforming. It is a hearing aid featuring 5G beamforming hearing technology, which helps in speech recognition in noisy environments, with advantages of highly concentrated coverage, energy-saving technology, and long-distance transmission [70; 71]. [PERSON] prepared the final research article, and it was edited and reviewed by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. The research work was supervised, and the manuscript was finalized by [PERSON] All authors have read and agreed to the published version of the manuscript. The authors would like to thank the Universiti Teknologi PETRONAS for supporting this research work. This work is funded by YUTP, through the research grant number 015 LC0-106. The authors declare that there is no conflict of interest.
## 2 Materials and Methods
### Testing and Measurement  
Compressive test: The compressive strength of the materials was evaluated using an Advantest 9 Controls Model 50-C9030 apparatus with a constant loading rate of 2.5 kN/s until failure of the cubic specimen. The two parallel ends of each specimen were polished using sandpaper to obtain flat surfaces before testing. Three specimens were tested for each sample investigated. The reported compressive strength was the average of the three specimens tested. The formula used to calculate compressive strength is F = P\({}_{\rm max}\)/A, where F represents the compressive strength (MPa), P\({}_{\rm max}\) is the maximum load (or load until failure) applied to the material (N), and A is the area of the material resisting the load (mm\({}^{2}\)).  
Microstructure investigation: X-ray diffraction (XRD) was employed to determine the crystalline phases present in the materials. XRD patterns of the materials were measured using a D8 Advance-Brucker diffractometer with CuK\(\alpha\) radiation (\(\lambda\) = 1.549 A). In order to observe the morphology of the materials, scanning electron microscopy (SEM) images were taken using an S4800-Hitachi apparatus. Fourier transformation infrared (FT-IR) spectroscopy of the materials was obtained using an IRAffinity-1S-Shimadzu spectrometer. The porosity properties of the materials were determined using the Brunauer-Emmett-Teller (BET) method with a TriStar II Plus analyzer.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**No.** & **Kaolin** & **Fly Ash** & **Red Mud** & **Ca(OH)\({}_{2}\)** & **NaOH** & **Water/Solid** \\  & **(g)** & **(g)** & **(g)** & **(g)** & **(M)** & **(_w1/w_)** \\ \hline
1 & 150 & 75 & 75 & 0 & 4 & 0.37 \\
2 & 150 & 75 & 75 & 15 & 4 & 0.37 \\
3 & 150 & 75 & 75 & 30 & 4
# Analyzing GPU Performance in Virtualized Environments: A Case Study
## 5 Use Cases
### Analysis of Contention for Resources  
This second use case looks into the extent to which the performance of a vGPU-enabled VM can be affected by the presence of other VMs. In our experiment, two collocated VMs shared host GPU resources via GVT-g, and their vGPUs were configured identically, as detailed in Table 3. On VM1, we ran the Gaussian benchmark, while VM2 executed a variety of benchmarks characterized by various intensities of host GPU resource usage (e.g., varying workload sizes, diverse request frequencies, etc.). For example, Figure 8 demonstrates the effects of workload sizes from these benchmarks on the Gaussian benchmark's execution time. Notably, the execution time of the Gaussian benchmark increased significantly when run concurrently with the Hotspot benchmark. Our framework view, which is depicted in Figure 9, further illustrates that during the Hotspot benchmark's execution, GPU usage for the Gaussian benchmark did not exceed 20%, but this rose to 40% after the Hotspot benchmark ended.  
Figure 8: The execution time of the Gaussian benchmark (VM1) increased when it was concurrently executed with the Hotspot benchmark (VM2). Contention for GPU resources between the two VMs is a plausible cause.  
This experiment shows that the performance of one vGPU can be considerably affected by another vGPU that is co-located. Our analysis indicates that this is primarily caused by the disparity in workload sizes: approximately 18 ms for vGPU1 (Gaussian benchmark) versus 800 ms for vGPU2 (Hotspot benchmark). The i915 GPU driver's scheduler lacks preemptive multitasking capabilities, meaning that it cannot interrupt an ongoing workload. Thus, the GVT-g scheduler uses a coarse-grained sharing approach for the host GPU time to compensate. Time slices are allocated to vGPUs in proportion to their weights, and the execution times of GPU requests are subtracted from these slices. Outstanding balances (debts and lefts) from previous periods are carried over for up to ten periods, explaining why the large workloads from vGPU2 adversely affected vGPU1's performance.  
Despite enhancing scheduling fairness among vGPUs and, thus, improving VM application responsiveness, this method's drawback lies in its coarse-grained approach to host GPU time-sharing. This becomes particularly problematic when a vGPU's workload size exceeds the period length.  
In summary, this use case demonstrates that the GVT-g scheduler struggles to maintain fairness between vGPUs under certain conditions, which is largely due to the i915 driver's lack of preemption support and the implementation of its coarse-grained sharing mechanism. Because the scheduling algorithm's focus is to maintain interactive program responsiveness, we suggest disabling the debt and leftover management feature when VMs are tasked with running non-interactive tasks.
## 5 Conclusions and Future Work  
A novel method for mitigating voltage unbalance at the PCC by tuning the volt-var curve of each PV inverter through a day-ahead DRL training platform with forecast data in a digital twin grid was proposed. The simulation results demonstrated the performance of the proposed method. The auxiliary service of DG, such as volt-var control, becomes necessary because of the increasing proportion of DG in the power system. Moreover, the improvement of auxiliary service of DG leads to an increased capacity for renewable energy. Related future works include the further application of the proposed method to ensure transient voltage stability. Moreover, we will develop the strategy for the incentives of this auxiliary service to encourage participation by PV system owners.  
Conceptualization, [PERSON], [PERSON] and Y.J.; methodology, C.H. and Y.J.; software, C.H., D.L. and Y.J.; validation, C.H., S.S. and Y.J.; formal analysis, G.J., C.H. and S.S.; investigation, C.H. and Y.J.; resources, C.H. and Y.J.; data curation, C.H. and Y.J.; writing--original draft preparation, Y.J.; writing--review and editing, C.H.; visualization, C.H., D.L. and Y.J.; supervision, [PERSON] and Y.J.; project administration, [PERSON] and Y.J.; funding acquisition, C.H. and G.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (No. 20191210301890) and Korea Electric Power Corporation (Grant number: R20X002-4).  
The authors declare no conflict of interest.
## 1 Introduction  
Mobile ad-hoc networks (MANETs) have gained a significant reputation in recent years due to the current proliferation of the latest technology (i.e., smartphones, tablets, personal digital assistants, etc.) [1]. Due to the dynamic environment, nodes are wirelessly connected with each other to transfer the data packets. The transportation of data packets between nodes occurs in an open medium without any central support, therefore, nodes exchange information at any time in the network. If the source and destination nodes are not in the same range, the reliability of the communication merely depends upon the intermediate nodes to forward the data packets in a trusted manner. An intermediate node acts as a host and communicates directly with the source node if it is near, while it works as a router if it is far away from the destination node [2]. Meanwhile, wireless nodes have constrained resources, in terms of possessing less battery power, a low memory, and limited bandwidth. The MANET is specifically designed for fast and easy communication between nodes. It is applied in battlefields, disaster management, rescue operations, maritime communications, personal or commercial information sharing, and in those areas where wired infrastructure is not possible. The MANET does not require any special infrastructure for deployment and it is cheap to set up anywhere [3,4]. When the source node wants to transmit the data packets to a targeted node through an open medium, it utilizes multi-hop with the help of intermediate nodes. Due to the dynamic topology, the unstructured network, the open medium, and the high mobility of the nodes, some malicious nodes may enter easily into the network. Malicious nodes try to disturb the network resources, in terms of dropping the data packets, stealing important information, or manipulating data packets, which produces undesirable situations, a type of phenomena called a Denial of Service (DoS) attack [5].
## 2 Related Work
### Linear Transformation Methods  
Prior work has explored many approaches to cross-lingual representation learning through linear transformation [22; 23; 24]. These methods represent semantic features across different languages by mapping words to continuous vector spaces. Subsequently, linear transformations are employed to project these language-specific representations into a shared, unified vector space [25]. [PERSON] et al. [22] proposed a strategy of bilingual sentiment word embeddings to bridge the semantic gap between English and Chinese. [PERSON] et al. [23] employed structural correspondence learning to effectively exploit target-language domain knowledge, minimizing knowledge degradation during semi-supervised transfer. [PERSON] et al. [24] proposed a method that maps languages into a common cross-lingual space through pairwise alignment. Although linear transformation methods are widely applied, they still have significant limitations. In particular, these methods struggle to address lexical gaps between languages, such as culturally specific vocabulary, and fail to effectively handle polysemous words. Furthermore, alignment at the word or sentence level tends to be insufficient, especially when handling culturally unique expressions or polysemous words. Additionally, these methods exhibit limited effectiveness in handling languages with substantial differences in word order or syntactic structures.
## 2 Materials and Methods  
### Proposed Feature Extraction Method  
The proposed unsupervised feature extraction method is to describe the information of a 3D local image patch as a feature vector. The 3D image patches here are local regions of images of interest extracted from a 3D medical image to be analyzed, such as lesion candidates. A flowchart of the proposed method is shown in Figure 1. First, the input 3D local image patches are transformed into four types of 2.5D image patches. Next, latent variables of the compressed pixel value information of each 2.5D image are extracted using multiple CAEs. Image features were calculated using the four extracted sets of latent variables. The feature sets of each of the four types of 2.5D images are integrated and output as a feature set of the input 3D image patches. The features obtained via the proposed method vary in content depending on the nature of the normal image data used for training. Therefore, the proposed method is generalized and can be applied to various types of 3D images by changing the normal training dataset. The following paragraphs describe, in detail, 2.5D image transformation, latent variable extraction using CAE, and feature extraction.