esa-sceva/satcom-chunk-collection huggingface dataset:
# RIS-Assisted Cooperative Time-Division Multiple Access
## 2 RIS-Assisted Cooperative Time-Division Multiple Access
### Proposed Algorithm
Note that the first time slot is allocated to the weakest-channel-conditioned UE, and the last time slot is allocated to the strongest-channel-conditioned UE, performed by line 3. When performing the user pairing for a given weak-channel-conditioned UE, the optimal strong-channel-conditioned UE is determined to maximize the SINR of line 24. In other words, the time slot assigned to the optimal strong-channel-conditioned UE becomes the cooperative time slot, and the paired weak-channel-conditioned UE and strong-channel-conditioned UE are served simultaneously in this cooperative time slot. The transmit beamforming vectors used in the cooperative time slot are updated in lines 25 to 28. Consequently, the optimal transmit beamforming vectors are determined, as shown in line 30.
# A Pineapple Target Detection Method in a Field Environment Based on Improved YOLOv7
###### Abstract  
The accurate detection of pineapples of different maturity levels in a complex field environment is the key step to achieving the early yield estimation and mechanized picking of pineapple. This study proposes a target detection model based on the improved YOLOv7 to achieve the accurate detection and maturity classification of pineapples in the field. First, the attention mechanism SimAM is inserted into the structure of the original YOLOv7 network to improve the feature extraction ability of the model. Then, the max-pooling convolution (MPConv) structure is improved to reduce the feature loss in the downsampling process. Finally, the non-maximum suppression (NMS) algorithm is replaced by the soft-NMS algorithm, with a better effect at improving the detection effect when pineapple is in occlusion or overlaps. According to the test, the mean average precision (mAP) and recall of the model proposed in this paper are 95.82% and 89.83%, which are 2.71% and 3.41% higher than those of the original YOLOv7, respectively. The maturity classification accuracy of the model and the detection performance under six different field scenarios were analyzed quantitatively. This method provides an effective scheme for the vision system of the field pineapple picking robot.  
pineapple detection; YOLOv7; deep learning; computer vision; CNN +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Mechanism Analysis  
The reactions occurring at the plasma-liquid interface are very complex. In the reaction system with N2 as the discharge gas, the only source of H and O is water. In the WF-DBD process, high-energy electrons directly enter the gas-liquid interface and collide with water molecules or collide with water molecules in the gas phase, leading to their excitation or disintegration (R4\(-\)R5) [34].  
\[\mathrm{H_{2}O+e}\ \rightarrow\ \mathrm{H_{2}O^{*}+e} \tag{4}\]  
\[\mathrm{H_{2}O+e}\ \rightarrow\ \mathrm{OH+H+e} \tag{5}\]  
In conjunction with the above experimental results and reported literature, Figure 8 illustrates the main reaction pathways of WF-DBD plasma nitrogen fixation at the gas-liquid interface. When the discharge gas is in the state of the flowing gas stream, NO3- is the main product of nitrogen fixation in the plasma liquid. Therefore, the reaction path for the synthesis of NO3- in the state of the flowing gas stream is the main path for the plasma gas-liquid reaction, and the detailed synthesis path can be found in the published literature [35]. In the sealed gas flow state, the primary product of plasma gas-liquid nitrogen fixation is NH4+, indicating that the reaction path of ammonia synthesis in this state represents the dominant pathway of the plasma gas-liquid reaction. It has been demonstrated that NH2 is the most significant precursor for ammonia synthesis, while NH serves as the main precursor for NH2 [36; 37; 38; 20; 39]. NH radicals can be generated from the reaction of N2+ and N atoms with hydrogen atoms produced during discharge, as  
Figure 7: **(a)** OES of the WF-DBD reactor under different peak–peak voltages. **(b)** The emission intensity of the N2 (C \(\rightarrow\) B) and the N2+ (B \(\rightarrow\) X) of sealed N2 at different peak–peak voltages.  
illustrated in equations (R6-R9) [40; 41]. Subsequently, ammonia produced in the WF-DBD reactor enters the water to form hydrated ammonia (R10-R11) [42].  
\[\mathrm{N_{2}^{+}+H} \rightarrow \mathrm{N_{2}H^{+}} \tag{6}\] \[\mathrm{N_{2}H^{+}+e} \rightarrow \mathrm{NH+N}\] (7) \[\mathrm{N+H} \rightarrow \mathrm{NH}\] (8) \[\mathrm{NH+H} \rightarrow \mathrm{NH_{2}}\] (9) \[\mathrm{NH_{2}+H} \rightarrow \mathrm{NH_{3}}\] (10) \[\mathrm{NH_{3}+H_{2}O(aq)} \rightarrow \mathrm{NH_{4}^{+}+OH^{-}} \tag{11}\]  
Consequently, the sealed gas flow state has a long residence time, and the reaction that maintains higher excitation energy levels/excited states of the reactive particles of nitrogen is used for subsequent hydrogenation, which is essential to the production of ammonia. In contrast, the excited state of the nitrogen molecules predominates in the state of flowing gas flow, so it tends to favor the formation of nitrate [33]. In addition, the experimental results showed that the ammonia synthesis in this pathway could be further enhanced by appropriately increasing the applied voltage.
# NGIoU Loss: Generalized Intersection over Union Loss Based on a New Bounding Box Regression
## 2 Related Work
### Loss Function for Bounding Box Regression  
Loss function, for bounding box regression, plays a significant role in object detection. The \(l_{n}\)-norm loss functions are usually used in bounding box regression. They are sensitive to different scales. Early works claim that the L-norm Loss is represented by L1 and L2 losses and assesses the error between the predicted bounding box and the ground truth box by computing the standard deviation or squared deviation between the two. Recent works replace them with IoU-based loss functions. The IoU Loss is the first attempt to include IoU in the regression loss, and it is a success. However, when there is no overlap between the two shapes, the IoU value is 0, so it is hard for the IoU Loss to optimize the regression result. GIoU solves this problem by using closed regions to ensure that the value between two shapes without overlapping regions is not zero, thus greatly improving the reliability of the IoU method. Following GIoU, a number of IoU-based bounding box representations and losses have evolved, including DIoU (which focuses on the distance between two shapes), CIoU (which considers the aspect ratio), PIoU [30] (which is recommended for the rotating target identification) and Updated-IoU [31] (which concentrates on the overlap areas and predicts object localization to obtain the higher position accuracy performance). Therefore, we propose NGIoU Loss by adding a new bounding box, which prevents the NGIoU Loss from completely degrading to IoU Loss.
The reason for combining several measured parameters for material characterization is the increased robustness against disturbing influences such as material variations and surface treatment. In previous work, a similar attempt was made: power scale laws (PSLs), magnetic Barkhausen noise (MBN), and MAT results were compared with each other on the same series of neutron-irradiated nuclear reactor pressure vessel steel material [24]. However, DBTT values were taken from the literature; they were not directly measured in the investigated specimens. In the present work, specimens of two different steels of eastern and western RPV design were investigated. The results obtained from measurements carried out using 3 MAX8 and MAT techniques on Charpy specimens were compared, before and after neutron irradiation at different neutron fluences, and were correlated with different measured mechanical properties. 3 MA-X8 and MAT data, measured on the same specimen series, were collected into a common database and normalized so that they could be quantitatively compared with each other to study their reliability and sensitivity. Finally, a regression analysis was performed to predict neutron-irradiation-induced damage, and conclusions were drawn about the potential applicability of the method for nondestructive evaluation of RPV steel degradation. These results provide information complementary to that obtained from destructive tests of surveillance specimens, which are currently assumed to represent the whole component and cannot account for possible local material variations.
###### Abstract  
Inkjet printing technology is widely used in the manufacture of conformal structures, such as load-bearing antennas or frequency-selective surface radomes. It is particularly promising for preparing conductive patterns on non-developable surfaces. Existing printing technologies employ a single nozzle and a five-axis linkage technique for printing, which is time-consuming. In this study, a conformal plane printing technology based on the arrayed nozzle was developed to prepare conductive patterns on a non-developable surface. The technique actualizes fast printing of passive circuits on a conformal surface, such as a microstrip antenna. Compared to printing techniques employing a single nozzle, the proposed method greatly improves the printing efficiency on conformal surfaces. Specifically, we first developed a model for the driver waveforms and the printing injection parameters via simulation. Subsequently, the accuracy of the computational fluid dynamic simulation results was validated by comparing them with the experimental measurements of droplet trajectory captured using a camera. Next, a droplet spreading model was established, considering energy conservation principles. Finally, a conformal surface printing technology using arrayed nozzles was developed based on the injection parameter and droplet spreading models. The effectiveness and feasibility of the proposed printing method were further validated via simulation and experimental tests of return loss.  
**Keywords:** arrayed nozzle; inkjet printing; conformal antenna; five-axis system; additive manufacturing +
Footnote †: journal: Applied Soft 2020, 10, 3082; doi:10.3390/app10093082
## 2 Materials and Methods
### Cost Analysis of Conventional Methods for Sensor Replacement and Calibration  
In conventional machining processes, sensor-based monitoring systems play a critical role in detecting tool wear, ensuring production quality, and maintaining operational efficiency. However, these systems come with significant costs, both in terms of initial setup and ongoing maintenance. The costs associated with replacing or calibrating sensors can become substantial over time, especially in dynamic production environments.  
One of the primary cost factors involves the periodic replacement of sensors. Sensors such as those used for acoustic emission, vibration analysis, and cutting torque measurement are subject to wear and degradation due to exposure to harsh machining conditions. Frequent replacement of these sensors is necessary to maintain accurate readings, which incurs direct material costs and the additional cost of halting production during the replacement process.  
Another considerable expense arises from the need for regular calibration. Precision monitoring requires sensors to be regularly recalibrated to ensure accuracy, especially in environments where conditions change frequently. Calibration typically requires skilled technicians and specialized equipment, contributing to both labor and operational costs. Additionally, calibration processes may necessitate machine downtime, further increasing the overall expense.  
A comparative study of conventional and AI-based monitoring systems highlights the cost-effectiveness of automated solutions. While AI-based approaches, such as those using Convolutional Neural Networks (CNNs) and Explainable AI (XAI) techniques, require initial investments in software and model training, they significantly reduce the ongoing costs associated with sensor management. AI-driven models can monitor tool wear and quality without the need for extensive sensor networks, thus minimizing both direct and indirect expenses.  
In summary, conventional sensor-based methods for tool condition monitoring are associated with recurring costs related to sensor replacement, calibration, and production downtime. By integrating AI-based solutions, manufacturers can mitigate these costs while improving the reliability and efficiency of their monitoring systems.
## 2 Technological Alternatives for Computing at the Network Edge  
Different technological alternatives have emerged in recent years promoting the deployment of distributed computing environments towards the edge of the network, looking to enable new advanced services. Industry and academia have been actively researching on this area where several architectures and approaches have been proposed, existing several works surveying their different aspects and characteristics (e.g., [14; 15; 16; 17; 18; 19]). Despite the purpose is common to all of them, which is essentially the extension centralized cloud capillarity, the approach taken is slightly different.  
Multi-access Edge Computing specifies a complete orchestration architectural framework, initially conceived for smooth integration with carrier's mobile networks and extended later in scope to fixed services. MEC defines a number of well-defined open and standard APIs for consuming information generated by distinct services and applications, which can be dynamically deployed on top of a virtualized infrastructure.  
Fog computing concept has been extensively proposed in the context of Internet of Things (IoT) and sensor networks, assuming the deployment of some computing and storage capabilities, even minimal, in IoT and sensor devices at the far edge (that is, on end-user or near-user edge devices). All those devices together can constitute a large base of compute substrate when considering the aggregation of their capabilities.  
Finally, the Cloudlet approach advocates for the deployment of localized micro data centers very close to mobile devices in support of the execution of certain applications, mainly assisting on computation offloading tasks.  
A wider discussion of the three approaches can be found in [6; 7; 20]. Table 1 summarizes some relevant aspects for each of these technological alternatives, as well as referring to specific surveys for each of them. From all of the alternatives, MEC emerges as the one more consolidated as solution for carrier networks, including an integration path with NFV and 3 GPP architectures as primary evolution paradigms for telecom operator networks. As consequence, this paper focus on the formal specification of ETSI MEC architecture as baseline for the analysis of multi-domain scenarios.
# Current Challenges Supporting School-Aged Children with Vision Problems: A Rapid Review
## 4 Visual Information Processing and Visual Cognition  
Visual information processing is a cognitive skill to extract, organize, and analyze the visual information acquired from the environment [79]. Researchers believe that visual cognition is a complex process that involves visual information processing and cognitive factors. These work together to recognize the object from the scene using prior knowledge and retina input [80; 81]. [PERSON] [7] divides the information processing mechanism into three components: visual analysis, visual-spatial, and visual-motor processes. Figure 4 provides a basic description of these three components. These components represent specific skills of the individuals involved in performing tasks or activities. The literature argues that poor information processing skills such as visual-spatial and visual-motor integration, and visual analysis skills, are critical for school children [82].  
Figure 3: Different paradigms of technologies assisting experts in vision screening.  
Vision experts test visual information processing skills in children using various objective or subjective standardized tests such as the Beery-Buktenica Developmental Test of Visual-Motor Integration (VMI), Motor-Free Visual Perceptual Tests (MVPT), and Rapid Automatized Naming (RAN) with the subset of the Developmental Eye Movement (DEM) test [82; 83]. Studies have shown that children have visual processing disorders if they fail in such tests. Insufficient visual information processing is further associated with lower academic outcomes [79; 84].  
While eye-tracking technologies are helpful in recording eye movements such as fixations and saccades [85], which contribute to examining gaze movement in relation to visual representations, these relations are not necessarily enough to understand a whole picture of problems. For a better interpretation of the results, the relationships between cognitive skills are also important. Furthermore, handling the direct interpretations depends on prior knowledge [86] or usage of the context [87; 88]. Other studies that may contribute to improved results would need a better understanding and interpretation of individual capabilities [89; 90], hand-eye coordination [91], or children's adaptability [92] to different situations.  
Information about the systems that observe brain activities [93; 94], or advanced methodologies that create new technologies to support health [95], requires a high level of knowledge about information technologies and human cognition. According to what we understand, the use of eye-tracking technology is not widespread for diagnosing vision problems. However, it has great potential for complementing the work of vision experts by recording and analyzing the eye movements, and comparing the stored data of the subjects. The newest eye-tracking technology allows for controlling the distance from the screen, adjusting the position of the child's head to the test, and informing the assistant about a wrong position, which is very helpful for collecting valid data.
## 3 Methodology
### Total Energy Consumption  
Figure 4(a),b compare NBEER's total energy consumption to that of other energy-efficient schemes. By collaborating with neighboring nodes, our scheme optimizes data forwarding and load balancing, resulting in more efficient energy consumption by the system's sensors. Additionally, weight implementation efficiency and data transmission efficiency contribute to overall energy savings. Co-UWSN and CEER, on the other hand, consume more energy due to their emphasis on time-sensitive application requirements and reliance on collaboration and depth differences between data forwarders. This frequently results in the consistent selection of high-energy nodes, reducing the likelihood of collaborative routing at any source node, and resulting in an increase in energy consumption.  
Figure 4: Average values of end-to-end delay.  
Figure 5: (a) TEC of Proposed Work (b) Total energy consumption of all protocols vs. network lifetime.  
to when all nodes are involved. The strategic NHN selection ensures efficient and energy-saving data transfer, making NBEER a highly energy-efficient protocol compared to Co-UWSN and CEER. Figure 6 shows a comparison of the average total energy consumption: Co-UWSN obtained a value of 13.39, CEER 12.01, and NBEER 10.33.  
As shown in Figure 5b and Table 8, the energy consumption of the NBEER protocol increased as the network lifetime progressed. This increase can be attributed to the following factors.  
Node cooperation and neighbor node selection: In the initial stages of network operation, when the network lifetime is relatively low, nodes have ample energy reserves. This allows for easier cooperation and the selection of neighbor head nodes (NHNs). However, as the network operates for a longer duration, the overall energy consumption increases due to the accelerated selection of NHNs. This is because the network becomes fully operational, and the selected NHNs actively send data to the sink nodes and the base station. The average values of each protocol has been given in Figure 6.
# Neutralization of Toxic Malodorous Gases from Cattle Slurry
## 3 Results
### Deodorization Effect of Cattle Slurry by Microorganisms
For all selected microorganisms, the hydrogen sulfide reduction compared to the initial sample (R2--reduction) ranged from 14 to 23.1 ppm, thereby achieving an 80-88% removal efficiency. On Day 4 of the process, hydrogen sulfide was reduced from 113 ppm to 6.4 ppm for KKP 3903, 7.2 ppm for KKP 3909, 8.8 ppm for KKP 3901, 9.0 ppm for KKP 3910, and 9.4 ppm for KKP 3897. Only three strains achieved values higher than 10 ppm: KKP 3905 (10.1 ppm), KKP 3809 (11.1 ppm), and KKP 3902 (17.9 ppm). The H\({}_{2}\)S reduction achieved then was 87-94% compared to the initial sample (R2--reduction) and 50-76% compared to the control sample (average concentration was 27.2 ppm). Figure 3: The hydrogen sulfide concentration in CS during the deodorization process. Among three strains of _Bacillus_ and three strains of _Pseudomonas_ bacteria, those with the highest ammonia and hydrogen sulfide reduction were selected and a bacterial consortium was prepared. Two selected strains were mixed in a 1:1 ratio. The consortium of organisms contained strains of _Bacillus subtilis_ KKP 3903 and _Pseudomonas fluorescens_ KKP 3909. The microbial consortium was applied to cultures including 8.4 log CFU/cm\({}^{3}\). Table 6 shows the effect of the consortium of microorganisms on the concentration of ammonia and hydrogen sulfide compared to the control sample. The research was conducted using cultures: unshaken and aerated. The deodorizing biopreparation effectively decreased the investigated volatile odorous compounds in CS in laboratory conditions. Especially hydrogen sulfide, whose concentration in exhaust air was reduced by more than 65% compared to the control sample, and 89% compared to the initial sample (so-called zero sample, whose H\({}_{2}\)S content was 113 ppm) after 2 days of the deodorization process in the unshaken culture. The results showed that with applied the applied bacterial consortium to CS, more than 95% of the hydrogen sulfide and 77% of the ammonia could be removed after 4 days of the deodorization (reduction compared to the initial sample). However, when calculating the NH\({}_{3}\) and H\({}_{2}\)S concentration reduction relative to the control sample, the values were 42.2% (ammonia) and 81.4% (hydrogen sulfide), as shown in Table 6. Complete removal of H\({}_{2}\)S from the slurry environment was noted on the 6 th day after inoculation with the microbial consortium (KKP 3903 + KKP 3909). At this time, a two-fold greater reduction of ammonia was also noted compared to the sample without microbial supplementation. The biopreparation reduced ammonia by 98% in the unshaken culture and 100% in the aeration culture, after 10 days of incubation (compared to the initial sample). At the end of the deodorization process (i.e., Day 10), the amount of ammonia was approx. 2 ppm, while in the control sample, the NH\({}_{3}\) concentration remained at the level of approx. 38 ppm.