esa-sceva/satcom-chunk-collection huggingface dataset:
# Atmospheric GNSS RO 1D-Var in Use at UCAR: Description and Validation
## 4 Discussion
In addition, we aim to monitor and improve our data products by disabling quality control and bias correction. Data assimilation is more attentive to quality control and bias correction because outlying or inconsistent observations can be detrimental to the quality of analysis and forecast. Furthermore, data assimilation systems usually have many other observations to substitute for rejected data. (7) Data assimilation must consider inter-platform balance to maximize the utilization of all observation platforms, such as determining the data amount to be assimilated for each platform and the relative weighting to give. It is thus challenging for data assimilation to improve the components specific to RO. Focusing exclusively on RO, 1D-Var has excellent potential for further improvement and augmentation. For example, 1D-Var can use dual-frequency observations to improve ionospheric correction and retrieve electron density and atmospheric profiles together [69; 85]. 1D-Var can also employ advanced observation operators, which might be too sophisticated for use in data assimilation. As an example, [PERSON] et al. [86] and [PERSON] and [PERSON] [87] used a curved ray tracing to model phase measurements directly while extending the computational grid to the orbital height of GNSS satellites (~20,000 km). Our future work will focus on implementing these improvement and augmentation.
# Explainable AI-Enhanced Human Activity Recognition for Human-Robot Collaboration in Agriculture
## 5 Conclusions  
The main objective of this study was to address the limitations of existing HAR models by incorporating XAI techniques. Specifically, its scientific novelty lies in the successful integration of SHAP into HAR within the context of human-robot collaboration in agriculture. By analyzing data and SHAP values collected from 20 participants wearing five IMUs on different body positions during outdoor agricultural tasks involving a UGV, this study offers valuable insights into the impact of sensor placement on activity recognition accuracy. The practical value of these findings is significant, as optimizing sensor placement improves the efficiency and cost-effectiveness of HAR systems, ultimately enhancing safety and performance in human-robot collaboration. In addition, the increased interpretability of HAR models through SHAP analysis strengthens the reliability and robustness of the system, leading to more trustworthy activity recognition.  
The main conclusions are outlined as follows:  
* The XGBoost model exhibited a relatively high level of classification accuracy (93%) for all ten investigated activities;
* The use of SHAP allowed for deeper insights into feature contributions, emphasizing the significance of magnetometer-based features across various activities. Remarkably, the features \"my_lumbar region\", \"my_cervix\", and \"my_chest\" were identified as the most impactful;
* Sensors mounted on the torso, particularly on the lumbar region, cervix, and chest, proved to be critical for capturing core movements, while wrist sensors provided complementary information, especially for load-specific tasks;
* Future work should focus on expanding the dataset to include more activities and diverse participants to improve generalizability. Exploring additional sensors and experimenting with different sensor placements could enhance accuracy. Real-world testing in agricultural environments and long-term studies on system adaptability are also essential for practical applications.  
Conceptualization, L.B., D.T. and D.B; methodology, L.B. and D.T.; software, L.B.; validation, D.T. and A.C.T.; writing--original draft preparation, L.B.; writing--review and editing, L.B., D.T., A.C.T., D.K., P.B. and D.B.; visualization, L.B., D.K. and P.B.; supervision, D.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethical Committee under the identification code 1660 on 3 June 2020.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in this study.  
The dataset used in this work is publicly available in [35].  
Author [PERSON] is employed by the company farmB Digital Agriculture S.A. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
## 4 Phenomenological and Statistical Analysis of Results
### Analysis of Ductility  
Ductility [26] reflects the deformation capacity of the joints following yielding. Its extent largely influences the seismic performance of the structure. It is typically represented by the ratio of the ultimate displacement \(\Delta_{u}\) to the yield displacement \(\Delta_{y}\). The equation for the ductility coefficient is as follows:  
\[\mu=\frac{\Delta_{u}}{\Delta_{y}} \tag{1}\]  
in which \(\Delta_{u}\) represents the failure displacement of the joint specimen, specifically, the corresponding displacement when the load value diminishes to 85% of the peak load. \(\Delta_{y}\) signifies the yield displacement of joint specimens. Both the load characteristic values and the ductility coefficients of each specimen are computed, and the findings are displayed in Table 4.  
Based on the data in Table 4, the ductility coefficient of ZP-1 is marginally higher than that of ZP-2 and ZP-3, suggesting that an increase in the length of L-shaped bars can effectively enhance the ductility of joint specimens. However, a decrease in concrete strength in the composite beam area can lead to a decline in ductility. The ductility coefficient of ZP-4 is significantly larger than the other three newly assembled joints, indicating that ductility performance is amplified by augmenting the post-cast area of the concrete in the joint. The ductility performance of the cast-in-place joints is notably superior than the other four joints due to the integrity performance. The bonding of new joints between old and new concrete can influence the overall mechanical properties of the joints.  
Figure 7: Hysteresis curve and skeleton curve: (**a**) XJ-1, (**b**) ZP-1, (**c**) ZP-2, (**d**) ZP-3, (**e**) ZP-4, (**f**) skeleton curve.  
Thus, it is observed that by enlarging the concrete post-cast area in the joint area, the length of the L-shape, and the concrete strength in the composite beam, the bonding capability between the post-cast area and the assembled component can be effectively realized with the goal of improving the ductility performance of the joints.
###### Abstract  
Large-scale (LS) multi-user (MU) multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) is considered to be a desirable signal transmission scheme because it can significantly improve the energy efficiency (EE) and spectral efficiency (SE) of the system. However, there are many difficulties in realizing an LS-MU-MIMO-OFDM system, and one of these is its high peak-to-average power ratio (PAPR), which results in serious nonlinear signal distortion and power inefficiency of the power amplifier (PA). LS-MIMO-OFDM systems require a lot of PAs, which are necessary to be connected to each antenna. To compensate for the PA nonlinearity and increase the efficiency, a digital pre-distorter (DPD) is very popular and has been successfully implemented in current base stations (BSs). However, a DPD is very difficult to use in an LS-MU-MIMO-OFDM system because it is expensive, but should be applied to each antenna. Therefore, a considerate scheme of signal processing is necessary to cope with the PA nonlinearity issue of the LS-MU-MIMO-OFDM system. In this paper, we propose an interference-aware iterative clipping and filtering peak-to-average power ratio (PAPR) reduction scheme for LS-MU-MIMO-OFDM systems. In the proposed scheme, the clipping level in the clipping process is adaptively adjusted based on any kind of interference level that exists in the general communication environment. In particular, when matched filtering (MF) precoding is used for the LS-MU-MIMO-OFDM, the inter-user interference (IUI) always exists with a practical number of transmitter (TX) antennas, and this inevitable IUI level can be a decision point for the clipping ratio (CR). Choosing a proper CR to make the clipping noise lower than IUI has a very high benefit for the EE improvement of the system. The results of numerical analysis show that the proposed scheme can induce a very effective peak-to-average power ratio (PAPR) performance with little SE loss.  
large-scale multi-user MIMO-OFDM; peak-to-average power ratio +
Footnote †: journal:  
_Article_
## 4 The Model of Packet Size Adaptation Using Bounded Multi-Armed Bandits  
This section introduces the bounded multi-armed bandits model, then describes the packet size adaptation problem. Finally, we show how to map packet size adaptation to MAB by exploration and exploitation.
## 2 Thick Level Set Damage Model
### Shrinkage Contribution  
To estimate the shrinkage strain, most studies in the literature neglect the heterogeneous nature of the concrete and its effect on the drying process. Semi-analytical and empirical models such as Eurocode [28] and the B3 model [29] provide a good approximation of the shrinkage evolution with time taking into account concrete composition, ambient humidity, geometry, drying surface area-to-volume (A/V) ratio and much more. These approximations are mean values per cross-section. In the present paper, the same logic is adopted. This point will be revisited in future works to reflect reality by computing heterogeneous shrinkage strain fields. Note that, in the present paper, free shrinkage data issued from experimental measurements are used exactly in the same manner.  
The total strain is expressed as the sum of the shrinkage strain and the mechanical one \(\varepsilon=\varepsilon^{shr}+\varepsilon^{mec}\). In other words, the mechanical strain is the sole responsible of generating (or not) stresses.  
Therefore, taking into account the shrinkage into the TLS formulation is equivalent to writing:  
\[\left\{\begin{array}{rcl}\sigma&=&\frac{\partial W(\varepsilon- \varepsilon^{shr},d)}{\partial\varepsilon}\\ Y&=&-\frac{\partial W(\varepsilon-\varepsilon^{shr},d)}{\partial d} \end{array}\right. \tag{15}\]  
with \(\varepsilon^{shr}=\left(\begin{array}{ccc}\varepsilon^{shr}_{\infty}&0&0\\ 0&\varepsilon^{shr}_{\infty}&0\\ 0&0&\varepsilon^{shr}_{\infty}\end{array}\right)\) and \(\varepsilon^{shr}_{\infty}\) being the shrinkage strain when \(t\rightarrow\infty\).  
Although the considered shrinkage strain is the one reached after an infinite drying time (i.e., maximum), another drying time could be as easily considered.  
When writing the variational formulation, considering shrinkage introduces additional volume integrations in Equations (8) and (9).
# An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs
## 4 Our Proposal
### Improving the Ant Clustering Algorithm for Sensor Node Clustering  
To improve and facilitate communication between each node and the sink utilizing an optimal path, we offer a metaheuristic strategy for forming clusters via the CH. The CH are determined by the SNs with the highest chance of picking factor. Finally, clusters are formed by the improved ant-clustering algorithm (IAC). Some nodes use a multihop routing mechanism inside clusters to interact with the cluster head, and once all of the cluster members receive data packets, the data along the precomputed path are transferred to the sink.  
Our approach improves the ant clustering algorithm by adding three elements that increase the quality and speed of clustering. Algorithms 1 and 2 depict the suggested IAC algorithm in two steps: initialization and main clustering steps, respectively. In the initialization step, clustering is performed based on the communication range and the distance between each sensor and the most similar cluster head. At the end of this step, we obtain k clusters and other nodes (outliers) that are not yet assigned to any cluster. The main clustering step continues to formulate clusters by using the pick-up and dropping probabilities and the ant's short memory to ameliorate the clusters' formation and assign each outlier node to the most similar cluster head. Therefore, this memory is checked upon selecting a new sensor (including the outlier node) to determine the ant's movement bias. Accordingly, the ant tends to move towards the site of the last similar cluster head it visited. We assume that each sensor can remember the CH relaying to the cluster and the number of clusters (CN) in which it is situated. The CN and CH are established in the initialization step, and the CN is later updated during the main clustering procedure, as described below.
# Revisiting the Analysis of Radiative Mid-Range Wireless Link for Pacemakers
###### Abstract  
The development of a wireless link for biomedical applications requires an accurate estimation of the delivered power to implanted devices. In particular, a variety of mid-range applications in the biomedical area have gained significant attention. An appropriate method for the mid-range wireless link is required to implement a continuous wireless link through human tissue. Even though formulas used in this work are all based on previous works, this paper presents an implementation of the diverse formulas for the mid-range wireless link of an implanted antenna used for a pacemaker system based on the understanding on radiation properties varied with the distances from the antenna. The formulas based on input far-field data are successfully applied to compute the power transmission for the implanted devices, whose range includes radiative near-field and far-field regions. The wireless link for a pacemaker system is evaluated through using a patch antenna immersed with different depths of human tissue. A comparison of the computed and measured results shows an excellent agreement where the validity of the evaluation is demonstrated.  
radiative near-field; wireless link; implanted biomedical devices; pacemakers; asymptotic Friis formula; Chu formula; integral coupling formula +
Footnote †: journal: _Sensors_  
0
Footnote 0: Copyright** © 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Efficiency Comparison of AC and DC Distribution Networks for Modern Residential Localities
## 6 Discussion
### Important Points from Efficiency Plot  
\(\bullet\)Average efficiency  
The average efficiency of the DC system with solar addition came out to be 87.5% compared to that of the AC system at 87.6%. However, the DC system efficiency varies between 72.9% and 93.1%, whereas the AC system efficiency is almost constant around 87%. However, average efficiencies of both the systems are comparable. Although it is believed that solar PV, being a DC generating module, supports DC distribution system, the picture changes as compared to what is expected when the effect of conversion efficiencies of involved power electronic converters is included.  
\(\bullet\)Fall of the DC efficiency at 11 th and 12 th hour  
'D' and 'I' category loads are supplied from solar panels, and the solar irradiance is at its peak. The DC grid has to supply A category loads only, which are already operating at the reduced loading. The inverters' efficiencies, which are quite low, define the system's efficiency during this interval.  
Figure 5: AC-DC system efficiency with the inclusion of 1-kW solar PV panels.  
In order to check the variation in system's efficiency with solar capacity, we simulated our system for different solar capacities installed in the home. The results are presented in Figure 6. The AC system's efficiency is almost unaltered, and the DC system shows variations in lower efficiency values. However, the higher values are almost constant. The fall in efficiency lies in that with increasing solar power, more 'D' type loads are driven by solar, which leaves a lower load on the DC-DC converters, driving their operating efficiencies to lower values.
###### Abstract  
To solve the problems of poor quality mechanical weeding and no obvious plowing effect in complex paddy field environments, the burying weeding operation mode was proposed. The height and force acting on the weeds were the main factors that altered the effectiveness of burying. The structure of the rake teeth weeding wheel was designed and matched with appropriate power, and the rake teeth weeding device was optimized. To verify the rationality of the designed device, the forward speed of the machine, rotating speed of the weeding wheels and weeding depth were selected as the experimental factors, and the inter row weeding rate was selected as the experimental index. A quadratic orthogonal rotation combination experiment with three factors and five levels was designed and optimized. The results showed that when the forward speed was 0.64 m/s, the rotational speed of the weeding wheel was 140 r/min, the weeding depth was 56.8 mm, the inter row weeding rate predicted by the model was 88.43%, and the inter row weeding rate was determined by a confirmatory experiment to be 87.06%, which met the weeding requirements for modern agonomy. To intuitively analyze how the soil was disturbed by the weeding wheel, the explicit dynamic analysis software LS-DYNA was used to build a fluid-solid coupling simulation model of the weeding wheel and water soil. The soil density and coupling stress were used to analyze the plowing state of paddy soil when the weeding wheel was operated. This study provides references for the design and development of paddy field weeding components and for mechanical and soil coupling simulation in paddy fields.  
self-propelled; paddy field weeding; mechanism; optimization; fluid-solid coupling simulation +
Footnote †: journal: Applied Sciences
# Gamma Ray Shielding Properties of Yb\({}^{3+}\)-Doped Calcium Borotellurite Glasses
## 3 Results and Discussion
The rapid reduction in the TVL in the low energy zone implies that the \(\rm TeO_{2}\) content has a remarkable influence on the attenuation competence of the \(\rm TeBYbX\) glasses, and this is due to the domination of the photoelectric effect. However, as we see from the ratio figure, the amount of \(\rm TeO_{2}\) has a lower effect on the TVL at higher energies. The mean free path, or MFP, was determined at 0.284 MeV and graphed in Figure 10. A smaller value is more desirable. An energy of 0.284 MeV was chosen as an example since all other energies obtained similar results. In addition to the five glass samples, the MFP of three commercial glasses and one concrete were plotted at the same energy for comparison, namely Rider, RS-360, PHP, and ordinary concrete. The results demonstrated that the five \(\rm Yb^{3+}\)-doped calcium borotellurite glasses had a lower MFP than Rider and PHP glass and ordinary concrete, with Rider having the greatest MFP at 4.44 cm, which indicates that the investigated glasses have greater shielding capabilities than the other three materials. However, RS-360 has the lowest MFP out of all the tested materials at 1.10 cm, which means it is a better shield than the \(\rm Yb^{3+}\)-doped calcium borotellurite glasses. When only comparing these glasses, \(\rm TeBYb5\) has the lowest MFP at 1.45 cm, while \(\rm TeBYb1\) has the greatest at 2.59 cm. The figure indicates that as the \(\rm TeO_{2}\) content in the glasses increases, and the density with it, the MFP decreases, improving the shielding abilities of the glasses. This trend is consistent with the other investigated parameters. From this  
Figure 9: The ratio between the tenth value layer for TeBYb1 and that of TeBYb5. graph, it can be concluded that TeBYb5 and RS-360 have the greatest potential to be used in radiation shielding applications.
## 5 Asymmetric Partition Support Monitoring System for the Final Mining Withdrawal Space
### Rock Pressure Monitoring of the Large-Section Withdrawal Space in the Final Mining Stage  
The deformation of surrounding rock and the stress of the anchor cable from the beginning of roadway excavation to the withdrawal of the working face are shown in Figure 16a. The maximum roof subsidence of the withdrawal space is 151 mm. The withdrawal space is completed on the fifth day and the stopping coal pillar is revealed. The maximum deformation of the stopping coal pillar is 82 mm before the withdrawal, and the deformation of the top wall meets the requirements of the safe and rapid withdrawal operation in the later period.  
The force of the top anchor cable in the withdrawal channel before the withdrawal of the support is 210 kN, the force of the top anchor cable in the support area of the hydraulic support is 163 kN, and the force of the anchor bolt in the stopping coal pillar is 106 kN. Obviously, the anchor cable shows a good anchoring effect but the change rate of anchorage  
Figure 16: Mine pressure monitoring in withdrawal space: (**a**) the deformation of surrounding rock and the stress of anchor cable; (**b**) on site support control effect.  
Figure 15: Pre-stressed field model of asymmetric partition support.  
force of the anchor cable on the roof of withdrawal channel is fast, the strength of the top anchor cable in the withdrawal channel area is better than that of the top anchor cable in the support area of the hydraulic support, which is in line with the principle of asymmetric partition support control in the final mining withdrawal space.  
The effect diagram of the on site support control is shown in Figure 16b. The section of the withdrawal space in the preparation and withdrawal sections of the working face remains flat, and there is no strong mine pressure phenomenon such as roof fall, rib spalling, support member failure, and support crushing in the roadway, which fully meets the needs of safe and efficient withdrawal in the later periods.  
In conclusion, the monitoring and support control measures have proven to be effective in managing the deformation and stress levels in the withdrawal space. The data collected and the subsequent adjustments made to the support system have ensured that the mining operation can proceed safely and efficiently, with the structural integrity of the roadway being maintained throughout the withdrawal process. This success is a testament to the careful planning and execution of the support strategy, which has been designed to accommodate the unique challenges of the final mining withdrawal space.