esa-sceva/satcom-chunk-collection huggingface dataset:
# Deflection Reduction Shaping Commands with Asymmetric First-Order Actuators
###### Abstract  
In this paper, two approaches for generating deflection reduction shaping commands are proposed to reduce the transient and residual deflections of flexible systems subject to asymmetric first-order actuators. The commands are limited-state in that they consist of two positive actuations of different magnitudes and one negative actuation, similar to on-off-on commands. Standard on-off commands that are commonly used in robots, cranes, and spacecrafts can degrade the control performance of conventional input-shaped commands and cause detrimental damage resulting from large transient deflections of flexible structures due to asymmetric first-order actuators. Therefore, to cope with the performance degradation resulting from the effects of first-order actuators, an approximated closed-form solution and a numerically optimized approach for deflection reduction shaping commands are presented with an exponential function, final impulse magnitude modification of an input shaper is determined by a transient deflection constraint and a phasor vector approach. The performance assessment showed that the approximated analytical approach has an advantage in real-time control applications. The characteristics of the proposed deflection reduction shaping commands are analyzed with respect to system parameters, deflection reduction ratios, and actuator time constants. The proposed command shaping techniques are numerically evaluated using a pendulum system and are experimentally validated on a mini-bridge crane.  
flexible systems; input shaping; parameter optimization; asymmetric first-order actuators; transient deflection +
Footnote †: journal: Applied Sciences
# Topic Modeling for Faster Literature Screening Using Transformer-Based Embeddings
## 4 Conclusions  
Our analysis focused on two separate datasets, and used BERTopic, a popular topic-modeling algorithm, to identify sets of articles to discard from datasets of the biomedical literature prior to manual screening to expedite evidence identification in systematic reviews. Taken together, our data show that encoding article titles using the all-mpnet-base-v2 model, followed by semantic clustering with BERTopic, is an inexpensive and quick way to categorize articles into topics and have an effective overview of the dataset's semantic structure. This procedure has sufficient granularity to identify article groups that can be safely removed from the dataset before it is processed by the investigators during the reliable but slow and labor-intensive process of manual inspection. The number of topics unrelated to the query varies according to the query itself, the keywords used to conduct it, and the database used to create the dataset, but in one of the two datasets we used, we were able to filter out more than 1800 articles, or 30% of the dataset. Furthermore, we also observed that the target articles that had been actually identified for the systematic reviews tended to be found in few clusters, This suggests that semantic search can help investigators not only identify unrelated articles for exclusion but also focus manual inspection on a smaller, relevant subset of the dataset, with faster processing.  
The key highlights of our study can be listed as follows:  
* We applied BERTopic to two datasets of biomedical literature (dentistry).
* BERTopic identified a significant number of papers unrelated to the query.
* Off-topic papers constituted up to 30% of the initial dataset.
* BERTopic effectively filtered out off-topic papers from datasets, saving review time.
* The excluded topic clusters contained no relevant manuscripts, ensuring safe exclusion.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/metrics1010002/s1](https://www.mdpi.com/article/10.3390/metrics1010002/s1)]([https://www.mdpi.com/article/10.3390/metrics1010002/s1](https://www.mdpi.com/article/10.3390/metrics1010002/s1)), Table S1: Full topic list peri-implantitis dataset; Table S2: Full topic list bone regeneration dataset; Table S3: Performance comparison of topic-modeling algorithms.  
Conceptualization, C.G. and E.C.; methodology, C.C.; software, C.C. and C.G.; formal analysis, C.C. and C.G.; resources, E.C. and N.D.; data curation, M.M.; writing--original draft preparation, C.G. and M.M.; writing--review and editing, N.D. and E.C.; supervision, E.C.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are available upon request.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Synthesis of Porous Polymer  
A hyper-crosslinked VBC-DVB microporous polymer was prepared and functionalized in two steps, as shown in the schema in Figure 1a. The first step involved the suspension polymerization of DVB and 4-vinylbenzyl chloride (VBC). The resulting polymer was then hyper-crosslinked under the conditions shown in Figure 1a. During this operation, certain chlorophyll groups within the polymer network were transformed into crosslinked methylene bridges. This additional crosslinking (hyper-crosslinking) induces phase separation within the polymer gel, resulting in the creation of pores between the hyper-crosslinked nodules. However, the conversion of a chloromethyl group into a methylene bridge can proceed only if a neighboring polymer chain is available in the vicinity of the chlorophyll group. The probability of finding such a reaction partner in the surface layer of the nodules, which is on the walls of the newly created pores, is low; hence, unreacted chloromethyl groups are available for further modification on the pore walls [24]. In the second step, various polyamine species were applied to react with the alkyl chloride groups and functionalize the pore surface to improve the selectivity of the material for CO\({}_{2}\) (see Figure 1b).  
Figure 1: Scheme of (**a**) the synthesis of the hyper-crosslinked porous polymer (HCLPP) and (**b**) post-polyamine functionalization by ethylenediamine, diethylenetriamine, and dendrimer (**c**).
## 2 Theories and Methods
### Fundamentals of the Water Injection Test  
According to the Chinese \"Code of water injection test for water resources and hydropower engineering\" (SL 345-2007) [20], when carrying out the variable water head water injection test in the test hole, it is necessary to first seal the water at the top and bottom of the test section. Then, inject a certain amount of water into the casing in order to make the water level in the casing higher than the groundwater level by a certain height, and, finally, record the change in water level in the casing with time. Draw the relation curve between the water head ratio and the time on semi-logarithmic coordinate paper. When the relationship between the water head ratio and the time is not linear, it should be checked and retested. The test can be ended when the water head of the test hole drops to 0.3 times the initial water head. The variable water head water injection test is applicable to the silt, cohesive soil or rock mass with a low permeability coefficient below the groundwater level.  
The aquifer permeability coefficient is calculated as follows:  
\[K_{2}=\frac{0.0523r_{c}^{2}}{AT_{0}} \tag{24}\]  
Among them, \(T_{0}\) is the characteristic time of the water injection test (\(\mathbf{s}\)), the value of \(t\) corresponding to \(H_{t}/H_{0}=0.37\). It can be determined on the \(\ln(H_{t}/H_{0})-t\) curve. \(H_{t}\) is the water head value when the time is \(t\) (m). \(H_{0}\) is the initial water head value of the water injection test (m). \(r_{c}\) is the inner radius of the well casing (m). \(A\) is the shape factor (m).
## 1 Introduction
### Mission Overview of GPM
This was a goal for IMERG, which provides estimates every half hour, _versus_ the 3-hourly interval for the satellite data contributing to 3B43. Usually, satellite estimation in regions subject to convective precipitation could be difficult whereas they expect that IMERG data sets will be more accurate [7]. Contrary to other satellites, such as TRMM, that could not measure light rain and snowfall, GPM-IMERG uses different sensors from different satellites to detect both light and heavy rain and snowfall. Three critical improvements in GPM are that (1) the orbital inclination has been increased from 35\({}^{\circ}\) to 65\({}^{\circ}\), affording coverage of important additional climate zones; (2) the radar has been upgraded to two frequencies, adding sensitivity to light precipitation; and (3) high-frequency channels (165.5 and 183.3 GHz) have been added to the passive microwave (PMW) imager, which are expected to facilitate sensing of light and solid precipitation. In brief, the input precipitation estimates computed from the various satellite passive microwave sensors are inter-calibrated to the GPM Combined Instrument (GCI, using GMI and DPR), because it is presumed to be the best snapshot GPM estimate, then morphed and combined with microwave precipitation-calibrated geosynchronous earth orbit (geo) infrared (IR) fields, and adjusted with monthly surface precipitation gauge analysis data (where available) to provide half-hourly and monthly precipitation estimates. Precipitation phase is diagnosed using analyses of surface temperature, humidity, and pressure. On the other hand, the TMPA combines microwave data from multiple satellites, each inter-calibrated to the TRMM Combined Instrument (TCI), using TRMM Microwave Imager (TMI) and TRMM Precipitation Radar (PR). Coverage gaps in space and time are filled in with calibrated infrared (IR) data (which are generally available with near-global coverage every 3 h); coefficients are derived from co-located IR brightness temperatures and the microwave-based precipitation estimates. The final data products reflect scaling the multi-satellite estimates to rain gauge data on a monthly basis, and ensuring that the 3-hourly averages in 3B42 sum to the monthly totals in 3B43 [7]. Other advantages for using satellite precipitation data in places such as Iran include generally insufficient spatial coverage of _in-situ_ data, long delay in data processing and transfer until they become accessible for the public and scientific use, and absence of data sharing in many trans-boundary basins. As a result, this study aims to assess the accuracy of the new generation of satellite precipitation products. To our knowledge, no report exists yet to study the GPM constellation satellites data over Iran.
## 3 Interference Mitigation Schemes
### MAC Approach
#### 3.2.8 Random Incomplete Coloring  
In [32], _inter_-WBASN interference is mitigated by considering inter-WBASN scheduling (IWS) as a problem of graph coloring. The graph coloring problem aims to achieve a tradeoff between spatial reuse and convergence.  
WBASNs consist of one central processing node (CPN) and wireless sensor nodes (WSN). A unit-disk graph in RIC is generated with the vertices representing the set of CPNs and the edges representing the conflict links between CPNs. This model is called CPN-based IWS.  
In random incomplete coloring (RIC), TDMA is used in both intra- and inter-WBASN channels. A superframe is equivalent to a coloring cycle in RIC. For intra-WBASN channels, the superframe consists of one beacon slot and \(p\) data slots. For inter-WBASN channels, there are \(r\) coloring rounds in one coloring cycle in which each coloring round is divided into coloring slots and winner notification slots. The superframe in RIC is shown in Figure 6.  
The RIC-coloring algorithm is an oriented coloring in which two nodes in one edge are assigned different colors. Each vertex in graph coloring adopts a random value from an available color set, and then, CPN or the vertex broadcasts the coloring message to other vertices. This message contains the color and the random value information. If any vertex receives a message that has the same the color and a higher random value, the vertex is uncolored; otherwise, it wins that color. When a vertex wins the color, it will send this information to all vertices.  
The two steps of IWS for CPNs are the negotiation between CPNs and the broadcast, which use the inter-WBASN channel and intra-WBASN channel, respectively. In the first step, CPNs randomly choose a color in every coloring round, and then, they exchange the coloring message through the coloring slots. In the second step, if the CPN wins the slot, it will broadcast the information on the winner notification slot and send a beacon to its WSNs to indicate the time slot for the WBASN. The number of data slots that can be scheduled in each coloring cycle is indicated as the winner notificationslots or the colored nodes. The remaining nodes that are not colored suspend transmission to avoid interference; they then may be activated in the next round.
# A Novel Resource Allocation Scheme in NOMA-Based Cellular Network with D2D Communications
## 4 Numerical Results and Discussions
### Data Transmission Rates of CUs in the Same SC in the Network  
Figure 9 illustrates that the increasing of the number of CUs on the same SC affects the data rates with arithmetic average. The arithmetic average can reflect the real average data rate of all the CUs in the network. In the four algorithms, TSA, FRPA, FIPA and fixed SINR power allocation algorithm (FSPA) [38], the arithmetic average of data rates of CUs decreases with a changing speed that gradually slows down as the number of the CUs increases.  
Figure 8: D2D data rate versus number of CUs on the same SC. We study how the number of users influences the data rates on each SC where the number of the users changes from 1 to 7.  
Figure 9: Arithmetic average of data rates versus number of CUs on the same SC. We study how the increasing of users influences the arithmetic average of the data rates on each SC where the number of the users changes from 1 to 7.  
Figure 10 illustrates how the increasing of the number of CUs on the same SC affects the data rates with geometry average. In the four algorithms, the geometry average of data rates of CUs decreases with a changing speed that gradually slows down as the number of the CUs increases. For the geometric average, the standard of the data rates is  
\[\Gamma_{s,m}=\prod\
olimits_{k=1}^{s}R_{s,m} \tag{32}\]  
which denotes the data rates of CUs from 1 to \(s\). Noticing that the geometry average is less affected by mutation data than the arithmetic average, from two different points of view, we can comprehensively evaluate the simulation results.  
From the two figures, we can note that the output performance is influenced by the number of users. The two figures also give comparison of the four algorithms to demonstrate the superiority of TSA. In FIPA and FSPA, the power is allocated to the CUs in fixed methods without considering the current channel states, whereas in FRPA the power is simply allocated referring to the path loss of each CU without concerning the sum rate of the whole system. In summary, the performance of FSPA is worst and FIPA and FRPA can improve the output performance. However, the proposed algorithm is the best of all the algorithms we considered.
###### Abstract  
The resistivity method and time-domain-induced polarization (TDIP) are two branches of electric exploration that are used to solve problems in mineral exploration, hydrogeology and engineering geology. In recent years, integrating different physical parameters for joint inversion to improve the accuracy of inversion results has been extensively examined; however, three-dimensional joint inversion of the two methods above has not been realized. To further address this issue, in this research, we used the limited-memory BFGS (L-BFGS) method to develop a three-dimensional joint inversion algorithm of the resistivity method and TDIP based on the cross-gradient constraints. In the new algorithm, the resistivity method and TDIP inversion were iteratively updated alternately to ensure that the inversion results can simultaneously meet the two conditions of obtaining minimum data misfits and finding structural similarity. The three-dimensional synthetic dataset inversion results showed that the models obtained by joint inversion are more accurate in the recovery of both the boundaries and the values of the anomalies. Especially in the background of high noise, joint inversion has higher resolution for the target body. The joint inversion algorithm was also successfully applied to a groundwater detection practice in Beijing, China, in which the practicability of the algorithm was confirmed.  
Keywords:cross-gradient constraints; three-dimensional joint inversion; the resistivity method; time-domain-induced polarization +
Footnote †: journal: _Applied Sciences_
## 3 The Improved SSD Networks
### Backbone with Attention Mechanism  
To promise the speed of the proposed method, we adopt VGG-16 as our backbone, which is the same as SSD. After the Conv4_3, Conv7, Conv8_2, Conv9_2, Conv10_2, and Conv11_2 layers, we add the global context module in GCNet as the attention module to improve the ability to express semantic information. Figure 3 shows the details of the GC block. It includes three parts, named context modeling module, feature conversion module, and fusion module. The context modeling module first uses a \(1\times 1\) convolution and softmax function to get the weights of each spatial location and then feeds the weights back to the original image. The feature conversion module performs feature conversion through two \(1\times 1\) convolutions for dimensionality reduction and dimensionality increase operations. To make the model convergence faster and improve the generalization of our method, a normalization operation is added between the two convolution operations.  
Finally, the fusion module uses the element addition method to integrate the global context features onto each location.  
Equation (1) is defined as follows,  
\[z_{i}=F\bigg{(}x_{i},\delta\bigg{(}\sum_{j=1}^{N_{p}}\alpha_{j}x_{j}\bigg{)} \bigg{)} \tag{1}\]  
Figure 3: The architecture of GC block. This structure consists of three parts: context modeling module, feature conversion module, and fusion module. \(\otimes\) denotes matrix multiplication, \(\oplus\) denotes broadcast elementwise addition.  
Figure 2: The framework of our improved SSD algorithm.  
where \(\sum_{j}\alpha_{j}x_{j}\) represents the context modeling module, which aggregates the features of all positions by the weight \(\alpha_{j}\) to obtain the global context features, \(\delta(\cdot)\) represents the feature transformation to capture the dependencies between the channels, \(F(\cdot,\cdot)\) represents the fusion module.
## 3 Results
### Comparing Truncation Term Coefficient Ratios for the NDM and the FDM  
FDM based coefficients for truncation terms of orders ranging from 8 to 22 were calculated in a manner identical to that of the sixth order truncation term coefficients [Equations (6)-(15)] above. Coefficient ratios (CIRD over LIIRD) for the full range of truncation term orders (6 to 22) were compared to the respective NDM based ratios calculated in [23]. The resulting comparison is presented in Figure 4.  
Relating the Difference in Truncation Term Coefficient Ratios to the Difference in TCRE Configurations Between the NDM and the FDM  
As seen in Figure 4, while the general shapes of the curves corresponding to the truncation term coefficient ratios based on the NDM and the FDM are consistent, there is a substantial difference in scale. This difference arises because the ratios for the NDM were based on TCRE setups presented in Figure 5[23].  
Comparing Figures 1 and 5 it can be noticed that the difference between the middle rings in two TCRE configurations (CIRD versus LIIRD) is equivalent to a distance of \(r\). However, while in Figure 1 that distance represents 1/9 of the radius of the TCRE (equal to 9\(r\)), in Figure 5 it represents 1/6 of the radius of the TCRE (equal to 6\(r\)). To make CRE configurations for the NDM and the FDM comparable, the latter would need to be scaled by a factor of 2/3 since (1/6) \(\cdot\) (2/3) = 1/9. The revised comparison from Figure 4 with the curve corresponding to the NDM based truncation term coefficient ratios scaled by a factor of 2/3 is presented in Figure 6.  
Figure 4: Ratios of truncation term coefficients for term orders 6 to 22 (CIRD TCRE over LIIRD TCRE) based on the negligible dimensions model (NDM) and the FDM respectively.  
Figure 5: Relative locations of rings with respect to the central disc for CIRD (**A**) and LIIRD (**B**) NDM based TCRE configurations.  
As shown in Figure 6, once the scale is adjusted for the NDM to make CRE configurations in Figs. 1 and 5 directly comparable, respective ratios of truncation term coefficients for the scaled NDM and the FDM differ by less than 5%. Non parametric Wilcoxon rank sum test (equivalent to a Mann-Whitney U-test) has been used to confirm that there is no statistically significant difference between the aforementioned ratios (\(p=0.077\)) after confirming that data did not come from a normal distribution using Lilliefors test.