esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper describes the design, development and characterisation of an electrochemical biosensor for the measurement of linoleic and \(\alpha\)-inolenic acid, as representative free polyunsaturated fatty acids (PUFAs), that may be implicated in food safety and food quality. Initial cyclic voltammetric studies were performed with solutions that contained enzyme-generated hydroperoxides of the two PUFAs. These were examined with plain screen-printed carbon electrodes (SPCEs) and screen-printed carbon electrodes containing the electrocataly cobalt phthalocyanine (CoPC). The electrocatalytic oxidation peaks obtained with the latter occurred at potentials about 300 mV lower than the those obtained by direct oxidation with the plain SPCEs and were better defined; as these attributes would lead to better selectivity and sensitivity for fatty acid determinations, the CoPC-SPCEs were used in the fabrication of amperometric biosensors. The enzyme lipoxygenase (LOX) was immobilised on the surface of these devices using the crosslinking agent glutaraldehyde. These biosensors were optimised for the measurement of linoleic and \(\alpha\)-inolenic acid using amperometry in stirred solution; the optimum conditions were deduced by studying the effect of enzyme loading, pH and temperature on the amperometric responses. These responses were examined over the concentration range 2.0 to 20 \(\upmu\)M and the results indicated that the following conditions were optimal: LOX loading 15 units; pH 8.0; temperature 37 \({}^{\circ}\)C. Low concentration calibration studies were performed with the two PUFAs and it was shown that the steady state currents were linear between 0.2 and 10 \(\upmu\)M for linoleic acid and 0.2 and 10 \(\upmu\)M for \(\alpha\)-linolenic acid; the detection limits were 24 and 100 \(\upmu\)M, respectively. The precision (coefficient of variation, \(n=6\)) was 5.3% for \(\alpha\)-linoleic acid and 3.3% for linoleic acid, which were calculated from the steady state current following additions (\(n=6\)) of 0.2 \(\upmu\)M PUFA. These results demonstrate that the novel amperometric biosensor holds promise for determining whether foods contain acceptable levels of free fatty acids.  
food safety; food quality; amperometric biosensor; cyclic voltammetry; screen-printed carbon electrode; PUFA; free fatty acid +
Footnote †: journal: Applied Sciences, 2020, 10, 7779; doi:10.3390/app10217779
# Smooth Information Criterion for Regularized Estimation of Item Response Models
## 3 Simulation Study 1: Differential Item Functioning
### Results  
Table 1 displays the average absolute bias and the average RMSE of model parameters as a function of the extent of DIF and sample size \(N\) for balanced and unbalanced DIF. It turned out that the mean \(\mu_{2}\) and the standard deviation \(\sigma_{2}\) of the second group were unbiasedly estimated in the balanced DIF condition. Moreover, while DIF effects for non-DIF items were unbiasedly estimated, DIF effects were biased for moderate sample sizes (i.e., for \(N=500\) and 1000). In general, there was a similar behavior of regularized estimation based on AIC and BIC compared to its smooth competitors SAIC and SBIC. However, smooth information criteria had some advantages in smaller samples with respect to the RMSE. Note that SAIC was the frontrunner in all balanced DIF conditions regarding the RMSE of the estimate of \(\mu_{2}\). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & \multicolumn{4}{c}{**(Average) Absolute Bias**} & \multicolumn{4}{c}{**(Average) RMSE**} \\ \cline{4-11}
**Par** & **DIF** & \(N\) & **AIC** & **SAIC** & **BIC** & **SBIC** & **AIC** & **SAIC** & **BIC** & **SBIC** \\ \hline \multirow{6}{*}{\(\mu_{2}\)} & & & \multicolumn{4}{c}{_Balanced DIF_} & & & & & & & \\  & small & 500 & 0.001 & 0.004 & 0.000 & 0.003 & 0.104 & 0.090 & 0.113 & 0.104 \\  & small & 1000 & 0.002 & 0.000 & 0.001 & 0.001 & 0.069 & 0.064 & 0.075 & 0.070  
In the unbalanced DIF condition, estimated group means and DIF effects were generally biased. However, the bias decreased with increased sample size and was smaller with large instead of small DIF effects. SBIC was the frontrunner on five out of six conditions for estimates of \(\mu_{2}\) with respect to the RMSE. Only for \(N=500\) and small DIF, SAIC outperformed the other estimators. Table 2 presents average type-I error and power rates for DIF effects of non-DIF and DIF items. It is evident that AIC and SAIC had inflated type-I error rates. Moreover, BIC and SBIC had acceptable type-I error rates. However, SBIC had an inflated type-I error rate for \(N=500\) in the unbalanced DIF condition with a small DIF. Overall, the power rates of regularized estimators AIC and BIC performed similarly to their smooth alternatives SAIC and SBIC. However, SBIC slightly outperformed BIC in terms of power rates.
# Query Rewriting for Incremental Continuous Query Evaluation in HIFUN
## 4 Methods
### System Implementation
#### 4.2.2 Continuous HIFUN Queries Using MapReduce
The value \(V_{b_{k}}\) is emitted as a new \(V^{\prime}>\) value for the key \(K^{\prime}>\). Finally, a reducing function is applied and produces the answer of the \"outer\" query in the form of key-value pairs \(<K_{q},V_{k}>\), where \(K_{q}\) is the query key and \(V_{k}\) its value. Incremental Evaluation  
We have to note that we maintain the state across the micro-batches (using the map-WithState method), using the key-value pairs produced for each micro-batch. Stateful transformation is a particular property used in this case, and it enables us to maintain the state between the various micro-batches across a period of time. That operation is able to execute partial updates for only the newly arrived keys in the current micro-batch. As such, computations are initiated only for the records that need to be updated. The state information is stored as a mapWithStateRDD, thereby benefiting from the distribution's effectiveness of Spark. Next we focus on how the incremental update mechanism leverages the rewriting rules and allows updating the state(s) between the micro-batches using the MapReduce programming model. We distinguish the incrementalization of the rewritings into two cases. **Case 1**. The first case includes the _Common Grouping and Measuring Rewriting Rule_, _Common Grouping Rewriting Rule_ and _Basic Rewriting Rule_. In these rules when a rewritten query is executed over a micro-batch, a DStream is created in a form of \(DStream[(K_{q},V_{k})]\). The \(K_{q}\) is the key of the query and \(V_{k}\) is the synthetic value of its \(K_{q}\) in the current micro-batch. The mapWithState method is used to update the current state, which is also in the form of \(DStream[(K_{s},V_{ks})]\), where \(K_{s}\) is the key of the aggregated query and \(V_{ks}\) is the synthetic value of its \(K_{s}\). In each micro-batch, this method is executed only for the keys of the state that need to be updated, which is a great performance optimization. **Case 2**. The second case includes the _Common Measuring and Operation Rewriting Rule_. In this rule when a rewritten query is executed over a micro-batch, \(n\) DStreams are created in a form of \(DStream[(K_{q},V_{k})]\). Here, the \(K_{q}\) denotes the key of the projection query and \(V_{k}\) denotes the value of its \(K_{q}\) in the current micro-batch. The \(n\) DStreams depend on the number of different grouping attributes appearing in the rewritten set \(Q\). A chain of mapWithState methods is used to update the \(n\) current states, which are also in the form of \(DStream[(K_{s},V_{ks})]\). The \(K_{s}\) denotes the key of the aggregated projection query and \(V_{ks}\) denotes the value of its \(K_{s}\).
## 8 Discussion
### Implementation of POCUS in LRS and Barriers  
These applications in LRS may face various barriers that hinder the implementation of POCUS, particularly due to limited training and accurate on-site analysis and data extrapolation [29]. Limited training is an ongoing challenge that is actively being explored through training exercises and regiments or telemedicine. A previous study by [PERSON] et al. was able to train midwives to utilize POCUS devices alongside mobile phones and tablets to capture high-enough resolution images to identify crucial features within images [79]. Similarly, [PERSON] et al. found that the majority of eighth graders were able to obtain adequate POCUS (FAST) images, even with minimal training, emphasizing the effectiveness of training and the ease of use of POCUS as an imaging modality [80]. Although the ability to teach the layperson is extremely helpful, these past methodologies rely on the continuous assurance that everyday citizens remember this training. An alternative for such a roadblock is the utilization of telemedicine. Despite limited studies within the realm of LRS POCUS, [PERSON] et al. offered a unique setup revolving around augmented reality telemedicine to allow for the continuous ability for real-time training [81]. Regardless, there has been little research into the further development of ultrasound telemedicine applications [82]. Further research should be encouraged to transition these past studies towards POCUS modalities that can be implemented in low-resource settings.
# Experimental Study on Repairing Corroded Cracks by Electrophoretic Deposition
## 4 Results and Discussion  
### Crack Surface Morphology  
Figure 4 shows evolution in the crack surface morphology of the mortar during repair. In the repair process, cationic epoxy resin molecules and curing agent molecules under the electric field drive continuously to the crack and surface electrophoresis deposition, so that the crack of mortar gradually healed, and epoxy resin film thickness gradually increased and tended to be uniform. The newly deposited epoxy resin was mainly milky gelatinous substance, which was eventually transformed into light-yellow epoxy resin curing substance with the extension of repair time. It can be seen from the picture that there are some holes on the surface of the epoxy resin cover. Through observation, we believe that there are three reasons. Firstly, there are some holes on the surface of the mortar, and the epoxy resin will sink into the holes. Secondly, the anode graphite plate electrolyzes water to produce oxygen, and oxygen bubbles float to the surface of the specimen from the bottom, resulting in epoxy resin loss in local areas, resulting in small holes. Finally, the cathode-corroded rebar will electrolyze water to produce hydrogen, which creates small holes in the crack area. Therefore, in pursuit of better repair effect, the concrete surface can be treated to reduce its surface holes, and the voltage should be appropriately reduced in the repair process, so as to avoid a large number of bubbles on the surface of the anode and cathode, resulting in uneven deposition of epoxy resin molecules on the surface of concrete.
## 4 Discussion
### \(\text{SIF}_{757}\) and fPAR Dynamics at the Scale of Climate Zones  
In our study, fPAR and \(\text{SIF}_{757}\) showed a temporal mismatch (Figure 2C), which suggests that the fluorescence quantum yield in the band of measurement (\(\Phi_{F}\)) and the probability that photons can escape the canopy (\(\Omega_{\text{C}}\)) play an important role in the SIF of plants compared to fPAR. SIF-fPAR temporal mismatches were found in 2015 [13], where the land surface temperature (proxy for PAR) and nadir BRDF (bidirectional reflectance distribution function) adjusted reflectance (NBAR) in the near-infrared band of the MODIS satellite (an fPAR proxy) showed a temporal mismatch with GOSAT SIF across the climate zones in Amazonia. We agree with the implications of previous findings on the seasonality of SIF in the Amazon [13], which showed that there is leaf phenological diversity [63] that cannot be captured at the scale of KG-climate zones [7; 29] and biomes [30; 44]. Thus, it may be valid to aggregate SIF time-series of retrievals over combined classes of ERs to capture SIF dynamics and magnitude differences. This is because ERs are based on biomes maps considering the vegetation's climatological and ecological dimensions (Table 1) that the OCO-2 captures in terms of \(\Phi_{F}\) and \(\Omega_{C}\)[9; 19].  
The reason behind the inverse relation observed between SIF\({}_{757}\) and the fPAR may be related to the structural effects at canopy level that the seasonal increase in leaf growth [63] can have on \(\Omega_{C}\) and fPAR. Research has been done on fitting the parameters of SIF\({}_{760}\) with reflectance between 400 and 900 nm from the canopy of crop systems in a SCOPE (Soil Canopy Observation, Photochemistry and Energy Fluxes) model [64]. They found that the reflectance properties alone fit well with the SIF in the model, while simulations including a control of \(\Phi_{F}\) according to the maximum rate of the carboxylation parameter performed marginally better. These results support the argument that SIF might be strongly determined by canopy structure, and thus \(\Omega_{C}\), and not as much by canopy biochemistry (i.e., \(\Phi_{F}\)) [9].  
Interestingly, canopies with similar fPAR can emit different levels of SIF and have different levels of GPP [65]. It has been reported that structural parameters, such as near-infrared reflectance (NIRv), correlate better with canopy GPP than does fPAR [65] because higher NIRv (as well as more SIF) corresponds to a larger fraction of sun leaves [65], which gives them a higher maximum rate of carboxylation than shade leaves. Thus, it is possible that SIF seasonality may be better explained by forest structural factors that determine the fraction of sun and shade leaves than with fPAR.
## 4 Materials and Methods
### WBC Model
#### 4.2.3 CRF Layer  
The CRF layer is used to analyze label dependencies between adjacent terms. Here, we use the BIOES markup method for sequence labeling of text. B is the beginning of the entity; I is the middle part of the entity; O is the non-entity used to mark the part that has nothing to do with the entity; E is the end of the entity; S is the single-character entity, that is, the entity represented by a single word. The sequence labeling task can achieve the conversion from text sequence to tag sequence by labeling each position in the given input sequence with a corresponding label. In the process of sequence labeling, there is an implicit semantic correlation between adjacent labels. After labeling, it is necessary to determine whether the labels in adjacent positions are reasonable. For example, the I tag represents the middle part of the entity, so it must come after the B tag that represents the beginning of the entity. If the S tag and the B tag appear next to each other after a B tag, this situation is not reasonable.  
The objective function is used for calculating the occurred probability for each tag, which shows that the larger the value it has, the greater the probability of selecting the tag it has at this location.  
Sequence Y uses the state labels at different times to predict the most likely sequence of label T, where \(\theta\) denotes the set of parameters in this layer, all of which can be obtained by the following log-likelihood ratio. T is the sequence of sequence labels, and p is the conditional probability of T given the set of sentences and parameters.  
\[\mathrm{L(\theta)}=\sum_{(S,\ T)}\mathrm{log}_{\mathrm{p}}(T\mid Y,\ \theta) \tag{7}\]  
\(S_{\theta}(B,\ T)\) denotes the score of a given input sentence to obtain a labeled sequence T, obtained from the transfer matrix A based on the output B of the previous layer. The normalization result of the score S could be considered as conditional probability p. \(B_{s_{t},t}\) is the probability that the current position character gets tag \(s_{t}\), and the other parameter is the probability that the contents of the previous location adjacent to the character has label \(s_{t-1}\).  
\[\mathrm{b_{t}} =\mathrm{tanh}\mathrm{h_{t}} \tag{8}\] \[\mathrm{S_{\theta}}(B,T) =\sum\left(B_{s_{t},t}+A_{s_{t-1},s_{t}}\right) \tag{9}\]  
In the end, since abbreviations in medicine can be identical to abbreviations in other fields, this situation can lead to errors in the recognition of named entities. To address the occurrence, we use Ab3p [25] to identify abbreviations in medical named entities; Ab3P is a biomedical text abbreviation recognition tool that has excellent results in abbreviation recognition, with an F1 score of 0.9 for its recognition effect on the Medstract corpus.
## 4 High-\(k\) Dielectric Nanocomposites
Since the high dielectric constant and high-breakdown strengths of high-\(k\) polymer nanocomposites are the key factors to consider in designing various sensors and actuators, the incorporation of two-dimensional (2D) dielectric fillers such as boron nitride nano-sheets (BNNs) significantly tune the dielectric properties by improving the breakdown strength of high-\(k\) dielectric polymers [155; 156; 157]. The successful approach in this direction may emphasize its utilization in high-temperature-operating electronic vehicle applications [158].
## 5 Conclusions  
The work concerns a study of thermomechanical processing of 0.24 C-0.9 Si-0.4 Al TRIP steel in laboratory- and semi-industrial scales. The possibility for obtaining acicular ferrite and corresponding mechanical properties during interrupted tensile tests were investigated. The main findings of the present study are as follows:  
* Accelerated controlled cooling after plastic deformation of the austenite combined with the realization of the ferritic transformation in a reduced temperature range (from 650 to 580 \({}^{\circ}\)C) allows acicular ferrite to be obtained in the microstructure.
* Both manufacturing methods allowed RA amounts at a level of about 10% to be obtained. However, pearlitic transformation was initiated due to the more difficult temperature control associated with the semi-industrial process, which consumed some carbon and thus decreased the stability of RA produced in this way,
* The steel produced here has the following properties: YS\({}_{0,2}\)-472 MPa, UTS\({}_{0}\)-690 MPa and UEI-15.6%, and the strain hardening exponent peak (0.2) at a strain of 0.05. The YS\({}_{0,2}\) and UTS are 70 and 40 MPa higher compared to steel containing polygonal ferrite,
* Insufficient enrichment of RA in carbon leads to the intense martensitic transformation in an early stage of deformation (confirmed by the strain hardening peak) and thus lowered plasticity,
* The martensitic transformation starts in the centers of the largest grains and affects their gradual fragmentation separating regions of higher stability. This leads to the formation of RA-M islands.  
Conceptualization, A.G. and A.S.; methodology, A.G.; validation, A.G. and A.S.; formal analysis, A.G.; investigation, A.S.; resources, A.G.; data curation, A.S.; writing--original draft preparation, A.S.; writing--review and editing, A.G.; visualization, A.S.; supervision, A.G.; project administration, A.G. and A.S. All authors have read and agreed to the published version of the manuscript.  
The financial support of the Silesian University of Technology, Poland, is gratefully acknowledged, grant no. 10/010/BKM21/1049.  
Not applicable.  
Co-financed by the European Union through the European Social Fund (grant POWR.03.05.00-00-Z305).  
The authors declare no conflict of interest.  
Figure 6: EBSD results showing the martensite formed in the blocky austenite grain during final cooling to room temperature (**a**) image quality map; (**b**) phase map; M—martensite; RA—retained austenite.
# A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows
## 3 Compliance Architecture
### Adding Workflow Verification to an Execution Entity  
To add compliance capabilities to an execution entity, we foresee the architecture described in Figure 2. On top of the execution entity, we add an execution controller. Its purpose is to orchestrate the execution of signed workflow queues. The authorization handler is tasked with verifying that the client has sufficient rights to perform a certain action. The compliance service is an external, trusted service that digitally signs workflows upon request of the client. The authorization handler requests a signature verification from the service upon invocation of a workflow. A request of a client who is not authorized or whose signature is invalid will not be invoked. By using digital certificates, the communication between authorization handler and compliance service can be skipped.
# Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars
## 4 Theoretical Basis of Performance Metrics
### Supplementary Performance Metrics
Essentially, the phase difference, therefore, becomes half, since only half the distance is considered in this scenario. Figure 6 shows that the reflected wave arrives at the second Rx antenna after covering a longer distance \(\Delta s\). Using fundamental geometry and the assumption that the EM waves are planar since the target lies in the far-field region of the antenna, the distance \(\Delta d\) can be written as  
\[\Delta d=lsin(\theta), \tag{38}\]  
where \(\theta\) is the AoA with reference to the radar boresight and \(l\) is the distance between the neighboring Rx antennae. Plugging (38) into (8) with half the RTOF and rearranging, the AoA can then be expressed as  
\[\theta=sin^{-1}\left(\frac{\lambda\Delta\varphi_{IF}}{2\pi l}\right). \tag{39}\]  
As already elaborated in the preceding text, the upper bound for the maximum phase difference for unambiguous measurement is \(|\Delta\varphi_{IF}|<\pi\). Moreover, (39) elucidates that the maximum AoA estimation, \(\theta_{max}=\pm 90^{\circ}\) degrees, is only possible when the antennae are separated by a distance of \(\frac{\lambda}{2}\). The derivation of the angular resolution requires a more detailed consideration and extends beyond the scope of this article. From a holistic perspective, its importance cannot be discounted in any radar application. Given AoAevaluation necessitates a third FFT along the antenna dimension, the angular resolution, \(\Delta\theta\), is expressed in radians as  
\[\Delta\varphi_{IF}=\frac{4\pi vT_{c}}{\lambda}. \tag{33}\]0  
where \(N\) is the number of Rx antennae, as well as the FFT order in the third dimension. In summary, this basic discussion on the AoA serves as a basis for a detailed explanation of TDM-MIMO. Nevertheless, the potential of the velocity and AoA in advancing the capabilities of modern mmWave radar altimeters for UASs requires further investigation to be carried out in future work.