esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating the Effectiveness of Natural Carbon Sinks Through a Temperature-Dependent Model
## 3 Results and Discussion
### Evaluation of the Two Sink Models
#### 3.2.1 Evaluation with the Simple Concentration-Dependent Sink Model  
The design of the simple concentration model has been extended compared to previous papers by allowing a time shift of the CO\({}_{2}\) concentration in least squares optimization (Equation (12)). The optimization is conducted for all time shifts between 0 and 22 months. The explained variance ratio \(R^{2}\) of the model is shown in Figure 8. After optimization for the two parameters \(a\) and \(n\), the \(R^{2}\) value of the regression has a maximum of nearly 0.59 at the optimal time shift of 16 months. This means that the simple model explains, at most, 59% of the original data variability.  
Table 1 displays the most relevant time shift dependent values, the explained variance ratio \(R^{2}\), the sink parameter \(a\), the model constant \(n\), and the equilibrium CO\({}_{2}\) concentration \(C^{0}\).  
The estimated model parameters \(a^{\prime}\) and \(n\) are not very sensitive to the applied time shift, but a maximum of the explained variance is clearly visible. As there are several underlying causal processes involved, the visible maximum is an indication of the most dominant process. Determining the mechanism in nature that causes this time delay is the subject of further research.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{8}{c}{**Shift**} \\ \cline{2-9}  & **11** & **12** & **13** & **14** & **15** & **16** & **17** & **18** \\ \hline \(R^{2}\) & 0.5880 & 0.5886 & 0.5890 & 0.5895 & 0.5896 & 0.5897 & 0.5896 & 0.5895 \\ \(a^{\prime}\) & 0.0175 & 0.0176 & 0.0176 & 0.0176 & 0.0176 & 0.0177 & 0.0177 \\ \(n\) & \(-4.93\) & \(-4.94\) & \(-4.95\) & \(-4.96\) & \(-4.97\) & \(-4.97\) & \(-4.98\) & \(-4.99\) \\ \(C^{0}\) & 282.2 & 282.7 & 283.2 & 283.7 & 284.1 & 284.5 & 284.9 & 285.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Shift and corresponding metrics (\(R^{2}\), \(a^{\prime}\), \(n\), and \(C^{0}\)).  
Figure 8: Explained variance of simple sink model as a function of CO\({}_{2}\) concentration shift \(lagC\), measured in months.  
With the optimal concentration time shift, the model Equation (4) becomes:  
\[\hat{S}_{i}(C)=0.018\cdot(\sum_{k=12i}^{12i+11}\frac{1}{12}C_{k-16}^{\text{ month}}-285\text{ ppm}) \tag{19}\]  
The value \(C^{0}=285\) ppm can be interpreted as the effective preindustrial equilibrium concentration. The 95% confidence interval for the absorption parameter \(a^{\prime}\) is [0.014, 0.021]. The 95% confidence interval for \(C^{0}\) is [192, 379] and is, therefore, very large. But it is plausible: when the relative error of \(a\) is about 10% and the relative error for \(n\) is 13%, then the relative error of their ratio is 17%. Therefore, it is not surprising that different authors arrive at different values depending on their data selection and model design.
## 2 Three-Dimensional Forward Modeling Algorithm
### Three-Dimensional Forward Modeling of TDIP  
The difference between the forward modeling of TDIP and that of the resistivity method lies in whether the IP effect is taken into consideration. Therefore, the total field of TDIP is a combination of the fields, with and without the IP effect. The calculation of the total field was carried out in two steps: first, the potential or potential difference without the polarization effect is obtained by forward modeling; second, the conductivity \(\sigma\) in (1) is replaced by the conductivity \(\sigma_{\eta}\) with the IP effect in (4), and then the potential or potential difference \(u_{\eta}\) in which the IP effect is considered is derived forward, and the apparent polarizability \(\eta_{a}\) is calculated with (5) [20]:  
\[\sigma_{\eta}=\sigma(1-\eta), \tag{4}\]  
where \(\eta\) is the polarizability of the anomaly, which is nondimensional.  
\[\eta_{a}=\frac{u_{\eta}-u}{u_{\eta}}, \tag{5}\]  
where \(u\) is the potential or potential difference in which the IP effect is not considered.
# Electronic Sensing Technologies in Food Quality Assessment: A Comprehensive Literature Review
## 4 e-Nose
The variety of sensors allows for accurate profiling and classification of the studied analytes. The graphene e-nose developed by [PERSON] et al. [133], which consists of 432 sensors and 36 different receptors, achieved an impressive 89% accuracy in recognizing six odorants at four different concentrations. Another graphene e-nose developed by [PERSON] et al. [134], featuring 56 sensors connected by 4 DNA oligomers, could visually distinguish eight chemical vapors. Additionally, the e-nose presented by [PERSON] et al. [72], which comprises five elements, effectively distinguished four VOCs. Furthermore, the semiconductor e-nose developed by [PERSON] et al. [114, 135] based on five sensors made of various metal oxides achieved a remarkable 99% accuracy in classifying six VOCs. The conductivity or resistivity of the sensing material changes due to charge transfer, ion exchange, or interaction with ions when odor molecules attach to its surface. The electrical signals generated by the sensors undergo processes such as noise reduction or amplification and are then converted from analog to digital form using an analog-to-digital converter (ADC). In the subsequent step, this information is further analyzed with machine learning algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), K-nearest neighbor (K-NN), artificial neural networks (ANNs), partial least squares regression (PLSR), and partial least squares discrimination analysis (PLS-DA) [136]. Integrating multiple transducers and sensitive nanomaterials in electronic noses (e-noses) increases complexity, power consumption, and overall size. To ensure diverse fingerprint profiling patterns of analytes, sensors are often coated with different nanomaterials [137], or the same nanomaterial undergoes various functionalization processes [133, 134, 138], which can be complex and time-consuming. Additionally, sophisticated sensors may require extra circuits and infrastructure to supply power and collect data from multiple channels [74]. The malfunction of even one component can lead to the failure of the entire system. In the case of wireless sensor networks (WSN), installing numerous sensors can result in high costs and added complexity [114]. Using non-selective chemical sensors enables a comprehensive response to the volatile compounds present in a sample, allowing for recognizing odor patterns [139]. Electronic noses (e-noses) detect various dairy products and meats, including beef, poultry, and fish. These odor-detecting devices electronically record the volatile substances produced during decomposition, facilitating the accurate assessment of spoilage levels. By monitoring the different odors emitted by meat as it deteriorates, e-noses assist in identifying early signs of spoilage, reducing health risks, and minimizing food waste [117].
# Bi-Objective Power Optimization of Radio Stripe Uplink Communications
## 1 Introduction
### Related Work  
The existing work in the literature has focused in developing sequential uplink (UL) processing algorithms to be employed in the APs over the fronthaul that are capable of increasing the UE SE [10; 11]. For this purpose, in [10], each AP computes local channel state information (CSI) and makes soft estimates of the desired signals using normalized linear minimum mean square error (NLMMSE) combining and then forwards the soft estimates, CSI and error statistics to the next AP, until the last AP is reached. In [11], a UL optimal sequential linear processing (OSLP) algorithm for an RS network is proposed and compared to the standard sequential maximum ratio combining (MRC), with the former being able of maximizing the SE and achieving the same performance of a centralized scheme [9; 13], while reducing the fronthaul signaling.  
Furthermore, CF mMIMO systems should be supported by efficient power control algorithms [14; 15]. Several SE metrics can be considered in the power optimization methods: max-min fairness, maximum overall sum rate, total power radiated, etc. In [16], the max-min fairness optimization problem is exploited in CF mMIMO by adapting the UE power coefficients and the AP receiver filter coefficients. Since the resulting problem is not convex, the original problem is divided into two optimization sub-problems that are iteratively solved. This means that the optimization problem is transformed into two decoupled single-objective (SO) optimization problems. In [17], the same optimization problem is explored with a zero-forcing combining scheme at the APs. In [18], a max-min fairness problem is considered by taking into account quality of service (QoS) constraints on specific UE, while in [19] the system total energy efficiency is optimized subject to per-UE power and per-UE QoS constraints. In [15], the downlink max-min fairness power optimization is performed using a deep neural network. The authors in [20] proposed the use of deep learning to employ the max-sum rate and max-min power allocation in the UL of CF mMIMO. In this case, the deep learning method is individually employed to solve each power optimization problem, which means that the max-sum rate and max-min fairness optimization problems are solved independently, i.e., there are two independent SO optimization problems. Finally, in [21], the authors proposed the use of three meta-heuristics (MHs) as alternative optimization approaches to solve the UL max-min fairness power optimization problem in CF mMIMO with a per-UE power constraint and MRC at APs. They showed that these algorithms are adaptive and capable of providing near-optimal solutions with affordable computation complexity, when compared to exact schemes, such as the bisection and geometric-programming-based algorithms.
The rather complex vibration of piezoelectric plates not only is composed by a thickness-extensional (TE) vibration mode but also includes contributions of radial modes [23; 24], edge waves [17], and Lamb waves [21; 25], whose effects are more noticeable under a continuous regime [17]; this vibration can be more complicated if we consider that the piezoelectric plate is not vibrating freely, but it is somehow clamped by its edge producing a higher vibration amplitude at the center of the plate with an attenuated vibration at the edge [26; 27]. This complex vibration occurs in both planar and concave plates, and it should be accounted for when producing models of acoustic fields for more accurate results. In this paper, we are proposing an approach to model the acoustic field produced by FUS transducers in a low-intensity regime (considering linear propagation) using polynomial-Bessel based functions as nonuniform radiating distributions on a curved surface. The reason of proposing these functions is based on the reported vibration patterns produced in piezoelectric curved disks composed of a main vibrating thickness-extensional (TE) mode generating a wave in the thickness direction and a second component of Lamb waves propagating radially [17; 28; 29]. These two components are modified when the disk is fixed to the transducer case, which produced a combined vibration pattern that can be approximated by a polynomial-Bessel based function, in accordance with the measurements for any specific transducer. With this approach, we got better results than the widely used SCUR, which can be comparable with the results using the intuitive approach for modeling the acoustic field using the velocity distribution measured on the radiating surface [29; 30]. Using analytical functions instead of measurements will permit to propose mathematical algorithms to produce realistic acoustic fields of actual FUS transducers; however, these demonstrations are beyond the scope of this paper. The future applications of these new models are open, since the assumptions taken for this work are not particular.
## 3 Model Construction
### Residual Correction Forecast for Future Intervals  
A hybrid residual correction prediction model based on optimal machine learning using the IOWA operator is employed to predict residuals and refine the prediction outcomes of the GRNN model. The weight vector for this model is determined by solving a nonlinear programming problem. Adhering to the principle of predictive consistency, the residual forecast value for the sample in the subsequent time interval \([n+1,n+2,\ldots\ldots]\) can be calculated as follows:  
\[\begin{array}{rl}\Delta E_{c,t}&=IOWA(<\delta E_{1,t},\Delta E_{1,t}>,<\delta E _{2,t},\Delta E_{2,t}>)\\ &=\sum\limits_{i=1}^{2}\omega_{i}\Delta E_{v-index(it)}\end{array}, \tag{10}\]  
where \(t=n+1,n+2,n+3,\ldots\); and \(\omega_{i}(i=1,2)\) represents the weighted vector associated with the residual correction prediction approach of an optimized neural network that utilizes the IOWA operator.  
This method generates a value on the prediction interval \([n+1,n+2,\ldots\ldots]\), which aligns with the sequence of prediction accuracies \(\delta E_{i,t}(i=1,2,t=n+1,n+2,\ldots)\). If the forecast is extended into the future for \(k\) periods, the prediction accuracy for those \(n+k\) periods on the prediction interval is determined based on the average accuracy of the most recent \(k\) periods. Then,  
\[\delta E_{i,n+k}=\frac{1}{k}\sum\limits_{t=n-k+1}^{n}\delta E_{i,t}\qquad(i=1,2). \tag{11}\]
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
## 4 Evaluation
### Wireless Energy Transfer
#### 4.1.2 Harvester Performance
These observations are consistent with previously reported harvester efficiencies with comparable input powers, e.g., as reported in [1]. When available power is reduced to only tens of microwatts, the efficiency shrinks to only several percent, as the harvester consumes most of the available power itself. Furthermore, the 300 mV nominal minimum turn-on voltage of the PMU is not a significant limitation. Typical piezoelectric sources have large internal series resistance; hence, higher voltages are required to provide sufficient power for the MCU. However, further optimization of the PMU might allow less leakage and enable more effective harvesting from low-power sources. When the harvester must work with sources providing very little power, the charging threshold at the storage capacitor could be reduced, and a larger capacitor should instead be chosen. A disadvantage of this strategy is that a larger cap increases the charge-up time required until the cap voltage exceeds the minimum required voltage for the MCU.
## 2 Sensor Design
### Fatigue Behavior
Although embedded bare fibers reach the best interface adhesion with the surrounding matrix and show an excellent gauge factor during static loading tests, during dynamic loading they show a limited fatigue life. The reason for failure of the patches is not a bad application. The patches still bonded perfectly even after failure. It was observed that the bare fiber broke inside the patch. Part of the coated fiber with an outer diameter of 250 \(\upmu\)m goes inside of the patch, and the coating was removed for a length of 25 mm so that the bare fiber with the sensitive FBG, outer diameter 125 \(\upmu\)m, lies inside of the patch. The change in diameter from coating to bare fiber is very abrupt, so that the internal stress of the fiber concentrates at the point of diameter change (see Figure 15). This leads to a fiber breakage exactly at the points of stress concentration, so that the patch fails long before the specimen cracks. Figure 13: Strain response of a patch with embedded acrylate-coated FBG compared to the strain response of the laser extensometer for maximum and minimum load. Therefore, it was decided to use patches with embedded Ormocer(r)-coated fibers to avoid early failure of the patch during dynamic fatigue tests. Ormocer(r)-coated fibers showed good interfacial adhesion and a relative good strain gauge factor. Figure 14: Two patches with embedded bare FBG were applied on the front and back of a specimen and loaded during tensile fatigue tests. Both patches failed clearly before reaching beginning crack initiation of the specimen. Figure 15: Schematic drawing of the stress concentrations of the embedded fiber inside a patch under dynamic loading. The parameters for the dynamic fatigue test were chosen according to the experimentally determined strain parameters from the tensile fatigue tests. The test was force-controlled. The tests were carried out with a frequency of 3 Hz and an alternating load between 2 kN and 20 kN. The loading was increased stepwise every 10,000 cycles. The maximum load corresponded to a theoretical strain of 6000 \(\upmu\)m/m. In Figure 16, the strain of the patch with an embedded Ormocer(r)-coated fiber and the strain of the referencing laser extensometer are shown for maximum and minimum load. Obviously, the patch is able to withstand up to 650,000 loading cycles without failure. It is assumed that the patch would bear up to 1,000,000 loading cycles, but this has to be proved yet. It can also be seen from Figure 16 that the strain of the specimen measured with both the patch and the laser extensometer increases over the whole load cycling. This behavior probably results from a beginning plastic material deformation that leads to a change in the Young's modulus of the specimen.
# Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues
## 3 Methods
### Object Shape Extraction Module
#### 3.2.3 Shape Spatio-Temporal Constraints  
Since the video object itself has a spatio-temporal constraint, i.e., the location of the object is approximately the same in two sequential frames, inspired by [26], this paper utilizes the mask information from the previous frame to make the network more focused on the shape of the object region. The prediction mask of the previous frame has a shape of \(H\times W\) and is connected to the current shape map, which has a size of \(H\times W\times 1\), forming a feature map of \(H\times W\times 2\). Subsequently, a convolution layer with a size of 3 \(\times\) 3 and the Sigmoid function generate the object's rough location prior with a size of \(H\times W\times 1\). Finally, the shape map features are multiplied by the positional prior to obtain the spatially and temporally constrained shape map. Next, we integrate the spatially and temporally constrained shape map with the memory readout features in a residual manner. Figure 4 illustrates the specific implementation of this module.  
To further enhance performance by leveraging shape information, especially at the boundaries, inspired by [26] we designed a shape-based refinement module. We used the feature maps before soft aggregation and the object boundary map as inputs to the refinement module. Next, we resampled them with a 3 \(\times\) 3 convolutional layer and the ReLU function, followed by upsampling to the original resolution and once again merging them through soft aggregation.
## 3 Results and Discussion
### Cure-Strain Sweep Analysis of NR/CR (BIIR) with CV and MF  
From the curing curves of the blends depicted in Figure 4a,b, it can be noticed that the torque corresponds to the \(T_{90}\) (15.2 min) of NR/CR-CV is 3.56 dNm. To reach the same level of torque, NR/CR-CVMF\({}_{3}\) took only 8 min. Similarly, the torque at \(T_{90}\) (9.95 min) of NR/BIIR-CV is 2.48 dNm. To reach the same torque, NR/BIIR-CVMF\({}_{3}\) took only 5 min. Based on the nature of these rheometer cure curves, it has been assumed that the strength of the cured network of the blends NR/CR-CVMF\({}_{3}\) and NR/BIIR-CVMF\({}_{3}\) might be higher than NR/CR-CV and NR/BIIR-CV, respectively, even if the magnitudes of their curing torque are the same. To check the validity of this assumption, a qualitative rheological test in the form of a cure-strain sweep was conducted as per the testing protocol described in the experimental Section 2.2.2 using the RPA. Depicted in Figure 6a are the RPA cure curves of NR/CR-CV up to its \(T_{90}\) (15 min) and NR/CR-CVMF\({}_{3}\) up to 8 min at 170 \({}^{\circ}\)C. Their shear storage modulus (G\({}^{\prime}\)) vs. strain sweep curves are depicted in Figure 6b. It is well known that the rheological parameter G\({}^{\prime}\) indicates the elastic response of viscoelastic material to an applied oscillatory strain. Hence, the term G\({}^{\prime}\) can be considered as the strength of the cured network. Generally, the higher the G\({}^{\prime}\) higher will be the strength of the cured network. It is interesting to note that at a given strain, the G\({}^{\prime}\) of NR/CR-CVMF\({}_{3}\) was considerably higher than NR/CR-CV even if the 15 min cured network of NR/CR-CVMF\({}_{3}\) and the 8 min cured network of NR/CR-CVMF\({}_{3}\) exhibited the same RPA \(\Delta\)\(M\) of around 0.13 dNm. For instance, at 10% strain, the NR/CR-CV exhibits a G\({}^{\prime}\) of 324 kPa. At the same strain, the G\({}^{\prime}\) of NR/CR-CVMF\({}_{3}\) was 3.7% higher than NR/CR-CV. Represented in Figure 6c,d are the RPA cure curves of NR/BIIR-CV up to its \(T_{90}\) (10 min) and NR/BIIR-CVMF\({}_{3}\) cured up to 5 min at 170 \({}^{\circ}\)C and their G\({}^{\prime}\) vs. strain sweep curves. As seen in the case of NR/CR-CV and NR/CR-CVMF\({}_{3}\), similar behaviors were also observed in the G\({}^{\prime}\) vs. strain sweep curves of NR/BIIR-CV and NR/BIIR-CVMF\({}_{3}\). For instance, at 10% strain, the G\({}^{\prime}\) of NR/BIIR-CVMF\({}_{3}\) was around 25% higher than NR/BIIR-CV even though both the \(T_{90}\) cured NR/BIIR-CV and the 5 min cured NR/BIIR-CVMF\({}_{3}\) exhibited the same RPA \(\Delta\)\(M\) of around 0.07 dNm. This RPA cure-strain sweep results support the fact that MF can act as a compatibilising (co-curing) agent through the formation of bismaleimide adduct by utilizing the in situ generated dienes on the chains of NR and CIIR (or BIIR) via Diels-Alder reaction as described in Figure 5.  
Figure 6: \(Cont.\)