esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Although a Gaussian distribution assumption of the posterior density of the simulated variable is also given, this method is capable of outperforming both the 4 DVar and EnKF methods under perfect- and imperfect-model scenarios with lower computational costs than EnKF, which improves accuracy and efficiency and produces correct estimates of prediction uncertainty in nonlinear and non-Gaussian crop-growth model data assimilation. Notably, few studies on POD-based ensemble 4 DVar (POD4 DVar) for regional crop yield estimations have been published. The main objective of this study was to introduce the POD4 DVar algorithm into crop model data assimilation and to evaluate its performance. The CERES-Wheat model was employed to design a feasible data assimilation strategy because it is an outstanding agro-ecological dynamic model that considers the effects of weather, management, genetics, soil water, carbon, and nitrogen on crop-yield simulations [40,41]. Moreover, the leaf area index (LAI) was used as an observational variable to couple observed LAI with simulated LAI. Two experiments for winter wheat yield estimation were performed. One experiment was performed using the LAI measured in field plots to determine the feasibility of the data assimilation strategy and to compare its performance with that of the EnKF-based data assimilation strategy. A second experiment was conducted on a regional scale by employing remotely sensed LAI to map regional winter wheat yields and to analyze the accuracy and efficiency of the strategy. In addition, the potentials and uncertainties were analyzed for the application of the CERES-Wheat model with POD4 DVar to an operational system for monitoring regional crop growth and predicting critical crop variables.
## 5 Experiments
### A TensorFlow Lite Model for Object Detection
#### 5.2.2 Additional TensorFlow Lite Model for Object Detection  
Guided by the suggestions of the method for Scenario I, we created a TensorFlow Lite model from scratch using the weights pre-trained on ImageNet [72] with the SSD MobileNet_v1 architecture, aimed at improving the efficiency and versatility of the model for object detection tasks. For training, we used the training data provided in the PASCAL VOC 2012 Dataset since this dataset is widely used for object detection tasks [73]. After training, we quantized the model to int8 to fit in the selected edge hardware. Then, we evaluated the model's performance on the validation set of the PASCAL VOC Dataset across various object categories such as airplane and bicycle. The results of the int8 quantized model for top three performing categories are provided in Figure 6. The results of all the investigated categories are provided in Appendix D, Evaluation Results for PASCAL VOC-Trained ST SSD Model.  
Figure 6: Evaluation results for the PASCAL VOC-trained ST SSD model.  
Figure 6 depicts the PR curves for the top three performing object detection classes: \"bus\", \"person\", and \"train\". The PR curve for the bus class (AP = 46.84%) shows that the model is more capable of handling relatively simpler and larger objects, like buses. The curve shows a high precision at the beginning, which remains relatively stable, but as recall increases, precision drops. This behavior is a typical trade-off observed in object detection models. The stability at the beginning and the gradual decline suggests that the model handles this trade-off reasonably well for the \"bus\" class. Given the resource constraints of STM32H7, which limit its ability to process more complex data, the model performs well on larger, more easily identifiable objects, suggesting that the STM32H7 is suitable for tasks where high accuracy and speed for easily detectable objects are prioritized.  
The PR curve for the \"train\" class follows a somewhat similar pattern to the \"bus\" class but with a slightly lower precision. The curve starts with high precision and then gradually decreases as recall increases. The lower AP of 43.14% suggests that the model struggles more with the detection of trains compared to buses, especially when recall exceeds 0.3. This indicates that the model may have more difficulty identifying trains, possibly due to the complexity of the class or variability in the appearance of trains in the dataset.  
The PR curve for the \"person\" class exhibits a steep initial drop in precision when recall is very low (around 0). This suggests that the model initially struggles to correctly identify person, which may be due to issues with distinguishing person from other objects. However, the curve flattens out, and precision stabilizes at a higher value, suggesting that the model becomes more reliable as more candidates are considered, but still with lower accuracy (AP = 35.89%) compared to other classes.
## 3 Results
In this way, \(N_{eq}\) is chosen to satisfy the constraint on the maximum distance between the equivalent sources, whereas \(N_{S}\) can be limited to a reasonable maximum number for practical reasons (measurement time, number of monopoles and cables, complexity of the switching network). The reconstruction of the electric field samples is very accurate, and the iterative procedure terminates after reaching the desired accuracy, as shown in Figure 5. For a better readability of the graphics, only the first 20 samples are shown. According to our experience, a value of \(err\leq\) 0.01 assures a good level of reliability of the algorithm. The same equivalent sources that reconstruct the samples of the electric field collected inside the RC are placed in free space, so that the electric field radiated at a desired distance can be calculated in every direction of the space. Figure 6 reports the emissions calculated at the frequency of 2 GHz. Two planes are considered; according to the reference system of Figure 4, the planes \(\phi=0^{\circ}\) and \(\phi=90^{\circ}\) are shown. Figure 5: Relative error (err) on the reconstruction of the electric field at samples’ position in function of iterations (\(\uppsi\)) and reference and reconstructed electric field samples (**down**). Figure 7 shows the 3D reconstruction of the emission of the EUT. The good level of agreement between numerical simulations and the prediction from RC measurements, especially in the directions where the radiated field in higher, assures the capability of the method to find the maximum level of electric field radiated over the entirety of the solid angle. This is the final value to be compared with the emission standard limits during a compliance EMC tests. In this sense, the non-perfect reconstruction of the backscattering does not significantly affect the result of an emission test. Figure 6: Radiated Emissions at the frequency of 2 GHz: (**a**) plane \(\phi\) = 0 and (**b**) plane \(\phi\) = 90\({}^{\circ}\) are shown. The comparison reports numerical simulations (blue line), the prediction based on measurements in RC (red line), and measurements in AE (green line). Figure 7: 3D reconstruction of the electric field radiated by the EUT; results of numerical simulations (**a**) and model prediction from measurement in RC environment (**b**) are reported.
## 4 Experiments
### Evaluation Metrics  
To evaluate whether the boundary of the entity is correctly recognized and whether the type of the entity is accurately labeled, Precision rate, Recall rate, and F1-score are chosen.  
There are four types of situation in entity recognition results, (1) If the predicted value is entity and the real value is entity, then the predicted result is true positive (TP). (2) If the predicted value is entity and the real value is non-entity, then the predicted result is false positive rate (FP). (3) If the predicted value is non-entity and the real value is entity, then the predicted result is False negative rate (FN). (4) If the predicted value is non-entity and the real value is non-entity, then the predicted result is a true negative (TN). Then, Precision rate, Recall rate and F1 score can thus be calculated as in Equations (9)-(11):  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
\[F1=\frac{2*Precision*Recall}{Precision+Recall} \tag{11}\]  
Precision is the rate of recognized entities to actual entities. Recall is the rate of the number of identified entities to the number of predicted entities. F1 value is the balance between precision rate and recall rate. The larger the precision, recall and F1 values are, the better the overall recognition performance is.
# An Automatic Transformer from Sequential to Parallel Java Code
## 7 Discussion  
### Performance Gain  
To properly evaluate the effectiveness of the approach, a performance benchmark was executed to measure the actual gain in terms of the execution time, and a suite of tests was run to check the correctness of the transformations performed. The average speed-up was 1.66 for all five methods benchmarked, from the highest one, extractFromDataset(), with 2.11, to the lowest one, getUserForAuthor(), with 1.44. Of course, the performance of the parallel version and, hence, the obtained speed-up, is mostly affected by the number of operations executed by the two parallel paths. When there is only a slight difference between the execution time of each path, then there is a maximum gain, since the paths run in parallel, without having one waiting for the other. As discussed in Section 5, the analysis built a CFG to estimate the number of operations for each path. We identified the actual number of operations and whether there were loops within them, but not an estimate of the execution time; hence, there could be some differences between the execution times of the two paths. This factor might negatively influence the performance gain since the path with less work will have to wait for the other one to complete. The five benchmarks executed for the application have shown varying speed-ups (see Table 1. For extractFromDataset() method, the two parallel threads had very similar execution times, and the performance gain was high. Conversely, for the methods with 1.44 and 1.46 speed-ups, there was a greater difference between the execution times of the two parallel paths. Despite this, the results have shown that our transformations were effective since they provided significant performance gains. The extractFromDataset() benchmark required a longer execution time compared to the other benchmarks because it performed several I/O operations to read the data from the _c_sv files, which, as presented in Section 6, consisted of more than 200,000 records for books and 300,000 records for reviews. Table 2 provides insight into the tests executed to check the correctness of our applied refactoring. Result column presents the values returned by both sequential and parallel versions, showing that--in every execution--the results were unchanged. This shows that the control flow and data dependence analysis (Section 4.2) were correctly performed to identify dependencies, and that synchronization statements were introduced in the proper positions. A misidentification could have led to desynchronization and, thus, test failure. We checked the code coverage of our test suite with JaCoCo ([[https://www.eclemma.org/jacoco/](https://www.eclemma.org/jacoco/)]([https://www.eclemma.org/jacoco/](https://www.eclemma.org/jacoco/)), accessed on 21 July 2023), an open-source library for Java that automatically performs code coverage analyses. The total code coverage of our test suite was 94%, as only the catch branches for try statements were not executed. All the branches that
# Mechanical Performance of Concrete Made with the Addition of Recycled Macro Plastic Fibres
## 5 Conclusions  
This study addressed the use of fibre from multilayer recycled plastic from food packaging plastic waste for reinforcement in concrete. Considering the results, the following conclusions can be drawn from this research:  
* The physical properties studied were not significantly affected by the addition of recycled plastic fibres;
* The addition of RPF produces a certain decrease in compressive and flexural strength, but an improvement in the post-cracking properties of the concrete was achieved. The presence of plastic fibres inside the matrix increased both the ability of the concrete to resist the advancement of cracks, and its ability to deform plastically before breaking, giving the concrete matrix a certain residual tensile strength during the post-crack phase;
* The increase in toughness index with an increase in the percentage of recycled plastic fibres confirmed their stitching action inside the cement matrix, which is fundamentally dependent on the type of fibre and its quantity;
* The toughness index presented by the concrete with commercial fibres was greater than that with the same dosage of RPF, due to its rougher surface and greater adherence to the cementitious matrix. Furthermore, it should be noted that the toughness index relative to the mixture with 6 kg of recycled plastic fibres per cubic meter of concrete was comparable to that with commercial plastic fibre (PFRC-REF). This suggests that manufacturing RPF with a rougher surface could lead to a reduction in the amount needed to achieve similar toughness index levels to PFRC-REF.  
In addition to the mechanical characteristics mentioned above, the use of recycled fibre from multilayer plastic waste derived from the packaging industry--which is currently difficult to recycle--in the production of this composite material could be an interesting approach to extend the life cycle of this material.  
Conceptualization, J.A. and A.P.G.; methodology, A.L.-U.; Software, A.L.-U. and P.A.V.; Validation, J.A. and A.P.G. formal analysis, P.A.V. and A.B.; investigation, P.A.V. and A.L.-U.; resources, A.P.G.; data curation, P.A.V. and A.L.-U.; writing--original draft preparation, P.A.V. and A.L.-U.; writing--review and editing, A.B. and J.A.; visualization, P.A.V.; supervision, A.L.-U., J.A. and A.B; project administration, J.A. and A.P.G.; funding acquisition, J.A. and A.P.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 7: Values of toughness (**a**) and toughness index (**b**).
# Efficient V2V Communications by Clustering-Based Collaborative Caching +
## 3 The Proposed Method
### Cache Management  
A CH manages how to cache content in its cluster. When a CH receives or forwards a data packet, it uses the following operation.  
* If the packet destination is its CM and the CM has free cache space, the CH adds the timestamp and the name of the content to the corresponding CM's entry in its CM table.
* Otherwise, the CH decides whether to save the content or not by checking if the content is already stored in the cluster. If it saves the content, it also decides which vehicle in the cluster to save it to. If the target vehicle is a CM, it sends a request-to-cache message to the CM and adds the timestamp and the name of the content to the entry of the corresponding CM in its CM table (step 8 in Figure 4). If the buffer of the CM has no free space, the CM decides which content to replace based on content popularity. Figure 6 shows this operation.  
Figure 5: Request counter table.  
A CM overhears data packets and stores content in its temporary buffer (step 6' in Figure 4). When a CM receives a request-to-cache message from its CH, it moves the specified content from its temporary buffer to its CS (Content Store). If a specified time has passed and the CM has not received a request-to-cache message, it discards overheard content silently.  
When a CH selects a CM to store content, it selects the vehicle with the maximum free space. If no CMs have free cache space, the CH selects a CM with content with the least popularity. Then, the selected vehicle replaces the content with the new content using the same cache policy as the CH.  
When a CM joins a cluster, it sends a sync message that contains information about the \(n_{\text{sync}}\) most popular content data (with content names and their timestamps) in its buffer to its CH. The CH updates its CM table accordingly. When a CH considers that a CM has left its cluster (cluster-keep-alive timer expires), it removes the CM's information from its CM table.  
Algorithm 3 is the pseudo-code for storing content in a cluster.  
```
1 ProcedureStoreContentInCluster//Assumption:ThecallerisACH Data:Input:content ifDoIhaveemptybuffers?then
2 Store the content ;
3 elseifDoesoneoftheCMshaveemptybuffers?then
4 Send a request-to-cache packet to the CM ;
5 else
6\(n\leftarrow\) ID of the vehicle in the cluster having the least popular content ;
7 ifIsntheIDofthecaller?then
8 Store the content ;
9
10 else
11 Send a request-to-cache packet to the vehicle with ID \(n\) ;
12
13 end if
14
15 end for
```  
**Algorithm 3**Storing content in a cluster  
Figure 6: Data packet forwarding operation.
## 2 Materials and Methods
### Methods
#### 2.2.5 Measurement of Water Vapor Transmission Rate (WVTR)  
The measurement of the water vapor transmission rate (WVTR) of the different samples was performed in triplicate according to the ASTM E96 standard, with modifications. Two grams of anhydrous CaCl\({}_{2}\) were placed in 25 mL glass bottles (55 mm high, 10 mm internal diameter mouth) to achieve a 0% internal RH. They were positioned in an oven at 45 \({}^{\circ}\)C, to remain completely dry and to be progressively sealed with the paper samples glued on the top of the vials, with the coated part inwards to prevent water vapor tangential diffusion. The vials were weighed at zero time, then placed in a desiccator with 90% RH obtained by BaCl\({}_{2}\) saturated solution at 45 \({}^{\circ}\)C.  
The vials were weighted daily for 4 days of storage. The WVTR value (g 24 h\({}^{-1}\) m\({}^{-2}\)) was calculated using the following formula:  
\[\text{WVTR}=\Delta\text{W}/(\Delta\text{t}\times\text{A}) \tag{1}\]  
where: \"\(\Delta\text{W}/\Delta\text{t}\)\" represents the weight gain as a function of time (g 24 h\({}^{-1}\)), obtained as the slope of the linear regression of the mass gain versus time; \"A\" corresponds to the exposed surface of the film (7.85 \(\times\) 10\({}^{-5}\) m\({}^{2}\)). The reported WVTR data are the average of three replicates.
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 4 Micro-Supercapacitor Electrolytes
### Aqueous Electrolytes  
Aqueous electrolytes are of interest because of their high conductivity, low cost, intrinsic non-flammable nature, and because they are environment friendly. Such electrolytes can be acids, alkalis, and inorganic salts. They can provide large capacity and high power density. However, the bottleneck with aqueous electrolytes is the low operating voltage because of the narrow electrochemical stability window of water, as pure water decomposes above 1.23 V. Recently, a new strategy was proposed to overcome this challenge, namely by the development of supersaturated water-in-salt electrolytes, which can widen the potential window of aqueous solutions to about 3 V [111]. In this type of electrolytes, the decomposition of water molecules near the cathode or anode is hindered because the content of free water in these super concentrated aqueous solutions is very low [112].  
A 1 M NaCl electrolyte was employed with binder-free reduced graphene oxide foam electrodes for a micro-supercapacitor with a high capacitance of 618 F/g at a current density of 3.0 A/g [113]. An aqueous H\({}_{2}\)SO\({}_{4}\) electrolyte was used for a flexible, simple, light-weight, and low-cost micro-supercapacitor, based on graphene printed on paper, which had a volumetric capacitance of 30 mF/cm\({}^{3}\) at 6.5 mA/cm\({}^{3}\)[114]. Similarly, aqueous H\({}_{2}\)SO\({}_{4}\) solution acted as the electrolyte for a micro-supercapacitor with laser-induced graphene patterned on fluorinated polyimides via a laser photothermal method and resulting in a very large specific surface area of 1126 m\({}^{2}\)/g, and a very high areal capacitance of 110 mF/cm\({}^{2}\), as determined by cyclic voltammetry [115]. The thin film supercapacitor performance of an aqueous electrolyte, Li\({}_{2}\)SO\({}_{4}\), was improved using a liquid-carbon-precursor nanoimprint lithography method, with liquid sucrose and lignin used as the precursors for nanoporous carbon materials with high specific surface area of more than 1000 m\({}^{2}\)/g [116]. An aqueous 1 M Na\({}_{2}\)SO\({}_{4}\) electrolyte was used to test a MnO\({}_{2}\)/PEDOT:PSS-rGO on a carbon fibre substrate for application in fibre-shaped micro-supercapacitors, resulting in a specific capacitance of 2.9 F/cm\({}^{2}\) at a current density of 5 mA/cm\({}^{2}\), with a 95% capacity retention after 5000 cycles. The energy density was increased, and a cell voltage of 2.8 V was achieved for such a fibre-shaped micro-supercapacitor with a water-in-salt electrolyte of super-concentrated potassium acetate instead of conventional dilute aqueous electrolyte [117].
## 3 Parametric Study for Determining the Optimal Sampling Strategy
### Sampling from a Single Point  
#### 3.1.1 Effect of Spatial Correlation Length (\(\theta\))  
Example charts showing the variation of \(p_{f}\) with respect to \(d_{p}/H\) for various \(\theta/H\) values are given in Figure 3. From this figure, it is inferred that the optimal sampling location for the active case is clearly for zero horizontal distance from the wall, both for the case of translating and rotating wall and for any \(\theta\) value. Isolating the curves for x/\(H\) = 0 (see Figure 4), it seems that there is a \"worst case spatial correlation length\"--see also [23], where the failure probability becomes maximum. For example, for the various \(\theta/H\) ratios shown in Figure 4 (ranging from \(\theta/H\) = 0.04 to 8.3), the \(\theta/H\) = 0.21  
Figure 2: Graphical representation of different sampling scenarios: Scenarios A and B refer to a single sampling point (each located at depth \(d_{p}\)), whilst Scenarios C and D refer to sampling domains (each having length \(d_{d}\)).  
case gives the higher \(p_{f}\) values. From Figure 4, it is also inferred that as \(\theta\) tends to zero, the \(p_{f}\) value tends to a single value for any depth (that is, \(p_{f}\) is independent of the sampling depth). However, as \(\theta\) increases, the \(p_{f}\) value becomes more dependent on the sampling depth.  
Figure 3: \(p_{f}\) versus \(d_{p}/H\) example curves for various \(\theta/H\) and x/\(H\) values for the case of sliding (figure (a,c,e)) and overturning wall (figure (b,d,f)).